ner-english-ontonotes-large

Maintainer: flair

Total Score

91

Last updated 5/28/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The ner-english-ontonotes-large model is a large 18-class Named Entity Recognition (NER) model for English that ships with the Flair NLP library. It is based on document-level XLM-R embeddings and the FLERT approach, and achieves an impressive F1-score of 90.93 on the Ontonotes dataset. This model can recognize 18 different entity types, including cardinal values, dates, events, facilities, geopolitical entities, languages, laws, locations, money, numeric values, organizations, people, products, and more.

The model was developed and trained by the Flair team. It can be used as a drop-in component for a variety of NLP tasks that require named entity recognition, and is a strong alternative to other popular English NER models like bert-large-NER and roberta-large-ner-english.

Model inputs and outputs

Inputs

  • Plain text: The model takes in raw text as input and performs NER on the entire document or sentence.

Outputs

  • Named entity spans: The model outputs a list of entity spans, each with a predicted entity type (e.g. 'PERSON', 'ORGANIZATION', 'LOCATION', etc.) and a confidence score.

Capabilities

The ner-english-ontonotes-large model excels at accurately identifying a wide range of entity types in English text. It can be used to extract valuable information from documents, social media posts, news articles, and other textual data. For example, you could use it to build applications that automatically catalog the people, places, and organizations mentioned in a corpus of legal documents, or to power a chatbot that can understand and respond to queries about current events by recognizing the relevant entities.

What can I use it for?

This Flair NER model is a powerful tool for a variety of NLP applications that require named entity extraction, such as:

  • Information Extraction: Automatically identify and extract key entities (people, organizations, locations, etc.) from large text corpora.

  • Question Answering: Use the recognized entities to help answer questions about who, what, where, and when in a given text.

  • Knowledge Graph Construction: Build knowledge graphs by linking the extracted entities and their relationships.

  • Sentiment Analysis: Combine entity recognition with sentiment analysis to understand how different entities are being discussed.

  • Chatbots and Conversational AI: Enable chatbots to understand and respond to user queries that reference specific entities.

The model's broad coverage of entity types and high accuracy make it a versatile tool that can be integrated into a wide range of NLP applications. By leveraging the Flair library, developers can easily incorporate this model into their projects.

Things to try

One interesting aspect of the ner-english-ontonotes-large model is its ability to recognize a diverse set of entity types, beyond just the common ones like people, organizations, and locations. For example, it can identify more specialized entities like laws, works of art, and languages.

You could try experimenting with the model on different types of text data to see how it performs. For instance, you might use it to extract entities from legal documents, news articles, or even fictional stories to explore how its capabilities vary across domains. Additionally, you could investigate how the model handles ambiguous or context-dependent entity references, and whether you need to perform any post-processing of the output to improve its accuracy for your specific use case.

Overall, this Flair NER model provides a robust and versatile entity extraction solution that can be a valuable addition to a wide range of NLP projects.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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