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"cells": [ |
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"cell_type": "markdown", |
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"metadata": { |
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"id": "view-in-github", |
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"colab_type": "text" |
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}, |
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"source": [ |
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"<a href=\"https://colab.research.google.com/github/jlopetegui98/NER-ClinicalTrials-Elegibility-Criteria/blob/main/Roberta%2BLLM/evaluate_roberta_chia.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 1, |
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"metadata": { |
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"id": "OmG4urkedeiv", |
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"outputId": "87a33dc4-f118-45a7-c8aa-aab80e9c76ca", |
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"colab": { |
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"base_uri": "https://localhost:8080/" |
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} |
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}, |
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"outputs": [ |
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{ |
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"output_type": "stream", |
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"name": "stdout", |
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"text": [ |
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"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" |
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] |
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} |
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], |
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"source": [ |
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"# uncomment if working in colab\n", |
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"from google.colab import drive\n", |
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"drive.mount('/content/drive')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 2, |
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"metadata": { |
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"id": "pG8-9pLtdeiv", |
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"outputId": "d3c710c2-a443-4b3b-db20-2fafe6746f0d", |
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"colab": { |
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"base_uri": "https://localhost:8080/" |
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} |
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}, |
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"outputs": [ |
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{ |
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"output_type": "stream", |
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"name": "stdout", |
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"text": [ |
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" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", |
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" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", |
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" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", |
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" Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", |
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", |
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", |
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", |
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", |
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"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", |
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" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", |
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" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", |
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" Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", |
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"Collecting seqeval\n", |
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" Downloading seqeval-1.2.2.tar.gz (43 kB)\n", |
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", |
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", |
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"Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.25.2)\n", |
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"Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.2.2)\n", |
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"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", |
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"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", |
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"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", |
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"Building wheels for collected packages: seqeval\n", |
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" Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n", |
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" Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16161 sha256=b571c9c4836027705ada02b905790e6d0b00dff2c033b522f11aeb9c3d0d66ed\n", |
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" Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n", |
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"Successfully built seqeval\n", |
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"Installing collected packages: seqeval\n", |
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"Successfully installed seqeval-1.2.2\n", |
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", |
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"\u001b[?25h" |
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] |
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} |
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], |
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"source": [ |
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"# uncomment if using colab\n", |
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"!pip install -q -U git+https://github.com/huggingface/transformers.git\n", |
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"!pip install -q -U datasets\n", |
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"!pip install -q -U git+https://github.com/huggingface/accelerate.git\n", |
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"!pip install seqeval\n", |
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"!pip install -q -U evaluate" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 2, |
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"metadata": { |
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"id": "cD_ebmTNdeiv" |
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}, |
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"outputs": [], |
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"source": [ |
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"import numpy as np\n", |
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"from transformers import AutoTokenizer, AutoModelForTokenClassification, DataCollatorForTokenClassification, Trainer, TrainingArguments\n", |
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"from datasets import load_dataset, load_metric\n", |
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"from seqeval.metrics import classification_report\n", |
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"from seqeval.scheme import IOB2\n", |
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"import evaluate\n", |
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"import torch" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 3, |
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"metadata": { |
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"id": "RTiSFJvzdeiw", |
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"outputId": "ed96941b-ab02-41bb-da96-c668cb8aac43", |
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"colab": { |
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"base_uri": "https://localhost:8080/", |
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"height": 145, |
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] |
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} |
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}, |
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"outputs": [ |
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{ |
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"output_type": "display_data", |
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"data": { |
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"text/plain": [ |
