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https://api.github.com/repos/huggingface/datasets/issues/3304 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3304/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3304/comments | https://api.github.com/repos/huggingface/datasets/issues/3304/events | https://github.com/huggingface/datasets/issues/3304 | 1,059,130,494 | I_kwDODunzps4_IQx- | 3,304 | Dataset object has no attribute `to_tf_dataset` | {
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"The issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n```\r\n# upgrade transformers and datasets to latest versions\r\n!pip install --upgrade transformers\r\n!pip install --upgrade datasets\r\n```\r\n\r\nRegards!"
] | 1,637,409,839,000 | 1,637,478,445,000 | 1,637,478,445,000 | NONE | null | I am following HuggingFace Course. I am at Fine-tuning a model.
Link: https://huggingface.co/course/chapter3/2?fw=tf
I use tokenize_function and `map` as mentioned in the course to process data.
`# define a tokenize function`
`def Tokenize_function(example):`
` return tokenizer(example['sentence'], truncation=True)`
`# tokenize entire data`
`tokenized_data = raw_data.map(Tokenize_function, batched=True)`
I get Dataset object at this point. When I try converting this to a TF dataset object as mentioned in the course, it throws the following error.
`# convert to TF dataset`
`train_data = tokenized_data["train"].to_tf_dataset( `
` columns = ['attention_mask', 'input_ids', 'token_type_ids'], `
` label_cols = ['label'], `
` shuffle = True, `
` collate_fn = data_collator, `
` batch_size = 8 `
`)`
Output:
`---------------------------------------------------------------------------`
`AttributeError Traceback (most recent call last)`
`/tmp/ipykernel_42/103099799.py in <module>`
` 1 # convert to TF dataset`
`----> 2 train_data = tokenized_data["train"].to_tf_dataset( \`
` 3 columns = ['attention_mask', 'input_ids', 'token_type_ids'], \`
` 4 label_cols = ['label'], \`
` 5 shuffle = True, \`
`AttributeError: 'Dataset' object has no attribute 'to_tf_dataset'`
When I look for `dir(tokenized_data["train"])`, there is no method or attribute in the name of `to_tf_dataset`.
Why do I get this error? And how to clear this?
Please help me. | {
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https://api.github.com/repos/huggingface/datasets/issues/3303 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3303/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3303/comments | https://api.github.com/repos/huggingface/datasets/issues/3303/events | https://github.com/huggingface/datasets/issues/3303 | 1,059,129,732 | I_kwDODunzps4_IQmE | 3,303 | DataCollatorWithPadding: TypeError | {
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"\r\n> \r\n> Input:\r\n> \r\n> ```\r\n> tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\r\n> ```\r\n> \r\n> Output:\r\n> \r\n> ```\r\n> TypeError Traceback (most recent call last)\r\n> /tmp/ipykernel_42/1563280798.py in <module>\r\n> 1 checkpoint = 'bert-base-uncased'\r\n> 2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"pt\")\r\n> TypeError: __init__() got an unexpected keyword argument 'return_tensors'\r\n> ```\r\n> \r\n\r\nThe issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n`# upgrade transformers and datasets to latest versions`\r\n`!pip install --upgrade transformers`\r\n`!pip install --upgrade datasets`\r\n\r\nCheers!"
] | 1,637,409,595,000 | 1,637,478,337,000 | 1,637,478,337,000 | NONE | null | Hi,
I am following the HuggingFace course. I am now at Fine-tuning [https://huggingface.co/course/chapter3/3?fw=tf](https://huggingface.co/course/chapter3/3?fw=tf). When I set up `DataCollatorWithPadding` as following I got an error while trying to reproduce the course code in Kaggle. This error occurs with either a CPU-only-device or a GPU-device.
Input:
```checkpoint = 'bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
```
Output:
```---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_42/1563280798.py in <module>
1 checkpoint = 'bert-base-uncased'
2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt")
TypeError: __init__() got an unexpected keyword argument 'return_tensors'
```
When I call `help` method, it too confirms that there is no argument `return_tensors`.
Input:
```
help(DataCollatorWithPadding.__init__)
```
Output:
```
Help on function __init__ in module transformers.data.data_collator:
__init__(self, tokenizer: transformers.tokenization_utils_base.PreTrainedTokenizerBase, padding: Union[bool, str, transformers.file_utils.PaddingStrategy] = True, max_length: Union[int, NoneType] = None, pad_to_multiple_of: Union[int, NoneType] = None) -> None
```
But, the source file *[Data Collator - docs](https://huggingface.co/transformers/main_classes/data_collator.html#datacollatorwithpadding)* says that there is such an argument. By default, it returns Pytorch tensors while I need TF tensors.
Where do I miss?
Please help me. | {
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https://api.github.com/repos/huggingface/datasets/issues/3302 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3302/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3302/comments | https://api.github.com/repos/huggingface/datasets/issues/3302/events | https://github.com/huggingface/datasets/pull/3302 | 1,058,907,168 | PR_kwDODunzps4uynjc | 3,302 | fix old_val typo in f-string | {
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} | [] | closed | false | null | [] | null | [] | 1,637,355,068,000 | 1,637,878,483,000 | 1,637,600,659,000 | CONTRIBUTOR | null |
This PR is to correct a typo in #3277 that @Carlosbogo revieled in a comment.
Related closed issue : #3257
Sorry about that π
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https://api.github.com/repos/huggingface/datasets/issues/3301 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3301/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3301/comments | https://api.github.com/repos/huggingface/datasets/issues/3301/events | https://github.com/huggingface/datasets/pull/3301 | 1,058,718,957 | PR_kwDODunzps4uyA9o | 3,301 | Add wikipedia tags | {
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} | [] | closed | false | null | [] | null | [] | 1,637,339,965,000 | 1,637,340,570,000 | 1,637,340,569,000 | MEMBER | null | Add the missing tags to the wikipedia dataset card.
I also added the missing languages code in our language codes list.
This should also fix the code snippet that is presented on the Hub to load the dataset: fix https://github.com/huggingface/datasets/issues/3292 | {
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https://api.github.com/repos/huggingface/datasets/issues/3300 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3300/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3300/comments | https://api.github.com/repos/huggingface/datasets/issues/3300/events | https://github.com/huggingface/datasets/issues/3300 | 1,058,644,459 | I_kwDODunzps4_GaHr | 3,300 | β Dataset loading script from Hugging Face Hub | {
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"Hi ! In the next version of `datasets`, your train and test splits will be correctly separated (changes from #3027) if you create a dataset repository with only your CSV files.\r\n\r\nAlso it seems that you overwrite the `data_files` and `data_dir` arguments in your code, when you instantiate the AGNewsConfig objects. Those parameters are not necessary since you already know which files you want to load.\r\n\r\nYou can find an example on how to specify which file the dataset has to download in this [example script](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107):\r\n```python\r\n_URLS = {\r\n \"train\": \"train-v1.1.json\", # you can use a URL or a relative path from the python script to your file in the repository\r\n \"dev\": \"dev-v1.1.json\",\r\n}\r\n```\r\n```python\r\n def _split_generators(self, dl_manager):\r\n downloaded_files = dl_manager.download_and_extract(_URLS)\r\n\r\n return [\r\n datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": downloaded_files[\"train\"]}),\r\n datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": downloaded_files[\"dev\"]}),\r\n ]\r\n```",
"Also I think the viewer will be updated when you fix the dataset script, let me know if it doesn't",
"Hi @lhoestq,\r\n\r\nThanks a lot for the super quick answer!\r\n\r\nYour suggestion solves my issue. I am now able to load the dataset properly π \r\nHowever, the dataviewer is not working yet.\r\n\r\nReally, thanks a lot for your help and consideration!\r\n\r\nBest,\r\nPietro",
"Great ! We'll take a look at the viewer to fix it",
"@lhoestq I think I am having a related problem.\r\nMy call to load_dataset() looks like this:\r\n\r\n```\r\n datasets = load_dataset(\r\n os.path.abspath(layoutlmft.data.datasets.xfun.__file__),\r\n f\"xfun.{data_args.lang}\",\r\n additional_langs=data_args.additional_langs,\r\n keep_in_memory=True,\r\n )\r\n\r\n```\r\n\r\nMy _split_generation code is:\r\n\r\n```\r\n def _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n\r\n downloaded_file = dl_manager.download_and_extract(\"https://guillaumejaume.github.io/FUNSD/dataset.zip\")\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": f\"{downloaded_file}/dataset/training_data/\"}\r\n ),\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TEST, gen_kwargs={\"filepath\": f\"{downloaded_file}/dataset/testing_data/\"}\r\n ),\r\n ]\r\n\r\n```\r\nHowever I get the error \"TypeError: _generate_examples() got an unexpected keyword argument 'filepath'\"\r\nThe path looks right and I see the data in the path so I think the only problem I have is that it doesn't like the key \"filepath\". However, the documentation (example [here](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107)) seems to show that this is the correct parameter. \r\n\r\nHere is the full stack trace:\r\n\r\n```\r\nDownloading and preparing dataset xfun/xfun.en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/caseygre/.cache/huggingface/datasets/xfun/xfun.en/0.0.0/96b8cb7c57f6f822f0ab37ae3be7b82d84ac57062e774c9361ccf0a4b9ef61cc...\r\nTraceback (most recent call last):\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 574, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 652, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 975, in _prepare_split\r\n generator = self._generate_examples(**split_generator.gen_kwargs)\r\nTypeError: _generate_examples() got an unexpected keyword argument 'filepath'\r\npython-BaseException\r\n```",
"Hi ! The `gen_kwargs` dictionary is passed to `_generate_examples`, so in your case it must be defined this way:\r\n```python\r\ndef _generate_examples(self, filepath):\r\n ...\r\n```\r\n\r\nAnd here is an additional tip: you can use `os.path.join(downloaded_file, \"dataset/testing_data\")` instead of `f\"downloaded_file}/dataset/testing_data/\"` to get compatibility with Windows and streaming.\r\n\r\nIndeed Windows uses a backslash separator, not a slash, and streaming uses chained URLs (like `zip://dataset/testing_data::https://https://guillaumejaume.github.io/FUNSD/dataset.zip` for example)",
"Thanks for you quick reply @lhoestq and so sorry for my very delayed response.\r\nWe have gotten around the error another way but I will try to duplicate this when I can. We may have had \"filepaths\" instead of \"filepath\" in our def of _generate_examples() and not noticed the difference. If I find a more useful answer for others I will add to this ticket so they know what the issue was.\r\nNote: we do have our own _generate_examples() defined with the same def as Quentin has. (But one version does have \"filepaths\".)\r\n",
"Fixed in the viewer: https://huggingface.co/datasets/pietrolesci/ag_news"
] | 1,637,335,252,000 | 1,640,170,676,000 | 1,640,170,676,000 | NONE | null | Hi there,
I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.
Issues I have encountered:
- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations
- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this
```python
load_dataset("pietrolesci/ag_news", name="my_configuration")
```
Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following
```
ag_news.py
train.csv
test.csv
```
and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.
Any suggestion is very much appreciated.
Best,
Pietro
Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news
BONUS: how can I make the data viewer work in this specific case? :) | {
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"Hi ! Thanks for reporting\r\nWe've already fixed the code that generates the preview for this dataset, we'll release the fix soon :)",
"Hi @lhoestq, thanks for your feedback!",
"Fixed in the viewer.\r\n\r\nhttps://huggingface.co/datasets/ag_news"
] | 1,637,320,739,000 | 1,640,103,845,000 | 1,640,103,845,000 | NONE | null | ## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/ag_news
Hi there, the `ag_news` dataset viewer is not working.
Am I the one who added this dataset? No
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https://api.github.com/repos/huggingface/datasets/issues/3297 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3297/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3297/comments | https://api.github.com/repos/huggingface/datasets/issues/3297/events | https://github.com/huggingface/datasets/issues/3297 | 1,058,263,859 | I_kwDODunzps4_E9Mz | 3,297 | .map() cache is wrongfully reused - only happens when the mapping function is imported | {
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"Hi ! Thanks for reporting. Indeed this is a current limitation of the usage we have of `dill` in `datasets`. I'd suggest you use your workaround for now until we find a way to fix this. Maybe functions that are not coming from a module not installed with pip should be dumped completely, rather than only taking their locations into account",
"I agree. Sounds like a solution for it would be pretty dirty, even [cloudpickle](https://stackoverflow.com/a/16891169) doesn't help in this case.\r\nIn the meanwhile I think that adding a warning and the workaround somewhere in the documentation can be helpful."
] | 1,637,309,916,000 | 1,638,834,340,000 | null | CONTRIBUTOR | null | ## Describe the bug
When `.map` is used with a mapping function that is imported, the cache is reused even if the mapping function has been modified.
The reason for this is that `dill` that is used for creating the fingerprint [pickles imported functions by reference](https://stackoverflow.com/a/67851411).
I guess it is not a widespread case, but it can still lead to unwanted results unnoticeably.
## Steps to reproduce the bug
Create files `a.py` and `b.py`:
```python
# a.py
from datasets import load_dataset
def main():
squad = load_dataset("squad")
squad.map(mapping_func, batched=True)
def mapping_func(examples):
ID_LENGTH = 4
examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]]
return examples
if __name__ == "__main__":
main()
```
```python
# b.py
from datasets import load_dataset
from a import mapping_func
def main():
squad = load_dataset("squad")
squad.map(mapping_func, batched=True)
if __name__ == "__main__":
main()
```
Run `python b.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...".
Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python b.py` again. You'll see that `.map` loads from the cache the result of the previous mapping function.
## Expected results
Run `python a.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...".
Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python a.py` again. You'll see that the dataset is being processed and that there's no reuse of the previous mapping function result.
## Workaround
Put the mapping function inside a dummy class as a static method:
```python
# a.py
class MappingFuncClass:
@staticmethod
def mapping_func(examples):
ID_LENGTH = 4
examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]]
return examples
```
```python
# b.py
from datasets import load_dataset
from a import MappingFuncClass
def main():
squad = load_dataset("squad")
squad.map(MappingFuncClass.mapping_func, batched=True)
if __name__ == "__main__":
main()
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
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"Hi ! Thanks for the fix :) \r\n\r\nI think this should be `extract_path_from_uri` 's responsibility to strip the extra `/` from a badly formatted path like `hdfs:///absolute/path` (or raise an error). Do you think you can simply do the changes in `extract_path_from_uri` ? This way this fix will be available for all the other parts of the lib that need to extract the inner path from an URI of a remote filesystem\r\n\r\nThen we can also keep your test cases but simply apply them to `extract_path_from_uri` instead",
"Hi @lhoestq! No problem! Thanks for your interest! :)\r\n\r\nI think stripping the 3rd `/` in `hdfs:///absolute/path` inside `extract_path_from_uri` is not the solution. When I provide `hdfs:///absolute/path` to `extract_path_from_uri` we want `/absolute/path` to be returned, as it does now (at least in the case of URIs with `hdfs` schemas, for `s3` is different as it should start with a bucket name).\r\n\r\nThe problem comes in line 1041 in the original code below:\r\n\r\nhttps://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1038-L1042\r\n\r\nLets assume the following parameters for line 1041 after `extract_path_from_uri` has removed the `hdfs` schema part and the `://` from `hdfs:///absolute/path`, and `get_temporary_cache_files_directory()` returns `/tmp/a1b2b3c4`, as it is shown below: \r\n\r\n```python\r\nsrc_dataset_path = '/absolute/path'\r\ntmp_dir = '/tmp/a1b2b3c4'\r\ndataset_path = Path(tmp_dir, src_dataset_path)\r\n```\r\n\r\nAfter passing those paths to the `Path` object, `dataset_path` contains only `/absolute/path`; that is, it has lost the temporary directory path. This is because, when two (or more) absolute paths are passed to the `Path` function, only the last one is taken. However, if the contents of those variables are:\r\n\r\n```python\r\nsrc_dataset_path = 'relative/path'\r\ntmp_dir = '/tmp/a1b2b3c4'\r\ndataset_path = Path(tmp_dir, src_dataset_path)\r\n```\r\n\r\nthen `dataset_path` contains `/tmp/a1b2b3c4/relative/path` as expected.\r\n\r\nAbsolute paths are allowed in hdfs URIs, so that's why I added the extra function `build_local_temp_path` in the PR; so in case the second argument is an absolute path, it still will create the correct absolute path by concatenating the temp dir and the path passed by converting it to a relative path (and it also works for windows paths too.) It also allows to add the tests, checking that the main combinations are ok.\r\n\r\nI've checked all the places where the result of `extract_path_from_uri` is used, and as far as I've seen this is the only place where it is concatenated with another possible absolute path, so no need to add `build_local_temp_path` anywhere else. \r\n"
] | 1,637,278,365,000 | 1,638,787,504,000 | 1,638,787,504,000 | CONTRIBUTOR | null | This aims to close #3295 | {
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"Hi ! Good catch and thanks for opening a PR :)\r\n\r\nI just responded in your PR"
] | 1,637,277,842,000 | 1,638,787,504,000 | 1,638,787,504,000 | CONTRIBUTOR | null | ## Describe the bug
When trying to build a temporary dataset path from a remote URI in this block of code:
https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1038-L1042
the result is not the expected when passing an absolute path in an URI like `hdfs:///absolute/path`.
