Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
License:
# 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, | |
} | |