{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OmG4urkedeiv",
"outputId": "564d7767-0fd9-4fe4-fa52-e627c853acf7"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"# uncomment if working in colab\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pG8-9pLtdeiv",
"outputId": "e1e4d250-1316-434b-b534-ef986c0cf754"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting seqeval\n",
" Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.25.2)\n",
"Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.2.2)\n",
"Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.11.4)\n",
"Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.4.0)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.4.0)\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=16161 sha256=fa0cb1d1af1f5ce87aea7c7a9c03b2c6d8e5ddc09f3f0154bca3aacfddb8730b\n",
" Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n",
"Successfully built seqeval\n",
"Installing collected packages: seqeval\n",
"Successfully installed seqeval-1.2.2\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"# uncomment if using colab\n",
"!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
"!pip install -q -U datasets\n",
"!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
"!pip install -q -U git+https://github.com/huggingface/peft.git\n",
"!pip install -q -U bitsandbytes\n",
"!pip install seqeval\n",
"!pip install -q -U evaluate"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "cD_ebmTNdeiv"
},
"outputs": [],
"source": [
"import numpy as np\n",
"from transformers import AutoTokenizer, AutoModelForTokenClassification, DataCollatorForTokenClassification, Trainer, TrainingArguments, AutoModelForCausalLM, BitsAndBytesConfig, TextGenerationPipeline\n",
"from datasets import load_dataset, load_metric\n",
"from seqeval.metrics import classification_report\n",
"import pandas as pd\n",
"from datasets import Dataset, DatasetDict, load_dataset\n",
"from seqeval.scheme import IOB2\n",
"import evaluate\n",
"import torch\n",
"from eval_file import *\n",
"from utils import *"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 159,
"referenced_widgets": [
"1f89a6e850d044d5bf0ff42826c9d380",
"4dd06c9a1c2844b098827fa425670805",
"dea4621e41dc4aed896525f0f5d2082f",
"6f3fc00b6eda41778fe7bd287d910258",
"c4909f7aefc8468d87c523bb3e3d8a9c",
"0f26ea65ecc2435ca79e5850a3039231",
"ef02f67c5c6449c1944f1905f36c3eac",
"3f81145a71a44933bd3f941796f56cee",
"a11d59e40d7c42deafe5acd6b4cc9870",
"1b998bb43270416e957b5201b5d89d6c",
"949fd3e1474a48db9f22d6c9b5009a5e",
"92c204333bf14600b38b7674400bf461",
"1340bc1629134745bca3f7a710089038",
"e3103093559b42129f9ccd2a7b629c29",
"7bbce2b680134eeeae6cc36624fb5944",
"3811c4d478584c578d7f04b4de5364b2",
"92eb2778add54b5b8470ea6a4e7cb69b",
"7d72b73db60b4c0c97db1fe318d7b80f",
"d60258b3a0c4458294a38b28751e01ca",
"cea64f9845bc45b78195b1aded785402",
"13d76452759a47c2a159506000b29f99",
"34e8cb0a1ce04f689c763e213b40a5a9",
"6c006df746754052a1d614e093420286",
"048c49c93dcf4f578960c02eef89564a",
"aa89b45433db4a95a24f2992ec216703",
"6fd4ab24b0424c46aea4ac2d0ddd4fd4",
"39da10287e3f4f26ab304e3b24b1ab14",
"22aa08e25b22442f8c08040836e5bfac",
"e33030643b8a447084c935a82958d763",
"a0e9fed85eab46d2bdbdadd7bd87357e",
"e567df5cf7714fdabf680d7051c8720a",
"c4addaa947f54b0cbf23d50611970a9f"
]
},
"id": "RTiSFJvzdeiw",
"outputId": "e379f9c4-8ba5-4671-b444-249fa27120b0"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"VBox(children=(HTML(value=' :2: 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",
" metric = load_metric(\"seqeval\")\n",
"/usr/local/lib/python3.10/dist-packages/datasets/load.py:756: FutureWarning: The repository for seqeval contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.18.0/metrics/seqeval/seqeval.py\n",
"You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
"Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading builder script: 0%| | 0.00/2.47k [00:00\" + ' '.join(tokens[start:end]) + f\"\"\n",
