Omkar2118/datasets huggingface dataset:
Harm-C

**Harm-C** is a dataset for detecting harmful memes related to Covid-19.

Introducing paper: MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets
Commonsense-Dialogues

**Commonsense-Dialogues** is a crowdsourced dataset of ~11K dialogues grounded in social contexts involving utilization of commonsense. The social contexts used were sourced from the train split of the [SocialIQA](social-iqa) dataset, a multiple-choice question-answering based social commonsense reasoning benchmark.

Introducing paper: Commonsense-Focused Dialogues for Response Generation: An Empirical Study
BenchIE

BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese and German. In contrast to existing OIE benchmarks, BenchIE takes into account informational equivalence of extractions: our gold standard consists of fact synsets, clusters in which we exhaustively list all surface forms of the same fact.

Introducing paper: BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation
DMO

A large scale dataset to pre-train optical flow prediction network. The data are generated from the DAVIS videos using as-rigid-as-possible principle from Deep-matching and MaskRCNN. The dataset has shown better performance compared to the FlyingChairs dataset.

Introducing paper: Automatic Generation of Dense Non-rigid Optical Flow
KVQA
Full name: Knowledge-aware VQA

It contains manually verified 183K question-answer pairs about more than 18K persons and 24K images. The questions in this dataset require multi-entity, multi-relation and multi-hop reasoning over KG to arrive at an answer. To enable visual named entity linking, it also provides a support set containing reference images of 69K persons harvested from Wikidata as part of the dataset.

Introducing paper: KVQA: Knowledge-Aware Visual Question Answering
BioLAMA

**BioLAMA** is a benchmark comprised of 49K biomedical factual knowledge triples for probing biomedical Language Models. It is used to assess the capabilities of Language Models for being valid biomedical knowledge bases.

Introducing paper: Can Language Models be Biomedical Knowledge Bases?
BLANCA

**BLANCA** (Benchmarks for LANguage models on Coding Artifacts) is a collection of benchmarks that assess code understanding based on tasks such as predicting the best answer to a question in a forum post, finding related forum posts, or predicting classes related in a hierarchy from class documentation.

Introducing paper: Can Machines Read Coding Manuals Yet? -- A Benchmark for Building Better Language Models for Code Understanding
ELITR ECA

The ELITR ECA corpus is a multilingual corpus derived from publications of the European Court of Auditors. We use automatic translation together with Bleualign to identify parallel sentence pairs in all 506 translation directions. The result is a corpus comprising 264k document pairs and 41.9M sentence pairs.

Description from: [The ELITR ECA Corpus](https://arxiv.org/pdf/2109.07351v1.pdf)

Introducing paper: The ELITR ECA Corpus
MindCraft

**MindCraft** is a fine-grained dataset of collaborative tasks performed by pairs of human subjects in the 3D virtual blocks world of Minecraft. It provides information that captures partners' beliefs of the world and of each other as an interaction unfolds, bringing abundant opportunities to study human collaborative behaviors in situated language communication.

Introducing paper: MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks
M5Product

The **M5Product** dataset is a large-scale multi-modal pre-training dataset with coarse and fine-grained annotations for E-products.

• 6 Million multi-modal samples, 5k properties with 24 Million values

• 5 modalities-image text table video audio

• 6 Million category annotations with 6k classes

• Wide data source (1 Million merchants provide)

Introducing paper: M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining
AnlamVer

In this paper, we present AnlamVer, which is a semantic model evaluation dataset for Turkish designed to evaluate word similarity and word relatedness tasks while discriminating those two relations from each other. Our dataset consists of 500 word-pairs annotated by 12 human subjects, and each pair has two distinct scores for similarity and relatedness. Word-pairs are selected to enable the evaluation of distributional semantic models by multiple attributes of words and word-pair relations such as frequency, morphology, concreteness and relation types (e.g., synonymy, antonymy). Our aim is to provide insights to semantic model researchers by evaluating models in multiple attributes. We balance dataset word-pairs by their frequencies to evaluate the robustness of semantic models concerning out-of-vocabulary and rare words problems, which are caused by the rich derivational and inflectional morphology of the Turkish language.
(from the original abstract of the dataset paper)

Introducing paper: AnlamVer: Semantic Model Evaluation Dataset for Turkish - Word Similarity and Relatedness