Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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.
+
+---