Update README.md

README.md

@@ -1,27 +1,52 @@
----
-license: mit
-dataset_info:
-  features:
-  - name: instruction
-    dtype: string
-  - name: output
-    dtype: string
-  - name: task
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 8972956600
-    num_examples: 503698
-  - name: validation
-    num_bytes: 1259708059
-    num_examples: 71638
-  download_size: 4925396868
-  dataset_size: 10232664659
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
----
+---
+license: mit
+dataset_info:
+  features:
+  - name: instruction
+    dtype: string
+  - name: output
+    dtype: string
+  - name: task
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 8972956600
+    num_examples: 503698
+  - name: validation
+    num_bytes: 1259708059
+    num_examples: 71638
+  download_size: 4925396868
+  dataset_size: 10232664659
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+---
+# Lawma fine-tuning dataset
+
+This fine-tuning dataset contains 260 legal classification tasks derived from the [Supreme Court](http://scdb.wustl.edu/data.php) and [Songer Court of Appeals](www.songerproject.org/us-courts-of-appeals-databases.html) databases, totalling over 500k training examples and 2B tokens. This dataset was used to train [Lawma 8B](https://huggingface.co/ricdomolm/lawma-8b) and [Lawma 70B](https://huggingface.co/ricdomolm/lawma-70b). The Lawma models outperform GPT-4 on 95\% of these legal tasks, on average by over 17 accuracy points. See our [arXiv preprint](https://arxiv.org/abs/2407.16615) and [GitHub repository](https://github.com/socialfoundations/lawma) for more details.
+
+Our reasons to study these legal classification tasks are both technical and substantive. From a technical machine learning perspective, these tasks provide highly non-trivial classification problems where
+even the best models leave much room for improvement. From a substantive legal perspective, efficient
+solutions to such classification problems have rich and important applications in legal research.
+
+This dataset was created for the project
+
+*Lawma: The Power of Specizalization for Legal Tasks. Ricardo Dominguez-Olmedo and Vedant Nanda and Rediet Abebe and Stefan Bechtold and Christoph Engel and Jens Frankenreiter and Krishna Gummadi and Moritz Hardt and Michael Livermore. 2024*
+
+Please cite as:
+
+```
+@misc{dominguezolmedo2024lawmapowerspecializationlegal,
+      title={Lawma: The Power of Specialization for Legal Tasks}, 
+      author={Ricardo Dominguez-Olmedo and Vedant Nanda and Rediet Abebe and Stefan Bechtold and Christoph Engel and Jens Frankenreiter and Krishna Gummadi and Moritz Hardt and Michael Livermore},
+      year={2024},
+      eprint={2407.16615},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2407.16615}, 
+}
+```