readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - tweet_eval
  metrics:
  - precision
  - recall
  model-index:
  - name: bert-emotion
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: tweet_eval
        type: tweet_eval
        config: emotion
        split: train
        args: emotion
      metrics:
      - name: Precision
        type: precision
        value: 0.7071669427034283
      - name: Recall
        type: recall
        value: 0.723286061789479
  ---
  
  <!-- 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-emotion
  
  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2053
  - Precision: 0.7072
  - Recall: 0.7233
  - Fscore: 0.7124
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
  | 0.8623        | 1.0   | 815  | 0.7198          | 0.7536    | 0.6312 | 0.6559 |
  | 0.5637        | 2.0   | 1630 | 0.8756          | 0.7213    | 0.7166 | 0.7160 |
  | 0.2845        | 3.0   | 2445 | 1.2053          | 0.7072    | 0.7233 | 0.7124 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  