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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "<a href=\"https://colab.research.google.com/github/jlopetegui98/NER-ClinicalTrials-Elegibility-Criteria/blob/main/Roberta%2BLLM/MLM%2Bgenerative.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+ ]
+ },
+ {
+ "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",
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+ "1340bc1629134745bca3f7a710089038",
+ "e3103093559b42129f9ccd2a7b629c29",
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+ "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='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1f89a6e850d044d5bf0ff42826c9d380"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "from huggingface_hub import notebook_login\n",
+ "\n",
+ "notebook_login()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "46CEQFSvdeiw"
+ },
+ "outputs": [],
+ "source": [
+ "# dict for the entities (entity to int value)\n",
+ "simple_ent = {\"Condition\", \"Value\", \"Drug\", \"Procedure\", \"Measurement\", \"Temporal\", \"Observation\", \"Person\", \"Device\"}\n",
+ "sel_ent = {\n",
+ " \"O\": 0,\n",
+ " \"B-Condition\": 1,\n",
+ " \"I-Condition\": 2,\n",
+ " \"B-Value\": 3,\n",
+ " \"I-Value\": 4,\n",
+ " \"B-Drug\": 5,\n",
+ " \"I-Drug\": 6,\n",
+ " \"B-Procedure\": 7,\n",
+ " \"I-Procedure\": 8,\n",
+ " \"B-Measurement\": 9,\n",
+ " \"I-Measurement\": 10,\n",
+ " \"B-Temporal\": 11,\n",
+ " \"I-Temporal\": 12,\n",
+ " \"B-Observation\": 13,\n",
+ " \"I-Observation\": 14,\n",
+ " \"B-Person\": 15,\n",
+ " \"I-Person\": 16,\n",
+ " \"B-Device\": 17,\n",
+ " \"I-Device\": 18\n",
+ "}\n",
+ "\n",
+ "entities_list = list(sel_ent.keys())\n",
+ "sel_ent_inv = {v: k for k, v in sel_ent.items()}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "8DChGuXtdeiw"
+ },
+ "outputs": [],
+ "source": [
+ "root = '..'\n",
+ "root = './drive/MyDrive/TER-LISN-2024'\n",
+ "data_path = f'{root}/data'\n",
+ "models_path = f'{root}/models'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "id": "azvAU6endeiw"
+ },
+ "outputs": [],
+ "source": [
+ "model_name = \"roberta-base\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 301,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "9Lj6yRsIdeiw",
+ "outputId": "1317466b-b7ea-4f58-baaa-b8d102e1304f"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+ "You will be able to reuse this secret in all of your notebooks.\n",
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer_config.json: 0%| | 0.00/25.0 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "7f6e3b3162bc4db1bccb0d1d01258349"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "config.json: 0%| | 0.00/481 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d0969975ed534564b4fd00a79f9287f5"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "vocab.json: 0%| | 0.00/899k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e21aa3b226584eefb0b542d1f70ec9ab"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e4935e5934e745e6bb96709008d6b75c"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer.json: 0%| | 0.00/1.36M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "7632e4e779814d168c34058ed58c74c3"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "tokenizer = AutoTokenizer.from_pretrained(model_name, add_prefix_space=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "id": "jGIUj0Wwdeiw"
+ },
+ "outputs": [],
+ "source": [
+ "# tokenize and align the labels in the dataset\n",
+ "def tokenize_and_align_labels(sentence, tokenizer, flag = 'I'):\n",
+ " \"\"\"\n",
+ " Tokenize the sentence and align the labels\n",
+ " inputs:\n",
+ " sentence: dict, the sentence from the dataset\n",
+ " flag: str, the flag to indicate how to deal with the labels for subwords\n",
+ " - 'I': use the label of the first subword for all subwords but as intermediate (I-ENT)\n",
+ " - 'B': use the label of the first subword for all subwords as beginning (B-ENT)\n",
+ " - None: use -100 for subwords\n",
+ " outputs:\n",
+ " tokenized_sentence: dict, the tokenized sentence now with a field for the labels\n",
+ " \"\"\"\n",
+ " tokenized_sentence = tokenizer(sentence['tokens'], is_split_into_words=True, truncation=True, padding='max_length', max_length=512)\n",
+ "\n",
+ " labels = []\n",
+ " all_word_ids = []\n",
+ " for i, labels_s in enumerate(sentence['ner_tags']):\n",
+ " word_ids = tokenized_sentence.word_ids(batch_index=i)\n",
+ " previous_word_idx = None\n",
+ " label_ids = []\n",
+ " for word_idx in word_ids:\n",
+ " # if the word_idx is None, assign -100\n",
+ " if word_idx is None:\n",
+ " label_ids.append(-100)\n",
+ " # if it is a new word, assign the corresponding label\n",
+ " elif word_idx != previous_word_idx:\n",
+ " label_ids.append(labels_s[word_idx])\n",
+ " # if it is the same word, check the flag to assign\n",
+ " else:\n",
+ " if flag == 'I':\n",
+ " if entities_list[labels_s[word_idx]].startswith('I'):\n",
+ " label_ids.append(labels_s[word_idx])\n",
+ " else:\n",
+ " label_ids.append(labels_s[word_idx] + 1)\n",
+ " elif flag == 'B':\n",
+ " label_ids.append(labels_s[word_idx])\n",
+ " elif flag == None:\n",
+ " label_ids.append(-100)\n",
+ " previous_word_idx = word_idx\n",
+ " labels.append(label_ids)\n",
+ " all_word_ids.append(word_ids)\n",
+ " tokenized_sentence['labels'] = labels\n",
