IslamicBank-HCDi-RoBERTa

IslamicBank-HCDi-RoBERTa

Model description

IslamicBank-HCDi-RoBERTa is a RoBERTa-based NLP transformer model fine-tuned to classify dimensions of human capital disclosure in Islamic banking. The model identifies whether a text segment is substantively related to human capital and assigns it to one of six human capital disclosure dimensions or to a separate Not related class.

The model is based on FacebookAI/roberta-base and was fine-tuned using a supervised seven-class text classification design. It is intended for research on human capital disclosure, Islamic banking, sustainability reporting, corporate disclosure, workforce disclosure and computational text analysis.

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Labels and Training data

The model was fine-tuned on a labelled text classification dataset developed from Islamic banking disclosure text. Candidate text segments were extracted using a human capital lexicon and then classified into human capital disclosure dimensions.

The final labelled dataset contained 13,218 observations across seven classes:

The dataset was split using a stratified 80:10:10 train-validation-test design:

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Training procedure

The model was fine-tuned using the Hugging Face Transformers library.

Class-weighted cross-entropy loss was used to address class imbalance.

Evaluation results

The selected RoBERTa model achieved the following performance:

Class-wise test performance

Model comparison

Four transformer models were fine-tuned and compared under the same experimental setup.

RoBERTa base was selected because it achieved the highest validation macro-F1 and strongest overall test performance.

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GitHub repository and related model

Pipeline notebooks, data preparation files, outputs and reproducible experiments are available on GitHub:

GitHub: bilalezafar/IslamicBank-HCDi-RoBERTa

A related binary human capital classifier is also available on Hugging Face. This model classifies text into two labels: HC and Non-HC.

Related model: bilalzafar/HC-BERT-IFIs

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Citation

If you use this model, please cite:

@misc{zafar2026_islamicbank_hcdi_roberta,
  title        = {IslamicBank-HCDi-RoBERTa: A Transformer Model for Classifying Dimensions of Human Capital Disclosure in Islamic Banking},
  author       = {Zafar, Muhammad Bilal},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/bilalzafar/IslamicBank-HCDi-RoBERTa}}
}

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How to use

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model_id = "bilalzafar/IslamicBank-HCDi-RoBERTa"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

classifier = pipeline(
    "text-classification",
    model=model,
    tokenizer=tokenizer,
    top_k=None,
    truncation=True,
    max_length=128
)

text = "The Bank provides regular training programmes to enhance employees' professional and Shariah-related competencies."

predictions = classifier(text)

best_prediction = max(predictions[0], key=lambda x: x["score"])

print(f"Dimension: {best_prediction['label']} | Prediction score: {best_prediction['score']:.4f}")

# Output {Dimension: Training, Development, and Human Capital Investment | Prediction score: 0.9986}