Datasets:

Duplicated from stanfordnlp/sst

kylam
/

sst

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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 os

	import datasets


	_CITATION = """\
	@inproceedings{socher-etal-2013-recursive,
	title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
	author = "Socher, Richard and Perelygin, Alex and Wu, Jean and
	Chuang, Jason and Manning, Christopher D. and Ng, Andrew and Potts, Christopher",
	booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
	month = oct,
	year = "2013",
	address = "Seattle, Washington, USA",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/D13-1170",
	pages = "1631--1642",
	}
	"""

	_DESCRIPTION = """\
	The Stanford Sentiment Treebank, the first corpus with fully labeled parse trees that allows for a
	complete analysis of the compositional effects of sentiment in language.
	"""

	_HOMEPAGE = "https://nlp.stanford.edu/sentiment/"

	_LICENSE = ""

	_DEFAULT_URL = "https://nlp.stanford.edu/~socherr/stanfordSentimentTreebank.zip"
	_PTB_URL = "https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip"


	class Sst(datasets.GeneratorBasedBuilder):
	"""The Stanford Sentiment Treebank"""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="default",
	version=VERSION,
	description="Sentences and relative parse trees annotated with sentiment labels.",
	),
	datasets.BuilderConfig(
	name="dictionary",
	version=VERSION,
	description="List of all possible sub-sentences (phrases) with their sentiment label.",
	),
	datasets.BuilderConfig(
	name="ptb", version=VERSION, description="Penn Treebank-formatted trees with labelled sub-sentences."
	),
	]

	DEFAULT_CONFIG_NAME = "default"

	def _info(self):

	if self.config.name == "default":
	features = datasets.Features(
	{
	"sentence": datasets.Value("string"),
	"label": datasets.Value("float"),
	"tokens": datasets.Value("string"),
	"tree": datasets.Value("string"),
	}
	)
	elif self.config.name == "dictionary":
	features = datasets.Features({"phrase": datasets.Value("string"), "label": datasets.Value("float")})
	else:
	features = datasets.Features(
	{
	"ptb_tree": datasets.Value("string"),
	}
	)

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

	def _split_generators(self, dl_manager):
	default_dir = dl_manager.download_and_extract(_DEFAULT_URL)
	ptb_dir = dl_manager.download_and_extract(_PTB_URL)

	file_paths = {}
	for split_index in range(0, 4):
	file_paths[split_index] = {
	"phrases_path": os.path.join(default_dir, "stanfordSentimentTreebank/dictionary.txt"),
	"labels_path": os.path.join(default_dir, "stanfordSentimentTreebank/sentiment_labels.txt"),
	"tokens_path": os.path.join(default_dir, "stanfordSentimentTreebank/SOStr.txt"),
	"trees_path": os.path.join(default_dir, "stanfordSentimentTreebank/STree.txt"),
	"splits_path": os.path.join(default_dir, "stanfordSentimentTreebank/datasetSplit.txt"),
	"sentences_path": os.path.join(default_dir, "stanfordSentimentTreebank/datasetSentences.txt"),
	"ptb_filepath": None,
	"split_id": str(split_index),
	}

	ptb_file_paths = {}
	for ptb_split in ["train", "dev", "test"]:
	ptb_file_paths[ptb_split] = {
	"phrases_path": None,
	"labels_path": None,
	"tokens_path": None,
	"trees_path": None,
	"splits_path": None,
	"sentences_path": None,
	"ptb_filepath": os.path.join(ptb_dir, "trees/" + ptb_split + ".txt"),
	"split_id": None,
	}

	if self.config.name == "default":
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=file_paths[1]),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=file_paths[3]),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=file_paths[2]),
	]
	elif self.config.name == "dictionary":
	return [datasets.SplitGenerator(name="dictionary", gen_kwargs=file_paths[0])]
	else:
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=ptb_file_paths["train"]),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=ptb_file_paths["dev"]),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=ptb_file_paths["test"]),
	]

	def _generate_examples(
	self, phrases_path, labels_path, tokens_path, trees_path, splits_path, sentences_path, split_id, ptb_filepath
	):

	if self.config.name == "ptb":
	with open(ptb_filepath, encoding="utf-8") as fp:
	ptb_reader = csv.reader(fp, delimiter="\t", quoting=csv.QUOTE_NONE)
	for id_, row in enumerate(ptb_reader):
	yield id_, {"ptb_tree": row[0]}
	else:
	labels = {}
	phrases = {}
	with open(labels_path, encoding="utf-8") as g, open(phrases_path, encoding="utf-8") as f:
	label_reader = csv.DictReader(g, delimiter="\|", quoting=csv.QUOTE_NONE)
	for row in label_reader:
	labels[row["phrase ids"]] = float(row["sentiment values"])

	phrase_reader = csv.reader(f, delimiter="\|", quoting=csv.QUOTE_NONE)
	if self.config.name == "dictionary":
	for id_, row in enumerate(phrase_reader):
	yield id_, {"phrase": row[0], "label": labels[row[1]]}
	else:
	for row in phrase_reader:
	phrases[row[0]] = labels[row[1]]

	# Case config=="default"
	# Read parse trees for each complete sentence
	trees = {}
	with open(tokens_path, encoding="utf-8") as tok, open(trees_path, encoding="utf-8") as tr:
	tok_reader = csv.reader(tok, delimiter="\t", quoting=csv.QUOTE_NONE)
	tree_reader = csv.reader(tr, delimiter="\t", quoting=csv.QUOTE_NONE)
	for i, row in enumerate(tok_reader, start=1):
	trees[i] = {}
	trees[i]["tokens"] = row[0]
	for i, row in enumerate(tree_reader, start=1):
	trees[i]["tree"] = row[0]

	with open(splits_path, encoding="utf-8") as spl, open(sentences_path, encoding="utf-8") as snt:
	splits_reader = csv.DictReader(spl, delimiter=",", quoting=csv.QUOTE_NONE)
	splits = {row["sentence_index"]: row["splitset_label"] for row in splits_reader}

	sentence_reader = csv.DictReader(snt, delimiter="\t", quoting=csv.QUOTE_NONE)
	for id_, row in enumerate(sentence_reader):
	# fix encoding, see https://github.com/huggingface/datasets/pull/1961#discussion_r585969890
	row["sentence"] = (
	row["sentence"]
	.encode("utf-8")
	.replace(b"\xc3\x83\xc2", b"\xc3")
	.replace(b"\xc3\x82\xc2", b"\xc2")
	.decode("utf-8")
	)
	row["sentence"] = row["sentence"].replace("-LRB-", "(").replace("-RRB-", ")")
	if splits[row["sentence_index"]] == split_id:
	tokens = trees[int(row["sentence_index"])]["tokens"]
	parse_tree = trees[int(row["sentence_index"])]["tree"]
	yield id_, {
	"sentence": row["sentence"],
	"label": phrases[row["sentence"]],
	"tokens": tokens,
	"tree": parse_tree,
	}