Regression_BERT_aug_MSEloss

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

• Loss: 0.1118
• Mse: 0.1118
• Mae: 0.2369
• R2: 0.7519
• Accuracy: 0.8733

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: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Mse	Mae	R2	Accuracy
No log	1.0	263	0.1491	0.1491	0.2707	0.6520	0.8367
0.1428	2.0	526	0.0948	0.0948	0.1805	0.7788	0.9033
0.1428	3.0	789	0.0596	0.0596	0.1209	0.8610	0.9533
0.0215	4.0	1052	0.0534	0.0534	0.1034	0.8755	0.9533
0.0215	5.0	1315	0.0464	0.0464	0.0882	0.8917	0.9567
0.0111	6.0	1578	0.0420	0.0420	0.0852	0.9019	0.9633
0.0111	7.0	1841	0.0419	0.0419	0.0744	0.9022	0.9633
0.0051	8.0	2104	0.0424	0.0424	0.0736	0.9010	0.96
0.0051	9.0	2367	0.0457	0.0457	0.0737	0.8935	0.9533
0.0034	10.0	2630	0.0396	0.0396	0.0692	0.9076	0.96
0.0034	11.0	2893	0.0419	0.0419	0.0740	0.9023	0.9633
0.0027	12.0	3156	0.0370	0.0370	0.0684	0.9136	0.9667
0.0027	13.0	3419	0.0389	0.0389	0.0688	0.9092	0.9633
0.0023	14.0	3682	0.0392	0.0392	0.0654	0.9085	0.9633
0.0023	15.0	3945	0.0382	0.0382	0.0663	0.9108	0.9633
0.0018	16.0	4208	0.0403	0.0403	0.0655	0.9059	0.96
0.0018	17.0	4471	0.0391	0.0391	0.0675	0.9087	0.96
0.0016	18.0	4734	0.0386	0.0386	0.0618	0.9099	0.9633
0.0016	19.0	4997	0.0389	0.0389	0.0640	0.9093	0.9633
0.0013	20.0	5260	0.0384	0.0384	0.0623	0.9104	0.9633

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.3