Downloads: 1
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Entities_NER.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# HuggingFace Installations"
],
"metadata": {
"id": "vawMUV8TT2dg"
}
},
{
"cell_type": "code",
"source": [
"!pip install datasets\n",
"!pip install transformers\n",
"!pip install seqeval"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6yD-WetFMkwS",
"outputId": "5880d2ef-ce47-4a34-a522-5d433e689f8a"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting datasets\n",
" Downloading datasets-2.6.1-py3-none-any.whl (441 kB)\n",
"\u001b[K |████████████████████████████████| 441 kB 4.9 MB/s \n",
"\u001b[?25hRequirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2.23.0)\n",
"Collecting huggingface-hub<1.0.0,>=0.2.0\n",
" Downloading huggingface_hub-0.10.1-py3-none-any.whl (163 kB)\n",
"\u001b[K |████████████████████████████████| 163 kB 84.5 MB/s \n",
"\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from datasets) (6.0)\n",
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"Collecting responses<0.19\n",
" Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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"Collecting multiprocess\n",
" Downloading multiprocess-0.70.13-py37-none-any.whl (115 kB)\n",
"\u001b[K |████████████████████████████████| 115 kB 93.2 MB/s \n",
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"Collecting xxhash\n",
" Downloading xxhash-3.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
"\u001b[K |████████████████████████████████| 212 kB 87.9 MB/s \n",
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"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (3.0.4)\n",
"Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
" Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
"\u001b[K |████████████████████████████████| 127 kB 91.7 MB/s \n",
"\u001b[?25hRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.9.0)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n",
"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.4)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
"Installing collected packages: urllib3, xxhash, responses, multiprocess, huggingface-hub, datasets\n",
" Attempting uninstall: urllib3\n",
" Found existing installation: urllib3 1.24.3\n",
" Uninstalling urllib3-1.24.3:\n",
" Successfully uninstalled urllib3-1.24.3\n",
"Successfully installed datasets-2.6.1 huggingface-hub-0.10.1 multiprocess-0.70.13 responses-0.18.0 urllib3-1.25.11 xxhash-3.1.0\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting transformers\n",
" Downloading transformers-4.23.1-py3-none-any.whl (5.3 MB)\n",
"\u001b[K |████████████████████████████████| 5.3 MB 5.0 MB/s \n",
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"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.1)\n",
"Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
" Downloading tokenizers-0.13.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
"\u001b[K |████████████████████████████████| 7.6 MB 71.4 MB/s \n",
"\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
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"Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.1)\n",
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"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.1.1)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.9.0)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.25.11)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.9.24)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
"Installing collected packages: tokenizers, transformers\n",
"Successfully installed tokenizers-0.13.1 transformers-4.23.1\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting seqeval\n",
" Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
"\u001b[K |████████████████████████████████| 43 kB 1.4 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from seqeval) (1.21.6)\n",
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"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.1.0)\n",
"Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.7.3)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.2.0)\n",
"Building wheels for collected packages: seqeval\n",
" Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16180 sha256=23a3a3ae907d4e838c06fc140623ceabf5f6a01792b4d0fc8b0bc856e17c3e53\n",
" Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7\n",
"Successfully built seqeval\n",
"Installing collected packages: seqeval\n",
"Successfully installed seqeval-1.2.2\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"id": "2LEFwSmbKpLP"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import spacy\n",
"import tqdm\n",
"import sys\n",
"from datasets import Dataset, DatasetDict\n",
"from transformers import Trainer\n",
"from transformers import AutoModelForTokenClassification\n",
"from transformers import AutoTokenizer\n",
"from transformers import TrainingArguments\n",
"from transformers import DataCollatorForTokenClassification\n",
"from datasets import load_metric\n",
"from transformers import pipeline\n",
"from transformers import EarlyStoppingCallback, IntervalStrategy"
]
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "G7DLsSK319zN",
"outputId": "6d759f8e-521e-4fcb-c859-0c4d4f620e58"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%cd /content/drive/MyDrive/IRE"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "czuvYXJp17x-",
"outputId": "c5fa02b9-9703-4ba6-8fe6-baa9c367f653"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/drive/MyDrive/IRE\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JP_EFEuNKpLT"
},
"source": [
"# Loading Data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
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"id": "V-_Zs-ZGKpLV",
"outputId": "aea55c01-65dd-4e58-916a-92e83b90ee95"
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{
"output_type": "execute_result",
"data": {
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" filename mark label offset1 offset2 \\\n",
"0 es-S0212-71992007000100007-1 T1 ENFERMEDAD 40 61 \n",
"1 es-S0212-71992007000100007-1 T2 ENFERMEDAD 66 79 \n",
"2 es-S0212-71992007000100007-1 T3 ENFERMEDAD 1682 1698 \n",
"3 es-S0212-71992007000100007-1 T4 ENFERMEDAD 1859 1875 \n",
"4 es-S0212-71992007000100007-1 T5 ENFERMEDAD 1626 1648 \n",
"\n",
" span code \n",
"0 arterial hypertension 38341003 \n",
"1 polyarthrosis 36186002 \n",
"2 pleural effusion 60046008 \n",
"3 pleural effusion 60046008 \n",
"4 lower lobe atelectasis 46621007 "
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},
"metadata": {},
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}
],
"source": [
"entities = pd.read_csv(\"data/entities.tsv\", delimiter=\"\\t\")\n",
"entities.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "9ZrFWsjmKpLW"
},
"outputs": [],
"source": [
"list_off0 = list(entities['offset1'])\n",
"list_off1 = list(entities['offset2'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "Mafcxoi4KpLW"
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{
"output_type": "stream",
"name": "stdout",
"text": [
"A 73-year-old patient with a history of arterial hypertension and polyarthrosis presented to the emergency department with abdominal distension and pain associated with constipation and febrile fever. The symptoms had started three weeks earlier and worsened during the four days prior to admission. During this period, an upper gastrointestinal fibroendoscopy (oesophagus, stomach and duodenum) and a colonoscopy (up to the splenic angle) were performed, but no abnormalities were found.\n",
"\n",
"Physical examination revealed a low-grade fever (37.6º C), a distended abdomen, diffusely painful on palpation, tympanised on percussion, with scant borborygmi but no evidence of peritonism, pulmonary auscultation with decreased ventilation in the lower half of the right hemithorax and the onset of intense pain on palpation and percussion of the last three dorsal spinous processes.\n",
"\n",
"Analyses showed 8.2 x 109 leukocytes / L, haemoglobin 136 g / L, platelets 186 x 109 / L. Except for glycaemia (123 mg/dl), the following laboratory parameters were normal or negative: urea, creatinine, bilirubin, transaminases, gamma-glutamyltranspeptidase, sodium, potassium, chlorine, calcium, phosphorus, creatine phosphokinase, amylase, lactate dehydrogenase (LDH), proteinogram, immunoglobulin dosage, alpha-fetoprotein, CA 19 antigens. 9 and CA 125 antigens, as well as general urinalysis. ESR and C-reactive protein were elevated, with values of 85 mm / 1 h and 133 mg / L (normal < 5 mg / L), respectively. Mantoux intradermal reaction (10 IU RT-23) was positive, with an induration of 25 mm. Chest X-ray showed an image compatible with right lower lobe atelectasis in the context of an ipsilateral pleural effusion. There were no signs suggestive of adenopathy or alterations in the cardiopericardial silhouette. A thoracoabdominal CT scan confirmed the existence of a right pleural effusion and identified prominent degenerative changes along the dorsolumbar spine but, above all, erosions in the vertebral plates adjacent to the D10-D11 disc space. A lumbar MRI showed hyposignal on T1-weighted sequences and hypersignal on T2-weighted sequences in these vertebrae and their corresponding disc, with morphological alterations typical of infectious spondylodiscitis D10-D11. Three serial blood cultures were negative. Samples obtained by aspiration of the D10-D11 space showed gram-positive cocci chains, which were subsequently recovered and typed as penicillin-sensitive Streptococcus pneumoniae. Pleural fluid analysis showed pH: 7.55; leucocytes: 8.4 x 109/L (58% neutrophils, 26% eosinophils, 16% lymphocytes), protein: 48 g/L (ratio to serum protein: 0.65), glucose: 125 mg/dl, ADA: 25.92 IU/ml, LDH: 362 U/L (pleural LDH/serum LDH ratio: 0.8). Both auramine-rhodamine staining and Löwenstein-Jensen medium culture of pleural fluid were negative and cytology showed no evidence of neoplastic cells.\n",
"\n",
"\n",
"\n",
"The patient was initially treated intravenously with amoxicillin + clavulanic acid (1 g / 200 mg, every 8 hours). After 21 days, she was switched to the oral route (875 / 125 mg, every 8 hours) for 6 weeks. The evolution was favourable and she was able to start walking with a dorsolumbar corset after the fourth week. One month after the end of antibiotic therapy, a control chest CT scan still showed a discrete pleural effusion, but the patient had only mild mechanical dorsalgia, her ESR had decreased to 21 mm / 1 h and her CRP was 2.4 mg/L. Outpatient follow-up continued for a further three years, during which time the evolution was favourable and a D10-D11 vertebral block was formed.\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"f = open(text_files_path + \"/\" + entities.iloc[1,0] + \".txt\", \"r\", encoding=\"UTF-8\")\n",
"for l in f:\n",
" print(l)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UeML6HJmKpLX",
"outputId": "de8c52d4-49f7-441b-89af-cce4183b18e9"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 6650/6650 [01:51<00:00, 59.70it/s]\n"
]
}
],
"source": [
"#Clinical cases\n",
"HCs = {}\n",
"for fid in tqdm.tqdm(range(len(entities[\"filename\"]))):\n",
" fname = entities[\"filename\"][fid]\n",
" with open(text_files_path + \"/\" + fname + \".txt\", \"r\", encoding=\"UTF-8\") as f:\n",
" HCs.update({fname: f.read()})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "nivRpUrBKpLY"
},
"outputs": [],
"source": [
"#Diseases\n",
"ENF = {}\n",
"enfermedades = []\n",
"fn = entities[\"filename\"][0]\n",
"for fname, enf in zip(entities[\"filename\"], entities[\"span\"]):\n",
" if fname!=fn:\n",
" enfermedades = []\n",
" enfermedades.append(enf)\n",
" ENF.update({fname: enfermedades})\n",
" fn = fname"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nYI19JFtKpLY",
"outputId": "68cd794c-7b51-488f-ccf8-af9e574794cb"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"741"
]
},
"metadata": {},
"execution_count": 11
}
],
"source": [
"len(ENF)"
]
},
{
"cell_type": "markdown",
"source": [
"# Preprocessing"
],
"metadata": {
"id": "qviw-SCgUDwK"
}
},
{
"cell_type": "code",
"source": [
"!python -m spacy download en_core_web_sm"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xtkwMJhYNP17",
"outputId": "4c9fdacb-c17f-4bef-909d-fe6b1117dc06"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting en-core-web-sm==3.4.1\n",
" Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.1/en_core_web_sm-3.4.1-py3-none-any.whl (12.8 MB)\n",
"\u001b[K |████████████████████████████████| 12.8 MB 2.6 MB/s \n",
"\u001b[?25hRequirement already satisfied: spacy<3.5.0,>=3.4.0 in /usr/local/lib/python3.7/dist-packages (from en-core-web-sm==3.4.1) (3.4.1)\n",
"Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.0.8)\n",
"Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (4.64.1)\n",
"Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.0.7)\n",
"Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.4.4)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (57.4.0)\n",
"Requirement already satisfied: pathy>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (0.6.2)\n",
"Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.23.0)\n",
"Requirement already satisfied: thinc<8.2.0,>=8.1.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (8.1.4)\n",
"Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.9 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (3.0.10)\n",
"Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (0.10.1)\n",
"Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (3.0.8)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.11.3)\n",
"Requirement already satisfied: typing-extensions<4.2.0,>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (4.1.1)\n",
"Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (3.3.0)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (21.3)\n",
"Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (1.0.9)\n",
"Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.10.0,>=1.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (1.9.2)\n",
"Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (1.0.3)\n",
"Requirement already satisfied: typer<0.5.0,>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (0.4.2)\n",
"Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (1.21.6)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from catalogue<2.1.0,>=2.0.6->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (3.9.0)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (3.0.9)\n",
"Requirement already satisfied: smart-open<6.0.0,>=5.2.1 in /usr/local/lib/python3.7/dist-packages (from pathy>=0.3.5->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (5.2.1)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.10)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2022.9.24)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (1.25.11)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (3.0.4)\n",
"Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.7/dist-packages (from thinc<8.2.0,>=8.1.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (0.0.3)\n",
"Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.7/dist-packages (from thinc<8.2.0,>=8.1.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (0.7.8)\n",
"Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer<0.5.0,>=0.3.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (7.1.2)\n",
"Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.0.1)\n",
"Installing collected packages: en-core-web-sm\n",
" Attempting uninstall: en-core-web-sm\n",
" Found existing installation: en-core-web-sm 3.4.0\n",
" Uninstalling en-core-web-sm-3.4.0:\n",
" Successfully uninstalled en-core-web-sm-3.4.0\n",
"Successfully installed en-core-web-sm-3.4.1\n",
"\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
"You can now load the package via spacy.load('en_core_web_sm')\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "6at1DC6zKpLZ"
},
"outputs": [],
"source": [
"nlp = spacy.load(\"en_core_web_sm\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "r-OrghwTKpLZ"
},
"outputs": [],
"source": [
"HCs_tokenized = []\n",
"for hc in HCs:\n",
" hl = []\n",
" tokens = nlp(HCs[hc])\n",
" #tokens = HCs[hc].split(\" \") #The simplest option\n",
" for t in tokens:\n",
" hl.append(str(t))\n",
" HCs_tokenized.append(hl)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "c4Nh9SJ9KpLa",
"outputId": "4cf4dd3e-e57b-4f52-98d3-ba2fc55632ab"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"741"
]
},
"metadata": {},
"execution_count": 15
}
],
"source": [
"len(HCs_tokenized)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "mJkuhsnTKpLa"
},
"outputs": [],
"source": [
"Ent_tokenized = []\n",
"for enf in ENF:\n",
" Tks = []\n",
" for e in ENF[enf]:\n",
" sl = []\n",
" tokens = nlp(e)\n",
" #tokens = e.split(\" \")\n",
" for t in tokens:\n",
" sl.append(str(t))\n",
" Tks.append(sl)\n",
" Ent_tokenized.append(Tks)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "195X74buKpLa",
"outputId": "ad159ed3-e174-4048-9d30-9a92e94dd653"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"741"
]
},
"metadata": {},
"execution_count": 17
}
],
"source": [
"len(Ent_tokenized)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mtLgqICDKpLb",
"outputId": "d6befaf5-1174-4e9e-8cb1-768df6a768ac"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[['arterial', 'hypertension'],\n",
" ['polyarthrosis'],\n",
" ['pleural', 'effusion'],\n",
" ['pleural', 'effusion'],\n",
" ['lower', 'lobe', 'atelectasis'],\n",
" ['infectious', 'spondylodiscitis', 'D10', '-', 'D11'],\n",
" ['pleural', 'effusion']]"
]
},
"metadata": {},
"execution_count": 18
}
],
"source": [
"Ent_tokenized[0]"
]
},
{
"cell_type": "markdown",
"source": [
"# Tagging Data with BIO scheme"
],
"metadata": {
"id": "ZapndudTUQvP"
}
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"id": "O94wXbu4KpLb"
},
"outputs": [],
"source": [
"def find_idx(list_to_check, item_to_find):\n",
" indices = []\n",
" for idx, value in enumerate(list_to_check):\n",
" if value == item_to_find:\n",
" indices.append(idx)\n",
" return indices"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xy-dmZodKpLb",
"outputId": "d34b67ea-5d01-4120-9cdb-4801dc7a8108"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['A', '43', '-', 'year', '-', 'old', 'man', 'was', 'admitted', 'to', 'the', 'emergency', 'department', 'due', 'to', 'sudden', 'left', 'lumbar', 'pain', ',', 'continuous', 'and', 'incapacitating', ',', 'without', 'antalgic', 'position', 'or', 'aggravating', 'factors', ',', 'without', 'irradiation', ',', 'with', 'approximately', '23', 'hours', 'of', 'evolution', '.', 'No', 'nausea', 'or', 'vomiting', ',', 'no', 'macroscopic', 'haematuria', 'or', 'lower', 'urinary', 'tract', 'discomfort', '.', 'Absence', 'of', 'precordial', 'pain', '.', 'Hypertension', 'controlled', 'with', 'verapamil', '.', 'He', 'reported', 'an', 'episode', ',', 'interpreted', 'as', 'a', 'transient', 'ischaemic', 'attack', ',', 'approximately', 'eight', 'weeks', 'earlier', '(', 'not', 'confirmed', ')', '.', 'No', 'history', 'of', 'cardiac', 'arrhythmia', 'or', 'valvular', 'heart', 'disease', '.', 'No', 'other', 'previous', 'thromboembolic', 'episodes', '.', 'No', 'known', 'history', 'of', 'urinary', 'lithiasis', '.', 'No', 'osteoarticular', 'or', 'respiratory', 'complaints', '.', 'No', 'cocaine', 'abuse', '.', 'No', 'history', 'of', 'hepatitis', 'B', 'or', 'C.', 'Medicated', 'with', '160', 'mg', '/', 'day', 'of', 'verapamil', '.', '\\n', 'Physical', 'examination', 'showed', 'diaphoretic', ',', 'BP', '150', '/', '110', 'mmHg', ',', '80', 'beats', 'per', 'minute', ',', 'rhythmic', 'and', 'wide', '.', 'Temperature', '37.8', 'ºC.', 'Abdomen', 'painful', 'on', 'deep', 'palpation', 'in', 'the', 'left', 'iliac', 'fossa', 'and', 'flank', ',', 'with', 'defence', ',', 'with', 'no', 'signs', 'of', 'peritoneal', 'irritation', '.', 'Decreased', 'RHA', '.', 'No', 'abdominal', 'murmurs', '.', 'Negative', 'bilateral', 'renal', 'Murphy', '.', 'Existence', 'of', 'symmetrical', 'arterial', 'pulses', '.', 'No', 'perfusion', 'deficit', 'in', 'the', 'extremities', '.', 'General', 'neurological', 'examination', 'without', 'alterations', '.', '\\n', 'Renal', 'ultrasound', 'showed', 'no', 'abnormalities', ',', 'especially', 'dilatation', 'of', 'the', 'urinary', 'tract', '.', 'Laboratory', 'tests', ':', 'Hb15.6', 'g', '/', 'dL', ',', 'Leuc', '13,800/µL', ',', 'Neut', '76.1', '%', ',', 'Creat', '1.4', 'mg', '/', 'dL', ',', 'TGO', '104', 'UI', '/', 'L', ',', 'TGP', '74', 'UI', '/', 'L', ',', 'LDH', '1,890', 'UI', '/', 'L.', 'Coagulation', 'parameters', 'showed', 'no', 'alterations', '.', 'The', 'ECG', 'showed', 'sinus', 'rhythm', ',', 'with', 'no', 'alterations', 'compatible', 'with', 'acute', 'myocardial', 'ischaemia', '.', 'An', 'abdominal', 'and', 'pelvic', 'CT', 'scan', 'was', 'requested', ',', 'which', 'showed', 'the', 'presence', 'of', 'multiple', 'areas', 'without', 'contrast', 'uptake', 'in', 'the', 'left', 'kidney', ',', 'without', 'morpho', '-', 'structural', 'alterations', ',', 'compatible', 'with', 'multifocal', 'areas', 'of', 'ischaemia', ',', 'with', 'multisegmental', 'distribution', ',', 'probably', 'of', 'embolic', 'aetiology', '.', 'No', 'aortic', 'dilatation', 'or', 'renal', 'artery', 'aneurysm', '.', 'No', 'intra', '-', 'peritoneal', 'alterations', '.', 'Taking', 'into', 'account', 'the', 'multi', '-', 'segmental', 'distribution', 'of', 'the', 'ischaemic', 'process', 'and', 'the', 'duration', 'of', 'discomfort', ',', 'we', 'decided', 'that', 'there', 'was', 'no', 'indication', 'for', 'invasive', 'manoeuvres', '.', 'The', 'patient', 'underwent', 'systemic', 'hypo', '-', 'coagulation', 'with', 'heparin', 'in', 'an', 'attempt', 'to', 'avoid', 'future', 'embolic', 'episodes', 'and', 'appropriate', 'analgesia', '.', '\\n\\n', 'He', 'then', 'underwent', 'multiple', 'examinations', 'in', 'an', 'attempt', 'to', 'identify', 'an', 'embolic', 'focus', 'and', 'the', 'aetiological', 'process', '.', 'Echocardiography', 'ruled', 'out', 'pathology', 'of', 'the', 'cardiac', 'valvular', 'apparatus', 'or', 'the', 'existence', 'of', 'valvular', 'vegetations', '.', 'Absence', 'of', 'areas', 'of', 'myocardial', 'dyskinesia', '.', 'Arteriography', 'showed', 'a', 'perfusion', 'deficit', 'of', 'the', 'lower', 'pole', 'of', 'the', 'left', 'kidney', 'with', 'multiple', 'other', 'less', 'prominent', 'areas', 'showing', 'perfusion', 'deficits', 'as', 'well', '.', 'No', 'other', 'alterations', 'such', 'as', 'macro', '/', 'microaneurysms', 'or', 'alterations', 'of', 'the', 'main', 'renal', 'artery', 'or', 'aorta', 'were', 'detected', '.', '\\n', 'The', 'study', 'to', 'rule', 'out', 'prothrombotic', 'and', 'vascular', 'disease', '(', 'lupus', 'anticoagulant', ',', 'anti', '-', 'cardiolipin', ',', 'ANCA', \"'s\", ',', 'detection', 'of', 'cryoglobulins', ',', 'ANA', \"'s\", ',', 'determination', 'of', 'immunoglobulins', 'and', 'complement', 'fractions', ')', 'was', 'negative', '.', '\\n', 'After', '15', 'months', 'of', 'follow', '-', 'up', ',', 'we', 'were', 'left', 'without', 'an', 'aetiological', 'diagnosis', '.', 'The', 'patient', 'remains', 'asymptomatic', ',', 'with', 'no', 'new', 'episodes', 'of', 'embolism', 'or', 'manifestations', 'of', 'systemic', 'disease', '.', 'Anti', '-', 'coagulation', 'therapy', 'has', 'been', 'discontinued', '.', 'He', 'maintains', 'controlled', 'hypertension', 'with', 'the', 'same', 'dose', 'of', 'verapamil', '.', 'The', 'last', 'analytical', 'control', 'had', 'a', 'serum', 'creatinine', 'of', '1.2', 'mg', '/', 'dL', ',', 'and', 'GFR', 'of', '93', 'ml', '/', 'min', '.', 'The', 'follow', '-', 'up', 'kinillogram', 'shows', 'a', 'functional', 'deficit', 'of', 'the', 'affected', 'renal', 'unit', '(', 'differential', 'function', '41', '%', ')', '.', '\\n\\n']\n",
