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-Eligibility-Criteria/blob/main/Roberta%2BLLM/mistral7b-full-evaluation.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": {
"id": "GREqJcq4OLO6",
"outputId": "511e84c9-0109-411a-b44e-0801766d0fe9",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\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": {
"id": "ICZaolBLOLO8",
"outputId": "c8c38b1c-e0e3-4258-cc8b-d7d684d9d089",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: seqeval in /usr/local/lib/python3.10/dist-packages (1.2.2)\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) (1.4.2)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.5.0)\n",
"Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.3.0)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.14.0)\n",
"Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.11.0)\n",
"Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
"Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
"Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch) (8.9.2.26)\n",
"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.3.1)\n",
"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch) (11.0.2.54)\n",
"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch) (10.3.2.106)\n",
"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch) (11.4.5.107)\n",
"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.0.106)\n",
"Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch) (2.20.5)\n",
"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
"Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.3.0)\n",
"Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch) (12.4.127)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\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",
" 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",
" 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"
]
}
],
"source": [
"# uncomment if using colab\n",
"!pip install -q -U datasets\n",
"!pip install seqeval\n",
"!pip install -U torch\n",
"!pip install -q -U evaluate\n",
"!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
"# !pip install -q -U transformers\n",
"!pip install -q -U bitsandbytes\n",
"!pip install -q -U git+https://github.com/huggingface/peft.git\n",
"!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
"# !pip install -q -U accelerate"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "Km86o_uHOLO8"
},
"outputs": [],
"source": [
"import torch\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextGenerationPipeline\n",
"import os\n",
"from utils import *"
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"from datasets import Dataset, DatasetDict, load_dataset"
],
"metadata": {
"id": "L0zkxHTmOxkJ"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import re"
],
"metadata": {
"id": "uoWkyegpPCOZ"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "L0zE4thvOLO9",
"outputId": "75716581-4307-4d98-fddf-887bc825e808",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145,
"referenced_widgets": [
"009541513b8e451886782e457d7cd479",
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"6ab04465b07747a592cb28f77c6b5e20",
"2379403cd2df4ffd9f7d4eecbf571d7e",
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"aec5500c07b54365ab252b5746e08127",
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]
}
},
"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": "009541513b8e451886782e457d7cd479"
}
},
"metadata": {}
}
],
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "6DQED9SWOLO9"
},
"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": 7,
"metadata": {
"id": "fLKSRg5XOLO9"
},
"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": 8,
"metadata": {
"id": "ZZnHifTsOLO9"
},
"outputs": [],
"source": [
"model_name = \"mistralai/Mistral-7B-v0.1\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "ytfHR4b2OLO-",
"outputId": "7f66d4d3-1867-4d9f-c298-42b2368c50df",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 173,
"referenced_widgets": [
"1ca11a0dedb44e6897fd914763d6731d",
"b418a6bbce1346bd9c0076f2d31bad26",
"b7975e8109d74252ae427c2507b70c2c",