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"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…" |
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], |
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"application/vnd.jupyter.widget-view+json": { |
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"version_major": 2, |
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"version_minor": 0, |
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"model_id": "aae03eda481e488eaf1ef5b0610cdc0b" |
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} |
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}, |
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"metadata": {} |
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} |
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], |
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"source": [ |
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"from huggingface_hub import notebook_login\n", |
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"\n", |
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"notebook_login()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 4, |
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"metadata": { |
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"id": "46CEQFSvdeiw" |
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}, |
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"outputs": [], |
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"source": [ |
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"# dict for the entities (entity to int value)\n", |
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"simple_ent = {\"Condition\", \"Value\", \"Drug\", \"Procedure\", \"Measurement\", \"Temporal\", \"Observation\", \"Person\", \"Device\"}\n", |
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"sel_ent = {\n", |
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" \"O\": 0,\n", |
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" \"B-Condition\": 1,\n", |
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" \"I-Condition\": 2,\n", |
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" \"B-Value\": 3,\n", |
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" \"I-Value\": 4,\n", |
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" \"B-Drug\": 5,\n", |
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" \"I-Drug\": 6,\n", |
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" \"B-Procedure\": 7,\n", |
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" \"I-Procedure\": 8,\n", |
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" \"B-Measurement\": 9,\n", |
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" \"I-Measurement\": 10,\n", |
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" \"B-Temporal\": 11,\n", |
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" \"I-Temporal\": 12,\n", |
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" \"B-Observation\": 13,\n", |
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" \"I-Observation\": 14,\n", |
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" \"B-Person\": 15,\n", |
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" \"I-Person\": 16,\n", |
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" \"B-Device\": 17,\n", |
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" \"I-Device\": 18\n", |
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"}\n", |
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"\n", |
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"entities_list = list(sel_ent.keys())\n", |
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"sel_ent_inv = {v: k for k, v in sel_ent.items()}" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 5, |
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"metadata": { |
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"id": "8DChGuXtdeiw" |
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}, |
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"outputs": [], |
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"source": [ |
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"root = '..'\n", |
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"root = './drive/MyDrive/TER-LISN-2024'\n", |
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"data_path = f'{root}/data'\n", |
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"models_path = f'{root}/models'" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 6, |
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"metadata": { |
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"id": "azvAU6endeiw" |
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}, |
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"outputs": [], |
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"source": [ |
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"model_name = \"roberta-base\"" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 7, |
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"metadata": { |
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"id": "9Lj6yRsIdeiw", |
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"outputId": "c140456c-c362-4eed-c946-67e626da7f52", |
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"colab": { |
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"base_uri": "https://localhost:8080/" |
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} |
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}, |
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"outputs": [ |
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{ |
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"output_type": "stream", |
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"name": "stderr", |
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"text": [ |
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"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", |
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"The secret `HF_TOKEN` does not exist in your Colab secrets.\n", |
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"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", |
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"You will be able to reuse this secret in all of your notebooks.\n", |
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"Please note that authentication is recommended but still optional to access public models or datasets.\n", |
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" warnings.warn(\n" |
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] |
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} |
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], |
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"source": [ |
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"tokenizer = AutoTokenizer.from_pretrained(model_name, add_prefix_space=True)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 8, |
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"metadata": { |
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"id": "jGIUj0Wwdeiw" |
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}, |
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"outputs": [], |
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"source": [ |
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"# tokenize and align the labels in the dataset\n", |
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"def tokenize_and_align_labels(sentence, tokenizer, flag = 'I'):\n", |
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" \"\"\"\n", |
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" Tokenize the sentence and align the labels\n", |
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" inputs:\n", |
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" sentence: dict, the sentence from the dataset\n", |
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" flag: str, the flag to indicate how to deal with the labels for subwords\n", |
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" - 'I': use the label of the first subword for all subwords but as intermediate (I-ENT)\n", |
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" - 'B': use the label of the first subword for all subwords as beginning (B-ENT)\n", |
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" - None: use -100 for subwords\n", |
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" outputs:\n", |
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" tokenized_sentence: dict, the tokenized sentence now with a field for the labels\n", |
|
|
288 |
" \"\"\"\n", |
|
|
289 |
" tokenized_sentence = tokenizer(sentence['tokens'], is_split_into_words=True, truncation=True, padding='max_length', max_length=512)\n", |
|
|
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"\n", |
|
|
291 |
" labels = []\n", |
|
|
292 |
" all_word_ids = []\n", |
|
|
293 |
" for i, labels_s in enumerate(sentence['ner_tags']):\n", |
|
|
294 |
" word_ids = tokenized_sentence.word_ids(batch_index=i)\n", |
|
|
295 |
" previous_word_idx = None\n", |
|
|
296 |
" label_ids = []\n", |
