Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

dataset_infos.json

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+{"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. 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. 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_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": 95823986, "num_examples": 1620, "dataset_name": "qangaroo"}, "validation": {"name": "validation", "num_bytes": 16802484, "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": 112626470, "size_in_bytes": 452469531}, "wikihop": {"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. 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": "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": 325994029, "num_examples": 43738, "dataset_name": "qangaroo"}, "validation": {"name": "validation", "num_bytes": 40869634, "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": 366863663, "size_in_bytes": 706706724}, "masked_wikihop": {"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. 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}}

dummy/medhop/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e2d534d9b8533c365d90ab73565697864e70bad23f1a441194ebb40a31e647f
+size 154091

qangaroo.py

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+"""TODO(qangaroo): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+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"],
+                }