my_awesome_model

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

• Loss: 1.0970
• Accuracy: 0.8681
• F1: 0.8376

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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	167	0.3828	0.8501	0.8031
No log	2.0	334	0.4787	0.8456	0.8275
0.2101	3.0	501	0.6186	0.8666	0.8367
0.2101	4.0	668	0.7201	0.8546	0.8265
0.2101	5.0	835	0.7675	0.8651	0.8346
0.0339	6.0	1002	0.8561	0.8681	0.8434
0.0339	7.0	1169	0.8898	0.8681	0.8382
0.0339	8.0	1336	0.9854	0.8711	0.8436
0.0069	9.0	1503	0.9919	0.8711	0.8407
0.0069	10.0	1670	1.0695	0.8561	0.8280
0.0069	11.0	1837	1.0542	0.8666	0.8349
0.0007	12.0	2004	1.0896	0.8681	0.8370
0.0007	13.0	2171	1.1001	0.8666	0.8349
0.0007	14.0	2338	1.0888	0.8606	0.8312
0.0012	15.0	2505	1.0970	0.8681	0.8376

Framework versions

• Transformers 4.29.2
• Pytorch 2.0.1+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3