NLP People

JANUARY 2, 2019

Hundreds of researchers, students, recruiters, and business professionals came to Brussels this November to learn about recent advances, and share their own findings, in computational linguistics and Natural Language Processing (NLP). So, what’s new in the world of machine translation and what can we expect in 2019?

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Explosion

DECEMBER 28, 2019

As 2019 draws to a close and we step into the 2020s, we thought we’d take a look back at the year and all we’ve accomplished. was released – our first major upgrade to Prodigy for 2019. Sep 15: Adriane Boyd makes up the second spaCy developer team hire in 2019. Got a question? ✨ Feb 18: Finally in February, Prodigy v1.7.0

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Sebastian Ruder

AUGUST 18, 2019

This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. 2017 ) and pretrained language models ( Peters et al., 2019 ) of recent years. A taxonomy that highlights the variations can be seen below: A taxonomy for transfer learning in NLP ( Ruder, 2019 ). 2018 ; Akbik et al., 2018 ; Baevski et al.,

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Explosion

AUGUST 1, 2019

Huge transformer models like BERT, GPT-2 and XLNet have set a new standard for accuracy on almost every NLP leaderboard. Transformers and transfer-learning Natural Language Processing (NLP) systems face a problem known as the “knowledge acquisition bottleneck”. We have updated our library and this blog post accordingly.

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John Snow Labs

JUNE 6, 2023

Many different transformer models have already been implemented in Spark NLP, and specifically for text classification, Spark NLP provides various annotators that are designed to work with pretrained language models. BERT (Bidirectional Encoder Representations from Transformers) is a language model that was introduced by Google in 2018.

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Snorkel AI

MARCH 1, 2023

This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

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Mlearning.ai

JUNE 14, 2023

Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube YouTube Introduction to Natural Language Processing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1)

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Sebastian Ruder

OCTOBER 26, 2019

This post expands on the ACL 2019 tutorial on Unsupervised Cross-lingual Representation Learning. In particular, I cover unsupervised deep multilingual models such as multilingual BERT. In particular, I cover unsupervised deep multilingual models such as multilingual BERT. The domains in this case are different languages.

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Sebastian Ruder

FEBRUARY 24, 2021

Fine-tuning a pre-trained language model (LM) has become the de facto standard for doing transfer learning in natural language processing. 2018 ) while pre-trained language models are favoured over models trained on translation ( McCann et al., 2018 ), natural language inference ( Conneau et al.,

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Heartbeat

OCTOBER 10, 2023

One of the most popular techniques for speech recognition is natural language processing (NLP), which entails training machine learning models on enormous amounts of text data to understand linguistic patterns and structures. It was developed by Facebook AI Research and released in 2019. Why Did RoBERTa Get Developed?

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Heartbeat

JUNE 28, 2023

In this article, we will explore about ALBERT ( A lite weighted version of BERT machine learning model) What is ALBERT? ALBERT (A Lite BERT) is a language model developed by Google Research in 2019. BERT, GPT-2, and XLNet are some examples of models that can be used as teacher models for ALBERT.

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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.

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Heartbeat

MARCH 22, 2023

RoBERTa: A Modified BERT Model for NLP — by Khushboo Kumari An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019.

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Sebastian Ruder

NOVEMBER 14, 2022

Models that allow interaction via natural language have become ubiquitious. 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. Transfer learning in natural language processing.

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Heartbeat

JUNE 6, 2023

In recent years, researchers have also explored using GCNs for natural language processing (NLP) tasks, such as text classification , sentiment analysis , and entity recognition. Once the GCN is trained, it is easier to process new graphs and make predictions about them. Richong, Z., Yongyi, M., & Xudong L.

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Snorkel AI

MAY 25, 2023

A brief history of large language models Large language models grew out of research and experiments with neural networks to allow computers to process natural language. In the 2010s, this research intersected with the then-bustling field of neural networks, setting the ground for the first large language model.

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Snorkel AI

MAY 25, 2023

A brief history of large language models Large language models grew out of research and experiments with neural networks to allow computers to process natural language. In the 2010s, this research intersected with the then-bustling field of neural networks, setting the ground for the first large language model.

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AWS Machine Learning Blog

JUNE 13, 2023

The first generation of AWS Inferentia, a purpose-built accelerator launched in 2019, is optimized to accelerate deep learning inference. Two models were used in this process––both large language models: ELECTRA large discriminator and BERT large uncased. With AWS Inferentia1, customers saw up to 2.3x PyTorch (1.13.1)

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Marek Rei

NOVEMBER 12, 2019

Below you will find short summaries of a number of different research papers published in the areas of Machine Learning and Natural Language Processing in the past couple of years (2017-2019). NAACL 2019. link] A bidirectional transformer architecture for pre-training language representations. ArXiv 2019.

