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ruRoberta-brand-text-classification
chocee/ruRoberta-brand-text-classification
ruRoberta-brand-text-classification is a machine learning model from chocee. 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 peft.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- Access
- Public
- Created
- Nov 3, 2024
- Updated
- Nov 3, 2024
- SHA
- f172ad62
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. -->
ruRoberta-large-brand-text-classification
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4496
- Accuracy: {'accuracy': 0.9166666666666666}
- F1 Score: {'f1': 0.8095238095238095}
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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use 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: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
|---|---|---|---|---|---|
| 0.0864 | 1.0 | 7 | 0.4496 | {'accuracy': 0.9166666666666666} | {'f1': 0.8095238095238095} |
| 0.5172 | 2.0 | 14 | 0.4940 | {'accuracy': 0.9166666666666666} | {'f1': 0.8095238095238095} |
| 0.2577 | 3.0 | 21 | 0.6420 | {'accuracy': 0.5833333333333334} | {'f1': 0.3686274509803922} |
| 0.5231 | 4.0 | 28 | 0.7110 | {'accuracy': 0.5833333333333334} | {'f1': 0.3686274509803922} |
| 0.4421 | 5.0 | 35 | 0.5916 | {'accuracy': 0.75} | {'f1': 0.5029239766081871} |
| 0.138 | 6.0 | 42 | 0.6586 | {'accuracy': 0.75} | {'f1': 0.5029239766081871} |
| 0.1561 | 7.0 | 49 | 0.6274 | {'accuracy': 0.6666666666666666} | {'f1': 0.4259259259259259} |
| 0.0271 | 8.0 | 56 | 0.6291 | {'accuracy': 0.6666666666666666} | {'f1': 0.48148148148148145} |
| 0.4528 | 9.0 | 63 | 0.5169 | {'accuracy': 0.9166666666666666} | {'f1': 0.8095238095238095} |
| 0.5633 | 10.0 | 70 | 0.4749 | {'accuracy': 0.75} | {'f1': 0.6210526315789473} |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
- Library
- peft
- Created
- Nov 3, 2024
- Updated
- Nov 3, 2024