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https://api.github.com/repos/huggingface/datasets/issues/4820 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4820/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4820/comments | https://api.github.com/repos/huggingface/datasets/issues/4820/events | https://github.com/huggingface/datasets/issues/4820 | 1,335,117,132 | I_kwDODunzps5PlEVM | 4,820 | Terminating: fork() called from a process already using GNU OpenMP, this is unsafe. | {
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"Fixed by installing either resampy<3 or resampy>=4"
] | "2022-08-10T19:42:33" | "2022-08-10T19:53:10" | "2022-08-10T19:53:10" | NONE | null | Hi, when i try to run prepare_dataset function in [fine tuning ASR tutorial 4](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb) , i got this error.
I got this error
Terminating: fork() called from a process already using GNU OpenMP, this is unsafe.
There is no other logs available, so i have no clue what is the cause of it.
```
def prepare_dataset(batch):
audio = batch["path"]
# batched output is "un-batched"
batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
batch["input_length"] = len(batch["input_values"])
with processor.as_target_processor():
batch["labels"] = processor(batch["text"]).input_ids
return batch
data = data.map(prepare_dataset, remove_columns=data.column_names["train"],
num_proc=4)
```
Specify the actual results or traceback.
There is no traceback except
`Terminating: fork() called from a process already using GNU OpenMP, this is unsafe.`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-5.15.0-43-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
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https://api.github.com/repos/huggingface/datasets/issues/4819 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4819/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4819/comments | https://api.github.com/repos/huggingface/datasets/issues/4819/events | https://github.com/huggingface/datasets/pull/4819 | 1,335,064,449 | PR_kwDODunzps48-xc6 | 4,819 | Add missing language tags to resources | {
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} | [] | closed | false | null | [] | null | [] | "2022-08-10T19:06:42" | "2022-08-10T19:45:49" | "2022-08-10T19:32:15" | MEMBER | null | Add missing language tags to resources, required by existing datasets on GitHub. | {
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https://api.github.com/repos/huggingface/datasets/issues/4818 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4818/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4818/comments | https://api.github.com/repos/huggingface/datasets/issues/4818/events | https://github.com/huggingface/datasets/pull/4818 | 1,334,941,810 | PR_kwDODunzps48-W7a | 4,818 | Add add cc-by-sa-2.5 license tag | {
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} | [] | closed | false | null | [] | null | [] | "2022-08-10T17:18:39" | "2022-10-04T13:47:24" | "2022-10-04T13:47:24" | CONTRIBUTOR | null | - [ ] add it to moon-landing
- [ ] add it to hub-docs | {
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https://api.github.com/repos/huggingface/datasets/issues/4817 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4817/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4817/comments | https://api.github.com/repos/huggingface/datasets/issues/4817/events | https://github.com/huggingface/datasets/issues/4817 | 1,334,572,163 | I_kwDODunzps5Pi_SD | 4,817 | Outdated Link for mkqa Dataset | {
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"Thanks for reporting @liaeh, we are investigating this. "
] | "2022-08-10T12:45:45" | "2022-08-11T09:37:52" | "2022-08-11T09:37:52" | NONE | null | ## Describe the bug
The URL used to download the mkqa dataset is outdated. It seems the URL to download the dataset is currently https://github.com/apple/ml-mkqa/blob/main/dataset/mkqa.jsonl.gz instead of https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz (master branch has been renamed to main).
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mkqa")
```
## Expected results
downloads the dataset
## Actual results
```python
Downloading builder script:
4.79k/? [00:00<00:00, 201kB/s]
Downloading metadata:
13.2k/? [00:00<00:00, 504kB/s]
Downloading and preparing dataset mkqa/mkqa (download: 11.35 MiB, generated: 34.29 MiB, post-processed: Unknown size, total: 45.65 MiB) to /home/lhr/.cache/huggingface/datasets/mkqa/mkqa/1.0.0/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d...
Downloading data files: 0%
0/1 [00:00<?, ?it/s]
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Input In [3], in <cell line: 3>()
1 from datasets import load_dataset
----> 3 dataset = load_dataset("mkqa")
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/load.py:1746, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1743 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1745 # Download and prepare data
-> 1746 builder_instance.download_and_prepare(
1747 download_config=download_config,
1748 download_mode=download_mode,
1749 ignore_verifications=ignore_verifications,
1750 try_from_hf_gcs=try_from_hf_gcs,
1751 use_auth_token=use_auth_token,
1752 )
1754 # Build dataset for splits
1755 keep_in_memory = (
1756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1757 )
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:1227, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1226 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/mkqa/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d/mkqa.py:130, in Mkqa._split_generators(self, dl_manager)
128 # download and extract URLs
129 urls_to_download = _URLS
--> 130 downloaded_files = dl_manager.download_and_extract(urls_to_download)
132 return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})]
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:431, in DownloadManager.download_and_extract(self, url_or_urls)
415 def download_and_extract(self, url_or_urls):
416 """Download and extract given url_or_urls.
417
418 Is roughly equivalent to:
(...)
429 extracted_path(s): `str`, extracted paths of given URL(s).
430 """
--> 431 return self.extract(self.download(url_or_urls))
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:309, in DownloadManager.download(self, url_or_urls)
306 download_func = partial(self._download, download_config=download_config)
308 start_time = datetime.now()
--> 309 downloaded_path_or_paths = map_nested(
310 download_func,
311 url_or_urls,
312 map_tuple=True,
313 num_proc=download_config.num_proc,
314 disable_tqdm=not is_progress_bar_enabled(),
315 desc="Downloading data files",
316 )
317 duration = datetime.now() - start_time
318 logger.info(f"Downloading took {duration.total_seconds() // 60} min")
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:393, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
391 num_proc = 1
392 if num_proc <= 1 or len(iterable) <= num_proc:
--> 393 mapped = [
394 _single_map_nested((function, obj, types, None, True, None))
395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
396 ]
397 else:
398 split_kwds = [] # We organize the splits ourselve (contiguous splits)
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:394, in <listcomp>(.0)
391 num_proc = 1
392 if num_proc <= 1 or len(iterable) <= num_proc:
393 mapped = [
--> 394 _single_map_nested((function, obj, types, None, True, None))
395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
396 ]
397 else:
398 split_kwds = [] # We organize the splits ourselve (contiguous splits)
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:330, in _single_map_nested(args)
328 # Singleton first to spare some computation
329 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 330 return function(data_struct)
332 # Reduce logging to keep things readable in multiprocessing with tqdm
333 if rank is not None and logging.get_verbosity() < logging.WARNING:
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:335, in DownloadManager._download(self, url_or_filename, download_config)
332 if is_relative_path(url_or_filename):
333 # append the relative path to the base_path
334 url_or_filename = url_or_path_join(self._base_path, url_or_filename)
--> 335 return cached_path(url_or_filename, download_config=download_config)
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:185, in cached_path(url_or_filename, download_config, **download_kwargs)
181 url_or_filename = str(url_or_filename)
183 if is_remote_url(url_or_filename):
184 # URL, so get it from the cache (downloading if necessary)
--> 185 output_path = get_from_cache(
186 url_or_filename,
187 cache_dir=cache_dir,
188 force_download=download_config.force_download,
189 proxies=download_config.proxies,
190 resume_download=download_config.resume_download,
191 user_agent=download_config.user_agent,
192 local_files_only=download_config.local_files_only,
193 use_etag=download_config.use_etag,
194 max_retries=download_config.max_retries,
195 use_auth_token=download_config.use_auth_token,
196 ignore_url_params=download_config.ignore_url_params,
197 download_desc=download_config.download_desc,
198 )
199 elif os.path.exists(url_or_filename):
200 # File, and it exists.
201 output_path = url_or_filename
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:530, in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
525 raise FileNotFoundError(
526 f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been"
527 " disabled. To enable file online look-ups, set 'local_files_only' to False."
528 )
529 elif response is not None and response.status_code == 404:
--> 530 raise FileNotFoundError(f"Couldn't find file at {url}")
531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
532 if head_error is not None:
FileNotFoundError: Couldn't find file at https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz
```
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.4.2
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Fix #4815. | {
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] | null | [] | "2022-08-10T05:12:34" | "2022-08-12T14:17:57" | "2022-08-12T14:17:57" | MEMBER | null | ## Describe the bug
Our loading script for OPUS ParaCrawl loads its 7.1 version. Current existing version is 9.
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https://api.github.com/repos/huggingface/datasets/issues/4813 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4813/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4813/comments | https://api.github.com/repos/huggingface/datasets/issues/4813/events | https://github.com/huggingface/datasets/pull/4813 | 1,333,287,756 | PR_kwDODunzps48446r | 4,813 | Fix loading example in opus dataset cards | {
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- fixes the examples to load the datasets, with the corrected dataset name, in their dataset cards for:
- opus_dgt
- opus_paracrawl
- opus_wikipedia
- fixes their dataset cards with the missing required information: title, data instances/fields/splits
- enumerates the supported languages
- adds a missing citation reference for opus_wikipedia
Related to:
- #4806 | {
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This fixes #4811 because:
```python
In [4]: typing.Optional[str]
Out[4]: typing.Optional[str]
In [5]: get_origin(typing.Optional[str])
Out[5]: typing.Union
```
Fix #4811. | {
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The function `validate_type` assumes that the type `typing.Optional[str]` is automatically transformed to `typing.Union[str, NoneType]`.
```python
In [4]: typing.Optional[str]
Out[4]: typing.Union[str, NoneType]
```
However, this is not the case for Python 3.9:
```python
In [3]: typing.Optional[str]
Out[3]: typing.Optional[str]
```
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} | [] | closed | false | null | [] | null | [] | "2022-08-09T07:38:06" | "2022-08-09T16:26:21" | "2022-08-09T13:26:43" | CONTRIBUTOR | null | I fixed the issue #4808
Details of PR:
- Added languages included in the dataset.
- Added task id and task category.
- Updated the citation information.
Fix #4808. | {
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"#self-assign",
"Fixed by:\r\n- #4809"
] | "2022-08-09T07:35:42" | "2022-08-09T13:33:23" | "2022-08-09T13:33:23" | CONTRIBUTOR | null | null | {
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} | [] | closed | false | null | [] | null | [] | "2022-08-09T06:38:13" | "2022-08-09T07:28:03" | "2022-08-09T07:28:03" | CONTRIBUTOR | null | I fixed a issue #4805.
I changed `"gnome"` to `"opus_gnome"` in[ README.md](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary). | {
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I changed `"gnome"` to `"opus_gnome"` in[ README.md](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary).
Fix #4805 | {
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] | closed | false | null | [] | null | [] | "2022-08-09T03:21:27" | "2022-08-09T11:52:05" | "2022-08-09T11:52:05" | CONTRIBUTOR | null | ## Describe the bug
I found that [the example on opus_gone dataset ](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary) doesn't work.
