Upload README.md

README.md

@@ -2,13 +2,14 @@
 
 ## Overview
 
-This is an image-text pair dataset constructed within the REEVLAUATE project, built upon the **REEVLAUATE KG ArtKB**. The dataset is designed for developing and evaluating information retrieval systems within the REEVLAUATE framework.
+This is an image-text pair dataset constructed for the **Knowledge-Enhanced Multimodal Retrieval System**, built upon the **REEVLAUATE KG ArtKB**. 
+The dataset is designed for training and evaluating the CLIP model for the retrieval system.
 
 ## Data Source
 
 The ArtKB knowledge base combines data from two primary sources:
-- **Wikidata**: Structured information from the Wikidata knowledge base
-- **Partner Collaborations**: Domain-specific data contributed by project partners
+- **Wikidata**
+- **Pilot Museums**
 
 ## Dataset Structure
 
@@ -56,15 +57,11 @@ Each sample in the Parquet files contains the following columns:
 |--------|------|-------------|
 | `image` | string | Relative path to the image file |
 | `uuid` | string | Unique identifier for the artwork |
-| `object_type` | string | Type of the cultural object (e.g., "Painting", "Sculpture") |
-| `content` | list[string] | 5 content descriptions generated by BLIP2 |
-| `metadata` | list[string] | 5 metadata descriptions constructed from KG fields |
-| `hybrid_o1` | list[string] | 5 descriptions combining metadata + content |
-| `hybrid_o2` | list[string] | 5 descriptions combining content + metadata |
-
+| `query_text` | string | User query-like text |
+| `target_text` | list[string] | Description text corresponding to the specific image including visual content and metadata information |
 ## Text Generation Methods
 
-### 1. Metadata Descriptions
+### 1. Metadata Portion
 The **metadata** descriptions are constructed by combining multiple metadata fields from the ArtKB knowledge base using different templates. Each template produces a different textual representation of the same metadata information. This results in 5 distinct variants that capture the same facts in different phrasings.
 
 **Example fields used:**
@@ -74,30 +71,20 @@ The **metadata** descriptions are constructed by combining multiple metadata fie
 - Dimensions
 - Current location/Museum
 - Object type and classification
+- ...
 
-### 2. Content Descriptions
+### 2. Content Portion
 The **content** descriptions are generated automatically using the **Salesforce/BLIP2-OPT-2.7B** vision-language model. These descriptions capture visual characteristics of the artwork observed directly from the image, such as composition, colors, subjects, and visual elements.
 
 **Model**: `Salesforce/blip2-opt-2.7b`
 
-### 3. Hybrid Descriptions (Order 1 - hybrid_o1)
-The **hybrid_o1** descriptions are created by concatenating metadata descriptions with content descriptions:
+### 3. Description Texts 
+The **description text** descriptions are created by concatenating content portion with metadata protion:
 
 ```
-[Metadata Description] + [Content Description]
+[Content Portion] + [Metadata Portion]
 ```
 
-This approach creates a unified description that combines structured knowledge (metadata) with visual observations (content), resulting in 5 variants.
-
-### 4. Hybrid Descriptions (Order 2 - hybrid_o2)
-The **hybrid_o2** descriptions follow the reverse concatenation order:
-
-```
-[Content Description] + [Metadata Description]
-```
-
-This alternative ordering explores how different sequencing of information affects downstream information retrieval tasks.
-
 ## Usage
 
 The dataset can be loaded and used with the Hugging Face `datasets` library:
@@ -117,10 +104,8 @@ test_set = load_dataset('xuemduan/reevaluate-image-text-pairs', split='test')
 for sample in train_set:
     image_path = sample['image']
     uuid = sample['uuid']
-    content_variants = sample['content']  # 5 variants
-    metadata_variants = sample['metadata']  # 5 variants
-    hybrid_o1_variants = sample['hybrid_o1']  # 5 variants
-    hybrid_o2_variants = sample['hybrid_o2']  # 5 variants
+    query_texts = sample['query_text']  
+    description_text = sample['target_txt']  
 ```
 
 ## Citation
@@ -129,7 +114,7 @@ If you use this dataset in your research, please cite this dataset.
 
 ## License
 
-TBD
+MIT
 
 ## Contact