readme: 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': 0.9690210656753407} {'recall': 0.9722337339411521} {'f1': 0.9706247414149772} {'accuracy': 0.9535340314136126}
  
  Training hyperparameters:
  
  learning_rate: 2e-5
  
  train_batch_size: 8
  
  eval_batch_size: 8
  
  optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
  
  weight_decay= 0.01
  
  lr_scheduler_type: linear
  
  num_epochs: 3
  
  
  It achieves the following results on the test set:
  
  Incorrect UD Padded:
  {'precision': 0.623370110330993} {'recall': 0.8415707515233581} {'f1': 0.7162201094785364} {'accuracy': 0.5828038966539602}
  Incorrect UD Unigram:
  {'precision': 0.6211431461810825} {'recall': 0.8314150304671631} {'f1': 0.7110596409959468} {'accuracy': 0.5772977551884795}
  Incorrect UD Bigram:
  {'precision': 0.6203980099502487} {'recall': 0.8442789438050101} {'f1': 0.7152279896759391} {'accuracy': 0.579415501905972}
  Incorrect UD All:
  {'precision': 0.605543710021322} {'recall': 0.1922816519972918} {'f1': 0.2918807810894142} {'accuracy': 0.4163490046590428}
  Incorrect Sentence:
  {'precision': 0.6411042944785276} {'recall': 0.4245091401489506} {'f1': 0.5107942973523422} {'accuracy': 0.4913172384582804}
  
  