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Data Visualization
Clinical NER Leaderboard

Clinical NER Leaderboard

Explore and submit NER models

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What is Clinical NER Leaderboard ?

Clinical NER Leaderboard is a platform designed to evaluate and compare Named Entity Recognition (NER) models specifically within the clinical domain. It serves as a centralized hub for researchers and developers to explore, submit, and benchmark their NER models. The leaderboard provides transparency into model performance, fostering innovation and advancements in clinical NLP.

Features

  • Comprehensive Model Comparison: Evaluate NER models across standardized datasets.
  • Support for Clinical Datasets: Access to curated clinical datasets for benchmarking.
  • Submission System: Easily submit your own NER model for evaluation.
  • Interactive Visualizations: Gain insights through detailed performance metrics and visualizations.
  • Community Engagement: Discuss with experts and learn from community-driven feedback.
  • Regular Updates: Stay current with the latest advancements in clinical NER.

How to use Clinical NER Leaderboard ?

  1. Visit the Platform: Access the leaderboard via the official website.
  2. Explore Models: Browse and compare existing NER models.
  3. Submit Your Model: Upload your model for evaluation using the submission guidelines.
  4. Review Results: Analyze your model's performance against benchmarks.
  5. Visualize Data: Use interactive tools to understand strengths and weaknesses.
  6. Engage with the Community: Participate in discussions and share insights.

Frequently Asked Questions

What types of models can I submit?
You can submit any NER model designed for clinical text processing, including rule-based, machine learning, and deep learning models.

How are models evaluated?
Models are evaluated using standardized metrics such as precision, recall, F1-score, and throughput on curated clinical datasets.

Can I access the datasets used for benchmarking?
Yes, the datasets used for benchmarking are available for download, allowing you to train and fine-tune your models effectively.

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