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README.md

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+---
+configs:
+  - config_name: candidate
+    data_files:
+      - split: train
+        path: "candidate/**/*.parquet"
+  - config_name: item_feat
+    data_files:
+      - split: train
+        path: "item_feat/**/*.parquet"
+  - config_name: seq
+    data_files:
+      - split: train
+        path: "seq/**/*.parquet"
+  - config_name: user_feat
+    data_files:
+      - split: train
+        path: "user_feat/**/*.parquet"
+  - config_name: mm_emb_81_32
+    data_files:
+      - split: train
+        path: "mm_emb/emb_81_32_parquet/**/*.parquet"
+  - config_name: mm_emb_82_1024
+    data_files:
+      - split: train
+        path: "mm_emb/emb_82_1024_parquet/**/*.parquet"
+  - config_name: mm_emb_83_3584
+    data_files:
+      - split: train
+        path: "mm_emb/emb_83_3584_parquet/**/*.parquet"
+  - config_name: mm_emb_84_4096
+    data_files:
+      - split: train
+        path: "mm_emb/emb_84_4096_parquet/**/*.parquet"
+  - config_name: mm_emb_85_3584
+    data_files:
+      - split: train
+        path: "mm_emb/emb_85_3584_parquet/**/*.parquet"
+  - config_name: mm_emb_86_3584
+    data_files:
+      - split: train
+        path: "mm_emb/emb_86_3584_parquet/**/*.parquet"
+---
+
+# 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.
+
+
+
+
+## Dataset Structure
+
+### Overview
+
+| Config Name | Path | Files | Approx. Size | Description |
+|---|---|---|---|---|
+| `candidate` | `candidate/` | 1 | ~22 MB | Candidate item set |
+| `item_feat` | `item_feat/` | 10 | ~104 MB | Item features |
+| `seq` | `seq/` | 10 | ~881 MB | User behavior sequences |
+| `user_feat` | `user_feat/` | 10 | ~8.4 MB | User features |
+| `mm_emb_81_32` | `mm_emb/emb_81_32_parquet/` | 5 | ~901 MB | Multimodal embedding (dim=32) |
+| `mm_emb_82_1024` | `mm_emb/emb_82_1024_parquet/` | 80 | ~9.4 GB | Multimodal embedding (dim=1024) |
+| `mm_emb_83_3584` | `mm_emb/emb_83_3584_parquet/` | 109 | ~31 GB | Multimodal embedding (dim=3584) |
+| `mm_emb_84_4096` | `mm_emb/emb_84_4096_parquet/` | 199 | ~30 GB | Multimodal embedding (dim=4096) |
+| `mm_emb_85_3584` | `mm_emb/emb_85_3584_parquet/` | 199 | ~31 GB | Multimodal embedding (dim=3584) |
+| `mm_emb_86_3584` | `mm_emb/emb_86_3584_parquet/` | 199 | ~26 GB | Multimodal embedding (dim=3584) |
+
+### Additional Files
+
+| File | Size | Description |
+|---|---|---|
+| `indexer.pkl` | ~142 MB | Index mapping file (From original ID to remapped ID) |
+
+### Data Format
+
+All data files are stored in **Snappy-compressed Parquet** format.
+
+### Schema
+
+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. 
+
+#### `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 |
+| `100` | int64 | An encrypted feature. | 4779045 |
+| `101` | int64 | An encrypted feature. | 4779045 |
+| `102` | int64 | An encrypted feature. | 4735917 |
+| `112` | int64 | An encrypted feature. | 4701740 |
+| `114` | int64 | An encrypted feature. | 4778327 |
+| `115` | int64 | An encrypted feature. | 1531415 |
+| `116` | int64 | An encrypted feature. | 4778146 |
+| `117` | int64 | An encrypted feature. | 4701740 |
+| `118` | int64 | An encrypted feature. | 4700703 |
+| `119` | int64 | An encrypted feature. | 4699894 |
+| `120` | int64 | An encrypted feature. | 4694982 |
+| `121` | int64 | An encrypted feature. | 4783154 |
+| `122` | int64 | An encrypted feature. | 4779045 |
+
+
+#### `user_feat`
+
+The `user_feat` table contains the features of each user appeared in the dataset.
