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https://api.github.com/repos/huggingface/datasets/issues/2496 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2496/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2496/comments | https://api.github.com/repos/huggingface/datasets/issues/2496/events | https://github.com/huggingface/datasets/issues/2496 | 920,216,314 | MDU6SXNzdWU5MjAyMTYzMTQ= | 2,496 | Dataset fingerprint changes after moving the cache directory, which prevent cache reload when using `map` | {
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However the fingerprint seems to change when someone moves the cache directory of the dataset.
This is because it uses the default fingerprint generation:
1. the dataset path is used to get the fingerprint
2. the modification times of the arrow file is also used to get the fingerprint
To fix that we could set the fingerprint of the dataset to be a hash of (<dataset_name>, <config_name>, <version>, <script_hash>), i.e. a hash of the the cache path relative to the cache directory. | {
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- How to make datasets the fastest
- How to make datasets take the less RAM
- How to make datasets take the less hard drive mem
cc: @thomwolf
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"@albertvillanova done!"
] | 1,623,601,208,000 | 1,623,747,186,000 | 1,623,747,186,000 | CONTRIBUTOR | null | ref - https://github.com/huggingface/datasets/issues/2068 | {
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} | [] | closed | false | null | [] | null | [] | 1,623,561,059,000 | 1,624,355,344,000 | 1,623,840,106,000 | CONTRIBUTOR | null | Hi, awesome folks behind the huggingface!
Here is my PR for the text classification dataset in Mongolian.
Please do let me know in case you have anything to clarify.
Thanks & Regards,
Enod | {
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"Closing this PR as I'll submit a new one - bug free"
] | 1,623,559,021,000 | 1,623,560,808,000 | 1,623,560,798,000 | CONTRIBUTOR | null | {
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Close #2489. | {
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https://api.github.com/repos/huggingface/datasets/issues/2489 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2489/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2489/comments | https://api.github.com/repos/huggingface/datasets/issues/2489/events | https://github.com/huggingface/datasets/issues/2489 | 919,569,749 | MDU6SXNzdWU5MTk1Njk3NDk= | 2,489 | Allow latest pyarrow version once segfault bug is fixed | {
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- it was fixed on 3 May 2021
- version 4.0.1 was released on 19 May 2021 with the bug fix | {
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"Let me push a small fix... 😉 ",
"Thanks !"
] | 1,623,476,849,000 | 1,623,663,017,000 | 1,623,661,376,000 | MEMBER | null | Part of #2480. | {
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"Hi ! Thanks for adding this dataset :)\r\n\r\nRegarding your questions:\r\n1. We can have them as different configuration of the `rico` dataset\r\n2. Yes please use the path to the image and not open the image directly, so that we can let users open the image one at at time during training if they want to for example. In the future we'll have an Image feature type that will decode the encoded image data on the fly when accessing the examples.\r\n3. Feel free to keep the hierarchies as strings if they don't follow a fixed format\r\n4. You can just return the path\r\n\r\n"
] | 1,623,442,661,000 | 1,631,176,166,000 | null | CONTRIBUTOR | null | Hi there!
I'm wanting to add the Rico datasets for software engineering type data to y'alls awesome library. However, as I have started coding, I've ran into a few hiccups so I thought it best to open the PR early to get a bit of discussion on how the Rico datasets should be added to the `datasets` lib.
1) There are 7 different datasets under Rico and so I was wondering, should I make a folder for each or should I put them as different configurations of a single dataset?
You can see the datasets available for Rico here: http://interactionmining.org/rico
2) As of right now, I have a semi working version of the first dataset which has pairs of screenshots and hierarchies from android applications. However, these screenshots are very large (1440, 2560, 3) and there are 66,000 of them so I am not able to perform the processing that the `datasets` lib does after downloading and extracting the dataset since I run out of memory very fast. Is there a way to have `datasets` lib not put everything into memory while it is processing the dataset?
2.1) If there is not a way, would it be better to just return the path to the screenshots instead of the actual image?
3) The hierarchies are JSON objects and looking through the documentation of `datasets`, I didn't see any feature that I could use for this type of data. So, for now I just have it being read in as a string, is this okay or should I be doing it differently?
4) One of the Rico datasets is a bunch of animations (GIFs), is there a `datasets` feature that I can put this type of data into or should I just return the path as a string?
I appreciate any and all help I can get for this PR, I think the Rico datasets will be an awesome addition to the library :nerd_face: ! | {
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> My suggestion for this would be to have this enabled by default.
>
> Plus I don't know if there should be a dedicated issue to that is another functionality. But I propose layered building rather than all at once. That is:
>
> 1. uncompress a handful of files via a generator enough to generate one arrow file
> 2. process arrow file 1
> 3. delete all the files that went in and aren't needed anymore.
>
> rinse and repeat.
>
> 1. This way much less disc space will be required - e.g. on JZ we won't be running into inode limitation, also it'd help with the collaborative hub training project
> 2. The user doesn't need to go and manually clean up all the huge files that were left after pre-processing
> 3. It would already include deleting temp files this issue is talking about
>
> I wonder if the new streaming API would be of help, except here the streaming would be into arrow files as the destination, rather than dataloaders. | {
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"#self-assign"
] | 1,623,437,242,000 | 1,625,481,957,000 | 1,625,481,957,000 | MEMBER | null | As discussed with @stas00 and @lhoestq, this would allow things like:
```python
from datasets import load_dataset_builder
dataset_name = "openwebtext"
builder = load_dataset_builder(dataset_name)
print(builder.cache_dir)
``` | {
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"I continue thinking that the origin of the issue has to do with tqdm (and not with Arrow): this issue only arises for version 4.50.0 (and later) of tqdm, not for previous versions of tqdm.\r\n\r\nMy guess is that tqdm made a change from version 4.50.0 that does not properly release the iterable. ",
"FR"
] | 1,623,424,170,000 | 1,624,044,306,000 | 1,623,425,496,000 | MEMBER | null | In https://github.com/huggingface/datasets/commit/42320a110d9d072703814e1f630a0d90d626a1e6 we added a call to gc.collect to resolve some issues on windows (see https://github.com/huggingface/datasets/pull/2482)
However calling gc.collect too often causes significant slow downs (the CI run time doubled).
So I just moved the gc.collect call to the exact place where it's actually needed: when post-processing a dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/2482 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2482/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2482/comments | https://api.github.com/repos/huggingface/datasets/issues/2482/events | https://github.com/huggingface/datasets/pull/2482 | 918,846,027 | MDExOlB1bGxSZXF1ZXN0NjY4MjMyMzI5 | 2,482 | Allow to use tqdm>=4.50.0 | {
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They were due to open arrow files not properly closed by pyarrow.
Since https://github.com/huggingface/datasets/commit/42320a110d9d072703814e1f630a0d90d626a1e6 gc.collect is called each time we don't need an arrow file to make sure that the files are closed.
close https://github.com/huggingface/datasets/issues/2471
cc @lewtun | {
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"My suggestion for this would be to have this enabled by default.\r\n\r\nPlus I don't know if there should be a dedicated issue to that is another functionality. But I propose layered building rather than all at once. That is:\r\n\r\n1. uncompress a handful of files via a generator enough to generate one arrow file\r\n2. process arrow file 1\r\n3. delete all the files that went in and aren't needed anymore.\r\n\r\nrinse and repeat.\r\n\r\n1. This way much less disc space will be required - e.g. on JZ we won't be running into inode limitation, also it'd help with the collaborative hub training project\r\n2. The user doesn't need to go and manually clean up all the huge files that were left after pre-processing\r\n3. It would already include deleting temp files this issue is talking about\r\n\r\nI wonder if the new streaming API would be of help, except here the streaming would be into arrow files as the destination, rather than dataloaders."
] | 1,623,414,112,000 | 1,626,685,698,000 | 1,626,685,698,000 | MEMBER | null | As discussed with @stas00 and @lhoestq, allowing the deletion of extracted files would save a great amount of disk space to typical user. | {
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"For example to be able to send uncompressed and temp build files to another volume/partition, so that the user gets the minimal disk usage on their primary setup - and ends up with just the downloaded compressed data + arrow files, but outsourcing the huge files and building to another partition. e.g. on JZ there is a special partition for fast data, but it's also volatile, so only temp files should go there.\r\n\r\nThink of it as `TMPDIR` so we need the equivalent for `datasets`."
] | 1,623,414,024,000 | 1,623,767,029,000 | null | MEMBER | null | As discussed with @stas00 and @lhoestq, setting these paths configurable may allow to overcome disk space limitation on different partitions/drives.
TODO:
- [x] Set configurable extracted datasets path: #2487
- [x] Set configurable downloaded datasets path: #2488
- [ ] Set configurable "incomplete" datasets path? | {
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https://api.github.com/repos/huggingface/datasets/issues/2477 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2477/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2477/comments | https://api.github.com/repos/huggingface/datasets/issues/2477/events | https://github.com/huggingface/datasets/pull/2477 | 918,334,431 | MDExOlB1bGxSZXF1ZXN0NjY3NzczMTY0 | 2,477 | Fix docs custom stable version | {
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"I see that @lhoestq overlooked this PR with his commit 07e2b05. 😢 \r\n\r\nI'm adding a script so that this issue does not happen again.\r\n",
"For the moment, the script only includes `update_custom_js`, but in a follow-up PR I will include all the required steps to make a package release.",
"I think we just need to clarify the release process in setup.py instead of adding a script that does the replacement",
"@lhoestq I really think we should implement a script that performs the release (instead of doing it manually as it is done now), as it is already the case in `transformers`. I will do it in a next PR.\r\n\r\nFor the moment, this PR includes one of the steps of the release script."
] | 1,623,396,363,000 | 1,623,662,060,000 | 1,623,658,818,000 | MEMBER | null | Currently docs default version is 1.5.0. This PR fixes this and sets the latest version instead. | {
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https://api.github.com/repos/huggingface/datasets/issues/2476 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2476/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2476/comments | https://api.github.com/repos/huggingface/datasets/issues/2476/events | https://github.com/huggingface/datasets/pull/2476 | 917,686,662 | MDExOlB1bGxSZXF1ZXN0NjY3MTg3OTk1 | 2,476 | Add TimeDial | {
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"Hi @lhoestq,\r\nI've pushed the updated README and tags. Let me know if anything is missing/needs some improvement!\r\n\r\n~PS. I don't know why it's not triggering the build~"
] | 1,623,349,987,000 | 1,627,649,874,000 | 1,627,649,874,000 | CONTRIBUTOR | null | Dataset: https://github.com/google-research-datasets/TimeDial
To-Do: Update README.md and add YAML tags | {
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https://api.github.com/repos/huggingface/datasets/issues/2475 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2475/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2475/comments | https://api.github.com/repos/huggingface/datasets/issues/2475/events | https://github.com/huggingface/datasets/issues/2475 | 917,650,882 | MDU6SXNzdWU5MTc2NTA4ODI= | 2,475 | Issue in timit_asr database | {
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"This bug was fixed in #1995. Upgrading datasets to version 1.6 fixes the issue!",
"Indeed was a fixed bug.\r\nWorks on version 1.8\r\nThanks "
] | 1,623,348,329,000 | 1,623,572,030,000 | 1,623,571,993,000 | NONE | null | ## Describe the bug
I am trying to load the timit_asr dataset however only the first record is shown (duplicated over all the rows).
I am using the next code line
dataset = load_dataset(“timit_asr”, split=“test”).shuffle().select(range(10))
The above code result with the same sentence duplicated ten times.
It also happens when I use the dataset viewer at Streamlit .
## Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset(“timit_asr”, split=“test”).shuffle().select(range(10))
data = dataset.to_pandas()
# Sample code to reproduce the bug
```
## Expected results
table with different row information
## 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: 1.4.1 (also occur in the latest version)
- Platform: Linux-4.15.0-143-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.8.1+cu102 (False)
- Tensorflow version (GPU?): 1.15.3 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
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"Hi ! `load_from_disk` doesn't move the data. If you specify a local path to your mounted drive, then the dataset is going to be loaded directly from the arrow file in this directory. The cache files that result from `map` operations are also stored in the same directory by default.\r\n\r\nHowever note than writing data to your google drive actually fills the VM's disk (see https://github.com/huggingface/datasets/issues/643)\r\n\r\nGiven that, I don't think that changing the cache directory changes anything.\r\n\r\nLet me know what you think",
"Thanks for your answer! I am a little surprised since I just want to read the dataset.\r\n\r\nAfter debugging a bit, I noticed that the VM’s disk fills up when the tables (generator) are converted to a list:\r\n\r\nhttps://github.com/huggingface/datasets/blob/5ba149773d23369617563d752aca922081277ec2/src/datasets/table.py#L850\r\n\r\nIf I try to iterate through the table’s generator e.g.: \r\n\r\n`length = sum(1 for x in tables)`\r\n\r\nthe VM’s disk fills up as well.\r\n\r\nI’m running out of Ideas 😄 ",
"Indeed reading the data shouldn't increase the VM's disk. Not sure what google colab does under the hood for that to happen"
] | 1,623,346,776,000 | 1,623,660,069,000 | null | NONE | null | **Is your feature request related to a problem? Please describe.**
When using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk:
`
from datasets import load_from_disk
myPreprocessedData = load_from_disk("/content/gdrive/MyDrive/ASR_data/myPreprocessedData")
`
I know that chaching on google drive could slow down learning. But at least it would run.
**Describe the solution you'd like**
Add cache_Dir parameter to the load_from_disk function.
**Describe alternatives you've considered**
It looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?
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"Sounds great! It'll make things easier for the user while accessing the dataset. I'll make some changes to the current file then.",
"I've updated with the suggested changes. Updated the README, YAML tags as well (not sure of Size category tag as I couldn't pass the path of `dataset_infos.json` for this dataset)\r\n"
] | 1,623,341,880,000 | 1,627,559,779,000 | 1,627,559,778,000 | CONTRIBUTOR | null | Dataset: https://github.com/google-research-datasets/disfl-qa
To-Do: Update README.md and add YAML tags | {
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"I have received a reply from Zenodo support:\r\n> We are currently investigating and fixing this issue related to GitHub releases. As soon as we have solved it we will reach back to you.",
"Other repo maintainers had the same problem with Zenodo. \r\n\r\nThere is an open issue on their GitHub repo: zenodo/zenodo#2181",
"I have received the following request from Zenodo support:\r\n> Could you send us the link to the repository as well as the release tag?\r\n\r\nMy reply:\r\n> Sure, here it is:\r\n> - Link to the repository: https://github.com/huggingface/datasets\r\n> - Link to the repository at the release tag: https://github.com/huggingface/datasets/releases/tag/1.8.0\r\n> - Release tag: 1.8.0",
"Zenodo issue has been fixed. The 1.8.0 release DOI can be found here: https://zenodo.org/record/4946100#.YMd6vKj7RPY"
] | 1,623,338,146,000 | 1,623,689,382,000 | 1,623,689,382,000 | MEMBER | null | After the last release of Datasets (1.8.0), the automatic generation of the Zenodo DOI failed: it appears in yellow as "Received", instead of in green as "Published".
I have contacted Zenodo support to fix this issue.
