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completeformer-masked / completeformer-masked.py

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	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the Semeru Lab and SEART research group.
	#
	# 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.
	"""TODO: Add a description here."""


	import csv
	import glob
	import os

	import datasets

	import numpy as np

	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@InProceedings{huggingface:dataset,
	title = {A great new dataset},
	author={huggingface, Inc.
	},
	year={2020}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = ""

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = ""

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_DATA_URLs = {
	"tokenizer": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/data_for_tokenizer_training.csv",
	"all": {
	"train": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_clean.csv",
	"valid": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/validation_clean.csv",
	"test": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/test_clean.csv",
	},
	"mix": {
	"train": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_mix.csv",
	"valid": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/validation_mix.csv",
	"test": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/test_mix.csv",
	},
	"length_short": {
	"train": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_short.csv",
	"train_long": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_long.csv",
	"valid": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/validation_length.csv",
	"test": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/test_short.csv",
	},
	"length_medium": {
	"train": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_medium.csv",
	"valid": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/validation_length.csv",
	"test": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/test_medium.csv",
	},
	"length_long": {
	"train": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_long.csv",
	"valid": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/validation_length.csv",
	"test": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/test_long.csv",
	},
	"length_mix": {
	"train": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_length_mix.csv",
	"valid": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/validation_length.csv",
	"test": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/test_length.csv",
	},
	}


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class CSNCHumanJudgementDataset(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	VERSION = datasets.Version("1.1.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="all",
	version=VERSION,
	description="",
	),
	datasets.BuilderConfig(
	name="mix",
	version=VERSION,
	description="",
	),
	datasets.BuilderConfig(
	name="length_short",
	version=VERSION,
	description="",
	),
	datasets.BuilderConfig(
	name="length_medium",
	version=VERSION,
	description="",
	),
	datasets.BuilderConfig(
	name="length_long",
	version=VERSION,
	description="",
	),
	datasets.BuilderConfig(
	name="length_mix",
	version=VERSION,
	description="",
	),
	datasets.BuilderConfig(
	name="tokenizer",
	version=VERSION,
	description="",
	),
	]

	DEFAULT_CONFIG_NAME = "all"

	def _info(self):
	if self.config.name == "tokenizer":
	features = datasets.Features(
	{
	"function": datasets.Value("string"),
	}
	)
	elif self.config.name == "all":
	features = datasets.Features(
	{
	"method": datasets.Value("string"),
	"block": datasets.Value("string"),
	"complex_masked_block": datasets.Value("string"),
	"complex_input": datasets.Value("string"),
	"complex_target": datasets.Value("string"),
	"medium_masked_block": datasets.Value("string"),
	"medium_input": datasets.Value("string"),
	"medium_target": datasets.Value("string"),
	"simple_masked_block": datasets.Value("string"),
	"simple_input": datasets.Value("string"),
	"simple_target": datasets.Value("string"),
	}
	)
	elif self.config.name == "mix":
	features = datasets.Features(
	{
	"input": datasets.Value("string"),
	"target": datasets.Value("string"),
	}
	)
	elif self.config.name.startswith("length_"):
	features = datasets.Features(
	{
	"input": datasets.Value("string"),
	"target": datasets.Value("string"),
	"size": datasets.Value("int64"),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	my_urls = _DATA_URLs[self.config.name]
	if self.config.name == "tokenizer":
	data_dir = dl_manager.download_and_extract(my_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": data_dir},
	),
	]
	else:
	data_dirs = {}
	for k, v in my_urls.items():
	data_dirs[k] = dl_manager.download_and_extract(v)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"file_path": data_dirs["train"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"file_path": data_dirs["valid"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"file_path": data_dirs["test"],
	},
	),
	]

	def _generate_examples(
	self,
	file_path,
	):
	"""Yields examples as (key, example) tuples."""
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.

	with open(file_path, encoding="utf-8") as f:
	csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True)
	next(csv_reader, None) # skip header

	for row_id, row in enumerate(csv_reader):
	if self.config.name == "tokenizer":
	yield row_id, {
	"function": row[1],
	}
	elif self.config.name == "all":
	_, method, block, complex_masked_block, complex_input, complex_target, medium_masked_block, medium_input, medium_target, simple_masked_block, simple_input, simple_target = row
	yield row_id, {
	"method": method,
	"block": block,
	"complex_masked_block": complex_masked_block,
	"complex_input": complex_input,
	"complex_target": complex_target,
	"medium_masked_block": medium_masked_block,
	"medium_input": medium_input,
	"medium_target": medium_target,
	"simple_masked_block": simple_masked_block,
	"simple_input": simple_input,
	"simple_target": simple_target,
	}
	elif self.config.name == "mix":
	_, input, target = row
	yield row_id, {
	"input": input,
	"target": target,
	}
	elif self.config.name.startswith("length_"):
	_, input, target, size = row
	yield row_id, {
	"input": input,
	"target": target,
	"size": int(size),
	}