KazBERT-NERD

KazBERT for Named Entity Recognition (NER)

This model is a fine-tuned version of KazBERT (a specialized BERT model for the Kazakh language) on the KazNERD dataset. It is designed to identify and categorize named entities in Kazakh text into 25 distinct classes.

Model Description

While multilingual models like XLM-RoBERTa cover many languages, KazBERT-NER is specifically optimized for the nuances of the Kazakh language. Despite having significantly fewer parameters than "Large" multilingual models, it achieves competitive performance, demonstrating superior efficiency and domain-specific knowledge (especially in complex categories like Adages).

• Base Model: Eraly-ml/KazBERT
• Task: Token Classification (NER)
• Dataset: issai/kaznerd
• Language: Kazakh (kk)

Training Hyperparameters

The model was trained with a focus on stability and fine-tuning the pre-existing semantic knowledge of KazBERT:

• Learning Rate:
• Batch Size: 32
• Epochs: 8
• Optimizer: AdamW
• Seed: 1
• Label Alignment: Sub-word labels were aligned with the first token of the word (label_all_tokens=False).

Evaluation Results

The model shows exceptional stability across both Validation and Test sets, proving its ability to generalize to unseen Kazakh text.

Overall Performance (Test Set)

Detailed Metrics by Category (Test Set)

The model excels in identifying core entities such as Persons, Dates, and Monetary values.

Comparison vs XLM-RoBERTa Large

While models like xlm-roberta-large-kaznerd (560M params) may show slightly higher overall F1, KazBERT-NER (~110M params) offers:

Efficiency: 5x fewer parameters, leading to much faster inference and lower deployment costs.

Native Understanding: Better performance on culture-specific entities like ADAGE compared to many multilingual alternatives.

Clean Embeddings: Contextual representations focused purely on Kazakh syntax and semantics.

How to Use

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_name = "Eraly-ml/KazBERT-NERD"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
example = "Қазақстан Республикасы — Орталық Азияда орналасқан мемлекет."

print(nlp(example))

# [{'entity_group': 'GPE', 'score': 0.9998361, 'word': 'қазақстан республикасы', 'start': 0, 'end': 22},
# {'entity_group': 'LOCATION', 'score': 0.9993429, 'word': 'орталық азияда', 'start': 25, 'end': 39}]