mirror of
https://gitee.com/fastnlp/fastNLP.git
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2615 lines
111 KiB
Plaintext
2615 lines
111 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fdd7ff16",
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"metadata": {},
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"source": [
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"# T4. fastNLP 中的预定义模型\n",
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"\n",
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"  1   fastNLP 中 modules 的介绍\n",
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" \n",
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"    1.1   modules 模块、models 模块 简介\n",
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"\n",
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"    1.2   示例一：modules 实现 LSTM 分类\n",
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"\n",
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"  2   fastNLP 中 models 的介绍\n",
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" \n",
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"    2.1   示例一：models 实现 CNN 分类\n",
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"\n",
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"    2.3   示例二：models 实现 BiLSTM 标注"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d3d65d53",
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"metadata": {},
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"source": [
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"## 1. fastNLP 中 modules 模块的介绍\n",
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"\n",
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"### 1.1 modules 模块、models 模块 简介\n",
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"\n",
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"在`fastNLP 0.8`中，**`modules.torch`路径下定义了一些基于`pytorch`实现的基础模块**\n",
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"\n",
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"    包括长短期记忆网络`LSTM`、条件随机场`CRF`、`transformer`的编解码器模块等，详见下表\n",
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"\n",
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"| <div align=\"center\">代码名称</div> | <div align=\"center\">简要介绍</div> | <div align=\"center\">代码路径</div> |\n",
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"|:--|:--|:--|\n",
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"| `LSTM` | 轻量封装`pytorch`的`LSTM` | `/modules/torch/encoder/lstm.py` |\n",
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"| `Seq2SeqEncoder` | 序列变换编码器，基类 | `/modules/torch/encoder/seq2seq_encoder.py` |\n",
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"| `LSTMSeq2SeqEncoder` | 序列变换编码器，基于`LSTM` | `/modules/torch/encoder/seq2seq_encoder.py` |\n",
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"| `TransformerSeq2SeqEncoder` | 序列变换编码器，基于`transformer` | `/modules/torch/encoder/seq2seq_encoder.py` |\n",
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"| `StarTransformer` | `Star-Transformer`的编码器部分 | `/modules/torch/encoder/star_transformer.py` |\n",
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"| `VarRNN` | 实现`Variational Dropout RNN` | `/modules/torch/encoder/variational_rnn.py` |\n",
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"| `VarLSTM` | 实现`Variational Dropout LSTM` | `/modules/torch/encoder/variational_rnn.py` |\n",
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"| `VarGRU` | 实现`Variational Dropout GRU` | `/modules/torch/encoder/variational_rnn.py` |\n",
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"| `MLP` | 多层感知机模型 | `/modules/torch/decoder/mlp.py` |\n",
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"| `ConditionalRandomField` | 条件随机场模型 | `/modules/torch/decoder/crf.py` |\n",
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"| `Seq2SeqDecoder` | 序列变换解码器，基类 | `/modules/torch/decoder/seq2seq_decoder.py` |\n",
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"| `LSTMSeq2SeqDecoder` | 序列变换解码器，基于`LSTM` | `/modules/torch/decoder/seq2seq_decoder.py` |\n",
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"| `TransformerSeq2SeqDecoder` | 序列变换解码器，基于`transformer` | `/modules/torch/decoder/seq2seq_decoder.py` |\n",
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"| `SequenceGenerator` | 序列生成，封装`Seq2SeqDecoder` | `/models/torch/sequence_labeling.py` |\n",
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"| `TimestepDropout` | 在每个`timestamp`上`dropout` | `/modules/torch/dropout.py` |"
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]
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},
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{
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"cell_type": "markdown",
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"id": "89ffcf07",
