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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-v0.0-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-v0.0-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: 0.3777 |
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- F1: 0.5004 |
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- Roc Auc: 0.6722 |
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- Accuracy: 0.0375 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- training_steps: 500 |
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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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| 0.3811 | 0.88 | 50 | 0.3716 | 0.4904 | 0.6685 | 0.0583 | |
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| 0.3257 | 1.77 | 100 | 0.3708 | 0.4953 | 0.6701 | 0.0583 | |
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| 0.3178 | 2.65 | 150 | 0.3745 | 0.4977 | 0.6712 | 0.0417 | |
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| 0.3091 | 3.54 | 200 | 0.3750 | 0.4989 | 0.6719 | 0.0417 | |
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| 0.3115 | 4.42 | 250 | 0.3768 | 0.5007 | 0.6724 | 0.0417 | |
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| 0.3092 | 5.31 | 300 | 0.3762 | 0.5021 | 0.6727 | 0.0458 | |
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| 0.3057 | 6.19 | 350 | 0.3772 | 0.5005 | 0.6723 | 0.0375 | |
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| 0.3062 | 7.08 | 400 | 0.3777 | 0.5002 | 0.6721 | 0.0417 | |
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| 0.3086 | 7.96 | 450 | 0.3777 | 0.5005 | 0.6723 | 0.0417 | |
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| 0.3075 | 8.85 | 500 | 0.3777 | 0.5004 | 0.6722 | 0.0375 | |
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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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