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End of preview. Expand in Data Studio
arithmetic-llm
MNIST 图像 → 字节级算术 训练数据集。
用于教学向的字节级算术大模型 (byte-omni-model-zh)：给定一个显式数字 (digit 0-9) 与一张 MNIST 图像 (隐式数字)，预测两者之和。
输入: digit_byte + MNIST_image_bytes
输出: result_bytes
示例:
digit = "1" + image(数字 "2") → 预测 "3"
Schema
| 列 | 类型 | 说明 |
|---|---|---|
id |
int | 序号 |
image_base64 |
str | MNIST 图像 PNG (base64) |
pixels |
float[] | 28×28 展平像素 (0-1), 784 维 |
label |
int | 图像数字标签 (0-9) |
split |
str | train (59134) / test (9943) |
image_hash |
str | SHA-256 逐字节指纹 |
phash |
str | 16×16 感知哈希位串 (与 label 综合去重) |
划分
| split | 行数 |
|---|---|
| train | 59134 |
| test | 9943 |
| 合计 | 69077 |
清洗说明 (去重)
- 原始 MNIST 无逐字节重复 (SHA-256 重复 = 0)。
- 但存在大量视觉近重复 (同图数字, 仅差少量像素)。
- 采用 16×16 感知哈希 + label 综合去重: 键 =
(phash, label), 保留首现。- 同图同数字 → 视为冗余删除; 同图异数字 → 作为难样本保留。
- 去重结果: train 60000 → 59134 (删 866), test 10000 → 9943 (删 57)。
来源与许可
- 原始数据: Yann LeCun 的 MNIST 手写数字数据集 (cc-by-sa-4.0)。
- 本数据集为加工版本：补充 PNG base64、784 维像素数组、SHA-256 指纹与 16×16 感知哈希，按 train/test 划分并去重。
- 用法示例见 byte-omni-model-zh。
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