Text Classification
Transformers
Safetensors
Russian
English
multilingual
modernbert
feature-extraction
zero-shot-classification
typed-decision
russian
routing
intent-classification
calibrated
calibration
on-prem
cpu
mmbert
text-embeddings-inference
Instructions to use besktech/ru-jev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use besktech/ru-jev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="besktech/ru-jev")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("besktech/ru-jev") model = AutoModel.from_pretrained("besktech/ru-jev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
card: state the held-out calibration error, the shipped 0.0054 is in-sample
Browse files
README.md
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## Results (development)
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Measured on the
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point of this model, and the expected calibration
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| Metric | Value |
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| ECE
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| Temperature | 1.82 |
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| Overall accuracy (micro) | 0.824 |
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Per task:
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| Task | Accuracy | n |
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## Results (development)
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Measured on the development split (17,501 items), which is held out from
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training. Calibration is the point of this model, and the expected calibration
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error is low.
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| Metric | Value |
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| ECE, temperature fitted on this split | 0.0054 |
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| ECE, temperature fitted on a disjoint half | 0.0089 |
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| Temperature | 1.82 |
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| Overall accuracy (micro) | 0.824 |
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Two ECE figures, because the number depends on where the temperature came from.
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The shipped temperature was fitted on this same split, so 0.0054 is an in-sample
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figure and optimistic. Fitting the temperature on one half and measuring on the
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other gives 0.0089, which is what to expect on new data of the same kind. Both
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are reproducible from the code repository; the second is the one to quote.
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Per task:
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| Task | Accuracy | n |
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