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Cybonto Distilbert Base Uncased Finetuned Ner Wnut17

Developed by theResearchNinja
This model is a named entity recognition (NER) model fine-tuned on the wnut_17 dataset based on distilbert-base-uncased, used to identify specific entity categories in text.
Downloads 18
Release Time : 4/15/2022

Model Overview

This is a fine-tuned DistilBERT model specifically designed for named entity recognition tasks, trained on the WNUT 17 dataset, capable of identifying specific entity categories in text.

Model Features

Efficient and Lightweight
Based on the DistilBERT architecture, it is more lightweight and efficient than standard BERT models while maintaining good performance.
Domain-Specific Optimization
Fine-tuned on the WNUT 17 dataset, optimized for specific entity recognition tasks.
Balanced Performance
Achieves a balance between precision and recall, with an F1 score of 0.5479.

Model Capabilities

Named Entity Recognition
Text Token Classification
Entity Category Prediction

Use Cases

Text Analysis
Social Media Entity Recognition
Identifies specific entities in social media text, such as products, organizations, etc.
F1 score 0.5479
Information Extraction
Extracts key entity information from unstructured text.
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