# Third-party notices: toxic

`toxic` is built for commercial use. It derives from the components below,
each used under a license that permits commercial use and derivative works.
Their license terms apply to those components; the attributions here satisfy
them. Evaluation suites are never trained on.

## Base encoder

### XLM-RoBERTa base (FacebookAI)
- [`FacebookAI/xlm-roberta-base`](https://huggingface.co/FacebookAI/xlm-roberta-base): **MIT**.
- Used as the encoder: vocabulary trimmed from 250k to 60,728 pieces,
  fine-tuned for multi-label hate-speech / abuse classification.

## Training datasets

- [`google/civil_comments`](https://huggingface.co/datasets/google/civil_comments)
  (Civil Comments / Jigsaw Unintended Bias): **CC0-1.0** (public domain).
  Courtesy citation: Borkan, Dixon, Sorensen, Thain, Vasserman, "Nuanced
  Metrics for Measuring Unintended Bias with Real Data for Text
  Classification", WWW 2019.
- [`ucberkeley-dlab/measuring-hate-speech`](https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech)
  (Measuring Hate Speech, MHS): **CC-BY-4.0**. Attribution: D-Lab, UC
  Berkeley. Citation: Kennedy, Bacon, Sahn, von Vacano, "Constructing
  interval variables via faceted Rasch measurement and multitask deep
  learning: a hate speech application", 2020.
- [GAHD (German Adversarial Hate speech Dataset)](https://github.com/jagol/gahd):
  **CC-BY-4.0**. Citation: Goldzycher, Röttger, Schneider, "Improving
  Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a
  German Hate Speech Dataset", NAACL 2024.

## Datasets used during development (earlier training iterations)

- [GermEval 2018](https://github.com/uds-lsv/GermEval-2018-Data): used under
  the maintainers' citation terms. Required citation: Wiegand, Siegel,
  Ruppenhofer, "Overview of the GermEval 2018 Shared Task on the
  Identification of Offensive Language", Proceedings of GermEval 2018,
  Vienna.
- [MLMA hate speech](https://huggingface.co/datasets/nedjmaou/MLMA_hate_speech):
  **MIT**. Citation: Ousidhoum, Lin, Zhang, Song, Yeung, "Multilingual and
  Multi-Aspect Hate Speech Analysis", EMNLP 2019.

## Evaluation suites (eval only, never trained on)

- [HateCheck](https://github.com/paul-rottger/hatecheck-data): **CC-BY-4.0**.
  Citation: Röttger, Vidgen, Nguyen, Waseem, Margetts, Pierrehumbert,
  "HateCheck: Functional Tests for Hate Speech Detection Models", ACL 2021.
- [Multilingual HateCheck](https://huggingface.co/Paul/hatecheck): **CC-BY-4.0**.
  Citation: Röttger, Seelawi, Nozza, Talat, Vidgen, "Multilingual HateCheck:
  Functional Tests for Multilingual Hate Speech Detection Models", WOAH 2022.

## Generator models (used to create synthetic training data)

- Gemma 4 family ([`google/gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it),
  [`google/gemma-4-12B-it`](https://huggingface.co/google/gemma-4-12B-it)):
  **Apache-2.0**. Outputs used as synthetic multilingual training text.
- xAI `grok-4.3` (API): outputs used as synthetic training text for the
  severity-tail, adversarial and clean-side minimal-pair slices, under xAI's
  commercial API terms.

No non-commercial or unlicensed datasets are used. None of the above datasets
are redistributed in this repository.