"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "wandb.init(\n",
+ " # set the wandb project where this run will be logged\n",
+ " project=\"nhs-classification\",\n",
+ "\n",
+ " # track hyperparameters and run metadata\n",
+ " config={\n",
+ " \"learning_rate\": 6.926e-6,\n",
+ " \"architecture\": \"transformer\",\n",
+ " \"dataset\": \"binary2-nhs-abstract-only\",\n",
+ " \"epochs\": 6,\n",
+ " \"weight_decay\": 1,\n",
+ " }\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "9f24e75c",
+ "metadata": {
+ "id": "00984d21"
+ },
+ "outputs": [],
+ "source": [
+ "df = pd.read_excel(\"../content/final_trainset.xlsx\")\n",
+ "# df = pd.read_excel(\"/kaggle/input/testing/final_trainset.xlsx\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "be0b7602",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " PMID | \n",
+ " Labeling_State | \n",
+ " Correct_Label | \n",
+ " binary_label | \n",
+ " binary2_label | \n",
+ " Agreement | \n",
+ " Explanation | \n",
+ " TITLE | \n",
+ " ABSTRACT | \n",
+ " vector | \n",
+ " curated | \n",
+ " y | \n",
+ "
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+ " \n",
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+ " PMID Labeling_State Correct_Label binary_label binary2_label \\\n",
+ "0 27504812 Gold Standard 2 1 1 \n",
+ "1 33564419 Labeled -1 0 0 \n",
+ "\n",
+ " Agreement Explanation TITLE \\\n",
+ "0 0.900 NaN The Natural History of Primary Sclerosing Chol... \n",
+ "1 0.545 NaN Recurrent acute interstitial nephritis: what l... \n",
+ "\n",
+ " ABSTRACT \\\n",
+ "0 Data regarding pediatric primary sclerosing ch... \n",
+ "1 Acute interstitial nephritis (AIN) is an emerg... \n",
+ "\n",
+ " vector curated y \n",
+ "0 [0.0, 0.35, 0.78, 12.03] 2 2 \n",
+ "1 [2.95, 0.0, 2.03, 0.54] -1 -1 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head(2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "32fc7037",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 81,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "dfba48cc",
+ "outputId": "3053556c-6240-421e-d983-5a5b0321ace5"
+ },
+ "outputs": [],
+ "source": [
+ "# Medical/biology oriented models\n",
+ "# tokenizer = AutoTokenizer.from_pretrained('microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract')\n",
+ "tokenizer = AutoTokenizer.from_pretrained('dmis-lab/biobert-base-cased-v1.2')\n",
+ "# tokenizer = AutoTokenizer.from_pretrained('dmis-lab/biobert-base-cased-v1.1')\n",
+ "# tokenizer = AutoTokenizer.from_pretrained('bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12')\n",
+ "# tokenizer = AutoTokenizer.from_pretrained('bionlp/bluebert_pubmed_uncased_L-24_H-1024_A-16')\n",
+ "# tokenizer = AutoTokenizer.from_pretrained('microsoft/biogpt')\n",
+ "\n",
+ "# General models\n",
+ "# tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')\n",
+ "# tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n",
+ "# tokenizer = AutoTokenizer.from_pretrained('roberta-base')\n",
+ "# tokenizer = AutoTokenizer.from_pretrained('roberta-large')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "083ed7a5",
+ "metadata": {
+ "id": "ce079752"
+ },
+ "outputs": [],
+ "source": [
+ "# for random train test split\n",
+ "abs_labels = df[[\"ABSTRACT\", \"binary2_label\"]].rename(columns={\"ABSTRACT\": \"text\", \"binary2_label\": \"label\"}).copy()\n",
+ "ds = Dataset.from_pandas(abs_labels)\n",
+ "ds = ds.train_test_split(test_size=0.2, shuffle=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "0e96aba5",
+ "metadata": {
+ "id": "9e906949"
+ },
+ "outputs": [],
+ "source": [
+ "def preprocess(e):\n",
+ " return tokenizer(e[\"text\"], max_length=256, truncation=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f20286a5",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "277e96b7",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 322,
+ "referenced_widgets": [
+ "7f8788f4a2cb4fafa88c2b5b445471bc",
+ "99048b0e09b24e2693df054effecc382",
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+ "da6313b899ff45569fa1f7667a61a9ea",
+ "2bc01f54871a4922b64068b679f0a597",
+ "185873fe72af4771ba10c546c0949d3b",
+ "9d9af7c5f1ac4b1381cefefe7b9840a4",
+ "ad824f99ce1a468ab911f04747dc887a",
