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| 1,358,611,513
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I_kwDODunzps5Q-sQ5
| 4,924
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Concatenate_datasets loads everything into RAM
|
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| null | null |
## Describe the bug
When loading the datasets seperately and saving them on disk, I want to concatenate them. But `concatenate_datasets` is filling up my RAM and the process gets killed. Is there a way to prevent this from happening or is this intended behaviour? Thanks in advance
## Steps to reproduce the bug
```python
gcs = gcsfs.GCSFileSystem(project='project')
datasets = [load_from_disk(f'path/to/slice/of/data/{i}', fs=gcs, keep_in_memory=False) for i in range(10)]
dataset = concatenate_datasets(datasets)
```
## Expected results
A concatenated dataset which is stored on my disk.
## Actual results
Concatenated dataset gets loaded into RAM and overflows it which gets the process killed.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 8.0.1
- Pandas version: 1.4.3
|
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I_kwDODunzps5Q7J0y
| 4,922
|
I/O error on Google Colab in streaming mode
|
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## Describe the bug
When trying to load a streaming dataset in Google Colab the loading fails with an I/O error
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
hf_ds = load_dataset(path='wmt19', name='cs-en', streaming=True, split=datasets.Split.VALIDATION)
list(hf_ds.take(5))
```
## Expected results
It should load five data points
## Actual results
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-13-7b5b8b1e7e58>](https://localhost:8080/#) in <module>
2 from datasets import load_dataset
3 hf_ds = load_dataset(path='wmt19', name='cs-en', streaming=True, split=datasets.Split.VALIDATION)
----> 4 list(hf_ds.take(5))
6 frames
[/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self)
716
717 def __iter__(self):
--> 718 for key, example in self._iter():
719 if self.features:
720 # `IterableDataset` automatically fills missing columns with None.
[/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in _iter(self)
706 else:
707 ex_iterable = self._ex_iterable
--> 708 yield from ex_iterable
709
710 def _iter_shard(self, shard_idx: int):
[/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self)
582
583 def __iter__(self):
--> 584 yield from islice(self.ex_iterable, self.n)
585
586 def shuffle_data_sources(self, generator: np.random.Generator) -> "TakeExamplesIterable":
[/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self)
110
111 def __iter__(self):
--> 112 yield from self.generate_examples_fn(**self.kwargs)
113
114 def shuffle_data_sources(self, generator: np.random.Generator) -> "ExamplesIterable":
[~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in _generate_examples(self, split_subsets, extraction_map, with_translation)
845 raise ValueError("Invalid number of files: %d" % len(files))
846
--> 847 for sub_key, ex in sub_generator(*sub_generator_args):
848 if not all(ex.values()):
849 continue
[~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in _parse_parallel_sentences(f1, f2, filename1, filename2)
923 l2_sentences, l2 = parse_file(f2_i, filename2)
924
--> 925 for line_id, (s1, s2) in enumerate(zip(l1_sentences, l2_sentences)):
926 key = f"{f_id}/{line_id}"
927 yield key, {l1: s1, l2: s2}
[~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in gen()
895
896 def gen():
--> 897 with open(path, encoding="utf-8") as f:
898 for line in f:
899 seg_match = re.match(seg_re, line)
ValueError: I/O operation on closed file.
```
## Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.4.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 9.0.0. (the same error happened with PyArrow version 6.0.0)
- Pandas version: 1.3.5
|
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| 1,357,564,589
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I_kwDODunzps5Q6sqt
| 4,920
|
Unable to load local tsv files through load_dataset method
|
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[
"Hi @DataNoob0723,\r\n\r\nUnder the hood, we use `pandas` to load CSV/TSV files. Therefore, you should use \"csv\" and pass `sep=\"\\t\"`, as explained in our docs: https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/loading_methods#from-files\r\n```python\r\nds = load_dataset('csv', sep=\"\\t\", data_files=data_files)\r\n``` "
] | 1,661,962,419,000
| 1,662,010,290,000
| 1,662,010,290,000
|
NONE
| null | null |
## Describe the bug
Unable to load local tsv files through load_dataset method.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
data_files = {
'train': 'train.tsv',
'test': 'test.tsv'
}
raw_datasets = load_dataset('tsv', data_files=data_files)
## Expected results
I am pretty sure the data files exist in the current directory. The above code should load them as Datasets, but threw exceptions.
## Actual results
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
[<ipython-input-9-24207899c1af>](https://localhost:8080/#) in <module>
----> 1 raw_datasets = load_dataset('tsv', data_files='train.tsv')
2 frames
[/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)
1244 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
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:
FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/main/datasets/tsv/tsv.py
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-5.4.188+-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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I_kwDODunzps5Q5eGF
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Dataset Viewer issue for pysentimiento/spanish-targeted-sentiment-headlines
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[
"Thanks for reporting, it's fixed now (I refreshed it manually). It's a known issue; we hope it will be fixed permanently in a few days.\r\n\r\n<img width=\"1508\" alt=\"Capture d’écran 2022-09-05 à 18 31 22\" src=\"https://user-images.githubusercontent.com/1676121/188489762-0ed86a7e-dfb3-46e8-a125-43b815a2c6f4.png\">\r\n",
"Thanks @severo! "
] | 1,661,947,747,000
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NONE
| null | null |
### Link
https://huggingface.co/datasets/pysentimiento/spanish-targeted-sentiment-headlines
### Description
After moving the dataset from my user (`finiteautomata`) to the `pysentimiento` organization, the dataset viewer says that it doesn't exist.
### Owner
_No response_
|
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I_kwDODunzps5Q5SJx
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Keys mismatch: make error message more informative
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[] |
[
"Good idea ! I think this can be improved in `Features.reorder_fields_as()` indeed at\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/features/features.py#L1739-L1740\r\n\r\nIs it something you would be interested in contributing ?",
"Is this open to work on? I'd love to take on this as my first issue.",
"Hi @daspartho I’ve opened a PR #4919 \r\nI don’t think there’s much left to do",
"ok : )"
] | 1,661,945,074,000
| 1,662,367,418,000
| 1,662,367,418,000
|
CONTRIBUTOR
| null | null |
**Is your feature request related to a problem? Please describe.**
When loading a dataset from disk with a defect in its `dataset_info.json` describing its features (I don’t know when/why/how this happens but it deserves its own issue), you will get an error message like:
`ValueError: Keys mismatch: between {'bar': Value(dtype='int64', id=None)} and {'foo': Value(dtype='int64', id=None)}`
Which is fine when you have only a few features like in the example but it gets very hard to read when you have a lot of features in your dataset.
**Describe the solution you'd like**
The error message should give the difference between the features (what keys are in A but missing in B and vice-versa). It should also tell which keys are inferred from `dataset.arrow` and which come from `dataset_info.json`.
Willing to help :)
|
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I_kwDODunzps5Q41nM
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Apache Beam unable to write the downloaded wikipedia dataset
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"See:\r\n- #4915"
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NONE
| null | null |
## Describe the bug
Hi, I am currently trying to download wikipedia dataset using
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner'). However, I end up in getting filenotfound error. I get this error for any language I try to download. It downloads the file but while saving it in hugging face cache it fails to write. This happens for any available date of any language in wikipedia dump. I had raised another issue earlier #4915 but probably was not that clear and the solution provider misunderstood my problem. Hence raising one more issue. Any help is appreciated.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner')
```
## Expected results
to load the dataset
## Actual results
I am pasting the error trace here:
Downloading builder script: 35.9kB [00:00, ?B/s]
Downloading metadata: 30.4kB [00:00, 1.94MB/s]
Using custom data configuration 20220401.aa-date=20220401,language=aa
Downloading and preparing dataset wikipedia/20220401.aa to C:\Users\Shilpa.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading data: 100%|████████████████████████████████████████████████████████████| 11.1k/11.1k [00:00<00:00, 712kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.82s/it]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]
Downloading data: 100%|███████████████████████████████████████████████████████████| 35.6k/35.6k [00:00<00:00, 84.3kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.93s/it]
Traceback (most recent call last):
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in init
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
FileNotFoundError: [Errno 2] No such file or directory: 'C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "G:/abc/temp.py", line 32, in
beam_runner='DirectRunner')
File "G:\Python3.7\lib\site-packages\datasets\load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 1394, in _download_and_prepare
pipeline_results = pipeline.run()
File "G:\Python3.7\lib\site-packages\apache_beam\pipeline.py", line 574, in run
return self.runner.run_pipeline(self, self._options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\direct\direct_runner.py", line 131, in run_pipeline
return runner.run_pipeline(pipeline, options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 201, in run_pipeline
options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 212, in run_via_runner_api
return self.run_stages(stage_context, stages)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 443, in run_stages
runner_execution_context, bundle_context_manager, bundle_input)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 776, in _execute_bundle
bundle_manager))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1000, in _run_bundle
data_input, data_output, input_timers, expected_timer_output)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1309, in process_bundle
result_future = self._worker_handler.control_conn.push(process_bundle_req)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\worker_handlers.py", line 380, in push
response = self.worker.do_instruction(request)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 598, in do_instruction
getattr(request, request_type), request.instruction_id)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 635, in process_bundle
bundle_processor.process_bundle(instruction_id))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 1004, in process_bundle
element.data)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 227, in process_encoded
self.output(decoded_value)
File "apache_beam\runners\worker\operations.py", line 526, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 528, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 237, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
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File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
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File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
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File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
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File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
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File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
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File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
RuntimeError: FileNotFoundError: [Errno 2] No such file or directory: 'C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
## Environment info
Python: 3.7.6
Windows 10 Pro
datasets :2.4.0
apache_beam: 2.41.0
mwparserfromhell: 0.6.4
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FileNotFoundError while downloading wikipedia dataset for any language
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[
"Hi @Shilpac20,\r\n\r\nAs explained in the Wikipedia dataset card: https://huggingface.co/datasets/wikipedia\r\n> You can find the full list of languages and dates [here](https://dumps.wikimedia.org/backup-index.html).\r\n\r\nThis means that, before passing a specific date, you should first make sure it is available online, as Wikimedia only keeps last X months (depending on the size of the corresponding language dump)): e.g. to see which dates \"aa\" Wikipedia is available online, see https://dumps.wikimedia.org/aawiki/ (as of today 2022-08-31, the available dates are from [20220401](https://dumps.wikimedia.org/aawiki/20220401/) to [20220820](https://dumps.wikimedia.org/aawiki/20220820/)).",
"Hi, the date that I have specified \"20220401\" is available for the language \"aa\". The error persists for any other available dates as present in https://dumps.wikimedia.org/aawiki/. The error is mainly due to apache beam not able to write the downloaded files. Any help on this?",
"I see, sorry, I misread your issue.\r\n\r\nWe are investigating this.",
"I am struggling with basically the same issue. I am trying to download the German Wikipedia dump.\r\n\r\nAs per the [documentation](https://huggingface.co/datasets/wikipedia), `\"20220301.de\"` should be available as a pre-processed dataset.\r\n\r\nIssuing the command mentioned in the documentation cited above\r\n\r\n from datasets import load_dataset\r\n load_dataset(\"wikipedia\", \"20220301.de\")\r\n\r\nraises the following `FileNotFound` error\r\n\r\n FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/dewiki/20220301/dumpstatus.json\r\n\r\nUsing the ([undocumented](https://huggingface.co/docs/datasets/v1.2.1/package_reference/loading_methods.html#datasets.load_dataset)?) call to `load_dataset()` with `language` and `date` parameters\r\n\r\n load_dataset(\"wikipedia\", language=\"de\", date=\"20220301\", beam_runner=\"DirectRunner\")\r\n\r\nproduces the same error.\r\n\r\nEDIT: as I am using `datasets` v2.7.1, I should be looking at [that version's documentation](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/loading_methods#datasets.load_dataset)! It is mentioned there, that additional `kwargs` are \"passed to the [BuilderConfig](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/builder_classes#datasets.BuilderConfig) and used in the [DatasetBuilder](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/builder_classes#datasets.DatasetBuilder)\". So I guess that is how `language` and `date` are used.\r\n\r\nAs I can see a folder `20221130` on `https://dumps.wikimedia.org/dewiki/`, I also tried\r\n\r\n from datasets import load_dataset\r\n load_dataset(\"wikipedia\", \"20221130.de\")\r\n\r\nwhich throws another error:\r\n\r\n ValueError: BuilderConfig 20221120.de not found. Available: ['20220301.aa', ... '20220301.de', ...\r\n\r\nbasically telling me that the dataset I originally requested (`'20220301.de'`) is available...\r\n\r\nIt seems that `load_dataset` is not handling the vanishing older dumps for Wikipedia correctly?",
"I am able to start downloading the dataset when trying anything with the recent dumps for 20221201. But obviously, those are the big wiki dumps and I need the smaller preloaded version.\r\n\r\nI am now getting some error when the files show up in my cache but it will say FileNotFoundError at the end of the download for some reason. The cache directory to the datasets\\wikipedia\\date.bn\\ had something in it, then when the error came up it disappeared. \r\n\r\nIt is easy to test with the langauge \"bn\" because the amount of files is low.\r\n\r\ndataset = load_dataset('wikipedia', date=\"20221201\", language=\"bn\", split='train', beam_runner='DirectRunner')"
] | 1,661,876,146,000
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NONE
| null | null |
## Describe the bug
Hi, I am currently trying to download wikipedia dataset using
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner'). However, I end up in getting filenotfound error. I get this error for any language I try to download.
Environment:
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner')
```
## Expected results
to load the dataset
## Actual results
I am pasting the error trace here:
Downloading builder script: 35.9kB [00:00, ?B/s]
Downloading metadata: 30.4kB [00:00, 1.94MB/s]
Using custom data configuration 20220401.aa-date=20220401,language=aa
Downloading and preparing dataset wikipedia/20220401.aa to C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading data: 100%|████████████████████████████████████████████████████████████| 11.1k/11.1k [00:00<00:00, 712kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.82s/it]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]
Downloading data: 100%|███████████████████████████████████████████████████████████| 35.6k/35.6k [00:00<00:00, 84.3kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.93s/it]
Traceback (most recent call last):
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in __init__
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Shilpa\\.cache\\huggingface\\datasets\\wikipedia\\20220401.aa-date=20220401,language=aa\\2.0.0\\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "G:/abc/temp.py", line 32, in <module>
beam_runner='DirectRunner')
File "G:\Python3.7\lib\site-packages\datasets\load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 1394, in _download_and_prepare
pipeline_results = pipeline.run()
File "G:\Python3.7\lib\site-packages\apache_beam\pipeline.py", line 574, in run
return self.runner.run_pipeline(self, self._options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\direct\direct_runner.py", line 131, in run_pipeline
return runner.run_pipeline(pipeline, options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 201, in run_pipeline
options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 212, in run_via_runner_api
return self.run_stages(stage_context, stages)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 443, in run_stages
runner_execution_context, bundle_context_manager, bundle_input)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 776, in _execute_bundle
bundle_manager))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1000, in _run_bundle
data_input, data_output, input_timers, expected_timer_output)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1309, in process_bundle
result_future = self._worker_handler.control_conn.push(process_bundle_req)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\worker_handlers.py", line 380, in push
response = self.worker.do_instruction(request)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 598, in do_instruction
getattr(request, request_type), request.instruction_id)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 635, in process_bundle
bundle_processor.process_bundle(instruction_id))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 1004, in process_bundle
element.data)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 227, in process_encoded
self.output(decoded_value)
File "apache_beam\runners\worker\operations.py", line 526, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 528, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 237, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
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File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
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File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
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File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
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File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in __init__
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
RuntimeError: FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Shilpa\\.cache\\huggingface\\datasets\\wikipedia\\20220401.aa-date=20220401,language=aa\\2.0.0\\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
## Environment info
Python: 3.7.6
Windows 10 Pro
datasets :2.4.0
apache_beam: 2.41.0
mwparserfromhell: 0.6.4
|
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I_kwDODunzps5QxNzQ
| 4,912
|
datasets map() handles all data at a stroke and takes long time
|
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[
"Hi ! Interesting question ;)\r\n\r\n> Which is better? Process in map() or in data-collator\r\n\r\nAs you said, both can be used in practice: map() if you want to preprocess before training, or a data-collator (or the equivalent `dataset.set_transform`) if you want to preprocess on-the-fly during training. Both options are great and really depend on your case.\r\n\r\nTo choose between the two, here are IMO the main caveats of each approach:\r\n- if your preprocessing takes too much CPU for example, using a data-collator may slow down your training and your GPUs may not work at full speed\r\n- on the other hand, map() may take a lot of time and disk space to run if your dataset is too big.\r\n\r\n> Why huggingface advises map() function? There should be some advantages to using map()\r\n\r\nTo get the best throughput when training a model, it is often recommended to preprocess your dataset before training. Note that preprocessing may include other steps before tokenization such as data filtering, cleaning, chunking etc. which are often done before training.",
"Thanks for your clear explanation @lhoestq ! \r\n> * if your preprocessing takes too much CPU for example, using a data-collator may slow down your training and your GPUs may not work at full speed\r\n> * on the other hand, map() may take a lot of time and disk space to run if your dataset is too big.\r\n\r\nI really agree with you. There should be some trade-off between processing before and during the train loop.\r\nBesides, I find `map()` function can cache the results once it has been executed. Very useful!",
"I'm closing this issue if you don't mind, feel free to reopen if needed ;)",
"@lhoestq How to preprocess on-the-fly during training？my data is about 1w hours, when I use map to preprocess, and It's not finished yet， but all disk space(2T) is full.",
"Hi ! You can do that using `set_transform`, see https://huggingface.co/docs/datasets/process#format-transform for more info :)",
"unfortunately , it not work.",
"Could you share more details ?"
] | 1,661,826,356,000
| 1,680,774,238,000
| 1,662,456,215,000
|
NONE
| null | null |
**1. Background**
Huggingface datasets package advises using `map()` to process data in batches. In the example code on pretraining masked language model, they use `map()` to tokenize all data at a stroke before the train loop.
The corresponding code:
```
with accelerator.main_process_first():
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc="Running tokenizer on every text in dataset"
)
```
**2. The problem**
Thus, when I try the same pertaining code with a much larger corpus, it takes quite a long time to tokenize.
Also, we can choose to tokenize data in `data-collator`. In this way, the program only tokenizes one batch in the next training step and avoids getting stuck in tokenization.
**3. My question**
As described above, my questions are:
* **Which is better? Process in `map()` or in `data-collator`**
* **Why huggingface advises `map()` function?** There should be some advantages to using `map()`
Thanks for your answers!
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[Tests] Ensure `datasets` supports renamed repositories
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"You could also switch to using `huggingface_hub` more directly, where such a guarantee is already tested =)\r\n\r\ncc @Wauplin "
] | 1,661,784,374,000
| 1,661,787,063,000
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MEMBER
| null | null |
On https://hf.co/datasets you can rename a dataset (or sometimes move it to another user/org). The website handles redirections correctly and AFAIK `datasets` does as well.
However it would be nice to have an integration test to make sure we don't break support for renamed datasets.
To implement this we can use the /api/repos/move endpoint on hub-ci to rename/move a repo (it is documented at https://huggingface.co/docs/hub/api)
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I_kwDODunzps5Quhy4
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|
Identical keywords in build_kwargs and config_kwargs lead to TypeError in load_dataset_builder()
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[
"I am getting similar error - `TypeError: type object got multiple values for keyword argument 'name'` while following this [tutorial](https://huggingface.co/docs/datasets/dataset_script#create-a-dataset-loading-script). I am getting this error with the `dataset-cli test` command.\r\n\r\n`datasets` version: 2.4.0",
"In my case, this was happening because I defined multiple `BuilderConfig` for multiple types, but didn't had all the data files that are requierd by those configs. \r\n\r\nI think this is different than the original issue by @bablf .",
"Hi ! I think this can be fixed by letting the config_kwargs take over the builder kwargs here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/load.py#L1533-L1534\r\n\r\nmaybe something like this ?\r\n```python\r\n **{**builder_kwargs, **config_kwargs}\r\n```\r\n\r\nLet me know if you'd like to contribute and fix this bug, so I can assign you :)\r\n\r\n> In my case, this was happening because I defined multiple BuilderConfig for multiple types, but didn't had all the data files that are requierd by those configs.\r\n> \r\n> I think this is different than the original issue by @bablf .\r\n\r\nFeel free to to open an new issue, I'd be happy to help\r\n",
"@lhoestq Yeah, I want to, please assign.",
"Cool thank you ! Let me know if you have questions or if I can help",
"@lhoestq On second thoughts, I think this might be expected behavior; although a better error message might help.\r\n\r\nReasoning: Given n configs, if no data file is provided for any config, then it should be an error. Then why it should not be the case if out of n configs, for some data files are provided but not for others. Also, I was using `--all_configs` flag with `dataset-cli test`.",
"Ok I see - maybe we should check the values of builder_kwargs raise an error if any key in config_kwargs tries to overwrite it ? The builder kwargs are determined from the builder's type and location (in some cases it forces the base_path, data_files and config name for example)"
] | 1,661,782,308,000
| 1,663,070,326,000
| null |
NONE
| null | null |
## Describe the bug
In `load_dataset_builder()`, `build_kwargs` and `config_kwargs` can contain the same keywords leading to a TypeError("type object got multiple values for keyword argument "xyz").
