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README.md

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+---
+license: mit
+task_categories:
+- text-generation
+- mathematical-modeling
+language:
+- en
+tags:
+- math
+- transformer
+- symbolic-math
+- jax
+- arithmetic
+size_categories:
+- 100M-1B
+---
+
+# Transformer Math Dataset (200,000,000 Samples Sharded)
+
+High-precision synthetic mathematical expression dataset generated for training sequence-to-sequence math Transformers in JAX/Flax.
+
+## Dataset Structure
+
+- **Total Samples**: 200,000,000
+- **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**: 80%
+
+## 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-intmax", streaming=True)
+for sample in dataset["train"]:
+    print(sample["expr"], "->", sample["val"], "ops:", sample["ops_used"])
+    break
+```