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https://github.com/GaoQ1/rasa_nlu_gq

turn natural language into structured data(支持中文，自定义了N种模型，支持不同的场景和任务)
https://github.com/GaoQ1/rasa_nlu_gq

bert bilstm-idcnn jieba natural-language nlp nlu rasa rasa-nlu rasa-nlu-gao tensorflow

Last synced: 30 days ago
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turn natural language into structured data(支持中文，自定义了N种模型，支持不同的场景和任务)

Host: GitHub
URL: https://github.com/GaoQ1/rasa_nlu_gq
Owner: GaoQ1
License: apache-2.0
Created: 2018-10-10T02:49:47.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2023-03-24T23:47:53.000Z (over 1 year ago)
Last Synced: 2024-03-14T16:22:56.601Z (4 months ago)
Topics: bert, bilstm-idcnn, jieba, natural-language, nlp, nlu, rasa, rasa-nlu, rasa-nlu-gao, tensorflow
Language: Python
Homepage:
Size: 5.66 MB
Stars: 302
Watchers: 21
Forks: 98
Open Issues: 11
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.rst
- License: LICENSE.txt

Lists

awesome-bert - GaoQ1/rasa_nlu_gq

README

# Rasa NLU GQ
Rasa NLU (Natural Language Understanding) 是一个自然语义理解的工具，举个官网的例子如下：

> *"I'm looking for a Mexican restaurant in the center of town"*

And returning structured data like:

```
intent: search_restaurant
entities:
- cuisine : Mexican
- location : center
```

## Introduction
原来的项目在分支0.2.7上，可自由切换。这个版本的修改是基于最新版本的rasa，将原来rasa_nlu_gao里面的component修改了下，并没有做新增。并且之前做法有些累赘，并不需要在rasa源码中修改。可以直接将原来的component当做addon加载，继承最新版本的rasa，可实时更新。

## New features
目前新增的特性如下（请下载最新的rasa-nlu-gao版本）(edit at 2019.06.24)：
- 新增了实体识别的模型，一个是bilstm+crf，一个是idcnn+crf膨胀卷积模型，对应的yml文件配置如下：
```
language: "zh"

pipeline:
- name: "JiebaTokenizer"
- name: "CountVectorsFeaturizer"
token_pattern: "(?u)\b\w+\b"
- name: "EmbeddingIntentClassifier"
- name: "rasa_nlu_gao.extractors.bilstm_crf_entity_extractor.BilstmCRFEntityExtractor"
lr: 0.001
char_dim: 100
lstm_dim: 100
batches_per_epoch: 10
seg_dim: 20
num_segs: 4
batch_size: 200
tag_schema: "iobes"
model_type: "bilstm" # 模型支持两种idcnn膨胀卷积模型或bilstm双向lstm模型
clip: 5
optimizer: "adam"
dropout_keep: 0.5
steps_check: 100
```
- 新增了jieba词性标注的模块，可以方便识别名字，地名，机构名等等jieba能够支持的词性，对应的yml文件配置如下：
```
language: "zh"

pipeline:
- name: "JiebaTokenizer"
- name: "CRFEntityExtractor"
- name: "rasa_nlu_gao.extractors.jieba_pseg_extractor.JiebaPsegExtractor"
part_of_speech: ["nr", "ns", "nt"]
- name: "CountVectorsFeaturizer"
OOV_token: oov
token_pattern: "(?u)\b\w+\b"
- name: "EmbeddingIntentClassifier"
```
- 新增了根据实体反向修改意图，对应的文件配置如下：
```
language: "zh"

pipeline:
- name: "JiebaTokenizer"
- name: "CRFEntityExtractor"
- name: "JiebaPsegExtractor"
- name: "CountVectorsFeaturizer"
OOV_token: oov
token_pattern: '(?u)\b\w+\b'
- name: "EmbeddingIntentClassifier"
- name: "rasa_nlu_gao.classifiers.entity_edit_intent.EntityEditIntent"
entity: ["nr"]
intent: ["enter_data"]
min_confidence: 0
```
- 新增了bert模型提取词向量特征，对应的配置文件如下：
```
language: "zh"

pipeline:
- name: "JiebaTokenizer"
- name: "rasa_nlu_gao.featurizers.bert_vectors_featurizer.BertVectorsFeaturizer"
ip: '127.0.0.1'
port: 5555
port_out: 5556
show_server_config: True
timeout: 10000
- name: "EmbeddingIntentClassifier"
- name: "CRFEntityExtractor"
```
- 新增了对CPU和GPU的利用率的配置，主要是`EmbeddingIntentClassifier`和`ner_bilstm_crf`这两个使用到tensorflow的组件，配置如下（当然config_proto可以不配置，默认值会将资源全部利用）：
```
language: "zh"

pipeline:
- name: "JiebaTokenizer"
- name: "CountVectorsFeaturizer"
token_pattern: '(?u)\b\w+\b'
- name: "EmbeddingIntentClassifier"
config_proto: {
"device_count": 4,
"inter_op_parallelism_threads": 0,
"intra_op_parallelism_threads": 0,
"allow_growth": True
}
- name: "rasa_nlu_gao.extractors.bilstm_crf_entity_extractor.BilstmCRFEntityExtractor"
config_proto: {
"device_count": 4,
"inter_op_parallelism_threads": 0,
"intra_op_parallelism_threads": 0,
"allow_growth": True
}
```
- 新增了`embedding_bert_intent_classifier`分类器，对应的配置文件如下：
```
language: "zh"

pipeline:
- name: "JiebaTokenizer"
- name: "rasa_nlu_gao.featurizers.bert_vectors_featurizer.BertVectorsFeaturizer"
ip: '127.0.0.1'
port: 5555
port_out: 5556
show_server_config: True
timeout: 10000
- name: "rasa_nlu_gao.classifiers.embedding_bert_intent_classifier.EmbeddingBertIntentClassifier"
- name: "CRFEntityExtractor"
```

- 在基础词向量使用bert的情况下，后端的分类器使用tensorflow高级api完成，tf.estimator,tf.data,tf.example,tf.saved_model
`intent_estimator_classifier_tensorflow_embedding_bert`分类器，对应的配置文件如下：
```
language: "zh"

- [rasa-nlu的究极形态](https://www.jianshu.com/p/553e37ffbac0)，对应的配置文件如下(edit at 2019.10.01)可参考上面的文章

## Quick Install
```
pip install rasa-nlu-gao
```

## Some Examples
具体的例子请看[rasa_chatbot_cn](https://github.com/GaoQ1/rasa_chatbot_cn)