delete notebook file

datasets.ipynb

@@ -1,1056 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "0729b762-3b84-474f-b82a-df7622b91ccb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch, html\n",
-    "from transformers import AutoTokenizer\n",
-    "from datasets import load_dataset, load_from_disk\n",
-    "from huggingface_hub import notebook_login\n",
-    "from dotenv import load_dotenv\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "92ee5f76-2cd3-4af0-8687-dca782aa38a3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "load_dotenv()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "97d33c57-b03b-4bee-b051-04d707a8d773",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "access_token = os.environ['HF_TOKEN']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4358520c-3d8c-42ef-967a-eddeef732ef1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'cuda'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
-    "device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "1c2ec24f-4c6d-4469-8e85-601a4b0d3e4e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['Unnamed: 0', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 161297\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['Unnamed: 0', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 53766\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset = load_dataset('csv', data_files={\n",
-    "    'train': 'data/drugsComTrain_raw.tsv',\n",
-    "    'test': 'data/drugsComTest_raw.tsv'\n",
-    "}, delimiter='\\t', num_proc=8)\n",
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "dbb81021-9acc-46b4-87c0-23f0f787fef5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'train': (161297, 7), 'test': (53766, 7)}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "a983147c-eb04-455f-bf02-0c57c2a549e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Unnamed: 0': 206461,\n",
-       " 'drugName': 'Valsartan',\n",
-       " 'condition': 'Left Ventricular Dysfunction',\n",
-       " 'review': '\"It has no side effect, I take it in combination of Bystolic 5 Mg and Fish Oil\"',\n",
-       " 'rating': 9.0,\n",
-       " 'date': 'May 20, 2012',\n",
-       " 'usefulCount': 27}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset['train'][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "ee2b8ddf-79d7-44d6-80ba-243bc2f04de8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['row_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 138514\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['row_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset = (\n",
-    "    dataset\n",
-    "    .filter(lambda x: x['condition'] is not None)\n",
-    "    .rename_column('Unnamed: 0', 'row_id')\n",
-    "    .map(lambda x: {'condition': [row.lower() for row in x['condition']]}, batched=True, num_proc=8, batch_size=3000)\n",
-    "    .map(lambda x: {'review': [html.unescape(row) for row in x['review']]}, batched=True, num_proc=8, batch_size=3000)\n",
-    "    .map(lambda x: {'review_length': [len(row.split()) for row in x['review']]}, batched=True, num_proc=8, batch_size=3000)\n",
-    "    # .filter(lambda x: {'review_length': [row > 30 for row in x['review_length']]}, batched=True, num_proc=8)\n",
-    "    .filter(lambda x: x['review_length'] > 30, num_proc=8, batch_size=3000)\n",
-    ")\n",
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e7c4daf2-36c1-4074-91ca-8871a581052d",
-   "metadata": {},
-   "source": [
-    "# Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea14b998-69f1-40a7-a200-7cc53b0e22fd",
-   "metadata": {},
-   "source": [
-    "## Predict patient condition based on drug review"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "dc6b299b-2d0b-4475-bfff-d0180dd672c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoTokenizer, AutoModel, DataCollatorWithPadding\n",
-    "from torch.utils.data import DataLoader\n",
-    "import evaluate, numpy as np\n",
-    "from huggingface_hub import HfApi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "77caa284-8307-40a0-8369-621195e5c7e9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def clean_condition_column(rows):\n",
-    "    target_text = 'users found this comment helpful'\n",
-    "    return {'condition': ['unknown' if target_text in condition else condition for condition in rows['condition']]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "058d4c64-428b-43bb-86c4-ba8f5c1b8a84",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['row_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 138514\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['row_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset = dataset.map(clean_condition_column, batched=True, batch_size=3000, num_proc=8)\n",
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "80dc20fe-cb66-4b0d-99dc-88e84413975b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['row_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['row_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['row_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "clean_data = dataset['train'].train_test_split(test_size=.2, seed=5, writer_batch_size=3000)\n",
