nllb-200-3.3B
- 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
3.3 B
Context
1,024
Downloads 30d
296 K
Likes
492
Download history
daily snapshots · 106 days
▲ 104 K in the last 30 days (54.0%)
298 K195 K
Oct 12Oct 22Nov 1Nov 10
298 K75 K
Aug 13Sep 12Oct 12Nov 10
298 K72 K
Jul 28Sep 1Oct 6Nov 10
Specifications
- Architecture
- M2M100ForConditionalGeneration
- Parameters
- 3.3 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