" if end < len(tokens):\n",
" sentence += ' '\n",
" else:\n",
" sentence += tokens[i]\n",
" i += 1\n",
" if i < len(tokens):\n",
" sentence += ' '\n",
" return sentence"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
},
"id": "3eJLxQmYMn_5",
"outputId": "666fedfa-8d76-4cc6-94ce-39a61420c51e"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'The patient has a fever of 38 degrees Celsius .'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 32
}
],
"source": [
"get_annotated_sentence(['The', 'patient', 'has', 'a', 'fever', 'of', '38', 'degrees', 'Celsius', '.'], ['O', 'O', 'O', 'O', 'B-Condition', 'O', 'B-Value', 'I-Value', 'I-Value', 'O'])"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"id": "y7pgTiPSMn_5"
},
"outputs": [],
"source": [
"def get_verification_prompt(sentence):\n",
" \"\"\"\n",
" Get the verification prompt\n",
" inputs:\n",
" sentence: str, the sentence\n",
" outputs:\n",
" verification_prompt: str, the verification prompt\n",
" \"\"\"\n",
" return f\"\"\"I am working in a named entity recognition task for Clinical trial\n",
"eligibility criteria. I have some annotations I need you to check. The list of possible entities is {','.join(simple_ent)}.\n",
"I want you to check the correcteness of the sentence and return the correct annotations in the exact same format as the original annotations.\n",
"Please use just the entities in the list. I will show you a first simple example for you to understand the task:\n",
"input: The patient has a fever of 38 degrees Celsius .\n",
"output: The patient has a fever of 38 degrees Celsius .\n",
"\n",
"Now it is your turn. Please check the following sentence:\n",
"input: {sentence}\n",
"output: \"\"\""
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Z2SKgzYaMn_5",
"outputId": "07b330b0-183e-4a44-a578-eb80a78d16c7"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"I am working in a named entity recognition task for Clinical trial\n",
"eligibility criteria. I have some annotations I need you to check. The list of possible entities is Measurement,Device,Procedure,Drug,Observation,Condition,Temporal,Value,Person.\n",
"I want you to check the correcteness of the sentence and return the correct annotations in the exact same format as the original annotations.\n",
"Please use just the entities in the list. I will show you a first simple example for you to understand the task:\n",
"input: The patient has a fever of 38 degrees Celsius .\n",
"output: The patient has a fever of 38 degrees Celsius .\n",
"\n",
"Now it is your turn. Please check the following sentence:\n",
"input: The patient has a fever of 38 degrees Celsius .\n",
"output: \n"
]
}
],
"source": [
"print(get_verification_prompt('The patient has a fever of 38 degrees Celsius .'))"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"id": "Gx2fT9CYMn_5"
},
"outputs": [],
"source": [
"verification_prompts = [get_verification_prompt(get_annotated_sentence(dataset['test'][i]['tokens'], pred_labels[i])) for i in range(len(dataset['test']))]"
]
},
{
"cell_type": "code",
"source": [
"print(verification_prompts[0])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6WFtKISlOgJ6",
"outputId": "4048659e-1fa4-4979-89fc-12fa31091f54"
},
"execution_count": 43,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"I am working in a named entity recognition task for Clinical trial\n",
"eligibility criteria. I have some annotations I need you to check. The list of possible entities is Measurement,Device,Procedure,Drug,Observation,Condition,Temporal,Value,Person.\n",
"I want you to check the correcteness of the sentence and return the correct annotations in the exact same format as the original annotations.\n",
"Please use just the entities in the list. I will show you a first simple example for you to understand the task:\n",
"input: The patient has a fever of 38 degrees Celsius .\n",
"output: The patient has a fever of 38 degrees Celsius .\n",
"\n",
