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:
+- expert-generated
+language_creators:
+- found
+languages:
+- en
+- fi
+licenses:
+- cc-by-4-0
+multilinguality:
+- multilingual
+size_categories:
+- n<1K
+source_datasets:
+- extended|other-OpenSubtitles2016
+task_categories:
+- text-classification
+task_ids:
+- intent-classification
+- multi-class-classification
+- multi-label-classification
+- sentiment-classification
+---
+
+# Dataset Card for xed_english_finnish
+
+## 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:**
+- **Repository:** [Github](https://github.com/Helsinki-NLP/XED)
+- **Paper:** [Arxiv](https://arxiv.org/abs/2011.01612)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+This is the XED dataset. The dataset consists of emotion annotated movie subtitles from OPUS. We use Plutchik's 8 core emotions to annotate. The data is multilabel. The original annotations have been sourced for mainly English and Finnish.
+For the English data we used Stanford NER (named entity recognition) (Finkel et al., 2005) to replace names and locations with the tags: [PERSON] and [LOCATION] respectively.
+For the Finnish data, we replaced names and locations using the Turku NER corpus (Luoma et al., 2020).
+
+### Supported Tasks and Leaderboards
+
+Sentiment Classification, Multilabel Classification, Multilabel Classification, Intent Classification
+
+### Languages
+
+English, Finnish
+
+## Dataset Structure
+
+### Data Instances
+
+```
+{ "sentence": "A confession that you hired [PERSON] ... and are responsible for my father's murder."
+   "labels": [1, 6]  # anger, sadness
+}
+```
+
+### Data Fields
+
+- sentence: a line from the dataset
+- labels: labels corresponding to the emotion as an integer
+
+Where the number indicates the emotion in ascending alphabetical order: anger:1, anticipation:2, disgust:3, fear:4, joy:5, sadness:6, surprise:7, trust:8, with neutral:0 where applicable.
+
+### Data Splits
+
+For English:
+Number of unique data points: 17528 ('en_annotated' config) + 9675 ('en_neutral' config)
+Number of emotions: 8 (+neutral)
+
+For Finnish:
+Number of unique data points: 14449 ('fi_annotated' config) + 10794 ('fi_neutral' config)
+Number of emotions: 8 (+neutral)
+
+## 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
+
+License: Creative Commons Attribution 4.0 International License (CC-BY)
+
+### Citation Information
+
+@inproceedings{ohman2020xed,
+  title={XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection},
+  author={{\"O}hman, Emily and P{\`a}mies, Marc and Kajava, Kaisla and Tiedemann, J{\"o}rg},
+  booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},
+  year={2020}
+}

dataset_infos.json

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+{"en_annotated": {"description": "A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik\u2019s\ncore emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass\ndataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to\nshow that XED performs on par with other similar datasets and is therefore a useful tool for\nsentiment analysis and emotion detection.\n", "citation": "@inproceedings{ohman2020xed,\n  title={XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection},\n  author={{\"O}hman, Emily and P{\"a}mies, Marc and Kajava, Kaisla and Tiedemann, J{\"o}rg},\n  booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},\n  year={2020}\n}\n", "homepage": "", "license": "License: Creative Commons Attribution 4.0 International License (CC-BY)", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "labels": {"feature": {"num_classes": 9, "names": ["neutral", "anger", "anticipation", "disgust", "fear", "joy", "sadness", "surprise", "trust"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "xed_en_fi", "config_name": "en_annotated", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1018485, "num_examples": 17528, "dataset_name": "xed_en_fi"}}, "download_checksums": {"https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/en-annotated.tsv": {"num_bytes": 783663, "checksum": "26ac0254fbdb8b09cf2b00d44041d9084ece31bd200ae7c0e0662fbd8b037d98"}, "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/fi-annotated.tsv": {"num_bytes": 560675, "checksum": "0b5ba26b2636c3a773b77a12b1b6a86a0b6ad49c6a50572ac43868d443992392"}, "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/neu_en.txt": {"num_bytes": 314079, "checksum": "6e8b38af83b80fb64ec1139007973b1f09ec16d48549e82de22423cdba259f08"}, "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/neu_fi.txt": {"num_bytes": 762818, "checksum": "05634a802d7ceb34b17a78c04bf240714b8b814524892cef85ffe67223e4c777"}}, "download_size": 2421235, "post_processing_size": null, "dataset_size": 1018485, "size_in_bytes": 3439720}, "en_neutral": {"description": "A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik\u2019s\ncore emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass\ndataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to\nshow that XED performs on par with other similar datasets and is therefore a useful tool for\nsentiment analysis and emotion detection.