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easytext's Introduction

easytext

让自然语言模型训练更容易

安装

pip install easytext-nlp

注意: pip repository 中存在一个 easytext,不是本项目,不要安装错了。 也就是 pip install easytext 并非安装的是本项目。

构建训练

训练的具体构建流程如下:

docs: 文档

mkdocs serve 启动文档服务。

其中:

  • uml: 文件夹下是 uml 设计文档,使用 "Visual Paradigm" 工具打开
  • 开发计划: 列出了已经开发出的功能和特性
  • 相关模型说明以及论文文档

ner: 命名实体识别

相关模型以及对应的配置文件

序号 模型描述 配置文件/config_file_path
1 rnn + crf data/ner/rnn_with_crf/config/config.json
2 rnn + softmax data/ner/rnn_with_crf/config/config_without_crf.json
3 bert + crf data/ner/bert_with_crf/config/config.json
4 bert + softmax data/ner/bert_with_crf/config/config_without_crf.json
5 bert + rnn + crf data/ner/bert_rnn_with_crf/config/config.json
6 bert + rnn + softmax data/ner/bert_rnn_with_crf/config/config_without_crf.json

启动命令

python -m ner.launcher --config {config_file_path}

  • config_file_path - 参考 "相关模型以及对应的配置文件" 中 "配置文件/config_file_path" 列内容。

event: 事件识别

事件识别以及事件要素识别模型。

event_detection_without_tirgger

ACL 2019 论文,忽略触发词的事件类型检测。

更加详细说明,请参考: docs/docs/event/event_detection_without_trigger.md

acsa

属性级情感分析,baseline 模型:

ATAELstm

2016 emnlp 基于 Attention Lstm 的 属性级情感分析模型.

  • 相关论文参考: docs/docs/acsa/相关文章及论文/2016-emnlp-Attention-based LSTM for Aspect-level Sentiment Classification.pdf

easytext's People

Contributors

cjopengler avatar

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