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Categorizing documents in one language using a model trained on a different language.
Distinct from Document Classification: Specifically handles cross-lingual transfer for categorization rather than general document organization
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- facebookresearch/laser
facebookresearch/LASER
3,659Ver en GitHubLASER is a cross-lingual sentence embedding library and multilingual text encoder. It functions as a parallel text mining tool that maps sentences from multiple languages into a shared vector space for similarity and classification tasks. The system converts raw text into fixed-length embeddings, enabling the discovery of translation pairs by calculating the vector distance between sentences. This shared representation allows for cross-lingual document classification, where a model trained on one language can be used to categorize documents in another. The library includes a sentence-piece t
Assigns categories to documents in one language using a model trained on another via shared embeddings.
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