distilbert-base-uncased-finetuned

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 7.2813

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: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
8.6309	1.0	76	7.4774
7.0806	2.0	152	6.9937
6.6842	3.0	228	6.9314
6.4592	4.0	304	6.9088
6.2936	5.0	380	6.9135
6.1301	6.0	456	6.9018
5.9878	7.0	532	6.8865
5.8071	8.0	608	6.8926
5.6372	9.0	684	6.8750
5.4791	10.0	760	6.9394
5.3365	11.0	836	6.9594
5.2117	12.0	912	6.9962
5.0887	13.0	988	7.0570
4.9288	14.0	1064	7.0549
4.8169	15.0	1140	7.0971
4.7008	16.0	1216	7.1439
4.6149	17.0	1292	7.1320
4.487	18.0	1368	7.1577
4.364	19.0	1444	7.1712
4.3208	20.0	1520	7.1959
4.2492	21.0	1596	7.2136
4.1423	22.0	1672	7.2304
4.0873	23.0	1748	7.2526
4.0261	24.0	1824	7.2681
3.9598	25.0	1900	7.2715
3.9562	26.0	1976	7.2648
3.8951	27.0	2052	7.2665
3.8772	28.0	2128	7.2781
3.8403	29.0	2204	7.2801
3.8275	30.0	2280	7.2813

Framework versions

• Transformers 4.21.3
• Pytorch 1.12.1
• Datasets 2.4.0
• Tokenizers 0.12.1