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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-v0.4-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-v0.4-k3
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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: 0.3407
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- F1: 0.4872
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- Roc Auc: 0.6726
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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: 0.0005
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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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| 0.3575 | 0.12 | 250 | 0.3463 | 0.5176 | 0.6948 | 0.0 |
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| 0.347 | 0.24 | 500 | 0.3424 | 0.4507 | 0.6503 | 0.0714 |
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| 0.347 | 0.36 | 750 | 0.3390 | 0.4507 | 0.6503 | 0.0714 |
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| 0.3398 | 0.48 | 1000 | 0.3248 | 0.4872 | 0.6726 | 0.0 |
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| 0.3485 | 0.6 | 1250 | 0.3322 | 0.5000 | 0.6785 | 0.0 |
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| 0.3355 | 0.71 | 1500 | 0.3407 | 0.4872 | 0.6726 | 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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size 16026
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