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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "NH518TfOBOwe"
},
"source": [
"# Finetuning Transformer Models for NER\n",
"\n",
"In this file following things will be done:\n",
"\n",
"- fine-tune BERT, RoBERTA and Bio_ClinicalBERT for NER\n",
"- evaluate and compare their performance\n",
"- choose best performaning model and optimize parameter\n",
"- continue evaluating with test set"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "A-VFH8lt_xcp"
},
"outputs": [],
"source": [
"import os\n",
"import itertools\n",
"import pandas as pd\n",
"import numpy as np\n",
"!pip3 install datasets\n",
"!pip3 install transformers\n",
"from datasets import Dataset\n",
"from datasets import load_metric\n",
"from transformers import AutoTokenizer\n",
"from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer\n",
"from transformers import DataCollatorForTokenClassification\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3yP25LVQCabA"
},
"outputs": [],
"source": [
"import ast"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mQ48t22uC6QI"
},
"outputs": [],
"source": [
"from datasets import DatasetDict, Dataset\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 744
},
"id": "rSAFyHMGB_SQ",
"outputId": "faf90fcc-95d5-48a2-8cd2-73c7d1ec9624"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
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" sentence \\\n",
"0 CASE: A 28-year-old previously healthy man pre... \n",
"1 The symptoms occurred during rest, 2–3 times p... \n",
"2 Except for a grade 2/6 holosystolic tricuspid ... \n",
"3 An electrocardiogram (ECG) revealed normal sin... \n",
"4 Transthoracic echocardiography demonstrated th... \n",
"... ... \n",
"4537 MHL was diagnosed (Fig.3). \n",
"4538 Immunohistochemistry results (Fig.4) were the ... \n",
"4539 After 9 days of recovery, the patient returned... \n",
"4540 A follow-up examination, which included blood ... \n",
"4541 No adverse or unanticipated event was presented. \n",
"\n",
" tags \\\n",
"0 ['O', 'O', 'B-Age', 'B-History', 'I-History', ... \n",
"1 ['O', 'B-Coreference', 'O', 'O', 'B-Clinical_e... \n",
"2 ['O', 'O', 'O', 'B-Lab_value', 'I-Lab_value', ... \n",
"3 ['O', 'B-Diagnostic_procedure', 'O', 'O', 'B-L... \n",
"4 ['B-Biological_structure', 'B-Diagnostic_proce... \n",
"... ... \n",
"4537 ['B-Disease_disorder', 'O', 'O', 'O'] \n",
"4538 ['B-Diagnostic_procedure', 'I-Diagnostic_proce... \n",
"4539 ['O', 'B-Duration', 'I-Duration', 'O', 'B-Ther... \n",
"4540 ['O', 'B-Clinical_event', 'O', 'O', 'O', 'B-Di... \n",
"4541 ['O', 'B-Sign_symptom', 'I-Sign_symptom', 'I-S... \n",
"\n",
" tokens \\\n",
"0 ['case', 'a', '28-year-old', 'previously', 'he... \n",
"1 ['the', 'symptoms', 'occurred', 'during', 'res... \n",
"2 ['except', 'for', 'a', 'grade', '2/6', 'holosy... \n",
"3 ['an', 'electrocardiogram', 'ecg', 'revealed',... \n",
"4 ['transthoracic', 'echocardiography', 'demonst... \n",
"... ... \n",
"4537 ['mhl', 'was', 'diagnosed', 'fig3'] \n",
"4538 ['immunohistochemistry', 'results', 'fig4', 'w... \n",
"4539 ['after', '9', 'days', 'of', 'recovery', 'the'... \n",
"4540 ['a', 'follow-up', 'examination', 'which', 'in... \n",
"4541 ['no', 'adverse', 'or', 'unanticipated', 'even... \n",
"\n",
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"4540 [0, 5, 0, 0, 0, 19, 20, 19, 20, 20, 19, 20, 0,... \n",
"4541 [0, 9, 10, 10, 10, 0, 0] \n",
"\n",
"[4542 rows x 4 columns]"
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},
"metadata": {},
"execution_count": 4
}
],
"source": [
"data = pd.read_csv(\"data.csv\")\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r29gex2BCP9u"
},
"source": [
"Because when reading the file in, the lists in columns 'tags', 'numeric_tags' and 'tokens' are read as strings, so they need to be converted into lists again."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mw8eII0oCjKo"
},
"outputs": [],
"source": [
"def convert_tags(tags_string):\n",
" return ast.literal_eval(tags_string)\n",
"\n",
"data['tags'] = data['tags'].apply(convert_tags)\n",
"data['numeric_tags'] = data['numeric_tags'].apply(convert_tags)\n",
"data['tokens'] = data['tokens'].apply(convert_tags)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T4ZzYWOuCvGM"
},
"source": [
"For later purposes, we will need the dictionary again."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "1p45Sf--Cex5"
},
"outputs": [],
"source": [
"label_dict = {'O': 0, 'B-Age': 1, 'I-Age': 2, 'B-Sex': 3, 'I-Sex': 4, 'B-Clinical_event': 5,\n",
" 'I-Clinical_event': 6, 'B-Nonbiological_location': 7, 'I-Nonbiological_location': 8,\n",
" 'B-Sign_symptom': 9, 'I-Sign_symptom': 10, 'B-Biological_structure': 11, 'I-Biological_structure': 12,\n",
" 'B-Detailed_description': 13, 'I-Detailed_description': 14, 'B-History': 15, 'I-History': 16, 'B-Family_history': 17,\n",
" 'I-Family_history': 18, 'B-Diagnostic_procedure': 19, 'I-Diagnostic_procedure': 20, 'B-Distance': 21,\n",
" 'I-Distance': 22, 'B-Lab_value': 23, 'I-Lab_value': 24, 'B-Disease_disorder': 25, 'I-Disease_disorder': 26,\n",
" 'B-Shape': 27, 'I-Shape': 28, 'B-Coreference': 29, 'I-Coreference': 30, 'B-Volume': 31, 'I-Volume': 32,\n",
" 'B-Therapeutic_procedure': 33, 'I-Therapeutic_procedure': 34, 'B-Area': 35, 'I-Area': 36, 'B-Duration': 37,\n",
" 'I-Duration': 38, 'B-Date': 39, 'I-Date': 40, 'B-Color': 41, 'I-Color': 42, 'B-Frequency': 43, 'I-Frequency': 44,\n",
" 'B-Texture': 45, 'I-Texture': 46, 'B-Biological_attribute': 47, 'I-Biological_attribute': 48, 'B-Severity': 49,\n",
" 'I-Severity': 50, 'B-Activity': 51, 'I-Activity': 52, 'B-Outcome': 53, 'I-Outcome': 54, 'B-Personal_background': 55,\n",
" 'I-Personal_background': 56, 'B-Medication': 57, 'I-Medication': 58, 'B-Dosage': 59, 'I-Dosage': 60, 'B-Other_event': 61,\n",
" 'I-Other_event': 62, 'B-Administration': 63, 'I-Administration': 64, 'B-Occupation': 65, 'I-Occupation': 66,\n",
" 'B-Other_entity': 67, 'I-Other_entity': 68, 'B-Time': 69, 'I-Time': 70, 'B-Subject': 71, 'I-Subject': 72,\n",
" 'B-Quantitative_concept': 73, 'I-Quantitative_concept': 74, 'B-Height': 75, 'I-Height': 76, 'B-Mass': 77, 'I-Mass': 78,\n",
" 'B-Weight': 79, 'I-Weight': 80, 'B-Qualitative_concept': 81, 'I-Qualitative_concept': 82}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Y26dEt6JI0a6"
},
"outputs": [],
"source": [
"id2label = {i: label for i, label in enumerate(label_dict)}\n",
"label2id = {v: k for k, v in id2label.items()}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iz3SCN6uDBm0"
},
"source": [
"Next, the dataset is splitted into training, validation, and test. It creates dataframes containing the corresponding data, which get converted into datasets using the Hugging Face `Dataset.from_pandas` method. Finally, it organizes these datasets into a DatasetDict named data_dict."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uxN4BM4xDRgT"
},
"outputs": [],
"source": [
"X = data[\"sentence\"]\n",
"y = data[\"tags\"]\n",
"numeric_tags = data[\"numeric_tags\"]\n",
"\n",
"X_train, X_rest, y_train, y_rest, numeric_tags_train, numeric_tags_rest = train_test_split(X, y, numeric_tags, test_size=0.2, random_state=42)\n",
"X_valid, X_test, y_valid, y_test, numeric_tags_valid, numeric_tags_test = train_test_split(X_rest, y_rest, numeric_tags_rest, test_size=0.5, random_state=42)\n",
"\n",
"train_df = pd.DataFrame({\"tags\": y_train, \"sentence\": X_train, \"numeric_tags\": numeric_tags_train, \"tokens\": data[\"tokens\"][X_train.index]})\n",
"valid_df = pd.DataFrame({\"tags\": y_valid, \"sentence\": X_valid, \"numeric_tags\": numeric_tags_valid, \"tokens\": data[\"tokens\"][X_valid.index]})\n",
"test_df = pd.DataFrame({\"tags\": y_test, \"sentence\": X_test, \"numeric_tags\": numeric_tags_test, \"tokens\": data[\"tokens\"][X_test.index]})\n",
"\n",
"train_dataset = Dataset.from_pandas(train_df)\n",
"valid_dataset = Dataset.from_pandas(valid_df)\n",
"test_dataset = Dataset.from_pandas(test_df)\n",
"\n",
"data_dict = DatasetDict({\n",
" \"train\": train_dataset,\n",
" \"validation\": valid_dataset,\n",
" \"test\": test_dataset\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bgc2aRxwDo3p"
},
"source": [
"Now it is in the right format: \"train\" for fine-tuning, \"eval\" for evaluation and \"test\" for testing the models performance."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "C5aMoeyBD20n"
},
"source": [
"# Prepare Data\n",
"\n",
"The sentences need to be converted to token ids before the model can make sense of them.\n",
"\n",
"The first processing will be more detailed, using the first pre-trained model: BERT\n",
"\n",
"For later, we need a framework for evaluating the token classification prediction, called seqeval."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iuiWHzw7D7h8"
},
"outputs": [],
"source": [
"from transformers import AutoTokenizer\n",
"from transformers import DataCollatorForTokenClassification\n",
"!pip install seqeval\n",
"!pip install evaluate\n",
"import evaluate\n",
"metric = evaluate.load(\"seqeval\")"
]
},
{
"cell_type": "markdown",
"source": [
"To begin, the tokenizer is initialized. To tokenize a pre-tokenized input, we add `is_split_into_words=True`:"
],
"metadata": {
"id": "6OssTa4HqQvi"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bs9XrJtQD72O"
},
"outputs": [],
"source": [
"checkpoint_bert = \"bert-base-uncased\"\n",
"tokenizer_bert = AutoTokenizer.from_pretrained(checkpoint_bert)\n",
"data_collator_bert = DataCollatorForTokenClassification(tokenizer=tokenizer_bert)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fn2vWI71EHJq",
"outputId": "3553e8a3-4673-471f-ad39-1ec46daf9625"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['[CLS]',\n",
" 'then',\n",
" 'the',\n",
" 'tram',\n",
" 'flap',\n",
" 'was',\n",
" 'harvested',\n",
" 'from',\n",
" 'right',\n",
" 'rec',\n",
" '##tus',\n",
" 'abd',\n",
" '##omi',\n",
" '##nis',\n",
" 'fig',\n",
" '##2',\n",
" '##b',\n",
" 'and',\n",
" 'was',\n",
" 'deep',\n",
" '##ith',\n",
" '##elial',\n",
" '##ized',\n",
" 'fig',\n",
" '##2',\n",
" '##c',\n",
" '[SEP]']"
]
},
"metadata": {},
"execution_count": 11
}
],
"source": [
"inputs = tokenizer_bert(data_dict[\"train\"][0][\"tokens\"], is_split_into_words=True)\n",
"inputs.tokens() # add start and end, and turn token into subtoken"
]
},
{
"cell_type": "markdown",
"source": [
"The tokenizer now added special tokens ([CLS] at the beginning and [SEP] at the end), left most of the words untouched, some got splitted into subtokens.\n",
"\n",
"It is necessary to do more processing on the labels as the input ids returned by the tokenizer are longer than the lists of labels of the dataset, which produces mismatches. We need another function to align all the labels with its word."
