Update README.md

README.md

@@ -1,76 +1,25 @@
 ---
 annotations_creators:
-- expert-generated
+  - expert-generated
 language:
-- en
+  - en
 license: cc-by-nc-4.0
 multilinguality: monolingual
-pretty_name: CaseReportBench - Clinical Dense Extraction Benchmark
+pretty_name: CaseReportBench_Clinical Dense Extraction Benchmark
 tags:
-- clinical-nlp
-- dense-information-extraction
-- medical
-- case-reports
-- rare-diseases
-- benchmarking
-- information-extraction
+  - clinical-nlp
+  - dense-information-extraction
+  - medical
+  - case-reports
+  - rare-diseases
+  - benchmarking
+  - information-extraction
 task_categories:
-- information-extraction
-- text-classification
-- question-answering
+  - information-extraction
+  - text-classification
+  - question-answering
 task_ids:
-- entity-extraction
-- multi-label-classification
-- open-domain-qa
+  - entity-extraction
+  - multi-label-classification
+  - open-domain-qa
 ---
-
-
-# CaseReportBench: Clinical Dense Extraction Benchmark
-
-**CaseReportBench** is a curated benchmark dataset designed to evaluate the ability of large language models to perform **dense information extraction** from **clinical case reports**, particularly in the context of **rare disease diagnosis**.
-
-This dataset supports fine-grained, system-wise phenotype extraction and structured diagnostic reasoning evaluation.
-
----
-
-## Key Features
-
-- Expert-annotated dense labels simulating comprehensive head-to-toe clinical assessments, capturing multi-system findings as encountered in real-world diagnostic reasoning
-- Domain: Clinical Case Reports (PubmedCentral indexed)
-- Use case: Medical IE, LLM evaluation, Rare disease diagnosis
-- Data type: JSON with structured system-wise output
-- Evaluation metrics: Token Selection Rate, Levenshtein Similarity, Exact Match
-
----
-
-## Dataset Structure
-
-Each record includes:
-
-- `id`: Unique document identifier
-- `text`: Raw case report
-- `extracted_labels`: Dense structured annotations by system (e.g., nervous system, metabolic)
-- `diagnosis`: Gold standard diagnosis
-- `source`: PubMed ID or citation
-
----
-
-## Usage
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("cxyzhang/caseReportBench_ClinicalDenseExtraction_Benchmark")
-print(ds["train"][0])
-```
-
-## Citation
-
-```bibtex
-@inproceedings{zhang2025casereportbench,
-  title={CaseReportBench: An LLM Benchmark Dataset for Dense Information Extraction in Clinical Case Reports},
-  author={Zhang, Cindy and Others},
-  booktitle={Conference on Health, Inference, and Learning (CHIL)},
-  year={2025}
-}
-```