bert-finetuned-am

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: 0.4629
• Precision: 0.3961
• Recall: 0.6021
• F1: 0.4779
• Accuracy: 0.8443

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	41	0.7587	0.2544	0.4027	0.3118	0.7235
No log	2.0	82	0.5670	0.2003	0.4082	0.2687	0.8011
No log	3.0	123	0.4773	0.2355	0.4525	0.3098	0.8238
No log	4.0	164	0.4514	0.2963	0.5166	0.3766	0.8292
No log	5.0	205	0.4409	0.3261	0.5491	0.4092	0.8384
No log	6.0	246	0.4426	0.3558	0.5839	0.4422	0.8460
No log	7.0	287	0.4629	0.3961	0.6021	0.4779	0.8443

Framework versions

• Transformers 4.39.2
• Pytorch 2.2.2+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2