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RuleBert-v0.1-k3
ribesstefano/RuleBert-v0.1-k3
RuleBert-v0.1-k3 is a text classification model from ribesstefano. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Jan 7, 2024
- Updated
- Jan 7, 2024
- SHA
- 5af1a404
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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. -->
ribesstefano/RuleBert-v0.1-k3
This model is a fine-tuned version of classla/xlm-roberta-base-multilingual-text-genre-classifier on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3285
- F1: 0.4638
- Roc Auc: 0.6576
- Accuracy: 0.0714
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: 4
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.3423 | 0.13 | 250 | 0.3539 | 0.4497 | 0.6562 | 0.0670 |
| 0.3231 | 0.27 | 500 | 0.3425 | 0.4596 | 0.6594 | 0.0670 |
| 0.3248 | 0.4 | 750 | 0.3364 | 0.4495 | 0.6541 | 0.0714 |
| 0.3283 | 0.54 | 1000 | 0.3351 | 0.4529 | 0.6555 | 0.0714 |
| 0.3237 | 0.67 | 1250 | 0.3315 | 0.4600 | 0.6581 | 0.0625 |
| 0.325 | 0.81 | 1500 | 0.3313 | 0.4681 | 0.6624 | 0.0312 |
| 0.3316 | 0.94 | 1750 | 0.3290 | 0.4595 | 0.6564 | 0.0714 |
| 0.3239 | 1.08 | 2000 | 0.3310 | 0.4592 | 0.6572 | 0.0625 |
| 0.3085 | 1.21 | 2250 | 0.3280 | 0.4614 | 0.6567 | 0.0670 |
| 0.3161 | 1.35 | 2500 | 0.3303 | 0.4623 | 0.6574 | 0.0670 |
| 0.314 | 1.48 | 2750 | 0.3289 | 0.4613 | 0.6566 | 0.0714 |
| 0.3187 | 1.62 | 3000 | 0.3293 | 0.4594 | 0.6554 | 0.0714 |
| 0.3145 | 1.75 | 3250 | 0.3295 | 0.4629 | 0.6569 | 0.0714 |
| 0.3128 | 1.89 | 3500 | 0.3285 | 0.4629 | 0.6569 | 0.0714 |
| 0.3135 | 2.02 | 3750 | 0.3285 | 0.4615 | 0.6566 | 0.0714 |
| 0.3171 | 2.16 | 4000 | 0.3285 | 0.4638 | 0.6576 | 0.0714 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Jan 7, 2024
- Updated
- Jan 7, 2024