Omkar2118/datasets huggingface dataset:
GoEmotions
Full name: GoEmotions

**GoEmotions** is a corpus of 58k carefully curated comments extracted from Reddit, with human annotations to 27 emotion categories or Neutral.

- Number of examples: 58,009.
- Number of labels: 27 + Neutral.
- Maximum sequence length in training and evaluation datasets: 30.

On top of the raw data, the dataset also includes a version filtered based on reter-agreement, which contains a train/test/validation split:

- Size of training dataset: 43,410.
- Size of test dataset: 5,427.
- Size of validation dataset: 5,426.

The emotion categories are: admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise.

Source: [Google Research](https://github.com/google-research/google-research/tree/master/goemotions)

Introducing paper: GoEmotions: A Dataset of Fine-Grained Emotions
RecipeNLG

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Introducing paper: RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation
MAMS
Full name: Multi Aspect Multi-Sentiment

MAMS is a challenge dataset for aspect-based sentiment analysis (ABSA), in which each sentences contain at least two aspects with different sentiment polarities. MAMS dataset contains two versions: one for aspect-term sentiment analysis (ATSA) and one for aspect-category sentiment analysis (ACSA).

Source: [MAMS](https://github.com/siat-nlp/MAMS-for-ABSA)

Introducing paper: A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis
Multi30K

Multi30K is a large-scale multilingual multimodal dataset for interdisciplinary machine learning research. It extends the Flickr30K dataset with German translations created by professional translators over a subset of the English descriptions, and descriptions crowdsourced independently of the original English descriptions. The dataset was introduced to stimulate multilingual multimodal research.

Introducing paper: Multi30K: Multilingual English-German Image Descriptions
DDRel

**DDRel** is a dataset for interpersonal relation classification in dyadic dialogues. It consists of 6,300 dyadic dialogue sessions between 694 pairs of speakers with 53,126 utterances in total. It is constructed by crawling movie scripts from IMSDb and annotating the relation labels for each session according to 13 pre-defines relationships.

Source: [https://github.com/JiaQiSJTU/DialogueRelationClassification](https://github.com/JiaQiSJTU/DialogueRelationClassification)

Introducing paper: DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
HateXplain

Covers multiple aspects of the issue. Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labelling decision (as hate, offensive or normal) is based.

Source: [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](/paper/hatexplain-a-benchmark-dataset-for)

Introducing paper: HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection
RECCON

RECCON is a dataset for the task of recognizing emotion cause in conversations.

Source: [Recognizing Emotion Cause in Conversations](https://arxiv.org/pdf/2012.11820.pdf)

Introducing paper: Recognizing Emotion Cause in Conversations