Upload ImageRewardDB.py with huggingface_hub

ImageRewardDB.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-# TODO: Address all TODOs and remove all explanatory comments
+
 """TODO: Add a description here."""
 
 
@@ -23,31 +23,33 @@ import datasets
 from huggingface_hub import hf_hub_url
 
 
-# TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.
-},
-year={2020}
+@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}
 }
 """
 
-# TODO: Add description of the dataset here
 # You can copy an official description
 _DESCRIPTION = """\
-This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+We systematically identify the challenges for text-to-image human preference annotation, and \
+consequently design a pipeline tailored for it, establishing criteria for quantitative assessment and \
+annotator training, optimizing labeling experience, and ensuring quality validation. We build this \
+text-to-image comparison dataset, ImageRewardDB, for training the ImageReward model based on the pipeline.\
+The ImageRewarDB covers both the rating and ranking components, collecting a dataset of 137k expert \
+comparisons to date.
 """
 
-# TODO: Add a link to an official homepage for the dataset here
 _HOMEPAGE = "https://huggingface.co/datasets/wuyuchen/ImageRewardDB"
 _VERSION = datasets.Version("1.0.0")
 
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
+_LICENSE = "apache-2.0"
 
-# TODO: Add link to the official dataset URLs here
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _REPO_ID = "wuyuchen/ImageRewardDB"
@@ -84,9 +86,8 @@ class ImageRewardDBConfig(datasets.BuilderConfig):
         super(ImageRewardDBConfig, self).__init__(version=_VERSION, **kwargs)
         self.part_ids = part_ids
 
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
 class ImageRewardDB(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
+    """A dataset of 137k expert comparisons to date, demonstrating the text-to-image human preference."""
 
     # This is an example of a dataset with multiple configurations.
     # If you don't want/need to define several sub-sets in your dataset,
@@ -99,6 +100,7 @@ class ImageRewardDB(datasets.GeneratorBasedBuilder):
     # You will be able to load one or the other configurations in the following list with
     # data = datasets.load_dataset('my_dataset', 'first_domain')
     # data = datasets.load_dataset('my_dataset', 'second_domain')
+    
     BUILDER_CONFIGS = []
     
     for num_k in [1,2,4,8]:
@@ -114,7 +116,6 @@ class ImageRewardDB(datasets.GeneratorBasedBuilder):
     DEFAULT_CONFIG_NAME = "8k"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
         features = datasets.Features(
             {
                 "image": datasets.Image(),
@@ -145,7 +146,6 @@ class ImageRewardDB(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
         # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
 
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
@@ -195,7 +195,6 @@ class ImageRewardDB(datasets.GeneratorBasedBuilder):
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, split, data_dirs, json_paths, metadata_path):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
         
         num_data_dirs = len(data_dirs)