---
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: domain
      dtype: string
    - name: post_id
      dtype: string
    - name: raw_score_chosen
      dtype: int64
    - name: raw_score_ratio
      dtype: float64
    - name: raw_score_rejected
      dtype: int64
    - name: seconds_difference
      dtype: float64
    - name: source
      dtype: string
    - name: upvote_ratio
      dtype: float64
  splits:
  - name: train
    num_bytes: 1815446429
    num_examples: 348718
  - name: validation
    num_bytes: 93098840
    num_examples: 18436
  - name: test
    num_bytes: 95879141
    num_examples: 18409
  download_size: 262070837
  dataset_size: 2004424410
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

# Dataset Card for Dataset Name

<!-- Provide a quick summary of the dataset. -->

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:
     ```python
     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.
## Dataset Details

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