Text Classification
Transformers
PyTorch
Safetensors
Russian
bert
russian
classification
emotion
emotion-detection
emotion-recognition
multilabel
Eval Results (legacy)
text-embeddings-inference
Instructions to use Aniemore/rubert-tiny-emotion-russian-cedr-m7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aniemore/rubert-tiny-emotion-russian-cedr-m7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aniemore/rubert-tiny-emotion-russian-cedr-m7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aniemore/rubert-tiny-emotion-russian-cedr-m7") model = AutoModelForSequenceClassification.from_pretrained("Aniemore/rubert-tiny-emotion-russian-cedr-m7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ef4a05394be79e1432b0e19358ece99c7ad68e1a2a995f2b80b06dd03257724
- Size of remote file:
- 117 MB
- SHA256:
- b7b52b5600434c50439ab3468b727b52bbc85409aa5a2a9874eeacbce0a5d563
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