"[['transient', 'ischaemic', 'attack'], ['cardiac', 'arrhythmia'], ['valvular', 'heart', 'disease'], ['thromboembolic', 'episodes'], ['urinary', 'lithiasis'], ['cocaine', 'abuse'], ['hepatitis', 'B', 'or', 'C'], ['Hypertension'], ['acute', 'myocardial', 'ischaemia'], ['aortic', 'dilatation'], ['renal', 'artery', 'aneurysm'], ['ischaemia'], ['ischaemia'], ['valvular', 'vegetations'], ['myocardial', 'dyskinesia'], ['macro', '/', 'microaneurysms', 'or', 'alterations', 'of', 'the', 'main', 'renal', 'artery', 'or', 'aorta'], ['prothrombotic', 'and', 'vascular', 'disease'], ['hypertension'], ['embolism'], ['systemic', 'disease']]\n",
"['hepatitis', 'B', 'or', 'C']\n",
"C\n",
"27\n",
"['A', 'four', '-', 'month', '-', 'old', 'boy', 'was', 'admitted', 'with', 'vomiting', ',', 'diarrhoea', 'and', 'severe', 'weight', 'and', 'body', 'weight', 'delay', '.', 'He', 'presented', 'hypotonia', ',', 'bilateral', 'convergent', 'strabismus', 'and', 'inverted', 'nipples', '.', 'Healthy', ',', 'non', '-', 'consanguineous', 'parents', ',', 'normal', 'pregnancy', 'and', 'delivery', '.', 'Since', 'the', 'age', 'of', 'one', 'month', 'she', 'has', 'had', 'poor', 'intake', ',', 'growth', 'failure', 'and', 'vomiting', ',', 'with', 'normal', 'abdominal', 'and', 'transfontanelar', 'ultrasound', ',', 'chest', 'X', '-', 'ray', ',', 'blood', 'tests', 'and', 'urine', 'culture', '.', '\\n\\n', 'On', 'admission', ',', 'rotavirus', 'was', 'detected', 'in', 'stool', '.', 'After', 'resolution', 'of', 'the', 'acute', 'process', ',', 'nutritional', 'support', 'was', 'started', 'with', 'artificial', 'protein', 'hydrolysate', 'formula', ';', 'later', ',', 'the', 'patient', 'was', 'switched', 'to', 'elemental', 'formula', 'as', 'he', 'continued', 'to', 'show', 'poor', 'weight', 'gain', '.', 'The', 'study', 'performed', 'detected', 'hypertransaminemia', ',', 'hypoalbuminemia', ',', 'decreased', 'transferrin', 'and', 'ceruloplasmin', ',', 'with', 'normal', 'hepatotropic', 'virus', 'serology', 'and', 'echocardiography', '.', 'Given', 'the', 'suspicion', 'of', 'a', 'possible', 'inborn', 'error', 'of', 'metabolism', '(', 'IEM', ')', ',', 'a', 'metabolic', 'study', 'was', 'requested', ',', 'while', 'the', 'patient', 'required', 'admission', 'to', 'the', 'Intensive', 'Care', 'Unit', 'due', 'to', 'the', 'onset', 'of', 'symptoms', 'compatible', 'with', 'sepsis', '(', 'fever', ',', 'hypoglycaemia', ',', 'poor', 'general', 'condition', 'and', 'poor', 'colouring', ')', '.', 'A', 'cranial', 'MRI', 'scan', 'was', 'performed', ',', 'which', 'showed', 'cerebellar', 'hypoplasia', '.', 'The', 'result', 'of', 'the', 'metabolic', 'study', 'confirms', 'a', 'congenital', 'protein', 'glycosylation', 'defect', '(', 'CGD', ')', 'type', 'Ia.', '\\n\\n\\n']\n",
"[['bilateral', 'convergent', 'strabismus'], ['inverted', 'nipples'], ['inborn', 'error', 'of', 'metabolism'], ['IEM'], ['sepsis'], ['cerebellar', 'hypoplasia'], ['congenital', 'protein', 'glycosylation', 'defect', '(', 'CGD', ')', 'type', 'Ia'], ['CGD'], ['hypoglycaemia']]\n",
"['congenital', 'protein', 'glycosylation', 'defect', '(', 'CGD', ')', 'type', 'Ia']\n",
"Ia\n",
"205\n",
"['Patient', 'aged', '53', 'years', 'at', 'the', 'time', 'of', 'diagnosis', 'with', 'a', 'personal', 'history', 'of', 'a', 'caesarean', 'section', ',', 'arterial', 'hypertension', 'and', 'tachycardia', 'treated', 'with', 'Atenolol', ',', 'who', 'came', 'to', 'the', 'Emergency', 'Department', 'with', 'a', '5', '-', 'month', 'history', 'of', 'progressive', 'dull', 'pain', 'in', 'the', 'left', 'flank', 'and', 'microhaematuria', '.', 'Ultrasound', 'and', 'then', 'abdominal', '-', 'pelvic', 'computerised', 'axial', 'tomography', '(', 'CAT', ')', 'scans', 'revealed', 'a', 'large', 'solid', 'mass', 'measuring', '20x16x13', 'cm', 'arising', 'from', 'the', 'left', 'renal', 'pole', ',', 'without', 'associated', 'venous', 'thrombosis', '.', 'Para', '-', 'aortic', 'adenopathies', 'were', 'also', 'observed', '.', 'All', 'this', 'was', 'compatible', 'with', 'a', 'left', 'renal', 'tumour', '.', '\\n\\n', 'In', 'view', 'of', 'this', 'finding', ',', 'an', 'extension', 'study', 'was', 'carried', 'out', 'with', 'a', 'general', 'analysis', 'showing', 'mild', 'iron', '-', 'deficiency', 'microcytic', 'anaemia', 'and', 'hyperuricaemia', ',', 'a', 'normal', 'chest', 'X', '-', 'ray', ',', 'a', 'bone', 'scan', 'showing', 'an', 'image', 'with', 'a', 'slight', 'increase', 'in', 'tracer', 'uptake', 'corresponding', 'to', 'soft', 'tissue', ',', 'with', 'a', 'rounded', 'morphology', ',', 'located', 'in', 'the', 'hypochondrium', 'and', 'in', 'the', 'hypochondrium', ',', 'located', 'in', 'the', 'left', 'hypochondrium', 'and', 'void', ',', 'exceeding', 'the', 'midline', ',', 'and', '3', 'foci', 'of', 'tracer', 'uptake', ',', 'one', 'in', 'the', 'left', 'iliac', 'blade', ',', 'the', 'second', 'in', 'the', 'left', 'hemivertebrae', 'L4', 'and', 'L5', 'and', 'the', 'third', 'at', 'the', 'level', 'of', 'the', 'right', 'hemivertebra', 'L2', ',', 'which', 'could', 'correspond', 'to', 'bone', 'involvement', 'due', 'to', 'contiguity', 'or', 'be', 'metastatic', ',', 'and', 'an', 'abdominal', 'MRI', 'scan', 'which', 'confirms', 'the', 'findings', 'of', 'the', 'CT', 'scan', '.', '\\n', 'With', 'the', 'presumptive', 'diagnosis', 'of', 'renal', 'cell', 'carcinoma', 'with', 'retroperitoneal', 'adenopathy', ',', 'the', 'patient', 'underwent', 'radical', 'left', 'nephrectomy', ',', 'removal', 'of', 'all', 'the', 'pararenal', 'fat', 'and', 'the', 'left', 'adrenal', 'gland', 'and', 'para', '-', 'aortic', 'and', 'interaortocaval', 'lymphadenectomy', 'from', 'the', 'renal', 'artery', 'to', '3', 'cm', 'from', 'the', 'common', 'iliac', 'artery', '.', 'Removal', 'of', 'the', 'mass', 'was', 'difficult', 'due', 'to', 'infiltration', 'of', 'the', 'transverse', 'mesocolon', 'and', 'tail', 'of', 'the', 'pancreas', ',', 'which', 'were', 'released', ',', 'leaving', 'the', 'mesocolon', 'untouched', '.', 'The', 'anatomo', '-', 'pathological', '(', 'A.P.', ')', 'result', 'was', 'as', 'follows', ':', 'Collision', 'renal', 'tumour', '(', 'Leiomyosarcoma', '(', '21', 'x', '15', 'cm', ')', 'and', 'renal', 'carcinoma', 'papillary', 'type', 'nuclear', 'grade', '3', '(', '7', 'x', '3.5', 'cm', ')', ')', '.', 'The', 'weight', 'of', 'the', 'whole', 'specimen', 'was', '2539', 'grams', '.', 'The', 'tumour', 'was', 'in', 'contact', 'with', 'the', 'surgical', 'edge', 'in', 'most', 'areas', '.', 'The', 'renal', 'parenchyma', 'was', 'microscopically', 'respected', 'and', 'no', 'tumour', 'infiltration', 'was', 'observed', '.', 'The', 'ureteral', 'fragment', 'and', 'renal', 'hilum', 'were', 'free', 'of', 'tumour', 'infiltration', '.', 'The', 'immunophenotypic', 'profile', 'of', 'the', 'tumour', 'was', 'as', 'follows', ':', 'Actin', ',', 'Desmin', ',', 'S-100', ',', 'Synaptofusin', 'and', 'CD', '56', 'and', 'c', '-', 'kit', 'negative', ';', 'Smooth', 'muscle', 'actin', 'positive', 'in', 'the', 'sarcomatous', 'zone', 'and', 'keratin', 'cocktail', '(', 'E1', ',', 'E3', ')', 'positive', 'in', 'the', 'carcinomatous', 'zone', '.', 'At', 'the', 'level', 'of', 'the', 'para', '-', 'aortic', 'chain', ',', '16', 'adenopathies', 'were', 'isolated', ',', 'the', 'largest', 'measuring', '2.5', 'cm', ',', 'with', 'metastases', 'in', '14', 'of', 'them', ',', '13', 'from', 'the', 'carcinoma', 'and', '1', 'with', 'mixed', 'metastases', '(', 'sarcoma+carcinoma', ')', '.', 'Six', 'adenopathies', 'were', 'isolated', 'in', 'the', 'interaortocaval', 'chain', ',', 'the', 'largest', 'measuring', '1.4', 'cm', ',', 'three', 'of', 'which', 'were', 'carcinoma', 'metastases', '.', 'The', 'mesocolic', 'bed', 'was', 'infiltrated', 'by', 'leiomyosarcoma', '.', 'In', 'the', 'perisuprarenal', 'adipose', 'tissue', '4', 'adenopathies', 'were', 'isolated', ',', '3', 'of', 'them', 'with', 'metastasis', 'of', 'the', 'carcinoma', 'and', 'another', 'with', 'mixed', 'metastasis', '(', 'carcinoma+sarcoma', ')', '.', 'The', 'left', 'adrenal', 'gland', ',', 'the', 'perirenal', 'fat', 'and', 'the', 'gall', 'bladder', 'showed', 'no', 'tumour', 'elements', '.', '\\n\\n', 'We', 'were', 'therefore', 'faced', 'with', 'a', 'renal', 'collision', 'tumour', 'consisting', 'of', 'a', 'stage', 'IV', 'papillary', 'type', 'renal', 'cancer', '(', 'pT3', '-', '4pN2', ')', 'according', 'to', 'the', 'TNM', 'classification', 'and', 'a', 'stage', 'IV', 'renal', 'leiomyosarcoma', '(', 'pT2bpN1', ')', 'according', 'to', 'the', 'AJCC', 'classification', ',', 'not', 'radically', 'resected', 'and', 'with', 'possible', 'bone', 'metastases', 'according', 'to', 'bone', 'scintigraphy', '.', '\\n', 'The', 'postoperative', 'period', 'was', 'uneventful', 'and', 'the', 'patient', 'was', 'referred', 'to', 'the', 'Medical', 'Oncology', 'Department', '.', 'It', 'was', 'decided', 'to', 'propose', 'complementary', 'chemotherapy', 'treatment', 'with', 'Ifosfamide', '5', 'g', '/', 'm2', 'in', 'a', 'continuous', 'infusion', 'of', '24h', 'x', '1', 'day', '+', 'Adriamycin', '60', 'mg', '/', 'm2', 'x', '1', 'day/21', 'days', 'against', 'the', 'sarcomatous', 'component', 'of', 'the', 'tumour', '.', 'A', 'CT', 'scan', 'was', 'previously', 'requested', 'in', 'which', 'a', 'small', 'soft', 'tissue', 'enlargement', 'was', 'observed', 'behind', 'the', 'pancreatic', 'tail', 'and', 'renal', 'bed', ',', 'which', 'could', 'be', 'compatible', 'with', 'present', 'disease', '.', '\\n', 'The', 'patient', 'started', 'treatment', 'according', 'to', 'the', 'planned', 'schedule', '3', 'weeks', 'after', 'surgery', '.', 'She', 'received', 'a', 'total', 'of', '6', 'cycles', 'with', 'good', 'clinical', 'tolerance', '.', 'After', 'the', '4th', 'cycle', ',', 'an', 'abdominal', 'CAT', 'scan', 'was', 'performed', ',', 'which', 'was', 'normal', ',', 'and', 'at', 'the', 'end', 'of', 'the', '6th', 'cycle', ',', 'a', 'bone', 'scan', 'was', 'performed', ',', 'which', 'showed', 'no', 'pathological', 'findings', '.', 'The', 'patient', 'underwent', 'regular', 'check', '-', 'ups', 'and', '3', 'months', 'later', 'a', 'chest', 'X', '-', 'ray', 'was', 'performed', 'showing', 'images', 'suggestive', 'of', 'bilateral', 'pulmonary', 'metastases', ',', 'which', 'were', 'confirmed', 'by', 'a', 'CT', 'scan', 'showing', 'multiple', 'bilateral', 'millimetric', 'pulmonary', 'metastases', '.', 'In', 'order', 'to', 'identify', 'the', 'origin', 'of', 'these', 'metastases', ',', 'Thoracic', 'Surgery', 'was', 'consulted', 'and', 'it', 'was', 'decided', 'to', 'perform', 'a', 'left', 'videothoracoscopy', 'with', 'biopsies', '.', 'The', 'P.A.', 'diagnosis', 'was', 'metastasis', 'of', 'poorly', 'differentiated', 'carcinoma', 'with', 'an', 'epithelial', 'component', '(', 'renal', ')', '.', 'In', 'view', 'of', 'this', 'diagnosis', ',', '6', 'months', 'after', 'finishing', 'the', 'first', 'chemotherapy', 'regimen', ',', 'it', 'was', 'decided', 'to', 'start', 'a', 'second', 'line', 'of', 'treatment', 'with', 'a', 'chemotherapy', 'regimen', 'with', 'Gemzitabine', 'and', 'Fluoropyrimidines9', 'that', 'had', 'proved', 'useful', 'in', 'stage', 'IV', 'renal', 'carcinoma', ':', 'Gemcitabine', '1000', 'mg', '/', 'm2', 'days', '1', 'and', '8', '+', 'Capecitabine', '1000', 'mg', '/', 'm2/12h', 'days', '1', '-', '14/21', 'days', ',', 'which', 'the', 'patient', 'accepted', '.', '\\n\\n', 'Treatment', 'was', 'started', 'with', 'a', '20', '%', 'dose', 'reduction', 'which', 'was', 'maintained', 'for', 'the', 'rest', 'of', 'the', 'treatment', 'given', 'the', 'patient', \"'s\", 'general', 'condition', '(', 'ECOG', ':', '1', '-', '2', ')', '.', 'After', 'the', 'second', 'cycle', ',', 'the', 'patient', 'suffered', 'a', 'complication', 'of', 'pulmonary', 'thromboembolism', 'from', 'which', 'she', 'recovered', 'but', 'which', 'caused', 'a', 'delay', 'of', '4', 'weeks', 'in', 'the', 'administration', 'of', 'the', 'third', 'cycle', '.', 'After', '6', 'cycles', 'of', 'treatment', ',', 'which', 'she', 'received', 'with', 'acceptable', 'tolerance', 'except', 'for', 'grade', '4', 'anaemia', ',', 'she', 'was', 're', '-', 'evaluated', 'with', 'a', 'body', 'CT', 'scan', 'which', 'showed', 'persistent', 'pulmonary', 'metastases', 'with', 'the', 'appearance', 'of', 'liver', 'and', 'spleen', 'metastases', 'and', 'local', 'relapse', '.', '\\n', 'In', 'view', 'of', 'this', 'progression', ',', 'treatment', 'with', 'IL-2', 'was', 'proposed', 'for', '6', 'weeks', '(', '1', 'week', 'of', 'induction', 'with', '18', 'Million', 'Units', '(', 'MU', ')', 'x', '5', 'days', 'and', '5', 'weeks', ':', '9', 'MU', 'days', '1', 'and', '2', 'and', '18', 'MU', 'days', '3', 'to', '5)10', '.', 'The', 'patient', 'accepted', 'the', 'treatment', 'with', 'moderate', 'toxicity', 'with', 'secondary', 'constitutional', 'symptoms', 'grade', '2', ',', 'anaemia', 'grade', '3', 'and', 'emesis', 'grade', '1', ',', 'maintaining', 'her', 'general', 'condition', '.', '\\n', 'At', 'the', 'end', 'of', 'treatment', ',', 'a', 'new', 're', '-', 'evaluation', 'was', 'performed', 'with', 'a', 'CT', 'scan', 'showing', 'progression', 'of', 'the', 'disease', 'with', 'a', 'large', 'mass', 'in', 'the', 'surgical', 'site', 'measuring', '19x10x5', 'cm', ',', 'which', 'had', 'grown', 'with', 'respect', 'to', 'the', 'previous', 'CT', 'scan', ',', 'and', 'persistent', 'metastases', 'in', 'the', 'rest', 'of', 'the', 'previous', 'sites', '.', 'The', 'patient', \"'s\", 'general', 'condition', 'worsened', ',', 'with', 'the', 'appearance', 'of', 'abdominal', 'and', 'lumbar', 'pain', ',', 'and', 'on', 'physical', 'examination', 'a', '5', 'cm', 'epigastric', 'mass', 'was', 'palpated', ',', 'corresponding', 'to', 'the', 'underlying', 'mass', '.', '\\n', 'Given', 'this', 'new', 'progression', ',', 'it', 'is', 'considered', 'that', 'the', 'tumour', 'is', 'resistant', 'to', 'chemotherapy', 'or', 'immunotherapy', 'schemes', 'against', 'renal', 'carcinoma', 'and', 'it', 'is', 'proposed', 'to', 'start', 'palliative', 'treatment', 'with', 'liposomal', 'Adriamycin', 'against', 'the', 'sarcomatous', 'component', 'of', 'the', 'tumour', '.', 'The', 'patient', 'accepted', 'the', 'proposal', 'and', 'received', 'a', 'first', 'cycle', '.', 'However', ',', 'a', 'week', 'later', 'she', 'went', 'to', 'the', 'emergency', 'department', 'for', 'hypovolemic', 'shock', 'with', 'metabolic', 'acidosis', 'and', 'pre', '-', 'renal', 'renal', 'failure', 'secondary', 'to', 'hyperemesis', 'of', '4', 'days', \"'\", 'evolution', 'and', 'grade', '4', 'anaemia', '.', 'The', 'patient', 'recovered', 'from', 'this', 'episode', 'but', 'a', 'week', 'later', 'she', 'began', 'with', 'faecal', 'vomiting', 'of', 'probable', 'obstructive', 'origin', 'due', 'to', 'compression', 'of', 'the', 'retroperitoneal', 'mass', ',', 'causing', 'progressive', 'deterioration', 'of', 'the', 'patient', 'and', 'the', 'patient', 'died', 'of', 'multi', '-', 'organ', 'failure', '19', 'months', 'after', 'diagnosis', '.', '\\n\\n\\n']\n",
"[['tumour'], ['arterial', 'hypertension'], ['venous', 'thrombosis'], ['Para', '-', 'aortic', 'adenopathies'], ['renal', 'tumour'], ['hyperuricaemia'], ['anaemia'], ['bone', 'involvement'], ['renal', 'cell', 'carcinoma', 'with', 'retroperitoneal', 'adenopathy'], ['tumour'], ['tumour'], ['tumour'], ['tumour'], ['tumour'], ['tumour'], ['renal', 'carcinoma'], ['metastases'], ['metastases'], ['metastases'], ['Leiomyosarcoma'], ['leiomyosarcoma'], ['renal', 'tumour'], ['tumour', 'infiltration'], ['tumour', 'infiltration'], ['sarcoma'], ['sarcoma'], ['carcinoma'], ['carcinoma'], ['carcinoma'], ['carcinoma'], ['carcinoma'], ['carcinoma'], ['carcinoma'], ['carcinoma', 'metastases'], ['renal', 'collision', 'tumour'], ['tumour'], ['papillary', 'type', 'renal', 'cancer'], ['renal', 'leiomyosarcoma'], ['metastases'], ['bone', 'metastases'], ['leiomyosarcoma'], ['tumour'], ['sarcomatous', 'component', 'of', 'the', 'tumour'], ['renal', 'carcinoma'], ['metastases'], ['metastases'], ['metastases'], ['bilateral', 'pulmonary', 'metastases'], ['carcinoma'], ['carcinoma'], ['pulmonary', 'metastases'], ['pulmonary', 'metastases'], ['pulmonary', 'thromboembolism'], ['metastases'], ['metastases'], ['anaemia'], ['pulmonary', 'metastases'], ['anaemia'], ['emesis'], ['metastases'], ['tumour'], ['tumour'], ['renal', 'carcinoma'], ['anaemia'], ['hypovolemic', 'shock'], ['metabolic', 'acidosis'], ['hyperemesis'], ['multi', '-', 'organ', 'failure'], ['carcinoma'], ['sarcomatous', 'component', 'of', 'the', 'tumour']]\n",
"['sarcoma']\n",
"sarcoma\n",
"231\n",
"['Patient', 'aged', '53', 'years', 'at', 'the', 'time', 'of', 'diagnosis', 'with', 'a', 'personal', 'history', 'of', 'a', 'caesarean', 'section', ',', 'arterial', 'hypertension', 'and', 'tachycardia', 'treated', 'with', 'Atenolol', ',', 'who', 'came', 'to', 'the', 'Emergency', 'Department', 'with', 'a', '5', '-', 'month', 'history', 'of', 'progressive', 'dull', 'pain', 'in', 'the', 'left', 'flank', 'and', 'microhaematuria', '.', 'Ultrasound', 'and', 'then', 'abdominal', '-', 'pelvic', 'computerised', 'axial', 'tomography', '(', 'CAT', ')', 'scans', 'revealed', 'a', 'large', 'solid', 'mass', 'measuring', '20x16x13', 'cm', 'arising', 'from', 'the', 'left', 'renal', 'pole', ',', 'without', 'associated', 'venous', 'thrombosis', '.', 'Para', '-', 'aortic', 'adenopathies', 'were', 'also', 'observed', '.', 'All', 'this', 'was', 'compatible', 'with', 'a', 'left', 'renal', 'tumour', '.', '\\n\\n', 'In', 'view', 'of', 'this', 'finding', ',', 'an', 'extension', 'study', 'was', 'carried', 'out', 'with', 'a', 'general', 'analysis', 'showing', 'mild', 'iron', '-', 'deficiency', 'microcytic', 'anaemia', 'and', 'hyperuricaemia', ',', 'a', 'normal', 'chest', 'X', '-', 'ray', ',', 'a', 'bone', 'scan', 'showing', 'an', 'image', 'with', 'a', 'slight', 'increase', 'in', 'tracer', 'uptake', 'corresponding', 'to', 'soft', 'tissue', ',', 'with', 'a', 'rounded', 'morphology', ',', 'located', 'in', 'the', 'hypochondrium', 'and', 'in', 'the', 'hypochondrium', ',', 'located', 'in', 'the', 'left', 'hypochondrium', 'and', 'void', ',', 'exceeding', 'the', 'midline', ',', 'and', '3', 'foci', 'of', 'tracer', 'uptake', ',', 'one', 'in', 'the', 'left', 'iliac', 'blade', ',', 'the', 'second', 'in', 'the', 'left', 'hemivertebrae', 'L4', 'and', 'L5', 'and', 'the', 'third', 'at', 'the', 'level', 'of', 'the', 'right', 'hemivertebra', 'L2', ',', 'which', 'could', 'correspond', 'to', 'bone', 'involvement', 'due', 'to', 'contiguity', 'or', 'be', 'metastatic', ',', 'and', 'an', 'abdominal', 'MRI', 'scan', 'which', 'confirms', 'the', 'findings', 'of', 'the', 'CT', 'scan', '.', '\\n', 'With', 'the', 'presumptive', 'diagnosis', 'of', 'renal', 'cell', 'carcinoma', 'with', 'retroperitoneal', 'adenopathy', ',', 'the', 'patient', 'underwent', 'radical', 'left', 'nephrectomy', ',', 'removal', 'of', 'all', 'the', 'pararenal', 'fat', 'and', 'the', 'left', 'adrenal', 'gland', 'and', 'para', '-', 'aortic', 'and', 'interaortocaval', 'lymphadenectomy', 'from', 'the', 'renal', 'artery', 'to', '3', 'cm', 'from', 'the', 'common', 'iliac', 'artery', '.', 'Removal', 'of', 'the', 'mass', 'was', 'difficult', 'due', 'to', 'infiltration', 'of', 'the', 'transverse', 'mesocolon', 'and', 'tail', 'of', 'the', 'pancreas', ',', 'which', 'were', 'released', ',', 'leaving', 'the', 'mesocolon', 'untouched', '.', 'The', 'anatomo', '-', 'pathological', '(', 'A.P.', ')', 'result', 'was', 'as', 'follows', ':', 'Collision', 'renal', 'tumour', '(', 'Leiomyosarcoma', '(', '21', 'x', '15', 'cm', ')', 'and', 'renal', 'carcinoma', 'papillary', 'type', 'nuclear', 'grade', '3', '(', '7', 'x', '3.5', 'cm', ')', ')', '.', 'The', 'weight', 'of', 'the', 'whole', 'specimen', 'was', '2539', 'grams', '.', 'The', 'tumour', 'was', 'in', 'contact', 'with', 'the', 'surgical', 'edge', 'in', 'most', 'areas', '.', 'The', 'renal', 'parenchyma', 'was', 'microscopically', 'respected', 'and', 'no', 'tumour', 'infiltration', 'was', 'observed', '.', 'The', 'ureteral', 'fragment', 'and', 'renal', 'hilum', 'were', 'free', 'of', 'tumour', 'infiltration', '.', 'The', 'immunophenotypic', 'profile', 'of', 'the', 'tumour', 'was', 'as', 'follows', ':', 'Actin', ',', 'Desmin', ',', 'S-100', ',', 'Synaptofusin', 'and', 'CD', '56', 'and', 'c', '-', 'kit', 'negative', ';', 'Smooth', 'muscle', 'actin', 'positive', 'in', 'the', 'sarcomatous', 'zone', 'and', 'keratin', 'cocktail', '(', 'E1', ',', 'E3', ')', 'positive', 'in', 'the', 'carcinomatous', 'zone', '.', 'At', 'the', 'level', 'of', 'the', 'para', '-', 'aortic', 'chain', ',', '16', 'adenopathies', 'were', 'isolated', ',', 'the', 'largest', 'measuring', '2.5', 'cm', ',', 'with', 'metastases', 'in', '14', 'of', 'them', ',', '13', 'from', 'the', 'carcinoma', 'and', '1', 'with', 'mixed', 'metastases', '(', 'sarcoma+carcinoma', ')', '.', 'Six', 'adenopathies', 'were', 'isolated', 'in', 'the', 'interaortocaval', 'chain', ',', 'the', 'largest', 'measuring', '1.4', 'cm', ',', 'three', 'of', 'which', 'were', 'carcinoma', 'metastases', '.', 'The', 'mesocolic', 'bed', 'was', 'infiltrated', 'by', 'leiomyosarcoma', '.', 'In', 'the', 'perisuprarenal', 'adipose', 'tissue', '4', 'adenopathies', 'were', 'isolated', ',', '3', 'of', 'them', 'with', 'metastasis', 'of', 'the', 'carcinoma', 'and', 'another', 'with', 'mixed', 'metastasis', '(', 'carcinoma+sarcoma', ')', '.', 'The', 'left', 'adrenal', 'gland', ',', 'the', 'perirenal', 'fat', 'and', 'the', 'gall', 'bladder', 'showed', 'no', 'tumour', 'elements', '.', '\\n\\n', 'We', 'were', 'therefore', 'faced', 'with', 'a', 'renal', 'collision', 'tumour', 'consisting', 'of', 'a', 'stage', 'IV', 'papillary', 'type', 'renal', 'cancer', '(', 'pT3', '-', '4pN2', ')', 'according', 'to', 'the', 'TNM', 'classification', 'and', 'a', 'stage', 'IV', 'renal', 'leiomyosarcoma', '(', 'pT2bpN1', ')', 'according', 'to', 'the', 'AJCC', 'classification', ',', 'not', 'radically', 'resected', 'and', 'with', 'possible', 'bone', 'metastases', 'according', 'to', 'bone', 'scintigraphy', '.', '\\n', 'The', 'postoperative', 'period', 'was', 'uneventful', 'and', 'the', 'patient', 'was', 'referred', 'to', 'the', 'Medical', 'Oncology', 'Department', '.', 'It', 'was', 'decided', 'to', 'propose', 'complementary', 'chemotherapy', 'treatment', 'with', 'Ifosfamide', '5', 'g', '/', 'm2', 'in', 'a', 'continuous', 'infusion', 'of', '24h', 'x', '1', 'day', '+', 'Adriamycin', '60', 'mg', '/', 'm2', 'x', '1', 'day/21', 'days', 'against', 'the', 'sarcomatous', 'component', 'of', 'the', 'tumour', '.', 'A', 'CT', 'scan', 'was', 'previously', 'requested', 'in', 'which', 'a', 'small', 'soft', 'tissue', 'enlargement', 'was', 'observed', 'behind', 'the', 'pancreatic', 'tail', 'and', 'renal', 'bed', ',', 'which', 'could', 'be', 'compatible', 'with', 'present', 'disease', '.', '\\n', 'The', 'patient', 'started', 'treatment', 'according', 'to', 'the', 'planned', 'schedule', '3', 'weeks', 'after', 'surgery', '.', 'She', 'received', 'a', 'total', 'of', '6', 'cycles', 'with', 'good', 'clinical', 'tolerance', '.', 'After', 'the', '4th', 'cycle', ',', 'an', 'abdominal', 'CAT', 'scan', 'was', 'performed', ',', 'which', 'was', 'normal', ',', 'and', 'at', 'the', 'end', 'of', 'the', '6th', 'cycle', ',', 'a', 'bone', 'scan', 'was', 'performed', ',', 'which', 'showed', 'no', 'pathological', 'findings', '.', 'The', 'patient', 'underwent', 'regular', 'check', '-', 'ups', 'and', '3', 'months', 'later', 'a', 'chest', 'X', '-', 'ray', 'was', 'performed', 'showing', 'images', 'suggestive', 'of', 'bilateral', 'pulmonary', 'metastases', ',', 'which', 'were', 'confirmed', 'by', 'a', 'CT', 'scan', 'showing', 'multiple', 'bilateral', 'millimetric', 'pulmonary', 'metastases', '.', 'In', 'order', 'to', 'identify', 'the', 'origin', 'of', 'these', 'metastases', ',', 'Thoracic', 'Surgery', 'was', 'consulted', 'and', 'it', 'was', 'decided', 'to', 'perform', 'a', 'left', 'videothoracoscopy', 'with', 'biopsies', '.', 'The', 'P.A.', 'diagnosis', 'was', 'metastasis', 'of', 'poorly', 'differentiated', 'carcinoma', 'with', 'an', 'epithelial', 'component', '(', 'renal', ')', '.', 'In', 'view', 'of', 'this', 'diagnosis', ',', '6', 'months', 'after', 'finishing', 'the', 'first', 'chemotherapy', 'regimen', ',', 'it', 'was', 'decided', 'to', 'start', 'a', 'second', 'line', 'of', 'treatment', 'with', 'a', 'chemotherapy', 'regimen', 'with', 'Gemzitabine', 'and', 'Fluoropyrimidines9', 'that', 'had', 'proved', 'useful', 'in', 'stage', 'IV', 'renal', 'carcinoma', ':', 'Gemcitabine', '1000', 'mg', '/', 'm2', 'days', '1', 'and', '8', '+', 