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RuleBert-v0.1-k4
ribesstefano/RuleBert-v0.1-k4
RuleBert-v0.1-k4 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
- 5f64a67c
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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-k4
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: 0.3547
- F1: 0.4940
- Roc Auc: 0.6712
- Accuracy: 0.0
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 |
|---|---|---|---|---|---|---|
| 0.3524 | 0.14 | 250 | 0.3526 | 0.4888 | 0.6731 | 0.0 |
| 0.3318 | 0.27 | 500 | 0.3489 | 0.4885 | 0.6698 | 0.0 |
| 0.3278 | 0.41 | 750 | 0.3517 | 0.4870 | 0.6709 | 0.0 |
| 0.3165 | 0.54 | 1000 | 0.3506 | 0.4953 | 0.6726 | 0.0 |
| 0.3243 | 0.68 | 1250 | 0.3501 | 0.4904 | 0.6693 | 0.0 |
| 0.3072 | 0.82 | 1500 | 0.3529 | 0.4979 | 0.6715 | 0.0 |
| 0.311 | 0.95 | 1750 | 0.3527 | 0.4855 | 0.6664 | 0.0 |
| 0.3277 | 1.09 | 2000 | 0.3542 | 0.4900 | 0.6693 | 0.0 |
| 0.3102 | 1.22 | 2250 | 0.3535 | 0.4881 | 0.6679 | 0.0 |
| 0.3159 | 1.36 | 2500 | 0.3533 | 0.4839 | 0.6663 | 0.0 |
| 0.3073 | 1.49 | 2750 | 0.3531 | 0.4994 | 0.6726 | 0.0 |
| 0.3108 | 1.63 | 3000 | 0.3542 | 0.4929 | 0.6701 | 0.0 |
| 0.3093 | 1.77 | 3250 | 0.3546 | 0.4925 | 0.6702 | 0.0 |
| 0.2981 | 1.9 | 3500 | 0.3547 | 0.4933 | 0.6703 | 0.0 |
| 0.3046 | 2.04 | 3750 | 0.3547 | 0.4929 | 0.6707 | 0.0 |
| 0.3085 | 2.17 | 4000 | 0.3547 | 0.4940 | 0.6712 | 0.0 |
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