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---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 75975910.63587219
num_examples: 185574
- name: test
num_bytes: 18994182.36412781
num_examples: 46394
download_size: 53587175
dataset_size: 94970093
license: mit
task_categories:
- text-classification
language:
- en
pretty_name: Suicidal Tendency Prediction Dataset
size_categories:
- 100K<n<1M
---
# Dataset Card for "vibhorag101/suicide_prediction_dataset_phr"
- The dataset is sourced from Reddit and is available on [Kaggle](https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch).
- The dataset contains text with binary labels for suicide or non-suicide.
- The dataset was cleaned and following steps were applied
- Converted to lowercase
- Removed numbers and special characters.
- Removed URLs, Emojis and accented characters.
- Removed any word contractions.
- Remove any extra white spaces and any extra spaces after a single space.
- Removed any consecutive characters repeated more than 3 times.
- Tokenised the text, then lemmatized it and then removed the stopwords (excluding not).
- The `class_label` column was renamed to `label` for use with trainer API.
- The evaluation set had ~23000 samples, while the training set had ~186k samples, i.e. a 80:10:10 (train:test:val) split. |