readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - f1
  - accuracy
  model-index:
  - name: glue_sst_classifier2
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        args: sst2
      metrics:
      - name: F1
        type: f1
        value: 0.9054054054054054
      - name: Accuracy
        type: accuracy
        value: 0.9036697247706422
  ---
  
  <!-- 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. -->
  
  # glue_sst_classifier2
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2350
  - F1: 0.9054
  - Accuracy: 0.9037
  
  ## 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: 1e-05
  - train_batch_size: 128
  - eval_batch_size: 128
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 1.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
  | 0.3592        | 0.19  | 100  | 0.3165          | 0.8690 | 0.8693   |
  | 0.2888        | 0.38  | 200  | 0.2565          | 0.8997 | 0.9002   |
  | 0.2279        | 0.57  | 300  | 0.2494          | 0.9042 | 0.9014   |
  | 0.2249        | 0.76  | 400  | 0.2358          | 0.9037 | 0.9025   |
  | 0.2276        | 0.95  | 500  | 0.2356          | 0.9054 | 0.9037   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  