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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- climate |
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--- |
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# Model Card for distilroberta-base-climate-d-s |
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## Model Description |
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This is the ClimateBERT language model based on the DIV-SELECT and SIM-SELECT sample selection strategy. |
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*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).* |
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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). |
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## Climate performance model card |
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| distilroberta-base-climate-d-s | | |
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|--------------------------------------------------------------------------|----------------| |
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| 1. Is the resulting model publicly available? | Yes | |
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| 2. How much time does the training of the final model take? | 48 hours | |
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| 3. How much time did all experiments take (incl. hyperparameter search)? | 350 hours | |
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| 4. What was the power of GPU and CPU? | 0.7 kW | |
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| 5. At which geo location were the computations performed? | Germany | |
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| 6. What was the energy mix at the geo location? | 470 gCO2eq/kWh | |
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| 7. How much CO2eq was emitted to train the final model? | 15.79 kg | |
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| 8. How much CO2eq was emitted for all experiments? | 115.15 kg | |
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| 9. What is the average CO2eq emission for the inference of one sample? | 0.62 mg | |
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| 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. | |
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| 11. Comments | Block pruning could decrease CO2eq emissions | |
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## Citation Information |
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```bibtex |
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@inproceedings{wkbl2022climatebert, |
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title={{ClimateBERT: A Pretrained Language Model for Climate-Related Text}}, |
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author={Webersinke, Nicolas and Kraus, Mathias and Bingler, Julia and Leippold, Markus}, |
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booktitle={Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges}, |
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year={2022}, |
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doi={https://doi.org/10.48550/arXiv.2212.13631}, |
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} |
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``` |
