
This is a smaller version of the [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) with only Russian and English embeddings left.

sentencepiece vocabulary was shrinked from 250k to 25k (most common 10k English tokens and most common 15k Russian tokens). The creation of this model is heavily based on David Dale's method described [here](https://cointegrated.medium.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90), but with some specific to MBart additions.
{"language": ["ru", "en"], "license": "mit", "library_name": "transformers", "tags": ["mbart", "mbart-50"], "pipeline_tag": "text2text-generation"}
transformers pytorch safetensors mbart text2text-generation mbart-50 ru en license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #mbart #text2text-generation #mbart-50 #ru #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 


This is a smaller version of the facebook/mbart-large-50 with only Russian and English embeddings left.

sentencepiece vocabulary was shrinked from 250k to 25k (most common 10k English tokens and most common 15k Russian tokens). The creation of this model is heavily based on David Dale's method described here, but with some specific to MBart additions.
TAGS
#transformers #pytorch #safetensors #mbart #text2text-generation #mbart-50 #ru #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #safetensors #mbart #text2text-generation #mbart-50 #ru #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