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"metadata": {},
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"source": [
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"  **`models.torch`路径下定义了一些基于`pytorch`、`modules`实现的预定义模型** \n",
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"\n",
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"    例如基于`CNN`的分类模型、基于`BiLSTM+CRF`的标注模型、基于[双仿射注意力机制](https://arxiv.org/pdf/1611.01734.pdf)的分析模型\n",
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"\n",
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"    基于`modules.torch`中的`LSTM`/`transformer`编/解码器模块的序列变换/生成模型，详见下表\n",
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"\n",
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"| <div align=\"center\">代码名称</div> | <div align=\"center\">简要介绍</div> | <div align=\"center\">代码路径</div> |\n",
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"|:--|:--|:--|\n",
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"| `BiaffineParser` | 句法分析模型，基于双仿射注意力 | `/models/torch/biaffine_parser.py` |\n",
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"| `CNNText` | 文本分类模型，基于`CNN` | `/models/torch/cnn_text_classification.py` |\n",
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"| `Seq2SeqModel` | 序列变换，基类`encoder+decoder` | `/models/torch/seq2seq_model.py` |\n",
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"| `LSTMSeq2SeqModel` | 序列变换，基于`LSTM` | `/models/torch/seq2seq_model.py` |\n",
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"| `TransformerSeq2SeqModel` | 序列变换，基于`transformer` | `/models/torch/seq2seq_model.py` |\n",
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"| `SequenceGeneratorModel` | 封装`Seq2SeqModel`，结合`SequenceGenerator` | `/models/torch/seq2seq_generator.py` |\n",
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"| `SeqLabeling` | 标注模型，基类`LSTM+FC+CRF` | `/models/torch/sequence_labeling.py` |\n",
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"| `BiLSTMCRF` | 标注模型，`BiLSTM+FC+CRF` | `/models/torch/sequence_labeling.py` |\n",
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"| `AdvSeqLabel` | 标注模型，`LN+BiLSTM*2+LN+FC+CRF` | `/models/torch/sequence_labeling.py` |"
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]
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},
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{
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"cell_type": "markdown",
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"id": "61318354",
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"metadata": {},
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"source": [
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"上述`fastNLP`模块，不仅**为入门级用户提供了简单易用的工具**，以解决各种`NLP`任务，或复现相关论文\n",
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"\n",
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"  同时**也为专业研究人员提供了便捷可操作的接口**，封装部分代码的同时，也能指定参数修改细节\n",
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"\n",
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"  在接下来的`tutorial`中，我们将通过`SST-2`分类和`CoNLL-2003`标注，展示相关模型使用\n",
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"\n",
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"注一：**`SST`**，**单句情感分类**数据集，包含电影评论和对应情感极性，1 对应正面情感，0 对应负面情感\n",
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"\n",
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"  数据集包括三部分：训练集 67350 条，验证集 873 条，测试集 1821 条，更多参考[下载链接](https://gluebenchmark.com/tasks)\n",
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"\n",
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"注二：**`CoNLL-2003`**，**文本语法标注**数据集，包含语句和对应的词性标签`pos_tags`（名动形数量代）\n",
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"\n",
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"  语法结构标签`chunk_tags`（主谓宾定状补）、命名实体标签`ner_tags`（人名、组织名、地名、时间等）\n",
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"\n",
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"  数据集包括三部分：训练集 14041 条，验证集 3250 条，测试集 3453 条，更多参考[原始论文](https://aclanthology.org/W03-0419.pdf)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2a36bbe4",
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"metadata": {},
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"source": [
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"### 1.2 示例一：modules 实现 LSTM 分类\n",
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"\n",
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"\n",
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"  本示例使用`fastNLP 0.8`中预定义模型`modules`模块，基于`LSTM`模型，实现`SST-2`文本二分类任务\n",
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"\n",
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"数据使用方面：首先，**使用`datasets`模块中的`load_dataset`函数**，以如下形式，指定`SST-2`数据集加载\n",
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"\n",
