Instructions to use citiusLTL/DisorBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use citiusLTL/DisorBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="citiusLTL/DisorBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("citiusLTL/DisorBERT") model = AutoModelForMaskedLM.from_pretrained("citiusLTL/DisorBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# DisorBERT
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[DisorBERT](https://aclanthology.org/2023.acl-long.853/) We propose a double-domain adaptation of a language model. First, we adapted the model to social media language, and then, we adapted it to the mental health domain. In both steps, we 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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# DisorBERT
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 [DisorBERT](https://aclanthology.org/2023.acl-long.853/) We propose a double-domain adaptation of a language model. First, we adapted the model to social media language, and then, we adapted it to the mental health domain. In both steps, we 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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