mirror of
https://gitee.com/fastnlp/fastNLP.git
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913 lines
26 KiB
Plaintext
913 lines
26 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 序列标注\n",
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"\n",
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"这一部分的内容主要展示如何使用fastNLP实现序列标注(Sequence labeling)任务。您可以使用fastNLP的各个组件快捷，方便地完成序列标注任务，达到出色的效果。 在阅读这篇教程前，希望您已经熟悉了fastNLP的基础使用，尤其是数据的载入以及模型的构建，通过这个小任务的能让您进一步熟悉fastNLP的使用。\n",
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"\n",
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"## 命名实体识别(name entity recognition, NER)\n",
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"\n",
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"命名实体识别任务是从文本中抽取出具有特殊意义或者指代性非常强的实体，通常包括人名、地名、机构名和时间等。 如下面的例子中\n",
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"\n",
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"*我来自复旦大学*\n",
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"\n",
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"其中“复旦大学”就是一个机构名，命名实体识别就是要从中识别出“复旦大学”这四个字是一个整体，且属于机构名这个类别。这个问题在实际做的时候会被 转换为序列标注问题\n",
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"\n",
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"针对\"我来自复旦大学\"这句话，我们的预测目标将是[O, O, O, B-ORG, I-ORG, I-ORG, I-ORG]，其中O表示out,即不是一个实体，B-ORG是ORG( organization的缩写)这个类别的开头(Begin)，I-ORG是ORG类别的中间(Inside)。\n",
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"\n",
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"在本tutorial中我们将通过fastNLP尝试写出一个能够执行以上任务的模型。\n",
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"\n",
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"## 载入数据\n",
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"\n",
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"fastNLP的数据载入主要是由Loader与Pipe两个基类衔接完成的，您可以通过《使用Loader和Pipe处理数据》了解如何使用fastNLP提供的数据加载函数。下面我们以微博命名实体任务来演示一下在fastNLP进行序列标注任务。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"+-----------------------------------+-----------------------------------+-----------------------------------+---------+\n",
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"| raw_chars | target | chars | seq_len |\n",
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"+-----------------------------------+-----------------------------------+-----------------------------------+---------+\n",
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"| ['科', '技', '全', '方', '位',... | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,... | [792, 1015, 156, 198, 291, 714... | 26 |\n",
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"| ['对', '，', '输', '给', '一',... | [0, 0, 0, 0, 0, 0, 3, 1, 0, 0,... | [123, 2, 1205, 115, 8, 24, 101... | 15 |\n",
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"+-----------------------------------+-----------------------------------+-----------------------------------+---------+\n"
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]
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}
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],
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"source": [
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"from fastNLP.io import WeiboNERPipe\n",
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"data_bundle = WeiboNERPipe().process_from_file()\n",
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"print(data_bundle.get_dataset('train')[:2])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 模型构建\n",
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"\n",
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"首先选择需要使用的Embedding类型。关于Embedding的相关说明可以参见《使用Embedding模块将文本转成向量》。 在这里我们使用通过word2vec预训练的中文汉字embedding。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Found 3321 out of 3471 words in the pre-training embedding.\n"
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]
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}
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],
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"source": [
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"from fastNLP.embeddings import StaticEmbedding\n",
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"\n",
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"embed = StaticEmbedding(vocab=data_bundle.get_vocab('chars'), model_dir_or_name='cn-char-fastnlp-100d')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"选择好Embedding之后，我们可以使用fastNLP中自带的 fastNLP.models.BiLSTMCRF 作为模型。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from fastNLP.models import BiLSTMCRF\n",
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"\n",
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"data_bundle.rename_field('chars', 'words') # 这是由于BiLSTMCRF模型的forward函数接受的words，而不是chars，所以需要把这一列重新命名\n",
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"model = BiLSTMCRF(embed=embed, num_classes=len(data_bundle.get_vocab('target')), num_layers=1, hidden_size=200, dropout=0.5,\n",
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" target_vocab=data_bundle.get_vocab('target'))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 进行训练\n",
