Best of NLP — August 2024

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Article
Machine Learning Mastery·2y
7 Machine Learning Projects That Can Add Value to Any Resume
Master essential ML skills by working on advanced projects like automatic image captioning, speech recognition, stock price forecasting, and reinforcement learning. Dive into fine-tuning models like Stable Diffusion XL and Llama 3, and building multi-step AI agents. These projects will help you handle complex neural network architectures and diverse datasets, making your resume more attractive to recruiters.
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Article
Machine Learning Mastery·2y
Free Tools Every ML Beginner Should Use
Starting in the machine learning field can be challenging, but several free tools can ease the process for beginners. Essential tools include Jupyter Notebook for creating and sharing documents with code and visuals, Hugging Face for Natural Language Processing (NLP) and large language models, LangChain for developing context-aware AI applications, Scikit-learn for implementing machine learning algorithms in Python, and Kaggle for accessing datasets and participating in competitions. Leveraging these tools can make the learning experience more interactive and efficient.
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Article
KDnuggets·2y
10 Free Resources to Learn LLMs
Large Language Models (LLMs) are pivotal in the current AI landscape, essential for various data-centric roles. This guide provides 10 free resources from organizations like Deeplearning.AI, Microsoft, and AWS to help you learn about LLMs. These include video tutorials, full courses, and practical guides covering topics from basic LLM concepts to advanced tasks like fine-tuning and deployment. Various resources cater to beginners as well as those with some prior knowledge in AI and NLP.
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4
Article
Machine Learning Mastery·2y
Everything You Need to Know About the Hugging Face Model Hub and Community
Hugging Face has revolutionized the machine learning landscape by creating a platform, the Hugging Face Hub, which allows easy access to models, tools, and datasets. The Hub is integral for hosting and sharing machine learning models for tasks like image classification and question-answering. The Model Hub features repositories for models, complete with version control, discussion threads, and inferential APIs. Additionally, the Hugging Face Community offers educational resources, forums, Discord chat, and a GitHub repository for collaborative work and learning.
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Article
Machine Learning News·2y
Top Artificial Intelligence (AI) Hallucination Detection Tools
Large Language Models (LLMs) often produce inaccurate information, known as hallucinations, which pose risks in industries like healthcare and finance. Tools like Pythia, Galileo, Cleanlab, Guardrail AI, and FacTool help detect and mitigate these hallucinations, ensuring the reliability of AI outputs. These tools leverage advanced techniques such as knowledge graphs, real-time monitoring, and customizable filters to enhance AI model accuracy and compliance. Additionally, benchmarks like TruthfulQA and FACTOR assess the factual correctness of AI systems across various domains, highlighting the importance of reliable AI applications.
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Article
GoPenAI·2y
RAG in Action: Enhancing AI with Real-Time Data Retrieval
Retrieval-Augmented Generation (RAG) enhances AI by combining real-time data retrieval with generative models, improving accuracy and relevance of responses. It integrates information retrieval and language generation to dynamically access and use up-to-date data, making AI outputs more precise and contextually appropriate. RAG's scalability and ability to use vast, current datasets make it versatile across various sectors such as customer support, healthcare, legal research, and more. The architecture consists of a retriever to find relevant documents and a generator to produce final responses.
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7
Article
Nordic APIs·2y
7 Ways to Test LLMs
Large language models (LLMs) have become essential tools for many organizations, but they have shortcomings, particularly in consistent performance and reliability. To address this, various methods and standards have been developed to test LLMs, including BERTScore, ROUGE, BLEU, MMLU, GLUE, G-Eval, and HELM. Each has its strengths and weaknesses, offering different approaches to measure the efficacy of these models. This overview provides a primer on these metrics, aiding organizations in selecting appropriate evaluation criteria for their LLM applications.
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Article
KDnuggets·2y
Building a Recommendation System with Hugging Face Transformers
Learn to build a recommendation system using Hugging Face Transformers. This guide walks through the essential steps, from setting up the environment and processing the dataset to using embeddings and cosine similarity for accurate recommendations. It also highlights using the sentence-transformers package for transforming text into numerical vectors.
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Article
Medium·2y
Beyond Fine-Tuning: Merging Specialized LLMs Without the Data Burden
The post discusses innovative methods to combine specialized large language models (LLMs) without requiring extensive datasets and intensive fine-tuning. By leveraging different model merging techniques, such as Linear Mode Connectivity, SLERP, task vectors, and evolutionary optimization, researchers can create robust models by combining pre-fine-tuned models. These approaches reduce computational costs and enhance the model's generalization across multiple tasks. Tools like WEBUI and MergeKit facilitate these merging processes, providing efficient implementations for various hardware configurations.
