Chinese Named Entity Recognization (BiLSTM with PyTorch)

Overview

BiLSTM-CRF for Name Entity Recognition PyTorch version

A PyTorch implemention of Bi-LSTM-CRF model for Chinese Named Entity Recognition.

使用 PyTorch 实现 Bi-LSTM-CRF 模型,用来完成中文命名实体识别任务。

Dataset

三甲医院肺结节数据集,20000+字,BIO格式,形如:

中	B-ORG
共	I-ORG
中	I-ORG
央	I-ORG
致	O
中	B-ORG
国	I-ORG
致	I-ORG
公	I-ORG
党	I-ORG
十	I-ORG
一	I-ORG
大	I-ORG
的	O
贺	O
词	O

ATTENTION: 在处理自己数据集的时候需要注意:

  • 字与标签之间用tab("\t")隔开
  • 其中句子与句子之间使用空行隔开
  • 文件最后以两个换行结束

训练数据和测试数据存放在 datasets 目录下,在 data.py 中有具体读取数据的代码。

Project Structure

./
├── README.md
├── __pycache__
├── config.py                       模型超参数配置
├── data.py                         数据转换 加载词表
├── datasets                        数据集
│   ├── dct.pkl                     词表
│   ├── test_data.txt               训练集
│   └── train_data.txt              测试集
├── evaluate.py                     评估模型
├── log                             nohup训练输出日志
│   └── nohup.out
├── main.py                         训练
├── metric.py                       计算f1评估指标
├── model.py                        模型构建
├── path.py                         所有路径
├── predict.py                      实体抽取预测
├── pulmonary_label2id.json         所有label
├── requirements.txt                pip包
├── statistic.py                    统计最长句子长度
├── trainer.py                      训练器构建
├── utils
│   ├── __init__.py
│   ├── __pycache__
│   ├── earlystopping.py            早停工具
│   └── load_data.py                构建label2id工具
└── weights                         权重
    └── xxx.pth

6 directories, 32 files

Steps

  1. 替换数据集
  2. 修改path.py中的地址
  3. 修改data.py中的文本最长长度SEQUENCE_MAX_LENGTH
  4. 使用utils/load_data.py生成label2id.txt文件,将其中的内容填到data.py的TAG_MAP中。注意:序号必须从1开始
  5. 修改data.py中的len_tag_dict,值等于TAG_MAP的最大值
  6. 修改data.py中build_dict(corpus, num_words = 6000)的num_words,为词表长度,词表按词频生成,超过num_words的将被忽略
  7. 根据需要修改model.py模型结构
  8. 修改config.py的超参数
  9. 训练前debug看下main.py的train_dl,train_ds对不对
  10. 训练,注意,必须传入验证集数据,如果没有验证集,就把测试集传入

Model

模型的结构大致如下,这里 BiLSTM 层的输入为字向量。Bi-LSTM 对每个字进行编码,然后经过 softmax 后,每个词对应一个长度为 len(tags) 的向量,在不使用 CRF 的方法中,就取这个向量中最大的值的位置作为预测的 tag 了,可能会不符合BIO标签的规则。

这里每个词的对应的向量作为 CRF 的输入,CRF 会最大化整个序列的概率,学习BIO的规则,保证输出格式是合法的。

在 PyTorch 中没有 CRF 层,这里使用了 AllenNLP 中的 CRF 实现

Config

在条件随机场中存在一个状态转移矩阵,在这里此状态转移矩阵就包含的是不同 tag 之间转移的概率。但并不是任何状态之间都能进行转移的,比如 B-PER 就不可能转移到 I-LOC 上。condtraints 就用来指明那些状态之间可以转移,这样将极大地减少可能性,在训练和解码过程中,能够大幅提升速度。请务必指定此参数,其创建方法见 data.py

