xlm_roberta_large_az_ner

Azerbaijani Named Entity Recognition with XLM-RoBERTa Large

Fine-tuned version of xlm-roberta-large for Named Entity Recognition (NER) on Azerbaijani text. Recognizes 12 entity types including persons, locations, organizations, dates, and more.

Hugging Face: IsmatS/xlm_roberta_large_az_ner

Quick Start

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_name = "IsmatS/xlm_roberta_large_az_ner"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Shahla Khuduyeva və Pasha Sığorta şirkəti haqqında məlumat."
entities = ner(text)
for e in entities:
    print(f"{e['entity_group']:15} {e['word']:25} ({e['score']:.2f})")

Output:

PERSON          Shahla Khuduyeva          (0.97)
ORGANISATION    Pasha Sığorta             (0.95)

Model Details

Supported Entity Types

Performance

Best Checkpoint (Epoch 6)

Training History

Entity-Level Metrics (Epoch 7)

Overall: Micro Avg F1 = 0.75 | Weighted Avg F1 = 0.74

Training Configuration

TrainingArguments(
    learning_rate=2e-5,
    per_device_train_batch_size=128,
    per_device_eval_batch_size=128,
    num_train_epochs=12,
    weight_decay=0.005,
    fp16=True,
    metric_for_best_model="f1",
    load_best_model_at_end=True,
)

• Optimizer: AdamW
• Early stopping: patience=5 on F1
• Infrastructure: Google Colab A100 GPU

Usage

Installation

pip install transformers torch

Inference

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
import torch

model_name = "IsmatS/xlm_roberta_large_az_ner"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

device = 0 if torch.cuda.is_available() else -1
ner = pipeline("ner", model=model, tokenizer=tokenizer,
               aggregation_strategy="simple", device=device)

# Single text
text = "Bakı şəhərində Azərbaycan Respublikasının prezidenti İlham Əliyev."
results = ner(text)
for e in results:
    print(f"[{e['entity_group']}] {e['word']} (score: {e['score']:.3f})")

Batch Inference

texts = [
    "Bakı şəhərində İlham Əliyev çıxış etdi.",
    "SOCAR şirkəti 2024-cü ildə rekord gəlir əldə etdi.",
    "Heydər Əliyev Beynəlxalq Hava Limanı yeni terminalı açıldı.",
]

results = ner(texts)
for text, entities in zip(texts, results):
    print(f"\nText: {text}")
    for e in entities:
        print(f"  [{e['entity_group']}] {e['word']}")

Dataset

Trained on LocalDoc/azerbaijani-ner-dataset with 25 entity categories annotated in IOB2 format.

from datasets import load_dataset
dataset = load_dataset("LocalDoc/azerbaijani-ner-dataset")

Model Comparison

Files

xlm_roberta_large_az_ner/
├── README.md                 # This file
├── config.json               # Model configuration
├── model-001.safetensors     # Model weights
├── sentencepiece.bpe.model   # SentencePiece tokenizer
├── special_tokens_map.json   # Special token mappings
├── tokenizer.json            # Tokenizer vocabulary
├── tokenizer_config.json     # Tokenizer configuration
├── xlm_roberta_large.ipynb   # Training notebook
└── xlm_roberta_large.py      # Training script

Citation

@model{samadov2024xlm_large_az_ner,
  author = {Ismat Samadov},
  title = {XLM-RoBERTa Large Azerbaijani NER},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/IsmatS/xlm_roberta_large_az_ner}
}

License

Apache 2.0 — see LICENSE for details.