def download_winogrande_data_and_write_config(
task_name: str, task_data_path: str, task_config_path: str
):
os.makedirs(task_data_path, exist_ok=True)
download_utils.download_and_unzip(
"https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip",
task_data_path,
)
task_data_path = os.path.join(task_data_path, "winogrande_1.1")
py_io.write_json(
data={
"task": task_name,
"paths": {
"train": os.path.join(task_data_path, "train_xl.jsonl"),
"train_labels": os.path.join(task_data_path, "train_xl-labels.lst"),
"train_xs": os.path.join(task_data_path, "train_xs.jsonl"),
"train_xs_labels": os.path.join(task_data_path, "train_xs-labels.lst"),
"train_s": os.path.join(task_data_path, "train_s.jsonl"),
"train_s_labels": os.path.join(task_data_path, "train_s-labels.lst"),
"train_m": os.path.join(task_data_path, "train_m.jsonl"),
"train_m_labels": os.path.join(task_data_path, "train_m-labels.lst"),
"train_l": os.path.join(task_data_path, "train_l.jsonl"),
"train_l_labels": os.path.join(task_data_path, "train_l-labels.lst"),
"train_xl": os.path.join(task_data_path, "train_xl.jsonl"),
"train_xl_labels": os.path.join(task_data_path, "train_xl-labels.lst"),
"val": os.path.join(task_data_path, "dev.jsonl"),
"val_labels": os.path.join(task_data_path, "dev-labels.lst"),
"test": os.path.join(task_data_path, "test.jsonl"),
},
"name": task_name,
},
path=task_config_path,
)
def download_swag_data_and_write_config(task_name: str, task_data_path: str,
task_config_path: str):
os.makedirs(task_data_path, exist_ok=True)
download_utils.download_and_unzip(
"https://github.com/rowanz/swagaf/archive/master.zip",
task_data_path,
)
for phase in ["train", "val", "test"]:
os.rename(
os.path.join(task_data_path, "swagaf-master", "data",
f"{phase}.csv"),
os.path.join(task_data_path, f"{phase}.csv"),
)
shutil.rmtree(os.path.join(task_data_path, "swagaf-master"))
py_io.write_json(
data={
"task": task_name,
"paths": {
"train": os.path.join(task_data_path, "train.csv"),
"val": os.path.join(task_data_path, "val.csv"),
"test": os.path.join(task_data_path, "test.csv"),
},
"name": task_name,
},
path=task_config_path,
)
def download_qamr_data_and_write_config(task_name: str, task_data_path: str, task_config_path: str):
os.makedirs(task_data_path, exist_ok=True)
download_utils.download_and_unzip(
"https://github.com/uwnlp/qamr/archive/master.zip", task_data_path,
)
data_phase_list = ["train", "dev", "test"]
jiant_phase_list = ["train", "val", "test"]
for data_phase, jiant_phase in zip(data_phase_list, jiant_phase_list):
os.rename(
os.path.join(task_data_path, "qamr-master", "data", "filtered", f"{data_phase}.tsv"),
os.path.join(task_data_path, f"{jiant_phase}.tsv"),
)
os.rename(
os.path.join(task_data_path, "qamr-master", "data", "wiki-sentences.tsv"),
os.path.join(task_data_path, "wiki-sentences.tsv"),
)
shutil.rmtree(os.path.join(task_data_path, "qamr-master"))
py_io.write_json(
data={
"task": task_name,
"paths": {
"train": os.path.join(task_data_path, "train.tsv"),
"val": os.path.join(task_data_path, "val.tsv"),
"test": os.path.join(task_data_path, "test.tsv"),
"wiki_dict": os.path.join(task_data_path, "wiki-sentences.tsv"),
},
"name": task_name,
},
path=task_config_path,
)
