readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: microsoft-deberta-v3-large_cls_subj
    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. -->
  
  # microsoft-deberta-v3-large_cls_subj
  
  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on [subj](https://huggingface.co/datasets/SetFit/subj) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1525
  - Accuracy: 0.976
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_ratio: 0.2
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.2629        | 1.0   | 500  | 0.1519          | 0.955    |
  | 0.1232        | 2.0   | 1000 | 0.1121          | 0.974    |
  | 0.0535        | 3.0   | 1500 | 0.1341          | 0.974    |
  | 0.0152        | 4.0   | 2000 | 0.1794          | 0.969    |
  | 0.0043        | 5.0   | 2500 | 0.1525          | 0.976    |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  