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https://api.github.com/repos/huggingface/datasets/issues/3475 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3475/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3475/comments | https://api.github.com/repos/huggingface/datasets/issues/3475/events | https://github.com/huggingface/datasets/issues/3475 | 1,087,352,041 | I_kwDODunzps5Az6zp | 3,475 | The rotten_tomatoes dataset of movie reviews contains some reviews in Spanish | {
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"Hi @puzzler10, thanks for reporting.\r\n\r\nPlease note this dataset is not hosted on Hugging Face Hub. See: \r\nhttps://github.com/huggingface/datasets/blob/c8f914473b041833fd47178fa4373cdcb56ac522/datasets/rotten_tomatoes/rotten_tomatoes.py#L42\r\n\r\nIf there are issues with the source data of a dataset, you should contact the data owners/creators instead. In the homepage associated with this dataset (http://www.cs.cornell.edu/people/pabo/movie-review-data/), you can find the authors of the dataset and how to contact them:\r\n> If you have any questions or comments regarding this site, please send email to Bo Pang or Lillian Lee.\r\n\r\nP.S.: Please also note that the example you gave of non-English review is in Portuguese (not Spanish). ;)",
"Maybe best to just put a quick sentence in the dataset description that highlights this? "
] | 2021-12-23T03:56:43 | 2021-12-24T00:23:03 | null | NONE | null | null | null | ## Describe the bug
See title. I don't think this is intentional and they probably should be removed. If they stay the dataset description should be at least updated to make it clear to the user.
## Steps to reproduce the bug
Go to the [dataset viewer](https://huggingface.co./datasets/viewer/?dataset=rotten_tomatoes) for the dataset, set the offset to 4160 for the train dataset, and scroll through the results. I found ones at index 4166 and 4173. There's others too (e.g. index 2888) but those two are easy to find like that.
## Expected results
English movie reviews only.
## Actual results
Example of a Spanish movie review (4173):
> "É uma pena que , mais tarde , o próprio filme abandone o tom de paródia e passe a utilizar os mesmos clichês que havia satirizado "
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} | If I iterate over a vision dataset, the images are not decoded, and the dictionary with the bytes is returned.
This PR enables image decoding in `Dataset.__iter__`
Close https://github.com/huggingface/datasets/issues/3473 | {
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"As discussed, I remember I set `decoded=False` here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed.",
"> I set decoded=False here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed\r\n\r\nhttps://github.com/huggingface/datasets/pull/3430 will add more control to decoding, so I think it's OK to enable decoding in `__iter__` for now. After we merge the linked PR, the user can easily disable it again.",
"@mariosasko I wonder why there is no issue in `Audio` feature with decoding disabled in `__iter__`, whereas there is in `Image` feature.\r\n\r\nEnabling decoding in `__iter__` will make fail Audio regressions tests: https://github.com/huggingface/datasets/runs/4608657230?check_suite_focus=true\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/features/test_audio.py::test_dataset_with_audio_feature_map_is_not_decoded\r\nFAILED tests/features/test_audio.py::test_dataset_with_audio_feature_map_is_decoded\r\n========================= 2 failed, 15 passed in 8.37s =========================",
"Please also note that the regression tests were implemented in accordance with the specifications:\r\n- when doing a `map` (wich calls `__iter__`) of a function that doesn't access the audio field, the decoding should be disabled; this is why the decoding is disabled in `__iter__` (and only enabled in `__getitem__`).",
"> I wonder why there is no issue in Audio feature with decoding disabled in __iter__, whereas there is in Image feature.\r\n\r\n@albertvillanova Not sure if I understand this part. Currently, both the Image and the Audio feature don't decode data in `__iter__`, so their behavior is aligned there.\r\n",
"Therefore, this is not an issue, neither for Audio nor Image feature.\r\n\r\nCould you please elaborate more on the expected use case? @lhoestq @NielsRogge \r\n\r\nThe expected use cases (in accordance with the specs: see #2324):\r\n- decoding should be enabled when accessing a specific item (`__getitem__`)\r\n- decoding should be disabled while iterating (`__iter__`) to allow preprocessing of non-audio/image features (like label or text, for example) using `.map`\r\n- decoding should be enabled in a `.map` only if the `.map` function accesses the audio/image feature (implemented using `LazyDict`)",
"For me it's not an issue, actually. I just (mistakenly) tried to iterate over a PyTorch Dataset instead of a PyTorch DataLoader, \r\n\r\ni.e. I did this:\r\n\r\n`batch = next(iter(train_ds)) `\r\n\r\nwhereas I actually wanted to do\r\n\r\n`batch = next(iter(train_dataloader))`\r\n\r\nand then it turned out that in the first case, the image was a string of bytes rather than a Pillow image, hence Quentin opened an issue.",
"Thanks @NielsRogge for the context.\r\n\r\nSo IMO everything is working as expected.\r\n\r\nI'm closing this issue. Feel free to reopen it again if further changes of the specs should be addressed.",
"Thanks for the details :)\r\n\r\nI still think that it's unexpected to get different results when doing\r\n```python\r\nfor i in range(len(dataset)):\r\n sample = dataset[i]\r\n```\r\nand\r\n```python\r\nfor sample in dataset:\r\n pass\r\n```\r\neven though I understand that if you don't need to decode the data, then decoding image or audio data when iterating is a waste of time and resources.\r\n\r\nBut in this case users can still drop the column that need decoding to get the full speed back no ?"
] | 2021-12-22T15:26:32 | 2021-12-27T14:13:21 | 2021-12-23T15:21:57 | MEMBER | null | null | null | ## Describe the bug
If I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.
## Steps to reproduce the bug
```python
from datasets import load_dataset
import PIL
mnist = load_dataset("mnist", split="train")
first_image = mnist[0]["image"]
assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # passes
first_image = next(iter(mnist))["image"]
assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # fails
```
## Expected results
The image should be decoded, as a PIL Image
## Actual results
We get a dictionary
```
{'bytes': b'\x89PNG\r\n\x1a\n\x00..., 'path': None}
```
## Environment info
- `datasets` version: 1.17.1.dev0
- Platform: Darwin-20.6.0-x86_64-i386-64bit
- Python version: 3.7.2
- PyArrow version: 6.0.0
The bug also exists in 1.17.0
## Investigation
I think the issue is that decoding is disabled in `__iter__`:
https://github.com/huggingface/datasets/blob/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6/src/datasets/arrow_dataset.py#L1651-L1661
Do you remember why it was disabled in the first place @albertvillanova ?
Also cc @mariosasko @NielsRogge
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Close bigscience-workshop/data_tooling#279 | {
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Currently, `ClassLabel` docstring rendering is not right. | {
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"I also modified the doctest call to raise the exception that doctest may catch, instead of `doctest.UnexpectedException`.\r\nThis will make debugging easier if it happens again"
] | 2021-12-21T14:19:11 | 2021-12-21T14:52:28 | 2021-12-21T14:49:28 | MEMBER | null | false | {
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} | NLTK 3.6.6 now requires `omw-1.4` to be downloaded for METEOR to work.
This should fix the CI on master | {
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https://api.github.com/repos/huggingface/datasets/issues/3468 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3468/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3468/comments | https://api.github.com/repos/huggingface/datasets/issues/3468/events | https://github.com/huggingface/datasets/pull/3468 | 1,085,871,301 | PR_kwDODunzps4wIozO | 3,468 | Add COCO dataset | {
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"The CI failures other than a missing dummy data file and missing fields in the card are unrelated to this PR. ",
"Thanks a lot for this great work and fixing TFDS based script @mariosasko 🤗 will generate the dummy dataset and write the model card tomorrow!",
"@mariosasko I added the dataset card, I'm on the dummy data rn. ",
"@merveenoyan Let me know if you need any help with the dummy data.\r\n\r\nI plan to split the current script/dataset into 4 smaller scripts/datasets to make sure they are properly indexed by Papers With Code later on. In this format:\r\n* the `*_image_captioning` configs will form the [COCO Captions](https://paperswithcode.com/sota/image-captioning-on-coco-captions) dataset (also present in TFDS, but only the 2017 version)\r\n* the `stuff_segmentation` config will form the [COCO Stuff](https://paperswithcode.com/dataset/coco-stuff) dataset\r\n* the `desnepose` config will form the [DensePose-COCO](https://paperswithcode.com/dataset/densepose) dataset\r\n* the rest will be [COCO](https://paperswithcode.com/dataset/coco) (+ will add the `minival` and the `valminusminival` splits to COCO 2014)\r\n\r\nAlso, if I find the time, I'll add preprocessing examples that rely on `pycocotools` to the README files.",
"@mariosasko I feel like we can just push main COCO and add Captions + Stuff later, WDYT?",
"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for your contribution, @mariosasko and @merveenoyan. 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."
] | 2021-12-21T14:07:50 | 2022-10-03T09:38:07 | 2022-10-03T09:36:08 | CONTRIBUTOR | null | true | {
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} | This PR adds the MS COCO dataset. Compared to the [TFDS](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/object_detection/coco.py) script, this implementation adds 8 additional configs to cover the tasks other than object detection.
Some notes:
* the data exposed by TFDS is contained in the `2014`, `2015`, `2017` and `2017_panoptic_segmentation` configs here
* I've updated `encode_nested_example` for easier handling of missing values (cc @lhoestq @albertvillanova; will add tests if you are OK with the changes in `features.py`)
* this implementation should fix https://github.com/huggingface/datasets/pull/3377#issuecomment-985559427
TODOs:
- [x] dataset card
- [ ] dummy data
cc @merveenoyan
Closes #2526 | {
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https://api.github.com/repos/huggingface/datasets/issues/3467 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3467/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3467/comments | https://api.github.com/repos/huggingface/datasets/issues/3467/events | https://github.com/huggingface/datasets/pull/3467 | 1,085,870,665 | PR_kwDODunzps4wIoqd | 3,467 | Push dataset infos.json to Hub | {
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"The change from `___` to `--` was allowed by https://github.com/huggingface/moon-landing/pull/1657"
] | 2021-12-21T14:07:13 | 2021-12-21T17:00:10 | 2021-12-21T17:00:09 | MEMBER | null | false | {
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} | When doing `push_to_hub`, the feature types are lost (see issue https://github.com/huggingface/datasets/issues/3394).
This PR fixes this by also pushing a `dataset_infos.json` file to the Hub, that stores the feature types.
Other minor changes:
- renamed the `___` separator to `--`, since `--` is now disallowed in a name in the back-end.
I tested this feature with datasets like conll2003 that has feature types like `ClassLabel` that were previously lost.
Close https://github.com/huggingface/datasets/issues/3394
I would like to include this in today's release (though not mandatory), so feel free to comment/suggest changes | {
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https://api.github.com/repos/huggingface/datasets/issues/3466 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3466/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3466/comments | https://api.github.com/repos/huggingface/datasets/issues/3466/events | https://github.com/huggingface/datasets/pull/3466 | 1,085,722,837 | PR_kwDODunzps4wII3w | 3,466 | Add CRASS dataset | {
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"Hi Albert,\r\nThank you for your comments.\r\nI hope I have uploaded my local git repo to include the dummy files and style reworkings.\r\nAdded YAML in Readme as well.\r\n\r\nPlease check again.\r\n\r\nHope it works now :)",
"Thanks for your contribution, @apergo-ai. \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. It's OK for you? Please, feel free to tell us if you need some help."
] | 2021-12-21T11:17:22 | 2022-10-03T09:37:06 | 2022-10-03T09:37:06 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/3465 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3465/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3465/comments | https://api.github.com/repos/huggingface/datasets/issues/3465/events | https://github.com/huggingface/datasets/issues/3465 | 1,085,400,432 | I_kwDODunzps5AseVw | 3,465 | Unable to load 'cnn_dailymail' dataset | {
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"Hi @talha1503, thanks for reporting.\r\n\r\nIt seems there is an issue with one of the data files hosted at Google Drive:\r\n```\r\nGoogle Drive - Quota exceeded\r\n\r\nSorry, you can't view or download this file at this time.\r\n\r\nToo many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.\r\n```\r\n\r\nAs you probably know, Hugging Face does not host the data, and in this case the data owner decided to host their data at Google Drive, which has quota limits.\r\n\r\nIs there anything we could do, @lhoestq @mariosasko?",
"This looks related to https://github.com/huggingface/datasets/issues/996",
"It seems that [this](https://huggingface.co./datasets/ccdv/cnn_dailymail) copy of the dataset has fixed the problem"
] | 2021-12-21T03:32:21 | 2022-02-17T14:13:57 | 2022-02-17T14:13:57 | NONE | null | null | null | ## Describe the bug
I wanted to load cnn_dailymail dataset from huggingface datasets on Google Colab, but I am getting an error while loading it.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0', ignore_verifications = True)
```
## Expected results
Expecting to load 'cnn_dailymail' dataset.
## Actual results
`NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
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"Hi ! Can you try setting `datasets.config.MAX_TABLE_NBYTES_FOR_PICKLING` to a smaller value than `4 << 30` (4GiB), for example `500 << 20` (500MiB) ? It should reduce the maximum size of the arrow table being pickled during multiprocessing.\r\n\r\nIf it fixes the issue, we can consider lowering the default value for everyone.",
"@lhoestq I tried that just now but didn't seem to help."
] | 2021-12-21T03:29:01 | 2022-11-21T19:55:11 | null | NONE | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
using latest datasets=datasets-1.16.1-py3-none-any.whl
process my own multilingual dataset by following codes, and the number of rows in all dataset is 306000, the max_length of each sentence is 256:
then I get this error:
I have seen the issue in #2134 and #2150, so I don't understand why latest repo still can't deal with big dataset.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: linux docker
- Python version: 3.6
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} | Update dataset with latest verion data files.
Fix #3462.
Close bigscience-workshop/data_tooling#107 | {
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] | null | [] | 2021-12-20T17:44:01 | 2021-12-21T06:24:02 | 2021-12-21T06:24:01 | MEMBER | null | null | null | Please note also: the HuggingFace version at https://huggingface.co./datasets/swahili_news is outdated. An updated version, with deduplicated text and official splits, can be found at https://zenodo.org/record/5514203.
## Adding a Dataset
- **Name:** swahili_news
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Related to:
- bigscience-workshop/data_tooling#107
| {
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https://api.github.com/repos/huggingface/datasets/issues/3461 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3461/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3461/comments | https://api.github.com/repos/huggingface/datasets/issues/3461/events | https://github.com/huggingface/datasets/pull/3461 | 1,085,007,346 | PR_kwDODunzps4wFzDP | 3,461 | Fix links in metrics description | {
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} | Remove Markdown syntax for links in metrics description, as it is not properly rendered.
Related to #3437. | {
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https://api.github.com/repos/huggingface/datasets/issues/3460 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3460/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3460/comments | https://api.github.com/repos/huggingface/datasets/issues/3460/events | https://github.com/huggingface/datasets/pull/3460 | 1,085,002,469 | PR_kwDODunzps4wFyCf | 3,460 | Don't encode lists as strings when using `Value("string")` | {
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} | Following https://github.com/huggingface/datasets/pull/3456#event-5792250497 it looks like `datasets` can silently convert lists to strings using `str()`, instead of raising an error.
This PR fixes this and should fix the issue with WER showing low values if the input format is not right. | {
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"I think this is a duplicate of [#3190](https://github.com/huggingface/datasets/issues/3190)?",
"Upgrading the datasets version as per #3190 fixes this bug. \r\nI'm Marking as closed."
] | 2021-12-20T16:16:49 | 2021-12-20T16:34:57 | 2021-12-20T16:34:57 | NONE | null | null | null | ## Describe the bug
When using dataset.select to select a subset of a dataset, dataset._indices are set to indicate which elements are now considered in the dataset.
The same thing happens when you shuffle the dataset; dataset._indices are set to indicate what the new order of the data is.
However, if you then use a dataset.filter, that filter interacts with those dataset._indices values in a non-intuitive manner.
https://huggingface.co./docs/datasets/_modules/datasets/arrow_dataset.html#Dataset.filter
Effectively, it looks like the original set of _indices were discared and overwritten by the set created during the filter operation.
I think this is actually an issue with how the map function handles dataset._indices. Ideally it should use the _indices it gets passed, and then return an updated _indices which reflect the map transformation applied to the starting _indices.
## Steps to reproduce the bug
```python
dataset = load_dataset('imdb', split='train', keep_in_memory=True)
dataset = dataset.shuffle(keep_in_memory=True)
dataset = dataset.select(range(0, 10), keep_in_memory=True)
print("initial 10 elements")
print(dataset['label']) # -> [1, 1, 0, 1, 0, 0, 0, 1, 0, 0]
dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True)
print("filtered 10 elements looking for label 0")
print(dataset['label']) # -> [1, 1, 1, 1, 1, 1]
```
## Actual results
```
$ python indices_bug.py
initial 10 elements
[1, 1, 0, 1, 0, 0, 0, 1, 0, 0]
filtered 10 elements looking for label 0
[1, 1, 1, 1, 1, 1]
```
This code block first shuffles the dataset (to get a mix of label 0 and label 1).
Then it selects just the first 10 elements (the number of elements does not matter, 10 is just easy to visualize). The important part is that you select some subset of the dataset.
Finally, a filter is applied to pull out just the elements with `label == 0`.
The bug is that you cannot combine any dataset operation which sets the dataset._indices with filter.
In this case I have 2, shuffle and subset.
If you just use a single dataset._indices operation (in this case shuffle) the bug still shows up.
The shuffle sets the dataset._indices and then filter uses those indices in the map, then overwrites dataset._indices with the filter results.
```python
dataset = load_dataset('imdb', split='train', keep_in_memory=True)
dataset = dataset.shuffle(keep_in_memory=True)
dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True)
dataset = dataset.select(range(0, 10), keep_in_memory=True)
print(dataset['label']) # -> [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
```
## Expected results
In an ideal world, the dataset filter would respect any dataset._indices values which had previously been set.
If you use dataset.filter with the base dataset (where dataset._indices has not been set) then the filter command works as expected.
## Environment info
Here are the commands required to rebuild the conda environment from scratch.
