readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - emotion
  metrics:
  - accuracy
  - f1
  model-index:
  - name: bert_uncased_L-2_H-128_A-2-finetuned-emotion
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: emotion
        type: emotion
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.913
      - name: F1
        type: f1
        value: 0.9131486432959599
  ---
  
  <!-- 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_uncased_L-2_H-128_A-2-finetuned-emotion
  
  This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the emotion dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2502
  - Accuracy: 0.913
  - F1: 0.9131
  
  ## 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: 3e-05
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: constant
  - num_epochs: 200
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 1.5953        | 1.0   | 250  | 1.4759          | 0.5055   | 0.3899 |
  | 1.3208        | 2.0   | 500  | 1.1113          | 0.628    | 0.5554 |
  | 1.0064        | 3.0   | 750  | 0.8224          | 0.79     | 0.7802 |
  | 0.7535        | 4.0   | 1000 | 0.6185          | 0.8455   | 0.8425 |
  | 0.5891        | 5.0   | 1250 | 0.5004          | 0.877    | 0.8758 |
  | 0.4783        | 6.0   | 1500 | 0.4260          | 0.8865   | 0.8862 |
  | 0.4078        | 7.0   | 1750 | 0.3787          | 0.8905   | 0.8903 |
  | 0.3554        | 8.0   | 2000 | 0.3432          | 0.891    | 0.8909 |
  | 0.3146        | 9.0   | 2250 | 0.3181          | 0.8925   | 0.8924 |
  | 0.2808        | 10.0  | 2500 | 0.2986          | 0.8965   | 0.8970 |
  | 0.2659        | 11.0  | 2750 | 0.2881          | 0.9      | 0.8999 |
  | 0.2487        | 12.0  | 3000 | 0.2740          | 0.907    | 0.9072 |
  | 0.2253        | 13.0  | 3250 | 0.2683          | 0.9045   | 0.9047 |
  | 0.2103        | 14.0  | 3500 | 0.2650          | 0.9095   | 0.9099 |
  | 0.1995        | 15.0  | 3750 | 0.2551          | 0.9105   | 0.9108 |
  | 0.1894        | 16.0  | 4000 | 0.2534          | 0.9085   | 0.9088 |
  | 0.1791        | 17.0  | 4250 | 0.2473          | 0.91     | 0.9102 |
  | 0.168         | 18.0  | 4500 | 0.2441          | 0.913    | 0.9134 |
  | 0.1563        | 19.0  | 4750 | 0.2459          | 0.9105   | 0.9107 |
  | 0.1511        | 20.0  | 5000 | 0.2497          | 0.9075   | 0.9076 |
  | 0.1363        | 21.0  | 5250 | 0.2502          | 0.913    | 0.9131 |
  
  
  ### Framework versions
  
  - Transformers 4.13.0
  - Pytorch 1.12.1+cu113
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  