aditeyabaral/calbert

CalBERT - Code-mixed Adaptive Language representations using BERT, published at AAAI-MAKE 2022

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This project helps developers and researchers working with code-mixed languages, like Hinglish, to create more accurate natural language processing (NLP) models. It takes in sentences from two related languages (e.g., English and Hindi) and outputs dense vector representations (embeddings) for words, sentences, or paragraphs, which can then be used for tasks like sentiment analysis or semantic search. This is for machine learning engineers, data scientists, and computational linguists.

No commits in the last 6 months. Available on PyPI.

Use this if you need to build or improve NLP models that understand and process text containing a blend of two languages.

Not ideal if your NLP tasks exclusively involve a single language or if you are not comfortable working with machine learning model training and development.

code-mixing multilingual-nlp natural-language-processing sentiment-analysis semantic-search
Stale 6m No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 25 / 25
Community 14 / 25

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Stars

13

Forks

3

Language

Python

License

MIT

Last pushed

Dec 18, 2023

Commits (30d)

0

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