---
license: afl-3.0
base_model: dlicari/Italian-Legal-BERT-SC
tags:
- generated_from_trainer
model-index:
- name: model
  results: []
language:
- it
library_name: transformers
pipeline_tag: fill-mask
---

<!-- 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. -->

# model

This model is a fine-tuned version of [dlicari/Italian-Legal-BERT-SC](https://huggingface.co/dlicari/Italian-Legal-BERT-SC) on a custom dataset about financial norms.


## Intended uses & limitations

This mode has just been further pre-trained, if you intend to use it for downstream task you should fine-tune it with your data.

## Usage

To use this model you can use the following script:
```python
from transformers import AutoTokenizer, AutoModel,CamembertModel,pipeline
import torch
model = CamembertModel.from_pretrained("PeppePasti/IT-FINANCIAL-BERT")
tokenizer = AutoTokenizer.from_pretrained("dlicari/Italian-Legal-BERT-SC")
tokenizer.model_max_length=512
classifier = pipeline("fill-mask", model=model, tokenizer=tokenizer)
classifier(" La Repubblica riconosce a tutti i <mask> il diritto al lavoro e promuove le condizioni che rendano effettivo questo diritto.")

```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 256
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Input Tokens Seen |
|:-------------:|:------:|:----:|:-----------------:|
| 0.4096        | 0.9982 | 202  | 211812352         |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.3.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1