],
"metadata": {
"id": "h05j9JV9qk9j"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cTZH71mREQI-",
"outputId": "c24bb54a-08de-4de9-946e-734045da975d"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[None,\n",
" 0,\n",
" 1,\n",
" 2,\n",
" 3,\n",
" 4,\n",
" 5,\n",
" 6,\n",
" 7,\n",
" 8,\n",
" 8,\n",
" 9,\n",
" 9,\n",
" 9,\n",
" 10,\n",
" 10,\n",
" 10,\n",
" 11,\n",
" 12,\n",
" 13,\n",
" 13,\n",
" 13,\n",
" 13,\n",
" 14,\n",
" 14,\n",
" 14,\n",
" None]"
]
},
"metadata": {},
"execution_count": 12
}
],
"source": [
"inputs.word_ids()"
]
},
{
"cell_type": "markdown",
"source": [
"The function `align_labels_with_tokens` expands the label list to match the tokens. Special tokens get a label of -100, which will be ignored in the loss function. Subtokens get the same tokens as their starting token."
],
"metadata": {
"id": "P5bfzUfyo244"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kUseDN3IESHA"
},
"outputs": [],
"source": [
"def align_labels_with_tokens(labels, word_ids):\n",
" new_labels = []\n",
" current_word = None\n",
" for word_id in word_ids:\n",
" if word_id != current_word: # New word\n",
" current_word = word_id\n",
" try:\n",
" label = -100 if word_id is None else labels[word_id]\n",
" except:\n",
" label = -100\n",
" new_labels.append(label)\n",
" elif word_id is None: # Special token\n",
" new_labels.append(-100)\n",
" else: # Same word as previous token\n",
" try:\n",
" label = labels[word_id]\n",
" if label % 2 == 1: # if praefix is B-, it gets changed to I-\n",
" label += 1\n",
" except:\n",
" label = -100\n",
" new_labels.append(label)\n",
"\n",
" return new_labels"
]
},
{
"cell_type": "markdown",
"source": [
"An example:"
],
"metadata": {
"id": "2umNMteGsAaz"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zUEUPv_4EdEV",
"outputId": "a44463d7-f402-4fe1-a35f-07c1c3fcff01"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0, 0, 33, 34, 0, 33, 0, 11, 12, 12, 0, 0, 0, 33, 0]\n",
"[-100, 0, 0, 33, 34, 0, 33, 0, 11, 12, 12, 12, 12, 12, 0, 0, 0, 0, 0, 33, 34, 34, 34, 0, 0, 0, -100]\n"
]
}
],
"source": [
"labels = data_dict[\"train\"][0][\"numeric_tags\"]\n",
"word_ids = inputs.word_ids()\n",
"print(labels)\n",
"print(align_labels_with_tokens(labels, word_ids))"
]
},
{
"cell_type": "markdown",
"source": [
"The special tokens in the beginning and end are now represented by -100, additional subwords are now laveled like its corresponding ancestor."
],
"metadata": {
"id": "gtQUGHqesCS2"
}
},
{
"cell_type": "markdown",
"source": [
"To preprocess the whole dataset, we need to tokenize all the inputs and apply `align_labels_with_tokens()` on all the labels."
],
"metadata": {
"id": "rLuSlPrUsPeZ"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wk_fvHsQEnjc"
},
"outputs": [],
"source": [
"def tokenize_and_align_labels(examples, tokenizer):\n",
" tokenized_inputs = tokenizer(\n",
" examples[\"tokens\"], truncation=True, is_split_into_words=True\n",
" )\n",
" all_labels = examples[\"numeric_tags\"]\n",
" new_labels = []\n",
" for i, labels in enumerate(all_labels):\n",
" word_ids = tokenized_inputs.word_ids(i)\n",
" new_labels.append(align_labels_with_tokens(labels, word_ids))\n",
"\n",
" tokenized_inputs[\"labels\"] = new_labels\n",
" return tokenized_inputs"
]
},
{
"cell_type": "markdown",
"source": [
"By calling the function `get_tokenized_data`, each dataset will be preprocessed in the shown way."
],
"metadata": {
"id": "DD8LrdU2saSU"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PHxKa8e-EovN"
},
"outputs": [],
"source": [
"def get_tokenized_data(tokenizer, data=data_dict ):\n",
" tokenized_datasets = data.map(\n",
" lambda examples: tokenize_and_align_labels(examples, tokenizer),\n",
" batched=True,\n",
" remove_columns=data[\"train\"].column_names,\n",
" )\n",
" return tokenized_datasets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 112,
"referenced_widgets": [
"132d50e305ad41cba46f741bbcbfa1c7",
"31e8527002894a35ae45ca6ad00dbd84",
"7c3f37c48ea44b0c839b67a475e6acc4",
"89e012739f3a4b89aed392c4d027ffcc",
"7d9ee9ba55594f48ada2a5702b520b0e",
"b55ce36b74cf43a6bdb28bb000c5f956",
"dbec304daddb40c9bf3b0b04c125c3ab",
"6cf03c2c9e1b461b848c0f66836c68b6",
"b92f527f50db4930ba904224a1758e13",
"7b15a9a0b8724a978bb55d1e348524b3",
"f561b6a559da44d3a488a071922d6d17",
"dbb4192b103745d5a7ddab96909ec7c0",
"c0790f4e440f4bb49acc3261d5cac2b6",
"39a37366486f4167a988099f60f9b27c",
"443817df0b8c4abf86f7682eb1a60065",
"1d0c811004624e938803b3cb91054f42",
"74881981c47a445b9c28fd11655c69be",
"ec39c298101141e7a569e890431da6bb",
"e76f84942f154450ad048cd45b504e44",
"686554421a4340718b16272f6ece2ec2",
"8f4b77da3f574cae81234e87c3487dd4",
"8614663eb74c4befa0aa63292b585ea3",
"781905b76f5c42d891f1a700874cbf18",
"a8d2ab3d9c244271957f480818efa2d5",
"0152f3e0e6d6415c9ec6d50ededbb53c",
"a7212c058b2d45f2a63fa4757df37b8b",
"8cb09a84d6e0434db8dfc5880dd640ff",
"01c197965c5947a38a9e57c24c4a5e2c",
"a4f03f0e15cc4cf0a62f520a83f6a05b",
"ac34b85703d34b87a1cd8e0839edb0cb",
"aab7946c991c4049b2da6b775ec8042d",
"08532c1665584873ade5a5ddd082ae34",
"2929c85d5ed44172b53839483197094e"
]
},
"id": "lwAcjZdsFU-v",
"outputId": "fac3311d-cf21-42de-dd5e-d3c107b95c8a"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/3633 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "132d50e305ad41cba46f741bbcbfa1c7"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/454 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "dbb4192b103745d5a7ddab96909ec7c0"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/455 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "781905b76f5c42d891f1a700874cbf18"
}
},
"metadata": {}
}
],
"source": [
"tokenized_datasets_bert = get_tokenized_data(tokenizer_bert)"
]
},
{
"cell_type": "markdown",
"source": [
"The data has now finally been preprocessed."
],
"metadata": {
"id": "NkDd1J1CsoWZ"
}
},
{
"cell_type": "markdown",
"source": [
"The labels should be padded the exact same way as the inputs so that they stay the same size, which can be achieved using a DataCollatorForTokenClassification. It takes the tokenizer used to preprocess the inputs. Here are some examples:"
],
"metadata": {
"id": "G0IzIKsLs0o2"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8hMvzmFZIISk",
"outputId": "9412e2b9-01c1-4f06-e4d8-5995dd96e693"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"tensor([[-100, 0, 0, 33, 34, 0, 33, 0, 11, 12, 12, 12,\n",
" 12, 12, 0, 0, 0, 0, 0, 33, 34, 34, 34, 0,\n",
" 0, 0, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
" -100],\n",
" [-100, 39, 40, 40, 0, 0, 0, 0, 0, 0, 49, 11,\n",
" 12, 9, 0, 9, 0, 0, 0, 0, 33, 0, 0, 7,\n",
" 8, 8, 0, 0, 0, 0, 0, 13, 25, 26, 26, 26,\n",
" -100]])"
]
},
"metadata": {},
"execution_count": 18
}
],
"source": [
"batch = data_collator_bert([tokenized_datasets_bert[\"train\"][i] for i in range(2)])\n",
"batch[\"labels\"]"
]
},
{
"cell_type": "markdown",
"source": [
" Seqeval takes the lists of labels as strings, not integers, so we need to fully decode the predictions and labels before passing them to the metric. The labels for our first training example looks like this:"
],
"metadata": {
"id": "vg1AoqiLt98V"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ElDPJNEPIRkf",
"outputId": "c98735c9-39f5-4f63-db7c-86b360514f01"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'tags': ['O',\n",
" 'O',\n",
" 'B-Therapeutic_procedure',\n",
" 'I-Therapeutic_procedure',\n",
" 'O',\n",
" 'B-Therapeutic_procedure',\n",
" 'O',\n",
" 'B-Biological_structure',\n",
" 'I-Biological_structure',\n",
" 'I-Biological_structure',\n",
" 'O',\n",
" 'O',\n",
" 'O',\n",
" 'B-Therapeutic_procedure',\n",
" 'O'],\n",
" 'sentence': 'Then the TRAM flap was harvested from right rectus abdominis (Fig.2B) and was deepithelialized (Fig.2C).',\n",
" 'numeric_tags': [0, 0, 33, 34, 0, 33, 0, 11, 12, 12, 0, 0, 0, 33, 0],\n",
" 'tokens': ['then',\n",
" 'the',\n",
" 'tram',\n",
" 'flap',\n",
" 'was',\n",
" 'harvested',\n",
" 'from',\n",
" 'right',\n",
" 'rectus',\n",
" 'abdominis',\n",
" 'fig2b',\n",
" 'and',\n",
" 'was',\n",
" 'deepithelialized',\n",
" 'fig2c'],\n",
" '__index_level_0__': 4418}"
]
},
"metadata": {},
"execution_count": 19
}
],
"source": [
"data_dict[\"train\"][0]"
]
},
{
"cell_type": "markdown",
"source": [
"We can then create fake predictions for those by just changing the value at index 2:"
],
"metadata": {
"id": "yFNbkvnQuNJn"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "i4mwIIY-IYdZ",
"outputId": "c5818ff4-f22a-466b-ac3b-6230c1d90c6a"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['O',\n",
" 'O',\n",
" 'B-Therapeutic_procedure',\n",
" 'I-Therapeutic_procedure',\n",
" 'O',\n",
" 'B-Therapeutic_procedure',\n",
" 'O',\n",
" 'B-Biological_structure',\n",
" 'I-Biological_structure',\n",
" 'I-Biological_structure',\n",
" 'O',\n",
" 'O',\n",
" 'O',\n",
" 'B-Therapeutic_procedure',\n",
" 'O']"
]
},
"metadata": {},
"execution_count": 20
}
],
"source": [
"labels = data_dict[\"train\"][0][\"tags\"]\n",
"labels"
]
},
{
"cell_type": "markdown",
"source": [
"Here’s the output:"
],
"metadata": {
"id": "SvtmyHzquQko"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QuINbaOoIh97",
"outputId": "44e292f9-7450-424d-8c28-aa2fc692b084"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'Biological_structure': {'precision': 1.0,\n",
" 'recall': 1.0,\n",
" 'f1': 1.0,\n",
" 'number': 1},\n",
" 'Therapeutic_procedure': {'precision': 0.6666666666666666,\n",
" 'recall': 0.6666666666666666,\n",
" 'f1': 0.6666666666666666,\n",
" 'number': 3},\n",
" 'overall_precision': 0.75,\n",
" 'overall_recall': 0.75,\n",
" 'overall_f1': 0.75,\n",
" 'overall_accuracy': 0.9333333333333333}"
]
},
"metadata": {},
"execution_count": 21
}
],
"source": [
"predictions = labels.copy()\n",
"predictions[2] = 'O'\n",
"metric.compute(predictions=[predictions], references=[labels])"
]
},
{
"cell_type": "markdown",
"source": [
"We get the precision, recall, and F1 score for each separate entity, as well as overall metrics."