def download_mlqa_data_and_write_config(task_data_base_path: str, task_config_base_path: str):
mlqa_temp_path = py_io.create_dir(task_data_base_path, "mlqa_temp")
download_utils.download_and_unzip(
"https://dl.fbaipublicfiles.com/MLQA/MLQA_V1.zip", mlqa_temp_path,
)
languages = "ar de en es hi vi zh".split()
for lang1, lang2 in itertools.product(languages, languages):
task_name = f"mlqa_{lang1}_{lang2}"
task_data_path = py_io.create_dir(task_data_base_path, task_name)
val_path = os.path.join(task_data_path, f"dev-context-{lang1}-question-{lang2}.json")
os.rename(
src=os.path.join(
mlqa_temp_path, "MLQA_V1", "dev", f"dev-context-{lang1}-question-{lang2}.json"
),
dst=val_path,
)
test_path = os.path.join(task_data_path, f"test-context-{lang1}-question-{lang2}.json")
os.rename(
src=os.path.join(
mlqa_temp_path, "MLQA_V1", "test", f"test-context-{lang1}-question-{lang2}.json"
),
dst=test_path,
)
py_io.write_json(
data={
"task": "mlqa",
"paths": {"val": val_path, "test": test_path},
"kwargs": {"context_language": lang1, "question_language": lang2},
"name": task_name,
},
path=os.path.join(task_config_base_path, f"{task_name}_config.json"),
)
shutil.rmtree(mlqa_temp_path)
def download_xnli_data_and_write_config(task_data_base_path: str, task_config_base_path: str):
xnli_temp_path = py_io.create_dir(task_data_base_path, "xnli_temp")
download_utils.download_and_unzip(
"https://dl.fbaipublicfiles.com/XNLI/XNLI-1.0.zip", xnli_temp_path,
)
full_val_data = py_io.read_jsonl(os.path.join(xnli_temp_path, "XNLI-1.0", "xnli.dev.jsonl"))
val_data = datastructures.group_by(full_val_data, key_func=lambda elem: elem["language"])
full_test_data = py_io.read_jsonl(os.path.join(xnli_temp_path, "XNLI-1.0", "xnli.test.jsonl"))
test_data = datastructures.group_by(full_test_data, lambda elem: elem["language"])
languages = sorted(list(val_data))
for lang in languages:
task_name = f"xnli_{lang}"
task_data_path = py_io.create_dir(task_data_base_path, task_name)
val_path = os.path.join(task_data_path, "val.jsonl")
test_path = os.path.join(task_data_path, "test.jsonl")
py_io.write_jsonl(data=val_data[lang], path=val_path)
py_io.write_jsonl(data=test_data[lang], path=test_path)
py_io.write_json(
data={
"task": "xnli",
"paths": {"val": val_path, "test": test_path},
"name": task_name,
"kwargs": {"language": lang},
},
path=os.path.join(task_config_base_path, f"{task_name}_config.json"),
)
shutil.rmtree(xnli_temp_path)
def download_fever_nli_data_and_write_config(
task_name: str, task_data_path: str, task_config_path: str
):
os.makedirs(task_data_path, exist_ok=True)
download_utils.download_and_unzip(
("https://www.dropbox.com/s/hylbuaovqwo2zav/nli_fever.zip?dl=1"), task_data_path,
)
# Since the FEVER NLI dataset doesn't have labels for the dev set, we also download the original
# FEVER dev set and match example CIDs to obtain labels.
orig_dev_path = os.path.join(task_data_path, "fever-dev-temp.jsonl")
download_utils.download_file(
"https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev.jsonl", orig_dev_path,
)
id_to_label = {}
for line in py_io.read_jsonl(orig_dev_path):
if "id" not in line:
logging.warning("FEVER dev dataset is missing ID.")
continue
if "label" not in line:
logging.warning("FEVER dev dataset is missing label.")