```
# create a virtual environment
conda create -n dataset_indices python=3.8 -y
# activate the virtual environment
conda activate dataset_indices
# install huggingface datasets
conda install datasets
```
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.11.0-41-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 3.0.0
### Full Conda Environment
```
$ conda env export
name: dasaset_indices
channels:
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=4.5=1_gnu
- abseil-cpp=20210324.2=h2531618_0
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- aws-c-common=0.4.57=he6710b0_1
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- aws-sdk-cpp=1.8.185=hce553d0_0
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- blas=1.0=mkl
- boost-cpp=1.73.0=h27cfd23_11
- bottleneck=1.3.2=py38heb32a55_1
- brotli=1.0.9=he6710b0_2
- brotli-python=1.0.9=py38heb0550a_2
- brotlicffi=1.0.9.2=py38h295c915_0
- brotlipy=0.7.0=py38h27cfd23_1003
- bzip2=1.0.8=h7b6447c_0
- c-ares=1.17.1=h27cfd23_0
- ca-certificates=2021.10.26=h06a4308_2
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- cryptography=36.0.0=py38h9ce1e76_0
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- double-conversion=3.1.5=he6710b0_1
- et_xmlfile=1.1.0=py38h06a4308_0
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- packaging=21.3=pyhd3eb1b0_0
- pip=21.2.4=py38h06a4308_0
- py7zr=0.16.1=pyhd3eb1b0_1
- pycparser=2.21=pyhd3eb1b0_0
- pycryptodomex=3.10.1=py38h27cfd23_1
- pyopenssl=21.0.0=pyhd3eb1b0_1
- pyparsing=3.0.4=pyhd3eb1b0_0
- pyppmd=0.16.1=py38h295c915_0
- pysocks=1.7.1=py38h06a4308_0
- python=3.8.12=h12debd9_0
- python-dateutil=2.8.2=pyhd3eb1b0_0
- python-xxhash=2.0.2=py38h7f8727e_0
- pyzstd=0.14.4=py38h7f8727e_3
- re2=2020.11.01=h2531618_1
- readline=8.1=h27cfd23_0
- requests=2.26.0=pyhd3eb1b0_0
- setuptools=58.0.4=py38h06a4308_0
- six=1.16.0=pyhd3eb1b0_0
- snappy=1.1.8=he6710b0_0
- sqlite=3.36.0=hc218d9a_0
- texttable=1.6.4=pyhd3eb1b0_0
- tk=8.6.11=h1ccaba5_0
- typing_extensions=3.10.0.2=pyh06a4308_0
- uriparser=0.9.3=he6710b0_1
- utf8proc=2.6.1=h27cfd23_0
- wheel=0.37.0=pyhd3eb1b0_1
- xxhash=0.8.0=h7f8727e_3
- xz=5.2.5=h7b6447c_0
- zipp=3.6.0=pyhd3eb1b0_0
- zlib=1.2.11=h7f8727e_4
- zstd=1.4.9=haebb681_0
- pip:
- async-timeout==4.0.2
- charset-normalizer==2.0.9
- datasets==1.16.1
- fsspec==2021.11.1
- huggingface-hub==0.2.1
- multidict==5.2.0
- pandas==1.3.5
- pyarrow==6.0.1
- pytz==2021.3
- pyyaml==6.0
- tqdm==4.62.3
- typing-extensions==4.0.1
- urllib3==1.26.7
- yarl==1.7.2
```
| {
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https://api.github.com/repos/huggingface/datasets/issues/3458 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3458/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3458/comments | https://api.github.com/repos/huggingface/datasets/issues/3458/events | https://github.com/huggingface/datasets/pull/3458 | 1,084,926,025 | PR_kwDODunzps4wFiRb | 3,458 | Fix duplicated tag in wikicorpus dataset card | {
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"CI is failing just because of empty sections - merging"
] | 2021-12-20T15:34:16 | 2021-12-20T16:03:25 | 2021-12-20T16:03:24 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/3457 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3457/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3457/comments | https://api.github.com/repos/huggingface/datasets/issues/3457/events | https://github.com/huggingface/datasets/issues/3457 | 1,084,862,121 | I_kwDODunzps5Aqa6p | 3,457 | Add CMU Graphics Lab Motion Capture dataset | {
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"This dataset has files in ASF/AMC format. [ The skeleton file is the ASF file (Acclaim Skeleton File). The motion file is the AMC file (Acclaim Motion Capture data). ] \r\n\r\nSome questions : \r\n1. How do we go about representing these features using datasets.Features and generate examples ?\r\n2. The dataset download link for ASF/AMC files does not have metadata information, for eg : category and subcategory information. We will need to crawl the website for this information. The authors mention \"Please don't crawl this database for all motions.\" Can we mail the authors for this information ?\r\nThe dataset structure is as follows : \r\n```\r\nsubjects\r\n\t- 01\r\n\t\t- 01_01.amc\r\n\t\t- 01_02.amc\r\n\t\t.\r\n\t\t.\r\n\t\t.\r\n\t\t- 01.asf\r\n\t- 02\r\n\t\t- 02_01.amc\r\n\t\t- 02_02.amc\r\n\t\t.\r\n\t\t.\r\n\t\t.\r\n\t\t- 02.asf\r\n```\r\nThere is no metadata regarding the category, sub-category and motion description.\r\n\r\nNeed your inputs. @mariosasko / @lhoestq \r\nThank you.\r\n",
"Hi @dnaveenr! Thanks for working on this!\r\n\r\n1. We can use the `Sequence(Value(\"string\"))` feature type for the subject's AMC files and `Value(\"string\")` for the subject's ASF file (`Value(\"string\")` represents the file paths) + the types for categories/subcategories and descriptions.\r\n2. We can use this URL to download the motion descriptions: http://mocap.cs.cmu.edu/search.php?subjectnumber=<subject_number>&motion=%%%&maincat=%&subcat=%&subtext=yes where `subject_number` is the number between 1 and 144. And to get categories/subcategories, feel free to contact the authors (they state in the FAQ they are happy to help) and ask them if they can provide the mapping from categories/subcategories to the AMC files to avoid crawling. You can also mention that your goal is to make their dataset more accessible by adding its loading script to the Hub.\r\n\r\nThe AMC files are also available in the tvd, c3d, mpg and avi formats (the links are in the [FAQ](http://mocap.cs.cmu.edu/faqs.php) section), so it would be nice to have one config for each of these additional formats. \r\n\r\nAnd additionally, we can add a `Data Preprocessing` section to the card where we explain how to load/process the files. I can help with that.",
"Hi @mariosasko ,\r\n\r\n1. Thanks for this, so we can add the file paths.\r\n2. Yes, I had already mailed the authors a couple of days back actually, asking for the metadata details[ i.e category, sub-category and motion description] . They are yet to respond though, I will wait for a couple of days and try to follow up with them again. :) Else we can use the workaround solution.\r\n\r\nYes. Supporting all the formats would be helpful. \r\n\r\n> And additionally, we can add a Data Preprocessing section to the card where we explain how to load/process the files. I can help with that.\r\n\r\nOkay. Got it."
] | 2021-12-20T14:34:39 | 2022-03-16T16:53:09 | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** CMU Graphics Lab Motion Capture database
- **Description:** The database contains free motions which you can download and use.
- **Data:** http://mocap.cs.cmu.edu/
- **Motivation:** Nice motion capture 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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"Hi ! I don't think this would solve this issue.\r\nCurrently it looks like there's a bug that converts the list `[\"hello it's nice\"]` to a string `'[\"hello it's nice\"]'` since this is what the metric expects as input. The conversion is done before the data are passed to `_compute()`.\r\n\r\nThis is `Value(\"string\").encode_example` that is called to do the conversion. Since `str()` encoding is too permissive we should consider raising an error if the example is not a string (even though it can be converted to string). ",
"> called\r\n\r\nAh yeah you're right",
"I just opened https://github.com/huggingface/datasets/pull/3460 to fix that. It now raises an error instead of computing the wrong WER",
"Thank you - that should be good enough!"
] | 2021-12-20T11:38:40 | 2021-12-20T16:53:37 | 2021-12-20T16:53:36 | MEMBER | null | false | {
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} | Currently we have the following problem when using the WER. When the input format to the WER metric is wrong, instead of throwing an error message a word-error-rate is computed which is incorrect. E.g. when doing the following:
```python
from datasets import load_metric
wer = load_metric("wer")
target_str = ["hello this is nice", "hello the weather is bloomy"]
pred_str = [["hello it's nice"], ["hello it's the weather"]]
print("Wrong:", wer.compute(predictions=pred_str, references=target_str))
print("Correct", wer.compute(predictions=[x[0] for x in pred_str], references=target_str))
```
We get:
```
Wrong: 1.0
Correct 0.5555555555555556
```
meaning that we get a word-error rate for incorrectly passed input formats. We should raise an error here instead so that people don't spent hours fixing a model while it's their incorrect evaluation metric is the problem for a low WER. | {
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https://api.github.com/repos/huggingface/datasets/issues/3455 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3455/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3455/comments | https://api.github.com/repos/huggingface/datasets/issues/3455/events | https://github.com/huggingface/datasets/issues/3455 | 1,084,599,650 | I_kwDODunzps5Apa1i | 3,455 | Easier information editing | {
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"Hi ! I guess you are talking about the dataset cards that are in this repository on github ?\r\n\r\nI think github allows to submit a PR even for 1 line though the `Edit file` button on the page of the dataset card.\r\n\r\nMaybe let's mention this in `CONTRIBUTING.md` ?",
"We now host all the datasets on the HF Hub, where you can easily edit them through UI (for single file changes) or Git workflow (for single/multiple file changes)"
] | 2021-12-20T10:10:43 | 2023-07-25T15:36:14 | 2023-07-25T15:36:14 | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
It requires a lot of effort to improve a datasheet.
**Describe the solution you'd like**
UI or at least a link to the place where the code that needs to be edited is (and an easy way to edit this code directly from the site, without cloning, branching, makefile etc.)
**Describe alternatives you've considered**
The current Ux is to have the 8 steps for contribution while One just wishes to change a line a type etc.
| {
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https://api.github.com/repos/huggingface/datasets/issues/3454 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3454/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3454/comments | https://api.github.com/repos/huggingface/datasets/issues/3454/events | https://github.com/huggingface/datasets/pull/3454 | 1,084,519,107 | PR_kwDODunzps4wENam | 3,454 | Fix iter_archive generator | {
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} | This PR:
- Adds tests to DownloadManager and StreamingDownloadManager `iter_archive` for both path and file inputs
- Fixes bugs in `iter_archive` introduced in:
- #3443
Fix #3453. | {
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https://api.github.com/repos/huggingface/datasets/issues/3453 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3453/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3453/comments | https://api.github.com/repos/huggingface/datasets/issues/3453/events | https://github.com/huggingface/datasets/issues/3453 | 1,084,515,911 | I_kwDODunzps5ApGZH | 3,453 | ValueError while iter_archive | {
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] | null | [] | 2021-12-20T08:46:18 | 2021-12-20T10:04:59 | 2021-12-20T10:04:59 | MEMBER | null | null | null | ## Describe the bug
After the merge of:
- #3443
the method `iter_archive` throws a ValueError:
```
ValueError: read of closed file
```
## Steps to reproduce the bug
```python
for path, file in dl_manager.iter_archive(archive_path):
pass
```
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https://api.github.com/repos/huggingface/datasets/issues/3452 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3452/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3452/comments | https://api.github.com/repos/huggingface/datasets/issues/3452/events | https://github.com/huggingface/datasets/issues/3452 | 1,083,803,178 | I_kwDODunzps5AmYYq | 3,452 | why the stratify option is omitted from test_train_split function? | {
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"Hi ! It's simply not added yet :)\r\n\r\nIf someone wants to contribute to add the `stratify` parameter I'd be happy to give some pointers.\r\n\r\nIn the meantime, I guess you can use `sklearn` or other tools to do a stratified train/test split over the **indices** of your dataset and then do\r\n```\r\ntrain_dataset = dataset.select(train_indices)\r\ntest_dataset = dataset.select(test_indices)\r\n```",
"Hi @lhoestq I would like to add `stratify` parameter, can you give me some pointers for adding the same ?",
"Hi ! Sure :)\r\n\r\nThe `train_test_split` method is defined here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/dc62232fa1b3bcfe2fbddcb721f2d141f8908943/src/datasets/arrow_dataset.py#L3253-L3253\r\n\r\nand inside `train_test_split ` we need to create the right `train_indices` and `test_indices` that are passed here to `.select()`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/dc62232fa1b3bcfe2fbddcb721f2d141f8908943/src/datasets/arrow_dataset.py#L3450-L3464\r\n\r\nFor example if your dataset is like\r\n| | label |\r\n|---:|--------:|\r\n| 0 | 1 |\r\n| 1 | 1 |\r\n| 2 | 0 |\r\n| 3 | 0 |\r\n\r\nand the user passes `stratify=dataset[\"label\"]`, then you should get indices that look like this\r\n```\r\ntrain_indices = [0, 2]\r\ntest_indices = [1, 3]\r\n```\r\n\r\nthese indices will be passed to `.select` to return the stratified train and test splits :)\r\n\r\nFeel free to îng me if you have any question !",
"@lhoestq \r\nI just added the implementation for `stratify` option here #4322 "
] | 2021-12-18T10:37:47 | 2022-05-25T20:43:51 | 2022-05-25T20:43:51 | NONE | null | null | null | why the stratify option is omitted from test_train_split function?
is there any other way implement the stratify option while splitting the dataset? as it is important point to be considered while splitting the dataset. | {
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https://api.github.com/repos/huggingface/datasets/issues/3451 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3451/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3451/comments | https://api.github.com/repos/huggingface/datasets/issues/3451/events | https://github.com/huggingface/datasets/pull/3451 | 1,083,459,137 | PR_kwDODunzps4wA5LP | 3,451 | [Staging] Update dataset repos automatically on the Hub | {
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"do keep us updated on how it's going in staging! cc @SBrandeis ",
"Sure ! For now it works smoothly. We'll also do a new release today.\r\n\r\nI can send you some repos to explore on staging, in case you want to see how they look like after being updated.\r\nFor example [swahili_news](https://moon-staging.huggingface.co/datasets/swahili_news/tree/main)"
] | 2021-12-17T17:12:11 | 2021-12-21T10:25:46 | 2021-12-20T14:09:51 | MEMBER | null | false | {
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} | Let's have a script that updates the dataset repositories on staging for now. This way we can make sure it works fine before going in prod.
Related to https://github.com/huggingface/datasets/issues/3341
The script runs on each commit on `master`. It checks the datasets that were changed, and it pushes the changes to the corresponding repositories on the Hub.
If there's a new dataset, then a new repository is created.
If the commit is a new release of `datasets`, it also pushes the tag to all the repositories. | {
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https://api.github.com/repos/huggingface/datasets/issues/3450 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3450/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3450/comments | https://api.github.com/repos/huggingface/datasets/issues/3450/events | https://github.com/huggingface/datasets/issues/3450 | 1,083,450,158 | I_kwDODunzps5AlCMu | 3,450 | Unexpected behavior doing Split + Filter | {
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"Hi ! This is an issue with `datasets` 1.12. Sorry for the inconvenience. Can you update to `>=1.13` ?\r\nsee https://github.com/huggingface/datasets/issues/3190\r\n\r\nMaybe we should also backport the bug fix to `1.12` (in a new version `1.12.2`)"
] | 2021-12-17T17:00:39 | 2023-07-25T15:38:47 | 2023-07-25T15:38:47 | NONE | null | null | null | ## Describe the bug
I observed unexpected behavior when applying 'train_test_split' followed by 'filter' on dataset. Elements of the training dataset eventually end up in the test dataset (after applying the 'filter')
## Steps to reproduce the bug
```
from datasets import Dataset
import pandas as pd
dic = {'x': [1,2,3,4,5,6,7,8,9], 'y':['q','w','e','r','t','y','u','i','o']}
df = pd.DataFrame.from_dict(dic)
dataset = Dataset.from_pandas(df)
split_dataset = dataset.train_test_split(test_size=0.5, shuffle=False, seed=42)
train_dataset = split_dataset["train"]
eval_dataset = split_dataset["test"]
eval_dataset_2 = eval_dataset.filter(lambda example: example['x'] % 2 == 0)
print( eval_dataset['x'])
print(eval_dataset_2['x'])
```
One observes that elements in eval_dataset2 are actually coming from the training dataset...
## Expected results
The expected results would be that the filtered eval dataset would only contain elements from the original eval dataset.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows 10
- Python version: 3.7
- PyArrow version: 5.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3449 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3449/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3449/comments | https://api.github.com/repos/huggingface/datasets/issues/3449/events | https://github.com/huggingface/datasets/issues/3449 | 1,083,373,018 | I_kwDODunzps5AkvXa | 3,449 | Add `__add__()`, `__iadd__()` and similar to `Dataset` class | {
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"I was going through the codebase, and I believe the implementation of __add__() and __iadd__() will be similar to concatenate_datasets() after the elimination of code for arguments other than the list of datasets (info, split, axis). \r\n(Assuming elimination of axis means concatenating over axis 1.)",
"Most data frame libraries (Polars, Pandas, ...) override `__add__` to perform (mathematical) summation, so having different behavior here is a bad idea."
] | 2021-12-17T15:29:11 | 2023-07-25T15:33:57 | 2023-07-25T15:33:56 | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
No.
**Describe the solution you'd like**
I would like to be able to concatenate datasets as follows:
```python
>>> dataset["train"] += dataset["validation"]
```
... instead of using `concatenate_datasets()`:
```python
>>> raw_datasets["train"] = concatenate_datasets([raw_datasets["train"], raw_datasets["validation"]])
>>> del raw_datasets["validation"]
```
**Describe alternatives you've considered**
Well, I have considered `concatenate_datasets()` 😀
**Additional context**
N.a.
