This is a version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model only for English with some embeddings left. 


* Using `sentencepiece` vocabulary was shrinking from 250K to 20K (top 20K English tokens) the number of model parameters reduced to 244M parameters, and model size reduced from 2.2GB to 0.9GB - 39% of the original one.

Approach was taken from article: [How to adapt a multilingual T5 model for a single language](https://cointegrated.medium.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90).
{"language": ["en"], "license": "mit", "tags": ["english"]}
transformers pytorch t5 text2text-generation english en license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #english #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

This is a version of the google/mt5-base model only for English with some embeddings left. 


* Using 'sentencepiece' vocabulary was shrinking from 250K to 20K (top 20K English tokens) the number of model parameters reduced to 244M parameters, and model size reduced from 2.2GB to 0.9GB - 39% of the original one.

Approach was taken from article: How to adapt a multilingual T5 model for a single language.
TAGS
#transformers #pytorch #t5 #text2text-generation #english #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

TAGS
#transformers #pytorch #t5 #text2text-generation #english #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
