readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: github-issue-classifier
    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. -->
  
  # github-issue-classifier
  
  This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0684
  - Accuracy: 0.875
  - F1: 0.0455
  - Precision: 1.0
  - Recall: 0.0233
  
  ## 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: 8e-05
  - train_batch_size: 256
  - eval_batch_size: 512
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | No log        | 1.0   | 6    | 0.0888          | 0.8720   | 0.0    | 0.0       | 0.0    |
  | No log        | 2.0   | 12   | 0.0700          | 0.8720   | 0.0    | 0.0       | 0.0    |
  | No log        | 3.0   | 18   | 0.0713          | 0.8720   | 0.0851 | 0.5       | 0.0465 |
  | No log        | 4.0   | 24   | 0.0684          | 0.875    | 0.0455 | 1.0       | 0.0233 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  