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doi: 10.5281/zenodo.5702383 , 10.5281/zenodo.5701281 , 10.5281/zenodo.5700994 , 10.5281/zenodo.5701154 , 10.5281/zenodo.5700941 , 10.5281/zenodo.5702713 , 10.5281/zenodo.5702796 , 10.5281/zenodo.5702552 , 10.5281/zenodo.5702202 , 10.5281/zenodo.5939004 , 10.5281/zenodo.5702945 , 10.5281/zenodo.5734384 , 10.5281/zenodo.5737987 , 10.5281/zenodo.5700995 , 10.5281/zenodo.5734903 , 10.5281/zenodo.5734383 , 10.5281/zenodo.5701282 , 10.5281/zenodo.5702382 , 10.5281/zenodo.5702494 , 10.5281/zenodo.5702495 , 10.5281/zenodo.5702944 , 10.5281/zenodo.5735391 , 10.5281/zenodo.5701153 , 10.5281/zenodo.5733882 , 10.5281/zenodo.5733883 , 10.5281/zenodo.5735686 , 10.5281/zenodo.5702797 , 10.5281/zenodo.5737988 , 10.5281/zenodo.5702714 , 10.5281/zenodo.5702201 , 10.5281/zenodo.5735687 , 10.5281/zenodo.5734904 , 10.5281/zenodo.5700942 , 10.5281/zenodo.5735390 , 10.5281/zenodo.5702551
doi: 10.5281/zenodo.5702383 , 10.5281/zenodo.5701281 , 10.5281/zenodo.5700994 , 10.5281/zenodo.5701154 , 10.5281/zenodo.5700941 , 10.5281/zenodo.5702713 , 10.5281/zenodo.5702796 , 10.5281/zenodo.5702552 , 10.5281/zenodo.5702202 , 10.5281/zenodo.5939004 , 10.5281/zenodo.5702945 , 10.5281/zenodo.5734384 , 10.5281/zenodo.5737987 , 10.5281/zenodo.5700995 , 10.5281/zenodo.5734903 , 10.5281/zenodo.5734383 , 10.5281/zenodo.5701282 , 10.5281/zenodo.5702382 , 10.5281/zenodo.5702494 , 10.5281/zenodo.5702495 , 10.5281/zenodo.5702944 , 10.5281/zenodo.5735391 , 10.5281/zenodo.5701153 , 10.5281/zenodo.5733882 , 10.5281/zenodo.5733883 , 10.5281/zenodo.5735686 , 10.5281/zenodo.5702797 , 10.5281/zenodo.5737988 , 10.5281/zenodo.5702714 , 10.5281/zenodo.5702201 , 10.5281/zenodo.5735687 , 10.5281/zenodo.5734904 , 10.5281/zenodo.5700942 , 10.5281/zenodo.5735390 , 10.5281/zenodo.5702551
mBERT models trained to classify targets belonging to SDG 6. We trained the mBERT multi-language model to classify the 169 individual SDG Targets, based on the English abstracts in the corpus of 1.4 million research papers we gathered using the Aurora SDG Query model v5. Read more in our report: https://doi.org/10.5281/zenodo.5603019 Fork / Contribute to our code: https://github.com/Aurora-Network-Global/sdgs_many_berts
Many thanks to Ma��va Vignes from University of South Denmark, to allow us to use their UCloud HPC facilities and budget to train the mBERT models on their GPU's. Funded by European Commission, Project ID: 101004013, Call: EAC-A02-2019-1, Programme: EPLUS2020, DG/Agency: EACEA [ Project website | Zenodo Community | Github ]
Machine Learning, mBERT, SDGs, Sustainable Development Goals, Scientific publications, Mapping, Research output
Machine Learning, mBERT, SDGs, Sustainable Development Goals, Scientific publications, Mapping, Research output
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 42 |

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