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"    首次下载保存至`~/.cache/huggingface/modules/datasets_modules/datasets/glue/`目录下"
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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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"id": "1aa5cf6d",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
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]
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},
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{
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"data": {
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"model_id": "b8bdfdc011d349e38a1aa2aff35b2482",
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from datasets import load_dataset\n",
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"\n",
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"sst2data = load_dataset('glue', 'sst2')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c476abe7",
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"metadata": {},
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"source": [
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"  接着，使用`tutorial-1`和`tutorial-2`中的知识，将数据集转化为`fastNLP`中的`DataSet`格式\n",
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"\n",
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"    **使用`apply_more`函数、`Vocabulary`模块的`from_/index_dataset`函数预处理数据**\n",
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"\n",
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"      并结合`delete_field`函数删除字段调整格式，`split`函数划分测试集和验证集\n",
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"\n",
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"    **仅保留`'words'`字段表示输入文本单词序号序列、`'target'`字段表示文本对应预测输出结果**\n",
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"\n",
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"      两者**对应到`CNNText`中`train_step`函数和`evaluate_step`函数的签名/输入参数**"
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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": 2,
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"id": "357ea748",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[38;5;2m[i 0604 16:19:46.727257 48 log.cc:351] Load log_sync: 1\u001b[m\n"
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]
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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"</pre>\n"
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"\n"
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"metadata": {},
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"output_type": "display_data"
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"Processing: 0%| | 0/6000 [00:00<?, ?it/s]"
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import sys\n",
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"sys.path.append('..')\n",
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"\n",
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"from fastNLP import DataSet\n",
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"\n",
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"dataset = DataSet.from_pandas(sst2data['train'].to_pandas())[:6000]\n",
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"\n",
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"dataset.apply_more(lambda ins:{'words': ins['sentence'].lower().split(), 'target': ins['label']}, \n",
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" progress_bar=\"tqdm\")\n",
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"dataset.delete_field('sentence')\n",
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"dataset.delete_field('label')\n",
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"dataset.delete_field('idx')\n",
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"\n",
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"from fastNLP import Vocabulary\n",
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"\n",
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"vocab = Vocabulary()\n",
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"vocab.from_dataset(dataset, field_name='words')\n",
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"vocab.index_dataset(dataset, field_name='words')\n",
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"\n",
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"train_dataset, evaluate_dataset = dataset.split(ratio=0.85)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "96380c67",
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"metadata": {},
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"source": [
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"  然后，使用`tutorial-3`中的知识，**通过`prepare_torch_dataloader`处理数据集得到`dataloader`**"
