---
language: en
license: mit
library_name: transformers
tags:
  - climate-change
  - domain-adaptation
  - masked-language-modeling
  - scientific-nlp
  - transformer
  - BERT
  - ClimateBERT
metrics:
  - f1
model-index:
  - name: SciClimateBERT
    results:
      - task:
          type: text-classification
          name: Climate NLP Tasks (ClimaBench)
        dataset:
          name: ClimaBench
          type: benchmark
        metrics:
          - type: f1
            name: Macro F1 (avg)
            value: 57.829
---

# SciClimateBERT 🌎🔬

**SciClimateBERT** is a domain-adapted version of [**ClimateBERT**](https://huggingface.co/climatebert/distilroberta-base-climate-f), further pretrained on peer-reviewed scientific papers focused on climate change. While ClimateBERT is tuned for general climate-related text, SciClimateBERT narrows the focus to high-quality academic content, improving performance in scientific NLP applications.

## 🔍 Overview

- **Base Model**: ClimateBERT (RoBERTa-based architecture)
- **Pretraining Method**: Continued pretraining (domain adaptation) with Masked Language Modeling (MLM)
- **Corpus**: Scientific climate change literature from top-tier journals
- **Tokenizer**: ClimateBERT tokenizer (unchanged)
- **Language**: English
- **Domain**: Scientific climate change research

## 📊 Performance

Evaluated on **ClimaBench**, a benchmark suite for climate-focused NLP tasks:

| Metric         | Value        |
|----------------|--------------|
| Macro F1 (avg) | 57.83|
| Tasks won      | 0/7  |
| Avg. Std Dev   | 0.01747|

While based on ClimateBERT, this model focuses on structured scientific input, making it ideal for downstream applications in climate science and research automation.

Climate performance model card:
|SciClimateBERT||
|---------------------------------|-----------------------------|
| 1. Model publicly available?    | Yes                         |
| 2. Time to train final model    |300h                        | 
| 3. Time for all experiments     | 1,226h ~ 51 days       |
| 4. Power of GPU and CPU         | 0.250 kW + 0.013 kW         |
| 5. Location for computations    | Croatia                     |
| 6. Energy mix at location       | 224.71 gCO<sub>2</sub>eq/kWh        |
| 7. CO$_2$eq for final model     | 18 kg CO<sub>2</sub>  |
| 8. CO$_2$eq for all experiments | 74 kg CO<sub>2</sub>                |

## 🧪 Intended Uses

**Use for:**
- Scientific climate change text classification and extraction
- Knowledge base and graph construction in climate policy and research domains

**Not suitable for:**
- Non-scientific general-purpose text
- Multilingual applications

Example:
``` python
from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline
import torch

# Load the pretrained model and tokenizer
model_name = "P0L3/clirebert_clirevocab_uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)

# Move model to GPU if available
device = 0 if torch.cuda.is_available() else -1

# Create a fill-mask pipeline
fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer, device=device)

# Example input from scientific climate literature
text = "The increase in greenhouse gas emissions has significantly affected the <mask> balance of the Earth."

# Run prediction
predictions = fill_mask(text)

# Show top predictions
print(text)
print(10*">")
for p in predictions:
    print(f"{p['sequence']} — {p['score']:.4f}")
```
Output:
``` shell
The increase in greenhouse gas emissions has significantly affected the <mask> balance of the Earth.
>>>>>>>>>>
The increase in greenhouse gas ... affected the energy balance of the Earth. — 0.7897
The increase in greenhouse gas ... affected the radiation balance of the Earth. — 0.0522
The increase in greenhouse gas ... affected the mass balance of the Earth. — 0.0401
The increase in greenhouse gas ... affected the water balance of the Earth. — 0.0359
The increase in greenhouse gas ... affected the carbon balance of the Earth. — 0.0190
```

## ⚠️ Limitations
- May reflect scientific publication biases

## 🧾 Citation

If you use this model, please cite:

```bibtex
@Article{Poleksić2025,
  author={Poleksi{\'{c}}, Andrija
  and Martin{\v{c}}i{\'{c}}-Ip{\v{s}}i{\'{c}}, Sanda},
  title={Pretraining and evaluation of BERT models for climate research},
  journal={Discover Applied Sciences},
  year={2025},
  month={Oct},
  day={24},
  volume={7},
  number={11},
  pages={1278},
  issn={3004-9261},
  doi={10.1007/s42452-025-07740-5},
  url={https://doi.org/10.1007/s42452-025-07740-5}
}