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I want to use cosine similarity to identify the intent and pass it to RASA Core
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I want to use cosine similarity to identify the intent and pass it to RASA Core. In other words, I want to replace the NLU part with some other similarity calculation method.
How to do it?
rasa-nlu rasa-core
edited Jan 3 at 10:27
Amir
8,09774277
asked Jan 3 at 9:34
SUBHOJEETSUBHOJEET
394
add a comment |
I want to use cosine similarity to identify the intent and pass it to RASA Core. In other words, I want to replace the NLU part with some other similarity calculation method.
How to do it?
rasa-nlu rasa-core
edited Jan 3 at 10:27
Amir
8,09774277
asked Jan 3 at 9:34
SUBHOJEETSUBHOJEET
394
add a comment |
I want to use cosine similarity to identify the intent and pass it to RASA Core. In other words, I want to replace the NLU part with some other similarity calculation method.
How to do it?
rasa-nlu rasa-core
edited Jan 3 at 10:27
Amir
8,09774277
asked Jan 3 at 9:34
SUBHOJEETSUBHOJEET
394
I want to use cosine similarity to identify the intent and pass it to RASA Core. In other words, I want to replace the NLU part with some other similarity calculation method.
How to do it?
rasa-nlu rasa-core
rasa-nlu rasa-core
edited Jan 3 at 10:27
Amir
8,09774277
asked Jan 3 at 9:34
SUBHOJEETSUBHOJEET
394
edited Jan 3 at 10:27
Amir
8,09774277
asked Jan 3 at 9:34
SUBHOJEETSUBHOJEET
394
edited Jan 3 at 10:27
Amir
8,09774277
edited Jan 3 at 10:27
Amir
8,09774277
edited Jan 3 at 10:27
Amir
8,09774277
8,09774277
asked Jan 3 at 9:34
SUBHOJEETSUBHOJEET
394
asked Jan 3 at 9:34
SUBHOJEETSUBHOJEET
394
asked Jan 3 at 9:34
SUBHOJEETSUBHOJEET
394
394
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Currently, there is four classifiers implemented in Rasa-NLU:
- sklearn_intent_classifier
- mitie_intent_classifier
- keyword_intent_classifier
- embedding_intent_classifier
If you use embedding_intent_classifier.py by default it is used cosine similarity:
"similarity_type": 'cosine', # string 'cosine' or 'inner'
How to customize your pipeline?
language: "en"
pipeline:
- name: "tokenizer_whitespace"
- name: "ner_crf"
- name: "ner_synonyms"
- name: "intent_featurizer_count_vectors"
- name: "intent_classifier_tensorflow_embedding"
See here for more details.
How to define my own Components?
Inherit from parent object Component and implement your own. If you need to define tfidf and cosine read here, and then compare your code with here.
from rasa_nlu.components import Component
class MyComponent(Component):
def __init__(self, component_config=None):
pass
def train(self, training_data, cfg, **kwargs):
pass
def process(self, message, **kwargs):
pass
def persist(self, model_dir):
pass
@classmethod
def load(cls, model_dir=None, model_metadata=None, cached_component=None,
**kwargs):
Also do not forget to add it into pipeline:
pipeline:
- name: "MyComponent"
answered Jan 3 at 10:40
AmirAmir
8,09774277
I want to use tf-idf vectorizer and then cosine similarity to find the best match. How to do it.
– SUBHOJEET
Jan 4 at 4:11
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Currently, there is four classifiers implemented in Rasa-NLU:
- sklearn_intent_classifier
- mitie_intent_classifier
- keyword_intent_classifier
- embedding_intent_classifier
If you use embedding_intent_classifier.py by default it is used cosine similarity:
"similarity_type": 'cosine', # string 'cosine' or 'inner'
How to customize your pipeline?
language: "en"
pipeline:
- name: "tokenizer_whitespace"
- name: "ner_crf"
- name: "ner_synonyms"
- name: "intent_featurizer_count_vectors"
- name: "intent_classifier_tensorflow_embedding"
See here for more details.
How to define my own Components?
Inherit from parent object Component and implement your own. If you need to define tfidf and cosine read here, and then compare your code with here.
from rasa_nlu.components import Component
class MyComponent(Component):
def __init__(self, component_config=None):
pass
def train(self, training_data, cfg, **kwargs):
pass
def process(self, message, **kwargs):
pass
def persist(self, model_dir):
pass
@classmethod
def load(cls, model_dir=None, model_metadata=None, cached_component=None,
**kwargs):
Also do not forget to add it into pipeline:
pipeline:
- name: "MyComponent"
answered Jan 3 at 10:40
AmirAmir
8,09774277
I want to use tf-idf vectorizer and then cosine similarity to find the best match. How to do it.
