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# Copyright (c) 2020, MeetKai Inc. All rights reserved.
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This script downloads Text2Sparql data and processes it into NeMo's neural machine translation dataset format.
Text2Sparql data consists of 3 files which are saved to source_data_dir:
- train_queries_v3.tsv
- test_easy_queries_v3.tsv
- test_hard_queries_v3.tsv
After processing, the script saves them to the target_data_dir as:
- train.tsv
- test_easy.tsv
- test_hard.tsv
You may run it with:
python import_datasets \
--source_data_dir ./text2sparql_src \
--target_data_dir ./text2sparql_tgt
"""
import argparse
import csv
import os
from urllib.request import Request, urlopen
from nemo.collections.nlp.data.data_utils.data_preprocessing import MODE_EXISTS_TMP, if_exist
from nemo.utils import logging
base_url = "https://m.meetkai.com/public_datasets/knowledge/"
prefix_map = {
"train_queries_v3.tsv": "train.tsv",
"test_easy_queries_v3.tsv": "test_easy.tsv",
"test_hard_queries_v3.tsv": "test_hard.tsv",
}
def download_text2sparql(infold: str):
"""Downloads text2sparql train, test_easy, and test_hard data
Args:
infold: save directory path
"""
os.makedirs(infold, exist_ok=True)
for prefix in prefix_map:
url = base_url + prefix
logging.info(f"Downloading: {url}")
if if_exist(infold, [prefix]):
logging.info("** Download file already exists, skipping download")
else:
req = Request(url, headers={"User-Agent": "Mozilla/5.0"})
with open(os.path.join(infold, prefix), "wb") as handle:
handle.write(urlopen(req, timeout=20).read())
def process_text2sparql(infold: str, outfold: str, do_lower_case: bool):
""" Process and convert MeetKai's text2sparql datasets to NeMo's neural machine translation format.
Args:
infold: directory path to raw text2sparql data containing
train.tsv, test_easy.tsv, test_hard.tsv
outfold: output directory path to save formatted data for NeuralMachineTranslationDataset
the first line is header (sentence [tab] label)
each line should be [sentence][tab][label]
do_lower_case: if true, convert all sentences and labels to lower
"""
logging.info(f"Processing Text2Sparql dataset and storing at: {outfold}")
os.makedirs(outfold, exist_ok=True)
dataset_name = "Text2Sparql"
for prefix in prefix_map:
input_file = os.path.join(infold, prefix)
output_file = os.path.join(outfold, prefix_map[prefix])
if if_exist(outfold, [prefix_map[prefix]]):
logging.info(f"** {MODE_EXISTS_TMP.format(prefix_map[prefix], dataset_name, output_file)}")
continue
if not if_exist(infold, [prefix]):
logging.info(f"** {prefix} of {dataset_name}" f" is skipped as it was not found")
continue
assert input_file != output_file, "input file cannot equal output file"
with open(input_file, "r") as in_file:
with open(output_file, "w") as out_file:
reader = csv.reader(in_file, delimiter="\t")
# replace headers
out_file.write("sentence\tlabel\n")
next(reader)
for line in reader:
sentence = line[0]
label = line[1]
if do_lower_case:
sentence = sentence.lower()
label = label.lower()
out_file.write(f"{sentence}\t{label}\n")
if __name__ == "__main__":
# Parse the command-line arguments.
parser = argparse.ArgumentParser(description="Process and convert datasets into NeMo's format")
parser.add_argument(
"--source_data_dir", required=True, type=str, help="Path to the folder containing the dataset files"
)
parser.add_argument("--target_data_dir", required=True, type=str, help="Path to save the processed dataset")
parser.add_argument("--do_lower_case", action="store_true")
args = parser.parse_args()
source_dir = args.source_data_dir
target_dir = args.target_data_dir
do_lower_case = args.do_lower_case
download_text2sparql(infold=source_dir)
process_text2sparql(infold=source_dir, outfold=target_dir, do_lower_case=do_lower_case)
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