Snorkel AI

MARCH 1, 2023

BERT BERT, an acronym that stands for “Bidirectional Encoder Representations from Transformers,” was one of the first foundation models and pre-dated the term by several years. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). This is less of a problem in NLP where unsupervised pre-training involves classification over thousands of word types.

NLP 52

NLP ML Computer Vision Natural Language Processing 52

Sebastian Ruder

JANUARY 24, 2022

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP). 6] such as W2v-BERT [7] as well as more powerful multilingual models such as XLS-R [8]. If CNNs are pre-trained the same way as transformer models, they achieve competitive performance on many NLP tasks [28]. What happened?  

NLP 52

NLP ML BERT Computational Linguistics 52

Sebastian Ruder

DECEMBER 6, 2021

Reading Comprehension assumes a gold paragraph is provided Standard approaches for reading comprehension build on pre-trained models such as BERT. Using BERT for reading comprehension involves fine-tuning it to predict a) whether a question is answerable and b) whether each token is the start and end of an answer span.

BERT 52

BERT NLP Natural Language Processing Computational Linguistics 52

Dlabs.ai

AUGUST 10, 2020

On principle, all chatbots work by utilising some form of natural language processing (NLP). Our recently published paper, Transformer-Capsule Model for Intent Detection , demonstrated the results of our long-term research into better NLP. One of the first widely discussed chatbots was the one deployed by SkyScanner in 2016.

Natural Language Processing 52

Natural Language Processing Chatbots Neural Network Machine Learning 52

Probably Approximately a Scientific Blog

JANUARY 12, 2021

The release of Google Translate’s neural models in 2016 reported large performance improvements: “60% reduction in translation errors on several popular language pairs”. In NLP, dialogue systems generate highly generic responses such as “I don’t know” even for simple questions. Open-ended generation is prone to repetition.

Natural Language Processing 52

Natural Language Processing BERT NLP Neural Network 52

Sebastian Ruder

OCTOBER 26, 2019

In particular, I cover unsupervised deep multilingual models such as multilingual BERT. Cross-lingual learning might be useful—but why should we care about applying NLP to other languages in the first place? A conceptual view of the NLP resource hierarchy This approximately corresponds with a language's presence online.

BERT 52

BERT NLP Natural Language Processing Neural Network 52

Lexalytics

APRIL 5, 2021

Fast-forward a couple of decades: I was (and still am) working at Lexalytics, a text-analytics company that has a comprehensive NLP stack developed over many years. Around this time (early 2016), our management team realized that to maintain relevance as a company, we would need to be able to incorporate even more ML into our product.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

The benchmark used is the RoBERTa-Base, a popular model used in natural language processing (NLP) applications, that uses the transformer architecture. For the latter instance type, they ran three tests: language pretraining with GPT2, token classification with BERT Large, and image classification with the Vision Transformer.

ML 96

ML Deep Learning Algorithm Large Language Models 96

Unite.AI

SEPTEMBER 20, 2023

Transformers in NLP In 2017, Cornell University published an influential paper that introduced transformers. These are deep learning models used in NLP. Hugging Face , started in 2016, aims to make NLP models accessible to everyone. It is based in New York and was founded in 2016."

LLM 342

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