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https://api.github.com/repos/huggingface/datasets/issues/3448 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3448/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3448/comments | https://api.github.com/repos/huggingface/datasets/issues/3448/events | https://github.com/huggingface/datasets/issues/3448 | 1,083,231,080 | I_kwDODunzps5AkMto | 3,448 | JSONDecodeError with HuggingFace dataset viewer | {
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"Hi ! I think the issue comes from the dataset_infos.json file: it has the \"flat\" field twice.\r\n\r\nCan you try deleting this file and regenerating it please ?",
"Thanks! That fixed that, but now I am getting:\r\nServer Error\r\nStatus code: 400\r\nException: KeyError\r\nMessage: 'feature'\r\n\r\nI checked the dataset_infos.json and pubmed_neg.py script, I don't use 'feature' anywhere as a key. Is the dataset viewer expecting that I do?",
"It seems that the `feature` key is missing from some feature type definition in your dataset_infos.json:\r\n```json\r\n\t\t\t\"tokens\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n\t\t\t},\r\n\t\t\t\"tags\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n\t\t\t}\r\n```\r\nThey should be\r\n```json\r\n\t\t\t\"tokens\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n \"feature\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"}\r\n\t\t\t},\r\n\t\t\t\"tags\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\",\r\n \"feature\": {\"num_classes\": 5, \"names\": [\"-\", \"S\", \"H\", \"N\", \"C\"], \"names_file\": null, \"id\": null, \"_type\": \"ClassLabel\"}\r\n\t\t\t}\r\n```\r\n\r\nNote that you can generate the dataset_infos.json automatically to avoid mistakes:\r\n```bash\r\ndatasets-cli test ./path/to/dataset --save_infos\r\n```"
] | 2021-12-17T12:52:41 | 2022-02-24T09:10:26 | 2022-02-24T09:10:26 | NONE | null | null | null | ## Dataset viewer issue for 'pubmed_neg'
**Link:** https://huggingface.co./datasets/IGESML/pubmed_neg
I am getting the error:
Status code: 400
Exception: JSONDecodeError
Message: Expecting property name enclosed in double quotes: line 61 column 2 (char 1202)
I have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue.
Am I the one who added this dataset ? Yes
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"Hi ! Indeed it says \"downloading and preparing\" but in your case it didn't need to download anything since you used local files (it would have thrown an error otherwise). I think we can improve the logging to make it clearer in this case",
"@lhoestq Thank you for explaining. I am sorry but I was not clear about my intention. I didn't want to kill internet traffic; I wanted to kill all write activity. In other words, you can imagine that my storage has only read access but crashes on write.\r\n\r\nWhen run_clm.py is invoked with the same parameters, the hash in the cache directory \"datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/...\" doesn't change, and my job can load cached data properly. This is great.\r\n\r\nUnfortunately, when params change (which happens sometimes), the hash changes and the old cache is invalid. datasets builder would create a new cache directory with the new hash and create JSON builder there, even though every JSON builder is the same. I didn't find a way to avoid such behavior.\r\n\r\nThis problem can be resolved when using datasets.map() for tokenizing and grouping text. This function allows me to specify output filenames with --cache_file_names, so that the cached files are always valid.\r\n\r\nThis is the code that I used to freeze cache filenames for tokenization. I wish I could do the same to datasets.load_dataset()\r\n```\r\n tokenized_datasets = raw_datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n num_proc=data_args.preprocessing_num_workers,\r\n remove_columns=column_names,\r\n load_from_cache_file=not data_args.overwrite_cache,\r\n desc=\"Running tokenizer on dataset\",\r\n cache_file_names={k: os.path.join(model_args.cache_dir, f'{k}-tokenized') for k in raw_datasets},\r\n )\r\n```",
"Hi ! `load_dataset` may re-generate your dataset if some parameters changed indeed. If you want to freeze a dataset loaded with `load_dataset`, I think the best solution is just to save it somewhere on your disk with `.save_to_disk(my_dataset_dir)` and reload it with `load_from_disk(my_dataset_dir)`. This way you will be able to reload the dataset without having to run `load_dataset`"
] | 2021-12-16T18:51:13 | 2022-02-17T14:16:27 | 2022-02-17T14:16:27 | NONE | null | null | null | ## Describe the bug
According to https://huggingface.co./docs/datasets/loading_datasets.html#loading-a-dataset-builder, setting HF_DATASETS_OFFLINE to 1 should make datasets to "run in full offline mode". It didn't work for me. At the very beginning, datasets still tried to download "custom data configuration" for JSON, despite I have run the program once and cached all data into the same --cache_dir.
"Downloading" is not an issue when running with local disk, but crashes often with cloud storage because (1) multiply GPU processes try to access the same file, AND (2) FileLocker fails to synchronize all processes, due to storage throttling. 99% of times, when the main process releases FileLocker, the file is not actually ready for access in cloud storage and thus triggers "FileNotFound" errors for all other processes. Well, another way to resolve the problem is to investigate super reliable cloud storage, but that's out of scope here.
## Steps to reproduce the bug
```
export HF_DATASETS_OFFLINE=1
python run_clm.py --model_name_or_path=models/gpt-j-6B --train_file=trainpy.v2.train.json --validation_file=trainpy.v2.eval.json --cache_dir=datacache/trainpy.v2
```
## Expected results
datasets should stop all "downloading" behavior but reuse the cached JSON configuration. I think the problem here is part of the cache directory path, "default-471372bed4b51b53", is randomly generated, and it could change if some parameters changed. And I didn't find a way to use a fixed path to ensure datasets to reuse cached data every time.
## Actual results
The logging shows datasets are still downloading into "datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426".
```
12/16/2021 10:25:59 - WARNING - datasets.builder - Using custom data configuration default-471372bed4b51b53
12/16/2021 10:25:59 - INFO - datasets.builder - Generating dataset json (datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426)
Downloading and preparing dataset json/default to datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...
100%|██████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 17623.13it/s]
12/16/2021 10:25:59 - INFO - datasets.utils.download_manager - Downloading took 0.0 min
12/16/2021 10:26:00 - INFO - datasets.utils.download_manager - Checksum Computation took 0.0 min
100%|███████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1206.99it/s]
12/16/2021 10:26:00 - INFO - datasets.utils.info_utils - Unable to verify checksums.
12/16/2021 10:26:00 - INFO - datasets.builder - Generating split train
12/16/2021 10:26:01 - INFO - datasets.builder - Generating split validation
12/16/2021 10:26:02 - INFO - datasets.utils.info_utils - Unable to verify splits sizes.
Dataset json downloaded and prepared to datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426. Subsequent calls will reuse this data.
100%|█████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 53.54it/s]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux
- Python version: 3.8.10
- PyArrow version: 6.0.1
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"Cool, I'm in favor of this PR. Our official examples in speech already make use of `\"audio\"` so no need to change anything there. It would be great if we could prominently feature how one can get the audio path without decoding in the docs.",
"@patrickvonplaten Yes, I agree.\r\n\r\ncc @stevhliu we should add an example where decoding is disabled (to read paths) to [this section](https://github.com/huggingface/datasets/blob/master/docs/source/audio_process.rst#audio-datasets) in the docs and remove the mentions of `path`/`file` (if we merge this PR).",
"Thanks @mariosasko. Are you planning to finish this before we remove the dataset scripts from GitHub? "
] | 2021-12-16T16:35:15 | 2022-09-23T13:02:04 | null | CONTRIBUTOR | null | true | {
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} | Remove the redundant path information in the audio/image dataset as discussed in https://github.com/huggingface/datasets/pull/3430#issuecomment-994734828
TODOs:
* [ ] merge https://github.com/huggingface/datasets/pull/3430
* [ ] merge https://github.com/huggingface/datasets/pull/3364
* [ ] re-generate the info files of the updated audio datasets
cc: @patrickvonplaten @anton-l @nateraw (I expect this to break the audio/vision examples in Transformers; after this change you'll be able to access underlying paths as follows `dset = dset.cast_column("audio", Audio(..., decode=False)); path = dset[0]["audio"]`)
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"Hi ! What's your question ?"
] | 2021-12-16T15:57:00 | 2022-01-03T10:09:00 | 2022-01-03T10:09:00 | NONE | null | null | null | ## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
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"Yes I agree, these should be as aligned as possible. Maybe we can also check the feedback in the survey at http://hf.co/oss-survey and see if people mentioned related things on the API (in particular if we go the breaking change way, it would be good to be sure we are taking the right direction for the community).",
"I like this proposal.\r\n\r\n> There is also an important difference in terms of behavior:\r\nDataset.map adds new columns (with dict.update)\r\nBUT\r\nIterableDataset discards previous columns (it overwrites the dict)\r\nIMO the two methods should have the same behavior. This would be an important breaking change though.\r\n\r\n> The main breaking change would be the change of behavior of IterableDataset.map, because currently it discards all the previous columns instead of keeping them.\r\n\r\nYes, this behavior of `IterableDataset.map` was surprising to me the first time I used it because I was expecting the same behavior as `Dataset.map`, so I'm OK with the breaking change here.\r\n\r\n> IterableDataset only supports \"torch\" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs\r\n\r\n\\+ it's also missing the actual formatting code (we return unformatted tensors)\r\n> We could have a completely aligned map method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.\r\n\r\n> For information, TFDS does lazy map by default, and has an additional .cache() method.\r\n\r\nIf I understand this part correctly, the idea would be for `Dataset.map` to behave similarly to `Dataset.with_transform` (lazy processing) and to have an option to cache processed data (with `.cache()`). This idea is really nice because it can also be applied to `IterableDataset` to fix https://github.com/huggingface/datasets/issues/3142 (again we get the aligned APIs). However, this change would break a lot of things, so I'm still not sure if this is a step in the right direction (maybe it's OK for Datasets 2.0?) \r\n> If the two APIs are more aligned it would be awesome for the examples in transformers, and it would create a satisfactory experience for users that want to switch from one mode to the other.\r\n\r\nYes, it would be amazing to have an option to easily switch between these two modes.\r\n\r\nI agree with the rest.\r\n",
"> If I understand this part correctly, the idea would be for Dataset.map to behave similarly to Dataset.with_transform (lazy processing) and to have an option to cache processed data (with .cache()). This idea is really nice because it can also be applied to IterableDataset to fix #3142 (again we get the aligned APIs). However, this change would break a lot of things, so I'm still not sure if this is a step in the right direction (maybe it's OK for Datasets 2.0?)\r\n\r\nYea this is too big of a change in my opinion. Anyway it's fine as it is right now with streaming=lazy and regular=eager.",
"Hi, IterableDataset is also missing set_format.",
"Yes indeed, thanks. I added it to the list of methods to align in the first post",
"I just encountered the problem of the missing `fn_kwargs` parameter in the `map` method. I am commenting to give a workaround in case someone has the same problem and does not find a solution.\r\nYou can wrap your function call inside a class that contains the other parameters needed by the function called by map, like this:\r\n\r\n```python\r\ndef my_func(x, y, z):\r\n # Do things\r\n\r\nclass MyFuncWrapper:\r\n def __init__(self, y, z):\r\n self.y = y\r\n self.z = z\r\n\r\n def __call__(self, x):\r\n return my_func(x, self.y, self.z)\r\n```\r\n\r\nThen, give an instance of the `MyFuncWrapper` to the map function.",
"Any update on this? It's almost 2024😂 @lhoestq ",
"The main differences have been addressed (map, formatting) but there are still a few things to implement like Dataset.take, Dataset.skip, IterableDataset.set_format, IterableDataset.formatted_as, IterableDataset.reset_format.\r\n\r\nThe rest cannot be implemented for the general case. E.g. train_test_split and select can only work on an iterable dataset if the underlying dataset format allows it (we need to know the number of rows and have some sort of random access)"
] | 2021-12-16T11:26:11 | 2023-08-16T09:28:17 | null | MEMBER | null | null | null | ## Intro
items marked like <s>this</s> are done already :)
Currently the two classes have two distinct API for processing:
### The `.map()` method
Both have those parameters in common: function, batched, batch_size
- IterableDataset is missing those parameters:
<s>with_indices</s>, with_rank, <s>input_columns</s>, <s>drop_last_batch</s>, <s>remove_columns</s>, features, disable_nullable, fn_kwargs, num_proc
- Dataset also has additional parameters that are exclusive, due to caching:
keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, suffix_template, new_fingerprint
- <s>There is also an important difference in terms of behavior:
**Dataset.map adds new columns** (with dict.update)
BUT
**IterableDataset discards previous columns** (it overwrites the dict)
IMO the two methods should have the same behavior. This would be an important breaking change though.</s>
- Dataset.map is eager while IterableDataset.map is lazy
### The `.shuffle()` method
- <s>Both have an optional seed parameter, but IterableDataset requires a mandatory parameter buffer_size to control the size of the local buffer used for approximate shuffling.</s>
- <s>IterableDataset is missing the parameter generator</s>
- Also Dataset has exclusive parameters due to caching: keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint
### The `.with_format()` method
- IterableDataset only supports "torch" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs
- other methods like `set_format`, `reset_format` or `formatted_as` are also missing
### Other methods
- Both have the same `remove_columns` method
- IterableDataset is missing: <s>cast</s>, <s>cast_column</s>, <s>filter</s>, <s>rename_column</s>, <s>rename_columns</s>, class_encode_column, flatten, prepare_for_task, train_test_split, shard
- Some other methods are missing but we can discuss them: set_transform, formatted_as, with_transform
- And others don't really make sense for an iterable dataset: select, sort, add_column, add_item
- Dataset is missing skip and take, that IterableDataset implements.
## Questions
I think it would be nice to be able to switch between streaming and regular dataset easily, without changing the processing code significantly.
1. What should be aligned and what shouldn't between those two APIs ?
IMO the minimum is to align the main processing methods.
It would mean aligning breaking the current `Iterable.map` to have the same behavior as `Dataset.map` (add columns with dict.update), and add multiprocessing as well as the missing parameters. DONE ✅
It would also mean implementing the missing methods: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard. WIP 🟠
2. What are the breaking changes for IterableDataset ?
The main breaking change would be the change of behavior of `IterableDataset.map`, because currently it discards all the previous columns instead of keeping them. DONE ✅
3. Shall we also do some changes for regular datasets ?
I agree the simplest would be to have the exact same methods for both Dataset and IterableDataset. However this is probably not a good idea because it would prevent users from using the best benefits of them. That's why we can keep some aspects of regular datasets as they are:
- keep the eager Dataset.map with caching
- keep the with_transform method for lazy processing
- keep Dataset.select (it could also be added to IterableDataset even though it's not recommended)
We could have a completely aligned `map` method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.
For information, TFDS does lazy map by default, and has an additional `.cache()` method.
## Opinions ?
I'd love to gather some opinions about this here. If the two APIs are more aligned it would be awesome for the examples in `transformers`, and it would create a satisfactory experience for users that want to switch from one mode to the other.
cc @mariosasko @albertvillanova @thomwolf @patrickvonplaten @sgugger | {
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} | This PR adds support to passing a file object to `[Streaming]DownloadManager.iter_archive`.
With this feature, we can iterate over a tar file inside another tar file. | {
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"The parameter can also be named `split_by` with values \"line\", \"paragraph\" or \"document\" (no 's' at the end)",
"> The parameter can also be named `split_by` with values \"line\", \"paragraph\" or \"document\" (no 's' at the end)\r\n\r\n@lhoestq @mariosasko I would avoid the term `split` in this context and keep it only for \"train\", \"validation\" and \"test\" splits.\r\n- https://huggingface.co./docs/datasets/process.html#split\r\n > datasets.Dataset.train_test_split() creates train and test splits, if your dataset doesn’t already have them.\r\n- https://huggingface.co./docs/datasets/process.html#process-multiple-splits\r\n > Many datasets have splits that you can process simultaneously with datasets.DatasetDict.map().\r\n\r\nPlease note that in the documentation, one of the terms more frequently used in this context is **\"row\"**:\r\n- https://huggingface.co./docs/datasets/access.html#features-and-columns\r\n > A dataset is a table of rows and typed columns.\r\n\r\n > Return the number of rows and columns with the following standard attributes:\r\n > dataset.num_columns\r\n > 4\r\n > dataset.num_rows\r\n > 3668\r\n\r\n- https://huggingface.co./docs/datasets/access.html#rows-slices-batches-and-columns\r\n > Get several rows of your dataset at a time with slice notation or a list of indices:\r\n- https://huggingface.co./docs/datasets/process.html#map\r\n > This function can even create new rows and columns.\r\n\r\nOther of the terms more frequently used in the docs (in the code as well) is **\"example\"**:\r\n- https://huggingface.co./docs/datasets/process.html#map\r\n > It allows you to apply a processing function to each example in a dataset, independently or in batches.\r\n- https://huggingface.co./docs/datasets/process.html#batch-processing\r\n > datasets.Dataset.map() also supports working with batches of examples.\r\n- https://huggingface.co./docs/datasets/process.html#split-long-examples\r\n > When your examples are too long, you may want to split them\r\n- https://huggingface.co./docs/datasets/process.html#data-augmentation\r\n > With batch processing, you can even augment your dataset with additional examples.\r\n\r\nLess frequently used: **\"item\"**:\r\n- https://huggingface.co./docs/datasets/package_reference/main_classes.html#datasets.Dataset.add_item\r\n > Add item to Dataset.\r\n\r\nOther term used in the docs (although less frequently) is **\"sample\"**. The advantage of this word is that it is also a verb, so we can use the parameter: \"sample_by\" (if you insist on using a verb instead of a noun).\r\n\r\nIn summary, these proposals:\r\n- config.row\r\n- config.example\r\n- config.item\r\n- config.sample\r\n- config.sample_by",
"I like `sample_by`. Another idea I had was `separate_by`.\r\n\r\nIt could also be `sampling`, `sampling_method`, `separation_method`.\r\n\r\nNot a big fan of the proposed nouns alone since they are very generic, that's why I tried to have something more specific.\r\n\r\nI also agree that we actually should avoid `split` to avoid any confusion",
"Thanks for the analysis of the used terms. I also like `sample_by` (`separate_by` is good too).",
"Thank you !! :D "
] | 2021-12-16T07:33:17 | 2021-12-20T16:59:10 | 2021-12-20T16:39:18 | MEMBER | null | false | {
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} | Add `config.row` option to `text` module to allow yielding lines (default, current case), paragraphs or documents.
