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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f32bea75780960a19afb8fbe89f7a1af7cf3d97ad066a9643bd83371de58a42f
- Size of remote file:
- 47.2 MB
- SHA256:
- e29f120dfe1f0e3bd60823967efe31eb9104ca546e40222fc4a63eaa554ea081
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.