],
"metadata": {
"id": "SFf7utyvuTm3"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "DPxDzJKNIllt"
},
"source": [
"# Prepare for Fine-tuning\n",
"\n",
"To fine-tune multiple models, general functions are defined, thus we can call them with adjusted parameters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vtC7gTcYI4R4"
},
"outputs": [],
"source": [
"from transformers import AutoModelForTokenClassification\n",
"! pip install -U accelerate\n",
"! pip install -U transformers\n",
"from transformers import TrainingArguments\n",
"from transformers import Trainer"
]
},
{
"cell_type": "markdown",
"source": [
"To enable the Trainer to calculate a metric after each epoch, we define the `compute_metrics()` function. This function receives arrays containing predictions and labels, and it returns a dictionary containing metric names and their values.\n",
"\n",
"Within the compute_metrics() function, we first convert the logits to predictions by taking the argmax. Following this, we convert both the labels and predictions to strings. We filter out all instances where the label is -100 and then proceed to utilize the `metric.compute()` method with the obtained results."
],
"metadata": {
"id": "Y_c7i9srtWPI"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RTVhOQKbIvmT"
},
"outputs": [],
"source": [
"def compute_metrics(eval_preds):\n",
" logits, labels = eval_preds\n",
" predictions = np.argmax(logits, axis=-1)\n",
" true_labels = [[id2label[l] for l in label if l != -100] for label in labels]\n",
" true_predictions = [\n",
" [id2label[p] for (p, l) in zip(prediction, label) if l != -100]\n",
" for prediction, label in zip(predictions, labels)\n",
" ]\n",
" all_metrics = metric.compute(predictions=true_predictions, references=true_labels)\n",
" return {\n",
" \"precision\": all_metrics[\"overall_precision\"],\n",
" \"recall\": all_metrics[\"overall_recall\"],\n",
" \"f1\": all_metrics[\"overall_f1\"],\n",
" \"accuracy\": all_metrics[\"overall_accuracy\"],\n",
" }"
]
},
{
"cell_type": "markdown",
"source": [
"Next, we define our TrainingArguments. There are already default-values for the hyperparameteres, which will be replaced when optimizing the model."
],
"metadata": {
"id": "jeq8lq5BvHse"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9YrseX4dI7hn"
},
"outputs": [],
"source": [
"def get_args(name_to_save, learning_rate=2e-5, num_train_epochs=3, weight_decay=0.01):\n",
" args = TrainingArguments(\n",
" name_to_save,\n",
" evaluation_strategy=\"epoch\",\n",
" save_strategy=\"epoch\",\n",
" learning_rate=learning_rate,\n",
" num_train_epochs=num_train_epochs,\n",
" weight_decay=weight_decay\n",
" )\n",
" return args"
]
},
{
"cell_type": "markdown",
"source": [
"When defining the model we have to pass along some information on the number of labels we have. id2label and label2id contain the mappings from ID to label and vice versa:"
],
"metadata": {
"id": "fPcCCwgQvPIR"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ht0gxChCJSvw"
},
"outputs": [],
"source": [
"def get_model(checkpoint):\n",
" return AutoModelForTokenClassification.from_pretrained(\n",
" checkpoint,\n",
" id2label=id2label,\n",
" label2id=label2id,\n",
")"
]
},
{
"cell_type": "markdown",
"source": [
" We just pass everything to the Trainer."
],
"metadata": {
"id": "ErEQ5eulvhQN"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xOo36d4TJVex"
},
"outputs": [],
"source": [
"def get_trainer(model, args, training_set, eval_set, data_collator, tokenizer):\n",
" return Trainer(\n",
" model=model,\n",
" args=args,\n",
" train_dataset=training_set,\n",
" eval_dataset=eval_set,\n",
" data_collator=data_collator,\n",
" compute_metrics=compute_metrics,\n",
" tokenizer=tokenizer\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QORdgsJGQqQ8"
},
"source": [
"## BERT"
]
},
{
"cell_type": "markdown",
"source": [
"First, we try BERT based uncased."
],
"metadata": {
"id": "I6gLhL9ov_cu"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZIOfngdsJb9_",
"outputId": "e7784816-2734-4027-df5a-1185dabc7b68"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased 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_bert = get_model(checkpoint_bert)"
]
},
{
"cell_type": "markdown",
"source": [
"All parameters are costumized with BERT's tokenizer."
],
"metadata": {
"id": "R8OFfXfbwCZo"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "s249ItIHJoNr"
},
"outputs": [],
"source": [
"trainer_bert = get_trainer(model_bert, get_args(\"bert_ner\"),\n",
" tokenized_datasets_bert[\"train\"],\n",
" tokenized_datasets_bert[\"validation\"],\n",
" data_collator_bert,\n",
" tokenizer_bert)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 350
},
"id": "0dVCjp2_QkEI",
"outputId": "0285d038-99e2-434a-9724-0558e0cdc29e"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 03:50, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.164763</td>\n",
" <td>0.376830</td>\n",
" <td>0.493333</td>\n",
" <td>0.427283</td>\n",
" <td>0.685781</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.665000</td>\n",
" <td>1.011111</td>\n",
" <td>0.439300</td>\n",
" <td>0.533750</td>\n",
" <td>0.481941</td>\n",
" <td>0.719987</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1.017200</td>\n",
" <td>0.983526</td>\n",
" <td>0.458201</td>\n",
" <td>0.541250</td>\n",
" <td>0.496275</td>\n",
" <td>0.728789</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=1365, training_loss=1.2219703534583906, metrics={'train_runtime': 233.4984, 'train_samples_per_second': 46.677, 'train_steps_per_second': 5.846, 'total_flos': 305075418355890.0, 'train_loss': 1.2219703534583906, 'epoch': 3.0})"
]
},
"metadata": {},
"execution_count": 29
}
],
"source": [
"trainer_bert.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 105
},
"id": "KhH5KQJ0QzGz",
"outputId": "38519365-dd0c-40cc-ce60-dbd6cc0e95a8"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='57' max='57' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [57/57 00:02]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'eval_loss': 0.9835257530212402, 'eval_precision': 0.4582010582010582, 'eval_recall': 0.54125, 'eval_f1': 0.4962750716332378, 'eval_accuracy': 0.7287894030851777, 'eval_runtime': 4.201, 'eval_samples_per_second': 108.069, 'eval_steps_per_second': 13.568, 'epoch': 3.0}\n"
]
}
],
"source": [
"metrics_bert = trainer_bert.evaluate()\n",
"print(metrics_bert)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "u4YTBtvMJwdS",
"outputId": "b2a49c15-311e-45f8-fa8d-d66ef49b10be"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('tokenizer_bert/tokenizer_config.json',\n",
" 'tokenizer_bert/special_tokens_map.json',\n",
" 'tokenizer_bert/vocab.txt',\n",
" 'tokenizer_bert/added_tokens.json',\n",
" 'tokenizer_bert/tokenizer.json')"
]
},
"metadata": {},
"execution_count": 31
}
],
"source": [
"model_bert.save_pretrained('model_bert')\n",
"tokenizer_bert.save_pretrained('tokenizer_bert')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5CjULMCQQrmq"
},
"source": [
"## RoBERTa"
]
},
{
"cell_type": "markdown",
"source": [
"Next, we use RoBERTa for finetuning."