TODO:
- [x] Check with Zenodo to fix the issue
- [x] Check BibTeX entry is right | {
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```
PermissionError: [WinError 32] The process cannot access the file because it is being used by another process
``` | {
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"Hi ! It looks like the issue comes from pyarrow. What version of pyarrow are you using ? How did you install it ?",
"Thank you for the quick reply! I have `pyarrow==4.0.0`, and I am installing with `pip`. It's not one of my explicit dependencies, so I assume it came along with something else.",
"Could you trying reinstalling pyarrow with pip ?\r\nI'm not sure why it would check in your multicurtural-sc directory for source files.",
"Sure! I tried reinstalling to get latest. pip was mad because it looks like Datasets currently wants <4.0.0 (which is interesting, because apparently I ended up with 4.0.0 already?), but I gave it a shot anyway:\r\n\r\n```bash\r\n$ pip install --upgrade --force-reinstall pyarrow\r\nCollecting pyarrow\r\n Downloading pyarrow-4.0.1-cp39-cp39-manylinux2014_x86_64.whl (21.9 MB)\r\n |████████████████████████████████| 21.9 MB 23.8 MB/s\r\nCollecting numpy>=1.16.6\r\n Using cached numpy-1.20.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.4 MB)\r\nInstalling collected packages: numpy, pyarrow\r\n Attempting uninstall: numpy\r\n Found existing installation: numpy 1.20.3\r\n Uninstalling numpy-1.20.3:\r\n Successfully uninstalled numpy-1.20.3\r\n Attempting uninstall: pyarrow\r\n Found existing installation: pyarrow 3.0.0\r\n Uninstalling pyarrow-3.0.0:\r\n Successfully uninstalled pyarrow-3.0.0\r\nERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\ndatasets 1.8.0 requires pyarrow<4.0.0,>=1.0.0, but you have pyarrow 4.0.1 which is incompatible.\r\nSuccessfully installed numpy-1.20.3 pyarrow-4.0.1\r\n```\r\n\r\nTrying it, the same issue:\r\n\r\n\r\n\r\nI tried installing `\"pyarrow<4.0.0\"`, which gave me 3.0.0. Running, still, same issue.\r\n\r\nI agree it's weird that pyarrow is checking the source code directory for its files. (There is no `pyarrow/` directory there.) To me, that makes it seem like an issue with how pyarrow is called.\r\n\r\nOut of curiosity, I tried running this with fewer workers to see when the error arises:\r\n\r\n- 1: ✅\r\n- 2: ✅\r\n- 4: ✅\r\n- 8: ✅\r\n- 10: ✅\r\n- 11: ❌ 🤔\r\n- 12: ❌\r\n- 16: ❌\r\n- 32: ❌\r\n\r\nchecking my datasets:\r\n\r\n```python\r\n>>> datasets\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text'],\r\n num_rows: 389290\r\n })\r\n validation.sc: Dataset({\r\n features: ['text'],\r\n num_rows: 10 # 🤔\r\n })\r\n validation.wvs: Dataset({\r\n features: ['text'],\r\n num_rows: 93928\r\n })\r\n})\r\n```\r\n\r\nNew hypothesis: crash if `num_proc` > length of a dataset? 😅\r\n\r\nIf so, this might be totally my fault, as the caller. Could be a docs fix, or maybe this library could do a check to limit `num_proc` for this case?",
"Good catch ! Not sure why it could raise such a weird issue from pyarrow though\r\nWe should definitely reduce num_proc to the length of the dataset if needed and log a warning.",
"This has been fixed in #2566, thanks @connor-mccarthy !\r\nWe'll make a new release soon that includes the fix ;)"
] | 1,623,278,422,000 | 1,625,132,094,000 | 1,625,130,673,000 | NONE | null | ## Describe the bug
Crash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.
I believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.
## Steps to reproduce the bug
```python
# this function will be applied with map()
def tokenize_function(examples):
return tokenizer(
examples["text"],
padding=PaddingStrategy.DO_NOT_PAD,
truncation=True,
)
# data_files is a Dict[str, str] mapping name -> path
datasets = load_dataset("text", data_files={...})
# this is where the error happens if num_proc = 16,
# but is fine if num_proc = 1
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=num_workers,
)
```
## Expected results
The `map()` function succeeds with `num_proc` > 1.
## Actual results
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31
- Python version: 3.9.5
- PyTorch version (GPU?): 1.8.1+cu111 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: Yes, but I think N/A for this issue
- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N/A for this issue
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https://api.github.com/repos/huggingface/datasets/issues/2469 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2469/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2469/comments | https://api.github.com/repos/huggingface/datasets/issues/2469/events | https://github.com/huggingface/datasets/pull/2469 | 916,440,418 | MDExOlB1bGxSZXF1ZXN0NjY2MTA1OTk1 | 2,469 | Bump tqdm version | {
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"i tried both the latest version of `tqdm` and the version required by `autonlp` - no luck with windows 😞 \r\n\r\nit's very weird that a progress bar would trigger these kind of errors, so i'll have a look to see if it's something unique to `datasets`",
"Closing since this is now fixed in #2482 "
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https://api.github.com/repos/huggingface/datasets/issues/2468 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2468/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2468/comments | https://api.github.com/repos/huggingface/datasets/issues/2468/events | https://github.com/huggingface/datasets/pull/2468 | 916,427,320 | MDExOlB1bGxSZXF1ZXN0NjY2MDk0ODI5 | 2,468 | Implement ClassLabel encoding in JSON loader | {
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"No, nevermind @lhoestq. Thanks to you for your reviews!"
] | 1,623,258,534,000 | 1,624,894,794,000 | 1,624,892,735,000 | MEMBER | null | Close #2365. | {
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https://api.github.com/repos/huggingface/datasets/issues/2466 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2466/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2466/comments | https://api.github.com/repos/huggingface/datasets/issues/2466/events | https://github.com/huggingface/datasets/pull/2466 | 915,914,098 | MDExOlB1bGxSZXF1ZXN0NjY1NjY1MjQy | 2,466 | change udpos features structure | {
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"Let's add the tags in another PR. Thanks again !",
"Close #2061 , close #2444."
] | 1,623,225,811,000 | 1,624,017,309,000 | 1,623,840,097,000 | CONTRIBUTOR | null | The structure is change such that each example is a sentence
The change is done for issues:
#2061
#2444
Close #2061 , close #2444. | {
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https://api.github.com/repos/huggingface/datasets/issues/2465 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2465/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2465/comments | https://api.github.com/repos/huggingface/datasets/issues/2465/events | https://github.com/huggingface/datasets/pull/2465 | 915,525,071 | MDExOlB1bGxSZXF1ZXN0NjY1MzMxMDMz | 2,465 | adding masahaner dataset | {
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"Thank you for the review. ",
"Thanks a lot for the corrections and comments. \r\n\r\nI have resolved point 2. The make style still throws some errors, please see below\r\n\r\nblack --line-length 119 --target-version py36 tests src benchmarks datasets/**/*.py metrics\r\n/bin/sh: 1: black: not found\r\nMakefile:13: recipe for target 'style' failed\r\nmake: *** [style] Error 127\r\n\r\nCan you help to resolve this?",
"Thank you very much @lhoestq for the help. "
] | 1,623,187,225,000 | 1,623,682,745,000 | 1,623,682,745,000 | CONTRIBUTOR | null | Adding Masakhane dataset https://github.com/masakhane-io/masakhane-ner
@lhoestq , can you please review | {
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https://api.github.com/repos/huggingface/datasets/issues/2464 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2464/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2464/comments | https://api.github.com/repos/huggingface/datasets/issues/2464/events | https://github.com/huggingface/datasets/pull/2464 | 915,485,601 | MDExOlB1bGxSZXF1ZXN0NjY1Mjk1MDE5 | 2,464 | fix: adjusting indexing for the labels. | {
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"> Good catch ! Thanks for fixing it\r\n\r\nMy pleasure🙏"
] | 1,623,185,245,000 | 1,623,233,746,000 | 1,623,229,828,000 | CONTRIBUTOR | null | The labels index were mismatching the actual ones used in the dataset. Specifically `0` is used for `SUPPORTS` and `1` is used for `REFUTES`
After this change, the `README.md` now reflects the content of `dataset_infos.json`.
Signed-off-by: Matteo Manica <drugilsberg@gmail.com> | {
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https://api.github.com/repos/huggingface/datasets/issues/2463 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2463/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2463/comments | https://api.github.com/repos/huggingface/datasets/issues/2463/events | https://github.com/huggingface/datasets/pull/2463 | 915,454,788 | MDExOlB1bGxSZXF1ZXN0NjY1MjY3NTA2 | 2,463 | Fix proto_qa download link | {
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Instead of updating the path, this PR fixes a commit hash as suggested by @lhoestq. | {
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https://api.github.com/repos/huggingface/datasets/issues/2462 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2462/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2462/comments | https://api.github.com/repos/huggingface/datasets/issues/2462/events | https://github.com/huggingface/datasets/issues/2462 | 915,384,613 | MDU6SXNzdWU5MTUzODQ2MTM= | 2,462 | Merge DatasetDict and Dataset | {
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} | [] | 1,623,180,124,000 | 1,630,560,812,000 | null | MEMBER | null | As discussed in #2424 and #2437 (please see there for detailed conversation):
- It would be desirable to improve UX with respect the confusion between DatasetDict and Dataset.
- The difference between Dataset and DatasetDict is an additional abstraction complexity that confuses "typical" end users.
- A user expects a "Dataset" (whatever it contains multiple or a single split) and maybe it could be interesting to try to simplify the user-facing API as much as possible to hide this complexity from the end user.
Here is a proposal for discussion and refined (and potential abandon if it's not good enough):
- let's consider that a DatasetDict is also a Dataset with the various split concatenated one after the other
- let's disallow the use of integers in split names (probably not a very big breaking change)
- when you index with integers you access the examples progressively in split after the other is finished (in a deterministic order)
- when you index with strings/split name you have the same behavior as now (full backward compat)
- let's then also have all the methods of a Dataset on the DatasetDict
The end goal would be to merge both Dataset and DatasetDict object in a single object that would be (pretty much totally) backward compatible with both.
There are a few things that we could discuss if we want to merge Dataset and DatasetDict:
1. what happens if you index by a string ? Does it return the column or the split ? We could disallow conflicts between column names and split names to avoid ambiguities. It can be surprising to be able to get a column or a split using the same indexing feature
```
from datasets import load_dataset
dataset = load_dataset(...)
dataset["train"]
dataset["input_ids"]
```
2. what happens when you iterate over the object ? I guess it should iterate over the examples as a Dataset object, but a DatasetDict used to iterate over the splits as they are the dictionary keys. This is a breaking change that we can discuss.
Moreover regarding your points:
- integers are not allowed as split names already
- it's definitely doable to have all the methods. Maybe some of them like train_test_split that is currently only available for Dataset can be tweaked to work for a split dataset
cc: @thomwolf @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/2461 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2461/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2461/comments | https://api.github.com/repos/huggingface/datasets/issues/2461/events | https://github.com/huggingface/datasets/pull/2461 | 915,286,150 | MDExOlB1bGxSZXF1ZXN0NjY1MTE3MTY4 | 2,461 | Support sliced list arrays in cast | {
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} | [] | closed | false | null | [] | null | [] | 1,623,173,927,000 | 1,623,174,984,000 | 1,623,174,983,000 | MEMBER | null | There is this issue in pyarrow:
```python
import pyarrow as pa
arr = pa.array([[i * 10] for i in range(4)])
arr.cast(pa.list_(pa.int32())) # works
arr = arr.slice(1)
arr.cast(pa.list_(pa.int32())) # fails
# ArrowNotImplementedError("Casting sliced lists (non-zero offset) not yet implemented")
```
However in `Dataset.cast` we slice tables to cast their types (it's memory intensive), so we have the same issue.
Because of this it is currently not possible to cast a Dataset with a Sequence feature type (unless the table is small enough to not be sliced).
In this PR I fixed this by resetting the offset of `pyarrow.ListArray` arrays to zero in the table before casting.
I used `pyarrow.compute.subtract` function to update the offsets of the ListArray.
cc @abhi1thakur @SBrandeis | {
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https://api.github.com/repos/huggingface/datasets/issues/2460 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2460/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2460/comments | https://api.github.com/repos/huggingface/datasets/issues/2460/events | https://github.com/huggingface/datasets/pull/2460 | 915,268,536 | MDExOlB1bGxSZXF1ZXN0NjY1MTAyMjA4 | 2,460 | Revert default in-memory for small datasets | {
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"Thank you for this welcome change guys!"
] | 1,623,172,463,000 | 1,623,175,454,000 | 1,623,174,943,000 | MEMBER | null | Close #2458 | {
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https://api.github.com/repos/huggingface/datasets/issues/2459 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2459/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2459/comments | https://api.github.com/repos/huggingface/datasets/issues/2459/events | https://github.com/huggingface/datasets/issues/2459 | 915,222,015 | MDU6SXNzdWU5MTUyMjIwMTU= | 2,459 | `Proto_qa` hosting seems to be broken | {
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"@VictorSanh , I think @mariosasko is already working on it. "
] | 1,623,168,992,000 | 1,623,313,869,000 | 1,623,313,869,000 | MEMBER | null | ## Describe the bug
The hosting (on Github) of the `proto_qa` dataset seems broken. I haven't investigated more yet, just flagging it for now.
@zaidalyafeai if you want to dive into it, I think it's just a matter of changing the links in `proto_qa.py`
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("proto_qa")
```
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/load.py", line 751, in load_dataset
use_auth_token=use_auth_token,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 630, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/hf/.cache/huggingface/modules/datasets_modules/datasets/proto_qa/445346efaad5c5f200ecda4aa7f0fb50ff1b55edde3003be424a2112c3e8102e/proto_qa.py", line 131, in _split_generators
train_fpath = dl_manager.download(_URLs[self.config.name]["train"])
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 199, in download
num_proc=download_config.num_proc,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 195, in map_nested
return function(data_struct)
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 218, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/iesl/protoqa-data/master/data/train/protoqa_train.jsonl
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2458 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2458/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2458/comments | https://api.github.com/repos/huggingface/datasets/issues/2458/events | https://github.com/huggingface/datasets/issues/2458 | 915,199,693 | MDU6SXNzdWU5MTUxOTk2OTM= | 2,458 | Revert default in-memory for small datasets | {
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"cc: @krandiash (pinged in reverted PR)."
] | 1,623,167,501,000 | 1,623,178,631,000 | 1,623,174,943,000 | MEMBER | null | Users are reporting issues and confusion about setting default in-memory to True for small datasets.
We see 2 clear use cases of Datasets:
- the "canonical" way, where you can work with very large datasets, as they are memory-mapped and cached (after every transformation)
- some edge cases (speed benchmarks, interactive/exploratory analysis,...), where default in-memory can explicitly be enabled, and no caching will be done
After discussing with @lhoestq we have agreed to:
- revert this feature (implemented in #2182)
- explain in the docs how to optimize speed/performance by setting default in-memory
cc: @stas00 https://github.com/huggingface/datasets/pull/2409#issuecomment-856210552 | {
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https://api.github.com/repos/huggingface/datasets/issues/2457 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2457/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2457/comments | https://api.github.com/repos/huggingface/datasets/issues/2457/events | https://github.com/huggingface/datasets/pull/2457 | 915,079,441 | MDExOlB1bGxSZXF1ZXN0NjY0OTQwMzQ0 | 2,457 | Add align_labels_with_mapping function | {
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"@lhoestq i think this is ready for another review 🙂 ",
"@lhoestq thanks for the feedback - it's now integrated :) \r\n\r\ni also added a comment about sorting the input label IDs",
"Created the PR here: https://github.com/huggingface/datasets/pull/2510",
"> Thanks ! Looks all good now :)\r\n> \r\n> We will also need to have the `DatasetDict.align_labels_with_mapping` method. Let me quickly add it\r\n\r\nthanks a lot! i always forget about `DatasetDict` - will be happy when it's just one \"dataset\" object :)"
] | 1,623,160,440,000 | 1,623,925,026,000 | 1,623,923,812,000 | MEMBER | null | This PR adds a helper function to align the `label2id` mapping between a `datasets.Dataset` and a classifier (e.g. a transformer with a `PretrainedConfig.label2id` dict), with the alignment performed on the dataset itself.
