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Distilcamembert Base Ner

Developed by cmarkea
A French named entity recognition model fine-tuned on DistilCamemBERT, with inference speed twice as fast as the original CamemBERT
Downloads 5,783
Release Time : 3/2/2022

Model Overview

Used for named entity recognition in French text, capable of identifying entity types such as persons, locations, and organizations

Model Features

Efficient Inference
Inference time is halved compared to the original CamemBERT model
Multi-category Recognition
Can identify various entity types such as persons (PER), locations (LOC), and organizations (ORG)
High Accuracy
Achieves an overall F1 score of 98.18% on the wikiner_fr dataset

Model Capabilities

French Text Processing
Named Entity Recognition
Entity Classification

Use Cases

Information Extraction
Financial Document Analysis
Extract company names, person names, and location information from financial documents
Accurately identifies financial institution names such as Crédit Mutuel Arkéa
News Analysis
Extract key entity information from news articles
Can identify entities such as person names (Louis Lichou) and locations (Bretagne)
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