Issue: Tip for recomputing metadata
State: closed
Author: stevhliu

From this [feedback](https://discuss.huggingface.co/t/nonmatchingsplitssizeserror/30033) on the forum, thought I'd include a tip for recomputing the metadata numbers if it is your own dataset.

Comments:
_The documentation is not available anymore as the PR was closed or merged._

<details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.008167 / 0.011353 (-0.003186) | 0.004404 / 0.011008 (-0.006605) | 0.100462 / 0.038508 (0.061954) | 0.028835 / 0.023109 (0.005726) | 0.326759 / 0.275898 (0.050861) | 0.355150 / 0.323480 (0.031670) | 0.007200 / 0.007986 (-0.000786) | 0.003293 / 0.004328 (-0.001035) | 0.078006 / 0.004250 (0.073756) | 0.033298 / 0.037052 (-0.003754) | 0.307119 / 0.258489 (0.048630) | 0.337689 / 0.293841 (0.043848) | 0.033016 / 0.128546 (-0.095530) | 0.011383 / 0.075646 (-0.064263) | 0.321989 / 0.419271 (-0.097283) | 0.039793 / 0.043533 (-0.003740) | 0.295388 / 0.255139 (0.040249) | 0.322694 / 0.283200 (0.039494) | 0.082989 / 0.141683 (-0.058694) | 1.496701 / 1.452155 (0.044546) | 1.548861 / 1.492716 (0.056145) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.176587 / 0.018006 (0.158580) | 0.397660 / 0.000490 (0.397170) | 0.001063 / 0.000200 (0.000863) | 0.000070 / 0.000054 (0.000015) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022386 / 0.037411 (-0.015025) | 0.096380 / 0.014526 (0.081854) | 0.103032 / 0.176557 (-0.073525) | 0.135050 / 0.737135 (-0.602086) | 0.105941 / 0.296338 (-0.190397) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.430989 / 0.215209 (0.215780) | 4.310309 / 2.077655 (2.232654) | 2.142596 / 1.504120 (0.638477) | 1.952043 / 1.541195 (0.410848) | 1.817803 / 1.468490 (0.349312) | 0.690026 / 4.584777 (-3.894751) | 3.315413 / 3.745712 (-0.430299) | 3.370336 / 5.269862 (-1.899525) | 1.668707 / 4.565676 (-2.896970) | 0.081860 / 0.424275 (-0.342415) | 0.012493 / 0.007607 (0.004886) | 0.527779 / 0.226044 (0.301735) | 5.318732 / 2.268929 (3.049804) | 2.467029 / 55.444624 (-52.977596) | 2.247171 / 6.876477 (-4.629306) | 2.270825 / 2.142072 (0.128752) | 0.802288 / 4.805227 (-4.002939) | 0.148895 / 6.500664 (-6.351770) | 0.064967 / 0.075469 (-0.010503) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.259304 / 1.841788 (-0.582484) | 13.662441 / 8.074308 (5.588133) | 14.074662 / 10.191392 (3.883270) | 0.152907 / 0.680424 (-0.527516) | 0.028340 / 0.534201 (-0.505861) | 0.397356 / 0.579283 (-0.181927) | 0.392600 / 0.434364 (-0.041764) | 0.467935 / 0.540337 (-0.072402) | 0.539890 / 1.386936 (-0.847046) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006156 / 0.011353 (-0.005197) | 0.004371 / 0.011008 (-0.006637) | 0.076391 / 0.038508 (0.037883) | 0.026455 / 0.023109 (0.003346) | 0.339816 / 0.275898 (0.063917) | 0.370032 / 0.323480 (0.046552) | 0.004614 / 0.007986 (-0.003372) | 0.003200 / 0.004328 (-0.001129) | 0.075408 / 0.004250 (0.071157) | 0.034100 / 0.037052 (-0.002953) | 0.341232 / 0.258489 (0.082743) | 0.380290 / 0.293841 (0.086449) | 0.031021 / 0.128546 (-0.097525) | 0.011562 / 0.075646 (-0.064084) | 0.085564 / 0.419271 (-0.333708) | 0.041431 / 0.043533 (-0.002102) | 0.359570 / 0.255139 (0.104431) | 0.366919 / 0.283200 (0.083719) | 0.088242 / 0.141683 (-0.053441) | 1.460703 / 1.452155 (0.008548) | 1.534351 / 1.492716 (0.041635) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.225703 / 0.018006 (0.207697) | 0.395014 / 0.000490 (0.394524) | 0.000385 / 0.000200 (0.000185) | 0.000060 / 0.000054 (0.000006) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023975 / 0.037411 (-0.013436) | 0.098658 / 0.014526 (0.084132) | 0.105043 / 0.176557 (-0.071513) | 0.139988 / 0.737135 (-0.597148) | 0.106854 / 0.296338 (-0.189484) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.442454 / 0.215209 (0.227245) | 4.430860 / 2.077655 (2.353205) | 2.084823 / 1.504120 (0.580704) | 1.870421 / 1.541195 (0.329226) | 1.901618 / 1.468490 (0.433128) | 0.699214 / 4.584777 (-3.885563) | 3.336911 / 3.745712 (-0.408801) | 1.856479 / 5.269862 (-3.413383) | 1.166496 / 4.565676 (-3.399180) | 0.083189 / 0.424275 (-0.341086) | 0.012293 / 0.007607 (0.004686) | 0.543147 / 0.226044 (0.317102) | 5.452030 / 2.268929 (3.183101) | 2.506689 / 55.444624 (-52.937936) | 2.168186 / 6.876477 (-4.708291) | 2.172277 / 2.142072 (0.030205) | 0.813554 / 4.805227 (-3.991673) | 0.152074 / 6.500664 (-6.348590) | 0.066891 / 0.075469 (-0.008579) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.278635 / 1.841788 (-0.563153) | 13.690232 / 8.074308 (5.615924) | 13.403201 / 10.191392 (3.211809) | 0.128171 / 0.680424 (-0.552253) | 0.016687 / 0.534201 (-0.517514) | 0.378645 / 0.579283 (-0.200638) | 0.382922 / 0.434364 (-0.051442) | 0.467483 / 0.540337 (-0.072854) | 0.559026 / 1.386936 (-0.827910) |

</details>
</details>

![](https://cml.dev/watermark.png#b262d411ec0e252615a140c4e3e60e7dbd38eef1 "CML watermark")