## Steps to reproduce the bug
```python
dataset_path = "hdfs:///absolute/path"
src_dataset_path = extract_path_from_uri(dataset_path)
tmp_dir = get_temporary_cache_files_directory()
dataset_path = Path(tmp_dir, src_dataset_path)
print(dataset_path)
```
## Expected results
With the code above, we would expect a value in `dataset_path` similar to:
`/tmp/tmpnwxyvao5/absolute/path`
## Actual results
However, we get a `dataset_path` value like:
`/absolute/path`
This is because this line here: https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1041
returns the last absolute path when two absolute paths (the one in `tmp_dir` and the one extracted from the URI in `src_dataset_path`) are passed as arguments.
## Environment info
- `datasets` version: 1.13.3
- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 5.0.0
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] | open | false | null | [] | null | [] | 1,637,249,684,000 | 1,638,964,802,000 | null | NONE | null | ## Adding a Dataset
- **Name:** Natural Adversarial Objects (NAO)
- **Description:** Natural Adversarial Objects (NAO) is a new dataset to evaluate the robustness of object detection models. NAO contains 7,934 images and 9,943 objects that are unmodified and representative of real-world scenarios, but cause state-of-the-art detection models to misclassify with high confidence.
- **Paper:** https://arxiv.org/abs/2111.04204v1
- **Data:** https://drive.google.com/drive/folders/15P8sOWoJku6SSEiHLEts86ORfytGezi8
- **Motivation:** interesting object detection dataset useful for miscclassifications
cc @NielsRogge
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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Related to #3289, #3286. | {
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"Hi ! Indeed it looks like the code snippet on the Hugging face Hub doesn't show the second parameter\r\n\r\n![image](https://user-images.githubusercontent.com/42851186/142649237-45ba55c5-1a64-4c30-8692-2c8120572f92.png)\r\n\r\nThanks for reporting, I'm taking a look\r\n"
] | 1,637,214,078,000 | 1,637,340,569,000 | 1,637,340,569,000 | NONE | null | ## Describe the bug
I am following the instruction for loading the wikipedia dataset using datasets. However getting the below error.
## Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset("wikipedia")
```
## Expected results
A clear and concise description of the expected results.
## Actual results
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)
339 "Config name is missing."
340 "\nPlease pick one among the available configs: %s" % list(self.builder_configs.keys())
--> 341 + "\nExample of usage:\n\t`{}`".format(example_of_usage)
342 )
343 builder_config = self.BUILDER_CONFIGS[0]
ValueError: Config name is missing.
Please pick one among the available configs: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']
Example of usage:
`load_dataset('wikipedia', '20200501.aa')`
I think the other parameter is missing in the load_dataset function that is not shown in the instruction. | {
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"Reading FLAC (for `librispeech_asr`) works OK for me (`soundfile` version: `0.10.3`):\r\n```python\r\nIn [2]: ds = load_dataset(\"datasets/librispeech_asr/librispeech_asr.py\", \"clean\", streaming=True, split=\"train.100\")\r\n\r\nIn [3]: item = next(iter(ds))\r\n\r\nIn [4]: item.keys()\r\nOut[4]: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])\r\n\r\nIn [5]: item[\"file\"]\r\nOut[5]: '374-180298-0000.flac'\r\n\r\nIn [6]: item[\"audio\"].keys()\r\nOut[6]: dict_keys(['path', 'array', 'sampling_rate'])\r\n\r\nIn [7]: item[\"audio\"][\"sampling_rate\"]\r\nOut[7]: 16000\r\n\r\nIn [8]: item[\"audio\"][\"path\"]\r\nOut[8]: '374-180298-0000.flac'\r\n\r\nIn [9]: item[\"audio\"][\"array\"].shape\r\nOut[9]: (232480,)\r\n```",
"Oh cool ! I think this might have come from an issue with my local `soundfile` installation then",
"I'll do `multilingual_librispeech` in a separate PR since it requires the data to be in another format (in particular separate the train/dev/test splits in different files)"
] | 1,637,171,021,000 | 1,637,334,538,000 | 1,637,334,537,000 | MEMBER | null | <s>Needs https://github.com/huggingface/datasets/pull/3129 to be merged first</s>
Make those audio datasets streamable:
- [x] common_voice
- [x] openslr
- [x] vivos
- [x] librispeech_asr <s>(still has some issues to read FLAC)</s> *actually it's ok*
- [ ] <s>multilingual_librispeech (yet to be converted)</S> *TODO in a separate PR* | {
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https://api.github.com/repos/huggingface/datasets/issues/3288 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3288/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3288/comments | https://api.github.com/repos/huggingface/datasets/issues/3288/events | https://github.com/huggingface/datasets/pull/3288 | 1,056,145,703 | PR_kwDODunzps4up6S5 | 3,288 | Allow datasets with indices table when concatenating along axis=1 | {
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} | [] | closed | false | null | [] | null | [] | 1,637,156,488,000 | 1,637,163,672,000 | 1,637,163,671,000 | CONTRIBUTOR | null | Calls `flatten_indices` on the datasets with indices table in `concatenate_datasets` to fix issues when concatenating along `axis=1`.
cc @lhoestq: I decided to flatten all the datasets instead of flattening all the datasets except the largest one in the end. The latter approach fails on the following example:
```python
a = Dataset.from_dict({"a": [10, 20, 30, 40]})
b = Dataset.from_dict({"b": [10, 20, 30, 40, 50, 60]}) # largest dataset
a = a.select([1, 2, 3])
b = b.select([1, 2, 3])
concatenate_datasets([a, b], axis=1) # fails at line concat_tables(...) because the real length of b's data is 6 and a's length is 3 after flattening (was 4 before flattening)
```
Also, it requires additional re-ordering of indices to prepare them for working with the indices table of the largest dataset. IMO not worth when we save only one `flatten_indices` call. (feel free to check the code of that approach at https://github.com/huggingface/datasets/commit/6acd10481c70950dcfdbfd2bab0bf0c74ad80bcb if you are interested)
Fixes #3273
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- The complete final version of The Pile dataset: "all" config
- PubMed Central subset of The Pile: "pubmed_central" config
Close #1675, close bigscience-workshop/data_tooling#74.
CC: @StellaAthena, @lewtun | {
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- **Name:** IEMOCAP
- **Description:** acted, multimodal and multispeaker database
- **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf
- **Data:** https://sail.usc.edu/iemocap/index.html
- **Motivation:** Useful multimodal dataset
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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"#self-assign"
] | 1,637,102,648,000 | 1,638,784,185,000 | null | NONE | null | ## Adding a Dataset
- **Name:** VoxLingua107
- **Description:** VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives.
- **Paper:** https://arxiv.org/abs/2011.12998
- **Data:** http://bark.phon.ioc.ee/voxlingua107/
- **Motivation:** Nice audio classification dataset
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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"#self-assign"
] | 1,637,102,396,000 | 1,639,132,215,000 | 1,639,132,215,000 | NONE | null | ## Adding a Dataset
- **Name:** Speech commands
- **Description:** A Dataset for Limited-Vocabulary Speech Recognition
- **Paper:** https://arxiv.org/abs/1804.03209
- **Data:** https://www.tensorflow.org/datasets/catalog/speech_commands, Available:
http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz
- **Motivation:** Nice dataset for audio classification training
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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https://api.github.com/repos/huggingface/datasets/issues/3282 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3282/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3282/comments | https://api.github.com/repos/huggingface/datasets/issues/3282/events | https://github.com/huggingface/datasets/issues/3282 | 1,055,054,898 | I_kwDODunzps4-4twy | 3,282 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py | {
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"Hi ! Thanks for reporting :)\r\nI think this is because the dataset is behind an access page. We can fix the dataset viewer\r\n\r\nIf you also have this error when you use the `datasets` library in python, you should probably pass `use_auth_token=True` to the `load_dataset()` function to use your account to access the dataset.",
"Ah ok, I didn't realise about the login page. I'll try `use_auth_token=True` and see if that solves it.\r\n\r\nRegards!",
"Hi, \r\n\r\nUsing `use_auth_token=True` and downloading the credentials with `huggingface-cli login` (stored in .huggingface/token) solved the issue.\r\n\r\nShould I leave the issue open until you fix the Dataset viewer issue?",
"Cool ! Yes let's keep this issue open until the viewer is fixed - I'll close it when this is fixed. Thanks",
"The error I get when trying to load OSCAR 21.09 is this\r\n```\r\nConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py\r\n```\r\n\r\nThe URL I get in the browser is this\r\n```\r\nhttps://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py\r\n```\r\n\r\nMaybe URL is the issue? (resolve vs blob)",
"> The error I get when trying to load OSCAR 21.09 is this\r\n> \r\n> ```\r\n> ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py\r\n> ```\r\n> \r\n> The URL I get in the browser is this\r\n> \r\n> ```\r\n> https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py\r\n> ```\r\n> \r\n> Maybe URL is the issue? (resolve vs blob)\r\n\r\nYou need to download your login credentials. See `huggingface-cli login` documentation and when loading the dataset use `use_auth_token=True`:\r\n`\r\nload_dataset(corpus, language, split=None, use_auth_token=True, cache_dir=cache_folder)`"
] | 1,637,078,719,000 | 1,638,173,849,000 | null | NONE | null | ## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library. | {
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https://api.github.com/repos/huggingface/datasets/issues/3281 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3281/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3281/comments | https://api.github.com/repos/huggingface/datasets/issues/3281/events | https://github.com/huggingface/datasets/pull/3281 | 1,055,018,876 | PR_kwDODunzps4umWZE | 3,281 | [Datasets] Improve Covost 2 | {
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Currenty the user has to:
1. Go on Common Voice website
2. Find the correct dataset which is **not** mentioned in the error message
3. Download it
4. Untar it
5. Create a language id folder (why? this folder does not exist in the `.tar` downloaded file)
6. pass the folder containing the created language id folder
This PR improves this to:
1. Go on Common Voice website
2. Find the correct dataset which **is** mentioned in the error message
3. Download it
4. Untar it
5. pass the untared folder
**Note**: This PR is not at all time-critical | {
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Fix #3167. | {
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Because of the possibility of insertions, the numerator in the WER formula can be larger than N, so the value of WER can be greater than 1.0:
```
>>> from datasets import load_metric
>>> metric = load_metric("wer")
>>> metric.compute(predictions=["hello how are you"], references=["hello"])
3.0
```
and similarly from the underlying jiwer module's `wer` function:
```
>>> from jiwer import wer
>>> wer("hello", "hello how are you")
3.0
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3277 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3277/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3277/comments | https://api.github.com/repos/huggingface/datasets/issues/3277/events | https://github.com/huggingface/datasets/pull/3277 | 1,054,122,656 | PR_kwDODunzps4ujk11 | 3,277 | f-string formatting | {
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"Hello @lhoestq, ```make style``` is applied as asked. :)"
] | 1,637,012,225,000 | 1,637,354,408,000 | 1,637,165,918,000 | CONTRIBUTOR | null | **Fix #3257**
Replaced _.format()_ and _%_ by f-strings in the following modules :
- [x] **tests**
- [x] **metrics**
- [x] **benchmarks**
- [x] **utils**
- [x] **templates**
- [x] **src/Datasets/\*.py**
Modules in **_src/Datasets/_**:
- [x] **commands**
- [x] **features**
- [x] **formatting**
- [x] **io**
- [x] **tasks**
- [x] **utils**
Module **datasets** will not be edited as asked by @mariosasko
-A correction of the first PR (#3267)-
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https://api.github.com/repos/huggingface/datasets/issues/3276 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3276/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3276/comments | https://api.github.com/repos/huggingface/datasets/issues/3276/events | https://github.com/huggingface/datasets/pull/3276 | 1,053,793,063 | PR_kwDODunzps4uihih | 3,276 | Update KILT metadata JSON | {
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https://api.github.com/repos/huggingface/datasets/issues/3275 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3275/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3275/comments | https://api.github.com/repos/huggingface/datasets/issues/3275/events | https://github.com/huggingface/datasets/pull/3275 | 1,053,698,898 | PR_kwDODunzps4uiN9t | 3,275 | Force data files extraction if download_mode='force_redownload' | {
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} | [] | closed | false | null | [] | null | [] | 1,636,984,824,000 | 1,636,987,523,000 | 1,636,987,523,000 | CONTRIBUTOR | null | Avoids weird issues when redownloading a dataset due to cached data not being fully updated.
With this change, issues #3122 and https://github.com/huggingface/datasets/issues/2956 (not a fix, but a workaround) can be fixed as follows:
```python
dset = load_dataset(..., download_mode="force_redownload")
``` | {
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"The CI fail are caused by some missing sections or tags, which is unrelated to this PR. Merging !"
] | 1,636,984,234,000 | 1,636,987,435,000 | 1,636,987,434,000 | MEMBER | null | As reported in https://github.com/huggingface/datasets/issues/3188 some contact information are not displayed correctly.
This PR fixes this for CoNLL-2002 and some other datasets with the same issue | {
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A minimal reproducible example:
```python
>>> from datasets import Dataset, concatenate_datasets
>>> a = Dataset.from_dict({"a": [30, 20, 10]})
>>> b = Dataset.from_dict({"b": [2, 1, 3]})
>>> d = concatenate_datasets([a.sort("a"), b.sort("b")], axis=1)
>>> print(d[:3]) # expected: {'a': [10, 20, 30], 'b': [1, 2, 3]}
{'a': [10, 20, 30], 'b': [3, 1, 2]}
```
I've noticed the bug while working on #3195. | {
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"Hello, is this issue open for any contributor ? can I work on it ?\r\n\r\n",
"Hi ! Sure this is open for any contributor. If you're interested feel free to self-assign this issue to you by commenting `#self-assign`. Then if you have any question or if I can help, feel free to ping me.\r\n\r\nTo begin with, feel free to take a look at both implementations of `iter_archive` for local downloads and for data streaming:\r\n\r\nIn the `DownloadManager` for local dowloads:\r\nhttps://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/download_manager.py#L218-L242\r\n\r\nIn the `StreamingDownloadManager` to stream the content of the archive directly from the remote file:\r\nhttps://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/streaming_download_manager.py#L502-L526\r\n\r\nNotice the call to `xopen` that opens and streams a file given either an URL or a local path :)",
"Okay thank you for the information. I will work on this :) ",
"#self-assign"
] | 1,636,973,442,000 | 1,637,798,927,000 | null | MEMBER | null | Currently users can use `dl_manager.iter_archive` in their dataset script to iterate over all the files of a TAR archive.
It would be nice if it could work with ZIP files too ! | {
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} | [] | closed | false | null | [] | null | [] | 1,636,971,956,000 | 1,637,062,558,000 | 1,637,062,558,000 | MEMBER | null | Currently the Audio feature type can only decode local audio files, not remote files.
