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import json |
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import os |
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import datasets |
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_CITATION = """\@inproceedings{parikh2020totto, |
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title={{ToTTo}: A Controlled Table-To-Text Generation Dataset}, |
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author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan}, |
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booktitle={Proceedings of EMNLP}, |
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year={2020} |
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} |
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""" |
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_DESCRIPTION = """\ |
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ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. |
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""" |
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_URLs = { |
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"totto": { |
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"data": "https://storage.googleapis.com/totto/totto_data.zip", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/totto.zip", |
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}, |
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} |
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class Mlsum(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="totto", |
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version=datasets.Version("1.0.0"), |
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description=f"GEM benchmark: struct2text task", |
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) |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features = datasets.Features( |
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{ |
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"gem_id": datasets.Value("string"), |
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"gem_parent_id": datasets.Value("string"), |
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"totto_id": datasets.Value("int32"), |
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"table_page_title": datasets.Value("string"), |
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"table_webpage_url": datasets.Value("string"), |
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"table_section_title": datasets.Value("string"), |
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"table_section_text": datasets.Value("string"), |
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"table": [ |
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[ |
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{ |
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"column_span": datasets.Value("int32"), |
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"is_header": datasets.Value("bool"), |
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"row_span": datasets.Value("int32"), |
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"value": datasets.Value("string"), |
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} |
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] |
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], |
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"highlighted_cells": [[datasets.Value("int32")]], |
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"example_id": datasets.Value("string"), |
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"sentence_annotations": [ |
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{ |
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"original_sentence": datasets.Value("string"), |
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"sentence_after_deletion": datasets.Value("string"), |
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"sentence_after_ambiguity": datasets.Value("string"), |
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"final_sentence": datasets.Value("string"), |
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} |
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], |
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"overlap_subset": datasets.Value("string"), |
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"target": datasets.Value("string"), |
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"references": [datasets.Value("string")], |
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}, |
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), |
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supervised_keys=None, |
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homepage="", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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dl_dir = dl_manager.download_and_extract(_URLs[self.config.name]) |
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challenge_sets = [ |
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("challenge_train_sample", "train_totto_RandomSample500.json"), |
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("challenge_validation_sample", "validation_totto_RandomSample500.json"), |
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("challenge_test_scramble", "test_totto_ScrambleInputStructure500.json"), |
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] |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": os.path.join(dl_dir["data"], "totto_data/totto_train_data.jsonl"), |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": os.path.join(dl_dir["data"], "totto_data/totto_dev_data.jsonl"), |
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"split": "validation", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": os.path.join(dl_dir["data"], "totto_data/unlabeled_totto_test_data.jsonl"), |
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"split": "test", |
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}, |
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), |
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] + [ |
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datasets.SplitGenerator( |
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name=challenge_split, |
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gen_kwargs={ |
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"filepath": os.path.join(dl_dir["challenge_set"], self.config.name, filename), |
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"split": challenge_split, |
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}, |
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) |
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for challenge_split, filename in challenge_sets |
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] |
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def _generate_examples(self, filepath, split, filepaths=None, lang=None): |
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"""Yields examples.""" |
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if "challenge" in split: |
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exples = json.load(open(filepath, encoding="utf-8")) |
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if isinstance(exples, dict): |
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assert len(exples) == 1, "multiple entries found" |
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exples = list(exples.values())[0] |
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for id_, exple in enumerate(exples): |
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if len(exple) == 0: |
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continue |
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exple["gem_parent_id"] = exple["gem_id"] |
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exple["gem_id"] = f"{self.config.name}-{split}-{id_}" |
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yield id_, exple |
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else: |
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with open(filepath, "r", encoding="utf-8") as json_file: |
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json_list = list(json_file) |
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id_ = -1 |
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i = -1 |
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for json_str in json_list: |
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result = json.loads(json_str) |
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if split == "train": |
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i += 1 |
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for sentence in result["sentence_annotations"]: |
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id_ += 1 |
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response = { |
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"gem_id": f"{self.config.name}-{split}-{id_}", |
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"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
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"totto_id": i, |
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"table_page_title": result["table_page_title"], |
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"table_webpage_url": result["table_webpage_url"], |
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"table_section_title": result["table_section_title"], |
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"table_section_text": result["table_section_text"], |
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"table": result["table"], |
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"highlighted_cells": result["highlighted_cells"], |
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"example_id": str(result["example_id"]), |
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"overlap_subset": "none", |
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"sentence_annotations": [sentence], |
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"references": [], |
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"target": sentence["final_sentence"], |
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} |
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yield id_, response |
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else: |
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id_ += 1 |
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response = { |
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"gem_id": f"{self.config.name}-{split}-{id_}", |
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"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
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"totto_id": id_, |
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"table_page_title": result["table_page_title"], |
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"table_webpage_url": result["table_webpage_url"], |
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"table_section_title": result["table_section_title"], |
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"table_section_text": result["table_section_text"], |
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"table": result["table"], |
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"highlighted_cells": result["highlighted_cells"], |
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"example_id": str(result["example_id"]), |
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"overlap_subset": str(result["overlap_subset"]), |
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} |
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response["sentence_annotations"] = [] if split == "test" else result["sentence_annotations"] |
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response["references"] = [ |
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sentence["final_sentence"] for sentence in response["sentence_annotations"] |
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] |
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response["target"] = response["references"][0] if len(response["references"]) > 0 else "" |
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yield id_, response |
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