– SUBHOJEET
Jan 4 at 4:11
add a comment |
Currently, there is four classifiers implemented in Rasa-NLU:
- sklearn_intent_classifier
- mitie_intent_classifier
- keyword_intent_classifier
- embedding_intent_classifier
If you use embedding_intent_classifier.py by default it is used cosine similarity:
"similarity_type": 'cosine', # string 'cosine' or 'inner'
How to customize your pipeline?
language: "en"
pipeline:
- name: "tokenizer_whitespace"
- name: "ner_crf"
- name: "ner_synonyms"
- name: "intent_featurizer_count_vectors"
- name: "intent_classifier_tensorflow_embedding"
See here for more details.
How to define my own Components?
Inherit from parent object Component and implement your own. If you need to define tfidf and cosine read here, and then compare your code with here.
from rasa_nlu.components import Component
class MyComponent(Component):
def __init__(self, component_config=None):
pass
def train(self, training_data, cfg, **kwargs):
pass
def process(self, message, **kwargs):
pass
def persist(self, model_dir):
pass
@classmethod
def load(cls, model_dir=None, model_metadata=None, cached_component=None,
**kwargs):
Also do not forget to add it into pipeline:
pipeline:
- name: "MyComponent"
answered Jan 3 at 10:40
AmirAmir
8,09774277
I want to use tf-idf vectorizer and then cosine similarity to find the best match. How to do it.
– SUBHOJEET
Jan 4 at 4:11
add a comment |
Currently, there is four classifiers implemented in Rasa-NLU:
- sklearn_intent_classifier
- mitie_intent_classifier
- keyword_intent_classifier
- embedding_intent_classifier
If you use embedding_intent_classifier.py by default it is used cosine similarity:
"similarity_type": 'cosine', # string 'cosine' or 'inner'
How to customize your pipeline?
language: "en"
pipeline:
- name: "tokenizer_whitespace"
- name: "ner_crf"
- name: "ner_synonyms"
- name: "intent_featurizer_count_vectors"
- name: "intent_classifier_tensorflow_embedding"
See here for more details.
How to define my own Components?
Inherit from parent object Component and implement your own. If you need to define tfidf and cosine read here, and then compare your code with here.
from rasa_nlu.components import Component
class MyComponent(Component):
def __init__(self, component_config=None):
pass
def train(self, training_data, cfg, **kwargs):
pass
def process(self, message, **kwargs):
pass
def persist(self, model_dir):
pass
@classmethod
def load(cls, model_dir=None, model_metadata=None, cached_component=None,
**kwargs):
Also do not forget to add it into pipeline:
pipeline:
- name: "MyComponent"
answered Jan 3 at 10:40
AmirAmir
8,09774277
Currently, there is four classifiers implemented in Rasa-NLU:
- sklearn_intent_classifier
- mitie_intent_classifier
- keyword_intent_classifier
- embedding_intent_classifier
If you use embedding_intent_classifier.py by default it is used cosine similarity:
"similarity_type": 'cosine', # string 'cosine' or 'inner'
How to customize your pipeline?
language: "en"
pipeline:
- name: "tokenizer_whitespace"
- name: "ner_crf"
- name: "ner_synonyms"
- name: "intent_featurizer_count_vectors"
- name: "intent_classifier_tensorflow_embedding"
See here for more details.
How to define my own Components?
Inherit from parent object Component and implement your own. If you need to define tfidf and cosine read here, and then compare your code with here.
from rasa_nlu.components import Component
class MyComponent(Component):
def __init__(self, component_config=None):
pass
def train(self, training_data, cfg, **kwargs):
pass
def process(self, message, **kwargs):
pass
def persist(self, model_dir):
pass
@classmethod
def load(cls, model_dir=None, model_metadata=None, cached_component=None,
**kwargs):
Also do not forget to add it into pipeline:
pipeline:
- name: "MyComponent"
answered Jan 3 at 10:40
AmirAmir
8,09774277
edited Jan 4 at 5:59
answered Jan 3 at 10:40
AmirAmir
8,09774277
answered Jan 3 at 10:40
AmirAmir
8,09774277
answered Jan 3 at 10:40
AmirAmir
8,09774277
8,09774277
I want to use tf-idf vectorizer and then cosine similarity to find the best match. How to do it.
– SUBHOJEET
Jan 4 at 4:11
add a comment |
I want to use tf-idf vectorizer and then cosine similarity to find the best match. How to do it.
– SUBHOJEET
Jan 4 at 4:11
I want to use tf-idf vectorizer and then cosine similarity to find the best match. How to do it.
– SUBHOJEET
Jan 4 at 4:11
I want to use tf-idf vectorizer and then cosine similarity to find the best match. How to do it.
– SUBHOJEET
Jan 4 at 4:11
add a comment |
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