Please refer to newest instructions at official Rasa NLU document
- 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.
- Clone this project, and run
python setup.py install
-
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"-
(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"-
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
- Run the rasa_nlu server:
python -m rasa_nlu.server -c sample_configs/config_jieba_mitie_sklearn.yml --path models
- 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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