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@@ -24,7 +24,7 @@ The model has been trained on paragraphs from German company websites using an e
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  1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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  2. Training a classification head with features from the fine-tuned Sentence Transformer.
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- The model is designed to predict the clean technology capabilities of German companies based on their website texts. It is intended to be used in conjunction with the [TwinTransitionMapper_AI](https://huggingface.co/LKriesch/TwinTransitionMapper_AI) model to identify companies contributing to the twin transition in Germany. For detailed information on the fine-tuning process and the results of these models, please refer to: [LINK TO WORKING PAPER]
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  ### Model Description
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  - **Model Type:** SetFit
 
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  1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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  2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+ The model is designed to predict the clean technology capabilities of German companies based on their website texts. It is intended to be used in conjunction with the [TwinTransitionMapper_AI](https://huggingface.co/LKriesch/TwinTransitionMapper_AI) model to identify companies contributing to the twin transition in Germany. For detailed information on the fine-tuning process and the results of these models, please refer to the [paper](https://drive.google.com/file/d/1MN0GSl1FExHYkDyN_VhEt8yFwMX1MM4x/view?usp=drive_link).
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  ### Model Description
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  - **Model Type:** SetFit