# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("raoulmago/codice_fiscale")
model = AutoModelForTokenClassification.from_pretrained("raoulmago/codice_fiscale", device_map="auto")Quick Links
codice_fiscale
This model is a fine-tuned version of 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
- Downloads last month
- 1
Model tree for raoulmago/codice_fiscale
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="raoulmago/codice_fiscale")