klotz: classification* + llm*

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  1. Firecrawl introduces pdf-inspector, a high-performance Rust library designed for rapid PDF classification, text extraction, and Markdown conversion. By sampling content streams to quickly distinguish between text-based and scanned documents, the tool enables intelligent routing that bypasses costly OCR services for standard PDFs. It delivers position-aware text extraction, automated table and column detection, and robust encoding handling while maintaining a lightweight footprint with no external ML dependencies or model training requirements.

    - Provides bindings for Python, Node.js, and browser WebAssembly environments.
    - Achieves sub-200ms processing times on large corpora while outperforming several established local parsers in reading order and table accuracy.
    - Features per-page OCR routing suggestions to optimize mixed-format document workflows.
    - Handles complex layouts including RTL text, multi-column newspapers, and CID-encoded fonts.
    - Released under the MIT license with active community contributions and CI/CD automation.
  2. This tutorial demonstrates how to implement an intelligent routing layer using NadirClaw to optimize Large Language Model (LLM) costs. The system classifies prompts into simple or complex tiers locally before selecting the most appropriate model, such as switching between Gemini Flash and Pro versions. It covers installation, local classification testing via CLI, visualizing decision boundaries through centroid-based similarity scores, running a proxy server for live routing, and calculating estimated cost savings compared to using high-end models exclusively.
    2026-05-11 Tags: , , , by klotz
  3. A practical pipeline for classifying messy free-text data into meaningful categories using a locally hosted LLM, no labeled training data required.
  4. The article discusses using Large Language Model (LLM) embeddings as features in traditional machine learning models built with scikit-learn. It covers the process of generating embeddings from text data using models like Sentence Transformers, and how these embeddings can be combined with existing features to improve model performance. It details practical steps including loading data, creating embeddings, and integrating them into a scikit-learn pipeline for tasks like classification.
  5. This page details the topic namers available in Turftopic, allowing automated assignment of human-readable names to topics. It covers Large Language Models (local and OpenAI), N-gram patterns, and provides API references for the `TopicNamer`, `LLMTopicNamer`, `OpenAITopicNamer`, and `NgramTopicNamer` classes.
  6. Python tutorial for reproducible labeling of cutting-edge topic models with GPT4-o-mini. The article details training a FASTopic model and labeling its results using GPT-4.0 mini, emphasizing reproducibility and control over the labeling process.
  7. This article provides a comprehensive guide on the basics of BERT (Bidirectional Encoder Representations from Transformers) models. It covers the architecture, use cases, and practical implementations, helping readers understand how to leverage BERT for natural language processing tasks.
  8. A tutorial on using LLM for text classification, addressing common challenges and providing practical tips to improve accuracy and usability.
  9. Replace traditional NLP approaches with prompt engineering and Large Language Models (LLMs) for Jira ticket text classification. A code sample walkthrough.
  10. A study investigating whether format restrictions like JSON or XML impact the performance of large language models (LLMs) in tasks like reasoning and domain knowledge comprehension.

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