readme: ---
  license: mit
  tags:
  - text-classification
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: deberta-v3-xsmall-finetuned-DAGPap22
    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-xsmall-finetuned-DAGPap22
  
  This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0798
  - Accuracy: 0.9907
  - F1: 0.9934
  
  ## 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: 4.5e-05
  - 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: 1000
  - num_epochs: 20
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | No log        | 1.0   | 402  | 0.1626          | 0.9477   | 0.9616 |
  | 0.4003        | 2.0   | 804  | 0.0586          | 0.9794   | 0.9853 |
  | 0.1075        | 3.0   | 1206 | 0.0342          | 0.9907   | 0.9933 |
  | 0.0581        | 4.0   | 1608 | 0.1140          | 0.9776   | 0.9838 |
  | 0.0245        | 5.0   | 2010 | 0.1409          | 0.9776   | 0.9842 |
  | 0.0245        | 6.0   | 2412 | 0.0732          | 0.9832   | 0.9881 |
  | 0.0167        | 7.0   | 2814 | 0.1996          | 0.9682   | 0.9778 |
  | 0.0139        | 8.0   | 3216 | 0.1219          | 0.9850   | 0.9894 |
  | 0.006         | 9.0   | 3618 | 0.0670          | 0.9907   | 0.9934 |
  | 0.0067        | 10.0  | 4020 | 0.1036          | 0.9869   | 0.9907 |
  | 0.0067        | 11.0  | 4422 | 0.1220          | 0.9776   | 0.9838 |
  | 0.0041        | 12.0  | 4824 | 0.1768          | 0.9776   | 0.9839 |
  | 0.0007        | 13.0  | 5226 | 0.0943          | 0.9888   | 0.9920 |
  | 0.0           | 14.0  | 5628 | 0.0959          | 0.9907   | 0.9934 |
  | 0.0054        | 15.0  | 6030 | 0.0915          | 0.9888   | 0.9921 |
  | 0.0054        | 16.0  | 6432 | 0.1618          | 0.9794   | 0.9855 |
  | 0.0019        | 17.0  | 6834 | 0.0794          | 0.9907   | 0.9934 |
  | 0.0           | 18.0  | 7236 | 0.0799          | 0.9907   | 0.9934 |
  | 0.0           | 19.0  | 7638 | 0.0797          | 0.9907   | 0.9934 |
  | 0.0           | 20.0  | 8040 | 0.0798          | 0.9907   | 0.9934 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  