The Microsoft work was published on Thursday on the Microsoft Research Blog. … In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks. Fill out the form to download this resource . We pro-vide a conservative … @inproceedings{wang2019glue, title={ {GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding}, author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.}, note={In the Proceedings of ICLR. Using the Report’s insights as a jumping off point, this checklist provides you with some of the key questions you should be able to answer about your business. The final score is the average score for the nine tasks. The GLUE benchmark is a suite of language understanding tasks which has seen dramatic progress in the past year, with average perfor-mance moving from 70.0 at launch to 82.9, state of the art at writing.
Since the launch of the GLUE benchmark in 2018, world-renowned companies, including Google, Microsoft, Facebook, Huawei, Alibaba and well-known research institutions have participated in the competition.In this competition, the model submitted by Ping An Technology is comprised of a pre-trained language model (ALBERT), the Data Augmentation and Auxiliary Feature (DAAF) and Neural Architecture Search (NAS). The GLUE benchmark, introduced a little over one year ago, offers a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently surpassed the level of non-expert humans, suggesting limited headroom for further research. Join them to grow your own development teams, manage permissions, and collaborate on projects.EasyTransfer is designed to make the development of transfer learning in NLP applications easier.中文语言理解基准测评 Chinese Language Understanding Evaluation Benchmark: datasets, baselines, pre-trained models, corpus and leaderboard,LGEB: Benchmark of Language Generation Evaluation,Chinese Language Generation Evaluation 中文生成任务基准测评,CLUENER2020 中文细粒度命名实体识别 Fine Grained Named Entity Recognition,高性能小模型测评 Shared Tasks in NLPCC 2020. (,Except as otherwise noted, the content of this page is licensed under the,TensorFlow Lite for mobile and embedded devices,TensorFlow Extended for end-to-end ML components,Resources and tools to integrate Responsible AI practices into your ML workflow,Pre-trained models and datasets built by Google and the community,Ecosystem of tools to help you use TensorFlow,Libraries and extensions built on TensorFlow,Differentiate yourself by demonstrating your ML proficiency,Educational resources to learn the fundamentals of ML with TensorFlow,Sign up for the TensorFlow monthly newsletter,https://nlp.stanford.edu/sentiment/index.html,https://www.microsoft.com/en-us/download/details.aspx?id=52398,https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs,http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark,http://www.nyu.edu/projects/bowman/multinli/,https://rajpurkar.github.io/SQuAD-explorer/,https://aclweb.org/aclwiki/Recognizing_Textual_Entailment,https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html. Download now to get started. Better Interaction Between Computers and Human Languages Has Widespread Applications Across Various Sectors.GLUE is the top competition in the field of NLP and one of the most important measures of the technical level of NLP technologies.NLP, which allows computers to understand human speech and text, is one of the core technologies supporting artificial intelligence applications.
Tags: Computer Vision & Machine Vision, Machine Learning & Artificial Intelligence, Speech & Audio Processing.
Here, we mea-sure human performance on the benchmark, in order to learn whether significant head- room remains for further progress. In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new …
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