Datasets:

arafatanam
/

Mental-Health-Couseling

license: apache-2.0
task_categories:
  - question-answering
  - text-generation
language:
  - en
tags:
  - mental-health
  - counseling
  - conversations
pretty_name: Mental Health Counseling Conversational Dataset
size_categories:
  - 1K<n<10K

Mental Health Counseling Conversations (Cleaned)

Dataset Overview

This dataset is derived from the original Amod/mental_health_counseling_conversations dataset, which contains mental health counseling conversations.

In this version, duplicate Context-Response pairs have been removed to improve data quality and usability.

Dataset Details

Dataset Name: arafatanam/Mental-Health-Counseling
Source Dataset: Amod/mental_health_counseling_conversations
Modifications: Removed redundant columns and duplicate Context-Response pairs
Format: JSON (newline-delimited)

Processing Steps

The dataset was prepared using the following steps:

Loaded the original dataset using the datasets library.
Identified and removed duplicate columns.
Dropped duplicate Context-Response pairs.
Computed statistics on response counts per prompt.
Saved the cleaned dataset as a JSON file.

Code Used for Cleaning

# Install necessary libraries
!pip install datasets

# Import required modules
from datasets import load_dataset
import pandas as pd

# Load dataset from Hugging Face
dataset = load_dataset("Amod/mental_health_counseling_conversations")

# Convert the dataset to a Pandas DataFrame (train split)
df = pd.DataFrame(dataset['train'])

# Remove duplicate Context-Response pairs
df_cleaned = df.drop_duplicates(subset=['Context', 'Response'])

# Calculate response count per prompt
response_counts = df_cleaned.groupby('Context').size().reset_index(name='response_count')

# Compute statistical insights
min_responses = response_counts['response_count'].min()
avg_responses = response_counts['response_count'].mean()
max_responses = response_counts['response_count'].max()

print(f"Minimum responses per prompt: {min_responses}")
print(f"Average responses per prompt: {avg_responses:.2f}")
print(f"Maximum responses per prompt: {max_responses}")

# Identify prompts with the highest and lowest number of responses
max_prompt = response_counts[response_counts['response_count'] == max_responses]['Context'].tolist()
min_prompt = response_counts[response_counts['response_count'] == min_responses]['Context'].tolist()

print(f"Prompt(s) with the highest responses ({max_responses}): {max_prompt}")
print(f"Prompt(s) with the lowest responses ({min_responses}): {min_prompt}")

# Calculate dataset reduction percentage
reduction = ((df.shape[0] - df_cleaned.shape[0]) / df.shape[0]) * 100

print(f"Original dataset shape: {df.shape}")
print(f"Cleaned dataset shape: {df_cleaned.shape}")
print(f"Percentage reduction in data: {reduction:.2f}%")

# Save cleaned dataset
df_cleaned.to_json('mental-health-counseling.json', orient='records', lines=True)

Usage

This dataset can be used for:

Mental Health Chatbots: Developing AI-driven support systems.
Sentiment Analysis: Analyzing emotional tones in counseling dialogues.
Natural Language Processing (NLP): Training models for mental health-related text understanding.

License

This dataset is released under the Apache 2.0 License.
Please ensure compliance with licensing terms, especially regarding modifications and redistribution.
For more details, refer to the original dataset's license.

Acknowledgments

Special thanks to the creators of Amod/mental_health_counseling_conversations for providing the original dataset.