Datasets:
File size: 3,599 Bytes
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---
license: cc-by-4.0
dataset_info:
features:
- name: original_id
dtype: int32
- name: edit_goal
dtype: string
- name: edit_type
dtype: string
- name: text
dtype: string
- name: food
dtype: string
- name: ambiance
dtype: string
- name: service
dtype: string
- name: noise
dtype: string
- name: counterfactual
dtype: bool
- name: rating
dtype: int64
splits:
- name: validation
num_bytes: 306529
num_examples: 1673
- name: test
num_bytes: 309751
num_examples: 1689
- name: train
num_bytes: 2282439
num_examples: 11728
download_size: 628886
dataset_size: 2898719
task_categories:
- text-classification
language:
- en
---
# Dataset Card for "CEBaB"
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'
])
``` |