
# Codebert (base) fine-tuned this [dataset](https://aclanthology.org/2020.acl-main.443/) for NER


## Eval metrics

eval_accuracy_score = 0.9430622955139325

eval_precision = 0.6047440699126092

eval_recall = 0.6100755667506297

eval_f1 = 0.607398119122257

{"language": "en", "license": "mit", "datasets": ["https://aclanthology.org/2020.acl-main.443/"], "widget": [{"text": "I want to create a table and ListView or ArrayList for Android or javascript in Windows 10"}]}
transformers pytorch jax roberta token-classification en license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #jax #roberta #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Codebert (base) fine-tuned this dataset for NER


## Eval metrics

eval_accuracy_score = 0.9430622955139325

eval_precision = 0.6047440699126092

eval_recall = 0.6100755667506297

eval_f1 = 0.607398119122257

# Codebert (base) fine-tuned this dataset for NER ## Eval metrics

eval_accuracy_score = 0.9430622955139325

eval_precision = 0.6047440699126092

eval_recall = 0.6100755667506297

eval_f1 = 0.607398119122257
TAGS
#transformers #pytorch #jax #roberta #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Codebert (base) fine-tuned this dataset for NER ## Eval metrics

eval_accuracy_score = 0.9430622955139325

eval_precision = 0.6047440699126092

eval_recall = 0.6100755667506297

eval_f1 = 0.607398119122257
passage: TAGS
#transformers #pytorch #jax #roberta #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Codebert (base) fine-tuned this dataset for NER## Eval metrics

eval_accuracy_score = 0.9430622955139325

eval_precision = 0.6047440699126092

eval_recall = 0.6100755667506297

eval_f1 = 0.607398119122257