I ran into this problem with the keyword: `base_path`. It might happen with other kwargs as well. I think a quickfix would be
```python
builder_cls = import_main_class(dataset_module.module_path)
builder_kwargs = dataset_module.builder_kwargs
data_files = builder_kwargs.pop("data_files", data_files)
config_name = builder_kwargs.pop("config_name", name)
hash = builder_kwargs.pop("hash")
base_path = builder_kwargs.pop("base_path")
```
and then pass base_path into `builder_cls`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("rotten_tomatoes", base_path="./sample_data")
```
## Expected results
The docs state: `**config_kwargs` — Keyword arguments to be passed to the [BuilderConfig](https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/builder_classes#datasets.BuilderConfig) and used in the [DatasetBuilder](https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/builder_classes#datasets.DatasetBuilder).
So I would expect to be able to pass the base_path into `load_dataset()`.
## Actual results
TypeError("type object got multiple values for keyword argument "base_path").
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: macOS-12.5-arm64-arm-64bit
- Python version: 3.8.9
- PyArrow version: 9.0.0
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I_kwDODunzps5QsXnc
| 4,907
|
None Type error for swda datasets
|
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[
"Thanks for reporting @hannan72 ! I couldn't reproduce the error on my side, can you share the full stack trace please ?",
"Thanks a lot for your response @lhoestq \r\nThe problem is solved accidentally today and I don't know exactly why it was happened yesterday.\r\nThe issue can be closed.",
"Ok, let us know if you encounter the issue again ;)"
] | 1,661,756,720,000
| 1,661,870,621,000
| 1,661,870,621,000
|
NONE
| null | null |
## Describe the bug
I got `'NoneType' object is not callable` error while calling the swda datasets.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("swda")
```
## Expected results
Run without error
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Python version: 3.8.10
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I_kwDODunzps5QqI71
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Can't import datasets AttributeError: partially initialized module 'datasets' has no attribute 'utils' (most likely due to a circular import)
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"Thanks for reporting, @OPterminator.\r\n\r\nHowever, we are not able to reproduce this issue.\r\n\r\nThere might be 2 reasons why you get this exception:\r\n- Either the name of your local Python file: if it is called `datasets.py` this could generate a circular import when trying to import the Hugging Face `datasets` library.\r\n - You could try to rename it and run it again.\r\n- Another cause could be the simultaneous use of the packages `nlp` and `datasets`. Please note that we renamed the Hugging Face `nlp` library to `datasets` more than 2 years ago: they are 2 versions of the same library.\r\n - Please try to update your script and use only `datasets` (`nlp` name is no longer in use and is out of date).",
"i am also facing this issue\r\n\r\n\r\n```\r\n----> 1 import datasets\r\n 3 dataset = datasets.load_dataset(\"ucberkeley-dlab/measuring-hate-speech\", \"binary\")\r\n 4 df = dataset[\"train\"].to_pandas()\r\n\r\nFile ~/.pyenv/versions/3.10.9/lib/python3.10/site-packages/datasets/__init__.py:52\r\n 50 from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled\r\n 51 from .info import DatasetInfo, MetricInfo\r\n---> 52 from .inspect import (\r\n 53 get_dataset_config_info,\r\n 54 get_dataset_config_names,\r\n 55 get_dataset_infos,\r\n 56 get_dataset_split_names,\r\n 57 inspect_dataset,\r\n 58 inspect_metric,\r\n 59 list_datasets,\r\n 60 list_metrics,\r\n 61 )\r\n 62 from .iterable_dataset import IterableDataset\r\n 63 from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric\r\n\r\nFile ~/.pyenv/versions/3.10.9/lib/python3.10/site-packages/datasets/inspect.py:30\r\n 28 from .download.streaming_download_manager import StreamingDownloadManager\r\n...\r\n---> 16 logger = datasets.utils.logging.get_logger(__name__)\r\n 19 if datasets.config.PYARROW_VERSION.major >= 7:\r\n 21 def pa_table_to_pylist(table):\r\n```",
"I am facing the same question. And this happens when i installing `evaluate` package while `jupyter notebook` running. I'm not sure if the error occured because of trying to import the package installed when the notebook is running. Surpringly when i stop the notebook and rerun, the issue has been solved itself. Hope this will be helpful : )",
"I also got this error.\r\nIt helped me to find the python process and kill it, then restart the kernel and the error disappeared."
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## Describe the bug
A clear and concise description of what the bug is.
Not able to import datasets
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import os
os.environ["WANDB_API_KEY"] = "0" ## to silence warning
import numpy as np
import random
import sklearn
import matplotlib.pyplot as plt
import pandas as pd
import sys
import tensorflow as tf
import plotly.express as px
import transformers
import tokenizers
import nlp as nlp
import utils
import datasets
```
## Expected results
A clear and concise description of the expected results.
import should work normal
## Actual results
Specify the actual results or traceback.
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-21-b3b5b0b62103> in <module>
13 import nlp as nlp
14 import utils
---> 15 import datasets
~\anaconda3\lib\site-packages\datasets\__init__.py in <module>
44 from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
45 from .info import DatasetInfo, MetricInfo
---> 46 from .inspect import (
47 get_dataset_config_info,
48 get_dataset_config_names,
~\anaconda3\lib\site-packages\datasets\inspect.py in <module>
28 from .download.streaming_download_manager import StreamingDownloadManager
29 from .info import DatasetInfo
---> 30 from .load import dataset_module_factory, import_main_class, load_dataset_builder, metric_module_factory
31 from .utils.file_utils import relative_to_absolute_path
32 from .utils.logging import get_logger
~\anaconda3\lib\site-packages\datasets\load.py in <module>
53 from .iterable_dataset import IterableDataset
54 from .metric import Metric
---> 55 from .packaged_modules import (
56 _EXTENSION_TO_MODULE,
57 _MODULE_SUPPORTS_METADATA,
~\anaconda3\lib\site-packages\datasets\packaged_modules\__init__.py in <module>
4 from typing import List
5
----> 6 from .csv import csv
7 from .imagefolder import imagefolder
8 from .json import json
~\anaconda3\lib\site-packages\datasets\packaged_modules\csv\csv.py in <module>
13
14
---> 15 logger = datasets.utils.logging.get_logger(__name__)
16
17 _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS = ["names", "prefix"]
AttributeError: partially initialized module 'datasets' has no attribute 'utils' (most likely due to a circular import)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.4.0
- Platform: Windows-10-10.0.22000-SP0
- Python version: 3.8.8
- PyArrow version: 9.0.0
- Pandas version: 1.2.4
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I_kwDODunzps5QnQrM
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Name the default config `default`
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CONTRIBUTOR
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Currently, if a dataset has no configuration, a default configuration is created from the dataset name.
For example, for a dataset loaded from the hub repository, such as https://huggingface.co/datasets/user/dataset (repo id is `user/dataset`), the default configuration will be `user--dataset`.
It might be easier to handle to set it to `default`, or another reserved word.
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Dataset Viewer issue for asaxena1990/Dummy_dataset
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"Seems to be linked to the use of the undocumented `_resolve_features` method in the dataset viewer backend:\r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"asaxena1990/Dummy_dataset\", name=\"asaxena1990--Dummy_dataset\", split=\"train\", streaming=True)\r\nUsing custom data configuration asaxena1990--Dummy_dataset-4a704ed7e5627563\r\n>>> dataset._resolve_features()\r\nFailed to read file 'https://huggingface.co/datasets/asaxena1990/Dummy_dataset/resolve/06885879a8bdd767d2d27695484fc6c83244617a/dummy_dataset_train.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Column() changed from object to array in row 0\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py\", line 109, in _generate_tables\r\n pa_table = paj.read_json(\r\n File \"pyarrow/_json.pyx\", line 246, in pyarrow._json.read_json\r\n File \"pyarrow/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 99, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0\r\n\r\nDuring handling of the above exception, another exception occurred:\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/iterable_dataset.py\", line 1261, in _resolve_features\r\n features = _infer_features_from_batch(self._head())\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 686, in _head\r\n return _examples_to_batch([x for key, x in islice(self._iter(), n)])\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 686, in <listcomp>\r\n return _examples_to_batch([x for key, x in islice(self._iter(), n)])\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 708, in _iter\r\n yield from ex_iterable\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 112, in __iter__\r\n yield from self.generate_examples_fn(**self.kwargs)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 651, in wrapper\r\n for key, table in generate_tables_fn(**kwargs):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py\", line 137, in _generate_tables\r\n f\"This JSON file contain the following fields: {str(list(dataset.keys()))}. \"\r\nAttributeError: 'list' object has no attribute 'keys'\r\n```\r\n\r\nPinging @huggingface/datasets",
"Hi ! JSON files containing a list of object are not supported yet, you can use JSON Lines files instead in the meantime\r\n```json\r\n{\"text\": \"can I know this?\", \"intent\": \"Know\", \"type\": \"Test\"}\r\n{\"text\": \"can I know this?\", \"intent\": \"Know\", \"type\": \"Test\"}\r\n...\r\n```"
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NONE
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### Link
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Dataset Viewer issue for timit_asr
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[
"Yes, the dataset viewer is based on `datasets`, and the following does not work:\r\n\r\n```\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names('timit_asr')\r\nDownloading builder script: 7.48kB [00:00, 6.69MB/s]\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/timit_asr/43f9448dd5db58e95ee48a277f466481b151f112ea53e27f8173784da9254fb2/timit_asr.py\", line 117, in _split_generators\r\n data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))\r\n File \"/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/posixpath.py\", line 231, in expanduser\r\n path = os.fspath(path)\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\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\ncc @huggingface/datasets ",
"Due to license restriction, this dataset needs manual downloading of the original data.\r\n\r\nThis information is in the dataset card: https://huggingface.co/datasets/timit_asr\r\n> The dataset needs to be downloaded manually from https://catalog.ldc.upenn.edu/LDC93S1",
"Maybe a better error message for datasets that need manual downloading? @severo \r\n\r\nMaybe we can raise a specific excpetion as done from `load_dataset`...",
"Yes, ideally something like https://github.com/huggingface/datasets/blob/main/src/datasets/builder.py#L81\r\n",
"The preview is now disabled (and a descriptive warning is displayed) for datasets requiring manual download. See:\r\n\r\n\r\n"
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| 1,351,784,727
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I_kwDODunzps5QkpkX
| 4,897
|
datasets generate large arrow file
|
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[
"Hi ! The cache files are the results of all the transforms you applied to the dataset using `map` for example.\r\nDid you run a transform that could potentially blow up the size of the dataset ?",
"@lhoestq,\r\nI don't remember, but I can't imagine what kind of transform may generate data that grow over 200 times in size. \r\nI think maybe it doesn' matter, it's just cache after all."
] | 1,661,493,076,000
| 1,663,477,672,000
| 1,663,477,672,000
|
NONE
| null | null |
Checking the large file in disk, and found the large cache file in the cifar10 data directory:
As we know, the size of cifar10 dataset is ~130MB, but the cache file has almost 30GB size, there may be some problems here.
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I_kwDODunzps5Qg4y_
| 4,895
|
load_dataset method returns Unknown split "validation" even if this dir exists
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[
"I don't know the main problem but it looks like, it is ignoring the last directory in your case. So, create a directory called 'zzz' in the same folder as train, validation and test. if it doesn't work, create a directory called \"aaa\". It worked for me.\r\n",
"@SamSamhuns could you please try to load it with the current main-branch version of `datasets`? I suppose the problem is that it tries to get splits names from filenames in this case, ignoring directories names, but `val` wasn't in keywords at that time, but it was fixed recently in this PR https://github.com/huggingface/datasets/pull/4844. ",
"I have a similar problem.\r\nWhen I try to create `data_infos.json` using `datasets-cli test Peter.py --save_infos --all_configs` I get an error:\r\n`ValueError: Unknown split \"test\". Should be one of ['train'].`\r\n\r\nThe `data_infos.json` is created perfectly fine when I use only one split - `datasets.Split.TRAIN`\r\n\r\n@polinaeterna Could you help here please?\r\n\r\nYou can find the code here: https://huggingface.co/datasets/sberbank-ai/Peter/tree/add_splits (add_splits branch)",
"@skalinin It seems the `dataset_infos.json` of your dataset is missing the info on the test split (and `datasets-cli` doesn't ignore the cached infos at the moment, which is a known bug), so your issue is not related to this one. I think you can fix your issue by deleting all the cached `dataset_infos.json` (in the local repo and in `~/.cache/huggingface/modules`) before running the `datasets-cli test` command. Let us know if that doesn't help, and I can try to generate it myself.",
"This code indeed behaves as expected on `main`. But suppose the `val_234.png` is renamed to some other value not containing one of [these](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L31) keywords, in that case, this issue becomes relevant again because the real cause of it is the order in which we check the predefined split patterns to assign data files to each split - first we assign data files based on filenames, and only if this fails meaning not a single split found (`val` is not recognized here in the older versions of `datasets`, which results in an empty `validation` split), do we assign based on directory names.\r\n\r\n@polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if `data_dir` is specified (or if `load_dataset(data_dir)` is called)? ",
"> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nyes that makes sense !",
"Looks like the `val/validation` dir name issue is fixed with the current main-branch version of the `datasets` repository. \r\n\r\n> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nI agree with this as well. I would expect higher precedence to the directory name over the file name. Right now if I place a single file named `train_00001.jpg` under the `validation` directory, `load_dataset` cannot find the validation split.",
"Thanks for the reply\r\n\r\nI've created a separate [issue](https://github.com/huggingface/datasets/issues/4982#issue-1375604693) for my problem.",
"> @polinaeterna @lhoestq Perhaps one way to fix this would be to swap the [order](https://github.com/huggingface/datasets/blob/38c8c725f3996ff1ff03f6fd461aa6d645321034/src/datasets/data_files.py#L78-L79) of the patterns if data_dir is specified (or if load_dataset(data_dir) is called)?\r\n\r\nSounds good to me! opened a PR: https://github.com/huggingface/datasets/pull/4985",
"Hi there @polinaeterna @mariosasko ! I have installed 5.2.3.dev0, which should have this fix. Unfortunately, I am still getting the error:\r\n`ValueError: Unknown split \"validation\". Should be one of ['train'].` When I call `load_dataset(\"csv\", data_files=files, split=split)`\r\n\r\nAny help would be greatly appreciated!",
"hi @shaneacton ! could you please show your dataset structure?",
"Hi there @polinaeterna . My local CSV files are stored as follows:\r\nbinding:\r\n---------- tune.csv\r\n---------- public_data:\r\n--------------------------- train.csv\r\n\r\n`self.list_shards(split)` sucessfully finds the relevant data files",
"@shaneacton do you have `validation.csv`/`val.csv`/`valid.csv`/`dev.csv` file in your data folder? I can't find it in the structure you provided",
"@polinaeterna no, does the name of the split need to match the name of the file exactly?\r\n\r\nBut my train file is not actually named 'train.py' its called 'XXXXXXXXX_train_XXXXXXXX.csv'\r\nAnd the code works fine for train, but fails for validation.\r\nDoes the file name need to _contain_ the split name?",
"@shaneacton what files do you expect to be included in \"validation\" split? yes, you should somehow indicate that a file belongs to a certain split - either by including split name in a filename or by putting it into a folder with split name, you can also check out [this documentation page](https://huggingface.co/docs/datasets/main/en/repository_structure) :)\r\nby default all the data goes to a single `train` split",
"@polinaeterna I have specified my train/test/tune files via the `split_to_filepattern` argument when initialising my `FileDataSource` class. This is how `list_shards` is able to find the right files.\r\nAfter your last message, I have tried renaminig my data files to simply `train.csv` and `validation.csv`, however I am still getting the same error: `Unknown split \"validation\". Should be one of ['train']`",
"@polinaeterna I have solved the issue. The solution was to call:\r\n`load_dataset(\"csv\", data_files={split: files}, split=split)`"
] | 1,661,429,460,000
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## Describe the bug
The `datasets.load_dataset` returns a `ValueError: Unknown split "validation". Should be one of ['train', 'test'].` when running `load_dataset(local_data_dir_path, split="validation")` even if the `validation` sub-directory exists in the local data path.
The data directories are as follows and attached to this issue:
```
test_data1
|_ train
|_ 1012.png
|_ metadata.jsonl
...
|_ test
...
|_ validation
|_ 234.png
|_ metadata.jsonl
...
test_data2
|_ train
|_ train_1012.png
|_ metadata.jsonl
...
|_ test
...
|_ validation
|_ val_234.png
|_ metadata.jsonl
...
```
They contain the same image files and `metadata.jsonl` but the images in `test_data2` have the split names prepended i.e.
`train_1012.png, val_234.png` and the images in `test_data1` do not have the split names prepended to the image names i.e. `1012.png, 234.png`
I actually saw in another issue `val` was not recognized as a split name but here I would expect the files to take the split from the parent directory name i.e. val should become part of the validation split?
## Steps to reproduce the bug
```python
import datasets
datasets.logging.set_verbosity_error()
from datasets import load_dataset, get_dataset_split_names
# the following only finds train, validation and test splits correctly
path = "./test_data1"
print("######################", get_dataset_split_names(path), "######################")
dataset_list = []
for spt in ["train", "test", "validation"]:
dataset = load_dataset(path, split=spt)
dataset_list.append(dataset)
# the following only finds train and test splits
path = "./test_data2"
print("######################", get_dataset_split_names(path), "######################")
dataset_list = []
for spt in ["train", "test", "validation"]:
dataset = load_dataset(path, split=spt)
dataset_list.append(dataset)
```
## Expected results
```
###################### ['train', 'test', 'validation'] ######################
###################### ['train', 'test', 'validation'] ######################
```
## Actual results
```
Traceback (most recent call last):
File "test_data_loader.py", line 11, in <module>
dataset = load_dataset(path, split=spt)
File "/home/venv/lib/python3.8/site-packages/datasets/load.py", line 1758, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 893, in as_dataset
datasets = map_nested(
File "/home/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 385, in map_nested
return function(data_struct)
File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 924, in _build_single_dataset
ds = self._as_dataset(
File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 993, in _as_dataset
dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 211, in read
files = self.get_file_instructions(name, instructions, split_infos)
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 184, in get_file_instructions
file_instructions = make_file_instructions(
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 107, in make_file_instructions
absolute_instructions = instruction.to_absolute(name2len)
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 616, in to_absolute
return [_rel_to_abs_instr(rel_instr, name2len) for rel_instr in self._relative_instructions]
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 616, in <listcomp>
return [_rel_to_abs_instr(rel_instr, name2len) for rel_instr in self._relative_instructions]
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 433, in _rel_to_abs_instr
raise ValueError(f'Unknown split "{split}". Should be one of {list(name2len)}.')
ValueError: Unknown split "validation". Should be one of ['train', 'test'].
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux Ubuntu 18.04
- Python version: 3.8.12
- PyArrow version: 9.0.0
Data files
[test_data1.zip](https://github.com/huggingface/datasets/files/9424463/test_data1.zip)
[test_data2.zip](https://github.com/huggingface/datasets/files/9424468/test_data2.zip)
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I_kwDODunzps5QgV66
| 4,893
|
Oversampling strategy for iterable datasets in `interleave_datasets`
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[
"Hi @lhoestq,\r\nI plunged into the code and it should be manageable for me to work on it!\r\n#take\r\n\r\nAlso, setting `d1`, `d2` and `d3` as you did raised a `SyntaxError: 'yield' inside list comprehension` for me, on Python 3.8.10.\r\nThe following snippet works for me though:\r\n```\r\nd1 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [0, 1, 2]])), {}))\r\nd2 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [10, 11, 12, 13]])), {}))\r\nd3 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [20, 21, 22, 23, 24]])), {}))\r\n```\r\n\r\n",
"Great @ylacombe thanks ! I'm assigning you this issue",
"Hi @ylacombe :) Is there anything I can do to help ? Feel free to ping me if you have any question :)",
"Hi @lhoestq,\r\n\r\nI actually have already wrote the code last time [on this commit](https://github.com/ylacombe/datasets/commit/84769db97facc78a33ec53f7b1b395951e1804df) but I still have to change the docs and write some tests though. I'm working on it.\r\n\r\nHowever, I still your advice on one matter. \r\nIn #4831, when using a `Dataset` list with probabilities, I had change the original behavior so that it stops as soon as one or all datasets are out of samples. By nature, this behavior can't be applied with an `IterableDataset` because one only knows an iterable dataset is out of sample when receiving a StopIteration error after calling the iterator once again. \r\nTo sum up, as it is right know, the behavior is not consistent with an `IterableDataset` list or a `Dataset` list, when using probabilities.\r\nTo be honest, I think that the current behavior with a `Dataset` list is desirable and avoid having too many samples, so I would recommand keeping that as it is, but I can understand the desire to have the same behavior for both classes. \r\nWhat do you think ? Please let me know if you need more details.\r\n\r\n\r\nEDIT:\r\nHere is an example:\r\n```\r\n>>> from tests.test_iterable_dataset import *\r\n>>> d1 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [0, 1, 2]])), {}))\r\n>>> d2 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [10, 11, 12, 13]])), {}))\r\n>>> d3 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {\"a\": i}) for i in [20, 21, 22, 23, 24]])), {}))\r\n>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)\r\n>>> [x[\"a\"] for x in dataset]\r\n[10, 0, 11, 1, 2, 20, 12, 13]\r\n>>> from tests.test_arrow_dataset import *\r\n>>> d1 = Dataset.from_dict({\"a\": [0, 1, 2]})\r\n>>> d2 = Dataset.from_dict({\"a\": [10, 11, 12]})\r\n>>> d3 = Dataset.from_dict({\"a\": [20, 21, 22]})\r\n>>> interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)[\"a\"]\r\n[10, 0, 11, 1, 2]\r\n[10, 0, 11, 1, 2]\r\n```\r\n ",
"Hi ! Awesome :) \r\n\r\nMaybe you can pre-load the next sample to know if the dataset is empty or not ?\r\nThis way it should be possible to have the same behavior for `IterableDataset`",
"Hi @ylacombe let us know if we can help with anything :)",
"Hi @lhoestq, I've finally made some advances in the matter. I've modified the `IterableDataset` behavior so that it aligns with the `Dataset` behavior as we have discussed. The documentation has been dealt with too. \r\nIt works as expected on my examples. However I'm having trouble figuring out how to test `interleave_datasets` on `test_iterable_datasets.py` as I have never worked with pytest. Could you help me on that or give me some indications? \r\n",
"Thanks @ylacombe :)\r\n\r\nUsing the `pytest` command, you can run all the functions in a python file that start with \"test_*\" and make sure they return not errors:\r\n```\r\npytest tests/test_iterable_dataset.py\r\n```\r\n\r\nIn our case it can be nice to define a `test_interleave_datasets_with_oversampling` function. This function can contain the code example that we mentioned earlier in this github issue to make sure it works as expected.",
"Resolved via #5036."
