Text Classification
Transformers
PyTorch
Safetensors
Russian
bert
sentiment-analysis
multi-class-classification
sentiment analysis
rubert
sentiment
tiny
russian
multiclass
classification
text-embeddings-inference
Instructions to use mikhmanoff/bert_ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mikhmanoff/bert_ru with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mikhmanoff/bert_ru")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mikhmanoff/bert_ru") model = AutoModelForSequenceClassification.from_pretrained("mikhmanoff/bert_ru", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mikhmanoff/bert_ru: direct link, hf CLI and curl.
- Browser
- Download file 117 MB
-
https://huggingface.co/mikhmanoff/bert_ru/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mikhmanoff/bert_ru/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mikhmanoff/bert_ru/resolve/main/pytorch_model.bin
117 MB
- Xet hash:
- 7a02342a224d0273a98297e728dfbb1ec4c1e04938cb5b3481e5a819d37416c1
- Size of remote file:
- 117 MB
- SHA256:
- f6cd179bc20f33ddf17fd16318da2257574f831363d46d6a77a21faafb2f2c0f
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