README.md

---
language:
- it
license: mit
tags:
- generated_from_trainer
datasets:
- tner/wikiann
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: 'Ciao, sono Giacomo. Vivo a Milano e lavoro da Armani. '
  example_title: Example 1
- text: 'Domenica andrò allo stadio con Giovanna a guardare la Fiorentina. '
  example_title: Example 2
base_model: dbmdz/bert-base-italian-cased
model-index:
- name: bert-italian-finetuned-ner
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: wiki_neural
      type: wiki_neural
      config: it
      split: validation
      args: it
    metrics:
    - type: precision
      value: 0.9438064759036144
      name: Precision
    - type: recall
      value: 0.954225352112676
      name: Recall
    - type: f1
      value: 0.9489873178118493
      name: F1
    - type: accuracy
      value: 0.9917883014379933
      name: Accuracy
---

<!-- 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. -->

# bert-italian-finetuned-ner

This model is a fine-tuned version of [dbmdz/bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italian-cased) on the wiki_neural dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0361
- Precision: 0.9438
- Recall: 0.9542
- F1: 0.9490
- Accuracy: 0.9918

## Model description

Token classification for italian language experiment, NER.

### Example

```python
from transformers import pipeline
ner_pipeline = pipeline("ner", model="nickprock/bert-italian-finetuned-ner", aggregation_strategy="simple")
text = "La sede storica della Olivetti è ad Ivrea"
output = ner_pipeline(text)
```

## Intended uses & limitations

The model can be used on token classification, in particular NER. It is fine tuned on italian language.

## Training and evaluation data

The dataset used is [wikiann](https://huggingface.co/datasets/tner/wikiann)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0297        | 1.0   | 11050 | 0.0323          | 0.9324    | 0.9420 | 0.9372 | 0.9908   |
| 0.0173        | 2.0   | 22100 | 0.0324          | 0.9445    | 0.9514 | 0.9479 | 0.9915   |
| 0.0057        | 3.0   | 33150 | 0.0361          | 0.9438    | 0.9542 | 0.9490 | 0.9918   |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2