slac-new-taste-none

trungpq/slac-new-taste-none

slac-new-taste-none is a machine learning model from trungpq. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
Model type: bert_model
Access: Public
Created: Nov 4, 2025
Updated: Nov 4, 2025
SHA: 70509be5

Model card

From the Hugging Face model README

slac-new-taste-none

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

Loss: 0.6326
Accuracy: 0.9121
F1 Macro: 0.8844
Precision Macro: 0.8807
Recall Macro: 0.8884
F1 Micro: 0.9121
Precision Micro: 0.9121
Recall Micro: 0.9121
Total Tf: [1411, 136, 1411, 136]

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: 64
eval_batch_size: 64
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
lr_scheduler_warmup_steps: 188
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro	F1 Micro	Precision Micro	Recall Micro	Total Tf
0.2328	1.0	189	0.2509	0.9089	0.8814	0.8742	0.8896	0.9089	0.9089	0.9089	[1406, 141, 1406, 141]
0.198	2.0	378	0.2336	0.9134	0.8874	0.8798	0.8960	0.9134	0.9134	0.9134	[1413, 134, 1413, 134]
0.1745	3.0	567	0.2694	0.9076	0.8815	0.8699	0.8956	0.9076	0.9076	0.9076	[1404, 143, 1404, 143]
0.1279	4.0	756	0.3168	0.9101	0.8810	0.8800	0.8819	0.9101	0.9101	0.9101	[1408, 139, 1408, 139]
0.0648	5.0	945	0.3551	0.9037	0.8757	0.8661	0.8870	0.9037	0.9037	0.9037	[1398, 149, 1398, 149]
0.0633	6.0	1134	0.4262	0.9095	0.8814	0.8766	0.8866	0.9095	0.9095	0.9095	[1407, 140, 1407, 140]
0.0354	7.0	1323	0.4898	0.9030	0.8748	0.8655	0.8857	0.9030	0.9030	0.9030	[1397, 150, 1397, 150]
0.0252	8.0	1512	0.5322	0.9101	0.8804	0.8813	0.8794	0.9101	0.9101	0.9101	[1408, 139, 1408, 139]
0.0187	9.0	1701	0.5827	0.9108	0.8811	0.8824	0.8798	0.9108	0.9108	0.9108	[1409, 138, 1409, 138]
0.015	10.0	1890	0.5678	0.9114	0.8817	0.8840	0.8794	0.9114	0.9114	0.9114	[1410, 137, 1410, 137]
0.0099	11.0	2079	0.5877	0.9121	0.8852	0.8792	0.8918	0.9121	0.9121	0.9121	[1411, 136, 1411, 136]
0.0092	12.0	2268	0.5962	0.9140	0.8865	0.8842	0.8888	0.9140	0.9140	0.9140	[1414, 133, 1414, 133]
0.0073	13.0	2457	0.6296	0.9121	0.8838	0.8819	0.8858	0.9121	0.9121	0.9121	[1411, 136, 1411, 136]
0.0082	14.0	2646	0.6375	0.9127	0.8842	0.8839	0.8845	0.9127	0.9127	0.9127	[1412, 135, 1412, 135]
0.0066	15.0	2835	0.6326	0.9121	0.8844	0.8807	0.8884	0.9121	0.9121	0.9121	[1411, 136, 1411, 136]

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2

Library: transformers
Type: bert_model
Created: Nov 4, 2025
Updated: Nov 4, 2025