readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - emotion
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert-base-uncased-finetuned-emotion
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: emotion
        type: emotion
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.937
      - name: F1
        type: f1
        value: 0.9372331942198677
    - task:
        type: text-classification
        name: Text Classification
      dataset:
        name: emotion
        type: emotion
        config: default
        split: test
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.924
        verified: true
      - name: Precision Macro
        type: precision
        value: 0.8811256547088461
        verified: true
      - name: Precision Micro
        type: precision
        value: 0.924
        verified: true
      - name: Precision Weighted
        type: precision
        value: 0.9250809835160841
        verified: true
      - name: Recall Macro
        type: recall
        value: 0.8882276452967225
        verified: true
      - name: Recall Micro
        type: recall
        value: 0.924
        verified: true
      - name: Recall Weighted
        type: recall
        value: 0.924
        verified: true
      - name: F1 Macro
        type: f1
        value: 0.8844059421244559
        verified: true
      - name: F1 Micro
        type: f1
        value: 0.924
        verified: true
      - name: F1 Weighted
        type: f1
        value: 0.9243911585312775
        verified: true
      - name: loss
        type: loss
        value: 0.15944455564022064
        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 emotion dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1413
  - Accuracy: 0.937
  - F1: 0.9372
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.7628        | 1.0   | 250  | 0.2489          | 0.9155   | 0.9141 |
  | 0.2014        | 2.0   | 500  | 0.1716          | 0.928    | 0.9283 |
  | 0.1351        | 3.0   | 750  | 0.1456          | 0.937    | 0.9374 |
  | 0.1046        | 4.0   | 1000 | 0.1440          | 0.9355   | 0.9349 |
  | 0.0877        | 5.0   | 1250 | 0.1413          | 0.937    | 0.9372 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  