readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: deberta-v3-large__sst2__train-8-1
    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. -->
  
  # deberta-v3-large__sst2__train-8-1
  
  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7020
  - Accuracy: 0.5008
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6773        | 1.0   | 3    | 0.7822          | 0.25     |
  | 0.6587        | 2.0   | 6    | 0.8033          | 0.25     |
  | 0.693         | 3.0   | 9    | 0.8101          | 0.25     |
  | 0.5979        | 4.0   | 12   | 1.1235          | 0.25     |
  | 0.4095        | 5.0   | 15   | 1.3563          | 0.25     |
  | 0.2836        | 6.0   | 18   | 1.5325          | 0.5      |
  | 0.1627        | 7.0   | 21   | 1.7786          | 0.25     |
  | 0.0956        | 8.0   | 24   | 2.0067          | 0.5      |
  | 0.0535        | 9.0   | 27   | 2.3351          | 0.5      |
  | 0.0315        | 10.0  | 30   | 2.6204          | 0.5      |
  | 0.0182        | 11.0  | 33   | 2.8483          | 0.5      |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2
  - Tokenizers 0.10.3
  