Chinese named entity recognization (bert/roberta/macbert/bert_wwm with Keras)

Related tags

Text Data & NLPBERT-NER-Keras
Overview

Chinese named entity recognization (bert/roberta/macbert/bert_wwm with Keras)

Project Structure

./
├── DataProcess
│   ├── __pycache__
│   ├── convert2bio.py
│   ├── convert_jsonl.py
│   ├── handle_numbers.py
│   ├── load_data.py
│   └── statistic.py
├── README.md
├── __pycache__
├── chinese_L-12_H-768_A-12                                    BERT权重
│   ├── bert_config.json
│   ├── bert_model.ckpt.data-00000-of-00001
│   ├── bert_model.ckpt.index
│   ├── bert_model.ckpt.meta
│   └── vocab.txt
├── chinese_bert_wwm                                           BERT_wwm权重
│   ├── bert_config.json
│   ├── bert_model.ckpt.data-00000-of-00001
│   ├── bert_model.ckpt.index
│   ├── bert_model.ckpt.meta
│   └── vocab.txt
├── chinese_macbert_base                                       macBERT权重
│   ├── chinese_macbert_base.ckpt.data-00000-of-00001
│   ├── chinese_macbert_base.ckpt.index
│   ├── chinese_macbert_base.ckpt.meta
│   ├── macbert_base_config.json
│   └── vocab.txt
├── chinese_roberta_wwm_ext_L-12_H-768_A-12                    roberta权重
│   ├── bert_config.json
│   ├── bert_model.ckpt.data-00000-of-00001
│   ├── bert_model.ckpt.index
│   ├── bert_model.ckpt.meta
│   └── vocab.txt
├── config                                                     
│   ├── __pycache__
│   ├── config.py                                              配置文件
│   └── pulmonary_label2id.json                                label id
├── data                                                       数据集
│   ├── pulmonary.test
│   ├── pulmonary.train
│   └── sict_train.txt
├── environment.yaml                                           conda环境配置文件
├── evaluate.py
├── generator_train.py
├── keras_bert                                                 keras_bert（可pip下）
├── keras_contrib                                              keras_contrib（可pip下）
├── log                                                        训练nohup日志
│   ├── chinese_L-12_H-768_A-12.out
│   ├── chinese_macbert_base.out
│   ├── chinese_roberta_wwm_ext_L-12_H-768_A-12.out
│   └── electra_180g_base.out
├── model.py                                                   模型构建文件
├── models                                                     保存的模型权重
│   ├── pulmonary_chinese_L-12_H-768_A-12_ner.h5
│   ├── pulmonary_chinese_bert_wwm_ner.h5
│   ├── pulmonary_chinese_macbert_base_ner.h5
│   └── pulmonary_chinese_roberta_wwm_ext_L-12_H-768_A-12_ner.h5
├── predict.py                                                 预测
├── report                                                     模型实体F1评估报告
│   ├── pulmonary_chinese_L-12_H-768_A-12_evaluate.txt
│   ├── pulmonary_chinese_L-12_H-768_A-12_predict.json
│   ├── pulmonary_chinese_bert_wwm_evaluate.txt
│   ├── pulmonary_chinese_bert_wwm_predict.json
│   ├── pulmonary_chinese_macbert_base_evaluate.txt
│   ├── pulmonary_chinese_macbert_base_predict.json
│   ├── pulmonary_chinese_roberta_wwm_ext_L-12_H-768_A-12_evaluate.txt
│   └── pulmonary_chinese_roberta_wwm_ext_L-12_H-768_A-12_predict.json
├── requirements.txt                                           pip环境
├── test.py                                                    
├── train.py                                                   训练
└── utils                                                      
    ├── FGM.py                                                 FGM对抗
    ├── __pycache__
    └── path.py                                                所有路径

56 directories, 193 files

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：

  • 字与标签之间用空格（"\ "）隔开
  • 其中句子与句子之间使用空行隔开

Steps

  1. 替换数据集
  2. 使用DataProcess/load_data.py生成label2id.txt文件
  3. 修改config/config.py中的MAX_SEQ_LEN（超过截断，少于填充，最好设置训练集、测试集中最长句子作为MAX_SEQ_LEN）
  4. 下载权重，放到项目中
  5. 修改public/path.py中的地址
  6. 根据需要修改model.py模型结构
  7. 修改config/config.py的参数
  8. 训练前debug看下input_train_labels,result_train对不对，input_train_types全是0
  9. 训练

