| | --- |
| | dataset_info: |
| | - config_name: 2023_conversational |
| | features: |
| | - name: id |
| | dtype: string |
| | - name: prompt |
| | list: |
| | - name: role |
| | dtype: string |
| | - name: content |
| | dtype: string |
| | - name: completion |
| | list: |
| | - name: role |
| | dtype: string |
| | - name: content |
| | dtype: string |
| | - name: Label |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 4686231 |
| | num_examples: 1700 |
| | - name: dev |
| | num_bytes: 522955 |
| | num_examples: 200 |
| | - name: test |
| | num_bytes: 1465772 |
| | num_examples: 500 |
| | download_size: 2386772 |
| | dataset_size: 6674958 |
| | - config_name: 2023_processed |
| | features: |
| | - name: id |
| | dtype: string |
| | - name: Type |
| | dtype: string |
| | - name: Section_id |
| | dtype: string |
| | - name: Primary_id |
| | dtype: string |
| | - name: Secondary_id |
| | dtype: string |
| | - name: Statement |
| | dtype: string |
| | - name: Label |
| | dtype: string |
| | - name: Primary_evidence_index |
| | sequence: int64 |
| | - name: Secondary_evidence_index |
| | sequence: int64 |
| | - name: Primary_Document |
| | struct: |
| | - name: Adverse Events |
| | sequence: string |
| | - name: Eligibility |
| | sequence: string |
| | - name: Intervention |
| | sequence: string |
| | - name: Results |
| | sequence: string |
| | - name: Primary_Section |
| | sequence: string |
| | - name: Secondary_Document |
| | struct: |
| | - name: Adverse Events |
| | sequence: string |
| | - name: Eligibility |
| | sequence: string |
| | - name: Intervention |
| | sequence: string |
| | - name: Results |
| | sequence: string |
| | - name: Secondary_Section |
| | sequence: string |
| | splits: |
| | - name: train |
| | num_bytes: 15485945 |
| | num_examples: 1700 |
| | - name: dev |
| | num_bytes: 1761256 |
| | num_examples: 200 |
| | - name: test |
| | num_bytes: 4873788 |
| | num_examples: 500 |
| | download_size: 7268052 |
| | dataset_size: 22120989 |
| | - config_name: 2023_source |
| | features: |
| | - name: id |
| | dtype: string |
| | - name: Type |
| | dtype: string |
| | - name: Section_id |
| | dtype: string |
| | - name: Primary_id |
| | dtype: string |
| | - name: Secondary_id |
| | dtype: string |
| | - name: Statement |
| | dtype: string |
| | - name: Label |
| | dtype: string |
| | - name: Primary_evidence_index |
| | sequence: int64 |
| | - name: Secondary_evidence_index |
| | sequence: int64 |
| | splits: |
| | - name: train |
| | num_bytes: 571852 |
| | num_examples: 1700 |
| | - name: dev |
| | num_bytes: 61121 |
| | num_examples: 200 |
| | - name: test |
| | num_bytes: 175674 |
| | num_examples: 500 |
| | download_size: 283972 |
| | dataset_size: 808647 |
| | - config_name: 2024_conversational |
| | features: |
| | - name: id |
| | dtype: string |
| | - name: prompt |
| | list: |
| | - name: role |
| | dtype: string |
| | - name: content |
| | dtype: string |
| | - name: completion |
| | list: |
| | - name: role |
| | dtype: string |
| | - name: content |
| | dtype: string |
| | - name: Label |
| | dtype: string |
| | splits: |
| | - name: test |
| | num_bytes: 16283587 |
| | num_examples: 5500 |
| | - name: dev |
| | num_bytes: 522955 |
| | num_examples: 200 |
| | - name: train |
| | num_bytes: 4686231 |
| | num_examples: 1700 |
| | download_size: 7652506 |
| | dataset_size: 21492773 |
| | - config_name: 2024_processed |
| | features: |
| | - name: id |
| | dtype: string |
| | - name: Type |
| | dtype: string |
| | - name: Section_id |
| | dtype: string |
| | - name: Primary_id |
| | dtype: string |
| | - name: Statement |
| | dtype: string |
| | - name: Label |
| | dtype: string |
| | - name: Intervention |
| | dtype: string |
| | - name: Causal_type |
| | sequence: string |
| | - name: Secondary_id |
| | dtype: string |
| | - name: Secondary_evidence_index |
| | dtype: 'null' |
| | - name: Primary_Document |
| | struct: |
| | - name: Adverse Events |
| | sequence: string |
| | - name: Eligibility |
| | sequence: string |
| | - name: Intervention |
| | sequence: string |
| | - name: Results |
| | sequence: string |
| | - name: Primary_Section |
| | sequence: string |
| | - name: Secondary_Document |
| | struct: |
| | - name: Adverse Events |
| | sequence: string |
| | - name: Eligibility |
| | sequence: string |
| | - name: Intervention |
| | sequence: string |
| | - name: Results |
| | sequence: string |
| | - name: Secondary_Section |
| | sequence: string |
| | splits: |
| | - name: test |
| | num_bytes: 53427368 |
| | num_examples: 5500 |
| | - name: dev |
| | num_bytes: 1748808 |
| | num_examples: 200 |
| | - name: train |
| | num_bytes: 15342665 |
| | num_examples: 1700 |
| | download_size: 13749622 |
| | dataset_size: 70518841 |
| | - config_name: 2024_source |
| | features: |
| | - name: id |
| | dtype: string |
| | - name: Type |
| | dtype: string |
| | - name: Section_id |
| | dtype: string |
| | - name: Primary_id |
| | dtype: string |
| | - name: Statement |
| | dtype: string |
| | - name: Label |
| | dtype: string |
| | - name: Intervention |
| | dtype: string |
| | - name: Causal_type |
| | sequence: string |
| | - name: Secondary_id |
| | dtype: string |
| | - name: Secondary_evidence_index |
| | dtype: 'null' |
| | splits: |
| | - name: test |
| | num_bytes: 1965546 |
| | num_examples: 5500 |
| | - name: dev |
| | num_bytes: 48673 |
| | num_examples: 200 |
| | - name: train |
| | num_bytes: 428572 |
| | num_examples: 1700 |
| | download_size: 1034926 |
| | dataset_size: 2442791 |
| | configs: |
| | - config_name: 2023_conversational |
| | data_files: |
| | - split: train |
| | path: 2023_conversational/train-* |
| | - split: dev |
| | path: 2023_conversational/dev-* |
| | - split: test |
| | path: 2023_conversational/test-* |
| | - config_name: 2023_processed |
| | data_files: |
| | - split: train |
| | path: 2023_processed/train-* |
| | - split: dev |
| | path: 2023_processed/dev-* |
| | - split: test |
| | path: 2023_processed/test-* |
| | - config_name: 2023_source |
| | data_files: |
| | - split: train |
| | path: 2023_source/train-* |
| | - split: dev |
| | path: 2023_source/dev-* |
| | - split: test |
| | path: 2023_source/test-* |
| | - config_name: 2024_conversational |
| | data_files: |
| | - split: test |
| | path: 2024_conversational/test-* |
| | - split: dev |
| | path: 2024_conversational/dev-* |
| | - split: train |
| | path: 2024_conversational/train-* |
| | - config_name: 2024_processed |
| | data_files: |
| | - split: test |
| | path: 2024_processed/test-* |
| | - split: dev |
| | path: 2024_processed/dev-* |
| | - split: train |
| | path: 2024_processed/train-* |
| | - config_name: 2024_source |
| | data_files: |
| | - split: test |
| | path: 2024_source/test-* |
| | - split: dev |
| | 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. |
