model = SentenceTransformer('all-MiniLM-L6-v2') embedding = model.encode(raw) features['sentence_embedding'] = embedding # 384-dim vector
: Represents the email domains contained within the text file stephen 52 yahoo com gmail com mail com 2020 21 txt
If by “make a deep feature” you meant (e.g., a neural net feature map, a regex to extract a password/username, or a data pipeline), let me know and I’ll adjust. a neural net feature map
It looks like you’re asking to build a from a raw string of mixed data: a regex to extract a password/username
: Indicates the years the data was supposedly collected or leaked : The standard file format for simple text data
model = SentenceTransformer('all-MiniLM-L6-v2') embedding = model.encode(raw) features['sentence_embedding'] = embedding # 384-dim vector
: Represents the email domains contained within the text file
If by “make a deep feature” you meant (e.g., a neural net feature map, a regex to extract a password/username, or a data pipeline), let me know and I’ll adjust.
It looks like you’re asking to build a from a raw string of mixed data:
: Indicates the years the data was supposedly collected or leaked : The standard file format for simple text data
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