Methods › Sequential › Sequence To Sequence Models › mBARTHez

mBARTHez

1 paper tagged archive 2025-07-28

Introduced by Moussa Kamal Eddine et al. in BARThez: a Skilled Pretrained French Sequence-to-Sequence Model

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

BARThez is a self-supervised transfer learning model for the French language based on BART. Compared to existing BERT-based French language models such as CamemBERT and FlauBERT, BARThez is well-suited for generative tasks, since not only its encoder but also its decoder is pretrained.

PaperSource

Papers archive 2025-07-28

1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
FLUE1
Natural Language Understanding1
OrangeSum1
Self-Supervised Learning1
Text Summarization1
Transfer Learning1

Usage over time archive 2025-07-28

Papers per year tagged with mBARTHez: 2020 to 2020, peak 1 1 0 2020: 1 paper 2020
Papers per year the archive tags with this method, by the paper's archive date (1 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Sequence To Sequence ModelsLanguage Models

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