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Albert Base V2 Finetuned Ner
This model is a Named Entity Recognition (NER) model fine-tuned on the conll2003 dataset based on the ALBERT-base-v2 architecture, demonstrating excellent performance in entity recognition tasks.
Downloads 20
Release Time : 3/2/2022
Model Overview
A model specifically designed for Named Entity Recognition (NER) tasks, capable of identifying entities such as person names, locations, and organizations in text.
Model Features
High-precision entity recognition
Achieves an F1 score of 93.38% on the conll2003 test set, demonstrating outstanding performance.
Lightweight architecture
Based on ALBERT's lightweight design with high parameter efficiency.
End-to-end training
Directly fine-tuned end-to-end on NER tasks.
Model Capabilities
Identify named entities in text
Classify entity types (e.g., person names, locations, organizations)
Process English text
Use Cases
Information extraction
Entity extraction from news articles
Extract person names, locations, and organization names from news articles
Can achieve over 93% accuracy
Knowledge graph construction
Entity recognition for knowledge graphs
Identify entities in text for knowledge graph construction
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