nllb-200-distilled-1.3B
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper. - Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Transla...
Params
1.3 B
Context
1,024
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Likes
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Specifications
- Architecture
- M2M100ForConditionalGeneration
- Parameters
- 1.3 B
- Context length
- 1,024
- Vocabulary
- 256,206
- Licence
- cc-by-nc-4.0
- First seen on the Hub
- 2022-07-08
- Training datasets
- flores-200
- Added to our catalog
- 2026-07-28
Compare with any translation model