Created README.md

README.md

@@ -289,4 +289,112 @@ configs:
     path: 2024_source/dev-*
   - split: train
     path: 2024_source/train-*
+license: cc-by-sa-4.0
+task_categories:
+- text-classification
+- question-answering
+language:
+- en
+tags:
+- medical
+pretty_name: 'NLI4CT: Natural Language Inference for Clinical Trial Reports'
+size_categories:
+- 1K<n<10K
 ---
+
+# NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports and SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials
+
+## Dataset Description
+
+|                                 | Links         | 
+|:-------------------------------:|:-------------:|
+| **Homepage:**                   |  [sites.google](https://sites.google.com/view/nli4ct/)  | 
+| **Repository:**                 |  [Github2024](https://github.com/ai-systems/Task-2-SemEval-2024)  | 
+| **Paper:**                      |  [arXiv2023](https://arxiv.org/abs/2305.02993) / [arXiv2024](https://arxiv.org/abs/2404.04963) | 
+| **Leaderboard:**                |  [Codalab2023](https://codalab.lisn.upsaclay.fr/competitions/8937)  | 
+| **Contact (Original Authors):** |  Maël Jullien (mael.jullien@postgrad.manchester.ac.uk) |
+| **Contact (Curator):**          |  [Artur Guimarães](https://araag2.netlify.app/) (artur.guimas@gmail.com) |
+
+  
+### Dataset Summary
+
+`The NLI4CT dataset introduces a challenging two-part benchmark designed to enable large-scale automated reasoning over full clinical trial reports (CTRs): (1) determining whether a natural language statement is entailed or contradicted by a CTR (textual entailment) and (2) retrieving the specific evidence sentences that justify that label. It covers 2,400 expert-annotated instances drawn from breast cancer trials, each mapped to one of four CTR sections—eligibility, intervention, results, or adverse events—and includes both single-trial and comparison scenarios.`
+
+### Data Instances
+
+#### Source Format
+
+```json
+{
+        "Type": "Comparison",
+        "Section_id": "Eligibility",
+        "Primary_id": "NCT01129622",
+        "Secondary_id": "NCT01156987",
+        "Statement": "Women suffering from both claustrophobia and IBS or not eligible for either the primary trial or the secondary trial.",
+        "Label": "Contradiction",
+        "Primary_evidence_index": [
+            2,
+            3
+        ],
+        "Secondary_evidence_index": [
+            2,
+            9
+        ]
+}
+```
+
+### Data Fields
+
+#### Source Format
+
+TO:DO
+
+### Data Splits
+
+TO:DO
+
+## Additional Information
+
+### Dataset Curators
+
+#### Original Paper
+
+- Maël Jullien - Department of Computer Science, University of Manchester, United Kingdom
+- Marco Valentino - Idiap Research Institute, Switzerland
+- Hannah Frost - Department of Computer Science, University of Manchester, United Kingdom, and Digital Experimental Cancer Medicine Team, Cancer Research UK Manchester Institute
+- Paul O’Regan - Digital Experimental Cancer Medicine Team, Cancer Research UK Manchester Institute
+- Donal Landers- Digital Experimental Cancer Medicine Team, Cancer Research UK Manchester Institute
+- André Freitas - Department of Computer Science, University of Manchester, United Kingdom and Digital Experimental Cancer Medicine Team, Cancer Research UK Manchester Institute and Idiap Research Institute, Switzerland
+
+#### Huggingface Curator
+
+- [Artur Guimarães](https://araag2.netlify.app/) (artur.guimas@gmail.com) - INESC-ID / University of Lisbon - Instituto Superior Técnico
+
+### Licensing Information
+
+[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.en)
+
+### Citation Information
+
+```bibtex
+@article{jullien2023semeval,
+  title={SemEval-2023 task 7: Multi-evidence natural language inference for clinical trial data},
+  author={Jullien, Ma{\"e}l and Valentino, Marco and Frost, Hannah and O'Regan, Paul and Landers, Donal and Freitas, Andr{\'e}},
+  journal={arXiv preprint arXiv:2305.02993},
+  year={2023}
+}
+
+@article{jullien2024semeval,
+  title={SemEval-2024 task 2: Safe biomedical natural language inference for clinical trials},
+  author={Jullien, Ma{\"e}l and Valentino, Marco and Freitas, Andr{\'e}},
+  journal={arXiv preprint arXiv:2404.04963},
+  year={2024}
+}
+```
+
+[10.48550/ARXIV.2305.02993](http://doi.org/10.48550/ARXIV.2305.02993)
+[10.48550/ARXIV.2404.04963](http://doi.org/110.48550/ARXIV.2404.04963)
+
+### Contributions
+
+Thanks to [araag2](https://github.com/araag2) for adding this dataset.