Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
Russian
bert
sentiment-analysis
multi-label-classification
sentiment analysis
rubert
sentiment
tiny
russian
multilabel
classification
emotion-classification
emotion-recognition
emotion
emotion-detection
text-embeddings-inference
Instructions to use seara/rubert-tiny2-russian-emotion-detection-ru-go-emotions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seara/rubert-tiny2-russian-emotion-detection-ru-go-emotions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="seara/rubert-tiny2-russian-emotion-detection-ru-go-emotions")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("seara/rubert-tiny2-russian-emotion-detection-ru-go-emotions") model = AutoModelForSequenceClassification.from_pretrained("seara/rubert-tiny2-russian-emotion-detection-ru-go-emotions") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf45c36d8510fd2769c827d5d287a6ac1fd2f4617b4abfd7cd4e9844c465a513
- Size of remote file:
- 117 MB
- SHA256:
- fef10eb40b8c4598fac6284413c76fef0b445c429d3f0584625134f22fabff03
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