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https://api.github.com/repos/huggingface/datasets/issues/3492
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Add `gzip` for `to_json`
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| 2022-07-10T14:36:52Z
| 2022-01-05T13:03:36Z
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CONTRIBUTOR
| null | 0
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(Partially) closes #3480. I have added `gzip` compression for `to_json`. I realised we can run into this compression problem with `to_csv` as well. `IOHandler` can be used for `to_csv` too. Please let me know if any changes are required.
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Dataset wikipedia and Bookcorpusopen cannot be fetched from dataloader.
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[
"Hi ! I think this issue is more related to the `transformers` project. Could you open an issue on https://github.com/huggingface/transformers ?\r\n\r\nAnyway I think the issue could be that both wikipedia and bookcorpusopen have an additional \"title\" column, contrary to wikitext which only has a \"text\" column. After calling `load_dataset`, can you try doing `dataset = dataset.remove_columns(\"title\")` ?",
"Removing the \"title\" column works! Thanks for your advice.\r\n\r\nMaybe I should still create an issue to `transformers' to mark this solution?"
] | 2021-10-11T11:54:59Z
| 2022-01-19T14:03:49Z
| 2022-01-19T14:03:49Z
|
NONE
| null | null | null |
## Describe the bug
I have used the previous version of `transformers` and `datasets`. The dataset `wikipedia` can be successfully used. Recently, I upgrade them to the newest version and find it raises errors. I also tried other datasets. The `wikitext` works and the `bookcorpusopen` raises the same errors as `wikipedia`.
## Steps to reproduce the bug
Run the `run_mlm_no_trainer.py` and the given script on this [link](https://github.com/huggingface/transformers/tree/master/examples/pytorch/language-modeling). Change the dataset from wikitext to wikipedia or bookcorpusopen. BTW, the library transformers is of version 4.11.3.
## Expected results
The data batchs are fetched from the data loader and train.
## Actual results
The first time to fetch data batch occurs error.
`Traceback (most recent call last):
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 705, in convert_to_tensors
tensor = as_tensor(value)
ValueError: too many dimensions 'str'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "src/original_run_mlm_no_trainer.py", line 528, in <module>
main()
File "src/original_run_mlm_no_trainer.py", line 488, in main
for step, batch in enumerate(train_dataloader):
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/accelerate/data_loader.py", line 303, in __iter__
for batch in super().__iter__():
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 517, in __next__
data = self._next_data()
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 557, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
return self.collate_fn(data)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 41, in __call__
return self.torch_call(features)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 671, in torch_call
batch = self.tokenizer.pad(examples, return_tensors="pt", pad_to_multiple_of=self.pad_to_multiple_of)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2774, in pad
return BatchEncoding(batch_outputs, tensor_type=return_tensors)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 210, in __init__
self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 722, in convert_to_tensors
"Unable to create tensor, you should probably activate truncation and/or padding "
ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.
`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.8.0-59-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.6
- PyArrow version: 5.0.0
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I_kwDODunzps5NehtD
| 4,669
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loading oscar-corpus/OSCAR-2201 raises an error
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[
"I had to use the appropriate token for use_auth_token. Thank you."
] | 2022-07-10T07:09:30Z
| 2022-07-11T09:27:49Z
| 2022-07-11T09:27:49Z
|
NONE
| null | null | null |
## Describe the bug
load_dataset('oscar-2201', 'af')
raises an error:
Traceback (most recent call last):
File "/usr/lib/python3.8/code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 1, in <module>
File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset
builder_instance = load_dataset_builder(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder
dataset_module = dataset_module_factory(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py
I've tried other permutations such as :
oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-2201', 'af')
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201')
with the same unfortunate result.
## Steps to reproduce the bug
oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-2201', 'af')
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201')
# Sample code to reproduce the bug
```
## Expected results
loaded data
## Actual results
Traceback (most recent call last):
File "/usr/lib/python3.8/code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 1, in <module>
File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset
builder_instance = load_dataset_builder(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder
dataset_module = dataset_module_factory(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.13.0-37-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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MDExOlB1bGxSZXF1ZXN0NjAwOTYxMDEz
| 2,113
|
Implement Dataset as context manager
|
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[] | 2021-03-25T18:18:30Z
| 2021-03-31T11:30:14Z
| 2021-03-31T08:30:11Z
|
MEMBER
| null | 0
|
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When used as context manager, it would be safely deleted if some exception is raised.
This will avoid
> During handling of the above exception, another exception occurred:
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| 1,179,658,611
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I_kwDODunzps5GUClz
| 4,009
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AMI load_dataset error: sndfile library not found
|
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[
"Issue unresolved, see [4000](https://github.com/huggingface/datasets/issues/4009#issue-1179658611)"
] | 2022-03-24T15:13:38Z
| 2022-03-24T15:46:38Z
| 2022-03-24T15:17:29Z
|
NONE
| null | null | null |
## Describe the bug
Getting error message when loading AMI dataset.
## Steps to reproduce the bug
`python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
`
## Expected results
A clear and concise description of the expected results.
## Actual results
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
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Added XL-Sum dataset
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"Thanks for adding this one ! I just did some minor changes and set the timeout back to 100sec instead of 1000",
"The CI failure is unrelated to this PR - let me take a look",
"> Thanks for adding this one! I just did some minor changes and set the timeout back to 100sec instead of 1000\r\n\r\nThank you for updating the language tags. I tried timeout values up to 300 sec on my local machine, but some of the larger files still get timed out. Although this could have been a network issue on my end, have you verified that 100 sec works for all files?",
"Well the main issue with google drive - even before the time out issues - is that it has a daily quota of downloads per file.\r\nTherefore if many people start downloading this dataset, it will be unavailable until the quota is reset the next day.\r\n\r\nSo ideally it would be nice if the data were hosted elsewhere than Google drive, to avoid the quota and time out issue.\r\nHF can probably help with hosting the data if needed",
"> Well the main issue with google drive - even before the time out issues - is that it has a daily quota of downloads per file.\r\n> Therefore if many people start downloading this dataset, it will be unavailable until the quota is reset the next day.\r\n> \r\n> So ideally it would be nice if the data were hosted elsewhere than Google drive, to avoid the quota and time out issue.\r\n> HF can probably help with hosting the data if needed\r\n\r\nIt'd be great if the dataset can be hosted in HF. How should I proceed here though? Upload the dataset files as a community dataset and update the links in this pull request or is there a more straightforward way?",
"Hi ! Ideally everything should be in the same place, so feel free to create a community dataset on the Hub and upload your data files as well as you dataset script (and also the readme.md and dataset_infos.json).\r\n\r\nThe only change you have to do in your dataset script is use a relative path to your data files instead of urls.\r\nFor example if your repository looks like this:\r\n```\r\nxlsum/\r\n├── data/\r\n│ ├── amharic_XLSum_v2.0.tar.bz2\r\n│ ├── ...\r\n│ └── yoruba_XLSum_v2.0.tar.bz2\r\n├── xlsum.py\r\n├── README.md\r\n└── dataset_infos.json\r\n```\r\nThen you just need to pass `\"data/amharic_XLSum_v2.0.tar.bz2\"` to `dl_manager.download_and_extract(...)`, instead of an url.\r\n\r\nLocally you can test that it's working as expected with\r\n```python\r\nload_dataset(\"path/to/my/directory/named/xlsum\")\r\n```\r\n\r\nThen once it's on the Hub, you can load it with\r\n```python\r\nload_dataset(\"username/xlsum\")\r\n```\r\n\r\nLet me know if you have questions :)",
"Thank you for your detailed response regarding the community dataset building process. However, will this pull request be merged into the main branch?",
"If XL-sum is available via the Hub we don't need to add it again in the `datasets` github repo ;)",
"The dataset has now been uploaded on HF hub. It's available at https://huggingface.co/datasets/csebuetnlp/xlsum. Closing this pull request. Thank you for your contributions. ",
"Thank you !"
] | 2021-08-19T13:47:45Z
| 2021-09-29T08:13:44Z
| 2021-09-23T17:49:05Z
|
NONE
| null | 0
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Added XL-Sum dataset published in ACL-IJCNLP 2021. (https://aclanthology.org/2021.findings-acl.413/). The default timeout values in `src/datasets/utils/file_utls.py` were increased to enable downloading from the original google drive links.
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push to hub fails with huggingface-hub 0.5.0
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"Hi ! Indeed there was a breaking change in `huggingface_hub` 0.5.0 in `HfApi.create_repo`, which is called here in `datasets` by passing the org name in both the `repo_id` and the `organization` arguments:\r\n\r\nhttps://github.com/huggingface/datasets/blob/2230f7f7d7fbaf102cff356f5a8f3bd1561bea43/src/datasets/arrow_dataset.py#L3363-L3369\r\n\r\nI think we should fix that in `huggingface_hub`, will keep you posted. In the meantime please use `huggingface_hub` 0.4.0",
"I'll be sending a fix for this later today on the `huggingface_hub` side.\r\n\r\nThe error would be converted to a `FutureWarning` if `datasets` uses kwargs instead of positional, for example here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/2230f7f7d7fbaf102cff356f5a8f3bd1561bea43/src/datasets/arrow_dataset.py#L3363-L3369\r\n\r\nto be:\r\n\r\n``` python\r\n api.create_repo(\r\n name=dataset_name,\r\n token=token,\r\n repo_type=\"dataset\",\r\n organization=organization,\r\n private=private,\r\n )\r\n```\r\n\r\nBut `name` and `organization` are deprecated in `huggingface_hub=0.5`, and people should pass `repo_id='org/name` instead. Note that `repo_id` was introduced in 0.5 and if `datasets` wants to support older `huggingface_hub` versions (which I encourage it to do), there needs to be a helper function to do that. It can be something like:\r\n\r\n\r\n```python\r\ndef create_repo(\r\n client,\r\n name: str,\r\n token: Optional[str] = None,\r\n organization: Optional[str] = None,\r\n private: Optional[bool] = None,\r\n repo_type: Optional[str] = None,\r\n exist_ok: Optional[bool] = False,\r\n space_sdk: Optional[str] = None,\r\n) -> str:\r\n try:\r\n return client.create_repo(\r\n repo_id=f\"{organization}/{name}\",\r\n token=token,\r\n private=private,\r\n repo_type=repo_type,\r\n exist_ok=exist_ok,\r\n space_sdk=space_sdk,\r\n )\r\n except TypeError:\r\n return client.create_repo(\r\n name=name,\r\n organization=organization,\r\n token=token,\r\n private=private,\r\n repo_type=repo_type,\r\n exist_ok=exist_ok,\r\n space_sdk=space_sdk,\r\n )\r\n```\r\n\r\nin a `utils/_fixes.py` kinda file and and be used internally.\r\n\r\nI'll be sending a patch to `huggingface_hub` to convert the error reported in this issue to a `FutureWarning`.",
"PR with the hotfix on the `huggingface_hub` side: https://github.com/huggingface/huggingface_hub/pull/822",
"We can definitely change `push_to_hub` to use `repo_id` in `datasets` and require `huggingface_hub>=0.5.0`.\r\n\r\nLet me open a PR :)",
"`huggingface_hub` 0.5.1 just got released with a fix, feel free to update `huggingface_hub` ;)"
] | 2022-04-06T08:59:57Z
| 2022-04-13T14:30:47Z
| 2022-04-13T14:30:47Z
|
NONE
| null | null | null |
## Describe the bug
`ds.push_to_hub` is failing when updating a dataset in the form "org_id/repo_id"
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("rubrix/news_test")
ds.push_to_hub("<your-user>/news_test", token="<your-token>")
```
## Expected results
The dataset is successfully uploaded
## Actual results
An error validation is raised:
```bash
if repo_id and (name or organization):
> raise ValueError(
"Only pass `repo_id` and leave deprecated `name` and "
"`organization` to be None."
E ValueError: Only pass `repo_id` and leave deprecated `name` and `organization` to be None.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.1
- `huggingface-hub`: 0.5
- Platform: macOS
- Python version: 3.8.12
- PyArrow version: 6.0.0
cc @adrinjalali
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I_kwDODunzps5gjglf
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load_dataset gives "403" error when using Financial phrasebank
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"Hi! You seem to be using an outdated version of `datasets` that downloads the older script version. To avoid the error, you can either pass `revision=\"main\"` to `load_dataset` (this can fail if a script uses newer features of the lib) or update your installation with `pip install -U datasets` (better solution)."
] | 2023-03-11T07:46:39Z
| 2023-03-13T18:27:26Z
| null |
NONE
| null | null | null |
When I try to load this dataset, I receive the following error:
ConnectionError: Couldn't reach https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip (error 403)
Has this been seen before? Thanks. The website loads when I try to access it manually.
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MDExOlB1bGxSZXF1ZXN0NTMyOTk2NjQ2
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Add BSD
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[
"Glad to have more Japanese data! Couple of comments:\r\n- the abbreviation might confuse some people as there is also an OPUS BSD corpus, would you mind renaming it as `bsd_ja_en`?\r\n- `flake8` is throwing some errors, you can run it locally (`flake8 datasets`) and fix what it tells you until it's happy :)\r\n- We're not using `os.path.join` for URLs as it's unstable across systems (introduces backslashes on Windows). Can you write the URLs explicitly instead?\r\n\r\nThanks!",
"Fantastic, looks great!",
"> Fantastic, looks great!\r\n\r\nThanks for your help @yjernite, really appreciate it!",
"The RemoteDatasetTest is fixed on master so it's fine",
"merging since the CI is fixed on master"
] | 2020-12-05T10:43:48Z
| 2020-12-07T09:27:46Z
| 2020-12-07T09:27:46Z
|
CONTRIBUTOR
| null | 0
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This PR adds BSD, the Japanese-English business dialogue corpus by
[Rikters et al., 2020](https://www.aclweb.org/anthology/D19-5204.pdf).
|
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I_kwDODunzps5o0Pa3
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IterableDataset: split by node and map may preprocess samples that will be skipped anyway
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[
"Does \"number of shards\" refer to the total number of data?\r\n\r\nmy config:\r\nnproc_per_node=2\r\nds=ds['train'] = load_dataset(streaming=True).take(50000)\r\n\r\nI'm test again: in prepare_data(), data have the same for each GPU\r\n",
"The number of shards is `ds.n_shards`. It corresponds generally to the number of files the dataset is made of, to be able to distribute to several nodes.\r\n\r\n**You don't end up with the same data per GPU**. But all the samples are going through your preprocessing function you pass to map. They are just skipped afterwards to only keep 1 sample out of n(GPUs)",
"For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end. \r\nIs my understanding correct?\r\n\r\nWhere can I print the actual training data for each GPU?",
"> For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end.\r\nIs my understanding correct?\r\n\r\nYes exactly :)\r\n\r\n> Where can I print the actual training data for each GPU?\r\n\r\nYou should call print in the data_collator",
"I print out n_shards, and under multiple GPUs, this value is always 1.\r\nIs this value correct?",
"Yes it's correct, and it explains why you always have the same data passed to your map function (the data can't be split).\r\n\r\nBut after being passed to `map`, each GPU keeps one example out of n(GPUs) so that you don't end up with duplicate data across GPUs",
"> > For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end.\r\n> > Is my understanding correct?\r\n> \r\n> Yes exactly :)\r\n> \r\n> > Where can I print the actual training data for each GPU?\r\n> \r\n> You should call print in the data_collator\r\n\r\nOK, when printing the train data in the data collator, each GPU sees different data.\r\n\r\nThanks for your reply",
"Do we have a solution for this one? Or it's required to get \"number of shards is a factor of number of GPUs: in that case the shards are evenly distributed per GPU\"",
"For now it's required to have a number of shards that is a factor of the number of GPUs to not have all the workers process the same data (and then skip the right ones to not end up training on duplicate data).\r\n\r\nIt would be quite complex to implement a strategy that would utilize all the GPUs with an arbitrary number of shards even at the end of training"
] | 2023-06-15T10:29:10Z
| 2023-09-01T10:35:11Z
| null |
NONE
| null | null | null |
There are two ways an iterable dataset can be split by node:
1. if the number of shards is a factor of number of GPUs: in that case the shards are evenly distributed per GPU
2. otherwise, each GPU iterate on the data and at the end keeps 1 sample out of n(GPUs) - skipping the others.
In case 2. it's therefore possible to have the same examples passed to `prepare_dataset` for each GPU.
This doesn't sound optimized though, because it runs the preprocessing on samples that won't be used in the end.
Could you open a new issue so that we can discuss about this and find a solution ?
_Originally posted by @lhoestq in https://github.com/huggingface/datasets/issues/5360#issuecomment-1592729051_
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Using a feature named "_type" fails with certain operations
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[
"Thanks for reporting !\r\n\r\nIndeed this is a keyword in the library that is used to encode/decode features to a python dictionary that we can save/load to json.\r\nWe can probably change `_type` to something that is less likely to collide with user feature names.\r\nIn this case we would want something backward compatible though.\r\n\r\nFeel free to try a fix and open a PR, and to ping me if I can help :) "
] | 2020-12-04T12:56:33Z
| 2022-01-14T18:07:00Z
| 2022-01-14T18:07:00Z
|
CONTRIBUTOR
| null | null | null |
A column named `_type` leads to a `TypeError: unhashable type: 'dict'` for certain operations:
```python
from datasets import Dataset, concatenate_datasets
ds = Dataset.from_dict({"_type": ["whatever"]}).map()
concatenate_datasets([ds])
# or simply
Dataset(ds._data)
```
Context: We are using datasets to persist data coming from elasticsearch to feed to our pipeline, and elasticsearch has a `_type` field, hence the strange name of the column.
Not sure if you wish to support this specific column name, but if you do i would be happy to try a fix and provide a PR. I already had a look into it and i think the culprit is the `datasets.features.generate_from_dict` function. It uses the hard coded `_type` string to figure out if it reached the end of the nested feature object from a serialized dict.
Best wishes and keep up the awesome work!
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Import Error in Kaggle notebook
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[
"This looks like an issue with PyArrow. Did you try reinstalling it ?",
"@lhoestq I did, and then let pip handle the installation in `pip import datasets`. I also tried using conda but it gives the same error.\r\n\r\nEdit: pyarrow version on kaggle is 4.0.0, it gets replaced with 4.0.1. So, I don't think uninstalling will change anything.\r\n```\r\nInstall Trace of datasets:\r\n\r\nCollecting datasets\r\n Downloading datasets-1.9.0-py3-none-any.whl (262 kB)\r\n |████████████████████████████████| 262 kB 834 kB/s eta 0:00:01\r\nRequirement already satisfied: dill in /opt/conda/lib/python3.7/site-packages (from datasets) (0.3.4)\r\nCollecting pyarrow!=4.0.0,>=1.0.0\r\n Downloading pyarrow-4.0.1-cp37-cp37m-manylinux2014_x86_64.whl (21.8 MB)\r\n |████████████████████████████████| 21.8 MB 6.2 MB/s eta 0:00:01\r\nRequirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from datasets) (3.4.0)\r\nRequirement already satisfied: huggingface-hub<0.1.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (0.0.8)\r\nRequirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from datasets) (1.2.4)\r\nRequirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2.25.1)\r\nRequirement already satisfied: fsspec>=2021.05.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2021.6.1)\r\nRequirement already satisfied: multiprocess in /opt/conda/lib/python3.7/site-packages (from datasets) (0.70.12.2)\r\nRequirement already satisfied: packaging in /opt/conda/lib/python3.7/site-packages (from datasets) (20.9)\r\nCollecting xxhash\r\n Downloading xxhash-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (243 kB)\r\n |████████████████████████████████| 243 kB 23.7 MB/s eta 0:00:01\r\nRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from datasets) (1.19.5)\r\nRequirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from datasets) (4.61.1)\r\nRequirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)\r\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (1.26.5)\r\nRequirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2.10)\r\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2021.5.30)\r\nRequirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (4.0.0)\r\nRequirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.7.4.3)\r\nRequirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.4.1)\r\nRequirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging->datasets) (2.4.7)\r\nRequirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2.8.1)\r\nRequirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2021.1)\r\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\r\nInstalling collected packages: xxhash, pyarrow, datasets\r\n Attempting uninstall: pyarrow\r\n Found existing installation: pyarrow 4.0.0\r\n Uninstalling pyarrow-4.0.0:\r\n Successfully uninstalled pyarrow-4.0.0\r\nSuccessfully installed datasets-1.9.0 pyarrow-4.0.1 xxhash-2.0.2\r\nWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\r\n```",
"You may need to restart your kaggle notebook after installing a newer version of `pyarrow`.\r\n\r\nIf it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail",
"> You may need to restart your kaggle notebook before after installing a newer version of `pyarrow`.\r\n> \r\n> If it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail\r\n\r\nIt works after restarting.\r\nMy bad, I forgot to restart the notebook. Sorry for the trouble!"
] | 2021-07-20T09:28:38Z
| 2021-07-21T13:59:26Z
| 2021-07-21T13:03:02Z
|
NONE
| null | null | null |
## Describe the bug
Not able to import datasets library in kaggle notebooks
## Steps to reproduce the bug
```python
!pip install datasets
import datasets
```
## Expected results
No such error
## Actual results
```
ImportError Traceback (most recent call last)
<ipython-input-9-652e886d387f> in <module>
----> 1 import datasets
/opt/conda/lib/python3.7/site-packages/datasets/__init__.py in <module>
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in <module>
36 import pandas as pd
37 import pyarrow as pa
---> 38 import pyarrow.compute as pc
39 from multiprocess import Pool, RLock
40 from tqdm.auto import tqdm
/opt/conda/lib/python3.7/site-packages/pyarrow/compute.py in <module>
16 # under the License.
17
---> 18 from pyarrow._compute import ( # noqa
19 Function,
20 FunctionOptions,
ImportError: /opt/conda/lib/python3.7/site-packages/pyarrow/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Kaggle
- Python version: 3.7.10
- PyArrow version: 4.0.1
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Add "Adversarial GLUE" dataset to datasets library
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"@lhoestq can you review when you have some time?",
"Hi @lhoestq -- thanks so much for your review! I just added the stuff you requested to the README.md, including an example from the dataset, the table of contents, and lots of section headers with \"More Information Needed\" below. Let me know if there's anything else I need to do!",
"Feel free to also merge `master` into your branch to get the latest updates for the tests ;)",
"thanks @lhoestq - just made all the updates you requested!"
] | 2022-03-08T00:47:11Z
| 2022-03-28T11:17:14Z
| 2022-03-28T11:12:04Z
|
CONTRIBUTOR
| null | 0
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Adds the Adversarial GLUE dataset: https://adversarialglue.github.io/
```python
>>> import datasets
>>> >>> datasets.load_dataset('adv_glue')
Using the latest cached version of the module from /home/jxm3/.cache/huggingface/modules/datasets_modules/datasets/adv_glue/26709a83facad2830d72d4419dd179c0be092f4ad3303ad0ebe815d0cdba5cb4 (last modified on Mon Mar 7 19:19:48 2022) since it couldn't be found locally at adv_glue., or remotely on the Hugging Face Hub.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jxm3/random/datasets/src/datasets/load.py", line 1657, in load_dataset
builder_instance = load_dataset_builder(
File "/home/jxm3/random/datasets/src/datasets/load.py", line 1510, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/home/jxm3/random/datasets/src/datasets/builder.py", line 1021, in __init__
super().__init__(*args, **kwargs)
File "/home/jxm3/random/datasets/src/datasets/builder.py", line 258, in __init__
self.config, self.config_id = self._create_builder_config(
File "/home/jxm3/random/datasets/src/datasets/builder.py", line 337, in _create_builder_config
raise ValueError(
ValueError: Config name is missing.
Please pick one among the available configs: ['adv_sst2', 'adv_qqp', 'adv_mnli', 'adv_mnli_mismatched', 'adv_qnli', 'adv_rte']
Example of usage:
`load_dataset('adv_glue', 'adv_sst2')`
>>> datasets.load_dataset('adv_glue', 'adv_sst2')['validation'][0]
Reusing dataset adv_glue (/home/jxm3/.cache/huggingface/datasets/adv_glue/adv_sst2/1.0.0/3719a903f606f2c96654d87b421bc01114c37084057cdccae65cd7bc24b10933)
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 604.11it/s]
{'sentence': "it 's an uneven treat that bores fun at the democratic exercise while also examining its significance for those who take part .", 'label': 1, 'idx': 0}
```
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[GEM] MultiWOZ dialogue dataset
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[
"Hi @yjernite can I help in adding this dataset? \r\n\r\nI am excited about this because this will be my first contribution to the datasets library as well as to hugginface.",
"Resolved via https://github.com/huggingface/datasets/pull/979"
] | 2020-11-10T14:57:50Z
| 2022-10-05T12:31:13Z
| 2022-10-05T12:31:13Z
|
MEMBER
| null | null | null |
## Adding a Dataset
- **Name:** MultiWOZ (Multi-Domain Wizard-of-Oz)
- **Description:** 10k annotated human-human dialogues. Each dialogue consists of a goal, multiple user and system utterances as well as a belief state. Only system utterances are annotated with dialogue acts – there are no annotations from the user side.
- **Paper:** https://arxiv.org/pdf/2007.12720.pdf
- **Data:** https://github.com/budzianowski/multiwoz
- **Motivation:** Will likely be part of the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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adding TabFact dataset
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Adding TabFact: A Large-scale Dataset for Table-based Fact Verification.
https://github.com/wenhuchen/Table-Fact-Checking
- The tables are stored as individual csv files, so need to download 16,573 🤯 csv files. As a result the `datasets_infos.json` file is huge (6.62 MB).
- Original dataset has nested structure where, where table is one example and each table has multiple statements,
flattening the structure here so that each statement is one example.
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Support pathlike obj in load dataset
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Fix #582
(I recreated the PR, I got an issue with git)
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Add msr_genomics_kbcomp dataset
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adding marathi-wiki dataset
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[
"Can you make it a draft PR until you've added the dataset please ? @ekdnam ",
"Done",
"Thanks for your contribution, @ekdnam. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if you need some help."
] | 2020-12-09T13:01:20Z
| 2022-10-03T09:39:09Z
| 2022-10-03T09:39:09Z
|
NONE
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Adding marathi-wiki-articles dataset.
