readme: ---
  tags:
  - text-classification
  - generated_from_trainer
  metrics:
  - f1
  - precision
  - recall
  model-index:
  - name: deberta-v3-large-finetuned-dagpap22-only-and-real
    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-finetuned-dagpap22-only-and-real
  
  This model is a fine-tuned version of [domenicrosati/deberta-v3-large-finetuned-dagpap22-only-and-real](https://huggingface.co/domenicrosati/deberta-v3-large-finetuned-dagpap22-only-and-real) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0008
  - F1: 0.9999
  - Precision: 1.0
  - Recall: 0.9997
  
  ## 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: 6e-06
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 50
  - num_epochs: 2
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | F1     | Precision | Recall |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:|
  | 0.0016        | 1.0   | 10567 | 0.0060          | 0.9982 | 0.9997    | 0.9967 |
  | 0.0           | 2.0   | 21134 | 0.0008          | 0.9999 | 1.0       | 0.9997 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  