annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
- text-generation
- question-answering
task_ids:
- sentiment-classification
- language-modeling
- open-domain-qa
Dataset Card for My Dataset
Table of Contents
Dataset Description
- Homepage:
- Repository:
- Paper: Bertagnolli, Nicolas (2020). Counsel chat: Bootstrapping high-quality therapy data. Towards Data Science. https://towardsdatascience.com/counsel-chat
- Leaderboard:
- Point of Contact:
Dataset Summary
This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. The dataset is intended to be used for fine-tuning language models to improve their ability to provide mental health advice.
Supported Tasks and Leaderboards
The dataset supports the task of text generation, particularly for generating advice or suggestions in response to a mental health-related question.
Languages
The text in the dataset is in English.
Dataset Structure
Data Instances
A data instance includes a 'Context' and a 'Response'. 'Context' contains the question asked by a user, and 'Response' contains the corresponding answer provided by a psychologist.
Data Fields
- 'Context': a string containing the question asked by a user
- 'Response': a string containing the corresponding answer provided by a psychologist
Data Splits
The dataset has no predefined splits. Users can create their own splits as needed.
Dataset Creation
Curation Rationale
This dataset was created to aid in the development of AI models that can provide mental health advice or guidance.
Source Data
The data was sourced from two online counseling and therapy platforms.
Annotations
The dataset does not contain any additional annotations.
Personal and Sensitive Information
The dataset may contain sensitive information related to mental health. All data was anonymized and no personally identifiable information is included.