readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: distilrubert-2ndfinetune-epru
    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. -->
  
  # distilrubert-2ndfinetune-epru
  
  This model is a fine-tuned version of [mmillet/distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear](https://huggingface.co/mmillet/distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3531
  - Accuracy: 0.9054
  - F1: 0.9034
  - Precision: 0.9074
  - Recall: 0.9054
  
  ## 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: 0.0001
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
  - lr_scheduler_type: linear
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.4716        | 1.0   | 11   | 0.2851          | 0.8986   | 0.8945 | 0.9029    | 0.8986 |
  | 0.2842        | 2.0   | 22   | 0.3041          | 0.8851   | 0.8796 | 0.8816    | 0.8851 |
  | 0.167         | 3.0   | 33   | 0.2996          | 0.8986   | 0.8914 | 0.8997    | 0.8986 |
  | 0.1527        | 4.0   | 44   | 0.2443          | 0.9189   | 0.9163 | 0.9222    | 0.9189 |
  | 0.0926        | 5.0   | 55   | 0.2777          | 0.9054   | 0.9016 | 0.9059    | 0.9054 |
  | 0.0897        | 6.0   | 66   | 0.3081          | 0.9122   | 0.9080 | 0.9147    | 0.9122 |
  | 0.0438        | 7.0   | 77   | 0.3332          | 0.8986   | 0.8952 | 0.8993    | 0.8986 |
  | 0.0433        | 8.0   | 88   | 0.3480          | 0.8851   | 0.8859 | 0.8896    | 0.8851 |
  | 0.0398        | 9.0   | 99   | 0.3531          | 0.9054   | 0.9034 | 0.9074    | 0.9054 |
  
  
  ### Framework versions
  
  - Transformers 4.19.3
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  