This will help us with the Hub evaluation, where we won't know in advance whether a model that is fine-tuned on say MNLI has the same mappings as the MNLI dataset we load from `datasets`.
An example where this is needed is if we naively try to evaluate `microsoft/deberta-base-mnli` on `mnli` because the model config has the following mappings:
```python
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
},
"label2id": {
"CONTRADICTION": 0,
"ENTAILMENT": 2,
"NEUTRAL": 1
}
```
while the `mnli` dataset has the `contradiction` and `neutral` labels swapped:
```python
id2label = {0: 'entailment', 1: 'neutral', 2: 'contradiction'}
label2id = {'contradiction': 2, 'entailment': 0, 'neutral': 1}
```
As a result, we get a much lower accuracy during evaluation:
```python
from datasets import load_dataset
from transformers.trainer_utils import EvalPrediction
from transformers import AutoModelForSequenceClassification, Trainer
# load dataset for evaluation
mnli = load_dataset("glue", "mnli", split="test")
# load model
model_ckpt = "microsoft/deberta-base-mnli"
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
# preprocess, create trainer ...
mnli_enc = ...
trainer = Trainer(model, args=args, tokenizer=tokenizer)
# generate preds
preds = trainer.predict(mnli_enc)
# preds.label_ids misalinged with model.config => returns wrong accuracy (too low)!
compute_metrics(EvalPrediction(preds.predictions, preds.label_ids))
```
The fix is to use the helper function before running the evaluation to make sure the label IDs are aligned:
```python
mnli_enc_aligned = mnli_enc.align_labels_with_mapping(label2id=config.label2id, label_column="label")
# preds now aligned and everyone is happy :)
preds = trainer.predict(mnli_enc_aligned)
```
cc @thomwolf @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/2456 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2456/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2456/comments | https://api.github.com/repos/huggingface/datasets/issues/2456/events | https://github.com/huggingface/datasets/pull/2456 | 914,709,293 | MDExOlB1bGxSZXF1ZXN0NjY0NjAwOTk1 | 2,456 | Fix cross-reference typos in documentation | {
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https://api.github.com/repos/huggingface/datasets/issues/2455 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2455/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2455/comments | https://api.github.com/repos/huggingface/datasets/issues/2455/events | https://github.com/huggingface/datasets/pull/2455 | 914,177,468 | MDExOlB1bGxSZXF1ZXN0NjY0MTEzNjg2 | 2,455 | Update version in xor_tydi_qa.py | {
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"Hi ! Thanks for updating the version\r\n\r\n> Should I revert to the old dummy/1.0.0 or delete it and keep only dummy/1.1.0?\r\n\r\nFeel free to delete the old dummy data files\r\n"
] | 1,623,119,025,000 | 1,623,684,925,000 | 1,623,684,925,000 | CONTRIBUTOR | null | Fix #2449
@lhoestq Should I revert to the old `dummy/1.0.0` or delete it and keep only `dummy/1.1.0`? | {
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https://api.github.com/repos/huggingface/datasets/issues/2454 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2454/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2454/comments | https://api.github.com/repos/huggingface/datasets/issues/2454/events | https://github.com/huggingface/datasets/pull/2454 | 913,883,631 | MDExOlB1bGxSZXF1ZXN0NjYzODUyODU1 | 2,454 | Rename config and environment variable for in memory max size | {
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"Thank you for the rename, @albertvillanova!"
] | 1,623,093,668,000 | 1,623,098,626,000 | 1,623,098,626,000 | MEMBER | null | As discussed in #2409, both config and environment variable have been renamed.
cc: @stas00, huggingface/transformers#12056 | {
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"The arrow writer was supposing that the columns were always in the sorted order. I just pushed a fix to reorder the arrays accordingly to the schema. It was failing for many datasets like squad",
"and obviously it broke everything",
"Feel free to revert my commit. I can investigate this in the coming days",
"@lhoestq I do not understand when you say:\r\n> It was failing for many datasets like squad\r\n\r\nAll the tests were green after my last commit.",
"> All the tests were green after my last commit.\r\n\r\nYes but loading the actual squad dataset was failing :/\r\n"
] | 1,623,083,198,000 | 1,623,780,336,000 | 1,623,771,828,000 | MEMBER | null | When loading a Dataset from a JSON file whose column names are not sorted alphabetically, we should get the same column name order, whether we pass features (in the same order as in the file) or not.
I found this issue while working on #2366. | {
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"Realized that `tensorflow_datasets` is not provided by Huggingface and should therefore raise the issue there."
] | 1,623,075,626,000 | 1,623,076,472,000 | 1,623,076,472,000 | NONE | null | ## Describe the bug
When using `load_dataset("glue", "mrpc")` to load the MRPC dataset, the test set includes the labels. When using `tensorflow_datasets.load('glue/{}'.format('mrpc'))` to load the dataset the test set does not contain the labels. There should be consistency between torch and tensorflow ways of importing the GLUE datasets.
## Steps to reproduce the bug
Minimal working code
```python
from datasets import load_dataset
import tensorflow as tf
import tensorflow_datasets
# torch
dataset = load_dataset("glue", "mrpc")
# tf
data = tensorflow_datasets.load('glue/{}'.format('mrpc'))
data = list(data['test'].as_numpy_iterator())
for i in range(40,50):
tf_sentence1 = data[i]['sentence1'].decode("utf-8")
tf_sentence2 = data[i]['sentence2'].decode("utf-8")
tf_label = data[i]['label']
index = data[i]['idx']
print('Index {}'.format(index))
torch_sentence1 = dataset['test']['sentence1'][index]
torch_sentence2 = dataset['test']['sentence2'][index]
torch_label = dataset['test']['label'][index]
print('Tensorflow: \n\tSentence1 {}\n\tSentence2 {}\n\tLabel {}'.format(tf_sentence1, tf_sentence2, tf_label))
print('Torch: \n\tSentence1 {}\n\tSentence2 {}\n\tLabel {}'.format(torch_sentence1, torch_sentence2, torch_label))
```
Sample output
```
Index 954
Tensorflow:
Sentence1 Sabri Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate yesterday on charges of violating U.S. arms-control laws .
Sentence2 The elder Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate Wednesday on charges of violating U.S. arms control laws .
Label -1
Torch:
Sentence1 Sabri Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate yesterday on charges of violating U.S. arms-control laws .
Sentence2 The elder Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate Wednesday on charges of violating U.S. arms control laws .
Label 1
Index 711
Tensorflow:
Sentence1 Others keep records sealed for as little as five years or as much as 30 .
Sentence2 Some states make them available immediately ; others keep them sealed for as much as 30 years .
Label -1
Torch:
Sentence1 Others keep records sealed for as little as five years or as much as 30 .
Sentence2 Some states make them available immediately ; others keep them sealed for as much as 30 years .
Label 0
```
## Expected results
I would expect the datasets to be independent of whether I am working with torch or tensorflow.
## Actual results
Test set labels are provided in the `datasets.load_datasets()` for MRPC. However MRPC is the only task where the test set labels are not -1.
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2451 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2451/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2451/comments | https://api.github.com/repos/huggingface/datasets/issues/2451/events | https://github.com/huggingface/datasets/pull/2451 | 913,263,340 | MDExOlB1bGxSZXF1ZXN0NjYzMzIwNDY1 | 2,451 | Mention that there are no answers in adversarial_qa test set | {
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} | [] | closed | false | null | [] | null | [] | 1,623,053,637,000 | 1,623,054,854,000 | 1,623,054,853,000 | MEMBER | null | As mention in issue https://github.com/huggingface/datasets/issues/2447, there are no answers in the test set | {
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https://api.github.com/repos/huggingface/datasets/issues/2450 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2450/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2450/comments | https://api.github.com/repos/huggingface/datasets/issues/2450/events | https://github.com/huggingface/datasets/issues/2450 | 912,890,291 | MDU6SXNzdWU5MTI4OTAyOTE= | 2,450 | BLUE file not found | {
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"Hi ! The `blue` metric doesn't exist, but the `bleu` metric does.\r\nYou can get the full list of metrics [here](https://github.com/huggingface/datasets/tree/master/metrics) or by running\r\n```python\r\nfrom datasets import list_metrics\r\n\r\nprint(list_metrics())\r\n```",
"Ah, my mistake. Thanks for correcting"
] | 1,622,998,914,000 | 1,623,062,775,000 | 1,623,062,775,000 | NONE | null | Hi, I'm having the following issue when I try to load the `blue` metric.
```shell
import datasets
metric = datasets.load_metric('blue')
Traceback (most recent call last):
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 320, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.7.0/metrics/blue/blue.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 332, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/metrics/blue/blue.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 605, in load_metric
dataset=False,
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 343, in prepare_module
combined_path, github_file_path
FileNotFoundError: Couldn't find file locally at blue/blue.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.7.0/metrics/blue/blue.py.
The file is also not present on the master branch on github.
```
Here is dataset installed version info
```shell
pip freeze | grep datasets
datasets==1.7.0
```
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https://api.github.com/repos/huggingface/datasets/issues/2449 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2449/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2449/comments | https://api.github.com/repos/huggingface/datasets/issues/2449/events | https://github.com/huggingface/datasets/pull/2449 | 912,751,752 | MDExOlB1bGxSZXF1ZXN0NjYyODg1ODUz | 2,449 | Update `xor_tydi_qa` url to v1.1 | {
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"Just noticed while \r\n```load_dataset('local_path/datastes/xor_tydi_qa')``` works,\r\n```load_dataset('xor_tydi_qa')``` \r\noutputs an error: \r\n`\r\nFileNotFoundError: Couldn't find file at https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_dev_retrieve_eng_span.jsonl\r\n`\r\n(the old url)\r\n\r\nI tired clearing the cache `.cache/huggingface/modules` and `.cache/huggingface/datasets`, didn't work.\r\n\r\nAnyone know how to fix this? Thanks.",
"It seems like the error is not on your end. By default, the lib tries to download the version of the dataset script that matches the version of the lib, and that version of the script is, in your case, broken because the old URL no longer works. Once this PR gets merged, you can wait for the new release or set `script_version` to `\"master\"` in `load_dataset` to get the fixed version of the script.",
"@mariosasko Thanks! It works now.\r\n\r\nPasting the docstring here for reference.\r\n```\r\n script_version (:class:`~utils.Version` or :obj:`str`, optional): Version of the dataset script to load:\r\n\r\n - For canonical datasets in the `huggingface/datasets` library like \"squad\", the default version of the module is the local version fo the lib.\r\n You can specify a different version from your local version of the lib (e.g. \"master\" or \"1.2.0\") but it might cause compatibility issues.\r\n - For community provided datasets like \"lhoestq/squad\" that have their own git repository on the Datasets Hub, the default version \"main\" corresponds to the \"main\" branch.\r\n You can specify a different version that the default \"main\" by using a commit sha or a git tag of the dataset repository.\r\n```\r\nBranch name didn't work, but commit sha works.",
"Regarding the issue you mentioned about the `--ignore_verifications` flag, I think we should actually change the current behavior of the `--save_infos` flag to make it ignore the verifications as well, so that you don't need to specific `--ignore_verifications` in this case.",
"@lhoestq I realized I forgot to change this:\r\n\r\nhttps://github.com/huggingface/datasets/blob/fdbf5a97d3393f4a91e4cddcabe364029508f7ce/datasets/xor_tydi_qa/xor_tydi_qa.py#L72-L73\r\n\r\nWhat should I do?",
"Oh indeed. Please open a PR to change this. This should be 1.1.0"
] | 1,622,972,698,000 | 1,623,078,981,000 | 1,623,054,664,000 | CONTRIBUTOR | null | The dataset is updated and the old url no longer works. So I updated it.
I faced a bug while trying to fix this. Documenting the solution here. Maybe we can add it to the doc (`CONTRIBUTING.md` and `ADD_NEW_DATASET.md`).
> And to make the command work without the ExpectedMoreDownloadedFiles error, you just need to use the --ignore_verifications flag.
https://github.com/huggingface/datasets/issues/2076#issuecomment-803904366 | {
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https://api.github.com/repos/huggingface/datasets/issues/2448 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2448/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2448/comments | https://api.github.com/repos/huggingface/datasets/issues/2448/events | https://github.com/huggingface/datasets/pull/2448 | 912,360,109 | MDExOlB1bGxSZXF1ZXN0NjYyNTI2NjA3 | 2,448 | Fix flores download link | {
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|
https://api.github.com/repos/huggingface/datasets/issues/2447 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2447/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2447/comments | https://api.github.com/repos/huggingface/datasets/issues/2447/events | https://github.com/huggingface/datasets/issues/2447 | 912,299,527 | MDU6SXNzdWU5MTIyOTk1Mjc= | 2,447 | dataset adversarial_qa has no answers in the "test" set | {
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"Hi ! I'm pretty sure that the answers are not made available for the test set on purpose because it is part of the DynaBench benchmark, for which you can submit your predictions on the website.\r\nIn any case we should mention this in the dataset card of this dataset.",
"Makes sense, but not intuitive for someone searching through the datasets. Thanks for adding the note to clarify."
] | 1,622,905,058,000 | 1,623,064,387,000 | 1,623,064,387,000 | NONE | null | ## Describe the bug
When loading the adversarial_qa dataset the 'test' portion has no answers. Only the 'train' and 'validation' portions do. This occurs with all four of the configs ('adversarialQA', 'dbidaf', 'dbert', 'droberta')
## Steps to reproduce the bug
```
from datasets import load_dataset
examples = load_dataset('adversarial_qa', 'adversarialQA', script_version="master")['test']
print('Loaded {:,} examples'.format(len(examples)))
has_answers = 0
for e in examples:
if e['answers']['text']:
has_answers += 1
print('{:,} have answers'.format(has_answers))
>>> Loaded 3,000 examples
>>> 0 have answers
examples = load_dataset('adversarial_qa', 'adversarialQA', script_version="master")['validation']
<...code above...>
>>> Loaded 3,000 examples
>>> 3,000 have answers
```
## Expected results
If 'test' is a valid dataset, it should have answers. Also note that all of the 'train' and 'validation' sets have answers, there are no "no answer" questions with this set (not sure if this is correct or not).
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.8.0-53-generic-x86_64-with-glibc2.29
- Python version: 3.8.5
- PyArrow version: 1.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2446 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2446/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2446/comments | https://api.github.com/repos/huggingface/datasets/issues/2446/events | https://github.com/huggingface/datasets/issues/2446 | 911,635,399 | MDU6SXNzdWU5MTE2MzUzOTk= | 2,446 | `yelp_polarity` is broken | {
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"```\r\nFile \"/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/script_runner.py\", line 332, in _run_script\r\n exec(code, module.__dict__)\r\nFile \"/home/sasha/nlp-viewer/run.py\", line 233, in <module>\r\n configs = get_confs(option)\r\nFile \"/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/caching.py\", line 604, in wrapped_func\r\n return get_or_create_cached_value()\r\nFile \"/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/caching.py\", line 588, in get_or_create_cached_value\r\n return_value = func(*args, **kwargs)\r\nFile \"/home/sasha/nlp-viewer/run.py\", line 148, in get_confs\r\n builder_cls = nlp.load.import_main_class(module_path[0], dataset=True)\r\nFile \"/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/datasets/load.py\", line 85, in import_main_class\r\n module = importlib.import_module(module_path)\r\nFile \"/usr/lib/python3.7/importlib/__init__.py\", line 127, in import_module\r\n return _bootstrap._gcd_import(name[level:], package, level)\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\nFile \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\r\nFile \"<frozen importlib._bootstrap_external>\", line 728, in exec_module\r\nFile \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\nFile \"/home/sasha/.cache/huggingface/modules/datasets_modules/datasets/yelp_polarity/a770787b2526bdcbfc29ac2d9beb8e820fbc15a03afd3ebc4fb9d8529de57544/yelp_polarity.py\", line 36, in <module>\r\n from datasets.tasks import TextClassification\r\n```",
"Solved by updating the `nlpviewer`"
] | 1,622,821,469,000 | 1,622,833,007,000 | 1,622,833,007,000 | MEMBER | null | 
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https://api.github.com/repos/huggingface/datasets/issues/2445 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2445/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2445/comments | https://api.github.com/repos/huggingface/datasets/issues/2445/events | https://github.com/huggingface/datasets/pull/2445 | 911,577,578 | MDExOlB1bGxSZXF1ZXN0NjYxODMzMTky | 2,445 | Fix broken URLs for bn_hate_speech and covid_tweets_japanese | {
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"Thanks ! To fix the CI you just have to rename the dummy data file in the dummy_data.zip files",
"thanks for the tip with the dummy data - all fixed now!"
] | 1,622,818,415,000 | 1,622,828,386,000 | 1,622,828,385,000 | MEMBER | null | Closes #2388 | {
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https://api.github.com/repos/huggingface/datasets/issues/2444 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2444/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2444/comments | https://api.github.com/repos/huggingface/datasets/issues/2444/events | https://github.com/huggingface/datasets/issues/2444 | 911,297,139 | MDU6SXNzdWU5MTEyOTcxMzk= | 2,444 | Sentence Boundaries missing in Dataset: xtreme / udpos | {
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"Hi,\r\n\r\nThis is a known issue. More info on this issue can be found in #2061. If you are looking for an open-source contribution, there are step-by-step instructions in the linked issue that you can follow to fix it.",
"Closed by #2466."
] | 1,622,797,826,000 | 1,624,017,223,000 | 1,624,017,223,000 | CONTRIBUTOR | null | I was browsing through annotation guidelines, as suggested by the datasets introduction.
The guidlines saids "There must be exactly one blank line after every sentence, including the last sentence in the file. Empty sentences are not allowed." in the [Sentence Boundaries and Comments section](https://universaldependencies.org/format.html#sentence-boundaries-and-comments)
But the sentence boundaries seems not to be represented by huggingface datasets features well. I found out that multiple sentence are concatenated together as a 1D array, without any delimiter.
PAN-x, which is another token classification subset from xtreme do represent the sentence boundary using a 2D array.
You may compare in PAN-x.en and udpos.English in the explorer:
https://huggingface.co/datasets/viewer/?dataset=xtreme | {
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https://api.github.com/repos/huggingface/datasets/issues/2443 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2443/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2443/comments | https://api.github.com/repos/huggingface/datasets/issues/2443/events | https://github.com/huggingface/datasets/issues/2443 | 909,983,574 | MDU6SXNzdWU5MDk5ODM1NzQ= | 2,443 | Some tests hang on Windows | {
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"Hi ! That would be nice indeed to at least have a warning, since we don't handle the max path length limit.\r\nAlso if we could have an error instead of an infinite loop I'm sure windows users will appreciate that",
"Unfortunately, I know this problem very well... 😅 \r\n\r\nI remember having proposed to throw an error instead of hanging in an infinite loop #2220: 60c7d1b6b71469599a27147a08100f594e7a3f84, 8c8ab60018b00463edf1eca500e434ff061546fc \r\nbut @lhoestq told me:\r\n> Note that the filelock module comes from this project that hasn't changed in years - while still being used by ten of thousands of projects:\r\nhttps://github.com/benediktschmitt/py-filelock\r\n> \r\n> Unless we have proper tests for this, I wouldn't recommend to change it\r\n\r\nI opened an Issue requesting a warning/error at startup for that case: #2224",
"@albertvillanova Thanks for additional info on this issue.\r\n\r\nYes, I think the best option is to throw an error instead of suppressing it in a loop. I've considered 2 more options, but I don't really like them:\r\n1. create a temporary file with a filename longer than 255 characters on import; if this fails, long paths are not enabled and raise a warning. I'm not sure about this approach because I don't like the idea of creating a temporary file on import for this purpose.\r\n2. check if long paths are enabled with [this code](https://stackoverflow.com/a/46546731/14095927). As mentioned in the comment, this code relies on an undocumented function and Win10-specific."
] | 1,622,680,050,000 | 1,624,870,059,000 | 1,624,870,059,000 | CONTRIBUTOR | null | Currently, several tests hang on Windows if the max path limit of 260 characters is not disabled. This happens due to the changes introduced by #2223 that cause an infinite loop in `WindowsFileLock` described in #2220. This can be very tricky to debug, so I think now is a good time to address these issues/PRs. IMO throwing an error is too harsh, but maybe we can emit a warning in the top-level `__init__.py ` on startup if long paths are not enabled.
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https://api.github.com/repos/huggingface/datasets/issues/2442 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2442/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2442/comments | https://api.github.com/repos/huggingface/datasets/issues/2442/events | https://github.com/huggingface/datasets/pull/2442 | 909,677,029 | MDExOlB1bGxSZXF1ZXN0NjYwMjE1ODY1 | 2,442 | add english language tags for ~100 datasets | {
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"Fixing the tags of all the datasets is out of scope for this PR so I'm merging even though the CI fails because of the missing tags"
] | 1,622,651,096,000 | 1,622,800,300,000 | 1,622,800,299,000 | MEMBER | null | As discussed on Slack, I have manually checked for ~100 datasets that they have at least one subset in English. This information was missing so adding into the READMEs.
Note that I didn't check all the subsets so it's possible that some of the datasets have subsets in other languages than English... | {
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https://api.github.com/repos/huggingface/datasets/issues/2441 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2441/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2441/comments | https://api.github.com/repos/huggingface/datasets/issues/2441/events | https://github.com/huggingface/datasets/issues/2441 | 908,554,713 | MDU6SXNzdWU5MDg1NTQ3MTM= | 2,441 | DuplicatedKeysError on personal dataset | {
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"Hi ! In your dataset script you must be yielding examples like\r\n```python\r\nfor line in file:\r\n ...\r\n yield key, {...}\r\n```\r\n\r\nSince `datasets` 1.7.0 we enforce the keys to be unique.\r\nHowever it looks like your examples generator creates duplicate keys: at least two examples have key 0.\r\n\r\nYou can fix that by making sure that your keys are unique.\r\n\r\nFor example if you use a counter to define the key of each example, make sure that your counter is not reset to 0 in during examples generation (between two open files for examples).\r\n\r\nLet me know if you have other questions :)",
"Yup, I indeed was generating duplicate keys. Fixed it and now it's working."
] | 1,622,570,381,000 | 1,622,850,603,000 | 1,622,850,603,000 | NONE | null | ## Describe the bug
Ever since today, I have been getting a DuplicatedKeysError while trying to load my dataset from my own script.
Error returned when running this line: `dataset = load_dataset('/content/drive/MyDrive/Thesis/Datasets/book_preprocessing/goodreads_maharjan_trimmed_and_nered/goodreadsnered.py')`
Note that my script was working fine with earlier versions of the Datasets library. Cannot say with 100% certainty if I have been doing something wrong with my dataset script this whole time or if this is simply a bug with the new version of datasets.
## Steps to reproduce the bug
I cannot provide code to reproduce the error as I am working with my own dataset. I can however provide my script if requested.
## Expected results
For my data to be loaded.
## Actual results
**DuplicatedKeysError** exception is raised
```
Downloading and preparing dataset good_reads_practice_dataset/main_domain (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/good_reads_practice_dataset/main_domain/1.1.0/64ff7c3fee2693afdddea75002eb6887d4fedc3d812ae3622128c8504ab21655...
---------------------------------------------------------------------------
DuplicatedKeysError Traceback (most recent call last)
<ipython-input-6-c342ea0dae9d> in <module>()
----> 1 dataset = load_dataset('/content/drive/MyDrive/Thesis/Datasets/book_preprocessing/goodreads_maharjan_trimmed_and_nered/goodreadsnered.py')
5 frames
/usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, **config_kwargs)
749 try_from_hf_gcs=try_from_hf_gcs,
750 base_path=base_path,
--> 751 use_auth_token=use_auth_token,
752 )
753
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
573 if not downloaded_from_gcs:
574 self._download_and_prepare(
--> 575 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
576 )
577 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
650 try:
651 # Prepare split will record examples associated to the split
--> 652 self._prepare_split(split_generator, **prepare_split_kwargs)
653 except OSError as e:
654 raise OSError(
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator)
990 writer.write(example, key)
991 finally:
--> 992 num_examples, num_bytes = writer.finalize()
993
994 split_generator.split_info.num_examples = num_examples
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in finalize(self, close_stream)
407 # In case current_examples < writer_batch_size, but user uses finalize()
408 if self._check_duplicates:
--> 409 self.check_duplicate_keys()
410 # Re-intializing to empty list for next batch
411 self.hkey_record = []
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self)
347 for hash, key in self.hkey_record:
348 if hash in tmp_record:
--> 349 raise DuplicatedKeysError(key)
350 else:
351 tmp_record.add(hash)
DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 0
Keys should be unique and deterministic in nature
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.7.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.9
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2440 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2440/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2440/comments | https://api.github.com/repos/huggingface/datasets/issues/2440/events | https://github.com/huggingface/datasets/issues/2440 | 908,521,954 | MDU6SXNzdWU5MDg1MjE5NTQ= | 2,440 | Remove `extended` field from dataset tagger | {
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"The tagger also doesn't insert the value for the `size_categories` field automatically, so this should be fixed too",
"Thanks for reporting. Indeed the `extended` tag doesn't exist. Not sure why we had that in the tagger.\r\nThe repo of the tagger is here if someone wants to give this a try: https://github.com/huggingface/datasets-tagging\r\nOtherwise I can probably fix it next week",
"I've opened a PR on `datasets-tagging` to fix the issue 🚀 ",
"thanks ! this is fixed now"
] | 1,622,567,922,000 | 1,623,229,591,000 | 1,623,229,590,000 | MEMBER | null | ## Describe the bug
While working on #2435 I used the [dataset tagger](https://huggingface.co/datasets/tagging/) to generate the missing tags for the YAML metadata of each README.md file. However, it seems that our CI raises an error when the `extended` field is included:
```
dataset_name = 'arcd'
@pytest.mark.parametrize("dataset_name", get_changed_datasets(repo_path))
def test_changed_dataset_card(dataset_name):
card_path = repo_path / "datasets" / dataset_name / "README.md"
assert card_path.exists()
error_messages = []
try:
ReadMe.from_readme(card_path)
except Exception as readme_error:
error_messages.append(f"The following issues have been found in the dataset cards:\nREADME:\n{readme_error}")
try:
DatasetMetadata.from_readme(card_path)
except Exception as metadata_error:
error_messages.append(
f"The following issues have been found in the dataset cards:\nYAML tags:\n{metadata_error}"
)
if error_messages:
> raise ValueError("\n".join(error_messages))
E ValueError: The following issues have been found in the dataset cards:
E YAML tags:
E __init__() got an unexpected keyword argument 'extended'
tests/test_dataset_cards.py:70: ValueError
```
Consider either removing this tag from the tagger or including it as part of the validation step in the CI.
cc @yjernite | {
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https://api.github.com/repos/huggingface/datasets/issues/2439 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2439/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2439/comments | https://api.github.com/repos/huggingface/datasets/issues/2439/events | https://github.com/huggingface/datasets/pull/2439 | 908,511,983 | MDExOlB1bGxSZXF1ZXN0NjU5MTkzMDE3 | 2,439 | Better error message when trying to access elements of a DatasetDict without specifying the split | {
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cc @thomwolf | {
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https://api.github.com/repos/huggingface/datasets/issues/2438 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2438/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2438/comments | https://api.github.com/repos/huggingface/datasets/issues/2438/events | https://github.com/huggingface/datasets/pull/2438 | 908,461,914 | MDExOlB1bGxSZXF1ZXN0NjU5MTQ5Njg0 | 2,438 | Fix NQ features loading: reorder fields of features to match nested fields order in arrow data | {
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} | [] | closed | false | null | [] | null | [] | 1,622,563,770,000 | 1,622,797,351,000 | 1,622,797,351,000 | MEMBER | null | As mentioned in #2401, there is an issue when loading the features of `natural_questions` since the order of the nested fields in the features don't match. The order is important since it matters for the underlying arrow schema.
To fix that I re-order the features based on the arrow schema:
```python
inferred_features = Features.from_arrow_schema(arrow_table.schema)
self.info.features = self.info.features.reorder_fields_as(inferred_features)
assert self.info.features.type == inferred_features.type
```
The re-ordering is a recursive function. It takes into account that the `Sequence` feature type is a struct of list and not a list of struct.
Now it's possible to load `natural_questions` again :) | {
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https://api.github.com/repos/huggingface/datasets/issues/2437 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2437/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2437/comments | https://api.github.com/repos/huggingface/datasets/issues/2437/events | https://github.com/huggingface/datasets/pull/2437 | 908,108,882 | MDExOlB1bGxSZXF1ZXN0NjU4ODUwNTkw | 2,437 | Better error message when using the wrong load_from_disk | {
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} | [] | closed | false | null | [] | null | [
"We also have other cases where people are lost between Dataset and DatasetDict, maybe let's gather and solve them all here?\r\n\r\nFor instance, I remember that some people thought they would request a single element of a split but are calling this on a DatasetDict. Maybe here also a better error message when the split requested in not in the dict? pointing to the list of split and the fact that this is a datasetdict containing several datasets?",
"Good idea, let me add a better error message for this case too",
"As a digression from the topic of this PR, IMHO I think that the difference between Dataset and DatasetDict is an additional abstraction complexity that confuses \"typical\" end users. I think a user expects a \"Dataset\" (whatever it contains multiple or a single split) and maybe it could be interesting to try to simplify the user-facing API as much as possible to hide this complexity from the end user.\r\n\r\nI don't know your opinion about this, but it might be worth discussing...\r\n\r\nFor example, I really like the line of the solution of using the function `load_from_disk`, which hides the previous mentioned complexity and handles under the hood whether Dataset/DatasetDict instances should be created...",
"I totally agree, I just haven't found a solution that doesn't imply major breaking changes x)",
"Yes I would also like to find a better solution. Do we have any solution actually? (even implying breaking changes)\r\n\r\nHere is a proposal for discussion and refined (and potential abandon if it's not good enough):\r\n- let's consider that a DatasetDict is also a Dataset with the various split concatenated one after the other\r\n- let's disallow the use of integers in split names (probably not a very big breaking change)\r\n- when you index with integers you access the examples progressively in split after the other is finished (in a deterministic order)\r\n- when you index with strings/split name you have the same behavior as now (full backward compat)\r\n- let's then also have all the methods of a Dataset on the DatasetDict",
"The end goal would be to merge both `Dataset` and `DatasetDict` object in a single object that would be (pretty much totally) backward compatible with both.",
"I like the direction :) I think it can make sense to concatenate them.\r\n\r\nThere are a few things that I we could discuss if we want to merge Dataset and DatasetDict:\r\n1. what happens if you index by a string ? Does it return the column or the split ? We could disallow conflicts between column names and split names to avoid ambiguities. It can be surprising to be able to get a column or a split using the same indexing feature\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(...)\r\ndataset[\"train\"]\r\ndataset[\"input_ids\"]\r\n```\r\n2. what happens when you iterate over the object ? I guess it should iterate over the examples as a Dataset object, but a DatasetDict used to iterate over the splits as they are the dictionary keys. This is a breaking change that we can discuss.\r\n\r\nMoreover regarding your points:\r\n- integers are not allowed as split names already\r\n- it's definitely doable to have all the methods. Maybe some of them like `train_test_split` that is currently only available for Dataset can be tweaked to work for a split dataset",
"Instead of suggesting the use of `Dataset.load_from_disk` and `DatasetDict.load_from_disk`, the error message now suggests to use `datasets.load_from_disk` directly",
"Merging the error message improvement, feel free to continue the discussion here or in a github issue"
] | 1,622,540,602,000 | 1,623,175,430,000 | 1,623,175,430,000 | MEMBER | null | As mentioned in #2424, the error message when one tries to use `Dataset.load_from_disk` to load a DatasetDict object (or _vice versa_) can be improved. I added a suggestion in the error message to let users know that they should use the other one. | {
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https://api.github.com/repos/huggingface/datasets/issues/2436 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2436/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2436/comments | https://api.github.com/repos/huggingface/datasets/issues/2436/events | https://github.com/huggingface/datasets/pull/2436 | 908,100,211 | MDExOlB1bGxSZXF1ZXN0NjU4ODQzMzQy | 2,436 | Update DatasetMetadata and ReadMe | {
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} | [] | closed | false | null | [] | null | [] | 1,622,539,957,000 | 1,623,677,007,000 | 1,623,677,006,000 | CONTRIBUTOR | null | This PR contains the changes discussed in #2395.