] | 1,661,422,015,000
| 1,664,800,666,000
| 1,664,800,666,000
|
MEMBER
| null | null |
In https://github.com/huggingface/datasets/pull/4831 @ylacombe added an oversampling strategy for `interleave_datasets`. However right now it doesn't work for datasets loaded using `load_dataset(..., streaming=True)`, which are `IterableDataset` objects.
It would be nice to expand `interleave_datasets` for iterable datasets as well to support this oversampling strategy
```python
>>> from datasets.iterable_dataset import IterableDataset, ExamplesIterable
>>> d1 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [0, 1, 2]], {}))
>>> d2 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [10, 11, 12, 13]], {}))
>>> d3 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [20, 21, 22, 23, 24]], {}))
>>> dataset = interleave_datasets([d1, d2, d3]) # is supported
>>> [x["a"] for x in dataset]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
>>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted") # is not supported yet
>>> [x["a"] for x in dataset]
[0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 0, 24]
```
This can be implemented by adding the strategy to both `CyclingMultiSourcesExamplesIterable` and `RandomlyCyclingMultiSourcesExamplesIterable` used in `_interleave_iterable_datasets` in `iterable_dataset.py`
I would be happy to share some guidance if anyone would like to give it a shot :)
|
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I_kwDODunzps5Qc649
| 4,889
|
torchaudio 11.0 yields different results than torchaudio 12.1 when loading MP3
|
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[
"Maybe we can just pass this along to torchaudio @lhoestq @albertvillanova ? It be great if you could investigate if the errors lies in datasets or in torchaudio.",
"torchaudio did a change in [0.12](https://github.com/pytorch/audio/releases/tag/v0.12.0) on MP3 decoding (which affects common voice):\r\n> MP3 decoding is now handled by FFmpeg in sox_io backend. (https://github.com/pytorch/audio/pull/2419, https://github.com/pytorch/audio/pull/2428)\r\n> - FFmpeg is now used as fallback in sox_io backend, and now MP3 decoding is handled by FFmpeg. To load MP3 audio with torchaudio.load, please install a compatible version of FFmpeg (Version 4 when using an official binary distribution).\r\n> - Note that, whereas the previous MP3 decoding scheme pads the output audio, the new scheme does not. As a consequence, the new version returns shorter audio tensors.",
"Do we have a solution for this now? Should we just upgrade to `torchaudio 0.12.0` then? ",
"`datasets` supports `torchaudio` 0.12 if you have an environment that supports reading MP3 with `torchaudio`, i.e. if you have `ffmpeg>=4`",
"Closing as we no longer use `torchaudio` for decoding."
] | 1,661,360,083,000
| 1,677,771,185,000
| 1,677,771,184,000
|
MEMBER
| null | null |
## Describe the bug
When loading Common Voice with torchaudio 0.11.0 the results are different to 0.12.1 which leads to problems in transformers see: https://github.com/huggingface/transformers/pull/18749
## Steps to reproduce the bug
If you run the following code once with `torchaudio==0.11.0+cu102` and `torchaudio==0.12.1+cu102` you can see that the tensors differ. This is a pretty big breaking change and makes some integration tests fail in Transformers.
```python
#!/usr/bin/env python3
from datasets import load_dataset
import datasets
import numpy as np
import torch
import torchaudio
print("torch vesion", torch.__version__)
print("torchaudio vesion", torchaudio.__version__)
save_audio = True
load_audios = False
if save_audio:
ds = load_dataset("common_voice", "en", split="train", streaming=True)
ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
ds_iter = iter(ds)
sample = next(ds_iter)
np.save(f"audio_sample_{torch.__version__}", sample["audio"]["array"])
print(sample["audio"]["array"])
if load_audios:
array_torch_11 = np.load("/home/patrick/audio_sample_1.11.0+cu102.npy")
print("Array 11 Shape", array_torch_11.shape)
print("Array 11 abs sum", np.sum(np.abs(array_torch_11)))
array_torch_12 = np.load("/home/patrick/audio_sample_1.12.1+cu102.npy")
print("Array 12 Shape", array_torch_12.shape)
print("Array 12 abs sum", np.sum(np.abs(array_torch_12)))
```
Having saved the tensors the print output yields:
```
torch vesion 1.12.1+cu102
torchaudio vesion 0.12.1+cu102
Array 11 Shape (122880,)
Array 11 abs sum 1396.4988
Array 12 Shape (123264,)
Array 12 abs sum 1396.5193
```
## Expected results
torchaudio 11.0 and 12.1 should yield same results.
## Actual results
See above.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.1.dev0
- Platform: Linux-5.18.10-76051810-generic-x86_64-with-glibc2.34
- Python version: 3.9.7
- PyArrow version: 6.0.1
- Pandas version: 1.4.2
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I_kwDODunzps5Qbu9h
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Dataset Viewer issue for subjqa
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[
"It's a bug in the viewer, thanks for reporting it. We're hoping to update to a new version in the next few days which should fix it.",
"Fixed \r\n\r\nhttps://huggingface.co/datasets/subjqa\r\n\r\n<img width=\"1040\" alt=\"Capture d’écran 2022-09-08 à 10 23 26\" src=\"https://user-images.githubusercontent.com/1676121/189073210-2a57ff88-8bb1-44bd-851e-0e75473cea3f.png\">\r\n"
] | 1,661,347,580,000
| 1,662,625,422,000
| 1,662,625,422,000
|
MEMBER
| null | null |
### Link
https://huggingface.co/datasets/subjqa
### Description
Getting the following error for this dataset:
```
Status code: 500
Exception: Status500Error
Message: 2 or more items returned, instead of 1
```
Not sure what's causing it though 🤔
### Owner
Yes
|
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I_kwDODunzps5QbHbB
| 4,886
|
Loading huggan/CelebA-HQ throws pyarrow.lib.ArrowInvalid
|
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[
"Hi! IIRC one of the files in this dataset is corrupted due to https://github.com/huggingface/datasets/pull/4081 (fixed now).\r\n\r\n@NielsRogge Could you please re-generate and re-push this dataset (or I can do it if you share the generation script)?",
"Could you put something in place to catch these problems? I'm seeing this on another dataset consistently too and I guess I can't fix it in code?",
"Hey,\r\n\r\nYes the notebook I used to upload this dataset can be found here: https://colab.research.google.com/drive/141LJCcM2XyqprPY83nIQ-Zk3BbxWeahq?usp=sharing.\r\n\r\nIf you have time to regenerate the dataset, would be great.",
"Sorry, maybe I wasn't clear enough that it's a different dataset `laion2B-multi-joined-translated-to-en`. I think there should be checks in the upload, tests on the server, or validation after download (hashes) to catch these problems.\r\n\r\nLots of bandwidth wasted otherwise! /cc @mariosasko",
"Yes @alexjc sorry was more a reply to @JeanKaddour.\r\n\r\nAnd indeed it'd be great to have additional checks to avoid these errors. ",
"cc @severo since such checks should probably be implemented on the datasets-server side.",
"Hi,\r\n\r\nIt seems the problem is still persist. I have encountered the exact same problem using just 2 line of code above. \r\n\r\nThe error code is as follows:\r\n\r\n```\r\n發生例外狀況: DatasetGenerationError\r\nAn error occurred while generating the dataset\r\npyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\n File \"/code/ddpm_learn/train.py\", line 65, in <module>\r\n dataset = load_dataset(\"huggan/CelebA-HQ\", cache_dir=\"./CelebA-HQ\"\r\ndatasets.builder.DatasetGenerationError: An error occurred while generating the dataset\r\n```",
"Yes for the moment refer to the notebook linked above if you want to create a HF dataset yourself",
"Hi @NielsRogge ,\r\nI can help to push the dataset to the cloud. However, I cannot locate the situation so far. I wonder if \r\n1. the downloaded files so far has corruption s.t. the file cannot generate properly, or\r\n2. the downloaded files has no bug, the bug is caused by buggy upload program so that I can use what I have just downloaded to re-upload to cloud\r\n\r\nThank, \r\nAllan"
] | 1,661,340,261,000
| 1,675,305,653,000
| null |
NONE
| null | null |
## Describe the bug
Loading huggan/CelebA-HQ throws pyarrow.lib.ArrowInvalid
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('huggan/CelebA-HQ')
```
## Expected results
See https://colab.research.google.com/drive/141LJCcM2XyqprPY83nIQ-Zk3BbxWeahq?usp=sharing#scrollTo=N3ml_7f8kzDd
## Actual results
```
File "/home/jean/projects/cold_diffusion/celebA.py", line 4, in <module>
dataset = load_dataset('huggan/CelebA-HQ')
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/load.py", line 1793, in load_dataset
builder_instance.download_and_prepare(
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 1274, in _prepare_split
for key, table in logging.tqdm(
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 67, in _generate_tables
parquet_file = pq.ParquetFile(f)
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/pyarrow/parquet/__init__.py", line 286, in __init__
self.reader.open(
File "pyarrow/_parquet.pyx", line 1227, in pyarrow._parquet.ParquetReader.open
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets-2.4.1.dev0
- Platform: Ubuntu 18.04
- Python version: 3.10
- PyArrow version: pyarrow 9.0.0
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I_kwDODunzps5QauAI
| 4,885
|
Create dataset from list of dicts
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"Hi @sanderland, thanks for your enhancement proposal.\r\n\r\nI agree with you that this would be useful.\r\n\r\nPlease note that under the hood, we use PyArrow tables as backend:\r\n- The implementation of `Dataset.from_dict` uses the PyArrow `Table.from_pydict`\r\n\r\nTherefore, I would suggest:\r\n- Implementing `Dataset.from_list` using the PyArrow `Table.from_pylist`\r\n\r\nWhat do you think?\r\nLet's see if other people have other suggestions...",
"Thanks for the quick and positive reply @albertvillanova! \r\n`from_list` seems sensible. Have opened a PR so we can discuss details there.",
"Resolved via #4890."
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CONTRIBUTOR
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I often find myself with data from a variety of sources, and a list of dicts is very common among these.
However, converting this to a Dataset is a little awkward, requiring either
```Dataset.from_pandas(pd.DataFrame(formatted_training_data))```
Which can error out on some more exotic values as 2-d arrays for reasons that are not entirely clear
> ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column labels with type object')
Alternatively:
```Dataset.from_dict({k: [s[k] for s in formatted_training_data] for k in formatted_training_data[0].keys()})```
Which works, but is a little ugly.
**Describe the solution you'd like**
Either `.from_dict` accepting a list of dicts, or a `.from_records` function accepting such.
I am happy to PR this, just wanted to check you are happy to accept this I haven't missed something obvious, and which of the solutions would be prefered.
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With dataloader RSS memory consumed by HF datasets monotonically increases
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"Are you sure there is a leak? How can I see it? You shared the script but not the output which you believe should indicate a leak.\r\n\r\nI modified your reproduction script to print only once per try as your original was printing too much info and you absolutely must add `gc.collect()` when doing any memory measurements, since python's GC is scheduled so you might be measuring the wrong thing. This gives us:\r\n\r\n```\r\nimport psutil\r\nimport os\r\nimport gc\r\nfrom transformers import BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\nBATCH_SIZE = 32\r\nNUM_TRIES = 100\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\ndef transform(x):\r\n x.update(tokenizer(x[\"text\"], return_tensors=\"pt\", max_length=64, padding=\"max_length\", truncation=True))\r\n x.pop(\"text\")\r\n x.pop(\"label\")\r\n return x\r\ndataset = load_dataset(\"imdb\", split=\"train\")\r\ndataset.set_transform(transform)\r\ntrain_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)\r\n\r\nmem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n\r\ncount = 0\r\nwhile count < NUM_TRIES:\r\n for idx, batch in enumerate(train_loader): pass\r\n gc.collect()\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(count, mem_after - mem_before)\r\n count += 1\r\n```\r\n\r\nNow running it:\r\n\r\n```\r\n$ python dl-leak.py \r\nReusing dataset imdb (/home/stas/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1)\r\n0 4.43359375\r\n1 4.4453125\r\n2 4.44921875\r\n3 4.44921875\r\n4 4.4609375\r\n5 4.46484375\r\n6 4.46484375\r\n7 4.46484375\r\n8 4.46484375\r\n9 4.46484375\r\n10 4.46484375\r\n11 4.46484375\r\n12 4.46484375\r\n13 4.46484375\r\n14 4.46484375\r\n15 4.46484375\r\n16 4.46484375\r\n```\r\n\r\nIt's normal that at the beginning there is a small growth in memory usage, but after 5 cycles it gets steady.",
"Unless of course you're referring the memory growth during the first try. Is that what you're referring to? And since your ds is small it's hard to see the growth - could it be just because some records are longer and it needs to allocate more memory for those?\r\n\r\nThough while experimenting with this I have observed a peculiar thing, if I concatenate 2 datasets, I don't see any growth at all. But that's probably because the program allocated additional peak RSS memory to concatenate and then is re-using the memory\r\n\r\nI basically tried to see if I make the dataset much longer, I'd expect not to see any memory growth once the 780 records of the imdb ds have been processed once.",
"It is hard to say if it is directly reproducible in this setup. Maybe it is specific to the images stored in the CM4 case which cause a memory leak. I am still running your script and seeing if I can reproduce that particular leak in this case.",
"I was able to reproduce the leak with:\r\n\r\n```\r\nimport psutil\r\nimport os\r\nimport gc\r\nfrom datasets import load_from_disk\r\nimport time\r\n\r\nDATASET_PATH = \"/hf/m4-master/data/cm4/cm4-10000-v0.1\"\r\n\r\ndataset = load_from_disk(DATASET_PATH)\r\n\r\n# truncate to a tiny dataset\r\ndataset = dataset.select(range(1000))\r\n\r\nprint(f\"dataset: {len(dataset)} records\")\r\n\r\nmem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\nfor idx, rec in enumerate(dataset):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n```\r\nYou need to adjust the DATASET_PATH record.\r\n\r\nwhich you get from\r\n\r\n```\r\ngsutil -m cp \"gs://hf-science-m4/cm4/cm4-10000-v0.1/dataset.arrow\" \"gs://hf-science-m4/cm4/cm4-10000-v0.1/dataset_info.json\" \"gs://hf-science-m4/cm4/cm4-10000-v0.1/state.json\" .\r\n```\r\n(I assume the hf folks have the perms) - it's a smallish dataset (10k)\r\n\r\nthen you run:\r\n```\r\n$ python ds.py\r\ndataset: 1000 records\r\n 0 1.0156MB\r\n 100 126.3906MB\r\n 200 142.8906MB\r\n 300 168.5586MB\r\n 400 218.3867MB\r\n 500 230.7070MB\r\n 600 238.9570MB\r\n 700 263.3789MB\r\n 800 288.1289MB\r\n 900 300.5039MB\r\n```\r\n\r\nyou should be able to see the leak ",
"This issue has nothing to do with `PIL`'s decoder. I removed it and the problem is still there.\r\n\r\nI then traced this leak to this single call: `pa_table.to_pydict()` here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/08a7b389cdd6fb49264a72aa8ccfc49a233494b6/src/datasets/formatting/formatting.py#L138-L140\r\n\r\nI can make it leak much faster by modifying that code to repeat `pa_table.to_pydict()` many times in a row. It shouldn't have that impact:\r\n\r\n```\r\nclass PythonArrowExtractor(BaseArrowExtractor[dict, list, dict]):\r\n def extract_row(self, pa_table: pa.Table) -> dict:\r\n x = [pa_table.to_pydict() for x in range(200)]\r\n return _unnest(pa_table.to_pydict())\r\n```\r\n\r\n@lhoestq - do you know what might be happening inside `pa_table.to_pydict()`, as this is in the `pyarrow` domain. Perhaps you know someone to tag from that project?\r\n\r\nProbably next need to remove `datasets` from the equation and make a reproducible case with just `pyarrow` directly.\r\n\r\nThe problem already happens with `pyarrow==6.0.0` or later (minimum for current `datasets`)\r\n\r\nI'm also trying to dig in with `objgraph` to see if there are any circular references which prevent objects from being freed, but no luck there so far. And I'm pretty sure `to_pydict` is not a python code, so the problem is likely to happen somewhere outside of python's GC.",
"This appears to be the same issue I think: https://github.com/huggingface/datasets/issues/4528\r\nI dug into the repro code there and it's the same behavior with the same leak, but it's a pure nlp dataset and thus much faster to work with. \r\n",
"I went all the way back to `pyarrow==1.0.0` and `datasets==1.12.0` and the problem is still there. How is it even possible that it wasn't noticed all this time. \r\n\r\nCould it be that the leak is in some 3rd party component `pyarrow` relies on? as while downgrading I have only downgraded the above 2 packages.\r\n",
"Also found this warning \r\n\r\n> Be careful: if you don't pass the ArrowArray struct to a consumer,\r\n> array memory will leak. This is a low-level function intended for\r\n> expert users.\r\n\r\nsee: https://github.com/apache/arrow/blob/99b57e84277f24e8ec1ddadbb11ef8b4f43c8c89/python/pyarrow/table.pxi#L2515-L2517\r\n\r\nperhaps something triggers this condition?\r\n\r\nI have no idea if it's related - this is just something that came up during my research.",
"Does it crash with OOM at some point? If it doesn't, it isn't a leak, just agressive caching or a custom allocator that doesn't like to give memory back (not uncommon). #4528 looks like it hits a steady state.\r\n\r\nI believe the underlying arrow libs use a custom C allocator. Some of those are designed not to give back to OS, but keep heap memory for themselves to re-use (hitting up the OS involves more expensive mutex locks, contention, etc). The greedy behaviour can be undesirable though. There are likely flags to change the allocator behaviour, and one could likely build without any custom allocators (or use a different one).",
"> Does it crash with OOM at some point?\r\n\r\nIn the original setup where we noticed this problem, it was indeed ending in an OOM",
"> https://github.com/huggingface/datasets/issues/4528 looks like it hits a steady state.\r\n\r\n@rwightman in the plot I shared, the steady state comes from the `time.sleep(100)` I added in the end of the script, to showcase that even the garbage collector couldn't free that allocated memory.\r\n",
"Could this be related to this discussion about a potential memory leak in pyarrow: https://issues.apache.org/jira/browse/ARROW-11007 ?\r\n\r\n(Note: I've tried `import pyarrow; pyarrow.jemalloc_set_decay_ms(0)` and the memory leak is still happening on your toy example)",
"> @lhoestq - do you know what might be happening inside pa_table.to_pydict(), as this is in the pyarrow domain. Perhaps you know someone to tag from that project?\r\n\r\n`to_pydict` calls `to_pylist` on each column (i.e. on each PyArrow Array). Then it iterates on the array and calls `as_py` on each element. The `as_py` implementation depends on the data type. For strings I think it simply gets the buffer that contains the binary string data that is defined in C++\r\n\r\nThe Arrow team is pretty responsive at user@arrow.apache.org if it can help\r\n\r\n> Probably next need to remove datasets from the equation and make a reproducible case with just pyarrow directly.\r\n\r\nThat would be ideal indeed. Would be happy to help on this, can you give me access to the bucket so I can try with your data ?",
"> That would be ideal indeed. Would be happy to help on this, can you give me access to the bucket so I can try with your data ?\r\n\r\nI added you to the bucket @lhoestq ",
"It looks like an issue with memory mapping:\r\n- the amount of memory used in the end corresponds to the size of the dataset\r\n- setting `keep_in_memory=True` in `load_from_disk` loads the dataset in RAM, and **doesn't cause any memory leak**",
"Here is a code to reproduce this issue using only PyArrow and a dummy arrow file:\r\n```python\r\nimport psutil\r\nimport os\r\nimport gc\r\nimport pyarrow as pa\r\nimport time\r\n\r\nARROW_PATH = \"tmp.arrow\"\r\n\r\nif not os.path.exists(ARROW_PATH):\r\n arr = pa.array([b\"a\" * (200 * 1024)] * 1000) # ~200MB\r\n table = pa.table({\"a\": arr})\r\n\r\n with open(ARROW_PATH, \"wb\") as f:\r\n writer = pa.RecordBatchStreamWriter(f, schema=table.schema)\r\n writer.write_table(table)\r\n writer.close()\r\n\r\n\r\ndef memory_mapped_arrow_table_from_file(filename: str) -> pa.Table:\r\n memory_mapped_stream = pa.memory_map(filename)\r\n opened_stream = pa.ipc.open_stream(memory_mapped_stream)\r\n pa_table = opened_stream.read_all()\r\n return pa_table\r\n\r\n\r\ntable = memory_mapped_arrow_table_from_file(ARROW_PATH)\r\narr = table[0]\r\n\r\nmem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\nfor idx, x in enumerate(arr):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n```\r\nprints\r\n```\r\n 0 0.2500MB\r\n 100 19.8008MB\r\n 200 39.3320MB\r\n 300 58.8633MB\r\n 400 78.3945MB\r\n 500 97.9258MB\r\n 600 117.4570MB\r\n 700 136.9883MB\r\n 800 156.5195MB\r\n 900 176.0508MB\r\n```\r\nNote that this example simply iterates over the `pyarrow.lib.BinaryScalar` objects in the array. Running `.as_py()` is not needed to experience the memory issue.",
"@lhoestq that does indeed increase in memory, but if you iterate over array again after the first time, or re-open and remap the same file (repeat `table = memory_mapped_arrow_table_from_file(ARROW_PATH)`) before re-iterating, it doesn't move pas 195MB.... it would appear another step is needed to continue consuming memory past that.. hmmm\r\n\r\nAre the pa_tables held on to anywhere after they are iterated in the real code?\r\n\r\nin my hack, if you do a bunch cut & paste and then change the arr name for each iter \r\n\r\n```\r\ntable = memory_mapped_arrow_table_from_file(ARROW_PATH)\r\narr = table[0]\r\n\r\nfor idx, x in enumerate(arr):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n\r\ntable = memory_mapped_arrow_table_from_file(ARROW_PATH)\r\narr1 = table[0]\r\n\r\nfor idx, x in enumerate(arr1):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n\r\ntable = memory_mapped_arrow_table_from_file(ARROW_PATH)\r\narr2 = table[0]\r\n\r\nfor idx, x in enumerate(arr2):\r\n if idx % 100 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB\")\r\n```\r\n\r\nit leaks, if all arr are the same name (so prev one gets cleaned up) it does not and goes back to 0, anything that could be holding onto a reference of an intermediary equivalent like arr in the real use case?\r\n\r\n\r\n\r\n",
"Yes, we have already established here https://github.com/huggingface/datasets/issues/4883#issuecomment-1232063891 that when one iterates over the whole dataset multiple times, it consumes a bit more memory in the next few repetitions and then remains steady. \r\n\r\nWhich means that when a new iterator is created over the same dataset, all the memory from the previous iterator is re-used.\r\n\r\nSo the leak happens primarily when the iterator is \"drained\" the first time. which tells me that either a circular reference is created somewhere which only gets released when the iterator is destroyed, or there is some global variable that keeps piling up the memory and doesn't release it in time.\r\n\r\nAlso I noticed some `__del__` methods which won't destroy objects automatically and there is usually a warning against using it https://stackoverflow.com/a/1481512/9201239\r\n\r\nThere are also some `weakref`s in the code which too may lead to leaks or weird problems at times.\r\n",
"@stas00 my point was, I'm not convinced @lhoestq last example illustrates the leak, but rather the differences between memory mapping and in memory usage patterns. If you destroy arr, memory map impl goes back to 0 each iteration. The amount of memory that 'looks' like it is leaked in first pass differes quite a bit between memory mapped vs in memory, but the underlying issue likely a circular reference, or reference(s) which were not cleaned up that would impact either case, but likely much more visible with mmap.",
"Thank you for clarifying, Ross. \r\n\r\nI think we agree that it's almost certain that the `datasets` iterator traps some inner variable that prevents object freeing, since if we create the iterator multiple times (and drain it) after a few runs no new memory is allocated. We could try to dig in more with `objgraph` - my main concern is if the problem happens somewhere outside of python, (i.e. in pyarrow cpp implementation) in which case it'd be much more difficult to trace. \r\n\r\nI wish there was a way on linux to tell the program to free no longer used memory at will.",
"FWIW, I revisted some code I had in the works to use HF datasets w/ timm train & val scripts. There is no leak there across multipe epochs. It uses the defaults. \r\n\r\nIt's worth noting that with imagenet `keep_in_memory=True` isn't even an option because the train arrow file is ~140GB and my local memory is less. The virtual address space reflects mmap (> 150GB) and doesn't increase over epochs that I noticed. I have some perf issues to bring up wrt to the current setup, but that's a separate and lower prio discussion to have elsewhere...",
"# Notes \r\n\r\nAfter reading many issues and trying many things here is the summary of my learning\r\n\r\nI'm now using @lhoestq repro case as it's pyarrow-isolated: https://github.com/huggingface/datasets/issues/4883#issuecomment-1242034985\r\n\r\n\r\n## 1. pyarrow memory backends\r\n\r\nit has 3 backends, I tried them all with the same results\r\n\r\n```\r\npa.set_memory_pool(pa.jemalloc_memory_pool())\r\npa.set_memory_pool(pa.mimalloc_memory_pool())\r\npa.set_memory_pool(pa.system_memory_pool())\r\n```\r\n\r\n## 2. quick release\r\n\r\nThe `jemalloc` backend supports quick release\r\n\r\n```\r\npa.jemalloc_set_decay_ms(0)\r\n```\r\n\r\nit doesn't make any difference in this case\r\n\r\n## 3. actual memory allocations\r\n\r\nthis is a useful tracer for PA memory allocators\r\n```\r\npa.log_memory_allocations(enable=True)\r\n```\r\n\r\nit nicely reports memory allocations and releases when the arrow file is created the first time.\r\n\r\nbut when we then try to do `enumerate(arr)` this logger reports 0 allocations.\r\n\r\nThis summary also reports no allocations when the script run the second time (post file creation):\r\n```\r\nmem_pool = pa.default_memory_pool()\r\nprint(f\"PyArrow mem pool info: {mem_pool.backend_name} backend, {mem_pool.bytes_allocated()} allocated, \"\r\n f\"{mem_pool.max_memory()} max allocated, \")\r\n\r\nprint(f\"PyArrow total allocated bytes: {pa.total_allocated_bytes()}\")\r\n```\r\n\r\nHowever it's easy to see by using `tracemalloc` which only measures python's memory allocations that it's PA that leaks, since `tracemalloc` shows fixed memory\r\n\r\n(this is bolted on top of the original repro script)\r\n\r\n```\r\nimport tracemalloc\r\ntracemalloc.start()\r\n\r\n[...]\r\nfor idx, x in enumerate(arr):\r\n if idx % 10 == 0:\r\n gc.collect()\r\n time.sleep(0.1)\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\n mem_use = pa.total_allocated_bytes() - start_use\r\n mem_peak = pool.max_memory() - start_peak_use\r\n\r\n second_size, second_peak = tracemalloc.get_traced_memory()\r\n mem_diff = (second_size - first_size) / 2**20\r\n mem_peak_diff = (second_peak - first_peak) / 2**20\r\n\r\n # pa.jemalloc_memory_pool().release_unused()\r\n # pa.mimalloc_memory_pool().release_unused()\r\n # pa.system_memory_pool().release_unused()\r\n\r\n print(f\"{idx:4d} {mem_after - mem_before:12.4f}MB {mem_diff:12.4f} {mem_peak_diff:12.4f} {memory_mapped_stream.size()/2**20:4.4}MB {mem_use/2**20:4.4}MB {mem_peak/2**20:4.4}MB\")\r\n\r\n```\r\n\r\ngives:\r\n\r\n```\r\n 0 5.4258MB 0.0110 0.0201 195.3MB 0.0MB 0.0MB\r\n 10 25.3672MB 0.0112 0.0202 195.3MB 0.0MB 0.0MB\r\n 20 45.9336MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 30 62.4336MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 40 83.0586MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 50 103.6836MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 60 124.3086MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 70 140.8086MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 80 161.4336MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n 90 182.0586MB 0.0112 0.0203 195.3MB 0.0MB 0.0MB\r\n```\r\n\r\nthe 3rd and 4th columns are `tracemalloc`'s report.\r\n\r\nthe 5th column is the size of mmaped stream - fixed.\r\n\r\nthe last 2 are the PA's malloc reports - you can see it's totally fixed and 0.\r\n\r\nSo what gives? PA's memory allocator says nothing was allocated and we can see python doesn't allocate any memory either.\r\n\r\nAs someone suggested in one of the PA issues that **IPC/GRPC could be the issue.** Any suggestions on how debug this one?\r\n\r\nThe main issue is that one can't step through with a python debugger as `arr` is an opaque cpp object binded to python.\r\n\r\nPlease see the next comment for a possible answer.\r\n\r\n# ref-count\r\n\r\nI also traced reference counts and they are all fixed using either `sys.getrefcount(x)` or `len(gc.get_referrers(x))`\r\n\r\nso it's not the python object\r\n\r\n# Important related discussions\r\n\r\nhttps://issues.apache.org/jira/browse/ARROW-11007 - looks very similar to our issue\r\nin particular this part of the report:\r\nhttps://issues.apache.org/jira/browse/ARROW-11007?focusedCommentId=17279642&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-17279642\r\n",
"# There is no leak, just badly communicated linux RSS memory usage stats\r\n\r\nNext, lets revisit @rwightman's suggestion that there is actually no leak.\r\n\r\nAfter all - we are using mmap which **will try to map** the file to RAM as much as it can and then page out if there is no memory. i.e. MMAP is only fast if you have a lot of CPU RAM.\r\n\r\nSo let's do it:\r\n\r\n# Memory mapping OOM test\r\n\r\nWe first quickly start a cgroups-controlled shell which will instantly kill any program that consumes more than 1GB of memory:\r\n\r\n```\r\n$ systemd-run --user --scope -p MemoryHigh=1G -p MemoryMax=1G -p MemorySwapMax=1G --setenv=\"MEMLIMIT=1GB\" bash\r\n```\r\n\r\nLet's check that it indeed does so. Let's change @lhoestq's script to allocate a 10GB arrow file:\r\n\r\n```\r\n$ python -c 'import pyarrow as pa; pa.array([b\"a\" * (2000 * 1024)] * 5000)'\r\nKilled\r\n```\r\noops, that didn't work, as we tried to allocate 10GB when only 1GB is allowed. This is what we want!\r\n\r\nLet's do a sanity check - can we allocate 0.1GB?\r\n```\r\npython -c 'import pyarrow as pa; pa.array([b\"a\" * (2000 * 1024)] * 50)'\r\n```\r\n\r\nYes. So the limited shell does the right thing. It let's allocate `< 1GB` of RSS RAM.\r\n\r\nNext let's go back to @lhoestq's script but with 10GB arrow file.\r\n\r\nwe change his repro script https://github.com/huggingface/datasets/issues/4883#issuecomment-1242034985 to 50x larger file\r\n```\r\n arr = pa.array([b\"a\" * (2000 * 1024)] * 5000) # ~10000MB\r\n```\r\nwe first have to run into a normal unlimited shell so that we don't get killed (as the script allocates 10GB)\r\n\r\nlet's run the script now in the 1GB-limited shell while running a monitor:\r\n\r\n```\r\n$ htop -F python -s M_RESIDENT -u `whoami`\r\n```\r\n\r\nso we have 2 sources of RSS info just in case.\r\n\r\n```\r\n$ python pyar.py\r\n 0 4.3516MB 0.0103 0.0194 9.766e+03MB 0.0MB 0.0MB\r\n 10 24.3008MB 0.0104 0.0195 9.766e+03MB 0.0MB 0.0MB\r\n[...]\r\n4980 9730.3672MB 0.0108 0.0199 9.766e+03MB 0.0MB 0.0MB\r\n4990 9750.9922MB 0.0108 0.0199 9.766e+03MB 0.0MB 0.0MB\r\nPyArrow mem pool info: jemalloc backend, 0 allocated, 0 max allocated,\r\nPyArrow total allocated bytes: 0\r\n```\r\n\r\nBut wait, it reported 10GB RSS both in `htop` and in our log!\r\n\r\nSo that means it never allocated 10GB otherwise it'd have been killed.\r\n\r\n**Which tells us that there is no leak whatsoever** and this is just a really difficult situation where MMAPPED memory is reported as part of RSS which it probably shouldn't. As now we have no way how to measure real memory usage.\r\n\r\nI also attached the script with all the different things I have tried in it, so it should be easy to turn them on/off if you want to reproduce any of my findings.\r\n\r\n[pyar.txt](https://github.com/huggingface/datasets/files/9539430/pyar.txt)\r\n\r\njust rename it to `pyra.py` as gh doesn't let attaching scripts...\r\n\r\n(I have to remember to exit that special mem-limited shell or else I won't be able to do anything serious there.)\r\n\r\n",
"The original leak in the multi-modal code is very likely something else. But of course now it'd be very difficult to trace it using mmap.\r\n\r\nI think to debug we have to set `keep_in_memory=True` in `load_from_disk` to load the small dataset in RAM, so there will be no mmap misleading reporting component and then continue searching for another source of a leak.",
"To add to what @stas00 found, I'm gonna leave some links to where I believe the confusion came from in pyarrow's APIs, for future reference:\r\n* In the section where they talk about [efficiently writing and reading arrow data](https://arrow.apache.org/docs/dev/python/ipc.html?#efficiently-writing-and-reading-arrow-data), they give an example of how \r\n\r\n> Arrow can directly reference the data mapped from disk and avoid having to allocate its own memory. \r\n\r\nAnd where their example shows 0 RSS memory allocation, the way we used to measure RSS shows 39.6719MB allocated. Here's the script to reproduce:\r\n```\r\nimport psutil\r\nimport os\r\nimport gc\r\nimport pyarrow as pa\r\nimport time\r\nimport sys\r\n\r\n# gc.set_debug(gc.DEBUG_LEAK)\r\n# gc.set_threshold(0,0,0)\r\n\r\n#pa.set_memory_pool(pa.mimalloc_memory_pool())\r\n#pa.set_memory_pool(pa.system_memory_pool())\r\n\r\nimport tracemalloc\r\n\r\n#pa.jemalloc_set_decay_ms(0)\r\n# pa.log_memory_allocations(enable=True)\r\n\r\nBATCH_SIZE = 10000\r\nNUM_BATCHES = 1000\r\nschema = pa.schema([pa.field('nums', pa.int32())])\r\nwith pa.OSFile('bigfile.arrow', 'wb') as sink:\r\n with pa.ipc.new_file(sink, schema) as writer:\r\n for row in range(NUM_BATCHES):\r\n batch = pa.record_batch([pa.array(range(BATCH_SIZE), type=pa.int32())], schema)\r\n writer.write(batch)\r\n\r\nstart_use = pa.total_allocated_bytes()\r\npool = pa.default_memory_pool()\r\nstart_peak_use = pool.max_memory()\r\ntracemalloc.start()\r\nfirst_size, first_peak = tracemalloc.get_traced_memory()\r\nmem_before = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\n\r\n\r\n# with pa.OSFile('bigfile.arrow', 'rb') as source:\r\n# loaded_array = pa.ipc.open_file(source).read_all()\r\n\r\nwith pa.memory_map('bigfile.arrow', 'rb') as source:\r\n loaded_array = pa.ipc.open_file(source).read_all()\r\n\r\n\r\nprint(\"LEN:\", len(loaded_array))\r\nprint(\"RSS: {}MB\".format(pa.total_allocated_bytes() >> 20))\r\n\r\ngc.collect()\r\ntime.sleep(0.1)\r\nmem_after = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\nmem_use = pa.total_allocated_bytes() - start_use\r\nmem_peak = pool.max_memory() - start_peak_use\r\nsecond_size, second_peak = tracemalloc.get_traced_memory()\r\nmem_diff = (second_size - first_size) / 2**20\r\nmem_peak_diff = (second_peak - first_peak) / 2**20\r\n\r\nidx = 0\r\nprint(f\"{idx:4d} {mem_after - mem_before:12.4f}MB {mem_diff:12.4f} {mem_peak_diff:12.4f} {mem_use/2**20:4.4}MB {mem_peak/2**20:4.4}MB\")\r\n```\r\ngives:\r\n```\r\n\r\nLEN: 10000000\r\nRSS: 0MB\r\n 0 39.6719MB 0.0132 0.0529 0.0MB 0.0MB\r\n```\r\nWhich again just proves that we uncorrectly measure RSS, in the case of MMAPPED memory\r\n\r\n\r\n* [The recommended way to do memory profiling from Arrow's docs](https://arrow.apache.org/docs/dev/cpp/memory.html#memory-profiling)\r\n",
"@lhoestq, I have been working on a detailed article that shows that MMAP doesn't leak and it's mostly ready. I will share when it's ready.\r\n\r\nThe issue is that we still need to be able to debug memory leaks by turning MMAP off.\r\n\r\nBut, once I tried to show the user that using `load_dataset(... keep_in_memory=True)` is the way to debug an actual memory leak - guess I what I discovered? A potential actual leak.\r\n\r\nHere is the repro:\r\n\r\n```\r\n$ cat ds-mmap.py\r\nfrom datasets import load_dataset\r\nimport gc\r\nimport os\r\nimport psutil\r\n\r\nproc = psutil.Process(os.getpid())\r\ndef mem_read():\r\n gc.collect()\r\n return proc.memory_info().rss / 2**20\r\n\r\ndataset = load_dataset(\"wmt19\", 'cs-en', keep_in_memory=True, streaming=False)['train']\r\n\r\nprint(f\"{'idx':>6} {'RSS':>10} {'Δ RSS':>15}\")\r\nstep = 20000\r\nfor i in range(0, 10*step, step):\r\n mem_before = mem_read()\r\n _ = dataset[i:i+step]\r\n mem_after = mem_read()\r\n print(f\"{i:6d} {mem_after:12.4f}MB {mem_after - mem_before:12.4f}MB \")\r\n```\r\n\r\n```\r\npython ds-io.py\r\nReusing dataset wmt19 (/home/stas/.cache/huggingface/datasets/wmt19/cs-en/1.0.0/c3db1bf4240362ed1ef4673b354f468d70aac66d4e67d45f536d493a0840f0d3)\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 5.66it/s]\r\n idx RSS Δ RSS\r\n 0 1398.4609MB 3.5195MB\r\n 20000 1398.5742MB 0.1133MB\r\n 40000 1398.6016MB 0.0273MB\r\n 60000 1398.6016MB 0.0000MB\r\n 80000 1398.6016MB 0.0000MB\r\n100000 1398.6328MB 0.0312MB\r\n120000 1398.6953MB 0.0625MB\r\n140000 1398.6953MB 0.0000MB\r\n160000 1398.7500MB 0.0547MB\r\n180000 1398.7500MB 0.0000MB\r\n```",
"as I suggested on slack perhaps it was due to dataset records length variation, so with your help I wrote another repro with synthetic records which are all identical - which should remove my hypothese from the equation and we should expect 0 incremental growth as we iterate over the datasets. But alas this is not the case. There is a tiny but definite leak-like behavior.\r\n\r\nHere is the new repro:\r\n\r\n```\r\n$ cat ds-synthetic-no-mmap.py\r\nfrom datasets import load_from_disk, Dataset\r\nimport gc\r\nimport sys\r\nimport os\r\nimport psutil\r\n\r\nproc = psutil.Process(os.getpid())\r\ndef mem_read():\r\n gc.collect()\r\n return proc.memory_info().rss / 2**20\r\n\r\nDS_PATH = \"synthetic-ds\"\r\nif not os.path.exists(DS_PATH):\r\n records = 1_000_000\r\n print(\"Creating a synthetic dataset\")\r\n row = dict(foo=[dict(a='a'*500, b='b'*1000)])\r\n ds = Dataset.from_dict({k: [v] * records for k, v in row.items()})\r\n ds.save_to_disk(DS_PATH)\r\n print(\"Done. Please restart the program\")\r\n sys.exit()\r\n\r\ndataset = load_from_disk(DS_PATH, keep_in_memory=True)\r\nprint(f\"Dataset len={len(dataset)}\")\r\n\r\nprint(f\"{'idx':>8} {'RSS':>10} {'Δ RSS':>15}\")\r\nmem_start = 0\r\nstep = 25_000\r\nwarmup_iterations = 4\r\nfor idx, i in enumerate(range(0, len(dataset), step)):\r\n if idx == warmup_iterations: # skip the first few iterations while things get set up\r\n mem_start = mem_read()\r\n mem_before = mem_read()\r\n _ = dataset[i:i+step]\r\n mem_after = mem_read()\r\n print(f\"{i:8d} {mem_after:12.4f}MB {mem_after - mem_before:12.4f}MB\")\r\nmem_end = mem_read()\r\n\r\nprint(f\"Total diff: {mem_end - mem_start:12.4f}MB (after {warmup_iterations} warmup iterations)\")\r\n```\r\n\r\nand the run:\r\n\r\n```\r\n$ python ds-synthetic-no-mmap.py\r\nDataset len=1000000\r\n idx RSS Δ RSS\r\n 0 1601.9258MB 47.9688MB\r\n 25000 1641.6289MB 39.7031MB\r\n 50000 1641.8594MB 0.2305MB\r\n 75000 1642.1289MB 0.2695MB\r\n 100000 1642.1289MB 0.0000MB\r\n 125000 1642.3789MB 0.2500MB\r\n 150000 1642.3789MB 0.0000MB\r\n 175000 1642.6289MB 0.2500MB\r\n 200000 1642.6289MB 0.0000MB\r\n 225000 1642.8789MB 0.2500MB\r\n 250000 1642.8828MB 0.0039MB\r\n 275000 1643.1328MB 0.2500MB\r\n 300000 1643.1328MB 0.0000MB\r\n 325000 1643.3828MB 0.2500MB\r\n 350000 1643.3828MB 0.0000MB\r\n 375000 1643.6328MB 0.2500MB\r\n 400000 1643.6328MB 0.0000MB\r\n 425000 1643.8828MB 0.2500MB\r\n 450000 1643.8828MB 0.0000MB\r\n 475000 1644.1328MB 0.2500MB\r\n 500000 1644.1328MB 0.0000MB\r\n 525000 1644.3828MB 0.2500MB\r\n 550000 1644.3828MB 0.0000MB\r\n 575000 1644.6328MB 0.2500MB\r\n 600000 1644.6328MB 0.0000MB\r\n 625000 1644.8828MB 0.2500MB\r\n 650000 1644.8828MB 0.0000MB\r\n 675000 1645.1328MB 0.2500MB\r\n 700000 1645.1328MB 0.0000MB\r\n 725000 1645.3828MB 0.2500MB\r\n 750000 1645.3828MB 0.0000MB\r\n 775000 1645.6328MB 0.2500MB\r\n 800000 1645.6328MB 0.0000MB\r\n 825000 1645.8828MB 0.2500MB\r\n 850000 1645.8828MB 0.0000MB\r\n 875000 1646.1328MB 0.2500MB\r\n 900000 1646.1328MB 0.0000MB\r\n 925000 1646.3828MB 0.2500MB\r\n 950000 1646.3828MB 0.0000MB\r\n 975000 1646.6328MB 0.2500MB\r\nTotal diff: 4.5039MB (after 4 warmup iterations)\r\n```\r\nso I'm still not sure why we get this.\r\n\r\nAs you can see I started skipping the first few iterations where memory isn't stable yet. As the actual diff is much larger if we count all iterations.\r\n\r\nWhat do you think?",
"@stas00 my 2 cents from having looked at a LOT of memory leaks over the years, esp in Python, .3% memory increase over that many iterations of something is difficult to say with certainty it is a leak. \r\n\r\nAlso, just looking at RSS makes it hard to analyze leaks. RSS can stay near constant while you are leaking. RSS is paged in mem, if you have a big leak your RSS might not increase much (leaked mem tends not to get used again so often paged out) while your virtual page allocation could be going through the roof...",
"yes, that's true, but unless the leak is big, I'm yet to find another measurement tool.\r\n\r\nTo prove your point here is a very simple IO in a loop program that also reads the same line all over again:\r\n\r\n```\r\n$ cat mmap-no-leak-debug.py\r\nimport gc\r\nimport mmap\r\nimport os\r\nimport psutil\r\nimport sys\r\n\r\nproc = psutil.Process(os.getpid())\r\n\r\nPATH = \"./tmp.txt\"\r\n\r\ndef mem_read():\r\n gc.collect()\r\n return proc.memory_info().rss / 2**20\r\n\r\n# create a large data file with a few long lines\r\nif not os.path.exists(PATH):\r\n with open(PATH, \"w\") as fh:\r\n s = 'a'* 2**27 + \"\\n\" # 128MB\r\n # write ~2GB file\r\n for i in range(16):\r\n fh.write(s)\r\n\r\nprint(f\"{'idx':>4} {'RSS':>10} {'Δ RSS':>12} {'Δ accumulated':>10}\")\r\n\r\ntotal_read = 0\r\ncontent = ''\r\nmem_after = mem_before_acc = mem_after_acc = mem_before = proc.memory_info().rss / 2**20\r\nprint(f\"{0:4d} {mem_after:10.2f}MB {mem_after - 0:10.2f}MB {0:10.2f}MB\")\r\n\r\nmmap_mode = True if \"--mmap\" in sys.argv else False\r\n\r\nwith open(PATH, \"r\") as fh:\r\n\r\n if mmap_mode:\r\n mm = mmap.mmap(fh.fileno(), 0, access=mmap.ACCESS_READ)\r\n\r\n idx = 0\r\n while True:\r\n idx += 1\r\n mem_before = mem_read()\r\n line = mm.readline() if mmap_mode else fh.readline()\r\n if not line:\r\n break\r\n\r\n #total_read += len(line)\r\n\r\n if \"--accumulate\" in sys.argv:\r\n mem_before_acc = mem_read()\r\n content += str(line)\r\n mem_after_acc = mem_read()\r\n\r\n mem_after = mem_read()\r\n\r\n print(f\"{idx:4d} {mem_after:10.2f}MB {mem_after - mem_before:10.2f}MB {mem_after_acc - mem_before_acc:10.2f}MB\")\r\n```\r\n\r\nit has some other instrumentations to do mmap and accumulate data, but let's ignore that for now.\r\n\r\nHere it is running in a simple non-mmap IO:\r\n\r\n```\r\n$ python mmap-no-leak-debug.py\r\n idx RSS Δ RSS Δ accumulated\r\n 0 12.43MB 12.43MB 0.00MB\r\n 1 269.72MB 257.29MB 0.00MB\r\n 2 269.73MB 0.02MB 0.00MB\r\n 3 269.73MB 0.00MB 0.00MB\r\n 4 269.74MB 0.01MB 0.00MB\r\n 5 269.74MB 0.00MB 0.00MB\r\n 6 269.75MB 0.01MB 0.00MB\r\n 7 269.75MB 0.00MB 0.00MB\r\n 8 269.76MB 0.01MB 0.00MB\r\n 9 269.76MB 0.00MB 0.00MB\r\n 10 269.77MB 0.01MB 0.00MB\r\n 11 269.77MB 0.00MB 0.00MB\r\n 12 269.77MB 0.00MB 0.00MB\r\n 13 269.77MB 0.00MB 0.00MB\r\n 14 269.77MB 0.00MB 0.00MB\r\n 15 269.77MB 0.00MB 0.00MB\r\n 16 146.02MB -123.75MB 0.00MB\r\n```\r\n\r\nas you can see even this super-simplistic program that just performs `readline()` slightly increases in RSS over iterations.\r\n\r\nIf you have a better tool for measurement other than RSS, I'm all ears.",
"@stas00 if you aren't using memory maps, you should be able to clearly see the increase in the virtual mem for the process as well. Even then, it could still be challenging to determine if it's leak vs fragmentation due to problematic allocation patterns (not uncommon with Python). Using a better mem allocator like tcmalloc via LD_PRELOAD hooks could reduce impact of fragmentation across both Python and c libs. Not sure that plays nice with any allocator that arrow might use itself though. "
] | 1,661,330,574,000
| 1,664,468,191,000
| null |
CONTRIBUTOR
| null | null |
## Describe the bug
When the HF datasets is used in conjunction with PyTorch Dataloader, the RSS memory of the process keeps on increasing when it should stay constant.
## Steps to reproduce the bug
Run and observe the output of this snippet which logs RSS memory.
```python
import psutil
import os
from transformers import BertTokenizer
from datasets import load_dataset
from torch.utils.data import DataLoader
BATCH_SIZE = 32
NUM_TRIES = 10
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def transform(x):
x.update(tokenizer(x["text"], return_tensors="pt", max_length=64, padding="max_length", truncation=True))
x.pop("text")
x.pop("label")
return x
dataset = load_dataset("imdb", split="train")
dataset.set_transform(transform)
train_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)
mem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
count = 0
while count < NUM_TRIES:
for idx, batch in enumerate(train_loader):
mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
print(count, idx, mem_after - mem_before)
count += 1
```
## Expected results
Memory should not increase after initial setup and loading of the dataset
## Actual results
Memory continuously increases as can be seen in the log.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 7.0.0
