Update README.md

README.md

@@ -31,7 +31,54 @@ dataset_info:
     num_examples: 59708
   download_size: 7351682183
   dataset_size: 7168446432
+task_categories:
+- image-classification
+tags:
+- art
+size_categories:
+- 100K<n<1M
 ---
-# Dataset Card for "hakatashi-pixiv-bookmark-deepdanbooru"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+The dataset for training classification model of pixiv artworks by my preference.
+
+## Schema
+
+* tag_probs: List of probabilities for each tag. Preprocessed by [RF5/danbooru-pretrained](https://github.com/RF5/danbooru-pretrained) model. The index of each probability corresponds to the index of the tag in the [class_names_6000.json](https://github.com/RF5/danbooru-pretrained/blob/master/config/class_names_6000.json) file.
+* class:
+  * not_bookmarked (0): Generated from images randomly-sampled from [animelover/danbooru2022](https://huggingface.co/datasets/animelover/danbooru2022) dataset. The images are filtered in advance to the post with pixiv source.
+  * bookmarked_public (1): Generated from publicly bookmarked images of [hakatashi](https://twitter.com/hakatashi).
+  * bookmarked_private (2): Generated from privately bookmarked images of [hakatashi](https://twitter.com/hakatashi).
+
+## Stats
+
+train:test:validation = 6:2:2
+
+* not_bookmarked (0): 202,290 images
+* bookmarked_public (1): 73,587 images
+* bookmarked_private (2): 22,659 images
+
+## Usage
+
+```
+>>> from datasets import load_dataset
+
+>>> dataset = load_dataset("hakatashi/hakatashi-pixiv-bookmark-deepdanbooru")
+>>> dataset
+DatasetDict({
+    test: Dataset({
+        features: ['tag_probs', 'class'],
+        num_rows: 59707
+    })
+    train: Dataset({
+        features: ['tag_probs', 'class'],
+        num_rows: 179121
+    })
+    validation: Dataset({
+        features: ['tag_probs', 'class'],
+        num_rows: 59708
+    })
+})
+>>> dataset['train'].features
+{'tag_probs': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None),
+ 'class': ClassLabel(names=['not_bookmarked', 'bookmarked_public', 'bookmarked_private'], id=None)}
+```