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README.md

@@ -1,30 +1,114 @@
 ---
-dataset_info:
-  features:
-  - name: question
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: reasoning
-    dtype: string
-  - name: category
-    dtype: string
-  - name: difficulty
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 386692
-    num_examples: 1350
-  - name: test
-    num_bytes: 44273
-    num_examples: 150
-  download_size: 145789
-  dataset_size: 430965
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+license: cc0-1.0
+language:
+- en
+tags:
+- math
+- reasoning
+- unit-conversion
+- synthetic
+- chain-of-thought
+size_categories:
+- 1K<n<10K
+task_categories:
+- text-generation
+- question-answering
 ---
+
+# Unit Conversion Reasoning
+
+A small, fully synthetic dataset of unit-conversion word problems with step-by-step
+worked solutions, covering **length, weight, volume, temperature, and time**.
+
+## Dataset Summary
+
+Each example presents a unit-conversion problem (a direct conversion instruction,
+a chained multi-step conversion, or a short word problem) along with a
+step-by-step "reasoning" trace and a final "answer" string. The dataset was
+generated programmatically from a fixed table of conversion factors (and the
+standard Celsius/Fahrenheit/Kelvin formulas for temperature) — there is no
+scraped or human-written content.
+
+- **Total examples:** 1,500 (deduplicated)
+- **Train / test split:** 90% / 10% (1,350 / 150)
+- **Categories:** `length`, `weight`, `volume`, `temperature`, `time`
+- **Difficulties:** `easy` (single-step), `medium` (chained, two-step), `hard` (word-problem phrasing)
+
+## Schema
+
+| Field        | Type   | Description                                                            |
+|--------------|--------|--------------------------------------------------------------------------|
+| `question`   | string | The conversion problem, either a direct instruction or a word problem. |
+| `answer`     | string | The final numeric answer with its unit, e.g. `"29.199475 feet"`.       |
+| `reasoning`  | string | A step-by-step worked solution explaining the conversion.              |
+| `category`   | string | One of `length`, `weight`, `volume`, `temperature`, `time`.            |
+| `difficulty` | string | One of `easy`, `medium`, `hard`.                                       |
+
+### Example row
+
+```json
+{
+  "question": "Convert 8.9 meters to feet.",
+  "answer": "29.199475 feet",
+  "reasoning": "Step 1: 1 meter = 1 meter, and 1 foot = 0.3048 of the same base unit, so 1 meter = 3.28084 feet.\nStep 2: 8.9 meters x 3.28084 = 29.199475 feet.",
+  "category": "length",
+  "difficulty": "easy"
+}
+```
+
+## Generation Method
+
+The dataset is produced by `generate_dataset.py`, using fixed, standard conversion
+factor tables for length, weight, volume, and time (each expressed relative to a
+base unit — meter, gram, liter, second), and the standard linear/affine formulas
+for Celsius/Fahrenheit/Kelvin temperature conversion.
+
+For each example, the script:
+1. Picks a category and a difficulty level.
+2. **Easy** — generates a single direct conversion between two random units in
+   that category.
+3. **Medium** — generates a chained conversion through an intermediate unit
+   (e.g. inches → meters → kilometers), requiring two reasoning steps.
+4. **Hard** — wraps a single or two-step conversion in a short word-problem
+   template (e.g. "A recipe calls for 2.5 liters of milk, how many cups is that?").
+5. Computes the exact numeric result from the conversion factors directly
+   (not from the displayed reasoning text), formats the question/answer/reasoning
+   strings, and records the category and difficulty.
+
+Problems are deduplicated by exact question text. After deduplication, every
+row is passed through an independent checker (`validate_row` in the generation
+script) that re-parses the numeric value out of the `answer` string and confirms
+it matches the actual computed conversion to within a small numeric tolerance;
+any row that fails this check is dropped before the dataset is saved.
+
+The dataset is purely synthetic and rule-based: no scraping, no external text
+sources, and no human annotation were used.
+
+## Intended Use
+
+This dataset is intended for:
+- Evaluating LLMs' quantitative reasoning and unit-conversion accuracy.
+- Fine-tuning or instruction-tuning models to produce step-by-step
+  arithmetic/conversion reasoning.
+- Benchmarking chain-of-thought consistency (does the reasoning trace actually
+  support the final answer?).
+
+It is **not** intended as a general-purpose math benchmark — it covers a narrow
+domain (unit conversion) and uses a fixed set of templates and units, so models
+can in principle overfit to its surface patterns.
+
+## Limitations
+
+- Templates and unit lists are fixed and limited, so question phrasing has
+  noticeable repetition across the dataset.
+- Numeric answers are rounded for display (typically to 6 decimal places or
+  fewer); exact-string-match evaluation should allow for reasonable numeric
+  tolerance rather than requiring exact text equality.
+- Only "everyday" units are covered (no scientific/imperial obscure units like
+  furlongs, slugs, etc.).
+
+## License
+
+This dataset is released under **CC0 1.0 (Public Domain Dedication)**. It is
+entirely synthetic, generated from arithmetic conversion formulas, and contains
+no personal, copyrighted, or scraped data.