
Fixed label mapping issue for textattack/bert-base-uncased-MNLI, if using the original model, the predicted label has systematic confusion with the huggingface MNLI dataset. See the Github issue: https://github.com/QData/TextAttack/issues/684. The fixed accuracy_mm is 84.44% and is 7% before the fix applied.
{"license": "mit", "tags": ["Issue_fixed", "textattack", "textclassification", "entailment"], "datasets": ["mnli"], "metrics": ["accuracy"]}
transformers pytorch bert text-classification Issue_fixed textattack textclassification entailment dataset:mnli license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #Issue_fixed #textattack #textclassification #entailment #dataset-mnli #license-mit #autotrain_compatible #endpoints_compatible #region-us 


Fixed label mapping issue for textattack/bert-base-uncased-MNLI, if using the original model, the predicted label has systematic confusion with the huggingface MNLI dataset. See the Github issue: URL The fixed accuracy_mm is 84.44% and is 7% before the fix applied.
TAGS
#transformers #pytorch #bert #text-classification #Issue_fixed #textattack #textclassification #entailment #dataset-mnli #license-mit #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #text-classification #Issue_fixed #textattack #textclassification #entailment #dataset-mnli #license-mit #autotrain_compatible #endpoints_compatible #region-us 