'Capecitabine', '1000', 'mg', '/', 'm2/12h', 'days', '1', '-', '14/21', 'days', ',', 'which', 'the', 'patient', 'accepted', '.', '\\n\\n', 'Treatment', 'was', 'started', 'with', 'a', '20', '%', 'dose', 'reduction', 'which', 'was', 'maintained', 'for', 'the', 'rest', 'of', 'the', 'treatment', 'given', 'the', 'patient', \"'s\", 'general', 'condition', '(', 'ECOG', ':', '1', '-', '2', ')', '.', 'After', 'the', 'second', 'cycle', ',', 'the', 'patient', 'suffered', 'a', 'complication', 'of', 'pulmonary', 'thromboembolism', 'from', 'which', 'she', 'recovered', 'but', 'which', 'caused', 'a', 'delay', 'of', '4', 'weeks', 'in', 'the', 'administration', 'of', 'the', 'third', 'cycle', '.', 'After', '6', 'cycles', 'of', 'treatment', ',', 'which', 'she', 'received', 'with', 'acceptable', 'tolerance', 'except', 'for', 'grade', '4', 'anaemia', ',', 'she', 'was', 're', '-', 'evaluated', 'with', 'a', 'body', 'CT', 'scan', 'which', 'showed', 'persistent', 'pulmonary', 'metastases', 'with', 'the', 'appearance', 'of', 'liver', 'and', 'spleen', 'metastases', 'and', 'local', 'relapse', '.', '\\n', 'In', 'view', 'of', 'this', 'progression', ',', 'treatment', 'with', 'IL-2', 'was', 'proposed', 'for', '6', 'weeks', '(', '1', 'week', 'of', 'induction', 'with', '18', 'Million', 'Units', '(', 'MU', ')', 'x', '5', 'days', 'and', '5', 'weeks', ':', '9', 'MU', 'days', '1', 'and', '2', 'and', '18', 'MU', 'days', '3', 'to', '5)10', '.', 'The', 'patient', 'accepted', 'the', 'treatment', 'with', 'moderate', 'toxicity', 'with', 'secondary', 'constitutional', 'symptoms', 'grade', '2', ',', 'anaemia', 'grade', '3', 'and', 'emesis', 'grade', '1', ',', 'maintaining', 'her', 'general', 'condition', '.', '\\n', 'At', 'the', 'end', 'of', 'treatment', ',', 'a', 'new', 're', '-', 'evaluation', 'was', 'performed', 'with', 'a', 'CT', 'scan', 'showing', 'progression', 'of', 'the', 'disease', 'with', 'a', 'large', 'mass', 'in', 'the', 'surgical', 'site', 'measuring', '19x10x5', 'cm', ',', 'which', 'had', 'grown', 'with', 'respect', 'to', 'the', 'previous', 'CT', 'scan', ',', 'and', 'persistent', 'metastases', 'in', 'the', 'rest', 'of', 'the', 'previous', 'sites', '.', 'The', 'patient', \"'s\", 'general', 'condition', 'worsened', ',', 'with', 'the', 'appearance', 'of', 'abdominal', 'and', 'lumbar', 'pain', ',', 'and', 'on', 'physical', 'examination', 'a', '5', 'cm', 'epigastric', 'mass', 'was', 'palpated', ',', 'corresponding', 'to', 'the', 'underlying', 'mass', '.', '\\n', 'Given', 'this', 'new', 'progression', ',', 'it', 'is', 'considered', 'that', 'the', 'tumour', 'is', 'resistant', 'to', 'chemotherapy', 'or', 'immunotherapy', 'schemes', 'against', 'renal', 'carcinoma', 'and', 'it', 'is', 'proposed', 'to', 'start', 'palliative', 'treatment', 'with', 'liposomal', 'Adriamycin', 'against', 'the', 'sarcomatous', 'component', 'of', 'the', 'tumour', '.', 'The', 'patient', 'accepted', 'the', 'proposal', 'and', 'received', 'a', 'first', 'cycle', '.', 'However', ',', 'a', 'week', 'later', 'she', 'went', 'to', 'the', 'emergency', 'department', 'for', 'hypovolemic', 'shock', 'with', 'metabolic', 'acidosis', 'and', 'pre', '-', 'renal', 'renal', 'failure', 'secondary', 'to', 'hyperemesis', 'of', '4', 'days', \"'\", 'evolution', 'and', 'grade', '4', 'anaemia', '.', 'The', 'patient', 'recovered', 'from', 'this', 'episode', 'but', 'a', 'week', 'later', 'she', 'began', 'with', 'faecal', 'vomiting', 'of', 'probable', 'obstructive', 'origin', 'due', 'to', 'compression', 'of', 'the', 'retroperitoneal', 'mass', ',', 'causing', 'progressive', 'deterioration', 'of', 'the', 'patient', 'and', 'the', 'patient', 'died', 'of', 'multi', '-', 'organ', 'failure', '19', 'months', 'after', 'diagnosis', '.', '\\n\\n\\n']\n",
"[['tumour'], ['arterial', 'hypertension'], ['venous', 'thrombosis'], ['Para', '-', 'aortic', 'adenopathies'], ['renal', 'tumour'], ['hyperuricaemia'], ['anaemia'], ['bone', 'involvement'], ['renal', 'cell', 'carcinoma', 'with', 'retroperitoneal', 'adenopathy'], ['tumour'], ['tumour'], ['tumour'], ['tumour'], ['tumour'], ['tumour'], ['renal', 'carcinoma'], ['metastases'], ['metastases'], ['metastases'], ['Leiomyosarcoma'], ['leiomyosarcoma'], ['renal', 'tumour'], ['tumour', 'infiltration'], ['tumour', 'infiltration'], ['sarcoma'], ['sarcoma'], ['carcinoma'], ['carcinoma'], ['carcinoma'], ['carcinoma'], ['carcinoma'], ['carcinoma'], ['carcinoma'], ['carcinoma', 'metastases'], ['renal', 'collision', 'tumour'], ['tumour'], ['papillary', 'type', 'renal', 'cancer'], ['renal', 'leiomyosarcoma'], ['metastases'], ['bone', 'metastases'], ['leiomyosarcoma'], ['tumour'], ['sarcomatous', 'component', 'of', 'the', 'tumour'], ['renal', 'carcinoma'], ['metastases'], ['metastases'], ['metastases'], ['bilateral', 'pulmonary', 'metastases'], ['carcinoma'], ['carcinoma'], ['pulmonary', 'metastases'], ['pulmonary', 'metastases'], ['pulmonary', 'thromboembolism'], ['metastases'], ['metastases'], ['anaemia'], ['pulmonary', 'metastases'], ['anaemia'], ['emesis'], ['metastases'], ['tumour'], ['tumour'], ['renal', 'carcinoma'], ['anaemia'], ['hypovolemic', 'shock'], ['metabolic', 'acidosis'], ['hyperemesis'], ['multi', '-', 'organ', 'failure'], ['carcinoma'], ['sarcomatous', 'component', 'of', 'the', 'tumour']]\n",
"['sarcoma']\n",
"sarcoma\n",
"231\n",
"['Male', 'patient', ',', 'black', ',', '21', 'years', 'of', 'age', ',', 'who', 'consulted', 'the', 'Maxillofacial', 'Surgery', 'Department', 'of', 'the', 'Hospital', 'Universitario', 'San', 'Vicente', 'Fundación', 'de', 'Medellín', 'attached', 'to', 'the', 'Universidad', 'de', 'Antioquia', ',', 'referred', 'by', 'a', 'dentist', 'from', 'a', 'public', 'health', 'institution', 'for', 'presenting', 'an', 'asymptomatic', 'increase', 'in', 'the', 'volume', 'of', 'the', 'mandible', ',', 'with', 'unknown', 'evolution', '.', '\\n', 'Intraorally', ',', 'excellent', 'dental', 'integrity', 'and', 'good', 'dental', 'occlusion', 'were', 'observed', ',', 'but', 'there', 'was', 'an', 'increase', 'in', 'volume', 'in', 'the', 'vestibular', 'region', 'involving', 'the', 'body', 'and', 'the', 'symphysis', ',', 'while', 'lingually', ',', 'the', 'expansion', 'of', 'the', 'table', 'was', 'only', 'in', 'the', 'area', 'of', 'the', 'lower', 'right', 'canine', 'and', 'bicuspids', '.', '\\n', 'The', 'initial', 'panoramic', 'X', '-', 'ray', 'showed', 'a', 'radiolucent', 'image', '10', 'cm', 'long', ',', 'multiloculated', ',', 'located', 'from', 'distal', 'tooth', '46', 'to', 'mesial', 'tooth', '33', '.', 'The', 'mesial', 'root', 'of', 'tooth', '46', 'and', 'the', 'root', 'of', 'tooth', '45', 'showed', 'rhizolysis', ',', 'with', 'possible', 'pulp', 'necrosis', '.', 'Teeth', '46', ',', '45', 'and', '44', 'had', 'minimal', 'mobility', ';', 'the', 'other', 'teeth', 'had', 'normal', 'vitality', 'and', 'the', 'lower', 'dental', 'canal', 'was', 'rejected', '.', 'There', 'is', 'no', 'alteration', 'in', 'the', 'sensitivity', 'of', 'the', 'mentonian', 'nerve', '.', '\\n\\n', 'The', 'patient', 'consented', 'to', 'the', 'entire', 'treatment', 'by', 'signing', 'the', 'informed', 'consent', 'form', '.', 'Prior', 'to', 'the', 'initial', 'biopsy', 'an', 'aspirate', 'was', 'taken', 'producing', 'a', 'citrine', 'fluid', 'and', 'the', 'first', 'histopathological', 'study', 'found', 'a', 'lax', 'connective', 'tissue', ',', 'some', 'multinucleated', 'giant', 'cells', 'with', 'few', 'nuclei', 'and', 'a', 'thin', 'band', 'of', 'keratin', ';', 'there', 'was', 'no', 'evidence', 'of', 'epithelial', 'tissue', 'but', 'it', 'was', 'considered', 'as', 'insufficient', 'sample', '.', 'Nevertheless', ',', 'a', 'diagnosis', 'of', 'keratocystic', 'odontogenic', 'tumour', 'was', 'made', ',', 'due', 'to', 'the', 'presence', 'of', 'a', 'keratin', 'band', '.', 'After', 'this', 'procedure', ',', 'the', 'lesion', 'became', 'superinfected', 'and', 'the', 'patient', 'had', 'to', 'be', 'hospitalised', 'due', 'to', 'the', 'severity', 'of', 'the', 'clinical', 'picture', ';', 'this', 'condition', 'was', 'used', 'to', 'perform', 'a', 'second', 'biopsy', 'under', 'general', 'anaesthesia', '8', 'days', 'later', ',', 'in', 'which', 'a', 'capsule', 'made', 'up', 'of', 'connective', 'tissue', 'with', 'an', 'intense', 'inflammatory', 'infiltrate', 'and', 'epithelial', 'tissue', 'with', 'several', 'layers', 'of', 'cells', 'was', 'found', ',', 'which', 'gave', 'the', 'diagnosis', ':', 'inflammatory', 'root', 'cyst', '.', 'Given', 'the', 'ambiguity', 'of', 'the', 'diagnosis', 'and', 'the', 'aggressiveness', 'of', 'the', 'lesion', ',', 'it', 'was', 'decided', 'to', 'perform', 'the', 'intervention', 'with', 'the', 'first', 'diagnosis', '(', 'keratocystic', 'odontogenic', 'tumour', '-exkeratocyst-', ')', '.', 'With', 'this', 'diagnosis', 'it', 'was', 'decided', 'to', 'plan', 'an', 'aggressive', 'surgical', 'treatment', 'and', 'it', 'was', 'decided', 'to', 'order', 'endodontic', 'treatment', 'from', '46', 'to', '33', '(', '9', 'teeth', ')', 'prior', 'to', 'the', 'surgical', 'procedure', ',', 'as', 'the', 'dental', 'apices', 'were', 'immersed', 'in', 'the', 'cavity', 'and', 'the', 'curettage', 'itself', 'would', 'cause', 'amputation', 'of', 'the', 'pulp', 'vascular', 'bundles', ',', 'in', 'addition', ',', 'according', 'to', 'the', 'diagnosis', ',', 'it', 'is', 'a', 'very', 'recurrent', 'lesion', '.', 'This', 'endodontic', 'treatment', 'lasted', '3', 'months', 'due', 'to', 'the', 'difficulty', 'of', 'sealing', 'the', 'canals', 'due', 'to', 'the', 'presence', 'of', 'an', 'amber', '-', 'coloured', 'liquid', 'draining', 'through', 'the', 'pulp', 'chambers', '.', 'After', 'this', 'time', ',', 'the', 'production', 'of', 'liquid', 'content', 'ceased', 'and', 'the', 'endodontic', 'treatment', 'was', 'completed', '.', '\\n\\n', 'The', 'surgical', 'phase', 'was', 'performed', 'under', 'general', 'anaesthesia', '.', 'A', 'trapezoidal', 'flap', 'was', 'raised', 'from', 'distal', '46', 'to', 'distal', '33', '.', 'When', 'the', 'flap', 'was', 'reflected', ',', 'it', 'was', 'found', 'to', 'be', 'expanded', 'in', 'its', 'entire', 'length', 'and', 'perforated', 'in', 'the', 'bicuspid', 'area', '(', 'teeth', '44', 'and', '45', ')', ',', 'where', 'the', 'biopsies', 'had', 'previously', 'been', 'taken', '.', 'We', 'proceeded', 'to', 'remove', 'all', 'the', 'expanded', 'vestibular', 'cortex', 'until', 'we', 'had', 'complete', 'access', 'to', 'the', 'cystic', 'cavity', '.', 'The', 'thick', 'fibrous', 'capsule', 'covering', 'the', 'osseous', 'defect', 'was', 'found', 'and', 'removed', '.', 'The', 'bone', 'cavity', 'is', 'reamed', 'with', 'rotary', 'cutting', 'instruments', 'and', 'the', 'entire', 'bone', 'defect', 'is', 'brushed', 'with', 'Carnoy', \"'s\", 'solution', '.', 'Some', 'perforations', 'of', 'the', 'lingual', 'cortex', 'are', 'visible', ',', 'which', 'are', 'cauterised', 'with', 'an', 'electroscalpel', 'due', 'to', 'the', 'risk', 'of', 'invasion', 'of', 'the', 'tumour', 'lesion', 'into', 'the', 'lingual', 'soft', 'tissues', '.', 'Due', 'to', 'the', 'weakening', 'of', 'the', 'mandibular', 'basilar', 'border', ',', 'a', 'reconstruction', 'plate', 'is', 'placed', 'to', 'avoid', 'intra-', 'or', 'postoperative', 'fracture', '.', 'Before', 'suturing', 'the', 'flap', ',', 'the', 'bone', 'defect', 'is', 'filled', 'with', 'fibrillar', 'collagen', 'and', 'medicated', 'with', 'analgesics', 'and', 'antibiotics', '.', 'The', 'tissue', 'obtained', 'is', 'sent', 'to', 'pathology', '.', 'The', 'histopathological', 'report', 'of', 'the', 'surgical', 'specimen', 'shows', 'fibroconnective', 'tissue', 'devoid', 'of', 'epithelium', 'and', 'a', 'post', '-', 'surgical', 'diagnosis', 'of', 'an', 'aneurysmal', 'bone', 'cyst', 'was', 'obtained', '.', '\\n', 'Because', 'the', 'second', 'biopsy', 'showed', 'an', 'epithelium', ',', 'the', 'diagnosis', 'was', 'misleading', 'but', ',', 'evaluating', 'the', 'three', 'histopathological', 'samples', ',', 'it', 'was', 'considered', 'that', 'the', 'epithelial', 'tissue', 'of', 'the', 'second', 'biopsy', 'was', 'the', 'product', 'of', 'the', 'inflammatory', 'reaction', ',', 'while', 'the', 'initial', 'biopsy', 'and', 'the', 'surgical', 'specimen', 'showed', 'no', 'epithelial', 'component', ';', 'it', 'was', 'decided', 'to', 'consider', 'it', 'an', 'aneurysmal', 'bone', 'cyst', '.', '\\n', 'The', 'patient', 'was', 'evaluated', 'at', '8', 'and', '15', 'days', 'and', 'then', 'at', '2', 'months', ',', '10', 'months', 'and', '2', 'years', '.', 'At', 'the', 'follow', '-', 'up', 'appointment', 'at', '2', 'months', 'pulp', 'necrosis', 'and', 'fistula', 'were', 'found', 'at', 'the', 'level', 'of', '47', ',', 'which', 'was', 'adjacent', 'to', 'the', 'lesion', 'and', 'the', 'mesial', 'root', 'apex', 'had', 'been', 'amputated', 'during', 'the', 'surgical', 'procedure', '.', 'Endodontics', 'was', 'performed', 'and', 'the', 'infection', 'resolved', '.', 'He', 'also', 'presented', 'with', 'right', 'mentonian', 'nerve', 'paraesthesia', 'and', 'was', 'prescribed', 'B', '-', 'complex', 'tablets', 'for', 'one', 'month', '.', 'A', 'new', 'post', '-', 'surgical', 'assessment', 'was', 'carried', 'out', '10', 'months', 'later', 'and', 'adequate', 'bone', 'healing', 'was', 'found', ',', 'but', 'there', 'was', 'an', 'occlusal', 'sequela', ',', 'as', 'the', 'teeth', 'that', 'were', 'left', 'without', 'bone', 'support', 'due', 'to', 'being', 'immersed', 'in', 'the', 'bone', 'defect', '(', 'teeth', '44', 'to', '33', ')', 'were', 'intruded', 'and', 'produced', 'an', 'open', 'bite', '.', 'Two', 'years', 'later', 'a', 'new', 'clinical', 'and', 'radiographic', 'check', '-', 'up', 'was', 'carried', 'out', 'where', 'it', 'was', 'found', 'that', ':', 'teeth', '44', 'to', '33', 'remained', 'in', 'open', 'bite', ',', 'tooth', '44', 'was', 'also', 'slightly', 'vestibularised', '.', 'The', 'panoramic', 'radiograph', 'shows', 'good', 'bone', 'filling', ',', 'but', 'the', 'three', '-', 'dimensional', 'tomography', 'shows', 'that', 'there', 'is', 'a', 'defect', 'of', 'about', '6', 'mm', 'in', 'diameter', 'that', 'compromises', 'the', 'apex', 'of', '44', 'and', '43', ',', 'and', 'that', 'the', 'bone', 'has', 'yet', 'to', 'regenerate', ',', 'and', 'the', 'paraesthesia', 'still', 'persists', 'two', 'years', 'later', '.', 'The', 'intraoral', 'image', 'shows', 'that', 'the', 'enlargement', 'produced', 'by', 'the', 'expansion', 'of', 'the', 'lesion', 'of', 'the', 'lingual', 'plate', 'at', 'the', 'level', 'of', 'teeth', '43', ',', '44', 'and', '45', 'still', 'persists', '.', '\\n\\n\\n']\n",
"[['pulp', 'necrosis'], ['keratocystic', 'odontogenic', 'tumour'], ['keratocystic', 'odontogenic', 'tumour'], ['inflammatory', 'root', 'cyst'], ['exkeratocyst'], ['lesion', 'became', 'superinfected'], ['lesion'], ['lesion'], ['lesion'], ['aneurysmal', 'bone', 'cyst'], ['tumour', 'lesion'], ['bone', 'defect'], ['bone', 'defect'], ['cystic', 'cavity'], ['lesion'], ['perforations', 'of', 'the', 'lingual', 'cortex'], ['aneurysmal', 'bone', 'cyst'], ['open', 'bite'], ['open', 'bite'], ['infection'], ['bone', 'defect'], ['pulp', 'necrosis'], ['lesion'], ['lesion']]\n",
"['exkeratocyst']\n",
"exkeratocyst\n",
"638\n"
]
}
],
"source": [
"labels_tokenized = []\n",
"idx =-1\n",
"for hct, et in zip(HCs_tokenized, Ent_tokenized):\n",
" idx+=1\n",
" labels = []\n",
" for i in range(len(hct)):\n",
" #Labels: 0->'O'; 1->'B'; 2->'I'\n",
" #labels.append('O')\n",
" labels.append(0)\n",
"\n",
" #For Entities (Diseases|Enfermedades)\n",
" for enf in et:\n",
" first = True\n",
" for e in enf:\n",
" if first == True:\n",
" try:\n",
" #labels[hct.index(e)] = 'B'\n",
" #labels[posLab] = 'B'\n",
" indices = find_idx(hct, e)\n",
" if len(indices) > 1:\n",
" for id in indices:\n",
" labels[id] = 1\n",
" else:\n",
" labels[hct.index(e)] = 1\n",
" \n",
" first = False\n",
" except:\n",
" first = False\n",
" if e == \"sarcoma+carcinoma\" or e == \"carcinoma+sarcoma\":\n",
" continue\n",
" print(hct)\n",
" print(et)\n",
" print(enf)\n",
" print(e)\n",
" print(idx)\n",
" else:\n",
" try:\n",
" #labels[hct.index(e)] = 'I'\n",
" #labels[posLab] = 'I'\n",
" indices = find_idx(hct, e)\n",
" if len(indices) > 1:\n",
" for id in indices:\n",
" if labels[id-1] != 0:\n",
" labels[id] = 2\n",
" else:\n",
" labels[hct.index(e)] = 2\n",
" except:\n",
" if e == \"sarcoma+carcinoma\" or e == \"carcinoma+sarcoma\":\n",
" continue\n",
" print(hct)\n",
" print(et)\n",
" print(enf)\n",
" print(e)\n",
" print(idx)\n",
"\n",
" labels_tokenized.append(labels)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jXKczS_fKpLc",
"outputId": "d385f71c-5507-4a33-8cce-f5d2195354f9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"patient\t0\n",
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"arterial\t1\n",
"hypertension\t2\n",
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"polyarthrosis\t1\n",
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"to\t0\n",
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"emergency\t0\n",
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"abdominal\t0\n",
"distension\t0\n",
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"constipation\t0\n",
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"febrile\t0\n",
"fever\t0\n",
".\t0\n",
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",\t0\n",
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",\t0\n",
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"(\t0\n",
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",\t0\n",
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".\t0\n",
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",\t0\n",
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",\t0\n",
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",\t0\n",
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".\t0\n",
"\n",
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"109\t0\n",
"leukocytes\t0\n",
"/\t0\n",
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",\t0\n",
"haemoglobin\t0\n",
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"/\t0\n",
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",\t0\n",
"platelets\t0\n",
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"x\t0\n",
"109\t0\n",
"/\t0\n",
"L.\t0\n",
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"glycaemia\t0\n",
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"123\t0\n",
"mg\t0\n",
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":\t0\n",
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",\t0\n",
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",\t0\n",
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",\t0\n",
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",\t0\n",
"gamma\t0\n",
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",\t0\n",
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",\t0\n",
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",\t0\n",
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",\t0\n",
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",\t0\n",
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",\t0\n",
"creatine\t0\n",
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",\t0\n",
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",\t0\n",
"lactate\t0\n",
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",\t0\n",
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",\t0\n",
"immunoglobulin\t0\n",
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",\t0\n",
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",\t0\n",
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".\t0\n",
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",\t0\n",
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".\t0\n",
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",\t0\n",
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".\t0\n",
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",\t0\n",
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".\t0\n",
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".\t0\n",
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".\t0\n",
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":\t0\n",
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";\t0\n",
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":\t0\n",
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",\t0\n",
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".\t0\n",
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"was\t0\n",
"switched\t0\n",
"to\t0\n",
"the\t0\n",
"oral\t0\n",
"route\t0\n",
"(\t0\n",
"875\t0\n",
"/\t0\n",
"125\t0\n",
"mg\t0\n",
",\t0\n",
"every\t0\n",
"8\t0\n",
"hours\t0\n",
")\t0\n",
"for\t0\n",
"6\t0\n",
"weeks\t0\n",
".\t0\n",
"The\t0\n",
"evolution\t0\n",
"was\t0\n",
"favourable\t0\n",
"and\t0\n",
"she\t0\n",
"was\t0\n",
"able\t0\n",
"to\t0\n",
"start\t0\n",
"walking\t0\n",
"with\t0\n",
"a\t0\n",
"dorsolumbar\t0\n",
"corset\t0\n",
"after\t0\n",
"the\t0\n",
"fourth\t0\n",
"week\t0\n",
".\t0\n",
"One\t0\n",
"month\t0\n",
"after\t0\n",
"the\t0\n",
"end\t0\n",
"of\t0\n",
"antibiotic\t0\n",
"therapy\t0\n",
",\t0\n",
"a\t0\n",
"control\t0\n",
"chest\t0\n",
"CT\t0\n",
"scan\t0\n",
"still\t0\n",
"showed\t0\n",
"a\t0\n",
"discrete\t0\n",
"pleural\t1\n",
"effusion\t2\n",
",\t0\n",
"but\t0\n",
"the\t0\n",
"patient\t0\n",
"had\t0\n",
"only\t0\n",
"mild\t0\n",
"mechanical\t0\n",
"dorsalgia\t0\n",
",\t0\n",
"her\t0\n",
"ESR\t0\n",
"had\t0\n",
"decreased\t0\n",
"to\t0\n",
"21\t0\n",
"mm\t0\n",
"/\t0\n",
"1\t0\n",
"h\t0\n",
"and\t0\n",
"her\t0\n",
"CRP\t0\n",
"was\t0\n",
"2.4\t0\n",
"mg\t0\n",
"/\t0\n",
"L.\t0\n",
"Outpatient\t0\n",
"follow\t0\n",
"-\t0\n",
"up\t0\n",
"continued\t0\n",
"for\t0\n",
"a\t0\n",
"further\t0\n",
"three\t0\n",
"years\t0\n",
",\t0\n",
"during\t0\n",
"which\t0\n",
"time\t0\n",
"the\t0\n",
"evolution\t0\n",
"was\t0\n",
"favourable\t0\n",
"and\t0\n",
"a\t0\n",
"D10\t0\n",
"-\t0\n",
"D11\t0\n",
"vertebral\t0\n",
"block\t0\n",
"was\t0\n",
"formed\t0\n",
".\t0\n",
"\n",
"\n",
"\n",
"\t0\n"
]
}
],
"source": [
"j = 0\n",
"for i in range(len(HCs_tokenized[j])):\n",
" print(str(HCs_tokenized[j][i]) + \"\\t\" + str(labels_tokenized[j][i]))"
]
},
{
"cell_type": "markdown",
"source": [
"# Validating tokenization and alignment with the BIO tags."
],
"metadata": {
"id": "laU64q79UYZy"
}
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jcJBsudYKpLc",
"outputId": "13ea2860-19b7-4ee1-ddf4-ce607a60e80f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Everything is aligned!\n"
]
}
],
"source": [
"flag = 0\n",
"for st, lt in zip(HCs_tokenized, labels_tokenized):\n",
" if len(st) != len(lt):\n",
" print(st)\n",
" print(lt)\n",
" flag = 1\n",
"if flag==0:\n",
" print(\"Everything is aligned!\")"
]
},
{
"cell_type": "markdown",
"source": [
"# Sentence tokenization"
],
"metadata": {
"id": "lvR3V8qXUbvR"
}
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "-nHHwKmUKpLd"
},
"outputs": [],
"source": [
"sent_tokenized = []\n",
"label_sent_tokenized = []\n",
"for ht, lht in zip(HCs_tokenized, labels_tokenized):\n",
" st = []; lbst = []\n",
" for h, l in zip(ht,lht):\n",
" if h != \".\":\n",
" st.append(h)\n",
" lbst.append(l)\n",
" else:\n",
" st.append(\".\")\n",
" lbst.append(0)\n",
" sent_tokenized.append(st)\n",
" label_sent_tokenized.append(lbst)\n",
" st = []; lbst = []"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8Lo6zzhGKpLd",
"outputId": "6d68e803-7ad4-4970-b4bf-b34be7fbdd37"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"11668"
]
},
"metadata": {},
"execution_count": 24
}
],
"source": [
"len(sent_tokenized)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5Ei7TrATKpLd",
"outputId": "93b623a3-ac12-4500-b6f4-506972dcad9f"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['A',\n",
" '73',\n",
" '-',\n",
" 'year',\n",
" '-',\n",
" 'old',\n",
" 'patient',\n",
" 'with',\n",
" 'a',\n",
" 'history',\n",
" 'of',\n",
" 'arterial',\n",
" 'hypertension',\n",
" 'and',\n",
" 'polyarthrosis',\n",
" 'presented',\n",
" 'to',\n",
" 'the',\n",
" 'emergency',\n",
" 'department',\n",
" 'with',\n",
" 'abdominal',\n",
" 'distension',\n",
" 'and',\n",
" 'pain',\n",
" 'associated',\n",
" 'with',\n",
" 'constipation',\n",
" 'and',\n",
" 'febrile',\n",
" 'fever',\n",
" '.']"