"Now it is your turn. Please check the following sentence:\n",
"input: self - reported healthy adults between the ages of 18 - 60 who are fluent in English .\n",
"output: \n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JQDNBa2DMn_5"
},
"source": [
"**load mistral-7b**"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"id": "WEdKf4ZhMn_8"
},
"outputs": [],
"source": [
"model_name = \"mistralai/Mistral-7B-v0.1\""
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297,
"referenced_widgets": [
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},
"id": "5wM2h3k8Mn_9",
"outputId": "f3c956c2-0c9f-4e54-ed75-c14656960f67"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/571 [00:00 i:\n",
" annotation.extend([(word, \"O\") for word in sentence[i:beg].split()])\n",
" entity = sentence[beg:end]\n",
" entity_name = re.search(pattern1, entity).group(1)\n",
" entity = entity.replace(f'<{entity_name}>', \"\").replace(f'', \"\")\n",
" split_entity = entity.split()\n",
" annotation.append((split_entity[0], \"B-\" + entity_name))\n",
" annotation.extend([(word, \"I-\" + entity_name) for word in split_entity[1:]])\n",
" i = end\n",
" annotation.extend([(word, \"O\") for word in sentence[i:].split()])\n",
"\n",
" # check punctuation sign in tokens and put them as individual tokens\n",
" ps = r'(\\.|\\,|\\:|\\;|\\!|\\?|\\-|\\(|\\)|\\[|\\]|\\{|\\}|\\\")'\n",
" new_annotation = []\n",
" for i,(word, tag) in enumerate(annotation):\n",
" if re.search(ps, word):\n",
" # find the ocurrences of the punctuation signs\n",
" occurrences = re.finditer(ps, word)\n",
" indexes = [(match.start(), match.end()) for match in occurrences]\n",
" # create the new tokens\n",
" last = 0\n",
" for j, (beg, end) in enumerate(indexes):\n",
" if beg > last:\n",
" new_annotation.append((word[last:beg], tag))\n",
" if tag != \"O\":\n",
" label = f'I-{tag.split(\"-\")[1]}'\n",
" else:\n",
" label = \"O\"\n",
" if end < len(word) or (i < len(annotation) - 1 and annotation[i+1][1] == label):\n",
" new_annotation.append((word[beg:end], label))\n",
" else:\n",
" new_annotation.append((word[beg:end], 'O'))\n",
" last = end\n",
" if last < len(word):\n",
" new_annotation.append((word[last:], label))\n",
"\n",
" else:\n",
" new_annotation.append((word, tag))\n",
"\n",
"\n",
" return new_annotation"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lk1DKNyDMn_9",
"outputId": "366a9ce2-0e06-40dc-c70f-2fc99bbc72b4"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1307"
]
},
"metadata": {},
"execution_count": 54
}
],
"source": [
"new_true_annotations = []\n",
"for sent in dataset['test']:\n",
" annotation = []\n",
" for word, tag in zip(sent['tokens'], sent['ner_tags']):\n",
" annotation.append((word, entities_list[tag]))\n",
" new_annotation = []\n",
" ps = r'(\\.|\\,|\\:|\\;|\\!|\\?|\\-|\\(|\\)|\\[|\\]|\\{|\\}|\\\")'\n",
" for i,(word, tag) in enumerate(annotation):\n",
" if re.search(ps, word):\n",
" # find the ocurrences of the punctuation signs\n",
" occurrences = re.finditer(ps, word)\n",
" indexes = [(match.start(), match.end()) for match in occurrences]\n",
" # create the new tokens\n",
" last = 0\n",
" for j, (beg, end) in enumerate(indexes):\n",
" if beg > last:\n",
" new_annotation.append((word[last:beg], tag))\n",
" if tag != \"O\":\n",
" label = f'I-{tag.split(\"-\")[1]}'\n",
" else:\n",
" label = \"O\"\n",
" if end < len(word) or (i < len(annotation) - 1 and annotation[i+1][1] == label):\n",
" new_annotation.append((word[beg:end], label))\n",
" else:\n",
" new_annotation.append((word[beg:end], 'O'))\n",
" last = end\n",
" if last < len(word):\n",
" new_annotation.append((word[last:], label))\n",
" else:\n",
" new_annotation.append((word, tag))\n",
" new_true_annotations.append(new_annotation)\n",
"len(new_true_annotations)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "c9GKzSwgMn_9",
"outputId": "1f814b96-996e-4aae-f77b-523f97a0df5c"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1307"
]
},
"metadata": {},
"execution_count": 55
