results

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

• Loss: 0.6067
• Accuracy: 0.925
• Precision: 0.8676
• Recall: 0.7415
• F1: 0.7869

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	50	0.5663	0.905	0.7726	0.8381	0.7997
No log	2.0	100	0.4798	0.92	0.8020	0.8646	0.8287
No log	3.0	150	0.4370	0.92	0.8424	0.7386	0.7778
No log	4.0	200	0.4873	0.93	0.85	0.7983	0.8212
No log	5.0	250	0.6067	0.925	0.8676	0.7415	0.7869

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Datasets 3.0.1
• Tokenizers 0.19.1