Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
Russian
bert
sentiment-analysis
multi-class-classification
sentiment analysis
rubert
sentiment
tiny
russian
multiclass
classification
text-embeddings-inference
Instructions to use seara/rubert-tiny2-russian-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seara/rubert-tiny2-russian-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="seara/rubert-tiny2-russian-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("seara/rubert-tiny2-russian-sentiment") model = AutoModelForSequenceClassification.from_pretrained("seara/rubert-tiny2-russian-sentiment") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8cb02f6392ac7a73f06eae5b0fad0a85fe5a50ef0d2beee4416eae15943fe59
- Size of remote file:
- 117 MB
- SHA256:
- 04a423fe991c23cc30f70d45d74cd4ad7eaa4f48e73b924dcd7891b643b123c5
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