To fix this I replaced `open` with our `xopen` functoin that is compatible with remote files in audio.py
cc @albertvillanova @mariosasko | {
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"Hi @ZhaofengWu, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your bug:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.91MB/s]\r\nDownloading: 1.79kB [00:00, 1.79MB/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to .cache\\coqa\\default\\1.0.0\\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 49.0M/49.0M [00:06<00:00, 7.17MB/s]\r\nDownloading: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 9.09M/9.09M [00:01<00:00, 6.08MB/s]\r\n100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:12<00:00, 6.48s/it]\r\n100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<00:00, 333.26it/s]\r\nDataset coqa downloaded and prepared to .cache\\coqa\\default\\1.0.0\\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0. Subsequent calls will reuse this data.\r\n100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<00:00, 285.49it/s]\r\n\r\nIn [3]: ds\r\nOut[3]:\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['source', 'story', 'questions', 'answers'],\r\n num_rows: 7199\r\n })\r\n validation: Dataset({\r\n features: ['source', 'story', 'questions', 'answers'],\r\n num_rows: 500\r\n })\r\n})\r\n```\r\n\r\nCould you please give more details about your development environment? You can run the command `datasets-cli env` and copy-and-paste its output:\r\n```\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n```\r\nIt might be because you are using an old version of `datasets`. Could you please update it (`pip install -U datasets`) and confirm if the problem parsists? ",
"I'm getting the same error in two separate environments:\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.4.0-84-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.11\r\n- PyArrow version: 6.0.0\r\n```\r\n\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.9.5\r\n- PyArrow version: 6.0.0\r\n```",
"I'm sorry, but don't get to reproduce the error in the Linux environment.\r\n\r\n@mariosasko @lhoestq can you reproduce it?",
"I also can't reproduce the error on Windows/Linux (tested both the master and the `1.15.1` version). ",
"Maybe the file had issues during the download ? Could you try to delete your cache and try again ?\r\nBy default the downloads cache is at `~/.cache/huggingface/datasets/downloads`\r\n\r\nAlso can you check if you have a proxy that could prevent the download to succeed ? Are you able to download those files via your browser ?",
"I got the same error in a third environment (google cloud) as well. The internet for these three environments are all different so I don't think that's the reason.\r\n```\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-1022-gcp-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n```\r\nI deleted the entire `~/.cache/huggingface/datasets` on my local mac, and got a different first time error.\r\n```\r\nPython 3.9.5 (default, May 18 2021, 12:31:01) \r\n[Clang 10.0.0 ] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.19MB/s] \r\nDownloading: 1.79kB [00:00, 712kB/s] \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 222/222 [00:00<00:00, 1.36MB/s]\r\n 50%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 1/2 [00:00<00:00, 2.47it/s]Traceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py\", line 1632, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py\", line 607, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py\", line 675, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/Users/zhaofengw/.cache/huggingface/modules/datasets_modules/datasets/coqa/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0/coqa.py\", line 70, in _split_generators\r\n downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 284, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 196, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 216, in map_nested\r\n mapped = [\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 217, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True))\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 152, in _single_map_nested\r\n return function(data_struct)\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 217, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 295, in cached_path\r\n output_path = get_from_cache(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 594, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json\r\n>>> dataset = load_dataset(\"coqa\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 222/222 [00:00<00:00, 1.38MB/s]\r\n100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<00:00, 6.26it/s]\r\n100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<00:00, 1087.45it/s]\r\n 50%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | 1/2 [00:45<00:45, 45.60s/it]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py\", line 1632, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py\", line 607, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py\", line 679, in _download_and_prepare\r\n verify_checksums(\r\n File \"/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py\", line 40, in verify_checksums\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']\r\n```\r\nI can access the URL using my browser, though I did notice a redirection -- could that have something to do with it?",
"Hi @ZhaofengWu, \r\n\r\nWhat about in Google Colab? Can you run this notebook without errors? \r\nhttps://colab.research.google.com/drive/1CCpiiHmtNlfO_4CZ3-fW-TSShr1M0rL4?usp=sharing",
"I can run your notebook fine, but if I create one myself, it has that error: https://colab.research.google.com/drive/107GIdhrauPO6ZiFDY7G9S74in4qqI2Kx?usp=sharing.\r\n\r\nIt's so funny -- it's like whenever you guys run it it's fine but whenever I run it it fails, whatever the environment is.",
"I guess it must be some connection issue: the data owner may be blocking requests coming from your country or IP range...",
"I mean, I don't think google colab sends the connection from my IP. Same applies to google cloud.",
"Hello, I am having the same error with @ZhaofengWu first with \"social bias frames\" dataset. As I found this report, I tried also \"coqa\" and it fails as well. \r\n\r\nI test this on Google Colab. \r\n\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n```\r\n\r\nThen another environment\r\n\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: macOS-12.0.1-arm64-arm-64bit\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n```\r\n\r\nI tried the notebook @albertvillanova provided earlier, and it fails...\r\n",
"Hi, still not able to reproduce the issue with `coqa`. If you still have this issue, could you please run these additional commands ?\r\n```python\r\n>>> import os\r\n>>> from hashlib import md5\r\n>>> from datasets.utils import DownloadManager, DownloadConfig\r\n>>> path = DownloadManager(download_config=DownloadConfig(use_etag=False)).download(\"https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json\") # it returns the cached file\r\n>>> os.path.getsize(path)\r\n9090845\r\n>>> m = md5()\r\n>>> m.update(open(path, \"rb\").read())\r\n>>> m.hexdigest()\r\n`95d427588e3733e4ebec55f6938dbba6`\r\n>>> open(path).read(500)\r\n'{\\n \"version\": \"1.0\",\\n \"data\": [\\n {\\n \"source\": \"mctest\",\\n \"id\": \"3dr23u6we5exclen4th8uq9rb42tel\",\\n \"filename\": \"mc160.test.41\",\\n \"story\": \"Once upon a time, in a barn near a farm house, there lived a little white kitten named Cotton. Cotton lived high up in a nice warm place above the barn where all of the farmer\\'s horses slept. But Cotton wasn\\'t alone in her little home above the barn, oh no. She shared her hay bed with her mommy and 5 other sisters. All of her sisters w'\r\n```\r\n\r\nThis way we can know whether you downloaded a corrupted file or an error file that could cause the `NonMatchingChecksumError` error to happen",
"```\r\n>>> import os\r\n>>> from hashlib import md5\r\n>>> from datasets.utils import DownloadManager, DownloadConfig\r\n>>> path = DownloadManager(download_config=DownloadConfig(use_etag=False)).download(\"https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json\") # it returns the cached file\r\n>>> os.path.getsize(path)\r\n222\r\n>>> m = md5()\r\n>>> m.update(open(path, \"rb\").read())\r\n>>> m.hexdigest()\r\n'1195812a37c01a4481a4748c85d0c6a9'\r\n>>> open(path).read(500)\r\n'<html>\\n<head><title>503 Service Temporarily Unavailable</title></head>\\n<body bgcolor=\"white\">\\n<center><h1>503 Service Temporarily Unavailable</h1></center>\\n<hr><center>nginx/1.10.3 (Ubuntu)</center>\\n</body>\\n</html>\\n'\r\n```\r\nLooks like there was a server-side error when downloading the dataset? But I don't believe this is a transient error given (a) deleting the cache and re-downloading gives the same error; (b) it happens on multiple platforms with different network configurations; (c) other people are getting this error too, see above. So I'm not sure why it works for some people but not others.",
"`wget https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json` does work. So I suspect there might be some problem in `datasets`' networking code? Can you give me some snippet that simulates how `datasets` requests the resource which I can run on my end?",
"There is a redirection -- I don't know if that's the cause.",
"Ok This is an issue with the server that hosts the data at `https://nlp.stanford.edu/nlp/data` that randomly returns 503 (by trying several times it also happens on my side), hopefully it can be fixed soon. I'll try to reach the people in charge of hosting the data",
"Thanks. Also it might help to display a more informative error message?",
"You're right. I just opened a PR that would show this error if it happens again:\r\n```python\r\nConnectionError: Couldn't reach https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json (error 503)\r\n```"
] | 1,636,952,647,000 | 1,638,358,090,000 | null | NONE | null | ```
>>> from datasets import load_dataset
>>> dataset = load_dataset("coqa")
Downloading: 3.82kB [00:00, 1.26MB/s]
Downloading: 1.79kB [00:00, 733kB/s]
Using custom data configuration default
Downloading and preparing dataset coqa/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to /Users/zhaofengw/.cache/huggingface/datasets/coqa/default/1.0.0/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...
Downloading: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 222/222 [00:00<00:00, 1.38MB/s]
Downloading: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 222/222 [00:00<00:00, 1.32MB/s]
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:01<00:00, 1.91it/s]
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<00:00, 1117.44it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/Users/zhaofengw/miniconda3/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json', 'https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json']
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"Hi ! I think the issue comes from this [line](https://huggingface.co/datasets/liweili/c4_200m/blob/main/c4_200m.py#L87):\r\n```python\r\npath = filepath + \"/*.tsv*\"\r\n```\r\n\r\nYou can fix this by doing this instead:\r\n```python\r\npath = os.path.join(filepath, \"/*.tsv*\")\r\n```\r\n\r\nHere is why:\r\n\r\nLocally you can append `\"/*.tsv*\"` to your local path, however it doesn't work in streaming mode, and the dataset viewer does use the streaming mode.\r\nIn streaming mode, the download and extract part is done lazily. It means that instead of using local paths, it's still passing around URLs and [chained URLs](https://filesystem-spec.readthedocs.io/en/latest/features.html#url-chaining)\r\n\r\nTherefore in streaming mode, `filepath` is not a local path, but instead is equal to\r\n```python\r\nzip://::https://huggingface.co/datasets/liweili/c4_200m/resolve/main/data.zip\r\n```\r\nThe `zip://` part means that we navigate inside the remote ZIP file.\r\n\r\nYou must use `os.path.join` to navigate inside it and get your TSV files:\r\n```python\r\n>>> os.path.join(filepath, \"/*.tsv*\")\r\nzip://*.tsv*::https://huggingface.co/datasets/liweili/c4_200m/resolve/main/data.zip\r\n```\r\n\r\n`datasets` extends `os.path.join`, `glob.glob`, etc. in your dataset scripts to work with remote files.",
"hi @lhoestq ! thanks for the tip! i've updated the line of code but it's still not working. am i doing something else wrong? thank you!",
"Hi ! Your dataset code is all good now :)\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: d = load_dataset(\"liweili/c4_200m\", streaming=True)\r\nDownloading: 100%|βββββββββββββββββββββββββββββββββββββββββββββ| 2.79k/2.79k [00:00<00:00, 4.83MB/s]\r\nUsing custom data configuration default\r\n\r\nIn [3]: next(iter(d[\"train\"]))\r\nOut[3]: \r\n{'input': 'Bitcoin is for $7,094 this morning, which CoinDesk says.',\r\n 'output': 'Bitcoin goes for $7,094 this morning, according to CoinDesk.'}\r\n```\r\nThough the viewer doesn't seem to be updated, I'll take a look at what's wrong",
"thank you @lhoestq! π ",
"It's working\r\n\r\n<img width=\"1424\" alt=\"Capture dβeΜcran 2021-12-21 aΜ 11 24 29\" src=\"https://user-images.githubusercontent.com/1676121/146914238-24bf87c0-c68d-4699-8d6c-fa3065656d1d.png\">\r\n\r\n"
] | 1,636,910,326,000 | 1,640,082,320,000 | 1,640,082,291,000 | NONE | null | ## Dataset viewer issue for '*liweili/c4_200m*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/liweili/c4_200m)*
*Server Error*
```
Status code: 404
Exception: Status404Error
Message: Not found. Maybe the cache is missing, or maybe the ressource does not exist.
```
Am I the one who added this dataset ? Yes
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https://api.github.com/repos/huggingface/datasets/issues/3267 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3267/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3267/comments | https://api.github.com/repos/huggingface/datasets/issues/3267/events | https://github.com/huggingface/datasets/pull/3267 | 1,052,750,084 | PR_kwDODunzps4ufQzB | 3,267 | Replacing .format() and % by f-strings | {
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"Hi ! It looks like most of your changes are just `black` changes. All those changes are not necessary. In particular if you want to use `black`, please use the `make style` command instead. It runs `black` with additional parameters and you shouldn't end up with that many changes\r\n\r\nFeel free to open a new PR that doesn't include all the unnecessary `black` changes that you have on your branch :)",
"> Hi ! It looks like most of your changes are just `black` changes. All those changes are not necessary. In particular if you want to use `black`, please use the `make style` command instead. It runs `black` with additional parameters and you shouldn't end up with that many changes\r\n> \r\n> Feel free to open a new PR that doesn't include all the unnecessary `black` changes that you have on your branch :)\r\n\r\nThank you for your answer :) , I will open a new PR with the correct changes.",
"Hi @lhoestq, I submitted 3 commits in a new PR (#3277) where I did not apply black.\r\n\r\nI can apply the ```make style``` command if asked.",
"Cool thanks ! Yes feel free to make sure you have `black==21.4b0` and run `make style`"
] | 1,636,830,722,000 | 1,637,096,426,000 | 1,637,074,543,000 | CONTRIBUTOR | null | **Fix #3257**
Replaced _.format()_ and _%_ by f-strings in the following modules :
- [x] **tests**
- [x] **metrics**
- [x] **benchmarks**
- [x] **utils**
- [x] **templates**
Will follow in the next PR the modules left :
- [ ] **src**
Module **datasets** will not be edited as asked by @mariosasko
PS : black and isort applied to files
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"There seems to be problems with datasets metadata, of which I dont have access to. I think one of the datasets is from reddit. Can anyone help?",
"Hello @LashaO , I think the errors were caused by `_DATA_FILES` in `definite_pronoun_resolution.py`. Here are details of the test error.\r\n```\r\nself = BuilderConfig(name='plain_text', version=1.0.0, data_dir=None, data_files={'train': 'train.c.txt', 'test': 'test.c.txt'}, description='Plain text import of the Definite Pronoun Resolution Dataset.')\r\n\r\n def __post_init__(self):\r\n # The config name is used to name the cache directory.\r\n invalid_windows_characters = r\"<>:/\\|?*\"\r\n for invalid_char in invalid_windows_characters:\r\n if invalid_char in self.name:\r\n raise InvalidConfigName(\r\n f\"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. \"\r\n f\"They could create issues when creating a directory for this config on Windows filesystem.\"\r\n )\r\n if self.data_files is not None and not isinstance(self.data_files, DataFilesDict):\r\n> raise ValueError(f\"Expected a DataFilesDict in data_files but got {self.data_files}\")\r\nE ValueError: Expected a DataFilesDict in data_files but got {'train': 'train.c.txt', 'test': 'test.c.txt'}\r\n```",
"Hi ! Thanks for the fixes :)\r\n\r\nInstead of uploading the `definite_pronoun_resolution` data files in this PR, maybe we can just update the URL ?\r\nThe old url was http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt, but now it's https://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt (https instead of http)",
"Actually the bad certificate creates an issue with the download\r\n```python\r\nimport datasets \r\ndatasets.DownloadManager().download(\"https://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt\")\r\n# raises: ConnectionError: Couldn't reach https://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt\r\n```\r\n\r\nLet me see if I can fix that",
"I uploaded them to these URLs, feel free to use them instead of having the text files here in the PR :)\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/definite_pronoun_resolution/train.c.txt\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/definite_pronoun_resolution/test.c.txt",
"Thank you for the tips! Having a busy week so anyone willing to commit the suggestions is welcome. Else, I will try to get back to this in a while.",
"@LashaO Thanks for working on this. Yes, I'll take over as we already have a request to fix the URL of the Jeopardy! dataset in a separate issue.",
"~~Still have to fix the error in the dummy data test of the WikiAuto dataset (so please don't merge).~~ Done! Ready for merging.",
"Thank you, Mario!",
"The CI failure is only related to missing tags in the dataset cards, merging :)"
] | 1,636,815,694,000 | 1,638,789,391,000 | 1,638,789,391,000 | CONTRIBUTOR | null | [#3264](https://github.com/huggingface/datasets/issues/3264) | {
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"Using `dataset = load_dataset(\"kilt_tasks\", \"wow\", ignore_verifications=True)` may fix it, but I do not think it is a elegant solution.",
"Hi @slyviacassell, thanks for reporting.\r\n\r\nYes, there is an issue with the checksum verification. I'm fixing it.\r\n\r\nAnd as you pointed out, in the meantime, you can circumvent the problem by passing `ignore_verifications=True`. "
] | 1,636,805,057,000 | 1,637,061,833,000 | 1,637,061,718,000 | NONE | null | ## Describe the bug
Checksum failed when downloads kilt_tasks_wow. See error output for details.
## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('kilt_tasks','wow')
```
## Expected results
Download successful
## Actual results
```
Downloading and preparing dataset kilt_tasks/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to /root/.cache/huggingface/datasets/kilt_tasks/wow/1.0.0/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a...
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 5121.25it/s]
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 1527.42it/s]
Traceback (most recent call last):
File "kilt_wow.py", line 30, in <module>
main()
File "kilt_wow.py", line 27, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "kilt_wow.py", line 21, in load_dataset
return datasets.load_dataset('kilt_tasks','wow')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl', 'http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3264 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3264/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3264/comments | https://api.github.com/repos/huggingface/datasets/issues/3264/events | https://github.com/huggingface/datasets/issues/3264 | 1,052,663,513 | I_kwDODunzps4-vl7Z | 3,264 | Downloading URL change for WikiAuto Manual, jeopardy and definite_pronoun_resolution | {
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"#take\r\nI am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy with new ones provided by authors.\r\n\r\nAs for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. I can include them in the dataset folder as the files are <1MB in size total.",
"> #take I am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy.\r\n> \r\n> As for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. Anyone has opinions on whether it is preferable for me to host them somewhere (e.g. personal GDrive account) or upload them to the dataset folder directly and use github raw URLs? The files are <1MB in size.\r\n\r\nI am planning to fix it next few days. But my to-do list is full and I do not have the cache of definite_pronoun_resolution. I am glad that you can take this. Thanks a lot!",
"No problem, buddy! Will submit a PR over this weekend."
] | 1,636,804,032,000 | 1,636,810,761,000 | null | NONE | null | ## Describe the bug
- WikiAuto Manual
The original manual datasets with the following downloading URL in this [repository](https://github.com/chaojiang06/wiki-auto) was [deleted](https://github.com/chaojiang06/wiki-auto/commit/0af9b066f2b4e02726fb8a9be49283c0ad25367f) by the author.
```
https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```
- jeopardy
The downloading URL for jeopardy may move from
```
http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
to
```
https://drive.google.com/file/d/0BwT5wj_P7BKXb2hfM3d2RHU1ckE/view?resourcekey=0-1abK4cJq-mqxFoSg86ieIg
```
- definite_pronoun_resolution
The following downloading URL for definite_pronoun_resolution cannot be reached for some reasons.
```
http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```
## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('wiki_auto','manual')
datasets.load_datasets('jeopardy')
datasets.load_datasets('definite_pronoun_resolution')
```
## Expected results
Download successfully
## Actual results
- WikiAuto Manual
```
Downloading and preparing dataset wiki_auto/manual (download: 151.65 MiB, generated: 155.97 MiB, post-processed: Unknown size, total: 307.61 MiB) to /root/.cache/huggingface/datasets/wiki_auto/manual/1.0.0/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8...
0%| | 0/3 [00:00<?, ?it/s]Traceback (most recent call last):
File "wiki_auto.py", line 43, in <module>
main()
File "wiki_auto.py", line 40, in main
train, dev, test = dataset.generate_k_shot_data(k=16, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 24, in generate_k_shot_data
dataset = self.load_dataset()
File "wiki_auto.py", line 34, in load_dataset
return datasets.load_dataset('wiki_auto', 'manual')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wiki_auto/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8/wiki_auto.py", line 193, in _split_generators
data_dir = dl_manager.download_and_extract(my_urls)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 592, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```
- jeopardy
```
Using custom data configuration default
Downloading and preparing dataset jeopardy/default (download: 12.13 MiB, generated: 34.46 MiB, post-processed: Unknown size, total: 46.59 MiB) to /root/.cache/huggingface/datasets/jeopardy/default/0.1.0/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810...
Traceback (most recent call last):
File "jeopardy.py", line 45, in <module>
main()
File "jeopardy.py", line 42, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "jeopardy.py", line 36, in load_dataset
return datasets.load_dataset("jeopardy")
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/jeopardy/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810/jeopardy.py", line 72, in _split_generators
filepath = dl_manager.download_and_extract(_DATA_URL)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 206, in map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
- definite_pronoun_resolution
```
Downloading and preparing dataset definite_pronoun_resolution/plain_text (download: 222.12 KiB, generated: 239.12 KiB, post-processed: Unknown size, total: 461.24 KiB) to /root/.cache/huggingface/datasets/definite_pronoun_resolution/plain_text/1.0.0/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff...
0%| | 0/2 [00:00<?, ?it/s]Traceback (most recent call last):
File "definite_pronoun_resolution.py", line 37, in <module>
main()
File "definite_pronoun_resolution.py", line 34, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "definite_pronoun_resolution.py", line 28, in load_dataset
return datasets.load_dataset('definite_pronoun_resolution')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/definite_pronoun_resolution/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff/definite_pronoun_resolution.py", line 76, in _split_generators
files = dl_manager.download_and_extract(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3263 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3263/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3263/comments | https://api.github.com/repos/huggingface/datasets/issues/3263/events | https://github.com/huggingface/datasets/issues/3263 | 1,052,552,516 | I_kwDODunzps4-vK1E | 3,263 | FET DATA | {
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"default": false,
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] | closed | false | null | [] | null | [] | 1,636,782,366,000 | 1,636,810,307,000 | 1,636,810,307,000 | NONE | null | ## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). | {
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https://api.github.com/repos/huggingface/datasets/issues/3262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3262/comments | https://api.github.com/repos/huggingface/datasets/issues/3262/events | https://github.com/huggingface/datasets/pull/3262 | 1,052,455,082 | PR_kwDODunzps4uej4t | 3,262 | asserts replaced with exception for image classification task, csv, json | {
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https://api.github.com/repos/huggingface/datasets/issues/3261 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3261/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3261/comments | https://api.github.com/repos/huggingface/datasets/issues/3261/events | https://github.com/huggingface/datasets/issues/3261 | 1,052,346,381 | I_kwDODunzps4-uYgN | 3,261 | Scifi_TV_Shows: Having trouble getting viewer to find appropriate files | {
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"Hi ! I think this is because `iter_archive` doesn't support ZIP files yet. See https://github.com/huggingface/datasets/issues/3272\r\n\r\nYou can navigate into the archive this way instead:\r\n```python\r\n# in split_generators\r\ndata_dir = dl_manager.download_and_extract(url)\r\ntrain_filepath = os.path.join(data_dir, \"all-sci-fi-data-train.txt\")\r\nreturn [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN,\r\n gen_kwargs={\r\n \"filepath\": train_filepath,\r\n },\r\n ),\r\n...\r\n])\r\n\r\n# in generate_examples\r\nwith open(filepath, encoding=\"utf-8\") as f:\r\n ...\r\n```",
"It's working: https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/viewer/Scifi_TV_Shows/test\r\n\r\n<img width=\"1494\" alt=\"Capture dβeΜcran 2021-12-21 aΜ 11 23 51\" src=\"https://user-images.githubusercontent.com/1676121/146914068-f4b7225f-42c5-471d-9c73-2adac722162f.png\">\r\n"
] | 1,636,745,119,000 | 1,640,082,250,000 | 1,640,082,250,000 | NONE | null | ## Dataset viewer issue for '*Science Fiction TV Show Plots Corpus (Scifi_TV_Shows)*'
**Link:** [link](https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows)
I tried adding both a script (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/blob/main/Scifi_TV_Shows.py) and some dummy examples (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/tree/main/dummy), but the viewer still has a 404 error ("Not found. Maybe the cache is missing, or maybe the ressource does not exist."). I'm not sure what to try next. Thanks in advance!
Am I the one who added this dataset? Yes
| {
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https://api.github.com/repos/huggingface/datasets/issues/3260 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3260/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3260/comments | https://api.github.com/repos/huggingface/datasets/issues/3260/events | https://github.com/huggingface/datasets/pull/3260 | 1,052,247,373 | PR_kwDODunzps4ueCIU | 3,260 | Fix ConnectionError in Scielo dataset | {
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"The CI error is unrelated to the change."
] | 1,636,740,157,000 | 1,637,086,697,000 | 1,637,085,322,000 | CONTRIBUTOR | null | This PR:
* allows 403 status code in HEAD requests to S3 buckets to fix the connection error in the Scielo dataset (instead of `url`, uses `response.url` to check the URL of the final endpoint)
* makes the Scielo dataset streamable
Fixes #3255. | {
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https://api.github.com/repos/huggingface/datasets/issues/3259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3259/comments | https://api.github.com/repos/huggingface/datasets/issues/3259/events | https://github.com/huggingface/datasets/pull/3259 | 1,052,189,775 | PR_kwDODunzps4ud5W3 | 3,259 | Updating details of IRC disentanglement data | {
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"Thank you for the cleanup!"
] | 1,636,737,418,000 | 1,637,255,973,000 | 1,637,255,973,000 | CONTRIBUTOR | null | I was pleasantly surprised to find that someone had already added my dataset to the huggingface library, but some details were missing or incorrect. This PR fixes the documentation. | {
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https://api.github.com/repos/huggingface/datasets/issues/3258 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3258/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3258/comments | https://api.github.com/repos/huggingface/datasets/issues/3258/events | https://github.com/huggingface/datasets/issues/3258 | 1,052,188,195 | I_kwDODunzps4-tx4j | 3,258 | Reload dataset that was already downloaded with `load_from_disk` from cloud storage | {
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] | open | false | null | [] | null | [] | 1,636,737,299,000 | 1,636,737,299,000 | null | MEMBER | null | `load_from_disk` downloads the dataset to a temporary directory without checking if the dataset has already been downloaded once.
It would be nice to have some sort of caching for datasets downloaded this way. This could leverage the fingerprint of the dataset that was saved in the `state.json` file. | {
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https://api.github.com/repos/huggingface/datasets/issues/3257 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3257/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3257/comments | https://api.github.com/repos/huggingface/datasets/issues/3257/events | https://github.com/huggingface/datasets/issues/3257 | 1,052,118,365 | I_kwDODunzps4-tg1d | 3,257 | Use f-strings for string formatting | {
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"Hi, I would be glad to help with this. Is there anyone else working on it?",
"Hi, I would be glad to work on this too.",
"#self-assign",
"Hi @Carlosbogo,\r\n\r\nwould you be interested in replacing the `.format` and `%` syntax with f-strings in the modules in the `datasets` directory since @Mehdi2402 has opened a PR that does that for all the other directories?",
"Oh I see. I will be glad to help with the `datasets` directory then."
] | 1,636,732,935,000 | 1,637,165,918,000 | 1,637,165,918,000 | CONTRIBUTOR | null | f-strings offer better readability/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.
> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets/*`. | {
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https://api.github.com/repos/huggingface/datasets/issues/3256 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3256/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3256/comments | https://api.github.com/repos/huggingface/datasets/issues/3256/events | https://github.com/huggingface/datasets/pull/3256 | 1,052,000,613 | PR_kwDODunzps4udTqg | 3,256 | asserts replaced by exception for text classification task with test. | {
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"Haha it looks like you got the chance of being reviewed twice at the same time and got the same suggestion twice x)\r\nAnyway it's all good now so we can merge !",
"Thanks for the feedback. "
] | 1,636,725,936,000 | 1,636,729,773,000 | 1,636,729,172,000 | CONTRIBUTOR | null | I have replaced only a single assert in text_classification.py along with a unit test to verify an exception is raised based on https://github.com/huggingface/datasets/issues/3171 .
I would like to first understand the code contribution workflow. So keeping the change to a single file rather than making too many changes. Once this gets approved, I will look into the rest.
Thanks. | {
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] | null | [] | 1,636,711,034,000 | 1,637,085,322,000 | 1,637,085,322,000 | NONE | null | ## Describe the bug
I get `ConnectionError` when I am trying to load the SciELO dataset.
When I try the URL with `requests` I get:
```
>>> requests.head("https://ndownloader.figstatic.com/files/14019287")
<Response [302]>
```
And as far as I understand redirections in `datasets` are not supported for downloads.
https://github.com/huggingface/datasets/blob/807341d0db0728073ab605c812c67f927d148f38/datasets/scielo/scielo.py#L45
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("scielo", "en-es")
```
## Expected results
Download SciELO dataset and load Dataset object
## Actual results
```
Downloading and preparing dataset scielo/en-es (download: 21.90 MiB, generated: 68.45 MiB, post-processed: Unknown size, total: 90.35 MiB) to /Users/test/.cache/huggingface/datasets/scielo/en-es/1.0.0/7e05d55a20257efeb9925ff5de65bd4884fc6ddb6d765f1ea3e8860449d90e0e...
Traceback (most recent call last):
File "scielo.py", line 3, in <module>
dataset = load_dataset("scielo", "en-es")
File "../lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "../lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "../lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/test/.cache/huggingface/modules/datasets_modules/datasets/scielo/7e05d55a20257efeb9925ff5de65bd4884fc6ddb6d765f1ea3e8860449d90e0e/scielo.py", line 77, in _split_generators
data_dir = dl_manager.download_and_extract(_URLS[self.config.name])
File "../lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "../lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "../lib/python3.8/site-packages/datasets/utils/py_utils.py", line 206, in map_nested
return function(data_struct)
File "../lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "../lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "../lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://ndownloader.figstatic.com/files/14019287
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.12
- PyArrow version: 6.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3254 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3254/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3254/comments | https://api.github.com/repos/huggingface/datasets/issues/3254/events | https://github.com/huggingface/datasets/pull/3254 | 1,051,351,172 | PR_kwDODunzps4ubPwR | 3,254 | Update xcopa dataset (fix checksum issues + add translated data) | {
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"The CI failures are unrelated to the changes (missing fields in the readme and the CER metric error fixed in #3252)."
] | 1,636,663,893,000 | 1,636,713,058,000 | 1,636,713,057,000 | CONTRIBUTOR | null | This PR updates the checksums (as reported [here](https://discuss.huggingface.co/t/how-to-load-dataset-locally/11601/2)) of the `xcopa` dataset. Additionally, it adds new configs that hold the translated data of the original set of configs. This data was not available at the time of adding this dataset to the lib. | {
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https://api.github.com/repos/huggingface/datasets/issues/3253 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3253/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3253/comments | https://api.github.com/repos/huggingface/datasets/issues/3253/events | https://github.com/huggingface/datasets/issues/3253 | 1,051,308,972 | I_kwDODunzps4-qbOs | 3,253 | `GeneratorBasedBuilder` does not support `None` values | {
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"Hi,\r\n\r\nthanks for reporting and providing a minimal reproducible example. \r\n\r\nThis line of the PR I've linked in our discussion on the Forum will add support for `None` values:\r\nhttps://github.com/huggingface/datasets/blob/a53de01842aac65c66a49b2439e18fa93ff73ceb/src/datasets/features/features.py#L835\r\n\r\nI expect that PR to be merged soon."
] | 1,636,660,281,000 | 1,639,060,018,000 | 1,639,060,018,000 | NONE | null | ## Describe the bug
`GeneratorBasedBuilder` does not support `None` values.
## Steps to reproduce the bug
See [this repository](https://github.com/pavel-lexyr/huggingface-datasets-bug-reproduction) for minimal reproduction.
## Expected results
Dataset is initialized with a `None` value in the `value` column.
## Actual results
```
Traceback (most recent call last):
File "main.py", line 3, in <module>
datasets.load_dataset("./bad-data")
File ".../datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File ".../datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File ".../datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File ".../datasets/builder.py", line 1103, in _prepare_split
example = self.info.features.encode_example(record)
File ".../datasets/features/features.py", line 1033, in encode_example
return encode_nested_example(self, example)
File ".../datasets/features/features.py", line 808, in encode_nested_example
return {
File ".../datasets/features/features.py", line 809, in <dictcomp>
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File ".../datasets/features/features.py", line 855, in encode_nested_example
return schema.encode_example(obj)
File ".../datasets/features/features.py", line 299, in encode_example
return float(value)
TypeError: float() argument must be a string or a number, not 'NoneType'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 6.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/3252 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3252/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3252/comments | https://api.github.com/repos/huggingface/datasets/issues/3252/events | https://github.com/huggingface/datasets/pull/3252 | 1,051,124,749 | PR_kwDODunzps4uagoy | 3,252 | Fix failing CER metric test in CI after update | {
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} | [] | closed | false | null | [] | null | [] | 1,636,646,236,000 | 1,636,726,004,000 | 1,636,726,003,000 | CONTRIBUTOR | null | Fixes the [failing CER metric test](https://app.circleci.com/pipelines/github/huggingface/datasets/8644/workflows/79816553-fa2f-4756-b022-d5937f00bf7b/jobs/53298) in CI by adding support for `jiwer==2.3.0`, which was released yesterday. Also, I verified that all the tests in `metrics/cer/test_cer.py` pass after the change, so the results should be the same irrespective of the `jiwer` version. | {
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https://api.github.com/repos/huggingface/datasets/issues/3250 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3250/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3250/comments | https://api.github.com/repos/huggingface/datasets/issues/3250/events | https://github.com/huggingface/datasets/pull/3250 | 1,050,541,348 | PR_kwDODunzps4uYmkr | 3,250 | Add ETHICS dataset | {
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} | [] | open | false | null | [] | null | [] | 1,636,602,334,000 | 1,637,087,545,000 | null | NONE | null | This PR adds the ETHICS dataset, including all 5 sub-datasets.
