assignment2_attempt11

This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6058
• Precision: 0.2642
• Recall: 0.1186
• F1: 0.1637
• Accuracy: 0.9370

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	128	0.3124	0.2308	0.0254	0.0458	0.9401
No log	2.0	256	0.2862	0.1636	0.0763	0.1040	0.9353
No log	3.0	384	0.3899	0.2093	0.0763	0.1118	0.9359
0.1996	4.0	512	0.4161	0.3095	0.1102	0.1625	0.9382
0.1996	5.0	640	0.4845	0.3077	0.1017	0.1529	0.9392
0.1996	6.0	768	0.4841	0.2692	0.1186	0.1647	0.9365
0.1996	7.0	896	0.4987	0.2258	0.1186	0.1556	0.9349
0.0254	8.0	1024	0.5512	0.2766	0.1102	0.1576	0.9370
0.0254	9.0	1152	0.5772	0.3171	0.1102	0.1635	0.9379
0.0254	10.0	1280	0.5764	0.2586	0.1271	0.1705	0.9342
0.0254	11.0	1408	0.5964	0.2917	0.1186	0.1687	0.9380
0.005	12.0	1536	0.5952	0.2642	0.1186	0.1637	0.9368
0.005	13.0	1664	0.5980	0.2593	0.1186	0.1628	0.9367
0.005	14.0	1792	0.6033	0.2642	0.1186	0.1637	0.9370
0.005	15.0	1920	0.6058	0.2642	0.1186	0.1637	0.9370

Framework versions

• Transformers 4.34.1
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1