readme: ---
  license: apache-2.0
  tags:
  - text-classification
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: jrtec-distilroberta-base-mrpc-glue-omar-espejel
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: datasetX
        type: glue
        config: mrpc
        split: train
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8161764705882353
      - name: F1
        type: f1
        value: 0.8747913188647747
  ---
  
  <!-- 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. -->
  
  # jrtec-distilroberta-base-mrpc-glue-omar-espejel
  
  This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4901
  - Accuracy: 0.8162
  - F1: 0.8748
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.4845        | 1.09  | 500  | 0.4901          | 0.8162   | 0.8748 |
  | 0.3706        | 2.18  | 1000 | 0.6421          | 0.8162   | 0.8691 |
  | 0.2003        | 3.27  | 1500 | 0.9711          | 0.8162   | 0.8760 |
  | 0.1281        | 4.36  | 2000 | 0.8224          | 0.8480   | 0.8893 |
  | 0.0717        | 5.45  | 2500 | 1.1803          | 0.8113   | 0.8511 |
  | 0.0344        | 6.54  | 3000 | 1.1759          | 0.8480   | 0.8935 |
  | 0.0277        | 7.63  | 3500 | 1.2140          | 0.8456   | 0.8927 |
  | 0.0212        | 8.71  | 4000 | 1.0895          | 0.8554   | 0.8974 |
  | 0.0071        | 9.8   | 4500 | 1.1849          | 0.8554   | 0.8991 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  