Rasa NLU
 | |
| Developer(s) | Rasa Technologies, various |
|---|---|
| Initial release | December 2016.[1] |
| Repository | github |
| Written in | Python |
| Engine | |
| Type | Natural language processing |
| License | Apache License |
| Website | Rasa Forum |
Search Rasa NLU on Amazon.
Rasa NLU is an open-source library for Natural Language Processing[2][3]. The library is published under the Apache 2.0 license and enables intent classification and entity extraction of natural language using word embeddings for use in AI assistants and chatbots.[4]
Unlike most NLU solutions, it is hosted completely on-premise, making it a viable option for companies handling sensitive data or developing in-house expertise.[5] Rasa NLU integrates with common backend systems, providing pre-trained word vectors like SpaCy or fastText. It is also possible to use tensorflow components to train new custom word vectors on a specific dataset.
Recently, it was announced as one of the top 10 open source machine learning projects on Github.[6]
Main Features
- Intent classification[7]
- Multi-intent classification[8].
- Named entity recognition[9].
- Pre-trained word vectors
- Custom supervised word embeddings[10]
See also
References
- ↑ Mannes, John. "Rasa NLU gives developers an open source solution for natural langauge processing". Techcrunch.
- ↑ "Open-source intent recognition in NLP & NLU". Nology. Retrieved 31 August 2018.
- ↑ Rodriguez, Jesus. "Technology Fridays: An overview of Rasa, the best NLP Platform you never heard of". Retrieved 31 August 2018.
- ↑ "Rasa NLU". Rasa. Retrieved 31 August 2018.
- ↑ Olson, Parmy. "Google, Microsoft And Startups Are Going To War On Chatbot Technology". Forbes. Forbes. Retrieved 18 February 2019.
- ↑ WIGGERS, KYLE. "Top ML Projects on Github". VentureBeat. Retrieved 18 February 2019.
- ↑ "Understanding the NLU pipleine". Rasa. Retrieved 31 August 2018.
- ↑ Petraityte, Justina. "How to handle multiple intents per input using Rasa NLU TensorFlow pipeline". Rasa. Retrieved 31 August 2018.
- ↑ "Entity Extraction". Rasa. Retrieved 31 August 2018.
- ↑ Nichol, Alan. "Supervised Word Vectors from Scratch". Medium.com. Rasa. Retrieved 18 February 2019.
External links
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