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--- |
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license: mit |
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base_model: classla/xlm-roberta-base-multilingual-text-genre-classifier |
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tags: |
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- Italian |
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- legal ruling |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: ribesstefano/RuleBert-v0.1-k3 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ribesstefano/RuleBert-v0.1-k3 |
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This model is a fine-tuned version of [classla/xlm-roberta-base-multilingual-text-genre-classifier](https://huggingface.co/classla/xlm-roberta-base-multilingual-text-genre-classifier) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3285 |
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- F1: 0.4638 |
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- Roc Auc: 0.6576 |
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- Accuracy: 0.0714 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 4000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| 0.3423 | 0.13 | 250 | 0.3539 | 0.4497 | 0.6562 | 0.0670 | |
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| 0.3231 | 0.27 | 500 | 0.3425 | 0.4596 | 0.6594 | 0.0670 | |
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| 0.3248 | 0.4 | 750 | 0.3364 | 0.4495 | 0.6541 | 0.0714 | |
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| 0.3283 | 0.54 | 1000 | 0.3351 | 0.4529 | 0.6555 | 0.0714 | |
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| 0.3237 | 0.67 | 1250 | 0.3315 | 0.4600 | 0.6581 | 0.0625 | |
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| 0.325 | 0.81 | 1500 | 0.3313 | 0.4681 | 0.6624 | 0.0312 | |
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| 0.3316 | 0.94 | 1750 | 0.3290 | 0.4595 | 0.6564 | 0.0714 | |
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| 0.3239 | 1.08 | 2000 | 0.3310 | 0.4592 | 0.6572 | 0.0625 | |
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| 0.3085 | 1.21 | 2250 | 0.3280 | 0.4614 | 0.6567 | 0.0670 | |
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| 0.3161 | 1.35 | 2500 | 0.3303 | 0.4623 | 0.6574 | 0.0670 | |
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| 0.314 | 1.48 | 2750 | 0.3289 | 0.4613 | 0.6566 | 0.0714 | |
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| 0.3187 | 1.62 | 3000 | 0.3293 | 0.4594 | 0.6554 | 0.0714 | |
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| 0.3145 | 1.75 | 3250 | 0.3295 | 0.4629 | 0.6569 | 0.0714 | |
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| 0.3128 | 1.89 | 3500 | 0.3285 | 0.4629 | 0.6569 | 0.0714 | |
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| 0.3135 | 2.02 | 3750 | 0.3285 | 0.4615 | 0.6566 | 0.0714 | |
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| 0.3171 | 2.16 | 4000 | 0.3285 | 0.4638 | 0.6576 | 0.0714 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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