Feel free to comment on the name of the config parameter `row`:
- Currently, the docs state datasets are made of rows and columns
- Other names I considered: `example`, `item` | {
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"I'll take this one if no one hasn't yet!"
] | 2021-12-15T22:26:19 | 2021-12-28T15:17:05 | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** QuALITY
- **Description:** A challenging question answering with very long contexts (Twitter [thread](https://twitter.com/sleepinyourhat/status/1471225421794529281?s=20))
- **Paper:** No ArXiv link yet, but draft is [here](https://github.com/nyu-mll/quality/blob/main/quality_preprint.pdf)
- **Data:** GitHub repo [here](https://github.com/nyu-mll/quality)
- **Motivation:** This dataset would serve as a nice way to benchmark long-range Transformer models like BigBird, Longformer and their descendants. In particular, it would be very interesting to see how the S4 model fares on this given it's impressive performance on the Long Range Arena
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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https://api.github.com/repos/huggingface/datasets/issues/3440 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3440/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3440/comments | https://api.github.com/repos/huggingface/datasets/issues/3440/events | https://github.com/huggingface/datasets/issues/3440 | 1,081,528,426 | I_kwDODunzps5AdtBq | 3,440 | datasets keeps reading from cached files, although I disabled it | {
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"Hi ! What version of `datasets` are you using ? Can you also provide the logs you get before it raises the error ?"
] | 2021-12-15T21:26:22 | 2022-02-24T09:12:22 | 2022-02-24T09:12:22 | NONE | null | null | null | ## Describe the bug
Hi,
I am trying to avoid dataset library using cached files, I get the following bug when this tried to read the cached files. I tried to do the followings:
```
from datasets import set_caching_enabled
set_caching_enabled(False)
```
also force redownlaod:
```
download_mode='force_redownload'
```
but none worked so far, this is on a cluster and on some of the machines this reads from the cached files, I really appreciate any idea on how to fully remove caching @lhoestq
many thanks
```
File "run_clm.py", line 496, in <module>
main()
File "run_clm.py", line 419, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 943, in train
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/transformers/trainer.py", line 1445, in _maybe_log_save_evaluate
metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 172, in evaluate
output = self.eval_loop(
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 241, in eval_loop
metrics = self.compute_pet_metrics(eval_datasets, model, self.extra_info[metric_key_prefix], task=task)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 268, in compute_pet_metrics
centroids = self._compute_per_token_train_centroids(model, task=task)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 353, in _compute_per_token_train_centroids
data = get_label_samples(self.get_per_task_train_dataset(task), label)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 350, in get_label_samples
return dataset.filter(lambda example: int(example['labels']) == label)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 470, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/fingerprint.py", line 406, in wrapper
out = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2519, in filter
indices = self.map(
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2036, in map
return self._map_single(
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 503, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 470, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/fingerprint.py", line 406, in wrapper
out = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2248, in _map_single
return Dataset.from_file(cache_file_name, info=info, split=self.split)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 654, in from_file
return cls(
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 593, in __init__
self.info.features = self.info.features.reorder_fields_as(inferred_features)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/features/features.py", line 1092, in reorder_fields_as
return Features(recursive_reorder(self, other))
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/features/features.py", line 1081, in recursive_reorder
raise ValueError(f"Keys mismatch: between {source} and {target}" + stack_position)
ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'candidates_ids': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'labels': Value(dtype='int64', id=None), 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'extra_fields': {}, 'task': Value(dtype='string', id=None)}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: linux
- Python version: 3.8.12
- PyArrow version: 6.0.1
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"Awesome thanks a lot @mariosasko "
] | 2021-12-15T19:00:45 | 2021-12-16T15:55:20 | 2021-12-16T15:55:19 | CONTRIBUTOR | null | false | {
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} | Closes #3369.
cc: @patrickvonplaten | {
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} | Update the list of supported versions of Python in `setup.py` to keep the PyPI project description updated. | {
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"seems like a very very low-prio improvement :)",
"@albertvillanova thanks for the feedback! I removed the formatting altogether since I think this is a bit simpler tor read than non-rendered Markdown"
] | 2021-12-15T16:34:47 | 2021-12-17T13:28:26 | 2021-12-17T13:28:25 | MEMBER | null | false | {
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} | The description of BLEURT on the hf.co website has a strange use of URL hyperlinking. This PR attempts to fix this, although I am not 100% sure Markdown syntax is allowed on the frontend or not.
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"I wanted to flag a change from since we discussed this: I initially wrote a function for using the Wikimedia APIs to collect namespace aliases, but decided that adding in more http requests to the script wasn't a great idea so instead used that code to build a static list that I just added directly to the code.\r\n\r\nAlso, an FYI that python library dependencies weren't working on my local end so I wasn't able to directly test the code. I tested a copy with the problematic elements stripped (beam etc.) that worked fine, but someone with a working local copy may want to test just to make sure I didn't accidentally break anything.",
"Also, while I would argue more strongly for some of the changes in this code, they are five distinct changes so not so hard to remove one or two if other folks think they aren't worth the overhead etc.",
"I also add a comment by @geohci in the Issue page:\r\n> See https://public.paws.wmcloud.org/User:Isaac_(WMF)/HuggingFace%20Wikipedia%20Processing.ipynb for more implementation details / some data around the overhead induced by adding the extra preprocessing steps (stripping link prefixes and magic words)",
"Hi ! Thanks a lot, this is very cool ! Note that unfortunately if we change the processing right now, users won't be able to load the \"big\" languages like english anymore, because it requires an Apache Beam runtime to process them. Some Wikipedia dumps have been processed by Hugging Face so that users don't need to run Apache Beam stuff.\r\n\r\nTherefore, we can merge this change after we have processed dumps using this new processing, and host them on the Hugging Face google storage.\r\n\r\nI think we can take care of this and let you know once this is ready ? What do you think @albertvillanova ?\r\n\r\nThis is also an opportunity to have the latest dumps ready, the current ones are from 2020",
"Related PR on updating to the latest dates: https://github.com/huggingface/datasets/pull/3612",
"@lhoestq if the additional processing steps are validated, we could go on generating the processed datasets for the big languages.\r\n\r\nThe only thing before doing that is that we should also validate other change (so that we include it also in the processed datasets):\r\n- #3398 ",
"> @lhoestq if the additional processing steps are validated, we could go on generating the processed datasets for the big languages.\r\n\r\nCool ! Looking forward to it :)\r\n\r\n> The only thing before doing that is that we should also validate other change (so that we include it also in the processed datasets):\r\n> \r\n> https://github.com/huggingface/datasets/issues/3398\r\n\r\nSounds good ! We can definitely add the URL as asked by the Wikipedia to provide credits to the authors.",
"@geohci I do not have push rights to this PR. See: [Enabling repository maintainer permissions on existing pull requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork#enabling-repository-maintainer-permissions-on-existing-pull-requests).\r\n\r\nI would like to merge the master branch so that all tests pass. Once done, I will be able approve this PR.",
"> @geohci I do not have push rights to this PR. See: [Enabling repository maintainer permissions on existing pull requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork#enabling-repository-maintainer-permissions-on-existing-pull-requests).\r\n> \r\n> I would like to merge the master branch so that all tests pass. Once done, I will be able approve this PR.\r\n\r\n@albertvillanova the `Allow edits by maintainers` box was already checked (what your instructions indicated) and indicates `If checked, users with write access to huggingface/datasets can add new commits to your wikipedia-updates branch. You can always change this setting later.` so you should have permissions already. If there's something else I'm missing or can do, please let me know. If it's not easy to resolve, I am plenty comfortable with you creating a new PR with these changes under your account too."
] | 2021-12-15T13:30:06 | 2022-03-04T08:16:00 | 2022-03-04T08:16:00 | CONTRIBUTOR | null | false | {
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} | * More structured approach to detecting redirects
* Remove redundant template filter code (covered by strip_code)
* Add language-specific lists of additional media namespace aliases for filtering
* Add language-specific lists of category namespace aliases for new link text cleaning step
* Remove magic words (parser directions like __TOC__ that occasionally occur in text)
Fix #3400
With support from @albertvillanova
CC @yjernite | {
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"This dataset is now available on the Hub here: https://huggingface.co./datasets/MLCommons/peoples_speech"
] | 2021-12-15T11:21:21 | 2023-02-28T16:22:29 | 2023-02-28T16:22:28 | 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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- **Name:** Multilingual Spoken Words
- **Description:** Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken words in 50 languages for academic research and commercial applications in keyword spotting and spoken term search, licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords, totaling 23.4 million 1-second spoken examples (over 6,000 hours).
Read more: https://mlcommons.org/en/news/spoken-words-blog/
- **Paper:** https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/fe131d7f5a6b38b23cc967316c13dae2-Paper-round2.pdf
- **Data:** https://mlcommons.org/en/multilingual-spoken-words/
- **Motivation:**
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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https://api.github.com/repos/huggingface/datasets/issues/3432 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3432/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3432/comments | https://api.github.com/repos/huggingface/datasets/issues/3432/events | https://github.com/huggingface/datasets/pull/3432 | 1,079,910,769 | PR_kwDODunzps4v1NGS | 3,432 | Correctly indent builder config in dataset script docs | {
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https://api.github.com/repos/huggingface/datasets/issues/3431 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3431/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3431/comments | https://api.github.com/repos/huggingface/datasets/issues/3431/events | https://github.com/huggingface/datasets/issues/3431 | 1,079,866,083 | I_kwDODunzps5AXXLj | 3,431 | Unable to resolve any data file after loading once | {
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"Hi ! `load_dataset` accepts as input either a local dataset directory or a dataset name from the Hugging Face Hub.\r\n\r\nSo here you are getting this error the second time because it tries to load the local `wiki_dpr` directory, instead of `wiki_dpr` from the Hub. It doesn't work since it's a **cache** directory, not a **dataset** directory in itself.\r\n\r\nTo fix that you can use another cache directory like `cache_dir=\"/data2/whr/lzy/open_domain_data/retrieval/cache\"`",
"thx a lot"
] | 2021-12-14T15:02:15 | 2022-12-11T10:53:04 | 2022-02-24T09:13:52 | NONE | null | null | null | when I rerun my program, it occurs this error
" Unable to resolve any data file that matches '['**train*']' at /data2/whr/lzy/open_domain_data/retrieval/wiki_dpr with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'zip']", so how could i deal with this problem?
thx.
And below is my code .
| {
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https://api.github.com/repos/huggingface/datasets/issues/3430 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3430/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3430/comments | https://api.github.com/repos/huggingface/datasets/issues/3430/events | https://github.com/huggingface/datasets/pull/3430 | 1,079,811,124 | PR_kwDODunzps4v033w | 3,430 | Make decoding of Audio and Image feature optional | {
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"Closing this PR for now due to https://github.com/huggingface/datasets/issues/3145#issuecomment-993664104.",
"Okay, after some more thinking, I'm re-opening this PR for three reasons:\r\n* This feature will allow us to remove the `image_file_path`/`audio_file_path` columns in our vision/audio datasets. Currently, it makes sense to keep those columns because it's not obvious how to access the underlying path information of the Image/Audio feature. However, if the user is not aware and does `dataset[0][\"image_file_path\"]` on our vision/audio datasets, this will be costly because the image/audio file data has to be decoded first (stored in `dataset[0][\"image\"]`/`dataset[0][\"audio\"]`)\r\n* In CV, we often work with the so-called \"half life\" datasets (RedCaps, WIT, ...) that only provide image URLs and not actual image data, and some of these image URLs even go down. Our solution to this problem is to give a note on how to efficiently download the image data using `map` in the datasets cards of such datasets. This feature will remove the need for a separate `image_url` column of type `Value(\"string\")` in such datasets. Instead, we will be able to use the `image` column of type `Image()` (the image feature knows how to decode image URLs using `xopen`), disable decoding and use `requests.get` for download, which I expect to be faster than `xopen`.\r\n* This feature should help us in implementing `push_to_hub` for the Image/Audio where we transfer actual image/audio data and not paths",
"> This feature will allow us to remove the image_file_path/audio_file_path columns in our vision/audio datasets. Currently, it makes sense to keep those columns because it's not obvious how to access the underlying path information of the Image/Audio feature. However, if the user is not aware and does dataset[0][\"image_file_path\"] on our vision/audio datasets, this will be costly because the image/audio file data has to be decoded first (stored in dataset[0][\"image\"]/dataset[0][\"audio\"])\r\n\r\nThat makes sense !\r\n\r\n> Instead, we will be able to use the image column of type Image() (the image feature knows how to decode image URLs using xopen), disable decoding and use requests.get for download, which I expect to be faster than xopen.\r\n\r\nI feel like it's a bit convoluted compared to having the `image_url` column as string, and say to users to `map` using `requests.get` with `image_url`.\r\n\r\nMoreover I'm not 100% sure that we should have `Image` features with both local paths and URLs, since this behavior is a bit hidden the users and they don't give the same performance at all.\r\n\r\n> This feature should help us in implementing push_to_hub for the Image/Audio where we transfer actual image/audio data and not paths\r\n\r\nCool !",
"Thanks, @lhoestq.\r\n\r\n> I feel like it's a bit convoluted compared to having the image_url column as string, and say to users to map using requests.get with image_url.\r\n\r\nYes, that makes sense. \r\n\r\n>Moreover I'm not 100% sure that we should have Image features with both local paths and URLs, since this behavior is a bit hidden the users and they don't give the same performance at all.\r\n\r\nDo you mean we should remove support for URLs in the Image feature? Because this is what we get for free by adding support for streaming (by using `xopen` instead of `open`) and this is also what the Audio feature does.",
"> Do you mean we should remove support for URLs in the Image feature? Because this is what we get for free by adding support for streaming (by using xopen instead of open) and this is also what the Audio feature does.\r\n\r\nI think it might not be ideal to have URLs in an `Image` type column for a dataset in **non-streaming** mode, since you'd expect to have everything locally. But for a streaming dataset it must use `xopen` indeed",
"Yes, I agree. Let's have the `image_url` columns as `Value(\"string\")` + a note with the map function to download images for local datasets and a note with `cast_column` (which is requested in https://github.com/huggingface/datasets/issues/3369) for streamed datasets (`ds.cast_column(\"image_url\", Image())`).",
"I fixed the merge conflicts and small bugs in nested decoding introduced by #3575. Additionally, I addressed https://github.com/huggingface/datasets/issues/3473 by adding the `_iter` method to `Dataset` (inspired by the `_getitem` method). For `iter(dset)` I set `_iter(dset, decoded=True)` to enable decoding and for `map` `_iter(dset, decoded=False)` to make it lazy."
] | 2021-12-14T14:15:08 | 2022-01-25T18:57:52 | 2022-01-25T18:57:52 | CONTRIBUTOR | null | false | {
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} | Add the `decode` argument (`True` by default) to the `Audio` and the `Image` feature to make it possible to toggle on/off decoding of these features.
Even though we've discussed that on Slack, I'm not removing the `_storage_dtype` argument of the Audio feature in this PR to avoid breaking the Audio feature tests. | {
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https://api.github.com/repos/huggingface/datasets/issues/3429 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3429/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3429/comments | https://api.github.com/repos/huggingface/datasets/issues/3429/events | https://github.com/huggingface/datasets/pull/3429 | 1,078,902,390 | PR_kwDODunzps4vx1gp | 3,429 | Make cast cacheable (again) on Windows | {
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} | `cast` currently emits the following warning when called on Windows:
```
Parameter 'function'=<function Dataset.cast.<locals>.<lambda> at 0x000001C930571EA0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting
and caching to work. If you reuse this transform, the caching mechanism will consider it to be different
from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
```
It seems like the issue stems from the `config.PYARROW_VERSION` object not being serializable on Windows (tested with `dumps(lambda: config.PYARROW_VERSION)`), so I'm fixing this by capturing `config.PYARROW_VERSION.major` before the lambda definition. | {
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} | Some unused files were remaining, this PR removes them. We just need to keep the dummy_data.zip file | {
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} | Add:
- Enron Emails subset of The Pile: "enron_emails" config
Close bigscience-workshop/data_tooling#310.
CC: @StellaAthena | {
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} | Fixes #3240, fixes #3416 | {
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https://api.github.com/repos/huggingface/datasets/issues/3425 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3425/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3425/comments | https://api.github.com/repos/huggingface/datasets/issues/3425/events | https://github.com/huggingface/datasets/issues/3425 | 1,078,598,140 | I_kwDODunzps5AShn8 | 3,425 | Getting configs names takes too long | {
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"maybe related to https://github.com/huggingface/datasets/issues/2859\r\n",
"It looks like it's currently calling `HfFileSystem.ls()` ~8 times at the root and for each subdirectory:\r\n- \"\"\r\n- \"en.noblocklist\"\r\n- \"en.noclean\"\r\n- \"en\"\r\n- \"multilingual\"\r\n- \"realnewslike\"\r\n\r\nCurrently `ls` is slow because it iterates on all the files inside the repository.\r\n\r\nAn easy optimization would be to cache the result of each call to `ls`.\r\nWe can also optimize `ls` by using a tree structure per directory instead of a list of all the files.\r\n",
"ok\r\n"
] | 2021-12-13T14:27:57 | 2021-12-13T14:53:33 | null | CONTRIBUTOR | null | null | null |
## Steps to reproduce the bug
```python
from datasets import get_dataset_config_names
get_dataset_config_names("allenai/c4")
```
## Expected results
I would expect to get the answer quickly, at least in less than 10s
## Actual results
It takes about 45s on my environment
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-5.11.0-1022-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/3424 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3424/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3424/comments | https://api.github.com/repos/huggingface/datasets/issues/3424/events | https://github.com/huggingface/datasets/pull/3424 | 1,078,543,625 | PR_kwDODunzps4vwpNt | 3,424 | 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."
