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\u001b[?25l\u001b[?25hdone\n", + " Created wheel for future: filename=future-0.18.2-cp37-none-any.whl size=491070 sha256=0cd9b2556c31ce15088c4c3a6d4de6b99be7667148ced598029e1e872362fc03\n", + " Stored in directory: /root/.cache/pip/wheels/8b/99/a0/81daf51dcd359a9377b110a8a886b3895921802d2fc1b2397e\n", + "Successfully built pathtools subprocess32 future\n", + "\u001b[31mERROR: tensorflow 2.5.0 has requirement tensorboard~=2.5, but you'll have tensorboard 2.4.1 which is incompatible.\u001b[0m\n", + "\u001b[31mERROR: pytorch-lightning 1.3.5 has requirement PyYAML<=5.4.1,>=5.1, but you'll have pyyaml 3.13 which is incompatible.\u001b[0m\n", + "Installing collected packages: sentencepiece, pathtools, smmap, gitdb, GitPython, configparser, docker-pycreds, subprocess32, sentry-sdk, shortuuid, wandb, multidict, yarl, async-timeout, aiohttp, fsspec, pyDeprecate, torchmetrics, tensorboard, future, pytorch-lightning, tokenizers, sacremoses, huggingface-hub, transformers, keytotext\n", + " Found existing installation: tensorboard 2.5.0\n", + " Uninstalling tensorboard-2.5.0:\n", + " Successfully uninstalled tensorboard-2.5.0\n", + " Found existing installation: future 0.16.0\n", + " Uninstalling future-0.16.0:\n", + " Successfully uninstalled future-0.16.0\n", + "Successfully installed GitPython-3.1.17 aiohttp-3.7.4.post0 async-timeout-3.0.1 configparser-5.0.2 docker-pycreds-0.4.0 fsspec-2021.5.0 future-0.18.2 gitdb-4.0.7 huggingface-hub-0.0.8 keytotext-0.3.0 multidict-5.1.0 pathtools-0.1.2 pyDeprecate-0.3.0 pytorch-lightning-1.3.5 sacremoses-0.0.45 sentencepiece-0.1.95 sentry-sdk-1.1.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 tensorboard-2.4.1 tokenizers-0.10.3 torchmetrics-0.3.2 transformers-4.6.1 wandb-0.10.32 yarl-1.6.3\n", + "Collecting datasets\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/08/a2/d4e1024c891506e1cee8f9d719d20831bac31cb5b7416983c4d2f65a6287/datasets-1.8.0-py3-none-any.whl (237kB)\n", + "\u001b[K 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pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->datasets) (2.4.7)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n", + "Installing collected packages: xxhash, datasets\n", + "Successfully installed datasets-1.8.0 xxhash-2.0.2\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 236, + "referenced_widgets": [ + "863777a1804a455ab5bccd3f96622181", + "8d97fcadd8664f2385c69be2985d031a", + "06127b15b1f14d4e8ee29f5bbe1b4b2c", + "5c2e43ce18ea4ef69c6fad9cb07382e8", + "1a4ab548cf3740f7bf80d4205e14e242", + "f8cd265843954ca88851a85ba28fb9b8", + "c03dafc7229c446485f0ed64e35c2f74", + "5040fc9f0e7d46798c8a9d398e28e7d2", + 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"83000468976549efb67a2656f2436d8e", + "a865efcc98184a139772cd3425a51667", + "51c6993212864684aa8d6d797333e9ea", + "3858915696604df2bff0502ab772b4e0", + "725f49140da842d58bd5e04720c51b02", + "5f947b4c79f74d2c8ad1a3a22c056b24", + "63dd5a7dbbe64921a1d77dd179d745fa", + "07a99c977f6f43b4969bd40759d35af5", + "e6fda914f9e14ebfa3a13ad6265e3d63", + "d824df465023430b8063571c74e4920b", + "c2284328a0f043d88a405a7295eed225", + "19529e196fb443aeb8eb0160daca6bb4", + "51500031138742469c8360aaeea56d49", + "9fd2923417c44899a5fd71757ca1d918" + ] + }, + "id": "4SfZt1zcSfMb", + "outputId": "2f0ff984-ac2f-4590-9326-32d925951f84" + }, + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset = load_dataset('common_gen',split='train')" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "863777a1804a455ab5bccd3f96622181", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1635.0, style=ProgressStyle(description…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "15fa6fe3e5ee4b0bbf7536b167453773", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1147.0, style=ProgressStyle(description…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Using custom data configuration default\n" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "\n", + "Downloading and preparing dataset common_gen/default (download: 1.76 MiB, generated: 6.88 MiB, post-processed: Unknown size, total: 8.64 MiB) to /root/.cache/huggingface/datasets/common_gen/default/2020.5.30/1a9e8bdc026c41ce7a9e96260debf7d2809cb7fd63fa02b017e4fac1b00c6b23...\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "feede812a58e43248e6f1e8c4a99447c", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1845699.0, style=ProgressStyle(descript…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "400d9deb6ec34ac6ab1876b4426b1295", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\r" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2cb69e41ee154c75bb56afbce907bf69", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\r" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "63dd5a7dbbe64921a1d77dd179d745fa", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\rDataset common_gen downloaded and prepared to /root/.cache/huggingface/datasets/common_gen/default/2020.5.30/1a9e8bdc026c41ce7a9e96260debf7d2809cb7fd63fa02b017e4fac1b00c6b23. Subsequent calls will reuse this data.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 205 + }, + "id": "IkKS6rFbSuQL", + "outputId": "72977168-f544-434d-bd3a-9d930f1dd4b3" + }, + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.DataFrame()\n", + "df['keywords']=dataset['concepts']\n", + "df['text'] = dataset['target']\n", + "df.head()" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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1ski mountain skierA skier is skiing down a mountain.
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" + ], + "text/plain": [ + " keywords text\n", + "0 ski mountain skier Skier skis down the mountain\n", + "1 ski mountain skier A skier is skiing down a mountain.\n", + "2 ski mountain skier Three skiers are skiing on a snowy mountain.\n", + "3 wag tail dog The dog is wagging his tail.\n", + "4 wag tail dog A dog wags his tail at the boy." + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 673, + "referenced_widgets": [ + "6165513df36c4896ae9b8684107b1915", + "e9def1bc23404455af521521cc0bb435", + "218a9d58501e4fee8e24f85b7eaea863", + "ae5622ced1f74cd7ab6a8e1d57d21d58", + "4dfc81aed4134bedaf3dba4f0709a4cd", + "9ced278b9c7448baba87700af8dfb287", + "431819b7d1604a739cc999d2995bc130", + "8ef8e24e73f846199365df72c0eb7830", + "5b9e114e2a604646be9ce095285bd1bf", + "9351c025963344e6b3a4b17a59aaa35b", + "4518845f4be948b6be0302aae5ec9b8b", + 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"d792037cf4284135b8c31688bc9625c3", + "e0996e868a544b48a73a0a1256cb726a", + "15a02dd317664e4e99255bf413c4ed28", + "7369e52d6fdd4faeb87ef29a32da2fe9", + "a684d16ab52e4118b41a2ce984773eb9", + "aeff8e23452c410c8bea236d94a30b44", + "acab66f46cd949158018f7f13a2d76fc", + "9c30745711104a0783bb13c4be39159d", + "78dd5f54bdd04efbb2aa60546e9916d2", + "0a2cc5a69f2f4c2c9f348ffe14c37164", + "51cbc35fac7746d5a895b35a63d252f7", + "df55689d9a09435dbc0f681b2ec9e0b9", + "b1003e4158e949659609fcfdeea53cd4", + "4cff72bf002840239dd7185fbc40448e", + "17fe43c0786640f4a5f6550b3de05283", + "e1a5d790e9ee42938d3474da89e72805", + "39e1e74768c642bdab6d172300bcbbd5", + "26972ae7f4864a0290368af174606a17", + "039ef582b4704d80bdbf2dde7e7a3d3e", + "95cd84efdf714922aea46cd688950214", + "8845c414434a4eb2b2ebd59008758804", + "c04bc7ae1b5d4c798596140ad585984d", + "764e93b41611473d836a6e0b14c0b20b", + "ab4b858603dc4b3885c772e61afd0cc7", + "1eb53b53634a4e7092ec02d57b3f0c8c", + "bfdfb693d0e6461ead4b33a4d87f6ff5", + "9614165401544974a54b55c3d44ffc10", + "89a4acd055004e8d9e999aab3625810d", + "5ade953ffaa247ec89212ab4b72a3da6", + "58053f740f1e4e6a9ad4b38b1b62a5fb", + "e763be034dda4853be06955aa7e6d89a", + "10664ffbf22a420ebff20add5d2db052", + "92fc861c9d35441fa51bddfbad06b4d3", + "178a0e3f5b114f6bb5df83ef14a3df42", + "6f4d3c1bc20e480b96abec2e9fef08c0" + ] + }, + "id": "3irTAFrBTEHw", + "outputId": "f002c537-7b1c-472e-8798-ffdeb308df78" + }, + "source": [ + "from keytotext import KeytotextTrainer\n", + "\n", + "model = KeytotextTrainer()\n", + "model.from_pretrained(model_name=\"t5-small\")\n", + "model.train(data_df=df[:1000],batch_size=4, max_epochs=3, use_gpu=True)\n", + "model.save_model()" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Global seed set to 42\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6165513df36c4896ae9b8684107b1915", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=791656.0, style=ProgressStyle(descripti…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5b9e114e2a604646be9ce095285bd1bf", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1389353.0, style=ProgressStyle(descript…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ac2ad4c5203746a88afcf35b5e664e8e", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1197.0, style=ProgressStyle(description…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cbe777ca7d8d403fb4021a037f18dd29", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=242065649.0, style=ProgressStyle(descri…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "GPU available: True, used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "application/javascript": [ + "\n", + " window._wandbApiKey = new Promise((resolve, reject) => {\n", + " function loadScript(url) {\n", + " return new Promise(function(resolve, reject) {\n", + " let newScript = document.createElement(\"script\");\n", + " newScript.onerror = reject;\n", + " newScript.onload = resolve;\n", + " document.body.appendChild(newScript);\n", + " newScript.src = url;\n", + " });\n", + " }\n", + " loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n", + " const iframe = document.createElement('iframe')\n", + " iframe.style.cssText = \"width:0;height:0;border:none\"\n", + " document.body.appendChild(iframe)\n", + " const handshake = new Postmate({\n", + " container: iframe,\n", + " url: 'https://wandb.ai/authorize'\n", + " });\n", + " const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n", + " handshake.then(function(child) {\n", + " child.on('authorize', data => {\n", + " clearTimeout(timeout)\n", + " resolve(data)\n", + " });\n", + " });\n", + " })\n", + " });\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + " Tracking run with wandb version 0.10.32
\n", + " Syncing run genial-donkey-27 to Weights & Biases (Documentation).
\n", + " Project page: https://wandb.ai/gagan3012/keytotext
\n", + " Run page: https://wandb.ai/gagan3012/keytotext/runs/1zg7lmeq
\n", + " Run data is saved locally in /content/wandb/run-20210615_074634-1zg7lmeq

\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + " | Name | Type | Params\n", + "-----------------------------------------------------\n", + "0 | model | T5ForConditionalGeneration | 60.5 M\n", + "-----------------------------------------------------\n", + "60.5 M Trainable params\n", + "0 Non-trainable params\n", + "60.5 M Total params\n", + "242.026 Total estimated model params size (MB)\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "91e1702d2b234409a9cba6acbb3bcafe", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validation sanity check', layout=Layout…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Global seed set to 42\n" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "\r" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7369e52d6fdd4faeb87ef29a32da2fe9", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Training', layout=Layout(flex='2'), max…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "df55689d9a09435dbc0f681b2ec9e0b9", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "95cd84efdf714922aea46cd688950214", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "89a4acd055004e8d9e999aab3625810d", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "xOqnNSWZTLNC", + "outputId": "f766cd14-c6f3-4253-fd09-35ba8748c7bb" + }, + "source": [ + "model.load_model(\"/content/outputs/keytotext-epoch-3\", use_gpu=True)\n", + "\n", + "keywords=[\"ski\", \"mountain\", \"sky\"]\n", + "model.predict(keywords)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'a sky on the mountain of skis.'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2oZypdkndmbb" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file