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16123e68dd232b088a20106987fe1b95731a0614..19e4fda8c54c867dd3e41db469ad46d0dab8d7e7 100644 GIT binary patch delta 64 zcmX?jhUwrLrVTBOhWSRt$w|gWCi;d+28PDThACz#scA+@rfDhWmZ`?6DM^V&DQ1?5 TNlB(jiHQb4;munadzJtI(Z?1X delta 64 zcmX?jhUwrLrVTBOh9x;U<|SD<#rla!h88B~mIf9nCT3=3.6.0\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:23:51.853675Z", - "iopub.status.busy": "2024-02-08T14:23:51.853160Z", - "iopub.status.idle": "2024-02-08T14:23:51.870601Z", - "shell.execute_reply": "2024-02-08T14:23:51.870190Z" + "iopub.execute_input": "2024-02-09T00:11:43.376613Z", + "iopub.status.busy": "2024-02-09T00:11:43.376195Z", + "iopub.status.idle": "2024-02-09T00:11:43.393510Z", + "shell.execute_reply": "2024-02-09T00:11:43.393091Z" } }, "outputs": [], @@ -157,10 +157,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:23:51.872492Z", - "iopub.status.busy": "2024-02-08T14:23:51.872251Z", - "iopub.status.idle": "2024-02-08T14:23:51.875252Z", - "shell.execute_reply": "2024-02-08T14:23:51.874804Z" + "iopub.execute_input": "2024-02-09T00:11:43.395569Z", + "iopub.status.busy": "2024-02-09T00:11:43.395185Z", + "iopub.status.idle": "2024-02-09T00:11:43.398136Z", + "shell.execute_reply": "2024-02-09T00:11:43.397691Z" }, "nbsphinx": "hidden" }, @@ -191,10 +191,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:23:51.877186Z", - "iopub.status.busy": "2024-02-08T14:23:51.876862Z", - "iopub.status.idle": "2024-02-08T14:23:52.181787Z", - "shell.execute_reply": "2024-02-08T14:23:52.181254Z" + "iopub.execute_input": "2024-02-09T00:11:43.400075Z", + "iopub.status.busy": "2024-02-09T00:11:43.399809Z", + "iopub.status.idle": "2024-02-09T00:11:43.651178Z", + "shell.execute_reply": "2024-02-09T00:11:43.650721Z" } }, "outputs": [ @@ -367,10 +367,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:23:52.184008Z", - "iopub.status.busy": "2024-02-08T14:23:52.183619Z", - "iopub.status.idle": "2024-02-08T14:23:52.360947Z", - "shell.execute_reply": "2024-02-08T14:23:52.360350Z" + "iopub.execute_input": "2024-02-09T00:11:43.653338Z", + "iopub.status.busy": "2024-02-09T00:11:43.653006Z", + "iopub.status.idle": "2024-02-09T00:11:43.829453Z", + "shell.execute_reply": "2024-02-09T00:11:43.828940Z" }, "nbsphinx": "hidden" }, @@ -410,10 +410,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:23:52.363384Z", - "iopub.status.busy": "2024-02-08T14:23:52.363193Z", - "iopub.status.idle": "2024-02-08T14:23:52.575076Z", - "shell.execute_reply": "2024-02-08T14:23:52.574445Z" + "iopub.execute_input": "2024-02-09T00:11:43.831827Z", + "iopub.status.busy": "2024-02-09T00:11:43.831488Z", + "iopub.status.idle": "2024-02-09T00:11:44.073327Z", + "shell.execute_reply": "2024-02-09T00:11:44.072738Z" } }, "outputs": [ @@ -449,10 +449,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:23:52.577356Z", - "iopub.status.busy": "2024-02-08T14:23:52.577010Z", - "iopub.status.idle": "2024-02-08T14:23:52.581336Z", - "shell.execute_reply": "2024-02-08T14:23:52.580885Z" + "iopub.execute_input": "2024-02-09T00:11:44.075472Z", + "iopub.status.busy": "2024-02-09T00:11:44.075122Z", + "iopub.status.idle": "2024-02-09T00:11:44.079226Z", + "shell.execute_reply": "2024-02-09T00:11:44.078761Z" } }, "outputs": [], @@ -470,10 +470,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:23:52.583396Z", - "iopub.status.busy": "2024-02-08T14:23:52.583092Z", - "iopub.status.idle": "2024-02-08T14:23:52.589537Z", - "shell.execute_reply": "2024-02-08T14:23:52.589107Z" + "iopub.execute_input": "2024-02-09T00:11:44.081202Z", + "iopub.status.busy": "2024-02-09T00:11:44.080801Z", + "iopub.status.idle": "2024-02-09T00:11:44.086947Z", + "shell.execute_reply": "2024-02-09T00:11:44.086396Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:23:52.591576Z", - "iopub.status.busy": "2024-02-08T14:23:52.591292Z", - "iopub.status.idle": "2024-02-08T14:23:52.593848Z", - "shell.execute_reply": "2024-02-08T14:23:52.593397Z" + "iopub.execute_input": "2024-02-09T00:11:44.088967Z", + "iopub.status.busy": "2024-02-09T00:11:44.088670Z", + "iopub.status.idle": "2024-02-09T00:11:44.091270Z", + "shell.execute_reply": "2024-02-09T00:11:44.090741Z" } }, "outputs": [], @@ -538,10 +538,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:23:52.595878Z", - "iopub.status.busy": "2024-02-08T14:23:52.595479Z", - "iopub.status.idle": "2024-02-08T14:24:00.798537Z", - "shell.execute_reply": "2024-02-08T14:24:00.797890Z" + "iopub.execute_input": "2024-02-09T00:11:44.093121Z", + "iopub.status.busy": "2024-02-09T00:11:44.092835Z", + "iopub.status.idle": "2024-02-09T00:11:52.218196Z", + "shell.execute_reply": "2024-02-09T00:11:52.217562Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:24:00.801551Z", - "iopub.status.busy": "2024-02-08T14:24:00.800885Z", - "iopub.status.idle": "2024-02-08T14:24:00.808006Z", - "shell.execute_reply": "2024-02-08T14:24:00.807446Z" + "iopub.execute_input": "2024-02-09T00:11:52.220871Z", + "iopub.status.busy": "2024-02-09T00:11:52.220503Z", + "iopub.status.idle": "2024-02-09T00:11:52.227499Z", + "shell.execute_reply": "2024-02-09T00:11:52.227069Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:24:00.810105Z", - "iopub.status.busy": "2024-02-08T14:24:00.809675Z", - "iopub.status.idle": "2024-02-08T14:24:00.813427Z", - "shell.execute_reply": "2024-02-08T14:24:00.812875Z" + "iopub.execute_input": "2024-02-09T00:11:52.229490Z", + "iopub.status.busy": "2024-02-09T00:11:52.229180Z", + "iopub.status.idle": "2024-02-09T00:11:52.232804Z", + "shell.execute_reply": "2024-02-09T00:11:52.232251Z" } }, "outputs": [], @@ -689,10 +689,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:24:00.815317Z", - "iopub.status.busy": "2024-02-08T14:24:00.814991Z", - "iopub.status.idle": "2024-02-08T14:24:00.818255Z", - "shell.execute_reply": "2024-02-08T14:24:00.817733Z" + "iopub.execute_input": "2024-02-09T00:11:52.234734Z", + "iopub.status.busy": "2024-02-09T00:11:52.234425Z", + "iopub.status.idle": "2024-02-09T00:11:52.237606Z", + "shell.execute_reply": "2024-02-09T00:11:52.237067Z" } }, "outputs": [ @@ -727,10 +727,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:24:00.820195Z", - "iopub.status.busy": "2024-02-08T14:24:00.819898Z", - "iopub.status.idle": "2024-02-08T14:24:00.822963Z", - "shell.execute_reply": "2024-02-08T14:24:00.822402Z" + "iopub.execute_input": "2024-02-09T00:11:52.239563Z", + "iopub.status.busy": "2024-02-09T00:11:52.239279Z", + "iopub.status.idle": "2024-02-09T00:11:52.242270Z", + "shell.execute_reply": "2024-02-09T00:11:52.241733Z" } }, "outputs": [], @@ -749,10 +749,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:24:00.824877Z", - "iopub.status.busy": "2024-02-08T14:24:00.824613Z", - "iopub.status.idle": "2024-02-08T14:24:00.832464Z", - "shell.execute_reply": "2024-02-08T14:24:00.831935Z" + "iopub.execute_input": "2024-02-09T00:11:52.244146Z", + "iopub.status.busy": "2024-02-09T00:11:52.243839Z", + "iopub.status.idle": "2024-02-09T00:11:52.251507Z", + "shell.execute_reply": "2024-02-09T00:11:52.250985Z" } }, "outputs": [ @@ -894,10 +894,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:24:00.834750Z", - "iopub.status.busy": "2024-02-08T14:24:00.834296Z", - "iopub.status.idle": "2024-02-08T14:24:00.955283Z", - "shell.execute_reply": "2024-02-08T14:24:00.954723Z" + "iopub.execute_input": "2024-02-09T00:11:52.253489Z", + "iopub.status.busy": "2024-02-09T00:11:52.253167Z", + "iopub.status.idle": "2024-02-09T00:11:52.373581Z", + "shell.execute_reply": "2024-02-09T00:11:52.373031Z" } }, "outputs": [ @@ -936,10 +936,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:24:00.957601Z", - "iopub.status.busy": "2024-02-08T14:24:00.957213Z", - "iopub.status.idle": "2024-02-08T14:24:01.062665Z", - "shell.execute_reply": "2024-02-08T14:24:01.062150Z" + "iopub.execute_input": "2024-02-09T00:11:52.375750Z", + "iopub.status.busy": "2024-02-09T00:11:52.375369Z", + "iopub.status.idle": "2024-02-09T00:11:52.480958Z", + "shell.execute_reply": "2024-02-09T00:11:52.480384Z" } }, "outputs": [ @@ -995,10 +995,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:24:01.065038Z", - "iopub.status.busy": "2024-02-08T14:24:01.064661Z", - "iopub.status.idle": "2024-02-08T14:24:01.552738Z", - "shell.execute_reply": "2024-02-08T14:24:01.552196Z" + "iopub.execute_input": "2024-02-09T00:11:52.483328Z", + "iopub.status.busy": "2024-02-09T00:11:52.483003Z", + "iopub.status.idle": "2024-02-09T00:11:52.971209Z", + "shell.execute_reply": "2024-02-09T00:11:52.970588Z" } }, "outputs": [], @@ -1014,10 +1014,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:24:01.555334Z", - "iopub.status.busy": "2024-02-08T14:24:01.555141Z", - "iopub.status.idle": "2024-02-08T14:24:01.632480Z", - "shell.execute_reply": "2024-02-08T14:24:01.631920Z" + "iopub.execute_input": "2024-02-09T00:11:52.973589Z", + "iopub.status.busy": "2024-02-09T00:11:52.973269Z", + "iopub.status.idle": "2024-02-09T00:11:53.049789Z", + "shell.execute_reply": "2024-02-09T00:11:53.049215Z" } }, "outputs": [ @@ -1055,10 +1055,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:24:01.634575Z", - "iopub.status.busy": "2024-02-08T14:24:01.634395Z", - "iopub.status.idle": "2024-02-08T14:24:01.644126Z", - "shell.execute_reply": "2024-02-08T14:24:01.643586Z" + "iopub.execute_input": "2024-02-09T00:11:53.052120Z", + "iopub.status.busy": "2024-02-09T00:11:53.051803Z", + "iopub.status.idle": "2024-02-09T00:11:53.061065Z", + "shell.execute_reply": "2024-02-09T00:11:53.060617Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index 6d37e6190..932d1d202 100644 --- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:24:04.325384Z", - "iopub.status.busy": "2024-02-08T14:24:04.325208Z", - "iopub.status.idle": "2024-02-08T14:24:06.264644Z", - "shell.execute_reply": "2024-02-08T14:24:06.263981Z" + "iopub.execute_input": "2024-02-09T00:11:55.706327Z", + "iopub.status.busy": "2024-02-09T00:11:55.705913Z", + "iopub.status.idle": "2024-02-09T00:11:58.409189Z", + "shell.execute_reply": "2024-02-09T00:11:58.408524Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:24:06.267222Z", - "iopub.status.busy": "2024-02-08T14:24:06.266849Z", - "iopub.status.idle": "2024-02-08T14:25:00.950515Z", - "shell.execute_reply": "2024-02-08T14:25:00.949868Z" + "iopub.execute_input": "2024-02-09T00:11:58.411644Z", + "iopub.status.busy": "2024-02-09T00:11:58.411453Z", + "iopub.status.idle": "2024-02-09T00:16:12.520745Z", + "shell.execute_reply": "2024-02-09T00:16:12.520095Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:25:00.953008Z", - "iopub.status.busy": "2024-02-08T14:25:00.952671Z", - "iopub.status.idle": "2024-02-08T14:25:01.985876Z", - "shell.execute_reply": "2024-02-08T14:25:01.985324Z" + "iopub.execute_input": "2024-02-09T00:16:12.523122Z", + "iopub.status.busy": "2024-02-09T00:16:12.522932Z", + "iopub.status.idle": "2024-02-09T00:16:13.561109Z", + "shell.execute_reply": "2024-02-09T00:16:13.560569Z" }, "nbsphinx": "hidden" }, @@ -111,7 +111,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -137,10 +137,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:25:01.988452Z", - "iopub.status.busy": "2024-02-08T14:25:01.987961Z", - "iopub.status.idle": "2024-02-08T14:25:01.991234Z", - "shell.execute_reply": "2024-02-08T14:25:01.990702Z" + "iopub.execute_input": "2024-02-09T00:16:13.563706Z", + "iopub.status.busy": "2024-02-09T00:16:13.563270Z", + "iopub.status.idle": "2024-02-09T00:16:13.566498Z", + "shell.execute_reply": "2024-02-09T00:16:13.566044Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:25:01.993300Z", - "iopub.status.busy": "2024-02-08T14:25:01.992908Z", - "iopub.status.idle": "2024-02-08T14:25:01.996930Z", - "shell.execute_reply": "2024-02-08T14:25:01.996404Z" + "iopub.execute_input": "2024-02-09T00:16:13.568560Z", + "iopub.status.busy": "2024-02-09T00:16:13.568238Z", + "iopub.status.idle": "2024-02-09T00:16:13.572059Z", + "shell.execute_reply": "2024-02-09T00:16:13.571602Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:25:01.999046Z", - "iopub.status.busy": "2024-02-08T14:25:01.998716Z", - "iopub.status.idle": "2024-02-08T14:25:02.002246Z", - "shell.execute_reply": "2024-02-08T14:25:02.001745Z" + "iopub.execute_input": "2024-02-09T00:16:13.573969Z", + "iopub.status.busy": "2024-02-09T00:16:13.573788Z", + "iopub.status.idle": "2024-02-09T00:16:13.577236Z", + "shell.execute_reply": "2024-02-09T00:16:13.576801Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:25:02.004444Z", - "iopub.status.busy": "2024-02-08T14:25:02.004125Z", - "iopub.status.idle": "2024-02-08T14:25:02.006724Z", - "shell.execute_reply": "2024-02-08T14:25:02.006292Z" + "iopub.execute_input": "2024-02-09T00:16:13.579148Z", + "iopub.status.busy": "2024-02-09T00:16:13.578896Z", + "iopub.status.idle": "2024-02-09T00:16:13.581549Z", + "shell.execute_reply": "2024-02-09T00:16:13.581134Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:25:02.008777Z", - "iopub.status.busy": "2024-02-08T14:25:02.008459Z", - "iopub.status.idle": "2024-02-08T14:26:17.950141Z", - "shell.execute_reply": "2024-02-08T14:26:17.949536Z" + "iopub.execute_input": "2024-02-09T00:16:13.583565Z", + "iopub.status.busy": "2024-02-09T00:16:13.583250Z", + "iopub.status.idle": "2024-02-09T00:17:29.534773Z", + "shell.execute_reply": "2024-02-09T00:17:29.534063Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f94cde7b85b8462293dd9990e3c52464", + "model_id": "b5fe880c9c944d13a507143c65c6969e", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7745ee901ff342c78d7afbc0c3fe2d5c", + "model_id": "436bcafa5cf8491c9ab4a7016809e293", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:26:17.952879Z", - "iopub.status.busy": "2024-02-08T14:26:17.952519Z", - "iopub.status.idle": "2024-02-08T14:26:18.607737Z", - "shell.execute_reply": "2024-02-08T14:26:18.607257Z" + "iopub.execute_input": "2024-02-09T00:17:29.537495Z", + "iopub.status.busy": "2024-02-09T00:17:29.537281Z", + "iopub.status.idle": "2024-02-09T00:17:30.196715Z", + "shell.execute_reply": "2024-02-09T00:17:30.196213Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:26:18.609963Z", - "iopub.status.busy": "2024-02-08T14:26:18.609520Z", - "iopub.status.idle": "2024-02-08T14:26:21.287502Z", - "shell.execute_reply": "2024-02-08T14:26:21.286905Z" + "iopub.execute_input": "2024-02-09T00:17:30.198853Z", + "iopub.status.busy": "2024-02-09T00:17:30.198533Z", + "iopub.status.idle": "2024-02-09T00:17:32.863728Z", + "shell.execute_reply": "2024-02-09T00:17:32.863158Z" } }, "outputs": [ @@ -519,10 +519,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:26:21.289696Z", - "iopub.status.busy": "2024-02-08T14:26:21.289392Z", - "iopub.status.idle": "2024-02-08T14:26:53.836268Z", - "shell.execute_reply": "2024-02-08T14:26:53.835780Z" + "iopub.execute_input": "2024-02-09T00:17:32.865723Z", + "iopub.status.busy": "2024-02-09T00:17:32.865543Z", + "iopub.status.idle": "2024-02-09T00:18:04.978574Z", + "shell.execute_reply": "2024-02-09T00:18:04.978087Z" } }, "outputs": [ @@ -539,7 +539,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 15453/4997817 [00:00<00:32, 154516.52it/s]" + " 0%| | 15587/4997817 [00:00<00:31, 155862.34it/s]" ] }, { @@ -547,7 +547,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 31025/4997817 [00:00<00:31, 155215.37it/s]" + " 1%| | 31247/4997817 [00:00<00:31, 156278.59it/s]" ] }, { @@ -555,7 +555,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 46565/4997817 [00:00<00:31, 155297.20it/s]" + " 1%| | 46886/4997817 [00:00<00:31, 156327.48it/s]" ] }, { @@ -563,7 +563,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 62143/4997817 [00:00<00:31, 155485.08it/s]" + " 1%|▏ | 62519/4997817 [00:00<00:32, 151825.80it/s]" ] }, { @@ -571,7 +571,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 77692/4997817 [00:00<00:31, 155225.21it/s]" + " 2%|▏ | 78064/4997817 [00:00<00:32, 153105.94it/s]" ] }, { @@ -579,7 +579,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 93215/4997817 [00:00<00:31, 154864.25it/s]" + " 2%|▏ | 93637/4997817 [00:00<00:31, 153983.79it/s]" ] }, { @@ -587,7 +587,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 108734/4997817 [00:00<00:31, 154969.22it/s]" + " 2%|▏ | 109311/4997817 [00:00<00:31, 154873.76it/s]" ] }, { @@ -595,7 +595,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 124232/4997817 [00:00<00:31, 154694.39it/s]" + " 2%|▏ | 124926/4997817 [00:00<00:31, 155276.59it/s]" ] }, { @@ -603,7 +603,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 139819/4997817 [00:00<00:31, 155058.79it/s]" + " 3%|▎ | 140521/4997817 [00:00<00:31, 155484.59it/s]" ] }, { @@ -611,7 +611,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 155359/4997817 [00:01<00:31, 155162.63it/s]" + " 3%|▎ | 156137/4997817 [00:01<00:31, 155690.63it/s]" ] }, { @@ -619,7 +619,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 170902/4997817 [00:01<00:31, 155243.06it/s]" + " 3%|▎ | 171824/4997817 [00:01<00:30, 156047.52it/s]" ] }, { @@ -627,7 +627,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▎ | 186427/4997817 [00:01<00:31, 155190.21it/s]" + " 4%|▍ | 187446/4997817 [00:01<00:30, 156096.57it/s]" ] }, { @@ -635,7 +635,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 201975/4997817 [00:01<00:30, 155276.41it/s]" + " 4%|▍ | 203084/4997817 [00:01<00:30, 156180.71it/s]" ] }, { @@ -643,7 +643,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 217591/4997817 [00:01<00:30, 155542.07it/s]" + " 4%|▍ | 218704/4997817 [00:01<00:30, 155874.61it/s]" ] }, { @@ -651,7 +651,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 233146/4997817 [00:01<00:30, 154998.56it/s]" + " 5%|▍ | 234338/4997817 [00:01<00:30, 156010.99it/s]" ] }, { @@ -659,7 +659,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 248647/4997817 [00:01<00:30, 154982.96it/s]" + " 5%|▌ | 250073/4997817 [00:01<00:30, 156412.31it/s]" ] }, { @@ -667,7 +667,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 264244/4997817 [00:01<00:30, 155277.77it/s]" + " 5%|▌ | 265770/4997817 [00:01<00:30, 156576.19it/s]" ] }, { @@ -675,7 +675,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 279773/4997817 [00:01<00:30, 155279.71it/s]" + " 6%|▌ | 281505/4997817 [00:01<00:30, 156804.67it/s]" ] }, { @@ -683,7 +683,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 295352/4997817 [00:01<00:30, 155430.31it/s]" + " 6%|▌ | 297194/4997817 [00:01<00:29, 156827.19it/s]" ] }, { @@ -691,7 +691,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 310896/4997817 [00:02<00:30, 155393.91it/s]" + " 6%|▋ | 312964/4997817 [00:02<00:29, 157087.40it/s]" ] }, { @@ -699,7 +699,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 326472/4997817 [00:02<00:30, 155500.21it/s]" + " 7%|▋ | 328819/4997817 [00:02<00:29, 157522.86it/s]" ] }, { @@ -707,7 +707,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 342210/4997817 [00:02<00:29, 156061.57it/s]" + " 7%|▋ | 344572/4997817 [00:02<00:29, 157417.61it/s]" ] }, { @@ -715,7 +715,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 357817/4997817 [00:02<00:29, 155940.57it/s]" + " 7%|▋ | 360354/4997817 [00:02<00:29, 157535.97it/s]" ] }, { @@ -723,7 +723,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 373511/4997817 [00:02<00:29, 156239.03it/s]" + " 8%|▊ | 376108/4997817 [00:02<00:29, 157370.22it/s]" ] }, { @@ -731,7 +731,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 389135/4997817 [00:02<00:30, 152838.94it/s]" + " 8%|▊ | 391846/4997817 [00:02<00:29, 157000.05it/s]" ] }, { @@ -739,7 +739,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 404605/4997817 [00:02<00:29, 153386.30it/s]" + " 8%|▊ | 407635/4997817 [00:02<00:29, 157262.76it/s]" ] }, { @@ -747,7 +747,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 419956/4997817 [00:02<00:29, 153175.78it/s]" + " 8%|▊ | 423362/4997817 [00:02<00:29, 156964.92it/s]" ] }, { @@ -755,7 +755,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▊ | 435378/4997817 [00:02<00:29, 153484.69it/s]" + " 9%|▉ | 439059/4997817 [00:02<00:29, 156774.87it/s]" ] }, { @@ -763,7 +763,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 450769/4997817 [00:02<00:29, 153610.00it/s]" + " 9%|▉ | 454737/4997817 [00:02<00:29, 156409.34it/s]" ] }, { @@ -771,7 +771,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 466192/4997817 [00:03<00:29, 153794.25it/s]" + " 9%|▉ | 470444/4997817 [00:03<00:28, 156602.77it/s]" ] }, { @@ -779,7 +779,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|▉ | 481575/4997817 [00:03<00:29, 153573.81it/s]" + " 10%|▉ | 486212/4997817 [00:03<00:28, 156922.73it/s]" ] }, { @@ -787,7 +787,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|▉ | 496938/4997817 [00:03<00:29, 153588.69it/s]" + " 10%|█ | 501905/4997817 [00:03<00:28, 156337.67it/s]" ] }, { @@ -795,7 +795,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 512299/4997817 [00:03<00:29, 153591.16it/s]" + " 10%|█ | 517540/4997817 [00:03<00:28, 156323.25it/s]" ] }, { @@ -803,7 +803,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 527959/4997817 [00:03<00:28, 154488.89it/s]" + " 11%|█ | 533196/4997817 [00:03<00:28, 156390.22it/s]" ] }, { @@ -811,7 +811,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 543588/4997817 [00:03<00:28, 155025.22it/s]" + " 11%|█ | 548903/4997817 [00:03<00:28, 156592.23it/s]" ] }, { @@ -819,7 +819,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 559092/4997817 [00:03<00:29, 149709.63it/s]" + " 11%|█▏ | 564601/4997817 [00:03<00:28, 156706.99it/s]" ] }, { @@ -827,7 +827,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 574751/4997817 [00:03<00:29, 151721.26it/s]" + " 12%|█▏ | 580277/4997817 [00:03<00:28, 156721.02it/s]" ] }, { @@ -835,7 +835,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 590241/4997817 [00:03<00:28, 152655.75it/s]" + " 12%|█▏ | 595950/4997817 [00:03<00:28, 156704.49it/s]" ] }, { @@ -843,7 +843,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 605871/4997817 [00:03<00:28, 153734.61it/s]" + " 12%|█▏ | 611722/4997817 [00:03<00:27, 157007.00it/s]" ] }, { @@ -851,7 +851,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 621582/4997817 [00:04<00:28, 154734.87it/s]" + " 13%|█▎ | 627423/4997817 [00:04<00:27, 156736.98it/s]" ] }, { @@ -859,7 +859,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 637225/4997817 [00:04<00:28, 155236.93it/s]" + " 13%|█▎ | 643136/4997817 [00:04<00:27, 156850.38it/s]" ] }, { @@ -867,7 +867,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 652929/4997817 [00:04<00:27, 155774.79it/s]" + " 13%|█▎ | 658822/4997817 [00:04<00:27, 156731.02it/s]" ] }, { @@ -875,7 +875,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 668514/4997817 [00:04<00:27, 155680.32it/s]" + " 13%|█▎ | 674520/4997817 [00:04<00:27, 156801.86it/s]" ] }, { @@ -883,7 +883,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▎ | 684105/4997817 [00:04<00:27, 155746.64it/s]" + " 14%|█▍ | 690201/4997817 [00:04<00:27, 156451.73it/s]" ] }, { @@ -891,7 +891,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 699696/4997817 [00:04<00:27, 155792.38it/s]" + " 14%|█▍ | 705868/4997817 [00:04<00:27, 156513.31it/s]" ] }, { @@ -899,7 +899,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 715278/4997817 [00:04<00:27, 155651.60it/s]" + " 14%|█▍ | 721572/4997817 [00:04<00:27, 156666.97it/s]" ] }, { @@ -907,7 +907,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 730845/4997817 [00:04<00:27, 154882.92it/s]" + " 15%|█▍ | 737239/4997817 [00:04<00:27, 156521.43it/s]" ] }, { @@ -915,7 +915,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 746461/4997817 [00:04<00:27, 155261.45it/s]" + " 15%|█▌ | 752946/4997817 [00:04<00:27, 156682.79it/s]" ] }, { @@ -923,7 +923,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 762124/4997817 [00:04<00:27, 155667.52it/s]" + " 15%|█▌ | 768687/4997817 [00:04<00:26, 156898.64it/s]" ] }, { @@ -931,7 +931,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 777861/4997817 [00:05<00:27, 156175.47it/s]" + " 16%|█▌ | 784568/4997817 [00:05<00:26, 157468.87it/s]" ] }, { @@ -939,7 +939,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 793610/4997817 [00:05<00:26, 156568.46it/s]" + " 16%|█▌ | 800444/4997817 [00:05<00:26, 157853.46it/s]" ] }, { @@ -947,7 +947,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 809410/4997817 [00:05<00:26, 156994.68it/s]" + " 16%|█▋ | 816237/4997817 [00:05<00:26, 157874.88it/s]" ] }, { @@ -955,7 +955,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 825117/4997817 [00:05<00:26, 157014.38it/s]" + " 17%|█▋ | 832128/4997817 [00:05<00:26, 158182.55it/s]" ] }, { @@ -963,7 +963,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 840872/4997817 [00:05<00:26, 157173.67it/s]" + " 17%|█▋ | 847947/4997817 [00:05<00:26, 157864.11it/s]" ] }, { @@ -971,7 +971,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 856681/4997817 [00:05<00:26, 157446.81it/s]" + " 17%|█▋ | 863734/4997817 [00:05<00:26, 157681.58it/s]" ] }, { @@ -979,7 +979,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 872426/4997817 [00:05<00:27, 152280.97it/s]" + " 18%|█▊ | 879503/4997817 [00:05<00:26, 157312.53it/s]" ] }, { @@ -987,7 +987,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 887885/4997817 [00:05<00:26, 152956.02it/s]" + " 18%|█▊ | 895235/4997817 [00:05<00:26, 156848.17it/s]" ] }, { @@ -995,7 +995,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 903350/4997817 [00:05<00:26, 153452.85it/s]" + " 18%|█▊ | 910921/4997817 [00:05<00:26, 156751.48it/s]" ] }, { @@ -1003,7 +1003,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 918902/4997817 [00:05<00:26, 154064.30it/s]" + " 19%|█▊ | 926597/4997817 [00:05<00:25, 156617.09it/s]" ] }, { @@ -1011,7 +1011,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▊ | 934453/4997817 [00:06<00:26, 154492.54it/s]" + " 19%|█▉ | 942385/4997817 [00:06<00:25, 156990.29it/s]" ] }, { @@ -1019,7 +1019,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 949976/4997817 [00:06<00:26, 154709.14it/s]" + " 19%|█▉ | 958148/4997817 [00:06<00:25, 157178.14it/s]" ] }, { @@ -1027,7 +1027,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 965479/4997817 [00:06<00:26, 154804.39it/s]" + " 19%|█▉ | 973881/4997817 [00:06<00:25, 157222.22it/s]" ] }, { @@ -1035,7 +1035,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 980967/4997817 [00:06<00:25, 154824.85it/s]" + " 20%|█▉ | 989673/4997817 [00:06<00:25, 157427.33it/s]" ] }, { @@ -1043,7 +1043,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 996519/4997817 [00:06<00:25, 155030.03it/s]" + " 20%|██ | 1005469/4997817 [00:06<00:25, 157584.24it/s]" ] }, { @@ -1051,7 +1051,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 1012029/4997817 [00:06<00:25, 155049.71it/s]" + " 20%|██ | 1021228/4997817 [00:06<00:25, 157399.00it/s]" ] }, { @@ -1059,7 +1059,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1027536/4997817 [00:06<00:26, 151315.26it/s]" + " 21%|██ | 1036969/4997817 [00:06<00:25, 157391.66it/s]" ] }, { @@ -1067,7 +1067,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1042689/4997817 [00:06<00:26, 149743.22it/s]" + " 21%|██ | 1052735/4997817 [00:06<00:25, 157468.09it/s]" ] }, { @@ -1075,7 +1075,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1058142/4997817 [00:06<00:26, 151147.20it/s]" + " 21%|██▏ | 1068482/4997817 [00:06<00:24, 157449.57it/s]" ] }, { @@ -1083,7 +1083,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██▏ | 1073486/4997817 [00:06<00:25, 151821.86it/s]" + " 22%|██▏ | 1084230/4997817 [00:06<00:24, 157456.60it/s]" ] }, { @@ -1091,7 +1091,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1089073/4997817 [00:07<00:25, 153021.80it/s]" + " 22%|██▏ | 1100100/4997817 [00:07<00:24, 157826.93it/s]" ] }, { @@ -1099,7 +1099,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1104725/4997817 [00:07<00:25, 154062.34it/s]" + " 22%|██▏ | 1115901/4997817 [00:07<00:24, 157878.83it/s]" ] }, { @@ -1107,7 +1107,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1120386/4997817 [00:07<00:25, 154820.51it/s]" + " 23%|██▎ | 1131689/4997817 [00:07<00:24, 157518.21it/s]" ] }, { @@ -1115,7 +1115,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1136079/4997817 [00:07<00:24, 155447.90it/s]" + " 23%|██▎ | 1147442/4997817 [00:07<00:24, 157322.29it/s]" ] }, { @@ -1123,7 +1123,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1151659/4997817 [00:07<00:24, 155551.21it/s]" + " 23%|██▎ | 1163213/4997817 [00:07<00:24, 157435.48it/s]" ] }, { @@ -1131,7 +1131,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1167343/4997817 [00:07<00:24, 155936.00it/s]" + " 24%|██▎ | 1179033/4997817 [00:07<00:24, 157661.50it/s]" ] }, { @@ -1139,7 +1139,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▎ | 1182942/4997817 [00:07<00:24, 155949.22it/s]" + " 24%|██▍ | 1194800/4997817 [00:07<00:24, 157468.21it/s]" ] }, { @@ -1147,7 +1147,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1198610/4997817 [00:07<00:24, 156165.63it/s]" + " 24%|██▍ | 1210626/4997817 [00:07<00:24, 157702.37it/s]" ] }, { @@ -1155,7 +1155,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1214228/4997817 [00:07<00:24, 156056.87it/s]" + " 25%|██▍ | 1226411/4997817 [00:07<00:23, 157744.17it/s]" ] }, { @@ -1163,7 +1163,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 1229891/4997817 [00:07<00:24, 156225.68it/s]" + " 25%|██▍ | 1242288/4997817 [00:07<00:23, 158049.57it/s]" ] }, { @@ -1171,7 +1171,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 1245515/4997817 [00:08<00:24, 156135.27it/s]" + " 25%|██▌ | 1258094/4997817 [00:08<00:23, 157760.66it/s]" ] }, { @@ -1179,7 +1179,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 1261129/4997817 [00:08<00:23, 156064.68it/s]" + " 25%|██▌ | 1273871/4997817 [00:08<00:23, 157612.58it/s]" ] }, { @@ -1187,7 +1187,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1276754/4997817 [00:08<00:23, 156118.04it/s]" + " 26%|██▌ | 1289633/4997817 [00:08<00:23, 157376.43it/s]" ] }, { @@ -1195,7 +1195,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1292428/4997817 [00:08<00:23, 156303.76it/s]" + " 26%|██▌ | 1305371/4997817 [00:08<00:23, 157120.80it/s]" ] }, { @@ -1203,7 +1203,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1308201/4997817 [00:08<00:23, 156730.13it/s]" + " 26%|██▋ | 1321084/4997817 [00:08<00:23, 156805.63it/s]" ] }, { @@ -1211,7 +1211,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▋ | 1323980/4997817 [00:08<00:23, 157046.57it/s]" + " 27%|██▋ | 1336765/4997817 [00:08<00:23, 156367.69it/s]" ] }, { @@ -1219,7 +1219,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1339758/4997817 [00:08<00:23, 157262.88it/s]" + " 27%|██▋ | 1352471/4997817 [00:08<00:23, 156552.79it/s]" ] }, { @@ -1227,7 +1227,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1355485/4997817 [00:08<00:23, 156797.70it/s]" + " 27%|██▋ | 1368265/4997817 [00:08<00:23, 156965.35it/s]" ] }, { @@ -1235,7 +1235,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1371166/4997817 [00:08<00:23, 156404.06it/s]" + " 28%|██▊ | 1384003/4997817 [00:08<00:23, 157086.82it/s]" ] }, { @@ -1243,7 +1243,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1386807/4997817 [00:08<00:23, 156266.74it/s]" + " 28%|██▊ | 1399743/4997817 [00:08<00:22, 157176.97it/s]" ] }, { @@ -1251,7 +1251,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1402444/4997817 [00:09<00:23, 156296.00it/s]" + " 28%|██▊ | 1415480/4997817 [00:09<00:22, 157231.15it/s]" ] }, { @@ -1259,7 +1259,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1418141/4997817 [00:09<00:22, 156497.04it/s]" + " 29%|██▊ | 1431205/4997817 [00:09<00:22, 157233.70it/s]" ] }, { @@ -1267,7 +1267,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▊ | 1433937/4997817 [00:09<00:22, 156934.55it/s]" + " 29%|██▉ | 1446929/4997817 [00:09<00:22, 157070.02it/s]" ] }, { @@ -1275,7 +1275,7 @@ "output_type": "stream", "text": [ "\r", - " 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156508.90it/s]" + " 31%|███ | 1541585/4997817 [00:09<00:22, 156987.65it/s]" ] }, { @@ -1323,7 +1323,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1544082/4997817 [00:09<00:22, 156843.13it/s]" + " 31%|███ | 1557287/4997817 [00:09<00:21, 156994.80it/s]" ] }, { @@ -1331,7 +1331,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1559788/4997817 [00:10<00:21, 156906.10it/s]" + " 31%|███▏ | 1572987/4997817 [00:10<00:21, 156278.25it/s]" ] }, { @@ -1339,7 +1339,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1575495/4997817 [00:10<00:21, 156952.78it/s]" + " 32%|███▏ | 1588684/4997817 [00:10<00:21, 156480.80it/s]" ] }, { @@ -1347,7 +1347,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1591191/4997817 [00:10<00:21, 156953.42it/s]" + " 32%|███▏ | 1604333/4997817 [00:10<00:21, 156413.89it/s]" ] }, { @@ -1355,7 +1355,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1606887/4997817 [00:10<00:21, 156861.14it/s]" + " 32%|███▏ | 1620080/4997817 [00:10<00:21, 156726.97it/s]" ] }, { @@ -1363,7 +1363,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1622574/4997817 [00:10<00:21, 156859.47it/s]" + " 33%|███▎ | 1635754/4997817 [00:10<00:21, 156711.45it/s]" ] }, { @@ -1371,7 +1371,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1638261/4997817 [00:10<00:21, 156646.46it/s]" + " 33%|███▎ | 1651426/4997817 [00:10<00:21, 156344.28it/s]" ] }, { @@ -1379,7 +1379,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1653926/4997817 [00:10<00:21, 156399.57it/s]" + " 33%|███▎ | 1667099/4997817 [00:10<00:21, 156458.18it/s]" ] }, { @@ -1387,7 +1387,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1669567/4997817 [00:10<00:21, 155713.44it/s]" + " 34%|███▎ | 1682955/4997817 [00:10<00:21, 157086.07it/s]" ] }, { @@ -1395,7 +1395,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▎ | 1685155/4997817 [00:10<00:21, 155761.78it/s]" + " 34%|███▍ | 1698732/4997817 [00:10<00:20, 157288.56it/s]" ] }, { @@ -1403,7 +1403,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1700875/4997817 [00:10<00:21, 156189.91it/s]" + " 34%|███▍ | 1714506/4997817 [00:10<00:20, 157422.90it/s]" ] }, { @@ -1411,7 +1411,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1716710/4997817 [00:11<00:20, 156833.16it/s]" + " 35%|███▍ | 1730335/4997817 [00:11<00:20, 157681.56it/s]" ] }, { @@ -1419,7 +1419,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 1732465/4997817 [00:11<00:20, 157046.12it/s]" + " 35%|███▍ | 1746275/4997817 [00:11<00:20, 158193.85it/s]" ] }, { @@ -1427,7 +1427,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 1748170/4997817 [00:11<00:20, 156751.34it/s]" + " 35%|███▌ | 1762095/4997817 [00:11<00:20, 158154.92it/s]" ] }, { @@ -1435,7 +1435,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 1763846/4997817 [00:11<00:20, 156341.54it/s]" + " 36%|███▌ | 1777926/4997817 [00:11<00:20, 158199.09it/s]" ] }, { @@ -1443,7 +1443,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1779481/4997817 [00:11<00:20, 155660.22it/s]" + " 36%|███▌ | 1793800/4997817 [00:11<00:20, 158357.63it/s]" ] }, { @@ -1451,7 +1451,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1795048/4997817 [00:11<00:20, 155306.10it/s]" + " 36%|███▌ | 1809636/4997817 [00:11<00:20, 158190.96it/s]" ] }, { @@ -1459,7 +1459,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1810580/4997817 [00:11<00:20, 155202.42it/s]" + " 37%|███▋ | 1825456/4997817 [00:11<00:20, 157679.67it/s]" ] }, { @@ -1467,7 +1467,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1826101/4997817 [00:11<00:20, 154937.79it/s]" + " 37%|███▋ | 1841225/4997817 [00:11<00:20, 157608.17it/s]" ] }, { @@ -1475,7 +1475,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1841596/4997817 [00:11<00:21, 147386.04it/s]" + " 37%|███▋ | 1857016/4997817 [00:11<00:19, 157696.41it/s]" ] }, { @@ -1483,7 +1483,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1857017/4997817 [00:11<00:21, 149355.03it/s]" + " 37%|███▋ | 1872927/4997817 [00:11<00:19, 158118.36it/s]" ] }, { @@ -1491,7 +1491,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1872515/4997817 [00:12<00:20, 150994.90it/s]" + " 38%|███▊ | 1888788/4997817 [00:12<00:19, 158263.58it/s]" ] }, { @@ -1499,7 +1499,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1887933/4997817 [00:12<00:20, 151929.03it/s]" + " 38%|███▊ | 1904615/4997817 [00:12<00:19, 157950.61it/s]" ] }, { @@ -1507,7 +1507,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1903381/4997817 [00:12<00:20, 152683.49it/s]" + " 38%|███▊ | 1920411/4997817 [00:12<00:19, 157870.86it/s]" ] }, { @@ -1515,7 +1515,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1918786/4997817 [00:12<00:20, 153087.07it/s]" + " 39%|███▊ | 1936221/4997817 [00:12<00:19, 157937.16it/s]" ] }, { @@ -1523,7 +1523,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▊ | 1934310/4997817 [00:12<00:19, 153727.24it/s]" + " 39%|███▉ | 1952030/4997817 [00:12<00:19, 157982.10it/s]" ] }, { @@ -1531,7 +1531,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1949834/4997817 [00:12<00:19, 154176.84it/s]" + " 39%|███▉ | 1967829/4997817 [00:12<00:19, 157336.75it/s]" ] }, { @@ -1539,7 +1539,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1965261/4997817 [00:12<00:19, 153979.69it/s]" + " 40%|███▉ | 1983564/4997817 [00:12<00:19, 157209.88it/s]" ] }, { @@ -1547,7 +1547,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 1980728/4997817 [00:12<00:19, 154184.83it/s]" + " 40%|████ | 1999340/4997817 [00:12<00:19, 157372.44it/s]" ] }, { @@ -1555,7 +1555,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 1996152/4997817 [00:12<00:19, 154181.91it/s]" + " 40%|████ | 2015193/4997817 [00:12<00:18, 157715.86it/s]" ] }, { @@ -1563,7 +1563,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 2011574/4997817 [00:12<00:19, 154133.60it/s]" + " 41%|████ | 2030992/4997817 [00:12<00:18, 157793.07it/s]" ] }, { @@ -1571,7 +1571,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2027070/4997817 [00:13<00:19, 154378.23it/s]" + " 41%|████ | 2046772/4997817 [00:13<00:18, 157519.57it/s]" ] }, { @@ -1579,7 +1579,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2042510/4997817 [00:13<00:19, 154258.26it/s]" + " 41%|████▏ | 2062543/4997817 [00:13<00:18, 157571.79it/s]" ] }, { @@ -1587,7 +1587,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2058008/4997817 [00:13<00:19, 154472.01it/s]" + " 42%|████▏ | 2078301/4997817 [00:13<00:18, 157377.47it/s]" ] }, { @@ -1595,7 +1595,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 2073482/4997817 [00:13<00:18, 154548.95it/s]" + " 42%|████▏ | 2094073/4997817 [00:13<00:18, 157477.39it/s]" ] }, { @@ -1603,7 +1603,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2088938/4997817 [00:13<00:18, 153521.68it/s]" + " 42%|████▏ | 2109884/4997817 [00:13<00:18, 157664.15it/s]" ] }, { @@ -1611,7 +1611,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2104383/4997817 [00:13<00:18, 153797.48it/s]" + " 43%|████▎ | 2125651/4997817 [00:13<00:18, 157551.79it/s]" ] }, { @@ -1619,7 +1619,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2119882/4997817 [00:13<00:18, 154152.23it/s]" + " 43%|████▎ | 2141407/4997817 [00:13<00:18, 157216.88it/s]" ] }, { @@ -1627,7 +1627,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2135299/4997817 [00:13<00:18, 154103.94it/s]" + " 43%|████▎ | 2157129/4997817 [00:13<00:18, 156618.92it/s]" ] }, { @@ -1635,7 +1635,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2150711/4997817 [00:13<00:19, 146362.48it/s]" + " 43%|████▎ | 2172834/4997817 [00:13<00:18, 156745.72it/s]" ] }, { @@ -1643,7 +1643,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2165992/4997817 [00:13<00:19, 148222.75it/s]" + " 44%|████▍ | 2188523/4997817 [00:13<00:17, 156787.80it/s]" ] }, { @@ -1651,7 +1651,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▎ | 2181411/4997817 [00:14<00:18, 149963.83it/s]" + " 44%|████▍ | 2204228/4997817 [00:14<00:17, 156863.54it/s]" ] }, { @@ -1659,7 +1659,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2196843/4997817 [00:14<00:18, 151244.25it/s]" + " 44%|████▍ | 2219915/4997817 [00:14<00:17, 156651.27it/s]" ] }, { @@ -1667,7 +1667,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2212218/4997817 [00:14<00:18, 151984.27it/s]" + " 45%|████▍ | 2235581/4997817 [00:14<00:17, 156458.37it/s]" ] }, { @@ -1675,7 +1675,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 2227577/4997817 [00:14<00:18, 152459.76it/s]" + " 45%|████▌ | 2251327/4997817 [00:14<00:17, 156754.31it/s]" ] }, { @@ -1683,7 +1683,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 2242916/4997817 [00:14<00:18, 152736.53it/s]" + " 45%|████▌ | 2267034/4997817 [00:14<00:17, 156844.20it/s]" ] }, { @@ -1691,7 +1691,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 2258281/4997817 [00:14<00:17, 153007.96it/s]" + " 46%|████▌ | 2282719/4997817 [00:14<00:17, 156334.00it/s]" ] }, { @@ -1699,7 +1699,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 2273592/4997817 [00:14<00:17, 152735.13it/s]" + " 46%|████▌ | 2298416/4997817 [00:14<00:17, 156522.24it/s]" ] }, { @@ -1707,7 +1707,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2288928/4997817 [00:14<00:17, 152919.34it/s]" + " 46%|████▋ | 2314113/4997817 [00:14<00:17, 156652.97it/s]" ] }, { @@ -1715,7 +1715,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2304260/4997817 [00:14<00:17, 153038.29it/s]" + " 47%|████▋ | 2329932/4997817 [00:14<00:16, 157111.06it/s]" ] }, { @@ -1723,7 +1723,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▋ | 2319568/4997817 [00:15<00:17, 152361.58it/s]" + " 47%|████▋ | 2345800/4997817 [00:14<00:16, 157577.78it/s]" ] }, { @@ -1731,7 +1731,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2335242/4997817 [00:15<00:17, 153668.16it/s]" + " 47%|████▋ | 2361559/4997817 [00:15<00:16, 157303.13it/s]" ] }, { @@ -1739,7 +1739,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2350813/4997817 [00:15<00:17, 154275.45it/s]" + " 48%|████▊ | 2377374/4997817 [00:15<00:16, 157554.08it/s]" ] }, { @@ -1747,7 +1747,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2366638/4997817 [00:15<00:16, 155464.59it/s]" + " 48%|████▊ | 2393130/4997817 [00:15<00:16, 157384.90it/s]" ] }, { @@ -1755,7 +1755,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2382187/4997817 [00:15<00:16, 155253.71it/s]" + " 48%|████▊ | 2409052/4997817 [00:15<00:16, 157932.54it/s]" ] }, { @@ -1763,7 +1763,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2398009/4997817 [00:15<00:16, 156141.03it/s]" + " 49%|████▊ | 2424895/4997817 [00:15<00:16, 158080.18it/s]" ] }, { @@ -1771,7 +1771,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2413776/4997817 [00:15<00:16, 156598.21it/s]" + " 49%|████▉ | 2440709/4997817 [00:15<00:16, 158095.22it/s]" ] }, { @@ -1779,7 +1779,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▊ | 2429486/4997817 [00:15<00:16, 156746.96it/s]" + " 49%|████▉ | 2456579/4997817 [00:15<00:16, 158275.12it/s]" ] }, { @@ -1787,7 +1787,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2445162/4997817 [00:15<00:16, 156741.95it/s]" + " 49%|████▉ | 2472409/4997817 [00:15<00:15, 158280.59it/s]" ] }, { @@ -1795,7 +1795,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2460837/4997817 [00:15<00:16, 156646.65it/s]" + " 50%|████▉ | 2488309/4997817 [00:15<00:15, 158492.99it/s]" ] }, { @@ -1803,7 +1803,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|████▉ | 2476522/4997817 [00:16<00:16, 156704.63it/s]" + " 50%|█████ | 2504159/4997817 [00:15<00:15, 158189.60it/s]" ] }, { @@ -1811,7 +1811,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|████▉ | 2492202/4997817 [00:16<00:15, 156729.90it/s]" + " 50%|█████ | 2519979/4997817 [00:16<00:15, 158143.52it/s]" ] }, { @@ -1819,7 +1819,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|█████ | 2507908/4997817 [00:16<00:15, 156826.69it/s]" + " 51%|█████ | 2535797/4997817 [00:16<00:15, 158153.03it/s]" ] }, { @@ -1827,7 +1827,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|█████ | 2523591/4997817 [00:16<00:15, 156803.29it/s]" + " 51%|█████ | 2551613/4997817 [00:16<00:15, 158116.86it/s]" ] }, { @@ -1835,7 +1835,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2539281/4997817 [00:16<00:15, 156828.97it/s]" + " 51%|█████▏ | 2567425/4997817 [00:16<00:15, 158039.46it/s]" ] }, { @@ -1843,7 +1843,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2554979/4997817 [00:16<00:15, 156873.53it/s]" + " 52%|█████▏ | 2583230/4997817 [00:16<00:15, 158040.07it/s]" ] }, { @@ -1851,7 +1851,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████▏ | 2570674/4997817 [00:16<00:15, 156895.05it/s]" + " 52%|█████▏ | 2599042/4997817 [00:16<00:15, 158061.14it/s]" ] }, { @@ -1859,7 +1859,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2586364/4997817 [00:16<00:15, 156771.57it/s]" + " 52%|█████▏ | 2614849/4997817 [00:16<00:15, 157782.62it/s]" ] }, { @@ -1867,7 +1867,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2602045/4997817 [00:16<00:15, 156781.68it/s]" + " 53%|█████▎ | 2630628/4997817 [00:16<00:15, 157661.82it/s]" ] }, { @@ -1875,7 +1875,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2617790/4997817 [00:16<00:15, 156979.88it/s]" + " 53%|█████▎ | 2646395/4997817 [00:16<00:14, 157613.22it/s]" ] }, { @@ -1883,7 +1883,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2633489/4997817 [00:17<00:15, 149239.21it/s]" + " 53%|█████▎ | 2662167/4997817 [00:16<00:14, 157641.54it/s]" ] }, { @@ -1891,7 +1891,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2649075/4997817 [00:17<00:15, 151153.23it/s]" + " 54%|█████▎ | 2677932/4997817 [00:17<00:14, 157610.33it/s]" ] }, { @@ -1899,7 +1899,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2664773/4997817 [00:17<00:15, 152856.17it/s]" + " 54%|█████▍ | 2693694/4997817 [00:17<00:14, 157542.23it/s]" ] }, { @@ -1907,7 +1907,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▎ | 2680434/4997817 [00:17<00:15, 153959.06it/s]" + " 54%|█████▍ | 2709452/4997817 [00:17<00:14, 157550.33it/s]" ] }, { @@ -1915,7 +1915,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2696089/4997817 [00:17<00:14, 154725.32it/s]" + " 55%|█████▍ | 2725308/4997817 [00:17<00:14, 157851.95it/s]" ] }, { @@ -1923,7 +1923,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2711706/4997817 [00:17<00:14, 155151.68it/s]" + " 55%|█████▍ | 2741117/4997817 [00:17<00:14, 157922.03it/s]" ] }, { @@ -1931,7 +1931,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 2727415/4997817 [00:17<00:14, 155727.99it/s]" + " 55%|█████▌ | 2756910/4997817 [00:17<00:14, 157844.78it/s]" ] }, { @@ -1939,7 +1939,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 2743027/4997817 [00:17<00:14, 155843.02it/s]" + " 55%|█████▌ | 2772695/4997817 [00:17<00:14, 157367.29it/s]" ] }, { @@ -1947,7 +1947,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▌ | 2758621/4997817 [00:17<00:14, 155672.15it/s]" + " 56%|█████▌ | 2788531/4997817 [00:17<00:14, 157663.15it/s]" ] }, { @@ -1955,7 +1955,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2774251/4997817 [00:17<00:14, 155856.87it/s]" + " 56%|█████▌ | 2804298/4997817 [00:17<00:13, 157655.34it/s]" ] }, { @@ -1963,7 +1963,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2789842/4997817 [00:18<00:14, 155664.72it/s]" + " 56%|█████▋ | 2820067/4997817 [00:17<00:13, 157662.73it/s]" ] }, { @@ -1971,7 +1971,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2805412/4997817 [00:18<00:14, 155552.46it/s]" + " 57%|█████▋ | 2835834/4997817 [00:18<00:13, 157565.64it/s]" ] }, { @@ -1979,7 +1979,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▋ | 2821032/4997817 [00:18<00:13, 155743.88it/s]" + " 57%|█████▋ | 2851591/4997817 [00:18<00:13, 157515.79it/s]" ] }, { @@ -1987,7 +1987,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2836692/4997817 [00:18<00:13, 155997.68it/s]" + " 57%|█████▋ | 2867343/4997817 [00:18<00:13, 157372.93it/s]" ] }, { @@ -1995,7 +1995,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2852360/4997817 [00:18<00:13, 156200.63it/s]" + " 58%|█████▊ | 2883104/4997817 [00:18<00:13, 157440.18it/s]" ] }, { @@ -2003,7 +2003,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2867995/4997817 [00:18<00:13, 156244.67it/s]" + " 58%|█████▊ | 2898849/4997817 [00:18<00:13, 157340.63it/s]" ] }, { @@ -2011,7 +2011,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2883678/4997817 [00:18<00:13, 156417.08it/s]" + " 58%|█████▊ | 2914589/4997817 [00:18<00:13, 157356.33it/s]" ] }, { @@ -2019,7 +2019,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2899321/4997817 [00:18<00:13, 156065.06it/s]" + " 59%|█████▊ | 2930325/4997817 [00:18<00:13, 157008.50it/s]" ] }, { @@ -2027,7 +2027,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2914928/4997817 [00:18<00:13, 155936.11it/s]" + " 59%|█████▉ | 2946027/4997817 [00:18<00:13, 156827.18it/s]" ] }, { @@ -2035,7 +2035,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▊ | 2930522/4997817 [00:18<00:13, 155694.35it/s]" + " 59%|█████▉ | 2961710/4997817 [00:18<00:12, 156774.03it/s]" ] }, { @@ -2043,7 +2043,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2946092/4997817 [00:19<00:13, 151006.40it/s]" + " 60%|█████▉ | 2977393/4997817 [00:18<00:12, 156787.03it/s]" ] }, { @@ -2051,7 +2051,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2961387/4997817 [00:19<00:13, 151573.39it/s]" + " 60%|█████▉ | 2993072/4997817 [00:19<00:12, 156785.50it/s]" ] }, { @@ -2059,7 +2059,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|█████▉ | 2976741/4997817 [00:19<00:13, 152153.11it/s]" + " 60%|██████ | 3008826/4997817 [00:19<00:12, 157010.11it/s]" ] }, { @@ -2067,7 +2067,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|█████▉ | 2992142/4997817 [00:19<00:13, 152700.31it/s]" + " 61%|██████ | 3024612/4997817 [00:19<00:12, 157262.71it/s]" ] }, { @@ -2075,7 +2075,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 3007577/4997817 [00:19<00:12, 153188.69it/s]" + " 61%|██████ | 3040423/4997817 [00:19<00:12, 157513.33it/s]" ] }, { @@ -2083,7 +2083,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 3022905/4997817 [00:19<00:12, 152563.41it/s]" + " 61%|██████ | 3056232/4997817 [00:19<00:12, 157683.16it/s]" ] }, { @@ -2091,7 +2091,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3038300/4997817 [00:19<00:12, 152973.07it/s]" + " 61%|██████▏ | 3072001/4997817 [00:19<00:12, 157564.81it/s]" ] }, { @@ -2099,7 +2099,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3053603/4997817 [00:19<00:12, 152920.78it/s]" + " 62%|██████▏ | 3087758/4997817 [00:19<00:12, 154988.29it/s]" ] }, { @@ -2107,7 +2107,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████▏ | 3069096/4997817 [00:19<00:12, 153517.73it/s]" + " 62%|██████▏ | 3103621/4997817 [00:19<00:12, 156066.74it/s]" ] }, { @@ -2115,7 +2115,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3084573/4997817 [00:19<00:12, 153890.60it/s]" + " 62%|██████▏ | 3119427/4997817 [00:19<00:11, 156657.83it/s]" ] }, { @@ -2123,7 +2123,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3100000/4997817 [00:20<00:12, 154000.73it/s]" + " 63%|██████▎ | 3135150/4997817 [00:19<00:11, 156826.72it/s]" ] }, { @@ -2131,7 +2131,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3115402/4997817 [00:20<00:12, 149191.61it/s]" + " 63%|██████▎ | 3150837/4997817 [00:20<00:11, 156803.62it/s]" ] }, { @@ -2139,7 +2139,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3130822/4997817 [00:20<00:12, 150659.08it/s]" + " 63%|██████▎ | 3166569/4997817 [00:20<00:11, 156954.30it/s]" ] }, { @@ -2147,7 +2147,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3146133/4997817 [00:20<00:12, 151379.07it/s]" + " 64%|██████▎ | 3182309/4997817 [00:20<00:11, 157086.53it/s]" ] }, { @@ -2155,7 +2155,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3161445/4997817 [00:20<00:12, 151891.88it/s]" + " 64%|██████▍ | 3198111/4997817 [00:20<00:11, 157363.04it/s]" ] }, { @@ -2163,7 +2163,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▎ | 3176831/4997817 [00:20<00:11, 152475.99it/s]" + " 64%|██████▍ | 3213937/4997817 [00:20<00:11, 157630.19it/s]" ] }, { @@ -2171,7 +2171,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3192250/4997817 [00:20<00:11, 152986.03it/s]" + " 65%|██████▍ | 3229701/4997817 [00:20<00:11, 157550.41it/s]" ] }, { @@ -2179,7 +2179,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3207636/4997817 [00:20<00:11, 153246.35it/s]" + " 65%|██████▍ | 3245457/4997817 [00:20<00:11, 156986.24it/s]" ] }, { @@ -2187,7 +2187,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3223036/4997817 [00:20<00:11, 153470.77it/s]" + " 65%|██████▌ | 3261157/4997817 [00:20<00:11, 156844.80it/s]" ] }, { @@ -2195,7 +2195,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▍ | 3238433/4997817 [00:20<00:11, 153619.52it/s]" + " 66%|██████▌ | 3276901/4997817 [00:20<00:10, 157020.32it/s]" ] }, { @@ -2203,7 +2203,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 3253892/4997817 [00:21<00:11, 153907.01it/s]" + " 66%|██████▌ | 3292688/4997817 [00:20<00:10, 157271.61it/s]" ] }, { @@ -2211,7 +2211,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 3269285/4997817 [00:21<00:11, 153551.97it/s]" + " 66%|██████▌ | 3308436/4997817 [00:21<00:10, 157333.30it/s]" ] }, { @@ -2219,7 +2219,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3284672/4997817 [00:21<00:11, 153641.17it/s]" + " 67%|██████▋ | 3324226/4997817 [00:21<00:10, 157500.37it/s]" ] }, { @@ -2227,7 +2227,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3300038/4997817 [00:21<00:11, 153524.07it/s]" + " 67%|██████▋ | 3339980/4997817 [00:21<00:10, 157509.03it/s]" ] }, { @@ -2235,7 +2235,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▋ | 3315392/4997817 [00:21<00:10, 153399.75it/s]" + " 67%|██████▋ | 3355732/4997817 [00:21<00:10, 157219.58it/s]" ] }, { @@ -2243,7 +2243,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3330815/4997817 [00:21<00:10, 153647.65it/s]" + " 67%|██████▋ | 3371455/4997817 [00:21<00:10, 157029.43it/s]" ] }, { @@ -2251,7 +2251,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3346284/4997817 [00:21<00:10, 153957.15it/s]" + " 68%|██████▊ | 3387159/4997817 [00:21<00:10, 156730.91it/s]" ] }, { @@ -2259,7 +2259,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3361681/4997817 [00:21<00:10, 153748.11it/s]" + " 68%|██████▊ | 3402833/4997817 [00:21<00:10, 152851.48it/s]" ] }, { @@ -2267,7 +2267,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3377171/4997817 [00:21<00:10, 154075.46it/s]" + " 68%|██████▊ | 3418461/4997817 [00:21<00:10, 153856.47it/s]" ] }, { @@ -2275,7 +2275,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3392597/4997817 [00:21<00:10, 154128.93it/s]" + " 69%|██████▊ | 3434044/4997817 [00:21<00:10, 154438.41it/s]" ] }, { @@ -2283,7 +2283,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3408011/4997817 [00:22<00:10, 154046.64it/s]" + " 69%|██████▉ | 3449699/4997817 [00:21<00:09, 155062.44it/s]" ] }, { @@ -2291,7 +2291,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3423416/4997817 [00:22<00:10, 153737.79it/s]" + " 69%|██████▉ | 3465354/4997817 [00:22<00:09, 155502.09it/s]" ] }, { @@ -2299,7 +2299,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3438822/4997817 [00:22<00:10, 153826.72it/s]" + " 70%|██████▉ | 3480911/4997817 [00:22<00:09, 155409.46it/s]" ] }, { @@ -2307,7 +2307,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3454286/4997817 [00:22<00:10, 154066.86it/s]" + " 70%|██████▉ | 3496571/4997817 [00:22<00:09, 155762.29it/s]" ] }, { @@ -2315,7 +2315,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3469693/4997817 [00:22<00:09, 153905.32it/s]" + " 70%|███████ | 3512151/4997817 [00:22<00:09, 155564.45it/s]" ] }, { @@ -2323,7 +2323,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|██████▉ | 3485084/4997817 [00:22<00:09, 153886.10it/s]" + " 71%|███████ | 3527750/4997817 [00:22<00:09, 155685.24it/s]" ] }, { @@ -2331,7 +2331,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3500473/4997817 [00:22<00:09, 153691.75it/s]" + " 71%|███████ | 3543419/4997817 [00:22<00:09, 155984.55it/s]" ] }, { @@ -2339,7 +2339,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3515848/4997817 [00:22<00:09, 153703.21it/s]" + " 71%|███████ | 3559019/4997817 [00:22<00:09, 155726.39it/s]" ] }, { @@ -2347,7 +2347,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3531294/4997817 [00:22<00:09, 153928.03it/s]" + " 72%|███████▏ | 3574719/4997817 [00:22<00:09, 156094.10it/s]" ] }, { @@ -2355,7 +2355,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3546733/4997817 [00:22<00:09, 154063.14it/s]" + " 72%|███████▏ | 3590367/4997817 [00:22<00:09, 156205.83it/s]" ] }, { @@ -2363,7 +2363,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████▏ | 3562200/4997817 [00:23<00:09, 154242.81it/s]" + " 72%|███████▏ | 3606131/4997817 [00:22<00:08, 156633.40it/s]" ] }, { @@ -2371,7 +2371,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3577625/4997817 [00:23<00:09, 153646.01it/s]" + " 72%|███████▏ | 3621795/4997817 [00:23<00:08, 156270.66it/s]" ] }, { @@ -2379,7 +2379,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3593024/4997817 [00:23<00:09, 153746.53it/s]" + " 73%|███████▎ | 3637423/4997817 [00:23<00:08, 156270.16it/s]" ] }, { @@ -2387,7 +2387,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3608430/4997817 [00:23<00:09, 153837.67it/s]" + " 73%|███████▎ | 3653356/4997817 [00:23<00:08, 157184.82it/s]" ] }, { @@ -2395,7 +2395,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3623815/4997817 [00:23<00:08, 153817.02it/s]" + " 73%|███████▎ | 3669201/4997817 [00:23<00:08, 157561.76it/s]" ] }, { @@ -2403,7 +2403,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3639197/4997817 [00:23<00:08, 153776.43it/s]" + " 74%|███████▎ | 3685042/4997817 [00:23<00:08, 157813.14it/s]" ] }, { @@ -2411,7 +2411,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3654657/4997817 [00:23<00:08, 154019.66it/s]" + " 74%|███████▍ | 3700824/4997817 [00:23<00:08, 157732.26it/s]" ] }, { @@ -2419,7 +2419,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3670060/4997817 [00:23<00:08, 153980.65it/s]" + " 74%|███████▍ | 3716598/4997817 [00:23<00:08, 157596.42it/s]" ] }, { @@ -2427,7 +2427,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▎ | 3685461/4997817 [00:23<00:08, 153987.18it/s]" + " 75%|███████▍ | 3732437/4997817 [00:23<00:08, 157831.67it/s]" ] }, { @@ -2435,7 +2435,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3700941/4997817 [00:23<00:08, 154228.64it/s]" + " 75%|███████▍ | 3748235/4997817 [00:23<00:07, 157874.83it/s]" ] }, { @@ -2443,7 +2443,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3716364/4997817 [00:24<00:08, 154025.79it/s]" + " 75%|███████▌ | 3764046/4997817 [00:23<00:07, 157943.12it/s]" ] }, { @@ -2451,7 +2451,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 3731767/4997817 [00:24<00:08, 153982.98it/s]" + " 76%|███████▌ | 3779841/4997817 [00:24<00:07, 157812.88it/s]" ] }, { @@ -2459,7 +2459,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 3747166/4997817 [00:24<00:08, 153765.28it/s]" + " 76%|███████▌ | 3795623/4997817 [00:24<00:07, 157797.13it/s]" ] }, { @@ -2467,7 +2467,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▌ | 3762545/4997817 [00:24<00:08, 153769.76it/s]" + " 76%|███████▋ | 3811403/4997817 [00:24<00:07, 157445.57it/s]" ] }, { @@ -2475,7 +2475,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3777923/4997817 [00:24<00:07, 153676.16it/s]" + " 77%|███████▋ | 3827181/4997817 [00:24<00:07, 157543.60it/s]" ] }, { @@ -2483,7 +2483,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3793299/4997817 [00:24<00:07, 153700.33it/s]" + " 77%|███████▋ | 3843001/4997817 [00:24<00:07, 157739.61it/s]" ] }, { @@ -2491,7 +2491,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3808757/4997817 [00:24<00:07, 153960.30it/s]" + " 77%|███████▋ | 3858776/4997817 [00:24<00:07, 157696.57it/s]" ] }, { @@ -2499,7 +2499,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3824154/4997817 [00:24<00:07, 153935.96it/s]" + " 78%|███████▊ | 3874546/4997817 [00:24<00:07, 155257.03it/s]" ] }, { @@ -2507,7 +2507,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3839548/4997817 [00:24<00:07, 153894.42it/s]" + " 78%|███████▊ | 3890428/4997817 [00:24<00:07, 156312.33it/s]" ] }, { @@ -2515,7 +2515,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3854978/4997817 [00:24<00:07, 154012.54it/s]" + " 78%|███████▊ | 3906076/4997817 [00:24<00:06, 156360.60it/s]" ] }, { @@ -2523,7 +2523,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3870510/4997817 [00:25<00:07, 154401.67it/s]" + " 78%|███████▊ | 3921718/4997817 [00:24<00:06, 156246.80it/s]" ] }, { @@ -2531,7 +2531,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3885951/4997817 [00:25<00:07, 154259.50it/s]" + " 79%|███████▉ | 3937352/4997817 [00:25<00:06, 156273.01it/s]" ] }, { @@ -2539,7 +2539,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3901377/4997817 [00:25<00:07, 153432.08it/s]" + " 79%|███████▉ | 3952984/4997817 [00:25<00:06, 156283.76it/s]" ] }, { @@ -2547,7 +2547,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3916722/4997817 [00:25<00:07, 153206.94it/s]" + " 79%|███████▉ | 3968615/4997817 [00:25<00:06, 155948.00it/s]" ] }, { @@ -2555,7 +2555,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▊ | 3932107/4997817 [00:25<00:06, 153394.08it/s]" + " 80%|███████▉ | 3984245/4997817 [00:25<00:06, 156050.08it/s]" ] }, { @@ -2563,7 +2563,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3947647/4997817 [00:25<00:06, 153992.29it/s]" + " 80%|████████ | 3999851/4997817 [00:25<00:06, 155967.61it/s]" ] }, { @@ -2571,7 +2571,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3963242/4997817 [00:25<00:06, 154577.23it/s]" + " 80%|████████ | 4015465/4997817 [00:25<00:06, 156018.05it/s]" ] }, { @@ -2579,7 +2579,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 3978912/4997817 [00:25<00:06, 155210.97it/s]" + " 81%|████████ | 4031068/4997817 [00:25<00:06, 155991.25it/s]" ] }, { @@ -2587,7 +2587,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 3994434/4997817 [00:25<00:06, 155023.75it/s]" + " 81%|████████ | 4046668/4997817 [00:25<00:06, 155709.01it/s]" ] }, { @@ -2595,7 +2595,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 4009937/4997817 [00:25<00:06, 154838.02it/s]" + " 81%|████████▏ | 4062240/4997817 [00:25<00:06, 155602.94it/s]" ] }, { @@ -2603,7 +2603,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4025422/4997817 [00:26<00:06, 154623.87it/s]" + " 82%|████████▏ | 4077954/4997817 [00:25<00:05, 156062.33it/s]" ] }, { @@ -2611,7 +2611,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4040954/4997817 [00:26<00:06, 154831.27it/s]" + " 82%|████████▏ | 4093561/4997817 [00:26<00:05, 155710.31it/s]" ] }, { @@ -2619,7 +2619,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4056438/4997817 [00:26<00:06, 154003.54it/s]" + " 82%|████████▏ | 4109133/4997817 [00:26<00:05, 155603.80it/s]" ] }, { @@ -2627,7 +2627,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████▏ | 4071840/4997817 [00:26<00:06, 153855.96it/s]" + " 83%|████████▎ | 4124694/4997817 [00:26<00:05, 154521.18it/s]" ] }, { @@ -2635,7 +2635,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4087227/4997817 [00:26<00:05, 153651.37it/s]" + " 83%|████████▎ | 4140225/4997817 [00:26<00:05, 154753.70it/s]" ] }, { @@ -2643,7 +2643,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4102593/4997817 [00:26<00:05, 153173.36it/s]" + " 83%|████████▎ | 4155873/4997817 [00:26<00:05, 155266.73it/s]" ] }, { @@ -2651,7 +2651,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4117988/4997817 [00:26<00:05, 153402.85it/s]" + " 83%|████████▎ | 4171401/4997817 [00:26<00:05, 155153.38it/s]" ] }, { @@ -2659,7 +2659,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4133406/4997817 [00:26<00:05, 153631.41it/s]" + " 84%|████████▍ | 4186918/4997817 [00:26<00:05, 154977.33it/s]" ] }, { @@ -2667,7 +2667,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4148796/4997817 [00:26<00:05, 153708.87it/s]" + " 84%|████████▍ | 4202417/4997817 [00:26<00:05, 154902.72it/s]" ] }, { @@ -2675,7 +2675,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4164242/4997817 [00:26<00:05, 153931.23it/s]" + " 84%|████████▍ | 4217908/4997817 [00:26<00:05, 154893.00it/s]" ] }, { @@ -2683,7 +2683,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▎ | 4179636/4997817 [00:27<00:05, 153647.64it/s]" + " 85%|████████▍ | 4233398/4997817 [00:26<00:04, 154856.77it/s]" ] }, { @@ -2691,7 +2691,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 4195002/4997817 [00:27<00:05, 153528.33it/s]" + " 85%|████████▌ | 4248984/4997817 [00:27<00:04, 155156.50it/s]" ] }, { @@ -2699,7 +2699,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 4210356/4997817 [00:27<00:05, 153165.84it/s]" + " 85%|████████▌ | 4264603/4997817 [00:27<00:04, 155464.19it/s]" ] }, { @@ -2707,7 +2707,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▍ | 4225762/4997817 [00:27<00:05, 153431.55it/s]" + " 86%|████████▌ | 4280309/4997817 [00:27<00:04, 155938.98it/s]" ] }, { @@ -2715,7 +2715,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▍ | 4241220/4997817 [00:27<00:04, 153772.52it/s]" + " 86%|████████▌ | 4295913/4997817 [00:27<00:04, 155967.76it/s]" ] }, { @@ -2723,7 +2723,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 4256605/4997817 [00:27<00:04, 153792.81it/s]" + " 86%|████████▋ | 4311510/4997817 [00:27<00:04, 155912.96it/s]" ] }, { @@ -2731,7 +2731,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 4272104/4997817 [00:27<00:04, 154150.40it/s]" + " 87%|████████▋ | 4327174/4997817 [00:27<00:04, 156130.11it/s]" ] }, { @@ -2739,7 +2739,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 4287571/4997817 [00:27<00:04, 154305.76it/s]" + " 87%|████████▋ | 4342943/4997817 [00:27<00:04, 156596.84it/s]" ] }, { @@ -2747,7 +2747,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 4303011/4997817 [00:27<00:04, 154331.08it/s]" + " 87%|████████▋ | 4358603/4997817 [00:27<00:04, 156099.77it/s]" ] }, { @@ -2755,7 +2755,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▋ | 4318531/4997817 [00:27<00:04, 154589.93it/s]" + " 88%|████████▊ | 4374286/4997817 [00:27<00:03, 156300.18it/s]" ] }, { @@ -2763,7 +2763,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4334038/4997817 [00:28<00:04, 154732.52it/s]" + " 88%|████████▊ | 4389917/4997817 [00:27<00:03, 156288.10it/s]" ] }, { @@ -2771,7 +2771,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4349604/4997817 [00:28<00:04, 155008.20it/s]" + " 88%|████████▊ | 4405547/4997817 [00:28<00:03, 156076.87it/s]" ] }, { @@ -2779,7 +2779,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4365105/4997817 [00:28<00:04, 154916.06it/s]" + " 88%|████████▊ | 4421155/4997817 [00:28<00:03, 156071.72it/s]" ] }, { @@ -2787,7 +2787,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4380597/4997817 [00:28<00:03, 154675.01it/s]" + " 89%|████████▉ | 4436828/4997817 [00:28<00:03, 156266.08it/s]" ] }, { @@ -2795,7 +2795,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4396234/4997817 [00:28<00:03, 155180.82it/s]" + " 89%|████████▉ | 4452455/4997817 [00:28<00:03, 156201.67it/s]" ] }, { @@ -2803,7 +2803,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4411901/4997817 [00:28<00:03, 155626.26it/s]" + " 89%|████████▉ | 4468085/4997817 [00:28<00:03, 156228.62it/s]" ] }, { @@ -2811,7 +2811,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▊ | 4427621/4997817 [00:28<00:03, 156095.97it/s]" + " 90%|████████▉ | 4483785/4997817 [00:28<00:03, 156458.13it/s]" ] }, { @@ -2819,7 +2819,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 4443378/4997817 [00:28<00:03, 156534.97it/s]" + " 90%|█████████ | 4499431/4997817 [00:28<00:03, 156221.96it/s]" ] }, { @@ -2827,7 +2827,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 4459130/4997817 [00:28<00:03, 156827.16it/s]" + " 90%|█████████ | 4515065/4997817 [00:28<00:03, 156254.25it/s]" ] }, { @@ -2835,7 +2835,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 4474862/4997817 [00:28<00:03, 156972.32it/s]" + " 91%|█████████ | 4530697/4997817 [00:28<00:02, 156272.99it/s]" ] }, { @@ -2843,7 +2843,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 4490562/4997817 [00:29<00:03, 156978.20it/s]" + " 91%|█████████ | 4546333/4997817 [00:28<00:02, 156296.44it/s]" ] }, { @@ -2851,7 +2851,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|█████████ | 4506275/4997817 [00:29<00:03, 157022.54it/s]" + " 91%|█████████▏| 4561963/4997817 [00:29<00:02, 156245.39it/s]" ] }, { @@ -2859,7 +2859,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|█████████ | 4521978/4997817 [00:29<00:03, 156867.20it/s]" + " 92%|█████████▏| 4577640/4997817 [00:29<00:02, 156399.48it/s]" ] }, { @@ -2867,7 +2867,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████ | 4537665/4997817 [00:29<00:02, 156330.11it/s]" + " 92%|█████████▏| 4593280/4997817 [00:29<00:02, 155023.78it/s]" ] }, { @@ -2875,7 +2875,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████ | 4553309/4997817 [00:29<00:02, 156362.47it/s]" + " 92%|█████████▏| 4608803/4997817 [00:29<00:02, 155083.53it/s]" ] }, { @@ -2883,7 +2883,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████▏| 4569051/4997817 [00:29<00:02, 156676.32it/s]" + " 93%|█████████▎| 4624351/4997817 [00:29<00:02, 155199.54it/s]" ] }, { @@ -2891,7 +2891,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4584719/4997817 [00:29<00:02, 155791.44it/s]" + " 93%|█████████▎| 4639882/4997817 [00:29<00:02, 155230.29it/s]" ] }, { @@ -2899,7 +2899,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4600422/4997817 [00:29<00:02, 156159.76it/s]" + " 93%|█████████▎| 4655537/4997817 [00:29<00:02, 155621.69it/s]" ] }, { @@ -2907,7 +2907,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4616049/4997817 [00:29<00:02, 156191.64it/s]" + " 93%|█████████▎| 4671100/4997817 [00:29<00:02, 155480.55it/s]" ] }, { @@ -2915,7 +2915,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4631744/4997817 [00:29<00:02, 156416.29it/s]" + " 94%|█████████▍| 4686716/4997817 [00:29<00:01, 155682.63it/s]" ] }, { @@ -2923,7 +2923,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4647387/4997817 [00:30<00:02, 156234.82it/s]" + " 94%|█████████▍| 4702285/4997817 [00:29<00:01, 155559.20it/s]" ] }, { @@ -2931,7 +2931,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4663040/4997817 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4741234/4997817 [00:30<00:01, 156267.49it/s]" + " 96%|█████████▌| 4795990/4997817 [00:30<00:01, 155406.63it/s]" ] }, { @@ -2979,7 +2979,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 4756907/4997817 [00:30<00:01, 156404.72it/s]" + " 96%|█████████▋| 4811531/4997817 [00:30<00:01, 155396.04it/s]" ] }, { @@ -2987,7 +2987,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 4772548/4997817 [00:30<00:01, 155900.28it/s]" + " 97%|█████████▋| 4827303/4997817 [00:30<00:01, 156090.29it/s]" ] }, { @@ -2995,7 +2995,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4788139/4997817 [00:30<00:01, 155878.33it/s]" + " 97%|█████████▋| 4842940/4997817 [00:30<00:00, 156171.14it/s]" ] }, { @@ -3003,7 +3003,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4803774/4997817 [00:31<00:01, 156018.59it/s]" + " 97%|█████████▋| 4858603/4997817 [00:30<00:00, 156305.27it/s]" ] }, { @@ -3011,7 +3011,7 @@ "output_type": "stream", "text": [ "\r", - " 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"@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_5031fba96d7f4e3fb13bd6d4dab9f87d", + "IPY_MODEL_9cd6691628984fd28dc0a7e3993bd16b", + "IPY_MODEL_481933b6b8cd400a92a3f19ab440935e" + ], + "layout": "IPY_MODEL_dd65649f5be54e6b97ee6f643cdf7e32", + "tabbable": null, + "tooltip": null } }, - "c178eb76910e4b9d9ba3c9a09ab5fad7": { + "b2f1df3f48844c8788be90b729cac970": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4448,7 +4403,49 @@ "width": null } }, - "c6e4b64f1bc04f8eb3f037b5afa72044": { + "b5fe880c9c944d13a507143c65c6969e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_44e6235c987844e0807867a16421c1a8", + "IPY_MODEL_122d2a7991f64576b6a411ed450090c2", + "IPY_MODEL_e960a8b36ba54e7ba4b931bf9d3818b8" + ], + "layout": "IPY_MODEL_713c73a157d24a71954f8c6081bf9cec", + "tabbable": null, + "tooltip": null + } + }, + "c1913e6699f540a08788926bff73a15b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "cac7b5641c884d65817f8743c76edbeb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4501,7 +4498,7 @@ "width": null } }, - "d28c21d2574a493da0c6590694f83b4a": { + "d85604126b41434384cc4a9366fe4c6e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4554,30 +4551,7 @@ "width": null } }, - "dbe3fedab0984c5b92e43bca0fdc37ef": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4f0735505f034cbaaf2475aeecf17edf", - "placeholder": "​", - "style": "IPY_MODEL_07c5448e57c84b749c403d74447a3a2c", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: 100%" - } - }, - "e918b7f4d27b4678bcc41d011713e058": { + "dd65649f5be54e6b97ee6f643cdf7e32": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4630,25 +4604,7 @@ "width": null } }, - "ed2289bbdf3d4fc49ce7af082bc53a4f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f33f447e3af24f1fb872fb78803f30c9": { + "e960a8b36ba54e7ba4b931bf9d3818b8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4663,36 +4619,48 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_734e0ce1fe0741fda251660722f3cd9d", + "layout": "IPY_MODEL_5f50671edfd8446e9492bca1328174da", "placeholder": "​", - "style": "IPY_MODEL_86b6a6f5404d4c3cb34b73d638090f14", + "style": "IPY_MODEL_4e96e892c9d94bcd8f3f2a3d59775eee", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:00<00:00, 442.81it/s]" + "value": " 30/30 [00:00<00:00, 431.01it/s]" } }, - "f94cde7b85b8462293dd9990e3c52464": { + "eeeaf33cbf6940c891e332adcf469321": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_06a1d799aed643ac89843400475f5e6c", - "IPY_MODEL_b77742aceb8e441ca7af3c3a6bbdf2e9", - "IPY_MODEL_f33f447e3af24f1fb872fb78803f30c9" - ], - "layout": "IPY_MODEL_4bfe821c9552467097c55c9a4d8773de", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "f948eab644594a3cb8da5eeff83ae080": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb index 1b33489a6..0659cb517 100644 --- a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb @@ -112,10 +112,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:23.910942Z", - "iopub.status.busy": "2024-02-08T14:27:23.910768Z", - "iopub.status.idle": "2024-02-08T14:27:24.940294Z", - "shell.execute_reply": "2024-02-08T14:27:24.939749Z" + "iopub.execute_input": "2024-02-09T00:18:34.821415Z", + "iopub.status.busy": "2024-02-09T00:18:34.821240Z", + "iopub.status.idle": "2024-02-09T00:18:35.853977Z", + "shell.execute_reply": "2024-02-09T00:18:35.853365Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -150,10 +150,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:24.942864Z", - "iopub.status.busy": "2024-02-08T14:27:24.942409Z", - "iopub.status.idle": "2024-02-08T14:27:24.960391Z", - "shell.execute_reply": "2024-02-08T14:27:24.959858Z" + "iopub.execute_input": "2024-02-09T00:18:35.856709Z", + "iopub.status.busy": "2024-02-09T00:18:35.856269Z", + "iopub.status.idle": "2024-02-09T00:18:35.874903Z", + "shell.execute_reply": "2024-02-09T00:18:35.874457Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:24.962618Z", - "iopub.status.busy": "2024-02-08T14:27:24.962240Z", - "iopub.status.idle": "2024-02-08T14:27:25.105038Z", - "shell.execute_reply": "2024-02-08T14:27:25.104546Z" + "iopub.execute_input": "2024-02-09T00:18:35.877027Z", + "iopub.status.busy": "2024-02-09T00:18:35.876667Z", + "iopub.status.idle": "2024-02-09T00:18:35.990307Z", + "shell.execute_reply": "2024-02-09T00:18:35.989781Z" } }, "outputs": [ @@ -304,10 +304,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:25.107096Z", - "iopub.status.busy": "2024-02-08T14:27:25.106688Z", - "iopub.status.idle": "2024-02-08T14:27:25.110892Z", - "shell.execute_reply": "2024-02-08T14:27:25.110370Z" + "iopub.execute_input": "2024-02-09T00:18:35.992500Z", + "iopub.status.busy": "2024-02-09T00:18:35.992081Z", + "iopub.status.idle": "2024-02-09T00:18:35.995641Z", + "shell.execute_reply": "2024-02-09T00:18:35.995109Z" } }, "outputs": [], @@ -328,10 +328,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:25.112912Z", - "iopub.status.busy": "2024-02-08T14:27:25.112535Z", - "iopub.status.idle": "2024-02-08T14:27:25.120751Z", - "shell.execute_reply": "2024-02-08T14:27:25.120347Z" + "iopub.execute_input": "2024-02-09T00:18:35.997629Z", + "iopub.status.busy": "2024-02-09T00:18:35.997299Z", + "iopub.status.idle": "2024-02-09T00:18:36.005226Z", + "shell.execute_reply": "2024-02-09T00:18:36.004712Z" } }, "outputs": [], @@ -383,10 +383,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:25.122791Z", - "iopub.status.busy": "2024-02-08T14:27:25.122459Z", - "iopub.status.idle": "2024-02-08T14:27:25.124896Z", - "shell.execute_reply": "2024-02-08T14:27:25.124469Z" + "iopub.execute_input": "2024-02-09T00:18:36.007559Z", + "iopub.status.busy": "2024-02-09T00:18:36.007251Z", + "iopub.status.idle": "2024-02-09T00:18:36.009871Z", + "shell.execute_reply": "2024-02-09T00:18:36.009320Z" } }, "outputs": [], @@ -408,10 +408,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:25.126805Z", - "iopub.status.busy": "2024-02-08T14:27:25.126493Z", - "iopub.status.idle": "2024-02-08T14:27:25.642785Z", - "shell.execute_reply": "2024-02-08T14:27:25.642180Z" + "iopub.execute_input": "2024-02-09T00:18:36.011745Z", + "iopub.status.busy": "2024-02-09T00:18:36.011570Z", + "iopub.status.idle": "2024-02-09T00:18:36.533177Z", + "shell.execute_reply": "2024-02-09T00:18:36.532558Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:25.645257Z", - "iopub.status.busy": "2024-02-08T14:27:25.645076Z", - "iopub.status.idle": "2024-02-08T14:27:27.286919Z", - "shell.execute_reply": "2024-02-08T14:27:27.286290Z" + "iopub.execute_input": "2024-02-09T00:18:36.535697Z", + "iopub.status.busy": "2024-02-09T00:18:36.535500Z", + "iopub.status.idle": "2024-02-09T00:18:38.139182Z", + "shell.execute_reply": "2024-02-09T00:18:38.138532Z" } }, "outputs": [ @@ -480,10 +480,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:27.289990Z", - "iopub.status.busy": "2024-02-08T14:27:27.289045Z", - "iopub.status.idle": "2024-02-08T14:27:27.299053Z", - "shell.execute_reply": "2024-02-08T14:27:27.298619Z" + "iopub.execute_input": "2024-02-09T00:18:38.141773Z", + "iopub.status.busy": "2024-02-09T00:18:38.141256Z", + "iopub.status.idle": "2024-02-09T00:18:38.151171Z", + "shell.execute_reply": "2024-02-09T00:18:38.150656Z" } }, "outputs": [ @@ -604,10 +604,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:27.301278Z", - "iopub.status.busy": "2024-02-08T14:27:27.300855Z", - "iopub.status.idle": "2024-02-08T14:27:27.304611Z", - "shell.execute_reply": "2024-02-08T14:27:27.304194Z" + "iopub.execute_input": "2024-02-09T00:18:38.153259Z", + "iopub.status.busy": "2024-02-09T00:18:38.152943Z", + "iopub.status.idle": "2024-02-09T00:18:38.156723Z", + "shell.execute_reply": "2024-02-09T00:18:38.156280Z" } }, "outputs": [], @@ -632,10 +632,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:27.306583Z", - "iopub.status.busy": "2024-02-08T14:27:27.306265Z", - "iopub.status.idle": "2024-02-08T14:27:27.313141Z", - "shell.execute_reply": "2024-02-08T14:27:27.312733Z" + "iopub.execute_input": "2024-02-09T00:18:38.158787Z", + "iopub.status.busy": "2024-02-09T00:18:38.158338Z", + "iopub.status.idle": "2024-02-09T00:18:38.165281Z", + "shell.execute_reply": "2024-02-09T00:18:38.164867Z" } }, "outputs": [], @@ -657,10 +657,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:27.315134Z", - "iopub.status.busy": "2024-02-08T14:27:27.314793Z", - "iopub.status.idle": "2024-02-08T14:27:27.426264Z", - "shell.execute_reply": "2024-02-08T14:27:27.425672Z" + "iopub.execute_input": "2024-02-09T00:18:38.167378Z", + "iopub.status.busy": "2024-02-09T00:18:38.167056Z", + "iopub.status.idle": "2024-02-09T00:18:38.277715Z", + "shell.execute_reply": "2024-02-09T00:18:38.277132Z" } }, "outputs": [ @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:27.428538Z", - "iopub.status.busy": "2024-02-08T14:27:27.428170Z", - "iopub.status.idle": "2024-02-08T14:27:27.431075Z", - "shell.execute_reply": "2024-02-08T14:27:27.430580Z" + "iopub.execute_input": "2024-02-09T00:18:38.280101Z", + "iopub.status.busy": "2024-02-09T00:18:38.279720Z", + "iopub.status.idle": "2024-02-09T00:18:38.282390Z", + "shell.execute_reply": "2024-02-09T00:18:38.281969Z" } }, "outputs": [], @@ -714,10 +714,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:27.433121Z", - "iopub.status.busy": "2024-02-08T14:27:27.432789Z", - "iopub.status.idle": "2024-02-08T14:27:29.373969Z", - "shell.execute_reply": "2024-02-08T14:27:29.373229Z" + "iopub.execute_input": "2024-02-09T00:18:38.284337Z", + "iopub.status.busy": "2024-02-09T00:18:38.284127Z", + "iopub.status.idle": "2024-02-09T00:18:40.250999Z", + "shell.execute_reply": "2024-02-09T00:18:40.250341Z" } }, "outputs": [], @@ -737,10 +737,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:29.376990Z", - "iopub.status.busy": "2024-02-08T14:27:29.376399Z", - "iopub.status.idle": "2024-02-08T14:27:29.387660Z", - "shell.execute_reply": "2024-02-08T14:27:29.387089Z" + "iopub.execute_input": "2024-02-09T00:18:40.253972Z", + "iopub.status.busy": "2024-02-09T00:18:40.253228Z", + "iopub.status.idle": "2024-02-09T00:18:40.264455Z", + "shell.execute_reply": "2024-02-09T00:18:40.263907Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:29.389725Z", - "iopub.status.busy": "2024-02-08T14:27:29.389357Z", - "iopub.status.idle": "2024-02-08T14:27:29.529033Z", - "shell.execute_reply": "2024-02-08T14:27:29.528506Z" + "iopub.execute_input": "2024-02-09T00:18:40.266531Z", + "iopub.status.busy": "2024-02-09T00:18:40.266229Z", + "iopub.status.idle": "2024-02-09T00:18:40.286648Z", + "shell.execute_reply": "2024-02-09T00:18:40.286215Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/text.ipynb b/master/.doctrees/nbsphinx/tutorials/text.ipynb index a5b2a02d5..097217444 100644 --- a/master/.doctrees/nbsphinx/tutorials/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/text.ipynb @@ -114,10 +114,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:32.204126Z", - "iopub.status.busy": "2024-02-08T14:27:32.203935Z", - "iopub.status.idle": "2024-02-08T14:27:34.742594Z", - "shell.execute_reply": "2024-02-08T14:27:34.741969Z" + "iopub.execute_input": "2024-02-09T00:18:43.092031Z", + "iopub.status.busy": "2024-02-09T00:18:43.091860Z", + "iopub.status.idle": "2024-02-09T00:18:45.719204Z", + "shell.execute_reply": "2024-02-09T00:18:45.718566Z" }, "nbsphinx": "hidden" }, @@ -134,7 +134,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:34.745277Z", - "iopub.status.busy": "2024-02-08T14:27:34.744930Z", - "iopub.status.idle": "2024-02-08T14:27:34.748478Z", - "shell.execute_reply": "2024-02-08T14:27:34.747942Z" + "iopub.execute_input": "2024-02-09T00:18:45.721879Z", + "iopub.status.busy": "2024-02-09T00:18:45.721579Z", + "iopub.status.idle": "2024-02-09T00:18:45.725027Z", + "shell.execute_reply": "2024-02-09T00:18:45.724581Z" } }, "outputs": [], @@ -184,10 +184,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:34.750440Z", - "iopub.status.busy": "2024-02-08T14:27:34.750133Z", - "iopub.status.idle": "2024-02-08T14:27:34.753219Z", - "shell.execute_reply": "2024-02-08T14:27:34.752696Z" + "iopub.execute_input": "2024-02-09T00:18:45.727032Z", + "iopub.status.busy": "2024-02-09T00:18:45.726723Z", + "iopub.status.idle": "2024-02-09T00:18:45.729765Z", + "shell.execute_reply": "2024-02-09T00:18:45.729325Z" }, "nbsphinx": "hidden" }, @@ -218,10 +218,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:34.755230Z", - "iopub.status.busy": "2024-02-08T14:27:34.754907Z", - "iopub.status.idle": "2024-02-08T14:27:34.906355Z", - "shell.execute_reply": "2024-02-08T14:27:34.905931Z" + "iopub.execute_input": "2024-02-09T00:18:45.731693Z", + "iopub.status.busy": "2024-02-09T00:18:45.731367Z", + "iopub.status.idle": "2024-02-09T00:18:45.845990Z", + "shell.execute_reply": "2024-02-09T00:18:45.845460Z" } }, "outputs": [ @@ -311,10 +311,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:34.908383Z", - "iopub.status.busy": "2024-02-08T14:27:34.908131Z", - "iopub.status.idle": "2024-02-08T14:27:34.911634Z", - "shell.execute_reply": "2024-02-08T14:27:34.911104Z" + "iopub.execute_input": "2024-02-09T00:18:45.848136Z", + "iopub.status.busy": "2024-02-09T00:18:45.847858Z", + "iopub.status.idle": "2024-02-09T00:18:45.851188Z", + "shell.execute_reply": "2024-02-09T00:18:45.850729Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:34.913686Z", - "iopub.status.busy": "2024-02-08T14:27:34.913377Z", - "iopub.status.idle": "2024-02-08T14:27:34.916837Z", - "shell.execute_reply": "2024-02-08T14:27:34.916382Z" + "iopub.execute_input": "2024-02-09T00:18:45.853196Z", + "iopub.status.busy": "2024-02-09T00:18:45.852875Z", + "iopub.status.idle": "2024-02-09T00:18:45.856174Z", + "shell.execute_reply": "2024-02-09T00:18:45.855631Z" } }, "outputs": [ @@ -341,7 +341,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_payment_fee_charged', 'getting_spare_card', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'change_pin', 'visa_or_mastercard', 'card_about_to_expire', 'supported_cards_and_currencies', 'cancel_transfer', 'apple_pay_or_google_pay'}\n" + "Classes: {'getting_spare_card', 'cancel_transfer', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'change_pin', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'card_about_to_expire'}\n" ] } ], @@ -364,10 +364,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:34.918764Z", - "iopub.status.busy": "2024-02-08T14:27:34.918447Z", - "iopub.status.idle": "2024-02-08T14:27:34.921398Z", - "shell.execute_reply": "2024-02-08T14:27:34.920863Z" + "iopub.execute_input": "2024-02-09T00:18:45.858156Z", + "iopub.status.busy": "2024-02-09T00:18:45.857843Z", + "iopub.status.idle": "2024-02-09T00:18:45.860792Z", + "shell.execute_reply": "2024-02-09T00:18:45.860270Z" } }, "outputs": [ @@ -408,10 +408,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:34.923447Z", - "iopub.status.busy": "2024-02-08T14:27:34.923127Z", - "iopub.status.idle": "2024-02-08T14:27:34.926192Z", - "shell.execute_reply": "2024-02-08T14:27:34.925770Z" + "iopub.execute_input": "2024-02-09T00:18:45.862822Z", + "iopub.status.busy": "2024-02-09T00:18:45.862466Z", + "iopub.status.idle": "2024-02-09T00:18:45.865679Z", + "shell.execute_reply": "2024-02-09T00:18:45.865242Z" } }, "outputs": [], @@ -452,10 +452,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:34.928180Z", - "iopub.status.busy": "2024-02-08T14:27:34.927859Z", - "iopub.status.idle": "2024-02-08T14:27:39.131556Z", - "shell.execute_reply": "2024-02-08T14:27:39.131002Z" + "iopub.execute_input": "2024-02-09T00:18:45.867706Z", + "iopub.status.busy": "2024-02-09T00:18:45.867392Z", + "iopub.status.idle": "2024-02-09T00:18:50.141569Z", + "shell.execute_reply": "2024-02-09T00:18:50.141035Z" } }, "outputs": [ @@ -510,10 +510,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:39.134249Z", - "iopub.status.busy": "2024-02-08T14:27:39.133873Z", - "iopub.status.idle": "2024-02-08T14:27:39.136873Z", - "shell.execute_reply": "2024-02-08T14:27:39.136390Z" + "iopub.execute_input": "2024-02-09T00:18:50.144320Z", + "iopub.status.busy": "2024-02-09T00:18:50.143885Z", + "iopub.status.idle": "2024-02-09T00:18:50.146801Z", + "shell.execute_reply": "2024-02-09T00:18:50.146226Z" } }, "outputs": [], @@ -535,10 +535,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:39.138829Z", - "iopub.status.busy": "2024-02-08T14:27:39.138502Z", - "iopub.status.idle": "2024-02-08T14:27:39.141136Z", - "shell.execute_reply": "2024-02-08T14:27:39.140702Z" + "iopub.execute_input": "2024-02-09T00:18:50.148918Z", + "iopub.status.busy": "2024-02-09T00:18:50.148575Z", + "iopub.status.idle": "2024-02-09T00:18:50.151317Z", + "shell.execute_reply": "2024-02-09T00:18:50.150770Z" } }, "outputs": [], @@ -553,10 +553,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:39.142971Z", - "iopub.status.busy": "2024-02-08T14:27:39.142659Z", - "iopub.status.idle": "2024-02-08T14:27:41.368853Z", - "shell.execute_reply": "2024-02-08T14:27:41.368128Z" + "iopub.execute_input": "2024-02-09T00:18:50.153241Z", + "iopub.status.busy": "2024-02-09T00:18:50.152913Z", + "iopub.status.idle": "2024-02-09T00:18:52.453367Z", + "shell.execute_reply": "2024-02-09T00:18:52.452720Z" }, "scrolled": true }, @@ -579,10 +579,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:41.371805Z", - "iopub.status.busy": "2024-02-08T14:27:41.371238Z", - "iopub.status.idle": "2024-02-08T14:27:41.378737Z", - "shell.execute_reply": "2024-02-08T14:27:41.378284Z" + "iopub.execute_input": "2024-02-09T00:18:52.456211Z", + "iopub.status.busy": "2024-02-09T00:18:52.455635Z", + "iopub.status.idle": "2024-02-09T00:18:52.463412Z", + "shell.execute_reply": "2024-02-09T00:18:52.462950Z" } }, "outputs": [ @@ -683,10 +683,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:41.380700Z", - "iopub.status.busy": "2024-02-08T14:27:41.380363Z", - "iopub.status.idle": "2024-02-08T14:27:41.384008Z", - "shell.execute_reply": "2024-02-08T14:27:41.383559Z" + "iopub.execute_input": "2024-02-09T00:18:52.465564Z", + "iopub.status.busy": "2024-02-09T00:18:52.465145Z", + "iopub.status.idle": "2024-02-09T00:18:52.469053Z", + "shell.execute_reply": "2024-02-09T00:18:52.468609Z" } }, "outputs": [], @@ -700,10 +700,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:41.385893Z", - "iopub.status.busy": "2024-02-08T14:27:41.385556Z", - "iopub.status.idle": "2024-02-08T14:27:41.388767Z", - "shell.execute_reply": "2024-02-08T14:27:41.388324Z" + "iopub.execute_input": "2024-02-09T00:18:52.471055Z", + "iopub.status.busy": "2024-02-09T00:18:52.470611Z", + "iopub.status.idle": "2024-02-09T00:18:52.473960Z", + "shell.execute_reply": "2024-02-09T00:18:52.473503Z" } }, "outputs": [ @@ -738,10 +738,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:41.390727Z", - "iopub.status.busy": "2024-02-08T14:27:41.390420Z", - "iopub.status.idle": "2024-02-08T14:27:41.393251Z", - "shell.execute_reply": "2024-02-08T14:27:41.392824Z" + "iopub.execute_input": "2024-02-09T00:18:52.475995Z", + "iopub.status.busy": "2024-02-09T00:18:52.475676Z", + "iopub.status.idle": "2024-02-09T00:18:52.478705Z", + "shell.execute_reply": "2024-02-09T00:18:52.478221Z" } }, "outputs": [], @@ -761,10 +761,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:41.395292Z", - "iopub.status.busy": "2024-02-08T14:27:41.394954Z", - "iopub.status.idle": "2024-02-08T14:27:41.401357Z", - "shell.execute_reply": "2024-02-08T14:27:41.400936Z" + "iopub.execute_input": "2024-02-09T00:18:52.480587Z", + "iopub.status.busy": "2024-02-09T00:18:52.480244Z", + "iopub.status.idle": "2024-02-09T00:18:52.487024Z", + "shell.execute_reply": "2024-02-09T00:18:52.486469Z" } }, "outputs": [ @@ -889,10 +889,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:41.403438Z", - "iopub.status.busy": "2024-02-08T14:27:41.403120Z", - "iopub.status.idle": "2024-02-08T14:27:41.626428Z", - "shell.execute_reply": "2024-02-08T14:27:41.625913Z" + "iopub.execute_input": "2024-02-09T00:18:52.489149Z", + "iopub.status.busy": "2024-02-09T00:18:52.488746Z", + "iopub.status.idle": "2024-02-09T00:18:52.751106Z", + "shell.execute_reply": "2024-02-09T00:18:52.750578Z" }, "scrolled": true }, @@ -931,10 +931,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:41.628842Z", - "iopub.status.busy": "2024-02-08T14:27:41.628467Z", - "iopub.status.idle": "2024-02-08T14:27:41.804111Z", - "shell.execute_reply": "2024-02-08T14:27:41.803643Z" + "iopub.execute_input": "2024-02-09T00:18:52.753638Z", + "iopub.status.busy": "2024-02-09T00:18:52.753273Z", + "iopub.status.idle": "2024-02-09T00:18:52.931483Z", + "shell.execute_reply": "2024-02-09T00:18:52.930895Z" }, "scrolled": true }, @@ -967,10 +967,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:41.806451Z", - "iopub.status.busy": "2024-02-08T14:27:41.806094Z", - "iopub.status.idle": "2024-02-08T14:27:41.809763Z", - "shell.execute_reply": "2024-02-08T14:27:41.809286Z" + "iopub.execute_input": "2024-02-09T00:18:52.935035Z", + "iopub.status.busy": "2024-02-09T00:18:52.934051Z", + "iopub.status.idle": "2024-02-09T00:18:52.939066Z", + "shell.execute_reply": "2024-02-09T00:18:52.938542Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index 669fea6f7..0c11fcccb 100644 --- a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:44.620201Z", - "iopub.status.busy": "2024-02-08T14:27:44.619732Z", - "iopub.status.idle": "2024-02-08T14:27:48.290139Z", - "shell.execute_reply": "2024-02-08T14:27:48.289531Z" + "iopub.execute_input": "2024-02-09T00:18:57.048750Z", + "iopub.status.busy": "2024-02-09T00:18:57.048302Z", + "iopub.status.idle": "2024-02-09T00:18:58.991824Z", + "shell.execute_reply": "2024-02-09T00:18:58.991242Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-02-08 14:27:44-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-02-09 00:18:57-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,9 +94,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "169.150.249.169, 2400:52e0:1a01::987:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|169.150.249.169|:443... connected.\r\n", - "HTTP request sent, awaiting response... 200 OK\r\n", + "143.244.50.84, 2400:52e0:1a01::1002:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|143.244.50.84|:443... connected.\r\n", + "HTTP request sent, awaiting response... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 OK\r\n", "Length: 982975 (960K) [application/zip]\r\n", "Saving to: ‘conll2003.zip’\r\n", "\r\n", @@ -109,9 +116,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K 5.77MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.05s \r\n", "\r\n", - "2024-02-08 14:27:44 (5.77 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-02-09 00:18:57 (18.8 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -131,9 +138,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-02-08 14:27:45-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.25.234, 16.182.34.9, 52.217.191.57, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.25.234|:443... " + "--2024-02-09 00:18:57-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.35.137, 54.231.223.49, 3.5.6.160, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.35.137|:443... " ] }, { @@ -167,71 +174,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 1%[ ] 263.08K 1.13MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 4%[ ] 806.73K 1.73MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 8%[> ] 1.42M 2.11MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 13%[=> ] 2.20M 2.44MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 19%[==> ] 3.14M 2.76MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 26%[====> ] 4.30M 3.15MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 35%[======> ] 5.70M 3.57MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 45%[========> ] 7.41M 4.06MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 58%[==========> ] 9.48M 4.61MB/s " + "pred_probs.npz 0%[ ] 126.53K 597KB/s " ] }, { @@ -239,7 +182,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 73%[=============> ] 11.98M 5.28MB/s " + "pred_probs.npz 7%[> ] 1.16M 2.74MB/s " ] }, { @@ -247,7 +190,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 92%[=================> ] 15.04M 6.01MB/s " + "pred_probs.npz 47%[========> ] 7.66M 12.0MB/s " ] }, { @@ -255,9 +198,10 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 6.33MB/s in 2.6s \r\n", + "pred_probs.npz 83%[===============> ] 13.58M 15.8MB/s \r", + "pred_probs.npz 100%[===================>] 16.26M 18.7MB/s in 0.9s \r\n", "\r\n", - "2024-02-08 14:27:48 (6.33 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-02-09 00:18:58 (18.7 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -274,10 +218,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:48.292712Z", - "iopub.status.busy": "2024-02-08T14:27:48.292351Z", - "iopub.status.idle": "2024-02-08T14:27:49.318807Z", - "shell.execute_reply": "2024-02-08T14:27:49.318266Z" + "iopub.execute_input": "2024-02-09T00:18:58.994407Z", + "iopub.status.busy": "2024-02-09T00:18:58.994052Z", + "iopub.status.idle": "2024-02-09T00:19:00.032835Z", + "shell.execute_reply": "2024-02-09T00:19:00.032278Z" }, "nbsphinx": "hidden" }, @@ -288,7 +232,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -314,10 +258,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:49.321441Z", - "iopub.status.busy": "2024-02-08T14:27:49.321001Z", - "iopub.status.idle": "2024-02-08T14:27:49.324649Z", - "shell.execute_reply": "2024-02-08T14:27:49.324219Z" + "iopub.execute_input": "2024-02-09T00:19:00.035412Z", + "iopub.status.busy": "2024-02-09T00:19:00.034998Z", + "iopub.status.idle": "2024-02-09T00:19:00.038580Z", + "shell.execute_reply": "2024-02-09T00:19:00.038037Z" } }, "outputs": [], @@ -367,10 +311,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:49.326803Z", - "iopub.status.busy": "2024-02-08T14:27:49.326369Z", - "iopub.status.idle": "2024-02-08T14:27:49.329270Z", - "shell.execute_reply": "2024-02-08T14:27:49.328862Z" + "iopub.execute_input": "2024-02-09T00:19:00.040669Z", + "iopub.status.busy": "2024-02-09T00:19:00.040361Z", + "iopub.status.idle": "2024-02-09T00:19:00.043148Z", + "shell.execute_reply": "2024-02-09T00:19:00.042725Z" }, "nbsphinx": "hidden" }, @@ -388,10 +332,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:49.331345Z", - "iopub.status.busy": "2024-02-08T14:27:49.330958Z", - "iopub.status.idle": "2024-02-08T14:27:58.410208Z", - "shell.execute_reply": "2024-02-08T14:27:58.409616Z" + "iopub.execute_input": "2024-02-09T00:19:00.045150Z", + "iopub.status.busy": "2024-02-09T00:19:00.044840Z", + "iopub.status.idle": "2024-02-09T00:19:09.194303Z", + "shell.execute_reply": "2024-02-09T00:19:09.193758Z" } }, "outputs": [], @@ -465,10 +409,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:58.412728Z", - "iopub.status.busy": "2024-02-08T14:27:58.412543Z", - "iopub.status.idle": "2024-02-08T14:27:58.418173Z", - "shell.execute_reply": "2024-02-08T14:27:58.417624Z" + "iopub.execute_input": "2024-02-09T00:19:09.196802Z", + "iopub.status.busy": "2024-02-09T00:19:09.196461Z", + "iopub.status.idle": "2024-02-09T00:19:09.201982Z", + "shell.execute_reply": "2024-02-09T00:19:09.201462Z" }, "nbsphinx": "hidden" }, @@ -508,10 +452,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:58.420195Z", - "iopub.status.busy": "2024-02-08T14:27:58.419875Z", - "iopub.status.idle": "2024-02-08T14:27:58.748360Z", - "shell.execute_reply": "2024-02-08T14:27:58.747727Z" + "iopub.execute_input": "2024-02-09T00:19:09.203935Z", + "iopub.status.busy": "2024-02-09T00:19:09.203620Z", + "iopub.status.idle": "2024-02-09T00:19:09.535937Z", + "shell.execute_reply": "2024-02-09T00:19:09.535333Z" } }, "outputs": [], @@ -548,10 +492,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:58.750811Z", - "iopub.status.busy": "2024-02-08T14:27:58.750619Z", - "iopub.status.idle": "2024-02-08T14:27:58.755066Z", - "shell.execute_reply": "2024-02-08T14:27:58.754523Z" + "iopub.execute_input": "2024-02-09T00:19:09.538380Z", + "iopub.status.busy": "2024-02-09T00:19:09.538182Z", + "iopub.status.idle": "2024-02-09T00:19:09.542321Z", + "shell.execute_reply": "2024-02-09T00:19:09.541826Z" } }, "outputs": [ @@ -623,10 +567,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:27:58.757107Z", - "iopub.status.busy": "2024-02-08T14:27:58.756716Z", - "iopub.status.idle": "2024-02-08T14:28:01.081800Z", - "shell.execute_reply": "2024-02-08T14:28:01.081151Z" + "iopub.execute_input": "2024-02-09T00:19:09.544346Z", + "iopub.status.busy": "2024-02-09T00:19:09.544034Z", + "iopub.status.idle": "2024-02-09T00:19:11.827569Z", + "shell.execute_reply": "2024-02-09T00:19:11.826938Z" } }, "outputs": [], @@ -648,10 +592,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:28:01.084631Z", - "iopub.status.busy": "2024-02-08T14:28:01.084106Z", - "iopub.status.idle": "2024-02-08T14:28:01.087890Z", - "shell.execute_reply": "2024-02-08T14:28:01.087366Z" + "iopub.execute_input": "2024-02-09T00:19:11.830609Z", + "iopub.status.busy": "2024-02-09T00:19:11.829877Z", + "iopub.status.idle": "2024-02-09T00:19:11.833766Z", + "shell.execute_reply": "2024-02-09T00:19:11.833237Z" } }, "outputs": [ @@ -687,10 +631,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:28:01.089754Z", - "iopub.status.busy": "2024-02-08T14:28:01.089582Z", - "iopub.status.idle": "2024-02-08T14:28:01.095038Z", - "shell.execute_reply": "2024-02-08T14:28:01.094482Z" + "iopub.execute_input": "2024-02-09T00:19:11.835782Z", + "iopub.status.busy": "2024-02-09T00:19:11.835464Z", + "iopub.status.idle": "2024-02-09T00:19:11.840749Z", + "shell.execute_reply": "2024-02-09T00:19:11.840197Z" } }, "outputs": [ @@ -868,10 +812,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:28:01.096888Z", - "iopub.status.busy": "2024-02-08T14:28:01.096718Z", - "iopub.status.idle": "2024-02-08T14:28:01.122512Z", - "shell.execute_reply": "2024-02-08T14:28:01.121960Z" + "iopub.execute_input": "2024-02-09T00:19:11.842787Z", + "iopub.status.busy": "2024-02-09T00:19:11.842520Z", + "iopub.status.idle": "2024-02-09T00:19:11.869333Z", + "shell.execute_reply": "2024-02-09T00:19:11.868916Z" } }, "outputs": [ @@ -973,10 +917,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:28:01.124389Z", - "iopub.status.busy": "2024-02-08T14:28:01.124221Z", - "iopub.status.idle": "2024-02-08T14:28:01.128790Z", - "shell.execute_reply": "2024-02-08T14:28:01.128336Z" + "iopub.execute_input": "2024-02-09T00:19:11.871431Z", + "iopub.status.busy": "2024-02-09T00:19:11.871119Z", + "iopub.status.idle": "2024-02-09T00:19:11.874995Z", + "shell.execute_reply": "2024-02-09T00:19:11.874443Z" } }, "outputs": [ @@ -1050,10 +994,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:28:01.130630Z", - "iopub.status.busy": "2024-02-08T14:28:01.130463Z", - "iopub.status.idle": "2024-02-08T14:28:02.551229Z", - "shell.execute_reply": "2024-02-08T14:28:02.550712Z" + "iopub.execute_input": "2024-02-09T00:19:11.877031Z", + "iopub.status.busy": "2024-02-09T00:19:11.876726Z", + "iopub.status.idle": "2024-02-09T00:19:13.283142Z", + "shell.execute_reply": "2024-02-09T00:19:13.282587Z" } }, "outputs": [ @@ -1225,10 +1169,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:28:02.553241Z", - "iopub.status.busy": "2024-02-08T14:28:02.553068Z", - "iopub.status.idle": "2024-02-08T14:28:02.556966Z", - "shell.execute_reply": "2024-02-08T14:28:02.556551Z" + "iopub.execute_input": "2024-02-09T00:19:13.285106Z", + "iopub.status.busy": "2024-02-09T00:19:13.284921Z", + "iopub.status.idle": "2024-02-09T00:19:13.288805Z", + "shell.execute_reply": "2024-02-09T00:19:13.288390Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/tutorials/audio.doctree 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  • report
    • +
  • task
    • diff --git a/master/_modules/cleanlab/classification.html b/master/_modules/cleanlab/classification.html index 00b24fba2..192917a5a 100644 --- a/master/_modules/cleanlab/classification.html +++ b/master/_modules/cleanlab/classification.html @@ -238,6 +238,7 @@
  • report
    • +
  • task
    • diff --git a/master/_modules/cleanlab/count.html b/master/_modules/cleanlab/count.html index 5564913d7..c3572adf8 100644 --- a/master/_modules/cleanlab/count.html +++ b/master/_modules/cleanlab/count.html @@ -238,6 +238,7 @@
  • report
    • +
  • task
    • diff --git a/master/_modules/cleanlab/datalab/datalab.html b/master/_modules/cleanlab/datalab/datalab.html index 4dd5e3ea2..15c4058c1 100644 --- a/master/_modules/cleanlab/datalab/datalab.html +++ b/master/_modules/cleanlab/datalab/datalab.html @@ -238,6 +238,7 @@
  • report
    • +
  • task
    • @@ -570,6 +571,7 @@

