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rlcc-new-palate-upsample_replacement-absa-min-sum_distribution
trungpq/rlcc-new-palate-upsample_replacement-absa-min-sum_distribution
rlcc-new-palate-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
- 4eeb0d1f
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
rlcc-new-palate-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: 1.0653
- Accuracy: 0.4382
- F1 Macro: 0.4432
- Precision Macro: 0.4696
- Recall Macro: 0.4362
- F1 Micro: 0.4382
- Precision Micro: 0.4382
- Recall Micro: 0.4382
- Total Tf: [78, 100, 256, 100]
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | F1 Micro | Precision Micro | Recall Micro | Total Tf |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.1714 | 1.0 | 22 | 1.1298 | 0.4045 | 0.3282 | 0.3038 | 0.3896 | 0.4045 | 0.4045 | 0.4045 | [72, 106, 250, 106] |
| 1.0578 | 2.0 | 44 | 1.0618 | 0.4494 | 0.4077 | 0.4278 | 0.4388 | 0.4494 | 0.4494 | 0.4494 | [80, 98, 258, 98] |
| 1.0138 | 3.0 | 66 | 1.0515 | 0.4775 | 0.4730 | 0.4812 | 0.4755 | 0.4775 | 0.4775 | 0.4775 | [85, 93, 263, 93] |
| 0.9738 | 4.0 | 88 | 1.0494 | 0.4551 | 0.4552 | 0.4587 | 0.4542 | 0.4551 | 0.4551 | 0.4551 | [81, 97, 259, 97] |
| 0.9238 | 5.0 | 110 | 1.0653 | 0.4382 | 0.4432 | 0.4696 | 0.4362 | 0.4382 | 0.4382 | 0.4382 | [78, 100, 256, 100] |
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