Difference or Relation between RASA and Spacy
Question:
I’m really new to Chatbots and starting to learn these stuff using frameworks. I’m starting to use this opensource framework RASA and learning about it. Then I found that this entity extraction tool Spacy, is used by RASA.
Can anybody explain what’s the actual relation between these ? What’s the role of Spacy within RASA ?
Answers:
The Rasa stack has two primary components: NLU and Core. Inside of Rasa NLU there are pipelines for extracting intents and entities. One of the pipeline components uses spaCy. For example in this Rasa pipeline:
pipeline:
- name: "nlp_spacy"
- name: "tokenizer_spacy"
- name: "intent_entity_featurizer_regex"
- name: "intent_featurizer_spacy"
- name: "ner_crf"
- name: "ner_synonyms"
- name: "intent_classifier_sklearn"
spaCy is used for pre-processing of the utterances, tokenization, and featurization. It also utilizes other python libraries like nltk and sklearn.
But Rasa NLU has several different pipeline options. So in the below pipeline:
pipeline:
- name: "tokenizer_whitespace"
- name: "ner_crf"
- name: "intent_featurizer_count_vectors"
- name: "intent_classifier_tensorflow_embedding"
spaCy is not used at all, but sklearn and tensorflow are.
Rasa NLU attempts to abstract some of the difficulties of working with spaCy and other libraries to make it easier and more focused on building a chatbot. Then there are other applications that are trying to take Rasa NLU and make it even easier to use by providing a more abstraction with a GUI. It’s a fairly common pattern in open source where one tool builds on another. Some of the GUI applications for Rasa are:
I’m really new to Chatbots and starting to learn these stuff using frameworks. I’m starting to use this opensource framework RASA and learning about it. Then I found that this entity extraction tool Spacy, is used by RASA.
Can anybody explain what’s the actual relation between these ? What’s the role of Spacy within RASA ?
The Rasa stack has two primary components: NLU and Core. Inside of Rasa NLU there are pipelines for extracting intents and entities. One of the pipeline components uses spaCy. For example in this Rasa pipeline:
pipeline:
- name: "nlp_spacy"
- name: "tokenizer_spacy"
- name: "intent_entity_featurizer_regex"
- name: "intent_featurizer_spacy"
- name: "ner_crf"
- name: "ner_synonyms"
- name: "intent_classifier_sklearn"
spaCy is used for pre-processing of the utterances, tokenization, and featurization. It also utilizes other python libraries like nltk and sklearn.
But Rasa NLU has several different pipeline options. So in the below pipeline:
pipeline:
- name: "tokenizer_whitespace"
- name: "ner_crf"
- name: "intent_featurizer_count_vectors"
- name: "intent_classifier_tensorflow_embedding"
spaCy is not used at all, but sklearn and tensorflow are.
Rasa NLU attempts to abstract some of the difficulties of working with spaCy and other libraries to make it easier and more focused on building a chatbot. Then there are other applications that are trying to take Rasa NLU and make it even easier to use by providing a more abstraction with a GUI. It’s a fairly common pattern in open source where one tool builds on another. Some of the GUI applications for Rasa are: