Update README.md

README.md

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----
-license: openrail
----
+---
+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)