ssarathi/Text_Summarizer_Using_Transformers

Text Summarization Model

Model Overview

This is a text summarization model built using a Seq2Seq architecture.
It was trained on the CNN/DailyMail dataset (3.0.0) and is capable of generating concise summaries of news articles or other long-form texts.

Intended Use:

• Summarizing articles, documents, or reports.
• Extracting key points from text for quick understanding.

Limitations & Biases:

• May struggle with extremely long articles or highly technical content.
• Generated summaries may occasionally miss nuanced details.

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Training Details

• Dataset: CNN/DailyMail (3.0.0 version)
• Preprocessing: Truncation at 512 tokens for input, summaries capped at 150 tokens.
• Hyperparameters:
  • Optimizer: AdamW (PyTorch)
  • Learning rate: 2e-5
  • Batch size: 4 (per device)
  • Epochs: 10
• Evaluation Metrics: ROUGE-1, ROUGE-2, ROUGE-L

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Evaluation Results

Metric	Score (%)
ROUGE-1	83.3
ROUGE-2	60.0
ROUGE-L	83.3
ROUGE-Lsum	83.3

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Example Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("your-username/your-model-name")
model = AutoModelForSeq2SeqLM.from_pretrained("your-username/your-model-name")

text = "The stock market saw a significant drop today due to rising inflation concerns. Investors are cautious ahead of the Federal Reserve's upcoming decision."

inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
summary_ids = model.generate(**inputs, max_length=150, num_beams=4, early_stopping=True)

print(tokenizer.decode(summary_ids[0], skip_special_tokens=True))