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https://api.github.com/repos/huggingface/datasets/issues/5533	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5533/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5533/comments	https://api.github.com/repos/huggingface/datasets/issues/5533/events	https://github.com/huggingface/datasets/pull/5533	1,585,885,871	PR_kwDODunzps5KCR5I	5,533	Add reduce function	[]	closed	false	null	19	2023-02-15T13:44:01Z	2023-02-28T14:46:13Z	2023-02-28T14:46:12Z	null	This PR closes #5496 . I tried to imitate the `reduce`-method from `functools`, i.e. the function input must be a binary operation. I assume that the input type has an empty element, i.e. `input_type()` is defined, as the acumulant is instantiated as this object - im not sure that is this a reasonable assumption? If `batched= True` the reduction of each shard is _not_ returned, but the reduction of the entire dataset. I was unsure wether this was an intuitive API, or it would make more sense to return the reduction of each shard?	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5533/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5533/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5533.diff", "html_url": "https://github.com/huggingface/datasets/pull/5533", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5533.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5533" }	true	[ "I agree that it would be a good idea to introduce a `combiner` argument in another PR.\r\n\r\nI did take quite a lot of inspiration from the implementation of `map`, but it did not seem obvious how to resuse `map` for the implementation. Do you have any suggestions, i could give a try?\r\n\r\nThose were exactly my thoughts, regarding the non-obvious initializer for batched and formatted datasets, so i agree! I'll introduce a `initializer` argument, and have it mandatory when `batched=True`.", "I added `initializer`. It is optional for `batched=False` and mandatory for `batched=True`. It has to be of the same length as `input_columns`, if `input_columns=None` it has to have the same length as `_data.column_names`. \r\n\r\nIf the initializer is not set for `batched=False` the first example is set as the `initializer`. \r\n\r\nThe initializer is used to initiliaze for each shard, so that means if that:\r\n```python\r\ndset = Dataset.from_dict({\"x\": [1, 2, 3]})\r\nsum_reduce = lambda x, y: x + y\r\nreduction = dset.reduce(sum_reduce, batched=True, initializer=1, input_columns='x', num_proc=2)\r\n# reduction is 8, i.e. reduction + num_proc * initializer\r\n```", "> I added initializer. It is optional for batched=False and mandatory for batched=True. It has to be of the same length as input_columns, if input_columns=None it has to have the same length as _data.column_names.\r\n> \r\n> If the initializer is not set for batched=False the first example is set as the initializer.\r\n\r\nSounds good to me !\r\n\r\n> The initializer is used to initiliaze for each shard, so that means if that:\r\n> \r\n> ```python\r\n> dset = Dataset.from_dict({\"x\": [1, 2, 3]})\r\n> sum_reduce = lambda x, y: x + y\r\n> reduction = dset.reduce(sum_reduce, batched=True, initializer=1, input_columns='x', num_proc=2)\r\n> # reduction is 8, i.e. reduction + num_proc * initializer\r\n> ```\r\n\r\nHmm this can be confusing for some users. Maybe we should consider making `combiner` mandatory for multiprocessing.\r\n\r\nIf we agree on this, maybe for this PR you can either:\r\n- remove multiprocessing (and we add combiner + multiprocessing in a subsequent PR)\r\n- OR add `combiner` directly\r\n\r\nMaybe we can get more feedback from @huggingface/datasets as well", "> > I added initializer. It is optional for batched=False and mandatory for batched=True. It has to be of the same length as input_columns, if input_columns=None it has to have the same length as _data.column_names.\r\n> > If the initializer is not set for batched=False the first example is set as the initializer.\r\n> \r\n> Sounds good to me !\r\n> \r\n> > The initializer is used to initiliaze for each shard, so that means if that:\r\n> > ```python\r\n> > dset = Dataset.from_dict({\"x\": [1, 2, 3]})\r\n> > sum_reduce = lambda x, y: x + y\r\n> > reduction = dset.reduce(sum_reduce, batched=True, initializer=1, input_columns='x', num_proc=2)\r\n> > # reduction is 8, i.e. reduction + num_proc * initializer\r\n> > ```\r\n> \r\n> Hmm this can be confusing for some users. Maybe we should consider making `combiner` mandatory for multiprocessing.\r\n> \r\n> If we agree on this, maybe for this PR you can either:\r\n> \r\n> * remove multiprocessing (and we add combiner + multiprocessing in a subsequent PR)\r\n> * OR add `combiner` directly\r\n> \r\n> Maybe we can get more feedback from @huggingface/datasets as well\r\n\r\nI think i prefer adding `combiner` in this PR. I think ill make `combiner` mandatory for `batched=True`, instead of assuming that `combiner=function`. Ill look at this one of the coming days. Also at some point i have to define `reduce` for `DatasetDict`, and not just `Dataset`.", "I added the `combiner` parameter as described. I added some examples in the docstring, as i felt it might still be a bit confusing what happens during multiprocessing / batching.\r\n\r\nStill need to look at `DatasetDict`.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5533). All of your documentation changes will be reflected on that endpoint.", "Feel free to merge `main` into your branch - we fixed some CI failures today", "The proposed API doesn't seem intuitive to me - one can already use `functools.reduce` or `Dataset.map` for this purpose ([Colab](https://colab.research.google.com/drive/1jCLv31Y4cDfqD0lhO0AnqEv3Or-LLvWe?usp=sharing) with examples), so perhaps we could have a section in the docs that uses these methods to perform reductions rather than introducing a new method (which needs to be maintained later)", "Thanks for sharing this google colab, it has nice examples !\r\n\r\nThough I still think `functools.reduce` with multiprocessing can be a pain - we offer something easier here:\r\n- no need to use a pool yourself\r\n- no need to use `map` just to iterate on the dataset (not its main purpose)\r\n- native support for lambdas (using dill)\r\n- the combiner is mandatory for multiprocessing to avoid ending up with an incorrect result as in your example\r\n\r\nHowever I agree that maintaining this can be challenging, especially if you think about how `map` already is, and if we also have to deal with dataset formatting.", "> native support for lambdas (using dill)\r\n\r\nReplacing `multiprocessing` with `multiprocess` in the example would allow that.\r\n\r\n> no need to use map just to iterate on the dataset (not its main purpose)\r\n\r\nNot the main purpose, but this was mentioned as a \"feature\" in the previous docs if I remember.\r\n\r\nAnd all this is related to the multi-processing case, which we can document.\r\n\r\nBesides the linked issue, I can't find requests for `Dataset.reduce`, which makes me think `functools.reduce` does the job for most users.", "> Besides the linked issue, I can't find requests for Dataset.reduce, which makes me think functools.reduce does the job for most users.\r\n\r\nI think @srush was looking for a way to do a word count but ended up using a single processed `map`. I also saw some users on the forum wanting to compute `max`\r\n\r\n> Not the main purpose, but this was mentioned as a \"feature\" in the previous docs if I remember.\r\n> \r\n> And all this is related to the multi-processing case, which we can document.\r\n\r\nYup indeed", "While counting is one example, I often find I want to compute different statistics over a dataset. This seems like a natural way to do it in a stateless manner.\n\n\nI guess you could use functools reduce, but that wouldn't allow batching, right?", "I've updated the [Colab](https://colab.research.google.com/drive/1jCLv31Y4cDfqD0lhO0AnqEv3Or-LLvWe?usp=sharing) with an example that reduces batches with `map` and then computes the final result. It would be nice to have a similar example (explained in detail) in the docs to show the full power of `map`.\r\n\r\nPlus, for simple reductions such as `max`, one can do `pc.max(ds.with_format(\"arrow\")[\"col\"])` to directly get the result (without loading the entire column in RAM).\r\n\r\n@srush \r\n\r\n> I guess you could use functools reduce, but that wouldn't allow batching, right?\r\n\r\nYou can use `.iter(batch_size)` to get batches\r\n ", "That `functools` tools example is clean. I didn't know about `iter`. That would handle my use case.\n\nThe stateful `map` with a global variable is pretty hairy. I don't think we should recommend people do that.\n\n", "Whenever I in the past wanted to calculate statistics for datasets I used `functools` similarly to how it's described in the colab, but I always felt it was a bit of a hassle to use it together with multiprocessing, which is why I picked up the issue, to do it \"once and for all\".", "Should i close this and open another PR, with descriptions of how to use `map` for reduction, or?", "Yes I think good documentation is the way to go here. @mariosasko 's examples are clear and efficient.\r\n\r\nMaybe we could have an `Aggregations` section in the `Process` page with some guides on how to:\r\n- use `.map()` to compute aggregates\r\n- use `.with_format(\"arrow\")` for max, min, etc. to save RAM and get max speed\r\n- use a multiprocessed `.map()` to get partial results in parallel and combine them (max text length example)\r\n- (advanced) use multiprocessing with an arbitrary accumulator (word count example)\r\n\r\nAnd also a new conceptual guide on `Multiprocessed mapping` to say that it helps speed up CPU intensive processing but why it may lead to incorrect results when computing aggregates.\r\n\r\ncc @stevhliu for visibility and if you have some comments", "I would create a `Reduce` - to be more exact - subsection under `Map` to demonstrate these examples since we're showing how they can be done with the `Dataset.map` function. It'd also be good to add a link to the new concept guide from this section to solidify user understanding :)", "Coolio. Ill close this PR and get going on another one adding what we've discussed during the next couple of days!" ]
https://api.github.com/repos/huggingface/datasets/issues/374	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/374/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/374/comments	https://api.github.com/repos/huggingface/datasets/issues/374/events	https://github.com/huggingface/datasets/pull/374	654,895,066	MDExOlB1bGxSZXF1ZXN0NDQ3NTMxMzUy	374	Add dataset post processing for faiss indexes	[]	closed	false	null	2	2020-07-10T16:25:59Z	2020-07-13T13:44:03Z	2020-07-13T13:44:01Z	null	# Post processing of datasets for faiss indexes Now that we can have datasets with embeddings (see `wiki_pr` for example), we can allow users to load the dataset + get the Faiss index that comes with it to do nearest neighbors queries. ## Implementation proposition - Faiss indexes have to be added to the `nlp.Dataset` object, and therefore it's in a different scope that what are doing the `_split_generators` and `_generate_examples` methods of `nlp.DatasetBuilder`. Therefore I added a new method for post processing of the `nlp.Dataset` object called `_post_process` (name could change) - The role of `_post_process` is to apply dataset transforms (filter/map etc.) or indexing functions (add_faiss_index) to modify/enrich the `nlp.Dataset` object. It is not part of the `download_and_prepare` process (that is focused on arrow files creation) so the post processing is run inside the `as_dataset` method. - `_post_process` can generate new files (cached files from dataset transforms or serialized faiss indexes) and their names are defined by `_post_processing_resources` - as we know what are the post processing resources, we can download them automatically from google storage instead of computing them if they're available (as we do for arrow files) I'd happy to discuss these choices ! ## The `wiki_dpr` index It takes 1h20 and ~7GB of memory to compute. The final index is 1.42GB and takes ~1.5GB of memory. This is pretty cool given that a naive flat index would take 170GB of memory to store the 21M vectors of dim 768. I couldn't use directly the Faiss `index_factory` as I needed to set the metric to inner product. ## Example of usage ```python import nlp dset = nlp.load_dataset( "wiki_dpr", "psgs_w100_with_nq_embeddings", split="train", with_index=True ) print(len(dset), dset.list_indexes()) # (21015300, ['embeddings']) ``` (it also works with the dataset configuration without the embeddings because I added the index file in google storage for this one too) ## Demo You can also check a demo on google colab that shows how to use it with the DPRQuestionEncoder from transformers: https://colab.research.google.com/drive/1FakNU8W5EPMcWff7iP1H6REg3XSS0YLp?usp=sharing	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/374/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/374/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/374.diff", "html_url": "https://github.com/huggingface/datasets/pull/374", "merged_at": "2020-07-13T13:44:01Z", "patch_url": "https://github.com/huggingface/datasets/pull/374.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/374" }	true	[ "I changed the `wiki_dpr` script to ignore the last 24 examples for now. Hopefully we'll have the full version soon.\r\nThe datasets_infos.json and the data on GCS are updated.\r\n\r\nAnd I also added a check to make sure we don't have post processing resources in sub-directories.", "I added a dummy config that can be loaded with:\r\n```python\r\nwiki = load_dataset(\"wiki_dpr\", \"dummy_psgs_w100_no_embeddings\", with_index=True, split=\"train\")\r\n```\r\nIt's only 6MB of arrow files and 30MB of index" ]
https://api.github.com/repos/huggingface/datasets/issues/3283	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3283/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3283/comments	https://api.github.com/repos/huggingface/datasets/issues/3283/events	https://github.com/huggingface/datasets/issues/3283	1,055,495,874	I_kwDODunzps4-6ZbC	3,283	Add Speech Commands dataset	[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" }, { "color": "d93f0b", "default": false, "description": "", "id": 2725241052, "name": "speech", "node_id": "MDU6TGFiZWwyNzI1MjQxMDUy", "url": "https://api.github.com/repos/huggingface/datasets/labels/speech" } ]	closed	false	null	1	2021-11-16T22:39:56Z	2021-12-10T10:30:15Z	2021-12-10T10:30:15Z	null	## Adding a Dataset - Name: Speech commands - Description: A Dataset for Limited-Vocabulary Speech Recognition - Paper: https://arxiv.org/abs/1804.03209 - Data: https://www.tensorflow.org/datasets/catalog/speech_commands, Available: http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz - Motivation: Nice dataset for audio classification training cc @anton-l Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3283/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3283/timeline	null	completed	null	null	false	[ "#self-assign" ]
https://api.github.com/repos/huggingface/datasets/issues/6019	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6019/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6019/comments	https://api.github.com/repos/huggingface/datasets/issues/6019/events	https://github.com/huggingface/datasets/pull/6019	1,799,532,822	PR_kwDODunzps5VPAlD	6,019	Improve logging	[]	closed	false	null	13	2023-07-11T18:30:23Z	2023-07-12T19:34:14Z	2023-07-12T17:19:28Z	null	Adds the StreamHandler (as `hfh` and `transformers` do) to the library's logger to log INFO messages and logs the messages about "loading a cached result" (and some other warnings) as INFO (Also removes the `leave=False` arg in the progress bars to be consistent with `hfh` and `transformers` - progress bars serve as an indicator that a result is not cached, so it makes more sense not to delete them) Fix #2832, fix https://github.com/huggingface/datasets/issues/1948, fix https://github.com/huggingface/datasets/issues/5444	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6019/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6019/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6019.diff", "html_url": "https://github.com/huggingface/datasets/pull/6019", "merged_at": "2023-07-12T17:19:28Z", "patch_url": "https://github.com/huggingface/datasets/pull/6019.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6019" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007782 / 0.011353 (-0.003571) \| 0.004451 / 0.011008 (-0.006557) \| 0.099928 / 0.038508 (0.061420) \| 0.081534 / 0.023109 (0.058425) \| 0.379382 / 0.275898 (0.103484) \| 0.410652 / 0.323480 (0.087172) \| 0.005967 / 0.007986 (-0.002019) \| 0.003702 / 0.004328 (-0.000627) \| 0.076359 / 0.004250 (0.072109) \| 0.066721 / 0.037052 (0.029669) \| 0.383595 / 0.258489 (0.125106) \| 0.423854 / 0.293841 (0.130013) \| 0.032796 / 0.128546 (-0.095750) \| 0.009728 / 0.075646 (-0.065918) \| 0.344347 / 0.419271 (-0.074925) \| 0.056320 / 0.043533 (0.012788) \| 0.379974 / 0.255139 (0.124835) \| 0.401294 / 0.283200 (0.118094) \| 0.024110 / 0.141683 (-0.117572) \| 1.804194 / 1.452155 (0.352039) \| 1.860240 / 1.492716 (0.367523) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.233803 / 0.018006 (0.215797) \| 0.506893 / 0.000490 (0.506404) \| 0.003894 / 0.000200 (0.003694) \| 0.000090 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033328 / 0.037411 (-0.004083) \| 0.098661 / 0.014526 (0.084136) \| 0.114971 / 0.176557 (-0.061586) \| 0.186815 / 0.737135 (-0.550321) \| 0.115490 / 0.296338 (-0.180848) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422590 / 0.215209 (0.207381) \| 4.277189 / 2.077655 (2.199535) \| 2.095565 / 1.504120 (0.591445) \| 2.040825 / 1.541195 (0.499630) \| 2.162562 / 1.468490 (0.694072) \| 0.578602 / 4.584777 (-4.006175) \| 4.203474 / 3.745712 (0.457762) \| 6.674595 / 5.269862 (1.404734) \| 3.913251 / 4.565676 (-0.652426) \| 0.067777 / 0.424275 (-0.356498) \| 0.008716 / 0.007607 (0.001109) \| 0.548704 / 0.226044 (0.322660) \| 5.162120 / 2.268929 (2.893192) \| 2.600250 / 55.444624 (-52.844374) \| 2.232730 / 6.876477 (-4.643747) \| 2.485617 / 2.142072 (0.343544) \| 0.650872 / 4.805227 (-4.154355) \| 0.148022 / 6.500664 (-6.352642) \| 0.064795 / 0.075469 (-0.010674) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.399439 / 1.841788 (-0.442349) \| 22.438959 / 8.074308 (14.364651) \| 16.447831 / 10.191392 (6.256439) \| 0.202003 / 0.680424 (-0.478421) \| 0.026200 / 0.534201 (-0.508001) \| 0.472966 / 0.579283 (-0.106317) \| 0.491621 / 0.434364 (0.057257) \| 0.551580 / 0.540337 (0.011242) \| 0.751420 / 1.386936 (-0.635516) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007241 / 0.011353 (-0.004112) \| 0.004434 / 0.011008 (-0.006574) \| 0.075872 / 0.038508 (0.037364) \| 0.080094 / 0.023109 (0.056985) \| 0.459244 / 0.275898 (0.183346) \| 0.492482 / 0.323480 (0.169002) \| 0.005791 / 0.007986 (-0.002194) \| 0.003657 / 0.004328 (-0.000671) \| 0.075214 / 0.004250 (0.070964) \| 0.064208 / 0.037052 (0.027156) \| 0.464195 / 0.258489 (0.205706) \| 0.497809 / 0.293841 (0.203968) \| 0.036301 / 0.128546 (-0.092245) \| 0.009855 / 0.075646 (-0.065791) \| 0.080826 / 0.419271 (-0.338445) \| 0.056700 / 0.043533 (0.013167) \| 0.452850 / 0.255139 (0.197711) \| 0.490738 / 0.283200 (0.207538) \| 0.024145 / 0.141683 (-0.117538) \| 1.689911 / 1.452155 (0.237757) \| 1.789803 / 1.492716 (0.297087) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.247741 / 0.018006 (0.229735) \| 0.486769 / 0.000490 (0.486279) \| 0.000418 / 0.000200 (0.000218) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036317 / 0.037411 (-0.001094) \| 0.104943 / 0.014526 (0.090417) \| 0.120972 / 0.176557 (-0.055585) \| 0.188461 / 0.737135 (-0.548674) \| 0.120926 / 0.296338 (-0.175412) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.465788 / 0.215209 (0.250579) \| 4.662369 / 2.077655 (2.584714) \| 2.442241 / 1.504120 (0.938121) \| 2.266328 / 1.541195 (0.725133) \| 2.438998 / 1.468490 (0.970508) \| 0.531384 / 4.584777 (-4.053393) \| 4.125286 / 3.745712 (0.379574) \| 3.920912 / 5.269862 (-1.348950) \| 2.292149 / 4.565676 (-2.273528) \| 0.070146 / 0.424275 (-0.354129) \| 0.008887 / 0.007607 (0.001280) \| 0.598181 / 0.226044 (0.372137) \| 5.726454 / 2.268929 (3.457526) \| 3.081836 / 55.444624 (-52.362788) \| 2.683508 / 6.876477 (-4.192969) \| 2.587350 / 2.142072 (0.445278) \| 0.604736 / 4.805227 (-4.200491) \| 0.141303 / 6.500664 (-6.359362) \| 0.065020 / 0.075469 (-0.010449) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.481850 / 1.841788 (-0.359938) \| 22.259592 / 8.074308 (14.185284) \| 16.304290 / 10.191392 (6.112898) \| 0.173514 / 0.680424 (-0.506909) \| 0.021590 / 0.534201 (-0.512611) \| 0.471753 / 0.579283 (-0.107531) \| 0.472132 / 0.434364 (0.037768) \| 0.563344 / 0.540337 (0.023007) \| 0.738509 / 1.386936 (-0.648427) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cb7ae56dbd814945a4982c63bf0e50859a7b93a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005910 / 0.011353 (-0.005443) \| 0.004372 / 0.011008 (-0.006636) \| 0.081583 / 0.038508 (0.043075) \| 0.069598 / 0.023109 (0.046488) \| 0.346360 / 0.275898 (0.070462) \| 0.360733 / 0.323480 (0.037254) \| 0.004725 / 0.007986 (-0.003261) \| 0.003106 / 0.004328 (-0.001222) \| 0.059916 / 0.004250 (0.055666) \| 0.053242 / 0.037052 (0.016189) \| 0.353551 / 0.258489 (0.095062) \| 0.373052 / 0.293841 (0.079211) \| 0.029036 / 0.128546 (-0.099510) \| 0.007894 / 0.075646 (-0.067753) \| 0.284131 / 0.419271 (-0.135140) \| 0.049348 / 0.043533 (0.005815) \| 0.347409 / 0.255139 (0.092270) \| 0.355029 / 0.283200 (0.071830) \| 0.022511 / 0.141683 (-0.119171) \| 1.454495 / 1.452155 (0.002340) \| 1.439551 / 1.492716 (-0.053166) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218889 / 0.018006 (0.200883) \| 0.478734 / 0.000490 (0.478244) \| 0.003758 / 0.000200 (0.003558) \| 0.000083 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025759 / 0.037411 (-0.011653) \| 0.082511 / 0.014526 (0.067985) \| 0.087578 / 0.176557 (-0.088979) \| 0.137760 / 0.737135 (-0.599375) \| 0.093312 / 0.296338 (-0.203027) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.378963 / 0.215209 (0.163754) \| 3.645846 / 2.077655 (1.568191) \| 1.741135 / 1.504120 (0.237015) \| 1.599166 / 1.541195 (0.057972) \| 1.610817 / 1.468490 (0.142327) \| 0.459209 / 4.584777 (-4.125568) \| 3.484857 / 3.745712 (-0.260855) \| 3.928109 / 5.269862 (-1.341752) \| 2.419784 / 4.565676 (-2.145892) \| 0.051987 / 0.424275 (-0.372288) \| 0.006495 / 0.007607 (-0.001112) \| 0.427311 / 0.226044 (0.201267) \| 4.226378 / 2.268929 (1.957450) \| 2.212331 / 55.444624 (-53.232293) \| 1.916213 / 6.876477 (-4.960264) \| 1.978809 / 2.142072 (-0.163263) \| 0.547351 / 4.805227 (-4.257876) \| 0.121110 / 6.500664 (-6.379554) \| 0.054163 / 0.075469 (-0.021306) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.228594 / 1.841788 (-0.613193) \| 19.410901 / 8.074308 (11.336593) \| 13.014722 / 10.191392 (2.823330) \| 0.156449 / 0.680424 (-0.523975) \| 0.021032 / 0.534201 (-0.513169) \| 0.403976 / 0.579283 (-0.175307) \| 0.413885 / 0.434364 (-0.020479) \| 0.470465 / 0.540337 (-0.069873) \| 0.641322 / 1.386936 (-0.745614) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007210 / 0.011353 (-0.004143) \| 0.003824 / 0.011008 (-0.007185) \| 0.058227 / 0.038508 (0.019719) \| 0.076211 / 0.023109 (0.053102) \| 0.336626 / 0.275898 (0.060728) \| 0.420542 / 0.323480 (0.097062) \| 0.006178 / 0.007986 (-0.001808) \| 0.003332 / 0.004328 (-0.000997) \| 0.058073 / 0.004250 (0.053823) \| 0.062485 / 0.037052 (0.025432) \| 0.386175 / 0.258489 (0.127686) \| 0.415659 / 0.293841 (0.121818) \| 0.031264 / 0.128546 (-0.097282) \| 0.007502 / 0.075646 (-0.068144) \| 0.072079 / 0.419271 (-0.347192) \| 0.055860 / 0.043533 (0.012327) \| 0.343508 / 0.255139 (0.088369) \| 0.437844 / 0.283200 (0.154645) \| 0.032852 / 0.141683 (-0.108831) \| 1.409241 / 1.452155 (-0.042913) \| 1.623949 / 1.492716 (0.131233) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207511 / 0.018006 (0.189504) \| 0.464149 / 0.000490 (0.463660) \| 0.003248 / 0.000200 (0.003048) \| 0.000226 / 0.000054 (0.000172) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030767 / 0.037411 (-0.006645) \| 0.079169 / 0.014526 (0.064643) \| 0.093111 / 0.176557 (-0.083445) \| 0.153369 / 0.737135 (-0.583767) \| 0.092939 / 0.296338 (-0.203400) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.375602 / 0.215209 (0.160392) \| 3.968612 / 2.077655 (1.890957) \| 2.081749 / 1.504120 (0.577629) \| 1.899772 / 1.541195 (0.358577) \| 1.847923 / 1.468490 (0.379433) \| 0.442867 / 4.584777 (-4.141910) \| 3.646664 / 3.745712 (-0.099048) \| 5.870600 / 5.269862 (0.600739) \| 3.356698 / 4.565676 (-1.208979) \| 0.051422 / 0.424275 (-0.372853) \| 0.006006 / 0.007607 (-0.001601) \| 0.442439 / 0.226044 (0.216395) \| 4.466256 / 2.268929 (2.197328) \| 2.483832 / 55.444624 (-52.960792) \| 2.105612 / 6.876477 (-4.770865) \| 2.060650 / 2.142072 (-0.081422) \| 0.531119 / 4.805227 (-4.274108) \| 0.123436 / 6.500664 (-6.377228) \| 0.059838 / 0.075469 (-0.015632) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283042 / 1.841788 (-0.558746) \| 19.688251 / 8.074308 (11.613943) \| 13.346386 / 10.191392 (3.154994) \| 0.197463 / 0.680424 (-0.482961) \| 0.018484 / 0.534201 (-0.515717) \| 0.391727 / 0.579283 (-0.187556) \| 0.425061 / 0.434364 (-0.009303) \| 0.448177 / 0.540337 (-0.092160) \| 0.653694 / 1.386936 (-0.733242) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01604752fe89d290479fa406b1a24ac1f346826e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008966 / 0.011353 (-0.002387) \| 0.005195 / 0.011008 (-0.005813) \| 0.102879 / 0.038508 (0.064371) \| 0.090902 / 0.023109 (0.067792) \| 0.434397 / 0.275898 (0.158498) \| 0.454013 / 0.323480 (0.130534) \| 0.008507 / 0.007986 (0.000521) \| 0.005000 / 0.004328 (0.000671) \| 0.075789 / 0.004250 (0.071538) \| 0.067608 / 0.037052 (0.030555) \| 0.435091 / 0.258489 (0.176602) \| 0.469411 / 0.293841 (0.175570) \| 0.050859 / 0.128546 (-0.077687) \| 0.013560 / 0.075646 (-0.062086) \| 0.345473 / 0.419271 (-0.073799) \| 0.094974 / 0.043533 (0.051441) \| 0.429626 / 0.255139 (0.174487) \| 0.434290 / 0.283200 (0.151090) \| 0.052269 / 0.141683 (-0.089413) \| 1.700549 / 1.452155 (0.248395) \| 1.890693 / 1.492716 (0.397976) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296618 / 0.018006 (0.278612) \| 0.613908 / 0.000490 (0.613419) \| 0.000484 / 0.000200 (0.000284) \| 0.000086 / 0.000054 (0.000032) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034346 / 0.037411 (-0.003065) \| 0.096836 / 0.014526 (0.082310) \| 0.113332 / 0.176557 (-0.063224) \| 0.194464 / 0.737135 (-0.542671) \| 0.111732 / 0.296338 (-0.184606) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.624954 / 0.215209 (0.409745) \| 6.442193 / 2.077655 (4.364538) \| 2.818331 / 1.504120 (1.314211) \| 2.529607 / 1.541195 (0.988413) \| 2.549026 / 1.468490 (1.080536) \| 0.967367 / 4.584777 (-3.617410) \| 5.446885 / 3.745712 (1.701173) \| 6.259099 / 5.269862 (0.989237) \| 3.652936 / 4.565676 (-0.912740) \| 0.106420 / 0.424275 (-0.317855) \| 0.011293 / 0.007607 (0.003686) \| 0.772026 / 0.226044 (0.545982) \| 7.823986 / 2.268929 (5.555057) \| 3.725328 / 55.444624 (-51.719297) \| 2.851489 / 6.876477 (-4.024988) \| 3.013722 / 2.142072 (0.871649) \| 1.045090 / 4.805227 (-3.760137) \| 0.213174 / 6.500664 (-6.287490) \| 0.077104 / 0.075469 (0.001635) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.657135 / 1.841788 (-0.184652) \| 24.547604 / 8.074308 (16.473296) \| 19.989533 / 10.191392 (9.798141) \| 0.257139 / 0.680424 (-0.423285) \| 0.028448 / 0.534201 (-0.505753) \| 0.490801 / 0.579283 (-0.088482) \| 0.628072 / 0.434364 (0.193708) \| 0.584873 / 0.540337 (0.044536) \| 0.825258 / 1.386936 (-0.561678) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009258 / 0.011353 (-0.002095) \| 0.005660 / 0.011008 (-0.005348) \| 0.080577 / 0.038508 (0.042069) \| 0.095786 / 0.023109 (0.072676) \| 0.473334 / 0.275898 (0.197436) \| 0.527962 / 0.323480 (0.204482) \| 0.006537 / 0.007986 (-0.001449) \| 0.004411 / 0.004328 (0.000083) \| 0.080702 / 0.004250 (0.076452) \| 0.077020 / 0.037052 (0.039968) \| 0.483205 / 0.258489 (0.224716) \| 0.556916 / 0.293841 (0.263076) \| 0.047670 / 0.128546 (-0.080877) \| 0.016647 / 0.075646 (-0.058999) \| 0.090653 / 0.419271 (-0.328619) \| 0.062122 / 0.043533 (0.018589) \| 0.498326 / 0.255139 (0.243187) \| 0.546572 / 0.283200 (0.263372) \| 0.037525 / 0.141683 (-0.104157) \| 1.869520 / 1.452155 (0.417365) \| 1.915335 / 1.492716 (0.422619) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.248287 / 0.018006 (0.230281) \| 0.611440 / 0.000490 (0.610950) \| 0.004102 / 0.000200 (0.003902) \| 0.000132 / 0.000054 (0.000078) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038228 / 0.037411 (0.000817) \| 0.103510 / 0.014526 (0.088984) \| 0.114337 / 0.176557 (-0.062219) \| 0.189662 / 0.737135 (-0.547473) \| 0.119078 / 0.296338 (-0.177260) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.606622 / 0.215209 (0.391413) \| 6.053900 / 2.077655 (3.976246) \| 2.857972 / 1.504120 (1.353852) \| 2.549756 / 1.541195 (1.008561) \| 2.584557 / 1.468490 (1.116067) \| 0.930431 / 4.584777 (-3.654346) \| 5.524077 / 3.745712 (1.778365) \| 7.858406 / 5.269862 (2.588545) \| 4.890697 / 4.565676 (0.325020) \| 0.095356 / 0.424275 (-0.328919) \| 0.008614 / 0.007607 (0.001007) \| 0.774227 / 0.226044 (0.548182) \| 7.470215 / 2.268929 (5.201287) \| 3.784820 / 55.444624 (-51.659805) \| 3.199364 / 6.876477 (-3.677113) \| 3.212002 / 2.142072 (1.069929) \| 1.054104 / 4.805227 (-3.751123) \| 0.226044 / 6.500664 (-6.274620) \| 0.092237 / 0.075469 (0.016768) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.801054 / 1.841788 (-0.040734) \| 24.220404 / 8.074308 (16.146096) \| 21.652936 / 10.191392 (11.461544) \| 0.247004 / 0.680424 (-0.433420) \| 0.029651 / 0.534201 (-0.504550) \| 0.475702 / 0.579283 (-0.103581) \| 0.621121 / 0.434364 (0.186757) \| 0.570489 / 0.540337 (0.030151) \| 0.768840 / 1.386936 (-0.618096) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2fc21eda345643fb57d1d1167ebed9043310911 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009223 / 0.011353 (-0.002130) \| 0.005750 / 0.011008 (-0.005258) \| 0.105264 / 0.038508 (0.066756) \| 0.088478 / 0.023109 (0.065369) \| 0.461119 / 0.275898 (0.185221) \| 0.481115 / 0.323480 (0.157636) \| 0.006366 / 0.007986 (-0.001619) \| 0.004515 / 0.004328 (0.000186) \| 0.079296 / 0.004250 (0.075045) \| 0.063483 / 0.037052 (0.026430) \| 0.444490 / 0.258489 (0.186001) \| 0.496474 / 0.293841 (0.202634) \| 0.048568 / 0.128546 (-0.079978) \| 0.013574 / 0.075646 (-0.062073) \| 0.379213 / 0.419271 (-0.040059) \| 0.086464 / 0.043533 (0.042932) \| 0.437526 / 0.255139 (0.182387) \| 0.447117 / 0.283200 (0.163917) \| 0.049502 / 0.141683 (-0.092180) \| 1.749146 / 1.452155 (0.296992) \| 1.831082 / 1.492716 (0.338365) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.268205 / 0.018006 (0.250199) \| 0.627406 / 0.000490 (0.626917) \| 0.005439 / 0.000200 (0.005239) \| 0.000128 / 0.000054 (0.000074) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030564 / 0.037411 (-0.006848) \| 0.096365 / 0.014526 (0.081840) \| 0.117484 / 0.176557 (-0.059072) \| 0.189104 / 0.737135 (-0.548032) \| 0.118073 / 0.296338 (-0.178266) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.618229 / 0.215209 (0.403019) \| 6.437853 / 2.077655 (4.360199) \| 2.789946 / 1.504120 (1.285826) \| 2.339245 / 1.541195 (0.798050) \| 2.588779 / 1.468490 (1.120289) \| 0.921008 / 4.584777 (-3.663769) \| 5.402940 / 3.745712 (1.657227) \| 4.818783 / 5.269862 (-0.451078) \| 3.162259 / 4.565676 (-1.403417) \| 0.108501 / 0.424275 (-0.315774) \| 0.009384 / 0.007607 (0.001777) \| 0.766811 / 0.226044 (0.540766) \| 7.624629 / 2.268929 (5.355701) \| 3.442420 / 55.444624 (-52.002204) \| 2.759967 / 6.876477 (-4.116510) \| 3.049644 / 2.142072 (0.907572) \| 1.113308 / 4.805227 (-3.691919) \| 0.223923 / 6.500664 (-6.276741) \| 0.079156 / 0.075469 (0.003687) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.683318 / 1.841788 (-0.158470) \| 25.062141 / 8.074308 (16.987833) \| 21.777131 / 10.191392 (11.585739) \| 0.266939 / 0.680424 (-0.413485) \| 0.029670 / 0.534201 (-0.504531) \| 0.476761 / 0.579283 (-0.102522) \| 0.622080 / 0.434364 (0.187716) \| 0.601781 / 0.540337 (0.061443) \| 0.785126 / 1.386936 (-0.601811) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010198 / 0.011353 (-0.001155) \| 0.005777 / 0.011008 (-0.005231) \| 0.083003 / 0.038508 (0.044495) \| 0.093411 / 0.023109 (0.070302) \| 0.496178 / 0.275898 (0.220280) \| 0.554670 / 0.323480 (0.231190) \| 0.008351 / 0.007986 (0.000365) \| 0.004678 / 0.004328 (0.000350) \| 0.083631 / 0.004250 (0.079381) \| 0.075538 / 0.037052 (0.038485) \| 0.492410 / 0.258489 (0.233921) \| 0.545209 / 0.293841 (0.251368) \| 0.048365 / 0.128546 (-0.080181) \| 0.014219 / 0.075646 (-0.061427) \| 0.100749 / 0.419271 (-0.318523) \| 0.063431 / 0.043533 (0.019898) \| 0.511115 / 0.255139 (0.255976) \| 0.532965 / 0.283200 (0.249765) \| 0.037968 / 0.141683 (-0.103715) \| 1.940268 / 1.452155 (0.488113) \| 2.032934 / 1.492716 (0.540217) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238179 / 0.018006 (0.220172) \| 0.605767 / 0.000490 (0.605277) \| 0.004033 / 0.000200 (0.003833) \| 0.000125 / 0.000054 (0.000071) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036436 / 0.037411 (-0.000975) \| 0.108034 / 0.014526 (0.093509) \| 0.118624 / 0.176557 (-0.057933) \| 0.183079 / 0.737135 (-0.554056) \| 0.121739 / 0.296338 (-0.174600) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.630538 / 0.215209 (0.415329) \| 6.552184 / 2.077655 (4.474529) \| 3.003412 / 1.504120 (1.499292) \| 2.669026 / 1.541195 (1.127832) \| 2.791109 / 1.468490 (1.322619) \| 0.884003 / 4.584777 (-3.700774) \| 5.538660 / 3.745712 (1.792947) \| 5.126708 / 5.269862 (-0.143154) \| 3.120825 / 4.565676 (-1.444852) \| 0.101178 / 0.424275 (-0.323097) \| 0.009027 / 0.007607 (0.001420) \| 0.785914 / 0.226044 (0.559869) \| 7.994720 / 2.268929 (5.725792) \| 4.061996 / 55.444624 (-51.382629) \| 3.263230 / 6.876477 (-3.613247) \| 3.288622 / 2.142072 (1.146550) \| 1.141867 / 4.805227 (-3.663360) \| 0.255287 / 6.500664 (-6.245378) \| 0.100637 / 0.075469 (0.025168) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.769821 / 1.841788 (-0.071967) \| 24.994008 / 8.074308 (16.919700) \| 21.765971 / 10.191392 (11.574579) \| 0.268493 / 0.680424 (-0.411931) \| 0.028047 / 0.534201 (-0.506154) \| 0.489472 / 0.579283 (-0.089811) \| 0.594809 / 0.434364 (0.160445) \| 0.613578 / 0.540337 (0.073241) \| 0.879360 / 1.386936 (-0.507576) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b85b1154aef2a9ab4d558f60d91623f2cc1583c4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006003 / 0.011353 (-0.005350) \| 0.003590 / 0.011008 (-0.007418) \| 0.084657 / 0.038508 (0.046149) \| 0.057884 / 0.023109 (0.034775) \| 0.318347 / 0.275898 (0.042449) \| 0.345976 / 0.323480 (0.022496) \| 0.004706 / 0.007986 (-0.003279) \| 0.002921 / 0.004328 (-0.001407) \| 0.061850 / 0.004250 (0.057600) \| 0.050558 / 0.037052 (0.013505) \| 0.320877 / 0.258489 (0.062388) \| 0.356062 / 0.293841 (0.062222) \| 0.027511 / 0.128546 (-0.101035) \| 0.007954 / 0.075646 (-0.067693) \| 0.260290 / 0.419271 (-0.158981) \| 0.051207 / 0.043533 (0.007674) \| 0.334423 / 0.255139 (0.079284) \| 0.338575 / 0.283200 (0.055375) \| 0.022330 / 0.141683 (-0.119353) \| 1.445446 / 1.452155 (-0.006709) \| 1.500626 / 1.492716 (0.007910) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192440 / 0.018006 (0.174433) \| 0.428455 / 0.000490 (0.427965) \| 0.000318 / 0.000200 (0.000118) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022933 / 0.037411 (-0.014478) \| 0.072795 / 0.014526 (0.058269) \| 0.081149 / 0.176557 (-0.095407) \| 0.142941 / 0.737135 (-0.594195) \| 0.082410 / 0.296338 (-0.213928) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405220 / 0.215209 (0.190011) \| 4.048585 / 2.077655 (1.970931) \| 2.027908 / 1.504120 (0.523788) \| 1.887828 / 1.541195 (0.346633) \| 2.131780 / 1.468490 (0.663290) \| 0.502847 / 4.584777 (-4.081930) \| 3.069498 / 3.745712 (-0.676215) \| 4.094774 / 5.269862 (-1.175088) \| 2.544004 / 4.565676 (-2.021673) \| 0.059540 / 0.424275 (-0.364735) \| 0.006501 / 0.007607 (-0.001106) \| 0.477218 / 0.226044 (0.251173) \| 4.764961 / 2.268929 (2.496032) \| 2.434594 / 55.444624 (-53.010030) \| 2.104833 / 6.876477 (-4.771644) \| 2.263059 / 2.142072 (0.120987) \| 0.591755 / 4.805227 (-4.213472) \| 0.131167 / 6.500664 (-6.369497) \| 0.061808 / 0.075469 (-0.013661) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.345364 / 1.841788 (-0.496424) \| 18.122584 / 8.074308 (10.048276) \| 13.318689 / 10.191392 (3.127297) \| 0.144526 / 0.680424 (-0.535898) \| 0.016997 / 0.534201 (-0.517204) \| 0.336036 / 0.579283 (-0.243247) \| 0.359532 / 0.434364 (-0.074832) \| 0.386945 / 0.540337 (-0.153392) \| 0.538659 / 1.386936 (-0.848277) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006088 / 0.011353 (-0.005265) \| 0.003684 / 0.011008 (-0.007324) \| 0.062340 / 0.038508 (0.023832) \| 0.058461 / 0.023109 (0.035352) \| 0.360134 / 0.275898 (0.084236) \| 0.393298 / 0.323480 (0.069818) \| 0.004664 / 0.007986 (-0.003322) \| 0.002909 / 0.004328 (-0.001420) \| 0.062668 / 0.004250 (0.058418) \| 0.050145 / 0.037052 (0.013092) \| 0.361897 / 0.258489 (0.103408) \| 0.402008 / 0.293841 (0.108167) \| 0.027491 / 0.128546 (-0.101055) \| 0.008113 / 0.075646 (-0.067534) \| 0.068114 / 0.419271 (-0.351157) \| 0.043303 / 0.043533 (-0.000230) \| 0.360569 / 0.255139 (0.105430) \| 0.387144 / 0.283200 (0.103944) \| 0.020194 / 0.141683 (-0.121489) \| 1.418066 / 1.452155 (-0.034089) \| 1.475640 / 1.492716 (-0.017076) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200291 / 0.018006 (0.182285) \| 0.432298 / 0.000490 (0.431809) \| 0.003303 / 0.000200 (0.003103) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027749 / 0.037411 (-0.009662) \| 0.081890 / 0.014526 (0.067364) \| 0.094319 / 0.176557 (-0.082238) \| 0.148646 / 0.737135 (-0.588490) \| 0.091830 / 0.296338 (-0.204509) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.433546 / 0.215209 (0.218337) \| 4.326855 / 2.077655 (2.249200) \| 2.230186 / 1.504120 (0.726066) \| 2.052524 / 1.541195 (0.511329) \| 2.117270 / 1.468490 (0.648779) \| 0.500331 / 4.584777 (-4.084446) \| 3.113662 / 3.745712 (-0.632050) \| 2.931540 / 5.269862 (-2.338322) \| 1.853615 / 4.565676 (-2.712062) \| 0.058250 / 0.424275 (-0.366025) \| 0.006546 / 0.007607 (-0.001061) \| 0.508850 / 0.226044 (0.282806) \| 5.081809 / 2.268929 (2.812880) \| 2.687037 / 55.444624 (-52.757588) \| 2.369317 / 6.876477 (-4.507160) \| 2.383549 / 2.142072 (0.241477) \| 0.587039 / 4.805227 (-4.218188) \| 0.125858 / 6.500664 (-6.374806) \| 0.062522 / 0.075469 (-0.012947) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.294929 / 1.841788 (-0.546858) \| 18.056312 / 8.074308 (9.982004) \| 13.755117 / 10.191392 (3.563725) \| 0.132037 / 0.680424 (-0.548387) \| 0.016866 / 0.534201 (-0.517335) \| 0.339040 / 0.579283 (-0.240243) \| 0.364371 / 0.434364 (-0.069993) \| 0.399533 / 0.540337 (-0.140804) \| 0.564524 / 1.386936 (-0.822412) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#64b811c13a7982015d7e078e3d693ce5359a05a2 \"CML watermark\")\n", "@lhoestq This bar comes from: https://github.com/huggingface/datasets/blob/b8067c0262073891180869f700ebef5ac3dc5cce/src/datasets/builder.py#L1156-L1166\r\n\r\nDo