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cls_10_backbone_100k_v2
ttqdunggg/cls_10_backbone_100k_v2
cls_10_backbone_100k_v2 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 23, 2025
- Updated
- Nov 23, 2025
- SHA
- 7acdb297
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. -->
cls_10_backbone_100k_v2
This model is a fine-tuned version of RonTon05/model_content_V2_test on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7842
- Accuracy: 0.7496
- F1: 0.7665
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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.4441 | 1.0 | 275 | 1.0155 | 0.6634 | 0.4158 |
| 0.8722 | 2.0 | 550 | 0.8439 | 0.7180 | 0.5978 |
| 0.6808 | 3.0 | 825 | 0.8420 | 0.7121 | 0.6219 |
| 0.5589 | 4.0 | 1100 | 0.7615 | 0.7480 | 0.7606 |
| 0.4804 | 5.0 | 1375 | 0.7842 | 0.7496 | 0.7665 |
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 23, 2025
- Updated
- Nov 23, 2025