checkpoints

This model is a fine-tuned version of UBC-NLP/MARBERTv2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.1854
• Accuracy: 0.7595
• Balanced Accuracy: 0.7595
• Mcc: 0.6402
• Macro F1: 0.7607
• Macro Precision: 0.7646
• Macro Recall: 0.7595
• Weighted F1: 0.7607
• Weighted Precision: 0.7646
• Weighted Recall: 0.7595
• Class 0 F1: 0.8113
• Class 0 Precision: 0.86
• Class 0 Recall: 0.7679
• Class 1 F1: 0.7235
• Class 1 Precision: 0.7177
• Class 1 Recall: 0.7293
• Class 2 F1: 0.7473
• Class 2 Precision: 0.7162
• Class 2 Recall: 0.7812

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 32
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 0.1
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Balanced Accuracy	Mcc	Macro F1	Macro Precision	Macro Recall	Weighted F1	Weighted Precision	Weighted Recall	Class 0 F1	Class 0 Precision	Class 0 Recall	Class 1 F1	Class 1 Precision	Class 1 Recall	Class 2 F1	Class 2 Precision	Class 2 Recall
0.5611	1.0	925	0.5775	0.7714	0.7714	0.6584	0.7699	0.7716	0.7714	0.7699	0.7716	0.7714	0.8281	0.8052	0.8523	0.7215	0.7737	0.6759	0.7602	0.7360	0.7861
0.4367	2.0	1850	0.6233	0.7757	0.7757	0.6641	0.7759	0.7774	0.7757	0.7759	0.7774	0.7757	0.8223	0.8418	0.8036	0.7355	0.7504	0.7212	0.7698	0.7399	0.8023
0.2949	3.0	2775	0.7892	0.7703	0.7703	0.6573	0.7717	0.7778	0.7703	0.7717	0.7777	0.7703	0.8132	0.8748	0.7597	0.7473	0.7044	0.7958	0.7547	0.7540	0.7553
0.2141	4.0	3700	1.0209	0.7584	0.7584	0.6389	0.7599	0.7646	0.7584	0.7599	0.7645	0.7584	0.8093	0.8595	0.7646	0.7281	0.6920	0.7682	0.7423	0.7423	0.7423
0.1405	5.0	4625	1.1854	0.7595	0.7595	0.6402	0.7607	0.7646	0.7595	0.7607	0.7646	0.7595	0.8113	0.86	0.7679	0.7235	0.7177	0.7293	0.7473	0.7162	0.7812

Framework versions

• Transformers 5.13.1
• Pytorch 2.11.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2