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---
language: en
license: apache-2.0
datasets: climatebert/environmental_claims
tags:
- ClimateBERT
- climate
---
# Model Card for environmental-claims
## Model Description
The environmental-claims model is fine-tuned on the [EnvironmentalClaims](https://huggingface.co/datasets/climatebert/environmental_claims) dataset by using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) model as pre-trained language model. The underlying methodology can be found in our [research paper](https://arxiv.org/abs/2209.00507).
## Climate Performance Model Card
| environmental-claims | |
|--------------------------------------------------------------------------|----------------|
| 1. Is the resulting model publicly available? | Yes |
| 2. How much time does the training of the final model take? | < 5 min |
| 3. How much time did all experiments take (incl. hyperparameter search)? | 60 hours |
| 4. What was the power of GPU and CPU? | 0.3 kW |
| 5. At which geo location were the computations performed? | Switzerland |
| 6. What was the energy mix at the geo location? | 89 gCO2eq/kWh |
| 7. How much CO2eq was emitted to train the final model? | 2.2 g |
| 8. How much CO2eq was emitted for all experiments? | 1.6 kg |
| 9. What is the average CO2eq emission for the inference of one sample? | 0.0067 mg |
| 10. Which positive environmental impact can be expected from this work? | This work can help detect and evaluate environmental claims and thus have a positive impact on the environment in the future. |
| 11. Comments | - |
## Citation Information
```bibtex
@misc{stammbach2022environmentalclaims,
title = {A Dataset for Detecting Real-World Environmental Claims},
author = {Stammbach, Dominik and Webersinke, Nicolas and Bingler, Julia Anna and Kraus, Mathias and Leippold, Markus},
year = {2022},
doi = {10.48550/ARXIV.2209.00507},
url = {https://arxiv.org/abs/2209.00507},
publisher = {arXiv},
}
``` |