Update parquet files

README.md

@@ -1,299 +0,0 @@
----
-language:
-- en
-paperswithcode_id: null
-pretty_name: qangaroo
-dataset_info:
-- config_name: medhop
-  features:
-  - name: query
-    dtype: string
-  - name: supports
-    sequence: string
-  - name: candidates
-    sequence: string
-  - name: answer
-    dtype: string
-  - name: id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 93947725
-    num_examples: 1620
-  - name: validation
-    num_bytes: 16463555
-    num_examples: 342
-  download_size: 339843061
-  dataset_size: 110411280
-- config_name: masked_medhop
-  features:
-  - name: query
-    dtype: string
-  - name: supports
-    sequence: string
-  - name: candidates
-    sequence: string
-  - name: answer
-    dtype: string
-  - name: id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 95823986
-    num_examples: 1620
-  - name: validation
-    num_bytes: 16802484
-    num_examples: 342
-  download_size: 339843061
-  dataset_size: 112626470
-- config_name: wikihop
-  features:
-  - name: query
-    dtype: string
-  - name: supports
-    sequence: string
-  - name: candidates
-    sequence: string
-  - name: answer
-    dtype: string
-  - name: id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 325994029
-    num_examples: 43738
-  - name: validation
-    num_bytes: 40869634
-    num_examples: 5129
-  download_size: 339843061
-  dataset_size: 366863663
-- config_name: masked_wikihop
-  features:
-  - name: query
-    dtype: string
-  - name: supports
-    sequence: string
-  - name: candidates
-    sequence: string
-  - name: answer
-    dtype: string
-  - name: id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 348290479
-    num_examples: 43738
-  - name: validation
-    num_bytes: 43689810
-    num_examples: 5129
-  download_size: 339843061
-  dataset_size: 391980289
----
-
-# Dataset Card for "qangaroo"
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [http://qangaroo.cs.ucl.ac.uk/index.html](http://qangaroo.cs.ucl.ac.uk/index.html)
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 1296.40 MB
-- **Size of the generated dataset:** 936.40 MB
-- **Total amount of disk used:** 2232.79 MB
-
-### Dataset Summary
-
-  We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.
-
-Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.
-
-Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents.
-
-The two QAngaroo datasets provide a training and evaluation resource for such methods.
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Dataset Structure
-
-### Data Instances
-
-#### masked_medhop
-
-- **Size of downloaded dataset files:** 324.10 MB
-- **Size of the generated dataset:** 107.41 MB
-- **Total amount of disk used:** 431.51 MB
-
-An example of 'validation' looks as follows.
-```
-
-```
-
-#### masked_wikihop
-
-- **Size of downloaded dataset files:** 324.10 MB
-- **Size of the generated dataset:** 373.82 MB
-- **Total amount of disk used:** 697.92 MB
-
-An example of 'validation' looks as follows.
-```
-
-```
-
-#### medhop
-
-- **Size of downloaded dataset files:** 324.10 MB
-- **Size of the generated dataset:** 105.30 MB
-- **Total amount of disk used:** 429.40 MB
-
-An example of 'validation' looks as follows.
-```
-
-```
-
-#### wikihop
-
-- **Size of downloaded dataset files:** 324.10 MB
-- **Size of the generated dataset:** 349.87 MB
-- **Total amount of disk used:** 673.97 MB
-
-An example of 'validation' looks as follows.
-```
-
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### masked_medhop
-- `query`: a `string` feature.
-- `supports`: a `list` of `string` features.
-- `candidates`: a `list` of `string` features.
-- `answer`: a `string` feature.
-- `id`: a `string` feature.
-
-#### masked_wikihop
-- `query`: a `string` feature.
-- `supports`: a `list` of `string` features.
-- `candidates`: a `list` of `string` features.
-- `answer`: a `string` feature.
-- `id`: a `string` feature.
-
-#### medhop
-- `query`: a `string` feature.
-- `supports`: a `list` of `string` features.
-- `candidates`: a `list` of `string` features.
-- `answer`: a `string` feature.
-- `id`: a `string` feature.
-
-#### wikihop
-- `query`: a `string` feature.
-- `supports`: a `list` of `string` features.
-- `candidates`: a `list` of `string` features.
-- `answer`: a `string` feature.
-- `id`: a `string` feature.
-
-### Data Splits
-
-|     name     |train|validation|
-|--------------|----:|---------:|
-|masked_medhop | 1620|       342|
-|masked_wikihop|43738|      5129|
-|medhop        | 1620|       342|
-|wikihop       |43738|      5129|
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Citation Information
-
-```
-
-```
-
-
-### Contributions
-
-Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
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In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.\n\nOur aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents.\n\nThe two QAngaroo datasets provide a training and evaluation resource for such methods.\n", "citation": "\n", "homepage": "http://qangaroo.cs.ucl.ac.uk/index.html", "license": "", "features": {"query": {"dtype": "string", "id": null, "_type": "Value"}, "supports": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "candidates": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "qangaroo", "config_name": "medhop", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 93947725, "num_examples": 1620, "dataset_name": "qangaroo"}, "validation": {"name": "validation", "num_bytes": 16463555, "num_examples": 342, "dataset_name": "qangaroo"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1ytVZ4AhubFDOEL7o7XrIRIyhU8g9wvKA": {"num_bytes": 339843061, "checksum": "2f512869760cdad76a022a1465f025b486ae79dc5b8f0bf3ad901a4caf2d3050"}}, "download_size": 339843061, "dataset_size": 110411280, "size_in_bytes": 450254341}, "masked_medhop": {"description": "  We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.\n\nSeveral pieces of information often jointly imply another fact. 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In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.\n\nOur aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents.\n\nThe two QAngaroo datasets provide a training and evaluation resource for such methods.\n", "citation": "\n", "homepage": "http://qangaroo.cs.ucl.ac.uk/index.html", "license": "", "features": {"query": {"dtype": "string", "id": null, "_type": "Value"}, "supports": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "candidates": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "qangaroo", "config_name": "masked_wikihop", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 348290479, "num_examples": 43738, "dataset_name": "qangaroo"}, "validation": {"name": "validation", "num_bytes": 43689810, "num_examples": 5129, "dataset_name": "qangaroo"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1ytVZ4AhubFDOEL7o7XrIRIyhU8g9wvKA": {"num_bytes": 339843061, "checksum": "2f512869760cdad76a022a1465f025b486ae79dc5b8f0bf3ad901a4caf2d3050"}}, "download_size": 339843061, "dataset_size": 391980289, "size_in_bytes": 731823350}}

