readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - sem_eval_2018_task_1
  metrics:
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-sem_eval-english
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: sem_eval_2018_task_1
        type: sem_eval_2018_task_1
        config: subtask5.english
        split: train
        args: subtask5.english
      metrics:
      - name: F1
        type: f1
        value: 0.7113731269958242
      - name: Accuracy
        type: accuracy
        value: 0.28103837471783294
  ---
  
  <!-- 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. -->
  
  # bert-finetuned-sem_eval-english
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the sem_eval_2018_task_1 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3131
  - F1: 0.7114
  - Roc Auc: 0.8046
  - Accuracy: 0.2810
  
  ## 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: 2e-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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
  | 0.4067        | 1.0   | 855  | 0.3205          | 0.6756 | 0.7766  | 0.2709   |
  | 0.2828        | 2.0   | 1710 | 0.3062          | 0.7058 | 0.7973  | 0.3014   |
  | 0.239         | 3.0   | 2565 | 0.3122          | 0.7100 | 0.8038  | 0.2810   |
  | 0.2145        | 4.0   | 3420 | 0.3131          | 0.7114 | 0.8046  | 0.2810   |
  | 0.1888        | 5.0   | 4275 | 0.3167          | 0.7096 | 0.8022  | 0.2844   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  