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https://api.github.com/repos/huggingface/datasets/issues/4772
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I_kwDODunzps5O1rID
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AssertionError when using label_cols in to_tf_dataset
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"cc @Rocketknight1 ",
"Hi @lehrig, this is caused by the data collator renaming \"label\" to \"labels\". If you set `label_cols=[\"labels\"]` in the call it will work correctly. However, I agree that the cause of the bug is not obvious, so I'll see if I can make a PR to clarify things when the collator renames columns.",
"Thanks - and wow, that appears like a strange side-effect of the data collator. Is that really needed?\r\n\r\nWhy not make it more explicit? For example, extend `DefaultDataCollator` with an optional property `label_col_name` to be used as label column; only when it is not provided default to `labels` (and document that this happens) for backwards-compatibility? ",
"Haha, I honestly have no idea why our data collators rename `\"label\"` (the standard label column name in our datasets) to `\"labels\"` (the standard label column name input to our models). It's been a pain point when I design TF data pipelines, though, because I don't want to hardcode things like that - especially in `datasets`, because the renaming is something that happens purely at the `transformers` end. I don't think I could make the change in the data collators themselves at this point, because it would break backward compatibility for everything in PyTorch as well as TF.\r\n\r\nIn the most recent version of `transformers` we added a [prepare_tf_dataset](https://huggingface.co/docs/transformers/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset) method to our models which takes care of these details for you, and even chooses appropriate columns and labels for the model you're using. In future we might make that the officially recommended way to convert HF datasets to `tf.data.Dataset`.",
"Interesting, that'd be great especially for clarity. https://huggingface.co/docs/datasets/use_with_tensorflow#data-loading already improved clarity, yet, all those options will still confuse people. Looking forward to those advances in the hope there'll be only 1 way in the future ;)\r\n\r\nAnyways, I am happy for the time being with the work-around you provided. Thank you!"
] | 2022-07-29T21:32:12Z
| 2022-09-12T11:24:46Z
| 2022-09-12T11:24:46Z
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## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator
dataset = load_dataset('glue', 'mrpc', split='train')
tf_dataset = dataset.to_tf_dataset(
columns=["sentence1", "sentence2", "idx"],
label_cols=["label"],
batch_size=16,
collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```
## Expected results
No assertion error.
## Actual results
```
AssertionError: in user code:
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels *
assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.3
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[
"Hi @SaulLu, thanks for reporting.\r\n\r\nI think offline mode is not supported for datasets containing only data files (without any loading script). I'm having a look into this...",
"Thanks for your feedback! \r\n\r\nTo give you a little more info, if you don't set the offline mode flag, the script will load the cache. I first noticed this behavior with the `evaluate` library, and while trying to understand the downloading flow I realized that I had a similar error with datasets.",
"This is an issue we have to fix.",
"This is related to https://github.com/huggingface/datasets/issues/3547",
"Still not fixed? ......",
"#5331 will be helpful to fix this, as it updates the cache directory template to be aligned with the other datasets",
"Any updates ?",
"I'm facing the same problem",
"This issue has been fixed in `datasets` 2.16 by https://github.com/huggingface/datasets/pull/6493. The cache is now working properly :)\r\n\r\nYou just have to update `datasets`:\r\n\r\n```\r\npip install -U datasets\r\n```",
"I'm on version 2.17.0, and this exact problem is still persisting.",
"Can you share some code to reproduce your issue ?\r\n\r\nAlso make sure your cache was populated with recent versions of `datasets`. Datasets cached with old versions may not be reloadable in offline mode, though we did our best to keep as much backward compatibility as possible.",
"I'm not sure if this is related @lhoestq but I am experiencing a similar issue when using offline mode:\r\n\r\n```bash\r\n$ python -c \"from datasets import load_dataset; load_dataset('openai_humaneval', split='test')\"\r\n$ HF_DATASETS_OFFLINE=1 python -c \"from datasets import load_dataset; load_dataset('openai_humaneval', split='test')\"\r\nUsing the latest cached version of the dataset since openai_humaneval couldn't be found on the Hugging Face Hub (offline mode is enabled).\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/dodrio/scratch/projects/2023_071/alignment-handbook/.venv/lib/python3.10/site-packages/datasets/load.py\", line 2556, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/dodrio/scratch/projects/2023_071/alignment-handbook/.venv/lib/python3.10/site-packages/datasets/load.py\", line 2265, in load_dataset_builder\r\n builder_instance: DatasetBuilder = builder_cls(\r\n File \"/dodrio/scratch/projects/2023_071/alignment-handbook/.venv/lib/python3.10/site-packages/datasets/packaged_modules/cache/cache.py\", line 122, in __init__\r\n config_name, version, hash = _find_hash_in_cache(\r\n File \"/dodrio/scratch/projects/2023_071/alignment-handbook/.venv/lib/python3.10/site-packages/datasets/packaged_modules/cache/cache.py\", line 48, in _find_hash_in_cache\r\n raise ValueError(\r\nValueError: Couldn't find cache for openai_humaneval for config 'default'\r\nAvailable configs in the cache: ['openai_humaneval']\r\n```",
"Thanks for reporting @BramVanroy, I managed to reproduce and I opened a fix here: https://github.com/huggingface/datasets/pull/6741",
"Awesome, thanks for the quick fix @lhoestq! Looking forward to update my dependency version list.",
"> Thanks for reporting @BramVanroy, I managed to reproduce and I opened a fix here: #6741\r\n\r\nThanks a lot！ I have faced the same problem. Can I use your fix code to directly replace the existing version code? I noticed that this fix has not been merged yet. Will it affect other functionalities?\r\n",
"I just merged the fix, you can install `datasets` from source or wait for the patch release which will be out in the coming days",
"Hi, a related issue here when loading a single file from a dataset, unable to access it in offline mode (datasets 3.2.0)\n\n```python\nimport os\n# os.environ[\"HF_HUB_OFFLINE\"] = \"1\"\nos.environ[\"HF_TOKEN\"] = \"xxxxxxxxxxxxxx\"\n\nimport datasets\n\ndataset_name = \"uonlp/CulturaX\"\ndata_files = \"fr/fr_part_00038.parquet\"\n\nds = datasets.load_dataset(dataset_name, split='train', data_files=data_files)\nprint(f\"Dataset loaded : {ds}\")\n```\nOnce the file has been cached, I rerun whit the HF_HUB_OFFLINE activated an get this error : \n```\nValueError: Couldn't find cache for uonlp/CulturaX for config 'default-1e725f978350254e'\nAvailable configs in the cache: ['default-2935e8cdcc21c613']\n```"
] | 2022-07-28T12:45:14Z
| 2025-05-04T16:44:59Z
| 2024-01-23T10:58:22Z
|
CONTRIBUTOR
| null | null |
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## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
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[
"I refreshed the dataset viewer manually, it's fixed now. Sorry for the inconvenience.\r\n<img width=\"1557\" alt=\"Capture d’écran 2022-07-28 à 09 17 39\" src=\"https://user-images.githubusercontent.com/1676121/181514666-92d7f8e1-ddc1-4769-84f3-f1edfdb902e8.png\">\r\n\r\n"
] | 2022-07-28T11:21:43Z
| 2022-07-28T13:17:56Z
| 2022-07-28T13:17:48Z
|
NONE
| null | null |
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### Link
https://huggingface.co/datasets/Toygar/turkish-offensive-language-detection
### Description
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
Hi, I provided train.csv, test.csv and valid.csv files. However, viewer says dataset does not exist.
Should I need to do anything else?
### Owner
Yes
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I_kwDODunzps5Otsuk
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Document better when relative paths are transformed to URLs
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[] | 2022-07-28T08:46:27Z
| 2022-08-25T18:34:24Z
| 2022-08-25T18:34:24Z
|
MEMBER
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As discussed with @ydshieh, when passing a relative path as `data_dir` to `load_dataset` of a dataset hosted on the Hub, the relative path is transformed to the corresponding URL of the Hub dataset.
Currently, we mention this in our docs here: [Create a dataset loading script > Download data files and organize splits](https://huggingface.co/docs/datasets/v2.4.0/en/dataset_script#download-data-files-and-organize-splits)
> If the data files live in the same folder or repository of the dataset script, you can just pass the relative paths to the files instead of URLs.
Maybe we should document better how relative paths are handled, not only when creating a dataset loading script, but also when passing to `load_dataset`:
- `data_dir`
- `data_files`
CC: @stevhliu
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Easily create loading script for benchmark comprising multiple huggingface datasets
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"Hi ! I think the simplest is to copy paste the `_split_generators` code from the other datasets and do a bunch of if-else, as in the glue dataset: https://huggingface.co/datasets/glue/blob/main/glue.py#L467",
"Ok, I see. Thank you"
] | 2022-07-27T10:13:38Z
| 2022-07-27T13:58:07Z
| 2022-07-27T13:58:07Z
|
CONTRIBUTOR
| null | null |
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Hi,
I would like to create a loading script for a benchmark comprising multiple huggingface datasets.
The function _split_generators needs to return the files for the respective dataset. However, the files are not always in the same location for each dataset. I want to just make a wrapper dataset that provides a single interface to all the underlying datasets.
I thought about downloading the files with the load_dataset function and then providing the link to the cached file. But this seems a bit inelegant to me. What approach would you propose to do this?
Please let me know if you have any questions.
Cheers,
Joel
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Dataset Viewer issue for yanekyuk/wikikey
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[
"The dataset is empty, as far as I can tell: there are no files in the repository at https://huggingface.co/datasets/yanekyuk/wikikey/tree/main\r\n\r\nMaybe the viewer can display a better message for empty datasets",
"OK. Closing as it's not an error. We will work on making the error message a lot clearer."
] | 2022-07-26T16:25:16Z
| 2022-09-08T08:15:22Z
| 2022-09-08T08:15:22Z
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NONE
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### Link
_No response_
### Description
_No response_
### Owner
_No response_
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I_kwDODunzps5Oj1aP
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|
Allow `list_datasets` to include private datasets
|
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[
"Thanks for opening this issue :)\r\n\r\nIf it can help, I think you can already use `huggingface_hub` to achieve this:\r\n```python\r\n>>> from huggingface_hub import HfApi\r\n>>> [ds_info.id for ds_info in HfApi().list_datasets(use_auth_token=token) if ds_info.private]\r\n['bigscience/xxxx', 'bigscience-catalogue-data/xxxxxxx', ... ]\r\n```\r\n\r\n---------\r\n\r\nThough the latest versions of `huggingface_hub` that contain this feature are not available on python 3.6, so maybe we should first drop support for python 3.6 (see #4460) to update `list_datasets` in `datasets` as well (or we would have to copy/paste some `huggingface_hub` code)",
"Great, thanks @lhoestq the workaround works! I think it would be intuitive to have the support directly in `datasets` but it makes sense to wait given that the workaround exists :)",
"i also think that going forward we should replace more and more implementations inside datasets with the corresponding ones from `huggingface_hub` (same as we're doing in `transformers`)",
"`datasets.list_datasets` is now deprecated in favor of `huggingface_hub.list_datasets` (returns private datasets when `token` is present), so I'm closing this issue."
] | 2022-07-26T10:16:08Z
| 2023-07-25T15:01:49Z
| 2023-07-25T15:01:49Z
|
NONE
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I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the list of all datasets I have permissions to view, including private ones. The only current alternative I see is to use the hub website to manually obtain the list of dataset names - this is in the context of BigScience where respective private spaces contain hundreds of datasets, so not very convenient to list manually.
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|
Remove instructions to generate dummy data from our docs
|
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[
"Note that for me personally, conceptually all the dummy data (even for \"canonical\" datasets) should be superseded by `datasets-server`, which performs some kind of CI/CD of datasets (including the canonical ones)",
"I totally agree: next step should be rethinking if dummy data makes sense for canonical datasets (once we have datasets-server) and eventually remove it.\r\n\r\nBut for now, we could at least start by removing the indication to generate dummy data from our docs."
] | 2022-07-26T07:32:58Z
| 2022-08-02T23:50:30Z
| 2022-08-02T23:50:30Z
|
MEMBER
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In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata
However:
- dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI
- for datasets on the Hub:
- they do not pass any CI test requiring dummy data
- there are no instructions on how they can test their dataset locally using the dummy data
- the generation of the dummy data assumes our GitHub directory structure:
- the dummy data will be generated under `./datasets/<dataset_name>/dummy` even if locally there is no `./datasets` directory (which is the usual case). See issue:
- #4742
CC: @stevhliu
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I_kwDODunzps5Og813
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Dummy data nowhere to be found
|
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"Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst of all, please note that you do not need the dummy data: this was the case when we were adding datasets to the `datasets` library (on this GitHub repo), so that we could test the correct loading of all datasets with our CI. However, this is no longer the case for datasets on the Hub.\r\n- We should definitely update our docs.\r\n\r\nSecond, the dummy data is generated locally:\r\n- in your case, the dummy data will be generated inside the directory: `./datasets/hebban-reviews/dummy`\r\n- please note the preceding `./datasets` directory: the reason for this is that the command to generate the dummy data was specifically created for our `datasets` library, and therefore assumes our directory structure: commands are run from the root directory of our GitHub repo, and datasets scripts are under `./datasets` \r\n\r\n\r\n ",
"I have opened an Issue to update the instructions on dummy data generation:\r\n- #4744",
"Dummy data generation is deprecated now, so I think we can close this issue."
] | 2022-07-25T19:18:42Z
| 2022-11-04T14:04:24Z
| 2022-11-04T14:04:10Z
|
CONTRIBUTOR
| null | null |
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## Describe the bug
To finalize my dataset, I wanted to create dummy data as per the guide and I ran
```shell
datasets-cli dummy_data datasets/hebban-reviews --auto_generate
```
where hebban-reviews is [this repo](https://huggingface.co/datasets/BramVanroy/hebban-reviews). And even though the scripts runs and shows a message at the end that it succeeded, I cannot find the dummy data anywhere. Where is it?
## Expected results
To see the dummy data in the datasets' folder or in the folder where I ran the command.
## Actual results
I see the following message but I cannot find the dummy data anywhere.
```
Dummy data generation done and dummy data test succeeded for config 'filtered''.
Automatic dummy data generation succeeded for all configs of '.\datasets\hebban-reviews\'
```
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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I_kwDODunzps5OYXm8
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|
Download error on scene_parse_150
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[
"Hi! The server with the data seems to be down. I've reported this issue (https://github.com/CSAILVision/sceneparsing/issues/34) in the dataset repo. ",
"The URL seems to work now, and therefore the script as well."
] | 2022-07-22T13:28:28Z
| 2022-09-01T15:37:11Z
| 2022-09-01T15:37:11Z
|
NONE
| null | null |
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```
from datasets import load_dataset
dataset = load_dataset("scene_parse_150", "scene_parsing")
FileNotFoundError: Couldn't find file at http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip
```
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I_kwDODunzps5OYEUc
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Dataset Viewer issue for deepklarity/huggingface-spaces-dataset
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[
"Thanks for reporting. You're right, workers were under-provisioned due to a manual error, and the job queue was full. It's fixed now."
] | 2022-07-22T12:14:18Z
| 2022-07-22T13:46:38Z
| 2022-07-22T13:46:38Z
|
NONE
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### Link
https://huggingface.co/datasets/deepklarity/huggingface-spaces-dataset/viewer/deepklarity--huggingface-spaces-dataset/train
### Description
Hi Team,
I'm getting the following error on a uploaded dataset. I'm getting the same status for a couple of hours now. The dataset size is `<1MB` and the format is csv, so I'm not sure if it's supposed to take this much time or not.
```
Status code: 400
Exception: Status400Error
Message: The split is being processed. Retry later.
```
Is there any explicit step to be taken to get the viewer to work?
### Owner
Yes
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Package rouge-score cannot be imported
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[
"We have added a comment on an existing issue opened in their repo: https://github.com/google-research/google-research/issues/1212#issuecomment-1192267130\r\n- https://github.com/google-research/google-research/issues/1212"
] | 2022-07-22T07:15:05Z
| 2022-07-22T07:45:19Z
| 2022-07-22T07:45:18Z
|
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## Describe the bug
After the today release of `rouge_score-0.0.7` it seems no longer importable. Our CI fails: https://github.com/huggingface/datasets/runs/7463218591?check_suite_focus=true
```
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_bigbench
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_configs_bigbench
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_bigbench
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_rouge
```
with errors:
```
> from rouge_score import rouge_scorer
E ModuleNotFoundError: No module named 'rouge_score'
```
```
E ImportError: To be able to use rouge, you need to install the following dependency: rouge_score.
E Please install it using 'pip install rouge_score' for instance'
```
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[
"Fixed by:\r\n- #4735"
] | 2022-07-22T07:06:51Z
| 2022-07-22T09:08:02Z
| 2022-07-22T09:05:35Z
|
NONE
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## Describe the bug
A clear and concise description of what the bug is.
Loading Rouge metric gives error after latest rouge-score==0.0.7 release.
Downgrading rougemetric==0.0.4 works fine.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
## Expected results
A clear and concise description of the expected results.
from rouge_score import rouge_scorer, scoring
should run
## Actual results
Specify the actual results or traceback.
File "/root/.cache/huggingface/modules/datasets_modules/metrics/rouge/0ffdb60f436bdb8884d5e4d608d53dbe108e82dac4f494a66f80ef3f647c104f/rouge.py", line 21, in <module>
from rouge_score import rouge_scorer, scoring
ImportError: cannot import name 'rouge_scorer' from 'rouge_score' (unknown location)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version:3.9
- PyArrow version:
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Document better that loading a dataset passing its name does not use the local script
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[
"Thanks for the feedback!\r\n\r\nI think since this issue is closely related to loading, I can add a clearer explanation under [Load > local loading script](https://huggingface.co/docs/datasets/main/en/loading#local-loading-script).",
"That makes sense but I think having a line about it under https://huggingface.co/docs/datasets/installation#source the \"source\" header here would be useful. My mental model of `pip install -e .` does not include the fact that the source files aren't actually being used. ",
"Thanks for sharing your perspective. I think the `load_dataset` function is the only one that pulls from GitHub, and since this use-case is very specific, I don't think we need to include such a broad clarification in the Installation section.\r\n\r\nFeel free to check out the linked PR and let me know if it needs any additional explanation 😊"
] | 2022-07-22T06:07:31Z
| 2022-08-23T16:32:23Z
| 2022-08-23T16:32:23Z
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MEMBER
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As reported by @TrentBrick here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191858596, it could be more clear that loading a dataset by passing its name does not use the (modified) local script of it.
What he did:
- he installed `datasets` from source
- he modified locally `datasets/the_pile/the_pile.py` loading script
- he tried to load it but using `load_dataset("the_pile")` instead of `load_dataset("datasets/the_pile")`
- as explained here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191040245:
- the former does not use the local script, but instead it downloads a copy of `the_pile.py` from our GitHub, caches it locally (inside `~/.cache/huggingface/modules`) and uses that.
He suggests adding a more clear explanation about this. He suggests adding it maybe in [Installation > source](https://huggingface.co/docs/datasets/installation))
CC: @stevhliu
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I_kwDODunzps5OSTfP
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Loading imagenet-1k validation split takes much more RAM than expected
|
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"My bad, `482 * 418 * 50000 * 3 / 1000000 = 30221 MB` ( https://stackoverflow.com/a/42979315 ).\r\n\r\nMeanwhile `256 * 256 * 50000 * 3 / 1000000 = 9830 MB`. We are loading the non-cropped images and that is why we take so much RAM."
] | 2022-07-21T15:14:06Z
| 2022-07-21T16:41:04Z
| 2022-07-21T16:41:04Z
|
CONTRIBUTOR
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## Describe the bug
Loading into memory the validation split of imagenet-1k takes much more RAM than expected. Assuming ImageNet-1k is 150 GB, split is 50000 validation images and 1,281,167 train images, I would expect only about 6 GB loaded in RAM.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imagenet-1k", split="validation")
print(dataset)
"""prints
Dataset({
features: ['image', 'label'],
num_rows: 50000
})
"""
pipe_inputs = dataset["image"]
# and wait :-)
```
## Expected results
Use only < 10 GB RAM when loading the images.
## Actual results
```
Using custom data configuration default
Reusing dataset imagenet-1k (/home/fxmarty/.cache/huggingface/datasets/imagenet-1k/default/1.0.0/a1e9bfc56c3a7350165007d1176b15e9128fcaf9ab972147840529aed3ae52bc)
Killed
```
## Environment info
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
- datasets commit: 4e4222f1b6362c2788aec0dd2cd8cede6dd17b80
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I_kwDODunzps5OQTmu
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load_dataset gives "403" error when using Financial Phrasebank
|
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[
"Hi @rohitvincent, thanks for reporting.\r\n\r\nUnfortunately I'm not able to reproduce your issue:\r\n```python\r\nIn [2]: from datasets import load_dataset, DownloadMode\r\n ...: load_dataset(path='financial_phrasebank',name='sentences_allagree', download_mode=\"force_redownload\")\r\nDownloading builder script: 6.04kB [00:00, 2.87MB/s] \r\nDownloading metadata: 13.7kB [00:00, 7.24MB/s] \r\nDownloading and preparing dataset financial_phrasebank/sentences_allagree (download: 665.91 KiB, generated: 296.26 KiB, post-processed: Unknown size, total: 962.17 KiB) to .../.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141...\r\nDownloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 682k/682k [00:00<00:00, 7.66MB/s]\r\nDataset financial_phrasebank downloaded and prepared to .../.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 918.80it/s]\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label'],\r\n num_rows: 2264\r\n })\r\n})\r\n```\r\n\r\nAre you able to access the link? https://www.researchgate.net/profile/Pekka-Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip",
"Yes was able to download from the link manually. But still, get the same error when I use load_dataset.",
"Fixed once data files are hosted on the Hub:\r\n- #4598"
] | 2022-07-21T08:43:32Z
| 2022-08-04T08:32:35Z
| 2022-08-04T08:32:35Z
|
NONE
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I tried both codes below to download the financial phrasebank dataset (https://huggingface.co/datasets/financial_phrasebank) with the sentences_allagree subset. However, the code gives a 403 error when executed from multiple machines locally or on the cloud.
```
from datasets import load_dataset, DownloadMode
load_dataset(path='financial_phrasebank',name='sentences_allagree',download_mode=DownloadMode.FORCE_REDOWNLOAD)
```
```
from datasets import load_dataset, DownloadMode
load_dataset(path='financial_phrasebank',name='sentences_allagree')
```
**Error**
ConnectionError: Couldn't reach https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip (error 403)
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Dataset Viewer issue for TheNoob3131/mosquito-data
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[
"The preview is working OK:\r\n\r\n\r\n\r\n"
] | 2022-07-21T05:24:48Z
| 2022-07-21T07:51:56Z
| 2022-07-21T07:45:01Z
|
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### Link
https://huggingface.co/datasets/TheNoob3131/mosquito-data/viewer/TheNoob3131--mosquito-data/test
### Description
Dataset preview not showing with large files. Says 'split cache is empty' even though there are train and test splits.
### Owner
_No response_
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the_pile datasets URL broken.
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[
"Thanks for reporting, @TrentBrick. We are addressing the change with their data host server.\r\n\r\nOn the meantime, if you would like to work with your fixed local copy of the_pile script, you should use:\r\n```python\r\nload_dataset(\"path/to/your/local/the_pile/the_pile.py\",...\r\n```\r\ninstead of just `load_dataset(\"the_pile\",...`.\r\n\r\nThe latter downloads a copy of `the_pile.py` from our GitHub, caches it locally (inside `~/.cache/huggingface/modules`) and uses that.",
"@TrentBrick, I have checked the URLs and both hosts work, the original (https://the-eye.eu/) and the mirror (https://mystic.the-eye.eu/). See e.g.:\r\n- https://mystic.the-eye.eu/public/AI/pile/\r\n- https://mystic.the-eye.eu/public/AI/pile_preliminary_components/\r\n\r\nPlease, let me know if you still find any issue loading this dataset by using current server URLs.",
"Great this is working now. Re the download from GitHub... I'm sure thought went into doing this but could it be made more clear maybe here? https://huggingface.co/docs/datasets/installation for example under installing from source? I spent over an hour questioning my sanity as I kept trying to edit this file, uninstall and reinstall the repo, git reset to previous versions of the file etc.",
"Thanks for the quick reply and help too\r\n",
"Thanks @TrentBrick for the suggestion about improving our docs: we should definitely do this if you find they are not clear enough.\r\n\r\nCurrently, our docs explain how to load a dataset from a local loading script here: [Load > Local loading script](https://huggingface.co/docs/datasets/loading#local-loading-script)\r\n\r\nI've opened an issue here:\r\n- #4732\r\n\r\nFeel free to comment on it any additional explanation/suggestion/requirement related to this problem."
] | 2022-07-20T20:57:30Z
| 2022-07-22T06:09:46Z
| 2022-07-21T07:38:19Z
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https://github.com/huggingface/datasets/pull/3627 changed the Eleuther AI Pile dataset URL from https://the-eye.eu/ to https://mystic.the-eye.eu/ but the latter is now broken and the former works again.
Note that when I git clone the repo and use `pip install -e .` and then edit the URL back the codebase doesn't seem to use this edit so the mystic URL is also cached somewhere else that I can't find?
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Dataset Viewer issue for shamikbose89/lancaster_newsbooks
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"It seems like the list of splits could not be obtained:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"shamikbose89/lancaster_newsbooks\", \"default\")\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 354, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/shamikbose89--lancaster_newsbooks/2d1c63d269bf7b9342accce0a95960b1710ab4bc774248878bd80eb96c1afaf7/lancaster_newsbooks.py\", line 73, in _split_generators\r\n data_dir = dl_manager.download_and_extract(_URL)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 916, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 879, in extract\r\n urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 348, in map_nested\r\n return function(data_struct)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 884, in _extract\r\n protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 388, in _get_extraction_protocol\r\n return _get_extraction_protocol_with_magic_number(f)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 354, in _get_extraction_protocol_with_magic_number\r\n f.seek(0)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 684, in seek\r\n raise ValueError(\"Cannot seek streaming HTTP file\")\r\nValueError: Cannot seek streaming HTTP file\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nping @huggingface/datasets ",
"Oh, I removed the 'split' key from `kwargs`. I put it back in, but there's still the same error",
"It looks like the data host doesn't support http range requests, which is necessary to glob inside a ZIP archive in streaming mode. Can you try hosting the dataset elsewhere ? Or download each file separately from https://ota.bodleian.ox.ac.uk/repository/xmlui/handle/20.500.12024/2531 ?",
"@lhoestq Thanks! That seems to have solved it. I can get the splits with the `get_dataset_split_names()` function. The dataset viewer is still not loading properly, though. The new error is\r\n```\r\nStatus code: 400\r\nException: BadZipFile\r\nMessage: File is not a zip file\r\n```\r\n\r\nPS. The dataset loads properly and can be accessed"
] | 2022-07-19T20:00:07Z
| 2022-09-08T16:47:21Z
| 2022-09-08T16:47:21Z
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### Link
https://huggingface.co/datasets/shamikbose89/lancaster_newsbooks
### Description
Status code: 400
Exception: ValueError
Message: Cannot seek streaming HTTP file
I am able to use the dataset loading script locally and it also runs when I'm using the one from the hub, but the viewer still doesn't load
### Owner
Yes
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I_kwDODunzps5OEssc
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Issue loading TheNoob3131/mosquito-data dataset
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[
"I am also getting a ValueError: 'Couldn't cast' at the bottom. Is this because of some delimiter issue? My dataset is on the Huggingface Hub. If you could look at it, that would be greatly appreciated.",
"Hi @thenerd31, thanks for reporting.\r\n\r\nPlease note that your issue is not caused by the Hugging Face Datasets library, but it has to do with the specific implementation of your dataset on the Hub.\r\n\r\nTherefore, I'm transferring this discussion to your own dataset Community tab: https://huggingface.co/datasets/TheNoob3131/mosquito-data/discussions/1"
] | 2022-07-19T17:47:37Z
| 2022-07-20T06:46:57Z
| 2022-07-20T06:46:02Z
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So my dataset is public in the Huggingface Hub, but when I try to load it using the load_dataset command, it shows that it is downloading the files, but throws a ValueError. When I went to my directory to see if the files were downloaded, the folder was blank.
Here is the error below:
ValueError Traceback (most recent call last)
Input In [8], in <cell line: 3>()
1 from datasets import load_dataset
----> 3 dataset = load_dataset("TheNoob3131/mosquito-data", split="train")
File ~\Anaconda3\lib\site-packages\datasets\load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
Is the dataset in the wrong format or is there some security permission that I should enable?
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Dataset Viewer issue for LawalAfeez/englishreview-ds-mini
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[
"It's currently working, as far as I understand\r\n\r\nhttps://huggingface.co/datasets/LawalAfeez/englishreview-ds-mini/viewer/LawalAfeez--englishreview-ds-mini/train\r\n\r\n<img width=\"1556\" alt=\"Capture d’écran 2022-07-19 à 09 24 01\" src=\"https://user-images.githubusercontent.com/1676121/179761130-2d7980b9-c0f6-4093-8b1d-f0a3872fef3f.png\">\r\n\r\n---\r\n\r\nWhat was your issue?"
] | 2022-07-19T13:19:39Z
| 2022-07-20T08:32:57Z
| 2022-07-20T08:32:57Z
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### Link
_No response_
### Description
Unable to view the split data
### Owner
_No response_
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Document how to create a dataset loading script for audio/vision
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"I'm closing this issue as both the Audio and Image sections now have a \"Create dataset\" page that contains the info about writing the loading script version of a dataset."
] | 2022-07-19T08:03:40Z
| 2023-07-25T16:07:52Z
| 2023-07-25T16:07:52Z
|
MEMBER
| null | null |
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Currently, in our docs for Audio/Vision/Text, we explain how to:
- Load data
- Process data
However we only explain how to *Create a dataset loading script* for text data.
I think it would be useful that we add the same for Audio/Vision as these have some specificities different from Text.
See, for example:
- #4697
- and comment there: https://github.com/huggingface/datasets/issues/4697#issuecomment-1191502492
CC: @stevhliu
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Dataset Viewer issue for TheNoob3131/mosquito-data
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[
"Thanks for reporting. I refreshed the dataset viewer and it now works as expected.\r\n\r\nhttps://huggingface.co/datasets/TheNoob3131/mosquito-data\r\n\r\n<img width=\"1135\" alt=\"Capture d’écran 2022-07-18 à 13 15 22\" src=\"https://user-images.githubusercontent.com/1676121/179566497-e47f1a27-fd84-4a8d-9d7f-2e0f2da803df.png\">\r\n\r\nWe will investigate why it occurred in the first place\r\n",
"By chance, could you provide some details about the operations done on the dataset: was it private? gated?",
"Yes, it was a private dataset, and when I made it public, the Dataset Preview did not work. \r\n\r\nHowever, now when I make the dataset private, it says that the Dataset Preview has been disabled. Why is this?",
"Thanks for the details. For now, the dataset viewer is always disabled on private datasets (see https://huggingface.co/docs/hub/datasets-viewer for more details)",
"Hi, it was working fine for a few hours, but then I can't see the dataset viewer again (public dataset). Why is this still happening?\r\nIt's the same error too:\r\n\r\n",
"OK? This is a bug, thanks for help spotting and reproducing it (it occurs when a dataset is switched to private, then to public). We will be working on it, meanwhile, I've restored the dataset viewer manually again."
] | 2022-07-18T17:07:19Z
| 2022-07-18T19:44:46Z
| 2022-07-18T17:15:50Z
|
NONE
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### Link
_No response_
### Description
Getting this error when trying to view dataset preview:
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/TheNoob3131/mosquito-data/resolve/8aceebd6c4a359d216d10ef020868bd9e8c986dd/0_Africa_train.csv')
### Owner
_No response_
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Trouble with streaming frgfm/imagenette vision dataset with TAR archive
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[
"Hi @frgfm, thanks for reporting.\r\n\r\nAs the error message says, streaming mode is not supported out of the box when the dataset contains TAR archive files.\r\n\r\nTo make the dataset streamable, you have to use `dl_manager.iter_archive`.\r\n\r\nThere are several examples in other datasets, e.g. food101: https://huggingface.co/datasets/food101/blob/main/food101.py\r\n\r\nAnd yes, as the link you pointed out, for the streaming to be possible, the metadata file must be loaded before all of the images:\r\n- either this is the case when iterating the archive (and you get the metadata file before the images)\r\n- or you have to extract the metadata file by hand and upload it separately to the Hub",
"Hi @albertvillanova :wave:\r\n\r\nThanks! Yeah I saw that but since I didn't have any metadata, I wasn't sure whether I should create them myself.\r\n\r\nSo one last question:\r\nWhat is the metadata supposed to be for archives? The relative path of all files in it?\r\n_(Sorry I'm a bit confused since it's quite hard to debug using the single error message from the data preview :sweat_smile: )_",
"Hi @frgfm, streaming a dataset that contains a TAR file requires some tweaks because (contrary to ZIP files), tha TAR archive does not allow random access to any of the contained member files. Instead they have to be accessed sequentially (in the order in which they were put into the TAR file when created) and yielded.\r\n\r\nSo when iterating over the TAR file content, when an image file is found, we need to yield it (and not keeping it in memory, which will require huge RAM memory for large datasets). But when yielding an image file, we also need to yield with it what we call \"metadata\": the class label, and other textual information (for example, for audio files, sometimes we also add info such as the speaker ID, their sex, their age,...).\r\n\r\nAll this information usually is stored in what we call the metadata file: either a JSON or a CSV/TSV file.\r\n\r\nBut if this is also inside the TAR archive, we need to find this file in the first place when iterating the TAR archive, so that we already have this information when we find an image file and we can yield the image file and its metadata info.\r\n\r\nTherefore:\r\n- either the TAR archive contains the metadata file as the first member when iterating it (something we cannot change as it is done at the creation of the TAR file)\r\n- or if not, then we need to have the metadata file elsewhere\r\n - in these cases, what we do (if the dataset license allows it) is:\r\n - we download the TAR file locally, we extract the metadata file and we host the metadata on the Hub\r\n - we modify the dataset loading script so that it first downloads the metadata file (and reads it) and only then starts iterating the content of the TAR archive file\r\n\r\nSee an example of this process we recently did for \"google/fleurs\" (their metadata files for \"train\" were at the end of the TAR archives, after all audio files): https://huggingface.co/datasets/google/fleurs/discussions/4\r\n- we uploaded the metadata file to the Hub\r\n- we adapted the loading script to use it",
"Hi @albertvillanova :wave: \r\n\r\nThanks, since my last message, I went through the repo of https://huggingface.co/datasets/food101/blob/main/food101.py and managed to get it to work in the end :pray: \r\n\r\nHere it is: https://huggingface.co/datasets/frgfm/imagenette\r\n\r\nI appreciate you opening an issue to document the process, it might help a few!",
"Great to see that you manage to make your dataset streamable. :rocket: \r\n\r\nI'm closing this issue, as for the docs update there is another issue opened:\r\n- #4711"
] | 2022-07-18T02:51:09Z
| 2022-08-01T15:10:57Z
| 2022-08-01T15:10:57Z
|
NONE
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### Link
https://huggingface.co/datasets/frgfm/imagenette
### Description
Hello there :wave:
Thanks for the amazing work you've done with HF Datasets! I've just started playing with it, and managed to upload my first dataset. But for the second one, I'm having trouble with the preview since there is some archive extraction involved :sweat_smile:
Basically, I get a:
```
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
I've tried several things and checked this issue https://github.com/huggingface/datasets/issues/4181 as well, but no luck so far!
Could you point me in the right direction please? :pray:
### Owner
Yes
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Cannot load LinCE dataset
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[
"Hi @finiteautomata, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"lince\", \"ner_spaeng\")\r\nDownloading builder script: 20.8kB [00:00, 9.09MB/s] \r\nDownloading metadata: 31.2kB [00:00, 13.5MB/s] \r\nDownloading and preparing dataset lince/ner_spaeng (download: 2.93 MiB, generated: 18.45 MiB, post-processed: Unknown size, total: 21.38 MiB) to .../.cache/huggingface/datasets/lince/ner_spaeng/1.0.0/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589...\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.08M/3.08M [00:01<00:00, 2.73MB/s]\r\nDataset lince downloaded and prepared to .../.cache/huggingface/datasets/lince/ner_spaeng/1.0.0/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 630.66it/s]\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['idx', 'words', 'lid', 'ner'],\r\n num_rows: 33611\r\n })\r\n validation: Dataset({\r\n features: ['idx', 'words', 'lid', 'ner'],\r\n num_rows: 10085\r\n })\r\n test: Dataset({\r\n features: ['idx', 'words', 'lid', 'ner'],\r\n num_rows: 23527\r\n })\r\n})\r\n``` \r\n\r\nPlease note that for this dataset, the original data files are not hosted on the Hugging Face Hub, but on https://ritual.uh.edu\r\nAnd sometimes, the server might be temporarily unavailable, as your error message said (trying to connect to the server timed out):\r\n```\r\nConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError(\"HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))\")))\r\n```\r\nIn these cases you could:\r\n- either contact the owners of the data server where the data is hosted to inform them about the issue in their server\r\n- or re-try after waiting some time: usually these issues are just temporary",
"Great, thanks for checking out!"
] | 2022-07-17T19:01:54Z
| 2022-07-18T09:20:40Z
| 2022-07-18T07:24:22Z
|
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## Describe the bug
Cannot load LinCE dataset due to a connection error
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("lince", "ner_spaeng")
```
A notebook with this code and corresponding error can be found at https://colab.research.google.com/drive/1pgX3bNB9amuUwAVfPFm-XuMV5fEg-cD2
## Expected results
It should load the dataset
## Actual results
```python
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-2-fc551ddcebef> in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("lince", "ner_spaeng")
10 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, revision, use_auth_token, task, streaming, **config_kwargs)
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
-> 1684 use_auth_token=use_auth_token,
1685 )
1686
/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)
703 if not downloaded_from_gcs:
704 self._download_and_prepare(
--> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1219
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1222
1223 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
772
773 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/lince/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589/lince.py in _split_generators(self, dl_manager)
481 def _split_generators(self, dl_manager):
482 """Returns SplitGenerators."""
--> 483 lince_dir = dl_manager.download_and_extract(f"{_LINCE_URL}/{self.config.name}.zip")
484 data_dir = os.path.join(lince_dir, self.config.data_dir)
485 return [
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download_and_extract(self, url_or_urls)
429 extracted_path(s): `str`, extracted paths of given URL(s).
430 """
--> 431 return self.extract(self.download(url_or_urls))
432
433 def get_recorded_sizes_checksums(self):
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download(self, url_or_urls)
313 num_proc=download_config.num_proc,
314 disable_tqdm=not is_progress_bar_enabled(),
--> 315 desc="Downloading data files",
316 )
317 duration = datetime.now() - start_time
/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
346 # Singleton
347 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 348 return function(data_struct)
349
350 disable_tqdm = disable_tqdm or not logging.is_progress_bar_enabled()
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in _download(self, url_or_filename, download_config)
333 # append the relative path to the base_path
334 url_or_filename = url_or_path_join(self._base_path, url_or_filename)
--> 335 return cached_path(url_or_filename, download_config=download_config)
336
337 def iter_archive(self, path_or_buf: Union[str, io.BufferedReader]):
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
195 use_auth_token=download_config.use_auth_token,
196 ignore_url_params=download_config.ignore_url_params,
--> 197 download_desc=download_config.download_desc,
198 )
199 elif os.path.exists(url_or_filename):
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
532 if head_error is not None:
--> 533 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
534 elif response is not None:
535 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError("HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))")))
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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Unable to cast a column with `Image()` by using the `cast_column()` feature
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[
"Hi, thanks for reporting! A PR (https://github.com/huggingface/datasets/pull/4614) has already been opened to address this issue."
] | 2022-07-15T22:56:03Z
| 2022-07-19T13:36:24Z
| 2022-07-19T13:36:24Z
|
NONE
| null | null |
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|
## Describe the bug
A clear and concise description of what the bug is.
When I create a dataset, then add a column to the created dataset through the `dataset.add_column` feature and then try to cast a column of the dataset (this column contains image paths) with `Image()` by using the `cast_column()` feature, I get the following error - ``` TypeError: Couldn't cast array of type
string
to
{'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)} ```
When I try and cast the same column, but without doing the `add_column` in the previous step, it works as expected.
## Steps to reproduce the bug
```python
from datasets import Dataset, Image
data_dict = {
"img_path": ["https://picsum.photos/200/300"]
}
dataset = Dataset.from_dict(data_dict)
#NOTE Comment out this line and use cast_column and it works properly
dataset = dataset.add_column("yeet", [1])
#NOTE This line fails to execute properly if `add_column` is called before
dataset = dataset.cast_column("img_path", Image())
# #NOTE This is my current workaround. This seems to work fine with/without `add_column`. While
# # running this, make sure to comment out the `cast_column` line
# new_features = dataset.features.copy()
# new_features["img_path"] = Image()
# dataset = dataset.cast(new_features)
print(dataset)
print(dataset.features)
print(dataset[0])
```
## Expected results
A clear and concise description of the expected results.
Able to successfully use `cast_column` to cast a column containing img_paths to now be Image() features after modifying the dataset using `add_column` in a previous step
## Actual results
Specify the actual results or traceback.
