Update README.md

README.md

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-# Model Card for SVLM
-
-This model is a Seq2Seq Language Model (SVLM) fine-tuned to answer questions from the ACL research paper dataset. It generates responses related to academic research questions, making it useful for research and academic inquiry.
+---
+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 Description
-
-- **Developed by:** @binarybardakshat
-- **Model type:** Seq2Seq Language Model (BART-based)
-- **Language(s) (NLP):** English
-- **License:** [More Information Needed]
-- **Finetuned from model:** facebook/bart-base
-
-### Model Sources
-
-- **Repository:** [More Information Needed]
-
-## Uses
-
-### Direct Use
-
-This model can be directly used to answer questions based on research data from ACL papers. It is suitable for academic and research purposes.
-
-### Out-of-Scope Use
+- **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.
 
-The model may not work well for general conversation or non-research-related queries.
+## Usage
 
-## Bias, Risks, and Limitations
-
-The model may carry biases present in the training data, which consists of ACL research papers. It might not generalize well outside this domain.
-
-### Recommendations
-
-Users should be cautious of biases and ensure that outputs align with their academic requirements.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model:
+To use this model with the Hugging Face Interface API:
 
 ```python
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("path_to_your_tokenizer")
-model = AutoModelForSeq2SeqLM.from_pretrained("path_to_your_model")
-````
-## Training Details
-
-### Training Data
-
-The model was trained using the ACL dataset, which consists of research papers focused on computational linguistics.
-
-### Training Procedure
-
-#### Training Hyperparameters
-
-- **Training regime:** fp32
-- **Learning rate:** 2e-5
-- **Epochs:** 3
-- **Batch size:** 8
-
-## Evaluation
-
-### Testing Data
-
-The model was evaluated on a subset of the ACL dataset, focusing on research-related questions.
-
-### Metrics
-
-- **Accuracy**
-- **Loss**
-
-### Results
-
-The model performs best in research-related question-answering tasks. Further evaluation metrics will be added as the model is used more widely.
-
-## Environmental Impact
-
-- **Hardware Type:** GPU (NVIDIA V100)
-- **Hours used:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications
-
-### Model Architecture and Objective
-
-The model is based on BART architecture, designed to perform sequence-to-sequence tasks like text summarization and translation.
+from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
 
-### Compute Infrastructure
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Binarybardakshat/SVLM")
+model = TFAutoModelForSeq2SeqLM.from_pretrained("Binarybardakshat/SVLM")
 
-#### Hardware
+# Example input
+input_text = "What is the main contribution of the paper titled 'Your Paper Title'?"
 
-- **NVIDIA V100 GPU**
+# Tokenize input
+inputs = tokenizer(input_text, return_tensors="tf", padding=True, truncation=True)
 
-#### Software
+# 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)
 
-- **TensorFlow**
-- **Transformers**
-- **Safetensors**
+print("Answer:", answer)