readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - tweet_eval
  metrics:
  - accuracy
  - f1
  model-index:
  - name: twitter-roberta-base-mar2022-finetuned-emotion
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: tweet_eval
        type: tweet_eval
        args: emotion
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8191414496833216
      - name: F1
        type: f1
        value: 0.8170974933422602
  ---
  
  <!-- 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. -->
  
  # twitter-roberta-base-mar2022-finetuned-emotion
  
  This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-mar2022](https://huggingface.co/cardiffnlp/twitter-roberta-base-mar2022) on the tweet_eval dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5146
  - Accuracy: 0.8191
  - F1: 0.8171
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 2
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.8945        | 1.0   | 102  | 0.5831          | 0.7995   | 0.7887 |
  | 0.5176        | 2.0   | 204  | 0.5266          | 0.8235   | 0.8200 |
  
  
  ### Framework versions
  
  - Transformers 4.19.3
  - Pytorch 1.11.0+cu102
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  