| --- |
| license: mit |
| task_categories: |
| - text-generation |
| - mathematical-modeling |
| language: |
| - en |
| tags: |
| - math |
| - transformer |
| - symbolic-math |
| - jax |
| - arithmetic |
| size_categories: |
| - 100M-1B |
| --- |
| |
| # Transformer Math Dataset (250,000,000 Samples Sharded) |
|
|
| High-precision synthetic mathematical expression dataset generated for training sequence-to-sequence math Transformers in JAX/Flax. |
|
|
| ## Dataset Structure |
|
|
| - **Total Samples**: 250,000,000 |
| - **Shard Format**: JSONL sharded files (100,000 samples per shard) |
| - **Supported Operations**: `+`, `-`, `*`, `/`, `^`, `sin`, `cos`, `tan`, `log`, `ln`, `exp`, `sqrt`, `abs` |
| - **Expression Depth Range**: Depth 4 to 6 |
| - **Integer Operand Ratio**: 20% |
|
|
| ## Data Fields |
|
|
| Each line in the `.jsonl` shard files is a JSON object with the following fields: |
|
|
| - `expr` (`str`): Syntactically valid mathematical expression (e.g. `"sin((3.5))+cos((1.2))"`) |
| - `val` (`str`): Target evaluated numerical result formatted to precision (e.g. `"0.6"`) |
| - `category` (`str`): Operation-depth category bucket (e.g. `"sin_d4"`) |
| - `ops_used` (`list[str]`): List of mathematical functions/operators present in the expression (e.g. `["sin", "+", "cos"]`) |
|
|
| ## Usage Example |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("saidurga001301/mathmetics-dataset-float-long", streaming=True) |
| for sample in dataset["train"]: |
| print(sample["expr"], "->", sample["val"], "ops:", sample["ops_used"]) |
| break |
| ``` |
|
|
