Fix a description error for interleave_datasets.
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2023-03-29T19:06:06Z
https://github.com/huggingface/datasets/pull/5680#issuecomment-1489151458
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2023-03-30T12:37:41Z
https://github.com/huggingface/datasets/pull/5680#issuecomment-1490229830
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<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) | 0.006772 / 0.011353 (-0.004581) | 0.004674 / 0.011008 (-0.006335) | 0.098702 / 0.038508 (0.060194) | 0.028257 / 0.023109 (0.005148) | 0.368008 / 0.275898 (0.092110) | 0.402825 / 0.323480 (0.079345) | 0.005158 / 0.007986 (-0.002828) | 0.003470 / 0.004328 (-0.000858) | 0.075541 / 0.004250 (0.071291) | 0.039755 / 0.037052 (0.002702) | 0.373431 / 0.258489 (0.114942) | 0.410159 / 0.293841 (0.116318) | 0.031355 / 0.128546 (-0.097192) | 0.011632 / 0.075646 (-0.064014) | 0.325475 / 0.419271 (-0.093797) | 0.042574 / 0.043533 (-0.000958) | 0.373629 / 0.255139 (0.118490) | 0.393921 / 0.283200 (0.110721) | 0.084669 / 0.141683 (-0.057013) | 1.459947 / 1.452155 (0.007792) | 1.529593 / 1.492716 (0.036877) |

### 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) | 0.189994 / 0.018006 (0.171988) | 0.409091 / 0.000490 (0.408602) | 0.003693 / 0.000200 (0.003493) | 0.000072 / 0.000054 (0.000018) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.024649 / 0.037411 (-0.012762) | 0.097702 / 0.014526 (0.083177) | 0.103650 / 0.176557 (-0.072906) | 0.167141 / 0.737135 (-0.569994) | 0.108460 / 0.296338 (-0.187879) |

### 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) | 0.429544 / 0.215209 (0.214335) | 4.277106 / 2.077655 (2.199451) | 2.018745 / 1.504120 (0.514625) | 1.814782 / 1.541195 (0.273587) | 1.897030 / 1.468490 (0.428540) | 0.700332 / 4.584777 (-3.884445) | 3.421761 / 3.745712 (-0.323951) | 3.008281 / 5.269862 (-2.261581) | 1.554230 / 4.565676 (-3.011446) | 0.082922 / 0.424275 (-0.341353) | 0.012312 / 0.007607 (0.004705) | 0.527757 / 0.226044 (0.301713) | 5.287450 / 2.268929 (3.018522) | 2.329083 / 55.444624 (-53.115542) | 2.016651 / 6.876477 (-4.859826) | 2.214510 / 2.142072 (0.072437) | 0.807676 / 4.805227 (-3.997551) | 0.151752 / 6.500664 (-6.348912) | 0.066819 / 0.075469 (-0.008651) |

### 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) | 1.239522 / 1.841788 (-0.602266) | 13.923672 / 8.074308 (5.849364) | 14.317394 / 10.191392 (4.126002) | 0.159379 / 0.680424 (-0.521045) | 0.016537 / 0.534201 (-0.517664) | 0.376808 / 0.579283 (-0.202475) | 0.376351 / 0.434364 (-0.058012) | 0.437124 / 0.540337 (-0.103213) | 0.520589 / 1.386936 (-0.866347) |

</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) | 0.006892 / 0.011353 (-0.004461) | 0.004671 / 0.011008 (-0.006337) | 0.075841 / 0.038508 (0.037333) | 0.028713 / 0.023109 (0.005604) | 0.345105 / 0.275898 (0.069207) | 0.380694 / 0.323480 (0.057214) | 0.005155 / 0.007986 (-0.002830) | 0.003379 / 0.004328 (-0.000949) | 0.075134 / 0.004250 (0.070883) | 0.039990 / 0.037052 (0.002938) | 0.345540 / 0.258489 (0.087051) | 0.389913 / 0.293841 (0.096072) | 0.032089 / 0.128546 (-0.096458) | 0.011583 / 0.075646 (-0.064063) | 0.085169 / 0.419271 (-0.334102) | 0.041847 / 0.043533 (-0.001686) | 0.341504 / 0.255139 (0.086365) | 0.367582 / 0.283200 (0.084382) | 0.092684 / 0.141683 (-0.048999) | 1.498647 / 1.452155 (0.046492) | 1.549056 / 1.492716 (0.056339) |

### 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) | 0.228643 / 0.018006 (0.210637) | 0.410680 / 0.000490 (0.410191) | 0.000398 / 0.000200 (0.000198) | 0.000059 / 0.000054 (0.000005) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.025354 / 0.037411 (-0.012057) | 0.101567 / 0.014526 (0.087041) | 0.108340 / 0.176557 (-0.068217) | 0.157804 / 0.737135 (-0.579332) | 0.113985 / 0.296338 (-0.182354) |

### 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) | 0.436427 / 0.215209 (0.221218) | 4.359331 / 2.077655 (2.281676) | 2.047877 / 1.504120 (0.543757) | 1.844242 / 1.541195 (0.303047) | 1.924553 / 1.468490 (0.456063) | 0.695986 / 4.584777 (-3.888791) | 3.435571 / 3.745712 (-0.310141) | 1.905189 / 5.269862 (-3.364673) | 1.198542 / 4.565676 (-3.367134) | 0.083386 / 0.424275 (-0.340889) | 0.012442 / 0.007607 (0.004835) | 0.542562 / 0.226044 (0.316517) | 5.416554 / 2.268929 (3.147625) | 2.499496 / 55.444624 (-52.945128) | 2.160658 / 6.876477 (-4.715819) | 2.210535 / 2.142072 (0.068462) | 0.803324 / 4.805227 (-4.001903) | 0.151735 / 6.500664 (-6.348929) | 0.068392 / 0.075469 (-0.007078) |

### 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) | 1.319915 / 1.841788 (-0.521873) | 14.176755 / 8.074308 (6.102446) | 14.376366 / 10.191392 (4.184974) | 0.141219 / 0.680424 (-0.539204) | 0.017181 / 0.534201 (-0.517020) | 0.383589 / 0.579283 (-0.195694) | 0.389352 / 0.434364 (-0.045012) | 0.474465 / 0.540337 (-0.065873) | 0.563047 / 1.386936 (-0.823889) |

</details>
</details>

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2023-03-30T13:14:19Z
https://github.com/huggingface/datasets/pull/5680#issuecomment-1490284559
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There is a description mistake in the annotation of interleave_dataset with "all_exhausted" stopping_strategy.
``` python
d1 = Dataset.from_dict({"a": [0, 1, 2]})
d2 = Dataset.from_dict({"a": [10, 11, 12, 13]})
d3 = Dataset.from_dict({"a": [20, 21, 22, 23, 24]})
dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted")
```
According to the interleave way, the correct output of `dataset["a"]` is `[0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 10, 24]`, not `[0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 0, 24]`