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# 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
{"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 andr\u00f2 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"}]}]}]}
transformers pytorch safetensors bert token-classification generated_from_trainer it dataset:tner/wikiann base_model:dbmdz/bert-base-italian-cased license:mit model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bert #token-classification #generated_from_trainer #it #dataset-tner/wikiann #base_model-dbmdz/bert-base-italian-cased #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

bert-italian-finetuned-ner
==========================


This model is a fine-tuned version of 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


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


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



### Framework versions


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



### Example


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


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 ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #safetensors #bert #token-classification #generated_from_trainer #it #dataset-tner/wikiann #base_model-dbmdz/bert-base-italian-cased #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Example


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


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 ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #safetensors #bert #token-classification #generated_from_trainer #it #dataset-tner/wikiann #base_model-dbmdz/bert-base-italian-cased #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Example


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


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### Framework versions


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