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README.md

@@ -1,3 +1,4 @@
+
 ---
 dataset_name: cleaned-plotqa-v2-difficulty
 tags:
@@ -6,53 +7,18 @@ tags:
 - 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)
+# Cleaned-PlotQA v2 with difficulty tiers (vectorized + calibrated)
 
-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.
+This repository augments jrc/cleaned-plotqa-v2 by adding a single column difficulty_tier ∈ {easy, medium, hard} computed with a vectorized, batch‑scored rule set and cutoffs calibrated on a 1,000‑example sample to avoid tier collapse.
 
 ## Tier counts
-- easy: 199293
-- medium: 0
-- hard: 0
+- easy: 77403
+- medium: 78521
+- hard: 43369
 - 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.
+- Only one new column is added; original fields remain unchanged.
+- The scoring runs batched (8192) with multiprocessing (num_proc=4) for speed; adjust to fit memory/CPU limits.