[ { "title": "Huggingface/datasets", "url": "https://annefou.github.io/opensource/huggingface-datasets/", "body": "🤗 Datasets is a lightweight library providing two main features:\none-line dataloaders for many public datasets: \n\none liners to download and pre-process any of the number of datasets major public datasets (in 467 languages and dialects!) provided on the HuggingFace Datasets Hub. With a simple command like squad_dataset = load_dataset("squad"), get any of these datasets ready to use in a dataloader for training/evaluating a ML model (Numpy/Pandas/PyTorch/TensorFlow/JAX),\nefficient data pre-processing: simple, fast and reproducible data pre-processing for the above public datasets as well as your own local datasets in CSV/JSON/text. With simple commands like tokenized_dataset = dataset.map(tokenize_example), efficiently prepare the dataset for inspection and ML model evaluation and training.\n\n" } , { "title": "EOSC-NORDIC", "url": "https://annefou.github.io/projects/eosc-nordic/", "body": "EOSC-NORDIC\nEOSC-Nordic project: it aims to facilitate the coordination of EOSC relevant initiatives within the Nordic and Baltic countries. I am involved in the WP5 on the climate Science demonstrator.\nFunding\nThe EOSC-Nordic project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 857652.\n" } , { "title": "RELIANCE", "url": "https://annefou.github.io/projects/reliance/", "body": "RELIANCE\nResearch Lifecycle Management technologies for Earth Science Communities and Copernicus users in EOSC (RELIANCE).\nMain activities\nReliance delivers a suite of innovative and interconnected services that extend EOSC’s capabilities to support the management of the research lifecycle within Earth Science Communities and Copernicus Users.\nFunding\nINFRAEOSC-07-2020 Research and Innovation action - Grant number 101017501. 2.5 years project starting from January 2021. \n" } , { "title": "MASSIVE", "url": "https://annefou.github.io/projects/massive/", "body": "MASSIVE\nMAchine learning, Surface mass balance of glaciers, Snow cover, In-situ data, Volume change, Earth observation (MASSIVE) project. \nMain activities\nThe project team aims at improving glacier mapping and surface glacier mass balance estimation techniques with the help of machine learning, especially deep learning. We will develop the methodology for glaciers in Norway, Svalbard, the European Alps and the Himalayas and then expand it to regions with different glacier characteristics.\nFunding\n4 years project starting 1st September 2021 until 31 August 2025. \nThis research project is funded by the “Forskerprosjekt for fornyelse-programme” of The Research Council of Norway. The project number at NFR is 315971.\n" } , { "title": "TSAR", "url": "https://annefou.github.io/projects/tsar/", "body": "TSAR\n\nT-SAR project: it aims at demonstrating that recent advances in Artificial Intelligence (AI) can be leveraged in the automatic detection of FDIA in transport infrastructures. Prototypes have been developed by researchers (PhD student) and I am working on the operationalization of the code base.\n\nFunding\nTSAR is funded by the Research Council of Norway.\n" } , { "title": "NICEST2", "url": "https://annefou.github.io/projects/nicest2/", "body": "NICEST2\nNICEST2 project: I am the project leader of the second phase of the Nordic Collaboration on e-Infrastructures for Earth System Modeling. This project focuses on strengthening the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoing initiatives.\nMain activities\n\nEnhance the performance and optimize and homogenize workflows used, so climate models (like EC-EARTH and NorESM) can be run in an efficient way on future computing resources (like EuroHPC).\nWiden the usage and expertise on evaluating Earth System Models and develop new diagnostic modules for the Nordic region within the ESMValTool.\nCreate a roadmap for FAIRification of Nordic climate model data.\n\nFunding\nNICEST2 is funded by the [Nordic e-Infrastructure Collaboration (NeIC)(https://neic.no) which facilitates development and operation of high-quality e-infrastructure solutions in areas of joint Nordic interest.\n" } , { "title": "N-LTP: A Open-source Neural Chinese Language Technology Platform with Pretrained Models", "url": "https://annefou.github.io/publications/n-ltp-a-open-source-neural-chinese-language-technology-platform-with-pretrained-models/", "body": "An open-source neural language technology platform supporting six fundamental Chinese NLP tasks: \n\nlexical analysis (Chinese word segmentation, part-of-speech tagging, and named entity recognition)\nsyntactic parsing (dependency parsing)\nsemantic parsing (semantic dependency parsing and semantic role labeling). \n\nUnlike the existing state-of-the-art toolkits, such as Stanza, that adopt an independent model for each task, N-LTP adopts the multi-task framework by using a shared pre-trained model, which has the advantage of capturing the shared knowledge across relevant Chinese tasks. \nIn addition, knowledge distillation where the single-task model teaches the multi-task model is further introduced to encourage the multi-task model to surpass its single-task teacher.\nFinally, we provide a collection of easy-to-use APIs and a visualization tool to make users easier to use and view the processing results directly. To the best of our knowledge, this is the first toolkit to support six Chinese NLP fundamental tasks. \n" } , { "title": "HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser", "url": "https://annefou.github.io/publications/hit-scir-at-mrp-2020-transition-based-parser-and-iterative-inference-parser/", "body": "This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing. \nThe task includes five frameworks for graph-based meaning representations, i.e., UCCA, EDS, PTG, AMR, and DRG. \nOur solution consists of two sub-systems: \n\ntransition-based parser for Flavor (1) frameworks (UCCA, EDS, PTG)\niterative inference parser for Flavor (2) frameworks (DRG, AMR). \n\nIn the final evaluation, our system is ranked 3rd among the seven team both in Cross-Framework Track and Cross-Lingual Track, with the macro-averaged MRP F1 score of 0.81/0.69.\n" } ]