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---
license: openrail
datasets:
- Binarybardakshat/SVLM-ACL-DATASET
language:
- en
library_name: transformers
tags:
- code
---
# 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:** @binarybard (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) |