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--- |
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license: apache-2.0 |
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language: |
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- it |
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widget: |
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- text: "Mi chiamo Marco Rossi, vivo a Roma e lavoro per l'Agenzia Spaziale Italiana" |
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example_title: "Example 1" |
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--- |
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<body> |
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<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span> |
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<br> |
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<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;"> Task: Named Entity Recognition</span> |
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<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;"> Model: BERT</span> |
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<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;"> Lang: IT</span> |
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<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;"> </span> |
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<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span> |
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</body> |
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<h3>Model description</h3> |
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This is a <b>BERT</b> <b>[1]</b> cased model for the <b>Italian</b> language, fine-tuned for <b>Named Entity Recognition</b> (<b>Person</b>, <b>Location</b>, <b>Organization</b> and <b>Miscellanea</b> classes) on the [WikiNER](https://figshare.com/articles/dataset/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) dataset <b>[2]</b>, using <b>BERT-ITALIAN</b> ([bert-base-italian-cased](https://huggingface.co/osiria/bert-base-italian-cased)) as a pre-trained model. |
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This is a cased, base size BERT model. If you are looking for a lighter (but slightly less accurate) cased model, you can refer to: https://huggingface.co/osiria/distilbert-italian-cased-ner |
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If you are looking for an uncased model, you can refer to: https://huggingface.co/osiria/bert-italian-uncased-ner |
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<h3>Training and Performances</h3> |
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The model is trained to perform entity recognition over 4 classes: <b>PER</b> (persons), <b>LOC</b> (locations), <b>ORG</b> (organizations), <b>MISC</b> (miscellanea, mainly events, products and services). It has been fine-tuned for Named Entity Recognition, using the WikiNER Italian dataset plus an additional custom dataset of manually annotated Wikipedia paragraphs. |
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The WikiNER dataset has been splitted in 102.352 training instances and 25.588 test instances, and the model has been trained for 1 epoch with a constant learning rate of 1e-5. |
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The performances on the test set are reported in the following table: |
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| Recall | Precision | F1 | |
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| ------ | ------ | ------ | |
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| 93.35 | 92.22 | 92.78 | |
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The metrics have been computed at the token level and then macro-averaged over the 4 classes. |
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Then, since WikiNER is an automatically annotated (silver standard) dataset, which sometimes contains imperfect annotations, an additional fine-tuning on ~3.500 manually annotated paragraphs has been performed. |
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<h3>Quick usage</h3> |
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```python |
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from transformers import BertTokenizerFast, BertForTokenClassification |
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tokenizer = BertTokenizerFast.from_pretrained("osiria/bert-italian-cased-ner") |
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model = BertForTokenClassification.from_pretrained("osiria/bert-italian-cased-ner") |
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from transformers import pipeline |
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ner = pipeline("ner", model = model, tokenizer = tokenizer, aggregation_strategy="first") |
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ner("Mi chiamo Marco Rossi, vivo a Roma e lavoro per l'Agenzia Spaziale Italiana nella missione Prisma") |
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[{'entity_group': 'PER', |
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'score': 0.99910736, |
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'word': 'Marco Rossi', |
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'start': 10, |
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'end': 21}, |
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{'entity_group': 'LOC', |
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'score': 0.9973786, |
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'word': 'Roma', |
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'start': 30, |
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'end': 34}, |
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{'entity_group': 'ORG', |
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'score': 0.9987071, |
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'word': 'Agenzia Spaziale Italiana', |
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'start': 50, |
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'end': 75}, |
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{'entity_group': 'MISC', |
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'score': 0.9625836, |
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'word': 'Prisma', |
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'start': 91, |
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'end': 97}] |
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``` |
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You can also try the model online using this web app: https://huggingface.co/spaces/osiria/bert-italian-cased-ner |
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<h3>References</h3> |
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[1] https://arxiv.org/abs/1810.04805 |
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[2] https://www.sciencedirect.com/science/article/pii/S0004370212000276 |
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<h3>Limitations</h3> |
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This model is mainly trained on Wikipedia, so it's particularly suitable for natively digital text from the world wide web, written in a correct and fluent form (like wikis, web pages, news, etc.). However, it may show limitations when it comes to chaotic text, containing errors and slang expressions |
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(like social media posts) or when it comes to domain-specific text (like medical, financial or legal content). |
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<h3>License</h3> |
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The model is released under <b>Apache-2.0</b> license |
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