Baligh Ged Crf Surface Morph V2
This is Baligh's morphology-aware Arabic grammatical error detection CRF. It
uses Baligh preprocessing tokens plus the disambiguated morphological features
from preprocessing output, and emits the mapped labels UC, OT, MO, SY,
PC, MG, SP, and UNK.
Selected development operating point
- Training datasets: qalb14, qalb15
- Development datasets: qalb14, qalb15
- Threshold:
0.35 - Binary error F1:
0.778608 - Precision:
0.861525 - Recall:
0.710251 - False positives per 1,000 tokens:
25.620425
The threshold applies to the strongest non-UC marginal. The selected error
category is emitted only when that marginal clears the threshold.
Files
model.joblib: trusted sklearn-crfsuite model artifactmanifest.json: model, feature, threshold, data, and dependency metadatalabel_mapping.json: QALB-to-Baligh mappingthreshold_sweep.json: development operating-point sweepsmoke_test.json: dependency-free inference proberequirements.txt: exact loading dependenciesSHA256SUMS: integrity checks
Safety and licensing
Joblib uses pickle internally. Load this model only from a trusted repository
and verify SHA256SUMS first. Keep the repository private until QALB-derived
model redistribution and the intended model license have been reviewed.