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README.md
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---
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license: mit
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base_model: papluca/xlm-roberta-base-language-detection
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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-v0.5-k2
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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-v0.5-k2
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This model is a fine-tuned version of [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2962
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- F1: 0.5103
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- Roc Auc: 0.6747
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- Accuracy: 0.0
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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-06
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- train_batch_size: 2
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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: 8000
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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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| 0.4278 | 0.06 | 250 | 0.3602 | 0.5103 | 0.6747 | 0.0 |
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| 0.3633 | 0.12 | 500 | 0.3142 | 0.5103 | 0.6747 | 0.0 |
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| 0.3532 | 0.18 | 750 | 0.3019 | 0.5103 | 0.6747 | 0.0 |
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| 0.3358 | 0.24 | 1000 | 0.3004 | 0.5103 | 0.6747 | 0.0 |
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| 0.3453 | 0.3 | 1250 | 0.2990 | 0.5103 | 0.6747 | 0.0 |
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| 0.3541 | 0.36 | 1500 | 0.2962 | 0.5103 | 0.6747 | 0.0 |
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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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model.safetensors
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