Macadam

Macadam是一个以Tensorflow(Keras)和bert4keras为基础，专注于文本分类、序列标注和关系抽取的自然语言处理工具包。支持RANDOM、WORD2VEC、FASTTEXT、BERT、ALBERT、ROBERTA、NEZHA、XLNET、ELECTRA、GPT-2等EMBEDDING嵌入; 支持FineTune、FastText、TextCNN、CharCNN、BiRNN、RCNN、DCNN、CRNN、DeepMoji、SelfAttention、HAN、Capsule等文本分类算法; 支持CRF、Bi-LSTM-CRF、CNN-LSTM、DGCNN、Bi-LSTM-LAN、Lattice-LSTM-Batch、MRC等序列标注算法。

目录

• 安装
• 使用方式
• 数据
• TODO
• paper
• 参考

安装

pip install Macadam

# 清华镜像源
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple Macadam

使用方式

更多样例sample详情见test目录

# !/usr/bin/python
# -*- coding: utf-8 -*-
# @time    : 2020/5/8 21:33
# @author  : Mo
# @function: test trainer of bert


# 适配linux
import sys
import os
path_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
sys.path.append(path_root)
# cpu-gpu与tf.keras
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
os.environ["TF_KERAS"] = "1"
# macadam
from macadam.conf.path_config import path_root, path_tc_baidu_qa_2019, path_tc_thucnews
from macadam.tc import trainer


if __name__=="__main__":
    # bert-embedding地址, 必传
    path_embed = "D:/soft_install/dataset/bert-model/chinese_L-12_H-768_A-12"
    path_checkpoint = path_embed + "/bert_model.ckpt"
    path_config = path_embed + "/bert_config.json"
    path_vocab = path_embed + "/vocab.txt"

    # 训练/验证数据地址, 必传
    # path_train = os.path.join(path_tc_thucnews, "train.json")
    # path_dev = os.path.join(path_tc_thucnews, "dev.json")
    path_train = os.path.join(path_tc_baidu_qa_2019, "train.json")
    path_dev = os.path.join(path_tc_baidu_qa_2019, "dev.json")

    # 网络结构, 嵌入模型, 大小写都可以, 必传
    # 网络模型架构(Graph), "FineTune", "FastText", "TextCNN", "CharCNN",
    # "BiRNN", "RCNN", "DCNN", "CRNN", "DeepMoji", "SelfAttention", "HAN", "Capsule"
    network_type = "TextCNN"
    # 嵌入(embedding)类型, "ROOBERTA", "ELECTRA", "RANDOM", "ALBERT", "XLNET", "NEZHA", "GPT2", "WORD", "BERT"
    embed_type = "BERT"
    # token级别, 一般为"char", 只有random和word的embedding时存在"word"
    token_type = "CHAR"
    # 任务, "TC"(文本分类), "SL"(序列标注), "RE"(关系抽取)
    task = "TC"
    
    # 模型保存目录, 必传
    path_model_dir = os.path.join(path_root, "data", "model", network_type)
    # 开始训练, 可能前几轮loss较大acc较低, 后边会好起来
    trainer(path_model_dir, path_embed, path_train, path_dev, path_checkpoint, path_config, path_vocab,
            network_type=network_type, embed_type=embed_type, token_type=token_type, task=task)
    mm = 0

数据

• ner_clue_2020, CLUENER2020中文细粒度命名实体识别
• ner_people_1998, 《人民日报》标注语料库中的语料, 1998.01
• baidu_qa_2019, 百度知道问答语料
• thucnews, 新浪新闻RSS订阅频道2005-2011年间的历史数据筛

#TODO

• 文本分类TC(TextGCN)
• 序列标注SL(MRC)
• 关系抽取RE
• 嵌入embed(xlnet)

paper

文本分类(TC, text-classification)

• FastText: Bag of Tricks for Efficient Text Classification
• TextCNN： Convolutional Neural Networks for Sentence Classification
• charCNN-kim： Character-Aware Neural Language Models
• charCNN-zhang: Character-level Convolutional Networks for Text Classification
• TextRNN： Recurrent Neural Network for Text Classification with Multi-Task Learning
• RCNN： Recurrent Convolutional Neural Networks for Text Classification
• DCNN: A Convolutional Neural Network for Modelling Sentences
• DPCNN: Deep Pyramid Convolutional Neural Networks for Text Categorization
• VDCNN: Very Deep Convolutional Networks
• CRNN: A C-LSTM Neural Network for Text Classification
• DeepMoji: Using millions of emojio ccurrences to learn any-domain represent ations for detecting sentiment, emotion and sarcasm
• SelfAttention: Attention Is All You Need
• HAN: Hierarchical Attention Networks for Document Classification
• CapsuleNet: Dynamic Routing Between Capsules
• Transformer(encode or decode): Attention Is All You Need
• Bert: BERT: Pre-trainingofDeepBidirectionalTransformersfor LanguageUnderstanding
• Xlnet: XLNet: Generalized Autoregressive Pretraining for Language Understanding
• Albert: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS
• RoBERTa: RoBERTa: A Robustly Optimized BERT Pretraining Approach
• ELECTRA: ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
• TextGCN: Graph Convolutional Networks for Text Classification

序列标注(SL, sequence-labeling)

• CRF: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
• Bi-LSTM-CRF: Bidirectional LSTM-CRF Models for Sequence Tagging
• CNN-LSTM: End-to-endSequenceLabelingviaBi-directionalLSTM-CNNs-CRF
• DGCNN: Multi-Scale Context Aggregation by Dilated Convolutions
• Bi-LSTM-LAN: Hierarchically-Refined Label Attention Network for Sequence Labeling
• LATTICE-LSTM-BATCH: An Encoding Strategy Based Word-Character LSTM for Chinese NER
• MRC: An Encoding Strategy Based Word-Character LSTM for Chinese NER

参考

• keras与tensorflow版本对应: https://docs.floydhub.com/guides/environments/
• bert4keras: https://github.com/bojone/bert4keras
• Kashgari: https://github.com/BrikerMan/Kashgari
• fastNLP: https://github.com/fastnlp/fastNLP
• HanLP: https://github.com/hankcs/HanLP

*希望对你有所帮助!