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3adapter_backbone_100k_cls_content
ttqdunggg/3adapter_backbone_100k_cls_content
3adapter_backbone_100k_cls_content is a machine learning model from ttqdunggg. 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. The card lists the license as agpl-3.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- agpl-3.0
- Model type
- roberta
- Access
- Public
- Created
- Nov 12, 2025
- Updated
- Nov 12, 2025
- SHA
- ea7592fd
Base models
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. -->
3adapter_backbone_100k_cls_content
This model is a fine-tuned version of ttqdunggg/3adapter_backbone_100k on the None dataset. It achieves the following results on the evaluation set:
- Acc Content: 0.9639
- F1 Content: 0.9366
- Acc Classification: 0.8401
- F1 Classification: 0.8136
- Loss: 0.2292
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Acc Content | F1 Content | Acc Classification | F1 Classification | Validation Loss |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 276 | 0.9612 | 0.9319 | 0.8013 | 0.7655 | 0.2009 |
| 0.2424 | 2.0 | 552 | 0.9601 | 0.9306 | 0.8190 | 0.7896 | 0.1794 |
| 0.2424 | 3.0 | 828 | 0.9630 | 0.9350 | 0.8265 | 0.7943 | 0.1772 |
| 0.1156 | 4.0 | 1104 | 0.9623 | 0.9331 | 0.8381 | 0.8060 | 0.1805 |
| 0.1156 | 5.0 | 1380 | 0.9617 | 0.9329 | 0.8374 | 0.8106 | 0.1834 |
| 0.0695 | 6.0 | 1656 | 0.9619 | 0.9332 | 0.8419 | 0.8131 | 0.1952 |
| 0.0695 | 7.0 | 1932 | 0.9617 | 0.9312 | 0.8378 | 0.8102 | 0.2066 |
| 0.0401 | 8.0 | 2208 | 0.9617 | 0.9314 | 0.8383 | 0.8141 | 0.2164 |
| 0.0401 | 9.0 | 2484 | 0.9639 | 0.9368 | 0.8376 | 0.8120 | 0.2261 |
| 0.0246 | 10.0 | 2760 | 0.9639 | 0.9366 | 0.8401 | 0.8136 | 0.2292 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.22.1
- Library
- transformers
- Type
- roberta
- License
- agpl-3.0
- Created
- Nov 12, 2025
- Updated
- Nov 12, 2025