|
|
297 |
" for word_idx in word_ids:\n", |
|
|
298 |
" # if the word_idx is None, assign -100\n", |
|
|
299 |
" if word_idx is None:\n", |
|
|
300 |
" label_ids.append(-100)\n", |
|
|
301 |
" # if it is a new word, assign the corresponding label\n", |
|
|
302 |
" elif word_idx != previous_word_idx:\n", |
|
|
303 |
" label_ids.append(labels_s[word_idx])\n", |
|
|
304 |
" # if it is the same word, check the flag to assign\n", |
|
|
305 |
" else:\n", |
|
|
306 |
" if flag == 'I':\n", |
|
|
307 |
" if entities_list[labels_s[word_idx]].startswith('I'):\n", |
|
|
308 |
" label_ids.append(labels_s[word_idx])\n", |
|
|
309 |
" else:\n", |
|
|
310 |
" label_ids.append(labels_s[word_idx] + 1)\n", |
|
|
311 |
" elif flag == 'B':\n", |
|
|
312 |
" label_ids.append(labels_s[word_idx])\n", |
|
|
313 |
" elif flag == None:\n", |
|
|
314 |
" label_ids.append(-100)\n", |
|
|
315 |
" previous_word_idx = word_idx\n", |
|
|
316 |
" labels.append(label_ids)\n", |
|
|
317 |
" all_word_ids.append(word_ids)\n", |
|
|
318 |
" tokenized_sentence['labels'] = labels\n", |
|
|
319 |
" tokenized_sentence['word_ids'] = all_word_ids\n", |
|
|
320 |
" return tokenized_sentence" |
|
|
321 |
] |
|
|
322 |
}, |
|
|
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{ |
|
|
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"cell_type": "code", |
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"execution_count": 9, |
|
|
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"metadata": { |
|
|
327 |
"id": "Vn6kc7ZJdeiw" |
|
|
328 |
}, |
|
|
329 |
"outputs": [], |
|
|
330 |
"source": [ |
|
|
331 |
"dataset = load_dataset('JavierLopetegui/chia_v1')" |
|
|
332 |
] |
|
|
333 |
}, |
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{ |
|
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"cell_type": "code", |
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"execution_count": 10, |
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|
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"metadata": { |
|
|
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"id": "ia9F2gixdeiw", |
|
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"outputId": "295daaec-e013-4539-c939-6996765bd8a8", |
|
|
340 |
"colab": { |
|
|
341 |
"base_uri": "https://localhost:8080/", |
|
|
342 |
"height": 49, |
|
|
343 |
"referenced_widgets": [ |
|
|
344 |
"c134904ded5f47aabca0dfa5b67049db", |
|
|
345 |
"3c1d79df90544bdd93873579d38116a4", |
|
|
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"3806b75490704196afde5977db7dca89", |
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347 |
"7665c9c809964b6eb6b4a9720c5d2dac", |
|
|
348 |
"1785e46465ec42ccbb933604963d5dd6", |
|
|
349 |
"7b7dd023dbc7472998c6aac7eee1490f", |
|
|
350 |
"62a0e81ff19a4162a67e5884ce678f2c", |
|
|
351 |
"24a85f5bd83441b4943299be2d145aff", |
|
|
352 |
"18eaa2134e8e4edeb41340cebbeb8572", |
|
|
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"68e1a741d3ec4fc3a26d759ecc357ac6", |
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"de3e98c73f9547bebdad9b4bcb21d9a1" |
|
|
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] |
|
|
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} |
|
|
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}, |
|
|
358 |
"outputs": [ |
|
|
359 |
{ |
|
|
360 |
"output_type": "display_data", |
|
|
361 |
"data": { |
|
|
362 |
"text/plain": [ |
|
|
363 |
"Map: 0%| | 0/1307 [00:00<?, ? examples/s]" |
|
|
364 |
], |
|
|
365 |
"application/vnd.jupyter.widget-view+json": { |
|
|
366 |
"version_major": 2, |
|
|
367 |
"version_minor": 0, |
|
|
368 |
"model_id": "c134904ded5f47aabca0dfa5b67049db" |
|
|
369 |
} |
|
|
370 |
}, |
|
|
371 |
"metadata": {} |
|
|
372 |
} |
|
|
373 |
], |
|
|
374 |
"source": [ |
|
|
375 |
"# tokenize and align the labels in the dataset\n", |
|
|
376 |
"dataset = dataset.map(lambda x: tokenize_and_align_labels(x, tokenizer, 'I'), batched = True)" |
|
|
377 |
] |
|
|
378 |
}, |
|
|
379 |
{ |
|
|
380 |
"cell_type": "code", |
|
|
381 |
"source": [ |
|
|
382 |
"dataset" |
|
|
383 |
], |
|
|
384 |
"metadata": { |
|
|
385 |
"id": "fGpxVHNyfRJw", |
|
|
386 |
"outputId": "d1268582-6fc8-4531-b5af-9ea4406f4ba9", |
|
|
387 |
"colab": { |
|
|
388 |
"base_uri": "https://localhost:8080/" |
|
|
389 |
} |
|
|
390 |
}, |
|
|
391 |
"execution_count": 11, |
|
|
392 |
"outputs": [ |
|
|
393 |
{ |
|
|
394 |
"output_type": "execute_result", |
|
|
395 |
"data": { |
|
|
396 |
"text/plain": [ |
|
|
397 |
"DatasetDict({\n", |
|
|
398 |
" train: Dataset({\n", |
|
|
399 |
" features: ['tokens', 'ner_tags', 'file', 'index', 'input_ids', 'attention_mask', 'labels', 'word_ids'],\n", |
|
|
400 |
" num_rows: 8881\n", |
|
|
401 |
" })\n", |
|
|
402 |
" test: Dataset({\n", |
|
|
403 |
" features: ['tokens', 'ner_tags', 'file', 'index', 'input_ids', 'attention_mask', 'labels', 'word_ids'],\n", |
|
|
404 |
" num_rows: 1307\n", |
|
|
405 |
" })\n", |
|
|
406 |
" val: Dataset({\n", |
|
|
407 |
" features: ['tokens', 'ner_tags', 'file', 'index', 'input_ids', 'attention_mask', 'labels', 'word_ids'],\n", |
|
|
408 |
" num_rows: 2221\n", |
|
|
409 |
" })\n", |
|
|
410 |
"})" |
|
|
411 |
] |
|
|
412 |
}, |
|
|
413 |
"metadata": {}, |
|
|
414 |
"execution_count": 11 |
|
|
415 |
} |
|
|
416 |
] |
|
|
417 |
}, |
|
|
418 |
{ |
|
|
419 |
"cell_type": "code", |
|
|
420 |
"execution_count": 12, |
|
|
421 |
"metadata": { |
|
|
422 |
"id": "smLH3_Andeiw" |
|
|
423 |
}, |
|
|
424 |
"outputs": [], |
|
|
425 |
"source": [ |
|
|
426 |
"# load model\n", |
|
|
427 |
"model = torch.load(f'{models_path}/roberta-ner-chia.pt')" |
|
|
428 |
] |
|
|
429 |
}, |
|
|
430 |
{ |
|
|
431 |
"cell_type": "code", |
|
|
432 |
"execution_count": 13, |
|
|
433 |
"metadata": { |
|
|
434 |
"id": "GiKIdJXedeiw" |
|
|
435 |
}, |
|
|
436 |
"outputs": [], |
|
|
437 |
"source": [ |
|
|
438 |
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')" |
|
|
439 |
] |
|
|
440 |
}, |
|
|
441 |
{ |
|
|
442 |
"cell_type": "code", |
|
|
443 |
"source": [ |
|
|
444 |
"data_for_model = dataset['test'].remove_columns(['file', 'tokens', 'labels', 'index', 'ner_tags', 'word_ids'])" |
|
|
445 |
], |
|
|
446 |
"metadata": { |
|
|
447 |
"id": "k-wSN1KUfNix" |
|
|
448 |
}, |
|
|
449 |
"execution_count": 14, |
|
|
450 |
"outputs": [] |
|
|
451 |
}, |
|
|
452 |
{ |
|
|
453 |
"cell_type": "code", |
|
|
454 |
"source": [ |
|
|
455 |
"data_for_model" |
|
|
456 |
], |
|
|
457 |
"metadata": { |
|
|
458 |
"id": "OAeZ75r-hPCH", |
|
|
459 |
"outputId": "0d8c7676-3471-4f74-d3ad-942b26595c62", |
|
|
460 |
"colab": { |
|
|
461 |
"base_uri": "https://localhost:8080/" |
|
|
462 |
} |
|
|
463 |
}, |
|
|
464 |
"execution_count": 15, |
|
|
465 |
"outputs": [ |
|
|
466 |
{ |
|
|
467 |
"output_type": "execute_result", |
|
|
468 |
"data": { |
|
|
469 |
"text/plain": [ |
|
|
470 |
"Dataset({\n", |
|
|
471 |
" features: ['input_ids', 'attention_mask'],\n", |
|
|
472 |
" num_rows: 1307\n", |
|
|
473 |
"})" |
|
|
474 |
] |
|
|
475 |
}, |
|
|
476 |
"metadata": {}, |
|
|
477 |
"execution_count": 15 |
|
|
478 |
} |
|
|
479 |
] |
|
|
480 |
}, |
|
|
481 |
{ |
|
|
482 |
"cell_type": "code", |
|
|
483 |
"execution_count": 16, |
|
|
484 |
"metadata": { |
|
|
485 |
"id": "b99O2uACdeiw" |
|
|
486 |
}, |
|
|
487 |
"outputs": [], |
|
|
488 |
"source": [ |
|
|
489 |
"data_loader = torch.utils.data.DataLoader(data_for_model, batch_size=8)" |
|
|
490 |
] |
|
|
491 |
}, |
|
|
492 |
{ |
|
|
493 |
"cell_type": "code", |
|
|
494 |
"source": [], |
|
|
495 |
"metadata": { |
|
|
496 |
"id": "MtnU6v5Je8X9" |
|
|
497 |
}, |
|
|
498 |
"execution_count": null, |
|
|
499 |
"outputs": [] |
|
|
500 |
}, |
|
|
501 |
{ |
|
|
502 |
"cell_type": "code", |
|
|
503 |
"execution_count": 17, |
|
|
504 |
"metadata": { |
|
|
505 |
"id": "enBj0Mn5deiw", |
|
|
506 |
"outputId": "2c99fd25-35ad-4cde-d2cd-05b558f6eaf4", |
|
|
507 |
"colab": { |
|
|
508 |
"base_uri": "https://localhost:8080/" |
|
|
509 |
} |
|
|
510 |
}, |
|
|
511 |
"outputs": [ |
|
|
512 |
{ |
|
|
513 |
"output_type": "execute_result", |
|
|
514 |
"data": { |
|
|
515 |
"text/plain": [ |
|
|
516 |
"RobertaForTokenClassification(\n", |
|
|
517 |
" (roberta): RobertaModel(\n", |
|
|
518 |
" (embeddings): RobertaEmbeddings(\n", |
|
|
519 |
" (word_embeddings): Embedding(50265, 768, padding_idx=1)\n", |
|
|
520 |
" (position_embeddings): Embedding(514, 768, padding_idx=1)\n", |
|
|
521 |
" (token_type_embeddings): Embedding(1, 768)\n", |
|
|
522 |
" (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", |
|
|
523 |
" (dropout): Dropout(p=0.1, inplace=False)\n", |
|
|
524 |
" )\n", |
|
|
525 |
" (encoder): RobertaEncoder(\n", |
|
|
526 |
" (layer): ModuleList(\n", |
|
|
527 |
" (0-11): 12 x RobertaLayer(\n", |
|
|
528 |
" (attention): RobertaAttention(\n", |
|
|
529 |
" (self): RobertaSelfAttention(\n", |
|
|
530 |
" (query): Linear(in_features=768, out_features=768, bias=True)\n", |
|
|
531 |
" (key): Linear(in_features=768, out_features=768, bias=True)\n", |
|
|
532 |
" (value): Linear(in_features=768, out_features=768, bias=True)\n", |
|
|
533 |
" (dropout): Dropout(p=0.1, inplace=False)\n", |
|
|
534 |
" )\n", |
|
|
535 |
" (output): RobertaSelfOutput(\n", |
|
|
536 |