}
],
"source": [
"true_annotations = []\n",
"for sent in new_true_annotations:\n",
" dicc_sent = {\"tokens\":[], \"ner_tags\":[]}\n",
" for word, tag in sent:\n",
" dicc_sent[\"tokens\"].append(word)\n",
" dicc_sent[\"ner_tags\"].append(sel_ent[tag])\n",
" true_annotations.append(dicc_sent)\n",
"len(true_annotations)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"id": "2pXdvWBdMn_9"
},
"outputs": [],
"source": [
"true_df = pd.DataFrame(true_annotations)\n",
"true_ann_dataset = Dataset.from_pandas(true_df)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"2be20107aa2c46bdb62bf0a7eabcaf25",
"a3f98ef220414c9f8bbb09b4184a902a",
"5023a5b4806e4b52ae78f6e81429339a",
"a0a041c1c72d48d3be9c2d0c0d5c95a9",
"4c440b954a884a43a0ed910af0adca87",
"f831738c507142b58416255b2ebb34b3",
"18aa574962a4454eae746d61a63f073c",
"fce075f8c48744908767fea2215bf2c7",
"87e152cdd7ba4f239b338f4ad7f743fc",
"010e5b4cb2ef4bff8db8f26989cfcd46",
"64224fa0af03497487ed000cdea62876"
]
},
"id": "wxyFRXPfMn_9",
"outputId": "d75650fc-15e1-45bc-a803-8fe0c011de03"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/1307 [00:00'\n",
"pattern2 = r''\n",
"pattern = f'{pattern1}.*?{pattern2}'"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DDCo-eq6Mn_9",
"outputId": "ebdd46a3-8fa0-4e0b-a7c8-118b1b154f7b"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1307"
]
},
"metadata": {},
"execution_count": 85
}
],
"source": [
"new_corrected_annotations = []\n",
"for sent in corrected_sentences:\n",
" annotation = parse_ann2bio(sent, pattern, pattern1, pattern2)\n",
" new_corrected_annotations.append(annotation)\n",
"len(new_corrected_annotations)"
]
},
{
"cell_type": "code",
"source": [
"print(new_corrected_annotations[0])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "baA3nbEVQqhR",
"outputId": "be8e098a-4aad-4895-970f-b277b819db75"
},
"execution_count": 86,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[('self', 'O'), ('-', 'O'), ('reported', 'O'), ('healthy', 'B-Condition'), ('adults', 'B-Person'), ('between', 'O'), ('the', 'O'), ('ages', 'B-Value'), ('of', 'I-Value'), ('18', 'I-Value'), ('-', 'I-Value'), ('60', 'I-Value'), ('who', 'O'), ('are', 'O'), ('fluent', 'O'), ('in', 'B-Observation'), ('English', 'O'), ('.', 'O')]\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "twoJF0QLMn_9",
"outputId": "3042e962-a84d-4851-a111-b24f84fb45b3"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1307"
]
},
"metadata": {},
"execution_count": 87
}
],
"source": [
"corrected_annotations = []\n",
"for sent in new_corrected_annotations:\n",
" dicc_sent = {\"tokens\":[], \"ner_tags\":[]}\n",
" for word, tag in sent:\n",
" dicc_sent[\"tokens\"].append(word)\n",
" dicc_sent[\"ner_tags\"].append(sel_ent[tag])\n",
" corrected_annotations.append(dicc_sent)\n",
"len(corrected_annotations)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"id": "l95lzFQZMn_9"
},
"outputs": [],
"source": [
"corrections_df = pd.DataFrame(corrected_annotations)\n",
"corrections_dataset = Dataset.from_pandas(corrections_df)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"57bf7373f6da4ce8a27602c76a9a8d16",
"9374130506904234a22483a72ca619ec",
"d37432965f7c424798202ca121f79771",
"c14a91b588b74a79be6747fbb691c133",
"ad8e028b01e641099949df8e60e38f1c",
"d70e0bcd92494269904a92aa9aa7a2ce",
"0725418c51134e4c97543aa3098d9083",
"c4f02802e3c64b61a4c65ae81237b154",
"4e74d47ea1934cfdbecc61dfdb9fa7d5",
"13e6a2351fb84c4aadf55a39da49d6b6",
"f71fb0cbe892417ba530e5d518dd32b7"
]
},
"id": "4n23GGasMn_-",