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FileNotFoundError when trying to load the downloaded dataset with `load_dataset(..., streaming=True)`
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[
"You can clone the `togethercomputer/RedPajama-Data-1T-Sample` repo and load the dataset with `load_dataset(\"path/to/cloned_repo\")` to use it offline.",
"@mariosasko Thank you for your kind reply! I'll try it as a workaround.\r\nDoes that mean that currently it's not supported to simply load with a short name?",
"It is, but manually downloading repo files to the cache can easily lead to failure (the HF cache is not meant to be modified by a user besides deleting the files 🙂), as in your case. Hence, the clone + `load_dataset(\"path/to/cloned_repo\")` workflow should be used instead."
] | 2023-10-21T12:27:03Z
| 2023-10-23T18:50:07Z
| 2023-10-23T18:50:07Z
|
NONE
| null | null | null |
### Describe the bug
Hi, I'm trying to load the dataset `togethercomputer/RedPajama-Data-1T-Sample` with `load_dataset` in streaming mode, i.e., `streaming=True`, but `FileNotFoundError` occurs.
### Steps to reproduce the bug
I've downloaded the dataset and save it to the cache dir in advance. My hope is loading the files in offline environment and without taking too much hours to prepross the entire data before running into the training process.
So I try the following code to load the files streamingly
```py
dataset = load_dataset('togethercomputer/RedPajama-Data-1T-Sample', streaming=True)
print(next(iter(dataset['train'])))
```
Sadly, it raises the following:
```
FileNotFoundError: [Errno 2] No such file or directory: 'CURRENT_CODE_PATH/arxiv_sample.jsonl'
```
I've noticed that the dataset can be properly found in the begining
```
Using the latest cached version of the module from /root/.cache/huggingface/modules/datasets_modules/datasets/togethercomputer--RedPajama-Data-1T-Sample/6ea3bc8ec2e84ec6d2df1930942e9028ace8c5b9d9143823cf911c50bbd92039 (last modified on Sat Oct 21 20:12:57 2023) since it couldn't be found locally at togethercomputer/RedPajama-Data-1T-Sample., or remotely on the Hugging Face Hub.
```
But it seems that the paths couldn't be properly parsed when loading iteratively.
How should I fix this error. I've tried specifying `data_files` or `data_dir` as `.../arxiv_sample.jsonl` but none of them works.
Thanks.
### Expected behavior
Properly load the dataset.
### Environment info
`datasets==2.14.5`
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`Invalid user token` even when correct user token is passed!
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[
"This is because `download_config.use_auth_token` is deprecated - you should use `download_config.token` instead",
"Works! Thanks for the quick fix! <3"
] | 2023-08-29T12:37:03Z
| 2023-08-29T13:01:10Z
| 2023-08-29T13:01:09Z
|
MEMBER
| null | null | null |
### Describe the bug
I'm working on a dataset which comprises other datasets on the hub.
URL: https://huggingface.co/datasets/open-asr-leaderboard/datasets-test-only
Note: Some of the sub-datasets in this metadataset require explicit access.
All the other datasets work fine, except, `common_voice`.
### Steps to reproduce the bug
https://github.com/Vaibhavs10/scratchpad/blob/main/cv_datasets_bug_repro.ipynb
### Expected behavior
It should work if the provided access token is valid (as it does for all the other datasets)
### Environment info
datasets version -> 2.14.4
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Fix MP3 resampling when a dataset's audio files have different sampling rates
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The resampler needs to be updated if the `orig_freq` doesn't match the audio file sampling rate
Fix https://github.com/huggingface/datasets/issues/3662
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Create README.md
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[
"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-03-15T19:16:59Z
| 2022-04-04T15:23:15Z
| 2022-04-04T15:17:28Z
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Creating a README for IndicGLUE
cc @mcmillanmajora for fact checking in terms of languages (also, are there any limitations of the dataset or eval metric that I'm not aware of?)
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Support creating new branch in push_to_hub
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This adds support for creating a new branch when pushing a dataset to the hub. Tested both methods locally and branches are created.
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Extend support for streaming datasets that use pathlib.Path stem/suffix
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This PR extends the support in streaming mode for datasets that use `pathlib`, by patching the properties `pathlib.Path.stem` and `pathlib.Path.suffix`.
Related to #2876, #2874, #2866.
CC: @severo
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rotten tomatoes movie review dataset taken down
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[
"found a mirror: https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz",
"fixed in #484 ",
"Closing this one. Thanks again @jxmorris12 for taking care of this :)"
] | 2020-08-07T15:12:01Z
| 2020-09-08T09:36:34Z
| 2020-09-08T09:36:33Z
|
CONTRIBUTOR
| null | null | null |
In an interesting twist of events, the individual who created the movie review seems to have left Cornell, and their webpage has been removed, along with the movie review dataset (http://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz). It's not downloadable anymore.
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convert can use manual dir as second argument
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@mariamabarham
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How to choose proper download_mode in function load_dataset?
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[
"`download_mode=datasets.GenerateMode.FORCE_REDOWNLOAD` should work.\r\nThis makes me think we we should rename this to DownloadMode.FORCE_REDOWNLOAD. Currently that's confusing",
"Can we just use `features=...` in `load_dataset` for this @lhoestq?",
"Indeed you should use `features` in this case. \r\n```python\r\nfeatures = Features({'text': Value('string'), 'label': Value('float32')})\r\ndataset = load_dataset('csv', data_files=['sst_test.csv'], features=features)\r\n```\r\nNote that because of an issue with the caching when you change the features (see #750 ) you still need to specify the `FORCE_REDOWNLOAD ` flag. I'm working on a fix for this one",
"https://github.com/huggingface/datasets/issues/769#issuecomment-717837832\r\n> This makes me think we we should rename this to DownloadMode.FORCE_REDOWNLOAD. Currently that's confusing\r\n\r\n@lhoestq do you still think we should rename it?\r\n",
"It's no big deal, but since it can be confusing to users I think it's worth renaming it, and deprecate `GenerateMode` until `datasets` 2.0 at least. IMO it's confusing to have `download_mode=GenerateMode.something`"
] | 2020-10-28T09:16:19Z
| 2022-02-22T12:22:52Z
| 2022-02-22T12:22:52Z
|
NONE
| null | null | null |
Hi, I am a beginner to datasets and I try to use datasets to load my csv file.
my csv file looks like this
```
text,label
"Effective but too-tepid biopic",3
"If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .",4
"Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .",5
```
First I try to use this command to load my csv file .
``` python
dataset=load_dataset('csv', data_files=['sst_test.csv'])
```
It seems good, but when i try to overwrite the convert_options to convert 'label' columns from int64 to float32 like this.
``` python
import pyarrow as pa
from pyarrow import csv
read_options = csv.ReadOptions(block_size=1024*1024)
parse_options = csv.ParseOptions()
convert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()})
dataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options,
parse_options=parse_options, convert_options=convert_options)
```
It keeps the same:
```shell
Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210)
```
I think this issue is caused by the parameter "download_mode" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right.
Is it a bug? How to choose proper download_mode to avoid this issue?
|
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Add xquad-r dataset
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| 2020-12-04T18:14:30Z
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"Hi @Zaker237, thanks for reporting.\r\n\r\nThis is weird: the error you get is only thrown if the installed pyarrow version is less than 3.0.0.\r\n\r\nCould you please check that you install pyarrow in the same Python virtual environment where you installed datasets?\r\n\r\nFrom the Python command line (or terminal) where you get the error, please type:\r\n```\r\nimport pyarrow\r\nprint(pyarrow.__version__)\r\nimport datasets\r\nprint(datasets.__version__)\r\n``` ",
"hi @albertvillanova i try yesterday to create a new python environement with python 7 and try it on the environement and it worked. so i think that the error was not the package but may be jupyter notebook on conda. still yet i'm not yet sure but it worked in an environment created with venv",
"OK, thanks @Zaker237 for your feedback.\r\n\r\nI close this issue then. Please, feel free to reopen it if the problem arises again."
] | 2022-02-08T12:53:59Z
| 2022-02-09T06:35:33Z
| 2022-02-09T06:35:32Z
|
NONE
| null | null | null |
## Describe the bug
I installed datasets(version 1.17.0, 1.18.0, 1.18.3) but i'm right now nor able to import it because of pyarrow. when i try to import it, i get the following error:
`To use datasets, the module pyarrow>=3.0.0 is required, and the current version of pyarrow doesn't match this condition`.
i tryed with all version of pyarrow execpt `4.0.0` but still get the same error.
## Steps to reproduce the bug
```python
import datasets
```
## Expected results
A clear and concise description of the expected results.
## Actual results
AttributeError Traceback (most recent call last)
<ipython-input-19-652e886d387f> in <module>
----> 1 import datasets
~\AppData\Local\Continuum\anaconda3\lib\site-packages\datasets\__init__.py in <module>
26
27
---> 28 if _version.parse(pyarrow.__version__).major < 3:
29 raise ImportWarning(
30 "To use `datasets`, the module `pyarrow>=3.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
AttributeError: 'Version' object has no attribute 'major'
## Environment info
Traceback (most recent call last):
File "c:\users\alex\appdata\local\continuum\anaconda3\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "c:\users\alex\appdata\local\continuum\anaconda3\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\Alex\AppData\Local\Continuum\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 5, in <module>
File "c:\users\alex\appdata\local\continuum\anaconda3\lib\site-packages\datasets\__init__.py", line 28, in <module>
if _version.parse(pyarrow.__version__).major < 3:
AttributeError: 'Version' object has no attribute 'major'
- `datasets` version:
- Platform: Linux(Ubuntu) and Windows: conda on the both
- Python version: 3.7
- PyArrow version: 7.0.0
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Support streaming mlsum dataset
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"_The documentation is not available anymore as the PR was closed or merged._",
"After unpinning `s3fs` and pinning `fsspec[http]>=2021.11.1`, the CI installs\r\n- `fsspec-2022.1.0`\r\n- `s3fs-0.5.1`\r\n\r\nand raises the following error:\r\n```\r\n ImportError while loading conftest '/home/runner/work/datasets/datasets/tests/conftest.py'.\r\ntests/conftest.py:13: in <module>\r\n import datasets\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/__init__.py:37: in <module>\r\n from .arrow_dataset import Dataset\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/arrow_dataset.py:62: in <module>\r\n from .arrow_reader import ArrowReader\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/arrow_reader.py:29: in <module>\r\n from .download.download_config import DownloadConfig\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/download/__init__.py:10: in <module>\r\n from .streaming_download_manager import StreamingDownloadManager\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/download/streaming_download_manager.py:20: in <module>\r\n from ..filesystems import COMPRESSION_FILESYSTEMS\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/filesystems/__init__.py:13: in <module>\r\n from .s3filesystem import S3FileSystem # noqa: F401\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/filesystems/s3filesystem.py:1: in <module>\r\n import s3fs\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/s3fs/__init__.py:1: in <module>\r\n from .core import S3FileSystem, S3File\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/s3fs/core.py:12: in <module>\r\n from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper, maybe_sync\r\nE ImportError: cannot import name 'maybe_sync'\r\n```\r\n\r\nThe installed `s3fs` version is too old. What about pinning a min version?",
"Maybe you can try setting the same minimum version as fsspec ? `s3fs>=2021.11.1`",
"Yes, I have checked that they both require to have the same version. \r\n\r\nThe issue then was coming from aiobotocore, boto3, botocore. I have changed them from strict to min version requirements.\r\n> s3fs 2021.11.1 depends on aiobotocore~=2.0.1",
"I have updated all min versions so that they are compatible one with each other. I'm pushing again...",
"Thanks !",
"Nice!"
] | 2022-06-27T07:37:03Z
| 2022-07-21T13:37:30Z
| 2022-07-21T12:40:00Z
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MEMBER
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Support streaming mlsum dataset.
This PR:
- pins `fsspec` min version with fixed BlockSizeError: `fsspec[http]>=2021.11.1`
- https://github.com/fsspec/filesystem_spec/pull/830
- unpins `s3fs==2021.08.1` to align it with `fsspec` requirement: `s3fs>=2021.11.1`
> s3fs 2021.8.1 requires fsspec==2021.08.1
- see discussion: https://github.com/huggingface/datasets/pull/2858/files#r700027326
- updates the following requirements to be compatible with the previous ones and one with each other:
- `aiobotocore==1.4.2` to `aiobotocore>=2.0.1` (required by s3fs>=2021.11.1)
- `boto3==1.17.106` to `boto3>=1.19.8` (to be compatible with aiobotocore>=2.0.1)
- `botocore==1.20.106` to `botocore>=1.22.8` (to be compatible with aiobotocore and boto3)
Fix #4572.
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Allow passing a subset of output features to Dataset.map
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[] | 2022-11-01T20:07:20Z
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| null | null | null |
### Feature request
Currently, map does one of two things to the features (if I'm not mistaken):
* when you do not pass features, types are assumed to be equal to the input if they can be cast, and inferred otherwise
* when you pass a full specification of features, output features are set to this
However, sometimes you want to just pass some of the output types, particularly when the first of these modes makes an incorrect type. This currently crashes.
### Motivation
To give a little background: this problem appears in converting labels to ids, where the labels happen to be floats rather than strings
Consider the following use of map to convert from float to int
```python
data = Dataset.from_dict({'y':[1.0,2.0,3.0]})
mapped = data.map(lambda r: {'y': int(r['y'])})
mapped['y'] # is floats, not ints
```
The result is a float again, since after the mapping operation it forces the old datatypes back on the data.
Passing `features=Features({"y": Value(dtype="int64")})` to map works in principle, but then extending it a little to e.g.
```python
def format_data(r):
return {**tokenizer(r["text"]), "y": int(r["y"])}
data = Dataset.from_dict({"y": [1.0, 2.0, 3.0], "text": ["one", "two", "three"]})
mapped = data.map(
format_data,
features=Features({'y': Value(dtype="int64")}),
remove_columns=["text"],
)
```
Results in a crash in dataset internals, as it expects either all or no output features to be specified.
Of course one can pass a full feature specification, but this becomes tokenizer specific and very awkward.
### Your contribution
I've looked at `write_batch` and particularly `col_type = features[col] if features else None`, but checking for `col in features` here makes it fail elsewhere, but the structure makes it hard to understand how and why. I do not think I would have the time myself to get to the bottom of this anytime soon.
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"Running into the same error with `wmt17/zh-en`, `wmt18/zh-en` and `wmt19/zh-en`.",
"@albertvillanova @lhoestq Could you take a look at this issue?",
"@winterfell2021 Hi, I wonder where the code you provided should be added. I tried to add them in the `datasets/table.py` in `array_cast` function, however, the 'zh' item is none.",
"I found some 'zh' item is none while 'c[hn]' is not.\r\nSo the code may change to:\r\n```python\r\nif 'c[hn]' in str(array.type):\r\n py_array = array.to_pylist()\r\n data_list = []\r\n for vo in py_array:\r\n tmp = {\r\n 'en': vo['en'],\r\n }\r\n if vo.get('zh'):\r\n tmp['zh'] = vo['zh']\r\n else:\r\n tmp['zh'] = vo['c[hn]']\r\n data_list.append(tmp)\r\n array = pa.array(data_list, type=pa.struct([\r\n pa.field('en', pa.string()),\r\n pa.field('zh', pa.string()),\r\n ]))\r\n```",
"I just pushed a fix, we'll do a new release of `datasets` soon to include this fix. In the meantime you can use the fixed dataset by passing `revision=\"main\"` to `load_dataset`"
] | 2022-06-27T08:35:42Z
| 2022-08-23T10:01:02Z
| 2022-08-23T10:00:21Z
|
NONE
| null | null | null |
It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <module>
data = load_dataset(args.dataset, "zh-en")
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 1684, in load_dataset
use_auth_token=use_auth_token,
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1221, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1215, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 533, in finalize
self.write_examples_on_file()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 410, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 503, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 198, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1846, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1756, in array_cast
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
TypeError: Couldn't cast array of type
struct<c[hn]: string, en: string, zh: string>
to
struct<en: string, zh: string>
```
So the solution of this problem is to change the original array manually:
```
if 'c[hn]' in str(array.type):
py_array = array.to_pylist()
data_list = []
for vo in py_array:
tmp = {
'en': vo['en'],
}
if 'zh' not in vo:
tmp['zh'] = vo['c[hn]']
else:
tmp['zh'] = vo['zh']
data_list.append(tmp)
array = pa.array(data_list, type=pa.struct([
pa.field('en', pa.string()),
pa.field('zh', pa.string()),
]))
```
Therefore, maybe a correct version of original casia2015 file need to be updated
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PR_kwDODunzps5OAY3A
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Implement sharding on merged iterable datasets
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"Hi ! What if one of the sub-iterables only has one shard ? In that case I don't think we'd end up with a correctly interleaved dataset, since only rank 0 would yield examples from this sub-iterable",
"Hi ! \r\nI just tested this out with the code below and it seems to be ok. Both datasets are alternating and we get all the examples with no duplicates.\r\n\r\nOn thing to keep in mind is that the max amount of workers is equal to the lowest amount of shard amongst the datasets to be merged (1 in this example).\r\n\r\n ```python\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom datasets import load_dataset, interleave_datasets\r\n\r\n\r\ndef process_dataset_train(batch):\r\n return {\"input\": f'train: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef process_dataset_test(batch):\r\n return {\"input\": f'test: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef identity_collator(x):\r\n return x\r\n\r\n\r\nif __name__ == \"__main__\":\r\n ds = load_dataset(\"lhoestq/demo1\")\r\n ds[\"train\"] = ds[\"train\"].map(process_dataset_train, remove_columns=ds[\"train\"].column_names)\r\n ds[\"test\"] = ds[\"test\"].map(process_dataset_test, remove_columns=ds[\"test\"].column_names)\r\n\r\n ds1 = ds[\"train\"].to_iterable_dataset(num_shards=5)\r\n ds2 = ds[\"test\"].to_iterable_dataset(num_shards=1)\r\n\r\n ds_merged = interleave_datasets([ds1, ds2], stopping_strategy=\"all_exhausted\")\r\n\r\n dataloader = DataLoader(ds_merged, collate_fn=identity_collator, num_workers=1, batch_size=1)\r\n\r\n for i, element in enumerate(dataloader):\r\n print(i, element)\r\n\r\n```\r\n\r\n```\r\n0 [{'input': 'train: Great app! The new v'}]\r\n1 [{'input': 'test: Works with RTL and N'}]\r\n2 [{'input': \"train: Great It's not fully\"}]\r\n3 [{'input': 'test: Works with RTL SDR W'}]\r\n4 [{'input': 'train: Works on a Nexus 6p '}]\r\n5 [{'input': 'test: Awsome App! Easy to '}]\r\n6 [{'input': 'train: The bandwidth seemed'}]\r\n7 [{'input': \"test: I'll forgo the refun\"}]\r\n8 [{'input': 'train: Works well with my H'}]\r\n9 [{'input': 'test: looks like a great p'}]\r\n```",
"<s> Could you try with `num_workers>1` ? </s>\r\n\r\nedit: Oh I see\r\n\r\n> On thing to keep in mind is that the max amount of workers is equal to the lowest amount of shard amongst the datasets to be merged (1 in this example).",
"Great ! It's ok to have the max amount of workers is equal to the lowest amount of shard :)\r\n\r\nSo in the case of `num_workers>min(n_shards_per_dataset)` maybe some workers should turn off, and a warning can probably be shown. This is already the case if you use a single dataset with a single shard and `num_workers>1`.\r\n\r\n\r\nRight now it seems to raise an error:\r\n\r\n```python\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 979, in __iter__\r\n yield from self._iter_pytorch(ex_iterable)\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 912, in _iter_pytorch\r\n for key, example in ex_iterable.shard_data_sources(worker_info.id, worker_info.num_workers):\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 259, in shard_data_sources\r\n [iterable.shard_data_sources(worker_id, num_workers) for iterable in self.ex_iterables],\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 259, in <listcomp>\r\n [iterable.shard_data_sources(worker_id, num_workers) for iterable in self.ex_iterables],\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 125, in shard_data_sources\r\n requested_gen_kwargs = _merge_gen_kwargs([gen_kwargs_list[i] for i in shard_indices])\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/utils/sharding.py\", line 76, in _merge_gen_kwargs\r\n for key in gen_kwargs_list[0]\r\nIndexError: list index out of range\r\n```",
"Good point. I have fixed the n_shards property of merged iterable datasets so that this warning is raised properly",
"Hey @lhoestq, what do you think of the last modifications ? ",
"Hello! No problem :)\r\n\r\n- About HorizontallyConcatenatedMultiSourcesExamplesIterable, I've haven't been able to create a bug with sharding. So either I missed something or it's working somehow:\r\n\r\n```python\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom datasets import load_dataset, interleave_datasets, concatenate_datasets\r\n\r\n\r\ndef process_dataset_train(batch):\r\n return {\"input\": f'train: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef process_dataset_test(batch):\r\n return {\"input\": f'test: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef identity_collator(x):\r\n return x\r\n\r\n\r\nif __name__ == \"__main__\":\r\n ds = load_dataset(\"lhoestq/demo1\")\r\n ds[\"train\"] = ds[\"train\"].map(process_dataset_train, remove_columns=ds[\"train\"].column_names)\r\n ds[\"test\"] = ds[\"test\"].map(process_dataset_test, remove_columns=ds[\"test\"].column_names)\r\n ds[\"test\"] = ds[\"test\"].rename_columns({\"input\": \"input2\"})\r\n\r\n ds1 = ds[\"train\"].to_iterable_dataset(num_shards=5)\r\n ds2 = ds[\"test\"].to_iterable_dataset(num_shards=3)\r\n\r\n ds_merged = concatenate_datasets([ds1, ds2], axis=1)\r\n\r\n #n_shards is always 1 for HorizontallyConcatenatedMultiSourcesExamplesIterable\r\n dataloader = DataLoader(ds_merged, collate_fn=identity_collator, num_workers=1, batch_size=1)\r\n\r\n for i, element in enumerate(dataloader):\r\n print(i, element)\r\n```\r\n\r\n```\r\n0 [{'input': 'train: Great app! The new v', 'input2': 'test: Works with RTL and N'}]\r\n1 [{'input': \"train: Great It's not fully\", 'input2': 'test: Works with RTL SDR W'}]\r\n2 [{'input': 'train: Works on a Nexus 6p ', 'input2': 'test: Awsome App! Easy to '}]\r\n3 [{'input': 'train: The bandwidth seemed', 'input2': \"test: I'll forgo the refun\"}]\r\n4 [{'input': 'train: Works well with my H', 'input2': 'test: looks like a great p'}]\r\n```\r\n\r\n- I've added a test but I'm not completely happy with it. My issue is that multiprocessing makes interleaving not completely deterministic as samples are yielded whenever ready by each process, if I'm correct.\r\nAs a result I opted to check for the amount of samples yielded and make that they are all unique, which should be equivalent.\r\nBut now my issue is that the \"first_exhausted\" method breaks the loop when one of the datasets of one of the shards is empty which means that all shards stop yielding and we could be missing up to n_workers samples. I don't know if this is the behaviour expected, but I had to modify the test to accomodate this.\r\n\r\nWhat are your thoughts about this ?",
"Ah indeed it works because it's set to be only 1 shard - my bad :)",
"> But now my issue is that the \"first_exhausted\" method breaks the loop when one of the datasets of one of the shards is empty which means that all shards stop yielding and we could be missing up to n_workers samples. I don't know if this is the behaviour expected, but I had to modify the test to accomodate this.\r\n\r\nThis looks reasonable, maybe this can be documented in the `interleave_datasets` docstring ?\r\n```\r\nNote for iterable datasets:\r\n\r\nIn a distributed setup or in PyTorch DataLoader workers, the stopping strategy is applied per process.\r\nTherefore the \"first_exhausted\" strategy on an sharded iterable dataset can generate less samples in total (up to 1 missing sample per subdataset per worker).\r\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) | 0.006441 / 0.011353 (-0.004912) | 0.004551 / 0.011008 (-0.006457) | 0.099144 / 0.038508 (0.060636) | 0.028163 / 0.023109 (0.005054) | 0.386342 / 0.275898 (0.110444) | 0.398347 / 0.323480 (0.074867) | 0.004836 / 0.007986 (-0.003150) | 0.004724 / 0.004328 (0.000395) | 0.076277 / 0.004250 (0.072027) | 0.036305 / 0.037052 (-0.000747) | 0.377179 / 0.258489 (0.118690) | 0.410694 / 0.293841 (0.116853) | 0.030196 / 0.128546 (-0.098351) | 0.011436 / 0.075646 (-0.064211) | 0.325911 / 0.419271 (-0.093360) | 0.043709 / 0.043533 (0.000177) | 0.375801 / 0.255139 (0.120662) | 0.396511 / 0.283200 (0.113311) | 0.088346 / 0.141683 (-0.053337) | 1.483427 / 1.452155 (0.031272) | 1.553708 / 1.492716 (0.060992) |\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) | 0.190974 / 0.018006 (0.172968) | 0.451309 / 0.000490 (0.450819) | 0.004045 / 0.000200 (0.003845) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023814 / 0.037411 (-0.013597) | 0.096922 / 0.014526 (0.082396) | 0.101506 / 0.176557 (-0.075050) | 0.164694 / 0.737135 (-0.572441) | 0.106899 / 0.296338 (-0.189439) |\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) | 0.432164 / 0.215209 (0.216954) | 4.308076 / 2.077655 (2.230421) | 2.092434 / 1.504120 (0.588314) | 1.937405 / 1.541195 (0.396210) | 1.988030 / 1.468490 (0.519540) | 0.695476 / 4.584777 (-3.889301) | 3.436413 / 3.745712 (-0.309299) | 2.892954 / 5.269862 (-2.376908) | 1.519906 / 4.565676 (-3.045771) | 0.082579 / 0.424275 (-0.341696) | 0.012233 / 0.007607 (0.004626) | 0.531329 / 0.226044 (0.305284) | 5.365272 / 2.268929 (3.096344) | 2.391452 / 55.444624 (-53.053172) | 2.051116 / 6.876477 (-4.825361) | 2.140663 / 2.142072 (-0.001410) | 0.807262 / 4.805227 (-3.997966) | 0.151290 / 6.500664 (-6.349374) | 0.066137 / 0.075469 (-0.009333) |\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) | 1.193106 / 1.841788 (-0.648682) | 13.577240 / 8.074308 (5.502932) | 14.280126 / 10.191392 (4.088734) | 0.142538 / 0.680424 (-0.537886) | 0.016641 / 0.534201 (-0.517560) | 0.386318 / 0.579283 (-0.192965) | 0.385991 / 0.434364 (-0.048373) | 0.440712 / 0.540337 (-0.099625) | 0.524189 / 1.386936 (-0.862747) |\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) | 0.006628 / 0.011353 (-0.004725) | 0.004664 / 0.011008 (-0.006344) | 0.077254 / 0.038508 (0.038746) | 0.028369 / 0.023109 (0.005259) | 0.343076 / 0.275898 (0.067178) | 0.376491 / 0.323480 (0.053011) | 0.005298 / 0.007986 (-0.002687) | 0.004853 / 0.004328 (0.000524) | 0.075927 / 0.004250 (0.071677) | 0.039951 / 0.037052 (0.002899) | 0.346225 / 0.258489 (0.087736) | 0.382367 / 0.293841 (0.088526) | 0.031133 / 0.128546 (-0.097413) | 0.011666 / 0.075646 (-0.063981) | 0.086383 / 0.419271 (-0.332889) | 0.042885 / 0.043533 (-0.000647) | 0.343885 / 0.255139 (0.088746) | 0.366840 / 0.283200 (0.083640) | 0.095942 / 0.141683 (-0.045741) | 1.528972 / 1.452155 (0.076817) | 1.586392 / 1.492716 (0.093676) |\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) | 0.223952 / 0.018006 (0.205946) | 0.410767 / 0.000490 (0.410277) | 0.001014 / 0.000200 (0.000814) | 0.000067 / 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) | 0.024210 / 0.037411 (-0.013201) | 0.100308 / 0.014526 (0.085782) | 0.106899 / 0.176557 (-0.069658) | 0.156514 / 0.737135 (-0.580621) | 0.109548 / 0.296338 (-0.186790) |\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) | 0.434763 / 0.215209 (0.219554) | 4.348485 / 2.077655 (2.270831) | 2.064255 / 1.504120 (0.560135) | 1.864394 / 1.541195 (0.323199) | 1.899732 / 1.468490 (0.431242) | 0.694147 / 4.584777 (-3.890630) | 3.357898 / 3.745712 (-0.387815) | 2.909155 / 5.269862 (-2.360707) | 1.424790 / 4.565676 (-3.140886) | 0.082597 / 0.424275 (-0.341678) | 0.012442 / 0.007607 (0.004835) | 0.538758 / 0.226044 (0.312713) | 5.390288 / 2.268929 (3.121359) | 2.532016 / 55.444624 (-52.912609) | 2.185724 / 6.876477 (-4.690753) | 2.274176 / 2.142072 (0.132104) | 0.804785 / 4.805227 (-4.000442) | 0.152649 / 6.500664 (-6.348015) | 0.067707 / 0.075469 (-0.007762) |\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) | 1.285219 / 1.841788 (-0.556568) | 13.958098 / 8.074308 (5.883790) | 14.043653 / 10.191392 (3.852261) | 0.144526 / 0.680424 (-0.535898) | 0.016813 / 0.534201 (-0.517388) | 0.390286 / 0.579283 (-0.188997) | 0.389184 / 0.434364 (-0.045180) | 0.470810 / 0.540337 (-0.069527) | 0.562391 / 1.386936 (-0.824545) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bb172c9772858c188f85ffc9a51f8cb1da292a0 \"CML watermark\")\n"
] | 2023-04-11T10:02:25Z
| 2023-04-27T16:39:04Z
| 2023-04-27T16:32:09Z
|
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| null | 0
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This PR allows sharding of merged iterable datasets.
Merged iterable datasets with for instance the `interleave_datasets` command are comprised of multiple sub-iterable, one for each dataset that has been merged.
With this PR, sharding a merged iterable will result in multiple merged datasets each comprised of sharded sub-iterable, ensuring that there is no duplication of data.
As a result it is now possible to set any amount of workers in the dataloader as long as it is lower or equal to the lowest amount of shards amongst the datasets. Before it had to be set to 0.
I previously talked about this issue on the forum [here](https://discuss.huggingface.co/t/interleaving-iterable-dataset-with-num-workers-0/35801)
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PR_kwDODunzps40sdVu
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Adding Roman Urdu Hate Speech dataset
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"@lhoestq can you review when you have some time? Also were the previous CI fails due to the Google Drive tests which were excluded by #3982 ?",
"> were the previous CI fails due to the Google Drive tests which were excluded by https://github.com/huggingface/datasets/pull/3982 ?\r\n\r\nYes exactly, merging `master` into your branch fixed the CI ;)",
"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-03-20T00:19:26Z
| 2022-03-25T15:56:19Z
| 2022-03-25T15:51:20Z
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This Pull request will add the Roman Urdu Hate speech Dataset.
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PR_kwDODunzps4uqzcv
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Make several audio datasets streamable
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"Reading FLAC (for `librispeech_asr`) works OK for me (`soundfile` version: `0.10.3`):\r\n```python\r\nIn [2]: ds = load_dataset(\"datasets/librispeech_asr/librispeech_asr.py\", \"clean\", streaming=True, split=\"train.100\")\r\n\r\nIn [3]: item = next(iter(ds))\r\n\r\nIn [4]: item.keys()\r\nOut[4]: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])\r\n\r\nIn [5]: item[\"file\"]\r\nOut[5]: '374-180298-0000.flac'\r\n\r\nIn [6]: item[\"audio\"].keys()\r\nOut[6]: dict_keys(['path', 'array', 'sampling_rate'])\r\n\r\nIn [7]: item[\"audio\"][\"sampling_rate\"]\r\nOut[7]: 16000\r\n\r\nIn [8]: item[\"audio\"][\"path\"]\r\nOut[8]: '374-180298-0000.flac'\r\n\r\nIn [9]: item[\"audio\"][\"array\"].shape\r\nOut[9]: (232480,)\r\n```",
"Oh cool ! I think this might have come from an issue with my local `soundfile` installation then",
"I'll do `multilingual_librispeech` in a separate PR since it requires the data to be in another format (in particular separate the train/dev/test splits in different files)",
"@lhoestq @albertvillanova - think it would have been nice to have added a big message at the top stating that this is a breaking change and ping `transformers` people a bit more here."
] | 2021-11-17T17:43:41Z
| 2022-02-01T21:00:52Z
| 2021-11-19T15:08:57Z
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<s>Needs https://github.com/huggingface/datasets/pull/3129 to be merged first</s>
Make those audio datasets streamable:
- [x] common_voice
- [x] openslr
- [x] vivos
- [x] librispeech_asr <s>(still has some issues to read FLAC)</s> *actually it's ok*
- [ ] <s>multilingual_librispeech (yet to be converted)</S> *TODO in a separate PR*
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Specify split post processing + Add post processing resources downloading
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"I was using a hack in `wiki_dpr` to download the index from GCS even for the configurations without the embeddings.\r\nHowever as GCS is something internal, I changed the logic to add a download step for indexes directly in the dataset script, using the `DownloadManager`.\r\n\r\nThis change was directly linked to the changes I did to take into account the split name in the post processing, so I included this change in this PR too.\r\n\r\nTo summarize:\r\n\r\nDataset builders can now implement\r\n- `_post_processing_resources(split)`: return a dict `resource_name -> resource_file_name`. It defines the additional resources such as indexes or arrow files that you need in post processing\r\n- `_download_post_processing_resources(split, resource_name, dl_manager))`: if some resources can be downloaded, you can use the download_manager to download them\r\n- `_post_process(dataset, resources_path)`: (main function for post processing) given a dataset, you can apply dataset transforms or add indexes. For resources that have been downloaded, you can load them. For the others, you can generate and save them. The paths to load/save resources are in `resources_path` which is a dictionary `resource_name -> resource_path`\r\n\r\nAbout the CI:\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_wiki_dpr\r\n```\r\nIt fails because I changed the input of post processing functions (to include the split name)",
"I started to add metadata in the DatasetInfo.\r\nNote that because there are new fields, **ALL the dataset_info[s].json generated after these changes won't be loadable from older versions of the lib**\r\n\r\nRight now it looks like this:\r\n```json\r\n \"post_processing_resources_checksums\": {\r\n \"train\": {\r\n \"embeddings_index\": {\r\n \"num_bytes\": 30720045,\r\n \"checksum\": \"b04fb4f4f3ab83b9d1b9f6f9eb236f1c04a9fd61bef7cee16b12df8ac911766a\"\r\n }\r\n }\r\n },\r\n \"post_processing_size\": 30720045,\r\n```",
"Good point. Should we anticipate already that we may add other fields in the future and change the code to support the addition of new fields without breaking backward compatibility in the future?",
"I added:\r\n- post processing features (inside a PostProcessedInfo object)\r\n- backward compatibility for dataset info\r\n- post processing tests (as_dataset and download_and_prepare) for map (change features), select (change number of elements) and add_faiss_index (add indexes)\r\nAnd I fixed a bug in `map` that I found thanks to the new tests\r\n\r\nNow I just have to move `post_processing_resources_checksums` to PostProcessedInfo as well and everything should be good :)\r\nEdit: done"
] | 2020-07-24T09:29:19Z
| 2020-07-31T09:05:04Z
| 2020-07-31T09:05:03Z
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Previously if you tried to do
```python
from nlp import load_dataset
wiki = load_dataset("wiki_dpr", "psgs_w100_with_nq_embeddings", split="train[:100]", with_index=True)
```
Then you'd get an error `Index size should match Dataset size...`
This was because it was trying to use the full index (21M elements).
To fix that I made it so post processing resources can be named according to the split.
I'm going to add tests on post processing too.
Note that the CI will fail as I added a new argument in `_post_processing_resources`: the AWS version of wiki_dpr fails, and there's also an error telling that it is not synced (it'll be synced once it's merged):
```
=========================== short test summary info ============================
FAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_wiki_dpr
FAILED tests/test_hf_gcp.py::TestDatasetSynced::test_script_synced_with_s3_wiki_dpr
```
EDIT: I did a change to ignore the script hash to locate the arrow files on GCS, so I removed the sync test. It was there just because of the hash logic for files on GCS
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Documentation Mistakes in Dataset: emotion
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"Hi,\r\n\r\nthis issue has been already reported in the dataset repo (https://github.com/dair-ai/emotion_dataset/issues/2), so this is a bug on their side.",
"The documentation has another bug in the dataset card [here](https://huggingface.co/datasets/emotion). \r\n\r\nIn the dataset summary **six** emotions are mentioned: *\"six basic emotions: anger, fear, joy, love, sadness, and surprise\"*, however, in the datafields section we have only **five**:\r\n```\r\nlabel: a classification label, with possible values including sadness (0), joy (1), love (2), anger (3), fear (4).\r\n```",
"@GDGauravDutta the dataset author replied in their issue: https://github.com/dair-ai/emotion_dataset/issues/2\r\n> The dataset released is a preprocessed six emotion variant released for educational and research purposes.\r\n\r\n@albertovilla the dataset card was fixed with 6 emotions."
] | 2021-06-19T07:08:57Z
| 2023-01-02T12:04:58Z
| 2023-01-02T12:04:58Z
|
NONE
| null | null | null |
As per documentation,
Dataset: emotion
Homepage: https://github.com/dair-ai/emotion_dataset
Dataset: https://github.com/huggingface/datasets/blob/master/datasets/emotion/emotion.py
Permalink: https://huggingface.co/datasets/viewer/?dataset=emotion
Emotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the paper.
But when we view the data, there are only 6 emotions, anger, fear, joy, sadness, surprise, and trust.
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Use auth to get parquet export
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6468). 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) | 0.005076 / 0.011353 (-0.006277) | 0.003510 / 0.011008 (-0.007499) | 0.062939 / 0.038508 (0.024431) | 0.049191 / 0.023109 (0.026082) | 0.259088 / 0.275898 (-0.016810) | 0.273523 / 0.323480 (-0.049957) | 0.003902 / 0.007986 (-0.004083) | 0.002699 / 0.004328 (-0.001630) | 0.049077 / 0.004250 (0.044827) | 0.037174 / 0.037052 (0.000121) | 0.256467 / 0.258489 (-0.002022) | 0.291235 / 0.293841 (-0.002606) | 0.028119 / 0.128546 (-0.100427) | 0.010404 / 0.075646 (-0.065243) | 0.205825 / 0.419271 (-0.213446) | 0.035741 / 0.043533 (-0.007792) | 0.253219 / 0.255139 (-0.001920) | 0.274986 / 0.283200 (-0.008214) | 0.018379 / 0.141683 (-0.123304) | 1.131139 / 1.452155 (-0.321016) | 1.175875 / 1.492716 (-0.316841) |\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) | 0.090717 / 0.018006 (0.072710) | 0.299285 / 0.000490 (0.298796) | 0.000217 / 0.000200 (0.000017) | 0.000050 / 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) | 0.018678 / 0.037411 (-0.018733) | 0.060558 / 0.014526 (0.046032) | 0.073828 / 0.176557 (-0.102728) | 0.119302 / 0.737135 (-0.617833) | 0.075261 / 0.296338 (-0.221078) |\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) | 0.277018 / 0.215209 (0.061809) | 2.713255 / 2.077655 (0.635601) | 1.427512 / 1.504120 (-0.076608) | 1.311374 / 1.541195 (-0.229821) | 1.348756 / 1.468490 (-0.119734) | 0.561777 / 4.584777 (-4.023000) | 2.393578 / 3.745712 (-1.352134) | 2.798109 / 5.269862 (-2.471753) | 1.754808 / 4.565676 (-2.810869) | 0.062302 / 0.424275 (-0.361973) | 0.004948 / 0.007607 (-0.002659) | 0.328468 / 0.226044 (0.102423) | 3.246558 / 2.268929 (0.977629) | 1.786816 / 55.444624 (-53.657808) | 1.482937 / 6.876477 (-5.393540) | 1.516109 / 2.142072 (-0.625963) | 0.634457 / 4.805227 (-4.170770) | 0.116505 / 6.500664 (-6.384159) | 0.042162 / 0.075469 (-0.033308) |\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) | 0.935312 / 1.841788 (-0.906476) | 11.540599 / 8.074308 (3.466291) | 10.512593 / 10.191392 (0.321201) | 0.129638 / 0.680424 (-0.550786) | 0.013994 / 0.534201 (-0.520207) | 0.291490 / 0.579283 (-0.287793) | 0.263641 / 0.434364 (-0.170722) | 0.328718 / 0.540337 (-0.211619) | 0.437598 / 1.386936 (-0.949338) |\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) | 0.005192 / 0.011353 (-0.006161) | 0.003454 / 0.011008 (-0.007554) | 0.049448 / 0.038508 (0.010940) | 0.050968 / 0.023109 (0.027859) | 0.273702 / 0.275898 (-0.002196) | 0.296934 / 0.323480 (-0.026545) | 0.004066 / 0.007986 (-0.003920) | 0.002611 / 0.004328 (-0.001718) | 0.048284 / 0.004250 (0.044034) | 0.041399 / 0.037052 (0.004346) | 0.283000 / 0.258489 (0.024511) | 0.302553 / 0.293841 (0.008712) | 0.029086 / 0.128546 (-0.099460) | 0.010510 / 0.075646 (-0.065137) | 0.058097 / 0.419271 (-0.361175) | 0.032992 / 0.043533 (-0.010541) | 0.271752 / 0.255139 (0.016613) | 0.293535 / 0.283200 (0.010335) | 0.016958 / 0.141683 (-0.124725) | 1.130126 / 1.452155 (-0.322028) | 1.187228 / 1.492716 (-0.305488) |\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) | 0.092321 / 0.018006 (0.074315) | 0.302599 / 0.000490 (0.302109) | 0.000215 / 0.000200 (0.000015) | 0.000051 / 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) | 0.021837 / 0.037411 (-0.015574) | 0.071148 / 0.014526 (0.056622) | 0.082448 / 0.176557 (-0.094108) | 0.128083 / 0.737135 (-0.609053) | 0.090864 / 0.296338 (-0.205474) |\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) | 0.296248 / 0.215209 (0.081039) | 2.881130 / 2.077655 (0.803476) | 1.580360 / 1.504120 (0.076240) | 1.454642 / 1.541195 (-0.086553) | 1.461453 / 1.468490 (-0.007037) | 0.567500 / 4.584777 (-4.017277) | 2.493708 / 3.745712 (-1.252004) | 2.756623 / 5.269862 (-2.513239) | 1.771319 / 4.565676 (-2.794358) | 0.062287 / 0.424275 (-0.361988) | 0.004917 / 0.007607 (-0.002691) | 0.348034 / 0.226044 (0.121990) | 3.426938 / 2.268929 (1.158010) | 1.954190 / 55.444624 (-53.490435) | 1.660870 / 6.876477 (-5.215607) | 1.675118 / 2.142072 (-0.466955) | 0.636843 / 4.805227 (-4.168384) | 0.115028 / 6.500664 (-6.385636) | 0.040702 / 0.075469 (-0.034767) |\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) | 0.988076 / 1.841788 (-0.853711) | 11.890867 / 8.074308 (3.816559) | 10.621169 / 10.191392 (0.429777) | 0.131568 / 0.680424 (-0.548856) | 0.014994 / 0.534201 (-0.519207) | 0.288900 / 0.579283 (-0.290384) | 0.272092 / 0.434364 (-0.162272) | 0.329397 / 0.540337 (-0.210940) | 0.569337 / 1.386936 (-0.817599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae3b4a2268adc2f21568ff63891e9a83530c7e29 \"CML watermark\")\n"
] | 2023-12-04T11:18:27Z
| 2023-12-04T17:21:22Z
| 2023-12-04T17:15:11Z
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MEMBER
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added `token` to the `_datasets_server` functions
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Setting log level higher than warning does not suppress progress bar
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"Hi,\r\n\r\nyou can suppress progress bars by patching logging as follows:\r\n```python\r\nimport datasets\r\nimport logging\r\ndatasets.logging.get_verbosity = lambda: logging.NOTSET\r\n# map call ...\r\n```\r\nEDIT: now you have to use `disable_progress_bar `",
"Thank you, it worked :)",
"See https://github.com/huggingface/datasets/issues/2528 for reference",
"Note also that you can disable the progress bar with\r\n\r\n```python\r\nfrom datasets.utils import disable_progress_bar\r\ndisable_progress_bar()\r\n```\r\n\r\nSee https://github.com/huggingface/datasets/blob/8814b393984c1c2e1800ba370de2a9f7c8644908/src/datasets/utils/tqdm_utils.py#L84",
"Now the library officially recommends `set_progress_bar_enabled(False)`\r\n\r\n```py\r\nfrom datasets.utils import set_progress_bar_enabled\r\n\r\nset_progress_bar_enabled(False)\r\n```\r\n\r\nsource:\r\n\r\nhttps://github.com/huggingface/datasets/blob/1fd47120ace13626c528367787ffa13e1a26e6c0/src/datasets/utils/tqdm_utils.py#L83-L88\r\n\r\n",
"From https://github.com/huggingface/datasets/pull/3897, `disable_progress_bar` is the function you should use",
"Now ``disable_progress_bar`` function is in ``datasets/src/datasets/utils/logging.py``.\r\nhttps://github.com/huggingface/datasets/blob/aa555a299ad73c65e3f997a764e9d211675ab05d/src/datasets/utils/logging.py#L233-L236\r\nAnd the method mentioned in https://github.com/huggingface/datasets/issues/2651#issuecomment-880270774 is not working now."
] | 2021-07-14T21:06:51Z
| 2022-07-08T14:51:57Z
| 2021-07-15T03:41:35Z
|
NONE
| null | null | null |
## Describe the bug
I would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well).
According to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0.
I also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work.
## Steps to reproduce the bug
```python
import datasets
from datasets.utils.logging import set_verbosity_error
set_verbosity_error()
def dummy_map(batch):
return batch
common_voice_train = datasets.load_dataset("common_voice", "de", split="train")
common_voice_test = datasets.load_dataset("common_voice", "de", split="test")
common_voice_train.map(dummy_map)
```
## Expected results
- The progress bar for `.map` call won't be shown
## Actual results
- The progress bar for `.map` is still shown
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.5
- PyArrow version: 4.0.1
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I_kwDODunzps5WZfr6
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|
Download only split data
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"Hi @capsabogdan! Unfortunately, it's hard to implement because quite often datasets data is being hosted in a single archive for all splits :( So we have to download the whole archive to split it into splits. This is the case for CommonVoice too. \r\n\r\nHowever, for cases when data is distributed in separate archives ащк different splits I suppose it can (and will) be implemented someday. \r\n\r\n\r\nBtw for quick check of the dataset you can use [streaming](https://huggingface.co/docs/datasets/stream):\r\n```python\r\ncv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)\r\ncv = iter(cv)\r\nprint(next(cv))\r\n\r\n>> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',\r\n>> 'path': None,\r\n>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',\r\n>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,\r\n>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),\r\n>> ...}\r\n\r\n```",
"thank you for the answer but am not sure if this will not be helpful, as we\nneed maybe just 10% of the datasets for some experiment\n\ncan we get just a portion of the dataset with stream?\n\n\nis there really no solution? :(\n\nAm Di., 15. Nov. 2022 um 16:55 Uhr schrieb Polina Kazakova <\n***@***.***>:\n\n> Hi @capsabogdan <https://github.com/capsabogdan>! Unfortunately, it's\n> hard to implement because quite often datasets data is being hosted in a\n> single archive for all splits :( So we have to download the whole archive\n> to split it into splits. This is the case for CommonVoice too.\n>\n> However, for cases when data is distributed in separate archives in\n> different splits I suppose it can be implemented someday.\n>\n> Btw for quick check of the dataset you can use streaming\n> <https://huggingface.co/docs/datasets/stream>:\n>\n> cv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)cv = iter(cv)print(next(cv))\n> >> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',>> 'path': None,>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),>> ...}\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5243#issuecomment-1315512887>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOC3JYRCTH54OBRUJULWIOW6PANCNFSM6AAAAAASAYO2LY>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n",
"maybe it would be nice if you guys ould do some sort of shard before\nloading the dataset, so users can download just chunks of data :)\n\nI think this would be very helpful\n\nAm Di., 15. Nov. 2022 um 19:24 Uhr schrieb Bogdan Capsa <\n***@***.***>:\n\n> thank you for the answer but am not sure if this will not be helpful, as\n> we need maybe just 10% of the datasets for some experiment\n>\n> can we get just a portion of the dataset with stream?\n>\n>\n> is there really no solution? :(\n>\n> Am Di., 15. Nov. 2022 um 16:55 Uhr schrieb Polina Kazakova <\n> ***@***.***>:\n>\n>> Hi @capsabogdan <https://github.com/capsabogdan>! Unfortunately, it's\n>> hard to implement because quite often datasets data is being hosted in a\n>> single archive for all splits :( So we have to download the whole archive\n>> to split it into splits. This is the case for CommonVoice too.\n>>\n>> However, for cases when data is distributed in separate archives in\n>> different splits I suppose it can be implemented someday.\n>>\n>> Btw for quick check of the dataset you can use streaming\n>> <https://huggingface.co/docs/datasets/stream>:\n>>\n>> cv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)cv = iter(cv)print(next(cv))\n>> >> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',>> 'path': None,>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),>> ...}\n>>\n>> —\n>> Reply to this email directly, view it on GitHub\n>> <https://github.com/huggingface/datasets/issues/5243#issuecomment-1315512887>,\n>> or unsubscribe\n>> <https://github.com/notifications/unsubscribe-auth/ALSIFOC3JYRCTH54OBRUJULWIOW6PANCNFSM6AAAAAASAYO2LY>\n>> .\n>> You are receiving this because you were mentioned.Message ID:\n>> ***@***.***>\n>>\n>\n",
"+1 on this feature request - I am running into the same problem, where I only need the test set for a dataset that has a huge training set",
"Hey, I'm also interested in that as a feature. I'm having the same problem with Common Voice 13.0. The dataset is super big but I only want the test data to benchmark multilingual models, but I don't have much Terabytes to store all the dataset..."
] | 2022-11-15T10:15:54Z
| 2023-05-02T09:27:51Z
| null |
NONE
| null | null | null |
### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test",
cache_dir="cache/path...",
use_auth_token=True,
download_config=DownloadConfig(delete_extracted='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')
)
### Motivation
efficiency improvement
### Your contribution
n/a
|
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Adding UKP Argument Aspect Similarity Corpus
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"Adding a link to the guide on adding a dataset if someone want to give it a try: https://github.com/huggingface/datasets#add-a-new-dataset-to-the-hub\r\n\r\nwe should add this guide to the issue template @lhoestq ",
"thanks @thomwolf , this is added now. The template is correct, sorry my mistake not to include it. ",
"Available here: https://huggingface.co/datasets/UKPLab/UKP_ASPECT"
] | 2020-12-24T11:01:31Z
| 2022-10-05T12:36:12Z
| 2022-10-05T12:36:12Z
|
CONTRIBUTOR
| null | null | null |
Hi, this would be great to have this dataset included.
## Adding a Dataset
- **Name:** UKP Argument Aspect Similarity Corpus
- **Description:** The UKP Argument Aspect Similarity Corpus (UKP ASPECT) includes 3,595 sentence pairs over 28 controversial topics. Each sentence pair was annotated via crowdsourcing as either “high similarity”, “some similarity”, “no similarity” or “not related” with respect to the topic.
- **Paper:** https://www.aclweb.org/anthology/P19-1054/
- **Data:** https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/1998
- **Motivation:** this is one of the datasets currently used frequently in recent adapter papers like https://arxiv.org/pdf/2005.00247.pdf
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Thank you
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Dataset can’t cache model’s outputs
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"Addressed in https://github.com/huggingface/datasets/pull/5191 (torch.Tensor objects now produce deterministic hashes)"
] | 2022-10-25T12:19:00Z
| 2022-11-03T16:12:52Z
| 2022-11-03T16:12:51Z
|
NONE
| null | null | null |
### Describe the bug
Hi,
I try to cache some outputs of teacher model( Knowledge Distillation ) by using map function of Dataset library, while every time I run my code, I still recompute all the sequences. I tested Bert Model like this, I got different hash every single run, so any idea to deal with this?
### Steps to reproduce the bug
1. run below code
2. get different hash
```
from transformers import BertModel
from transformers import AutoTokenizer
import torch
token = ['hello']
model = BertModel.from_pretrained("bert-base-uncased").eval()
tok = AutoTokenizer.from_pretrained("bert-base-uncased")
def abcd():
with torch.no_grad():
out = model(**tok(token,return_tensors='pt'))[0]
# out = tok(token)
return out
from datasets.fingerprint import Hasher
my_func = abcd
print(Hasher.hash(my_func))
print(abcd())
```
### Expected behavior
I wanna cache all the model output
### Environment info
datasets:2.5.0
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| 752
|
Clicking on a metric in the search page points to datasets page giving "Missing dataset" warning
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[
"Thanks for the report, can reproduce. Will fix",
"Fixed now @ogabrielluiz "
] | 2020-10-21T22:56:23Z
| 2020-10-22T16:19:42Z
| 2020-10-22T16:19:42Z
|
NONE
| null | null | null |
Hi! Sorry if this isn't the right place to talk about the website, I just didn't exactly where to write this.
Searching a metric in https://huggingface.co/metrics gives the right results but clicking on a metric (E.g ROUGE) points to https://huggingface.co/datasets/rouge. Clicking on a metric without searching points to the right page.
Thanks for all the great work!
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Warn about checksums
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"_The documentation is not available anymore as the PR was closed or merged._",
"I'm also in favor of disabling this by default - it's kinda impractical",
"Great, thanks for the quick turnaround on this!"
] | 2022-11-22T10:58:48Z
| 2022-11-23T11:43:50Z
| 2022-11-23T09:47:02Z
|
MEMBER
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It takes a lot of time on big datasets to compute the checksums, we should at least add a warning to notify the user about this step. I also mentioned how to disable it, and added a tqdm bar (delay=5 seconds)
cc @ola13
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Add sberquad dataset
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[] | 2021-10-06T12:32:02Z
| 2021-10-13T10:19:11Z
| 2021-10-13T10:16:04Z
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CONTRIBUTOR
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PR_kwDODunzps4vwpNt
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Add RedCaps dataset
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[
"Cool ! If you want you can include `dataset_infos.json` but only for the main configurations. That's what we do for example for translation datasets when there are too many configs",
"@lhoestq I've added an example that uses `map` to download the images."
] | 2021-12-13T13:38:13Z
| 2022-01-12T14:13:16Z
| 2022-01-12T14:13:15Z
|
CONTRIBUTOR
| null | 0
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Add the RedCaps dataset. I'm not adding the generated `dataset_infos.json` file for now due to its size (11 MB).
TODOs:
- [x] dummy data
- [x] dataset card
Close #3316
|
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Add 1.5 billion words Arabic corpus
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[
"Can't pass dummy data tests. For the instructions, it asks me to generate the following file `dummy_data/Youm7_XML_utf_8.rar/Youm7_utf_8.xml` which is strange, any ideas @lhoestq ?\r\n\r\ncc: I tested the data locally and it works, maybe the dummy tests doesn't support `rar` ? ",
"In the dummy_data.zip files you must include the rar file as if is was already extracted.\r\nIn particular here `Youm7_XML_utf_8.rar` is a directory (not an archive).",
"Also I'm getting `BadRarFile: Failed the read enough data: req=16384 got=51` while trying to download and extract the `Alittihad_XML_utf_8.rar` file. Do you have this issue as well ?\r\n\r\nI have rarfile 4.0",
"Sorry it was my mistake, I missed up the directories, it works now. Not sure why you got that error. I have the same version of `rarfile`. Between, there were some suggestions to change the dataset from `1bn_words_arabic` to `arabic_billion_words` like https://github.com/huggingface/datasets/tree/master/datasets/spanish_billion_words. \r\n",
"I'm ok with renaming the dataset `arabic_billion_words` if you want.\r\nNote that you will need to rename class name `ArabicBillionWords` instead of `BillionWords`\r\n(though `BillionWords` was not matching `1bn_words_arabic` anyway)\r\n\r\nYou will need to regenerate the dataset_infos.json file after this change.\r\nOR alternatively just replace all mentions of `billion_words` with `arabic_billion_words` in dataset_infos.json <- this trick should save you some time :)",
"Hmmm I'm still not able to run it on my side because of the rar error (I'm running macos)\r\nI just tried with rarfile 3.1 and it didn't work either.\r\nI would like to be able to run it end-to-end on my side before merging if you don't mind. Let me investigate this issue a little bit",
"No worries, I will investigate it as well. ",
"I created a minimal example in [colab ](https://colab.research.google.com/drive/11ijesuGbrQylANka0VdsZ5vXwIuxkheY?usp=sharing).",
"Nice thanks, maybe it's just an issue on my side then",
"Ok I managed to solve the BadRarFile issue on my side :) \r\nTo fix it I had to install the `unrar` tool for macos (though it seems it's not available with `brew install` anymore, I had to install it from elsewhere)."
] | 2020-12-10T00:32:18Z
| 2020-12-22T10:03:59Z
| 2020-12-22T10:03:59Z
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Needs https://github.com/huggingface/datasets/pull/1429 to work.
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ConnectionError in IndicGLUE dataset
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[
"@sahoodib, thanks for reporting.\r\n\r\nIndeed, none of the data links appearing in the IndicGLUE website are working, e.g.: https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/soham-articles.tar.gz\r\n```\r\n<Error>\r\n<Code>UserProjectAccountProblem</Code>\r\n<Message>User project billing account not in good standing.</Message>\r\n<Details>\r\nThe billing account for the owning project is disabled in state delinquent\r\n</Details>\r\n</Error>\r\n```\r\n\r\nWe have contacted the data owners to inform them about their issue and ask them if they plan to fix it.",
"Yesterday I resent a reminder email with more AI4Bharat-related people in the loop.\r\n\r\nI also opened an issue in their repos:\r\n- https://github.com/AI4Bharat/indicnlp_corpus/issues/14\r\n- https://github.com/AI4Bharat/ai4bharat.org/issues/71",
"We have received a reply from the authors reporting they have updated the URLs of their data files and opened a PR. See:\r\n- #4978 "
] | 2022-01-12T17:59:36Z
| 2022-09-15T21:57:34Z
| 2022-09-15T21:57:34Z
|
NONE
| null | null | null |
While I am trying to load IndicGLUE dataset (https://huggingface.co/datasets/indic_glue) it is giving me with the error:
```
ConnectionError: Couldn't reach https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/wikiann-ner.tar.gz (error 403)
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[SEO improvement] Add Dataset Metadata to make datasets indexable
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[] | 2021-12-02T20:21:07Z
| 2022-03-18T09:36:48Z
| 2022-03-18T09:36:48Z
|
CONTRIBUTOR
| null | null | null |
Some people who host datasets on github seem to include a table of metadata at the end of their README.md to make the dataset indexable by [Google Dataset Search](https://datasetsearch.research.google.com/) (See [here](https://github.com/google-research/google-research/tree/master/goemotions#dataset-metadata) and [here](https://github.com/cvdfoundation/google-landmark#dataset-metadata)). This could be a useful addition to canonical datasets; perhaps even community datasets.
I'll include a screenshot (as opposed to markdown) as an example so as not to have a github issue indexed as a dataset:
> 
**_PS: It might very well be the case that this is already covered by some other markdown magic I'm not aware of._**
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Use GitHub instead of aws in remote dataset tests
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[] | 2020-10-01T13:07:50Z
| 2020-10-02T07:47:28Z
| 2020-10-02T07:47:27Z
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MEMBER
| null | 0
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Recently we switched from aws s3 to github to download dataset scripts.
However in the tests, the dummy data were still downloaded from s3.
So I changed that to download them from github instead, in the MockDownloadManager.
Moreover I noticed that `anli`'s dummy data were quite heavy (18MB compressed, i.e. the entire dataset) so I replaced them with dummy data with few examples.
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GermEval 2014: new download urls
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[
"/cc: @vblagoje",
"Closing this one as #594 is merged (same changes except the dummy data hack)",
"Awesome @stefan-it ! @lhoestq how soon can I use the fixed GermEval dataset in HF token classification examples?",
"I've manually updated the script on S3, so you can actually use it right now with\r\n```python\r\nfrom nlp import load_dataset\r\n\r\ngermeval = load_dataset(\"germeval_14\")\r\n```\r\n\r\nnot sure if it's used in token classification examples already",
"Awesome. Not used yet but I am going to use it now. I've been working on an update for token classification examples and this was a missing piece. Thanks @stefan-it @lhoestq "
] | 2020-09-09T10:07:29Z
| 2020-09-09T14:16:54Z
| 2020-09-09T13:35:15Z
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Hi,
unfortunately, the download links for the GermEval 2014 dataset have changed: they're now located on a Google Drive.
I changed the URLs and bump version from 1.0.0 to 2.0.0.
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Add HKCanCor
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(Apologies, didn't manage the branches properly and the PR got too messy. Going to open a new PR with everything in order)
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ExamplesIterable fixes
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"_The documentation is not available anymore as the PR was closed or merged._"
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fix typing and ExamplesIterable.shard_data_sources
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datasets card-creator link added
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dataset card creator link has been added
link: https://huggingface.co/datasets/card-creator/
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apply offset to indices in multiprocessed map
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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)
```
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Add a new metric: CTC_Consistency
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"Thanks for your contribution, @YEdenZ.\r\n\r\nPlease note that our old `metrics` module is in the process of being incorporated to a separate library called `evaluate`: https://github.com/huggingface/evaluate\r\n\r\nTherefore, I would ask you to transfer your PR to that repository. Thank you."
] | 2022-05-13T17:31:19Z
| 2022-05-19T10:23:04Z
| 2022-05-19T10:23:03Z
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Add CTC_Consistency metric
Do I also need to modify the `test_metric_common.py` file to make it run on test?
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add story_cloze dataset
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"Will create a new one, this one seems to be missed up. "
] | 2021-09-14T12:36:53Z
| 2021-10-08T21:41:42Z
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@lhoestq I have spent some time but I still I can't succeed in correctly testing the dummy_data.
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[
"Good catch ! Yes I think passing `format=\"wav\"` is the right thing to do",
"Thanks @polinaeterna for reporting this issue.\r\n\r\nIn relation to the decoding of MP3 audio files without torchaudio, I remember Patrick made some tests and these had quite bad performance. That is why he proposed to support MP3 files only with torchaudio. But yes, nice to give an alternative to non-torchaudio users (with a big warning on performance).",
"> I remember Patrick made some tests and these had quite bad performance. That is why he proposed to support MP3 files only with torchaudio.\r\n\r\nYeah, I know, but as far as I understand, some users just categorically don't want to have torchaudio in their environment. Anyway, it's just a more or less random example, they can use any library they like following the same logic (I'm just not a big expert in decoding utils so if you can give me some presentation / resources about that I would really appreciate it 🤗)"
] | 2022-03-23T17:11:47Z
| 2022-03-29T14:16:13Z
| 2022-03-29T14:16:13Z
|
CONTRIBUTOR
| null | null | null |
## Describe the bug
When trying to do `Audio().encode_example()` with preexisting array (see [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L73)), `sf.write()` throws you an error:
`TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7f4218c0db30>`
## Steps to reproduce the bug
### Sample code to reproduce the bug
```python
# download sample file
!wget https://huggingface.co/datasets/polinaeterna/test_encode_example/resolve/main/common_voice_vi_21824030.mp3
arr, sr = librosa.load("common_voice_vi_21824030.mp3")
Audio().encode_example({
"path": "common_voice_vi_21824030.mp3",
"array": arr,
"sampling_rate":sr
})
```
## Expected results
An encoded example (`{"bytes": b'....', "path": 'path'}`)
## Actual results
```python
TypeError Traceback (most recent call last)
Input In [3], in <module>
1 arr, sr = librosa.load("common_voice_vi_21824030.mp3")
----> 3 Audio().encode_example({
4 "path": "common_voice_vi_21824030.mp3",
5 "array": arr,
6 "sampling_rate":sr
7 })
File ~/workspace/datasets/src/datasets/features/audio.py:75, in Audio.encode_example(self, value)
73 elif isinstance(value, dict) and "array" in value:
74 buffer = BytesIO()
---> 75 sf.write(buffer, value["array"], value["sampling_rate"])
76 return {"bytes": buffer.getvalue(), "path": value.get("path")}
77 elif value.get("bytes") is not None or value.get("path") is not None:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:314, in write(file, data, samplerate, subtype, endian, format, closefd)
312 else:
313 channels = data.shape[1]
--> 314 with SoundFile(file, 'w', samplerate, channels,
315 subtype, endian, format, closefd) as f:
316 f.write(data)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:627, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)
625 mode_int = _check_mode(mode)
626 self._mode = mode
--> 627 self._info = _create_info_struct(file, mode, samplerate, channels,
628 format, subtype, endian)
629 self._file = self._open(file, mode_int, closefd)
630 if set(mode).issuperset('r+') and self.seekable():
631 # Move write position to 0 (like in Python file objects)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1416, in _create_info_struct(file, mode, samplerate, channels, format, subtype, endian)
1414 original_format = format
1415 if format is None:
-> 1416 format = _get_format_from_filename(file, mode)
1417 assert isinstance(format, (_unicode, str))
1418 else:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1457, in _get_format_from_filename(file, mode)
1455 pass
1456 if format.upper() not in _formats and 'r' not in mode:
-> 1457 raise TypeError("No format specified and unable to get format from "
1458 "file extension: {0!r}".format(file))
1459 return format
TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7fd8daf88180>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets master
- Platform: Ubuntu 20.04
- Python version: python 3.8.12
- PyArrow version: 6.0.1
## Solution
I guess we just need to add `format` arg in [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L75) like this:
```python
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
```
BTW discovered this when trying to decode audio in mp3 format without torchaudio (would be useful for TensorFlow users), like this:
```python
from datasets import load_dataset, Features, Audio
ds = load_dataset("common_voice", "vi", split="test")
ds = ds.remove_columns("audio")
ds.select(range(3)) # 3 samples just for testing
def load_mp3_with_librosa(example):
arr, sr = librosa.load(example["path"])
example["audio"] = {
"path": example["path"],
"array": arr,
"sampling_rate": sr
}
return example
updated_dataset = ds.map(lambda example: load_mp3_with_librosa(example),
features=Features(
{"audio": Audio(decode=False)}
))
```
@lhoestq @mariosasko @albertvillanova am I right in my logic? do we agree that we can set wav as the format? 🤗
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6480). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005392 / 0.011353 (-0.005960) | 0.003120 / 0.011008 (-0.007888) | 0.062017 / 0.038508 (0.023509) | 0.048824 / 0.023109 (0.025715) | 0.232300 / 0.275898 (-0.043598) | 0.262045 / 0.323480 (-0.061435) | 0.002909 / 0.007986 (-0.005077) | 0.003916 / 0.004328 (-0.000413) | 0.049469 / 0.004250 (0.045218) | 0.038965 / 0.037052 (0.001913) | 0.247841 / 0.258489 (-0.010648) | 0.268259 / 0.293841 (-0.025582) | 0.027588 / 0.128546 (-0.100958) | 0.010334 / 0.075646 (-0.065312) | 0.205811 / 0.419271 (-0.213460) | 0.035456 / 0.043533 (-0.008077) | 0.242774 / 0.255139 (-0.012365) | 0.260377 / 0.283200 (-0.022823) | 0.017469 / 0.141683 (-0.124214) | 1.199665 / 1.452155 (-0.252489) | 1.259316 / 1.492716 (-0.233400) |\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) | 0.092357 / 0.018006 (0.074350) | 0.303745 / 0.000490 (0.303255) | 0.000212 / 0.000200 (0.000012) | 0.000052 / 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) | 0.018820 / 0.037411 (-0.018592) | 0.061548 / 0.014526 (0.047022) | 0.072527 / 0.176557 (-0.104030) | 0.119696 / 0.737135 (-0.617440) | 0.074153 / 0.296338 (-0.222185) |\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) | 0.283952 / 0.215209 (0.068743) | 2.769844 / 2.077655 (0.692189) | 1.526100 / 1.504120 (0.021980) | 1.417584 / 1.541195 (-0.123611) | 1.440523 / 1.468490 (-0.027967) | 0.556994 / 4.584777 (-4.027783) | 2.400392 / 3.745712 (-1.345320) | 2.727794 / 5.269862 (-2.542068) | 1.724671 / 4.565676 (-2.841006) | 0.062111 / 0.424275 (-0.362164) | 0.004925 / 0.007607 (-0.002682) | 0.342748 / 0.226044 (0.116704) | 3.376790 / 2.268929 (1.107862) | 1.856498 / 55.444624 (-53.588127) | 1.574143 / 6.876477 (-5.302334) | 1.591828 / 2.142072 (-0.550245) | 0.644416 / 4.805227 (-4.160811) | 0.116862 / 6.500664 (-6.383802) | 0.041484 / 0.075469 (-0.033985) |\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) | 0.975704 / 1.841788 (-0.866084) | 11.196447 / 8.074308 (3.122139) | 10.567518 / 10.191392 (0.376126) | 0.126786 / 0.680424 (-0.553638) | 0.013768 / 0.534201 (-0.520433) | 0.284531 / 0.579283 (-0.294752) | 0.260855 / 0.434364 (-0.173509) | 0.328888 / 0.540337 (-0.211450) | 0.439911 / 1.386936 (-0.947025) |\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) | 0.005108 / 0.011353 (-0.006245) | 0.003006 / 0.011008 (-0.008003) | 0.048673 / 0.038508 (0.010165) | 0.051066 / 0.023109 (0.027957) | 0.279578 / 0.275898 (0.003680) | 0.298356 / 0.323480 (-0.025123) | 0.003965 / 0.007986 (-0.004020) | 0.002662 / 0.004328 (-0.001667) | 0.049037 / 0.004250 (0.044786) | 0.039385 / 0.037052 (0.002333) | 0.284545 / 0.258489 (0.026055) | 0.314240 / 0.293841 (0.020399) | 0.028493 / 0.128546 (-0.100053) | 0.010400 / 0.075646 (-0.065247) | 0.057375 / 0.419271 (-0.361896) | 0.032382 / 0.043533 (-0.011151) | 0.283163 / 0.255139 (0.028024) | 0.298967 / 0.283200 (0.015768) | 0.017564 / 0.141683 (-0.124119) | 1.172425 / 1.452155 (-0.279730) | 1.219975 / 1.492716 (-0.272742) |\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) | 0.090664 / 0.018006 (0.072658) | 0.298419 / 0.000490 (0.297929) | 0.000211 / 0.000200 (0.000011) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021739 / 0.037411 (-0.015672) | 0.068274 / 0.014526 (0.053748) | 0.080820 / 0.176557 (-0.095736) | 0.119809 / 0.737135 (-0.617326) | 0.081612 / 0.296338 (-0.214727) |\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) | 0.303346 / 0.215209 (0.088137) | 2.971648 / 2.077655 (0.893993) | 1.634828 / 1.504120 (0.130708) | 1.510851 / 1.541195 (-0.030344) | 1.515236 / 1.468490 (0.046745) | 0.558487 / 4.584777 (-4.026289) | 2.436263 / 3.745712 (-1.309449) | 2.718525 / 5.269862 (-2.551336) | 1.727421 / 4.565676 (-2.838255) | 0.061396 / 0.424275 (-0.362879) | 0.004951 / 0.007607 (-0.002656) | 0.352950 / 0.226044 (0.126906) | 3.473766 / 2.268929 (1.204838) | 1.971299 / 55.444624 (-53.473325) | 1.712173 / 6.876477 (-5.164304) | 1.711334 / 2.142072 (-0.430738) | 0.627291 / 4.805227 (-4.177936) | 0.113779 / 6.500664 (-6.386885) | 0.046561 / 0.075469 (-0.028908) |\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) | 0.989507 / 1.841788 (-0.852280) | 11.777883 / 8.074308 (3.703575) | 10.525453 / 10.191392 (0.334061) | 0.129118 / 0.680424 (-0.551306) | 0.014989 / 0.534201 (-0.519212) | 0.282324 / 0.579283 (-0.296959) | 0.280688 / 0.434364 (-0.153676) | 0.322579 / 0.540337 (-0.217758) | 0.554327 / 1.386936 (-0.832609) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79e94fcdf3d4378ddcdf7e130bb1ae23d99c6fce \"CML watermark\")\n"
] | 2023-12-07T16:31:50Z
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Example for glue sst2:
Dataset
```
DatasetDict({
test: Dataset({
features: ['sentence', 'label', 'idx'],
num_rows: 1821
})
train: Dataset({
features: ['sentence', 'label', 'idx'],
num_rows: 67349
})
validation: Dataset({
features: ['sentence', 'label', 'idx'],
num_rows: 872
})
})
```
IterableDataset (new)
```
IterableDatasetDict({
test: IterableDataset({
features: ['sentence', 'label', 'idx'],
n_shards: 1
})
train: IterableDataset({
features: ['sentence', 'label', 'idx'],
n_shards: 1
})
validation: IterableDataset({
features: ['sentence', 'label', 'idx'],
n_shards: 1
})
})
```
IterableDataset (before)
```
{'test': <datasets.iterable_dataset.IterableDataset object at 0x130d421f0>, 'train': <datasets.iterable_dataset.IterableDataset object at 0x136f3aaf0>, 'validation': <datasets.iterable_dataset.IterableDataset object at 0x136f4b100>}
{'sentence': 'hide new secretions from the parental units ', 'label': 0, 'idx': 0}
```
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added saudinewsnet
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"Hi @abdulelahsm - This is an interesting dataset! But there are multiple issues with the PR. Some of them are listed below: \r\n- default builder config is not defined. There should be atleast one builder config \r\n- URL is incorrectly constructed so the data files are not being downloaded \r\n- dataset_info.json file was not created\r\n\r\nPlease have a look at some existing merged datasets to get a reference on building the data loader. If you are still stuck, reach out. \r\n",
"@skyprince999 I totally agree. Thx for the feedback!",
"Hi @abdulelahsm ! Thanks for adding this one :) \r\nyou don't actually have to add builder configurations if you don't need them. It's fine as it is now\r\n\r\nAnd as @skyprince999 noticed, the current URLs don't work. to download files.\r\nYou can use this one for example for the first batch instead:\r\nhttps://github.com/parallelfold/SaudiNewsNet/raw/master/dataset/2015-07-21.zip\r\n\r\nFeel free to ping me if you have questions or if you're ready for a review :) ",
"@lhoestq Hey, I tried using the first batch instead, the data was downloaded but I got this error, not sure why it can't find the path?\r\n\r\nfor content, I ran ``` \"./datasets/saudinewsnet/test.py\"```\r\n\r\nwhich is a local test I'm running for the dataset, it contains the following code\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndata = load_dataset(\"./datasets/saudinewsnet\", split= \"train\")\r\n\r\nprint(data)\r\n\r\nprint(data[1])\r\n```\r\n\r\nthis is the error I got \r\n\r\n```\r\n2020-12-18 21:45:39.403908: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory\r\n2020-12-18 21:45:39.403953: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\nUsing custom data configuration default\r\nDownloading and preparing dataset saudi_news_net/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/mesfas/.cache/huggingface/datasets/saudi_news_net/default/0.0.0/62ece5ef0a991415352d4b1efac681d75b5b3404064fd4f6a1d659499dab18f4...\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.42M/3.42M [00:03<00:00, 1.03MB/s]\r\nTraceback (most recent call last):\r\n File \"/home/mesfas/opensource/datasets/src/datasets/builder.py\", line 604, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/mesfas/opensource/datasets/src/datasets/builder.py\", line 902, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"/home/mesfas/environments/ar_res_reviews/lib/python3.8/site-packages/tqdm/std.py\", line 1133, in __iter__\r\n for obj in iterable:\r\n File \"/home/mesfas/.cache/huggingface/modules/datasets_modules/datasets/saudinewsnet/62ece5ef0a991415352d4b1efac681d75b5b3404064fd4f6a1d659499dab18f4/saudinewsnet.py\", line 108, in _generate_examples\r\n with open(filepath, encoding=\"utf-8\").read() as f:\r\nIsADirectoryError: [Errno 21] Is a directory: '/home/mesfas/.cache/huggingface/datasets/downloads/extracted/314fd983aa07d3dada9429911a805270c3285f48759d3584a1343c2d86260765'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"./datasets/saudinewsnet/test.py\", line 3, in <module>\r\n data = load_dataset(\"./datasets/saudinewsnet\", split= \"train\")\r\n File \"/home/mesfas/opensource/datasets/src/datasets/load.py\", line 607, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/mesfas/opensource/datasets/src/datasets/builder.py\", line 526, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/mesfas/opensource/datasets/src/datasets/builder.py\", line 606, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 21] Is a directory: '/home/mesfas/.cache/huggingface/datasets/downloads/extracted/314fd983aa07d3dada9429911a805270c3285f48759d3584a1343c2d86260765'\r\n```\r\n\r\n\r\nthis is the split code \r\n\r\n```\r\n def _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n my_urls = _URL\r\n datadir = dl_manager.download_and_extract(my_urls)\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN,\r\n # These kwargs will be passed to _generate_examples\r\n gen_kwargs={\r\n \"filepath\": datadir,\r\n \"split\": \"train\"\r\n },\r\n ),\r\n ]\r\n```\r\nand this is how I'm generating the examples\r\n\r\n```\r\n def _generate_examples(self, filepath, split):\r\n \r\n #logging.info(\"generating examples from = %s\", filepath)\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n articles = json.load(f)\r\n for article in articles:\r\n title = article.get(\"title\", \"\").strip()\r\n source = article.get(\"source\", \"\").strip()\r\n date = article.get(\"date_extracted\", \"\").strip()\r\n link = article.get(\"url\", \"\").strip()\r\n author = article.get(\"author\", \"\").strip()\r\n content = article.get(\"content\", \"\").strip()\r\n\r\n yield id_, {\r\n \"title\": title,\r\n \"source\": source,\r\n \"date\": date,\r\n \"link\": link,\r\n \"author\": author,\r\n \"content\": content\r\n }\r\n```",
"What's `_URL` ?\r\n\r\nIt looks like you are downloading an archive.\r\nTherefore you may need to get to the file path using `filepath = os.path.join(datadir, \"actual_file_name_inside_the_downloaded_archive\")`",
"@lhoestq you were 100% right. Thank you. All fixed",
"@lhoestq ping!",
"@lhoestq added the remaining 17 batches and modified the readme.md to reflect that + resolved the camel case comment",
"merging since the CI is fixed on master"
] | 2020-12-14T10:35:09Z
| 2020-12-22T09:51:04Z
| 2020-12-22T09:51:04Z
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I'm having issues in creating the dummy data. I'm still investigating how to fix it. I'll close the PR if I couldn't find a solution
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| 2021-09-12T16:13:13Z
| 2021-09-12T16:13:13Z
|
NONE
| null | null | 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).
