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Ner English Ontonotes Large

Developed by flair
Flair's built-in large English named entity recognition model for 18 classes, trained on the Ontonotes dataset using XLM-R embeddings and FLERT technology.
Downloads 176.21k
Release Time : 3/2/2022

Model Overview

This model is used for named entity recognition in English texts, capable of identifying 18 different types of named entities such as persons, locations, dates, etc.

Model Features

Multi-category recognition
Capable of identifying 18 different types of named entities, including persons, locations, dates, monetary amounts, etc.
High performance
Achieves an F1 score of 90.93 on the Ontonotes dataset, demonstrating excellent performance.
Document-level context
Utilizes FLERT technology to leverage document-level contextual information for improved recognition accuracy.

Model Capabilities

Named entity recognition
Multi-category entity labeling
English text processing

Use Cases

Text analysis
News text entity recognition
Identifying entities such as persons, locations, and dates in news texts.
Accurately labels various entities for subsequent analysis and processing.
Financial text analysis
Identifying entities like monetary amounts and percentages in financial texts.
Facilitates automated processing of financial data.
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