you prefer not showing it or, e.g., having `desc=\"Generating splits\"`?", "No strong opinion. Since there is a \"Generating\" progress bar already, maybe it can be \"Preparing splits\" (ref to download_and_prepare)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006348 / 0.011353 (-0.005005) \| 0.003721 / 0.011008 (-0.007287) \| 0.084039 / 0.038508 (0.045531) \| 0.067627 / 0.023109 (0.044517) \| 0.308372 / 0.275898 (0.032474) \| 0.335131 / 0.323480 (0.011652) \| 0.005157 / 0.007986 (-0.002829) \| 0.003266 / 0.004328 (-0.001062) \| 0.065374 / 0.004250 (0.061124) \| 0.055550 / 0.037052 (0.018498) \| 0.314001 / 0.258489 (0.055512) \| 0.350510 / 0.293841 (0.056669) \| 0.030859 / 0.128546 (-0.097688) \| 0.008286 / 0.075646 (-0.067361) \| 0.287122 / 0.419271 (-0.132149) \| 0.051494 / 0.043533 (0.007961) \| 0.309868 / 0.255139 (0.054729) \| 0.325845 / 0.283200 (0.042645) \| 0.022622 / 0.141683 (-0.119061) \| 1.468730 / 1.452155 (0.016575) \| 1.547871 / 1.492716 (0.055155) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.202763 / 0.018006 (0.184757) \| 0.456403 / 0.000490 (0.455914) \| 0.003116 / 0.000200 (0.002916) \| 0.000079 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027297 / 0.037411 (-0.010114) \| 0.081204 / 0.014526 (0.066678) \| 0.094274 / 0.176557 (-0.082282) \| 0.154391 / 0.737135 (-0.582744) \| 0.094312 / 0.296338 (-0.202026) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.387382 / 0.215209 (0.172173) \| 3.865597 / 2.077655 (1.787943) \| 1.855959 / 1.504120 (0.351839) \| 1.685411 / 1.541195 (0.144216) \| 1.732127 / 1.468490 (0.263637) \| 0.482230 / 4.584777 (-4.102547) \| 3.664947 / 3.745712 (-0.080765) \| 5.114379 / 5.269862 (-0.155482) \| 3.102803 / 4.565676 (-1.462873) \| 0.056509 / 0.424275 (-0.367766) \| 0.007230 / 0.007607 (-0.000377) \| 0.456788 / 0.226044 (0.230744) \| 4.575831 / 2.268929 (2.306902) \| 2.335249 / 55.444624 (-53.109375) \| 2.003805 / 6.876477 (-4.872672) \| 2.141788 / 2.142072 (-0.000285) \| 0.577501 / 4.805227 (-4.227726) \| 0.130264 / 6.500664 (-6.370400) \| 0.058889 / 0.075469 (-0.016580) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.252673 / 1.841788 (-0.589115) \| 18.676897 / 8.074308 (10.602589) \| 13.988101 / 10.191392 (3.796709) \| 0.151376 / 0.680424 (-0.529048) \| 0.018104 / 0.534201 (-0.516097) \| 0.388413 / 0.579283 (-0.190870) \| 0.414841 / 0.434364 (-0.019523) \| 0.456078 / 0.540337 (-0.084259) \| 0.641715 / 1.386936 (-0.745221) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006315 / 0.011353 (-0.005038) \| 0.003847 / 0.011008 (-0.007162) \| 0.063989 / 0.038508 (0.025481) \| 0.068244 / 0.023109 (0.045135) \| 0.416201 / 0.275898 (0.140303) \| 0.438446 / 0.323480 (0.114966) \| 0.005820 / 0.007986 (-0.002166) \| 0.003165 / 0.004328 (-0.001163) \| 0.064143 / 0.004250 (0.059892) \| 0.056529 / 0.037052 (0.019477) \| 0.414916 / 0.258489 (0.156427) \| 0.450771 / 0.293841 (0.156930) \| 0.030611 / 0.128546 (-0.097935) \| 0.008289 / 0.075646 (-0.067357) \| 0.070725 / 0.419271 (-0.348546) \| 0.047998 / 0.043533 (0.004465) \| 0.405609 / 0.255139 (0.150470) \| 0.421895 / 0.283200 (0.138696) \| 0.022135 / 0.141683 (-0.119548) \| 1.444238 / 1.452155 (-0.007916) \| 1.515823 / 1.492716 (0.023107) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227043 / 0.018006 (0.209037) \| 0.439732 / 0.000490 (0.439242) \| 0.001267 / 0.000200 (0.001067) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029082 / 0.037411 (-0.008329) \| 0.086201 / 0.014526 (0.071675) \| 0.098653 / 0.176557 (-0.077903) \| 0.152574 / 0.737135 (-0.584561) \| 0.100696 / 0.296338 (-0.195642) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.411243 / 0.215209 (0.196034) \| 4.100170 / 2.077655 (2.022515) \| 2.118310 / 1.504120 (0.614190) \| 1.935646 / 1.541195 (0.394451) \| 1.970798 / 1.468490 (0.502307) \| 0.478635 / 4.584777 (-4.106142) \| 3.589396 / 3.745712 (-0.156316) \| 3.312462 / 5.269862 (-1.957399) \| 1.963081 / 4.565676 (-2.602595) \| 0.056392 / 0.424275 (-0.367883) \| 0.007134 / 0.007607 (-0.000473) \| 0.485131 / 0.226044 (0.259086) \| 4.838946 / 2.268929 (2.570017) \| 2.624550 / 55.444624 (-52.820075) \| 2.223046 / 6.876477 (-4.653431) \| 2.230642 / 2.142072 (0.088570) \| 0.594892 / 4.805227 (-4.210335) \| 0.130523 / 6.500664 (-6.370141) \| 0.059585 / 0.075469 (-0.015884) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.329941 / 1.841788 (-0.511847) \| 19.199057 / 8.074308 (11.124748) \| 14.166009 / 10.191392 (3.974617) \| 0.190595 / 0.680424 (-0.489829) \| 0.018419 / 0.534201 (-0.515782) \| 0.392031 / 0.579283 (-0.187252) \| 0.409395 / 0.434364 (-0.024969) \| 0.475930 / 0.540337 (-0.064408) \| 0.654412 / 1.386936 (-0.732524) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#42fdfbd567674d075c3a9148ec3c95221eb62cfe \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007500 / 0.011353 (-0.003853) \| 0.004328 / 0.011008 (-0.006681) \| 0.086718 / 0.038508 (0.048209) \| 0.098638 / 0.023109 (0.075529) \| 0.335308 / 0.275898 (0.059409) \| 0.369163 / 0.323480 (0.045683) \| 0.005733 / 0.007986 (-0.002253) \| 0.003738 / 0.004328 (-0.000590) \| 0.066452 / 0.004250 (0.062202) \| 0.066245 / 0.037052 (0.029192) \| 0.337609 / 0.258489 (0.079120) \| 0.388584 / 0.293841 (0.094744) \| 0.031742 / 0.128546 (-0.096804) \| 0.008721 / 0.075646 (-0.066925) \| 0.290820 / 0.419271 (-0.128452) \| 0.053323 / 0.043533 (0.009790) \| 0.329192 / 0.255139 (0.074053) \| 0.350560 / 0.283200 (0.067360) \| 0.025402 / 0.141683 (-0.116281) \| 1.476174 / 1.452155 (0.024020) \| 1.578194 / 1.492716 (0.085478) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.256160 / 0.018006 (0.238154) \| 0.560315 / 0.000490 (0.559825) \| 0.005287 / 0.000200 (0.005088) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029164 / 0.037411 (-0.008247) \| 0.084881 / 0.014526 (0.070356) \| 0.100979 / 0.176557 (-0.075577) \| 0.156539 / 0.737135 (-0.580597) \| 0.101510 / 0.296338 (-0.194828) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.381138 / 0.215209 (0.165929) \| 3.791573 / 2.077655 (1.713918) \| 1.841954 / 1.504120 (0.337834) \| 1.672463 / 1.541195 (0.131268) \| 1.785769 / 1.468490 (0.317279) \| 0.483263 / 4.584777 (-4.101514) \| 3.617391 / 3.745712 (-0.128322) \| 5.607794 / 5.269862 (0.337933) \| 3.359530 / 4.565676 (-1.206147) \| 0.056826 / 0.424275 (-0.367449) \| 0.007375 / 0.007607 (-0.000232) \| 0.455853 / 0.226044 (0.229809) \| 4.548965 / 2.268929 (2.280037) \| 2.412716 / 55.444624 (-53.031908) \| 1.991456 / 6.876477 (-4.885021) \| 2.242851 / 2.142072 (0.100778) \| 0.573070 / 4.805227 (-4.232157) \| 0.134658 / 6.500664 (-6.366006) \| 0.061539 / 0.075469 (-0.013930) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.278306 / 1.841788 (-0.563481) \| 20.634317 / 8.074308 (12.560009) \| 15.164246 / 10.191392 (4.972854) \| 0.167487 / 0.680424 (-0.512937) \| 0.019006 / 0.534201 (-0.515195) \| 0.394617 / 0.579283 (-0.184666) \| 0.423385 / 0.434364 (-0.010979) \| 0.469968 / 0.540337 (-0.070370) \| 0.630058 / 1.386936 (-0.756878) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006793 / 0.011353 (-0.004559) \| 0.004260 / 0.011008 (-0.006748) \| 0.065398 / 0.038508 (0.026890) \| 0.077850 / 0.023109 (0.054741) \| 0.371754 / 0.275898 (0.095855) \| 0.400652 / 0.323480 (0.077172) \| 0.005729 / 0.007986 (-0.002256) \| 0.003660 / 0.004328 (-0.000669) \| 0.065119 / 0.004250 (0.060869) \| 0.060714 / 0.037052 (0.023661) \| 0.384592 / 0.258489 (0.126103) \| 0.412806 / 0.293841 (0.118965) \| 0.031865 / 0.128546 (-0.096681) \| 0.008807 / 0.075646 (-0.066839) \| 0.071156 / 0.419271 (-0.348115) \| 0.049571 / 0.043533 (0.006038) \| 0.367381 / 0.255139 (0.112242) \| 0.386713 / 0.283200 (0.103513) \| 0.024838 / 0.141683 (-0.116845) \| 1.492986 / 1.452155 (0.040831) \| 1.559243 / 1.492716 (0.066526) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269737 / 0.018006 (0.251730) \| 0.565177 / 0.000490 (0.564687) \| 0.000404 / 0.000200 (0.000204) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031631 / 0.037411 (-0.005780) \| 0.087289 / 0.014526 (0.072764) \| 0.102798 / 0.176557 (-0.073759) \| 0.158977 / 0.737135 (-0.578158) \| 0.105495 / 0.296338 (-0.190843) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425067 / 0.215209 (0.209858) \| 4.243121 / 2.077655 (2.165466) \| 2.234567 / 1.504120 (0.730447) \| 2.070810 / 1.541195 (0.529615) \| 2.176802 / 1.468490 (0.708312) \| 0.484987 / 4.584777 (-4.099790) \| 3.647000 / 3.745712 (-0.098712) \| 3.574843 / 5.269862 (-1.695019) \| 2.092581 / 4.565676 (-2.473095) \| 0.057299 / 0.424275 (-0.366976) \| 0.007480 / 0.007607 (-0.000128) \| 0.507838 / 0.226044 (0.281794) \| 5.076594 / 2.268929 (2.807666) \| 2.718858 / 55.444624 (-52.725766) \| 2.362793 / 6.876477 (-4.513684) \| 2.451962 / 2.142072 (0.309890) \| 0.581355 / 4.805227 (-4.223872) \| 0.133723 / 6.500664 (-6.366941) \| 0.061896 / 0.075469 (-0.013573) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.325814 / 1.841788 (-0.515974) \| 20.614502 / 8.074308 (12.540194) \| 14.769422 / 10.191392 (4.578029) \| 0.193797 / 0.680424 (-0.486627) \| 0.018379 / 0.534201 (-0.515822) \| 0.394153 / 0.579283 (-0.185130) \| 0.409585 / 0.434364 (-0.024779) \| 0.479107 / 0.540337 (-0.061231) \| 0.668397 / 1.386936 (-0.718539) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2d892237169bad5512c91cae453d257ebefc201 \"CML watermark\")\n", "In the end, I decided to remove the progress bar to avoid having it displayed when loading a cached dataset.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006673 / 0.011353 (-0.004680) \| 0.004162 / 0.011008 (-0.006846) \| 0.084017 / 0.038508 (0.045509) \| 0.079536 / 0.023109 (0.056426) \| 0.313594 / 0.275898 (0.037695) \| 0.349200 / 0.323480 (0.025720) \| 0.005544 / 0.007986 (-0.002441) \| 0.003472 / 0.004328 (-0.000857) \| 0.064742 / 0.004250 (0.060491) \| 0.056857 / 0.037052 (0.019805) \| 0.318635 / 0.258489 (0.060146) \| 0.354378 / 0.293841 (0.060537) \| 0.030856 / 0.128546 (-0.097690) \| 0.008759 / 0.075646 (-0.066887) \| 0.287760 / 0.419271 (-0.131511) \| 0.052307 / 0.043533 (0.008775) \| 0.316396 / 0.255139 (0.061257) \| 0.351408 / 0.283200 (0.068208) \| 0.024914 / 0.141683 (-0.116769) \| 1.484592 / 1.452155 (0.032437) \| 1.560662 / 1.492716 (0.067945) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280938 / 0.018006 (0.262932) \| 0.580236 / 0.000490 (0.579747) \| 0.003369 / 0.000200 (0.003169) \| 0.000090 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028736 / 0.037411 (-0.008675) \| 0.082916 / 0.014526 (0.068390) \| 0.097761 / 0.176557 (-0.078796) \| 0.153515 / 0.737135 (-0.583620) \| 0.099282 / 0.296338 (-0.197057) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401244 / 0.215209 (0.186035) \| 4.019866 / 2.077655 (1.942211) \| 2.029642 / 1.504120 (0.525522) \| 1.849591 / 1.541195 (0.308396) \| 1.946829 / 1.468490 (0.478339) \| 0.479750 / 4.584777 (-4.105027) \| 3.482822 / 3.745712 (-0.262890) \| 3.955859 / 5.269862 (-1.314003) \| 2.370747 / 4.565676 (-2.194930) \| 0.056905 / 0.424275 (-0.367370) \| 0.007319 / 0.007607 (-0.000288) \| 0.485310 / 0.226044 (0.259266) \| 4.858228 / 2.268929 (2.589299) \| 2.500476 / 55.444624 (-52.944148) \| 2.171156 / 6.876477 (-4.705320) \| 2.427266 / 2.142072 (0.285194) \| 0.570199 / 4.805227 (-4.235029) \| 0.130855 / 6.500664 (-6.369809) \| 0.060269 / 0.075469 (-0.015200) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.258044 / 1.841788 (-0.583743) \| 20.218657 / 8.074308 (12.144349) \| 13.597970 / 10.191392 (3.406578) \| 0.167656 / 0.680424 (-0.512768) \| 0.018137 / 0.534201 (-0.516064) \| 0.395309 / 0.579283 (-0.183975) \| 0.406325 / 0.434364 (-0.028039) \| 0.467457 / 0.540337 (-0.072880) \| 0.613636 / 1.386936 (-0.773300) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006846 / 0.011353 (-0.004507) \| 0.004207 / 0.011008 (-0.006802) \| 0.064525 / 0.038508 (0.026017) \| 0.081329 / 0.023109 (0.058220) \| 0.399838 / 0.275898 (0.123940) \| 0.431305 / 0.323480 (0.107825) \| 0.005859 / 0.007986 (-0.002127) \| 0.003568 / 0.004328 (-0.000760) \| 0.065262 / 0.004250 (0.061011) \| 0.064796 / 0.037052 (0.027744) \| 0.406858 / 0.258489 (0.148369) \| 0.440971 / 0.293841 (0.147130) \| 0.031421 / 0.128546 (-0.097125) \| 0.008777 / 0.075646 (-0.066870) \| 0.071418 / 0.419271 (-0.347853) \| 0.049263 / 0.043533 (0.005730) \| 0.384279 / 0.255139 (0.129140) \| 0.410745 / 0.283200 (0.127546) \| 0.024467 / 0.141683 (-0.117216) \| 1.522379 / 1.452155 (0.070224) \| 1.581636 / 1.492716 (0.088920) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276161 / 0.018006 (0.258155) \| 0.548842 / 0.000490 (0.548352) \| 0.004523 / 0.000200 (0.004324) \| 0.000098 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030747 / 0.037411 (-0.006664) \| 0.087493 / 0.014526 (0.072967) \| 0.106563 / 0.176557 (-0.069993) \| 0.162949 / 0.737135 (-0.574186) \| 0.105303 / 0.296338 (-0.191036) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425854 / 0.215209 (0.210645) \| 4.244797 / 2.077655 (2.167142) \| 2.269006 / 1.504120 (0.764886) \| 2.097428 / 1.541195 (0.556234) \| 2.181038 / 1.468490 (0.712548) \| 0.477286 / 4.584777 (-4.107491) \| 3.591452 / 3.745712 (-0.154260) \| 3.481281 / 5.269862 (-1.788580) \| 2.066895 / 4.565676 (-2.498782) \| 0.056576 / 0.424275 (-0.367699) \| 0.007409 / 0.007607 (-0.000199) \| 0.498411 / 0.226044 (0.272367) \| 4.994873 / 2.268929 (2.725945) \| 2.749148 / 55.444624 (-52.695476) \| 2.378544 / 6.876477 (-4.497932) \| 2.452859 / 2.142072 (0.310786) \| 0.571340 / 4.805227 (-4.233887) \| 0.132174 / 6.500664 (-6.368490) \| 0.061507 / 0.075469 (-0.013962) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.370773 / 1.841788 (-0.471015) \| 20.493342 / 8.074308 (12.419034) \| 14.809886 / 10.191392 (4.618494) \| 0.175730 / 0.680424 (-0.504693) \| 0.018617 / 0.534201 (-0.515583) \| 0.393808 / 0.579283 (-0.185476) \| 0.416419 / 0.434364 (-0.017945) \| 0.477183 / 0.540337 (-0.063155) \| 0.668060 / 1.386936 (-0.718876) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2de7a2a4af5d94b0f98a7a6db94e78984af40602 \"CML watermark\")\n", "Nice one :)" ]
https://api.github.com/repos/huggingface/datasets/issues/1044	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/1044/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/1044/comments	https://api.github.com/repos/huggingface/datasets/issues/1044/events	https://github.com/huggingface/datasets/pull/1044	756,111,647	MDExOlB1bGxSZXF1ZXN0NTMxNzA5MTg0	1,044	Add AMTTL Chinese Word Segmentation Dataset	[]	closed	false	null	0	2020-12-03T11:27:52Z	2020-12-03T17:13:14Z	2020-12-03T17:13:13Z	null		{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/1044/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/1044/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/1044.diff", "html_url": "https://github.com/huggingface/datasets/pull/1044", "merged_at": "2020-12-03T17:13:13Z", "patch_url": "https://github.com/huggingface/datasets/pull/1044.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/1044" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/2142	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2142/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2142/comments	https://api.github.com/repos/huggingface/datasets/issues/2142/events	https://github.com/huggingface/datasets/pull/2142	843,919,420	MDExOlB1bGxSZXF1ZXN0NjAzMjQwMzUy	2,142	Gem V1.1	[]	closed	false	null	0	2021-03-29T23:47:02Z	2021-03-30T00:10:02Z	2021-03-30T00:10:02Z	null	This branch updates the GEM benchmark to its 1.1 version which includes: - challenge sets for most tasks - detokenized TurkCorpus to match the rest of the text simplification subtasks - fixed inputs for TurkCorpus and ASSET test sets - 18 languages in WikiLingua cc @sebastianGehrmann	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 1, "hooray": 1, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/2142/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2142/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/2142.diff", "html_url": "https://github.com/huggingface/datasets/pull/2142", "merged_at": "2021-03-30T00:10:02Z", "patch_url": "https://github.com/huggingface/datasets/pull/2142.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/2142" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/5193	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5193/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5193/comments	https://api.github.com/repos/huggingface/datasets/issues/5193/events	https://github.com/huggingface/datasets/issues/5193	1,433,883,780	I_kwDODunzps5Vd1SE	5,193	"One or several metadata. were found, but not in the same directory or in a parent directory"	[]	closed	false	null	5	2022-11-02T22:46:25Z	2022-11-03T13:39:16Z	2022-11-03T13:35:44Z	null	### Describe the bug When loading my own dataset, on loading it I get an error. Here is my dataset link: https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data And the error after loading with: ```python from datasets import load_dataset load_dataset("corentinm7/MyoQuant-SDH-Data") ``` ```python Downloading readme: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3.34k/3.34k [00:00<00:00, 4.45MB/s] Using custom data configuration SDH_16k-53e7301a92ab0025 Downloading and preparing dataset None/SDH_16k to /home/corentin/.cache/huggingface/datasets/corentinm7___imagefolder/SDH_16k-53e7301a92ab0025/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f... Downloading data: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3.28M/3.28M [00:00<00:00, 4.31MB/s] Downloading data files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:01<00:00, 1.75s/it] Downloading data: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1.13G/1.13G [00:15<00:00, 74.3MB/s] Downloading data files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:16<00:00, 16.09s/it] Extracting data files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:13<00:00, 13.16s/it] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/load.py", line 1742, in load_dataset builder_instance.download_and_prepare( File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 814, in download_and_prepare self._download_and_prepare( File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1423, in _download_and_prepare super()._download_and_prepare( File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 905, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1374, in _prepare_split for key, record in logging.tqdm( File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 394, in _generate_examples raise ValueError( ValueError: One or several metadata. were found, but not in the same directory or in a parent directory of /home/corentin/.cache/huggingface/datasets/downloads/extracted/60c4aa8d4da3065bb3d310de4373dffd73bd4dc331aedcb4ee867febe4fdb7cd/validation/sick/2_CG_SDH_TAM_Bin1cKO_ko_pla_4_1640.tif. ``` However the test command is working fine. ```datasets-cli test hugging_face_play/ds_test/SDH_16k.py --save_info --all_configs --force_redownload``` ``` Using custom data configuration SDH_16k Testing builder 'SDH_16k' (1/1) Downloading and preparing dataset sdh_16k/SDH_16k to /home/corentin/.cache/huggingface/datasets/sdh_16k/SDH_16k/1.0.0/21b584239a638aeeda33cba1ac2ca4869d48e4b4f20fb22274d5a5ddc487659d... Downloading data: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1.13G/1.13G [00:14<00:00, 76.5MB/s] Downloading data files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:15<00:00, 15.66s/it] Downloading data: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3.28M/3.28M [00:02<00:00, 1.44MB/s] Downloading data files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:03<00:00, 3.21s/it] Downloading data files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:00<00:00, 11586.48it/s] Extracting data files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:13<00:00, 13.42s/it] Dataset sdh_16k downloaded and prepared to /home/corentin/.cache/huggingface/datasets/sdh_16k/SDH_16k/1.0.0/21b584239a638aeeda33cba1ac2ca4869d48e4b4f20fb22274d5a5ddc487659d. Subsequent calls will reuse this data. 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 605.27it/s] Dataset card saved at hugging_face_play/ds_test/README.md Test successful. ``` ### Steps to reproduce the bug Simply run on python ```python from datasets import load_dataset load_dataset("corentinm7/MyoQuant-SDH-Data") ``` ### Expected behavior As the test command worked, this error should not appear ### Environment info - `datasets` version: 2.6.1 - Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.6 - PyArrow version: 10.0.0 - Pandas version: 1.5.1	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5193/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5193/timeline	null	completed	null	null	false	[ "Also unrelated but still: https://huggingface.co/docs/datasets/image_dataset#generate-the-dataset\r\n```If your loading script passed the test, you should now have a dataset_infos.json file in your dataset folder.```\r\nIt's not the case anymore as it's now in the readme.md, it was confusing to me", "And here is my data loader script: https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data/blob/main/SDH_16k.py\r\nI have one file archive to download that contains the images for all splits and one `metadata.jsonl` to download that contains the informations about what image goes into what split.", "Hi @lambda-science! It seems that your repo is recognized as a packaged module [ImageFolder](https://huggingface.co/docs/datasets/main/en/image_dataset#imagefolder), not as a dataset with the custom loading script, because loader looks for a script that has the same name as the dataset repo. So please try to rename your script to `MyoQuant-SDH-Data.py`, this should help.", "> Hi @lambda-science! It seems that your repo is recognized as a packaged module [ImageFolder](https://huggingface.co/docs/datasets/main/en/image_dataset#imagefolder), not as a dataset with the custom loading script, because loader looks for a script that has the same name as the dataset repo. So please try to rename your script to `MyoQuant-SDH-Data.py`, this should help.\r\n\r\nHi !\r\n\r\nThank you for your answer. That was... embarrassingly easy, sorry for this issue, everything is fixed now ! \r\n\r\nHave a nice day ! :)", "@lambda-science that's not embarrassing at all! it's actually not clear from the documentation that the script should have the same name, so thank you for the issue, we'll add this information to the docs :) " ]
https://api.github.com/repos/huggingface/datasets/issues/5336	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5336/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5336/comments	https://api.github.com/repos/huggingface/datasets/issues/5336/events	https://github.com/huggingface/datasets/pull/5336	1,479,649,900	PR_kwDODunzps5Egzed	5,336	Set `IterableDataset.map` param `batch_size` typing as optional	[]	closed	false	null	3	2022-12-06T17:08:10Z	2022-12-07T14:14:56Z	2022-12-07T14:06:27Z	null	This PR solves #5325 ~Indeed we're using the typing for optional values as `Union[type, None]` as it's similar to how Python 3.10 handles optional values as `type \| None`, instead of using `Optional[type]`.~ ~Do we want to start using `Union[type, None]` for type-hinting optional values or just keep on using `Optional`?~ -> Keeping `Optional` still for consistency with the rest of the code in `datasets` Also we now allow `batch_size` to be `None` for `IterableDataset.map` and `IterableDataset.filter`e.g. `MappedExamplesIterable` as `map` is internally instantiating those and propagating the `batch_size` param so if it can be `None` for `map` it should also do so for `MappedExamplesIterable`, as well as for `FilteredExamplesIterable` when calling `IterableDataset.filter`. ## TODOs - [x] Add integration tests - [x] Handle scenario where `batched=True` and `batch_size=None` or `batch_size<=0`	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5336/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5336/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5336.diff", "html_url": "https://github.com/huggingface/datasets/pull/5336", "merged_at": "2022-12-07T14:06:27Z", "patch_url": "https://github.com/huggingface/datasets/pull/5336.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5336" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5336). All of your documentation changes will be reflected on that endpoint.", "Hi @mariosasko, @lhoestq I was wondering whether we should include `batched` as a `pytest.mark` param for the functions testing `IterableDataset.map` so as to ensure that the changes done in this PR work fine without breaking anything of the actual functionality.\r\n\r\nI've pushed updated tests just for one of the unit testing functions to be run as `pytest tests/test_iterable_dataset.py::test_mapped_examples_iterable -s --durations 0`, but some are still missing `batched` param, it was just to ask you whether we're supposed to do this for the rest of the functions or not, if it's a yes I'll push the commit as it's ready, but didn't want to push extra stuff that may be discarded later!\r\n\r\nThanks :hugs:", "Thanks for the feedback @lhoestq, I agree with keeping `Optional` instead of `Union[type, None]` for now 👍🏻" ]
https://api.github.com/repos/huggingface/datasets/issues/4471	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4471/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4471/comments	https://api.github.com/repos/huggingface/datasets/issues/4471/events	https://github.com/huggingface/datasets/issues/4471	1,267,475,268	I_kwDODunzps5LjCNE	4,471	CI error with repo lhoestq/_dummy	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	null	1	2022-06-10T12:26:06Z	2022-06-10T13:24:53Z	2022-06-10T13:24:53Z	null	## Describe the bug CI is failing because of repo "lhoestq/_dummy". See: https://app.circleci.com/pipelines/github/huggingface/datasets/12461/workflows/1b040b45-9578-4ab9-8c44-c643c4eb8691/jobs/74269 ``` requests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/api/datasets/lhoestq/_dummy?full=true ``` The repo seems to no longer exist: https://huggingface.co/api/datasets/lhoestq/_dummy ``` error: "Repository not found" ``` CC: @lhoestq	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4471/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4471/timeline	null	completed	null	null	false	[ "fixed by https://github.com/huggingface/datasets/pull/4472" ]
https://api.github.com/repos/huggingface/datasets/issues/2070	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2070/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2070/comments	https://api.github.com/repos/huggingface/datasets/issues/2070/events	https://github.com/huggingface/datasets/issues/2070	833,799,035	MDU6SXNzdWU4MzM3OTkwMzU=	2,070	ArrowInvalid issue for squad v2 dataset	[]	closed	false	null	1	2021-03-17T13:51:49Z	2021-08-04T17:57:16Z	2021-08-04T17:57:16Z	null	Hello, I am using the huggingface official question answering example notebook (https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb). In the prepare_validation_features function, I made some modifications to tokenize a new set of quesions with the original contexts and save them in three different list called candidate_input_dis, candidate_attetion_mask and candidate_token_type_ids. When I try to run the next cell for dataset.map, I got the following error: `ArrowInvalid: Column 1 named candidate_attention_mask expected length 1180 but got length 1178` My code is as follows: ``` def generate_candidate_questions(examples): val_questions = examples["question"] candididate_questions = random.sample(datasets["train"]["question"], len(val_questions)) candididate_questions = [x[:max_length] for x in candididate_questions] return candididate_questions def prepare_validation_features(examples, use_mixing=False): pad_on_right = tokenizer.padding_side == "right" tokenized_examples = tokenizer( examples["question" if pad_on_right else "context"], examples["context" if pad_on_right else "question"], truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) if use_mixing: candidate_questions = generate_candidate_questions(examples) tokenized_candidates = tokenizer( candidate_questions if pad_on_right else examples["context"], examples["context"] if pad_on_right else candidate_questions, truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") tokenized_examples["example_id"] = [] if use_mixing: tokenized_examples["candidate_input_ids"] = tokenized_candidates["input_ids"] tokenized_examples["candidate_attention_mask"] = tokenized_candidates["attention_mask"] tokenized_examples["candidate_token_type_ids"] = tokenized_candidates["token_type_ids"] for i in range(len(tokenized_examples["input_ids"])): sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) tokenized_examples["offset_mapping"][i] = [ (o if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples validation_features = datasets["validation"].map( lambda xs: prepare_validation_features(xs, True), batched=True, remove_columns=datasets["validation"].column_names ) ``` I guess this might happen because of the batched=True. I see similar issues in this repo related to arrow table length mismatch error, but in their cases, the numbers vary a lot. In my case, this error always happens when the expected length and unexpected length are very close. Thanks for the help!	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/2070/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2070/timeline	null	completed	null	null	false	[ "Hi ! This error happens when you use `map` in batched mode and then your function doesn't return the same number of values per column.\r\n\r\nIndeed since you're using `map` in batched mode, `prepare_validation_features` must take a batch as input (i.e. a dictionary of multiple rows of the dataset), and return a batch.\r\n\r\nHowever it seems like `tokenized_examples` doesn't have the same number of elements in each field. One field seems to have `1180` elements while `candidate_attention_mask` only has `1178`." ]
https://api.github.com/repos/huggingface/datasets/issues/3082	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3082/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3082/comments	https://api.github.com/repos/huggingface/datasets/issues/3082/events	https://github.com/huggingface/datasets/pull/3082	1,026,388,994	PR_kwDODunzps4tM2BV	3,082	Fix error related to huggingface_hub timeout parameter	[]	closed	false	null	0	2021-10-14T13:17:47Z	2021-10-14T14:39:52Z	2021-10-14T14:39:51Z	null	The `huggingface_hub` package added the parameter `timeout` from version 0.0.19. This PR bumps this minimal version. Fix #3080.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3082/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3082/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/3082.diff", "html_url": "https://github.com/huggingface/datasets/pull/3082", "merged_at": "2021-10-14T14:39:51Z", "patch_url": "https://github.com/huggingface/datasets/pull/3082.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3082" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/6043	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6043/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6043/comments	https://api.github.com/repos/huggingface/datasets/issues/6043/events	https://github.com/huggingface/datasets/issues/6043	1,807,771,750	I_kwDODunzps5rwGhm	6,043	Compression kwargs have no effect when saving datasets as csv	[]	open	false	null	3	2023-07-17T13:19:21Z	2023-07-22T17:34:18Z	null	null	### Describe the bug Attempting to save a dataset as a compressed csv file, the compression kwargs provided to `.to_csv()` that get piped to panda's `pandas.DataFrame.to_csv` do not have any effect - resulting in the dataset not getting compressed. A warning is raised if explicitly providing a `compression` kwarg, but no warnings are raised if relying on the defaults. This can lead to datasets secretly not getting compressed for users expecting the behaviour to match panda's `.to_csv()`, where the compression format is automatically inferred from the destination path suffix. ### Steps to reproduce the bug ```python # dataset is not compressed (but at least a warning is emitted) import datasets dataset = datasets.load_dataset("rotten_tomatoes", split="train") dataset.to_csv("uncompressed.csv") print(os.path.getsize("uncompressed.csv")) # 1008607 dataset.to_csv("compressed.csv.gz", compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}) print(os.path.getsize("compressed.csv.gz")) # 1008607 ``` ```shell >>> RuntimeWarning: compression has no effect when passing a non-binary object as input. csv_str = batch.to_pandas().to_csv( ``` ```python # dataset is not compressed and no warnings are emitted dataset.to_csv("compressed.csv.gz") print(os.path.getsize("compressed.csv.gz")) # 1008607 # compare with dataset.to_pandas().to_csv("pandas.csv.gz") print(os.path.getsize("pandas.csv.gz")) # 418561 ``` --- I think that this is because behind the scenes `pandas.DataFrame.to_csv` is always called with a buf-like `path_or_buf`, but users that are providing a path-like to `datasets.Dataset.to_csv` are likely not to expect / know that - leading to a mismatch in their understanding of the expected behaviour of the `compression` kwarg. ### Expected behavior The dataset to be saved as a compressed csv file when providing a `compression` kwarg, or when relying on the default `compression='infer'` ### Environment info `datasets == 2.13.1`	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6043/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6043/timeline	null	null	null	null	false	[ "Hello @exs-avianello, I have reproduced the bug successfully and have understood the problem. But I am confused regarding this part of the statement, \"`pandas.DataFrame.to_csv` is always called with a buf-like `path_or_buf`\".\r\n\r\nCan you please elaborate on it?\r\n\r\nThanks!", "Hi @aryanxk02 ! Sure, what I actually meant is that when passing a path-like `path_or_buf` here\r\n\r\nhttps://github.com/huggingface/datasets/blob/14f6edd9222e577dccb962ed5338b79b73502fa5/src/datasets/arrow_dataset.py#L4708-L4714 \r\n\r\nit gets converted to a file object behind the scenes here\r\n\r\nhttps://github.com/huggingface/datasets/blob/14f6edd9222e577dccb962ed5338b79b73502fa5/src/datasets/io/csv.py#L92-L94\r\n\r\nand the eventual pandas `.to_csv()` calls that write to it always get `path_or_buf=None`, making pandas ignore the `compression` kwarg in the `to_csv_kwargs`\r\n\r\nhttps://github.com/huggingface/datasets/blob/14f6edd9222e577dccb962ed5338b79b73502fa5/src/datasets/io/csv.py#L107-L109", "@exs-avianello When `path_or_buf` is set to None, the `to_csv()` method will return the CSV data as a string instead of saving it to a file. Hence the compression doesn't take place. I think setting `path_or_buf=self.path_or_buf` should work. What you say?" ]
https://api.github.com/repos/huggingface/datasets/issues/3149	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3149/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3149/comments	https://api.github.com/repos/huggingface/datasets/issues/3149/events	https://github.com/huggingface/datasets/pull/3149	1,033,747,625	PR_kwDODunzps4tjuUt	3,149	Add CMU Hinglish DoG Dataset for MT	[]	closed	false	null	2	2021-10-22T16:17:25Z	2021-11-15T11:36:42Z	2021-11-15T10:27:45Z	null	Address part of #2841 Added the CMU Hinglish DoG Dataset as in GLUECoS. Added it as a seperate dataset as unlike other tasks of GLUE CoS this can't be evaluated for a BERT like model. Consists of parallel dataset between Hinglish (Hindi-English) and English, can be used for Machine Translation between the two. The data processing part is inspired from the GLUECoS repo [here](https://github.com/microsoft/GLUECoS/blob/7fdc51653e37a32aee17505c47b7d1da364fa77e/Data/Preprocess_Scripts/preprocess_mt_en_hi.py) The dummy data part is not working properly, it shows ``` UnboundLocalError: local variable 'generator_splits' referenced before assignment ``` when I run without ``--auto_generate``. Please let me know how I can fix that. Thanks	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3149/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3149/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/3149.diff", "html_url": "https://github.com/huggingface/datasets/pull/3149", "merged_at": "2021-11-15T10:27:45Z", "patch_url": "https://github.com/huggingface/datasets/pull/3149.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3149" }	true	[ "Hi @lhoestq, thanks a lot for the help. I have moved the part as suggested. \r\nAlthough still while running the dummy data script, I face this issue\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/ishan/anaconda3/bin/datasets-cli\", line 8, in <module>\r\n sys.exit(main())\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/datasets_cli.py\", line 33, in main\r\n service.run()\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/dummy_data.py\", line 318, in run\r\n self._autogenerate_dummy_data(\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/dummy_data.py\", line 363, in _autogenerate_dummy_data\r\n dataset_builder._prepare_split(split_generator)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/builder.py\", line 1103, in _prepare_split\r\n example = self.info.features.encode_example(record)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py\", line 981, in encode_example\r\n return encode_nested_example(self, example)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py\", line 775, in encode_nested_example\r\n return {\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py\", line 775, in <dictcomp>\r\n return {\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 99, in zip_dict\r\n yield key, tuple(d[key] for d in dicts)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 99, in <genexpr>\r\n yield key, tuple(d[key] for d in dicts)\r\nKeyError: 'status'\r\n```\r\nThis KeyError is at times different from 'status' also.\r\nwhen I run \r\n```\r\ndatasets-cli dummy_data datasets/cmu_hinglish_dog --auto_generate --json_field='history'\r\n```\r\nI have tried removing unnecessary feature type definition, but that didn't help. Please let me know if I am missing something, thanks!", "The CI fail is unrelated to this PR and fixed on master. Merging !" ]
https://api.github.com/repos/huggingface/datasets/issues/314	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/314/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/314/comments	https://api.github.com/repos/huggingface/datasets/issues/314/events	https://github.com/huggingface/datasets/pull/314	645,461,174	MDExOlB1bGxSZXF1ZXN0NDM5OTM4MTMw	314	Fixed singlular very minor spelling error	[]	closed	false	null	1	2020-06-25T10:45:59Z	2020-06-26T08:46:41Z	2020-06-25T12:43:59Z	null	An instance of "independantly" was changed to "independently". That's all.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/314/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/314/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/314.diff", "html_url": "https://github.com/huggingface/datasets/pull/314", "merged_at": "2020-06-25T12:43:59Z", "patch_url": "https://github.com/huggingface/datasets/pull/314.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/314" }	true	[ "Thank you BatJeti! The storm-joker, aka the typo, finally got caught!" ]
