From ec53a9c4134291fe64fc44aaf7786cbe16dbcee7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=C5=81ukasz=20Augustyniak?=
 <aisolutions@lukaszaugustyniak.com>
Date: Tue, 14 Nov 2023 11:01:22 +0000
Subject: [PATCH] fix cell outputs

---
 nbs/03_training_example.ipynb | 158 ++++++++++++++++++++++++++++++----
 1 file changed, 140 insertions(+), 18 deletions(-)

diff --git a/nbs/03_training_example.ipynb b/nbs/03_training_example.ipynb
index 6e38aca..feea3c5 100644
--- a/nbs/03_training_example.ipynb
+++ b/nbs/03_training_example.ipynb
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -99,6 +99,27 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset mms downloaded and prepared to /root/.cache/huggingface/datasets/Brand24___mms/default/0.2.0/70532fdd01f149ff84a280b7d9cfb661643abf4837b4f0f3aa1128064e870d65. Subsequent calls will reuse this data.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5663e74add1d415c853bcb257a15963a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -115,19 +136,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'mms_dataset' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m/app/nbs/03_training_example.ipynb Cell 7\u001b[0m line \u001b[0;36m2\n\u001b[1;32m      <a href='vscode-notebook-cell://attached-container%2B7b22636f6e7461696e65724e616d65223a222f6d6d732d62656e63686d61726b227d/app/nbs/03_training_example.ipynb#X36sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m#| eval: false\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://attached-container%2B7b22636f6e7461696e65724e616d65223a222f6d6d732d62656e63686d61726b227d/app/nbs/03_training_example.ipynb#X36sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m mms_dataset\u001b[39m.\u001b[39mcolumn_names\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'mms_dataset' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "{'train': ['_id',\n",
+       "  'text',\n",
+       "  'label',\n",
+       "  'original_dataset',\n",
+       "  'domain',\n",
+       "  'language',\n",
+       "  'Family',\n",
+       "  'Genus',\n",
+       "  'Definite articles',\n",
+       "  'Indefinite articles',\n",
+       "  'Number of cases',\n",
+       "  'Order of subject, object, verb',\n",
+       "  'Negative morphemes',\n",
+       "  'Polar questions',\n",
+       "  'Position of negative word wrt SOV',\n",
+       "  'Prefixing vs suffixing',\n",
+       "  'Coding of nominal plurality',\n",
+       "  'Grammatical genders',\n",
+       "  'cleanlab_self_confidence']}"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -146,7 +184,22 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31c9a75e3c5e4e349120702866a1ca1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Filter:   0%|          | 0/6165262 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#| eval: false\n",
     "pl_sm = mms_dataset[\"train\"].filter(lambda x: x[\"language\"] == \"pl\" and x[\"domain\"] == \"social_media\")"
@@ -163,7 +216,22 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3728a8e151e94b6392ea27436255e6d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Filter:   0%|          | 0/169576 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#| eval: false\n",
     "pl_sm_high_confidence = pl_sm.filter(lambda x: x[\"cleanlab_self_confidence\"] > 0.6)"
@@ -173,7 +241,18 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "73227"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#| eval: false\n",
     "len(pl_sm_high_confidence)"
@@ -203,7 +282,22 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28e61f6f723044e2bc8824d7f808f63e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/73227 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#| eval: false\n",
     "tokenized_dataset = pl_sm_high_confidence.map(tokenize, batched=True, batch_size=512)"
@@ -213,7 +307,19 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at allegro/herbert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.sso.sso_relationship.weight', 'cls.predictions.decoder.bias', 'cls.sso.sso_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias']\n",
+      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at allegro/herbert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    }
+   ],
    "source": [
     "#| eval: false\n",
     "model = AutoModelForSequenceClassification.from_pretrained(\"allegro/herbert-base-cased\", num_labels=3)"
@@ -241,7 +347,7 @@
     "training_args = TrainingArguments(\n",
     "    output_dir=\"PL_SM_SENT\",\n",
     "    evaluation_strategy=\"epoch\",\n",
-    "    num_train_epochs=5,\n",
+    "    num_train_epochs=1,\n",
     ")\n",
     "metric = evaluate.load(\"accuracy\")\n",
     "\n",
@@ -272,11 +378,27 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "#| eval: false\n",
     "trainer.train()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {