readme: ---
  license: mit
  tags:
  - text-classification
  - generated_from_trainer
  metrics:
  - f1
  - precision
  - recall
  model-index:
  - name: deberta-v3-large-finetuned-synthetic-multi-class
    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-synthetic-multi-class
  
  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: 0.0223
  - F1: 0.9961
  - Precision: 0.9961
  - Recall: 0.9961
  
  ## 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: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | F1     | Precision | Recall |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:|
  | 0.0278        | 1.0   | 10953 | 0.0352          | 0.9936 | 0.9935    | 0.9936 |
  | 0.0143        | 2.0   | 21906 | 0.0252          | 0.9952 | 0.9952    | 0.9953 |
  | 0.0014        | 3.0   | 32859 | 0.0267          | 0.9955 | 0.9955    | 0.9955 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  