# Rasa_NLU_Chi

**Repository Path**: jackyAtUstc/Rasa_NLU_Chi

## Basic Information

- **Project Name**: Rasa_NLU_Chi
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-24
- **Last Updated**: 2025-04-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README



# Rasa NLU for Chinese, a fork from RasaHQ/rasa_nlu.

## Please refer to newest instructions at [official Rasa NLU document](https://nlu.rasa.com/)

## [中文Blog](http://www.crownpku.com/2017/07/27/%E7%94%A8Rasa_NLU%E6%9E%84%E5%BB%BA%E8%87%AA%E5%B7%B1%E7%9A%84%E4%B8%AD%E6%96%87NLU%E7%B3%BB%E7%BB%9F.html)

![](http://www.crownpku.com/images/201707/5.jpg)
![](http://www.crownpku.com/images/201707/4.jpg)



### Files you should have:

* data/total_word_feature_extractor_zh.dat

Trained from Chinese corpus by MITIE wordrep tools (takes 2-3 days for training)

For training, please build the [MITIE Wordrep Tool](https://github.com/mit-nlp/MITIE/tree/master/tools/wordrep). Note that Chinese corpus should be tokenized first before feeding into the tool for training. Close-domain corpus that best matches user case works best.

A trained model from Chinese Wikipedia Dump and Baidu Baike can be downloaded from [中文Blog](http://www.crownpku.com/2017/07/27/%E7%94%A8Rasa_NLU%E6%9E%84%E5%BB%BA%E8%87%AA%E5%B7%B1%E7%9A%84%E4%B8%AD%E6%96%87NLU%E7%B3%BB%E7%BB%9F.html).


* data/examples/rasa/demo-rasa_zh.json

Should add as much examples as possible.

### Usage:

1. Clone this project, and run
```
python setup.py install
```

2. Modify configuration. 

   Currently for Chinese we have two pipelines:

   Use MITIE+Jieba (sample_configs/config_jieba_mitie.yml):
```yaml
language: "zh"

pipeline:
- name: "nlp_mitie"
  model: "data/total_word_feature_extractor_zh.dat"
- name: "tokenizer_jieba"
- name: "ner_mitie"
- name: "ner_synonyms"
- name: "intent_entity_featurizer_regex"
- name: "intent_classifier_mitie"
```

   RECOMMENDED: Use MITIE+Jieba+sklearn (sample_configs/config_jieba_mitie_sklearn.yml):
```yaml
language: "zh"

pipeline:
- name: "nlp_mitie"
  model: "data/total_word_feature_extractor_zh.dat"
- name: "tokenizer_jieba"
- name: "ner_mitie"
- name: "ner_synonyms"
- name: "intent_entity_featurizer_regex"
- name: "intent_featurizer_mitie"
- name: "intent_classifier_sklearn"
```

3. (Optional) Use Jieba User Defined Dictionary or Switch Jieba Default Dictionoary:

   You can put in **file path** or **directory path** as the "user_dicts" value. (sample_configs/config_jieba_mitie_sklearn_plus_dict_path.yml)

```yaml
language: "zh"

pipeline:
- name: "nlp_mitie"
  model: "data/total_word_feature_extractor_zh.dat"
- name: "tokenizer_jieba"
  default_dict: "./default_dict.big"
  user_dicts: "./jieba_userdict"
#  user_dicts: "./jieba_userdict/jieba_userdict.txt"
- name: "ner_mitie"
- name: "ner_synonyms"
- name: "intent_entity_featurizer_regex"
- name: "intent_featurizer_mitie"
- name: "intent_classifier_sklearn"
```

4. Train model by running:

   If you specify your project name in configure file, this will save your model at /models/your_project_name. 

   Otherwise, your model will be saved at /models/default

```
python -m rasa_nlu.train -c sample_configs/config_jieba_mitie_sklearn.yml --data data/examples/rasa/demo-rasa_zh.json --path models
```


5. Run the rasa_nlu server:

```
python -m rasa_nlu.server -c sample_configs/config_jieba_mitie_sklearn.yml --path models
```


6. Open a new terminal and now you can curl results from the server, for example:

```
$ curl -XPOST localhost:5000/parse -d '{"q":"我发烧了该吃什么药？", "project": "rasa_nlu_test", "model": "model_20170921-170911"}' | python -mjson.tool
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   652    0   552  100   100    157     28  0:00:03  0:00:03 --:--:--   157
{
    "entities": [
        {
            "end": 3,
            "entity": "disease",
            "extractor": "ner_mitie",
            "start": 1,
            "value": "发烧"
        }
    ],
    "intent": {
        "confidence": 0.5397186422631861,
        "name": "medical"
    },
    "intent_ranking": [
        {
            "confidence": 0.5397186422631861,
            "name": "medical"
        },
        {
            "confidence": 0.16206323981749196,
            "name": "restaurant_search"
        },
        {
            "confidence": 0.1212448457737397,
            "name": "affirm"
        },
        {
            "confidence": 0.10333600028547868,
            "name": "goodbye"
        },
        {
            "confidence": 0.07363727186010374,
            "name": "greet"
        }
    ],
    "text": "我发烧了该吃什么药？"
}
```