Train

runfile('/Volumes/Riesling/TRAIN/Torch-base/src/BiLSTM_CRF/main.py', wdir='/Volumes/Riesling/TRAIN/Torch-base/src/BiLSTM_CRF')
-----------------------------------------------------------------------------------------------------------
               Layer (type)                                    Input Shape         Param #     Tr. Param #
===========================================================================================================
                Embedding-1                                      [100, 32]         180,300         180,300
                     LSTM-2     [100, 32, 300], [2, 32, 256], [2, 32, 256]       1,142,784       1,142,784
                     LSTM-3     [100, 32, 512], [2, 32, 128], [2, 32, 128]         657,408         657,408
                  Dropout-4                                 [100, 32, 256]               0               0
                   Linear-5                                 [32, 100, 256]           7,453           7,453
   ConditionalRandomField-6            [32, 100, 29], [32, 100], [32, 100]           1,860             899
===========================================================================================================
Total params: 1,989,805
Trainable params: 1,988,844
Non-trainable params: 961
-----------------------------------------------------------------------------------------------------------
training on  cpu
epoch [1]: 100%|██████████| 29/29 [00:26<00:00,  1.09it/s, loss=42.7]
2021-12-17 14:52:37,207 - epoch 1 - loss: 42.6906 acc: 0.2409 - test_acc: 0.1804
epoch [2]: 100%|██████████| 29/29 [00:28<00:00,  1.02it/s, loss=17.6]
2021-12-17 14:53:19,099 - epoch 2 - loss: 17.5649 acc: 0.6874 - test_acc: 0.6554
epoch [3]: 100%|██████████| 29/29 [00:32<00:00,  1.10s/it, loss=10.7]
2021-12-17 14:54:01,811 - epoch 3 - loss: 10.6861 acc: 0.7702 - test_acc: 0.6908
epoch [4]: 100%|██████████| 29/29 [00:31<00:00,  1.07s/it, loss=7.76]
2021-12-17 14:54:43,429 - epoch 4 - loss: 7.7551 acc: 0.8336 - test_acc: 0.7633
epoch [5]: 100%|██████████| 29/29 [00:29<00:00,  1.03s/it, loss=5.89]
2021-12-17 14:55:23,635 - epoch 5 - loss: 5.8919 acc: 0.8689 - test_acc: 0.7907
epoch [6]: 100%|██████████| 29/29 [00:29<00:00,  1.00s/it, loss=4.68]
2021-12-17 14:56:01,725 - epoch 6 - loss: 4.6774 acc: 0.8798 - test_acc: 0.7858
epoch [7]: 100%|██████████| 29/29 [00:38<00:00,  1.32s/it, loss=4.03]
2021-12-17 14:56:53,304 - epoch 7 - loss: 4.0329 acc: 0.9052 - test_acc: 0.7858
epoch [8]: 100%|██████████| 29/29 [00:35<00:00,  1.21s/it, loss=3.34]
2021-12-17 14:57:41,694 - epoch 8 - loss: 3.3428 acc: 0.9118 - test_acc: 0.8180
epoch [9]: 100%|██████████| 29/29 [00:38<00:00,  1.32s/it, loss=2.98]
2021-12-17 14:58:29,565 - epoch 9 - loss: 2.9814 acc: 0.9217 - test_acc: 0.7762
epoch [10]: 100%|██████████| 29/29 [00:36<00:00,  1.26s/it, loss=2.53]
2021-12-17 14:59:15,809 - epoch 10 - loss: 2.5263 acc: 0.9298 - test_acc: 0.7971

Evaluate

metric_test = evaluate(model, test_dl, device, verbose = True)
print(metric_test.report())

测试集上的表现:

predicting training set: 100%|██████████| 29/29 [00:08<00:00,  3.46it/s]
Train set
            ANATOMY     SIGN        QUANTITY    ORGAN       TEXTURE     DISEASE     DENSITY     BOUNDARY    MARGIN      DIAMETER    SHAPE       TREATMENT   LUNGFIELD   NATURE      
precision   0.92        0.93        0.97        0.86        0.93        0.95        0.90        1.00        1.00        0.95        0.88        0.91        1.00        1.00        
recall      0.92        0.93        0.94        0.79        0.88        0.93        0.90        1.00        1.00        0.95        0.77        0.89        0.92        1.00        
f1          0.92        0.93        0.95        0.82        0.90        0.94        0.90        1.00        1.00        0.95        0.82        0.90        0.96        1.00        
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
precision   0.93
recall      0.92
f1          0.93

predicting test set: 100%|██████████| 5/5 [00:01<00:00,  3.85it/s]
Test set
            ANATOMY     SIGN        QUANTITY    ORGAN       TEXTURE     DISEASE     DENSITY     BOUNDARY    MARGIN      DIAMETER    SHAPE       TREATMENT   LUNGFIELD   NATURE      
precision   0.83        0.72        0.87        0.69        0.86        0.72        1.00        0.75        0.83        1.00        0.71        0.56        0.83        1.00        
recall      0.81        0.76        0.85        0.64        1.00        0.61        1.00        1.00        0.83        1.00        0.83        0.38        1.00        0.86        
f1          0.82        0.74        0.86        0.67        0.92        0.66        1.00        0.86        0.83        1.00        0.77        0.45        0.91        0.92        
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
precision   0.79
recall      0.78
f1          0.79

Predict

model = BiLSTM_CRF(Config())
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.load_state_dict(torch.load(path.weights_path + path.saved_model_name, map_location = torch.device(device)))
sentence = '右横隔见数枚肿大淋巴结较前退缩,现显示不清(4:9)。左肺下叶后基底段见不规则结节灶较前稍缩小,现最大截面约1.1cm*0.9cm(7.15),边界尚清;右肺中下叶见散在数枚直径小于0.5cm的模糊小结节影与前大致相仿(7:18、30、36);双肺尖见少许斑片、条索影较前无明显变化,余肺野未见明显实质性病变。'
tags = predict_sentence_tags(model, sentence, dct, device)
print(get_entity(sentence, tags))

预测结果:

{
    'ANATOMY': {'9mm', '右肺中下叶', '左肺下叶后基底段', '右横隔', '双肺尖'}, 
    'SIGN': {'明显实质性病变', '肿大淋巴结较前退缩', '斑片、条索影较前无明显变化', '较前稍缩小', '不规则结节灶', '数枚', '小结节影'}, 
    'DIAMETER': {'1.1*'}, 
    'BOUNDARY': {'尚清'}, 
    'QUANTITY': {'少许', '未见'}
}
Simple, Fast, Powerful and Easily extensible python package for extracting patterns from text, with over than 60 predefined Regular Expressions.

patterns-finder Simple, Fast, Powerful and Easily extensible python package for extracting patterns from text, with over than 60 predefined Regular Ex

22 Dec 19, 2022
Exploration of BERT-based models on twitter sentiment classifications

twitter-sentiment-analysis Explore the relationship between twitter sentiment of Tesla and its stock price/return. Explore the effect of different BER

Sammy Cui 2 Oct 02, 2022
BeautyNet is an AI powered model which can tell you whether you're beautiful or not.

BeautyNet BeautyNet is an AI powered model which can tell you whether you're beautiful or not. Download Dataset from here:https://www.kaggle.com/gpios

Ansh Gupta 0 May 06, 2022
GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates

GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates Vibhor Agarwal, Sagar Joglekar, Anthony P. Young an

Vibhor Agarwal 2 Jun 30, 2022
Dual languaged (rus+eng) tool for packing and unpacking archives of Silky Engine.

SilkyArcTool English Dual languaged (rus+eng) GUI tool for packing and unpacking archives of Silky Engine. It is not the same arc as used in Ai6WIN. I

Tester 5 Sep 15, 2022
a test times augmentation toolkit based on paddle2.0.

Patta Image Test Time Augmentation with Paddle2.0! Input | # input batch of images / / /|\ \ \ # apply

AgentMaker 110 Dec 03, 2022
The official repository of the ISBI 2022 KNIGHT Challenge

KNIGHT The official repository holding the data for the ISBI 2022 KNIGHT Challenge About The KNIGHT Challenge asks teams to develop models to classify

Nicholas Heller 4 Jan 22, 2022
Malaya-Speech is a Speech-Toolkit library for bahasa Malaysia, powered by Deep Learning Tensorflow.

Malaya-Speech is a Speech-Toolkit library for bahasa Malaysia, powered by Deep Learning Tensorflow. Documentation Proper documentation is available at

HUSEIN ZOLKEPLI 151 Jan 05, 2023
Training code of Spatial Time Memory Network. Semi-supervised video object segmentation.

Training-code-of-STM This repository fully reproduces Space-Time Memory Networks Performance on Davis17 val set&Weights backbone training stage traini

haochen wang 128 Dec 11, 2022
A Python package implementing a new model for text classification with visualization tools for Explainable AI :octocat:

A Python package implementing a new model for text classification with visualization tools for Explainable AI 🍣 Online live demos: http://tworld.io/s

Sergio Burdisso 285 Jan 02, 2023
Long text token classification using LongFormer

Long text token classification using LongFormer

abhishek thakur 161 Aug 07, 2022
The projects lets you extract glossary words and their definitions from a given piece of text automatically using NLP techniques

Unsupervised technique to Glossary and Definition Extraction Code Files GPT2-DefinitionModel.ipynb - GPT-2 model for definition generation. Data_Gener

Prakhar Mishra 28 May 25, 2021
BookNLP, a natural language processing pipeline for books

BookNLP BookNLP is a natural language processing pipeline that scales to books and other long documents (in English), including: Part-of-speech taggin

654 Jan 02, 2023
Natural Language Processing library built with AllenNLP 🌲🌱

Custom Natural Language Processing with big and small models 🌲🌱

Recognai 65 Sep 13, 2022
Lumped-element impedance calculator and frequency-domain plotter.

fastZ: Lumped-Element Impedance Calculator fastZ is a small tool for calculating and visualizing electrical impedance in Python. Features include: Sup

Wesley Hileman 47 Nov 18, 2022
Training open neural machine translation models

Train Opus-MT models This package includes scripts for training NMT models using MarianNMT and OPUS data for OPUS-MT. More details are given in the Ma

Language Technology at the University of Helsinki 167 Jan 03, 2023
Arabic speech recognition, classification and text-to-speech.

klaam Arabic speech recognition, classification and text-to-speech using many advanced models like wave2vec and fastspeech2. This repository allows tr

ARBML 177 Dec 27, 2022
Scene Text Retrieval via Joint Text Detection and Similarity Learning

This is the code of "Scene Text Retrieval via Joint Text Detection and Similarity Learning". For more details, please refer to our CVPR2021 paper.

79 Nov 29, 2022
Code for PED: DETR For (Crowd) Pedestrian Detection

Code for PED: DETR For (Crowd) Pedestrian Detection

36 Sep 13, 2022
The swas programming language

The Swas programming language This is a language that was made for fun. Installation Step 0: Make sure you have python installed Step 1. Clone this re

Swas.py 19 Jul 18, 2022