# 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,
}