" (dense): Linear(in_features=768, out_features=768, bias=True)\n", |
|
|
537 |
" (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", |
|
|
538 |
" (dropout): Dropout(p=0.1, inplace=False)\n", |
|
|
539 |
" )\n", |
|
|
540 |
" )\n", |
|
|
541 |
" (intermediate): RobertaIntermediate(\n", |
|
|
542 |
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n", |
|
|
543 |
" (intermediate_act_fn): GELUActivation()\n", |
|
|
544 |
" )\n", |
|
|
545 |
" (output): RobertaOutput(\n", |
|
|
546 |
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n", |
|
|
547 |
" (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", |
|
|
548 |
" (dropout): Dropout(p=0.1, inplace=False)\n", |
|
|
549 |
" )\n", |
|
|
550 |
" )\n", |
|
|
551 |
" )\n", |
|
|
552 |
" )\n", |
|
|
553 |
" )\n", |
|
|
554 |
" (dropout): Dropout(p=0.1, inplace=False)\n", |
|
|
555 |
" (classifier): Linear(in_features=768, out_features=19, bias=True)\n", |
|
|
556 |
")" |
|
|
557 |
] |
|
|
558 |
}, |
|
|
559 |
"metadata": {}, |
|
|
560 |
"execution_count": 17 |
|
|
561 |
} |
|
|
562 |
], |
|
|
563 |
"source": [ |
|
|
564 |
"model.to(device)" |
|
|
565 |
] |
|
|
566 |
}, |
|
|
567 |
{ |
|
|
568 |
"cell_type": "code", |
|
|
569 |
"source": [ |
|
|
570 |
"from tqdm import tqdm" |
|
|
571 |
], |
|
|
572 |
"metadata": { |
|
|
573 |
"id": "F6dReMqdeva6" |
|
|
574 |
}, |
|
|
575 |
"execution_count": 18, |
|
|
576 |
"outputs": [] |
|
|
577 |
}, |
|
|
578 |
{ |
|
|
579 |
"cell_type": "code", |
|
|
580 |
"source": [ |
|
|
581 |
"len(data_loader.dataset[2]['attention_mask'])" |
|
|
582 |
], |
|
|
583 |
"metadata": { |
|
|
584 |
"id": "vPBZwZGnfzG1", |
|
|
585 |
"outputId": "60979f60-e8f8-4fe9-8959-9a8ca30027cc", |
|
|
586 |
"colab": { |
|
|
587 |
"base_uri": "https://localhost:8080/" |
|
|
588 |
} |
|
|
589 |
}, |
|
|
590 |
"execution_count": 19, |
|
|
591 |
"outputs": [ |
|
|
592 |
{ |
|
|
593 |
"output_type": "execute_result", |
|
|
594 |
"data": { |
|
|
595 |
"text/plain": [ |
|
|
596 |
"512" |
|
|
597 |
] |
|
|
598 |
}, |
|
|
599 |
"metadata": {}, |
|
|
600 |
"execution_count": 19 |
|
|
601 |
} |
|
|
602 |
] |
|
|
603 |
}, |
|
|
604 |
{ |
|
|
605 |
"cell_type": "code", |
|
|
606 |
"source": [], |
|
|
607 |
"metadata": { |
|
|
608 |
"id": "UtZKgTQcgORE" |
|
|
609 |
}, |
|
|
610 |
"execution_count": null, |
|
|
611 |
"outputs": [] |
|
|
612 |
}, |
|
|
613 |
{ |
|
|
614 |
"cell_type": "code", |
|
|
615 |
"execution_count": 20, |
|
|
616 |
"metadata": { |
|
|
617 |
"id": "QzmBnKxjdeiw", |
|
|
618 |
"outputId": "7d40eef4-7f54-4faf-c2e0-29f48313a3f9", |
|
|
619 |
"colab": { |
|
|
620 |
"base_uri": "https://localhost:8080/" |
|
|
621 |
} |
|
|
622 |
}, |
|
|
623 |
"outputs": [ |
|
|
624 |
{ |
|
|
625 |
"output_type": "stream", |
|
|
626 |
"name": "stderr", |
|
|
627 |
"text": [ |
|
|
628 |
"100%|██████████| 164/164 [00:50<00:00, 3.22it/s]\n" |
|
|
629 |
] |
|
|
630 |
} |
|
|
631 |
], |
|
|
632 |
"source": [ |
|
|
633 |
"labels = []\n", |
|
|
634 |
"for batch in tqdm(data_loader):\n", |
|
|
635 |
"\n", |
|
|
636 |
" batch['input_ids'] = torch.LongTensor(np.column_stack(np.array(batch['input_ids']))).to(device)\n", |
|
|
637 |
" batch['attention_mask'] = torch.LongTensor(np.column_stack(np.array(batch['attention_mask']))).to(device)\n", |
|
|
638 |
" batch_tokenizer = {'input_ids': batch['input_ids'], 'attention_mask': batch['attention_mask']}\n", |
|
|
639 |
" # break\n", |
|
|
640 |
" with torch.no_grad():\n", |
|
|
641 |
" outputs = model(**batch_tokenizer)\n", |
|
|
642 |
"\n", |
|
|
643 |
" labels_batch = torch.argmax(outputs.logits, dim=2).to('cpu').numpy()\n", |
|
|
644 |
" labels.extend([list(labels_batch[i]) for i in range(labels_batch.shape[0])])\n", |
|
|
645 |
"\n", |
|
|
646 |
" del batch\n", |
|
|
647 |
" del outputs\n", |
|
|
648 |
" torch.cuda.empty_cache()" |
|
|
649 |
] |
|
|
650 |
}, |
|
|
651 |
{ |
|
|
652 |
"cell_type": "code", |
|
|
653 |
"source": [ |
|
|
654 |
"def annotate_sentences(dataset, labels, entities_list,criteria = 'first_label'):\n", |
|
|
655 |
" \"\"\"\n", |
|
|
656 |
" Annotate the sentences with the predicted labels\n", |
|
|
657 |
" inputs:\n", |
|
|
658 |
" dataset: dataset, dataset with the sentences\n", |
|
|
659 |
" labels: list, list of labels\n", |
|
|
660 |
" entities_list: list, list of entities\n", |
|
|
661 |
" criteria: str, criteria to use to select the label when the words pices have different labels\n", |
|
|
662 |
" - first_label: select the first label\n", |
|
|
663 |
" - majority: select the label with the majority\n", |
|
|
664 |
" outputs:\n", |
|
|
665 |
" annotated_sentences: list, list of annotated sentences\n", |
|
|
666 |
" \"\"\"\n", |
|
|
667 |
" annotated_sentences = []\n", |
|
|
668 |
" for i in range(len(dataset)):\n", |
|
|
669 |
" # get just the tokens different from None\n", |
|
|
670 |
" sentence = dataset[i]\n", |
|
|
671 |
" word_ids = sentence['word_ids']\n", |
|
|
672 |
" sentence_labels = labels[i]\n", |
|
|
673 |
" annotated_sentence = [[] for _ in range(len(dataset[i]['tokens']))]\n", |
|
|
674 |
" for word_id, label in zip(word_ids, sentence_labels):\n", |
|
|
675 |
" if word_id is not None:\n", |
|
|
676 |
" annotated_sentence[word_id].append(label)\n", |
|
|
677 |
" annotated_sentence_filtered = []\n", |
|
|
678 |
" if criteria == 'first_label':\n", |
|
|
679 |
" annotated_sentence_filtered = [annotated_sentence[i][0] for i in range(len(annotated_sentence))]\n", |
|
|
680 |
" elif criteria == 'majority':\n", |
|
|
681 |
" annotated_sentence_filtered = []\n", |
|
|
682 |
" for j in range(len(annotated_sentence)):\n", |
|
|
683 |
" starts_flag = entities_list[annotated_sentence[j][0]].startswith('B')\n", |
|
|
684 |
"\n", |
|
|
685 |
" ent = max(set(annotated_sentence[j]), key=annotated_sentence[j].count)\n", |
|
|
686 |
" if starts_flag and ent != 0:\n", |
|
|
687 |
" label = entities_list[ent][2:]\n", |
|
|
688 |
" label = 'B-' + label\n", |
|
|
689 |
" annotated_sentence_filtered.append(sel_ent[label])\n", |
|
|
690 |
" else:\n", |
|
|
691 |
" annotated_sentence_filtered.append(ent)\n", |
|
|
692 |
" annotated_sentences.append(annotated_sentence_filtered)\n", |
|
|
693 |
" return annotated_sentences" |
|
|
694 |
], |
|
|
695 |
"metadata": { |
|
|
696 |
"id": "w6MABN-NsXVR" |
|
|
697 |
}, |
|
|
698 |
"execution_count": 21, |
|
|
699 |
"outputs": [] |
|
|
700 |
}, |
|
|
701 |
{ |
|
|
702 |
"cell_type": "code", |
|
|
703 |
"source": [ |
|
|
704 |
"annotated_sentences_first = annotate_sentences(dataset['test'], labels, entities_list, criteria='first_label')\n", |
|
|
705 |
"annotated_sentences_max = annotate_sentences(dataset['test'], labels, entities_list, criteria='majority')" |
|
|
706 |
], |
|
|
707 |
"metadata": { |
|
|
708 |
"id": "UZVjibUbsasA" |
|
|
709 |
}, |
|
|
710 |
"execution_count": 22, |
|
|
711 |
"outputs": [] |
|
|
712 |
}, |
|
|
713 |
{ |
|
|
714 |
"cell_type": "code", |
|
|
715 |
"execution_count": null, |
|
|
716 |
"metadata": { |
|
|
717 |
"id": "h06Yl1uTdeix", |
|
|
718 |
"outputId": "f23119cd-e70b-44e3-b069-b6d2781a06f7", |
|
|
719 |
"colab": { |
|
|
720 |
"base_uri": "https://localhost:8080/", |
|
|
721 |
"height": 176, |
|
|
722 |
"referenced_widgets": [ |
|
|
723 |
"99188e0dd50f4d5a874eaec3101ceab6", |
|
|
724 |
"b289d32f0876481b97a080552549f7d1", |
|
|
725 |
"499e568745f743aea5c0b2ac64ef160f", |
|
|
726 |
"12eafe87e2b84edfa7d72b93361d3e6a", |
|
|
727 |
"61a45966b4654a7db975880b0a193139", |
|
|
728 |
"95909a3c6f6245d1acd056f28bfe5ba8", |
|
|
729 |
"feebbbdfbc3740bba039e95ddf31e4c1", |
|
|
730 |
"1eed478ce7694c4cb7e2c998963cafcc", |
|
|
731 |
"677a609737064c3c86de6503530b6268", |
|
|
732 |
"3736022fd7b5476dbfe03fee20d19f09", |
|
|
733 |
"0846105c8d484656848ff41c571eb71f" |
|
|
734 |
] |
|
|
735 |
} |
|
|
736 |
}, |
|
|
737 |
"outputs": [ |
|
|
738 |
{ |
|
|
739 |
"output_type": "stream", |
|
|
740 |
"name": "stderr", |
|
|
741 |
"text": [ |
|
|
742 |
"<ipython-input-38-653dc96d1cff>: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", |
|
|
743 |
" metric = load_metric(\"seqeval\")\n", |
|
|
744 |
"/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", |
|
|
745 |
"You can avoid this message in future by passing the argument `trust_remote_code=True`.\n", |
|
|
746 |
"Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n", |
|
|
747 |
" warnings.warn(\n" |
|
|
748 |
] |
|
|
749 |
}, |
|
|
750 |
{ |
|
|
751 |
"output_type": "display_data", |
|
|
752 |
"data": { |
|
|
753 |
"text/plain": [ |
|
|
754 |
"Downloading builder script: 0%| | 0.00/2.47k [00:00<?, ?B/s]" |
|
|
755 |
], |
|
|
756 |
"application/vnd.jupyter.widget-view+json": { |
|
|
757 |
"version_major": 2, |
|
|
758 |
"version_minor": 0, |
|
|
759 |
"model_id": "99188e0dd50f4d5a874eaec3101ceab6" |
|
|
760 |
} |
|
|
761 |
}, |
|
|
762 |
"metadata": {} |
|
|
763 |
} |
|
|
764 |
], |
|
|
765 |
"source": [ |
|
|
766 |
"#load seqeval metric for evaluation\n", |
|
|
767 |
"metric = load_metric(\"seqeval\")" |
|
|
768 |
] |
|
|
769 |
}, |
|
|
770 |
{ |
|
|
771 |
"cell_type": "code", |
|
|
772 |
"execution_count": null, |
|
|
773 |
"metadata": { |
|
|
774 |
"id": "0jHgq7Dldeix" |
|
|
775 |
}, |
|
|
776 |
"outputs": [], |
|
|
777 |
"source": [ |
|
|
778 |
"def compute_metrics_tr(p):\n", |
|
|
779 |
" \"\"\"\n", |
|
|
