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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from seara/rubert-tiny2-russian-emotion-detection-cedr: direct link, hf CLI and curl.
- Browser
- Download file 2.41 MB
-
https://huggingface.co/seara/rubert-tiny2-russian-emotion-detection-cedr/resolve/main/tokenizer.json
- Command line
-
hf download hf://seara/rubert-tiny2-russian-emotion-detection-cedr/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/seara/rubert-tiny2-russian-emotion-detection-cedr/resolve/main/tokenizer.json
2.41 MB
File too large to display, you can check the raw version instead.