+ " tokenized_sentence['word_ids'] = all_word_ids\n",
+ " return tokenized_sentence"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "Vn6kc7ZJdeiw",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 241,
+ "referenced_widgets": [
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+ ]
+ },
+ "outputId": "2dacda87-20e9-4589-8890-cbfe0d75df68"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading readme: 0%| | 0.00/577 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "55cf1bff90e847638de184e675d5d44b"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading data: 0%| | 0.00/590k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d636ee8d39374dcfa0ed3e73a0da5a16"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading data: 0%| | 0.00/84.8k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "82b3f4c560c94593b51f245bb2057882"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading data: 0%| | 0.00/151k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "68d04c46bc7c407790ddcea30cb3b427"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0%| | 0/8881 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "37ee53f85fad4257ac5b1dddf28679b7"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating test split: 0%| | 0/1307 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "8e205a49f3144185892f94ef061dc066"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating val split: 0%| | 0/2221 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "da83c24a80ea4c07891cdd693dd9f3c7"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "dataset = load_dataset('JavierLopetegui/chia_v1')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 113,
+ "referenced_widgets": [
+ "65d02c907e4642299e61ab6cfac17bf1",
+ "b44b98ce48d24006a61e63e498e65aac",
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+ "78bc864906964434b15738224fbbad5a",
+ "5c71d3ef1ed0408fb357581db5f1c526",
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+ "64d1dcc6a8ce4b3dab7f83a3a212615b",
+ "9632866091d647feabc3dcf377a77843",
+ "23aaca123cbf49c0a8e6d2456d97e101",
+ "4ebe63db09e24848bcb2e4bf68b48995",
+ "58b432793c3d48cb968a01922bf89f6c",
+ "d883e82e6f4b485a843ea678a906f5b3",
+ "7fee3af388be4a7095c6fbc84294a8a3",
+ "0bebc0d8847f45d78b2c976672f7d9a3",
+ "05975afd11b14f24831c1c1de5e9e6f7",
+ "65ba347cb18a4c90bb51b497722f4cc5",
+ "58300ac1877048a989d9e36db6362c1b",
+ "d5a1b102fbc048b68693bb1136eba9f9",
+ "48a440617ccb4a89bb49fff3cbcd6647"
+ ]
+ },
+ "id": "ia9F2gixdeiw",
+ "outputId": "b5807906-cba2-40e6-9207-c6b41a6c9c1c"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/8881 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "65d02c907e4642299e61ab6cfac17bf1"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/1307 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "7f4322aeb1f845398e939cfdc23a3aad"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/2221 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "23aaca123cbf49c0a8e6d2456d97e101"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "# tokenize and align the labels in the dataset\n",
+ "dataset = dataset.map(lambda x: tokenize_and_align_labels(x, tokenizer, 'I'), batched = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "fGpxVHNyfRJw",
+ "outputId": "3aa95e37-b794-4ac5-b751-4df45b81386f"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "DatasetDict({\n",
+ " train: Dataset({\n",
+ " features: ['tokens', 'ner_tags', 'file', 'index', 'input_ids', 'attention_mask', 'labels', 'word_ids'],\n",
+ " num_rows: 8881\n",
+ " })\n",
+ " test: Dataset({\n",
+ " features: ['tokens', 'ner_tags', 'file', 'index', 'input_ids', 'attention_mask', 'labels', 'word_ids'],\n",
+ " num_rows: 1307\n",
+ " })\n",
+ " val: Dataset({\n",
+ " features: ['tokens', 'ner_tags', 'file', 'index', 'input_ids', 'attention_mask', 'labels', 'word_ids'],\n",
+ " num_rows: 2221\n",
+ " })\n",
+ "})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 12
+ }
+ ],
+ "source": [
+ "dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "smLH3_Andeiw"
+ },
+ "outputs": [],
+ "source": [
+ "# load model\n",
+ "model = torch.load(f'{models_path}/roberta-ner-chia.pt')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "id": "GiKIdJXedeiw"
+ },
+ "outputs": [],
+ "source": [
+ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "id": "k-wSN1KUfNix"
+ },
+ "outputs": [],
+ "source": [
+ "data_for_model = dataset['test'].remove_columns(['file', 'tokens', 'labels', 'index', 'ner_tags', 'word_ids'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "OAeZ75r-hPCH",
+ "outputId": "628993fc-7a95-492d-fbe8-ac4cd6415866"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "Dataset({\n",
+ " features: ['input_ids', 'attention_mask'],\n",
+ " num_rows: 1307\n",
+ "})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 16
+ }
+ ],
+ "source": [
+ "data_for_model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "id": "b99O2uACdeiw"
+ },
+ "outputs": [],
+ "source": [
+ "data_loader = torch.utils.data.DataLoader(data_for_model, batch_size=8)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "MtnU6v5Je8X9"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "enBj0Mn5deiw",
+ "outputId": "ddb91815-2e9a-488a-aeff-12ebefc5d31a"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "RobertaForTokenClassification(\n",
+ " (roberta): RobertaModel(\n",
+ " (embeddings): RobertaEmbeddings(\n",
