Bert-RAdam-Large

This model is a fine-tuned version of bert-base-cased on a subset of the PLODv2-filtered dataset. It achieves the following results on the evaluation set:

• Loss: 0.2110
• Precision: 0.7864
• Recall: 0.8598
• F1: 0.8215
• Accuracy: 0.9403

It achieves the following results on the test set:

• Loss: 0.1825
• Precision: 0.8017
• Recall: 0.8902
• F1: 0.8436
• Accuracy: 0.9500

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2244	1.0	500	0.1675	0.7653	0.8651	0.8121	0.9355
0.1231	2.0	1000	0.1673	0.7433	0.9011	0.8146	0.9375
0.0923	3.0	1500	0.1698	0.7867	0.8539	0.8189	0.9391
0.0657	4.0	2000	0.1865	0.7857	0.8405	0.8122	0.9394
0.0431	5.0	2500	0.2110	0.7864	0.8598	0.8215	0.9403

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.1
• Tokenizers 0.21.1