John Snow Labs

MAY 18, 2023

Word embeddings are considered as a type of representation used in natural language processing (NLP) to capture the meaning of words in a numerical form. Word embeddings are used in natural language processing (NLP) as a technique to represent words in a numerical format.

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Explosion

MAY 17, 2023

But if you’re working on the same sort of Natural Language Processing (NLP) problems that businesses have been trying to solve for a long time, what’s the best way to use them? In 2014 I started working on spaCy , and here’s an excerpt of how I explained the motivation for the library: Computers don’t understand text.

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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

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Mlearning.ai

JUNE 8, 2023

A few embeddings for different data type For text data, models such as Word2Vec , GLoVE , and BERT transform words, sentences, or paragraphs into vector embeddings. What are Vector Embeddings? Pinecone Used a picture of phrase vector to explain vector embedding. All we need is the vectors for the words.

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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. The base model of BERT [ 103 ] had 12 (!) As with ULMFiT and ELMo, these contextual word vectors could be incorporated into any NLP application.

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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. At the same time, a wave of NLP startups has started to put this technology to practical use. Data is based on: ml_nlp_paper_data by Marek Rei.

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ODSC - Open Data Science

JANUARY 29, 2024

GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. NLP skills have long been essential for dealing with textual data. Tokenization & Transformers These are specific techniques in NLP and popularized by LLMs. Tokenization involves converting text into a format understandable by models.

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Sebastian Ruder

FEBRUARY 24, 2021

2020) fine-tune BERT for quality evaluation with a range of sentence similarity signals. 2014 ) fine-tunes only the last layer of the model. There is increasing evidence that large pre-trained language models learn representations that compress NLP tasks well ( Li et al., Sellam et al. Aghajanyan et al. 2020 ; Guo et al.,

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The MLOps Blog

JANUARY 30, 2025

Weight decay has been applied to transformer-based NLP models since the beginning. The 2017 DeepMind study on Population-Based Training (PBT) showcased its potential for LLMs by fine-tuning the f irst transformer model on the WMT 2014 English-German machine translation benchmark. validation loss).

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NLP People

JULY 24, 2019

We took the opportunity to review major research trends in the animated NLP space and formulate some implications from the business perspective. The article is backed by a statistical and – guess what – NLP-based analysis of ACL papers from the last 20 years. But what is the substance behind the buzz?

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