readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  model-index:
  - name: sentence_eval1
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # sentence_eval1
  
  This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4766
  - Precision: {'precision': 0.863681451041519}
  - Recall: {'recall': 0.8702170188463735}
  - F1: {'f1': 0.8669369177156675}
  - Acc: {'accuracy': 0.8073120494335736}
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 48
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision                         | Recall                         | F1                         | Acc                              |
  |:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:------------------------------:|:--------------------------:|:--------------------------------:|
  | 0.437         | 1.0   | 1771 | 0.4753          | {'precision': 0.9119431443924424} | {'recall': 0.7511422044545973} | {'f1': 0.8237688874970641} | {'accuracy': 0.7681771369721936} |
  | 0.367         | 2.0   | 3542 | 0.4342          | {'precision': 0.8658256880733946} | {'recall': 0.8623643632210166} | {'f1': 0.8640915593705294} | {'accuracy': 0.8043254376930999} |
  | 0.2915        | 3.0   | 5313 | 0.4766          | {'precision': 0.863681451041519}  | {'recall': 0.8702170188463735} | {'f1': 0.8669369177156675} | {'accuracy': 0.8073120494335736} |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  