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qangaroo.py

@@ -1,126 +0,0 @@
-"""TODO(qangaroo): Add a description here."""
-
-
-import json
-import os
-
-import datasets
-
-
-# TODO(qangaroo): BibTeX citation
-
-_CITATION = """
-"""
-
-# TODO(quangaroo):
-_DESCRIPTION = """\
-  We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.
-
-Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.
-
-Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents.
-
-The two QAngaroo datasets provide a training and evaluation resource for such methods.
-"""
-
-_MEDHOP_DESCRIPTION = """\
-  With the same format as WikiHop, this dataset is based on research paper abstracts from PubMed, and the queries are about interactions between pairs of drugs.
-  The correct answer has to be inferred by combining information from a chain of reactions of drugs and proteins.
-  """
-_WIKIHOP_DESCRIPTION = """\
-  With the same format as WikiHop, this dataset is based on research paper abstracts from PubMed, and the queries are about interactions between pairs of drugs.
-   The correct answer has to be inferred by combining information from a chain of reactions of drugs and proteins.
-  """
-
-_URL = "https://drive.google.com/uc?export=download&id=1ytVZ4AhubFDOEL7o7XrIRIyhU8g9wvKA"
-
-
-class QangarooConfig(datasets.BuilderConfig):
-    def __init__(self, data_dir, **kwargs):
-        """BuilderConfig for qangaroo dataset
-
-        Args:
-          data_dir: directory for the given dataset name
-          **kwargs: keyword arguments forwarded to super.
-
-        """
-
-        super(QangarooConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-
-        self.data_dir = data_dir
-
-
-class Qangaroo(datasets.GeneratorBasedBuilder):
-    """TODO(qangaroo): Short description of my dataset."""
-
-    # TODO(qangaroo): Set up version.
-    VERSION = datasets.Version("0.1.0")
-    BUILDER_CONFIGS = [
-        QangarooConfig(name="medhop", description=_MEDHOP_DESCRIPTION, data_dir="medhop"),
-        QangarooConfig(name="masked_medhop", description=_MEDHOP_DESCRIPTION, data_dir="medhop"),
-        QangarooConfig(name="wikihop", description=_WIKIHOP_DESCRIPTION, data_dir="wikihop"),
-        QangarooConfig(name="masked_wikihop", description=_WIKIHOP_DESCRIPTION, data_dir="wikihop"),
-    ]
-
-    def _info(self):
-        # TODO(qangaroo): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    # These are the features of your dataset like images, labels ...
-                    "query": datasets.Value("string"),
-                    "supports": datasets.features.Sequence(datasets.Value("string")),
-                    "candidates": datasets.features.Sequence(datasets.Value("string")),
-                    "answer": datasets.Value("string"),
-                    "id": datasets.Value("string")
-                    # 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="http://qangaroo.cs.ucl.ac.uk/index.html",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(qangaroo): 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, "qangaroo_v1.1")
-        train_file = "train.masked.json" if "masked" in self.config.name else "train.json"
-        dev_file = "dev.masked.json" if "masked" in self.config.name else "dev.json"
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, self.config.data_dir, train_file)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, self.config.data_dir, dev_file)},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(quangaroo): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for example in data:
-                id_ = example["id"]
-                yield id_, {
-                    "id": example["id"],
-                    "query": example["query"],
-                    "supports": example["supports"],
-                    "candidates": example["candidates"],
-                    "answer": example["answer"],
-                }

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