Sebastian Ruder

NOVEMBER 14, 2022

Developing models that work for more languages is important in order to offset the existing language divide and to ensure that speakers of non-English languages are not left behind, among many other reasons. In particular, languages and scripts that were never seen during pre-training often lead to poor performance [29] [30].

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AWS Machine Learning Blog

OCTOBER 24, 2023

Amazon Comprehend is a natural language processing (NLP) service that uses ML to extract insights from text. LLMs are helpful in document classification because they can analyze the text, patterns, and contextual elements in the document using natural language understanding.

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Heartbeat

AUGUST 23, 2023

But what if there was a technique to quickly and accurately solve this language puzzle? Enter Natural Language Processing (NLP) and its transformational power. But what if there was a way to unravel this language puzzle swiftly and accurately? However, in this sea of complexity, NLP offers a ray of hope.

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AWS Machine Learning Blog

JANUARY 13, 2023

Based on experimental results, the collaborative models demonstrated a 4% improvement in categorizing molecules as either pharmacologically or toxicologically active or inactive. Nat Mach Intell 2, 305–311 (2020). It also led to a 10% increase in its ability to yield confident predictions when applied to new types of molecules.

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The MLOps Blog

MARCH 23, 2023

The size of large NLP models is increasing | Source Such large natural language processing models require significant computational power and memory, which is often the leading cause of high infrastructure costs. 2020 or Hoffman et al., 2022 where they show how to train a model on a fixed-compute budget.

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ODSC - Open Data Science

SEPTEMBER 12, 2024

Now that artificial intelligence has become more widely accepted, some daring companies are looking at natural language processing (NLP) technology as the solution. billion in 2020 — a 1,549% increase in only a decade. Conventional techniques may be standard, but they’re tedious and expensive.

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Bugra Akyildiz

NOVEMBER 17, 2024

Vision Transformers(ViT) ViT is a type of machine learning model that applies the transformer architecture, originally developed for natural language processing, to image recognition tasks. 2020) EBM : Explainable Boosting Machine (Nori, et al. and 8B base and chat models, supporting both English and Chinese languages.

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Heartbeat

MAY 16, 2023

Sentiment analysis is a common natural language processing (NLP) task that involves determining the sentiment of a given piece of text, such as a tweet, product review, or customer feedback.

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Explosion

MAY 17, 2023

A lot of people are building truly new things with Large Language Models (LLMs), like wild interactive fiction experiences that weren’t possible before. 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?

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How to Learn Machine Learning

APRIL 23, 2023

The technology of AI has been categorized as narrow ai vs general, and super artificial intelligence. Data Processing Narrow AI analyses data by using ML, Natural Language Processing, Deep Learning, and Artificial Neural Networks. In this blog, we will try to learn more about these types of artificial intelligence.

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John Snow Labs

SEPTEMBER 4, 2023

Often in Natural Language Processing (NLP), we often judge a model’s success based on how accurate it is. But this approach can be misleading because it doesn’t show how well the model handles real-world language. What is Named Entity Recognition?

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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. Next, OpenAI released GPT-3 in June of 2020. At 175 billion parameters, GPT-3 set the new size standard for large language models.

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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. Next, OpenAI released GPT-3 in June of 2020. At 175 billion parameters, GPT-3 set the new size standard for large language models.

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John Snow Labs

DECEMBER 26, 2023

Sentiment analysis, a branch of natural language processing (NLP), has evolved as an effective method for determining the underlying attitudes, emotions, and views represented in textual information. References Langtest GitHub Repository Large Movie Review Dataset v1.0 Gopalakrishnan, K., & Salem, F. abs/2005.03993 Andrew L.

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Explosion

JUNE 18, 2021

We’ve been running Explosion for about five years now, which has given us a lot of insights into what Natural Language Processing looks like in industry contexts. Like most of the world, I spent even more time indoors in 2020 than I usually do. All of this can lead to higher revenue , or save the company a bunch of money.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

The recent history of PBAs begins in 1999, when NVIDIA released its first product expressly marketed as a GPU, designed to accelerate computer graphics and image processing. In 2018, other forms of PBAs became available, and by 2020, PBAs were being widely used for parallel problems, such as training of NN.

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Explosion

DECEMBER 14, 2021

Create better access to health with machine learning and natural language processing. The idea evolved throughout 2020 until we built the very first version of Healthsea with spaCy which also became the topic of my bachelor thesis. Hello everyone, my name is Edward!

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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. Namely, the way GPU-rich use HFRL is categorically beyond the rich of GPU-poor.

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Unite.AI

AUGUST 8, 2023

Information Retrieval: Using LLMs, such as BERT or GPT, as part of larger architectures to develop systems that can fetch and categorize information. GPT-3, launched in May 2020 had 96 layers, 96 attention heads, and a massive parameter count of 175 billion. However, relying on a single attention mechanism can be limiting.

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AWS Machine Learning Blog

OCTOBER 11, 2024

Content categorization – Metadata can provide information about the content or category of a document, such as the subject matter, domain, or topic. These identifiers can be used to uniquely reference and retrieve specific documents from the vector data store. data # Assing local directory path to a python variable local_data_path = ". .

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