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: 0.1696
• Accuracy: 0.9308
• Class 0 Precision: 0.9947
• Class 0 Recall: 0.9319
• Class 0 F1: 0.9623
• Class 0 Support: 132570
• Class 1 Precision: 0.4316
• Class 1 Recall: 0.9118
• Class 1 F1: 0.5859
• Class 1 Support: 7517

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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Class 0 Precision	Class 0 Recall	Class 0 F1	Class 0 Support	Class 1 Precision	Class 1 Recall	Class 1 F1	Class 1 Support
0.2116	0.9998	2830	0.1709	0.9437	0.9334	0.9671	0.9500	6265	0.9574	0.9146	0.9355	5058

Framework versions

• Transformers 4.40.0
• Pytorch 2.2.1+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1