rlcc-new-aroma-upsample_replacement-aspect_classifier

trungpq/rlcc-new-aroma-upsample_replacement-aspect_classifier

rlcc-new-aroma-upsample_replacement-aspect_classifier 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
Access: Public
Created: Nov 7, 2025
Updated: Nov 7, 2025
SHA: c4efb1d0

Model card

From the Hugging Face model README

rlcc-new-aroma-upsample_replacement-aspect_classifier

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

Loss: 1.5190
Accuracy: 0.4549
F1 Macro: 0.4173
Precision Macro: 0.4299
Recall Macro: 0.4139
F1 Micro: 0.4549
Precision Micro: 0.4549
Recall Micro: 0.4549
Total Tf: [116, 139, 371, 139]

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: 40
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro	F1 Micro	Precision Micro	Recall Micro	Total Tf
1.1063	1.0	41	1.1152	0.2588	0.1736	0.3440	0.3490	0.2588	0.2588	0.2588	[66, 189, 321, 189]
1.0222	2.0	82	1.1515	0.2980	0.2990	0.3347	0.3179	0.2980	0.2980	0.2980	[76, 179, 331, 179]
0.8089	3.0	123	1.1543	0.4	0.3963	0.3976	0.4065	0.4000	0.4	0.4	[102, 153, 357, 153]
0.6457	4.0	164	1.1887	0.4549	0.4204	0.4207	0.4293	0.4549	0.4549	0.4549	[116, 139, 371, 139]
0.4898	5.0	205	1.2712	0.4471	0.4351	0.4419	0.4361	0.4471	0.4471	0.4471	[114, 141, 369, 141]
0.3316	6.0	246	1.4476	0.4471	0.4220	0.4353	0.4177	0.4471	0.4471	0.4471	[114, 141, 369, 141]
0.2647	7.0	287	1.5190	0.4549	0.4173	0.4299	0.4139	0.4549	0.4549	0.4549	[116, 139, 371, 139]

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2

Library: transformers
Type: bert
Created: Nov 7, 2025
Updated: Nov 7, 2025