Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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README.md

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+---
+annotations_creators:
+  - crowdsourced
+language_creators:
+  - found
+languages:
+  - ko
+licenses:
+  - cc-by-1-0
+multilinguality:
+  - monolingual
+size_categories:
+  - 100K<n<1M
+source_datasets:
+  - original
+task_categories:
+  - text-classification
+task_ids:
+  - sentiment-classification
+---
+
+# Dataset Card for Naver sentiment movie corpus
+
+## Table of Contents
+
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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)
+
+## Dataset Description
+
+- **Homepage:** [Github](https://github.com/e9t/nsmc/)
+- **Repository:** [Github](https://github.com/e9t/nsmc/)
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+Each instance is a movie review written by Korean internet users on Naver, the most commonly used search engine in Korea. Each row can be broken down into the following fields:
+
+- `id`: A unique review ID, provided by Naver
+- `document`: The actual movie review
+- `label`: Binary labels for sentiment analysis, where `0` denotes negative, and `1`, positive
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### 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
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@InProceedings{Park:2016,
+  title        = "Naver Sentiment Movie Corpus",
+  author       = "Lucy Park",
+  year         = "2016",
+  howpublished = {\\url{https://github.com/e9t/nsmc}}
+}
+```

dataset_infos.json

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+{"default": {"description": "This is a movie review dataset in the Korean language. Reviews were scraped from Naver movies. The dataset construction is based on the method noted in Large movie review dataset from Maas et al., 2011.\n", "citation": "@InProceedings{Park:2016,\n  title        = \"Naver Sentiment Movie Corpus\",\n  author       = \"Lucy Park\",\n  year         = \"2016\",\n  howpublished = {\\url{https://github.com/e9t/nsmc}}\n}\n", "homepage": "https://github.com/e9t/nsmc/", "license": "CC0 1.0 Universal (CC0 1.0)", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "document": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["negative", "positive"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "nsmc", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 16423803, "num_examples": 150000, "dataset_name": "nsmc"}, "test": {"name": "test", "num_bytes": 5491417, "num_examples": 50000, "dataset_name": "nsmc"}}, "download_checksums": {"https://raw.githubusercontent.com/e9t/nsmc/master/ratings_train.txt": {"num_bytes": 14628807, "checksum": "e03b7d14e9e41be8d464a28057cd25d7396c53e67aa7fd5f7e552c59b0ee2940"}, "https://raw.githubusercontent.com/e9t/nsmc/master/ratings_test.txt": {"num_bytes": 4893335, "checksum": "8ac9f64052f11dbf6ae0acb5e038f03d90a76f0eda7820cfb3a92d02edfcebda"}}, "download_size": 19522142, "post_processing_size": null, "dataset_size": 21915220, "size_in_bytes": 41437362}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a1400fb173581fdfdb166cd9af0706a739b062e0989553c286ef2e8bfa83bf3c
+size 886

nsmc.py

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+# 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.
+"""Naver movie review corpus for binary sentiment classification"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{Park:2016,
+  title        = "Naver Sentiment Movie Corpus",
+  author       = "Lucy Park",
+  year         = "2016",
+  howpublished = {\\url{https://github.com/e9t/nsmc}}
+}
+"""
+
+_DESCRIPTION = """\
+This is a movie review dataset in the Korean language. Reviews were scraped from Naver movies. The dataset construction is based on the method noted in Large movie review dataset from Maas et al., 2011.
+"""
+
+_HOMEPAGE = "https://github.com/e9t/nsmc/"
+
+_LICENSE = "CC0 1.0 Universal (CC0 1.0)"
+
+
+_URL = "https://raw.githubusercontent.com/e9t/nsmc/master/"
+_URLs = {
+    "train": _URL + "ratings_train.txt",
+    "test": _URL + "ratings_test.txt",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class NSMC(datasets.GeneratorBasedBuilder):
+    """Korean Naver movie review dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=["negative", "positive"]),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": downloaded_files["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": downloaded_files["test"],
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        with open(filepath, encoding="utf-8") as f:
+            next(f)
+            reader = csv.reader(f, delimiter="\t")
+            for id_, row in enumerate(reader):
+                yield id_, {
+                    "id": row[0],
+                    "document": row[1],
+                    "label": int(row[2]),
+                }