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LangChain’s String Evaluators: How to Assess Language Model Output

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An In-depth Look into Evaluating AI Outputs, Custom Criteria, and the Integration of Constitutional Principles Photo by Markus Winkler on Unsplash Introduction In the age of conversational AI, chatbots, and advanced natural language processing, the need for systematic evaluation of language models has never been more pronounced.

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Evaluating RAG Pipelines: Practical Insights with ragas

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This post is designed for those with a technical background in natural language processing and AI, offering detailed guidance on optimizing and evaluating RAG pipelines for improved performance. These help individuals to cultivate the wisdom and virtue necessary to live in harmony with nature and their own rational nature.

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Image-to-Text Conversion for Performance Rating of an E-commerce Product

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Problem Statement In this article, we will use OCR to fetch useful pieces of information from an image of customer reviews from Amazon. This model uses the VADER module of the NLTK (Natural Language Toolkit) library. We’ll then use this information, along with various machine learning concepts, to determine the rating of a product.

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Choosing the Right Prompt for Language Models: A Key to Task-Specific Performance

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Photo by Gabriel Heinzer on Unsplash Language models have revolutionized natural language processing, but their generic nature often falls short when applied to specific tasks. Choosing between domain-specific and general-purpose prompts depends on the nature of the task and the available resources.

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Accessing GLUE datasets with the Hugging Face API

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Image from Hugging Face Hub Introduction Most natural language processing models are built to address a particular problem, such as responding to inquiries regarding a specific area. This task analyzes a pair of statements to determine whether or not they are semantically similar to one another. QQP : The Quora Question Pairs dataset.