azwikiner-xlmr
Named entity recognition for Azerbaijani: XLM-RoBERTa base fine-tuned on azWikiNER, a manually corrected corpus of 16,740 Azerbaijani Wikipedia segments with 16 entity categories. Trained in April 2021; construction and evaluation are described in Ibiyev & Novák, TSD 2021 (doi:10.1007/978-3-030-83527-9_26) and in the author's PhD dissertation (PPKE ITK, 2026).
Entity categories
CARDINAL, EVENT, FAC, GPE, LOC, MONEY, NORP, ORDINAL, ORG, PER, PERCENT, PROD, QUANTITY, TIME, WORK_OF_ART, and TITLE (professions, roles and military ranks attached to person names, e.g. aktyor 'actor', şair 'poet'). TIME covers both times and dates; PROD corresponds to OntoNotes PRODUCT. The classifier head has two vestigial classes that do not occur in practice: an interim OTHER label (one training span) and an empty label inherited from a trailing newline in the label file.
Results
Entity-level scores on the azWikiNER test set (1,618 segments), computed with the standard conlleval script:
| Tagset | Precision | Recall | F1 |
|---|---|---|---|
| Full (16 types) | 80.21 | 83.38 | 81.77 |
| Common (15 types shared with zero-shot OntoNotes models) | 81.30 | 84.37 | 82.81 |
Per-type F1 on the test set (full tagset):
| Type | Gold spans | F1 |
|---|---|---|
| GPE | 1354 | 92.05 |
| PER | 519 | 86.45 |
| LOC | 254 | 66.93 |
| ORG | 248 | 68.95 |
| TITLE | 210 | 67.44 |
| CARDINAL | 146 | 77.74 |
| TIME | 131 | 77.61 |
| WORK_OF_ART | 55 | 43.64 |
| FAC | 52 | 57.41 |
| NORP | 46 | 55.77 |
| EVENT | 37 | 81.16 |
| ORDINAL | 34 | 81.69 |
| QUANTITY | 29 | 81.36 |
| PERCENT | 10 | 100.00 |
| MONEY | 8 | 40.00 |
| PROD | 8 | 16.67 |
Training
Fine-tuned with the token-classification example of transformers 4.5.0 on 4 GPUs: AdamW, learning rate 1e-4 with linear decay, no warmup, no weight decay, per-device batch size 8 (effective 32), mixed precision (fp16), seed 42, 6 epochs. Splits: 13,483 train / 1,639 dev / 1,618 test segments. The full training configuration is included in this repository as training_args.bin.
Usage
from transformers import pipeline
ner = pipeline("ner", model="kamranibiyev/azwikiner-xlmr", aggregation_strategy="simple")
ner("Elnur Hüseynov Bakıda anadan olub.")
Limitations
The training text comes from Wikipedia; expect lower accuracy on other genres. The numeric and date-like categories entered the corpus through a zero-shot OntoNotes pass with rule-based merging rather than span-by-span human verification, so scores on those types should be read with care. Long inputs are truncated at the tokenizer limit. There is no DATE tag; dates fall under TIME.
Citation
@inproceedings{ibiyev2021azwikiner,
author = {Kamran Ibiyev and Attila Nov{\'a}k},
title = {Using Zero-Shot Transfer to Initialize azWikiNER, a Gold
Standard Named Entity Corpus for the Azerbaijani Language},
booktitle = {Text, Speech, and Dialogue (TSD 2021)},
pages = {305--317},
publisher = {Springer},
year = {2021},
doi = {10.1007/978-3-030-83527-9_26},
}