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--- |
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language: |
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- it |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- tner/wikiann |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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widget: |
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- text: 'Ciao, sono Giacomo. Vivo a Milano e lavoro da Armani. ' |
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example_title: Example 1 |
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- text: 'Domenica andrò allo stadio con Giovanna a guardare la Fiorentina. ' |
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example_title: Example 2 |
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base_model: dbmdz/bert-base-italian-cased |
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model-index: |
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- name: bert-italian-finetuned-ner |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: wiki_neural |
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type: wiki_neural |
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config: it |
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split: validation |
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args: it |
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metrics: |
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- type: precision |
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value: 0.9438064759036144 |
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name: Precision |
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- type: recall |
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value: 0.954225352112676 |
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name: Recall |
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- type: f1 |
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value: 0.9489873178118493 |
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name: F1 |
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- type: accuracy |
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value: 0.9917883014379933 |
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name: Accuracy |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-italian-finetuned-ner |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0361 |
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- Precision: 0.9438 |
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- Recall: 0.9542 |
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- F1: 0.9490 |
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- Accuracy: 0.9918 |
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## Model description |
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Token classification for italian language experiment, NER. |
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### Example |
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```python |
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from transformers import pipeline |
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ner_pipeline = pipeline("ner", model="nickprock/bert-italian-finetuned-ner", aggregation_strategy="simple") |
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text = "La sede storica della Olivetti è ad Ivrea" |
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output = ner_pipeline(text) |
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``` |
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## Intended uses & limitations |
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The model can be used on token classification, in particular NER. It is fine tuned on italian language. |
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## Training and evaluation data |
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The dataset used is [wikiann](https://huggingface.co/datasets/tner/wikiann) |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0297 | 1.0 | 11050 | 0.0323 | 0.9324 | 0.9420 | 0.9372 | 0.9908 | |
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| 0.0173 | 2.0 | 22100 | 0.0324 | 0.9445 | 0.9514 | 0.9479 | 0.9915 | |
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| 0.0057 | 3.0 | 33150 | 0.0361 | 0.9438 | 0.9542 | 0.9490 | 0.9918 | |
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### Framework versions |
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- Transformers 4.27.3 |
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- Pytorch 1.13.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.2 |
