bert-finetuned-ner-requirements

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: 0.9697
• Precision: 0.2404
• Recall: 0.3788
• F1: 0.2941
• Accuracy: 0.75

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	10	1.5976	0.0	0.0	0.0	0.7226
No log	2.0	20	1.2824	0.1364	0.1364	0.1364	0.7555
No log	3.0	30	1.1017	0.1829	0.2273	0.2027	0.7464
No log	4.0	40	1.0788	0.1321	0.2121	0.1628	0.7464
No log	5.0	50	1.0091	0.1651	0.2727	0.2057	0.7482
No log	6.0	60	0.9949	0.1667	0.2879	0.2111	0.7427
No log	7.0	70	0.9766	0.2	0.3182	0.2456	0.7536
No log	8.0	80	0.9734	0.2202	0.3636	0.2743	0.7482
No log	9.0	90	0.9744	0.2336	0.3788	0.2890	0.75
No log	10.0	100	0.9697	0.2404	0.3788	0.2941	0.75

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Datasets 3.2.0
• Tokenizers 0.19.1