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license: apache-2.0 |
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language: |
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- en |
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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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<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: EN</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>English</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 Google's <b>bert-base-cased</b> 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 English dataset. |
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The model has been trained for 1 epoch with a constant learning rate of 1e-5. |
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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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