Datasets:

araag2
/

MedNLI

@@ -114,117 +114,66 @@ tags:
 pretty_name: MedNLI
 size_categories:
 - 10K<n<100K
 ---
-# Dataset Card for MedNLI
-<!-- Provide a quick summary of the dataset. -->
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
-## Dataset Details
-### Dataset Description
-<!-- Provide a longer summary of what this dataset is. -->
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the dataset is intended to be used. -->
-### Direct Use
-<!-- This section describes suitable use cases for the dataset. -->
-[More Information Needed]
-### Out-of-Scope Use
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-## Dataset Structure
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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-## Dataset Creation
-### Curation Rationale
-<!-- Motivation for the creation of this dataset. -->
-[More Information Needed]
-### Source Data
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-#### Data Collection and Processing
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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-#### Who are the source data producers?
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-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### Annotation process
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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-#### Who are the annotators?
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-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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-## Bias, Risks, and Limitations
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
 ```
 @article{romanov2018lessons,
@@ -240,30 +189,8 @@ Users should be made aware of the risks, biases and limitations of the dataset.
 }
 ```
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
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-## Dataset Card Contact
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 pretty_name: MedNLI
 size_categories:
 - 10K<n<100K
+license: cc-by-sa-4.0
+task_categories:
+- question-answering
+- text-classification
 ---
+# MedNLI — A Natural Language Inference Dataset For The Clinical Domain
+## Dataset Description
+|                                 | Links         |
+|:-------------------------------:|:-------------:|
+| **Homepage:**                   |  [Github.io](https://jgc128.github.io/mednli/)  |
+| **Repository:**                 |  [Github](https://github.com/jgc128/mednli)  |
+| **Paper:**                      |  [arXiv](https://arxiv.org/abs/1808.06752)  |
+| **Leaderboard:**                |  [Papers with Code](https://paperswithcode.com/dataset/medmcqa)  |
+| **Contact (Original Authors):** |  Alexey Romanov aromanov@cs.uml.edu, Chaitanya Shivade cshivade@us.ibm.com |
+| **Contact (Curator):**          |  [Artur Guimarães](https://araag2.netlify.app/) (artur.guimas@gmail.com) |
+### Dataset Summary
+`Natural Language Inference (NLI) is one of the critical tasks for understanding natural language. The objective of NLI is to determine if a given hypothesis can be inferred from a given premise. NLI systems have made significant progress over the years, and has gained popularity since the recent release of datasets such as the Stanford Natural Language Inference (SNLI) (Bowman et al. 2015) and Multi-NLI (Nangia et al. 2017).
+We introduce MedNLI - a dataset annotated by doctors, performing a natural language inference task), grounded in the medical history of patients. We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g. SNLI) and 2) incorporate domain knowledge from external data and lexical sources (e.g. medical terminologies). Our results demonstrate performance gains using both strategies.`
+### Data Instances
+#### Source Format
+TO:DO
+### Data Fields
+#### Source Format
+TO:DO
+### Data Splits
+TO:DO
+## Additional Information
+### Dataset Curators
+#### Original Paper
+- Alexey Romanov (aromanov@cs.uml.edu) - Department of Computer Science, University of Massachusetts Lowell
+- Chaitanya Shivade (cshivade@us.ibm.com) - IBM Almaden Research Center
+#### 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
 ```
 @article{romanov2018lessons,
 }
 ```
+[10.13026/C2RS98](http://doi.org/10.13026/C2RS98)
+### Contributions
+Thanks to [araag2](https://github.com/araag2) for adding this dataset.