"f0af7ea71ed14275bb36b55c87d6c785",
"ddb109aa9fed447b9e8929f2ce49b8d2",
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"e7c31cc6d3a64a4fb911f3d2023ecfb1"
]
}
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "1ca11a0dedb44e6897fd914763d6731d"
}
},
"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": 10,
"metadata": {
"id": "ageLiTUKOLO-",
"outputId": "8f9850bc-2dd1-4230-9b0d-62503154247e",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(True, False)"
]
},
"metadata": {},
"execution_count": 10
}
],
"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 = False\n",
"tokenizer.add_bos_token, tokenizer.add_eos_token"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "qMfZMKhlOLO-"
},
"outputs": [],
"source": [
"pipe = TextGenerationPipeline(model = model, tokenizer = tokenizer)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "E1T2wFahOLO-"
},
"outputs": [],
"source": [
"dataset = load_dataset('JavierLopetegui/chia_v1')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "U37CsADiOLO-"
},
"outputs": [],
"source": [
"# for each sentence save the text\n",
"def generate_sentences_from_tokens(sentences):\n",
" texts_sentences = []\n",
" sentences_tokens = sentences['tokens']\n",
" for sentence in sentences_tokens:\n",
" sent_text = \" \".join(sentence)\n",
" texts_sentences.append(sent_text)\n",
" sentences['text'] = texts_sentences\n",
" return sentences"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "423oQ2BQOLO-"
},
"outputs": [],
"source": [
"def build_prompts(sentences, prompt_type=2):\n",
" sentences_prompts = []\n",
" for sent in sentences['text']:\n",
" prompt = build_prompt(sent, prompt_type)\n",
" sentences_prompts.append(prompt)\n",
" sentences['prompt'] = sentences_prompts\n",
" return sentences"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "kC1PSUY9OLO_"
},
"outputs": [],
"source": [
"dataset = dataset.map(lambda x: generate_sentences_from_tokens(x), batched = True)\n",
"dataset_prompt2 = dataset.map(lambda x: build_prompts(x, prompt_type=2), batched = True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "IEyo7EiUOLO_"
},
"outputs": [],
"source": [
"test_dataset_p2 = dataset_prompt2['test']"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"id": "47SUjSaWOLO_"
},
"outputs": [],
"source": [
"# keep just the prompt column\n",
"test_dataset_p2 = test_dataset_p2.remove_columns(['tokens', 'text', 'ner_tags', 'file'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "SAJgHjqnOLO_"
},
"outputs": [],
"source": [
"# data_loader_p2 = DataLoader(test_dataset_p2, batch_size=4, shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ps3MQNmIOLO_"
},
"outputs": [],
"source": [
"# generated_sentences_p2 = pipe(batch['prompt'], max_new_tokens = 500, return_full_text = False, handle_long_generation = \"hole\"))"
]
},
{
"cell_type": "code",
"source": [
"from tqdm import tqdm"
],
"metadata": {
"id": "VrXXsuAsSGVR"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(test_dataset_p2['prompt'][0])"
],
"metadata": {
"id": "pULuqZPEB5Y4",
"outputId": "75d27b13-4396-4232-b8af-ca5aad7c3b5e",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 59,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"I am working on a named entity recognition problem, in the context of clinical\n",
" trials eligibility criteria. I will show you the list of entities:\n",
" - Condition\n",
" - Value\n",
" - Drug\n",
" - Procedure\n",
" - Measurement\n",
" - Temporal\n",
" - Observation\n",
" - Person\n",
" - Device\n",
"\n",
" Your task consists in annotate the named entities in a given sentence in the format I will explain you.\n",
" I will explain you with some examples:\n",
"\n",
" Example 1:\n",
" Input: Patients who have received prior chemotherapy for unresectable disease.\n",
" Output: Patients who have received prior <Procedure>chemotherapy</Procedure> for <Condition>unresectable disease</Condition>.\n",
"\n",
" Example 2:\n",
" Input: Patients with any other severe concurrent disease, which in the judgment of the investigator, would make the patient inappropriate for entry into this study.\n",
