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-cedr 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-cedr 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-cedr")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("seara/rubert-tiny2-russian-emotion-detection-cedr") model = AutoModelForSequenceClassification.from_pretrained("seara/rubert-tiny2-russian-emotion-detection-cedr") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25a337d5f111462ea1b28c1a16a1f8b7f4e5afedea3764c50ed80f6a07076383
- Size of remote file:
- 117 MB
- SHA256:
- ebc2059a89bd3905886c727800f1371b79bb8f4ff1023191eddc41d8624b32a5
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