readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert-base-uncased-finetuned-emotion
    results: []
  ---
  
  <!-- 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 an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2055
  - Accuracy: 0.9355
  - F1: 0.9354
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.1775        | 1.0   | 250  | 0.1765          | 0.929    | 0.9287 |
  | 0.1205        | 2.0   | 500  | 0.1516          | 0.9395   | 0.9393 |
  | 0.0981        | 3.0   | 750  | 0.1530          | 0.9345   | 0.9351 |
  | 0.0799        | 4.0   | 1000 | 0.1654          | 0.935    | 0.9348 |
  | 0.0641        | 5.0   | 1250 | 0.1638          | 0.937    | 0.9364 |
  | 0.0495        | 6.0   | 1500 | 0.1695          | 0.937    | 0.9369 |
  | 0.0417        | 7.0   | 1750 | 0.1873          | 0.935    | 0.9350 |
  | 0.0332        | 8.0   | 2000 | 0.1941          | 0.935    | 0.9351 |
  | 0.0275        | 9.0   | 2250 | 0.1977          | 0.9385   | 0.9385 |
  | 0.0224        | 10.0  | 2500 | 0.2055          | 0.9355   | 0.9354 |
  
  
  ### Framework versions
  
  - Transformers 4.13.0
  - Pytorch 1.11.0
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  