https://api.github.com/repos/huggingface/datasets/issues/5073	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5073/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5073/comments	https://api.github.com/repos/huggingface/datasets/issues/5073/events	https://github.com/huggingface/datasets/pull/5073	1,397,832,183	PR_kwDODunzps5AN3Gn	5,073	Restore saved format state in `load_from_disk`	[]	closed	false	null	1	2022-10-05T13:51:47Z	2022-10-11T16:55:07Z	2022-10-11T16:49:23Z	null	Hello! @mariosasko This pull request relates to issue #5050 and intends to add the format to datasets loaded from disk. All I did was add a set_format in the Dataset.load_from_disk, as DatasetDict.load_from_disk relies on the first. I don't know if I should add a test and where, so let me know if I should and I can work on that as well!	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5073/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5073/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5073.diff", "html_url": "https://github.com/huggingface/datasets/pull/5073", "merged_at": "2022-10-11T16:49:23Z", "patch_url": "https://github.com/huggingface/datasets/pull/5073.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5073" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._" ]
https://api.github.com/repos/huggingface/datasets/issues/4201	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4201/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4201/comments	https://api.github.com/repos/huggingface/datasets/issues/4201/events	https://github.com/huggingface/datasets/pull/4201	1,212,086,420	PR_kwDODunzps42nIRm	4,201	Update GH template for dataset viewer issues	[]	closed	false	null	2	2022-04-22T09:34:44Z	2022-05-06T08:38:43Z	2022-04-26T08:45:55Z	null	Update template to use new issue forms instead. With this PR we can check if this new feature is useful for us. Once validated, we can update the other templates. CC: @severo	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4201/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4201/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/4201.diff", "html_url": "https://github.com/huggingface/datasets/pull/4201", "merged_at": "2022-04-26T08:45:55Z", "patch_url": "https://github.com/huggingface/datasets/pull/4201.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4201" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "You can see rendering at: https://github.com/huggingface/datasets/blob/6b48fedbdafe12a42c7b6edcecc32820af1a4822/.github/ISSUE_TEMPLATE/dataset-viewer.yml" ]
https://api.github.com/repos/huggingface/datasets/issues/3781	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3781/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3781/comments	https://api.github.com/repos/huggingface/datasets/issues/3781/events	https://github.com/huggingface/datasets/pull/3781	1,148,599,680	PR_kwDODunzps4zXr_O	3,781	Reddit dataset card additions	[]	closed	false	null	1	2022-02-23T21:29:16Z	2022-02-28T18:00:40Z	2022-02-28T11:21:14Z	null	The changes proposed are based on the "TL;DR: Mining Reddit to Learn Automatic Summarization" paper & https://zenodo.org/record/1043504#.YhaKHpbQC38 It is a Reddit dataset indeed, but the name given to the dataset by the authors is Webis-TLDR-17 (corpus), so perhaps it should be modified as well. The task at which the dataset is aimed is abstractive summarization.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3781/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3781/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/3781.diff", "html_url": "https://github.com/huggingface/datasets/pull/3781", "merged_at": "2022-02-28T11:21:14Z", "patch_url": "https://github.com/huggingface/datasets/pull/3781.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3781" }	true	[ "Hello! I added the tags and created a PR. Just to note, regarding the paperswithcode_id tag, that currently has the value \"reddit\"; the dataset described as reddit in paperswithcode is https://paperswithcode.com/dataset/reddit and it isn't the Webis-tldr-17. I could not find Webis-tldr-17 in paperswithcode neither in the Summarization category nor using the keywords reddit, webis, & tldr. I didn't change this tag." ]
https://api.github.com/repos/huggingface/datasets/issues/3425	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3425/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3425/comments	https://api.github.com/repos/huggingface/datasets/issues/3425/events	https://github.com/huggingface/datasets/issues/3425	1,078,598,140	I_kwDODunzps5AShn8	3,425	Getting configs names takes too long	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	open	false	null	3	2021-12-13T14:27:57Z	2021-12-13T14:53:33Z	null	null	## Steps to reproduce the bug ```python from datasets import get_dataset_config_names get_dataset_config_names("allenai/c4") ``` ## Expected results I would expect to get the answer quickly, at least in less than 10s ## Actual results It takes about 45s on my environment ## Environment info - `datasets` version: 1.16.1 - Platform: Linux-5.11.0-1022-aws-x86_64-with-glibc2.31 - Python version: 3.9.6 - PyArrow version: 4.0.1	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3425/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3425/timeline	null	null	null	null	false	[ "maybe related to https://github.com/huggingface/datasets/issues/2859\r\n", "It looks like it's currently calling `HfFileSystem.ls()` ~8 times at the root and for each subdirectory:\r\n- \"\"\r\n- \"en.noblocklist\"\r\n- \"en.noclean\"\r\n- \"en\"\r\n- \"multilingual\"\r\n- \"realnewslike\"\r\n\r\nCurrently `ls` is slow because it iterates on all the files inside the repository.\r\n\r\nAn easy optimization would be to cache the result of each call to `ls`.\r\nWe can also optimize `ls` by using a tree structure per directory instead of a list of all the files.\r\n", "ok\r\n" ]
https://api.github.com/repos/huggingface/datasets/issues/5052	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5052/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5052/comments	https://api.github.com/repos/huggingface/datasets/issues/5052/events	https://github.com/huggingface/datasets/pull/5052	1,393,076,765	PR_kwDODunzps4_-PZw	5,052	added from_generator method to IterableDataset class.	[]	closed	false	null	3	2022-09-30T22:14:05Z	2022-10-05T12:51:48Z	2022-10-05T12:10:48Z	null	Hello, This resolves issues #4988. I added a method `from_generator` to class `IterableDataset`. I modified the `read` method of input stream generator to also return Iterable_dataset.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5052/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5052/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5052.diff", "html_url": "https://github.com/huggingface/datasets/pull/5052", "merged_at": "2022-10-05T12:10:48Z", "patch_url": "https://github.com/huggingface/datasets/pull/5052.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5052" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "I added a test and moved the `streaming` param from `read` to `__init_`. Then, I also decided to update the `read` method of the rest of the packaged modules to account for this param. \r\n\r\n@hamid-vakilzadeh Are you OK with these changes? ", "@mariosasko these all look great! Thanks for the updates." ]
https://api.github.com/repos/huggingface/datasets/issues/4680	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4680/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4680/comments	https://api.github.com/repos/huggingface/datasets/issues/4680/events	https://github.com/huggingface/datasets/issues/4680	1,304,534,770	I_kwDODunzps5NwZ7y	4,680	Dataset Viewer issue for codeparrot/xlcost-text-to-code	[]	closed	false	null	5	2022-07-14T09:45:50Z	2022-07-18T16:37:00Z	2022-07-18T16:04:36Z	null	### Link https://huggingface.co/datasets/codeparrot/xlcost-text-to-code ### Description Error ``` Server Error Status code: 400 Exception: TypeError Message: 'NoneType' object is not iterable ``` Before I did a minor change in the dataset script (removing some comments), the viewer was working but not properely, it wasn't showing the dataset subsets. But the data can be loaded successfully. Thanks! ### Owner Yes	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4680/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4680/timeline	null	completed	null	null	false	[ "There seems to be an issue with the `C++-snippet-level` config:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"codeparrot/xlcost-text-to-code\", \"C++-snippet-level\")\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 352, in get_dataset_config_info\r\n info.splits = {\r\nTypeError: 'NoneType' object is not iterable\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nI remove the dataset-viewer tag since it's not directly related.\r\n\r\nPinging @huggingface/datasets ", "Thanks I found that this subset wasn't properly defined the the config, I fixed it. Now I can see the subsets but I get this error for the viewer\r\n````\r\nStatus code: 400\r\nException: Status400Error\r\nMessage: The split cache is empty.\r\n```", "Yes, the cache is being refreshed, hopefully, it will work in some minutes for all the splits. Some are already here:\r\n\r\nhttps://huggingface.co/datasets/codeparrot/xlcost-text-to-code/viewer/Python-snippet-level/train\r\n\r\n<img width=\"1533\" alt=\"Capture d’écran 2022-07-18 à 12 04 06\" src=\"https://user-images.githubusercontent.com/1676121/179553933-64d874fa-ada9-4b82-900e-082619523c20.png\">\r\n", "I think all the splits are working as expected now", "Perfect, thank you!" ]
https://api.github.com/repos/huggingface/datasets/issues/3767	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3767/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3767/comments	https://api.github.com/repos/huggingface/datasets/issues/3767/events	https://github.com/huggingface/datasets/pull/3767	1,146,036,648	PR_kwDODunzps4zPahh	3,767	Expose method and fix param	[]	closed	false	null	0	2022-02-21T16:57:47Z	2022-02-22T08:35:03Z	2022-02-22T08:35:02Z	null	A fix + expose a new method, following https://github.com/huggingface/datasets/pull/3670	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3767/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3767/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/3767.diff", "html_url": "https://github.com/huggingface/datasets/pull/3767", "merged_at": "2022-02-22T08:35:02Z", "patch_url": "https://github.com/huggingface/datasets/pull/3767.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3767" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/465	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/465/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/465/comments	https://api.github.com/repos/huggingface/datasets/issues/465/events	https://github.com/huggingface/datasets/pull/465	669,889,779	MDExOlB1bGxSZXF1ZXN0NDYwMjEwODYw	465	Keep features after transform	[]	closed	false	null	3	2020-07-31T14:43:21Z	2020-07-31T18:27:33Z	2020-07-31T18:27:32Z	null	When applying a transform like `map`, some features were lost (and inferred features were used). It was the case for ClassLabel, Translation, etc. To fix that, I did some modifications in the `ArrowWriter`: - added the `update_features` parameter. When it's `True`, then the features specified by the user (if any) can be updated with inferred features if their type don't match. `map` transform sets `update_features=True` when writing to cache file or buffer. Features won't change by default in `map`. - added the `with_metadata` parameter. If `True`, the `features` (after update) will be written inside the metadata of the schema in this format: ``` { "huggingface": {"features" : <serialized Features exactly like dataset_info.json>} } ``` Then, once a dataset is instantiated without info/features, these metadata are used to set the features of the dataset.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/465/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/465/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/465.diff", "html_url": "https://github.com/huggingface/datasets/pull/465", "merged_at": "2020-07-31T18:27:32Z", "patch_url": "https://github.com/huggingface/datasets/pull/465.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/465" }	true	[ "One note on features inference:\r\n\r\nif an arrow type is `struct of items` where each item is a `list`, then we return a `dict` in which each item is a `Sequence`.\r\nIt means that we don't use the Sequence <-> dict swap when we infer features.\r\n\r\nIt's fine because the swap is generally used in dataset scripts, in which features are defined (inferred features are discarded)", "If it's fine for you @thomwolf we can merge this one :) ", "Yes this is fine I think!" ]
https://api.github.com/repos/huggingface/datasets/issues/1216	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/1216/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/1216/comments	https://api.github.com/repos/huggingface/datasets/issues/1216/events	https://github.com/huggingface/datasets/pull/1216	758,005,982	MDExOlB1bGxSZXF1ZXN0NTMzMjU0ODE2	1,216	Add limit	[]	closed	false	null	1	2020-12-06T19:46:18Z	2020-12-08T07:52:11Z	2020-12-08T07:52:11Z	null	This PR adds [LiMiT](https://github.com/ilmgut/limit_dataset), a dataset for literal motion classification/extraction by [Manotas et al., 2020](https://www.aclweb.org/anthology/2020.findings-emnlp.88.pdf).	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/1216/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/1216/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/1216.diff", "html_url": "https://github.com/huggingface/datasets/pull/1216", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/1216.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/1216" }	true	[ "My bad, didn't see this on the open dataset list. Closing this since it overlaps with PR#1256" ]
https://api.github.com/repos/huggingface/datasets/issues/28	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/28/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/28/comments	https://api.github.com/repos/huggingface/datasets/issues/28/events	https://github.com/huggingface/datasets/pull/28	610,241,907	MDExOlB1bGxSZXF1ZXN0NDExNzE5MTQy	28	[Circle ci] Adds circle ci config	[]	closed	false	null	0	2020-04-30T17:03:35Z	2020-04-30T19:51:09Z	2020-04-30T19:51:08Z	null	@thomwolf can you take a look and set up circle ci on: https://app.circleci.com/projects/project-dashboard/github/huggingface I think for `nlp` only admins can set it up, which I guess is you :-)	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/28/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/28/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/28.diff", "html_url": "https://github.com/huggingface/datasets/pull/28", "merged_at": "2020-04-30T19:51:08Z", "patch_url": "https://github.com/huggingface/datasets/pull/28.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/28" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/4939	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4939/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4939/comments	https://api.github.com/repos/huggingface/datasets/issues/4939/events	https://github.com/huggingface/datasets/pull/4939	1,363,468,679	PR_kwDODunzps4-cw4A	4,939	Fix NonMatchingChecksumError in adv_glue dataset	[]	closed	false	null	1	2022-09-06T15:31:16Z	2022-09-06T17:42:10Z	2022-09-06T17:39:16Z	null	Fix issue reported on the Hub: https://huggingface.co/datasets/adv_glue/discussions/1	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4939/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4939/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/4939.diff", "html_url": "https://github.com/huggingface/datasets/pull/4939", "merged_at": "2022-09-06T17:39:16Z", "patch_url": "https://github.com/huggingface/datasets/pull/4939.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4939" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._" ]
https://api.github.com/repos/huggingface/datasets/issues/3291	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3291/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3291/comments	https://api.github.com/repos/huggingface/datasets/issues/3291/events	https://github.com/huggingface/datasets/pull/3291	1,056,689,876	PR_kwDODunzps4urikR	3,291	Use f-strings in the dataset scripts	[]	closed	false	null	0	2021-11-17T22:20:19Z	2021-11-22T16:40:16Z	2021-11-22T16:40:16Z	null	Uses f-strings to format the .py files in the dataset folder	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3291/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3291/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/3291.diff", "html_url": "https://github.com/huggingface/datasets/pull/3291", "merged_at": "2021-11-22T16:40:16Z", "patch_url": "https://github.com/huggingface/datasets/pull/3291.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3291" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/4911	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4911/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4911/comments	https://api.github.com/repos/huggingface/datasets/issues/4911/events	https://github.com/huggingface/datasets/issues/4911	1,354,426,978	I_kwDODunzps5Quupi	4,911	[Tests] Ensure `datasets` supports renamed repositories	[ { "color": "BDE59C", "default": false, "description": "Issues a bit more difficult than \"Good First\" issues", "id": 3761482852, "name": "good second issue", "node_id": "LA_kwDODunzps7gM6xk", "url": "https://api.github.com/repos/huggingface/datasets/labels/good%20second%20issue" } ]	open	false	null	1	2022-08-29T14:46:14Z	2022-08-29T15:31:03Z	null	null	On https://hf.co/datasets you can rename a dataset (or sometimes move it to another user/org). The website handles redirections correctly and AFAIK `datasets` does as well. However it would be nice to have an integration test to make sure we don't break support for renamed datasets. To implement this we can use the /api/repos/move endpoint on hub-ci to rename/move a repo (it is documented at https://huggingface.co/docs/hub/api)	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4911/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4911/timeline	null	null	null	null	false	[ "You could also switch to using `huggingface_hub` more directly, where such a guarantee is already tested =)\r\n\r\ncc @Wauplin " ]
https://api.github.com/repos/huggingface/datasets/issues/5835	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5835/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5835/comments	https://api.github.com/repos/huggingface/datasets/issues/5835/events	https://github.com/huggingface/datasets/pull/5835	1,702,522,620	PR_kwDODunzps5QHquR	5,835	Always set nullable fields in the writer	[]	closed	false	null	4	2023-05-09T18:16:59Z	2023-05-23T16:10:29Z	2023-05-19T13:04:30Z	null	This fixes loading of e.g. parquet data with non-nullable fields. Indeed `datasets.Features` doesn't support non-nullable fields, which can lead to data not concatenable due to arrow schema mismatch.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5835/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5835/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5835.diff", "html_url": "https://github.com/huggingface/datasets/pull/5835", "merged_at": "2023-05-19T13:04:30Z", "patch_url": "https://github.com/huggingface/datasets/pull/5835.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5835" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006640 / 0.011353 (-0.004713) \| 0.004606 / 0.011008 (-0.006402) \| 0.098870 / 0.038508 (0.060362) \| 0.028201 / 0.023109 (0.005092) \| 0.304396 / 0.275898 (0.028498) \| 0.339804 / 0.323480 (0.016324) \| 0.005011 / 0.007986 (-0.002974) \| 0.003530 / 0.004328 (-0.000799) \| 0.075223 / 0.004250 (0.070973) \| 0.037922 / 0.037052 (0.000870) \| 0.310273 / 0.258489 (0.051784) \| 0.348324 / 0.293841 (0.054483) \| 0.030181 / 0.128546 (-0.098365) \| 0.011584 / 0.075646 (-0.064062) \| 0.322637 / 0.419271 (-0.096635) \| 0.043119 / 0.043533 (-0.000414) \| 0.314514 / 0.255139 (0.059375) \| 0.334384 / 0.283200 (0.051185) \| 0.092551 / 0.141683 (-0.049132) \| 1.496694 / 1.452155 (0.044539) \| 1.555426 / 1.492716 (0.062710) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.205078 / 0.018006 (0.187072) \| 0.399200 / 0.000490 (0.398710) \| 0.004881 / 0.000200 (0.004681) \| 0.000200 / 0.000054 (0.000146) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025042 / 0.037411 (-0.012369) \| 0.101501 / 0.014526 (0.086975) \| 0.107430 / 0.176557 (-0.069127) \| 0.170107 / 0.737135 (-0.567028) \| 0.111253 / 0.296338 (-0.185086) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.460358 / 0.215209 (0.245149) \| 4.592037 / 2.077655 (2.514383) \| 2.222612 / 1.504120 (0.718493) \| 2.022804 / 1.541195 (0.481610) \| 2.040824 / 1.468490 (0.572334) \| 0.700485 / 4.584777 (-3.884292) \| 3.427847 / 3.745712 (-0.317866) \| 2.836916 / 5.269862 (-2.432946) \| 1.505055 / 4.565676 (-3.060621) \| 0.083206 / 0.424275 (-0.341069) \| 0.046492 / 0.007607 (0.038885) \| 0.555562 / 0.226044 (0.329518) \| 5.563574 / 2.268929 (3.294645) \| 2.635273 / 55.444624 (-52.809351) \| 2.299377 / 6.876477 (-4.577100) \| 2.394512 / 2.142072 (0.252440) \| 0.809541 / 4.805227 (-3.995686) \| 0.151814 / 6.500664 (-6.348850) \| 0.067241 / 0.075469 (-0.008228) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.188396 / 1.841788 (-0.653392) \| 13.714596 / 8.074308 (5.640288) \| 14.076906 / 10.191392 (3.885514) \| 0.143447 / 0.680424 (-0.536977) \| 0.016514 / 0.534201 (-0.517687) \| 0.383075 / 0.579283 (-0.196209) \| 0.386997 / 0.434364 (-0.047367) \| 0.441941 / 0.540337 (-0.098396) \| 0.522145 / 1.386936 (-0.864791) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006266 / 0.011353 (-0.005086) \| 0.004562 / 0.011008 (-0.006446) \| 0.077472 / 0.038508 (0.038964) \| 0.027596 / 0.023109 (0.004486) \| 0.400498 / 0.275898 (0.124600) \| 0.406728 / 0.323480 (0.083248) \| 0.004745 / 0.007986 (-0.003241) \| 0.003375 / 0.004328 (-0.000954) \| 0.076645 / 0.004250 (0.072394) \| 0.037756 / 0.037052 (0.000703) \| 0.415183 / 0.258489 (0.156694) \| 0.413758 / 0.293841 (0.119917) \| 0.030624 / 0.128546 (-0.097922) \| 0.011525 / 0.075646 (-0.064121) \| 0.086033 / 0.419271 (-0.333238) \| 0.039307 / 0.043533 (-0.004226) \| 0.418192 / 0.255139 (0.163053) \| 0.403152 / 0.283200 (0.119952) \| 0.094141 / 0.141683 (-0.047542) \| 1.459012 / 1.452155 (0.006857) \| 1.546493 / 1.492716 (0.053777) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239494 / 0.018006 (0.221488) \| 0.420918 / 0.000490 (0.420428) \| 0.000411 / 0.000200 (0.000211) \| 0.000057 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024525 / 0.037411 (-0.012886) \| 0.099793 / 0.014526 (0.085267) \| 0.105888 / 0.176557 (-0.070669) \| 0.155912 / 0.737135 (-0.581223) \| 0.109937 / 0.296338 (-0.186401) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.470108 / 0.215209 (0.254899) \| 4.696390 / 2.077655 (2.618735) \| 2.467841 / 1.504120 (0.963721) \| 2.275012 / 1.541195 (0.733818) \| 2.430736 / 1.468490 (0.962245) \| 0.700442 / 4.584777 (-3.884335) \| 3.458451 / 3.745712 (-0.287261) \| 1.921120 / 5.269862 (-3.348742) \| 1.183292 / 4.565676 (-3.382384) \| 0.083985 / 0.424275 (-0.340290) \| 0.012510 / 0.007607 (0.004903) \| 0.589066 / 0.226044 (0.363022) \| 5.896070 / 2.268929 (3.627141) \| 2.935379 / 55.444624 (-52.509245) \| 2.599524 / 6.876477 (-4.276953) \| 2.663426 / 2.142072 (0.521354) \| 0.812096 / 4.805227 (-3.993131) \| 0.152559 / 6.500664 (-6.348105) \| 0.066906 / 0.075469 (-0.008563) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.333341 / 1.841788 (-0.508446) \| 14.441667 / 8.074308 (6.367359) \| 14.754069 / 10.191392 (4.562677) \| 0.155707 / 0.680424 (-0.524716) \| 0.016983 / 0.534201 (-0.517218) \| 0.389386 / 0.579283 (-0.189897) \| 0.394106 / 0.434364 (-0.040258) \| 0.447355 / 0.540337 (-0.092982) \| 0.533142 / 1.386936 (-0.853794) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#99ee4467ce77f8f718159a535e237dd8790b5bed \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007801 / 0.011353 (-0.003552) \| 0.004884 / 0.011008 (-0.006124) \| 0.114754 / 0.038508 (0.076245) \| 0.040427 / 0.023109 (0.017318) \| 0.402064 / 0.275898 (0.126166) \| 0.428830 / 0.323480 (0.105350) \| 0.006429 / 0.007986 (-0.001556) \| 0.004394 / 0.004328 (0.000066) \| 0.087681 / 0.004250 (0.083431) \| 0.053684 / 0.037052 (0.016632) \| 0.399967 / 0.258489 (0.141478) \| 0.445298 / 0.293841 (0.151457) \| 0.033194 / 0.128546 (-0.095352) \| 0.010288 / 0.075646 (-0.065359) \| 0.390719 / 0.419271 (-0.028552) \| 0.059311 / 0.043533 (0.015778) \| 0.393651 / 0.255139 (0.138512) \| 0.418395 / 0.283200 (0.135196) \| 0.121494 / 0.141683 (-0.020189) \| 1.735470 / 1.452155 (0.283315) \| 1.820485 / 1.492716 (0.327769) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012887 / 0.018006 (-0.005119) \| 0.491652 / 0.000490 (0.491162) \| 0.005481 / 0.000200 (0.005281) \| 0.000127 / 0.000054 (0.000073) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030931 / 0.037411 (-0.006480) \| 0.125212 / 0.014526 (0.110686) \| 0.136004 / 0.176557 (-0.040552) \| 0.201686 / 0.737135 (-0.535449) \| 0.140181 / 0.296338 (-0.156157) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.475003 / 0.215209 (0.259794) \| 4.743918 / 2.077655 (2.666263) \| 2.149422 / 1.504120 (0.645302) \| 1.925016 / 1.541195 (0.383821) \| 2.061441 / 1.468490 (0.592951) \| 0.619845 / 4.584777 (-3.964932) \| 4.534691 / 3.745712 (0.788979) \| 2.248198 / 5.269862 (-3.021664) \| 1.409868 / 4.565676 (-3.155808) \| 0.080265 / 0.424275 (-0.344010) \| 0.014455 / 0.007607 (0.006848) \| 0.597810 / 0.226044 (0.371765) \| 5.845492 / 2.268929 (3.576564) \| 2.729139 / 55.444624 (-52.715486) \| 2.313879 / 6.876477 (-4.562598) \| 2.418763 / 2.142072 (0.276690) \| 0.748687 / 4.805227 (-4.056540) \| 0.165278 / 6.500664 (-6.335387) \| 0.076848 / 0.075469 (0.001379) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.416349 / 1.841788 (-0.425439) \| 17.440903 / 8.074308 (9.366595) \| 17.025733 / 10.191392 (6.834341) \| 0.167428 / 0.680424 (-0.512995) \| 0.020484 / 0.534201 (-0.513717) \| 0.470273 / 0.579283 (-0.109010) \| 0.494380 / 0.434364 (0.060016) \| 0.566131 / 0.540337 (0.025794) \| 0.690444 / 1.386936 (-0.696492) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007695 / 0.011353 (-0.003657) \| 0.005551 / 0.011008 (-0.005457) \| 0.087812 / 0.038508 (0.049304) \| 0.039107 / 0.023109 (0.015998) \| 0.436461 / 0.275898 (0.160563) \| 0.465116 / 0.323480 (0.141636) \| 0.006590 / 0.007986 (-0.001396) \| 0.004672 / 0.004328 (0.000343) \| 0.087109 / 0.004250 (0.082858) \| 0.054227 / 0.037052 (0.017175) \| 0.442660 / 0.258489 (0.184171) \| 0.484296 / 0.293841 (0.190455) \| 0.033308 / 0.128546 (-0.095238) \| 0.010780 / 0.075646 (-0.064866) \| 0.095255 / 0.419271 (-0.324016) \| 0.054399 / 0.043533 (0.010866) \| 0.431734 / 0.255139 (0.176595) \| 0.453583 / 0.283200 (0.170383) \| 0.116067 / 0.141683 (-0.025616) \| 1.780701 / 1.452155 (0.328546) \| 1.851077 / 1.492716 (0.358360) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228000 / 0.018006 (0.209994) \| 0.485733 / 0.000490 (0.485243) \| 0.003955 / 0.000200 (0.003755) \| 0.000109 / 0.000054 (0.000054) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033974 / 0.037411 (-0.003437) \| 0.134504 / 0.014526 (0.119978) \| 0.144421 / 0.176557 (-0.032135) \| 0.202171 / 0.737135 (-0.534964) \| 0.152015 / 0.296338 (-0.144323) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.520462 / 0.215209 (0.305253) \| 5.233339 / 2.077655 (3.155684) \| 2.575013 / 1.504120 (1.070893) \| 2.384119 / 1.541195 (0.842924) \| 2.403856 / 1.468490 (0.935366) \| 0.618656 / 4.584777 (-3.966121) \| 4.663582 / 3.745712 (0.917870) \| 3.738594 / 5.269862 (-1.531268) \| 1.794903 / 4.565676 (-2.770773) \| 0.077903 / 0.424275 (-0.346372) \| 0.014681 / 0.007607 (0.007074) \| 0.648615 / 0.226044 (0.422570) \| 6.503721 / 2.268929 (4.234792) \| 3.326239 / 55.444624 (-52.118386) \| 2.989791 / 6.876477 (-3.886685) \| 2.995479 / 2.142072 (0.853407) \| 0.765483 / 4.805227 (-4.039744) \| 0.169783 / 6.500664 (-6.330882) \| 0.077533 / 0.075469 (0.002064) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.518736 / 1.841788 (-0.323051) \| 17.989119 / 8.074308 (9.914811) \| 15.484365 / 10.191392 (5.292973) \| 0.168507 / 0.680424 (-0.511917) \| 0.020289 / 0.534201 (-0.513912) \| 0.467491 / 0.579283 (-0.111793) \| 0.501714 / 0.434364 (0.067350) \| 0.553418 / 0.540337 (0.013081) \| 0.662199 / 1.386936 (-0.724737) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ebda17e4362bd5f6123543a14fa526a3b54481a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007044 / 0.011353 (-0.004309) \| 0.004750 / 0.011008 (-0.006258) \| 0.096694 / 0.038508 (0.058186) \| 0.035682 / 0.023109 (0.012573) \| 0.300613 / 0.275898 (0.024715) \| 0.334831 / 0.323480 (0.011351) \| 0.006428 / 0.007986 (-0.001558) \| 0.004456 / 0.004328 (0.000128) \| 0.075060 / 0.004250 (0.070810) \| 0.053166 / 0.037052 (0.016114) \| 0.299601 / 0.258489 (0.041112) \| 0.359521 / 0.293841 (0.065680) \| 0.028072 / 0.128546 (-0.100474) \| 0.009216 / 0.075646 (-0.066430) \| 0.328895 / 0.419271 (-0.090377) \| 0.050881 / 0.043533 (0.007349) \| 0.298265 / 0.255139 (0.043126) \| 0.318095 / 0.283200 (0.034896) \| 0.116046 / 0.141683 (-0.025637) \| 1.491312 / 1.452155 (0.039157) \| 1.556053 / 1.492716 (0.063337) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.014248 / 0.018006 (-0.003758) \| 0.551455 / 0.000490 (0.550965) \| 0.006096 / 0.000200 (0.005897) \| 0.000145 / 0.000054 (0.000091) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030598 / 0.037411 (-0.006813) \| 0.109549 / 0.014526 (0.095023) \| 0.123207 / 0.176557 (-0.053350) \| 0.181940 / 0.737135 (-0.555195) \| 0.128965 / 0.296338 (-0.167374) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404552 / 0.215209 (0.189343) \| 4.030674 / 2.077655 (1.953020) \| 1.841819 / 1.504120 (0.337699) \| 1.650055 / 1.541195 (0.108860) \| 1.763208 / 1.468490 (0.294718) \| 0.532715 / 4.584777 (-4.052062) \| 3.774810 / 3.745712 (0.029098) \| 3.221927 / 5.269862 (-2.047934) \| 1.607974 / 4.565676 (-2.957702) \| 0.067160 / 0.424275 (-0.357116) \| 0.012479 / 0.007607 (0.004872) \| 0.498801 / 0.226044 (0.272757) \| 4.980567 / 2.268929 (2.711638) \| 2.356017 / 55.444624 (-53.088608) \| 2.018975 / 6.876477 (-4.857502) \| 2.218343 / 2.142072 (0.076270) \| 0.645714 / 4.805227 (-4.159514) \| 0.145470 / 6.500664 (-6.355195) \| 0.065666 / 0.075469 (-0.009803) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.205756 / 1.841788 (-0.636031) \| 15.682779 / 8.074308 (7.608470) \| 14.748987 / 10.191392 (4.557595) \| 0.167105 / 0.680424 (-0.513319) \| 0.017554 / 0.534201 (-0.516647) \| 0.393924 / 0.579283 (-0.185359) \| 0.432659 / 0.434364 (-0.001705) \| 0.502033 / 0.540337 (-0.038304) \| 0.602244 / 1.386936 (-0.784692) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007077 / 0.011353 (-0.004276) \| 0.004911 / 0.011008 (-0.006097) \| 0.075120 / 0.038508 (0.036612) \| 0.035460 / 0.023109 (0.012351) \| 0.362569 / 0.275898 (0.086671) \| 0.398995 / 0.323480 (0.075515) \| 0.006587 / 0.007986 (-0.001398) \| 0.004571 / 0.004328 (0.000242) \| 0.074647 / 0.004250 (0.070397) \| 0.057331 / 0.037052 (0.020279) \| 0.365123 / 0.258489 (0.106634) \| 0.408617 / 0.293841 (0.114776) \| 0.028911 / 0.128546 (-0.099635) \| 0.009533 / 0.075646 (-0.066113) \| 0.081566 / 0.419271 (-0.337705) \| 0.048841 / 0.043533 (0.005308) \| 0.367245 / 0.255139 (0.112106) \| 0.375975 / 0.283200 (0.092776) \| 0.123211 / 0.141683 (-0.018472) \| 1.471588 / 1.452155 (0.019433) \| 1.569342 / 1.492716 (0.076625) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.328443 / 0.018006 (0.310436) \| 0.541402 / 0.000490 (0.540912) \| 0.000440 / 0.000200 (0.000240) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030772 / 0.037411 (-0.006639) \| 0.115833 / 0.014526 (0.101307) \| 0.127837 / 0.176557 (-0.048719) \| 0.180897 / 0.737135 (-0.556238) \| 0.132458 / 0.296338 (-0.163881) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445979 / 0.215209 (0.230770) \| 4.453101 / 2.077655 (2.375447) \| 2.276625 / 1.504120 (0.772505) \| 2.102167 / 1.541195 (0.560972) \| 2.181583 / 1.468490 (0.713093) \| 0.525069 / 4.584777 (-4.059708) \| 3.803446 / 3.745712 (0.057734) \| 1.954173 / 5.269862 (-3.315688) \| 1.088734 / 4.565676 (-3.476942) \| 0.066020 / 0.424275 (-0.358255) \| 0.012158 / 0.007607 (0.004551) \| 0.546828 / 0.226044 (0.320783) \| 5.454060 / 2.268929 (3.185132) \| 2.756154 / 55.444624 (-52.688470) \| 2.476501 / 6.876477 (-4.399976) \| 2.525875 / 2.142072 (0.383803) \| 0.647515 / 4.805227 (-4.157712) \| 0.144511 / 6.500664 (-6.356153) \| 0.067060 / 0.075469 (-0.008409) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.306456 / 1.841788 (-0.535332) \| 15.822623 / 8.074308 (7.748315) \| 14.929114 / 10.191392 (4.737721) \| 0.168650 / 0.680424 (-0.511773) \| 0.018043 / 0.534201 (-0.516158) \| 0.396712 / 0.579283 (-0.182572) \| 0.425800 / 0.434364 (-0.008564) \| 0.466452 / 0.540337 (-0.073885) \| 0.564370 / 1.386936 (-0.822566) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ebda17e4362bd5f6123543a14fa526a3b54481a \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/463	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/463/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/463/comments	https://api.github.com/repos/huggingface/datasets/issues/463/events	https://github.com/huggingface/datasets/pull/463	669,735,455	MDExOlB1bGxSZXF1ZXN0NDYwMDcyNjQ1	463	Add dataset/mlsum	[]	closed	false	null	3	2020-07-31T11:50:52Z	2020-08-24T14:54:42Z	2020-08-24T14:54:42Z	null	New pull request that should correct the previous errors. The load_real_data stills fails because it is looking for a default dataset URL that does not exists, this does not happen when loading the dataset with load_dataset	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/463/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/463/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/463.diff", "html_url": "https://github.com/huggingface/datasets/pull/463", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/463.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/463" }	true	[ "I think the problem is related to `wiki_dpr` dataset which is making the circle CI failed as you can see:\r\n```\r\nFAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_wiki_dpr\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/dummy_psgs_w100_no_embeddings\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/dummy_psgs_w100_with_nq_embeddings\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/psgs_w100_no_embeddings\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/psgs_w100_with_nq_embeddings\r\n\r\n```\r\nI'm facing the same issues with my last commits, I tried to rebase from master but it still not working. Maybe @lhoestq can help with.", "Hello, I am confused about the next steps I need to do. Did the forced merge solve the issue ?", "Hello :)\r\nI think you can just rebase from master and it should solve the CI error" ]
