**BARThez** is a self-supervised transfer learning model for the French language based on [BART](https://paperswithcode.com/method/bart). Compared to existing [BERT](https://paperswithcode.com/method/bert)-based French language models such as [CamemBERT](https://paperswithcode.com/paper/camembert-a-tasty-french-language-model) and [FlauBERT](https://paperswithcode.com/paper/flaubert-unsupervised-language-model-pre), BARThez is well-suited for generative tasks, since not only its encoder but also its decoder is pretrained.

**OPT** is a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters. The model uses an AdamW optimizer and weight decay of 0.1. It follows a linear learning rate schedule, warming up from 0 to the maximum learning rate over the first 2000 steps in OPT-175B, or over 375M tokens in the smaller models, and decaying down to 10% of the maximum LR over 300B tokens. The batch sizes range from 0.5M to 4M depending on the model size and is kept constant throughout the course of training.

OPT: Open Pre-trained Transformer Language Models

mBARTHez

BARThez: a Skilled Pretrained French Sequence-to-Sequence Model

BCI MI framework to classifiy brain signals using a multimodal decission making phase, with an addtional differentiation of the signal.

Source	BARThez: a Skilled Pretrained French Sequence-to-Sequence Model
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com