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ribesstefano
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RuleBert-v0.4-k4

Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Italian
legal ruling
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
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  1. README.md +78 -0
  2. model.safetensors +1 -1
  3. runs/Jan07_23-04-15_68142e52d7cf/events.out.tfevents.1704668656.68142e52d7cf.255.13 +2 -2
README.md ADDED
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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-k4
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+ results: []
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+ ---
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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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+
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+ # ribesstefano/RuleBert-v0.4-k4
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+
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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: 0.3517
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+ - F1: 0.5190
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+ - Roc Auc: 0.6864
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+ - Accuracy: 0.0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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+ | 0.3447 | 0.12 | 250 | 0.3402 | 0.4810 | 0.6688 | 0.0 |
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+ | 0.3501 | 0.24 | 500 | 0.3548 | 0.4884 | 0.6786 | 0.0 |
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+ | 0.3433 | 0.36 | 750 | 0.3596 | 0.4946 | 0.6885 | 0.0 |
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+ | 0.3521 | 0.48 | 1000 | 0.3762 | 0.4861 | 0.6648 | 0.0 |
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+ | 0.3466 | 0.6 | 1250 | 0.3496 | 0.4861 | 0.6648 | 0.0 |
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+ | 0.3285 | 0.72 | 1500 | 0.3519 | 0.4861 | 0.6648 | 0.0 |
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+ | 0.333 | 0.84 | 1750 | 0.3550 | 0.4861 | 0.6648 | 0.0 |
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+ | 0.3268 | 0.96 | 2000 | 0.3436 | 0.5190 | 0.6864 | 0.0 |
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+ | 0.3376 | 1.08 | 2250 | 0.3637 | 0.4978 | 0.6891 | 0.0 |
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+ | 0.3319 | 1.19 | 2500 | 0.3459 | 0.5190 | 0.6864 | 0.0 |
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+ | 0.3169 | 1.31 | 2750 | 0.3430 | 0.4810 | 0.6688 | 0.0 |
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+ | 0.3293 | 1.43 | 3000 | 0.3480 | 0.4861 | 0.6648 | 0.0 |
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+ | 0.3293 | 1.55 | 3250 | 0.3517 | 0.5190 | 0.6864 | 0.0 |
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+
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+
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+ ### Framework versions
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+
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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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