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Dheerajsahi/
hf-summarization-pipeline

Name: hf-summarization-pipeline
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Summarization Pipeline Project

This project demonstrates how to use the Hugging Face Pipeline API to load a pre-trained summarization model and generate summaries from example text.

What Was Done

Model Selection: Used facebook/bart-large-cnn, a pre-trained BART (Bidirectional and Auto-Regressive Transformers) model that was fine-tuned on the CNN/DailyMail dataset for abstractive summarization.
Pipeline API: Leveraged Hugging Face's pipeline API, which provides a simple interface for:
- Loading pre-trained models
- Handling tokenization automatically
- Running inference with minimal code
Examples: Tested the model on multiple example texts covering different topics:
- Artificial intelligence and machine learning
- Climate change
- The Renaissance period
- A longer article-style text
Configuration: Used parameters to control summary length:
- max_length: Maximum number of tokens in the summary
- min_length: Minimum number of tokens in the summary
- do_sample: Set to False for deterministic (greedy) decoding

Files

summarization_pipeline.py: Main script demonstrating the pipeline API
README.md: This file

Usage

Installation

pip install transformers torch

Running the Scripts

Option 1: Simple version (recommended, avoids dependency issues)

python simple_summarization.py

Option 2: Pipeline API version

python summarization_pipeline.py

Note: May have dependency issues in some environments. See SETUP_GUIDE.md

Option 3: Using Inference API

bash

pip install requests
python summarization_api.py

Using in Your Own Code

python

from transformers import pipeline

# Load the summarization pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

# Summarize text
text = "Your long article or text here..."
summary = summarizer(text, max_length=100, min_length=30)
print(summary[0]['summary_text'])

Model Information

Model: facebook/bart-large-cnn
Task: Abstractive Summarization
Training Data: CNN/DailyMail dataset
Architecture: BART (Bidirectional and Auto-Regressive Transformers)

Alternative Models

You can easily switch to other pre-trained summarization models:

google/pegasus-xsum: Trained on XSum dataset
t5-small: General-purpose T5 model
sshleifer/distilbart-cnn-12-6: Smaller, faster version

Simply change the model parameter in the pipeline:

summarizer = pipeline("summarization", model="google/pegasus-xsum")

Notes

The model requires significant memory (the large BART model is ~1.6GB)
For GPU acceleration, change device=-1 to device=0 in the pipeline
The first run will download the model, which may take some time

Dheerajsahi/hf-summarization-pipeline