TalSchuster/CrossLingualContextualEmb

Cross-Lingual Alignment of Contextual Word Embeddings

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This project helps natural language processing (NLP) researchers and practitioners work with text across different languages. It takes text-based data in various languages and provides 'aligned' word embeddings, which are numerical representations of words that can be directly compared and used interchangeably across languages. This means you can apply an NLP model trained on English data to, say, Spanish text, without needing to retrain the model from scratch.

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Use this if you need to perform natural language processing tasks, like text classification or sentiment analysis, on data in multiple languages without extensive retraining for each new language.

Not ideal if you are a non-developer and are looking for an off-the-shelf application to process text, as this requires some programming knowledge.

cross-lingual NLP zero-shot learning multilingual text analysis language technology text embeddings
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 9 / 25
Maturity 16 / 25
Community 11 / 25

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99

Forks

8

Language

Python

License

MIT

Last pushed

Feb 12, 2020

Commits (30d)

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