---
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.

### Note
Since this dataset was preprocessed, and stopwords and punctuation marks such as "?!" were removed from it, it might be possible that in some cases
that, the text is having incorrect labels, as the meaning changed against the original text after preprocessing.