https://api.github.com/repos/huggingface/datasets/issues/5583	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5583/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5583/comments	https://api.github.com/repos/huggingface/datasets/issues/5583/events	https://github.com/huggingface/datasets/pull/5583	1,601,583,625	PR_kwDODunzps5K2mIz	5,583	Do no write index by default when exporting a dataset	[]	closed	false	null	3	2023-02-27T17:04:46Z	2023-02-28T13:52:15Z	2023-02-28T13:44:04Z	null	Ensures all the writers that use Pandas for conversion (JSON, CSV, SQL) do not export `index` by default (https://github.com/huggingface/datasets/pull/5490 only did this for CSV)	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5583/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5583/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5583.diff", "html_url": "https://github.com/huggingface/datasets/pull/5583", "merged_at": "2023-02-28T13:44:04Z", "patch_url": "https://github.com/huggingface/datasets/pull/5583.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5583" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009044 / 0.011353 (-0.002309) \| 0.004244 / 0.011008 (-0.006765) \| 0.106705 / 0.038508 (0.068197) \| 0.029779 / 0.023109 (0.006670) \| 0.289684 / 0.275898 (0.013786) \| 0.347100 / 0.323480 (0.023620) \| 0.007071 / 0.007986 (-0.000915) \| 0.003734 / 0.004328 (-0.000595) \| 0.077971 / 0.004250 (0.073720) \| 0.035323 / 0.037052 (-0.001730) \| 0.334520 / 0.258489 (0.076031) \| 0.375804 / 0.293841 (0.081964) \| 0.049211 / 0.128546 (-0.079335) \| 0.016992 / 0.075646 (-0.058654) \| 0.337208 / 0.419271 (-0.082064) \| 0.053700 / 0.043533 (0.010167) \| 0.295750 / 0.255139 (0.040611) \| 0.330157 / 0.283200 (0.046958) \| 0.097017 / 0.141683 (-0.044666) \| 1.379353 / 1.452155 (-0.072802) \| 1.402670 / 1.492716 (-0.090047) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012685 / 0.018006 (-0.005321) \| 0.474541 / 0.000490 (0.474051) \| 0.006752 / 0.000200 (0.006552) \| 0.000097 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025735 / 0.037411 (-0.011676) \| 0.092507 / 0.014526 (0.077982) \| 0.100275 / 0.176557 (-0.076281) \| 0.180359 / 0.737135 (-0.556777) \| 0.104312 / 0.296338 (-0.192026) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456558 / 0.215209 (0.241349) \| 4.786667 / 2.077655 (2.709012) \| 1.873169 / 1.504120 (0.369050) \| 1.640935 / 1.541195 (0.099741) \| 1.614543 / 1.468490 (0.146053) \| 0.936144 / 4.584777 (-3.648633) \| 4.699886 / 3.745712 (0.954174) \| 2.398545 / 5.269862 (-2.871317) \| 1.642808 / 4.565676 (-2.922868) \| 0.124803 / 0.424275 (-0.299472) \| 0.011848 / 0.007607 (0.004241) \| 0.631684 / 0.226044 (0.405639) \| 6.096052 / 2.268929 (3.827124) \| 2.463052 / 55.444624 (-52.981572) \| 1.928551 / 6.876477 (-4.947926) \| 1.927790 / 2.142072 (-0.214283) \| 1.098912 / 4.805227 (-3.706315) \| 0.196343 / 6.500664 (-6.304321) \| 0.063296 / 0.075469 (-0.012173) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.255032 / 1.841788 (-0.586755) \| 13.853623 / 8.074308 (5.779315) \| 16.303280 / 10.191392 (6.111888) \| 0.227287 / 0.680424 (-0.453137) \| 0.037527 / 0.534201 (-0.496674) \| 0.449345 / 0.579283 (-0.129938) \| 0.522054 / 0.434364 (0.087690) \| 0.552848 / 0.540337 (0.012511) \| 0.642994 / 1.386936 (-0.743942) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008470 / 0.011353 (-0.002883) \| 0.005167 / 0.011008 (-0.005841) \| 0.077794 / 0.038508 (0.039286) \| 0.029228 / 0.023109 (0.006119) \| 0.340828 / 0.275898 (0.064930) \| 0.400170 / 0.323480 (0.076691) \| 0.005485 / 0.007986 (-0.002500) \| 0.003854 / 0.004328 (-0.000475) \| 0.077597 / 0.004250 (0.073346) \| 0.036519 / 0.037052 (-0.000533) \| 0.335522 / 0.258489 (0.077033) \| 0.412622 / 0.293841 (0.118781) \| 0.044587 / 0.128546 (-0.083959) \| 0.016024 / 0.075646 (-0.059623) \| 0.092312 / 0.419271 (-0.326960) \| 0.055660 / 0.043533 (0.012127) \| 0.343140 / 0.255139 (0.088001) \| 0.386403 / 0.283200 (0.103203) \| 0.098634 / 0.141683 (-0.043049) \| 1.326126 / 1.452155 (-0.126029) \| 1.430316 / 1.492716 (-0.062400) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222807 / 0.018006 (0.204801) \| 0.473622 / 0.000490 (0.473132) \| 0.000376 / 0.000200 (0.000176) \| 0.000066 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024599 / 0.037411 (-0.012813) \| 0.100743 / 0.014526 (0.086217) \| 0.112086 / 0.176557 (-0.064471) \| 0.198294 / 0.737135 (-0.538842) \| 0.111210 / 0.296338 (-0.185129) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.494120 / 0.215209 (0.278911) \| 5.117958 / 2.077655 (3.040303) \| 2.305131 / 1.504120 (0.801011) \| 2.015591 / 1.541195 (0.474396) \| 2.027284 / 1.468490 (0.558794) \| 1.014241 / 4.584777 (-3.570536) \| 4.738836 / 3.745712 (0.993124) \| 2.519718 / 5.269862 (-2.750143) \| 1.706379 / 4.565676 (-2.859298) \| 0.122452 / 0.424275 (-0.301824) \| 0.011500 / 0.007607 (0.003893) \| 0.632864 / 0.226044 (0.406820) \| 6.295457 / 2.268929 (4.026529) \| 2.824897 / 55.444624 (-52.619727) \| 2.324359 / 6.876477 (-4.552117) \| 2.281046 / 2.142072 (0.138974) \| 1.173570 / 4.805227 (-3.631657) \| 0.197195 / 6.500664 (-6.303469) \| 0.064845 / 0.075469 (-0.010624) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.273224 / 1.841788 (-0.568563) \| 14.531155 / 8.074308 (6.456847) \| 15.892176 / 10.191392 (5.700784) \| 0.208051 / 0.680424 (-0.472373) \| 0.023119 / 0.534201 (-0.511082) \| 0.422317 / 0.579283 (-0.156966) \| 0.519946 / 0.434364 (0.085582) \| 0.544517 / 0.540337 (0.004179) \| 0.605955 / 1.386936 (-0.780981) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#337a4a91d0268c68f26760321c9b45bb4a98832a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010806 / 0.011353 (-0.000547) \| 0.005631 / 0.011008 (-0.005378) \| 0.113166 / 0.038508 (0.074657) \| 0.042980 / 0.023109 (0.019871) \| 0.344856 / 0.275898 (0.068958) \| 0.404417 / 0.323480 (0.080938) \| 0.012222 / 0.007986 (0.004236) \| 0.004470 / 0.004328 (0.000141) \| 0.088072 / 0.004250 (0.083822) \| 0.049815 / 0.037052 (0.012763) \| 0.366532 / 0.258489 (0.108043) \| 0.392558 / 0.293841 (0.098717) \| 0.045411 / 0.128546 (-0.083135) \| 0.014118 / 0.075646 (-0.061529) \| 0.392894 / 0.419271 (-0.026378) \| 0.067713 / 0.043533 (0.024181) \| 0.353013 / 0.255139 (0.097874) \| 0.378375 / 0.283200 (0.095175) \| 0.123686 / 0.141683 (-0.017996) \| 1.665272 / 1.452155 (0.213118) \| 1.748383 / 1.492716 (0.255667) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011672 / 0.018006 (-0.006335) \| 0.481667 / 0.000490 (0.481178) \| 0.003644 / 0.000200 (0.003444) \| 0.000092 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030436 / 0.037411 (-0.006976) \| 0.122577 / 0.014526 (0.108052) \| 0.135409 / 0.176557 (-0.041148) \| 0.220385 / 0.737135 (-0.516750) \| 0.143140 / 0.296338 (-0.153199) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.471146 / 0.215209 (0.255937) \| 4.645023 / 2.077655 (2.567368) \| 2.126783 / 1.504120 (0.622663) \| 1.907905 / 1.541195 (0.366710) \| 1.969561 / 1.468490 (0.501071) \| 0.798670 / 4.584777 (-3.786107) \| 4.394787 / 3.745712 (0.649075) \| 2.353535 / 5.269862 (-2.916327) \| 1.501013 / 4.565676 (-3.064664) \| 0.097472 / 0.424275 (-0.326803) \| 0.014015 / 0.007607 (0.006408) \| 0.589365 / 0.226044 (0.363320) \| 5.897331 / 2.268929 (3.628402) \| 2.656198 / 55.444624 (-52.788427) \| 2.256082 / 6.876477 (-4.620395) \| 2.271122 / 2.142072 (0.129050) \| 0.961566 / 4.805227 (-3.843661) \| 0.188303 / 6.500664 (-6.312361) \| 0.073258 / 0.075469 (-0.002211) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.445266 / 1.841788 (-0.396522) \| 16.876710 / 8.074308 (8.802402) \| 16.004287 / 10.191392 (5.812895) \| 0.212252 / 0.680424 (-0.468172) \| 0.033186 / 0.534201 (-0.501015) \| 0.520564 / 0.579283 (-0.058719) \| 0.516865 / 0.434364 (0.082501) \| 0.638482 / 0.540337 (0.098144) \| 0.761959 / 1.386936 (-0.624977) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008101 / 0.011353 (-0.003252) \| 0.005512 / 0.011008 (-0.005497) \| 0.086138 / 0.038508 (0.047630) \| 0.038605 / 0.023109 (0.015496) \| 0.413082 / 0.275898 (0.137184) \| 0.444016 / 0.323480 (0.120536) \| 0.006196 / 0.007986 (-0.001790) \| 0.005736 / 0.004328 (0.001408) \| 0.086938 / 0.004250 (0.082688) \| 0.052307 / 0.037052 (0.015255) \| 0.415206 / 0.258489 (0.156717) \| 0.481510 / 0.293841 (0.187669) \| 0.041469 / 0.128546 (-0.087077) \| 0.013481 / 0.075646 (-0.062165) \| 0.101528 / 0.419271 (-0.317744) \| 0.056507 / 0.043533 (0.012974) \| 0.418166 / 0.255139 (0.163027) \| 0.443834 / 0.283200 (0.160634) \| 0.116434 / 0.141683 (-0.025249) \| 1.651223 / 1.452155 (0.199068) \| 1.746429 / 1.492716 (0.253713) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242381 / 0.018006 (0.224375) \| 0.478826 / 0.000490 (0.478337) \| 0.000463 / 0.000200 (0.000264) \| 0.000067 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031743 / 0.037411 (-0.005668) \| 0.126141 / 0.014526 (0.111616) \| 0.134539 / 0.176557 (-0.042018) \| 0.216546 / 0.737135 (-0.520590) \| 0.143513 / 0.296338 (-0.152825) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.486915 / 0.215209 (0.271706) \| 4.833812 / 2.077655 (2.756158) \| 2.317785 / 1.504120 (0.813666) \| 2.114181 / 1.541195 (0.572986) \| 2.153896 / 1.468490 (0.685406) \| 0.797490 / 4.584777 (-3.787287) \| 4.369950 / 3.745712 (0.624238) \| 2.305492 / 5.269862 (-2.964370) \| 1.488860 / 4.565676 (-3.076816) \| 0.098071 / 0.424275 (-0.326204) \| 0.014129 / 0.007607 (0.006522) \| 0.611311 / 0.226044 (0.385266) \| 6.087482 / 2.268929 (3.818554) \| 2.837676 / 55.444624 (-52.606948) \| 2.451819 / 6.876477 (-4.424657) \| 2.456763 / 2.142072 (0.314690) \| 0.957637 / 4.805227 (-3.847590) \| 0.190974 / 6.500664 (-6.309690) \| 0.074497 / 0.075469 (-0.000972) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.466214 / 1.841788 (-0.375574) \| 17.063925 / 8.074308 (8.989617) \| 14.630326 / 10.191392 (4.438934) \| 0.170570 / 0.680424 (-0.509854) \| 0.023794 / 0.534201 (-0.510407) \| 0.509175 / 0.579283 (-0.070108) \| 0.506485 / 0.434364 (0.072121) \| 0.616965 / 0.540337 (0.076628) \| 0.718176 / 1.386936 (-0.668760) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4f14de325e26910d026f377756dd8a231150398 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/1556	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/1556/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/1556/comments	https://api.github.com/repos/huggingface/datasets/issues/1556/events	https://github.com/huggingface/datasets/pull/1556	765,689,730	MDExOlB1bGxSZXF1ZXN0NTM5MDM2OTYz	1,556	add bswac	[]	closed	false	null	1	2020-12-13T22:55:35Z	2020-12-18T15:14:28Z	2020-12-18T15:14:27Z	null		{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/1556/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/1556/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/1556.diff", "html_url": "https://github.com/huggingface/datasets/pull/1556", "merged_at": "2020-12-18T15:14:27Z", "patch_url": "https://github.com/huggingface/datasets/pull/1556.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/1556" }	true	[ "merging since the CI is fixed on master" ]
https://api.github.com/repos/huggingface/datasets/issues/696	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/696/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/696/comments	https://api.github.com/repos/huggingface/datasets/issues/696/events	https://github.com/huggingface/datasets/pull/696	712,942,977	MDExOlB1bGxSZXF1ZXN0NDk2MzQzMjEy	696	Elasticsearch index docs	[]	closed	false	null	0	2020-10-01T15:18:58Z	2020-10-02T07:48:19Z	2020-10-02T07:48:18Z	null	I added the docs for ES indexes. I also added a `load_elasticsearch_index` method to load an index that has already been built. I checked the tests for the ES index and we have tests that mock ElasticSearch. I think this is good for now but at some point it would be cool to have an end-to-end test with a real ES running.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/696/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/696/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/696.diff", "html_url": "https://github.com/huggingface/datasets/pull/696", "merged_at": "2020-10-02T07:48:18Z", "patch_url": "https://github.com/huggingface/datasets/pull/696.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/696" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/3003	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3003/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3003/comments	https://api.github.com/repos/huggingface/datasets/issues/3003/events	https://github.com/huggingface/datasets/pull/3003	1,014,137,933	PR_kwDODunzps4smExP	3,003	common_language: Fix license in README.md	[]	closed	false	null	0	2021-10-02T18:47:37Z	2021-10-04T09:27:01Z	2021-10-04T09:27:01Z	null	...it's correct elsewhere	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3003/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3003/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/3003.diff", "html_url": "https://github.com/huggingface/datasets/pull/3003", "merged_at": "2021-10-04T09:27:01Z", "patch_url": "https://github.com/huggingface/datasets/pull/3003.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3003" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/289	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/289/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/289/comments	https://api.github.com/repos/huggingface/datasets/issues/289/events	https://github.com/huggingface/datasets/pull/289	641,934,194	MDExOlB1bGxSZXF1ZXN0NDM3MDc0MTM3	289	update xsum	[]	closed	false	null	3	2020-06-19T12:28:32Z	2020-06-22T13:27:26Z	2020-06-22T07:20:07Z	null	This PR makes the following update to the xsum dataset: - Manual download is not required anymore - dataset can be loaded as follow: `nlp.load_dataset('xsum')` Important Instead of using on outdated url to download the data: "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json" a more up-to-date url stored here: https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz is used , so that the user does not need to manually download the data anymore. There might be slight breaking changes here for xsum.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/289/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/289/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/289.diff", "html_url": "https://github.com/huggingface/datasets/pull/289", "merged_at": "2020-06-22T07:20:07Z", "patch_url": "https://github.com/huggingface/datasets/pull/289.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/289" }	true	[ "Looks cool!\r\n@mariamabarham can you add a detailed description here what exactly is changed and how the user can load xsum now?", "And a rebase should solve the conflicts", "This is a super useful PR :-) @sshleifer - maybe you can take a look at the updated version of xsum if you can use it for your use case. Now, one should be able to just load it with:\r\n\r\n```python \r\nnlp.load_datasets(\"xsum\", ....) # no manual dir required anymore\r\n```\r\n" ]
https://api.github.com/repos/huggingface/datasets/issues/2742	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2742/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2742/comments	https://api.github.com/repos/huggingface/datasets/issues/2742/events	https://github.com/huggingface/datasets/issues/2742	958,114,064	MDU6SXNzdWU5NTgxMTQwNjQ=	2,742	Improve detection of streamable file types	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" }, { "color": "E5583E", "default": false, "description": "Related to the dataset viewer on huggingface.co", "id": 3470211881, "name": "dataset-viewer", "node_id": "LA_kwDODunzps7O1zsp", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset-viewer" } ]	closed	false	null	1	2021-08-02T12:55:09Z	2021-11-12T17:18:10Z	2021-11-12T17:18:10Z	null	Is your feature request related to a problem? Please describe. ```python from datasets import load_dataset_builder from datasets.utils.streaming_download_manager import StreamingDownloadManager builder = load_dataset_builder("journalists_questions", name="plain_text") builder._split_generators(StreamingDownloadManager(base_path=builder.base_path)) ``` raises ``` NotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet ``` But the file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is a text file and it can be streamed: ```bash curl --header "Range: bytes=0-100" -L https://drive.google.com/uc\?export\=download\&id\=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U 506938088174940160 yes 1 302221719412830209 yes 1 289761704907268096 yes 1 513820885032378369 yes % ``` Yet, it's wrongly categorized as a file type that cannot be streamed because the test is currently based on 1. the presence of a file extension at the end of the URL (here: no extension), and 2. the inclusion of this extension in a list of supported formats. Describe the solution you'd like In the case of an URL (instead of a local path), ask for the MIME type, and decide on that value? Note that it would not work in that case, because the value of `content_type` is `text/html; charset=UTF-8`. Describe alternatives you've considered Add a variable in the dataset script to set the data format by hand.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/2742/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2742/timeline	null	completed	null	null	false	[ "maybe we should rather attempt to download a `Range` from the server and see if it works?" ]
https://api.github.com/repos/huggingface/datasets/issues/1454	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/1454/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/1454/comments	https://api.github.com/repos/huggingface/datasets/issues/1454/events	https://github.com/huggingface/datasets/pull/1454	761,199,862	MDExOlB1bGxSZXF1ZXN0NTM1OTAxNjk4	1,454	Add kinnews_kirnews	[]	closed	false	null	1	2020-12-10T12:29:08Z	2020-12-17T18:34:16Z	2020-12-17T18:34:16Z	null	Add kinnews and kirnews	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/1454/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/1454/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/1454.diff", "html_url": "https://github.com/huggingface/datasets/pull/1454", "merged_at": "2020-12-17T18:34:16Z", "patch_url": "https://github.com/huggingface/datasets/pull/1454.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/1454" }	true	[ "merging since the CI is fixed on master" ]
https://api.github.com/repos/huggingface/datasets/issues/1329	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/1329/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/1329/comments	https://api.github.com/repos/huggingface/datasets/issues/1329/events	https://github.com/huggingface/datasets/pull/1329	759,654,174	MDExOlB1bGxSZXF1ZXN0NTM0NjIxNzg0	1,329	Add yoruba ner corpus	[]	closed	false	null	0	2020-12-08T17:54:00Z	2020-12-08T23:11:12Z	2020-12-08T23:11:12Z	null		{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/1329/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/1329/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/1329.diff", "html_url": "https://github.com/huggingface/datasets/pull/1329", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/1329.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/1329" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/4326	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4326/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4326/comments	https://api.github.com/repos/huggingface/datasets/issues/4326/events	https://github.com/huggingface/datasets/pull/4326	1,233,818,489	PR_kwDODunzps43tjWy	4,326	Fix type hint and documentation for `new_fingerprint`	[]	closed	false	null	1	2022-05-12T11:05:08Z	2022-06-01T13:04:45Z	2022-06-01T12:56:18Z	null	Currently, there are no type hints nor `Optional` for the argument `new_fingerprint` in several methods of `datasets.arrow_dataset.Dataset`. There was some documentation missing as well. Note that pylance is happy with the type hints, but pyright does not detect that `new_fingerprint` is set within the decorator. The modifications in this PR are fine since here https://github.com/huggingface/datasets/blob/aa743886221d76afb409d263e1b136e7a71fe2b4/src/datasets/fingerprint.py#L446-L454 for the non-inplace case we make sure to auto-generate a new fingerprint (as indicated in the doc).	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4326/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4326/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/4326.diff", "html_url": "https://github.com/huggingface/datasets/pull/4326", "merged_at": "2022-06-01T12:56:18Z", "patch_url": "https://github.com/huggingface/datasets/pull/4326.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4326" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._" ]
https://api.github.com/repos/huggingface/datasets/issues/393	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/393/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/393/comments	https://api.github.com/repos/huggingface/datasets/issues/393/events	https://github.com/huggingface/datasets/pull/393	657,330,911	MDExOlB1bGxSZXF1ZXN0NDQ5NDY1MTAz	393	Fix extracted files directory for the DownloadManager	[]	closed	false	null	0	2020-07-15T12:59:55Z	2020-07-17T17:02:16Z	2020-07-17T17:02:14Z	null	The cache dir was often cluttered by extracted files because of the download manager. For downloaded files, we are using the `downloads` directory to make things easier to navigate, but extracted files were still placed at the root of the cache directory. To fix that I changed the directory for extracted files to cache_dir/downloads/extracted.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/393/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/393/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/393.diff", "html_url": "https://github.com/huggingface/datasets/pull/393", "merged_at": "2020-07-17T17:02:14Z", "patch_url": "https://github.com/huggingface/datasets/pull/393.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/393" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/3575	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3575/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3575/comments	https://api.github.com/repos/huggingface/datasets/issues/3575/events	https://github.com/huggingface/datasets/pull/3575	1,101,947,955	PR_kwDODunzps4w8Usm	3,575	Add Arrow type casting to struct for Image and Audio + Support nested casting	[]	closed	false	null	9	2022-01-13T15:36:59Z	2022-11-29T11:14:16Z	2022-01-21T13:22:27Z	null	## Intro 1. Currently, it's not possible to have nested features containing Audio or Image. 2. Moreover one can keep an Arrow array as a StringArray to store paths to images, but such arrays can't be directly concatenated to another image array if it's stored an another Arrow type (typically, a StructType). 3. Allowing several Arrow types for a single HF feature type also leads to bugs like this one #3497 4. Issues like #3247 are quite frequent and happen when Arrow fails to reorder StructArrays. 5. Casting Audio feature type is blocking preparation for the ASR task template: https://github.com/huggingface/datasets/pull/3364 All those issues are linked together by the fact that: - we are limited by the Arrow type casting which is lacking features for nested types. - and especially for Audio and Image: they are not robust enough for concatenation and feature inference. ## Proposed solution To fix 1 and 4 I implemented nested array type casting (which is missing in PyArrow). To fix 2, 3 and 5 while having a simple implementation for nested array type casting, I changed the storage type of Audio and Image to always be a StructType. Also casting from StringType is directly implemented via a new function `cast_storage` that is defined individually for Audio and Image. I also added nested decoding. ## Implementation details ### I. Better Arrow data type casting for nested data structures I implemented new functions `array_cast` and `table_cast` that do the exact same as `pyarrow.Array.cast` or `pyarrow.Table.cast` but support nested struct casting and array re-ordering. These functions can be used on PyArrow objects, and are already integrated in our own `datasets.table.Table.cast` functions. So one can do `my_dataset.data.cast(pyarrow_schema_with_custom_hf_types)` directly. ### II. New image and audio extension types with custom casting I used PyArrow extension types to be able to define what casting is allowed or not. For example both StringType->ImageExtensionType and StructType->ImageExtensionType are allowed, via the `cast_storage` method. I factorized all the PyArrow + Pandas extension stuff in the `base_extension.py` file. This aims at separating the front-facing API code of `datasets` from the Arrow back-end which requires advanced knowledge. ### III. Nested feature decoding I added a new function `decode_nested_example` to decode image and audio data in nested data structures. For optimization's sake, this function is only called if a column has at least one feature that requires decoding. ## Alternative considered The casting to struct type could have been done directly with python objects using some Audio and Image methods, but bringing arrow data to python objects is expensive. The Audio and Image types could also have been able to convert the arrow data directly, but this is not convenient to use when casting a full Arrow Table with nested fields. Therefore I decided to keep the Arrow data casting logic in Arrow extension types. ## Future work This work can be used to allow the ArrayND feature types to be nested too (see issue #887) ## TODO - [x] fix current tests - [x] add new tests - [x] docstrings/comments	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 2, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/3575/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3575/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/3575.diff", "html_url": "https://github.com/huggingface/datasets/pull/3575", "merged_at": "2022-01-21T13:22:27Z", "patch_url": "https://github.com/huggingface/datasets/pull/3575.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3575" }	true	[ "Regarding the tests I'm just missing the FixedSizeListType type casting for ListArray objects, will to it tomorrow as well as adding new tests + docstrings\r\n\r\nand also adding soundfile in the CI", "While writing some tests I noticed that the ExtensionArray can't be directly concatenated - maybe we can get rid of the extension types/arrays and only keep their storages in native arrow types.\r\n\r\nIn this case the `cast_storage` functions should be the responsibility of the Image and Audio classes directly. And therefore we would need to never cast to a pyarrow type again but to a HF feature - since they'd end up being the one able to tell what's castable or not. This is fine in my opinion but let me know what you think. I can take care of this on monday I think", "Alright I got rid of all the extension type stuff, I'm writing the new tests now :)", "Tests are done, I'll finish the comments and docstrings tomorrow and set the PR on ready for review once it's done !", "> While writing some tests I noticed that the ExtensionArray can't be directly concatenated - maybe we can get rid of the extension types/arrays and only keep their storages in native arrow types.\r\n>\r\n>In this case the cast_storage functions should be the responsibility of the Image and Audio classes directly. And therefore we would need two never cast to a pyarrow type again but to a HF feature - since they'd end up being the one able to tell what's castable or not. This is fine in my opinion but let me know what you think. I can take care of this on monday I think\r\n\r\nDoes this change affect performance?", "> Does this change affect performance?\r\n\r\nIn general it shouldn't have a significant impact on performance since the structure of the features is rarely complex (in general we have <20 features and <4 levels of nesting)\r\n\r\nRegarding Audio and Image specifically, casting from a StringArray is a little bit more costly since it creates the \"bytes\" BinaryArray with `None` values with the same length as the \"path\" array. From the tests I did locally this is very fast though and shouldn't affect the user experience at the current scale of the audio/image datasets we have. It also requires a little bit of RAM though\r\n", "Alright this is ready for review now ! Let me know if you have comments and/or improvements :)", "I am facing the issue ArrowNotImplementedError but no solution is working. Please help me", "Can you open an new issue and share the error message as well as the script you used ? We'd be happy to help :)" ]
https://api.github.com/repos/huggingface/datasets/issues/1435	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/1435/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/1435/comments	https://api.github.com/repos/huggingface/datasets/issues/1435/events	https://github.com/huggingface/datasets/pull/1435	760,867,325	MDExOlB1bGxSZXF1ZXN0NTM1NjIwODE4	1,435	Add FreebaseQA dataset	[]	closed	false	null	12	2020-12-10T04:03:27Z	2021-02-05T09:47:30Z	2021-02-05T09:47:30Z	null	This PR adds the FreebaseQA dataset: A Trivia-type QA Data Set over the Freebase Knowledge Graph Repo: https://github.com/kelvin-jiang/FreebaseQA Paper: https://www.aclweb.org/anthology/N19-1028.pdf ## TODO: create dummy data Error encountered when running `python datasets-cli dummy_data datasets/freebase_qa --auto_generate` ``` f"Couldn't parse columns {list(json_data.keys())}. " ValueError: Couldn't parse columns ['Dataset', 'Version', 'Questions']. Maybe specify which json field must be used to read the data with --json_field <my_field>. ```	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/1435/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/1435/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/1435.diff", "html_url": "https://github.com/huggingface/datasets/pull/1435", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/1435.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/1435" }	true	[ "@yjernite @lhoestq Any suggestions on how to get the dummy data generator to recognize the columns? The structure of the json is:\r\n```\r\n{\r\n \"Dataset\": \"FreebaseQA-eval\", \r\n \"Version\": \"1.0\", \r\n \"Questions\": [\r\n {\r\n \"Question-ID\": \"FreebaseQA-eval-0\", \r\n \"RawQuestion\": \"Who is the female presenter of the Channel 4 quiz show '1001 things you should know'?\", \r\n \"ProcessedQuestion\": \"who is the female presenter of the channel 4 quiz show '1001 things you should know'\", \r\n \"Parses\": [\r\n {\r\n \"Parse-Id\": \"FreebaseQA-eval-0.P0\", \r\n \"PotentialTopicEntityMention\": \"1001 things you should know\", \r\n \"TopicEntityName\": \"1001 things you should know\", \r\n \"TopicEntityMid\": \"m.0nd3t34\", \r\n \"InferentialChain\": \"tv.tv_program.regular_personal_appearances..tv.tv_regular_personal_appearance.person\", \r\n \"Answers\": [\r\n {\r\n \"AnswersMid\": \"m.0216y_\", \r\n \"AnswersName\": [\r\n \"sandi toksvig\"\r\n ]\r\n }\r\n ]\r\n }\r\n ]\r\n }, \r\n ...\r\n ]\r\n}\r\n```\r\n\r\nThanks!", "Unfortunately this json structure is not recognized by the auto-generation yet, so you'd have to create the dummy data manually. \r\nYou can get some instructions on how to do that with: `python datasets-cli dummy_data datasets/freebase_qa`\r\nWe can definitely help you with that if there are too many files! ", "@yjernite Thanks for the instructions. I manually added dummy data and created the zip file but one of the splits seem to return an empty list.\r\n\r\n```\r\ntests/test_dataset_common.py F [100%]\r\n\r\n========================= FAILURES ==========================\r\n_ LocalDatasetTest.test_load_dataset_all_configs_freebase_qa _\r\n\r\nself = <tests.test_dataset_common.LocalDatasetTest testMethod=test_load_dataset_all_configs_freebase_qa>\r\ndataset_name = 'freebase_qa'\r\n\r\n @slow\r\n def test_load_dataset_all_configs(self, dataset_name):\r\n configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)\r\n> self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True)\r\n\r\ntests/test_dataset_common.py:237:\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\r\ntests/test_dataset_common.py:198: in check_load_dataset\r\n self.parent.assertTrue(len(dataset[split]) > 0)\r\nE AssertionError: False is not true\r\n```\r\n\r\nNote that the dataset has `train`, `eval`, and `dev` (no test split). I am not sure if I am mapping them correctly when I called the Split Generator.\r\n", "The dummy json files must follow the exact same structure as the original json files.\r\n\r\nHowever it looks like the dummy json files you have in your dummy_data.zip file are not structured the same way.\r\nFor example the original json is a dict with a field \"Questions\" that is a list of items.\r\nHowever your dummy json is simply a list of items.\r\n\r\nCan you update your dummy json files to follow the same structure ?", "And I'm pretty sure that this structure is supported by the dummy data auto-generation tool\r\n```\r\npython datasets-cli dummy_data ./datasets/freebase_qa --json_field \"Questions\"\r\n```", "Hi @anaerobeth did you manage to get the dummy data right ?\r\n\r\nFeel free to ping me if you have questions or when you're ready for a review", "Thanks for your help! I am able to create the dummy data with the dict structure as suggested. I'll add the tags and update this PR shortly.", "Also don't forget to run `make style` to fix the code formatting check in the CI :)", "Hi @anaerobeth ! Have you had a chance to consider updating the dataset script to yield one example per question ?\r\n\r\nFeel free to ping me if you have questions or if I can help :) ", "Hi @lhoestq,\r\n\r\nI am willing to take this forward if you and @anaerobeth don't mind.\r\n", "Hi @gchhablani thanks for proposing your help :) \r\nSure if you want to take this forward feel free to do so.\r\nAlso pinging @anaerobeth to make sure that you both don't work on the same thing at the same time", "Hi ! Closing this one since the dataset was added in #1814 \r\n\r\nThanks you two @anaerobeth and @gchhablani for adding this dataset !" ]
https://api.github.com/repos/huggingface/datasets/issues/5013	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5013/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5013/comments	https://api.github.com/repos/huggingface/datasets/issues/5013/events	https://github.com/huggingface/datasets/issues/5013	1,383,415,971	I_kwDODunzps5SdUCj	5,013	would huggingface like publish cpp binding for datasets package ?	[ { "color": "ffffff", "default": true, "description": "This will not be worked on", "id": 1935892913, "name": "wontfix", "node_id": "MDU6TGFiZWwxOTM1ODkyOTEz", "url": "https://api.github.com/repos/huggingface/datasets/labels/wontfix" } ]	closed	false	null	5	2022-09-23T07:42:49Z	2023-02-24T16:20:57Z	2023-02-24T16:20:57Z	null	HI: I use cpp env libtorch, I like use hugggingface ,but huggingface not cpp binding, would you like publish cpp binding for it. thanks	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5013/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5013/timeline	null	completed	null	null	false	[ "Hi ! Can you share more information about your use case ? How could it help you to have cpp bindings versus using the python libraries ?", "> Hi ! Can you share more information about your use case ? How could it help you to have cpp bindings versus using the python libraries ?\r\n\r\nfor example ,the huggingface load_model() and load_dataset() can execute in cpp env", "If it's a viable option for you, you can check [tch-rs](https://github.com/LaurentMazare/tch-rs) to load models in Rust. Regarding datasets, you can first download them in python and then use Arrow C++ or Rust to load them", "If you are more adventurous, another option is to embed python calls inside c++ e.g. with `pybind11`.", "> pybind11\r\n\r\nI think it is not the best solution" ]
https://api.github.com/repos/huggingface/datasets/issues/4731	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4731/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4731/comments	https://api.github.com/repos/huggingface/datasets/issues/4731/events	https://github.com/huggingface/datasets/pull/4731	1,313,773,348	PR_kwDODunzps474dlZ	4,731	docs: ✏️ fix TranslationVariableLanguages example	[]	closed	false	null	1	2022-07-21T20:35:41Z	2022-07-22T07:01:00Z	2022-07-22T06:48:42Z	null	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4731/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4731/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/4731.diff", "html_url": "https://github.com/huggingface/datasets/pull/4731", "merged_at": "2022-07-22T06:48:42Z", "patch_url": "https://github.com/huggingface/datasets/pull/4731.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4731" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._" ]
https://api.github.com/repos/huggingface/datasets/issues/5742	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5742/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5742/comments	https://api.github.com/repos/huggingface/datasets/issues/5742/events	https://github.com/huggingface/datasets/pull/5742	1,666,209,738	PR_kwDODunzps5OOH-W	5,742	Warning specifying future change in to_tf_dataset behaviour	[]	closed	false	null	2	2023-04-13T11:10:00Z	2023-04-21T13:18:14Z	2023-04-21T13:11:09Z	null	Warning specifying future changes happening to `to_tf_dataset` behaviour when #5602 is merged in	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5742/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5742/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5742.diff", "html_url": "https://github.com/huggingface/datasets/pull/5742", "merged_at": "2023-04-21T13:11:09Z", "patch_url": "https://github.com/huggingface/datasets/pull/5742.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5742" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006693 / 0.011353 (-0.004660) \| 0.004586 / 0.011008 (-0.006422) \| 0.097238 / 0.038508 (0.058730) \| 0.027912 / 0.023109 (0.004802) \| 0.347339 / 0.275898 (0.071441) \| 0.393847 / 0.323480 (0.070368) \| 0.005105 / 0.007986 (-0.002880) \| 0.004750 / 0.004328 (0.000422) \| 0.074671 / 0.004250 (0.070421) \| 0.037912 / 0.037052 (0.000860) \| 0.368973 / 0.258489 (0.110483) \| 0.403983 / 0.293841 (0.110142) \| 0.030817 / 0.128546 (-0.097730) \| 0.011813 / 0.075646 (-0.063833) \| 0.324470 / 0.419271 (-0.094802) \| 0.044232 / 0.043533 (0.000699) \| 0.347623 / 0.255139 (0.092484) \| 0.382458 / 0.283200 (0.099259) \| 0.086603 / 0.141683 (-0.055080) \| 1.485778 / 1.452155 (0.033623) \| 1.549776 / 1.492716 (0.057059) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200154 / 0.018006 (0.182147) \| 0.440645 / 0.000490 (0.440155) \| 0.003664 / 0.000200 (0.003464) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023635 / 0.037411 (-0.013776) \| 0.094969 / 0.014526 (0.080443) \| 0.103630 / 0.176557 (-0.072927) \| 0.168655 / 0.737135 (-0.568480) \| 0.105850 / 0.296338 (-0.190488) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425224 / 0.215209 (0.210015) \| 4.236618 / 2.077655 (2.158963) \| 1.917091 / 1.504120 (0.412971) \| 1.746984 / 1.541195 (0.205789) \| 1.817766 / 1.468490 (0.349276) \| 0.700989 / 4.584777 (-3.883788) \| 3.412577 / 3.745712 (-0.333135) \| 3.049311 / 5.269862 (-2.220551) \| 1.607692 / 4.565676 (-2.957984) \| 0.083410 / 0.424275 (-0.340865) \| 0.012601 / 0.007607 (0.004994) \| 0.528244 / 0.226044 (0.302200) \| 5.284134 / 2.268929 (3.015206) \| 2.391885 / 55.444624 (-53.052740) \| 2.020018 / 6.876477 (-4.856459) \| 2.105908 / 2.142072 (-0.036164) \| 0.801262 / 4.805227 (-4.003965) \| 0.151467 / 6.500664 (-6.349197) \| 0.066529 / 0.075469 (-0.008940) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.203894 / 1.841788 (-0.637894) \| 13.827561 / 8.074308 (5.753253) \| 14.136730 / 10.191392 (3.945338) \| 0.143829 / 0.680424 (-0.536595) \| 0.016410 / 0.534201 (-0.517791) \| 0.378194 / 0.579283 (-0.201089) \| 0.391235 / 0.434364 (-0.043129) \| 0.439261 / 0.540337 (-0.101076) \| 0.527181 / 1.386936 (-0.859755) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006639 / 0.011353 (-0.004714) \| 0.004469 / 0.011008 (-0.006540) \| 0.076495 / 0.038508 (0.037987) \| 0.027880 / 0.023109 (0.004771) \| 0.342807 / 0.275898 (0.066909) \| 0.374258 / 0.323480 (0.050778) \| 0.005543 / 0.007986 (-0.002443) \| 0.003362 / 0.004328 (-0.000966) \| 0.075064 / 0.004250 (0.070813) \| 0.039209 / 0.037052 (0.002156) \| 0.342490 / 0.258489 (0.084001) \| 0.382135 / 0.293841 (0.088294) \| 0.030356 / 0.128546 (-0.098191) \| 0.011762 / 0.075646 (-0.063884) \| 0.086031 / 0.419271 (-0.333241) \| 0.041991 / 0.043533 (-0.001542) \| 0.340323 / 0.255139 (0.085184) \| 0.364160 / 0.283200 (0.080961) \| 0.088483 / 0.141683 (-0.053200) \| 1.502836 / 1.452155 (0.050681) \| 1.570438 / 1.492716 (0.077722) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218486 / 0.018006 (0.200480) \| 0.405251 / 0.000490 (0.404761) \| 0.000398 / 0.000200 (0.000198) \| 0.000062 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025738 / 0.037411 (-0.011673) \| 0.100390 / 0.014526 (0.085864) \| 0.109913 / 0.176557 (-0.066644) \| 0.161310 / 0.737135 (-0.575826) \| 0.113269 / 0.296338 (-0.183069) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438083 / 0.215209 (0.222874) \| 4.377742 / 2.077655 (2.300087) \| 2.069949 / 1.504120 (0.565829) \| 1.857807 / 1.541195 (0.316613) \| 1.881315 / 1.468490 (0.412825) \| 0.695373 / 4.584777 (-3.889404) \| 3.440287 / 3.745712 (-0.305425) \| 1.842888 / 5.269862 (-3.426973) \| 1.146655 / 4.565676 (-3.419022) \| 0.083386 / 0.424275 (-0.340889) \| 0.012290 / 0.007607 (0.004683) \| 0.545672 / 0.226044 (0.319628) \| 5.469568 / 2.268929 (3.200639) \| 2.511886 / 55.444624 (-52.932739) \| 2.184210 / 6.876477 (-4.692267) \| 2.329822 / 2.142072 (0.187749) \| 0.804114 / 4.805227 (-4.001114) \| 0.151651 / 6.500664 (-6.349013) \| 0.067269 / 0.075469 (-0.008200) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272564 / 1.841788 (-0.569223) \| 14.180708 / 8.074308 (6.106400) \| 14.181657 / 10.191392 (3.990265) \| 0.131443 / 0.680424 (-0.548981) \| 0.016513 / 0.534201 (-0.517688) \| 0.383786 / 0.579283 (-0.195497) \| 0.397678 / 0.434364 (-0.036686) \| 0.447003 / 0.540337 (-0.093334) \| 0.539453 / 1.386936 (-0.847483) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#649d5a3315f9e7666713b6affe318ee00c7163a0 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/4740	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4740/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4740/comments	https://api.github.com/repos/huggingface/datasets/issues/4740/events	https://github.com/huggingface/datasets/pull/4740	1,316,478,007	PR_kwDODunzps48BX5l	4,740	Fix multiprocessing in map_nested	[]	closed	false	null	3	2022-07-25T08:44:19Z	2022-07-28T10:53:23Z	2022-07-28T10:40:31Z	null	As previously discussed: Before, multiprocessing was not used in `map_nested` if `num_proc` was greater than or equal to `len(iterable)`. - Multiprocessing was not used e.g. when passing `num_proc=20` but having 19 files to download - As by default, `DownloadManager` sets `num_proc=16`, before multiprocessing was only used when `len(iterable)>16` by default Now, if `num_proc` is greater than or equal to ``len(iterable)``, `num_proc` is set to ``len(iterable)`` and multiprocessing is used. - We pass the variable `parallel_min_length=16`, so that multiprocessing is only used if at least 16 files to be downloaded - ~As by default, `DownloadManager` sets `num_proc=16`, now multiprocessing is used when `len(iterable)>1` by default~ See discussion below. ~After having had to fix some tests (87602ac), I am wondering:~ - ~do we want to have multiprocessing by default?~ - ~please note that `DownloadManager.download` sets `num_proc=16` by default~ - ~or would it be better to ask the user to set it explicitly if they want multiprocessing (and default to `num_proc=1`)?~ Fix #4636. CC: @nateraw	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4740/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4740/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/4740.diff", "html_url": "https://github.com/huggingface/datasets/pull/4740", "merged_at": "2022-07-28T10:40:31Z", "patch_url": "https://github.com/huggingface/datasets/pull/4740.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4740" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq as a workaround to preserve previous behavior, the parameter `multiprocessing_min_length=16` is passed from `download` to `map_nested`, so that multiprocessing is only used if at least 16 files to be downloaded.\r\n\r\nNote that there is a small breaking change (I think previously it was unintended behavior, so that I have fixed it):\r\n- Before (with default `num_proc=16`) if there were 16 files to be downloaded, multiprocessing was not used\r\n- Now (with default `num_proc=16`) if there are 16 files to be downloaded, multiprocessing is used", "Thanks for the workaround !" ]