] | 2021-12-13T13:38:13 | 2022-01-12T14:13:16 | 2022-01-12T14:13:15 | CONTRIBUTOR | null | false | {
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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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https://api.github.com/repos/huggingface/datasets/issues/3423 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3423/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3423/comments | https://api.github.com/repos/huggingface/datasets/issues/3423/events | https://github.com/huggingface/datasets/issues/3423 | 1,078,049,638 | I_kwDODunzps5AQbtm | 3,423 | data duplicate when setting num_works > 1 with streaming data | {
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"Hi ! Thanks for reporting :)\r\n\r\nWhen using a PyTorch's data loader with `num_workers>1` and an iterable dataset, each worker streams the exact same data by default, resulting in duplicate data when iterating using the data loader.\r\n\r\nWe can probably fix this in `datasets` by checking `torch.utils.data.get_worker_info()` which gives the worker id if it happens.",
"> Hi ! Thanks for reporting :)\r\n> \r\n> When using a PyTorch's data loader with `num_workers>1` and an iterable dataset, each worker streams the exact same data by default, resulting in duplicate data when iterating using the data loader.\r\n> \r\n> We can probably fix this in `datasets` by checking `torch.utils.data.get_worker_info()` which gives the worker id if it happens.\r\nHi ! Thanks for reply\r\n\r\nDo u have some plans to fix the problem?\r\n",
"Isn’t that somehow a bug on PyTorch side? (Just asking because this behavior seems quite general and maybe not what would be intended)",
"From PyTorch's documentation [here](https://pytorch.org/docs/stable/data.html#dataset-types):\r\n\r\n> When using an IterableDataset with multi-process data loading. The same dataset object is replicated on each worker process, and thus the replicas must be configured differently to avoid duplicated data. See [IterableDataset](https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset) documentations for how to achieve this.\r\n\r\nIt looks like an intended behavior from PyTorch\r\n\r\nAs suggested in the [docstring of the IterableDataset class](https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset), we could pass a `worker_init_fn` to the DataLoader to fix this. It could be called `streaming_worker_init_fn` for example.\r\n\r\nHowever, while this solution works, I'm worried that many users simply don't know about this parameter and just start their training with duplicate data without knowing it. That's why I'm more in favor of integrating the check on the worker id directly in `datasets` in our implementation of `IterableDataset.__iter__`.",
"Fixed by https://github.com/huggingface/datasets/pull/4375",
"> Fixed by #4375\r\n\r\nThanks!",
"Hi there @lhoestq @cloudyuyuyu \r\nI met that problem recently, and #4375 is really useful because I finally found out I am training with duplicate data.\r\nHowever, in multi-GPU training, I'm using DDP mode and IterableDataset, which still yields duplicate data for each progress. And this is dangerous because users maybe not realize this behavior.",
"If the worker_info.id is unique per process it should work fine, could you check that they're unique ?\r\n\r\nThe code to get the worker_info in each worker is `torch.utils.data.get_worker_info()`",
"test.py\r\n```python\r\nimport json\r\nimport os\r\n\r\nimport torch\r\nfrom torch.utils.data import IterableDataset, DataLoader\r\nfrom transformers import PreTrainedTokenizer, TrainingArguments\r\n\r\nfrom common.arguments import DataTrainingArguments, ModelArguments\r\n\r\n\r\nclass MyIterableDataset(IterableDataset):\r\n def __iter__(self):\r\n worker_info = torch.utils.data.get_worker_info()\r\n print(worker_info)\r\n return iter(range(3))\r\n\r\n\r\nif __name__ == '__main__':\r\n dataset = MyIterableDataset()\r\n dataloader = DataLoader(dataset, num_workers=1)\r\n for i in dataloader:\r\n print(i)\r\n\r\n```\r\n\r\n\r\n```sh\r\n$ python3 -m torch.distributed.launch \\\r\n --nproc_per_node=2 test.py\r\nWorkerInfo(id=0, num_workers=1, seed=5545685212307804959, dataset=<__main__.MyIterableDataset object at 0x7f92648cf6a0>)\r\nWorkerInfo(id=0, num_workers=1, seed=3174108029709729025, dataset=<__main__.MyIterableDataset object at 0x7f19ab961670>)\r\ntensor([0])\r\ntensor([1])\r\ntensor([2])\r\ntensor([0])\r\ntensor([1])\r\ntensor([2])\r\n```\r\n\r\n@lhoestq they are not unique",
"It looks like a bug from pytorch no ? How can we know which data should go in which process when using DDP ?\r\n\r\nI guess we need to check `torch.distributed.get_world_size()` and `torch.distributed.get_rank()` as well. Not fan of the design here tbh, but that's how it is",
"> It looks like a bug from pytorch no ? How can we know which data should go in which process when using DDP ?\r\n> \r\n> I guess we need to check `torch.distributed.get_world_size()` and `torch.distributed.get_rank()` as well. Not fan of the design here tbh, but that's how it is\r\n\r\nMaybe we should document it?",
"Never mind. After reading the code, `IterableDatasetShard` has solved this problem.",
"I'm re-opening this one since I think it should be supported by `datasets` natively",
"hmm actually let me open a new issue on DDP - original post was for single node"
] | 2021-12-13T03:43:17 | 2022-12-14T16:04:22 | 2022-12-14T16:04:22 | NONE | null | null | null | ## Describe the bug
The data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import pandas as pd
import numpy as np
import os
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
import shutil
NUM_OF_USER = 1000000
NUM_OF_ACTION = 50000
NUM_OF_SEQUENCE = 10000
NUM_OF_FILES = 32
NUM_OF_WORKERS = 16
if __name__ == "__main__":
shutil.rmtree("./dataset")
for i in range(NUM_OF_FILES):
sequence_data = pd.DataFrame(
{
"imei": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),
"sequence": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)
}
)
if not os.path.exists("./dataset"):
os.makedirs("./dataset")
sequence_data.to_csv(f"./dataset/sequence_data_{i}.csv",
index=False)
dataset = load_dataset("csv",
data_files=[os.path.join("./dataset",file) for file in os.listdir("./dataset") if file.endswith(".csv")],
split="train",
streaming=True).with_format("torch")
data_loader = DataLoader(dataset,
batch_size=1024,
num_workers=NUM_OF_WORKERS)
result = pd.DataFrame()
for i, batch in tqdm(enumerate(data_loader)):
result = pd.concat([result,
pd.DataFrame(batch)],
axis=0)
result.to_csv(f"num_work_{NUM_OF_WORKERS}.csv", index=False)
```
## Expected results
data do not duplicate
## Actual results
data duplicate NUM_OF_WORKERS = 16
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:datasets==1.14.0
- Platform:transformers==4.11.3
- Python version:3.8
- PyArrow version:
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https://api.github.com/repos/huggingface/datasets/issues/3422 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3422/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3422/comments | https://api.github.com/repos/huggingface/datasets/issues/3422/events | https://github.com/huggingface/datasets/issues/3422 | 1,078,022,619 | I_kwDODunzps5AQVHb | 3,422 | Error about load_metric | {
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"Hi ! I wasn't able to reproduce your error.\r\n\r\nCan you try to clear your cache at `~/.cache/huggingface/modules` and try again ?"
] | 2021-12-13T02:49:51 | 2022-01-07T14:06:47 | 2022-01-07T14:06:47 | NONE | null | null | null | ## Describe the bug
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1371, in load_metric
metric = metric_cls(
TypeError: 'NoneType' object is not callable
## Steps to reproduce the bug
```python
metric = load_metric("glue", "sst2")
```
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3421 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3421/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3421/comments | https://api.github.com/repos/huggingface/datasets/issues/3421/events | https://github.com/huggingface/datasets/pull/3421 | 1,077,966,571 | PR_kwDODunzps4vuvJK | 3,421 | Adding mMARCO dataset | {
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"Hi @albertvillanova we've made a major overhaul of the loading script including all configurations we're making available. Could you please review it again?",
"@albertvillanova :ping_pong: ",
"Thanks @lhbonifacio for adding this dataset.\r\nHi there, i got an error about mmarco:\r\nConnectionError: Couldn't reach 'unicamp-dl/mmarco' on the Hub (ConnectionError)\r\ncode:\r\n`from datasets import list_datasets, load_dataset\r\ndataset = load_dataset('unicamp-dl/mmarco', language='portuguese')`\r\n\r\nAny help will be appreciated!",
"Hi @catqaq, we updated the loading script. Now you can load the datasets with:\r\n\r\n```python\r\ndataset = load_dataset('unicamp-dl/mmarco', 'portuguese')\r\n```\r\n\r\nYou can check the list of supported languages and usage examples in [this link](https://huggingface.co./datasets/unicamp-dl/mmarco). Feel free to contact us if you have any issues.",
"\r\n\r\n\r\n> \r\n\r\n\r\n\r\n> Hi @catqaq, we updated the loading script. Now you can load the datasets with:\r\n> \r\n> ```python\r\n> dataset = load_dataset('unicamp-dl/mmarco', 'portuguese')\r\n> ```\r\n> \r\n> You can check the list of supported languages and usage examples in [this link](https://huggingface.co./datasets/unicamp-dl/mmarco). Feel free to contact us if you have any issues.\r\n\r\nThanks for your quick updates. So, how can i get the fixed version, install from the source? It seems that the merging is blocked.",
"@catqaq you can load mMARCO using the namespace `unicamp-dl/mmarco` while this PR remains under review.",
"Thanks for your contribution, @lhbonifacio and @hugoabonizio. And sorry for the late response.\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\nAs you already created this dataset under your organization namespace (https://huggingface.co./datasets/unicamp-dl/mmarco), I think we can safely close this PR.\r\n\r\nWe would suggest you complete your dataset card with the YAML tags, to make it searchable and discoverable.\r\n\r\nPlease, feel free to tell us if you need some help."
] | 2021-12-13T00:56:43 | 2022-10-03T09:37:15 | 2022-10-03T09:37:15 | NONE | null | false | {
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"> Thanks a lot for adding this dataset ! Good job with the dataset card and the dataset scripts - they're really good :)\r\n> \r\n> I just added minor changes\r\n\r\nThanks for fixing typos!"
] | 2021-12-12T21:30:45 | 2021-12-14T17:53:03 | 2021-12-14T17:53:02 | CONTRIBUTOR | null | false | {
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} | This pull request adds a categorized Long-form question answering dataset `ELI5_Category`. It's a new variant of the [ELI5](https://huggingface.co./datasets/eli5) dataset that uses the Reddit tags to alleviate the training/validation overlapping in the origin ELI5 dataset.
A [report](https://celeritasml.netlify.app/posts/2021-12-01-eli5c/)(Section 2) on this dataset. | {
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https://api.github.com/repos/huggingface/datasets/issues/3419 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3419/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3419/comments | https://api.github.com/repos/huggingface/datasets/issues/3419/events | https://github.com/huggingface/datasets/issues/3419 | 1,077,350,974 | I_kwDODunzps5ANxI- | 3,419 | `.to_json` is extremely slow after `.select` | {
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"Hi ! It's slower indeed because a datasets on which `select`/`shard`/`train_test_split`/`shuffle` has been called has to do additional steps to retrieve the data of the dataset table in the right order.\r\n\r\nIndeed, if you call `dataset.select([0, 5, 10])`, the underlying table of the dataset is not altered to keep the examples at index 0, 5, and 10. Instead, an indices mapping is added on top of the table, that says that the first example is at index 0, the second at index 5 and the last one at index 10.\r\n\r\nTherefore accessing the examples of the dataset is slower because of the additional step that uses the indices mapping.\r\n\r\nThe step that takes the most time is to query the dataset table from a list of indices here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/047dc756ed20fbf06e6bcaf910464aba0e20610a/src/datasets/formatting/formatting.py#L61-L63\r\n\r\nIn your case it can be made significantly faster by checking if the indices are contiguous. If they're contiguous, we could pass a python `slice` or `range` instead of a list of integers to `_query_table`. This way `_query_table` will do only one lookup to get the queried batch instead of `batch_size` lookups.\r\n\r\nGiven that calling `select` with contiguous indices is a common use case I'm in favor of implementing such an optimization :)\r\nLet me know what you think",
"Hi, thanks for the response!\r\nI still don't understand why it is so much slower than iterating and saving:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\") # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\") # Takes 99 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) / 2))])\r\nselected_subset2.to_json(\"from_select2.json\") # Takes 47 seconds\r\n\r\nselected_subset3 = original.select([i for i in range(len(original)) if i % 2 == 0])\r\nselected_subset3.to_json(\"from_select3.json\") # Takes 49 seconds\r\n\r\nimport json\r\nimport time\r\ndef fast_to_json(dataset, path):\r\n start = time.time()\r\n with open(path, mode=\"w\") as f:\r\n for example in dataset:\r\n f.write(json.dumps(example, separators=(',', ':')) + \"\\n\")\r\n end = time.time()\r\n print(f\"Saved {len(dataset)} examples to {path} in {end - start} seconds.\")\r\n\r\nfast_to_json(original, \"from_original_fast.json\")\r\nfast_to_json(selected_subset1, \"from_select1_fast.json\")\r\nfast_to_json(selected_subset2, \"from_select2_fast.json\")\r\nfast_to_json(selected_subset3, \"from_select3_fast.json\")\r\n```\r\n```\r\nSaved 87599 examples to from_original_fast.json in 8 seconds.\r\nSaved 87599 examples to from_select1_fast.json in 10 seconds.\r\nSaved 43799 examples to from_select2_fast.json in 6 seconds.\r\nSaved 43800 examples to from_select3_fast.json in 5 seconds.\r\n```",
"There are slight differences between what you're doing and what `to_json` is actually doing.\r\nIn particular `to_json` currently converts batches of rows (as an arrow table) to a pandas dataframe, and then to JSON Lines. From your benchmark it looks like it's faster if we don't use pandas.\r\n\r\nThanks for investigating, I think we can optimize `to_json` significantly thanks to your test.",
"Thanks for your observations, @eladsegal! I spent some time with this and tried different approaches. Turns out that https://github.com/huggingface/datasets/blob/bb13373637b1acc55f8a468a8927a56cf4732230/src/datasets/io/json.py#L100 is giving the problem when we use `to_json` after `select`. This is when `indices` parameter in `query_table` is not `None` (if it is `None` then `to_json` should work as expected)\r\n\r\nIn order to circumvent this problem, I found out instead of doing Arrow Table -> Pandas-> JSON we can directly go to JSON by using `to_pydict()` which is a little slower than the current approach but at least `select` works properly now. Lmk what you guys think of it @lhoestq, @eladsegal?",
"Sounds good to me ! Feel free to also share your benchmarks for reference @bhavitvyamalik ",
"Posting it in @eladsegal's format:\r\n\r\nFor `squad`:\r\nSaving examples using current `to_json` in 3.63 secs\r\nSaving examples to `from_select1_fast.json` in 5.00 secs\r\nSaving examples to `from_select2_fast.json` in 2.45 secs\r\nSaving examples to `from_select3_fast.json` in 2.50 secs\r\n\r\nFor `squad_v2`:\r\nSaving examples using current `to_json` in 5.26 secs\r\nSaving examples to `from_select1_fast.json` in 7.54 secs\r\nSaving examples to `from_select2_fast.json` in 3.80 secs\r\nSaving examples to `from_select3_fast.json` in 3.67 secs"
] | 2021-12-11T01:36:31 | 2021-12-21T15:49:07 | null | CONTRIBUTOR | null | null | null | ## Describe the bug
Saving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.
## Steps to reproduce the bug
```python
from datasets import load_dataset
original = load_dataset("squad", split="train")
original.to_json("from_original.json") # Takes 0 seconds
selected_subset1 = original.select([i for i in range(len(original))])
selected_subset1.to_json("from_select1.json") # Takes 212 seconds
selected_subset2 = original.select([i for i in range(int(len(original) / 2))])
selected_subset2.to_json("from_select2.json") # Takes 90 seconds
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master (https://github.com/huggingface/datasets/commit/6090f3cfb5c819f441dd4a4bb635e037c875b044)
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27
- Python version: 3.9.7
- PyArrow version: 6.0.0
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"As we are removing the dataset scripts from GitHub and moving them to the Hugging Face Hub, I am going to transfer this script to the repo: https://huggingface.co./datasets/wikimedia/wikisource"
] | 2021-12-10T17:04:44 | 2022-10-04T09:35:56 | 2022-10-03T09:37:20 | MEMBER | null | false | {
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} | Add loading script for Wikisource dataset.
Fix #3399.
CC: @geohci, @yjernite | {
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} | Use `Value("string")` instead of `Value("bool")` for the feature type of the `"bridge"` field in the QED dataset. If the value is `False`, set to `None`.
The following paragraph in the QED repo explains the purpose of this field:
>Each annotation in referential_equalities is a pair of spans, the question_reference and the sentence_reference, corresponding to an entity mention in the question and the selected_sentence respectively. As described in the paper, sentence_references can be "bridged in", in which case they do not correspond with any actual span in the selected_sentence. Hence, sentence_reference spans contain an additional field, bridge, which is a prepositional phrase when a reference is bridged, and is False otherwise. Prepositional phrases serve to link bridged references to an anchoring phrase in the selected_sentence. In the case a sentence_reference is bridged, the start and end, as well as the span string, map to such an anchoring phrase in the selected_sentence.
Fix #3346
cc @VictorSanh | {
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"Hi, thanks for reporting! This is a duplicate of https://github.com/huggingface/datasets/issues/3240. We are working on a fix.\r\n\r\n"
] | 2021-12-10T13:49:17 | 2021-12-14T14:38:29 | 2021-12-14T14:38:29 | NONE | null | null | null | ## Dataset viewer issue for '* disaster_response_messages*'
**Link:** https://huggingface.co./datasets/disaster_response_messages
Dataset unavailable. Link dead: https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv
Am I the one who added this dataset ?No
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https://api.github.com/repos/huggingface/datasets/issues/3415 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3415/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3415/comments | https://api.github.com/repos/huggingface/datasets/issues/3415/events | https://github.com/huggingface/datasets/issues/3415 | 1,076,472,534 | I_kwDODunzps5AKarW | 3,415 | Non-deterministic tests: CI tests randomly fail | {
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"I think it might come from two different issues:\r\n1. Google Drive is an unreliable host, mainly because of quota limitations\r\n2. the staging environment can sometimes raise some errors\r\n\r\nFor Google Drive tests we could set up some retries with backup URLs if necessary I guess.\r\nFor staging on the other hand, I guess we can investigate what causes this and discuss with the back-end team",
"Closed by:\r\n- #3982"
] | 2021-12-10T06:08:59 | 2022-03-31T16:38:51 | 2022-03-31T16:38:51 | MEMBER | null | null | null | ## Describe the bug
Some CI tests fail randomly.
