BERT 101 - State Of The Art NLP Model Explained

Scaling up BERT-like model Inference on modern CPU - Part 2

Scaling-up BERT Inference on CPU (Part 1)

Train and Fine-Tune Sentence Transformers Models

Introduction to Transformers: an NLP Perspective

arXiv:2311. 17633v2 Announce Type: replace-cross Abstract: Transformers have dominated empirical machine learning models of natural language processing.

Finally, a Replacement for BERT: Introducing ModernBERT

Welcome PaliGemma 2 – New vision language models by Google

Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa

arXiv:2608. 19200v1 Announce Type: cross Abstract: Text summarization refers to the task of condensing a document into a shorter version while preserving its key information.

Training and Finetuning Reranker Models with Sentence Transformers

Foundations of Large Language Models

Foundations of Large Language Models is a book that focuses on core concepts of large language models rather than exhaustive coverage of the latest technologies. It is organized into six chapters covering pre‑training, generative models, prompting, alignment, inference, and reasoning. The book targets college students, professionals, and practitioners in NLP and related fields, serving as a reference for anyone interested in large language models.

Very Large Language Models and How to Evaluate Them

GottBERT: a pure German Language Model

arXiv:2012. 02110v2 Announce Type: replace-cross Abstract: Pre-trained language models have significantly advanced natural language processing (NLP), especially with the introduction of BERT and its optimized version, RoBERTa.

Leveraging Natural Language Processing to Unravel the Mystery of Life: A Review of NLP Approaches in Genomics, Transcriptomics, and Proteomics

arXiv:2506. 02212v2 Announce Type: replace-cross Abstract: Natural Language Processing (NLP) has transformed various fields beyond linguistics by applying techniques originally developed for human language to the analysis of biological sequences.