Prompt-based language models have produced encouraging results in numerous applications, including Named Entity Recognition (NER) tasks. NER aims to identify entities in a sentence and provide their types. However, the strong performance of most available NER approaches is heavily dependent on the design of discrete prompts and a verbalizer to map the model-predicted outputs to entity categories, which are complicated undertakings. To address these challenges, we present ContrastNER, a prompt-based NER framework that employs both discrete and continuous tokens in prompts and uses a contrastive learning approach to learn the continuous prompts and forecast entity types. The experimental results demonstrate that ContrastNER obtains competitive performance to the state-of-the-art NER methods in high-resource settings and outperforms the state-of-the-art models in low-resource circumstances without requiring extensive manual prompt engineering and verbalizer design.

This paper describes the DATACLOUDDSL language and the DEF-PIPE tool for describing Big Data pipelines. DAT-ACLOUDDSL has both a textual and a visual form and supports requirements obtained both from analyzing existing data pipeline specification tools and from interviews with relevant industrial actors. Particularly, DATACLOUDDSL supports (i) separation of concerns between design and run-time issues, (ii) reuse of previously developed pipeline steps and pipelines in designing new pipelines, (iii) flexible data transfer between pipelines steps and containerization of pipelines and pipeline steps, and (iv) integration of description and simulation components in Big Data pipeline orchestration systems. Additionally, it provides an interface to the discovery and deployment tools of the DataCloud toolbox.