Rasa NLU a natural language parser for bots
Project description
# 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
```
## Intent of this project
这个项目的目的和初衷,是由于官方的rasa nlu里面提供的components和models并不能满足实际需求,有些models精确度不是很乐观。所以我自定义了几个components,而为什么不直接提个pr到rasa nlu官网呢,因为要写太多test。所以在我自己的github上开源并发布到Pypi上,这样后续也能不断往里面填充和优化模型,方便别人也方便自己。
## New features
目前新增了两个特性,支持版本为rasa-nlu-gao==v0.1.2
- 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: "tokenizer_jieba"
- name: "intent_featurizer_count_vectors"
token_pattern: '(?u)\b\w+\b'
- name: "intent_classifier_tensorflow_embedding"
- name: "ner_bilstm_crf"
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: "tokenizer_jieba"
- name: "ner_crf"
- name: "jieba_pseg_extractor"
part_of_speech: ["nr", "ns", "nt"]
- name: "intent_featurizer_count_vectors"
OOV_token: oov
token_pattern: '(?u)\b\w+\b'
- name: "intent_classifier_tensorflow_embedding"
```
## Quick Install
```
pip install rasa-nlu-gao
```
## 🤖 Running of the bot
To train the NLU model:
```
python -m rasa_nlu_gao.train -c sample_configs/config_embedding_bilstm.yml --data data/examples/rasa/rasa_dataset_training.json --path models
```
To run the NLU model:
```
python -m rasa_nlu_gao.server -c sample_configs/config_embedding_bilstm.yml --path models
```
## Some Examples
具体的例子请看[rasa_chatbot_cn](https://github.com/GaoQ1/rasa_chatbot_cn)
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
```
## Intent of this project
这个项目的目的和初衷,是由于官方的rasa nlu里面提供的components和models并不能满足实际需求,有些models精确度不是很乐观。所以我自定义了几个components,而为什么不直接提个pr到rasa nlu官网呢,因为要写太多test。所以在我自己的github上开源并发布到Pypi上,这样后续也能不断往里面填充和优化模型,方便别人也方便自己。
## New features
目前新增了两个特性,支持版本为rasa-nlu-gao==v0.1.2
- 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: "tokenizer_jieba"
- name: "intent_featurizer_count_vectors"
token_pattern: '(?u)\b\w+\b'
- name: "intent_classifier_tensorflow_embedding"
- name: "ner_bilstm_crf"
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: "tokenizer_jieba"
- name: "ner_crf"
- name: "jieba_pseg_extractor"
part_of_speech: ["nr", "ns", "nt"]
- name: "intent_featurizer_count_vectors"
OOV_token: oov
token_pattern: '(?u)\b\w+\b'
- name: "intent_classifier_tensorflow_embedding"
```
## Quick Install
```
pip install rasa-nlu-gao
```
## 🤖 Running of the bot
To train the NLU model:
```
python -m rasa_nlu_gao.train -c sample_configs/config_embedding_bilstm.yml --data data/examples/rasa/rasa_dataset_training.json --path models
```
To run the NLU model:
```
python -m rasa_nlu_gao.server -c sample_configs/config_embedding_bilstm.yml --path models
```
## Some Examples
具体的例子请看[rasa_chatbot_cn](https://github.com/GaoQ1/rasa_chatbot_cn)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
rasa-nlu-gao-0.1.3.tar.gz
(109.5 kB
view hashes)
Built Distribution
Close
Hashes for rasa_nlu_gao-0.1.3-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | cf4a02d5afc0b277e16d7260fdd56f4ee64ba1f9b6c499f2ee5a545bee2c27ca |
|
MD5 | 092225eb32784c5f7c67f4c42eb0b014 |
|
BLAKE2b-256 | addf73e0cc650fbff017b9d373c698bf6474fb88fd3d356a8ad0d65245d504b6 |