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[BugFix] fix wrong import of DEFAULT_TOKENIZER
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| 2020-07-28T12:58:01Z
| 2020-07-28T12:52:05Z
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Fixed the path to `DEFAULT_TOKENIZER`
#445
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MDExOlB1bGxSZXF1ZXN0NTg2MjgwNzE3
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MOROCO
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[
"@lhoestq Thank you for all the feedback. I've added the suggested changes in my last commit."
] | 2021-03-07T16:22:17Z
| 2021-03-19T09:52:06Z
| 2021-03-19T09:52:06Z
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Add MOROCO to huggingface datasets.
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fix ner_tag bugs in thainer
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[
"> Thanks :)\r\n> \r\n> Apparently the dummy_data.zip got removed. Is this expected ?\r\n> Also can you remove the `data-pos.conll` file that you added ?\r\n\r\nNot expected. I forgot to remove the `dummy_data` folder used to create `dummy_data.zip`. \r\nChanged to only `dummy_data.zip`."
] | 2021-01-07T02:12:33Z
| 2021-01-07T14:43:45Z
| 2021-01-07T14:43:28Z
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fix bug that results in `ner_tag` always equal to 'O'.
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PR_kwDODunzps43lKpm
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[WIP] Add TEDLIUM dataset
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"_The documentation is not available anymore as the PR was closed or merged._",
"```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('./datasets/tedlium', 'release1', cache_dir='/home/sanchitgandhi/cache')\r\n```\r\n\r\n```\r\nDownloading and preparing dataset tedlium/release1 to /home/sanchitgandhi/cache/tedlium/release1/1.0.1/5a9fcb97b4b52d5a1c9dc7bde4b1d5994cd89c4a3425ea36c789bf6096fee4f0...\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/home/sanchit_huggingface_co/datasets/src/datasets/load.py\", line 1703, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/sanchit_huggingface_co/datasets/src/datasets/builder.py\", line 605, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/sanchit_huggingface_co/datasets/src/datasets/builder.py\", line 1240, in _download_and_prepare\r\n raise MissingBeamOptions(\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n `load_dataset('tedlium', 'release1', beam_runner='DirectRunner')`\r\n```\r\nSpecifying the `beam_runner='DirectRunner'` works:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('./datasets/tedlium', 'release1', cache_dir='/home/sanchitgandhi/cache', beam_runner='DirectRunner')\r\n```",
"Extra Python imports/Linux packages:\r\n```\r\npip install pydub\r\nsudo apt install ffmpeg\r\n```",
"Script heavily inspired by the TF datasets script at: https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/audio/tedlium.py\r\n\r\nThe TF datasets script uses the module AudioSegment from the package `pydub` (https://github.com/jiaaro/pydub), which is used to to open the audio files (stored in .sph format):\r\nhttps://github.com/huggingface/datasets/blob/61bf6123634bf6e7c7287cd6097909eb26118c58/datasets/tedlium/tedlium.py#L167-L170\r\nThis package requires the pip install of `pydub` and the system installation of `ffmpeg`: https://github.com/jiaaro/pydub#installation\r\nIs it ok to use these packages? Or do we tend to avoid introducing additional dependencies?\r\n\r\nThe TF datasets script also uses `_build_pcollection`:\r\nhttps://github.com/huggingface/datasets/blob/8afbbb6fe66b40d05574e2e72e65e974c72ae769/datasets/tedlium/tedlium.py#L200-L206\r\nHowever, I was advised against using `beam` logic. Thus, I have reverted to generating the examples file-by-file: https://github.com/huggingface/datasets/blob/61bf6123634bf6e7c7287cd6097909eb26118c58/datasets/tedlium/tedlium.py#L112-L138\r\n\r\nI am now able to generate examples by running the `load_dataset` command:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('./datasets/tedlium', 'release1', cache_dir='/home/sanchitgandhi/cache')\r\n```\r\n\r\nHere, generating examples is **extremely** slow: it takes ~1 second per example, so ~60k seconds for the train set (~16 hours). Is there a way of paralleling this to make it faster?",
"> This package requires the pip install of pydub and the system installation of ffmpeg: https://github.com/jiaaro/pydub#installation\r\nIs it ok to use these packages? Or do we tend to avoid introducing additional dependencies?\r\n\r\nIt's ok, windows users will have have a bad time but I'm not sure we can do much about it.\r\n\r\n> Here, generating examples is extremely slow: it takes ~1 second per example, so ~60k seconds for the train set (~16 hours). Is there a way of paralleling this to make it faster?\r\n\r\nNot at the moment. For such cases we advise hosting the dataset ourselves in a processed format. The license doesn't allow this since the license is \"NoDerivatives\". Currently the only way to parallelize it is by keeping is as a beam dataset and let users pay Google Dataflow to process it (or use spark or whatever).",
"Thanks for your super speedy reply @lhoestq!\r\n\r\nI’ve uploaded the script and README.md to the org here: https://huggingface.co/datasets/LIUM/tedlium\r\nIs any modification of the script required to be able to use it from the Hub? When I run:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntedlium = load_dataset(\"LIUM/tedlium\", \"release1\") # for Release 1\r\n```\r\nI get the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\nInput In [2], in <cell line: 1>()\r\n----> 1 load_dataset(\"LIUM/tedlium\", \"release1\")\r\n\r\nFile ~/datasets/src/datasets/load.py:1676, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)\r\n 1673 ignore_verifications = ignore_verifications or save_infos\r\n 1675 # Create a dataset builder\r\n-> 1676 builder_instance = load_dataset_builder(\r\n 1677 path=path,\r\n 1678 name=name,\r\n 1679 data_dir=data_dir,\r\n 1680 data_files=data_files,\r\n 1681 cache_dir=cache_dir,\r\n 1682 features=features,\r\n 1683 download_config=download_config,\r\n 1684 download_mode=download_mode,\r\n 1685 revision=revision,\r\n 1686 use_auth_token=use_auth_token,\r\n 1687 **config_kwargs,\r\n 1688 )\r\n 1690 # Return iterable dataset in case of streaming\r\n 1691 if streaming:\r\n\r\nFile ~/datasets/src/datasets/load.py:1502, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)\r\n 1500 download_config = download_config.copy() if download_config else DownloadConfig()\r\n 1501 download_config.use_auth_token = use_auth_token\r\n-> 1502 dataset_module = dataset_module_factory(\r\n 1503 path,\r\n 1504 revision=revision,\r\n 1505 download_config=download_config,\r\n 1506 download_mode=download_mode,\r\n 1507 data_dir=data_dir,\r\n 1508 data_files=data_files,\r\n 1509 )\r\n 1511 # Get dataset builder class from the processing script\r\n 1512 builder_cls = import_main_class(dataset_module.module_path)\r\n\r\nFile ~/datasets/src/datasets/load.py:1254, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n 1249 if isinstance(e1, FileNotFoundError):\r\n 1250 raise FileNotFoundError(\r\n 1251 f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n 1252 f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n 1253 ) from None\r\n-> 1254 raise e1 from None\r\n 1255 else:\r\n 1256 raise FileNotFoundError(\r\n 1257 f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory.\"\r\n 1258 )\r\n\r\nFile ~/datasets/src/datasets/load.py:1227, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n 1225 raise e\r\n 1226 if filename in [sibling.rfilename for sibling in dataset_info.siblings]:\r\n-> 1227 return HubDatasetModuleFactoryWithScript(\r\n 1228 path,\r\n 1229 revision=revision,\r\n 1230 download_config=download_config,\r\n 1231 download_mode=download_mode,\r\n 1232 dynamic_modules_path=dynamic_modules_path,\r\n 1233 ).get_module()\r\n 1234 else:\r\n 1235 return HubDatasetModuleFactoryWithoutScript(\r\n 1236 path,\r\n 1237 revision=revision,\r\n (...)\r\n 1241 download_mode=download_mode,\r\n 1242 ).get_module()\r\n\r\nFile ~/datasets/src/datasets/load.py:940, in HubDatasetModuleFactoryWithScript.get_module(self)\r\n 938 def get_module(self) -> DatasetModule:\r\n 939 # get script and other files\r\n--> 940 local_path = self.download_loading_script()\r\n 941 dataset_infos_path = self.download_dataset_infos_file()\r\n 942 imports = get_imports(local_path)\r\n\r\nFile ~/datasets/src/datasets/load.py:918, in HubDatasetModuleFactoryWithScript.download_loading_script(self)\r\n 917 def download_loading_script(self) -> str:\r\n--> 918 file_path = hf_hub_url(path=self.name, name=self.name.split(\"/\")[1] + \".py\", revision=self.revision)\r\n 919 download_config = self.download_config.copy()\r\n 920 if download_config.download_desc is None:\r\n\r\nTypeError: hf_hub_url() got an unexpected keyword argument 'name'\r\n```\r\n\r\nNote that I am able to load the dataset from the `datasets` repo with the following lines of code:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('./datasets/tedlium', 'release1', cache_dir='/home/sanchitgandhi/cache')\r\n```",
"What version of `datasets` do you have ?\r\nUpdating `datasets` should fix the error ;)\r\n",
"> This package requires the pip install of pydub and the system installation of ffmpeg: https://github.com/jiaaro/pydub#installation\r\nIs it ok to use these packages? Or do we tend to avoid introducing additional dependencies?\r\n\r\n`soundfile`, which is a required audio dependency, should also work with `.sph` files, no?",
"> `soundfile`, which is a required audio dependency, should also work with `.sph` files, no?\r\n\r\nAwesome, thanks for the pointer @mariosasko! Switched `pydub` to `soundfile`, and having specifying the `dtype` argument in `soundfile.read` as `np.int16`, the arrays match with those from `pydub` ✅\r\n\r\nI also did some heavy optimising of the script with the processing of the `.stm` and `.sph` files - it now runs 2000x faster than before, so there probably isn't a need to upload the data to the Hub @lhoestq. The total processing time is just ~2mins now 🚀\r\n",
"TEDLIUM completed and uploaded to the HF Hub: https://huggingface.co/datasets/LIUM/tedlium",
"Awesome !"
] | 2022-05-10T14:12:47Z
| 2022-06-17T12:54:40Z
| 2022-06-17T11:44:01Z
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Adds the TED-LIUM dataset https://www.tensorflow.org/datasets/catalog/tedlium#tedliumrelease3
TODO:
- [x] Port `tedium.py` from TF datasets using `convert_dataset.sh` script
- [x] Make `load_dataset` work
- [ ] ~~Run `datasets-cli` command to generate `dataset_infos.json`~~
- [ ] ~~Create dummy data for continuous testing~~
- [ ] ~~Dummy data tests~~
- [ ] ~~Real data tests~~
- [ ] Create the metadata JSON
- [ ] Close PR and add directly to the Hub under LIUM org
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Fixes #712 Attribute error in cell 3 of the overview notebook
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"Referencing the wrong issue # in the commit message. Closing this to fix it again."
] | 2020-10-05T15:42:09Z
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| 2020-10-05T15:46:32Z
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Fixes the Attribute error in cell 3 of the overview notebook
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PR_kwDODunzps5edaO6
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Fix for continuation behaviour on broken dataset archives due to starving download connections via HTTP-GET
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This PR proposes a (slightly hacky) fix for an Issue that can occur when downloading large dataset parts over unstable connections.
The underlying issue is also being discussed in https://github.com/huggingface/datasets/issues/5594.
Issue Symptoms & Behaviour:
- Download of a large archive file during dataset download via HTTP-GET fails.
- An silent net exception (which I was unable to identify) is thrown within the `tqdm` download progress.
- Due to missing exception catch code, the above process just continues processing, assuming `http_get` completed successfully.
- Pending Archive file gets renamed to remove the `.incomplete` extension, despite not all data has been downloaded.
- Also, for reasons I did not investigate, there seems to be no real integrity check for the downloaded files; or it does not detect this problem. This is especially problematic, since the downloader script won't retry downloading this archive after CRC-Checking, even if it is being manually restarted / executed again after running into errors on extraction.
Fix proposal: Adding a retry mechanic for HTTP-GET downloads, which adds the following behaviour:
- Download Progress Thread checks for download size validity in case the HTTP connection starves mid download. If the check fails, a RuntimeError is thrown
- Cache Downloader code with retry mechanic monitors for an exception thrown by the download progress thread, and retries download with updated `resume_size`.
- Cache Downloader will not mark incomplete files which have thrown an exception during download, and exceeded retries, as complete.
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Rearrange JSON field names to match passed features schema field names
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This PR depends on PR #2453 (which must be merged first).
Close #2366.
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Some functions use a param named `type` shouldn't that be avoided since it's a Python reserved name?
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[
"Hi! Let's not do this - renaming it would be a breaking change, and going through the deprecation cycle is only worth it if it improves user experience.",
"Hi @mariosasko, ok it makes sense. Anyway, don't you think it's worth it at some point to start a deprecation cycle e.g. `fs` in `load_from_disk`? It doesn't affect user experience but it's for sure a bad practice IMO, but's up to you 😄 Feel free to close this issue otherwise!",
"I don't think deprecating a param name in this particular instance is worth the hassle, so I'm closing the issue 🙂.",
"Sure, makes sense @mariosasko thanks!"
] | 2023-02-08T15:13:46Z
| 2023-07-24T16:02:18Z
| 2023-07-24T14:27:59Z
|
CONTRIBUTOR
| null | null | null |
Hi @mariosasko, @lhoestq, or whoever reads this! :)
After going through `ArrowDataset.set_format` I found out that the `type` param is actually named `type` which is a Python reserved name as you may already know, shouldn't that be renamed to `format_type` before the 3.0.0 is released?
Just wanted to get your input, and if applicable, tackle this issue myself! Thanks 🤗
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Better error message when download fails
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From our discussions in https://github.com/huggingface/datasets/issues/3269 and https://github.com/huggingface/datasets/issues/3282 it would be nice to have better messages if a download fails.
In particular the error now shows:
- the error from the HEAD request if there's one
- otherwise the response code of the HEAD request
I also added an error to tell users to pass `use_auth_token` when the Hugging Face Hub returns 401 (Unauthorized).
While paying around with this I also fixed a minor issue with the `force_download` parameter that was not always taken into account
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Trouble with streaming frgfm/imagenette vision dataset with TAR archive
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"Hi @frgfm, thanks for reporting.\r\n\r\nAs the error message says, streaming mode is not supported out of the box when the dataset contains TAR archive files.\r\n\r\nTo make the dataset streamable, you have to use `dl_manager.iter_archive`.\r\n\r\nThere are several examples in other datasets, e.g. food101: https://huggingface.co/datasets/food101/blob/main/food101.py\r\n\r\nAnd yes, as the link you pointed out, for the streaming to be possible, the metadata file must be loaded before all of the images:\r\n- either this is the case when iterating the archive (and you get the metadata file before the images)\r\n- or you have to extract the metadata file by hand and upload it separately to the Hub",
"Hi @albertvillanova :wave:\r\n\r\nThanks! Yeah I saw that but since I didn't have any metadata, I wasn't sure whether I should create them myself.\r\n\r\nSo one last question:\r\nWhat is the metadata supposed to be for archives? The relative path of all files in it?\r\n_(Sorry I'm a bit confused since it's quite hard to debug using the single error message from the data preview :sweat_smile: )_",
"Hi @frgfm, streaming a dataset that contains a TAR file requires some tweaks because (contrary to ZIP files), tha TAR archive does not allow random access to any of the contained member files. Instead they have to be accessed sequentially (in the order in which they were put into the TAR file when created) and yielded.\r\n\r\nSo when iterating over the TAR file content, when an image file is found, we need to yield it (and not keeping it in memory, which will require huge RAM memory for large datasets). But when yielding an image file, we also need to yield with it what we call \"metadata\": the class label, and other textual information (for example, for audio files, sometimes we also add info such as the speaker ID, their sex, their age,...).\r\n\r\nAll this information usually is stored in what we call the metadata file: either a JSON or a CSV/TSV file.\r\n\r\nBut if this is also inside the TAR archive, we need to find this file in the first place when iterating the TAR archive, so that we already have this information when we find an image file and we can yield the image file and its metadata info.\r\n\r\nTherefore:\r\n- either the TAR archive contains the metadata file as the first member when iterating it (something we cannot change as it is done at the creation of the TAR file)\r\n- or if not, then we need to have the metadata file elsewhere\r\n - in these cases, what we do (if the dataset license allows it) is:\r\n - we download the TAR file locally, we extract the metadata file and we host the metadata on the Hub\r\n - we modify the dataset loading script so that it first downloads the metadata file (and reads it) and only then starts iterating the content of the TAR archive file\r\n\r\nSee an example of this process we recently did for \"google/fleurs\" (their metadata files for \"train\" were at the end of the TAR archives, after all audio files): https://huggingface.co/datasets/google/fleurs/discussions/4\r\n- we uploaded the metadata file to the Hub\r\n- we adapted the loading script to use it",
"Hi @albertvillanova :wave: \r\n\r\nThanks, since my last message, I went through the repo of https://huggingface.co/datasets/food101/blob/main/food101.py and managed to get it to work in the end :pray: \r\n\r\nHere it is: https://huggingface.co/datasets/frgfm/imagenette\r\n\r\nI appreciate you opening an issue to document the process, it might help a few!",
"Great to see that you manage to make your dataset streamable. :rocket: \r\n\r\nI'm closing this issue, as for the docs update there is another issue opened:\r\n- #4711"
] | 2022-07-18T02:51:09Z
| 2022-08-01T15:10:57Z
| 2022-08-01T15:10:57Z
|
NONE
| null | null | null |
### Link
https://huggingface.co/datasets/frgfm/imagenette
### Description
Hello there :wave:
Thanks for the amazing work you've done with HF Datasets! I've just started playing with it, and managed to upload my first dataset. But for the second one, I'm having trouble with the preview since there is some archive extraction involved :sweat_smile:
Basically, I get a:
```
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
I've tried several things and checked this issue https://github.com/huggingface/datasets/issues/4181 as well, but no luck so far!
Could you point me in the right direction please? :pray:
### Owner
Yes
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Allow dataset implement .take
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"```\r\n dataset = IterableDataset(dataset) if type(dataset) != IterableDataset else dataset # to force dataset.take(batch_size) to work in non-streaming mode\r\n ```\r\n",
"hf discuss: https://discuss.huggingface.co/t/how-does-one-make-dataset-take-512-work-with-streaming-false-with-hugging-face-data-set/50770",
"so: https://stackoverflow.com/questions/76902824/how-does-one-make-dataset-take512-work-with-streaming-false-with-hugging-fac",
"Feel free to work on this. In addition, `IterableDataset` supports `skip`, so we should also add this method to `Dataset`."
] | 2023-08-15T00:17:51Z
| 2023-08-17T13:49:37Z
| null |
NONE
| null | null | null |
### Feature request
I want to do:
```
dataset.take(512)
```
but it only works with streaming = True
### Motivation
uniform interface to data sets. Really surprising the above only works with streaming = True.
### Your contribution
Should be trivial to copy paste the IterableDataset .take to use the local path in the data (when streaming = False)
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not being able to get wikipedia es language
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"@lhoestq I really appreciate if you could help me providiing processed datasets, I do not really have access to enough resources to run the apache-beam and need to run the codes on these datasets. Only en/de/fr currently works, but I need all the languages more or less. thanks ",
"Hi @dorost1234, I think I can help you a little. I’ve processed some Wikipedia datasets (Spanish inclusive) using the HF/datasets library during recent research.\r\n\r\n@lhoestq Could you help me to upload these preprocessed datasets to Huggingface's repositories? To be more precise, I've built datasets from the following languages using the 20201201 dumps: Spanish, Portuguese, Russian, French, Japanese, Chinese, and Turkish. Process these datasets have high costs that most of the community can't afford. I think these preprocessed datasets I have could be helpful for someone without access to high-resource machines to process Wikipedia's dumps like @dorost1234\r\n\r\n",
"Thank you so much @jonatasgrosman , I greatly appreciate your help with them. \r\nYes, I unfortunately does not have access to a good resource and need it for my\r\nresearch. I greatly appreciate @lhoestq your help with uploading the processed datasets in huggingface datasets. This would be really helpful for some users like me with not access to high-memory GPU resources.\r\n\r\nthank you both so much again.\r\n\r\nOn Sat, Mar 6, 2021 at 12:55 AM Jonatas Grosman <notifications@github.com>\r\nwrote:\r\n\r\n> Hi @dorost1234 <https://github.com/dorost1234>, I think I can help you a\r\n> little. I’ve processed some Wikipedia datasets (Spanish inclusive) using\r\n> the HF/datasets library during recent research.\r\n>\r\n> @lhoestq <https://github.com/lhoestq> Could you help me to upload these\r\n> preprocessed datasets to Huggingface's repositories? To be more precise,\r\n> I've built datasets from the following languages using the 20201201 dumps:\r\n> Spanish, Portuguese, Russian, French, Japanese, Chinese, and Turkish.\r\n> Process these datasets have high costs that most of the community can't\r\n> afford. I think these preprocessed datasets I have could be helpful for\r\n> someone without access to high-resource machines to process Wikipedia's\r\n> dumps like @dorost1234 <https://github.com/dorost1234>\r\n>\r\n> —\r\n> You are receiving this because you were mentioned.\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/1994#issuecomment-791798195>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AS37NMWMK5GFJFU3ACCJFUDTCFVNZANCNFSM4YUZIF4A>\r\n> .\r\n>\r\n",
"Hi @dorost1234, so sorry, but looking at my files here, I figure out that I've preprocessed files using the HF/datasets for all the languages previously listed by me (Portuguese, Russian, French, Japanese, Chinese, and Turkish) except the Spanish (on my tests I've used the [wikicorpus](https://www.cs.upc.edu/~nlp/wikicorpus/) instead).\r\n\r\nOnly with the Spanish Wikipedia's dump, I had the same `KeyError: '000nbsp'` problem already reported here https://github.com/huggingface/datasets/issues/577\r\n\r\nSo nowadays, even with access to a high resource machine, you couldn't be able to get Wikipedia's Spanish data using the HF/datasets :(\r\n\r\n\r\n\r\n\r\n",
"Thanks a lot for the information and help. This would be great to have\nthese datasets.\n@lhoestq <https://github.com/lhoestq> Do you know a way I could get\nsmaller amount of these data like 1 GBtype of each language to deal with\ncomputatioanl requirements? thanks\n\nOn Sat, Mar 6, 2021 at 5:36 PM Jonatas Grosman <notifications@github.com>\nwrote:\n\n> Hi @dorost1234 <https://github.com/dorost1234>, so sorry, but looking at\n> my files here, I figure out that I've preprocessed files using the\n> HF/datasets for all the languages previously listed by me (Portuguese,\n> Russian, French, Japanese, Chinese, and Turkish) except the Spanish (on my\n> tests I've used the wikicorpus <https://www.cs.upc.edu/~nlp/wikicorpus/>\n> instead).\n>\n> Only with the Spanish Wikipedia's dump, I had the same KeyError: '000nbsp'\n> problem already reported here #577\n> <https://github.com/huggingface/datasets/issues/577>\n>\n> So nowadays, even with access to a high resource machine, you couldn't be\n> able to get Wikipedia's Spanish data using the HF/datasets :(\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/1994#issuecomment-791985546>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AS37NMWMO7WOHWLOROPD6Q3TCJKXPANCNFSM4YUZIF4A>\n> .\n>\n",
"Hi ! As mentioned above the Spanish configuration have parsing issues from `mwparserfromhell`. I haven't tested with the latest `mwparserfromhell` >=0.6 though. Which version of `mwparserfromhell` are you using ?\r\n\r\n> @lhoestq Could you help me to upload these preprocessed datasets to Huggingface's repositories? To be more precise, I've built datasets from the following languages using the 20201201 dumps: Spanish, Portuguese, Russian, French, Japanese, Chinese, and Turkish. Process these datasets have high costs that most of the community can't afford. I think these preprocessed datasets I have could be helpful for someone without access to high-resource machines to process Wikipedia's dumps like @dorost1234\r\n\r\nThat would be awesome ! Feel free to ping me on slack so we can put the processed wikipedia files on google storage with the other ones we've already preprocessed.\r\n\r\n> Do you know a way I could get smaller amount of these data like 1 GBtype of each language to deal with computatioanl requirements? thanks\r\n\r\nI'd suggest to copy the [wikipedia.py](https://github.com/huggingface/datasets/blob/master/datasets/wikipedia/wikipedia.py) to a new script `custom_wikipedia.py` and modify it to only download and process only a subset of the raw data files.\r\nYou can for example replace [this line](https://github.com/huggingface/datasets/blob/64e59fc45ca2134218b3e42e83fddddbe840ff74/datasets/wikipedia/wikipedia.py#L446) by:\r\n```python\r\n if total_bytes >= (1 << 30): # stop if the total amount of data is >= 1GB\r\n break\r\n else:\r\n xml_urls.append(_base_url(lang) + fname)\r\n```\r\n\r\nThen you can load your custom wikipedia dataset with\r\n```python\r\nload_dataset(\"path/to/my/custom_wikipedia.py\", f\"{date}.{language}\")\r\n```",
"Hi @lhoestq!\r\n\r\n> Hi ! As mentioned above the Spanish configuration have parsing issues from mwparserfromhell. I haven't tested with the latest mwparserfromhell >=0.6 though. Which version of mwparserfromhell are you using ?\r\n\r\nI'm using the latest mwparserfromhell version (0.6)\r\n\r\n> That would be awesome ! Feel free to ping me on slack so we can put the processed wikipedia files on google storage with the other ones we've already preprocessed.\r\n\r\nI'll ping you there 👍 ",
"Thank you so much @jonatasgrosman and @lhoestq this would be a great help. I am really thankful to you both and to wonderful Huggingface dataset library allowing us to train models at scale."