+
+| **Field** | **Type** | **Description** | \# Non-None Values |
+|:---:|:---:|:---:|:---:|
+| `user_id` | int64 | RID for each user. | 1001845 |
+| `103` | int64 | An encrypted feature. | 1000964 |
+| `104` | int64 | An encrypted feature.         | 998043 |
+| `105` | int64 | An encrypted feature.         | 859602 |
+| `106` | List\[int64\] | An encrypted feature. | 880754 |
+| `107` | List\[int64\] | An encrypted feature. | 387686 |
+| `108` | List\[int64\] | An encrypted feature. | 170678 |
+| `109` | int64 | An encrypted feature.         | 1001467 |
+| `110` | List\[int64\] | An encrypted feature. | 430598 |
+
+#### `seq`
+
+The `seq` table contains the behavior sequence for each user.
+
+| **Field** | **Type** | **Description** | \# Non-None Values |
+|:---:|:---:|:---:|:---:|
+| `user_id` | int64 | RID for each user. | 1001845 |
+| `seq` | List\[Dict\] | The behavior sequence for each user, each dict contains 3 keys: `item_id`(RID), `action_type`, and `timestamp`, where the values are all integers | 1001845 |
+
+#### `candidate`
+
+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 |
+|:---:|:---:|:---:|:---:|
+| `creative_id` | int64 | OID for each item. | 660000 |
+| `reid_creative_id` | int64 | RID for each item. | 511029 |
+| `retrieval_id` | int64 | The remapped ID for faiss retrieval (Start from 0). | 660000 |
+| `100` | Dict\[ "cold\_start": int64, "feature_value": string \] | An encrypted feature. | 659206 |
+| `101` | Dict\[ "cold\_start": int64, "feature_value": string \] | An encrypted feature. | 659206 |
+| `102` | Dict\[ "cold\_start": int64, "feature_value": string \] | An encrypted feature. | 653852 |
+| `112` | Dict\[ "cold\_start": int64, "feature_value": string \] | An encrypted feature. | 654893 |
+| `114` | Dict\[ "cold\_start": int64, "feature_value": string \] | An encrypted feature. | 659093 |
+| `115` | Dict\[ "cold\_start": int64, "feature_value": string \] | An encrypted feature. | 195552 |
+| `116` | Dict\[ "cold\_start": int64, "feature_value": string \] | An encrypted feature. | 659090 |
+| `117` | Dict\[ "cold\_start": int64, "feature_value": string \] | An encrypted feature. | 654893 |
+| `118` | Dict\[ "cold\_start": int64, "feature_value": string \] | An encrypted feature. | 654886 |
+| `119` | Dict\[ "cold\_start": int64, "feature_value": string \] | An encrypted feature. | 654882 |
+| `120` | Dict\[ "cold\_start": int64, "feature_value": string \] | An encrypted feature. | 654870 |
+| `121` | Dict\[ "cold\_start": int64, "feature_value": string \] | An encrypted feature. | 660000 |
+| `122` | Dict\[ "cold\_start": int64, "feature_value": string \] | An encrypted feature. | 659206 |
+
+#### `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.
+
+Take the `81` embedding as an example:
+
+| **Field** | **Type** | **Description** | \# Non-None Values |
+|:---:|:---:|:---:|:---:|
+| `anonymous_cid` | string | OID for each item. | 4742961 |
+| `emb` | List\[ double \] | Embedding for each item. | 4742961 |
+
+#### `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,
+                    ...
+                },
+            ...
+        }
+}
+```
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load a specific config
+ds = load_dataset("TAAC2025/TencentGR-1M", name="candidate", split="train")
+
+# Load item features
+ds_item = load_dataset("TAAC2025/TencentGR-1M", name="item_feat", split="train")
+
+# Load user behavior sequences
+ds_seq = load_dataset("TAAC2025/TencentGR-1M", name="seq", split="train")
+
+# Load user features
+ds_user = load_dataset("TAAC2025/TencentGR-1M", name="user_feat", split="train")
+
+# Load multimodal embeddings
+ds_emb = load_dataset("TAAC2025/TencentGR-1M", name="mm_emb_81_32", split="train")
+```

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