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

Please refer to newest instructions at official Rasa NLU document

中文Blog

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. 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.

  • 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
  1. Modify configuration.

    Currently for Chinese we have two pipelines:

    Use MITIE+Jieba (sample_configs/config_jieba_mitie.yml):

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):

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"
  1. (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)

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"
  1. 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
  1. Run the rasa_nlu server:
python -m rasa_nlu.server -c sample_configs/config_jieba_mitie_sklearn.yml --path models
  1. 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?"
}

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Turn Chinese natural language into structured data 中文自然语言理解

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