1. In https://github.com/huggingface/datasets/pull/3375/commits/c10275fe36085601cb7bdb9daee9a8f1fc734f48, there were 3 failing tests, only on Linux:
```
=========================== short test summary info ============================
FAILED tests/test_streaming_download_manager.py::test_streaming_dl_manager_get_extraction_protocol[https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh-zip]
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive - Fi...
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped
= 3 failed, 3553 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 192.79s (0:03:12) =
```
2. After re-running the CI (without any change in the code) in https://github.com/huggingface/datasets/pull/3375/commits/57bfe1f342cd3c59d2510b992d5f06a0761eb147, there was only 1 failing test (one on Linux and a different one on Windows):
- On Linux:
```
=========================== short test summary info ============================
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped
= 1 failed, 3555 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 199.76s (0:03:19) =
```
- On Windows:
```
=========================== short test summary info ===========================
FAILED tests/test_load.py::test_load_dataset_builder_for_community_dataset_without_script
= 1 failed, 3551 passed, 2954 skipped, 2 xfailed, 1 xpassed, 121 warnings in 478.58s (0:07:58) =
```
The test `tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped` passes locally.
3. After re-running again the CI (without any change in the code) in https://github.com/huggingface/datasets/pull/3375/commits/39f32f2119cf91b86867216bb5c356c586503c6a, ALL the tests passed.
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https://api.github.com/repos/huggingface/datasets/issues/3414 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3414/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3414/comments | https://api.github.com/repos/huggingface/datasets/issues/3414/events | https://github.com/huggingface/datasets/pull/3414 | 1,076,028,998 | PR_kwDODunzps4voyaq | 3,414 | Skip None encoding (line deleted by accident in #3195) | {
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} | Return the line deleted by accident in #3195 while [resolving merge conflicts](https://github.com/huggingface/datasets/pull/3195/commits/8b0ed15be08559056b817836a07d47acda0c4510).
Fix #3181 (finally :))
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https://api.github.com/repos/huggingface/datasets/issues/3413 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3413/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3413/comments | https://api.github.com/repos/huggingface/datasets/issues/3413/events | https://github.com/huggingface/datasets/pull/3413 | 1,075,854,325 | PR_kwDODunzps4voNZv | 3,413 | Add WIDER FACE dataset | {
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} | Adds the WIDER FACE face detection benchmark.
TODOs:
* [x] dataset card
* [x] dummy data | {
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https://api.github.com/repos/huggingface/datasets/issues/3412 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3412/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3412/comments | https://api.github.com/repos/huggingface/datasets/issues/3412/events | https://github.com/huggingface/datasets/pull/3412 | 1,075,846,368 | PR_kwDODunzps4voLs4 | 3,412 | Fix flaky test again for s3 serialization | {
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} | Following https://github.com/huggingface/datasets/pull/3388 that wasn't enough (see CI error [here](https://app.circleci.com/pipelines/github/huggingface/datasets/9080/workflows/b971fb27-ff20-4220-9416-c19acdfdf6f4/jobs/55985)) | {
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https://api.github.com/repos/huggingface/datasets/issues/3411 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3411/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3411/comments | https://api.github.com/repos/huggingface/datasets/issues/3411/events | https://github.com/huggingface/datasets/issues/3411 | 1,075,846,272 | I_kwDODunzps5AIByA | 3,411 | [chinese wwm] load_datasets behavior not as expected when using run_mlm_wwm.py script | {
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"@LysandreJik not so sure who to @\r\nCould you help?",
"Hi @hyusterr, I believe it is @wlhgtc from https://github.com/huggingface/transformers/pull/9887"
] | 2021-12-09T17:54:35 | 2021-12-22T11:21:33 | null | NONE | null | null | null | ## Describe the bug
Model I am using (Bert, XLNet ...): bert-base-chinese
The problem arises when using:
* [https://github.com/huggingface/transformers/blob/master/examples/research_projects/mlm_wwm/run_mlm_wwm.py] the official example scripts: `rum_mlm_wwm.py`
The tasks I am working on is: pretraining whole word masking with my own dataset and ref.json file
I tried follow the run_mlm_wwm.py procedure to do whole word masking on pretraining task. my file is in .txt form, where one line represents one sample, with `9,264,784` chinese lines in total. the ref.json file is also contains 9,264,784 lines of whole word masking reference data for my chinese corpus. but when I try to adapt the run_mlm_wwm.py script, it shows that somehow after
`datasets["train"] = load_dataset(...`
`len(datasets["train"])` returns `9,265,365`
then, after `tokenized_datasets = datasets.map(...`
`len(tokenized_datasets["train"])` returns `9,265,279`
I'm really confused and tried to trace code by myself but can't know what happened after a week trial.
I want to know what happened in the `load_dataset()` function and `datasets.map` here and how did I get more lines of data than I input. so I'm here to ask.
## To reproduce
Sorry that I can't provide my data here since it did not belong to me. but I'm sure I remove the blank lines.
## Expected behavior
I expect the code run as it should. but the AssertionError in line 167 keeps raise as the line of reference json and datasets['train'] differs.
Thanks for your patient reading!
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3410 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3410/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3410/comments | https://api.github.com/repos/huggingface/datasets/issues/3410/events | https://github.com/huggingface/datasets/pull/3410 | 1,075,815,415 | PR_kwDODunzps4voFG7 | 3,410 | Fix dependencies conflicts in Windows CI after conda update to 4.11 | {
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} | For some reason the CI wasn't using python 3.6 but python 3.7 after the update to conda 4.11 | {
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https://api.github.com/repos/huggingface/datasets/issues/3409 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3409/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3409/comments | https://api.github.com/repos/huggingface/datasets/issues/3409/events | https://github.com/huggingface/datasets/pull/3409 | 1,075,684,593 | PR_kwDODunzps4vnpU0 | 3,409 | Pass new_fingerprint in multiprocessing | {
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"@lhoestq Hi~, does this support that `datasets.map(func, batched=True, batch_size, num_proc>1, new_fingerprint=\"func_v1\")` even if `func` can't pickle. I also notice that you said \"Unfortunately you need picklable mapping functions to make multiprocessing work :confused: Also feel free to open an issue or send me a dm if you are in a situation where the caching fails. I can help you with that :slight_smile:\" in [here](https://discuss.huggingface.co/t/how-to-deal-with-unpickable-objects-in-map/1547/8). So, I want to ask that is there a way for users to use multiprocessing in `datasets.map` when the `func` can't pickle? \r\nThanks in advance!",
"Yea you need your function to be picklable for multiprocessing, otherwise the main process is not able to pass your function to the child processes."
] | 2021-12-09T15:12:00 | 2022-08-19T10:41:04 | 2021-12-09T17:38:43 | MEMBER | null | false | {
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} | Following https://github.com/huggingface/datasets/pull/3045
Currently one can pass `new_fingerprint` to `.map()` to use a custom fingerprint instead of the one computed by hashing the map transform. However it's ignored if `num_proc>1`.
In this PR I fixed that by passing `new_fingerprint` to `._map_single()` when `num_proc>1`.
More specifically, `new_fingerprint` with a suffix based on the process `rank` is passed, so that each process has a different `new_fingerprint`
cc @TevenLeScao @vlievin | {
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"Fixed, thanks\r\n<img width=\"661\" alt=\"Capture d’écran 2021-12-22 à 12 02 30\" src=\"https://user-images.githubusercontent.com/1676121/147082881-cf700e8d-0511-4431-b214-d6cf8137db10.png\">\r\n"
] | 2021-12-09T14:34:02 | 2021-12-22T11:02:53 | 2021-12-22T11:02:53 | MEMBER | null | null | null | ## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
When creating an empty dataset repo, the Dataset Preview provides a helpful message that no files were found. There is a tiny typo in that message: "ressource" should be "resource"
Am I the one who added this dataset ?
N/A
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"Cool thanks :) Feel free to merge if it's all good for you"
] | 2021-12-08T14:58:43 | 2021-12-14T17:08:42 | 2021-12-14T17:08:42 | MEMBER | null | false | {
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} | When inferring the module for datasets without script, set a maximum number of iterations over data files.
This PR fixes the issue of taking too long when hundred of data files present.
Please, feel free to agree on both numbers:
```
# Datasets without script
DATA_FILES_MAX_NUMBER = 10
ARCHIVED_DATA_FILES_MAX_NUMBER = 5
```
Fix #3404. | {
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} | Fix module inference for an archive file that contains files within a directory.
Fix #3405. | {
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] | null | [] | 2021-12-08T12:32:15 | 2021-12-08T13:03:29 | 2021-12-08T13:03:29 | MEMBER | null | null | null | ## Describe the bug
When a zipped file contains archived files within a directory, the function `infer_module_for_data_files_in_archives` does not work.
It only works for files located in the root directory of the ZIP file.
## Steps to reproduce the bug
```python
infer_module_for_data_files_in_archives(["path/to/zip/file.zip"], False)
``` | {
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] | null | [] | 2021-12-07T18:44:49 | 2021-12-14T17:08:41 | 2021-12-14T17:08:41 | MEMBER | null | null | null | **Is your feature request related to a problem? Please describe.**
When hundreds of ZIP files are present in a dataset, format inference takes too long.
See: https://github.com/bigscience-workshop/data_tooling/issues/232#issuecomment-986685497
**Describe the solution you'd like**
Iterate over a maximum number of files.
CC: @lhoestq
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"Hi ! Can you try updating `fsspec` ? The minimum version is `2021.05.0`",
"hey @lhoestq. I'm using `fsspec-2021.11.1` but still getting that error.",
"Maybe this discussion can help:\r\n\r\nhttps://github.com/fsspec/filesystem_spec/issues/597#issuecomment-958646964",
"Thanks @lhoestq. Downgrading `fsspec and s3fs` to `2021.10` fixed this issue!"
] | 2021-12-07T17:57:59 | 2021-12-17T07:00:35 | 2021-12-17T07:00:35 | CONTRIBUTOR | null | null | null | ## Describe the bug
Cannot seem to import datasets when running run_summarizer.py script on a VM set up on ovhcloud
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
No error
## Actual results
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/conda/lib/python3.7/site-packages/datasets/__init__.py", line 34, in <module>
from .arrow_dataset import Dataset, concatenate_datasets
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 48, in <module>
from .arrow_writer import ArrowWriter, OptimizedTypedSequence
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_writer.py", line 27, in <module>
from .features import (
File "/opt/conda/lib/python3.7/site-packages/datasets/features/__init__.py", line 2, in <module>
from .audio import Audio
File "/opt/conda/lib/python3.7/site-packages/datasets/features/audio.py", line 8, in <module>
from ..utils.streaming_download_manager import xopen
File "/opt/conda/lib/python3.7/site-packages/datasets/utils/streaming_download_manager.py", line 16, in <module>
from ..filesystems import COMPRESSION_FILESYSTEMS
File "/opt/conda/lib/python3.7/site-packages/datasets/filesystems/__init__.py", line 13, in <module>
from .s3filesystem import S3FileSystem # noqa: F401
File "/opt/conda/lib/python3.7/site-packages/datasets/filesystems/s3filesystem.py", line 1, in <module>
import s3fs
File "/opt/conda/lib/python3.7/site-packages/s3fs/__init__.py", line 1, in <module>
from .core import S3FileSystem, S3File
File "/opt/conda/lib/python3.7/site-packages/s3fs/core.py", line 11, in <module>
from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper, maybe_sync
ImportError: cannot import name 'maybe_sync' from 'fsspec.asyn' (/opt/conda/lib/python3.7/site-packages/fsspec/asyn.py)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.0
- Platform: OVH Cloud Tesla V100 Machine
- Python version: 3.7.9
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3402 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3402/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3402/comments | https://api.github.com/repos/huggingface/datasets/issues/3402/events | https://github.com/huggingface/datasets/pull/3402 | 1,073,614,815 | PR_kwDODunzps4vg5Ff | 3,402 | More robust first elem check in encode/cast example | {
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https://api.github.com/repos/huggingface/datasets/issues/3401 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3401/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3401/comments | https://api.github.com/repos/huggingface/datasets/issues/3401/events | https://github.com/huggingface/datasets/issues/3401 | 1,073,603,508 | I_kwDODunzps4__eO0 | 3,401 | Add Wikimedia pre-processed datasets | {
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] | open | false | null | [] | null | [] | 2021-12-07T17:33:19 | 2021-12-07T17:43:37 | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** Add pre-processed data to:
- *wikimedia/wikipedia*: https://huggingface.co./datasets/wikimedia/wikipedia
- *wikimedia/wikisource*: https://huggingface.co./datasets/wikimedia/wikisource
- **Description:** Add pre-processed data to the Hub for all languages
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** This will be very useful for the NLP community, as the pre-processing has a high cost for lot of researchers (both in computation and in knowledge)
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
CC: @geohci, @yjernite | {
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https://api.github.com/repos/huggingface/datasets/issues/3400 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3400/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3400/comments | https://api.github.com/repos/huggingface/datasets/issues/3400/events | https://github.com/huggingface/datasets/issues/3400 | 1,073,600,382 | I_kwDODunzps4__dd- | 3,400 | Improve Wikipedia loading script | {
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"Thanks! See https://public.paws.wmcloud.org/User:Isaac_(WMF)/HuggingFace%20Wikipedia%20Processing.ipynb for more implementation details / some data around the overhead induced by adding the extra preprocessing steps (stripping link prefixes and magic words)",
"Closed by:\r\n- #3435"
] | 2021-12-07T17:29:25 | 2022-03-22T16:52:28 | 2022-03-22T16:52:28 | MEMBER | null | null | null | As reported by @geohci, the "wikipedia" processing/loading script could be improved by some additional small suggested processing functions:
- _extract_content(filepath):
- Replace .startswith("#redirect") with more structured approach: if elem.find(f"./{namespace}redirect") is None: continue
- _parse_and_clean_wikicode(raw_content, parser):
- Remove rm_template from cleaning -- this is redundant with .strip_code() from mwparserformhell
- Build a language-specific list of namespace prefixes to filter out per below get_namespace_prefixes
- Optional: strip prefixes like categories -- e.g., Category:Towns in Tianjin becomes Towns in Tianjin
- Optional: strip magic words
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https://api.github.com/repos/huggingface/datasets/issues/3399 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3399/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3399/comments | https://api.github.com/repos/huggingface/datasets/issues/3399/events | https://github.com/huggingface/datasets/issues/3399 | 1,073,593,861 | I_kwDODunzps4__b4F | 3,399 | Add Wikisource dataset | {
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"See notebook by @geohci: https://public.paws.wmcloud.org/User:Isaac_(WMF)/HuggingFace%20Wikisource%20Processing.ipynb"
] | 2021-12-07T17:21:31 | 2021-12-10T17:26:26 | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** *wikisource*
- **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:** Additional high quality textual data, besides Wikipedia.
Add loading script as "canonical" dataset (as it is the case for ""wikipedia").
