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Nuner V1 Orgs

Developed by guishe
A model fine-tuned from FewNERD-fine-supervised based on numind/NuNER-v1.0 for recognizing organizational entities (ORG) in text
Downloads 6,836
Release Time : 3/28/2024

Model Overview

This model is a fine-tuned NuNER model on the NER-ORGS dataset, specifically designed for named entity recognition tasks, particularly identifying organization names in text. The NuNER model uses RoBERTa-base as its backbone encoder and has been pre-trained on a large, diverse dataset.

Model Features

High-quality Pre-training
Pre-trained on a large, diverse dataset of 1 million sentences synthetically annotated by GPT-3.5-turbo-0301, generating high-quality token embeddings
Domain-specific Fine-tuning
Fine-tuned on the NER-ORGS dataset, specifically optimized for organizational entity recognition
Balanced Performance
Achieves a good balance between precision (0.76) and recall (0.80), with an F1 score of 0.78

Model Capabilities

Recognition of organizational entities in text
Named entity tag classification

Use Cases

News Analysis
Extraction of Organizational Entities in News
Identify mentioned companies, government agencies, and other organizational entities from news texts
Can accurately recognize organization names such as CNN, Apple, Google, etc.
Business Intelligence
Business Document Analysis
Analyze relevant organizations mentioned in business documents, contracts, or reports
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