-    "clean_data['validation'] = clean_data.pop('test')\n",
-    "clean_data['test'] = dataset['test']\n",
-    "\n",
-    "clean_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "8be33fbb-143f-45b5-9e18-c5662a7e0dad",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "751"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_conditions = sorted(set(clean_data['train']['condition']).union(set(clean_data['validation']['condition'])))\n",
-    "len(all_conditions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "912ef7d5-149a-48ed-ac6b-1ff2f3c2556a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "id2label = dict(enumerate(all_conditions))\n",
-    "label2id = {v:k for k, v in id2label.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "aca4a239-3f07-44bf-905e-2743b8f0889d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(label2id) == len(id2label)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "024d5faa-88f1-41b7-9f52-8178ad731089",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['row_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'labels'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['row_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'labels'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['row_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'labels'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "clean_data = clean_data.map(lambda x: {'labels': [label2id.get(condition, label2id['unknown']) for condition in x['condition']]}, batched=True, batch_size=3000, num_proc=8)\n",
-    "clean_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "2f71cacc-9fb4-4436-b32b-8f172bcc19b1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# checkpoint = 'distilbert/distilbert-base-uncased-finetuned-sst-2-english'\n",
-    "checkpoint = 'distilbert-base-uncased'\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=len(id2label)).to(device)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "e9b2c2bd-52d4-47e0-aaaf-eb76b3bab9fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.config.id2label = id2label\n",
-    "model.config.label2id = label2id\n",
-    "model.num_labels = len(label2id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "2d3bb44b-e635-4e7c-b984-6379510b60b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "collator = DataCollatorWithPadding(tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "c22a17ab-4a43-45f6-ba99-62cdb94103c5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    tokens = tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=512,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )\n",
-    "    mappings = tokens.pop('overflow_to_sample_mapping')\n",
-    "    for key, values in examples.items():\n",
-    "        tokens[key] = [values[idx] for idx in mappings]\n",
-    "    return tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "5a1b9eb6-87a1-4d7f-855b-f1c9e5ae63c2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['row_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'labels', 'input_ids', 'attention_mask'],\n",
-       "        num_rows: 110857\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['row_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'labels', 'input_ids', 'attention_mask'],\n",
-       "        num_rows: 27717\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['row_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'labels', 'input_ids', 'attention_mask'],\n",
-       "        num_rows: 46118\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset = clean_data.map(tokenize_and_split, batched=True, batch_size=3000, num_proc=8)\n",
-    "tokenized_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2729d5c2-499d-41f0-8ddb-27df3cf82475",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3488692d-44ef-4b99-af4c-8fa32d6ed3b2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset.save_to_disk('data/drugs', num_proc=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2bc7b3ea-5f48-4298-b625-d313c4dc1ea3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = load_from_disk('data/drugs/')\n",
-    "tokenized_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "001bb28a-9ff1-463f-90af-22dc7f6bce53",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "344a5505-f143-4389-8be3-282219f29d74",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['input_ids', 'attention_mask', 'labels'],\n",
-       "        num_rows: 110857\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['input_ids', 'attention_mask', 'labels'],\n",
-       "        num_rows: 27717\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['input_ids', 'attention_mask', 'labels'],\n",
-       "        num_rows: 46118\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filtered = tokenized_dataset.select_columns(['input_ids', 'attention_mask', 'labels'])\n",
-    "filtered"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "b31de787-0312-4d67-8b41-ce85732308ea",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accuracy = evaluate.load('accuracy')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "f6d0543e-06d5-4930-93f6-8028e4e4ead5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_metrics(eval_preds):\n",
