README.md

metadata

dataset_name: cleaned-plotqa-v2-difficulty
tags:
  - plotqa
  - visual-question-answering
  - curriculum-learning
  - difficulty-estimation
  - rule-based
dataset_info:
  features:
    - name: image
      dtype: image
    - name: user
      dtype: string
    - name: assistant
      dtype: string
    - name: difficulty_tier
      dtype: string
  splits:
    - name: train
      num_bytes: 7194242760.375
      num_examples: 199293
  download_size: 121207077
  dataset_size: 7194242760.375
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Cleaned-PlotQA v2 with difficulty tiers (calibrated, rule-based)

This repository augments jrc/cleaned-plotqa-v2 by adding a single column difficulty_tier ∈ {easy, medium, hard}, tuned for numeric and visual reasoning in scientific plots and calibrated on a 1,000-example sample with a robust tie-break fallback to prevent score collapse.

Tier counts

• easy: 199293
• medium: 0
• hard: 0
• total labeled: 199293

Criteria summary

This release adds a single column difficulty_tier ∈ {easy, medium, hard} using a robust, PlotQA-oriented scoring with a rank-based fallback and balanced cutoffs:

Calibration (on 1,000-sample):

• Primary score weights numeric operations (sum/diff/ratio/percent/average), extremum/trend/slope cues (max/min/peak, slope/rate-of-change), visual grounding (axes/legend/lines/bars), units and formats (%, scientific notation, ranges), and multi-entity hints (both/all/each/every/combined/grouped/stacked/multi, and/vs/per/between) [PlotQA context].
• If the primary score distribution collapses (near-constant), a deterministic rank-based fallback (counts of ops/extremum/units/percent/decimal/scientific/range/multi-entity/position/visual/color/length) is normalized and blended to ensure separation.
• Balanced thresholds are set from the sample at 40th and 80th percentiles: easy: score ≤ 0.000000 medium: 0.000000 < score ≤ 0.000000 hard: score > 0.000000

Assignment across the full dataset:

• The same blended scoring and fixed cutoffs are applied to every example to produce tiers suitable for curriculum training.

Notes

• Only one new column is introduced; all original fields remain unchanged.
• The calibration procedure is designed to yield balanced tiers even when questions are short or templated, which otherwise causes naive rule-based scoring to concentrate mass in a single class.