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Language names and language codes: connecting to a big database (rather than slow enrichment of custom list)
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"Thanks for opening this discussion, @alexis-michaud.\r\n\r\nAs the language validation procedure is shared with other Hugging Face projects, I'm tagging them as well.\r\n\r\nCC: @huggingface/moon-landing ",
"on the Hub side, there is not fine grained validation we just check that `language:` contains an array of lowercase strings between 2 and 3 characters long =)\r\n\r\nand for `language_bcp47:` we just check it's an array of strings.\r\n\r\nThe only page where we have a hardcoded list of languages is https://huggingface.co/languages and I've been thinking of hooking that page on an external database of languages (so any suggestion is super interesting), but it's not used for validation.\r\n\r\nThat being said, in `datasets` this file https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/languages.json is not really used no? Or just in the tagging tool? What about just removing it?\r\n\r\nalso cc'ing @lbourdois who's been active and helpful on those subjects in the past!",
"PS @alexis-michaud is there a DB of language codes you would recommend? That would contain all `ISO 639-1, 639-2 or 639-3 codes` and be kept up to date, and ideally that would be accessible as a Node.js npm package?\r\n\r\ncc @albertvillanova too",
"> PS @alexis-michaud is there a DB of language codes you would recommend? That would contain all `ISO 639-1, 639-2 or 639-3 codes` and be kept up to date, and ideally that would be accessible as a Node.js npm package?\r\n> \r\n> cc @albertvillanova too\r\n\r\nMany thanks for your answer! \r\n\r\nThe Glottolog database is kept up to date, and has information on the closest ISO code for each Glottocode. So providing a clean table with equivalences sounds (to me) like something perfectly reasonable to expect from their team. \r\nTo what extent would [pyglottolog](https://github.com/glottolog/pyglottolog) fit the bill / do the job? (API documentation [here](https://pyglottolog.readthedocs.io/en/latest/index.html)) I'm reaching my technical limitations here: I can't assess the distance between what they offer and what the HF team needs. \r\nI have opened an Issue in [their repo](https://github.com/glottolog/glottolog-cldf/issues/13). \r\n\r\nVery interested to see where it goes from there.",
"I just tried pyglottolog to generate a file with all the current IDs (first column).\r\n\r\n`glottolog languoids` inside the `glottolog` repository.\r\n\r\n[glottolog-languoids-v4.6-10-g5c66eec874.csv](https://github.com/huggingface/datasets/files/9417456/glottolog-languoids-v4.6-10-g5c66eec874.csv)\r\n\r\n",
"Greetings @alexis-michaud and others,\r\nI think perhaps a standards-based approach here would help everyone out both at the technical and social layers of technical innovations. \r\n\r\nLet me say a few things: \r\n1. there are multiple kinds of assets in AI that should have associated language codes. \r\n * AI Training Data sets\r\n * AI models\r\n * AI outputs\r\nThese are all distinct components which should be tagged for the language and encoding methods they operate on or enhance. For example, an AI based cross-language tool from French to English (UK variety) still needs to consider if it is operating on oral language speech or written text. This is where [IANA language sub-tags](https://www.iana.org/assignments/language-subtag-registry/language-subtag-registry) come in and are so important. I link to the official source. If one wants to use middleware such as a python package or npm package to manage strings then please make sure those packages are updating codes as they are being revised. I see that @julien-c mentioned BCP-47. BCP-47 is the current standard for language tagging. Following it will make the resources you create more findable and let future users better understand or expect any biases which may have been introduced in the different AI based products.\r\n2. BCP-47 is a technical read. However, you will notice that it identifies when to use an ISO 639-1, ISO 639-2, or ISO 639-3. code. This is important for interoperability with many systems. If you are using library systems then you should likely just stick with ISO 639-3 codes.\r\n3. If you are going to use Glottolog codes use them after an `-x-` tag in the BCP-47 format to maintain BCP-47 validity. \r\n4. You should source ISO 639-3 codes directly from the [ISO 639-3 registrar](https://iso639-3.sil.org/code_tables/639/data) as these codes are updated annually, usually in February or March. ISO 639-3 codes have multiple classes: `Active`, `Deprecated`, and `Unassigned`. This means that string length checking is not a sufficient strategy for validation.\r\n5. The names of smaller languages often change depending on the language used to describe them. The [ISO639-2 documentation](https://www.loc.gov/standards/iso639-2/php/code_list.php) has a list of language names for languages with smaller populations for languages in which descriptions about these languages are often written. For example, ISO 639-2's documentation contains the names of languages as they are used in French, German, and English. ISO 639-2 rarely is updated as it is now tied to ISO 639-3's evolution and modern systems should just use ISO 639-3, but these additional names of languages in other languages may not appear in the ISO 369-3 tables.\r\n6. Glottolog codes are also updated at least annually. Usually sometime after ISO 639-3 updates.\r\n7. Please, if the material is in a written mode, please indicate which script is used unless the IANA field has a `suppress script` value. Please use the script tag that BCP-47 calls for from [ISO 15924](https://unicode.org/iso15924/iso15924-codes.html). This also updates at least annually. \r\n8. Another great place to look for language names is the [Unicode CLDR database for locales](https://cldr.unicode.org/translation/displaynames/languagelocale-names). These ought to be congruent with ISO 639-3 but, sometimes CLDR has additional references to languages (such as the french name for a language) which is not contained in ISO 639-2 or ISO 639-3.\r\n9. Wikidata for language names is not always a great source of authoritative information. Language names are asymmetrical. Many times they are contrived because there is no actual name for the language in the language referring... e.g. French doesn't have a name for every language in the world, often they say something like: the language of 'x' people. — English does the same. When a language name standard does not have the best name for a language the best way to handle that is to make a change request with the standards registrar. Keeping track of the source list and the version of your source list for your language codes is very important. \r\n10. Finally, It would be a great service to technologist, minority language communities, and linguists if for all resources of the three types mentioned in number 1 above you added a record to [OLAC](http://www.language-archives.org/). — I can help you with that. OLAC is a search interface for language resources.\r\n",
"Hi everybody!\r\n\r\nAbout the point:\r\n> also cc'ing @lbourdois who's been active and helpful on those subjects in the past!\r\n\r\nDiscussions on the need to improve the Hub's tagging system (applying to both datasets and models) can be found in the following discussion: https://github.com/huggingface/hub-docs/issues/193\r\nOnce this system has been redone and satisfies the identified needs, a redesign of the [Languages page](https://huggingface.co/languages) would also be relevant: https://github.com/huggingface/hub-docs/issues/194. \r\nI invite you to read them. But as a quick summary, the exchanges were oriented towards the ISO standard (the first HF system was based on it and it is generally the standard indicated in AI/DL papers) by favouring ISO 639-1 if it exists, and fallback to ISO 639-2 or ISO 639-3 if it doesn't. In addition, it is possible to add BCP-47 tags to consider existing varieties/regionalisms within a language (https://huggingface.co/datasets/AmazonScience/massive/discussions/1). If a language does not belong to either of these two standards, then a request should be made to the HF team to add it manually.\r\n\r\n\r\nTo return to the present discussion, thank you for the various databases and methodologies you mention. It makes a big difference to have linguists in the loop 🚀.\r\n\r\nI have a couple of questions where I think an expert perspective would be appreciated:\r\n- Do you think it's possible to easily handle tags that have been deprecated potentially for decades?\r\nFor example (I'm taking the case of Hebrew but this has happened for other languages) I tagged Google models with the \"iw\" [tag](https://huggingface.co/models?language=iw&sort=downloads) because I based it on what the authors gave in their [paper](https://arxiv.org/pdf/2010.11934.pdf) see table 6 page 12). It turns out that this ISO tag has in fact been deprecated since 1989 in favour of the \"he\" tag. It would therefore be necessary to have a verification that transforms the old tags into the most recent ones.\r\n\r\n- When you look up a language on Wikipedia, it usually shows, in addition to the ISO standard, the codes in the Glottolog (which you have already mentioned), [ELP](https://www.endangeredlanguages.com/?hl=en) and [Linguasphere](http://www.linguasphere.info/jr/index.php?l1=home&l2=welcome) databases. Would you have any opinion about these two other databases?\r\n\r\n- On the Hub, there is the following dataset where French people speak in English: https://huggingface.co/datasets/Datatang/French_Speaking_English_Speech_Data_by_Mobile_Phone \r\nIs there a database to take this case into account? I have not found any code in the Glottolog database. If based on an IETF BCP-47 standard, I would tend to tag the dataset with \"en-fr\" but would this be something accepted by linguists?\r\nBased on the first post in this thread that there are about 8000 languages, if one considers that a given language can be pronounced by a speaker of the other 7999, that would theoretically make about 64 million BCP-47 language1-language2 codes existing. And even much more if we consider regionalists with language1_regionalism_x-language2_regionalism_y. I guess there is no such database.\r\n\r\n- Are there any databases that take into account all the existing sign languages in the world?\r\nIt would be nice to have them included on the Hub.\r\n\r\n- Is there an international classification of languages?\r\nA bit like the [International Classification of Diseases](https://en.wikipedia.org/wiki/International_Classification_of_Diseases) in medicine, which is established by the WHO and used as a reference throughout the world. The idea would be to have a precise number of languages to which we would then have to assign a unique tag in order to find them later. \r\n\r\n- Finally for the CNRS team, when can we expect to see all the datasets of [Pangloss](https://pangloss.cnrs.fr/) on HF? 👀 And I don't know if you have a way to help to add also the datasets of [CoCoON](https://cocoon.huma-num.fr/exist/crdo/).",
"> I invite you to read them. But as a quick summary, the exchanges were oriented towards the ISO standard (the first HF system was based on it and it is generally the standard indicated in AI/DL papers) by favouring ISO 639-1 if it exists, and fallback to ISO 639-2 or ISO 639-3 if it doesn't. In addition, it is possible to add BCP-47 tags to consider existing varieties/regionalisms within a language (https://huggingface.co/datasets/AmazonScience/massive/discussions/1). If a language does not belong to either of these two standards, then a request should be made to the HF team to add it manually.\r\n\r\nOne comment on this fall back system (which generally follows the BCP-47 process). ISO 639-2 has some codes which refer to a language ambiguously. For example, I believe code `ara` is used for arabic. In some contexts arabic is considered a single language, however, Egyptian Arabic is quite different from Moroccan Arabic, which are both considered separate languages. These ambiguous codes are valid ISO 639-3 codes but they have a special status. They are called `macro codes`. They exist inside the ISO 639-3 standard to provide absolute fallback compatibility between ISO 639-2 and ISO 639-3. However, when considering AI and MT applications with language data, the unforeseen potential applications and the potential for bias using macro codes should be avoided for new applications of language tags to resources. For historical cases where it is not clear what resources were used to create the AI tools or datasets then I understand the use of ambiguous tag uses. So for clarity in language tagging I suggest:\r\n\r\n1. Strictly following BCP-47\r\n2. Whenever possible avoid the use of macro tags in the ISO 639-3 standard. These are BCP-47 valid, but could introduce biases in the application of their use in society. (Generally there are more specific tags available to use in the ISO 639-3 standard.)",
"> * Are there any databases that take into account all the existing sign languages in the world?\r\n> It would be nice to have them included on the Hub.\r\n\r\nSign Languages present an interesting case. As I understand the situation. The identification of sign languages has been identified as a component of their endangerment. Some sign languages do exist in ISO 639-3. For further discussion on the issue I refer readers to the following publications: \r\n\r\n* https://doi.org/10.3390/languages7010049\r\n* https://www.academia.edu/35870983/The_ethics_of_of_language_identification_and_ISO_639\r\n\r\nOne way to be BCP-47 compliant and identify a sign language which is not identified in any of the BCP-47 referenced standards is to use the ISO 639-3 code for undetermined language `und` and then apply a custom suffix indicator (as explained in BCP-47) `-x-` and a custom code, such as the ones used in https://doi.org/10.3390/languages7010049",
"> * Is there an international classification of languages?\r\n> A bit like the [International Classification of Diseases](https://en.wikipedia.org/wiki/International_Classification_of_Diseases) in medicine, which is established by the WHO and used as a reference throughout the world. The idea would be to have a precise number of languages to which we would then have to assign a unique tag in order to find them later.\r\n\r\nYes that would be the function of ISO 639-3. It is the reference standard for languages. It includes a code and its name and the status of the code. Many technical metadata standards for file and computer interoperability reference it, many technical library metadata standards reference it. Some linguists use it. Many governments reference it. \r\n\r\nIndexing diseases are different from indexing languages in several ways, one way is that diseases are the impact of a pathogen not the pathogen itself. If we take COVID-19 as an example, there are many varieties of the pathogen but broadly speaking there is only one disease — with many symptoms.\r\n\r\n",
">* When you look up a language on Wikipedia, it usually shows, in addition to the ISO standard, the codes in the Glottolog (which you have already mentioned), [ELP](https://www.endangeredlanguages.com/?hl=en) and [Linguasphere](http://www.linguasphere.info/jr/index.php?l1=home&l2=welcome) databases. Would you have any opinion about these two other databases?\r\n\r\nWhile these do appear on wikipedia, I don't know of any information system which uses these codes. I do know that glottolog did import ELP data at one time and its database does contain ELP data I'm not sure if Glottolog regularly ingests new versions of ELP data. I suspect that the use of Linguasphere data may be relevant to users of wikidata as a linked data attribute but I haven't heard of any linked data projects using Linguasphere data for analysis or product development. My impression is that it is fairly unused.",
"> * Do you think it's possible to easily handle tags that have been deprecated potentially for decades?\r\n>For example (I'm taking the case of Hebrew but this has happened for other languages) I [tag](https://huggingface.co/models?language=iw&sort=downloads)ged Google models with the \"iw\" tag because I based it on what the authors gave in their [paper](https://arxiv.org/pdf/2010.11934.pdf) see table 6 page 12). It turns out that this ISO tag has in fact been deprecated since 1989 in favour of the \"he\" tag. It would therefore be necessary to have a verification that transforms the old tags into the most recent ones.\r\n\r\nYes. You can parse the IANA file linked to above (it is regularly updated). All deprecated tags are marked as such in that file. The new prefered tag if there is one, is indicated. ISO 639-3 also indicates a code's status but their list is relevant only codes within their domain (ISO 639-3).",
"> * On the Hub, there is the following dataset where French people speak in English: https://huggingface.co/datasets/Datatang/French_Speaking_English_Speech_Data_by_Mobile_Phone\r\nIs there a database to take this case into account? I have not found any code in the Glottolog database. If based on an IETF BCP-47 standard, I would tend to tag the dataset with \"en-fr\" but would this be something accepted by linguists?\r\n\r\nI would interpret `en-fr` as english as spoken in France. `fr`in this position refers to the geo-political entity not a second language. I see no reason that other linguists should have a different option after having read BCP-47 and understood how it works.\r\n\r\nThe functional goal here is to tag a language resource as being produced by nonnative speakers, while tagging both languages. There are several problems here. The first is that BCP-47 has no way explicit way to do this. One could use the sub code `x-` with a private use code to indicate a second language and infer some meaning as to that language's role. However, there is another problem here which complexifies the situation greatly... how do we know that those english speakers (in France, or from France, or who were native French speakers) were not speaking their third or fourth language rather than their second language. So to conceptualize a sub-tag which indicates the first language of a speech act for speakers in a second (or other) language would need to be carefully crafted. It might then be proposed to the appropriate authorities. For example three sub-tags exist.\r\n\r\nThere are three registered sub-tags out of a BCP-47 allowed 35. These are `x-`, `u-`, and `t-`. `u-` and `t-` are defined in [RFC6067 ](https://www.rfc-editor.org/rfc/rfc6067)and [RFC6497](https://www.rfc-editor.org/rfc/rfc6497) . For more information see the [Unicode CLDR documentation](https://cldr.unicode.org/index/bcp47-extension) where it says: \r\n\r\n\r\n>[IETF BCP 47 ](http://www.google.com/url?q=http%3A%2F%2Ftools.ietf.org%2Fhtml%2Fbcp47&sa=D&sntz=1&usg=AOvVaw1DoMN1IBGg-JHgECBvdW1t)[Tags for Identifying Languages](http://www.google.com/url?q=http%3A%2F%2Ftools.ietf.org%2Fhtml%2Fbcp47&sa=D&sntz=1&usg=AOvVaw1DoMN1IBGg-JHgECBvdW1t) defines the language identifiers (tags) used on the Internet and in many standards. It has an extension mechanism that allows additional information to be included. The Unicode Consortium is the maintainer of the extension ‘u’ for Locale Extensions, as described in [rfc6067](https://www.google.com/url?q=https%3A%2F%2Ftools.ietf.org%2Fhtml%2Frfc6067&sa=D&sntz=1&usg=AOvVaw0gGWi0EjHfy1WId8k8oKAi), and the extension 't' for Transformed Content, as described in [rfc6497](https://www.google.com/url?q=https%3A%2F%2Ftools.ietf.org%2Fhtml%2Frfc6497&sa=D&sntz=1&usg=AOvVaw0w-OUsFX1PtaKYIq31P64I).\r\n>\r\n>The subtags available for use in the 'u' extension provide language tag extensions that provide for additional information needed for identifying locales. The 'u' subtags consist of a set of keys and associated values (types). For example, a locale identifier for British English with numeric collation has the following form: en-GB-u-kn-true\r\n>\r\n>The subtags available for use in the 't' extension provide language tag extensions that provide for additional information needed for identifying transformed content, or a request to transform content in a certain way. For example, the language tag \"ja-Kana-t-it\" can be used as a content tag indicates Japanese Katakana transformed from Italian. It can also be used as a request for a given transformation.\r\n>\r\n>For more details on the valid subtags for these extensions, their syntax, and their meanings, see LDML Section 3.7 [Unicode BCP 47 Extension Data](http://www.google.com/url?q=http%3A%2F%2Fwww.unicode.org%2Freports%2Ftr35%2F%23Locale_Extension_Key_and_Type_Data&sa=D&sntz=1&usg=AOvVaw0lMthb9KbTJtoOd5mvv3Ha).",
