Create README.md

README.md

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+---
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- reddit
+- law
+pretty_name: Legal Advice Reddit
+---
+
+# Dataset Card for Legal Advice Reddit Dataset
+
+## Dataset Description
+
+- **Paper: [Parameter-Efficient Legal Domain Adaptation](https://arxiv.org/abs/2210.13712)** 
+- **Point of Contact: `jxl@queensu.ca`** 
+
+### Dataset Summary
+
+We introduce a new dataset from the Legal Advice Reddit community (known as "/r/legaldvice"), sourcing the Reddit posts from the Pushshift
+Reddit dataset. The dataset maps the text and title of each legal question posted into one of eleven classes, based on the original Reddit
+post's "flair" (i.e., tag). Questions are typically informal and use non-legal-specific language. Per the Legal Advice Reddit rules, posts 
+must be about actual personal circumstances or situations. We limit the number of labels to the top eleven classes and remove the other 
+samples from the dataset.
+
+
+### Citation Information
+
+```
+@misc{https://doi.org/10.48550/arxiv.2210.13712,
+  doi = {10.48550/ARXIV.2210.13712},
+  url = {https://arxiv.org/abs/2210.13712},
+  author = {Li, Jonathan and Bhambhoria, Rohan and Zhu, Xiaodan},
+  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {Parameter-Efficient Legal Domain Adaptation},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+```