AntiIsraelBERT

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

• Loss: 0.3801
• Accuracy: 0.8417
• F1: 0.8571
• Precision: 0.8143
• Recall: 0.9048

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: 1e-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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	90	0.3758	0.8333	0.8540	0.7697	0.9590
No log	2.0	180	0.3031	0.8542	0.8679	0.8042	0.9426
No log	3.0	270	0.3224	0.8792	0.8880	0.8394	0.9426
No log	4.0	360	0.4631	0.8667	0.8788	0.8169	0.9508
No log	5.0	450	0.3999	0.9042	0.9076	0.8898	0.9262
0.2639	6.0	540	0.6296	0.875	0.8864	0.8239	0.9590
0.2639	7.0	630	0.6210	0.8667	0.8769	0.8261	0.9344

Framework versions

• Transformers 4.36.1
• Pytorch 2.0.1+cu117
• Datasets 2.19.2
• Tokenizers 0.15.0