https://api.github.com/repos/huggingface/datasets/issues/4878	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4878/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4878/comments	https://api.github.com/repos/huggingface/datasets/issues/4878/events	https://github.com/huggingface/datasets/issues/4878	1,348,270,141	I_kwDODunzps5QXPg9	4,878	[not really a bug] `identical_ok` is deprecated in huggingface-hub's `upload_file`	[ { "color": "008672", "default": true, "description": "Extra attention is needed", "id": 1935892884, "name": "help wanted", "node_id": "MDU6TGFiZWwxOTM1ODkyODg0", "url": "https://api.github.com/repos/huggingface/datasets/labels/help%20wanted" }, { "color": "d876e3", "default": true, "description": "Further information is requested", "id": 1935892912, "name": "question", "node_id": "MDU6TGFiZWwxOTM1ODkyOTEy", "url": "https://api.github.com/repos/huggingface/datasets/labels/question" } ]	closed	false	null	1	2022-08-23T17:09:55Z	2022-09-13T14:00:06Z	2022-09-13T14:00:05Z	null	In the huggingface-hub dependency, the `identical_ok` argument has no effect in `upload_file` (and it will be removed soon) See https://github.com/huggingface/huggingface_hub/blob/43499582b19df1ed081a5b2bd7a364e9cacdc91d/src/huggingface_hub/hf_api.py#L2164-L2169 It's used here: https://github.com/huggingface/datasets/blob/fcfcc951a73efbc677f9def9a8707d0af93d5890/src/datasets/dataset_dict.py#L1373-L1381 https://github.com/huggingface/datasets/blob/fdcb8b144ce3ef241410281e125bd03e87b8caa1/src/datasets/arrow_dataset.py#L4354-L4362 https://github.com/huggingface/datasets/blob/fdcb8b144ce3ef241410281e125bd03e87b8caa1/src/datasets/arrow_dataset.py#L4197-L4213 We should remove it. Maybe the third code sample has an unexpected behavior since it uses the non-default value `identical_ok = False`, but the argument is ignored.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4878/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4878/timeline	null	completed	null	null	false	[ "Resolved via https://github.com/huggingface/datasets/pull/4937." ]
https://api.github.com/repos/huggingface/datasets/issues/3854	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3854/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3854/comments	https://api.github.com/repos/huggingface/datasets/issues/3854/events	https://github.com/huggingface/datasets/issues/3854	1,162,434,199	I_kwDODunzps5FSVaX	3,854	load only England English dataset from common voice english dataset	[ { "color": "d876e3", "default": true, "description": "Further information is requested", "id": 1935892912, "name": "question", "node_id": "MDU6TGFiZWwxOTM1ODkyOTEy", "url": "https://api.github.com/repos/huggingface/datasets/labels/question" } ]	closed	false	null	1	2022-03-08T09:40:52Z	2022-03-09T08:13:33Z	2022-03-09T08:13:33Z	null	training_data = load_dataset("common_voice", "en",split='train[:250]+validation[:250]') testing_data = load_dataset("common_voice", "en", split="test[:200]") I'm trying to load only 8% of the English common voice data with accent == "England English." Can somebody assist me with this? Typical Voice Accent Proportions: - 24% United States English - 8% England English - 5% India and South Asia (India, Pakistan, Sri Lanka) - 3% Australian English - 3% Canadian English - 2% Scottish English - 1% Irish English - 1% Southern African (South Africa, Zimbabwe, Namibia) - 1% New Zealand English Can we replicate this for Age as well? Age proportions of the common voice:- - 24% 19 - 29 - 14% 30 - 39 - 10% 40 - 49 - 6% < 19 - 4% 50 - 59 - 4% 60 - 69 - 1% 70 – 79	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3854/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3854/timeline	null	completed	null	null	false	[ "Hi @amanjaiswal777,\r\n\r\nFirst note that the dataset you are trying to load is deprecated: it was the Common Voice dataset release as of Dec 2020.\r\n\r\nCurrently, Common Voice dataset releases are directly hosted on the Hub, under the Mozilla Foundation organization: https://huggingface.co/mozilla-foundation\r\n\r\nFor example, to get their latest Common Voice relase (8.0):\r\n- Go to the dataset page and request access permission (Mozilla Foundation requires this for people willing to use their datasets): https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0\r\n- Looking at the dataset card, you can check that data instances have, among other fields, the ones you are interested in: \"accent\", \"age\",... \r\n- Then you can load their \"en\" language dataset as usual, besides passing your authentication token (more info on auth token here: https://huggingface.co/docs/hub/security)\r\n ```python\r\n from datasets import load_dataset\r\n ds_en = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"en\", use_auth_token=True)\r\n ```\r\n- Finally, you can filter only the data instances you are interested in (more info on `filter` here: https://huggingface.co/docs/datasets/process#select-and-filter):\r\n ```python\r\n ds_england_en = ds_en.filter(lambda item: item[\"accent\"] == \"England English\")\r\n ```\r\n\r\nFeel free to reopen this issue if you need further assistance." ]
https://api.github.com/repos/huggingface/datasets/issues/198	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/198/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/198/comments	https://api.github.com/repos/huggingface/datasets/issues/198/events	https://github.com/huggingface/datasets/issues/198	625,200,627	MDU6SXNzdWU2MjUyMDA2Mjc=	198	Index outside of table length	[]	closed	false	null	2	2020-05-26T21:09:40Z	2020-05-26T22:43:49Z	2020-05-26T22:43:49Z	null	The offset input box warns of numbers larger than a limit (like 2000) but then the errors start at a smaller value than that limit (like 1955). > ValueError: Index (2000) outside of table length (2000). > Traceback: > File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/ScriptRunner.py", line 322, in _run_script > exec(code, module.__dict__) > File "/home/sasha/nlp_viewer/run.py", line 116, in <module> > v = d[item][k] > File "/home/sasha/.local/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 338, in __getitem__ > output_all_columns=self._output_all_columns, > File "/home/sasha/.local/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 290, in _getitem > raise ValueError(f"Index ({key}) outside of table length ({self._data.num_rows}).")	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/198/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/198/timeline	null	completed	null	null	false	[ "Sounds like something related to the nlp viewer @srush ", "Fixed. " ]
https://api.github.com/repos/huggingface/datasets/issues/3626	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3626/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3626/comments	https://api.github.com/repos/huggingface/datasets/issues/3626/events	https://github.com/huggingface/datasets/issues/3626	1,113,534,436	I_kwDODunzps5CXy_k	3,626	The Pile cannot connect to host	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	null	0	2022-01-25T07:43:33Z	2022-02-14T08:40:58Z	2022-02-14T08:40:58Z	null	## Describe the bug The Pile had issues with their previous host server and have mirrored its content to another server. The new URL server should be updated.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3626/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3626/timeline	null	completed	null	null	false	[]
https://api.github.com/repos/huggingface/datasets/issues/2211	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2211/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2211/comments	https://api.github.com/repos/huggingface/datasets/issues/2211/events	https://github.com/huggingface/datasets/issues/2211	855,988,410	MDU6SXNzdWU4NTU5ODg0MTA=	2,211	Getting checksum error when trying to load lc_quad dataset	[]	closed	false	null	2	2021-04-12T13:38:58Z	2021-04-14T13:42:25Z	2021-04-14T13:42:25Z	null	I'm having issues loading the [lc_quad](https://huggingface.co/datasets/fquad) dataset by running: ```Python lc_quad = load_dataset("lc_quad") ``` which is giving me the following error: ``` Using custom data configuration default Downloading and preparing dataset lc_quad/default (download: 3.69 MiB, generated: 19.77 MiB, post-processed: Unknown size, total: 23.46 MiB) to /root/.cache/huggingface/datasets/lc_quad/default/2.0.0/5a98fe174603f5dec6df07edf1c2b4d2317210d2ad61f5a393839bca4d64e5a7... --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-42-404ace83f73c> in <module>() ----> 1 lc_quad = load_dataset("lc_quad") 3 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 37 if len(bad_urls) > 0: 38 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 39 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 40 logger.info("All the checksums matched successfully" + for_verification_name) 41 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/AskNowQA/LC-QuAD2.0/archive/master.zip'] ``` Does anyone know why this could be and how I fix it?	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/2211/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2211/timeline	null	completed	null	null	false	[ "Hi,\r\n\r\nI've already opened a PR with the fix. If you are in a hurry, just build the project from source and run:\r\n```bash\r\ndatasets-cli test datasets/lc_quad --save_infos --all_configs --ignore_verifications\r\n```\r\n\r\n", "Ah sorry, I tried searching but couldn't find any related PR. \r\n\r\nThank you! " ]
https://api.github.com/repos/huggingface/datasets/issues/6026	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6026/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6026/comments	https://api.github.com/repos/huggingface/datasets/issues/6026/events	https://github.com/huggingface/datasets/pull/6026	1,802,929,222	PR_kwDODunzps5VanI8	6,026	Fix style with ruff 0.0.278	[]	closed	false	null	3	2023-07-13T12:34:24Z	2023-07-13T12:46:26Z	2023-07-13T12:37:01Z	null	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6026/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6026/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/6026.diff", "html_url": "https://github.com/huggingface/datasets/pull/6026", "merged_at": "2023-07-13T12:37:01Z", "patch_url": "https://github.com/huggingface/datasets/pull/6026.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6026" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6026). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006444 / 0.011353 (-0.004909) \| 0.003768 / 0.011008 (-0.007240) \| 0.079625 / 0.038508 (0.041117) \| 0.064490 / 0.023109 (0.041381) \| 0.313858 / 0.275898 (0.037960) \| 0.350810 / 0.323480 (0.027330) \| 0.004804 / 0.007986 (-0.003182) \| 0.002904 / 0.004328 (-0.001425) \| 0.061728 / 0.004250 (0.057477) \| 0.052265 / 0.037052 (0.015213) \| 0.321246 / 0.258489 (0.062757) \| 0.353873 / 0.293841 (0.060032) \| 0.027510 / 0.128546 (-0.101036) \| 0.007942 / 0.075646 (-0.067704) \| 0.260518 / 0.419271 (-0.158754) \| 0.045686 / 0.043533 (0.002153) \| 0.316821 / 0.255139 (0.061682) \| 0.337086 / 0.283200 (0.053886) \| 0.022188 / 0.141683 (-0.119495) \| 1.427345 / 1.452155 (-0.024810) \| 1.476059 / 1.492716 (-0.016657) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.189640 / 0.018006 (0.171634) \| 0.429724 / 0.000490 (0.429235) \| 0.005314 / 0.000200 (0.005114) \| 0.000076 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024412 / 0.037411 (-0.013000) \| 0.073488 / 0.014526 (0.058962) \| 0.083843 / 0.176557 (-0.092714) \| 0.147849 / 0.737135 (-0.589286) \| 0.085465 / 0.296338 (-0.210873) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405314 / 0.215209 (0.190105) \| 4.071471 / 2.077655 (1.993816) \| 1.916252 / 1.504120 (0.412132) \| 1.721616 / 1.541195 (0.180422) \| 1.807187 / 1.468490 (0.338697) \| 0.498045 / 4.584777 (-4.086732) \| 3.057526 / 3.745712 (-0.688187) \| 4.451424 / 5.269862 (-0.818437) \| 2.764020 / 4.565676 (-1.801656) \| 0.057665 / 0.424275 (-0.366610) \| 0.006679 / 0.007607 (-0.000928) \| 0.485733 / 0.226044 (0.259688) \| 4.844367 / 2.268929 (2.575438) \| 2.435359 / 55.444624 (-53.009265) \| 2.111478 / 6.876477 (-4.764999) \| 2.377448 / 2.142072 (0.235375) \| 0.587997 / 4.805227 (-4.217230) \| 0.125545 / 6.500664 (-6.375120) \| 0.061509 / 0.075469 (-0.013960) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.229210 / 1.841788 (-0.612577) \| 18.553994 / 8.074308 (10.479686) \| 14.037877 / 10.191392 (3.846485) \| 0.144230 / 0.680424 (-0.536194) \| 0.016891 / 0.534201 (-0.517310) \| 0.329039 / 0.579283 (-0.250244) \| 0.357269 / 0.434364 (-0.077095) \| 0.384222 / 0.540337 (-0.156115) \| 0.521292 / 1.386936 (-0.865644) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006359 / 0.011353 (-0.004994) \| 0.003721 / 0.011008 (-0.007287) \| 0.062047 / 0.038508 (0.023539) \| 0.065267 / 0.023109 (0.042158) \| 0.360164 / 0.275898 (0.084266) \| 0.402292 / 0.323480 (0.078812) \| 0.005603 / 0.007986 (-0.002382) \| 0.002966 / 0.004328 (-0.001363) \| 0.062580 / 0.004250 (0.058330) \| 0.053634 / 0.037052 (0.016582) \| 0.362210 / 0.258489 (0.103721) \| 0.404285 / 0.293841 (0.110444) \| 0.027567 / 0.128546 (-0.100979) \| 0.008119 / 0.075646 (-0.067528) \| 0.067577 / 0.419271 (-0.351694) \| 0.042867 / 0.043533 (-0.000666) \| 0.361576 / 0.255139 (0.106437) \| 0.389061 / 0.283200 (0.105862) \| 0.021923 / 0.141683 (-0.119760) \| 1.446259 / 1.452155 (-0.005895) \| 1.490724 / 1.492716 (-0.001992) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206433 / 0.018006 (0.188427) \| 0.424178 / 0.000490 (0.423688) \| 0.002340 / 0.000200 (0.002140) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024955 / 0.037411 (-0.012456) \| 0.077446 / 0.014526 (0.062920) \| 0.088540 / 0.176557 (-0.088017) \| 0.141225 / 0.737135 (-0.595910) \| 0.089747 / 0.296338 (-0.206592) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.443738 / 0.215209 (0.228529) \| 4.208887 / 2.077655 (2.131233) \| 2.155127 / 1.504120 (0.651007) \| 2.028178 / 1.541195 (0.486983) \| 2.084903 / 1.468490 (0.616413) \| 0.497530 / 4.584777 (-4.087247) \| 3.069012 / 3.745712 (-0.676700) \| 3.025184 / 5.269862 (-2.244678) \| 1.904687 / 4.565676 (-2.660990) \| 0.057526 / 0.424275 (-0.366749) \| 0.006482 / 0.007607 (-0.001125) \| 0.494692 / 0.226044 (0.268647) \| 4.944437 / 2.268929 (2.675508) \| 2.655989 / 55.444624 (-52.788635) \| 2.331677 / 6.876477 (-4.544800) \| 2.382396 / 2.142072 (0.240324) \| 0.582019 / 4.805227 (-4.223209) \| 0.125866 / 6.500664 (-6.374799) \| 0.062908 / 0.075469 (-0.012561) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.294612 / 1.841788 (-0.547176) \| 19.016152 / 8.074308 (10.941844) \| 14.088828 / 10.191392 (3.897436) \| 0.160842 / 0.680424 (-0.519582) \| 0.017054 / 0.534201 (-0.517146) \| 0.333647 / 0.579283 (-0.245636) \| 0.348094 / 0.434364 (-0.086270) \| 0.394970 / 0.540337 (-0.145367) \| 0.551141 / 1.386936 (-0.835795) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e9cfe886792b30b5000808072a0f91ec8536749 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007442 / 0.011353 (-0.003911) \| 0.004302 / 0.011008 (-0.006707) \| 0.087159 / 0.038508 (0.048651) \| 0.095094 / 0.023109 (0.071985) \| 0.315422 / 0.275898 (0.039524) \| 0.346672 / 0.323480 (0.023192) \| 0.005811 / 0.007986 (-0.002174) \| 0.003597 / 0.004328 (-0.000731) \| 0.066400 / 0.004250 (0.062150) \| 0.065947 / 0.037052 (0.028894) \| 0.323269 / 0.258489 (0.064780) \| 0.353309 / 0.293841 (0.059468) \| 0.032268 / 0.128546 (-0.096278) \| 0.008696 / 0.075646 (-0.066950) \| 0.291486 / 0.419271 (-0.127786) \| 0.054609 / 0.043533 (0.011076) \| 0.321061 / 0.255139 (0.065922) \| 0.336907 / 0.283200 (0.053707) \| 0.027338 / 0.141683 (-0.114345) \| 1.496442 / 1.452155 (0.044287) \| 1.576946 / 1.492716 (0.084229) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229140 / 0.018006 (0.211134) \| 0.487500 / 0.000490 (0.487010) \| 0.002425 / 0.000200 (0.002225) \| 0.000089 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029351 / 0.037411 (-0.008060) \| 0.089610 / 0.014526 (0.075084) \| 0.097880 / 0.176557 (-0.078676) \| 0.155947 / 0.737135 (-0.581189) \| 0.098593 / 0.296338 (-0.197745) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382911 / 0.215209 (0.167702) \| 3.820363 / 2.077655 (1.742708) \| 1.866385 / 1.504120 (0.362265) \| 1.712910 / 1.541195 (0.171716) \| 1.813863 / 1.468490 (0.345373) \| 0.484884 / 4.584777 (-4.099893) \| 3.678911 / 3.745712 (-0.066801) \| 5.249908 / 5.269862 (-0.019953) \| 3.099614 / 4.565676 (-1.466063) \| 0.057449 / 0.424275 (-0.366826) \| 0.007728 / 0.007607 (0.000120) \| 0.462123 / 0.226044 (0.236078) \| 4.603942 / 2.268929 (2.335014) \| 2.380957 / 55.444624 (-53.063668) \| 2.059621 / 6.876477 (-4.816856) \| 2.293764 / 2.142072 (0.151691) \| 0.636471 / 4.805227 (-4.168756) \| 0.150112 / 6.500664 (-6.350552) \| 0.063705 / 0.075469 (-0.011764) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.358099 / 1.841788 (-0.483689) \| 20.193750 / 8.074308 (12.119442) \| 14.297350 / 10.191392 (4.105958) \| 0.164477 / 0.680424 (-0.515947) \| 0.018259 / 0.534201 (-0.515942) \| 0.399010 / 0.579283 (-0.180273) \| 0.417306 / 0.434364 (-0.017058) \| 0.456961 / 0.540337 (-0.083377) \| 0.631068 / 1.386936 (-0.755868) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007324 / 0.011353 (-0.004028) \| 0.004463 / 0.011008 (-0.006545) \| 0.066148 / 0.038508 (0.027640) \| 0.093909 / 0.023109 (0.070799) \| 0.399122 / 0.275898 (0.123224) \| 0.430226 / 0.323480 (0.106746) \| 0.005505 / 0.007986 (-0.002481) \| 0.003579 / 0.004328 (-0.000749) \| 0.066529 / 0.004250 (0.062278) \| 0.063471 / 0.037052 (0.026418) \| 0.406351 / 0.258489 (0.147862) \| 0.439987 / 0.293841 (0.146146) \| 0.032640 / 0.128546 (-0.095906) \| 0.008770 / 0.075646 (-0.066877) \| 0.072592 / 0.419271 (-0.346680) \| 0.050429 / 0.043533 (0.006896) \| 0.390873 / 0.255139 (0.135734) \| 0.412438 / 0.283200 (0.129239) \| 0.027113 / 0.141683 (-0.114570) \| 1.458281 / 1.452155 (0.006126) \| 1.536819 / 1.492716 (0.044103) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228309 / 0.018006 (0.210303) \| 0.454042 / 0.000490 (0.453552) \| 0.000387 / 0.000200 (0.000187) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029573 / 0.037411 (-0.007838) \| 0.086433 / 0.014526 (0.071907) \| 0.097992 / 0.176557 (-0.078565) \| 0.152464 / 0.737135 (-0.584671) \| 0.099901 / 0.296338 (-0.196437) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.413807 / 0.215209 (0.198598) \| 4.126395 / 2.077655 (2.048740) \| 2.113544 / 1.504120 (0.609424) \| 1.967829 / 1.541195 (0.426635) \| 2.037123 / 1.468490 (0.568633) \| 0.489403 / 4.584777 (-4.095374) \| 3.689508 / 3.745712 (-0.056204) \| 3.503909 / 5.269862 (-1.765952) \| 2.113812 / 4.565676 (-2.451864) \| 0.057988 / 0.424275 (-0.366287) \| 0.007336 / 0.007607 (-0.000271) \| 0.490840 / 0.226044 (0.264795) \| 4.885040 / 2.268929 (2.616112) \| 2.627864 / 55.444624 (-52.816760) \| 2.231467 / 6.876477 (-4.645010) \| 2.251307 / 2.142072 (0.109235) \| 0.577370 / 4.805227 (-4.227857) \| 0.131770 / 6.500664 (-6.368895) \| 0.061313 / 0.075469 (-0.014156) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.362052 / 1.841788 (-0.479735) \| 21.332694 / 8.074308 (13.258386) \| 15.562019 / 10.191392 (5.370627) \| 0.170874 / 0.680424 (-0.509550) \| 0.019226 / 0.534201 (-0.514975) \| 0.400311 / 0.579283 (-0.178972) \| 0.423060 / 0.434364 (-0.011304) \| 0.469946 / 0.540337 (-0.070391) \| 0.647745 / 1.386936 (-0.739191) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aec567c2f224f192e6e1f9799e3afc755eb517b2 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/5134	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5134/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5134/comments	https://api.github.com/repos/huggingface/datasets/issues/5134/events	https://github.com/huggingface/datasets/issues/5134	1,413,623,687	I_kwDODunzps5UQi-H	5,134	Raise ImportError instead of OSError if required extraction library is not installed	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" }, { "color": "7057ff", "default": true, "description": "Good for newcomers", "id": 1935892877, "name": "good first issue", "node_id": "MDU6TGFiZWwxOTM1ODkyODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/good%20first%20issue" }, { "color": "DF8D62", "default": false, "description": "", "id": 4614514401, "name": "hacktoberfest", "node_id": "LA_kwDODunzps8AAAABEwvm4Q", "url": "https://api.github.com/repos/huggingface/datasets/labels/hacktoberfest" } ]	closed	false	null	2	2022-10-18T17:53:46Z	2022-10-25T15:56:59Z	2022-10-25T15:56:59Z	null	According to the official Python docs, `OSError` should be thrown in the following situations: > This exception is raised when a system function returns a system-related error, including I/O failures such as “file not found” or “disk full” (not for illegal argument types or other incidental errors). Hence, it makes more sense to raise `ImportError` instead of `OSError` when the required extraction/decompression library is not installed.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5134/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5134/timeline	null	completed	null	null	false	[ "hey ,i would like to work on this issue . Please assign it to me.", "hey @mariosasko , i made a pr for this issue. Could you please review it.\r\nAlso i found multiple `OSError` in `extract.py` file which i thought could be replaced too but wasn't sure about them.\r\nPlease do tell if that also needs to be done." ]
https://api.github.com/repos/huggingface/datasets/issues/3608	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3608/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3608/comments	https://api.github.com/repos/huggingface/datasets/issues/3608/events	https://github.com/huggingface/datasets/issues/3608	1,109,310,981	I_kwDODunzps5CHr4F	3,608	Add support for continuous metrics (RMSE, MAE)	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" }, { "color": "7057ff", "default": true, "description": "Good for newcomers", "id": 1935892877, "name": "good first issue", "node_id": "MDU6TGFiZWwxOTM1ODkyODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/good%20first%20issue" } ]	closed	false	null	3	2022-01-20T13:35:36Z	2022-03-09T17:18:20Z	2022-03-09T17:18:20Z	null	Is your feature request related to a problem? Please describe. I am uploading our dataset and models for the "Constructing interval measures" method we've developed, which uses item response theory to convert multiple discrete labels into a continuous spectrum for hate speech. Once we have this outcome our NLP models conduct regression rather than classification, so binary metrics are not relevant. The only continuous metrics available at https://huggingface.co/metrics are pearson & spearman correlation, which don't ensure that the prediction is on the same scale as the outcome. Describe the solution you'd like I would like to be able to tag our models on the Hub with the following metrics: - RMSE - MAE Describe alternatives you've considered I don't know if there are any alternatives. Additional context Our preprint is available here: https://arxiv.org/abs/2009.10277 . We are making it available for use in Jigsaw's Toxic Severity Rating Kaggle competition: https://www.kaggle.com/c/jigsaw-toxic-severity-rating/overview . I have our first model uploaded to the Hub at https://huggingface.co/ucberkeley-dlab/hate-measure-roberta-large Thanks, Chris	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3608/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3608/timeline	null	completed	null	null	false	[ "Hey @ck37 \r\n\r\nYou can always use a custom metric as explained [in this guide from HF](https://huggingface.co/docs/datasets/master/loading_metrics.html#using-a-custom-metric-script).\r\n\r\nIf this issue needs to be contributed to (for enhancing the metric API) I think [this link](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html) would be helpful for the `MAE` metric.", "You can use a local metric script just by providing its path instead of the usual shortcut name ", "#self-assign I have starting working on this issue to enhance the metric API." ]
https://api.github.com/repos/huggingface/datasets/issues/2970	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2970/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2970/comments	https://api.github.com/repos/huggingface/datasets/issues/2970/events	https://github.com/huggingface/datasets/issues/2970	1,007,340,089	I_kwDODunzps48Cso5	2,970	Magnet’s	[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" } ]	closed	false	null	0	2021-09-26T09:50:29Z	2021-09-26T10:38:59Z	2021-09-26T10:38:59Z	null	## Adding a Dataset - Name: name of the dataset - Description: short description of the dataset (or link to social media or blog post) - Paper: link to the dataset paper if available - Data: link to the Github repository or current dataset location - Motivation: what are some good reasons to have this dataset Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/2970/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2970/timeline	null	completed	null	null	false	[]
https://api.github.com/repos/huggingface/datasets/issues/719	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/719/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/719/comments	https://api.github.com/repos/huggingface/datasets/issues/719/events	https://github.com/huggingface/datasets/pull/719	716,492,263	MDExOlB1bGxSZXF1ZXN0NDk5MjE5Mjg2	719	Fix train_test_split output format	[]	closed	false	null	0	2020-10-07T12:39:01Z	2020-10-07T13:38:08Z	2020-10-07T13:38:06Z	null	There was an issue in the `transmit_format` wrapper that returned bad formats when using train_test_split. This was due to `column_names` being handled as a List[str] instead of Dict[str, List[str]] when the dataset transform (train_test_split) returns a DatasetDict (one set of column names per split). This should fix @timothyjlaurent 's issue in #620 and fix #676 I added tests for `transmit_format` so that it doesn't happen again	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/719/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/719/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/719.diff", "html_url": "https://github.com/huggingface/datasets/pull/719", "merged_at": "2020-10-07T13:38:06Z", "patch_url": "https://github.com/huggingface/datasets/pull/719.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/719" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/5284	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5284/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5284/comments	https://api.github.com/repos/huggingface/datasets/issues/5284/events	https://github.com/huggingface/datasets/issues/5284	1,461,519,733	I_kwDODunzps5XHQV1	5,284	Features of IterableDataset set to None by remove column	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" }, { "color": "fef2c0", "default": false, "description": "", "id": 3287858981, "name": "streaming", "node_id": "MDU6TGFiZWwzMjg3ODU4OTgx", "url": "https://api.github.com/repos/huggingface/datasets/labels/streaming" } ]	closed	false	null	18	2022-11-23T10:54:59Z	2023-02-02T09:05:51Z	2022-11-28T12:53:24Z	null	### Describe the bug The `remove_column` method of the IterableDataset sets the dataset features to None. ### Steps to reproduce the bug ```python from datasets import Audio, load_dataset # load LS in streaming mode dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True) # check original features print("Original features: ", dataset.features.keys()) # define features to remove: we KEEP audio and text COLUMNS_TO_REMOVE = ['chapter_id', 'speaker_id', 'file', 'id'] dataset = dataset.remove_columns(COLUMNS_TO_REMOVE) # check processed features, uh-oh! print("Processed features: ", dataset.features) # streaming the first audio sample still works print("First sample:", next(iter(ds))) ``` Print Output: ``` Original features: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id']) Processed features: None First sample: {'audio': {'path': '2277-149896-0000.flac', 'array': array([ 0.00186157, 0.0005188 , 0.00024414, ..., -0.00097656, -0.00109863, -0.00146484]), 'sampling_rate': 16000}, 'text': "HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE"} ``` ### Expected behavior The features should be those not removed by the `remove_column` method, i.e. audio and text. ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.15 - PyArrow version: 9.0.0 - Pandas version: 1.3.5 (Running on Google Colab for a blog post: https://colab.research.google.com/drive/1ySCQREPZEl4msLfxb79pYYOWjUZhkr9y#scrollTo=8pRDGiVmH2ml) cc @polinaeterna @lhoestq	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5284/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5284/timeline	null	completed	null	null	false	[ "Related to https://github.com/huggingface/datasets/issues/5245", "#self-assign", "Thanks @lhoestq and @alvarobartt!\r\n\r\nThis would be extremely helpful to have working for the Whisper fine-tuning event - we're only training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!\r\n\r\n_c.f._ https://twitter.com/sanchitgandhi99/status/1592188332171493377", "> Thanks @lhoestq and @alvarobartt!\n> \n> \n> \n> This would be extremely helpful to have working for the Whisper fine-tuning event - we're only training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!\n> \n> \n> \n> _c.f._ https://twitter.com/sanchitgandhi99/status/1592188332171493377\n\nI'm almost done with at least a temporary fix to `rename_column`, `rename_columns`, and `remove_columns`, just trying to figure out how to extend it to the `map` function itself!\n\nI'll probably open the PR for review either tomorrow or Sunday hopefully! Glad I can help you and HuggingFace 🤗 ", "Awesome - thank you so much for this PR @alvarobartt! Is much appreciated!", "@sanchit-gandhi PR is ready and open for review at #5287, but there's still one issue I may need @lhoestq's input :hugs:", "Let us know @sanchit-gandhi if you need a new release of `datasets` soon with this fix included :)", "Thanks for the fix guys! We can direct people to install `datasets` from main if that's easier!", "Hey guys, any update around this? I'm facing the same issue with a streamable dataset. ", "Hi @asennoussi so this was already fixed and released as part of https://github.com/huggingface/datasets/releases/tag/2.8.0, so you should be able to install it as `pip install datasets==2.8.0` or just to use `pip install datasets --upgrade` to get the latest version, as of now, the https://github.com/huggingface/datasets/releases/tag/2.9.0 released last week! 🤗", "Still facing the same issue though: \r\n```\r\nfrom datasets import IterableDatasetDict, load_dataset\r\n\r\nraw_datasets = vectorized_datasets = IterableDatasetDict()\r\n\r\n\r\nraw_datasets[\"train\"] = load_dataset(\"asennoussi/private\", split=\"train\", use_auth_token=True, streaming=True)\r\nraw_datasets[\"test\"] = load_dataset(\"asennoussi/private\", split=\"test\", use_auth_token=True, streaming=True)\r\n\r\nprint(\"Original features: \", raw_datasets['train'].features.keys())\r\n\r\n...\r\n\r\ndef prepare_dataset(batch):\r\n\r\n # load and (possibly) resample audio datato 16kHz\r\n audio = batch[\"audio\"]\r\n\r\n # compute log-Mel input features from input audio array \r\n batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\r\n # compute input length of audio sample in seconds\r\n batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\r\n \r\n # optional pre-processing steps\r\n transcription = batch[\"sentence\"]\r\n \r\n # encode target text to label ids\r\n batch[\"labels\"] = processor.tokenizer(transcription).input_ids\r\n batch[\"labels_length\"] = len(batch[\"labels\"])\r\n return batch\r\n...\r\nvectorized_datasets = vectorized_datasets.remove_columns(['input_length', 'labels_length']+list(next(iter(raw_datasets.values())).features))\r\nprint(\"Processed features: \", vectorized_datasets['train'].features)\r\nprint(\"First sample:\", next(iter(vectorized_datasets['train'])))\r\n\r\n```\r\n\r\nOutput: \r\n```\r\nOriginal features: dict_keys(['path', 'audio', 'sentence'])\r\nProcessed features: None\r\n```", "Hmm weird, could you try to print\r\n\r\n```python\r\nprint(\"Processed features: \", vectorized_datasets['train'].features)\r\n```\r\n\r\nagain after iterating over the `vectorized_datasets`? In the code above, should be last line :)", "Didn't seem to fix it: \r\n```\r\nOriginal features: dict_keys(['path', 'audio', 'sentence'])\r\nProcessed features: None\r\nProcessed features: None\r\n```", "Actually the culprit looks to be this one: \r\n`vectorized_datasets = raw_datasets.map(prepare_dataset).with_format(\"torch\")`\r\nWhen I remove this line: `vectorized_datasets = vectorized_datasets.remove_columns(['input_length', 'labels_length']+list(next(iter(raw_datasets.values())).features))`\r\n\r\nI still get \r\n```\r\nProcessed features: None\r\n```", "The culprit is definitely `.map` \r\nJust validated it. \r\nAny idea please? ", "> The culprit is definitely `.map` Just validated it. Any idea please?\r\n\r\nYes, indeed `.map` losses the features, because AFAIK pre-fetching the data to infer the features is expensive and not ideal, that's part of this issue https://github.com/huggingface/datasets/issues/3888\r\n\r\nAnyway, now you can pass the `features` as a param to `.map` as follows:\r\n\r\n```python\r\nfrom datasets import Features\r\nvectorized_datasets = raw_datasets.map(\r\n prepare_dataset,\r\n features=Features(\r\n {\"path\": raw_datasets[\"train\"].info.features[\"path\"], \"audio\": raw_datasets[\"train\"].info.features[\"audio\"], \"sentence\": raw_datasets[\"train\"].info.features[\"sentence\"]}\r\n ),\r\n).with_format(\"torch\")\r\n```\r\n\r\nAlso, to let you know, when calling `.remove_columns` over an `IterableDataset`, the `features` are not lost, as well as `.rename_column` and `rename_columns` :)\r\n\r\nMore information about the latter at https://github.com/huggingface/datasets/pull/5287", "@asennoussi alternatively you can just call `._resolve_features()` from your `IterableDataset` and it will pre-fetch the data to resolve the features, but note that feature-inference is not as accurate as if you manually specify which features and feature-types the `IterableDataset` has, as mentioned in the comment above, the alternative is to provide `features` param to `.map` :hugs:", "Got it thanks a lot! " ]
https://api.github.com/repos/huggingface/datasets/issues/5146	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5146/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5146/comments	https://api.github.com/repos/huggingface/datasets/issues/5146/events	https://github.com/huggingface/datasets/pull/5146	1,418,331,282	PR_kwDODunzps5BSUWW	5,146	Delete duplicate issue template file	[]	closed	false	null	1	2022-10-21T13:18:46Z	2022-10-21T13:52:30Z	2022-10-21T13:50:04Z	null	A conflict between two PRs: - #5116 - #5136 was not properly resolved, resulting in a duplicate issue template. This PR removes the duplicate template.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5146/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5146/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5146.diff", "html_url": "https://github.com/huggingface/datasets/pull/5146", "merged_at": "2022-10-21T13:50:04Z", "patch_url": "https://github.com/huggingface/datasets/pull/5146.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5146" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._" ]
https://api.github.com/repos/huggingface/datasets/issues/64	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/64/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/64/comments	https://api.github.com/repos/huggingface/datasets/issues/64/events	https://github.com/huggingface/datasets/pull/64	614,737,057	MDExOlB1bGxSZXF1ZXN0NDE1MjMwMjYy	64	[Datasets] Make master ready for datasets adding	[]	closed	false	null	0	2020-05-08T13:17:00Z	2020-05-08T13:17:31Z	2020-05-08T13:17:30Z	null	Add all relevant files so that datasets can now be added on master	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/64/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/64/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/64.diff", "html_url": "https://github.com/huggingface/datasets/pull/64", "merged_at": "2020-05-08T13:17:30Z", "patch_url": "https://github.com/huggingface/datasets/pull/64.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/64" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/756	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/756/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/756/comments	https://api.github.com/repos/huggingface/datasets/issues/756/events	https://github.com/huggingface/datasets/pull/756	728,211,373	MDExOlB1bGxSZXF1ZXN0NTA4OTYwNTc3	756	Start community-provided dataset docs	[]	closed	false	null	1	2020-10-23T13:17:41Z	2020-10-26T12:55:20Z	2020-10-26T12:55:19Z	null	Continuation of #736 with clean fork. #### Old description This is what I did to get the pseudo-labels updated. Not sure if it generalizes, but I figured I would write it down. It was pretty easy because all I had to do was make properly formatted directories and change URLs. In slack @thomwolf called it a user-namespace dataset, but the docs call it community dataset. I think the first naming is clearer, but I didn't address that here. I didn't add metadata, will try that.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/756/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/756/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/756.diff", "html_url": "https://github.com/huggingface/datasets/pull/756", "merged_at": "2020-10-26T12:55:19Z", "patch_url": "https://github.com/huggingface/datasets/pull/756.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/756" }	true	[ "Oh, really cool @sshleifer!" ]
https://api.github.com/repos/huggingface/datasets/issues/1347	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/1347/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/1347/comments	https://api.github.com/repos/huggingface/datasets/issues/1347/events	https://github.com/huggingface/datasets/pull/1347	759,845,231	MDExOlB1bGxSZXF1ZXN0NTM0Nzc3NjQ0	1,347	Add spanish billion words corpus	[]	closed	false	null	4	2020-12-08T22:51:38Z	2020-12-11T11:26:39Z	2020-12-11T11:15:28Z	null	Add an unannotated Spanish corpus of nearly 1.5 billion words, compiled from different resources from the web.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 2, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/1347/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/1347/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/1347.diff", "html_url": "https://github.com/huggingface/datasets/pull/1347", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/1347.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/1347" }	true	[ "Thank you for your feedback! I've reduced the dummy data size to 2KB.\r\n\r\nI had to rebase to fix `RemoteDatasetTest` fails, sorry about the 80 commits. \r\nI could create a new clean PR if you prefer.", "I have seen that in similar cases you have suggested to other contributors to create another branch and another PR, so I will do that.", "Yes thank you !", "The new PR is #1476 :)" ]
