def _info(self):
# TODO(blended_skill_talk): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
"personas":
nlp.features.Sequence(nlp.Value("string")),
"additional_context":
nlp.Value("string"),
"previous_utterance":
nlp.features.Sequence(nlp.Value("string")),
"context":
nlp.Value("string"),
"free_messages":
nlp.features.Sequence(nlp.Value("string")),
"guided_messages":
nlp.features.Sequence(nlp.Value("string")),
"suggestions":
nlp.features.Sequence(
{task: nlp.Value("string")
for task in _TASK})
# 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://parl.ai/projects/bst/",
citation=_CITATION,
)
def _info(self):
# TODO(race): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
"article":
nlp.Value("string"),
"answer":
nlp.Value("string"),
"question":
nlp.Value("string"),
"options":
nlp.features.Sequence({"option": nlp.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://www.cs.cmu.edu/~glai1/data/race/",
citation=_CITATION,
)
def _info(self):
# TODO(wiqa): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features(
{
# These are the features of your dataset like images, labels ...
"question_stem": nlp.Value("string"),
"question_para_step": nlp.features.Sequence(nlp.Value("string")),
"answer_label": nlp.Value("string"),
"answer_label_as_choice": nlp.Value("string"),
"choices": nlp.features.Sequence({"text": nlp.Value("string"), "label": nlp.Value("string")}),
"metadata_question_id": nlp.Value("string"),
"metadata_graph_id": nlp.Value("string"),
"metadata_para_id": nlp.Value("string"),
"metadata_question_type": nlp.Value("string"),
"metadata_path_len": nlp.Value("int32"),
}
),
# 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://allenai.org/data/wiqa",
citation=_CITATION,
)
def read_batch_unformated(feats, tmp_dir):
batch_size = 10
dataset = nlp.Dataset.from_file(filename=os.path.join(
tmp_dir, "beta.arrow"),
info=nlp.DatasetInfo(features=feats))
for i in range(0, len(dataset), batch_size):
_ = dataset[i:i + batch_size]
def _info(self):
# TODO(coqa): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
"source":
nlp.Value("string"),
"story":
nlp.Value("string"),
"questions":
nlp.features.Sequence(nlp.Value("string")),
"answers":
nlp.features.Sequence({
"input_text": nlp.Value("string"),
"answer_start": nlp.Value("int32"),
"answer_end": nlp.Value("int32"),
}),
}),
# 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://stanfordnlp.github.io/coqa/",
citation=_CITATION,
)
def _info(self):
# TODO(x_stance): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
"question": nlp.Value("string"),
"id": nlp.Value("int32"),
"question_id": nlp.Value("int32"),
"language": nlp.Value("string"),
"comment": nlp.Value("string"),
"label": nlp.Value("string"),
"numerical_label": nlp.Value("int32"),
"author": nlp.Value("string"),
"topic": nlp.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="https://github.com/ZurichNLP/xstance",
citation=_CITATION,
)
def _info(self):
# TODO(math_qa): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features(
{
# These are the features of your dataset like images, labels ...
"Problem": nlp.Value("string"),
"Rationale": nlp.Value("string"),
"options": nlp.Value("string"),
"correct": nlp.Value("string"),
"annotated_formula": nlp.Value("string"),
"linear_formula": nlp.Value("string"),
"category": nlp.Value("string"),
}
),
# 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://math-qa.github.io/math-QA/",
citation=_CITATION,
)
def _info(self):
# TODO(cosmos_qa): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
"id": nlp.Value("string"),
"context": nlp.Value("string"),
"question": nlp.Value("string"),
"answer0": nlp.Value("string"),
"answer1": nlp.Value("string"),
"answer2": nlp.Value("string"),
"answer3": nlp.Value("string"),
"label": nlp.Value("int32")
# 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://wilburone.github.io/cosmos/",
citation=_CITATION,
)
def _info(self):
