Nemotron-4 15B Technical Report

We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens. Nemotron-4 15B demonstrates strong performance when assessed on English, multilingual, and coding tasks: it outperforms all existing similarly-sized open models on 4 out of 7 downstream evaluation areas and achieves competitive performance to the leading open models in the remaining ones. Specifically, Nemotron-4 15B exhibits the best multilingual capabilities of all similarly-sized models, even outperforming models over four times larger and those explicitly specialized for multilingual tasks.

DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations

We present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations, a self-supervised method for learning universal sentence embeddings that transfer to a wide variety of natural language processing (NLP) tasks. Our objective leverages recent advances in deep metric learning (DML) and has the advantage of being conceptually simple and easy to implement, requiring no specialized architectures or labelled training data. We demonstrate that our objective can be used to pretrain transformers to state-of-the-art performance on SentEval, a popular benchmark for evaluating universal sentence embeddings, outperforming existing supervised, semi-supervised and unsupervised methods. We perform extensive ablations to determine which factors contribute to the quality of the learned embeddings. Our code will be publicly available and can be easily adapted to new datasets or used to embed unseen text.