],
"metadata": {
"id": "gJb-2qLJwK9w"
}
},
{
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"execution_count": null,
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"colab": {
"base_uri": "https://localhost:8080/",
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},
"id": "0wSMoGglT6po",
"outputId": "19a55a6f-3b9e-4ab3-c26e-446fb62245ce"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/481 [00:00<?, ?B/s]"
],
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},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)olve/main/vocab.json: 0%| | 0.00/899k [00:00<?, ?B/s]"
],
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"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)olve/main/merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9c509d28edd24476a08b7e88f540252c"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)/main/tokenizer.json: 0%| | 0.00/1.36M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a411211e541248ff87613395067ca518"
}
},
"metadata": {}
}
],
"source": [
"checkpoint_roberta = \"roberta-base\"\n",
"tokenizer_roberta = AutoTokenizer.from_pretrained(checkpoint_roberta, add_prefix_space=True)\n",
"data_collator_roberta = DataCollatorForTokenClassification(tokenizer=tokenizer_roberta)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 223,
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"outputId": "b251f2ed-3626-4f46-c1cc-7ff240564199"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/3633 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/454 [00:00<?, ? examples/s]"
],
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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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"Map: 0%| | 0/455 [00:00<?, ? examples/s]"
],
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"version_major": 2,
"version_minor": 0,
"model_id": "fad79d37fdd74493b0a1212bc76cf851"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading model.safetensors: 0%| | 0.00/499M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "eb38cf044d7b48e0bec1dac883cb7901"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at roberta-base 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": [
"tokenized_datasets_roberta = get_tokenized_data(tokenizer_roberta)\n",
"model_roberta = get_model(checkpoint_roberta)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "467K0pQSUJiJ"
},
"outputs": [],
"source": [
"trainer_roberta = get_trainer(model_roberta,\n",
" get_args(\"roberta_ner\"),\n",
" tokenized_datasets_roberta[\"train\"],\n",
" tokenized_datasets_roberta[\"validation\"],\n",
" data_collator_roberta,\n",
" tokenizer_roberta)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 334
},
"id": "hlSDnh4gUaiL",
"outputId": "db33b8cf-c0ca-4c4c-d616-83b5992831d1"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 04:01, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.120588</td>\n",
" <td>0.409740</td>\n",
" <td>0.525833</td>\n",
" <td>0.460584</td>\n",
" <td>0.692872</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.606100</td>\n",
" <td>0.963525</td>\n",
" <td>0.472543</td>\n",
" <td>0.562917</td>\n",
" <td>0.513786</td>\n",
" <td>0.734035</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.989300</td>\n",
" <td>0.935671</td>\n",
" <td>0.497052</td>\n",
" <td>0.562083</td>\n",
" <td>0.527571</td>\n",
" <td>0.739817</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=1365, training_loss=1.1860921000386333, metrics={'train_runtime': 241.4337, 'train_samples_per_second': 45.143, 'train_steps_per_second': 5.654, 'total_flos': 297221582158254.0, 'train_loss': 1.1860921000386333, 'epoch': 3.0})"
]
},
"metadata": {},
"execution_count": 35
}
],
"source": [
"trainer_roberta.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 105
},
"id": "rClLdkjOVPiy",
"outputId": "5a29c44e-fc47-4807-a628-55f151718176"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='57' max='57' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [57/57 00:01]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'eval_loss': 0.9356706142425537, 'eval_precision': 0.4970523212969786, 'eval_recall': 0.5620833333333334, 'eval_f1': 0.5275713727023855, 'eval_accuracy': 0.7398170521228857, 'eval_runtime': 2.461, 'eval_samples_per_second': 184.474, 'eval_steps_per_second': 23.161, 'epoch': 3.0}\n"
]
}
],
"source": [
"metrics_roberta = trainer_roberta.evaluate()\n",
"print(metrics_roberta)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wv6oR6JIUgXE",
"outputId": "a396895d-9f6a-43f5-9322-a03522e9f1c4"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('tokenizer_roberta/tokenizer_config.json',\n",
" 'tokenizer_roberta/special_tokens_map.json',\n",
" 'tokenizer_roberta/vocab.json',\n",
" 'tokenizer_roberta/merges.txt',\n",
" 'tokenizer_roberta/added_tokens.json',\n",
" 'tokenizer_roberta/tokenizer.json')"
]
},
"metadata": {},
"execution_count": 37
}
],
"source": [
"model_roberta.save_pretrained('model_roberta')\n",
"tokenizer_roberta.save_pretrained('tokenizer_roberta')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tiHPg5gAQtUh"
},
"source": [
"## BioClinicalBERT"
]
},
{
"cell_type": "markdown",
"source": [
"Lastly, we choose BioClinicalBERT."
],
"metadata": {
"id": "gdCEqdd8wPtW"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 185,
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},
"id": "KkDKCP6lSTaZ",
"outputId": "22dd3f30-220b-47cc-d57e-370f49225103"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/385 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "c0f3883db48b4f23927d4f6d97696571"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading pytorch_model.bin: 0%| | 0.00/436M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "ee2e5c71c2a146d6a305a8e073cadd76"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at emilyalsentzer/Bio_ClinicalBERT 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": [
"checkpoint_clinicalBERT = \"emilyalsentzer/Bio_ClinicalBERT\"\n",
"model_clinicalBERT = get_model(checkpoint_clinicalBERT)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75,
"referenced_widgets": [
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"13969498661d4169bea6af86b392e7b1",
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"id": "R8WtJfAlTE4w",
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},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)solve/main/vocab.txt: 0%| | 0.00/213k [00:00<?, ?B/s]"
],
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"version_major": 2,
"version_minor": 0,
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}
},
"metadata": {}
}
],
"source": [
"tokenizer_clinicalBERT = AutoTokenizer.from_pretrained(checkpoint_clinicalBERT)\n",
"data_collator_clinicalBERT = DataCollatorForTokenClassification(tokenizer=tokenizer_clinicalBERT)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 149,
"referenced_widgets": [
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},
"id": "z6kXf673TGQx",
"outputId": "09aa53f6-cb64-453d-9d67-bc94bd894b56"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/3633 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "e5c752d8d6d54305979b9deb92ef3011"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/454 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "ae17b075122f4a1f80ee672f143a8b63"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/455 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8c4d27f105434a5ab471f763aa04ccad"
}
},
"metadata": {}
}
],
"source": [
"tokenized_datasets_clinicalBERT = get_tokenized_data(tokenizer_clinicalBERT)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "t2nXbd7_SfMC"
},
"outputs": [],
"source": [
"trainer_clinicalBERT = get_trainer(model_clinicalBERT,\n",
" get_args(\"bioclinicalBERT_ner\"),\n",
" tokenized_datasets_clinicalBERT[\"train\"],\n",
" tokenized_datasets_clinicalBERT[\"validation\"],\n",
" data_collator_clinicalBERT,\n",
" tokenizer_clinicalBERT)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 334
},
"id": "ICHg3E1bTeqf",
"outputId": "b7c215c0-1fac-4dd8-e296-b4ee5f26e1aa"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 03:35, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.086799</td>\n",
" <td>0.414810</td>\n",
" <td>0.527500</td>\n",
" <td>0.464417</td>\n",
" <td>0.704842</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.608300</td>\n",
" <td>0.952119</td>\n",
" <td>0.466690</td>\n",
" <td>0.554583</td>\n",
" <td>0.506855</td>\n",
" <td>0.737384</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.944900</td>\n",
" <td>0.940906</td>\n",
" <td>0.471297</td>\n",
" <td>0.554167</td>\n",
" <td>0.509383</td>\n",
" <td>0.741550</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=1365, training_loss=1.158196775555174, metrics={'train_runtime': 215.9306, 'train_samples_per_second': 50.475, 'train_steps_per_second': 6.321, 'total_flos': 326628269271252.0, 'train_loss': 1.158196775555174, 'epoch': 3.0})"
]
},
"metadata": {},
"execution_count": 42
}
],
"source": [
"trainer_clinicalBERT.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 105
},
"id": "NiSfJENrTgUM",
"outputId": "cb84f8d2-67dd-400d-f0ba-7352df728864"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='57' max='57' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [57/57 00:02]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'eval_loss': 0.9409064054489136, 'eval_precision': 0.4712969525159461, 'eval_recall': 0.5541666666666667, 'eval_f1': 0.5093833780160858, 'eval_accuracy': 0.7415500707435938, 'eval_runtime': 2.8944, 'eval_samples_per_second': 156.853, 'eval_steps_per_second': 19.693, 'epoch': 3.0}\n"
]
}
],
"source": [
"metrics_clinicalBERT = trainer_clinicalBERT.evaluate()\n",
"print(metrics_clinicalBERT)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RO-g13GpTqF_",
"outputId": "c4a7cc37-8181-4875-daec-0a18bd5fc435"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('tokenizer_clinicalBERT/tokenizer_config.json',\n",
" 'tokenizer_clinicalBERT/special_tokens_map.json',\n",
" 'tokenizer_clinicalBERT/vocab.txt',\n",
" 'tokenizer_clinicalBERT/added_tokens.json',\n",
" 'tokenizer_clinicalBERT/tokenizer.json')"
]
},
"metadata": {},
"execution_count": 44
}
],
"source": [
"model_clinicalBERT.save_pretrained('model_clinicalBERT')\n",
"tokenizer_clinicalBERT.save_pretrained('tokenizer_clinicalBERT')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CtcF6KPfVVca"
},
"source": [
"**Which model is the best?**"
]
},
{
"cell_type": "markdown",
"source": [
"To get a fast overview, the first metrics will be compared:"
],
"metadata": {
"id": "gELtmO38wV80"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FpkKAFK2VXaJ",
"outputId": "41337c90-28a7-40ca-97d7-a6fd1163368d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Bert:\n",
"Accuracy: 0.7287894030851777\n",
"F1-Score: 0.4962750716332378\n",
"Bio_ClinicalBERT:\n",
"Accuracy: 0.7415500707435938\n",
"F1-Score: 0.5093833780160858\n",
"RoBERTa:\n",
"Accuracy: 0.7398170521228857\n",
"F1-Score: 0.5275713727023855\n"
]
}
],
"source": [
"print(f\"Bert:\\nAccuracy: {metrics_bert['eval_accuracy']}\\nF1-Score: {metrics_bert['eval_f1']}\")\n",
"print(f\"Bio_ClinicalBERT:\\nAccuracy: {metrics_clinicalBERT['eval_accuracy']}\\nF1-Score: {metrics_clinicalBERT['eval_f1']}\")\n",
"print(f\"RoBERTa:\\nAccuracy: {metrics_roberta['eval_accuracy']}\\nF1-Score: {metrics_roberta['eval_f1']}\")"
]
},
{
"cell_type": "markdown",
"source": [
"It seems that all models perform similar good. To figure out if the values are differing significantly from each other, we will perform statistical tests, which can be done using metric values by applying 5-fold Cross Validation"
],
"metadata": {
"id": "qmlcih4uwaAQ"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oew-SFTTtu4Y"
},
"outputs": [],
"source": [
"from sklearn.model_selection import KFold\n",
"from statistics import mean"
]
},
{
"cell_type": "code",
"source": [
"def get_dataset(data_frame, tokenizer):\n",
" dataset = Dataset.from_pandas(data_frame)\n",
"\n",
" tokenized_dataset = dataset.map(\n",
" lambda examples: tokenize_and_align_labels(examples, tokenizer),\n",
" batched=True\n",
" )\n",
"\n",
" return tokenized_dataset"
],
"metadata": {
"id": "Fk2rp83EQXiT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kb5CPtxGtLsN"
},
"outputs": [],
"source": [
"def get_cross_validation_scores(checkpoint):\n",
" kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
" if checkpoint=='roberta-base':\n",
" tokenizer = AutoTokenizer.from_pretrained('roberta-base', add_prefix_space=True)\n",
" else:\n",
" tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
" accuracy_scores = []\n",
" f1_scores = []\n",
"\n",
" for train, test in kf.split(data):\n",
" train_data = data.iloc[train]\n",
" test_data = data.iloc[test]\n",
"\n",
" train_fold = get_dataset(train_data, tokenizer)\n",
" valid_fold = get_dataset(test_data, tokenizer)\n",
"\n",
" model = get_model(checkpoint)\n",
" args = get_args(\"checkpoint_\" + \"for_cv\")\n",
" data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)\n",
" trainer = get_trainer(model, args, train_fold, valid_fold, data_collator, tokenizer)\n",
" trainer.train()\n",
" metrics = trainer.evaluate()\n",
" accuracy_scores.append(metrics['eval_accuracy'])\n",
" f1_scores.append(metrics['eval_f1'])\n",
" return [accuracy_scores, f1_scores]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tKr_4uRdx4ho"
},
"outputs": [],
"source": [
"def print_mean_scores(scores):\n",
" avg_accuracy = sum(scores[0]) / len(scores[0])\n",
" avg_f1 = sum(scores[1]) / len(scores[1])\n",
"\n",
" print(f'Avg Accuracy: {avg_accuracy}, Avg F1-Score: {avg_f1}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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},
"id": "29qjAyqSwukC",
"outputId": "3b7edefd-b8ca-45a7-ba62-14516a4b6712"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/3633 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "ee33ce2b23234990a5a87fe5fecbbe5f"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/909 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "dffa8294b1074d78bc1434a4d1d8135f"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased 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",
"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 03:41, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.156941</td>\n",
" <td>0.382589</td>\n",