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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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"id": "b9dd1273",
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"metadata": {},
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"outputs": [],
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"source": [
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"from fastNLP import prepare_torch_dataloader\n",
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"\n",
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"train_dataloader = prepare_torch_dataloader(train_dataset, batch_size=16, shuffle=True)\n",
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"evaluate_dataloader = prepare_torch_dataloader(evaluate_dataset, batch_size=16)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eb75aaba",
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"metadata": {},
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"source": [
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"模型使用方面，这里使用`Embedding`、`LSTM`、`MLP`等模块搭建模型，方法类似`pytorch`，结构如下所示\n",
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"\n",
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"```\n",
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"ClsByModules(\n",
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" (embedding): Embedding(\n",
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" (embed): Embedding(8458, 100)\n",
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" (dropout): Dropout(p=0.0, inplace=False)\n",
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" )\n",
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" (lstm): LSTM(\n",
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" (lstm): LSTM(100, 64, num_layers=2, batch_first=True, bidirectional=True)\n",
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" )\n",
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" (mlp): MLP(\n",
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" (hiddens): ModuleList()\n",
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" (output): Linear(in_features=128, out_features=2, bias=True)\n",
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" (dropout): Dropout(p=0.5, inplace=False)\n",
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" )\n",
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" (loss_fn): CrossEntropyLoss()\n",
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")\n",
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"```"
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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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"id": "0b25b25c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"\n",
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"from fastNLP.modules.torch import LSTM, MLP\n",
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"from fastNLP.embeddings.torch import Embedding\n",
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"\n",
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"\n",
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"class ClsByModules(nn.Module):\n",
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" def __init__(self, vocab_size, embedding_dim, output_dim, hidden_dim=64, num_layers=2, dropout=0.5):\n",
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" nn.Module.__init__(self)\n",
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"\n",
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" self.embedding = Embedding((vocab_size, embedding_dim))\n",
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" self.lstm = LSTM(embedding_dim, hidden_dim, num_layers=num_layers, bidirectional=True)\n",
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" self.mlp = MLP([hidden_dim * 2, output_dim], dropout=dropout)\n",
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" \n",
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" self.loss_fn = nn.CrossEntropyLoss()\n",
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"\n",
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" def forward(self, words):\n",
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" output = self.embedding(words)\n",
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" output, (hidden, cell) = self.lstm(output)\n",
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" output = self.mlp(torch.cat((hidden[-1], hidden[-2]), dim=1))\n",
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" return output\n",
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" \n",
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" def train_step(self, words, target):\n",
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" pred = self(words)\n",
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" return {\"loss\": self.loss_fn(pred, target)}\n",
