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Build an automated insight extraction framework for customer feedback analysis with Amazon Bedrock and Amazon QuickSight

AWS Machine Learning Blog

Manually analyzing and categorizing large volumes of unstructured data, such as reviews, comments, and emails, is a time-consuming process prone to inconsistencies and subjectivity. The definition of our end-to-end orchestration is detailed in the GitHub repo. We provide a prompt example for feedback categorization.

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Churn prediction using multimodality of text and tabular features with Amazon SageMaker Jumpstart

AWS Machine Learning Blog

In addition to textual inputs, this model uses traditional structured data inputs such as numerical and categorical fields. We show you how to train, deploy and use a churn prediction model that has processed numerical, categorical, and textual features to make its prediction. BERT + Random Forest.

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The potential of Large Language Models for Revolutions in Healthcare

John Snow Labs

In the general language domain, there are two main branches of pre-trained language models: BERT (and its variants) and GPT (and its variants). The first one, BERT (and its variants), has received the most attention in the biomedical domain; examples include BioBERT and PubMedBERT, while the second one has received less attention.

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Evaluate the text summarization capabilities of LLMs for enhanced decision-making on AWS

AWS Machine Learning Blog

It uses BERT, a popular NLP technique, to understand the meaning and context of words in the candidate summary and reference summary. The more similar the words and meanings captured by BERT, the higher the BERTScore. It uses neural networks like BERT to measure semantic similarity beyond just exact word or phrase matching.

BERT 137
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Generative vs Predictive AI: Key Differences & Real-World Applications

Topbots

Basic Definitions Generative AI and predictive AI are two powerful types of artificial intelligence with a wide range of applications in business and beyond. Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g.,

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Introducing spaCy v3.1

Explosion

For example, you’ll be able to use the information that certain spans of text are definitely not PERSON entities, without having to provide the complete gold-standard annotations for the given example. spacy-dbpedia-spotlight Use DBpedia Spotlight to link entities ✍️ contextualSpellCheck Contextual spell correction using BERT ?

BERT 52
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Against LLM maximalism

Explosion

We want to aggregate it, link it, filter it, categorize it, generate it and correct it. That’s definitely new. Recognizing sentence boundaries in English isn’t entirely trivial (you don’t want to just use regular expressions), but it’s definitely not something you need an LLM to do. The results in Section 3.7,

LLM 135