    Source code for cleanlab.datalab.datalab

         list_possible_issue_types as _list_possible_issue_types,
 )
 from cleanlab.datalab.internal.serialize import _Serializer
+from cleanlab.datalab.internal.task import Task
 
 if TYPE_CHECKING:  # pragma: no cover
     import numpy.typing as npt
@@ -609,7 +611,11 @@ 
    Source code for cleanlab.datalab.datalab
     
     task : str
         The type of machine learning task that the dataset is used for.
-        By default, this is set to "classification", but you can also set it to "regression" if you are working with a regression dataset.
+
+        Supported tasks:
+          - "classification" (default): Multiclass classification
+          - "regression" : Regression
+          - "multilabel" : Multilabel classification
 
     label_name : str, optional
         The name of the label column in the dataset.
@@ -640,17 +646,11 @@ 
    Source code for cleanlab.datalab.datalab
             image_key: Optional[str] = None,
         verbosity: int = 1,
     ) -> None:
-        self._validate_task(task)
         # Assume continuous values of labels for regression task
         # Map labels to integers for classification task
-        map_labels_to_int = task == "classification"  # TODO: handle more generally
-        is_multilabel = task == "multilabel"
-
-        self._data = Data(
-            data, label_name, map_to_int=map_labels_to_int, is_multilabel=is_multilabel
-        )
+        self.task = Task.from_str(task)
+        self._data = Data(data, self.task, label_name)
         self.data = self._data._data
-        self.task = task
         self._labels = self._data.labels
         self._label_map = self._labels.label_map
         self.label_name = self._labels.label_name
@@ -661,7 +661,7 @@ 
    Source code for cleanlab.datalab.datalab
     
         # Create the builder for DataIssues
         builder = _DataIssuesBuilder(self._data)
-        builder.set_imagelab(self._imagelab).set_task(task)
+        builder.set_imagelab(self._imagelab).set_task(self.task)
         self.data_issues = builder.build()
 
     # todo: check displayer methods
@@ -671,13 +671,6 @@ 
    Source code for cleanlab.datalab.datalab
         def __str__(self) -> str:
         return _Displayer(data_issues=self.data_issues).__str__()
 
-    def _validate_task(self, task: str) -> None:
-        """Validates the task parameter passed to the Datalab constructor."""
-        _valid_tasks = ["classification", "regression", "multilabel"]
-        if task not in _valid_tasks:
-            error_msg = f"Invalid task: {task}. Datalab only supports {_valid_tasks}."
-            raise ValueError(error_msg)
-
     @property
     def labels(self) -> Union[np.ndarray, List[List[int]]]:
         """Labels of the dataset, in a [0, 1, ..., K-1] format."""
diff --git a/master/_modules/cleanlab/datalab/internal/data.html b/master/_modules/cleanlab/datalab/internal/data.html
index 405cbacb1..948704e75 100644
--- a/master/_modules/cleanlab/datalab/internal/data.html
+++ b/master/_modules/cleanlab/datalab/internal/data.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
@@ -547,6 +548,8 @@ 
    Source code for cleanlab.datalab.internal.data
    import os
 from typing import Any, Callable, Dict, List, Mapping, Optional, Union, cast, TYPE_CHECKING, Tuple
 
+from cleanlab.datalab.internal.task import Task
+
 try:
     import datasets
 except ImportError as error:
@@ -646,9 +649,21 @@ 
    Source code for cleanlab.datalab.internal.data
        label_name : Union[str, List[str]]
         Name of the label column in the dataset.
 
-    map_to_int : bool
-        Whether to map the labels to integers, e.g. [0, 1, ..., K-1] where K is the number of classes.
-        If False, the labels are not mapped to integers, e.g. for regression tasks.
+    task :
+        The task associated with the dataset. This is used to determine how to
+        to format the labels.
+
+        Note:
+
+          - If the task is a classification task, the labels
+          will be mapped to integers, e.g. [0, 1, ..., K-1] where K is the number
+          of classes. If the task is a regression task, the labels will not be
+          mapped to integers.
+
+          - If the task is a multilabel task, the labels will be formatted as a
+            list of lists, e.g. [[0, 1], [1, 2], [0, 2]] where each sublist contains
+            the labels for a single example. If the task is not a multilabel task,
+            the labels will be formatted as a 1D numpy array.
 
     Warnings
     --------
@@ -663,15 +678,15 @@ 
    Source code for cleanlab.datalab.internal.data
    def __init__(
         self,
         data: "DatasetLike",
+        task: Task,
         label_name: Optional[str] = None,
-        map_to_int: bool = True,
-        is_multilabel: bool = False,
     ) -> None:
         self._validate_data(data)
         self._data = self._load_data(data)
         self._data_hash = hash(self._data)
         self.labels: Label
-        label_class = MultiLabel if is_multilabel else MultiClass
+        label_class = MultiLabel if task.is_multilabel else MultiClass
+        map_to_int = task.is_classification
         self.labels = label_class(data=self._data, label_name=label_name, map_to_int=map_to_int)
 
     def _load_data(self, data: "DatasetLike") -> Dataset:
diff --git a/master/_modules/cleanlab/datalab/internal/data_issues.html b/master/_modules/cleanlab/datalab/internal/data_issues.html
index 5c5b3ecc4..912eb9d53 100644
--- a/master/_modules/cleanlab/datalab/internal/data_issues.html
+++ b/master/_modules/cleanlab/datalab/internal/data_issues.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
@@ -559,7 +560,7 @@ 
    Source code for cleanlab.datalab.internal.data_issues
    import warnings
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type, Union
 import numpy as np
 
 import pandas as pd
@@ -727,7 +728,7 @@ 
    Source code for cleanlab.datalab.internal.data_issues
            A dictionary that contains information and statistics about the data and each issue type.
     """
 
-    def __init__(self, data: Data, strategy: _InfoStrategy) -> None:
+    def __init__(self, data: Data, strategy: Type[_InfoStrategy]) -> None:
         self.issues: pd.DataFrame = pd.DataFrame(index=range(len(data)))
         self.issue_summary: pd.DataFrame = pd.DataFrame(
             columns=["issue_type", "score", "num_issues"]
diff --git a/master/_modules/cleanlab/datalab/internal/issue_finder.html b/master/_modules/cleanlab/datalab/internal/issue_finder.html
index 8d440d56a..0ab7a38a3 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_finder.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_finder.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
@@ -573,6 +574,7 @@ 
    Source code for cleanlab.datalab.internal.issue_finder
    RegressionPredictions,
     MultiLabelPredProbs,
 )
+from cleanlab.datalab.internal.task import Task
 
 if TYPE_CHECKING:  # pragma: no cover
     import numpy.typing as npt
@@ -700,7 +702,7 @@ 
    Source code for cleanlab.datalab.internal.issue_finder
    return args_dict
 
 
-def _select_strategy_for_resolving_required_args(task: str) -> Callable:
+def _select_strategy_for_resolving_required_args(task: Task) -> Callable:
     """Helper function that selects the strategy for resolving required arguments for each issue type.
 