Model

BERT

roberta

macBERT

BERT_wwm

Train

运行train.py

Evaluate

运行evaluate/f1_score.py

BERT

           precision    recall  f1-score   support

     SIGN     0.6651    0.7354    0.6985       189
  ANATOMY     0.8333    0.8409    0.8371       220
 DIAMETER     1.0000    1.0000    1.0000        16
  DISEASE     0.4915    0.6744    0.5686        43
 QUANTITY     0.8837    0.9157    0.8994        83
TREATMENT     0.3571    0.5556    0.4348         9
  DENSITY     1.0000    1.0000    1.0000         8
    ORGAN     0.4500    0.6923    0.5455        13
LUNGFIELD     1.0000    0.5000    0.6667         6
    SHAPE     0.5714    0.5714    0.5714         7
   NATURE     1.0000    1.0000    1.0000         6
 BOUNDARY     1.0000    0.6250    0.7692         8
   MARGIN     0.8333    0.8333    0.8333         6
  TEXTURE     1.0000    0.8571    0.9231         7

micro avg     0.7436    0.7987    0.7702       621
macro avg     0.7610    0.7987    0.7760       621

roberta

           precision    recall  f1-score   support

  ANATOMY     0.8624    0.8545    0.8584       220
  DENSITY     0.8000    1.0000    0.8889         8
     SIGN     0.7347    0.7619    0.7481       189
 QUANTITY     0.8977    0.9518    0.9240        83
  DISEASE     0.5690    0.7674    0.6535        43
 DIAMETER     1.0000    1.0000    1.0000        16
TREATMENT     0.3333    0.5556    0.4167         9
 BOUNDARY     1.0000    0.6250    0.7692         8
LUNGFIELD     1.0000    0.6667    0.8000         6
   MARGIN     0.8333    0.8333    0.8333         6
  TEXTURE     1.0000    0.8571    0.9231         7
    SHAPE     0.5714    0.5714    0.5714         7
   NATURE     1.0000    1.0000    1.0000         6
    ORGAN     0.6250    0.7692    0.6897        13

micro avg     0.7880    0.8261    0.8066       621
macro avg     0.8005    0.8261    0.8104       621

macBERT

           precision    recall  f1-score   support

  ANATOMY     0.8773    0.8773    0.8773       220
     SIGN     0.6538    0.7196    0.6851       189
  DISEASE     0.5893    0.7674    0.6667        43
 QUANTITY     0.9070    0.9398    0.9231        83
    ORGAN     0.5882    0.7692    0.6667        13
  TEXTURE     1.0000    0.8571    0.9231         7
 DIAMETER     1.0000    1.0000    1.0000        16
TREATMENT     0.3750    0.6667    0.4800         9
LUNGFIELD     1.0000    0.5000    0.6667         6
    SHAPE     0.4286    0.4286    0.4286         7
   NATURE     1.0000    1.0000    1.0000         6
  DENSITY     1.0000    1.0000    1.0000         8
 BOUNDARY     1.0000    0.6250    0.7692         8
   MARGIN     0.8333    0.8333    0.8333         6

micro avg     0.7697    0.8180    0.7931       621
macro avg     0.7846    0.8180    0.7977       621

BERT_wwm

           precision    recall  f1-score   support

  DISEASE     0.5667    0.7907    0.6602        43
  ANATOMY     0.8676    0.8636    0.8656       220
 QUANTITY     0.8966    0.9398    0.9176        83
     SIGN     0.7358    0.7513    0.7435       189
LUNGFIELD     1.0000    0.6667    0.8000         6
TREATMENT     0.3571    0.5556    0.4348         9
 DIAMETER     0.9375    0.9375    0.9375        16
 BOUNDARY     1.0000    0.6250    0.7692         8
  TEXTURE     1.0000    0.8571    0.9231         7
   MARGIN     0.8333    0.8333    0.8333         6
    ORGAN     0.5882    0.7692    0.6667        13
  DENSITY     1.0000    1.0000    1.0000         8
   NATURE     1.0000    1.0000    1.0000         6
    SHAPE     0.5000    0.5714    0.5333         7

micro avg     0.7889    0.8245    0.8063       621
macro avg     0.8020    0.8245    0.8104       621

Predict

运行predict/predict_bio.py

Owner
GitHub Repository
Official implementation of Meta-StyleSpeech and StyleSpeech

Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation Dongchan Min, Dong Bok Lee, Eunho Yang, and Sung Ju Hwang This is an official code

min95 169 Jan 05, 2023
A simple Streamlit App to classify swahili news into different categories.