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"\n",
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" def evaluate_step(self, words, target):\n",
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" pred = self(words)\n",
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||
" pred = torch.max(pred, dim=-1)[1]\n",
|
||
" return {\"pred\": pred, \"target\": target}"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
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||
"id": "4890de5a",
|
||
"metadata": {},
|
||
"source": [
|
||
"  接着，初始化模型`model`实例，同时，使用`torch.optim.AdamW`初始化`optimizer`实例"
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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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||
"id": "9dbbf50d",
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"model = ClsByModules(vocab_size=len(vocab), embedding_dim=100, output_dim=2)\n",
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"\n",
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||
"from torch.optim import AdamW\n",
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"\n",
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||
"optimizers = AdamW(params=model.parameters(), lr=5e-5)"
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||
]
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||
},
|
||
{
|
||
"cell_type": "markdown",
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||
"id": "054538f5",
|
||
"metadata": {},
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||
"source": [
|
||
"  最后，使用`trainer`模块，集成`model`、`optimizer`、`dataloader`、`metric`训练"
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||
]
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},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 6,
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||
"id": "7a93432f",
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
||
"from fastNLP import Trainer, Accuracy\n",
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"\n",
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||
"trainer = Trainer(\n",
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" model=model,\n",
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" driver='torch',\n",
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" device=0, # 'cuda'\n",
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" n_epochs=10,\n",
|
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" optimizers=optimizers,\n",
|
||
" train_dataloader=train_dataloader,\n",
|
||
" evaluate_dataloaders=evaluate_dataloader,\n",
|
||
" metrics={'acc': Accuracy()}\n",
|
||
")"
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||
]
|
||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 7,
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"id": "31102e0f",
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|
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],
|
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"text/plain": []
|
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},
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},
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"data": {
|
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"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"trainer.run(num_eval_batch_per_dl=10)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "8bc4bfb2",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Output()"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
|
||
],
|
||
"text/plain": []
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'acc#acc': 0.712222, 'total#acc': 900.0, 'correct#acc': 641.0}"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"trainer.evaluator.run()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "07538876",
|
||
"metadata": {},
|
||
"source": [
|
||
"  注：此处使用`gc`模块删除相关变量，释放内存，为接下来新的模型训练预留存储空间，下同"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "1b52eafd",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"383"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import gc\n",
|
||
"\n",
|
||
"del model\n",
|
||
"del trainer\n",
|
||
"del dataset\n",
|
||
"del sst2data\n",
|
||
"\n",
|
||
"gc.collect()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d9443213",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 2. fastNLP 中 models 模块的介绍\n",
|
||
"\n",
|
||
"### 2.1 示例一：models 实现 CNN 分类\n",
|
||
"\n",
|
||
"  本示例使用`fastNLP 0.8`中预定义模型`models`中的`CNNText`模型，实现`SST-2`文本二分类任务\n",
|
||
"\n",
|
||
"数据使用方面，此处沿用在上个示例中展示的`SST-2`数据集，数据加载过程相同且已经执行过了，因此简略\n",
|
||
"\n",
|
||
"模型使用方面，如上所述，这里使用**基于卷积神经网络`CNN`的预定义文本分类模型`CNNText`**，结构如下所示\n",
|
||
"\n",
|
||
"  首先是内置的`100`维嵌入层、`dropout`层、紧接着是三个一维卷积，将`100`维嵌入特征，分别通过\n",
|
||
"\n",
|
||
"    **感受野为`1`、`3`、`5`的卷积算子变换至`30`维、`40`维、`50`维的卷积特征**，再将三者拼接\n",
|
||
"\n",
