Add dataset card, paper link, and task metadata

README.md

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+---
+task_categories:
+- text-classification
+- text-generation
+language:
+- en
+tags:
+- biomedical
+- causal-relation-extraction
+- nlp
+---
+
+# PubMedCausal: A Span-Level Annotated Corpus for Causal Relation Extraction in Biomedical Text
+
+PubMedCausal is a comprehensive benchmark suite for causal relation extraction (CRE) and detection on biomedical text. Built from PubMed abstracts, it features 30,000 annotated rows designed to evaluate models on their ability to detect and extract causal claims as they are actually expressed in scientific literature.
+
+- **Paper:** [PubMedCausal: A Span-Level Annotated Corpus for Causal Relation Extraction in Biomedical Text](https://huggingface.co/papers/2605.28363)
+- **Repository:** [https://github.com/josiahpaul07/PubMedCausal_Exp](https://github.com/josiahpaul07/PubMedCausal_Exp)
+
+## Dataset Summary
+
+The corpus contains 30,000 paragraph-level rows, including 3,945 causal rows and 6,491 adjudicated cause–effect pairs. Each causal relation is annotated with:
+- **Full-text cause and effect spans.**
+- **Causality type:** (e.g., Explicit vs. Implicit).
+- **Sententiality:** (Intra-sentential vs. Inter-sentential).
+
+It supports two primary tasks:
+1. **Causal Detection:** Binary classification to identify if a text contains a causal claim.
+2. **Causal Extraction:** Identifying specific cause-effect spans and their attributes.
+
+### Dataset Statistics
+
+| Task | Split | Train | Test |
+|------|-------|-------|------|
+| Detection | - | 15,000 | 15,000 |
+| Extraction | Combined | 1,972 | 1,973 |
+| Extraction | X_only (multi-pair) | 1,206 | 1,207 |
+| Extraction | Y_only (single-pair) | 766 | 766 |
+
+## Data Format
+
+### Raw Data (30k_train.json / 30k_test.json)
+Each sample contains a sentence and up to 16 cause-effect pairs:
+
+```json
+{
+  "s/n": 6350,
+  "Sentence": "Insulin resistance causes hyperglycemia.",
+  "Cause 1": "Insulin resistance",
+  "Effect 1": "hyperglycemia",
+  "Sententiality 1": "Intra",
+  "Causality 1": "Explicit",
+  "Cause 2": "",
+  ...
+}
+```
+
+### Extraction Task Format
+```json
+{
+  "s/n": 17500,
+  "sentence": "Insulin resistance causes hyperglycemia.",
+  "pairs": [
+    {
+      "cause": "Insulin resistance",
+      "effect": "hyperglycemia",
+      "sententiality": "Intra",
+      "causality": "Explicit"
+    }
+  ],
+  "num_pairs": 1
+}
+```
+
+## Citation
+If you use this dataset in your research, please cite the following paper:
+```bibtex
+@article{paul2025pubmedcausal,
+  title={PubMedCausal: A Span-Level Annotated Corpus for Causal Relation Extraction in Biomedical Text},
+  author={Paul, Josiah and others},
+  journal={arXiv preprint arXiv:2605.28363},
+  year={2025}
+}
+```