README.md

metadata

task_categories:
  - text-classification
language:
  - en
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: dev
        path: data/dev-*
      - split: test
        path: data/test-*
      - split: validation
        path: data/validation-*
dataset_info:
  features:
    - name: text
      dtype: string
    - name: label
      dtype: string
    - name: label_id
      dtype: int64
    - name: gender
      dtype: string
    - name: group_id
      dtype: int64
    - name: author_id
      dtype: string
    - name: source_id
      dtype: string
    - name: job
      dtype: string
    - name: age
      dtype: int64
    - name: age_band
      dtype: string
    - name: sign
      dtype: string
  splits:
    - name: train
      num_bytes: 42000916
      num_examples: 25956
    - name: dev
      num_bytes: 5995268
      num_examples: 3755
    - name: test
      num_bytes: 11989095
      num_examples: 7425
    - name: validation
      num_bytes: 5995268
      num_examples: 3755
  download_size: 39778969
  dataset_size: 65980547

BlogTopic

Prepared for gender-fairness (TPR-gap) evaluation of fine-tuned language models.

Schema

column	type	meaning
text	str	input text
label	str	task class name (Y), used as the generation target in prompted fine-tuning
label_id	int	contiguous class id (Y)
gender	str	protected attribute (Z), female or male
group_id	int	0 = female, 1 = male

Splits: train, dev (aliased as validation), test.

Key statistics

• Examples: 37,136
• Classes: 26
• Test P(female|Y) range: 0.186 - 0.792
• Test Cramer's V (Y vs Z): 0.309
• Smallest test Y x Z cell: 21

Full per-class Y x Z tables are in dataset_stats.json.

Provenance

• raw_path: /projects/u6cg/sshao/code/peft/rep_under_control/data/raw/blog/data/blogs.zip
• files_seen: 19320
• files_skipped_bad_filename: 0
• bloggers_skipped_unknown_industry: 6827
• unit: post
• raw_industry_counts: {'indunk': 6827, 'student': 5120, 'education': 980, 'technology': 943, 'arts': 721, 'communications-media': 479, 'internet': 397, 'non-profit': 372, 'engineering': 312, 'government': 236, 'law': 197, 'consulting': 191, 'science': 184, 'marketing': 180, 'businessservices': 163, 'publishing': 150, 'advertising': 145, 'religion': 139, 'telecommunications': 119, 'military': 116, 'banking': 112, 'accounting': 105, 'fashion': 98, 'humanresources': 94, 'tourism': 94, 'transportation': 91, 'sports-recreation': 90, 'manufacturing': 87, 'architecture': 69, 'chemicals': 62, 'biotech': 57, 'lawenforcement-security': 57, 'realestate': 55, 'museums-libraries': 55, 'construction': 55, 'automotive': 54, 'agriculture': 36, 'investmentbanking': 33, 'environment': 28, 'maritime': 17}
• cleaning: {'input_rows': 76872, 'dropped_unusable_gender': 0, 'dropped_too_short': 6736, 'dropped_duplicate_text': 1713, 'output_rows': 68423}
• label_field: industry
• carried_metadata: ['job', 'age', 'age_band', 'sign']
• citation: Schler, Koppel, Argamon, Pennebaker (2006). Effects of Age and Gender on Blogging. AAAI Spring Symposium.
• license: Non-commercial research use only (per the corpus authors).
• gender_source: self-reported by the blogger at registration

Gender is a binary proxy attribute used only to measure disparate model performance. It is not an endorsement of a binary model of gender, and for some sources it is inferred rather than self-reported (see provenance above).