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MENTHOS-rootcause
LHRS-UM-FERI/MENTHOS-rootcause
MENTHOS-rootcause is a text classification model from LHRS-UM-FERI. Use it when you need a label for a piece of text. It is set up for transformers.
MENTHOS-RootCause is a binary classifier fine-tuned from answerdotai/ModernBERT-base to detect whether a log line indicates a root-cause issue. It uses a maximum sequence length of 256.
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Updated Apr 5, 2026
Repo size
598 MB
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At a glance
- Task
- Text Classification
- Library
- transformers
- Access
- Public
- Created
- Apr 5, 2026
- Updated
- Apr 5, 2026
- SHA
- e5628c18
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Model card
From the Hugging Face model README
MENTHOS-rootcause
English
Model Description
MENTHOS-RootCause is a binary classifier fine-tuned from answerdotai/ModernBERT-base to detect whether a log line indicates a root-cause issue.
It uses a maximum sequence length of 256.
Intended Use
- Binary classification of logs for root-cause detection.
- Labels:
0/1from the prepared dataset.
Training Data
Trained on the MENTHOS root-cause dataset.
Benchmark Results
Benchmark results for the MENTHOS evaluation set.
| model | samples | accuracy | precision | recall | f1 | roc_auc | p50 latency (ms) | throughput (samples/s) |
|---|---|---|---|---|---|---|---|---|
| MENTHOS-rootcause | 296 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 6.0751 | 164.09 |
Reference baseline (Morpheus ONNX):
| baseline model | accuracy | f1 | roc_auc | p50 latency (ms) | throughput (samples/s) |
|---|---|---|---|---|---|
| root-cause-binary-bert-20230517.onnx | 0.861486 | 0.839216 | 0.724343 | 65.7515 | 15.13 |
Benchmark Plots
Limitations
- Perfect benchmark scores may reflect dataset construction and domain overlap.
- Generalization to unseen infrastructures/log formats should be validated separately.
Citation
@misc{borovic_li-dobnik_kranjec_ferme_2026,
title = {MENTHOS-rootcause},
author = {Borovic, Li Dobnik, Kranjec, Ferme},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/LHRS-UM-FERI/MENTHOS-rootcause}}
}
Slovenščina
Opis modela
MENTHOS-RootCause je binarni klasifikator, naučen iz answerdotai/ModernBERT-base, za zaznavanje root-cause dogodkov v dnevniških zapisih.
Uporablja maksimalno dolžino zaporedja 256.
Namen uporabe
- Binarna klasifikacija logov za root-cause detekcijo.
- Oznake:
0/1iz pripravljenega dataseta.
Učni podatki
Učenje je potekalo na MENTHOS root-cause datasetu.
Rezultati benchmarka
| model | vzorcev | accuracy | precision | recall | f1 | roc_auc | p50 latenca (ms) | prepustnost (vzorcev/s) |
|---|---|---|---|---|---|---|---|---|
| MENTHOS-rootcause | 296 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 6.0751 | 164.09 |
Referenčni baseline (Morpheus ONNX):
| baseline model | accuracy | f1 | roc_auc | p50 latenca (ms) | prepustnost (vzorcev/s) |
|---|---|---|---|---|---|
| root-cause-binary-bert-20230517.onnx | 0.861486 | 0.839216 | 0.724343 | 65.7515 | 15.13 |
Grafi benchmarka
Omejitve
- Popolni rezultati na benchmarku lahko odražajo strukturo podatkov in domensko podobnost.
- Posploševanje na druge sisteme/log formate je treba dodatno validirati.
Citiranje
@misc{borovic_li-dobnik_kranjec_ferme_2026,
title = {MENTHOS-rootcause},
author = {Borovic, Li Dobnik, Kranjec, Ferme},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/LHRS-UM-FERI/MENTHOS-rootcause}}
}
- Task
- Text Classification
- Library
- transformers
- Languages
- en, sl
- Created
- Apr 5, 2026
- Updated
- Apr 5, 2026