ReadyTensorCertification/bart-highlightsum-merged

HuggingFace Model Card — BART-HighlightSum (Merged Model)

BART-HighlightSum (Merged Model)

Fine-tuned BART-Large on the HighlightSum dialogue summarization dataset (Merged LoRA → Full Model)

Model type: Seq2Seq Summarization
Base model: facebook/bart-large-cnn
Dataset: HighlightSum (dialogue summarization)
Finetuning method: LoRA → merged into full FP16 BART

Model Summary

This model is a merged BART-Large fine-tuned on 2,000 training + 200 validation samples from the HighlightSum dataset. It produces concise, accurate summaries of multi-turn dialogues.

✔ LoRA fine-tuning
✔ LoRA weights merged into base BART
✔ No PEFT required for inference
✔ Lightweight, fast, and deployment-ready

This version is recommended for production, as it scores highest among all variants (Baseline, LoRA, Merged).

Performance/Evaluation Results

Evaluation on HighlightSum (Validation 200 samples)
The following results were obtained using 200 validation samples from the HighlightSum dataset.

Merged Model Performance

Metric	Score
ROUGE-1	0.383
ROUGE-2	0.179
ROUGE-L	0.301
BERTScore (F1)	0.335
BLEU	0.0014

Comparison with Baseline and LoRA Models

Metric	Baseline BART	LoRA Model	Merged Model
ROUGE-1	0.275	0.337	0.383
ROUGE-2	0.090	0.152	0.179
ROUGE-L	0.204	0.252	0.301
BERTScore (F1)	0.163	0.298	0.335
BLEU	0.0052	0.0111	0.0014

Conclusion

The merged model performs best, achieving the highest ROUGE-1, ROUGE-2, ROUGE-L and BERTScore among all variants. It is therefore the recommended model for deployment, inference, and user-facing applications.

🧪 Example Input / Output

Using Example #1 from the HighlightSum dataset:

Dialogue

A: What are you getting him?
B: Something cool.
A: What about a Lego?
B: He is too old for that now.
A: What about a book?
B: He hates reading.
A: Then I give up. I have no idea what to get him.

Human Gold Summary

They discuss gift ideas for someone's son.

Merged Model Summary

They talk about what to get a boy as a gift but can't decide.

→ The model captures the intent, context, and key meaning with improved fluency and coherence.

Intended Use

Suitable for

• Dialogue summarization
• Customer service chat compression
• Meeting note extraction
• Educational tools

Not suitable for

• Factual QA
• Domain-specific technical summaries without fine-tuning
• Safety-critical use

How to Use

Python Inference

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "dlaima/bart-highlightsum-merged"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

text = """A: Hi Tom, are you busy tomorrow afternoon?
B: I think I am. Why?
A: I want to go to the animal shelter.
B: For what?
A: I'm getting a puppy for my son."""

inputs = tokenizer(text, return_tensors="pt", truncation=True)
summary = model.generate(**inputs, max_new_tokens=192)
print(tokenizer.decode(summary[0], skip_special_tokens=True))

Training Details

• Method: LoRA (rank 8)
• Model: BART-Large
• Batch size: 8 (micro-batch 4 × grad-accumulation 2)
• Epochs: ~2.4 (capped by 2000 examples)
• Max input length: 768 tokens
• Max summary length: 192 tokens
• Precision: FP16
• Optimizer: AdamW
• Learning rate: 3e-4
• Hardware: NVIDIA T4

📚 Dataset: HighlightSum

A dataset of dialogue → summary pairs from multiple conversational sources.

• Multi-turn dialogues
• Short, medium, or long
• Realistic conversational structure
• Human-written summaries

Subset used here:

• 2,000 samples for training
• 200 samples for validation

Files Included in This Repo

File	Description
pytorch_model.bin	Final merged FP16 BART model
config.json	Standard HuggingFace config
generation_config.json	Beam search config
tokenizer.json / tokenizer.model	Tokenizer files
README.md	This model card

Limitations & Recommendations

Limitations

• May shorten overly long dialogues excessively
• Not designed for domain-specific jargon
• Occasionally omits rare names or details
• Not a factual QA model
• Can hallucinate minor details in complex dialogues

Recommendations

• Use merged model for production
• Apply additional fine-tuning for domain-specific tasks
• For 100% reproducibility, fix random seeds and HF transformers version
• Consider quantization (INT8 or GGUF) for mobile deployment

Maintenance

This model will be updated as:

• Additional training data becomes available
• Larger LoRA variants are tested
• Better merging & evaluation pipelines are developed

Contact

For questions, improvements, or collaboration, feel free to reach out via GitHub or HuggingFace (@dlaima).