## Steps to reproduce the bug
```python
load_dataset("gnome", lang1="it", lang2="pl")
```
`"gnome"` should be `"opus_gnome"`
## Expected results
```bash
100%
1/1 [00:00<00:00, 42.09it/s]
DatasetDict({
train: Dataset({
features: ['id', 'translation'],
num_rows: 8368
})
})
```
## Actual results
```bash
Couldn't find 'gnome' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/main/datasets/gnome/gnome.py
```
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.27
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
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- replaces the fine labels, so that there are 50 instead of 47
- once more labels are added, all they (fine and coarse) have been re-ordered, so that they align with the order in: https://cogcomp.seas.upenn.edu/Data/QA/QC/definition.html
- the feature names have been fixed: `fine_label` instead of `label-fine`
- to sneak-case (underscores instead of hyphens)
- words have been reordered
Fix #4790. | {
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https://api.github.com/repos/huggingface/datasets/issues/4799 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4799/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4799/comments | https://api.github.com/repos/huggingface/datasets/issues/4799/events | https://github.com/huggingface/datasets/issues/4799 | 1,330,889,854 | I_kwDODunzps5PU8R- | 4,799 | video dataset loader/parser | {
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"Hi! We've just started discussing the video support in `datasets` (decoding backends, video feature type, etc.), so I believe we should have something tangible by the end of this year.\r\n\r\nAlso, if you have additional video features in mind that you would like to see, feel free to let us know",
"Coool thanks @mariosasko "
] | "2022-08-07T01:54:12" | "2022-08-09T16:42:51" | "2022-08-09T16:42:51" | CONTRIBUTOR | null | you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)?
could you please add functionality to load a video dataset? it would be really cool if i could point it to a bunch of video files and use pytorch to start looping through batches of videos. like if my batch size is 16, each sample in the batch is a frame from a video. i'm competing in the [minerl challenge](https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition), and it would be awesome to use the HF ecosystem. | {
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https://api.github.com/repos/huggingface/datasets/issues/4798 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4798/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4798/comments | https://api.github.com/repos/huggingface/datasets/issues/4798/events | https://github.com/huggingface/datasets/pull/4798 | 1,330,699,942 | PR_kwDODunzps48wVEG | 4,798 | Shard generator | {
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Example:
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="validation")
>>> ds
Dataset({
features: ['text', 'label'],
num_rows: 1066
})
>>> next(ds.shard_generator(300))
Dataset({
features: ['text', 'label'],
num_rows: 300
})
```
I hope it can be helpful to someone. Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/4797 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4797/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4797/comments | https://api.github.com/repos/huggingface/datasets/issues/4797/events | https://github.com/huggingface/datasets/pull/4797 | 1,330,000,998 | PR_kwDODunzps48uL-t | 4,797 | Torgo dataset creation | {
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https://api.github.com/repos/huggingface/datasets/issues/4795 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4795/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4795/comments | https://api.github.com/repos/huggingface/datasets/issues/4795/events | https://github.com/huggingface/datasets/issues/4795 | 1,329,525,732 | I_kwDODunzps5PPvPk | 4,795 | Missing MBPP splits | {
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"Thanks for reporting this as well, @stadlerb.\r\n\r\nI suggest waiting for the answer of the data owners... ",
"@albertvillanova The first author of the paper responded to the upstream issue:\r\n> Task IDs 11-510 are the 500 test problems. We use 90 problems (511-600) for validation and then remaining 374 for fine-tuning (601-974). The other problems can be used as desired, either for training or few-shot prompting (although this should be specified).",
"Thanks for the follow-up, @stadlerb.\r\n\r\nWould you be willing to open a Pull Request to address this issue? :wink: ",
"Opened a [PR](https://github.com/huggingface/datasets/pull/4943) to implement this--lmk if you have any feedback"
] | "2022-08-05T06:51:01" | "2022-09-13T12:27:24" | "2022-09-13T12:27:24" | NONE | null | (@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later in the paper, we hold out 10 problems for **few-shot prompting**, another 500 as our **test** dataset (which is used to evaluate both few-shot inference and fine-tuned models), 374 problems for **fine-tuning**, and the rest for **validation**.
If the dataset on the Hub should reproduce most closely what the original authors use, I guess this four-way split should be reflected.
The paper doesn't explicitly state the task_id ranges of the splits, but the [GitHub readme](https://github.com/google-research/google-research/tree/master/mbpp) referenced in the paper specifies exact task_id ranges, although it misstates the total number of samples:
> We specify a train and test split to use for evaluation. Specifically:
>
> * Task IDs 11-510 are used for evaluation.
> * Task IDs 1-10 and 511-1000 are used for training and/or prompting. We typically used 1-10 for few-shot prompting, although you can feel free to use any of the training examples.
I.e. the few-shot, train and validation splits are combined into one split, with a soft suggestion of using the first ten for few-shot prompting. It is not explicitly stated whether the 374 fine-tuning samples mentioned in the paper have task_id 511 to 784 or 601 to 974 or are randomly sampled from task_id 511 to 974.
Regarding the "sanitized" split the paper states the following:
> For evaluations involving the edited dataset, we perform comparisons with 100 problems that appear in both the original and edited dataset, using the same held out 10 problems for few-shot prompting and 374 problems for fine-tuning.
The statement doesn't appear to be very precise, as among the 10 few-shot problems, those with task_id 1, 5 and 10 are not even part of the sanitized variant, and many from the task_id range from 511 to 974 are missing (e.g. task_id 511 to 553). I suppose the idea the task_id ranges for each split remain the same, even if some of the task_ids are not present. That would result in 7 few-shot, 257 test, 141 train and 22 validation examples in the sanitized split. | {
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https://api.github.com/repos/huggingface/datasets/issues/4791 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4791/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4791/comments | https://api.github.com/repos/huggingface/datasets/issues/4791/events | https://github.com/huggingface/datasets/issues/4791 | 1,328,571,064 | I_kwDODunzps5PMGK4 | 4,791 | Dataset Viewer issue for Team-PIXEL/rendered-wikipedia-english | {
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"Thanks for reporting. It's a known issue that should be fixed soon. Meanwhile, I had to manually trigger the dataset viewer. It's OK now.\r\nNote that the extreme aspect ratio of the images generates another issue, that we're inspecting."
] | "2022-08-04T12:49:16" | "2022-08-04T13:43:16" | "2022-08-04T13:43:16" | NONE | null | ### Link
https://huggingface.co/datasets/Team-PIXEL/rendered-wikipedia-english/viewer/rendered-wikipedia-en/train
### Description
The dataset can be loaded fine but the viewer shows this error:
```
Server Error
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
```
I'm guessing this is because I recently renamed the dataset. Based on related issues (e.g. https://github.com/huggingface/datasets/issues/4759) , is there something server-side that needs to be refreshed?
### Owner
Yes | {
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] | null | [] | "2022-08-04T12:28:51" | "2022-08-22T16:14:16" | "2022-08-22T16:14:16" | MEMBER | null | ## Describe the bug
According to their paper, the TREC dataset contains 2 kinds of classes:
- 6 coarse classes: TREC-6
- 50 fine classes: TREC-50
However, our implementation only has 47 (instead of 50) fine classes. The reason for this is that we only considered the last segment of the label, which is repeated for several coarse classes:
- We have one `desc` fine label instead of 2:
- `DESC:desc`
- `HUM:desc`
- We have one `other` fine label instead of 3:
- `ENTY:other`
- `LOC:other`
- `NUM:other`
From their paper:
> We define a two-layered taxonomy, which represents a natural semantic classification for typical answers in the TREC task. The hierarchy contains 6 coarse classes and 50 fine classes,
> Each coarse class contains a non-overlapping set of fine classes.
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Fix #4787. | {
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] | null | [] | "2022-08-04T08:15:51" | "2022-08-04T17:21:01" | "2022-08-04T17:21:01" | MEMBER | null | ## Describe the bug
As reported on the Hub [Fix Checksum Mismatch](https://huggingface.co/datasets/mbpp/discussions/1), there is a `NonMatchingChecksumError` when loading mbpp dataset
## Steps to reproduce the bug
```python
ds = load_dataset("mbpp", "full")
```
## Expected results
Loading of the dataset without any exception raised.
## Actual results
```
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-1-a3fbdd3ed82e> in <module>
----> 1 ds = load_dataset("mbpp", "full")
.../huggingface/datasets/src/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1791
1792 # Download and prepare data
-> 1793 builder_instance.download_and_prepare(
1794 download_config=download_config,
1795 download_mode=download_mode,
.../huggingface/datasets/src/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
.../huggingface/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1225
1226 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1228
1229 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
.../huggingface/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
--> 775 verify_checksums(
776 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
777 )
.../huggingface/datasets/src/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://raw.githubusercontent.com/google-research/google-research/master/mbpp/mbpp.jsonl']
```
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```python
import datasets
train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"])
s3 = datasets.filesystems.S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key)
train_dataset.save_to_disk("s3://datasets/", fs=s3)
```
produces following traceback:
```shell
File "C:\Users\Hong Knop\AppData\Local\Programs\Python\Python310\lib\site-packages\botocore\auth.py", line 374, in scope
return '/'.join(scope)
```
I invoke print(scope) in <auth.py> (line 373) and find this:
```python
[('4VA08VLL3VTKQJKCAI8M',), '20220803', 'us-east-1', 's3', 'aws4_request']
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Subsequent to PR:
- #4777 | {
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] | closed | false | null | [] | null | [] | "2022-08-02T20:36:03" | "2022-09-09T17:08:14" | "2022-09-07T19:07:34" | MEMBER | null | This PR is a first draft of creating a loading script for image datasets. Feel free to let me know if there are any specificities I'm missing for this. π
To do:
- [x] Document how to create different configurations. | {
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https://api.github.com/repos/huggingface/datasets/issues/4782 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4782/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4782/comments | https://api.github.com/repos/huggingface/datasets/issues/4782/events | https://github.com/huggingface/datasets/issues/4782 | 1,326,247,158 | I_kwDODunzps5PDOz2 | 4,782 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 | {
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] | closed | false | null | [] | null | [
"Thanks for reporting @conceptofmind.\r\n\r\nCould you please give details about your environment? \r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n```",
"Hi @albertvillanova ,\r\n\r\nHere is the environment information:\r\n```\r\n- `datasets` version: 2.3.2\r\n- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.12\r\n- PyArrow version: 7.0.0\r\n- Pandas version: 1.4.2\r\n```\r\nThanks,\r\n\r\nEnrico",
"I think this issue is solved here https://discuss.huggingface.co/t/minhash-deduplication/19992/12?u=loubnabnl, this only happens for very large datasets we will update it in CodeParrot code",
"Hi @loubnabnl,\r\n\r\nYes, the issue is solved in the discussion thread.\r\n\r\nI will close this issue.\r\n\r\nThank you again for all of your help.\r\n\r\nEnrico",
"Thanks @loubnabnl for pointing out the solution to this issue."
] | "2022-08-02T18:36:05" | "2022-08-22T09:46:28" | "2022-08-20T02:11:53" | NONE | null | ## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.unique("hash"))
```
Gists for minimum reproducible example:
https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a
https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2
## Expected results
Chunking and writing out a deduplicated dataset.