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
CC: @geohci, @yjernite | {
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https://api.github.com/repos/huggingface/datasets/issues/3398 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3398/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3398/comments | https://api.github.com/repos/huggingface/datasets/issues/3398/events | https://github.com/huggingface/datasets/issues/3398 | 1,073,590,384 | I_kwDODunzps4__bBw | 3,398 | Add URL field to Wikimedia dataset instances: wikipedia,... | {
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"@geohci, I think the field \"url\" does not appear in the Wikimedia dumps. Therefore I guess we should generate it, using the \"title\" field and making some transformation of it (replacing spaces with underscores) and prepending the domain (created using the language)?",
"Indeed:\r\n\r\n> To re-distribute text on Wikipedia in any form, provide credit to the authors either by including a) a [hyperlink](https://en.wikipedia.org/wiki/Hyperlink) (where possible) or [URL](https://en.wikipedia.org/wiki/URL) to the page or pages you are re-using, b) a hyperlink (where possible) or URL to an alternative, stable online copy which is freely accessible, which conforms with the license, and which provides credit to the authors in a manner equivalent to the credit given on this website, or c) a list of all authors. (Any list of authors may be filtered to exclude very small or irrelevant contributions.) This applies to text developed by the Wikipedia community. Text from external sources may attach additional attribution requirements to the work, which should be indicated on an article's face or on its talk page. For example, a page may have a banner or other notation indicating that some or all of its content was originally published somewhere else. Where such notations are visible in the page itself, they should generally be preserved by re-users.\r\n\r\nsource: https://en.wikipedia.org/wiki/Wikipedia:Copyrights\r\n\r\nI guess it's fine to add the URL field - it can be constructed easily from the title page IIRC.",
"yep, sorry forgot that that wasn't already in the dumps. specifically `f\"https://{language}.wikipedia.org/wiki/{title.replace(' ', '_')}` should do it",
"Thanks @geohci.\r\n\r\nI had already been looking for information about the conversion from title to URL and I found that apart from replacing blanks with underscores, some other special character must also be percent-encoded (e.g. `\"` to `%22`): https://meta.wikimedia.org/wiki/Help:URL\r\n\r\nTherefore, I have finally used `urllib.parse.quote` function. This additionally percent-encodes non-ASCII characters, but Wikimedia docs say these are equivalent:\r\n> For the other characters either the code or the character can be used in internal and external links, they are equivalent. The system does a conversion when needed.\r\n> [[%C3%80_propos_de_M%C3%A9ta]]\r\n> is rendered as [À_propos_de_Méta](https://meta.wikimedia.org/wiki/%C3%80_propos_de_M%C3%A9ta), almost like [À propos de Méta](https://meta.wikimedia.org/wiki/%C3%80_propos_de_M%C3%A9ta), which leads to this page on Meta with in the address bar the URL\r\n> [http://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta](https://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta)\r\n> while [http://meta.wikipedia.org/wiki/À_propos_de_Méta](https://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta) leads to the same. ",
"Closed by:\r\n- #3789 "
] | 2021-12-07T17:17:27 | 2022-03-22T16:53:27 | 2022-03-22T16:53:27 | MEMBER | null | null | null | As reported by @geohci, in order to host pre-processed data in the Hub, we should add the full URL to data instances (new field "url"), so that we conform to proper attribution from license requirement. See, e.g.: https://fair-trec.github.io/docs/Fair_Ranking_2021_Participant_Instructions.pdf#subsection.3.2
This should be done for all pre-processed datasets under "wikimedia" org in the Hub: https://huggingface.co./wikimedia
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"\r\n> Also, maybe calling the dataset as \"bnl_historical_newspapers\" and setting \"processed\" as one configuration name?\r\n\r\nThis sounds like a good idea but my only question around this is how easy it would be to use the same approach for processing the other newspaper collections [https://data.bnl.lu/data/historical-newspapers/](). \r\n\r\nFor example, the \"BIG DATA PACK\" is `257GB` of ALTO XML. This format is slightly more annoying to process because the metadata and text are contained in different files but the bigger issue might be that processing this XML using the Python XML libraries will probably be quite slow? I had thought for those larger datasets it might be more appropriate to use the Beam datasets? I don't have any experience using Beam so I'm not sure what that would involve and there is a reason to not include it in a dataset script alongside a non Beam dataset? \r\n\r\nIf there isn't an issue with potentially later adding other datasets (which may require Beam) into the same script I'll add one config for the processed version now which leaves open the option for later adding the other datasets. If this makes sense I'll also change the name as you suggest. \r\n\r\nThere is another dataset that could be a good candidate for inclusion here is the \"Monograph Text pack\" which is also processed into a simpler XML format however as the name suggests this isn't newspapers so might be confusing to include under a 'newspapers' script. One option would be to put everything under a `BNL` collection but it might be better to keep the monographs separate if they are added as a dataset so a single script doesn't end up including too much variety of content types? \r\n\r\n\r\n\r\n",
"> My initial idea was to contribute the script also as \"community\" datasets (instead of canonical), i.e. in this case, pushing the script to the repo [huggingface.co/datasets/bigscience-catalogue-data/bnl_historical_newspapers](https://huggingface.co./datasets/bigscience-catalogue-data/bnl_historical_newspapers)\r\n\r\nSorry to respond to this late - happy for this to go in the community datasets. I think it would be nice to include in the canonical datasets at some point but since there is less urgency with this I could try and first work on improving the Datacard before doing that (i.e. make this a draft PR) - let me know if you think that makes more sense? \r\n\r\n\r\n",
"> My initial idea was to contribute the script also as \"community\" datasets (instead of canonical), i.e. in this case, pushing the script to the repo https://huggingface.co./datasets/bigscience-catalogue-data/bnl_historical_newspapers\r\n> One of the advantages is that no dummy data is required, so the addition can be made faster\r\n> On the other hand, one disadvantage is that contributions cannot be made through PRs\r\n> Therefore, we should use the Issue page for discussions, reviews, decisions,...\r\n\r\nSure we can use the issues to discuss/review community datasets. Maybe let's have an issue template for that ?\r\nFor this dataset in particular I'll let @albertvillanova decide whether it's best as community dataset or not. IMO both are fine :)\r\n\r\n> I had thought for those larger datasets it might be more appropriate to use the Beam datasets? I don't have any experience using Beam so I'm not sure what that would involve and there is a reason to not include it in a dataset script alongside a non Beam dataset?\r\n\r\nBeam is nice to process a dataset once and for all and store the resulting processed data on the Hugging Face Hub or elsewhere. However for big datasets it must run on a distributed processing runtime like Google DataFlow, which is often inconvenient for many users. We've been using it though for datasets like Wikipedia and sharing the processed data in a GCS bucket.\r\n\r\nSo feel free to use the tools you like to process the datasets, but in the end I think we just need to host the processed data in a convenient format on the Hugging Face Hub to share it with the community. The processing script you used can also be shared with the community for reproducibility and documentation. But maybe @albertvillanova already has something in mind",
"> > My initial idea was to contribute the script also as \"community\" datasets (instead of canonical), i.e. in this case, pushing the script to the repo [huggingface.co/datasets/bigscience-catalogue-data/bnl_historical_newspapers](https://huggingface.co./datasets/bigscience-catalogue-data/bnl_historical_newspapers)\r\n> > One of the advantages is that no dummy data is required, so the addition can be made faster\r\n> > On the other hand, one disadvantage is that contributions cannot be made through PRs\r\n> > Therefore, we should use the Issue page for discussions, reviews, decisions,...\r\n> \r\n> Sure we can use the issues to discuss/review community datasets. Maybe let's have an issue template for that ? For this dataset in particular I'll let @albertvillanova decide whether it's best as community dataset or not. IMO both are fine :)\r\n\r\nThanks, I'll hold off and let @albertvillanova decide best place for this. \r\n\r\n> > I had thought for those larger datasets it might be more appropriate to use the Beam datasets? I don't have any experience using Beam so I'm not sure what that would involve and there is a reason to not include it in a dataset script alongside a non Beam dataset?\r\n> \r\n> Beam is nice to process a dataset once and for all and store the resulting processed data on the Hugging Face Hub or elsewhere. However for big datasets it must run on a distributed processing runtime like Google DataFlow, which is often inconvenient for many users. We've been using it though for datasets like Wikipedia and sharing the processed data in a GCS bucket.\r\n> \r\n> So feel free to use the tools you like to process the datasets, but in the end I think we just need to host the processed data in a convenient format on the Hugging Face Hub to share it with the community. The processing script you used can also be shared with the community for reproducibility and documentation. But maybe @albertvillanova already has something in mind\r\n\r\nThat's useful, my own 2 cents are that it would make sense to do as @albertvillanova suggested and:-\r\n\r\n- rename the dataset to 'bnl_newspapers' \r\n- make the 'processed dataset' a config \r\n\r\nI won't try and include all the other datasets now but this leaves open the option of adding those later. The actual ALTO processing should be okay to do but I think it makes sense to do this as a one-off process and make the plain text + some associated metadata available elsewere so the dataset script can be kept simple and the processing doesn't get done multiple times. \r\n\r\n@albertvillanova if that sounds okay I'll update pull request to include those changes. \r\n",
"@albertvillanova I've now created a config (currently with only one option) and renamed the dataset. This should keep the option to add other configs based on different bnl newspapers in the future. \r\n",
"@mariosasko thanks for those suggestions ",
"I just merged `master` into your branch to fix the CI :)",
"@albertvillanova do you have additional comments ? Otherwise I think this PR is ready to merge :)",
"> @davanstrien you did an awsome job!!! Thanks a lot!\r\n> \r\n> Just some very minor comments (mainly about the README documentation), and we merge this to master!\r\n\r\nThanks! Hopefully all addressed now. Thanks again for all the support with this pull request! "
] | 2021-12-07T15:43:21 | 2022-01-17T18:35:34 | 2022-01-17T18:35:34 | MEMBER | null | false | {
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} | This pull request adds the BNL's [processed newspaper collections](https://data.bnl.lu/data/historical-newspapers/) as a dataset. This is partly done to support BigScience see: https://github.com/bigscience-workshop/data_tooling/issues/192.
The Datacard is more sparse than I would like but I plan to make a separate pull request to try and make this more complete at a later date.
I had to manually add the `dummy_data` but I believe I've done this correctly (the tests pass locally).
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"https://huggingface.co./datasets/projecte-aina/parlament_parla -> works (but we still have to show an audio player)\r\n\r\nhttps://huggingface.co./datasets/openslr -> another issue: `Message: [Errno 2] No such file or directory: '/home/hf/datasets-preview-backend/zip:/asr_javanese/data/00/00004fe6aa.flac'`",
"Done",
"https://huggingface.co./datasets/projecte-aina/parlament_parla/viewer/clean/train works\r\n\r\n<img width=\"1535\" alt=\"Capture d’écran 2022-04-12 à 13 58 47\" src=\"https://user-images.githubusercontent.com/1676121/162957855-cb3d9e2e-4b61-488c-99ca-8065cd8fe377.png\">\r\n",
"But https://huggingface.co./datasets/openslr/viewer does not work\r\n\r\n<img width=\"678\" alt=\"Capture d’écran 2022-04-12 à 13 59 46\" src=\"https://user-images.githubusercontent.com/1676121/162958013-e31ef2ae-f886-47b7-9f27-664ed3d4b5a1.png\">\r\n\r\nSame issue as #4126:\r\n\r\n```\r\nStatus code: 400\r\nException: TypeError\r\nMessage: __init__() got an unexpected keyword argument 'audio_column'\r\n```",
"Fixed:\r\n<img width=\"1561\" alt=\"Capture d’écran 2022-04-25 à 18 11 51\" src=\"https://user-images.githubusercontent.com/1676121/165129813-018ece9e-8b20-4544-844d-4e88148e738f.png\">\r\n"
] | 2021-12-07T15:11:36 | 2022-04-25T16:12:22 | 2022-04-25T16:12:01 | MEMBER | null | null | null | ## Dataset viewer issue for '*openslr*', '*projecte-aina/parlament_parla*'
**Link:** *https://huggingface.co./datasets/openslr*
**Link:** *https://huggingface.co./datasets/projecte-aina/parlament_parla*
Error:
```
Status code: 400
Exception: ImportError
Message: To support decoding audio files, please install 'librosa'.
```
Am I the one who added this dataset ? Yes-No
- openslr: No
- projecte-aina/parlament_parla: Yes
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"According to this [comment in the forum](https://discuss.huggingface.co/t/save-datasetdict-to-huggingface-hub/12075/8?u=lhoestq), using `push_to_hub` on a dataset with `ClassLabel` can also make the feature simply disappear when it's reloaded !",
"Maybe we can also fix https://github.com/huggingface/datasets/issues/3035 while working on this because, as pointed out in my initial post, `save_to_disk` also saves the `dataset_info.json` file."
] | 2021-12-07T14:08:30 | 2021-12-21T17:00:09 | 2021-12-21T17:00:09 | CONTRIBUTOR | null | null | null | Currently, if one of the dataset features is of type `ClassLabel`, saving the dataset with `push_to_hub` and reloading the dataset with `load_dataset` will return the feature of type `Value`. To fix this, we should do something similar to `save_to_disk` (which correctly preserves the types) and not only push the parquet files in `push_to_hub`, but also the dataset `info` (stored in a JSON file). | {
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https://api.github.com/repos/huggingface/datasets/issues/3393 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3393/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3393/comments | https://api.github.com/repos/huggingface/datasets/issues/3393/events | https://github.com/huggingface/datasets/issues/3393 | 1,073,189,777 | I_kwDODunzps4_95OR | 3,393 | Common Voice Belarusian Dataset | {
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- **Name:** *Common Voice Belarusian Dataset*
- **Description:** *[commonvoice.mozilla.org/be](https://commonvoice.mozilla.org/be)*
- **Data:** *[commonvoice.mozilla.org/be/datasets](https://commonvoice.mozilla.org/be/datasets)*
- **Motivation:** *It has more than 7GB of data, so it will be great to have it in this package so anyone can try to train something for Belarusian language.*
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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"This issue was fixed by me calling `all_datasets.push_to_hub(\"hackernews_hiring_posts\")`.\r\n\r\nThe previous problems were from calling `all_datasets.save_to_disk` and then pushing with `my_repo.git_add` and `my_repo.push_to_hub`.\r\n"
] | 2021-12-07T08:41:01 | 2021-12-07T14:04:28 | 2021-12-07T14:04:28 | CONTRIBUTOR | null | null | null | ## Dataset viewer issue for `dansbecker/hackernews_hiring_posts`
**Link:** https://huggingface.co./datasets/dansbecker/hackernews_hiring_posts
*short description of the issue*
Dataset preview not showing for uploaded DatasetDict. See https://discuss.huggingface.co/t/dataset-preview-not-showing-for-uploaded-datasetdict/12603
Am I the one who added this dataset ?
No -> @dansbecker | {
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https://api.github.com/repos/huggingface/datasets/issues/3391 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3391/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3391/comments | https://api.github.com/repos/huggingface/datasets/issues/3391/events | https://github.com/huggingface/datasets/issues/3391 | 1,072,849,055 | I_kwDODunzps4_8mCf | 3,391 | method to select columns | {
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"duplicate of #2655"
] | 2021-12-07T02:44:19 | 2021-12-07T02:45:27 | 2021-12-07T02:45:27 | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
* There is currently no way to select some columns of a dataset. In pandas, one can use `df[['col1', 'col2']]` to select columns, but in `datasets`, it results in error.
**Describe the solution you'd like**
* A new method that can be used to create a new dataset with only a list of specified columns.
**Describe alternatives you've considered**
`.remove_columns(self, columns: Union[str, List[str]], inverse: bool = False)`
Or
`.select(self, indices: Iterable = None, columns: List[str] = None)`
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https://api.github.com/repos/huggingface/datasets/issues/3390 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3390/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3390/comments | https://api.github.com/repos/huggingface/datasets/issues/3390/events | https://github.com/huggingface/datasets/issues/3390 | 1,072,462,456 | I_kwDODunzps4_7Hp4 | 3,390 | Loading dataset throws "KeyError: 'Field "builder_name" does not exist in table schema'" | {
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"Got solved it with push_to_hub, closing"
] | 2021-12-06T18:22:49 | 2021-12-06T20:22:05 | 2021-12-06T20:22:05 | NONE | null | null | null | ## Describe the bug
I have prepared dataset to datasets and now I am trying to load it back Finnish-NLP/voxpopuli_fi
I get "KeyError: 'Field "builder_name" does not exist in table schema'"
My dataset folder and files should be like @patrickvonplaten has here https://huggingface.co./datasets/flax-community/german-common-voice-processed
How my voxpopuli dataset looks like:
Part of the processing (path column is the absolute path to audio files)
```
def add_audio_column(example):
example['audio'] = example['path']
return example
voxpopuli = voxpopuli.map(add_audio_column)
voxpopuli.cast_column("audio", Audio())
voxpopuli["audio"] <-- to my knowledge this does load the local files and prepares those arrays
voxpopuli = voxpopuli.cast_column("audio", Audio(sampling_rate=16_000)) resampling 16kHz
```
I have then saved it to disk_
`voxpopuli.save_to_disk('/asr_disk/datasets_processed_new/voxpopuli')`
and made folder structure same as @patrickvonplaten
I also get same error while trying to load_dataset from his repo:
## Steps to reproduce the bug
```python
dataset = load_dataset("Finnish-NLP/voxpopuli_fi")
```
## Expected results
Dataset is loaded correctly and looks like in the first picture
## Actual results
Loading throws keyError:
KeyError: 'Field "builder_name" does not exist in table schema'
Resources I have been trying to follow:
https://huggingface.co./docs/datasets/audio_process.html
https://huggingface.co./docs/datasets/share_dataset.html
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.2.dev0
- Platform: Ubuntu 20.04.2 LTS
- Python version: 3.8.12
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3389 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3389/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3389/comments | https://api.github.com/repos/huggingface/datasets/issues/3389/events | https://github.com/huggingface/datasets/issues/3389 | 1,072,191,865 | I_kwDODunzps4_6Fl5 | 3,389 | Add EDGAR | {
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"cc @juliensimon ",
"Datasets are not tracked in this repository anymore. But you can make your own dataset in the huggingface hub"
] | 2021-12-06T14:06:11 | 2022-10-05T10:40:22 | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** EDGAR Database
- **Description:** https://www.sec.gov/edgar/about EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system, is the primary system for companies and others submitting documents under the Securities Act of 1933, the Securities Exchange Act of 1934, the Trust Indenture Act of 1939, and the Investment Company Act of 1940. Containing millions of company and individual filings, EDGAR benefits investors, corporations, and the U.S. economy overall by increasing the efficiency, transparency, and fairness of the securities markets. The system processes about 3,000 filings per day, serves up 3,000 terabytes of data to the public annually, and accommodates 40,000 new filers per year on average. EDGAR® and EDGARLink® are registered trademarks of the SEC.
- **Data:** https://www.sec.gov/os/accessing-edgar-data
- **Motivation:** Enabling and improving FSI (Financial Services Industry) datasets to increase ease of use
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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"CI failed because of a server error - merging"
] | 2021-12-06T11:09:31 | 2021-12-06T11:25:03 | 2021-12-06T11:24:49 | MEMBER | null | false | {
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} | The test is flaky, here is an example of random CI failure:
https://github.com/huggingface/datasets/commit/73ed6615b4b3eb74d5311684f7b9e05cdb76c989
I fixed that by not checking the content of the random part of the temporary directory name | {
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} | Create Language Modeling task to be able to specify the input "text" column in a dataset.
This can be useful for datasets which are not exclusively used for language modeling and have more than one column:
- for text classification datasets (with columns "review" and "rating", for example), the Language Modeling task can be used to specify the "text" column ("review" in this case).