780 |
" Compute the metrics for the model\n", |
|
|
781 |
" inputs:\n", |
|
|
782 |
" p: tuple, the predictions and the labels\n", |
|
|
783 |
" outputs:\n", |
|
|
784 |
" dict: the metrics\n", |
|
|
785 |
" \"\"\"\n", |
|
|
786 |
" predictions, labels = p\n", |
|
|
787 |
"\n", |
|
|
788 |
" # Remove ignored index (special tokens)\n", |
|
|
789 |
" true_predictions = [\n", |
|
|
790 |
" [entities_list[p] for (p, l) in zip(prediction, label) if l != -100]\n", |
|
|
791 |
" for prediction, label in zip(predictions, labels)\n", |
|
|
792 |
" ]\n", |
|
|
793 |
" true_labels = [\n", |
|
|
794 |
" [entities_list[l] for (p, l) in zip(prediction, label) if l != -100]\n", |
|
|
795 |
" for prediction, label in zip(predictions, labels)\n", |
|
|
796 |
" ]\n", |
|
|
797 |
"\n", |
|
|
798 |
" results = metric.compute(predictions=true_predictions, references=true_labels)\n", |
|
|
799 |
" resutls_strict = metric.compute(predictions=true_predictions, references=true_labels, mode='strict', scheme='IOB2')\n", |
|
|
800 |
"\n", |
|
|
801 |
" cr1 = classification_report(true_labels, true_predictions)\n", |
|
|
802 |
" cr2 = classification_report(true_labels, true_predictions, mode='strict', scheme=IOB2)\n", |
|
|
803 |
"\n", |
|
|
804 |
" return results, resutls_strict,cr1,cr2" |
|
|
805 |
] |
|
|
806 |
}, |
|
|
807 |
{ |
|
|
808 |
"cell_type": "code", |
|
|
809 |
"source": [], |
|
|
810 |
"metadata": { |
|
|
811 |
"id": "wYo2jYRfp-qK" |
|
|
812 |
}, |
|
|
813 |
"execution_count": null, |
|
|
814 |
"outputs": [] |
|
|
815 |
}, |
|
|
816 |
{ |
|
|
817 |
"cell_type": "code", |
|
|
818 |
"source": [ |
|
|
819 |
"def get_labels(p):\n", |
|
|
820 |
" predictions, labels = p\n", |
|
|
821 |
" # Remove ignored index (special tokens)\n", |
|
|
822 |
" predictions = [\n", |
|
|
823 |
" [entities_list[p] for (p, l) in zip(prediction, label) if l != -100]\n", |
|
|
824 |
" for prediction, label in zip(predictions, labels)\n", |
|
|
825 |
" ]\n", |
|
|
826 |
" labels = [\n", |
|
|
827 |
" [entities_list[l] for (p, l) in zip(prediction, label) if l != -100]\n", |
|
|
828 |
" for prediction, label in zip(predictions, labels)\n", |
|
|
829 |
" ]\n", |
|
|
830 |
"\n", |
|
|
831 |
" return predictions, labels\n", |
|
|
832 |
"\n", |
|
|
833 |
"\n", |
|
|
834 |
"\n", |
|
|
835 |
"\n" |
|
|
836 |
], |
|
|
837 |
"metadata": { |
|
|
838 |
"id": "kW2qWh4XppbT" |
|
|
839 |
}, |
|
|
840 |
"execution_count": 23, |
|
|
841 |
"outputs": [] |
|
|
842 |
}, |
|
|
843 |
{ |
|
|
844 |
"cell_type": "code", |
|
|
845 |
"source": [ |
|
|
846 |
"pred_labels, true_labels = get_labels((annotated_sentences_first, dataset['test']['ner_tags']))" |
|
|
847 |
], |
|
|
848 |
"metadata": { |
|
|
849 |
"id": "T8EIf4d83TXK" |
|
|
850 |
}, |
|
|
851 |
"execution_count": 24, |
|
|
852 |
"outputs": [] |
|
|
853 |
}, |
|
|
854 |
{ |
|
|
855 |
"cell_type": "code", |
|
|
856 |
"source": [ |
|
|
857 |
"pred_labels[0]" |
|
|
858 |
], |
|
|
859 |
"metadata": { |
|
|
860 |
"id": "MvgnYYJh608X", |
|
|
861 |
"outputId": "8e666e13-0733-4af0-cf6e-516da4407bb2", |
|
|
862 |
"colab": { |
|
|
863 |
"base_uri": "https://localhost:8080/" |
|
|
864 |
} |
|
|
865 |
}, |
|
|
866 |
"execution_count": 40, |
|
|
867 |
"outputs": [ |
|
|
868 |
{ |
|
|
869 |
"output_type": "execute_result", |
|
|
870 |
"data": { |
|
|
871 |
"text/plain": [ |
|
|
872 |
"['O',\n", |
|
|
873 |
" 'O',\n", |
|
|
874 |
" 'O',\n", |
|
|
875 |
" 'B-Condition',\n", |
|
|
876 |
" 'B-Person',\n", |
|
|
877 |
" 'B-Value',\n", |
|
|
878 |
" 'O',\n", |
|
|
879 |
" 'I-Value',\n", |
|
|
880 |
" 'I-Value',\n", |
|
|
881 |
" 'I-Value',\n", |
|
|
882 |
" 'I-Value',\n", |
|
|
883 |
" 'I-Value',\n", |
|
|
884 |
" 'O',\n", |
|
|
885 |
" 'O',\n", |
|
|
886 |
" 'O',\n", |
|
|
887 |
" 'I-Observation',\n", |
|
|
888 |
" 'O',\n", |
|
|
889 |
" 'O']" |
|
|
890 |
] |
|
|
891 |
}, |
|
|
892 |
"metadata": {}, |
|
|
893 |
"execution_count": 40 |
|
|
894 |
} |
|
|
895 |
] |
|
|
896 |
}, |
|
|
897 |
{ |
|
|
898 |
"cell_type": "code", |
|
|
899 |
"source": [ |
|
|
900 |
"# from eval_file import *\n", |
|
|
901 |
"\n", |
|
|
902 |
"import argparse\n", |
|
|
903 |
"from collections import defaultdict\n", |
|
|
904 |
"from itertools import chain\n", |
|
|
905 |
"from math import pow\n", |
|
|
906 |
"from pathlib import Path\n", |
|
|
907 |
"\n", |
|
|
908 |
"# from common_utils.common_io import load_bio_file_into_sents\n", |
|
|
909 |
"# from common_utils.common_log import create_logger\n", |
|
|
910 |
"# -*- coding: utf-8 -*-\n", |
|
|
911 |
"\n", |
|
|
912 |
"# -*- coding: utf-8 -*-\n", |
|
|
913 |
"\n", |
|
|
914 |
"import json\n", |
|
|
915 |
"import pickle as pkl\n", |
|
|
916 |
"\n", |
|
|
917 |
"\n", |
|
|
918 |
"def read_from_file(ifn):\n", |
|
|
919 |
" with open(ifn, \"r\") as f:\n", |
|
|
920 |
" text = f.read()\n", |
|
|
921 |
" return text\n", |
|
|
922 |
"\n", |
|
|
923 |
"\n", |
|
|
924 |
"def write_to_file(text, ofn):\n", |
|
|
925 |
" with open(ofn, \"w\") as f:\n", |
|
|
926 |
" f.write(text)\n", |
|
|
927 |
" return True\n", |
|
|
928 |
"\n", |
|
|
929 |
"\n", |
|
|
930 |
"def pkl_load(ifn):\n", |
|
|
931 |
" with open(ifn, \"rb\") as f:\n", |
|
|
932 |
" pdata = pkl.load(f)\n", |
|
|
933 |
" return pdata\n", |
|
|
934 |
"\n", |
|
|
935 |
"\n", |
|
|
936 |
"def pkl_dump(pdata, ofn):\n", |
|
|
937 |
" with open(ofn, \"wb\") as f:\n", |
|
|
938 |
" pkl.dump(pdata, f)\n", |
|
|
939 |
" return True\n", |
|
|
940 |
"\n", |
|
|
941 |
"\n", |
|
|
942 |
"def json_load(ifn):\n", |
|
|
943 |
" with open(ifn, \"r\") as f:\n", |
|
|
944 |
" jdata = json.load(f)\n", |
|
|
945 |
" return jdata\n", |
|
|
946 |
"\n", |
|
|
947 |
"\n", |
|
|
948 |
"def json_dump(jdata, ofn):\n", |
|
|
949 |
" with open(ofn, \"w\") as f:\n", |
|
|
950 |
" json.dump(jdata, f)\n", |
|
|
951 |
" return True\n", |
|
|
952 |
"\n", |
|
|
953 |
"\n", |
|
|
954 |
"def load_bio_file_into_sents(bio_file, word_sep=\" \", do_lower=False):\n", |
|
|
955 |
" bio_text = read_from_file(bio_file)\n", |
|
|
956 |
" bio_text = bio_text.strip()\n", |
|
|
957 |
" if do_lower:\n", |
|
|
958 |
" bio_text = bio_text.lower()\n", |
|
|
959 |
"\n", |
|
|
960 |
" new_sents = []\n", |
|
|
961 |
" sents = bio_text.split(\"\\n\\n\")\n", |
|
|
962 |
"\n", |
|
|
963 |
" for sent in sents:\n", |
|
|
964 |
" new_sent = []\n", |
|
|
965 |
" words = sent.split(\"\\n\")\n", |
|
|
966 |
" for word in words:\n", |
|
|
967 |
" new_word = word.split(word_sep)\n", |
|
|
968 |
" new_sent.append(new_word)\n", |
|
|
969 |
" new_sents.append(new_sent)\n", |
|
|
970 |
"\n", |
|
|
971 |
" return new_sents\n", |
|
|
972 |
"\n", |
|
|
973 |
"\n", |
|
|
974 |
"def output_bio(bio_data, output_file, sep=\" \"):\n", |
|
|
975 |
" with open(output_file, \"w\") as f:\n", |
|
|
976 |
" for sent in bio_data:\n", |
|
|
977 |
" for word in sent:\n", |
|
|
978 |
" line = sep.join(word)\n", |
|
|
979 |
" f.write(line)\n", |
|
|
980 |
" f.write(\"\\n\")\n", |
|
|
981 |
" f.write(\"\\n\")\n", |
|
|
982 |
"\n", |
|
|
983 |
"\n", |
|
|
984 |
"class PRF:\n", |
|
|
985 |
" def __init__(self):\n", |
|
|
986 |
" self.true = 0\n", |
|
|
987 |
" self.false = 0\n", |
|
|
988 |
"\n", |
|
|
989 |
" def add_true_case(self):\n", |
|
|
990 |
" self.true += 1\n", |
|
|
991 |
"\n", |
|
|
992 |
" def add_false_case(self):\n", |
|
|
993 |
" self.false += 1\n", |
|
|
994 |
"\n", |
|
|
995 |
" def get_true_false_counts(self):\n", |
|
|
996 |
" return self.true, self.false\n", |
|
|
997 |
"\n", |
|
|
998 |
" def __str__(self):\n", |
|
|
999 |
" return str(self.__dict__)\n", |
|
|
1000 |
"\n", |
|
|
1001 |
"\n", |
|
|
1002 |
"class BioEval:\n", |
|
|
1003 |
" def __init__(self):\n", |
|
|
1004 |
" self.acc = PRF()\n", |
|
|
1005 |
" # prediction\n", |
|
|
1006 |
" self.all_strict = PRF()\n", |
|
|
1007 |
" self.all_relax = PRF()\n", |
|
|
1008 |
" self.cat_strict = defaultdict(PRF)\n", |
|
|
1009 |
" self.cat_relax = defaultdict(PRF)\n", |
|
|
1010 |
" # gold standard\n", |
|
|
1011 |
" self.gs_all = 0\n", |
|
|
1012 |
" self.gs_cat = defaultdict(int)\n", |
|
|
1013 |
" self.performance = dict()\n", |
|
|
1014 |
" self.counts = dict()\n", |
|
|
1015 |
" self.beta = 1\n", |
|
|
1016 |
" self.label_not_for_eval = {'o'}\n", |
|
|
1017 |
"\n", |
|
|
1018 |
" def reset(self):\n", |
|
|
1019 |
" self.acc = PRF()\n", |
|
|
1020 |
" self.all_strict = PRF()\n", |
|
|
1021 |
" self.all_relax = PRF()\n", |
|
|
1022 |
" self.cat_strict = defaultdict(PRF)\n", |
|
|
1023 |
" self.cat_relax = defaultdict(PRF)\n", |
|
|
1024 |
" self.gs_all = 0\n", |
|
|
1025 |
" self.gs_cat = defaultdict(int)\n", |
|
|
1026 |
" self.performance = dict()\n", |
|
|
1027 |
" self.counts = dict()\n", |
|
|
1028 |
"\n", |
|
|
1029 |
" def set_beta_for_f_score(self, beta):\n", |
|
|
1030 |
" print(\"Using beta={} for calculating F-score\".format(beta))\n", |
|
|
1031 |
" self.beta = beta\n", |
|
|
1032 |
"\n", |
|
|
1033 |
" # def set_logger(self, logger):\n", |
|
|
1034 |
" # self.logger = logger\n", |
|
|
1035 |
"\n", |
|
|
1036 |
" def add_labels_not_for_eval(self, *labels):\n", |
|
|
1037 |
" for each in labels:\n", |
|
|
1038 |
" self.label_not_for_eval.add(each.lower())\n", |
|
|
1039 |
"\n", |
|
|
1040 |
" def __calc_prf(self, tp, fp, tp_tn):\n", |
|
|
1041 |
