---
language: en
license: apache-2.0
library_name: transformers
pipeline_tag: summarization
tags:
- scihigh-2026
- research-highlight-generation
- scientific-summarization
- text2text-generation
- seq2seq
- bart
base_model: facebook/bart-large-cnn
---

# Origin Task 1 — BART-large-CNN

This is a full-parameter fine-tuned checkpoint of `facebook/bart-large-cnn` for **SciHigh-2026 Task 1**. It generates concise research highlights from scientific paper abstracts.

## Model Details
- **Parameters:** 406M trainable
- **Framework:** Transformers 4.57.6, PyTorch 2.13.0+cu130
- **Training Data:** 10,000 examples (SciHigh Task 1 MixSub-SciHigh split)
- **Hyperparameters:** 3 epochs, lr `3e-5`, batch size 32, max input/target lengths 1024/320, bfloat16, SDPA attention.

## Validation Results (1,985 examples)
| Metric | Score |
|---|---|
| ROUGE-1 | 0.3808 |
| ROUGE-2 | 0.1403 |
| ROUGE-L / Lsum | 0.2503 / 0.2505 |
| METEOR | 0.3114 |
| BERTScore F1 | 0.8721 |

## Inference

```python
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_id = "uthayamurthy/origin-task1-bart-large"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForSeq2SeqLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    attn_implementation="sdpa",
).to("cuda").eval()

abstract = "..."
inputs = tokenizer(abstract, return_tensors="pt", truncation=True, max_length=1024)
inputs = {name: value.to("cuda") for name, value in inputs.items()}

with torch.inference_mode():
    generated = model.generate(
        **inputs,
        max_length=128,
        min_length=0,
        num_beams=4,
        length_penalty=1.0,
        no_repeat_ngram_size=3,
        early_stopping=True,
    )
print(tokenizer.decode(generated[0], skip_special_tokens=True))
```