]
},
"metadata": {},
"execution_count": 25
}
],
"source": [
"sent_tokenized[0]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KistaXEmKpLe",
"outputId": "b07ce5d3-df79-474e-a7fb-983632bb38d3"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"11668"
]
},
"metadata": {},
"execution_count": 26
}
],
"source": [
"len(label_sent_tokenized)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "m9qR1AxbKpLe",
"outputId": "66600bb9-dcae-4717-9cc9-9422115b4e2e"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 0,\n",
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},
"metadata": {},
"execution_count": 27
}
],
"source": [
"label_sent_tokenized[0]"
]
},
{
"cell_type": "markdown",
"source": [
"# Disease mentions identification as a Token classification problem"
],
"metadata": {
"id": "l5-_fyqPUh3b"
}
},
{
"cell_type": "markdown",
"source": [
"# Building the Dataset"
],
"metadata": {
"id": "098UDE8VUjXf"
}
},
{
"cell_type": "markdown",
"source": [
"## Case as a whole is given as input"
],
"metadata": {
"id": "8FT7wAx4VeaU"
}
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"id": "5qVPJ2LVKpLe"
},
"outputs": [],
"source": [
"dic = {\"tokens\": HCs_tokenized, \"ner_tags\": labels_tokenized} #For the whole clinical case. We used this option for our paper.\n",
"#dic = {\"tokens\": sent_tokenized, \"ner_tags\": label_sent_tokenized} #Use this option if you want to check the model performance with sentences tokenized by \". \" b"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"id": "UzXp6PO2KpLe"
},
"outputs": [],
"source": [
"dataset = Dataset.from_dict(dic)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NhqH78gAKpLf",
"outputId": "e3fe5c5f-764c-4bf8-c599-6bda653a4944"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 741\n",
"})"
]
},
"metadata": {},
"execution_count": 30
}
],
"source": [
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"id": "jspsCq2OKpLf"
},
"outputs": [],
"source": [
"#For training, validation, and test partitions\n",
"\"\"\"\n",
"#Train, val, test partitions\n",
"train_test = dataset.train_test_split()\n",
"test_val = train_test['test'].train_test_split()\n",
"raw_datasets = DatasetDict({\n",
" 'train': train_test['train'],\n",
" 'validation': test_val['train'],\n",
" 'test': test_val['test']\n",
" })\n",
"\"\"\"\n",
"\n",
"#Just for training and validation partitions\n",
"train_test = dataset.train_test_split()\n",
"raw_datasets = DatasetDict({\n",
" 'train': train_test['train'],\n",
" 'validation': train_test['test']\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "isqyq9VIKpLf",
"outputId": "7539fb10-1ab0-43cd-d7a5-bdb30514b891"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 555\n",
" })\n",
" validation: Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 186\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 32
}
],
"source": [
"raw_datasets"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pEJhTKi8KpLf",
"outputId": "fe3f3c2b-226c-42eb-9674-f69fce8a61e9"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
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]
},
"metadata": {},
"execution_count": 33
}
],
"source": [
"raw_datasets[\"train\"][0][\"ner_tags\"]\n",
"#raw_datasets[\"train\"][0][\"pos_tags\"]\n",
"#raw_datasets[\"train\"][0][\"chunk_tags\"]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gtrDybrCKpLf",
"outputId": "1217e93b-928c-4db1-bd22-8f1e3c2ebf2c"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 555\n",
"})"
]
},
"metadata": {},
"execution_count": 34
}
],
"source": [
"raw_datasets['train']"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QjdG8yi0KpLf",
"outputId": "20bc5bed-36bf-4e05-f80b-f75638f5196b"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['O', 'B', 'I']"
]
},
"metadata": {},
"execution_count": 35
}
],
"source": [
"label_names = ['O','B','I']\n",
"label_names"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9kGXdiCsKpLg",
"outputId": "754c7613-c7c3-46fc-a109-d8a67d6ab072"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"This is a 70 - year - old male patient , who was admitted to the emergency department of the Hospital Pablo Tobón Uribe , with symptoms of approximately one hour of evolution consisting of chest tightness , general malaise , asthenia and diaphoresis ; which began after having ingested 100 mg of sildenafil , denies ingestion of another sexual stimulant or cocaine and without sexual intercourse after its consumption . The patient 's only clinical history was arterial hypertension , pharmacologically controlled , and he denies previous episodes of angina or nitrate consumption . The clinical examination and vital signs were normal ; however , after the initial assessment he presented cardiorespiratory arrest secondary to ventricular fibrillation with response to a single defibrillation of 200 joules . \n",
" The initial electrocardiogram showed ST - segment elevation in the inferior ( II , III and aVF ) and anterior ( V2 - V4 ) leads with reciprocal changes in aVL , with no electrocardiographic extension to the right ventricle . \n",
"\n",
" Cardiac enzymes on admission revealed a creatine kinase ( CK ) of 170 and a creatine phosphokinase - MB fraction ( CK - MB ) of 6 . Electrolytes , coagulation tests and blood cell counts were normal . \n",
" Initial management was with aspirin 100 mg , lovastatin 40 mg daily , metoprolol 25 mg every 12 hours , enoxaparin 60 mg every 12 hours , oxygen at 3 lt / min and streptokinase 1'500,000 units administered over 30 minutes . No changes secondary to reperfusion were demonstrated . The patient was transferred to the intensive care unit , where episodes of complete A - V block with spontaneous resolution were documented during the first hours of evolution . \n",
" The EKG taken at 24 hours of evolution revealed QS in the inferior face and a late progression of the R wave in the anterior face . Enzyme monitoring showed increased CK and MB fraction at 6 hours ( 4476 and 165 ) and 12 hours ( 3839 and 136 ) . \n",
" The next day coronary angiography showed diffuse disease of the anterior descending artery with 50 % lesion in the distal third and 40 % lesion in the proximal third of the first diagonal branch . The circumflex artery had a 50 % lesion in the middle third and diffuse disease of its obtuse marginal branches . The right coronary artery had an irregular lesion suggestive of a partially resolved thrombus producing a maximum stenosis of 50 % ; distally the posterior descending artery had two 40 % lesions . \n",
"\n",
" The patient evolved satisfactorily without further complications and pain - free . He was discharged for outpatient follow - up . \n",
"\n",
"\n",
" \n",
"O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B I I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I I I I I I I I I I I I I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O \n"
]
}
],
"source": [
"words = raw_datasets[\"train\"][0][\"tokens\"]\n",
"labels = [int(n) for n in raw_datasets[\"train\"][0][\"ner_tags\"]]\n",
"#labels = raw_datasets[\"train\"][0][\"pos_tags\"]\n",
"#labels = raw_datasets[\"train\"][0][\"chunk_tags\"]\n",
"line1 = \"\"\n",
"line2 = \"\"\n",
"for word, label in zip(words, labels):\n",
" full_label = label_names[label]\n",
" max_length = max(len(word), len(full_label))\n",
" line1 += word + \" \" * (max_length - len(word) + 1)\n",
" line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n",
"\n",
"print(line1)\n",
"print(line2)"
]
},
{
"cell_type": "markdown",
"source": [
"## Helper Functions"
],
"metadata": {
"id": "0ABWC5YnTZzp"
}
},
{
"cell_type": "code",
"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:\n",
" # Start of a new word!\n",
" current_word = word_id\n",
" label = -100 if word_id is None else labels[word_id]\n",
" new_labels.append(label)\n",
" elif word_id is None:\n",
" # Special token\n",
" new_labels.append(-100)\n",
" else:\n",
" # Same word as previous token\n",
" label = labels[word_id]\n",
" # If the label is B-XXX we change it to I-XXX\n",
" if label % 2 == 1:\n",
" label += 1\n",
" new_labels.append(label)\n",
"\n",
" return new_labels"
],
"metadata": {
"id": "LQUKQDvxTNHn"
},
"execution_count": 37,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def tokenize_and_align_labels(examples):\n",
" tokenized_inputs = tokenizer(\n",
" examples[\"tokens\"], truncation=True, is_split_into_words=True\n",
" )\n",
" all_labels = examples[\"ner_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"
],
"metadata": {
"id": "qT-6oXUoTYah"
},
"execution_count": 38,
"outputs": []
},
{
"cell_type": "code",
"source": [
"metric = load_metric(\"seqeval\")\n",
"def compute_metrics(eval_preds):\n",
" logits, labels = eval_preds\n",
" predictions = np.argmax(logits, axis=-1)\n",
"\n",
" # Remove ignored index (special tokens) and convert to labels\n",
" true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n",
" try:\n",
" true_predictions = [\n",
" [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
" for prediction, label in zip(predictions, labels)\n",
" ]\n",
" except:\n",
" true_predictions = []\n",
" for prediction, label in zip(predictions, labels):\n",
" label_list = []\n",
" for (p, l) in zip(prediction, label):\n",
" if l != -100:\n",
" if p not in range(len(label_names)):\n",
" p = 0\n",
" \n",
" label_list.append(label_names[p])\n",
" true_predictions.append(label_list)\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",
" }"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 104,
"referenced_widgets": [
"b3fe12ca95e84b198d16bdb4d20f9ad9",
"1b7f8f1786394c01bad4a8589ad16513",
"70e437b3ba294189b4799c6607532ebd",
"0fb47d91dbf9497cac1ffc1c5dfd4519",
"9cfec0f21c0a459f9f5888c389a6a479",
"ef66098fb5f748eabe11abc3fe4ad54d",
"563d8b35192240be960bc08909984119",
"b94385d1423e47f5a9e2351bf873c3e0",
"e1b6e7774bc94a87ad23fb53d6c9b985",
"a0b523772cf04a85b0ac000cc9a83c67",
"71a3f1b2112344ea81721e59cce14cec"
]
},
"id": "EZMRI9ATTzc9",
"outputId": "14e38b3a-86a7-49b1-817f-92a7304747a2"
},
"execution_count": 39,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading builder script: 0%| | 0.00/2.47k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
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}
},
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}
]
},
{
"cell_type": "markdown",
"source": [
"# Loading BERT as a pre-trained model"
],
"metadata": {
"id": "6D0P7PztVnCP"
}
},
{
"cell_type": "markdown",
"source": [
"## Model 1 - d4data/biomedical-ner-all\n",
"\n",
"Complete document wise tokenization"
],
"metadata": {
"id": "mmPReh2mSux9"
}
},
{
"cell_type": "code",
"source": [
"model_checkpoint = \"d4data/biomedical-ner-all\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, add_prefix_space=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145,
"referenced_widgets": [
"a275c54cbefb4438a3015080e8b57999",
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"5ddf799b2fd94edc9949d36450a2d5e9",
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"414d466fed0b42378d8b38f10c720eba",
"e432f1e3e5c54358a321a21e9c7aad1f",
"cf931d70dc1a4d2ba5f10dba7bf90ece",
"58f1edc459ef4f5bab25544474897db3",
"8894005504364c36964d283cf58bb223",
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"cb3c438fb3a6412d80b5ba673a6455cb",
"bb189f5bc189462cad4824a1c30335c0",
"e98b7218049f4310951a1608c52c14e0",
"3dd1f27ff0d24a1294534ff7e69a7abb",
"4128d82e19f14e9d9be5416ebc974d0d",
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"b75e2a47db2b47dd8740f77b337c308f",
"d8849516ccb44011a7f9e7e745b30c60",
"ae5928c8da4243fba06ae9bf5086ba31",
"e613455bcbb24e36a31666acd83d7b24",
"bac45a33f9b444a1985ef56a9be85c52",
"91ac0673e600400f904b1b10deb86cee",
"289f23dd30814993afde0f5e987fdd9e",
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"b58c9bfa30b3421496adb52e082cb50a",
"6cbfa925d26e47139365d10b9b28d96a"
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},
"id": "RfwVBXFAS1Dc",
"outputId": "3a7c9806-3828-4ab7-c73d-d0e8039e1417"
},
"execution_count": 40,
"outputs": [
{
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"text/plain": [
"Downloading: 0%| | 0.00/373 [00:00<?, ?B/s]"
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{
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},
{
"cell_type": "code",
"source": [
"tokenizer.is_fast"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a9DegC0yS_Gp",
"outputId": "083e5505-007e-43c8-fe31-3b202c84cc76"
},
"execution_count": 41,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 41
}
]
},
{
"cell_type": "code",
"source": [
"inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n",
"inputs.tokens()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "v-7VQWw5TB-c",
"outputId": "0922ac3e-61af-43ae-f3a8-29f291e7c19b"
},
"execution_count": 42,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Token indices sequence length is longer than the specified maximum sequence length for this model (567 > 512). Running this sequence through the model will result in indexing errors\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['[CLS]',\n",
" 'this',\n",
" 'is',\n",
" 'a',\n",
" '70',\n",
" '-',\n",
" 'year',\n",
" '-',\n",
" 'old',\n",
" 'male',\n",
" 'patient',\n",
" ',',\n",
" 'who',\n",
" 'was',\n",
" 'admitted',\n",
" 'to',\n",
" 'the',\n",
" 'emergency',\n",
" 'department',\n",
" 'of',\n",
" 'the',\n",
" 'hospital',\n",
" 'pablo',\n",
" 'to',\n",
" '##bon',\n",
" 'ur',\n",
" '##ibe',\n",
" ',',\n",
" 'with',\n",
" 'symptoms',\n",
" 'of',\n",
" 'approximately',\n",
" 'one',\n",
" 'hour',\n",
" 'of',\n",
" 'evolution',\n",
" 'consisting',\n",
" 'of',\n",
" 'chest',\n",
" 'tight',\n",
" '##ness',\n",
" ',',\n",
" 'general',\n",
" 'mala',\n",
" '##ise',\n",
" ',',\n",
" 'as',\n",
" '##the',\n",
" '##nia',\n",
" 'and',\n",
" 'dia',\n",
" '##ph',\n",
" '##ores',\n",
" '##is',\n",
" ';',\n",
" 'which',\n",
" 'began',\n",
" 'after',\n",
" 'having',\n",
" 'ing',\n",
" '##ested',\n",
" '100',\n",
" 'mg',\n",
" 'of',\n",
" 'si',\n",
" '##lden',\n",
" '##af',\n",
" '##il',\n",
" ',',\n",
" 'denies',\n",
" 'ing',\n",
" '##est',\n",
" '##ion',\n",
" 'of',\n",
" 'another',\n",
" 'sexual',\n",
" 'st',\n",
" '##im',\n",
" '##ula',\n",
" '##nt',\n",
" 'or',\n",
" 'cocaine',\n",
" 'and',\n",
" 'without',\n",
" 'sexual',\n",
" 'intercourse',\n",
" 'after',\n",
" 'its',\n",
" 'consumption',\n",
" '.',\n",
" 'the',\n",
" 'patient',\n",
" \"'\",\n",
" 's',\n",
" 'only',\n",
" 'clinical',\n",
" 'history',\n",
" 'was',\n",
" 'arterial',\n",
" 'hyper',\n",
" '##tension',\n",
" ',',\n",
" 'ph',\n",
" '##arm',\n",
" '##aco',\n",
" '##logical',\n",
" '##ly',\n",
" 'controlled',\n",
" ',',\n",
" 'and',\n",
" 'he',\n",
" 'denies',\n",
" 'previous',\n",
" 'episodes',\n",
" 'of',\n",
" 'ang',\n",
" '##ina',\n",
" 'or',\n",
" 'nitrate',\n",
" 'consumption',\n",
" '.',\n",
" 'the',\n",
" 'clinical',\n",
" 'examination',\n",
" 'and',\n",
" 'vital',\n",
" 'signs',\n",
" 'were',\n",
" 'normal',\n",
" ';',\n",
" 'however',\n",
" ',',\n",
" 'after',\n",
" 'the',\n",
" 'initial',\n",
" 'assessment',\n",
" 'he',\n",
" 'presented',\n",
" 'card',\n",
" '##ior',\n",
" '##es',\n",
" '##pi',\n",
" '##rator',\n",
" '##y',\n",
" 'arrest',\n",
" 'secondary',\n",
" 'to',\n",
" 'vent',\n",
" '##ric',\n",
" '##ular',\n",
" 'fi',\n",
" '##bri',\n",
" '##llation',\n",
" 'with',\n",
" 'response',\n",
" 'to',\n",
" 'a',\n",
" 'single',\n",
" 'def',\n",
" '##ib',\n",
" '##rill',\n",
" '##ation',\n",
" 'of',\n",
" '200',\n",
" 'jo',\n",
" '##ules',\n",
" '.',\n",
" 'the',\n",
" 'initial',\n",
" 'electro',\n",
" '##card',\n",
" '##io',\n",
" '##gram',\n",
" 'showed',\n",
" 'st',\n",
" '-',\n",
" 'segment',\n",
" 'elevation',\n",
" 'in',\n",
" 'the',\n",
" 'inferior',\n",
" '(',\n",
" 'ii',\n",
" ',',\n",
" 'iii',\n",
" 'and',\n",
" 'av',\n",
" '##f',\n",
" ')',\n",
" 'and',\n",
" 'anterior',\n",
" '(',\n",
" 'v',\n",
" '##2',\n",
" '-',\n",
" 'v',\n",
" '##4',\n",
" ')',\n",
" 'leads',\n",
" 'with',\n",
" 'reciprocal',\n",
" 'changes',\n",
" 'in',\n",
" 'av',\n",
" '##l',\n",
" ',',\n",
" 'with',\n",
" 'no',\n",
" 'electro',\n",
" '##card',\n",
" '##io',\n",
" '##graphic',\n",
" 'extension',\n",
" 'to',\n",
" 'the',\n",
" 'right',\n",
" 'vent',\n",
" '##ric',\n",
" '##le',\n",
" '.',\n",
" 'cardiac',\n",
" 'enzymes',\n",
" 'on',\n",
" 'admission',\n",
" 'revealed',\n",
" 'a',\n",
" 'cr',\n",
" '##ea',\n",
" '##tine',\n",
" 'kinase',\n",
" '(',\n",
" 'ck',\n",
" ')',\n",
" 'of',\n",
" '170',\n",
" 'and',\n",
" 'a',\n",
" 'cr',\n",
" '##ea',\n",
" '##tine',\n",
" 'ph',\n",
" '##os',\n",
" '##ph',\n",
" '##oki',\n",
" '##nas',\n",
" '##e',\n",
" '-',\n",
" 'mb',\n",
" 'fraction',\n",
" '(',\n",
" 'ck',\n",
" '-',\n",
" 'mb',\n",
" ')',\n",
" 'of',\n",
" '6',\n",
" '.',\n",
" 'electro',\n",
" '##ly',\n",
" '##tes',\n",
" ',',\n",
" 'coa',\n",
" '##gul',\n",
" '##ation',\n",
" 'tests',\n",
" 'and',\n",
" 'blood',\n",
" 'cell',\n",
" 'counts',\n",
" 'were',\n",
" 'normal',\n",
" '.',\n",
" 'initial',\n",
" 'management',\n",
" 'was',\n",
" 'with',\n",
" 'as',\n",
" '##pi',\n",
" '##rin',\n",
" '100',\n",
" 'mg',\n",
" ',',\n",
" 'lo',\n",
" '##vas',\n",
" '##tat',\n",
" '##in',\n",
" '40',\n",
" 'mg',\n",
" 'daily',\n",
" ',',\n",
" 'met',\n",
" '##op',\n",
" '##rol',\n",
" '##ol',\n",
" '25',\n",
" 'mg',\n",
" 'every',\n",
" '12',\n",
" 'hours',\n",
" ',',\n",
" 'en',\n",
" '##ox',\n",
" '##apa',\n",
" '##rin',\n",
" '60',\n",
" 'mg',\n",
" 'every',\n",
" '12',\n",
" 'hours',\n",
" ',',\n",
" 'oxygen',\n",
" 'at',\n",
" '3',\n",
" 'lt',\n",
" '/',\n",
" 'min',\n",
" 'and',\n",
" 'st',\n",
" '##re',\n",
" '##pt',\n",
" '##oki',\n",
" '##nas',\n",
" '##e',\n",
" '1',\n",
" \"'\",\n",
" '500',\n",
" ',',\n",
" '000',\n",
" 'units',\n",
" 'administered',\n",
" 'over',\n",
" '30',\n",
" 'minutes',\n",
" '.',\n",
" 'no',\n",
" 'changes',\n",
" 'secondary',\n",
" 'to',\n",
" 'rep',\n",
" '##er',\n",
" '##fusion',\n",
" 'were',\n",
" 'demonstrated',\n",
" '.',\n",
" 'the',\n",
" 'patient',\n",
" 'was',\n",
" 'transferred',\n",
" 'to',\n",
" 'the',\n",
" 'intensive',\n",
" 'care',\n",
" 'unit',\n",
" ',',\n",
" 'where',\n",
" 'episodes',\n",
" 'of',\n",
" 'complete',\n",
" 'a',\n",
" '-',\n",
" 'v',\n",
" 'block',\n",
" 'with',\n",
" 'spontaneous',\n",
" 'resolution',\n",
" 'were',\n",
" 'documented',\n",
" 'during',\n",
" 'the',\n",
" 'first',\n",
" 'hours',\n",
" 'of',\n",
" 'evolution',\n",
" '.',\n",
" 'the',\n",
" 'ek',\n",
" '##g',\n",
" 'taken',\n",
" 'at',\n",
" '24',\n",
" 'hours',\n",
" 'of',\n",
" 'evolution',\n",
" 'revealed',\n",
" 'q',\n",
" '##s',\n",
" 'in',\n",
" 'the',\n",
" 'inferior',\n",
" 'face',\n",
" 'and',\n",
" 'a',\n",
" 'late',\n",
" 'progression',\n",
" 'of',\n",
" 'the',\n",
" 'r',\n",
" 'wave',\n",
" 'in',\n",
" 'the',\n",
" 'anterior',\n",
" 'face',\n",
" '.',\n",
" 'enzyme',\n",
" 'monitoring',\n",
" 'showed',\n",
" 'increased',\n",
" 'ck',\n",
" 'and',\n",
" 'mb',\n",
" 'fraction',\n",
" 'at',\n",
" '6',\n",
" 'hours',\n",
" '(',\n",
" '44',\n",
" '##7',\n",
" '##6',\n",
" 'and',\n",
" '165',\n",
" ')',\n",
" 'and',\n",
" '12',\n",
" 'hours',\n",
" '(',\n",
" '38',\n",
" '##39',\n",
" 'and',\n",
" '136',\n",
" ')',\n",
" '.',\n",
" 'the',\n",
" 'next',\n",
" 'day',\n",
" 'corona',\n",
" '##ry',\n",
" 'ang',\n",
" '##iography',\n",
" 'showed',\n",
" 'diffuse',\n",
" 'disease',\n",
" 'of',\n",
" 'the',\n",
" 'anterior',\n",
" 'descending',\n",
" 'artery',\n",
" 'with',\n",
" '50',\n",
" '%',\n",
" 'les',\n",
" '##ion',\n",
" 'in',\n",
" 'the',\n",
" 'distal',\n",
" 'third',\n",
" 'and',\n",
" '40',\n",
" '%',\n",
" 'les',\n",
" '##ion',\n",
" 'in',\n",
" 'the',\n",
" 'pro',\n",
" '##xi',\n",
" '##mal',\n",
" 'third',\n",
" 'of',\n",
" 'the',\n",
" 'first',\n",
" 'diagonal',\n",
" 'branch',\n",
" '.',\n",
" 'the',\n",
" 'ci',\n",
" '##rc',\n",
" '##um',\n",
" '##fle',\n",
" '##x',\n",
" 'artery',\n",
" 'had',\n",
" 'a',\n",
" '50',\n",
" '%',\n",
" 'les',\n",
" '##ion',\n",
" 'in',\n",
" 'the',\n",
" 'middle',\n",
" 'third',\n",
" 'and',\n",
" 'diffuse',\n",
" 'disease',\n",
" 'of',\n",
" 'its',\n",
" 'ob',\n",
" '##tus',\n",
" '##e',\n",
" 'marginal',\n",
" 'branches',\n",
" '.',\n",
" 'the',\n",
" 'right',\n",
" 'corona',\n",
" '##ry',\n",
" 'artery',\n",
" 'had',\n",
" 'an',\n",
" 'irregular',\n",
" 'les',\n",
" '##ion',\n",
" 'suggest',\n",
" '##ive',\n",
" 'of',\n",
" 'a',\n",
" 'partially',\n",
" 'resolved',\n",
" 'th',\n",
" '##rom',\n",
" '##bus',\n",
" 'producing',\n",
" 'a',\n",
" 'maximum',\n",
" 'ste',\n",
" '##nosis',\n",
" 'of',\n",
" '50',\n",
" '%',\n",
" ';',\n",
" 'distal',\n",
" '##ly',\n",
" 'the',\n",
" 'posterior',\n",
" 'descending',\n",
" 'artery',\n",
" 'had',\n",
" 'two',\n",
" '40',\n",
" '%',\n",
" 'lesions',\n",
" '.',\n",
" 'the',\n",
" 'patient',\n",
" 'evolved',\n",
" 'sat',\n",
" '##is',\n",
" '##fa',\n",
" '##ctor',\n",
" '##ily',\n",
" 'without',\n",
" 'further',\n",
" 'complications',\n",
" 'and',\n",
" 'pain',\n",
" '-',\n",
" 'free',\n",
" '.',\n",
" 'he',\n",
" 'was',\n",
" 'discharged',\n",
" 'for',\n",
" 'out',\n",
" '##patient',\n",
" 'follow',\n",
" '-',\n",
" 'up',\n",
" '.',\n",
" '[SEP]']"
]
},
"metadata": {},
"execution_count": 42
}
]
},
{
"cell_type": "code",
"source": [
"labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
"word_ids = inputs.word_ids()\n",
"print(labels)\n",
"print(align_labels_with_tokens(labels, word_ids))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "w77KW9-3TD4u",
"outputId": "eae68b43-ff6a-48cb-a380-930f156c24c6"
},
"execution_count": 43,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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]
}
]
},
{
"cell_type": "code",
"source": [
"tokenized_datasets = raw_datasets.map(\n",
" tokenize_and_align_labels,\n",
" batched=True,\n",
" remove_columns=raw_datasets[\"train\"].column_names,\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"d161016f9fea41e6b27eb537c12d0703",
"f315bcfdc76848cb8e851a2698e0248b",
"ef152955607540f2a7d38bf9e2207eec",
"456c36425ac94dc294f8402c07668a51",
"4d305d32efdf4b639e65e816a7132597",
"5c3a10b039c344509be9867ca40a8472",
"6663eac35b7a4043b97edb90a555e3d9",
"30bce58edba74043abc1a2625c492d4a",
"d4b6dbbad9c946ed99b6c6e587bfb6da",
"8976a59e4ea049088f92a37f7547e16e",
"6a689955d9b3463abaaaa03b62d3cf69",
"9d65a59161cd401aad05f4a52d51c724",
"6f244b91a3884eb5b0fbd577ed5d1710",
"2b6ad660dd1f4c78855433118b9fb61e",
"101fa9a9581a46d8b1e0951f03796740",
"7ffe4378bc7b410780780dd51d0705ea",
"77c422e831944566a6529da37645ef6d",
"9a88121d0138438980f1c7e4341f480a",
"52600cdbf4804b148e02724ae4902de5",
"11f16a0c34e64d6494ac1d2550d18f8f",
"645616ac236e479c8303a56100d26d51",
"859e35e323f0407fbdea9eb7ae953742"
]
},
"id": "5BPyKS51TWGK",
"outputId": "f7ada6c5-8860-40d3-f32b-4b79abe14ae8"
},
"execution_count": 44,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/1 [00:00<?, ?ba/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d161016f9fea41e6b27eb537c12d0703"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/1 [00:00<?, ?ba/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9d65a59161cd401aad05f4a52d51c724"
}
},
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}
]
},
{
"cell_type": "code",
"source": [
"data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
],
"metadata": {
"id": "_EZvP3kyTfms"
},
"execution_count": 45,
"outputs": []
},
{
"cell_type": "code",
"source": [
"batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
"batch[\"labels\"]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IkQdm1ODTiWJ",
"outputId": "5d18c54e-c5aa-4d89-ed8a-e1b7664350f8"
},
"execution_count": 46,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"You're using a DistilBertTokenizerFast 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, ..., 0, 0, -100],\n",
" [-100, 0, 0, ..., 0, 0, -100]])"
]
},
"metadata": {},
"execution_count": 46
}
]
},
{
"cell_type": "code",
"source": [
"id2label = {str(i): label for i, label in enumerate(label_names)}\n",
"label2id = {v: k for k, v in id2label.items()}"
],
"metadata": {
"id": "QPom2dyaTkdf"
},
"execution_count": 47,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model = AutoModelForTokenClassification.from_pretrained( \n",
" model_checkpoint\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LSRqpzXDTnpt",
"outputId": "f87c31de-e40d-49f8-81a1-53a2a9905083"
},
"execution_count": 55,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--d4data--biomedical-ner-all/snapshots/e87917020da1384aed6e93b1b46d68771f65ddab/config.json\n",
"Model config DistilBertConfig {\n",
" \"_name_or_path\": \"d4data/biomedical-ner-all\",\n",
" \"activation\": \"gelu\",\n",
" \"architectures\": [\n",
" \"DistilBertForTokenClassification\"\n",
" ],\n",
" \"attention_dropout\": 0.1,\n",
" \"dim\": 768,\n",
" \"dropout\": 0.1,\n",
" \"hidden_dim\": 3072,\n",
" \"id2label\": {\n",
" \"0\": \"O\",\n",
" \"1\": \"B-Activity\",\n",
" \"2\": \"B-Administration\",\n",
" \"3\": \"B-Age\",\n",
" \"4\": \"B-Area\",\n",
" \"5\": \"B-Biological_attribute\",\n",
" \"6\": \"B-Biological_structure\",\n",
" \"7\": \"B-Clinical_event\",\n",
" \"8\": \"B-Color\",\n",
" \"9\": \"B-Coreference\",\n",
" \"10\": \"B-Date\",\n",
" \"11\": \"B-Detailed_description\",\n",
" \"12\": \"B-Diagnostic_procedure\",\n",
" \"13\": \"B-Disease_disorder\",\n",
" \"14\": \"B-Distance\",\n",
" \"15\": \"B-Dosage\",\n",
" \"16\": \"B-Duration\",\n",
" \"17\": \"B-Family_history\",\n",
" \"18\": \"B-Frequency\",\n",
" \"19\": \"B-Height\",\n",
" \"20\": \"B-History\",\n",
" \"21\": \"B-Lab_value\",\n",
" \"22\": \"B-Mass\",\n",
" \"23\": \"B-Medication\",\n",
" \"24\": \"B-Non[biological](Detailed_description\",\n",
" \"25\": \"B-Nonbiological_location\",\n",
" \"26\": \"B-Occupation\",\n",
" \"27\": \"B-Other_entity\",\n",
" \"28\": \"B-Other_event\",\n",
" \"29\": \"B-Outcome\",\n",
" \"30\": \"B-Personal_[back](Biological_structure\",\n",
" \"31\": \"B-Personal_background\",\n",
" \"32\": \"B-Qualitative_concept\",\n",
" \"33\": \"B-Quantitative_concept\",\n",
" \"34\": \"B-Severity\",\n",
" \"35\": \"B-Sex\",\n",
" \"36\": \"B-Shape\",\n",
" \"37\": \"B-Sign_symptom\",\n",
" \"38\": \"B-Subject\",\n",
" \"39\": \"B-Texture\",\n",
" \"40\": \"B-Therapeutic_procedure\",\n",
" \"41\": \"B-Time\",\n",
" \"42\": \"B-Volume\",\n",
