Hi Communities,
I am using tensorflow and CounVector to featurize the input text.
Here is my pipeline configure:
language: "en"
pipeline:
- name: "nlp_spacy"
- name: "tokenizer_spacy"
- name: "intent_featurizer_count_vectors"
stop_words: ['how','what','hows','is','the','whats']
min_df: 0.0
max_df: 1.0
min_ngram: 1
max_ngram: 2
- name: "intent_entity_featurizer_regex"
- name: "ner_crf"
BILOU_flag: true
features: [["low",'title'],
["bias", "low", "title","pos",'pattern','prefix5','prefix2', 'suffix5', 'suffix3'],
["low", "title"]]
- name: "ner_duckling_http"
url: "http://localhost:8000"
dimensions: ["time", "number", "duration", "ordinal"]
locale: "en_US"
timezone: "US/Pacific"
- name: "ner_synonyms"
- name: "intent_classifier_tensorflow_embedding"
But when I run my model in a Docker container, it would try to access my local file of /home/<my_local_path>/rasa_nlu/rasa_nlu/featurizers/count_vectors_featurizer.py, other than the related file in the docker which is /app/rasa_nlu_chatbot/rasa_nlu/rasa_nlu/featurizers/count_vectors_featurizer.py. And it leads to some strange error message like:
File "/usr/local/lib/python3.5/site-packages/sklearn/feature_extraction/text.py", line 266, in <lambda>
tokenize(preprocess(self.decode(doc))), stop_words)
File "/home/<my_local_path>/rasa_nlu/rasa_nlu/featurizers/count_vectors_featurizer.py", line 140, in _tokenizer
NameError: name 'T' is not defined
After some debugging, I found that in the generated intent_featurizer_count_vectors.pkl, there is a string of my local path /home/<my_local_path>/rasa_nlu/rasa_nlu/featurizers/count_vectors_featurizer.py.
My question is, why is the model in the docker trying to acces my local path ? Why is a local path in the generated model?
My RASA_nlu version is : “0.14.0a1”