rlcc-new-appearance-class-weight-absa-None

trungpq/rlcc-new-appearance-class-weight-absa-None

rlcc-new-appearance-class-weight-absa-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. --

Downloads · 30 days

10% of all-time downloads

All-time downloads

Public

Parameters

109M

3.5 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors438 MB · 100%

At a glance

Library: transformers
Model type: bert_with_absa
Access: Public
Created: Sep 10, 2025
Updated: Sep 17, 2025
SHA: 0c7175b8

Model card

From the Hugging Face model README

rlcc-new-appearance-class-weight-absa-None

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

Loss: 1.3573
Accuracy: 0.6426
F1 Macro: 0.6384
Precision Macro: 0.6867
Recall Macro: 0.6338
F1 Micro: 0.6426
Precision Micro: 0.6426
Recall Micro: 0.6426
Total Tf: [178, 99, 455, 99]

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: 34
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
1.0802	1.0	35	1.0869	0.3899	0.1938	0.1978	0.3253	0.3899	0.3899	0.3899	[108, 169, 385, 169]
1.0591	2.0	70	1.0717	0.4007	0.1907	0.1336	0.3333	0.4007	0.4007	0.4007	[111, 166, 388, 166]
0.9575	3.0	105	0.9362	0.5379	0.5375	0.5440	0.5356	0.5379	0.5379	0.5379	[149, 128, 426, 128]
0.87	4.0	140	0.8764	0.6101	0.6123	0.6269	0.6048	0.6101	0.6101	0.6101	[169, 108, 446, 108]
0.7307	5.0	175	0.8619	0.5993	0.6005	0.6006	0.6049	0.5993	0.5993	0.5993	[166, 111, 443, 111]
0.6103	6.0	210	0.8720	0.6390	0.6449	0.6471	0.6501	0.6390	0.6390	0.6390	[177, 100, 454, 100]
0.5319	7.0	245	0.9234	0.6101	0.6021	0.6493	0.5987	0.6101	0.6101	0.6101	[169, 108, 446, 108]
0.4465	8.0	280	0.9005	0.6679	0.6697	0.6809	0.6640	0.6679	0.6679	0.6679	[185, 92, 462, 92]
0.3507	9.0	315	0.9280	0.6715	0.6716	0.7087	0.6639	0.6715	0.6715	0.6715	[186, 91, 463, 91]
0.268	10.0	350	0.9575	0.6606	0.6649	0.6689	0.6620	0.6606	0.6606	0.6606	[183, 94, 460, 94]
0.2634	11.0	385	1.0887	0.6570	0.6477	0.7135	0.6438	0.6570	0.6570	0.6570	[182, 95, 459, 95]
0.1824	12.0	420	1.0807	0.6787	0.6807	0.7100	0.6719	0.6787	0.6787	0.6787	[188, 89, 465, 89]
0.1747	13.0	455	1.1452	0.6354	0.6353	0.6881	0.6217	0.6354	0.6354	0.6354	[176, 101, 453, 101]
0.1663	14.0	490	1.1585	0.6715	0.6722	0.6935	0.6680	0.6715	0.6715	0.6715	[186, 91, 463, 91]
0.1129	15.0	525	1.1543	0.6643	0.6670	0.6838	0.6599	0.6643	0.6643	0.6643	[184, 93, 461, 93]
0.1127	16.0	560	1.2225	0.6534	0.6556	0.6672	0.6519	0.6534	0.6534	0.6534	[181, 96, 458, 96]
0.1198	17.0	595	1.3573	0.6426	0.6384	0.6867	0.6338	0.6426	0.6426	0.6426	[178, 99, 455, 99]

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