Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
Russian
bert
sentiment-analysis
multi-class-classification
sentiment analysis
rubert
sentiment
russian
multiclass
classification
text-embeddings-inference
Instructions to use seara/rubert-base-cased-russian-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seara/rubert-base-cased-russian-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="seara/rubert-base-cased-russian-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("seara/rubert-base-cased-russian-sentiment") model = AutoModelForSequenceClassification.from_pretrained("seara/rubert-base-cased-russian-sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a7dbb01d91257715155ba8b155893878ac28e952d836babd2e674d8facab33b
- Size of remote file:
- 711 MB
- SHA256:
- ec97bb55cf8578c0e47bc9889295d6de55f409428e6e20c6d446c98bca46de0f
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