From https://arxiv.org/abs/2008.02275 | {
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https://api.github.com/repos/huggingface/datasets/issues/3249 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3249/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3249/comments | https://api.github.com/repos/huggingface/datasets/issues/3249/events | https://github.com/huggingface/datasets/pull/3249 | 1,050,193,138 | PR_kwDODunzps4uXeea | 3,249 | Fix streaming for id_newspapers_2018 | {
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} | [] | closed | false | null | [] | null | [] | 1,636,570,530,000 | 1,636,725,692,000 | 1,636,725,691,000 | MEMBER | null | To be compatible with streaming, this dataset must use `dl_manager.iter_archive` since the data are in a .tgz file | {
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https://api.github.com/repos/huggingface/datasets/issues/3248 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3248/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3248/comments | https://api.github.com/repos/huggingface/datasets/issues/3248/events | https://github.com/huggingface/datasets/pull/3248 | 1,050,171,082 | PR_kwDODunzps4uXZzU | 3,248 | Stream from Google Drive and other hosts | {
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"I just tried some datasets and noticed that `spider` is not working for some reason (the compression type is not recognized), resulting in FileNotFoundError. I can take a look tomorrow",
"I'm fixing the remaining files based on TAR archives",
"THANKS A LOT"
] | 1,636,569,152,000 | 1,638,288,223,000 | 1,636,737,491,000 | MEMBER | null | Streaming from Google Drive is a bit more challenging than the other host we've been supporting:
- the download URL must be updated to add the confirm token obtained by HEAD request
- it requires to use cookies to keep the connection alive
- the URL doesn't tell any information about whether the file is compressed or not
Therefore I did two things:
- I added a step for URL and headers/cookies preparation in the StreamingDownloadManager
- I added automatic compression type inference by reading the [magic number](https://en.wikipedia.org/wiki/List_of_file_signatures)
This allows to do do fancy things like
```python
from datasets.utils.streaming_download_manager import StreamingDownloadManager, xopen, xjoin, xglob
# zip file containing a train.tsv file
url = "https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh"
extracted = StreamingDownloadManager().download_and_extract(url)
for inner_file in xglob(xjoin(extracted, "*.tsv")):
with xopen(inner_file) as f:
# streaming starts here
for line in f:
print(line)
```
This should make around 80 datasets streamable. It concerns those hosted on Google Drive but also any dataset for which the URL doesn't give any information about compression. Here is the full list:
```
amazon_polarity, ami, arabic_billion_words, ascent_kb, asset, big_patent, billsum, capes, cmrc2018, cnn_dailymail,
code_x_glue_cc_code_completion_token, code_x_glue_cc_code_refinement, code_x_glue_cc_code_to_code_trans,
code_x_glue_tt_text_to_text, conll2002, craigslist_bargains, dbpedia_14, docred, ehealth_kd, emo, euronews, germeval_14,
gigaword, grail_qa, great_code, has_part, head_qa, health_fact, hope_edi, id_newspapers_2018,
igbo_english_machine_translation, irc_disentangle, jfleg, jnlpba, journalists_questions, kor_ner, linnaeus, med_hop, mrqa,
mt_eng_vietnamese, multi_news, norwegian_ner, offcombr, offenseval_dravidian, para_pat, peoples_daily_ner, pn_summary,
poleval2019_mt, pubmed_qa, qangaroo, reddit_tifu, refresd, ro_sts_parallel, russian_super_glue, samsum, sberquad, scielo,
search_qa, species_800, spider, squad_adversarial, tamilmixsentiment, tashkeela, ted_talks_iwslt, trec, turk, turkish_ner,
twi_text_c3, universal_morphologies, web_of_science, weibo_ner, wiki_bio, wiki_hop, wiki_lingua, wiki_summary, wili_2018,
wisesight1000, wnut_17, yahoo_answers_topics, yelp_review_full, yoruba_text_c3
```
Some of them may not work if the host doesn't support HTTP range requests for example
Fix https://github.com/huggingface/datasets/issues/2742
Fix https://github.com/huggingface/datasets/issues/3188 | {
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https://api.github.com/repos/huggingface/datasets/issues/3247 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3247/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3247/comments | https://api.github.com/repos/huggingface/datasets/issues/3247/events | https://github.com/huggingface/datasets/issues/3247 | 1,049,699,088 | I_kwDODunzps4-kSMQ | 3,247 | Loading big json dataset raises pyarrow.lib.ArrowNotImplementedError | {
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] | open | false | null | [] | null | [
"Hi,\r\n\r\nthis issue is similar to https://github.com/huggingface/datasets/issues/3093, so you can either use the solution provided there or try to load the data in one chunk (you can control the chunk size by specifying the `chunksize` parameter (`int`) in `load_dataset`).\r\n\r\n@lhoestq Is this worth opening an issue on Jira? Basically, PyArrow doesn't allow casts that change the order of the struct fields because they treat `pa.struct` as an ordered sequence. Reordering fields manually in Python is probably too slow, so I think this needs to be fixed by them to be usable on our side.",
"I agree I would expect PyArrow to be able to handle this, do you want to open the issue @mariosasko ?\r\nAlthough maybe it's possible to fix struct casting on our side without hurting performance too much, if it's simply a matter of reordering the arrays in the StructArray"
] | 1,636,543,079,000 | 1,636,712,753,000 | null | NONE | null | ## Describe the bug
When trying to create a dataset from a json file with around 25MB, the following error is raised `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`
Splitting the big file into smaller ones and then loading it with the `load_dataset` method did also not work.
Creating a pandas dataframe from it and then loading it with `Dataset.from_pandas` works
## Steps to reproduce the bug
```python
load_dataset("json", data_files="test.json")
```
test.json ~25MB
```json
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
...
```
working.json ~160bytes
```json
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
```
## Expected results
It should load the dataset from the json file without error.
## Actual results
It raises Exception `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`
```
Traceback (most recent call last):
File "/Users/m/workspace/xxx/project/main.py", line 60, in <module>
dataset = load_dataset("json", data_files="result.json")
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/load.py", line 1627, in load_dataset
builder_instance.download_and_prepare(
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 1159, in _prepare_split
writer.write_table(table)
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/arrow_writer.py", line 428, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1685, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 630, in pyarrow.lib._sanitize_arrays
File "pyarrow/array.pxi", line 338, in pyarrow.lib.asarray
File "pyarrow/table.pxi", line 304, in pyarrow.lib.ChunkedArray.cast
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/pyarrow/compute.py", line 309, in cast
return call_function("cast", [arr], options)
File "pyarrow/_compute.pyx", line 528, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 327, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct
```
## Environment info
- `datasets` version: 1.14.0
- Platform: macOS-12.0.1-arm64-arm-64bit
- Python version: 3.9.7
- PyArrow version: 6.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3246 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3246/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3246/comments | https://api.github.com/repos/huggingface/datasets/issues/3246/events | https://github.com/huggingface/datasets/pull/3246 | 1,049,662,746 | PR_kwDODunzps4uVvaW | 3,246 | [tiny] fix typo in stream docs | {
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https://api.github.com/repos/huggingface/datasets/issues/3245 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3245/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3245/comments | https://api.github.com/repos/huggingface/datasets/issues/3245/events | https://github.com/huggingface/datasets/pull/3245 | 1,048,726,062 | PR_kwDODunzps4uSqqq | 3,245 | Fix load_from_disk temporary directory | {
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} | [] | closed | false | null | [] | null | [] | 1,636,470,915,000 | 1,636,471,852,000 | 1,636,471,851,000 | MEMBER | null | `load_from_disk` uses `tempfile.TemporaryDirectory()` instead of our `get_temporary_cache_files_directory()` function. This can cause the temporary directory to be deleted before the dataset object is garbage collected.
In practice, it prevents anyone from using methods like `shuffle` on a dataset loaded this way, because it can't write the shuffled indices in a directory that doesn't exist anymore.
In this PR I switch to using `get_temporary_cache_files_directory()` and I update the tests.
cc @mariosasko since you worked on `get_temporary_cache_files_directory()` | {
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} | [] | closed | false | null | [] | null | [] | 1,636,465,830,000 | 1,636,725,765,000 | 1,636,725,765,000 | CONTRIBUTOR | null | `isort` can place modules by itself from [version 5.0.0](https://pycqa.github.io/isort/docs/upgrade_guides/5.0.0.html#module-placement-changes-known_third_party-known_first_party-default_section-etc) onwards, making the `known_first_party` and `known_third_party` fields in `setup.cfg` redundant (this is why our CI works, even though we haven't touched these options in a while). | {
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"Adding ANERcorp dataset\r\n\r\n## Adding a Dataset\r\n- **Name:** *ANERcorp-CAMeLLab*\r\n- **Description:** *Since its creation in 2008, the ANERcorp dataset (Benajiba & Rosso, 2008) has been a standard reference used by Arabic named entity recognition researchers around the world. However, over time, this dataset was copied over from user to user, modified slightly here and there, and split in many different configurations that made it hard to compare fairly across papers and systems.\r\n\r\nIn 2020, a group of researchers from CAMeL Lab (Habash, Alhafni and Oudah), and Mind Lab (Antoun and Baly) met with the creator of the corpus, Yassine Benajiba, to consult with him and collectively agree on an exact split, and accepted minor corrections from the original dataset. Bashar Alhafni from CAMeL Lab working with Nizar Habash implemented the decisions provided in this release.*\r\n\r\n- **Paper:** *(a) Benajiba, Yassine, Paolo Rosso, and JosΓ© Miguel BenedΓ Ruiz. \"Anersys: An Arabic named entity recognition system based on maximum entropy.\" In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 143-153. Springer, Berlin, Heidelberg, 2007.\r\n\r\n(b)Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, and Nizar Habash. \"CAMeL Tools: An Open Source Python Toolkit, for Arabic Natural Language Processing.\" In Proceedings of the Conference on Language Resources and Evaluation (LREC 2020), Marseille, 2020.*\r\n- **Data:** *https://camel.abudhabi.nyu.edu/anercorp/*\r\n- **Motivation:** This is the standard dataset for evaluating NER performance in Arabic*\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)."
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"It looks like the dataset isn't available anymore on appen.com\r\n\r\nThe CSV files appear to still be available at https://www.kaggle.com/landlord/multilingual-disaster-response-messages though. It says that the data are under the CC0 license so I guess we can host the dataset elsewhere instead ?"
] | 1,636,450,002,000 | 1,639,492,709,000 | 1,639,492,709,000 | NONE | null | ## Describe the bug
Following command gives an ConnectionError.
## Steps to reproduce the bug
```python
disaster = load_dataset('disaster_response_messages')
```
## Error
```
ConnectionError: Couldn't reach https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv
```
## Expected results
It should load dataset without an error
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Google Colab
- Python version: 3.7
- PyArrow version:
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When downloaded from macine 1 the dataset is downloaded and parsed correctly.
When downloaded from machine two (which has a different cache directory),
the following script:
import datasets
from datasets import load_dataset
raw_dataset_elkhair_1 = load_dataset('arabic_billion_words', 'Alittihad', split="train",download_mode='force_redownload')
gives the following error:
**Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 1.49 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/arabic_billion_words/Alittihad/1.1.0/687a1f963284c8a766558661375ea8f7ab3fa3633f8cd9c9f42a53ebe83bfe17...
Downloading: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 348M/348M [00:24<00:00, 14.0MB/s]
Traceback (most recent call last):
File ".../why_mismatch.py", line 3, in <module>
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 709, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=1601790302, num_examples=349342, dataset_name='arabic_billion_words'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='arabic_billion_words')}]**
Note that the package versions of datasets (1.15.1) and rarfile (4.0) are identical.
## Steps to reproduce the bug
import datasets
from datasets import load_dataset
raw_dataset_elkhair_1 = load_dataset('arabic_billion_words', 'Alittihad', split="train",download_mode='force_redownload')
# Sample code to reproduce the bug
## Expected results
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 1.49 GiB, post-processed: Unknown size, total: 1.82 GiB) to .../.cache/huggingface/datasets/arabic_billion_words/Alittihad/1.1.0/687a1f963284c8a766558661375ea8f7ab3fa3633f8cd9c9f42a53ebe83bfe17...
Downloading: 100%|βββββββββββββββββββββββββββ| 348M/348M [00:22<00:00, 15.8MB/s]
Dataset arabic_billion_words downloaded and prepared to .../.cache/huggingface/datasets/arabic_billion_words/Alittihad/1.1.0/687a1f963284c8a766558661375ea8f7ab3fa3633f8cd9c9f42a53ebe83bfe17. Subsequent calls will reuse this data.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
Machine 1:
- `datasets` version: 1.15.1
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1
Machine 2 (the bugged one)
- `datasets` version: 1.15.1
- Platform: Linux-4.4.0-210-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 6.0.0
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"Hi ! The URL works fine on my side today, could you try again ?",
"thank you @lhoestq \r\nit works"
] | 1,636,438,136,000 | 1,636,588,977,000 | 1,636,588,977,000 | NONE | null | ``## Adding a Dataset
- **Name:** *Reuters21578*
- **Description:** *ConnectionError: Couldn't reach https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz*
- **Data:** *https://huggingface.co/datasets/reuters21578*
`from datasets import load_dataset`
`dataset = load_dataset("reuters21578", 'ModLewis')`
ConnectionError: Couldn't reach https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz
And I try to request the link as follow:
`import requests`
`requests.head('https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz')`
SSLError: HTTPSConnectionPool(host='kdd.ics.uci.edu', port=443): Max retries exceeded with url: /databases/reuters21578/reuters21578.tar.gz (Caused by SSLError(SSLError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:852)'),))
This problem likes #575
What should I do ?
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"Hi @hongyuanmei, thanks for reporting.\r\n\r\nI'm fixing it."
] | 1,636,430,812,000 | 1,636,465,768,000 | 1,636,465,768,000 | NONE | null | ## Describe the bug
Descriptions of the wikitext datasests are wrong.
## Steps to reproduce the bug
Please see: https://github.com/huggingface/datasets/blob/f6dcafce996f39b6a4bbe3a9833287346f4a4b68/datasets/wikitext/wikitext.py#L50
## Expected results
The descriptions for raw-v1 and v1 should be switched. | {
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"Hi @eladsegal, thanks for reporting.\r\n\r\nI am sorry, but I can't reproduce the bug:\r\n```\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"qasper\")\r\nDownloading: 5.11kB [00:00, ?B/s]\r\nDownloading and preparing dataset qasper/qasper (download: 9.88 MiB, generated: 35.11 MiB, post-processed: Unknown size, total: 44.99 MiB) to .cache\\qasper\\qasper\\0.1.0\\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8...\r\nDataset qasper downloaded and prepared to .cache\\qasper\\qasper\\0.1.0\\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8. Subsequent calls will reuse this data.\r\n100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<?, ?it/s]\r\n\r\nIn [3]: ds\r\nOut[3]:\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n num_rows: 888\r\n })\r\n validation: Dataset({\r\n features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n num_rows: 281\r\n })\r\n})\r\n``` \r\n\r\nThis makes me suspect that the origin of the problem might be the cache: I didn't have this dataset in my cache, although I guess you already had it, before the code change introduced by #3110.\r\n\r\n@lhoestq might it be possible that the code change introduced by #3110 makes \"inaccessible\" all previously cached TAR-based datasets?\r\n- Before the caching system downloaded and extracted the tar dataset\r\n- Now it only downloads the tar dataset (no extraction is done)",
"I can't reproduce either in my environment (macos, python 3.7).\r\n\r\nIn your case it generates zero examples. This can only happen if the extraction of the TAR archive doesn't output the right filenames. Indeed if the `qasper` script can't find the right file to load, it's currently ignored and it returns zero examples. This case was not even considered when #3110 was developed since we considered the file names to be deterministic - and not depend on your environment.\r\n\r\nTherefore here is my hypothesis:\r\n- either the cache is corrupted somehow with an empty TAR archive\r\n- OR I suspect that the issue comes from python 3.8\r\n",
"I just tried again on python 3.8 and I was able to reproduce the issue. Let me work on a fix",
"Ok I found the issue. It's not related to python 3.8 in itself though. This issue happens because your local installation of `datasets` is outdated compared to the changes to datasets in #3110\r\n\r\nTo fix this you just have to pull the latest changes from `master` :)\r\n\r\nLet me know if that helps !\r\n\r\n--------------\r\n\r\nHere are more details about my investigation:\r\n\r\nIt's possible to reproduce this issue if you use `datasets<=1.15.1` or before b6469baa22c174b3906c631802a7016fedea6780 and if you load the dataset after revision b6469baa22c174b3906c631802a7016fedea6780. This is because `dl_manager.iter_archive` had issues at that time (and it was not used anywhere anyway).\r\n\r\nIn particular it was returning the absolute path to extracted files instead of the relative path of the file inside the archive. This was an issue because `dl_manager.iter_archive` isn't supposed to extract the TAR archive. Instead, it iterates over all the files inside the archive, without creating a directory with the extracted content.\r\n\r\nTherefore if you want to use the datasets on `master`, make sure that you have an up-to-date local installation of `datasets` as well, or you may face incompatibilities like this.",
"Thanks!\r\nBut what about code that is already using older version of datasets? \r\nThe reason I encountered this issue was that suddenly one of my repos with version 1.12.1 started getting 0 examples.\r\nI handled it by adding `revision` to `load_dataset`, but I guess it would still be an issue for other users who doesn't know this.",
"Hi, in 1.12.1 it uses the dataset scripts from that time, not the one on master.\r\n\r\nIt only uses the datasets from master if you installed `datasets` from source, or if the dataset isn't available in your local version (in this case it shows a warning and it loads from master).\r\n",
"OK, I understand the issue a bit better now.\r\nI see I wasn't on 1.12.1, but on 1.12.1.dev0 and since it is a dev version it uses master.\r\nSo users that use an old dev version must specify revision or else they'll encounter this problem.\r\n\r\nBTW, when I opened the issue I installed the latest master version with\r\n```\r\npip install git+git://github.com/huggingface/datasets@master#egg=datasets\r\n```\r\nand also used `download_mode=\"force_redownload\"`, and it still returned 0 examples.\r\nNow I deleted all of the cache and ran the code again, and it worked.\r\nI'm not sure what exactly happened here, but looks like it was due to a mix of an unofficial version and its cache.\r\n\r\nThanks again!"
