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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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<br> |
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<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;"> Model: BLAZE 🔥</span> |
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<br> |
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<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;"> Lang: IT</span> |
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<br> |
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<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;"> Type: Uncased</span> |
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<br> |
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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 lightweight and uncased 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>[1]</b>, using <b>Blaze-IT</b> ([blaze-it](https://huggingface.co/osiria/blaze-it)) as a pre-trained model. |
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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 model has been trained for 1 epoch with a constant learning rate of 1e-5. |
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The 5-fold cross-validated 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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| 89.29 | 89.84 | 89.53 | |
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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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You can try the model online using this web app: https://huggingface.co/spaces/osiria/blaze-it-demo |
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<h3>References</h3> |
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[1] 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 |
