Instructions to use devesh1011/bart-large-cnn-finetuned-news-summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use devesh1011/bart-large-cnn-finetuned-news-summarizer with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn") model = PeftModel.from_pretrained(base_model, "devesh1011/bart-large-cnn-finetuned-news-summarizer") - Notebooks
- Google Colab
- Kaggle
Model Card for News Article Summarizer
Model Details
Model Description
This is a fine-tuned text summarization model based on facebook/bart-large-cnn, optimized using LoRA (Low-Rank Adaptation) on a custom dataset of global news articles. The model is designed to generate concise, high-quality summaries from long-form news content.
- Developed by: [Devesh]
- Model type: Text Summarization
- Language(s) (NLP): English
- License: [Specify license, e.g., Apache 2.0]
- Finetuned from model: facebook/bart-large-cnn
Model Sources
- Repository: https://github.com/devesh1011/ai-news-summarizer
Uses
Direct Use
The model is suitable for summarizing news articles, reports, and other long-form textual content into concise summaries for applications like:
- News aggregation platforms
- Content summarization for journalism
- Automated report generation
- Downloads last month
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Base model
facebook/bart-large-cnn