```
Traceback (most recent call last):
File "/home/surya/Desktop/hf_bug_test.py", line 14, in <module>
dataset = dataset.cast_column("img_path", Image())
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1580, in cast_column
dataset._data = dataset._data.cast(dataset.features.arrow_schema)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1487, in cast
new_tables.append(subtable.cast(subschema, *args, **kwargs))
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 834, in cast
return InMemoryTable(table_cast(self.table, *args, **kwargs))
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1897, in table_cast
return cast_table_to_schema(table, schema)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1846, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
string
to
{'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Ubuntu 20.04.3 LTS
- Python version: 3.9.7
- PyArrow version: 7.0.0
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Dataset Viewer issue for rajistics/indian_food_images
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[
"Hi, thanks for reporting. I triggered a refresh of the preview for this dataset, and it works now. I'm not sure what occurred.\r\n<img width=\"1019\" alt=\"Capture d’écran 2022-07-18 à 11 01 52\" src=\"https://user-images.githubusercontent.com/1676121/179541327-f62ecd5e-a18a-4d91-b316-9e2ebde77a28.png\">\r\n\r\n"
] | 2022-07-15T19:03:15Z
| 2022-07-18T15:02:03Z
| 2022-07-18T15:02:03Z
|
NONE
| null | null |
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### Link
https://huggingface.co/datasets/rajistics/indian_food_images/viewer/rajistics--indian_food_images/train
### Description
I have a train/test split in my dataset
<img width="410" alt="Screen Shot 2022-07-15 at 11 44 42 AM" src="https://user-images.githubusercontent.com/6808012/179293215-7b419ec3-3527-46f2-8dad-adbc5568cfa0.png">
t
The dataset viewer works for the test split (images of indian food), but does not show my train split. My guess is maybe there is some corrupt image file that is guessing this. But I have no idea.
The original dataset was pulled from here: https://www.kaggle.com/datasets/l33tc0d3r/indian-food-classification?resource=download-directory
### Owner
Yes
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How to assign new values to Dataset?
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[
"Hi! One option is use `map` with a function that overwrites the labels (`dset = dset.map(lamba _: {\"label\": 0}, features=dset.features`)). Or you can use the `remove_column` + `add_column` combination (`dset = dset.remove_columns(\"label\").add_column(\"label\", [0]*len(data)).cast(dset.features)`, but note that this approach creates an in-memory table for the added column instead of writing to disk, which could be problematic for large datasets.",
"Hi! I tried your proposed solution, but it does not solve my problem unfortunately. I am working with a set of protein sequences that have been tokenized with ESM, but some sequences are longer than `max_length`, they have been truncated in the tokenization. So now I want to truncate my labels as well, but that does not work with a mapping (e.g. `dset.map` as you suggested). Specifically, what I did was the following:\r\n\r\n```\r\ndef postprocess_tokenize(tokenized_data):\r\n \"\"\"\r\n adjust label lengths if they dont match.\r\n \"\"\"\r\n if len(tokenized_data['input_ids']) < len(tokenized_data['labels']):\r\n new_labels = tokenized_data['labels'][:len(tokenized_data['input_ids'])]\r\n tokenized_data[\"labels\"] = new_labels\r\n return tokenized_data\r\n\r\ntokenized_data = tokenized_data.map(postprocess_tokenize, batched=True) # this does not adjust the labels...\r\n```\r\n\r\nAny tips on how to do this properly?\r\n\r\nMore generally, I am wondering why the DataCollator supports padding but does not support truncation? Seems odd to me.\r\n\r\nThanks in advance!"
] | 2022-07-15T04:17:57Z
| 2023-03-20T15:50:41Z
| 2022-10-10T11:53:38Z
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Hi, if I want to change some values of the dataset, or add new columns to it, how can I do it?
For example, I want to change all the labels of the SST2 dataset to `0`:
```python
from datasets import load_dataset
data = load_dataset('glue','sst2')
data['train']['label'] = [0]*len(data)
```
I will get the error:
```
TypeError: 'Dataset' object does not support item assignment
```
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[
"Hi, thanks for reporting! If there are no examples in ImageFolder, the `label` column is of type `ClassLabel(names=[])`, which leads to an error in [this line](https://github.com/huggingface/datasets/blob/c15b391942764152f6060b59921b09cacc5f22a6/src/datasets/arrow_writer.py#L387) as `asdict(info)` calls `Features({..., \"label\": {'num_classes': 0, 'names': [], 'id': None, '_type': 'ClassLabel'}})`, which then calls `require_decoding` [here](https://github.com/huggingface/datasets/blob/c15b391942764152f6060b59921b09cacc5f22a6/src/datasets/features/features.py#L1516) on the dict value it does not expect.\r\n\r\nI see two ways to fix this:\r\n* custom `asdict` where `dict_factory` is also applied on the `dict` object itself besides dataclasses (the built-in implementation calls `type(dict_obj)` - this means we also need to fix `Features.to_dict` btw) \r\n* implement `DatasetInfo.to_dict` (though adding `to_dict` to a data class is a bit weird IMO)\r\n\r\n@lhoestq Which one of these approaches do you like more?\r\n",
"Small pref for the first option, it feels weird to know that `Features()` can be called with a dictionary of types defined as dictionaries instead of type instances."
] | 2022-07-14T10:57:55Z
| 2022-07-25T12:37:54Z
| 2022-07-25T12:37:54Z
|
NONE
| null | null |
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## Describe the bug
Loading an image dataset with `imagefolder` throws `IndexError: list index out of range` when the given folder contains a non-image file (like a csv).
## Steps to reproduce the bug
Put a csv file in a folder with images and load it:
```python
import datasets
datasets.load_dataset("imagefolder", data_dir=path/to/folder)
```
## Expected results
I would expect a better error message, like `Unsupported file` or even the dataset loader just ignoring every file that is not an image in that case.
## Actual results
Here is the whole traceback:
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.11.0-051100-generic-x86_64-with-glibc2.27
- Python version: 3.9.9
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
|
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| 1,304,534,770
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I_kwDODunzps5NwZ7y
| 4,680
|
Dataset Viewer issue for codeparrot/xlcost-text-to-code
|
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[
"There seems to be an issue with the `C++-snippet-level` config:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"codeparrot/xlcost-text-to-code\", \"C++-snippet-level\")\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 352, in get_dataset_config_info\r\n info.splits = {\r\nTypeError: 'NoneType' object is not iterable\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nI remove the dataset-viewer tag since it's not directly related.\r\n\r\nPinging @huggingface/datasets ",
"Thanks I found that this subset wasn't properly defined the the config, I fixed it. Now I can see the subsets but I get this error for the viewer\r\n````\r\nStatus code: 400\r\nException: Status400Error\r\nMessage: The split cache is empty.\r\n```",
"Yes, the cache is being refreshed, hopefully, it will work in some minutes for all the splits. Some are already here:\r\n\r\nhttps://huggingface.co/datasets/codeparrot/xlcost-text-to-code/viewer/Python-snippet-level/train\r\n\r\n<img width=\"1533\" alt=\"Capture d’écran 2022-07-18 à 12 04 06\" src=\"https://user-images.githubusercontent.com/1676121/179553933-64d874fa-ada9-4b82-900e-082619523c20.png\">\r\n",
"I think all the splits are working as expected now",
"Perfect, thank you!"
] | 2022-07-14T09:45:50Z
| 2022-07-18T16:37:00Z
| 2022-07-18T16:04:36Z
|
NONE
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### Link
https://huggingface.co/datasets/codeparrot/xlcost-text-to-code
### Description
Error
```
Server Error
Status code: 400
Exception: TypeError
Message: 'NoneType' object is not iterable
```
Before I did a minor change in the dataset script (removing some comments), the viewer was working but not properely, it wasn't showing the dataset subsets. But the data can be loaded successfully.
Thanks!
### Owner
Yes
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| 4,677
|
Random 400 Client Error when pushing dataset
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[] | null |
[
"did you ever fix this? I'm experiencing the same",
"I am having the same issue. Even the simple example from the documentation gives me the 400 Error\r\n\r\n\r\n> from datasets import load_dataset\r\n> \r\n> dataset = load_dataset(\"stevhliu/demo\")\r\n> dataset.push_to_hub(\"processed_demo\")\r\n\r\n\r\n`requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/REDACTED/commit/main (Request ID: e-tPnYTiCdB5KPmSL86dQ)`\r\n\r\nI \"fixed\" it by initializing a new virtual environment with only datasets==2.5.2 installed.\r\n\r\nThe workaround consists of saving to disk then loading from disk and pushing to hub but from the new clean virtual environment."
] | 2022-07-12T15:56:44Z
| 2023-02-07T13:54:10Z
| 2023-02-07T13:54:10Z
|
NONE
| null | null |
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## Describe the bug
When pushing a dataset, the client errors randomly with `Bad Request for url:...`.
At the next call, a new parquet file is created for each shard.
The client may fail at any random shard.
## Steps to reproduce the bug
```python
dataset.push_to_hub("ORG/DATASET", private=True, branch="main")
```
## Expected results
Push all the dataset to the Hub with no duplicates.
If it fails, it should retry or fail, but continue from the last failed shard.
## Actual results
```
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
testing.ipynb Cell 29 in <cell line: 1>()
----> [1](testing.ipynb?line=0) dataset.push_to_hub("ORG/DATASET", private=True, branch="main")
File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4297, in Dataset.push_to_hub(self, repo_id, split, private, token, branch, max_shard_size, shard_size, embed_external_files)
4291 warnings.warn(
4292 "'shard_size' was renamed to 'max_shard_size' in version 2.1.1 and will be removed in 2.4.0.",
4293 FutureWarning,
4294 )
4295 max_shard_size = shard_size
-> 4297 repo_id, split, uploaded_size, dataset_nbytes, repo_files, deleted_size = self._push_parquet_shards_to_hub(
4298 repo_id=repo_id,
4299 split=split,
4300 private=private,
4301 token=token,
4302 branch=branch,
4303 max_shard_size=max_shard_size,
4304 embed_external_files=embed_external_files,
4305 )
4306 organization, dataset_name = repo_id.split("/")
4307 info_to_dump = self.info.copy()
File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4195, in Dataset._push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, embed_external_files)
4193 shard.to_parquet(buffer)
4194 uploaded_size += buffer.tell()
-> 4195 _retry(
4196 api.upload_file,
4197 func_kwargs=dict(
4198 path_or_fileobj=buffer.getvalue(),
4199 path_in_repo=shard_path_in_repo,
4200 repo_id=repo_id,
4201 token=token,
4202 repo_type="dataset",
4203 revision=branch,
4204 identical_ok=False,
4205 ),
4206 exceptions=HTTPError,
4207 status_codes=[504],
4208 base_wait_time=2.0,
4209 max_retries=5,
4210 max_wait_time=20.0,
4211 )
4212 shards_path_in_repo.append(shard_path_in_repo)
4214 # Cleanup to remove unused files
File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:284, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
282 except exceptions as err:
283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
--> 284 raise err
285 else:
286 sleep_time = min(max_wait_time, base_wait_time * 2**retry) # Exponential backoff
File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:281, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
279 while True:
280 try:
--> 281 return func(*func_args, **func_kwargs)
282 except exceptions as err:
283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1967, in HfApi.upload_file(self, path_or_fileobj, path_in_repo, repo_id, token, repo_type, revision, identical_ok, commit_message, commit_description, create_pr)
1957 commit_message = (
1958 commit_message
1959 if commit_message is not None
1960 else f"Upload {path_in_repo} with huggingface_hub"
1961 )
1962 operation = CommitOperationAdd(
1963 path_or_fileobj=path_or_fileobj,
1964 path_in_repo=path_in_repo,
1965 )
-> 1967 pr_url = self.create_commit(
1968 repo_id=repo_id,
1969 repo_type=repo_type,
1970 operations=[operation],
1971 commit_message=commit_message,
1972 commit_description=commit_description,
1973 token=token,
1974 revision=revision,
1975 create_pr=create_pr,
1976 )
1977 if pr_url is not None:
1978 re_match = re.match(REGEX_DISCUSSION_URL, pr_url)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1844, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads)
1836 commit_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/commit/{revision}"
1838 commit_resp = requests.post(
1839 url=commit_url,
1840 headers={"Authorization": f"Bearer {token}"},
1841 json=commit_payload,
1842 params={"create_pr": 1} if create_pr else None,
1843 )
-> 1844 _raise_for_status(commit_resp)
1845 return commit_resp.json().get("pullRequestUrl", None)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:84, in _raise_for_status(request)
76 if request.status_code == 401:
77 # The repo was not found and the user is not Authenticated
78 raise RepositoryNotFoundError(
79 f"401 Client Error: Repository Not Found for url: {request.url}. If the"
80 " repo is private, make sure you are authenticated. (Request ID:"
81 f" {request_id})"
82 )
---> 84 _raise_with_request_id(request)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:95, in _raise_with_request_id(request)
92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str):
93 e.args = (e.args[0] + f" (Request ID: {request_id})",) + e.args[1:]
---> 95 raise e
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:90, in _raise_with_request_id(request)
88 request_id = request.headers.get("X-Request-Id")
89 try:
---> 90 request.raise_for_status()
91 except Exception as e:
92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str):
File ~/.local/lib/python3.9/site-packages/requests/models.py:1021, in Response.raise_for_status(self)
1016 http_error_msg = (
1017 f"{self.status_code} Server Error: {reason} for url: {self.url}"
1018 )
1020 if http_error_msg:
-> 1021 raise HTTPError(http_error_msg, response=self)
HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/ORG/DATASET/commit/main (Request ID: a_F0IQAHJdxGKVRYyu1cF)
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.13.0-1025-aws-x86_64-with-glibc2.31
- Python version: 3.9.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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Dataset.map gets stuck on _cast_to_python_objects
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"Are you able to reproduce this? My example is small enough that it should be easy to try.",
"Hi! Thanks for reporting and providing a reproducible example. Indeed, by default, `datasets` performs an expensive cast on the values returned by `map` to convert them to one of the types supported by PyArrow (the underlying storage format used by `datasets`). This cast is not needed on NumPy arrays as PyArrow supports them natively, so one way to make this transform faster is to add `return_tensors=\"np\"` to the tokenizer call. \r\n\r\nI think we should mention this in the docs (cc @stevhliu)",
"I tested this tokenize function and indeed noticed a casting. However it seems to only concerns the `offset_mapping` field, which contains a list of tuples, that is converted to a list of lists. Since `pyarrow` also supports tuples, we actually don't need to convert the tuples to lists. \r\n\r\nI think this can be changed here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/ede72d3f9796339701ec59899c7c31d2427046fb/src/datasets/features/features.py#L382-L383\r\n\r\n```diff\r\n- if isinstance(obj, list): \r\n+ if isinstance(obj, (list, tuple)): \r\n```\r\n\r\nand here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/ede72d3f9796339701ec59899c7c31d2427046fb/src/datasets/features/features.py#L386-L387\r\n\r\n```diff\r\n- return obj if isinstance(obj, list) else [], isinstance(obj, tuple)\r\n+ return obj, False\r\n```\r\n\r\n@srobertjames can you try applying these changes and let us know if it helps ? If so, feel free to open a Pull Request to contribute this improvement if you want :)",
"Wow, adding `return_tensors=\"np\"` sped up my example by a **factor 17x** of and completely eliminated the casting! I'd recommend not only to document it, but to make that the default.\r\n\r\nThe code at https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb does not specify `return_tensors=\"np\"` but yet avoids the casting penalty. How does it do that? (The ntbk seems to do `return_overflowing_tokens=True, return_offsets_mapping=True,`).\r\n\r\nAlso, surprisingly enough, using `return_tensors=\"pt\"` (which is my eventual application) yields this error:\r\n```\r\nTypeError: Provided `function` which is applied to all elements of table returns a `dict` of types \r\n[<class 'torch.Tensor'>, <class 'torch.Tensor'>, <class 'torch.Tensor'>, <class 'torch.Tensor'>]. \r\nWhen using `batched=True`, make sure provided `function` returns a `dict` of types like \r\n`(<class 'list'>, <class 'numpy.ndarray'>)`.\r\n```",
"Setting the output to `\"np\"` makes the whole pipeline fast because it moves the data buffers from rust to python to arrow using zero-copy, and also because it does eliminate the casting completely ;)\r\n\r\nHave you had a chance to try eliminating the tuple casting using the trick above ?",
"@lhoestq I just benchmarked the two edits to `features.py` above, and they appear to solve the problem, bringing my original example to within 20% the speed of the output `\"np\"` example. Nice!\r\n\r\nFor a pull request, do you suggest simply following https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md ?",
"Cool ! Sure feel free to follow these instructions to open a PR :) thanks !",
"#take",
"Resolved via https://github.com/huggingface/datasets/pull/4993."
] | 2022-07-12T15:09:58Z
| 2022-10-03T13:01:04Z
| 2022-10-03T13:01:03Z
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## Describe the bug
`Dataset.map`, when fed a Huggingface Tokenizer as its map func, can sometimes spend huge amounts of time doing casts. A minimal example follows.
Not all usages suffer from this. For example, I profiled the preprocessor at https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb , and it did _not_ have this problem. However, I'm at a loss to figure out how it avoids it, as the example below is simple and minimal and still has this problem.
This casting, where it occurs, causes the `Dataset.map` to run approximately 7x slower than it runs for code which does not cause this casting.
This may be related to https://github.com/huggingface/datasets/issues/1046 . However, the tokenizer is _not_ set to return Tensors.
## Steps to reproduce the bug
A minimal, self-contained example to reproduce is below:
```python
import transformers
from transformers import AutoTokenizer
from datasets import load_dataset
import torch
import cProfile
pretrained = 'distilbert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(pretrained)
squad = load_dataset('squad')
squad_train = squad['train']
squad_tiny = squad_train.select(range(5000))
assert isinstance(tokenizer, transformers.PreTrainedTokenizerFast)
def tokenize(ds):
tokens = tokenizer(text=ds['question'],
text_pair=ds['context'],
add_special_tokens=True,
padding='max_length',
truncation='only_second',
max_length=160,
stride=32,
return_overflowing_tokens=True,
return_offsets_mapping=True,
)
return tokens
cmd = 'squad_tiny.map(tokenize, batched=True, remove_columns=squad_tiny.column_names)'
cProfile.run(cmd, sort='tottime')
```
## Actual results
The code works, but takes 10-25 sec per batch (about 7x slower than non-casting code), with the following profile. Note that `_cast_to_python_objects` is the culprit.
```
63524075 function calls (58206482 primitive calls) in 121.836 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
5274034/40 68.751 0.000 111.060 2.776 features.py:262(_cast_to_python_objects)
42223832 24.077 0.000 33.310 0.000 {built-in method builtins.isinstance}
16338/20 5.121 0.000 111.053 5.553 features.py:361(<listcomp>)
5274135 4.747 0.000 4.749 0.000 {built-in method _abc._abc_instancecheck}
80/40 4.731 0.059 116.292 2.907 {pyarrow.lib.array}
5274135 4.485 0.000 9.234 0.000 abc.py:96(__instancecheck__)
2661564/2645196 2.959 0.000 4.298 0.000 features.py:1081(_check_non_null_non_empty_recursive)
5 2.786 0.557 2.786 0.557 {method 'encode_batch' of 'tokenizers.Tokenizer' objects}
2668052 0.930 0.000 0.930 0.000 {built-in method builtins.len}
5000 0.930 0.000 0.938 0.000 tokenization_utils_fast.py:187(_convert_encoding)
5 0.750 0.150 0.808 0.162 {method 'to_pydict' of 'pyarrow.lib.Table' objects}
1 0.444 0.444 121.749 121.749 arrow_dataset.py:2501(_map_single)
40 0.375 0.009 116.291 2.907 arrow_writer.py:151(__arrow_array__)
10 0.066 0.007 0.066 0.007 {method 'write_batch' of 'pyarrow.lib._CRecordBatchWriter' objects}
1 0.060 0.060 121.835 121.835 fingerprint.py:409(wrapper)
11387/5715 0.049 0.000 0.175 0.000 {built-in method builtins.getattr}
36 0.049 0.001 0.049 0.001 {pyarrow._compute.call_function}
15000 0.040 0.000 0.040 0.000 _collections_abc.py:719(__iter__)
3 0.023 0.008 0.023 0.008 {built-in method _imp.create_dynamic}
77 0.020 0.000 0.020 0.000 {built-in method builtins.dir}
37 0.019 0.001 0.019 0.001 socket.py:543(send)
15 0.017 0.001 0.017 0.001 tokenization_utils_fast.py:460(<listcomp>)
432/421 0.015 0.000 0.024 0.000 traitlets.py:1388(_notify_observers)
5000 0.015 0.000 0.018 0.000 _collections_abc.py:672(keys)
51 0.014 0.000 0.042 0.001 traitlets.py:276(getmembers)
5 0.014 0.003 3.775 0.755 tokenization_utils_fast.py:392(_batch_encode_plus)
3/1 0.014 0.005 0.035 0.035 {built-in method _imp.exec_dynamic}
5 0.012 0.002 0.950 0.190 tokenization_utils_fast.py:438(<listcomp>)
31626 0.012 0.000 0.012 0.000 {method 'append' of 'list' objects}
1532/1001 0.011 0.000 0.189 0.000 traitlets.py:643(get)
5 0.009 0.002 3.796 0.759 arrow_dataset.py:2631(apply_function_on_filtered_inputs)
51 0.009 0.000 0.062 0.001 traitlets.py:1766(traits)
5 0.008 0.002 3.784 0.757 tokenization_utils_base.py:2632(batch_encode_plus)
368 0.007 0.000 0.044 0.000 traitlets.py:1715(_get_trait_default_generator)
26 0.007 0.000 0.022 0.001 traitlets.py:1186(setup_instance)
51 0.006 0.000 0.010 0.000 traitlets.py:1781(<listcomp>)
80/32 0.006 0.000 0.052 0.002 table.py:1758(cast_array_to_feature)
684 0.006 0.000 0.007 0.000 {method 'items' of 'dict' objects}
4344/1794 0.006 0.000 0.192 0.000 traitlets.py:675(__get__)
...
```
## Environment info
I observed this on both Google colab and my local workstation:
### Google colab
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
### Local
- `datasets` version: 2.3.2
- Platform: Windows-7-6.1.7601-SP1
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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Issue loading datasets -- pyarrow.lib has no attribute
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"Hi @margotwagner, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your bug: in an environment with datasets-2.3.2 and pyarrow-8.0.0, I can load the datasets without any problem:\r\n```python\r\n>>> ds = load_dataset(\"glue\", \"cola\")\r\n>>> ds\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 8551\r\n })\r\n validation: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1043\r\n })\r\n test: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1063\r\n })\r\n})\r\n\r\n>>> import pyarrow\r\n>>> pyarrow.__version__\r\n8.0.0\r\n>>> from pyarrow.lib import IpcReadOptions\r\n>>> IpcReadOptions\r\npyarrow.lib.IpcReadOptions\r\n```\r\n\r\nI think you may have a problem in your Python environment: maybe you have also an old version of pyarrow that has precedence when importing it.\r\n\r\nCould you please check this (just after you tried to load the dataset and got the error)?\r\n```python\r\n>>> import pyarrow\r\n>>> pyarrow.__version__\r\n``` "
] | 2022-07-11T22:10:44Z
| 2023-02-28T18:06:55Z
| 2023-02-28T18:06:55Z
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## Describe the bug
I am trying to load sentiment analysis datasets from huggingface, but any dataset I try to use via load_dataset, I get the same error:
`AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'`
## Steps to reproduce the bug
```python
dataset = load_dataset("glue", "cola")
```
## Expected results
Download datasets without issue.
## Actual results
`AttributeError: module 'pyarrow.lib' has no attribute 'IpcReadOptions'`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 8.0.0
- Pandas version: 1.1.0
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load_datasets on csv returns everything as a string
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"Hi @courtneysprouse, thanks for reporting.\r\n\r\nYes, you are right: by default the \"csv\" loader loads all columns as strings. \r\n\r\nYou could tweak this behavior by passing the `feature` argument to `load_dataset`, but it is also true that currently it is not possible to perform some kind of casts, due to lacking of implementation in PyArrow. For example:\r\n```python\r\nimport datasets\r\n\r\nfeatures = datasets.Features(\r\n {\r\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n \"ner_tags\": datasets.Sequence(datasets.Value(\"int32\")),\r\n }\r\n)\r\n\r\nnew_conll = datasets.load_dataset(\"csv\", data_files=\"ner_conll.csv\", features=features)\r\n```\r\ngives `ArrowNotImplementedError` error:\r\n```\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: Unsupported cast from string to list using function cast_list\r\n```\r\n\r\nOn the other hand, if you just would like to save and afterwards load your dataset, you could use `save_to_disk` and `load_from_disk` instead. These functions preserve all data types.\r\n```python\r\n>>> orig_conll.save_to_disk(\"ner_conll\")\r\n\r\n>>> from datasets import load_from_disk\r\n\r\n>>> new_conll = load_from_disk(\"ner_conll\")\r\n>>> new_conll\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 14042\r\n })\r\n validation: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 3251\r\n })\r\n test: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 3454\r\n })\r\n})\r\n>>> new_conll[\"train\"][0]\r\n{'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\r\n 'id': '0',\r\n 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0],\r\n 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\r\n 'tokens': ['EU',\r\n 'rejects',\r\n 'German',\r\n 'call',\r\n 'to',\r\n 'boycott',\r\n 'British',\r\n 'lamb',\r\n '.']}\r\n>>> new_conll[\"train\"].features\r\n{'chunk_tags': Sequence(feature=ClassLabel(num_classes=23, names=['O', 'B-ADJP', 'I-ADJP', 'B-ADVP', 'I-ADVP', 'B-CONJP', 'I-CONJP', 'B-INTJ', 'I-INTJ', 'B-LST', 'I-LST', 'B-NP', 'I-NP', 'B-PP', 'I-PP', 'B-PRT', 'I-PRT', 'B-SBAR', 'I-SBAR', 'B-UCP', 'I-UCP', 'B-VP', 'I-VP'], id=None), length=-1, id=None),\r\n 'id': Value(dtype='string', id=None),\r\n 'ner_tags': Sequence(feature=ClassLabel(num_classes=9, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], id=None), length=-1, id=None),\r\n 'pos_tags': Sequence(feature=ClassLabel(num_classes=47, names=['\"', \"''\", '#', '$', '(', ')', ',', '.', ':', '``', 'CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NN', 'NNP', 'NNPS', 'NNS', 'NN|SYM', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WDT', 'WP', 'WP$', 'WRB'], id=None), length=-1, id=None),\r\n 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}\r\n```",
"Hi @albertvillanova!\r\n\r\nThanks so much for your suggestions! That worked! ",
"> Hi @courtneysprouse, thanks for reporting.\r\n> \r\n> Yes, you are right: by default the \"csv\" loader loads all columns as strings.\r\n> \r\n> You could tweak this behavior by passing the `feature` argument to `load_dataset`, but it is also true that currently it is not possible to perform some kind of casts, due to lacking of implementation in PyArrow. For example:\r\n> \r\n> ```python\r\n> import datasets\r\n> \r\n> features = datasets.Features(\r\n> {\r\n> \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n> \"ner_tags\": datasets.Sequence(datasets.Value(\"int32\")),\r\n> }\r\n> )\r\n> \r\n> new_conll = datasets.load_dataset(\"csv\", data_files=\"ner_conll.csv\", features=features)\r\n> ```\r\n> \r\n> gives `ArrowNotImplementedError` error:\r\n> \r\n> ```\r\n> /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n> \r\n> ArrowNotImplementedError: Unsupported cast from string to list using function cast_list\r\n> ```\r\n> \r\n> On the other hand, if you just would like to save and afterwards load your dataset, you could use `save_to_disk` and `load_from_disk` instead. These functions preserve all data types.\r\n> \r\n> ```python\r\n> >>> orig_conll.save_to_disk(\"ner_conll\")\r\n> \r\n> >>> from datasets import load_from_disk\r\n> \r\n> >>> new_conll = load_from_disk(\"ner_conll\")\r\n> >>> new_conll\r\n> DatasetDict({\r\n> train: Dataset({\r\n> features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n> num_rows: 14042\r\n> })\r\n> validation: Dataset({\r\n> features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n> num_rows: 3251\r\n> })\r\n> test: Dataset({\r\n> features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n> num_rows: 3454\r\n> })\r\n> })\r\n> >>> new_conll[\"train\"][0]\r\n> {'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\r\n> 'id': '0',\r\n> 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0],\r\n> 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\r\n> 'tokens': ['EU',\r\n> 'rejects',\r\n> 'German',\r\n> 'call',\r\n> 'to',\r\n> 'boycott',\r\n> 'British',\r\n> 'lamb',\r\n> '.']}\r\n> >>> new_conll[\"train\"].features\r\n> {'chunk_tags': Sequence(feature=ClassLabel(num_classes=23, names=['O', 'B-ADJP', 'I-ADJP', 'B-ADVP', 'I-ADVP', 'B-CONJP', 'I-CONJP', 'B-INTJ', 'I-INTJ', 'B-LST', 'I-LST', 'B-NP', 'I-NP', 'B-PP', 'I-PP', 'B-PRT', 'I-PRT', 'B-SBAR', 'I-SBAR', 'B-UCP', 'I-UCP', 'B-VP', 'I-VP'], id=None), length=-1, id=None),\r\n> 'id': Value(dtype='string', id=None),\r\n> 'ner_tags': Sequence(feature=ClassLabel(num_classes=9, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], id=None), length=-1, id=None),\r\n> 'pos_tags': Sequence(feature=ClassLabel(num_classes=47, names=['\"', \"''\", '#', '$', '(', ')', ',', '.', ':', '``', 'CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NN', 'NNP', 'NNPS', 'NNS', 'NN|SYM', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WDT', 'WP', 'WP$', 'WRB'], id=None), length=-1, id=None),\r\n> 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}\r\n> ```\r\n\r\nIt seems that by default, the 'csv' loader doesn’t load all columns as strings. When I load a column with numbers that start with 0, datasets removes the leading 0 and converts this column to an integer type. How can I set it to load all columns as strings?"
] | 2022-07-11T17:30:24Z
| 2024-11-05T03:55:10Z
| 2022-07-12T13:33:08Z
|
NONE
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## Describe the bug
If you use:
`conll_dataset.to_csv("ner_conll.csv")`
It will create a csv file with all of your data as expected, however when you load it with:
`conll_dataset = load_dataset("csv", data_files="ner_conll.csv")`
everything is read in as a string. For example if I look at everything in 'ner_tags' I get back `['[3 0 7 0 0 0 7 0 0]', '[1 2]', '[5 0]']` instead of what I originally saved which was `[[3, 0, 7, 0, 0, 0, 7, 0, 0], [1, 2], [5, 0]]`
I think maybe there is something funky going on with the csv delimiter
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
#load original conll dataset
orig_conll = load_dataset("conll2003")
#save original conll as a csv
orig_conll.to_csv("ner_conll.csv")
#reload conll data as a csv
new_conll = load_dataset("csv", data_files="ner_conll.csv")`
```
## Expected results
A clear and concise description of the expected results.
I would expect the data be returned as the data type I saved it as. I.e. if I save a list of ints
[[3, 0, 7, 0, 0, 0, 7, 0, 0]], I shouldnt get back a string ['[3 0 7 0 0 0 7 0 0]']
I also get back a string when I pass a list of strings ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']
## Actual results
A list of strings `['[3 0 7 0 0 0 7 0 0]', '[1 2]', '[5 0]']`
A string "['EU' 'rejects' 'German' 'call' 'to' 'boycott' 'British' 'lamb' '.']"
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
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Dataset Viewer issue for wmt16
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[
"Thanks for reporting, @lewtun.\r\n\r\n~We can't load the dataset locally, so I think this is an issue with the loading script (not the viewer).~\r\n\r\n We are investigating...",
"Recently, there was a merged PR related to this dataset:\r\n- #4554\r\n\r\nWe are looking at this...",
"Indeed, the above mentioned PR fixed the loading script (it was not working before).\r\n\r\nI'm forcing the refresh of the Viewer.",
"Please note that the above mentioned PR also made an enhancement in the `datasets` library, required by this loading script. This enhancement will only be available to the Viewer once we make our next release.",
"OK, it's working now.\r\n\r\nhttps://huggingface.co/datasets/wmt16/viewer/ro-en/test\r\n\r\n<img width=\"1434\" alt=\"Capture d’écran 2022-09-08 à 10 15 55\" src=\"https://user-images.githubusercontent.com/1676121/189071665-17d2d149-9b22-42bf-93ac-1a966c3f637a.png\">\r\n",
"Thank you @severo !!"
] | 2022-07-11T08:34:11Z
| 2022-09-13T13:27:02Z
| 2022-09-08T08:16:06Z
|
MEMBER
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### Link
https://huggingface.co/datasets/wmt16
### Description
[Reported](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/12#62cb83f14c7f35284e796f9c) by a user of AutoTrain Evaluate. AFAIK this dataset was working 1-2 weeks ago, and I'm not sure how to interpret this error.
```
Status code: 400
Exception: NotImplementedError
Message: This is a abstract method
```
Thanks!
### Owner
No
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Can't extract files from `.7z` zipfile using `download_and_extract`
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[
"Hi @bhavitvyamalik, thanks for reporting.\r\n\r\nYes, currently we do not support 7zip archive compression: I think we should.\r\n\r\nAs a workaround, you could uncompress it explicitly, like done in e.g. `samsum` dataset: \r\n\r\nhttps://github.com/huggingface/datasets/blob/fedf891a08bfc77041d575fad6c26091bc0fce52/datasets/samsum/samsum.py#L106-L110\r\n",
"Related to this issue: https://github.com/huggingface/datasets/issues/3541",
"Sure, let me look into and check what can be done. Will keep you guys updated here!",
"Initially, I thought of solving this without any external dependency. Almost everywhere I saw `lzma` can be used for this but there is a caveat that lzma doesn’t work with 7z archives but only single files. In my case the 7z archive has multiple files so it didn't work. Is it fine to use external library here?",
"Hi @bhavitvyamalik, thanks for your investigation.\r\n\r\nOn Monday, I started a PR that will eventually close this issue as well: I'm linking it to this.\r\n- #4672\r\n\r\nLet me know what you think. "
] | 2022-07-10T18:16:49Z
| 2022-07-15T13:02:07Z
| 2022-07-15T13:02:07Z
|
CONTRIBUTOR
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## Describe the bug
I'm adding a new dataset which is a `.7z` zip file in Google drive and contains 3 json files inside. I'm able to download the data files using `download_and_extract` but after downloading it throws this error:
```
>>> dataset = load_dataset("./datasets/mantis/")
Using custom data configuration default
Downloading and preparing dataset mantis/default to /Users/bhavitvyamalik/.cache/huggingface/datasets/mantis/default/1.1.0/611affa804ec53e2055a335cc1b8b213bb5a0b5142d919967729d5ee23c6bab4...
Downloading data: 100%|█████████████████████████████████████████████████████████| 77.2M/77.2M [00:23<00:00, 3.28MB/s]
/Users/bhavitvyamalik/.cache/huggingface/datasets/downloads/fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/load.py", line 1745, in load_dataset
use_auth_token=use_auth_token,
File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/Users/bhavitvyamalik/Desktop/work/hf/datasets/src/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
[Errno 20] Not a directory: '/Users/bhavitvyamalik/.cache/huggingface/datasets/downloads/fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6/merged_train.json'
```
just before generating the splits. I checked `fc3d70123c9de8407587a59aa426c37819cf2bf016795d33270e8a1d558a34e6` file and it's `7z` zip file (similar to downloaded Google drive file) which means it didn't get unzip. Do I need to unzip it separately and then pass the paths for train,dev,test files in `SplitGenerator`?
## Environment info
- `datasets` version: 1.18.4.dev0
- Platform: Darwin-19.6.0-x86_64-i386-64bit
- Python version: 3.7.8
- PyArrow version: 5.0.0
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I_kwDODunzps5NehtD
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loading oscar-corpus/OSCAR-2201 raises an error
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[
"I had to use the appropriate token for use_auth_token. Thank you."
] | 2022-07-10T07:09:30Z
| 2022-07-11T09:27:49Z
| 2022-07-11T09:27:49Z
|
NONE
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## Describe the bug
load_dataset('oscar-2201', 'af')
raises an error:
Traceback (most recent call last):
File "/usr/lib/python3.8/code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 1, in <module>
File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset
builder_instance = load_dataset_builder(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder
dataset_module = dataset_module_factory(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py
I've tried other permutations such as :
oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-2201', 'af')
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201')
with the same unfortunate result.
## Steps to reproduce the bug
oscar_22 = load_dataset('oscar-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201', 'af',use_auth_token=True)
oscar_22 = load_dataset('oscar-2201', 'af')
oscar_22 = load_dataset('oscar-corpus/OSCAR-2201')
# Sample code to reproduce the bug
```
## Expected results
loaded data
## Actual results
Traceback (most recent call last):
File "/usr/lib/python3.8/code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 1, in <module>
File "..python3.8/site-packages/datasets/load.py", line 1656, in load_dataset
builder_instance = load_dataset_builder(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder
dataset_module = dataset_module_factory(
File ".../lib/python3.8/site-packages/datasets/load.py", line 1189, in dataset_module_factory
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at .../oscar-2201/oscar-2201.py or any data file in the same directory. Couldn't find 'oscar-2201' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/oscar-2201/oscar-2201.py
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.13.0-37-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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I_kwDODunzps5NeGVV
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Dataset Viewer issue for hungnm/multilingual-amazon-review-sentiment-processed
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[
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] | 2022-07-09T18:04:13Z
| 2022-07-11T07:47:47Z
| 2022-07-11T07:47:47Z
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NONE
| null | null |
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### Link
https://huggingface.co/hungnm/multilingual-amazon-review-sentiment
### Description
_No response_
### Owner
Yes
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Dataset Viewer issue for hungnm/multilingual-amazon-review-sentiment-processed
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[] | 2022-07-09T18:03:15Z
| 2022-07-11T07:47:15Z
| 2022-07-11T07:47:15Z
|
NONE
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### Link
_No response_
### Description
_No response_
### Owner
_No response_
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Issues with concatenating datasets
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[
"Hi! I agree we should improve the features equality checks to account for this particular case. However, your code fails due to `answer_start` having the dtype `int64` instead of `int32` after loading from JSON (it's not possible to embed type precision info into a JSON file; `save_to_disk` does that for arrow files), which would lead to the concatenation error as PyArrow does not support this sort of type promotion. This can be fixed as follows:\r\n```python\r\ntemp = load_dataset(\"json\", data_files={\"train\": \"output.jsonl\"}, features=squad[\"train\"].features)\r\n``` ",
"That makes sense. I totally missed the `int64` and `int32` part. Thanks for pointing it out! Will close this issue for now."
] | 2022-07-09T17:45:14Z
| 2022-07-12T17:16:15Z
| 2022-07-12T17:16:14Z
|
NONE
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## Describe the bug
It is impossible to concatenate datasets if a feature is sequence of dict in one dataset and a dict of sequence in another. But based on the document, it should be automatically converted.
> A [datasets.Sequence](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Sequence) with a internal dictionary feature will be automatically converted into a dictionary of lists. This behavior is implemented to have a compatilbity layer with the TensorFlow Datasets library but may be un-wanted in some cases. If you don’t want this behavior, you can use a python list instead of the [datasets.Sequence](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Sequence).
## Steps to reproduce the bug
```python
from datasets import concatenate_datasets, load_dataset
squad = load_dataset("squad_v2")
squad["train"].to_json("output.jsonl", lines=True)
temp = load_dataset("json", data_files={"train": "output.jsonl"})
concatenate_datasets([temp["train"], squad["train"]])
```
## Expected results
No error executing that code
## Actual results
```
ValueError: The features can't be aligned because the key answers of features {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} has unexpected type - Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) (expected either {'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'answer_start': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)} or Value("null").
```
## Environment info
- `datasets` version: 2.3.2
- Platform: macOS-12.4-arm64-arm-64bit
- Python version: 3.8.11
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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[
"Hi @aleSuglia, thanks for reporting.\r\n\r\nWe are having a look at it. \r\n\r\nWe transfer this issue to the Community tab of the corresponding Hub dataset: https://huggingface.co/datasets/Heriot-WattUniversity/dialog-babi/discussions"
] | 2022-07-09T11:45:46Z
| 2022-07-11T07:10:09Z
| 2022-07-11T07:10:01Z
|
CONTRIBUTOR
| null | null |
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## Describe the bug
Hi there,
I'm trying to add the following dataset to Huggingface datasets: https://huggingface.co/datasets/Heriot-WattUniversity/dialog-babi/blob/
I'm trying to do so using the CLI commands but seems that this command generates the wrong `dataset_info.json` file (you can find it in the repo already):
```
datasets-cli test Heriot-WattUniversity/dialog-babi/dialog_babi.py --save_infos --all-configs
```
while it errors when I remove the python script:
```
datasets-cli test Heriot-WattUniversity/dialog-babi/ --save_infos --all-configs
```
The error message is the following:
```
FileNotFoundError: Unable to resolve any data file that matches '['**']' at /Users/as2180/workspace/Heriot-WattUniversity/dialog-babi with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```
## Environment info
- `datasets` version: 2.3.2
- Platform: macOS-12.4-arm64-arm-64bit
- Python version: 3.9.9
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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Transfer CI tests to GitHub Actions
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[] | 2022-07-07T08:10:50Z
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| 2022-07-12T11:18:25Z
|
MEMBER
| null | null |
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Let's try CI tests using GitHub Actions to see if they are more stable than on CircleCI.
