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metadata
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: deberta_biodiversite
    results: []

deberta_biodiversite

This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0237
  • Accuracy: 0.9926
  • F1: 0.9927

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: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
1.2788 1.0 128 0.6534 0.7828 0.7762
0.5887 2.0 256 0.3681 0.8926 0.8884
0.4035 3.0 384 0.2719 0.9142 0.9123
0.2674 4.0 512 0.1562 0.9564 0.9563
0.2189 5.0 640 0.1438 0.9613 0.9610
0.152 6.0 768 0.1300 0.9701 0.9701
0.1403 7.0 896 0.0879 0.9794 0.9792
0.1072 8.0 1024 0.0673 0.9873 0.9872
0.1236 9.0 1152 0.0499 0.9887 0.9887
0.0803 10.0 1280 0.0490 0.9907 0.9907
0.0746 11.0 1408 0.0501 0.9912 0.9912
0.0568 12.0 1536 0.0314 0.9912 0.9912
0.0397 13.0 1664 0.0285 0.9917 0.9916
0.0445 14.0 1792 0.0204 0.9926 0.9927
0.0479 15.0 1920 0.0250 0.9922 0.9922
0.0363 16.0 2048 0.0244 0.9922 0.9922
0.0343 17.0 2176 0.0237 0.9926 0.9927

Framework versions

  • Transformers 4.48.3
  • Pytorch 2.5.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.21.0