Add SST-2 dataset (#4473)

* Add SST-2 dataset

* Add dataset card

* Add metadata JSON

* Add dummy data

* Fix style

* Fix dataset card

* Remove default config from dataset card

Commit from https://github.com/huggingface/datasets/commit/5eac250e652118dff0ba3d528fb9b336a75ade47

README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+languages:
+- en
+licenses:
+- 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 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

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+{"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}}

dummy/2.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6cbcdd7df5dc2856008783c13b5cc7d1817b317c26776c44ef55f5814326ec28
+size 4694

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,
+                        }