|
||
"  最终再次通过`dropout`层、线性变换层，映射至二元的输出值，对应两个分类结果上的几率`logits`\n",
|
||
"\n",
|
||
"```\n",
|
||
"CNNText(\n",
|
||
" (embed): Embedding(\n",
|
||
" (embed): Embedding(5194, 100)\n",
|
||
" (dropout): Dropout(p=0.0, inplace=False)\n",
|
||
" )\n",
|
||
" (conv_pool): ConvMaxpool(\n",
|
||
" (convs): ModuleList(\n",
|
||
" (0): Conv1d(100, 30, kernel_size=(1,), stride=(1,), bias=False)\n",
|
||
" (1): Conv1d(100, 40, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
|
||
" (2): Conv1d(100, 50, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=120, out_features=2, bias=True)\n",
|
||
")\n",
|
||
"```\n",
|
||
"\n",
|
||
"对应到代码上，**从`fastNLP.models.torch`路径下导入`CNNText`**，初始化`CNNText`和`optimizer`实例\n",
|
||
"\n",
|
||
"  注意：初始化`CNNText`时，**二元组参数`embed`、分类数量`num_classes`是必须传入的**，其中\n",
|
||
"\n",
|
||
"    **`embed`表示嵌入层的嵌入抽取矩阵大小**，因此第二个元素对应的是默认隐藏层维度 `100`维"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "f6e76e2e",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from fastNLP.models.torch import CNNText\n",
|
||
"\n",
|
||
"model = CNNText(embed=(len(vocab), 100), num_classes=2, dropout=0.1)\n",
|
||
"\n",
|
||
"from torch.optim import AdamW\n",
|
||
"\n",
|
||
"optimizers = AdamW(params=model.parameters(), lr=5e-4)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0cc5ca10",
|
||
"metadata": {},
|
||
"source": [
|
||
"  最后，使用`trainer`模块，集成`model`、`optimizer`、`dataloader`、`metric`训练"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "50a13ee5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from fastNLP import Trainer, Accuracy\n",
|
||
"\n",
|
||
"trainer = Trainer(\n",
|
||
" model=model,\n",
|
||
" driver='torch',\n",
|
||
" device=0, # 'cuda'\n",
|
||
" n_epochs=10,\n",
|
||
" optimizers=optimizers,\n",
|
||
" train_dataloader=train_dataloader,\n",
|
||
" evaluate_dataloaders=evaluate_dataloader,\n",
|
||
" metrics={'acc': Accuracy()}\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "28903a7d",
|
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"metadata": {},
|
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],
|
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"text/plain": []
|
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},
|
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|
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"data": {
|
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"</pre>\n"
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|
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"metadata": {},
|
||
"output_type": "display_data"
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}
|
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],
|
||
"source": [
|
||
"trainer.run()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "f47a6a35",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Output()"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
|
||
],
|
||
"text/plain": []
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'acc#acc': 0.806667, 'total#acc': 900.0, 'correct#acc': 726.0}"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"trainer.evaluator.run()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5b5c0446",
|
||
"metadata": {},
|
||
"source": [
|
||
"  注：此处使用`gc`模块删除相关变量，释放内存，为接下来新的模型训练预留存储空间，下同"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "e9e70f88",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"344"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import gc\n",
|
||
"\n",
|
||
"del model\n",
|
||
"del trainer\n",
|
||
"\n",
|
||
"gc.collect()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6aec2a19",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 2.2 示例二：models 实现 BiLSTM 标注\n",
|
||
"\n",
|
||
"  通过两个示例一的对比可以发现，得益于`models`对模型结构的封装，使用`models`明显更加便捷\n",
|
||
"\n",
|
||
"    针对更加复杂的模型时，编码更加轻松；本示例将使用`models`中的`BiLSTMCRF`模型\n",
|
||
"\n",
|
||
"  避免`CRF`和`Viterbi`算法代码书写的困难，轻松实现`CoNLL-2003`中的命名实体识别`NER`任务\n",
|
||
"\n",
|
||
"模型使用方面，如上所述，这里使用**基于双向`LSTM`+条件随机场`CRF`的标注模型`BiLSTMCRF`**，结构如下所示\n",
|
||
"\n",
|
||
"  其中，隐藏层维度默认`100`维，因此对应双向`LSTM`输出`200`维，`dropout`层退学概率、`LSTM`层数可调\n",
|
||
"\n",
|
||
"```\n",
|
||
"BiLSTMCRF(\n",
|
||
" (embed): Embedding(7590, 100)\n",
|
||
" (lstm): LSTM(\n",
|
||
" (lstm): LSTM(100, 100, batch_first=True, bidirectional=True)\n",
|
||
" )\n",
|
||
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
||
" (fc): Linear(in_features=200, out_features=9, bias=True)\n",
|
||
" (crf): ConditionalRandomField()\n",
|
||
")\n",
|
||
"```\n",
|
||
"\n",
|
||
"数据使用方面，此处仍然**使用`datasets`模块中的`load_dataset`函数**，以如下形式，加载`CoNLL-2003`数据集\n",
|
||
"\n",
|
||
"  首次下载后会保存至`~.cache/huggingface/datasets/conll2003/conll2003/1.0.0/`目录下"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "03e66686",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Reusing dataset conll2003 (/remote-home/xrliu/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/63f4ebd1bcb7148b1644497336fd74643d4ce70123334431a3c053b7ee4e96ee)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "593bc03ed5914953ab94268ff2f01710",
