python -m rasa_nlu.train -c sample_configs/config_jieba_mitie_sklearn.json

[root@localhost Rasa_NLU_Chi]# python -m rasa_nlu.train -c sample_configs/config_jieba_mitie_sklearn.json
Building prefix dict from the default dictionary ...
DEBUG:jieba:Building prefix dict from the default dictionary ...
Loading model from cache /tmp/jieba.cache
DEBUG:jieba:Loading model from cache /tmp/jieba.cache
Loading model cost 0.155 seconds.
DEBUG:jieba:Loading model cost 0.155 seconds.
Prefix dict has been built succesfully.
DEBUG:jieba:Prefix dict has been built succesfully.
Start load dict...
Load dict from: /tmp/yaha.cache
End load dict  0.367727994919 seconds.
INFO:rasa_nlu.components:Added 'nlp_mitie' to component cache. Key 'nlp_mitie-/data/python/Rasa_NLU_Chi/data/total_word_feature_extractor_zh.dat'.
INFO:rasa_nlu.converters:Training data format at ./data/examples/rasa/demo-rasa_zh.json is rasa_nlu
INFO:rasa_nlu.training_data:Training data stats: 
	- intent examples: 42 (5 distinct intents)
	- found intents: 'affirm', 'goodbye', 'greet', 'medical', 'restaurant_search'
	- entity examples: 9 (2 distinct entities)
	- found entities: 'disease', 'food'

INFO:rasa_nlu.model:Starting to train component nlp_mitie
INFO:rasa_nlu.model:Finished training component.
INFO:rasa_nlu.model:Starting to train component tokenizer_jieba
INFO:rasa_nlu.model:Finished training component.
INFO:rasa_nlu.model:Starting to train component ner_mitie
Training to recognize 2 labels: 'food', 'disease'
Part I: train segmenter
words in dictionary: 200000
num features: 271
now do training
C:           20
epsilon:     0.01
num threads: 1
cache size:  5
max iterations: 2000
loss per missed segment:  3
C: 20   loss: 3 	0.444444
C: 35   loss: 3 	0.444444
C: 20   loss: 4.5 	0.555556
C: 5   loss: 3 	0.444444
C: 20   loss: 1.5 	0.444444
C: 20   loss: 6 	0.555556
C: 20   loss: 5.25 	0.555556
C: 21.5   loss: 4.65 	0.555556
C: 16.9684   loss: 4.72073 	0.555556
C: 18.2577   loss: 4.43072 	0.555556
C: 18.2131   loss: 4.55681 	0.555556
C: 20   loss: 4.4 	0.555556
C: 20.9694   loss: 4.47547 	0.555556
best C: 20
best loss: 4.5
num feats in chunker model: 4095
train: precision, recall, f1-score: 1 1 1 
Part I: elapsed time: 2 seconds.

Part II: train segment classifier
now do training
num training samples: 9
C: 200   f-score: 1
C: 400   f-score: 1
C: 300   f-score: 1
C: 100   f-score: 1
C: 0.01   f-score: 1
C: 50.005   f-score: 1
C: 25.0075   f-score: 1
C: 12.5088   f-score: 1
C: 6.25938   f-score: 1
C: 3.13469   f-score: 1
C: 1.57234   f-score: 1
C: 0.791172   f-score: 1
C: 0.400586   f-score: 1
best C: 0.791172
test on train: 
3 0 
0 6 

overall accuracy: 1
Part II: elapsed time: 2 seconds.
df.number_of_classes(): 2
INFO:rasa_nlu.model:Finished training component.
INFO:rasa_nlu.model:Starting to train component ner_synonyms
INFO:rasa_nlu.model:Finished training component.
INFO:rasa_nlu.model:Starting to train component intent_entity_featurizer_regex
INFO:rasa_nlu.model:Finished training component.
INFO:rasa_nlu.model:Starting to train component intent_featurizer_mitie
INFO:rasa_nlu.model:Finished training component.
INFO:rasa_nlu.model:Starting to train component intent_classifier_sklearn
Fitting 2 folds for each of 6 candidates, totalling 12 fits
/root/anaconda2/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s finished
INFO:rasa_nlu.model:Finished training component.
INFO:rasa_nlu.model:Successfully saved model into '/data/python/Rasa_NLU_Chi/models/default/model_20180125-101110'
INFO:__main__:Finished training
[root@localhost Rasa_NLU_Chi]# python -m rasa_n