+ " (word_embeddings): Embedding(50265, 768, padding_idx=1)\n",
+ " (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
+ " (token_type_embeddings): Embedding(1, 768)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (encoder): RobertaEncoder(\n",
+ " (layer): ModuleList(\n",
+ " (0-11): 12 x RobertaLayer(\n",
+ " (attention): RobertaAttention(\n",
+ " (self): RobertaSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (output): RobertaSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): RobertaIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): RobertaOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " (classifier): Linear(in_features=768, out_features=19, bias=True)\n",
+ ")"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 18
+ }
+ ],
+ "source": [
+ "model.to(device)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "id": "F6dReMqdeva6"
+ },
+ "outputs": [],
+ "source": [
+ "from tqdm import tqdm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "vPBZwZGnfzG1",
+ "outputId": "966f4812-7a43-4def-a8e8-053185eaa21d"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "512"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 20
+ }
+ ],
+ "source": [
+ "len(data_loader.dataset[2]['attention_mask'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "UtZKgTQcgORE"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "QzmBnKxjdeiw",
+ "outputId": "feed4ab8-0a23-4c37-e47f-4c8ae5120ebc"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 164/164 [00:46<00:00, 3.56it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "labels = []\n",
+ "for batch in tqdm(data_loader):\n",
+ "\n",
+ " batch['input_ids'] = torch.LongTensor(np.column_stack(np.array(batch['input_ids']))).to(device)\n",
+ " batch['attention_mask'] = torch.LongTensor(np.column_stack(np.array(batch['attention_mask']))).to(device)\n",
+ " batch_tokenizer = {'input_ids': batch['input_ids'], 'attention_mask': batch['attention_mask']}\n",
+ " # break\n",
+ " with torch.no_grad():\n",
+ " outputs = model(**batch_tokenizer)\n",
+ "\n",
+ " labels_batch = torch.argmax(outputs.logits, dim=2).to('cpu').numpy()\n",
+ " labels.extend([list(labels_batch[i]) for i in range(labels_batch.shape[0])])\n",
+ "\n",
+ " del batch\n",
+ " del outputs\n",
+ " torch.cuda.empty_cache()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "id": "w6MABN-NsXVR"
+ },
+ "outputs": [],
+ "source": [
+ "def annotate_sentences(dataset, labels, entities_list,criteria = 'first_label'):\n",
+ " \"\"\"\n",
+ " Annotate the sentences with the predicted labels\n",
+ " inputs:\n",
+ " dataset: dataset, dataset with the sentences\n",
+ " labels: list, list of labels\n",
+ " entities_list: list, list of entities\n",
+ " criteria: str, criteria to use to select the label when the words pices have different labels\n",
+ " - first_label: select the first label\n",
+ " - majority: select the label with the majority\n",
+ " outputs:\n",
+ " annotated_sentences: list, list of annotated sentences\n",
+ " \"\"\"\n",
+ " annotated_sentences = []\n",
+ " for i in range(len(dataset)):\n",
+ " # get just the tokens different from None\n",
+ " sentence = dataset[i]\n",
+ " word_ids = sentence['word_ids']\n",
+ " sentence_labels = labels[i]\n",
+ " annotated_sentence = [[] for _ in range(len(dataset[i]['tokens']))]\n",
+ " for word_id, label in zip(word_ids, sentence_labels):\n",
+ " if word_id is not None:\n",
+ " annotated_sentence[word_id].append(label)\n",
+ " annotated_sentence_filtered = []\n",
+ " if criteria == 'first_label':\n",
+ " annotated_sentence_filtered = [annotated_sentence[i][0] for i in range(len(annotated_sentence))]\n",
+ " elif criteria == 'majority':\n",
+ " annotated_sentence_filtered = []\n",
+ " for j in range(len(annotated_sentence)):\n",
+ " starts_flag = entities_list[annotated_sentence[j][0]].startswith('B')\n",
+ "\n",
+ " ent = max(set(annotated_sentence[j]), key=annotated_sentence[j].count)\n",
+ " if starts_flag and ent != 0:\n",
+ " label = entities_list[ent][2:]\n",
+ " label = 'B-' + label\n",
+ " annotated_sentence_filtered.append(sel_ent[label])\n",
+ " else:\n",
+ " annotated_sentence_filtered.append(ent)\n",
+ " annotated_sentences.append(annotated_sentence_filtered)\n",
+ " return annotated_sentences"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "id": "in70vBBeMn_4"
+ },
+ "outputs": [],
+ "source": [
+ "del model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "id": "UZVjibUbsasA"
+ },
+ "outputs": [],
+ "source": [
+ "annotated_sentences_first = annotate_sentences(dataset['test'], labels, entities_list, criteria='first_label')\n",
+ "annotated_sentences_max = annotate_sentences(dataset['test'], labels, entities_list, criteria='majority')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 173,
+ "referenced_widgets": [
+ "46d3589427554442a4934800c7e4c9f2",
+ "8e07ddce809e4ebe9f7e7ae7ec2b3a64",
+ "50ab4cca6cb84afe839fd7ac4451d43c",
+ "88ad55ec778844708c7f139bb3e03787",
+ "1c8e79a0ed54454488d95beaf1522904",
+ "efda8c27c4fc401d9d9fa91f10631a0c",
+ "fd55a4c8814946299635aa3cd436c321",
+ "308a525977a8430cbf950b862513d491",
+ "cf4e02193d0a4a6fbec4c5d66483dd50",
+ "d0c52edda517426197c6875aa0c1c11f",
+ "d3f12730794e402495704e67bb2a3ab0"
+ ]
+ },
+ "id": "h06Yl1uTdeix",
