distilbert-base-uncased-feverous-nei-binary-v2

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trainer_output

This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6104
• Accuracy: 0.6868

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Test results against FEVEROUS

          precision    recall  f1-score   support

       0       0.96      0.73      0.83      7389
       1       0.12      0.52      0.19       501

accuracy                           0.72      7890

macro avg 0.54 0.63 0.51 7890 weighted avg 0.90 0.72 0.79 7890

Framework versions

• Transformers 4.57.1
• Pytorch 2.9.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1