Swahili News Classifier Streamlit App A simple app to classify swahili news into different categories. Installation Install all streamlit requirements

Davis David 4 May 01, 2022
Transformer-based Text Auto-encoder (T-TA) using TensorFlow 2.

T-TA (Transformer-based Text Auto-encoder) This repository contains codes for Transformer-based Text Auto-encoder (T-TA, paper: Fast and Accurate Deep

Jeong Ukjae 13 Dec 13, 2022
SAINT PyTorch implementation

SAINT-pytorch A Simple pyTorch implementation of "Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing" based on https://arx

Arshad Shaikh 63 Dec 25, 2022
This is the main repository of open-sourced speech technology by Huawei Noah's Ark Lab.

Speech-Backbones This is the main repository of open-sourced speech technology by Huawei Noah's Ark Lab. Grad-TTS Official implementation of the Grad-

HUAWEI Noah's Ark Lab 295 Jan 07, 2023
Neural-Machine-Translation - Implementation of revolutionary machine translation models

Neural Machine Translation Framework: PyTorch Repository contaning my implementa

Utkarsh Jain 1 Feb 17, 2022
Fuzzy String Matching in Python

FuzzyWuzzy Fuzzy string matching like a boss. It uses Levenshtein Distance to calculate the differences between sequences in a simple-to-use package.

SeatGeek 8.8k Jan 01, 2023
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

ALBERT ***************New March 28, 2020 *************** Add a colab tutorial to run fine-tuning for GLUE datasets. ***************New January 7, 2020

Google Research 3k Dec 26, 2022
This code is the implementation of Text Emotion Recognition (TER) with linguistic features

APSIPA-TER This code is the implementation of Text Emotion Recognition (TER) with linguistic features. The network model is BERT with a pretrained mod

kenro515 1 Feb 08, 2022
Collection of scripts to pinpoint obfuscated code

Obfuscation Detection (v1.0) Author: Tim Blazytko Automatically detect control-flow flattening and other state machines Description: Scripts and binar

Tim Blazytko 230 Nov 26, 2022
Create a semantic search engine with a neural network (i.e. BERT) whose knowledge base can be updated

Create a semantic search engine with a neural network (i.e. BERT) whose knowledge base can be updated. This engine can later be used for downstream tasks in NLP such as Q&A, summarization, generation

Diego 1 Mar 20, 2022
FireFlyer Record file format, writer and reader for DL training samples.

FFRecord The FFRecord format is a simple format for storing a sequence of binary records developed by HFAiLab, which supports random access and Linux

77 Jan 04, 2023
端到端的长本文摘要模型（法研杯2020司法摘要赛道）

端到端的长文本摘要模型（法研杯2020司法摘要赛道）

苏剑林(Jianlin Su) 334 Jan 08, 2023
translate using your voice

speech-to-text-translator Usage translate using your voice description this project makes translating a word easy, all you have to do is speak and...

1 Oct 18, 2021
Linking data between GBIF, Biodiverse, and Open Tree of Life

GBIF-biodiverse-OpenTree Linking data between GBIF, Biodiverse, and Open Tree of Life The python scripts will rely on opentree and Dendropy. To set up

2 Oct 03, 2022
KLUE-baseline contains the baseline code for the Korean Language Understanding Evaluation (KLUE) benchmark.

KLUE Baseline Korean(한국어) KLUE-baseline contains the baseline code for the Korean Language Understanding Evaluation (KLUE) benchmark. See our paper fo

74 Dec 13, 2022
A Transformer Implementation that is easy to understand and customizable.

Simple Transformer I've written a series of articles on the transformer architecture and language models on Medium. This repository contains an implem

Naoki Shibuya 4 Jan 20, 2022
EasyTransfer is designed to make the development of transfer learning in NLP applications easier.

EasyTransfer is designed to make the development of transfer learning in NLP applications easier. The literature has witnessed the success of applying

Alibaba 819 Jan 03, 2023
Code Implementation of "Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction".

Span-ASTE: Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction ***** New March 31th, 2022: Scikit-Style API for Easy Usage *****

Chia Yew Ken 111 Dec 23, 2022
Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"

T5: Text-To-Text Transfer Transformer The t5 library serves primarily as code for reproducing the experiments in Exploring the Limits of Transfer Lear

Google Research 4.6k Jan 01, 2023