"Hi @lbourdois ! Many thanks for the detailed information.\r\n\r\n> Discussions on the need to improve the Hub's tagging system (applying to both datasets and models) can be found in the following discussion: [huggingface/hub-docs#193](https://github.com/huggingface/hub-docs/issues/193) \r\nFascinating topic! To me, the following suggestion has a lot of appeal:\r\n\"if consider that it was necessary to create an ISO 639-3 because ISO 639-1 was deficient, it would be to do the reverse and thus convert the tags from ISO 639-1 to ISO 639-2 or 3 (https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes or https://iso639-3.sil.org/code_tables/639/data).\"\r\n\r\nYes, ISO 639-1 is unsuitable because it has so few codes: less than 200. To address linguistic diversity in 'unrestricted mode', a list of all languages is wanted. \r\n\r\nThe idea of letting people use their favourite nomenclature and automatically adding the ISO 639-3 three-letter code as a tag is appealing. Thus all the HF datasets would have three-letter language tags (handy for basic search), alongside the authors' preferred tags and language names (including Glottolog tags as well as ISO 639-{1, 2}, and all other options allowed by BCP-47). \r\n\r\nRetaining the authors' original tags and language names would be best. \r\n* For language names: some people favour one name over another and it is important to respect their choice. In the case of Yongning Na: alternative names include 'Mosuo', 'Narua', 'Eastern Naxi'... and the names carry implications: people have been reported to come to blows about the use of the term 'Mosuo'. \r\n* For language tags: Glottocodes can be more fine-grained than Ethnologue (ISO 639-3), and some colleagues feel strongly about those. \r\n\r\nThus there would be a BCP-47 tag (sounds like a solid technical choice, though not 'passer-by-friendly': requiring some expertise to interpret) **plus** an ISO 639-3 tag that could be grabbed easily, and (last but not least) language names spelled out in full. Searches would be easier. No information would be lost. \r\n\r\nAre industry practices so conservative that many people are happy with two-letter codes, and consider ISO 639-3 three-letter codes an unnecessary complication? That would be a pity, since there are so many advantages to using longer lists. (Somewhat like the transition to Unicode: sooo much better!) But maybe that conservative attitude _is_ widespread, and it would then need to be taken into account. In which case, one could consider offering two-letter codes as a search option. Internally, the search engine would look up the corresponding 3-letter codes, and produce the search results accordingly. \r\n\r\nNow to the other questions:\r\n\r\n> * Do you think it's possible to easily handle tags that have been deprecated potentially for decades?\r\n> For example (I'm taking the case of Hebrew but this has happened for other languages) I tagged Google models with the \"iw\" [tag](https://huggingface.co/models?language=iw&sort=downloads) because I based it on what the authors gave in their [paper](https://arxiv.org/pdf/2010.11934.pdf) see table 6 page 12). It turns out that this ISO tag has in fact been deprecated since 1989 in favour of the \"he\" tag. It would therefore be necessary to have a verification that transforms the old tags into the most recent ones.\r\nI guess that the above suggestion takes care of this case. The original tag (in this example, \"iw\") is retained (facilitating cross-reference with the published paper, and respecting the real: the way the dataset was originally tagged). This old tag goes into the `BCP-47` field of the dataset, which can handle quirks & oddities like this one. And a new tag is added in the `ISO 639-3` field: the 3-letter code \"heb\". \r\n\r\n> * When you look up a language on Wikipedia, it usually shows, in addition to the ISO standard, the codes in the Glottolog (which you have already mentioned), [ELP](https://www.endangeredlanguages.com/?hl=en) and [Linguasphere](http://www.linguasphere.info/jr/index.php?l1=home&l2=welcome) databases. Would you have any opinion about these two other databases?\r\n\r\nI'm afraid I never heard about Linguasphere. The [online register for Linguasphere (PDF)](http://www.linguasphere.info/jr/pdf/index/LS_index_n-n.pdf) seems to be from 1999-2000. It seems that the level of interoperability is not very high right now. (By contrast, Glottolog has [pyglottolog](https://github.com/glottolog/pyglottolog) and in my experience contacts flow well.) \r\n\r\nThe Endangered Languages Project is something Google started but initially did not 'push' very strongly, it seems. Just airing an opinion on the public Internet, it seems that the project is now solidly rooted at University of Hawaiʻi at Mānoa. It seems that they do not generate codes of their own. They refer to ISO 639-3 (Ethnologue) as a code authority when applicable, and otherwise provide comments in so many words, such as that language L currently lacks an Ethnologue code of its own (example [here](https://www.endangeredlanguages.com/lang/10624)). \r\n\r\n> * On the Hub, there is the following dataset where French people speak in English: https://huggingface.co/datasets/Datatang/French_Speaking_English_Speech_Data_by_Mobile_Phone\r\n> Is there a database to take this case into account? I have not found any code in the Glottolog database. If based on an IETF BCP-47 standard, I would tend to tag the dataset with \"en-fr\" but would this be something accepted by linguists?\r\n> Based on the first post in this thread that there are about 8000 languages, if one considers that a given language can be pronounced by a speaker of the other 7999, that would theoretically make about 64 million BCP-47 language1-language2 codes existing. And even much more if we consider regionalists with language1_regionalism_x-language2_regionalism_y. I guess there is no such database.\r\n\r\nYes, you noted the difficulty here: that there are so many possible situations. Eventually, each dataset would required descriptors of its own. @BenjaminGalliot points out that, in addition to specifying the speakers' native languages, the degree of language proficiency would also be relevant. How many years did the speakers spend in which area? Talking which languages? In what chronological order? Etc. The complexity defies encoding. The purpose of language codes is to allow for searches that group resources into sets that make sense. Additional information is very important, but would seem to be a matter for 'comments' fields. \r\n\r\n> * Is there an international classification of languages?\r\n> A bit like the [International Classification of Diseases](https://en.wikipedia.org/wiki/International_Classification_of_Diseases) in medicine, which is established by the WHO and used as a reference throughout the world. The idea would be to have a precise number of languages to which we would then have to assign a unique tag in order to find them later.\r\n\r\nAs I understand, Ethnologue and Glottolog both try to do that, each in its own way. The simile with diseases seems interesting, to some extent: in both cases it's about human classification of phenomena that have complexity (though some diseases are simpler than others, whereas all languages have much complexity, in different ways).\r\n\r\n> * Finally, when can we expect to see all the datasets of [Pangloss](https://pangloss.cnrs.fr/) on HF? eyes And I don't know if you have a way to help to add also the datasets of [CoCoON](https://cocoon.huma-num.fr/exist/crdo/).\r\n\r\nThree concerns: (i) Technical specifications: we have not yet received feedback on the Japhug and Na datasets in HF. There may be technical considerations that we have not yet thought of and that would need to be taken into account before 'bulk upload'. (ii) Would there be a way to automate the process? The way @BenjaminGalliot did it for Japhug and Na, there was a manual component involved, and doing it by hand for all 200 datasets would not be an ideal workflow, given that the metadata are all clearly arranged. (iii) Some datasets are currently under a 'No derivatives' CreativeCommons license. We could go back to the depositors and argue that the 'No derivatives' mention were best omitted (see [here a similar argument about publications](https://creativecommons.org/2020/04/21/academic-publications-under-no-derivatives-licenses-is-misguided/)): again, we'd want to be sure about the way forward before we set the process into motion.\r\n\r\nOur hope would be that some colleagues try out the [OutilsPangloss](https://gitlab.com/lacito/outilspangloss) download tool, assemble datasets from Pangloss/Cocoon as they wish, then deposit them to HF.",
"> The idea of letting people use their favourite nomenclature and automatically adding the ISO 639-3 three-letter code as a tag is appealing. Thus all the HF datasets would have three-letter language tags (handy for basic search), alongside the authors' preferred tags and language names (including Glottolog tags as well as ISO 639-{1, 2}, and all other options allowed by BCP-47).\r\n> \r\n> Retaining the authors' original tags and language names would be best.\r\n> \r\n> * For language names: some people favour one name over another and it is important to respect their choice. In the case of Yongning Na: alternative names include 'Mosuo', 'Narua', 'Eastern Naxi'... and the names carry implications: people have been reported to come to blows about the use of the term 'Mosuo'.\r\n> * For language tags: Glottocodes can be more fine-grained than Ethnologue (ISO 639-3), and some colleagues feel strongly about those.\r\n> \r\n> Thus there would be a BCP-47 tag (sounds like a solid technical choice, though not 'passer-by-friendly': requiring some expertise to interpret) **plus** an ISO 639-3 tag that could be grabbed easily, and (last but not least) language names spelled out in full. Searches would be easier. No information would be lost.\r\n\r\n@alexis-michaud raises an excellent point. Language Resource users have varying search habits (or approaches). This includes cases where two or more language names refer to a single language. A search utility/interface needs to be flexible and able to present results from various kinds of input in the search process. This could be like how the terms French/Français/Franzosisch (en/fr/de) are names for the same language or it could be a variety of the following: autoglottonyms (how the speakers of the language refer to their language), or exoglottonyms (how others refer to the language). Additionally, in web based searches I have also needed to implement diacritic sensitive and insensitive logic so that users can type with or without diacritics and not have results unnecessarily excluded. \r\n\r\nDepending on how detailed of a search problem HF seeks to solve. It may be better to off load complex search to search engines like OLAC which aggregate a lot of language resources. — as I mentioned above I can assist with the informatics on creating an OLAC feed.\r\n\r\nAbstracting search logic from actual metadata may prove a useful way to lower the technical debt overhead. Technical tools and library standards use ISO and BCP-47 Standards. So, from a bibliographic metadata perspective this seems to be the way forward with the widest set of use cases. ",
"To get a visual idea of these first exchanges, I coded a Streamlit app that I put online on Spaces: https://huggingface.co/spaces/lbourdois/Language-tags-demo. \r\nThe code is in Python so I don't know if it can be used by HF who seems to need something in Node.js but it serves as a proof of concept. The advantage is also that you can directly test ideas by enter things in a search bar and see what comes up. \r\n\r\nThis application is divided into 3 points:\r\n- The first is to enter a language in natural language to get its code which can then be filled in the YAML file of the README.MD files of the HF datasets or models in order to be referenced and found by everyone.\r\nIn practice, enter the language (e.g: `English`) you are interested in to get its associated tag (e.g: `en`). You can enter several languages by separating them with a comma (e.g `French,English,German`). You will be given priority to the ISO 639-3 code if it exists otherwise the Glottocode or the BCP47 code (for varieties in particular). If none of these codes are available, it links to a page where the user can contact HF to request to add this tag. \r\nIf you enter a BCP47 code, it must be entered as follows: `Language(Territory)`, for example `French(Canada)`. Attention! If you enter a BCP-47 language, it must be entered first, otherwise the plant code will be displayed. I have to fix this problem but I am moving to a new place, I don't have an internet connection when I want and I prefer to push this first version so that you can already test things now and not have to wait days or weeks.\r\nThis point is intended to simulate the user's side of the equation, which wonders which tag he should fill in for his language.\r\n\r\n\r\n- The second is to enter a language code to obtain the name of the language in natural language.\r\nIn practice, enter the tag (ISO 639-1/2/3, Glottolog or BCP-47) you are interested in (e.g: `fra`) to get its associated language (e.g: French). You can enter several languages by separating them with a comma (e.g `fra,eng,deu`). Attention! If you enter a BCP-47 code, it must be entered first, otherwise the plant code will be displayed. Same as the other bug above (it's actually the same one).\r\nThis point is intended to simulate the side of HF that for a given tag must return the correct language.\r\n\r\n\r\n\r\nTo code these two points, I tested two approaches. \r\n\r\n1. The first one (internal DB in the app) consists in querying a database that HF would have locally at their place. To create this database, I merged the ISO 639 database (https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab) and the Glottolog database (https://glottolog.org/meta/downloads). The result of this merge is visible in the 3rd point of the application qui is an overview of the database.\r\nIn the image below, on line 1 of the database, we can see that the Glottocode database gives an ISO 639-3 code (column ISO639P3code) but not the ISO 639 database (column 639-3). Do you have an explanation for this phenomenon?\r\n\r\n\r\n\r\nFor BCP 47 codes of the type `fr-CA`, I have retrieved the ISO-3166 alpha 1 codes of the territories (https://www.iso.org/iso-3166-country-codes.html).\r\nIn practice, what I do is if we enter `fr-CA` is that the letters before the `-` refer to a language in the `Name` column for a `639-1` == `fr` (`639-3` for `fra` or `fre`) in the base of my image above. Then I look at the letters after the `-` which refers to a territory. It comes out `French (Canada)`. I used https://cldr.unicode.org/translation/displaynames/languagelocale-name-patterns for the pattern that came up.\r\n\r\n\r\n2. The second approach (with langcodes lib in the app) consists in using the Python `langcodes` library (https://github.com/rspeer/langcodes) which offers a lot of features in ready-made functions. It manages for example deprecated codes, the validity of an entered code, gives languages from code in the language of your choice (by default in English, but also autoglottonyms), etc. I invite you to read the README of the library. The only negative point is that it hasn't been updated for 10 months so basing your tag system on an external tool that isn't necessarily up to date can cause problems in the long run. But it is certainly an interesting source.\r\n\r\nFinally, I have added some information on the number of people speaking/reading the language(s) searched (figures provided by langcodes which are based on those given by ISO). This is not relevant for our topic but it could be figures that could be added as information on the https://huggingface.co/languages page. \r\n\r\n\r\n\r\nWhat could be done to improve the app if I have time:\r\n- Write the text for the app's homepage to describe what it does. This could serve as a basis for a documentation that I think will be necessary to add somewhere on the HF website to explain how the language tagging system works.\r\n- Deal with the bug mentioned above\r\n- Integrate ISO 3166-1 alpha 2 territories (https://www.iso.org/obp/ui#iso:pub:PUB500001:en)? They offer a finer granularity than ISO 3166-1 alpha 1 which is limited to the country level, but they are very administrative (for French, ISO 3166-1 alpha 2 gives us the \"départements\" for example).\r\n- Add autoglottonyms? (I only handle English language names for the moment)\r\n- For each language indicate to which family it belongs, in practice this could help to make data augmentation, but especially to classify the languages and find them more easily on the page https://huggingface.co/languages.",
"Very impressive! Using the prompt 'Japhug' (a language name), the app finds the intended language:\r\n\r\n\r\nA first question: based on the Glottocode, would it be possible to grab the closest ISO639-3 code? In case there is no match for the exact language variety, one needs to explore the higher-level groupings, level by level. For this language (Japhug), the information provided in the extracted CSV file (`glottolog-languoids-v4.6.csv`) is: \r\n`sino1245/burm1265/naqi1236/qian1263/rgya1241/core1262/jiar1240` \r\nOne need not look further than the first higher-level grouping, [`jiar1240`](https://glottolog.org/resource/languoid/id/jiar1240), to get an ISO639-3 code, namely `jya`.\r\n\r\nThus users searching by language names would get ISO639-3 (often less fine-grained than Glottolog) as a bonus.\r\nIt might be possible to ask the Glottolog team to provide this piece of information as part of an export from their database.",
"> on line 1 of the database, we can see that the Glottocode database gives an ISO 639-3 code (column ISO639P3code) but not the ISO 639 database (column 639-3). Do you have an explanation for this phenomenon?\r\n\r\nThat is because the language name 'Aewa' is not found in the Ethnologue (ISO 639-3) export that you are using. [This export in table form](https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab) only has one reference name (`Ref_Name`). For the language at issue, it is not 'Aewa' but ['Awishira'](https://www.ethnologue.com/language/ash).\r\n\r\nBy contrast, the language on line 0 of the database is called 'Abinomn' by both Ethnologue and Glottolog, and accordingly, columns `ISO639P3code` and `639-3` both contain the ISO 639-3 code, `bsa`.\r\n \r\nThe full Ethnologue database records alternate names for each language, and I'd bet that 'Aewa' is recorded among alternate names for the 'Ashiwira' language. I can't check because the full Ethnologue database is paywalled. \r\n\r\n\r\n[Glottolog](https://glottolog.org/resource/languoid/id/abis1238) does provide the corresponding ISO 639-3 code for 'Aewa', `ash`, which is an exact match (it refers to the same variety as Glottolog `abis1238`).\r\nIn this specific case, Glottolog provides all the relevant information. I'd say that Glottolog can be trusted for all the codes they provide, including ISO 639-3 codes: they only include them when the match is good. \r\n\r\nSee previous comment about the cases where there is no exact match between Glottolog and ISO 639-3 (suggested workaround: look at a higher-level grouping to get an ISO 639-3 code).",
"I will add these two points to my TODO list.\r\n- Since Glottolog can be trust, I will add a condition to the code that if there is no ISO 639-3 code in the \"official\" database (https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab), look for it in the \"ISO639P3code\" column of Glottolog.\r\n- For the point of adding the closest ISO 639-3 code for a Glottolog code, what convention should be adopted for the output? Just the ISO 639-3 code, or the ISO 639-3 code - Glottolog code, or the ISO 639-3 code - language name?\r\nTo use the example of `Japhug` , should it be just `jya`, or `jya-japh1234` or `jya-Japhug`?",
"> * Integrate ISO 3166-1 alpha 2 territories (https://www.iso.org/obp/ui#iso:pub:PUB500001:en)? They offer a finer granularity than ISO 3166-1 alpha 1 which is limited to the country level, but they are very administrative (for French, ISO 3166-1 alpha 2 gives us the \"départements\" for example).\r\n\r\nI'm concerned with this sort of exploration. Not because I am against innovation. In fact this is an interesting thought exercise. However, to explore this thought further creates cognitive dissidence between BCP-47 authorized codes and other code sets which are not BP-47 compliant. For that reason, I think adding additional codes is a waste of time both for HF devs and for future users who get a confusing idea about language tagging. ",
"Good job for the application!\r\n\r\n> On the Hub, there is the following dataset where French people speak in English: https://huggingface.co/datasets/Datatang/French_Speaking_English_Speech_Data_by_Mobile_Phone\r\n Is there a database to take this case into account? I have not found any code in the Glottolog database. If based on an IETF BCP-47 standard, I would tend to tag the dataset with \"en-fr\" but would this be something accepted by linguists?\r\n Based on the first post in this thread that there are about 8000 languages, if one considers that a given language can be pronounced by a speaker of the other 7999, that would theoretically make about 64 million BCP-47 language1-language2 codes existing. And even much more if we consider regionalists with language1_regionalism_x-language2_regionalism_y. I guess there is no such database.\r\n\r\n> Yes, you noted the difficulty here: that there are so many possible situations. Eventually, each dataset would required descriptors of its own. @BenjaminGalliot points out that, in addition to specifying the speakers' native languages, the degree of language proficiency would also be relevant. How many years did the speakers spend in which area? Talking which languages? In what chronological order? Etc. The complexity defies encoding. The purpose of language codes is to allow for searches that group resources into sets that make sense. Additional information is very important, but would seem to be a matter for 'comments' fields.\r\n\r\nTo briefly complete what I said on this subject in a private discussion group, there is a lot of (meta)data associated with each element of a corpus (which language level, according to which criteria, knowing that even among native speakers there are differences, some of which may go beyond what seems obvious to us from a linguistic point of view, such as socio-professional category, life history, environment in the broad sense, etc.), which can be placed in ad-hoc columns, or more freely in a comment/note column. And it is the role of the researcher (in this case a linguist, in all likelihood) to do analyses (statistics...) to determine the relevant data, including criteria that may justify separating different languages (in the broad sense), making separate corpora, etc. Putting this information in the language code is in my opinion doing the job in the opposite and wrong direction, as well as bringing other problems, like where to stop in the list of multidimensional criteria to be integrated, so in my opinion, here, the minimum is the best (the important thing is in my opinion to have well-documented data, globally, by sub-corpus or by line)...\r\n\r\n> If you are going to use Glottolog codes use them after an -x- tag in the BCP-47 format to maintain BCP-47 validity.\r\n\r\nYes, for the current corpora, I have written:\r\n\r\n```\r\nlanguage:\r\n- jya\r\n- nru\r\nlanguage_bcp47:\r\n- x-japh1234\r\n- x-yong1288\r\n```\r\n\r\n> * Add autoglottonyms? (I only handle English language names for the moment)\r\n\r\nAutoglossonyms are useful (I use them prior to other glossonyms), but I'm not sure there is an easy way to retrieve them. We can find some of them in the \"Alternative Names\" panel of Glottolog, but even if we have an API to retrieve them easily, their associated language code will often not be the one we are in (hence the need to do several cycles to find one, which might not be the right one...). Maybe this problem needs more investigation...\r\n\r\n> For the point of adding the closest ISO 639-3 code for a Glottolog code, what convention should be adopted for the output? Just the ISO 639-3 code, or the ISO 639-3 code - Glottolog code, or the ISO 639-3 code - language name?\r\nTo use the example of Japhug , should it be just jya, or jya-japh1234 or jya-Japhug?\r\n\r\nI strongly insist not to add **a** language name after the code, it would restart a spiral of problems, notably the choice of the language in question:\r\n* the autoglossonym: in my opinion the best choice, but you have to know it…\r\n* the English name: iniquitous,\r\n* the name in the administratively/politically dominant language of the target language if it is relevant (strictly localized without overlapping, for example): iniquitous and tendentious (and in a way a special case of the previous one)...\r\n* etc.\r\n",
"> To get a visual idea of these first exchanges, I coded a Streamlit app that I put online on Spaces: https://huggingface.co/spaces/lbourdois/Language-tags-demo.\r\n> The code is in Python so I don't know if it can be used by HF who seems to need something in Node.js but it serves as a proof of concept. The advantage is also that you can directly test ideas by enter things in a search bar and see what comes up.\r\n\r\nThis is really great. You're doing a fantastic job. I love watching the creative process evolve. It is exciting. Let me provide some links to some search interfaces for further inspiration. I always find it helpful to know how others have approached a problem when figuring out my approach. I will link to three examples Glottolog, r12a's language sub-tag chooser, and the FLEx project builder wizard. The first two are online, but the last one is in an application which must be downloaded and works only on windows or linux. I have placed some notes on each of the screenshots.\r\n\r\n* **[Glottolog](https://glottolog.org/)** | [Search Query](https://glottolog.org/glottolog?name=en&namequerytype=part&multilingual=on#2/20.9/150.0) \r\n\r\n\r\n\r\n\r\n\r\n* **[r12a language sub-tag chooser](https://r12a.github.io/app-subtags/)** | [Code on github](https://github.com/r12a/app-subtags)\r\n\r\n\r\n\r\n\r\n* **FLEx Language Chooser** | [application page](https://software.sil.org/fieldworks/)\r\n\r\n\r\n",
"> In practice, what I do is if we enter `fr-CA` is that the letters before the `-` refer to a language in the `Name` column for a `639-1` == `fr` (`639-3` for `fra` or `fre`) in the base of my image above. Then I look at the letters after the `-` which refers to a territory. It comes out `French (Canada)`. I used https://cldr.unicode.org/translation/displaynames/languagelocale-name-patterns for the pattern that came up.\r\n\r\nWhat you are doing is looking at the algorithm for Locale generation rather than BCP-47's original documentation. I'm not sure there are difference, there might be. I know that locale IDs generally follow BCP-47 But I think there are some differences such as the use of `_` vs. `-`. ",
"> A first question: based on the Glottocode, would it be possible to grab the closest ISO639-3 code? In case there is no match for the exact language variety, one needs to explore the higher-level groupings, level by level. For this language (Japhug), the information provided in the extracted CSV file (`glottolog-languoids-v4.6.csv`) is: `sino1245/burm1265/naqi1236/qian1263/rgya1241/core1262/jiar1240` One need not look further than the first higher-level grouping, [`jiar1240`](https://glottolog.org/resource/languoid/id/jiar1240), to get an ISO639-3 code, namely `jya`.\r\n> \r\n> Thus users searching by language names would get ISO639-3 (often less fine-grained than Glottolog) as a bonus. It might be possible to ask the Glottolog team to provide this piece of information as part of an export from their database.\r\n\r\nThis is logical, but the fine grained assertions are not the same. That is just because they are in a hierarchical structure today doesn't mean they will be tomorrow. In some cases the glottolog is clearly referring to sub-language variants which will never receive full language status, whereas in other cases glottolog is referencing to unequal entities one or more of which should be a language. Many of the codes in glottolog have no associated documentation indicating what sort of speech variety they are. ",
"@lbourdois \r\n> * Since Glottolog can be trust, I will add a condition to the code that if there is no ISO 639-3 code in the \"official\" database (https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab), look for it in the \"ISO639P3code\" column of Glottolog.\r\n\r\nI'm confused here... if there is no ISO639-3 code in the official database from the registrar, why would you look for an \"unofficial\" code from someone else? What is the use case here?",
"> For the point of adding the closest ISO 639-3 code for a Glottolog code, what convention should be adopted for the output? Just the ISO 639-3 code, or the ISO 639-3 code - Glottolog code, or the ISO 639-3 code - language name?\r\nTo use the example of Japhug , should it be just jya, or jya-japh1234 or jya-Japhug?\r\n\r\n(answer edited in view of [Benjamin Galliot's comment](https://github.com/huggingface/datasets/issues/4881#issuecomment-1237420600) \r\nEasy part of the answer first: jya-Japhug is out, because, as @BenjaminGalliot pointed out above, mixing language names with language codes will make trouble. For Japhug, `jya-Japhug` looks rather good: the pair looks nice, the one (`jya`) packed together, the other (`Japhug`) good and complete while still pretty compact. But think about languages like 'Yongning Na' or 'Yucatán Maya': a code with a space in the middle, like `nru-Yongning Na`, is really unsightly and unwieldy, not?\r\n\r\nSome [principles for language naming in English](http://hdl.handle.net/10125/24725) have been put forward, with some linguistic arguments, but always supposing that such standardization is desirable, actual standardization of language names in English may well never happen.\r\n\r\nAs for `jya-japh1234`: again, at first sight it seems cute, combining two fierce competitors (Ethnologue and Glottolog) into something that gets the best of both worlds. \r\nBut @HughP has a point: _adding additional codes is a waste of time both for HF devs and for future users who get a confusing idea about language tagging_ Strong wording, for an important comment: better stick with BCP 47. \r\n\r\nSo the solution pointed out by Benjamin, from Frances Gillis-Webber and Sabine Tittel, looks attractive: \r\njya-x-japh1234\r\n\r\nOn the other hand, if the idea for HF Datasets is simply to add the closest ISO 639-3 code for a Glottolog code, maybe it could be provided simply in three letters: providing the 'raw' ISO 639-3 code `jya`. Availability of 'straight' ISO 639-3 codes could save trouble for some users, and those who want more detail could look at the rest of the metadata and general information associated with datasets.",
"The problem seems to have already been raised here: https://drops.dagstuhl.de/opus/volltexte/2019/10368/pdf/OASIcs-LDK-2019-4.pdf\r\n\r\nAn example can be seen here :\r\n\r\n> 3.1.2 The use of privateuse sub-tag\r\nIn light of unambiguous language codes being available for the two Khoisan varieties, we\r\npropose to combine the ISO 639-3 code for the parent language N‖ng, i.e., ‘ngh’, with the\r\nprivateuse sub-tag ‘x-’ and the respective Glottocodes stated above.\r\nThe language tags for N|uu and ‖’Au can then be defined accordingly:\r\nN|uu: ngh-x-nuuu1242\r\n‖’Au: ngh-x-auni1243\r\n\r\nBy the way, while searching for this, I came across this application: https://huggingface.co/spaces/cdleong/langcode-search",
"> > * Since Glottolog can be trust, I will add a condition to the code that if there is no ISO 639-3 code in the \"official\" database (https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab), look for it in the \"ISO639P3code\" column of Glottolog.\r\n> \r\n> I'm confused here... if there is no ISO639-3 code in the official database from the registrar, why would you look for an \"unofficial\" code from someone else? What is the use case here?\r\n\r\nHi @HughP, I'm happy to clear what confusion may exist here :innocent: Here is the use case. \r\nGuillaume Jacques (@rgyalrong) put together a sizeable corpus of the Japhug language. It is up on HF Datasets ([here](https://huggingface.co/datasets/Lacito/pangloss/viewer/japh1234)) as well as on Zenodo. \r\n\r\nZenodo is an all-purpose repository without adequate domain-specific metadata (\"[métadonnées métier](https://www.cines.fr/archivage/des-expertises/les-metadonnees/metadonnees-metier/)\"), and the deposits in there are not easy to locate. The Zenodo deposit is intended for a highly specific user case: someone reads about the dataset in a paper, goes to the address on Zenodo and grabs the dataset at one go. \r\n\r\nHF Datasets, on the other hand, allows users to look around among corpora. The Japhug corpus needs proper tagging so that HF Datasets users can find out about it. \r\nJaphug has an entry of its own in Glottolog, whereas it lacks an entry of its own in Ethnologue. Hence the practical usefulness of Glottolog. Ethnologue pools together, under the code `jya`, three different languages (Japhug, Tshobdun `tsho1240` and Zbu `zbua1234`). \r\n\r\nI hope that this helps.",
"> By the way, while searching for this, I came across this application: https://huggingface.co/spaces/cdleong/langcode-search\r\n\r\nReally relevant Space, so tagging its author @cdleong, just in case!",
"@cdleong A one-stop shop for language codes: terrific!\r\nHow do you feel about the use of Glottocodes? When searching the language names 'Japhug' and 'Yongning Na' (real examples, related to a HF Datasets deposit & various research projects), the relevant Glottocodes are retrieved, and that is great (and not that easy, notably with the space in the middle of 'Yongning Na'). But this positive result is 'hidden' in the results page. Specifically: \r\n\r\n- for Japhug: when searching by language name ('Japhug'), the result in big print is 'Failure', even though there is an available Glottocode (at bottom).\r\n\r\nWhen searching by Glottocode (japh1234), same outcome: 'Result: failure!' (even though this _is_ the right Glottocode\r\nWhen searching by x-japh1234 (Glottocode encapsulated in BCP 47 syntax), one gets the message \r\n\r\n> ''x-japh1234' parses meaningfully as a language tag according to IANA\"\r\n\r\nbut there is paradoxically no link provided to Glottolog: the 'Glottolog' part of the results page is empty\r\n\r\n\r\n- Yongning Na: the correct code is identified (yong1288) but instead of foregrounding this exact match, the first result that comes up is a completely different language, called 'Yong'. \r\n\r\nTrying to formulate a conclusion (admittedly, this note is not based on intensive testing, it is just feedback on initial contact): from a user perspective, it seems that the tool could make more extensive use of Glottolog. `langcode-search` does a great job querying Glottolog, why not make more extensive use of that information? (including: to arrive at the nearest ISO 639-3 code)"
] | 1,661,285,664,000
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NONE
| null | null |
**The problem:**
Language diversity is an important dimension of the diversity of datasets. To find one's way around datasets, being able to search by language name and by standardized codes appears crucial.
Currently the list of language codes is [here](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/languages.json), right? At about 1,500 entries, it is roughly at 1/4th of the world's diversity of extant languages. (Probably less, as the list of 1,418 contains variants that are linguistically very close: 108 varieties of English, for instance.)
Looking forward to ever increasing coverage, how will the list of language names and language codes improve over time?
Enrichment of the custom list by HFT contributors (like [here](https://github.com/huggingface/datasets/pull/4880)) has several issues:
* progress is likely to be slow:
(input required from reviewers, etc.)
* the more contributors, the less consistency can be expected among contributions. No need to elaborate on how much confusion is likely to ensue as datasets accumulate.
* there is no information on which language relates with which: no encoding of the special closeness between the languages of the Northwestern Germanic branch (English+Dutch+German etc.), for instance. Information on phylogenetic closeness can be relevant to run experiments on transfer of technology from one language to its close relatives.
**A solution that seems desirable:**
Connecting to an established database that (i) aims at full coverage of the world's languages and (ii) has information on higher-level groupings, alternative names, etc.
It takes a lot of hard work to do such databases. Two important initiatives are [Ethnologue](https://www.ethnologue.com/) (ISO standard) and [Glottolog](https://glottolog.org/). Both have pros and cons. Glottolog contains references to Ethnologue identifiers, so adopting Glottolog entails getting the advantages of both sets of language codes.
Both seem technically accessible & 'developer-friendly'. Glottolog has a [GitHub repo](https://github.com/glottolog/glottolog). For Ethnologue, harvesting tools have been devised (see [here](https://github.com/lyy1994/ethnologue); I did not try it out).
In case a conversation with linguists seemed in order here, I'd be happy to participate ('pro bono', of course), & to rustle up more colleagues as useful, to help this useful development happen.
With appreciation of HFT,
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I_kwDODunzps5QXPg9
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[not really a bug] `identical_ok` is deprecated in huggingface-hub's `upload_file`
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"Resolved via https://github.com/huggingface/datasets/pull/4937."
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CONTRIBUTOR
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In the huggingface-hub dependency, the `identical_ok` argument has no effect in `upload_file` (and it will be removed soon)
See
https://github.com/huggingface/huggingface_hub/blob/43499582b19df1ed081a5b2bd7a364e9cacdc91d/src/huggingface_hub/hf_api.py#L2164-L2169
It's used here:
https://github.com/huggingface/datasets/blob/fcfcc951a73efbc677f9def9a8707d0af93d5890/src/datasets/dataset_dict.py#L1373-L1381
https://github.com/huggingface/datasets/blob/fdcb8b144ce3ef241410281e125bd03e87b8caa1/src/datasets/arrow_dataset.py#L4354-L4362
https://github.com/huggingface/datasets/blob/fdcb8b144ce3ef241410281e125bd03e87b8caa1/src/datasets/arrow_dataset.py#L4197-L4213
We should remove it.
Maybe the third code sample has an unexpected behavior since it uses the non-default value `identical_ok = False`, but the argument is ignored.
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Move DatasetInfo from `datasets_infos.json` to the YAML tags in `README.md`
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[
"also @osanseviero @Pierrci @SBrandeis potentially",
"Love this in principle 🚀 \r\n\r\nLet's keep in mind users might rely on `dataset_infos.json` already.\r\n\r\nI'm not convinced by the two-syntax solution, wouldn't it be simpler to have only one syntax with a `default` config for datasets with only one config? ie, always having the `configs` field. This makes parsing the metadata easier IMO.\r\n\r\nMight also be good to wrap the tags under a `datasets_info` tag as follows:\r\n\r\n```yaml\r\ndescription: ...\r\ncitation: ...\r\ndataset_infos:\r\n download_size: 35142551\r\n dataset_size: 89789763\r\n version: 1.0.0\r\n configs:\r\n - ...\r\n[...]\r\n```\r\n\r\nLet's also keep in mind that extracting YAML metadata from a markdown readme is a bit more fastidious for users than just parsing a JSON file.",
"> Let's keep in mind users might rely on dataset_infos.json already.\r\n\r\nYea we'll full full backward compatibility\r\n\r\n> Let's also keep in mind that extracting YAML metadata from a markdown readme is a bit more fastidious for users than just parsing a JSON file.\r\n\r\nThe main things that may use or ingest these data IMO are:\r\n- users in the UI or IDE\r\n- `datasets` to populate `DatasetInfo` python object\r\n- moon landing which is already parsing YAML\r\n\r\nAm I missing something ? If not I think it's ok to use YAML\r\n\r\n> Might also be good to wrap the tags under a datasets_info tag as follows:\r\n\r\nMaybe one single syntax like this then ?\r\n```yaml\r\ndataset_infos:\r\n- config: unlabeled\r\n download_size: 35142551\r\n dataset_size: 89789763\r\n version: 1.0.0\r\n splits:\r\n - name: train\r\n num_examples: 10000\r\n features:\r\n - name: text\r\n dtype: string\r\n- config: labeled\r\n download_size: 35142551\r\n dataset_size: 89789763\r\n version: 1.0.0\r\n splits:\r\n - name: train\r\n num_examples: 100\r\n features:\r\n - name: text\r\n dtype: string\r\n - name: label\r\n dtype: ClassLabel\r\n names:\r\n - negative\r\n - positive\r\n```\r\nand when you have only one config\r\n```yaml\r\ndataset_infos:\r\n- config: default\r\n splits:\r\n - name: train\r\n num_examples: 10000\r\n features:\r\n - name: text\r\n dtype: string\r\n```",
"love the idea, and the trend in general to move more things (like tasks) to a single place (YAML).\r\n\r\nalso, if you browse files on a dataset's page (in \"Files and versions\"), raw `README.md` files looks nice and readable, while `.json` files are just one long line that users need to scroll. \r\n\r\n> Let's also keep in mind that extracting YAML metadata from a markdown readme is a bit more fastidious for users than just parsing a JSON file.\r\n\r\ndo users often parse `datasets_infos.json` file themselves? ",
"> do users often parse datasets_infos.json file themselves?\r\n\r\nNot AFAIK, but I'm sure there should be a few users.\r\nUsers that access these info via the `DatasetInfo` from `datasets` won't see the change though e.g.\r\n```python\r\n>> from datasets import get_datasets_infos\r\n>>> get_datasets_infos(\"squad\")\r\n{'plain_text': DatasetInfo(description='Stanford Question Answering Dataset...\r\n```",
"> Maybe one single syntax like this then ?\r\n\r\nLGTM!\r\n\r\n> The main things that may use or ingest these data IMO are:\r\n> - users in the UI or IDE\r\n> - datasets to populate DatasetInfo python object\r\n> - moon landing which is already parsing YAML\r\n\r\nFair point!\r\n\r\nHaving dataset info in the README's YAML is great for API / `huggingface_hub` consumers as well as it will be inserted in the `cardData` field out of the box 🔥 \r\n",
"Very supportive of this!\r\n\r\nNesting an array of configs inside `dataset_infos: ` sounds good to me. One small tweak is that `config: default` can be optional for the default config (which can be the first one by convention)\r\n\r\nWe'll be able to implement metadata validation on the Hub side so we ensure that those metadata are always in the right format (maybe for @coyotte508 ? cc @Pierrci). From a quick glance the `features` might be the harder part to validate here, any doc will be welcome.\r\n\r\n### Other high-level points:\r\n- as we move from mostly academic datasets to *all* datasets (which include the data inside the repos), my intuition is that more and more datasets (Hub-stored) are going to be **single-config**\r\n- similarly, less and less datasets will have a loading script, **just the data + some metadata**\r\n- to lower the barrier to entry to contribution, in the long term users shouldn't need to compute/update this data via a command line. It could be filled automatically on the Hub through a \"bot\" inside Discussions & Pull requests for instance.",
"re: `config: default`\r\n\r\nNote also that the default config is not named `default`, afaiu, but create from the repo name, eg: https://huggingface.co/datasets/nbtpj/bionlp2021SAS default config is `nbtpj--bionlp2021SAS` (which is awful)",
"> Note also that the default config is not named default, afaiu, but create from the repo name, eg: https://huggingface.co/datasets/nbtpj/bionlp2021SAS default config is nbtpj--bionlp2021SAS (which is awful)\r\n\r\nWe can change this to `default` I think or something else",
"> From a quick glance the features might be the harder part to validate here, any doc will be welcome.\r\n\r\nI dug into features validation, see:\r\n\r\n- the OpenAPI spec: https://github.com/huggingface/datasets-server/blob/main/chart/static-files/openapi.json#L460-L697\r\n- the node.js code: https://github.com/huggingface/moon-landing/blob/upgrade-datasets-server-client/server/lib/datasets/FeatureType.ts",
"> We can change this to default I think or something else\r\n\r\nI created https://github.com/huggingface/datasets/issues/4902 to discuss that",
"> Note also that the default config is not named `default`, afaiu, but create from the repo name\r\n\r\nin case of single-config you can even hide the config name from the UI IMO\r\n\r\n> I dug into features validation, see: the OpenAPI spec\r\n\r\nin moon-landing we use [Joi](https://joi.dev/api/) to validate metadata so we would need to generate from Joi code from the OpenAPI spec (or from somewhere else) but I guess that's doable – or just rewrite it manually, as it won't change often",
"I remember there was an ongoing discussion on this topic:\r\n- #3507\r\n\r\nI recall some of the concerns raised on that discussion:\r\n- @lhoestq: Tensorflow Datasets catalog includes a community catalog where you can find and use HF datasets. They are using the exported dataset_infos.json files from github to get the metadata: [#3507 (comment)](https://github.com/huggingface/datasets/issues/3507#issuecomment-1056997627)\r\n- @severo: [#3507 (comment)](https://github.com/huggingface/datasets/issues/3507#issuecomment-1042779776)\r\n - the metadata header might be very long, before reaching the start of the README/dataset card. \r\n - It also somewhat prevents including large strings like the checksums\r\n - two concepts are mixed in the same file (metadata and documentation). This means that if you're interested only in one of them, you still have to know how to parse the whole file. \r\n- @severo: the future \"datasets server\" could be in charge of generating the dataset-info.json file: [#3507 (comment)](https://github.com/huggingface/datasets/issues/3507#issuecomment-1033752157)",
"Thanks for bringing these points up !\r\n\r\n> @lhoestq: Tensorflow Datasets catalog includes a community catalog where you can find and use HF datasets. They are using the exported dataset_infos.json files from github to get the metadata: https://github.com/huggingface/datasets/issues/3507#issuecomment-1056997627\r\n\r\nThe TFDS implementation is not super advanced, so it's ok IMO as long as we don't break all the dataset scripts. Note that users can still use `to_tf_dataset`.\r\n\r\nWe had a chance to discuss the two nexts points with @julien-c as well:\r\n\r\n> @severo: https://github.com/huggingface/datasets/issues/3507#issuecomment-1042779776\r\nthe metadata header might be very long, before reaching the start of the README/dataset card.\r\n\r\nIf we don't add the checksums we should be fine. We can also set a maximum number of supported configs in the README to keep it readable.\r\n\r\n> @severo: the future \"datasets server\" could be in charge of generating the dataset-info.json file: https://github.com/huggingface/datasets/issues/3507#issuecomment-1033752157\r\n\r\nI guess the \"HF Hub actions\" could open PRs to do the same in the YAML directly\r\n",
"Thanks for linking that similar discussion for context, @albertvillanova!"
