bert-base-cased-ner-en

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<body> <span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span> <br> <span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;">    Task: Named Entity Recognition</span> <br> <span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;">    Model: BERT</span> <br> <span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;">    Lang: EN</span> <br> <span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;">  </span> <br> <span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span> </body>

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<h3>Model description</h3>

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 dataset <b>[2]</b>, using Google's <b>bert-base-cased</b> as a pre-trained model.

<h3>Training and Performances</h3>

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. The model has been trained for 1 epoch with a constant learning rate of 1e-5.

<h3>References</h3>

[1] https://arxiv.org/abs/1810.04805

[2] https://www.sciencedirect.com/science/article/pii/S0004370212000276

<h3>Limitations</h3>

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 (like social media posts) or when it comes to domain-specific text (like medical, financial or legal content).

<h3>License</h3>

The model is released under <b>Apache-2.0</b> license