https://api.github.com/repos/huggingface/datasets/issues/2986	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2986/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2986/comments	https://api.github.com/repos/huggingface/datasets/issues/2986/events	https://github.com/huggingface/datasets/pull/2986	1,010,792,783	PR_kwDODunzps4scSHR	2,986	Refac module factory + avoid etag requests for hub datasets	[]	closed	false	null	6	2021-09-29T10:42:00Z	2021-10-11T11:05:53Z	2021-10-11T11:05:52Z	null	## Refactor the module factory When trying to extend the `data_files` logic to avoid doing unnecessary ETag requests, I noticed that the module preparation mechanism needed a refactor: - the function was 600 lines long - it was not readable - it contained many different cases that made it complex to maintain - it was hard to properly test it - it was hard to extend without breaking anything The module preparation mechanism is in charge of taking the name of a dataset or a metric given by the user (ex: "squad", "accuracy", "lhoestq/test", "path/to/my/script.py", "path/to/my/data/directory", "json", "csv") and return a module (possibly downloaded from the Hub) that contains the dataset builder or the metric class to use. ### Implementation details I decided to separate all these use cases into different dataset/metric module factories. First, the metric module factories: - CanonicalMetricModuleFactory: "accuracy", "rouge", ... - LocalMetricModuleFactory: "path/to/my/metric.py" Then, the dataset module factories: - CanonicalDatasetModuleFactory: "squad", "glue", ... - CommunityDatasetModuleFactoryWithScript: "lhoestq/test" - CommunityDatasetModuleFactoryWithoutScript: "lhoestq/demo1" - PackagedDatasetModuleFactory: "json", "csv", ... - LocalDatasetModuleFactoryWithScript: "path/to/my/script.py" - LocalDatasetModuleFactoryWithoutScript: "path/to/my/data/directory" And finally, additional factories when users have no internet: - CachedDatasetModuleFactory - CachedMetricModuleFactory ### Breaking changes One thing is that I still don't know at what extent we want to keep backward compatibility for `prepare_module`. For now I just kept it (except I removed two parameters) just in case, but it's not used anywhere anymore. ## Avoid etag requests for hub datasets To do this I added a class `DataFilesDict` that can be hashed to define the cache directory of the dataset. It contains the usual data files formatted as `{"train": ["train.txt"]}` for example. But each list of file is a `DataFilesList` that also has a `origin_metadata` attribute that contains metadata about the origin of each file: - for URLs: it stores the ETags of the files - for local files: it stores the last modification data - for files from a Hugging Face repository on the Hub: it stores the pattern (``, `.csv`, "train.txt", etc.) and the commit sha of the repository (so there're no ETag requests !) This way if any file changes, the hash of the `DataFilesDict` changes too ! You can instantiate a `DataFilesDict` by using patterns for local/remote files or files in a HF repository: - for local/remote files: `DataFilesDict.from_local_or_remote(patterns)` - for files in a HF repository: `DataFilesDict.from_hf_repo(patterns, dataset_info)` Fix #2859 ## TODO Fix the latest test: - [x] fix the call to dataset_info in offline mode (related to https://github.com/huggingface/huggingface_hub/issues/372) Add some more tests: - [x] test all the factories - [x] test the new data files logic Other: - [x] docstrings - [x] comments	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 1, "hooray": 2, "laugh": 0, "rocket": 0, "total_count": 3, "url": "https://api.github.com/repos/huggingface/datasets/issues/2986/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2986/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/2986.diff", "html_url": "https://github.com/huggingface/datasets/pull/2986", "merged_at": "2021-10-11T11:05:51Z", "patch_url": "https://github.com/huggingface/datasets/pull/2986.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/2986" }	true	[ "> One thing is that I still don't know at what extent we want to keep backward compatibility for prepare_module. For now I just kept it (except I removed two parameters) just in case, but it's not used anywhere anymore.\r\n\r\nFYI, various other projects currently use it, thus clearly a major version would be required:\r\n\r\nhttps://github.com/search?q=org%3Ahuggingface+prepare_module&type=code", "Yea so I kept `prepare_module` and changed it to use all the factories I added, so all the use cases in the link you shared are still working. The only two parameters I removed are minor IMO and were a bit hacky anyway (return_resolved_file_path and return_associated_base_path). I think they were only used internally in `datasets` but let me know if you're aware of a use case I didn't think of.", "I think I'm done with the tests :) I'll do the comments/docs and then we just wait for https://github.com/huggingface/huggingface_hub/pull/373 to get merged", "When there's a new release of `huggingface_hub` (probably on monday), it will fix the CI.\r\n\r\nThe PR is ready for review. Let me know if I need to clarify some parts", "One additional change I did: the tests won't affect the number of downloads on the website anymore. And users can choose to not update the number of downloads with `HF_UPDATE_DOWNLOAD_COUNTS=0`", "CI failures are simply due to RAM issues with circleci workers.\r\nAnd on windows there is an issue with installing `ruamel.yaml` from the bump of `huggingface_hub` (fixed on master)" ]
https://api.github.com/repos/huggingface/datasets/issues/5617	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5617/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5617/comments	https://api.github.com/repos/huggingface/datasets/issues/5617/events	https://github.com/huggingface/datasets/pull/5617	1,612,947,422	PR_kwDODunzps5LcvI-	5,617	Fix CI by temporarily pinning fsspec < 2023.3.0	[]	closed	false	null	2	2023-03-07T08:18:20Z	2023-03-07T08:44:55Z	2023-03-07T08:37:28Z	null	As a hotfix for our CI, temporarily pin `fsspec`: Fix #5616. Until root cause is fixed, see: - #5614	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5617/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5617/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5617.diff", "html_url": "https://github.com/huggingface/datasets/pull/5617", "merged_at": "2023-03-07T08:37:28Z", "patch_url": "https://github.com/huggingface/datasets/pull/5617.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5617" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008771 / 0.011353 (-0.002582) \| 0.004665 / 0.011008 (-0.006343) \| 0.101645 / 0.038508 (0.063137) \| 0.030190 / 0.023109 (0.007081) \| 0.298581 / 0.275898 (0.022683) \| 0.371206 / 0.323480 (0.047727) \| 0.007272 / 0.007986 (-0.000714) \| 0.003432 / 0.004328 (-0.000896) \| 0.078645 / 0.004250 (0.074395) \| 0.037640 / 0.037052 (0.000588) \| 0.314014 / 0.258489 (0.055525) \| 0.345682 / 0.293841 (0.051841) \| 0.033675 / 0.128546 (-0.094871) \| 0.011513 / 0.075646 (-0.064134) \| 0.320683 / 0.419271 (-0.098589) \| 0.041633 / 0.043533 (-0.001900) \| 0.302697 / 0.255139 (0.047558) \| 0.323560 / 0.283200 (0.040361) \| 0.089309 / 0.141683 (-0.052374) \| 1.477570 / 1.452155 (0.025415) \| 1.528004 / 1.492716 (0.035287) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.184710 / 0.018006 (0.166704) \| 0.412794 / 0.000490 (0.412305) \| 0.001421 / 0.000200 (0.001221) \| 0.000069 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023133 / 0.037411 (-0.014278) \| 0.099492 / 0.014526 (0.084967) \| 0.104806 / 0.176557 (-0.071751) \| 0.150765 / 0.737135 (-0.586370) \| 0.110127 / 0.296338 (-0.186211) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438642 / 0.215209 (0.223433) \| 4.349753 / 2.077655 (2.272098) \| 2.178754 / 1.504120 (0.674634) \| 1.952839 / 1.541195 (0.411645) \| 1.840574 / 1.468490 (0.372084) \| 0.694016 / 4.584777 (-3.890761) \| 3.375186 / 3.745712 (-0.370526) \| 1.892391 / 5.269862 (-3.377470) \| 1.177643 / 4.565676 (-3.388033) \| 0.082328 / 0.424275 (-0.341947) \| 0.012280 / 0.007607 (0.004673) \| 0.534478 / 0.226044 (0.308434) \| 5.377043 / 2.268929 (3.108114) \| 2.645273 / 55.444624 (-52.799351) \| 2.336391 / 6.876477 (-4.540086) \| 2.387917 / 2.142072 (0.245845) \| 0.814399 / 4.805227 (-3.990828) \| 0.149226 / 6.500664 (-6.351438) \| 0.066614 / 0.075469 (-0.008855) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.205467 / 1.841788 (-0.636321) \| 13.857481 / 8.074308 (5.783173) \| 14.269958 / 10.191392 (4.078566) \| 0.152199 / 0.680424 (-0.528225) \| 0.029083 / 0.534201 (-0.505118) \| 0.397590 / 0.579283 (-0.181693) \| 0.410587 / 0.434364 (-0.023777) \| 0.480479 / 0.540337 (-0.059858) \| 0.576014 / 1.386936 (-0.810922) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006956 / 0.011353 (-0.004397) \| 0.004914 / 0.011008 (-0.006094) \| 0.077571 / 0.038508 (0.039063) \| 0.028309 / 0.023109 (0.005200) \| 0.344523 / 0.275898 (0.068625) \| 0.383039 / 0.323480 (0.059560) \| 0.005202 / 0.007986 (-0.002783) \| 0.003513 / 0.004328 (-0.000816) \| 0.076393 / 0.004250 (0.072142) \| 0.042035 / 0.037052 (0.004982) \| 0.342950 / 0.258489 (0.084461) \| 0.387432 / 0.293841 (0.093591) \| 0.032267 / 0.128546 (-0.096280) \| 0.011914 / 0.075646 (-0.063732) \| 0.087140 / 0.419271 (-0.332131) \| 0.042624 / 0.043533 (-0.000909) \| 0.342391 / 0.255139 (0.087253) \| 0.367016 / 0.283200 (0.083817) \| 0.091757 / 0.141683 (-0.049926) \| 1.515845 / 1.452155 (0.063690) \| 1.607929 / 1.492716 (0.115213) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.234461 / 0.018006 (0.216455) \| 0.420430 / 0.000490 (0.419941) \| 0.000403 / 0.000200 (0.000203) \| 0.000059 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026639 / 0.037411 (-0.010772) \| 0.101860 / 0.014526 (0.087334) \| 0.109696 / 0.176557 (-0.066860) \| 0.160902 / 0.737135 (-0.576233) \| 0.112431 / 0.296338 (-0.183907) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438444 / 0.215209 (0.223235) \| 4.378881 / 2.077655 (2.301226) \| 2.063975 / 1.504120 (0.559855) \| 1.863069 / 1.541195 (0.321874) \| 1.955684 / 1.468490 (0.487193) \| 0.694106 / 4.584777 (-3.890671) \| 3.467683 / 3.745712 (-0.278029) \| 2.882441 / 5.269862 (-2.387421) \| 1.484533 / 4.565676 (-3.081143) \| 0.082682 / 0.424275 (-0.341593) \| 0.012597 / 0.007607 (0.004990) \| 0.539219 / 0.226044 (0.313174) \| 5.384838 / 2.268929 (3.115909) \| 2.528273 / 55.444624 (-52.916351) \| 2.190332 / 6.876477 (-4.686145) \| 2.252573 / 2.142072 (0.110500) \| 0.801047 / 4.805227 (-4.004180) \| 0.151082 / 6.500664 (-6.349582) \| 0.067564 / 0.075469 (-0.007905) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.306469 / 1.841788 (-0.535319) \| 14.220154 / 8.074308 (6.145846) \| 13.300979 / 10.191392 (3.109586) \| 0.153827 / 0.680424 (-0.526597) \| 0.016818 / 0.534201 (-0.517383) \| 0.383528 / 0.579283 (-0.195755) \| 0.393970 / 0.434364 (-0.040394) \| 0.468395 / 0.540337 (-0.071943) \| 0.558748 / 1.386936 (-0.828188) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#824860ca204a3bd84a7d63f71df5df4c56c2432f \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/3564	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3564/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3564/comments	https://api.github.com/repos/huggingface/datasets/issues/3564/events	https://github.com/huggingface/datasets/pull/3564	1,099,214,403	PR_kwDODunzps4wzSOL	3,564	Add the KMWP & DKTC dataset.	[]	closed	false	null	3	2022-01-11T14:14:08Z	2022-01-12T15:33:49Z	2022-01-12T15:33:28Z	null	Add the DKTC dataset. - https://github.com/tunib-ai/DKTC	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3564/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3564/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/3564.diff", "html_url": "https://github.com/huggingface/datasets/pull/3564", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/3564.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3564" }	true	[ "I reflect your review. cc. @lhoestq ", "Ah sorry, I missed KMWP comment, wait.", "I request 2 new pull requests. #3569 #3570" ]
https://api.github.com/repos/huggingface/datasets/issues/5053	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5053/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5053/comments	https://api.github.com/repos/huggingface/datasets/issues/5053/events	https://github.com/huggingface/datasets/issues/5053	1,393,739,882	I_kwDODunzps5TEshq	5,053	Intermittent JSON parse error when streaming the Pile	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	open	false	null	3	2022-10-02T11:56:46Z	2022-10-04T17:59:03Z	null	null	## Describe the bug I have an intermittent error when streaming the Pile, where I get a JSON parse error which causes my program to crash. This is intermittent - when I rerun the program with the same random seed it does not crash in the same way. The exact point this happens also varied - it happened to me 11B tokens and 4 days into a training run, and now just happened 2 minutes into one, but I can't reliably reproduce it. I'm using a remote machine with 8 A6000 GPUs via runpod.io ## Expected results I have a DataLoader which can iterate through the whole Pile ## Actual results Stack trace: ``` Failed to read file 'zstd://12.jsonl::https://the-eye.eu/public/AI/pile/train/12.jsonl.zst' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Invalid value. in row 0 ``` I'm currently using HuggingFace accelerate, which also gave me the following stack trace, but I've also experienced this problem intermittently when using DataParallel, so I don't think it's to do with parallelisation ``` Traceback (most recent call last): File "ddp_script.py", line 1258, in <module> main() File "ddp_script.py", line 1143, in main for c, batch in tqdm.tqdm(enumerate(data_iter)): File "/opt/conda/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 503, in __iter__ next_batch, next_batch_info, next_skip = self._fetch_batches(main_iterator) File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 454, in _fetch_batches broadcast_object_list(batch_info) File "/opt/conda/lib/python3.7/site-packages/accelerate/utils/operations.py", line 333, in broadcast_object_list torch.distributed.broadcast_object_list(object_list, src=from_process) File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1900, in broadcast_object_list object_list[i] = _tensor_to_object(obj_view, obj_size) File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1571, in _tensor_to_object return _unpickler(io.BytesIO(buf)).load() _pickle.UnpicklingError: invalid load key, '@'. ``` ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset( cfg["dataset_name"], streaming=True, split="train") dataset = dataset.remove_columns("meta") dataset = dataset.map(tokenize_and_concatenate, batched=True) dataset = dataset.with_format(type="torch") train_data_loader = DataLoader( dataset, batch_size=cfg["batch_size"], num_workers=3) for batch in train_data_loader: continue ``` `tokenize_and_concatenate` is a custom tokenization function I defined on the GPT-NeoX tokenizer to tokenize the text, separated by endoftext tokens, and reshape to have length batch_size, I don't think this is related to tokenization: ``` import numpy as np import einops import torch def tokenize_and_concatenate(examples): texts = examples["text"] full_text = tokenizer.eos_token.join(texts) div = 20 length = len(full_text) // div text_list = [full_text[i * length: (i + 1) * length] for i in range(div)] tokens = tokenizer(text_list, return_tensors="np", padding=True)[ "input_ids" ].flatten() tokens = tokens[tokens != tokenizer.pad_token_id] n = len(tokens) curr_batch_size = n // (seq_len - 1) tokens = tokens[: (seq_len - 1) * curr_batch_size] tokens = einops.rearrange( tokens, "(batch_size seq) -> batch_size seq", batch_size=curr_batch_size, seq=seq_len - 1, ) prefix = np.ones((curr_batch_size, 1), dtype=np.int64) * \ tokenizer.bos_token_id return { "text": np.concatenate([prefix, tokens], axis=1) } ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-5.4.0-105-generic-x86_64-with-debian-buster-sid - Python version: 3.7.13 - PyArrow version: 9.0.0 - Pandas version: 1.3.5 ZStandard data: Version: 0.18.0 Summary: Zstandard bindings for Python Home-page: https://github.com/indygreg/python-zstandard Author: Gregory Szorc Author-email: gregory.szorc@gmail.com License: BSD Location: /opt/conda/lib/python3.7/site-packages Requires: Required-by:	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5053/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5053/timeline	null	null	null	null	false	[ "Maybe #2838 can help. In this PR we allow to skip bad chunks of JSON data to not crash the training\r\n\r\nDid you have warning messages before the error ?\r\n\r\nsomething like this maybe ?\r\n```\r\n03/24/2022 02:19:46 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host. Retrying in 5sec [1/20]\r\n03/24/2022 02:20:01 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host. Retrying in 5sec [2/20]\r\n03/24/2022 02:20:09 - ERROR - datasets.packaged_modules.json.json - Failed to read file 'gzip://file-000000000007.json::https://huggingface.co/datasets/lvwerra/codeparrot-clean-train/resolve/1d740acb9d09cf7a3307553323e2c677a6535407/file-000000000007.json.gz' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Invalid value. in row 0\r\n```", "Ah, thanks! I did get errors like that. Sad that PR wasn't merged in! \r\n\r\nI'm currently just downloading 200GB of the Pile locally to avoid streaming (I have space and it's faster anyway), but that's really useful! I can probably apply the dumb patch of just commenting out the bits that raise the JSON Parse Error lol, based on your code - if I continue the loop should it be fine?", "Yup you can get some inspiration from this PR. It simply ignores the bad chunks (a chunk is ~a few MBs of data).\r\nWe'll try to merge this PR soon" ]
https://api.github.com/repos/huggingface/datasets/issues/3314	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3314/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3314/comments	https://api.github.com/repos/huggingface/datasets/issues/3314/events	https://github.com/huggingface/datasets/pull/3314	1,061,448,227	PR_kwDODunzps4u6mdX	3,314	Adding arg to pass process rank to `map`	[]	closed	false	null	1	2021-11-23T15:55:21Z	2021-11-24T11:54:13Z	2021-11-24T11:54:13Z	null	This PR adds a `with_rank` argument to `map` that gives the user the possibility to pass the rank of each process to their function. This is mostly designed for multi-GPU map (each process can be sent to a different device thanks to the rank). I've also added tests. I'm putting the PR up so you can check the code, I'll add a multi-GPU example to the doc (+ write a bit in the doc for the new arg)	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3314/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3314/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/3314.diff", "html_url": "https://github.com/huggingface/datasets/pull/3314", "merged_at": "2021-11-24T11:54:13Z", "patch_url": "https://github.com/huggingface/datasets/pull/3314.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3314" }	true	[ "Some commits seem to be there twice (made the mistake of rebasing because I wasn't sure whether the doc had changed), is this an issue @lhoestq ?" ]
https://api.github.com/repos/huggingface/datasets/issues/4744	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4744/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4744/comments	https://api.github.com/repos/huggingface/datasets/issues/4744/events	https://github.com/huggingface/datasets/issues/4744	1,317,822,345	I_kwDODunzps5OjF-J	4,744	Remove instructions to generate dummy data from our docs	[ { "color": "0075ca", "default": true, "description": "Improvements or additions to documentation", "id": 1935892861, "name": "documentation", "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation" } ]	closed	false	null	2	2022-07-26T07:32:58Z	2022-08-02T23:50:30Z	2022-08-02T23:50:30Z	null	In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata However: - dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI - for datasets on the Hub: - they do not pass any CI test requiring dummy data - there are no instructions on how they can test their dataset locally using the dummy data - the generation of the dummy data assumes our GitHub directory structure: - the dummy data will be generated under `./datasets/<dataset_name>/dummy` even if locally there is no `./datasets` directory (which is the usual case). See issue: - #4742 CC: @stevhliu	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/4744/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4744/timeline	null	completed	null	null	false	[ "Note that for me personally, conceptually all the dummy data (even for \"canonical\" datasets) should be superseded by `datasets-server`, which performs some kind of CI/CD of datasets (including the canonical ones)", "I totally agree: next step should be rethinking if dummy data makes sense for canonical datasets (once we have datasets-server) and eventually remove it.\r\n\r\nBut for now, we could at least start by removing the indication to generate dummy data from our docs." ]
https://api.github.com/repos/huggingface/datasets/issues/5872	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5872/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5872/comments	https://api.github.com/repos/huggingface/datasets/issues/5872/events	https://github.com/huggingface/datasets/pull/5872	1,713,174,662	PR_kwDODunzps5QrQ5o	5,872	Fix infer module for uppercase extensions	[]	closed	false	null	2	2023-05-17T05:56:45Z	2023-05-17T14:26:59Z	2023-05-17T14:19:18Z	null	Fix the `infer_module_for_data_files` and `infer_module_for_data_files_in_archives` functions when passed a data file name with uppercase extension, e.g. `filename.TXT`. Before, `None` module was returned.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5872/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5872/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5872.diff", "html_url": "https://github.com/huggingface/datasets/pull/5872", "merged_at": "2023-05-17T14:19:18Z", "patch_url": "https://github.com/huggingface/datasets/pull/5872.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5872" }	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007049 / 0.011353 (-0.004304) \| 0.005034 / 0.011008 (-0.005974) \| 0.097737 / 0.038508 (0.059229) \| 0.033280 / 0.023109 (0.010170) \| 0.301017 / 0.275898 (0.025119) \| 0.336593 / 0.323480 (0.013113) \| 0.005567 / 0.007986 (-0.002419) \| 0.005384 / 0.004328 (0.001056) \| 0.072980 / 0.004250 (0.068730) \| 0.045030 / 0.037052 (0.007978) \| 0.303280 / 0.258489 (0.044791) \| 0.367528 / 0.293841 (0.073687) \| 0.034131 / 0.128546 (-0.094415) \| 0.012118 / 0.075646 (-0.063528) \| 0.331677 / 0.419271 (-0.087594) \| 0.049211 / 0.043533 (0.005678) \| 0.297535 / 0.255139 (0.042396) \| 0.318136 / 0.283200 (0.034936) \| 0.101574 / 0.141683 (-0.040109) \| 1.472769 / 1.452155 (0.020615) \| 1.541724 / 1.492716 (0.049007) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.014646 / 0.018006 (-0.003360) \| 0.439050 / 0.000490 (0.438560) \| 0.008575 / 0.000200 (0.008375) \| 0.000297 / 0.000054 (0.000242) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027591 / 0.037411 (-0.009820) \| 0.111639 / 0.014526 (0.097113) \| 0.117098 / 0.176557 (-0.059458) \| 0.173281 / 0.737135 (-0.563855) \| 0.123197 / 0.296338 (-0.173141) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397507 / 0.215209 (0.182298) \| 3.971457 / 2.077655 (1.893803) \| 1.781158 / 1.504120 (0.277038) \| 1.590419 / 1.541195 (0.049224) \| 1.716374 / 1.468490 (0.247884) \| 0.687150 / 4.584777 (-3.897627) \| 3.691009 / 3.745712 (-0.054703) \| 2.050900 / 5.269862 (-3.218961) \| 1.304893 / 4.565676 (-3.260784) \| 0.084507 / 0.424275 (-0.339768) \| 0.012231 / 0.007607 (0.004624) \| 0.493033 / 0.226044 (0.266988) \| 4.929957 / 2.268929 (2.661028) \| 2.209069 / 55.444624 (-53.235555) \| 1.885992 / 6.876477 (-4.990485) \| 2.007004 / 2.142072 (-0.135069) \| 0.827265 / 4.805227 (-3.977963) \| 0.168225 / 6.500664 (-6.332439) \| 0.064988 / 0.075469 (-0.010481) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.182341 / 1.841788 (-0.659447) \| 14.691983 / 8.074308 (6.617674) \| 14.350720 / 10.191392 (4.159328) \| 0.164307 / 0.680424 (-0.516117) \| 0.017480 / 0.534201 (-0.516720) \| 0.421843 / 0.579283 (-0.157441) \| 0.417481 / 0.434364 (-0.016883) \| 0.496587 / 0.540337 (-0.043751) \| 0.581208 / 1.386936 (-0.805728) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007070 / 0.011353 (-0.004283) \| 0.005083 / 0.011008 (-0.005926) \| 0.075009 / 0.038508 (0.036500) \| 0.032343 / 0.023109 (0.009234) \| 0.366788 / 0.275898 (0.090890) \| 0.392273 / 0.323480 (0.068794) \| 0.005512 / 0.007986 (-0.002474) \| 0.003999 / 0.004328 (-0.000329) \| 0.073743 / 0.004250 (0.069492) \| 0.046203 / 0.037052 (0.009151) \| 0.367874 / 0.258489 (0.109385) \| 0.409154 / 0.293841 (0.115313) \| 0.035227 / 0.128546 (-0.093319) \| 0.012223 / 0.075646 (-0.063424) \| 0.087149 / 0.419271 (-0.332122) \| 0.045648 / 0.043533 (0.002115) \| 0.362414 / 0.255139 (0.107275) \| 0.379970 / 0.283200 (0.096770) \| 0.100631 / 0.141683 (-0.041052) \| 1.439733 / 1.452155 (-0.012422) \| 1.506266 / 1.492716 (0.013550) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227071 / 0.018006 (0.209065) \| 0.451243 / 0.000490 (0.450753) \| 0.000406 / 0.000200 (0.000206) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028952 / 0.037411 (-0.008459) \| 0.111934 / 0.014526 (0.097408) \| 0.124080 / 0.176557 (-0.052477) \| 0.174022 / 0.737135 (-0.563113) \| 0.126811 / 0.296338 (-0.169527) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436423 / 0.215209 (0.221214) \| 4.331959 / 2.077655 (2.254304) \| 2.111914 / 1.504120 (0.607794) \| 1.921338 / 1.541195 (0.380143) \| 1.994425 / 1.468490 (0.525935) \| 0.699164 / 4.584777 (-3.885613) \| 3.722143 / 3.745712 (-0.023569) \| 3.516538 / 5.269862 (-1.753323) \| 1.867245 / 4.565676 (-2.698431) \| 0.085923 / 0.424275 (-0.338352) \| 0.012059 / 0.007607 (0.004452) \| 0.586147 / 0.226044 (0.360102) \| 5.395823 / 2.268929 (3.126894) \| 2.594430 / 55.444624 (-52.850194) \| 2.275021 / 6.876477 (-4.601456) \| 2.347810 / 2.142072 (0.205737) \| 0.835118 / 4.805227 (-3.970109) \| 0.167089 / 6.500664 (-6.333575) \| 0.064893 / 0.075469 (-0.010576) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| 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 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291423 / 1.841788 (-0.550365) \| 14.992696 / 8.074308 (6.918388) \| 13.307842 / 10.191392 (3.116450) \| 0.163799 / 0.680424 (-0.516625) \| 0.017315 / 0.534201 (-0.516886) \| 0.461319 / 0.579283 (-0.117965) \| 0.430474 / 0.434364 (-0.003889) \| 0.568115 / 0.540337 (0.027777) \| 0.647909 / 1.386936 (-0.739027) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5161c9ecdcdde9cc99c7f212da13523d5ba6bdb \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/2952	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2952/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2952/comments	https://api.github.com/repos/huggingface/datasets/issues/2952/events	https://github.com/huggingface/datasets/pull/2952	1,002,704,096	PR_kwDODunzps4sDU8S	2,952	Fix missing conda deps	[]	closed	false	null	0	2021-09-21T15:23:01Z	2021-09-22T04:39:59Z	2021-09-21T15:30:44Z	null	`aiohttp` was added as a dependency in #2662 but was missing for the conda build, which causes the 1.12.0 and 1.12.1 to fail. Fix #2932.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/2952/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2952/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/2952.diff", "html_url": "https://github.com/huggingface/datasets/pull/2952", "merged_at": "2021-09-21T15:30:44Z", "patch_url": "https://github.com/huggingface/datasets/pull/2952.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/2952" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/602	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/602/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/602/comments	https://api.github.com/repos/huggingface/datasets/issues/602/events	https://github.com/huggingface/datasets/pull/602	697,636,605	MDExOlB1bGxSZXF1ZXN0NDgzNTU3NDM0	602	apply offset to indices in multiprocessed map	[]	closed	false	null	0	2020-09-10T08:54:30Z	2020-09-10T11:03:39Z	2020-09-10T11:03:37Z	null	Fix #597 I fixed the indices by applying an offset. I added the case to our tests to make sure it doesn't happen again. I also added the message proposed by @thomwolf in #597 ```python >>> d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2, load_from_cache_file=False) Done writing 10 indices in 80 bytes . Testing the mapped function outputs [0, 1] Testing finished, running the mapping function on the dataset Done writing 5 indices in 41 bytes . Done writing 5 indices in 41 bytes . Spawning 2 processes [0, 1, 2, 3, 4] [5, 6, 7, 8, 9] #0: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:00<00:00, 377.90ba/s] #1: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:00<00:00, 378.92ba/s] Concatenating 2 shards from multiprocessing # Dataset(features: {'label': ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None), 'text': Value(dtype='string', id=None)}, num_rows: 10) ```	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/602/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/602/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/602.diff", "html_url": "https://github.com/huggingface/datasets/pull/602", "merged_at": "2020-09-10T11:03:37Z", "patch_url": "https://github.com/huggingface/datasets/pull/602.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/602" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/4922	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4922/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4922/comments	https://api.github.com/repos/huggingface/datasets/issues/4922/events	https://github.com/huggingface/datasets/issues/4922	1,357,684,018	I_kwDODunzps5Q7J0y	4,922	I/O error on Google Colab in streaming mode	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	null	0	2022-08-31T18:08:26Z	2022-08-31T18:15:48Z	2022-08-31T18:15:48Z	null	## Describe the bug When trying to load a streaming dataset in Google Colab the loading fails with an I/O error ## Steps to reproduce the bug ```python import datasets from datasets import load_dataset hf_ds = load_dataset(path='wmt19', name='cs-en', streaming=True, split=datasets.Split.VALIDATION) list(hf_ds.take(5)) ``` ## Expected results It should load five data points ## Actual results ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [<ipython-input-13-7b5b8b1e7e58>](https://localhost:8080/#) in <module> 2 from datasets import load_dataset 3 hf_ds = load_dataset(path='wmt19', name='cs-en', streaming=True, split=datasets.Split.VALIDATION) ----> 4 list(hf_ds.take(5)) 6 frames [/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self) 716 717 def __iter__(self): --> 718 for key, example in self._iter(): 719 if self.features: 720 # `IterableDataset` automatically fills missing columns with None. [/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in _iter(self) 706 else: 707 ex_iterable = self._ex_iterable --> 708 yield from ex_iterable 709 710 def _iter_shard(self, shard_idx: int): [/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self) 582 583 def __iter__(self): --> 584 yield from islice(self.ex_iterable, self.n) 585 586 def shuffle_data_sources(self, generator: np.random.Generator) -> "TakeExamplesIterable": [/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self) 110 111 def __iter__(self): --> 112 yield from self.generate_examples_fn(*self.kwargs) 113 114 def shuffle_data_sources(self, generator: np.random.Generator) -> "ExamplesIterable": [~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in _generate_examples(self, split_subsets, extraction_map, with_translation) 845 raise ValueError("Invalid number of files: %d" % len(files)) 846 --> 847 for sub_key, ex in sub_generator(sub_generator_args): 848 if not all(ex.values()): 849 continue [~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in _parse_parallel_sentences(f1, f2, filename1, filename2) 923 l2_sentences, l2 = parse_file(f2_i, filename2) 924 --> 925 for line_id, (s1, s2) in enumerate(zip(l1_sentences, l2_sentences)): 926 key = f"{f_id}/{line_id}" 927 yield key, {l1: s1, l2: s2} [~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in gen() 895 896 def gen(): --> 897 with open(path, encoding="utf-8") as f: 898 for line in f: 899 seg_match = re.match(seg_re, line) ValueError: I/O operation on closed file. ``` ## Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.4.0 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 9.0.0. (the same error happened with PyArrow version 6.0.0) - Pandas version: 1.3.5	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4922/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4922/timeline	null	not_planned	null	null	false	[]
https://api.github.com/repos/huggingface/datasets/issues/3368	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3368/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3368/comments	https://api.github.com/repos/huggingface/datasets/issues/3368/events	https://github.com/huggingface/datasets/pull/3368	1,069,403,624	PR_kwDODunzps4vTObo	3,368	Fix dict source_datasets tagset validator	[]	closed	false	null	0	2021-12-02T10:52:20Z	2021-12-02T15:48:38Z	2021-12-02T15:48:37Z	null	Currently, the `source_datasets` tag validation does not support passing a dict with configuration keys. This PR: - Extends `tagset_validator` to support regex tags - Uses `tagset_validator` to validate dict `source_datasets`	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3368/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3368/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/3368.diff", "html_url": "https://github.com/huggingface/datasets/pull/3368", "merged_at": "2021-12-02T15:48:37Z", "patch_url": "https://github.com/huggingface/datasets/pull/3368.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3368" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/2256	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2256/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2256/comments	https://api.github.com/repos/huggingface/datasets/issues/2256/events	https://github.com/huggingface/datasets/issues/2256	866,708,609	MDU6SXNzdWU4NjY3MDg2MDk=	2,256	Running `datase.map` with `num_proc > 1` uses a lot of memory	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	null	2	2021-04-24T09:56:20Z	2021-04-26T17:12:15Z	2021-04-26T17:12:15Z	null	## Describe the bug Running `datase.map` with `num_proc > 1` leads to a tremendous memory usage that requires swapping on disk and it becomes very slow. ## Steps to reproduce the bug ```python from datasets import load_dataset dstc8_datset = load_dataset("roskoN/dstc8-reddit-corpus", keep_in_memory=False) def _prepare_sample(batch): return {"input_ids": list(), "attention_mask": list()} for split_name, dataset_split in list(dstc8_datset.items()): print(f"Processing {split_name}") encoded_dataset_split = dataset_split.map( function=_prepare_sample, batched=True, num_proc=4, remove_columns=dataset_split.column_names, batch_size=10, writer_batch_size=10, keep_in_memory=False, ) print(encoded_dataset_split) path = f"./data/encoded_{split_name}" encoded_dataset_split.save_to_disk(path) ``` ## Expected results Memory usage should stay within reasonable boundaries. ## Actual results This is htop-output from running the provided script. ![image](https://user-images.githubusercontent.com/8143425/115954836-66954980-a4f3-11eb-8340-0153bdc3a475.png) ## Versions ``` - Datasets: 1.6.0 - Python: 3.8.8 (default, Apr 13 2021, 19:58:26) [GCC 7.3.0] - Platform: Linux-4.19.128-microsoft-standard-x86_64-with-glibc2.10 ``` Running on WSL2	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/2256/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2256/timeline	null	completed	null	null	false	[ "Thanks for reporting ! We are working on this and we'll do a patch release very soon.", "We did a patch release to fix this issue.\r\nIt should be fixed in the new version 1.6.1\r\n\r\nThanks again for reporting and for the details :)" ]
https://api.github.com/repos/huggingface/datasets/issues/3556	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3556/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3556/comments	https://api.github.com/repos/huggingface/datasets/issues/3556/events	https://github.com/huggingface/datasets/pull/3556	1,097,907,724	PR_kwDODunzps4wvALx	3,556	Preserve encoding/decoding with features in `Iterable.map` call	[]	closed	false	null	0	2022-01-10T13:32:20Z	2022-01-18T19:54:08Z	2022-01-18T19:54:07Z	null	As described in https://github.com/huggingface/datasets/issues/3505#issuecomment-1004755657, this PR uses a generator expression to encode/decode examples with `features` (which are set to None in `map`) before applying a map transform. Fix #3505	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3556/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3556/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/3556.diff", "html_url": "https://github.com/huggingface/datasets/pull/3556", "merged_at": "2022-01-18T19:54:07Z", "patch_url": "https://github.com/huggingface/datasets/pull/3556.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3556" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/2405	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2405/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2405/comments	https://api.github.com/repos/huggingface/datasets/issues/2405/events	https://github.com/huggingface/datasets/pull/2405	901,227,658	MDExOlB1bGxSZXF1ZXN0NjUyNzA2OTk1	2,405	Add dataset tags	[]	closed	false	null	1	2021-05-25T18:57:29Z	2021-05-26T16:54:16Z	2021-05-26T16:40:07Z	null	The dataset tags were provided by Peter Clark following the guide.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/2405/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2405/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/2405.diff", "html_url": "https://github.com/huggingface/datasets/pull/2405", "merged_at": "2021-05-26T16:40:07Z", "patch_url": "https://github.com/huggingface/datasets/pull/2405.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/2405" }	true	[ "Thanks!" ]