     Each strategy resolves the required arguments for each issue type.
@@ -721,9 +723,9 @@ 
    Source code for cleanlab.datalab.internal.issue_finder
            Dictionary of required arguments for each issue type, if available.
     """
     strategies = {
-        "classification": _resolve_required_args_for_classification,
-        "regression": _resolve_required_args_for_regression,
-        "multilabel": _resolve_required_args_for_multilabel,
+        Task.CLASSIFICATION: _resolve_required_args_for_classification,
+        Task.REGRESSION: _resolve_required_args_for_regression,
+        Task.MULTILABEL: _resolve_required_args_for_multilabel,
     }
     selected_strategy = strategies.get(task, None)
     if selected_strategy is None:
@@ -762,7 +764,7 @@ 
    Source code for cleanlab.datalab.internal.issue_finder
        `Datalab.find_issues` method which internally utilizes an IssueFinder instance.
     """
 
-    def __init__(self, datalab: "Datalab", task: str, verbosity=1):
+    def __init__(self, datalab: "Datalab", task: Task, verbosity=1):
         self.datalab = datalab
         self.task = task
         self.verbosity = verbosity
@@ -943,9 +945,9 @@ 
    Source code for cleanlab.datalab.internal.issue_finder
    model_output = None
         if pred_probs is not None:
             model_output_dict = {
-                "regression": RegressionPredictions,
-                "classification": MultiClassPredProbs,
-                "multilabel": MultiLabelPredProbs,
+                Task.REGRESSION: RegressionPredictions,
+                Task.CLASSIFICATION: MultiClassPredProbs,
+                Task.MULTILABEL: MultiLabelPredProbs,
             }
 
             model_output_class = model_output_dict.get(self.task)
@@ -976,7 +978,7 @@ 
    Source code for cleanlab.datalab.internal.issue_finder
    drop_label_check = (
             "label" in issue_types_copy
             and not self.datalab.has_labels
-            and self.task != "regression"
+            and self.task != Task.REGRESSION
         )
 
         if drop_label_check:
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/data_valuation.html b/master/_modules/cleanlab/datalab/internal/issue_manager/data_valuation.html
index 6f79850ee..d9246cc91 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/data_valuation.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/data_valuation.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/duplicate.html b/master/_modules/cleanlab/datalab/internal/issue_manager/duplicate.html
index b14bea6d1..aa67fab16 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/duplicate.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/duplicate.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/imbalance.html b/master/_modules/cleanlab/datalab/internal/issue_manager/imbalance.html
index 32c84c3ca..d602083ce 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/imbalance.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/imbalance.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/issue_manager.html b/master/_modules/cleanlab/datalab/internal/issue_manager/issue_manager.html
index 95b1bd4f8..45a8ed9d3 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/issue_manager.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/issue_manager.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/label.html b/master/_modules/cleanlab/datalab/internal/issue_manager/label.html
index b4e58eca3..aaa8ff2a1 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/label.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/label.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/noniid.html b/master/_modules/cleanlab/datalab/internal/issue_manager/noniid.html
index f5b422638..a57a0e954 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/noniid.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/noniid.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/null.html b/master/_modules/cleanlab/datalab/internal/issue_manager/null.html
index dac92a906..93062fd4b 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/null.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/null.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/outlier.html b/master/_modules/cleanlab/datalab/internal/issue_manager/outlier.html
index df9be4f26..cc74ba8f5 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/outlier.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/outlier.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/regression/label.html b/master/_modules/cleanlab/datalab/internal/issue_manager/regression/label.html
index dfb385275..26e273d73 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/regression/label.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/regression/label.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/underperforming_group.html b/master/_modules/cleanlab/datalab/internal/issue_manager/underperforming_group.html
index 670dfe8a7..8028633c2 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/underperforming_group.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/underperforming_group.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager_factory.html b/master/_modules/cleanlab/datalab/internal/issue_manager_factory.html
index debe5ff0e..7c98ea668 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager_factory.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager_factory.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
@@ -584,10 +585,11 @@ 
    Source code for cleanlab.datalab.internal.issue_manager_factory
    )
 from cleanlab.datalab.internal.issue_manager.regression import RegressionLabelIssueManager
 from cleanlab.datalab.internal.issue_manager.multilabel.label import MultilabelIssueManager
+from cleanlab.datalab.internal.task import Task
 
 
-REGISTRY: Dict[str, Dict[str, Type[IssueManager]]] = {
-    "classification": {
+REGISTRY: Dict[Task, Dict[str, Type[IssueManager]]] = {
+    Task.CLASSIFICATION: {
         "outlier": OutlierIssueManager,
         "label": LabelIssueManager,
         "near_duplicate": NearDuplicateIssueManager,
@@ -597,14 +599,14 @@ 
    Source code for cleanlab.datalab.internal.issue_manager_factory
    "data_valuation": DataValuationIssueManager,
         "null": NullIssueManager,
     },
-    "regression": {
+    Task.REGRESSION: {
         "label": RegressionLabelIssueManager,
         "outlier": OutlierIssueManager,
         "near_duplicate": NearDuplicateIssueManager,
         "non_iid": NonIIDIssueManager,
         "null": NullIssueManager,
     },
-    "multilabel": {
+    Task.MULTILABEL: {
         "label": MultilabelIssueManager,
         "outlier": OutlierIssueManager,
         "near_duplicate": NearDuplicateIssueManager,
@@ -612,16 +614,31 @@ 
    Source code for cleanlab.datalab.internal.issue_manager_factory
    "null": NullIssueManager,
     },
 }
-"""Registry of issue managers that can be constructed from a string
+"""Registry of issue managers that can be constructed from a task and issue type
 and used in the Datalab class.
 
 :meta hide-value:
 
-Currently, the following issue managers are registered by default:
+Currently, the following issue managers are registered by default for a given task:
 
-- ``"outlier"``: :py:class:`OutlierIssueManager <cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager>`
-- ``"near_duplicate"``: :py:class:`NearDuplicateIssueManager <cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager>`
-- ``"non_iid"``: :py:class:`NonIIDIssueManager <cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager>`
+- Classification:
+
+    - ``"outlier"``: :py:class:`OutlierIssueManager <cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager>`
+    - ``"label"``: :py:class:`LabelIssueManager <cleanlab.datalab.internal.issue_manager.label.LabelIssueManager>`
+    - ``"near_duplicate"``: :py:class:`NearDuplicateIssueManager <cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager>`
+    - ``"non_iid"``: :py:class:`NonIIDIssueManager <cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager>`
+    - ``"class_imbalance"``: :py:class:`ClassImbalanceIssueManager <cleanlab.datalab.internal.issue_manager.class_imbalance.ClassImbalanceIssueManager>`
+    - ``"underperforming_group"``: :py:class:`UnderperformingGroupIssueManager <cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager>`
+    - ``"data_valuation"``: :py:class:`DataValuationIssueManager <cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager>`
+    - ``"null"``: :py:class:`NullIssueManager <cleanlab.datalab.internal.issue_manager.null.NullIssueManager>`
+    
+- Regression:
+
+    - ``"label"``: :py:class:`RegressionLabelIssueManager <cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager>`
+
+- Multilabel:
+
+    - ``"label"``: :py:class:`MultilabelIssueManager <cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager>`
 
 Warning
 -------
@@ -634,7 +651,7 @@ 
    Source code for cleanlab.datalab.internal.issue_manager_factory
        """Factory class for constructing concrete issue managers."""
 
     @classmethod
-    def from_str(cls, issue_type: str, task: str) -> Type[IssueManager]:
+    def from_str(cls, issue_type: str, task: Task) -> Type[IssueManager]:
         """Constructs a concrete issue manager class from a string."""
         if isinstance(issue_type, list):
             raise ValueError(
@@ -649,12 +666,12 @@ 
    Source code for cleanlab.datalab.internal.issue_manager_factory
    return REGISTRY[task][issue_type]
 
     @classmethod
-    def from_list(cls, issue_types: List[str], task: str) -> List[Type[IssueManager]]:
+    def from_list(cls, issue_types: List[str], task: Task) -> List[Type[IssueManager]]:
         """Constructs a list of concrete issue manager classes from a list of strings."""
         return [cls.from_str(issue_type, task) for issue_type in issue_types]
 
 
-
    [docs]def register(cls: Type[IssueManager], task: str = "classification") -> Type[IssueManager]:
+
    [docs]def register(cls: Type[IssueManager], task: str = str(Task.CLASSIFICATION)) -> Type[IssueManager]:
     """Registers the issue manager factory.
 
     Parameters
@@ -665,6 +682,8 @@ 
    Source code for cleanlab.datalab.internal.issue_manager_factory
        task :
         Specific machine learning task like classification or regression.
+        See :py:meth:`Task.from_str <cleanlab.datalab.internal.task.Task.from_str>`` for more details,
+        to see which task type corresponds to which string.
 
     Returns
     -------
@@ -711,20 +730,24 @@ 
    Source code for cleanlab.datalab.internal.issue_manager_factory
    name: str = str(cls.issue_name)
 
-    if task not in REGISTRY:
+    try:
+        _task = Task.from_str(task)
+        if _task not in REGISTRY:
+            raise ValueError(f"Invalid task type: {_task}, must be in {list(REGISTRY.keys())}")
+    except KeyError:
         raise ValueError(f"Invalid task type: {task}, must be in {list(REGISTRY.keys())}")
 
-    if name in REGISTRY[task]:
+    if name in REGISTRY[_task]:
         print(
-            f"Warning: Overwriting existing issue manager {name} with {cls} for task {task}."
+            f"Warning: Overwriting existing issue manager {name} with {cls} for task {_task}."
             "This may cause unexpected behavior."
         )
 
-    REGISTRY[task][name] = cls
+    REGISTRY[_task][name] = cls
     return cls
    
 
 
-
    [docs]def list_possible_issue_types(task: str) -> List[str]:
+
    [docs]def list_possible_issue_types(task: Task) -> List[str]:
     """Returns a list of all registered issue types.
 
     Any issue type that is not in this list cannot be used in the :py:meth:`find_issues` method.
@@ -736,16 +759,19 @@ 
    Source code for cleanlab.datalab.internal.issue_manager_factory
    return list(REGISTRY.get(task, []))
    
 
 
-
    [docs]def list_default_issue_types(task: str) -> List[str]:
+
    [docs]def list_default_issue_types(task: Task) -> List[str]:
     """Returns a list of the issue types that are run by default
     when :py:meth:`find_issues` is called without specifying `issue_types`.
 
+    task :
+        Specific machine learning task supported by Datalab.
+
     See Also
     --------
     :py:class:`REGISTRY <cleanlab.datalab.internal.issue_manager_factory.REGISTRY>` : All available issue types and their corresponding issue managers can be found here.
     """
     default_issue_types_dict = {
-        "classification": [
+        Task.CLASSIFICATION: [
             "null",
             "label",
             "outlier",
@@ -753,14 +779,14 @@ 
    Source code for cleanlab.datalab.internal.issue_manager_factory
    "non_iid",
             "class_imbalance",
         ],
-        "regression": [
+        Task.REGRESSION: [
             "null",
             "label",
             "outlier",
             "near_duplicate",
             "non_iid",
         ],
-        "multilabel": [
+        Task.MULTILABEL: [
             "null",
             "label",
             "outlier",
@@ -769,7 +795,7 @@ 
    Source code for cleanlab.datalab.internal.issue_manager_factory
    ],
     }
     if task not in default_issue_types_dict:
-        task = "classification"
+        task = Task.CLASSIFICATION
     default_issue_types = default_issue_types_dict[task]
     return default_issue_types
    
 
     
diff --git a/master/_modules/cleanlab/datalab/internal/report.html b/master/_modules/cleanlab/datalab/internal/report.html
index 76298e5c2..24b19b43e 100644
--- a/master/_modules/cleanlab/datalab/internal/report.html
+++ b/master/_modules/cleanlab/datalab/internal/report.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
@@ -552,6 +553,7 @@ 
    Source code for cleanlab.datalab.internal.report
    
 
 from cleanlab.datalab.internal.adapter.constants import DEFAULT_CLEANVISION_ISSUES
 from cleanlab.datalab.internal.issue_manager_factory import _IssueManagerFactory
+from cleanlab.datalab.internal.task import Task
 
 if TYPE_CHECKING:  # pragma: no cover
     from cleanlab.datalab.internal.data_issues import DataIssues
@@ -568,7 +570,8 @@ 
    Source code for cleanlab.datalab.internal.report
    
         and then passed to the :py:class:`Reporter` class to generate a report.
 
     task :
-        Specific machine learning task like classification or regression.
+        Specific machine learning task that the datset is intended for.
+        See details about supported tasks in :py:class:`Task <cleanlab.datalab.internal.task.Task>`.
 
     verbosity :
         The default verbosity of the report to generate. Each :py:class`IssueManager`
@@ -588,7 +591,7 @@ 
    Source code for cleanlab.datalab.internal.report
    
     def __init__(
         self,
         data_issues: "DataIssues",
-        task: str,
+        task: Task,
         verbosity: int = 1,
         include_description: bool = True,
         show_summary_score: bool = False,
diff --git a/master/_modules/cleanlab/datalab/internal/task.html b/master/_modules/cleanlab/datalab/internal/task.html
new file mode 100644
index 000000000..1c092e013
--- /dev/null
+++ b/master/_modules/cleanlab/datalab/internal/task.html
@@ -0,0 +1,733 @@
+
+
+  
+    
+    
+        
+        
+    
+
+    
+        cleanlab.datalab.internal.task - cleanlab
+      
+    
+    
+    
+    
+    
+    
+    
+    
+    
+
+
+
+  
+    
+    
+    
+
+
+  
+    Contents
+    
+      
+    
+  
+  
+    Menu
+    
+      
+      
+      
+    
+  
+  
+    Expand
+    
+      
+    
+  
+  
+    Light mode
+    
+      
+      
+      
+      
+      
+      
+      
+      
+      
+    
+  
+  
+    Dark mode
+    
+      
+      
+    
+  
+  
+    Auto light/dark mode
+    
+      
+      
+      
+      
+      
+      
+      
+    
+  
+
+
+
+
+
+
+
+
+
+
    
+  
    
+    
    
+      
+    
    
+    
    
+      
    cleanlab
    
+    
    
+    
    
+      
    
+        
+      
    
+      
+    
    
+  
    
+  
+  
    
+    
    
+      
    
+        
+          
+            
+          
+          Back to top
+        
+        
    
+          
    
+            
+          
    
+          
+        
    
+        
    
+          
+
+
+
+
+
+
+
+
    
+
+
+
+
+
+
+ 
    Source code for cleanlab.datalab.internal.task
    +# Copyright (C) 2017-2024  Cleanlab Inc.
+# This file is part of cleanlab.
+#
+# cleanlab is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published
+# by the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# cleanlab is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with cleanlab.  If not, see <https://www.gnu.org/licenses/>.
+from enum import Enum
+
+
+
    [docs]class Task(Enum):
+    """
+    Represents a task supported by Datalab.
+
+    Datalab supports the following tasks:
+
+    * **Classification**: for predicting discrete class labels.
+    * **Regression**: for predicting continuous numerical values.
+    * **Multilabel**: for predicting multiple binary labels simultaneously.
+
+    Example
+    -------
+    >>> task = Task.CLASSIFICATION
+    >>> task
+    <Task.CLASSIFICATION: 'classification'>
+    """
+
+    CLASSIFICATION = "classification"
+    """Classification task."""
+    REGRESSION = "regression"
+    """Regression task."""
+    MULTILABEL = "multilabel"
+    """Multilabel task."""
+
+    def __str__(self):
+        """
+        Returns the string representation of the task.
+
+        Returns:
+            str: The string representation of the task.
+        """
+        return self.value
+
+
    [docs]    @classmethod
+    def from_str(cls, task_str: str) -> "Task":
+        """
+        Converts a string representation of a task to a Task enum value.
+
+        Parameters
+        ----------
+        task_str :
+            The string representation of the task.
+
+        Returns
+        -------
+        Task :
+            The corresponding Task enum value.
+
+        Raises
+        ------
+        ValueError :
+            If the provided task_str is not a valid task supported by Datalab.
+
+        Examples
+        --------
+        >>> Task.from_str("classification")
+        <Task.CLASSIFICATION: 'classification'>
+        >>> print(Task.from_str("regression"))
+        regression
+        """
+        _value_to_enum = {task.value: task for task in Task}
+        try:
+            return _value_to_enum[task_str]
+        except KeyError:
+            valid_tasks = list(_value_to_enum.keys())
+            raise ValueError(f"Invalid task: {task_str}. Datalab only supports {valid_tasks}.")
    
+
+    @property
+    def is_classification(self):
+        """
+        Checks if the task is classification.
+
+        Returns
+        -------
+        bool :
+            True if the task is classification, False otherwise.
+
+        Examples
+        --------
+        >>> task = Task.CLASSIFICATION
+        >>> print(task.is_classification)
+        True
+        """
+        return self == Task.CLASSIFICATION
+
+    @property
+    def is_regression(self):
+        """
+        Checks if the task is regression.
+
+        Returns
+        -------
+        bool :
+            True if the task is regression, False otherwise.
+
+        Examples
+        --------
+        >>> task = Task.CLASSIFICATION
+        >>> print(task.is_regression)
+        False
+        """
+        return self == Task.REGRESSION
+
+    @property
+    def is_multilabel(self):
+        """
+        Checks if the task is multilabel.
+
+        Returns
+        -------
+        bool :
+            True if the task is multilabel, False otherwise.
+
+        Examples
+        --------
+        >>> task = Task.CLASSIFICATION
+        >>> print(task.is_multilabel)
+        False
+        """
+        return self == Task.MULTILABEL
    
+
     
+        
    
+      
    
+      
    
+         
+        
    
+          
+          
+        
    
+        
    
+          
    
+            
    
+                Copyright © 2024, Cleanlab Inc.
+            
    
+            Made with Sphinx and @pradyunsg's
+            
+            Furo
+            
+          
    
+          
    
+            
    
+              
+                
+                    
+                
+            
+              
+            
    
+          
    
+        
    
+        
+
+
+
+
+      
    
+    
    
+    
+  
    
+
    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+
+
+
\ No newline at end of file
diff --git a/master/_modules/cleanlab/dataset.html b/master/_modules/cleanlab/dataset.html
index 738f8f55c..f613a9fea 100644
--- a/master/_modules/cleanlab/dataset.html
+++ b/master/_modules/cleanlab/dataset.html
@@ -238,6 +238,7 @@
 
 
 
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+
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    • 
 
 
 
diff --git a/master/_modules/cleanlab/experimental/cifar_cnn.html b/master/_modules/cleanlab/experimental/cifar_cnn.html
index 84edc9eba..53f343d19 100644
--- a/master/_modules/cleanlab/experimental/cifar_cnn.html
+++ b/master/_modules/cleanlab/experimental/cifar_cnn.html
@@ -238,6 +238,7 @@
 
 
 
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+
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    • 
 
 
 
diff --git a/master/_modules/cleanlab/experimental/coteaching.html b/master/_modules/cleanlab/experimental/coteaching.html
index 278204bd7..cfe201d20 100644
--- a/master/_modules/cleanlab/experimental/coteaching.html
+++ b/master/_modules/cleanlab/experimental/coteaching.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/experimental/label_issues_batched.html b/master/_modules/cleanlab/experimental/label_issues_batched.html
index 96673cc6b..376288183 100644
--- a/master/_modules/cleanlab/experimental/label_issues_batched.html
+++ b/master/_modules/cleanlab/experimental/label_issues_batched.html
@@ -238,6 +238,7 @@
 
 
 
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+
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    • 
 
 
 
diff --git a/master/_modules/cleanlab/experimental/mnist_pytorch.html b/master/_modules/cleanlab/experimental/mnist_pytorch.html
index 6ef027e5b..b685fee38 100644
--- a/master/_modules/cleanlab/experimental/mnist_pytorch.html
+++ b/master/_modules/cleanlab/experimental/mnist_pytorch.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/filter.html b/master/_modules/cleanlab/filter.html
index bd6edb9b6..f33a309c6 100644
--- a/master/_modules/cleanlab/filter.html
+++ b/master/_modules/cleanlab/filter.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/internal/label_quality_utils.html b/master/_modules/cleanlab/internal/label_quality_utils.html
index aa1821739..4e6618dea 100644
--- a/master/_modules/cleanlab/internal/label_quality_utils.html
+++ b/master/_modules/cleanlab/internal/label_quality_utils.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/internal/latent_algebra.html b/master/_modules/cleanlab/internal/latent_algebra.html
index baef74ae9..cb636bd16 100644
--- a/master/_modules/cleanlab/internal/latent_algebra.html
+++ b/master/_modules/cleanlab/internal/latent_algebra.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/internal/multiannotator_utils.html b/master/_modules/cleanlab/internal/multiannotator_utils.html
index fd93b9ee5..3e66fdf51 100644
--- a/master/_modules/cleanlab/internal/multiannotator_utils.html
+++ b/master/_modules/cleanlab/internal/multiannotator_utils.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/internal/multilabel_scorer.html b/master/_modules/cleanlab/internal/multilabel_scorer.html
index e4df0e9d6..f292889cd 100644
--- a/master/_modules/cleanlab/internal/multilabel_scorer.html
+++ b/master/_modules/cleanlab/internal/multilabel_scorer.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/internal/multilabel_utils.html b/master/_modules/cleanlab/internal/multilabel_utils.html
index 6ffe1c292..9b73a0ca4 100644
--- a/master/_modules/cleanlab/internal/multilabel_utils.html
+++ b/master/_modules/cleanlab/internal/multilabel_utils.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/internal/outlier.html b/master/_modules/cleanlab/internal/outlier.html
index a8183a41f..a03a6aec5 100644
--- a/master/_modules/cleanlab/internal/outlier.html
+++ b/master/_modules/cleanlab/internal/outlier.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/internal/token_classification_utils.html b/master/_modules/cleanlab/internal/token_classification_utils.html
index 7a27fa947..98f44066d 100644
--- a/master/_modules/cleanlab/internal/token_classification_utils.html
+++ b/master/_modules/cleanlab/internal/token_classification_utils.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/internal/util.html b/master/_modules/cleanlab/internal/util.html
index 8e71a20f3..b9ead498a 100644
--- a/master/_modules/cleanlab/internal/util.html
+++ b/master/_modules/cleanlab/internal/util.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/internal/validation.html b/master/_modules/cleanlab/internal/validation.html
index 01c2f68fb..03dbd9e82 100644
--- a/master/_modules/cleanlab/internal/validation.html
+++ b/master/_modules/cleanlab/internal/validation.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/models/keras.html b/master/_modules/cleanlab/models/keras.html
index 4ed0e32a8..89ca1d80c 100644
--- a/master/_modules/cleanlab/models/keras.html
+++ b/master/_modules/cleanlab/models/keras.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/multiannotator.html b/master/_modules/cleanlab/multiannotator.html
index 0864cc174..a5a831d36 100644
--- a/master/_modules/cleanlab/multiannotator.html
+++ b/master/_modules/cleanlab/multiannotator.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/multilabel_classification/dataset.html b/master/_modules/cleanlab/multilabel_classification/dataset.html
index 7a782ff33..d51a0968c 100644
--- a/master/_modules/cleanlab/multilabel_classification/dataset.html
+++ b/master/_modules/cleanlab/multilabel_classification/dataset.html
@@ -238,6 +238,7 @@
 
 
 
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+
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    • 
 
 
 
diff --git a/master/_modules/cleanlab/multilabel_classification/filter.html b/master/_modules/cleanlab/multilabel_classification/filter.html
index c0f7b1234..7798f7251 100644
--- a/master/_modules/cleanlab/multilabel_classification/filter.html
+++ b/master/_modules/cleanlab/multilabel_classification/filter.html
@@ -238,6 +238,7 @@
 
 
 
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+
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    • 
 
 
 
diff --git a/master/_modules/cleanlab/multilabel_classification/rank.html b/master/_modules/cleanlab/multilabel_classification/rank.html
index 8e18e1c6d..46ccae06d 100644
--- a/master/_modules/cleanlab/multilabel_classification/rank.html
+++ b/master/_modules/cleanlab/multilabel_classification/rank.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/object_detection/filter.html b/master/_modules/cleanlab/object_detection/filter.html
index 97ef5c75c..fcc59a390 100644
--- a/master/_modules/cleanlab/object_detection/filter.html
+++ b/master/_modules/cleanlab/object_detection/filter.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/object_detection/rank.html b/master/_modules/cleanlab/object_detection/rank.html
index 4d6c79990..79c1c7fc4 100644
--- a/master/_modules/cleanlab/object_detection/rank.html
+++ b/master/_modules/cleanlab/object_detection/rank.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/object_detection/summary.html b/master/_modules/cleanlab/object_detection/summary.html
index d8c3ca714..022fcc370 100644
--- a/master/_modules/cleanlab/object_detection/summary.html
+++ b/master/_modules/cleanlab/object_detection/summary.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/outlier.html b/master/_modules/cleanlab/outlier.html
index 8a607766d..ced745e1b 100644
--- a/master/_modules/cleanlab/outlier.html
+++ b/master/_modules/cleanlab/outlier.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/rank.html b/master/_modules/cleanlab/rank.html
index e54247f19..4deb09878 100644
--- a/master/_modules/cleanlab/rank.html
+++ b/master/_modules/cleanlab/rank.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/regression/learn.html b/master/_modules/cleanlab/regression/learn.html
index 9db04ed12..d0d5e3489 100644
--- a/master/_modules/cleanlab/regression/learn.html
+++ b/master/_modules/cleanlab/regression/learn.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/regression/rank.html b/master/_modules/cleanlab/regression/rank.html
index de1377cf2..20beb65f0 100644
--- a/master/_modules/cleanlab/regression/rank.html
+++ b/master/_modules/cleanlab/regression/rank.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/segmentation/filter.html b/master/_modules/cleanlab/segmentation/filter.html
index 5017285f7..f05cc2d8f 100644
--- a/master/_modules/cleanlab/segmentation/filter.html
+++ b/master/_modules/cleanlab/segmentation/filter.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/segmentation/rank.html b/master/_modules/cleanlab/segmentation/rank.html
index 9ada56b19..52c4dc808 100644
--- a/master/_modules/cleanlab/segmentation/rank.html
+++ b/master/_modules/cleanlab/segmentation/rank.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/segmentation/summary.html b/master/_modules/cleanlab/segmentation/summary.html
index b597ac2a6..d3498d354 100644
--- a/master/_modules/cleanlab/segmentation/summary.html
+++ b/master/_modules/cleanlab/segmentation/summary.html
@@ -238,6 +238,7 @@
 
 
 
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+
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    • 
 
 
 
diff --git a/master/_modules/cleanlab/token_classification/filter.html b/master/_modules/cleanlab/token_classification/filter.html
index 1f9a8e945..43ae890e4 100644
--- a/master/_modules/cleanlab/token_classification/filter.html
+++ b/master/_modules/cleanlab/token_classification/filter.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/token_classification/rank.html b/master/_modules/cleanlab/token_classification/rank.html
index c56f0b1aa..cb89d85bf 100644
--- a/master/_modules/cleanlab/token_classification/rank.html
+++ b/master/_modules/cleanlab/token_classification/rank.html
@@ -238,6 +238,7 @@
 
 
 
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+
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diff --git a/master/_modules/cleanlab/token_classification/summary.html b/master/_modules/cleanlab/token_classification/summary.html
index 953d124e2..dde86da10 100644
--- a/master/_modules/cleanlab/token_classification/summary.html
+++ b/master/_modules/cleanlab/token_classification/summary.html
@@ -238,6 +238,7 @@
 
 
 
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+
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    • 
 
 
 
diff --git a/master/_modules/index.html b/master/_modules/index.html
index db00cc094..be778fc59 100644
--- a/master/_modules/index.html
+++ b/master/_modules/index.html
@@ -238,6 +238,7 @@
 
 
 
  • report
    • 
+
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    • 
 
 
 
@@ -546,6 +547,7 @@ 
    All modules for which code is available
    
 
  • cleanlab.datalab.internal.issue_manager.underperforming_group
    • 
 
  • cleanlab.datalab.internal.issue_manager_factory
    • 
 
  • cleanlab.datalab.internal.report
    • 
+
  • cleanlab.datalab.internal.task
    • 
 
  • cleanlab.dataset
    • 
 
  • cleanlab.experimental.cifar_cnn
    • 
 
  • cleanlab.experimental.coteaching
    • 
diff --git a/master/_sources/cleanlab/datalab/internal/index.rst b/master/_sources/cleanlab/datalab/internal/index.rst
index 2a5f6050c..6e8d335f7 100644
--- a/master/_sources/cleanlab/datalab/internal/index.rst
+++ b/master/_sources/cleanlab/datalab/internal/index.rst
@@ -21,3 +21,4 @@ internal
     factory
     issue_manager/index
     report
+    task
diff --git a/master/_sources/cleanlab/datalab/internal/task.rst b/master/_sources/cleanlab/datalab/internal/task.rst
new file mode 100644
index 000000000..42ded02f5
--- /dev/null
+++ b/master/_sources/cleanlab/datalab/internal/task.rst
@@ -0,0 +1,11 @@
+task
+====
+
+.. note:: This module is not intended to be used directly by users. It is used by the :mod:`cleanlab.datalab.datalab` module.
+
+.. automodule:: cleanlab.datalab.internal.task
+   :autosummary:
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :ignore-module-all:
\ No newline at end of file
diff --git a/master/_sources/tutorials/audio.ipynb b/master/_sources/tutorials/audio.ipynb
index 14895a485..a2c987fb9 100644
--- a/master/_sources/tutorials/audio.ipynb
+++ b/master/_sources/tutorials/audio.ipynb
@@ -91,7 +91,7 @@
     "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_advanced.ipynb b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
index 57628de2b..ef09fc76f 100644
--- a/master/_sources/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
@@ -87,7 +87,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]  # TODO: make sure this list is updated\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
index 7d51cb908..2f9a3eb5c 100644
--- a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
@@ -85,7 +85,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]  # TODO: make sure this list is updated\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb
index 9a1588077..b2db9adb1 100644
--- a/master/_sources/tutorials/datalab/tabular.ipynb
+++ b/master/_sources/tutorials/datalab/tabular.ipynb
@@ -81,7 +81,7 @@
     "dependencies = [\"cleanlab\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/datalab/text.ipynb b/master/_sources/tutorials/datalab/text.ipynb
index 13339a083..5a14ef4c3 100644
--- a/master/_sources/tutorials/datalab/text.ipynb
+++ b/master/_sources/tutorials/datalab/text.ipynb
@@ -90,7 +90,7 @@
     "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"  # disable parallelism to avoid deadlocks with huggingface\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb
index 75b5b8936..a5797f6ee 100644
--- a/master/_sources/tutorials/dataset_health.ipynb
+++ b/master/_sources/tutorials/dataset_health.ipynb
@@ -77,7 +77,7 @@
     "dependencies = [\"cleanlab\", \"requests\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/indepth_overview.ipynb b/master/_sources/tutorials/indepth_overview.ipynb
index 1979e19cb..1b95dff44 100644
--- a/master/_sources/tutorials/indepth_overview.ipynb
+++ b/master/_sources/tutorials/indepth_overview.ipynb
@@ -62,7 +62,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/multiannotator.ipynb b/master/_sources/tutorials/multiannotator.ipynb
index 06fb97345..3faabb648 100644
--- a/master/_sources/tutorials/multiannotator.ipynb
+++ b/master/_sources/tutorials/multiannotator.ipynb
@@ -96,7 +96,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
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diff --git a/master/_sources/tutorials/multilabel_classification.ipynb b/master/_sources/tutorials/multilabel_classification.ipynb
index 8dfb97daa..3a2b29659 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
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     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb
index f491f2948..7163db06a 100644
--- a/master/_sources/tutorials/object_detection.ipynb
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     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
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diff --git a/master/_sources/tutorials/outliers.ipynb b/master/_sources/tutorials/outliers.ipynb
index 492030165..12eccde38 100644
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-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
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diff --git a/master/_sources/tutorials/regression.ipynb b/master/_sources/tutorials/regression.ipynb
index d3e9e9f07..cc4da388c 100644
--- a/master/_sources/tutorials/regression.ipynb
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     "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\n",
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-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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diff --git a/master/_sources/tutorials/segmentation.ipynb b/master/_sources/tutorials/segmentation.ipynb
index 1ec4a4b86..59432150c 100644
--- a/master/_sources/tutorials/segmentation.ipynb
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-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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diff --git a/master/_sources/tutorials/tabular.ipynb b/master/_sources/tutorials/tabular.ipynb
index 8b8491d07..b17411a2f 100644
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-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
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diff --git a/master/_sources/tutorials/text.ipynb b/master/_sources/tutorials/text.ipynb
index 3e23a3669..e8f4d7239 100644
--- a/master/_sources/tutorials/text.ipynb
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     "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"  # disable parallelism to avoid deadlocks with huggingface\n",
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-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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diff --git a/master/_sources/tutorials/token_classification.ipynb b/master/_sources/tutorials/token_classification.ipynb
index 44816420d..7f1825644 100644
--- a/master/_sources/tutorials/token_classification.ipynb
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     "dependencies = [\"cleanlab\"]\n",
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-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
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diff --git a/master/cleanlab/benchmarking/index.html b/master/cleanlab/benchmarking/index.html
index 162210136..8a9bb9d26 100644
--- a/master/cleanlab/benchmarking/index.html
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index bbd1d658c..8836c2f5c 100644
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index f120dc7b1..60c7cdacd 100644
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  • task (str) – The type of machine learning task that the dataset is used for. -By default, this is set to “classification”, but you can also set it to “regression” if you are working with a regression dataset.

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  • task (str) –

    The type of machine learning task that the dataset is used for.

    +
    +
    Supported tasks:
      +

    • ”classification” (default): Multiclass classification

      • +

    • ”regression” : Regression

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    • ”multilabel” : Multilabel classification

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    +
    +
    +

  • label_name (str, optional) – The name of the label column in the dataset.

  • image_key (str, optional) – Optional key that can be specified for image datasets to point to the field containing the actual images themselves. If specified, additional image-specific issue types can be detected in the dataset. diff --git a/master/cleanlab/datalab/guide/custom_issue_manager.html b/master/cleanlab/datalab/guide/custom_issue_manager.html index 615989478..bb3974dcf 100644 --- a/master/cleanlab/datalab/guide/custom_issue_manager.html +++ b/master/cleanlab/datalab/guide/custom_issue_manager.html @@ -247,6 +247,7 @@

  • report
    • +
  • task
    • diff --git a/master/cleanlab/datalab/guide/generating_cluster_ids.html b/master/cleanlab/datalab/guide/generating_cluster_ids.html index 350d429e2..f2f9a165a 100644 --- a/master/cleanlab/datalab/guide/generating_cluster_ids.html +++ b/master/cleanlab/datalab/guide/generating_cluster_ids.html @@ -247,6 +247,7 @@
  • report
    • +
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    • diff --git a/master/cleanlab/datalab/guide/index.html b/master/cleanlab/datalab/guide/index.html index ee426e8da..e445bf691 100644 --- a/master/cleanlab/datalab/guide/index.html +++ b/master/cleanlab/datalab/guide/index.html @@ -247,6 +247,7 @@
  • report
    • +
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    • diff --git a/master/cleanlab/datalab/guide/issue_type_description.html b/master/cleanlab/datalab/guide/issue_type_description.html index d56f01635..840646935 100644 --- a/master/cleanlab/datalab/guide/issue_type_description.html +++ b/master/cleanlab/datalab/guide/issue_type_description.html @@ -247,6 +247,7 @@
  • report
    • +
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    • diff --git a/master/cleanlab/datalab/index.html b/master/cleanlab/datalab/index.html index f655d21e5..7827d1462 100644 --- a/master/cleanlab/datalab/index.html +++ b/master/cleanlab/datalab/index.html @@ -247,6 +247,7 @@
  • report
    • +
  • task
    • @@ -576,6 +577,7 @@

    API Referencefactory
  • issue_manager
  • report
    • +
  • task
    • diff --git a/master/cleanlab/datalab/internal/data.html b/master/cleanlab/datalab/internal/data.html index e28272cf0..ee68c98ce 100644 --- a/master/cleanlab/datalab/internal/data.html +++ b/master/cleanlab/datalab/internal/data.html @@ -247,6 +247,7 @@
  • report
    • +
  • task
    • @@ -559,7 +560,7 @@
    - + @@ -661,7 +662,7 @@
    -class cleanlab.datalab.internal.data.Data(data, label_name=None, map_to_int=True, is_multilabel=False)[source]#
    +class cleanlab.datalab.internal.data.Data(data, task, label_name=None)[source]#

    Bases: object

    Class that holds and validates datasets for Datalab.

    Internally, the data is stored as a datasets.Dataset object and the labels @@ -714,8 +715,24 @@

  • label_name (Union[str, List[str]]) – Name of the label column in the dataset.

    • -

  • map_to_int (bool) – Whether to map the labels to integers, e.g. [0, 1, …, K-1] where K is the number of classes. -If False, the labels are not mapped to integers, e.g. for regression tasks.

    • +

  • task (Task) –

    The task associated with the dataset. This is used to determine how to +to format the labels.

    +

    Note:

    +
    +
      +

    • If the task is a classification task, the labels

      • +
    +

    will be mapped to integers, e.g. [0, 1, …, K-1] where K is the number +of classes. If the task is a regression task, the labels will not be +mapped to integers.

    +
      +

    • If the task is a multilabel task, the labels will be formatted as a +list of lists, e.g. [[0, 1], [1, 2], [0, 2]] where each sublist contains +the labels for a single example. If the task is not a multilabel task, +the labels will be formatted as a 1D numpy array.

      • +
    +
    +

    • diff --git a/master/cleanlab/datalab/internal/data_issues.html b/master/cleanlab/datalab/internal/data_issues.html index 5ce254609..fb2d67592 100644 --- a/master/cleanlab/datalab/internal/data_issues.html +++ b/master/cleanlab/datalab/internal/data_issues.html @@ -247,6 +247,7 @@
  • report
    • +
  • task
    • @@ -576,7 +577,7 @@
    Parameters:

    • data (Data) – The data object for which the issues are being collected.

      • -

    • strategy (_InfoStrategy) – Strategy used for processing info dictionaries.

      • +

    • strategy (Type[_InfoStrategy]) – Strategy used for processing info dictionaries.

    diff --git a/master/cleanlab/datalab/internal/factory.html b/master/cleanlab/datalab/internal/factory.html index bc686db09..d8b6d1571 100644 --- a/master/cleanlab/datalab/internal/factory.html +++ b/master/cleanlab/datalab/internal/factory.html @@ -247,6 +247,7 @@
  • report
    • +
  • task
    • @@ -562,7 +563,7 @@

    Data(data[, label_name, map_to_int, ...])

    Data(data, task[, label_name])

    Class that holds and validates datasets for Datalab.

    Label(*, data[, label_name, map_to_int])
    - +

    REGISTRY

    Registry of issue managers that can be constructed from a string and used in the Datalab class.

    Registry of issue managers that can be constructed from a task and issue type and used in the Datalab class.
    @@ -585,16 +586,41 @@
    -cleanlab.datalab.internal.issue_manager_factory.REGISTRY: Dict[str, Dict[str, Type[IssueManager]]]#
    -

    Registry of issue managers that can be constructed from a string +cleanlab.datalab.internal.issue_manager_factory.REGISTRY: Dict[Task, Dict[str, Type[IssueManager]]]# +

    Registry of issue managers that can be constructed from a task and issue type and used in the Datalab class.

    -

    Currently, the following issue managers are registered by default:

    -
      +

      Currently, the following issue managers are registered by default for a given task:

      +

      Warning

      @@ -611,7 +637,9 @@

      • cls (Type[IssueManager]) – A subclass of IssueManager.

        • -

      • task (str) – Specific machine learning task like classification or regression.

        • +

      • task (str) – Specific machine learning task like classification or regression. +See Task.from_str <cleanlab.datalab.internal.task.Task.from_str>`() for more details, +to see which task type corresponds to which string.

      Return type:
      @@ -667,8 +695,16 @@
      cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types(task)[source]#

      Returns a list of the issue types that are run by default -when find_issues() is called without specifying issue_types. -:rtype: List[str]

      +when find_issues() is called without specifying issue_types.

      +
      +
      Return type:
      +

      List[str]

      +
      +
      +
      +
      task :

      Specific machine learning task supported by Datalab.