" \"\"\"\n", |
|
|
1042 |
" Using this function to calculate F-beta score, beta=1 is f_score-score, set beta=2 favor recall, and set beta=0.5 favor precision.\n", |
|
|
1043 |
" Using set_beta_for_f_score function to change beta value.\n", |
|
|
1044 |
" \"\"\"\n", |
|
|
1045 |
" tp_fp = tp + fp\n", |
|
|
1046 |
" pre = 1.0 * tp / tp_fp if tp_fp > 0 else 0.0\n", |
|
|
1047 |
" rec = 1.0 * tp / tp_tn if tp_tn > 0 else 0.0\n", |
|
|
1048 |
" beta2 = pow(self.beta, 2)\n", |
|
|
1049 |
" f_beta = (1 + beta2) * pre * rec / (beta2 * pre + rec) if (pre + rec) > 0 else 0.0\n", |
|
|
1050 |
" return pre, rec, f_beta\n", |
|
|
1051 |
"\n", |
|
|
1052 |
" def __measure_performance(self):\n", |
|
|
1053 |
" self.performance['overall'] = dict()\n", |
|
|
1054 |
"\n", |
|
|
1055 |
" acc_true_num, acc_false_num = self.acc.get_true_false_counts()\n", |
|
|
1056 |
" total_acc_num = acc_true_num + acc_false_num\n", |
|
|
1057 |
" # calc acc\n", |
|
|
1058 |
" overall_acc = round(1.0 * acc_true_num / total_acc_num, 4) if total_acc_num > 0 else 0.0\n", |
|
|
1059 |
" self.performance['overall']['acc'] = overall_acc\n", |
|
|
1060 |
"\n", |
|
|
1061 |
" strict_true_counts, strict_false_counts = self.all_strict.get_true_false_counts()\n", |
|
|
1062 |
" strict_pre, strict_rec, strict_f_score = self.__calc_prf(strict_true_counts, strict_false_counts, self.gs_all)\n", |
|
|
1063 |
" self.performance['overall']['strict'] = dict()\n", |
|
|
1064 |
" self.performance['overall']['strict']['precision'] = strict_pre\n", |
|
|
1065 |
" self.performance['overall']['strict']['recall'] = strict_rec\n", |
|
|
1066 |
" self.performance['overall']['strict']['f_score'] = strict_f_score\n", |
|
|
1067 |
"\n", |
|
|
1068 |
" relax_true_counts, relax_false_counts = self.all_relax.get_true_false_counts()\n", |
|
|
1069 |
" relax_pre, relax_rec, relax_f_score = self.__calc_prf(relax_true_counts, relax_false_counts, self.gs_all)\n", |
|
|
1070 |
" self.performance['overall']['relax'] = dict()\n", |
|
|
1071 |
" self.performance['overall']['relax']['precision'] = relax_pre\n", |
|
|
1072 |
" self.performance['overall']['relax']['recall'] = relax_rec\n", |
|
|
1073 |
" self.performance['overall']['relax']['f_score'] = relax_f_score\n", |
|
|
1074 |
"\n", |
|
|
1075 |
" self.performance['category'] = dict()\n", |
|
|
1076 |
" self.performance['category']['strict'] = dict()\n", |
|
|
1077 |
" for k, v in self.cat_strict.items():\n", |
|
|
1078 |
" self.performance['category']['strict'][k] = dict()\n", |
|
|
1079 |
" stc, sfc = v.get_true_false_counts()\n", |
|
|
1080 |
" p, r, f = self.__calc_prf(stc, sfc, self.gs_cat[k])\n", |
|
|
1081 |
" self.performance['category']['strict'][k]['precision'] = p\n", |
|
|
1082 |
" self.performance['category']['strict'][k]['recall'] = r\n", |
|
|
1083 |
" self.performance['category']['strict'][k]['f_score'] = f\n", |
|
|
1084 |
"\n", |
|
|
1085 |
" self.performance['category']['relax'] = dict()\n", |
|
|
1086 |
" for k, v in self.cat_relax.items():\n", |
|
|
1087 |
" self.performance['category']['relax'][k] = dict()\n", |
|
|
1088 |
" rtc, rfc = v.get_true_false_counts()\n", |
|
|
1089 |
" p, r, f = self.__calc_prf(rtc, rfc, self.gs_cat[k])\n", |
|
|
1090 |
" self.performance['category']['relax'][k]['precision'] = p\n", |
|
|
1091 |
" self.performance['category']['relax'][k]['recall'] = r\n", |
|
|
1092 |
" self.performance['category']['relax'][k]['f_score'] = f\n", |
|
|
1093 |
"\n", |
|
|
1094 |
" def __measure_counts(self):\n", |
|
|
1095 |
" # gold standard\n", |
|
|
1096 |
" self.counts['expect'] = dict()\n", |
|
|
1097 |
" self.counts['expect']['overall'] = self.gs_all\n", |
|
|
1098 |
" for k, v in self.gs_cat.items():\n", |
|
|
1099 |
" self.counts['expect'][k] = v\n", |
|
|
1100 |
" # prediction\n", |
|
|
1101 |
" self.counts['prediction'] = {'strict': dict(), 'relax': dict()}\n", |
|
|
1102 |
" # strict\n", |
|
|
1103 |
" strict_true_counts, strict_false_counts = self.all_strict.get_true_false_counts()\n", |
|
|
1104 |
" self.counts['prediction']['strict']['overall'] = dict()\n", |
|
|
1105 |
" self.counts['prediction']['strict']['overall']['total'] = strict_true_counts + strict_false_counts\n", |
|
|
1106 |
" self.counts['prediction']['strict']['overall']['true'] = strict_true_counts\n", |
|
|
1107 |
" self.counts['prediction']['strict']['overall']['false'] = strict_false_counts\n", |
|
|
1108 |
" for k, v in self.cat_strict.items():\n", |
|
|
1109 |
" t, f = v.get_true_false_counts()\n", |
|
|
1110 |
" self.counts['prediction']['strict'][k] = dict()\n", |
|
|
1111 |
" self.counts['prediction']['strict'][k]['total'] = t + f\n", |
|
|
1112 |
" self.counts['prediction']['strict'][k]['true'] = t\n", |
|
|
1113 |
" self.counts['prediction']['strict'][k]['false'] = f\n", |
|
|
1114 |
" # relax\n", |
|
|
1115 |
" relax_true_counts, relax_false_counts = self.all_relax.get_true_false_counts()\n", |
|
|
1116 |
" self.counts['prediction']['relax']['overall'] = dict()\n", |
|
|
1117 |
" self.counts['prediction']['relax']['overall']['total'] = relax_true_counts + relax_false_counts\n", |
|
|
1118 |
" self.counts['prediction']['relax']['overall']['true'] = relax_true_counts\n", |
|
|
1119 |
" self.counts['prediction']['relax']['overall']['false'] = relax_false_counts\n", |
|
|
1120 |
" for k, v in self.cat_relax.items():\n", |
|
|
1121 |
" t, f = v.get_true_false_counts()\n", |
|
|
1122 |
" self.counts['prediction']['relax'][k] = dict()\n", |
|
|
1123 |
" self.counts['prediction']['relax'][k]['total'] = t + f\n", |
|
|
1124 |
" self.counts['prediction']['relax'][k]['true'] = t\n", |
|
|
1125 |
" self.counts['prediction']['relax'][k]['false'] = f\n", |
|
|
1126 |
"\n", |
|
|
1127 |
" @staticmethod\n", |
|
|
1128 |
" def __strict_match(gs, pred, s_idx, e_idx, en_type):\n", |
|
|
1129 |
" if e_idx < len(gs) and gs[e_idx] == f\"i-{en_type}\":\n", |
|
|
1130 |
" # check token after end in GS is not continued entity token\n", |
|
|
1131 |
" return False\n", |
|
|
1132 |
" elif gs[s_idx] != f\"b-{en_type}\" or pred[s_idx] != f\"b-{en_type}\":\n", |
|
|
1133 |
" # force first token to be B-\n", |
|
|
1134 |
" return False\n", |
|
|
1135 |
" # check every token in span is the same\n", |
|
|
1136 |
" for idx in range(s_idx, e_idx):\n", |
|
|
1137 |
" if gs[idx] != pred[idx]:\n", |
|
|
1138 |
" return False\n", |
|
|
1139 |
" return True\n", |
|
|
1140 |
"\n", |
|
|
1141 |
" @staticmethod\n", |
|
|
1142 |
" def __relax_match(gs, pred, s_idx, e_idx, en_type):\n", |
|
|
1143 |
" # we adopt the partial match strategy which is very loose compare to right-left or approximate match\n", |
|
|
1144 |
" for idx in range(s_idx, e_idx):\n", |
|
|
1145 |
" gs_cate = gs[idx].split(\"-\")[-1]\n", |
|
|
1146 |
" pred_bound, pred_cate = pred[idx].split(\"-\")\n", |
|
|
1147 |
" if gs_cate == pred_cate == en_type:\n", |
|
|
1148 |
" return True\n", |
|
|
1149 |
" return False\n", |
|
|
1150 |
"\n", |
|
|
1151 |
" @staticmethod\n", |
|
|
1152 |
" def __check_evaluated_already(gs_dict, cate, start_idx, end_idx):\n", |
|
|
1153 |
" for k, v in gs_dict.items():\n", |
|
|
1154 |
" c, s, e = k\n", |
|
|
1155 |
" if not (e < start_idx or s > end_idx) and c == cate:\n", |
|
|
1156 |
" if v == 0:\n", |
|
|
1157 |
" return True\n", |
|
|
1158 |
" else:\n", |
|
|
1159 |
" gs_dict[k] -= 1\n", |
|
|
1160 |
" return False\n", |
|
|
1161 |
" return False\n", |
|
|
1162 |
"\n", |
|
|
1163 |
" def __process_bio(self, gs_bio, pred_bio):\n", |
|
|
1164 |
" # measure acc\n", |
|
|
1165 |
" for w_idx, (gs_word, pred_word) in enumerate(zip(gs_bio, pred_bio)):\n", |
|
|
1166 |
" # measure acc\n", |
|
|
1167 |
" if gs_word == pred_word:\n", |
|
|
1168 |
" self.acc.add_true_case()\n", |
|
|
1169 |
" else:\n", |
|
|
1170 |
" self.acc.add_false_case()\n", |
|
|
1171 |
"\n", |
|
|
1172 |
" # process gold standard\n", |
|
|
1173 |
" llen = len(gs_bio)\n", |
|
|
1174 |
" gs_dict = defaultdict(int)\n", |
|
|
1175 |
" cur_idx = 0\n", |
|
|
1176 |
" while cur_idx < llen:\n", |
|
|
1177 |
" if gs_bio[cur_idx].strip() in self.label_not_for_eval:\n", |
|
|
1178 |
" cur_idx += 1\n", |
|
|
1179 |
" else:\n", |
|
|
1180 |
" start_idx = cur_idx\n", |
|
|
1181 |
" end_idx = start_idx + 1\n", |
|
|
1182 |
" _, cate = gs_bio[start_idx].strip().split('-')\n", |
|
|
1183 |
" while end_idx < llen and gs_bio[end_idx].strip() == f\"i-{cate}\":\n", |
|
|
1184 |
" end_idx += 1\n", |
|
|
1185 |
" self.gs_all += 1\n", |
|
|
1186 |
" self.gs_cat[cate] += 1\n", |
|
|
1187 |
" gs_dict[(cate, start_idx, end_idx)] += 1\n", |
|
|
1188 |
" cur_idx = end_idx\n", |
|
|
1189 |
" # process predictions\n", |
|
|
1190 |
" cur_idx = 0\n", |
|
|
1191 |
" while cur_idx < llen:\n", |
|
|
1192 |
" if pred_bio[cur_idx].strip() in self.label_not_for_eval:\n", |
|
|
1193 |
" cur_idx += 1\n", |
|
|
1194 |
" else:\n", |
|
|
1195 |
" start_idx = cur_idx\n", |
|
|
1196 |
" end_idx = start_idx + 1\n", |
|
|
1197 |
" _, cate = pred_bio[start_idx].strip().split(\"-\")\n", |
|
|
1198 |
" while end_idx < llen and pred_bio[end_idx].strip() == f\"i-{cate}\":\n", |
|
|
1199 |
" end_idx += 1\n", |
|
|
1200 |
" if self.__strict_match(gs_bio, pred_bio, start_idx, end_idx, cate):\n", |
|
|
1201 |
