Add task category and links to paper, project page, and code

README.md

@@ -1,4 +1,7 @@
 ---
+license: cc-by-4.0
+task_categories:
+- other
 configs:
 - config_name: candidate
   data_files:
@@ -40,19 +43,17 @@ configs:
   data_files:
   - split: train
     path: mm_emb/emb_86_3584_parquet/**/*.parquet
-license: cc-by-4.0
 ---
 
 # TencentGR-1M Dataset
 
-TAAC2025 Preliminary Round Dataset(2025年腾讯广告算法大赛初赛数据集) TencentGR-1M Dataset is a large-scale, all-modality dataset designed specifically for generative recommendation (GR) in industrial advertising. Constructed from real, de-identified Tencent Ads logs, it aims to address the lack of realistic, public multi-modal datasets in the GR field.
-
-- Data Features: Contains rich collaborative IDs and multi-modal representations (text and vision) extracted using state-of-the-art embedding models.
-- Dataset Size: Provides 1 million user sequences, with each user sequence containing up to 100 interacted items.
-- Labels: Each interaction within the sequence is explicitly labeled with **exposure(0)** and **click(1)** signals.
-
+[**Paper**](https://huggingface.co/papers/2604.04976) | [**Project Page**](https://algo.qq.com/2025) | [**Code**](https://github.com/TencentAdvertisingAlgorithmCompetition/baseline_2025)
 
+TAAC2025 Preliminary Round Dataset (2025年腾讯广告算法大赛初赛数据集) TencentGR-1M Dataset is a large-scale, all-modality dataset designed specifically for generative recommendation (GR) in industrial advertising. Constructed from real, de-identified Tencent Ads logs, it aims to address the lack of realistic, public multi-modal datasets in the GR field.
 
+- **Data Features:** Contains rich collaborative IDs and multi-modal representations (text and vision) extracted using state-of-the-art embedding models.
+- **Dataset Size:** Provides 1 million user sequences, with each user sequence containing up to 100 interacted items.
+- **Labels:** Each interaction within the sequence is explicitly labeled with **exposure(0)** and **click(1)** signals.
 
 ## Dataset Structure
 
@@ -85,13 +86,12 @@ All data files are stored in **Snappy-compressed Parquet** format.
 
 For clarity and brevity, we provide detailed schema descriptions for each table below. 
 
-Need to notice that we use two types of IDs in the dataset: the original IDs and the remapped IDs, for simplicity, we will denote them as OID and RID. OIDs are used in `mm_emb`, and RIDs are used in all the training data and can be used for building models. The mapping between OIDs and RIDs can be found in the `indexer.pkl` file. 
+Note that we use two types of IDs in the dataset: the original IDs (OID) and the remapped IDs (RID). OIDs are used in `mm_emb`, and RIDs are used in all the training data and can be used for building models. The mapping between OIDs and RIDs can be found in the `indexer.pkl` file. 
 
 #### `item_feat`
 
 The `item_feat` table contains the features of each item appeared in the `seq` set.
 
-<!-- 15 x 3 table: -->
 | **Field** | **Type** | **Description** | \# Non-None Values |
 |:---:|:---:|:---:|:---:|
 | `item_id` | int64 | RID for each item. |4783154 |
@@ -139,11 +139,6 @@ The `seq` table contains the behavior sequence for each user.
 
 The `candidate` table contains the candidate items for the competition.
 
-Note: 
-
-- This `candidate` is for the competition, but we do not provide the ground truth labels. People may refer to this format to build their own candidate set.
-- The `candidate` contains some items that are not in the `seq`.
-
 | **Field** | **Type** | **Description** | \# Non-None Values |
 |:---:|:---:|:---:|:---:|
 | `item_id` | int64 | OID for each item. | 660000 |
@@ -164,7 +159,7 @@ Note:
 
 #### `mm_emb`
 
-The `mm_emb` tables contain the multimodal embeddings for each item. There are 6 different embedding dimensions(`[32, 1024, 3584, 4096, 3584, 3584]`) for 6 different embeddings(`[81, 82, 83, 84, 85, 86]`) placed in 6 files.
+The `mm_emb` tables contain the multimodal embeddings for each item. There are 6 different embedding dimensions(`[32, 1024, 3584, 4096, 3584, 3584]`) for 6 different embeddings(`[81, 82, 83, 84, 85, 86]`).
 
 Take the `81` embedding as an example:
 
@@ -175,39 +170,7 @@ Take the `81` embedding as an example:
 
 #### `indexer.pkl`
 
-This is a remapping file that maps the original IDs/Values to the remapped IDs/Values. The format is a dictionary with the following structure:
-
-```json
-{
-    "u": 
-        { 
-            OID: RID, 
-            ... 
-        },
-    "i": 
-        { 
-            OID: RID, 
-            ... 
-        },
-    "f":
-        {
-            101: 
-                {
-                    10100000000: 1, // original value: remapped value
-                    10100000001: 2, 
-                    ...
-                },
-            102:
-                {
-                    1020000000: 1,
-                    1020000001: 2,
-                    ...
-                },
-            ...
-        }
-}
-```
-
+This is a remapping file that maps the original IDs/Values to the remapped IDs/Values.
 
 ## Usage