" Ouput: Patients with any other severe <Condition>concurrent disease</Condition>, which in the judgment of the investigator, would make the patient inappropriate for <Observation>entry into this study</Observation>.\n",
"\n",
" As you can see, in each example, the extracted entities are enclosed using the sintax: <ENT>text of the entity</ENT>.\n",
"\n",
" Please now annotate as explained before the following sentence:\n",
"\n",
" Input: self - reported healthy adults between the ages of 18 - 60 who are fluent in English .\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"id": "QwPSO9NvOLO_",
"outputId": "b9b7841d-781a-47bf-92f0-a32c080343d1",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 1307/1307 [15:16:55<00:00, 42.09s/it]\n"
]
}
],
"source": [
"generated_sentences_p2 = []\n",
"for sentence in tqdm(test_dataset_p2['prompt']):\n",
" sentence += '\\noutput: '\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",
" generated_sentences_p2.append(output)"
]
},
{
"cell_type": "code",
"source": [
"len(generated_sentences_p2)"
],
"metadata": {
"id": "uTSOYC9UhKxw",
"outputId": "3799dd2c-4f62-4856-a407-0825469f89d4",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 89,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1307"
]
},
"metadata": {},
"execution_count": 89
}
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"id": "x0L_xzJXOLO_"
},
"outputs": [],
"source": [
"tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"id": "6i9B0a6_OLO_"
},
"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": "markdown",
"metadata": {
"id": "ZpemgZm_OLPA"
},
"source": [
"**Standarizing true annotations**"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"id": "-bbV-kgyOLPB"
},
"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",
"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)"
],
"metadata": {
"id": "Kgi4KQythezT",
"outputId": "51229f7b-ae1e-4b5c-d1dc-b477a365ab45",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 93,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1307"
]
},
"metadata": {},
"execution_count": 93
}
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"id": "ng4AD9cxOLPB",
"outputId": "3c9b6d49-4b24-4377-8017-4dbc7aba9a3f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1307"
]
},
"metadata": {},
"execution_count": 94
}
],
"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": 95,
"metadata": {
"id": "T3gzF1mvOLPB"
},
"outputs": [],
"source": [
"true_df = pd.DataFrame(true_annotations)\n",
"true_ann_dataset = Dataset.from_pandas(true_df)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"id": "gtFN5ix8OLPB",
"outputId": "e2ccd19f-b58b-4a4a-c7e1-c3f118cda808",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"4a35141fd2544fd08d1c5748a16c28f6",
"d6e6a89659834daa88bcef28c1d5d220",
"b4f30646efe04849afd1b45f50dd3931",
"9a90159ea82b4c3f921839ea7b6f9c86",
"8698beb6d78342caaa49ea61c9ebcc58",
"5562bbdb7f064ba4867374297f2c7ef4",
"f5f58531d6474340a7e5d4ccdc171969",
"57e9591c1f1a4be58f8a826059e27d05",
"e52d41b0f7084b83abfc2dce289e9757",
"49662c4803d846d6bb15e8ba44e4b818",
"a09956aac8fb4447b241f1dc646c6429"
]
}
},
"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": "4a35141fd2544fd08d1c5748a16c28f6"
}
},
"metadata": {}
}
],
"source": [
"true_ann_dataset = true_ann_dataset.map(tokenize_and_align_labels, batched=True)"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"id": "qhcwK4tTOLPB"
},
"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",
" predictions_ = []\n",
" for (pred,lab) in zip(predictions, labels):\n",
" predictions_.append([])\n",
" for (p,l) in zip(pred, lab):\n",
" if l != -100:\n",
" if p == -100:\n",
" predictions_[-1].append(entities_list[0])\n",
" else:\n",
" predictions_[-1].append(entities_list[p])\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"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"id": "kZqPzsPEOLPB"
},
"outputs": [],
"source": [
"from eval_file import *"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ICsodrKvOLPB"
},
"source": [
"**Evaluating prompt 2**"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"id": "ox3BUHhrOLPC"
