Update README.md

README.md

@@ -134,64 +134,42 @@ Below are an example row from metadata-train.parquet.
 |:---|:---|:---|:---|:---|:---|:---|:---|:---|
 |images/train/train_1/1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp|001324-0093|a magical forest that separates the good world from the dark world, fantasy art by greg rutkowski, loish, rhads, ferdinand knab, makoto shinkai and lois van baarle, ilya kuvshinov, rossdraws, tom bagshaw, global illumination, radiant light, detailed and intricate environment|Outdoor Scenes|9|3|6|6|6|
 
-## Dataset Creation
+## Loading ImageRewardDB
 
-### Curation Rationale
+You can use the Hugging Face [Datasets](https://huggingface.co/docs/datasets/quickstart) library to easily load the ImageRewardDB.
+As we mentioned before, we provide four subsets in the scales of 1k, 2k, 4k, and 8k. You can load them using as following:
+```python
+from datasets import load_dataset
 
-[More Information Needed]
+# Load the 1K-scale dataset
+dataset = load_dataset("wuyuchen/ImageRewardDB", "1k")
 
-### Source Data
+# Load the 2K-scale dataset
+dataset = load_dataset("wuyuchen/ImageRewardDB", "2k")
 
-#### Initial Data Collection and Normalization
+# Load the 4K-scale dataset
+dataset = load_dataset("wuyuchen/ImageRewardDB", "4K")
 
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
+# Load the 8K-scale dataset
+dataset = load_dataset("wuyuchen/ImageRewardDB", "8k")
+```
 
 ## Additional Information
 
-### Dataset Curators
-
-[More Information Needed]
-
 ### Licensing Information
 
-[More Information Needed]
+The ImageRewardDB dataset is available under the [Apache license 2.0](https://www.apache.org/licenses/LICENSE-2.0.html). 
+The Python code in this repository is available under the [MIT License](https://github.com/poloclub/diffusiondb/blob/main/LICENSE).
 
 ### Citation Information
 
-[More Information Needed]
-
-### Contributions
-
-[More Information Needed]
+```
+@misc{xu2023imagereward,
+      title={ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation}, 
+      author={Jiazheng Xu and Xiao Liu and Yuchen Wu and Yuxuan Tong and Qinkai Li and Ming Ding and Jie Tang and Yuxiao Dong},
+      year={2023},
+      eprint={2304.05977},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```