readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: microsoft-deberta-v3-large_cls_sst2
    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_sst2
  
  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on [sst2](https://huggingface.co/datasets/sst2) unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2206
  - Accuracy: 0.9576
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 1.0   | 433  | 0.2420          | 0.9415   |
  | 0.3716        | 2.0   | 866  | 0.2387          | 0.9404   |
  | 0.2001        | 3.0   | 1299 | 0.2379          | 0.9461   |
  | 0.1187        | 4.0   | 1732 | 0.2007          | 0.9610   |
  | 0.0555        | 5.0   | 2165 | 0.2206          | 0.9576   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  