readme: ---
  language: en
  license: apache-2.0
  ---
  
  # Model Card for distilroberta-base-climate-s
  
  ## Model Description
  
  This is the ClimateBERT language model based on the SIM-SELECT sample selection strategy.
  
  *Note: We generally recommend choosing the [distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model over this language model (unless you have good reasons not to).*
  
  Using the [DistilRoBERTa](https://huggingface.co/distilroberta-base) model as starting point, the ClimateBERT Language Model is additionally pre-trained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. The underlying methodology can be found in our [language model research paper](https://arxiv.org/abs/2110.12010).
  
  ## Climate performance model card
  
  | distilroberta-base-climate-s                                             |                |
  |--------------------------------------------------------------------------|----------------|
  | 1. Is the resulting model publicly available?                            | Yes            |
  | 2. How much time does the training of the final model take?              | 48 hours        |
  | 3. How much time did all experiments take (incl. hyperparameter search)? | 350 hours      |
  | 4. What was the power of GPU and CPU?                                    | 0.7 kW         |
  | 5. At which geo location were the computations performed?                | Germany        |
  | 6. What was the energy mix at the geo location?                          | 470 gCO2eq/kWh |
  | 7. How much CO2eq was emitted to train the final model?                  | 15.79 kg        |
  | 8. How much CO2eq was emitted for all experiments?                       | 115.15 kg       |
  | 9. What is the average CO2eq emission for the inference of one sample?   | 0.62 mg        |
  | 10. Which positive environmental impact can be expected from this work?  | This work can be categorized as a building block tools following Jin et al (2021). It supports the training of NLP models in the field of climate change and, thereby, have a positive environmental impact in the future. |
  | 11. Comments                                                             | Block pruning could decrease CO2eq emissions |
  
  ## Citation Information
  
  ```bibtex
  @article{wkbl2021,
          title={ClimateBERT: A Pretrained Language Model for Climate-Related Text},
          author={Webersinke, Nicolas and Kraus, Mathias and Bingler, Julia and Leippold, Markus},
          journal={arXiv preprint arXiv:2110.12010},
          year={2021}
  }
  ```