Datasets:
Fix viewer: json array -> jsonl lines + move Problem Types above Quickstart
Browse files- README.md +23 -21
- json/test.json +0 -0
- json/train.json +2 -2
- json/val.json +0 -0
README.md
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@@ -46,6 +46,28 @@ This dataset was built to train **100M-ish models** that benefit disproportionat
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Small models (25M-100M) trained on our earlier 5-minute budget went from 0.5% → 89.8% with scratchpad formats and to **98.3%** with `reverse-answers + scratchpad` - format and verification matter more than scale. This release distills those findings into a publishable, 100% verified dataset.
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## Quickstart
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```python
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@@ -83,6 +105,7 @@ ds_json = load_dataset("SagheerLab/Arithmetic-Reasoning", data_files={
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# or directly: load_dataset("json", data_files="hf://datasets/SagheerLab/Arithmetic-Reasoning/json/train.json")
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```
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# Dataset Summary
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- **Size:** 150,000 examples (135K train / 7.5K val / 7.5K test), 100% programmatically verified
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- **Tiers:** `easy 30% (1-2 digits, 1 step) | intermediate 35% (3-4 digits, 2-3 steps) | hard 25% (5-6 digits, 3-8 steps) | very hard 10% (competition/GSM8K-style)`
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- **License:** Apache 2.0 - see [Licensing](#licensing-information)
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# Problem Types - Top 10 + 8 more
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We cover 18+ operation families. Each `instruction` is sampled from 20+ phrasings with varied names/objects so at 1M tokens <1% duplicates.
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| # | Operation | Example Instruction | Reasoning | Answer |
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|---|-----------|---------------------|-----------|--------|
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| 1 | **Addition** | `Calculate 313921 + 108618.` | `313921 + 108618 = 422539` | `422539` |
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| 2 | **Subtraction** | `Please compute 964047 - 52578.9.` | `964047-50000=914047 | ... | 911468.1` | `911468.1` |
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| 3 | **Multiplication** | `Calculate 3088 x 1685.` | `3088*5*10^0=15440 | running=15440 | ... | 5203280` | `5203280` |
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| 4 | **Division** | `Divide 7763506128 by 8856.` | `77635/8856=8 | 8*8856=70848 | ... | 876638` | `876638` |
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| 5 | **Percentages** | `What is 86 percent of 5.6.` | `5.6*86=481.6 | 481.6/100=4.816` | `4.816` |
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| 6 | **Comparison** | `Which is smaller, 107473 or 709570?` | `107473 < 709570 | 107473 is smaller` | `107473 is smaller` |
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| 7 | **Fractions** | `Find 3/4 of 8324.` | `8324/4=2081 | 2081*3=6243` | `6243` |
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| 8 | **Word Problems** | `Layla has 95 bags with 608 lemons each and found 152 more lemons. How many lemons total?` | `95*608=57760 | 57760+152=57912` | `57912 lemons` |
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| 9 | **Algebra** | `Solve for x: x + 39 = 150` | `x = 150 - 39 = 111` | `111` |
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| 10 | **Geometry** | `What is the area of a rectangle 884 by 752?` | `884*752=664768` | `664768` |
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| | *+ 8 more:* `ratios, powers/roots, decimals, halving/doubling, sequences, multi-step, hard arithmetic, quantitative reasoning` - see `operation` field for full list. | | | |
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Full per-operation counts in `150K`:
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`word problems 13.6% | multi-step 10.7% | ratios 9.7% | hard arithmetic 5.7% | algebra 7.4% | fractions 4.6% | sequences 5.3% | geometry 5.1% | ...` (see `Per-operation` in generation log)
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# Dataset Structure
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### Data Instances
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Small models (25M-100M) trained on our earlier 5-minute budget went from 0.5% → 89.8% with scratchpad formats and to **98.3%** with `reverse-answers + scratchpad` - format and verification matter more than scale. This release distills those findings into a publishable, 100% verified dataset.
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# Problem Types - Top 10 + 8 more
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We cover 18+ operation families. Each `instruction` is sampled from 20+ phrasings with varied names/objects so at 1M tokens <1% duplicates.
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| # | Operation | Example Instruction | Reasoning | Answer |
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|---|-----------|---------------------|-----------|--------|
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| 1 | **Addition** | `Calculate 313921 + 108618.` | `313921 + 108618 = 422539` | `422539` |
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| 2 | **Subtraction** | `Please compute 964047 - 52578.9.` | `964047-50000=914047 | ... | 911468.1` | `911468.1` |
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| 3 | **Multiplication** | `Calculate 3088 x 1685.` | `3088*5*10^0=15440 | running=15440 | ... | 5203280` | `5203280` |
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| 4 | **Division** | `Divide 7763506128 by 8856.` | `77635/8856=8 | 8*8856=70848 | ... | 876638` | `876638` |
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| 5 | **Percentages** | `What is 86 percent of 5.6.` | `5.6*86=481.6 | 481.6/100=4.816` | `4.816` |
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| 6 | **Comparison** | `Which is smaller, 107473 or 709570?` | `107473 < 709570 | 107473 is smaller` | `107473 is smaller` |
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| 7 | **Fractions** | `Find 3/4 of 8324.` | `8324/4=2081 | 2081*3=6243` | `6243` |
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| 8 | **Word Problems** | `Layla has 95 bags with 608 lemons each and found 152 more lemons. How many lemons total?` | `95*608=57760 | 57760+152=57912` | `57912 lemons` |
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| 9 | **Algebra** | `Solve for x: x + 39 = 150` | `x = 150 - 39 = 111` | `111` |
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| 10 | **Geometry** | `What is the area of a rectangle 884 by 752?` | `884*752=664768` | `664768` |
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| | *+ 8 more:* `ratios, powers/roots, decimals, halving/doubling, sequences, multi-step, hard arithmetic, quantitative reasoning` - see `operation` field for full list. | | | |
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Full per-operation counts in `150K`:
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`word problems 13.6% | multi-step 10.7% | ratios 9.7% | hard arithmetic 5.7% | algebra 7.4% | fractions 4.6% | sequences 5.3% | geometry 5.1% | ...` (see `Per-operation` in generation log)
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## Quickstart
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```python
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# or directly: load_dataset("json", data_files="hf://datasets/SagheerLab/Arithmetic-Reasoning/json/train.json")
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```
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# Dataset Summary
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- **Size:** 150,000 examples (135K train / 7.5K val / 7.5K test), 100% programmatically verified
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- **Tiers:** `easy 30% (1-2 digits, 1 step) | intermediate 35% (3-4 digits, 2-3 steps) | hard 25% (5-6 digits, 3-8 steps) | very hard 10% (competition/GSM8K-style)`
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- **License:** Apache 2.0 - see [Licensing](#licensing-information)
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# Dataset Structure
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### Data Instances
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json/test.json
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json/train.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:cf5e3a3b9726cccd7062d2a1f55e4665da7ec5125076484fd33c83363b60a518
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size 42214948
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json/val.json
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