\n", "citation": "@inproceedings{ohman2020xed,\n  title={XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection},\n  author={{\"O}hman, Emily and P{\"a}mies, Marc and Kajava, Kaisla and Tiedemann, J{\"o}rg},\n  booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},\n  year={2020}\n}\n", "homepage": "", "license": "License: Creative Commons Attribution 4.0 International License (CC-BY)", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "labels": {"num_classes": 9, "names": ["neutral", "anger", "anticipation", "disgust", "fear", "joy", "sadness", "surprise", "trust"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "xed_en_fi", "config_name": "en_neutral", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 401129, "num_examples": 9675, "dataset_name": "xed_en_fi"}}, "download_checksums": {"https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/en-annotated.tsv": {"num_bytes": 783663, "checksum": "26ac0254fbdb8b09cf2b00d44041d9084ece31bd200ae7c0e0662fbd8b037d98"}, "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/fi-annotated.tsv": {"num_bytes": 560675, "checksum": "0b5ba26b2636c3a773b77a12b1b6a86a0b6ad49c6a50572ac43868d443992392"}, "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/neu_en.txt": {"num_bytes": 314079, "checksum": "6e8b38af83b80fb64ec1139007973b1f09ec16d48549e82de22423cdba259f08"}, "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/neu_fi.txt": {"num_bytes": 762818, "checksum": "05634a802d7ceb34b17a78c04bf240714b8b814524892cef85ffe67223e4c777"}}, "download_size": 2421235, "post_processing_size": null, "dataset_size": 401129, "size_in_bytes": 2822364}, "fi_annotated": {"description": "A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik\u2019s\ncore emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass\ndataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to\nshow that XED performs on par with other similar datasets and is therefore a useful tool for\nsentiment analysis and emotion detection.\n", "citation": "@inproceedings{ohman2020xed,\n  title={XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection},\n  author={{\"O}hman, Emily and P{\"a}mies, Marc and Kajava, Kaisla and Tiedemann, J{\"o}rg},\n  booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},\n  year={2020}\n}\n", "homepage": "", "license": "License: Creative Commons Attribution 4.0 International License (CC-BY)", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "labels": {"feature": {"num_classes": 9, "names": ["neutral", "anger", "anticipation", "disgust", "fear", "joy", "sadness", "surprise", "trust"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "xed_en_fi", "config_name": "fi_annotated", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 756224, "num_examples": 14449, "dataset_name": "xed_en_fi"}}, "download_checksums": {"https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/en-annotated.tsv": {"num_bytes": 783663, "checksum": "26ac0254fbdb8b09cf2b00d44041d9084ece31bd200ae7c0e0662fbd8b037d98"}, "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/fi-annotated.tsv": {"num_bytes": 560675, "checksum": "0b5ba26b2636c3a773b77a12b1b6a86a0b6ad49c6a50572ac43868d443992392"}, "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/neu_en.txt": {"num_bytes": 314079, "checksum": "6e8b38af83b80fb64ec1139007973b1f09ec16d48549e82de22423cdba259f08"}, "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/neu_fi.txt": {"num_bytes": 762818, "checksum": "05634a802d7ceb34b17a78c04bf240714b8b814524892cef85ffe67223e4c777"}}, "download_size": 2421235, "post_processing_size": null, "dataset_size": 756224, "size_in_bytes": 3177459}, "fi_neutral": {"description": "A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik\u2019s\ncore emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass\ndataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to\nshow that XED performs on par with other similar datasets and is therefore a useful tool for\nsentiment analysis and emotion detection.\n", "citation": "@inproceedings{ohman2020xed,\n  title={XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection},\n  author={{\"O}hman, Emily and P{\"a}mies, Marc and Kajava, Kaisla and Tiedemann, J{\"o}rg},\n  booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},\n  year={2020}\n}\n", "homepage": "", "license": "License: Creative Commons Attribution 4.0 International License (CC-BY)", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "labels": {"num_classes": 9, "names": ["neutral", "anger", "anticipation", "disgust", "fear", "joy", "sadness", "surprise", "trust"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "xed_en_fi", "config_name": "fi_neutral", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 427499, "num_examples": 10794, "dataset_name": "xed_en_fi"}}, "download_checksums": {"https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/en-annotated.tsv": {"num_bytes": 783663, "checksum": "26ac0254fbdb8b09cf2b00d44041d9084ece31bd200ae7c0e0662fbd8b037d98"}, "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/fi-annotated.tsv": {"num_bytes": 560675, "checksum": "0b5ba26b2636c3a773b77a12b1b6a86a0b6ad49c6a50572ac43868d443992392"}, "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/neu_en.txt": {"num_bytes": 314079, "checksum": "6e8b38af83b80fb64ec1139007973b1f09ec16d48549e82de22423cdba259f08"}, "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/neu_fi.txt": {"num_bytes": 762818, "checksum": "05634a802d7ceb34b17a78c04bf240714b8b814524892cef85ffe67223e4c777"}}, "download_size": 2421235, "post_processing_size": null, "dataset_size": 427499, "size_in_bytes": 2848734}}

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xed_en_fi.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.
