[docs] troubleshooting guide
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6424). All of your documentation changes will be reflected on that endpoint. <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) | 0.005323 / 0.011353 (-0.006030) | 0.003560 / 0.011008 (-0.007448) | 0.062572 / 0.038508 (0.024064) | 0.049549 / 0.023109 (0.026440) | 0.236522 / 0.275898 (-0.039376) | 0.260601 / 0.323480 (-0.062879) | 0.002887 / 0.007986 (-0.005099) | 0.003225 / 0.004328 (-0.001103) | 0.048210 / 0.004250 (0.043960) | 0.038783 / 0.037052 (0.001731) | 0.242506 / 0.258489 (-0.015983) | 0.273906 / 0.293841 (-0.019935) | 0.027202 / 0.128546 (-0.101344) | 0.010577 / 0.075646 (-0.065069) | 0.211669 / 0.419271 (-0.207603) | 0.035727 / 0.043533 (-0.007806) | 0.242303 / 0.255139 (-0.012836) | 0.260468 / 0.283200 (-0.022732) | 0.020109 / 0.141683 (-0.121573) | 1.089603 / 1.452155 (-0.362552) | 1.149899 / 1.492716 (-0.342817) |

### 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) | 0.088768 / 0.018006 (0.070761) | 0.300300 / 0.000490 (0.299810) | 0.000212 / 0.000200 (0.000013) | 0.000050 / 0.000054 (-0.000005) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018758 / 0.037411 (-0.018653) | 0.060097 / 0.014526 (0.045571) | 0.074060 / 0.176557 (-0.102496) | 0.119977 / 0.737135 (-0.617158) | 0.075298 / 0.296338 (-0.221040) |

### 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) | 0.278640 / 0.215209 (0.063431) | 2.715574 / 2.077655 (0.637919) | 1.466644 / 1.504120 (-0.037476) | 1.344470 / 1.541195 (-0.196725) | 1.386984 / 1.468490 (-0.081506) | 0.575796 / 4.584777 (-4.008981) | 2.392324 / 3.745712 (-1.353388) | 2.826284 / 5.269862 (-2.443578) | 1.758997 / 4.565676 (-2.806679) | 0.062474 / 0.424275 (-0.361801) | 0.004930 / 0.007607 (-0.002678) | 0.332595 / 0.226044 (0.106551) | 3.240076 / 2.268929 (0.971147) | 1.785283 / 55.444624 (-53.659341) | 1.527594 / 6.876477 (-5.348882) | 1.562840 / 2.142072 (-0.579233) | 0.655474 / 4.805227 (-4.149754) | 0.116682 / 6.500664 (-6.383983) | 0.042664 / 0.075469 (-0.032805) |

### 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) | 0.936306 / 1.841788 (-0.905481) | 11.561239 / 8.074308 (3.486931) | 10.341918 / 10.191392 (0.150526) | 0.140602 / 0.680424 (-0.539822) | 0.013857 / 0.534201 (-0.520344) | 0.294241 / 0.579283 (-0.285042) | 0.268359 / 0.434364 (-0.166005) | 0.326344 / 0.540337 (-0.213993) | 0.430936 / 1.386936 (-0.956000) |

</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) | 0.005197 / 0.011353 (-0.006156) | 0.003543 / 0.011008 (-0.007465) | 0.049051 / 0.038508 (0.010542) | 0.052742 / 0.023109 (0.029633) | 0.277032 / 0.275898 (0.001134) | 0.300799 / 0.323480 (-0.022681) | 0.003922 / 0.007986 (-0.004064) | 0.002573 / 0.004328 (-0.001755) | 0.047270 / 0.004250 (0.043019) | 0.039782 / 0.037052 (0.002730) | 0.282780 / 0.258489 (0.024291) | 0.308858 / 0.293841 (0.015017) | 0.028641 / 0.128546 (-0.099905) | 0.010516 / 0.075646 (-0.065131) | 0.056367 / 0.419271 (-0.362904) | 0.032346 / 0.043533 (-0.011186) | 0.277591 / 0.255139 (0.022452) | 0.298539 / 0.283200 (0.015339) | 0.018168 / 0.141683 (-0.123515) | 1.104331 / 1.452155 (-0.347823) | 1.187691 / 1.492716 (-0.305025) |

### 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) | 0.089511 / 0.018006 (0.071505) | 0.301309 / 0.000490 (0.300820) | 0.000213 / 0.000200 (0.000013) | 0.000049 / 0.000054 (-0.000005) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021466 / 0.037411 (-0.015945) | 0.069917 / 0.014526 (0.055391) | 0.081105 / 0.176557 (-0.095452) | 0.119619 / 0.737135 (-0.617516) | 0.083928 / 0.296338 (-0.212410) |

### 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) | 0.296471 / 0.215209 (0.081262) | 2.912139 / 2.077655 (0.834484) | 1.588861 / 1.504120 (0.084741) | 1.452148 / 1.541195 (-0.089047) | 1.475388 / 1.468490 (0.006898) | 0.555779 / 4.584777 (-4.028998) | 2.425599 / 3.745712 (-1.320113) | 2.792848 / 5.269862 (-2.477013) | 1.718757 / 4.565676 (-2.846919) | 0.077687 / 0.424275 (-0.346588) | 0.007522 / 0.007607 (-0.000085) | 0.348254 / 0.226044 (0.122210) | 3.439315 / 2.268929 (1.170386) | 1.925907 / 55.444624 (-53.518717) | 1.646163 / 6.876477 (-5.230314) | 1.662148 / 2.142072 (-0.479924) | 0.637277 / 4.805227 (-4.167950) | 0.116159 / 6.500664 (-6.384505) | 0.041518 / 0.075469 (-0.033952) |

### 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) | 0.966358 / 1.841788 (-0.875430) | 12.125201 / 8.074308 (4.050892) | 10.629939 / 10.191392 (0.438547) | 0.132439 / 0.680424 (-0.547984) | 0.015622 / 0.534201 (-0.518579) | 0.288824 / 0.579283 (-0.290459) | 0.277634 / 0.434364 (-0.156730) | 0.327200 / 0.540337 (-0.213138) | 0.549679 / 1.386936 (-0.837257) |

</details>
</details>

![](https://cml.dev/watermark.png#0850f663f5498e0f296461e99a345dfd65e3358f "CML watermark")

Hi all! This is a PR adding a troubleshooting guide for Datasets docs. 
I went through the library's GitHub Issues and Forum questions and identified a few issues that are common enough that I think it would be valuable to include them in the troubleshooting guide. These are: 
- creating a dataset from a folder and not following the required format
- authentication issues when using `push_to_hub`
- `Too Many Requests` with `push_to_hub`
- Pickling issues when using Dataset.from_generator()

There's also a section on asking for help. Please let me know if there are other common issues or advice that we can include here. 