Bitcoin addresses and their categories
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'The dataset contains Bitcoin addresses that have been identified and belong to one of particular categories: mining pools, miners, coinjoin services, gambling services, exchanges, other services - 8,008 addresses in total. The assignment of labels comes from two sources: plausible assumptions and external services and is not guaranteed to be error prone. These labels have been used for training and validating the performance of machine learning algorithms for discovering the types of addresses' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Bitcoin","blockchain","machine learning"]} <|eot_id|>
 Here is the Description of a dataset. Description:'The dataset contains Bitcoin addresses that have been identified and belong to one of particular categories: mining pools, miners, coinjoin services, gambling services, exchanges, other services - 8,008 addresses in total. The assignment of labels comes from two sources: plausible assumptions and external services and is not guaranteed to be error prone. These labels have been used for training and validating the performance of machine learning algorithms for discovering the types of addresses' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
The dataset contains Bitcoin addresses that have been identified and belong to one of particular categories: mining pools, miners, coinjoin services, gambling services, exchanges, other services - 8,008 addresses in total. The assignment of labels comes from two sources: plausible assumptions and external services and is not guaranteed to be error prone. These labels have been used for training and validating the performance of machine learning algorithms for discovering the types of addresses
Bitcoin; blockchain; machine learning