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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("citiusLTL/DisorBERT") model = AutoModelForMaskedLM.from_pretrained("citiusLTL/DisorBERT") - Notebooks
- Google Colab
- Kaggle
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<h1>Click
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<a href="https://www.geeksforgeeks.org/"
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target="_blank">here
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</a> to visit GeeksForGeeks website.
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[DisorBERT](https://aclanthology.org/2023.acl-long.853/)
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is a double-domain adaptation of a 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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<img style="float: left;" src="https://cdn-uploads.huggingface.co/production/uploads/64b946226b5ee8c388730ec1/uXCiWXUGrzhh6SE7ymBy_.png" width="150"/>
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[DisorBERT](https://aclanthology.org/2023.acl-long.853/)
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is a double-domain adaptation of a 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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