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README.md

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+---
+annotations_creators:
+- machine-generated
+language_creators:
+- machine-generated
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+paperswithcode_id: emotion
+pretty_name: Emotion
+tags:
+- emotion-classification
+dataset_info:
+- config_name: split
+  features:
+  - name: text
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': sadness
+          '1': joy
+          '2': love
+          '3': anger
+          '4': fear
+          '5': surprise
+  splits:
+  - name: train
+    num_bytes: 1741597
+    num_examples: 16000
+  - name: validation
+    num_bytes: 214703
+    num_examples: 2000
+  - name: test
+    num_bytes: 217181
+    num_examples: 2000
+  download_size: 740883
+  dataset_size: 2173481
+- config_name: unsplit
+  features:
+  - name: text
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': sadness
+          '1': joy
+          '2': love
+          '3': anger
+          '4': fear
+          '5': surprise
+  splits:
+  - name: train
+    num_bytes: 45445685
+    num_examples: 416809
+  download_size: 15388281
+  dataset_size: 45445685
+train-eval-index:
+- config: default
+  task: text-classification
+  task_id: multi_class_classification
+  splits:
+    train_split: train
+    eval_split: test
+  col_mapping:
+    text: text
+    label: target
+  metrics:
+  - type: accuracy
+    name: Accuracy
+  - type: f1
+    name: F1 macro
+    args:
+      average: macro
+  - type: f1
+    name: F1 micro
+    args:
+      average: micro
+  - type: f1
+    name: F1 weighted
+    args:
+      average: weighted
+  - type: precision
+    name: Precision macro
+    args:
+      average: macro
+  - type: precision
+    name: Precision micro
+    args:
+      average: micro
+  - type: precision
+    name: Precision weighted
+    args:
+      average: weighted
+  - type: recall
+    name: Recall macro
+    args:
+      average: macro
+  - type: recall
+    name: Recall micro
+    args:
+      average: micro
+  - type: recall
+    name: Recall weighted
+    args:
+      average: weighted
+---
+
+# Dataset Card for "emotion"
+
+## 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://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset)
+- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of downloaded dataset files:** 16.13 MB
+- **Size of the generated dataset:** 47.62 MB
+- **Total amount of disk used:** 63.75 MB
+
+### Dataset Summary
+
+Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Dataset Structure
+
+### Data Instances
+
+An example looks as follows.
+```
+{
+  "text": "im feeling quite sad and sorry for myself but ill snap out of it soon",
+  "label": 0
+}
+```
+
+### Data Fields
+
+The data fields are:
+- `text`: a `string` feature.
+- `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5).
+
+### Data Splits
+
+The dataset has 2 configurations:
+- split: with a total of 20_000 examples split into train, validation and split
+- unsplit: with a total of 416_809 examples in a single train split
+
+| name    |  train | validation | test |
+|---------|-------:|-----------:|-----:|
+| split   |  16000 |       2000 | 2000 |
+| unsplit | 416809 |        n/a |  n/a |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+The dataset should be used for educational and research purposes only.
+
+### Citation Information
+
+ If you use this dataset, please cite:
+```
+@inproceedings{saravia-etal-2018-carer,
+    title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
+    author = "Saravia, Elvis  and
+      Liu, Hsien-Chi Toby  and
+      Huang, Yen-Hao  and
+      Wu, Junlin  and
+      Chen, Yi-Shin",
+    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
+    month = oct # "-" # nov,
+    year = "2018",
+    address = "Brussels, Belgium",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/D18-1404",
+    doi = "10.18653/v1/D18-1404",
+    pages = "3687--3697",
+    abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
+}
+```
+
+### Contributions
+
+Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.

dataset_infos.json

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+{"default": {"description": "Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.\n", "citation": "@inproceedings{saravia-etal-2018-carer,\n    title = \"{CARER}: Contextualized Affect Representations for Emotion Recognition\",\n    author = \"Saravia, Elvis  and\n      Liu, Hsien-Chi Toby  and\n      Huang, Yen-Hao  and\n      Wu, Junlin  and\n      Chen, Yi-Shin\",\n    booktitle = \"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing\",\n    month = oct # \"-\" # nov,\n    year = \"2018\",\n    address = \"Brussels, Belgium\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D18-1404\",\n    doi = \"10.18653/v1/D18-1404\",\n    pages = \"3687--3697\",\n    abstract = \"Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.\",\n}\n", "homepage": "https://github.com/dair-ai/emotion_dataset", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 6, "names": ["sadness", "joy", "love", "anger", "fear", "surprise"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "text", "output": "label"}, "task_templates": [{"task": "text-classification", "text_column": "text", "label_column": "label", "labels": ["anger", "fear", "joy", "love", "sadness", "surprise"]}], "builder_name": "emotion", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1741541, "num_examples": 16000, "dataset_name": "emotion"}, "validation": {"name": "validation", "num_bytes": 214699, "num_examples": 2000, "dataset_name": "emotion"}, "test": {"name": "test", "num_bytes": 217177, "num_examples": 2000, "dataset_name": "emotion"}}, "download_checksums": {"https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1": {"num_bytes": 1658616, "checksum": "3ab03d945a6cb783d818ccd06dafd52d2ed8b4f62f0f85a09d7d11870865b190"}, "https://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt?dl=1": {"num_bytes": 204240, "checksum": "34faaa31962fe63cdf5dbf6c132ef8ab166c640254ab991af78f3aea375e79ef"}, "https://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt?dl=1": {"num_bytes": 206760, "checksum": "60f531690d20127339e7f054edc299a82c627b5ec0dd5d552d53d544e0cfcc17"}}, "download_size": 2069616, "post_processing_size": null, "dataset_size": 2173417, "size_in_bytes": 4243033}}

emotion.py

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+import json
+
+import datasets
+from datasets.tasks import TextClassification
+
+
+_CITATION = """\
+@inproceedings{saravia-etal-2018-carer,
+    title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
+    author = "Saravia, Elvis  and
+      Liu, Hsien-Chi Toby  and
+      Huang, Yen-Hao  and
+      Wu, Junlin  and
+      Chen, Yi-Shin",
+    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
+    month = oct # "-" # nov,
+    year = "2018",
+    address = "Brussels, Belgium",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/D18-1404",
+    doi = "10.18653/v1/D18-1404",
+    pages = "3687--3697",
+    abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
+}
+"""
+
+_DESCRIPTION = """\
+Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
+"""
+
+_HOMEPAGE = "https://github.com/dair-ai/emotion_dataset"
+
+_LICENSE = "The dataset should be used for educational and research purposes only"
+
+_URLS = {
+    "split": {
+        "train": "data/train.jsonl.gz",
+        "validation": "data/validation.jsonl.gz",
+        "test": "data/test.jsonl.gz",
+    },
+    "unsplit": {
+        "train": "data/data.jsonl.gz",
+    },
+}
+
+
+class Emotion(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="split", version=VERSION, description="Dataset split in train, validation and test"
+        ),
+        datasets.BuilderConfig(name="unsplit", version=VERSION, description="Unsplit dataset"),
+    ]
+    DEFAULT_CONFIG_NAME = "split"
+
+    def _info(self):
+        class_names = ["sadness", "joy", "love", "anger", "fear", "surprise"]
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {"text": datasets.Value("string"), "label": datasets.ClassLabel(names=class_names)}
+            ),
+            supervised_keys=("text", "label"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+            task_templates=[TextClassification(text_column="text", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        paths = dl_manager.download_and_extract(_URLS[self.config.name])
+        if self.config.name == "split":
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": paths["train"]}),
+                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": paths["validation"]}),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": paths["test"]}),
+            ]
+        else:
+            return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": paths["train"]})]
+
+    def _generate_examples(self, filepath):
+        """Generate examples."""
+        with open(filepath, encoding="utf-8") as f:
+            for idx, line in enumerate(f):
+                example = json.loads(line)
+                yield idx, example