## Actual results
```
return dataset._data.column(column).unique().to_pylist()
File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique
File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4781 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4781/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4781/comments | https://api.github.com/repos/huggingface/datasets/issues/4781/events | https://github.com/huggingface/datasets/pull/4781 | 1,326,114,161 | PR_kwDODunzps48hOie | 4,781 | Fix label renaming and add a battery of tests | {
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} | [] | closed | false | null | [] | null | [] | "2022-08-02T16:42:07" | "2022-09-12T11:27:06" | "2022-09-12T11:24:45" | MEMBER | null | This PR makes some changes to label renaming in `to_tf_dataset()`, both to fix some issues when users input something we weren't expecting, and also to make it easier to deprecate label renaming in future, if/when we want to move this special-casing logic to a function in `transformers`.
The main changes are:
- Label renaming now only happens when the `auto_rename_labels` argument is set. For backward compatibility, this defaults to `True` for now.
- If the user requests "label" but the data collator renames that column to "labels", the label renaming logic will now handle that case correctly.
- Added a battery of tests to make this more reliable in future.
- Adds an optimization to loading in `to_tf_dataset()` for unshuffled datasets (uses slicing instead of a list of indices)
Fixes #4772 | {
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https://api.github.com/repos/huggingface/datasets/issues/4780 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4780/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4780/comments | https://api.github.com/repos/huggingface/datasets/issues/4780/events | https://github.com/huggingface/datasets/pull/4780 | 1,326,034,767 | PR_kwDODunzps48g9oA | 4,780 | Remove apache_beam import from module level in natural_questions dataset | {
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} | [] | closed | false | null | [] | null | [] | "2022-08-02T15:34:54" | "2022-08-02T16:16:33" | "2022-08-02T16:03:17" | MEMBER | null | Instead of importing `apache_beam` at the module level, import it in the method `_build_pcollection`.
Fix #4779. | {
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https://api.github.com/repos/huggingface/datasets/issues/4779 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4779/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4779/comments | https://api.github.com/repos/huggingface/datasets/issues/4779/events | https://github.com/huggingface/datasets/issues/4779 | 1,325,997,225 | I_kwDODunzps5PCRyp | 4,779 | Loading natural_questions requires apache_beam even with existing preprocessed data | {
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] | closed | false | null | [] | null | [] | "2022-08-02T15:06:57" | "2022-08-02T16:03:18" | "2022-08-02T16:03:18" | MEMBER | null | ## Describe the bug
When loading "natural_questions", the package "apache_beam" is required:
```
ImportError: To be able to use natural_questions, you need to install the following dependency: apache_beam.
Please install it using 'pip install apache_beam' for instance'
```
This requirement is unnecessary, once there exists preprocessed data and the script just needs to download it.
## Steps to reproduce the bug
```python
load_dataset("natural_questions", "dev", split="validation", revision="main")
```
## Expected results
No ImportError raised.
## Actual results
```
ImportError Traceback (most recent call last)
[<ipython-input-3-c938e7c05d02>](https://localhost:8080/#) in <module>()
----> 1 from datasets import load_dataset; ds = load_dataset("natural_questions", "dev", split="validation", revision="main")
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1732 revision=revision,
1733 use_auth_token=use_auth_token,
-> 1734 **config_kwargs,
1735 )
1736
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1504 download_mode=download_mode,
1505 data_dir=data_dir,
-> 1506 data_files=data_files,
1507 )
1508
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1245 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1246 ) from None
-> 1247 raise e1 from None
1248 else:
1249 raise FileNotFoundError(
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1180 download_config=download_config,
1181 download_mode=download_mode,
-> 1182 dynamic_modules_path=dynamic_modules_path,
1183 ).get_module()
1184 elif path.count("/") == 1: # community dataset on the Hub
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self)
490 base_path=hf_github_url(path=self.name, name="", revision=revision),
491 imports=imports,
--> 492 download_config=self.download_config,
493 )
494 additional_files = [(config.DATASETDICT_INFOS_FILENAME, dataset_infos_path)] if dataset_infos_path else []
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in _download_additional_modules(name, base_path, imports, download_config)
214 _them_str = "them" if len(needs_to_be_installed) > 1 else "it"
215 raise ImportError(
--> 216 f"To be able to use {name}, you need to install the following {_depencencies_str}: "
217 f"{', '.join(needs_to_be_installed)}.\nPlease install {_them_str} using 'pip install "
218 f"{' '.join(needs_to_be_installed.values())}' for instance'"
ImportError: To be able to use natural_questions, you need to install the following dependency: apache_beam.
Please install it using 'pip install apache_beam' for instance'
```
## Environment info
Colab notebook.
| {
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https://api.github.com/repos/huggingface/datasets/issues/4778 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4778/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4778/comments | https://api.github.com/repos/huggingface/datasets/issues/4778/events | https://github.com/huggingface/datasets/pull/4778 | 1,324,928,750 | PR_kwDODunzps48dRPh | 4,778 | Update local loading script docs | {
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] | closed | false | null | [] | null | [] | "2022-08-01T20:21:07" | "2022-08-23T16:32:26" | "2022-08-23T16:32:22" | MEMBER | null | This PR clarifies the local loading script section to include how to load a dataset after you've modified the local loading script (closes #4732). | {
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} | [] | closed | false | null | [] | null | [] | "2022-08-01T14:50:50" | "2022-08-02T17:35:14" | "2022-08-02T17:21:39" | MEMBER | null | Related to:
- https://github.com/huggingface/transformers/issues/18379
Fix partially #4776. | {
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"Requiring torchaudio<0.12.0 isn't really a viable solution because that implies torch<0.12.0 which means no sm_86 CUDA support which means no RTX 3090 support in PyTorch.\r\n\r\nBut in my case, the error only occurs if `_fallback_load` resolves to `_fail_load` inside torchaudio 0.12.0 which is only the case if FFMPEG initialization failed: https://github.com/pytorch/audio/blob/b1f510fa5681e92ee82bdc6b2d1ed896799fc32c/torchaudio/backend/sox_io_backend.py#L36-L47\r\n\r\nThat means the proper solution for torchaudio>=0.12.0 is to check `torchaudio._extension._FFMPEG_INITIALIZED` and if it is False, then we need to remind the user to install a dynamically linked ffmpeg 4.1.8 and then maybe call `torchaudio._extension._init_ffmpeg()` to force a user-visible exception showing the missing ffmpeg dynamic library name.\r\n\r\nOn my system, installing \r\n\r\n- libavcodec.so.58 \r\n- libavdevice.so.58 \r\n- libavfilter.so.7 \r\n- libavformat.so.58 \r\n- libavutil.so.56 \r\n- libswresample.so.3 \r\n- libswscale.so.5\r\n\r\nfrom ffmpeg 4.1.8 made HF datasets 2.3.2 work just fine with torchaudio 0.12.1+cu116:\r\n\r\n```python3\r\nimport sox, torchaudio, datasets\r\nprint('torchaudio', torchaudio.__version__)\r\nprint('datasets', datasets.__version__)\r\ntorchaudio._extension._init_ffmpeg()\r\nprint(torchaudio._extension._FFMPEG_INITIALIZED)\r\nwaveform, sample_rate = torchaudio.load('/workspace/.cache/huggingface/datasets/downloads/extracted/8e5aa88585efa2a4c74c6664b576550d32b7ff9c3d1d17cc04f44f11338c3dc6/cv-corpus-8.0-2022-01-19/en/clips/common_voice_en_100038.mp3', format='mp3')\r\nprint(waveform.shape)\r\n```\r\n\r\n```\r\ntorchaudio 0.12.1+cu116\r\ndatasets 2.3.2\r\nTrue\r\ntorch.Size([1, 369792])\r\n```",
"Related: https://github.com/huggingface/datasets/issues/4889",
"Closing as we no longer use `torchaudio` for decoding MP3 files."
] | "2022-08-01T14:11:23" | "2023-03-02T15:58:16" | "2023-03-02T15:58:15" | MEMBER | null | Current version of `torchaudio` (0.12.0) raises a RuntimeError when trying to use `sox_io` backend but non-Python dependency `sox` is not installed:
https://github.com/pytorch/audio/blob/2e1388401c434011e9f044b40bc8374f2ddfc414/torchaudio/backend/sox_io_backend.py#L21-L29
```python
def _fail_load(
filepath: str,
frame_offset: int = 0,
num_frames: int = -1,
normalize: bool = True,
channels_first: bool = True,
format: Optional[str] = None,
) -> Tuple[torch.Tensor, int]:
raise RuntimeError("Failed to load audio from {}".format(filepath))
```
Maybe we should raise a more actionable error message so that the user knows how to fix it.
UPDATE:
- this is an incompatibility of latest torchaudio (0.12.0) and the sox backend
TODO:
- [x] as a temporary solution, we should recommend installing torchaudio<0.12.0
- #4777
- #4785
- [ ] however, a stable solution must be found for torchaudio>=0.12.0
Related to:
- https://github.com/huggingface/transformers/issues/18379 | {
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"Thanks for reporting @NitishkKarra.\r\n\r\nThe root source of the issue is that streaming mode is not supported out-of-the-box for that dataset, because it contains a TAR file.\r\n\r\nWe have opened a discussion in the corresponding Hub dataset page, pointing out this issue: https://huggingface.co/datasets/Theivaprakasham/wildreceipt/discussions/1\r\n\r\nI'm closing this issue here, so this discussion is transferred there instead."
] | "2022-08-01T09:46:17" | "2022-08-01T10:30:29" | "2022-08-01T10:30:29" | NONE | null | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | {
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"cc @Rocketknight1 ",
"Hi @lehrig, this is caused by the data collator renaming \"label\" to \"labels\". If you set `label_cols=[\"labels\"]` in the call it will work correctly. However, I agree that the cause of the bug is not obvious, so I'll see if I can make a PR to clarify things when the collator renames columns.",
"Thanks - and wow, that appears like a strange side-effect of the data collator. Is that really needed?\r\n\r\nWhy not make it more explicit? For example, extend `DefaultDataCollator` with an optional property `label_col_name` to be used as label column; only when it is not provided default to `labels` (and document that this happens) for backwards-compatibility? ",
"Haha, I honestly have no idea why our data collators rename `\"label\"` (the standard label column name in our datasets) to `\"labels\"` (the standard label column name input to our models). It's been a pain point when I design TF data pipelines, though, because I don't want to hardcode things like that - especially in `datasets`, because the renaming is something that happens purely at the `transformers` end. I don't think I could make the change in the data collators themselves at this point, because it would break backward compatibility for everything in PyTorch as well as TF.\r\n\r\nIn the most recent version of `transformers` we added a [prepare_tf_dataset](https://huggingface.co/docs/transformers/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset) method to our models which takes care of these details for you, and even chooses appropriate columns and labels for the model you're using. In future we might make that the officially recommended way to convert HF datasets to `tf.data.Dataset`.",
"Interesting, that'd be great especially for clarity. https://huggingface.co/docs/datasets/use_with_tensorflow#data-loading already improved clarity, yet, all those options will still confuse people. Looking forward to those advances in the hope there'll be only 1 way in the future ;)\r\n\r\nAnyways, I am happy for the time being with the work-around you provided. Thank you!"
