
# byt5-large-wmt14-deen

This model is released as part of the work from [Are Character-level Translations Worth the Wait? Comparing Character- and Subword-level Models for Machine Translation](https://arxiv.org/abs/2302.14220).
It is a ByT5 model finetuned on German-->English translation the WMT14 dataset. 

To use the model correctly, you must prepend the prompt with "translate X to Y: ", where X and Y are your source and target languages (e.g. German, English).


NOTE: The decoder_start_token_id is 259 for byt5 models and 250099 for mt5 models, which is different from the default token from google's byt5 and mt5 models (which is 0).
{"language": ["de", "en"], "datasets": ["wmt14"], "pipeline_tag": "translation"}
transformers pytorch t5 text2text-generation translation de en dataset:wmt14 arxiv:2302.14220 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #translation #de #en #dataset-wmt14 #arxiv-2302.14220 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# byt5-large-wmt14-deen

This model is released as part of the work from Are Character-level Translations Worth the Wait? Comparing Character- and Subword-level Models for Machine Translation.
It is a ByT5 model finetuned on German-->English translation the WMT14 dataset. 

To use the model correctly, you must prepend the prompt with "translate X to Y: ", where X and Y are your source and target languages (e.g. German, English).


NOTE: The decoder_start_token_id is 259 for byt5 models and 250099 for mt5 models, which is different from the default token from google's byt5 and mt5 models (which is 0).
# byt5-large-wmt14-deen

This model is released as part of the work from Are Character-level Translations Worth the Wait? Comparing Character- and Subword-level Models for Machine Translation.
It is a ByT5 model finetuned on German-->English translation the WMT14 dataset. 

To use the model correctly, you must prepend the prompt with "translate X to Y: ", where X and Y are your source and target languages (e.g. German, English).


NOTE: The decoder_start_token_id is 259 for byt5 models and 250099 for mt5 models, which is different from the default token from google's byt5 and mt5 models (which is 0).
TAGS
#transformers #pytorch #t5 #text2text-generation #translation #de #en #dataset-wmt14 #arxiv-2302.14220 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # byt5-large-wmt14-deen

This model is released as part of the work from Are Character-level Translations Worth the Wait? Comparing Character- and Subword-level Models for Machine Translation.
It is a ByT5 model finetuned on German-->English translation the WMT14 dataset. 

To use the model correctly, you must prepend the prompt with "translate X to Y: ", where X and Y are your source and target languages (e.g. German, English).


NOTE: The decoder_start_token_id is 259 for byt5 models and 250099 for mt5 models, which is different from the default token from google's byt5 and mt5 models (which is 0).