---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: codice_fiscale
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# codice_fiscale

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2024
- Precision: 0.8316
- Recall: 0.5374
- F1: 0.6529
- Accuracy: 0.9405

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 4    | 1.0541          | 0.0       | 0.0    | 0.0    | 0.8445   |
| No log        | 2.0   | 8    | 0.6374          | 0.0       | 0.0    | 0.0    | 0.8445   |
| No log        | 3.0   | 12   | 0.5150          | 0.0       | 0.0    | 0.0    | 0.8445   |
| No log        | 4.0   | 16   | 0.4235          | 0.0       | 0.0    | 0.0    | 0.8445   |
| No log        | 5.0   | 20   | 0.3564          | 0.5       | 0.0850 | 0.1453 | 0.8667   |
| No log        | 6.0   | 24   | 0.3024          | 0.5       | 0.0850 | 0.1453 | 0.8667   |
| No log        | 7.0   | 28   | 0.2609          | 0.6835    | 0.1837 | 0.2895 | 0.8796   |
| No log        | 8.0   | 32   | 0.2299          | 0.8264    | 0.4048 | 0.5434 | 0.9085   |
| No log        | 9.0   | 36   | 0.2104          | 0.7826    | 0.4898 | 0.6025 | 0.9280   |
| No log        | 10.0  | 40   | 0.2024          | 0.8316    | 0.5374 | 0.6529 | 0.9405   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1