] | 2021-03-05T08:31:48Z
| 2021-03-11T20:46:21Z
| null |
NONE
| null | null | null |
Hi
I am trying to run a code with wikipedia of config 20200501.es, getting:
Traceback (most recent call last):
File "run_mlm_t5.py", line 608, in <module>
main()
File "run_mlm_t5.py", line 359, in main
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
File "/dara/libs/anaconda3/envs/success432/lib/python3.7/site-packages/datasets-1.2.1-py3.7.egg/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/dara/libs/anaconda3/envs/success432/lib/python3.7/site-packages/datasets-1.2.1-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/dara/libs/anaconda3/envs/success432/lib/python3.7/site-packages/datasets-1.2.1-py3.7.egg/datasets/builder.py", line 1050, in _download_and_prepare
"\n\t`{}`".format(usage_example)
datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`
thanks @lhoestq for any suggestion/help
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PR_kwDODunzps4z9p4b
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Update version and date in Wikipedia dataset
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3825). All of your documentation changes will be reflected on that endpoint."
] | 2022-03-04T16:05:27Z
| 2022-03-04T17:24:37Z
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CC: @geohci
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Add The People's Speech
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"This dataset is now available on the Hub here: https://huggingface.co/datasets/MLCommons/peoples_speech"
] | 2021-12-15T11:21:21Z
| 2023-02-28T16:22:29Z
| 2023-02-28T16:22:28Z
|
CONTRIBUTOR
| null | null | null |
## Adding a Dataset
- **Name:** The People's Speech
- **Description:** a massive English-language dataset of audio transcriptions of full sentences.
- **Paper:** https://openreview.net/pdf?id=R8CwidgJ0yT
- **Data:** https://mlcommons.org/en/peoples-speech/
- **Motivation:** With over 30,000 hours of speech, this dataset is the largest and most diverse freely available English speech recognition corpus today.
[The article](https://thegradient.pub/new-datasets-to-democratize-speech-recognition-technology-2/) which may be useful when working on the dataset.
cc: @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Fix deprecation warning when building conda package
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"<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) | 0.004811 / 0.011353 (-0.006542) | 0.002478 / 0.011008 (-0.008530) | 0.062241 / 0.038508 (0.023733) | 0.031153 / 0.023109 (0.008044) | 0.248896 / 0.275898 (-0.027002) | 0.276860 / 0.323480 (-0.046620) | 0.002934 / 0.007986 (-0.005052) | 0.002428 / 0.004328 (-0.001901) | 0.048507 / 0.004250 (0.044257) | 0.044567 / 0.037052 (0.007515) | 0.253570 / 0.258489 (-0.004919) | 0.280762 / 0.293841 (-0.013079) | 0.023549 / 0.128546 (-0.104997) | 0.006985 / 0.075646 (-0.068661) | 0.206227 / 0.419271 (-0.213044) | 0.054027 / 0.043533 (0.010494) | 0.257655 / 0.255139 (0.002516) | 0.273498 / 0.283200 (-0.009702) | 0.018997 / 0.141683 (-0.122685) | 1.111732 / 1.452155 (-0.340422) | 1.162078 / 1.492716 (-0.330639) |\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) | 0.091816 / 0.018006 (0.073810) | 0.299428 / 0.000490 (0.298938) | 0.000211 / 0.000200 (0.000012) | 0.000048 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018503 / 0.037411 (-0.018908) | 0.062933 / 0.014526 (0.048407) | 0.076349 / 0.176557 (-0.100208) | 0.123291 / 0.737135 (-0.613844) | 0.077491 / 0.296338 (-0.218847) |\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) | 0.280770 / 0.215209 (0.065561) | 2.762185 / 2.077655 (0.684530) | 1.429124 / 1.504120 (-0.074996) | 1.303162 / 1.541195 (-0.238033) | 1.307523 / 1.468490 (-0.160967) | 0.405593 / 4.584777 (-4.179184) | 2.396992 / 3.745712 (-1.348721) | 2.550968 / 5.269862 (-2.718894) | 1.557358 / 4.565676 (-3.008318) | 0.046149 / 0.424275 (-0.378126) | 0.004808 / 0.007607 (-0.002799) | 0.341870 / 0.226044 (0.115825) | 3.362478 / 2.268929 (1.093550) | 1.786360 / 55.444624 (-53.658264) | 1.483419 / 6.876477 (-5.393058) | 1.493463 / 2.142072 (-0.648609) | 0.470605 / 4.805227 (-4.334623) | 0.098372 / 6.500664 (-6.402292) | 0.041722 / 0.075469 (-0.033748) |\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) | 0.938148 / 1.841788 (-0.903640) | 11.219184 / 8.074308 (3.144876) | 10.454439 / 10.191392 (0.263047) | 0.139645 / 0.680424 (-0.540778) | 0.014453 / 0.534201 (-0.519748) | 0.268975 / 0.579283 (-0.310308) | 0.262060 / 0.434364 (-0.172304) | 0.313652 / 0.540337 (-0.226686) | 0.423992 / 1.386936 (-0.962944) |\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) | 0.004829 / 0.011353 (-0.006524) | 0.002426 / 0.011008 (-0.008582) | 0.049064 / 0.038508 (0.010555) | 0.049728 / 0.023109 (0.026619) | 0.273263 / 0.275898 (-0.002635) | 0.295645 / 0.323480 (-0.027835) | 0.004156 / 0.007986 (-0.003830) | 0.002397 / 0.004328 (-0.001932) | 0.048902 / 0.004250 (0.044652) | 0.038414 / 0.037052 (0.001362) | 0.276176 / 0.258489 (0.017687) | 0.306844 / 0.293841 (0.013003) | 0.024546 / 0.128546 (-0.104000) | 0.006946 / 0.075646 (-0.068701) | 0.054024 / 0.419271 (-0.365247) | 0.032444 / 0.043533 (-0.011089) | 0.274125 / 0.255139 (0.018986) | 0.293226 / 0.283200 (0.010027) | 0.018003 / 0.141683 (-0.123680) | 1.130402 / 1.452155 (-0.321752) | 1.195969 / 1.492716 (-0.296748) |\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) | 0.090043 / 0.018006 (0.072037) | 0.298699 / 0.000490 (0.298209) | 0.000214 / 0.000200 (0.000014) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021284 / 0.037411 (-0.016127) | 0.069954 / 0.014526 (0.055428) | 0.080445 / 0.176557 (-0.096111) | 0.119461 / 0.737135 (-0.617674) | 0.080632 / 0.296338 (-0.215706) |\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) | 0.302246 / 0.215209 (0.087037) | 2.991936 / 2.077655 (0.914281) | 1.662969 / 1.504120 (0.158850) | 1.533141 / 1.541195 (-0.008054) | 1.583183 / 1.468490 (0.114693) | 0.402864 / 4.584777 (-4.181913) | 2.424119 / 3.745712 (-1.321593) | 2.489558 / 5.269862 (-2.780303) | 1.502196 / 4.565676 (-3.063481) | 0.045980 / 0.424275 (-0.378295) | 0.004768 / 0.007607 (-0.002839) | 0.356089 / 0.226044 (0.130044) | 3.481333 / 2.268929 (1.212404) | 2.009713 / 55.444624 (-53.434912) | 1.730021 / 6.876477 (-5.146455) | 1.704656 / 2.142072 (-0.437416) | 0.470832 / 4.805227 (-4.334395) | 0.097473 / 6.500664 (-6.403191) | 0.040437 / 0.075469 (-0.035032) |\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) | 0.981497 / 1.841788 (-0.860291) | 11.827242 / 8.074308 (3.752933) | 10.888324 / 10.191392 (0.696932) | 0.129249 / 0.680424 (-0.551174) | 0.015812 / 0.534201 (-0.518389) | 0.269657 / 0.579283 (-0.309626) | 0.275585 / 0.434364 (-0.158779) | 0.305698 / 0.540337 (-0.234639) | 0.411497 / 1.386936 (-0.975439) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bcde318293af04fd5044b42ddfcb650f9b092d45 \"CML watermark\")\n",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6425). 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) | 0.005402 / 0.011353 (-0.005951) | 0.003955 / 0.011008 (-0.007053) | 0.064096 / 0.038508 (0.025588) | 0.062330 / 0.023109 (0.039221) | 0.254729 / 0.275898 (-0.021169) | 0.276259 / 0.323480 (-0.047221) | 0.003052 / 0.007986 (-0.004934) | 0.003474 / 0.004328 (-0.000854) | 0.048938 / 0.004250 (0.044687) | 0.038635 / 0.037052 (0.001583) | 0.267953 / 0.258489 (0.009464) | 0.293725 / 0.293841 (-0.000116) | 0.028266 / 0.128546 (-0.100280) | 0.011188 / 0.075646 (-0.064458) | 0.221204 / 0.419271 (-0.198067) | 0.036549 / 0.043533 (-0.006984) | 0.252484 / 0.255139 (-0.002655) | 0.273855 / 0.283200 (-0.009345) | 0.017975 / 0.141683 (-0.123708) | 1.112265 / 1.452155 (-0.339890) | 1.185647 / 1.492716 (-0.307069) |\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) | 0.096223 / 0.018006 (0.078217) | 0.305010 / 0.000490 (0.304520) | 0.000227 / 0.000200 (0.000027) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018924 / 0.037411 (-0.018488) | 0.061910 / 0.014526 (0.047384) | 0.073751 / 0.176557 (-0.102806) | 0.120956 / 0.737135 (-0.616179) | 0.075090 / 0.296338 (-0.221249) |\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) | 0.293277 / 0.215209 (0.078068) | 2.867468 / 2.077655 (0.789813) | 1.518218 / 1.504120 (0.014098) | 1.393741 / 1.541195 (-0.147454) | 1.424979 / 1.468490 (-0.043511) | 0.579766 / 4.584777 (-4.005011) | 2.434951 / 3.745712 (-1.310761) | 2.909924 / 5.269862 (-2.359937) | 1.838123 / 4.565676 (-2.727554) | 0.064260 / 0.424275 (-0.360015) | 0.005169 / 0.007607 (-0.002438) | 0.348228 / 0.226044 (0.122184) | 3.447558 / 2.268929 (1.178629) | 1.884988 / 55.444624 (-53.559636) | 1.570921 / 6.876477 (-5.305556) | 1.646341 / 2.142072 (-0.495732) | 0.660189 / 4.805227 (-4.145038) | 0.120026 / 6.500664 (-6.380638) | 0.043715 / 0.075469 (-0.031754) |\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) | 0.953253 / 1.841788 (-0.888535) | 12.576112 / 8.074308 (4.501804) | 11.132637 / 10.191392 (0.941245) | 0.132870 / 0.680424 (-0.547553) | 0.014720 / 0.534201 (-0.519481) | 0.291866 / 0.579283 (-0.287417) | 0.265456 / 0.434364 (-0.168908) | 0.338629 / 0.540337 (-0.201709) | 0.456323 / 1.386936 (-0.930613) |\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) | 0.005644 / 0.011353 (-0.005709) | 0.003624 / 0.011008 (-0.007384) | 0.049043 / 0.038508 (0.010535) | 0.059572 / 0.023109 (0.036463) | 0.277159 / 0.275898 (0.001261) | 0.303933 / 0.323480 (-0.019547) | 0.004294 / 0.007986 (-0.003692) | 0.002744 / 0.004328 (-0.001584) | 0.048187 / 0.004250 (0.043937) | 0.043655 / 0.037052 (0.006603) | 0.282441 / 0.258489 (0.023952) | 0.317130 / 0.293841 (0.023289) | 0.030159 / 0.128546 (-0.098387) | 0.011300 / 0.075646 (-0.064346) | 0.057451 / 0.419271 (-0.361821) | 0.033666 / 0.043533 (-0.009866) | 0.274554 / 0.255139 (0.019415) | 0.292470 / 0.283200 (0.009270) | 0.018757 / 0.141683 (-0.122926) | 1.170094 / 1.452155 (-0.282060) | 1.244626 / 1.492716 (-0.248090) |\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) | 0.094920 / 0.018006 (0.076914) | 0.304156 / 0.000490 (0.303666) | 0.000226 / 0.000200 (0.000026) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022297 / 0.037411 (-0.015115) | 0.068908 / 0.014526 (0.054383) | 0.081520 / 0.176557 (-0.095037) | 0.122422 / 0.737135 (-0.614714) | 0.082533 / 0.296338 (-0.213806) |\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) | 0.296080 / 0.215209 (0.080871) | 2.883120 / 2.077655 (0.805465) | 1.607950 / 1.504120 (0.103830) | 1.496191 / 1.541195 (-0.045004) | 1.520549 / 1.468490 (0.052059) | 0.562081 / 4.584777 (-4.022696) | 2.453447 / 3.745712 (-1.292265) | 2.943676 / 5.269862 (-2.326186) | 1.820581 / 4.565676 (-2.745096) | 0.064518 / 0.424275 (-0.359757) | 0.005406 / 0.007607 (-0.002201) | 0.349022 / 0.226044 (0.122978) | 3.472117 / 2.268929 (1.203188) | 2.006928 / 55.444624 (-53.437696) | 1.704800 / 6.876477 (-5.171677) | 1.719025 / 2.142072 (-0.423048) | 0.643719 / 4.805227 (-4.161508) | 0.117723 / 6.500664 (-6.382941) | 0.043158 / 0.075469 (-0.032311) |\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) | 0.981229 / 1.841788 (-0.860559) | 12.637620 / 8.074308 (4.563312) | 10.848775 / 10.191392 (0.657383) | 0.143981 / 0.680424 (-0.536443) | 0.015950 / 0.534201 (-0.518251) | 0.287542 / 0.579283 (-0.291741) | 0.278989 / 0.434364 (-0.155375) | 0.331786 / 0.540337 (-0.208552) | 0.607238 / 1.386936 (-0.779698) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#06fb2f9973962ee97d1af7888209819b8ba7de37 \"CML watermark\")\n"
] | 2023-11-15T18:00:11Z
| 2023-12-13T14:22:30Z
| 2023-12-13T14:16:00Z
|
MEMBER
| null | 0
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When building/releasing conda package, we get this deprecation warning:
```
/usr/share/miniconda/envs/build-datasets/bin/conda-build:11: DeprecationWarning: conda_build.cli.main_build.main is deprecated and will be removed in 4.0.0. Use `conda build` instead.
```
This PR fixes the deprecation warning by using `conda build` instead.
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| 1,557,510,618
|
I_kwDODunzps5c1bna
| 5,465
|
audiofolder creates empty dataset even though the dataset passed in follows the correct structure
|
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[] | 2023-01-26T01:45:45Z
| 2023-01-26T08:48:45Z
| 2023-01-26T08:48:45Z
|
NONE
| null | null | null |
### Describe the bug
The structure of my dataset folder called "my_dataset" is : data metadata.csv
The data folder consists of all mp3 files and metadata.csv consist of file locations like 'data/...mp3 and transcriptions. There's 400+ mp3 files and corresponding transcriptions for my dataset.
When I run the following:
ds = load_dataset("audiofolder", data_dir="my_dataset")
I get:
Using custom data configuration default-...
Downloading and preparing dataset audiofolder/default to /...
Downloading data files: 0%| | 0/2 [00:00<?, ?it/s]
Downloading data files: 0it [00:00, ?it/s]
Extracting data files: 0it [00:00, ?it/s]
Generating train split: 0 examples [00:00, ? examples/s]
Dataset audiofolder downloaded and prepared to /.... Subsequent calls will reuse this data.
0%| | 0/1 [00:00<?, ?it/s]
DatasetDict({
train: Dataset({
features: ['audio', 'transcription'],
num_rows: 1
})
})
### Steps to reproduce the bug
Create a dataset folder called 'my_dataset' with a subfolder called 'data' that has mp3 files. Also, create metadata.csv that has file locations like 'data/...mp3' and their corresponding transcription.
Run:
ds = load_dataset("audiofolder", data_dir="my_dataset")
### Expected behavior
It should generate a dataset with numerous rows.
### Environment info
Run on Jupyter notebook
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MDExOlB1bGxSZXF1ZXN0NTQ1NzI2NzY4
| 1,640
|
Fix "'BertTokenizerFast' object has no attribute 'max_len'"
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Tensorflow 2.3.0 gives:
FutureWarning: The `max_len` attribute has been deprecated and will be removed in a future version, use `model_max_length` instead.
Tensorflow 2.4.0 gives:
AttributeError 'BertTokenizerFast' object has no attribute 'max_len'
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Fix DuplicatedKeysError in omp
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Close #2598.
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[WIP][Common Voice 7] Add common voice 7.0
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"Hi ! I think the name `common_voice_7` is fine :)\r\nMoreover if the dataset_infos.json is missing I'm pretty sure you don't need to specify `ignore_verifications=True`",
"Hi, how about to add a new parameter \"version\" in the function load_dataset, something like: \r\n`load_dataset(\"common_voice\", \"lg\", version=\"7.0\") `\r\nThis is to avoid creating a new common_voice_? dataset (with almost the same code) every time \r\nMozilla updates their Common Voice dataset.\r\n"
] | 2021-08-07T16:01:10Z
| 2021-12-06T23:24:02Z
| 2021-12-06T23:24:02Z
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This PR allows to load the new common voice dataset manually as explained when doing:
```python
from datasets import load_dataset
ds = load_dataset("./datasets/datasets/common_voice_7", "ab")
```
=>
```
Please follow the manual download instructions:
You need to manually the dataset from `https://commonvoice.mozilla.org/en/datasets`.
Make sure you choose the version `Common Voice Corpus 7.0`.
Choose a language of your choice and find the corresponding language-id, *e.g.*, `Abkhaz` with language-id `ab`. The following language-ids are available:
['ab', 'ar', 'as', 'az', 'ba', 'bas', 'be', 'bg', 'br', 'ca', 'cnh', 'cs', 'cv', 'cy', 'de', 'dv', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy-NL', 'ga-IE', 'gl', 'gn', 'ha', 'hi', 'hsb', 'hu', 'hy-AM', 'ia', 'id', 'it', 'ja', 'ka', 'kab', 'kk', 'kmr', 'ky', 'lg', 'lt', 'lv', 'mn', 'mt', 'nl', 'or', 'pa-IN', 'pl', 'pt', 'rm-sursilv', 'rm-vallader', 'ro', 'ru', 'rw', 'sah', 'sk', 'sl', 'sr', 'sv-SE', 'ta', 'th', 'tr', 'tt', 'ug', 'uk', 'ur', 'uz', 'vi', 'vot', 'zh-CN', 'zh-HK', 'zh-TW']
Next, you will have to enter your email address to download the dataset in the `tar.gz` format. Save the file under <path-to-file>.
The file should then be extracted with: ``tar -xvzf <path-to-file>`` which will extract a folder called ``cv-corpus-7.0-2021-07-21``.
The dataset can then be loaded with `datasets.load_dataset("common_voice", <language-id>, data_dir="<path-to-'cv-corpus-7.0-2021-07-21'-folder>", ignore_verifications=True).
```
Having followed those instructions one can then download the data as follows:
```python
from datasets import load_dataset
ds = load_dataset("./datasets/datasets/common_voice_7", "ab", data_dir="./cv-corpus-7.0-2021-07-21/", ignore_verifications=True)
```
## TODO
- [ ] Discuss naming. Is the name ok here "common_voice_7"? The dataset script differs only really in one point from `common_voice.py` in that all the metadata is different (more hours etc...) and that it has to use manual data dir for now
- [ ] Ideally we should get a bundled download link. For `common_voice.py` there is a bundled download link: `https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/{}.tar.gz` that allows one to directly download the data. However such a link is missing for Common Voice 7. I guess we should try to contact common voice about it and ask whether we could host the data or help otherwise somehow. See: https://github.com/common-voice/common-voice-bundler/issues/15 cc @yjernite
- [ ] I did not compute the dataset.json and it would mean that I'd have to download 76 datasets totalling around 1TB manually before running the checksum command. This just takes too much time. For now the user will have to add a `ignore_verifications=True` to download the data. This step would also be much easier if we could get a bundled link
- [ ] Add dummy data
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Add CANER Corpus
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| 2021-01-25T09:09:20Z
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What does this PR do?
Adds the following dataset:
https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus
Who can review?
@lhoestq
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Fix import in `image_load` doc
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"_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) | 0.009072 / 0.011353 (-0.002281) | 0.006088 / 0.011008 (-0.004920) | 0.134520 / 0.038508 (0.096011) | 0.074935 / 0.023109 (0.051826) | 0.480364 / 0.275898 (0.204466) | 0.568943 / 0.323480 (0.245464) | 0.006821 / 0.007986 (-0.001164) | 0.004941 / 0.004328 (0.000612) | 0.083274 / 0.004250 (0.079023) | 0.061080 / 0.037052 (0.024028) | 0.478960 / 0.258489 (0.220471) | 0.542720 / 0.293841 (0.248879) | 0.058023 / 0.128546 (-0.070524) | 0.020120 / 0.075646 (-0.055526) | 0.492680 / 0.419271 (0.073409) | 0.079118 / 0.043533 (0.035585) | 0.425087 / 0.255139 (0.169948) | 0.603228 / 0.283200 (0.320028) | 0.044102 / 0.141683 (-0.097581) | 2.138848 / 1.452155 (0.686693) | 2.454418 / 1.492716 (0.961702) |\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) | 0.255745 / 0.018006 (0.237738) | 0.587559 / 0.000490 (0.587069) | 0.006872 / 0.000200 (0.006672) | 0.000111 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038480 / 0.037411 (0.001069) | 0.115479 / 0.014526 (0.100953) | 0.138395 / 0.176557 (-0.038161) | 0.218007 / 0.737135 (-0.519129) | 0.128866 / 0.296338 (-0.167472) |\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) | 0.756089 / 0.215209 (0.540880) | 7.754631 / 2.077655 (5.676976) | 3.615716 / 1.504120 (2.111596) | 2.994327 / 1.541195 (1.453132) | 3.196169 / 1.468490 (1.727679) | 1.066937 / 4.584777 (-3.517840) | 6.079595 / 3.745712 (2.333883) | 5.455523 / 5.269862 (0.185661) | 3.559036 / 4.565676 (-1.006640) | 0.113044 / 0.424275 (-0.311231) | 0.011401 / 0.007607 (0.003794) | 0.961475 / 0.226044 (0.735430) | 8.664226 / 2.268929 (6.395298) | 4.203804 / 55.444624 (-51.240821) | 3.122437 / 6.876477 (-3.754039) | 3.549168 / 2.142072 (1.407095) | 1.213035 / 4.805227 (-3.592193) | 0.274725 / 6.500664 (-6.225939) | 0.094499 / 0.075469 (0.019030) |\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) | 1.770299 / 1.841788 (-0.071489) | 27.644591 / 8.074308 (19.570283) | 23.239529 / 10.191392 (13.048137) | 0.270185 / 0.680424 (-0.410238) | 0.033563 / 0.534201 (-0.500638) | 0.588301 / 0.579283 (0.009018) | 0.658746 / 0.434364 (0.224382) | 0.644476 / 0.540337 (0.104139) | 0.834314 / 1.386936 (-0.552622) |\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) | 0.011021 / 0.011353 (-0.000332) | 0.006719 / 0.011008 (-0.004289) | 0.087669 / 0.038508 (0.049161) | 0.088905 / 0.023109 (0.065796) | 0.594230 / 0.275898 (0.318332) | 0.620929 / 0.323480 (0.297449) | 0.006776 / 0.007986 (-0.001210) | 0.004725 / 0.004328 (0.000396) | 0.082006 / 0.004250 (0.077756) | 0.072164 / 0.037052 (0.035111) | 0.604489 / 0.258489 (0.346000) | 0.598520 / 0.293841 (0.304679) | 0.057534 / 0.128546 (-0.071013) | 0.016799 / 0.075646 (-0.058847) | 0.115029 / 0.419271 (-0.304243) | 0.070013 / 0.043533 (0.026481) | 0.561773 / 0.255139 (0.306634) | 0.624097 / 0.283200 (0.340897) | 0.043518 / 0.141683 (-0.098164) | 2.017089 / 1.452155 (0.564934) | 2.188159 / 1.492716 (0.695443) |\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) | 0.386476 / 0.018006 (0.368469) | 0.633195 / 0.000490 (0.632705) | 0.028469 / 0.000200 (0.028269) | 0.000159 / 0.000054 (0.000104) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.040020 / 0.037411 (0.002609) | 0.112927 / 0.014526 (0.098402) | 0.143663 / 0.176557 (-0.032894) | 0.205931 / 0.737135 (-0.531204) | 0.177814 / 0.296338 (-0.118524) |\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) | 0.711542 / 0.215209 (0.496333) | 7.518535 / 2.077655 (5.440880) | 3.714930 / 1.504120 (2.210810) | 3.031999 / 1.541195 (1.490804) | 3.328497 / 1.468490 (1.860006) | 0.858912 / 4.584777 (-3.725865) | 6.108384 / 3.745712 (2.362672) | 5.184329 / 5.269862 (-0.085532) | 3.622589 / 4.565676 (-0.943087) | 0.096933 / 0.424275 (-0.327342) | 0.008727 / 0.007607 (0.001120) | 0.830102 / 0.226044 (0.604057) | 8.331959 / 2.268929 (6.063030) | 4.165106 / 55.444624 (-51.279519) | 3.477003 / 6.876477 (-3.399474) | 3.794225 / 2.142072 (1.652153) | 1.237667 / 4.805227 (-3.567561) | 0.233731 / 6.500664 (-6.266933) | 0.076682 / 0.075469 (0.001213) |\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) | 1.944813 / 1.841788 (0.103026) | 27.666997 / 8.074308 (19.592689) | 24.562677 / 10.191392 (14.371285) | 0.279320 / 0.680424 (-0.401104) | 0.037802 / 0.534201 (-0.496399) | 0.553579 / 0.579283 (-0.025704) | 0.718229 / 0.434364 (0.283865) | 0.623456 / 0.540337 (0.083118) | 0.856777 / 1.386936 (-0.530159) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c2a9d31d5e720e85976af8b457d45755a7e6911 \"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) | 0.007716 / 0.011353 (-0.003637) | 0.004624 / 0.011008 (-0.006384) | 0.099987 / 0.038508 (0.061479) | 0.082651 / 0.023109 (0.059542) | 0.376277 / 0.275898 (0.100379) | 0.401210 / 0.323480 (0.077730) | 0.004528 / 0.007986 (-0.003458) | 0.003763 / 0.004328 (-0.000566) | 0.076274 / 0.004250 (0.072024) | 0.062933 / 0.037052 (0.025881) | 0.393881 / 0.258489 (0.135392) | 0.431695 / 0.293841 (0.137854) | 0.036795 / 0.128546 (-0.091752) | 0.009935 / 0.075646 (-0.065712) | 0.343638 / 0.419271 (-0.075634) | 0.061456 / 0.043533 (0.017923) | 0.372235 / 0.255139 (0.117096) | 0.412994 / 0.283200 (0.129794) | 0.027993 / 0.141683 (-0.113690) | 1.798018 / 1.452155 (0.345863) | 1.898502 / 1.492716 (0.405786) |\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) | 0.237330 / 0.018006 (0.219324) | 0.494956 / 0.000490 (0.494467) | 0.003543 / 0.000200 (0.003343) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034084 / 0.037411 (-0.003327) | 0.093407 / 0.014526 (0.078881) | 0.108378 / 0.176557 (-0.068179) | 0.177016 / 0.737135 (-0.560119) | 0.108622 / 0.296338 (-0.187716) |\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) | 0.456449 / 0.215209 (0.241240) | 4.522405 / 2.077655 (2.444750) | 2.206564 / 1.504120 (0.702444) | 1.994185 / 1.541195 (0.452990) | 2.083785 / 1.468490 (0.615295) | 0.563352 / 4.584777 (-4.021425) | 4.207295 / 3.745712 (0.461583) | 3.783061 / 5.269862 (-1.486800) | 2.372874 / 4.565676 (-2.192802) | 0.066907 / 0.424275 (-0.357368) | 0.009013 / 0.007607 (0.001406) | 0.537852 / 0.226044 (0.311808) | 5.349928 / 2.268929 (3.081000) | 2.759409 / 55.444624 (-52.685215) | 2.345972 / 6.876477 (-4.530505) | 2.630559 / 2.142072 (0.488486) | 0.681134 / 4.805227 (-4.124093) | 0.157898 / 6.500664 (-6.342766) | 0.071638 / 0.075469 (-0.003831) |\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) | 1.470730 / 1.841788 (-0.371058) | 22.479252 / 8.074308 (14.404944) | 16.543080 / 10.191392 (6.351688) | 0.191943 / 0.680424 (-0.488481) | 0.021641 / 0.534201 (-0.512560) | 0.467571 / 0.579283 (-0.111712) | 0.486728 / 0.434364 (0.052364) | 0.543359 / 0.540337 (0.003021) | 0.733968 / 1.386936 (-0.652968) |\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) | 0.008135 / 0.011353 (-0.003218) | 0.004662 / 0.011008 (-0.006347) | 0.077218 / 0.038508 (0.038710) | 0.092220 / 0.023109 (0.069111) | 0.481219 / 0.275898 (0.205321) | 0.530373 / 0.323480 (0.206893) | 0.006418 / 0.007986 (-0.001568) | 0.003924 / 0.004328 (-0.000404) | 0.076681 / 0.004250 (0.072431) | 0.068693 / 0.037052 (0.031641) | 0.491938 / 0.258489 (0.233449) | 0.540501 / 0.293841 (0.246660) | 0.038106 / 0.128546 (-0.090441) | 0.010035 / 0.075646 (-0.065611) | 0.084502 / 0.419271 (-0.334769) | 0.057234 / 0.043533 (0.013701) | 0.483239 / 0.255139 (0.228100) | 0.510026 / 0.283200 (0.226826) | 0.028770 / 0.141683 (-0.112913) | 1.854937 / 1.452155 (0.402783) | 1.948268 / 1.492716 (0.455552) |\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) | 0.380192 / 0.018006 (0.362186) | 0.523318 / 0.000490 (0.522828) | 0.051153 / 0.000200 (0.050953) | 0.000691 / 0.000054 (0.000637) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036838 / 0.037411 (-0.000573) | 0.109202 / 0.014526 (0.094676) | 0.124110 / 0.176557 (-0.052446) | 0.186717 / 0.737135 (-0.550419) | 0.124088 / 0.296338 (-0.172250) |\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) | 0.506411 / 0.215209 (0.291202) | 5.045421 / 2.077655 (2.967766) | 2.711911 / 1.504120 (1.207791) | 2.531668 / 1.541195 (0.990474) | 2.635680 / 1.468490 (1.167190) | 0.578395 / 4.584777 (-4.006382) | 4.206891 / 3.745712 (0.461178) | 3.851063 / 5.269862 (-1.418799) | 2.388327 / 4.565676 (-2.177350) | 0.068041 / 0.424275 (-0.356234) | 0.008769 / 0.007607 (0.001162) | 0.594170 / 0.226044 (0.368125) | 5.953138 / 2.268929 (3.684210) | 3.290586 / 55.444624 (-52.154038) | 2.877086 / 6.876477 (-3.999390) | 3.138600 / 2.142072 (0.996528) | 0.686393 / 4.805227 (-4.118834) | 0.156541 / 6.500664 (-6.344123) | 0.071514 / 0.075469 (-0.003955) |\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) | 1.613514 / 1.841788 (-0.228274) | 23.593185 / 8.074308 (15.518877) | 17.146647 / 10.191392 (6.955255) | 0.177230 / 0.680424 (-0.503193) | 0.023661 / 0.534201 (-0.510540) | 0.472367 / 0.579283 (-0.106916) | 0.484614 / 0.434364 (0.050250) | 0.547150 / 0.540337 (0.006813) | 0.843726 / 1.386936 (-0.543210) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dba64cd381bfe384cb64ab9826f6054a0f1df1ff \"CML watermark\")\n"
] | 2023-08-25T13:12:19Z
| 2023-08-25T16:12:46Z
| 2023-08-25T16:02:24Z
|
CONTRIBUTOR
| null | 0
|
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Reported on [Discord](https://discord.com/channels/879548962464493619/1144295822209581168/1144295822209581168)
|
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https://api.github.com/repos/huggingface/datasets/issues/6181/timeline
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https://api.github.com/repos/huggingface/datasets/issues/4619
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https://github.com/huggingface/datasets/issues/4619
| 1,292,107,275
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I_kwDODunzps5NA_4L
| 4,619
|
np arrays get turned into native lists
|
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"If you add the line `dataset2.set_format('np')` before calling `dataset2[0]['tmp']` it should return `np.ndarray`.\r\nI believe internally it will not store it as a list, it is only returning a list when you index it.\r\n\r\n```\r\nIn [1]: import datasets, numpy as np\r\nIn [2]: dataset = datasets.load_dataset(\"glue\", \"mrpc\")[\"validation\"]\r\nIn [3]: dataset2 = dataset.map(lambda x: {\"tmp\": np.array([0.5])}, batched=False)\r\nIn [4]: dataset2[0][\"tmp\"]\r\nOut[4]: [0.5]\r\n\r\nIn [5]: dataset2.set_format('np')\r\n\r\nIn [6]: dataset2[0][\"tmp\"]\r\nOut[6]: array([0.5])\r\n```",
"I see, thanks! Any idea if the default numpy → list conversion might cause precision loss?",
"I'm not super familiar with our datasets works internally, but I think your `np` array will be stored in a `pyarrow` format, and then you take a view of this as a python array. In which case, I think the precision should be preserved."
] | 2022-07-02T17:54:57Z
| 2022-07-03T20:27:07Z
| null |
NONE
| null | null | null |
## Describe the bug
When attaching an `np.array` field, it seems that it automatically gets turned into a list (see below). Why is this happening? Could it lose precision? Is there a way to make sure this doesn't happen?
## Steps to reproduce the bug
```python
>>> import datasets, numpy as np
>>> dataset = datasets.load_dataset("glue", "mrpc")["validation"]
Reusing dataset glue (...)
100%|███████████████████████████████████████████████| 3/3 [00:00<00:00, 1360.61it/s]
>>> dataset2 = dataset.map(lambda x: {"tmp": np.array([0.5])}, batched=False)
100%|██████████████████████████████████████████| 408/408 [00:00<00:00, 10819.97ex/s]
>>> dataset2[0]["tmp"]
[0.5]
>>> type(dataset2[0]["tmp"])
<class 'list'>
```
## Expected results
`dataset2[0]["tmp"]` should be an `np.ndarray`.
## Actual results
It's a list.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: mac, though I'm pretty sure it happens on a linux machine too
- Python version: 3.9.7
- PyArrow version: 6.0.1
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|
Update: Openwebtext - update size
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"merging since the CI error in unrelated to this PR and fixed on master"
] | 2021-08-31T17:11:03Z
| 2022-02-15T10:38:03Z
| 2021-09-07T09:44:32Z
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Update the size of the Openwebtext dataset
I also regenerated the dataset_infos.json but the data file checksum didn't change, and the number of examples either (8013769 examples)
Close #2839, close #726.
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Improve inconsistency of `Dataset.map` interface for `load_from_cache_file`
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"Hi, thanks for noticing this! We can't just remove the cache control as this allows us to control where the arrow files generated by the ops are written (cached on disk if enabled or a temporary directory if disabled). The right way to address this inconsistency would be by having `load_from_cache_file=None` by default everywhere.",
"Hi! Yes, this seems more plausible. I can implement that. One last thing is the type annotation `load_from_cache_file: bool = None`. Which I then would change to `load_from_cache_file: Optional[bool] = None`.",
"PR #5515 ",
"Yes, `Optional[bool]` is the correct type annotation and thanks for the PR."
] | 2023-02-08T16:40:44Z
| 2023-02-14T14:26:44Z
| 2023-02-14T14:26:44Z
|
CONTRIBUTOR
| null | null | null |
### Feature request
1. Replace the `load_from_cache_file` default value to `True`.
2. Remove or alter checks from `is_caching_enabled` logic.
### Motivation
I stumbled over an inconsistency in the `Dataset.map` interface. The documentation (and source) states for the parameter `load_from_cache_file`:
```
load_from_cache_file (`bool`, defaults to `True` if caching is enabled):
If a cache file storing the current computation from `function`
can be identified, use it instead of recomputing.
```
1. `load_from_cache_file` default value is `None`, while being annotated as `bool`
2. It is inconsistent with other method signatures like `filter`, that have the default value `True`
3. The logic is inconsistent, as the `map` method checks if caching is enabled through `is_caching_enabled`. This logic is not used for other similar methods.
### Your contribution
I am not fully aware of the logic behind caching checks. If this is just a inconsistency that historically grew, I would suggest to remove the `is_caching_enabled` logic as the "default" logic. Maybe someone can give insights, if environment variables have a higher priority than local variables or vice versa.
If this is clarified, I could adjust the source according to the "Feature request" section of this issue.
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to_tf_dataset with a TF collator causes bizarrely persistent slowdown
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"wtf",
"Couldn't find what's causing this, this will need more investigation",
"A possible hint: The function it seems to be spending a lot of time in (when iterating over the original dataset) is `_get_mp` in the PIL JPEG decoder: \r\n\r\n",
"If \"mp\" is multiprocessing, this might suggest some kind of negative interaction between the JPEG decoder and TF's handling of processes/threads. Note that we haven't merged the parallel `to_tf_dataset` PR yet, so it's not caused by that PR!",
"Update: MP isn't multiprocessing at all, it's an internal PIL method for loading metadata from JPEG files. No idea why that would be a bottleneck, but I'll see if a Python profiler can't figure out where the time is actually being spent.",
"After further profiling, the slowdown is in the C methods for JPEG decoding that are included as part of PIL. Because Python profilers can't inspect inside that, I don't have any further information on which lines exactly are responsible for the slowdown or why.\r\n\r\nIn the meantime, I'm going to suggest switching from `return_tensors=\"tf\"` to `return_tensors=\"np\"` in most of our `transformers` code - this generally works better for pre-processing. Two relevant PRs are [here](https://github.com/huggingface/transformers/pull/21266) and [here](https://github.com/huggingface/notebooks/pull/308).",
"Closing this issue as we've done what we can with this one! "
] | 2023-01-20T16:08:37Z
| 2023-02-13T14:13:34Z
| 2023-02-13T14:13:34Z
|
MEMBER
| null | null | null |
### Describe the bug
This will make more sense if you take a look at [a Colab notebook that reproduces this issue.](https://colab.research.google.com/drive/1rxyeciQFWJTI0WrZ5aojp4Ls1ut18fNH?usp=sharing)
Briefly, there are several datasets that, when you iterate over them with `to_tf_dataset` **and** a data collator that returns `tf` tensors, become very slow. We haven't been able to figure this one out - it can be intermittent, and we have no idea what could possibly cause it. The weirdest thing is that **the slowdown affects other attempts to access the underlying dataset**. If you try to iterate over the `tf.data.Dataset`, then interrupt execution, and then try to iterate over the original dataset, the original dataset is now also very slow! This is true even if the dataset format is not set to `tf` - the iteration is slow even though it's not calling TF at all!
There is a simple workaround for this - we can simply get our data collators to return `np` tensors. When we do this, the bug is never triggered and everything is fine. In general, `np` is preferred for this kind of preprocessing work anyway, when the preprocessing is not going to be compiled into a pure `tf.data` pipeline! However, the issue is fascinating, and the TF team were wondering if anyone in datasets (cc @lhoestq @mariosasko) might have an idea of what could cause this.
### Steps to reproduce the bug
Run the attached Colab.
### Expected behavior
The slowdown should go away, or at least not persist after we stop iterating over the `tf.data.Dataset`
### Environment info
The issue occurs on multiple versions of Python and TF, both on local machines and on Colab.
All testing was done using the latest versions of `transformers` and `datasets` from `main`
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Add OSCAR dataset card
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[
"@lhoestq Thanks for the suggestions! I agree with all of them. Should I accept them one by one or can I accept them all at once? When I try to load the whole diff GitHub is complaining and it does no render them well (probably my browser?) 😅 ",
"I just merged the tables as suggested 😄 . However I noticed something weird, the train sizes are identical for both the original and deduplicated files ... This is not normal, in general the original files are almost twice as big as the deduplicated ones 🤔 ",
"Good catch @pjox ! I just checked and this is because the scripts doesn't handle having several blank lines in a row.\r\nBlank lines introduced by deduplication are currently not ignored so we end up with the same number of examples in the dataset as the original version (but with empty examples...)\r\nI fixed that in this [commit](https://github.com/huggingface/datasets/commit/837a152e4724adc5308e2c4481908c00a8d93383). I'm re-running the metadata generation for deduplicated configs.",
"I got the new sizes today, will update the dataset_infos.json and the dataset card tomorrow",
"> I got the new sizes today, will update the dataset_infos.json and the dataset card tomorrow\r\n\r\ngreat, I just wanted to report that I got error message \"NonMatchingSplitsSizesError\" when I tried to load one of the oscar dataset.",
"Hi @cahya-wirawan, which configuration of oscar do you have this issue with ?",
"Ok I see you're having this issue because I haven't updated the sizes yet ! I'm opening a PR\r\n\r\nI just checked and indeed there's an issue with the `deduplicated` configurations since the commit I mentioned above.\r\nI'm fixing this by using the new sizes I got yesterday :) \r\n",
"I just updated the size in the table @pjox it should be good now :) \r\nI also updated the sizes in the dataset_infos.json in https://github.com/huggingface/datasets/pull/1868 (merged)",
"Thanks @lhoestq for fixing the issue, it works now",
"Thank you so much @lhoestq !"
] | 2021-02-08T01:39:49Z
| 2021-02-12T14:09:25Z
| 2021-02-12T14:08:24Z
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I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824).
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Add XOR QA Dataset
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"Hi @sumanthd17 \r\n\r\nLooks like a good start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card)",
"I followed the instructions mentioned there but my dataset isn't showing up in the dropdown list. Am I missing something here? @yjernite ",
"> I followed the instructions mentioned there but my dataset isn't showing up in the dropdown list. Am I missing something here? @yjernite\r\n\r\nThe best way is to run the tagging app locally and provide it the location to the `dataset_infos.json` after you've run the CLI:\r\nhttps://github.com/huggingface/datasets-tagging\r\n",
"This is a really good data card!!\r\n\r\nSmall changes to make it even better:\r\n- Tags: the dataset has both \"original\" data and data that is \"extended\" from a source dataset: TydiQA - you should choose both options in the tagging apps\r\n- The language and annotation creator tags are off: the language here is the questions: I understand it's a mix of crowd-sourced and expert-generated? Is there any machine translation involved? The annotations are the span selections: is that crowd-sourced?\r\n- Personal and sensitive information: there should be a statement there, even if only to say that none could be found or that it only mentions public figures"
] | 2020-11-30T15:10:54Z
| 2020-12-02T03:12:21Z
| 2020-12-02T03:12:21Z
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Added XOR Question Answering Dataset. The link to the dataset can be found [here](https://nlp.cs.washington.edu/xorqa/)
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data
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Connection error of the HuggingFace's dataset Hub due to SSLError with proxy
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[
"Hi ! It looks like an issue with your python environment, can you make sure you're able to run GET requests to https://huggingface.co using `requests` in python ?",
"Thanks for your reply. Does this mean that I have to use the `do_dataset `function and the `requests `function to download the dataset from the company's proxy environment?\r\n\r\n\r\n* Reference: \r\n```bash\r\n### How to load this dataset directly with the [datasets](https://github.com/huggingface/datasets) library\r\n\r\n\r\n* https://huggingface.co/datasets/moyix/debian_csrc\r\n\r\n* from datasets import load_dataset\r\ndataset = load_dataset(\"moyix/debian_csrc\")\r\n\r\n\r\n\r\n### Or just clone the dataset repo\r\n\r\n\r\ngit lfs install\r\ngit clone https://huggingface.co/datasets/moyix/debian_csrc\r\n# if you want to clone without large files – just their pointers\r\n# prepend your git clone with the following env var:\r\nGIT_LFS_SKIP_SMUDGE=1\r\n```",
"You can use `requests` to see if downloading a file from the Hugging Face Hub works. If so, then `datasets` should work as well. If not, then you have to find another way using an internet connection that works",
"I resolved this issue by applying to \"unblock websites\" at https://huggingface.com in a corporate network environment with a firewall. \r\n",
"> Hi ! It looks like an issue with your python environment, can you make sure you're able to run GET requests to https://huggingface.co using `requests` in python ?\r\n\r\nyes，but still not work\r\n\r\n\r\n\r\n",
"I read https://github.com/huggingface/datasets/blob/main/src/datasets/load.py, it fail when get the dataset metadata, so download_config has not worked.\r\n```python\r\n hf_api = HfApi(config.HF_ENDPOINT)\r\n try:\r\n dataset_info = hf_api.dataset_info(\r\n repo_id=path,\r\n revision=revision,\r\n token=download_config.token,\r\n timeout=100.0,\r\n )\r\n except Exception as e: # noqa catch any exception of hf_hub and consider that the dataset doesn't exist\r\n if isinstance(\r\n e,\r\n (\r\n OfflineModeIsEnabled,\r\n requests.exceptions.ConnectTimeout,\r\n requests.exceptions.ConnectionError,\r\n ),\r\n ):\r\n raise ConnectionError(f\"Couldn't reach '{path}' on the Hub ({type(e).__name__})\")\r\n```\r\nI configure the huggingface_hub api, use configure_http_backend\r\n```python\r\nfrom huggingface_hub import configure_http_backend\r\ndef backend_factory() -> requests.Session:\r\n session = requests.Session()\r\n session.proxies = proxy\r\n session.verify = False\r\n return session\r\n\r\nconfigure_http_backend(backend_factory=backend_factory)\r\n```\r\nIt works."
] | 2022-11-07T06:56:23Z
| 2023-12-08T05:41:10Z
| null |
NONE
| null | null | null |
### Describe the bug
It's weird. I could not normally connect the dataset Hub of HuggingFace due to a SSLError in my office.
Even when I try to connect using my company's proxy address (e.g., http_proxy and https_proxy),
I'm getting the SSLError issue. What should I do to download the datanet stored in HuggingFace normally?
I welcome any comments. I think those comments will be helpful to me.
* Dataset address - https://huggingface.co/datasets/moyix/debian_csrc/viewer/moyix--debian_csrc
* Log message
```
............ OMISSION ..............
Traceback (most recent call last):
File "/data/home/geunsik-lim/qtlab/./transformers/examples/pytorch/language-modeling/run_clm.py", line 587, in <module>
main()
File "/data/home/geunsik-lim/qtlab/./transformers/examples/pytorch/language-modeling/run_clm.py", line 278, in main
raw_datasets = load_dataset(
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1719, in load_dataset
builder_instance = load_dataset_builder(
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1497, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1222, in dataset_module_factory
raise e1 from None
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1179, in dataset_module_factory
raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})")
ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError)
[2022-11-07 15:23:38,476] [INFO] [launch.py:318:sigkill_handler] Killing subprocess 6760
[2022-11-07 15:23:38,476] [ERROR] [launch.py:324:sigkill_handler] ['/home/geunsik-lim/anaconda3/envs/deepspeed/bin/python', '-u', './transformers/examples/pytorch/language-modeling/run_clm.py', '--local_rank=0', '--model_name_or_path=Salesforce/codegen-350M-multi', '--per_device_train_batch_size=1', '--learning_rate', '2e-5', '--num_train_epochs', '1', '--output_dir=./codegen-350M-finetuned', '--overwrite_output_dir', '--dataset_name', 'moyix/debian_csrc', '--cache_dir', '/data/home/geunsik-lim/.cache', '--tokenizer_name', 'Salesforce/codegen-350M-multi', '--block_size', '2048', '--gradient_accumulation_steps', '32', '--do_train', '--fp16', '--deepspeed', 'ds_config_zero2.json'] exits with return code = 1
real 0m7.742s
user 0m4.930s
```
### Steps to reproduce the bug
Steps to reproduce this behavior.
```
(deepspeed) geunsik-lim@ai02:~/qtlab$ ./test_debian_csrc_dataset.py
Traceback (most recent call last):
File "/data/home/geunsik-lim/qtlab/./test_debian_csrc_dataset.py", line 6, in <module>
dataset = load_dataset("moyix/debian_csrc")
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1719, in load_dataset
builder_instance = load_dataset_builder(
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1497, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1222, in dataset_module_factory
raise e1 from None
File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1179, in dataset_module_factory
raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})")
ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError)
(deepspeed) geunsik-lim@ai02:~/qtlab$
(deepspeed) geunsik-lim@ai02:~/qtlab$
(deepspeed) geunsik-lim@ai02:~/qtlab$
(deepspeed) geunsik-lim@ai02:~/qtlab$ cat ./test_debian_csrc_dataset.py
#!/usr/bin/env python
from datasets import load_dataset
dataset = load_dataset("moyix/debian_csrc")
```
1. Adde proxy address of a company in /etc/profile
2. Download dataset with load_dataset() function of datasets package that is provided by HuggingFace.
3. In this case, the address would be "moyix--debian_csrc".
4. I get the "`ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError`)" error message.
### Expected behavior
* error message:
ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError)
### Environment info
* software version information:
```
(deepspeed) geunsik-lim@ai02:~$
(deepspeed) geunsik-lim@ai02:~$ conda list -f pytorch
# packages in environment at /home/geunsik-lim/anaconda3/envs/deepspeed:
#
# Name Version Build Channel
pytorch 1.13.0 py3.10_cuda11.7_cudnn8.5.0_0 pytorch
(deepspeed) geunsik-lim@ai02:~$ conda list -f python
# packages in environment at /home/geunsik-lim/anaconda3/envs/deepspeed:
#
# Name Version Build Channel
python 3.10.6 haa1d7c7_1
(deepspeed) geunsik-lim@ai02:~$ conda list -f datasets
# packages in environment at /home/geunsik-lim/anaconda3/envs/deepspeed:
#
# Name Version Build Channel
datasets 2.6.1 py_0 huggingface
(deepspeed) geunsik-lim@ai02:~$ uname -a
Linux ai02 5.4.0-131-generic #147-Ubuntu SMP Fri Oct 14 17:07:22 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux
(deepspeed) geunsik-lim@ai02:~$ cat /etc/lsb-release
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=20.04
DISTRIB_CODENAME=focal
DISTRIB_DESCRIPTION="Ubuntu 20.04.5 LTS"
```
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MDExOlB1bGxSZXF1ZXN0NDkzOTgyMDMx
| 679
|
Fix negative ids when slicing with an array
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[] | 2020-09-28T08:39:08Z
| 2020-09-28T14:42:20Z
| 2020-09-28T14:42:19Z
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MEMBER
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```python
from datasets import Dataset
d = ds.Dataset.from_dict({"a": range(10)})
print(d[[0, -1]])
# OverflowError
```
raises an error because of the negative id.
This PR fixes that.
Fix #668
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Add SLR52, SLR53 and SLR54 to OpenSLR
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[
"Hi @lhoestq , I am not sure about the error message:\r\n```\r\n#!/bin/bash -eo pipefail\r\n./scripts/datasets_metadata_validator.py\r\nWARNING:root:❌ Failed to validate 'datasets/openslr/README.md':\r\n__init__() got an unexpected keyword argument 'SLR32'\r\nINFO:root:❌ Failed on 1 files.\r\n\r\nExited with code exit status 1\r\nCircleCI received exit code 1 \r\n```\r\nCould you have a look please? Thanks.",
"Hi ! The error is unrelated to your PR and has been fixed on master\r\nNext time feel free to merge master into your branch to fix the CI error ;)"
] | 2021-05-04T09:08:03Z
| 2021-05-07T09:50:55Z
| 2021-05-07T09:50:55Z
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Add large speech datasets for Sinhala, Bengali and Nepali.
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Switch to huggingface_hub's HfFileSystem
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MEMBER
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instead of the current datasets.filesystems.hffilesystem.HfFileSystem which can be slow in some cases
related to https://github.com/huggingface/datasets/issues/5846 and https://github.com/huggingface/datasets/pull/5919
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Update: SWDA - Fixed code to use all metadata features. Added comments and cleaned c…
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"@yjernite Pushed all the changes you recommended. Thank you for your help!"
] | 2021-01-31T19:18:55Z
| 2021-02-09T22:06:13Z
| 2021-02-09T15:49:58Z
|
CONTRIBUTOR
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This is a dataset I currently use my research and I realized some features are not being returned.
Previous code was not using all available metadata and was kind of messy
I fixed code to use all metadata and made some modification to be more efficient and better formatted.
