rlcc-new-palate-upsample_replacement-absa-max

trungpq/rlcc-new-palate-upsample_replacement-absa-max

rlcc-new-palate-upsample_replacement-absa-max 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_with_absa
Access: Public
Created: Sep 10, 2025
Updated: Sep 17, 2025
SHA: 6ffdf5bc

Model card

From the Hugging Face model README

rlcc-new-palate-upsample_replacement-absa-max

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

Loss: 1.5839
Accuracy: 0.5449
F1 Macro: 0.5505
Precision Macro: 0.5946
Recall Macro: 0.5420
F1 Micro: 0.5449
Precision Micro: 0.5449
Recall Micro: 0.5449
Total Tf: [97, 81, 275, 81]

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: 21
num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro	F1 Micro	Precision Micro	Recall Micro	Total Tf
1.1769	1.0	22	1.1092	0.3371	0.1886	0.1971	0.3291	0.3371	0.3371	0.3371	[60, 118, 238, 118]
1.0296	2.0	44	1.1047	0.3539	0.2005	0.3156	0.3390	0.3539	0.3539	0.3539	[63, 115, 241, 115]
0.8965	3.0	66	1.0251	0.4494	0.4440	0.4374	0.4562	0.4494	0.4494	0.4494	[80, 98, 258, 98]
0.7047	4.0	88	1.0048	0.5281	0.5123	0.5123	0.5331	0.5281	0.5281	0.5281	[94, 84, 272, 84]
0.5384	5.0	110	1.0360	0.5337	0.5366	0.5379	0.5369	0.5337	0.5337	0.5337	[95, 83, 273, 83]
0.4743	6.0	132	1.1596	0.5337	0.5378	0.5474	0.5387	0.5337	0.5337	0.5337	[95, 83, 273, 83]
0.3305	7.0	154	1.2948	0.5	0.5026	0.5492	0.4972	0.5	0.5	0.5	[89, 89, 267, 89]
0.248	8.0	176	1.2884	0.5618	0.5644	0.6166	0.5576	0.5618	0.5618	0.5618	[100, 78, 278, 78]
0.2021	9.0	198	1.4366	0.5449	0.5492	0.5942	0.5419	0.5449	0.5449	0.5449	[97, 81, 275, 81]
0.1374	10.0	220	1.4121	0.5506	0.5538	0.6038	0.5467	0.5506	0.5506	0.5506	[98, 80, 276, 80]
0.1042	11.0	242	1.4868	0.5337	0.5374	0.5801	0.5329	0.5337	0.5337	0.5337	[95, 83, 273, 83]
0.0927	12.0	264	1.5962	0.5281	0.5321	0.5937	0.5249	0.5281	0.5281	0.5281	[94, 84, 272, 84]
0.1098	13.0	286	1.5839	0.5449	0.5505	0.5946	0.5420	0.5449	0.5449	0.5449	[97, 81, 275, 81]

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2

Library: transformers
Type: bert_with_absa
Created: Sep 10, 2025
Updated: Sep 17, 2025