      +
      +

      See also

      REGISTRY : All available issue types and their corresponding issue managers can be found here.

      diff --git a/master/cleanlab/datalab/internal/index.html b/master/cleanlab/datalab/internal/index.html index cabbd4c07..631e9246e 100644 --- a/master/cleanlab/datalab/internal/index.html +++ b/master/cleanlab/datalab/internal/index.html @@ -247,6 +247,7 @@
  • report
    • +
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    • @@ -580,6 +581,10 @@

    internalReporter +
  • task +
    • diff --git a/master/cleanlab/datalab/internal/issue_finder.html b/master/cleanlab/datalab/internal/issue_finder.html index 1fd909454..bee50ae2d 100644 --- a/master/cleanlab/datalab/internal/issue_finder.html +++ b/master/cleanlab/datalab/internal/issue_finder.html @@ -247,6 +247,7 @@
  • report
    • +
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    • diff --git a/master/cleanlab/datalab/internal/issue_manager/_notices/not_registered.html b/master/cleanlab/datalab/internal/issue_manager/_notices/not_registered.html index 8ee23a120..b1e56428b 100644 --- a/master/cleanlab/datalab/internal/issue_manager/_notices/not_registered.html +++ b/master/cleanlab/datalab/internal/issue_manager/_notices/not_registered.html @@ -245,6 +245,7 @@
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    • diff --git a/master/cleanlab/datalab/internal/issue_manager/data_valuation.html b/master/cleanlab/datalab/internal/issue_manager/data_valuation.html index f891af754..f4f62278e 100644 --- a/master/cleanlab/datalab/internal/issue_manager/data_valuation.html +++ b/master/cleanlab/datalab/internal/issue_manager/data_valuation.html @@ -247,6 +247,7 @@
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    • diff --git a/master/cleanlab/datalab/internal/issue_manager/duplicate.html b/master/cleanlab/datalab/internal/issue_manager/duplicate.html index ed90d1972..cedf87269 100644 --- a/master/cleanlab/datalab/internal/issue_manager/duplicate.html +++ b/master/cleanlab/datalab/internal/issue_manager/duplicate.html @@ -247,6 +247,7 @@
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    • diff --git a/master/cleanlab/datalab/internal/issue_manager/imbalance.html b/master/cleanlab/datalab/internal/issue_manager/imbalance.html index d37348f25..934ba4d41 100644 --- a/master/cleanlab/datalab/internal/issue_manager/imbalance.html +++ b/master/cleanlab/datalab/internal/issue_manager/imbalance.html @@ -247,6 +247,7 @@
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    • diff --git a/master/cleanlab/datalab/internal/issue_manager/index.html b/master/cleanlab/datalab/internal/issue_manager/index.html index 3b05d49d4..819f2fd4a 100644 --- a/master/cleanlab/datalab/internal/issue_manager/index.html +++ b/master/cleanlab/datalab/internal/issue_manager/index.html @@ -247,6 +247,7 @@
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    • diff --git a/master/cleanlab/datalab/internal/issue_manager/issue_manager.html b/master/cleanlab/datalab/internal/issue_manager/issue_manager.html index 6fbb7dd33..8afc6ac1a 100644 --- a/master/cleanlab/datalab/internal/issue_manager/issue_manager.html +++ b/master/cleanlab/datalab/internal/issue_manager/issue_manager.html @@ -247,6 +247,7 @@
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    • diff --git a/master/cleanlab/datalab/internal/issue_manager/label.html b/master/cleanlab/datalab/internal/issue_manager/label.html index 5132926ac..2e6f06ca3 100644 --- a/master/cleanlab/datalab/internal/issue_manager/label.html +++ b/master/cleanlab/datalab/internal/issue_manager/label.html @@ -247,6 +247,7 @@
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    • diff --git a/master/cleanlab/datalab/internal/issue_manager/noniid.html b/master/cleanlab/datalab/internal/issue_manager/noniid.html index 9e7f2509d..c2c0961da 100644 --- a/master/cleanlab/datalab/internal/issue_manager/noniid.html +++ b/master/cleanlab/datalab/internal/issue_manager/noniid.html @@ -247,6 +247,7 @@
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    • diff --git a/master/cleanlab/datalab/internal/issue_manager/null.html b/master/cleanlab/datalab/internal/issue_manager/null.html index 2f07f81b2..cb56357f0 100644 --- a/master/cleanlab/datalab/internal/issue_manager/null.html +++ b/master/cleanlab/datalab/internal/issue_manager/null.html @@ -247,6 +247,7 @@
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    • diff --git a/master/cleanlab/datalab/internal/issue_manager/outlier.html b/master/cleanlab/datalab/internal/issue_manager/outlier.html index 14e79ef2f..737e4c382 100644 --- a/master/cleanlab/datalab/internal/issue_manager/outlier.html +++ b/master/cleanlab/datalab/internal/issue_manager/outlier.html @@ -247,6 +247,7 @@
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    • diff --git a/master/cleanlab/datalab/internal/issue_manager/regression/index.html b/master/cleanlab/datalab/internal/issue_manager/regression/index.html index 85a9c569f..8462621af 100644 --- a/master/cleanlab/datalab/internal/issue_manager/regression/index.html +++ b/master/cleanlab/datalab/internal/issue_manager/regression/index.html @@ -247,6 +247,7 @@
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    • diff --git a/master/cleanlab/datalab/internal/issue_manager/regression/label.html b/master/cleanlab/datalab/internal/issue_manager/regression/label.html index c7ee7d744..d632871ce 100644 --- a/master/cleanlab/datalab/internal/issue_manager/regression/label.html +++ b/master/cleanlab/datalab/internal/issue_manager/regression/label.html @@ -247,6 +247,7 @@
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    • diff --git a/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html b/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html index db285bdfa..68132f3c5 100644 --- a/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html +++ b/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html @@ -247,6 +247,7 @@
  • report
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    • diff --git a/master/cleanlab/datalab/internal/report.html b/master/cleanlab/datalab/internal/report.html index 1b1138e9e..009722b39 100644 --- a/master/cleanlab/datalab/internal/report.html +++ b/master/cleanlab/datalab/internal/report.html @@ -21,7 +21,7 @@ - + report - cleanlab @@ -247,6 +247,7 @@
  • report
    • +
  • task
    • @@ -565,7 +566,8 @@

    report

    data_issues (DataIssues) – The DataIssues object containing the issues to report on. This is usually generated by the Datalab class, stored in the data_issues attribute, and then passed to the Reporter class to generate a report.

    -

  • task (str) – Specific machine learning task like classification or regression.

    • +

  • task (Task) – Specific machine learning task that the datset is intended for. +See details about supported tasks in Task.

  • verbosity (int) – The default verbosity of the report to generate. Each :py:class`IssueManager` specifies the available verbosity levels and what additional information is included at each level.

    • @@ -639,12 +641,12 @@

    report
    - +
    Next
    -
    classification
    +
    task
     diff --git a/master/cleanlab/datalab/internal/task.html b/master/cleanlab/datalab/internal/task.html new file mode 100644 index 000000000..b35011415 --- /dev/null +++ b/master/cleanlab/datalab/internal/task.html @@ -0,0 +1,859 @@ + + + + + + + + + + + + + + + + + + + + task - cleanlab + + + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
    +
    +
    + +
    +
    +
    cleanlab
     +
    +
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    + +
    +
    + +
    +
    +
    + + + + + Back to top + +
    + +
    + +
    + +
    +
    + + + + + + + + +

    + + + + + + +
    +

    task#

    +
    +

    Note

    +

    This module is not intended to be used directly by users. It is used by the cleanlab.datalab.datalab module.

    +
    +

    Classes:

    +
    + + + + + + +

    Task(value[, names, module, qualname, type, ...])

    Represents a task supported by Datalab.

    +
    +
    +
    +class cleanlab.datalab.internal.task.Task(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
    +

    Bases: Enum

    +

    Represents a task supported by Datalab.

    +

    Datalab supports the following tasks:

    +
      +

    • Classification: for predicting discrete class labels.

      • +

    • Regression: for predicting continuous numerical values.

      • +

    • Multilabel: for predicting multiple binary labels simultaneously.

      • +
    +

    Example

    +
    >>> task = Task.CLASSIFICATION
+>>> task
+<Task.CLASSIFICATION: 'classification'>
+
    +
    +

    Attributes:

    +
    + + + + + + + + + + + + + + + + + + + + + +

    CLASSIFICATION

    Classification task.

    REGRESSION

    Regression task.

    MULTILABEL

    Multilabel task.

    is_classification

    Checks if the task is classification.

    is_regression

    Checks if the task is regression.

    is_multilabel

    Checks if the task is multilabel.

    +
    +

    Methods:

    +
    + + + + + + + + + + + + + + + + + + +

    from_str(task_str)

    Converts a string representation of a task to a Task enum value.

    __contains__(member)

    Return True if member is a member of this enum raises TypeError if member is not an enum member

    __getitem__(name)

    Return the member matching name.

    __iter__()

    Return members in definition order.

    __len__()

    Return the number of members (no aliases)

    +
    +
    +
    +CLASSIFICATION = 'classification'#
    +

    Classification task.

    +
    + +
    +
    +REGRESSION = 'regression'#
    +

    Regression task.

    +
    + +
    +
    +MULTILABEL = 'multilabel'#
    +

    Multilabel task.

    +
    + +
    +
    +classmethod from_str(task_str)[source]#
    +

    Converts a string representation of a task to a Task enum value.

    +
    +
    Parameters:
    +

    task_str (str) – The string representation of the task.

    +
    +
    Return type:
    +

    Task

    +
    +
    Returns:
    +

    Task – The corresponding Task enum value.

    +
    +
    Raises:
    +

    ValueError : – If the provided task_str is not a valid task supported by Datalab.

    +
    +
    +

    Examples

    +
    >>> Task.from_str("classification")
+<Task.CLASSIFICATION: 'classification'>
+>>> print(Task.from_str("regression"))
+regression
+
    +
    +
    + +
    +
    +property is_classification#
    +

    Checks if the task is classification.

    +
    +
    Returns:
    +

    bool – True if the task is classification, False otherwise.

    +
    +
    +

    Examples

    +
    >>> task = Task.CLASSIFICATION
+>>> print(task.is_classification)
+True
+
    +
    +
    + +
    +
    +classmethod __contains__(member)#
    +

    Return True if member is a member of this enum +raises TypeError if member is not an enum member

    +

    note: in 3.12 TypeError will no longer be raised, and True will also be +returned if member is the value of a member in this enum

    +
    + +
    +
    +classmethod __getitem__(name)#
    +

    Return the member matching name.

    +
    + +
    +
    +classmethod __iter__()#
    +

    Return members in definition order.

    +
    + +
    +
    +classmethod __len__()#
    +

    Return the number of members (no aliases)

    +
    + +
    +
    +property is_regression#
    +

    Checks if the task is regression.

    +
    +
    Returns:
    +

    bool – True if the task is regression, False otherwise.

    +
    +
    +

    Examples

    +
    >>> task = Task.CLASSIFICATION
+>>> print(task.is_regression)
+False
+
    +
    +
    + +
    +
    +property is_multilabel#
    +

    Checks if the task is multilabel.

    +
    +
    Returns:
    +

    bool – True if the task is multilabel, False otherwise.

    +
    +
    +

    Examples

    +
    >>> task = Task.CLASSIFICATION
+>>> print(task.is_multilabel)
+False
+
    +
    +
    + +
    + +
    + +
    +
    +
    + +
    + +
    +
    + Next +
    +
    classification
    +
    + +     + + +
    +
    + Previous +
    + +
    report
    + +
    +     +
    +
    +
    +
    + Copyright © 2024, Cleanlab Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
    +
    + + + + + + +
    +
    +
    + + + + + +
    +
    + +
    +
    + + + + + + + + + + + \ No newline at end of file diff --git a/master/cleanlab/datalab/optional_dependencies.html b/master/cleanlab/datalab/optional_dependencies.html index 95d7556d6..c3adafe53 100644 --- a/master/cleanlab/datalab/optional_dependencies.html +++ b/master/cleanlab/datalab/optional_dependencies.html @@ -247,6 +247,7 @@
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    • diff --git a/master/cleanlab/dataset.html b/master/cleanlab/dataset.html index 5cd768e1a..c49153d5c 100644 --- a/master/cleanlab/dataset.html +++ b/master/cleanlab/dataset.html @@ -247,6 +247,7 @@
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    • diff --git a/master/cleanlab/experimental/cifar_cnn.html b/master/cleanlab/experimental/cifar_cnn.html index 72c51cedb..a0800d4d8 100644 --- a/master/cleanlab/experimental/cifar_cnn.html +++ b/master/cleanlab/experimental/cifar_cnn.html @@ -247,6 +247,7 @@
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Install and import required dependencies": [[76, "1.-Install-and-import-required-dependencies"], [82, "1.-Install-and-import-required-dependencies"], [85, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[76, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[76, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[76, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[76, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[76, "Get-additional-information"]], "Near duplicate issues": [[76, "Near-duplicate-issues"], [82, "Near-duplicate-issues"]], "Datalab Tutorials": [[77, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[78, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "1. Install required dependencies": [[78, "1.-Install-required-dependencies"], [79, "1.-Install-required-dependencies"], [90, "1.-Install-required-dependencies"], [92, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"]], "2. Load and process the data": [[78, "2.-Load-and-process-the-data"], [90, "2.-Load-and-process-the-data"], [92, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[78, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [92, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Construct K nearest neighbours graph": [[78, "4.-Construct-K-nearest-neighbours-graph"]], "Label issues": [[78, "Label-issues"], [79, "Label-issues"], [82, "Label-issues"]], "Outlier issues": [[78, "Outlier-issues"], [79, "Outlier-issues"], [82, "Outlier-issues"]], "Near-duplicate issues": [[78, "Near-duplicate-issues"], [79, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[79, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "2. Load and format the text dataset": [[79, "2.-Load-and-format-the-text-dataset"], [93, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[79, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[79, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[79, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[80, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[80, "Install-dependencies-and-import-them"], [83, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[80, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[80, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[81, "FAQ"]], "What data can cleanlab detect issues in?": [[81, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[81, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[81, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[81, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[81, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[81, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[81, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[81, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[81, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[81, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[81, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[81, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[81, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[81, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[82, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[82, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[82, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[82, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[82, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[82, "7.-Use-cleanlab-to-find-issues"]], "View report": [[82, "View-report"]], "View most likely examples with label errors": [[82, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[82, "View-most-severe-outliers"]], "View sets of near duplicate images": [[82, "View-sets-of-near-duplicate-images"]], "Dark images": [[82, "Dark-images"]], "View top examples of dark images": [[82, "View-top-examples-of-dark-images"]], "Low information images": [[82, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[83, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[83, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[83, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[83, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[83, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[83, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[83, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[83, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[83, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[83, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[83, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[83, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[83, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[83, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[83, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[83, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[83, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[83, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[83, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[83, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[83, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[83, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[84, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[85, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[85, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[85, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[85, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[85, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[85, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[85, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[85, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[85, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[86, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[86, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[86, "2.-Format-data,-labels,-and-model-predictions"], [87, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[86, "3.-Use-cleanlab-to-find-label-issues"], [87, "3.-Use-cleanlab-to-find-label-issues"], [91, "3.-Use-cleanlab-to-find-label-issues"], [94, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[86, "Label-quality-scores"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[86, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Finding Label Errors in Object Detection Datasets": [[87, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[87, "1.-Install-required-dependencies-and-download-data"], [91, "1.-Install-required-dependencies-and-download-data"], [94, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[87, "Get-label-quality-scores"], [91, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[87, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[87, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[87, "Other-uses-of-visualize"]], "Exploratory data analysis": [[87, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[88, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[88, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[88, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[88, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[88, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[88, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[89, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[89, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[89, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[90, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[90, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[90, "4.-Train-a-more-robust-model-from-noisy-labels"], [93, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[90, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[91, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[91, "2.-Get-data,-labels,-and-pred_probs"], [94, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[91, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[91, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[91, "Focusing-on-one-specific-class"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[92, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "4. Use cleanlab to find label issues": [[92, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[92, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[93, "Text-Classification-with-Noisy-Labels"]], "3. Define a classification model and use cleanlab to find potential label errors": [[93, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "Find Label Errors in Token Classification (Text) Datasets": [[94, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[94, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[94, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[94, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[94, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.datalab.datalab"], [9, 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cleanlab.datalab.internal.issue_manager.data_valuation)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info 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attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[20, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], 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"module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[42, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[43, "module-cleanlab.internal.multilabel_utils"]], "token_classification_utils": [[45, "module-cleanlab.internal.token_classification_utils"]], "util": [[46, "module-cleanlab.internal.util"]], "validation": [[47, "module-cleanlab.internal.validation"]], "fasttext": [[48, "fasttext"]], "models": [[49, "models"]], "keras": [[50, "module-cleanlab.models.keras"]], "multiannotator": [[51, "module-cleanlab.multiannotator"]], "multilabel_classification": [[54, "multilabel-classification"]], "rank": [[55, "module-cleanlab.multilabel_classification.rank"], [58, "module-cleanlab.object_detection.rank"], [61, "module-cleanlab.rank"], [67, "module-cleanlab.segmentation.rank"], [71, "module-cleanlab.token_classification.rank"]], "object_detection": [[57, "object-detection"]], "summary": [[59, "summary"], [68, "module-cleanlab.segmentation.summary"], [72, "module-cleanlab.token_classification.summary"]], "regression.learn": [[63, "module-cleanlab.regression.learn"]], "regression.rank": [[64, "module-cleanlab.regression.rank"]], "segmentation": [[66, "segmentation"]], "token_classification": [[70, "token-classification"]], "cleanlab open-source documentation": [[73, "cleanlab-open-source-documentation"]], "Quickstart": [[73, "quickstart"]], "1. Install cleanlab": [[73, "install-cleanlab"]], "2. Find common issues in your data": [[73, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[73, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[73, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[73, "improve-your-data-via-many-other-techniques"]], "Contributing": [[73, "contributing"]], "Easy Mode": [[73, "easy-mode"], [79, "Easy-Mode"], [80, "Easy-Mode"], [83, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[74, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[74, "function-and-class-name-changes"]], "Module name changes": [[74, "module-name-changes"]], "New modules": [[74, "new-modules"]], "Removed modules": [[74, "removed-modules"]], "Common argument and variable name changes": [[74, "common-argument-and-variable-name-changes"]], "Audio Classification with SpeechBrain and Cleanlab": [[75, "Audio-Classification-with-SpeechBrain-and-Cleanlab"]], "1. Install dependencies and import them": [[75, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[75, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[75, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[75, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[75, "5.-Use-cleanlab-to-find-label-issues"], [79, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[76, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[76, "Install-and-import-required-dependencies"]], "Create and load the data": [[76, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[76, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[76, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[76, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[76, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[76, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[76, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[77, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[77, "1.-Install-and-import-required-dependencies"], [83, "1.-Install-and-import-required-dependencies"], [86, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[77, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[77, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[77, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[77, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[77, "Get-additional-information"]], "Near duplicate issues": [[77, "Near-duplicate-issues"], [83, "Near-duplicate-issues"]], "Datalab Tutorials": [[78, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[79, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "1. Install required dependencies": [[79, "1.-Install-required-dependencies"], [80, "1.-Install-required-dependencies"], [91, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"]], "2. Load and process the data": [[79, "2.-Load-and-process-the-data"], [91, "2.-Load-and-process-the-data"], [93, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[79, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [93, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Construct K nearest neighbours graph": [[79, "4.-Construct-K-nearest-neighbours-graph"]], "Label issues": [[79, "Label-issues"], [80, "Label-issues"], [83, "Label-issues"]], "Outlier issues": [[79, "Outlier-issues"], [80, "Outlier-issues"], [83, "Outlier-issues"]], "Near-duplicate issues": [[79, "Near-duplicate-issues"], [80, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[80, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "2. Load and format the text dataset": [[80, "2.-Load-and-format-the-text-dataset"], [94, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[80, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[80, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[80, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[81, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[81, "Install-dependencies-and-import-them"], [84, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[81, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[81, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[82, "FAQ"]], "What data can cleanlab detect issues in?": [[82, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[82, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[82, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[82, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[82, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[82, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[82, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[82, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[82, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[82, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[82, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[82, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[82, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[82, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[83, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[83, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[83, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[83, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[83, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[83, "7.-Use-cleanlab-to-find-issues"]], "View report": [[83, "View-report"]], "View most likely examples with label errors": [[83, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[83, "View-most-severe-outliers"]], "View sets of near duplicate images": [[83, "View-sets-of-near-duplicate-images"]], "Dark images": [[83, "Dark-images"]], "View top examples of dark images": [[83, "View-top-examples-of-dark-images"]], "Low information images": [[83, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[84, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[84, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[84, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[84, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[84, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[84, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[84, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[84, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[84, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[84, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[84, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[84, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[84, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[84, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[84, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[84, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[84, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[84, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[84, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[84, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[84, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[84, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[85, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[86, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[86, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[86, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[86, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[86, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[86, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[86, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[86, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[86, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[87, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[87, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[87, "2.-Format-data,-labels,-and-model-predictions"], [88, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[87, "3.-Use-cleanlab-to-find-label-issues"], [88, "3.-Use-cleanlab-to-find-label-issues"], [92, "3.-Use-cleanlab-to-find-label-issues"], [95, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[87, "Label-quality-scores"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[87, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Finding Label Errors in Object Detection Datasets": [[88, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[88, "1.-Install-required-dependencies-and-download-data"], [92, "1.-Install-required-dependencies-and-download-data"], [95, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[88, "Get-label-quality-scores"], [92, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[88, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[88, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[88, "Other-uses-of-visualize"]], "Exploratory data analysis": [[88, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[89, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[89, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[89, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[89, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[89, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[89, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[90, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[90, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[90, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[91, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[91, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[91, "4.-Train-a-more-robust-model-from-noisy-labels"], [94, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[91, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[92, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[92, "2.-Get-data,-labels,-and-pred_probs"], [95, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[92, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[92, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[92, "Focusing-on-one-specific-class"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[93, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "4. Use cleanlab to find label issues": [[93, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[93, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[94, "Text-Classification-with-Noisy-Labels"]], "3. Define a classification model and use cleanlab to find potential label errors": [[94, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "Find Label Errors in Token Classification (Text) Datasets": [[95, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[95, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[95, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[95, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[95, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.datalab.datalab"], [9, 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(cleanlab.datalab.internal.data.multilabel property)": [[10, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[10, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[10, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[10, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[10, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[10, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[10, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[10, 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"set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[12, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[13, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[14, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[17, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() 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[[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager 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"cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[42, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[42, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[43, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[43, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[43, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[43, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[43, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.outlier": [[44, 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"get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[61, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[61, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[61, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[62, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[63, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[63, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[63, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[63, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[63, "cleanlab.regression.learn.CleanLearning.fit"]], 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a/master/tutorials/audio.ipynb +++ b/master/tutorials/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:16.706668Z", - "iopub.status.busy": "2024-02-08T14:16:16.706135Z", - "iopub.status.idle": "2024-02-08T14:16:21.469375Z", - "shell.execute_reply": "2024-02-08T14:16:21.468815Z" + "iopub.execute_input": "2024-02-09T00:04:09.983398Z", + "iopub.status.busy": "2024-02-09T00:04:09.982990Z", + "iopub.status.idle": "2024-02-09T00:04:15.080345Z", + "shell.execute_reply": "2024-02-09T00:04:15.079734Z" }, "nbsphinx": "hidden" }, @@ -97,7 +97,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -131,10 +131,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:21.472086Z", - "iopub.status.busy": "2024-02-08T14:16:21.471643Z", - "iopub.status.idle": "2024-02-08T14:16:21.474958Z", - "shell.execute_reply": "2024-02-08T14:16:21.474511Z" + "iopub.execute_input": "2024-02-09T00:04:15.083132Z", + "iopub.status.busy": "2024-02-09T00:04:15.082725Z", + "iopub.status.idle": "2024-02-09T00:04:15.086054Z", + "shell.execute_reply": "2024-02-09T00:04:15.085531Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:21.476951Z", - "iopub.status.busy": "2024-02-08T14:16:21.476627Z", - "iopub.status.idle": "2024-02-08T14:16:21.481318Z", - "shell.execute_reply": "2024-02-08T14:16:21.480899Z" + "iopub.execute_input": "2024-02-09T00:04:15.088084Z", + "iopub.status.busy": "2024-02-09T00:04:15.087776Z", + "iopub.status.idle": "2024-02-09T00:04:15.092579Z", + "shell.execute_reply": "2024-02-09T00:04:15.092051Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-02-08T14:16:21.483380Z", - "iopub.status.busy": "2024-02-08T14:16:21.482960Z", - "iopub.status.idle": "2024-02-08T14:16:23.517207Z", - "shell.execute_reply": "2024-02-08T14:16:23.516586Z" + "iopub.execute_input": "2024-02-09T00:04:15.094795Z", + "iopub.status.busy": "2024-02-09T00:04:15.094518Z", + "iopub.status.idle": "2024-02-09T00:04:16.867434Z", + "shell.execute_reply": "2024-02-09T00:04:16.866687Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-02-08T14:16:23.519855Z", - "iopub.status.busy": "2024-02-08T14:16:23.519460Z", - "iopub.status.idle": "2024-02-08T14:16:23.530252Z", - "shell.execute_reply": "2024-02-08T14:16:23.529727Z" + "iopub.execute_input": "2024-02-09T00:04:16.870850Z", + "iopub.status.busy": "2024-02-09T00:04:16.870344Z", + "iopub.status.idle": "2024-02-09T00:04:16.882323Z", + "shell.execute_reply": "2024-02-09T00:04:16.881733Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:23.560826Z", - "iopub.status.busy": "2024-02-08T14:16:23.560433Z", - "iopub.status.idle": "2024-02-08T14:16:23.565962Z", - "shell.execute_reply": "2024-02-08T14:16:23.565519Z" + "iopub.execute_input": "2024-02-09T00:04:16.915555Z", + "iopub.status.busy": "2024-02-09T00:04:16.915146Z", + "iopub.status.idle": "2024-02-09T00:04:16.920899Z", + "shell.execute_reply": "2024-02-09T00:04:16.920335Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-02-08T14:16:23.567988Z", - "iopub.status.busy": "2024-02-08T14:16:23.567661Z", - "iopub.status.idle": "2024-02-08T14:16:24.017237Z", - "shell.execute_reply": "2024-02-08T14:16:24.016742Z" + "iopub.execute_input": "2024-02-09T00:04:16.922935Z", + "iopub.status.busy": "2024-02-09T00:04:16.922596Z", + "iopub.status.idle": "2024-02-09T00:04:17.370996Z", + "shell.execute_reply": "2024-02-09T00:04:17.370407Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:24.019363Z", - "iopub.status.busy": "2024-02-08T14:16:24.019005Z", - "iopub.status.idle": "2024-02-08T14:16:25.951969Z", - "shell.execute_reply": "2024-02-08T14:16:25.951408Z" + "iopub.execute_input": "2024-02-09T00:04:17.373133Z", + "iopub.status.busy": "2024-02-09T00:04:17.372825Z", + "iopub.status.idle": "2024-02-09T00:04:18.459078Z", + "shell.execute_reply": "2024-02-09T00:04:18.458559Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-02-08T14:16:25.954331Z", - "iopub.status.busy": "2024-02-08T14:16:25.953925Z", - "iopub.status.idle": "2024-02-08T14:16:25.974868Z", - "shell.execute_reply": "2024-02-08T14:16:25.974349Z" + "iopub.execute_input": "2024-02-09T00:04:18.461555Z", + "iopub.status.busy": "2024-02-09T00:04:18.461293Z", + "iopub.status.idle": "2024-02-09T00:04:18.481600Z", + "shell.execute_reply": "2024-02-09T00:04:18.481092Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:25.976899Z", - "iopub.status.busy": "2024-02-08T14:16:25.976515Z", - "iopub.status.idle": "2024-02-08T14:16:25.979525Z", - "shell.execute_reply": "2024-02-08T14:16:25.979083Z" + "iopub.execute_input": "2024-02-09T00:04:18.483551Z", + "iopub.status.busy": "2024-02-09T00:04:18.483236Z", + "iopub.status.idle": "2024-02-09T00:04:18.486122Z", + "shell.execute_reply": "2024-02-09T00:04:18.485710Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:25.981413Z", - "iopub.status.busy": "2024-02-08T14:16:25.981105Z", - "iopub.status.idle": "2024-02-08T14:16:39.974956Z", - "shell.execute_reply": "2024-02-08T14:16:39.974329Z" + "iopub.execute_input": "2024-02-09T00:04:18.488110Z", + "iopub.status.busy": "2024-02-09T00:04:18.487796Z", + "iopub.status.idle": "2024-02-09T00:04:32.986572Z", + "shell.execute_reply": "2024-02-09T00:04:32.986041Z" }, "id": "2FSQ2GR9R_YA" }, @@ -627,10 +627,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-02-08T14:16:39.977858Z", - "iopub.status.busy": "2024-02-08T14:16:39.977387Z", - "iopub.status.idle": "2024-02-08T14:16:39.981171Z", - "shell.execute_reply": "2024-02-08T14:16:39.980626Z" + "iopub.execute_input": "2024-02-09T00:04:32.989352Z", + "iopub.status.busy": "2024-02-09T00:04:32.988816Z", + "iopub.status.idle": "2024-02-09T00:04:32.992607Z", + "shell.execute_reply": "2024-02-09T00:04:32.992136Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -689,10 +689,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:39.983316Z", - "iopub.status.busy": "2024-02-08T14:16:39.982964Z", - "iopub.status.idle": "2024-02-08T14:16:40.681904Z", - "shell.execute_reply": "2024-02-08T14:16:40.681309Z" + "iopub.execute_input": "2024-02-09T00:04:32.994721Z", + "iopub.status.busy": "2024-02-09T00:04:32.994330Z", + "iopub.status.idle": "2024-02-09T00:04:33.687787Z", + "shell.execute_reply": "2024-02-09T00:04:33.687211Z" }, "id": "i_drkY9YOcw4" }, @@ -726,10 +726,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-02-08T14:16:40.685601Z", - "iopub.status.busy": "2024-02-08T14:16:40.684659Z", - "iopub.status.idle": "2024-02-08T14:16:40.691330Z", - "shell.execute_reply": "2024-02-08T14:16:40.690821Z" + "iopub.execute_input": "2024-02-09T00:04:33.690624Z", + "iopub.status.busy": "2024-02-09T00:04:33.690250Z", + "iopub.status.idle": "2024-02-09T00:04:33.694895Z", + "shell.execute_reply": "2024-02-09T00:04:33.694413Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -776,10 +776,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:40.694784Z", - "iopub.status.busy": "2024-02-08T14:16:40.693891Z", - "iopub.status.idle": "2024-02-08T14:16:40.807297Z", - "shell.execute_reply": "2024-02-08T14:16:40.806610Z" + "iopub.execute_input": "2024-02-09T00:04:33.697194Z", + "iopub.status.busy": "2024-02-09T00:04:33.696840Z", + "iopub.status.idle": "2024-02-09T00:04:33.824254Z", + "shell.execute_reply": "2024-02-09T00:04:33.823745Z" } }, "outputs": [ @@ -816,10 +816,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:40.809569Z", - "iopub.status.busy": "2024-02-08T14:16:40.809330Z", - "iopub.status.idle": "2024-02-08T14:16:40.819013Z", - "shell.execute_reply": "2024-02-08T14:16:40.818580Z" + "iopub.execute_input": "2024-02-09T00:04:33.826518Z", + "iopub.status.busy": "2024-02-09T00:04:33.826154Z", + "iopub.status.idle": "2024-02-09T00:04:33.835869Z", + "shell.execute_reply": "2024-02-09T00:04:33.835416Z" }, "scrolled": true }, @@ -874,10 +874,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:40.821140Z", - "iopub.status.busy": "2024-02-08T14:16:40.820735Z", - "iopub.status.idle": "2024-02-08T14:16:40.828511Z", - "shell.execute_reply": "2024-02-08T14:16:40.827967Z" + "iopub.execute_input": "2024-02-09T00:04:33.837811Z", + "iopub.status.busy": "2024-02-09T00:04:33.837484Z", + "iopub.status.idle": "2024-02-09T00:04:33.845010Z", + "shell.execute_reply": "2024-02-09T00:04:33.844563Z" } }, "outputs": [ @@ -981,10 +981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:40.830582Z", - "iopub.status.busy": "2024-02-08T14:16:40.830213Z", - "iopub.status.idle": "2024-02-08T14:16:40.834337Z", - "shell.execute_reply": "2024-02-08T14:16:40.833913Z" + "iopub.execute_input": "2024-02-09T00:04:33.847004Z", + "iopub.status.busy": "2024-02-09T00:04:33.846747Z", + "iopub.status.idle": "2024-02-09T00:04:33.850633Z", + "shell.execute_reply": "2024-02-09T00:04:33.850090Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-02-08T14:16:40.836276Z", - "iopub.status.busy": "2024-02-08T14:16:40.836021Z", - "iopub.status.idle": "2024-02-08T14:16:40.841502Z", - "shell.execute_reply": "2024-02-08T14:16:40.841016Z" + "iopub.execute_input": "2024-02-09T00:04:33.852538Z", + "iopub.status.busy": "2024-02-09T00:04:33.852276Z", + "iopub.status.idle": "2024-02-09T00:04:33.857781Z", + "shell.execute_reply": "2024-02-09T00:04:33.857304Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1152,10 +1152,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-02-08T14:16:40.843515Z", - "iopub.status.busy": "2024-02-08T14:16:40.843258Z", - "iopub.status.idle": "2024-02-08T14:16:40.954512Z", - "shell.execute_reply": "2024-02-08T14:16:40.954025Z" + "iopub.execute_input": "2024-02-09T00:04:33.859846Z", + "iopub.status.busy": "2024-02-09T00:04:33.859521Z", + "iopub.status.idle": "2024-02-09T00:04:33.984872Z", + "shell.execute_reply": "2024-02-09T00:04:33.984316Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1209,10 +1209,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-02-08T14:16:40.956724Z", - "iopub.status.busy": "2024-02-08T14:16:40.956377Z", - "iopub.status.idle": "2024-02-08T14:16:41.061361Z", - "shell.execute_reply": "2024-02-08T14:16:41.060831Z" + "iopub.execute_input": "2024-02-09T00:04:33.987258Z", + "iopub.status.busy": "2024-02-09T00:04:33.987046Z", + "iopub.status.idle": "2024-02-09T00:04:34.090532Z", + "shell.execute_reply": "2024-02-09T00:04:34.090018Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1257,10 +1257,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-02-08T14:16:41.063543Z", - "iopub.status.busy": "2024-02-08T14:16:41.063216Z", - "iopub.status.idle": "2024-02-08T14:16:41.163077Z", - "shell.execute_reply": "2024-02-08T14:16:41.162560Z" + "iopub.execute_input": "2024-02-09T00:04:34.092619Z", + "iopub.status.busy": "2024-02-09T00:04:34.092336Z", + "iopub.status.idle": "2024-02-09T00:04:34.192557Z", + "shell.execute_reply": "2024-02-09T00:04:34.192042Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1301,10 +1301,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:41.165246Z", - "iopub.status.busy": 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a/master/tutorials/datalab/datalab_advanced.ipynb b/master/tutorials/datalab/datalab_advanced.ipynb index 7dcba895d..65d832198 100644 --- a/master/tutorials/datalab/datalab_advanced.ipynb +++ b/master/tutorials/datalab/datalab_advanced.ipynb @@ -80,10 +80,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:44.667503Z", - "iopub.status.busy": "2024-02-08T14:16:44.667333Z", - "iopub.status.idle": "2024-02-08T14:16:45.765513Z", - "shell.execute_reply": "2024-02-08T14:16:45.764988Z" + "iopub.execute_input": "2024-02-09T00:04:37.584876Z", + "iopub.status.busy": "2024-02-09T00:04:37.584705Z", + "iopub.status.idle": "2024-02-09T00:04:38.652724Z", + "shell.execute_reply": "2024-02-09T00:04:38.652123Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -118,10 +118,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:45.767834Z", - "iopub.status.busy": "2024-02-08T14:16:45.767566Z", - "iopub.status.idle": "2024-02-08T14:16:45.770485Z", - "shell.execute_reply": "2024-02-08T14:16:45.769973Z" + "iopub.execute_input": "2024-02-09T00:04:38.655296Z", + "iopub.status.busy": "2024-02-09T00:04:38.655043Z", + "iopub.status.idle": "2024-02-09T00:04:38.658034Z", + "shell.execute_reply": "2024-02-09T00:04:38.657603Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:45.772405Z", - "iopub.status.busy": "2024-02-08T14:16:45.772232Z", - "iopub.status.idle": "2024-02-08T14:16:45.780526Z", - "shell.execute_reply": "2024-02-08T14:16:45.780085Z" + "iopub.execute_input": "2024-02-09T00:04:38.660161Z", + "iopub.status.busy": "2024-02-09T00:04:38.659801Z", + "iopub.status.idle": "2024-02-09T00:04:38.668370Z", + "shell.execute_reply": "2024-02-09T00:04:38.667820Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:45.782381Z", - "iopub.status.busy": "2024-02-08T14:16:45.782206Z", - "iopub.status.idle": "2024-02-08T14:16:45.787036Z", - "shell.execute_reply": "2024-02-08T14:16:45.786625Z" + "iopub.execute_input": "2024-02-09T00:04:38.670230Z", + "iopub.status.busy": "2024-02-09T00:04:38.669940Z", + "iopub.status.idle": "2024-02-09T00:04:38.674660Z", + "shell.execute_reply": "2024-02-09T00:04:38.674118Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:45.789006Z", - "iopub.status.busy": "2024-02-08T14:16:45.788746Z", - "iopub.status.idle": "2024-02-08T14:16:45.972508Z", - "shell.execute_reply": "2024-02-08T14:16:45.971957Z" + "iopub.execute_input": "2024-02-09T00:04:38.676959Z", + "iopub.status.busy": "2024-02-09T00:04:38.676636Z", + "iopub.status.idle": "2024-02-09T00:04:38.857443Z", + "shell.execute_reply": "2024-02-09T00:04:38.856853Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:45.974779Z", - "iopub.status.busy": "2024-02-08T14:16:45.974504Z", - "iopub.status.idle": "2024-02-08T14:16:46.345529Z", - "shell.execute_reply": "2024-02-08T14:16:46.344993Z" + "iopub.execute_input": "2024-02-09T00:04:38.859687Z", + "iopub.status.busy": "2024-02-09T00:04:38.859477Z", + "iopub.status.idle": "2024-02-09T00:04:39.231483Z", + "shell.execute_reply": "2024-02-09T00:04:39.230915Z" } }, 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+ "shell.execute_reply": "2024-02-09T00:04:39.271877Z" } }, "outputs": [], @@ -641,10 +641,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:46.389160Z", - "iopub.status.busy": "2024-02-08T14:16:46.388784Z", - "iopub.status.idle": "2024-02-08T14:16:48.015391Z", - "shell.execute_reply": "2024-02-08T14:16:48.014807Z" + "iopub.execute_input": "2024-02-09T00:04:39.274218Z", + "iopub.status.busy": "2024-02-09T00:04:39.273960Z", + "iopub.status.idle": "2024-02-09T00:04:40.864317Z", + "shell.execute_reply": "2024-02-09T00:04:40.863709Z" } }, "outputs": [ @@ -708,10 +708,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:48.017953Z", - "iopub.status.busy": "2024-02-08T14:16:48.017287Z", - "iopub.status.idle": "2024-02-08T14:16:48.039678Z", - "shell.execute_reply": "2024-02-08T14:16:48.039212Z" + "iopub.execute_input": "2024-02-09T00:04:40.866936Z", + "iopub.status.busy": 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"version_major": 2, diff --git a/master/tutorials/datalab/datalab_quickstart.html b/master/tutorials/datalab/datalab_quickstart.html index fbf1392d0..ea8a1b546 100644 --- a/master/tutorials/datalab/datalab_quickstart.html +++ b/master/tutorials/datalab/datalab_quickstart.html @@ -248,6 +248,7 @@
  • report
    • +
  • task
    • diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index cb07825cf..4da0c16b5 100644 --- a/master/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/tutorials/datalab/datalab_quickstart.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:50.936026Z", - "iopub.status.busy": "2024-02-08T14:16:50.935853Z", - "iopub.status.idle": "2024-02-08T14:16:52.025764Z", - "shell.execute_reply": "2024-02-08T14:16:52.025235Z" + "iopub.execute_input": "2024-02-09T00:04:43.655042Z", + "iopub.status.busy": "2024-02-09T00:04:43.654875Z", + "iopub.status.idle": "2024-02-09T00:04:44.728830Z", + "shell.execute_reply": "2024-02-09T00:04:44.728239Z" }, "nbsphinx": "hidden" }, @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -116,10 +116,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:52.028077Z", - "iopub.status.busy": "2024-02-08T14:16:52.027825Z", - "iopub.status.idle": "2024-02-08T14:16:52.030713Z", - "shell.execute_reply": "2024-02-08T14:16:52.030287Z" + "iopub.execute_input": "2024-02-09T00:04:44.731406Z", + "iopub.status.busy": "2024-02-09T00:04:44.731145Z", + "iopub.status.idle": "2024-02-09T00:04:44.734162Z", + "shell.execute_reply": "2024-02-09T00:04:44.733732Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:52.032663Z", - "iopub.status.busy": "2024-02-08T14:16:52.032488Z", - "iopub.status.idle": "2024-02-08T14:16:52.041252Z", - "shell.execute_reply": "2024-02-08T14:16:52.040830Z" + "iopub.execute_input": "2024-02-09T00:04:44.736253Z", + "iopub.status.busy": "2024-02-09T00:04:44.735995Z", + "iopub.status.idle": "2024-02-09T00:04:44.744979Z", + "shell.execute_reply": "2024-02-09T00:04:44.744562Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:52.043122Z", - "iopub.status.busy": "2024-02-08T14:16:52.042929Z", - "iopub.status.idle": "2024-02-08T14:16:52.047314Z", - "shell.execute_reply": "2024-02-08T14:16:52.046886Z" + "iopub.execute_input": "2024-02-09T00:04:44.746967Z", + "iopub.status.busy": "2024-02-09T00:04:44.746627Z", + "iopub.status.idle": "2024-02-09T00:04:44.751229Z", + "shell.execute_reply": "2024-02-09T00:04:44.750667Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:52.049232Z", - "iopub.status.busy": "2024-02-08T14:16:52.049057Z", - "iopub.status.idle": "2024-02-08T14:16:52.230515Z", - "shell.execute_reply": "2024-02-08T14:16:52.229917Z" + "iopub.execute_input": "2024-02-09T00:04:44.753538Z", + "iopub.status.busy": "2024-02-09T00:04:44.753219Z", + "iopub.status.idle": "2024-02-09T00:04:44.935735Z", + "shell.execute_reply": "2024-02-09T00:04:44.935238Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:52.232957Z", - "iopub.status.busy": "2024-02-08T14:16:52.232747Z", - "iopub.status.idle": "2024-02-08T14:16:52.547982Z", - "shell.execute_reply": "2024-02-08T14:16:52.547415Z" + "iopub.execute_input": "2024-02-09T00:04:44.937927Z", + "iopub.status.busy": "2024-02-09T00:04:44.937604Z", + "iopub.status.idle": "2024-02-09T00:04:45.249989Z", + "shell.execute_reply": "2024-02-09T00:04:45.249396Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:52.550352Z", - "iopub.status.busy": "2024-02-08T14:16:52.549893Z", - "iopub.status.idle": "2024-02-08T14:16:52.552804Z", - "shell.execute_reply": "2024-02-08T14:16:52.552384Z" + "iopub.execute_input": "2024-02-09T00:04:45.252341Z", + "iopub.status.busy": "2024-02-09T00:04:45.251920Z", + "iopub.status.idle": "2024-02-09T00:04:45.254608Z", + "shell.execute_reply": "2024-02-09T00:04:45.254176Z" } }, "outputs": [], @@ -601,10 +601,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:52.554966Z", - "iopub.status.busy": "2024-02-08T14:16:52.554647Z", - "iopub.status.idle": "2024-02-08T14:16:52.590225Z", - "shell.execute_reply": "2024-02-08T14:16:52.589747Z" + "iopub.execute_input": "2024-02-09T00:04:45.256737Z", + "iopub.status.busy": "2024-02-09T00:04:45.256317Z", + "iopub.status.idle": "2024-02-09T00:04:45.291712Z", + "shell.execute_reply": "2024-02-09T00:04:45.291157Z" } }, "outputs": [ @@ -646,10 +646,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:52.592197Z", - "iopub.status.busy": "2024-02-08T14:16:52.592024Z", - "iopub.status.idle": "2024-02-08T14:16:54.212232Z", - "shell.execute_reply": "2024-02-08T14:16:54.211591Z" + "iopub.execute_input": "2024-02-09T00:04:45.293784Z", + "iopub.status.busy": "2024-02-09T00:04:45.293485Z", + "iopub.status.idle": "2024-02-09T00:04:46.926137Z", + "shell.execute_reply": "2024-02-09T00:04:46.925480Z" } }, "outputs": [ @@ -701,10 +701,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:54.214675Z", - "iopub.status.busy": "2024-02-08T14:16:54.214207Z", - "iopub.status.idle": "2024-02-08T14:16:54.230421Z", - "shell.execute_reply": "2024-02-08T14:16:54.229990Z" + "iopub.execute_input": "2024-02-09T00:04:46.928787Z", + "iopub.status.busy": "2024-02-09T00:04:46.928180Z", + "iopub.status.idle": "2024-02-09T00:04:46.944520Z", + "shell.execute_reply": "2024-02-09T00:04:46.944048Z" } }, "outputs": [ @@ -834,10 +834,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:54.232401Z", - "iopub.status.busy": "2024-02-08T14:16:54.232223Z", - "iopub.status.idle": "2024-02-08T14:16:54.238968Z", - "shell.execute_reply": "2024-02-08T14:16:54.238432Z" + "iopub.execute_input": "2024-02-09T00:04:46.946610Z", + "iopub.status.busy": "2024-02-09T00:04:46.946216Z", + "iopub.status.idle": "2024-02-09T00:04:46.952498Z", + "shell.execute_reply": "2024-02-09T00:04:46.952070Z" } }, "outputs": [ @@ -941,10 +941,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:54.240992Z", - "iopub.status.busy": "2024-02-08T14:16:54.240678Z", - "iopub.status.idle": "2024-02-08T14:16:54.246287Z", - "shell.execute_reply": "2024-02-08T14:16:54.245836Z" + "iopub.execute_input": "2024-02-09T00:04:46.954457Z", + "iopub.status.busy": "2024-02-09T00:04:46.954156Z", + "iopub.status.idle": "2024-02-09T00:04:46.959669Z", + "shell.execute_reply": "2024-02-09T00:04:46.959153Z" } }, "outputs": [ @@ -1011,10 +1011,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:54.248242Z", - "iopub.status.busy": "2024-02-08T14:16:54.247927Z", - "iopub.status.idle": "2024-02-08T14:16:54.257266Z", - "shell.execute_reply": "2024-02-08T14:16:54.256843Z" + "iopub.execute_input": "2024-02-09T00:04:46.961633Z", + "iopub.status.busy": "2024-02-09T00:04:46.961340Z", + "iopub.status.idle": "2024-02-09T00:04:46.970711Z", + "shell.execute_reply": "2024-02-09T00:04:46.970195Z" } }, "outputs": [ @@ -1187,10 +1187,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:54.259335Z", - "iopub.status.busy": "2024-02-08T14:16:54.259005Z", - "iopub.status.idle": "2024-02-08T14:16:54.267368Z", - "shell.execute_reply": "2024-02-08T14:16:54.266873Z" + "iopub.execute_input": "2024-02-09T00:04:46.972620Z", + "iopub.status.busy": "2024-02-09T00:04:46.972318Z", + "iopub.status.idle": "2024-02-09T00:04:46.981035Z", + "shell.execute_reply": "2024-02-09T00:04:46.980499Z" } }, "outputs": [ @@ -1306,10 +1306,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:54.269447Z", - "iopub.status.busy": "2024-02-08T14:16:54.269138Z", - "iopub.status.idle": "2024-02-08T14:16:54.275808Z", - "shell.execute_reply": "2024-02-08T14:16:54.275364Z" + "iopub.execute_input": "2024-02-09T00:04:46.983088Z", + "iopub.status.busy": "2024-02-09T00:04:46.982767Z", + "iopub.status.idle": "2024-02-09T00:04:46.989509Z", + "shell.execute_reply": "2024-02-09T00:04:46.989058Z" }, "scrolled": true }, @@ -1434,10 +1434,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:54.277832Z", - "iopub.status.busy": "2024-02-08T14:16:54.277521Z", - "iopub.status.idle": "2024-02-08T14:16:54.286073Z", - "shell.execute_reply": "2024-02-08T14:16:54.285606Z" + "iopub.execute_input": "2024-02-09T00:04:46.991356Z", + "iopub.status.busy": "2024-02-09T00:04:46.991097Z", + "iopub.status.idle": "2024-02-09T00:04:46.999994Z", + "shell.execute_reply": "2024-02-09T00:04:46.999548Z" } }, "outputs": [ diff --git a/master/tutorials/datalab/index.html b/master/tutorials/datalab/index.html index d0fb4fdf9..1ba3fc8a6 100644 --- a/master/tutorials/datalab/index.html +++ b/master/tutorials/datalab/index.html @@ -247,6 +247,7 @@
  • report
    • +
  • task
    • diff --git a/master/tutorials/datalab/tabular.html b/master/tutorials/datalab/tabular.html index 0c9c6d3a0..445a7afad 100644 --- a/master/tutorials/datalab/tabular.html +++ b/master/tutorials/datalab/tabular.html @@ -248,6 +248,7 @@
  • report
    • +
  • task
    • diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index b153c5e94..fcfa5142e 100644 --- a/master/tutorials/datalab/tabular.ipynb +++ b/master/tutorials/datalab/tabular.ipynb @@ -74,10 +74,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:56.799250Z", - "iopub.status.busy": "2024-02-08T14:16:56.799084Z", - "iopub.status.idle": "2024-02-08T14:16:57.842049Z", - "shell.execute_reply": "2024-02-08T14:16:57.841511Z" + "iopub.execute_input": "2024-02-09T00:04:49.569629Z", + "iopub.status.busy": "2024-02-09T00:04:49.569470Z", + "iopub.status.idle": "2024-02-09T00:04:50.581224Z", + "shell.execute_reply": "2024-02-09T00:04:50.580689Z" }, "nbsphinx": "hidden" }, @@ -87,7 +87,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:57.844683Z", - "iopub.status.busy": "2024-02-08T14:16:57.844278Z", - "iopub.status.idle": "2024-02-08T14:16:57.877483Z", - "shell.execute_reply": "2024-02-08T14:16:57.877041Z" + "iopub.execute_input": "2024-02-09T00:04:50.583712Z", + "iopub.status.busy": "2024-02-09T00:04:50.583366Z", + "iopub.status.idle": "2024-02-09T00:04:50.616984Z", + "shell.execute_reply": "2024-02-09T00:04:50.616573Z" } }, "outputs": [], @@ -155,10 +155,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:57.879807Z", - "iopub.status.busy": "2024-02-08T14:16:57.879347Z", - "iopub.status.idle": "2024-02-08T14:16:58.025248Z", - "shell.execute_reply": "2024-02-08T14:16:58.024590Z" + "iopub.execute_input": "2024-02-09T00:04:50.619168Z", + "iopub.status.busy": "2024-02-09T00:04:50.618794Z", + "iopub.status.idle": "2024-02-09T00:04:50.992473Z", + "shell.execute_reply": "2024-02-09T00:04:50.992035Z" } }, "outputs": [ @@ -265,10 +265,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:58.027533Z", - "iopub.status.busy": "2024-02-08T14:16:58.027283Z", - "iopub.status.idle": "2024-02-08T14:16:58.032275Z", - "shell.execute_reply": "2024-02-08T14:16:58.031769Z" + "iopub.execute_input": "2024-02-09T00:04:50.994441Z", + "iopub.status.busy": "2024-02-09T00:04:50.994103Z", + "iopub.status.idle": "2024-02-09T00:04:50.997515Z", + "shell.execute_reply": "2024-02-09T00:04:50.997089Z" } }, "outputs": [], @@ -289,10 +289,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:58.034505Z", - "iopub.status.busy": "2024-02-08T14:16:58.034117Z", - "iopub.status.idle": "2024-02-08T14:16:58.042432Z", - "shell.execute_reply": "2024-02-08T14:16:58.042014Z" + "iopub.execute_input": "2024-02-09T00:04:50.999530Z", + "iopub.status.busy": "2024-02-09T00:04:50.999205Z", + "iopub.status.idle": "2024-02-09T00:04:51.006892Z", + "shell.execute_reply": "2024-02-09T00:04:51.006421Z" } }, "outputs": [], @@ -337,10 +337,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:58.044590Z", - "iopub.status.busy": "2024-02-08T14:16:58.044252Z", - "iopub.status.idle": "2024-02-08T14:16:58.046877Z", - "shell.execute_reply": "2024-02-08T14:16:58.046428Z" + "iopub.execute_input": "2024-02-09T00:04:51.008965Z", + "iopub.status.busy": "2024-02-09T00:04:51.008654Z", + "iopub.status.idle": "2024-02-09T00:04:51.011050Z", + "shell.execute_reply": "2024-02-09T00:04:51.010615Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:16:58.048928Z", - "iopub.status.busy": "2024-02-08T14:16:58.048614Z", - "iopub.status.idle": "2024-02-08T14:17:01.044642Z", - "shell.execute_reply": "2024-02-08T14:17:01.044123Z" + "iopub.execute_input": "2024-02-09T00:04:51.012938Z", + "iopub.status.busy": "2024-02-09T00:04:51.012624Z", + "iopub.status.idle": "2024-02-09T00:04:53.945241Z", + "shell.execute_reply": "2024-02-09T00:04:53.944719Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:01.047435Z", - "iopub.status.busy": "2024-02-08T14:17:01.047035Z", - "iopub.status.idle": "2024-02-08T14:17:01.056867Z", - "shell.execute_reply": "2024-02-08T14:17:01.056425Z" + "iopub.execute_input": "2024-02-09T00:04:53.947815Z", + "iopub.status.busy": "2024-02-09T00:04:53.947441Z", + "iopub.status.idle": "2024-02-09T00:04:53.957455Z", + "shell.execute_reply": "2024-02-09T00:04:53.957041Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:01.058938Z", - "iopub.status.busy": "2024-02-08T14:17:01.058610Z", - "iopub.status.idle": "2024-02-08T14:17:02.762335Z", - "shell.execute_reply": "2024-02-08T14:17:02.761715Z" + "iopub.execute_input": "2024-02-09T00:04:53.959424Z", + "iopub.status.busy": "2024-02-09T00:04:53.959104Z", + "iopub.status.idle": "2024-02-09T00:04:55.647645Z", + "shell.execute_reply": "2024-02-09T00:04:55.647018Z" } }, "outputs": [ @@ -475,10 +475,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:02.765376Z", - "iopub.status.busy": "2024-02-08T14:17:02.764607Z", - "iopub.status.idle": "2024-02-08T14:17:02.784523Z", - "shell.execute_reply": "2024-02-08T14:17:02.784051Z" + "iopub.execute_input": "2024-02-09T00:04:55.651444Z", + "iopub.status.busy": "2024-02-09T00:04:55.650131Z", + "iopub.status.idle": "2024-02-09T00:04:55.671769Z", + "shell.execute_reply": "2024-02-09T00:04:55.671289Z" }, "scrolled": true }, @@ -604,10 +604,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:02.786857Z", - "iopub.status.busy": "2024-02-08T14:17:02.786492Z", - "iopub.status.idle": "2024-02-08T14:17:02.795412Z", - "shell.execute_reply": "2024-02-08T14:17:02.794907Z" + "iopub.execute_input": "2024-02-09T00:04:55.675134Z", + "iopub.status.busy": "2024-02-09T00:04:55.674223Z", + "iopub.status.idle": "2024-02-09T00:04:55.685089Z", + "shell.execute_reply": "2024-02-09T00:04:55.684624Z" } }, "outputs": [ @@ -711,10 +711,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:02.797695Z", - "iopub.status.busy": "2024-02-08T14:17:02.797331Z", - "iopub.status.idle": "2024-02-08T14:17:02.807642Z", - "shell.execute_reply": "2024-02-08T14:17:02.807170Z" + "iopub.execute_input": "2024-02-09T00:04:55.688421Z", + "iopub.status.busy": "2024-02-09T00:04:55.687538Z", + "iopub.status.idle": "2024-02-09T00:04:55.699838Z", + "shell.execute_reply": "2024-02-09T00:04:55.699374Z" } }, "outputs": [ @@ -843,10 +843,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:02.810673Z", - "iopub.status.busy": "2024-02-08T14:17:02.809765Z", - "iopub.status.idle": "2024-02-08T14:17:02.820597Z", - "shell.execute_reply": "2024-02-08T14:17:02.820115Z" + "iopub.execute_input": "2024-02-09T00:04:55.703253Z", + "iopub.status.busy": "2024-02-09T00:04:55.702288Z", + "iopub.status.idle": "2024-02-09T00:04:55.713082Z", + "shell.execute_reply": "2024-02-09T00:04:55.712614Z" } }, "outputs": [ @@ -960,10 +960,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:02.823989Z", - "iopub.status.busy": "2024-02-08T14:17:02.823100Z", - "iopub.status.idle": "2024-02-08T14:17:02.835383Z", - "shell.execute_reply": "2024-02-08T14:17:02.834892Z" + "iopub.execute_input": "2024-02-09T00:04:55.716308Z", + "iopub.status.busy": "2024-02-09T00:04:55.715458Z", + "iopub.status.idle": "2024-02-09T00:04:55.727537Z", + "shell.execute_reply": "2024-02-09T00:04:55.727073Z" } }, "outputs": [ @@ -1074,10 +1074,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:02.838698Z", - "iopub.status.busy": "2024-02-08T14:17:02.837812Z", - "iopub.status.idle": "2024-02-08T14:17:02.846936Z", - "shell.execute_reply": "2024-02-08T14:17:02.846460Z" + "iopub.execute_input": "2024-02-09T00:04:55.730683Z", + "iopub.status.busy": "2024-02-09T00:04:55.729835Z", + "iopub.status.idle": "2024-02-09T00:04:55.738860Z", + "shell.execute_reply": "2024-02-09T00:04:55.738451Z" } }, "outputs": [ @@ -1161,10 +1161,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:02.850303Z", - "iopub.status.busy": "2024-02-08T14:17:02.849419Z", - "iopub.status.idle": "2024-02-08T14:17:02.857439Z", - "shell.execute_reply": "2024-02-08T14:17:02.857050Z" + "iopub.execute_input": "2024-02-09T00:04:55.740941Z", + "iopub.status.busy": "2024-02-09T00:04:55.740570Z", + "iopub.status.idle": "2024-02-09T00:04:55.747459Z", + "shell.execute_reply": "2024-02-09T00:04:55.747065Z" } }, "outputs": [ @@ -1257,10 +1257,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:02.860182Z", - "iopub.status.busy": "2024-02-08T14:17:02.859474Z", - "iopub.status.idle": "2024-02-08T14:17:02.866031Z", - "shell.execute_reply": "2024-02-08T14:17:02.865631Z" + "iopub.execute_input": "2024-02-09T00:04:55.749215Z", + "iopub.status.busy": "2024-02-09T00:04:55.749058Z", + "iopub.status.idle": "2024-02-09T00:04:55.755466Z", + "shell.execute_reply": "2024-02-09T00:04:55.755063Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index 5527f84cc..33a64fa25 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -248,6 +248,7 @@
  • report
    • +
  • task
    • @@ -706,7 +707,7 @@

