slac-new-taste-class_weight

trungpq/slac-new-taste-class_weight

slac-new-taste-class_weight 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: Sep 10, 2025
Updated: Sep 17, 2025
SHA: b08be38a

Model card

From the Hugging Face model README

slac-new-taste-class_weight

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

Loss: 1.1930
Accuracy: 0.9082
F1 Macro: 0.8797
Precision Macro: 0.8749
Recall Macro: 0.8849
F1 Micro: 0.9082
Precision Micro: 0.9082
Recall Micro: 0.9082
Total Tf: [1405, 142, 1405, 142]

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
0.4197	1.0	189	0.4260	0.8914	0.8645	0.8474	0.8899	0.8914	0.8914	0.8914	[1379, 168, 1379, 168]
0.3343	2.0	378	0.3918	0.9076	0.8832	0.8678	0.9041	0.9076	0.9076	0.9076	[1404, 143, 1404, 143]
0.2901	3.0	567	0.4618	0.8875	0.8615	0.8424	0.8924	0.8875	0.8875	0.8875	[1373, 174, 1373, 174]
0.2328	4.0	756	0.5757	0.9076	0.8803	0.8716	0.8905	0.9076	0.9076	0.9076	[1404, 143, 1404, 143]
0.1465	5.0	945	0.6024	0.9101	0.8827	0.8765	0.8896	0.9101	0.9101	0.9101	[1408, 139, 1408, 139]
0.1468	6.0	1134	0.7507	0.9101	0.8825	0.8768	0.8888	0.9101	0.9101	0.9101	[1408, 139, 1408, 139]
0.0861	7.0	1323	0.7162	0.8992	0.8714	0.8588	0.8874	0.8992	0.8992	0.8992	[1391, 156, 1391, 156]
0.0482	8.0	1512	0.9931	0.9134	0.8833	0.8890	0.8781	0.9134	0.9134	0.9134	[1413, 134, 1413, 134]
0.0426	9.0	1701	1.0074	0.9134	0.8850	0.8850	0.8850	0.9134	0.9134	0.9134	[1413, 134, 1413, 134]
0.0251	10.0	1890	1.0537	0.9082	0.8793	0.8756	0.8832	0.9082	0.9082	0.9082	[1405, 142, 1405, 142]
0.0111	11.0	2079	1.0158	0.9089	0.8827	0.8721	0.8956	0.9089	0.9089	0.9089	[1406, 141, 1406, 141]
0.0212	12.0	2268	1.0897	0.9095	0.8824	0.8748	0.8909	0.9095	0.9095	0.9095	[1407, 140, 1407, 140]
0.0102	13.0	2457	1.1812	0.9056	0.8755	0.8730	0.8781	0.9056	0.9056	0.9056	[1401, 146, 1401, 146]
0.0188	14.0	2646	1.1855	0.9076	0.8790	0.8739	0.8845	0.9076	0.9076	0.9076	[1404, 143, 1404, 143]
0.0153	15.0	2835	1.1930	0.9082	0.8797	0.8749	0.8849	0.9082	0.9082	0.9082	[1405, 142, 1405, 142]

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: Sep 10, 2025
Updated: Sep 17, 2025