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Daily Dose of Data Science | Avi Chawla | Substack·2y
The Evolution of Embeddings
The post discusses the evolution of embeddings in natural language processing. It explores the shift from static embeddings like Glove and Word2Vec to contextualized embeddings powered by Transformer models such as BERT, DistilBERT, and ALBERT. The latter can generate context-aware representations, addressing limitations where a word's meaning changes based on context. Examples and comparisons illustrate how these models capture word semantics and syntactics more effectively.
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Article
KDnuggets·2y
Cleaning and Preprocessing Text Data in Pandas for NLP Tasks
This guide provides a comprehensive step-by-step process for cleaning and preprocessing text data using pandas for NLP tasks. It covers handling missing values, normalizing text, removing noise, tokenizing, removing stopwords, stemming, and converting text into numerical representations, preparing your data for use in language models.
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Article
Ardan Labs·2y
Categorizing Data with Large Language Models in Rust
LibreQoS is an open-source project for monitoring and ensuring quality-of-experience for ISPs by tracking individual data flows. To make the data understandable, ASN (Autonomous System Numbers) needs categorization, which is automated using Rust and large language models (LLMs). The post explains how to obtain ASN data, load and deduplicate it, set up a local LLM, and categorize data using context scraped from associated websites. The process involves using crates like Serde, CSV, Itertools, Reqwest, and Scraper for efficient data handling, and leveraging Tokio for parallel processing to speed up the categorization task.
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Article
Machine Learning News·2y
MLPs vs KANs: Evaluating Performance in Machine Learning, Computer Vision, NLP, and Symbolic Tasks
Multi-layer perceptrons (MLPs) and Kolmogorov-Arnold Networks (KANs) were compared across diverse domains, including machine learning, computer vision, and natural language processing. The study found that MLPs generally outperformed KANs in most tasks, particularly in audio and text classification, and computer vision. However, KANs showed superior performance in representing symbolic formulas. Both network types were tested with varied configurations and activation functions under controlled conditions to offer a balanced assessment. The research provides insights for future neural network architecture improvements.
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Article
Towards AI·2y
Build Product Knowledge Graph using LLM
A Product Knowledge Graph organizes and connects details about products to improve user experiences in e-commerce. This tutorial explains how to use the Zepto product catalog and LLM-based named entity recognition to build a Product Knowledge Graph. Leveraging frameworks like Tensorlake's Indexify, it addresses the cold-start problem and effectively extracts product attributes. The system identifies new brands, checks for duplicates, and updates the database for better catalog management.
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Article
GoPenAI·2y
Building a Database-Driven Chatbot with LangChain and OpenAI: A Practical Approach (Part 1, Warm-up)
The post provides a step-by-step guide to building a database-driven chatbot using LangChain and OpenAI. It covers setting up the project, initializing necessary APIs, and creating a basic LangChain application. Key aspects include generating SQL queries from natural language inputs, connecting to an SQLite database, and parsing query outputs for execution. By the end, readers will have a basic chatbot capable of aiding airline ground staff in tracking passenger baggage, with a promise of more advanced features in future sections.
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Machine Learning News·2y
Jina AI Introduced ‘Late Chunking’: A Simple AI Approach to Embed Short Chunks by Leveraging the Power of Long-Context Embedding Models
Retrieval-augmented generation (RAG) involves breaking large documents into smaller text chunks for efficient information retrieval using embedding models. The release of jina-embeddings-v2-base-en, an open-source model with 8K context length, highlighted practical limitations in handling long documents. Late Chunking, a new approach, addresses these issues by applying the transformer layer to the whole text first, preserving contextual information and improving retrieval efficiency. Tests showed that late chunking outperforms traditional methods, especially for longer texts.
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Machine Learning News·2y
Nvidia AI Released Llama-Minitron 3.1 4B: A New Language Model Built by Pruning and Distilling Llama 3.1 8B
Nvidia has released the Llama-3.1-Minitron 4B, a smaller and more efficient version of the Llama-3.1 8B language model, by using pruning and knowledge distillation techniques. This model offers high performance with reduced computational resources and excels in various benchmarks for reasoning, coding, and math. It is optimized for deployment with Nvidia's TensorRT-LLM toolkit, enhancing its inference performance and efficiency, making it a viable option for resource-constrained environments.