" \"43\": \"B-Weight\",\n",
" \"44\": \"I-Activity\",\n",
" \"45\": \"I-Administration\",\n",
" \"46\": \"I-Age\",\n",
" \"47\": \"I-Area\",\n",
" \"48\": \"I-Biological_attribute\",\n",
" \"49\": \"I-Biological_structure\",\n",
" \"50\": \"I-Clinical_event\",\n",
" \"51\": \"I-Color\",\n",
" \"52\": \"I-Coreference\",\n",
" \"53\": \"I-Date\",\n",
" \"54\": \"I-Detailed_description\",\n",
" \"55\": \"I-Diagnostic_procedure\",\n",
" \"56\": \"I-Disease_disorder\",\n",
" \"57\": \"I-Distance\",\n",
" \"58\": \"I-Dosage\",\n",
" \"59\": \"I-Duration\",\n",
" \"60\": \"I-Family_history\",\n",
" \"61\": \"I-Frequency\",\n",
" \"62\": \"I-Height\",\n",
" \"63\": \"I-History\",\n",
" \"64\": \"I-Lab_value\",\n",
" \"65\": \"I-Mass\",\n",
" \"66\": \"I-Medication\",\n",
" \"67\": \"I-Nonbiological_location\",\n",
" \"68\": \"I-Occupation\",\n",
" \"69\": \"I-Other_entity\",\n",
" \"70\": \"I-Other_event\",\n",
" \"71\": \"I-Outcome\",\n",
" \"72\": \"I-Personal_background\",\n",
" \"73\": \"I-Qualitative_concept\",\n",
" \"74\": \"I-Quantitative_concept\",\n",
" \"75\": \"I-Severity\",\n",
" \"76\": \"I-Shape\",\n",
" \"77\": \"I-Sign_symptom\",\n",
" \"78\": \"I-Subject\",\n",
" \"79\": \"I-Texture\",\n",
" \"80\": \"I-Therapeutic_procedure\",\n",
" \"81\": \"I-Time\",\n",
" \"82\": \"I-Volume\",\n",
" \"83\": \"I-Weight\"\n",
" },\n",
" \"initializer_range\": 0.02,\n",
" \"label2id\": {\n",
" \"B-Activity\": 1,\n",
" \"B-Administration\": 2,\n",
" \"B-Age\": 3,\n",
" \"B-Area\": 4,\n",
" \"B-Biological_attribute\": 5,\n",
" \"B-Biological_structure\": 6,\n",
" \"B-Clinical_event\": 7,\n",
" \"B-Color\": 8,\n",
" \"B-Coreference\": 9,\n",
" \"B-Date\": 10,\n",
" \"B-Detailed_description\": 11,\n",
" \"B-Diagnostic_procedure\": 12,\n",
" \"B-Disease_disorder\": 13,\n",
" \"B-Distance\": 14,\n",
" \"B-Dosage\": 15,\n",
" \"B-Duration\": 16,\n",
" \"B-Family_history\": 17,\n",
" \"B-Frequency\": 18,\n",
" \"B-Height\": 19,\n",
" \"B-History\": 20,\n",
" \"B-Lab_value\": 21,\n",
" \"B-Mass\": 22,\n",
" \"B-Medication\": 23,\n",
" \"B-Non[biological](Detailed_description\": 24,\n",
" \"B-Nonbiological_location\": 25,\n",
" \"B-Occupation\": 26,\n",
" \"B-Other_entity\": 27,\n",
" \"B-Other_event\": 28,\n",
" \"B-Outcome\": 29,\n",
" \"B-Personal_[back](Biological_structure\": 30,\n",
" \"B-Personal_background\": 31,\n",
" \"B-Qualitative_concept\": 32,\n",
" \"B-Quantitative_concept\": 33,\n",
" \"B-Severity\": 34,\n",
" \"B-Sex\": 35,\n",
" \"B-Shape\": 36,\n",
" \"B-Sign_symptom\": 37,\n",
" \"B-Subject\": 38,\n",
" \"B-Texture\": 39,\n",
" \"B-Therapeutic_procedure\": 40,\n",
" \"B-Time\": 41,\n",
" \"B-Volume\": 42,\n",
" \"B-Weight\": 43,\n",
" \"I-Activity\": 44,\n",
" \"I-Administration\": 45,\n",
" \"I-Age\": 46,\n",
" \"I-Area\": 47,\n",
" \"I-Biological_attribute\": 48,\n",
" \"I-Biological_structure\": 49,\n",
" \"I-Clinical_event\": 50,\n",
" \"I-Color\": 51,\n",
" \"I-Coreference\": 52,\n",
" \"I-Date\": 53,\n",
" \"I-Detailed_description\": 54,\n",
" \"I-Diagnostic_procedure\": 55,\n",
" \"I-Disease_disorder\": 56,\n",
" \"I-Distance\": 57,\n",
" \"I-Dosage\": 58,\n",
" \"I-Duration\": 59,\n",
" \"I-Family_history\": 60,\n",
" \"I-Frequency\": 61,\n",
" \"I-Height\": 62,\n",
" \"I-History\": 63,\n",
" \"I-Lab_value\": 64,\n",
" \"I-Mass\": 65,\n",
" \"I-Medication\": 66,\n",
" \"I-Nonbiological_location\": 67,\n",
" \"I-Occupation\": 68,\n",
" \"I-Other_entity\": 69,\n",
" \"I-Other_event\": 70,\n",
" \"I-Outcome\": 71,\n",
" \"I-Personal_background\": 72,\n",
" \"I-Qualitative_concept\": 73,\n",
" \"I-Quantitative_concept\": 74,\n",
" \"I-Severity\": 75,\n",
" \"I-Shape\": 76,\n",
" \"I-Sign_symptom\": 77,\n",
" \"I-Subject\": 78,\n",
" \"I-Texture\": 79,\n",
" \"I-Therapeutic_procedure\": 80,\n",
" \"I-Time\": 81,\n",
" \"I-Volume\": 82,\n",
" \"I-Weight\": 83,\n",
" \"O\": 0\n",
" },\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"distilbert\",\n",
" \"n_heads\": 12,\n",
" \"n_layers\": 6,\n",
" \"pad_token_id\": 0,\n",
" \"qa_dropout\": 0.1,\n",
" \"seq_classif_dropout\": 0.2,\n",
" \"sinusoidal_pos_embds\": false,\n",
" \"tie_weights_\": true,\n",
" \"torch_dtype\": \"float32\",\n",
" \"transformers_version\": \"4.23.1\",\n",
" \"vocab_size\": 30522\n",
"}\n",
"\n",
"loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--d4data--biomedical-ner-all/snapshots/e87917020da1384aed6e93b1b46d68771f65ddab/pytorch_model.bin\n",
"All model checkpoint weights were used when initializing DistilBertForTokenClassification.\n",
"\n",
"All the weights of DistilBertForTokenClassification were initialized from the model checkpoint at d4data/biomedical-ner-all.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use DistilBertForTokenClassification for predictions without further training.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"args = TrainingArguments(\n",
" \"NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased\",\n",
" evaluation_strategy = IntervalStrategy.STEPS,\n",
" eval_steps = 50,\n",
" learning_rate=5e-5,\n",
" num_train_epochs=50,\n",
" weight_decay=0.01,\n",
" metric_for_best_model = 'f1',\n",
" load_best_model_at_end=True\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Z4DTxHPoTsGs",
"outputId": "9b8c9d65-4d5a-4fb6-b91e-d41efb1ac68c"
},
"execution_count": 56,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"PyTorch: setting up devices\n",
"The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"trainer = Trainer(\n",
" model=model,\n",
" args=args,\n",
" train_dataset=tokenized_datasets[\"train\"],\n",
" eval_dataset=tokenized_datasets[\"validation\"],\n",
" data_collator=data_collator,\n",
" compute_metrics=compute_metrics,\n",
" tokenizer=tokenizer,\n",
" callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]\n",
")\n",
"trainer.train()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "rVfzrBZMTv6x",
"outputId": "3cdd94b4-ab62-40dc-e180-1f7a22a4ef1b"
},
"execution_count": 57,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"***** Running training *****\n",
" Num examples = 555\n",
" Num Epochs = 50\n",
" Instantaneous batch size per device = 8\n",
" Total train batch size (w. parallel, distributed & accumulation) = 8\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 3500\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='900' max='3500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [ 900/3500 02:39 < 07:41, 5.63 it/s, Epoch 12/50]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>No log</td>\n",
" <td>0.162818</td>\n",
" <td>0.270997</td>\n",
" <td>0.309661</td>\n",
" <td>0.289042</td>\n",
" <td>0.937595</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>No log</td>\n",
" <td>0.166674</td>\n",
" <td>0.348107</td>\n",
" <td>0.517594</td>\n",
" <td>0.416259</td>\n",
" <td>0.938953</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>No log</td>\n",
" <td>0.165462</td>\n",
" <td>0.376270</td>\n",
" <td>0.521433</td>\n",
" <td>0.437115</td>\n",
" <td>0.938915</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>No log</td>\n",
" <td>0.146949</td>\n",
" <td>0.404802</td>\n",
" <td>0.463852</td>\n",
" <td>0.432320</td>\n",
" <td>0.945101</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>No log</td>\n",
" <td>0.165257</td>\n",
" <td>0.440503</td>\n",
" <td>0.492642</td>\n",
" <td>0.465116</td>\n",
" <td>0.944807</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>No log</td>\n",
" <td>0.187354</td>\n",
" <td>0.421611</td>\n",
" <td>0.431862</td>\n",
" <td>0.426675</td>\n",
" <td>0.945729</td>\n",
" </tr>\n",
" <tr>\n",
" <td>350</td>\n",
" <td>No log</td>\n",
" <td>0.203230</td>\n",
" <td>0.445104</td>\n",
" <td>0.479846</td>\n",
" <td>0.461823</td>\n",
" <td>0.945396</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>No log</td>\n",
" <td>0.215578</td>\n",
" <td>0.419140</td>\n",
" <td>0.523992</td>\n",
" <td>0.465738</td>\n",
" <td>0.944269</td>\n",
" </tr>\n",
" <tr>\n",
" <td>450</td>\n",
" <td>No log</td>\n",
" <td>0.228786</td>\n",
" <td>0.456535</td>\n",
" <td>0.467051</td>\n",
" <td>0.461733</td>\n",
" <td>0.945178</td>\n",
" </tr>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>0.103800</td>\n",
" <td>0.261185</td>\n",
" <td>0.427002</td>\n",
" <td>0.522073</td>\n",
" <td>0.469775</td>\n",
" <td>0.943641</td>\n",
" </tr>\n",
" <tr>\n",
" <td>550</td>\n",
" <td>0.103800</td>\n",
" <td>0.265154</td>\n",
" <td>0.424972</td>\n",
" <td>0.485605</td>\n",
" <td>0.453270</td>\n",
" <td>0.945165</td>\n",
" </tr>\n",
" <tr>\n",
" <td>600</td>\n",
" <td>0.103800</td>\n",
" <td>0.269958</td>\n",
" <td>0.383793</td>\n",
" <td>0.551504</td>\n",
" <td>0.452612</td>\n",
" <td>0.941015</td>\n",
" </tr>\n",
" <tr>\n",
" <td>650</td>\n",
" <td>0.103800</td>\n",
" <td>0.283041</td>\n",
" <td>0.449687</td>\n",
" <td>0.506078</td>\n",
" <td>0.476219</td>\n",
" <td>0.945652</td>\n",
" </tr>\n",
" <tr>\n",
" <td>700</td>\n",
" <td>0.103800</td>\n",
" <td>0.296066</td>\n",
" <td>0.423529</td>\n",
" <td>0.552783</td>\n",
" <td>0.479600</td>\n",
" <td>0.941335</td>\n",
" </tr>\n",
" <tr>\n",
" <td>750</td>\n",
" <td>0.103800</td>\n",
" <td>0.282410</td>\n",
" <td>0.444382</td>\n",
" <td>0.503519</td>\n",
" <td>0.472106</td>\n",
" <td>0.944884</td>\n",
" </tr>\n",
" <tr>\n",
" <td>800</td>\n",
" <td>0.103800</td>\n",
" <td>0.295058</td>\n",
" <td>0.400769</td>\n",
" <td>0.533589</td>\n",
" <td>0.457739</td>\n",
" <td>0.943615</td>\n",
" </tr>\n",
" <tr>\n",
" <td>850</td>\n",
" <td>0.103800</td>\n",
" <td>0.294637</td>\n",
" <td>0.432018</td>\n",
" <td>0.504159</td>\n",
" <td>0.465309</td>\n",
" <td>0.944320</td>\n",
" </tr>\n",
" <tr>\n",
" <td>900</td>\n",
" <td>0.103800</td>\n",
" <td>0.296676</td>\n",
" <td>0.411402</td>\n",
" <td>0.512476</td>\n",
" <td>0.456410</td>\n",
" <td>0.942796</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"Saving model checkpoint to NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500\n",
"Configuration saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/config.json\n",
"Model weights saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/pytorch_model.bin\n",
"tokenizer config file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/tokenizer_config.json\n",
"Special tokens file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/special_tokens_map.json\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n",
"Loading best model from NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500 (score: 0.4697754749568221).\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=900, training_loss=0.06402364306979709, metrics={'train_runtime': 159.6722, 'train_samples_per_second': 173.794, 'train_steps_per_second': 21.92, 'total_flos': 932785215873192.0, 'train_loss': 0.06402364306979709, 'epoch': 12.86})"
]
},
"metadata": {},
"execution_count": 57
}
]
},
{
"cell_type": "code",
"source": [
"trainer.save_model('model/distilbert-base-uncased-all-tokens')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "g_r4zDTuT6n0",
"outputId": "d9cf6e0f-4d72-4dc3-bdaf-fe162acfde8f"
},
"execution_count": 58,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Saving model checkpoint to model/distilbert-base-uncased-all-tokens\n",
"Configuration saved in model/distilbert-base-uncased-all-tokens/config.json\n",
"Model weights saved in model/distilbert-base-uncased-all-tokens/pytorch_model.bin\n",
"tokenizer config file saved in model/distilbert-base-uncased-all-tokens/tokenizer_config.json\n",
"Special tokens file saved in model/distilbert-base-uncased-all-tokens/special_tokens_map.json\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Model 1 - d4data/biomedical-ner-all\n",
"\n",
"### Sentence Based Modelling"
],
"metadata": {
"id": "U9LTC6H7Ut3-"
}
},
{
"cell_type": "code",
"source": [
"dic = {\"tokens\": sent_tokenized, \"ner_tags\": label_sent_tokenized} #Use this option if you want to check the model performance with sentences tokenized by \". \" b"
],
"metadata": {
"id": "YntZAcxIUpmM"
},
"execution_count": 59,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"id": "c0p6sqVqVDhK"
},
"outputs": [],
"source": [
"dataset = Dataset.from_dict(dic)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "54fb3085-2261-496a-f6d0-60f93f654540",
"id": "dKokCRtaVDhK"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 11668\n",
"})"
]
},
"metadata": {},
"execution_count": 61
}
],
"source": [
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"id": "yaiKWzNRVDhK"
},
"outputs": [],
"source": [
"#For training, validation, and test partitions\n",
"\"\"\"\n",
"#Train, val, test partitions\n",
"train_test = dataset.train_test_split()\n",
"test_val = train_test['test'].train_test_split()\n",
"raw_datasets = DatasetDict({\n",
" 'train': train_test['train'],\n",
" 'validation': test_val['train'],\n",
" 'test': test_val['test']\n",
" })\n",
"\"\"\"\n",
"\n",
"#Just for training and validation partitions\n",
"train_test = dataset.train_test_split()\n",
"raw_datasets = DatasetDict({\n",
" 'train': train_test['train'],\n",
" 'validation': train_test['test']\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3f04c836-fa6c-4f6b-9be8-1a3b77910f74",
"id": "bJryyZX2VDhL"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 8751\n",
" })\n",
" validation: Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 2917\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 63
}
],
"source": [
"raw_datasets"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7cb91446-41f5-4257-9d36-aac32e1a2d8b",
"id": "p9q9WmGpVDhL"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
]
},
"metadata": {},
"execution_count": 64
}
],
"source": [
"raw_datasets[\"train\"][0][\"ner_tags\"]\n",
"#raw_datasets[\"train\"][0][\"pos_tags\"]\n",
"#raw_datasets[\"train\"][0][\"chunk_tags\"]"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0dd389dd-cb44-4235-c327-f58e5d8f2f1a",
"id": "K7Sip5njVDhL"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 8751\n",
"})"
]
},
"metadata": {},
"execution_count": 65
}
],
"source": [
"raw_datasets['train']"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fc667def-0a5f-481d-85c3-8ff5f80a5eb5",
"id": "BI420tEFVDhL"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['O', 'B', 'I']"
]
},
"metadata": {},
"execution_count": 66
}
],
"source": [
"label_names = ['O','B','I']\n",
"label_names"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d11245fb-f724-440f-954e-064b90d32579",
"id": "fvbDPubIVDhL"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Since 2006 she had tried several times to lose weight , without success . \n",
"O O O O O O O O O O O O O O \n"
]
}
],
"source": [
"words = raw_datasets[\"train\"][0][\"tokens\"]\n",
"labels = [int(n) for n in raw_datasets[\"train\"][0][\"ner_tags\"]]\n",
"#labels = raw_datasets[\"train\"][0][\"pos_tags\"]\n",
"#labels = raw_datasets[\"train\"][0][\"chunk_tags\"]\n",
"line1 = \"\"\n",
"line2 = \"\"\n",
"for word, label in zip(words, labels):\n",
" full_label = label_names[label]\n",
" max_length = max(len(word), len(full_label))\n",
" line1 += word + \" \" * (max_length - len(word) + 1)\n",
" line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n",
"\n",
"print(line1)\n",
"print(line2)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9d061e6c-4cd9-472d-b873-814d709efb63",
"id": "LXmlD43QVDhL"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--d4data--biomedical-ner-all/snapshots/e87917020da1384aed6e93b1b46d68771f65ddab/vocab.txt\n",
"loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--d4data--biomedical-ner-all/snapshots/e87917020da1384aed6e93b1b46d68771f65ddab/tokenizer.json\n",
"loading file added_tokens.json from cache at None\n",
"loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--d4data--biomedical-ner-all/snapshots/e87917020da1384aed6e93b1b46d68771f65ddab/special_tokens_map.json\n",
"loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--d4data--biomedical-ner-all/snapshots/e87917020da1384aed6e93b1b46d68771f65ddab/tokenizer_config.json\n"
]
}
],
"source": [
"model_checkpoint = \"d4data/biomedical-ner-all\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, add_prefix_space=True)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e11cebbd-2fd8-4db3-f014-d52f80ae104c",
"id": "rUn3zUd9VDhM"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 69
}
],
"source": [
"tokenizer.is_fast"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0196fde7-250b-4486-f63f-2dcea8848e5d",
"id": "LXxYSaDLVDhM"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['[CLS]',\n",
" 'since',\n",
" '2006',\n",
" 'she',\n",
" 'had',\n",
" 'tried',\n",
" 'several',\n",
" 'times',\n",
" 'to',\n",
" 'lose',\n",
" 'weight',\n",
" ',',\n",
" 'without',\n",
" 'success',\n",
" '.',\n",
" '[SEP]']"
]
},
"metadata": {},
"execution_count": 70
}
],
"source": [
"inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n",
"inputs.tokens()"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "48b11a85-7ca4-4589-dcba-21833e5b6c55",
"id": "-bG1VI2NVDhM"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"[-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]\n"
]
}
],
"source": [
"labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
"word_ids = inputs.word_ids()\n",
"print(labels)\n",
"print(align_labels_with_tokens(labels, word_ids))"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"40c2b37fa07f44648cecc9b7e406e7e2",
"69ed5fe8ed6046acb4202689c065f858",
"bbd9cf7a77aa48fda3a648583ed02b08",
"ebe8b2b35e884fd28bb42eacf01ff07c",
"ddb14bc1d5d4437a9ee4a895846e7d29",
"669c24c6309f46cbbdcd0c764143e74f",
"d1e4665beafa4bbeb25d0e9e8447a5a9",
"09217bdc1e2145eb84cc97207595e6f0",
"b294af01ac5f483dacbe2e1c40fdf223",
"c1e27e9184204d618ce59b97f7302335",
"2e63f2af443d448aaaddf81127def048",
"9239cc2fd1d94d86986b7f395de70fca",
"f1e8d31b67db4089ab1b036bda341617",
"33be40ebcab54ff68855f1145cf5e1d6",
"d96c111f09d74a0c9816328f88d9e45b",
"3907dc2aaa484877aee9beab8a6888d4",
"aaacfb0f3bd1427ea44ec84c28a2aaf7",
"cb92e843491142e8a2a4008223a90d02",
"52169f264141463e94a7761a4ffb3f7a",
"e509b790873740b59aa2f52875ca2038",
"a395318bce7348d78ca83a308552f042",
"a2bb171f700743559e1d2c472c8289ef"
]
},
"outputId": "aad10f99-e5fa-423c-f259-a12310b18d1d",
"id": "0OVZq1BtVDhM"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/9 [00:00<?, ?ba/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "40c2b37fa07f44648cecc9b7e406e7e2"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/3 [00:00<?, ?ba/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9239cc2fd1d94d86986b7f395de70fca"
}
},
"metadata": {}
}
],
"source": [
"tokenized_datasets = raw_datasets.map(\n",
" tokenize_and_align_labels,\n",
" batched=True,\n",
" remove_columns=raw_datasets[\"train\"].column_names,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"id": "rdl_EpWuVDhN"
},
"outputs": [],
"source": [
"data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d2768d3a-b399-4236-a9c4-cfff18f31d1c",
"id": "gY7K46A2VDhN"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"You're using a DistilBertTokenizerFast 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, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
" -100, -100, -100, -100, -100, -100, -100, -100, -100, -100],\n",
" [-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 1, 2, 2, 2, 2, 2, 2, 2, 0, -100]])"
]
},
"metadata": {},
"execution_count": 74
}
],
"source": [
"batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
"batch[\"labels\"]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"id": "PNC5_yuKVDhN"
},
"outputs": [],
"source": [
"id2label = {str(i): label for i, label in enumerate(label_names)}\n",
"label2id = {v: k for k, v in id2label.items()}"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ba1aa394-ff7a-471e-a8dc-d35755f501ed",
"id": "AbRkJHvbVDhN"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--d4data--biomedical-ner-all/snapshots/e87917020da1384aed6e93b1b46d68771f65ddab/config.json\n",
"Model config DistilBertConfig {\n",
" \"_name_or_path\": \"d4data/biomedical-ner-all\",\n",
" \"activation\": \"gelu\",\n",
" \"architectures\": [\n",
" \"DistilBertForTokenClassification\"\n",
" ],\n",
" \"attention_dropout\": 0.1,\n",
" \"dim\": 768,\n",
" \"dropout\": 0.1,\n",
" \"hidden_dim\": 3072,\n",
" \"id2label\": {\n",
" \"0\": \"O\",\n",
" \"1\": \"B-Activity\",\n",
" \"2\": \"B-Administration\",\n",
" \"3\": \"B-Age\",\n",
" \"4\": \"B-Area\",\n",
" \"5\": \"B-Biological_attribute\",\n",
" \"6\": \"B-Biological_structure\",\n",
" \"7\": \"B-Clinical_event\",\n",
" \"8\": \"B-Color\",\n",
" \"9\": \"B-Coreference\",\n",
" \"10\": \"B-Date\",\n",
" \"11\": \"B-Detailed_description\",\n",
" \"12\": \"B-Diagnostic_procedure\",\n",
" \"13\": \"B-Disease_disorder\",\n",
" \"14\": \"B-Distance\",\n",
" \"15\": \"B-Dosage\",\n",
" \"16\": \"B-Duration\",\n",
" \"17\": \"B-Family_history\",\n",
" \"18\": \"B-Frequency\",\n",
" \"19\": \"B-Height\",\n",
" \"20\": \"B-History\",\n",
" \"21\": \"B-Lab_value\",\n",
" \"22\": \"B-Mass\",\n",
" \"23\": \"B-Medication\",\n",
" \"24\": \"B-Non[biological](Detailed_description\",\n",
" \"25\": \"B-Nonbiological_location\",\n",
" \"26\": \"B-Occupation\",\n",
" \"27\": \"B-Other_entity\",\n",
" \"28\": \"B-Other_event\",\n",
" \"29\": \"B-Outcome\",\n",
" \"30\": \"B-Personal_[back](Biological_structure\",\n",
" \"31\": \"B-Personal_background\",\n",
" \"32\": \"B-Qualitative_concept\",\n",
" \"33\": \"B-Quantitative_concept\",\n",
" \"34\": \"B-Severity\",\n",
" \"35\": \"B-Sex\",\n",
" \"36\": \"B-Shape\",\n",
" \"37\": \"B-Sign_symptom\",\n",
" \"38\": \"B-Subject\",\n",
" \"39\": \"B-Texture\",\n",
" \"40\": \"B-Therapeutic_procedure\",\n",
" \"41\": \"B-Time\",\n",
" \"42\": \"B-Volume\",\n",
" \"43\": \"B-Weight\",\n",
" \"44\": \"I-Activity\",\n",
" \"45\": \"I-Administration\",\n",
" \"46\": \"I-Age\",\n",
" \"47\": \"I-Area\",\n",
" \"48\": \"I-Biological_attribute\",\n",
" \"49\": \"I-Biological_structure\",\n",
" \"50\": \"I-Clinical_event\",\n",
" \"51\": \"I-Color\",\n",
" \"52\": \"I-Coreference\",\n",
" \"53\": \"I-Date\",\n",
" \"54\": \"I-Detailed_description\",\n",
" \"55\": \"I-Diagnostic_procedure\",\n",
" \"56\": \"I-Disease_disorder\",\n",
" \"57\": \"I-Distance\",\n",
" \"58\": \"I-Dosage\",\n",
" \"59\": \"I-Duration\",\n",
" \"60\": \"I-Family_history\",\n",
" \"61\": \"I-Frequency\",\n",
" \"62\": \"I-Height\",\n",
" \"63\": \"I-History\",\n",
" \"64\": \"I-Lab_value\",\n",
" \"65\": \"I-Mass\",\n",
" \"66\": \"I-Medication\",\n",
" \"67\": \"I-Nonbiological_location\",\n",
" \"68\": \"I-Occupation\",\n",
" \"69\": \"I-Other_entity\",\n",
" \"70\": \"I-Other_event\",\n",
" \"71\": \"I-Outcome\",\n",
" \"72\": \"I-Personal_background\",\n",
" \"73\": \"I-Qualitative_concept\",\n",
" \"74\": \"I-Quantitative_concept\",\n",
" \"75\": \"I-Severity\",\n",
" \"76\": \"I-Shape\",\n",
" \"77\": \"I-Sign_symptom\",\n",
" \"78\": \"I-Subject\",\n",
" \"79\": \"I-Texture\",\n",
" \"80\": \"I-Therapeutic_procedure\",\n",
" \"81\": \"I-Time\",\n",
" \"82\": \"I-Volume\",\n",
" \"83\": \"I-Weight\"\n",
" },\n",
" \"initializer_range\": 0.02,\n",
" \"label2id\": {\n",
" \"B-Activity\": 1,\n",
" \"B-Administration\": 2,\n",
" \"B-Age\": 3,\n",
" \"B-Area\": 4,\n",
" \"B-Biological_attribute\": 5,\n",
" \"B-Biological_structure\": 6,\n",
" \"B-Clinical_event\": 7,\n",
" \"B-Color\": 8,\n",
" \"B-Coreference\": 9,\n",
" \"B-Date\": 10,\n",
" \"B-Detailed_description\": 11,\n",
" \"B-Diagnostic_procedure\": 12,\n",
" \"B-Disease_disorder\": 13,\n",
" \"B-Distance\": 14,\n",
" \"B-Dosage\": 15,\n",
" \"B-Duration\": 16,\n",
" \"B-Family_history\": 17,\n",
" \"B-Frequency\": 18,\n",
" \"B-Height\": 19,\n",
" \"B-History\": 20,\n",
" \"B-Lab_value\": 21,\n",
" \"B-Mass\": 22,\n",
" \"B-Medication\": 23,\n",
" \"B-Non[biological](Detailed_description\": 24,\n",
" \"B-Nonbiological_location\": 25,\n",
" \"B-Occupation\": 26,\n",
" \"B-Other_entity\": 27,\n",
" \"B-Other_event\": 28,\n",
" \"B-Outcome\": 29,\n",
" \"B-Personal_[back](Biological_structure\": 30,\n",
" \"B-Personal_background\": 31,\n",
" \"B-Qualitative_concept\": 32,\n",
" \"B-Quantitative_concept\": 33,\n",
" \"B-Severity\": 34,\n",
" \"B-Sex\": 35,\n",
" \"B-Shape\": 36,\n",
" \"B-Sign_symptom\": 37,\n",
" \"B-Subject\": 38,\n",
" \"B-Texture\": 39,\n",
" \"B-Therapeutic_procedure\": 40,\n",
" \"B-Time\": 41,\n",
" \"B-Volume\": 42,\n",
" \"B-Weight\": 43,\n",
" \"I-Activity\": 44,\n",
" \"I-Administration\": 45,\n",
" \"I-Age\": 46,\n",
" \"I-Area\": 47,\n",
" \"I-Biological_attribute\": 48,\n",
" \"I-Biological_structure\": 49,\n",
" \"I-Clinical_event\": 50,\n",
" \"I-Color\": 51,\n",
" \"I-Coreference\": 52,\n",
" \"I-Date\": 53,\n",
" \"I-Detailed_description\": 54,\n",
" \"I-Diagnostic_procedure\": 55,\n",
" \"I-Disease_disorder\": 56,\n",
" \"I-Distance\": 57,\n",
" \"I-Dosage\": 58,\n",
" \"I-Duration\": 59,\n",
" \"I-Family_history\": 60,\n",
" \"I-Frequency\": 61,\n",
" \"I-Height\": 62,\n",
" \"I-History\": 63,\n",
" \"I-Lab_value\": 64,\n",
" \"I-Mass\": 65,\n",
" \"I-Medication\": 66,\n",
" \"I-Nonbiological_location\": 67,\n",
" \"I-Occupation\": 68,\n",
" \"I-Other_entity\": 69,\n",
" \"I-Other_event\": 70,\n",
" \"I-Outcome\": 71,\n",
" \"I-Personal_background\": 72,\n",
" \"I-Qualitative_concept\": 73,\n",
" \"I-Quantitative_concept\": 74,\n",
" \"I-Severity\": 75,\n",
" \"I-Shape\": 76,\n",
" \"I-Sign_symptom\": 77,\n",
" \"I-Subject\": 78,\n",
" \"I-Texture\": 79,\n",
" \"I-Therapeutic_procedure\": 80,\n",
" \"I-Time\": 81,\n",
" \"I-Volume\": 82,\n",
" \"I-Weight\": 83,\n",
" \"O\": 0\n",
" },\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"distilbert\",\n",
" \"n_heads\": 12,\n",
" \"n_layers\": 6,\n",
" \"pad_token_id\": 0,\n",
" \"qa_dropout\": 0.1,\n",
" \"seq_classif_dropout\": 0.2,\n",
" \"sinusoidal_pos_embds\": false,\n",
" \"tie_weights_\": true,\n",
" \"torch_dtype\": \"float32\",\n",
" \"transformers_version\": \"4.23.1\",\n",
" \"vocab_size\": 30522\n",
"}\n",
"\n",
"loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--d4data--biomedical-ner-all/snapshots/e87917020da1384aed6e93b1b46d68771f65ddab/pytorch_model.bin\n",
"All model checkpoint weights were used when initializing DistilBertForTokenClassification.\n",
"\n",
