mtg-convnext-final

This model is a fine-tuned version of facebook/convnext-tiny-224 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0220
• Micro F1: 0.5651
• Macro F1: 0.5659
• Exact Match Accuracy: 0.3788
• Hamming Accuracy: 0.7976
• W Precision: 0.4796
• W Recall: 0.5995
• W F1: 0.5329
• U Precision: 0.4912
• U Recall: 0.6608
• U F1: 0.5635
• B Precision: 0.4514
• B Recall: 0.6469
• B F1: 0.5318
• R Precision: 0.5227
• R Recall: 0.6533
• R F1: 0.5808
• G Precision: 0.5662
• G Recall: 0.6866
• G F1: 0.6206

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: 32
• 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: cosine
• lr_scheduler_warmup_steps: 0.1
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Micro F1	Macro F1	Exact Match Accuracy	Hamming Accuracy	W Precision	W Recall	W F1	U Precision	U Recall	U F1	B Precision	B Recall	B F1	R Precision	R Recall	R F1	G Precision	G Recall	G F1
0.8734	1.0	1424	0.8249	0.5420	0.5411	0.2755	0.7544	0.4220	0.6825	0.5215	0.4425	0.7290	0.5508	0.4156	0.6275	0.5	0.4469	0.7462	0.5590	0.4475	0.8015	0.5744
0.7340	2.0	2848	0.7887	0.5641	0.5651	0.3081	0.7696	0.4208	0.7463	0.5382	0.4268	0.7917	0.5546	0.4415	0.6509	0.5262	0.4980	0.7241	0.5901	0.5166	0.7643	0.6165
0.5528	3.0	4272	0.8278	0.5657	0.5661	0.3288	0.7765	0.4584	0.6637	0.5423	0.4606	0.7256	0.5635	0.4218	0.7026	0.5271	0.4831	0.7335	0.5825	0.5121	0.7703	0.6152
0.3567	4.0	5696	0.9603	0.5666	0.5674	0.3623	0.7921	0.4679	0.6254	0.5353	0.4838	0.6793	0.5652	0.4407	0.6686	0.5312	0.5198	0.6695	0.5852	0.5492	0.7126	0.6203
0.2507	5.0	7120	1.0220	0.5651	0.5659	0.3788	0.7976	0.4796	0.5995	0.5329	0.4912	0.6608	0.5635	0.4514	0.6469	0.5318	0.5227	0.6533	0.5808	0.5662	0.6866	0.6206

Framework versions

• Transformers 5.14.1
• Pytorch 2.13.0+cu126
• Datasets 5.0.1
• Tokenizers 0.22.2