when2rl/SHP_reformatted

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Reformatted from stanfordnlp/SHP dataset. To make it consistent with other preference dsets, we:

1. convert upvotes to scores in a [1, 10] scale. This is achieved by 1) convert the better response's upvotes to score of [5.0, 10.0] by:

   def shp_map_score(score, threshold=78):
      # 78 is chosen because about the best 10% data has score > 78
      if score > threshold:
          return 10.0
      # linearly map the rest
      # start with 5.0 because we assume that any human written reponses that can receive any upvote should already reflect decent quality 
      return 5.0 + (score / 78) * 5.0
   

   to respect the score_ratio in the original dataset, we use it to model score difference between the chosen and the rejected score. Therefore, the rejected score is calculated by:

   remaped_chosen_score = # from above
   ratio_diff = data_row['score_ratio'] - 1.0
   rejected_score = max(remaped_chosen_score - ratio_diff, 0.0)
   

2. all other information is kept intact: since the original data is already paired, we simply reformat to use the better response as chosen, and the other as rejected.

convert all scores to a [1, 10] scale by np.mean([helpfulness+1, correctness+1, coherence+1, complexity+1, 4-verbosity])*2.0 the original dset considers 4 responses per prompt. We construct preference pairs by 1) take the best scoring response as chosen, and 2) randomly sample responses with score lower than best response as rejected. We skip prompts/data rows where all responses have the same score.

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