},
"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": 100,
"metadata": {
"id": "NF6iDWPeOLPC",
"outputId": "cad977da-3c36-43ec-9bfa-482971505810",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1307"
]
},
"metadata": {},
"execution_count": 100
}
],
"source": [
"new_p2_annotations = []\n",
"for sent in generated_sentences_p2:\n",
" annotation = parse_ann2bio(sent, pattern, pattern1, pattern2)\n",
" new_p2_annotations.append(annotation)\n",
"len(new_p2_annotations)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"id": "kRxCaGoFOLPC",
"outputId": "6e580bb9-a3ca-40d6-8d23-3414e2da292f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1307"
]
},
"metadata": {},
"execution_count": 101
}
],
"source": [
"p2_annotations = []\n",
"for sent in new_p2_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",
" p2_annotations.append(dicc_sent)\n",
"len(p2_annotations)"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"id": "JJapA5HhOLPC"
},
"outputs": [],
"source": [
"p2_df = pd.DataFrame(p2_annotations)\n",
"p2_dataset = Dataset.from_pandas(p2_df)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"id": "Wh2C3LSmOLPC",
"outputId": "ebd4d143-619c-44f1-e84e-8a06cc2d0ef0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"162bfe1f36444ce1861ceaf2c8f6a3f3",
"d33896d2c9c546e1b9d5cc12bb866d51",
"af607d56eb2c4de88e84315ebf4e5bed",
"0f469022363b4b0d96762dc3aafebffe",
"dc63518146884067aaebf37d682e83b5",
"676f0e00bfba466ea183f41ba064ff95",
"91796acd865b49e39500cbc7fdfa2e1e",
"48df188dce924c32b510d1ceb4d2450c",
"78810abaada343a998519b4cb128bf3a",
"5f6c93a23a55413f86aa4778e7d2a6c3",
"9b21d0ab5e874bf3aa723bb2114eef80"
]
}
},
"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": "162bfe1f36444ce1861ceaf2c8f6a3f3"
}
},
"metadata": {}
}
],
"source": [
"p2_dataset = p2_dataset.map(tokenize_and_align_labels, batched=True)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"id": "wu_XrafjOLPC",
"outputId": "b9bd9c26-892e-45e8-eb58-b24c8e760d3f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1.0\n"
]
}
],
"source": [
"# keep just sentences with the same length\n",
"sentences_to_evaluate_p2 = []\n",
"sentences_to_evaluate_true = []\n",
"\n",
"for i in range(len(p2_dataset)):\n",
" # keep the min len among the true and annotated sentence\n",
" l = min(len(p2_dataset['labels'][i]), len(true_ann_dataset['labels'][i]))\n",
" sentences_to_evaluate_p2.append(p2_dataset['labels'][i][:l])\n",
" sentences_to_evaluate_true.append(true_ann_dataset['labels'][i][:l])\n",
" # if len(p2_dataset['labels'][i]) != len(true_ann_dataset['labels'][i]):\n",
" # print(p2_dataset['tokens'][i])\n",
" # print(true_ann_dataset['tokens'][i])\n",
" # print(get_labels(([p2_dataset['labels'][i]], [true_ann_dataset['labels'][i]])))\n",
" # # print(true_ann_dataset['labels'][i])\n",
" # # sentences_to_evaluate_p2.append(p2_dataset['labels'][i])\n",
" # # sentences_to_evaluate_true.append(true_ann_dataset['labels'][i])\n",
"\n",
"print(len(sentences_to_evaluate_p2)/len(p2_dataset))"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"id": "BvXAM4BrOLPC"
},
"outputs": [],
"source": [
"evaluator = BioEval()"
]
},
{
"cell_type": "code",
"source": [
"# sentences_to_evaluate_p2[:2]\n",
"for sentence in sentences_to_evaluate_p2:\n",
" if len(sentence) == 0:\n",
" print('len000')\n"
],
"metadata": {
"id": "FgrghkwqfrGo"
},
"execution_count": 113,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"id": "i3WlQ9nNOLPC"
},
"outputs": [],
"source": [
"pred_labels, true_labels = get_labels((sentences_to_evaluate_p2, sentences_to_evaluate_true))"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"id": "50h006TSOLPC"
},
"outputs": [],
"source": [
"evaluator.evaluate_annotations(true_labels, pred_labels, do_lower=True)"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"id": "iYOfAO8qOLPK",
"outputId": "d630bfdc-faa4-4ba3-f817-a2885bc6e768",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'overall': {'acc': 0.6039,\n",
" 'strict': {'precision': 0.539911466514351,\n",
" 'recall': 0.5364642451759364,\n",
