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lora-samsum-summarizer
Sasmita03/lora-samsum-summarizer
lora-samsum-summarizer is a machine learning model from Sasmita03. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Flan-T5-base fine-tuned with LoRA on the SAMSum dataset (dialogue → summary).
Access
Public
Updated Sep 19, 2026
Repo size
3.6 MB
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At a glance
- Access
- Public
- Created
- Sep 19, 2026
- Updated
- Sep 19, 2026
- SHA
- e2723328
Model card
From the Hugging Face model README
LoRA-tuned Flan-T5 for Dialogue Summarization
Flan-T5-base fine-tuned with LoRA on the SAMSum dataset (dialogue → summary).
Training details
- Base model: google/flan-t5-base
- Method: LoRA (r=8, alpha=32, dropout=0.1, targeting q and v attention projections)
- Dataset: SAMSum (800-example subset, 1 epoch)
- Trained on a free Google Colab T4 GPU
Results
Evaluated on 100 held-out validation examples, ROUGE F1 vs. the untrained base model:
| Metric | Base model | Fine-tuned | Difference |
|---|---|---|---|
| ROUGE-1 | 0.4406 | 0.4749 | +0.0343 |
| ROUGE-2 | 0.2147 | 0.2237 | +0.0090 |
| ROUGE-L | 0.3705 | 0.3887 | +0.0182 |
Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from peft import PeftModel
base_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
model = PeftModel.from_pretrained(base_model, "Sasmita03/lora-samsum-summarizer")
tokenizer = AutoTokenizer.from_pretrained("Sasmita03/lora-samsum-summarizer")
- Created
- Sep 19, 2026
- Updated
- Sep 19, 2026