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---
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 |