---
dataset_info:
  features:
  - name: messages
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  splits:
  - name: train
    num_bytes: 902953
    num_examples: 2446
  download_size: 246445
  dataset_size: 902953
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- text-generation
- text2text-generation
- question-answering
language:
- en
size_categories:
- 1K<n<10K
---


## Description

The dataset is from [medalpaca/medical_meadow_pubmed_causal](https://huggingface.co/datasets/medalpaca/medical_meadow_pubmed_causal), formatted as dialogues for speed and ease of use. Many thanks to author for releasing it.
Importantly, this format is easy to use via the default chat template of `transformers`, meaning you can use [huggingface/alignment-handbook](https://github.com/huggingface/alignment-handbook) immediately, [unsloth](https://github.com/unslothai/unsloth).

## Structure

*View online through viewer.*

## Note

We advise you to reconsider before use, thank you. If you find it useful, please like and follow this account.

## Reference

The **Ghost X** was developed with the goal of researching and developing artificial intelligence useful to humans.

- HuggingFace: [ghost-x](https://huggingface.co/ghost-x)
- Github: [ghost-x-ai](https://github.com/ghost-x-ai)
- X / Twitter: [ghostx_ai](https://twitter.com/ghostx_ai)
- Website: [ghost-x.org](https://ghost-x.org/)

## Citation

```json
@inproceedings{yu-etal-2019-detecting,
    title = "Detecting Causal Language Use in Science Findings",
    author = "Yu, Bei  and
      Li, Yingya  and
      Wang, Jun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-1473",
    doi = "10.18653/v1/D19-1473",
    pages = "4664--4674",
}
```

### ~