rlcc-new-appearance-upsample_replacement-absa-max

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

rlcc-new-appearance-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: c2073ce0

Model card

From the Hugging Face model README

rlcc-new-appearance-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: 1.5657
Accuracy: 0.6354
F1 Macro: 0.6369
Precision Macro: 0.6629
Recall Macro: 0.6268
F1 Micro: 0.6354
Precision Micro: 0.6354
Recall Micro: 0.6354
Total Tf: [176, 101, 453, 101]

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: 44
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
1.1464	1.0	45	1.0885	0.3971	0.1965	0.2437	0.3313	0.3971	0.3971	0.3971	[110, 167, 387, 167]
0.9655	2.0	90	0.9673	0.4729	0.4539	0.4671	0.5079	0.4729	0.4729	0.4729	[131, 146, 408, 146]
0.76	3.0	135	0.9955	0.5018	0.4890	0.5171	0.5319	0.5018	0.5018	0.5018	[139, 138, 416, 138]
0.5537	4.0	180	0.9048	0.5884	0.5963	0.6021	0.5928	0.5884	0.5884	0.5884	[163, 114, 440, 114]
0.3566	5.0	225	1.0518	0.5921	0.5953	0.6080	0.5887	0.5921	0.5921	0.5921	[164, 113, 441, 113]
0.282	6.0	270	1.0173	0.6282	0.6336	0.6361	0.6400	0.6282	0.6282	0.6282	[174, 103, 451, 103]
0.265	7.0	315	1.1646	0.5993	0.5967	0.6270	0.5917	0.5993	0.5993	0.5993	[166, 111, 443, 111]
0.1113	8.0	360	1.1762	0.6282	0.6307	0.6508	0.6238	0.6282	0.6282	0.6282	[174, 103, 451, 103]
0.093	9.0	405	1.2829	0.6354	0.6408	0.6499	0.6349	0.6354	0.6354	0.6354	[176, 101, 453, 101]
0.0896	10.0	450	1.3545	0.6065	0.6086	0.6380	0.5977	0.6065	0.6065	0.6065	[168, 109, 445, 109]
0.0994	11.0	495	1.4229	0.6209	0.6184	0.6453	0.6128	0.6209	0.6209	0.6209	[172, 105, 449, 105]
0.0687	12.0	540	1.5555	0.6173	0.6195	0.6502	0.6077	0.6173	0.6173	0.6173	[171, 106, 448, 106]
0.0768	13.0	585	1.5729	0.6245	0.6203	0.6660	0.6117	0.6245	0.6245	0.6245	[173, 104, 450, 104]
0.0411	14.0	630	1.5657	0.6354	0.6369	0.6629	0.6268	0.6354	0.6354	0.6354	[176, 101, 453, 101]

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