---
base_model: facebook/bart-large
library_name: transformers
pipeline_tag: summarization
language:
- en
tags:
- base_model:facebook/bart-large
- transformers
- bart
- scihigh-2026
- task2
---

# Fine-Tuned BART-Large for SciHigh 2026 (Task 2)

This repository contains a fine-tuned **BART-Large** model trained specifically for the **SciHigh 2026 (Subtask 2)** competition to automatically generate concise and accurate scientific titles given research paper abstracts.

## Model Details

- **Developed by:** Vino1502
- **Model Type:** Sequence-to-Sequence (Seq2Seq) Transformer
- **Language:** English
- **Base Model:** `facebook/bart-large`
- **Task:** Scientific Abstract-to-Title Generation (SciHigh 2026 - Subtask 2)

## Uses

### Direct Use
This model is intended for scientific title generation. Given a research paper abstract as input, the model generates a concise scientific title summarizing the abstract's core findings.

## How to Get Started with the Model

You can load and run inference directly with this model using the following code:

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

# Configuration
MODEL_REPO_ID = "Vino1502/scihigh-2026-task2-bart"

# Load Tokenizer & Model directly from HF Hub
tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO_ID)
model = AutoModelForSeq2SeqLM.from_pretrained(
    MODEL_REPO_ID,
    dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto"
)
model.eval()

# Sample Inference
abstract_text = "Your scientific abstract goes here..."

# Tokenize and place tensors on the model's device
inputs = tokenizer(
    abstract_text, 
    return_tensors="pt", 
    max_length=512, 
    truncation=True
).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=64,
        num_beams=2,
        early_stopping=True
    )

predicted_title = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Predicted Title:", predicted_title)