] | "2022-07-29T21:32:12" | "2022-09-12T11:24:46" | "2022-09-12T11:24:46" | NONE | null | ## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator
dataset = load_dataset('glue', 'mrpc', split='train')
tf_dataset = dataset.to_tf_dataset(
columns=["sentence1", "sentence2", "idx"],
label_cols=["label"],
batch_size=16,
collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```
## Expected results
No assertion error.
## Actual results
```
AssertionError: in user code:
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels *
assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.3
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Close #4744 | {
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https://api.github.com/repos/huggingface/datasets/issues/4768 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4768/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4768/comments | https://api.github.com/repos/huggingface/datasets/issues/4768/events | https://github.com/huggingface/datasets/pull/4768 | 1,321,913,645 | PR_kwDODunzps48TRUH | 4,768 | Unpin rouge_score test dependency | {
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Related to:
- #4735 | {
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https://api.github.com/repos/huggingface/datasets/issues/4765 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4765/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4765/comments | https://api.github.com/repos/huggingface/datasets/issues/4765/events | https://github.com/huggingface/datasets/pull/4765 | 1,321,787,428 | PR_kwDODunzps48S2rM | 4,765 | Fix version in map_nested docstring | {
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https://api.github.com/repos/huggingface/datasets/issues/4763 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4763/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4763/comments | https://api.github.com/repos/huggingface/datasets/issues/4763/events | https://github.com/huggingface/datasets/pull/4763 | 1,321,295,876 | PR_kwDODunzps48RMKi | 4,763 | More rigorous shape inference in to_tf_dataset | {
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} | [] | closed | false | null | [] | null | [] | "2022-07-28T18:04:15" | "2022-09-08T19:17:54" | "2022-09-08T19:15:41" | MEMBER | null | `tf.data` needs to know the shape of tensors emitted from a `tf.data.Dataset`. Although `None` dimensions are possible, overusing them can cause problems - Keras uses the dataset tensor spec at compile-time, and so saying that a dimension is `None` when it's actually constant can hurt performance, or even cause training to fail for dimensions that are needed to determine the shape of weight tensors!
The compromise I used here was to sample several batches from the underlying dataset and apply the `collate_fn` to them, and then to see which dimensions were "empirically variable". There's an obvious problem here, though - if you sample 10 batches and they all have the same shape on a certain dimension, there's still a small chance that the 11th batch will be different, and Keras will throw an error if a dataset tries to emit a tensor whose shape doesn't match the spec.
I encountered this bug in practice once or twice for datasets that were mostly-but-not-totally constant on a given dimension, and I still don't have a perfect solution, but this PR should greatly reduce the risk. It samples many more batches, and also samples very small batches (size 2) - this increases the variability, making it more likely that a few outlier samples will be detected.
Ideally, of course, we'd determine the full output shape analytically, but that's surprisingly tricky when the `collate_fn` can be any arbitrary Python code! | {
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https://api.github.com/repos/huggingface/datasets/issues/4762 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4762/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4762/comments | https://api.github.com/repos/huggingface/datasets/issues/4762/events | https://github.com/huggingface/datasets/pull/4762 | 1,321,261,733 | PR_kwDODunzps48RE56 | 4,762 | Improve features resolution in streaming | {
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I also fixed `interleave_datasets` that was not filling missing columns with None, because it was not using the columns from `IterableDataset._resolve_features`
cc @severo | {
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"I refreshed the dataset viewer manually, it's fixed now. Sorry for the inconvenience.\r\n<img width=\"1557\" alt=\"Capture dβeΜcran 2022-07-28 aΜ 09 17 39\" src=\"https://user-images.githubusercontent.com/1676121/181514666-92d7f8e1-ddc1-4769-84f3-f1edfdb902e8.png\">\r\n\r\n"
] | "2022-07-28T11:21:43" | "2022-07-28T13:17:56" | "2022-07-28T13:17:48" | NONE | null | ### Link
https://huggingface.co/datasets/Toygar/turkish-offensive-language-detection
### Description
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
Hi, I provided train.csv, test.csv and valid.csv files. However, viewer says dataset does not exist.
Should I need to do anything else?
### Owner
Yes | {
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] | null | [] | "2022-07-28T08:46:27" | "2022-08-25T18:34:24" | "2022-08-25T18:34:24" | MEMBER | null | As discussed with @ydshieh, when passing a relative path as `data_dir` to `load_dataset` of a dataset hosted on the Hub, the relative path is transformed to the corresponding URL of the Hub dataset.
Currently, we mention this in our docs here: [Create a dataset loading script > Download data files and organize splits](https://huggingface.co/docs/datasets/v2.4.0/en/dataset_script#download-data-files-and-organize-splits)
> If the data files live in the same folder or repository of the dataset script, you can just pass the relative paths to the files instead of URLs.
Maybe we should document better how relative paths are handled, not only when creating a dataset loading script, but also when passing to `load_dataset`:
- `data_dir`
- `data_files`
CC: @stevhliu | {
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https://api.github.com/repos/huggingface/datasets/issues/4754 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4754/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4754/comments | https://api.github.com/repos/huggingface/datasets/issues/4754/events | https://github.com/huggingface/datasets/pull/4754 | 1,319,681,541 | PR_kwDODunzps48L9p6 | 4,754 | Remove "unkown" language tags | {
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https://api.github.com/repos/huggingface/datasets/issues/4753 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4753/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4753/comments | https://api.github.com/repos/huggingface/datasets/issues/4753/events | https://github.com/huggingface/datasets/pull/4753 | 1,319,571,745 | PR_kwDODunzps48Ll8G | 4,753 | Add `language_bcp47` tag | {
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After this PR is merged I think we can simplify the language validation from the DatasetMetadata class (and keep it bare-bone just for the tagging app)
PS: the CI is failing because of missing content in dataset cards that are unrelated to this PR | {
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https://api.github.com/repos/huggingface/datasets/issues/4751 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4751/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4751/comments | https://api.github.com/repos/huggingface/datasets/issues/4751/events | https://github.com/huggingface/datasets/pull/4751 | 1,319,440,903 | PR_kwDODunzps48LJ7U | 4,751 | Added dataset information in clinic oos dataset card | {
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https://api.github.com/repos/huggingface/datasets/issues/4750 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4750/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4750/comments | https://api.github.com/repos/huggingface/datasets/issues/4750/events | https://github.com/huggingface/datasets/issues/4750 | 1,319,333,645 | I_kwDODunzps5Oo28N | 4,750 | Easily create loading script for benchmark comprising multiple huggingface datasets | {
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"Hi ! I think the simplest is to copy paste the `_split_generators` code from the other datasets and do a bunch of if-else, as in the glue dataset: https://huggingface.co/datasets/glue/blob/main/glue.py#L467",
"Ok, I see. Thank you"
] | "2022-07-27T10:13:38" | "2022-07-27T13:58:07" | "2022-07-27T13:58:07" | CONTRIBUTOR | null | Hi,
I would like to create a loading script for a benchmark comprising multiple huggingface datasets.
The function _split_generators needs to return the files for the respective dataset. However, the files are not always in the same location for each dataset. I want to just make a wrapper dataset that provides a single interface to all the underlying datasets.
I thought about downloading the files with the load_dataset function and then providing the link to the cached file. But this seems a bit inelegant to me. What approach would you propose to do this?
Please let me know if you have any questions.
Cheers,
Joel | {
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It's good practice to shard parquet files to enable parallelism with spark/dask/etc.
I added the `max_shard_size` parameter to `download_and_prepare` (default to 500MB for parquet, and None for arrow).
```python
from datasets import *
output_dir = "./output_dir" # also supports "s3://..."
builder = load_dataset_builder("squad")
builder.download_and_prepare(output_dir, file_format="parquet", max_shard_size="5MB")
```
### Implementation details
The examples are written to a parquet file until `ParquetWriter._num_bytes > max_shard_size`. When this happens, a new writer is instantiated to start writing the next shard. At the end, all the shards are renamed to include the total number of shards in their names: `{builder.name}-{split}-{shard_id:05d}-of-{num_shards:05d}.parquet`
I also added the `MAX_SHARD_SIZE` config variable (default to 500MB)
TODO:
- [x] docstrings
- [x] docs
- [x] tests
cc @severo | {
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"The dataset is empty, as far as I can tell: there are no files in the repository at https://huggingface.co/datasets/yanekyuk/wikikey/tree/main\r\n\r\nMaybe the viewer can display a better message for empty datasets",
"OK. Closing as it's not an error. We will work on making the error message a lot clearer."
] | "2022-07-26T16:25:16" | "2022-09-08T08:15:22" | "2022-09-08T08:15:22" | NONE | null | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | {
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"Note that for me personally, conceptually all the dummy data (even for \"canonical\" datasets) should be superseded by `datasets-server`, which performs some kind of CI/CD of datasets (including the canonical ones)",
"I totally agree: next step should be rethinking if dummy data makes sense for canonical datasets (once we have datasets-server) and eventually remove it.\r\n\r\nBut for now, we could at least start by removing the indication to generate dummy data from our docs."
] | "2022-07-26T07:32:58" | "2022-08-02T23:50:30" | "2022-08-02T23:50:30" | MEMBER | null | In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata
However:
- dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI
- for datasets on the Hub:
- they do not pass any CI test requiring dummy data
- there are no instructions on how they can test their dataset locally using the dummy data
- the generation of the dummy data assumes our GitHub directory structure:
- the dummy data will be generated under `./datasets/<dataset_name>/dummy` even if locally there is no `./datasets` directory (which is the usual case). See issue:
- #4742
CC: @stevhliu | {
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"Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst of all, please note that you do not need the dummy data: this was the case when we were adding datasets to the `datasets` library (on this GitHub repo), so that we could test the correct loading of all datasets with our CI. However, this is no longer the case for datasets on the Hub.\r\n- We should definitely update our docs.\r\n\r\nSecond, the dummy data is generated locally:\r\n- in your case, the dummy data will be generated inside the directory: `./datasets/hebban-reviews/dummy`\r\n- please note the preceding `./datasets` directory: the reason for this is that the command to generate the dummy data was specifically created for our `datasets` library, and therefore assumes our directory structure: commands are run from the root directory of our GitHub repo, and datasets scripts are under `./datasets` \r\n\r\n\r\n ",
"I have opened an Issue to update the instructions on dummy data generation:\r\n- #4744",
"Dummy data generation is deprecated now, so I think we can close this issue."
] | "2022-07-25T19:18:42" | "2022-11-04T14:04:24" | "2022-11-04T14:04:10" | CONTRIBUTOR | null | ## Describe the bug
To finalize my dataset, I wanted to create dummy data as per the guide and I ran
```shell
datasets-cli dummy_data datasets/hebban-reviews --auto_generate
```
where hebban-reviews is [this repo](https://huggingface.co/datasets/BramVanroy/hebban-reviews). And even though the scripts runs and shows a message at the end that it succeeded, I cannot find the dummy data anywhere. Where is it?
## Expected results
To see the dummy data in the datasets' folder or in the folder where I ran the command.