+ "0529b7ebba7a47afbc4c917bec5122c4",
+ "fa02e63c309540ce8e9db2e2ffb1b5c5"
+ ]
+ },
+ "id": "4556658f",
+ "outputId": "d6f8c2f4-2c3e-449b-81d2-f1f2feb5cb3e"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "0ce03b25d1b44605abd1e851a71260a4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Map: 0%| | 0/6337 [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ac55d6c0b69342fb893874942ee92589",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Map: 0%| | 0/1585 [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "DatasetDict({\n",
+ " train: Dataset({\n",
+ " features: ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
+ " num_rows: 6337\n",
+ " })\n",
+ " test: Dataset({\n",
+ " features: ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
+ " num_rows: 1585\n",
+ " })\n",
+ "})"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokenized_ds = ds.map(preprocess, batched=True)\n",
+ "tokenized_ds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "cae306cf",
+ "metadata": {
+ "id": "04dafd3c"
+ },
+ "outputs": [],
+ "source": [
+ "train_ds = tokenized_ds[\"train\"]\n",
+ "eval_ds = tokenized_ds[\"test\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "8a55ddd9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Dataset({\n",
+ " features: ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
+ " num_rows: 6337\n",
+ "})"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_ds "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "2ccdc80b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Dataset({\n",
+ " features: ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
+ " num_rows: 1585\n",
+ "})"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "eval_ds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "7cbc7458",
+ "metadata": {
+ "id": "bf1f0c77"
+ },
+ "outputs": [],
+ "source": [
+ "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "417721d7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DataCollatorWithPadding(tokenizer=BertTokenizerFast(name_or_path='dmis-lab/biobert-base-cased-v1.2', vocab_size=28996, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True), added_tokens_decoder={\n",
+ "\t0: AddedToken(\"[PAD]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t100: AddedToken(\"[UNK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t101: AddedToken(\"[CLS]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t102: AddedToken(\"[SEP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t103: AddedToken(\"[MASK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "}, padding=True, max_length=None, pad_to_multiple_of=None, return_tensors='pt')"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_collator"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "33bb708d",
+ "metadata": {
+ "id": "6d0f6580"
+ },
+ "outputs": [],
+ "source": [
+ "def compute_metrics(eval_pred):\n",
+ " #taken from hf docs\n",
+ " labels = eval_pred.label_ids\n",
+ " preds = eval_pred.predictions.argmax(axis=1)\n",
+ "\n",
+ " accuracy = accuracy_score(labels, preds)\n",
+ " precision = precision_score(labels, preds, average='weighted')\n",
+ " recall = recall_score(labels, preds, average='weighted')\n",
+ " f1 = f1_score(labels, preds, average='weighted')\n",
+ "\n",
+ " wandb.log({\"acc\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1})\n",
+ " \n",
+ " return {\n",
+ " 'accuracy': accuracy,\n",
+ " 'precision': precision,\n",
+ " 'recall': recall,\n",
+ " 'f1': f1\n",
+ " }\n",
+ "# return metrics.compute(predictions=predictions, references=labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2a3c9bfc",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "aa6cd9c9",
+ "metadata": {
+ "id": "4bd68ddc"
+ },
+ "outputs": [],
+ "source": [
+ "id2label = {\n",
+ " 0: \"the paper is not a primary experimental study in rare disease or the study is not directly investigating the natural history of a disease\",\n",
+ " 1: \"its primary contribution centers on observing the time course of a rare disease\"\n",
+ "}\n",
+ "label2id = {v: k for k, v in id2label.items()}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "878228dc",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 133,