# TODO(openBookQA): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
# These are the features of your dataset like images, labels ...
'id':
nlp.Value('string'),
'question_stem':
nlp.Value('string'),
'choices':
nlp.features.Sequence({
'text': nlp.Value('string'),
'label': nlp.Value('string')
}),
'answerKey':
nlp.Value('string')
}),
# 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://allenai.org/data/open-book-qa',
citation=_CITATION,
)
def _info(self):
return nlp.DatasetInfo(
description=_DESCRIPTION + '\n' + self.config.description,
features=nlp.Features(
{
"category": nlp.Value("string"),
"air_date": nlp.Value("string"),
"question": nlp.Value("string"),
"value": nlp.Value("string"),
"answer": nlp.Value("string"),
"round": nlp.Value("string"),
"category": nlp.Value("string"),
"show_number": nlp.Value("int32"),
"search_results": nlp.features.Sequence({
'urls': nlp.Value('string'),
"snippets": nlp.Value('string'),
"titles": nlp.Value('string'),
"related_links": nlp.Value('string')
})
# These are the features of your dataset like images, labels ...
}
),
homepage="https://github.com/nyu-dl/dl4ir-searchQA",
citation=_CITATION,
)
def _info(self):
return nlp.DatasetInfo(
description=_DESCRIPTION,
features=nlp.Features({
"id":
nlp.Value("string"),
"title":
nlp.Value("string"),
"context":
nlp.Value("string"),
"question":
nlp.Value("string"),
"answers":
nlp.features.Sequence({
"text": nlp.Value("string"),
"answer_start": nlp.Value("int32")
}),
}),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage=
"https://github.com/husseinmozannar/SOQAL/tree/master/data",
citation=_CITATION,
)
def _info(self):
# TODO(reclor): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
# These are the features of your dataset like images, labels ...
"context":
nlp.Value("string"),
"question":
nlp.Value("string"),
"answers":
nlp.features.Sequence(nlp.Value("string")),
"label":
nlp.Value("string"),
"id_string":
nlp.Value("string"),
}),
# 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://whyu.me/reclor/",
citation=_CITATION,
)
def _info(self):
return nlp.DatasetInfo(
features=nlp.Features(
{"a": nlp.Sequence({"b": nlp.Value("string")})}),
# No default supervised_keys.
supervised_keys=None,
)
def _info(self):
return nlp.DatasetInfo(
description=_DESCRIPTION + self.config.description,
features=nlp.Features(self.config.features),
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _info(self):
# TODO(fquad): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
'context':
nlp.Value('string'),
'questions':
nlp.features.Sequence({
'question': nlp.Value('string'),
}),
'answers':
nlp.features.Sequence({
'texts': nlp.Value('string'),
'answers_starts': nlp.Value('int32')
}),
# 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://fquad.illuin.tech/',
citation=_CITATION,
)
def _info(self):
if self.config.name == 'wiki_pages':
features = {
'id': nlp.Value('string'),
'text': nlp.Value('string'),
'lines': nlp.Value('string'),
}
else:
features = {
'id': nlp.Value('int32'),
'label': nlp.Value('string'),
'claim': nlp.Value('string'),
'evidence_annotation_id': nlp.Value('int32'),
'evidence_id': nlp.Value('int32'),
'evidence_wiki_url': nlp.Value('string'),
'evidence_sentence_id': nlp.Value('int32')
}
return nlp.DatasetInfo(
description=_DESCRIPTION + '\n' + self.config.description,
features=nlp.Features(features),
homepage="https://fever.ai/",
citation=_CITATION,
)
def _info(self):
