rlcc-new-taste-upsample_replacement-absa-min-sum_distribution

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

rlcc-new-taste-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. --

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At a glance

Library: transformers
Model type: bert_with_absa_no_sum_dis
Access: Public
Created: Nov 4, 2025
Updated: Nov 6, 2025
SHA: 9764427a

Model card

From the Hugging Face model README

rlcc-new-taste-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: 1.1678
Accuracy: 0.3945
F1 Macro: 0.3957
Precision Macro: 0.4002
Recall Macro: 0.3945
F1 Micro: 0.3945
Precision Micro: 0.3945
Recall Micro: 0.3945
Total Tf: [144, 221, 509, 221]

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: 46
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
1.1104	1.0	47	1.0882	0.4055	0.3280	0.3028	0.4096	0.4055	0.4055	0.4055	[148, 217, 513, 217]
1.0567	2.0	94	1.0595	0.4	0.3731	0.4140	0.3973	0.4000	0.4	0.4	[146, 219, 511, 219]
0.9225	3.0	141	1.0962	0.4301	0.3937	0.4009	0.4242	0.4301	0.4301	0.4301	[157, 208, 522, 208]
0.8112	4.0	188	1.1196	0.4247	0.4095	0.4402	0.4201	0.4247	0.4247	0.4247	[155, 210, 520, 210]
0.6918	5.0	235	1.1678	0.3945	0.3957	0.4002	0.3945	0.3945	0.3945	0.3945	[144, 221, 509, 221]

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