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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). 2019 ) and work that focuses on making them smaller has gained momentum: Recent approaches rely on pruning ( Sajjad et al.,

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Lexalytics

APRIL 5, 2021

This is the sort of representation that is useful for natural language processing. ELMo would also be the first of the Muppet-themed language models that would come to include ERNIE [ 120 ], Grover [ 121 ]….and The base model of BERT [ 103 ] had 12 (!) layers of bidirectional Transformers.

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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.

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Viso.ai

JUNE 12, 2024

Their applications include various Natural Language Processing ( NLP ) tasks, including question answering, text summarization, sentiment analysis , etc. Previous ChatGPT models The GPT-1 version was introduced in June 2018 as a method for language understanding by using generative pre-training. billion parameters.

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Applied Data Science

DECEMBER 23, 2022

In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. The debate was on again: maybe language generation is really just a prediction task?

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Explosion

MARCH 17, 2019

of the spaCy Natural Language Processing library includes a huge number of features, improvements and bug fixes. spaCy is an open-source library for industrial-strength natural language processing in Python. Clearly, we couldn’t use a model such as BERT or GPT-2 directly. Version 2.1

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Marek Rei

NOVEMBER 4, 2024

I have written short summaries of 68 different research papers published in the areas of Machine Learning and Natural Language Processing. However, performance is considerably lower on a benchmark that focuses on minimal edits and only fixing grammaticality (BEA-2019). Manning, Percy Liang. Imperial, Google Research.

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Chatbots Life

MAY 12, 2023

In practice, we can ask a language model to continue the sequence with the word it thinks is the most probable. To sum everything up we know that ChatGPT is: A language model — a model trained to complete the sequence of words with the most probable continuation. Why is ChatGPT so effective?

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

The Inferentia chip became generally available (GA) in December 2019, followed by Trainium GA in October 2022, and Inferentia2 GA in April 2023. The benchmark used is the RoBERTa-Base, a popular model used in natural language processing (NLP) applications, that uses the transformer architecture.

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AWS Machine Learning Blog

APRIL 29, 2024

Historically, natural language processing (NLP) would be a primary research and development expense. In 2024, however, organizations are using large language models (LLMs), which require relatively little focus on NLP, shifting research and development from modeling to the infrastructure needed to support LLM workflows.

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Unite.AI

OCTOBER 8, 2024

The paper states*: ‘Gender biases present in train data are known to bleed into natural language processing (NLP) systems, resulting in dissemination and potential amplification of those biases. Such biases are often also the root cause of errors.

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deepsense.ai

NOVEMBER 14, 2023

Within Natural Language Processing (NLP), ‘pseudo-evaluation’ approaches that we call ‘Superficial Utility Comparison Kriterion’ ( SUCK ) methods, like BLEU [32], METEOR [33], ROUGE [34], or BLEURT [35], attempt to salvage the situation. 2023 [link] [link] [link] BERTScore: Evaluating text generation with BERT , Zhang T.,

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Unite.AI

AUGUST 8, 2023

These advanced AI deep learning models have seamlessly integrated into various applications, from Google's search engine enhancements with BERT to GitHub’s Copilot, which harnesses the capability of Large Language Models (LLMs) to convert simple code snippets into fully functional source codes.

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AWS Machine Learning Blog

AUGUST 7, 2023

As an added inherent challenge, natural language processing (NLP) classifiers are historically known to be very costly to train and require a large set of vocabulary, known as a corpus , to produce accurate predictions. Thus the basis of the transformer model was born. LLMs are truly capable of learning from internet-scale data.

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NLP People

JULY 24, 2019

The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) is starting this week in Florence, Italy. Especially pre-trained word embeddings such as Word2Vec, FastText and BERT allow NLP developers to jump to the next level. Follow us for a review of ACL 2019 and more updates on NLP trends! References E.

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Topbots

JUNE 29, 2023

4] In the open-source camp, initial attempts at solving the Text2SQL puzzle were focussed on auto-encoding models such as BERT, which excel at NLU tasks.[5, Content Enhanced BERT-based Text-to-SQL Generation [8] Torsten Scholak et al. Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback [16] Janna Lipenkova.

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Viso.ai

DECEMBER 15, 2023

Microsoft launched the project in 2019 in Asia. Symbolic Music Understanding ( MusicBERT ): MusicBERT is based on the BERT (Bidirectional Encoder Representations from Transformers) NLP model. Muzic utilizes this tool to interpret and process musical symbols at a granular level.

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Topbots

JUNE 6, 2023

Autoencoding models, which are better suited for information extraction, distillation and other analytical tasks, are resting in the background — but let’s not forget that the initial LLM breakthrough in 2018 happened with BERT, an autoencoding model. Unified language model pre-training for natural language understanding and generation.

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