source languages: nl; target languages: en; OPUS readme: nl-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.
This is the model card of NLLB-200's 3.3B variant. Here are the metrics for that particular checkpoint. - Information about training algorithms, parameters, fairness constraints or other applied approaches, and features.
Parameters—
Context1,024
Weights17.6 GB
Licensecc-by-nc-4.0
AccessOpen weights
Monthly Downloads186.7k
Model Card
This is the model card of NLLB-200's 3.3B variant. Here are the metrics for that particular checkpoint. - 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 Translation, Arxiv, 2022 - Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues • Model performance measures: NLLB-200 model was…
Excerpt from the card by AI at Meta, licensed cc-by-nc-4.0.
Configuration
- Architecture
- M2M100ForConditionalGeneration
- Context length (tokens)
- 1,024
- Layers
- 24
- Vocabulary size
- 256,206
- Stored precision
- float32
- Model type
- m2m_100
Identity and Version
- Repository
- facebook/nllb-200-3.3B
- Publisher
- AI at Meta
- Task
- Translation
- Modality
- Text
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- ace, acm, acq, aeb, af, ajp, ak, als
- Revision
- 1a07f7d195896b2114afcb79b7b57ab512e7b43e
- First published
- 2022-07-08
- Last updated
- 2023-02-11
Files and Weights
12 files, 17.6 GB in total. The weights are 3 files totalling 17.6 GB in bin.
Weights3 files · 17.6 GB
Configuration4 files · 94.6 KB
Tokenizer2 files · 17.3 MB
Documentation1 file · 7.6 KB
Other1 file · 4.9 MB
Repository1 file · 1.2 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| pytorch_model-00001-of-00003.bin | Weights | 6.9 GB | 06aec913c891 |
| pytorch_model-00002-of-00003.bin | Weights | 8.5 GB | 1c5a44acb028 |
| pytorch_model-00003-of-00003.bin | Weights | 2.1 GB | 2cf744009996 |
| config.json | Configuration | 808 B | — |
| generation_config.json | Configuration | 189 B | — |
| pytorch_model.bin.index.json | Configuration | 90.0 KB | — |
| special_tokens_map.json | Configuration | 3.5 KB | — |
| README.md | Documentation | 7.6 KB | — |
| sentencepiece.bpe.model | Other | 4.9 MB | 14bb8dfb35c0 |
| .gitattributes | Repository | 1.2 KB | — |
| tokenizer.json | Tokenizer | 17.3 MB | e316b82de11d |
| tokenizer_config.json | Tokenizer | 564 B | — |
License and Download
- License
- cc-by-nc-4.0
- Access
- Open weights, no gate
- Download size
- 17.6 GB
Download from AI at Meta
Released by AI at Meta through its official repository on Hugging Face. Read the license.
Built From
- Trained on (disclosed) flores-200
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 17.6 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About nllb-200-3.3B
Can I use nllb-200-3.3B commercially?
Not without separate permission. nllb-200-3.3B is released under Creative Commons Attribution-NonCommercial 4.0. CC BY-NC 4.0 permits sharing and adapting with credit for non-commercial purposes only. Commercial use needs separate permission from the rights holder.
What is nllb-200-3.3B's context length?
1,024 tokens, from the maximum position embeddings in its published configuration.
Similar Models
This is the model card of NLLB-200's distilled 600M variant. Here are the metrics for that particular checkpoint. - 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 Translation, Arxiv, 2022 - Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues • Model performance measures: NLLB-200…
source languages: en; target languages: ru; OPUS readme: en-ru; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.
This model can be used for translation and text-to-text generation. CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes. Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Further details about the dataset for this model can be found in the OPUS readme: en-de
hfname: kor-eng - sourcelanguages: kor - targetlanguages: eng - opusreadmeurl: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kor-eng/README.md - originalrepo: Tatoeba-Challenge - srcconstituents: {'korHani', 'korHang', 'korLatn', 'kor'} - tgtconstituents: {'eng'} - srcmultilingual: False - tgtmultilingual: False - urlmodel: https://object.pouta.csc.fi/Tatoeba-MT-models/kor-eng/opus-2020-06-17.zip - urltestset: https://object.pouta.csc.fi/Tatoeba-MT-models/kor-eng/opus-2020-06-17.test.txt - srcalpha3: kor - tgtalpha3: eng - shortpair: ko-en - chrF2score: 0.588 - brevitypenalty: 0.9590000000000001 - reflen: 17711.0 - srcname: Korean - tgtname: English - traindate…
source languages: de; target languages: en; OPUS readme: de-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.