Tag: nlp

This article provides a comprehensive guide to fine-tuning pre-trained Transformer models, a crucial technique for adapting large language models to specific tasks. It covers the setup process, demonstrates fine-tuning BERT using the Hugging Face Trainer, and discusses essential considerations for practical application.

This tutorial guides you through using GPT-2 with the Hugging Face Transformers library to generate creative content, offering practical steps and customization options for enhancing your creative output.

This article delves into the critical role of prompt tokens in Large Language Model (LLM) instruction tuning, exploring the impact of masking versus weighting on model performance and convergence. It analyzes the trade-offs and provides insights into optimizing fine-tuning strategies.

This tutorial guides you through building an extractive question-answering system using BERT and the Hugging Face Transformers library. Learn to set up your environment, prepare data, train a model, and generate answers to your questions.

Explore how GPT-3 and GPT-J revolutionize Named Entity Recognition (NER) by enabling advanced entity extraction without traditional data annotation and training, offering a more efficient approach for data science projects.

This tutorial demonstrates how to fine-tune NVIDIA's NV-Embed-v1 model on the Amazon Polarity dataset using LoRA and PEFT for memory-efficient adaptation, making advanced NLP tasks accessible on lower-VRAM GPUs.

This tutorial demonstrates how to deploy Hugging Face models on AWS Lambda for efficient, serverless machine learning inference. It covers setting up the environment, deploying models using container images, and leveraging Amazon EFS for caching to optimize performance and reduce latency.

Discover 10 essential Python one-liners to supercharge your Hugging Face Transformers pipelines. Learn to boost inference speed, manage memory efficiently, and enhance code robustness with simple yet powerful code snippets.

This article explores the process of fine-tuning Vision Language Models (VLMs) for improved document understanding and data extraction. It covers the motivation, advantages of VLMs over traditional OCR, dataset preparation, annotation strategies, and technical details of supervised fine-tuning (SFT). The guide emphasizes the importance of data quality, meticulous parameter tuning, and presents results demonstrating the effectiveness of fine-tuning for tasks like handwriting recognition and text extraction from images.

This tutorial details the LLM-AIx pipeline, an open-source solution for extracting structured clinical information from unstructured oncology text using privacy-preserving large language models. It requires no programming skills and runs on local infrastructure, making it accessible for clinical research and decision-making.

MIT researchers have identified the underlying mechanism of "position bias" in large language models (LLMs), a phenomenon where models overemphasize information at the beginning or end of text while neglecting the middle. This breakthrough, utilizing a novel theoretical framework, promises more accurate and reliable AI systems across various applications.

This article explores the crucial role of keywords in AI training for creative content generation. It provides a practical, instructional guide for leveraging keywords effectively to enhance AI models and produce high-quality, relevant content.