"All the weights of DistilBertForTokenClassification were initialized from the model checkpoint at d4data/biomedical-ner-all.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use DistilBertForTokenClassification for predictions without further training.\n"
]
}
],
"source": [
"model = AutoModelForTokenClassification.from_pretrained( \n",
" model_checkpoint\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MQILaROUVDhN",
"outputId": "a548d6da-abe4-417a-99eb-f65d2c5b3273"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"PyTorch: setting up devices\n",
"The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
]
}
],
"source": [
"args = TrainingArguments(\n",
" \"NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased\",\n",
" evaluation_strategy = IntervalStrategy.STEPS,\n",
" eval_steps = 50,\n",
" learning_rate=5e-5,\n",
" num_train_epochs=50,\n",
" weight_decay=0.01,\n",
" metric_for_best_model = 'f1',\n",
" load_best_model_at_end=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "cb0a12c3-0ce4-4d59-d773-f2f31ff05b68",
"id": "MRJatT86VDhN"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" FutureWarning,\n",
"***** Running training *****\n",
" Num examples = 8751\n",
" Num Epochs = 50\n",
" Instantaneous batch size per device = 8\n",
" Total train batch size (w. parallel, distributed & accumulation) = 8\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 54700\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1650' max='54700' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [ 1650/54700 04:10 < 2:14:23, 6.58 it/s, Epoch 1/50]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>No log</td>\n",
" <td>0.220473</td>\n",
" <td>0.223507</td>\n",
" <td>0.240041</td>\n",
" <td>0.231479</td>\n",
" <td>0.923262</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>No log</td>\n",
" <td>0.178907</td>\n",
" <td>0.233188</td>\n",
" <td>0.249353</td>\n",
" <td>0.241000</td>\n",
" <td>0.932774</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>No log</td>\n",
" <td>0.170712</td>\n",
" <td>0.209794</td>\n",
" <td>0.210554</td>\n",
" <td>0.210173</td>\n",
" <td>0.934965</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>No log</td>\n",
" <td>0.161026</td>\n",
" <td>0.308015</td>\n",
" <td>0.341956</td>\n",
" <td>0.324099</td>\n",
" <td>0.937756</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>No log</td>\n",
" <td>0.167800</td>\n",
" <td>0.290117</td>\n",
" <td>0.282462</td>\n",
" <td>0.286239</td>\n",
" <td>0.940074</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>No log</td>\n",
" <td>0.179418</td>\n",
" <td>0.388704</td>\n",
" <td>0.302638</td>\n",
" <td>0.340314</td>\n",
" <td>0.939863</td>\n",
" </tr>\n",
" <tr>\n",
" <td>350</td>\n",
" <td>No log</td>\n",
" <td>0.160154</td>\n",
" <td>0.333333</td>\n",
" <td>0.443870</td>\n",
" <td>0.380741</td>\n",
" <td>0.938609</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>No log</td>\n",
" <td>0.199905</td>\n",
" <td>0.207132</td>\n",
" <td>0.204346</td>\n",
" <td>0.205729</td>\n",
" <td>0.933585</td>\n",
" </tr>\n",
" <tr>\n",
" <td>450</td>\n",
" <td>No log</td>\n",
" <td>0.154181</td>\n",
" <td>0.349614</td>\n",
" <td>0.422142</td>\n",
" <td>0.382470</td>\n",
" <td>0.940611</td>\n",
" </tr>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>0.213900</td>\n",
" <td>0.154374</td>\n",
" <td>0.376731</td>\n",
" <td>0.422142</td>\n",
" <td>0.398146</td>\n",
" <td>0.941137</td>\n",
" </tr>\n",
" <tr>\n",
" <td>550</td>\n",
" <td>0.213900</td>\n",
" <td>0.154931</td>\n",
" <td>0.426748</td>\n",
" <td>0.432488</td>\n",
" <td>0.429599</td>\n",
" <td>0.943276</td>\n",
" </tr>\n",
" <tr>\n",
" <td>600</td>\n",
" <td>0.213900</td>\n",
" <td>0.147228</td>\n",
" <td>0.402466</td>\n",
" <td>0.472840</td>\n",
" <td>0.434824</td>\n",
" <td>0.942075</td>\n",
" </tr>\n",
" <tr>\n",
" <td>650</td>\n",
" <td>0.213900</td>\n",
" <td>0.154059</td>\n",
" <td>0.274133</td>\n",
" <td>0.265908</td>\n",
" <td>0.269958</td>\n",
" <td>0.939326</td>\n",
" </tr>\n",
" <tr>\n",
" <td>700</td>\n",
" <td>0.213900</td>\n",
" <td>0.158030</td>\n",
" <td>0.389341</td>\n",
" <td>0.544232</td>\n",
" <td>0.453937</td>\n",
" <td>0.940095</td>\n",
" </tr>\n",
" <tr>\n",
" <td>750</td>\n",
" <td>0.213900</td>\n",
" <td>0.137712</td>\n",
" <td>0.373972</td>\n",
" <td>0.399897</td>\n",
" <td>0.386500</td>\n",
" <td>0.946952</td>\n",
" </tr>\n",
" <tr>\n",
" <td>800</td>\n",
" <td>0.213900</td>\n",
" <td>0.145364</td>\n",
" <td>0.385280</td>\n",
" <td>0.530781</td>\n",
" <td>0.446475</td>\n",
" <td>0.941485</td>\n",
" </tr>\n",
" <tr>\n",
" <td>850</td>\n",
" <td>0.213900</td>\n",
" <td>0.148491</td>\n",
" <td>0.401239</td>\n",
" <td>0.535954</td>\n",
" <td>0.458915</td>\n",
" <td>0.940895</td>\n",
" </tr>\n",
" <tr>\n",
" <td>900</td>\n",
" <td>0.213900</td>\n",
" <td>0.149123</td>\n",
" <td>0.374374</td>\n",
" <td>0.464046</td>\n",
" <td>0.414414</td>\n",
" <td>0.946784</td>\n",
" </tr>\n",
" <tr>\n",
" <td>950</td>\n",
" <td>0.213900</td>\n",
" <td>0.142598</td>\n",
" <td>0.405747</td>\n",
" <td>0.365235</td>\n",
" <td>0.384427</td>\n",
" <td>0.946963</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1000</td>\n",
" <td>0.153200</td>\n",
" <td>0.143212</td>\n",
" <td>0.449354</td>\n",
" <td>0.341956</td>\n",
" <td>0.388367</td>\n",
" <td>0.945857</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1050</td>\n",
" <td>0.153200</td>\n",
" <td>0.146789</td>\n",
" <td>0.435653</td>\n",
" <td>0.495603</td>\n",
" <td>0.463698</td>\n",
" <td>0.947300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1100</td>\n",
" <td>0.153200</td>\n",
" <td>0.137099</td>\n",
" <td>0.421190</td>\n",
" <td>0.501811</td>\n",
" <td>0.457979</td>\n",
" <td>0.948090</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1150</td>\n",
" <td>0.153200</td>\n",
" <td>0.144017</td>\n",
" <td>0.437966</td>\n",
" <td>0.485773</td>\n",
" <td>0.460633</td>\n",
" <td>0.948532</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1200</td>\n",
" <td>0.153200</td>\n",
" <td>0.142823</td>\n",
" <td>0.459889</td>\n",
" <td>0.344025</td>\n",
" <td>0.393608</td>\n",
" <td>0.947416</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1250</td>\n",
" <td>0.153200</td>\n",
" <td>0.163925</td>\n",
" <td>0.380834</td>\n",
" <td>0.476979</td>\n",
" <td>0.423519</td>\n",
" <td>0.947732</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1300</td>\n",
" <td>0.153200</td>\n",
" <td>0.153968</td>\n",
" <td>0.461087</td>\n",
" <td>0.447491</td>\n",
" <td>0.454187</td>\n",
" <td>0.948711</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1350</td>\n",
" <td>0.153200</td>\n",
" <td>0.157536</td>\n",
" <td>0.465220</td>\n",
" <td>0.432488</td>\n",
" <td>0.448257</td>\n",
" <td>0.949680</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1400</td>\n",
" <td>0.153200</td>\n",
" <td>0.149706</td>\n",
" <td>0.421693</td>\n",
" <td>0.394206</td>\n",
" <td>0.407487</td>\n",
" <td>0.947236</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1450</td>\n",
" <td>0.153200</td>\n",
" <td>0.178025</td>\n",
" <td>0.472966</td>\n",
" <td>0.448008</td>\n",
" <td>0.460149</td>\n",
" <td>0.947447</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1500</td>\n",
" <td>0.113500</td>\n",
" <td>0.146669</td>\n",
" <td>0.440809</td>\n",
" <td>0.529747</td>\n",
" <td>0.481203</td>\n",
" <td>0.947689</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1550</td>\n",
" <td>0.113500</td>\n",
" <td>0.144341</td>\n",
" <td>0.480186</td>\n",
" <td>0.426280</td>\n",
" <td>0.451631</td>\n",
" <td>0.950344</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1600</td>\n",
" <td>0.113500</td>\n",
" <td>0.140071</td>\n",
" <td>0.446570</td>\n",
" <td>0.495085</td>\n",
" <td>0.469578</td>\n",
" <td>0.948690</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1650</td>\n",
" <td>0.113500</td>\n",
" <td>0.175828</td>\n",
" <td>0.432393</td>\n",
" <td>0.309364</td>\n",
" <td>0.360676</td>\n",
" <td>0.945825</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
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" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"Saving model checkpoint to NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500\n",
"Configuration saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/config.json\n",
"Model weights saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/pytorch_model.bin\n",
"tokenizer config file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/tokenizer_config.json\n",
"Special tokens file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/special_tokens_map.json\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
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" Batch size = 8\n",
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" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
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"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"Saving model checkpoint to NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000\n",
"Configuration saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000/config.json\n",
"Model weights saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000/pytorch_model.bin\n",
"tokenizer config file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000/tokenizer_config.json\n",
"Special tokens file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000/special_tokens_map.json\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"Saving model checkpoint to NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1500\n",
"Configuration saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1500/config.json\n",
"Model weights saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1500/pytorch_model.bin\n",
"tokenizer config file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1500/tokenizer_config.json\n",
"Special tokens file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1500/special_tokens_map.json\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n",
"Loading best model from NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1500 (score: 0.48120300751879697).\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=1650, training_loss=0.15572210947672527, metrics={'train_runtime': 250.5398, 'train_samples_per_second': 1746.429, 'train_steps_per_second': 218.329, 'total_flos': 244690187133744.0, 'train_loss': 0.15572210947672527, 'epoch': 1.51})"
]
},
"metadata": {},
"execution_count": 78
}
],
"source": [
"trainer = Trainer(\n",
" model=model,\n",
" args=args,\n",
" train_dataset=tokenized_datasets[\"train\"],\n",
" eval_dataset=tokenized_datasets[\"validation\"],\n",
" data_collator=data_collator,\n",
" compute_metrics=compute_metrics,\n",
" tokenizer=tokenizer,\n",
" callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]\n",
")\n",
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "cb8d8bbc-fe8e-4926-d201-8a64dc0edcdb",
"id": "P2GfgJz0VDhO"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Saving model checkpoint to model/distilbert-base-uncased-sentence\n",
"Configuration saved in model/distilbert-base-uncased-sentence/config.json\n",
"Model weights saved in model/distilbert-base-uncased-sentence/pytorch_model.bin\n",
"tokenizer config file saved in model/distilbert-base-uncased-sentence/tokenizer_config.json\n",
"Special tokens file saved in model/distilbert-base-uncased-sentence/special_tokens_map.json\n"
]
}
],
"source": [
"trainer.save_model('model/distilbert-base-uncased-sentence')"
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "lzL_QhxW7Dha"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Model 2 - pucpr/clinicalnerpt-medical\n",
"\n",
"Whole document based tokenization"
],
"metadata": {
"id": "GgGucecD600w"
}
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
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"e33897e8fa4841308c55b19352876ab1",
"909d7d349769402a8c20b02b35eafbd9",
"6139e3d551044671a79e15e8adf351ab",
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"804bf9b8a2154399a05dd0860f4dfd89",
"66fca4d7946240c3b08ba51fac82f2ae",
"6cefb3be5be9488ca033ed9908c6a8f5",
"2d0f2804db004da0914e4733ce96b749",
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"838416bfbee0400299abff324c4825bc",
"f7584b77d97f4d48aa5b50bae2df49f8",
"0a5cd97f8a914ff89fd27aed7b38164d",
"ad4c308ebd574e909d31b161580b9064",
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]
},
"id": "axyP0XOOKpLg",
"outputId": "da8d34ae-66c7-459a-99be-56d7a434ec80"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading: 0%| | 0.00/151 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f0fc94c6df4c432f9e1edcfceaf44edd"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading: 0%| | 0.00/1.05k [00:00<?, ?B/s]"
],
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{
"output_type": "stream",
"name": "stderr",
"text": [
"loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/config.json\n",
"Model config BertConfig {\n",
" \"_name_or_path\": \"pucpr/clinicalnerpt-medical\",\n",
" \"_num_labels\": 3,\n",
" \"architectures\": [\n",
" \"BertForTokenClassification\"\n",
" ],\n",
" \"attention_probs_dropout_prob\": 0.1,\n",
" \"classifier_dropout\": null,\n",
" \"directionality\": \"bidi\",\n",
" \"eos_token_ids\": null,\n",
" \"gradient_checkpointing\": false,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"id2label\": {\n",
" \"0\": \"O\",\n",
" \"1\": \"B-MedicalDevice\",\n",
" \"2\": \"I-MedicalDevice\"\n",
" },\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"label2id\": {\n",
" \"B-MedicalDevice\": 1,\n",
" \"I-MedicalDevice\": 2,\n",
" \"O\": 0\n",
" },\n",
" \"layer_norm_eps\": 1e-12,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"bert\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_hidden_layers\": 12,\n",
" \"output_past\": true,\n",
" \"pad_token_id\": null,\n",
" \"pooler_fc_size\": 768,\n",
" \"pooler_num_attention_heads\": 12,\n",
" \"pooler_num_fc_layers\": 3,\n",
" \"pooler_size_per_head\": 128,\n",
" \"pooler_type\": \"first_token_transform\",\n",
" \"position_embedding_type\": \"absolute\",\n",
" \"transformers_version\": \"4.23.1\",\n",
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" \"use_cache\": true,\n",
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"}\n",
"\n"
]
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"output_type": "stream",
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"text": [
"loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/vocab.txt\n",
"loading file tokenizer.json from cache at None\n",
"loading file added_tokens.json from cache at None\n",
"loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/special_tokens_map.json\n",
"loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/tokenizer_config.json\n",
"loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/config.json\n",
"Model config BertConfig {\n",
" \"_name_or_path\": \"pucpr/clinicalnerpt-medical\",\n",
" \"_num_labels\": 3,\n",
" \"architectures\": [\n",
" \"BertForTokenClassification\"\n",
" ],\n",
" \"attention_probs_dropout_prob\": 0.1,\n",
" \"classifier_dropout\": null,\n",
" \"directionality\": \"bidi\",\n",
" \"eos_token_ids\": null,\n",
" \"gradient_checkpointing\": false,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"id2label\": {\n",
" \"0\": \"O\",\n",
" \"1\": \"B-MedicalDevice\",\n",
" \"2\": \"I-MedicalDevice\"\n",
" },\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"label2id\": {\n",
" \"B-MedicalDevice\": 1,\n",
" \"I-MedicalDevice\": 2,\n",
" \"O\": 0\n",
" },\n",
" \"layer_norm_eps\": 1e-12,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"bert\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_hidden_layers\": 12,\n",
" \"output_past\": true,\n",
" \"pad_token_id\": null,\n",
" \"pooler_fc_size\": 768,\n",
" \"pooler_num_attention_heads\": 12,\n",
" \"pooler_num_fc_layers\": 3,\n",
" \"pooler_size_per_head\": 128,\n",
" \"pooler_type\": \"first_token_transform\",\n",
" \"position_embedding_type\": \"absolute\",\n",
" \"transformers_version\": \"4.23.1\",\n",
" \"type_vocab_size\": 2,\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 119547\n",
"}\n",
"\n",
"loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/config.json\n",
"Model config BertConfig {\n",
" \"_name_or_path\": \"pucpr/clinicalnerpt-medical\",\n",
" \"_num_labels\": 3,\n",
" \"architectures\": [\n",
" \"BertForTokenClassification\"\n",
" ],\n",
" \"attention_probs_dropout_prob\": 0.1,\n",
" \"classifier_dropout\": null,\n",
" \"directionality\": \"bidi\",\n",
" \"eos_token_ids\": null,\n",
" \"gradient_checkpointing\": false,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"id2label\": {\n",
" \"0\": \"O\",\n",
" \"1\": \"B-MedicalDevice\",\n",
" \"2\": \"I-MedicalDevice\"\n",
" },\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"label2id\": {\n",
" \"B-MedicalDevice\": 1,\n",
" \"I-MedicalDevice\": 2,\n",
" \"O\": 0\n",
" },\n",
" \"layer_norm_eps\": 1e-12,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"bert\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_hidden_layers\": 12,\n",
" \"output_past\": true,\n",
" \"pad_token_id\": null,\n",
" \"pooler_fc_size\": 768,\n",
" \"pooler_num_attention_heads\": 12,\n",
" \"pooler_num_fc_layers\": 3,\n",
" \"pooler_size_per_head\": 128,\n",
" \"pooler_type\": \"first_token_transform\",\n",
" \"position_embedding_type\": \"absolute\",\n",
" \"transformers_version\": \"4.23.1\",\n",
" \"type_vocab_size\": 2,\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 119547\n",
"}\n",
"\n"
]
}
],
"source": [
"model_checkpoint = \"pucpr/clinicalnerpt-medical\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, add_prefix_space=True)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JPTd6vUPKpLg",
"outputId": "84451d5e-eb3a-4763-dc29-5ffebafc4c3d"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 81
}
],
"source": [
"tokenizer.is_fast"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"id": "q8o2kvS69K2h"
},
"outputs": [],
"source": [
"dic = {\"tokens\": HCs_tokenized, \"ner_tags\": labels_tokenized} #For the whole clinical case. We used this option for our paper.\n",
"#dic = {\"tokens\": sent_tokenized, \"ner_tags\": label_sent_tokenized} #Use this option if you want to check the model performance with sentences tokenized by \". \" b"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"id": "6JXCzYF49K2h"
},
"outputs": [],
"source": [
"dataset = Dataset.from_dict(dic)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f01f435d-ced1-4002-cc23-05e42329c8df",
"id": "AGI1Hf_E9K2h"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 741\n",
"})"
]
},
"metadata": {},
"execution_count": 84
}
],
"source": [
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"id": "CUH94L-29K2h"
},
"outputs": [],
"source": [
"#For training, validation, and test partitions\n",
"\"\"\"\n",
"#Train, val, test partitions\n",
"train_test = dataset.train_test_split()\n",
"test_val = train_test['test'].train_test_split()\n",
"raw_datasets = DatasetDict({\n",
" 'train': train_test['train'],\n",
" 'validation': test_val['train'],\n",
" 'test': test_val['test']\n",
" })\n",
"\"\"\"\n",
"\n",
"#Just for training and validation partitions\n",
"train_test = dataset.train_test_split()\n",
"raw_datasets = DatasetDict({\n",
" 'train': train_test['train'],\n",
" 'validation': train_test['test']\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "654a2798-e4f3-49ce-c59c-747c7538352b",
"id": "zRYC70NF9K2i"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 555\n",
" })\n",
" validation: Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 186\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 86
}
],
"source": [
"raw_datasets"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e1de590a-ea92-4b0b-9da3-c65152d08872",
"id": "dCZcYkCa9K2i"
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"outputs": [
{
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},
"metadata": {},
"execution_count": 87
}
],
"source": [
"raw_datasets[\"train\"][0][\"ner_tags\"]\n",
"#raw_datasets[\"train\"][0][\"pos_tags\"]\n",
"#raw_datasets[\"train\"][0][\"chunk_tags\"]"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6b0070ab-8b15-4750-e848-a122ca47e050",
"id": "wWinDHdc9K2i"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 555\n",
"})"
]
},
"metadata": {},
"execution_count": 88
}
],
"source": [
"raw_datasets['train']"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ea900262-0ead-4843-da63-d1b736a2ad29",
"id": "NeEdChWM9K2i"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['O', 'B', 'I']"
]
},
"metadata": {},
"execution_count": 89
}
],
"source": [
"label_names = ['O','B','I']\n",
"label_names"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e264924f-3c29-4f64-d8f6-4441ca35c9b1",
"id": "FwWqjcpJ9K2i"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"This is a 79 - year - old woman with a history of hypertension , osteoporosis and hysterectomy for myomatosis at the age of 50 . She underwent transurethral resection of infiltrating bladder carcinoma in October 2006 . She subsequently received radiotherapy sessions up to a total of 50 Gy due to persistence of an external tumour mass in the right angle of the bladder , finishing this treatment in June 2007 . In August 2007 she began chemotherapy treatment due to persistence of the bladder lesion and metastases in the spine detected by follow - up CT scan and bone scintigraphy . Her digestive history began in February 2008 when she was admitted for episodes of rectorrhagia , initially scarce and distal , but which soon became more frequent and profuse , accompanied by symptoms of haemodynamic instability and severe anaemia with extensive transfusion requirements . \n",
" Total colonoscopy was performed , showing only changes typical of actinic proctitis with large friable and bleeding neovascular lesions ; treatment was carried out with argon plasma ( APC ) . The patient 's clinical course was unfavourable : she received consecutive treatment with steroid enemas , 5 - aminosalicylic acid and sucralfate ; in addition , three more therapeutic rectoscopies were performed , applying APC treatment , despite which the episodes of profuse rectorrhagia with haemodynamic instability persisted , requiring transfusion of a total of 21 red blood cell concentrates throughout the patient 's hospitalisation period , despite also receiving oral and intravenous ferrotherapy . \n",
" In the absence of response to these treatments , the case was discussed with the surgeon for topical treatment with formalin , who , using spinal anaesthesia and anal dilatation , treated the rectal ampulla for 10 minutes with a 10 % formalin solution 200 ml + 300 ml of water ; The patient 's tolerance to the procedure was excellent , it was carried out without any complications and from that moment onwards the patient was completely asymptomatic without new episodes of haemorrhagic externalisation , haemodynamic instability or new transfusion requirements ; she was discharged and a follow - up colonoscopy was proposed after the treatment , but she did not accept . \n",
" Four months later , she was admitted again for clinical signs of tumour progression , and symptomatic treatment was decided by the Oncology Department , and the patient died , but without recurrence of the rectorrhagia . \n",
"\n",
"\n",
" \n",
"O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O B I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O \n"
]
}
],
"source": [
"words = raw_datasets[\"train\"][0][\"tokens\"]\n",
"labels = [int(n) for n in raw_datasets[\"train\"][0][\"ner_tags\"]]\n",
"#labels = raw_datasets[\"train\"][0][\"pos_tags\"]\n",
"#labels = raw_datasets[\"train\"][0][\"chunk_tags\"]\n",
"line1 = \"\"\n",
"line2 = \"\"\n",
"for word, label in zip(words, labels):\n",
" full_label = label_names[label]\n",
" max_length = max(len(word), len(full_label))\n",
" line1 += word + \" \" * (max_length - len(word) + 1)\n",
" line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n",
"\n",
"print(line1)\n",
"print(line2)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Bhx4OJLrKpLg",
"outputId": "08eb850e-d3a5-41e0-9dcf-89f7e7748b02"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Token indices sequence length is longer than the specified maximum sequence length for this model (578 > 512). Running this sequence through the model will result in indexing errors\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['[CLS]',\n",
" 'this',\n",
" 'is',\n",
" 'a',\n",
" '79',\n",
" '-',\n",
" 'year',\n",
" '-',\n",
" 'old',\n",
" 'woman',\n",
" 'with',\n",
" 'a',\n",
" 'history',\n",
" 'of',\n",
" 'hy',\n",
" '##pert',\n",
" '##ension',\n",
" ',',\n",
" 'os',\n",
" '##te',\n",
" '##op',\n",
" '##oros',\n",
" '##is',\n",
" 'and',\n",
" 'hy',\n",
" '##ster',\n",
" '##ect',\n",
" '##omy',\n",
" 'for',\n",
" 'my',\n",
" '##oma',\n",
" '##tos',\n",
" '##is',\n",
" 'at',\n",
" 'the',\n",
" 'age',\n",
" 'of',\n",
" '50',\n",
" '.',\n",
" 'she',\n",
" 'underwent',\n",
" 'trans',\n",
" '##ure',\n",
" '##th',\n",
" '##ral',\n",
" 'res',\n",
" '##ection',\n",
" 'of',\n",
" 'in',\n",
" '##fil',\n",
" '##trat',\n",
" '##ing',\n",
" 'blad',\n",
" '##der',\n",
" 'car',\n",
" '##cino',\n",
" '##ma',\n",
" 'in',\n",
" 'o',\n",
" '##cto',\n",
" '##ber',\n",
" '2006',\n",
" '.',\n",
" 'she',\n",
" 'subsequently',\n",
" 'received',\n",
" 'radio',\n",
" '##ther',\n",
" '##ap',\n",
" '##y',\n",
" 'sessions',\n",
" 'up',\n",
" 'to',\n",
" 'a',\n",
" 'total',\n",
" 'of',\n",
" '50',\n",
" 'g',\n",
" '##y',\n",
" 'due',\n",
" 'to',\n",
" 'pers',\n",
" '##isten',\n",
" '##ce',\n",
" 'of',\n",
" 'an',\n",
" 'external',\n",
" 'tu',\n",
" '##mou',\n",
" '##r',\n",
" 'mass',\n",
" 'in',\n",
" 'the',\n",
" 'right',\n",
" 'angle',\n",
" 'of',\n",
" 'the',\n",
" 'blad',\n",
" '##der',\n",
" ',',\n",
" 'finishing',\n",
" 'this',\n",
" 'treatment',\n",
" 'in',\n",
" 'ju',\n",
" '##ne',\n",
" '2007',\n",
" '.',\n",
" 'in',\n",
" 'august',\n",
" '2007',\n",
" 'she',\n",
" 'began',\n",
" 'che',\n",
" '##mot',\n",
" '##hera',\n",
" '##py',\n",
" 'treatment',\n",
" 'due',\n",
" 'to',\n",
" 'pers',\n",
" '##isten',\n",
" '##ce',\n",
" 'of',\n",
" 'the',\n",
" 'blad',\n",
" '##der',\n",
" 'les',\n",
" '##ion',\n",
" 'and',\n",
