Update README.md to include details about ViVQA-X LSTM-Generative model, usage instructions, and relevant metadata

README.md

@@ -1,3 +1,56 @@
 ---
 license: apache-2.0
+language: vi
+library_name: pytorch
+tags:
+- visual-question-answering
+- text-generation
+- image-to-text
+- vietnamese
+- vivqa-x
+datasets:
+- VLAI-AIVN/ViVQA-X
 ---
+
+# ViVQA-X: LSTM-Generative Baseline Model
+
+This repository contains the `LSTM-Generative` baseline model from the research paper **"An Automated Pipeline for Constructing a Vietnamese VQA-NLE Dataset"**. The model is designed for Visual Question Answering with Natural Language Explanations (VQA-NLE) in Vietnamese.
+
+Given an image and a question in Vietnamese, it predicts an answer and generates a sentence explaining its reasoning.
+
+**Main Project Repository:** [duongtruongbinh/ViVQA-X](https://github.com/duongtruongbinh/ViVQA-X)
+
+---
+
+## 🚀 How to Use
+
+This model is a custom PyTorch checkpoint and requires the model class definition from the main project. The intended use is to download the checkpoint and load it using the project's source code.
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+# You need to have the model definition file, for example: from src.models.baseline_model.vivqax_model import ViVQAX_Model
+
+# Download checkpoint from Hub
+checkpoint_path = hf_hub_download(
+    repo_id="VLAI-AIVN/ViVQA-X_LSTM-Generative",  # <-- REPLACE WITH YOUR MODEL REPO NAME
+    filename="best_model.pth"                 # Checkpoint file name
+)
+
+# Load checkpoint
+checkpoint = torch.load(checkpoint_path, map_location='cpu')
+
+# Initialize model with structure and vocab from checkpoint
+# (This is an example, you need to adjust it to your code)
+# model = ViVQAX_Model(
+#     vocab_size=len(checkpoint['word2idx']),
+#     embed_size=checkpoint['config']['model']['embed_size'],
+#     # ... other parameters
+# )
+
+# Load trained weights
+# model.load_state_dict(checkpoint['model_state_dict'])
+# model.eval()
+
+print("Model loaded successfully!")
+# Now you can use the model to predict