add checkpoint and resume title in docs
State: closed | Author: lhoestq (MEMBER)

(minor) just to make it more prominent in the docs page for the soon-to-be-released new torchdata

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7050). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

<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) | 0.005707 / 0.011353 (-0.005646) | 0.004381 / 0.011008 (-0.006627) | 0.063711 / 0.038508 (0.025202) | 0.031882 / 0.023109 (0.008772) | 0.250056 / 0.275898 (-0.025842) | 0.287616 / 0.323480 (-0.035863) | 0.003327 / 0.007986 (-0.004658) | 0.003717 / 0.004328 (-0.000611) | 0.049103 / 0.004250 (0.044853) | 0.048821 / 0.037052 (0.011769) | 0.259688 / 0.258489 (0.001199) | 0.311469 / 0.293841 (0.017628) | 0.030667 / 0.128546 (-0.097879) | 0.013091 / 0.075646 (-0.062555) | 0.204737 / 0.419271 (-0.214534) | 0.038312 / 0.043533 (-0.005221) | 0.250055 / 0.255139 (-0.005084) | 0.272199 / 0.283200 (-0.011001) | 0.021161 / 0.141683 (-0.120522) | 1.116095 / 1.452155 (-0.336060) | 1.153588 / 1.492716 (-0.339129) |

### 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) | 0.107828 / 0.018006 (0.089822) | 0.315898 / 0.000490 (0.315408) | 0.000228 / 0.000200 (0.000028) | 0.000048 / 0.000054 (-0.000006) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018873 / 0.037411 (-0.018539) | 0.063374 / 0.014526 (0.048848) | 0.076424 / 0.176557 (-0.100133) | 0.123468 / 0.737135 (-0.613667) | 0.077432 / 0.296338 (-0.218906) |

### 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) | 0.288931 / 0.215209 (0.073722) | 2.828745 / 2.077655 (0.751091) | 1.471061 / 1.504120 (-0.033059) | 1.332289 / 1.541195 (-0.208906) | 1.379797 / 1.468490 (-0.088693) | 0.708053 / 4.584777 (-3.876724) | 2.382431 / 3.745712 (-1.363281) | 2.952672 / 5.269862 (-2.317190) | 1.957517 / 4.565676 (-2.608160) | 0.078730 / 0.424275 (-0.345546) | 0.005093 / 0.007607 (-0.002514) | 0.338147 / 0.226044 (0.112102) | 3.340841 / 2.268929 (1.071912) | 1.857083 / 55.444624 (-53.587541) | 1.533659 / 6.876477 (-5.342818) | 1.750549 / 2.142072 (-0.391523) | 0.804125 / 4.805227 (-4.001103) | 0.134618 / 6.500664 (-6.366046) | 0.042517 / 0.075469 (-0.032952) |

### 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) | 0.968608 / 1.841788 (-0.873180) | 12.326994 / 8.074308 (4.252686) | 9.464889 / 10.191392 (-0.726503) | 0.143979 / 0.680424 (-0.536445) | 0.014577 / 0.534201 (-0.519624) | 0.303205 / 0.579283 (-0.276078) | 0.269866 / 0.434364 (-0.164498) | 0.344846 / 0.540337 (-0.195491) | 0.443794 / 1.386936 (-0.943142) |

</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) | 0.006452 / 0.011353 (-0.004900) | 0.004264 / 0.011008 (-0.006745) | 0.051355 / 0.038508 (0.012847) | 0.035188 / 0.023109 (0.012079) | 0.267697 / 0.275898 (-0.008201) | 0.295853 / 0.323480 (-0.027627) | 0.004611 / 0.007986 (-0.003374) | 0.005395 / 0.004328 (0.001066) | 0.049903 / 0.004250 (0.045652) | 0.044582 / 0.037052 (0.007530) | 0.284706 / 0.258489 (0.026217) | 0.321623 / 0.293841 (0.027782) | 0.033228 / 0.128546 (-0.095318) | 0.013077 / 0.075646 (-0.062569) | 0.061867 / 0.419271 (-0.357405) | 0.034625 / 0.043533 (-0.008908) | 0.269088 / 0.255139 (0.013949) | 0.284899 / 0.283200 (0.001699) | 0.019972 / 0.141683 (-0.121710) | 1.157976 / 1.452155 (-0.294178) | 1.181658 / 1.492716 (-0.311058) |

### 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) | 0.111072 / 0.018006 (0.093066) | 0.333310 / 0.000490 (0.332820) | 0.000251 / 0.000200 (0.000051) | 0.000059 / 0.000054 (0.000005) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023760 / 0.037411 (-0.013652) | 0.080746 / 0.014526 (0.066221) | 0.090231 / 0.176557 (-0.086326) | 0.132200 / 0.737135 (-0.604936) | 0.095679 / 0.296338 (-0.200660) |

### 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) | 0.297404 / 0.215209 (0.082195) | 2.919779 / 2.077655 (0.842124) | 1.577470 / 1.504120 (0.073350) | 1.452924 / 1.541195 (-0.088271) | 1.523683 / 1.468490 (0.055193) | 0.743801 / 4.584777 (-3.840976) | 1.006944 / 3.745712 (-2.738768) | 3.218161 / 5.269862 (-2.051701) | 2.069762 / 4.565676 (-2.495914) | 0.082900 / 0.424275 (-0.341375) | 0.005239 / 0.007607 (-0.002368) | 0.360124 / 0.226044 (0.134080) | 3.505349 / 2.268929 (1.236420) | 1.959324 / 55.444624 (-53.485300) | 1.663782 / 6.876477 (-5.212694) | 1.725745 / 2.142072 (-0.416327) | 0.825268 / 4.805227 (-3.979959) | 0.138577 / 6.500664 (-6.362087) | 0.042716 / 0.075469 (-0.032753) |

### 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) | 1.021138 / 1.841788 (-0.820650) | 13.907954 / 8.074308 (5.833646) | 11.023796 / 10.191392 (0.832404) | 0.135224 / 0.680424 (-0.545200) | 0.016232 / 0.534201 (-0.517969) | 0.330389 / 0.579283 (-0.248894) | 0.131702 / 0.434364 (-0.302662) | 0.372499 / 0.540337 (-0.167838) | 0.472702 / 1.386936 (-0.914234) |

</details>
</details>

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