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Alibaba Cloud Content Moderation vs Detoxify

Alibaba Cloud Content Moderation

alibabacloud.com

Detoxify

github.com

Side by side

FigureAlibaba Cloud Content ModerationDetoxify
Price from$0.09/moFree
Free plan—Yes
Free trial——
Open source——
WebYes—
Linux—Yes
iOSYes—
APIYes—
Self-hosted—Yes
Plans published121
Spec score · Content Moderation Tools Software8.7 (#2)7.6 (#12)
Spec score · AI Content Moderation Software8.7 (#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

Alibaba Cloud Content Moderation pricing →

Detoxify

  • Open-source libraryFree

Detoxify pricing →

In their own words

Alibaba Cloud Content Moderation

Alibaba Cloud Content Moderation has no write-up on Specifiction yet; its figures are above.

Visit alibabacloud.com

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.

Visit github.com