End of training
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README.md
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---
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license: apache-2.0
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base_model: distilbert/distilbert-base-uncased
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: codice_fiscale
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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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# codice_fiscale
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2024
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- Precision: 0.8316
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- Recall: 0.5374
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- F1: 0.6529
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- Accuracy: 0.9405
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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: 16
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- eval_batch_size: 16
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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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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| No log | 1.0 | 4 | 1.0541 | 0.0 | 0.0 | 0.0 | 0.8445 |
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| No log | 2.0 | 8 | 0.6374 | 0.0 | 0.0 | 0.0 | 0.8445 |
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| No log | 3.0 | 12 | 0.5150 | 0.0 | 0.0 | 0.0 | 0.8445 |
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| No log | 4.0 | 16 | 0.4235 | 0.0 | 0.0 | 0.0 | 0.8445 |
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| No log | 5.0 | 20 | 0.3564 | 0.5 | 0.0850 | 0.1453 | 0.8667 |
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| No log | 6.0 | 24 | 0.3024 | 0.5 | 0.0850 | 0.1453 | 0.8667 |
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| No log | 7.0 | 28 | 0.2609 | 0.6835 | 0.1837 | 0.2895 | 0.8796 |
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| No log | 8.0 | 32 | 0.2299 | 0.8264 | 0.4048 | 0.5434 | 0.9085 |
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| No log | 9.0 | 36 | 0.2104 | 0.7826 | 0.4898 | 0.6025 | 0.9280 |
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| No log | 10.0 | 40 | 0.2024 | 0.8316 | 0.5374 | 0.6529 | 0.9405 |
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### Framework versions
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- Transformers 4.40.2
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- Pytorch 2.2.1+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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