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

Loss: 0.5219
Accuracy: 0.7358
F1: 0.7562

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: 9.426559053989038e-05
train_batch_size: 64
eval_batch_size: 16
seed: 10
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	122	0.5219	0.7358	0.7562
No log	2.0	244	0.5235	0.7513	0.7400
No log	3.0	366	0.7853	0.7193	0.6691
No log	4.0	488	0.9671	0.7245	0.7030
0.332	5.0	610	1.1997	0.7172	0.7128
0.332	6.0	732	1.3708	0.7245	0.7245
0.332	7.0	854	1.5733	0.7234	0.7118
0.332	8.0	976	1.7145	0.7245	0.7056
0.0365	9.0	1098	1.7506	0.7307	0.7172
0.0365	10.0	1220	1.8002	0.7245	0.7107

Framework versions

Transformers 4.41.0
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

varadsrivastava/distilbert_finetuned

distilbert_finetuned

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

varadsrivastava/
distilbert_finetuned