Omkar2118/datasets huggingface dataset:
ACLUE
Full name: Ancient Chinese Language Understanding Evaluation

The Ancient Chinese Language Understanding Evaluation (ACLUE) is an evaluation benchmark focused on ancient Chinese language comprehension. It aims to assess the performance of large-scale language models on understanding ancient Chinese. The benchmark comprises 15 tasks spanning various domains, including lexical, syntactic, semantic, inference, and knowledge. We encourage researchers to use ACLUE to test and enhance their models' abilities in ancient Chinese language understanding. ACLUE's tasks are derived from a combination of manually curated questions from publicly available resources, and automatically generated questions from classical Chinese language corpora. The range of questions span from the Xia dynasty (2070 BCE) to the Ming dynasty (1368 CE). ACLUE adopts a multiple-choice question format for all tasks.

Introducing paper: Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE
SHREC'20
Full name: Shape Correspondence with Non-Isometric Deformations

A dataset of highly non-isometric non-rigid quadruped shapes with consensus-based ground-truth correspondences.

The paper is available [here](http://orca.cf.ac.uk/134584/7/1-s2.0-S0097849320301266-main.pdf)
APIBench

**APIBench** is a benchmark dataset designed for evaluating the performance of **API recommendation approaches**. It was introduced in the paper titled "Revisiting, Benchmarking, and Exploring API Recommendation: How Far Are We?"¹. Let's delve into the details:

1. **Purpose and Context**:
   - **APIBench** serves as a standardized evaluation platform for assessing the effectiveness of various API recommendation techniques.
   - Researchers and practitioners can use it to compare and analyze different approaches in the field of API recommendation.

2. **Components**:
   - **APIBench** comprises two sub-datasets:
     - **APIBench-Q**: This dataset focuses on **query-based API recommendation**. It involves providing relevant APIs to developers based on natural language queries that describe programming requirements.
     - **APIBench-C**: This dataset deals with **code-based API recommendation**. It aims to predict the next API to use based on the code context.

3. **Contents**:
   - **APIBench-Q**:
     - Contains both **Java** and **Python** versions.
     - Includes original queries along with corresponding APIs and API classes.
     - Also provides reformulated queries derived from the original ones using various techniques (such as RACK, NLP2API, SEQUER, Google Prediction Service, and NLPAUG).
     - The dataset size:
       - Original Queries:
         - Python: 4,309
         - Java: 6,563
       - Expanded Queries (after reformulation):
         - Python: 173,517
         - Java: 400,126

   - **APIBench-C**:
     - Similar to **APIBench-Q**, it has both **Java** and **Python** versions.
     - Contains metadata files for code in different domains.
     - Metadata includes information about function lengths (long, normal, and short keywords).

4. **Access**:
   - Since GitHub does not host large datasets, you can download the **APIBench** dataset from **Zenodo**².

In summary, **APIBench** provides a valuable resource for evaluating and advancing API recommendation techniques, fostering research and development in this area. 🚀🔍

Source: Conversation with Bing, 3/20/2024
(1) GitHub - JohnnyPeng18/APIBench: APIBench is a benchmark for evaluating .... https://github.com/JohnnyPeng18/APIBench.
(2) APIBench: A Benchmark Dataset for Evaluating API ... - Zenodo. https://zenodo.org/records/5797297.
(3) api-benchmark - npm. https://www.npmjs.com/package/api-benchmark.
(4) The world's #1 API & code benchmark platform | CyBench. https://cybench.io/.
(5) undefined. https://github.com/masud-technope/RACK-Replication-Package.
(6) undefined. https://github.com/masud-technope/NLP2API-Replication-Package.
(7) undefined. https://github.com/kbcao/sequer.
(8) undefined. http://suggestqueries.google.com/complete/search?.
(9) undefined. https://github.com/makcedward/nlpaug.

Introducing paper: Revisiting, Benchmarking and Exploring API Recommendation: How Far Are We?