Omkar2118/datasets huggingface dataset:
YACLC
Full name: Yet Another Chinese Learner Corpus

**YACLC** is a large scale, multidimensional annotated Chinese learner corpus. To construct the corpus, the aurhots first obtain a large number of topic-rich texts generated by Chinese as Foreign Language (CFL) learners. The authors collected and annotated 32,124 sentences written by CFL learners from the lang-8 platform. Each sentence is annotated by 10 annotators. After post processing, a total of 469,000 revised sentences are obtained.

Introducing paper: YACLC: A Chinese Learner Corpus with Multidimensional Annotation
The Benchmark

**The Benchmark** is a collection of datasets for Monocular Height Estimation. It consists of two datasets: GTAH and AHN.

**GTAH** (Grand Theft Auto for Height estimation) is a large-scale synthetic dataset which is obtained from the game Grand Theft Auto, under different imaging conditions. GTAH contains 28,627 height maps in total and each with a resolution of 1920×1080. For each height map, there are three corresponding RGB images that are captured under different weather conditions.

Introducing paper: THE Benchmark: Transferable Representation Learning for Monocular Height Estimation
CUGE

**CUGE** is a Chinese Language Understanding and Generation Evaluation benchmark with the following features: (1) Hierarchical benchmark framework, where datasets are principally selected and organized with a language capability-task-dataset hierarchy. (2) Multi-level scoring strategy, where different levels of model performance are provided based on the hierarchical framework.

CUGE covers 7 important language capabilities, 17 mainstream NLP tasks and 19 representative datasets. It includes tasks like: word segmentation, part of speech tagging, reading comprehension and document retrieval.

Introducing paper: CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
N-Omniglot

**N-Omniglot** is a neuromorphic dataset for few-shot learning. It contains 1,623 categories of handwritten characters, with only 20 samples per class.

Introducing paper: N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning
nvBench

**nvBench** is a large-scale NL2VIS (natural languagge to visualisations) benchmark, containing 25,750 (NL, VIS) pairs from 750 tables over 105 domains, synthesized from (NL, SQL) benchmarks to support cross-domain NLPVIS (Natural Language Query to Visualization) task.

Introducing paper: nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task