Update README.md

README.md

@@ -3,7 +3,10 @@ configs:
 - config_name: default
   data_files:
   - split: train
-    path: "images.zip"
+    path:
+    - "MATVB_train.json"
+    - "MATVB_query_nl.json"
+    - "MATVB_query_caption.json"
 task_categories:
 - image-to-text
 - text-retrieval
@@ -24,7 +27,7 @@ size_categories:
 
 🏆 **Best Resource Paper Award** — *2025 ACM/IEEE Joint Conference on Digital Libraries (JCDL)*
 
-Official Hugging Face dataset repository for **MAT-VB**, a benchmark built on Wikipedia math images designed to advance research in multi-modal mathematical tasks, including visual-text retrieval and math image captioning.
+Official Hugging Face repository for **MAT-VB**, a multimodal benchmark constructed from Wikipedia mathematical content designed to evaluate mathematical image captioning and text-to-image retrieval models.
 
 ---
 
@@ -35,26 +38,63 @@ Official Hugging Face dataset repository for **MAT-VB**, a benchmark built on Wi
 
 ---
 
-## 📁 Dataset Contents
+## 📁 Dataset Files Overview
 
-MAT-VB provides a curated collection of mathematical visual data from Wikipedia for evaluating multimodal AI models:
+| File Name | Format | Description |
+| :--- | :--- | :--- |
+| `images.zip` | Archive (`.zip`) | Contains all ~4,000 Wikipedia math images (216 MB uncompressed). |
+| `MATVB_train.json` | JSON | Training annotations. Maps `image_id` (key) to its ground-truth `caption` (value). |
+| `MATVB_query_nl.json` | JSON | Natural language query set for text-to-image search (human-authored intent queries). |
+| `MATVB_query_caption.json` | JSON | Caption query set for text-to-image search (original Wikipedia captions). |
+| `MATVB_qrel.qrel` | TREC format | Evaluation relevance file mapping query IDs to target image IDs. |
 
-* **`images.zip`**: Contains all ~4,000 Wikipedia math image files (216 MB uncompressed archive).
-* **Metadata & Annotations**: Includes descriptive captions, query-image mappings, and split indexes for training and evaluation pipelines.
+---
+
+## 🎯 Task Definitions & Splits
+
+### 1. Mathematical Image Captioning
+* **Training Set:** Use all entries in `MATVB_train.json` (3,600 image-caption pairs).
+* **Test Set:** Any image in `images.zip` whose ID is **not** present in `MATVB_train.json` serves as a test sample (400 test images). Models generate captions for these test images during evaluation.
+
+### 2. Text-to-Image Retrieval
+The benchmark provides 400 test evaluation queries under two modalities sharing identical query IDs:
+* **Natural Language Queries (`MATVB_query_nl.json`):** Human-authored search queries representing intent-based user searches.
+* **Caption Queries (`MATVB_query_caption.json`):** Queries built using original, detailed Wikipedia captions.
+* **Ground Truth (`MATVB_qrel.qrel`):** Use standard TREC evaluation tools (e.g., `trec_eval`) to score retrieval runs against this relevance file.
 
 ---
 
-## 🚀 Usage with Python `datasets`
+## 🚀 How to Load and Use
 
-Hugging Face automatically extracts and parses `images.zip` when loading through the `datasets` library:
+You can download and parse the dataset files directly using `huggingface_hub` and standard Python libraries:
 
 ```python
-from datasets import load_dataset
+import json
+import zipfile
+from huggingface_hub import hf_hub_download
+
+# 1. Download dataset files
+repo_id = "AIIRLab/MATVB"
+
+train_path = hf_hub_download(repo_id=repo_id, filename="MATVB_train.json", repo_type="dataset")
+query_nl_path = hf_hub_download(repo_id=repo_id, filename="MATVB_query_nl.json", repo_type="dataset")
+query_cap_path = hf_hub_download(repo_id=repo_id, filename="MATVB_query_caption.json", repo_type="dataset")
+qrel_path = hf_hub_download(repo_id=repo_id, filename="MATVB_qrel.qrel", repo_type="dataset")
+images_zip_path = hf_hub_download(repo_id=repo_id, filename="images.zip", repo_type="dataset")
+
+# 2. Extract image archive
+with zipfile.ZipFile(images_zip_path, 'r') as zip_ref:
+    zip_ref.extractall("./images")
+
+# 3. Load JSON annotations
+with open(train_path, "r") as f:
+    train_data = json.load(f)  # dict: {image_id: caption}
+
+with open(query_nl_path, "r") as f:
+    queries_nl = json.load(f)  # dict: {query_id: query_text}
 
-# Load the dataset directly
-dataset = load_dataset("AIIRLab/MATVB")
+with open(query_cap_path, "r") as f:
+    queries_caption = json.load(f)  # dict: {query_id: query_text}
 
-# Access a sample
-example = dataset["train"][0]
-image = example["image"]
-caption = example.get("caption", "")
+print(f"Loaded {len(train_data)} training samples.")
+print(f"Loaded {len(queries_nl)} NL retrieval queries.")