+ "referenced_widgets": [
+ "2623c735399a4fcba9cadd65601af15f",
+ "3aadee097dde46aa98d1b74807f6bc9e",
+ "31d890762776455dbaf80dcc1c20c68d",
+ "8b1e45d0c8cf473d8975c3efb2441d80",
+ "54c8516b9a214709b43f638f209a5caf",
+ "04bb5a6a019548fbaf42b64c3bf29da5",
+ "a1ac4bc471ac46838f098f48bcd652f6",
+ "91d53da59475454cbb6f0b5dcc3377eb",
+ "032a116a6da6406a9167490cc9b5db7d",
+ "f3a924475d4f4c5aa105df74f9e8b31b",
+ "5fd3406092384b7a9c242662cba12cf7"
+ ]
+ },
+ "id": "f04efded",
+ "outputId": "dfc41b32-a553-422f-c243-22a3355b6f59",
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at dmis-lab/biobert-base-cased-v1.2 and are newly initialized: ['classifier.bias', '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": [
+ "model = AutoModelForSequenceClassification.from_pretrained(\n",
+ " \"dmis-lab/biobert-base-cased-v1.2\", num_labels=2, id2label=id2label, label2id=label2id\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "2899adc2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# !pip install accelerate -U"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "b01b85b8",
+ "metadata": {
+ "id": "8bd0804b"
+ },
+ "outputs": [],
+ "source": [
+ "# normal training args with hyperparameters\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir=\"NHS-dmis-hypop\",\n",
+ " learning_rate=3e-5,\n",
+ " per_device_train_batch_size=16,\n",
+ " per_device_eval_batch_size=16,\n",
+ " num_train_epochs=6,\n",
+ " weight_decay=1,\n",
+ " evaluation_strategy=\"epoch\",\n",
+ " logging_steps=1,\n",
+ " save_strategy=\"epoch\",\n",
+ " metric_for_best_model=\"eval_loss\",\n",
+ " greater_is_better=False,\n",
+ " load_best_model_at_end=True,\n",
+ " push_to_hub=True,\n",
+ " hub_model_id=\"NIHNCATS/NHS-dmis-hypop\", \n",
+ " hub_private_repo=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "02144629",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# training_args for hyperparameter optimization\n",
+ "# training_args = TrainingArguments(\n",
+ "# output_dir=\"NHS-dmis-hpop\",\n",
+ "# evaluation_strategy=\"epoch\", \n",
+ "# save_strategy=\"epoch\",\n",
+ "# save_total_limit=1,\n",
+ "# disable_tqdm=False,\n",
+ "# weight_decay=1,\n",
+ "# per_device_train_batch_size=16,\n",
+ "# per_device_eval_batch_size=16,\n",
+ "# metric_for_best_model=\"eval_loss\",\n",
+ "# greater_is_better=False,\n",
+ "# load_best_model_at_end = True,\n",
+ "# )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "66ab7345",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# from transformers import EarlyStoppingCallback\n",
+ "# # trainer for hp optimization\n",
+ "# def model_init():\n",
+ "# return AutoModelForSequenceClassification.from_pretrained(\n",
+ "# 'dmis-lab/biobert-base-cased-v1.2', \n",
+ "# num_labels=2, id2label=id2label, label2id=label2id, return_dict=True)\n",
+ "# trainer = Trainer(\n",
+ "# args=training_args,\n",
+ "# tokenizer=tokenizer,\n",
+ "# train_dataset=train_ds,\n",
+ "# eval_dataset=eval_ds,\n",
+ "# model_init=model_init,\n",
+ "# compute_metrics=compute_metrics,\n",
+ "# data_collator=data_collator,\n",
+ "# callbacks=[EarlyStoppingCallback(2)],\n",
+ "# )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "944bd96d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# def optuna_hp_space(trial):\n",
+ "# return {\n",
+ "# \"learning_rate\": trial.suggest_float(\"learning_rate\", 1e-6, 1e-4, log=True),\n",
+ "# # \"weight_decay\": trial.suggest_float(\"weight_decay\", 0, 1),\n",
+ "# \"num_train_epochs\": trial.suggest_int(\"num_train_epochs\", 3, 6)\n",
+ "# }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "49342790",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# trainer.hyperparameter_search(\n",
+ "# direction=\"maximize\", \n",
+ "# backend=\"optuna\", \n",
+ "# hp_space=optuna_hp_space,\n",
+ "# n_trials=10 # number of trials\n",
+ "# )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9a82c9cb",
+ "metadata": {},
+ "source": [
+ "BestRun(run_id='1', objective=3.2861456636636093, hyperparameters={'learning_rate': 6.926443850533433e-06, 'num_train_epochs': 6}, run_summary=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "d2867f25",