" self.all_strict.add_true_case()\n", |
|
|
1202 |
" self.cat_strict[cate].add_true_case()\n", |
|
|
1203 |
" self.all_relax.add_true_case()\n", |
|
|
1204 |
" self.cat_relax[cate].add_true_case()\n", |
|
|
1205 |
" elif self.__relax_match(gs_bio, pred_bio, start_idx, end_idx, cate):\n", |
|
|
1206 |
" if self.__check_evaluated_already(gs_dict, cate, start_idx, end_idx):\n", |
|
|
1207 |
" cur_idx = end_idx\n", |
|
|
1208 |
" continue\n", |
|
|
1209 |
" self.all_strict.add_false_case()\n", |
|
|
1210 |
" self.cat_strict[cate].add_false_case()\n", |
|
|
1211 |
" self.all_relax.add_true_case()\n", |
|
|
1212 |
" self.cat_relax[cate].add_true_case()\n", |
|
|
1213 |
" else:\n", |
|
|
1214 |
" self.all_strict.add_false_case()\n", |
|
|
1215 |
" self.cat_strict[cate].add_false_case()\n", |
|
|
1216 |
" self.all_relax.add_false_case()\n", |
|
|
1217 |
" self.cat_relax[cate].add_false_case()\n", |
|
|
1218 |
" cur_idx = end_idx\n", |
|
|
1219 |
"\n", |
|
|
1220 |
" def eval_file(self, gs_file, pred_file):\n", |
|
|
1221 |
" print(\"processing gold standard file: {} and prediciton file: {}\".format(gs_file, pred_file))\n", |
|
|
1222 |
" pred_bio_sents = load_bio_file_into_sents(pred_file, do_lower=True)\n", |
|
|
1223 |
" gs_bio_sents = load_bio_file_into_sents(gs_file, do_lower=True)\n", |
|
|
1224 |
" # process bio data\n", |
|
|
1225 |
" # check two data have same amount of sents\n", |
|
|
1226 |
" assert len(gs_bio_sents) == len(pred_bio_sents), \\\n", |
|
|
1227 |
" \"gold standard and prediction have different dimension: gs: {}; pred: {}\".format(len(gs_bio_sents), len(pred_bio_sents))\n", |
|
|
1228 |
" # measure performance\n", |
|
|
1229 |
" for s_idx, (gs_sent, pred_sent) in enumerate(zip(gs_bio_sents, pred_bio_sents)):\n", |
|
|
1230 |
" # check two sents have same No. of words\n", |
|
|
1231 |
" assert len(gs_sent) == len(pred_sent), \\\n", |
|
|
1232 |
" \"In {}th sentence, the words counts are different; gs: {}; pred: {}\".format(s_idx, gs_sent, pred_sent)\n", |
|
|
1233 |
" gs_sent = list(map(lambda x: x[-1], gs_sent))\n", |
|
|
1234 |
" pred_sent = list(map(lambda x: x[-1], pred_sent))\n", |
|
|
1235 |
" self.__process_bio(gs_sent, pred_sent)\n", |
|
|
1236 |
" # get the evaluation matrix\n", |
|
|
1237 |
" self.__measure_performance()\n", |
|
|
1238 |
" self.__measure_counts()\n", |
|
|
1239 |
"\n", |
|
|
1240 |
" def eval_mem(self, gs, pred, do_flat=False):\n", |
|
|
1241 |
" # flat sents to sent; we assume input sequences only have 1 dimension (only labels)\n", |
|
|
1242 |
" if do_flat:\n", |
|
|
1243 |
" print('Sentences have been flatten to 1 dim.')\n", |
|
|
1244 |
" gs = list(chain(*gs))\n", |
|
|
1245 |
" pred = list(chain(*pred))\n", |
|
|
1246 |
" gs = list(map(lambda x: x.lower(), gs))\n", |
|
|
1247 |
" pred = list(map(lambda x: x.lower(), pred))\n", |
|
|
1248 |
" self.__process_bio(gs, pred)\n", |
|
|
1249 |
" else:\n", |
|
|
1250 |
" for sidx, (gs_s, pred_s) in enumerate(zip(gs, pred)):\n", |
|
|
1251 |
" gs_s = list(map(lambda x: x.lower(), gs_s))\n", |
|
|
1252 |
" pred_s = list(map(lambda x: x.lower(), pred_s))\n", |
|
|
1253 |
" self.__process_bio(gs_s, pred_s)\n", |
|
|
1254 |
"\n", |
|
|
1255 |
" self.__measure_performance()\n", |
|
|
1256 |
" self.__measure_counts()\n", |
|
|
1257 |
"\n", |
|
|
1258 |
" def evaluate_annotations(self, gs, pred, do_lower=False):\n", |
|
|
1259 |
" for gs_sent, pred_sent in zip(gs, pred):\n", |
|
|
1260 |
" if do_lower:\n", |
|
|
1261 |
" gs_sent = list(map(lambda x: x.lower(), gs_sent))\n", |
|
|
1262 |
" pred_sent = list(map(lambda x: x.lower(), pred_sent))\n", |
|
|
1263 |
" self.__process_bio(gs_sent, pred_sent)\n", |
|
|
1264 |
"\n", |
|
|
1265 |
" self.__measure_performance()\n", |
|
|
1266 |
" self.__measure_counts()\n", |
|
|
1267 |
"\n", |
|
|
1268 |
" def get_performance(self):\n", |
|
|
1269 |
" return self.performance\n", |
|
|
1270 |
"\n", |
|
|
1271 |
" def get_counts(self):\n", |
|
|
1272 |
" return self.counts\n", |
|
|
1273 |
"\n", |
|
|
1274 |
" def save_evaluation(self, file):\n", |
|
|
1275 |
" with open(file, \"w\") as f:\n", |
|
|
1276 |
" json.dump(self.performance, f)\n", |
|
|
1277 |
"\n", |
|
|
1278 |
" def show_evaluation(self, digits=4):\n", |
|
|
1279 |
" if len(self.performance) == 0:\n", |
|
|
1280 |
" raise RuntimeError('call eval_mem() first to get the performance attribute')\n", |
|
|
1281 |
"\n", |
|
|
1282 |
" cate = self.performance['category']['strict'].keys()\n", |
|
|
1283 |
"\n", |
|
|
1284 |
" headers = ['precision', 'recall', 'f1']\n", |
|
|
1285 |
" width = max(max([len(c) for c in cate]), len('overall'), digits)\n", |
|
|
1286 |
" head_fmt = '{:>{width}s} ' + ' {:>9}' * len(headers)\n", |
|
|
1287 |
"\n", |
|
|
1288 |
" report = head_fmt.format(u'', *headers, width=width)\n", |
|
|
1289 |
" report += '\\n\\nstrict\\n'\n", |
|
|
1290 |
"\n", |
|
|
1291 |
" row_fmt = '{:>{width}s} ' + ' {:>9.{digits}f}' * 3 + '\\n'\n", |
|
|
1292 |
" for c in cate:\n", |
|
|
1293 |
" precision = self.performance['category']['strict'][c]['precision']\n", |
|
|
1294 |
" recall = self.performance['category']['strict'][c]['recall']\n", |
|
|
1295 |
" f1 = self.performance['category']['strict'][c]['f_score']\n", |
|
|
1296 |
" report += row_fmt.format(c, *[precision, recall, f1], width=width, digits=digits)\n", |
|
|
1297 |
"\n", |
|
|
1298 |
" report += '\\nrelax\\n'\n", |
|
|
1299 |
"\n", |
|
|
1300 |
" for c in cate:\n", |
|
|
1301 |
" precision = self.performance['category']['relax'][c]['precision']\n", |
|
|
1302 |
" recall = self.performance['category']['relax'][c]['recall']\n", |
|
|
1303 |
" f1 = self.performance['category']['relax'][c]['f_score']\n", |
|
|
1304 |
" report += row_fmt.format(c, *[precision, recall, f1], width=width, digits=digits)\n", |
|
|
1305 |
"\n", |
|
|
1306 |
" report += '\\n\\noverall\\n'\n", |
|
|
1307 |
" report += 'acc: ' + str(self.performance['overall']['acc'])\n", |
|
|
1308 |
" report += '\\nstrict\\n'\n", |
|
|
1309 |
" report += row_fmt.format('', *[self.performance['overall']['strict']['precision'],\n", |
|
|
1310 |
" self.performance['overall']['strict']['recall'],\n", |
|
|
1311 |
" self.performance['overall']['strict']['f_score']], width=width, digits=digits)\n", |
|
|
1312 |
"\n", |
|
|
1313 |
" report += '\\nrelax\\n'\n", |
|
|
1314 |
" report += row_fmt.format('', *[self.performance['overall']['relax']['precision'],\n", |
|
|
1315 |
" self.performance['overall']['relax']['recall'],\n", |
|
|
1316 |
" self.performance['overall']['relax']['f_score']], width=width, digits=digits)\n", |
|
|
1317 |
" return report\n" |
|
|
1318 |
], |
|
|
1319 |
"metadata": { |
|
|
1320 |
"id": "c0uiL0XA3dnz" |
|
|
1321 |
}, |
|
|
1322 |
"execution_count": 32, |
|
|
1323 |
"outputs": [] |
|
|
1324 |
}, |
|
|
1325 |
{ |
|
|
1326 |
"cell_type": "code", |
|
|
1327 |
"source": [ |
|
|
1328 |
"s = \"i-\"" |
|
|
1329 |
], |
|
|
1330 |
"metadata": { |
|
|
1331 |
"id": "WFBRwTMN8v2d" |
|
|
1332 |
}, |
|
|
1333 |
"execution_count": null, |
|
|
1334 |
"outputs": [] |
|
|
1335 |
}, |
|
|
1336 |
{ |
|
|
1337 |
"cell_type": "code", |
|
|
1338 |
"source": [ |
|
|
1339 |
"evaluator = BioEval()" |
|
|
1340 |
], |
|
|
1341 |
"metadata": { |
|
|
1342 |
"id": "6l6fW5Bd6MMK" |
|
|
1343 |
}, |
|
|
1344 |
"execution_count": 33, |
|
|
1345 |
"outputs": [] |
|
|
1346 |
}, |
|
|
1347 |
{ |
|
|
1348 |
"cell_type": "code", |
|
|
1349 |
"source": [ |
|
|
1350 |
"evaluator.evaluate_annotations(true_labels, pred_labels, do_lower=True)" |
|
|
1351 |
], |
|
|
1352 |
"metadata": { |
|
|
1353 |
"id": "obqUWCw-6T90" |
|
|
1354 |
}, |
|
|
1355 |
"execution_count": 34, |
|
|
1356 |
"outputs": [] |
|
|
1357 |
}, |
|
|
1358 |
{ |
|
|
1359 |
"cell_type": "code", |
|
|
1360 |
"source": [ |
|
|
1361 |
"evaluator.performance" |
|
|
1362 |
], |
|
|
1363 |
"metadata": { |
|
|
1364 |
"id": "XAdRmNx39MYa", |
|
|
1365 |
"outputId": "f2b1b26b-9ae1-4ce1-f513-8002bbc4d809", |
|
|
1366 |
"colab": { |
|
|
1367 |
"base_uri": "https://localhost:8080/" |
|
|
1368 |
} |
|
|
1369 |
}, |
|
|
1370 |
"execution_count": 38, |
|
|
1371 |
"outputs": [ |
|
|
1372 |
{ |
|
|
1373 |
"output_type": "execute_result", |
|
|
1374 |
"data": { |
|
|
1375 |
"text/plain": [ |
|
|
1376 |
"{'overall': {'acc': 0.8351,\n", |
|
|
1377 |
" 'strict': {'precision': 0.6225968648328897,\n", |
|
|
1378 |
" 'recall': 0.6740313800832533,\n", |
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|
1379 |
" 'f_score': 0.6472939729397292},\n", |
|
|
1380 |
" 'relax': {'precision': 0.7580597456373854,\n", |
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|
1381 |
" 'recall': 0.8206852385526737,\n", |
|
|
1382 |
" 'f_score': 0.7881303813038131}},\n", |
|
|
1383 |
" 'category': {'strict': {'condition': {'precision': 0.6648394675019577,\n", |
|
|
1384 |
" 'recall': 0.7683257918552037,\n", |
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|
1385 |
" 'f_score': 0.7128463476070528},\n", |
|
|
1386 |
" 'person': {'precision': 0.7133757961783439,\n", |
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1387 |
" 'recall': 0.8296296296296296,\n", |
|
|
1388 |
" 'f_score': 0.7671232876712328},\n", |
|
|
1389 |
" 'value': {'precision': 0.7067039106145251,\n", |
