Bilingual (EN-PT) corpus acquired from the website (https://www.europarl.europa.eu/) of the European Parliament (9th May 2020)
EN-PT Bilingual COVID-19-related corpus acquired from the website (https://globalvoices.org/) of GlobalVoices (28th April 2020)
Bilingual (EN-PT) corpus acquired from website (https://ec.europa.eu/commission/presscorner/) of the EU portal (8th July 2020).
Portuguese-English parallel from release 7 of the ParaCrawl project, specifically "Broader Web-Scale Provision of Parallel Corpora for European Languages". This version is filtered with BiCleaner with a threshold of 0.5. Data was crawled from the web following robots.txt, as is standard practice....
Bilingual (EN-PT) corpus acquired from website (https://eur-lex.europa.eu/legal-content) of the EU portal (9th July 2020)
This dataset has been created within the framework of the European Language Resource Coordination (ELRC) Connecting Europe Facility - Automated Translation (CEF.AT) action. For further information on the project: http://lr-coordination.eu. Complete text of the Portuguese Constitution in Portugue...
GistSumm (GIST SUMMarizer) is a summarization tool for Portuguese. It uses the gist as a guideline to identify and select text segments to include in the final extract. Automatically produced extracts have been evaluated under the light of gist preservation and textuality.
The EUROPARL Corpus (subpart Portuguese-English of the parallel corpora), available at http://www.statmt.org/europarl/, was extracted from the proceedings of the European Parliament (Koehn, 2005). It contains transcriptions of sessions dating back from 1996 to 2011, in a total of approximately 58...
Technical Description: http://qtleap.eu/wp-content/uploads/2015/05/Pilot1_technical_description.pdf http://qtleap.eu/wp-content/uploads/2015/05/TechnicalDescriptionPilot2_D2.7.pdf http://qtleap.eu/wp-content/uploads/2016/11/TechnicalDescriptionPilot3_D2.10.pdf
HuggingFace (pytorch) pre-trained roBERTa model in Portuguese, with 6 layers and 12 attention-heads, totaling 68M parameters. Pre-training was done on 10 million Portuguese sentences and 10 million English sentences from the Oscar corpus. Please cite: Santos, Rodrigo, João Rodrigues, Antóni...