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")# pip install -U transformers accelerate # 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
Commit History
-
Update README.md
Verified signature5d37eff
-
add the training notebook
Verified signaturefd5e387
-
Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (9d488c18edb1209c0e188490146c0f79bcb5e28a) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>9e6d94e
-
Update config.json
635187b
-
Update README.md
6bc8969 -
Update README.md
77f6923 -
Train the model for 15 epochs
f9844b9
-
Update README.md
01d80d4 -
Update README.md
b31c78a -
Create README.md
4ad85b8 -
The first commit
d8dad32 -
initial commit
b9a86cb