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rasa NLU vs SpaCy: What are the differences?
Developers describe rasa NLU as "Open source, drop-in replacement for NLP tools like wit.ai". rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries. On the other hand, SpaCy is detailed as "Industrial-Strength Natural Language Processing in Python". It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.
rasa NLU and SpaCy can be categorized as "NLP / Sentiment Analysis" tools.
rasa NLU is an open source tool with 5.77K GitHub stars and 1.7K GitHub forks. Here's a link to rasa NLU's open source repository on GitHub.
Pros of rasa NLU
- Open Source8
- Docker Image6
- Self Hosted6
- Comes with rasa_core3
- Enterprise Ready1
Pros of SpaCy
- No vendor lock-in2
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Cons of rasa NLU
- No interface provided4
Cons of SpaCy
- Requires creating a training set and managing training1