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Add SQuAD2.0 Dataset
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"Hey, It's already present [here](https://huggingface.co/datasets/squad_v2) ",
"Hi! This dataset is indeed already available on the Hub. Closing."
] | 2022-07-07T03:19:36Z
| 2022-07-12T16:14:52Z
| 2022-07-12T16:14:52Z
|
NONE
| null | null |
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## Adding a Dataset
- **Name:** *SQuAD2.0*
- **Description:** *Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.*
- **Paper:** *https://aclanthology.org/P18-2124.pdf*
- **Data:** *https://rajpurkar.github.io/SQuAD-explorer/*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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Add Amazon-QA Dataset
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"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/Amazon-QA)."
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| 2022-07-14T02:20:12Z
| 2022-07-14T02:20:12Z
|
NONE
| null | null |
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## Adding a Dataset
- **Name:** *Amazon-QA*
- **Description:** *The dataset is .jsonl format, where each line in the file is a json string that corresponds to a question, existing answers to the question and the extracted review snippets (relevant to the question).*
- **Paper:** *https://github.com/amazonqa/amazonqa/tree/master/paper*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/amazon-qa.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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Simple Wikipedia
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"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/simple-wiki)."
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| 2022-07-14T02:16:33Z
| 2022-07-14T02:16:33Z
|
NONE
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## Adding a Dataset
- **Name:** *Simple Wikipedia*
- **Description:** *Two different versions of the data set now exist. Both were generated by aligning Simple English Wikipedia and English Wikipedia. A complete description of the extraction process can be found in "Simple English Wikipedia: A New Simplification Task", William Coster and David Kauchak (2011).*
- **Paper:** *https://aclanthology.org/P11-2117/*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/SimpleWiki.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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Add Quora Question Triplets Dataset
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"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/QQP_triplets)."
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| 2022-07-14T02:13:50Z
| 2022-07-14T02:13:50Z
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NONE
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## Adding a Dataset
- **Name:** *Quora Question Triplets*
- **Description:** *This dataset consists of over 400,000 lines of potential question duplicate pairs. Each line contains IDs for each question in the pair, the full text for each question, and a binary value that indicates whether the line truly contains a duplicate pair.*
- **Paper:**
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/quora_duplicates_triplets.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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Add Altlex dataset
|
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"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/altlex)."
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| 2022-07-14T02:12:39Z
| 2022-07-14T02:12:39Z
|
NONE
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## Adding a Dataset
- **Name:** *Altlex*
- **Description:** *Git repository for software associated with the 2016 ACL paper "Identifying Causal Relations Using Parallel Wikipedia Articles.”*
- **Paper:** *https://aclanthology.org/P16-1135.pdf*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/altlex.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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I_kwDODunzps5NSgia
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|
Add Sentence Compression Dataset
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"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/sentence-compression)."
] | 2022-07-07T02:13:46Z
| 2022-07-14T02:11:48Z
| 2022-07-14T02:11:48Z
|
NONE
| null | null |
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## Adding a Dataset
- **Name:** *Sentence Compression*
- **Description:** *Large corpus of uncompressed and compressed sentences from news articles.*
- **Paper:** *https://www.aclweb.org/anthology/D13-1155/*
- **Data:** *https://github.com/google-research-datasets/sentence-compression/tree/master/data*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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I_kwDODunzps5NSekG
| 4,651
|
Add Flickr 30k Dataset
|
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| 2022-07-14T02:09:45Z
| 2022-07-14T02:09:45Z
|
NONE
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## Adding a Dataset
- **Name:** *Flickr 30k*
- **Description:** *To produce the denotation graph, we have created an image caption corpus consisting of 158,915 crowd-sourced captions describing 31,783 images. This is an extension of our previous Flickr 8k Dataset. The new images and captions focus on people involved in everyday activities and events.*
- **Paper:** *https://transacl.org/ojs/index.php/tacl/article/view/229/33*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/flickr30k_captions.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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Add PAQ dataset
|
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"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/PAQ_pairs)"
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| 2022-07-14T02:06:27Z
| 2022-07-14T02:06:27Z
|
NONE
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## Adding a Dataset
- **Name:** *PAQ*
- **Description:** *This repository contains code and models to support the research paper PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them*
- **Paper:** *https://arxiv.org/abs/2102.07033*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/PAQ_pairs.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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Add WikiAnswers dataset
|
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"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/WikiAnswers)"
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| 2022-07-14T02:03:40Z
| 2022-07-14T02:03:40Z
|
NONE
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## Adding a Dataset
- **Name:** *WikiAnswers*
- **Description:** *The WikiAnswers corpus contains clusters of questions tagged by WikiAnswers users as paraphrases. Each cluster optionally contains an answer provided by WikiAnswers users.*
- **Paper:** *https://dl.acm.org/doi/10.1145/2623330.2623677*
- **Data:** *https://github.com/afader/oqa#wikianswers-corpus*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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Streaming issue for ccdv/pubmed-summarization
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[
"Thanks for reporting @lewtun.\r\n\r\nI confirm there is an issue with streaming: it does not stream locally. ",
"Oh, after investigation, the source of the issue is in the Hub dataset loading script.\r\n\r\nI'm opening a PR on the Hub dataset.",
"I've opened a PR on their Hub dataset to support streaming: https://huggingface.co/datasets/ccdv/pubmed-summarization/discussions/2"
] | 2022-07-06T12:13:07Z
| 2022-07-06T14:17:34Z
| 2022-07-06T14:17:34Z
|
MEMBER
| null | null |
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### Link
https://huggingface.co/datasets/ccdv/pubmed-summarization
### Description
This was reported by a [user of AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/7). It seems like streaming doesn't work due to the way the dataset loading script is defined?
```
Status code: 400
Exception: FileNotFoundError
Message: https://huggingface.co/datasets/ccdv/pubmed-summarization/resolve/main/train.zip/train.txt
```
### Owner
No
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I_kwDODunzps5NOcti
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Dataset Viewer issue for kmfoda/booksum
|
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[
"Thanks for reporting, @lewtun.\r\n\r\nIt works locally in streaming mode:\r\n```\r\n{'bid': 27681,\r\n 'is_aggregate': True,\r\n 'source': 'cliffnotes',\r\n 'chapter_path': 'all_chapterized_books/27681-chapters/chapters_1_to_2.txt',\r\n 'summary_path': 'finished_summaries/cliffnotes/The Last of the Mohicans/section_1_part_0.txt',\r\n 'book_id': 'The Last of the Mohicans.chapters 1-2',\r\n 'summary_id': 'chapters 1-2',\r\n 'content': None,\r\n 'summary': '{\"name\": \"Chapters 1-2\", \"url\": \"https://web.archive.org/web/20201101053205/https://www.cliffsnotes.com/literature/l/the-last-of-the-mohicans/summary-and-analysis/chapters-12\", \"summary\": \"Before any characters appear, the time and geography are made clear. Though it is the last war that England and France waged for a country that neither would retain, the wilderness between the forces still has to be...\r\n```\r\n\r\nI'm forcing the refresh of the preview. ",
"The preview appears as expected once the refresh forced.",
"Thank you @albertvillanova 🤗 !"
] | 2022-07-06T10:38:16Z
| 2022-07-06T13:25:28Z
| 2022-07-06T11:58:06Z
|
MEMBER
| null | null |
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### Link
https://huggingface.co/datasets/kmfoda/booksum
### Description
A [user of AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/9) discovered this dataset cannot be streamed due to:
```
Status code: 400
Exception: ClientResponseError
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/kmfoda/booksum/resolve/47953f583d6967f086cb16a2f4d2346e9834024d/test.csv')
```
I'm not sure why it says "Unauthorized" since it's just a bunch of CSV files in a repo
### Owner
No
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I_kwDODunzps5NKTt8
| 4,636
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Add info in docs about behavior of download_config.num_proc
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[] | 2022-07-05T17:01:00Z
| 2022-07-28T10:40:32Z
| 2022-07-28T10:40:32Z
|
CONTRIBUTOR
| null | null |
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**Is your feature request related to a problem? Please describe.**
I went to override `download_config.num_proc` and was confused about what was happening under the hood. It would be nice to have the behavior documented a bit better so folks know what's happening when they use it.
**Describe the solution you'd like**
- Add note about how the default number of workers is 16. Related code:
https://github.com/huggingface/datasets/blob/7bcac0a6a0fc367cc068f184fa132b8de8dfa11d/src/datasets/download/download_manager.py#L299-L302
- Add note that if the number of workers is higher than the number of files to download, it won't use multiprocessing.
**Describe alternatives you've considered**
maybe it would also be nice to set `num_proc` = `num_files` when `num_proc` > `num_files`.
**Additional context**
...
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I_kwDODunzps5NKCKb
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Dataset Viewer issue for vadis/sv-ident
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[
"Thanks for reporting, @e-tornike \r\n\r\nSome context:\r\n- #4527 \r\n\r\nThe dataset loads locally in streaming mode:\r\n```python\r\nIn [2]: from datasets import load_dataset; ds = load_dataset(\"vadis/sv-ident\", split=\"validation\", streaming=True); item = next(iter(ds)); item\r\nUsing custom data configuration default\r\nOut[2]: \r\n{'sentence': 'Im Falle von Umweltbelastungen kann selten eindeutig entschieden werden, ob Unbedenklichkeitswerte bereits erreicht oder überschritten sind, die die menschliche Gesundheit oder andere Wohlfahrts»güter« beeinträchtigen.',\r\n 'is_variable': 0,\r\n 'variable': [],\r\n 'research_data': [],\r\n 'doc_id': '51971',\r\n 'uuid': 'ee3d7f88-1a3e-4a59-997f-e986b544a604',\r\n 'lang': 'de'}\r\n```",
"~~I have forced the refresh of the split in the preview without success.~~\r\n\r\nI have forced the refresh of the split in the preview, and now it works.",
"Preview seems to work now. \r\n\r\nhttps://huggingface.co/datasets/vadis/sv-ident/viewer/default/validation",
"OK, thank you @e-tornike.\r\n\r\nApparently, after forcing the refresh, we just had to wait a little until it is effectively refreshed. ",
"I'm closing this issue as it was solved after forcing the refresh of the split in the preview.",
"Thanks a lot! :)"
] | 2022-07-05T15:48:13Z
| 2022-07-06T07:13:33Z
| 2022-07-06T07:12:14Z
|
NONE
| null | null |
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### Link
https://huggingface.co/datasets/vadis/sv-ident/viewer/default/validation
### Description
Error message when loading validation split in the viewer:
```
Status code: 400
Exception: Status400Error
Message: The split cache is empty.
```
### Owner
_No response_
|
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I_kwDODunzps5NJw6D
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|
Can't load the Hausa audio dataset
|
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[
"Could you provide the error details. It is difficult to debug otherwise. Also try other config. `ha` is not a valid."
] | 2022-07-05T14:47:36Z
| 2022-09-13T14:07:32Z
| 2022-09-13T14:07:32Z
|
NONE
| null | null |
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common_voice_train = load_dataset("common_voice", "ha", split="train+validation")
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I_kwDODunzps5NI2tg
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|
'sort' method sorts one column only
|
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[
"Hi ! `ds.sort()` does sort the full dataset, not just one column:\r\n```python\r\nfrom datasets import *\r\n\r\nds = Dataset.from_dict({\"foo\": [3, 2, 1], \"bar\": [\"c\", \"b\", \"a\"]})\r\nprint(d.sort(\"foo\").to_pandas()\r\n# foo bar\r\n# 0 1 a\r\n# 1 2 b\r\n# 2 3 c\r\n```\r\n\r\nWhat made you think it was not the case ? Did you experience a situation where it was only sorting one column ?",
"Hi! thank you for your quick reply!\r\nI wanted to sort the `cnn_dailymail` dataset by the length of the labels (num of characters). I added a new column to the dataset (`ds.add_column`) with the lengths and then sorted by this new column. Only the new length column was sorted, the reset left in their original order. ",
"That's unexpected, can you share the code you used to get this ?"
] | 2022-07-05T11:25:26Z
| 2023-07-25T15:04:27Z
| 2023-07-25T15:04:27Z
|
NONE
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The 'sort' method changes the order of one column only (the one defined by the argument 'column'), thus creating a mismatch between a sample fields. I would expect it to change the order of the samples as a whole, based on the 'column' order.
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I_kwDODunzps5NGAEw
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|
Rename repo default branch to main
|
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[] | 2022-07-04T17:16:10Z
| 2022-07-06T15:49:57Z
| 2022-07-06T15:49:57Z
|
MEMBER
| null | null |
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Rename repository default branch to `main` (instead of current `master`).
Once renamed, users will have to manually update their local repos:
- [ ] Upstream:
```
git branch -m master main
git fetch upstream main
git branch -u upstream/main main
git remote set-head upstream -a
```
- [ ] Origin:
Rename fork default branch as well at: https://github.com/USERNAME/lam/settings/branches
Then:
```
git fetch origin main
git remote set-head origin -a
```
CC: @sgugger
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I_kwDODunzps5NEhLw
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ImageFolder raises an error with parameters drop_metadata=True and drop_labels=False when metadata.jsonl is present
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[] | 2022-07-04T11:21:44Z
| 2022-07-15T14:24:24Z
| 2022-07-15T14:24:24Z
|
CONTRIBUTOR
| null | null |
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## Describe the bug
If you pass `drop_metadata=True` and `drop_labels=False` when a `data_dir` contains at least one `matadata.jsonl` file, you will get a KeyError. This is probably not a very useful case but we shouldn't get an error anyway. Asking users to move metadata files manually outside `data_dir` or pass features manually (when there is a tool that can infer them automatically) don't look like a good idea to me either.
## Steps to reproduce the bug
### Clone an example dataset from the Hub
```bash
git clone https://huggingface.co/datasets/nateraw/test-imagefolder-metadata
```
### Try to load it
```python
from datasets import load_dataset
ds = load_dataset("test-imagefolder-metadata", drop_metadata=True, drop_labels=False)
```
or even just
```python
ds = load_dataset("test-imagefolder-metadata", drop_metadata=True)
```
as `drop_labels=False` is a default value.
## Expected results
A DatasetDict object with two features: `"image"` and `"label"`.
## Actual results
```
Traceback (most recent call last):
File "/home/polina/workspace/datasets/debug.py", line 18, in <module>
ds = load_dataset(
File "/home/polina/workspace/datasets/src/datasets/load.py", line 1732, in load_dataset
builder_instance.download_and_prepare(
File "/home/polina/workspace/datasets/src/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/polina/workspace/datasets/src/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/polina/workspace/datasets/src/datasets/builder.py", line 1218, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1596, in encode_example
return encode_nested_example(self, example)
File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in encode_nested_example
{
File "/home/polina/workspace/datasets/src/datasets/features/features.py", line 1165, in <dictcomp>
{
File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in zip_dict
yield key, tuple(d[key] for d in dicts)
File "/home/polina/workspace/datasets/src/datasets/utils/py_utils.py", line 249, in <genexpr>
yield key, tuple(d[key] for d in dicts)
KeyError: 'label'
```
## Environment info
`datasets` master branch
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.14.0-1042-oem-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.1
- Pandas version: 1.4.1
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Data type is not recognized when using datetime.time
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[
"cc @mariosasko ",
"Hi, thanks for reporting! I'm investigating the issue."
] | 2022-07-04T08:13:38Z
| 2022-07-07T13:57:11Z
| 2022-07-07T13:57:11Z
|
COLLABORATOR
| null | null |
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## Describe the bug
Creating a dataset from a pandas dataframe with `datetime.time` format generates an error.
## Steps to reproduce the bug
```python
import pandas as pd
from datetime import time
from datasets import Dataset
df = pd.DataFrame({"feature_name": [time(1, 1, 1)]})
dataset = Dataset.from_pandas(df)
```
## Expected results
The dataset should be created.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 823, in from_pandas
return cls(table, info=info, split=split)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 679, in __init__
inferred_features = Features.from_arrow_schema(arrow_table.schema)
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1551, in from_arrow_schema
obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema}
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1551, in <dictcomp>
obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema}
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1315, in generate_from_arrow_type
return Value(dtype=_arrow_to_datasets_dtype(pa_type))
File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 83, in _arrow_to_datasets_dtype
return f"time64[{arrow_type.unit}]"
AttributeError: 'pyarrow.lib.DataType' object has no attribute 'unit'
```
## Environment info
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.13.0-1031-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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[
"Apparently, `fsspec` does not allow access to attribute-based modules anymore, such as `fsspec.async`.\r\n\r\nHowever, this is a fairly simple fix:\r\n- Change the import to: `from fsspec import asyn`;\r\n- Change line 18 to: `asyn.iothread[0] = None`;\r\n- Change line 19 to `asyn.loop[0] = None`.",
"Hi! I think it's easier to replace `import fsspec` with `import fsspec.asyn` and leave the rest unchanged. @gugarosa Are you interested in submitting a PR?",
"Perfect, it is even better!\r\n\r\nJust submitted the PR: #4630.\r\n\r\nThank you!"
] | 2022-07-01T06:46:07Z
| 2022-07-05T15:08:21Z
| 2022-07-05T15:08:21Z
|
NONE
| null | null |
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## Describe the bug
Trying to iterate examples from custom iterable dataset fails to bug introduced in `torch_iterable_dataset.py` since the release of 2.3.0.
## Steps to reproduce the bug
```python
next(iter(custom_iterable_dataset))
```
## Expected results
`next(iter(custom_iterable_dataset))` should return examples from the dataset
## Actual results
```
/usr/local/lib/python3.7/dist-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py in _set_fsspec_for_multiprocess()
16 See https://github.com/fsspec/gcsfs/issues/379
17 """
---> 18 fsspec.asyn.iothread[0] = None
19 fsspec.asyn.loop[0] = None
20
AttributeError: module 'fsspec' has no attribute 'asyn'
```
## Environment info
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- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
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- Pandas version: 1.3.5
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[
"I believe the issue is in `codeparrot/github-code`. `base_path` param is missing - https://huggingface.co/datasets/codeparrot/github-code/blob/main/github-code.py#L169\r\n\r\nFunction definition has changed.\r\nhttps://github.com/huggingface/datasets/blob/0e1c629cfb9f9ba124537ba294a0ec451584da5f/src/datasets/data_files.py#L547\r\n\r\n@mariosasko could you please confirm my finding? And are there any changes that need to be done from my side?",
"Good catch ! We recently did a breaking change in `get_patterns_in_dataset_repository`, I think we can revert it",
"> Good catch ! We recently did a breaking change in `get_patterns_in_dataset_repository`, I think we can revert it\n\nI can't wait for that releasee. Broke my application",
"This simple workaround should fix: https://huggingface.co/datasets/codeparrot/github-code/discussions/2\r\n\r\n`get_patterns_in_dataset_repository` can treat whether `base_path=None`, so we just need to make sure that codeparrot/github-code `_split_generators` calls with such an argument.",
"I am afraid your suggested change @gugarosa will break compatibility with older datasets versions that don't have `base_path` argument in `get_patterns_in_dataset_repository`, as a workaround while the issue gets resolved in `datasets` can you downgrade your datasets version to `<=2.1.0` ? \r\n@lvwerra do you think we should adapt the script to check the datasets version before calling `get_patterns_in_dataset_repository`?",
"Actually I think it's just simpler to fix it in the dataset itself, let me open a PR\r\n\r\nEDIT: PR opened here: https://huggingface.co/datasets/codeparrot/github-code/discussions/3",
"PR is merged, it's working now ! Closing this one :)",
"> I am afraid your suggested change @gugarosa will break compatibility with older datasets versions that don't have `base_path` argument in `get_patterns_in_dataset_repository`, as a workaround while the issue gets resolved in `datasets` can you downgrade your datasets version to `<=2.1.0` ?\r\n> @lvwerra do you think we should adapt the script to check the datasets version before calling `get_patterns_in_dataset_repository`?\r\n\r\nYou are definitely right, sorry about it. I always keep forgetting that we need to keep in mind users from past versions, my bad."
] | 2022-06-30T20:24:48Z
| 2022-07-05T14:24:13Z
| 2022-07-05T09:19:56Z
|
NONE
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## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.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)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
|
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|
librispeech dataset has to download whole subset when specifing the split to use
|
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[
"Hi! You can use streaming to fetch only a subset of the data:\r\n```python\r\nraw_dataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"train.100\", streaming=True)\r\n```\r\nAlso, we plan to make it possible to download a particular split in the non-streaming mode, but this task is not easy due to how our dataset scripts are structured.",
"Hi,\r\n\r\nThat's a great help. Thank you very much."
