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YAML Metadata Warning: The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
π BluePrint
BluePrint is a large-scale dataset of social media conversation threads designed for evaluating and training LLM-based social media agents. It provides realistic, thread-structured data clustered into representative user personas at various levels of granularity.
β Key Features
Thread-Based Structure: Each example is a list of messages representing a user thread.
Persona Clustering: Users are clustered into 2, 25, 100, and 1000 representative personas to enable safe and scalable simulations.
Behavioral Signals: Each message includes relative timestamps, user ID, and social actions such as likes, reposts, and replies.
π― Use Cases
Text Generation
Next-Action Prediction
Behavioral Simulation
Persona Modeling
π Dataset Structure
Each record contains:
relative_integer_time: Relative timestamp of the message.text: Text content of the message.actions: Next action (bool). Will have the following:like,unlike,repost,unrepost,follow,unfollow,block,unblock,post_update,post_delete,quote,post,reply
user_id: Anonymized user identifier.
Example:
{
"thread": [
{
"relative_integer_time": 9884584,
"text": "BTW, Mark Cuban is the second-most followed person on Bluesky. Back in 2017 there was a lot of talk about who could be the Democrats' Trump, and plenty of chest-puffing in response that there couldn't be one. Cuban is it. He's not as dim and he's not a sociopath but he is talking out of his ass.",
"actions": {
"like": false,
"unlike": false,
"repost": false,
"unrepost": false,
"follow": false,
"unfollow": false,
"block": false,
"unblock": false,
"post_update": false,
"post_delete": false,
"quote": false,
"post": true,
"reply": false
},
"user_id": "7ebac0bdfe8f0925bf1e5dd672e3288e3b1e05ba0620285f7d7f7ed89e33ee9e"
}
]
}
Statistics of each cluster split can be found at the folder stats/.
ποΈ Configurations
| Config Name | Description | Size (examples) |
|---|---|---|
2_clusters |
2 representative user personas | 6.8M |
25_clusters |
25 representative user personas | 6.8M |
100_clusters |
100 representative user personas | 6.8M |
1000_clusters |
1000 representative user personas | 6.8M |
πΎ Loading Example
from datasets import load_dataset
dataset = load_dataset("ComplexDataLab/BluePrint", name="2_clusters")
for i, example in enumerate(dataset["full"]):
print(f"Example {i}: {example}")
break
Example 0: {'thread': [{'relative_integer_time': 9884584, 'text': "BTW, Mark Cuban is the second-most followed person on Bluesky. Back in 2017 there was a lot of talk about who could be the Democrats' Trump, and plenty of chest-puffing in response that there couldn't be one. Cuban is it. He's not as dim and he's not a sociopath but he is talking out of his ass.", 'actions': {'like': False, 'unlike': False, 'repost': False, 'unrepost': False, 'follow': False, 'unfollow': False, 'block': False, 'unblock': False, 'post_update': False, 'post_delete': False, 'quote': False, 'post': True, 'reply': False}, 'user_id': '7ebac0bdfe8f0925bf1e5dd672e3288e3b1e05ba0620285f7d7f7ed89e33ee9e'}]}
π License
This dataset is released under the MIT License.
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