Remove Categorization Remove Data Quality Remove Natural Language Processing
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Decoding the DNA of Large Language Models: A Comprehensive Survey on Datasets, Challenges, and Future Directions

Marktechpost

Developing and refining Large Language Models (LLMs) has become a focal point of cutting-edge research in the rapidly evolving field of artificial intelligence, particularly in natural language processing. A significant innovation in this domain is creating a specialized tool to refine the dataset compilation process.

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Training Improved Text Embeddings with Large Language Models

Unite.AI

They serve as a core building block in many natural language processing (NLP) applications today, including information retrieval, question answering, semantic search and more. With further research intoprompt engineering and synthetic data quality, this methodology could greatly advance multilingual text embeddings.

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Build a classification pipeline with Amazon Comprehend custom classification (Part I)

AWS Machine Learning Blog

Amazon Comprehend is a natural-language processing (NLP) service that uses machine learning to uncover valuable insights and connections in text. Knowledge management – Categorizing documents in a systematic way helps to organize an organization’s knowledge base. This allows for better monitoring and auditing.

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Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction

Towards AI

If you want an overview of the Machine Learning Process, it can be categorized into 3 wide buckets: Collection of Data: Collection of Relevant data is key for building a Machine learning model. It isn't easy to collect a good amount of quality data. How Machine Learning Works?

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Building Domain-Specific Custom LLM Models: Harnessing the Power of Open Source Foundation Models

Towards AI

Challenges of building custom LLMs Building custom Large Language Models (LLMs) presents an array of challenges to organizations that can be broadly categorized under data, technical, ethical, and resource-related issues. Ensuring data quality during collection is also important.

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5 Key Open-Source Datasets for Named Entity Recognition

Becoming Human

In this article, we’ll talk about what named entity recognition is and why it holds such an integral position in the world of natural language processing. Introduction about NER Named entity recognition (NER) is a fundamental aspect of natural language processing (NLP). Disadvantages 1.Data

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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

As a first step, they wanted to transcribe voice calls and analyze those interactions to determine primary call drivers, including issues, topics, sentiment, average handle time (AHT) breakdowns, and develop additional natural language processing (NLP)-based analytics.