metadata

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.
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.