
# WMT 21 En-X
WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2108.03265) and first released in [this](https://github.com/pytorch/fairseq/tree/main/examples/wmt21) repository.

The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de).

To translate into a target language, the target language id is forced as the first generated token.
To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method.

*Note: `M2M100Tokenizer` depends on `sentencepiece`, so make sure to install it before running the example.*

To install `sentencepiece` run `pip install sentencepiece`

Since the model was trained with domain tags, you should prepend them to the input as well.
* "wmtdata newsdomain": Use for sentences in the news domain
* "wmtdata otherdomain": Use for sentences in all other domain

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("facebook/wmt21-dense-24-wide-en-x")
tokenizer = AutoTokenizer.from_pretrained("facebook/wmt21-dense-24-wide-en-x")

inputs = tokenizer("wmtdata newsdomain One model for many languages.", return_tensors="pt")

# translate English to German
generated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.get_lang_id("de"))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
# => "Ein Modell für viele Sprachen."

# translate English to Icelandic
generated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.get_lang_id("is"))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
# => "Ein fyrirmynd fyrir mörg tungumál."
```

See the [model hub](https://huggingface.co/models?filter=wmt21) to look for more fine-tuned versions.


## Languages covered
English (en), Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)


## BibTeX entry and citation info
```
@inproceedings{tran2021facebook
  title={Facebook AI’s WMT21 News Translation Task Submission},
  author={Chau Tran and Shruti Bhosale and James Cross and Philipp Koehn and Sergey Edunov and Angela Fan},
  booktitle={Proc. of WMT},
  year={2021},
}
```
{"language": ["multilingual", "ha", "is", "ja", "cs", "ru", "zh", "de", "en"], "license": "mit", "tags": ["translation", "wmt21"]}
transformers pytorch m2m_100 text2text-generation translation wmt21 multilingual ha is ja cs ru zh de en arxiv:2108.03265 license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #m2m_100 #text2text-generation #translation #wmt21 #multilingual #ha #is #ja #cs #ru #zh #de #en #arxiv-2108.03265 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 


# WMT 21 En-X
WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this paper and first released in this repository.

The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de).

To translate into a target language, the target language id is forced as the first generated token.
To force the target language id as the first generated token, pass the 'forced_bos_token_id' parameter to the 'generate' method.

*Note: 'M2M100Tokenizer' depends on 'sentencepiece', so make sure to install it before running the example.*

To install 'sentencepiece' run 'pip install sentencepiece'

Since the model was trained with domain tags, you should prepend them to the input as well.
* "wmtdata newsdomain": Use for sentences in the news domain
* "wmtdata otherdomain": Use for sentences in all other domain



See the model hub to look for more fine-tuned versions.


## Languages covered
English (en), Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)


## BibTeX entry and citation info

# WMT 21 En-X
WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this paper and first released in this repository.

The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de).

To translate into a target language, the target language id is forced as the first generated token.
To force the target language id as the first generated token, pass the 'forced_bos_token_id' parameter to the 'generate' method.

*Note: 'M2M100Tokenizer' depends on 'sentencepiece', so make sure to install it before running the example.*

To install 'sentencepiece' run 'pip install sentencepiece'

Since the model was trained with domain tags, you should prepend them to the input as well.
* "wmtdata newsdomain": Use for sentences in the news domain
* "wmtdata otherdomain": Use for sentences in all other domain



See the model hub to look for more fine-tuned versions. ## Languages covered
English (en), Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de) ## BibTeX entry and citation info
TAGS
#transformers #pytorch #m2m_100 #text2text-generation #translation #wmt21 #multilingual #ha #is #ja #cs #ru #zh #de #en #arxiv-2108.03265 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # WMT 21 En-X
WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this paper and first released in this repository.

The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de).

To translate into a target language, the target language id is forced as the first generated token.
To force the target language id as the first generated token, pass the 'forced_bos_token_id' parameter to the 'generate' method.

*Note: 'M2M100Tokenizer' depends on 'sentencepiece', so make sure to install it before running the example.*

To install 'sentencepiece' run 'pip install sentencepiece'

Since the model was trained with domain tags, you should prepend them to the input as well.
* "wmtdata newsdomain": Use for sentences in the news domain
* "wmtdata otherdomain": Use for sentences in all other domain



See the model hub to look for more fine-tuned versions. ## Languages covered
English (en), Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de) ## BibTeX entry and citation info
passage: TAGS
#transformers #pytorch #m2m_100 #text2text-generation #translation #wmt21 #multilingual #ha #is #ja #cs #ru #zh #de #en #arxiv-2108.03265 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
# WMT 21 En-X
WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this paper and first released in this repository.

The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de).

To translate into a target language, the target language id is forced as the first generated token.
To force the target language id as the first generated token, pass the 'forced_bos_token_id' parameter to the 'generate' method.

*Note: 'M2M100Tokenizer' depends on 'sentencepiece', so make sure to install it before running the example.*

To install 'sentencepiece' run 'pip install sentencepiece'

Since the model was trained with domain tags, you should prepend them to the input as well.
* "wmtdata newsdomain": Use for sentences in the news domain
* "wmtdata otherdomain": Use for sentences in all other domain



See the model hub to look for more fine-tuned versions.## Languages covered
English (en), Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de)## BibTeX entry and citation info