Update README.md

README.md

@@ -141,200 +141,79 @@ pretty_name: Danbooru 2025 Metadata
 size_categories:
 - 1M<n<10M
 ---
-
 # Dataset Card for Danbooru 2025 Metadata
 
-**Current max id: `8,877,698` (Feb.18, 2025)**
+**Latest Post ID**: 8,877,698 (as of February 18, 2025)
 
-This dataset repo provides comprehensive, up-to-date metadata for the Danbooru booru site. All metadata was freshly scraped starting on January 2, 2025, resulting in more extensive tag annotations for older posts, fewer errors, and reduced occurrences of non-labelled AI-generated images in the data.
+This repository provides a comprehensive, up-to-date metadata dump for Danbooru. The metadata was freshly scraped starting January 2, 2025, featuring more extensive tag annotations for older posts, fewer errors, and fewer unlabeled AI-generated images compared to previous scrapes.
 
 ## Dataset Details
 
-**What is this?**  
-A refreshed, Parquet-formatted metadata dump of Danbooru, current as of Feb.18, 2025.
-
-
-**Why choose this over other Danbooru scrapes?**
+**Overview**
+Danbooru is a well-known imageboard focusing on anime-style artwork, hosting millions of user-submitted images with extensive tagging. This dataset offers metadata (in Parquet format) for all posts up to the specified date, including details such as tags, upload timestamps, and file properties.
 
-The dataset includes all available metadata in one place, eliminating the need to gather and merge data from multiple sources manually.
+**Key Advantages**
 
-It features more annotations and fewer untagged or mislabeled AI-generated images than older scrapes. Additionally, historical tag renames and additions are accurately reflected, ensuring easier and more reliable downstream use.
+- **Consolidated Metadata**: All available metadata is contained within this single dataset, eliminating the need to merge multiple partial scrapes.
+- **Improved Tag Accuracy**: Historical tag renames and additions are accurately reflected, reducing the potential mismatch or redundancy often found in older metadata dumps.
+- **Less AI Noise**: Compared to many legacy scrapes, the 2025 data incorporates updated annotations and filters out many unlabeled AI-generated images.
 
+## Usage
 
-## Uses
-
-The dataset can be loaded or filtered with the Huggingface `datasets` library:
+You can load and filter this dataset using the Hugging Face `datasets` library:
 
 ```python
-from datasets import Dataset, load_dataset
+from datasets import load_dataset
 
-danbooru_dataset = load_dataset("trojblue/danbooru2025-metadata", split="train")
-df = danbooru_dataset.to_pandas()
+danbooru_metadata = load_dataset("trojblue/danbooru2025-metadata", split="train")
+df = danbooru_metadata.to_pandas()
 ```
 
+This metadata can be used for research, indexing, or as a foundation for building image-based machine learning pipelines. However, please be mindful of any copyright, content, or platform-specific policies.
 
