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Lm Ner Linkedin Skills Recognition
DistilBERT-based LinkedIn skill recognition model for identifying professional skills from text
Downloads 1,194
Release Time : 7/7/2023
Model Overview
This model is a Named Entity Recognition (NER) model fine-tuned on LinkedIn data based on distilbert-base-uncased, specifically designed to identify professional skill keywords in text.
Model Features
High-precision skill recognition
Achieves an F1 score of 0.9214 on the evaluation set, accurately identifying professional skill keywords in text.
LinkedIn domain optimization
Fine-tuned on LinkedIn data, providing better understanding of skill expressions in professional contexts.
Lightweight model
Based on DistilBERT architecture, reducing computational resource requirements while maintaining high performance.
Model Capabilities
Skill keyword recognition in text
Professional entity extraction
Resume content analysis
Use Cases
HR technology
Automatic resume skill extraction
Automatically extracts skill keywords from job applicants' resumes
99.12% accuracy, helping HR quickly screen candidates
Job matching system
Analyzes job descriptions and candidate skills for automatic matching
High recall rate (93.12%) ensures no relevant skills are missed
Career analysis
Skill trend analysis
Extracts skills from LinkedIn profiles for market trend analysis
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