Unite.AI

NOVEMBER 8, 2023

Developed by a team at Google led by Tomas Mikolov in 2013, Word2Vec represented words in a dense vector space, capturing syntactic and semantic word relationships based on their context within large corpora of text. GPT Architecture Here's a more in-depth comparison of the T5, BERT, and GPT models across various dimensions: 1.

BERT 298

BERT NLP Neural Network Natural Language Processing 298

Towards AI

APRIL 12, 2023

All of these companies were founded between 2013–2016 in various parts of the world. Soon to be followed by large general language models like BERT (Bidirectional Encoder Representations from Transformers).

LLM 77

LLM BERT NLP OpenAI 77

O'Reilly Media

APRIL 23, 2024

But in 2013 and 2014, it remained stuck at 83% , and while in the ten years since, it has reached 95% , it had become clear that the easy money that came from acquiring more users was ending. It was certainly obvious to outsiders how disruptive BERT could be to Google Search. The market was maturing. Will History Repeat Itself?

BERT 108

BERT Algorithm AI AI 108

The Stanford AI Lab Blog

MAY 31, 2022

Language Model Pretraining Language models (LMs), like BERT 1 and the GPT series 2 , achieve remarkable performance on many natural language processing (NLP) tasks. To achieve this, we first chunk each document into segments of roughly 256 tokens, which is half of the maximum BERT LM input length.

BERT 40

BERT Natural Language Processing NLP Neural Network 40

Sebastian Ruder

AUGUST 18, 2019

2013 ) learned a single representation for every word independent of its context. In contrast, current models like BERT-Large and GPT-2 consist of 24 Transformer blocks and recent models are even deeper. Multilingual BERT in particular has been the subject of much recent attention ( Pires et al., 2019 ; Wu and Dredze, 2019 ).

NLP 75

NLP BERT Natural Language Processing Computational Linguistics 75

Lexalytics

APRIL 5, 2021

The base model of BERT [ 103 ] had 12 (!) If you gave BERT a chunk of input text, it produced word vectors that encoded each word’s context, so that now it was finally possible to disambiguate “bank” (the financial institution) from “bank” (the edge of a river). BERT is just too good not to use. Socher, L.-J.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Sebastian Ruder

OCTOBER 26, 2019

In particular, I cover unsupervised deep multilingual models such as multilingual BERT. Joint models The most prominent example in this line of work is multilingual BERT (mBERT), a BERT-base model that was jointly trained on the corpora of 104 languages with a shared vocabulary of 110k subword tokens. 2015 , Artetxe et al.,

BERT 52

BERT NLP Natural Language Processing Neural Network 52