---
license: mit
language:
- en
metrics:
- accuracy
- f1
base_model:
- climatebert/distilroberta-base-climate-detector
pipeline_tag: text-classification
tags:
- islamic finance
- islamic banks
- text classification
- climate
- binary classification
- NLP
- finance
---


# Islamic-FinClimateBERT: Fine-Tuned ClimateBERT for Islamic Finance Climate Discourse

A domain-adapted binary classifier fine-tuned on *climate-related vs. non-climate* sentences from Islamic finance corpora. This model is based on [`ClimateBERT`](https://huggingface.co/climatebert/distilroberta-base-climate-detector) and is specialized for detecting climate relevance in **Islamic financial narratives**.


## Model Summary

- **Base model**: [`ClimateBERT`](https://huggingface.co/climatebert/distilroberta-base-climate-detector)
- **Architecture**: RoBERTa-based, distilled
- **Task**: Binary sentence classification
- **Domain**: Islamic Finance + Climate Discourse
- **Labels**: 
  - `0` → Not Climate-Relevant  
  - `1` → Climate-Relevant
- **Language**: English (Islamic finance-specific vocabulary)
- **Training Data Size**: 1,132 manually annotated sentences


## Training Pipeline
- **Framework**: Hugging Face `transformers` + `datasets`
- **Tokenizer**: ClimateBERT tokenizer (BPE)
- **Training split**: Stratified 80/20 (train/test)
- **Evaluation metric**: F1 (macro), accuracy
- **Optimizer**: AdamW with weight decay
- **Epochs**: 4
- **Batch size**: 16
- **Precision**: FP16 enabled

### Evaluation
| Metric     | Value     |
|------------|-----------|
| Accuracy   | 0.9868    |
| F1-score   | 0.9868    |
| Eval loss  | 0.0553    |

---

## Evaluation & Domain Comparison

The **Islamic-FinClimateBERT** model was evaluated against the original [`ClimateBERT`](https://huggingface.co/climatebert/distilroberta-base-climate-detector) using **79,876** sentence-level samples extracted from 838 annual reports of 103 Islamic banks across 25 jurisdictions (2015–2024).

This comparative evaluation assesses how domain fine-tuning affects climate relevance detection within Islamic finance discourse.

### Evaluation Summary

| Metric           | Fine-Tuned | Original | Description |
|------------------|------------|-----------|--------------|
| **Total Sentences** | 79,876 | – | Sentences compared 1-to-1 |
| **Agreements** | 70,209 | – | Sentences where both models agreed |
| **Disagreements** | 9,667 | – | Sentences with differing predictions |
| **Overall Accuracy** | 0.88 | – | Agreement between models |


### Classification Report (Fine-Tuned vs. Original)

| Label | Precision | Recall | F1-score | Support |
|:------|:-----------:|:--------:|:----------:|:---------:|
| **Climate** | 0.92 | 0.83 | 0.87 | 39,558 |
| **Non-Climate** | 0.85 | 0.93 | 0.89 | 40,318 |
| **Overall Accuracy** | – | – | **0.88** | 79,876 |
| **Macro Avg** | 0.88 | 0.88 | 0.88 | – |

### Confusion Matrix

|                      | **Fine = Climate** | **Fine = Non-Climate** |
|----------------------:|------------------:|-----------------------:|
| **Orig = Climate**    | 32,887 | 6,671 |
| **Orig = Non-Climate**| 2,996 | 37,322 |

- The fine-tuned model shows **strong domain adaptation**, improving contextual sensitivity to Islamic finance climate narratives.  
- It tends to **classify fewer sentences as “climate-relevant”** compared to the base model, reflecting a **more conservative and context-aware** understanding of climate-related terminology in Islamic finance reporting.  

---
## GitHub Repository

The full project repository, including training notebooks, dataset scripts, and evaluation pipelines, is available at [https://github.com/bilalezafar/Islamic-FinClimateBERT](https://github.com/bilalezafar/Islamic-FinClimateBERT).

---

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("bilalzafar/Islamic-FinClimateBERT")
model = AutoModelForSequenceClassification.from_pretrained("bilalzafar/Islamic-FinClimateBERT")

# Define classifier function
def clf(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=-1)
    label = probs.argmax().item()
    score = probs.max().item()
    return [{"label": "Climate" if label == 1 else "Not Climate", "score": round(score, 4)}]

# Example usage
text = "The bank’s green sukuk issuance aims to support renewable energy projects in the country."
print(clf(text)[0])

# Example output: {'label': 'Climate', 'score': 0.9995}
```

---
## Citation

```bibtex
@article{zafar2026islamicfinclimatebert,
  title   = {Talk or Action? Unveiling the Nature and Depth of Climate Disclosures in Islamic Banks Using Machine Learning},
  author  = {Zafar, Muhammad Bilal},
  journal = {Borsa Istanbul Review},
  year    = {2026},
  doi     = {10.1016/j.bir.2026.100789}
}
```

Zafar, M. B. (2026). Talk or action? Unveiling the nature and depth of climate disclosures in Islamic banks using machine learning. Borsa Istanbul Review. https://doi.org/10.1016/j.bir.2026.100789