https://api.github.com/repos/huggingface/datasets/issues/2760	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2760/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2760/comments	https://api.github.com/repos/huggingface/datasets/issues/2760/events	https://github.com/huggingface/datasets/issues/2760	961,372,667	MDU6SXNzdWU5NjEzNzI2Njc=	2,760	Add Nuswide dataset	[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" }, { "color": "bfdadc", "default": false, "description": "Vision datasets", "id": 3608941089, "name": "vision", "node_id": "LA_kwDODunzps7XHBIh", "url": "https://api.github.com/repos/huggingface/datasets/labels/vision" } ]	open	false	null	0	2021-08-05T03:00:41Z	2021-12-08T12:06:23Z	null	null	## Adding a Dataset - Name: NUSWIDE - Description: [A Real-World Web Image Dataset from National University of Singapore](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html) - Paper: [here](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/nuswide-civr2009.pdf) - Data: [here](https://github.com/wenting-zhao/nuswide) - Motivation: This dataset is a benchmark in the Text Retrieval task. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/2760/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2760/timeline	null	null	null	null	false	[]
https://api.github.com/repos/huggingface/datasets/issues/3677	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3677/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3677/comments	https://api.github.com/repos/huggingface/datasets/issues/3677/events	https://github.com/huggingface/datasets/issues/3677	1,123,192,866	I_kwDODunzps5C8pAi	3,677	Discovery cannot be streamed anymore	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	null	2	2022-02-03T15:02:03Z	2022-02-10T16:51:24Z	2022-02-10T16:51:24Z	null	## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python from datasets import load_dataset iterable_dataset = load_dataset("discovery", name="discovery", split="train", streaming=True) list(iterable_dataset.take(1)) ``` ## Expected results The first row of the train split. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 365, in __iter__ for key, example in self._iter(): File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 362, in _iter yield from ex_iterable File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 272, in __iter__ yield from islice(self.ex_iterable, self.n) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 79, in __iter__ yield from self.generate_examples_fn(*self.kwargs) File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/discovery/542fab7a9ddc1d9726160355f7baa06a1ccc44c40bc8e12c09e9bc743aca43a2/discovery.py", line 333, in _generate_examples with open(data_file, encoding="utf8") as f: File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 64, in wrapper return function(args, use_auth_token=use_auth_token, *kwargs) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py", line 369, in xopen file_obj = fsspec.open(file, mode=mode, args, kwargs).open() File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/core.py", line 456, in open return open_files( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/core.py", line 288, in open_files fs, fs_token, paths = get_fs_token_paths( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/core.py", line 611, in get_fs_token_paths fs = filesystem(protocol, inkwargs) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 253, in filesystem return cls(*storage_options) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 68, in __call__ obj = super().__call__(args, **kwargs) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 57, in __init__ self.zip = zipfile.ZipFile(self.fo) File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/zipfile.py", line 1257, in __init__ self._RealGetContents() File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/zipfile.py", line 1320, in _RealGetContents endrec = _EndRecData(fp) File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/zipfile.py", line 263, in _EndRecData fpin.seek(0, 2) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 676, in seek raise ValueError("Cannot seek streaming HTTP file") ValueError: Cannot seek streaming HTTP file ``` ## Environment info - `datasets` version: 1.18.3 - Platform: Linux-5.11.0-1027-aws-x86_64-with-glibc2.31 - Python version: 3.9.6 - PyArrow version: 6.0.1	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3677/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3677/timeline	null	completed	null	null	false	[ "Seems like a regression from https://github.com/huggingface/datasets/pull/2843\r\n\r\nOr maybe it's an issue with the hosting. I don't think so, though, because https://www.dropbox.com/s/aox84z90nyyuikz/discovery.zip seems to work as expected\r\n\r\n", "Hi @severo, thanks for reporting.\r\n\r\nSome servers do not support HTTP range requests, and those are required to stream some file formats (like ZIP in this case).\r\n\r\nLet me try to propose a workaround. " ]
https://api.github.com/repos/huggingface/datasets/issues/4542	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4542/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4542/comments	https://api.github.com/repos/huggingface/datasets/issues/4542/events	https://github.com/huggingface/datasets/issues/4542	1,280,269,445	I_kwDODunzps5MT1yF	4,542	[to_tf_dataset] Use Feather for better compatibility with TensorFlow ?	[ { "color": "c5def5", "default": false, "description": "Generic discussion on the library", "id": 2067400324, "name": "generic discussion", "node_id": "MDU6TGFiZWwyMDY3NDAwMzI0", "url": "https://api.github.com/repos/huggingface/datasets/labels/generic%20discussion" } ]	open	false	null	48	2022-06-22T14:42:00Z	2022-10-11T08:45:45Z	null	null	To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory. It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library. Here are a few points to explore - [ ] check the performance of ArrowFeatherDataset in tf.data - [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc. We would also need to implement sharding when loading a dataset (this will be done anyway for #546) cc @Rocketknight1 @gante feel free to comment in case I missed anything ! I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4542/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4542/timeline	null	null	null	null	false	[ "This has so much potential to be great! Also I think you tagged some poor random dude on the internet whose name is also Joao, lol, edited that for you! ", "cc @sayakpaul here too, since he was interested in our new approaches to converting datasets!", "Noted and I will look into the thread in detail tomorrow once I log back in. ", "@lhoestq I have used TFRecords with `tf.data` for both vision and text and I can say that they are quite performant. I haven't worked with Feather yet as similarly as I have with TFRecords. If you haven't started the benchmarking script yet, I can prepare a Colab notebook that loads Feather files, converts them into a `tf.data` pipeline, and does some basic preprocessing. \r\n\r\nBut in my limited understanding, Feather might be better suited for CSV files. Not yet sure if it's good for modalities like images. ", "> Not yet sure if it's good for modalities like images.\r\n\r\nWe store images pretty much the same way as tensorflow_datasets (i.e. storing the encoded image bytes, or a path to the local image, so that the image can be decoded on-the-fly), so as long as we use something similar as TFDS for image decoding it should be ok", "So for image datasets, we could potentially store the paths in the feather format and decode and read them on the fly? But it introduces an I/O redundancy of having to read the images every time.\r\n\r\nWith caching it could be somewhat mitigated but it's not a good solution for bigger image datasets. ", "> So for image datasets, we could potentially store the paths in the feather format and decode and read them on the fly?\r\n\r\nhopefully yes :) \r\n\r\nI double-checked the TFDS source code and they always save the bytes actually, not the path. Anyway we'll see if we run into issues or not (as a first step we can require the bytes to be in the feather file)", "Yes. For images, TFDS actually prepares TFRecords first for encoding and then reuses them for every subsequent call. ", "@lhoestq @Rocketknight1 I worked on [this PoC](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59) that\r\n\r\n* Creates Feather files from a medium resolution dataset (`tf_flowers`).\r\n* Explores different options with TensorFlow IO to load the Feather files. \r\n\r\nI haven't benchmarked those different options yet. There's also a gotcha that I have noted in the PoC. I hope it gets us started but I'm sorry if this is redundant. ", "Cool thanks ! If I understand correctly in your PoC you store the flattened array of pixels in the feather file. This will take a lot of disk space.\r\n\r\nMaybe we could just save the encoded bytes and let users apply a `map` to decode/transform them into the format they need for training ? Users can use tf.image to do so for example", "@lhoestq this is what I tried:\r\n\r\n```py\r\ndef read_image(path):\r\n with open(path, \"rb\") as f:\r\n return f.read()\r\n\r\n\r\ntotal_images_written = 0\r\n\r\nfor step in tqdm.tnrange(int(math.ceil(len(image_paths) / batch_size))):\r\n batch_image_paths = image_paths[step * batch_size : (step + 1) * batch_size]\r\n batch_image_labels = all_integer_labels[step * batch_size : (step + 1) * batch_size]\r\n\r\n data = [read_image(path) for path in batch_image_paths]\r\n table = pa.Table.from_arrays([data, batch_image_labels], [\"data\", \"labels\"])\r\n write_feather(table, f\"/tmp/flowers_feather_{step}.feather\", chunksize=chunk_size)\r\n total_images_written += len(batch_image_paths)\r\n print(f\"Total images written: {total_images_written}.\")\r\n\r\n del data\r\n```\r\n\r\nI got the feather files done (no resizing required as you can see):\r\n\r\n```sh\r\nls -lh /tmp/.feather\r\n\r\n-rw-r--r-- 1 sayakpaul wheel 64M Jun 24 09:28 /tmp/flowers_feather_0.feather\r\n-rw-r--r-- 1 sayakpaul wheel 59M Jun 24 09:28 /tmp/flowers_feather_1.feather\r\n-rw-r--r-- 1 sayakpaul wheel 51M Jun 24 09:28 /tmp/flowers_feather_2.feather\r\n-rw-r--r-- 1 sayakpaul wheel 45M Jun 24 09:28 /tmp/flowers_feather_3.feather\r\n```\r\n\r\nNow there seems to be a problem with `tfio.arrow`:\r\n\r\n```py\r\nimport tensorflow_io.arrow as arrow_io\r\n\r\n\r\ndataset = arrow_io.ArrowFeatherDataset(\r\n [\"/tmp/flowers_feather_0.feather\"],\r\n columns=(0, 1),\r\n output_types=(tf.string, tf.int64),\r\n output_shapes=([], []),\r\n batch_mode=\"auto\",\r\n)\r\n\r\nprint(dataset.element_spec) \r\n```\r\n\r\nPrints:\r\n\r\n```\r\n(TensorSpec(shape=(None,), dtype=tf.string, name=None),\r\n TensorSpec(shape=(None,), dtype=tf.int64, name=None))\r\n```\r\n\r\nBut when I do `sample = next(iter(dataset))` it goes into:\r\n\r\n```py\r\nInternalError Traceback (most recent call last)\r\nInput In [30], in <cell line: 1>()\r\n----> 1 sample = next(iter(dataset))\r\n\r\nFile ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/data/ops/iterator_ops.py:766, in OwnedIterator.__next__(self)\r\n 764 def __next__(self):\r\n 765 try:\r\n--> 766 return self._next_internal()\r\n 767 except errors.OutOfRangeError:\r\n 768 raise StopIteration\r\n\r\nFile ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/data/ops/iterator_ops.py:749, in OwnedIterator._next_internal(self)\r\n 746 # TODO(b/77291417): This runs in sync mode as iterators use an error status\r\n 747 # to communicate that there is no more data to iterate over.\r\n 748 with context.execution_mode(context.SYNC):\r\n--> 749 ret = gen_dataset_ops.iterator_get_next(\r\n 750 self._iterator_resource,\r\n 751 output_types=self._flat_output_types,\r\n 752 output_shapes=self._flat_output_shapes)\r\n 754 try:\r\n 755 # Fast path for the case `self._structure` is not a nested structure.\r\n 756 return self._element_spec._from_compatible_tensor_list(ret) # pylint: disable=protected-access\r\n\r\nFile ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/ops/gen_dataset_ops.py:3017, in iterator_get_next(iterator, output_types, output_shapes, name)\r\n 3015 return _result\r\n 3016 except _core._NotOkStatusException as e:\r\n-> 3017 _ops.raise_from_not_ok_status(e, name)\r\n 3018 except _core._FallbackException:\r\n 3019 pass\r\n\r\nFile ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:7164, in raise_from_not_ok_status(e, name)\r\n 7162 def raise_from_not_ok_status(e, name):\r\n 7163 e.message += (\" name: \" + name if name is not None else \"\")\r\n-> 7164 raise core._status_to_exception(e) from None\r\n\r\nInternalError: Invalid: INVALID_ARGUMENT: arrow data type 0x7ff9899d8038 is not supported: Type error: Arrow data type is not supported [Op:IteratorGetNext]\r\n```\r\n\r\nSome additional notes:\r\n\r\n I can actually decode an image encoded with `read_image()` (shown earlier):\r\n\r\n ```py\r\n sample_image_path = image_paths[0]\r\n encoded_image = read_image(sample_image_path)\r\n image = tf.image.decode_png(encoded_image, 3)\r\n print(image.shape)\r\n ```\r\n\r\n* If the above `tf.data.Dataset` object would have succeeded my plan was to just map the decoder like so:\r\n\r\n ```py\r\n autotune = tf.data.AUTOTUNE\r\n dataset = dataset.map(lambda x, y: (tf.image.decode_png(x, 3), y), num_parallel_calls=autotune)\r\n ```", "@lhoestq I think I was able to make it work in the way you were envisioning. Here's the PoC:\r\nhttps://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb\r\n\r\nSome details:\r\n\r\n* I am currently serializing the images as strings with `base64`). In comparison to the flattened arrays as before, the size of the individual feather files has reduced (144 MB -> 85 MB, largest).\r\n* When decoding, I am first decoding the base64 string and then decoding that string (with `tf.io.decode_base64`) as an image with `tf.image.decode_png()`. \r\n* The entire workflow (from generating the Feather files to loading them and preparing the batched `tf.data` pipeline) involves the following libraries: `pyarraow`, `tensorflow-io`, and `tensorflow`. \r\n\r\nCc: @Rocketknight1 @gante ", "Cool thanks ! Too bad the Arrow binary type doesn't seem to be supported in `arrow_io.ArrowFeatherDataset` :/ We would also need it to support Arrow struct type. Indeed images in `datasets` are represented using an Arrow type\r\n```python\r\npa.struct({\"path\": pa.string(), \"bytes\": pa.binary()})\r\n```\r\nnot sure yet how hard it is to support this though.\r\n\r\nChanging the typing on our side would create concerning breaking changes, that's why it would be awesome if it could work using these types", "If the ArrowFeatherDataset doesn't yet support it, I guess our hands are a bit tied at the moment. \r\n\r\nIIUC, in my [latest PoC notebook](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb), you wanted to see each entry in the feather file to be represented like so?\r\n\r\n```\r\npa.struct({\"path\": pa.string(), \"bytes\": pa.binary()})\r\n``` \r\n\r\nIn that case, `pa.binary()` isn't yet supported.", "> IIUC, in my [latest PoC notebook](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb), you wanted to see each entry in the feather file to be represented like so?\r\n> \r\n> pa.struct({\"path\": pa.string(), \"bytes\": pa.binary()})\r\n\r\nYea because that's the data format we're using. If we were to use base64, then we would have to process the full dataset to convert it, which can take some time. Converting to TFRecords would be simpler than converting to base64 in Feather files.\r\n\r\nMaybe it would take too much time to be worth exploring, but according to https://github.com/tensorflow/io/issues/1361#issuecomment-819029002 it's possible to add support for binary type in ArrowFeatherDataset. What do you think ? Any other alternative in mind ?", "> Maybe it would take too much time to be worth exploring, but according to https://github.com/tensorflow/io/issues/1361#issuecomment-819029002 it's possible to add support for binary type in ArrowFeatherDataset.\r\n\r\nShould be possible as per the comment but there hasn't been any progress and it's been more than a year. \r\n\r\n> If we were to use base64, then we would have to process the full dataset to convert it, which can take some time.\r\n\r\nI don't understand this. I would think TFRecords would also need something similar but I need the context you're coming from. \r\n\r\n> What do you think ? Any other alternative in mind ?\r\n\r\nTFRecords since the TensorFlow ecosystem has developed good support for it over the years. ", "> I don't understand this. I would think TFRecords would also need something similar but I need the context you're coming from.\r\n\r\nUsers already have a copy of the dataset in Arrow format (we can change this to Feather). So to load the Arrow/feather files to a TF dataset we need TF IO or something like that. Otherwise the user has to convert all the files from Arrow to TFRecords to use TF data efficiently. But the conversion needs resources: CPU, disk, time. Converting the images to base64 require the same sort of resources.\r\n\r\nSo the issue we're trying to tackle is how to load the Arrow data in TF without having to convert anything ^^", "Yeah, it looks like in its current state the tfio support for `Feather` is incomplete, so we'd end up having to write a lot of it, or do a conversion that defeats the whole point (because if we're going to convert the whole dataset we might as well convert to `TFRecord`).", "Understood @lhoestq. Thanks for explaining!\r\n\r\nAgreed with @Rocketknight1. ", "@lhoestq Although I think this is a dead-end for now unfortunately, because of the limitations at TF's end, we could still explore automatic conversion to TFRecord, or I could dive into refining `to_tf_dataset()` to yield unbatched samples and/or load samples with multiprocessing to improve throughput. Do you have any preferences there?", "> @lhoestq Although I think this is a dead-end for now unfortunately, because of the limitations at TF's end, we could still explore automatic conversion to TFRecord, or I could dive into refining `to_tf_dataset()` to yield unbatched samples and/or load samples with multiprocessing to improve throughput. Do you have any preferences there?\r\n\r\nHappy to take part there @Rocketknight1.", "If `to_tf_dataset` can be unbatched, then it should be fairly easy for users to convert the TF dataset to TFRecords right ?", "@lhoestq why one would convert to TFRecords after unbatching? ", "> If to_tf_dataset can be unbatched, then it should be fairly easy for users to convert the TF dataset to TFRecords right ?\r\n\r\nSort of! A `tf.data.Dataset` is more like an iterator, and does not support sample indexing. `to_tf_dataset()` creates an iterator, but to convert that to `TFRecord`, the user would have to iterate over the whole thing and manually save the stream of samples to files. ", "Someone would like to try to dive into tfio to fix this ? Sounds like a good opportunity to learn what are the best ways to load a dataset for TF, and also the connections between Arrow and TF.\r\n\r\nIf we can at least have the Arrow `binary` type working for TF that would be awesome already (issue https://github.com/tensorflow/io/issues/1361)\r\n\r\nalso cc @nateraw in case you'd be interested ;)", "> Sounds like a good opportunity to learn what are the best ways to load a dataset for TF\r\n\r\nThe recommended way would likely be a combination of TFRecords and `tf.data`. \r\n\r\nExploring the connection between Arrow and TensorFlow is definitely worth pursuing though. But I am not sure about the implications of storing images in a format supported by Arrow. I guess we'll know more once we have at least figured out the support for `binary` type for TFIO. I will spend some time on it and keep this thread updated. ", "I am currently working on a fine-tuning notebook for the TFSegFormer model (Semantic Segmentation). The resolution is high for both the input images and the labels - (512, 512, 3). Here's the [Colab Notebook](https://colab.research.google.com/drive/1jAtR7Z0lYX6m6JsDI5VByh5vFaNhHIbP?usp=sharing) (it's a WIP so please bear that in mind).\r\n\r\nI think the current implementation of `to_tf_dataset()` does create a bottleneck here since the GPU utilization is quite low. ", "Here's a notebook showing the performance difference: https://colab.research.google.com/gist/sayakpaul/d7ca67c90beb47e354942c9d8c0bd8ef/scratchpad.ipynb. \r\n\r\nNote that I acknowledge that it's not an apples-to-apples comparison in many aspects (the dataset isn't the same, data serialization format isn't the same, etc.) but this is the best I could do. ", "Thanks ! I think the speed difference can be partly explained: you use ds.shuffle in your dataset, which is an exact shuffling (compared to TFDS which does buffer shuffling): it slows down query time by 2x to 10x since it has to play with data that are not contiguous.\r\n\r\nThe rest of the speed difference seems to be caused by image decoding (from 330µs/image to 30ms/image)", "Fair enough. Can do one without shuffling too. But it's an important one to consider I guess. " ]
https://api.github.com/repos/huggingface/datasets/issues/3123	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3123/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3123/comments	https://api.github.com/repos/huggingface/datasets/issues/3123/events	https://github.com/huggingface/datasets/issues/3123	1,031,793,207	I_kwDODunzps49f-o3	3,123	Segmentation fault when loading datasets from file	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	null	2	2021-10-20T20:16:11Z	2021-11-02T14:57:07Z	2021-11-02T14:57:07Z	null	## Describe the bug Custom dataset loading sometimes segfaults and kills the process if chunks contain a variety of features/ ## Steps to reproduce the bug Download an example file: ``` wget https://gist.githubusercontent.com/TevenLeScao/11e2184394b3fa47d693de2550942c6b/raw/4232704d08fbfcaf93e5b51def9e5051507651ad/tiny_kelm.jsonl ``` Then in Python: ``` import datasets tiny_kelm = datasets.load_dataset("json", data_files="tiny_kelm.jsonl", chunksize=100000) ``` ## Expected results a `tiny_kelm` functional dataset ## Actual results ☠️ `Segmentation fault (core dumped)` ☠️ ## Environment info - `datasets` version: 1.14.0 - Platform: Linux-5.11.0-38-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 5.0.0	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3123/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3123/timeline	null	completed	null	null	false	[ "Hi ! I created an issue on Arrow's JIRA after making a minimum reproducible example\r\n\r\nhttps://issues.apache.org/jira/browse/ARROW-14439\r\n\r\n```python\r\nimport io\r\n\r\nimport pyarrow.json as paj\r\n\r\nbatch = b'{\"a\": [], \"b\": 1}\\n{\"b\": 1}'\r\nblock_size = 12\r\n\r\npaj.read_json(\r\n io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)\r\n)\r\n```\r\n\r\nI don't see a way to workaround this properly now without hurting the performance of the JSON loader significantly though", "The issue has been fixed in pyarrow 6.0.0, please update pyarrow :)\r\n\r\nThe issue was due to missing fields in the JSON data of type list. Now it's working fine and missing list fields are replaced with empty lists" ]
https://api.github.com/repos/huggingface/datasets/issues/1016	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/1016/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/1016/comments	https://api.github.com/repos/huggingface/datasets/issues/1016/events	https://github.com/huggingface/datasets/pull/1016	755,521,862	MDExOlB1bGxSZXF1ZXN0NTMxMjE3MjM3	1,016	Add CLINC150 dataset	[]	closed	false	null	0	2020-12-02T18:44:30Z	2020-12-03T10:32:04Z	2020-12-03T10:32:04Z	null	Added CLINC150 Dataset. The link to the dataset can be found [here](https://github.com/clinc/oos-eval) and the paper can be found [here](https://www.aclweb.org/anthology/D19-1131.pdf) - [x] Followed the instructions in CONTRIBUTING.md - [x] Ran the tests successfully - [x] Created the dummy data	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/1016/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/1016/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/1016.diff", "html_url": "https://github.com/huggingface/datasets/pull/1016", "merged_at": "2020-12-03T10:32:04Z", "patch_url": "https://github.com/huggingface/datasets/pull/1016.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/1016" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/1193	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/1193/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/1193/comments	https://api.github.com/repos/huggingface/datasets/issues/1193/events	https://github.com/huggingface/datasets/pull/1193	757,840,830	MDExOlB1bGxSZXF1ZXN0NTMzMTM1NDAy	1,193	add taskmaster-1	[]	closed	false	null	0	2020-12-06T04:09:57Z	2020-12-07T15:23:24Z	2020-12-07T15:08:39Z	null	Adding Taskmaster-1 dataset https://github.com/google-research-datasets/Taskmaster/tree/master/TM-1-2019	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/1193/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/1193/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/1193.diff", "html_url": "https://github.com/huggingface/datasets/pull/1193", "merged_at": "2020-12-07T15:08:39Z", "patch_url": "https://github.com/huggingface/datasets/pull/1193.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/1193" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/2324	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2324/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2324/comments	https://api.github.com/repos/huggingface/datasets/issues/2324/events	https://github.com/huggingface/datasets/pull/2324	876,602,064	MDExOlB1bGxSZXF1ZXN0NjMwNzE1NTQz	2,324	Create Audio feature	[]	closed	false	{ "closed_at": null, "closed_issues": 2, "created_at": "2021-07-21T15:34:56Z", "creator": { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }, "description": "Next minor release", "due_on": "2021-08-30T07:00:00Z", "html_url": "https://github.com/huggingface/datasets/milestone/8", "id": 6968069, "labels_url": "https://api.github.com/repos/huggingface/datasets/milestones/8/labels", "node_id": "MI_kwDODunzps4AalMF", "number": 8, "open_issues": 4, "state": "open", "title": "1.12", "updated_at": "2021-10-13T10:26:33Z", "url": "https://api.github.com/repos/huggingface/datasets/milestones/8" }	30	2021-05-05T15:55:22Z	2021-10-13T10:26:33Z	2021-10-13T10:26:33Z	null	Create `Audio` feature to handle raw audio files. Some decisions to be further discussed: - I have chosen `soundfile` as the audio library; another interesting library is `librosa`, but this requires `soundfile` (see [here](https://github.com/librosa/librosa/blob/main/setup.cfg#L53)). If we require some more advanced functionalities, we could eventually switch the library. - I have implemented the audio feature as an extra: `pip install datasets[audio]`. For the moment, the typical datasets user uses only text datasets, and there is no need for them for additional package requirements for audio/image if they do not need them. - For tests, I require audio dependencies (so that all audio functionalities are checked with our CI test suite); I exclude Linux platforms, which require an additional library to be installed with the distribution package manager - I also require `pytest-datadir`, which allow to have (audio) data files for tests - The audio data contain: array and sample_rate. - The array is reshaped as 1D array (expected input for `Wav2Vec2`). Note that to install `soundfile` on Linux, you need to install `libsndfile` using your distribution’s package manager, for example `sudo apt-get install libsndfile1`. ## Requirements Specification - Access example with audio loading and resampling: ```python ds[0]["audio"] ``` - Map with audio loading & resampling: ```python def preprocess(batch): batch["input_values"] = processor(batch["audio"]).input_values return batch ds = ds.map(preprocess) ``` - Map without audio loading and resampling: ```python def preprocess(batch): batch["labels"] = processor(batch["target_text"]).input_values return batch ds = ds.map(preprocess) ``` - Additional requirement specification (see https://github.com/huggingface/datasets/pull/2324#pullrequestreview-768864998): Cast audio column to change sampling sate: ```python ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) ```	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 3, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 3, "url": "https://api.github.com/repos/huggingface/datasets/issues/2324/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2324/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/2324.diff", "html_url": "https://github.com/huggingface/datasets/pull/2324", "merged_at": "2021-10-13T10:26:33Z", "patch_url": "https://github.com/huggingface/datasets/pull/2324.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/2324" }	true	[ "For optimal storage, it would be better to:\r\n- store only the audio file path in the cache Arrow file\r\n- perform decoding of the audio file (into audio array and sample rate) on the fly, while loading the dataset from cache (or by adding a convenient `load_audio` function)", "Thanks a lot @lhoestq for your helpful insights! 🤗 ", "Just one step before having a first running example to benchmark.\r\n\r\nDecision to make: how to call the function `dataset.features.decode_example`:\r\n- The usual approach until now in speech applications: call it in a subsequent `.map` function\r\n - Pros: multiprocessing can be used out of the box\r\n - Cons: large disk storage required for caching decoded audio files, although having it cached will enhance speed for further usage\r\n- Approach suggested by @lhoestq (see above: https://github.com/huggingface/datasets/pull/2324#discussion_r660758683): doing it in formatting\r\n - Pros: no large disk storage required, as it will be done on the fly while iterating on the dataset\r\n - Cons: it is not cached; need to implement multiprocessing for this case\r\n- Other pros/cons for the previous options?\r\n- Other options?\r\n\r\ncc: @lhoestq @patrickvonplaten @anton-l ", "@albertvillanova I'm in two minds about this, to be honest. For example, if we consider CommonVoice, which is encoded in lossy `mp3`:\n\n- If we decompress `mp3` into raw `wav` arrays, loading a batch will speed up about 40x.\n- However, a 60gb English mp3 dataset will blow up to about 600gb raw (iirc), which is why loading on-the-fly (optionally?) could be very beneficial as well.", "Users can do the conversion from mp3 to wav by themselves if they want to using `map`.\r\n\r\nIMO it's better if we can keep the decoding part with the minimal features to be both easy to understand and flexible, i.e. just having the on-the-fly decoding of the audio data (with the sampling rate parameter)\r\n\r\nDecompressing from mp3 to wav sounds like an optimization that depends on the problem that the user wants to solve, the constrains from its environment (disk space, IO speed), and other parameters (optimal training speed for example). Therefore I would leave this to the user to decide whether it has to do it or not.\r\n\r\nLet me know what you think about this", "@albertvillanova, In my opinion the pros strongly outweigh the cons in the @lhoestq's suggestion which is why I think we should go forward with it. \r\n\r\nThe cons:\r\n- \"the operation won't be cached\" is not to important as the user will most likely access just a couple of audio array to see how it looks like and then for the \"full\" feature extraction she/he will make use of `.map(...)` anyways which means that the result will be cached. \r\n- Regarding the multi-processing - if I understand correctly it'll follow the same logic here -> the user will only access some audio arrays for testing playing around with the model but use `.map(...)` for larger operations where multi-processing would still work as before.\r\n\r\nThe advantages mostly solve the main poinpoints being:\r\n- exploding disk space\r\n- bad user experience since the audio is not loaded on the go\r\n\r\n=> So I'm very much in favor of the \"direct-access\" feature", "Update: I've retaken this issue.\r\n\r\nIf the decoding logic is implemented when \"examples are accessed\", then if afterwards we use the `.map`, it tries to apply the decoding twice (as maps iterates over the examples, thus \"accessing them\", before trying to apply the map function)...\r\n\r\nI'm thinking on some other approach...", "I have reimplemented the previous approach, so that we can discuss about it: examples are decoded when accessed.", "What about creating a new specific formatting, just for decoding? This would be only active within a context manager.", "Hi @lhoestq, as we discussed, I've followed your suggestion of implementing the decoding step within the formatting logic: extract-decode-format. Feel free to tell me what you think.\r\n\r\n@patrickvonplaten and @anton-l, could you have a look at the use case in the test (https://github.com/huggingface/datasets/pull/2324/files#diff-58e348f6e4deaa5f3119e420a5d48ebb82875a78c28628831748fb54f59b2c78R34-R50) and tell me if this is aligned with your needs? Thanks.", "Hi @lhoestq, if you validate this approach, we could merge the Audio feature this (or early next) week.", "Sure it looks nice this way :) Feel free to continue !", "As discussed, we should pay attention when applying `map` to a dataset with `Audio` feature, in order to avoid decoding the audio data twice.\r\n\r\nOne proposed solution is to pass `input_columns` to `map`. Just, note that the field containing the Audio feature should not be passed in `input_columns` (not possible, for example, to map the audio file path to a new directory).\r\n\r\nI suggest again (3rd time, sorry, lol) using a formatting context manager (as we already use for PyTorch/TensorFlow: https://huggingface.co/docs/datasets/torch_tensorflow.html).\r\n\r\nAbove (https://github.com/huggingface/datasets/pull/2324#issuecomment-915244003), I suggested to define a formatting just for decoding: the decoding of the audio data is only performed if this specific formatting is set (`ds.set_format(\"decoding\")`) or within a context manager (`with ds.formatted_as(\"decoding\"): ...`)\r\n\r\nNow, I would like also to suggest an alternative formatting for non-decoding (if decoding is the default behavior), for a use case like this:\r\n```python\r\ndef change_dir(example):\r\n example[\"audio\"] = \"dir/\" + example[\"audio\"]\r\n\r\n\r\nwith ds.formatted_as(\"no_decoding\"):\r\n print(ds[0]) # {\"audio\": \"path/to/file.wav\"}\r\n ds.map(change_dir)\r\n print(ds[0]) # {\"audio\": \"dir/path/to/file.wav\"}\r\n\r\nprint(ds[0]) # {\"audio\": {\"path\": \"dir/path/to/file.wav\", \"array\": np.array([1., 2., 3...]), \"sampling_rate\": 44100}}\r\n```\r\n\r\nPlease, just tell me what you think.\r\nCC: @lhoestq @patrickvonplaten @anton-l ", "> As discussed, we should pay attention when applying `map` to a dataset with `Audio` feature, in order to avoid decoding the audio data twice.\r\n> \r\n> One proposed solution is to pass `input_columns` to `map`. Just, note that the field containing the Audio feature should not be passed in `input_columns` (not possible, for example, to map the audio file path to a new directory).\r\n> \r\n> I suggest again (3rd time, sorry, lol) using a formatting context manager (as we already use for PyTorch/TensorFlow: https://huggingface.co/docs/datasets/torch_tensorflow.html).\r\n> \r\n> Above ([#2324 (comment)](https://github.com/huggingface/datasets/pull/2324#issuecomment-915244003)), I suggested to define a formatting just for decoding: the decoding of the audio data is only performed if this specific formatting is set (`ds.set_format(\"decoding\")`) or within a context manager (`with ds.formatted_as(\"decoding\"): ...`)\r\n> \r\n> Now, I would like also to suggest an alternative formatting for non-decoding (if decoding is the default behavior), for a use case like this:\r\n> \r\n> ```python\r\n> def change_dir(example):\r\n> example[\"audio\"] = \"dir/\" + example[\"audio\"]\r\n> \r\n> \r\n> with ds.formatted_as(\"no_decoding\"):\r\n> print(ds[0]) # {\"audio\": \"path/to/file.wav\"}\r\n> ds.map(change_dir)\r\n> print(ds[0]) # {\"audio\": \"dir/path/to/file.wav\"}\r\n> \r\n> print(ds[0]) # {\"audio\": {\"path\": \"dir/path/to/file.wav\", \"array\": np.array([1., 2., 3...]), \"sampling_rate\": 44100}}\r\n> ```\r\n> \r\n> Please, just tell me what you think.\r\n> CC: @lhoestq @patrickvonplaten @anton-l\r\n\r\nI'm fine with a context manager! There is no way to not decode the audio if its key is not accessed no?\r\n\r\nE.g.\r\n\r\n```python\r\ndef load(batch):\r\n batch[\"speech_array\"] = torchaudio.load(batch[\"file\"])\r\n return batch\r\n\r\nds.map(load)\r\n```\r\n\r\ndoes e.g. not access the \"audio\" key `batch[\"audio\"}` but there is no way to not decode it without major changes no? \r\n\r\n=> I'm happy with both the context manager and using `input_colmuns`. Both of those solutions are equally good to me if a \"not-access-key-no-decoding\" solution is just not feasible. I let you guys decide :-)", "> \r\n> There is no way to not decode the audio if its key is not accessed no?\r\n> \r\n> E.g...\r\n> \r\n> does _e.g._ not access the \"audio\" key `batch[\"audio\"}` but there is no way to not decode it without major changes no?\r\n\r\n@patrickvonplaten I think therefore we should rethink the implementation of the Audio feature: its goal is to enrich/simplify the user experience when working with audio files. If on the other hand, you see that the current implementation may be problematic/unsatisfying/not-optimal, then we miss the point of creating this feature. This feature should be useful to users, not inconvenient.", "> > There is no way to not decode the audio if its key is not accessed no?\r\n> > E.g...\r\n> > does _e.g._ not access the \"audio\" key `batch[\"audio\"}` but there is no way to not decode it without major changes no?\r\n> \r\n> @patrickvonplaten I think therefore we should rethink the implementation of the Audio feature: its goal is to enrich/simplify the user experience when working with audio files. If on the other hand, you see that the current implementation may be problematic/unsatisfying/not-optimal, then we miss the point of creating this feature. This feature should be useful to users, not inconvenient.\r\n\r\nThanks a lot for the message! I'm discussing a bit with @anton-l at the moment - will share our results as soon as possible", "Current implementation: see use cases in file https://github.com/huggingface/datasets/blob/0f80e6eaa6f596ff6287eb33587e2d9c69af0e73/tests/features/test_audio.py\r\n\r\nAutomatic decoding:\r\n- when directly accessing an example or a batch\r\n ```python\r\n dset[0]\r\n dset[:2]\r\n ```\r\n- during map, only if audio field is accessed:\r\n ```python\r\n def process_audio_sampling_rate(example):\r\n example[\"double_sampling_rate\"] = 2 * example[\"audio\"][\"sampling_rate\"]\r\n return example\r\n\r\n decoded_dset = dset.map(process_audio_sampling_rate)\r\n ```\r\n\r\nNo automatic decoding:\r\n- during map if audio field is not accessed:\r\n ```python\r\n def process_text(example):\r\n example[\"text\"] = example[\"text\"] + \" World!\"\r\n return example\r\n\r\n decoded_dset = dset.map(process_text)\r\n ```\r\n\r\nThe types of example and batch are kept as usual, `dict[str, Any]` and `dict[str, list[Any]]` respectively.\r\n\r\nCC: @patrickvonplaten @anton-l @lhoestq ", "That's awesome! Thanks so much for your work on this @albertvillanova!", "Oh and maybe have a test to make sure that casting the Audio feature to change the sampling rate works as expected ?", "@lhoestq the test for the resampling is already in place in `test_audio_resampling`: \r\nhttps://github.com/huggingface/datasets/pull/2324/files#diff-58e348f6e4deaa5f3119e420a5d48ebb82875a78c28628831748fb54f59b2c78R48-R56", "Please note that we should agree in the API: see 53d6d73\r\n\r\nThis is just a proposal implementation:\r\n- Create a new method named `cast_column`, which performs a shallow kind of cast (without using `map()` or caching)\r\n\r\nWe should agree in the name, because as it is, it might be confused with `cast` (and users might think `cast_column` caches the result as `cast`)\r\n\r\nCC: @lhoestq @patrickvonplaten @anton-l ", "IMO cast and cast_column should have the exact same behavior, to make the experience simple for the user (no distinction between shallow or deep cast).\r\n\r\nMaybe we should change `cast` to use `cast_column` on every column and make `cast_column` use `map` if and only if it's necessary. For Audio for example `map` is not needed.\r\n\r\nWe just need to do some tests to know which casts always need map and which ones don't. This implies either looking at the PyArrow source code (the documentation doesn't mention all these details) or playing with PyArrow to figure it out.\r\n\r\nI guess for now we can just have the simplest `cast_column` which always uses map unless it's an Audio feature type.\r\n\r\nLet me know what you think !", "@lhoestq I totally agree: `cast` and `cast_column` should be analog to each other.\r\n\r\nFor the implementation, let me try something simpler than the one suggested by you...", "@lhoestq what do you think of an approach like this 633ef09?\r\n\r\nIf it's OK, then we should implement passing parameters to `cast`.", "@lhoestq maybe for now we could make a simple implementation and finish this PR. Then we could make a follow-up PR to deal specifically with the optimal implementation of `cast_column` and `cast`, as this issue is not specific to the Audio feature.", "> @lhoestq what do you think of an approach like this 633ef09?\r\n\r\nYea that's good enough for the time being :)\r\n\r\nI think the last thing we need to do is make sure that `cast_column` changes the fingerprint of the dataset. Feel free to use the `fingerprint_transform` decorator, as for `remove_columns`.\r\n\r\n(note that cast currently doesn't use the decorator since it's based on `map` that already updates the fingerprint).", "> \r\n> I think the last thing we need to do is make sure that `cast_column` changes the fingerprint of the dataset. Feel free to use the `fingerprint_transform` decorator, as for `remove_columns`.\r\n> \r\n> (note that cast currently doesn't use the decorator since it's based on `map` that already updates the fingerprint).\r\n\r\n@lhoestq note that `cast_column` may call `cast` in some cases, and the decorator would not be necessary for these cases...\r\n- I did it by setting `inplace=False`, and updating fingerprint explicitly only when `cast` is not called.", "I think current state of this PR could be included in our next release, as experimental feature, for stress testing it and try to find all potential issues. What do you think?\r\n\r\nCC: @lhoestq @patrickvonplaten @anton-l ", "Looks great! Ready to try it out on the transformers examples after the release :)", "Think we are good to merge here no? :-)" ]
