anti_semic_test_trainer

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: 0.6848
• Accuracy: 0.8214
• F1: 0.8148
• Precision: 0.825
• Recall: 0.8049

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: 0.0001
• 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: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	68	0.4601	0.8370	0.7963	0.8776	0.7288
No log	2.0	136	0.4343	0.7926	0.8056	0.6824	0.9831
No log	3.0	204	0.4401	0.8370	0.8333	0.7534	0.9322
No log	4.0	272	0.4575	0.8889	0.8780	0.8438	0.9153
No log	5.0	340	0.5199	0.8444	0.8108	0.8654	0.7627
No log	6.0	408	0.5788	0.8222	0.7692	0.8889	0.6780
No log	7.0	476	0.5212	0.8519	0.8214	0.8679	0.7797

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cpu
• Datasets 2.16.1
• Tokenizers 0.14.1