This model was finetuned with the base model microsoft/xtremedistil-l6-h256-uncased on the TriviQA dataset for the course NLP4Web at TU Darmstadt 2023.
{"language": ["en"], "license": "mit", "datasets": ["trivia_qa"], "pipeline_tag": "question-answering"}
transformers pytorch bert question-answering en dataset:trivia_qa license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #question-answering #en #dataset-trivia_qa #license-mit #endpoints_compatible #region-us 

This model was finetuned with the base model microsoft/xtremedistil-l6-h256-uncased on the TriviQA dataset for the course NLP4Web at TU Darmstadt 2023.
TAGS
#transformers #pytorch #bert #question-answering #en #dataset-trivia_qa #license-mit #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #question-answering #en #dataset-trivia_qa #license-mit #endpoints_compatible #region-us 