    2. Load and format the text dataset 
     This dataset has 10 classes.
-Classes: {'cancel_transfer', 'visa_or_mastercard', 'getting_spare_card', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'card_about_to_expire', 'apple_pay_or_google_pay', 'change_pin'}
+Classes: {'visa_or_mastercard', 'cancel_transfer', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'getting_spare_card', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'card_about_to_expire'}

    Let’s view the i-th example in the dataset:

    @@ -753,43 +754,43 @@

    2. Load and format the text dataset
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    @@ -1523,7 +1524,7 @@

    Easy ModeCleanlab Studio which will automatically produce one for you. Super easy to use, Cleanlab Studio is no-code platform for data-centric AI that automatically: detects data issues (more types of issues than this cleanlab package), helps you quickly correct these data issues, confidently labels large subsets of an unlabeled dataset, and provides other smart metadata about each of your data points – all powered by a system that automatically trains/deploys the best ML model for your data. Try it for free!

    diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 5d9d173ae..16c84ba59 100644 --- a/master/tutorials/datalab/text.ipynb +++ b/master/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:05.451285Z", - "iopub.status.busy": "2024-02-08T14:17:05.451126Z", - "iopub.status.idle": "2024-02-08T14:17:08.245342Z", - "shell.execute_reply": "2024-02-08T14:17:08.244841Z" + "iopub.execute_input": "2024-02-09T00:04:58.357840Z", + "iopub.status.busy": "2024-02-09T00:04:58.357376Z", + "iopub.status.idle": "2024-02-09T00:05:01.260489Z", + "shell.execute_reply": "2024-02-09T00:05:01.259942Z" }, "nbsphinx": "hidden" }, @@ -96,7 +96,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -121,10 +121,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:08.247881Z", - "iopub.status.busy": "2024-02-08T14:17:08.247474Z", - "iopub.status.idle": "2024-02-08T14:17:08.250677Z", - "shell.execute_reply": "2024-02-08T14:17:08.250241Z" + "iopub.execute_input": "2024-02-09T00:05:01.263091Z", + "iopub.status.busy": "2024-02-09T00:05:01.262622Z", + "iopub.status.idle": "2024-02-09T00:05:01.265882Z", + "shell.execute_reply": "2024-02-09T00:05:01.265443Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:08.252664Z", - "iopub.status.busy": "2024-02-08T14:17:08.252343Z", - "iopub.status.idle": "2024-02-08T14:17:08.255363Z", - "shell.execute_reply": "2024-02-08T14:17:08.254911Z" + "iopub.execute_input": "2024-02-09T00:05:01.267841Z", + "iopub.status.busy": "2024-02-09T00:05:01.267550Z", + "iopub.status.idle": "2024-02-09T00:05:01.270524Z", + "shell.execute_reply": "2024-02-09T00:05:01.270008Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:08.257430Z", - "iopub.status.busy": "2024-02-08T14:17:08.257106Z", - "iopub.status.idle": "2024-02-08T14:17:08.424748Z", - "shell.execute_reply": "2024-02-08T14:17:08.424259Z" + "iopub.execute_input": "2024-02-09T00:05:01.272500Z", + "iopub.status.busy": "2024-02-09T00:05:01.272129Z", + "iopub.status.idle": "2024-02-09T00:05:01.392937Z", + "shell.execute_reply": "2024-02-09T00:05:01.392395Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:08.426890Z", - "iopub.status.busy": "2024-02-08T14:17:08.426531Z", - "iopub.status.idle": "2024-02-08T14:17:08.430340Z", - "shell.execute_reply": "2024-02-08T14:17:08.429886Z" + "iopub.execute_input": "2024-02-09T00:05:01.394884Z", + "iopub.status.busy": "2024-02-09T00:05:01.394668Z", + "iopub.status.idle": "2024-02-09T00:05:01.398469Z", + "shell.execute_reply": "2024-02-09T00:05:01.397997Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'cancel_transfer', 'visa_or_mastercard', 'getting_spare_card', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'card_about_to_expire', 'apple_pay_or_google_pay', 'change_pin'}\n" + "Classes: {'visa_or_mastercard', 'cancel_transfer', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'getting_spare_card', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'card_about_to_expire'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:08.432207Z", - "iopub.status.busy": "2024-02-08T14:17:08.432026Z", - "iopub.status.idle": "2024-02-08T14:17:08.435144Z", - "shell.execute_reply": "2024-02-08T14:17:08.434605Z" + "iopub.execute_input": "2024-02-09T00:05:01.400525Z", + "iopub.status.busy": "2024-02-09T00:05:01.400102Z", + "iopub.status.idle": "2024-02-09T00:05:01.403228Z", + "shell.execute_reply": "2024-02-09T00:05:01.402789Z" } }, "outputs": [ @@ -365,17 +365,17 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:08.437094Z", - "iopub.status.busy": "2024-02-08T14:17:08.436917Z", - "iopub.status.idle": "2024-02-08T14:17:13.883350Z", - "shell.execute_reply": "2024-02-08T14:17:13.882787Z" + "iopub.execute_input": "2024-02-09T00:05:01.405216Z", + "iopub.status.busy": "2024-02-09T00:05:01.404896Z", + "iopub.status.idle": "2024-02-09T00:05:06.902588Z", + "shell.execute_reply": "2024-02-09T00:05:06.902058Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "708f8274a8a04e618ed2c60cac2c4f77", + "model_id": "aaf2fc7de10f404d810a4e679a934b70", "version_major": 2, "version_minor": 0 }, @@ -389,7 +389,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4c9a52ff837a404f8ae8df40faf98275", + "model_id": "35d3a52ac9ce444290777c2b2e2515e8", "version_major": 2, "version_minor": 0 }, @@ -403,7 +403,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "21addfebbd504430a62b84e96bbeee90", + "model_id": "86a2171f7aef4b02a3e7f5c0400b9aeb", "version_major": 2, "version_minor": 0 }, @@ -417,7 +417,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a64ac1a511c421884ed86b3d64fe233", + "model_id": "6d881896432542caad00ab959525d424", "version_major": 2, "version_minor": 0 }, @@ -431,7 +431,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7c90e973f0e64c94826638f83390d75e", + "model_id": "21b04accbdc5441a8e385d6ba6d9d131", "version_major": 2, "version_minor": 0 }, @@ -445,7 +445,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d0902af33a554dfc9217ca13e47e1efd", + "model_id": "7cc762a56a284b1e9925c17f5c05183d", "version_major": 2, "version_minor": 0 }, @@ -459,7 +459,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4fc8634bd99c4bd78d9ff0247c343408", + "model_id": "b1c7a03a165e40b891da544a1eb97a0a", "version_major": 2, "version_minor": 0 }, @@ -521,10 +521,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:13.885868Z", - "iopub.status.busy": "2024-02-08T14:17:13.885675Z", - "iopub.status.idle": "2024-02-08T14:17:14.800048Z", - "shell.execute_reply": "2024-02-08T14:17:14.799447Z" + "iopub.execute_input": "2024-02-09T00:05:06.905337Z", + "iopub.status.busy": "2024-02-09T00:05:06.904954Z", + "iopub.status.idle": "2024-02-09T00:05:07.783973Z", + "shell.execute_reply": "2024-02-09T00:05:07.783407Z" }, "scrolled": true }, @@ -556,10 +556,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:14.803064Z", - "iopub.status.busy": "2024-02-08T14:17:14.802643Z", - "iopub.status.idle": "2024-02-08T14:17:14.805581Z", - "shell.execute_reply": "2024-02-08T14:17:14.805093Z" + "iopub.execute_input": "2024-02-09T00:05:07.786875Z", + "iopub.status.busy": "2024-02-09T00:05:07.786541Z", + "iopub.status.idle": "2024-02-09T00:05:07.789230Z", + "shell.execute_reply": "2024-02-09T00:05:07.788771Z" } }, "outputs": [], @@ -579,10 +579,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:14.807931Z", - "iopub.status.busy": "2024-02-08T14:17:14.807566Z", - "iopub.status.idle": "2024-02-08T14:17:16.326751Z", - "shell.execute_reply": "2024-02-08T14:17:16.326053Z" + "iopub.execute_input": "2024-02-09T00:05:07.791510Z", + "iopub.status.busy": "2024-02-09T00:05:07.791208Z", + "iopub.status.idle": "2024-02-09T00:05:09.293292Z", + "shell.execute_reply": "2024-02-09T00:05:09.292628Z" }, "scrolled": true }, @@ -626,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:16.329746Z", - "iopub.status.busy": "2024-02-08T14:17:16.329165Z", - "iopub.status.idle": "2024-02-08T14:17:16.350147Z", - "shell.execute_reply": "2024-02-08T14:17:16.349670Z" + "iopub.execute_input": "2024-02-09T00:05:09.296862Z", + "iopub.status.busy": "2024-02-09T00:05:09.296273Z", + "iopub.status.idle": "2024-02-09T00:05:09.319076Z", + "shell.execute_reply": "2024-02-09T00:05:09.318583Z" }, "scrolled": true }, @@ -756,10 +756,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:16.352597Z", - "iopub.status.busy": "2024-02-08T14:17:16.352287Z", - "iopub.status.idle": "2024-02-08T14:17:16.361472Z", - "shell.execute_reply": "2024-02-08T14:17:16.361002Z" + "iopub.execute_input": "2024-02-09T00:05:09.322420Z", + "iopub.status.busy": "2024-02-09T00:05:09.321539Z", + "iopub.status.idle": "2024-02-09T00:05:09.332879Z", + "shell.execute_reply": "2024-02-09T00:05:09.332408Z" }, "scrolled": true }, @@ -869,10 +869,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:16.363977Z", - "iopub.status.busy": "2024-02-08T14:17:16.363673Z", - "iopub.status.idle": "2024-02-08T14:17:16.367987Z", - "shell.execute_reply": "2024-02-08T14:17:16.367518Z" + "iopub.execute_input": "2024-02-09T00:05:09.336221Z", + "iopub.status.busy": "2024-02-09T00:05:09.335342Z", + "iopub.status.idle": "2024-02-09T00:05:09.341614Z", + "shell.execute_reply": "2024-02-09T00:05:09.341133Z" } }, "outputs": [ @@ -910,10 +910,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:16.370467Z", - "iopub.status.busy": "2024-02-08T14:17:16.370104Z", - "iopub.status.idle": "2024-02-08T14:17:16.377237Z", - "shell.execute_reply": "2024-02-08T14:17:16.376758Z" + "iopub.execute_input": "2024-02-09T00:05:09.344907Z", + "iopub.status.busy": "2024-02-09T00:05:09.344014Z", + "iopub.status.idle": "2024-02-09T00:05:09.353075Z", + "shell.execute_reply": "2024-02-09T00:05:09.352596Z" } }, "outputs": [ @@ -1030,10 +1030,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:16.379572Z", - "iopub.status.busy": "2024-02-08T14:17:16.379218Z", - "iopub.status.idle": "2024-02-08T14:17:16.387621Z", - "shell.execute_reply": "2024-02-08T14:17:16.387114Z" + "iopub.execute_input": "2024-02-09T00:05:09.355063Z", + "iopub.status.busy": "2024-02-09T00:05:09.354887Z", + "iopub.status.idle": "2024-02-09T00:05:09.362386Z", + "shell.execute_reply": "2024-02-09T00:05:09.361833Z" } }, "outputs": [ @@ -1116,10 +1116,10 @@ "execution_count": 16, "metadata": { "execution": { - 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"layout": "IPY_MODEL_d940b72d51744bf988a85b93fb45c2e3", - "max": 231508.0, + "layout": "IPY_MODEL_783128bb9a9243ec949e72a39b066c6c", + "max": 466062.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_2c217bbffebf480cba0d5a6e0066f7ff", + "style": "IPY_MODEL_1c6a1c36a6c24ff7a09e54380476835a", "tabbable": null, "tooltip": null, - "value": 231508.0 + "value": 466062.0 } } }, diff --git a/master/tutorials/dataset_health.html b/master/tutorials/dataset_health.html index cbddb38ef..e770d7f34 100644 --- a/master/tutorials/dataset_health.html +++ b/master/tutorials/dataset_health.html @@ -248,6 +248,7 @@
  • report
    • +
  • task
    • diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index 752fa628d..8bdf70ba7 100644 --- a/master/tutorials/dataset_health.ipynb +++ b/master/tutorials/dataset_health.ipynb @@ -68,10 +68,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:20.507284Z", - "iopub.status.busy": "2024-02-08T14:17:20.507108Z", - "iopub.status.idle": "2024-02-08T14:17:21.540730Z", - "shell.execute_reply": "2024-02-08T14:17:21.540169Z" + "iopub.execute_input": "2024-02-09T00:05:12.623029Z", + "iopub.status.busy": "2024-02-09T00:05:12.622604Z", + "iopub.status.idle": "2024-02-09T00:05:13.654489Z", + "shell.execute_reply": "2024-02-09T00:05:13.653939Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -108,10 +108,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:21.543062Z", - "iopub.status.busy": "2024-02-08T14:17:21.542778Z", - "iopub.status.idle": "2024-02-08T14:17:21.545763Z", - "shell.execute_reply": "2024-02-08T14:17:21.545204Z" + "iopub.execute_input": "2024-02-09T00:05:13.657156Z", + "iopub.status.busy": "2024-02-09T00:05:13.656802Z", + "iopub.status.idle": "2024-02-09T00:05:13.659440Z", + "shell.execute_reply": "2024-02-09T00:05:13.659012Z" }, "id": "_UvI80l42iyi" }, @@ -201,10 +201,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:21.547870Z", - "iopub.status.busy": "2024-02-08T14:17:21.547572Z", - "iopub.status.idle": "2024-02-08T14:17:21.559100Z", - "shell.execute_reply": "2024-02-08T14:17:21.558630Z" + "iopub.execute_input": "2024-02-09T00:05:13.661557Z", + "iopub.status.busy": "2024-02-09T00:05:13.661244Z", + "iopub.status.idle": "2024-02-09T00:05:13.672927Z", + "shell.execute_reply": "2024-02-09T00:05:13.672512Z" }, "nbsphinx": "hidden" }, @@ -283,10 +283,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:21.560898Z", - "iopub.status.busy": "2024-02-08T14:17:21.560724Z", - "iopub.status.idle": "2024-02-08T14:17:28.152620Z", - "shell.execute_reply": "2024-02-08T14:17:28.152155Z" + "iopub.execute_input": "2024-02-09T00:05:13.674915Z", + "iopub.status.busy": "2024-02-09T00:05:13.674571Z", + "iopub.status.idle": "2024-02-09T00:05:21.391567Z", + "shell.execute_reply": "2024-02-09T00:05:21.390983Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index 5c906d758..e931c2b32 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -248,6 +248,7 @@
  • report
    • +
  • task
    • @@ -700,13 +701,13 @@

    How can I find label issues in big datasets with limited memory?

    -
    +
    -
    +
    @@ -1578,7 +1579,7 @@

    How to handle near-duplicate data identified by cleanlab? 
    -/tmp/ipykernel_5881/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
+/tmp/ipykernel_5947/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
   to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values

    @@ -1620,7 +1621,7 @@

    Can’t find an answer to your question?new Github issue. Our developers may also provide personalized assistance in our Slack Community.

    Professional support and services are also available from our ML experts, learn more by emailing: info@cleanlab.ai

    diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index 04a5e2f21..0185e23f9 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:30.122511Z", - "iopub.status.busy": "2024-02-08T14:17:30.122025Z", - "iopub.status.idle": "2024-02-08T14:17:31.138719Z", - "shell.execute_reply": "2024-02-08T14:17:31.138168Z" + "iopub.execute_input": "2024-02-09T00:05:23.356191Z", + "iopub.status.busy": "2024-02-09T00:05:23.355787Z", + "iopub.status.idle": "2024-02-09T00:05:24.377950Z", + "shell.execute_reply": "2024-02-09T00:05:24.377400Z" }, "nbsphinx": "hidden" }, @@ -97,10 +97,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:31.141331Z", - "iopub.status.busy": "2024-02-08T14:17:31.140895Z", - "iopub.status.idle": "2024-02-08T14:17:31.144171Z", - "shell.execute_reply": "2024-02-08T14:17:31.143720Z" + "iopub.execute_input": "2024-02-09T00:05:24.380666Z", + "iopub.status.busy": "2024-02-09T00:05:24.380249Z", + "iopub.status.idle": "2024-02-09T00:05:24.383578Z", + "shell.execute_reply": "2024-02-09T00:05:24.383094Z" } }, "outputs": [], @@ -136,10 +136,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:31.146141Z", - "iopub.status.busy": "2024-02-08T14:17:31.145806Z", - "iopub.status.idle": "2024-02-08T14:17:34.074162Z", - "shell.execute_reply": "2024-02-08T14:17:34.073437Z" + "iopub.execute_input": "2024-02-09T00:05:24.385569Z", + "iopub.status.busy": "2024-02-09T00:05:24.385185Z", + "iopub.status.idle": "2024-02-09T00:05:27.324010Z", + "shell.execute_reply": "2024-02-09T00:05:27.323272Z" } }, "outputs": [], @@ -162,10 +162,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:34.077255Z", - "iopub.status.busy": "2024-02-08T14:17:34.076648Z", - "iopub.status.idle": "2024-02-08T14:17:34.113431Z", - "shell.execute_reply": "2024-02-08T14:17:34.112729Z" + "iopub.execute_input": "2024-02-09T00:05:27.327139Z", + "iopub.status.busy": "2024-02-09T00:05:27.326356Z", + "iopub.status.idle": "2024-02-09T00:05:27.367045Z", + "shell.execute_reply": "2024-02-09T00:05:27.366280Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "90c10e18", "metadata": { "execution": { - 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"model_id": "f195e2798ebf44cf8d2027e25f93fdb2", + "model_id": "2b1c9d6e104e40a1a0ff8382e6c51ae0", "version_major": 2, "version_minor": 0 }, @@ -329,7 +329,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fbe3605a8fec45139ab8d1029db0780a", + "model_id": "672cd986abca4a98b9c66f6076d86507", "version_major": 2, "version_minor": 0 }, @@ -387,10 +387,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:34.188085Z", - "iopub.status.busy": "2024-02-08T14:17:34.187540Z", - "iopub.status.idle": "2024-02-08T14:17:34.193766Z", - "shell.execute_reply": "2024-02-08T14:17:34.193357Z" + "iopub.execute_input": "2024-02-09T00:05:27.448213Z", + "iopub.status.busy": "2024-02-09T00:05:27.447809Z", + "iopub.status.idle": "2024-02-09T00:05:27.454176Z", + "shell.execute_reply": "2024-02-09T00:05:27.453658Z" }, "nbsphinx": "hidden" }, @@ -421,10 +421,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:34.195676Z", - "iopub.status.busy": "2024-02-08T14:17:34.195359Z", - "iopub.status.idle": "2024-02-08T14:17:34.198563Z", - "shell.execute_reply": "2024-02-08T14:17:34.198154Z" + "iopub.execute_input": "2024-02-09T00:05:27.456279Z", + "iopub.status.busy": "2024-02-09T00:05:27.455978Z", + "iopub.status.idle": "2024-02-09T00:05:27.459401Z", + "shell.execute_reply": "2024-02-09T00:05:27.458925Z" }, "nbsphinx": "hidden" }, @@ -447,10 +447,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:34.200561Z", - "iopub.status.busy": "2024-02-08T14:17:34.200252Z", - "iopub.status.idle": "2024-02-08T14:17:34.206257Z", - "shell.execute_reply": "2024-02-08T14:17:34.205801Z" + "iopub.execute_input": "2024-02-09T00:05:27.461260Z", + "iopub.status.busy": "2024-02-09T00:05:27.461086Z", + "iopub.status.idle": "2024-02-09T00:05:27.467236Z", + "shell.execute_reply": "2024-02-09T00:05:27.466779Z" } }, "outputs": [], @@ -500,10 +500,10 @@ "id": "b0a01109", "metadata": { "execution": { - 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"iopub.execute_input": "2024-02-08T14:17:37.474987Z", - "iopub.status.busy": "2024-02-08T14:17:37.474771Z", - "iopub.status.idle": "2024-02-08T14:17:37.532706Z", - "shell.execute_reply": "2024-02-08T14:17:37.532188Z" + "iopub.execute_input": "2024-02-09T00:05:30.729357Z", + "iopub.status.busy": "2024-02-09T00:05:30.729006Z", + "iopub.status.idle": "2024-02-09T00:05:30.786098Z", + "shell.execute_reply": "2024-02-09T00:05:30.785552Z" } }, "outputs": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "markdown", - "id": "4b08fe83", + "id": "f6cca8f6", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1214,7 +1214,7 @@ }, { "cell_type": "markdown", - "id": "dc4d9608", + "id": "f98e37b9", "metadata": {}, "source": [ "When detecting underperforming groups in a dataset, Datalab provides the option for passing pre-computed\n", @@ -1227,13 +1227,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "a6bcb0ac", + "id": "00f83abc", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:37.534729Z", - "iopub.status.busy": "2024-02-08T14:17:37.534551Z", - "iopub.status.idle": "2024-02-08T14:17:37.636069Z", - "shell.execute_reply": "2024-02-08T14:17:37.635525Z" + "iopub.execute_input": "2024-02-09T00:05:30.788411Z", + "iopub.status.busy": "2024-02-09T00:05:30.787979Z", + "iopub.status.idle": "2024-02-09T00:05:30.889807Z", + "shell.execute_reply": "2024-02-09T00:05:30.889205Z" } }, "outputs": [ @@ -1274,7 +1274,7 @@ }, { "cell_type": "markdown", - "id": "e9fe17af", + "id": "a746198d", "metadata": {}, "source": [ "For a tabular dataset, you can alternatively use a categorical column's values as cluster IDs:" @@ -1283,13 +1283,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "ce424dc2", + "id": "d467e08f", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:37.638736Z", - "iopub.status.busy": "2024-02-08T14:17:37.638521Z", - "iopub.status.idle": "2024-02-08T14:17:37.714330Z", - "shell.execute_reply": "2024-02-08T14:17:37.713901Z" + "iopub.execute_input": "2024-02-09T00:05:30.892949Z", + "iopub.status.busy": "2024-02-09T00:05:30.892036Z", + "iopub.status.idle": "2024-02-09T00:05:30.967994Z", + "shell.execute_reply": "2024-02-09T00:05:30.967399Z" } }, "outputs": [ @@ -1325,7 +1325,7 @@ }, { "cell_type": "markdown", - "id": "298d89e1", + "id": "cb22d225", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by cleanlab?\n", @@ -1336,13 +1336,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "a623bd21", + "id": "d4dc7076", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:37.716464Z", - "iopub.status.busy": "2024-02-08T14:17:37.716069Z", - "iopub.status.idle": "2024-02-08T14:17:37.723314Z", - "shell.execute_reply": "2024-02-08T14:17:37.722778Z" + "iopub.execute_input": "2024-02-09T00:05:30.970084Z", + "iopub.status.busy": "2024-02-09T00:05:30.969900Z", + "iopub.status.idle": "2024-02-09T00:05:30.977565Z", + "shell.execute_reply": "2024-02-09T00:05:30.976999Z" } }, "outputs": [], @@ -1444,7 +1444,7 @@ }, { "cell_type": "markdown", - "id": "9330130d", + "id": "56fbdb38", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1459,13 +1459,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "b22bd75d", + "id": "27bb27ed", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:37.725452Z", - "iopub.status.busy": "2024-02-08T14:17:37.725147Z", - "iopub.status.idle": "2024-02-08T14:17:37.744615Z", - "shell.execute_reply": "2024-02-08T14:17:37.744057Z" + "iopub.execute_input": "2024-02-09T00:05:30.979455Z", + "iopub.status.busy": "2024-02-09T00:05:30.979281Z", + "iopub.status.idle": "2024-02-09T00:05:30.998554Z", + "shell.execute_reply": "2024-02-09T00:05:30.997990Z" } }, "outputs": [ @@ -1482,7 +1482,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_5881/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_5947/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values\n" ] } @@ -1516,13 +1516,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "9efce442", + "id": "3b57f4b1", "metadata": { "execution": { - "iopub.execute_input": "2024-02-08T14:17:37.746658Z", - "iopub.status.busy": "2024-02-08T14:17:37.746337Z", - "iopub.status.idle": "2024-02-08T14:17:37.749661Z", - "shell.execute_reply": "2024-02-08T14:17:37.749194Z" + "iopub.execute_input": "2024-02-09T00:05:31.000731Z", + "iopub.status.busy": "2024-02-09T00:05:31.000421Z", + "iopub.status.idle": "2024-02-09T00:05:31.003699Z", + "shell.execute_reply": "2024-02-09T00:05:31.003153Z" } }, "outputs": [ @@ -1617,7 +1617,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0f423d009fea4b3fb8a28f87511002b2": { + "0ea4e3c19be44131b2184fa4ecf9d1e1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1670,30 +1670,67 @@ "width": null } }, - "1a309551352340fbaa037984aef52286": { + "115e8dfcd5ac4c05a0b1f643182a3a93": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "1fbc3ce6c95a4ba2a6db1546cb37b51e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "2b1c9d6e104e40a1a0ff8382e6c51ae0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_21a7ecfb88db4c238907f432b7d1103e", - "placeholder": "​", - "style": "IPY_MODEL_c12b723b1efa43ecb41496715043e8af", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b17cce279ae547838745f492e19bec79", + "IPY_MODEL_7e60dfbae2da49c990257b378382f01e", + "IPY_MODEL_d95943b52fdd49c190e48977f72539a9" + ], + "layout": "IPY_MODEL_4da99d07c10041bcaaf9d7ca86362f5d", "tabbable": null, - "tooltip": null, - "value": " 10000/? 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  • report
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    2. Fetch and normalize the Fashion-MNIST dataset
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    -epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.831
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 Computing feature embeddings ...

