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---
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",
}
```
### ~ |