] | 1,636,414,186,000 | 1,636,476,365,000 | 1,636,476,347,000 | CONTRIBUTOR | null | ## Describe the bug
Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3235 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3235/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3235/comments | https://api.github.com/repos/huggingface/datasets/issues/3235/events | https://github.com/huggingface/datasets/pull/3235 | 1,047,808,263 | PR_kwDODunzps4uPr9Z | 3,235 | Addd options to use updated bleurt checkpoints | {
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Updated checkpoints are described in https://github.com/google-research/bleurt/blob/master/checkpoints.md#the-recommended-checkpoint-bleurt-20
This change won't affect the default behavior of metrics/bleurt. It only adds option to load newer checkpoints as
`datasets.load_metric('bleurt', 'bleurt-20')`
`bluert-20` generates scores roughly between 0 and 1, which wasn't the case for the previous checkpoints. | {
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"That's good to have a way to disable this easily :)\r\nI just find it a bit unfortunate that users would have to experience the error once and then do `DISABLE_PYARROW_TYPES_OPTIMIZATION=1`. Do you know if there's a way to simply fallback on disabling it automatically when it fails ?",
"@lhoestq Actually, I agree a fallback makes more sense. The current approach is not very practical indeed and would require a mention in the docs.\r\n",
"Replaced the env variable with a fallback!",
"Hmm if the fallback automatically happens without the user knowing it, then I don't think we really need to mention it. But if you really wanted to, I think the [Improve performance](https://huggingface.co/docs/datasets/cache.html#improve-performance) section would be a great place for it! ",
"Yea I think this could just end up in a note that says that `datasets` automatically picks the most optimized integer precision for your tokenized text data to save you disk space. Maybe later if we have a page on text processing we can add this note, but for now I agree it doesn't fit well into the doc.\r\n\r\nIn particular in the \"Improve performance\" section we mention what users can do to speed up their computations, while this behavior is just some internal feature that users don't have control over anyway."
] | 1,636,387,827,000 | 1,636,545,869,000 | 1,636,545,868,000 | CONTRIBUTOR | null | Adds a new variable, `DISABLE_PYARROW_TYPES_OPTIMIZATION`, to `config.py` for easier control of the Arrow type optimization.
Fix #2206 | {
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https://api.github.com/repos/huggingface/datasets/issues/3232 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3232/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3232/comments | https://api.github.com/repos/huggingface/datasets/issues/3232/events | https://github.com/huggingface/datasets/issues/3232 | 1,047,361,573 | I_kwDODunzps4-bXgl | 3,232 | The Xsum datasets seems not able to download. | {
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"Hi ! On my side the URL is working fine, could you try again ?",
"> Hi ! On my side the URL is working fine, could you try again ?\r\n\r\nI try it again and cannot download the file (might because of my location). Could you please provide another download link(such as google drive)? :>",
"I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.",
"> I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.\r\n\r\n:> ok. Thanks for your reply."
] | 1,636,372,734,000 | 1,636,470,436,000 | 1,636,470,436,000 | NONE | null | ## Describe the bug
The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.
## Steps to reproduce the bug
```python
load_dataset('xsum')
```
## Actual results
``` python
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz
```
| {
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} | [] | closed | false | null | [] | null | [] | 1,636,361,163,000 | 1,636,377,558,000 | 1,636,361,984,000 | MEMBER | null | By grouping tests by test file, we make sure that all the tests in `test_load.py` are sent to the same worker.
Therefore, the fixture `hf_token` will be called only once (and from the same worker).
Related to: #3200.
Fix #3219. | {
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"I also added the missing `pretty_name` tag in the dataset card to fix the CI"
] | 1,636,358,764,000 | 1,636,454,918,000 | 1,636,454,458,000 | CONTRIBUTOR | null | Even though it is possible to manually get the tagset list with
```python
dset.features[field_name].feature.names
```
I think it is useful to have an overview of the used tagset on the dataset card. This is particularly useful in light of the **dataset viewer**: the tags are encoded, so it is not immediately obvious what they are for a given sample. Adding a label-int mapping should make it easier for visitors to get a grasp of what they mean.
From user-experience perspective, I would urge the full tagsets to always be available in the README's but I understand that that would take a lot of work, probably. Perhaps it can be automated?
closes #3189 | {
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} | [] | closed | false | null | [] | null | [] | 1,636,279,475,000 | 1,636,279,486,000 | 1,636,279,485,000 | MEMBER | null | Currently the BibTeX citation parsed from the CITATION file has wrong URL (it shows the repo URL instead of the proceedings paper URL):
```
@inproceedings{Lhoest_Datasets_A_Community_2021,
author = {Lhoest, Quentin and Villanova del Moral, Albert and von Platen, Patrick and Wolf, Thomas and Šaőko, Mario and Jernite, Yacine and Thakur, Abhishek and Tunstall, Lewis and Patil, Suraj and Drame, Mariama and Chaumond, Julien and Plu, Julien and Davison, Joe and Brandeis, Simon and Sanh, Victor and Le Scao, Teven and Canwen Xu, Kevin and Patry, Nicolas and Liu, Steven and McMillan-Major, Angelina and Schmid, Philipp and Gugger, Sylvain and Raw, Nathan and Lesage, Sylvain and Lozhkov, Anton and Carrigan, Matthew and Matussière, Théo and von Werra, Leandro and Debut, Lysandre and Bekman, Stas and Delangue, Clément},
booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
month = {11},
pages = {175--184},
publisher = {Association for Computational Linguistics},
title = {{Datasets: A Community Library for Natural Language Processing}},
url = {https://github.com/huggingface/datasets},
year = {2021}
}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3227 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3227/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3227/comments | https://api.github.com/repos/huggingface/datasets/issues/3227/events | https://github.com/huggingface/datasets/issues/3227 | 1,046,667,845 | I_kwDODunzps4-YuJF | 3,227 | Error in `Json(datasets.ArrowBasedBuilder)` class | {
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"I have additionally identified the source of the error, being that [this condition](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/src/datasets/packaged_modules/json/json.py#L124-L126) in the file\r\n`python3.8/site-packages/datasets/packaged_modules/json/json.py` is not being entered correctly:\r\n```python\r\n if (\r\n isinstance(e, pa.ArrowInvalid)\r\n and \"straddling\" not in str(e)\r\n or block_size > len(batch)\r\n ):\r\n```\r\n\r\nFrom what I can tell, in my case the block_size simply needs to be increased, but the error message does not contain \"straddling\" so the condition does trigger correctly and we fail to reach [the line to increase block_size](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/src/datasets/packaged_modules/json/json.py#L135).\r\n\r\nChanging the condition above to simply\r\n```python\r\n if (\r\n block_size > len(batch)\r\n ):\r\n```\r\n\r\nFixes the error for me. I'm happy to create a PR containing this fix if the developers deem the other conditions unnecessary.",
"Hi ! I think the issue comes from the fact that your JSON file is not a valid JSON Lines file.\r\nEach example should be on one single line.\r\n\r\nCan you try fixing the format to have one line per example and try again ?",
":open_mouth: you're right, that did it! I just put everything on a single line (my file only has a single example) and that fixed the error. Thank you so much!"
] | 1,636,264,232,000 | 1,636,484,955,000 | 1,636,484,955,000 | NONE | null | ## Describe the bug
When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.
## Steps to reproduce the bug
Create a folder that contains the following:
```
.
βββ testdata
βΒ Β βββ mydata.json
βββ test.py
```
Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)
:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!
`test.py` simply contains:
```python
from datasets import load_dataset
my_dataset = load_dataset("testdata")
```
To reproduce the error, simply run
```
python test.py
```
## Expected results
The data should load correctly without error.
## Actual results
The dataset builder fails with:
```
Using custom data configuration testdata-d490389b8ab4fd82
Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 2264.74it/s]
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 447.01it/s]
Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0
Traceback (most recent call last):
File "test.py", line 28, in <module>
my_dataset = load_dataset("testdata")
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split
for key, table in utils.tqdm(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__
for obj in iterable:
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables
raise ValueError(
ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3226 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3226/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3226/comments | https://api.github.com/repos/huggingface/datasets/issues/3226/events | https://github.com/huggingface/datasets/pull/3226 | 1,046,584,518 | PR_kwDODunzps4uL0ma | 3,226 | Fix paper BibTeX citation with proceedings reference | {
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https://api.github.com/repos/huggingface/datasets/issues/3225 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3225/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3225/comments | https://api.github.com/repos/huggingface/datasets/issues/3225/events | https://github.com/huggingface/datasets/pull/3225 | 1,046,530,493 | PR_kwDODunzps4uLrB3 | 3,225 | Update tatoeba to v2021-07-22 | {
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"How about this? @lhoestq @abhishekkrthakur ",
"Hi ! I think it would be nice if people could still be able to load the old version.\r\nMaybe this can be a parameter ? For example to load the old version they could do\r\n```python\r\nload_dataset(\"tatoeba\", lang1=\"en\", lang2=\"mr\", date=\"v2020-11-09\")\r\n```\r\n\r\nIf it sounds good to you, we can add this parameter to the TatoebaConfig:\r\n```python\r\nclass TatoebaConfig(datasets.BuilderConfig):\r\n def __init__(self, *args, lang1=None, lang2=None, date=\"v2021-07-22\", **kwargs):\r\n self.date = date\r\n```\r\nand then pass the date to the URL\r\n```python\r\n_BASE_URL = \"https://object.pouta.csc.fi/OPUS-Tatoeba/{}/moses/{}-{}.txt.zip\"\r\n```\r\n```python\r\n def _base_url(lang1, lang2, date):\r\n return _BASE_URL.format(date, lang1, lang2)\r\n```\r\n\r\nWhat do you think ?",
"`_DATE = \"v\" + \"-\".join(s.zfill(2) for s in _VERSION.split(\".\"))` seems rather tricky but works well. How about this? @lhoestq \r\n",
"The CI is only failing because of the missing sections in the dataset card, and because of an issue with the CER metric that is unrelated to this PR"
] | 1,636,211,671,000 | 1,636,715,593,000 | 1,636,715,593,000 | CONTRIBUTOR | null | Tatoeba's latest version is v2021-07-22 | {
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https://api.github.com/repos/huggingface/datasets/issues/3224 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3224/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3224/comments | https://api.github.com/repos/huggingface/datasets/issues/3224/events | https://github.com/huggingface/datasets/pull/3224 | 1,046,495,831 | PR_kwDODunzps4uLk2q | 3,224 | User-pickling with dynamic sub-classing | {
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"@lhoestq Feel free to have a look. The implementation is slightly different from what you suggested. I have opted to overwrite `save` instead of meddling with `save_global`. `save_global` is called very late down in dill/pickle so it is hard to control for what is happening there. I might be wrong. Pickling is more complex than I thought! \r\n\r\nThe linked issue (`map` with spaCy) also works now!\r\n\r\n```python\r\nimport pickle\r\nimport spacy\r\nfrom spacy import Language\r\nfrom datasets import load_dataset\r\nfrom datasets.utils.py_utils import dumps, pklregister\r\n\r\n@pklregister(Language, allow_subclasses=True)\r\ndef hash_spacy_language(pickler, nlp: Language):\r\n pickler.save(nlp.to_bytes())\r\n\r\ndef main():\r\n fin = r\"large/file.txt\"\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n\r\n def tokenize(l):\r\n return {\"tok\": [t.text for t in nlp(l[\"text\"])]}\r\n\r\n ds = load_dataset(\"text\", data_files=fin)\r\n ds = ds[\"train\"].map(tokenize)\r\n\r\n # Sanity check: load NLP from pickle created with our own `dumps`\r\n config = nlp.config\r\n lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n nlp2 = lang_cls.from_config(config)\r\n nlp2.from_bytes(pickle.loads(dumps(nlp)))\r\n\r\n assert isinstance(nlp2, type(nlp))\r\n assert dumps(nlp) == dumps(nlp2)\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```\r\n\r\nIf this all looks good to you, I'll start writing on some documentation and examples.\r\n",
"One more thing. This is a reduction function for SpaCy Language that should work with the new API:\r\n```python\r\n@pklregister(Language, allow_subclasses=True)\r\ndef hash_spacy_language(pickler, obj):\r\n def create_language(config, bytes_data):\r\n lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n nlp = lang_cls.from_config(config)\r\n return nlp.from_bytes(bytes_data)\r\n\r\n args = (obj.config, obj.to_bytes())\r\n pickler.save_reduce(create_language, args, obj=obj)\r\n```\r\nso IMO we are missing a test with `pickler.save_reduce`. ",
"> One more thing. This is a reduction function for SpaCy Language that should work with the new API:\r\n> \r\n> ```python\r\n> @pklregister(Language, allow_subclasses=True)\r\n> def hash_spacy_language(pickler, obj):\r\n> def create_language(config, bytes_data):\r\n> lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n> nlp = lang_cls.from_config(config)\r\n> return nlp.from_bytes(bytes_data)\r\n> \r\n> args = (obj.config, obj.to_bytes())\r\n> pickler.save_reduce(create_language, args, obj=obj)\r\n> ```\r\n> \r\n> so IMO we are missing a test with `pickler.save_reduce`.\r\n\r\nSure that seems a good idea, but I do not quite understand what `save_reduce` does. Could you give some more info about what reduce functions do and how they differ from regular `save` and `save_global`? I've read about it but the docs nor the built-in `pickle` code seem really helpful.",
"I'm no pickle expect, but here is my understanding. I believe the pickler uses the reduce function when you do `loads` to reconstructs the original object from the parameters/arguments that were saved with `dumps`.\r\n\r\nFor example your sanity check could be simplified from\r\n```python\r\n config = nlp.config\r\n lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n nlp2 = lang_cls.from_config(config)\r\n nlp2 = nlp2.from_bytes(pickle.loads(dumps(nlp)))\r\n```\r\nto\r\nEDIT: <s>pickle.loads(pickle.dumps(nlp))</s>\r\n```python\r\n nlp2 = loads(dumps(nlp)) # using our custom pickler\r\n```\r\n\r\nThough note that while it can be convenient for tests, we actually don't care about the reconstruction of the object since we're only using the pickler for `dumps` to compute hashes.",
"> I'm no pickle expect, but here is my understanding. I believe the pickler uses the reduce function when you do `loads` to reconstructs the original object from the parameters/arguments that were saved with `dumps`.\r\n> \r\n> For example your sanity check could be simplified from\r\n> \r\n> ```python\r\n> config = nlp.config\r\n> lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n> nlp2 = lang_cls.from_config(config)\r\n> nlp2 = nlp2.from_bytes(pickle.loads(dumps(nlp)))\r\n> ```\r\n> \r\n> to\r\n> \r\n> ```python\r\n> nlp2 = pickle.loads(pickle.dumps(nlp))\r\n> ```\r\n> \r\n> Though note that while it can be convenient for tests, we actually don't care about the reconstruction of the object since we're only using the pickler for `dumps` to compute hashes.\r\n\r\nYes, the sanity check can be simplified like that _if_ we use `pickle.dumps` - but that would not test our own `dumps` functionality and would do a naive dump instead of using `to_bytes`. It won't work if we use our own `dumps`, exactly because of the reason that we want custom pickling and being able to call `to_bytes`. To reconstruct the object from the pickled bytes from `to_bytes` we need `from_bytes`. The result of pickle/dill loads will therefore always be a `bytes` object and not a `Language` object.\r\n\r\nBut `save_reduce` is called when saving, right? Not when loading, AFAICT. I am just not sure what exactly it is saving. It is _potentially_ called [at the end of `save`](https://github.com/python/cpython/blob/24af9a40a8f85af813ea89998aa4e931fcc78cd9/Lib/pickle.py#L603) but only if we haven't returned by then. I just can't figure out what that base case is.",
"I don't think we expect users to write the reduce function that isn't going to be used anyway. So maybe let's stick with `save` ?",
"@BramVanroy \r\nAs I understand `save_reduce` is very similar to `copyreg.pickle`, so I'd suggest you to check the following links:\r\n* https://docs.python.org/3/library/copyreg.html#copyreg.pickle\r\n* https://docs.python.org/3/library/pickle.html#object.__reduce__\r\n\r\n\r\n@lhoestq \r\n> I don't think we expect users to write the reduce function that isn't going to be used anyway. So maybe let's stick with save ?\r\n\r\nI agree. \r\n\r\n`save_reduce` is very similar to `copyreg.pickle` and `object.__reduce__`, which are part of public API (and `save` isn't), so I expect more advanced users to know how to write their own reduction functions. But, as you say, `pklregister` should also work with `save` (even though I think `save` is a bit lower-level, and harder to understand than `save_reduce`).\r\n\r\nAll our examples in `py_utils` that use `pklregister` also use `save_reduce` in the last step, so my reduction for SpaCy is meant to be added there, and not to be written by users (because SpaCy is very popular, so the official support by us makes sense :)).\r\n\r\nAnd in the tests, let's ignore the reconstruction part of pickle/dill, because it's not important for us, and focus on the generated dumps. What do you think?",
"@mariosasko What exactly do you mean with \"isn't part of the public API\"? It is [a public method](https://github.com/python/cpython/blob/24af9a40a8f85af813ea89998aa4e931fcc78cd9/Lib/pickle.py#L535) in base pickle, just like `dump` is but maybe you mean something else.",
"@BramVanroy Oh sorry, it's public (not prefixed with `\"_\"`) but it's not documented in the docs. `save_reduce` is also not in the docs, but its signature/functionality is similar to `copyreg.pickle` and I see it more often being used in the projects on GH, so it's seems \"more public\" to me. ",
"Unfortunately I feel that pickle in general is under-documented. π \r\n\r\nFor the documentation, I can add a brief example, maybe under \"How-to Guides\"? The only thing that isn't immediately obvious to me is how I can add that doc page to the TOC?",
"Yes great idea ! To add that doc page to the TOC, you just have to add it to the index.rst file in the \"How-to guides\" TOC section",
"@mariosasko @lhoestq Feel free to make any edits or suggestions in the text!",
"Hi @mariosasko. I wish you'd told me sooner, as I spent quite some time writing on this.\r\n\r\nI'm also not sure whether it is too advanced to have in the documentation. The spaCy use-case seems potentially frequent. Or do you wish to add that case to the defaults, and whenever new issues come up that seem like frequent/obvious cases, add those internally as well?",
"Documenting the internal `pklregister` is overkill IMO (and it can be kept in docstrings), but we can document something higher level like `register_hash_func` once it's implemented.\r\n\r\nSo we keep the nice documentation you've written (thank you!), except we can rename it to \"Advanced caching\" and show an API that is similar to\r\n```python\r\n>>> @register_hash_func(Language, allow_subclasses=True)\r\n>>> def hash_spacy_language(nlp: Language):\r\n>>> return (nlp.to_bytes(),)\r\n```\r\nThis way we keep the documentation centered around the public API rather than the internals that may evolve/be too complicated to fit only one section.\r\n\r\n> Or do you wish to add that case to the defaults, and whenever new issues come up that seem like frequent/obvious cases, add those internally as well?\r\n\r\nLet's add it to the defaults since it's a frequent use-case. And also allow users to control the hashing using the API mentioned above if they face other non-trivially-hashable objects",
"Sure, I can have a go at implementing spaCy as a built-in. Should it be included in the tests? (Therefore adding spaCy to the tests requirements.)\r\n\r\nNext, from your example, it seems that the return value of `register_hash_func` will be used in pickler.save automatically (calling pklregister a bit deeper). Any reason why it returns a tuple? I can work on this as well, if needed.",
"> Sure, I can have a go at implementing spaCy as a built-in. Should it be included in the tests? (Therefore adding spaCy to the tests requirements.)\r\n\r\nThat would be perfect !\r\n\r\n> Next, from your example, it seems that the return value of register_hash_func will be used in pickler.save automatically (calling pklregister a bit deeper). \r\n\r\nYes I think so. For example register_hash_func can call pklregister with the user's function, but wrapped to use pickler.save.\r\n\r\n> Any reason why it returns a tuple? I can work on this as well, if needed.\r\n\r\nIt can either return an arbitrary object or a tuple. I like it a bit better if it's a tuple, so users understand more easily how to make the function take into account more than one item for the hash. It's also consistent with the streamlit caching functions, that also require a tuple. No strong opinion on this though\r\n\r\nLet me know if I can help with anything"
] | 1,636,200,504,000 | 1,636,990,640,000 | null | CONTRIBUTOR | null | This is a continuation of the now closed PR in https://github.com/huggingface/datasets/pull/3206. The discussion there has shaped a new approach to do this.
