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

Loss: 0.0231
F1: 0.9734

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	F1
3.8235	0.5	185	3.7551	0.0022
3.5949	0.99	370	3.1246	0.0454
2.8705	1.49	555	2.4379	0.1543
2.3444	1.99	740	1.7732	0.2967
1.7151	2.49	925	1.2983	0.4403
1.3959	2.98	1110	0.9965	0.5490
0.9919	3.48	1295	0.7098	0.6880
0.9495	3.98	1480	0.5798	0.7014
0.6	4.48	1665	0.4419	0.7408
0.5952	4.97	1850	0.3653	0.7522
0.3715	5.47	2035	0.3077	0.7957
0.3783	5.97	2220	0.2050	0.8453
0.196	6.47	2405	0.1532	0.8386
0.22	6.96	2590	0.0968	0.8871
0.1117	7.46	2775	0.0725	0.9057
0.1065	7.96	2960	0.0458	0.9265
0.0644	8.45	3145	0.0378	0.9336
0.0526	8.95	3330	0.0324	0.9616
0.0521	9.45	3515	0.0251	0.9708
0.0302	9.95	3700	0.0231	0.9734

Framework versions

Transformers 4.38.1
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2

afaji/distilbert-massive-cheat

results

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

afaji/
distilbert-massive-cheat