Delete loading script

break_data.py

@@ -1,261 +0,0 @@
-"""TODO(break_data): Add a description here."""
-
-
-import csv
-import json
-import os
-import textwrap
-
-import datasets
-
-
-# TODO(break): BibTeX citation
-_CITATION = """\
-@article{Wolfson2020Break,
-  title={Break It Down: A Question Understanding Benchmark},
-  author={Wolfson, Tomer and Geva, Mor and Gupta, Ankit and Gardner, Matt and Goldberg, Yoav and Deutch, Daniel and Berant, Jonathan},
-  journal={Transactions of the Association for Computational Linguistics},
-  year={2020},
-}
-"""
-
-# TODO(break):
-_DESCRIPTION = """\
-Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations
-(QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases.
-This repository contains the Break dataset along with information on the exact data format.
-"""
-_URL = "https://github.com/allenai/Break/raw/master/break_dataset/Break-dataset.zip"
-
-
-class BreakDataConfig(datasets.BuilderConfig):
-
-    """BuilderConfig for Break"""
-
-    def __init__(self, text_features, lexicon_tokens, **kwargs):
-        """
-
-        Args:
-            text_features: `dict[string, string]`, map from the name of the feature
-        dict for each text field to the name of the column in the tsv file
-            lexicon_tokens: to define if we want to load the lexicon_tokens files or not
-            **kwargs: keyword arguments forwarded to super.
-        """
-        super(BreakDataConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-        self.text_features = text_features
-        self.lexicon_tokens = lexicon_tokens
-
-
-class BreakData(datasets.GeneratorBasedBuilder):
-    """TODO(break_data): Short description of my dataset."""
-
-    # TODO(break_data): Set up version.
-    VERSION = datasets.Version("0.1.0")
-    BUILDER_CONFIGS = [
-        BreakDataConfig(
-            name="QDMR-high-level",
-            description=textwrap.dedent(
-                """
-             Contains questions annotated with the high-level variant of QDMR. These decomposition are exclusive to Reading
-             Comprehension tasks (Section 2). lexicon_tokens files are also provided."""
-            ),
-            text_features={
-                "question_id": "question_id",
-                "question_text": "question_text",
-                "decomposition": "decomposition",
-                "operators": "operators",
-                "split": "split",
-            },
-            lexicon_tokens=False,
-        ),
-        BreakDataConfig(
-            name="QDMR-high-level-lexicon",
-            description=textwrap.dedent(
-                """
-               Contains questions annotated with the high-level variant of QDMR. These decomposition are exclusive to Reading
-               Comprehension tasks (Section 2). lexicon_tokens files are also provided."""
-            ),
-            text_features={
-                "source": "source",
-                "allowed_tokens": "allowed_tokens",
-            },
-            lexicon_tokens=True,
-        ),
-        BreakDataConfig(
-            name="QDMR",
-            description=textwrap.dedent(
-                """
-               Contains questions over text, images and databases annotated with their Question Decomposition Meaning
-               Representation. In addition to the train, dev and (hidden) test sets we provide lexicon_tokens files. For
-               each question, the lexicon file contains the set of valid tokens that could potentially appear in its
-               decomposition """
-            ),
-            text_features={
-                "question_id": "question_id",
-                "question_text": "question_text",
-                "decomposition": "decomposition",
-                "operators": "operators",
-                "split": "split",
-            },
-            lexicon_tokens=False,
-        ),
-        BreakDataConfig(
-            name="QDMR-lexicon",
-            description=textwrap.dedent(
-                """
-                 Contains questions over text, images and databases annotated with their Question Decomposition Meaning
-               Representation. In addition to the train, dev and (hidden) test sets we provide lexicon_tokens files. For
-               each question, the lexicon file contains the set of valid tokens that could potentially appear in its
-               decomposition """
-            ),
-            text_features={
-                "source": "source",
-                "allowed_tokens": "allowed_tokens",
-            },
-            lexicon_tokens=True,
-        ),
-        BreakDataConfig(
-            name="logical-forms",
-            description=textwrap.dedent(
-                """
-               Contains questions and QDMRs annotated with full logical-forms of QDMR operators + arguments. Full logical-forms
-               were inferred by the annotation-consistency algorithm described in """
-            ),
-            lexicon_tokens=False,
-            text_features={
-                "question_id": "question_id",
-                "question_text": "question_text",
-                "decomposition": "decomposition",
-                "operators": "operators",
-                "split": "split",
-                "program": "program",
-            },
-        ),
-    ]
-
-    def _info(self):
-        # TODO(break_data): Specifies the datasets.DatasetInfo object
-        features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                features
-                # These are the features of your dataset like images, labels ...
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="https://github.com/allenai/Break",
-            citation=_CITATION,
-        )
-        # if
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(break_data): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        dl_dir = dl_manager.download_and_extract(_URL)
-        data_dir = os.path.join(dl_dir, "Break-dataset")
-        qdmr_high_level = os.path.join(data_dir, "QDMR-high-level")
-        qdmr = os.path.join(data_dir, "QDMR")
-        logical = os.path.join(data_dir, "logical-forms")
-        if self.config.name == "QDMR" or self.config.name == "QDMR-lexicon":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(qdmr, "train.csv")
-                        if not self.config.lexicon_tokens
-                        else os.path.join(qdmr, "train_lexicon_tokens.json")
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(qdmr, "dev.csv")
-                        if not self.config.lexicon_tokens
-                        else os.path.join(qdmr, "dev_lexicon_tokens.json")
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(qdmr, "test.csv")
-                        if not self.config.lexicon_tokens
-                        else os.path.join(qdmr, "test_lexicon_tokens.json")
-                    },
-                ),
-            ]
-        elif self.config.name == "QDMR-high-level" or self.config.name == "QDMR-high-level-lexicon":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(qdmr_high_level, "train.csv")
-                        if not self.config.lexicon_tokens
-                        else os.path.join(qdmr_high_level, "train_lexicon_tokens.json")
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(qdmr_high_level, "dev.csv")
-                        if not self.config.lexicon_tokens
-                        else os.path.join(qdmr_high_level, "dev_lexicon_tokens.json")
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(qdmr_high_level, "test.csv")
-                        if not self.config.lexicon_tokens
-                        else os.path.join(qdmr_high_level, "test_lexicon_tokens.json")
-                    },
-                ),
-            ]
-        elif self.config.name == "logical-forms":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(logical, "train.csv")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(logical, "dev.csv")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(logical, "test.csv")},
-                ),
-            ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(break_data): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            if (
-                self.config.name == "QDMR-high-level"
-                or self.config.name == "QDMR"
-                or self.config.name == "logical-forms"
-            ):
-                data = csv.DictReader(f)
-                for id_, row in enumerate(data):
-                    yield id_, row
-            elif self.config.name == "QDMR-high-level-lexicon" or self.config.name == "QDMR-lexicon":
-                for id_, row in enumerate(f):
-                    data = json.loads(row)
-                    yield id_, data