158 lines
8.1 KiB
Python
158 lines
8.1 KiB
Python
""" Script for downloading all GLUE data.
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Original source: https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e
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Note: for legal reasons, we are unable to host MRPC.
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You can either use the version hosted by the SentEval team, which is already tokenized,
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or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
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For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
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You should then rename and place specific files in a folder (see below for an example).
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mkdir MRPC
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cabextract MSRParaphraseCorpus.msi -d MRPC
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cat MRPC/_2DEC3DBE877E4DB192D17C0256E90F1D | tr -d $'\r' > MRPC/msr_paraphrase_train.txt
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cat MRPC/_D7B391F9EAFF4B1B8BCE8F21B20B1B61 | tr -d $'\r' > MRPC/msr_paraphrase_test.txt
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rm MRPC/_*
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rm MSRParaphraseCorpus.msi
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1/30/19: It looks like SentEval is no longer hosting their extracted and tokenized MRPC data, so you'll need to download the data from the original source for now.
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2/11/19: It looks like SentEval actually *is* hosting the extracted data. Hooray!
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"""
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import argparse
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import os
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import sys
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import urllib.request
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import zipfile
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TASKS = ["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"]
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TASK2PATH = {
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"CoLA": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4",
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"SST": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8",
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"MRPC": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc",
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"QQP": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5",
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"STS": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5",
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"MNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce",
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"SNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df",
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"QNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601",
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"RTE": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb",
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"WNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf",
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"diagnostic": "https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D",
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}
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MRPC_TRAIN = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
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MRPC_TEST = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"
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def download_and_extract(task, data_dir):
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print(f"Downloading and extracting {task}...")
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data_file = f"{task}.zip"
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urllib.request.urlretrieve(TASK2PATH[task], data_file)
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with zipfile.ZipFile(data_file) as zip_ref:
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zip_ref.extractall(data_dir)
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os.remove(data_file)
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print("\tCompleted!")
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def format_mrpc(data_dir, path_to_data):
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print("Processing MRPC...")
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mrpc_dir = os.path.join(data_dir, "MRPC")
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if not os.path.isdir(mrpc_dir):
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os.mkdir(mrpc_dir)
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if path_to_data:
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mrpc_train_file = os.path.join(path_to_data, "msr_paraphrase_train.txt")
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mrpc_test_file = os.path.join(path_to_data, "msr_paraphrase_test.txt")
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else:
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print("Local MRPC data not specified, downloading data from %s" % MRPC_TRAIN)
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mrpc_train_file = os.path.join(mrpc_dir, "msr_paraphrase_train.txt")
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mrpc_test_file = os.path.join(mrpc_dir, "msr_paraphrase_test.txt")
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urllib.request.urlretrieve(MRPC_TRAIN, mrpc_train_file)
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urllib.request.urlretrieve(MRPC_TEST, mrpc_test_file)
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if not os.path.isfile(mrpc_train_file):
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raise ValueError(f"Train data not found at {mrpc_train_file}")
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if not os.path.isfile(mrpc_test_file):
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raise ValueError(f"Test data not found at {mrpc_test_file}")
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urllib.request.urlretrieve(TASK2PATH["MRPC"], os.path.join(mrpc_dir, "dev_ids.tsv"))
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dev_ids = []
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with open(os.path.join(mrpc_dir, "dev_ids.tsv"), encoding="utf8") as ids_fh:
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for row in ids_fh:
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dev_ids.append(row.strip().split("\t"))
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with open(mrpc_train_file, encoding="utf8") as data_fh, open(
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os.path.join(mrpc_dir, "train.tsv"), "w", encoding="utf8"
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) as train_fh, open(os.path.join(mrpc_dir, "dev.tsv"), "w", encoding="utf8") as dev_fh:
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header = data_fh.readline()
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train_fh.write(header)
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dev_fh.write(header)
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for row in data_fh:
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label, id1, id2, s1, s2 = row.strip().split("\t")
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if [id1, id2] in dev_ids:
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dev_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2))
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else:
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train_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2))
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with open(mrpc_test_file, encoding="utf8") as data_fh, open(
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os.path.join(mrpc_dir, "test.tsv"), "w", encoding="utf8"
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) as test_fh:
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header = data_fh.readline()
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test_fh.write("index\t#1 ID\t#2 ID\t#1 String\t#2 String\n")
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for idx, row in enumerate(data_fh):
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label, id1, id2, s1, s2 = row.strip().split("\t")
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test_fh.write("%d\t%s\t%s\t%s\t%s\n" % (idx, id1, id2, s1, s2))
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print("\tCompleted!")
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def download_diagnostic(data_dir):
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print("Downloading and extracting diagnostic...")
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if not os.path.isdir(os.path.join(data_dir, "diagnostic")):
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os.mkdir(os.path.join(data_dir, "diagnostic"))
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data_file = os.path.join(data_dir, "diagnostic", "diagnostic.tsv")
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urllib.request.urlretrieve(TASK2PATH["diagnostic"], data_file)
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print("\tCompleted!")
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return
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def get_tasks(task_names):
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task_names = task_names.split(",")
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if "all" in task_names:
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tasks = TASKS
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else:
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tasks = []
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for task_name in task_names:
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if task_name not in TASKS:
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raise ValueError(f"Task {task_name} not found!")
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tasks.append(task_name)
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return tasks
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def main(arguments):
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parser = argparse.ArgumentParser()
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parser.add_argument("--data_dir", help="directory to save data to", type=str, default="glue_data")
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parser.add_argument(
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"--tasks", help="tasks to download data for as a comma separated string", type=str, default="all"
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)
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parser.add_argument(
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"--path_to_mrpc",
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help="path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt",
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type=str,
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default="",
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)
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args = parser.parse_args(arguments)
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if not os.path.isdir(args.data_dir):
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os.mkdir(args.data_dir)
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tasks = get_tasks(args.tasks)
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for task in tasks:
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if task == "MRPC":
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format_mrpc(args.data_dir, args.path_to_mrpc)
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elif task == "diagnostic":
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download_diagnostic(args.data_dir)
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else:
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download_and_extract(task, args.data_dir)
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if __name__ == "__main__":
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sys.exit(main(sys.argv[1:]))
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