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  • Adityajaiswal9050 Fine-Tuning-BERT-for-Mental-Health-Text . . .
    Key Steps: Data Collection Preprocessing: Gather mental health-related datasets, clean the data, and preprocess text (e g , tokenization, removing special characters) Labels such as "Anxiety" and "Depression" are encoded for training Model Setup: Load a pre-trained BERT model from Hugging Face's Transformers library
  • mental mental-bert-base-uncased - Hugging Face
    We train and release masked language models for mental health to facilitate the automatic detection of mental disorders in online social content for non-clinical use The models may help social workers find potential individuals in need of early prevention
  • Mental Health Text Classification with HuggingFace BERT . . .
    #machinelearning #datascience #python #llm Discover how to leverage HuggingFace Transformers and BERT for mental health text classification In this tutorial, we fine-tune BERT to
  • Looking for Mental Health Support Datasets for building a . . .
    I’m currently developing a multi-turn chatbot aimed at helping users manage anxiety, depression, and other mental health challenges I’m seeking datasets related to mental health support conversations to train the chatbot So far, I have found two datasets: ESConv and AUGESC However, these kinds of datasets seem to be quite rare
  • tanusrich Mental_Health_Chatbot · Hugging Face
    Fine-Tuned Dataset: A specialized dataset curated for mental health conversations Purpose: Provide empathetic, supportive responses for individuals seeking mental health assistance This model is based on LLaMA-2-7b architecture
  • Fine-Tuning BERT for Text Classification Using Hugging Face . . .
    In this article, we’ll walk through how to fine-tune BERT for a text classification task using Hugging Face Transformers and the IMDB dataset Why Fine-Tune a Pre-Trained Model?
  • [2306. 16891] Harnessing the Power of Hugging Face . . .
    Using social media and pre-trained language models, this study explores how user-generated data can be used to predict mental disorder symptoms Our study compares four different BERT models of Hugging Face with standard machine learning techniques used in automatic depression diagnosis in recent literature





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