Datasets
Models
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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": [
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},
"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,
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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]"
],
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"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]"
],
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"version_major": 2,
"version_minor": 0,
"model_id": "e21aa3b226584eefb0b542d1f70ec9ab"
}
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},
{
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"data": {
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],
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"model_id": "e4935e5934e745e6bb96709008d6b75c"
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"metadata": {}
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{
"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,
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"outputId": "2dacda87-20e9-4589-8890-cbfe0d75df68"
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{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading readme: 0%| | 0.00/577 [00:00<?, ?B/s]"
],
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{
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{
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"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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"version_major": 2,
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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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"version_major": 2,
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"model_id": "8e205a49f3144185892f94ef061dc066"
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{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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],
"source": [
"dataset = load_dataset('JavierLopetegui/chia_v1')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 113,
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},
"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"
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},
"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,
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}
},
"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": [
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"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",
"2cc4a2c9d483483e8abe499d88eba846",
"29d81a56e87e4c7ba9f3dd33bcd5fa50",
"5c51fe3c3b5d4353909b4b91b71ff2ec",
"34299f5e28034eafa17884d89b98d96d",
"3aee02f3e8e743d78ad0888167ae29ea",
"927cbad9304b45e08f5b9a2c06d7d9c6",
"3186d4d15d7548ca980ecae4a01d977f",
"c9c2d2316a70469693917ccb2c5951ea",
"96d52af8e3ba477190dc40962b930661",
"2f6c999a966d4a978dea4afa93d525f3",
"9b88d1dfbd264eccbcb818d22aa812fc",
"e5bb79ba8c5e447099f7735dfe5f1423",
"05a657854c024e818b98e03c23c06d05",
"324ff1636c7e49ab961221bbbb1d524d",
"f4887636b8f449e49c8e142e9edc3781",
"64d9db0ce11442929e105ee4fbd9214f",
"677683c5d1ba411daef0e2f40fa3b082",
"fed63fe1dbda430eb4fd66c54c7d0501",
"95811d9cd5674bb591b16002f605a117",
"373669491f534b868f5801b87f28804b",
"1e6d09f7493f4548ab61799a9f85ab7e",
"e557cca0a4c6420883792ada77ddcc55",
"6bc9615cc57f45b0bb346d30b452a8ad",
"19db2f75802e4301a46982dfd7f8d6b2",
"63e2e78e9db24044a6ad658b91ff77b0",
"c0eaf812183146c78d1272381a0d29d4",
"6404fca71188422096164ec4041b1958",
"5ddc689413f44e3bad4306ce37436298",
"a1666a7dc89249fda1b5a236d358de41",
"cab0db9836e8479b92e53da67b984f1d",
"701a2cbbc4b04e69a47c1d05ef1c1495",
"35cd001dd8634a438a3d166b30e57b8e",
"988b957a991344b29d2bb2c072cd62e9",
"e9e54e03b9204f96b67ba56353aa5544",
"bb65493aa12b46539f8b0cd6d2319877",
"6dae7fda565c4ea3b857f3ab86ebe10f",
"2767af4f599f4601977b0a73778e9586",
"1aca93d32e0145f195b06cca013555f5",
"289dca3658b849be94420db16c07abaf",
"292e8e98cb9d41d89e4344ccb11c4fc5",
"ffdceee99b4d448894ed89d91931fc61",
"2ae1d02eb31a4558b87a0e011ba6e01c",
"7ba23b6cb7e74511a104d45bbfc5c651",
"2639ce3e4c5b49fba5d1293c88f1211b",
"61b5508f4aaa4132b016184584e67004",
"a7469f65d65e41aeb7e6e89185a5b8e2",
"3bcd298a6c344a9597b943df5f338401",
"81ab28318e894d69a27bf7b6cfaa38bb",
"78968d136fed4ad58a90996efefb1f94",
"ad4dc1b65c9c4847a65953264f47a3c2",
"da79aac0af0a44ab8a95c1fe4ea79551",
"7a64d901a05b4b2c8d69b580a6cde512",
"6877e10c104445a3a75562a0c7768d24",
"62bf58ddf89b4ec1b221d058f2df4ceb",
"84071152296e41c7a1e516e9cda6c669",
"4f04ac285d8d4a03a48369f5b2506b14",
"1cd5a05f73f3423eb8295ece24cb9e26",
"4161aa829b0b4a88b3eaef39471d9d79",
"aa3264b095454513a5a156447b38ca0c",
"9e1c908ba8c04f3ebba4e2c8f75f6353",
"f620ffd58d6d4ef5adfa78a3b614ab8e",
"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",
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},
"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]"
],
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"version_major": 2,
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},
{
"output_type": "display_data",
"data": {
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],
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"version_major": 2,
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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],
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},
{
"output_type": "display_data",
"data": {
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],
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},
{
"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)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Z8y0JvL3Mn_-",
"outputId": "f91e3704-406f-4d95-88f5-580852cc4125"
},
"outputs": [
{
"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",
" 'recall': 0.8627450980392157,\n",
" 'f_score': 0.8441247002398082},\n",
" 'observation': {'precision': 0.5333333333333333,\n",
" 'recall': 0.32653061224489793,\n",
" 'f_score': 0.40506329113924044},\n",
" 'temporal': {'precision': 0.7168674698795181,\n",
" 'recall': 0.7727272727272727,\n",
" '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",
" 'f_score': 0.7904191616766467},\n",
" 'procedure': {'precision': 0.7046979865771812,\n",
" 'recall': 0.75,\n",
" 'f_score': 0.726643598615917},\n",
" '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_-"
},
"outputs": [],
"source": [
"evaluator.save_evaluation('eval_MLM+generative.json')"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yQOwpABZMn_-",
"outputId": "1d2c4ffd-bfbb-4cba-ac35-4512a1f1da59"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
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