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 SetFit/emotion.
  It achieves the following results on the evaluation set:
  - Loss: 0.2276
  - Accuracy: 0.921
  - F1: 0.9209
  
  ## Model description
  
  This model follows chapter 2 of https://github.com/nlp-with-transformers/notebooks. A few things that were changed from the original notebook:
  - the emotion dataset has moved to SetFit/emotion https://github.com/nlp-with-transformers/notebooks/issues/77
  - the new dataset doesn't have ClassLabel feature so needed to change int2str method https://github.com/nlp-with-transformers/notebooks/issues/77
  - made the label names on inference API human-readable with https://discuss.huggingface.co/t/change-label-names-on-inference-api/3063/3
  - function to inspect dataset for existence of certain strings
  
  ## Intended uses & limitations
  
  ## Training and evaluation data
  
  ## 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: 2
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.8732        | 1.0   | 250  | 0.3279          | 0.9055   | 0.9037 |
  | 0.259         | 2.0   | 500  | 0.2276          | 0.921    | 0.9209 |
  
  
  ### Framework versions
  
  - Transformers 4.13.0
  - Pytorch 1.13.0+cu116
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  