] | 2022-06-30T16:38:24Z
| 2022-07-12T21:44:32Z
| 2022-07-12T21:44:32Z
|
NONE
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## Describe the bug
librispeech dataset has to download whole subset when specifing the split to use
## Steps to reproduce the bug
see below
# Sample code to reproduce the bug
```
!pip install datasets
from datasets import load_dataset
raw_dataset = load_dataset("librispeech_asr", "clean", split="train.100")
```
## Expected results
The split "train.clean.100" is downloaded.
## Actual results
All four splits in "clean" subset is downloaded.
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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evaluation result changes after `datasets` version change
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"Hi! The GH/no-namespace datasets versioning is synced with the version of the `datasets` lib, which means that the `wikiann` script was modified between the two compared versions. In this scenario, you can ensure reproducibility by pinning the script version, which is done by passing `revision=\"x.y.z\"` (e.g. `revision=\"2.2.0\"`) to `load_dataset.`\r\n"
] | 2022-06-30T12:43:26Z
| 2023-07-25T15:05:26Z
| 2023-07-25T15:05:26Z
|
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## Describe the bug
evaluation result changes after `datasets` version change
## Steps to reproduce the bug
1. Train a model on WikiAnn
2. reload the ckpt -> test accuracy becomes same as eval accuracy
3. such behavior is gone after downgrading `datasets`
https://colab.research.google.com/drive/1kYz7-aZRGdayaq-gDTt30tyEgsKlpYOw?usp=sharing
## Expected results
evaluation result shouldn't change before/after `datasets` version changes
## Actual results
evaluation result changes before/after `datasets` version changes
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: colab
- Python version: 3.7.13
- PyArrow version: 6.0.1
Q. How could the evaluation result change before/after `datasets` version changes?
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Dataset Viewer issue for boris/gis_filtered
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[
"Yes, this dataset is \"gated\": you first have to go to https://huggingface.co/datasets/boris/gis_filtered and click \"Access repository\" (if you accept to share your contact information with the repository authors).",
"I already did that, it returns error when using streaming",
"Oh, sorry, I misread. Looking at it. Maybe @huggingface/datasets or @SBrandeis ",
"I could reproduce the error, even though I provided my token and accepted the gate form. It looks like an error from `datasets`",
"This is indeed a bug in `datasets`. Parquet datasets in gated/private repositories can't be streamed properly, which caused the viewer to fail. I opened a PR at https://github.com/huggingface/datasets/pull/4608"
] | 2022-06-30T12:23:34Z
| 2022-07-06T12:34:19Z
| 2022-07-06T12:34:19Z
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|
### Link
https://huggingface.co/datasets/boris/gis_filtered/viewer/boris--gis_filtered/train
### Description
When I try to access this from the website I get this error:
Status code: 400
Exception: ClientResponseError
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/boris/gis_filtered/resolve/80b805053ce61d4eb487b6b8d9095d775c2c466e/data/train/0000.parquet')
If I try to load with code I also get the same issue:
```python
dataset2_train=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"],split="train",streaming=True)
dataset2_validation=load_dataset("boris/gis_filtered", use_auth_token=os.environ["HF_TOKEN"], split="validation",streaming=True)
```
### Owner
No
|
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| 1,289,963,331
|
I_kwDODunzps5M40dD
| 4,603
|
CI fails recurrently and randomly on Windows
|
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[] | 2022-06-30T10:59:58Z
| 2022-06-30T13:22:25Z
| 2022-06-30T13:22:25Z
|
MEMBER
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As reported by @lhoestq,
The windows CI is currently flaky: some dependencies like `aiobotocore`, `multiprocess` and `seqeval` sometimes fail to install.
In particular it seems that building the wheels fail. Here is an example of logs:
```
Building wheel for seqeval (setup.py): started
Running command 'C:\tools\miniconda3\envs\py37\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"'; __file__='"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6'
No parent package detected, impossible to derive `name`
running bdist_wheel
running build
running build_py
package init file 'seqeval\__init__.py' not found (or not a regular file)
package init file 'seqeval\metrics\__init__.py' not found (or not a regular file)
C:\tools\miniconda3\envs\py37\lib\site-packages\setuptools\command\install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
setuptools.SetuptoolsDeprecationWarning,
installing to build\bdist.win-amd64\wheel
running install
running install_lib
warning: install_lib: 'build\lib' does not exist -- no Python modules to install
running install_egg_info
running egg_info
creating UNKNOWN.egg-info
writing UNKNOWN.egg-info\PKG-INFO
writing dependency_links to UNKNOWN.egg-info\dependency_links.txt
writing top-level names to UNKNOWN.egg-info\top_level.txt
writing manifest file 'UNKNOWN.egg-info\SOURCES.txt'
reading manifest file 'UNKNOWN.egg-info\SOURCES.txt'
writing manifest file 'UNKNOWN.egg-info\SOURCES.txt'
Copying UNKNOWN.egg-info to build\bdist.win-amd64\wheel\.\UNKNOWN-0.0.0-py3.7.egg-info
running install_scripts
creating build\bdist.win-amd64\wheel\UNKNOWN-0.0.0.dist-info\WHEEL
creating 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6\UNKNOWN-0.0.0-py3-none-any.whl' and adding 'build\bdist.win-amd64\wheel' to it
adding 'UNKNOWN-0.0.0.dist-info/METADATA'
adding 'UNKNOWN-0.0.0.dist-info/WHEEL'
adding 'UNKNOWN-0.0.0.dist-info/top_level.txt'
adding 'UNKNOWN-0.0.0.dist-info/RECORD'
removing build\bdist.win-amd64\wheel
Building wheel for seqeval (setup.py): finished with status 'done'
Created wheel for seqeval: filename=UNKNOWN-0.0.0-py3-none-any.whl size=963 sha256=67eb93a6e1ff4796c5882a13f9fa25bb0d3d103796e2525f9cecf3b2ef26d4b1
Stored in directory: c:\users\circleci\appdata\local\pip\cache\wheels\05\96\ee\7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7
WARNING: Built wheel for seqeval is invalid: Wheel has unexpected file name: expected 'seqeval', got 'UNKNOWN'
```
|
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I_kwDODunzps5Mz5MH
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|
Streaming issue for financial_phrasebank
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[
"cc @huggingface/datasets: it seems like https://www.researchgate.net/ is flaky for datasets hosting (I put the \"hosted-on-google-drive\" tag since it's the same kind of issue I think)",
"Let's see if their license allows hosting their data on the Hub.",
"License is Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0).\r\n\r\nWe can host their data on the Hub."
] | 2022-06-29T12:45:43Z
| 2022-07-01T09:29:36Z
| 2022-07-01T09:29:36Z
|
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### Link
https://huggingface.co/datasets/financial_phrasebank/viewer/sentences_allagree/train
### Description
As reported by a community member using [AutoTrain Evaluate](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions/5#62bc217436d0e5d316a768f0), there seems to be a problem streaming this dataset:
```
Server error
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No
|
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I_kwDODunzps5MyyUn
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|
Dataset Viewer issue for universal_dependencies
|
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[
"Thanks, looking at it!",
"Finally fixed! We updated the dataset viewer and it fixed the issue.\r\n\r\nhttps://huggingface.co/datasets/universal_dependencies/viewer/aqz_tudet/train\r\n\r\n<img width=\"1561\" alt=\"Capture d’écran 2022-09-07 à 13 29 18\" src=\"https://user-images.githubusercontent.com/1676121/188867795-4f7dd438-d4f2-46cd-8a92-20a37fb2d6bc.png\">\r\n"
] | 2022-06-29T08:50:29Z
| 2022-09-07T11:29:28Z
| 2022-09-07T11:29:27Z
|
NONE
| null | null |
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### Link
https://huggingface.co/datasets/universal_dependencies
### Description
invalid json response body at https://datasets-server.huggingface.co/splits?dataset=universal_dependencies reason: Unexpected token I in JSON at position 0
### Owner
_No response_
|
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I_kwDODunzps5MyYgI
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Dataset Viewer issue with False positive PII redaction
|
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[
"The value is in the data, it's not an issue with the \"dataset-viewer\".\r\n\r\n<img width=\"1161\" alt=\"Capture d’écran 2022-06-29 à 10 25 51\" src=\"https://user-images.githubusercontent.com/1676121/176389325-4d2a9a7f-1583-45b8-aa7a-960ffaa6a36a.png\">\r\n\r\n Maybe open a PR: https://huggingface.co/datasets/cakiki/rosetta-code/discussions\r\n",
"This was indeed a scraping issue which I assumed was a display issue; sorry about that!"
] | 2022-06-29T07:15:57Z
| 2022-06-29T08:29:41Z
| 2022-06-29T08:27:49Z
|
CONTRIBUTOR
| null | null |
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### Link
https://huggingface.co/datasets/cakiki/rosetta-code
### Description
Hello, I just noticed an entry being redacted that shouldn't have been:
`RootMeanSquare@Range[10]` is being displayed as `[email protected][10]`
### Owner
_No response_
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I_kwDODunzps5MxmOH
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load_from_disk suggests incorrect fix when used to load DatasetDict
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[] | 2022-06-29T01:40:01Z
| 2022-06-29T04:03:44Z
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Edit: Please feel free to remove this issue. The problem was not the error message but the fact that the DatasetDict.load_from_disk does not support loading nested splits, i.e. if one of the splits is itself a DatasetDict. If nesting splits is an antipattern, perhaps the load_from_disk function can throw a warning indicating that?
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Issue with jalFaizy/detect_chess_pieces when running datasets-cli test
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[
"Hi @faizankshaikh\r\n\r\nPlease note that we have recently launched the Community feature, specifically targeted to create Discussions (about issues/questions/asking-for-help) on each Dataset on the Hub:\r\n- Blog post: https://huggingface.co/blog/community-update\r\n- Docs: https://huggingface.co/docs/hub/repositories-pull-requests-discussions\r\n\r\nThe Discussion tab for your \"jalFaizy/detect_chess_pieces\" dataset is here: https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions\r\nYou can use it to ask for help by pinging the Datasets maintainers: see our docs here: https://huggingface.co/docs/datasets/master/en/share#ask-for-a-help-and-reviews\r\n\r\nI'm transferring this discussion to your Discussion tab and trying to address it: https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions/1",
"Thank you @albertvillanova , I will keep that in mind.\r\n\r\nJust a quick note - I posted the issue on Github because the dataset viewer suggested me to \"open an issue for direct support\". Maybe it can be updated with your suggestion\r\n\r\n\r\n\r\n\r\n",
"Thank you pointing this out: yes, definitely, we should fix the error message. We are working on this."
] | 2022-06-29T00:15:54Z
| 2022-06-29T10:30:03Z
| 2022-06-29T07:49:27Z
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### Link
https://huggingface.co/datasets/jalFaizy/detect_chess_pieces
### Description
I am trying to write a appropriate data loader for [a custom dataset](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces) using [this script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py)
When I run the command
`$ datasets-cli test "D:\workspace\HF\detect_chess_pieces" --save_infos --all_configs`
It gives the following error
```
Using custom data configuration default
Traceback (most recent call last):
File "c:\users\faiza\anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\faiza\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\faiza\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 7, in <module>
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 132, in run
for j, builder in enumerate(get_builders()):
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 125, in get_builders
yield builder_cls(
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 1148, in __init__
super().__init__(*args, **kwargs)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 306, in __init__
info = self.get_exported_dataset_info()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 405, in get_exported_dataset_info
return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 390, in get_all_exported_dataset_infos
return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 309, in from_directory
dataset_infos_dict = {
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 310, in <dictcomp>
config_name: DatasetInfo.from_dict(dataset_info_dict)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 272, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 160, in __post_init__
templates = [
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 161, in <listcomp>
template if isinstance(template, TaskTemplate) else task_template_from_dict(template)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\tasks\__init__.py", line 43, in task_template_from_dict
return template.from_dict(task_template_dict)
AttributeError: 'NoneType' object has no attribute 'from_dict'
```
My assumption is that there is some kind of issue in how the "task_templates" are read, because even if I keep them as None, or not include the argument at all, the same error occurs
### Owner
Yes
|
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I_kwDODunzps5MxaVU
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Can't push Images to hub with manual Dataset
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[
"Hi, thanks for reporting! This issue stems from the changes introduced in https://github.com/huggingface/datasets/pull/4282 (cc @lhoestq), in which list casts are ignored if they don't change the list type (required to preserve `null` values). And `push_to_hub` does a special cast to embed external image files but doesn't change the types, hence the failure."
] | 2022-06-29T00:01:23Z
| 2022-07-08T12:01:36Z
| 2022-07-08T12:01:35Z
|
CONTRIBUTOR
| null | null |
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## Describe the bug
If I create a dataset including an 'Image' feature manually, when pushing to hub decoded images are not pushed,
instead it looks for image where image local path is/used to be.
This doesn't (at least didn't used to) happen with imagefolder. I want to build dataset manually because it is complicated.
This happens even though the dataset is looking like decoded images:
and I use `embed_external_files=True` while `push_to_hub` (same with false)
## Steps to reproduce the bug
```python
from PIL import Image
from datasets import Image as ImageFeature
from datasets import Features,Dataset
#manually create dataset
feats=Features(
{
"images": [ImageFeature()], #same even if explicitly ImageFeature(decode=True)
"input_image": ImageFeature(),
}
)
test_data={"images":[[Image.open("test.jpg"),Image.open("test.jpg"),Image.open("test.jpg")]], "input_image":[Image.open("test.jpg")]}
test_dataset=Dataset.from_dict(test_data,features=feats)
print(test_dataset)
test_dataset.push_to_hub("ceyda/image_test_public",private=False,token="",embed_external_files=True)
# clear cache rm -r ~/.cache/huggingface
# remove "test.jpg" # remove to see that it is looking for image on the local path
test_dataset=load_dataset("ceyda/image_test_public",use_auth_token="")
print(test_dataset)
print(test_dataset['train'][0])
```
## Expected results
should be able to push image bytes if dataset has `Image(decode=True)`
## Actual results
errors because it is trying to decode file from the non existing local path.
```
----> print(test_dataset['train'][0])
File ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py:2154, in Dataset.__getitem__(self, key)
2152 def __getitem__(self, key): # noqa: F811
2153 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2154 return self._getitem(
2155 key,
2156 )
File ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py:2139, in Dataset._getitem(self, key, decoded, **kwargs)
2137 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
2138 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 2139 formatted_output = format_table(
2140 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
2141 )
2142 return formatted_output
File ~/.local/lib/python3.8/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
...
-> 3068 fp = builtins.open(filename, "rb")
3069 exclusive_fp = True
3071 try:
FileNotFoundError: [Errno 2] No such file or directory: 'test.jpg'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-1074-azure-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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[
"linked to https://github.com/huggingface/datasets/issues/4580#issuecomment-1168373066?",
"Note that I refreshed twice this dataset, and I still have (another) error on one of the splits\r\n\r\n```\r\nStatus code: 400\r\nException: ClientResponseError\r\nMessage: 403, message='Forbidden', url=URL('https://doc-14-4c-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/pgotjmcuh77q0lk7p44rparfrhv459kp/1656403650000/11771870722949762109/*/16OgJ_OrfzUF_i3ftLjFn9kpcyoi7UJeO?e=download')\r\n```\r\n\r\nLike the three splits are processed in parallel by the workers, I imagine that the Google hosting is rate-limiting us.\r\n\r\ncc @albertvillanova \r\n\r\n",
"Exactly, Google Drive bans our loading scripts.\r\n\r\nWhen possible, we should host somewhere else."
] | 2022-06-27T20:56:12Z
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### Link
https://huggingface.co/datasets/pn_summary/viewer/1.0.0/validation
### Description
Getting an index error on the `validation` and `test` splits:
```
Server error
Status code: 400
Exception: IndexError
Message: list index out of range
```
### Owner
No
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[
"Thanks for reporting, @lewtun.\r\n\r\nI forced the refreshing of the preview and it worked OK for train and validation splits.\r\n\r\nI guess the error has to do with the data files being hosted at Google Drive: this gives errors when requested automatically using scripts.\r\nWe should host them to fix the error. Let's see if the license allows that.",
"I guess we can host the data: https://github.com/Alex-Fabbri/Multi-News/blob/master/LICENSE.txt"
] | 2022-06-27T20:25:25Z
| 2022-06-28T14:08:48Z
| 2022-06-28T14:08:48Z
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### Link
https://huggingface.co/datasets/multi_news
### Description
Not sure what the index error is referring to here:
```
Status code: 400
Exception: IndexError
Message: list index out of range
```
### Owner
No
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https://api.github.com/repos/huggingface/datasets/issues/4575
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Problem about wmt17 zh-en dataset
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[
"Running into the same error with `wmt17/zh-en`, `wmt18/zh-en` and `wmt19/zh-en`.",
"@albertvillanova @lhoestq Could you take a look at this issue?",
"@winterfell2021 Hi, I wonder where the code you provided should be added. I tried to add them in the `datasets/table.py` in `array_cast` function, however, the 'zh' item is none.",
"I found some 'zh' item is none while 'c[hn]' is not.\r\nSo the code may change to:\r\n```python\r\nif 'c[hn]' in str(array.type):\r\n py_array = array.to_pylist()\r\n data_list = []\r\n for vo in py_array:\r\n tmp = {\r\n 'en': vo['en'],\r\n }\r\n if vo.get('zh'):\r\n tmp['zh'] = vo['zh']\r\n else:\r\n tmp['zh'] = vo['c[hn]']\r\n data_list.append(tmp)\r\n array = pa.array(data_list, type=pa.struct([\r\n pa.field('en', pa.string()),\r\n pa.field('zh', pa.string()),\r\n ]))\r\n```",
"I just pushed a fix, we'll do a new release of `datasets` soon to include this fix. In the meantime you can use the fixed dataset by passing `revision=\"main\"` to `load_dataset`"
] | 2022-06-27T08:35:42Z
| 2022-08-23T10:01:02Z
| 2022-08-23T10:00:21Z
|
NONE
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It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <module>
data = load_dataset(args.dataset, "zh-en")
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 1684, in load_dataset
use_auth_token=use_auth_token,
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1221, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/builder.py", line 1215, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 533, in finalize
self.write_examples_on_file()
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 410, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 503, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py", line 198, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1846, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/datasets/table.py", line 1756, in array_cast
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
TypeError: Couldn't cast array of type
struct<c[hn]: string, en: string, zh: string>
to
struct<en: string, zh: string>
```
So the solution of this problem is to change the original array manually:
```
if 'c[hn]' in str(array.type):
py_array = array.to_pylist()
data_list = []
for vo in py_array:
tmp = {
'en': vo['en'],
}
if 'zh' not in vo:
tmp['zh'] = vo['c[hn]']
else:
tmp['zh'] = vo['zh']
data_list.append(tmp)
array = pa.array(data_list, type=pa.struct([
pa.field('en', pa.string()),
pa.field('zh', pa.string()),
]))
```
Therefore, maybe a correct version of original casia2015 file need to be updated
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Dataset Viewer issue for mlsum
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[
"Thanks for reporting, @lewtun.\r\n\r\nAfter investigation, it seems that the server https://gitlab.lip6.fr does not allow HTTP Range requests.\r\n\r\nWe are trying to find a workaround..."
] | 2022-06-26T20:24:17Z
| 2022-07-21T12:40:01Z
| 2022-07-21T12:40:01Z
|
MEMBER
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### Link
https://huggingface.co/datasets/mlsum/viewer/de/train
### Description
There's seems to be a problem with the download / streaming of this dataset:
```
Server error
Status code: 400
Exception: BadZipFile
Message: File is not a zip file
```
### Owner
No
|
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Dataset sharding non-contiguous?
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[
"This was silly; I was sure I'd looked for a `contiguous` argument, and was certain there wasn't one the first time I looked :smile:\r\n\r\nSorry about that.",
"Hi! You can pass `contiguous=True` to `.shard()` get contiguous shards. More info on this and the default behavior can be found in the [docs](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.shard).\r\n\r\nEDIT: Answered as you closed the thread 😄 ",
"Hahaha I'm sorry; my excuse is: it's Sunday. (Which makes me all the more grateful for your response :smiley: ",
"@mariosasko Sorry for reviving this, but I was curious as to why `contiguous=False` was the default. This might be a personal bias, but I feel that a user would expect the opposite to be the default. :thinking: ",
"This project started as a fork of TFDS, and `contiguous=False` is the default behavior [there](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shard)."
] | 2022-06-26T08:34:05Z
| 2022-06-30T11:00:47Z
| 2022-06-26T14:36:20Z
|
CONTRIBUTOR
| null | null |
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## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggingface/datasets/pull/4466) but I have to admit I did not properly look into the changes made.
