Marek Rei

NOVEMBER 12, 2019

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. Evaluations on CoNLL 2014 and JFLEG show a considerable improvement over previous best results of neural models, making this work comparable to state-of-the art on error correction. NAACL 2019.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

John Snow Labs

MAY 18, 2023

Please check our similar post about “Embeddings with Transformers” for BERT family embeddings. An annotator takes an input text document and produces an output document with additional metadata, which can be used for further processing or analysis. It is developed as an open-source project at Stanford and was launched in 2014.

NLP 52

NLP Machine Learning Python Algorithm 52

Sebastian Ruder

NOVEMBER 14, 2022

Research models such as BERT and T5 have become much more accessible while the latest generation of language and multi-modal models are demonstrating increasingly powerful capabilities. Writing System and Speaker Metadata for 2,800+ Language Varieties. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Natural Language Processing 52

Natural Language Processing NLP Computational Linguistics BERT 52

Bugra Akyildiz

AUGUST 10, 2024

Large models like GPT-3 (175B parameters) or BERT-Large (340M parameters) can be reduced by 75% or more. Running BERT models on smartphones for on-device natural language processing requires much less energy due to resource constrained in smartphones than server deployments. million per year in 2014 currency) in Shanghai.

BERT 59

BERT Large Language Models Categorization Deep Learning 59