Categories: AI/ML Research

Text Summarization with DistillBart Model

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.
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Recent Posts

Some recent Chroma renders

Model: https://huggingface.co/silveroxides/Chroma-GGUF/blob/main/chroma-unlocked-v38-detail-calibrated/chroma-unlocked-v38-detail-calibrated-Q8_0.gguf Workflow: https://huggingface.co/lodestones/Chroma/resolve/main/simple_workflow.json Prompts used: High detail photo showing an abandoned Renaissance painter’s studio…

45 mins ago

A Gentle Introduction to Multi-Head Latent Attention (MLA)

This post is divided into three parts; they are: • Low-Rank Approximation of Matrices •…

45 mins ago

Converting Pandas DataFrames to PyTorch DataLoaders for Custom Deep Learning Model Training

Pandas DataFrames are powerful and versatile data manipulation and analysis tools.

45 mins ago

Securing America’s Defense Industrial Base

Palantir FedStart and the Path to CMMC ComplianceSecuring the Defense Industrial BaseNever has the imperative…

46 mins ago

No-code data preparation for time series forecasting using Amazon SageMaker Canvas

Time series forecasting helps businesses predict future trends based on historical data patterns, whether it’s…

46 mins ago

Beyond static AI: MIT’s new framework lets models teach themselves

MIT researchers developed SEAL, a framework that lets language models continuously learn new knowledge and…

2 hours ago