Omkar2118/datasets huggingface dataset:
COSIAN
Full name: a collection of singing voice annotation

**COSIAN** is an annotation collection of Japanese popular (J-POP) songs, focusing on singing style and expression of famous solo-singers.

It consists of various 168 songs. There are 21 female- and 21 male singers. Each singer has four songs that have different moods from each other.

Source: [Analysis and Detection of Singing Techniques in Repertoires of J-POP Solo Singers](https://yamathcy.github.io/ISMIR2022J-POP/)

Image Source: [https://yamathcy.github.io/ISMIR2022J-POP/](https://yamathcy.github.io/ISMIR2022J-POP/)

Introducing paper: Analysis and Detection of Singing Techniques in Repertoires of J-POP Solo Singers
SpaRTUN

**SpaRTUN** a dataset synthesized for transfer learning on spatial question answering (SQA) and spatial role labeling (SpRL).

Recent research shows synthetic data as a source of supervision helps pretrained language models (PLM) transfer learning to new target tasks/domains. However, this idea is  less explored for spatial language. We provide two new data resources on multiple spatial language processing tasks. The first dataset is synthesized for transfer learning on spatial question answering (SQA) and spatial role labeling (SpRL). Compared to previous SQA datasets, we include a larger variety of spatial relation types and spatial expressions. Our data generation process is easily extendable with new spatial expression lexicons. The second one is a real-world SQA dataset with human-generated questions built on an existing corpus with SPRL annotations. This dataset can be used to evaluate spatial language processing models in realistic situations. We show pretraining with automatically generated data significantly improves the SOTA results on several SQA and SPRL benchmarks, particularly when the training data in the target domain is small.

Introducing paper: Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning
Cornell (48%/32%/20% fixed splits)

Node classification on Cornell with the fixed 48%/32%/20% splits provided by Geom-GCN.

Introducing paper: Geom-GCN: Geometric Graph Convolutional Networks
Wisconsin (48%/32%/20% fixed splits)

Node classification on Wisconsin  with the fixed 48%/32%/20% splits provided by Geom-GCN.

Introducing paper: Geom-GCN: Geometric Graph Convolutional Networks
Texas (48%/32%/20% fixed splits)

Node classification on Texas with the fixed 48%/32%/20% splits provided by Geom-GCN.

Introducing paper: Geom-GCN: Geometric Graph Convolutional Networks
Film(48%/32%/20% fixed splits)

Node classification on Film with the fixed 48%/32%/20% splits provided by Geom-GCN.

Introducing paper: Geom-GCN: Geometric Graph Convolutional Networks
Chameleon (48%/32%/20% fixed splits)

Node classification on Chameleon with the fixed 48%/32%/20% splits provided by Geom-GCN.

Introducing paper: Geom-GCN: Geometric Graph Convolutional Networks
Squirrel (48%/32%/20% fixed splits)

Node classification on Squirrel with the fixed 48%/32%/20% splits provided by Geom-GCN.

Introducing paper: Geom-GCN: Geometric Graph Convolutional Networks
HumSet
Full name: HumSet

Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data, a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain. To enable creation of such NLP systems, we introduce and release **HumSet**, a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. The dataset provides documents in three languages (English, French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021 across the globe. For each document, **HumSet** provides selected snippets (entries) as well as assigned classes to each entry annotated using common humanitarian information analysis frameworks. **HumSet** also provides novel and challenging entry extraction and multi-label entry classification tasks. In this paper, we take a first step towards approaching these tasks and conduct a set of experiments on  Pre-trained Language Models (PLM) to establish strong baselines for future research in this domain. The dataset is available at [https://blog.thedeep.io/humset/](https://blog.thedeep.io/humset/).

Introducing paper: HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response
Cora (48%/32%/20% fixed splits)

Node classification on Cora with the fixed 48%/32%/20% splits provided by Geom-GCN.

Introducing paper: Geom-GCN: Geometric Graph Convolutional Networks
Citeseer (48%/32%/20% fixed splits)

Node classification on Citeseer with the fixed 48%/32%/20% splits provided by Geom-GCN.

Introducing paper: Geom-GCN: Geometric Graph Convolutional Networks
PubMed (48%/32%/20% fixed splits)

Node classification on PubMed with the fixed 48%/32%/20% splits provided by Geom-GCN.

Introducing paper: Geom-GCN: Geometric Graph Convolutional Networks