test/biaffine_bert.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import os\n",
    "import jieba\n",
    "import torch\n",
    "import pickle\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import pandas as pd\n",
    "\n",
    "from ark_nlp.model.ner.biaffine_bert import BiaffineBert\n",
    "from ark_nlp.model.ner.biaffine_bert import BiaffineBertConfig\n",
    "from ark_nlp.model.ner.biaffine_bert import Dataset\n",
    "from ark_nlp.model.ner.biaffine_bert import Task\n",
    "from ark_nlp.model.ner.biaffine_bert import get_default_model_optimizer\n",
    "from ark_nlp.model.ner.biaffine_bert import Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 目录地址\n",
    "\n",
    "train_data_path = '../data/source_datasets/CMeEE/CMeEE_train.json'\n",
    "dev_data_path = '../data/source_datasets/CMeEE/CMeEE_dev.json'\n",
    "test_data_path = '../data/source_datasets/CMeEE/CMeEE_test.json'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一、数据读入与处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. 数据读入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_df = pd.read_json(train_data_path)\n",
    "dev_data_df = pd.read_json(dev_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_df = train_data_df.rename(columns={'entities': 'label'})\n",
    "dev_data_df = dev_data_df.rename(columns={'entities': 'label'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_df = train_data_df.loc[:,['text', 'label']]\n",
    "train_data_df['label'] = train_data_df['label'].apply(lambda x: str(x))\n",
    "dev_data_df = dev_data_df.loc[:,['text', 'label']]\n",
    "dev_data_df['label'] = dev_data_df['label'].apply(lambda x: str(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ner_train_dataset = Dataset(train_data_df)\n",
    "ner_dev_dataset = Dataset(dev_data_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. 词典创建和生成分词器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(vocab='nghuyong/ernie-1.0', max_seq_len=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. ID化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ner_train_dataset.convert_to_ids(tokenizer)\n",
    "ner_dev_dataset.convert_to_ids(tokenizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 二、模型构建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. 模型参数设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = BiaffineBertConfig.from_pretrained('nghuyong/ernie-1.0', \n",
    "                                         num_labels=len(ner_train_dataset.cat2id))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. 模型创建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_module = BiaffineBert.from_pretrained('nghuyong/ernie-1.0', \n",
    "                                         config=config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 三、任务构建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. 任务参数和必要部件设定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置运行次数\n",
    "num_epoches = 1\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = get_default_model_optimizer(dl_module)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. 任务创建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Task(dl_module, optimizer, torch.nn.CrossEntropyLoss(reduction=\"none\"), cuda_device=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(ner_train_dataset, \n",
    "          ner_dev_dataset,\n",
    "          lr=1e-5,\n",
    "          epochs=5, \n",
    "          batch_size=batch_size\n",
    "         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 四、模型验证与保存"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. 模型验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ark_nlp.model.ner.biaffine_bert import Predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ner_predictor_instance = Predictor(model.module, tokenizer, ner_train_dataset.cat2id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ner_predictor_instance.predict_one_sample('（5）房室结消融和起搏器植入作为反复发作或难治性心房内折返性心动过速的替代疗法。')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. 多样本验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_df = pd.read_json(test_data_path)\n",
    "test_data_df = test_data_df.loc[:,['text']]\n",
    "\n",
    "record_ = []\n",
    "for text_ in test_data_df['text'].to_list():\n",
    "    record_.append([text_, ner_predictor_instance.predict_one_sample(text_)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "record_[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Batch模型验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_df = pd.read_json(test_data_path)\n",
    "test_data_df = test_data_df.loc[:,['text']]\n",
    "\n",
    "ner_test_dataset = Dataset(test_data_df, categories=ner_train_dataset.categories, is_test=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 五、模型测试报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "1. 基本功能测试 通过\n",
    "2. one sample predict 通过\n",
    "3. batch predict  无batch方法\n",
    "4. 多样本验证  通过"
   ]
  }
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