In this demo paper, we present a new open-source python module for building information retrieval pipelines with transformers namely CHERCHE. Our aim is to propose an easy to plug tool capable to execute, simple but strong, state-of-the-art information retrieval models. To do so, we have integrated classical models based on lexical matching but also recent models based on semantic matching. Indeed, a large number of models available on public hubs can be now tested on information retrieval tasks with only a few lines. CHERCHE is oriented to newcomers into the neural information retrieval field that want to use transformer-based models in small collections without struggling with heavy tools. The code and documentation of CHERCHE is public available at https://github.com/raphaelsty/cherche

River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result from the merger of two popular packages for stream learning in Python: Creme and scikit-multiflow. River introduces a revamped architecture based on the lessons learnt from the seminal packages. River's ambition is to be the go-to library for doing machine learning on streaming data. Additionally, this open source package brings under the same umbrella a large community of practitioners and researchers. The source code is available at https://github.com/online-ml/river.

Knowledge bases are increasingly exploited as gold standard data sources which benefit various knowledge-driven NLP tasks.In this paper, we explore a new research direction to perform knowledge base (KB) representation learning grounded with the recent theoretical framework of knowledge distillation over neural networks.Given a set of KBs, our proposed approach KD-MKB, learns KB embeddings by mutually and jointly distilling knowledge within a dynamic teacher-student setting.Experimental results on two standard datasets show that knowledge distillation between KBs through entity and relation inference is actually observed.We also show that cooperative learning significantly outperforms the two proposed baselines, namely traditional and sequential distillation.