readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - go_emotions
  metrics:
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-uncased-finetuned-go_emotions_20220608_1
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: go_emotions
        type: go_emotions
        args: simplified
      metrics:
      - name: F1
        type: f1
        value: 0.5575026333429091
      - name: Accuracy
        type: accuracy
        value: 0.43641725027644673
  ---
  
  <!-- 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-go_emotions_20220608_1
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the go_emotions dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0857
  - F1: 0.5575
  - Roc Auc: 0.7242
  - Accuracy: 0.4364
  
  ## 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 | F1     | Roc Auc | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
  | 0.173         | 1.0   | 679  | 0.1074          | 0.4245 | 0.6455  | 0.2976   |
  | 0.0989        | 2.0   | 1358 | 0.0903          | 0.5199 | 0.6974  | 0.3972   |
  | 0.0865        | 3.0   | 2037 | 0.0868          | 0.5504 | 0.7180  | 0.4263   |
  | 0.0806        | 4.0   | 2716 | 0.0860          | 0.5472 | 0.7160  | 0.4233   |
  | 0.0771        | 5.0   | 3395 | 0.0857          | 0.5575 | 0.7242  | 0.4364   |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  