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Towards Data Science·2y
A visual explanation of LLM hyperparameters
Understanding LLM hyperparameters like temperature, Top-k, Top-p, frequency, and presence penalties is essential for effective prompt engineering. Temperature controls output randomness; Top-k sampling limits next-word choices to the top probabilities, while Top-p focuses on cumulative probabilities. Frequency and presence penalties prevent repetition to promote diversity in model responses.
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GoPenAI·2y
Steps to Fine-Tune a Llama-3–8B Model Using LLaMA Factory
Fine-tuning large language models (LLMs) like Llama-3–8B using LLaMA Factory involves several steps including data collection, preprocessing, setting up the environment in Google Colab, and running the fine-tuning process. LLaMA Factory offers tools for supervised fine-tuning, policy optimization, and reward modeling, supporting over 100 datasets and 50 different LLMs. With easy-to-use features for model evaluation and deployment, it empowers both beginners and experts to efficiently customize models for specific tasks.
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KDnuggets·2y
5 Tips for Getting Started with Language Models
Language Models (LMs) have reshaped NLP and AI. Beginners should grasp foundational concepts like NLP basics, probability and statistics, embeddings, and transformer architecture. Practical steps include learning tools like Hugging Face, PyTorch, and TensorFlow, exploring quality datasets, and starting with simple tasks like sentiment analysis. Pre-trained LMs from Hugging Face can save time and resources for various language tasks.
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GoPenAI·2y
The Future of RAG will be with Vision: End to End Example with ColPali and a Vision Language Model
The post explores the concept of Retrieval-Augmented Generation (RAG) and its application in enterprise settings. It highlights the benefits and challenges of traditional text-based RAG and introduces Vision Language Models (VLMs) as a more effective solution. The post provides a detailed end-to-end example using the ColPali model for document retrieval and GPT-4o-mini for answer generation, emphasizing the advantages of integrating vision capabilities into RAG to handle complex document layouts and multimodal information.
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Machine Learning News·2y
FocusLLM: A Scalable AI Framework for Efficient Long-Context Processing in Language Models
FocusLLM, developed by researchers from Tsinghua and Xiamen Universities, is designed to extend the context length for language models. It processes long texts by dividing them into chunks and uses parallel decoding to extract and integrate relevant information efficiently. This approach enables handling texts up to 400K tokens with reduced computational costs. FocusLLM outperforms other methods in long-text comprehension tasks while maintaining low perplexity and high training efficiency, making it a valuable solution for long-context applications.
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Article
Eli Bendersky·2y
SentencePiece BPE Tokenizer in Go
This post discusses the development of go-sentencepiece, a pure Go implementation of the SentencePiece tokenizer, which is used in Google AI's models like Gemma and Gemini. Unlike the C++ and Python bindings, go-sentencepiece doesn't require a C compiler. It focuses on BPE tokenization and only supports encoding & decoding, not the training phase. The implementation leverages advanced algorithms, which significantly improve performance. A protobuf file configures the tokenizer, and an online demo is available for testing.
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Machine Learning News·2y
Transformer Explainer: An Innovative Web-Based Tool for Interactive Learning and Visualization of Complex AI Models for Non-Experts
Transformers are revolutionizing AI, especially in natural language processing and machine learning, but their complexity poses a learning barrier. Georgia Tech and IBM Research have introduced Transformer Explainer, an accessible, web-based tool for understanding AI models. This open-source platform allows users to interact with a live GPT-2 model, visualize processes via Sankey diagrams, and adjust parameters in real-time without needing specialized hardware or software, thereby making it easier for non-experts to learn about Transformers.
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Machine Learning News·2y
RAGate: Enhancing Conversational AI with Adaptive Knowledge Retrieval
The rapid advancement of Large Language Models (LLMs) has greatly improved conversational systems, but they still face issues such as outdated knowledge and non-factual content. RAGate is proposed as an adaptive solution to enhance conversational AI by selectively augmenting responses with external knowledge based on context and human judgments. This approach aims to make responses more accurate, reliable, and contextually appropriate, utilizing variants like RAGate-Prompt, RAGate-PEFT, and RAGate-MHA. Extensive experiments demonstrate that RAGate improves response quality and reduces hallucinated outputs.
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