This model was produced by fine-tuning DeBERTa XL on a dataset produced by contractors labeling a bunch of LM-generated completions to snippets of fanfiction that were selected by various heuristics to have a high probability of being completed violently.
I think you might have better performance if you train your own DeBERTa XL-like model with classification of different snippets as a secondary objective alongside masked token prediction, rather than just fine-tuning with that classification after the initial model training. (You might use different snippets in each step to avoid double-dipping the information in that sample, analogous to splitting text data for causal inference, e.g., Egami et al 2018.) The Hugging Face DeBERTa XL might not contain the features that would be most useful for the follow-up task of nonviolence fine-tuning. However, that might be a less interesting exercise if you want to build tools for working with more naturalistic models.
I think you might have better performance if you train your own DeBERTa XL-like model with classification of different snippets as a secondary objective alongside masked token prediction, rather than just fine-tuning with that classification after the initial model training. (You might use different snippets in each step to avoid double-dipping the information in that sample, analogous to splitting text data for causal inference, e.g., Egami et al 2018.) The Hugging Face DeBERTa XL might not contain the features that would be most useful for the follow-up task of nonviolence fine-tuning. However, that might be a less interesting exercise if you want to build tools for working with more naturalistic models.