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  license: apache-2.0
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  ---
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  Using the [DistilRoBERTa](https://huggingface.co/distilroberta-base) model as starting point, the ClimateBERT Language Model is additionally pretrained 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-f | |
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  |--------------------------------------------------------------------------|----------------|
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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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- ### BibTeX entry and citation info
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  ```bibtex
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  @article{wkbl2021,
 
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  license: apache-2.0
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  ---
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+ # Model Card for distilroberta-base-climate-f
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+ ## Model Description
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+ This is the ClimateBERT language model based on the FULL-SELECT sample selection strategy.
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+ Note: *We generally recommend choosing this language model over those based on the other sample selection strategies (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 pretrained 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 card
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  | distilroberta-base-climate-f | |
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  |--------------------------------------------------------------------------|----------------|
 
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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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  @article{wkbl2021,