Datasets:

dair-ai
/

emotion

@@ -1,277 +0,0 @@
----
-pretty_name: Emotion
-annotations_creators:
-- machine-generated
-language_creators:
-- machine-generated
-language:
-- en
-license:
-- unknown
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- text-classification
-task_ids:
-- multi-class-classification
-paperswithcode_id: emotion
-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
-tags:
-- emotion-classification
-dataset_info:
-  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: 1741541
-    num_examples: 16000
-  - name: validation
-    num_bytes: 214699
-    num_examples: 2000
-  - name: test
-    num_bytes: 217177
-    num_examples: 2000
-  download_size: 2069616
-  dataset_size: 2173417
----
-# 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:** 3.95 MB
-- **Size of the generated dataset:** 4.16 MB
-- **Total amount of disk used:** 8.11 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
-#### default
-- **Size of downloaded dataset files:** 1.97 MB
-- **Size of the generated dataset:** 2.07 MB
-- **Total amount of disk used:** 4.05 MB
-An example of 'train' looks as follows.
-```
-{
-    "label": 0,
-    "text": "im feeling quite sad and sorry for myself but ill snap out of it soon"
-}
-```
-#### emotion
-- **Size of downloaded dataset files:** 1.97 MB
-- **Size of the generated dataset:** 2.09 MB
-- **Total amount of disk used:** 4.06 MB
-An example of 'validation' looks as follows.
-```
-```
-### Data Fields
-The data fields are the same among all splits.
-#### default
-- `text`: a `string` feature.
-- `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5).
-#### emotion
-- `text`: a `string` feature.
-- `label`: a `string` feature.
-### Data Splits
-| name    | train | validation | test |
-| ------- | ----: | ---------: | ---: |
-| default | 16000 |       2000 | 2000 |
-| emotion | 16000 |       2000 | 2000 |
-## 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
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-### Citation Information
-```
-@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 DELETED Viewed

@@ -1 +0,0 @@

- {"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 DELETED Viewed

@@ -1,68 +0,0 @@
-import csv
-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.
-"""
-_URL = "https://github.com/dair-ai/emotion_dataset"
-# use dl=1 to force browser to download data instead of displaying it
-_TRAIN_DOWNLOAD_URL = "https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1"
-_VALIDATION_DOWNLOAD_URL = "https://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt?dl=1"
-_TEST_DOWNLOAD_URL = "https://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt?dl=1"
-class Emotion(datasets.GeneratorBasedBuilder):
-    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=_URL,
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
-        valid_path = dl_manager.download_and_extract(_VALIDATION_DOWNLOAD_URL)
-        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
-        ]
-    def _generate_examples(self, filepath):
-        """Generate examples."""
-        with open(filepath, encoding="utf-8") as csv_file:
-            csv_reader = csv.reader(csv_file, delimiter=";")
-            for id_, row in enumerate(csv_reader):
-                text, label = row
-                yield id_, {"text": text, "label": label}

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