**Edit**:
In addition to those changes, I'll be updating the `ReadMe` as follows:
Currently, `Section` has separate parsing and validation error lists. In `.validate()`, we add these lists to the final lists and throw errors.
One way to make `ReadMe` consistent with `DatasetMetadata` and add a separate `.validate()` method is to throw separate parsing and validation errors.
This way, we don't have to throw validation errors, but only parsing errors in `__init__ ()`. We can have an option in `__init__()` to suppress parsing errors so that an object is created for validation. Doing this will allow the user to get all the errors in one go.
In `test_dataset_cards` , we are already catching error messages and appending to a list. This can be done for `ReadMe()` for parsing errors, and `ReadMe(...,suppress_errors=True); readme.validate()` for validation, separately.
**Edit 2**:
The only parsing issue we have as of now is multiple headings at the same level with the same name. I assume this will happen very rarely, but it is still better to throw an error than silently pick one of them. It should be okay to separate it this way.
Wdyt @lhoestq ?
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https://api.github.com/repos/huggingface/datasets/issues/2435 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2435/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2435/comments | https://api.github.com/repos/huggingface/datasets/issues/2435/events | https://github.com/huggingface/datasets/pull/2435 | 907,505,531 | MDExOlB1bGxSZXF1ZXN0NjU4MzQzNDE2 | 2,435 | Insert Extractive QA templates for SQuAD-like datasets | {
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"hi @lhoestq @SBrandeis i've now added the missing YAML tags, so this PR should be good to go :)",
"urgh, the windows tests are failing because of encoding issues 😢 \r\n\r\n```\r\ndataset_name = 'squad_kor_v1'\r\n\r\n @pytest.mark.parametrize(\"dataset_name\", get_changed_datasets(repo_path))\r\n def test_changed_dataset_card(dataset_name):\r\n card_path = repo_path / \"datasets\" / dataset_name / \"README.md\"\r\n assert card_path.exists()\r\n error_messages = []\r\n try:\r\n ReadMe.from_readme(card_path)\r\n except Exception as readme_error:\r\n error_messages.append(f\"The following issues have been found in the dataset cards:\\nREADME:\\n{readme_error}\")\r\n try:\r\n DatasetMetadata.from_readme(card_path)\r\n except Exception as metadata_error:\r\n error_messages.append(\r\n f\"The following issues have been found in the dataset cards:\\nYAML tags:\\n{metadata_error}\"\r\n )\r\n \r\n if error_messages:\r\n> raise ValueError(\"\\n\".join(error_messages))\r\nE ValueError: The following issues have been found in the dataset cards:\r\nE README:\r\nE 'charmap' codec can't decode byte 0x90 in position 2283: character maps to <undefined>\r\nE The following issues have been found in the dataset cards:\r\nE YAML tags:\r\nE 'charmap' codec can't decode byte 0x90 in position 2283: character maps to <undefined>\r\n```",
"Seems like the encoding issues on windows is also being tackled in #2418 - will see if this solves the problem in the current PR"
] | 1,622,470,151,000 | 1,622,730,870,000 | 1,622,730,747,000 | MEMBER | null | This PR adds task templates for 9 SQuAD-like templates with the following properties:
* 1 config
* A schema that matches the `squad` one (i.e. same column names, especially for the nested `answers` column because the current implementation does not support casting with mismatched columns. see #2434)
* Less than 20GB (my laptop can't handle more right now)
The aim of this PR is to provide a few datasets to experiment with the task template integration in other libraries / services.
PR #2429 should be merged before this one.
cc @abhi1thakur | {
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https://api.github.com/repos/huggingface/datasets/issues/2434 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2434/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2434/comments | https://api.github.com/repos/huggingface/datasets/issues/2434/events | https://github.com/huggingface/datasets/issues/2434 | 907,503,557 | MDU6SXNzdWU5MDc1MDM1NTc= | 2,434 | Extend QuestionAnsweringExtractive template to handle nested columns | {
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] | open | false | null | [] | null | [
"this is also the case for the following datasets and configurations:\r\n\r\n* `mlqa` with config `mlqa-translate-train.ar`\r\n\r\n"
] | 1,622,470,011,000 | 1,623,918,090,000 | null | MEMBER | null | Currently the `QuestionAnsweringExtractive` task template and `preprare_for_task` only support "flat" features. We should extend the functionality to cover QA datasets like:
* `iapp_wiki_qa_squad`
* `parsinlu_reading_comprehension`
where the nested features differ with those from `squad` and trigger an `ArrowNotImplementedError`:
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-12-50e5b8f69c20> in <module>
----> 1 ds.prepare_for_task("question-answering-extractive")[0]
~/git/datasets/src/datasets/arrow_dataset.py in prepare_for_task(self, task)
1436 # We found a template so now flush `DatasetInfo` to skip the template update in `DatasetInfo.__post_init__`
1437 dataset.info.task_templates = None
-> 1438 dataset = dataset.cast(features=template.features)
1439 return dataset
1440
~/git/datasets/src/datasets/arrow_dataset.py in cast(self, features, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, num_proc)
977 format = self.format
978 dataset = self.with_format("arrow")
--> 979 dataset = dataset.map(
980 lambda t: t.cast(schema),
981 batched=True,
~/git/datasets/src/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1600
1601 if num_proc is None or num_proc == 1:
-> 1602 return self._map_single(
1603 function=function,
1604 with_indices=with_indices,
~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
176 }
177 # apply actual function
--> 178 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
179 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
180 # re-apply format to the output
~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs)
395 # Call actual function
396
--> 397 out = func(self, *args, **kwargs)
398
399 # Update fingerprint of in-place transforms + update in-place history of transforms
~/git/datasets/src/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)
1940 ) # Something simpler?
1941 try:
-> 1942 batch = apply_function_on_filtered_inputs(
1943 batch,
1944 indices,
~/git/datasets/src/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)
1836 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset
1837 processed_inputs = (
-> 1838 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
1839 )
1840 if update_data is None:
~/git/datasets/src/datasets/arrow_dataset.py in <lambda>(t)
978 dataset = self.with_format("arrow")
979 dataset = dataset.map(
--> 980 lambda t: t.cast(schema),
981 batched=True,
982 batch_size=batch_size,
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.cast()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.ChunkedArray.cast()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/compute.py in cast(arr, target_type, safe)
241 else:
242 options = CastOptions.unsafe(target_type)
--> 243 return call_function("cast", [arr], options)
244
245
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: Unsupported cast from struct<answer_end: list<item: int32>, answer_start: list<item: int32>, text: list<item: string>> to struct using function cast_struct
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2433 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2433/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2433/comments | https://api.github.com/repos/huggingface/datasets/issues/2433/events | https://github.com/huggingface/datasets/pull/2433 | 907,488,711 | MDExOlB1bGxSZXF1ZXN0NjU4MzI5MDQ4 | 2,433 | Fix DuplicatedKeysError in adversarial_qa | {
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} | [] | closed | false | null | [] | null | [] | 1,622,468,927,000 | 1,622,537,531,000 | 1,622,537,531,000 | CONTRIBUTOR | null | Fixes #2431 | {
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https://api.github.com/repos/huggingface/datasets/issues/2432 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2432/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2432/comments | https://api.github.com/repos/huggingface/datasets/issues/2432/events | https://github.com/huggingface/datasets/pull/2432 | 907,462,881 | MDExOlB1bGxSZXF1ZXN0NjU4MzA3MTE1 | 2,432 | Fix CI six installation on linux | {
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} | [] | closed | false | null | [] | null | [] | 1,622,466,936,000 | 1,622,467,027,000 | 1,622,467,026,000 | MEMBER | null | For some reason we end up with this error in the linux CI when running pip install .[tests]
```
pip._vendor.resolvelib.resolvers.InconsistentCandidate: Provided candidate AlreadyInstalledCandidate(six 1.16.0 (/usr/local/lib/python3.6/site-packages)) does not satisfy SpecifierRequirement('six>1.9'), SpecifierRequirement('six>1.9'), SpecifierRequirement('six>=1.11'), SpecifierRequirement('six~=1.15'), SpecifierRequirement('six'), SpecifierRequirement('six>=1.5.2'), SpecifierRequirement('six>=1.9.0'), SpecifierRequirement('six>=1.11.0'), SpecifierRequirement('six'), SpecifierRequirement('six>=1.6.1'), SpecifierRequirement('six>=1.9'), SpecifierRequirement('six>=1.5'), SpecifierRequirement('six<2.0'), SpecifierRequirement('six<2.0'), SpecifierRequirement('six'), SpecifierRequirement('six'), SpecifierRequirement('six~=1.15.0'), SpecifierRequirement('six'), SpecifierRequirement('six<2.0,>=1.6.1'), SpecifierRequirement('six'), SpecifierRequirement('six>=1.5.2'), SpecifierRequirement('six>=1.9.0')
```
example CI failure here:
https://app.circleci.com/pipelines/github/huggingface/datasets/6200/workflows/b64fdec9-f9e6-431c-acd7-e9f2c440c568/jobs/38247
The main version requirement comes from tensorflow: `six~=1.15.0`
So I pinned the six version to this. | {
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https://api.github.com/repos/huggingface/datasets/issues/2431 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2431/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2431/comments | https://api.github.com/repos/huggingface/datasets/issues/2431/events | https://github.com/huggingface/datasets/issues/2431 | 907,413,691 | MDU6SXNzdWU5MDc0MTM2OTE= | 2,431 | DuplicatedKeysError when trying to load adversarial_qa | {
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] | closed | false | null | [] | null | [
"Thanks for reporting !\r\n#2433 fixed the issue, thanks @mariosasko :)\r\n\r\nWe'll do a patch release soon of the library.\r\nIn the meantime, you can use the fixed version of adversarial_qa by adding `script_version=\"master\"` in `load_dataset`"
] | 1,622,463,079,000 | 1,622,537,643,000 | 1,622,537,531,000 | NONE | null | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
dataset = load_dataset('adversarial_qa', 'adversarialQA')
```
## Expected results
The dataset should be loaded into memory
## Actual results
>DuplicatedKeysError: FAILURE TO GENERATE DATASET !
>Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4
>Keys should be unique and deterministic in nature
>
>
>During handling of the above exception, another exception occurred:
>
>DuplicatedKeysError Traceback (most recent call last)
>
>/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self)
> 347 for hash, key in self.hkey_record:
> 348 if hash in tmp_record:
>--> 349 raise DuplicatedKeysError(key)
> 350 else:
> 351 tmp_record.add(hash)
>
>DuplicatedKeysError: FAILURE TO GENERATE DATASET !
>Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4
>Keys should be unique and deterministic in nature
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2430 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2430/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2430/comments | https://api.github.com/repos/huggingface/datasets/issues/2430/events | https://github.com/huggingface/datasets/pull/2430 | 907,322,595 | MDExOlB1bGxSZXF1ZXN0NjU4MTg3Njkw | 2,430 | Add version-specific BibTeX | {
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"Maybe we should only keep one citation ?\r\ncc @thomwolf @yjernite ",
"For info:\r\n- The one automatically generated by Zenodo is version-specific, and a new one will be generated after each release.\r\n- Zenodo has also generated a project-specific DOI (they call it *Concept DOI* as opposed to *Version DOI*), but currently this only redirects to the DOI page of the latest version.\r\n- All the information automatically generated by Zenodo can be corrected/customized if necessary.\r\n - If we decide to correct/update metadata, take into account that there are the following fields (among others): Authors, Contributors, Title, Description, Keywords, Additional Notes, License,...\r\n\r\nAccording to Zenodo: https://help.zenodo.org/#versioning\r\n> **Which DOI should I use in citations?**\r\n> \r\n> You should normally always use the DOI for the specific version of your record in citations. This is to ensure that other researchers can access the exact research artefact you used for reproducibility. By default, Zenodo uses the specific version to generate citations.\r\n> \r\n> You can use the Concept DOI representing all versions in citations when it is desirable to cite an evolving research artifact, without being specific about the version.",
"Thanks for the details ! As zenodo says we should probably just show the versioned DOI. And we can remove the old citation.",
"I have removed the old citation.\r\n\r\nWhat about the new one? Should we customize it? I have fixed some author names (replaced nickname with first and family names). Note that the list of authors is created automatically by Zenodo from this list: https://github.com/huggingface/datasets/graphs/contributors\r\nI do not know if this default automatic list of authors is what we want to show in the citation..."
] | 1,622,455,542,000 | 1,623,138,802,000 | 1,623,138,802,000 | MEMBER | null | As pointed out by @lhoestq in #2411, after the creation of the Zenodo DOI for Datasets, a new BibTeX entry is created with each release.
This PR adds a version-specific BibTeX entry, besides the existing one which is generic for the project.