-    "    logits, labels = eval_preds\n",
-    "    preds = np.argmax(logits, axis=-1)\n",
-    "    return accuracy.compute(predictions=preds, references=labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "ec5be835-e194-47a4-8c2a-3eb7500645ad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lr = 3e-5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "d04e4bae-8bb0-4e5e-be0f-2ce41db1bbe6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_args = TrainingArguments(\n",
-    "    'medical_condition_classification', \n",
-    "    overwrite_output_dir=True, \n",
-    "    eval_strategy='steps', eval_steps=2000, \n",
-    "    per_device_train_batch_size=24, \n",
-    "    per_device_eval_batch_size=24, \n",
-    "    fp16=True, num_train_epochs=5,\n",
-    "    learning_rate=lr,\n",
-    "    push_to_hub=True,\n",
-    "    hub_token=access_token\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "e26faf3f-03ab-411d-97a2-c1a3b6e2b425",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer = Trainer(model, train_args, collator, filtered['train'], filtered['validation'], tokenizer, compute_metrics=compute_metrics)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "52c55095-4761-4353-8222-887cdf309431",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      \n",
-       "      <progress value='23100' max='23100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      [23100/23100 1:14:13, Epoch 5/5]\n",
-       "    </div>\n",
-       "    <table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       " <tr style=\"text-align: left;\">\n",
-       "      <th>Step</th>\n",
-       "      <th>Training Loss</th>\n",
-       "      <th>Validation Loss</th>\n",
-       "      <th>Accuracy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>2000</td>\n",
-       "      <td>1.862500</td>\n",
-       "      <td>1.719871</td>\n",
-       "      <td>0.639680</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4000</td>\n",
-       "      <td>1.459000</td>\n",
-       "      <td>1.369566</td>\n",
-       "      <td>0.688963</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6000</td>\n",
-       "      <td>1.173700</td>\n",
-       "      <td>1.213141</td>\n",
-       "      <td>0.717249</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>8000</td>\n",
-       "      <td>1.042000</td>\n",
-       "      <td>1.101419</td>\n",
-       "      <td>0.732908</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>10000</td>\n",
-       "      <td>0.843100</td>\n",
-       "      <td>1.032237</td>\n",
-       "      <td>0.750983</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>12000</td>\n",
-       "      <td>0.801200</td>\n",
-       "      <td>0.988939</td>\n",
-       "      <td>0.758668</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>14000</td>\n",
-       "      <td>0.731200</td>\n",
-       "      <td>0.949687</td>\n",
-       "      <td>0.772703</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>16000</td>\n",
-       "      <td>0.656100</td>\n",
-       "      <td>0.933845</td>\n",
-       "      <td>0.780496</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>18000</td>\n",
-       "      <td>0.613200</td>\n",
-       "      <td>0.907262</td>\n",
-       "      <td>0.787531</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>20000</td>\n",
-       "      <td>0.519500</td>\n",
-       "      <td>0.901089</td>\n",
-       "      <td>0.792943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>22000</td>\n",
-       "      <td>0.501500</td>\n",
-       "      <td>0.892959</td>\n",
-       "      <td>0.795072</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table><p>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "TrainOutput(global_step=23100, training_loss=1.0162131207949154, metrics={'train_runtime': 4454.3937, 'train_samples_per_second': 124.436, 'train_steps_per_second': 5.186, 'total_flos': 2.958796560013029e+16, 'train_loss': 1.0162131207949154, 'epoch': 5.0})"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "7c8d06d3-ef08-42ca-9dad-651c3a7c45fc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with torch.no_grad():\n",
-    "    preds = trainer.predict(filtered['test'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "cab2f41e-d00f-41cb-a5a6-daf9e713077d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'test_loss': 0.8813542127609253,\n",
-       " 'test_accuracy': 0.8004249967474739,\n",
-       " 'test_runtime': 87.98,\n",
-       " 'test_samples_per_second': 524.188,\n",
-       " 'test_steps_per_second': 21.846}"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preds.metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "1e323be4-ac78-498e-99fc-3133b11dc241",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "model_id": "d45b5b475cce4bb09298a7278ec51c64",
