RoBERTa is a pre-trained language model for Central Kurdish (Sorani) that provides high-quality contextual word embeddings. This model serves as a feature extractor .

Model Details

Architecture

Base Model: XLM-RoBERTa-large
Hidden Size: 1024
Layers: 24
Attention Heads: 16

Training Data

1B token Kurdish corpus (KurdishTextCorpus)
Covers various domains including news, literature, and web text

Pretraining

Objective: Masked language modeling (15% dynamic masking)
Batch Size: 128
Sequence Length: 512 tokens
Training Hardware: 4× NVIDIA A100 GPUs

Uses

Direct Use

Feature extraction for Kurdish text
Contextual word embeddings

Downstream Use

Fine-tuning for:
- Named Entity Recognition (NER)
- Text classification
- Question answering
- Other sequence labeling tasks The corpus data tables and the detailed methodology can be found in the full research paper and are summarized here for quick reference:

Corpus Data Tables Summary

Table 1: AsoSoft Kurdish Text Corpus

Source	Number of Tokens
Crawled From Websites	95M
Text Books	45M
Magazines	48M
Sum	188M

Table 2: Muhammad Azizi and AramRafeq Text Corpus

Source	Number of Tokens
Wikipedia	13.5M
Wishe Website	11M
Speemedia Website	6.5M
Kurdiu Website	19M
Dengiamerika Website	2M
Chawg Website	8M
Sum	60M

Table 3: The Kurdish Text Corpus Used to Train BERT

Corpus Name	Number of Tokens
Oscar 2019 corpus	48.5M
AsoSoft corpus	188M
Muhammad Azizi and AramRafeq corpus	60M
Sum	296.5M

How to Use

Feature Extraction

python

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("abdulhade/RoBERTa-large-SizeCorpus_1B")
model = AutoModel.from_pretrained("abdulhade/RoBERTa-large-SizeCorpus_1B")

text = "لیژنەی فتوا دەلێن سینگڵ زەکات وسەرفیترەی پێ دەشێت."
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

# Contextual embeddings
embeddings = outputs.last_hidden_state

Cite

If you are using our text corpus cite us.

@article{abdullah2024ner,
  title={NER-RoBERTa: Fine-Tuning RoBERTa for Named Entity Recognition (NER) within Low-Resource Languages},
  author={Abdullah, Aso A and Abdulla, Sana H and Toufiq, Darya M and others},
  journal={arXiv preprint arXiv:2412.15252},
  year={2024}
}