region:us
---
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: output
    dtype: string
  - name: kind
    dtype: string
  - name: prob
    dtype: float64
  splits:
  - name: train
    num_bytes: 694061788.6849711
    num_examples: 613214
  download_size: 372451511
  dataset_size: 694061788.6849711
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# Dataset Card for "reverse_instruct"

This is a reverse instruction dataset, designed to be used in cases where we're trying to predict the `instruction` given the `output`.  

This is useful to train a model that can generate instructions for a raw dataset (useful to quickly instruction tune on a new domain).

This data was created by aggregating [clean alpaca data](https://github.com/gururise/AlpacaDataCleaned), [evol-instruct](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1), [clean instruct](https://huggingface.co/datasets/crumb/Clean-Instruct-3M), and [orca](https://huggingface.co/datasets/Open-Orca/OpenOrca).

The combined dataset was filtered using heuristics to remove instructions unlikely to be predictable from the output (multiple choice, continue this passage, etc.).  The dataset was then filtered using [instruct_rater](https://huggingface.co/vikp/instruct_rater), a trained classifier that predicts how likely an instruction is to be able to be recreated from an output.