AWS Machine Learning Blog

OCTOBER 30, 2024

These embeddings are useful for various natural language processing (NLP) tasks such as text classification, clustering, semantic search, and information retrieval. M5 LLMS are BERT-based LLMs fine-tuned on internal Amazon product catalog data using product title, bullet points, description, and more. str.split("|").str[0]

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Sebastian Ruder

JANUARY 6, 2020

This post gathers ten ML and NLP research directions that I found exciting and impactful in 2019.  Unsupervised pretraining was prevalent in NLP this year, mainly driven by BERT ( Devlin et al., 2019 ) and other variants.  Unsupervised pretraining was prevalent in NLP this year, mainly driven by BERT ( Devlin et al.,

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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. This article provides a brief overview of GCNs for NLP tasks and how to implement them using PyTorch and Comet.

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John Snow Labs

JUNE 27, 2023

Are you curious about the groundbreaking advancements in Natural Language Processing (NLP)? Prepare to be amazed as we delve into the world of Large Language Models (LLMs) – the driving force behind NLP’s remarkable progress. Ever wondered how machines can understand and generate human-like text?

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John Snow Labs

MAY 15, 2023

The underlying principles behind the NLP Test library: Enabling data scientists to deliver reliable, safe and effective language models. However, today there is a gap between these principles and current state-of-the-art NLP models. These findings suggest that the current NLP systems are unreliable and flawed.

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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?  

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Marek Rei

NOVEMBER 4, 2024

They annotate a new test set of news data from 2020 and find that performance of certain models holds up very well and the field luckily hasn’t overfitted to the CoNLL 2003 test set. Analysis shows that the final layers of ELECTRA and BERT capture subject-verb agreement errors best. Imperial, Google Research.

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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.

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Mlearning.ai

JUNE 7, 2023

Photo by Eugene Zhyvchik on Unsplash I wanted to share a short perspective of the radical evolution we have seen in NLP. I’ve been working on NLP problems since word2vec was released, and it has been remarkable to see how quickly the models, problems, and applications have evolved. GPT-2 released with 1.5 GPT-2 released with 1.5

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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? However, LLMs are not a direct solution to most of the NLP use-cases companies have been working on. That’s definitely new.

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AssemblyAI

NOVEMBER 8, 2023

We’ve used the DistilBertTokenizer , which inherits from the BERT WordPiece tokenization scheme. 2020 (AAAI2020) perform Truecasing as an auxiliary task supporting the main NER one. Training Data : We trained this neural network on a total of 3.7 billion words). Susanto et al., This accounts for mixed-case words. Mayhew et al.,

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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

JUNE 9, 2023

The last 12 years though, is where some of the big magic has happened in NLP. Word vectorization is an NLP methodology used to map words or phrases from a vocabulary to a corresponding vector of real numbers used to find word predictions and word similarities or semantics. BERT was designed to understand the meanings of sentences.

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Explosion

DECEMBER 28, 2019

Jan 15: The year started out with us as guests on the NLP Highlights podcast , hosted by Matt Gardner and Waleed Ammar of Allen AI. In the interview, Matt and Ines talked about Prodigy , where training corpora come from and the challenges of annotating data for an NLP system – with some ideas about how to make it easier. ?

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Sebastian Ruder

FEBRUARY 24, 2021

2020 ), they are still poorly equipped to deal with data that is substantially different from the one they have been pre-trained on. 2020) show that adapting to data of the target domain and target task are complementary. 2020) proposed language-adaptive fine-tuning to adapt a model to new languages. 2020 ; Phang et al.,

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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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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. They are now the foundation of today’s NLP systems. LinkBERT improves previous BERT models on many applications.

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Explosion

JANUARY 31, 2021

It’s much easier to configure and train your pipeline, and there are lots of new and improved integrations with the rest of the NLP ecosystem. And since modern NLP workflows often consist of multiple steps, there’s a new workflow system to help you keep your work organized. See NLP-progress for more results. Flair 2 89.7

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NVIDIA

FEBRUARY 11, 2025

That work inspired researchers who created BERT and other large language models , making 2018 a watershed moment for natural language processing, a report on AI said at the end of that year. Google released BERT as open-source software , spawning a family of follow-ons and setting off a race to build ever larger, more powerful LLMs.

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Viso.ai

FEBRUARY 25, 2023

Transformer models have become the de-facto status quo in Natural Language Processing (NLP). Vision Transformer (ViT) in Image Recognition While the Transformer architecture has become the highest standard for tasks involving Natural Language Processing (NLP) , its use cases relating to Computer Vision (CV) remain only a few.

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Snorkel AI

MAY 25, 2023

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). By making BERT bidirectional, it allowed the inputs and outputs to take each others’ context into account. Next, OpenAI released GPT-3 in June of 2020.

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Snorkel AI

MAY 25, 2023

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). By making BERT bidirectional, it allowed the inputs and outputs to take each others’ context into account. Next, OpenAI released GPT-3 in June of 2020.

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Viso.ai

JUNE 12, 2024

The authors described how to improve language understanding performances in NLP by using GPT. Their applications include various Natural Language Processing ( NLP ) tasks, including question answering, text summarization, sentiment analysis , etc. GPT-2 is not just a language model like BERT, it can also generate text.

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Topbots

JUNE 15, 2023

Language Disparity in Natural Language Processing This digital divide in natural language processing (NLP) is an active area of research. 2 ] Multilingual models perform worse on several NLP tasks on low resource languages than on high resource languages such as English.[ Are All Languages Created Equal in Multilingual BERT?

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Probably Approximately a Scientific Blog

JANUARY 12, 2021

This long-overdue blog post is based on the Commonsense Tutorial taught by Maarten Sap, Antoine Bosselut, Yejin Choi, Dan Roth, and myself at ACL 2020. In NLP, dialogue systems generate highly generic responses such as “I don’t know” even for simple questions. In the last 3 years, language models have been ubiquitous in NLP.

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Dlabs.ai

AUGUST 10, 2020

Despite 80% of surveyed businesses wanting to use chatbots in 2020 , how many do you think will implement them well? On principle, all chatbots work by utilising some form of natural language processing (NLP). It also topped the Intent Recognition Leaderboard , with an accuracy result of 0.9889. But what does it all mean?

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Sebastian Ruder

DECEMBER 5, 2023

This post was first published in  NLP News. EMNLP 2023 , one of the biggest NLP conferences takes place this week from Dec 6–10 in Singapore.  Similar to  earlier years  where BERT was ubiquitous, instruction-tuned language models (LMs) and large language models (LLMs) are used in almost every paper.

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Sebastian Ruder

FEBRUARY 12, 2024

It’s crazy how the AI and NLP landscape has evolved over the last five years. 5 years ago, around the time I finished my PhD, if you wanted to work on cutting-edge natural language processing (NLP), your choice was relatively limited. It is written from my perspective as a Europe-based researcher focused on NLP.

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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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The Stanford AI Lab Blog

JANUARY 21, 2022

self-supervised or cheaply supervised learning) has been the backbone of the many recent successes of foundation models 3 in NLP 4 5 6 7 8 9 and vision 10 11 12. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. RoBERTa: A Robustly Optimized BERT Pretraining Approach. Toutanova, K. Goyal, N.,

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Pickl AI

NOVEMBER 26, 2024

billion in 2020 to an expected $152.61 Applications include NLP, speech recognition, and forecasting. Natural Language Processing (NLP) RNNs excel in NLP tasks due to their ability to process text sequences. In parallel, transformers have emerged as a dominant architecture in many NLP tasks.

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ML Review

DECEMBER 16, 2019

Conversational AI & Graphs Let’s have a short break from hardcore ML algorithms and talk about NLP applications a bit. The authors also outline that brute-force BERT decoding without semantic parsing works much worse, so use language models wisely ?‍ And works in different domains, i.e., from CV to NLP and Reinforcement Learning.

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Applied Data Science

MARCH 11, 2022

For machine learning researchers there are many reasons why this is more than a philosophical question: Natural Language Processing (NLP) is an area containing some of the most important machine learning applications, like translation and text summarisation. The topic is in red. The agents won this one!

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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. Just wait until you hear what happened in 2022. What happened?

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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. For this approach, the authors made use of a lattice rescoring method from an earlier 2020 work.

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deepsense.ai

NOVEMBER 14, 2023

This disruptive tendency manifests every few months and shows no sign of slowing down, with the recent releases of Llama 2 [25] and Mistral [26] (the great hopes of open source NLP [27, 28]) and two proprietary game-changers seemingly just around the corner: Gemini [29] and GPT-5 [30]. Garrido-Merchán E.C., Hertzmann A., Kishore V.,

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Jay Alammar

DECEMBER 16, 2020

Multiple methods exist for assigning importance scores to the inputs of an NLP model. Previous work has examined neuron firings inside deep neural networks in both the NLP and computer vision domains. A breakdown of this architecture is provided here. Figure: Input saliency map attributing a model's prediction to input pixels.

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