+ "outputId": "2623f14e-4c5c-4f71-802a-ea55bbbf58d8"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "<ipython-input-25-653dc96d1cff>: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<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "46d3589427554442a4934800c7e4c9f2"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "#load seqeval metric for evaluation\n",
+ "metric = load_metric(\"seqeval\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "id": "kW2qWh4XppbT"
+ },
+ "outputs": [],
+ "source": [
+ "def get_labels(p):\n",
+ " predictions, labels = p\n",
+ " # Remove ignored index (special tokens)\n",
+ " predictions = [\n",
+ " [entities_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
+ " for prediction, label in zip(predictions, labels)\n",
+ " ]\n",
+ " labels = [\n",
+ " [entities_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
+ " for prediction, label in zip(predictions, labels)\n",
+ " ]\n",
+ "\n",
+ " return predictions, labels\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "id": "T8EIf4d83TXK"
+ },
+ "outputs": [],
+ "source": [
+ "pred_labels, true_labels = get_labels((annotated_sentences_first, dataset['test']['ner_tags']))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oBjOyohXMn_4"
+ },
+ "source": [
+ "**Evaluating MLM outputs**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "id": "6l6fW5Bd6MMK"
+ },
+ "outputs": [],
+ "source": [
+ "evaluator = BioEval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "id": "obqUWCw-6T90"
+ },
+ "outputs": [],
+ "source": [
+ "evaluator.evaluate_annotations(true_labels, pred_labels, do_lower=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "XAdRmNx39MYa",
+ "outputId": "6403f520-901f-4fc2-d2ef-2cbd7dc141ad"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'overall': {'acc': 0.834,\n",
+ " 'strict': {'precision': 0.6155223880597015,\n",
+ " 'recall': 0.6602625680435479,\n",
+ " 'f_score': 0.6371079870230187},\n",
+ " 'relax': {'precision': 0.7602985074626866,\n",
+ " 'recall': 0.8155619596541787,\n",
+ " 'f_score': 0.7869612235439518}},\n",
+ " 'category': {'strict': {'condition': {'precision': 0.6619385342789598,\n",
+ " 'recall': 0.7601809954751131,\n",
+ " 'f_score': 0.7076663858466722},\n",
+ " 'person': {'precision': 0.7655172413793103,\n",
+ " 'recall': 0.8222222222222222,\n",
+ " 'f_score': 0.7928571428571428},\n",
+ " 'value': {'precision': 0.6925207756232687,\n",
+ " 'recall': 0.7122507122507122,\n",
+ " 'f_score': 0.702247191011236},\n",
+ " 'observation': {'precision': 0.33,\n",
+ " 'recall': 0.19879518072289157,\n",
+ " 'f_score': 0.24812030075187974},\n",
+ " 'procedure': {'precision': 0.4537037037037037,\n",
+ " 'recall': 0.4696485623003195,\n",
+ " 'f_score': 0.46153846153846156},\n",
+ " 'drug': {'precision': 0.6989010989010989,\n",
+ " 'recall': 0.7178329571106095,\n",
+ " 'f_score': 0.7082405345211581},\n",
+ " 'temporal': {'precision': 0.49702380952380953,\n",
+ " 'recall': 0.5622895622895623,\n",
+ " 'f_score': 0.5276461295418641},\n",
+ " 'measurement': {'precision': 0.5662650602409639,\n",
+ " 'recall': 0.6482758620689655,\n",
+ " 'f_score': 0.6045016077170419},\n",
+ " 'device': {'precision': 0.2857142857142857,\n",
+ " 'recall': 0.34782608695652173,\n",
+ " 'f_score': 0.3137254901960784}},\n",
+ " 'relax': {'condition': {'precision': 0.8006304176516943,\n",
+ " 'recall': 0.9194570135746606,\n",
+ " 'f_score': 0.8559393428812131},\n",
+ " 'person': {'precision': 0.7724137931034483,\n",
+ " 'recall': 0.8296296296296296,\n",
+ " 'f_score': 0.7999999999999999},\n",
+ " 'value': {'precision': 0.8448753462603878,\n",
+ " 'recall': 0.8689458689458689,\n",
+ " 'f_score': 0.8567415730337079},\n",
+ " 'observation': {'precision': 0.54,\n",
+ " 'recall': 0.3253012048192771,\n",
+ " 'f_score': 0.40601503759398494},\n",
+ " 'procedure': {'precision': 0.6419753086419753,\n",
+ " 'recall': 0.6645367412140575,\n",
+ " 'f_score': 0.6530612244897959},\n",
+ " 'drug': {'precision': 0.8241758241758241,\n",
+ " 'recall': 0.8465011286681715,\n",
+ " 'f_score': 0.8351893095768373},\n",
+ " 'temporal': {'precision': 0.6636904761904762,\n",
+ " 'recall': 0.7508417508417509,\n",
+ " 'f_score': 0.7045813586097945},\n",
+ " 'measurement': {'precision': 0.7319277108433735,\n",
+ " 'recall': 0.8379310344827586,\n",
+ " 'f_score': 0.7813504823151125},\n",
+ " 'device': {'precision': 0.39285714285714285,\n",
+ " 'recall': 0.4782608695652174,\n",
+ " 'f_score': 0.4313725490196078}}}}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 30
+ }
+ ],
+ "source": [
+ "evaluator.performance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "id": "FqrRcxjTMn_5"
+ },
+ "outputs": [],
+ "source": [
+ "def get_annotated_sentence(tokens, labels):\n",
+ " \"\"\"\n",
+ " Get the annotated sentence\n",
+ " inputs:\n",
+ " tokens: list, list of tokens\n",
+ " labels: list, list of labels\n",
+ " outputs:\n",
+ " annotated_sentence: str, the annotated sentence\n",
+ " \"\"\"\n",
+ " annotated_sentence = []\n",
+ " i = 0\n",
+ " sentence = \"\"\n",
+ " while i < len(tokens):\n",
+ " if labels[i] != 'O':\n",
+ " start = i\n",
+ " while i < len(tokens) and labels[i] != 'O' and labels[i].split('-')[1] == labels[start].split('-')[1]:\n",
+ " i += 1\n",
+ " end = i\n",
+ " sentence += f\"<{labels[start].split('-')[1]}>\" + ' '.join(tokens[start:end]) + f\"</{labels[start].split('-')[1]}>\"\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 <Condition>fever</Condition> of <Value>38 degrees Celsius</Value> .'"
