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Azerbaijani NER Benchmark

This dataset is the evaluation benchmark used to test and compare four Azerbaijani Named Entity Recognition (NER) models trained on the LocalDoc/azerbaijani-ner-dataset.

Entity Types

The dataset uses IOB2 annotation with 12 entity categories:

Tag	Description
O	Outside (non-entity token)
B-PERSON / I-PERSON	Person names (e.g., İlham Əliyev)
B-LOCATION / I-LOCATION	Geographic locations (e.g., Bakı, Azərbaycan)
B-ORGANISATION / I-ORGANISATION	Organizations (e.g., universitetlər, şirkətlər)
B-DATE / I-DATE	Date expressions (e.g., 2014-cü il, yanvar ayı)

Model Comparison

The following four models were evaluated on this benchmark:

Model	Parameters	F1-Score	Hugging Face
mBERT Azerbaijani NER	180M	67.70%	IsmatS/mbert-az-ner
XLM-RoBERTa Base Azerbaijani NER	125M	75.22%	IsmatS/xlm-roberta-az-ner
XLM-RoBERTa Large Azerbaijani NER	355M	75.48%	IsmatS/xlm_roberta_large_az_ner
Azerbaijani-Turkish BERT Base NER	110M	73.55%	IsmatS/azeri-turkish-bert-ner

XLM-RoBERTa Large achieves the highest F1-score of 75.48% and is used in the production deployment at named-entity-recognition.fly.dev.

How to Use for Evaluation

Quick Start

from datasets import load_dataset

dataset = load_dataset("IsmatS/azerbaijani-ner-benchmark", split="test")
print(dataset)
# Dataset({features: ['tokens', 'ner_tags'], num_rows: 2915})

Evaluate a Model

Use the provided evaluate_models.py script to reproduce benchmark results:

pip install transformers datasets seqeval
python evaluate_models.py

Or evaluate a single model programmatically:

from transformers import pipeline
from datasets import load_dataset
from seqeval.metrics import f1_score

# Load benchmark
dataset = load_dataset("IsmatS/azerbaijani-ner-benchmark", split="test")

# Load model
ner_pipeline = pipeline(
    "token-classification",
    model="IsmatS/xlm-roberta-az-ner",
    aggregation_strategy="simple"
)

# Run evaluation
# See evaluate_models.py for the full evaluation loop

Evaluation Script

The full evaluation script (evaluate_models.py) in this repository:

Loads each of the 4 Azerbaijani NER models from Hugging Face Hub
Runs inference on all 2,915 benchmark sentences
Computes precision, recall, and F1-score using seqeval
Prints a comparison table with all results

Dataset Loading

from datasets import load_dataset

# Load test split (the full benchmark)
benchmark = load_dataset("IsmatS/azerbaijani-ner-benchmark", split="test")

# Inspect a sample
print(benchmark[0])
# {
#   'tokens': ['2014-cü', 'ildə', 'Azərbaycan', ...],
#   'ner_tags': [7, 8, 3, ...]
# }

Citation

If you use this benchmark in your research, please cite the original dataset:

@dataset{azerbaijani_ner_benchmark,
  title     = {Azerbaijani NER Benchmark},
  author    = {Ismat Samadov},
  year      = {2024},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/IsmatS/azerbaijani-ner-benchmark},
  note      = {Derived from LocalDoc/azerbaijani-ner-dataset}
}