TODO:
- [ ] Add the LanguageModeling task to all dataset scripts which can be used for language modeling | {
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} | This PR:
- Fix typos in dataset cards
- Fix Papers With Code ID for:
- Bilingual Corpus of Arabic-English Parallel Tweets
- Tweets Hate Speech Detection
- Add pretty name tags | {
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"Hi ! Thanks for the suggestion :)\r\nIt makes sense to me, and it can surely be implemented by wrapping the user's function to make it a batched function. However I'm not a big fan of the inconsistency it would create with `map`: `with_transform` is batched by default while `map` isn't.\r\n\r\nIs there something you would like to contribute ? I can give you some pointers if you want",
"Hi @lhoestq ,\r\nSorry I missed your reply.\r\n\r\nI would love to contribute. But I don't know which solution would be the best for this repo.\r\n\r\n> However I'm not a big fan of the inconsistency it would create with map: with_transform is batched by default while map isn't.\r\n\r\nI agree. What do you think about the alternative solutions?\r\n\r\n> * Convert a non-batched transform function to batched one myself.\r\n\r\nThis won't be able to use torch loader multi-worker.\r\n\r\n> * Wrap a 🤗 Dataset with torch Dataset, and add a __getitem__. 🙄\r\n\r\nThis is actually pretty simple.\r\n\r\n```python\r\nimport torch\r\n\r\nclass LazyMapTorchDataset(torch.utils.data.Dataset):\r\n def __init__(self, ds, fn):\r\n self.ds = ds\r\n self.fn = fn\r\n def __getitem__(self, i):\r\n return self.fn(self.ds[i])\r\n\r\nd = [{1:2, 2:3}, {1:3, 2:4}]\r\nds = LazyMapTorchDataset(d, lambda x:{k:v*2 for k,v in x.items()})\r\nfor i in range(2):\r\n print(f'before {d[i]}')\r\n print(f'after {ds[i]}')\r\n```\r\n```\r\nbefore {1: 2, 2: 3}\r\nafter {1: 4, 2: 6}\r\nbefore {1: 3, 2: 4}\r\nafter {1: 6, 2: 8}\r\n```\r\n\r\nBut this requires converting data to torch tensor myself. And this is really similar to `.map()`, why not just use it? So I have the next solution.\r\n\r\n> * Have lazy=False in Dataset.map, and returns a LazyDataset if lazy=True. This way the same map interface can be used, and existing code can be updated with one argument change.\r\n\r\nI think I like this solution best. Because `.with_transform` is entangled with `.with_format`, so seems more flexible to modify the `.map` than to modify `.with_transform`.\r\n\r\nThe usage looks nice, too.\r\n```python\r\n# lazy, one to one, can be parallelized via torch loader, no need to set `num_worker` beforehand.\r\ndataset = dataset.map(fn, lazy=True, batched=False)\r\n# collate_fn\r\ndataloader = Dataloader(dataset.with_format('torch'), collate_fn=collate_fn, num_workers=...) \r\n```\r\n\r\nThere are some minor decisions like whether a lazy map should be allowed before another map, but I think we can work it out later. The implementation can probably borrow from `IterableDataset`.",
"I like the idea of lazy map. On the other hand we should only have either lazy map or `with_transform` (not both). That's why I'd rather stick with `with_transform` for now (but maybe we can consider it for later major releases like `datasets` v2).\r\n\r\nI understand the issue with `with_transform` and `with_format` being exclusive, maybe we can separate them: first transform, them format.\r\n\r\nFinally I think what's also going to be important in the end will be the addition of multiprocessing to transforms"
] | 2021-12-06T05:20:54 | 2022-01-17T15:25:01 | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
A `torch.utils.data.Dataset.__getitem__` operates on a single example.
But 🤗 `Datasets.with_transform` doesn't seem to allow non-batched transform.
**Describe the solution you'd like**
Have a `batched=True` argument in `Datasets.with_transform`
**Describe alternatives you've considered**
* Convert a non-batched transform function to batched one myself.
* Wrap a 🤗 Dataset with torch Dataset, and add a `__getitem__`. 🙄
* Have `lazy=False` in `Dataset.map`, and returns a `LazyDataset` if `lazy=True`. This way the same `map` interface can be used, and existing code can be updated with one argument change. | {
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This way, all the languages covered in the translation are available in a easy way. | {
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} | update cc100 dataset to support loading Georgian (ka) data which is originally available in CC100 dataset source.
All tests are passed.
Dummy data generated.
metadata generated. | {
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"Locally the `make quality` passes with the same dependencies. I would suggest upgrading flake8. (I can take care of it in another PR)\r\ncc @lhoestq ",
"Thank you for fixing flake8! I think we are ready to merge then. "
] | 2021-12-04T20:54:49 | 2021-12-14T10:28:55 | 2021-12-14T10:28:55 | CONTRIBUTOR | null | false | {
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} | Add typing overloads to Dataset.__getitem__ for mypy
Fixes #3337
**Iterable**
Iterable from `collections` cannot have a type, so you can't do `Iterable[int]` for example. `typing` has a Generic version that builds upon the one from `collections`.
**Flake8**
I had to add `# noqa: F811`, this is a bug from Flake8.
datasets uses flake8==3.7.9 which released in October 2019 if I update flake8 (4.0.1), I no longer get these errors, but I did not want to make the update without your approval. (It also triggers other errors like no args in f-strings.) | {
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"Hi ! Feel free to access `batch[\"audio\"][\"array\"]` and `batch[\"audio\"][\"sampling_rate\"]` instead\r\n\r\n`datasets` 1.16 introduced some changes in `common_voice` and now the `path` field is no longer a path to a local file (but rather the path to the file in the archive it's extracted from)",
"Thanks for the information. It works.",
"Cool ! Closing this issue then"
] | 2021-12-04T19:59:11 | 2021-12-06T17:52:42 | 2021-12-06T17:52:42 | NONE | null | null | null | ## Describe the bug
I am not able to load audio features from common_voice dataset
## Steps to reproduce the bug
```
from datasets import load_dataset
import torchaudio
test_dataset = load_dataset("common_voice", "hi", split="test[:2%]")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
```
## Expected results
This piece of code should return test_dataset after loading audio features.
## Actual results
Reusing dataset common_voice (/home/jovyan/.cache/huggingface/datasets/common_voice/hi/6.1.0/b879a355caa529b11f2249400b61cadd0d9433f334d5c60f8c7216ccedfecfe1)
/opt/conda/lib/python3.7/site-packages/transformers/configuration_utils.py:341: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.
"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 "
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
0%| | 0/3 [00:00<?, ?ex/s]formats: can't open input file `common_voice_hi_23795358.mp3': No such file or directory
0%| | 0/3 [00:00<?, ?ex/s]
Traceback (most recent call last):
File "demo_file.py", line 23, in <module>
test_dataset = test_dataset.map(speech_file_to_array_fn)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2036, in map
desc=desc,
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 518, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 485, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py", line 411, in wrapper
out = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2368, in _map_single
example = apply_function_on_filtered_inputs(example, i, offset=offset)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2277, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1978, in decorated
result = f(decorated_item, *args, **kwargs)
File "demo_file.py", line 19, in speech_file_to_array_fn
speech_array, sampling_rate = torchaudio.load(batch["path"])
File "/opt/conda/lib/python3.7/site-packages/torchaudio/backend/sox_io_backend.py", line 154, in load
filepath, frame_offset, num_frames, normalize, channels_first, format)
RuntimeError: Error loading audio file: failed to open file common_voice_hi_23795358.mp3
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-4.14.243 with-debian-bullseye-sid
- Python version: 3.7.9
- PyArrow version: 6.0.1
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"Hello @lhoestq, thank you for your answer.\r\n\r\nI don't use pytest a lot so I think I might need some help on it :) but I tried some tests for `streaming_download_manager.py` only. I don't know how to test `download_manager.py` since we need to use local files.\r\n\r\n# Comments : \r\n* In **download_manager.py** I removed some unnecessary imports after the simplification of `_get_extraction_protocol_local`.\r\n* In **streaming_download_manager** I moved the raised Error as suggested.\r\n \r\n### I also started some tests on `StreamingDownloadManager()` :\r\n* Used an existing zipfile url and added a new one that has a folder and many files : \r\n```python\r\nTEST_GG_DRIVE_ZIPPED_URL = \"https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh\"\r\nTEST_GG_DRIVE2_ZIPPED_URL = \"https://drive.google.com/uc?export=download&id=1X4jyUBBbShyCRfD-vCO1ZvfqFXP3NEeU\"\r\n``` \r\n* **For now is being tested :**\r\n * Return type of the function : should be tuple\r\n * Files names\r\n * Files content\r\n * Added an `xfail` test for the gzip file, because I get a `zipfile.BadZipFile exception`.\r\n\r\n\r\n * And lastly, changed the test for `_get_extraction_protocol_throws` since it was moved to `_extract` : \r\n ```diff\r\[email protected](raises=NotImplementedError)\r\ndef test_streaming_dl_manager_get_extraction_protocol_throws(urlpath):\r\n- _get_extraction_protocol(urlpath)\r\n\r\[email protected](raises=NotImplementedError)\r\ndef test_streaming_dl_manager_get_extraction_protocol_throws(urlpath):\r\n+ StreamingDownloadManager()._extract(urlpath)\r\n```\r\n\r\n\r\n",
"Hello,\r\nIn this Commit was taken into account all the comment escept the `test_download _manager.py`.\r\nI will work on that for the next commit.\r\n\r\nSorry again for being inactive lately in this PR.\r\n\r\n",
"thanks a lot ! This CI seems to have import errors now though ?",
"> thanks a lot ! This CI seems to have import errors now though ?\r\n\r\nYes sorry about that, it's due to a cyclic import I didn't pay attention to.\r\n\r\nWill fix that in the next Commit along with adding the tests to download_manager.\r\n\r\n",
"Hello @Mehdi2402, are you still interested in working on this further? ",
"> Hello @Mehdi2402, are you still interested in working on this further?\r\n\r\nHello @albertvillanova, yes I would like to resume work on this.",
"Great, we would like to have this feature.\r\n\r\nFirst, you should resolve the conflicts with the main branch, by merging main into your feature branch and then fixing the conflicts by hand. Let us know if you would need some help on this: we can resolve the conflicts for you, so that you can continue your contribution afterwards.",
"_The documentation is not available anymore as the PR was closed or merged._",
"I refactored the code to make this PR ready for the final review.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009475 / 0.011353 (-0.001878) | 0.005249 / 0.011008 (-0.005759) | 0.099713 / 0.038508 (0.061205) | 0.036328 / 0.023109 (0.013219) | 0.295955 / 0.275898 (0.020057) | 0.368779 / 0.323480 (0.045299) | 0.007796 / 0.007986 (-0.000190) | 0.005635 / 0.004328 (0.001306) | 0.077351 / 0.004250 (0.073100) | 0.045290 / 0.037052 (0.008238) | 0.306634 / 0.258489 (0.048145) | 0.345025 / 0.293841 (0.051184) | 0.038241 / 0.128546 (-0.090306) | 0.012338 / 0.075646 (-0.063308) | 0.335184 / 0.419271 (-0.084088) | 0.047737 / 0.043533 (0.004204) | 0.295092 / 0.255139 (0.039953) | 0.319810 / 0.283200 (0.036610) | 0.102777 / 0.141683 (-0.038906) | 1.399444 / 1.452155 (-0.052711) | 1.450239 / 1.492716 (-0.042478) |\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) | 0.202919 / 0.018006 (0.184912) | 0.447493 / 0.000490 (0.447003) | 0.004187 / 0.000200 (0.003987) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028570 / 0.037411 (-0.008841) | 0.113536 / 0.014526 (0.099010) | 0.120525 / 0.176557 (-0.056031) | 0.162732 / 0.737135 (-0.574404) | 0.130195 / 0.296338 (-0.166144) |\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) | 0.408831 / 0.215209 (0.193622) | 4.094929 / 2.077655 (2.017274) | 1.810356 / 1.504120 (0.306236) | 1.618532 / 1.541195 (0.077337) | 1.681310 / 1.468490 (0.212820) | 0.705157 / 4.584777 (-3.879620) | 3.789040 / 3.745712 (0.043327) | 2.121842 / 5.269862 (-3.148020) | 1.522505 / 4.565676 (-3.043171) | 0.085443 / 0.424275 (-0.338832) | 0.012065 / 0.007607 (0.004458) | 0.521176 / 0.226044 (0.295132) | 5.201899 / 2.268929 (2.932970) | 2.303055 / 55.444624 (-53.141569) | 1.971721 / 6.876477 (-4.904756) | 2.053827 / 2.142072 (-0.088245) | 0.864810 / 4.805227 (-3.940418) | 0.168040 / 6.500664 (-6.332624) | 0.063332 / 0.075469 (-0.012138) |\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) | 1.208105 / 1.841788 (-0.633683) | 14.722757 / 8.074308 (6.648449) | 14.396695 / 10.191392 (4.205303) | 0.152702 / 0.680424 (-0.527722) | 0.028828 / 0.534201 (-0.505373) | 0.439573 / 0.579283 (-0.139710) | 0.438891 / 0.434364 (0.004527) | 0.509043 / 0.540337 (-0.031295) | 0.603531 / 1.386936 (-0.783405) |\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) | 0.007337 / 0.011353 (-0.004016) | 0.005080 / 0.011008 (-0.005929) | 0.097916 / 0.038508 (0.059408) | 0.032722 / 0.023109 (0.009612) | 0.338925 / 0.275898 (0.063027) | 0.372945 / 0.323480 (0.049465) | 0.005464 / 0.007986 (-0.002522) | 0.004031 / 0.004328 (-0.000297) | 0.076761 / 0.004250 (0.072511) | 0.046804 / 0.037052 (0.009752) | 0.336088 / 0.258489 (0.077599) | 0.403704 / 0.293841 (0.109863) | 0.036928 / 0.128546 (-0.091618) | 0.012204 / 0.075646 (-0.063442) | 0.335467 / 0.419271 (-0.083804) | 0.049158 / 0.043533 (0.005625) | 0.342040 / 0.255139 (0.086901) | 0.356729 / 0.283200 (0.073530) | 0.101280 / 0.141683 (-0.040403) | 1.432540 / 1.452155 (-0.019614) | 1.545228 / 1.492716 (0.052512) |\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) | 0.226003 / 0.018006 (0.207997) | 0.445601 / 0.000490 (0.445112) | 0.000408 / 0.000200 (0.000208) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028861 / 0.037411 (-0.008551) | 0.112083 / 0.014526 (0.097557) | 0.130843 / 0.176557 (-0.045713) | 0.159275 / 0.737135 (-0.577861) | 0.127582 / 0.296338 (-0.168756) |\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) | 0.446357 / 0.215209 (0.231148) | 4.448568 / 2.077655 (2.370914) | 2.197861 / 1.504120 (0.693741) | 2.004675 / 1.541195 (0.463480) | 2.052082 / 1.468490 (0.583592) | 0.710770 / 4.584777 (-3.874007) | 3.868936 / 3.745712 (0.123224) | 2.095008 / 5.269862 (-3.174854) | 1.363064 / 4.565676 (-3.202613) | 0.086734 / 0.424275 (-0.337541) | 0.012272 / 0.007607 (0.004665) | 0.546378 / 0.226044 (0.320334) | 5.475189 / 2.268929 (3.206260) | 2.702742 / 55.444624 (-52.741882) | 2.335880 / 6.876477 (-4.540597) | 2.396194 / 2.142072 (0.254121) | 0.856249 / 4.805227 (-3.948978) | 0.170466 / 6.500664 (-6.330198) | 0.063585 / 0.075469 (-0.011884) |\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) | 1.236981 / 1.841788 (-0.604807) | 15.046616 / 8.074308 (6.972307) | 14.551781 / 10.191392 (4.360389) | 0.144485 / 0.680424 (-0.535939) | 0.017774 / 0.534201 (-0.516427) | 0.446274 / 0.579283 (-0.133010) | 0.436871 / 0.434364 (0.002507) | 0.504503 / 0.540337 (-0.035834) | 0.602014 / 1.386936 (-0.784922) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#697b6d6e718ebec777061a803582864e31486dba \"CML watermark\")\n"
] | 2021-12-04T01:04:48 | 2023-01-24T13:00:19 | 2023-01-24T12:53:08 | CONTRIBUTOR | null | false | {
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"merged_at": "2023-01-24T12:53:08"
} | Hello @lhoestq , thank you for your detailed answer on previous PR !
I made this new PR because I misused git on the previous one #3347.
Related issue #3272.
# Comments :
* For extension check I used the `_get_extraction_protocol` function in **download_manager.py** with a slight change and called it `_get_extraction_protocol_local`:
**I removed this part :**
```python
elif path.endswith(".tar.gz") or path.endswith(".tgz"):
raise NotImplementedError(
f"Extraction protocol for TAR archives like '{urlpath}' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead."
)
```
**And also changed :**
```diff
- extension = path.split(".")[-1]
+ extension = "tar" if path.endswith(".tar.gz") else path.split(".")[-1]
```
The reason for this is a compression like **.tar.gz** will be considered a **.gz** which is handled with **zipfile**, though **tar.gz** can only be opened using **tarfile**.
Please tell me if there's anything to change.
# Tasks :
- [x] download_manager.py
- [x] streaming_download_manager.py | {
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https://api.github.com/repos/huggingface/datasets/issues/3378 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3378/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3378/comments | https://api.github.com/repos/huggingface/datasets/issues/3378/events | https://github.com/huggingface/datasets/pull/3378 | 1,070,580,126 | PR_kwDODunzps4vXF1D | 3,378 | Add The Pile subsets | {
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} | Add The Pile subsets:
- pubmed
- ubuntu_irc
- europarl
- hacker_news
- nih_exporter
Close bigscience-workshop/data_tooling#301.
CC: @StellaAthena | {
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"@mariosasko I fixed couple of bugs",
"TO-DO: \r\n- [x] Add unlabeled 2017 splits, train and validation splits of 2015\r\n- [x] Add Class Labels as list instead",
"@mariosasko added fine & coarse grained labels, will fix the bugs (currently getting set up with VM, my internet is too slow to run the tests and download the data 🥲)",
"migrated to here https://github.com/huggingface/datasets/tree/coco"
] | 2021-12-03T12:55:27 | 2021-12-20T14:14:01 | 2021-12-20T14:14:00 | CONTRIBUTOR | null | true | {
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} | This is a draft PR since I ran into few small problems.
I referred to this TFDS code: https://github.com/tensorflow/datasets/blob/2538a08c184d53b37bfcf52cc21dd382572a88f4/tensorflow_datasets/object_detection/coco.py
cc: @mariosasko | {
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"The CI error is due to missing tags in the CLUE dataset card - merging !"
] | 2021-12-03T12:06:01 | 2021-12-08T14:14:42 | 2021-12-08T14:14:41 | CONTRIBUTOR | null | false | {
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