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2A2I/H4_no_robots

Dataset Card for "No Robots" 🙅‍♂️🤖 Summary "No Robots" is a dataset consisting of 10,000 instructions and demonstrations, created by professional annotators. It was translated using the Google Cloud Platform Translation API. This dataset can be used to train language models to follow instructions more accurately (instruction-tuned fine-tuning - SFT). The "No Robots" dataset was created based on the dataset described in OpenAI's InstructGPT paper, and includes… See the full description on the dataset page: https://huggingface.co/datasets/2A2I/H4_no_robots.

sourceHugging Facecc-by-nc-4.0updated 3y agoView on Hugging Face
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Dataset Card

Dataset Card for "No Robots" 🙅‍♂️🤖

Summary

"No Robots" is a dataset consisting of 10,000 instructions and demonstrations, created by professional annotators. It was translated using the Google Cloud Platform Translation API. This dataset can be used to train language models to follow instructions more accurately (instruction-tuned fine-tuning - SFT). The "No Robots" dataset was created based on the dataset described in OpenAI's InstructGPT paper, and includes the following categories:

CategoryCount
Creation4560
Open Questions1240
Brainstorming1120
Chatting850
Rewriting660
Summarization420
Programming350
Classification350
Closed Questions260
Extraction190
Languages

This dataset is available in Arabic only. The original version in English can be found at this link, and the Turkish version at this link.

Data Fields

Columns as follows:

  • prompt: Specifies the instruction that the model should follow.
  • prompt_id: A unique identifier.
  • messages: A list containing dictionaries, each dictionary describes a message (key: content) and who sent it (key: role).
  • category: The task category, I did not translate this.
Splits
traintest
No Robots9500500
License

The dataset is available under the (CC BY-NC 4.0) license.

Citation Information
@misc{no_robots,
  author = {Nazneen Rajani and Lewis Tunstall and Edward Beeching and Nathan Lambert and Alexander M. Rush and Thomas Wolf},
  title = {No Robots},
  year = {2023},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\url{https://huggingface.co/datasets/HuggingFaceH4/no_robots}}
}