Update README.md

README.md

@@ -36,4 +36,79 @@ dataset_info:
 ---
 # Dataset Card for "CEBaB"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+This is a lightly cleaned and simplified version of the CEBaB counterfactual restaurant review dataset from [this paper](https://arxiv.org/abs/2205.14140).
+The most important difference from the original dataset is that the `rating` column corresponds to the _median_ rating provided by the Mechanical Turkers,
+rather than the majority rating. These are the same whenever a majority rating exists, but when there is no majority rating (e.g. because there were two 1s,
+two 2s, and one 3), the original dataset used a `"no majority"` placeholder whereas we are able to provide an aggregate rating for all reviews.
+
+The exact code used to process the original dataset is provided below:
+```py
+from ast import literal_eval
+from datasets import DatasetDict, Value, load_dataset
+
+
+def compute_median(x: str):
+    """Compute the median rating given a multiset of ratings."""
+    # Decode the dictionary from string format
+    dist = literal_eval(x)
+
+    # Should be a dictionary whose keys are string-encoded integer ratings
+    # and whose values are the number of times that the rating was observed
+    assert isinstance(dist, dict)
+    assert sum(dist.values()) % 2 == 1, "Number of ratings should be odd"
+
+    ratings = []
+    for rating, count in dist.items():
+        ratings.extend([int(rating)] * count)
+
+    ratings.sort()
+    return ratings[len(ratings) // 2]
+
+
+cebab = load_dataset('CEBaB/CEBaB')
+assert isinstance(cebab, DatasetDict)
+
+# Remove redundant splits
+cebab['train'] = cebab.pop('train_inclusive')
+del cebab['train_exclusive']
+del cebab['train_observational']
+
+cebab = cebab.cast_column(
+    'original_id', Value('int32')
+).map(
+    lambda x: {
+        # New column with inverted label for counterfactuals
+        'counterfactual': not x['is_original'],
+        # Reduce the rating multiset into a single median rating
+        'rating': compute_median(x['review_label_distribution'])
+    }
+).map(
+    # Replace the empty string and 'None' with Apache Arrow nulls
+    lambda x: {
+        k: v if v not in ('', 'no majority', 'None') else None
+        for k, v in x.items()
+    }
+)
+
+# Sanity check that all the splits have the same columns
+cols = next(iter(cebab.values())).column_names
+assert all(split.column_names == cols for split in cebab.values())
+
+# Clean up the names a bit
+cebab = cebab.rename_columns({
+    col: col.removesuffix('_majority').removesuffix('_aspect')
+    for col in cols if col.endswith('_majority')
+}).rename_column(
+    'description', 'text'
+)
+
+# Drop the unimportant columns
+cebab = cebab.remove_columns([
+    col for col in cols if col.endswith('_distribution') or col.endswith('_workers')
+] + [
+    'edit_id', 'edit_worker', 'id', 'is_original', 'opentable_metadata', 'review'
+]).sort([
+    # Make sure counterfactual reviews come immediately after each original review
+    'original_id', 'counterfactual'
+])
+```