Enhanced NER with Optimized Transformers and Tokenization

📅 2025-03-01 — Session: Enhanced NER with Optimized Transformers and Tokenization

🕒 04:10–04:25
🏷️ Labels: NER, Transformers, Tokenization, Python, Machine Learning
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance the performance of Named Entity Recognition (NER) by optimizing Transformer models and addressing subword tokenization issues.

Key Activities

• Model Selection: Recommended smaller Transformer models like dbmdz/bert-base-cased-finetuned-conll03-english for better speed and accuracy balance.
• Subword Tokenization: Discussed the impact of subword tokenization on NER and provided solutions to merge subwords and map unclear labels to meaningful entity types.
• Code Implementation: Provided code snippets for fixing NER output issues, addressing unwanted labels, and incorrect entity groupings.

Achievements

• Identified optimal Transformer models for fast NER applications.
• Developed strategies and code implementations to improve entity recognition by fixing subword tokenization and label mapping issues.

Pending Tasks

• Further testing and validation of the proposed solutions and code implementations in diverse datasets to ensure robustness and accuracy.