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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from seara/rubert-tiny2-russian-emotion-detection-cedr: direct link, hf CLI and curl.
- Browser
- Download file 117 MB
-
https://huggingface.co/seara/rubert-tiny2-russian-emotion-detection-cedr/resolve/main/tf_model.h5
- Command line
-
hf download hf://seara/rubert-tiny2-russian-emotion-detection-cedr/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/seara/rubert-tiny2-russian-emotion-detection-cedr/resolve/main/tf_model.h5
117 MB
- Xet hash:
- 76e5893cb203cd7aebbfa93a78e278f4f764d5267ff8cdec52832baa35184807
- Size of remote file:
- 117 MB
- SHA256:
- 47db4227bce06626fc78a267a68d8e0482d89366f5fb0b638667d70909f1e896
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