bert-base-uncased-finetuned-classification_ds30

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: 41.1515
• Mse: 41.1515
• Mae: 4.7002
• R2: 0.7675
• Accuracy: 0.2685
• Msev: 0.0243

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: 1e-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	Mse	Mae	R2	Accuracy	Msev
10.1514	1.0	5215	40.1844	40.1844	4.6065	0.7730	0.2644	0.0249
3.7754	2.0	10430	39.4067	39.4067	4.5926	0.7774	0.2803	0.0254
2.2314	3.0	15645	44.9527	44.9527	4.8825	0.7460	0.2680	0.0222
1.6468	4.0	20860	40.3435	40.3435	4.6496	0.7721	0.2702	0.0248
1.2442	5.0	26075	40.8178	40.8178	4.6934	0.7694	0.2657	0.0245
1.0992	6.0	31290	42.6644	42.6644	4.7802	0.7590	0.2620	0.0234
0.9911	7.0	36505	40.0627	40.0627	4.6277	0.7737	0.2751	0.0250
0.8167	8.0	41720	40.6918	40.6918	4.6755	0.7701	0.2693	0.0246
0.7862	9.0	46935	41.9593	41.9593	4.7363	0.7629	0.2693	0.0238
0.8136	10.0	52150	41.1515	41.1515	4.7002	0.7675	0.2685	0.0243

Framework versions

• Transformers 4.22.0
• Pytorch 1.12.1+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1