In this PR, behavior of `pklregister` and `Pickler` is extended. Earlier, users were already able to register custom pickle functions. That is useful if they have objects that are not easily picklable with default methods. When one registers a custom function to a type, an object of that type will be pickled with the given function by `Pickler` which looks up the type in its `dispatch` table. The downside of this method, and of `pickle` in general, is that it is limited to direct type-matching and does not allow sub-classes. In many, default, cases that is not an issue. But when you are using external libraries where classes (e.g. parsers, models) are sub-classed this is not ideal.
```python
from datasets.fingerprint import Hasher
from datasets.utils.py_utils import pklregister
class BaseParser:
pass
class EnglishParser(BaseParser):
pass
@pklregister(BaseParser)
def custom_pkl_func(pickler, obj):
print(f"Called the custom pickle function for type {type(obj)}!")
# do something with the obj and ultimately save with the pickler
base = BaseParser()
en = EnglishParser()
# Hasher.hash uses the Pickler behind the scenes
# `custom_pkl_func` called for base
Hasher.hash(base)
# `custom_pkl_func` not called for en :-(
Hasher.hash(en)
```
In the example above we'd want to sub-class `EnglishParser` to be handled in the same way as its super-class `BaseParser`. This PR solves that by allowing for a keyword-argument `allow_subclasses` in `pklregister` (default: `False`).
```python
@pklregister(BaseParser, allow_subclasses=True)
```
When this option is enabled, we not only save the function in `Pickler.dispatch` but also save it in a custom table `Pickler.subclass_dispatch` **which allows us to dynamically add sub-classes of that class to the real dispatch table**. Then, if we want to pickle an object `obj` with `Pickler.dump()` (which ultimately will call `Pickler.save()`) we _first_ check whether any of the object's super-classes exist in `Pickler.sublcass_dispatch` and get the related custom pickle function. If we find one, we add the type of `obj` alongside the function to `Pickler.dispatch`. All of this happens at the start of the call to `Pickler.save()`. _Only then_ dill.Pickler's `save` will be called, which in turn will call `pickle._Pickler.save` which handles everything. Here, the `Pickler.dispatch` table will be used to look up custom pickler functions - and it now also includes the function for `obj`, which was copied from its super-class, which we added at the very start of our custom `Pickler.save()`.
For edge cases and, especially, for testing, a contextmanager class `TempPickleRegistry` is included that resets the pickle registry on exit to its previous state.
```python
with TempPickleRegistry():
@pklregister(MyObjClass)
def pickle_registry_test_false(pickler, obj):
pickler.save(obj.fancy_method())
some_obj = MyObjClass()
dumps(some_obj)
# `MyObjClass` is in Pickler.dispatch
# ... `MyObjClass` is _not_ in Pickler.dispatch anymore
```
closes https://github.com/huggingface/datasets/issues/3178
To Do
====
- [x] Write tests
- [ ] Write documentation/examples? | {
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https://api.github.com/repos/huggingface/datasets/issues/3223 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3223/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3223/comments | https://api.github.com/repos/huggingface/datasets/issues/3223/events | https://github.com/huggingface/datasets/pull/3223 | 1,046,445,507 | PR_kwDODunzps4uLb1E | 3,223 | Update BibTeX entry | {
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https://api.github.com/repos/huggingface/datasets/issues/3222 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3222/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3222/comments | https://api.github.com/repos/huggingface/datasets/issues/3222/events | https://github.com/huggingface/datasets/pull/3222 | 1,046,299,725 | PR_kwDODunzps4uK_uG | 3,222 | Add docs for audio processing | {
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"Nice ! love it this way. I guess you can set this PR to \"ready for review\" ?",
"I guess we can merge this one now :)"
] | 1,636,153,679,000 | 1,637,771,528,000 | 1,637,768,152,000 | CONTRIBUTOR | null | This PR adds documentation for the `Audio` feature. It describes:
- The difference between loading `path` and `audio`, as well as use-cases/best practices for each of them.
- Resampling audio files with `cast_column`, and then calling `ds[0]["audio"]` to automatically decode and resample to the desired sampling rate.
- Resampling with `map`.
Preview [here](https://52969-250213286-gh.circle-artifacts.com/0/docs/_build/html/audio_process.html), let me know if I'm missing anything! | {
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https://api.github.com/repos/huggingface/datasets/issues/3221 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3221/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3221/comments | https://api.github.com/repos/huggingface/datasets/issues/3221/events | https://github.com/huggingface/datasets/pull/3221 | 1,045,890,512 | PR_kwDODunzps4uJp4Z | 3,221 | Resolve data_files by split name | {
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"Really cool!\r\nWhen splitting by folder, what do we use for validation set (\"valid\", \"validation\" or both)?",
"> When splitting by folder, what do we use for validation set (\"valid\", \"validation\" or both)?\r\n\r\nBoth are fine :) As soon as it has \"valid\" in it",
"Merging for now, if you have comments about the documentation we can address them in subsequent PRs :)",
"Thanks for the comments @stevhliu :) I just opened https://github.com/huggingface/datasets/pull/3233 to take them into account"
] | 1,636,121,255,000 | 1,636,379,540,000 | 1,636,134,598,000 | MEMBER | null | As discussed in https://github.com/huggingface/datasets/issues/3027 we should automatically infer what file is supposed to go to what split automatically, based on filenames.
I added the support for different kinds of patterns, for both dataset repositories and local directories:
```
Input structure:
my_dataset_repository/
βββ README.md
βββ dataset.csv
Output patterns:
{"train": ["*"]}
```
```
Input structure:
my_dataset_repository/
βββ README.md
βββ train.csv
βββ test.csv
my_dataset_repository/
βββ README.md
βββ data/
βββ train.csv
βββ test.csv
my_dataset_repository/
βββ README.md
βββ train_0.csv
βββ train_1.csv
βββ train_2.csv
βββ train_3.csv
βββ test_0.csv
βββ test_1.csv
Output patterns:
{"train": ["*train*"], "test": ["*test*"]}
```
```
Input structure:
my_dataset_repository/
βββ README.md
βββ data/
βββ train/
β βββ shard_0.csv
β βββ shard_1.csv
β βββ shard_2.csv
β βββ shard_3.csv
βββ test/
βββ shard_0.csv
βββ shard_1.csv
Output patterns:
{"train": ["*train*/*", "*train*/**/*"], "test": ["*test*/*", "*test*/**/*"]}
```
and also this pattern that allows to have custom split names, and that is the structure used by #3098 for `push_to_hub` (cc @LysandreJik ):
```
Input structure:
my_dataset_repository/
βββ README.md
βββ data/
βββ train-00000-of-00003.csv
βββ train-00001-of-00003.csv
βββ train-00002-of-00003.csv
βββ test-00000-of-00001.csv
βββ random-00000-of-00003.csv
βββ random-00001-of-00003.csv
βββ random-00002-of-00003.csv
Output patterns:
{
"train": ["data/train-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"],
"test": ["data/test-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"],
"random": ["data/random-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"],
}
```
You can check the documentation about structuring your repository [here](https://52640-250213286-gh.circle-artifacts.com/0/docs/_build/html/repository_structure.html). cc @stevhliu
Fix https://github.com/huggingface/datasets/issues/3027
Fix https://github.com/huggingface/datasets/issues/3212
In the future we can also add support for dataset configurations. | {
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https://api.github.com/repos/huggingface/datasets/issues/3220 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3220/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3220/comments | https://api.github.com/repos/huggingface/datasets/issues/3220/events | https://github.com/huggingface/datasets/issues/3220 | 1,045,549,029 | I_kwDODunzps4-Uc_l | 3,220 | Add documentation about dataset viewer feature | {
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] | open | false | null | [] | null | [] | 1,636,099,879,000 | 1,636,099,879,000 | null | MEMBER | null | Add to the docs more details about the dataset viewer feature in the Hub.
CC: @julien-c
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From time to time, there appear Invalid Token errors with private datasets:
- https://app.circleci.com/pipelines/github/huggingface/datasets/8520/workflows/d44629f2-4749-40f8-a657-50931d0b3434/jobs/52534
```
____________ ERROR at setup of test_load_streaming_private_dataset _____________
ValueError: Invalid token passed!
____ ERROR at setup of test_load_streaming_private_dataset_with_zipped_data ____
ValueError: Invalid token passed!
=========================== short test summary info ============================
ERROR tests/test_load.py::test_load_streaming_private_dataset - ValueError: I...
ERROR tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data
```
- https://app.circleci.com/pipelines/github/huggingface/datasets/8557/workflows/a8383181-ba6d-4487-9d0a-f750b6dcb936/jobs/52763
```
____ ERROR at setup of test_load_streaming_private_dataset_with_zipped_data ____
[gw1] linux -- Python 3.6.15 /home/circleci/.pyenv/versions/3.6.15/bin/python3.6
hf_api = <huggingface_hub.hf_api.HfApi object at 0x7f4899bab908>
hf_token = 'vgNbyuaLNEBuGbgCEtSBCOcPjZnngJufHkTaZvHwkXKGkHpjBPwmLQuJVXRxBuaRzNlGjlMpYRPbthfHPFWXaaEDTLiqTTecYENxukRYVAAdpeApIUPxcgsowadkTkPj'
zip_csv_path = PosixPath('/tmp/pytest-of-circleci/pytest-0/popen-gw1/data16/dataset.csv.zip')
@pytest.fixture(scope="session")
def hf_private_dataset_repo_zipped_txt_data_(hf_api: HfApi, hf_token, zip_csv_path):
repo_name = "repo_zipped_txt_data-{}".format(int(time.time() * 10e3))
hf_api.create_repo(token=hf_token, name=repo_name, repo_type="dataset", private=True)
repo_id = f"{USER}/{repo_name}"
hf_api.upload_file(
token=hf_token,
path_or_fileobj=str(zip_csv_path),
path_in_repo="data.zip",
repo_id=repo_id,
> repo_type="dataset",
)
tests/hub_fixtures.py:68:
...
ValueError: Invalid token passed!
=========================== short test summary info ============================
ERROR tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data
```
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Fix #3217. | {
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"To give more context: https://github.com/psf/black/issues/318. `black` doesn't treat this as a bug, but `flake8` does. \r\n"
] | 1,636,047,632,000 | 1,636,048,202,000 | 1,636,048,202,000 | MEMBER | null | ## Describe the bug
```
datasets/riddle_sense/riddle_sense.py:36:21: W291 trailing whitespace
``` | {
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- https://github.com/tensorflow/tensorflow/commit/c1867f3bfdd1042f694df7a9870be51ba80543cb
- https://pypi.org/project/tensorflow/2.6.2/
with the patch:
- tensorflow/tensorflow#52927
we can remove the temporary fix we introduced in:
- #3208
Fix #3209. | {
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"@stevhliu Accepted both suggestions, thanks for the review!"
] | 1,636,046,521,000 | 1,636,052,138,000 | 1,636,052,137,000 | CONTRIBUTOR | null | I added a little more description about `to_tf_dataset` compared to just setting the format | {
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- **Name:** *ACAV100M*
- **Description:** *contains 100 million videos with high audio-visual correspondence, ideal for self-supervised video representation learning.*
- **Paper:** *https://arxiv.org/abs/2101.10803*
- **Data:** *https://github.com/sangho-vision/acav100m*
- **Motivation:** *The largest dataset (to date) for audio-visual learning.*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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"This will be fixed by https://github.com/huggingface/datasets/pull/3221"
] | 1,636,024,111,000 | 1,636,134,598,000 | 1,636,134,598,000 | CONTRIBUTOR | null | When loading a dataset that consists of several files (e.g. `my_data/data_001.json`, `my_data/data_002.json` etc.) they are not loaded in order when using `load_dataset("my_data")`.
This could lead to counter-intuitive results if, for example, the data files are sorted by date or similar since they would appear in different order in the `Dataset`.