## Actual results
I see the following message but I cannot find the dummy data anywhere.
```
Dummy data generation done and dummy data test succeeded for config 'filtered''.
Automatic dummy data generation succeeded for all configs of '.\datasets\hebban-reviews\'
```
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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https://api.github.com/repos/huggingface/datasets/issues/4740 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4740/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4740/comments | https://api.github.com/repos/huggingface/datasets/issues/4740/events | https://github.com/huggingface/datasets/pull/4740 | 1,316,478,007 | PR_kwDODunzps48BX5l | 4,740 | Fix multiprocessing in map_nested | {
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Before, multiprocessing was not used in `map_nested` if `num_proc` was greater than or equal to `len(iterable)`.
- Multiprocessing was not used e.g. when passing `num_proc=20` but having 19 files to download
- As by default, `DownloadManager` sets `num_proc=16`, before multiprocessing was only used when `len(iterable)>16` by default
Now, if `num_proc` is greater than or equal to ``len(iterable)``, `num_proc` is set to ``len(iterable)`` and multiprocessing is used.
- We pass the variable `parallel_min_length=16`, so that multiprocessing is only used if at least 16 files to be downloaded
- ~As by default, `DownloadManager` sets `num_proc=16`, now multiprocessing is used when `len(iterable)>1` by default~
See discussion below.
~After having had to fix some tests (87602ac), I am wondering:~
- ~do we want to have multiprocessing by default?~
- ~please note that `DownloadManager.download` sets `num_proc=16` by default~
- ~or would it be better to ask the user to set it explicitly if they want multiprocessing (and default to `num_proc=1`)?~
Fix #4636.
CC: @nateraw | {
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https://api.github.com/repos/huggingface/datasets/issues/4739 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4739/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4739/comments | https://api.github.com/repos/huggingface/datasets/issues/4739/events | https://github.com/huggingface/datasets/pull/4739 | 1,316,400,915 | PR_kwDODunzps48BHdE | 4,739 | Deprecate metrics | {
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- deprecate public functions: `load_metric`, `list_metrics` and `inspect_metric`: docstring and warning
- test deprecation warnings are issues
- deprecate metrics in all docs
- remove mentions to metrics in docs and README
- deprecate internal functions/classes
Maybe we should also stop testing metrics? | {
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https://api.github.com/repos/huggingface/datasets/issues/4738 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4738/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4738/comments | https://api.github.com/repos/huggingface/datasets/issues/4738/events | https://github.com/huggingface/datasets/pull/4738 | 1,315,222,166 | PR_kwDODunzps479hq4 | 4,738 | Use CI unit/integration tests | {
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- Implements separate unit/integration tests
- A fail in integration tests does not cancel the rest of the jobs
- We should implement more robust integration tests: work in progress in a subsequent PR
- For the moment, test involving network requests are marked as integration: to be evolved | {
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https://api.github.com/repos/huggingface/datasets/issues/4737 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4737/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4737/comments | https://api.github.com/repos/huggingface/datasets/issues/4737/events | https://github.com/huggingface/datasets/issues/4737 | 1,315,011,004 | I_kwDODunzps5OYXm8 | 4,737 | Download error on scene_parse_150 | {
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"Hi! The server with the data seems to be down. I've reported this issue (https://github.com/CSAILVision/sceneparsing/issues/34) in the dataset repo. ",
"The URL seems to work now, and therefore the script as well."
] | "2022-07-22T13:28:28" | "2022-09-01T15:37:11" | "2022-09-01T15:37:11" | NONE | null | ```
from datasets import load_dataset
dataset = load_dataset("scene_parse_150", "scene_parsing")
FileNotFoundError: Couldn't find file at http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip
```
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"Thanks for reporting. You're right, workers were under-provisioned due to a manual error, and the job queue was full. It's fixed now."
] | "2022-07-22T12:14:18" | "2022-07-22T13:46:38" | "2022-07-22T13:46:38" | NONE | null | ### Link
https://huggingface.co/datasets/deepklarity/huggingface-spaces-dataset/viewer/deepklarity--huggingface-spaces-dataset/train
### Description
Hi Team,
I'm getting the following error on a uploaded dataset. I'm getting the same status for a couple of hours now. The dataset size is `<1MB` and the format is csv, so I'm not sure if it's supposed to take this much time or not.
```
Status code: 400
Exception: Status400Error
Message: The split is being processed. Retry later.
```
Is there any explicit step to be taken to get the viewer to work?
### Owner
Yes | {
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https://api.github.com/repos/huggingface/datasets/issues/4735 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4735/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4735/comments | https://api.github.com/repos/huggingface/datasets/issues/4735/events | https://github.com/huggingface/datasets/pull/4735 | 1,314,501,641 | PR_kwDODunzps477CuP | 4,735 | Pin rouge_score test dependency | {
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Fix #4734 | {
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"We have added a comment on an existing issue opened in their repo: https://github.com/google-research/google-research/issues/1212#issuecomment-1192267130\r\n- https://github.com/google-research/google-research/issues/1212"
] | "2022-07-22T07:15:05" | "2022-07-22T07:45:19" | "2022-07-22T07:45:18" | MEMBER | null | ## Describe the bug
After the today release of `rouge_score-0.0.7` it seems no longer importable. Our CI fails: https://github.com/huggingface/datasets/runs/7463218591?check_suite_focus=true
```
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_bigbench
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_configs_bigbench
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_bigbench
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_rouge
```
with errors:
```
> from rouge_score import rouge_scorer
E ModuleNotFoundError: No module named 'rouge_score'
```
```
E ImportError: To be able to use rouge, you need to install the following dependency: rouge_score.
E Please install it using 'pip install rouge_score' for instance'
```
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"Fixed by:\r\n- #4735"
] | "2022-07-22T07:06:51" | "2022-07-22T09:08:02" | "2022-07-22T09:05:35" | NONE | null | ## Describe the bug
A clear and concise description of what the bug is.
Loading Rouge metric gives error after latest rouge-score==0.0.7 release.
Downgrading rougemetric==0.0.4 works fine.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
## Expected results
A clear and concise description of the expected results.
from rouge_score import rouge_scorer, scoring
should run
## Actual results
Specify the actual results or traceback.
File "/root/.cache/huggingface/modules/datasets_modules/metrics/rouge/0ffdb60f436bdb8884d5e4d608d53dbe108e82dac4f494a66f80ef3f647c104f/rouge.py", line 21, in <module>
from rouge_score import rouge_scorer, scoring
ImportError: cannot import name 'rouge_scorer' from 'rouge_score' (unknown location)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version:3.9
- PyArrow version:
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"Thanks for the feedback!\r\n\r\nI think since this issue is closely related to loading, I can add a clearer explanation under [Load > local loading script](https://huggingface.co/docs/datasets/main/en/loading#local-loading-script).",
"That makes sense but I think having a line about it under https://huggingface.co/docs/datasets/installation#source the \"source\" header here would be useful. My mental model of `pip install -e .` does not include the fact that the source files aren't actually being used. ",
"Thanks for sharing your perspective. I think the `load_dataset` function is the only one that pulls from GitHub, and since this use-case is very specific, I don't think we need to include such a broad clarification in the Installation section.\r\n\r\nFeel free to check out the linked PR and let me know if it needs any additional explanation π"
] | "2022-07-22T06:07:31" | "2022-08-23T16:32:23" | "2022-08-23T16:32:23" | MEMBER | null | As reported by @TrentBrick here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191858596, it could be more clear that loading a dataset by passing its name does not use the (modified) local script of it.
What he did:
- he installed `datasets` from source
- he modified locally `datasets/the_pile/the_pile.py` loading script
- he tried to load it but using `load_dataset("the_pile")` instead of `load_dataset("datasets/the_pile")`
- as explained here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191040245:
- the former does not use the local script, but instead it downloads a copy of `the_pile.py` from our GitHub, caches it locally (inside `~/.cache/huggingface/modules`) and uses that.
He suggests adding a more clear explanation about this. He suggests adding it maybe in [Installation > source](https://huggingface.co/docs/datasets/installation))
CC: @stevhliu | {
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"My bad, `482 * 418 * 50000 * 3 / 1000000 = 30221 MB` ( https://stackoverflow.com/a/42979315 ).\r\n\r\nMeanwhile `256 * 256 * 50000 * 3 / 1000000 = 9830 MB`. We are loading the non-cropped images and that is why we take so much RAM."
] | "2022-07-21T15:14:06" | "2022-07-21T16:41:04" | "2022-07-21T16:41:04" | CONTRIBUTOR | null | ## Describe the bug
Loading into memory the validation split of imagenet-1k takes much more RAM than expected. Assuming ImageNet-1k is 150 GB, split is 50000 validation images and 1,281,167 train images, I would expect only about 6 GB loaded in RAM.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imagenet-1k", split="validation")
print(dataset)
"""prints
Dataset({
features: ['image', 'label'],
num_rows: 50000
})
"""
pipe_inputs = dataset["image"]
# and wait :-)
```
## Expected results
Use only < 10 GB RAM when loading the images.
## Actual results
```
Using custom data configuration default
Reusing dataset imagenet-1k (/home/fxmarty/.cache/huggingface/datasets/imagenet-1k/default/1.0.0/a1e9bfc56c3a7350165007d1176b15e9128fcaf9ab972147840529aed3ae52bc)
Killed
```
## Environment info
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
- datasets commit: 4e4222f1b6362c2788aec0dd2cd8cede6dd17b80
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- `ci_hub_config`
- `set_ci_hub_access_token`: to replace setUp/tearDown
- `temporary_repo` context manager: to replace `try... finally`
- `cleanup_repo`: to delete repo accidentally created if one of the tests fails
This is a preliminary work done to manage unit/integration tests separately. | {
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https://api.github.com/repos/huggingface/datasets/issues/4728 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4728/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4728/comments | https://api.github.com/repos/huggingface/datasets/issues/4728/events | https://github.com/huggingface/datasets/issues/4728 | 1,312,897,454 | I_kwDODunzps5OQTmu | 4,728 | load_dataset gives "403" error when using Financial Phrasebank | {
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"Hi @rohitvincent, thanks for reporting.\r\n\r\nUnfortunately I'm not able to reproduce your issue:\r\n```python\r\nIn [2]: from datasets import load_dataset, DownloadMode\r\n ...: load_dataset(path='financial_phrasebank',name='sentences_allagree', download_mode=\"force_redownload\")\r\nDownloading builder script: 6.04kB [00:00, 2.87MB/s] \r\nDownloading metadata: 13.7kB [00:00, 7.24MB/s] \r\nDownloading and preparing dataset financial_phrasebank/sentences_allagree (download: 665.91 KiB, generated: 296.26 KiB, post-processed: Unknown size, total: 962.17 KiB) to .../.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141...\r\nDownloading data: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 682k/682k [00:00<00:00, 7.66MB/s]\r\nDataset financial_phrasebank downloaded and prepared to .../.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141. Subsequent calls will reuse this data.\r\n100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 918.80it/s]\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label'],\r\n num_rows: 2264\r\n })\r\n})\r\n```\r\n\r\nAre you able to access the link? https://www.researchgate.net/profile/Pekka-Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip",
"Yes was able to download from the link manually. But still, get the same error when I use load_dataset.",
"Fixed once data files are hosted on the Hub:\r\n- #4598"
] | "2022-07-21T08:43:32" | "2022-08-04T08:32:35" | "2022-08-04T08:32:35" | NONE | null | I tried both codes below to download the financial phrasebank dataset (https://huggingface.co/datasets/financial_phrasebank) with the sentences_allagree subset. However, the code gives a 403 error when executed from multiple machines locally or on the cloud.