+"""XED: A multilingual fine-grained emotion dataset. The dataset consists of humanannotated Finnish (25k) and English sentences (30k)."""
+
+from __future__ import absolute_import, division, print_function
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{ohman2020xed,
+  title={XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection},
+  author={{\"O}hman, Emily and P{\"a}mies, Marc and Kajava, Kaisla and Tiedemann, J{\"o}rg},
+  booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},
+  year={2020}
+}
+"""
+
+_DESCRIPTION = """\
+A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
+core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
+dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
+show that XED performs on par with other similar datasets and is therefore a useful tool for
+sentiment analysis and emotion detection.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = "License: Creative Commons Attribution 4.0 International License (CC-BY)"
+
+_URLs = {
+    "en_annotated": "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/en-annotated.tsv",
+    "fi_annotated": "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/fi-annotated.tsv",
+    "en_neutral": "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/neu_en.txt",
+    "fi_neutral": "https://raw.githubusercontent.com/Helsinki-NLP/XED/master/AnnotatedData/neu_fi.txt",
+}
+
+
+class XedEnFi(datasets.GeneratorBasedBuilder):
+    """XED: A multilingual fine-grained emotion dataset. The dataset consists of humanannotated Finnish (25k) and English sentences (30k)."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="en_annotated", version=VERSION, description="English, Covers 8 classes without neutral"
+        ),
+        datasets.BuilderConfig(name="en_neutral", version=VERSION, description="English, Covers neutral"),
+        datasets.BuilderConfig(
+            name="fi_annotated", version=VERSION, description="Finnish, Covers 8 classes without neutral"
+        ),
+        datasets.BuilderConfig(name="fi_neutral", version=VERSION, description="Finnish, Covers neutral"),
+    ]
+
+    def _info(self):
+        if self.config.name == "en_annotated" or self.config.name == "fi_annotated":
+            features = datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "labels": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "neutral",
+                                "anger",
+                                "anticipation",
+                                "disgust",
+                                "fear",
+                                "joy",
+                                "sadness",
+                                "surprise",
+                                "trust",
+                            ]
+                        )
+                    )
+                    # the number indicates the emotion in ascending alphabetical order: neutral:0, anger:1, anticipation:2, disgust:3, fear:4, joy:5, #sadness:6, surprise:7, trust:8 in the text.
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "labels": datasets.features.ClassLabel(
+                        names=[
+                            "neutral",
+                            "anger",
+                            "anticipation",
+                            "disgust",
+                            "fear",
+                            "joy",
+                            "sadness",
+                            "surprise",
+                            "trust",
+                        ]
+                    ),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        my_urls = _URLs
+        data_dir = dl_manager.download_and_extract(my_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": data_dir[self.config.name]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """ Yields examples. """
+        with open(filepath, encoding="utf-8") as f:
+            for id_, line in enumerate(f):
+                if self.config.name == "en_neutral":
+                    sentence = line[1:].strip()
+                    labels = "neutral"
+                elif self.config.name == "fi_neutral":
+                    sentence = line.split("\t")[1].strip()
+                    labels = "neutral"
+                else:
+                    sentence = line.split("\t")[0]
+                    labels = list(map(int, line.split("\t")[1].split(",")))
+
+                yield id_, {"sentence": sentence, "labels": labels}