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") - Inference
- Notebooks
- Google Colab
- Kaggle
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- emotion
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|neutral |0.64 |0.58 |0.61 |0.81 |1787 |
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|micro avg |0.68 |0.43 |0.53 |0.93 |6329 |
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|macro avg |0.51 |0.29 |0.33 |0.87 |6329 |
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|weighted avg |0.62 |0.43 |0.48 |0.86 |6329 |
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- emotion-classification
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- emotion-recognition
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- emotion
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- emotion-detection
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datasets:
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- seara/ru_go_emotions
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|neutral |0.64 |0.58 |0.61 |0.81 |1787 |
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|micro avg |0.68 |0.43 |0.53 |0.93 |6329 |
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|macro avg |0.51 |0.29 |0.33 |0.87 |6329 |
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|weighted avg |0.62 |0.43 |0.48 |0.86 |6329 |
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