sandeepsaga/Transcript_Summerizer

Fine-Tuned Pegasus Transcript Summarizer

Model Details

Model Description

This model is a fine-tuned version of google/pegasus-large optimized for text summarization, specifically designed for generating concise and structured summaries from spoken transcripts. It captures key takeaways, removes filler content, and retains core semantic coherence.

• Developed by: Sandeep Ganga
• Model type: Transformer-based sequence-to-sequence summarization model
• Language(s): English
• License: Apache 2.0
• Finetuned from model: google/pegasus-large

Model Sources

• Repository: https://huggingface.co/sandeepsaga/Transcript_Summerizer

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Uses

Direct Use

• Meeting and lecture transcript summarization
• Podcast and interview summaries
• Long-form unstructured text condensor

Downstream Use

The model can be fine-tuned further for:

• Domain-specific summarization (e.g., medical, legal, educational transcripts)
• Integration into AI-powered note-taking tools

Out-of-Scope Use

• Generating fictional or creative stories
• Processing highly degraded or unsegmented audio transcripts
• Producing legally binding or medical summaries without human review

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Bias, Risks, and Limitations

• Dataset Bias: The model may reflect domain biases present in the fine-tuning transcript dataset.
• Loss of Context: Highly complex or lengthy transcripts may experience minor context compression.
• Accuracy: Generated output should be validated for mission-critical applications to avoid hallucinations.

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How to Get Started with the Model

Use the code below to run inference with PyTorch:

from transformers import PegasusForConditionalGeneration, PegasusTokenizer

model_id = "sandeepsaga/Transcript_Summerizer"

tokenizer = PegasusTokenizer.from_pretrained(model_id)
model = PegasusForConditionalGeneration.from_pretrained(model_id)

def summarize_text(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding="longest")
    summary_ids = model.generate(**inputs, max_length=150, min_length=30, length_penalty=2.0)
    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)

# Example usage
sample_transcript = "The team met to review quarterly progress. Overall performance met expectations..."
print(summarize_text(sample_transcript))