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README.md
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[DisorBERT](https://aclanthology.org/2023.acl-long.853/)
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is a double-domain adaptation of a BERT language model. First, is adapted to social media language, and then, adapted to the mental health domain. In both steps, it incorporated a lexical resource to guide the masking process of the language model and, therefore, to help it in paying more attention to words related to mental disorders.
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We follow the standard fine-tuning a masked language model of [Huggingface’s NLP Course](https://huggingface.co/learn/nlp-course/chapter7/3?fw=pt)
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We used the models provided by HuggingFace v4.24.0, and Pytorch v1.13.0.
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In particular, for training the model we used a batch size of 256, Adam optimizer, with a learning rate of 1e<sup>-5</sup>, and cross-entropy as a loss function. We trained the model for three epochs using a GPU NVIDIA Tesla V100 32GB SXM2.
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[DisorBERT](https://aclanthology.org/2023.acl-long.853/)
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is a double-domain adaptation of a BERT language model. First, is adapted to social media language, and then, adapted to the mental health domain. In both steps, it incorporated a lexical resource to guide the masking process of the language model and, therefore, to help it in paying more attention to words related to mental disorders.
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We follow the standard fine-tuning a masked language model of [Huggingface’s NLP Course](https://huggingface.co/learn/nlp-course/chapter7/3?fw=pt) 🤗.
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We used the models provided by HuggingFace v4.24.0, and Pytorch v1.13.0.
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In particular, for training the model we used a batch size of 256, Adam optimizer, with a learning rate of 1e<sup>-5</sup>, and cross-entropy as a loss function. We trained the model for three epochs using a GPU NVIDIA Tesla V100 32GB SXM2.
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