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alibiserikbay/JevK5-Lite

AI model by alibiserikbay

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JevK5-Lite is a CPU classifier. It reads a text and any number of label sets ("heads") in one encoder pass. It returns a calibrated probability for every label: a softmax within a single-label head, and a sigmoid per label in a multi-label head. It is a 437M-parameter DeBERTa-v3-large, fine-tuned by the JevK5 project. This is the preview-1 release; the runtime is jevk5 0.3.1. Where it is better: calibration. On a neutral test of seven public datasets, chosen before either model was run, its probabilities are better calibrated than GLiNER2.5-Decide's. The top-label expected calibration error is lower on all six single-label sets: 0.035-0.103 against 0.091-0.250. Where it is not: accuracy. This is a preview, and it does not beat the model it was built against. zero. tweeteval sentiment is a tie. its mean over the seven sets is higher (0.643 against 0.629). GLiNER2.5-Decide is stronger: head accuracy 0.637 against 0.587, all…

alibiserikbay/JevK5-Lite on Hugging Face A short preview, not the full document.

Read the full model card ↗ · Preview checked 2026-09-25T06:55:39.337Z

Inside the original model card — Document outline
  1. JevK5-Lite (preview, experimental)
  2. Use
  3. How it reads
  4. Evaluation
  5. fastino/fast-decisions dev
  6. Neutral test (seven public datasets)
  7. Calibration
  8. Training
  9. Limitations
  10. License and notices

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Model overview

alibiserikbay/JevK5-Lite is a task-unspecified model repository published by alibiserikbay on Hugging Face. A library was not reported.

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Created
2026-09-25T06:34:04.000Z
Last modified
2026-09-25T06:34:04.000Z

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