model1-thesis-3

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: 1.1377
• Precision: 0.4527
• Recall: 0.5051
• F1: 0.4774
• Accuracy: 0.6190

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: 5e-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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	45	1.3105	0.3737	0.4765	0.4189	0.5364
No log	2.0	90	1.0783	0.4009	0.4523	0.4250	0.5781
No log	3.0	135	1.0601	0.4444	0.4750	0.4592	0.6127
No log	4.0	180	1.0953	0.4745	0.4876	0.4809	0.6266
No log	5.0	225	1.1377	0.4527	0.5051	0.4774	0.6190

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1+cu116
• Datasets 2.9.0
• Tokenizers 0.13.2