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"下面我们选择用来评估模型的metric，以及优化用到的优化函数。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"from fastNLP import SpanFPreRecMetric\n",
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"from torch.optim import Adam\n",
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"from fastNLP import LossInForward\n",
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"\n",
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"metric = SpanFPreRecMetric(tag_vocab=data_bundle.get_vocab('target'))\n",
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"optimizer = Adam(model.parameters(), lr=1e-2)\n",
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"loss = LossInForward()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"使用Trainer进行训练, 您可以通过修改 device 的值来选择显卡。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"input fields after batch(if batch size is 2):\n",
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"\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 26]) \n",
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"\tseq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
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"\twords: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 26]) \n",
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"target fields after batch(if batch size is 2):\n",
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"\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 26]) \n",
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"\tseq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
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"\n",
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"training epochs started 2020-02-27-13-53-24\n"
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]
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\r",
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"Evaluate data in 0.89 seconds!\n",
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"\r",
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"Evaluation on dev at Epoch 1/10. Step:43/430: \n",
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"\r",
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"SpanFPreRecMetric: f=0.067797, pre=0.192771, rec=0.041131\n",
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"\n"
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"text": [
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"\r",
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"Evaluate data in 0.9 seconds!\n",
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"\r",
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"Evaluation on dev at Epoch 2/10. Step:86/430: \n",
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"\r",
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"SpanFPreRecMetric: f=0.344086, pre=0.568047, rec=0.246787\n",
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"\n"
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"text": [
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"\r",
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"Evaluate data in 0.88 seconds!\n",
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"\r",
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"Evaluation on dev at Epoch 3/10. Step:129/430: \n",
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"\r",
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"SpanFPreRecMetric: f=0.446701, pre=0.653465, rec=0.339332\n",
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"text": [
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"\r",
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"Evaluate data in 0.81 seconds!\n",
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"\r",
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"Evaluation on dev at Epoch 4/10. Step:172/430: \n",
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"\r",
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"SpanFPreRecMetric: f=0.479871, pre=0.642241, rec=0.383033\n",
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"text": [
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"\r",
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"Evaluate data in 0.91 seconds!\n",
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"\r",
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"Evaluation on dev at Epoch 5/10. Step:215/430: \n",
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"\r",
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"SpanFPreRecMetric: f=0.486312, pre=0.650862, rec=0.388175\n",
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"text": [
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"\r",
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"Evaluate data in 0.87 seconds!\n",
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"\r",
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"Evaluation on dev at Epoch 6/10. Step:258/430: \n",