" 'f_score': 0.5381823357768131},\n",
" 'relax': {'precision': 0.6547194059688705,\n",
" 'recall': 0.6505391600454029,\n",
" 'f_score': 0.652622589139563}},\n",
" 'category': {'strict': {'condition': {'precision': 0.6026216555934638,\n",
" 'recall': 0.6669316375198728,\n",
" 'f_score': 0.6331478162437506},\n",
" 'measurement': {'precision': 0.14698162729658792,\n",
" 'recall': 0.19310344827586207,\n",
" 'f_score': 0.16691505216095381},\n",
" 'drug': {'precision': 0.562358276643991,\n",
" 'recall': 0.5598194130925508,\n",
" 'f_score': 0.5610859728506786},\n",
" 'procedure': {'precision': 0.3992248062015504,\n",
" 'recall': 0.329073482428115,\n",
" 'f_score': 0.36077057793345},\n",
" 'temporal': {'precision': 0.030303030303030304,\n",
" 'recall': 0.003367003367003367,\n",
" 'f_score': 0.006060606060606061},\n",
" 'person': {'precision': 0.053763440860215055,\n",
" 'recall': 0.03759398496240601,\n",
" 'f_score': 0.04424778761061946},\n",
" 'observation': {'precision': 0.016216216216216217,\n",
" 'recall': 0.018072289156626505,\n",
" 'f_score': 0.017094017094017092},\n",
" 'value': {'precision': 0.05555555555555555,\n",
" 'recall': 0.002849002849002849,\n",
" 'f_score': 0.005420054200542005},\n",
" 'device': {'precision': 0.32,\n",
" 'recall': 0.34782608695652173,\n",
" 'f_score': 0.3333333333333333}},\n",
" 'relax': {'condition': {'precision': 0.7141318010414797,\n",
" 'recall': 0.790341812400636,\n",
" 'f_score': 0.750306574851429},\n",
" 'measurement': {'precision': 0.25196850393700787,\n",
" 'recall': 0.3310344827586207,\n",
" 'f_score': 0.28614008941877794},\n",
" 'drug': {'precision': 0.6893424036281179,\n",
" 'recall': 0.6862302483069977,\n",
" 'f_score': 0.6877828054298643},\n",
" 'procedure': {'precision': 0.5271317829457365,\n",
" 'recall': 0.43450479233226835,\n",
" 'f_score': 0.4763572679509632},\n",
" 'temporal': {'precision': 0.5757575757575758,\n",
" 'recall': 0.06397306397306397,\n",
" 'f_score': 0.11515151515151514},\n",
" 'person': {'precision': 0.12903225806451613,\n",
" 'recall': 0.09022556390977443,\n",
" 'f_score': 0.10619469026548671},\n",
" 'observation': {'precision': 0.0918918918918919,\n",
" 'recall': 0.10240963855421686,\n",
" 'f_score': 0.09686609686609687},\n",
" 'value': {'precision': 0.7222222222222222,\n",
" 'recall': 0.037037037037037035,\n",
" 'f_score': 0.07046070460704607},\n",
" 'device': {'precision': 0.44,\n",
" 'recall': 0.4782608695652174,\n",
" 'f_score': 0.4583333333333333}}}}"
]
},
"metadata": {},
"execution_count": 141
}
],
"source": [
"evaluator.performance"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"id": "vikGKa6fOLPK"
},
"outputs": [],
"source": [
"evaluator.save_evaluation('eval_paper_full_generative.json')"
]
},
{
"cell_type": "code",
"source": [
"evaluator.get_counts()"
],
"metadata": {
"id": "_nF89z1JiAqF",
"outputId": "94c401df-5c0a-46d7-b6f4-3a2b12aa39cd",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 142,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'expect': {'overall': 7048,\n",
" 'condition': 5032,\n",
" 'person': 133,\n",
" 'value': 351,\n",
" 'observation': 166,\n",
" 'drug': 443,\n",
" 'measurement': 290,\n",
" 'procedure': 313,\n",
" 'temporal': 297,\n",
" 'device': 23},\n",
" 'prediction': {'strict': {'overall': {'total': 7003,\n",
" 'true': 3781,\n",
" 'false': 3222},\n",
" 'condition': {'total': 5569, 'true': 3356, 'false': 2213},\n",
" 'measurement': {'total': 381, 'true': 56, 'false': 325},\n",
" 'drug': {'total': 441, 'true': 248, 'false': 193},\n",
" 'procedure': {'total': 258, 'true': 103, 'false': 155},\n",
" 'temporal': {'total': 33, 'true': 1, 'false': 32},\n",
" 'person': {'total': 93, 'true': 5, 'false': 88},\n",
" 'observation': {'total': 185, 'true': 3, 'false': 182},\n",
" 'value': {'total': 18, 'true': 1, 'false': 17},\n",
" 'device': {'total': 25, 'true': 8, 'false': 17}},\n",
" 'relax': {'overall': {'total': 7003, 'true': 4585, 'false': 2418},\n",
" 'condition': {'total': 5569, 'true': 3977, 'false': 1592},\n",
" 'measurement': {'total': 381, 'true': 96, 'false': 285},\n",
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