File size: 3,934 Bytes
1bb0e99 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
---
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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/arafatanam/Mental-Health-Counseling)
- **Source Dataset**: [Amod/mental_health_counseling_conversations](https://huggingface.co/datasets/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:
1. Loaded the original dataset using the `datasets` library.
2. Identified and removed duplicate columns.
3. Dropped duplicate Context-Response pairs.
4. Computed statistics on response counts per prompt.
5. Saved the cleaned dataset as a JSON file.
## Code Used for Cleaning
```python
# 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](https://huggingface.co/datasets/Amod/mental_health_counseling_conversations).
## Acknowledgments
Special thanks to the creators of **Amod/mental_health_counseling_conversations** for providing the original dataset.
--- |