README.md

metadata

dataset_info:
  features:
    - name: data_id
      dtype: string
    - name: text
      dtype: string
    - name: exact
      dtype: string
    - name: group
      dtype: string
    - name: ternary
      dtype: string
    - name: binary
      dtype: string
    - name: label
      dtype: string
  splits:
    - name: train
      num_bytes: 14338723
      num_examples: 31318
    - name: val
      num_bytes: 1545579
      num_examples: 3355
    - name: test
      num_bytes: 3285868
      num_examples: 7282
  download_size: 4686461
  dataset_size: 19170170
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: val
        path: data/val-*
      - split: test
        path: data/test-*
license: mit
task_categories:
  - text-classification
language:
  - ru
size_categories:
  - 10K<n<100K

This is a derivative dataset from psytechlab/presuisidal_antisuisidal_dataset-master that used in the paper experiments "The methodology of constructing the large-scale dataset for detecting presuicidal and anti-suicidal signals in social media texts in Russian".

The preprocessing list:

• removed all classes that have less than 100 examples
• the re.sub("[^А-Яа-я ]+", "", x) has been applied to all texts
• all multilabel text was removed

The dataset has all class granularity levels:

• exact - the full class name with upper group and specialization
• group - the group class only (applicable to a presuisidal part)
• ternary - presuisidal, antisuisidal and irrelevant
• binary - relevant and irrelevant

In the paper experiments the presuisidal and antisuisidal parts share the irrelevant texts. The texts from presuisidal part are not marked as irrelevant for antisuisidal dataset and vice versa, only natural irrelevant texts are used.