```
from datasets import load_dataset, DownloadMode
load_dataset(path='financial_phrasebank',name='sentences_allagree',download_mode=DownloadMode.FORCE_REDOWNLOAD)
```
```
from datasets import load_dataset, DownloadMode
load_dataset(path='financial_phrasebank',name='sentences_allagree')
```
**Error**
ConnectionError: Couldn't reach https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip (error 403)
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https://api.github.com/repos/huggingface/datasets/issues/4727 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4727/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4727/comments | https://api.github.com/repos/huggingface/datasets/issues/4727/events | https://github.com/huggingface/datasets/issues/4727 | 1,312,645,391 | I_kwDODunzps5OPWEP | 4,727 | Dataset Viewer issue for TheNoob3131/mosquito-data | {
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"The preview is working OK:\r\n\r\n\r\n\r\n"
] | "2022-07-21T05:24:48" | "2022-07-21T07:51:56" | "2022-07-21T07:45:01" | NONE | null | ### Link
https://huggingface.co/datasets/TheNoob3131/mosquito-data/viewer/TheNoob3131--mosquito-data/test
### Description
Dataset preview not showing with large files. Says 'split cache is empty' even though there are train and test splits.
### Owner
_No response_ | {
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https://api.github.com/repos/huggingface/datasets/issues/4726 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4726/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4726/comments | https://api.github.com/repos/huggingface/datasets/issues/4726/events | https://github.com/huggingface/datasets/pull/4726 | 1,312,082,175 | PR_kwDODunzps47ykPI | 4,726 | Fix broken link to the Hub | {
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https://api.github.com/repos/huggingface/datasets/issues/4725 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4725/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4725/comments | https://api.github.com/repos/huggingface/datasets/issues/4725/events | https://github.com/huggingface/datasets/issues/4725 | 1,311,907,096 | I_kwDODunzps5OMh0Y | 4,725 | the_pile datasets URL broken. | {
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"Thanks for reporting, @TrentBrick. We are addressing the change with their data host server.\r\n\r\nOn the meantime, if you would like to work with your fixed local copy of the_pile script, you should use:\r\n```python\r\nload_dataset(\"path/to/your/local/the_pile/the_pile.py\",...\r\n```\r\ninstead of just `load_dataset(\"the_pile\",...`.\r\n\r\nThe latter downloads a copy of `the_pile.py` from our GitHub, caches it locally (inside `~/.cache/huggingface/modules`) and uses that.",
"@TrentBrick, I have checked the URLs and both hosts work, the original (https://the-eye.eu/) and the mirror (https://mystic.the-eye.eu/). See e.g.:\r\n- https://mystic.the-eye.eu/public/AI/pile/\r\n- https://mystic.the-eye.eu/public/AI/pile_preliminary_components/\r\n\r\nPlease, let me know if you still find any issue loading this dataset by using current server URLs.",
"Great this is working now. Re the download from GitHub... I'm sure thought went into doing this but could it be made more clear maybe here? https://huggingface.co/docs/datasets/installation for example under installing from source? I spent over an hour questioning my sanity as I kept trying to edit this file, uninstall and reinstall the repo, git reset to previous versions of the file etc.",
"Thanks for the quick reply and help too\r\n",
"Thanks @TrentBrick for the suggestion about improving our docs: we should definitely do this if you find they are not clear enough.\r\n\r\nCurrently, our docs explain how to load a dataset from a local loading script here: [Load > Local loading script](https://huggingface.co/docs/datasets/loading#local-loading-script)\r\n\r\nI've opened an issue here:\r\n- #4732\r\n\r\nFeel free to comment on it any additional explanation/suggestion/requirement related to this problem."
] | "2022-07-20T20:57:30" | "2022-07-22T06:09:46" | "2022-07-21T07:38:19" | NONE | null | https://github.com/huggingface/datasets/pull/3627 changed the Eleuther AI Pile dataset URL from https://the-eye.eu/ to https://mystic.the-eye.eu/ but the latter is now broken and the former works again.
Note that when I git clone the repo and use `pip install -e .` and then edit the URL back the codebase doesn't seem to use this edit so the mystic URL is also cached somewhere else that I can't find? | {
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https://api.github.com/repos/huggingface/datasets/issues/4724 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4724/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4724/comments | https://api.github.com/repos/huggingface/datasets/issues/4724/events | https://github.com/huggingface/datasets/pull/4724 | 1,311,127,404 | PR_kwDODunzps47vLrP | 4,724 | Download and prepare as Parquet for cloud storage | {
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} | [] | closed | false | null | [] | null | [] | "2022-07-20T13:39:02" | "2022-09-05T17:27:25" | "2022-09-05T17:25:27" | MEMBER | null | Download a dataset as Parquet in a cloud storage can be useful for streaming mode and to use with spark/dask/ray.
This PR adds support for `fsspec` URIs like `s3://...`, `gcs://...` etc. and ads the `file_format` to save as parquet instead of arrow:
```python
from datasets import *
cache_dir = "s3://..."
builder = load_dataset_builder("crime_and_punish", cache_dir=cache_dir)
builder.download_and_prepare(file_format="parquet")
```
EDIT: actually changed the API to
```python
from datasets import *
builder = load_dataset_builder("crime_and_punish")
builder.download_and_prepare("s3://...", file_format="parquet")
```
credentials to cloud storage can be passed using the `storage_options` argument in
For consistency with the BeamBasedBuilder, I name the parquet files `{builder.name}-{split}-xxxxx-of-xxxxx.parquet`. I think this is fine since we'll need to implement parquet sharding after this PR, so that a dataset can be used efficiently with dask for example.
Note that images/audio files are not embedded yet in the parquet files, this will added in a subsequent PR
TODO:
- [x] docs
- [x] tests | {
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https://api.github.com/repos/huggingface/datasets/issues/4723 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4723/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4723/comments | https://api.github.com/repos/huggingface/datasets/issues/4723/events | https://github.com/huggingface/datasets/pull/4723 | 1,310,970,604 | PR_kwDODunzps47uoSj | 4,723 | Refactor conftest fixtures | {
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} | [] | closed | false | null | [] | null | [] | "2022-07-20T12:15:22" | "2022-07-21T14:37:11" | "2022-07-21T14:24:18" | MEMBER | null | Previously, fixture modules `hub_fixtures` and `s3_fixtures`:
- were both at the root test directory
- were imported using `import *`
- as a side effect, the modules `os` and `pytest` were imported from `s3_fixtures` into `conftest`
This PR:
- puts both fixture modules in a dedicated directory `fixtures`
- renames both to: `fixtures.hub` and `fixtures.s3`
- imports them into `conftest` as plugins, using the `pytest_plugins`: this avoids the `import *`
- additionally creates a new fixture module `fixtures.files` with all file-related fixtures | {
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} | [] | closed | false | null | [] | null | [] | "2022-07-20T10:04:37" | "2022-07-20T17:02:33" | "2022-07-20T16:49:36" | CONTRIBUTOR | null | `href="/docs/datasets/quickstart#audio"` implicitly goes to `href="/docs/datasets/{$LATEST_STABLE_VERSION}/quickstart#audio"`. Therefore, https://huggingface.co/docs/datasets/quickstart#audio #audio hashlink does not work since the new docs were not added to v2.3.2 (LATEST_STABLE_VERSION)
to preserve the version, it should be just `href="#audio"`, which will implicilty go to curren_page + #audio element | {
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https://api.github.com/repos/huggingface/datasets/issues/4720 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4720/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4720/comments | https://api.github.com/repos/huggingface/datasets/issues/4720/events | https://github.com/huggingface/datasets/issues/4720 | 1,309,980,195 | I_kwDODunzps5OFLYj | 4,720 | Dataset Viewer issue for shamikbose89/lancaster_newsbooks | {
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"It seems like the list of splits could not be obtained:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"shamikbose89/lancaster_newsbooks\", \"default\")\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 354, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/shamikbose89--lancaster_newsbooks/2d1c63d269bf7b9342accce0a95960b1710ab4bc774248878bd80eb96c1afaf7/lancaster_newsbooks.py\", line 73, in _split_generators\r\n data_dir = dl_manager.download_and_extract(_URL)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 916, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 879, in extract\r\n urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 348, in map_nested\r\n return function(data_struct)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 884, in _extract\r\n protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 388, in _get_extraction_protocol\r\n return _get_extraction_protocol_with_magic_number(f)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 354, in _get_extraction_protocol_with_magic_number\r\n f.seek(0)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 684, in seek\r\n raise ValueError(\"Cannot seek streaming HTTP file\")\r\nValueError: Cannot seek streaming HTTP file\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nping @huggingface/datasets ",
"Oh, I removed the 'split' key from `kwargs`. I put it back in, but there's still the same error",
"It looks like the data host doesn't support http range requests, which is necessary to glob inside a ZIP archive in streaming mode. Can you try hosting the dataset elsewhere ? Or download each file separately from https://ota.bodleian.ox.ac.uk/repository/xmlui/handle/20.500.12024/2531 ?",
"@lhoestq Thanks! That seems to have solved it. I can get the splits with the `get_dataset_split_names()` function. The dataset viewer is still not loading properly, though. The new error is\r\n```\r\nStatus code: 400\r\nException: BadZipFile\r\nMessage: File is not a zip file\r\n```\r\n\r\nPS. The dataset loads properly and can be accessed"
] | "2022-07-19T20:00:07" | "2022-09-08T16:47:21" | "2022-09-08T16:47:21" | NONE | null | ### Link
https://huggingface.co/datasets/shamikbose89/lancaster_newsbooks
### Description
Status code: 400
Exception: ValueError
Message: Cannot seek streaming HTTP file
I am able to use the dataset loading script locally and it also runs when I'm using the one from the hub, but the viewer still doesn't load
### Owner
Yes | {
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https://api.github.com/repos/huggingface/datasets/issues/4719 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4719/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4719/comments | https://api.github.com/repos/huggingface/datasets/issues/4719/events | https://github.com/huggingface/datasets/issues/4719 | 1,309,854,492 | I_kwDODunzps5OEssc | 4,719 | Issue loading TheNoob3131/mosquito-data dataset | {
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"I am also getting a ValueError: 'Couldn't cast' at the bottom. Is this because of some delimiter issue? My dataset is on the Huggingface Hub. If you could look at it, that would be greatly appreciated.",
"Hi @thenerd31, thanks for reporting.\r\n\r\nPlease note that your issue is not caused by the Hugging Face Datasets library, but it has to do with the specific implementation of your dataset on the Hub.\r\n\r\nTherefore, I'm transferring this discussion to your own dataset Community tab: https://huggingface.co/datasets/TheNoob3131/mosquito-data/discussions/1"
] | "2022-07-19T17:47:37" | "2022-07-20T06:46:57" | "2022-07-20T06:46:02" | NONE | null | 
So my dataset is public in the Huggingface Hub, but when I try to load it using the load_dataset command, it shows that it is downloading the files, but throws a ValueError. When I went to my directory to see if the files were downloaded, the folder was blank.
