File size: 3,511 Bytes
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dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: batch_1
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num_examples: 50000
- name: batch_2
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- name: batch_3
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num_examples: 50000
- name: batch_4
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num_examples: 50000
- name: batch_5
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num_examples: 50000
- name: batch_6
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num_examples: 50000
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- name: batch_9
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num_examples: 50000
- name: batch_10
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num_examples: 15051
- name: validation
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num_examples: 92976
- name: test
num_bytes: 21842704
num_examples: 23190
download_size: 328660016
dataset_size: 541504152
configs:
- config_name: default
data_files:
- split: batch_1
path: data/batch_1-*
- split: batch_2
path: data/batch_2-*
- split: batch_3
path: data/batch_3-*
- split: batch_4
path: data/batch_4-*
- split: batch_5
path: data/batch_5-*
- split: batch_6
path: data/batch_6-*
- split: batch_7
path: data/batch_7-*
- split: batch_8
path: data/batch_8-*
- split: batch_9
path: data/batch_9-*
- split: batch_10
path: data/batch_10-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
license: cc-by-4.0
task_categories:
- text-classification
language:
- en
tags:
- medical
size_categories:
- 100K<n<1M
---
---
- annotations_creators:
- original
- language:
- en
- license: cc-by-4.0
- multilinguality: monolingual
- pretty_name: DDCBot Mental Disorders Dataset (Split)
- size_categories:
- 100K<n<1M
- source_datasets:
- Kanakmi/mental-disorders
- task_categories:
- text-classification
- task_ids:
- multi-class-classification
---
# DDCBot Mental Disorders Dataset (Split Batches)
This dataset is derived from [`Kanakmi/mental-disorders`](https://huggingface.co/datasets/Kanakmi/mental-disorders), and has been **split into 10 batches** of 50,000 records each to facilitate efficient Large Language Model (LLM) fine-tuning for the DDCBot project.
## 🧠 About DDCBot
**DDCBot** (Disorder Detection & Care Bot) is an intelligent mental health assistant that uses fine-tuned LLMs to identify patterns in mental health-related textual data. It aims to:
- Detect early signs of various disorders.
- Provide classification support for mental health professionals.
- Understand linguistic features in user expressions that may relate to psychological conditions.
## 📊 Dataset Structure
The dataset is split into the following subsets:
- `batch_1` to `batch_10`: 50k training records each from the Kanakmi/mental-disorders dataset
- `validation`: Original validation set
- `test`: Original test set
Each batch (e.g., `batch_1`, `batch_2`, ..., `batch_10`) contains ~50,000 samples with the following fields:
- `text`: The user's expression or social media post.
- `label`: An integer representing one of several mental health conditions (e.g., depression, anxiety, PTSD, etc.).
## 🔢 Label Classes
The dataset includes multiple classes such as:
Labels:
- 0:'BPD'
- 1:'bipolar'
- 2:'depression'
- 3:'Anxiety'
- 4:'schizophrenia'
- 5:'mentalillness'
*(Label mapping can be extracted from the original dataset.)*
--- |