" 'meta',\n",
" '##stas',\n",
" '##es',\n",
" 'in',\n",
" 'the',\n",
" 'spin',\n",
" '##e',\n",
" 'det',\n",
" '##ected',\n",
" 'by',\n",
" 'follow',\n",
" '-',\n",
" 'up',\n",
" 'c',\n",
" '##t',\n",
" 's',\n",
" '##can',\n",
" 'and',\n",
" 'bone',\n",
" 'sci',\n",
" '##nti',\n",
" '##graphy',\n",
" '.',\n",
" 'her',\n",
" 'dig',\n",
" '##esti',\n",
" '##ve',\n",
" 'history',\n",
" 'began',\n",
" 'in',\n",
" 'februar',\n",
" '##y',\n",
" '2008',\n",
" 'when',\n",
" 'she',\n",
" 'was',\n",
" 'admitted',\n",
" 'for',\n",
" 'episodes',\n",
" 'of',\n",
" 'rector',\n",
" '##r',\n",
" '##ha',\n",
" '##gia',\n",
" ',',\n",
" 'initially',\n",
" 's',\n",
" '##car',\n",
" '##ce',\n",
" 'and',\n",
" 'dis',\n",
" '##tal',\n",
" ',',\n",
" 'but',\n",
" 'which',\n",
" 'soon',\n",
" 'became',\n",
" 'more',\n",
" 'frequent',\n",
" 'and',\n",
" 'prof',\n",
" '##use',\n",
" ',',\n",
" 'accompanied',\n",
" 'by',\n",
" 'symptoms',\n",
" 'of',\n",
" 'hae',\n",
" '##mo',\n",
" '##dyn',\n",
" '##ami',\n",
" '##c',\n",
" 'ins',\n",
" '##tab',\n",
" '##ility',\n",
" 'and',\n",
" 'severe',\n",
" 'ana',\n",
" '##emia',\n",
" 'with',\n",
" 'extensive',\n",
" 'trans',\n",
" '##fus',\n",
" '##ion',\n",
" 'requirements',\n",
" '.',\n",
" 'total',\n",
" 'colonos',\n",
" '##co',\n",
" '##py',\n",
" 'was',\n",
" 'performed',\n",
" ',',\n",
" 'showing',\n",
" 'only',\n",
" 'changes',\n",
" 'typical',\n",
" 'of',\n",
" 'act',\n",
" '##ini',\n",
" '##c',\n",
" 'pro',\n",
" '##cti',\n",
" '##tis',\n",
" 'with',\n",
" 'large',\n",
" 'fri',\n",
" '##able',\n",
" 'and',\n",
" 'ble',\n",
" '##eding',\n",
" 'neo',\n",
" '##vas',\n",
" '##cular',\n",
" 'les',\n",
" '##ions',\n",
" ';',\n",
" 'treatment',\n",
" 'was',\n",
" 'carried',\n",
" 'out',\n",
" 'with',\n",
" 'ar',\n",
" '##gon',\n",
" 'plasma',\n",
" '(',\n",
" 'ap',\n",
" '##c',\n",
" ')',\n",
" '.',\n",
" 'the',\n",
" 'patient',\n",
" \"'\",\n",
" 's',\n",
" 'clinical',\n",
" 'course',\n",
" 'was',\n",
" 'un',\n",
" '##fa',\n",
" '##vour',\n",
" '##able',\n",
" ':',\n",
" 'she',\n",
" 'received',\n",
" 'consecutive',\n",
" 'treatment',\n",
" 'with',\n",
" 'ster',\n",
" '##oid',\n",
" 'ene',\n",
" '##mas',\n",
" ',',\n",
" '5',\n",
" '-',\n",
" 'amino',\n",
" '##sal',\n",
" '##icy',\n",
" '##lic',\n",
" 'acid',\n",
" 'and',\n",
" 'su',\n",
" '##cra',\n",
" '##lfa',\n",
" '##te',\n",
" ';',\n",
" 'in',\n",
" 'addition',\n",
" ',',\n",
" 'three',\n",
" 'more',\n",
" 'the',\n",
" '##rap',\n",
" '##eu',\n",
" '##tic',\n",
" 're',\n",
" '##ctos',\n",
" '##co',\n",
" '##pies',\n",
" 'were',\n",
" 'performed',\n",
" ',',\n",
" 'apply',\n",
" '##ing',\n",
" 'ap',\n",
" '##c',\n",
" 'treatment',\n",
" ',',\n",
" 'despite',\n",
" 'which',\n",
" 'the',\n",
" 'episodes',\n",
" 'of',\n",
" 'prof',\n",
" '##use',\n",
" 'rector',\n",
" '##r',\n",
" '##ha',\n",
" '##gia',\n",
" 'with',\n",
" 'hae',\n",
" '##mo',\n",
" '##dyn',\n",
" '##ami',\n",
" '##c',\n",
" 'ins',\n",
" '##tab',\n",
" '##ility',\n",
" 'pers',\n",
" '##isted',\n",
" ',',\n",
" 'requiring',\n",
" 'trans',\n",
" '##fus',\n",
" '##ion',\n",
" 'of',\n",
" 'a',\n",
" 'total',\n",
" 'of',\n",
" '21',\n",
" 'red',\n",
" 'blood',\n",
" 'cell',\n",
" 'con',\n",
" '##centra',\n",
" '##tes',\n",
" 'throughout',\n",
" 'the',\n",
" 'patient',\n",
" \"'\",\n",
" 's',\n",
" 'hospital',\n",
" '##isation',\n",
" 'period',\n",
" ',',\n",
" 'despite',\n",
" 'also',\n",
" 'receiving',\n",
" 'oral',\n",
" 'and',\n",
" 'intra',\n",
" '##veno',\n",
" '##us',\n",
" 'ferro',\n",
" '##ther',\n",
" '##ap',\n",
" '##y',\n",
" '.',\n",
" 'in',\n",
" 'the',\n",
" 'absence',\n",
" 'of',\n",
" 'response',\n",
" 'to',\n",
" 'these',\n",
" 'treatment',\n",
" '##s',\n",
" ',',\n",
" 'the',\n",
" 'case',\n",
" 'was',\n",
" 'discussed',\n",
" 'with',\n",
" 'the',\n",
" 'surgeon',\n",
" 'for',\n",
" 'topic',\n",
" '##al',\n",
" 'treatment',\n",
" 'with',\n",
" 'formal',\n",
" '##in',\n",
" ',',\n",
" 'who',\n",
" ',',\n",
" 'using',\n",
" 'spin',\n",
" '##al',\n",
" 'ana',\n",
" '##est',\n",
" '##hes',\n",
" '##ia',\n",
" 'and',\n",
" 'anal',\n",
" 'dil',\n",
" '##ata',\n",
" '##tion',\n",
" ',',\n",
" 'treated',\n",
" 'the',\n",
" 'recta',\n",
" '##l',\n",
" 'am',\n",
" '##pul',\n",
" '##la',\n",
" 'for',\n",
" '10',\n",
" 'minutes',\n",
" 'with',\n",
" 'a',\n",
" '10',\n",
" '%',\n",
" 'formal',\n",
" '##in',\n",
" 'solution',\n",
" '200',\n",
" 'ml',\n",
" '+',\n",
" '300',\n",
" 'ml',\n",
" 'of',\n",
" 'water',\n",
" ';',\n",
" 'the',\n",
" 'patient',\n",
" \"'\",\n",
" 's',\n",
" 'tol',\n",
" '##erance',\n",
" 'to',\n",
" 'the',\n",
" 'procedure',\n",
" 'was',\n",
" 'excellent',\n",
" ',',\n",
" 'it',\n",
" 'was',\n",
" 'carried',\n",
" 'out',\n",
" 'without',\n",
" 'any',\n",
" 'com',\n",
" '##plications',\n",
" 'and',\n",
" 'from',\n",
" 'that',\n",
" 'moment',\n",
" 'onwards',\n",
" 'the',\n",
" 'patient',\n",
" 'was',\n",
" 'completely',\n",
" 'as',\n",
" '##ym',\n",
" '##pt',\n",
" '##oma',\n",
" '##tic',\n",
" 'without',\n",
" 'new',\n",
" 'episodes',\n",
" 'of',\n",
" 'hae',\n",
" '##mor',\n",
" '##r',\n",
" '##ha',\n",
" '##gic',\n",
" 'external',\n",
" '##isation',\n",
" ',',\n",
" 'hae',\n",
" '##mo',\n",
" '##dyn',\n",
" '##ami',\n",
" '##c',\n",
" 'ins',\n",
" '##tab',\n",
" '##ility',\n",
" 'or',\n",
" 'new',\n",
" 'trans',\n",
" '##fus',\n",
" '##ion',\n",
" 'requirements',\n",
" ';',\n",
" 'she',\n",
" 'was',\n",
" 'disc',\n",
" '##harge',\n",
" '##d',\n",
" 'and',\n",
" 'a',\n",
" 'follow',\n",
" '-',\n",
" 'up',\n",
" 'colonos',\n",
" '##co',\n",
" '##py',\n",
" 'was',\n",
" 'proposed',\n",
" 'after',\n",
" 'the',\n",
" 'treatment',\n",
" ',',\n",
" 'but',\n",
" 'she',\n",
" 'did',\n",
" 'not',\n",
" 'accept',\n",
" '.',\n",
" 'four',\n",
" 'months',\n",
" 'later',\n",
" ',',\n",
" 'she',\n",
" 'was',\n",
" 'admitted',\n",
" 'again',\n",
" 'for',\n",
" 'clinical',\n",
" 'signs',\n",
" 'of',\n",
" 'tu',\n",
" '##mou',\n",
" '##r',\n",
" 'progression',\n",
" ',',\n",
" 'and',\n",
" 'sy',\n",
" '##mpt',\n",
" '##oma',\n",
" '##tic',\n",
" 'treatment',\n",
" 'was',\n",
" 'decided',\n",
" 'by',\n",
" 'the',\n",
" 'on',\n",
" '##cology',\n",
" 'department',\n",
" ',',\n",
" 'and',\n",
" 'the',\n",
" 'patient',\n",
" 'died',\n",
" ',',\n",
" 'but',\n",
" 'without',\n",
" 're',\n",
" '##cu',\n",
" '##rren',\n",
" '##ce',\n",
" 'of',\n",
" 'the',\n",
" 'rector',\n",
" '##r',\n",
" '##ha',\n",
" '##gia',\n",
" '.',\n",
" '[SEP]']"
]
},
"metadata": {},
"execution_count": 91
}
],
"source": [
"inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n",
"inputs.tokens()"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EQskMV19KpLh",
"outputId": "9fbf1919-5853-4cd7-ea80-475edeb3b482"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"[-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]\n"
]
}
],
"source": [
"labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
"word_ids = inputs.word_ids()\n",
"print(labels)\n",
"print(align_labels_with_tokens(labels, word_ids))"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"84631a71d2ca4ae8a781019ea3ce6da9",
"9666c2ff4f32449ea3cbef076a166836",
"efc4af4547804d9daae235691942e73a",
"c8194cd34f554a789359eab6e7596291",
"54f649d297ec4456be5b5df14497fb93",
"e35bc6f834c4490e85ac2ae25d9e922f",
"b166c61328bb49ea803f0d3a7d515d81",
"a2aac740ef3b4f3c913b71c82b408c2c",
"d77e188e43dc4e01b82054f2a6a8e832",
"266b2c90bb4d41198784f016e996066a",
"cc62b20cee8c4a4b8e24576d1c854fbf",
"98bebe04cb254369bb3b6b991d4b2648",
"40080df663cc43749963657150cf632d",
"a04f157a98db4d47b75094b6ef1b0990",
"ba2967950f4c483ea399827046f52963",
"a9e0ad6a141a462fb9bea1c18d447332",
"31fbaf0ffb0845f5800e6fca0353b929",
"4a2c17e757d34547a4a68718ef064073",
"f74d219071ab49479194f1061bf343be",
"a92097360dba4d5c848b48e345b0028e",
"24a164b22a8f4e00944ef05bcec5d032",
"976ade0b37cd43e2aa5aa272dac2445b"
]
},
"id": "z_6q0eitKpLh",
"outputId": "4c9c0ce3-5855-49e3-c4da-bbe068361c63"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/1 [00:00<?, ?ba/s]"
],
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"version_major": 2,
"version_minor": 0,
"model_id": "84631a71d2ca4ae8a781019ea3ce6da9"
}
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},
{
"output_type": "display_data",
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}
},
"metadata": {}
}
],
"source": [
"tokenized_datasets = raw_datasets.map(\n",
" tokenize_and_align_labels,\n",
" batched=True,\n",
" remove_columns=raw_datasets[\"train\"].column_names,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"id": "K3GGBsIIKpLh"
},
"outputs": [],
"source": [
"data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1M1bHT_pKpLi",
"outputId": "19fc29d4-cd88-4fe7-e963-71a59d0434ca"
},
"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, ..., 0, 0, -100],\n",
" [-100, 0, 0, ..., -100, -100, -100]])"
]
},
"metadata": {},
"execution_count": 95
}
],
"source": [
"batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
"batch[\"labels\"]"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"id": "maAO4nNXKpLi"
},
"outputs": [],
"source": [
"id2label = {str(i): label for i, label in enumerate(label_names)}\n",
"label2id = {v: k for k, v in id2label.items()}"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 954,
"referenced_widgets": [
"21b02caa5dc146b8ac2bd1a282381c7f",
"0c01b39e34744f74ae47d0d4e70638ce",
"82e7357418144359abba3548449c0c08",
"2ff5e18b6d684b99a82676dbf3db6d32",
"cd75e771337843d9b55838502bed9a1b",
"b6a4250c705f4dd8b9f52731cce2a23d",
"4549eb0838864025ac6a0f3da9192818",
"b2d377844c1a4bc09433a94088f5213e",
"fe7e058b9a6944969d83f7e72b398bb1",
"47d5ccd1eafe4ea1a3476e06d998bd74",
"5a17027205cb4c2bbe140e1e96e4b495"
]
},
"id": "Q0T8WOGBKpLi",
"outputId": "d950423b-480b-495e-f02e-667ec124dcb4"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/config.json\n",
"Model config BertConfig {\n",
" \"_name_or_path\": \"pucpr/clinicalnerpt-medical\",\n",
" \"_num_labels\": 3,\n",
" \"architectures\": [\n",
" \"BertForTokenClassification\"\n",
" ],\n",
" \"attention_probs_dropout_prob\": 0.1,\n",
" \"classifier_dropout\": null,\n",
" \"directionality\": \"bidi\",\n",
" \"eos_token_ids\": null,\n",
" \"gradient_checkpointing\": false,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"id2label\": {\n",
" \"0\": \"O\",\n",
" \"1\": \"B-MedicalDevice\",\n",
" \"2\": \"I-MedicalDevice\"\n",
" },\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"label2id\": {\n",
" \"B-MedicalDevice\": 1,\n",
" \"I-MedicalDevice\": 2,\n",
" \"O\": 0\n",
" },\n",
" \"layer_norm_eps\": 1e-12,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"bert\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_hidden_layers\": 12,\n",
" \"output_past\": true,\n",
" \"pad_token_id\": null,\n",
" \"pooler_fc_size\": 768,\n",
" \"pooler_num_attention_heads\": 12,\n",
" \"pooler_num_fc_layers\": 3,\n",
" \"pooler_size_per_head\": 128,\n",
" \"pooler_type\": \"first_token_transform\",\n",
" \"position_embedding_type\": \"absolute\",\n",
" \"transformers_version\": \"4.23.1\",\n",
" \"type_vocab_size\": 2,\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 119547\n",
"}\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading: 0%| | 0.00/709M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "21b02caa5dc146b8ac2bd1a282381c7f"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/pytorch_model.bin\n",
"All model checkpoint weights were used when initializing BertForTokenClassification.\n",
"\n",
"All the weights of BertForTokenClassification were initialized from the model checkpoint at pucpr/clinicalnerpt-medical.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForTokenClassification for predictions without further training.\n"
]
}
],
"source": [
"model = AutoModelForTokenClassification.from_pretrained( \n",
" model_checkpoint\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"id": "k6ExcF0UKpLi",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "68661940-56bd-4adc-ec64-37f1db50604c"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"PyTorch: setting up devices\n",
"The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
]
}
],
"source": [
"args = TrainingArguments(\n",
" \"NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased\",\n",
" evaluation_strategy = IntervalStrategy.STEPS,\n",
" eval_steps = 50,\n",
" learning_rate=5e-5,\n",
" num_train_epochs=50,\n",
" weight_decay=0.01,\n",
" metric_for_best_model = 'f1',\n",
" load_best_model_at_end=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "gQSIB3FfKpLj",
"outputId": "72ede653-dc87-4230-94b2-805a52f1f5d2"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" FutureWarning,\n",
"***** Running training *****\n",
" Num examples = 555\n",
" Num Epochs = 50\n",
" Instantaneous batch size per device = 8\n",
" Total train batch size (w. parallel, distributed & accumulation) = 8\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 3500\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1150' max='3500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1150/3500 05:32 < 11:20, 3.45 it/s, Epoch 16/50]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>No log</td>\n",
" <td>0.160077</td>\n",
" <td>0.281675</td>\n",
" <td>0.325786</td>\n",
" <td>0.302129</td>\n",
" <td>0.934798</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>No log</td>\n",
" <td>0.156098</td>\n",
" <td>0.440468</td>\n",
" <td>0.425786</td>\n",
" <td>0.433003</td>\n",
" <td>0.941619</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>No log</td>\n",
" <td>0.175201</td>\n",
" <td>0.430540</td>\n",
" <td>0.491195</td>\n",
" <td>0.458872</td>\n",
" <td>0.943305</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>No log</td>\n",
" <td>0.181505</td>\n",
" <td>0.465023</td>\n",
" <td>0.438994</td>\n",
" <td>0.451634</td>\n",
" <td>0.944750</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>No log</td>\n",
" <td>0.198468</td>\n",
" <td>0.437112</td>\n",
" <td>0.487421</td>\n",
" <td>0.460898</td>\n",
" <td>0.942113</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>No log</td>\n",
" <td>0.213199</td>\n",
" <td>0.454245</td>\n",
" <td>0.518239</td>\n",
" <td>0.484136</td>\n",
" <td>0.943191</td>\n",
" </tr>\n",
" <tr>\n",
" <td>350</td>\n",
" <td>No log</td>\n",
" <td>0.225870</td>\n",
" <td>0.439457</td>\n",
" <td>0.529560</td>\n",
" <td>0.480319</td>\n",
" <td>0.941974</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>No log</td>\n",
" <td>0.252911</td>\n",
" <td>0.509901</td>\n",
" <td>0.453459</td>\n",
" <td>0.480027</td>\n",
" <td>0.945802</td>\n",
" </tr>\n",
" <tr>\n",
" <td>450</td>\n",
" <td>No log</td>\n",
" <td>0.248783</td>\n",
" <td>0.475821</td>\n",
" <td>0.501258</td>\n",
" <td>0.488208</td>\n",
" <td>0.945092</td>\n",
" </tr>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>0.078000</td>\n",
" <td>0.230288</td>\n",
" <td>0.441730</td>\n",
" <td>0.545912</td>\n",
" <td>0.488326</td>\n",
" <td>0.941631</td>\n",
" </tr>\n",
" <tr>\n",
" <td>550</td>\n",
" <td>0.078000</td>\n",
" <td>0.257168</td>\n",
" <td>0.470556</td>\n",
" <td>0.537736</td>\n",
" <td>0.501908</td>\n",
" <td>0.944002</td>\n",
" </tr>\n",
" <tr>\n",
" <td>600</td>\n",
" <td>0.078000</td>\n",
" <td>0.264445</td>\n",
" <td>0.509816</td>\n",
" <td>0.506289</td>\n",
" <td>0.508047</td>\n",
" <td>0.946145</td>\n",
" </tr>\n",
" <tr>\n",
" <td>650</td>\n",
" <td>0.078000</td>\n",
" <td>0.277278</td>\n",
" <td>0.448800</td>\n",
" <td>0.576101</td>\n",
" <td>0.504544</td>\n",
" <td>0.943115</td>\n",
" </tr>\n",
" <tr>\n",
" <td>700</td>\n",
" <td>0.078000</td>\n",
" <td>0.278563</td>\n",
" <td>0.504016</td>\n",
" <td>0.473585</td>\n",
" <td>0.488327</td>\n",
" <td>0.946145</td>\n",
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" <tr>\n",
" <td>750</td>\n",
" <td>0.078000</td>\n",
" <td>0.295773</td>\n",
" <td>0.501239</td>\n",
" <td>0.508805</td>\n",
" <td>0.504994</td>\n",
" <td>0.946436</td>\n",
" </tr>\n",
" <tr>\n",
" <td>800</td>\n",
" <td>0.078000</td>\n",
" <td>0.300880</td>\n",
" <td>0.491329</td>\n",
" <td>0.534591</td>\n",
" <td>0.512048</td>\n",
" <td>0.945333</td>\n",
" </tr>\n",
" <tr>\n",
" <td>850</td>\n",
" <td>0.078000</td>\n",
" <td>0.315745</td>\n",
" <td>0.503695</td>\n",
" <td>0.514465</td>\n",
" <td>0.509023</td>\n",
" <td>0.944154</td>\n",
" </tr>\n",
" <tr>\n",
" <td>900</td>\n",
" <td>0.078000</td>\n",
" <td>0.303611</td>\n",
" <td>0.461154</td>\n",
" <td>0.522642</td>\n",
" <td>0.489976</td>\n",
" <td>0.944839</td>\n",
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" <tr>\n",
" <td>950</td>\n",
" <td>0.078000</td>\n",
" <td>0.309813</td>\n",
" <td>0.502151</td>\n",
" <td>0.513836</td>\n",
" <td>0.507927</td>\n",
" <td>0.947552</td>\n",
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" <tr>\n",
" <td>1000</td>\n",
" <td>0.009800</td>\n",
" <td>0.335969</td>\n",
" <td>0.520309</td>\n",
" <td>0.507547</td>\n",
" <td>0.513849</td>\n",
" <td>0.946829</td>\n",
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" <tr>\n",
" <td>1050</td>\n",
" <td>0.009800</td>\n",
" <td>0.313057</td>\n",
" <td>0.484645</td>\n",
" <td>0.545912</td>\n",
" <td>0.513458</td>\n",
" <td>0.946018</td>\n",
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" <tr>\n",
" <td>1100</td>\n",
" <td>0.009800</td>\n",
" <td>0.297767</td>\n",
" <td>0.478187</td>\n",
" <td>0.530818</td>\n",
" <td>0.503130</td>\n",
" <td>0.947780</td>\n",
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" <tr>\n",
" <td>1150</td>\n",
" <td>0.009800</td>\n",
" <td>0.317451</td>\n",
" <td>0.496936</td>\n",
" <td>0.510063</td>\n",
" <td>0.503414</td>\n",
" <td>0.946601</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"Saving model checkpoint to NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500\n",
"Configuration saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/config.json\n",
"Model weights saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/pytorch_model.bin\n",
"tokenizer config file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/tokenizer_config.json\n",
"Special tokens file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/special_tokens_map.json\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"Saving model checkpoint to NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000\n",
"Configuration saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000/config.json\n",
"Model weights saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000/pytorch_model.bin\n",
"tokenizer config file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000/tokenizer_config.json\n",
"Special tokens file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000/special_tokens_map.json\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 186\n",
" Batch size = 8\n",
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n",
"Loading best model from NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000 (score: 0.5138490926456541).\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=1150, training_loss=0.03881311048632083, metrics={'train_runtime': 332.8101, 'train_samples_per_second': 83.381, 'train_steps_per_second': 10.517, 'total_flos': 2382353890443360.0, 'train_loss': 0.03881311048632083, 'epoch': 16.43})"
]
},
"metadata": {},
"execution_count": 99
}
],
"source": [
"trainer = Trainer(\n",
" model=model,\n",
" args=args,\n",
" train_dataset=tokenized_datasets[\"train\"],\n",
" eval_dataset=tokenized_datasets[\"validation\"],\n",
" data_collator=data_collator,\n",
" compute_metrics=compute_metrics,\n",
" tokenizer=tokenizer,\n",
" callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]\n",
")\n",
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "arJA0rVIKpLj",
"outputId": "12b3b4d0-2f59-4f46-9f20-e2aa3cd35398"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Saving model checkpoint to model/multilingual-BERT-all-tokens\n",
"Configuration saved in model/multilingual-BERT-all-tokens/config.json\n",
"Model weights saved in model/multilingual-BERT-all-tokens/pytorch_model.bin\n",
"tokenizer config file saved in model/multilingual-BERT-all-tokens/tokenizer_config.json\n",
"Special tokens file saved in model/multilingual-BERT-all-tokens/special_tokens_map.json\n"
]
}
],
"source": [
"trainer.save_model('model/multilingual-BERT-all-tokens')"
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "D3Bdj1H9TxOp"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "kCThHmWQWZ57"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Model 2 - pucpr/clinicalnerpt-medical\n",
"\n",
"### Sentence Based Modelling"
],
"metadata": {
"id": "5fN4MRRx_fFZ"
}
},
{
"cell_type": "code",
"source": [
"dic = {\"tokens\": sent_tokenized, \"ner_tags\": label_sent_tokenized} #Use this option if you want to check the model performance with sentences tokenized by \". \" b"
],
"metadata": {
"id": "_E60UP1w_fFZ"
},
"execution_count": 101,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"id": "DY7gaYbj_fFa"
},
"outputs": [],
"source": [
"dataset = Dataset.from_dict(dic)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "44a9f0cc-c1cf-4064-aa89-baffa435cf52",
"id": "FcLEVVcn_fFa"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 11668\n",
"})"
]
},
"metadata": {},
"execution_count": 103
}
],
"source": [
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"id": "vZqWyVo2_fFa"
},
"outputs": [],
"source": [
"#For training, validation, and test partitions\n",
"\"\"\"\n",
"#Train, val, test partitions\n",
"train_test = dataset.train_test_split()\n",
"test_val = train_test['test'].train_test_split()\n",
"raw_datasets = DatasetDict({\n",
" 'train': train_test['train'],\n",
" 'validation': test_val['train'],\n",
" 'test': test_val['test']\n",
" })\n",
"\"\"\"\n",
"\n",
"#Just for training and validation partitions\n",
"train_test = dataset.train_test_split()\n",
"raw_datasets = DatasetDict({\n",
" 'train': train_test['train'],\n",
" 'validation': train_test['test']\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ddadc450-b61a-43f9-e5fd-90c8db32de6b",
"id": "GDv9hwpm_fFa"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 8751\n",
" })\n",
" validation: Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 2917\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 105
}
],
"source": [
"raw_datasets"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "54b152d5-edaa-482d-afb5-a209403c7dbc",
"id": "ZnB_fXql_fFa"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
]
},
"metadata": {},
"execution_count": 106
}
],
"source": [
"raw_datasets[\"train\"][0][\"ner_tags\"]\n",
"#raw_datasets[\"train\"][0][\"pos_tags\"]\n",
"#raw_datasets[\"train\"][0][\"chunk_tags\"]"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a7beafe3-27ce-4e03-904a-3b8a8e06d831",
"id": "egmgQAVt_fFa"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['tokens', 'ner_tags'],\n",
" num_rows: 8751\n",
"})"
]
},
"metadata": {},
"execution_count": 107
}
],
"source": [
"raw_datasets['train']"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e0cd0121-e53b-4d8c-9b33-2d093d604378",
"id": "Hize89uK_fFa"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['O', 'B', 'I']"
]
},
"metadata": {},
"execution_count": 108
}
],
"source": [
"label_names = ['O','B','I']\n",
"label_names"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a9fe31c8-45c9-4873-fb2c-cf24bd7c64a9",
"id": "bQf54Sst_fFa"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Since 2006 she had tried several times to lose weight , without success . \n",
"O O O O O O O O O O O O O O \n"
]
}
],
"source": [
"words = raw_datasets[\"train\"][0][\"tokens\"]\n",
"labels = [int(n) for n in raw_datasets[\"train\"][0][\"ner_tags\"]]\n",
"#labels = raw_datasets[\"train\"][0][\"pos_tags\"]\n",
"#labels = raw_datasets[\"train\"][0][\"chunk_tags\"]\n",
"line1 = \"\"\n",
"line2 = \"\"\n",
"for word, label in zip(words, labels):\n",
" full_label = label_names[label]\n",
" max_length = max(len(word), len(full_label))\n",
" line1 += word + \" \" * (max_length - len(word) + 1)\n",
" line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n",
"\n",
"print(line1)\n",
"print(line2)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "53863be6-1112-4c14-b34b-529492b236dc",
"id": "cNKxSKCT_fFb"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/config.json\n",
"Model config BertConfig {\n",
" \"_name_or_path\": \"pucpr/clinicalnerpt-medical\",\n",
" \"_num_labels\": 3,\n",
" \"architectures\": [\n",
" \"BertForTokenClassification\"\n",
" ],\n",
" \"attention_probs_dropout_prob\": 0.1,\n",
" \"classifier_dropout\": null,\n",
" \"directionality\": \"bidi\",\n",
" \"eos_token_ids\": null,\n",
" \"gradient_checkpointing\": false,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"id2label\": {\n",
" \"0\": \"O\",\n",
" \"1\": \"B-MedicalDevice\",\n",
" \"2\": \"I-MedicalDevice\"\n",
" },\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"label2id\": {\n",
" \"B-MedicalDevice\": 1,\n",
" \"I-MedicalDevice\": 2,\n",
" \"O\": 0\n",
" },\n",
" \"layer_norm_eps\": 1e-12,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"bert\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_hidden_layers\": 12,\n",
" \"output_past\": true,\n",
" \"pad_token_id\": null,\n",
" \"pooler_fc_size\": 768,\n",
" \"pooler_num_attention_heads\": 12,\n",
" \"pooler_num_fc_layers\": 3,\n",
" \"pooler_size_per_head\": 128,\n",
" \"pooler_type\": \"first_token_transform\",\n",
" \"position_embedding_type\": \"absolute\",\n",
" \"transformers_version\": \"4.23.1\",\n",
" \"type_vocab_size\": 2,\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 119547\n",
"}\n",
"\n",
"loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/vocab.txt\n",
"loading file tokenizer.json from cache at None\n",
"loading file added_tokens.json from cache at None\n",
"loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/special_tokens_map.json\n",
"loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/tokenizer_config.json\n",
"loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/config.json\n",
"Model config BertConfig {\n",
" \"_name_or_path\": \"pucpr/clinicalnerpt-medical\",\n",
" \"_num_labels\": 3,\n",
" \"architectures\": [\n",
" \"BertForTokenClassification\"\n",
" ],\n",
" \"attention_probs_dropout_prob\": 0.1,\n",
" \"classifier_dropout\": null,\n",
" \"directionality\": \"bidi\",\n",
