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--- |
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dataset_name: cleaned-plotqa-v2-difficulty |
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tags: |
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- plotqa |
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- visual-question-answering |
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- curriculum-learning |
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- difficulty-estimation |
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- rule-based |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: user |
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dtype: string |
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- name: assistant |
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dtype: string |
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- name: difficulty_tier |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 7194242760.375 |
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num_examples: 199293 |
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download_size: 121207077 |
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dataset_size: 7194242760.375 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# Cleaned-PlotQA v2 with difficulty tiers (calibrated, rule-based) |
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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. |
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## Tier counts |
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- easy: 199293 |
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- medium: 0 |
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- hard: 0 |
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- total labeled: 199293 |
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## Criteria summary |
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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: |
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Calibration (on 1,000-sample): |
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- 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]. |
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- 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. |
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- Balanced thresholds are set from the sample at 40th and 80th percentiles: |
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easy: score ≤ 0.000000 |
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medium: 0.000000 < score ≤ 0.000000 |
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hard: score > 0.000000 |
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Assignment across the full dataset: |
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- The same blended scoring and fixed cutoffs are applied to every example to produce tiers suitable for curriculum training. |
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## Notes |
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- Only one new column is introduced; all original fields remain unchanged. |
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- 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. |
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