dataset_info:
features:

• name: prompt
  dtype: string
• name: prompt_id
  dtype: string
• name: chosen
  list:
  • name: content
    dtype: string
  • name: role
    dtype: string
• name: rejected
  list:
  • name: content
    dtype: string
  • name: role
    dtype: string
• name: messages
  list:
  • name: content
    dtype: string
  • name: role
    dtype: string
• name: score_chosen
  dtype: float64
• name: score_rejected
  dtype: float64
• name: other_info
  struct:
  • name: in_gsm8k_train
    dtype: bool
  • name: labeling_model
    dtype: string
  • name: labeling_prompt
    list:
    • name: content
      dtype: string
    • name: role
      dtype: string
  • name: original_chosen
    dtype: string
  • name: original_rejected
    dtype: string
  • name: score_is_flipped
    dtype: bool
  • name: system
    dtype: string
    splits:
• name: train
  num_bytes: 145232429.99453852
  num_examples: 12815
  download_size: 74069814
  dataset_size: 145232429.99453852
  configs:
• config_name: default
  data_files:
  • split: train
    path: data/train-*

Dataset Card for distilabel-intel-orca-dpo-pairs_cleaned_reformatted

This is a "cleaned" and reformatted version of argilla's distilabel-intel-orca-dpo-pairs:

1. remove all samples where rating where none. There are 42 samples where there were no ratings information.
2. reformatted the daatset to be consistent with ultrafeedback_binarized.
3. (new) fliped "system" with "assistant" role in the chosen, rejected, and message columns. Somehow the original dataset used "systems" to indicate chatbot responses.
4. (new) removed all rows where the chosen is the same as rejected. This removed 2 rows from the training set.

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