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from datasets import load_dataset
load_dataset("path/to/my_dataset")
datasets.Features(
{
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.Sequence(
{
"text": datasets.Value("string"),
"answer_start": datasets.Value("int32"),
}
),
}
)
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.features.Sequence(
{"text": datasets.Value("string"), "answer_start": datasets.Value("int32"),}
),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage="https://rajpurkar.github.io/SQuAD-explorer/",
citation=_CITATION,
)
class SuperGlueConfig(datasets.BuilderConfig):
"""BuilderConfig for SuperGLUE."""
def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
"""BuilderConfig for SuperGLUE.
Args:
features: *list[string]*, list of the features that will appear in the
feature dict. Should not include "label".
data_url: *string*, url to download the zip file from.
citation: *string*, citation for the data set.
url: *string*, url for information about the data set.
label_classes: *list[string]*, the list of classes for the label if the
label is present as a string. Non-string labels will be cast to either
'False' or 'True'.
**kwargs: keyword arguments forwarded to super.
"""
# Version history:
# 1.0.2: Fixed non-nondeterminism in ReCoRD.
# 1.0.1: Change from the pre-release trial version of SuperGLUE (v1.9) to
# the full release (v2.0).
# 1.0.0: S3 (new shuffling, sharding and slicing mechanism).
# 0.0.2: Initial version.
super(SuperGlueConfig, self).__init__(version=datasets.Version("1.0.2"), **kwargs)
self.features = features
self.label_classes = label_classes
self.data_url = data_url
self.citation = citation
self.url = url
class SuperGlue(datasets.GeneratorBasedBuilder):
"""The SuperGLUE benchmark."""
BUILDER_CONFIGS = [
SuperGlueConfig(
name="boolq",
description=_BOOLQ_DESCRIPTION,
features=["question", "passage"],
data_url="https://dl.fbaipublicfiles.com/glue/superglue/data/v2/BoolQ.zip",
citation=_BOOLQ_CITATION,
url="https://github.com/google-research-datasets/boolean-questions",
),
...
...
SuperGlueConfig(
name="axg",
description=_AXG_DESCRIPTION,
features=["premise", "hypothesis"],
label_classes=["entailment", "not_entailment"],
data_url="https://dl.fbaipublicfiles.com/glue/superglue/data/v2/AX-g.zip",
citation=_AXG_CITATION,
url="https://github.com/rudinger/winogender-schemas",
),
from datasets import load_dataset
dataset = load_dataset('super_glue', 'boolq')
class NewDataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
]
DEFAULT_CONFIG_NAME = "first_domain"
_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
_URLS = {
"train": _URL + "train-v1.1.json",
"dev": _URL + "dev-v1.1.json",
}
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
urls_to_download = self._URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
with open(filepath) as f:
squad = json.load(f)
for article in squad["data"]:
title = article.get("title", "").strip()
for paragraph in article["paragraphs"]:
context = paragraph["context"].strip()
for qa in paragraph["qas"]:
question = qa["question"].strip()
id_ = qa["id"]
answer_starts = [answer["answer_start"] for answer in qa["answers"]]
answers = [answer["text"].strip() for answer in qa["answers"]]
# Features currently used are "context", "question", and "answers".
# Others are extracted here for the ease of future expansions.
yield id_, {
"title": title,
"context": context,
"question": question,
"id": id_,
"answers": {"answer_start": answer_starts, "text": answers,},
}
datasets-cli test datasets/<your-dataset-folder> --save_infos --all_configs
datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate
datasets-cli dummy_data datasets/<your-dataset-folder>
==============================DUMMY DATA INSTRUCTIONS==============================
- In order to create the dummy data for my-dataset, please go into the folder './datasets/my-dataset/dummy/1.1.0' with *cd ./datasets/my-dataset/dummy/1.1.0* .
- Please create the following dummy data files 'dummy_data/TREC_10.label, dummy_data/train_5500.label' from the folder './datasets/my-dataset/dummy/1.1.0'
- For each of the splits 'train, test', make sure that one or more of the dummy data files provide at least one example
- If the method *_generate_examples(...)* includes multiple *open()* statements, you might have to create other files in addition to 'dummy_data/TREC_10.label, dummy_data/train_5500.label'. In this case please refer to the *_generate_examples(...)* method
- After all dummy data files are created, they should be zipped recursively to 'dummy_data.zip' with the command *zip -r dummy_data.zip dummy_data/*
- You can now delete the folder 'dummy_data' with the command *rm -r dummy_data*
- To get the folder 'dummy_data' back for further changes to the dummy data, simply unzip dummy_data.zip with the command *unzip dummy_data.zip*
- Make sure you have created the file 'dummy_data.zip' in './datasets/my-dataset/dummy/1.1.0'
===================================================================================
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_<your_dataset_name>
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<your_dataset_name> |