|
||
"version_major": 2,
|
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"version_minor": 0
|
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},
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]
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||
},
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||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from datasets import load_dataset\n",
|
||
"\n",
|
||
"ner2data = load_dataset('conll2003', 'conll2003')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fc505631",
|
||
"metadata": {},
|
||
"source": [
|
||
"紧接着，使用`tutorial-1`和`tutorial-2`中的知识，将数据集转化为`fastNLP`中的`DataSet`格式\n",
|
||
"\n",
|
||
"  完成数据集格式调整、文本序列化等操作；此处**需要`'words'`、`'seq_len'`、`'target'`三个字段**\n",
|
||
"\n",
|
||
"此外，**需要定义`NER`标签到标签序号的映射**（**词汇表`label_vocab`**），数据集中标签已经完成了序号映射\n",
|
||
"\n",
|
||
"  所以需要人工定义**`9`个标签对应之前的`9`个分类目标**；数据集说明中规定，`'O'`表示其他标签\n",
|
||
"\n",
|
||
"  **后缀`'-PER'`、`'-ORG'`、`'-LOC'`、`'-MISC'`对应人名、组织名、地名、时间等其他命名**\n",
|
||
"\n",
|
||
"  **前缀`'B-'`表示起始标签、`'I-'`表示终止标签**；例如，`'B-PER'`表示人名实体的起始标签"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "1f88cad4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Processing: 0%| | 0/4000 [00:00<?, ?it/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import sys\n",
|
||
"sys.path.append('..')\n",
|
||
"\n",
|
||
"from fastNLP import DataSet\n",
|
||
"\n",
|
||
"dataset = DataSet.from_pandas(ner2data['train'].to_pandas())[:4000]\n",
|
||
"\n",
|
||
"dataset.apply_more(lambda ins:{'words': ins['tokens'], 'seq_len': len(ins['tokens']), 'target': ins['ner_tags']}, \n",
|
||
" progress_bar=\"tqdm\")\n",
|
||
"dataset.delete_field('tokens')\n",
|
||
"dataset.delete_field('ner_tags')\n",
|
||
"dataset.delete_field('pos_tags')\n",
|
||
"dataset.delete_field('chunk_tags')\n",
|
||
"dataset.delete_field('id')\n",
|
||
"\n",
|
||
"from fastNLP import Vocabulary\n",
|
||
"\n",
|
||
"token_vocab = Vocabulary()\n",
|
||
"token_vocab.from_dataset(dataset, field_name='words')\n",
|
||
"token_vocab.index_dataset(dataset, field_name='words')\n",
|
||
"label_vocab = Vocabulary(padding=None, unknown=None)\n",
|
||
"label_vocab.add_word_lst(['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'])\n",
|
||
"\n",
|
||
"train_dataset, evaluate_dataset = dataset.split(ratio=0.85)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d9889427",
|
||
"metadata": {},
|
||
"source": [
|
||
"然后，同样使用`tutorial-3`中的知识，通过`prepare_torch_dataloader`处理数据集得到`dataloader`"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "7802a072",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from fastNLP import prepare_torch_dataloader\n",
|
||
"\n",
|
||
"train_dataloader = prepare_torch_dataloader(train_dataset, batch_size=16, shuffle=True)\n",
|
||
"evaluate_dataloader = prepare_torch_dataloader(evaluate_dataset, batch_size=16)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2bc7831b",
|
||
"metadata": {},
|
||
"source": [
|
||
"接着，**从`fastNLP.models.torch`路径下导入`BiLSTMCRF`**，初始化`BiLSTMCRF`实例和优化器\n",
|
||
"\n",
|
||
"  注意：初始化`BiLSTMCRF`时，和`CNNText`相同，**参数`embed`、`num_classes`是必须传入的**\n",
|
||
"\n",
|
||
"    隐藏层维度`hidden_size`默认`100`维，调整`150`维；退学概率默认`0.1`，调整`0.2`"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "4e12c09f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from fastNLP.models.torch import BiLSTMCRF\n",
|
||
"\n",
|
||
"model = BiLSTMCRF(embed=(len(token_vocab), 150), num_classes=len(label_vocab), \n",
|
||
" num_layers=1, hidden_size=150, dropout=0.2)\n",
|
||
"\n",
|
||
"from torch.optim import AdamW\n",
|
||
"\n",
|
||
"optimizers = AdamW(params=model.parameters(), lr=1e-3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bf30608f",
|
||
"metadata": {},
|
||
"source": [
|
||
"最后，使用`trainer`模块，集成`model`、`optimizer`、`dataloader`、`metric`训练\n",
|
||
"\n",
|
||
"  **使用`SpanFPreRecMetric`作为`NER`的评价标准**，详细请参考接下来的`tutorial-5`\n",
|
||
"\n",
|
||
"  同时，**初始化时需要添加`vocabulary`形式的标签与序号之间的映射`tag_vocab`**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "cbd6c205",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from fastNLP import Trainer, SpanFPreRecMetric\n",
|
||
"\n",
|
||
"trainer = Trainer(\n",
|
||
" model=model,\n",
|
||
" driver='torch',\n",
|
||
" device=0, # 'cuda'\n",
|
||
" n_epochs=10,\n",
|
||
" optimizers=optimizers,\n",
|
||
" train_dataloader=train_dataloader,\n",
|
||
" evaluate_dataloaders=evaluate_dataloader,\n",
|
||
" metrics={'F1': SpanFPreRecMetric(tag_vocab=label_vocab)}\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "0f8eff34",
|
||
"metadata": {},
|
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"outputs": [
|
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