## Steps to reproduce the bug
```python
max_shard_size = convert_file_size_to_int('300MB')
dataset_nbytes = dataset.data.nbytes
num_shards = int(dataset_nbytes / max_shard_size) + 1
num_shards = max(num_shards, 1)
print(f"{num_shards=}")
for shard_index in range(num_shards):
shard = dataset.shard(num_shards=num_shards, index=shard_index)
shard.to_parquet(f"tokenized/tokenized-{shard_index:03d}.parquet")
os.listdir('tokenized/')
```
## Expected results
I expected the shards to match the order of the data of the original dataset; i.e. `dataset[10]` being the same as `shard_1[10]` for example
## Actual results
Only the first element is the same; i.e. `dataset[0]` is the same as `shard_1[0]`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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Dataset Viewer issue for sst2
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[
"Hi @lewtun, thanks for reporting.\r\n\r\nI have checked locally and refreshed the preview and it seems working smooth now:\r\n```python\r\nIn [8]: ds\r\nOut[8]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['idx', 'sentence', 'label'],\r\n num_rows: 67349\r\n })\r\n validation: Dataset({\r\n features: ['idx', 'sentence', 'label'],\r\n num_rows: 872\r\n })\r\n test: Dataset({\r\n features: ['idx', 'sentence', 'label'],\r\n num_rows: 1821\r\n })\r\n})\r\n```\r\n\r\nCould you confirm? ",
"Thanks @albertvillanova - it is indeed working now (not sure what caused the error in the first place). Closing this :)"
] | 2022-06-26T07:32:54Z
| 2022-06-27T06:37:48Z
| 2022-06-27T06:37:48Z
|
MEMBER
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### Link
https://huggingface.co/datasets/sst2
### Description
Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):
```
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No
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XNLI cache reload is very slow
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[
"Hi,\r\nCould you tell us how you are running this code?\r\nI tested on my machine (M1 Mac). And it is running fine both on and off internet.\r\n\r\n<img width=\"1033\" alt=\"Screen Shot 2022-07-03 at 1 32 25 AM\" src=\"https://user-images.githubusercontent.com/8711912/177026364-4ad7cedb-e524-4513-97f7-7961bbb34c90.png\">\r\nTested on both stable and dev version. ",
"Sure, I was running it on a Linux machine.\r\nI found that if I turn the Internet off, it would still try to make a HTTPS call which would slow down the cache loading. If you can't reproduce then we can close the issue.",
"Hi @Muennighoff! You can set the env variable `HF_DATASETS_OFFLINE` to `1` to avoid this behavior in offline mode. More info is available [here](https://huggingface.co/docs/datasets/master/en/loading#offline)."
] | 2022-06-25T16:43:56Z
| 2022-07-04T14:29:40Z
| 2022-07-04T14:29:40Z
|
CONTRIBUTOR
| null | null |
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### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the library trying to download when there is no Internet. If I leave it running it works but takes way longer than when there is Internet. I would expect loading from cache to take the same amount of time regardless of whether there is Internet.
```
---------------------------------------------------------------------------
gaierror Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
/opt/conda/lib/python3.7/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
71
---> 72 for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
73 af, socktype, proto, canonname, sa = res
/opt/conda/lib/python3.7/socket.py in getaddrinfo(host, port, family, type, proto, flags)
751 addrlist = []
--> 752 for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
753 af, socktype, proto, canonname, sa = res
gaierror: [Errno -3] Temporary failure in name resolution
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
/tmp/ipykernel_33/3594208039.py in <module>
----> 1 load_dataset("xnli", "en")
/opt/conda/lib/python3.7/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, revision, use_auth_token, task, streaming, **config_kwargs)
1673 revision=revision,
1674 use_auth_token=use_auth_token,
-> 1675 **config_kwargs,
1676 )
1677
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1494 download_mode=download_mode,
1495 data_dir=data_dir,
-> 1496 data_files=data_files,
1497 )
1498
/opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1182 download_config=download_config,
1183 download_mode=download_mode,
-> 1184 dynamic_modules_path=dynamic_modules_path,
1185 ).get_module()
1186 elif path.count("/") == 1: # community dataset on the Hub
/opt/conda/lib/python3.7/site-packages/datasets/load.py in __init__(self, name, revision, download_config, download_mode, dynamic_modules_path)
506 self.dynamic_modules_path = dynamic_modules_path
507 assert self.name.count("/") == 0
--> 508 increase_load_count(name, resource_type="dataset")
509
510 def download_loading_script(self, revision: Optional[str]) -> str:
/opt/conda/lib/python3.7/site-packages/datasets/load.py in increase_load_count(name, resource_type)
166 if not config.HF_DATASETS_OFFLINE and config.HF_UPDATE_DOWNLOAD_COUNTS:
167 try:
--> 168 head_hf_s3(name, filename=name + ".py", dataset=(resource_type == "dataset"))
169 except Exception:
170 pass
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in head_hf_s3(identifier, filename, use_cdn, dataset, max_retries)
93 return http_head(
94 hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset),
---> 95 max_retries=max_retries,
96 )
97
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
445 allow_redirects=allow_redirects,
446 timeout=timeout,
--> 447 max_retries=max_retries,
448 )
449 return response
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
366 tries += 1
367 try:
--> 368 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
369 success = True
370 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/opt/conda/lib/python3.7/site-packages/requests/api.py in request(method, url, **kwargs)
59 # cases, and look like a memory leak in others.
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
63
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
527 }
528 send_kwargs.update(settings)
--> 529 resp = self.send(prep, **send_kwargs)
530
531 return resp
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in send(self, request, **kwargs)
643
644 # Send the request
--> 645 r = adapter.send(request, **kwargs)
646
647 # Total elapsed time of the request (approximately)
/opt/conda/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 decode_content=False,
449 retries=self.max_retries,
--> 450 timeout=timeout
451 )
452
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
708 body=body,
709 headers=headers,
--> 710 chunked=chunked,
711 )
712
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
384 # Trigger any extra validation we need to do.
385 try:
--> 386 self._validate_conn(conn)
387 except (SocketTimeout, BaseSSLError) as e:
388 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
1038 # Force connect early to allow us to validate the connection.
1039 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
-> 1040 conn.connect()
1041
1042 if not conn.is_verified:
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in connect(self)
356 def connect(self):
357 # Add certificate verification
--> 358 self.sock = conn = self._new_conn()
359 hostname = self.host
360 tls_in_tls = False
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
173 try:
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
177
KeyboardInterrupt:
```
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Document link #load_dataset_enhancing_performance points to nowhere
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[
"Hi! This is indeed the link the docstring should point to. Are you interested in submitting a PR to fix this?",
"https://github.com/huggingface/datasets/blame/master/docs/source/cache.mdx#L93\r\n\r\nThere seems already an anchor here. Somehow it doesn't work. I am not very familiar with how this online documentation works."
] | 2022-06-25T01:18:19Z
| 2023-01-24T16:33:40Z
| 2023-01-24T16:33:40Z
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## Describe the bug
A clear and concise description of what the bug is.
The [load_dataset_enhancing_performance](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#load_dataset_enhancing_performance) link [here](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.load_from_disk.keep_in_memory) points to nowhere, I guess it should point to https://huggingface.co/docs/datasets/v2.3.2/en/cache#improve-performance?
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I_kwDODunzps5MinJi
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Add UFSC OCPap dataset
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[
"I will add this directly on the hub (same as #4486)—in https://huggingface.co/lapix"
] | 2022-06-24T20:07:54Z
| 2022-07-06T19:03:02Z
| 2022-07-06T19:03:02Z
|
NONE
| null | null |
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## Adding a Dataset
- **Name:** UFSC OCPap: Papanicolaou Stained Oral Cytology Dataset (v4)
- **Description:** The UFSC OCPap dataset comprises 9,797 labeled images of 1200x1600 pixels acquired from 5 slides of cancer diagnosed and 3 healthy of oral brush samples, from distinct patients.
- **Paper:** https://dx.doi.org/10.2139/ssrn.4119212
- **Data:** https://data.mendeley.com/datasets/dr7ydy9xbk/1
- **Motivation:** real data of pap stained oral cytology samples
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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I_kwDODunzps5MhOvl
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Dataset Viewer issue for allocine
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[
"I removed my assignment as @huggingface/datasets should be able to answer better than me\r\n",
"Let me have a look...",
"Thanks for the quick fix @albertvillanova ",
"Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content *sequentially* (no random access).",
"> Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content _sequentially_ (no random access).\r\n\r\nAh thanks for the clarification! I'll look out for this next time and implement the fix myself :)"
] | 2022-06-24T13:50:38Z
| 2022-06-27T06:39:32Z
| 2022-06-24T16:44:41Z
|
MEMBER
| null | null |
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### Link
https://huggingface.co/datasets/allocine
### Description
Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:
```
Status code: 400
Exception: AttributeError
Message: 'TarContainedFile' object has no attribute 'readable'
```
### Owner
No
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I_kwDODunzps5MgBbh
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Dataset Viewer issue for conll2003
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[
"Fixed, thanks."
] | 2022-06-24T08:55:18Z
| 2022-06-24T09:50:39Z
| 2022-06-24T09:50:39Z
|
MEMBER
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### Link
https://huggingface.co/datasets/conll2003/viewer/conll2003/test
### Description
Seems like a cache problem with this config / split:
```
Server error
Status code: 400
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/conll2003/__init__.py'
```
### Owner
No
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Dataset Viewer issue for xtreme
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[
"Fixed, thanks."
] | 2022-06-24T08:46:08Z
| 2022-06-24T09:50:45Z
| 2022-06-24T09:50:45Z
|
MEMBER
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### Link
https://huggingface.co/datasets/xtreme/viewer/PAN-X.de/test
### Description
There seems to be a problem with the cache of this config / split:
```
Server error
Status code: 400
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/xtreme/349258adc25bb45e47de193222f95e68a44f7a7ab53c4283b3f007208a11bf7e/xtreme.py'
```
### Owner
No
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I_kwDODunzps5Mb3sp
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imdb source error
|
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[
"Thanks for reporting, @Muhtasham.\r\n\r\nIndeed IMDB dataset is not accessible from yesterday, because the data is hosted on the data owners servers at Stanford (http://ai.stanford.edu/) and these are down due to a power outage originated by a fire: https://twitter.com/StanfordAILab/status/1539472302399623170?s=20&t=1HU1hrtaXprtn14U61P55w\r\n\r\nAs a temporary workaroud, you can load the IMDB dataset with this tweak:\r\n```python\r\nds = load_dataset(\"imdb\", revision=\"tmp-fix-imdb\")\r\n```\r\n"
] | 2022-06-23T13:02:52Z
| 2022-06-23T13:47:05Z
| 2022-06-23T13:47:04Z
|
NONE
| null | null |
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## Describe the bug
imdb dataset not loading
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imdb")
```
## Expected results
## Actual results
```bash
06/23/2022 14:45:18 - INFO - datasets.builder - Dataset not on Hf google storage. Downloading and preparing it from source
06/23/2022 14:46:34 - INFO - datasets.utils.file_utils - HEAD request to http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz timed out, retrying... [1.0]
.....
ConnectionError: Couldn't reach http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz (ConnectTimeout(MaxRetryError("HTTPConnectionPool(host='ai.stanford.edu', port=80): Max retries exceeded with url: /~amaas/data/sentiment/aclImdb_v1.tar.gz (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x7f2d750cf690>, 'Connection to ai.stanford.edu timed out. (connect timeout=100)'))")))
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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FileNotFoundError when passing a data_file inside a directory starting with double underscores
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[
"I have consistently experienced this bug on GitHub actions when bumping to `2.3.2`",
"We're working on a fix ;)"
] | 2022-06-23T12:19:24Z
| 2022-06-30T14:38:18Z
| 2022-06-30T14:38:18Z
|
MEMBER
| null | null |
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Bug experienced in the `accelerate` CI: https://github.com/huggingface/accelerate/runs/7016055148?check_suite_focus=true
This is related to https://github.com/huggingface/datasets/pull/4505 and the changes from https://github.com/huggingface/datasets/pull/4412
|
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Metadata.jsonl for Imagefolder is ignored if it's in a parent directory to the splits directories/do not have "{split}_" prefix
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[
"I agree it would be nice to support this. It doesn't fit really well in the current data_files.py, where files of each splits are separated in different folder though, maybe we have to modify a bit the logic here. \r\n\r\nOne idea would be to extend `get_patterns_in_dataset_repository` and `get_patterns_locally` to additionally check for `metadata.json`, but feel free to comment if you have better ideas (I feel like we're reaching the limits of what the current implementation IMO, so we could think of a different way of resolving the data files if necessary)"
] | 2022-06-23T10:58:57Z
| 2022-06-30T10:15:32Z
| 2022-06-30T10:15:32Z
|
CONTRIBUTOR
| null | null |
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If data contains a single `metadata.jsonl` file for several splits, it won't be included in a dataset's `data_files` and therefore ignored.
This happens when a directory is structured like as follows:
```
train/
file_1.jpg
file_2.jpg
test/
file_3.jpg
file_4.jpg
metadata.jsonl
```
or like as follows:
```
train_file_1.jpg
train_file_2.jpg
test_file_3.jpg
test_file_4.jpg
metadata.jsonl
```
The same for HF repos.
because it's ignored by the patterns [here](https://github.com/huggingface/datasets/blob/master/src/datasets/data_files.py#L29)
@lhoestq @mariosasko Do you think it's better to add this functionality in `data_files.py` or just specifically in imagefolder/audiofolder code? In `data_files.py` would me more general but I don't know if there are any other cases when that might be needed.
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[CI] seqeval installation fails sometimes on python 3.6
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[] | 2022-06-22T16:35:23Z
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The CI sometimes fails to install seqeval, which cause the `seqeval` metric tests to fail.
The installation fails because of this error:
```
Collecting seqeval
Downloading seqeval-1.2.2.tar.gz (43 kB)
|███████▌ | 10 kB 42.1 MB/s eta 0:00:01
|███████████████ | 20 kB 53.3 MB/s eta 0:00:01
|██████████████████████▌ | 30 kB 67.2 MB/s eta 0:00:01
|██████████████████████████████ | 40 kB 76.1 MB/s eta 0:00:01
|████████████████████████████████| 43 kB 10.0 MB/s
Preparing metadata (setup.py) ... - error
ERROR: Command errored out with exit status 1:
command: /home/circleci/.pyenv/versions/3.6.15/bin/python3.6 -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py'"'"'; __file__='"'"'/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-pip-egg-info-pf54_vqy
cwd: /tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/
Complete output (22 lines):
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py", line 56, in <module>
'Programming Language :: Python :: Implementation :: PyPy'
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/__init__.py", line 143, in setup
return distutils.core.setup(**attrs)
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/distutils/core.py", line 108, in setup
_setup_distribution = dist = klass(attrs)
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/dist.py", line 442, in __init__
k: v for k, v in attrs.items()
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/distutils/dist.py", line 281, in __init__
self.finalize_options()
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/dist.py", line 601, in finalize_options
ep.load()(self, ep.name, value)
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/pkg_resources/__init__.py", line 2346, in load
return self.resolve()
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/pkg_resources/__init__.py", line 2352, in resolve
module = __import__(self.module_name, fromlist=['__name__'], level=0)
File "/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/.eggs/setuptools_scm-7.0.2-py3.6.egg/setuptools_scm/__init__.py", line 5
from __future__ import annotations
^
SyntaxError: future feature annotations is not defined
----------------------------------------
WARNING: Discarding https://files.pythonhosted.org/packages/9d/2d/233c79d5b4e5ab1dbf111242299153f3caddddbb691219f363ad55ce783d/seqeval-1.2.2.tar.gz#sha256=f28e97c3ab96d6fcd32b648f6438ff2e09cfba87f05939da9b3970713ec56e6f (from https://pypi.org/simple/seqeval/). Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
```
for example in https://app.circleci.com/pipelines/github/huggingface/datasets/12665/workflows/93878eb9-a923-4b35-b2e7-c5e9b22f10ad/jobs/75300
Here is a diff of the pip install logs until the error is reached: https://www.diffchecker.com/VkQDLeQT
This could be caused by the latest updates of setuptools-scm
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I_kwDODunzps5MTW5e
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Avoid splitting by` .py` for the file.
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[
"Hi @espoirMur, thanks for reporting.\r\n\r\nYou are right: that code line could be improved and made more generically valid.\r\n\r\nOn the other hand, I would suggest using `os.path.splitext` instead.\r\n\r\nAre you willing to open a PR? :)",
"I will have a look.. \r\n\r\nThis weekend .. ",
"@albertvillanova , Can you have a look at #4590. \r\n\r\nThanks ",
"#self-assign"
] | 2022-06-22T13:26:55Z
| 2022-07-07T13:17:44Z
| 2022-07-07T13:17:44Z
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https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272
Hello,
Thanks you for this library .
I was using it and I had one edge case. my home folder name ends with `.py` it is `/home/espoir.py` so anytime I am running the code to load a local module this code here it is failing because after splitting it is trying to save the code to my home directory.
Step to reproduce.
- If you have a home folder which ends with `.py`
- load a module with a local folder
`qa_dataset = load_dataset("src/data/build_qa_dataset.py")`
it is failed
A possible workaround would be to use pathlib at the mentioned line
` meta_path = Path(importable_local_file).parent.joinpath("metadata.json")` this can alivate the issue .
Let me what are your thought on this and I can try to fix it by A PR.
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[
"Hi @Breakend, yes – we'll propose a solution today",
"Thanks so much, I appreciate it!",
"Thanks so much for adding the docs. I was able to successfully hide the viewer using the \r\n```\r\nviewer: false\r\n```\r\nflag in the README.md of the dataset. I'm closing the issue because this is resolved. Thanks again!",
"Awesome! Thanks for confirming. cc @severo ",
"Just for the record:\r\n\r\n- the doc\r\n \r\n<img width=\"1430\" alt=\"Capture d’écran 2022-06-27 à 09 29 27\" src=\"https://user-images.githubusercontent.com/1676121/175884089-bca6c0d5-6387-473e-98ca-86a910ede4bd.png\">\r\n\r\n- the dataset main page\r\n\r\n<img width=\"1134\" alt=\"Capture d’écran 2022-06-27 à 09 29 05\" src=\"https://user-images.githubusercontent.com/1676121/175884152-5f285bf0-3471-45de-927a-e141b00ebb33.png\">\r\n\r\n- the dataset viewer page\r\n\r\n<img width=\"567\" alt=\"Capture d’écran 2022-06-27 à 09 29 16\" src=\"https://user-images.githubusercontent.com/1676121/175884191-ab6a297b-1c11-417e-bbde-0b7623278a79.png\">\r\n"
] | 2022-06-22T02:48:40Z
| 2022-06-27T07:30:23Z
| 2022-06-26T22:26:22Z
|
NONE
| null | null |
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### Link
https://huggingface.co/datasets/pile-of-law/pile-of-law
### Description
Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines and so would like to turn off dataset previews. But we do not want to collect user emails because it would violate single blind review, allowing us to deduce potential reviewers' identities. Is there a way that we can turn off the dataset viewer without collecting identity information?
Thanks so much!
### Owner
Yes
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Timestamp not returned as datetime objects in streaming mode
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[] | 2022-06-20T17:28:47Z
| 2022-06-22T16:29:09Z
| 2022-06-22T16:29:09Z
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MEMBER
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As reported in (internal) https://github.com/huggingface/datasets-server/issues/397
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("ett", name="h2", split="test", streaming=True)
>>> d = next(iter(dataset))
>>> d['start']
Timestamp('2016-07-01 00:00:00')
```
while loading in non-streaming mode it returns `datetime.datetime(2016, 7, 1, 0, 0)`
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Dataset Viewer issue for CSV datasets
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[
"this should now be fixed",
"Confirmed, it's fixed now. Thanks for reporting, and thanks @coyotte508 for fixing it\r\n\r\n<img width=\"1123\" alt=\"Capture d’écran 2022-06-21 à 10 28 05\" src=\"https://user-images.githubusercontent.com/1676121/174753833-1b453a5a-6a90-4717-bca1-1b5fc6b75e4a.png\">\r\n"
] | 2022-06-20T14:56:24Z
| 2022-06-21T08:28:46Z
| 2022-06-21T08:28:27Z
|
CONTRIBUTOR
| null | null |
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### Link
https://huggingface.co/datasets/scikit-learn/breast-cancer-wisconsin
### Description
I'm populating CSV datasets [here](https://huggingface.co/scikit-learn) but the viewer is not enabled and it looks for a dataset loading script, the datasets aren't on queue as well.
You can replicate the problem by simply uploading any CSV dataset.
