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-ru-go-emotions 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-ru-go-emotions 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-ru-go-emotions")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("seara/rubert-tiny2-russian-emotion-detection-ru-go-emotions") model = AutoModelForSequenceClassification.from_pretrained("seara/rubert-tiny2-russian-emotion-detection-ru-go-emotions") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8cedcf64d5a408c069bd16d90715e7f5cc139850af70d5aa4c37c3a559b91ee
- Size of remote file:
- 117 MB
- SHA256:
- d372dd6a9c7e5a4cd745e65c36ca0a2ca9c78f529b00b0294e4c518a48153bdb
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