readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - tweet_eval
  metrics:
  - accuracy
  - f1
  model-index:
  - name: mini-vanilla-target-tweet
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: tweet_eval
        type: tweet_eval
        config: emotion
        split: train
        args: emotion
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7540106951871658
      - name: F1
        type: f1
        value: 0.7568814825340653
  ---
  
  <!-- 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. -->
  
  # mini-vanilla-target-tweet
  
  This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the tweet_eval dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.5603
  - Accuracy: 0.7540
  - F1: 0.7569
  
  ## 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: 32
  - eval_batch_size: 32
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.9285        | 4.9   | 500  | 0.7493          | 0.7273   | 0.7207 |
  | 0.4468        | 9.8   | 1000 | 0.7630          | 0.7460   | 0.7437 |
  | 0.2194        | 14.71 | 1500 | 0.8997          | 0.7406   | 0.7455 |
  | 0.1062        | 19.61 | 2000 | 1.0822          | 0.7433   | 0.7435 |
  | 0.0568        | 24.51 | 2500 | 1.2225          | 0.7620   | 0.7622 |
  | 0.0439        | 29.41 | 3000 | 1.3475          | 0.7513   | 0.7527 |
  | 0.0304        | 34.31 | 3500 | 1.4999          | 0.7433   | 0.7399 |
  | 0.0247        | 39.22 | 4000 | 1.5603          | 0.7540   | 0.7569 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  