" <td>0.470197</td>\n",
" <td>0.421893</td>\n",
" <td>0.685283</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.626900</td>\n",
" <td>1.030604</td>\n",
" <td>0.455342</td>\n",
" <td>0.512650</td>\n",
" <td>0.482300</td>\n",
" <td>0.717830</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.998900</td>\n",
" <td>1.024186</td>\n",
" <td>0.454118</td>\n",
" <td>0.539022</td>\n",
" <td>0.492941</td>\n",
" <td>0.724816</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='114' max='114' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [114/114 00:03]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/3633 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "fead456f72f1477a915708ba60e2041c"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/909 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "c55e4148a8d440ceaaa3a795e677317e"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased 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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 03:49, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.156984</td>\n",
" <td>0.396164</td>\n",
" <td>0.482838</td>\n",
" <td>0.435228</td>\n",
" <td>0.689652</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.632200</td>\n",
" <td>1.049778</td>\n",
" <td>0.455980</td>\n",
" <td>0.535481</td>\n",
" <td>0.492543</td>\n",
" <td>0.716842</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1.001100</td>\n",
" <td>1.012401</td>\n",
" <td>0.469376</td>\n",
" <td>0.537376</td>\n",
" <td>0.501080</td>\n",
" <td>0.725277</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='114' max='114' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [114/114 00:03]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/3634 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "7474ac12fd31476bb868645cbf7b9b92"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/908 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "00959e1b50264101bebad05458e97d9f"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased 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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 03:43, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.176537</td>\n",
" <td>0.405291</td>\n",
" <td>0.460868</td>\n",
" <td>0.431297</td>\n",
" <td>0.686747</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.610800</td>\n",
" <td>1.039953</td>\n",
" <td>0.450009</td>\n",
" <td>0.519076</td>\n",
" <td>0.482081</td>\n",
" <td>0.712596</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1.001500</td>\n",
" <td>1.019376</td>\n",
" <td>0.472344</td>\n",
" <td>0.538042</td>\n",
" <td>0.503057</td>\n",
" <td>0.720570</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='114' max='114' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [114/114 00:03]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
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},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased 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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
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"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 03:46, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.145955</td>\n",
" <td>0.410415</td>\n",
" <td>0.485792</td>\n",
" <td>0.444933</td>\n",
" <td>0.694829</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.628900</td>\n",
" <td>1.039630</td>\n",
" <td>0.442393</td>\n",
" <td>0.540744</td>\n",
" <td>0.486649</td>\n",
" <td>0.715414</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1.006800</td>\n",
" <td>1.021600</td>\n",
" <td>0.468283</td>\n",
" <td>0.536774</td>\n",
" <td>0.500195</td>\n",
" <td>0.722399</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
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"\n",
" <div>\n",
" \n",
" <progress value='114' max='114' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [114/114 00:03]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
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"Map: 0%| | 0/3634 [00:00<?, ? examples/s]"
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},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased 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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 03:47, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.110955</td>\n",
" <td>0.410031</td>\n",
" <td>0.427358</td>\n",
" <td>0.418515</td>\n",
" <td>0.695315</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.643800</td>\n",
" <td>0.999179</td>\n",
" <td>0.439949</td>\n",
" <td>0.528207</td>\n",
" <td>0.480055</td>\n",
" <td>0.716991</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1.013800</td>\n",
" <td>0.979439</td>\n",
" <td>0.459369</td>\n",
" <td>0.535613</td>\n",
" <td>0.494570</td>\n",
" <td>0.727154</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
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"\n",
" <div>\n",
" \n",
" <progress value='114' max='114' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [114/114 00:03]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
}
],
"source": [
"cross_validation_scores_bert = get_cross_validation_scores(\"bert-base-uncased\")"
]
},
{
"cell_type": "code",
"source": [
"cross_validation_scores_bert"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_nPP4khTaj2i",
"outputId": "41b05126-f4c5-4d0d-b5b7-8816e9e2a203"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[[0.7248159303882196,\n",
" 0.7252774251668646,\n",
" 0.7205695509309967,\n",
" 0.7223990410449304,\n",
" 0.727153881836967],\n",
" [0.4929411764705882,\n",
" 0.5010799136069115,\n",
" 0.5030574806359559,\n",
" 0.5001947040498443,\n",
" 0.4945695897023331]]"
]
},
"metadata": {},
"execution_count": 71
}
]
},
{
"cell_type": "code",
"source": [
"print_mean_scores(cross_validation_scores_bert)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tWdYeqHjRAZx",
"outputId": "9e8cc5d1-8c4e-41a3-d063-cc8a91439b44"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Avg Accuracy: 0.723926030666099, Avg F1-Score: 0.49762739069204354\n"
]
}
]
},
{
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{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/3633 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
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},
{
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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at roberta-base 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",
"You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 03:42, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.097066</td>\n",
" <td>0.415056</td>\n",
" <td>0.483491</td>\n",
" <td>0.446667</td>\n",
" <td>0.698825</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.582200</td>\n",
" <td>0.991852</td>\n",
" <td>0.476973</td>\n",
" <td>0.535163</td>\n",
" <td>0.504395</td>\n",
" <td>0.723353</td>\n",
" </tr>\n",
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" <td>3</td>\n",
" <td>0.986500</td>\n",
" <td>0.976402</td>\n",
" <td>0.480274</td>\n",
" <td>0.555961</td>\n",
" <td>0.515353</td>\n",
" <td>0.730453</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
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" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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"name": "stderr",
"text": [
"Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at roberta-base 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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
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"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 04:00, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.107128</td>\n",
" <td>0.423097</td>\n",
" <td>0.504527</td>\n",
" <td>0.460238</td>\n",
" <td>0.696007</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.574000</td>\n",
" <td>1.016745</td>\n",
" <td>0.473899</td>\n",
" <td>0.539061</td>\n",
" <td>0.504384</td>\n",
" <td>0.716966</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.979800</td>\n",
" <td>0.979545</td>\n",
" <td>0.494173</td>\n",
" <td>0.553590</td>\n",
" <td>0.522197</td>\n",
" <td>0.729516</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
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" [114/114 00:03]\n",
" </div>\n",
" "
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"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
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},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at roberta-base 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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 03:54, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.109957</td>\n",
" <td>0.444037</td>\n",
" <td>0.475692</td>\n",
" <td>0.459320</td>\n",
" <td>0.700108</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.540400</td>\n",
" <td>0.983117</td>\n",
" <td>0.483996</td>\n",
" <td>0.543928</td>\n",
" <td>0.512215</td>\n",
" <td>0.723806</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.982000</td>\n",
" <td>0.959463</td>\n",
" <td>0.498349</td>\n",
" <td>0.559407</td>\n",
" <td>0.527116</td>\n",
" <td>0.730821</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
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{
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" \n",
" <progress value='114' max='114' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [114/114 00:02]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
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"Map: 0%| | 0/3634 [00:00<?, ? examples/s]"
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{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at roberta-base 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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 03:53, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.104467</td>\n",
" <td>0.442596</td>\n",
" <td>0.511492</td>\n",
" <td>0.474557</td>\n",
" <td>0.702290</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.553700</td>\n",
" <td>0.989619</td>\n",
" <td>0.471260</td>\n",
" <td>0.551609</td>\n",
" <td>0.508279</td>\n",
" <td>0.722118</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.985000</td>\n",
" <td>0.957092</td>\n",
" <td>0.496408</td>\n",
" <td>0.563101</td>\n",
" <td>0.527655</td>\n",
" <td>0.731903</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
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"data": {
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],
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"\n",
" <div>\n",
" \n",
" <progress value='114' max='114' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [114/114 00:03]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/3634 [00:00<?, ? examples/s]"
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},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at roberta-base 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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 04:00, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.066230</td>\n",
" <td>0.429740</td>\n",
" <td>0.446308</td>\n",
" <td>0.437867</td>\n",
" <td>0.706020</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.558500</td>\n",
" <td>0.948279</td>\n",
" <td>0.472836</td>\n",
" <td>0.549771</td>\n",
" <td>0.508410</td>\n",
" <td>0.729094</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.987400</td>\n",
" <td>0.939218</td>\n",
" <td>0.488368</td>\n",
" <td>0.557831</td>\n",
" <td>0.520793</td>\n",
" <td>0.738636</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
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"\n",
" <div>\n",
" \n",
" <progress value='114' max='114' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [114/114 00:03]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
}
],
"source": [
"cross_validation_scores_roberta = get_cross_validation_scores('roberta-base')"
]
},
{
"cell_type": "code",
"source": [
"cross_validation_scores_roberta"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GK1s5W8ogU0N",
"outputId": "19014341-889b-444e-d2a9-cc2f956c6a7c"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[[0.7304531176040278,\n",
" 0.7295161631100782,\n",
" 0.7308211170069251,\n",
" 0.7319032561319545,\n",
" 0.7386363636363636],\n",
" [0.5153532743714597,\n",
" 0.5221968417916377,\n",
" 0.5271158586688579,\n",
" 0.5276554087126775,\n",
" 0.5207930859176411]]"
]
},
"metadata": {},
"execution_count": 75
}
]
},
{
"cell_type": "code",
"source": [
"print_mean_scores(cross_validation_scores_roberta)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KHlxCAkDRBtI",
"outputId": "da614e59-7ebb-49de-8fe2-700d110bb2ee"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Avg Accuracy: 0.7322660034978699, Avg F1-Score: 0.5226228938924548\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": null,
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"text": [
"Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
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"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at emilyalsentzer/Bio_ClinicalBERT 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",
"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
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" [1365/1365 03:57, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
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" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.091431</td>\n",
" <td>0.415973</td>\n",
" <td>0.503645</td>\n",
" <td>0.455630</td>\n",
" <td>0.703202</td>\n",
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" <td>0.529803</td>\n",
" <td>0.494645</td>\n",
" <td>0.725022</td>\n",
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" <td>3</td>\n",
" <td>0.935500</td>\n",
" <td>0.983385</td>\n",
" <td>0.467625</td>\n",
" <td>0.551244</td>\n",
" <td>0.506003</td>\n",
" <td>0.733577</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
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"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
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"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
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{
