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README.md

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----
-annotations_creators:
-- crowdsourced
-language_creators:
-- found
-language:
-- en
-license:
-- unknown
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- text-classification
-task_ids:
-- sentiment-classification
-paperswithcode_id: sst
-pretty_name: Stanford Sentiment Treebank v2
-dataset_info:
-  features:
-  - name: idx
-    dtype: int32
-  - name: sentence
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: negative
-          1: positive
-  splits:
-  - name: train
-    num_bytes: 4690022
-    num_examples: 67349
-  - name: validation
-    num_bytes: 106361
-    num_examples: 872
-  - name: test
-    num_bytes: 216868
-    num_examples: 1821
-  download_size: 7439277
-  dataset_size: 5013251
----
-
-# Dataset Card for [Dataset Name]
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** https://nlp.stanford.edu/sentiment/
-- **Repository:**
-- **Paper:** [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://www.aclweb.org/anthology/D13-1170/)
-- **Leaderboard:**
-- **Point of Contact:**
-
-### Dataset Summary
-
-The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the
-compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005)
-and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and
-includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges.
-
-Binary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive
-with neutral sentences discarded) refer to the dataset as SST-2 or SST binary.
-
-### Supported Tasks and Leaderboards
-
-- `sentiment-classification`
-
-### Languages
-
-The text in the dataset is in English (`en`).
-
-## Dataset Structure
-
-### Data Instances
-
-```
-{'idx': 0,
- 'sentence': 'hide new secretions from the parental units ',
- 'label': 0}
-```
-
-### Data Fields
-
-- `idx`: Monotonically increasing index ID.
-- `sentence`: Complete sentence expressing an opinion about a film.
-- `label`: Sentiment of the opinion, either "negative" (0) or positive (1).
-
-### Data Splits
-
-|                    |    train | validation | test |
-|--------------------|---------:|-----------:|-----:|
-| Number of examples |    67349 |        872 | 1821 |
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-Rotten Tomatoes reviewers.
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed]
-
-### Licensing Information
-
-Unknown.
-
-### Citation Information
-
-```bibtex
-@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",
-}
-```
-
-### Contributions
-
-Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "The Stanford Sentiment Treebank consists of sentences from movie reviews and\nhuman annotations of their sentiment. The task is to predict the sentiment of a\ngiven sentence. We use the two-way (positive/negative) class split, and use only\nsentence-level labels.\n", "citation": "@inproceedings{socher2013recursive,\n  title={Recursive deep models for semantic compositionality over a sentiment treebank},\n  author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},\n  booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},\n  pages={1631--1642},\n  year={2013}\n}\n", "homepage": "https://nlp.stanford.edu/sentiment/", "license": "Unknown", "features": {"idx": {"dtype": "int32", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["negative", "positive"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "sst2", "config_name": "default", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4690022, "num_examples": 67349, "dataset_name": "sst2"}, "validation": {"name": "validation", "num_bytes": 106361, "num_examples": 872, "dataset_name": "sst2"}, "test": {"name": "test", "num_bytes": 216868, "num_examples": 1821, "dataset_name": "sst2"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/SST-2.zip": {"num_bytes": 7439277, "checksum": "d67e16fb55739c1b32cdce9877596db1c127dc322d93c082281f64057c16deaa"}}, "download_size": 7439277, "post_processing_size": null, "dataset_size": 5013251, "size_in_bytes": 12452528}}

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sst2.py

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-# 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.
-"""SST-2 (Stanford Sentiment Treebank v2) dataset."""
-
-
-import csv
-import os
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{socher2013recursive,
-  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},
-  pages={1631--1642},
-  year={2013}
-}
-"""
-
-_DESCRIPTION = """\
-The Stanford Sentiment Treebank consists of sentences from movie reviews and
-human annotations of their sentiment. The task is to predict the sentiment of a
-given sentence. We use the two-way (positive/negative) class split, and use only
-sentence-level labels.
-"""
-
-_HOMEPAGE = "https://nlp.stanford.edu/sentiment/"
-
-_LICENSE = "Unknown"
-
-_URL = "https://dl.fbaipublicfiles.com/glue/data/SST-2.zip"
-
-
-class Sst2(datasets.GeneratorBasedBuilder):
-    """SST-2 dataset."""
-
-    VERSION = datasets.Version("2.0.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "idx": datasets.Value("int32"),
-                "sentence": datasets.Value("string"),
-                "label": datasets.features.ClassLabel(names=["negative", "positive"]),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "file_paths": dl_manager.iter_files(dl_dir),
-                    "data_filename": "train.tsv",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "file_paths": dl_manager.iter_files(dl_dir),
-                    "data_filename": "dev.tsv",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "file_paths": dl_manager.iter_files(dl_dir),
-                    "data_filename": "test.tsv",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, file_paths, data_filename):
-        for file_path in file_paths:
-            filename = os.path.basename(file_path)
-            if filename == data_filename:
-                with open(file_path, encoding="utf8") as f:
-                    reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-                    for idx, row in enumerate(reader):
-                        yield idx, {
-                            "idx": row["index"] if "index" in row else idx,
-                            "sentence": row["sentence"],
-                            "label": int(row["label"]) if "label" in row else -1,
-                        }