rubert-ner-toxicity
| Property | Value |
|---|---|
| Author | tesemnikov-av |
| Language | Russian |
| Model Type | Named Entity Recognition for Toxicity |
| Base Model | rubert-tiny-toxicity |
What is rubert-ner-toxicity?
rubert-ner-toxicity is a specialized Named Entity Recognition (NER) model designed for identifying toxic content in Russian text. It's built upon the rubert-tiny-toxicity foundation model and has been fine-tuned specifically for detecting toxic entities within Russian language content.
Implementation Details
The model utilizes the Transformers library and implements AutoModelForTokenClassification architecture. It's designed to process Russian text input and identify toxic elements through named entity recognition. The implementation requires minimal setup and can be easily integrated using the Hugging Face transformers library.
- Built on AutoModelForTokenClassification architecture
- Uses specialized tokenizer for Russian language
- Implements pipeline-based inference with average aggregation strategy
Core Capabilities
- Russian language toxic content detection
- Named entity recognition for toxic elements
- Real-time text analysis and classification
- Support for batch processing of text inputs
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in Russian language toxicity detection using NER approach, making it particularly effective for identifying specific toxic entities within Russian text content. Its fine-tuning on toxic_dataset_ner makes it highly specialized for this task.
Q: What are the recommended use cases?
The model is ideal for content moderation systems, social media monitoring, and automated toxic content detection in Russian language platforms. It can be used for both real-time and batch processing of text content.