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"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at emilyalsentzer/Bio_ClinicalBERT 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"
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{
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" [1365/1365 03:40, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.133353</td>\n",
" <td>0.411875</td>\n",
" <td>0.506844</td>\n",
" <td>0.454451</td>\n",
" <td>0.699443</td>\n",
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" <td>0.537797</td>\n",
" <td>0.493765</td>\n",
" <td>0.721098</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.949900</td>\n",
" <td>1.004915</td>\n",
" <td>0.472793</td>\n",
" <td>0.552537</td>\n",
" <td>0.509564</td>\n",
" <td>0.725859</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
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{
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"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
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"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
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"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at emilyalsentzer/Bio_ClinicalBERT 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"
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{
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" [1365/1365 03:57, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
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" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.114807</td>\n",
" <td>0.442840</td>\n",
" <td>0.488119</td>\n",
" <td>0.464378</td>\n",
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" <td>0.465235</td>\n",
" <td>0.538260</td>\n",
" <td>0.499090</td>\n",
" <td>0.722519</td>\n",
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" <td>0.951400</td>\n",
" <td>0.977412</td>\n",
" <td>0.481189</td>\n",
" <td>0.557663</td>\n",
" <td>0.516611</td>\n",
" <td>0.730365</td>\n",
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" </tbody>\n",
"</table><p>"
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"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
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" _warn_prf(average, modifier, msg_start, len(result))\n"
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"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at emilyalsentzer/Bio_ClinicalBERT 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"
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"data": {
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" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 03:49, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
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" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.098683</td>\n",
" <td>0.436341</td>\n",
" <td>0.516298</td>\n",
" <td>0.472964</td>\n",
" <td>0.701761</td>\n",
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" <td>2</td>\n",
" <td>1.597500</td>\n",
" <td>0.990811</td>\n",
" <td>0.460678</td>\n",
" <td>0.559340</td>\n",
" <td>0.505237</td>\n",
" <td>0.725541</td>\n",
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" <td>0.956900</td>\n",
" <td>0.973427</td>\n",
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" <td>0.567697</td>\n",
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"</table><p>"
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" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
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"Some weights of BertForTokenClassification were not initialized from the model checkpoint at emilyalsentzer/Bio_ClinicalBERT 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"
]
},
{
"output_type": "display_data",
"data": {
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" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 03:48, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>1.054887</td>\n",
" <td>0.441123</td>\n",
" <td>0.478981</td>\n",
" <td>0.459273</td>\n",
" <td>0.714280</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.590800</td>\n",
" <td>0.953492</td>\n",
" <td>0.453199</td>\n",
" <td>0.539970</td>\n",
" <td>0.492794</td>\n",
" <td>0.729980</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.954000</td>\n",
" <td>0.952928</td>\n",
" <td>0.470479</td>\n",
" <td>0.551949</td>\n",
" <td>0.507968</td>\n",
" <td>0.736268</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
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},
"metadata": {}
},
{
"output_type": "stream",
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"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
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" "
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},
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"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
}
],
"source": [
"cross_validation_scores_bio_clinicalBERT = get_cross_validation_scores('emilyalsentzer/Bio_ClinicalBERT')"
]
},
{
"cell_type": "code",
"source": [
"cross_validation_scores_bio_clinicalBERT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "v9jQDWaJgg1R",
"outputId": "9031bc90-3d03-4a79-d581-61a06294686d"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[[0.7335766423357665,\n",
" 0.7258590900364753,\n",
" 0.7303647716069668,\n",
" 0.7317092094033673,\n",
" 0.736267647407759],\n",
" [0.5060027553631176,\n",
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" 0.5166111279410279,\n",
" 0.5183630640083946,\n",
" 0.5079683271524508]]"
]
},
"metadata": {},
"execution_count": 78
}
]
},
{
"cell_type": "code",
"source": [
"print_mean_scores(cross_validation_scores_bio_clinicalBERT)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BDbsk8bsRC1A",
"outputId": "c9ff1730-6f27-42e8-86a8-f2297759fc7b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Avg Accuracy: 0.7315554721580669, Avg F1-Score: 0.5117018619616457\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j7o-HRrs3BUd"
},
"source": [
"To find out if there is a significant difference in the means of the metrics data, we will use the python library Pingouin to perform three paired t-test."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "g8v7M5ju3BCx"
},
"outputs": [],
"source": [
"!pip install pingouin\n",
"import pingouin as pg"
]
},
{
"cell_type": "code",
"source": [
"bert_acc = [0.7248159303882196, 0.7252774251668646, 0.7205695509309967, 0.7223990410449304, 0.727153881836967] # eig accuracy\n",
"roberta_acc = [0.7304531176040278, 0.7295161631100782, 0.7308211170069251, 0.7319032561319545, 0.7386363636363636]\n",
"clinicalbert_acc = [0.7335766423357665, 0.7258590900364753, 0.7303647716069668, 0.7317092094033673, 0.736267647407759]"
],
"metadata": {
"id": "9tKrO6VOLPCj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Bonferroni correction must be performed because multiple groups are compared in multiple t tests. This results in a new alpha level."
],
"metadata": {
"id": "nE6cGuEVMBLP"
}
},
{
"cell_type": "code",
"source": [
"alpha_adjusted = 0.05 / 3\n",
"alpha_adjusted"
],
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},
"id": "ylvDRBORJyRX",
"outputId": "747dee75-3059-4b03-ec0d-8c21de45f634"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.016666666666666666"
]
},
"metadata": {},
"execution_count": 4
}
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"pg.ttest(bert_acc, roberta_acc, paired=True)"
],
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"outputId": "aae8301a-2d95-465e-8fd9-bde8ed74d669"
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" T dof alternative p-val CI95% cohen-d BF10 \\\n",
"T-test -5.892911 4 two-sided 0.004147 [-0.01, -0.0] 2.596799 13.337 \n",
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],
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" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-8da1acaa-1b00-4bea-ba89-61193ab80527 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-8da1acaa-1b00-4bea-ba89-61193ab80527');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kej7D_dR3GBD"
},
"source": [
"The last t-test with RoBERTa and BioClinicalBERT showed that neither model is significantly better, as p>.016. This means that we can decide which model we proceed with. We choose BioClinicalBERT."
]
},
{
"cell_type": "markdown",
"source": [
"# Model optimization\n",
"\n",
"Tough we could not find a outstanding model, we will use BioClinicalBERT and optimize its parameter, trying to better its performance.\n",
"\n",
"We will vary the following hyperparameters: learning rate, number of epochs and weight decay. Their values for fine-tuning can be easily adjusted calling the get_args method with different values.\n",
"\n",
"For comparison the previous values:"
],
"metadata": {
"id": "qNYTnNHwZPgg"
}
},
{
"cell_type": "code",
"source": [
"metrics_clinicalBERT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9nuPzDSmneVN",
"outputId": "40617a08-c9b2-4b44-a255-64867f6d41e8"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'eval_loss': 0.9409064054489136,\n",
" 'eval_precision': 0.4712969525159461,\n",
" 'eval_recall': 0.5541666666666667,\n",
" 'eval_f1': 0.5093833780160858,\n",
" 'eval_accuracy': 0.7415500707435938,\n",
" 'eval_runtime': 2.8944,\n",
" 'eval_samples_per_second': 156.853,\n",
" 'eval_steps_per_second': 19.693,\n",
" 'epoch': 3.0}"
]
},
"metadata": {},
"execution_count": 91
}
]
},
{
"cell_type": "markdown",
"source": [
"### Learning rate\n",
"\n",
"\n",
"Until now we used a learning rate of 2e-5, which will be reduced to 1e-4 for fine-tuning."
],
"metadata": {
"id": "OQXJh0HPZUmB"
}
},
{
"cell_type": "code",
"source": [
"model_1 = get_model('emilyalsentzer/Bio_ClinicalBERT')\n",
"trainer_1 = get_trainer(model_1,\n",
" get_args(\"model_1\", 1e-4),\n",
" tokenized_datasets_clinicalBERT[\"train\"],\n",
" tokenized_datasets_clinicalBERT[\"validation\"],\n",
" data_collator_clinicalBERT,\n",
" tokenizer_clinicalBERT\n",
" )\n",
"trainer_1.train()\n",
"trainer_1.evaluate()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 518
},
"id": "Xd3EEHEGZPIU",
"outputId": "638d09e7-f398-4651-990c-a2ac0a489ab1"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at emilyalsentzer/Bio_ClinicalBERT 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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 04:02, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>0.921146</td>\n",
" <td>0.439488</td>\n",
" <td>0.587083</td>\n",
" <td>0.502676</td>\n",
" <td>0.726851</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.181500</td>\n",
" <td>0.863842</td>\n",
" <td>0.519725</td>\n",
" <td>0.598333</td>\n",
" <td>0.556266</td>\n",
" <td>0.756092</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.636900</td>\n",
" <td>0.888697</td>\n",
" <td>0.526278</td>\n",
" <td>0.613333</td>\n",
" <td>0.566481</td>\n",
" <td>0.760887</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='57' max='57' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [57/57 00:01]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'eval_loss': 0.8886973261833191,\n",
" 'eval_precision': 0.5262781551662495,\n",
" 'eval_recall': 0.6133333333333333,\n",
" 'eval_f1': 0.5664806619203385,\n",
" 'eval_accuracy': 0.7608866530419746,\n",
" 'eval_runtime': 2.1777,\n",
" 'eval_samples_per_second': 208.479,\n",
" 'eval_steps_per_second': 26.175,\n",
" 'epoch': 3.0}"
]
},
"metadata": {},
"execution_count": 89
}
]
},
{
"cell_type": "markdown",
"source": [
"With an adjusted learning rate of 1e-4, the model performs even better than before. All metrics improved, for example accuracy increased from 0.74 to 0.76."
],
"metadata": {
"id": "QuqJFFdoNno4"
}
},
{
"cell_type": "markdown",
"source": [
"### Number of Epochs\n",
"\n",
"From the results of the evaluation metrics after each epoch, it can be seen that from the 2nd to the 3rd epoch only limited improvements are visible. As a test, 4 epochs are now trained."
],
"metadata": {
"id": "v2nF68LtamvF"
}
},
{
"cell_type": "code",
"source": [
"model_2 = get_model('emilyalsentzer/Bio_ClinicalBERT')\n",
"trainer_2 = get_trainer(model_2,\n",
" get_args(\"model_2\", learning_rate=1e-4, num_train_epochs=4),\n",
" tokenized_datasets_clinicalBERT[\"train\"],\n",
" tokenized_datasets_clinicalBERT[\"validation\"],\n",
" data_collator_clinicalBERT,\n",
" tokenizer_clinicalBERT\n",
" )\n",
"trainer_2.train()\n",
"trainer_2.evaluate()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 582
},
"id": "ldmZU8Ncatg9",
"outputId": "d65574d0-5cec-43ef-a75c-3be1d79ca1d5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at emilyalsentzer/Bio_ClinicalBERT 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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1820' max='1820' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1820/1820 05:08, Epoch 4/4]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>0.942180</td>\n",
" <td>0.438356</td>\n",
" <td>0.573333</td>\n",
" <td>0.496841</td>\n",
" <td>0.723000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.212700</td>\n",
" <td>0.870196</td>\n",
" <td>0.518204</td>\n",
" <td>0.610833</td>\n",
" <td>0.560719</td>\n",
" <td>0.750904</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.672200</td>\n",
" <td>0.902824</td>\n",
" <td>0.533818</td>\n",
" <td>0.611667</td>\n",
" <td>0.570097</td>\n",
" <td>0.760022</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.410000</td>\n",
" <td>0.988483</td>\n",
" <td>0.534858</td>\n",
" <td>0.613750</td>\n",
" <td>0.571595</td>\n",
" <td>0.757507</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='57' max='57' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [57/57 00:01]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'eval_loss': 0.9884827733039856,\n",
" 'eval_precision': 0.5348583877995643,\n",
" 'eval_recall': 0.61375,\n",
" 'eval_f1': 0.5715948777648429,\n",
" 'eval_accuracy': 0.757506681339412,\n",
" 'eval_runtime': 2.1422,\n",
" 'eval_samples_per_second': 211.929,\n",
" 'eval_steps_per_second': 26.608,\n",
" 'epoch': 4.0}"
]
},
"metadata": {},
"execution_count": 92
}
]
},
{
"cell_type": "markdown",
"source": [
"The fine-tuning with 4 epochs clearly shows a decreasing accuracy, indicating that the model tends to overfit the data. Thus we will not change this parameter and keep 3 epochs."
