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

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	199	1.0316	0.2811
No log	2.0	398	0.4969	0.7560
0.525	3.0	597	0.6104	0.6868

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

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

trainer_output

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Test results against FEVEROUS

Framework versions

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