Omkar2118/datasets huggingface dataset:
BiRdQA

BiRdQA is a bilingual multiple-choice question answering dataset with 6614 English riddles and 8751 Chinese riddles.

Image source: [https://arxiv.org/pdf/2109.11087v1.pdf](https://arxiv.org/pdf/2109.11087v1.pdf)

Introducing paper: BiRdQA: A Bilingual Dataset for Question Answering on Tricky Riddles
ParaShoot

ParaShoot is the first question answering dataset in modern Hebrew. The dataset follows the format and crowdsourcing methodology of [SQuAD](/dataset/squad), and contains approximately 3000 annotated examples, similar to other question-answering datasets in low-resource languages.

Introducing paper: ParaShoot: A Hebrew Question Answering Dataset
GermEval

The **GermEval dataset** is a valuable resource for natural language processing (NLP) tasks, specifically **named entity recognition (NER)**, conducted in the German language. Here are some key details about this dataset:

- **Task**: **Token Classification** (specifically, named entity recognition)
- **Language**: **German**
- **Size**: The dataset falls within the category of **100K < n < 1M** tokens.
- **Source**: The data was sampled from **German Wikipedia** and **News Corpora**, comprising a collection of citations.
- **Annotations**: The annotations were created through **crowdsourcing** efforts.
- **License**: The dataset is available under the **cc-by-4.0** license.
- **Content**: It covers over **31,000 sentences**, corresponding to more than **590,000 tokens**.
- **Purpose**: Researchers and practitioners can use this dataset to train and evaluate NER models for German text.

You can find more information and explore the dataset on the [Hugging Face Datasets page](https://huggingface.co/datasets/germeval_14) ¹.

(1) germeval_14 · Datasets at Hugging Face. https://huggingface.co/datasets/germeval_14.
(2) GermEval-2018 Corpus (DE) - Empirical Linguistics and ... - heiDATA. https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/0B5VML.
(3) GermEval 2014 Named Entity Recognition Shared Task - Data and Task Setup. https://sites.google.com/site/germeval2014ner/data.
(4) 6 Best German Language Datasets of 2022 | Twine - Twine Blog. https://www.twine.net/blog/best-german-language-datasets/.
(5) germeval_14 | TensorFlow Datasets. https://www.tensorflow.org/datasets/community_catalog/huggingface/germeval_14.
(6) undefined. http://www.stern.de/sport/fussball/krawalle-in-der-fussball-bundesliga-dfb-setzt-auf-falsche-konzepte-1553657.html.
(7) undefined. http://www.fr-online.de/in_und_ausland/sport/aktuell/1618625_Frings-schaut-finster-in-die-Zukunft.html.
Hilti SLAM Challenge

Hilti SLAM Challenge is a dataset for Simultaneous Localization and Mapping (SLAM) algorithms due to sparsity, varying illumination conditions, and dynamic objects. The sensor platform used to collect this dataset contains a number of visual, lidar and inertial sensors which have all been rigorously calibrated. All data is temporally aligned to support precise multi-sensor fusion. Each dataset includes accurate ground truth to allow direct testing of SLAM results. Raw data as well as intrinsic and extrinsic sensor calibration data from twelve datasets in various environments is provided. Each environment represents common scenarios found in building construction sites in various stages of completion.

Introducing paper: The Hilti SLAM Challenge Dataset
ERATO

**ERATO** is a large-scale multi-modal dataset for Pairwise Emotional Relationship Recognition (PERR). It has 31,182 video clips, lasting about 203 video hours. Different from the existing datasets, ERATO contains interaction-centric videos with multi-shots, varied video length, and multiple modalities including visual, audio and text

Introducing paper: Pairwise Emotional Relationship Recognition in Drama Videos: Dataset and Benchmark
CI-ToD

**CI-ToD** is a dataset for Consistency Identification in Task-oriented Dialog system.

Introducing paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
Wrench

Wrench is a benchmark platform for thorough and standardized evaluation of Weak Supervision (WS). It consists of 22 varied real-world datasets for classification and sequence tagging; a range of real, synthetic, and procedurally-generated weak supervision sources; and a modular, extensible framework for WS evaluation, including implementations for popular WS methods.

Introducing paper: WRENCH: A Comprehensive Benchmark for Weak Supervision
CoWeSe
Full name: Corpus Web Salud Espanol

**CoWeSe** is a Spanish biomedical corpus consisting of 4.5GB (about 750M tokens) of clean plain text. CoWeSe is the result of a massive crawler on 3000 Spanish domains executed in 2020.

Introducing paper: Spanish Biomedical Crawled Corpus: A Large, Diverse Dataset for Spanish Biomedical Language Models
METEOR

**METEOR** is a complex traffic dataset which captures traffic patterns in unstructured scenarios in India. METEOR consists of more than 1000 one-minute video clips, over 2 million annotated frames with ego-vehicle trajectories, and more than 13 million bounding boxes for surrounding vehicles or traffic agents. METEOR is a unique dataset in terms of capturing the heterogeneity of microscopic and macroscopic traffic characteristics.

Introducing paper: METEOR:A Dense, Heterogeneous, and Unstructured Traffic Dataset With Rare Behaviors
SketchHairSalon

**SketchHairSalon** is a dataset for hair generation containing thousands of annotated hair sketch-image pairs and corresponding hair mattes.

Introducing paper: SketchHairSalon: Deep Sketch-based Hair Image Synthesis
XTD10

**XTD10** is a dataset for cross-lingual image retrieval and tagging consisting of the MSCOCO2014 caption test dataset annotated in 7 languages that were collected using a crowdsourcing platform.

Introducing paper: Towards Zero-shot Cross-lingual Image Retrieval and Tagging