],
"metadata": {
"id": "4PpmWo1iNvHA"
}
},
{
"cell_type": "markdown",
"source": [
"### Weight Decay\n",
"\n",
"Because the dataset is rather small and has many labels, it may happen that the model tends to overfit faster. By using a smaller weight decay value, you can increase regularization and force the model to restrict weights more. We will use a value of 0.001."
],
"metadata": {
"id": "qG16HwkqazBC"
}
},
{
"cell_type": "code",
"source": [
"model_3 = get_model('emilyalsentzer/Bio_ClinicalBERT')\n",
"trainer_3 = get_trainer(model_3,\n",
" get_args(\"model_3\", learning_rate=1e-4, weight_decay=0.001),\n",
" tokenized_datasets_clinicalBERT[\"train\"],\n",
" tokenized_datasets_clinicalBERT[\"validation\"],\n",
" data_collator_clinicalBERT,\n",
" tokenizer_clinicalBERT\n",
" )\n",
"trainer_3.train()\n",
"trainer_3.evaluate()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 518
},
"id": "8CLiyvR-a3KK",
"outputId": "fa898981-696b-4e30-99d2-d244f8f0177f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of BertForTokenClassification were not initialized from the model checkpoint at emilyalsentzer/Bio_ClinicalBERT 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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1365' max='1365' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1365/1365 03:40, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>0.924542</td>\n",
" <td>0.442520</td>\n",
" <td>0.585417</td>\n",
" <td>0.504036</td>\n",
" <td>0.728895</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.176200</td>\n",
" <td>0.841584</td>\n",
" <td>0.525887</td>\n",
" <td>0.617917</td>\n",
" <td>0.568199</td>\n",
" <td>0.761830</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.636200</td>\n",
" <td>0.895939</td>\n",
" <td>0.528126</td>\n",
" <td>0.614167</td>\n",
" <td>0.567906</td>\n",
" <td>0.759865</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='57' max='57' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [57/57 00:01]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'eval_loss': 0.8959393501281738,\n",
" 'eval_precision': 0.528126119670369,\n",
" 'eval_recall': 0.6141666666666666,\n",
" 'eval_f1': 0.5679059911385089,\n",
" 'eval_accuracy': 0.7598648011318975,\n",
" 'eval_runtime': 2.1635,\n",
" 'eval_samples_per_second': 209.845,\n",
" 'eval_steps_per_second': 26.346,\n",
" 'epoch': 3.0}"
]
},
"metadata": {},
"execution_count": 93
}
]
},
{
"cell_type": "markdown",
"source": [
"In this case, a weight decay of 0.001 ensures a faster growing accuracy, but there is also a trend towards overfitting. That's why we keep the original value."
],
"metadata": {
"id": "bUaDTp5xp4Ng"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q6rpVfB8Q3YS"
},
"source": [
"# Precise Evaluation of the best Model with the Test Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JrBSsteEQ7ps"
},
"outputs": [],
"source": [
"from transformers import pipeline\n",
"from collections import Counter\n",
"from sklearn.metrics import classification_report"
]
},
{
"cell_type": "markdown",
"source": [
"To use the finetuned model for inference we apply it in a pipeline(). After instantiating the pipeline for NER with the model, we will pass the text."
],
"metadata": {
"id": "d2n4NHBwRa9Q"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DKSkVT9NQ-Tz"
},
"outputs": [],
"source": [
"classifier = pipeline(\"ner\", model=model_1, tokenizer=tokenizer_clinicalBERT)\n",
"model_1.to(\"cpu\") # Oder \"cpu\", je nachdem, was verfügbar ist\n",
"\n",
"def predict_tags(sentence):\n",
" return classifier(sentence)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "THTr3_57RChE",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "cb68c55e-6cc0-43f4-b693-df88f48a603c"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'entity': 'B-Biological_structure',\n",
" 'score': 0.99034506,\n",
" 'index': 2,\n",
" 'word': 'left',\n",
" 'start': 4,\n",
" 'end': 8},\n",
" {'entity': 'I-Biological_structure',\n",
" 'score': 0.99015266,\n",
" 'index': 3,\n",
" 'word': 'f',\n",
" 'start': 9,\n",
" 'end': 10},\n",
" {'entity': 'I-Biological_structure',\n",
" 'score': 0.9918446,\n",
" 'index': 4,\n",
" 'word': '##em',\n",
" 'start': 10,\n",
" 'end': 12},\n",
" {'entity': 'I-Biological_structure',\n",
" 'score': 0.9898303,\n",
" 'index': 5,\n",
" 'word': '##oral',\n",
" 'start': 12,\n",
" 'end': 16},\n",
" {'entity': 'I-Biological_structure',\n",
" 'score': 0.9824426,\n",
" 'index': 6,\n",
" 'word': 'artery',\n",
" 'start': 17,\n",
" 'end': 23},\n",
" {'entity': 'B-Therapeutic_procedure',\n",
" 'score': 0.9038626,\n",
" 'index': 8,\n",
" 'word': 'can',\n",
" 'start': 28,\n",
" 'end': 31},\n",
" {'entity': 'I-Therapeutic_procedure',\n",
" 'score': 0.91639614,\n",
" 'index': 9,\n",
" 'word': '##nu',\n",
" 'start': 31,\n",
" 'end': 33},\n",
" {'entity': 'I-Therapeutic_procedure',\n",
" 'score': 0.95666164,\n",
" 'index': 10,\n",
" 'word': '##lated',\n",
" 'start': 33,\n",
" 'end': 38}]"
]
},
"metadata": {},
"execution_count": 100
}
],
"source": [
"classifier(\"The left femoral artery was cannulated.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gSGm_hPKREO8"
},
"source": [
"The classifier correctly extracts the entities from the sentence. However, these are not returned in lists, but with its entity information. In order to be able to compare the output with the ground truth lists from the original dataset, the results have to be processed.\n",
"\n",
"A first step is to extract each token from each sentence with its start and end value."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "F6bBDyrfRJAr"
},
"outputs": [],
"source": [
"def create_token_table(sentence):\n",
" sentence = sentence.replace(\"\\u2005\", \"\").replace(\"\\u200a\", \"\").replace(\"\\u2009\", \"\").replace(\"\\n\", \"\")\n",
" tokens = sentence.split()\n",
"\n",
" token_table = []\n",
" start = 0\n",
"\n",
" for token in tokens:\n",
" end = start + len(token) Läng\n",
"\n",
" token_info = {\n",
" \"token\": token,\n",
" \"start\": start,\n",
" \"end\": end\n",
" }\n",
" token_table.append(token_info)\n",
"\n",
" start = end + 1\n",
"\n",
" return token_table"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1l6sROD0RKZu"
},
"source": [
"Now the pipeline predictions are processed. Only entities with a confident prediction of 0.4 or higher are used. In addition, they are combined and the tokenization into subtokens is reversed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RlMKhugPROe3"
},
"outputs": [],
"source": [
"def preprocess_entities(sentence_info):\n",
" sentence_info = [info for info in sentence_info if info['score'] >= 0.4]\n",
"\n",
" cleaned_entities = []\n",
" current_entity = None\n",
" current_start = None\n",
" current_end = None\n",
"\n",
" for info in sentence_info:\n",
" entity = info['entity']\n",
" start = info['start']\n",
" end = info['end']\n",
"\n",
" if current_entity is None:\n",
" current_entity = entity.replace('B-', '').replace('I-', '')\n",
" current_start = start\n",
" current_end = end\n",
" elif current_end == start:\n",
" current_entity = entity.replace('B-', '').replace('I-', '')\n",
" current_end = end\n",
" else:\n",
" cleaned_entities.append((current_entity, current_start, current_end))\n",
" current_entity = entity.replace('B-', '').replace('I-', '')\n",
" current_start = start\n",
" current_end = end\n",
"\n",
" if current_entity is not None:\n",
" cleaned_entities.append((current_entity, current_start, current_end))\n",
"\n",
" return cleaned_entities"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qtMwuxkBRP1Y"
},
"outputs": [],
"source": [
"def generate_tags(predicted_entities, sentence_entities):\n",
" tags = [\"O\"] * len(sentence_entities)\n",
"\n",
" for entity, start, end in predicted_entities:\n",
" for i, token_info in enumerate(sentence_entities):\n",
" token_start = token_info['start']\n",
" token_end = token_info['end']\n",
"\n",
" if token_start == start and token_end == end:\n",
" tags[i] = entity\n",
" return tags"
]
},
{
"cell_type": "markdown",
"source": [
"With the `format_tags` function, we turn the raw tags into BOI-tags, just like in the original data."
],
"metadata": {
"id": "p_Ss6z-yTQHd"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "S0bhOyPHRRKN"
},
"outputs": [],
"source": [
"def format_tags(tags):\n",
" formatted_tags = []\n",
" current_entity = None\n",
"\n",
" for tag in tags:\n",
" if tag == 'O':\n",
" formatted_tags.append(tag)\n",
" current_entity = None\n",
" else:\n",
" entity_type = tag\n",
" if current_entity == entity_type:\n",
" formatted_tags.append('I-' + entity_type)\n",
" else:\n",
" formatted_tags.append('B-' + entity_type)\n",
" current_entity = entity_type\n",
"\n",
" return formatted_tags"
]
},
{
"cell_type": "markdown",
"source": [
"The function `get_sentence_tags` automates the whole process from turning a single sentence into a list of tags in BOI-format."
