Text-to-Text Transfer Transformer Quantized Model for Earnings Calls Text Summarization

This repository hosts a quantized version of the T5 model, fine-tuned for text summarization tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.

Model Details

Model Architecture: T5
Task: Earnings Calls Text Summarization
Dataset: Hugging Face's `cnn_dailymail'
Quantization: Float16
Fine-tuning Framework: Hugging Face Transformers

Usage

Installation

pip install transformers torch

Loading the Model

from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "AventIQ-AI/t5-text-summarization-for-earnings-calls"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)

def test_summarization(model, tokenizer):
    user_text = input("\nEnter your text for summarization:\n")
    input_text = "summarize: " + user_text
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512).to(device)

    output = model.generate(
        **inputs,
        max_new_tokens=100,
        num_beams=5,
        length_penalty=0.8,
        early_stopping=True
    )

    summary = tokenizer.decode(output[0], skip_special_tokens=True)
    return summary

print("\n📝 **Model Summary:**")
print(test_summarization(model, tokenizer))

📊 ROUGE Evaluation Results

After fine-tuning the T5-Small model for text summarization, we obtained the following ROUGE scores:

Metric	Score	Meaning
ROUGE-1	0.3061 (~30%)	Measures overlap of unigrams (single words) between the reference and generated summary.
ROUGE-2	0.1241 (~12%)	Measures overlap of bigrams (two-word phrases), indicating coherence and fluency.
ROUGE-L	0.2233 (~22%)	Measures longest matching word sequences, testing sentence structure preservation.
ROUGE-Lsum	0.2620 (~26%)	Similar to ROUGE-L but optimized for summarization tasks.

Fine-Tuning Details

Dataset

The Hugging Face's cnn_dailymail dataset was used, containing the text and their summarization examples.

Training

Number of epochs: 3
Batch size: 4
Evaluation strategy: epoch
Learning rate: 3e-5

Quantization

Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.

Repository Structure

.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safetensors/   # Quantized Model
├── README.md            # Model documentation

Limitations

The model may not generalize well to domains outside the fine-tuning dataset.
Quantization may result in minor accuracy degradation compared to full-precision models.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.