+ ],
+ "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 <Condition>fever</Condition> of 38 degrees Celsius .\n",
+ "output: The patient has a <Condition>fever</Condition> of <Value>38 degrees Celsius</Value> .\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 <Condition>fever</Condition> of 38 degrees Celsius .\n",
+ "output: The patient has a <Condition>fever</Condition> of <Value>38 degrees Celsius</Value> .\n",
+ "\n",
+ "Now it is your turn. Please check the following sentence:\n",
+ "input: The patient has a <Condition>fever</Condition> of 38 degrees Celsius .\n",
+ "output: \n"
+ ]
+ }
+ ],
+ "source": [
+ "print(get_verification_prompt('The patient has a <Condition>fever</Condition> 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 <Condition>fever</Condition> of 38 degrees Celsius .\n",
+ "output: The patient has a <Condition>fever</Condition> of <Value>38 degrees Celsius</Value> .\n",
+ "\n",
+ "Now it is your turn. Please check the following sentence:\n",
+ "input: self - reported <Condition>healthy</Condition> <Person>adults</Person> between the <Value>ages of 18 - 60</Value> who are fluent <Observation>in</Observation> 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": [
+ "37038ecae0034a1d89a82ddd6e03f09e",
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+ "62bf58ddf89b4ec1b221d058f2df4ceb",
+ "84071152296e41c7a1e516e9cda6c669",
+ "4f04ac285d8d4a03a48369f5b2506b14",
+ "1cd5a05f73f3423eb8295ece24cb9e26",
+ "4161aa829b0b4a88b3eaef39471d9d79",
+ "aa3264b095454513a5a156447b38ca0c",
+ "9e1c908ba8c04f3ebba4e2c8f75f6353",
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+ "05a4f02211364716895776fe88ee59ed",
+ "7d8997a11a914760b72ecaaf9021fd36",
+ "ada6824d412e48198d91a0d1ba4c4a99",
+ "dab25f05c65e4d029751c62588e6559a",
+ "e56c62bed7034268b448a2a7e22929c2",
+ "291d8cbb98a4480da789b4b6e32bf98d",
+ "4348750ac71942e387584a7e0f7b198e",
+ "3ed28ccb08924878b11d6ae571f38ae6",
+ "78ac8626794d413a8ad6779a71fcb99b",
+ "31290b8d07f74512b9a1b8da544620e4",
+ "55eee0f96f05458cbab37adc1e220752",
+ "9d5d17fd233b49fca8c4a33a3470adc1",
+ "858d1ba3133c43138dc1cf72296ee16b",
+ "87aefa546e4b4891b07c4f3206b06f39",
+ "c046b1d37f89430fa916e82793c7dd60"
+ ]
+ },
+ "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<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "37038ecae0034a1d89a82ddd6e03f09e"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "model.safetensors.index.json: 0%| | 0.00/25.1k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "9b88d1dfbd264eccbcb818d22aa812fc"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading shards: 0%| | 0/2 [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e557cca0a4c6420883792ada77ddcc55"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "model-00001-of-00002.safetensors: 0%| | 0.00/9.94G [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "988b957a991344b29d2bb2c072cd62e9"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "model-00002-of-00002.safetensors: 0%| | 0.00/4.54G [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "2639ce3e4c5b49fba5d1293c88f1211b"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "84071152296e41c7a1e516e9cda6c669"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "generation_config.json: 0%| | 0.00/116 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e56c62bed7034268b448a2a7e22929c2"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "# Load base model(Mistral 7B)\n",
+ "bnb_config = BitsAndBytesConfig(\n",
+ " load_in_4bit= True,\n",
+ " bnb_4bit_quant_type= \"nf4\",\n",
+ " bnb_4bit_compute_dtype= torch.bfloat16,\n",
+ " bnb_4bit_use_double_quant= False,\n",
+ ")\n",
+ "model = AutoModelForCausalLM.from_pretrained(\n",
+ " model_name,\n",
+ " quantization_config=bnb_config,\n",
+ " device_map={\"\": 0}\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 162,
+ "referenced_widgets": [
+ "64ddac7afa644ee6ad803b9c9a296343",
+ "b1203bbf2ff44cf69a54ece1cdf5c1cd",
+ "2ada5e03a29047e4ab1ceb0ad18cdf16",
+ "5db40b14baf1473a886f8de74984c6d7",
+ "c9cbf5c4bfcf4456bffc65f88dbb02a6",
+ "e29465fc32a549418211fb77e22ac135",
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+ "01f7c52aa8634f21b8222511d6503555",
+ "1bcc26c4325d4e2a908de8d2be84920f",
+ "f3f94a18fb9844908a7b004e7658b867",
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+ "c808f90f59eb4ed1ad280eba5a969a71",
+ "305d4e356acf478c9d532502e3767b37",
+ "3e9e7d5006644b3aa5cb666adf597f04",
+ "68e727d9435142c88e41e5be0947e8a5",
+ "ce768c91d4c24d2485fccd6b02bb08e1",
+ "54783062ec7a4415b0b8a778f7131711",
+ "1e68bef97b0045d5beb4ae3fd8b84f36",
+ "029372d5d5bb4c338486753528a2d3a1",
+ "d5e75b2e82b94f21ac4d0d5ea3225811",
+ "f4a6e1dbae1741bfae9b157c71ed462c",
+ "6baeed3156e647679ebfc1ff90bd8547",
+ "429bd02d0830435a909a850e366667a2",
+ "7f75e2d9329547acb4dedbce353380b7",
+ "26a7b47611024ab0a4f063bf5b8fa7c1",
+ "fb89370bbd3740dd8fee212d9d576a37",
+ "3118bbe8ce0845ea8fa4e02432ea0337",
+ "73feb101732c4fd48e68fb08ca2a357b",
+ "03d4eea3a8064cf0af7c9ee195717ffa",
+ "685cfe686a8e4facbd5c741b882f714a",
+ "93375395a517473d971cadd33196063f",
+ "77f68f07570348088ee764564f24af4a",
+ "1719ce4960b84b5796b6f71f8ebaa52c",
+ "c01058158092408492e978226bfeddeb",
+ "43b8a44cdd764efb827641d74d2a4166",
+ "3eadfd342316495684587e30afdb44e1",
+ "b4ed02324e3e4018ab65a9093ca8f2b5",
+ "f478fb9889de4618b12c295c94c581f6",
+ "03f11c5b6dab4bf9ab7ae14e58fc9396",
+ "95d9217f200b40c78c83c6359a154df4",
+ "b0651ca2afd54d349bbbe31bdb38a6d8",
+ "ddc322d6c4f645588da1ae6cb0f668ef",
+ "ce650d381bf8426b87650c2dcd9157b3"
+ ]
+ },
+ "id": "tNqGo_XMMn_9",