https://api.github.com/repos/huggingface/datasets/issues/2833	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2833/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2833/comments	https://api.github.com/repos/huggingface/datasets/issues/2833/events	https://github.com/huggingface/datasets/issues/2833	978,296,140	MDU6SXNzdWU5NzgyOTYxNDA=	2,833	IndexError when accessing first element of a Dataset if first RecordBatch is empty	[]	closed	false	null	0	2021-08-24T16:49:20Z	2021-08-24T17:21:17Z	2021-08-24T17:21:17Z	null	The computation of the offsets of the underlying Table of a Dataset has some issues if the first RecordBatch is empty. ```python from datasets import Dataset import pyarrow as pa pa_table = pa.Table.from_pydict({"a": [1]}) pa_table2 = pa.Table.from_pydict({"a": []}, schema=pa_table.schema) ds_table = pa.concat_tables([pa_table2, pa_table]) dataset = Dataset(ds_table) print([len(b) for b in dataset.data._batches]) # [0, 1] print(dataset.data._offsets) # [0 0 1] (should be [0, 1]) dataset[0] ``` raises ```python --------------------------------------------------------------------------- IndexError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/datasets/table.py in _interpolation_search(arr, x) 90 else: 91 i, j = i, k ---> 92 raise IndexError(f"Invalid query '{x}' for size {arr[-1] if len(arr) else 'none'}.") 93 94 IndexError: Invalid query '0' for size 1. ``` This can be fixed by ignoring empty batches when computing `table._batches` and `table._offsets` cc @SaulLu	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 1, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/2833/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2833/timeline	null	completed	null	null	false	[]
https://api.github.com/repos/huggingface/datasets/issues/343	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/343/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/343/comments	https://api.github.com/repos/huggingface/datasets/issues/343/events	https://github.com/huggingface/datasets/pull/343	651,419,630	MDExOlB1bGxSZXF1ZXN0NDQ0Njc4NDEw	343	Fix nested tensorflow format	[]	closed	false	null	0	2020-07-06T10:13:45Z	2020-07-06T13:11:52Z	2020-07-06T13:11:51Z	null	In #339 and #337 we are thinking about adding a way to export datasets to tfrecords. However I noticed that it was not possible to do `dset.set_format("tensorflow")` on datasets with nested features like `squad`. I fixed that using a nested map operations to convert features to `tf.ragged.constant`. I also added tests on the `set_format` function.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/343/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/343/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/343.diff", "html_url": "https://github.com/huggingface/datasets/pull/343", "merged_at": "2020-07-06T13:11:51Z", "patch_url": "https://github.com/huggingface/datasets/pull/343.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/343" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/1033	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/1033/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/1033/comments	https://api.github.com/repos/huggingface/datasets/issues/1033/events	https://github.com/huggingface/datasets/pull/1033	755,921,927	MDExOlB1bGxSZXF1ZXN0NTMxNTUxNzYw	1,033	Add support for ".txm" format	[]	closed	false	null	5	2020-12-03T06:52:08Z	2021-02-21T19:47:11Z	2021-02-21T19:47:11Z	null	In dummy data generation, add support for XML-like ".txm" file format. Also support filenames with additional compression extension: ".txm.gz".	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/1033/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/1033/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/1033.diff", "html_url": "https://github.com/huggingface/datasets/pull/1033", "merged_at": "2021-02-21T19:47:11Z", "patch_url": "https://github.com/huggingface/datasets/pull/1033.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/1033" }	true	[ "Neat! Looks like you need a rebase and then should be good to go :) ", "Done, @yjernite, @lhoestq.", "If you agree, we could merge this.", "Hi ! yes sure :) can you just merge master into this branch before we merge ?", "Done @lhoestq " ]
https://api.github.com/repos/huggingface/datasets/issues/5926	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5926/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5926/comments	https://api.github.com/repos/huggingface/datasets/issues/5926/events	https://github.com/huggingface/datasets/issues/5926	1,743,922,028	I_kwDODunzps5n8iNs	5,926	Uncaught exception when generating the splits from a dataset that miss data	[]	open	false	null	1	2023-06-06T13:51:01Z	2023-06-07T07:53:16Z	null	null	### Describe the bug Dataset https://huggingface.co/datasets/blog_authorship_corpus has an issue with its hosting platform, since https://drive.google.com/u/0/uc?id=1cGy4RNDV87ZHEXbiozABr9gsSrZpPaPz&export=download returns 404 error. But when trying to generate the split names, we get an exception which is now correctly caught. Seen originally in https://github.com/huggingface/datasets-server/blob/adbdcd6710ffed4e2eb2e4cd905b5e0dff530a15/services/worker/src/worker/job_runners/config/parquet_and_info.py#L435 ### Steps to reproduce the bug ```python >>> from datasets import StreamingDownloadManager, load_dataset_builder >>> builder = load_dataset_builder(path="blog_authorship_corpus") Downloading builder script: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 5.60k/5.60k [00:00<00:00, 23.1MB/s] Downloading metadata: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2.81k/2.81k [00:00<00:00, 14.7MB/s] Downloading readme: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 7.30k/7.30k [00:00<00:00, 30.8MB/s] >>> dl_manager = StreamingDownloadManager(base_path=builder.base_path) >>> builder._split_generators(dl_manager) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/blog_authorship_corpus/6f5d78241afd8313111956f877a57db7a0e9fc6718255dc85df0928197feb683/blog_authorship_corpus.py", line 79, in _split_generators data = dl_manager.download_and_extract(_DATA_URL) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1087, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1039, in extract urlpaths = map_nested(self._extract, url_or_urls, map_tuple=True) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 435, in map_nested return function(data_struct) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 1044, in _extract protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token) File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 433, in _get_extraction_protocol with fsspec.open(urlpath, **kwargs) as f: File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 439, in open return open_files( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 194, in __getitem__ out = super().__getitem__(item) IndexError: list index out of range ``` ### Expected behavior We should have an Exception raised by the datasets library. ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.19.0-1026-aws-x86_64-with-glibc2.35 - Python version: 3.9.15 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 2.0.2	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5926/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5926/timeline	null	null	null	null	false	[ "Thanks for reporting, @severo.\r\n\r\nThis is a known issue with `fsspec`:\r\n- #5862\r\n- https://github.com/fsspec/filesystem_spec/issues/1265" ]
https://api.github.com/repos/huggingface/datasets/issues/1753	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/1753/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/1753/comments	https://api.github.com/repos/huggingface/datasets/issues/1753/events	https://github.com/huggingface/datasets/pull/1753	789,867,685	MDExOlB1bGxSZXF1ZXN0NTU4MTQ3Njkx	1,753	fix comet citations	[]	closed	false	null	0	2021-01-20T10:52:38Z	2021-01-20T14:39:30Z	2021-01-20T14:39:30Z	null	I realized COMET citations were not showing in the hugging face metrics page: <img width="814" alt="Screenshot 2021-01-20 at 09 48 44" src="https://user-images.githubusercontent.com/17256847/105164848-8b9da900-5b0d-11eb-9e20-a38f559d2037.png"> This pull request is intended to fix that. Thanks!	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/1753/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/1753/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/1753.diff", "html_url": "https://github.com/huggingface/datasets/pull/1753", "merged_at": "2021-01-20T14:39:30Z", "patch_url": "https://github.com/huggingface/datasets/pull/1753.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/1753" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/1877	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/1877/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/1877/comments	https://api.github.com/repos/huggingface/datasets/issues/1877/events	https://github.com/huggingface/datasets/issues/1877	808,462,272	MDU6SXNzdWU4MDg0NjIyNzI=	1,877	Allow concatenation of both in-memory and on-disk datasets	[]	closed	false	null	6	2021-02-15T11:39:46Z	2021-03-26T16:51:58Z	2021-03-26T16:51:58Z	null	This is a prerequisite for the addition of the `add_item` feature (see #1870). Currently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files). This assumption is used for pickling for example: - in-memory dataset can just be pickled/unpickled in-memory - on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling Maybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future One idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table. Then the dataset would be the concatenation of all these tables. Depending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data. If you have some ideas you would like to share about the design/API feel free to do so :) cc @albertvillanova	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 1, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/1877/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/1877/timeline	null	completed	null	null	false	[ "I started working on this. My idea is to first add the pyarrow Table wrappers InMemoryTable and MemoryMappedTable that both implement what's necessary regarding copy/pickle. Then have another wrapper that takes the concatenation of InMemoryTable/MemoryMappedTable objects.\r\n\r\nWhat's important here is that concatenating two tables into one doesn't double the memory used (`total_allocated_bytes()` stays the same).", "Hi @lhoestq @albertvillanova,\r\n\r\nI checked the linked issues and PR, this seems like a great idea. Would you mind elaborating on the in-memory and memory-mapped datasets? \r\nBased on my understanding, it is something like this, please correct me if I am wrong:\r\n1. For in-memory datasets, we don't have any dataset files so the entire dataset is pickled to the cache during loading, and then whenever required it is unpickled .\r\n2. For on-disk/memory-mapped datasets, we have the data files provided, so they can be re-loaded from the paths, and only the file-paths are stored while pickling.\r\n\r\nIf this is correct, will the feature also handle pickling/unpickling of a concatenated dataset? Will this be cached?\r\n\r\nThis also leads me to ask whether datasets are chunked during pickling? \r\n\r\nThanks,\r\nGunjan", "Hi ! Yes you're totally right about your two points :)\r\n\r\nAnd in the case of a concatenated dataset, then we should reload each sub-table depending on whether it's in-memory or memory mapped. That means the dataset will be made of several blocks in order to keep track of what's from memory and what's memory mapped. This allows to pickle/unpickle concatenated datasets", "Hi @lhoestq\r\n\r\nThanks, that sounds nice. Can you explain where the issue of the double memory may arise? Also, why is the existing `concatenate_datasets` not sufficient for this purpose?", "Hi @lhoestq,\r\n\r\nWill the `add_item` feature also help with lazy writing (or no caching) during `map`/`filter`?", "> Can you explain where the issue of the double memory may arise?\r\n\r\nWe have to keep each block (in-memory vs memory mapped) separated in order to be able to reload them with pickle.\r\nOn the other hand we also need to have the full table from mixed in-memory and memory mapped data in order to iterate or extract data conveniently. That means that each block is accessible twice: once in the full table, and once in the separated blocks. But since pyarrow tables concatenation doesn't double the memory, then building the full table doesn't cost memory which is what we want :)\r\n\r\n> Also, why is the existing concatenate_datasets not sufficient for this purpose?\r\n\r\nThe existing `concatenate_datasets` doesn't support having both in-memory and memory mapped data together (there's no fancy block separation logic). It works for datasets fully in-memory or fully memory mapped but not a mix of the two.\r\n\r\n> Will the add_item feature also help with lazy writing (or no caching) during map/filter?\r\n\r\nIt will enable the implementation of the fast, masked filter from this discussion: https://github.com/huggingface/datasets/issues/1949\r\nHowever I don't think this will affect map." ]
https://api.github.com/repos/huggingface/datasets/issues/5510	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5510/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5510/comments	https://api.github.com/repos/huggingface/datasets/issues/5510/events	https://github.com/huggingface/datasets/pull/5510	1,575,191,549	PR_kwDODunzps5JehbR	5,510	Milvus integration for search	[]	open	false	null	5	2023-02-07T23:30:26Z	2023-02-24T16:45:09Z	null	null	Signed-off-by: Filip Haltmayer <filip.haltmayer@zilliz.com>	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5510/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5510/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/5510.diff", "html_url": "https://github.com/huggingface/datasets/pull/5510", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5510.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5510" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5510). All of your documentation changes will be reflected on that endpoint.", "To the maintainer, sorry about the repeated run requests for formatting. Missed the `make style` outlined in contributing guidelines. ", "Anything I can do to get the workflow to run? @lhoestq ", "cc @mariosasko \r\n\r\n> Anything I can do to get the workflow to run?\r\n\r\nYou can merge `main` into your branch to fix code formatting (we switched from isort+flake8 to ruff this week), and then run `make style`", "I believe that should be good. @mariosasko" ]
https://api.github.com/repos/huggingface/datasets/issues/402	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/402/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/402/comments	https://api.github.com/repos/huggingface/datasets/issues/402/events	https://github.com/huggingface/datasets/pull/402	658,001,288	MDExOlB1bGxSZXF1ZXN0NDUwMDI2NTE0	402	Search qa	[]	closed	false	null	0	2020-07-16T09:00:10Z	2020-07-16T14:27:00Z	2020-07-16T14:26:59Z	null	add SearchQA dataset #336	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/402/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/402/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/402.diff", "html_url": "https://github.com/huggingface/datasets/pull/402", "merged_at": "2020-07-16T14:26:59Z", "patch_url": "https://github.com/huggingface/datasets/pull/402.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/402" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/5738	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5738/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5738/comments	https://api.github.com/repos/huggingface/datasets/issues/5738/events	https://github.com/huggingface/datasets/issues/5738	1,663,477,690	I_kwDODunzps5jJqe6	5,738	load_dataset("text","dataset.txt") loads the wrong dataset!	[]	closed	false	null	1	2023-04-12T01:07:46Z	2023-04-19T12:08:27Z	2023-04-19T12:08:27Z	null	### Describe the bug I am trying to load my own custom text dataset using the load_dataset function. My dataset is a bunch of ordered text, think along the lines of shakespeare plays. However, after I load the dataset and I inspect it, the dataset is a table with a bunch of latitude and longitude values! What in the world?? ### Steps to reproduce the bug my_dataset = load_dataset("text","TextFile.txt") my_dataset ### Expected behavior I expected the dataset to contain the actual data from the text document that I used. ### Environment info Google Colab	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5738/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5738/timeline	null	completed	null	null	false	[ "You need to provide a text file as `data_files`, not as a configuration:\r\n\r\n```python\r\nmy_dataset = load_dataset(\"text\", data_files=\"TextFile.txt\")\r\n```\r\n\r\nOtherwise, since `data_files` is `None`, it picks up Colab's sample datasets from the `content` dir." ]
https://api.github.com/repos/huggingface/datasets/issues/375	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/375/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/375/comments	https://api.github.com/repos/huggingface/datasets/issues/375/events	https://github.com/huggingface/datasets/issues/375	655,023,307	MDU6SXNzdWU2NTUwMjMzMDc=	375	TypeError when computing bertscore	[]	closed	false	null	2	2020-07-10T20:37:44Z	2022-06-01T15:15:59Z	2022-06-01T15:15:59Z	null	Hi, I installed nlp 0.3.0 via pip, and my python version is 3.7. When I tried to compute bertscore with the code: ``` import nlp bertscore = nlp.load_metric('bertscore') # load hyps and refs ... print (bertscore.compute(hyps, refs, lang='en')) ``` I got the following error. ``` Traceback (most recent call last): File "bert_score_evaluate.py", line 16, in <module> print (bertscore.compute(hyps, refs, lang='en')) File "/home/willywsm/anaconda3/envs/torcher/lib/python3.7/site-packages/nlp/metric.py", line 200, in compute output = self._compute(predictions=predictions, references=references, **metrics_kwargs) File "/home/willywsm/anaconda3/envs/torcher/lib/python3.7/site-packages/nlp/metrics/bertscore/fb176889831bf0ce995ed197edc94b2e9a83f647a869bb8c9477dbb2d04d0f08/bertscore.py", line 105, in _compute hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline) TypeError: get_hash() takes 3 positional arguments but 4 were given ``` It seems like there is something wrong with get_hash() function?	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/375/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/375/timeline	null	completed	null	null	false	[ "I am not able to reproduce this issue on my side.\r\nCould you give us more details about the inputs you used ?\r\n\r\nI do get another error though:\r\n```\r\n~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/bert_score/utils.py in bert_cos_score_idf(model, refs, hyps, tokenizer, idf_dict, verbose, batch_size, device, all_layers)\r\n 371 return sorted(list(set(l)), key=lambda x: len(x.split(\" \")))\r\n 372 \r\n--> 373 sentences = dedup_and_sort(refs + hyps)\r\n 374 embs = []\r\n 375 iter_range = range(0, len(sentences), batch_size)\r\n\r\nValueError: operands could not be broadcast together with shapes (0,) (2,)\r\n```\r\nThat's because it gets numpy arrays as input and not lists. See #387 ", "The other issue was fixed by #403 \r\n\r\nDo you still get this issue @willywsm1013 ?\r\n" ]
https://api.github.com/repos/huggingface/datasets/issues/3725	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3725/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3725/comments	https://api.github.com/repos/huggingface/datasets/issues/3725/events	https://github.com/huggingface/datasets/pull/3725	1,138,835,625	PR_kwDODunzps4y3bOG	3,725	Pin pandas to avoid bug in streaming mode	[]	closed	false	null	0	2022-02-15T15:21:00Z	2022-02-15T15:52:38Z	2022-02-15T15:52:37Z	null	Temporarily pin pandas version to avoid bug in streaming mode (patching no longer works). Related to #3724.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/3725/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3725/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/3725.diff", "html_url": "https://github.com/huggingface/datasets/pull/3725", "merged_at": "2022-02-15T15:52:37Z", "patch_url": "https://github.com/huggingface/datasets/pull/3725.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3725" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/2076	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2076/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2076/comments	https://api.github.com/repos/huggingface/datasets/issues/2076/events	https://github.com/huggingface/datasets/issues/2076	834,445,296	MDU6SXNzdWU4MzQ0NDUyOTY=	2,076	Issue: Dataset download error	[ { "color": "2edb81", "default": false, "description": "A bug in a dataset script provided in the library", "id": 2067388877, "name": "dataset bug", "node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug" } ]	open	false	null	7	2021-03-18T06:36:06Z	2021-03-22T11:52:31Z	null	null	The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link?	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/2076/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2076/timeline	null	null	null	null	false	[ "Hi @XuhuiZhou, thanks for reporting this issue. \r\n\r\nIndeed, the old links are no longer valid (404 Not Found error), and the script must be updated with the new links to Google Drive.", "It would be nice to update the urls indeed !\r\n\r\nTo do this, you just need to replace the urls in `iwslt2017.py` and then update the dataset_infos.json file with\r\n```\r\ndatasets-cli test ./datasets/iwslt2017 --all_configs --save_infos --ignore_verifications\r\n```", "Is this a command to update my local files or fix the file Github repo in general? (I am not so familiar with the datasets-cli command here)\r\n\r\nI also took a brief look at the Sharing your dataset section, looks like I could fix that locally and push it to the repo? I guess we are \"canonical\" category?", "This command will update your local file. Then you can open a Pull Request to push your fix to the github repo :)\r\nAnd yes you are right, it is a \"canonical\" dataset, i.e. a dataset script defined in this github repo (as opposed to dataset repositories of users on the huggingface hub)", "Hi, thanks for the answer. \r\n\r\nI gave a try to the problem today. But I encountered an upload error: \r\n\r\n```\r\ngit push -u origin fix_link_iwslt\r\nEnter passphrase for key '/home2/xuhuizh/.ssh/id_rsa': \r\nERROR: Permission to huggingface/datasets.git denied to XuhuiZhou.\r\nfatal: Could not read from remote repository.\r\n\r\nPlease make sure you have the correct access rights\r\nand the repository exists.\r\n```\r\n\r\nAny insight here? \r\n\r\nBy the way, when I run the datasets-cli command, it shows the following error, but does not seem to be the error coming from `iwslt.py`\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home2/xuhuizh/anaconda3/envs/UMT/bin/datasets-cli\", line 33, in <module>\r\n sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/commands/datasets_cli.py\", line 35, in main\r\n service.run()\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/commands/test.py\", line 141, in run\r\n try_from_hf_gcs=False,\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/builder.py\", line 579, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/builder.py\", line 639, in _download_and_prepare\r\n self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/utils/info_utils.py\", line 32, in verify_checksums\r\n raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\ndatasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://wit3.fbk.eu/archive/2017-01-trnmted//texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.tgz'}\r\n```", "Hi ! To create a PR on this repo your must fork it and create a branch on your fork. See how to fork the repo [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#start-by-preparing-your-environment).\r\nAnd to make the command work without the `ExpectedMoreDownloadedFiles` error, you just need to use the `--ignore_verifications` flag.", "Hi @XuhuiZhou,\r\n\r\nAs @lhoestq has well explained, you need to fork HF's repository, create a feature branch in your fork, push your changes to it and then open a Pull Request to HF's upstream repository. This is so because at HuggingFace Datasets we follow a development model called \"Fork and Pull Model\". You can find more information here:\r\n- [Understanding the GitHub flow](https://guides.github.com/introduction/flow/)\r\n- [Forking Projects](https://guides.github.com/activities/forking/)\r\n\r\nAlternatively, if you find all these steps too complicated, you can use the GitHub official command line tool: [GitHub CLI](https://cli.github.com/). Once installed, in order to create a Pull Request, you only need to use this command:\r\n```shell\r\ngh pr create --web\r\n```\r\nThis utility will automatically create the fork, push your changes and open a Pull Request, under the hood." ]
https://api.github.com/repos/huggingface/datasets/issues/2085	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2085/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2085/comments	https://api.github.com/repos/huggingface/datasets/issues/2085/events	https://github.com/huggingface/datasets/pull/2085	835,870,994	MDExOlB1bGxSZXF1ZXN0NTk2NDYyOTc2	2,085	Fix max_wait_time in requests	[]	closed	false	null	0	2021-03-19T11:22:26Z	2021-03-23T15:36:38Z	2021-03-23T15:36:37Z	null	it was handled as a min time, not max cc @SBrandeis	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/2085/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2085/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/2085.diff", "html_url": "https://github.com/huggingface/datasets/pull/2085", "merged_at": "2021-03-23T15:36:37Z", "patch_url": "https://github.com/huggingface/datasets/pull/2085.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/2085" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/5624	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5624/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5624/comments	https://api.github.com/repos/huggingface/datasets/issues/5624/events	https://github.com/huggingface/datasets/issues/5624	1,617,400,192	I_kwDODunzps5gZ5GA	5,624	glue datasets returning -1 for test split	[]	closed	false	null	1	2023-03-09T14:47:18Z	2023-03-09T16:49:29Z	2023-03-09T16:49:29Z	null	### Describe the bug Downloading any dataset from GLUE has -1 as class labels for test split. Train and validation have regular 0/1 class labels. This is also present in the dataset card online. ### Steps to reproduce the bug ``` dataset = load_dataset("glue", "sst2") for d in dataset: # prints out -1 print(d["label"] ``` ### Expected behavior Expected behavior should be 0/1 instead of -1. ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.15.0-46-generic-x86_64-with-glibc2.17 - Python version: 3.8.16 - PyArrow version: 8.0.0 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5624/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5624/timeline	null	completed	null	null	false	[ "Hi @lithafnium, thanks for reporting.\r\n\r\nPlease note that you can use the \"Community\" tab in the corresponding dataset page to start any discussion: https://huggingface.co/datasets/glue/discussions\r\n\r\nIndeed this issue was already raised there (https://huggingface.co/datasets/glue/discussions/5) and answered: https://huggingface.co/datasets/glue/discussions/5#63907885937867f0cb3cde31\r\n> The test labels are not public.\r\n>\r\n> Note this dataset belongs to a benchmark: people send their predictions for the test split to GLUE (https://gluebenchmark.com/) and then they get a score in their leaderboard...\r\n" ]
https://api.github.com/repos/huggingface/datasets/issues/2124	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2124/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2124/comments	https://api.github.com/repos/huggingface/datasets/issues/2124/events	https://github.com/huggingface/datasets/issues/2124	842,627,729	MDU6SXNzdWU4NDI2Mjc3Mjk=	2,124	Adding ScaNN library to do MIPS?	[]	open	false	null	1	2021-03-28T00:07:00Z	2021-03-29T13:23:43Z	null	null	@lhoestq Hi I am thinking of adding this new google library to do the MIPS similar to add_faiss_idex. As the paper suggests, it is really fast when it comes to retrieving the nearest neighbors. https://github.com/google-research/google-research/tree/master/scann ![image](https://user-images.githubusercontent.com/16892570/112738294-78ec9800-8fc6-11eb-9a5f-3d7ee5818e76.png)	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/2124/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2124/timeline	null	null	null	null	false	[ "I haven't played with it (yet) but it sounds really cool !\r\n" ]
https://api.github.com/repos/huggingface/datasets/issues/1841	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/1841/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/1841/comments	https://api.github.com/repos/huggingface/datasets/issues/1841/events	https://github.com/huggingface/datasets/issues/1841	803,561,123	MDU6SXNzdWU4MDM1NjExMjM=	1,841	Add ljspeech	[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" }, { "color": "d93f0b", "default": false, "description": "", "id": 2725241052, "name": "speech", "node_id": "MDU6TGFiZWwyNzI1MjQxMDUy", "url": "https://api.github.com/repos/huggingface/datasets/labels/speech" } ]	closed	false	null	0	2021-02-08T13:22:26Z	2021-03-15T05:59:02Z	2021-03-15T05:59:02Z	null	## Adding a Dataset - Name: ljspeech - Description: This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours. The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.) - Paper: Homepage: https://keithito.com/LJ-Speech-Dataset/ - Data: https://keithito.com/LJ-Speech-Dataset/ - Motivation: Important speech dataset - TFDatasets Implementation: https://www.tensorflow.org/datasets/catalog/ljspeech If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/1841/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/1841/timeline	null	completed	null	null	false	[]
https://api.github.com/repos/huggingface/datasets/issues/798	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/798/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/798/comments	https://api.github.com/repos/huggingface/datasets/issues/798/events	https://github.com/huggingface/datasets/issues/798	735,518,805	MDU6SXNzdWU3MzU1MTg4MDU=	798	Cannot load TREC dataset: ConnectionError	[ { "color": "2edb81", "default": false, "description": "A bug in a dataset script provided in the library", "id": 2067388877, "name": "dataset bug", "node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug" } ]	closed	false	null	9	2020-11-03T17:45:22Z	2022-02-14T15:34:22Z	2022-02-14T15:34:22Z	null	## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here.	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/798/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/798/timeline	null	completed	null	null	false	[ "Hi ! Indeed there's an issue with those links.\r\nWe should probably use the target urls of the redirections instead", "Hi, the same issue here, could you tell me how to download it through datasets? thanks ", "Same issue. ", "Actually it's already fixed on the master branch since #740 \r\nI'll do the 1.1.3 release soon", "Hi\nthanks, but I did tried to install from the pip install git+... and it does\nnot work for me,. thanks for the help. I have the same issue with wmt16,\n\"ro-en\"\nthanks.\nBest\nRabeeh\n\nOn Mon, Nov 16, 2020 at 10:29 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Actually it's already fixed on the master branch since #740\n> <https://github.com/huggingface/datasets/pull/740>\n> I'll do the 1.1.3 release soon\n>\n> —\n> You are receiving this because you commented.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/798#issuecomment-727854736>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ABP4ZCEUBJKPOCLABXCKMPDSQDWH3ANCNFSM4TJBUKSA>\n> .\n>\n", "I just tested on google colab using\r\n```python\r\n!pip install git+https://github.com/huggingface/datasets.git\r\nfrom datasets import load_dataset\r\nload_dataset(\"trec\")\r\n```\r\nand it works.\r\nCan you detail how you got the issue even when using the latest version on master ?\r\n\r\nAlso about wmt we'll look into it, thanks for reporting !", "I think the new url with .edu is also broken:\r\n```\r\nConnectionError: Couldn't reach https://cogcomp.seas.upenn.edu/Data/QA/QC/train_5500.label\r\n```\r\nCant download the dataset anymore.", "Hi ! The URL seems to work fine on my side, can you try again ?", "Forgot to update, i wrote an email to the webmaster of seas.upenn.edu because i couldnt reach the url on any machine. This was the answer:\r\n```\r\nThank you for your report. The server was offline for maintenance and is now available again.\r\n```\r\nGuess all back to normal now 🙂 " ]
https://api.github.com/repos/huggingface/datasets/issues/2248	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/2248/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/2248/comments	https://api.github.com/repos/huggingface/datasets/issues/2248/events	https://github.com/huggingface/datasets/pull/2248	864,853,447	MDExOlB1bGxSZXF1ZXN0NjIxMDEyNzg5	2,248	Implement Dataset to JSON	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	{ "closed_at": "2021-05-31T16:20:53Z", "closed_issues": 3, "created_at": "2021-04-09T13:16:31Z", "creator": { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }, "description": "Next minor release", "due_on": "2021-05-14T07:00:00Z", "html_url": "https://github.com/huggingface/datasets/milestone/3", "id": 6644287, "labels_url": "https://api.github.com/repos/huggingface/datasets/milestones/3/labels", "node_id": "MDk6TWlsZXN0b25lNjY0NDI4Nw==", "number": 3, "open_issues": 0, "state": "closed", "title": "1.7", "updated_at": "2021-05-31T16:20:53Z", "url": "https://api.github.com/repos/huggingface/datasets/milestones/3" }	0	2021-04-22T11:46:51Z	2021-04-27T15:29:21Z	2021-04-27T15:29:20Z	null	Implement `Dataset.to_json`.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/2248/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/2248/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/2248.diff", "html_url": "https://github.com/huggingface/datasets/pull/2248", "merged_at": "2021-04-27T15:29:20Z", "patch_url": "https://github.com/huggingface/datasets/pull/2248.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/2248" }	true	[]
https://api.github.com/repos/huggingface/datasets/issues/3791	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/3791/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/3791/comments	https://api.github.com/repos/huggingface/datasets/issues/3791/events	https://github.com/huggingface/datasets/pull/3791	1,150,733,475	PR_kwDODunzps4zevU2	3,791	Add `data_dir` to `data_files` resolution and misc improvements to HfFileSystem	[]	closed	false	null	0	2022-02-25T18:26:35Z	2022-03-01T13:10:43Z	2022-03-01T13:10:42Z	null	As discussed in https://github.com/huggingface/datasets/pull/2830#issuecomment-1048989764, this PR adds a QOL improvement to easily reference the files inside a directory in `load_dataset` using the `data_dir` param (very handy for ImageFolder because it avoids globbing, but also useful for the other loaders). Additionally, it fixes the issue with `HfFileSystem.isdir`, which would previously always return `False`, and aligns the path-handling logic in `HfFileSystem` with `fsspec.GitHubFileSystem`.	{ "+1": 2, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/3791/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/3791/timeline	null	null	false	{ "diff_url": "https://github.com/huggingface/datasets/pull/3791.diff", "html_url": "https://github.com/huggingface/datasets/pull/3791", "merged_at": "2022-03-01T13:10:42Z", "patch_url": "https://github.com/huggingface/datasets/pull/3791.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3791" }	true	[]

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