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
中文无监督SimCSE Pytorch实现

A PyTorch implementation of unsupervised SimCSE SimCSE: Simple Contrastive Learning of Sentence Embeddings 1. 用法 无监督训练 python train_unsup.py ./data/ne

99 Dec 23, 2022
NLP Core Library and Model Zoo based on PaddlePaddle 2.0

PaddleNLP 2.0拥有丰富的模型库、简洁易用的API与高性能的分布式训练的能力，旨在为飞桨开发者提升文本建模效率，并提供基于PaddlePaddle 2.0的NLP领域最佳实践。

6.9k Jan 01, 2023
SAVI2I: Continuous and Diverse Image-to-Image Translation via Signed Attribute Vectors

SAVI2I: Continuous and Diverse Image-to-Image Translation via Signed Attribute Vectors [Paper] [Project Website] Pytorch implementation for SAVI2I. We

Qi Mao 44 Dec 30, 2022
MILES is a multilingual text simplifier inspired by LSBert - A BERT-based lexical simplification approach proposed in 2018. Unlike LSBert, MILES uses the bert-base-multilingual-uncased model, as well as simple language-agnostic approaches to complex word identification (CWI) and candidate ranking.

MILES Multilingual Lexical Simplifier Explore the docs » Read LSBert Paper · Report Bug · Request Feature About The Project MILES is a multilingual te

Kane 45 Oct 19, 2022
A python framework to transform natural language questions to queries in a database query language.

__ _ _ _ ___ _ __ _ _ / _` | | | |/ _ \ '_ \| | | | | (_| | |_| | __/ |_) | |_| | \__, |\__,_|\___| .__/ \__, | |_| |_| |___/

Machinalis 1.2k Dec 18, 2022
LSTC: Boosting Atomic Action Detection with Long-Short-Term Context

LSTC: Boosting Atomic Action Detection with Long-Short-Term Context This Repository contains the code on AVA of our ACM MM 2021 paper: LSTC: Boosting

Tencent YouTu Research 9 Oct 11, 2022
News-Articles-and-Essays - NLP (Topic Modeling and Clustering)

NLP T5 Project proposal Topic Modeling and Clustering of News-Articles-and-Essays Students: Nasser Alshehri Abdullah Bushnag Abdulrhman Alqurashi OVER

2 Jan 18, 2022
PyTorch Implementation of the paper Single Image Texture Translation for Data Augmentation

SITT The repo contains official PyTorch Implementation of the paper Single Image Texture Translation for Data Augmentation. Authors: Boyi Li Yin Cui T

Boyi Li 52 Jan 05, 2023
NLP, Machine learning

Netflix-recommendation-system NLP, Machine learning About Recommendation algorithms are at the core of the Netflix product. It provides their members

Harshith VH 6 Jan 12, 2022
Create a machine learning model which will predict if the mortgage will be approved or not based on 5 variables

Mortgage-Application-Analysis Create a machine learning model which will predict if the mortgage will be approved or not based on 5 variables: age, in

1 Jan 29, 2022
NL. The natural language programming language.

NL A Natural-Language programming language. Built using Codex. A few examples are inside the nl_projects directory. How it works Write any code in pur

2 Jan 17, 2022
vits chinese, tts chinese, tts mandarin

vits chinese, tts chinese, tts mandarin 史上训练最简单，音质最好的语音合成系统

AmorTX 12 Dec 14, 2022
This is an incredibly powerful calculator that is capable of many useful day-to-day functions.

Description 💻 This is an incredibly powerful calculator that is capable of many useful day-to-day functions. Such functions include solving basic ari

Jordan Leich 37 Nov 19, 2022
A curated list of efficient attention modules

awesome-fast-attention A curated list of efficient attention modules

Sepehr Sameni 891 Dec 22, 2022
Intent parsing and slot filling in PyTorch with seq2seq + attention

PyTorch Seq2Seq Intent Parsing Reframing intent parsing as a human - machine translation task. Work in progress successor to torch-seq2seq-intent-pars

Sean Robertson 159 Apr 04, 2022
Poetry PEP 517 Build Backend & Core Utilities

Poetry Core A PEP 517 build backend implementation developed for Poetry. This project is intended to be a light weight, fully compliant, self-containe

Poetry 293 Jan 02, 2023
Knowledge Management for Humans using Machine Learning & Tags

HyperTag helps humans intuitively express how they think about their files using tags and machine learning. Represent how you think using tags. Find what you look for using semantic search for your t

Ravn Tech, Inc. 166 Jan 07, 2023
Code for the Findings of NAACL 2022(Long Paper): AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks

AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks arXiv link: upcoming To be published in Findings of NA

Allen 16 Nov 12, 2022
Sentiment Classification using WSD, Maximum Entropy & Naive Bayes Classifiers

Sentiment Classification using WSD, Maximum Entropy & Naive Bayes Classifiers

Pulkit Kathuria 173 Jan 04, 2023
Official codebase for Can Wikipedia Help Offline Reinforcement Learning?

Official codebase for Can Wikipedia Help Offline Reinforcement Learning?

Machel Reid 82 Dec 19, 2022