yakshithk/t5-small-baseline

📌 Model Description

This model is a fine-tuned version of google/t5-small trained on a custom text-to-text dataset (derived from cleaned news articles). The objective was to build a baseline encoder-decoder model that can transform raw article text into simplified outputs (summaries / cleaned text).

• Architecture: T5-small (60M parameters)
• Task: Text-to-Text Generation (text2text-generation)
• Framework: Hugging Face Transformers (PyTorch backend)
• Training Logs: Tracked with Weights & Biases (W&B)

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🛠 Intended Uses & Limitations

✅ Intended uses

• Text simplification
• News article summarization
• As a baseline for experimenting with encoder-decoder fine-tuning

⚠️ Limitations

• Small model size → may hallucinate or underperform on complex tasks
• Not suitable for production-level summarization without further fine-tuning
• Dataset size was limited (baseline experiment), so generalization may be weak

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📊 Training and Evaluation

Dataset

The model was fine-tuned on a custom dataset of news articles scraped and cleaned in [Week 1’s pipeline](link to repo).

• Input: Raw article text
• Output: Processed / summarized text

👉 Future versions will be trained on larger, more diverse datasets.

Hyperparameters

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• gradient_accumulation_steps: 2
• optimizer: adamw_torch_fused
• num_epochs: 20
• mixed_precision_training: Native AMP

Results

Epoch	Validation Loss
1	9.7087
5	4.3784
10	2.3668
15	1.9624
20	1.8684

• Final Validation Loss: 1.8684
• Qualitative evaluation: outputs were shorter, cleaner summaries compared to baseline t5-small.

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🚀 How to Use

from transformers import pipeline

model = "yakshithk/t5-small-baseline"
summarizer = pipeline("text2text-generation", model=model)

input_text = "The quick brown fox jumped over the lazy dog."
output = summarizer(input_text, max_length=50, do_sample=False)

print(output[0]["generated_text"])

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🔍 Future Work

• Fine-tune larger models (flan-t5-base, flan-t5-large)
• Train on domain-specific datasets (finance, healthcare)
• Deploy via Hugging Face Spaces for live demo

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📚 Citation

If you use this model in your research or project:

@misc{yakshithk_t5small_baseline,
  author = {Yakshith K},
  title = {t5-small-baseline: Fine-tuned T5-small on custom dataset},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/yakshithk/t5-small-baseline}},
}

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