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Flair Ner
The Flair NER model is a sequence labeling model trained on the CoNLL-2003 dataset for named entity recognition tasks.
Downloads 1,412
Release Time : 3/2/2022
Model Overview
This model is a named entity recognition (NER) model capable of identifying entities such as person names, locations, and organization names in text.
Model Features
Trained on CoNLL-2003 Dataset
Trained on the standard NER benchmark dataset CoNLL-2003, offering strong generalization capabilities.
Flair Framework Support
Built on the Flair framework, making it easy to integrate and use.
Multi-Type Entity Recognition
Capable of recognizing various entity types such as person names (PER), locations (LOC), and organization names (ORG).
Model Capabilities
Named Entity Recognition
Text Sequence Labeling
Use Cases
Information Extraction
News Text Analysis
Extracting key information such as person names, locations, and organization names from news articles.
Accurately identifies entities and their types in the text.
Document Processing
Processing named entities in legal or medical documents.
Aids in document classification and information retrieval.
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