16052025_xlm_roberta_large_test_linsearch_only_abstract

This model is a fine-tuned version of FacebookAI/xlm-roberta-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.3318
• Accuracy: 0.5931
• F1 Macro: 0.5650
• Precision Macro: 0.5755
• Recall Macro: 0.5602

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: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro
1.3362	0.9999	7024	1.3026	0.5520	0.4904	0.5266	0.4959
1.2241	1.9999	14048	1.2036	0.5773	0.5348	0.5535	0.5395
1.1337	2.9999	21072	1.1903	0.5760	0.5303	0.5466	0.5278
1.069	3.9999	28096	1.1570	0.5876	0.5418	0.5564	0.5439
0.9832	4.9999	35120	1.1723	0.5872	0.5461	0.5570	0.5461
0.9197	5.9999	42144	1.1752	0.5871	0.5455	0.5456	0.5572
0.8278	6.9999	49168	1.2078	0.5928	0.5537	0.5616	0.5589
0.7568	7.9999	56192	1.2398	0.5923	0.5563	0.5612	0.5568
0.6863	8.9999	63216	1.2840	0.5937	0.5556	0.5723	0.5465
0.63	9.9999	70240	1.3318	0.5931	0.5650	0.5755	0.5602

Framework versions

• Transformers 4.48.0
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0