" \"eos_token_ids\": null,\n",
" \"gradient_checkpointing\": false,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"id2label\": {\n",
" \"0\": \"O\",\n",
" \"1\": \"B-MedicalDevice\",\n",
" \"2\": \"I-MedicalDevice\"\n",
" },\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"label2id\": {\n",
" \"B-MedicalDevice\": 1,\n",
" \"I-MedicalDevice\": 2,\n",
" \"O\": 0\n",
" },\n",
" \"layer_norm_eps\": 1e-12,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"bert\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_hidden_layers\": 12,\n",
" \"output_past\": true,\n",
" \"pad_token_id\": null,\n",
" \"pooler_fc_size\": 768,\n",
" \"pooler_num_attention_heads\": 12,\n",
" \"pooler_num_fc_layers\": 3,\n",
" \"pooler_size_per_head\": 128,\n",
" \"pooler_type\": \"first_token_transform\",\n",
" \"position_embedding_type\": \"absolute\",\n",
" \"transformers_version\": \"4.23.1\",\n",
" \"type_vocab_size\": 2,\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 119547\n",
"}\n",
"\n",
"loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/config.json\n",
"Model config BertConfig {\n",
" \"_name_or_path\": \"pucpr/clinicalnerpt-medical\",\n",
" \"_num_labels\": 3,\n",
" \"architectures\": [\n",
" \"BertForTokenClassification\"\n",
" ],\n",
" \"attention_probs_dropout_prob\": 0.1,\n",
" \"classifier_dropout\": null,\n",
" \"directionality\": \"bidi\",\n",
" \"eos_token_ids\": null,\n",
" \"gradient_checkpointing\": false,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"id2label\": {\n",
" \"0\": \"O\",\n",
" \"1\": \"B-MedicalDevice\",\n",
" \"2\": \"I-MedicalDevice\"\n",
" },\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"label2id\": {\n",
" \"B-MedicalDevice\": 1,\n",
" \"I-MedicalDevice\": 2,\n",
" \"O\": 0\n",
" },\n",
" \"layer_norm_eps\": 1e-12,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"bert\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_hidden_layers\": 12,\n",
" \"output_past\": true,\n",
" \"pad_token_id\": null,\n",
" \"pooler_fc_size\": 768,\n",
" \"pooler_num_attention_heads\": 12,\n",
" \"pooler_num_fc_layers\": 3,\n",
" \"pooler_size_per_head\": 128,\n",
" \"pooler_type\": \"first_token_transform\",\n",
" \"position_embedding_type\": \"absolute\",\n",
" \"transformers_version\": \"4.23.1\",\n",
" \"type_vocab_size\": 2,\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 119547\n",
"}\n",
"\n"
]
}
],
"source": [
"model_checkpoint = \"pucpr/clinicalnerpt-medical\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, add_prefix_space=True)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "1b408280-d763-4487-e0c0-06823ddc8daa",
"id": "Gm0lsFPN_fFb"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 111
}
],
"source": [
"tokenizer.is_fast"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a3d8f036-bf64-4f4d-e793-9244e9bd1043",
"id": "5Awp28lM_fFb"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['[CLS]',\n",
" 'since',\n",
" '2006',\n",
" 'she',\n",
" 'had',\n",
" 'tried',\n",
" 'several',\n",
" 'times',\n",
" 'to',\n",
" 'lose',\n",
" 'weight',\n",
" ',',\n",
" 'without',\n",
" 'success',\n",
" '.',\n",
" '[SEP]']"
]
},
"metadata": {},
"execution_count": 112
}
],
"source": [
"inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n",
"inputs.tokens()"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "65b0481e-76fe-4f0a-e7e6-daa6cee662d1",
"id": "Oqi5M1ll_fFb"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"[-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]\n"
]
}
],
"source": [
"labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
"word_ids = inputs.word_ids()\n",
"print(labels)\n",
"print(align_labels_with_tokens(labels, word_ids))"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"49642b493a2d4c3592ed010663ef789c",
"fcc50d75dfb04fccb26c9b93bf8f1efa",
"dc630459e6564d69833d46b63493a160",
"31d0648af26b4fe797f2cb2ff21336a8",
"90e567bcc88445f695a896af6d8da649",
"2e815baaae8940ffb90e2aaf5c6f7e2a",
"32250602bfc140d18859e6b48f9dbfbc",
"a85179eeb7d94bba8c79a10a734d8c6b",
"7346605d4df548afb179d489217990ff",
"6b1a06b7d6ea43da8b9af7f92a882455",
"a1b4b4cc26ff4c50b4ef5c99d8c7cb3e",
"7b3ce57b6b5e4253b8219adc2c6ff47e",
"ee7bc9576ea44b97a6ad9d2ae8adaec7",
"c012f774a21e44188365f5b0646b422e",
"5899086ed64c4d3185374a7f541e22fe",
"3aaf8990b0574a9183b9902eb33670a5",
"cc3d6c6b95ab4c80983b3ce2175acb8a",
"873c876ae8f64b9bba4f25efa3a3859a",
"d727d1396df2443e9e46ab7e0c7d5276",
"cda8e993793c4b949910e309a3f50a03",
"5f38c6b988b44678a7b7f06a99daa983",
"7b53cf7c82a6439eb94d5f0635afe2f3"
]
},
"outputId": "774134e7-c481-40c4-df12-902897c28ad6",
"id": "fVvvny2D_fFb"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/9 [00:00<?, ?ba/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "49642b493a2d4c3592ed010663ef789c"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/3 [00:00<?, ?ba/s]"
],
"application/vnd.jupyter.widget-view+json": {
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"model_id": "7b3ce57b6b5e4253b8219adc2c6ff47e"
}
},
"metadata": {}
}
],
"source": [
"tokenized_datasets = raw_datasets.map(\n",
" tokenize_and_align_labels,\n",
" batched=True,\n",
" remove_columns=raw_datasets[\"train\"].column_names,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"id": "MKc1_p7W_fFb"
},
"outputs": [],
"source": [
"data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e4e50bbf-a7bd-485b-834b-05949304f311",
"id": "-u-nUkGH_fFb"
},
"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, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
" -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100],\n",
" [-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, -100]])"
]
},
"metadata": {},
"execution_count": 116
}
],
"source": [
"batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
"batch[\"labels\"]"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"id": "_hW6-lz2_fFb"
},
"outputs": [],
"source": [
"id2label = {str(i): label for i, label in enumerate(label_names)}\n",
"label2id = {v: k for k, v in id2label.items()}"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4b8291fc-97e1-4d18-9713-7350ec2f075e",
"id": "7zhu1mtB_fFb"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/config.json\n",
"Model config BertConfig {\n",
" \"_name_or_path\": \"pucpr/clinicalnerpt-medical\",\n",
" \"_num_labels\": 3,\n",
" \"architectures\": [\n",
" \"BertForTokenClassification\"\n",
" ],\n",
" \"attention_probs_dropout_prob\": 0.1,\n",
" \"classifier_dropout\": null,\n",
" \"directionality\": \"bidi\",\n",
" \"eos_token_ids\": null,\n",
" \"gradient_checkpointing\": false,\n",
" \"hidden_act\": \"gelu\",\n",
" \"hidden_dropout_prob\": 0.1,\n",
" \"hidden_size\": 768,\n",
" \"id2label\": {\n",
" \"0\": \"O\",\n",
" \"1\": \"B-MedicalDevice\",\n",
" \"2\": \"I-MedicalDevice\"\n",
" },\n",
" \"initializer_range\": 0.02,\n",
" \"intermediate_size\": 3072,\n",
" \"label2id\": {\n",
" \"B-MedicalDevice\": 1,\n",
" \"I-MedicalDevice\": 2,\n",
" \"O\": 0\n",
" },\n",
" \"layer_norm_eps\": 1e-12,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"bert\",\n",
" \"num_attention_heads\": 12,\n",
" \"num_hidden_layers\": 12,\n",
" \"output_past\": true,\n",
" \"pad_token_id\": null,\n",
" \"pooler_fc_size\": 768,\n",
" \"pooler_num_attention_heads\": 12,\n",
" \"pooler_num_fc_layers\": 3,\n",
" \"pooler_size_per_head\": 128,\n",
" \"pooler_type\": \"first_token_transform\",\n",
" \"position_embedding_type\": \"absolute\",\n",
" \"transformers_version\": \"4.23.1\",\n",
" \"type_vocab_size\": 2,\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 119547\n",
"}\n",
"\n",
"loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--pucpr--clinicalnerpt-medical/snapshots/0d889f90b203734b0ba45904781a6779c8eac2b9/pytorch_model.bin\n",
"All model checkpoint weights were used when initializing BertForTokenClassification.\n",
"\n",
"All the weights of BertForTokenClassification were initialized from the model checkpoint at pucpr/clinicalnerpt-medical.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForTokenClassification for predictions without further training.\n"
]
}
],
"source": [
"model = AutoModelForTokenClassification.from_pretrained( \n",
" model_checkpoint\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7f9279a0-4c0a-41ea-c39c-caf895886d2a",
"id": "-4pqS6QR_fFc"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"PyTorch: setting up devices\n",
"The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
]
}
],
"source": [
"args = TrainingArguments(\n",
" \"NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased\",\n",
" evaluation_strategy = IntervalStrategy.STEPS,\n",
" eval_steps = 50,\n",
" learning_rate=5e-5,\n",
" num_train_epochs=50,\n",
" weight_decay=0.01,\n",
" metric_for_best_model = 'f1',\n",
" load_best_model_at_end=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "438ca67b-ebf6-49f3-f3ad-03827a51e196",
"id": "Xrf-cYnW_fFc"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" FutureWarning,\n",
"***** Running training *****\n",
" Num examples = 8751\n",
" Num Epochs = 50\n",
" Instantaneous batch size per device = 8\n",
" Total train batch size (w. parallel, distributed & accumulation) = 8\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 54700\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='2750' max='54700' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [ 2750/54700 10:53 < 3:26:01, 4.20 it/s, Epoch 2/50]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>No log</td>\n",
" <td>0.213414</td>\n",
" <td>0.235529</td>\n",
" <td>0.183135</td>\n",
" <td>0.206054</td>\n",
" <td>0.927267</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>No log</td>\n",
" <td>0.185129</td>\n",
" <td>0.241594</td>\n",
" <td>0.200724</td>\n",
" <td>0.219271</td>\n",
" <td>0.927296</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>No log</td>\n",
" <td>0.175822</td>\n",
" <td>0.340069</td>\n",
" <td>0.304708</td>\n",
" <td>0.321419</td>\n",
" <td>0.933018</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>No log</td>\n",
" <td>0.175697</td>\n",
" <td>0.278355</td>\n",
" <td>0.332644</td>\n",
" <td>0.303087</td>\n",
" <td>0.932284</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>No log</td>\n",
" <td>0.183824</td>\n",
" <td>0.331652</td>\n",
" <td>0.442318</td>\n",
" <td>0.379073</td>\n",
" <td>0.931647</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>No log</td>\n",
" <td>0.197462</td>\n",
" <td>0.431220</td>\n",
" <td>0.228660</td>\n",
" <td>0.298851</td>\n",
" <td>0.932342</td>\n",
" </tr>\n",
" <tr>\n",
" <td>350</td>\n",
" <td>No log</td>\n",
" <td>0.167895</td>\n",
" <td>0.386070</td>\n",
" <td>0.433006</td>\n",
" <td>0.408193</td>\n",
" <td>0.937046</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>No log</td>\n",
" <td>0.190235</td>\n",
" <td>0.179594</td>\n",
" <td>0.201242</td>\n",
" <td>0.189802</td>\n",
" <td>0.930726</td>\n",
" </tr>\n",
" <tr>\n",
" <td>450</td>\n",
" <td>No log</td>\n",
" <td>0.182239</td>\n",
" <td>0.341333</td>\n",
" <td>0.463528</td>\n",
" <td>0.393155</td>\n",
" <td>0.936134</td>\n",
" </tr>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>0.197700</td>\n",
" <td>0.161157</td>\n",
" <td>0.377800</td>\n",
" <td>0.383859</td>\n",
" <td>0.380806</td>\n",
" <td>0.938074</td>\n",
" </tr>\n",
" <tr>\n",
" <td>550</td>\n",
" <td>0.197700</td>\n",
" <td>0.158413</td>\n",
" <td>0.422494</td>\n",
" <td>0.375065</td>\n",
" <td>0.397369</td>\n",
" <td>0.940534</td>\n",
" </tr>\n",
" <tr>\n",
" <td>600</td>\n",
" <td>0.197700</td>\n",
" <td>0.155527</td>\n",
" <td>0.395566</td>\n",
" <td>0.350750</td>\n",
" <td>0.371812</td>\n",
" <td>0.939897</td>\n",
" </tr>\n",
" <tr>\n",
" <td>650</td>\n",
" <td>0.197700</td>\n",
" <td>0.159286</td>\n",
" <td>0.323269</td>\n",
" <td>0.321262</td>\n",
" <td>0.322263</td>\n",
" <td>0.938202</td>\n",
" </tr>\n",
" <tr>\n",
" <td>700</td>\n",
" <td>0.197700</td>\n",
" <td>0.171928</td>\n",
" <td>0.381213</td>\n",
" <td>0.503880</td>\n",
" <td>0.434046</td>\n",
" <td>0.933910</td>\n",
" </tr>\n",
" <tr>\n",
" <td>750</td>\n",
" <td>0.197700</td>\n",
" <td>0.147573</td>\n",
" <td>0.323820</td>\n",
" <td>0.305225</td>\n",
" <td>0.314248</td>\n",
" <td>0.942298</td>\n",
" </tr>\n",
" <tr>\n",
" <td>800</td>\n",
" <td>0.197700</td>\n",
" <td>0.148027</td>\n",
" <td>0.425638</td>\n",
" <td>0.439731</td>\n",
" <td>0.432570</td>\n",
" <td>0.943326</td>\n",
" </tr>\n",
" <tr>\n",
" <td>850</td>\n",
" <td>0.197700</td>\n",
" <td>0.147193</td>\n",
" <td>0.408436</td>\n",
" <td>0.395758</td>\n",
" <td>0.401997</td>\n",
" <td>0.943993</td>\n",
" </tr>\n",
" <tr>\n",
" <td>900</td>\n",
" <td>0.197700</td>\n",
" <td>0.164249</td>\n",
" <td>0.404822</td>\n",
" <td>0.495085</td>\n",
" <td>0.445427</td>\n",
" <td>0.943277</td>\n",
" </tr>\n",
" <tr>\n",
" <td>950</td>\n",
" <td>0.197700</td>\n",
" <td>0.155629</td>\n",
" <td>0.408925</td>\n",
" <td>0.289188</td>\n",
" <td>0.338788</td>\n",
" <td>0.940769</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1000</td>\n",
" <td>0.165700</td>\n",
" <td>0.185534</td>\n",
" <td>0.289720</td>\n",
" <td>0.080186</td>\n",
" <td>0.125608</td>\n",
" <td>0.925875</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1050</td>\n",
" <td>0.165700</td>\n",
" <td>0.163427</td>\n",
" <td>0.402687</td>\n",
" <td>0.511640</td>\n",
" <td>0.450672</td>\n",
" <td>0.936379</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1100</td>\n",
" <td>0.165700</td>\n",
" <td>0.148432</td>\n",
" <td>0.446659</td>\n",
" <td>0.428867</td>\n",
" <td>0.437582</td>\n",
" <td>0.944943</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1150</td>\n",
" <td>0.165700</td>\n",
" <td>0.160113</td>\n",
" <td>0.457825</td>\n",
" <td>0.426798</td>\n",
" <td>0.441767</td>\n",
" <td>0.943307</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1200</td>\n",
" <td>0.165700</td>\n",
" <td>0.167280</td>\n",
" <td>0.462996</td>\n",
" <td>0.265391</td>\n",
" <td>0.337389</td>\n",
" <td>0.939074</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1250</td>\n",
" <td>0.165700</td>\n",
" <td>0.147611</td>\n",
" <td>0.443478</td>\n",
" <td>0.448526</td>\n",
" <td>0.445988</td>\n",
" <td>0.946579</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1300</td>\n",
" <td>0.165700</td>\n",
" <td>0.147158</td>\n",
" <td>0.447046</td>\n",
" <td>0.434558</td>\n",
" <td>0.440714</td>\n",
" <td>0.945070</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1350</td>\n",
" <td>0.165700</td>\n",
" <td>0.184782</td>\n",
" <td>0.432701</td>\n",
" <td>0.495603</td>\n",
" <td>0.462021</td>\n",
" <td>0.942092</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1400</td>\n",
" <td>0.165700</td>\n",
" <td>0.164378</td>\n",
" <td>0.409772</td>\n",
" <td>0.381790</td>\n",
" <td>0.395287</td>\n",
" <td>0.944522</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1450</td>\n",
" <td>0.165700</td>\n",
" <td>0.161122</td>\n",
" <td>0.489035</td>\n",
" <td>0.461459</td>\n",
" <td>0.474847</td>\n",
" <td>0.946138</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1500</td>\n",
" <td>0.133400</td>\n",
" <td>0.151911</td>\n",
" <td>0.395260</td>\n",
" <td>0.483187</td>\n",
" <td>0.434823</td>\n",
" <td>0.944914</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1550</td>\n",
" <td>0.133400</td>\n",
" <td>0.160618</td>\n",
" <td>0.470175</td>\n",
" <td>0.485256</td>\n",
" <td>0.477597</td>\n",
" <td>0.945551</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1600</td>\n",
" <td>0.133400</td>\n",
" <td>0.153152</td>\n",
" <td>0.458456</td>\n",
" <td>0.485256</td>\n",
" <td>0.471475</td>\n",
" <td>0.944796</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1650</td>\n",
" <td>0.133400</td>\n",
" <td>0.159076</td>\n",
" <td>0.481323</td>\n",
" <td>0.406622</td>\n",
" <td>0.440830</td>\n",
" <td>0.946981</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1700</td>\n",
" <td>0.133400</td>\n",
" <td>0.147369</td>\n",
" <td>0.451056</td>\n",
" <td>0.486291</td>\n",
" <td>0.468011</td>\n",
" <td>0.948882</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1750</td>\n",
" <td>0.133400</td>\n",
" <td>0.157782</td>\n",
" <td>0.416149</td>\n",
" <td>0.554578</td>\n",
" <td>0.475493</td>\n",
" <td>0.942082</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1800</td>\n",
" <td>0.133400</td>\n",
" <td>0.139047</td>\n",
" <td>0.469262</td>\n",
" <td>0.473875</td>\n",
" <td>0.471557</td>\n",
" <td>0.948186</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1850</td>\n",
" <td>0.133400</td>\n",
" <td>0.169168</td>\n",
" <td>0.467036</td>\n",
" <td>0.392137</td>\n",
" <td>0.426322</td>\n",
" <td>0.947804</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1900</td>\n",
" <td>0.133400</td>\n",
" <td>0.190142</td>\n",
" <td>0.451332</td>\n",
" <td>0.482152</td>\n",
" <td>0.466233</td>\n",
" <td>0.946511</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1950</td>\n",
" <td>0.133400</td>\n",
" <td>0.142872</td>\n",
" <td>0.489979</td>\n",
" <td>0.480600</td>\n",
" <td>0.485244</td>\n",
" <td>0.948431</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2000</td>\n",
" <td>0.127700</td>\n",
" <td>0.141305</td>\n",
" <td>0.490028</td>\n",
" <td>0.444904</td>\n",
" <td>0.466377</td>\n",
" <td>0.948823</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2050</td>\n",
" <td>0.127700</td>\n",
" <td>0.154673</td>\n",
" <td>0.441645</td>\n",
" <td>0.516813</td>\n",
" <td>0.476281</td>\n",
" <td>0.944257</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2100</td>\n",
" <td>0.127700</td>\n",
" <td>0.161482</td>\n",
" <td>0.559639</td>\n",
" <td>0.417486</td>\n",
" <td>0.478222</td>\n",
" <td>0.948402</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2150</td>\n",
" <td>0.127700</td>\n",
" <td>0.155931</td>\n",
" <td>0.482567</td>\n",
" <td>0.494051</td>\n",
" <td>0.488241</td>\n",
" <td>0.946942</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2200</td>\n",
" <td>0.127700</td>\n",
" <td>0.180343</td>\n",
" <td>0.503902</td>\n",
" <td>0.467667</td>\n",
" <td>0.485109</td>\n",
" <td>0.947011</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2250</td>\n",
" <td>0.127700</td>\n",
" <td>0.173788</td>\n",
" <td>0.474372</td>\n",
" <td>0.469219</td>\n",
" <td>0.471782</td>\n",
" <td>0.945678</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2300</td>\n",
" <td>0.127700</td>\n",
" <td>0.172453</td>\n",
" <td>0.415626</td>\n",
" <td>0.533885</td>\n",
" <td>0.467391</td>\n",
" <td>0.942180</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2350</td>\n",
" <td>0.127700</td>\n",
" <td>0.177914</td>\n",
" <td>0.406309</td>\n",
" <td>0.553026</td>\n",
" <td>0.468449</td>\n",
" <td>0.935782</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2400</td>\n",
" <td>0.127700</td>\n",
" <td>0.177702</td>\n",
" <td>0.452438</td>\n",
" <td>0.484739</td>\n",
" <td>0.468032</td>\n",
" <td>0.947794</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2450</td>\n",
" <td>0.127700</td>\n",
" <td>0.149222</td>\n",
" <td>0.504098</td>\n",
" <td>0.445422</td>\n",
" <td>0.472947</td>\n",
" <td>0.947657</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2500</td>\n",
" <td>0.106000</td>\n",
" <td>0.176770</td>\n",
" <td>0.487315</td>\n",
" <td>0.476979</td>\n",
" <td>0.482092</td>\n",
" <td>0.947148</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2550</td>\n",
" <td>0.106000</td>\n",
" <td>0.159687</td>\n",
" <td>0.442256</td>\n",
" <td>0.511123</td>\n",
" <td>0.474202</td>\n",
" <td>0.944669</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2600</td>\n",
" <td>0.106000</td>\n",
" <td>0.185752</td>\n",
" <td>0.529092</td>\n",
" <td>0.503363</td>\n",
" <td>0.515907</td>\n",
" <td>0.949421</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2650</td>\n",
" <td>0.106000</td>\n",
" <td>0.166756</td>\n",
" <td>0.449671</td>\n",
" <td>0.494568</td>\n",
" <td>0.471052</td>\n",
" <td>0.945943</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2700</td>\n",
" <td>0.106000</td>\n",
" <td>0.174073</td>\n",
" <td>0.432521</td>\n",
" <td>0.477496</td>\n",
" <td>0.453897</td>\n",
" <td>0.946609</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2750</td>\n",
" <td>0.106000</td>\n",
" <td>0.186960</td>\n",
" <td>0.526675</td>\n",
" <td>0.439214</td>\n",
" <td>0.478984</td>\n",
" <td>0.947981</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
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"name": "stderr",
"text": [
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
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" Num examples = 2917\n",
" Batch size = 8\n",
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" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
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"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"Saving model checkpoint to NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500\n",
"Configuration saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/config.json\n",
"Model weights saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/pytorch_model.bin\n",
"tokenizer config file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/tokenizer_config.json\n",
"Special tokens file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-500/special_tokens_map.json\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
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" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"Saving model checkpoint to NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000\n",
"Configuration saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000/config.json\n",
"Model weights saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000/pytorch_model.bin\n",
"tokenizer config file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000/tokenizer_config.json\n",
"Special tokens file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1000/special_tokens_map.json\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
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" Num examples = 2917\n",
" Batch size = 8\n",
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" Batch size = 8\n",
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" Num examples = 2917\n",
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" Num examples = 2917\n",
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" Num examples = 2917\n",
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" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"Saving model checkpoint to NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1500\n",
"Configuration saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1500/config.json\n",
"Model weights saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1500/pytorch_model.bin\n",
"tokenizer config file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1500/tokenizer_config.json\n",
"Special tokens file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-1500/special_tokens_map.json\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
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" Num examples = 2917\n",
" Batch size = 8\n",
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"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"Saving model checkpoint to NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-2000\n",
"Configuration saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-2000/config.json\n",
"Model weights saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-2000/pytorch_model.bin\n",
"tokenizer config file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-2000/tokenizer_config.json\n",
"Special tokens file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-2000/special_tokens_map.json\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
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"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"Saving model checkpoint to NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-2500\n",
"Configuration saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-2500/config.json\n",
"Model weights saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-2500/pytorch_model.bin\n",
"tokenizer config file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-2500/tokenizer_config.json\n",
"Special tokens file saved in NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-2500/special_tokens_map.json\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"***** Running Evaluation *****\n",
" Num examples = 2917\n",
" Batch size = 8\n",
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n",
"Loading best model from NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased/checkpoint-2500 (score: 0.48209150326797384).\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=2750, training_loss=0.14159858218106355, metrics={'train_runtime': 653.9792, 'train_samples_per_second': 669.058, 'train_steps_per_second': 83.642, 'total_flos': 861581789561556.0, 'train_loss': 0.14159858218106355, 'epoch': 2.51})"
]
},
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}
],
"source": [
"trainer = Trainer(\n",
" model=model,\n",
" args=args,\n",
" train_dataset=tokenized_datasets[\"train\"],\n",
" eval_dataset=tokenized_datasets[\"validation\"],\n",
" data_collator=data_collator,\n",
" compute_metrics=compute_metrics,\n",
" tokenizer=tokenizer,\n",
" callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]\n",
")\n",
"trainer.train()"
]
},
{
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"execution_count": 121,
"metadata": {
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},
"outputId": "977619c0-4fc0-423f-f47a-b308c9fb1ecd",
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},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Saving model checkpoint to model/multilingual-BERT-sentence\n",
"Configuration saved in model/multilingual-BERT-sentence/config.json\n",
"Model weights saved in model/multilingual-BERT-sentence/pytorch_model.bin\n",
"tokenizer config file saved in model/multilingual-BERT-sentence/tokenizer_config.json\n",
"Special tokens file saved in model/multilingual-BERT-sentence/special_tokens_map.json\n"
]
}
],
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]
},
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