mBERT base uncased model trained on 50% SQUAD data. This model can further be used to fine-tune using dev data for QA system on a specific language. The process is similar to what was followed in MLQA paper[https://aclanthology.org/2020.acl-main.421.pdf].


{}
transformers pytorch bert question-answering endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us 

mBERT base uncased model trained on 50% SQUAD data. This model can further be used to fine-tune using dev data for QA system on a specific language. The process is similar to what was followed in MLQA paper[URL


TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us 
