
mBART (a pre-trained model by Facebook) is pre-trained to de-noise multiple languages simultaneously with BART objective.

Checkpoint available in this repository is obtained after fine-tuning `facebook/mbart-large-cc25` on all samples (~60K) from Bhasha (pib_v1.3) Gujarati-English parallel corpus. This checkpoint gives decent results for Gujarati-english translation.
{"datasets": "pib", "widget": [{"text": "\u0ab9\u0ac7\u0aaf! \u0ab9\u0ac1\u0a82 \u0ab5\u0abe\u0ab8\u0ac1\u0aa6\u0ac7\u0ab5 \u0a97\u0ac1\u0aaa\u0acd\u0aa4\u0abe \u0a9b\u0ac1\u0a82"}]}
transformers pytorch mbart text2text-generation dataset:pib autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mbart #text2text-generation #dataset-pib #autotrain_compatible #endpoints_compatible #region-us 


mBART (a pre-trained model by Facebook) is pre-trained to de-noise multiple languages simultaneously with BART objective.

Checkpoint available in this repository is obtained after fine-tuning 'facebook/mbart-large-cc25' on all samples (~60K) from Bhasha (pib_v1.3) Gujarati-English parallel corpus. This checkpoint gives decent results for Gujarati-english translation.
TAGS
#transformers #pytorch #mbart #text2text-generation #dataset-pib #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #mbart #text2text-generation #dataset-pib #autotrain_compatible #endpoints_compatible #region-us 
