This post is divided into five parts; they are: • Recommendation Systems • Cross-Lingual Applications • Text Classification • Zero-Shot Classification • Visualizing Text Embeddings A simple recommendation system can be created by finding a few of the most similar items to the target item. Source link

Nowadays, everyone across AI and related communities talks about generative AI models, particularly the large language models (LLMs) behind widespread applications like ChatGPT, as if they have completely taken over the field of machine learning. Source link

Retrieval augmented generation (RAG) encompasses a family of systems that extend conventional language models , large and otherwise (LLMs), to incorporate context based on retrieved knowledge from a document base, thereby leading to more truthful and relevant responses being generated upon user queries. Source link

This post is divided into three parts; they are: • Fine-tuning DistilBERT for Custom Q&A • Dataset and Preprocessing • Running the Training The simplest way to use a model in the transformers library is to create a pipeline, which hides many details about how to interact with it. Source link

Organizations increasingly adopt machine learning solutions into their daily operations and long-term strategies, and, as a result, the need for effective standards for deploying and maintaining machine learning systems has become critical. Source link

Clustering is a widely applied method in many domains like customer and image segmentation, image recognition, bioinformatics, and anomaly detection, all to group data into clusters in terms of similarity. Source link

This post is divided into three parts; they are: • What Is Auto Classes • How to Use Auto Classes • Limitations of the Auto Classes There is no class called “AutoClass” in the transformers library. Source link