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: 0.1523
Accuracy: 0.941
F1: 0.9410

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: 64
eval_batch_size: 64
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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.148	1.0	250	0.1697	0.9325	0.9333
0.1078	2.0	500	0.1539	0.9315	0.9317
0.0921	3.0	750	0.1519	0.941	0.9417
0.0697	4.0	1000	0.1489	0.943	0.9429
0.0586	5.0	1250	0.1523	0.941	0.9410

Framework versions

Transformers 4.53.0
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2

dhrumil2312/dhrumil-bert-classifier

dhrumil-bert-classifier

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dhrumil2312/
dhrumil-bert-classifier