Here is the error below:
ValueError Traceback (most recent call last)
Input In [8], in <cell line: 3>()
1 from datasets import load_dataset
----> 3 dataset = load_dataset("TheNoob3131/mosquito-data", split="train")
File ~\Anaconda3\lib\site-packages\datasets\load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
Is the dataset in the wrong format or is there some security permission that I should enable? | {
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https://api.github.com/repos/huggingface/datasets/issues/4718 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4718/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4718/comments | https://api.github.com/repos/huggingface/datasets/issues/4718/events | https://github.com/huggingface/datasets/pull/4718 | 1,309,520,453 | PR_kwDODunzps47prWR | 4,718 | Make Extractor accept Path as input | {
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- Makes `Extractor` accept instance of `Path` as input
- Removes unnecessary castings of `Path` to `str` | {
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https://api.github.com/repos/huggingface/datasets/issues/4717 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4717/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4717/comments | https://api.github.com/repos/huggingface/datasets/issues/4717/events | https://github.com/huggingface/datasets/issues/4717 | 1,309,512,483 | I_kwDODunzps5ODZMj | 4,717 | Dataset Viewer issue for LawalAfeez/englishreview-ds-mini | {
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"It's currently working, as far as I understand\r\n\r\nhttps://huggingface.co/datasets/LawalAfeez/englishreview-ds-mini/viewer/LawalAfeez--englishreview-ds-mini/train\r\n\r\n<img width=\"1556\" alt=\"Capture dβeΜcran 2022-07-19 aΜ 09 24 01\" src=\"https://user-images.githubusercontent.com/1676121/179761130-2d7980b9-c0f6-4093-8b1d-f0a3872fef3f.png\">\r\n\r\n---\r\n\r\nWhat was your issue?"
] | "2022-07-19T13:19:39" | "2022-07-20T08:32:57" | "2022-07-20T08:32:57" | NONE | null | ### Link
_No response_
### Description
Unable to view the split data
### Owner
_No response_ | {
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https://api.github.com/repos/huggingface/datasets/issues/4716 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4716/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4716/comments | https://api.github.com/repos/huggingface/datasets/issues/4716/events | https://github.com/huggingface/datasets/pull/4716 | 1,309,455,838 | PR_kwDODunzps47pdbh | 4,716 | Support "tags" yaml tag | {
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https://api.github.com/repos/huggingface/datasets/issues/4715 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4715/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4715/comments | https://api.github.com/repos/huggingface/datasets/issues/4715/events | https://github.com/huggingface/datasets/pull/4715 | 1,309,405,980 | PR_kwDODunzps47pSui | 4,715 | Fix POS tags | {
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https://api.github.com/repos/huggingface/datasets/issues/4714 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4714/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4714/comments | https://api.github.com/repos/huggingface/datasets/issues/4714/events | https://github.com/huggingface/datasets/pull/4714 | 1,309,265,682 | PR_kwDODunzps47o0YG | 4,714 | Fix named split sorting and remove unnecessary casting | {
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} | [] | closed | false | null | [] | null | [] | "2022-07-19T09:48:28" | "2022-07-22T09:39:45" | "2022-07-22T09:10:57" | MEMBER | null | This PR:
- makes `NamedSplit` sortable: so that `sorted()` can be called on them
- removes unnecessary `sorted()` on `dict.keys()`: `dict_keys` view is already like a `set`
- removes unnecessary casting of `NamedSplit` to `str` | {
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https://api.github.com/repos/huggingface/datasets/issues/4713 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4713/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4713/comments | https://api.github.com/repos/huggingface/datasets/issues/4713/events | https://github.com/huggingface/datasets/pull/4713 | 1,309,184,756 | PR_kwDODunzps47ojC1 | 4,713 | Document installation of sox OS dependency for audio | {
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} | [] | closed | false | null | [] | null | [] | "2022-07-19T08:42:35" | "2022-07-21T08:16:59" | "2022-07-21T08:04:15" | MEMBER | null | The `sox` OS package needs being installed manually using the distribution package manager.
This PR adds this explanation to the docs. | {
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https://api.github.com/repos/huggingface/datasets/issues/4712 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4712/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4712/comments | https://api.github.com/repos/huggingface/datasets/issues/4712/events | https://github.com/huggingface/datasets/pull/4712 | 1,309,177,302 | PR_kwDODunzps47ohdr | 4,712 | Highlight non-commercial license in amazon_reviews_multi dataset card | {
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} | [] | closed | false | null | [] | null | [] | "2022-07-19T08:36:20" | "2022-07-27T16:09:40" | "2022-07-27T15:57:41" | CONTRIBUTOR | null | Highlight that the licence granted by Amazon only covers non-commercial research use. | {
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https://api.github.com/repos/huggingface/datasets/issues/4710 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4710/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4710/comments | https://api.github.com/repos/huggingface/datasets/issues/4710/events | https://github.com/huggingface/datasets/pull/4710 | 1,308,958,525 | PR_kwDODunzps47ny0L | 4,710 | Add object detection processing tutorial | {
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} | [] | closed | false | null | [] | null | [] | "2022-07-19T04:23:46" | "2022-07-21T20:10:35" | "2022-07-21T19:56:42" | CONTRIBUTOR | null | The following adds a quick guide on how to process object detection datasets with `albumentations`. | {
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https://api.github.com/repos/huggingface/datasets/issues/4708 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4708/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4708/comments | https://api.github.com/repos/huggingface/datasets/issues/4708/events | https://github.com/huggingface/datasets/pull/4708 | 1,308,279,700 | PR_kwDODunzps47lewm | 4,708 | Fix require torchaudio and refactor test requirements | {
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} | [] | closed | false | null | [] | null | [] | "2022-07-18T17:24:28" | "2022-07-22T06:30:56" | "2022-07-22T06:18:11" | MEMBER | null | Currently there is a bug in `require_torchaudio` (indeed it is requiring `sox` instead):
```python
def require_torchaudio(test_case):
if find_spec("sox") is None:
...
```
The bug was introduced by:
- #3685
- Commit: https://github.com/huggingface/datasets/pull/3685/commits/b5a3e7122d49c4dcc9333b1d8d18a833fc04b940
which moved
```python
require_sndfile = pytest.mark.skipif(
# In Windows and OS X, soundfile installs sndfile
(sys.platform != "linux" and find_spec("soundfile") is None)
# In Linux, soundfile throws RuntimeError if sndfile not installed with distribution package manager
or (sys.platform == "linux" and find_library("sndfile") is None),
reason="Test requires 'sndfile': `pip install soundfile`; "
"Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`",
)
require_sox = pytest.mark.skipif(
find_library("sox") is None,
reason="Test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`",
)
require_torchaudio = pytest.mark.skipif(find_spec("torchaudio") is None, reason="Test requires 'torchaudio'")
```
to
```python
def require_sndfile(test_case):
"""
Decorator marking a test that requires soundfile.
These tests are skipped when soundfile isn't installed.
"""
if (sys.platform != "linux" and find_spec("soundfile") is None) or (
sys.platform == "linux" and find_library("sndfile") is None
):
test_case = unittest.skip(
"test requires 'sndfile': `pip install soundfile`; "
"Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`",
)(test_case)
return test_case
def require_sox(test_case):
"""
Decorator marking a test that requires sox.
These tests are skipped when sox isn't installed.
"""
if find_library("sox") is None:
return unittest.skip("test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`")(
test_case
)
return test_case
def require_torchaudio(test_case):
"""
Decorator marking a test that requires torchaudio.
These tests are skipped when torchaudio isn't installed.
"""
if find_spec("sox") is None:
return unittest.skip("test requires 'torchaudio'")(test_case)
return test_case
```
This PR;
- fixes the bug in `require_torchaudio`
- refactors the test requirements back to using `pytest` instead of `unittest`
- the text in `pytest.skipif` `reason` can be used if needed in a test body: `require_torchaudio.kwargs["reason"]` | {
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"Thanks for reporting. I refreshed the dataset viewer and it now works as expected.\r\n\r\nhttps://huggingface.co/datasets/TheNoob3131/mosquito-data\r\n\r\n<img width=\"1135\" alt=\"Capture dβeΜcran 2022-07-18 aΜ 13 15 22\" src=\"https://user-images.githubusercontent.com/1676121/179566497-e47f1a27-fd84-4a8d-9d7f-2e0f2da803df.png\">\r\n\r\nWe will investigate why it occurred in the first place\r\n",
"By chance, could you provide some details about the operations done on the dataset: was it private? gated?",
"Yes, it was a private dataset, and when I made it public, the Dataset Preview did not work. \r\n\r\nHowever, now when I make the dataset private, it says that the Dataset Preview has been disabled. Why is this?",
"Thanks for the details. For now, the dataset viewer is always disabled on private datasets (see https://huggingface.co/docs/hub/datasets-viewer for more details)",
"Hi, it was working fine for a few hours, but then I can't see the dataset viewer again (public dataset). Why is this still happening?\r\nIt's the same error too:\r\n\r\n",
"OK? This is a bug, thanks for help spotting and reproducing it (it occurs when a dataset is switched to private, then to public). We will be working on it, meanwhile, I've restored the dataset viewer manually again."
] | "2022-07-18T17:07:19" | "2022-07-18T19:44:46" | "2022-07-18T17:15:50" | NONE | null | ### Link
_No response_
### Description
Getting this error when trying to view dataset preview:
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/TheNoob3131/mosquito-data/resolve/8aceebd6c4a359d216d10ef020868bd9e8c986dd/0_Africa_train.csv')
### Owner
_No response_ | {
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I applied your fix @mustaszewski
I also used a dict to make the dataset generation much faster | {
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https://api.github.com/repos/huggingface/datasets/issues/4705 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4705/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4705/comments | https://api.github.com/repos/huggingface/datasets/issues/4705/events | https://github.com/huggingface/datasets/pull/4705 | 1,308,161,794 | PR_kwDODunzps47lFDo | 4,705 | Fix crd3 | {
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} | [] | closed | false | null | [] | null | [] | "2022-07-18T15:53:44" | "2022-07-21T17:18:44" | "2022-07-21T17:06:30" | MEMBER | null | As reported in https://huggingface.co/datasets/crd3/discussions/1#62cc377073b2512b81662794, each split of the dataset was containing the same data. This issues comes from a bug in the dataset script
I fixed it and also uploaded the data to hf.co to make the dataset work in streaming mode | {
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https://api.github.com/repos/huggingface/datasets/issues/4704 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4704/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4704/comments | https://api.github.com/repos/huggingface/datasets/issues/4704/events | https://github.com/huggingface/datasets/pull/4704 | 1,308,147,876 | PR_kwDODunzps47lCFt | 4,704 | Skip tests only for lz4/zstd params if not installed | {
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} | [] | closed | false | null | [] | null | [] | "2022-07-18T15:41:40" | "2022-07-19T13:02:31" | "2022-07-19T12:49:18" | MEMBER | null | Currently, if `zstandard` or `lz4` are not installed, `test_compression_filesystems` and `test_streaming_dl_manager_extract_all_supported_single_file_compression_types` are skipped for all compression format parameters.
