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--- |
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license: cc-by-4.0 |
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dataset_info: |
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features: |
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- name: original_id |
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dtype: int32 |
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- name: edit_goal |
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dtype: string |
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- name: edit_type |
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dtype: string |
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- name: text |
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dtype: string |
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- name: food |
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dtype: string |
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- name: ambiance |
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dtype: string |
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- name: service |
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dtype: string |
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- name: noise |
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dtype: string |
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- name: counterfactual |
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dtype: bool |
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- name: rating |
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dtype: int64 |
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splits: |
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- name: validation |
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num_bytes: 306529 |
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num_examples: 1673 |
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- name: test |
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num_bytes: 309751 |
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num_examples: 1689 |
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- name: train |
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num_bytes: 2282439 |
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num_examples: 11728 |
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download_size: 628886 |
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dataset_size: 2898719 |
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task_categories: |
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- text-classification |
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language: |
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- en |
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--- |
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# Dataset Card for "CEBaB" |
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|
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This is a lightly cleaned and simplified version of the CEBaB counterfactual restaurant review dataset from [this paper](https://arxiv.org/abs/2205.14140). |
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The most important difference from the original dataset is that the `rating` column corresponds to the _median_ rating provided by the Mechanical Turkers, |
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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, |
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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. |
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The exact code used to process the original dataset is provided below: |
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```py |
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from ast import literal_eval |
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from datasets import DatasetDict, Value, load_dataset |
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def compute_median(x: str): |
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"""Compute the median rating given a multiset of ratings.""" |
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# Decode the dictionary from string format |
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dist = literal_eval(x) |
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|
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# Should be a dictionary whose keys are string-encoded integer ratings |
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# and whose values are the number of times that the rating was observed |
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assert isinstance(dist, dict) |
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assert sum(dist.values()) % 2 == 1, "Number of ratings should be odd" |
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ratings = [] |
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for rating, count in dist.items(): |
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ratings.extend([int(rating)] * count) |
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ratings.sort() |
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return ratings[len(ratings) // 2] |
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cebab = load_dataset('CEBaB/CEBaB') |
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assert isinstance(cebab, DatasetDict) |
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# Remove redundant splits |
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cebab['train'] = cebab.pop('train_inclusive') |
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del cebab['train_exclusive'] |
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del cebab['train_observational'] |
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cebab = cebab.cast_column( |
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'original_id', Value('int32') |
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).map( |
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lambda x: { |
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# New column with inverted label for counterfactuals |
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'counterfactual': not x['is_original'], |
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# Reduce the rating multiset into a single median rating |
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'rating': compute_median(x['review_label_distribution']) |
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} |
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).map( |
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# Replace the empty string and 'None' with Apache Arrow nulls |
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lambda x: { |
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k: v if v not in ('', 'no majority', 'None') else None |
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for k, v in x.items() |
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} |
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) |
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# Sanity check that all the splits have the same columns |
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cols = next(iter(cebab.values())).column_names |
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assert all(split.column_names == cols for split in cebab.values()) |
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|
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# Clean up the names a bit |
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cebab = cebab.rename_columns({ |
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col: col.removesuffix('_majority').removesuffix('_aspect') |
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for col in cols if col.endswith('_majority') |
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}).rename_column( |
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'description', 'text' |
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) |
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# Drop the unimportant columns |
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cebab = cebab.remove_columns([ |
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col for col in cols if col.endswith('_distribution') or col.endswith('_workers') |
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] + [ |
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'edit_id', 'edit_worker', 'id', 'is_original', 'opentable_metadata', 'review' |
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]).sort([ |
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# Make sure counterfactual reviews come immediately after each original review |
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'original_id', 'counterfactual' |
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]) |
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``` |
