---
license: mit
base_model: microsoft/deberta-v3-base
language:
  - en
pipeline_tag: text-classification
tags:
  - generated_from_trainer
  - climate
  - un-general-assembly
  - text-classification
  - fine-tuned
metrics:
  - accuracy
model-index:
  - name: unga-climate-classifier
    results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# unga-climate-classifier

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) trained to classify climate-related sentences in English using a dataset of 5,600 annotated sentences from the United Nations General Assembly Corpus. It was developed to build the Executive Comparative Climate Attention (ECCA) indicator, introduced in a [paper](https://doi.org/10.1162/glep.a.1
) published in Global Environmental Politics.


# How to use

```python 
from transformers import pipeline classifier = pipeline("text-classification", model="mljn/unga-climate-classifier")

text = "Climate change poses a fundamental threat to our future."

result = classifier(text)

print(result)

[{'label': 'climate', 'score': 0.9988275170326233}]

```



# How to cite

If you use this model or the underlying dataset or indicator, please cite:

> Emiliano Grossman, Malo Jan; Executive Climate Change Attention: Toward an Indicator of Comparative Climate Change Attention. Global Environmental Politics 2025; doi: https://doi.org/10.1162/glep.a.1


```bibtex
@article{grossman2025executive,
  title={Executive Climate Change Attention: Toward an Indicator of Comparative Climate Change Attention},
  author={Grossman, Emiliano and Jan, Malo},
  journal={Global Environmental Politics},
  pages={1--14},
  year={2025},
  publisher={MIT Press 255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA~…}
}
```

### Model evaluation

It achieves the following results on the evaluation set:
- Loss: 0.0807
- Accuracy: 0.975
- F1 Macro: 0.9710
- Accuracy Balanced: 0.9715
- F1 Micro: 0.975
- Precision Macro: 0.9705
- Recall Macro: 0.9715
- Precision Micro: 0.975
- Recall Micro: 0.975


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 80
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Accuracy Balanced | F1 Micro | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| No log        | 1.0   | 123  | 0.1057          | 0.9726   | 0.9675   | 0.9583            | 0.9726   | 0.9783          | 0.9583       | 0.9726          | 0.9726       |
| No log        | 2.0   | 246  | 0.1102          | 0.9726   | 0.9683   | 0.9697            | 0.9726   | 0.9669          | 0.9697       | 0.9726          | 0.9726       |
| No log        | 3.0   | 369  | 0.0894          | 0.9798   | 0.9763   | 0.9729            | 0.9798   | 0.9800          | 0.9729       | 0.9798          | 0.9798       |
| No log        | 4.0   | 492  | 0.1098          | 0.9762   | 0.9723   | 0.9723            | 0.9762   | 0.9723          | 0.9723       | 0.9762          | 0.9762       |
| 0.1374        | 5.0   | 615  | 0.1026          | 0.9798   | 0.9763   | 0.9729            | 0.9798   | 0.9800          | 0.9729       | 0.9798          | 0.9798       |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.5.0+cu121
- Datasets 2.6.0
- Tokenizers 0.15.2