---
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.