See version-specific BibTeX entry here: https://zenodo.org/record/4817769/export/hx#.YLSyd6j7RPY | {
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https://api.github.com/repos/huggingface/datasets/issues/2429 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2429/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2429/comments | https://api.github.com/repos/huggingface/datasets/issues/2429/events | https://github.com/huggingface/datasets/pull/2429 | 907,321,665 | MDExOlB1bGxSZXF1ZXN0NjU4MTg2ODc0 | 2,429 | Rename QuestionAnswering template to QuestionAnsweringExtractive | {
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"> I like having \"extractive\" in the name to make things explicit. However this creates an inconsistency with transformers.\r\n> \r\n> See\r\n> https://huggingface.co/transformers/task_summary.html#extractive-question-answering\r\n> \r\n> But this is minor IMO and I'm ok with this renaming\r\n\r\nyes i chose this convention because it allows us to match the `QuestionAnsweringXxx` naming and i think it's better to have `task_name-subtask_name` should auto-complete ever become part of the Hub :)"
] | 1,622,455,482,000 | 1,622,476,646,000 | 1,622,476,644,000 | MEMBER | null | Following the discussion with @thomwolf in #2255, this PR renames the QA template to distinguish extractive vs abstractive QA. The abstractive template will be added in a future PR. | {
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https://api.github.com/repos/huggingface/datasets/issues/2428 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2428/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2428/comments | https://api.github.com/repos/huggingface/datasets/issues/2428/events | https://github.com/huggingface/datasets/pull/2428 | 907,169,746 | MDExOlB1bGxSZXF1ZXN0NjU4MDU2MjI3 | 2,428 | Add copyright info for wiki_lingua dataset | {
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"Build fails but this change should not be the reason...",
"rebased on master",
"rebased on master"
] | 1,622,445,772,000 | 1,622,802,153,000 | 1,622,802,153,000 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2427 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2427/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2427/comments | https://api.github.com/repos/huggingface/datasets/issues/2427/events | https://github.com/huggingface/datasets/pull/2427 | 907,162,923 | MDExOlB1bGxSZXF1ZXN0NjU4MDUwMjAx | 2,427 | Add copyright info to MLSUM dataset | {
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"Build fails but this change should not be the reason...",
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https://api.github.com/repos/huggingface/datasets/issues/2426 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2426/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2426/comments | https://api.github.com/repos/huggingface/datasets/issues/2426/events | https://github.com/huggingface/datasets/issues/2426 | 906,473,546 | MDU6SXNzdWU5MDY0NzM1NDY= | 2,426 | Saving Graph/Structured Data in Datasets | {
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"It should probably work out of the box to save structured data. If you want to show an example we can help you.",
"An example of a toy dataset is like:\r\n```json\r\n[\r\n {\r\n \"name\": \"mike\",\r\n \"friends\": [\r\n \"tom\",\r\n \"lily\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"aaaaa\",\r\n \"reader\": [\r\n \"tom\",\r\n \"lucy\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"tom\",\r\n \"friends\": [\r\n \"mike\",\r\n \"bbb\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"xxxxx\",\r\n \"reader\": [\r\n \"tom\",\r\n \"qqqq\"\r\n ]\r\n }\r\n ]\r\n }\r\n]\r\n```\r\nWe can use the friendship relation to build a directional graph, and a user node can be represented using the articles written by himself. And the relationship between articles can be built when the article has read by the same user.\r\nThis dataset can be used to model the heterogeneous relationship between users and articles, and this graph can be used to build recommendation systems to recommend articles to the user, or potential friends to the user.",
"Hi,\r\n\r\nyou can do the following to load this data into a `Dataset`:\r\n```python\r\nfrom datasets import Dataset\r\nexamples = [\r\n {\r\n \"name\": \"mike\",\r\n \"friends\": [\r\n \"tom\",\r\n \"lily\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"aaaaa\",\r\n \"reader\": [\r\n \"tom\",\r\n \"lucy\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"tom\",\r\n \"friends\": [\r\n \"mike\",\r\n \"bbb\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"xxxxx\",\r\n \"reader\": [\r\n \"tom\",\r\n \"qqqq\"\r\n ]\r\n }\r\n ]\r\n }\r\n]\r\n\r\nkeys = examples[0].keys()\r\nvalues = [ex.values() for ex in examples]\r\ndataset = Dataset.from_dict({k: list(v) for k, v in zip(keys, zip(*values))})\r\n```\r\n\r\nLet us know if this works for you.",
"Thank you so much, and that works! I also have a question that if the dataset is very large, that cannot be loaded into the memory. How to create the Dataset?",
"If your dataset doesn't fit in memory, store it in a local file and load it from there. Check out [this chapter](https://huggingface.co/docs/datasets/master/loading_datasets.html#from-local-files) in the docs for more info.",
"Nice! Thanks for your help."
] | 1,622,295,321,000 | 1,622,596,863,000 | 1,622,596,863,000 | NONE | null | Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''.
Although I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets.
Thank you very much for your help. | {
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https://api.github.com/repos/huggingface/datasets/issues/2425 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2425/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2425/comments | https://api.github.com/repos/huggingface/datasets/issues/2425/events | https://github.com/huggingface/datasets/pull/2425 | 906,385,457 | MDExOlB1bGxSZXF1ZXN0NjU3NDAwMjM3 | 2,425 | Fix Docstring Mistake: dataset vs. metric | {
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"IMO this PR is ready for review. I do not know why tests fail...",
"The CI fail is unrelated to this PR, and it has been fixed on master, merging :)",
"> I just have one comment: we use rouge, not rogue :p\r\n\r\nOops!",
"rebased on master"
] | 1,622,268,593,000 | 1,622,535,484,000 | 1,622,535,484,000 | CONTRIBUTOR | null | PR to fix #2412 | {
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https://api.github.com/repos/huggingface/datasets/issues/2424 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2424/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2424/comments | https://api.github.com/repos/huggingface/datasets/issues/2424/events | https://github.com/huggingface/datasets/issues/2424 | 906,193,679 | MDU6SXNzdWU5MDYxOTM2Nzk= | 2,424 | load_from_disk and save_to_disk are not compatible with each other | {
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"Hi,\r\n\r\n`load_dataset` returns an instance of `DatasetDict` if `split` is not specified, so instead of `Dataset.load_from_disk`, use `DatasetDict.load_from_disk` to load the dataset from disk.",
"Thanks it worked!",
"Though I see a stream of issues open by people lost between datasets and datasets dicts so maybe there is here something that could be better in terms of UX. Could be better error handling or something else smarter to even avoid said errors but maybe we should think about this. Reopening to use this issue as a discussion place but feel free to open a new open if you prefer @lhoestq @albertvillanova ",
"We should probably improve the error message indeed.\r\n\r\nAlso note that there exists a function `load_from_disk` that can load a Dataset or a DatasetDict. Under the hood it calls either `Dataset.load_from_disk` or `DatasetDict.load_from_disk`:\r\n\r\n\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ndataset_dict = load_from_disk(\"path/to/dataset/dict\")\r\nsingle_dataset = load_from_disk(\"path/to/single/dataset\")\r\n```",
"I just opened #2437 to improve the error message",
"Superseded by #2462 "
] | 1,622,243,230,000 | 1,623,180,152,000 | 1,623,180,152,000 | NONE | null | ## Describe the bug
load_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("art")
dataset.save_to_disk("mydir")
d = Dataset.load_from_disk("mydir")
```
## Expected results
It is expected that these two functions be the reverse of each other without more manipulation
## Actual results
FileNotFoundError: [Errno 2] No such file or directory: 'mydir/art/state.json'
## Environment info
- `datasets` version: 1.6.2
- Platform: Linux-5.4.0-73-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
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https://api.github.com/repos/huggingface/datasets/issues/2423 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2423/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2423/comments | https://api.github.com/repos/huggingface/datasets/issues/2423/events | https://github.com/huggingface/datasets/pull/2423 | 905,935,753 | MDExOlB1bGxSZXF1ZXN0NjU2OTc5MjA5 | 2,423 | add `desc` in `map` for `DatasetDict` object | {
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"The CI error is unrelated to the PR, merging",
"@lhoestq, can we release this feature if you guys are planning for any patch release for Datasets. It'll slow down [#11927](https://github.com/huggingface/transformers/pull/11927) otherwise :/ ",
"Sure definitely, having a discrepancy between Dataset.map and DatasetDict.map is an issue that we should fix and include in a patch release. Will do it in the coming days"
] | 1,622,230,124,000 | 1,622,472,683,000 | 1,622,466,484,000 | CONTRIBUTOR | null | `desc` in `map` currently only works with `Dataset` objects. This PR adds support for `DatasetDict` objects as well | {
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https://api.github.com/repos/huggingface/datasets/issues/2422 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2422/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2422/comments | https://api.github.com/repos/huggingface/datasets/issues/2422/events | https://github.com/huggingface/datasets/pull/2422 | 905,568,548 | MDExOlB1bGxSZXF1ZXN0NjU2NjM3MzY1 | 2,422 | Fix save_to_disk nested features order in dataset_info.json | {
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The order of the nested features matters (pyarrow limitation), but the save_to_disk method was saving the features types as JSON with `sort_keys=True`, which was breaking the order of the nested features. | {
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https://api.github.com/repos/huggingface/datasets/issues/2421 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2421/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2421/comments | https://api.github.com/repos/huggingface/datasets/issues/2421/events | https://github.com/huggingface/datasets/pull/2421 | 905,549,756 | MDExOlB1bGxSZXF1ZXN0NjU2NjIwMTM3 | 2,421 | doc: fix typo HF_MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES | {
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https://api.github.com/repos/huggingface/datasets/issues/2420 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2420/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2420/comments | https://api.github.com/repos/huggingface/datasets/issues/2420/events | https://github.com/huggingface/datasets/pull/2420 | 904,821,772 | MDExOlB1bGxSZXF1ZXN0NjU1OTQ1ODgw | 2,420 | Updated Dataset Description | {
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https://api.github.com/repos/huggingface/datasets/issues/2419 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2419/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2419/comments | https://api.github.com/repos/huggingface/datasets/issues/2419/events | https://github.com/huggingface/datasets/pull/2419 | 904,347,339 | MDExOlB1bGxSZXF1ZXN0NjU1NTA1OTM1 | 2,419 | adds license information for DailyDialog. | {
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"Thanks! Can you also add it as metadata in the YAML block at the top of the file?\r\n\r\nShould be in the form:\r\n\r\n```\r\nlicenses:\r\n- cc-by-sa-4.0\r\n```",
"seems like we need to add all the other tags ? \r\n``` \r\nif error_messages:\r\n> raise ValueError(\"\\n\".join(error_messages))\r\nE ValueError: The following issues have been found in the dataset cards:\r\nE YAML tags:\r\nE __init__() missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'languages', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'\r\n```",
"I'll let @lhoestq or @yjernite chime in (and maybe complete/merge). Thanks!",
"Looks like CircleCI has an incident. Let's wait for it to be working again and make sure the CI is green",
"The remaining error is unrelated to this PR, merging"
] | 1,622,156,622,000 | 1,622,467,012,000 | 1,622,467,012,000 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2418 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2418/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2418/comments | https://api.github.com/repos/huggingface/datasets/issues/2418/events | https://github.com/huggingface/datasets/pull/2418 | 904,051,497 | MDExOlB1bGxSZXF1ZXN0NjU1MjM2OTEz | 2,418 | add utf-8 while reading README | {
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"Can you please add encoding to this line as well to fix the issue (and maybe replace `path.open(...)` with `open(path, ...)`)?\r\nhttps://github.com/huggingface/datasets/blob/7bee4be44706a59b084b9b69c4cd00f73ee72f76/src/datasets/utils/metadata.py#L58",
"Sure, in fact even I was thinking of adding this in order to maintain the consistency!"
] | 1,622,139,148,000 | 1,622,800,501,000 | 1,622,800,500,000 | CONTRIBUTOR | null | It was causing tests to fail in Windows (see #2416). In Windows, the default encoding is CP1252 which is unable to decode the character byte 0x9d | {
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https://api.github.com/repos/huggingface/datasets/issues/2417 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2417/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2417/comments | https://api.github.com/repos/huggingface/datasets/issues/2417/events | https://github.com/huggingface/datasets/pull/2417 | 903,956,071 | MDExOlB1bGxSZXF1ZXN0NjU1MTU3NTI4 | 2,417 | Make datasets PEP-561 compliant | {
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"This is super cool, I love that ❤️ "
] | 1,622,132,177,000 | 1,622,207,410,000 | 1,622,207,356,000 | CONTRIBUTOR | null | Allows to type-check datasets with `mypy` when imported as a third-party library
PEP-561: https://www.python.org/dev/peps/pep-0561
MyPy doc on the subject: https://mypy.readthedocs.io/en/stable/installed_packages.html
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https://api.github.com/repos/huggingface/datasets/issues/2416 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2416/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2416/comments | https://api.github.com/repos/huggingface/datasets/issues/2416/events | https://github.com/huggingface/datasets/pull/2416 | 903,932,299 | MDExOlB1bGxSZXF1ZXN0NjU1MTM3NDUy | 2,416 | Add KLUE dataset | {
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"I'm not sure why I got error like below when I auto-generate dummy data \"mrc\" \r\n```\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 0\r\nKeys should be unique and deterministic in nature\r\n```",
"> I'm not sure why I got error like below when I auto-generate dummy data \"mrc\"\r\n> \r\n> ```\r\n> datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\n> Found duplicate Key: 0\r\n> Keys should be unique and deterministic in nature\r\n> ```\r\n\r\nPlease check out the suggestion below. I think it might be a cause.",
"> > I'm not sure why I got error like below when I auto-generate dummy data \"mrc\"\r\n> > ```\r\n> > datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\n> > Found duplicate Key: 0\r\n> > Keys should be unique and deterministic in nature\r\n> > ```\r\n> \r\n> Please check out the suggestion below. I think it might be a cause.\r\n\r\nThe problem was `id_` in mrc when yield was not unique. (I used index in `enumerate(paragraphs)` by mistake)\r\nI fixed it and update all the things",
"To fix the CI you can just merge master into your branch and it should be all green hopefully :)",
"@lhoestq\r\nThanks for reviewing!\r\n\r\nIt's harder than I thought to add dataset card. 😅 \r\nI checked and updated your suggestion (script, readme details, dummy data). \r\n\r\ndummy data is little bit larger than expected because `ner` dataset is about 80 lines and `dp` dataset is about 25 lines to avoid 0 examples.\r\n\r\nI'm not sure why some CI keep fails, can u check for this?",
"Thanks ! That makes sense for ner and dp\r\n\r\nFor mrc on the other hand there are still too many examples, maybe you can generate the dummy data for 5 examples for all tasks except ner and dp ?",
"> Thanks ! That makes sense for ner and dp\r\n> \r\n> For mrc on the other hand there are still too many examples, maybe you can generate the dummy data for 5 examples for all tasks except ner and dp ?\r\n\r\nYes, I generate default lines in dataset-cli for other dataset except \"dp\" and \"ner\"\r\nI fixed mrc dataset, hope it's fine now :)\r\n\r\nthe reason CI failed was I forgot to merge master into my branch 😅 "
] | 1,622,130,591,000 | 1,623,250,802,000 | 1,622,828,715,000 | CONTRIBUTOR | null | Add `KLUE (Korean Language Understanding Evaluation)` dataset released recently from [paper](https://arxiv.org/abs/2105.09680), [github](https://github.com/KLUE-benchmark/KLUE) and [webpage](https://klue-benchmark.com/tasks).
Please let me know if there's anything missing in the code or README.
Thanks!
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"It actually seems to happen all the time in above configuration:\r\n* the function `filter_by_duration` correctly loads cached processed dataset\r\n* the function `prepare_dataset` is always reexecuted\r\n\r\nI end up solving the issue by saving to disk my dataset at the end but I'm still wondering if it's a bug or limitation here.",
"Hi ! The hash used for caching `map` results is the fingerprint of the resulting dataset. It is computed using three things:\r\n- the old fingerprint of the dataset\r\n- the hash of the function\r\n- the hash of the other parameters passed to `map`\r\n\r\nYou can compute the hash of your function (or any python object) with\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nmy_func = lambda x: x + 1\r\nprint(Hasher.hash(my_func))\r\n```\r\n\r\nIf `prepare_dataset` is always executed, maybe this is because your `processor` has a different hash each time you want to execute it.",
"> If `prepare_dataset` is always executed, maybe this is because your `processor` has a different hash each time you want to execute it.\r\n\r\nYes I think that was the issue.\r\n\r\nFor the hash of the function:\r\n* does it consider just the name or the actual code of the function\r\n* does it consider variables that are not passed explicitly as parameters to the functions (such as the processor here)",
"> does it consider just the name or the actual code of the function\r\n\r\nIt looks at the name and the actual code and all variables such as recursively. It uses `dill` to do so, which is based on `pickle`.\r\nBasically the hash is computed using the pickle bytes of your function (computed using `dill` to support most python objects).\r\n\r\n> does it consider variables that are not passed explicitly as parameters to the functions (such as the processor here)\r\n\r\nYes it does thanks to recursive pickling.",
"Thanks for these explanations. I'm closing the issue."
