Uzbek exact-span NER (RemBERT + domain-adaptive MLM)
Named entity recognition for Uzbek text with exact character offsets.
Three classes: ORG (organizations and brands), NAME (people),
GEO (geographic entities).
Handles both Latin and Cyrillic scripts, plus Russian and English fragments.
Metrics
Exact-span match on a held-out set (1500 documents, 7698 entities). An entity counts only when the class and both boundaries match exactly.
| class | precision | recall | F1 |
|---|---|---|---|
| ORG | 0.8872 | 0.8665 | 0.8767 |
| NAME | 0.9182 | 0.8814 | 0.8994 |
| GEO | 0.9153 | 0.9096 | 0.9124 |
| micro | 0.9065 | 0.8862 | 0.8962 |
Intended use
Extracting people, organizations and locations from Uzbek news and social media text. Exact character offsets make the output suitable for downstream analytics: entity frequency and co-occurrence statistics, media monitoring, building entity indexes over document collections, and linking mentions back to their exact position in the source text.
Important: weights alone are not enough
Tags are predicted in the BILOU scheme and decoded with the Viterbi
algorithm using the transition matrix in transitions.json. That file is not a
standard Hugging Face artifact, so AutoModelForTokenClassification.from_pretrained
will ignore it. Without it decoding falls back to per-token argmax and F1 drops by
roughly one point. Boundary post-processing (quotes, brackets, affixes) is also
applied and also matters for exact-span scoring.
Run the model through the solution repository instead:
python -m solution.predict \
--model-dir <directory with these files> \
--input texts.jsonl \
--output predictions.jsonl
Input is JSON Lines with hash and text. Output is hash plus a list of
{"label", "start", "end"}, where offsets are zero-based Unicode character
positions and end is exclusive.
Inference settings come from run_config.json: window 512 with stride 128, BILOU,
Viterbi in conditional mode with transition weight 0.4.
Training
Two stages.
Domain adaptation. google/rembert was further pretrained with masked language
modeling on an Uzbek corpus of 405,585 passages (~220M tokens): Telegram blogs and
news from tahrirchi/uz-crawl, Cyrillic and Latin books from
tahrirchi/uz-books-v2, plus 8% Russian and English Wikipedia to preserve
multilinguality. Whole-word masking at 15%, 16,800 steps, held-out perplexity 11.5.
Fine-tuning. Token classification in BILOU, window 512 with stride 128, effective batch 128, learning rate 3e-5, checkpoint selected by exact-span micro-F1.
Full reproduction protocol is in REPRODUCE.md of the solution repository.
Limitations
Trained on news and blog text; other genres were not evaluated.
Attribution
The base model google/rembert is licensed under Apache 2.0. The adaptation corpus
was assembled from tahrirchi/uz-crawl, tahrirchi/uz-books-v2 and
wikimedia/wikipedia; their respective licenses apply to the underlying texts.
The annotated NER dataset used for fine-tuning was provided by Brand Analytics.