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "model.safetensors:   0%|          | 0.00/270M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "CommitInfo(commit_url='https://huggingface.co/samsaara/medical_condition_classification/commit/73d201a08dd78ce2afea66736b188d271e652052', commit_message='End of training', commit_description='', oid='73d201a08dd78ce2afea66736b188d271e652052', pr_url=None, repo_url=RepoUrl('https://huggingface.co/samsaara/medical_condition_classification', endpoint='https://huggingface.co', repo_type='model', repo_id='samsaara/medical_condition_classification'), pr_revision=None, pr_num=None)"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "6e754047-4b6c-4b3c-80a1-8493009ac7ca",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9f853954de9b4fe5bcf797878413702f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "README.md:   0%|          | 0.00/2.11k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "CommitInfo(commit_url='https://huggingface.co/samsaara/medical_condition_classification/commit/9a28b6773707f2b88e7eeb37bc811c642bb524c7', commit_message='tokenizer', commit_description='', oid='9a28b6773707f2b88e7eeb37bc811c642bb524c7', pr_url=None, repo_url=RepoUrl('https://huggingface.co/samsaara/medical_condition_classification', endpoint='https://huggingface.co', repo_type='model', repo_id='samsaara/medical_condition_classification'), pr_revision=None, pr_num=None)"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.push_to_hub('medical_condition_classification', commit_message='tokenizer')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "2940e50a-328d-4f30-8cb7-30dd047d2f92",
-   "metadata": {},
-   "outputs": [
-    {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      },
-      "text/plain": [
-       "Creating parquet from Arrow format:   0%|          | 0/111 [00:00<?, ?ba/s]"
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "Creating parquet from Arrow format:   0%|          | 0/28 [00:00<?, ?ba/s]"
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-     "metadata": {},
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-    },
-    {
-     "data": {
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "Creating parquet from Arrow format:   0%|          | 0/47 [00:00<?, ?ba/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "CommitInfo(commit_url='https://huggingface.co/datasets/samsaara/medical_condition_classification/commit/7aea5155fcba521a02ec3e9e8fb4e86d09dc44ba', commit_message='Upload dataset', commit_description='', oid='7aea5155fcba521a02ec3e9e8fb4e86d09dc44ba', pr_url=None, repo_url=RepoUrl('https://huggingface.co/datasets/samsaara/medical_condition_classification', endpoint='https://huggingface.co', repo_type='dataset', repo_id='samsaara/medical_condition_classification'), pr_revision=None, pr_num=None)"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset.push_to_hub('medical_condition_classification')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "4bbf39bb-fea9-4b5b-8d87-7a9835975358",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "api = HfApi(token=access_token)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "16e0625b-5518-463a-96e2-2d008341b1f1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CommitInfo(commit_url='https://huggingface.co/samsaara/medical_condition_classification/commit/98ede0386065880a4cfefcb0ab1c9d7bfc9d081d', commit_message='update README', commit_description='', oid='98ede0386065880a4cfefcb0ab1c9d7bfc9d081d', pr_url=None, repo_url=RepoUrl('https://huggingface.co/samsaara/medical_condition_classification', endpoint='https://huggingface.co', repo_type='model', repo_id='samsaara/medical_condition_classification'), pr_revision=None, pr_num=None)"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "api.upload_file(\n",
-    "    path_or_fileobj='./medical_condition_classification/README.md', \n",
-    "    path_in_repo='README.md',\n",
-    "    repo_id='samsaara/medical_condition_classification', \n",
-    "    commit_message='update README'\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "4144a617-f0d6-41d3-9a44-90833b8bb1f5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "CommitInfo(commit_url='https://huggingface.co/samsaara/medical_condition_classification/commit/53b029a816883983962ebe0050977be8ee501d82', commit_message='notebook for training & evaluation', commit_description='', oid='53b029a816883983962ebe0050977be8ee501d82', pr_url=None, repo_url=RepoUrl('https://huggingface.co/samsaara/medical_condition_classification', endpoint='https://huggingface.co', repo_type='model', repo_id='samsaara/medical_condition_classification'), pr_revision=None, pr_num=None)"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "api.upload_file(\n",
-    "    path_or_fileobj='datasets.ipynb', \n",
-    "    path_in_repo='datasets.ipynb',\n",
-    "    repo_id='samsaara/medical_condition_classification', \n",
-    "    commit_message='notebook for training & evaluation'\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2de298eb-82f2-482b-acee-36c6a1e630b8",
-   "metadata": {},
-   "outputs": [],
-   "source": []
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- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
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- "nbformat": 4,
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