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: 0.7162
Accuracy: 0.8616

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: 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
No log	1.0	57	0.3736	0.8428
No log	2.0	114	0.4061	0.8553
No log	3.0	171	0.5411	0.8742
No log	4.0	228	0.5767	0.8491
No log	5.0	285	0.5931	0.8616
No log	6.0	342	0.6440	0.8742
No log	7.0	399	0.6649	0.8742
No log	8.0	456	0.7300	0.8491
0.0548	9.0	513	0.7114	0.8616
0.0548	10.0	570	0.7162	0.8616

Framework versions

Transformers 4.38.1
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2

vaibhav1/DistilBertPolitiFact

DistilBertPolitiFact

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vaibhav1/
DistilBertPolitiFact