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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) |