### Owner
Yes
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Ecoset
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[] | null |
[
"Hi! Very cool dataset! I answered your questions on the forum. Also, feel free to comment `#self-assign` on this issue to self-assign it.",
"The dataset lives on the Hub [here](https://huggingface.co/datasets/kietzmannlab/ecoset), so I'm closing this issue.",
"Hey There, thanks for closing 🤗 \r\n\r\nForgot the issue existed, so I didn't close it after implementing the downloader :)"
] | 2022-06-20T10:39:34Z
| 2023-10-26T09:12:32Z
| 2023-10-04T18:19:52Z
|
NONE
| null | null |
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## Adding a Dataset
- **Name:** *Ecoset*
- **Description:** *https://www.kietzmannlab.org/ecoset/*
- **Paper:** *https://doi.org/10.1073/pnas.2011417118*
- **Data:** *https://codeocean.com/capsule/9570390/tree/v1*
- **Motivation:**
**Ecoset** was created as a clean and ecologically valid alternative to **Imagenet**.
It is a large image recognition dataset, similar to Imagenet in size and structure. However, the authors of ecoset claim several improvements over Imagenet, like:
- more ecologically valid classes (e.g. not over-focussed on distinguishing different dog breeds)
- less NSFW content
- 'pre-packed image recognition models' that come with the dataset and can be used for validation of other models.
I am working for one of the authors of the paper with the aim of bringing Ecoset to huggingface datasets. Therefore I can work on this issue personally, but could use some help from devs and experienced users if the dataset is of interest to them. I phrased some of my questions on [discuss.huggingface](https://discuss.huggingface.co/t/handling-large-image-datasets/19373).
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Memory leak when iterating a Dataset
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"Is someone assigned to this issue?",
"The same issue is being debugged here: https://github.com/huggingface/datasets/issues/4883\r\n",
"Here is a modified repro example that makes it easier to see the leak:\r\n\r\n```\r\n$ cat ds2.py\r\nimport gc, sys\r\nimport time\r\nfrom datasets import load_dataset\r\nimport os, psutil\r\n\r\nprocess = psutil.Process(os.getpid())\r\n\r\nprint(process.memory_info().rss/2**20)\r\n\r\ncorpus = load_dataset(\"BeIR/msmarco\", 'corpus', keep_in_memory=False, streaming=False)['corpus']\r\ncorpus = corpus.select(range(200000))\r\n\r\nprint(process.memory_info().rss/2**20)\r\n\r\nbatch = None\r\n\r\nmem_before_start = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\n\r\nstep = 20000\r\nfor i in range(0, 10*step, step):\r\n mem_before = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\n batch = corpus[i:i+step]\r\n import objgraph\r\n #objgraph.show_refs([batch])\r\n #objgraph.show_refs([corpus])\r\n #sys.exit()\r\n gc.collect()\r\n\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\n print(f\"{i:6d} {mem_after - mem_before:12.4f} {mem_after - mem_before_start:12.4f}\")\r\n\r\n```\r\n\r\nLet's run:\r\n\r\n```\r\n$ python ds2.py\r\n 0 36.5391 36.5391\r\n 20000 10.4609 47.0000\r\n 40000 5.9766 52.9766\r\n 60000 7.8906 60.8672\r\n 80000 6.0586 66.9258\r\n100000 8.4453 75.3711\r\n120000 6.7422 82.1133\r\n140000 8.5664 90.6797\r\n160000 5.7344 96.4141\r\n180000 8.3398 104.7539\r\n```\r\n\r\nYou can see the last column of total RSS memory keeps on growing in MBs. The mid column is by how much it was grown during a single iteration of the repro script (20000 items)",
"@NouamaneTazi, please check my analysis here https://github.com/huggingface/datasets/issues/4883#issuecomment-1242599722 so if you agree with my research this Issue can be closed as well.\r\n\r\nI also made a suggestion at how to proceed to hunt for a real leak here https://github.com/huggingface/datasets/issues/4883#issuecomment-1242600626\r\n\r\nyou may find this one to be useful as well https://github.com/huggingface/datasets/issues/4883#issuecomment-1242597966",
"Amazing job! Thanks for taking time to debug this 🤗\r\n\r\nFor my side, I tried to do some more research as well, but to no avail. https://github.com/huggingface/datasets/issues/4883#issuecomment-1243415957"
] | 2022-06-20T10:03:14Z
| 2022-09-12T08:51:39Z
| 2022-09-12T08:51:39Z
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e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
process = psutil.Process(os.getpid())
print(process.memory_info().rss) # output: 633507840 bytes
corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus'] # or "BeIR/trec-covid" for a smaller dataset
print(process.memory_info().rss) # output: 698601472 bytes
logger.info("Applying method to all examples in all splits")
for i in trange(0, len(corpus), 1000):
batch = corpus[i:i+1000]
data = pyarrow.total_allocated_bytes()
if data > 0:
logger.info(f"{i}/{len(corpus)}: {data}")
print(process.memory_info().rss) # output: 3788247040 bytes
del batch
gc.collect()
print(process.memory_info().rss) # output: 3788247040 bytes
logger.info("Done...")
time.sleep(100)
```
## Expected results
Limited memory usage, and memory to be freed after processing
## Actual results
Memory leak
You can see how the memory allocation keeps increasing until it reaches a steady state when we hit the `time.sleep(100)`, which showcases that even the garbage collector couldn't free the allocated memory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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Dataset Viewer issue for vadis/sv-ident
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[
"Fixed, thanks!\r\n![Uploading Capture d’écran 2022-06-21 à 18.42.40.png…]()\r\n\r\n"
] | 2022-06-20T08:47:42Z
| 2022-06-21T16:42:46Z
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### Link
https://huggingface.co/datasets/vadis/sv-ident
### Description
The dataset preview does not work:
```
Server Error
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
```
However, the dataset is streamable and works locally:
```python
In [1]: from datasets import load_dataset; ds = load_dataset("sv-ident.py", split="train", streaming=True); item = next(iter(ds)); item
Using custom data configuration default
Out[1]:
{'sentence': 'Our point, however, is that so long as downward (favorable) comparisons overwhelm the potential for unfavorable comparisons, system justification should be a likely outcome amongst the disadvantaged.',
'is_variable': 1,
'variable': ['exploredata-ZA5400_VarV66', 'exploredata-ZA5400_VarV53'],
'research_data': ['ZA5400'],
'doc_id': '73106',
'uuid': 'b9fbb80f-3492-4b42-b9d5-0254cc33ac10',
'lang': 'en'}
```
CC: @e-tornike
### Owner
No
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Out of memory error on workers while running Beam+Dataflow
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[
"Some naive ideas to cope with this:\r\n- enable more RAM on each worker\r\n- force the spanning of more workers\r\n- others?",
"@albertvillanova We were finally able to process the full NQ dataset on our machines using 600 gb with 5 workers. Maybe these numbers will work for you as well.",
"Thanks a lot for the hint, @seirasto.\r\n\r\nI have one question: what runner did you use? Direct, Apache Flink/Nemo/Samza/Spark, Google Dataflow...? Thank you.",
"I asked my colleague who ran the code and he said apache beam.",
"@albertvillanova Since we have already processed the NQ dataset on our machines can we upload it to datasets so the NQ PR can be merged?",
"Maybe @lhoestq can give a more accurate answer as I am not sure about the authentication requirements to upload those files to our cloud bucket.\r\n\r\nAnyway I propose to continue this discussion on the dedicated PR for Natural questions dataset:\r\n- #4368",
"> I asked my colleague who ran the code and he said apache beam.\r\n\r\nHe looked into it further and he just used DirectRunner. @albertvillanova ",
"OK, thank you @seirasto for your hint.\r\n\r\nThat explains why you did not encounter the out of memory error: this only appears when the processing is distributed (on workers memory) and DirectRunner does not distribute the processing (all is done in a single machine). ",
"@albertvillanova Doesn't DirectRunner offer distributed processing through?\r\n\r\nhttps://beam.apache.org/documentation/runners/direct/\r\n\r\n```\r\nSetting parallelism\r\n\r\nNumber of threads or subprocesses is defined by setting the direct_num_workers pipeline option. From 2.22.0, direct_num_workers = 0 is supported. When direct_num_workers is set to 0, it will set the number of threads/subprocess to the number of cores of the machine where the pipeline is running.\r\n\r\nSetting running mode\r\n\r\nIn Beam 2.19.0 and newer, you can use the direct_running_mode pipeline option to set the running mode. direct_running_mode can be one of ['in_memory', 'multi_threading', 'multi_processing'].\r\n\r\nin_memory: Runner and workers’ communication happens in memory (not through gRPC). This is a default mode.\r\n\r\nmulti_threading: Runner and workers communicate through gRPC and each worker runs in a thread.\r\n\r\nmulti_processing: Runner and workers communicate through gRPC and each worker runs in a subprocess.\r\n```",
"Unrelated to the OOM issue, but we deprecated datasets with Beam scripts in #6474. I think we can close this issue"
] | 2022-06-20T07:28:12Z
| 2024-10-09T16:09:50Z
| 2024-10-09T16:09:50Z
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## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently workers run out of memory while processing them.
Any help/hint is welcome!
Error message:
```
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
Info from the Diagnostics tab:
```
Out of memory: Killed process 1882 (python) total-vm:6041764kB, anon-rss:3290928kB, file-rss:0kB, shmem-rss:0kB, UID:0 pgtables:9520kB oom_score_adj:900
The worker VM had to shut down one or more processes due to lack of memory.
```
## Additional information
### Stack trace
```
Traceback (most recent call last):
File "/home/albert_huggingface_co/natural_questions/venv/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/run_beam.py", line 127, in run
builder.download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 1389, in _download_and_prepare
pipeline_results.wait_until_finish()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/apache_beam/runners/dataflow/dataflow_runner.py", line 1667, in wait_until_finish
raise DataflowRuntimeException(
apache_beam.runners.dataflow.dataflow_runner.DataflowRuntimeException: Dataflow pipeline failed. State: FAILED, Error:
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
### Logs
```
Error message from worker: Data channel closed, unable to receive additional data from SDK sdk-0-0
Workflow failed. Causes: S30:train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/Read+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/GroupByWindow+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/FlatMap(restore_timestamps)+train/ReadAllFromText/ReadAllFiles/Reshard/RemoveRandomKeys+train/ReadAllFromText/ReadAllFiles/ReadRange+train/Map(_parse_example)+train/Encode+train/Count N. Examples+train/Get values/Values+train/Save to parquet/Write/WriteImpl/WindowInto(WindowIntoFn)+train/Save to parquet/Write/WriteImpl/WriteBundles+train/Save to parquet/Write/WriteImpl/Pair+train/Save to parquet/Write/WriteImpl/GroupByKey/Write failed., The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors. The work item was attempted on these workers: beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: Data channel closed, unable to receive additional data from SDK sdk-0-0, beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-bwsj Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-5052 Root cause: The worker lost contact with the service.
```
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I_kwDODunzps5L_boN
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Datasets method `.map` not hashing
|
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[
"Fix posted: https://github.com/huggingface/datasets/issues/4506#issuecomment-1157417219",
"Didn't realize it's a bug when I asked the question yesterday! Free free to post an answer if you are sure the cause has been addressed.\r\n\r\nhttps://stackoverflow.com/questions/72664827/can-pickle-dill-foo-but-not-lambda-x-foox",
"Thank @nalzok . That works for me:\r\n\r\n`pip install \"dill<0.3.5\"`"
] | 2022-06-17T11:31:10Z
| 2022-08-04T12:08:16Z
| 2022-06-28T13:23:05Z
|
CONTRIBUTOR
| null | null |
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## Describe the bug
Datasets method `.map` not hashing, even with an empty no-op function
## Steps to reproduce the bug
```python
from datasets import load_dataset
# download 9MB dummy dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")
def prepare_dataset(batch):
return(batch)
ds = ds.map(
prepare_dataset,
num_proc=1,
desc="preprocess train dataset",
)
```
## Expected results
Hashed and cached dataset preprocessing
## Actual results
Does not hash properly:
```
Parameter 'function'=<function prepare_dataset at 0x7fccb68e9280> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
cc @lhoestq
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I_kwDODunzps5L_RzM
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Failure to hash `dataclasses` - results in functions that cannot be hashed or cached in `.map`
|
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[
"I think this has been fixed by #4516, let me know if you encounter this again :)\r\n\r\nI re-ran your code in 3.7 and 3.9 and it works fine",
"Thank you!"
] | 2022-06-17T10:47:17Z
| 2022-06-28T14:47:17Z
| 2022-06-28T14:04:29Z
|
CONTRIBUTOR
| null | null |
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Dataclasses cannot be hashed. As a result, they cannot be hashed or cached if used in the `.map` method. Dataclasses are used extensively in Transformers examples scripts: (c.f. [CTC example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py)). Since dataclasses cannot be hashed, one has to define separate variables prior to passing dataclass attributes to the `.map` method:
```python
phoneme_language = data_args.phoneme_language
```
in the example https://github.com/huggingface/transformers/blob/3c7e56fbb11f401de2528c1dcf0e282febc031cd/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L603-L630
## Steps to reproduce the bug
```python
from dataclasses import dataclass, field
from datasets.fingerprint import Hasher
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
phoneme_language: str = field(
default=None, metadata={"help": "The name of the phoneme language to use."}
)
data_args = DataTrainingArguments(phoneme_language ="foo")
Hasher.hash(data_args)
phoneme_language = data_args.phoneme_language
Hasher.hash(phoneme_language)
```
## Expected results
A hash.
## Actual results
<details>
<summary> Traceback </summary>
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Input In [1], in <cell line: 16>()
10 phoneme_language: str = field(
11 default=None, metadata={"help": "The name of the phoneme language to use."}
12 )
14 data_args = DataTrainingArguments(phoneme_language ="foo")
---> 16 Hasher.hash(data_args)
18 phoneme_language = data_args. phoneme_language
20 Hasher.hash(phoneme_language)
File ~/datasets/src/datasets/fingerprint.py:237, in Hasher.hash(cls, value)
235 return cls.dispatch[type(value)](cls, value)
236 else:
--> 237 return cls.hash_default(value)
File ~/datasets/src/datasets/fingerprint.py:230, in Hasher.hash_default(cls, value)
228 @classmethod
229 def hash_default(cls, value: Any) -> str:
--> 230 return cls.hash_bytes(dumps(value))
File ~/datasets/src/datasets/utils/py_utils.py:564, in dumps(obj)
562 file = StringIO()
563 with _no_cache_fields(obj):
--> 564 dump(obj, file)
565 return file.getvalue()
File ~/datasets/src/datasets/utils/py_utils.py:539, in dump(obj, file)
537 def dump(obj, file):
538 """pickle an object to a file"""
--> 539 Pickler(file, recurse=True).dump(obj)
540 return
File ~/hf/lib/python3.8/site-packages/dill/_dill.py:620, in Pickler.dump(self, obj)
618 raise PicklingError(msg)
619 else:
--> 620 StockPickler.dump(self, obj)
621 return
File /usr/lib/python3.8/pickle.py:487, in _Pickler.dump(self, obj)
485 if self.proto >= 4:
486 self.framer.start_framing()
--> 487 self.save(obj)
488 self.write(STOP)
489 self.framer.end_framing()
File /usr/lib/python3.8/pickle.py:603, in _Pickler.save(self, obj, save_persistent_id)
599 raise PicklingError("Tuple returned by %s must have "
600 "two to six elements" % reduce)
602 # Save the reduce() output and finally memoize the object
--> 603 self.save_reduce(obj=obj, *rv)
File /usr/lib/python3.8/pickle.py:687, in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
684 raise PicklingError(
685 "args[0] from __newobj__ args has the wrong class")
686 args = args[1:]
--> 687 save(cls)
688 save(args)
689 write(NEWOBJ)
File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id)
558 f = self.dispatch.get(t)
559 if f is not None:
--> 560 f(self, obj) # Call unbound method with explicit self
561 return
563 # Check private dispatch table if any, or else
564 # copyreg.dispatch_table
File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1838, in save_type(pickler, obj, postproc_list)
1836 postproc_list = []
1837 postproc_list.append((setattr, (obj, '__qualname__', obj_name)))
-> 1838 _save_with_postproc(pickler, (_create_type, (
1839 type(obj), obj.__name__, obj.__bases__, _dict
1840 )), obj=obj, postproc_list=postproc_list)
1841 log.info("# %s" % _t)
1842 else:
File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1140, in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list)
1137 pickler._postproc[id(obj)] = postproc_list
1139 # TODO: Use state_setter in Python 3.8 to allow for faster cPickle implementations
-> 1140 pickler.save_reduce(*reduction, obj=obj)
1142 if is_pickler_dill:
1143 # pickler.x -= 1
1144 # print(pickler.x*' ', 'pop', obj, id(obj))
1145 postproc = pickler._postproc.pop(id(obj))
File /usr/lib/python3.8/pickle.py:692, in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
690 else:
691 save(func)
--> 692 save(args)
693 write(REDUCE)
695 if obj is not None:
696 # If the object is already in the memo, this means it is
697 # recursive. In this case, throw away everything we put on the
698 # stack, and fetch the object back from the memo.
File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id)
558 f = self.dispatch.get(t)
559 if f is not None:
--> 560 f(self, obj) # Call unbound method with explicit self
561 return
563 # Check private dispatch table if any, or else
564 # copyreg.dispatch_table
File /usr/lib/python3.8/pickle.py:901, in _Pickler.save_tuple(self, obj)
899 write(MARK)
900 for element in obj:
--> 901 save(element)
903 if id(obj) in memo:
904 # Subtle. d was not in memo when we entered save_tuple(), so
905 # the process of saving the tuple's elements must have saved
(...)
909 # could have been done in the "for element" loop instead, but
910 # recursive tuples are a rare thing.
911 get = self.get(memo[id(obj)][0])
File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id)
558 f = self.dispatch.get(t)
559 if f is not None:
--> 560 f(self, obj) # Call unbound method with explicit self
561 return
563 # Check private dispatch table if any, or else
564 # copyreg.dispatch_table
File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1251, in save_module_dict(pickler, obj)
1248 if is_dill(pickler, child=False) and pickler._session:
1249 # we only care about session the first pass thru
1250 pickler._first_pass = False
-> 1251 StockPickler.save_dict(pickler, obj)
1252 log.info("# D2")
1253 return
File /usr/lib/python3.8/pickle.py:971, in _Pickler.save_dict(self, obj)
968 self.write(MARK + DICT)
970 self.memoize(obj)
--> 971 self._batch_setitems(obj.items())
File /usr/lib/python3.8/pickle.py:997, in _Pickler._batch_setitems(self, items)
995 for k, v in tmp:
996 save(k)
--> 997 save(v)
998 write(SETITEMS)
999 elif n:
File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id)
558 f = self.dispatch.get(t)
559 if f is not None:
--> 560 f(self, obj) # Call unbound method with explicit self
561 return
563 # Check private dispatch table if any, or else
564 # copyreg.dispatch_table
File ~/datasets/src/datasets/utils/py_utils.py:862, in save_function(pickler, obj)
859 if state_dict:
860 state = state, state_dict
--> 862 dill._dill._save_with_postproc(
863 pickler,
864 (
865 dill._dill._create_function,
866 (obj.__code__, globs, obj.__name__, obj.__defaults__, closure),
867 state,
868 ),
869 obj=obj,
870 postproc_list=postproc_list,
871 )
872 else:
873 closure = obj.func_closure
File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1153, in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list)
1151 dest, source = reduction[1]
1152 if source:
-> 1153 pickler.write(pickler.get(pickler.memo[id(dest)][0]))
1154 pickler._batch_setitems(iter(source.items()))
1155 else:
1156 # Updating with an empty dictionary. Same as doing nothing.
KeyError: 140434581781568
```
</details>
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
cc @lhoestq
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Allow .JPEG as a file extension
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[
"Hi, thanks for reporting! I've opened a PR with the fix.",
"Wow, that was quick! Thank you very much 🙏 "
] | 2022-06-16T12:36:20Z
| 2022-06-20T08:18:46Z
| 2022-06-16T17:11:40Z
|
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## Describe the bug
When loading image data, HF datasets seems to recognize `.jpg` and `.jpeg` file extensions, but not e.g. .JPEG. As the naming convention .JPEG is used in important datasets such as imagenet, I would welcome if according extensions like .JPEG or .JPG would be allowed.
## Steps to reproduce the bug
```python
# use bash to create 2 sham datasets with jpeg and JPEG ext
!mkdir dataset_a
!mkdir dataset_b
!wget https://upload.wikimedia.org/wikipedia/commons/7/71/Dsc_%28179253513%29.jpeg -O example_img.jpeg
!cp example_img.jpeg ./dataset_a/
!mv example_img.jpeg ./dataset_b/example_img.JPEG
from datasets import load_dataset
# working
df1 = load_dataset("./dataset_a", ignore_verifications=True)
#not working
df2 = load_dataset("./dataset_b", ignore_verifications=True)
# show
print(df1, df2)
```
## Expected results
```
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 1
})
}) DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 1
})
})
```
## Actual results
```
FileNotFoundError: Unable to resolve any data file that matches '['**']' at /..PATH../dataset_b with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```
I know that it can be annoying to allow seemingly arbitrary numbers of file extensions. But I think this one would be really welcome.
|
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