Summary
High Performance Language Technologies (HPLT) is a space combining petabytes of natural language data with large-scale model training. With trillions of words of text, the space will be the largest open text collection. Cleaning and privacy protecting services improve the quality and ethical properties of the text. Going beyond static repositories that require the user to individually analyze each data set, the project will rate data sets by how much they improve end-to-end language models and machine translation systems. Continuous integration of models and data will result in free downloadable high-quality models for all official European Union languages and beyond. The models will be reproducible with information and evaluation metrics shown in a publicly available dashboard. By focusing on training at scale, the project complements the inference-focused European Language Grid, which in turn will be used for model deployment. Datasets, models and information about them will be published in recognized FAIR data repositories, aggregation catalogues and marketplaces for easy discovery, access, replication, and exploitation.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101070350 |
| Start date: | 01-09-2022 |
| End date: | 31-08-2025 |
| Total budget - Public funding: | 4 058 287,50 Euro - 3 880 687,00 Euro |
Cordis data
Original description
High Performance Language Technologies (HPLT) is a space combining petabytes of natural language data with large-scale model training. With trillions of words of text, the space will be the largest open text collection. Cleaning and privacy protecting services improve the quality and ethical properties of the text. Going beyond static repositories that require the user to individually analyze each data set, the project will rate data sets by how much they improve end-to-end language models and machine translation systems. Continuous integration of models and data will result in free downloadable high-quality models for all official European Union languages and beyond. The models will be reproducible with information and evaluation metrics shown in a publicly available dashboard. By focusing on training at scale, the project complements the inference-focused European Language Grid, which in turn will be used for model deployment. Datasets, models and information about them will be published in recognized FAIR data repositories, aggregation catalogues and marketplaces for easy discovery, access, replication, and exploitation.Status
SIGNEDCall topic
HORIZON-CL4-2021-DATA-01-03Update Date
09-02-2023
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