"outputId": "87336418-b7f8-4e10-f641-0f327a0a1df8"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/1307 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpred_labels_mlm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrue_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentences_to_eval_mlm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msentences_to_evaluate_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m\u001b[0m in \u001b[0;36mget_labels\u001b[0;34m(p)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Remove ignored index (special tokens)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m predictions = [\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mentities_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ml\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Remove ignored index (special tokens)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m predictions = [\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mentities_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ml\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m ]\n",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Remove ignored index (special tokens)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m predictions = [\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mentities_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ml\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m ]\n",
"\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not str"
]
}
]
},
{
"cell_type": "code",
"source": [
"len(true_labels[0])"
],
"metadata": {
"id": "Ip6QcrIzax2V",
"outputId": "4fcffb96-e91e-4452-9090-786c8e74628a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 111,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"20"
]
},
"metadata": {},
"execution_count": 111
}
]
},
{
"cell_type": "code",
"source": [
"len(sentences_to_eval_mlm[0])"
],
"metadata": {
"id": "h_Z1EIvra0fV",
"outputId": "4e6fbafa-d514-4273-8266-ff8ee6ed126b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 112,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"18"
]
},
"metadata": {},
"execution_count": 112
}
]
},
{
"cell_type": "code",
"source": [
"evaluator_mlm.evaluate_annotations(true_2, sentences_to_eval_mlm, do_lower=True)"
],
"metadata": {
"id": "p_74Cm5eaRTD"
},
"execution_count": 115,
"outputs": []
},
{
"cell_type": "code",
"source": [
"evaluator_mlm.performance"
],
"metadata": {
"id": "PTjX_eNZaaxf",
"outputId": "ff1880b4-92a9-43e1-946e-8ac21c883b0e",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 116,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'overall': {'acc': 0.8273,\n",
" 'strict': {'precision': 0.6645091693635383,\n",
" 'recall': 0.696045197740113,\n",
" 'f_score': 0.6799116997792495},\n",
" 'relax': {'precision': 0.7982740021574973,\n",
" 'recall': 0.8361581920903954,\n",
" 'f_score': 0.8167770419426049}},\n",
" 'category': {'strict': {'condition': {'precision': 0.6894150417827298,\n",
" 'recall': 0.7722308892355694,\n",
" 'f_score': 0.728476821192053},\n",
" 'person': {'precision': 0.8130841121495327,\n",
" 'recall': 0.8613861386138614,\n",
" 'f_score': 0.8365384615384616},\n",
" 'value': {'precision': 0.7227272727272728,\n",
" 'recall': 0.7607655502392344,\n",
" 'f_score': 0.7412587412587412},\n",
" 'observation': {'precision': 0.3225806451612903,\n",
" 'recall': 0.2,\n",
" 'f_score': 0.24691358024691357},\n",
" 'temporal': {'precision': 0.5670731707317073,\n",
" 'recall': 0.6038961038961039,\n",
" 'f_score': 0.5849056603773586},\n",
" 'drug': {'precision': 0.74609375,\n",
" 'recall': 0.7670682730923695,\n",
" 'f_score': 0.7564356435643564},\n",
" 'measurement': {'precision': 0.6176470588235294,\n",
" 'recall': 0.6441717791411042,\n",
" 'f_score': 0.6306306306306307},\n",
" 'procedure': {'precision': 0.5302013422818792,\n",
" 'recall': 0.5524475524475524,\n",
" 'f_score': 0.5410958904109587},\n",
" 'device': {'precision': 0.375,\n",
" 'recall': 0.3,\n",
" 'f_score': 0.33333333333333326}},\n",
" 'relax': {'condition': {'precision': 0.8217270194986073,\n",
" 'recall': 0.9204368174726989,\n",
" 'f_score': 0.8682855040470935},\n",
" 'person': {'precision': 0.8130841121495327,\n",
" 'recall': 0.8613861386138614,\n",
" 'f_score': 0.8365384615384616},\n",
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" 'measurement': {'precision': 0.8,\n",
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