This PR fixes these tests, so that if `zstandard` or `lz4` are not installed, the tests are skipped only for the corresponding compression parameters (`zstd` or `lz4`), whereas the tests are not skipped for all the other compression parameters (`gzip`, `xz` and `bz2`).
Related to:
- #4688 | {
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} | [] | closed | false | null | [] | null | [] | "2022-07-18T11:55:49" | "2022-07-22T11:17:42" | "2022-07-22T11:05:24" | CONTRIBUTOR | null | Make the cast in `from_pandas` more robust (as it was done for the packaged modules in https://github.com/huggingface/datasets/pull/4364)
This should be useful in situations like [this one](https://discuss.huggingface.co/t/loading-custom-audio-dataset-and-fine-tuning-model/8836/4). | {
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https://api.github.com/repos/huggingface/datasets/issues/4701 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4701/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4701/comments | https://api.github.com/repos/huggingface/datasets/issues/4701/events | https://github.com/huggingface/datasets/pull/4701 | 1,307,689,625 | PR_kwDODunzps47jeE9 | 4,701 | Added more information in the README about contributors of the Arabic Speech Corpus | {
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https://api.github.com/repos/huggingface/datasets/issues/4699 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4699/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4699/comments | https://api.github.com/repos/huggingface/datasets/issues/4699/events | https://github.com/huggingface/datasets/pull/4699 | 1,307,555,592 | PR_kwDODunzps47jA6Z | 4,699 | Fix Authentification Error while streaming | {
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```
from datasets import load_dataset
dataset = load_dataset(<repo_id>, use_auth_token=<private_token>, streaming=True)
for d in dataset['train']:
print(d)
break # this is for checking
```
This code is an example for streaming private datasets.
when the version of the datasets is 2.2.2, it works well but datasets>2.2.2 occurs error like this,
```
/usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self)
1007 status=self.status,
1008 message=self.reason,
β 1009 headers=self.headers,
1010 )
1011
ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/.../train-00000-of-00001-168b451062c67c34.parquet')
```
(this is an example on the dataset has `parquet` extenstion)
It seems that the `xisfile `module in `download/streaming_download_manager.py` couldn't recognize the file on "https://huggingface.co/~".
so I add three lines.
With this change, there is no error anymore(but this code is ad-hoc). | {
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https://api.github.com/repos/huggingface/datasets/issues/4697 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4697/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4697/comments | https://api.github.com/repos/huggingface/datasets/issues/4697/events | https://github.com/huggingface/datasets/issues/4697 | 1,307,332,253 | I_kwDODunzps5N7E6d | 4,697 | Trouble with streaming frgfm/imagenette vision dataset with TAR archive | {
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"Hi @frgfm, thanks for reporting.\r\n\r\nAs the error message says, streaming mode is not supported out of the box when the dataset contains TAR archive files.\r\n\r\nTo make the dataset streamable, you have to use `dl_manager.iter_archive`.\r\n\r\nThere are several examples in other datasets, e.g. food101: https://huggingface.co/datasets/food101/blob/main/food101.py\r\n\r\nAnd yes, as the link you pointed out, for the streaming to be possible, the metadata file must be loaded before all of the images:\r\n- either this is the case when iterating the archive (and you get the metadata file before the images)\r\n- or you have to extract the metadata file by hand and upload it separately to the Hub",
"Hi @albertvillanova :wave:\r\n\r\nThanks! Yeah I saw that but since I didn't have any metadata, I wasn't sure whether I should create them myself.\r\n\r\nSo one last question:\r\nWhat is the metadata supposed to be for archives? The relative path of all files in it?\r\n_(Sorry I'm a bit confused since it's quite hard to debug using the single error message from the data preview :sweat_smile: )_",
"Hi @frgfm, streaming a dataset that contains a TAR file requires some tweaks because (contrary to ZIP files), tha TAR archive does not allow random access to any of the contained member files. Instead they have to be accessed sequentially (in the order in which they were put into the TAR file when created) and yielded.\r\n\r\nSo when iterating over the TAR file content, when an image file is found, we need to yield it (and not keeping it in memory, which will require huge RAM memory for large datasets). But when yielding an image file, we also need to yield with it what we call \"metadata\": the class label, and other textual information (for example, for audio files, sometimes we also add info such as the speaker ID, their sex, their age,...).\r\n\r\nAll this information usually is stored in what we call the metadata file: either a JSON or a CSV/TSV file.\r\n\r\nBut if this is also inside the TAR archive, we need to find this file in the first place when iterating the TAR archive, so that we already have this information when we find an image file and we can yield the image file and its metadata info.\r\n\r\nTherefore:\r\n- either the TAR archive contains the metadata file as the first member when iterating it (something we cannot change as it is done at the creation of the TAR file)\r\n- or if not, then we need to have the metadata file elsewhere\r\n - in these cases, what we do (if the dataset license allows it) is:\r\n - we download the TAR file locally, we extract the metadata file and we host the metadata on the Hub\r\n - we modify the dataset loading script so that it first downloads the metadata file (and reads it) and only then starts iterating the content of the TAR archive file\r\n\r\nSee an example of this process we recently did for \"google/fleurs\" (their metadata files for \"train\" were at the end of the TAR archives, after all audio files): https://huggingface.co/datasets/google/fleurs/discussions/4\r\n- we uploaded the metadata file to the Hub\r\n- we adapted the loading script to use it",
"Hi @albertvillanova :wave: \r\n\r\nThanks, since my last message, I went through the repo of https://huggingface.co/datasets/food101/blob/main/food101.py and managed to get it to work in the end :pray: \r\n\r\nHere it is: https://huggingface.co/datasets/frgfm/imagenette\r\n\r\nI appreciate you opening an issue to document the process, it might help a few!",
"Great to see that you manage to make your dataset streamable. :rocket: \r\n\r\nI'm closing this issue, as for the docs update there is another issue opened:\r\n- #4711"
] | "2022-07-18T02:51:09" | "2022-08-01T15:10:57" | "2022-08-01T15:10:57" | NONE | null | ### Link
https://huggingface.co/datasets/frgfm/imagenette
### Description
Hello there :wave:
Thanks for the amazing work you've done with HF Datasets! I've just started playing with it, and managed to upload my first dataset. But for the second one, I'm having trouble with the preview since there is some archive extraction involved :sweat_smile:
Basically, I get a:
```
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
I've tried several things and checked this issue https://github.com/huggingface/datasets/issues/4181 as well, but no luck so far!
Could you point me in the right direction please? :pray:
### Owner
Yes | {
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"Hi @finiteautomata, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"lince\", \"ner_spaeng\")\r\nDownloading builder script: 20.8kB [00:00, 9.09MB/s] \r\nDownloading metadata: 31.2kB [00:00, 13.5MB/s] \r\nDownloading and preparing dataset lince/ner_spaeng (download: 2.93 MiB, generated: 18.45 MiB, post-processed: Unknown size, total: 21.38 MiB) to .../.cache/huggingface/datasets/lince/ner_spaeng/1.0.0/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589...\r\nDownloading data: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3.08M/3.08M [00:01<00:00, 2.73MB/s]\r\nDataset lince downloaded and prepared to .../.cache/huggingface/datasets/lince/ner_spaeng/1.0.0/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589. Subsequent calls will reuse this data.\r\n100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 630.66it/s]\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['idx', 'words', 'lid', 'ner'],\r\n num_rows: 33611\r\n })\r\n validation: Dataset({\r\n features: ['idx', 'words', 'lid', 'ner'],\r\n num_rows: 10085\r\n })\r\n test: Dataset({\r\n features: ['idx', 'words', 'lid', 'ner'],\r\n num_rows: 23527\r\n })\r\n})\r\n``` \r\n\r\nPlease note that for this dataset, the original data files are not hosted on the Hugging Face Hub, but on https://ritual.uh.edu\r\nAnd sometimes, the server might be temporarily unavailable, as your error message said (trying to connect to the server timed out):\r\n```\r\nConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError(\"HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))\")))\r\n```\r\nIn these cases you could:\r\n- either contact the owners of the data server where the data is hosted to inform them about the issue in their server\r\n- or re-try after waiting some time: usually these issues are just temporary",
"Great, thanks for checking out!"
] | "2022-07-17T19:01:54" | "2022-07-18T09:20:40" | "2022-07-18T07:24:22" | NONE | null | ## Describe the bug
Cannot load LinCE dataset due to a connection error
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("lince", "ner_spaeng")
```
A notebook with this code and corresponding error can be found at https://colab.research.google.com/drive/1pgX3bNB9amuUwAVfPFm-XuMV5fEg-cD2
## Expected results
It should load the dataset
## Actual results
```python
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-2-fc551ddcebef> in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("lince", "ner_spaeng")
10 frames
/usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
-> 1684 use_auth_token=use_auth_token,
1685 )
1686
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
703 if not downloaded_from_gcs:
704 self._download_and_prepare(
--> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1219
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1222
1223 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
772
773 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/lince/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589/lince.py in _split_generators(self, dl_manager)
481 def _split_generators(self, dl_manager):
482 """Returns SplitGenerators."""
--> 483 lince_dir = dl_manager.download_and_extract(f"{_LINCE_URL}/{self.config.name}.zip")
484 data_dir = os.path.join(lince_dir, self.config.data_dir)
485 return [
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download_and_extract(self, url_or_urls)
429 extracted_path(s): `str`, extracted paths of given URL(s).
430 """
--> 431 return self.extract(self.download(url_or_urls))
432
433 def get_recorded_sizes_checksums(self):
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download(self, url_or_urls)
313 num_proc=download_config.num_proc,
314 disable_tqdm=not is_progress_bar_enabled(),
--> 315 desc="Downloading data files",
316 )
317 duration = datetime.now() - start_time
/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
346 # Singleton
347 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 348 return function(data_struct)
349
350 disable_tqdm = disable_tqdm or not logging.is_progress_bar_enabled()
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in _download(self, url_or_filename, download_config)
333 # append the relative path to the base_path
334 url_or_filename = url_or_path_join(self._base_path, url_or_filename)
--> 335 return cached_path(url_or_filename, download_config=download_config)
336
337 def iter_archive(self, path_or_buf: Union[str, io.BufferedReader]):
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
195 use_auth_token=download_config.use_auth_token,
196 ignore_url_params=download_config.ignore_url_params,
--> 197 download_desc=download_config.download_desc,
198 )
199 elif os.path.exists(url_or_filename):
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
532 if head_error is not None:
--> 533 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
534 elif response is not None:
535 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError("HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))")))
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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