Please let me know if I need to make any changes.
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Fix push to hub to allow individual split push
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"This has been addressed in https://github.com/huggingface/datasets/pull/4415. Closing."
] | 2022-01-12T12:42:58Z
| 2023-09-24T09:54:19Z
| 2022-07-27T12:11:11Z
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# Description of the issue
If one decides to push a split on a datasets repo, he uploads the dataset and overrides the config. However previous config splits end up being lost despite still having the dataset necessary.
The new flow is the following:
- query the old config from the repo
- update into a new config (add/overwrite new split for example)
- push the new config
# Side fix
- `repo_id` in HfFileSystem was wrongly typed.
- I've added `indent=2` as it becomes much easier to read now.
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PR_kwDODunzps5AMG3g
| 5,071
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Support DEFAULT_CONFIG_NAME when no BUILDER_CONFIGS
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"_The documentation is not available anymore as the PR was closed or merged._",
"Super, thanks a lot for adding this support, Albert!"
] | 2022-10-05T06:28:39Z
| 2022-10-06T14:43:12Z
| 2022-10-06T14:40:26Z
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This PR supports defining a default config name, even if no predefined allowed config names are set.
Fix #5070.
CC: @stas00
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Multiple dataloader memory error
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"Hi!\r\n\r\n200+ data loaders is a lot. Have you tried to reduce the number of datasets by concatenating/interleaving the ones with the same structure/task (the API is `{concatenate_datasets/interleave_datasets}([dset1, ..., dset_N])`)?",
"Hi @mariosasko, thank you for your reply. I tried pre-concatenating different datasets into one, but one key need is to keep each batch the same data type. Considering that the concatenate-then-segment operation for prefetched samples may span across different data types after concatenating/interleaving (cuz different data sources are mixed), any solution to remain the same data source for each batch?",
"@cyk1337 have you found any solutions to it?\r\n@mariosasko I tried with interleave_datasets to sample batches from two large datasets (wikipedia alike) and it results in out-of-memory error during data loading (16gpus, >1TB physical memory). Do you have any idea about it?"
] | 2022-08-23T08:59:50Z
| 2023-01-26T02:01:11Z
| null |
NONE
| null | null | null |
For the use of multiple datasets and tasks, we use around more than 200+ dataloaders, then pass it into `dataloader1, dataloader2, ..., dataloader200=accelerate.prepare(dataloader1, dataloader2, ..., dataloader200)`
It causes the memory error when generating batches. Any solutions to it?
```bash
File "/home/xxx/my_code/src/utils/data_utils.py", line 54, in generate_batch
x = next(iterator)
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/accelerate/data_loader.py", line 301, in __iter__
for batch in super().__iter__():
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
data = self._next_data()
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 28, in fetch
data.append(next(self.dataset_iter))
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/accelerate/data_loader.py", line 249, in __iter__
for element in self.dataset:
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 503, in __iter__
for key, example in self._iter():
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 500, in _iter
yield from ex_iterable
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 231, in __iter__
new_key = "_".join(str(key) for key in keys)
MemoryError
```
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Release 2.14.2
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"<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) | 0.006543 / 0.011353 (-0.004810) | 0.003894 / 0.011008 (-0.007115) | 0.084742 / 0.038508 (0.046234) | 0.072942 / 0.023109 (0.049833) | 0.310722 / 0.275898 (0.034824) | 0.346806 / 0.323480 (0.023326) | 0.005373 / 0.007986 (-0.002613) | 0.003270 / 0.004328 (-0.001059) | 0.064379 / 0.004250 (0.060128) | 0.054876 / 0.037052 (0.017824) | 0.316794 / 0.258489 (0.058305) | 0.350353 / 0.293841 (0.056512) | 0.030683 / 0.128546 (-0.097863) | 0.008275 / 0.075646 (-0.067371) | 0.288747 / 0.419271 (-0.130525) | 0.051892 / 0.043533 (0.008359) | 0.315060 / 0.255139 (0.059921) | 0.331664 / 0.283200 (0.048464) | 0.023334 / 0.141683 (-0.118349) | 1.499734 / 1.452155 (0.047579) | 1.542006 / 1.492716 (0.049290) |\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) | 0.210488 / 0.018006 (0.192482) | 0.462187 / 0.000490 (0.461697) | 0.001280 / 0.000200 (0.001080) | 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) | 0.027812 / 0.037411 (-0.009599) | 0.082492 / 0.014526 (0.067966) | 0.096504 / 0.176557 (-0.080053) | 0.158164 / 0.737135 (-0.578972) | 0.096678 / 0.296338 (-0.199661) |\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) | 0.403317 / 0.215209 (0.188108) | 4.008367 / 2.077655 (1.930713) | 2.033067 / 1.504120 (0.528947) | 1.869484 / 1.541195 (0.328290) | 1.947450 / 1.468490 (0.478960) | 0.494048 / 4.584777 (-4.090729) | 3.631673 / 3.745712 (-0.114039) | 5.322167 / 5.269862 (0.052306) | 3.125570 / 4.565676 (-1.440107) | 0.057341 / 0.424275 (-0.366934) | 0.007318 / 0.007607 (-0.000289) | 0.483990 / 0.226044 (0.257945) | 4.830573 / 2.268929 (2.561645) | 2.543267 / 55.444624 (-52.901358) | 2.217890 / 6.876477 (-4.658587) | 2.435111 / 2.142072 (0.293038) | 0.597920 / 4.805227 (-4.207307) | 0.132690 / 6.500664 (-6.367974) | 0.060160 / 0.075469 (-0.015309) |\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) | 1.247656 / 1.841788 (-0.594131) | 19.436984 / 8.074308 (11.362675) | 14.504249 / 10.191392 (4.312857) | 0.167444 / 0.680424 (-0.512980) | 0.018214 / 0.534201 (-0.515987) | 0.394790 / 0.579283 (-0.184493) | 0.413770 / 0.434364 (-0.020594) | 0.474290 / 0.540337 (-0.066048) | 0.646782 / 1.386936 (-0.740154) |\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) | 0.006575 / 0.011353 (-0.004778) | 0.003924 / 0.011008 (-0.007084) | 0.064402 / 0.038508 (0.025893) | 0.072569 / 0.023109 (0.049460) | 0.361981 / 0.275898 (0.086083) | 0.398660 / 0.323480 (0.075180) | 0.005380 / 0.007986 (-0.002605) | 0.003355 / 0.004328 (-0.000974) | 0.065173 / 0.004250 (0.060923) | 0.057120 / 0.037052 (0.020067) | 0.366347 / 0.258489 (0.107858) | 0.402723 / 0.293841 (0.108882) | 0.031258 / 0.128546 (-0.097288) | 0.008499 / 0.075646 (-0.067147) | 0.070558 / 0.419271 (-0.348714) | 0.050089 / 0.043533 (0.006556) | 0.361280 / 0.255139 (0.106141) | 0.384497 / 0.283200 (0.101297) | 0.024789 / 0.141683 (-0.116893) | 1.492577 / 1.452155 (0.040422) | 1.572242 / 1.492716 (0.079525) |\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) | 0.228054 / 0.018006 (0.210048) | 0.448317 / 0.000490 (0.447828) | 0.000368 / 0.000200 (0.000168) | 0.000056 / 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) | 0.030575 / 0.037411 (-0.006836) | 0.088604 / 0.014526 (0.074078) | 0.099317 / 0.176557 (-0.077239) | 0.152455 / 0.737135 (-0.584680) | 0.100444 / 0.296338 (-0.195894) |\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) | 0.411876 / 0.215209 (0.196667) | 4.108187 / 2.077655 (2.030532) | 2.096371 / 1.504120 (0.592251) | 1.923532 / 1.541195 (0.382337) | 1.998345 / 1.468490 (0.529855) | 0.483853 / 4.584777 (-4.100924) | 3.622433 / 3.745712 (-0.123279) | 3.254430 / 5.269862 (-2.015431) | 2.044342 / 4.565676 (-2.521334) | 0.056756 / 0.424275 (-0.367519) | 0.007720 / 0.007607 (0.000113) | 0.487656 / 0.226044 (0.261612) | 4.882024 / 2.268929 (2.613096) | 2.585008 / 55.444624 (-52.859616) | 2.229251 / 6.876477 (-4.647225) | 2.408318 / 2.142072 (0.266246) | 0.617537 / 4.805227 (-4.187691) | 0.132102 / 6.500664 (-6.368562) | 0.061694 / 0.075469 (-0.013775) |\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) | 1.362077 / 1.841788 (-0.479711) | 19.750714 / 8.074308 (11.676406) | 14.545299 / 10.191392 (4.353907) | 0.168666 / 0.680424 (-0.511758) | 0.018606 / 0.534201 (-0.515595) | 0.394760 / 0.579283 (-0.184523) | 0.410030 / 0.434364 (-0.024334) | 0.464742 / 0.540337 (-0.075596) | 0.610881 / 1.386936 (-0.776055) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53e8007baeff133aaad8cbb366196be18a5e57fd \"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) | 0.005836 / 0.011353 (-0.005517) | 0.003493 / 0.011008 (-0.007515) | 0.079877 / 0.038508 (0.041369) | 0.057299 / 0.023109 (0.034190) | 0.332945 / 0.275898 (0.057047) | 0.386615 / 0.323480 (0.063135) | 0.004437 / 0.007986 (-0.003548) | 0.002758 / 0.004328 (-0.001571) | 0.062668 / 0.004250 (0.058418) | 0.046135 / 0.037052 (0.009083) | 0.346160 / 0.258489 (0.087671) | 0.416720 / 0.293841 (0.122879) | 0.026678 / 0.128546 (-0.101868) | 0.007893 / 0.075646 (-0.067753) | 0.260427 / 0.419271 (-0.158845) | 0.044240 / 0.043533 (0.000707) | 0.328101 / 0.255139 (0.072963) | 0.380072 / 0.283200 (0.096872) | 0.020813 / 0.141683 (-0.120870) | 1.400202 / 1.452155 (-0.051952) | 1.475627 / 1.492716 (-0.017089) |\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) | 0.174479 / 0.018006 (0.156473) | 0.413810 / 0.000490 (0.413320) | 0.003059 / 0.000200 (0.002860) | 0.000212 / 0.000054 (0.000157) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023422 / 0.037411 (-0.013990) | 0.071519 / 0.014526 (0.056993) | 0.080555 / 0.176557 (-0.096001) | 0.143825 / 0.737135 (-0.593311) | 0.081182 / 0.296338 (-0.215157) |\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) | 0.406858 / 0.215209 (0.191648) | 4.161475 / 2.077655 (2.083820) | 1.991800 / 1.504120 (0.487680) | 1.811224 / 1.541195 (0.270030) | 1.828809 / 1.468490 (0.360318) | 0.504882 / 4.584777 (-4.079895) | 2.985010 / 3.745712 (-0.760703) | 3.984856 / 5.269862 (-1.285006) | 2.477936 / 4.565676 (-2.087740) | 0.057553 / 0.424275 (-0.366722) | 0.006436 / 0.007607 (-0.001172) | 0.488061 / 0.226044 (0.262016) | 4.805501 / 2.268929 (2.536573) | 2.446508 / 55.444624 (-52.998116) | 2.051406 / 6.876477 (-4.825071) | 2.177696 / 2.142072 (0.035623) | 0.588021 / 4.805227 (-4.217207) | 0.125118 / 6.500664 (-6.375546) | 0.060885 / 0.075469 (-0.014584) |\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) | 1.197130 / 1.841788 (-0.644658) | 17.867450 / 8.074308 (9.793142) | 13.536895 / 10.191392 (3.345503) | 0.137603 / 0.680424 (-0.542821) | 0.016706 / 0.534201 (-0.517495) | 0.327642 / 0.579283 (-0.251641) | 0.347201 / 0.434364 (-0.087163) | 0.379570 / 0.540337 (-0.160768) | 0.517825 / 1.386936 (-0.869111) |\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) | 0.005769 / 0.011353 (-0.005584) | 0.003414 / 0.011008 (-0.007594) | 0.063198 / 0.038508 (0.024690) | 0.056020 / 0.023109 (0.032911) | 0.393333 / 0.275898 (0.117435) | 0.421166 / 0.323480 (0.097686) | 0.004360 / 0.007986 (-0.003626) | 0.002860 / 0.004328 (-0.001469) | 0.062712 / 0.004250 (0.058461) | 0.045363 / 0.037052 (0.008311) | 0.413156 / 0.258489 (0.154667) | 0.422897 / 0.293841 (0.129056) | 0.027092 / 0.128546 (-0.101455) | 0.007960 / 0.075646 (-0.067687) | 0.068531 / 0.419271 (-0.350740) | 0.041402 / 0.043533 (-0.002131) | 0.377008 / 0.255139 (0.121869) | 0.409142 / 0.283200 (0.125942) | 0.019707 / 0.141683 (-0.121976) | 1.440556 / 1.452155 (-0.011599) | 1.487403 / 1.492716 (-0.005314) |\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) | 0.224355 / 0.018006 (0.206349) | 0.397855 / 0.000490 (0.397365) | 0.000363 / 0.000200 (0.000163) | 0.000056 / 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) | 0.025107 / 0.037411 (-0.012305) | 0.076404 / 0.014526 (0.061878) | 0.083194 / 0.176557 (-0.093362) | 0.135347 / 0.737135 (-0.601789) | 0.084786 / 0.296338 (-0.211553) |\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) | 0.433024 / 0.215209 (0.217815) | 4.323879 / 2.077655 (2.246224) | 2.263004 / 1.504120 (0.758884) | 2.072053 / 1.541195 (0.530858) | 2.113916 / 1.468490 (0.645426) | 0.502742 / 4.584777 (-4.082035) | 3.001716 / 3.745712 (-0.743996) | 2.777960 / 5.269862 (-2.491901) | 1.826514 / 4.565676 (-2.739162) | 0.057735 / 0.424275 (-0.366540) | 0.006671 / 0.007607 (-0.000937) | 0.503347 / 0.226044 (0.277303) | 5.037308 / 2.268929 (2.768380) | 2.679146 / 55.444624 (-52.765478) | 2.410899 / 6.876477 (-4.465577) | 2.467341 / 2.142072 (0.325268) | 0.589824 / 4.805227 (-4.215403) | 0.125529 / 6.500664 (-6.375135) | 0.061950 / 0.075469 (-0.013520) |\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) | 1.304128 / 1.841788 (-0.537659) | 17.950215 / 8.074308 (9.875907) | 13.673768 / 10.191392 (3.482376) | 0.129863 / 0.680424 (-0.550561) | 0.016720 / 0.534201 (-0.517481) | 0.329795 / 0.579283 (-0.249488) | 0.339057 / 0.434364 (-0.095307) | 0.382279 / 0.540337 (-0.158059) | 0.507337 / 1.386936 (-0.879599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef05b6f99a2b19990c6f5e4e28d95d28781570db \"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) | 0.006199 / 0.011353 (-0.005154) | 0.003749 / 0.011008 (-0.007259) | 0.080600 / 0.038508 (0.042092) | 0.061017 / 0.023109 (0.037908) | 0.319966 / 0.275898 (0.044067) | 0.354937 / 0.323480 (0.031457) | 0.004854 / 0.007986 (-0.003131) | 0.002996 / 0.004328 (-0.001333) | 0.063100 / 0.004250 (0.058849) | 0.050063 / 0.037052 (0.013011) | 0.316744 / 0.258489 (0.058255) | 0.358001 / 0.293841 (0.064160) | 0.027503 / 0.128546 (-0.101043) | 0.007876 / 0.075646 (-0.067771) | 0.262211 / 0.419271 (-0.157060) | 0.045717 / 0.043533 (0.002184) | 0.317188 / 0.255139 (0.062049) | 0.342404 / 0.283200 (0.059205) | 0.020194 / 0.141683 (-0.121489) | 1.498672 / 1.452155 (0.046517) | 1.545479 / 1.492716 (0.052762) |\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) | 0.210985 / 0.018006 (0.192979) | 0.433592 / 0.000490 (0.433102) | 0.002864 / 0.000200 (0.002664) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023463 / 0.037411 (-0.013948) | 0.073375 / 0.014526 (0.058850) | 0.083082 / 0.176557 (-0.093475) | 0.142583 / 0.737135 (-0.594552) | 0.084267 / 0.296338 (-0.212071) |\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) | 0.412890 / 0.215209 (0.197681) | 4.131421 / 2.077655 (2.053766) | 1.969164 / 1.504120 (0.465044) | 1.772379 / 1.541195 (0.231185) | 1.834154 / 1.468490 (0.365664) | 0.496290 / 4.584777 (-4.088487) | 3.056504 / 3.745712 (-0.689208) | 3.400962 / 5.269862 (-1.868900) | 2.120575 / 4.565676 (-2.445101) | 0.056932 / 0.424275 (-0.367343) | 0.006412 / 0.007607 (-0.001195) | 0.484521 / 0.226044 (0.258477) | 4.817474 / 2.268929 (2.548545) | 2.464075 / 55.444624 (-52.980549) | 2.085056 / 6.876477 (-4.791421) | 2.324516 / 2.142072 (0.182444) | 0.592013 / 4.805227 (-4.213214) | 0.132232 / 6.500664 (-6.368432) | 0.062825 / 0.075469 (-0.012645) |\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) | 1.228080 / 1.841788 (-0.613708) | 18.555385 / 8.074308 (10.481077) | 13.939565 / 10.191392 (3.748173) | 0.145979 / 0.680424 (-0.534445) | 0.016823 / 0.534201 (-0.517377) | 0.330569 / 0.579283 (-0.248714) | 0.358094 / 0.434364 (-0.076270) | 0.384642 / 0.540337 (-0.155696) | 0.518347 / 1.386936 (-0.868589) |\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) | 0.006198 / 0.011353 (-0.005155) | 0.003670 / 0.011008 (-0.007338) | 0.062502 / 0.038508 (0.023994) | 0.064339 / 0.023109 (0.041229) | 0.428414 / 0.275898 (0.152516) | 0.463899 / 0.323480 (0.140420) | 0.005524 / 0.007986 (-0.002462) | 0.002915 / 0.004328 (-0.001413) | 0.062521 / 0.004250 (0.058270) | 0.051182 / 0.037052 (0.014130) | 0.431144 / 0.258489 (0.172655) | 0.469465 / 0.293841 (0.175624) | 0.027463 / 0.128546 (-0.101083) | 0.007974 / 0.075646 (-0.067673) | 0.068029 / 0.419271 (-0.351242) | 0.042123 / 0.043533 (-0.001409) | 0.428667 / 0.255139 (0.173528) | 0.455917 / 0.283200 (0.172717) | 0.023264 / 0.141683 (-0.118419) | 1.426986 / 1.452155 (-0.025168) | 1.500049 / 1.492716 (0.007332) |\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) | 0.207264 / 0.018006 (0.189258) | 0.440738 / 0.000490 (0.440248) | 0.000802 / 0.000200 (0.000602) | 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) | 0.026245 / 0.037411 (-0.011166) | 0.078749 / 0.014526 (0.064223) | 0.087873 / 0.176557 (-0.088684) | 0.141518 / 0.737135 (-0.595617) | 0.089811 / 0.296338 (-0.206527) |\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) | 0.418955 / 0.215209 (0.203746) | 4.177881 / 2.077655 (2.100226) | 2.162678 / 1.504120 (0.658558) | 1.998969 / 1.541195 (0.457775) | 2.066720 / 1.468490 (0.598230) | 0.496850 / 4.584777 (-4.087927) | 3.041179 / 3.745712 (-0.704534) | 4.126039 / 5.269862 (-1.143823) | 2.740507 / 4.565676 (-1.825169) | 0.058025 / 0.424275 (-0.366250) | 0.006846 / 0.007607 (-0.000761) | 0.493281 / 0.226044 (0.267237) | 4.930196 / 2.268929 (2.661268) | 2.685152 / 55.444624 (-52.759472) | 2.378247 / 6.876477 (-4.498230) | 2.469103 / 2.142072 (0.327031) | 0.585346 / 4.805227 (-4.219882) | 0.126099 / 6.500664 (-6.374565) | 0.062946 / 0.075469 (-0.012523) |\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) | 1.313892 / 1.841788 (-0.527896) | 19.177117 / 8.074308 (11.102809) | 14.081321 / 10.191392 (3.889929) | 0.133948 / 0.680424 (-0.546476) | 0.017128 / 0.534201 (-0.517073) | 0.332241 / 0.579283 (-0.247042) | 0.373218 / 0.434364 (-0.061145) | 0.395308 / 0.540337 (-0.145030) | 0.529883 / 1.386936 (-0.857053) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#16f7c7677942083436062b904b74643accb9bcac \"CML watermark\")\n"
] | 2023-07-31T06:05:36Z
| 2023-07-31T06:33:00Z
| 2023-07-31T06:18:17Z
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MEMBER
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I_kwDODunzps5S7V05
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Automatically revert to last successful commit to hub when a push_to_hub is interrupted
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[
"Could you share the error you got please ? Maybe the full stack trace if you have it ?\r\n\r\nMaybe `push_to_hub` be implemented as a single commit @Wauplin ? This way if it fails, the repo is still at the previous (valid) state instead of ending-up in an invalid/incimplete state.",
"> Maybe push_to_hub be implemented as a single commit ? \r\n\r\nI think that would definitely be the way to go. Do you know the reasons why not implementing it like this in the first place ? I guess it is because of not been able to upload all at once with `huggingface_hub` but if there was another reason, please let me know.\r\nAbout pushing all at once, it seems to be a more and more requested feature. I have created this issue https://github.com/huggingface/huggingface_hub/issues/1085 recently but other discussions already happened in the past. The `moon-landing` team is working on it (cc @coyotte508). The `huggingface_hub` integration will come afterwards.\r\n\r\nFor now, maybe it's best to wait for a proper implementation instead of creating a temporary workaround :)\r\n",
"> I think that would definitely be the way to go. Do you know the reasons why not implementing it like this in the first place ? I guess it is because of not been able to upload all at once with huggingface_hub but if there was another reason, please let me know.\r\n\r\nIdeally we would want to upload the files iteratively - and then once everything is uploaded we proceed to commit. When we implemented `push_to_hub`, using `upload_file` for each shard was the only option.\r\n\r\nFor more context: for each shard to upload we do:\r\n1. load the arrow shard in memory\r\n2. convert to parquet\r\n3. upload\r\n\r\nSo to avoid OOM we need to upload the files iteratively.\r\n\r\n> For now, maybe it's best to wait for a proper implementation instead of creating a temporary workaround :)\r\n\r\nLet us know if we can help !",
"> Ideally we would want to upload the files iteratively - and then once everything is uploaded we proceed to commit. \r\n\r\nOh I see. So maybe this has to be done in an implementation specific to `datasets/` as it is not a very common case (upload a bunch of files on the fly).\r\n\r\nYou can maybe have a look at how `huggingface_hub` is implemented for LFS files (arrow shards are LFS anyway, right?).\r\nIn [`upload_lfs_files`](https://github.com/huggingface/huggingface_hub/blob/e28646c977fc9304a4c3576ce61ff07f9778950b/src/huggingface_hub/_commit_api.py#L164) LFS files are uploaded 1 by 1 (multithreaded) and then [the commit is pushed](https://github.com/huggingface/huggingface_hub/blob/e28646c977fc9304a4c3576ce61ff07f9778950b/src/huggingface_hub/hf_api.py#L1926) to the Hub once all files have been uploaded. This is pretty much what you need, right ?\r\n\r\nI can help you if you have questions how to do it in `datasets`. If that makes sense we could then move the implementation from `datasets` to `huggingface_hub` once it's mature. Next week I'm on holidays but feel free to start without my input.\r\n\r\n(also cc @coyotte508 and @SBrandeis who implemented LFS upload in `hfh`)",
"> Could you share the error you got please ? Maybe the full stack trace if you have it ?\r\n\r\nHere’s part of the stack trace, that I can reproduce at the moment from a photo I took (potential typos from OCR):\r\n```\r\nValueError\r\nTraceback (most recent call last)\r\n<ipython-input-4-274613b7d3f5> in <module>\r\nfrom datasets import load dataset\r\nds = load_dataset('jrahn/chessv6', use_auth_token-True)\r\n\r\n/us/local/1ib/python3.7/dist-packages/datasets/table.py in cast_table _to_schema (table, schema)\r\nLine 2005 raise ValueError()\r\n\r\nValueError: Couldn't cast \r\nfen: string \r\nmove: string \r\nres: string \r\neco: string \r\nmove_id: int64\r\nres_num: int64 to\r\n{ 'fen': Value(dtype='string', id=None), \r\n'move': Value(dtype=' string', id=None),\r\n'res': Value(dtype='string', id=None),\r\n'eco': Value(dtype='string', id=None), \r\n'hc': Value(dtype='string', id=None), \r\n'move_ id': Value(dtype='int64', id=None),\r\n'res_num': Value(dtype= 'int64' , id=None) }\r\nbecause column names don't match \r\n```\r\n\r\nThe column 'hc' was removed before the interrupted push_to_hub(). It appears in the column list in curly brackets but not in the column list above.\r\n\r\nLet me know, if I can be of any help."
] | 2022-09-29T18:08:12Z
| 2023-10-16T13:30:49Z
| 2023-10-16T13:30:49Z
|
NONE
| null | null | null |
**Is your feature request related to a problem? Please describe.**
I pushed a modification of a large dataset (remove a column) to the hub. The push was interrupted after some files were committed to the repo. This left the dataset to raise an error on load_dataset() (ValueError couldn’t cast … because column names don’t match). Only by specifying the previous (complete) commit as revision=commit_hash in load_data(), I was able to repair this and after a successful, complete push, the dataset loads without error again.
**Describe the solution you'd like**
Would it make sense to detect an incomplete push_to_hub() and automatically revert to the previous commit/revision?
**Describe alternatives you've considered**
Leave everything as is, the revision parameter in load_dataset() allows to manually fix this problem.
**Additional context**
Provide useful defaults
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PR_kwDODunzps4wlH1d
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added PII statements and license links to data cards
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[] | 2022-01-05T20:59:21Z
| 2022-01-05T22:02:37Z
| 2022-01-05T22:02:37Z
|
CONTRIBUTOR
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Updates for the following datacards:
multilingual_librispeech
openslr
speech commands
superb
timit_asr
vctk
|
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