Alibaba Cloud Content Moderation vs Detoxify
Alibaba Cloud Content Moderation
alibabacloud.comDetoxify
github.comSide by side
| Figure | Alibaba Cloud Content Moderation | Detoxify |
|---|---|---|
| Price from | $0.09/mo | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Linux | — | Yes |
| iOS | Yes | — |
| API | Yes | — |
| Self-hosted | — | Yes |
| Plans published | 12 | 1 |
| Spec score · Content Moderation Tools Software | 8.7 (#2) | 7.6 (#12) |
| Spec score · AI Content Moderation Software | 8.7 (#3) | 7.6 (#17) |
Plans
Alibaba Cloud Content Moderation
- Pay-as-you-go — Standard text moderation$0.30 a month
- Pay-as-you-go — Standard image moderation$0.60 a month
- Pay-as-you-go — Standard voice moderation$9 a month
- Pay-as-you-go — Standard video moderation$8.10 a month
- Pay-as-you-go — Standard document moderation$0.09 a month
- Document Moderation 2.0 standard$0.09 a month
- Content Moderation 2.0 standard text$0.30 a month
- Content Moderation 2.0 advanced text$0.60 a month
- Content Moderation 2.0 standard image$0.60 a month
- Content Moderation 2.0 advanced image$1.20 a month
In their own words
Alibaba Cloud Content Moderation
Alibaba Cloud Content Moderation has no write-up on Specifiction yet; its figures are above.
Detoxify
Detoxify is a free, self-hosted library that provides trained models and code to classify toxic comments. Its original, unbiased, and multilingual models are based on BERT, RoBERTa, and XLM-RoBERTa, respectively. Depending on the model, outputs include categories such as threats, obscenity, insults, identity attacks, and explicit sexual content. The multilingual model is intended for English, French, Spanish, Italian, Portuguese, Turkish, and Russian. The project describes research, fine-tuning on carefully constructed representative datasets, and helping moderators flag harmful content faster as intended uses. It can be installed with pip and called from Python to predict labels for one string or a list. Inference uses CPU by default and accepts a PyTorch device such as CUDA. The project warns that profanity-associated words may be labeled toxic regardless of tone or intent, potentially biasing results against vulnerable minority groups. Inference uses Transformers and PyTorch Lightning; training also requires the Kaggle API to download data. API access can be requested by email. The library is distributed under Apache-2.0.