license:apache-2.0 size_categories:100K<n<1M format:parquet modality:text library:datasets library:dask library:mlcroissant library:polars region:us
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license: apache-2.0
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Combines textual datasets from multiple sources including:
- aegis safety dataset
- open ai moderation dataset
- ALERT + ALERT jailbreaking datasets
- Real toxicity prompts
- Toxic Chat
- Trawling for Trolling

Part 2 includes sources from (filtering for bad labels only):
- toxic uncensored lgbtq
- conan
- salad data
- wikitoxic
- hatespeech curated

I clean and reformat all of these into a dataset with 4 main columns including: 
1. `text`
2. `binary_label` - if the prompt/text is unsafe (1) or safe (0)
3. `label_cat` - the respective type of label from the source dataset e.g. toxicity, hate speech etc
4. `source` - name of the raw dataset it derives from

## Considerations 

You will notice the main value of the dataset is everything that is unsafe. You can replace or augment the safe examples using "happy path" examples 
from your own domain or dataset. 

Note: you will also likely want to standardise the label categories if you will be using them for your model training or fine-tuning. 
They currently are all disparate and only make sense to their source taxonomies, so you can work towards unifying and standardising them 
based on your desired risk taxonomy. 

