The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets
library). If this is not possible, please
open a discussion
for direct help.
Infinite Blue Skies
A streaming dataset providing real-time access to public posts from the Bluesky social network via the AtProto API.
Dataset Summary
The Bluesky Posts dataset provides streaming access to public posts from the Bluesky social network through the AtProto API. This dataset is particularly useful for researchers and developers interested in social media analysis, content moderation, language modeling, and trend detection.
Supported Tasks and Leaderboards
The dataset can be used for various tasks including:
- Text Generation: Training language models on social media content
- Text Classification: Content moderation, topic classification, sentiment analysis
- Social Media Analysis: Trend detection, user behavior analysis
- Content Analysis: Hashtag analysis, URL pattern analysis
Dataset Structure
Data Instances
Each instance in the dataset represents a Bluesky post with the following fields:
{
'uri': 'at://did:plc:..../app.bsky.feed.post/...',
'cid': 'baf...',
'text': 'The content of the post...',
'created_at': '2024-03-21T12:34:56.789Z',
'author_did': 'did:plc:...',
}
Data Fields
uri
: Unique identifier for the postcid
: Content identifiertext
: Content of the postcreated_at
: ISO timestamp of when the post was createdauthor_did
: Decentralized identifier of the author
Data Splits
This is a streaming dataset and does not have traditional splits. Data is accessed in real-time through an iterator.
How to Use
This dataset is designed to be used with the Hugging Face Datasets library. Here's how to get started:
from datasets import load_dataset
dataset = load_dataset(
"serpxe/infinite_blue_skies",
streaming=True,
trust_remote_code=True,
split="train",
batch_size=5,
)
# Iterate one-by-one
for i in range(10):
print(next(iter(dataset)))
# Returns 10 posts
# Batched iteration
iterable_dataset = iter(dataset)
for i in range(10):
print(next(iterable_dataset))
# Returns 10 posts, but in batches of 5
- Downloads last month
- 63