+ "outputId": "008b0fd3-2512-4d37-c6d4-4ec6cdcf1059"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer_config.json: 0%| | 0.00/967 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "64ddac7afa644ee6ad803b9c9a296343"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer.model: 0%| | 0.00/493k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "3d08f1972a2241208870b37227b19d74"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer.json: 0%| | 0.00/1.80M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "6baeed3156e647679ebfc1ff90bd8547"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "special_tokens_map.json: 0%| | 0.00/72.0 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1719ce4960b84b5796b6f71f8ebaa52c"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(True, True)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 46
+ }
+ ],
+ "source": [
+ "# import tokenizer for mistral-7B\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
+ "tokenizer.padding_side = 'left'\n",
+ "tokenizer.pad_token = tokenizer.eos_token\n",
+ "tokenizer.add_eos_token = True\n",
+ "tokenizer.add_bos_token, tokenizer.add_eos_token"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "id": "IudZ0h25Mn_9"
+ },
+ "outputs": [],
+ "source": [
+ "pipe = TextGenerationPipeline(model = model, tokenizer = tokenizer)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "cZHmpVMvMn_9",
+ "outputId": "c5ef019d-a33e-4eb5-8832-506443ccc31e"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ " 0%| | 5/1307 [03:35<15:06:33, 41.78s/it]You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
+ "100%|██████████| 1307/1307 [14:24:36<00:00, 39.69s/it]\n"
+ ]
+ }
+ ],
+ "source": [
+ "corrected_sentences = []\n",
+ "for sentence in tqdm(verification_prompts):\n",
+ " output = pipe(sentence, max_new_tokens = 500, return_full_text = False, handle_long_generation = \"hole\")[0]['generated_text']\n",
+ " output = output.split('\\n')[0]\n",
+ " corrected_sentences.append(output)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "5_VGPxGrMn_9",
+ "outputId": "b50d9ba3-28b7-425a-80d2-8f95751516a6"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "1307"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 79
+ }
+ ],
+ "source": [
+ "len(corrected_sentences)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {
+ "id": "U0I9Wkw6Mn_9"
+ },
+ "outputs": [],
+ "source": [
+ "tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {
+ "id": "WXwdjxd4Mn_9"
+ },
+ "outputs": [],
+ "source": [
+ "# tokenize and align the labels in the dataset\n",
+ "def tokenize_and_align_labels(sentence, flag = 'I'):\n",
+ " \"\"\"\n",
+ " Tokenize the sentence and align the labels\n",
+ " inputs:\n",
+ " sentence: dict, the sentence from the dataset\n",
+ " flag: str, the flag to indicate how to deal with the labels for subwords\n",
+ " - 'I': use the label of the first subword for all subwords but as intermediate (I-ENT)\n",
+ " - 'B': use the label of the first subword for all subwords as beginning (B-ENT)\n",
+ " - None: use -100 for subwords\n",
+ " outputs:\n",
+ " tokenized_sentence: dict, the tokenized sentence now with a field for the labels\n",
+ " \"\"\"\n",
+ " tokenized_sentence = tokenizer(sentence['tokens'], is_split_into_words=True, truncation=True)\n",
+ "\n",
+ " labels = []\n",
+ " for i, labels_s in enumerate(sentence['ner_tags']):\n",
+ " word_ids = tokenized_sentence.word_ids(batch_index=i)\n",
+ " previous_word_idx = None\n",
+ " label_ids = []\n",
+ " for word_idx in word_ids:\n",
+ " # if the word_idx is None, assign -100\n",
+ " if word_idx is None:\n",
+ " label_ids.append(-100)\n",
+ " # if it is a new word, assign the corresponding label\n",
+ " elif word_idx != previous_word_idx:\n",
+ " label_ids.append(labels_s[word_idx])\n",
+ " # if it is the same word, check the flag to assign\n",
+ " else:\n",
+ " if flag == 'I':\n",
+ " if entities_list[labels_s[word_idx]].startswith('I'):\n",
+ " label_ids.append(labels_s[word_idx])\n",
+ " else:\n",
+ " label_ids.append(labels_s[word_idx] + 1)\n",
+ " elif flag == 'B':\n",
+ " label_ids.append(labels_s[word_idx])\n",
+ " elif flag == None:\n",
+ " label_ids.append(-100)\n",
+ " previous_word_idx = word_idx\n",
+ " labels.append(label_ids)\n",
+ " tokenized_sentence['labels'] = labels\n",
+ " return tokenized_sentence"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import re"
+ ],
+ "metadata": {
+ "id": "DOQ8F0tPQkPI"
+ },
+ "execution_count": 82,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {
+ "id": "FrwuZs8oMn_9"
+ },
+ "outputs": [],
+ "source": [
+ "def parse_ann2bio(sentence, pattern, pattern1, pattern2):\n",
+ " # if sentence[-1] == \"\\n\":\n",
+ " # sentence = sentence[:-2] # remove the \\n and a final point wrongly added\n",
+ " # else:\n",
+ " # sentence = sentence[:-1] # remove the final point wrongly added\n",
+ "\n",
+ " # find the entities\n",
+ " occurrences = re.finditer(pattern, sentence)\n",
+ " indexes = [(match.start(), match.end()) for match in occurrences]\n",
+ "\n",
+ " annotation = []\n",
+ " i = 0\n",
+ "\n",
+ "\n",
+ " # create the bio list\n",
+ " for beg, end in indexes:\n",
+ " if beg > 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'</{entity_name}>', \"\")\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<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "2be20107aa2c46bdb62bf0a7eabcaf25"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "true_ann_dataset = true_ann_dataset.map(tokenize_and_align_labels, batched=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {
