This post is in two parts; they are: • Understanding the Encoder-Decoder Architecture • Evaluating the Result of Summarization using ROUGE DistilBart is a “distilled” version of the BART model, a powerful sequence-to-sequence model for natural language generation, translation, and comprehension.
Investment professionals face the mounting challenge of processing vast amounts of data to make timely, informed decisions. The traditional approach of manually sifting through countless research documents, industry reports, and financial statements is not only time-consuming but can also lead to missed opportunities and incomplete analysis. This challenge is particularly acute in credit markets, where …
GenLayer is betting that AI-driven contracts, enforced on the blockchain, will be the foundation for a trillion-dollar marketplace.Read More
The acting inspector general says the Office of Personnel Management is investigating whether any “emerging threats” have arisen as Elon Musk’s DOGE works to rapidly transform government systems.
AI models often rely on “spurious correlations,” making decisions based on unimportant and potentially misleading information. Researchers have now discovered these learned spurious correlations can be traced to a very small subset of the training data and have demonstrated a technique that overcomes the problem.
Surreal September was more than just a challenge—it was about elevating your AI art skills through the imaginative world of surrealism. We provided structured subthemes and a detailed workbook to teach actual surreal design principles, guiding participants through an iterative creative process similar to a condensed AI art course. Surrealism made for the perfect theme because it …
Read more “Surreal September: Celebrating Our Winners and Highlights”
Gen AI is not just another technology layer; it has the potential to eat the entire software industry from the inside-out.Read More
From beach days to board meetings, these top totes are designed to protect your valuables, and then some.
This tutorial is in two parts; they are: • Using DistilBart for Summarization • Improving the Summarization Process Let’s start with a fundamental implementation that demonstrates the key concepts of text summarization with DistilBart: import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM class TextSummarizer: def __init__(self, model_name=”sshleifer/distilbart-cnn-12-6″): “””Initialize the summarizer with a pre-trained model.
Those clicks and pops aren’t supposed to be there! Give your music a bath with this handy guide.