continue
id_to_label[line["id"]] = line["label"]
os.remove(orig_dev_path)
dev_path = os.path.join(task_data_path, "nli_fever", "dev_fitems.jsonl")
dev_examples = []
for line in py_io.read_jsonl(dev_path):
if "cid" not in line:
logging.warning("Data in {} is missing CID.".format(dev_path))
continue
if int(line["cid"]) not in id_to_label:
logging.warning("Could not match CID {} to dev data.".format(line["cid"]))
continue
dev_example = line
dev_example["label"] = id_to_label[int(line["cid"])]
dev_examples.append(dev_example)
py_io.write_jsonl(dev_examples, os.path.join(task_data_path, "val.jsonl"))
os.remove(dev_path)
for phase in ["train", "test"]:
os.rename(
os.path.join(task_data_path, "nli_fever", f"{phase}_fitems.jsonl"),
os.path.join(task_data_path, f"{phase}.jsonl"),
)
shutil.rmtree(os.path.join(task_data_path, "nli_fever"))
py_io.write_json(
data={
"task": task_name,
"paths": {
"train": os.path.join(task_data_path, "train.jsonl"),
"val": os.path.join(task_data_path, "val.jsonl"),
"test": os.path.join(task_data_path, "test.jsonl"),
},
"name": task_name,
},
path=task_config_path,
)
def download_superglue_data_and_write_config(task_name: str,
task_data_path: str,
task_config_path: str):
# Applies to ReCoRD, MultiRC and WSC
assert task_name not in SQUAD_TASKS
os.makedirs(task_data_path, exist_ok=True)
download_utils.download_and_unzip(
DIRECT_SUPERGLUE_TASKS_TO_DATA_URLS[task_name], task_data_path)
# Move task data up one folder (nested under task name when unzipped)
# ie: mv ./record/ReCoRD/* ./record
nested_task_dir = os.path.join(
task_data_path,
filesystem.find_case_insensitive_filename(task_name, task_data_path))
task_data_files = os.listdir(nested_task_dir)
for f in task_data_files:
# Overwrite file if it exists (overwrite by full path specification)
shutil.move(os.path.join(nested_task_dir, f),
os.path.join(task_data_path, f))
shutil.rmtree(nested_task_dir)
# Supports datasets with non-standard dev dataset name
if os.path.isfile(os.path.join(task_data_path, "dev.jsonl")):
dev_data_name = "dev.jsonl"
elif os.path.isfile(os.path.join(task_data_path, "val.jsonl")):
dev_data_name = "val.jsonl"
else:
raise RuntimeError("Unsupported dev dataset name in downloaded task.")
val_path = os.path.join(task_data_path, dev_data_name)
train_path = os.path.join(task_data_path, "train.jsonl")
test_path = os.path.join(task_data_path, "test.jsonl")
py_io.write_json(
data={
"task": task_name,
"paths": {
"train": train_path,
"val": val_path,
"test": test_path
},
"name": task_name,
},
path=task_config_path,
)
def download_mctest500_data_and_write_config(task_name: str,
task_data_path: str,
task_config_path: str):
os.makedirs(task_data_path, exist_ok=True)
download_utils.download_and_unzip(
"https://mattr1.github.io/mctest/data/MCTest.zip",
task_data_path,
)
download_utils.download_and_unzip(
"https://mattr1.github.io/mctest/data/MCTestAnswers.zip",
task_data_path,
)
os.rename(
os.path.join(task_data_path, "MCTestAnswers", f"mc500.test.ans"),
os.path.join(task_data_path, "MCTest", f"mc500.test.ans"),
)
shutil.rmtree(os.path.join(task_data_path, "MCTestAnswers"))
for phase in ["train", "dev", "test"]:
os.rename(
os.path.join(task_data_path, "MCTest", f"mc500.{phase}.tsv"),
os.path.join(task_data_path, f"mc500.{phase}.tsv"),
)
os.rename(
os.path.join(task_data_path, "MCTest", f"mc500.{phase}.ans"),
os.path.join(task_data_path, f"mc500.{phase}.ans"),
)
shutil.rmtree(os.path.join(task_data_path, "MCTest"))
py_io.write_json(
data={
"task": task_name,
"paths": {
"train": os.path.join(task_data_path, "mc500.train.tsv"),
"train_ans": os.path.join(task_data_path, "mc500.train.ans"),
"val": os.path.join(task_data_path, "mc500.dev.tsv"),
