Update dataset_dict.py
thanks ! <details>
<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) | 0.005050 / 0.011353 (-0.006303) | 0.003786 / 0.011008 (-0.007222) | 0.062406 / 0.038508 (0.023898) | 0.029459 / 0.023109 (0.006349) | 0.262388 / 0.275898 (-0.013510) | 0.274119 / 0.323480 (-0.049361) | 0.004085 / 0.007986 (-0.003901) | 0.002754 / 0.004328 (-0.001574) | 0.048779 / 0.004250 (0.044529) | 0.046187 / 0.037052 (0.009135) | 0.263513 / 0.258489 (0.005024) | 0.294260 / 0.293841 (0.000419) | 0.027391 / 0.128546 (-0.101155) | 0.010567 / 0.075646 (-0.065080) | 0.200225 / 0.419271 (-0.219046) | 0.036165 / 0.043533 (-0.007367) | 0.251757 / 0.255139 (-0.003382) | 0.268271 / 0.283200 (-0.014928) | 0.018446 / 0.141683 (-0.123237) | 1.125787 / 1.452155 (-0.326368) | 1.163172 / 1.492716 (-0.329544) |

### 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) | 0.004428 / 0.018006 (-0.013578) | 0.301730 / 0.000490 (0.301241) | 0.000215 / 0.000200 (0.000015) | 0.000045 / 0.000054 (-0.000010) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019424 / 0.037411 (-0.017987) | 0.062269 / 0.014526 (0.047743) | 0.074289 / 0.176557 (-0.102268) | 0.121069 / 0.737135 (-0.616067) | 0.076485 / 0.296338 (-0.219853) |

### 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) | 0.277315 / 0.215209 (0.062106) | 2.742027 / 2.077655 (0.664372) | 1.472970 / 1.504120 (-0.031150) | 1.350065 / 1.541195 (-0.191130) | 1.378806 / 1.468490 (-0.089684) | 0.567742 / 4.584777 (-4.017035) | 2.376752 / 3.745712 (-1.368960) | 2.662459 / 5.269862 (-2.607402) | 1.750396 / 4.565676 (-2.815280) | 0.063589 / 0.424275 (-0.360686) | 0.004987 / 0.007607 (-0.002620) | 0.326441 / 0.226044 (0.100397) | 3.224125 / 2.268929 (0.955197) | 1.801623 / 55.444624 (-53.643001) | 1.534712 / 6.876477 (-5.341765) | 1.652365 / 2.142072 (-0.489708) | 0.647624 / 4.805227 (-4.157603) | 0.117161 / 6.500664 (-6.383504) | 0.041908 / 0.075469 (-0.033561) |

### 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) | 0.954879 / 1.841788 (-0.886909) | 11.571875 / 8.074308 (3.497567) | 9.489146 / 10.191392 (-0.702246) | 0.141630 / 0.680424 (-0.538794) | 0.014764 / 0.534201 (-0.519437) | 0.285003 / 0.579283 (-0.294280) | 0.266138 / 0.434364 (-0.168226) | 0.323527 / 0.540337 (-0.216810) | 0.419658 / 1.386936 (-0.967278) |

</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) | 0.005359 / 0.011353 (-0.005994) | 0.003615 / 0.011008 (-0.007393) | 0.050692 / 0.038508 (0.012184) | 0.033632 / 0.023109 (0.010522) | 0.273614 / 0.275898 (-0.002284) | 0.303780 / 0.323480 (-0.019700) | 0.004171 / 0.007986 (-0.003814) | 0.002687 / 0.004328 (-0.001642) | 0.050002 / 0.004250 (0.045751) | 0.040824 / 0.037052 (0.003772) | 0.287759 / 0.258489 (0.029270) | 0.324144 / 0.293841 (0.030303) | 0.029101 / 0.128546 (-0.099445) | 0.010244 / 0.075646 (-0.065402) | 0.059599 / 0.419271 (-0.359672) | 0.033146 / 0.043533 (-0.010387) | 0.276592 / 0.255139 (0.021453) | 0.293670 / 0.283200 (0.010470) | 0.018270 / 0.141683 (-0.123413) | 1.126216 / 1.452155 (-0.325939) | 1.155658 / 1.492716 (-0.337058) |

### 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) | 0.093537 / 0.018006 (0.075530) | 0.302706 / 0.000490 (0.302216) | 0.000216 / 0.000200 (0.000016) | 0.000052 / 0.000054 (-0.000003) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023118 / 0.037411 (-0.014293) | 0.076995 / 0.014526 (0.062469) | 0.089476 / 0.176557 (-0.087080) | 0.130705 / 0.737135 (-0.606430) | 0.090258 / 0.296338 (-0.206081) |

### 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) | 0.285920 / 0.215209 (0.070710) | 2.830581 / 2.077655 (0.752927) | 1.561695 / 1.504120 (0.057575) | 1.522791 / 1.541195 (-0.018403) | 1.429875 / 1.468490 (-0.038615) | 0.566683 / 4.584777 (-4.018094) | 0.957157 / 3.745712 (-2.788555) | 2.663718 / 5.269862 (-2.606143) | 1.748885 / 4.565676 (-2.816791) | 0.063697 / 0.424275 (-0.360578) | 0.004996 / 0.007607 (-0.002611) | 0.340042 / 0.226044 (0.113998) | 3.352792 / 2.268929 (1.083863) | 1.907189 / 55.444624 (-53.537435) | 1.608177 / 6.876477 (-5.268300) | 1.775438 / 2.142072 (-0.366634) | 0.645264 / 4.805227 (-4.159963) | 0.116441 / 6.500664 (-6.384223) | 0.040671 / 0.075469 (-0.034798) |

### 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) | 1.005050 / 1.841788 (-0.836738) | 12.040057 / 8.074308 (3.965749) | 10.213560 / 10.191392 (0.022168) | 0.138383 / 0.680424 (-0.542041) | 0.015409 / 0.534201 (-0.518792) | 0.283509 / 0.579283 (-0.295774) | 0.125501 / 0.434364 (-0.308863) | 0.318816 / 0.540337 (-0.221521) | 0.415454 / 1.386936 (-0.971482) |

</details>
</details>

![](https://cml.dev/watermark.png#cbb29cea0e21dc0eb8f7de01d0c6ed5718d6ce4e "CML watermark")

shape returns (number of rows, number of columns)