readme: ---
  license: mit
  tags:
  - fill-mask
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: deberta-v3-large-dapt-scientific-papers-pubmed
    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-dapt-scientific-papers-pubmed
  
  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.4729
  - Accuracy: 0.3510
  
  ## 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: 1e-06
  - train_batch_size: 12
  - eval_batch_size: 12
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 10000
  - training_steps: 21600
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 12.0315       | 0.02  | 500   | 11.6840         | 0.0      |
  | 11.0675       | 0.05  | 1000  | 8.9471          | 0.0226   |
  | 8.6646        | 0.07  | 1500  | 8.0093          | 0.0344   |
  | 8.3625        | 0.09  | 2000  | 7.9624          | 0.0274   |
  | 8.2467        | 0.12  | 2500  | 7.6599          | 0.0376   |
  | 7.9714        | 0.14  | 3000  | 7.6716          | 0.0316   |
  | 7.9852        | 0.16  | 3500  | 7.4535          | 0.0385   |
  | 7.7502        | 0.19  | 4000  | 7.4293          | 0.0429   |
  | 7.7016        | 0.21  | 4500  | 7.3576          | 0.0397   |
  | 7.5789        | 0.23  | 5000  | 7.3124          | 0.0513   |
  | 7.4141        | 0.25  | 5500  | 7.1353          | 0.0634   |
  | 7.2365        | 0.28  | 6000  | 6.8600          | 0.0959   |
  | 7.0725        | 0.3   | 6500  | 6.5743          | 0.1150   |
  | 6.934         | 0.32  | 7000  | 6.3674          | 0.1415   |
  | 6.7219        | 0.35  | 7500  | 6.3467          | 0.1581   |
  | 6.5039        | 0.37  | 8000  | 6.1312          | 0.1815   |
  | 6.3096        | 0.39  | 8500  | 5.9080          | 0.2134   |
  | 6.1835        | 0.42  | 9000  | 5.8414          | 0.2137   |
  | 6.0939        | 0.44  | 9500  | 5.5137          | 0.2553   |
  | 6.0457        | 0.46  | 10000 | 5.5881          | 0.2545   |
  | 5.8851        | 0.49  | 10500 | 5.5134          | 0.2497   |
  | 5.7277        | 0.51  | 11000 | 5.3023          | 0.2699   |
  | 5.6183        | 0.53  | 11500 | 5.0074          | 0.3019   |
  | 5.4978        | 0.56  | 12000 | 5.1822          | 0.2814   |
  | 5.5916        | 0.58  | 12500 | 5.1211          | 0.2808   |
  | 5.4749        | 0.6   | 13000 | 4.9126          | 0.2972   |
  | 5.3765        | 0.62  | 13500 | 5.0468          | 0.2899   |
  | 5.3529        | 0.65  | 14000 | 4.8160          | 0.3037   |
  | 5.2993        | 0.67  | 14500 | 4.8598          | 0.3141   |
  | 5.2929        | 0.69  | 15000 | 4.9669          | 0.3052   |
  | 5.2649        | 0.72  | 15500 | 4.7849          | 0.3270   |
  | 5.162         | 0.74  | 16000 | 4.6819          | 0.3357   |
  | 5.1639        | 0.76  | 16500 | 4.6056          | 0.3275   |
  | 5.1245        | 0.79  | 17000 | 4.5473          | 0.3311   |
  | 5.1596        | 0.81  | 17500 | 4.7008          | 0.3212   |
  | 5.1346        | 0.83  | 18000 | 4.7932          | 0.3192   |
  | 5.1174        | 0.86  | 18500 | 4.7624          | 0.3208   |
  | 5.1152        | 0.88  | 19000 | 4.6388          | 0.3274   |
  | 5.0852        | 0.9   | 19500 | 4.5247          | 0.3305   |
  | 5.0564        | 0.93  | 20000 | 4.6982          | 0.3161   |
  | 5.0179        | 0.95  | 20500 | 4.5363          | 0.3389   |
  | 5.07          | 0.97  | 21000 | 4.6647          | 0.3307   |
  | 5.0781        | 1.0   | 21500 | 4.4729          | 0.3510   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  