],
"metadata": {
"id": "mo1G9Q9-TY0d"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "a_fZoiCpRSfu"
},
"outputs": [],
"source": [
"def get_sentence_tags(sentence):\n",
" token_table = create_token_table(sentence) # convert sentence to info table (where which word starts)\n",
" sentence_info = predict_tags(sentence) # predict tags of sentence\n",
" predicted_entities = preprocess_entities(sentence_info) # change format of pred tags\n",
" general_tags = generate_tags(predicted_entities, token_table) # use sentence info and tags to get list\n",
" boi_tags = format_tags(general_tags) # bring data in boi format\n",
" return boi_tags"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sNw8C0iORTrK"
},
"source": [
"The predicted values are stored in a list, to be able to compare them with the original data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0y3sBO11RUp7"
},
"outputs": [],
"source": [
"predicted_entities = []\n",
"for sentence in data_dict[\"test\"][\"sentence\"]:\n",
" predicted_entities.append(get_sentence_tags(sentence))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4dhM-y8NRVs2"
},
"source": [
"For performing the classification report, we save the predicted and ground truth tags into lists."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YmTn7qb3RWvx"
},
"outputs": [],
"source": [
"ground_truth_tags = []\n",
"\n",
"for sublist in data_dict[\"test\"][\"tags\"]:\n",
" ground_truth_tags.extend(sublist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "eS4ahoLuRXtf"
},
"outputs": [],
"source": [
"pred_tags = []\n",
"\n",
"for sublist in predicted_entities:\n",
" pred_tags.extend(sublist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dhGy8tGlRbYA",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e5768f5e-0aed-4b9d-9777-2bf5d4d4bf05"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Counter({'B-Diagnostic_procedure': 442,\n",
" 'I-Diagnostic_procedure': 377,\n",
" 'O': 4521,\n",
" 'B-Biological_structure': 248,\n",
" 'B-Sign_symptom': 312,\n",
" 'B-Detailed_description': 295,\n",
" 'I-Detailed_description': 189,\n",
" 'B-Clinical_event': 60,\n",
" 'B-Nonbiological_location': 27,\n",
" 'I-Biological_structure': 212,\n",
" 'B-Therapeutic_procedure': 78,\n",
" 'B-Lab_value': 213,\n",
" 'I-Lab_value': 209,\n",
" 'B-Volume': 3,\n",
" 'I-Volume': 5,\n",
" 'I-Sign_symptom': 131,\n",
" 'B-Disease_disorder': 116,\n",
" 'I-Disease_disorder': 78,\n",
" 'B-Distance': 5,\n",
" 'I-Distance': 8,\n",
" 'B-Severity': 26,\n",
" 'I-Therapeutic_procedure': 33,\n",
" 'B-Administration': 12,\n",
" 'B-Medication': 76,\n",
" 'B-Date': 51,\n",
" 'I-Date': 106,\n",
" 'B-Coreference': 23,\n",
" 'I-Coreference': 4,\n",
" 'B-History': 31,\n",
" 'I-History': 141,\n",
" 'B-Duration': 23,\n",
" 'I-Duration': 31,\n",
" 'B-Outcome': 4,\n",
" 'B-Shape': 10,\n",
" 'B-Other_event': 1,\n",
" 'I-Other_event': 11,\n",
" 'B-Frequency': 5,\n",
" 'I-Frequency': 4,\n",
" 'B-Texture': 5,\n",
" 'B-Dosage': 11,\n",
" 'I-Dosage': 22,\n",
" 'I-Nonbiological_location': 25,\n",
" 'B-Age': 19,\n",
" 'B-Sex': 17,\n",
" 'B-Color': 5,\n",
" 'I-Color': 2,\n",
" 'I-Texture': 1,\n",
" 'B-Other_entity': 6,\n",
" 'I-Other_entity': 48,\n",
" 'B-Family_history': 5,\n",
" 'I-Family_history': 35,\n",
" 'I-Medication': 16,\n",
" 'I-Age': 9,\n",
" 'B-Personal_background': 7,\n",
" 'B-Subject': 5,\n",
" 'I-Clinical_event': 6,\n",
" 'B-Activity': 6,\n",
" 'I-Activity': 7,\n",
" 'B-Biological_attribute': 2,\n",
" 'I-Biological_attribute': 1,\n",
" 'B-Time': 6,\n",
" 'I-Time': 9,\n",
" 'B-Qualitative_concept': 1,\n",
" 'I-Subject': 1,\n",
" 'I-Shape': 3,\n",
" 'I-Personal_background': 1,\n",
" 'I-Severity': 1,\n",
" 'B-Quantitative_concept': 1})"
]
},
"metadata": {},
"execution_count": 112
}
],
"source": [
"count_ground_truth = Counter(ground_truth_tags)\n",
"count_ground_truth"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ESRlNwyyRc3K",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0215434d-7780-404c-835a-bc2026453df2"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Counter({'B-Diagnostic_procedure': 422,\n",
" 'I-Diagnostic_procedure': 273,\n",
" 'O': 5627,\n",
" 'B-Biological_structure': 238,\n",
" 'I-Biological_structure': 149,\n",
" 'B-Sign_symptom': 232,\n",
" 'B-Detailed_description': 227,\n",
" 'I-Detailed_description': 89,\n",
" 'B-Clinical_event': 43,\n",
" 'B-Therapeutic_procedure': 68,\n",
" 'B-Lab_value': 173,\n",
" 'I-Therapeutic_procedure': 24,\n",
" 'B-Date': 67,\n",
" 'I-Date': 87,\n",
" 'B-Nonbiological_location': 28,\n",
" 'I-Lab_value': 62,\n",
" 'B-Volume': 1,\n",
" 'B-Disease_disorder': 95,\n",
" 'I-Sign_symptom': 50,\n",
" 'B-Distance': 2,\n",
" 'B-Severity': 22,\n",
" 'B-History': 39,\n",
" 'I-History': 71,\n",
" 'B-Duration': 23,\n",
" 'B-Medication': 67,\n",
" 'B-Coreference': 17,\n",
" 'I-Medication': 16,\n",
" 'I-Disease_disorder': 44,\n",
" 'I-Nonbiological_location': 15,\n",
" 'B-Administration': 9,\n",
" 'I-Duration': 14,\n",
" 'I-Coreference': 2,\n",
" 'B-Dosage': 8,\n",
" 'I-Dosage': 12,\n",
" 'B-Age': 15,\n",
" 'B-Sex': 17,\n",
" 'B-Color': 2,\n",
" 'B-Family_history': 14,\n",
" 'I-Age': 2,\n",
" 'B-Personal_background': 5,\n",
" 'I-Family_history': 25,\n",
" 'B-Outcome': 2,\n",
" 'I-Clinical_event': 1,\n",
" 'I-Administration': 2,\n",
" 'B-Frequency': 1,\n",
" 'I-Frequency': 1,\n",
" 'B-Time': 1})"
]
},
"metadata": {},
"execution_count": 113
}
],
"source": [
"count_predictions = Counter(pred_tags)\n",
"count_predictions"
]
},
{
"cell_type": "markdown",
"source": [
"Finally, we can print the classification report."
],
"metadata": {
"id": "qYt5EchTTyPf"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0P291AjpRgo1",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b544ea0c-0a99-4087-fdbd-9b60fc7222a2"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" B-Activity 0.00 0.00 0.00 6\n",
" B-Administration 0.78 0.58 0.67 12\n",
" B-Age 1.00 0.79 0.88 19\n",
" B-Biological_attribute 0.00 0.00 0.00 2\n",
" B-Biological_structure 0.69 0.66 0.67 248\n",
" B-Clinical_event 0.86 0.62 0.72 60\n",
" B-Color 1.00 0.40 0.57 5\n",
" B-Coreference 0.29 0.22 0.25 23\n",
" B-Date 0.61 0.80 0.69 51\n",
" B-Detailed_description 0.53 0.41 0.46 295\n",
" B-Diagnostic_procedure 0.75 0.72 0.73 442\n",
" B-Disease_disorder 0.55 0.45 0.49 116\n",
" B-Distance 0.00 0.00 0.00 5\n",
" B-Dosage 0.50 0.36 0.42 11\n",
" B-Duration 0.70 0.70 0.70 23\n",
" B-Family_history 0.07 0.20 0.11 5\n",
" B-Frequency 1.00 0.20 0.33 5\n",
" B-History 0.49 0.61 0.54 31\n",
" B-Lab_value 0.57 0.46 0.51 213\n",
" B-Medication 0.76 0.67 0.71 76\n",
"B-Nonbiological_location 0.71 0.74 0.73 27\n",
" B-Other_entity 0.00 0.00 0.00 6\n",
" B-Other_event 0.00 0.00 0.00 1\n",
" B-Outcome 1.00 0.50 0.67 4\n",
" B-Personal_background 0.80 0.57 0.67 7\n",
" B-Qualitative_concept 0.00 0.00 0.00 1\n",
" B-Quantitative_concept 0.00 0.00 0.00 1\n",
" B-Severity 0.59 0.50 0.54 26\n",
" B-Sex 1.00 1.00 1.00 17\n",
" B-Shape 0.00 0.00 0.00 10\n",
" B-Sign_symptom 0.64 0.47 0.54 312\n",
" B-Subject 0.00 0.00 0.00 5\n",
" B-Texture 0.00 0.00 0.00 5\n",
" B-Therapeutic_procedure 0.57 0.50 0.53 78\n",
" B-Time 0.00 0.00 0.00 6\n",
" B-Volume 0.00 0.00 0.00 3\n",
" I-Activity 0.00 0.00 0.00 7\n",
" I-Administration 0.00 0.00 0.00 0\n",
" I-Age 1.00 0.22 0.36 9\n",
" I-Biological_attribute 0.00 0.00 0.00 1\n",
" I-Biological_structure 0.79 0.55 0.65 212\n",
" I-Clinical_event 1.00 0.17 0.29 6\n",
" I-Color 0.00 0.00 0.00 2\n",
" I-Coreference 0.00 0.00 0.00 4\n",
" I-Date 0.74 0.60 0.66 106\n",
" I-Detailed_description 0.42 0.20 0.27 189\n",
" I-Diagnostic_procedure 0.79 0.57 0.66 377\n",
" I-Disease_disorder 0.66 0.37 0.48 78\n",
" I-Distance 0.00 0.00 0.00 8\n",
" I-Dosage 0.75 0.41 0.53 22\n",
" I-Duration 0.71 0.32 0.44 31\n",
" I-Family_history 0.60 0.43 0.50 35\n",
" I-Frequency 1.00 0.25 0.40 4\n",
" I-History 0.89 0.45 0.59 141\n",
" I-Lab_value 0.74 0.22 0.34 209\n",
" I-Medication 0.25 0.25 0.25 16\n",
"I-Nonbiological_location 0.67 0.40 0.50 25\n",
" I-Other_entity 0.00 0.00 0.00 48\n",
" I-Other_event 0.00 0.00 0.00 11\n",
" I-Personal_background 0.00 0.00 0.00 1\n",
" I-Severity 0.00 0.00 0.00 1\n",
" I-Shape 0.00 0.00 0.00 3\n",
" I-Sign_symptom 0.72 0.27 0.40 131\n",
" I-Subject 0.00 0.00 0.00 1\n",
" I-Texture 0.00 0.00 0.00 1\n",
" I-Therapeutic_procedure 0.54 0.39 0.46 33\n",
" I-Time 0.00 0.00 0.00 9\n",
" I-Volume 0.00 0.00 0.00 5\n",
" O 0.75 0.93 0.83 4521\n",
"\n",
" accuracy 0.73 8404\n",
" macro avg 0.43 0.29 0.33 8404\n",
" weighted avg 0.71 0.73 0.70 8404\n",
"\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
}
],
"source": [
"report = classification_report(ground_truth_tags, pred_tags)\n",
"print(report)"
]
},
{
"cell_type": "markdown",
"source": [
"The Bio_ClinicalBERT model achieved an average accuracy of 0.73 when applied to the test dataset, indicating that it correctly identified 73% of the entities.\n",
"\n",
"The model has some challenges when dealing with certain entities, such as 'B-Activity' or 'I-Shape.' These entities are relatively rare in the dataset, which may explain the model's difficulties in learning them well.\n",
"\n",
"In contrast, the model performs very well in identifying specific entities, such as 'B-Clinical_event,' 'B-Diagnostic_procedure,' and 'B-Sex.' For these categories, the model demonstrates high precision, recall, and F1-scores, often approaching a value of 1."
],
"metadata": {
"id": "u0fEdpEoT1Y5"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "LhWCWb8JRkkq"
},
"source": [
"Lastly, one example of a sentence, its ground truth tags and its predicted one:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vGlZfUTQRohy",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "3d48f951-a970-424c-caf0-e2498cc5b20f"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'The negative cardiolipin test excluded tabetic crises.'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 122
}
],
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