Text Classification
Transformers
ONNX
Safetensors
Russian
roberta
Text Classification
emotion-classification
emotion-recognition
emotion-detection
emotion
text-embeddings-inference
Instructions to use fyaronskiy/ruRoberta-large-ru-go-emotions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fyaronskiy/ruRoberta-large-ru-go-emotions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fyaronskiy/ruRoberta-large-ru-go-emotions")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fyaronskiy/ruRoberta-large-ru-go-emotions") model = AutoModelForSequenceClassification.from_pretrained("fyaronskiy/ruRoberta-large-ru-go-emotions") - Notebooks
- Google Colab
- Kaggle
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README.md
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INT8 quantized model (onnx/model_quantized.onnx) - 2.5x faster than Transformer model, quality is almost the same.
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In table below results of tests of inference of
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I tested inference with batch_size 1 on Intel Xeon CPU with 2 vCPUs (Google Colab).
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|Model |Size |f1 macro|acceleration|Time of inference|
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INT8 quantized model (onnx/model_quantized.onnx) - 2.5x faster than Transformer model, quality is almost the same.
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In table below results of tests of inference of 5427 samples of test_set.
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I tested inference with batch_size 1 on Intel Xeon CPU with 2 vCPUs (Google Colab).
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|Model |Size |f1 macro|acceleration|Time of inference|
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