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"\r",
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"SpanFPreRecMetric: f=0.541401, pre=0.711297, rec=0.437018\n",
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"\r",
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"Evaluate data in 0.86 seconds!\n",
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"\r",
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"Evaluation on dev at Epoch 7/10. Step:301/430: \n",
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"\r",
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"SpanFPreRecMetric: f=0.430335, pre=0.685393, rec=0.313625\n",
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"text": [
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"\r",
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"Evaluate data in 0.82 seconds!\n",
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"\r",
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"Evaluation on dev at Epoch 8/10. Step:344/430: \n",
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"\r",
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"SpanFPreRecMetric: f=0.477759, pre=0.665138, rec=0.372751\n",
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"\n"
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
||
"\r",
|
||
"Evaluate data in 0.81 seconds!\n",
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"\r",
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"Evaluation on dev at Epoch 9/10. Step:387/430: \n",
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"\r",
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"SpanFPreRecMetric: f=0.500759, pre=0.611111, rec=0.424165\n",
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"\n"
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"metadata": {},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
||
"\r",
|
||
"Evaluate data in 0.8 seconds!\n",
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"\r",
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||
"Evaluation on dev at Epoch 10/10. Step:430/430: \n",
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||
"\r",
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"SpanFPreRecMetric: f=0.496025, pre=0.65, rec=0.401028\n",
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||
"\n",
|
||
"\r\n",
|
||
"In Epoch:6/Step:258, got best dev performance:\n",
|
||
"SpanFPreRecMetric: f=0.541401, pre=0.711297, rec=0.437018\n",
|
||
"Reloaded the best model.\n"
|
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]
|
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},
|
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{
|
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"data": {
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"text/plain": [
|
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"{'best_eval': {'SpanFPreRecMetric': {'f': 0.541401,\n",
|
||
" 'pre': 0.711297,\n",
|
||
" 'rec': 0.437018}},\n",
|
||
" 'best_epoch': 6,\n",
|
||
" 'best_step': 258,\n",
|
||
" 'seconds': 121.39}"
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from fastNLP import Trainer\n",
|
||
"import torch\n",
|
||
"\n",
|
||
"device= 0 if torch.cuda.is_available() else 'cpu'\n",
|
||
"trainer = Trainer(data_bundle.get_dataset('train'), model, loss=loss, optimizer=optimizer,\n",
|
||
" dev_data=data_bundle.get_dataset('dev'), metrics=metric, device=device)\n",
|
||
"trainer.train()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 进行测试\n",
|
||
"训练结束之后过，可以通过 Tester 测试其在测试集上的性能"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=17.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
"Evaluate data in 1.54 seconds!\n",
|
||
"[tester] \n",
|
||
"SpanFPreRecMetric: f=0.439024, pre=0.685279, rec=0.322967\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'SpanFPreRecMetric': {'f': 0.439024, 'pre': 0.685279, 'rec': 0.322967}}"
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from fastNLP import Tester\n",
|
||
"tester = Tester(data_bundle.get_dataset('test'), model, metrics=metric)\n",
|
||
"tester.test()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 使用更强的Bert做序列标注\n",
|
||
"\n",
|
||
"在fastNLP使用Bert进行任务，您只需要把fastNLP.embeddings.StaticEmbedding 切换为 fastNLP.embeddings.BertEmbedding（可修改 device 选择显卡）。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"loading vocabulary file /remote-home/ynzheng/.fastNLP/embedding/bert-chinese-wwm/vocab.txt\n",
|
||
"Load pre-trained BERT parameters from file /remote-home/ynzheng/.fastNLP/embedding/bert-chinese-wwm/chinese_wwm_pytorch.bin.\n",
|
||
"Start to generate word pieces for word.\n",
|
||
"Found(Or segment into word pieces) 3384 words out of 3471.\n",
|
||
"input fields after batch(if batch size is 2):\n",
|
||
"\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 26]) \n",
|
||
"\tseq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
|
||
"\twords: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 26]) \n",
|
||
"target fields after batch(if batch size is 2):\n",
|
||
"\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 26]) \n",
|
||
