Explore and submit NER models
Display a welcome message on a webpage
Finance chatbot using vectara-agentic
Transfer GitHub repositories to Hugging Face Spaces
Parse bilibili bvid to aid / cid
statistics analysis for linear regression
Gather data from websites
Generate a data profile report
NSFW Text Generator for Detecting NSFW Text
Generate plots for GP and PFN posterior approximations
Display CLIP benchmark results for inference performance
Predict linear relationships between numbers
Generate synthetic dataset files (JSON Lines)
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.
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.