
# squad_it_xxl_cased
This is a model, based on **BERT** trained on cased Italian, that can be used for [Extractive Q&A](https://huggingface.co/tasks/question-answering) on Italian texts.

## Model description
This model has been trained on **squad_it** dataset starting from the pre-trained model [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased).

These are the metrics computed on evaluation set:

- EM:	63.95
- F1:	75.27

#### How to use

```python
from transformers import pipeline

pipe_qa = pipeline('question-answering', model='luigisaetta/squad_it_xxl_cased_hub1')

pipe_qa(context="Io sono nato a Napoli. Il mare bagna Napoli. Napoli è la più bella città del mondo", 
        question="Qual è la più bella città del mondo?")
```

## Intended uses & limitations

This model can be used for Extractive Q&A on Italian Text

## Training and evaluation data

[squad_it](https://huggingface.co/datasets/squad_it)

## Training procedure
 see code in this [NoteBook](https://github.com/luigisaetta/nlp-qa-italian/blob/main/train_squad_it_final1.ipynb)
 
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1234
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.12.1

{"language": ["it"], "tags": ["Q&A"], "datasets": ["squad_it"], "metrics": ["type squad"], "widget": [{"text": "Come si chiama il primo re di Roma?", "context": "Roma \u00e8 una delle pi\u00f9 belle ed antiche citt\u00e0 del mondo. Il pi\u00f9 famoso monumento di Roma \u00e8 il Colosseo. Un altro monumento molto bello \u00e8 la Colonna Traiana. Il primo re di Roma \u00e8 stato Romolo. Roma ha avuto tanti re: Numa Pompilio, Tullio Ostilio."}, {"text": "Qual \u00e8 il pi\u00f9 famoso monumento di Roma?", "context": "Roma \u00e8 una delle pi\u00f9 belle ed antiche citt\u00e0 del mondo. Il pi\u00f9 famoso monumento di Roma \u00e8 il Colosseo. Un altro monumento molto bello \u00e8 la Colonna Traiana. Il primo re di Roma \u00e8 stato Romolo. Roma ha avuto tanti re: Numa Pompilio, Tullio Ostilio."}], "model-index": [{"name": "squad_it_xxl_cased_hub1", "results": []}]}
transformers pytorch bert question-answering Q&A it dataset:squad_it endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #question-answering #Q&A #it #dataset-squad_it #endpoints_compatible #has_space #region-us 


# squad_it_xxl_cased
This is a model, based on BERT trained on cased Italian, that can be used for Extractive Q&A on Italian texts.

## Model description
This model has been trained on squad_it dataset starting from the pre-trained model dbmdz/bert-base-italian-xxl-cased.

These are the metrics computed on evaluation set:

- EM:	63.95
- F1:	75.27

#### How to use



## Intended uses & limitations

This model can be used for Extractive Q&A on Italian Text

## Training and evaluation data

squad_it

## Training procedure
 see code in this NoteBook
 
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1234
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.12.1

# squad_it_xxl_cased
This is a model, based on BERT trained on cased Italian, that can be used for Extractive Q&A on Italian texts. ## Model description
This model has been trained on squad_it dataset starting from the pre-trained model dbmdz/bert-base-italian-xxl-cased.

These are the metrics computed on evaluation set:

- EM:	63.95
- F1:	75.27 #### How to use ## Intended uses & limitations

This model can be used for Extractive Q&A on Italian Text ## Training and evaluation data

squad_it ## Training procedure
 see code in this NoteBook ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1234
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #question-answering #Q&A #it #dataset-squad_it #endpoints_compatible #has_space #region-us 
 # squad_it_xxl_cased
This is a model, based on BERT trained on cased Italian, that can be used for Extractive Q&A on Italian texts. ## Model description
This model has been trained on squad_it dataset starting from the pre-trained model dbmdz/bert-base-italian-xxl-cased.

These are the metrics computed on evaluation set:

- EM:	63.95
- F1:	75.27 #### How to use ## Intended uses & limitations

This model can be used for Extractive Q&A on Italian Text ## Training and evaluation data

squad_it ## Training procedure
 see code in this NoteBook ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1234
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #bert #question-answering #Q&A #it #dataset-squad_it #endpoints_compatible #has_space #region-us 
# squad_it_xxl_cased
This is a model, based on BERT trained on cased Italian, that can be used for Extractive Q&A on Italian texts.## Model description
This model has been trained on squad_it dataset starting from the pre-trained model dbmdz/bert-base-italian-xxl-cased.

These are the metrics computed on evaluation set:

- EM:	63.95
- F1:	75.27#### How to use## Intended uses & limitations

This model can be used for Extractive Q&A on Italian Text## Training and evaluation data

squad_it## Training procedure
 see code in this NoteBook### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1234
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.12.1