"\tseq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
|
||
"\n",
|
||
"training epochs started 2020-02-27-13-58-51\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=1130.0), HTML(value='')), layout=Layout(d…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=23.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Evaluate data in 2.7 seconds!\n",
|
||
"Evaluation on dev at Epoch 1/10. Step:113/1130: \n",
|
||
"SpanFPreRecMetric: f=0.008114, pre=0.019231, rec=0.005141\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=23.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Evaluate data in 2.49 seconds!\n",
|
||
"Evaluation on dev at Epoch 2/10. Step:226/1130: \n",
|
||
"SpanFPreRecMetric: f=0.467866, pre=0.467866, rec=0.467866\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=23.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Evaluate data in 2.6 seconds!\n",
|
||
"Evaluation on dev at Epoch 3/10. Step:339/1130: \n",
|
||
"SpanFPreRecMetric: f=0.566879, pre=0.482821, rec=0.686375\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=23.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Evaluate data in 2.56 seconds!\n",
|
||
"Evaluation on dev at Epoch 4/10. Step:452/1130: \n",
|
||
"SpanFPreRecMetric: f=0.651972, pre=0.59408, rec=0.722365\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=23.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
"Evaluate data in 2.69 seconds!\n",
|
||
"\r",
|
||
"Evaluation on dev at Epoch 5/10. Step:565/1130: \n",
|
||
"\r",
|
||
"SpanFPreRecMetric: f=0.640909, pre=0.574338, rec=0.724936\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=23.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Evaluate data in 2.52 seconds!\n",
|
||
"Evaluation on dev at Epoch 6/10. Step:678/1130: \n",
|
||
"SpanFPreRecMetric: f=0.661836, pre=0.624146, rec=0.70437\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=23.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Evaluate data in 2.67 seconds!\n",
|
||
"Evaluation on dev at Epoch 7/10. Step:791/1130: \n",
|
||
"SpanFPreRecMetric: f=0.683429, pre=0.615226, rec=0.768638\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=23.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
"Evaluate data in 2.37 seconds!\n",
|
||
"\r",
|
||
"Evaluation on dev at Epoch 8/10. Step:904/1130: \n",
|
||
"\r",
|
||
"SpanFPreRecMetric: f=0.674699, pre=0.634921, rec=0.719794\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=23.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Evaluate data in 2.42 seconds!\n",
|
||
"Evaluation on dev at Epoch 9/10. Step:1017/1130: \n",
|
||
"SpanFPreRecMetric: f=0.693878, pre=0.650901, rec=0.742931\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=23.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
"Evaluate data in 2.46 seconds!\n",
|
||
"\r",
|
||
"Evaluation on dev at Epoch 10/10. Step:1130/1130: \n",
|
||
"\r",
|
||
"SpanFPreRecMetric: f=0.686845, pre=0.62766, rec=0.758355\n",
|
||
"\n",
|
||
"\r\n",
|
||
"In Epoch:9/Step:1017, got best dev performance:\n",
|
||
"SpanFPreRecMetric: f=0.693878, pre=0.650901, rec=0.742931\n",
|
||
"Reloaded the best model.\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=17.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
"Evaluate data in 1.96 seconds!\n",
|
||
"[tester] \n",
|
||
"SpanFPreRecMetric: f=0.626561, pre=0.596112, rec=0.660287\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'SpanFPreRecMetric': {'f': 0.626561, 'pre': 0.596112, 'rec': 0.660287}}"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"from fastNLP.io import WeiboNERPipe\n",
|
||
"data_bundle = WeiboNERPipe().process_from_file()\n",
|
||
"data_bundle.rename_field('chars', 'words')\n",
|
||
"\n",
|
||
"from fastNLP.embeddings import BertEmbedding\n",
|
||
"embed = BertEmbedding(vocab=data_bundle.get_vocab('words'), model_dir_or_name='cn')\n",
|
||
"model = BiLSTMCRF(embed=embed, num_classes=len(data_bundle.get_vocab('target')), num_layers=1, hidden_size=200, dropout=0.5,\n",
|
||
" target_vocab=data_bundle.get_vocab('target'))\n",
|
||
"\n",
|
||
"from fastNLP import SpanFPreRecMetric\n",
|
||
"from torch.optim import Adam\n",
|
||
"from fastNLP import LossInForward\n",
|
||
"metric = SpanFPreRecMetric(tag_vocab=data_bundle.get_vocab('target'))\n",
|
||
"optimizer = Adam(model.parameters(), lr=2e-5)\n",
|
||
"loss = LossInForward()\n",
|
||
"\n",
|
||
"from fastNLP import Trainer\n",
|
||
"import torch\n",
|
||
"device= 5 if torch.cuda.is_available() else 'cpu'\n",
|
||
"trainer = Trainer(data_bundle.get_dataset('train'), model, loss=loss, optimizer=optimizer, batch_size=12,\n",
|
||
" dev_data=data_bundle.get_dataset('dev'), metrics=metric, device=device)\n",
|
||
"trainer.train()\n",
|
||
"\n",
|
||
"from fastNLP import Tester\n",
|
||
"tester = Tester(data_bundle.get_dataset('test'), model, metrics=metric)\n",
|
||
"tester.test()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python Now",
|
||
"language": "python",
|
||
"name": "now"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.8.0"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|