Omkar2118/datasets huggingface dataset:
MMChat

- A large scale Chinese multi-modal dialogue corpus (120.84K dialogues and 198.82 K images).
- MMCHAT contains image-grounded dialogues collected from real conversations on social media.
- We manually annotate 100K dialogues from MMCHAT with the dialogue quality and whether the dialogues are related to the given image.
- We provide the rule-filtered raw dialogues  that are used to create MMChat (Rule Filtered Raw MMChat). It contains 4.257 M dialogue sessions and 4.874 M images
- We provide a version of MMChat that is filtered based on LCCC (LCCC Filtered MMChat). This version contain much cleaner dialogues (492.6 K dialogue sessions and 1.066 M images)

Introducing paper: MMChat: Multi-Modal Chat Dataset on Social Media
Avicenna: Deductive Commonsense Reasoning

A syllogism is a common form of deductive reasoning that requires precisely two premises and one conclusion. The Avicenna corpus is a benchmark for syllogistic NLI and syllogistic NLG: 


- syllogistic NLI: Identifying the possibility of inferring between pairs of inputted sentences. 
- syllogistic NLG: Generating a conclusion sentence for two sentences with a syllogistic relation.
CER Smart Metering Project - Electricity Customer Behaviour Trial
Full name: CER Smart Metering Project - Electricity Customer Behaviour Trial, 2009-2010. Irish Social Science Data Archive. SN: 0012-00

The CER initiated the Smart Metering Project in 2007 with the purpose of undertaking trials to assess the performance of Smart Meters, their impact on consumers’ energy consumption and the economic case for a wider national rollout. It is a collaborative energy industry-wide project managed by the CER and actively involving energy industry participants including the Sustainable Energy Authority of Ireland (SEAI), the Department of Communications, Energy and Natural Resources (DCENR), ESB Networks, Bord Gáis Networks, Electric Ireland, Bord Gáis Energy and other energy suppliers.
Unitail
Full name: The United Retail Datasets

The United Retail Datasets (Unitail) is a large-scale benchmark of basic visual tasks on products that challenges algorithms for detecting, reading, and matching. It offers the Unitial-Det, with 1.8M quadrilateral-shaped instances annotated; and the Unitial-OCR, containing 1454 product categories, 30k text regions, and 21k transcriptions to enable robust reading on products and motivate enhanced product matching.

Introducing paper: Unitail: Detecting, Reading, and Matching in Retail Scene
HFFD
Full name: Hybrid Fake Face Dataset

We build a hybrid fake face (HFF) dataset, which contains eight types of face images. For real face images, three types of face images are randomly selected from three open datasets. They are low-resolution face images from CelebA, high-resolution face images from CelebA-HQ, and face video frames from FaceForensics, respectively. Thus, real face images under internet scenarios are simulated as real as possible. Then, some most representative face manipulation techniques, which include PGGAN and StyleGAN for identity manipulation, Face2Face and Glow for face expression manipulation, and StarGAN for face attribute transfer, are selected to produce fake face images. The HFF dataset is a large fake face dataset, which contains more than 155k face images.

Introducing paper: Fake face detection via adaptive manipulation traces extraction network