 ## Dataset Structure
 
-For better compatability, the columns are converted from danbooru jsons with minimal change:
+The metadata schema is closely aligned with Danbooru’s JSON structure, ensuring familiarity for those who have used other Danbooru scrapes. Below are the main columns:
 
 ```
-Index(['approver_id', 'bit_flags', 'created_at', 'down_score', 'fav_count',
-       'file_ext', 'file_size', 'file_url', 'has_active_children',
-       'has_children', 'has_large', 'has_visible_children', 'id',
-       'image_height', 'image_width', 'is_banned', 'is_deleted', 'is_flagged',
-       'is_pending', 'large_file_url', 'last_comment_bumped_at',
-       'last_commented_at', 'last_noted_at', 'md5', 'media_asset_created_at',
-       'media_asset_duration', 'media_asset_file_ext', 'media_asset_file_key',
-       'media_asset_file_size', 'media_asset_id', 'media_asset_image_height',
-       'media_asset_image_width', 'media_asset_is_public', 'media_asset_md5',
-       'media_asset_pixel_hash', 'media_asset_status',
-       'media_asset_updated_at', 'media_asset_variants', 'parent_id',
-       'pixiv_id', 'preview_file_url', 'rating', 'score', 'source',
-       'tag_count', 'tag_count_artist', 'tag_count_character',
-       'tag_count_copyright', 'tag_count_general', 'tag_count_meta',
-       'tag_string', 'tag_string_artist', 'tag_string_character',
-       'tag_string_copyright', 'tag_string_general', 'tag_string_meta',
-       'up_score', 'updated_at', 'uploader_id'],
-      dtype='object')
+Index([
+  'approver_id', 'bit_flags', 'created_at', 'down_score', 'fav_count',
+  'file_ext', 'file_size', 'file_url', 'has_active_children', 'has_children',
+  'has_large', 'has_visible_children', 'id', 'image_height', 'image_width',
+  'is_banned', 'is_deleted', 'is_flagged', 'is_pending', 'large_file_url',
+  'last_comment_bumped_at', 'last_commented_at', 'last_noted_at', 'md5',
+  'media_asset_created_at', 'media_asset_duration', 'media_asset_file_ext',
+  'media_asset_file_key', 'media_asset_file_size', 'media_asset_id',
+  'media_asset_image_height', 'media_asset_image_width',
+  'media_asset_is_public', 'media_asset_md5', 'media_asset_pixel_hash',
+  'media_asset_status', 'media_asset_updated_at', 'media_asset_variants',
+  'parent_id', 'pixiv_id', 'preview_file_url', 'rating', 'score', 'source',
+  'tag_count', 'tag_count_artist', 'tag_count_character',
+  'tag_count_copyright', 'tag_count_general', 'tag_count_meta', 'tag_string',
+  'tag_string_artist', 'tag_string_character', 'tag_string_copyright',
+  'tag_string_general', 'tag_string_meta', 'up_score', 'updated_at',
+  'uploader_id'
+], dtype='object')
 ```
 
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>approver_id</th>
-      <th>bit_flags</th>
-      <th>created_at</th>
-      <th>down_score</th>
-      <th>fav_count</th>
-      <th>file_ext</th>
-      <th>file_size</th>
-      <th>file_url</th>
-      <th>has_active_children</th>
-      <th>has_children</th>
-      <th>...</th>
-      <th>tag_count_meta</th>
-      <th>tag_string</th>
-      <th>tag_string_artist</th>
-      <th>tag_string_character</th>
-      <th>tag_string_copyright</th>
-      <th>tag_string_general</th>
-      <th>tag_string_meta</th>
-      <th>up_score</th>
-      <th>updated_at</th>
-      <th>uploader_id</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>NaN</td>
-      <td>0</td>
-      <td>2015-08-07T23:23:45.072-04:00</td>
-      <td>0</td>
-      <td>66</td>
-      <td>jpg</td>
-      <td>4134797</td>
-      <td>https://cdn.donmai.us/original/a1/b3/a1b3d0fa9...</td>
-      <td>False</td>
-      <td>False</td>
-      <td>...</td>
-      <td>3</td>
-      <td>1girl absurdres ass bangle bikini black_bikini...</td>
-      <td>kyouka.</td>
-      <td>marie_(splatoon)</td>
-      <td>splatoon_(series) splatoon_1</td>
-      <td>1girl ass bangle bikini black_bikini blush bra...</td>
-      <td>absurdres commentary_request highres</td>
-      <td>15</td>
-      <td>2024-06-25T15:32:44.291-04:00</td>
-      <td>420773</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>NaN</td>
-      <td>0</td>
-      <td>2008-03-05T01:52:28.194-05:00</td>
-      <td>0</td>
-      <td>7</td>
-      <td>jpg</td>
-      <td>380323</td>
-      <td>https://cdn.donmai.us/original/d6/10/d6107a13b...</td>
-      <td>False</td>
-      <td>False</td>
-      <td>...</td>
-      <td>2</td>
-      <td>1girl aqua_hair bad_id bad_pixiv_id guitar hat...</td>
-      <td>shimeko</td>
-      <td>hatsune_miku</td>
-      <td>vocaloid</td>
-      <td>1girl aqua_hair guitar instrument long_hair so...</td>
-      <td>bad_id bad_pixiv_id</td>
-      <td>4</td>
-      <td>2018-01-23T00:32:10.080-05:00</td>
-      <td>1309</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>85307.0</td>
-      <td>0</td>
-      <td>2015-08-07T23:26:12.355-04:00</td>
-      <td>0</td>
-      <td>10</td>
-      <td>jpg</td>
-      <td>208409</td>
-      <td>https://cdn.donmai.us/original/a1/2c/a12ce629f...</td>
-      <td>False</td>
-      <td>False</td>
-      <td>...</td>
-      <td>1</td>
-      <td>1boy 1girl blush boots carrying closed_eyes co...</td>
-      <td>yuuryuu_nagare</td>
-      <td>jon_(pixiv_fantasia_iii) race_(pixiv_fantasia)</td>
-      <td>pixiv_fantasia pixiv_fantasia_3</td>
-      <td>1boy 1girl blush boots carrying closed_eyes da...</td>
-      <td>commentary_request</td>
-      <td>3</td>
-      <td>2022-05-25T02:26:06.588-04:00</td>
-      <td>95963</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-
-
 ## Dataset Creation
 
