license:cc-by-4.0 region:us
---
license:
- cc-by-4.0
---
This dataset combines Atomic 10X, Atomic 2020 and SODA into a single dataset. It creates a story for a single head, in the head->relation->tail triplets. There are about 2.2M stories. This dataset is intended as a seed dataset for synthetic data generation using a model. For example, you could create a story as follows:

```
Revise this story to make it compelling and more logical and detailed. Keep as much of the feelings and actions as possibe, but remove anything that doesn't make sense:
{text}
```

There are 4 types of stories:
1) Basic stories that describes the main character, their feelings, the "head" action - PersonX [action] [object]. It describes PersonX's reactions, intentions, etc. It also describes other's reactions.
2) Stories like the above except where PersonX is hindered from performing the "head" action.
3) Stories like (1) above, but includes a related SODA narrative and dialouge.
4) Stories like (1) except at least some of the details, such as feelings, reactions, intentions are less likely to occur. In these stories, we use terms like "suprisingly", "in a twist", "strangely" in the story to signal these unlikely details. 

WARNING:
The seed data from the atomic dataset may contain NSFW materials, including stories about suicide, killing, sex crimes, and so forth. We keep the data in here for completeness. 
We recommend using alignment techniques in generating stories and/or creating refusals as appropriate depending on your policies for data and training.

This dataset should not be used AS IS to train a model. It has grammatical errors, is repetitive and some logical inconsistencies. This will hopefully be fixed by a model when generating a story from the seed data.

Also, please note that while we have performed some gender swapping for stories, there may still be bias in the data as well.

See: 
* https://huggingface.co/datasets/allenai/atomic
* https://huggingface.co/datasets/allenai/soda
* https://github.com/peterwestai2/symbolic-knowledge-distillation