Text Classification
Transformers
PyTorch
Safetensors
Russian
bert
russian
classification
sentiment
emotion-classification
multiclass
text-embeddings-inference
Instructions to use cointegrated/rubert-tiny2-cedr-emotion-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cointegrated/rubert-tiny2-cedr-emotion-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cointegrated/rubert-tiny2-cedr-emotion-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2-cedr-emotion-detection") model = AutoModelForSequenceClassification.from_pretrained("cointegrated/rubert-tiny2-cedr-emotion-detection") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c400fb5cc97540689488d404f7a04ce6c03b8085ebd33d47fe7f25736816df0c
- Size of remote file:
- 117 MB
- SHA256:
- b43e25fc5e624a0b3d8b14bfb488f6939f23440e07adbf46b7deb1a0ed0cb309
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