Text Classification
Transformers
PyTorch
Safetensors
Russian
bert
russian
classification
toxicity
multilabel
text-embeddings-inference
Instructions to use cointegrated/rubert-tiny-toxicity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cointegrated/rubert-tiny-toxicity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cointegrated/rubert-tiny-toxicity")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny-toxicity") model = AutoModelForSequenceClassification.from_pretrained("cointegrated/rubert-tiny-toxicity") - Inference
- Notebooks
- Google Colab
- Kaggle
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language: ["ru"
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tags:
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- russian
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- classification
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This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of toxicity and inappropriateness.
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The problem is formulated as multilabel classification with the following classes:
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- `non-toxic`: the text does NOT contain insults, obscenities, and threats, in the sense of the [OK ML Cup](https://cups.mail.ru/ru/tasks/1048) competition.
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language: ["ru"]
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tags:
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- russian
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- classification
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widget:
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- text: "Иди ты нафиг!"
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---
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This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of toxicity and inappropriateness for short informal Russian texts, such as comments in social networks.
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The problem is formulated as multilabel classification with the following classes:
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- `non-toxic`: the text does NOT contain insults, obscenities, and threats, in the sense of the [OK ML Cup](https://cups.mail.ru/ru/tasks/1048) competition.
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