+ "metadata": {
+ "id": "dc710cef"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import EarlyStoppingCallback\n",
+ "\n",
+ "trainer = Trainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=train_ds,\n",
+ " eval_dataset=eval_ds,\n",
+ " tokenizer=tokenizer,\n",
+ " data_collator=data_collator,\n",
+ " compute_metrics=compute_metrics,\n",
+ " callbacks=[EarlyStoppingCallback(2)],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "6022c7b9",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
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| precision | 0.8156 |
| recall | 0.81577 |
| train/epoch | 3.0 |
| train/global_step | 1191 |
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| train/learning_rate | 2e-05 |
| train/loss | 0.2024 |
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+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
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+ "trainer.push_to_hub(\"NHS-dmis-hypop\")"
+ ]
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+ "text": [
+ "Test Results: {'eval_loss': 0.4118824005126953, 'eval_accuracy': 0.8173076923076923, 'eval_precision': 0.8188430938430938, 'eval_recall': 0.8173076923076923, 'eval_f1': 0.8177715636896312, 'eval_runtime': 336.0728, 'eval_samples_per_second': 1.238, 'eval_steps_per_second': 0.077, 'epoch': 3.0}\n"
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| acc | ▁ |
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| acc | 0.81731 |
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| train/epoch | 3.0 |
| train/global_step | 1191 |
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+ },
+ {
+ "data": {
+ "text/html": [
+ " View run effortless-grass-7 at: https://wandb.ai/binary_transformer/nhs-classification/runs/2k4hpdtf
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+ ]
+ },
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+ "output_type": "display_data"
+ },
+ {
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+ },
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+ },
+ {
+ "data": {
+ "text/plain": [
+ "CommitInfo(commit_url='https://huggingface.co/NIHNCATS/NHS-dmis-hypop/commit/f9a7e8ac9fa13c04b9c9742d4cd4a9331f516b74', commit_message='NHS-dmis-hypop', commit_description='', oid='f9a7e8ac9fa13c04b9c9742d4cd4a9331f516b74', pr_url=None, pr_revision=None, pr_num=None)"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# After the training code\n",
+ "# trainer.train()\n",
+ "\n",
+ "# Load and preprocess the new test dataset\n",
+ "df_test = pd.read_excel(\"content/final_testset.xlsx\")\n",
+ "test_labels = df_test[[\"ABSTRACT\", \"binary2_label\"]].rename(columns={\"ABSTRACT\": \"text\", \"binary2_label\": \"label\"}).copy()\n",
+ "test_dataset = Dataset.from_pandas(test_labels)\n",
+ "tokenized_test_ds = test_dataset.map(preprocess, batched=True)\n",
+ "\n",
+ "# Evaluate the model on the new test dataset\n",
+ "test_results = trainer.evaluate(tokenized_test_ds)\n",
+ "print(\"Test Results:\", test_results)\n",
+ "\n",
+ "# Log the results\n",
+ "wandb.log(test_results)\n",
+ "\n",
+ "# Finish wandb session\n",
+ "wandb.finish()\n",
+ "\n",
+ "# Push the trained model to Hugging Face Hub\n",
+ "trainer.push_to_hub(\"NHS-dmis-hypop\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "63061fb2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#save modle for API"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "d0618d5d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('saved_model/my_bert_model\\\\tokenizer_config.json',\n",
+ " 'saved_model/my_bert_model\\\\special_tokens_map.json',\n",
+ " 'saved_model/my_bert_model\\\\vocab.txt',\n",
+ " 'saved_model/my_bert_model\\\\added_tokens.json',\n",
+ " 'saved_model/my_bert_model\\\\tokenizer.json')"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model_path = \"saved_model/my_bert_model\"\n",
+ "trainer.model.save_pretrained(model_path)\n",
+ "trainer.tokenizer.save_pretrained(model_path)\n"
+ ]
+ },
+ {
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+ "execution_count": null,
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