IC-AnnoMI: In-Context MI Dialogues - GitHub Repository

IC-AnnoMI: In-Context MI Dialogues - GitHub Repository

The IC-AnnoMI repository contains source code and a synthetic dataset generated through in-context zero-shot LLM prompting for mental health and therapeutic counselling. IC-AnnoMI is a project that generates contextual MI dialogues using large language models (LLMs). The project contains source code and a synthetic dataset generated through zero-shot prompts, aiming to address the data scarcity and inherent bias problems in mental health and therapeutic consultation.

IC-AnnoMI: In-Context MI Dialogues - GitHub Repository

Részletes Bevezetés

IC-AnnoMI is an official repository that employs Large Language Models (LLMs) to generate in-context Motivational Interviewing (MI) dialogues. The repository includes a dataset folder with annotated MI dialogues across psychological and linguistic dimensions. It also provides a test set for experiments. The project aims to address scarce data and inherent bias challenges in mental health and therapeutic counselling by leveraging the capabilities of LLMs. The IC-AnnoMI project generates contextual MI dialogues through large language models and provides a synthetic dataset for training and testing MI dialogue systems. The project contains detailed annotation files covering dialogue annotations in psychological and linguistic dimensions, suitable for research in mental health and therapeutic consultation.

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Adatkészlet

National Study of Mental Health and Wellbeing, 2020-2022
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National Study of Mental Health and Wellbeing, 2020-2022

The National Study of Mental Health and Wellbeing provides key statistics on mental health issues in Australia, including the prevalence of mental disorders, consultations with health professionals, and the use of mental health-related medications. The study covers a wide range of mental health conditions and offers insights into the impact of mental health on individuals and society.

HuggingFaceFW/fineweb-2
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HuggingFaceFW/fineweb-2

FineWeb-2 is a dataset of over 15 trillion tokens of cleaned and deduplicated English web data from CommonCrawl. This is the second iteration of the popular 🍷 FineWeb dataset, bringing high quality pretraining data to over 1000 🗣️ languages.The 🥂 FineWeb2 dataset is fully reproducible, available under the permissive ODC-By 1.0 license and extensively validated through hundreds of ablation experiments.In particular, on the set of 9 diverse languages we used to guide our processing decisions, 🥂 FineWeb2 outperforms other popular pretraining datasets covering multiple languages (such as CC-100, mC4, CulturaX or HPLT, while being substantially larger) and, in some cases, even performs better than some datasets specifically curated for a single one of these languages, in our diverse set of carefully selected evaluation tasks: FineTasks.

Hugging Face Dataset - lsy641/PsyQA
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Hugging Face Dataset - lsy641/PsyQA

The data is originally source from (Sun et al,2021). (Liu et al, 2023) processed the data to make it a dataset vis huggingface api with taining/validation/testing splitting

Kulcsszavak

IC-AnnoMIMental HealthTherapeutic CounsellingLLMsMotivational InterviewingSynthetic Dataset

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