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    7. Use cleanlab to find issues
 
    
 
    
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+
 
 
    
 
    
@@ -3277,7 +3278,7 @@ 
    Easy ModeCleanlab Studio which will automatically produce one for you. Super easy to use, Cleanlab Studio is no-code platform for data-centric AI that automatically: detects data
 issues (more types of issues than this cleanlab package), helps you quickly correct these data issues, confidently labels large subsets of an unlabeled dataset, and provides other smart metadata about each of your data points – all powered by a system that automatically trains/deploys the best ML model for your data. Try it for free!
    
 
 
 
diff --git a/master/tutorials/image.ipynb b/master/tutorials/image.ipynb
index 77ef40263..65c66d269 100644
--- a/master/tutorials/image.ipynb
+++ b/master/tutorials/image.ipynb
@@ -71,10 +71,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:17:40.806484Z",
-     "iopub.status.busy": "2024-02-08T14:17:40.806013Z",
-     "iopub.status.idle": "2024-02-08T14:17:43.580086Z",
-     "shell.execute_reply": "2024-02-08T14:17:43.579471Z"
+     "iopub.execute_input": "2024-02-09T00:05:34.086062Z",
+     "iopub.status.busy": "2024-02-09T00:05:34.085584Z",
+     "iopub.status.idle": "2024-02-09T00:05:36.778309Z",
+     "shell.execute_reply": "2024-02-09T00:05:36.777771Z"
     },
     "nbsphinx": "hidden"
    },
@@ -112,10 +112,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:17:43.582827Z",
-     "iopub.status.busy": "2024-02-08T14:17:43.582457Z",
-     "iopub.status.idle": "2024-02-08T14:17:43.585954Z",
-     "shell.execute_reply": "2024-02-08T14:17:43.585530Z"
+     "iopub.execute_input": "2024-02-09T00:05:36.780858Z",
+     "iopub.status.busy": "2024-02-09T00:05:36.780441Z",
+     "iopub.status.idle": "2024-02-09T00:05:36.783942Z",
+     "shell.execute_reply": "2024-02-09T00:05:36.783411Z"
     }
    },
    "outputs": [],
@@ -152,17 +152,17 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:17:43.587947Z",
-     "iopub.status.busy": "2024-02-08T14:17:43.587634Z",
-     "iopub.status.idle": "2024-02-08T14:17:51.288727Z",
-     "shell.execute_reply": "2024-02-08T14:17:51.288236Z"
+     "iopub.execute_input": "2024-02-09T00:05:36.785915Z",
+     "iopub.status.busy": "2024-02-09T00:05:36.785589Z",
+     "iopub.status.idle": "2024-02-09T00:05:40.896608Z",
+     "shell.execute_reply": "2024-02-09T00:05:40.896138Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "17b04100ae3b4041b2cc5308f56299a7",
+       "model_id": "98404c51963941e6a9027aec614a324e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -176,7 +176,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c0a94ea07c964f5891347ce2d7fb94f6",
+       "model_id": "d6c58ccae87e44019ca4cfe64749f04a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -190,7 +190,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "51457fe63fa946abbc8dda560e84b2e3",
+       "model_id": "d286ca1e292243abbdf29581379754d9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -204,7 +204,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dfe7aa75add44b1091d88c90e3775523",
+       "model_id": "ba75d1285b8445f0b5c5ae6b0b1f36e6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -246,10 +246,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:17:51.290917Z",
-     "iopub.status.busy": "2024-02-08T14:17:51.290572Z",
-     "iopub.status.idle": "2024-02-08T14:17:51.294208Z",
-     "shell.execute_reply": "2024-02-08T14:17:51.293750Z"
+     "iopub.execute_input": "2024-02-09T00:05:40.898739Z",
+     "iopub.status.busy": "2024-02-09T00:05:40.898537Z",
+     "iopub.status.idle": "2024-02-09T00:05:40.902081Z",
+     "shell.execute_reply": "2024-02-09T00:05:40.901593Z"
     }
    },
    "outputs": [
@@ -274,17 +274,17 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:17:51.296153Z",
-     "iopub.status.busy": "2024-02-08T14:17:51.295851Z",
-     "iopub.status.idle": "2024-02-08T14:18:02.620567Z",
-     "shell.execute_reply": "2024-02-08T14:18:02.619907Z"
+     "iopub.execute_input": "2024-02-09T00:05:40.903968Z",
+     "iopub.status.busy": "2024-02-09T00:05:40.903795Z",
+     "iopub.status.idle": "2024-02-09T00:05:51.732374Z",
+     "shell.execute_reply": "2024-02-09T00:05:51.731862Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f7e97750241d49189b8937ce52c37d4c",
+       "model_id": "e7cfdd1ec2504a228b70e506edc27e26",
        "version_major": 2,
        "version_minor": 0
       },
@@ -322,10 +322,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:18:02.623264Z",
-     "iopub.status.busy": "2024-02-08T14:18:02.622844Z",
-     "iopub.status.idle": "2024-02-08T14:18:20.865373Z",
-     "shell.execute_reply": "2024-02-08T14:18:20.864826Z"
+     "iopub.execute_input": "2024-02-09T00:05:51.734827Z",
+     "iopub.status.busy": "2024-02-09T00:05:51.734572Z",
+     "iopub.status.idle": "2024-02-09T00:06:09.571696Z",
+     "shell.execute_reply": "2024-02-09T00:06:09.571103Z"
     }
    },
    "outputs": [],
@@ -358,10 +358,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:18:20.868021Z",
-     "iopub.status.busy": "2024-02-08T14:18:20.867643Z",
-     "iopub.status.idle": "2024-02-08T14:18:20.873485Z",
-     "shell.execute_reply": "2024-02-08T14:18:20.873029Z"
+     "iopub.execute_input": "2024-02-09T00:06:09.574501Z",
+     "iopub.status.busy": "2024-02-09T00:06:09.574157Z",
+     "iopub.status.idle": "2024-02-09T00:06:09.578844Z",
+     "shell.execute_reply": "2024-02-09T00:06:09.578393Z"
     }
    },
    "outputs": [],
@@ -399,10 +399,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:18:20.875457Z",
-     "iopub.status.busy": "2024-02-08T14:18:20.875132Z",
-     "iopub.status.idle": "2024-02-08T14:18:20.879143Z",
-     "shell.execute_reply": "2024-02-08T14:18:20.878592Z"
+     "iopub.execute_input": "2024-02-09T00:06:09.580846Z",
+     "iopub.status.busy": "2024-02-09T00:06:09.580470Z",
+     "iopub.status.idle": "2024-02-09T00:06:09.584478Z",
+     "shell.execute_reply": "2024-02-09T00:06:09.583971Z"
     },
     "nbsphinx": "hidden"
    },
@@ -539,10 +539,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:18:20.881199Z",
-     "iopub.status.busy": "2024-02-08T14:18:20.880901Z",
-     "iopub.status.idle": "2024-02-08T14:18:20.889694Z",
-     "shell.execute_reply": "2024-02-08T14:18:20.889161Z"
+     "iopub.execute_input": "2024-02-09T00:06:09.586434Z",
+     "iopub.status.busy": "2024-02-09T00:06:09.586261Z",
+     "iopub.status.idle": "2024-02-09T00:06:09.594996Z",
+     "shell.execute_reply": "2024-02-09T00:06:09.594545Z"
     },
     "nbsphinx": "hidden"
    },
@@ -667,10 +667,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:18:20.891671Z",
-     "iopub.status.busy": "2024-02-08T14:18:20.891363Z",
-     "iopub.status.idle": "2024-02-08T14:18:20.918515Z",
-     "shell.execute_reply": "2024-02-08T14:18:20.917921Z"
+     "iopub.execute_input": "2024-02-09T00:06:09.597019Z",
+     "iopub.status.busy": "2024-02-09T00:06:09.596713Z",
+     "iopub.status.idle": "2024-02-09T00:06:09.622661Z",
+     "shell.execute_reply": "2024-02-09T00:06:09.622257Z"
     }
    },
    "outputs": [],
@@ -707,10 +707,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:18:20.921128Z",
-     "iopub.status.busy": "2024-02-08T14:18:20.920761Z",
-     "iopub.status.idle": "2024-02-08T14:18:53.149225Z",
-     "shell.execute_reply": "2024-02-08T14:18:53.148467Z"
+     "iopub.execute_input": "2024-02-09T00:06:09.624531Z",
+     "iopub.status.busy": "2024-02-09T00:06:09.624358Z",
+     "iopub.status.idle": "2024-02-09T00:06:41.952403Z",
+     "shell.execute_reply": "2024-02-09T00:06:41.951644Z"
     }
    },
    "outputs": [
@@ -726,14 +726,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.831\n"
+      "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.742\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.600\n",
+      "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.545\n",
       "Computing feature embeddings ...\n"
      ]
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+      "  2%|▎         | 1/40 [00:00<00:04,  8.63it/s]"
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+      " 18%|█▊        | 7/40 [00:00<00:00, 35.02it/s]"
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+      " 35%|███▌      | 14/40 [00:00<00:00, 46.77it/s]"
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+      " 52%|█████▎    | 21/40 [00:00<00:00, 52.86it/s]"
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+      " 72%|███████▎  | 29/40 [00:00<00:00, 59.36it/s]"
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+      " 92%|█████████▎| 37/40 [00:00<00:00, 64.33it/s]"
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+      " 30%|███       | 12/40 [00:00<00:00, 42.35it/s]"
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+      " 62%|██████▎   | 25/40 [00:00<00:00, 53.72it/s]"
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+      " 82%|████████▎ | 33/40 [00:00<00:00, 59.91it/s]"
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@@ -898,14 +898,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.765\n"
+      "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.753\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.515\n",
+      "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.676\n",
       "Computing feature embeddings ...\n"
      ]
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@@ -922,7 +922,7 @@
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+      " 22%|██▎       | 9/40 [00:00<00:00, 46.68it/s]"
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+      " 20%|██        | 8/40 [00:00<00:00, 40.29it/s]"
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+      " 38%|███▊      | 15/40 [00:00<00:00, 50.94it/s]"
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      ]
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     {
@@ -1070,14 +1070,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.786\n"
+      "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.754\n"
      ]
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     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.487\n",
+      "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.532\n",
       "Computing feature embeddings ...\n"
      ]
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@@ -1094,7 +1094,7 @@
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+      " 20%|██        | 8/40 [00:00<00:00, 39.99it/s]"
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+      " 35%|███▌      | 14/40 [00:00<00:00, 47.18it/s]"
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+      "100%|██████████| 40/40 [00:00<00:00, 55.30it/s]"
      ]
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     {
@@ -1297,10 +1297,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:18:53.151737Z",
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-     "shell.execute_reply": "2024-02-08T14:18:53.166213Z"
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@@ -1325,10 +1325,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:18:53.168949Z",
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@@ -1348,10 +1348,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:18:53.629949Z",
-     "iopub.status.busy": "2024-02-08T14:18:53.629561Z",
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-     "shell.execute_reply": "2024-02-08T14:22:19.448237Z"
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     }
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    "outputs": [
@@ -1390,7 +1390,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fafb64243d2944a0a47cbb6ce547dbda",
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        "version_major": 2,
        "version_minor": 0
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@@ -1429,10 +1429,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:19.451461Z",
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-     "shell.execute_reply": "2024-02-08T14:22:19.897320Z"
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@@ -1580,10 +1580,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:19.900408Z",
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@@ -1687,10 +1687,10 @@
    "execution_count": 17,
    "metadata": {
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-     "shell.execute_reply": "2024-02-08T14:22:19.972469Z"
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@@ -1820,10 +1820,10 @@
    "execution_count": 18,
    "metadata": {
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-     "iopub.execute_input": "2024-02-08T14:22:19.974921Z",
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-     "shell.execute_reply": "2024-02-08T14:22:19.979393Z"
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@@ -1869,10 +1869,10 @@
    "execution_count": 19,
    "metadata": {
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-     "iopub.execute_input": "2024-02-08T14:22:19.981725Z",
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@@ -1907,10 +1907,10 @@
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    "metadata": {
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-     "shell.execute_reply": "2024-02-08T14:22:20.491098Z"
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@@ -2077,10 +2077,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:20.493847Z",
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-     "shell.execute_reply": "2024-02-08T14:22:20.501137Z"
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@@ -2156,10 +2156,10 @@
    "execution_count": 22,
    "metadata": {
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-     "shell.execute_reply": "2024-02-08T14:22:20.968021Z"
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    "outputs": [
@@ -2196,10 +2196,10 @@
    "execution_count": 23,
    "metadata": {
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-     "shell.execute_reply": "2024-02-08T14:22:20.985081Z"
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     }
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    "outputs": [
@@ -2356,10 +2356,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:20.987663Z",
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-     "shell.execute_reply": "2024-02-08T14:22:20.993248Z"
+     "iopub.execute_input": "2024-02-09T00:10:10.804907Z",
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@@ -2404,10 +2404,10 @@
    "execution_count": 25,
    "metadata": {
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-     "shell.execute_reply": "2024-02-08T14:22:21.455773Z"
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    "outputs": [
@@ -2489,10 +2489,10 @@
    "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:21.459247Z",
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-     "shell.execute_reply": "2024-02-08T14:22:21.467266Z"
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     }
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    "outputs": [
@@ -2620,10 +2620,10 @@
    "execution_count": 27,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:21.470181Z",
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@@ -2660,10 +2660,10 @@
    "execution_count": 28,
    "metadata": {
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-     "shell.execute_reply": "2024-02-08T14:22:21.677543Z"
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     }
    },
    "outputs": [
@@ -2705,10 +2705,10 @@
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diff --git a/master/tutorials/indepth_overview.html b/master/tutorials/indepth_overview.html
index b4c9dbb69..fa64dbfb8 100644
--- a/master/tutorials/indepth_overview.html
+++ b/master/tutorials/indepth_overview.html
@@ -248,6 +248,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb
index d49bb267c..f819765e3 100644
--- a/master/tutorials/indepth_overview.ipynb
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     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
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-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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@@ -340,10 +340,10 @@
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@@ -393,10 +393,10 @@
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@@ -427,10 +427,10 @@
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@@ -473,10 +473,10 @@
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@@ -603,10 +603,10 @@
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@@ -725,10 +725,10 @@
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@@ -777,10 +777,10 @@
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@@ -887,10 +887,10 @@
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@@ -927,10 +927,10 @@
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@@ -1396,10 +1396,10 @@
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@@ -1546,10 +1546,10 @@
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@@ -1630,10 +1630,10 @@
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@@ -1693,10 +1693,10 @@
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@@ -1734,10 +1734,10 @@
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@@ -1792,10 +1792,10 @@
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@@ -1846,10 +1846,10 @@
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@@ -1918,10 +1918,10 @@
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@@ -1953,10 +1953,10 @@
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@@ -2013,10 +2013,10 @@
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@@ -2051,10 +2051,10 @@
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@@ -2216,10 +2216,10 @@
    "execution_count": 24,
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-     "shell.execute_reply": "2024-02-08T14:22:32.622406Z"
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@@ -2273,10 +2273,10 @@
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-     "shell.execute_reply": "2024-02-08T14:22:32.841761Z"
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@@ -2323,10 +2323,10 @@
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-     "shell.execute_reply": "2024-02-08T14:22:33.902681Z"
+     "iopub.execute_input": "2024-02-09T00:10:22.109310Z",
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     },
     "id": "wL3ngCnuLEWd"
    },
diff --git a/master/tutorials/index.html b/master/tutorials/index.html
index f8f61b2ff..be4818365 100644
--- a/master/tutorials/index.html
+++ b/master/tutorials/index.html
@@ -247,6 +247,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/tutorials/multiannotator.html b/master/tutorials/multiannotator.html
index 8f7b6b9aa..21512e852 100644
--- a/master/tutorials/multiannotator.html
+++ b/master/tutorials/multiannotator.html
@@ -248,6 +248,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb
index e73ea405d..71257d6f5 100644
--- a/master/tutorials/multiannotator.ipynb
+++ b/master/tutorials/multiannotator.ipynb
@@ -89,10 +89,10 @@
    "id": "a3ddc95f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:37.136864Z",
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-     "iopub.status.idle": "2024-02-08T14:22:38.171469Z",
-     "shell.execute_reply": "2024-02-08T14:22:38.170896Z"
+     "iopub.execute_input": "2024-02-09T00:10:27.224779Z",
+     "iopub.status.busy": "2024-02-09T00:10:27.224228Z",
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+     "shell.execute_reply": "2024-02-09T00:10:28.322344Z"
     },
     "nbsphinx": "hidden"
    },
@@ -102,7 +102,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -136,10 +136,10 @@
    "id": "c4efd119",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:38.173847Z",
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@@ -264,10 +264,10 @@
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@@ -351,10 +351,10 @@
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@@ -380,10 +380,10 @@
    "id": "8f241c16",
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    "outputs": [
@@ -598,10 +598,10 @@
    "id": "4f0819ba",
    "metadata": {
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     }
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    "outputs": [
@@ -672,10 +672,10 @@
    "id": "d009f347",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:38.260398Z",
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-     "shell.execute_reply": "2024-02-08T14:22:38.288112Z"
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@@ -699,10 +699,10 @@
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-     "shell.execute_reply": "2024-02-08T14:22:38.315890Z"
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@@ -739,10 +739,10 @@
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@@ -772,10 +772,10 @@
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@@ -886,10 +886,10 @@
    "id": "14251ee0",
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@@ -1139,10 +1139,10 @@
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@@ -1316,10 +1316,10 @@
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    "outputs": [],
@@ -1341,10 +1341,10 @@
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-     "shell.execute_reply": "2024-02-08T14:22:40.051548Z"
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@@ -1396,10 +1396,10 @@
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-     "shell.execute_reply": "2024-02-08T14:22:40.056015Z"
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@@ -1423,10 +1423,10 @@
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-     "shell.execute_reply": "2024-02-08T14:22:40.061826Z"
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@@ -1481,10 +1481,10 @@
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@@ -1527,10 +1527,10 @@
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-     "iopub.execute_input": "2024-02-08T14:22:40.094570Z",
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     },
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diff --git a/master/tutorials/multilabel_classification.html b/master/tutorials/multilabel_classification.html
index 11d6c8ada..dcda94a79 100644
--- a/master/tutorials/multilabel_classification.html
+++ b/master/tutorials/multilabel_classification.html
@@ -248,6 +248,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb
index 2aec9ad39..91736dacc 100644
--- a/master/tutorials/multilabel_classification.ipynb
+++ b/master/tutorials/multilabel_classification.ipynb
@@ -64,10 +64,10 @@
    "id": "7383d024-8273-4039-bccd-aab3020d331f",
    "metadata": {
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-     "shell.execute_reply": "2024-02-08T14:22:43.956173Z"
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@@ -79,7 +79,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -105,10 +105,10 @@
    "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69",
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-     "shell.execute_reply": "2024-02-08T14:22:44.149246Z"
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@@ -268,10 +268,10 @@
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-     "shell.execute_reply": "2024-02-08T14:22:44.164687Z"
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@@ -407,10 +407,10 @@
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@@ -452,10 +452,10 @@
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-     "shell.execute_reply": "2024-02-08T14:22:48.122619Z"
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@@ -497,10 +497,10 @@
    "id": "ac1a60df",
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-     "iopub.execute_input": "2024-02-08T14:22:48.126271Z",
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@@ -542,10 +542,10 @@
    "id": "d09115b6-ad44-474f-9c8a-85a459586439",
    "metadata": {
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-     "iopub.execute_input": "2024-02-08T14:22:48.131671Z",
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-     "shell.execute_reply": "2024-02-08T14:22:49.820801Z"
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@@ -592,10 +592,10 @@
    "id": "c18dd83b",
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-     "iopub.execute_input": "2024-02-08T14:22:49.824131Z",
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@@ -631,10 +631,10 @@
    "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
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     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:49.833119Z",
-     "iopub.status.busy": "2024-02-08T14:22:49.832696Z",
-     "iopub.status.idle": "2024-02-08T14:22:52.366638Z",
-     "shell.execute_reply": "2024-02-08T14:22:52.366053Z"
+     "iopub.execute_input": "2024-02-09T00:10:40.571499Z",
+     "iopub.status.busy": "2024-02-09T00:10:40.571107Z",
+     "iopub.status.idle": "2024-02-09T00:10:43.163241Z",
+     "shell.execute_reply": "2024-02-09T00:10:43.162614Z"
     }
    },
    "outputs": [
@@ -669,10 +669,10 @@
    "id": "c1198575",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:52.368851Z",
-     "iopub.status.busy": "2024-02-08T14:22:52.368522Z",
-     "iopub.status.idle": "2024-02-08T14:22:52.371773Z",
-     "shell.execute_reply": "2024-02-08T14:22:52.371250Z"
+     "iopub.execute_input": "2024-02-09T00:10:43.165634Z",
+     "iopub.status.busy": "2024-02-09T00:10:43.165250Z",
+     "iopub.status.idle": "2024-02-09T00:10:43.169030Z",
+     "shell.execute_reply": "2024-02-09T00:10:43.168555Z"
     }
    },
    "outputs": [
@@ -717,10 +717,10 @@
    "id": "49161b19-7625-4fb7-add9-607d91a7eca1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:52.373800Z",
-     "iopub.status.busy": "2024-02-08T14:22:52.373517Z",
-     "iopub.status.idle": "2024-02-08T14:22:52.377287Z",
-     "shell.execute_reply": "2024-02-08T14:22:52.376879Z"
+     "iopub.execute_input": "2024-02-09T00:10:43.171166Z",
+     "iopub.status.busy": "2024-02-09T00:10:43.170830Z",
+     "iopub.status.idle": "2024-02-09T00:10:43.175024Z",
+     "shell.execute_reply": "2024-02-09T00:10:43.174559Z"
     }
    },
    "outputs": [],
@@ -743,10 +743,10 @@
    "id": "d1a2c008",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:52.379296Z",
-     "iopub.status.busy": "2024-02-08T14:22:52.378971Z",
-     "iopub.status.idle": "2024-02-08T14:22:52.381834Z",
-     "shell.execute_reply": "2024-02-08T14:22:52.381413Z"
+     "iopub.execute_input": "2024-02-09T00:10:43.177052Z",
+     "iopub.status.busy": "2024-02-09T00:10:43.176724Z",
+     "iopub.status.idle": "2024-02-09T00:10:43.179767Z",
+     "shell.execute_reply": "2024-02-09T00:10:43.179312Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/object_detection.html b/master/tutorials/object_detection.html
index b7949217f..619e3e3de 100644
--- a/master/tutorials/object_detection.html
+++ b/master/tutorials/object_detection.html
@@ -248,6 +248,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb
index 1c1d3591c..13ae402e5 100644
--- a/master/tutorials/object_detection.ipynb
+++ b/master/tutorials/object_detection.ipynb
@@ -70,10 +70,10 @@
    "id": "0ba0dc70",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:54.789790Z",
-     "iopub.status.busy": "2024-02-08T14:22:54.789619Z",
-     "iopub.status.idle": "2024-02-08T14:22:55.867460Z",
-     "shell.execute_reply": "2024-02-08T14:22:55.866922Z"
+     "iopub.execute_input": "2024-02-09T00:10:45.803290Z",
+     "iopub.status.busy": "2024-02-09T00:10:45.803124Z",
+     "iopub.status.idle": "2024-02-09T00:10:46.944656Z",
+     "shell.execute_reply": "2024-02-09T00:10:46.944065Z"
     },
     "nbsphinx": "hidden"
    },
@@ -83,7 +83,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -109,10 +109,10 @@
    "id": "c90449c8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:55.870105Z",
-     "iopub.status.busy": "2024-02-08T14:22:55.869576Z",
-     "iopub.status.idle": "2024-02-08T14:22:58.466883Z",
-     "shell.execute_reply": "2024-02-08T14:22:58.466207Z"
+     "iopub.execute_input": "2024-02-09T00:10:46.947693Z",
+     "iopub.status.busy": "2024-02-09T00:10:46.947073Z",
+     "iopub.status.idle": "2024-02-09T00:10:49.613006Z",
+     "shell.execute_reply": "2024-02-09T00:10:49.612291Z"
     }
    },
    "outputs": [],
@@ -130,10 +130,10 @@
    "id": "df8be4c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:58.469267Z",
-     "iopub.status.busy": "2024-02-08T14:22:58.469069Z",
-     "iopub.status.idle": "2024-02-08T14:22:58.472238Z",
-     "shell.execute_reply": "2024-02-08T14:22:58.471790Z"
+     "iopub.execute_input": "2024-02-09T00:10:49.615627Z",
+     "iopub.status.busy": "2024-02-09T00:10:49.615396Z",
+     "iopub.status.idle": "2024-02-09T00:10:49.618657Z",
+     "shell.execute_reply": "2024-02-09T00:10:49.618200Z"
     }
    },
    "outputs": [],
@@ -169,10 +169,10 @@
    "id": "2e9ffd6f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:58.474254Z",
-     "iopub.status.busy": "2024-02-08T14:22:58.473928Z",
-     "iopub.status.idle": "2024-02-08T14:22:58.480218Z",
-     "shell.execute_reply": "2024-02-08T14:22:58.479806Z"
+     "iopub.execute_input": "2024-02-09T00:10:49.620834Z",
+     "iopub.status.busy": "2024-02-09T00:10:49.620497Z",
+     "iopub.status.idle": "2024-02-09T00:10:49.627030Z",
+     "shell.execute_reply": "2024-02-09T00:10:49.626551Z"
     }
    },
    "outputs": [],
@@ -198,10 +198,10 @@
    "id": "56705562",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:58.482113Z",
-     "iopub.status.busy": "2024-02-08T14:22:58.481933Z",
-     "iopub.status.idle": "2024-02-08T14:22:58.980033Z",
-     "shell.execute_reply": "2024-02-08T14:22:58.979409Z"
+     "iopub.execute_input": "2024-02-09T00:10:49.629211Z",
+     "iopub.status.busy": "2024-02-09T00:10:49.628864Z",
+     "iopub.status.idle": "2024-02-09T00:10:50.124582Z",
+     "shell.execute_reply": "2024-02-09T00:10:50.123953Z"
     },
     "scrolled": true
    },
@@ -242,10 +242,10 @@
    "id": "b08144d7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:58.982550Z",
-     "iopub.status.busy": "2024-02-08T14:22:58.982360Z",
-     "iopub.status.idle": "2024-02-08T14:22:58.987621Z",
-     "shell.execute_reply": "2024-02-08T14:22:58.987174Z"
+     "iopub.execute_input": "2024-02-09T00:10:50.127122Z",
+     "iopub.status.busy": "2024-02-09T00:10:50.126706Z",
+     "iopub.status.idle": "2024-02-09T00:10:50.132043Z",
+     "shell.execute_reply": "2024-02-09T00:10:50.131509Z"
     }
    },
    "outputs": [
@@ -497,10 +497,10 @@
    "id": "3d70bec6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:58.989592Z",
-     "iopub.status.busy": "2024-02-08T14:22:58.989301Z",
-     "iopub.status.idle": "2024-02-08T14:22:58.993229Z",
-     "shell.execute_reply": "2024-02-08T14:22:58.992795Z"
+     "iopub.execute_input": "2024-02-09T00:10:50.134295Z",
+     "iopub.status.busy": "2024-02-09T00:10:50.133966Z",
+     "iopub.status.idle": "2024-02-09T00:10:50.138010Z",
+     "shell.execute_reply": "2024-02-09T00:10:50.137484Z"
     }
    },
    "outputs": [
@@ -557,10 +557,10 @@
    "id": "4caa635d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:58.995032Z",
-     "iopub.status.busy": "2024-02-08T14:22:58.994841Z",
-     "iopub.status.idle": "2024-02-08T14:22:59.634808Z",
-     "shell.execute_reply": "2024-02-08T14:22:59.634169Z"
+     "iopub.execute_input": "2024-02-09T00:10:50.140242Z",
+     "iopub.status.busy": "2024-02-09T00:10:50.139940Z",
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+     "shell.execute_reply": "2024-02-09T00:10:50.844562Z"
     }
    },
    "outputs": [
@@ -616,10 +616,10 @@
    "id": "a9b4c590",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:59.637152Z",
-     "iopub.status.busy": "2024-02-08T14:22:59.636724Z",
-     "iopub.status.idle": "2024-02-08T14:22:59.837142Z",
-     "shell.execute_reply": "2024-02-08T14:22:59.836698Z"
+     "iopub.execute_input": "2024-02-09T00:10:50.847495Z",
+     "iopub.status.busy": "2024-02-09T00:10:50.847286Z",
+     "iopub.status.idle": "2024-02-09T00:10:51.019140Z",
+     "shell.execute_reply": "2024-02-09T00:10:51.018538Z"
     }
    },
    "outputs": [
@@ -660,10 +660,10 @@
    "id": "ffd9ebcc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:59.839101Z",
-     "iopub.status.busy": "2024-02-08T14:22:59.838788Z",
-     "iopub.status.idle": "2024-02-08T14:22:59.842850Z",
-     "shell.execute_reply": "2024-02-08T14:22:59.842340Z"
+     "iopub.execute_input": "2024-02-09T00:10:51.021572Z",
+     "iopub.status.busy": "2024-02-09T00:10:51.021101Z",
+     "iopub.status.idle": "2024-02-09T00:10:51.025473Z",
+     "shell.execute_reply": "2024-02-09T00:10:51.025037Z"
     }
    },
    "outputs": [
@@ -700,10 +700,10 @@
    "id": "4dd46d67",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:22:59.844707Z",
-     "iopub.status.busy": "2024-02-08T14:22:59.844529Z",
-     "iopub.status.idle": "2024-02-08T14:23:00.296156Z",
-     "shell.execute_reply": "2024-02-08T14:23:00.295577Z"
+     "iopub.execute_input": "2024-02-09T00:10:51.027519Z",
+     "iopub.status.busy": "2024-02-09T00:10:51.027343Z",
+     "iopub.status.idle": "2024-02-09T00:10:51.498859Z",
+     "shell.execute_reply": "2024-02-09T00:10:51.498219Z"
     }
    },
    "outputs": [
@@ -762,10 +762,10 @@
    "id": "ceec2394",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:23:00.298830Z",
-     "iopub.status.busy": "2024-02-08T14:23:00.298610Z",
-     "iopub.status.idle": "2024-02-08T14:23:00.628065Z",
-     "shell.execute_reply": "2024-02-08T14:23:00.627518Z"
+     "iopub.execute_input": "2024-02-09T00:10:51.501949Z",
+     "iopub.status.busy": "2024-02-09T00:10:51.501565Z",
+     "iopub.status.idle": "2024-02-09T00:10:51.840251Z",
+     "shell.execute_reply": "2024-02-09T00:10:51.839663Z"
     }
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    "outputs": [
@@ -812,10 +812,10 @@
    "id": "94f82b0d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:23:00.630528Z",
-     "iopub.status.busy": "2024-02-08T14:23:00.630315Z",
-     "iopub.status.idle": "2024-02-08T14:23:00.987729Z",
-     "shell.execute_reply": "2024-02-08T14:23:00.987117Z"
+     "iopub.execute_input": "2024-02-09T00:10:51.843343Z",
+     "iopub.status.busy": "2024-02-09T00:10:51.842973Z",
+     "iopub.status.idle": "2024-02-09T00:10:52.183410Z",
+     "shell.execute_reply": "2024-02-09T00:10:52.182752Z"
     }
    },
    "outputs": [
@@ -862,10 +862,10 @@
    "id": "1ea18c5d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:23:00.990737Z",
-     "iopub.status.busy": "2024-02-08T14:23:00.990515Z",
-     "iopub.status.idle": "2024-02-08T14:23:01.425886Z",
-     "shell.execute_reply": "2024-02-08T14:23:01.425318Z"
+     "iopub.execute_input": "2024-02-09T00:10:52.187667Z",
+     "iopub.status.busy": "2024-02-09T00:10:52.187252Z",
+     "iopub.status.idle": "2024-02-09T00:10:52.635281Z",
+     "shell.execute_reply": "2024-02-09T00:10:52.634587Z"
     }
    },
    "outputs": [
@@ -925,10 +925,10 @@
    "id": "7e770d23",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:23:01.429577Z",
-     "iopub.status.busy": "2024-02-08T14:23:01.429143Z",
-     "iopub.status.idle": "2024-02-08T14:23:01.846868Z",
-     "shell.execute_reply": "2024-02-08T14:23:01.846266Z"
+     "iopub.execute_input": "2024-02-09T00:10:52.639934Z",
+     "iopub.status.busy": "2024-02-09T00:10:52.639416Z",
+     "iopub.status.idle": "2024-02-09T00:10:53.070984Z",
+     "shell.execute_reply": "2024-02-09T00:10:53.070367Z"
     }
    },
    "outputs": [
@@ -971,10 +971,10 @@
    "id": "57e84a27",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:23:01.850069Z",
-     "iopub.status.busy": "2024-02-08T14:23:01.849692Z",
-     "iopub.status.idle": "2024-02-08T14:23:02.041760Z",
-     "shell.execute_reply": "2024-02-08T14:23:02.041181Z"
+     "iopub.execute_input": "2024-02-09T00:10:53.074513Z",
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     }
    },
    "outputs": [
@@ -1017,10 +1017,10 @@
    "id": "0302818a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:23:02.044327Z",
-     "iopub.status.busy": "2024-02-08T14:23:02.043915Z",
-     "iopub.status.idle": "2024-02-08T14:23:02.225719Z",
-     "shell.execute_reply": "2024-02-08T14:23:02.225171Z"
+     "iopub.execute_input": "2024-02-09T00:10:53.294783Z",
+     "iopub.status.busy": "2024-02-09T00:10:53.294392Z",
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     }
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    "outputs": [
@@ -1067,10 +1067,10 @@
    "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:23:02.228455Z",
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-     "shell.execute_reply": "2024-02-08T14:23:02.230502Z"
+     "iopub.execute_input": "2024-02-09T00:10:53.499564Z",
+     "iopub.status.busy": "2024-02-09T00:10:53.499191Z",
+     "iopub.status.idle": "2024-02-09T00:10:53.502253Z",
+     "shell.execute_reply": "2024-02-09T00:10:53.501783Z"
     }
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    "outputs": [],
@@ -1090,10 +1090,10 @@
    "id": "3335b8a3-d0b4-415a-a97d-c203088a124e",
    "metadata": {
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-     "iopub.execute_input": "2024-02-08T14:23:02.232668Z",
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-     "iopub.status.idle": "2024-02-08T14:23:03.182630Z",
-     "shell.execute_reply": "2024-02-08T14:23:03.182052Z"
+     "iopub.execute_input": "2024-02-09T00:10:53.504217Z",
+     "iopub.status.busy": "2024-02-09T00:10:53.503942Z",
+     "iopub.status.idle": "2024-02-09T00:10:54.485995Z",
+     "shell.execute_reply": "2024-02-09T00:10:54.485393Z"
     }
    },
    "outputs": [
@@ -1172,10 +1172,10 @@
    "id": "9d4b7677-6ebd-447d-b0a1-76e094686628",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:23:03.185707Z",
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-     "iopub.status.idle": "2024-02-08T14:23:03.375924Z",
-     "shell.execute_reply": "2024-02-08T14:23:03.375383Z"
+     "iopub.execute_input": "2024-02-09T00:10:54.489059Z",
+     "iopub.status.busy": "2024-02-09T00:10:54.488687Z",
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+     "shell.execute_reply": "2024-02-09T00:10:54.604552Z"
     }
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    "outputs": [
@@ -1214,10 +1214,10 @@
    "id": "59d7ee39-3785-434b-8680-9133014851cd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:23:03.377942Z",
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-     "shell.execute_reply": "2024-02-08T14:23:03.543484Z"
+     "iopub.execute_input": "2024-02-09T00:10:54.607408Z",
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+     "shell.execute_reply": "2024-02-09T00:10:54.729966Z"
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    "outputs": [],
@@ -1266,10 +1266,10 @@
    "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d",
    "metadata": {
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    3. Use cleanlab and feature embeddings to find outliers in the data
 
    
 
    
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+
 
 
    
 
    
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    4. Use cleanlab and here.
    
 
 
  
diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb
index 374b8fcfb..f2b0a4460 100644
--- a/master/tutorials/outliers.ipynb
+++ b/master/tutorials/outliers.ipynb
@@ -109,10 +109,10 @@
    "id": "2bbebfc8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:23:06.345281Z",
-     "iopub.status.busy": "2024-02-08T14:23:06.345109Z",
-     "iopub.status.idle": "2024-02-08T14:23:08.942692Z",
-     "shell.execute_reply": "2024-02-08T14:23:08.942154Z"
+     "iopub.execute_input": "2024-02-09T00:10:58.031021Z",
+     "iopub.status.busy": "2024-02-09T00:10:58.030844Z",
+     "iopub.status.idle": "2024-02-09T00:11:00.830378Z",
+     "shell.execute_reply": "2024-02-09T00:11:00.829767Z"
     },
     "nbsphinx": "hidden"
    },
@@ -125,7 +125,7 @@
     "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -159,10 +159,10 @@
    "id": "4396f544",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:23:08.945321Z",
-     "iopub.status.busy": "2024-02-08T14:23:08.944923Z",
-     "iopub.status.idle": "2024-02-08T14:23:09.258956Z",
-     "shell.execute_reply": "2024-02-08T14:23:09.258358Z"
+     "iopub.execute_input": "2024-02-09T00:11:00.833294Z",
+     "iopub.status.busy": "2024-02-09T00:11:00.832822Z",
+     "iopub.status.idle": "2024-02-09T00:11:01.181010Z",
+     "shell.execute_reply": "2024-02-09T00:11:01.180405Z"
     }
    },
    "outputs": [],
@@ -188,10 +188,10 @@
    "id": "3792f82e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:23:09.261422Z",
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-     "iopub.status.idle": "2024-02-08T14:23:09.264974Z",
-     "shell.execute_reply": "2024-02-08T14:23:09.264561Z"
+     "iopub.execute_input": "2024-02-09T00:11:01.183715Z",
+     "iopub.status.busy": "2024-02-09T00:11:01.183384Z",
+     "iopub.status.idle": "2024-02-09T00:11:01.187746Z",
+     "shell.execute_reply": "2024-02-09T00:11:01.187308Z"
     },
     "nbsphinx": "hidden"
    },
@@ -225,10 +225,10 @@
    "id": "fd853a54",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:23:09.266785Z",
-     "iopub.status.busy": "2024-02-08T14:23:09.266612Z",
-     "iopub.status.idle": "2024-02-08T14:23:16.912806Z",
-     "shell.execute_reply": "2024-02-08T14:23:16.912219Z"
+     "iopub.execute_input": "2024-02-09T00:11:01.189740Z",
+     "iopub.status.busy": "2024-02-09T00:11:01.189524Z",
+     "iopub.status.idle": "2024-02-09T00:11:08.466862Z",
+     "shell.execute_reply": "2024-02-09T00:11:08.466311Z"
     }
    },
    "outputs": [
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@@ -706,10 +666,10 @@
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@@ -760,10 +720,10 @@
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@@ -837,10 +797,10 @@
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@@ -863,17 +823,17 @@
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@@ -932,10 +892,10 @@
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@@ -979,10 +939,10 @@
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@@ -1018,10 +978,10 @@
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@@ -1071,10 +1031,10 @@
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diff --git a/master/tutorials/pred_probs_cross_val.html b/master/tutorials/pred_probs_cross_val.html
index bd2580f70..c611fdd25 100644
--- a/master/tutorials/pred_probs_cross_val.html
+++ b/master/tutorials/pred_probs_cross_val.html
@@ -247,6 +247,7 @@
 
 
 
  • report
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+
  • task
    • 
 
 
 
diff --git a/master/tutorials/regression.html b/master/tutorials/regression.html
index f6bd70c97..5f6e6e39a 100644
--- a/master/tutorials/regression.html
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@@ -248,6 +248,7 @@
 
 
 
  • report
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+
  • task
    • 
 
 
 
diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb
index 6c12e2de3..6e74c8790 100644
--- a/master/tutorials/regression.ipynb
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@@ -410,10 +410,10 @@
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@@ -449,10 +449,10 @@
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@@ -470,10 +470,10 @@
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@@ -520,10 +520,10 @@
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@@ -538,10 +538,10 @@
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@@ -565,10 +565,10 @@
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@@ -671,10 +671,10 @@
    "id": "f7385336",
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@@ -689,10 +689,10 @@
    "id": "59fc3091",
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@@ -727,10 +727,10 @@
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@@ -749,10 +749,10 @@
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@@ -894,10 +894,10 @@
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@@ -936,10 +936,10 @@
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@@ -995,10 +995,10 @@
    "id": "7021bd68",
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@@ -1014,10 +1014,10 @@
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@@ -1055,10 +1055,10 @@
    "id": "95531cda",
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-     "shell.execute_reply": "2024-02-08T14:24:01.643586Z"
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+     "shell.execute_reply": "2024-02-09T00:11:53.060617Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/segmentation.html b/master/tutorials/segmentation.html
index 536dbaa21..2539e4198 100644
--- a/master/tutorials/segmentation.html
+++ b/master/tutorials/segmentation.html
@@ -248,6 +248,7 @@
 
 
 
  • report
    • 
+
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    • 
 
 
 
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    3. Use cleanlab to find label issues
 
    
 
    
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@@ -9198,14 +9112,6 @@ 
    Classes which are commonly mislabeled overall
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@@ -9428,7 +9334,7 @@ 
    Get label quality scores
 
    
 
    
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    Beyond scoring the overall label quality of each image, the above method produces a (0 to 1) quality score for each pixel. We can apply a thresholding function to these scores in order to extract the same style True or False mask as find_label_issues().
    
 
    
@@ -9484,7 +9390,7 @@ 
    Get label quality scores
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diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb
index 6d37e6190..932d1d202 100644
--- a/master/tutorials/segmentation.ipynb
+++ b/master/tutorials/segmentation.ipynb
@@ -61,10 +61,10 @@
    "id": "ae8a08e0",
    "metadata": {
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-     "iopub.execute_input": "2024-02-08T14:24:04.325384Z",
-     "iopub.status.busy": "2024-02-08T14:24:04.325208Z",
-     "iopub.status.idle": "2024-02-08T14:24:06.264644Z",
-     "shell.execute_reply": "2024-02-08T14:24:06.263981Z"
+     "iopub.execute_input": "2024-02-09T00:11:55.706327Z",
+     "iopub.status.busy": "2024-02-09T00:11:55.705913Z",
+     "iopub.status.idle": "2024-02-09T00:11:58.409189Z",
+     "shell.execute_reply": "2024-02-09T00:11:58.408524Z"
     }
    },
    "outputs": [],
@@ -79,10 +79,10 @@
    "id": "58fd4c55",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:24:06.267222Z",
-     "iopub.status.busy": "2024-02-08T14:24:06.266849Z",
-     "iopub.status.idle": "2024-02-08T14:25:00.950515Z",
-     "shell.execute_reply": "2024-02-08T14:25:00.949868Z"
+     "iopub.execute_input": "2024-02-09T00:11:58.411644Z",
+     "iopub.status.busy": "2024-02-09T00:11:58.411453Z",
+     "iopub.status.idle": "2024-02-09T00:16:12.520745Z",
+     "shell.execute_reply": "2024-02-09T00:16:12.520095Z"
     }
    },
    "outputs": [],
@@ -97,10 +97,10 @@
    "id": "439b0305",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:25:00.953008Z",
-     "iopub.status.busy": "2024-02-08T14:25:00.952671Z",
-     "iopub.status.idle": "2024-02-08T14:25:01.985876Z",
-     "shell.execute_reply": "2024-02-08T14:25:01.985324Z"
+     "iopub.execute_input": "2024-02-09T00:16:12.523122Z",
+     "iopub.status.busy": "2024-02-09T00:16:12.522932Z",
+     "iopub.status.idle": "2024-02-09T00:16:13.561109Z",
+     "shell.execute_reply": "2024-02-09T00:16:13.560569Z"
     },
     "nbsphinx": "hidden"
    },
@@ -111,7 +111,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -137,10 +137,10 @@
    "id": "a1349304",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:25:01.988452Z",
-     "iopub.status.busy": "2024-02-08T14:25:01.987961Z",
-     "iopub.status.idle": "2024-02-08T14:25:01.991234Z",
-     "shell.execute_reply": "2024-02-08T14:25:01.990702Z"
+     "iopub.execute_input": "2024-02-09T00:16:13.563706Z",
+     "iopub.status.busy": "2024-02-09T00:16:13.563270Z",
+     "iopub.status.idle": "2024-02-09T00:16:13.566498Z",
+     "shell.execute_reply": "2024-02-09T00:16:13.566044Z"
     }
    },
    "outputs": [],
@@ -203,10 +203,10 @@
    "id": "07dc5678",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:25:01.993300Z",
-     "iopub.status.busy": "2024-02-08T14:25:01.992908Z",
-     "iopub.status.idle": "2024-02-08T14:25:01.996930Z",
-     "shell.execute_reply": "2024-02-08T14:25:01.996404Z"
+     "iopub.execute_input": "2024-02-09T00:16:13.568560Z",
+     "iopub.status.busy": "2024-02-09T00:16:13.568238Z",
+     "iopub.status.idle": "2024-02-09T00:16:13.572059Z",
+     "shell.execute_reply": "2024-02-09T00:16:13.571602Z"
     }
    },
    "outputs": [
@@ -247,10 +247,10 @@
    "id": "25ebe22a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:25:01.999046Z",
-     "iopub.status.busy": "2024-02-08T14:25:01.998716Z",
-     "iopub.status.idle": "2024-02-08T14:25:02.002246Z",
-     "shell.execute_reply": "2024-02-08T14:25:02.001745Z"
+     "iopub.execute_input": "2024-02-09T00:16:13.573969Z",
+     "iopub.status.busy": "2024-02-09T00:16:13.573788Z",
+     "iopub.status.idle": "2024-02-09T00:16:13.577236Z",
+     "shell.execute_reply": "2024-02-09T00:16:13.576801Z"
     }
    },
    "outputs": [
@@ -290,10 +290,10 @@
    "id": "3faedea9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:25:02.004444Z",
-     "iopub.status.busy": "2024-02-08T14:25:02.004125Z",
-     "iopub.status.idle": "2024-02-08T14:25:02.006724Z",
-     "shell.execute_reply": "2024-02-08T14:25:02.006292Z"
+     "iopub.execute_input": "2024-02-09T00:16:13.579148Z",
+     "iopub.status.busy": "2024-02-09T00:16:13.578896Z",
+     "iopub.status.idle": "2024-02-09T00:16:13.581549Z",
+     "shell.execute_reply": "2024-02-09T00:16:13.581134Z"
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@@ -333,17 +333,17 @@
    "id": "2c2ad9ad",
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-     "shell.execute_reply": "2024-02-08T14:26:17.949536Z"
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@@ -357,7 +357,7 @@
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@@ -400,10 +400,10 @@
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@@ -446,10 +446,10 @@
    "id": "57fed473",
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-     "shell.execute_reply": "2024-02-08T14:26:21.286905Z"
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@@ -519,10 +519,10 @@
    "id": "e4a006bd",
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+     "shell.execute_reply": "2024-02-09T00:18:04.978087Z"
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-       "value": "number of examples processed for checking labels: 100%"
-      }
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-     "e918b7f4d27b4678bcc41d011713e058": {
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       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -4630,25 +4604,7 @@
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-     "f33f447e3af24f1fb872fb78803f30c9": {
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@@ -4663,36 +4619,48 @@
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        "description_allow_html": false,
-       "layout": "IPY_MODEL_734e0ce1fe0741fda251660722f3cd9d",
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        "placeholder": "​",
-       "style": "IPY_MODEL_86b6a6f5404d4c3cb34b73d638090f14",
+       "style": "IPY_MODEL_4e96e892c9d94bcd8f3f2a3d59775eee",
        "tabbable": null,
        "tooltip": null,
-       "value": " 30/30 [00:00<00:00, 442.81it/s]"
+       "value": " 30/30 [00:00<00:00, 431.01it/s]"
       }
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-     "f94cde7b85b8462293dd9990e3c52464": {
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        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
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-       "_view_module": "@jupyter-widgets/controls",
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-        "IPY_MODEL_f33f447e3af24f1fb872fb78803f30c9"
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-       "layout": "IPY_MODEL_4bfe821c9552467097c55c9a4d8773de",
-       "tabbable": null,
-       "tooltip": null
+       "_view_name": "StyleView",
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+       "description_width": "",
+       "font_size": null,
+       "text_color": null
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+      "model_name": "HTMLStyleModel",
+      "state": {
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+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
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diff --git a/master/tutorials/tabular.html b/master/tutorials/tabular.html
index 00bf81a5f..6d5990f49 100644
--- a/master/tutorials/tabular.html
+++ b/master/tutorials/tabular.html
@@ -248,6 +248,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
diff --git a/master/tutorials/tabular.ipynb b/master/tutorials/tabular.ipynb
index 1b33489a6..0659cb517 100644
--- a/master/tutorials/tabular.ipynb
+++ b/master/tutorials/tabular.ipynb
@@ -112,10 +112,10 @@
    "execution_count": 1,
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-     "shell.execute_reply": "2024-02-08T14:27:24.939749Z"
+     "iopub.execute_input": "2024-02-09T00:18:34.821415Z",
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@@ -125,7 +125,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -150,10 +150,10 @@
    "execution_count": 2,
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-     "iopub.status.idle": "2024-02-08T14:27:24.960391Z",
-     "shell.execute_reply": "2024-02-08T14:27:24.959858Z"
+     "iopub.execute_input": "2024-02-09T00:18:35.856709Z",
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+     "shell.execute_reply": "2024-02-09T00:18:35.874457Z"
     }
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@@ -194,10 +194,10 @@
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-     "shell.execute_reply": "2024-02-08T14:27:25.104546Z"
+     "iopub.execute_input": "2024-02-09T00:18:35.877027Z",
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     }
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@@ -304,10 +304,10 @@
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-     "shell.execute_reply": "2024-02-08T14:27:25.110370Z"
+     "iopub.execute_input": "2024-02-09T00:18:35.992500Z",
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     }
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@@ -328,10 +328,10 @@
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-     "shell.execute_reply": "2024-02-08T14:27:25.120347Z"
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@@ -383,10 +383,10 @@
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@@ -408,10 +408,10 @@
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@@ -445,10 +445,10 @@
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-     "shell.execute_reply": "2024-02-08T14:27:27.286290Z"
+     "iopub.execute_input": "2024-02-09T00:18:36.535697Z",
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@@ -480,10 +480,10 @@
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-     "shell.execute_reply": "2024-02-08T14:27:27.298619Z"
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     }
    },
    "outputs": [
@@ -604,10 +604,10 @@
    "execution_count": 10,
    "metadata": {
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-     "shell.execute_reply": "2024-02-08T14:27:27.304194Z"
+     "iopub.execute_input": "2024-02-09T00:18:38.153259Z",
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     }
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    "outputs": [],
@@ -632,10 +632,10 @@
    "execution_count": 11,
    "metadata": {
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-     "shell.execute_reply": "2024-02-08T14:27:27.312733Z"
+     "iopub.execute_input": "2024-02-09T00:18:38.158787Z",
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@@ -657,10 +657,10 @@
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-     "shell.execute_reply": "2024-02-08T14:27:27.425672Z"
+     "iopub.execute_input": "2024-02-09T00:18:38.167378Z",
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@@ -690,10 +690,10 @@
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-     "shell.execute_reply": "2024-02-08T14:27:27.430580Z"
+     "iopub.execute_input": "2024-02-09T00:18:38.280101Z",
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     }
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@@ -714,10 +714,10 @@
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-     "shell.execute_reply": "2024-02-08T14:27:29.373229Z"
+     "iopub.execute_input": "2024-02-09T00:18:38.284337Z",
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     }
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    "outputs": [],
@@ -737,10 +737,10 @@
    "execution_count": 15,
    "metadata": {
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-     "iopub.status.idle": "2024-02-08T14:27:29.387660Z",
-     "shell.execute_reply": "2024-02-08T14:27:29.387089Z"
+     "iopub.execute_input": "2024-02-09T00:18:40.253972Z",
+     "iopub.status.busy": "2024-02-09T00:18:40.253228Z",
+     "iopub.status.idle": "2024-02-09T00:18:40.264455Z",
+     "shell.execute_reply": "2024-02-09T00:18:40.263907Z"
     }
    },
    "outputs": [
@@ -770,10 +770,10 @@
    "execution_count": 16,
    "metadata": {
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-     "iopub.execute_input": "2024-02-08T14:27:29.389725Z",
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-     "iopub.status.idle": "2024-02-08T14:27:29.529033Z",
-     "shell.execute_reply": "2024-02-08T14:27:29.528506Z"
+     "iopub.execute_input": "2024-02-09T00:18:40.266531Z",
+     "iopub.status.busy": "2024-02-09T00:18:40.266229Z",
+     "iopub.status.idle": "2024-02-09T00:18:40.286648Z",
+     "shell.execute_reply": "2024-02-09T00:18:40.286215Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/text.html b/master/tutorials/text.html
index 94c0ba151..b9cc5c8b8 100644
--- a/master/tutorials/text.html
+++ b/master/tutorials/text.html
@@ -248,6 +248,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
@@ -732,7 +733,7 @@ 
    2. Load and format the text dataset
 
     This dataset has 10 classes.
-Classes: {'card_payment_fee_charged', 'getting_spare_card', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'change_pin', 'visa_or_mastercard', 'card_about_to_expire', 'supported_cards_and_currencies', 'cancel_transfer', 'apple_pay_or_google_pay'}
+Classes: {'getting_spare_card', 'cancel_transfer', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'change_pin', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'card_about_to_expire'}
 
    
 
 
    Let’s print the first example in the train set.
    
diff --git a/master/tutorials/text.ipynb b/master/tutorials/text.ipynb
index a5b2a02d5..097217444 100644
--- a/master/tutorials/text.ipynb
+++ b/master/tutorials/text.ipynb
@@ -114,10 +114,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:32.204126Z",
-     "iopub.status.busy": "2024-02-08T14:27:32.203935Z",
-     "iopub.status.idle": "2024-02-08T14:27:34.742594Z",
-     "shell.execute_reply": "2024-02-08T14:27:34.741969Z"
+     "iopub.execute_input": "2024-02-09T00:18:43.092031Z",
+     "iopub.status.busy": "2024-02-09T00:18:43.091860Z",
+     "iopub.status.idle": "2024-02-09T00:18:45.719204Z",
+     "shell.execute_reply": "2024-02-09T00:18:45.718566Z"
     },
     "nbsphinx": "hidden"
    },
@@ -134,7 +134,7 @@
     "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"  # disable parallelism to avoid deadlocks with huggingface\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -159,10 +159,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:34.745277Z",
-     "iopub.status.busy": "2024-02-08T14:27:34.744930Z",
-     "iopub.status.idle": "2024-02-08T14:27:34.748478Z",
-     "shell.execute_reply": "2024-02-08T14:27:34.747942Z"
+     "iopub.execute_input": "2024-02-09T00:18:45.721879Z",
+     "iopub.status.busy": "2024-02-09T00:18:45.721579Z",
+     "iopub.status.idle": "2024-02-09T00:18:45.725027Z",
+     "shell.execute_reply": "2024-02-09T00:18:45.724581Z"
     }
    },
    "outputs": [],
@@ -184,10 +184,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:34.750440Z",
-     "iopub.status.busy": "2024-02-08T14:27:34.750133Z",
-     "iopub.status.idle": "2024-02-08T14:27:34.753219Z",
-     "shell.execute_reply": "2024-02-08T14:27:34.752696Z"
+     "iopub.execute_input": "2024-02-09T00:18:45.727032Z",
+     "iopub.status.busy": "2024-02-09T00:18:45.726723Z",
+     "iopub.status.idle": "2024-02-09T00:18:45.729765Z",
+     "shell.execute_reply": "2024-02-09T00:18:45.729325Z"
     },
     "nbsphinx": "hidden"
    },
@@ -218,10 +218,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:34.755230Z",
-     "iopub.status.busy": "2024-02-08T14:27:34.754907Z",
-     "iopub.status.idle": "2024-02-08T14:27:34.906355Z",
-     "shell.execute_reply": "2024-02-08T14:27:34.905931Z"
+     "iopub.execute_input": "2024-02-09T00:18:45.731693Z",
+     "iopub.status.busy": "2024-02-09T00:18:45.731367Z",
+     "iopub.status.idle": "2024-02-09T00:18:45.845990Z",
+     "shell.execute_reply": "2024-02-09T00:18:45.845460Z"
     }
    },
    "outputs": [
@@ -311,10 +311,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:34.908383Z",
-     "iopub.status.busy": "2024-02-08T14:27:34.908131Z",
-     "iopub.status.idle": "2024-02-08T14:27:34.911634Z",
-     "shell.execute_reply": "2024-02-08T14:27:34.911104Z"
+     "iopub.execute_input": "2024-02-09T00:18:45.848136Z",
+     "iopub.status.busy": "2024-02-09T00:18:45.847858Z",
+     "iopub.status.idle": "2024-02-09T00:18:45.851188Z",
+     "shell.execute_reply": "2024-02-09T00:18:45.850729Z"
     }
    },
    "outputs": [],
@@ -329,10 +329,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:34.913686Z",
-     "iopub.status.busy": "2024-02-08T14:27:34.913377Z",
-     "iopub.status.idle": "2024-02-08T14:27:34.916837Z",
-     "shell.execute_reply": "2024-02-08T14:27:34.916382Z"
+     "iopub.execute_input": "2024-02-09T00:18:45.853196Z",
+     "iopub.status.busy": "2024-02-09T00:18:45.852875Z",
+     "iopub.status.idle": "2024-02-09T00:18:45.856174Z",
+     "shell.execute_reply": "2024-02-09T00:18:45.855631Z"
     }
    },
    "outputs": [
@@ -341,7 +341,7 @@
      "output_type": "stream",
      "text": [
       "This dataset has 10 classes.\n",
-      "Classes: {'card_payment_fee_charged', 'getting_spare_card', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'change_pin', 'visa_or_mastercard', 'card_about_to_expire', 'supported_cards_and_currencies', 'cancel_transfer', 'apple_pay_or_google_pay'}\n"
+      "Classes: {'getting_spare_card', 'cancel_transfer', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'change_pin', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'card_about_to_expire'}\n"
      ]
     }
    ],
@@ -364,10 +364,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:34.918764Z",
-     "iopub.status.busy": "2024-02-08T14:27:34.918447Z",
-     "iopub.status.idle": "2024-02-08T14:27:34.921398Z",
-     "shell.execute_reply": "2024-02-08T14:27:34.920863Z"
+     "iopub.execute_input": "2024-02-09T00:18:45.858156Z",
+     "iopub.status.busy": "2024-02-09T00:18:45.857843Z",
+     "iopub.status.idle": "2024-02-09T00:18:45.860792Z",
+     "shell.execute_reply": "2024-02-09T00:18:45.860270Z"
     }
    },
    "outputs": [
@@ -408,10 +408,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:34.923447Z",
-     "iopub.status.busy": "2024-02-08T14:27:34.923127Z",
-     "iopub.status.idle": "2024-02-08T14:27:34.926192Z",
-     "shell.execute_reply": "2024-02-08T14:27:34.925770Z"
+     "iopub.execute_input": "2024-02-09T00:18:45.862822Z",
+     "iopub.status.busy": "2024-02-09T00:18:45.862466Z",
+     "iopub.status.idle": "2024-02-09T00:18:45.865679Z",
+     "shell.execute_reply": "2024-02-09T00:18:45.865242Z"
     }
    },
    "outputs": [],
@@ -452,10 +452,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:34.928180Z",
-     "iopub.status.busy": "2024-02-08T14:27:34.927859Z",
-     "iopub.status.idle": "2024-02-08T14:27:39.131556Z",
-     "shell.execute_reply": "2024-02-08T14:27:39.131002Z"
+     "iopub.execute_input": "2024-02-09T00:18:45.867706Z",
+     "iopub.status.busy": "2024-02-09T00:18:45.867392Z",
+     "iopub.status.idle": "2024-02-09T00:18:50.141569Z",
+     "shell.execute_reply": "2024-02-09T00:18:50.141035Z"
     }
    },
    "outputs": [
@@ -510,10 +510,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:39.134249Z",
-     "iopub.status.busy": "2024-02-08T14:27:39.133873Z",
-     "iopub.status.idle": "2024-02-08T14:27:39.136873Z",
-     "shell.execute_reply": "2024-02-08T14:27:39.136390Z"
+     "iopub.execute_input": "2024-02-09T00:18:50.144320Z",
+     "iopub.status.busy": "2024-02-09T00:18:50.143885Z",
+     "iopub.status.idle": "2024-02-09T00:18:50.146801Z",
+     "shell.execute_reply": "2024-02-09T00:18:50.146226Z"
     }
    },
    "outputs": [],
@@ -535,10 +535,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:39.138829Z",
-     "iopub.status.busy": "2024-02-08T14:27:39.138502Z",
-     "iopub.status.idle": "2024-02-08T14:27:39.141136Z",
-     "shell.execute_reply": "2024-02-08T14:27:39.140702Z"
+     "iopub.execute_input": "2024-02-09T00:18:50.148918Z",
+     "iopub.status.busy": "2024-02-09T00:18:50.148575Z",
+     "iopub.status.idle": "2024-02-09T00:18:50.151317Z",
+     "shell.execute_reply": "2024-02-09T00:18:50.150770Z"
     }
    },
    "outputs": [],
@@ -553,10 +553,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:39.142971Z",
-     "iopub.status.busy": "2024-02-08T14:27:39.142659Z",
-     "iopub.status.idle": "2024-02-08T14:27:41.368853Z",
-     "shell.execute_reply": "2024-02-08T14:27:41.368128Z"
+     "iopub.execute_input": "2024-02-09T00:18:50.153241Z",
+     "iopub.status.busy": "2024-02-09T00:18:50.152913Z",
+     "iopub.status.idle": "2024-02-09T00:18:52.453367Z",
+     "shell.execute_reply": "2024-02-09T00:18:52.452720Z"
     },
     "scrolled": true
    },
@@ -579,10 +579,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:41.371805Z",
-     "iopub.status.busy": "2024-02-08T14:27:41.371238Z",
-     "iopub.status.idle": "2024-02-08T14:27:41.378737Z",
-     "shell.execute_reply": "2024-02-08T14:27:41.378284Z"
+     "iopub.execute_input": "2024-02-09T00:18:52.456211Z",
+     "iopub.status.busy": "2024-02-09T00:18:52.455635Z",
+     "iopub.status.idle": "2024-02-09T00:18:52.463412Z",
+     "shell.execute_reply": "2024-02-09T00:18:52.462950Z"
     }
    },
    "outputs": [
@@ -683,10 +683,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:41.380700Z",
-     "iopub.status.busy": "2024-02-08T14:27:41.380363Z",
-     "iopub.status.idle": "2024-02-08T14:27:41.384008Z",
-     "shell.execute_reply": "2024-02-08T14:27:41.383559Z"
+     "iopub.execute_input": "2024-02-09T00:18:52.465564Z",
+     "iopub.status.busy": "2024-02-09T00:18:52.465145Z",
+     "iopub.status.idle": "2024-02-09T00:18:52.469053Z",
+     "shell.execute_reply": "2024-02-09T00:18:52.468609Z"
     }
    },
    "outputs": [],
@@ -700,10 +700,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:41.385893Z",
-     "iopub.status.busy": "2024-02-08T14:27:41.385556Z",
-     "iopub.status.idle": "2024-02-08T14:27:41.388767Z",
-     "shell.execute_reply": "2024-02-08T14:27:41.388324Z"
+     "iopub.execute_input": "2024-02-09T00:18:52.471055Z",
+     "iopub.status.busy": "2024-02-09T00:18:52.470611Z",
+     "iopub.status.idle": "2024-02-09T00:18:52.473960Z",
+     "shell.execute_reply": "2024-02-09T00:18:52.473503Z"
     }
    },
    "outputs": [
@@ -738,10 +738,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:41.390727Z",
-     "iopub.status.busy": "2024-02-08T14:27:41.390420Z",
-     "iopub.status.idle": "2024-02-08T14:27:41.393251Z",
-     "shell.execute_reply": "2024-02-08T14:27:41.392824Z"
+     "iopub.execute_input": "2024-02-09T00:18:52.475995Z",
+     "iopub.status.busy": "2024-02-09T00:18:52.475676Z",
+     "iopub.status.idle": "2024-02-09T00:18:52.478705Z",
+     "shell.execute_reply": "2024-02-09T00:18:52.478221Z"
     }
    },
    "outputs": [],
@@ -761,10 +761,10 @@
    "execution_count": 17,
    "metadata": {
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-     "iopub.execute_input": "2024-02-08T14:27:41.395292Z",
-     "iopub.status.busy": "2024-02-08T14:27:41.394954Z",
-     "iopub.status.idle": "2024-02-08T14:27:41.401357Z",
-     "shell.execute_reply": "2024-02-08T14:27:41.400936Z"
+     "iopub.execute_input": "2024-02-09T00:18:52.480587Z",
+     "iopub.status.busy": "2024-02-09T00:18:52.480244Z",
+     "iopub.status.idle": "2024-02-09T00:18:52.487024Z",
+     "shell.execute_reply": "2024-02-09T00:18:52.486469Z"
     }
    },
    "outputs": [
@@ -889,10 +889,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:41.403438Z",
-     "iopub.status.busy": "2024-02-08T14:27:41.403120Z",
-     "iopub.status.idle": "2024-02-08T14:27:41.626428Z",
-     "shell.execute_reply": "2024-02-08T14:27:41.625913Z"
+     "iopub.execute_input": "2024-02-09T00:18:52.489149Z",
+     "iopub.status.busy": "2024-02-09T00:18:52.488746Z",
+     "iopub.status.idle": "2024-02-09T00:18:52.751106Z",
+     "shell.execute_reply": "2024-02-09T00:18:52.750578Z"
     },
     "scrolled": true
    },
@@ -931,10 +931,10 @@
    "execution_count": 19,
    "metadata": {
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-     "iopub.execute_input": "2024-02-08T14:27:41.628842Z",
-     "iopub.status.busy": "2024-02-08T14:27:41.628467Z",
-     "iopub.status.idle": "2024-02-08T14:27:41.804111Z",
-     "shell.execute_reply": "2024-02-08T14:27:41.803643Z"
+     "iopub.execute_input": "2024-02-09T00:18:52.753638Z",
+     "iopub.status.busy": "2024-02-09T00:18:52.753273Z",
+     "iopub.status.idle": "2024-02-09T00:18:52.931483Z",
+     "shell.execute_reply": "2024-02-09T00:18:52.930895Z"
     },
     "scrolled": true
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@@ -967,10 +967,10 @@
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-     "iopub.execute_input": "2024-02-08T14:27:41.806451Z",
-     "iopub.status.busy": "2024-02-08T14:27:41.806094Z",
-     "iopub.status.idle": "2024-02-08T14:27:41.809763Z",
-     "shell.execute_reply": "2024-02-08T14:27:41.809286Z"
+     "iopub.execute_input": "2024-02-09T00:18:52.935035Z",
+     "iopub.status.busy": "2024-02-09T00:18:52.934051Z",
+     "iopub.status.idle": "2024-02-09T00:18:52.939066Z",
+     "shell.execute_reply": "2024-02-09T00:18:52.938542Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html
index 179f1876e..5c4e23ecf 100644
--- a/master/tutorials/token_classification.html
+++ b/master/tutorials/token_classification.html
@@ -248,6 +248,7 @@
 
 
 
  • report
    • 
+
  • task
    • 
 
 
 
@@ -625,7 +626,7 @@ 
    1. Install required dependencies and download data
 
    
 
    ---2024-02-08 14:27:44--  https://data.deepai.org/conll2003.zip
+--2024-02-09 00:18:57--  https://data.deepai.org/conll2003.zip
 Resolving data.deepai.org (data.deepai.org)...
 
    
 
@@ -634,9 +635,17 @@ 
    1. Install required dependencies and download data
 
    
 
    -169.150.249.169, 2400:52e0:1a01::987:1
-Connecting to data.deepai.org (data.deepai.org)|169.150.249.169|:443... connected.
-HTTP request sent, awaiting response... 200 OK
+143.244.50.84, 2400:52e0:1a01::1002:1
+Connecting to data.deepai.org (data.deepai.org)|143.244.50.84|:443... connected.
+HTTP request sent, awaiting response...
+
    
+
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+
    
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+
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    1. Install required dependencies and download data
 
    
 
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    1. Install required dependencies and download data
 
    
 
    ---2024-02-08 14:27:45--  https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz
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    [3]:
diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb
index 669fea6f7..0c11fcccb 100644
--- a/master/tutorials/token_classification.ipynb
+++ b/master/tutorials/token_classification.ipynb
@@ -75,10 +75,10 @@
    "id": "ae8a08e0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:44.620201Z",
-     "iopub.status.busy": "2024-02-08T14:27:44.619732Z",
-     "iopub.status.idle": "2024-02-08T14:27:48.290139Z",
-     "shell.execute_reply": "2024-02-08T14:27:48.289531Z"
+     "iopub.execute_input": "2024-02-09T00:18:57.048750Z",
+     "iopub.status.busy": "2024-02-09T00:18:57.048302Z",
+     "iopub.status.idle": "2024-02-09T00:18:58.991824Z",
+     "shell.execute_reply": "2024-02-09T00:18:58.991242Z"
     }
    },
    "outputs": [
@@ -86,7 +86,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "--2024-02-08 14:27:44--  https://data.deepai.org/conll2003.zip\r\n",
+      "--2024-02-09 00:18:57--  https://data.deepai.org/conll2003.zip\r\n",
       "Resolving data.deepai.org (data.deepai.org)... "
      ]
     },
@@ -94,9 +94,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "169.150.249.169, 2400:52e0:1a01::987:1\r\n",
-      "Connecting to data.deepai.org (data.deepai.org)|169.150.249.169|:443... connected.\r\n",
-      "HTTP request sent, awaiting response... 200 OK\r\n",
+      "143.244.50.84, 2400:52e0:1a01::1002:1\r\n",
+      "Connecting to data.deepai.org (data.deepai.org)|143.244.50.84|:443... connected.\r\n",
+      "HTTP request sent, awaiting response... "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "200 OK\r\n",
       "Length: 982975 (960K) [application/zip]\r\n",
       "Saving to: ‘conll2003.zip’\r\n",
       "\r\n",
@@ -109,9 +116,9 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "conll2003.zip       100%[===================>] 959.94K  5.77MB/s    in 0.2s    \r\n",
+      "conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.05s   \r\n",
       "\r\n",
-      "2024-02-08 14:27:44 (5.77 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
+      "2024-02-09 00:18:57 (18.8 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
       "\r\n",
       "mkdir: cannot create directory ‘data’: File exists\r\n"
      ]
@@ -131,9 +138,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "--2024-02-08 14:27:45--  https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
-      "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.25.234, 16.182.34.9, 52.217.191.57, ...\r\n",
-      "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.25.234|:443... "
+      "--2024-02-09 00:18:57--  https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
+      "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.35.137, 54.231.223.49, 3.5.6.160, ...\r\n",
+      "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.35.137|:443... "
      ]
     },
     {
@@ -167,71 +174,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "pred_probs.npz        1%[                    ] 263.08K  1.13MB/s               "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "pred_probs.npz        4%[                    ] 806.73K  1.73MB/s               "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "pred_probs.npz        8%[>                   ]   1.42M  2.11MB/s               "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "pred_probs.npz       13%[=>                  ]   2.20M  2.44MB/s               "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "pred_probs.npz       19%[==>                 ]   3.14M  2.76MB/s               "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "pred_probs.npz       26%[====>               ]   4.30M  3.15MB/s               "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
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-      "\r",
-      "pred_probs.npz       45%[========>           ]   7.41M  4.06MB/s               "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "pred_probs.npz       58%[==========>         ]   9.48M  4.61MB/s               "
+      "pred_probs.npz        0%[                    ] 126.53K   597KB/s               "
      ]
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@@ -239,7 +182,7 @@
      "output_type": "stream",
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+      "pred_probs.npz        7%[>                   ]   1.16M  2.74MB/s               "
      ]
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@@ -247,7 +190,7 @@
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+      "pred_probs.npz       47%[========>           ]   7.66M  12.0MB/s               "
      ]
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@@ -255,9 +198,10 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "pred_probs.npz      100%[===================>]  16.26M  6.33MB/s    in 2.6s    \r\n",
+      "pred_probs.npz       83%[===============>    ]  13.58M  15.8MB/s               \r",
+      "pred_probs.npz      100%[===================>]  16.26M  18.7MB/s    in 0.9s    \r\n",
       "\r\n",
-      "2024-02-08 14:27:48 (6.33 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+      "2024-02-09 00:18:58 (18.7 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
       "\r\n"
      ]
     }
@@ -274,10 +218,10 @@
    "id": "439b0305",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:48.292712Z",
-     "iopub.status.busy": "2024-02-08T14:27:48.292351Z",
-     "iopub.status.idle": "2024-02-08T14:27:49.318807Z",
-     "shell.execute_reply": "2024-02-08T14:27:49.318266Z"
+     "iopub.execute_input": "2024-02-09T00:18:58.994407Z",
+     "iopub.status.busy": "2024-02-09T00:18:58.994052Z",
+     "iopub.status.idle": "2024-02-09T00:19:00.032835Z",
+     "shell.execute_reply": "2024-02-09T00:19:00.032278Z"
     },
     "nbsphinx": "hidden"
    },
@@ -288,7 +232,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -314,10 +258,10 @@
    "id": "a1349304",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:49.321441Z",
-     "iopub.status.busy": "2024-02-08T14:27:49.321001Z",
-     "iopub.status.idle": "2024-02-08T14:27:49.324649Z",
-     "shell.execute_reply": "2024-02-08T14:27:49.324219Z"
+     "iopub.execute_input": "2024-02-09T00:19:00.035412Z",
+     "iopub.status.busy": "2024-02-09T00:19:00.034998Z",
+     "iopub.status.idle": "2024-02-09T00:19:00.038580Z",
+     "shell.execute_reply": "2024-02-09T00:19:00.038037Z"
     }
    },
    "outputs": [],
@@ -367,10 +311,10 @@
    "id": "ab9d59a0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:49.326803Z",
-     "iopub.status.busy": "2024-02-08T14:27:49.326369Z",
-     "iopub.status.idle": "2024-02-08T14:27:49.329270Z",
-     "shell.execute_reply": "2024-02-08T14:27:49.328862Z"
+     "iopub.execute_input": "2024-02-09T00:19:00.040669Z",
+     "iopub.status.busy": "2024-02-09T00:19:00.040361Z",
+     "iopub.status.idle": "2024-02-09T00:19:00.043148Z",
+     "shell.execute_reply": "2024-02-09T00:19:00.042725Z"
     },
     "nbsphinx": "hidden"
    },
@@ -388,10 +332,10 @@
    "id": "519cb80c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:49.331345Z",
-     "iopub.status.busy": "2024-02-08T14:27:49.330958Z",
-     "iopub.status.idle": "2024-02-08T14:27:58.410208Z",
-     "shell.execute_reply": "2024-02-08T14:27:58.409616Z"
+     "iopub.execute_input": "2024-02-09T00:19:00.045150Z",
+     "iopub.status.busy": "2024-02-09T00:19:00.044840Z",
+     "iopub.status.idle": "2024-02-09T00:19:09.194303Z",
+     "shell.execute_reply": "2024-02-09T00:19:09.193758Z"
     }
    },
    "outputs": [],
@@ -465,10 +409,10 @@
    "id": "202f1526",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:58.412728Z",
-     "iopub.status.busy": "2024-02-08T14:27:58.412543Z",
-     "iopub.status.idle": "2024-02-08T14:27:58.418173Z",
-     "shell.execute_reply": "2024-02-08T14:27:58.417624Z"
+     "iopub.execute_input": "2024-02-09T00:19:09.196802Z",
+     "iopub.status.busy": "2024-02-09T00:19:09.196461Z",
+     "iopub.status.idle": "2024-02-09T00:19:09.201982Z",
+     "shell.execute_reply": "2024-02-09T00:19:09.201462Z"
     },
     "nbsphinx": "hidden"
    },
@@ -508,10 +452,10 @@
    "id": "a4381f03",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:58.420195Z",
-     "iopub.status.busy": "2024-02-08T14:27:58.419875Z",
-     "iopub.status.idle": "2024-02-08T14:27:58.748360Z",
-     "shell.execute_reply": "2024-02-08T14:27:58.747727Z"
+     "iopub.execute_input": "2024-02-09T00:19:09.203935Z",
+     "iopub.status.busy": "2024-02-09T00:19:09.203620Z",
+     "iopub.status.idle": "2024-02-09T00:19:09.535937Z",
+     "shell.execute_reply": "2024-02-09T00:19:09.535333Z"
     }
    },
    "outputs": [],
@@ -548,10 +492,10 @@
    "id": "7842e4a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:58.750811Z",
-     "iopub.status.busy": "2024-02-08T14:27:58.750619Z",
-     "iopub.status.idle": "2024-02-08T14:27:58.755066Z",
-     "shell.execute_reply": "2024-02-08T14:27:58.754523Z"
+     "iopub.execute_input": "2024-02-09T00:19:09.538380Z",
+     "iopub.status.busy": "2024-02-09T00:19:09.538182Z",
+     "iopub.status.idle": "2024-02-09T00:19:09.542321Z",
+     "shell.execute_reply": "2024-02-09T00:19:09.541826Z"
     }
    },
    "outputs": [
@@ -623,10 +567,10 @@
    "id": "2c2ad9ad",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:27:58.757107Z",
-     "iopub.status.busy": "2024-02-08T14:27:58.756716Z",
-     "iopub.status.idle": "2024-02-08T14:28:01.081800Z",
-     "shell.execute_reply": "2024-02-08T14:28:01.081151Z"
+     "iopub.execute_input": "2024-02-09T00:19:09.544346Z",
+     "iopub.status.busy": "2024-02-09T00:19:09.544034Z",
+     "iopub.status.idle": "2024-02-09T00:19:11.827569Z",
+     "shell.execute_reply": "2024-02-09T00:19:11.826938Z"
     }
    },
    "outputs": [],
@@ -648,10 +592,10 @@
    "id": "95dc7268",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:28:01.084631Z",
-     "iopub.status.busy": "2024-02-08T14:28:01.084106Z",
-     "iopub.status.idle": "2024-02-08T14:28:01.087890Z",
-     "shell.execute_reply": "2024-02-08T14:28:01.087366Z"
+     "iopub.execute_input": "2024-02-09T00:19:11.830609Z",
+     "iopub.status.busy": "2024-02-09T00:19:11.829877Z",
+     "iopub.status.idle": "2024-02-09T00:19:11.833766Z",
+     "shell.execute_reply": "2024-02-09T00:19:11.833237Z"
     }
    },
    "outputs": [
@@ -687,10 +631,10 @@
    "id": "e13de188",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:28:01.089754Z",
-     "iopub.status.busy": "2024-02-08T14:28:01.089582Z",
-     "iopub.status.idle": "2024-02-08T14:28:01.095038Z",
-     "shell.execute_reply": "2024-02-08T14:28:01.094482Z"
+     "iopub.execute_input": "2024-02-09T00:19:11.835782Z",
+     "iopub.status.busy": "2024-02-09T00:19:11.835464Z",
+     "iopub.status.idle": "2024-02-09T00:19:11.840749Z",
+     "shell.execute_reply": "2024-02-09T00:19:11.840197Z"
     }
    },
    "outputs": [
@@ -868,10 +812,10 @@
    "id": "e4a006bd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:28:01.096888Z",
-     "iopub.status.busy": "2024-02-08T14:28:01.096718Z",
-     "iopub.status.idle": "2024-02-08T14:28:01.122512Z",
-     "shell.execute_reply": "2024-02-08T14:28:01.121960Z"
+     "iopub.execute_input": "2024-02-09T00:19:11.842787Z",
+     "iopub.status.busy": "2024-02-09T00:19:11.842520Z",
+     "iopub.status.idle": "2024-02-09T00:19:11.869333Z",
+     "shell.execute_reply": "2024-02-09T00:19:11.868916Z"
     }
    },
    "outputs": [
@@ -973,10 +917,10 @@
    "id": "c8f4e163",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:28:01.124389Z",
-     "iopub.status.busy": "2024-02-08T14:28:01.124221Z",
-     "iopub.status.idle": "2024-02-08T14:28:01.128790Z",
-     "shell.execute_reply": "2024-02-08T14:28:01.128336Z"
+     "iopub.execute_input": "2024-02-09T00:19:11.871431Z",
+     "iopub.status.busy": "2024-02-09T00:19:11.871119Z",
+     "iopub.status.idle": "2024-02-09T00:19:11.874995Z",
+     "shell.execute_reply": "2024-02-09T00:19:11.874443Z"
     }
    },
    "outputs": [
@@ -1050,10 +994,10 @@
    "id": "db0b5179",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:28:01.130630Z",
-     "iopub.status.busy": "2024-02-08T14:28:01.130463Z",
-     "iopub.status.idle": "2024-02-08T14:28:02.551229Z",
-     "shell.execute_reply": "2024-02-08T14:28:02.550712Z"
+     "iopub.execute_input": "2024-02-09T00:19:11.877031Z",
+     "iopub.status.busy": "2024-02-09T00:19:11.876726Z",
+     "iopub.status.idle": "2024-02-09T00:19:13.283142Z",
+     "shell.execute_reply": "2024-02-09T00:19:13.282587Z"
     }
    },
    "outputs": [
@@ -1225,10 +1169,10 @@
    "id": "a18795eb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-08T14:28:02.553241Z",
-     "iopub.status.busy": "2024-02-08T14:28:02.553068Z",
-     "iopub.status.idle": "2024-02-08T14:28:02.556966Z",
-     "shell.execute_reply": "2024-02-08T14:28:02.556551Z"
+     "iopub.execute_input": "2024-02-09T00:19:13.285106Z",
+     "iopub.status.busy": "2024-02-09T00:19:13.284921Z",
+     "iopub.status.idle": "2024-02-09T00:19:13.288805Z",
+     "shell.execute_reply": "2024-02-09T00:19:13.288390Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/versioning.js b/versioning.js
index 501159886..dbe66d849 100644
--- a/versioning.js
+++ b/versioning.js
@@ -1,4 +1,4 @@
 var Version = {
   version_number: "v2.5.0",
-  commit_hash: "f03cdd8f3c75a5f550d00a20cb320ddc53d49705",
+  commit_hash: "16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd",
 };
\ No newline at end of file