File size: 2,398 Bytes
4464cb4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | ---
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
|