upload hub_repos/mediqa_nli/README.md to hub from bigbio repo

README.md

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+
+---
+language: 
+- en
+license: other
+license_bigbio_shortname: PHYSIONET_LICENSE_1p5
+pretty_name: MEDIQA NLI
+---
+
+
+# Dataset Card for MEDIQA NLI
+
+## Dataset Description
+
+- **Homepage:** https://physionet.org/content/mednli-bionlp19/1.0.1/
+- **Pubmed:** False
+- **Public:** False
+- **Tasks:** Textual Entailment
+
+
+Natural Language Inference (NLI) is the task of determining whether a given hypothesis can be
+inferred from a given premise. Also known as Recognizing Textual Entailment (RTE), this task has
+enjoyed popularity among researchers for some time. However, almost all datasets for this task
+focused on open domain data such as as news texts, blogs, and so on. To address this gap, the MedNLI
+dataset was created for language inference in the medical domain. MedNLI is a derived dataset with
+data sourced from MIMIC-III v1.4. In order to stimulate research for this problem, a shared task on
+Medical Inference and Question Answering (MEDIQA) was organized at the workshop for biomedical
+natural language processing (BioNLP) 2019. The dataset provided herein is a test set of 405 premise
+hypothesis pairs for the NLI challenge in the MEDIQA shared task. Participants of the shared task
+are expected to use the MedNLI data for development of their models and this dataset was used as an
+unseen dataset for scoring each participant submission.
+
+
+
+## Citation Information
+
+```
+@misc{https://doi.org/10.13026/gtv4-g455,
+    title        = {MedNLI for Shared Task at ACL BioNLP 2019},
+    author       = {Shivade,  Chaitanya},
+    year         = 2019,
+    publisher    = {physionet.org},
+    doi          = {10.13026/GTV4-G455},
+    url          = {https://physionet.org/content/mednli-bionlp19/}
+}
+
+
+```