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Electra Base Discriminator Finetuned Conll03 English
A named entity recognition model based on the ELECTRA architecture, fine-tuned on the CoNLL-2003 English dataset for token classification tasks.
Downloads 99
Release Time : 4/2/2022
Model Overview
This model is a fine-tuned version of the ELECTRA base discriminator on the CoNLL-2003 English named entity recognition dataset, specifically designed for token classification tasks. It can identify entities such as person names, locations, and organizations in text.
Model Features
High-precision entity recognition
Achieves an F1 score of 94.8% on the CoNLL-2003 test set, demonstrating excellent performance.
Advantages of ELECTRA architecture
Utilizes ELECTRA's discriminative pre-training method, which is more efficient compared to traditional generative pre-training.
Lightweight deployment
The base version model has moderate parameters, making it suitable for production environment deployment.
Model Capabilities
Named Entity Recognition
Text token classification
English text processing
Use Cases
Information extraction
News entity extraction
Automatically identifies people, places, and organizations from news articles
Accuracy 93.9%, F1 score 94.8%
Knowledge graph construction
Knowledge graph entity labeling
Provides automated entity labeling for knowledge graph construction
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