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-tiny2-russian-emotion-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aniemore/rubert-tiny2-russian-emotion-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aniemore/rubert-tiny2-russian-emotion-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aniemore/rubert-tiny2-russian-emotion-detection") model = AutoModelForSequenceClassification.from_pretrained("Aniemore/rubert-tiny2-russian-emotion-detection", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Aniemore/rubert-tiny2-russian-emotion-detection: direct link, hf CLI and curl.
- Browser
- Download file 117 MB
-
https://huggingface.co/Aniemore/rubert-tiny2-russian-emotion-detection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Aniemore/rubert-tiny2-russian-emotion-detection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Aniemore/rubert-tiny2-russian-emotion-detection/resolve/main/pytorch_model.bin
117 MB
- Xet hash:
- 8bad58de7495a05e651c5d168e421499bc055acb5b4ec8b81bfcf057c6b1b082
- Size of remote file:
- 117 MB
- SHA256:
- c4dd562e8e2b5e6e4f22ef8d8c819de2fb94ca9ba9c9233b5dd3c34aa0f82618
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