rlcc-new-aroma-upsample_replacement-absa-min-sum_distribution

trungpq/rlcc-new-aroma-upsample_replacement-absa-min-sum_distribution

rlcc-new-aroma-upsample_replacement-absa-min-sum_distribution 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

14% of all-time downloads

All-time downloads

Public

Parameters

109M

1.8 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_no_sum_dis
Access: Public
Created: Nov 4, 2025
Updated: Nov 6, 2025
SHA: ed06231a

Model card

From the Hugging Face model README

rlcc-new-aroma-upsample_replacement-absa-min-sum_distribution

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

Loss: 0.9507
Accuracy: 0.5490
F1 Macro: 0.5136
Precision Macro: 0.5274
Recall Macro: 0.5106
F1 Micro: 0.5490
Precision Micro: 0.5490
Recall Micro: 0.5490
Total Tf: [140, 115, 395, 115]

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro	F1 Micro	Precision Micro	Recall Micro	Total Tf
1.0966	1.0	41	1.1196	0.3765	0.3762	0.3950	0.3942	0.3765	0.3765	0.3765	[96, 159, 351, 159]
0.9695	2.0	82	1.0105	0.5020	0.4672	0.4754	0.4713	0.5020	0.5020	0.5020	[128, 127, 383, 127]
0.8561	3.0	123	0.9987	0.5255	0.5049	0.5045	0.5069	0.5255	0.5255	0.5255	[134, 121, 389, 121]
0.745	4.0	164	1.0131	0.5412	0.5224	0.5344	0.5371	0.5412	0.5412	0.5412	[138, 117, 393, 117]
0.671	5.0	205	0.9507	0.5490	0.5136	0.5274	0.5106	0.5490	0.5490	0.5490	[140, 115, 395, 115]

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_no_sum_dis
Created: Nov 4, 2025
Updated: Nov 6, 2025