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|
1390 |
" 'recall': 0.7207977207977208,\n", |
|
|
1391 |
" 'f_score': 0.7136812411847672},\n", |
|
|
1392 |
" 'drug': {'precision': 0.7180043383947939,\n", |
|
|
1393 |
" 'recall': 0.7471783295711061,\n", |
|
|
1394 |
" 'f_score': 0.7323008849557522},\n", |
|
|
1395 |
" 'temporal': {'precision': 0.49279538904899134,\n", |
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|
1396 |
" 'recall': 0.5757575757575758,\n", |
|
|
1397 |
" 'f_score': 0.5310559006211181},\n", |
|
|
1398 |
" 'measurement': {'precision': 0.5473372781065089,\n", |
|
|
1399 |
" 'recall': 0.6379310344827587,\n", |
|
|
1400 |
" 'f_score': 0.5891719745222931},\n", |
|
|
1401 |
" 'procedure': {'precision': 0.5241157556270096,\n", |
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|
1402 |
" 'recall': 0.5207667731629393,\n", |
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|
1403 |
" 'f_score': 0.5224358974358974},\n", |
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|
1404 |
" 'observation': {'precision': 0.31683168316831684,\n", |
|
|
1405 |
" 'recall': 0.1927710843373494,\n", |
|
|
1406 |
" 'f_score': 0.2397003745318352},\n", |
|
|
1407 |
" 'device': {'precision': 0.2903225806451613,\n", |
|
|
1408 |
" 'recall': 0.391304347826087,\n", |
|
|
1409 |
" 'f_score': 0.33333333333333337}},\n", |
|
|
1410 |
" 'relax': {'condition': {'precision': 0.7956147220046985,\n", |
|
|
1411 |
" 'recall': 0.9194570135746606,\n", |
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|
1412 |
" 'f_score': 0.8530646515533165},\n", |
|
|
1413 |
" 'person': {'precision': 0.7261146496815286,\n", |
|
|
1414 |
" 'recall': 0.8444444444444444,\n", |
|
|
1415 |
" 'f_score': 0.7808219178082192},\n", |
|
|
1416 |
" 'value': {'precision': 0.8547486033519553,\n", |
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|
1417 |
" 'recall': 0.8717948717948718,\n", |
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|
1418 |
" 'f_score': 0.8631875881523272},\n", |
|
|
1419 |
" 'drug': {'precision': 0.841648590021692,\n", |
|
|
1420 |
" 'recall': 0.8758465011286681,\n", |
|
|
1421 |
" 'f_score': 0.8584070796460177},\n", |
|
|
1422 |
" 'temporal': {'precision': 0.6368876080691642,\n", |
|
|
1423 |
" 'recall': 0.7441077441077442,\n", |
|
|
1424 |
" 'f_score': 0.686335403726708},\n", |
|
|
1425 |
" 'measurement': {'precision': 0.7189349112426036,\n", |
|
|
1426 |
" 'recall': 0.8379310344827586,\n", |
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|
1427 |
" 'f_score': 0.7738853503184714},\n", |
|
|
1428 |
" 'procedure': {'precision': 0.6752411575562701,\n", |
|
|
1429 |
" 'recall': 0.670926517571885,\n", |
|
|
1430 |
" 'f_score': 0.6730769230769232},\n", |
|
|
1431 |
" 'observation': {'precision': 0.5148514851485149,\n", |
|
|
1432 |
" 'recall': 0.3132530120481928,\n", |
|
|
1433 |
" 'f_score': 0.3895131086142322},\n", |
|
|
1434 |
" 'device': {'precision': 0.41935483870967744,\n", |
|
|
1435 |
" 'recall': 0.5652173913043478,\n", |
|
|
1436 |
" 'f_score': 0.4814814814814815}}}}" |
|
|
1437 |
] |
|
|
1438 |
}, |
|
|
1439 |
"metadata": {}, |
|
|
1440 |
"execution_count": 38 |
|
|
1441 |
} |
|
|
1442 |
] |
|
|
1443 |
}, |
|
|
1444 |
{ |
|
|
1445 |
"cell_type": "code", |
|
|
1446 |
"source": [ |
|
|
1447 |
"evaluator.save_evaluation('eval.json')" |
|
|
1448 |
], |
|
|
1449 |
"metadata": { |
|
|
1450 |
"id": "kbc_sKVo901C" |
|
|
1451 |
}, |
|
|
1452 |
"execution_count": 40, |
|
|
1453 |
"outputs": [] |
|
|
1454 |
}, |
|
|
1455 |
{ |
|
|
1456 |
"cell_type": "code", |
|
|
1457 |
"execution_count": null, |
|
|
1458 |
"metadata": { |
|
|
1459 |
"id": "EY9TwGjjdeix" |
|
|
1460 |
}, |
|
|
1461 |
"outputs": [], |
|
|
1462 |
"source": [ |
|
|
1463 |
"results, results_strict,cr1,cr2 = compute_metrics_tr((annotated_sentences_first, dataset['test']['ner_tags']))" |
|
|
1464 |
] |
|
|
1465 |
}, |
|
|
1466 |
{ |
|
|
1467 |
"cell_type": "code", |
|
|
1468 |
"source": [ |
|
|
1469 |
"print(cr1)" |
|
|
1470 |
], |
|
|
1471 |
"metadata": { |
|
|
1472 |
"id": "t8cITFjd2947", |
|
|
1473 |
"outputId": "363f869d-a417-49f3-85a2-d14fc4296b59", |
|
|
1474 |
"colab": { |
|
|
1475 |
"base_uri": "https://localhost:8080/" |
|
|
1476 |
} |
|
|
1477 |
}, |
|
|
1478 |
"execution_count": null, |
|
|
1479 |
"outputs": [ |
|
|
1480 |
{ |
|
|
1481 |
"output_type": "stream", |
|
|
1482 |
"name": "stdout", |
|
|
1483 |
"text": [ |
|
|
1484 |
" precision recall f1-score support\n", |
|
|
1485 |
"\n", |
|
|
1486 |
" Condition 0.64 0.77 0.70 1105\n", |
|
|
1487 |
" Device 0.24 0.30 0.27 23\n", |
|
|
1488 |
" Drug 0.68 0.73 0.70 443\n", |
|
|
1489 |
" Measurement 0.53 0.62 0.57 290\n", |
|
|
1490 |
" Observation 0.30 0.18 0.23 166\n", |
|
|
1491 |
" Person 0.76 0.84 0.80 135\n", |
|
|
1492 |
" Procedure 0.46 0.49 0.48 313\n", |
|
|
1493 |
" Temporal 0.48 0.58 0.52 297\n", |
|
|
1494 |
" Value 0.65 0.70 0.68 351\n", |
|
|
1495 |
"\n", |
|
|
1496 |
" micro avg 0.60 0.67 0.63 3123\n", |
|
|
1497 |
" macro avg 0.53 0.58 0.55 3123\n", |
|
|
1498 |
"weighted avg 0.59 0.67 0.62 3123\n", |
|
|
1499 |
"\n" |
|
|
1500 |
] |
|
|
1501 |
} |
|
|
1502 |
] |
|
|
1503 |
}, |
|
|
1504 |
{ |
|
|
1505 |
"cell_type": "code", |
|
|
1506 |
"source": [ |
|
|
1507 |
"print(cr2)" |
|
|
1508 |
], |
|
|
1509 |
"metadata": { |
|
|
1510 |
"id": "FOYL6Awc3Cp5", |
|
|
1511 |
"outputId": "07c1fd7c-c56f-4adc-f3d5-5c4bf7f0ed7d", |
|
|
1512 |
"colab": { |
|
|
1513 |
"base_uri": "https://localhost:8080/" |
|
|
1514 |
} |
|
|
1515 |
}, |
|
|
1516 |
"execution_count": null, |
|
|
1517 |
"outputs": [ |
|
|
1518 |
{ |
|
|
1519 |
"output_type": "stream", |
|
|
1520 |
"name": "stdout", |
|
|
1521 |
"text": [ |
|
|
1522 |
" precision recall f1-score support\n", |
|
|
1523 |
"\n", |
|
|
1524 |
" Condition 0.69 0.76 0.72 1104\n", |
|
|
1525 |
" Device 0.29 0.30 0.30 23\n", |
|
|
1526 |
" Drug 0.73 0.73 0.73 443\n", |
|
|
1527 |
" Measurement 0.59 0.61 0.60 288\n", |
|
|
1528 |
" Observation 0.40 0.17 0.24 166\n", |
|
|
1529 |
" Person 0.76 0.84 0.80 135\n", |
|
|
1530 |
" Procedure 0.53 0.49 0.51 311\n", |
|
|
1531 |
" Temporal 0.58 0.57 0.58 295\n", |
|
|
1532 |
" Value 0.70 0.72 0.71 345\n", |
|
|
1533 |
"\n", |
|
|
1534 |
" micro avg 0.66 0.66 0.66 3110\n", |
|
|
1535 |
" macro avg 0.59 0.58 0.58 3110\n", |
|
|
1536 |
"weighted avg 0.65 0.66 0.65 3110\n", |
|
|
1537 |
"\n" |
|
|
1538 |
] |
|
|
1539 |
} |
|
|
1540 |
] |
|
|
1541 |
} |
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|
1542 |
], |
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1543 |
"metadata": { |
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1544 |
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"display_name": "Python 3", |
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"name": "python3" |
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}, |
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"mimetype": "text/x-python", |
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|
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"name": "python", |
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"nbconvert_exporter": "python", |
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|
1557 |
"pygments_lexer": "ipython3", |
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"version": "3.10.13" |
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}, |
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"colab": { |
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"provenance": [], |
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|
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"gpuType": "T4", |
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|
1563 |
"include_colab_link": true |
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|
1564 |
}, |
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"accelerator": "GPU", |
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"widgets": { |
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"application/vnd.jupyter.widget-state+json": { |
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"_view_name": "HBoxView", |
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"box_style": "", |
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"children": [ |
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|
1583 |
"IPY_MODEL_b289d32f0876481b97a080552549f7d1", |
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"IPY_MODEL_499e568745f743aea5c0b2ac64ef160f", |
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], |
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"layout": "IPY_MODEL_61a45966b4654a7db975880b0a193139" |
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"description": "", |
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"layout": "IPY_MODEL_95909a3c6f6245d1acd056f28bfe5ba8", |
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"style": "IPY_MODEL_feebbbdfbc3740bba039e95ddf31e4c1", |
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"value": "Downloading builder script: " |
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} |
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"499e568745f743aea5c0b2ac64ef160f": { |
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