] | 1,622,130,006,000 | 1,622,639,747,000 | 1,622,639,747,000 | CONTRIBUTOR | null | ## Describe the bug
I have a large dataset (common_voice, english) where I use several map and filter functions.
Sometimes my cached datasets after specific functions are not loaded.
I always use the same arguments, same functions, no seed…
## Steps to reproduce the bug
```python
def filter_by_duration(batch):
return (
batch["duration"] <= 10
and batch["duration"] >= 1
and len(batch["target_text"]) > 5
)
def prepare_dataset(batch):
batch["input_values"] = processor(
batch["speech"], sampling_rate=batch["sampling_rate"][0]
).input_values
with processor.as_target_processor():
batch["labels"] = processor(batch["target_text"]).input_ids
return batch
train_dataset = train_dataset.filter(
filter_by_duration,
remove_columns=["duration"],
num_proc=data_args.preprocessing_num_workers,
)
# PROBLEM HERE -> below function is reexecuted and cache is not loaded
train_dataset = train_dataset.map(
prepare_dataset,
remove_columns=train_dataset.column_names,
batch_size=training_args.per_device_train_batch_size,
batched=True,
num_proc=data_args.preprocessing_num_workers,
)
# Later in script
set_caching_enabled(False)
# apply map on trained model to eval/test sets
```
## Expected results
The cached dataset should always be reloaded.
## Actual results
The function is reexecuted.
I have access to cached files `cache-xxxxx.arrow`.
Is there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)?
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29
- Python version: 3.8.5
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No | {
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https://api.github.com/repos/huggingface/datasets/issues/2414 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2414/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2414/comments | https://api.github.com/repos/huggingface/datasets/issues/2414/events | https://github.com/huggingface/datasets/pull/2414 | 903,877,096 | MDExOlB1bGxSZXF1ZXN0NjU1MDg5OTIw | 2,414 | Update README.md | {
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"Merging since the CI error is unrelated to this PR and has been fixed on master",
"Thank you for taking a look at the CI error - I was a bit confused with that. Thanks!"
] | 1,622,127,199,000 | 1,624,887,974,000 | 1,624,885,496,000 | CONTRIBUTOR | null | Provides description of data instances and dataset features
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https://api.github.com/repos/huggingface/datasets/issues/2413 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2413/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2413/comments | https://api.github.com/repos/huggingface/datasets/issues/2413/events | https://github.com/huggingface/datasets/issues/2413 | 903,777,557 | MDU6SXNzdWU5MDM3Nzc1NTc= | 2,413 | AttributeError: 'DatasetInfo' object has no attribute 'task_templates' | {
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"Hi ! Can you try using a more up-to-date version ? We added the task_templates in `datasets` 1.7.0.\r\n\r\nIdeally when you're working on new datasets, you should install and use the local version of your fork of `datasets`. Here I think you tried to run the 1.7.0 tests with the 1.6.2 code"
] | 1,622,123,068,000 | 1,622,509,547,000 | 1,622,509,547,000 | CONTRIBUTOR | null | ## Describe the bug
Hello,
I'm trying to add dataset and contribute, but test keep fail with below cli.
` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<my_dataset>`
## Steps to reproduce the bug
It seems like a bug when I see an error with the existing dataset, not the dataset I'm trying to add.
` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<any_dataset>`
## Expected results
All test passed
## Actual results
```
# check that dataset is not empty
self.parent.assertListEqual(sorted(dataset_builder.info.splits.keys()), sorted(dataset))
for split in dataset_builder.info.splits.keys():
# check that loaded datset is not empty
self.parent.assertTrue(len(dataset[split]) > 0)
# check that we can cast features for each task template
> task_templates = dataset_builder.info.task_templates
E AttributeError: 'DatasetInfo' object has no attribute 'task_templates'
tests/test_dataset_common.py:175: AttributeError
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Darwin-20.4.0-x86_64-i386-64bit
- Python version: 3.7.7
- PyTorch version (GPU?): 1.7.0 (False)
- Tensorflow version (GPU?): 2.3.0 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
| {
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https://api.github.com/repos/huggingface/datasets/issues/2412 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2412/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2412/comments | https://api.github.com/repos/huggingface/datasets/issues/2412/events | https://github.com/huggingface/datasets/issues/2412 | 903,769,151 | MDU6SXNzdWU5MDM3NjkxNTE= | 2,412 | Docstring mistake: dataset vs. metric | {
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"> I can provide a PR l8er...\r\n\r\nSee #2425 "
] | 1,622,122,751,000 | 1,622,535,484,000 | 1,622,535,484,000 | CONTRIBUTOR | null | This:
https://github.com/huggingface/datasets/blob/d95b95f8cf3cb0cff5f77a675139b584dcfcf719/src/datasets/load.py#L582
Should better be something like:
`a metric identifier on HuggingFace AWS bucket (list all available metrics and ids with ``datasets.list_metrics()``)`
I can provide a PR l8er... | {
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https://api.github.com/repos/huggingface/datasets/issues/2411 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2411/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2411/comments | https://api.github.com/repos/huggingface/datasets/issues/2411/events | https://github.com/huggingface/datasets/pull/2411 | 903,671,778 | MDExOlB1bGxSZXF1ZXN0NjU0OTAzNjg2 | 2,411 | Add DOI badge to README | {
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https://api.github.com/repos/huggingface/datasets/issues/2410 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2410/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2410/comments | https://api.github.com/repos/huggingface/datasets/issues/2410/events | https://github.com/huggingface/datasets/pull/2410 | 903,613,676 | MDExOlB1bGxSZXF1ZXN0NjU0ODUwMjY4 | 2,410 | fix #2391 add original answers in kilt-TriviaQA | {
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"LGTM, but I'm not sure what's going on with the Unix tests @lhoestq ",
"The CI error is unrelated to this PR, it's been fixed now on master.",
"Thanks @PaulLerner !",
"> #- [ ] - Hey@fr[fr_fr**fr~~fr `fr```\nFR\n````~~**_]()",
"Oh that was unexpected. I didn't know pokemons were into NLP"
] | 1,622,116,469,000 | 1,623,760,557,000 | 1,623,691,750,000 | CONTRIBUTOR | null | cc @yjernite is it ok like this? | {
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https://api.github.com/repos/huggingface/datasets/issues/2409 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2409/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2409/comments | https://api.github.com/repos/huggingface/datasets/issues/2409/events | https://github.com/huggingface/datasets/pull/2409 | 903,441,398 | MDExOlB1bGxSZXF1ZXN0NjU0Njk3NjA0 | 2,409 | Add HF_ prefix to env var MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES | {
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"I thought the renaming was suggested only for the env var, and not for the config variable... As you think is better! ;)",
"I think it's better if they match, so that users understand directly that they're directly connected",
"Well, if you're not concerned about back-compat here, perhaps it could be renamed and shortened too ;)\r\n\r\nI'd suggest one of:\r\n\r\n* `HF_DATASETS_IN_MEMORY_MAX_SIZE`\r\n* `HF_DATASETS_MAX_IN_MEMORY_SIZE`\r\n\r\nthe itention is to:\r\n1. make it consistent with all the other `datasets` env vars which all start with `HF_DATASETS_`, e.g.:\r\n```\r\nHF_DATASETS_CACHE\r\nHF_DATASETS_OFFLINE \r\n```\r\n2. allow to recode in the future to support 1M, 4K, 1T and not just bytes - bytes is not a great choice for this type of variable since it will be at least X Mbytes for most reasonable uses.\r\n\r\nAnd I agree with @albertvillanova that the config variable name shouldn't have the HF prefix - it's preaching to the choir - the user already knows it's a local variable. \r\n\r\nThe only reason we prefix env vars, is because they are used outside of the software.\r\n\r\nBut I do see a good point of you trying to make things consistent too. How about this:\r\n\r\n`config.IN_MEMORY_MAX_SIZE` (or whatever the final env var will be minus `HF_DATASETS_` prefix).\r\n\r\nThis is of course just my opinion.\r\n\r\n",
"Thanks for the comment :)\r\nI like both propositions, and I agree this would be better in order to allow support for 1M, 1T etc. \r\nRegarding the prefix of the variable in config.py I don't have a strong opinion. I just added it for consistency with the other variables that default to the env variables like HF_DATASETS_CACHE. However I agree this would be nice to have shorter names so I'm not against removing the prefix either. Since the feature is relatively new, I think we can still allow ourself to rename it",
"Awesome, \r\n\r\nLet's use then:\r\n\r\n- `HF_DATASETS_IN_MEMORY_MAX_SIZE` for the env var\r\n- `config.IN_MEMORY_MAX_SIZE` for config.\r\n\r\nand for now bytes will be documented as the only option and down the road add support for K/M/G.\r\n\r\n@albertvillanova, does that sound good to you?",
"Great!!! 🤗 ",
"Did I miss a PR with this change?\r\n\r\nI want to make sure to add it to transformers tests to avoid the overheard of rebuilding the datasets.\r\n\r\nThank you!",
"@stas00 I'm taking on this now that I have finally finished the collaborative training experiment. Sorry for the delay.",
"Yes, of course! Thank you for taking care of it, @albertvillanova ",
"Actually, why is this feature on by default? \r\n\r\nUsers are very unlikely to understand what is going on or to know where to look. Should it at the very least emit a warning that this was done w/o asking the user to do so and how to turn it off?\r\n\r\nIMHO, this feature should be enabled explicitly by those who want it and not be On by default. This is an optimization that benefits only select users and is a burden on the rest.\r\n\r\nIn my line of dev/debug work (multiple short runs that have to be very fast) now I have to remember to disable this feature explicitly on every machine I work :(\r\n",
"Having the dataset in memory is nice to get the speed but I agree that the lack of caching for dataset in memory is an issue. By default we always had caching on.\r\nHere the issue is that in-memory datasets are still not able to use the cache - we should fix this asap IMO.\r\n\r\nHere is the PR that fixes this: https://github.com/huggingface/datasets/pull/2329",
"But why do they have to be datasets in memory in the first place? Why not just have the default that all datasets are normal and are cached which seems to be working solidly. And only enable in memory datasets explicitly if the user chooses to and then it doesn't matter if it's cached on not for the majority of the users who will not make this choice.\r\n\r\nI mean the definition of in-memory-datasets is very arbitrary - why 250MB and not 5GB? It's very likely that the user will want to set this threshold based on their RAM availability. So while doing that they can enable the in-memory-datasets. Unless I'm missing something here.\r\n\r\nThe intention here is that things work well in general out of the box, and further performance optimizations are available to those who know what they are doing.\r\n",
"This is just for speed improvements, especially for data exploration/experiments in notebooks. Ideally it shouldn't have changed anything regarding caching behavior in the first place (i.e. have the caching enabled by default).\r\n\r\nThe 250MB limit has also been chosen to not create unexpected high memory usage on small laptops.",
"Won't it be more straight-forward to create a performance optimization doc and share all these optimizations there? That way the user will be in the knowing and will be able to get faster speeds if their RAM is large. \r\n\r\nIt is hard for me to tell the average size of a dataset an average user will have, but my gut feeling is that many NLP datasets are larger than 250MB. Please correct me if I'm wrong.\r\n\r\nBut at the same time what you're saying is that once https://github.com/huggingface/datasets/pull/2329 is completed and merged, the in-memory-datasets will be cached too. So if I wait long enough the whole issue will go away altogether, correct?"
] | 1,622,106,420,000 | 1,623,168,055,000 | 1,622,108,021,000 | MEMBER | null | As mentioned in https://github.com/huggingface/datasets/pull/2399 the env var should be prefixed by HF_ | {
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https://api.github.com/repos/huggingface/datasets/issues/2408 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2408/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2408/comments | https://api.github.com/repos/huggingface/datasets/issues/2408/events | https://github.com/huggingface/datasets/pull/2408 | 903,422,648 | MDExOlB1bGxSZXF1ZXN0NjU0NjgxMzE4 | 2,408 | Fix head_qa keys | {
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https://api.github.com/repos/huggingface/datasets/issues/2407 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2407/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2407/comments | https://api.github.com/repos/huggingface/datasets/issues/2407/events | https://github.com/huggingface/datasets/issues/2407 | 903,111,755 | MDU6SXNzdWU5MDMxMTE3NTU= | 2,407 | .map() function got an unexpected keyword argument 'cache_file_name' | {
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"Hi @cindyxinyiwang,\r\nDid you try adding `.arrow` after `cache_file_name` argument? Here I think they're expecting something like that only for a cache file:\r\nhttps://github.com/huggingface/datasets/blob/e08362256fb157c0b3038437fc0d7a0bbb50de5c/src/datasets/arrow_dataset.py#L1556-L1558",
"Hi ! `cache_file_name` is an argument of the `Dataset.map` method. Can you check that your `dataset` is indeed a `Dataset` object ?\r\n\r\nIf you loaded several splits, then it would actually be a `DatasetDict` (one dataset per split, in a dictionary).\r\nIn this case, since there are several datasets in the dict, the `DatasetDict.map` method requires a `cache_file_names` argument (with an 's'), so that you can provide one file name per split.",
"I think you are right. I used cache_file_names={data1: name1, data2: name2} and it works. Thank you!"
] | 1,622,080,466,000 | 1,622,123,200,000 | 1,622,123,200,000 | NONE | null | ## Describe the bug
I'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that ".map() function got an unexpected keyword argument 'cache_file_name'".
I believe I'm using the latest dataset 1.6.2. Also seems like the document and the actual code indicates there is an argument 'cache_file_name' for the .map() function.
Here is the code I use
## Steps to reproduce the bug
```datasets = load_from_disk(dataset_path=my_path)
[...]
def tokenize_function(examples):
return tokenizer(examples[text_column_name])
logger.info("Mapping dataset to tokenized dataset.")
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=True,
cache_file_name="my_tokenized_file"
)
```
## Actual results
tokenized_datasets = datasets.map(
TypeError: map() got an unexpected keyword argument 'cache_file_name'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:1.6.2
- Platform:Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.10
- Python version:3.8.5
- PyArrow version:3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2406 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2406/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2406/comments | https://api.github.com/repos/huggingface/datasets/issues/2406/events | https://github.com/huggingface/datasets/issues/2406 | 902,643,844 | MDU6SXNzdWU5MDI2NDM4NDQ= | 2,406 | Add guide on using task templates to documentation | {
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] | null | [] | 1,622,046,506,000 | 1,622,046,506,000 | null | MEMBER | null | Once we have a stable API on the text classification and question answering task templates, add a guide on how to use them in the documentation.
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https://api.github.com/repos/huggingface/datasets/issues/2405 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2405/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2405/comments | https://api.github.com/repos/huggingface/datasets/issues/2405/events | https://github.com/huggingface/datasets/pull/2405 | 901,227,658 | MDExOlB1bGxSZXF1ZXN0NjUyNzA2OTk1 | 2,405 | Add dataset tags | {
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"Thanks!"
