Update create_dataset.mdx
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<summary>Show benchmarks</summary>

PyArrow==8.0.0

<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) | 0.008892 / 0.011353 (-0.002461) | 0.005140 / 0.011008 (-0.005868) | 0.110951 / 0.038508 (0.072442) | 0.086159 / 0.023109 (0.063050) | 0.391117 / 0.275898 (0.115218) | 0.440884 / 0.323480 (0.117404) | 0.006562 / 0.007986 (-0.001423) | 0.003711 / 0.004328 (-0.000618) | 0.081848 / 0.004250 (0.077598) | 0.063187 / 0.037052 (0.026135) | 0.369771 / 0.258489 (0.111282) | 0.447685 / 0.293841 (0.153844) | 0.046623 / 0.128546 (-0.081923) | 0.014024 / 0.075646 (-0.061622) | 0.418556 / 0.419271 (-0.000715) | 0.064660 / 0.043533 (0.021127) | 0.379416 / 0.255139 (0.124277) | 0.415800 / 0.283200 (0.132600) | 0.036899 / 0.141683 (-0.104784) | 1.710280 / 1.452155 (0.258125) | 1.932326 / 1.492716 (0.439610) |

### 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) | 0.311351 / 0.018006 (0.293345) | 0.621121 / 0.000490 (0.620631) | 0.013677 / 0.000200 (0.013477) | 0.000543 / 0.000054 (0.000488) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.031310 / 0.037411 (-0.006102) | 0.099546 / 0.014526 (0.085020) | 0.122100 / 0.176557 (-0.054457) | 0.186477 / 0.737135 (-0.550659) | 0.116634 / 0.296338 (-0.179704) |

### 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) | 0.574639 / 0.215209 (0.359430) | 5.976678 / 2.077655 (3.899023) | 2.535482 / 1.504120 (1.031362) | 2.248873 / 1.541195 (0.707678) | 2.361696 / 1.468490 (0.893205) | 0.866700 / 4.584777 (-3.718077) | 5.298018 / 3.745712 (1.552306) | 4.753240 / 5.269862 (-0.516622) | 3.124698 / 4.565676 (-1.440979) | 0.101852 / 0.424275 (-0.322423) | 0.009117 / 0.007607 (0.001510) | 0.723730 / 0.226044 (0.497685) | 7.172649 / 2.268929 (4.903720) | 3.400410 / 55.444624 (-52.044214) | 2.626619 / 6.876477 (-4.249857) | 2.948692 / 2.142072 (0.806620) | 0.991589 / 4.805227 (-3.813638) | 0.208902 / 6.500664 (-6.291762) | 0.076172 / 0.075469 (0.000703) |

### 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) | 1.621880 / 1.841788 (-0.219907) | 22.735673 / 8.074308 (14.661365) | 20.376990 / 10.191392 (10.185598) | 0.232219 / 0.680424 (-0.448204) | 0.028616 / 0.534201 (-0.505585) | 0.455725 / 0.579283 (-0.123558) | 0.562796 / 0.434364 (0.128432) | 0.545344 / 0.540337 (0.005007) | 0.759440 / 1.386936 (-0.627496) |

</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) | 0.009845 / 0.011353 (-0.001508) | 0.005289 / 0.011008 (-0.005719) | 0.083117 / 0.038508 (0.044609) | 0.098467 / 0.023109 (0.075357) | 0.532345 / 0.275898 (0.256447) | 0.571000 / 0.323480 (0.247520) | 0.007223 / 0.007986 (-0.000763) | 0.004442 / 0.004328 (0.000114) | 0.081710 / 0.004250 (0.077459) | 0.071132 / 0.037052 (0.034080) | 0.540093 / 0.258489 (0.281604) | 0.582244 / 0.293841 (0.288403) | 0.048509 / 0.128546 (-0.080038) | 0.013897 / 0.075646 (-0.061749) | 0.092579 / 0.419271 (-0.326692) | 0.073409 / 0.043533 (0.029876) | 0.537369 / 0.255139 (0.282230) | 0.551403 / 0.283200 (0.268203) | 0.038847 / 0.141683 (-0.102835) | 1.940848 / 1.452155 (0.488693) | 2.045597 / 1.492716 (0.552881) |

### 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) | 0.303883 / 0.018006 (0.285877) | 0.600237 / 0.000490 (0.599748) | 0.006030 / 0.000200 (0.005830) | 0.000124 / 0.000054 (0.000070) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.036633 / 0.037411 (-0.000778) | 0.105853 / 0.014526 (0.091327) | 0.126289 / 0.176557 (-0.050267) | 0.190022 / 0.737135 (-0.547113) | 0.123251 / 0.296338 (-0.173087) |

### 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) | 0.711893 / 0.215209 (0.496684) | 6.979781 / 2.077655 (4.902126) | 3.491514 / 1.504120 (1.987394) | 3.268077 / 1.541195 (1.726882) | 3.241777 / 1.468490 (1.773287) | 0.875913 / 4.584777 (-3.708864) | 5.458421 / 3.745712 (1.712709) | 4.818355 / 5.269862 (-0.451507) | 3.256046 / 4.565676 (-1.309631) | 0.095000 / 0.424275 (-0.329275) | 0.009072 / 0.007607 (0.001465) | 0.818468 / 0.226044 (0.592424) | 8.027702 / 2.268929 (5.758773) | 4.363234 / 55.444624 (-51.081390) | 3.695269 / 6.876477 (-3.181207) | 3.902601 / 2.142072 (1.760528) | 1.039007 / 4.805227 (-3.766220) | 0.212050 / 6.500664 (-6.288614) | 0.081438 / 0.075469 (0.005969) |

### 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) | 1.746945 / 1.841788 (-0.094842) | 25.274283 / 8.074308 (17.199975) | 23.514717 / 10.191392 (13.323325) | 0.232580 / 0.680424 (-0.447843) | 0.032083 / 0.534201 (-0.502118) | 0.482873 / 0.579283 (-0.096410) | 0.585730 / 0.434364 (0.151366) | 0.602066 / 0.540337 (0.061729) | 0.796391 / 1.386936 (-0.590546) |

</details>
</details>

![](https://cml.dev/watermark.png#0d7cb68fe37dbfd81e5f82e19d8f9847c337788d "CML watermark")

modified , as AudioFolder and ImageFolder not in Dataset Library. 

``` from datasets import AudioFolder ```  and ```from datasets import ImageFolder```  to ```from datasets import load_dataset```

``` 
cannot import name 'AudioFolder' from 'datasets' (/home/eswardivi/miniconda3/envs/Hugformers/lib/python3.10/site-packages/datasets/__init__.py)

```