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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-k0 |
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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-k0 |
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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: 0.3765 |
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- F1: 0.5087 |
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- Roc Auc: 0.6757 |
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- Accuracy: 0.0333 |
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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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| 0.3621 | 0.14 | 250 | 0.3791 | 0.4861 | 0.6678 | 0.0458 | |
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| 0.3199 | 0.28 | 500 | 0.3754 | 0.4969 | 0.6730 | 0.0375 | |
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| 0.3135 | 0.41 | 750 | 0.3785 | 0.4965 | 0.6715 | 0.0458 | |
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| 0.3176 | 0.55 | 1000 | 0.3755 | 0.4988 | 0.6725 | 0.0333 | |
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| 0.3056 | 0.69 | 1250 | 0.3734 | 0.5100 | 0.6756 | 0.0667 | |
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| 0.3016 | 0.83 | 1500 | 0.3749 | 0.5076 | 0.6755 | 0.0333 | |
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| 0.3044 | 0.97 | 1750 | 0.3763 | 0.5014 | 0.6738 | 0.0125 | |
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| 0.3009 | 1.11 | 2000 | 0.3781 | 0.5073 | 0.6751 | 0.0333 | |
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| 0.3044 | 1.24 | 2250 | 0.3782 | 0.5089 | 0.6755 | 0.0417 | |
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| 0.2979 | 1.38 | 2500 | 0.3770 | 0.5047 | 0.6745 | 0.0167 | |
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| 0.2977 | 1.52 | 2750 | 0.3772 | 0.5114 | 0.6764 | 0.0417 | |
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| 0.2998 | 1.66 | 3000 | 0.3747 | 0.5107 | 0.6762 | 0.0375 | |
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| 0.2916 | 1.8 | 3250 | 0.3741 | 0.5096 | 0.6758 | 0.0375 | |
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| 0.3107 | 1.94 | 3500 | 0.3757 | 0.5056 | 0.6748 | 0.0208 | |
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| 0.3023 | 2.07 | 3750 | 0.3767 | 0.5069 | 0.6752 | 0.025 | |
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| 0.3069 | 2.21 | 4000 | 0.3765 | 0.5087 | 0.6757 | 0.0333 | |
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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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