Omkar2118/datasets huggingface dataset:
KnowledgeNet
Full name: KnowledgeNet

KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction).

For instance, the dataset contains text expressing the fact (Gennaro Basile; RESIDENCE; Moravia), in the passage: "Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn, in Moravia, and lived about 1756..."

Source: [KnowledgeNet](https://github.com/diffbot/knowledge-net)

Introducing paper: KnowledgeNet: A Benchmark Dataset for Knowledge Base Population
CLINC150
Full name: CLINC150

This dataset is for evaluating the performance of intent classification systems in the presence of "out-of-scope" queries, i.e., queries that do not fall into any of the system-supported intent classes. The dataset includes both in-scope and out-of-scope data.

Source: [CLINC150](https://github.com/clinc/oos-eval)

Introducing paper: An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
WikiCREM
Full name: WikiCREM

An unsupervised dataset for co-reference resolution. Presented in the publication: Kocijan et. al, WikiCREM: A Large Unsupervised Corpus for Coreference Resolution, presented at EMNLP 2019.

Source: [WikiCREM](https://ora.ox.ac.uk/objects/uuid:c83e94bb-7584-41a1-aef9-85b0e764d9e3)
Image Source: [https://arxiv.org/pdf/1908.08025v3.pdf](https://arxiv.org/pdf/1908.08025v3.pdf)

Introducing paper: WikiCREM: A Large Unsupervised Corpus for Coreference Resolution
BiPaR
Full name: BiPaR

**BiPaR** is a manually annotated bilingual parallel novel-style machine reading comprehension (MRC) dataset, developed to support monolingual, multilingual and cross-lingual reading comprehension on novels. The biggest difference between BiPaR and existing reading comprehension datasets is that each triple (Passage, Question, Answer) in BiPaR is written in parallel in two languages. BiPaR is diverse in prefixes of questions, answer types and relationships between questions and passages. Answering the questions requires reading comprehension skills of coreference resolution, multi-sentence reasoning, and understanding of implicit causality.

Source: [BiPaR](https://multinlp.github.io/BiPaR/)

Introducing paper: BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels
PASTEL
Full name: PASTEL

PASTEL is a parallelly annotated stylistic language dataset. The dataset consists of ~41K parallel sentences and 8.3K parallel stories annotated across different personas.

Source: [PASTEL](https://github.com/dykang/PASTEL)

Introducing paper: (Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas
JuICe
Full name: JuICe Dataset

JuICe is a corpus of 1.5 million examples with a curated test set of 3.7K instances based on online programming assignments. Compared with existing contextual code generation datasets, JuICe provides refined human-curated data, open-domain code, and an order of magnitude more training data.

Source: [JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation](https://arxiv.org/pdf/1910.02216v2.pdf)

Introducing paper: JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation
VisPro
Full name: VisPro

VisPro dataset contains coreference annotation of 29,722 pronouns from 5,000 dialogues.

Source: [VisPro](https://github.com/HKUST-KnowComp/Visual_PCR)

Introducing paper: What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues
RUN
Full name: The RUN Dataset

The RUN dataset  is based on OpenStreetMap (OSM). The map contains rich layers and an abundance of entities of different types. Each entity is complex and can contain (at least) four labels: name, type, is building=y/n, and house number. An entity can spread over several tiles. As the maps do not overlap, only very few entities are shared among them. The RUN dataset aligns NL navigation instructions to coordinates of their corresponding route on the OSM map.

Source: [RUN](https://github.com/OnlpLab/RUN)

Introducing paper: RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation
CrossWOZ
Full name: CrossWOZ

**CrossWOZ** is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides.

Source: [CrossWOZ](https://github.com/thu-coai/CrossWOZ)

Introducing paper: CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset
OLPBENCH
Full name: OLPBENCH

OLPBENCH is a large Open Link Prediction benchmark, which was derived from the state-of-the-art Open Information Extraction corpus OPIEC (Gashteovski et al., 2019). OLPBENCH contains 30M open triples, 1M distinct open relations and 2.5M distinct mentions of approximately 800K entities. 

Open Link Prediction is defined as follows: Given an Open Knowledge Graph and a question consisting of an entity mention and an open relation, predict mentions as answers. A predicted mention is correct if it is a mention of the correct answer entity. For example, given the question (“NBC-TV”, “has office in”, ?), correct answers include “NYC” and “New York”.

Source: [OLPBENCH](https://www.uni-mannheim.de/dws/research/resources/olpbench/)

Introducing paper: Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction