This model is a fine-tuned version of bert-base-uncased on an NLI dataset. It achieves the following results on the evaluation set:

{'precision': 0.8384560400285919} {'recall': 0.9536585365853658} {'f1': 0.892354507417269} {'accuracy': 0.8345996493278784}

Training hyperparameters:

learning_rate=2e-5
batch_size=32
epochs = 4
warmup_steps=10% training data number
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear



{}
transformers pytorch bert text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

This model is a fine-tuned version of bert-base-uncased on an NLI dataset. It achieves the following results on the evaluation set:

{'precision': 0.8384560400285919} {'recall': 0.9536585365853658} {'f1': 0.892354507417269} {'accuracy': 0.8345996493278784}

Training hyperparameters:

learning_rate=2e-5
batch_size=32
epochs = 4
warmup_steps=10% training data number
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear



TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
