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 In this work, we propose ME²-BERT, the first holistic  framework for fine-tuning a pre-trained language model like BERT to the task of moral foundation prediction. ME²-BERT integrates events and emotions for learning domain-invariant morality-relevant text representations.
 Our extensive experiments show that ME²-BERT outperforms existing state-of-the-art methods for moral foundation prediction, with an average percentage increase up to 35% in the out-of-domain scenario.
-The link to the paper is [here](https://aclanthology.org/2025.coling-main.638.pdf). The source code is available [here](https://github.com/lorenzozangari/ME2-BERT).
 ## Training Data
 ME²-BERT was fine-tuned on the [**E2MoCase dataset**](https://arxiv.org/pdf/2409.09001) (available upon request), which consists of 97,251 paragraphs from news articles encompassing both event-based and event-free samples. It includes annotations for:

 In this work, we propose ME²-BERT, the first holistic  framework for fine-tuning a pre-trained language model like BERT to the task of moral foundation prediction. ME²-BERT integrates events and emotions for learning domain-invariant morality-relevant text representations.
 Our extensive experiments show that ME²-BERT outperforms existing state-of-the-art methods for moral foundation prediction, with an average percentage increase up to 35% in the out-of-domain scenario.
+[Paper](https://aclanthology.org/2025.coling-main.638.pdf) | [Source code](https://github.com/lorenzozangari/ME2-BERT) | [WebApp](https://huggingface.co/spaces/lorenzozan/ME2-BERT)
 ## Training Data
 ME²-BERT was fine-tuned on the [**E2MoCase dataset**](https://arxiv.org/pdf/2409.09001) (available upon request), which consists of 97,251 paragraphs from news articles encompassing both event-based and event-free samples. It includes annotations for: