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Ner English Fast

Developed by flair
Flair's built-in fast English 4-class named entity recognition model, based on Flair embeddings and LSTM-CRF architecture, achieving an F1 score of 92.92 on the CoNLL-03 dataset.
Downloads 978.01k
Release Time : 3/2/2022

Model Overview

This model is used for named entity recognition in English text, capable of identifying four types of entities: person, location, organization, and other names.

Model Features

Fast Inference
The model is optimized to provide fast named entity recognition capabilities.
High Accuracy
Achieves an F1 score of 92.92 on the CoNLL-03 dataset.
Multi-type Recognition
Can simultaneously identify four types of entities: person, location, organization, and other names.

Model Capabilities

English Text Named Entity Recognition
Sequence Labeling
Entity Classification

Use Cases

Information Extraction
News Text Analysis
Extract information about people, locations, and organizations from news texts.
Accurately identifies named entities and their categories in the text.
Document Processing
Process named entities in legal or business documents.
Automatically annotates key entity information in documents.
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