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README.md DELETED
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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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-
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- # Dataset Card for "emotion"
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-
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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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-
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- ## Dataset Description
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-
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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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-
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- ### Dataset Summary
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-
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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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- ### Supported Tasks and Leaderboards
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-
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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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-
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- ### Languages
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-
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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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-
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- ## Dataset Structure
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-
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- ### Data Instances
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-
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- #### default
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-
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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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-
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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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-
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- #### emotion
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-
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- - **Size of downloaded dataset files:** 1.97 MB
170
- - **Size of the generated dataset:** 2.09 MB
171
- - **Total amount of disk used:** 4.06 MB
172
-
173
- An example of 'validation' looks as follows.
174
- ```
175
-
176
- ```
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-
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- ### Data Fields
179
-
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- The data fields are the same among all splits.
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-
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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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-
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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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-
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- ### Data Splits
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-
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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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-
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- ## Dataset Creation
198
-
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- ### Curation Rationale
200
-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
202
-
203
- ### Source Data
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-
205
- #### Initial Data Collection and Normalization
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-
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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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-
209
- #### Who are the source language producers?
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-
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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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-
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- ### Annotations
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-
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- #### Annotation process
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-
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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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-
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- #### Who are the annotators?
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-
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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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-
223
- ### Personal and Sensitive Information
224
-
225
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
226
-
227
- ## Considerations for Using the Data
228
-
229
- ### Social Impact of Dataset
230
-
231
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
232
-
233
- ### Discussion of Biases
234
-
235
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
236
-
237
- ### Other Known Limitations
238
-
239
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
240
-
241
- ## Additional Information
242
-
243
- ### Dataset Curators
244
-
245
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
246
-
247
- ### Licensing Information
248
-
249
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
250
-
251
- ### Citation Information
252
-
253
- ```
254
- @inproceedings{saravia-etal-2018-carer,
255
- title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
256
- author = "Saravia, Elvis and
257
- Liu, Hsien-Chi Toby and
258
- Huang, Yen-Hao and
259
- Wu, Junlin and
260
- Chen, Yi-Shin",
261
- booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
262
- month = oct # "-" # nov,
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- year = "2018",
264
- address = "Brussels, Belgium",
265
- publisher = "Association for Computational Linguistics",
266
- url = "https://www.aclweb.org/anthology/D18-1404",
267
- doi = "10.18653/v1/D18-1404",
268
- pages = "3687--3697",
269
- 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.",
270
- }
271
-
272
- ```
273
-
274
-
275
- ### Contributions
276
-
277
- 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
@@ -1 +0,0 @@
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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 DELETED
@@ -1,68 +0,0 @@
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- import csv
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-
3
- import datasets
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- from datasets.tasks import TextClassification
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-
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-
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- _CITATION = """\
8
- @inproceedings{saravia-etal-2018-carer,
9
- title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
10
- author = "Saravia, Elvis and
11
- Liu, Hsien-Chi Toby and
12
- Huang, Yen-Hao and
13
- Wu, Junlin and
14
- Chen, Yi-Shin",
15
- booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
16
- month = oct # "-" # nov,
17
- year = "2018",
18
- address = "Brussels, Belgium",
19
- publisher = "Association for Computational Linguistics",
20
- url = "https://www.aclweb.org/anthology/D18-1404",
21
- doi = "10.18653/v1/D18-1404",
22
- pages = "3687--3697",
23
- 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.",
24
- }
25
- """
26
-
27
- _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.
29
- """
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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"
35
-
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-
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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(
41
- description=_DESCRIPTION,
42
- features=datasets.Features(
43
- {"text": datasets.Value("string"), "label": datasets.ClassLabel(names=class_names)}
44
- ),
45
- supervised_keys=("text", "label"),
46
- 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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-
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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 [
57
- datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
58
- datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
59
- datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
60
- ]
61
-
62
- def _generate_examples(self, filepath):
63
- """Generate examples."""
64
- with open(filepath, encoding="utf-8") as csv_file:
65
- csv_reader = csv.reader(csv_file, delimiter=";")
66
- for id_, row in enumerate(csv_reader):
67
- text, label = row
68
- yield id_, {"text": text, "label": label}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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