ProbeLoop Laya stated limits finds explicit knowledge gaps in Italian practice answers. Its provisional ONNX classifier uses synthetic training data.
By submsk · Hugging Face weight release: 10 Nov 2026
A provisional Italian sentence classifier fine-tuned from Laya multilingual. It finds explicit statements of missing knowledge or experience in interview-practice answers. This is a fixed two-class encoder and head, separate from Laya’s general typed-question heads; it does not assess competence or choose interview moves.
306,941,186 parameters; full float32 weights and a 308 MB dynamic-int8 ONNX export
Inputs
One answer sentence paired with the last 300 characters of the interviewer’s question; 192-token truncation
Runtime
ONNX Runtime, Tokenizers and NumPy on CPU; no Laya SDK required
Training
2,549 synthetic Italian sentence pairs, 362 positive; final checkpoint trained on all rows
Calibration
Threshold 0.5; no calibrated confidence guarantee or real-interview acceptance test
Licence
Apache-2.0 fine-tuning and exports; retain Laya and mmBERT upstream attribution
Claims and sources
The maker reports 288/294 positive answers found and 8/312 negative answers flagged in a synthetic leave-one-profession-out pilot. Those results concern held-out-group models, not an unseen test of the final checkpoint trained on all rows. vendor reportedsource
Uses a fine-tuned Laya ONNX classifier to find explicit knowledge or experience limits in Italian practice answers. A recorded synthetic HTTP/WebSocket turn uses the actual classifier and scripted LLMs. Interview decisions and evaluation remain separate; real-speech quality and physical Mac performance are still open.