Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
multi-class-classification
Languages:
English
Size:
100K - 1M
Tags:
emotion-classification
License:
parquet-converter
commited on
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Parent(s):
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Update parquet files
Browse files- README.md +0 -277
- dataset_infos.json +0 -1
- emotion.py +0 -68
- split/emotion-test.parquet +3 -0
- split/emotion-train.parquet +3 -0
- split/emotion-validation.parquet +3 -0
- unsplit/emotion-train.parquet +3 -0
README.md
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---
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pretty_name: Emotion
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annotations_creators:
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- machine-generated
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language_creators:
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- machine-generated
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language:
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- en
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license:
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- unknown
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multilinguality:
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- monolingual
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size_categories:
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- 10K<n<100K
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source_datasets:
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- original
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task_categories:
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- text-classification
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task_ids:
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- multi-class-classification
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paperswithcode_id: emotion
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train-eval-index:
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- config: default
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task: text-classification
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task_id: multi_class_classification
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splits:
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train_split: train
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eval_split: test
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col_mapping:
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text: text
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label: target
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metrics:
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- type: accuracy
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name: Accuracy
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- type: f1
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name: F1 macro
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args:
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average: macro
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- type: f1
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name: F1 micro
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args:
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average: micro
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- type: f1
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name: F1 weighted
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args:
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average: weighted
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- type: precision
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name: Precision macro
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args:
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average: macro
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- type: precision
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name: Precision micro
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args:
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average: micro
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- type: precision
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name: Precision weighted
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args:
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average: weighted
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- type: recall
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name: Recall macro
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args:
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average: macro
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- type: recall
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name: Recall micro
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args:
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average: micro
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- type: recall
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name: Recall weighted
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args:
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average: weighted
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tags:
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- emotion-classification
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dataset_info:
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features:
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- name: text
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dtype: string
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- name: label
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dtype:
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class_label:
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names:
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0: sadness
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1: joy
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2: love
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3: anger
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4: fear
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5: surprise
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splits:
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- name: train
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num_bytes: 1741541
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num_examples: 16000
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- name: validation
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num_bytes: 214699
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num_examples: 2000
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- name: test
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num_bytes: 217177
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num_examples: 2000
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download_size: 2069616
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dataset_size: 2173417
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---
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# Dataset Card for "emotion"
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:** [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset)
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- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- **Size of downloaded dataset files:** 3.95 MB
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- **Size of the generated dataset:** 4.16 MB
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- **Total amount of disk used:** 8.11 MB
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### Dataset Summary
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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.
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### Supported Tasks and Leaderboards
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Languages
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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## Dataset Structure
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### Data Instances
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#### default
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- **Size of downloaded dataset files:** 1.97 MB
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- **Size of the generated dataset:** 2.07 MB
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- **Total amount of disk used:** 4.05 MB
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An example of 'train' looks as follows.
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```
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{
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"label": 0,
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"text": "im feeling quite sad and sorry for myself but ill snap out of it soon"
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}
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```
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#### emotion
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- **Size of downloaded dataset files:** 1.97 MB
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- **Size of the generated dataset:** 2.09 MB
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- **Total amount of disk used:** 4.06 MB
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An example of 'validation' looks as follows.
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```
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```
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### Data Fields
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The data fields are the same among all splits.
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#### default
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- `text`: a `string` feature.
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- `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5).
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#### emotion
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- `text`: a `string` feature.
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- `label`: a `string` feature.
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### Data Splits
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| name | train | validation | test |
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| ------- | ----: | ---------: | ---: |
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| default | 16000 | 2000 | 2000 |
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| emotion | 16000 | 2000 | 2000 |
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## Dataset Creation
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### Curation Rationale
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Source Data
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#### Initial Data Collection and Normalization
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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#### Who are the source language producers?
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Annotations
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#### Annotation process
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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#### Who are the annotators?
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Personal and Sensitive Information
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Discussion of Biases
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Other Known Limitations
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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## Additional Information
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### Dataset Curators
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Licensing Information
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Citation Information
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```
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@inproceedings{saravia-etal-2018-carer,
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title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
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author = "Saravia, Elvis and
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Liu, Hsien-Chi Toby and
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Huang, Yen-Hao and
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Wu, Junlin and
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Chen, Yi-Shin",
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booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
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month = oct # "-" # nov,
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year = "2018",
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address = "Brussels, Belgium",
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publisher = "Association for Computational Linguistics",
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url = "https://www.aclweb.org/anthology/D18-1404",
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doi = "10.18653/v1/D18-1404",
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pages = "3687--3697",
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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.",
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}
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```
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### Contributions
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Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.
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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}}
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emotion.py
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import csv
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import datasets
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from datasets.tasks import TextClassification
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_CITATION = """\
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@inproceedings{saravia-etal-2018-carer,
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title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
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author = "Saravia, Elvis and
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Liu, Hsien-Chi Toby and
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Huang, Yen-Hao and
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Wu, Junlin and
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Chen, Yi-Shin",
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booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
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month = oct # "-" # nov,
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year = "2018",
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address = "Brussels, Belgium",
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publisher = "Association for Computational Linguistics",
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url = "https://www.aclweb.org/anthology/D18-1404",
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doi = "10.18653/v1/D18-1404",
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pages = "3687--3697",
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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.",
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}
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"""
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_DESCRIPTION = """\
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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.
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"""
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_URL = "https://github.com/dair-ai/emotion_dataset"
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# use dl=1 to force browser to download data instead of displaying it
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_TRAIN_DOWNLOAD_URL = "https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1"
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_VALIDATION_DOWNLOAD_URL = "https://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt?dl=1"
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_TEST_DOWNLOAD_URL = "https://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt?dl=1"
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class Emotion(datasets.GeneratorBasedBuilder):
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def _info(self):
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class_names = ["sadness", "joy", "love", "anger", "fear", "surprise"]
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{"text": datasets.Value("string"), "label": datasets.ClassLabel(names=class_names)}
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),
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supervised_keys=("text", "label"),
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homepage=_URL,
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citation=_CITATION,
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task_templates=[TextClassification(text_column="text", label_column="label")],
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
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valid_path = dl_manager.download_and_extract(_VALIDATION_DOWNLOAD_URL)
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test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
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]
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def _generate_examples(self, filepath):
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"""Generate examples."""
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with open(filepath, encoding="utf-8") as csv_file:
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csv_reader = csv.reader(csv_file, delimiter=";")
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for id_, row in enumerate(csv_reader):
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text, label = row
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yield id_, {"text": text, "label": label}
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split/emotion-test.parquet
ADDED
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+
version https://git-lfs.github.com/spec/v1
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+
oid sha256:257214cbcf098ce0b7571c0dcb73e017ec551fac280ccc99c506f58fd2810488
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size 128986
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split/emotion-train.parquet
ADDED
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+
version https://git-lfs.github.com/spec/v1
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+
oid sha256:55b47443ac1cc2bb55a583c9c9d7339de70607e3ebdc6a093e63ecbdd7fb5d2c
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size 1030739
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split/emotion-validation.parquet
ADDED
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+
version https://git-lfs.github.com/spec/v1
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+
oid sha256:8d56663dc63c545553664bff5d809c3f7eee3f36e796861bbcd3b5bab8161e98
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size 127465
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unsplit/emotion-train.parquet
ADDED
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+
version https://git-lfs.github.com/spec/v1
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+
oid sha256:4986a5b94c3bd18bd31884d4745c53c5cf167bd80b1e69ad0f17fc7be7d4fb6a
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3 |
+
size 26888537
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