+ "id": "PMPKCcLoMn_9"
+ },
+ "outputs": [],
+ "source": [
+ "pattern1 = r'<(Person|Condition|Value|Drug|Procedure|Measurement|Temporal|Observation|Device)>'\n",
+ "pattern2 = r'</(Person|Condition|Value|Drug|Procedure|Measurement|Temporal|Observation|Device)>'\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<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "57bf7373f6da4ce8a27602c76a9a8d16"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "corrections_dataset = corrections_dataset.map(tokenize_and_align_labels, batched=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "corrections_dataset"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "HV6qafbhQ45e",
+ "outputId": "5ab12a74-0977-4e80-96fe-2eb3412af434"
+ },
+ "execution_count": 90,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "Dataset({\n",
+ " features: ['tokens', 'ner_tags', 'input_ids', 'attention_mask', 'labels'],\n",
+ " num_rows: 1307\n",
+ "})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 90
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7Qvu77UwMn_-",
+ "outputId": "5e7977b5-5862-4f97-8243-fa85a601c83c"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "0.6725325172149962\n"
+ ]
+ }
+ ],
+ "source": [
+ "# keep just sentences with the same length\n",
+ "sentences_to_evaluate_corr = []\n",
+ "sentences_to_evaluate_true = []\n",
+ "\n",
+ "for i in range(len(corrections_dataset)):\n",
+ " if len(corrections_dataset['labels'][i]) == len(true_ann_dataset['labels'][i]):\n",
+ " sentences_to_evaluate_corr.append(corrections_dataset['labels'][i])\n",
+ " sentences_to_evaluate_true.append(true_ann_dataset['labels'][i])\n",
+ "\n",
+ "print(len(sentences_to_evaluate_corr)/len(corrections_dataset))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "labels_mlm, _ = get_labels((annotated_sentences_first, annotated_sentences_first))"
+ ],
+ "metadata": {
+ "id": "laB7Zc0LZpDe"
+ },
+ "execution_count": 101,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# keep just sentences with the same length\n",
+ "sentences_to_evaluate_corr = []\n",
+ "sentences_to_evaluate_true = []\n",
+ "sentences_to_eval_mlm = []\n",
+ "true_2 = []\n",
+ "\n",
+ "for i in range(len(corrections_dataset)):\n",
+ " if len(corrections_dataset['labels'][i]) == len(true_ann_dataset['labels'][i]):\n",
+ " sentences_to_evaluate_corr.append(corrections_dataset['labels'][i])\n",
+ " sentences_to_evaluate_true.append(true_ann_dataset['labels'][i])\n",
+ " sentences_to_eval_mlm.append(labels_mlm[i])\n",
+ " true_2.append(dataset['test']['ner_tags'][i])\n",
+ "print(len(sentences_to_evaluate_corr)/len(corrections_dataset))"
+ ],
+ "metadata": {
+ "id": "Rdej41NbZNdW",
+ "outputId": "71e3fbc4-f9e2-4fa8-e66e-5cbf6932f1b2",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": 113,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "0.6725325172149962\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "true_2, _ = get_labels((true_2, true_2))"
+ ],
+ "metadata": {
+ "id": "5S86BJ8QbVqv"
+ },
+ "execution_count": 114,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "print(len(sentences_to_eval_mlm)/len(labels_mlm))\n"
+ ],
+ "metadata": {
+ "id": "jf9KXjj4afPK",
+ "outputId": "cb54a976-1679-4ac8-8c38-6bc94c802373",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": 103,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "0.6725325172149962\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uxT9puNBMn_-"
+ },
+ "source": [
+ "**Evaluate new annotations**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "evaluator_mlm = BioEval()"
+ ],
+ "metadata": {
+ "id": "we7Rj-yoaG6d"
+ },
+ "execution_count": 104,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# pred_labels_mlm, true_labels = get_labels((sentences_to_eval_mlm, sentences_to_evaluate_true))"
+ ],
+ "metadata": {
+ "id": "UA282scaaJib",
+ "outputId": "26a3cd41-77e4-4d90-b6e3-3619d291a66e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 356
+ }
+ },
+ "execution_count": 105,
+ "outputs": [
+ {
+ "output_type": "error",
+ "ename": "TypeError",
+ "evalue": "list indices must be integers or slices, not str",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-105-a48f8ee9138e>\u001b[0m in \u001b[0;36m<cell line: 1>\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<ipython-input-26-d65102fa91b2>\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<ipython-input-26-d65102fa91b2>\u001b[0m in \u001b[0;36m<listcomp>\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<ipython-input-26-d65102fa91b2>\u001b[0m in \u001b[0;36m<listcomp>\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",
+ " 'value': {'precision': 0.8590909090909091,\n",
+ " 'recall': 0.9043062200956937,\n",
+ " 'f_score': 0.881118881118881},\n",
+ " 'observation': {'precision': 0.5483870967741935,\n",
+ " 'recall': 0.34,\n",
+ " 'f_score': 0.4197530864197531},\n",
+ " 'temporal': {'precision': 0.725609756097561,\n",
+ " 'recall': 0.7727272727272727,\n",
+ " 'f_score': 0.7484276729559748},\n",
+ " 'drug': {'precision': 0.84765625,\n",
+ " 'recall': 0.8714859437751004,\n",
+ " 'f_score': 0.8594059405940594},\n",
+ " 'measurement': {'precision': 0.8,\n",
+ " 'recall': 0.8343558282208589,\n",
+ " 'f_score': 0.816816816816817},\n",
+ " 'procedure': {'precision': 0.7046979865771812,\n",
+ " 'recall': 0.7342657342657343,\n",
+ " 'f_score': 0.7191780821917809},\n",
+ " 'device': {'precision': 0.375,\n",
+ " 'recall': 0.3,\n",
+ " 'f_score': 0.33333333333333326}}}}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 116
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {
+ "id": "LWAePW06Mn_-"
+ },
+ "outputs": [],
+ "source": [
+ "evaluator = BioEval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {
+ "id": "5zSXJWziMn_-"
+ },
+ "outputs": [],
+ "source": [
+ "pred_labels, true_labels = get_labels((sentences_to_evaluate_corr, sentences_to_evaluate_true))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {
+ "id": "Co8lMIAWMn_-"
+ },
+ "outputs": [],
+ "source": [
+ "evaluator.evaluate_annotations(true_labels, pred_labels, do_lower=True)"
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+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Z8y0JvL3Mn_-",
+ "outputId": "f91e3704-406f-4d95-88f5-580852cc4125"
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+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'overall': {'acc': 0.8156,\n",
+ " 'strict': {'precision': 0.735303776683087,\n",
+ " 'recall': 0.738943894389439,\n",
+ " 'f_score': 0.737119341563786},\n",
+ " 'relax': {'precision': 0.8308702791461412,\n",
+ " 'recall': 0.834983498349835,\n",
+ " 'f_score': 0.8329218106995884}},\n",
+ " 'category': {'strict': {'condition': {'precision': 0.7823834196891192,\n",
+ " 'recall': 0.783601453035807,\n",
+ " 'f_score': 0.7829919626652839},\n",
+ " 'person': {'precision': 0.7444444444444445,\n",
+ " 'recall': 0.7701149425287356,\n",
+ " 'f_score': 0.7570621468926554},\n",
+ " 'value': {'precision': 0.7230046948356808,\n",
+ " 'recall': 0.7549019607843137,\n",
+ " 'f_score': 0.7386091127098322},\n",
+ " 'observation': {'precision': 0.35,\n",
+ " 'recall': 0.21428571428571427,\n",
+ " 'f_score': 0.26582278481012656},\n",
+ " 'temporal': {'precision': 0.5783132530120482,\n",
+ " 'recall': 0.6233766233766234,\n",
+ " 'f_score': 0.6000000000000001},\n",
+ " 'drug': {'precision': 0.754863813229572,\n",
+ " 'recall': 0.7854251012145749,\n",
+ " 'f_score': 0.7698412698412699},\n",
+ " 'measurement': {'precision': 0.631578947368421,\n",
+ " 'recall': 0.6625766871165644,\n",
+ " 'f_score': 0.6467065868263473},\n",
+ " 'procedure': {'precision': 0.5771812080536913,\n",
+ " 'recall': 0.6142857142857143,\n",
+ " 'f_score': 0.5951557093425606},\n",
+ " 'device': {'precision': 0.3333333333333333,\n",
+ " 'recall': 0.3,\n",
+ " 'f_score': 0.3157894736842105}},\n",
+ " 'relax': {'condition': {'precision': 0.866839378238342,\n",
+ " 'recall': 0.868188894654904,\n",
+ " 'f_score': 0.867513611615245},\n",
+ " 'person': {'precision': 0.7888888888888889,\n",
+ " 'recall': 0.8160919540229885,\n",
+ " 'f_score': 0.8022598870056497},\n",
+ " 'value': {'precision': 0.8262910798122066,\n",
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+ " 'f_score': 0.40506329113924044},\n",
+ " 'temporal': {'precision': 0.7168674698795181,\n",
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+ " 'f_score': 0.74375},\n",
+ " 'drug': {'precision': 0.8521400778210116,\n",
+ " 'recall': 0.8866396761133604,\n",
+ " 'f_score': 0.8690476190476192},\n",
+ " 'measurement': {'precision': 0.7719298245614035,\n",
+ " 'recall': 0.8098159509202454,\n",
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+ " 'procedure': {'precision': 0.7046979865771812,\n",
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+ " 'device': {'precision': 0.3333333333333333,\n",
+ " 'recall': 0.3,\n",
+ " 'f_score': 0.3157894736842105}}}}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 95
+ }
+ ],
+ "source": [
+ "evaluator.performance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "metadata": {
+ "id": "N6c22z-KMn_-"
+ },
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+ "source": [
+ "evaluator.save_evaluation('eval_MLM+generative.json')"
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+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "metadata": {
+ "colab": {
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+ },
+ "id": "yQOwpABZMn_-",
+ "outputId": "1d2c4ffd-bfbb-4cba-ac35-4512a1f1da59"
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+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'expect': {'overall': 3030,\n",
+ " 'condition': 1927,\n",
+ " 'person': 87,\n",
+ " 'value': 204,\n",
+ " 'observation': 98,\n",
+ " 'drug': 247,\n",
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+ " 'temporal': 154,\n",
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+ " 'false': 806},\n",
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+ " 'value': {'total': 213, 'true': 154, 'false': 59},\n",
+ " 'observation': {'total': 60, 'true': 21, 'false': 39},\n",
+ " 'temporal': {'total': 166, 'true': 96, 'false': 70},\n",
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+ " 'relax': {'overall': {'total': 3045, 'true': 2530, 'false': 515},\n",
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+ " 'observation': {'total': 60, 'true': 32, 'false': 28},\n",
+ " 'temporal': {'total': 166, 'true': 119, 'false': 47},\n",
+ " 'drug': {'total': 257, 'true': 219, 'false': 38},\n",
+ " 'measurement': {'total': 171, 'true': 132, 'false': 39},\n",
+ " 'procedure': {'total': 149, 'true': 105, 'false': 44},\n",
+ " 'device': {'total': 9, 'true': 3, 'false': 6}}}}"
+ ]
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+ "metadata": {},
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+ "metadata": {
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+ "outputId": "c0fe9cd8-c544-4615-db1f-7634e198ffe7"
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "done\n"
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+ }
+ ],
+ "source": [
+ "print('done')"
+ ]
+ }
+ ],
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