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Not all columns needs to be encoded by llm tokenizers.
author: definitely encoder or index
time: no need to put in the model for the current stage, but can do some descriptive stats
title, selftext: input values, llm tokenizer
note: intermediate value? llm tokenizer
jurisdictions: MultiLabelBinarizer
relevance: one-hot
poster's legal status: one-hot
predictions (misconception and unclear knowledge): llm tokenizer or MultiLabelBinarizer?
category: one-hot
background: llm tokenizer or one-hot or MultiLabelBinarizer?
how about prompting? tokenize everything with llm? or other ways to predict + decode?
The text was updated successfully, but these errors were encountered:
Not all columns needs to be encoded by llm tokenizers.
author: definitely encoder or index
time: no need to put in the model for the current stage, but can do some descriptive stats
title, selftext: input values, llm tokenizer
note: intermediate value? llm tokenizer
jurisdictions: MultiLabelBinarizer
relevance: one-hot
poster's legal status: one-hot
predictions (misconception and unclear knowledge): llm tokenizer or MultiLabelBinarizer?
category: one-hot
background: llm tokenizer or one-hot or MultiLabelBinarizer?
how about prompting? tokenize everything with llm? or other ways to predict + decode?
The text was updated successfully, but these errors were encountered: