NL_Parser_using_Spacy VS Multimodal

Compare NL_Parser_using_Spacy vs Multimodal and see what are their differences.

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NL_Parser_using_Spacy Multimodal
1 1
22 8
- -
0.0 0.0
over 1 year ago about 2 years ago
Jupyter Notebook Jupyter Notebook
- MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

NL_Parser_using_Spacy

Posts with mentions or reviews of NL_Parser_using_Spacy. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-01.

Multimodal

Posts with mentions or reviews of Multimodal. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing NL_Parser_using_Spacy and Multimodal you can also consider the following projects:

bert - TensorFlow code and pre-trained models for BERT

silero-models - Silero Models: pre-trained speech-to-text, text-to-speech and text-enhancement models made embarrassingly simple

txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

NLU-engine-prototype-benchmarks - Demo and benchmarks for building an NLU engine similar to those in voice assistants. Several intent classifiers are implemented and benchmarked. Conditional Random Fields (CRFs) are used for entity extraction.