"val_ans": os.path.join(task_data_path, "mc500.dev.ans"),
"test": os.path.join(task_data_path, "mc500.test.tsv"),
"test_ans": os.path.join(task_data_path, "mc500.test.ans"),
},
"name": task_name,
},
path=task_config_path,
)
def download_abductive_nli_data_and_write_config(
task_name: str, task_data_path: str, task_config_path: str
):
os.makedirs(task_data_path, exist_ok=True)
download_utils.download_and_unzip(
"https://storage.googleapis.com/ai2-mosaic/public/alphanli/alphanli-train-dev.zip",
task_data_path,
)
py_io.write_json(
data={
"task": task_name,
"paths": {
"train_inputs": os.path.join(task_data_path, "train.jsonl"),
"train_labels": os.path.join(task_data_path, "train-labels.lst"),
"val_inputs": os.path.join(task_data_path, "dev.jsonl"),
"val_labels": os.path.join(task_data_path, "dev-labels.lst"),
},
"name": task_name,
},
path=task_config_path,
)
def download_tatoeba_data_and_write_config(task_data_base_path: str,
task_config_base_path: str):
tatoeba_temp_path = py_io.create_dir(task_data_base_path, "tatoeba_temp")
download_utils.download_and_unzip(
"https://github.com/facebookresearch/LASER/archive/master.zip",
tatoeba_temp_path,
)
languages_dict = {
"afr": "af",
"ara": "ar",
"bul": "bg",
"ben": "bn",
"deu": "de",
"ell": "el",
"spa": "es",
"est": "et",
"eus": "eu",
"pes": "fa",
"fin": "fi",
"fra": "fr",
"heb": "he",
"hin": "hi",
"hun": "hu",
"ind": "id",
"ita": "it",
"jpn": "ja",
"jav": "jv",
"kat": "ka",
"kaz": "kk",
"kor": "ko",
"mal": "ml",
"mar": "mr",
"nld": "nl",
"por": "pt",
"rus": "ru",
"swh": "sw",
"tam": "ta",
"tel": "te",
"tha": "th",
"tgl": "tl",
"tur": "tr",
"urd": "ur",
"vie": "vi",
"cmn": "zh",
"eng": "en",
}
raw_base_path = os.path.join(tatoeba_temp_path, "LASER-master", "data",
"tatoeba", "v1")
for full_lang, lang in languages_dict.items():
task_name = f"tatoeba_{lang}"
if lang == "en":
continue
task_data_path = py_io.create_dir(task_data_base_path, task_name)
eng_src = os.path.join(raw_base_path, f"tatoeba.{full_lang}-eng.eng")
other_src = os.path.join(raw_base_path,
f"tatoeba.{full_lang}-eng.{full_lang}")
eng_out = os.path.join(task_data_path, f"{lang}-en.en")
other_out = os.path.join(task_data_path, f"{lang}-en.{lang}")
labels_out = os.path.join(task_data_path, f"{lang}-en.labels")
tgts = [line.strip() for line in py_io.read_file_lines(eng_src)]
os.rename(src=other_src, dst=other_out)
idx = range(len(tgts))
data = zip(tgts, idx)
# Tatoeba is a retrieval dataset where you have a set of sentences in English and another
# set in another language, and you need to match them. It also doesn't have training
# data, so it's pretty much evaluation only. However, the dataset is distributed with the
# sentences in order, i.e. the retrieval pairing is the sentence order.
#
# The XTREME authors intentionally scramble the order by sorting one of the two
# sets alphabetically. We're following their recipe, but also retaining the labels for
# internal scoring.
with py_io.get_lock(eng_out):
with py_io.get_lock(labels_out):
if os.path.exists(eng_out) and os.path.exists(labels_out):
logger.info('Skip writing to %s since it already exists.',
eng_out)
logger.info('Skip writing to %s since it already exists.',
labels_out)
else:
with open(eng_out, "w") as ftgt, open(labels_out,
"w") as flabels:
for t, i in sorted(data, key=lambda x: x[0]):
ftgt.write(f"{t}\n")
flabels.write(f"{i}\n")
py_io.write_json(
data={
"task": "tatoeba",
"paths": {
"eng": eng_out,
"other": other_out,
"labels_path": labels_out
},
"kwargs": {
"language": lang
},
"name": task_name,
},
path=os.path.join(task_config_base_path,
f"{task_name}_config.json"),
skip_if_exists=True,
)
shutil.rmtree(tatoeba_temp_path)