] | 1,621,969,049,000 | 1,622,048,056,000 | 1,622,047,207,000 | CONTRIBUTOR | null | The dataset tags were provided by Peter Clark following the guide. | {
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https://api.github.com/repos/huggingface/datasets/issues/2404 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2404/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2404/comments | https://api.github.com/repos/huggingface/datasets/issues/2404/events | https://github.com/huggingface/datasets/pull/2404 | 901,179,832 | MDExOlB1bGxSZXF1ZXN0NjUyNjYzOTcz | 2,404 | Paperswithcode dataset mapping | {
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"messed up my branch, repushing",
"live mapping can be found at https://huggingface.co/api/pwc/datasets-mapping and will be kept up to date going forward"
] | 1,621,966,466,000 | 1,622,028,085,000 | 1,622,027,838,000 | MEMBER | null | This is a continuation of https://github.com/huggingface/huggingface_hub/pull/43, encoded directly inside dataset cards.
As discussed:
- `paperswithcode_id: null` when the dataset doesn't exist on paperswithcode's side.
- I've added this new key at the end of the yaml instead of ordering all keys alphabetically as pyyaml's default. No strong opinion on that one though
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https://api.github.com/repos/huggingface/datasets/issues/2403 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2403/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2403/comments | https://api.github.com/repos/huggingface/datasets/issues/2403/events | https://github.com/huggingface/datasets/pull/2403 | 900,059,014 | MDExOlB1bGxSZXF1ZXN0NjUxNjcxMTMw | 2,403 | Free datasets with cache file in temp dir on exit | {
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} | [] | closed | false | null | [] | null | [] | 1,621,894,511,000 | 1,622,049,919,000 | 1,622,047,169,000 | CONTRIBUTOR | null | This PR properly cleans up the memory-mapped tables that reference the cache files inside the temp dir.
Since the built-in `_finalizer` of `TemporaryDirectory` can't be modified, this PR defines its own `TemporaryDirectory` class that accepts a custom clean-up function.
Fixes #2402 | {
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https://api.github.com/repos/huggingface/datasets/issues/2402 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2402/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2402/comments | https://api.github.com/repos/huggingface/datasets/issues/2402/events | https://github.com/huggingface/datasets/issues/2402 | 900,025,329 | MDU6SXNzdWU5MDAwMjUzMjk= | 2,402 | PermissionError on Windows when using temp dir for caching | {
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] | closed | false | null | [] | null | [] | 1,621,891,379,000 | 1,622,047,169,000 | 1,622,047,169,000 | CONTRIBUTOR | null | Currently, the following code raises a PermissionError on master if working on Windows:
```python
# run as a script or call exit() in REPL to initiate the temp dir cleanup
from datasets import *
d = load_dataset("sst", split="train", keep_in_memory=False)
set_caching_enabled(False)
d.map(lambda ex: ex)
```
Error stack trace:
```
Traceback (most recent call last):
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 624, in _exitfunc
f()
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 548, in __call__
return info.func(*info.args, **(info.kwargs or {}))
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\tempfile.py", line 799, in _cleanup
_shutil.rmtree(name)
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 500, in rmtree
return _rmtree_unsafe(path, onerror)
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 395, in _rmtree_unsafe
onerror(os.unlink, fullname, sys.exc_info())
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 393, in _rmtree_unsafe
os.unlink(fullname)
PermissionError: [WinError 5] Access is denied: 'C:\\Users\\Mario\\AppData\\Local\\Temp\\tmp20epyhmq\\cache-87a87ffb5a956e68.arrow'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2401 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2401/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2401/comments | https://api.github.com/repos/huggingface/datasets/issues/2401/events | https://github.com/huggingface/datasets/issues/2401 | 899,910,521 | MDU6SXNzdWU4OTk5MTA1MjE= | 2,401 | load_dataset('natural_questions') fails with "ValueError: External features info don't match the dataset" | {
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"I faced the similar problem. Downgrading datasets to 1.5.0 fixed it.",
"Thanks for reporting, I'm looking into it",
"I just opened #2438 to fix this :)",
"Hi ! This has been fixed in the 1.8.0 release of `datasets`"
] | 1,621,881,533,000 | 1,623,229,645,000 | 1,623,229,645,000 | NONE | null | ## Describe the bug
load_dataset('natural_questions') throws ValueError
## Steps to reproduce the bug
```python
from datasets import load_dataset
datasets = load_dataset('natural_questions', split='validation[:10]')
```
## Expected results
Call to load_dataset returns data.
## Actual results
```
Using custom data configuration default
Reusing dataset natural_questions (/mnt/d/huggingface/datasets/natural_questions/default/0.0.2/19bc04755018a3ad02ee74f7045cde4ba9b4162cb64450a87030ab786b123b76)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-d55ab8a8cc1c> in <module>
----> 1 datasets = load_dataset('natural_questions', split='validation[:10]', cache_dir='/mnt/d/huggingface/datasets')
~/miniconda3/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)
756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
757 )
--> 758 ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
759 if save_infos:
760 builder_instance._save_infos()
~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in as_dataset(self, split, run_post_process, ignore_verifications, in_memory)
735
736 # Create a dataset for each of the given splits
--> 737 datasets = utils.map_nested(
738 partial(
739 self._build_single_dataset,
~/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)
193 # Singleton
194 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 195 return function(data_struct)
196
197 disable_tqdm = bool(logger.getEffectiveLevel() > INFO)
~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _build_single_dataset(self, split, run_post_process, ignore_verifications, in_memory)
762
763 # Build base dataset
--> 764 ds = self._as_dataset(
765 split=split,
766 in_memory=in_memory,
~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _as_dataset(self, split, in_memory)
838 in_memory=in_memory,
839 )
--> 840 return Dataset(**dataset_kwargs)
841
842 def _post_process(self, dataset: Dataset, resources_paths: Dict[str, str]) -> Optional[Dataset]:
~/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)
271 assert self._fingerprint is not None, "Fingerprint can't be None in a Dataset object"
272 if self.info.features.type != inferred_features.type:
--> 273 raise ValueError(
274 "External features info don't match the dataset:\nGot\n{}\nwith type\n{}\n\nbut expected something like\n{}\nwith type\n{}".format(
275 self.info.features, self.info.features.type, inferred_features, inferred_features.type
ValueError: External features info don't match the dataset:
Got
{'id': Value(dtype='string', id=None), 'document': {'title': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None), 'html': Value(dtype='string', id=None), 'tokens': Sequence(feature={'token': Value(dtype='string', id=None), 'is_html': Value(dtype='bool', id=None)}, length=-1, id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': Sequence(feature={'id': Value(dtype='string', id=None), 'long_answer': {'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None)}, 'short_answers': Sequence(feature={'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}, length=-1, id=None), 'yes_no_answer': ClassLabel(num_classes=2, names=['NO', 'YES'], names_file=None, id=None)}, length=-1, id=None)}
with type
struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<start_token: int64, end_token: int64, start_byte: int64, end_byte: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<title: string, url: string, html: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>>, id: string, question: struct<text: string, tokens: list<item: string>>>
but expected something like
{'id': Value(dtype='string', id=None), 'document': {'html': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'tokens': {'is_html': Sequence(feature=Value(dtype='bool', id=None), length=-1, id=None), 'token': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'url': Value(dtype='string', id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': {'id': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'long_answer': [{'end_byte': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'start_token': Value(dtype='int64', id=None)}], 'short_answers': [{'end_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'end_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}], 'yes_no_answer': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}}
with type
struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<end_byte: int64, end_token: int64, start_byte: int64, start_token: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<html: string, title: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>, url: string>, id: string, question: struct<text: string, tokens: list<item: string>>>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyTorch version (GPU?): 1.6.0 (False)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
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https://api.github.com/repos/huggingface/datasets/issues/2400 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2400/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2400/comments | https://api.github.com/repos/huggingface/datasets/issues/2400/events | https://github.com/huggingface/datasets/issues/2400 | 899,867,212 | MDU6SXNzdWU4OTk4NjcyMTI= | 2,400 | Concatenate several datasets with removed columns is not working. | {
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] | closed | false | null | [] | null | [
"Hi,\r\n\r\ndid you fill out the env info section manually or by copy-pasting the output of the `datasets-cli env` command?\r\n\r\nThis code should work without issues on 1.6.2 version (I'm working on master (1.6.2.dev0 version) and can't reproduce this error).",
"@mariosasko you are right I was still on `1.5.0`. "
] | 1,621,878,015,000 | 1,621,921,921,000 | 1,621,921,919,000 | MEMBER | null | ## Describe the bug
You can't concatenate datasets when you removed columns before.
## Steps to reproduce the bug
```python
from datasets import load_dataset, concatenate_datasets
wikiann= load_dataset("wikiann","en")
wikiann["train"] = wikiann["train"].remove_columns(["langs","spans"])
wikiann["test"] = wikiann["test"].remove_columns(["langs","spans"])
assert wikiann["train"].features.type == wikiann["test"].features.type
concate = concatenate_datasets([wikiann["train"],wikiann["test"]])
```
## Expected results
Merged dataset
## Actual results
```python
ValueError: External features info don't match the dataset:
Got
{'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ner_tags': Sequence(feature=ClassLabel(num_classes=7, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'], names_file=None, id=None), length=-1, id=None), 'langs': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'spans': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
with type
struct<langs: list<item: string>, ner_tags: list<item: int64>, spans: list<item: string>, tokens: list<item: string>>
but expected something like
{'ner_tags': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
with type
struct<ner_tags: list<item: int64>, tokens: list<item: string>>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: ~1.6.2~ 1.5.0
- Platform: macos
- Python version: 3.8.5
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2399 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2399/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2399/comments | https://api.github.com/repos/huggingface/datasets/issues/2399/events | https://github.com/huggingface/datasets/pull/2399 | 899,853,610 | MDExOlB1bGxSZXF1ZXN0NjUxNDk0OTc2 | 2,399 | Add env variable for MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES | {
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"Thank you for clarifying the precedence, @albertvillanova \r\n\r\nIsn't it typically the case where env vars have the highest precedence? \r\n\r\nIn my understanding the point of env vars is to be able to override software w/o needing to touch the code. \r\n\r\nPlease correct me if this is not so in the general case.",
"Hi @stas00, \r\n\r\nWell, I'm not an expert on this topic, but the precedence hierarchy I have normally found is from higher to lower:\r\n- command line parameters\r\n- env vars\r\n- config files\r\nSo yes, normally env vars have precedence over configuration files.\r\n\r\nAnyway, for Datasets, there are no configuration files. The _in-memory_ config is set from default values or env vars (which have precedence over default values). But this is done at import.\r\n\r\nHowever, once the library is imported, the user can modify the in-memory config, and this will have precedence over the rest of mechanisms (which take place only at import).",
"In my limited experience env vars are typically above cmd line args, so that one can override scripts with cmd lines using env vars, but usually one then uses env vars inside cmd line args, so it's loud and clear.\r\n\r\nFor example specifying a specific gpu number on a command line will depend on `CUDA_VISIBLE_DEVICES` so gpu0 will be different if someone sets `CUDA_VISIBLE_DEVICES=2,3` vs `CUDA_VISIBLE_DEVICES=0,1`.\r\n\r\n> However, once the library is imported, the user can modify the in-memory config, and this will have precedence over the rest of mechanisms (which take place only at import).\r\n\r\nAnd this is exactly the problem we are trying to solve here. For a good reason HF examples don't want to use `keep_in_memory=False`, and they may choose to now set `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` and which means we again can't override it via env var.\r\n\r\n",
"oops, sorry, didn't think earlier - do we need to prefix this with `HF_DATASETS` or `HF_` like all the other env vars? or is it long enough already to be unique - it's just not telling the user in the config file what projet this variable is for...",
"You're right, I just opened https://github.com/huggingface/datasets/pull/2409"
] | 1,621,876,755,000 | 1,622,106,435,000 | 1,622,045,274,000 | MEMBER | null | Add env variable for `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES`.
This will allow to turn off default behavior: loading in memory (and not caching) small datasets.
Fix #2387. | {
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https://api.github.com/repos/huggingface/datasets/issues/2398 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2398/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2398/comments | https://api.github.com/repos/huggingface/datasets/issues/2398/events | https://github.com/huggingface/datasets/issues/2398 | 899,511,837 | MDU6SXNzdWU4OTk1MTE4Mzc= | 2,398 | News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs | {
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] | open | false | null | [] | null | [] | 1,621,850,614,000 | 1,621,850,614,000 | null | NONE | null | I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi.
```
train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]')
val_ds = load_dataset("news_commentary", "ar-en", split='train[98%:]')
# filtering out examples that are not ar-en translations but ar-hi
val_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True)
```
* I'm fairly new to using datasets so I might be doing something wrong | {
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https://api.github.com/repos/huggingface/datasets/issues/2397 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2397/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2397/comments | https://api.github.com/repos/huggingface/datasets/issues/2397/events | https://github.com/huggingface/datasets/pull/2397 | 899,427,378 | MDExOlB1bGxSZXF1ZXN0NjUxMTMxMTY0 | 2,397 | Fix number of classes in indic_glue sna.bn dataset | {
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"@lhoestq there are many things missing in the README.md file, but this correction is right despite not passing the validation tests...",
"Yes indeed. We run the validation in all modified readme because we think that it is the time when contributors are the most likely to fix a dataset card - or it will never be done"
] | 1,621,844,335,000 | 1,621,960,336,000 | 1,621,960,336,000 | MEMBER | null | As read in the [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.445.pdf), Table 11. | {
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https://api.github.com/repos/huggingface/datasets/issues/2396 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2396/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2396/comments | https://api.github.com/repos/huggingface/datasets/issues/2396/events | https://github.com/huggingface/datasets/issues/2396 | 899,016,308 | MDU6SXNzdWU4OTkwMTYzMDg= | 2,396 | strange datasets from OSCAR corpus | {
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"Hi ! Thanks for reporting\r\ncc @pjox is this an issue from the data ?\r\n\r\nAnyway we should at least mention that OSCAR could contain such contents in the dataset card, you're totally right @jerryIsHere ",
"Hi @jerryIsHere , sorry for the late response! Sadly this is normal, the problem comes form fasttext's classifier which we used to create the original corpus. In general the classifier is not really capable of properly recognizing Yue Chineese so the file ends un being just noise from Common Crawl. Some of these problems with OSCAR were already discussed [here](https://arxiv.org/pdf/2103.12028.pdf) but we are working on explicitly documenting the problems by language on our website. In fact, could please you open an issue on [our repo](https://github.com/oscar-corpus/oscar-website/issues) as well so that we can track it?"
] | 1,621,775,162,000 | 1,623,938,077,000 | null | CONTRIBUTOR | null | 
From the [official site ](https://oscar-corpus.com/), the Yue Chinese dataset should have 2.2KB data.
7 training instances is obviously not a right number.
As I can read Yue Chinese, I call tell the last instance is definitely not something that would appear on Common Crawl.
And even if you don't read Yue Chinese, you can tell the first six instance are problematic.
(It is embarrassing, as the 7 training instances look exactly like something from a pornographic novel or flitting messages in a chat of a dating app)
It might not be the problem of the huggingface/datasets implementation, because when I tried to download the dataset from the official site, I found out that the zip file is corrupted.
I will try to inform the host of OSCAR corpus later.
Awy a remake about this dataset in huggingface/datasets is needed, perhaps after the host of the dataset fixes the issue.
> Hi @jerryIsHere , sorry for the late response! Sadly this is normal, the problem comes form fasttext's classifier which we used to create the original corpus. In general the classifier is not really capable of properly recognizing Yue Chineese so the file ends un being just noise from Common Crawl. Some of these problems with OSCAR were already discussed [here](https://arxiv.org/pdf/2103.12028.pdf) but we are working on explicitly documenting the problems by language on our website. In fact, could please you open an issue on [our repo](https://github.com/oscar-corpus/oscar-website/issues) as well so that we can track it?
Thanks a lot, the new post is here:
https://github.com/oscar-corpus/oscar-website/issues/11 | {
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