# TODO(quarel): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
# These are the features of your dataset like images, labels ...
'id':
nlp.Value('string'),
'answer_index':
nlp.Value('int32'),
'logical_forms':
nlp.features.Sequence({'logical_form': nlp.Value('string')}),
'logical_form_pretty':
nlp.Value('string'),
'world_literals':
nlp.features.Sequence({
'world1': nlp.Value('string'),
'world2': nlp.Value('string')
}),
'question':
nlp.Value('string')
}),
# 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://allenai.org/data/quarel',
citation=_CITATION,
)
def _info(self):
# TODO(wiki_qa): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
"question_id":
nlp.Value("string"),
"question":
nlp.Value("string"),
"document_title":
nlp.Value("string"),
"answer":
nlp.Value("string"),
"label":
nlp.features.ClassLabel(num_classes=2),
# 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://www.microsoft.com/en-us/download/details.aspx?id=52419",
citation=_CITATION,
)
def _info(self):
return nlp.DatasetInfo(
description=_DESCRIPTION,
features=nlp.Features(
{
"text": nlp.Value("string"),
"topics": nlp.Sequence(nlp.Value("string")),
"lewis_split": nlp.Value("string"),
"cgis_split": nlp.Value("string"),
"old_id": nlp.Value("string"),
"new_id": nlp.Value("string"),
"places": nlp.Sequence(nlp.Value("string")),
"people": nlp.Sequence(nlp.Value("string")),
"orgs": nlp.Sequence(nlp.Value("string")),
"exchanges": nlp.Sequence(nlp.Value("string")),
"date": nlp.Value("string"),
"title": nlp.Value("string"),
}
),
# No default supervised_keys (as we have to pass both premise
# and hypothesis as input).
supervised_keys=None,
homepage="https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html",
citation=_CITATION,
)
def _info(self):
return nlp.DatasetInfo(
description=_DESCRIPTION,
features=nlp.Features({
"q_id":
nlp.Value("string"),
"title":
nlp.Value("string"),
"selftext":
nlp.Value("string"),
"document":
nlp.Value("string"),
"subreddit":
nlp.Value("string"),
"answers":
nlp.features.Sequence({
"a_id": nlp.Value("string"),
"text": nlp.Value("string"),
"score": nlp.Value("int32")
}),
"title_urls":
nlp.features.Sequence({"url": nlp.Value("string")}),
"selftext_urls":
nlp.features.Sequence({"url": nlp.Value("string")}),
"answers_urls":
nlp.features.Sequence({"url": nlp.Value("string")}),
}),
supervised_keys=None,
homepage="https://facebookresearch.github.io/ELI5/explore.html",
citation=_CITATION,
)
def _info(self):
features = {
feature: nlp.Value("string")
for feature in self.config.features
}
if self.config.name == 'task1':
features["id"] = nlp.Value("int64")
features["text"] = nlp.Value("string")
features["url"] = nlp.Value("string")
features["label"] = nlp.ClassLabel(names=["0", "1"])
elif self.config.name == 'task2':
features["id"] = nlp.Value("int64")
features["label"] = nlp.ClassLabel(names=["0", "1"])
features["last"] = nlp.Value("bool")
features["sent_num"] = nlp.Value("int64")
features["sentence"] = nlp.Value("string")
elif self.config.name in ['task3_document', 'task3_sentence']:
features['token'] = nlp.Sequence(nlp.Value("string"))
features['label'] = nlp.Sequence(
nlp.ClassLabel(names=[
'B-etime', 'B-fname', 'B-loc', 'B-organizer',
'B-participant', 'B-place', 'B-target', 'B-trigger',
'I-etime', 'I-fname', 'I-loc', 'I-organizer',
'I-participant', 'I-place', 'I-target', 'I-trigger', 'O'
]))
else:
raise SystemExit('Invalid task name')
return nlp.DatasetInfo(features=nlp.Features(features), )
def _info(self):
# TODO(ai2_arc): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
"id":
nlp.Value("string"),
"question":
nlp.Value("string"),
"choices":
nlp.features.Sequence({
"text": nlp.Value("string"),
"label": nlp.Value("string")
}),
"answerKey":
nlp.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="https://allenai.org/data/arc",
citation=_CITATION,
)
def read_col_formatted_as_numpy(feats, tmp_dir):
dataset = nlp.Dataset.from_file(filename=os.path.join(
tmp_dir, "beta.arrow"),
info=nlp.DatasetInfo(features=feats))
dataset.set_format("numpy")
for col in feats:
_ = dataset[col]
def _info(self):
# TODO(xtreme): Specifies the nlp.DatasetInfo object
features = {
text_feature: nlp.Value("string")
for text_feature in six.iterkeys(self.config.text_features)
}
if "answers" in features.keys():
features["answers"] = nlp.features.Sequence({
"answer_start":
nlp.Value("int32"),
"text":
nlp.Value("string")
})
if self.config.name.startswith("PAWS-X"):
features["label"] = nlp.Value("string")
if self.config.name == "XNLI":
features["gold_label"] = nlp.Value("string")
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=self.config.description + "\n" + _DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.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/google-research/xtreme" + "\t" +
self.config.url,
citation=self.config.citation + "\n" + _CITATION,
)
def _info(self):
# TODO(lc_quad): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
"NNQT_question": nlp.Value('string'),
"uid": nlp.Value('int32'),
"subgraph": nlp.Value('string'),
"template_index": nlp.Value('int32'),
"question": nlp.Value('string'),
"sparql_wikidata": nlp.Value('string'),
"sparql_dbpedia18": nlp.Value('string'),
"template": nlp.Value('string'),
#"template_id": nlp.Value('string'),
"paraphrased_question": nlp.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://lc-quad.sda.tech/',
citation=_CITATION,
)
def _info(self):
# TODO(empathetic_dialogues): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
"conv_id": nlp.Value("string"),
"utterance_idx": nlp.Value("int32"),
"context": nlp.Value("string"),
"prompt": nlp.Value("string"),
"speaker_idx": nlp.Value("int32"),
"utterance": nlp.Value("string"),
"selfeval": nlp.Value("string"),
"tags": nlp.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="https://github.com/facebookresearch/EmpatheticDialogues",
citation=_CITATION,
)
def _info(self):
# TODO(squad_it): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
"id":
nlp.Value('string'),
"context":
nlp.Value('string'),
"question":
nlp.Value('string'),
"answers":
nlp.features.Sequence({
"text": nlp.Value('string'),
"answer_start": nlp.Value('int32'),
}),
# 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/crux82/squad-it',
citation=_CITATION,
)
def _info(self):
# TODO(quoref): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
"id":
nlp.Value("string"),
"question":
nlp.Value("string"),
"context":
nlp.Value("string"),
"title":
nlp.Value("string"),
"url":
nlp.Value("string"),
"answers":
nlp.features.Sequence({
"answer_start": nlp.Value("int32"),
"text": nlp.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=
"https://leaderboard.allenai.org/quoref/submissions/get-started",
citation=_CITATION,
)
def _info(self):
# TODO(art): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features({
"observation_1":
nlp.Value("string"),
"observation_2":
nlp.Value("string"),
"hypothesis_1":
nlp.Value("string"),
"hypothesis_2":
nlp.Value("string"),
"label":
nlp.features.ClassLabel(num_classes=3)
# 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://leaderboard.allenai.org/anli/submissions/get-started",
citation=_CITATION,
)
def _info(self):
# TODO(coqa): Specifies the nlp.DatasetInfo object
return nlp.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# nlp.features.FeatureConnectors
features=nlp.Features(
{
"source": nlp.Value("string"),
"story": nlp.Value("string"),
"questions": nlp.features.Sequence({"input_text": nlp.Value("string"),}),
"answers": nlp.features.Sequence(
{
"input_text": nlp.Value("string"),
"answer_start": nlp.Value("int32"),
"answer_end": nlp.Value("int32"),
}
),
# ##the foloowing feature allows to take into account additional answers in the validation set
# 'additional_answers': nlp.features.Sequence({
# "input_texts": nlp.Value('int32'),
# "answers_start": nlp.Value('int32'),
# "answers_end": nlp.Value('int32')
# }),
# 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://stanfordnlp.github.io/coqa/",
citation=_CITATION,
)