-We scraped all post IDs on Danbooru from 1 up to the latest. Some restricted tags (e.g. `loli`) were hidden by the site and require a gold account to access, so they are not present.  
-For a more complete (but older) metadata reference, you may wish to combine this with Danbooru2021 or similar previous scrapes.
+**Scraping Process**
+
+- Post IDs from 1 to the latest ID (8,877,698) were retrieved using a distributed scraping approach.
+- Certain restricted tags (e.g., `loli`) are inaccessible without special permissions and are therefore absent in this dataset.
+- If you require more comprehensive metadata (including hidden or restricted tags), consider merging this data with older scrapes such as Danbooru2021.
 
-The scraping process used a pool of roughly 400 IPs over six hours, ensuring consistent tag definitions. Below is a simplified example of the process used to convert the metadata into Parquet:
+Below is a simplified example of how the raw JSON was converted into a flattened Parquet file:
 
 ```python
 import pandas as pd
 from pandarallel import pandarallel
 
-# Initialize pandarallel
 pandarallel.initialize(nb_workers=4, progress_bar=True)
 
 def flatten_dict(d, parent_key='', sep='_'):
-    """
-    Flattens a nested dictionary.
-    """
+    """Recursively flattens a nested dictionary."""
     items = []
     for k, v in d.items():
         new_key = f"{parent_key}{sep}{k}" if parent_key else k
@@ -347,22 +226,27 @@ def flatten_dict(d, parent_key='', sep='_'):
     return dict(items)
 
 def extract_all_illust_info(json_content):
-    """
-    Parses and flattens Danbooru JSON into a pandas Series.
-    """
+    """Parses and flattens Danbooru JSON into a pandas Series."""
     flattened_data = flatten_dict(json_content)
     return pd.Series(flattened_data)
 
 def dicts_to_dataframe_parallel(dicts):
-    """
-    Converts a list of dicts to a flattened DataFrame using pandarallel.
-    """
+    """Converts a list of dicts to a flattened DataFrame using pandarallel."""
     df = pd.DataFrame(dicts)
     flattened_df = df.parallel_apply(lambda row: extract_all_illust_info(row.to_dict()), axis=1)
     return flattened_df
 ```
 
+## Considerations & Recommendations
+
+- **Adult/NSFW Content**: Danbooru includes adult imagery and explicit tags. Exercise caution, especially if sharing or using this data in public-facing contexts.
+- **Licensing & Copyright**: Images referenced by this metadata may be copyrighted. Refer to Danbooru’s Terms of Service and respect artists’ rights.
+- **Potential Bias**: Tags are community-curated and can reflect the inherent biases of the user base (e.g., under- or over-tagging certain categories).
+- **Missing or Restricted Tags**: Some tags require special permissions on Danbooru; hence they do not appear in this dataset.
+
+For further integration of historical data, consider merging with previous Danbooru scrapes.
+ If you use this dataset in research or production, please cite appropriately and abide by all relevant terms and conditions.
 
-### Recommendations
+------
 
-Users should be aware of potential biases and limitations, including the presence of adult content in some tags. More details and mitigations may be needed.
+*Last Updated: February 18, 2025*