readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - emotion
  metrics:
  - accuracy
  - f1
  model-index:
  - name: bertweet-base-finetuned-emotion
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: emotion
        type: emotion
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9365
      - name: F1
        type: f1
        value: 0.9371
    - task:
        type: text-classification
        name: Text Classification
      dataset:
        name: emotion
        type: emotion
        config: default
        split: test
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.923
        verified: true
      - name: Precision Macro
        type: precision
        value: 0.8676576686813523
        verified: true
      - name: Precision Micro
        type: precision
        value: 0.923
        verified: true
      - name: Precision Weighted
        type: precision
        value: 0.9268406401714973
        verified: true
      - name: Recall Macro
        type: recall
        value: 0.8945488803260702
        verified: true
      - name: Recall Micro
        type: recall
        value: 0.923
        verified: true
      - name: Recall Weighted
        type: recall
        value: 0.923
        verified: true
      - name: F1 Macro
        type: f1
        value: 0.8798961895301041
        verified: true
      - name: F1 Micro
        type: f1
        value: 0.923
        verified: true
      - name: F1 Weighted
        type: f1
        value: 0.9241278880972197
        verified: true
      - name: loss
        type: loss
        value: 0.24626904726028442
        verified: true
  ---
  
  <!-- 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. -->
  
  # distilbert-base-uncased-finetuned-emotion
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1995
  - Accuracy: 0.9365
  - F1: 0.9371
  
  ## 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: 64
  - eval_batch_size: 64
  - 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 | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.475         | 1.0   | 503  | 0.2171          | 0.928    | 0.9292 |
  | 0.1235        | 2.0   | 1006 | 0.1764          | 0.9365   | 0.9372 |
  | 0.0802        | 3.0   | 1509 | 0.1788          | 0.938    | 0.9388 |
  | 0.0531        | 4.0   | 2012 | 0.2005          | 0.938    | 0.9388 |
  | 0.0367        | 5.0   | 2515 | 0.1995          | 0.9365   | 0.9371 |
  
  
  ### Framework versions
  
  - Transformers 4.13.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  