---
license: openrail
datasets:
- Binarybardakshat/SVLM-ACL-DATASET
language:
- en
library_name: transformers
---
# SVLM: A Question-Answering Model for ACL Research Papers

This model, `SVLM`, is designed to answer questions based on research papers from the ACL dataset. It leverages the BART architecture to generate precise answers from scientific abstracts.

## Model Details

- **Model Architecture:** BART (Bidirectional and Auto-Regressive Transformers)
- **Framework:** TensorFlow
- **Dataset:** [Binarybardakshat/SVLM-ACL-DATASET](https://huggingface.co/datasets/Binarybardakshat/SVLM-ACL-DATASET)
- **Author:** @binarybardakshat (Akshat Shukla)
- **Purpose:** The model is trained to provide answers to questions from the ACL research paper dataset.

## Usage

To use this model with the Hugging Face Interface API:

```python
from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("binarybardakshat/SVLM")
model = TFAutoModelForSeq2SeqLM.from_pretrained("binarybardakshat/SVLM")

# Example input
input_text = "What is the main contribution of the paper titled 'Your Paper Title'?"

# Tokenize input
inputs = tokenizer(input_text, return_tensors="tf", padding=True, truncation=True)

# Generate answer
outputs = model.generate(inputs.input_ids, max_length=50, num_beams=5, early_stopping=True)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Answer:", answer)