The straightforward solution is to sort the list of files alphabetically before loading them.
cc @lhoestq
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https://api.github.com/repos/huggingface/datasets/issues/3211 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3211/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3211/comments | https://api.github.com/repos/huggingface/datasets/issues/3211/events | https://github.com/huggingface/datasets/pull/3211 | 1,044,617,913 | PR_kwDODunzps4uFkBx | 3,211 | Fix disable_nullable default value to False | {
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This creates unexpected behaviors like this
```python
from datasets import Dataset, concatenate_datasets
d1 = Dataset.from_dict({"a": [0, 1, 2, 3]})
d2 = d1.filter(lambda x: x["a"] < 2).flatten_indices()
d1.data.schema == d2.data.schema # False
```
This can cause issues when concatenating datasets for example.
For consistency I set `disable_nullable` to `False` in `flatten_indices` and I fixed some docstrings
cc @SBrandeis | {
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"Hi ! Do you have some kind of proxy in your browser that gives you access to internet ?\r\n\r\nMaybe you're having this error because you don't have access to this URL from python ?"
] | 1,636,022,846,000 | 1,640,170,625,000 | null | NONE | null | when I use python examples/pytorch/translation/run_translation.py --model_name_or_path examples/pytorch/translation/opus-mt-en-ro --do_train --do_eval --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config_name ro-en --output_dir /tmp/tst-translation --per_device_train_batch_size=4 --per_device_eval_batch_size=4 --overwrite_output_dir --predict_with_generate to finetune translation model on huggingface, I get the issue"ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py".But I can open the https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py by using website. What should I do to solve the issue? | {
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https://api.github.com/repos/huggingface/datasets/issues/3209 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3209/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3209/comments | https://api.github.com/repos/huggingface/datasets/issues/3209/events | https://github.com/huggingface/datasets/issues/3209 | 1,044,505,771 | I_kwDODunzps4-QeSr | 3,209 | Unpin keras once TF fixes its release | {
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- #3208 | {
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] | null | [] | 1,636,016,651,000 | 1,636,018,254,000 | 1,636,018,254,000 | MEMBER | null | ## Describe the bug
Release of TensorFlow 2.7.0 contains an incompatibility with Keras. See:
- keras-team/keras#15579
This breaks our CI test suite: https://app.circleci.com/pipelines/github/huggingface/datasets/8493/workflows/055c7ae2-43bc-49b4-9f11-8fc71f35a25c/jobs/52363
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"Hi @BramVanroy, thanks for your PR.\r\n\r\nThere was a bug in TensorFlow/Keras. We have made a temporary fix in master branch. Please, merge master into your PR branch, so that the CI tests pass.\r\n\r\n```\r\ngit checkout registry\r\ngit fetch upstream master\r\ngit merge upstream/master\r\n```",
"@albertvillanova Done. Although new tests will need to be added. I am looking for some feedback on my initial proposal in this PR. Reviews and ideas welcome!",
"Hi ! Thanks for diving into this :)\r\n\r\nWith this approach you get the right hash when doing `Hasher.hash(nlp)` but if you try to hash an object that has `nlp` as one of its attributes for example you will get different hashes every time.\r\n\r\nThis is because `Hasher.hash` is not recursive itself. Indeed what happens when you try to hash an object is that:\r\n1. it is dumped with our custom `dill` pickler (which is recursive)\r\n2. the bytes of the dump are hashed\r\n\r\nTo fix this we must integrate the custom hashing as a custom pickler dumping instead.\r\n\r\nNote that we're only using the `pickler.dumps` method and not `pickler.loads` since we only use it to get hashes, so it doesn't matter if `loads` doesn't reconstruct the object exactly. What's important it only to capture all the necessary information that defines how the object transforms the data (here `nlp.to_bytes()` determines how the spacy pipeline transforms the text).\r\n\r\nOur pickler already has a registry and you can register new dump functions with:\r\n```python\r\nimport dill\r\nimport spacy\r\nfrom datasets.utils.py_utils import pklregister\r\n\r\n@pklregister(spacy.Language)\r\ndef _save_spacy_language(pickler, nlp):\r\n pickler.save_reduce(...) # I think we can use nlp.to_bytes() here\r\n dill._dill.log.info(...)\r\n```\r\n\r\nYou can find some examples of custom dump functions in `py_utils.py`",
"Ah, darn it. Completely missed that register. Time wasted, unfortunately. \r\n\r\nTo better understand what you mean, I figured I'd try the basis of your snippet and I've noticed quite an annoying side-effect of how the pickle dispatch table seems to work. It explicitly uses an object's [`type()`](https://github.com/python/cpython/blob/87032cfa3dc975d7442fd57dea2c6a56d31c911a/Lib/pickle.py#L557-L558), which makes sense for pickling some (primitive) types it is not ideal for more complex ones, I think. `Hasher.hash` has the same issue as far as I can tell.\r\n\r\nhttps://github.com/huggingface/datasets/blob/d21ce54f2c2782f854f975eb1dc2be6f923b4314/src/datasets/fingerprint.py#L187-L191\r\n\r\nThis is very restrictive, and won't work for subclasses. In the case of spaCy, for instance, we register `Language`, but `nlp` is an instance of `English`, which is a _subclass_ of `Language`. These are different types, and so they will not match in the dispatch table. Maybe this is more general approach to cover such cases? Something like this is a start but too broad, but ideally a hierarchy is constructed and traversed of all classes in the table and the lowest class is selected to ensure that the most specific class function is dispatched.\r\n\r\n```python\r\n def hash(cls, value: Any) -> str:\r\n # Try to match the exact type\r\n if type(value) in cls.dispatch:\r\n return cls.dispatch[type(value)](cls, value)\r\n\r\n # Try to match instance (superclass)\r\n for type_cls, func in cls.dispatch.items():\r\n if isinstance(value, type_cls):\r\n return cls.dispatch[type_cls](cls, value)\r\n\r\n return cls.hash_default(value)\r\n```\r\n\r\nThis does not solve the problem for pickling, though. That is quite unfortunate IMO because that implies that users always have to specify the most specific class, which is not always obvious. (For instance, `spacy.load`'s signature returns `Language`, but as said before a subclass might be returned.)\r\n\r\nSecond, I am trying to understand `save_reduce` but I can find very little documentation about it, only the source code which is quite cryptic. Can you explain it a bit? The required arguments are not very clear to me and there is no docstring.\r\n\r\n```python\r\n def save_reduce(self, func, args, state=None, listitems=None, dictitems=None, obj=None):\r\n```",
"Here is an example illustrating the problem with sub-classes.\r\n\r\n```python\r\nimport spacy\r\n\r\nfrom spacy import Language\r\nfrom spacy.lang.en import English\r\n\r\nfrom datasets.utils.py_utils import Pickler, pklregister\r\n\r\n# Only useful in the registry (matching with `nlp`)\r\n# if you swap it out for very specific `English`\r\n@pklregister(English)\r\ndef hash_spacy_language(pickler, nlp):\r\n pass\r\n\r\n\r\ndef main():\r\n print(Pickler.dispatch)\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n print(f\"NLP type {type(nlp)} in dispatch table? \", type(nlp) in Pickler.dispatch)\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```",
"Indeed that's not ideal.\r\nMaybe we could integrate all the subclasses directly in `datasets`. That's simple to do but the catch is that if users have new subclasses of `Language` it won't work.\r\n\r\nOtherwise we can see how to make the API simpler for users by allowing subclasses\r\n```python\r\n# if you swap it out for very specific `English`\r\n@pklregister(Language, allow_subclasses=True)\r\ndef hash_spacy_language(pickler, nlp):\r\n pass\r\n```\r\n\r\nHere is an idea how to make this work, let me know what you think:\r\n\r\nWhen `Pickler.dumps` is called, it uses `Pickler.save_global` which is a method that is going to be called recursively on all the objects. We can customize this part, and make it work as we want when it encounters a subclass of `Language`.\r\n\r\nFor example when it encounters a subclass of `Language`, we can dynamically register the hashing function for the subclass (`English` for example) in `Pickler.save_global`, right before calling the actual `dill.Pickler.save_global(self, obj, name=name)`:\r\n```python\r\npklregister(type(obj))(hash_function_registered_for_parent_class)\r\ndill.Pickler.save_global(self, obj, name=name)\r\n```\r\n\r\nIn practice that means we can have an additional dispatch dictionary (similar to `Pickler.dispatch`) to store the hashing functions when `allow_subclasses=True`, and use this dictionary in `Pickler.save_global` to check if we need to use a hashing function registered with `allow_subclasses=True` and get `hash_function_registered_for_parent_class`.",
"If I understood you correctly, I do not think that that is enough because you are only doing this for a type and its direct parent class. You could do this for all superclasses (so traverse all ancestors and find the registered function for the first that is encountered). I can work on that, if you agree. The one thing that I am not sure about is how you want to create the secondary dispatch table. An empty dict as class variable in Pickler? (It doesn't have to be a true dispatcher, I think.)\r\n\r\nI do not think that dynamic registration is the ideal situation (it feels a bit hacky). An alternative would be to subclass Pickle and Dill to make sure that instead of just type() checking in the dispatch table also superclasses are considered. But that is probably overkill.",
"> You could do this for all superclasses (so traverse all ancestors and find the registered function for the first that is encountered)\r\n\r\nThat makes sense indeed !\r\n\r\n> The one thing that I am not sure about is how you want to create the secondary dispatch table. An empty dict as class variable in Pickler? (It doesn't have to be a true dispatcher, I think.)\r\n\r\nSure, let's try to not use too complicated stuff\r\n\r\n> I do not think that dynamic registration is the ideal situation (it feels a bit hacky). An alternative would be to subclass Pickle and Dill to make sure that instead of just type() checking in the dispatch table also superclasses are considered. But that is probably overkill.\r\n\r\nIndeed that would feel less hacky, but maybe it's too complex just for this. I feel like this part of the library is already hard to understand when you're not familiar with pickle. IMO having only a few changes that are simpler to understand is better than having a rewrite of `dill`'s core code.\r\n\r\nThanks a lot for your insights, it looks like we're going to have something that works well and that unlocks some nice flexibility for users :) Feel free to ping me anytime if I can help on this",
"Sure, thanks for brainstorming! I'll try to work on it this weekend. Will also revert the current changes in this PR and rename it. ",
"It seems like this is going in the right direction :). \r\n\r\n@BramVanroy Just one small suggestion for future contributions: instead of using `WIP` in the PR title, you can create a draft PR if you're still working on it.",
"Maybe I should just create a new (draft) PR then, seeing that I'll have to rename and revert the changes anyway? I'll link to this PR so that the discussion is at least referenced.",
"I can convert this PR to a draft PR. Let me know what would you prefer.",
"I think reverting my previous commits would make for a dirty (or confusing) commit history, so I'll just create a new one. Thanks."
] | 1,635,981,942,000 | 1,636,115,891,000 | 1,636,115,884,000 | CONTRIBUTOR | null | Inspired by the discussion on hashing in https://github.com/huggingface/datasets/issues/3178#issuecomment-959016329, @lhoestq suggested that it would be neat to allow users more control over the hashing process. Specifically, it would be great if users can specify specific hashing functions depending on the **class** of the object.
As an example, we found in the linked topic that loaded spaCy models (`Language` objects) have different hashes when `dump`'d, but their byte representation with `Language.to_bytes()` _is_ deterministic. It would therefore be great if we could specify that for `Language` objects, the hasher should hash the objects `to_bytes()` return value instead of the object itself.
This PR adds a new, but tiny, dependency to manage the registry, namely [`catalogue`](https://github.com/explosion/catalogue).
Two files have been changed (apart from the added dependency in `setup.py`) and one file has been added.
**utils.registry** (added)
This file defines our custom Registry and builds a registry called "hashers". A Registry is basically dictionary from names (str) to functions. A function can be added to the registry by a decorator, e.g.
```python
@hashers.register(spacy.Language)
def hash_spacy_language(nlp):
return Hasher.hash(nlp.to_bytes())
```
You'll notice that `spacy.Language` is not a string, even though the registry holds a str->func mapping. To accomplish this with classes in a dynamic way, catalogue.Registry needed to be subclassed and modified as `DatasetsRegistry`. All methods that use a name as an input are now modified so that classes are deterministically converted in strings in such a way that we can later retrieve the actual class from the string (below).
**utils.py_utils** (modified)
Added two functions to deal with classes and their qualified names, that is, their full descriptive name including the module. On the one hand it allows us to retrieve a string from a given class, e.g. given `Module` class, return `torch.nn.Module` str. Conversly, a function is added to convert such a full qualified name into a class. For instance, given the string `torch.nn.Module`, return the `Module` class. These straightforward methods allow us to interchangeably use classes and strings without any needed user interaction - they can just register a class, and behind the scenes `DatasetsRegistry` converts these to deterministic strings.
**fingerprint** (modified)
Updated Hasher.hash so that if the object to hash is an instance of a class in the registry, the registered function is used to hash the object instead of the default behavior. To do so we iterate over the registry `hashers` and convert its keys (strings) into classes, and then we can use `isinstance`.
```python
# Check if the current object is an instance that is
# applicable to the user-defined hashers. If so, hash
# with the user-defined function
for full_module_name, func in hashers.get_all().items():
registered_cls = get_cls_from_qualname(full_module_name)
if isinstance(value, registered_cls):
return func(value)
```
**Putting it all together**
To test this, you can try the following example with spaCy. First install spaCy from source and checkout a specific commit.
```shell
git clone https://github.com/explosion/spaCy.git
cd spaCy/
git checkout cab9209c3dfcd1b75dfe5657f10e52c4d847a3cf
cd ..
git clone https://github.com/BramVanroy/datasets.git
cd datasets
git checkout registry
pip install -e .
pip install ../spaCy
spacy download en_core_web_sm
```
Now you can run the following script. By default it will use the custom hasher function for the Language object. You can enable the default behavior by commenting out `@hashers.register...`.
```python
import spacy
from datasets.fingerprint import Hasher
from datasets.utils.registry import hashers
# Register a function so that when the Hasher encounters a spacy.Language object
# it uses this custom function to hash instead of the default
@hashers.register(spacy.Language)
def hash_spacy_language(nlp):
return Hasher.hash(nlp.to_bytes())
def main():
print(hashers.get_all())
nlp = spacy.load("en_core_web_sm")
dump1 = Hasher.hash(nlp)
nlp = spacy.load("en_core_web_sm")
dump2 = Hasher.hash(nlp)
print(dump1)
# succeeds when using the registered custom function
# fails if using the default
assert dump1 == dump2
if __name__ == '__main__':
main()
```
To do
====
- The above is just a proof-of-concept. I am open to changes/suggestions
- Tests still need to be written
- We should consider whether we can make `DatasetsRegistry` very restrictive and ONLY allowing classes. That would make testing easier - otherwise we also need to test for other sorts of objects.
- Maybe the `hashers` definition is better suited in `fingerprint`?
- Documentation/examples need to be updated
- Not sure why the logger is not working in `hash()`
- `get_cls_from_qualname` might need a fail-safe: is it possible for a full_qualname to not have a module, and if so how do we deal with that?
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"@songfeng cc",
"Hi @sivasankalpp, thanks for your PR.\r\n\r\nThere was a bug in TensorFlow/Keras. We have made a temporary fix in our master branch. Please, merge master into your PR branch, so that the CI tests pass.\r\n\r\n```\r\ngit checkout multidoc2dial\r\ngit fetch upstream master\r\ngit merge upstream/master\r\n```",
"Hi @albertvillanova, I have merged master into my PR branch. All tests are passing. \r\nPlease take a look when you get a chance, thanks! \r\n",
"Thanks for your feedback @lhoestq. We addressed your comments in the latest commit. Let us know if everything looks okay :) "
] | 1,635,972,511,000 | 1,637,775,169,000 | 1,637,772,908,000 | CONTRIBUTOR | null | This PR adds the MultiDoc2Dial dataset introduced in this [paper](https://arxiv.org/pdf/2109.12595v1.pdf ) | {
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"@mariosasko @lhoestq Could you give me an update on how to load the dataset after the fix?\r\nThanks.",
"Hi @arda-vianai,\r\n\r\nfirst, you can try:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('tuple_ie', 'all', revision=\"master\")\r\n```\r\nIf this doesn't work, your version of `datasets` is missing some features that are required to run the dataset script, so install the master version with the following command:\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```\r\nand then:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('tuple_ie', 'all')\r\n```\r\nshould work (even without `revision`).",
"@mariosasko \r\nThanks, it is working now. I actually did that before but I didn't restart the kernel. I restarted it and it works now. My bad!!!\r\nMany thanks and great job!\r\n-arda"
] | 1,635,951,415,000 | 1,636,127,475,000 | 1,636,121,765,000 | NONE | null | Hi,
`dataset = datasets.load_dataset('tuple_ie', 'all')`
returns a FileNotFound error. Is the data not available?
Many thanks.
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