] | 1,661,271,401,000
| 1,664,788,273,000
| 1,664,788,273,000
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MEMBER
| null | null |
Currently there are two places to find metadata for datasets:
- datasets_infos.json, which contains **per dataset config**
- description
- citation
- license
- splits and sizes
- checksums of the data files
- feature types
- and more
- YAML tags, which contain
- license
- language
- train-eval-index
- and more
It would be nice to have a single place instead. We can rely on the YAML tags more than the JSON file for consistency with models. And it would all be indexed by our back-end directly, which is nice to have.
One way would be to move everything to the YAML tags except the checksums (there can be tens of thousands of them). The description/citation is already in the dataset card so we probably don't need to have them in the YAML card, it would be redundant.
Here is an example for SQuAD
```yaml
download_size: 35142551
dataset_size: 89789763
version: 1.0.0
splits:
- name: train
num_examples: 87599
num_bytes: 79317110
- name: validation
num_examples: 10570
num_bytes: 10472653
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: text
list:
dtype: string
- name: answer_start
list:
dtype: int32
```
Since there is only one configuration for SQuAD, this structure is ok. For datasets with several configs we can see in a second step, but IMO it would be ok to have these fields per config using another syntax
```yaml
configs:
- config: unlabeled
splits:
- name: train
num_examples: 10000
features:
- name: text
dtype: string
- config: labeled
splits:
- name: train
num_examples: 100
features:
- name: text
dtype: string
- name: label
dtype: ClassLabel
names:
- negative
- positive
```
So in the end you could specify a YAML tag either at the top level (for all configs) or per config in the `configs` field
Alternatively we could keep config specific stuff in the `dataset_infos.json` as it it today
Not sure yet what's the best approach here but cc @julien-c @mariosasko @albertvillanova @polinaeterna for feedback :)
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`_resolve_features` ignores the token
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[
"Hi ! Your HF_ENDPOINT seems wrong because of the extra \"/\"\r\n```diff\r\n- os.environ[\"HF_ENDPOINT\"] = \"https://hub-ci.huggingface.co/\"\r\n+ os.environ[\"HF_ENDPOINT\"] = \"https://hub-ci.huggingface.co\"\r\n```\r\n\r\ncan you try again without the extra \"/\" ?",
"Oh, yes, sorry, but it's not the issue.\r\n\r\nIn my code, I set `HF_ENDPOINT=https://hub-ci.huggingface.co`. I added `os.environ[\"HF_ENDPOINT\"] = \"https://hub-ci.huggingface.co/\"` afterward just to indicate that we had to have this env var and made a mistake there",
"I can't reproduce on my side. I tried using a private dataset repo with a CSV file on hub-ci\r\n\r\nWhat's your version of `huggingface_hub` ?",
"I can't reproduce either... Not sure what has occurred, very sorry to have made you lost your time on that ",
"I got something similar in https://github.com/huggingface/datasets-server/pull/608. Look how changing the order of the tests (https://github.com/huggingface/datasets-server/pull/608/commits/2c50fe833323de3dfdc76c5cd68639279e0887f8) change the result, which means that something has a side-effect:\r\n- https://github.com/huggingface/datasets-server/actions/runs/3264636253/jobs/5365612918 works\r\n- https://github.com/huggingface/datasets-server/actions/runs/3264651839/jobs/5365654924 does not work\r\n\r\nI still couldn't reproduce it with a simpler script... ",
"The issue happens because `extend_module_for_streaming` can't be used several times on packaged builders like `csv` to apply a new auth token. Indeed `extend_module_for_streaming` only applies authentication once, and on subsequent calls does nothing:\r\n\r\nhttps://github.com/huggingface/datasets/blob/07b7c38d9e9c72c74b02524c432ca64d0d3738f4/src/datasets/streaming.py#L62-L64\r\n\r\nThis behavior exists because the authenticatoin wrapper only supports one token. This is an issue for packaged builders which can be used to load several datasets, so it may require several tokens.\r\n\r\nThis can be fixed by storing a dict `token_per_repo_id` instead of `use_auth_token` in the authentication wrapper, and by making it possible to update the authentication wrapper with a new token",
"I fixed the datasets-server CI with: https://github.com/huggingface/datasets-server/pull/608\r\n\r\nSee https://github.com/huggingface/datasets-server/actions/runs/3265359326/jobs/5367445018\r\n\r\nThanks for the help @lhoestq !",
"> This can be fixed by storing a dict token_per_repo_id instead of use_auth_token in the authentication wrapper, and by making it possible to update the authentication wrapper with a new token\r\n\r\nIf I call the module on the same repo twice: first with authentication, then without authentication, would the second call use authentication anyway? It sounds like a bug: the argument passed to the function would be silently ignored.",
"Yes exactly, this is a known bug",
"And do you think this bug could be solved as well when fixing this issue?",
"yes definitely !"
] | 1,661,266,656,000
| 1,666,014,347,000
| null |
CONTRIBUTOR
| null | null |
## Describe the bug
When calling [`_resolve_features()`](https://github.com/huggingface/datasets/blob/54b532a8a2f5353fdb0207578162153f7b2da2ec/src/datasets/iterable_dataset.py#L1255) on a gated dataset, ie. a dataset which requires a token to be loaded, the token seems to be ignored even if it has been provided to `load_dataset` before.
## Steps to reproduce the bug
```python
import os
os.environ["HF_ENDPOINT"] = "https://hub-ci.huggingface.co/"
hf_token = "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"
from datasets import load_dataset
# public
dataset_name = "__DUMMY_DATASETS_SERVER_USER__/repo_csv_data-16612654226756"
config_name = "__DUMMY_DATASETS_SERVER_USER__--repo_csv_data-16612654226756"
split_name = "train"
iterable_dataset = load_dataset(
dataset_name,
name=config_name,
split=split_name,
streaming=True,
use_auth_token=hf_token,
)
iterable_dataset = iterable_dataset._resolve_features()
print(iterable_dataset.features)
# gated
dataset_name = "__DUMMY_DATASETS_SERVER_USER__/repo_csv_data-16612654317644"
config_name = "__DUMMY_DATASETS_SERVER_USER__--repo_csv_data-16612654317644"
split_name = "train"
iterable_dataset = load_dataset(
dataset_name,
name=config_name,
split=split_name,
streaming=True,
use_auth_token=hf_token,
)
try:
iterable_dataset = iterable_dataset._resolve_features()
except FileNotFoundError as e:
print("FAILS")
```
## Expected results
I expect to have the same result on a public dataset and on a gated (or private) dataset, if the token has been provided.
## Actual results
An exception is thrown on gated datasets.
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.15.0-1017-aws-x86_64-with-glibc2.35
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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Multiple dataloader memory error
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[
"Hi!\r\n\r\n200+ data loaders is a lot. Have you tried to reduce the number of datasets by concatenating/interleaving the ones with the same structure/task (the API is `{concatenate_datasets/interleave_datasets}([dset1, ..., dset_N])`)?",
"Hi @mariosasko, thank you for your reply. I tried pre-concatenating different datasets into one, but one key need is to keep each batch the same data type. Considering that the concatenate-then-segment operation for prefetched samples may span across different data types after concatenating/interleaving (cuz different data sources are mixed), any solution to remain the same data source for each batch?",
"@cyk1337 have you found any solutions to it?\r\n@mariosasko I tried with interleave_datasets to sample batches from two large datasets (wikipedia alike) and it results in out-of-memory error during data loading (16gpus, >1TB physical memory). Do you have any idea about it?"
] | 1,661,245,190,000
| 1,674,698,471,000
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NONE
| null | null |
For the use of multiple datasets and tasks, we use around more than 200+ dataloaders, then pass it into `dataloader1, dataloader2, ..., dataloader200=accelerate.prepare(dataloader1, dataloader2, ..., dataloader200)`
It causes the memory error when generating batches. Any solutions to it?
```bash
File "/home/xxx/my_code/src/utils/data_utils.py", line 54, in generate_batch
x = next(iterator)
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/accelerate/data_loader.py", line 301, in __iter__
for batch in super().__iter__():
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
data = self._next_data()
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 28, in fetch
data.append(next(self.dataset_iter))
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/accelerate/data_loader.py", line 249, in __iter__
for element in self.dataset:
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 503, in __iter__
for key, example in self._iter():
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 500, in _iter
yield from ex_iterable
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 231, in __iter__
new_key = "_".join(str(key) for key in keys)
MemoryError
```
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[
"Thanks for reporting @MoritzLaurer.\r\n\r\nCurrently, the dataset preview is working properly: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli\r\n\r\nPlease note that when a dataset is modified, it might take some time until the preview is completely updated.\r\n\r\n@severo might it be worth adding a clearer error message, something like \"The preview is updating, please retry later\"?",
"Thanks for your response. You are right, its now working well. I had waited for 30 min or so and refreshed several times and thought there was some other error. Yeah, a different error message sounds like a good idea to avoid confusion. ",
"I'm closing this issue then.",
"> @severo might it be worth adding a clearer error message, something like \"The preview is updating, please retry later\"?\r\n\r\nYes, it's a known issue, and we're about to ship a better version"
] | 1,660,920,920,000
| 1,661,179,634,000
| 1,661,148,800,000
|
NONE
| null | null |
### Link
_No response_
### Description
I've just uploaded a new dataset to the hub and the viewer does not work for some reason, see here: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli
It displays the error:
```
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
```
Weirdly enough the dataviewer works for an earlier version of the same dataset. The only difference is that it is smaller, but I'm not aware of other changes I have made: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli_test
Do you know why the dataviewer is not working?
### Owner
_No response_
|
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I_kwDODunzps5QIhG7
| 4,864
|
Allow pathlib PoxisPath in Dataset.read_json
|
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[
"This same error will occur using `ds = datasets.load_dataset('json', data_files=['test.jsonl'])`",
"@cccntu I want to make a quick fix for this, but I am struggling to find where the json dataset builder is. Do you know?",
"@vvvm23 I think you mean think:\r\n```python\r\nds = datasets.load_dataset('json', data_files=[Path('test.jsonl')])\r\n```\r\nAnd the place you want to modify is here:\r\n```\r\nutils/file_utils.py:64, in is_remote_url(url_or_filename)\r\n 63 def is_remote_url(url_or_filename: str) -> bool:\r\n---> 64 parsed = urlparse(url_or_filename)\r\n 65 return parsed.scheme in (\"http\", \"https\", \"s3\", \"gs\", \"hdfs\", \"ftp\")\r\n```\r\n\r\nProbably just need to check first if `url_or_filename` is [PathLike](https://docs.python.org/3/library/os.html#os.PathLike) and return False early.\r\n\r\nBtw, I tried installing from main, and ran my code above and got a different error. Probably because the API have changed.\r\n`AttributeError: module 'datasets' has no attribute 'read_json'`\r\n",
"> @vvvm23 I think you mean think:\r\n\r\nYou are correct, thanks!\r\n\r\n> Probably just need to check first if url_or_filename is [PathLike](https://docs.python.org/3/library/os.html#os.PathLike) and return False early.\r\n\r\nIs PathLike sufficient, or should I check the file exists here? Or is that handled later?",
"I think here we just want to avoid passing Path to urlparse. A simpler solution is to add a str() call and convert the input to string before passing to the next step. No need to check anything.",
"Above PR should do your first suggestion. Hope that works for you, as I am going on holiday and won't be able to change much :wink: "
] | 1,660,913,957,000
| 1,678,620,349,000
| null |
CONTRIBUTOR
| null | null |
**Is your feature request related to a problem? Please describe.**
```
from pathlib import Path
from datasets import Dataset
ds = Dataset.read_json(Path('data.json'))
```
causes an error
```
AttributeError: 'PosixPath' object has no attribute 'decode'
```
**Describe the solution you'd like**
It should be able to accept PosixPath and read the json from inside.
|
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| 1,343,737,668
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I_kwDODunzps5QF89E
| 4,863
|
TFDS wiki_dialog dataset to Huggingface dataset
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[
"@albertvillanova any help ? The linked dataset is in beam format which is similar to wikipedia dataset in huggingface that you scripted..",
"Nvm, I was able to port it to huggingface datasets, will upload to the hub soon",
"https://huggingface.co/datasets/djaym7/wiki_dialog",
"Thanks for the addition, @djaym7."
] | 1,660,863,990,000
| 1,661,161,305,000
| 1,661,145,533,000
|
NONE
| null | null |
## Adding a Dataset
- **Name:** *Wiki_dialog*
- **Description: https://github.com/google-research/dialog-inpainting#:~:text=JSON%20object%2C%20for-,example,-%3A
- **Paper: https://arxiv.org/abs/2205.09073
- **Data: https://github.com/google-research/dialog-inpainting
- **Motivation:** *Research and Development on biggest corpus of dialog data*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
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I_kwDODunzps5QE6T7
| 4,862
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Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code
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[
"What's more, the downloaded data is actually a folder instead of an excel file.",
"Hi hi, instead of using `download_and_extract` function, I only use `download` function: `base_dir = Path(dl_manager.download(urls))`. It turns out that the code works for `datasets==2.2.2`, however, it doesn't work with `datasets==2.4.0`. ",
"Hi @yana-xuyan, thanks for reporting.\r\n\r\nIndeed you already found the answer: an Excel file should be just downloaded and not downloaded-and-extracted.\r\n\r\nThe reason why is that if you call also extract, our library will try to infer the compression format (and extract it). And Excel files are viewed as ZIP files and extracted as so (into a directory). This is because the Office Open XML is indeed a zipped file under the hood): https://en.wikipedia.org/wiki/Office_Open_XML\r\n> Office Open XML (also informally known as OOXML) is a **zipped**, XML-based file format\r\n```python\r\nimport zipfile\r\n\r\nzipfile.is_zipfile(\"filename.xlsx\")\r\n```\r\nreturns `True`.",
"Hi @albertvillanova, thank you for your reply! Do you have any clue on why the same error still exists with `datasets==2.4.0` even after I don't extract the downloaded file? FYI, if I downgrade to `datasets==2.2.2`, the code works fine.",
"I guess this has to do with the cache: you should remove the previously-wrongly generated directory from the cache; otherwise `datasets` tries to re-use it."
] | 1,660,847,774,000
| 1,661,937,908,000
| 1,661,937,908,000
|
NONE
| null | null |
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""
_DATASETNAME = "jadi_ide"
_DESCRIPTION = """\
"""
_HOMEPAGE = ""
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx",
}
_SOURCE_VERSION = "1.0.0"
class JaDi_Ide(datasets.GeneratorBasedBuilder):
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BUILDER_CONFIGS = [
NusantaraConfig(
name="jadi_ide_source",
version=SOURCE_VERSION,
description="JaDi-Ide source schema",
schema="source",
subset_id="jadi_ide",
),
]
DEFAULT_CONFIG_NAME = "source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
# Dataset does not have predetermined split, putting all as TRAIN
urls = _URLS[_DATASETNAME]
base_dir = Path(dl_manager.download_and_extract(urls))
data_files = {"train": base_dir}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_files["train"],
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_excel(filepath, engine='openpyxl')
df.columns = ["id", "text", "label"]
if self.config.schema == "source":
for row in df.itertuples():
ex = {
"id": str(row.id),
"text": row.text,
"label": row.label,
}
yield row.id, ex
```
## Expected results
Expecting to load the dataset smoothly.
## Actual results
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1216, in _prepare_split
desc=f"Generating {split_info.name} split",
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/xuyan/.cache/huggingface/modules/datasets_modules/datasets/jadi_ide/7a539f2b6f726defea8fbe36ceda17bae66c370f6d6c418e3a08d760ebef7519/jadi_ide.py", line 107, in _generate_examples
df = pd.read_excel(filepath, engine='openpyxl')
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/download/streaming_download_manager.py", line 701, in xpandas_read_excel
return pd.read_excel(BytesIO(filepath_or_buffer.read()), **kwargs)
AttributeError: 'xPath' object has no attribute 'read'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-142-generic-x86_64-with-debian-stretch-sid
- Python version: 3.7.4
- PyArrow version: 9.0.0
- Pandas version: 0.25.1
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I_kwDODunzps5QEIY8
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Using disk for memory with the method `from_dict`
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"This issue was also causing an OOM in @nateraw 's workflow and shows again that behavior is confusing - we should definitely switch to using the disk IMO"
] | 1,660,835,898,000
| 1,674,758,188,000
| null |
MEMBER
| null | null |
**Is your feature request related to a problem? Please describe.**
I start with an empty dataset. In a loop, at each iteration, I create a new dataset with the method `from_dict` (based on some data I load) and I concatenate this new dataset with the one at the previous iteration. After some iterations, I have an OOM error.
**Describe the solution you'd like**
The method `from_dict` loads the data in RAM. It could be good to add an option to use the disk instead.
**Describe alternatives you've considered**
To solve the problem, I have to do an intermediate step where I save the new datasets at each iteration with `save_to_disk`. Once it's done, I open them all and concatenate them.
|
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| 1,342,231,016
|
I_kwDODunzps5QANHo
| 4,859
|
can't install using conda on Windows 10
|
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[] | 1,660,766,257,000
| 1,660,766,257,000
| null |
NONE
| null | null |
## Describe the bug
I wanted to install using conda or Anaconda navigator. That didn't work, so I had to install using pip.
## Steps to reproduce the bug
conda install -c huggingface -c conda-forge datasets
## Expected results
Should have indicated successful installation.
## Actual results
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
... took forever, so I cancelled it with ctrl-c
## Environment info
- `datasets` version: 2.4.0 # after installing with pip
- Platform: Windows-10-10.0.19044-SP0
- Python version: 3.9.12
- PyArrow version: 9.0.0
- Pandas version: 1.4.2
- conda version: 4.13.0
conda info
active environment : base
active env location : G:\anaconda2022
shell level : 1
user config file : C:\Users\michael\.condarc
populated config files : C:\Users\michael\.condarc
conda version : 4.13.0
conda-build version : 3.21.8
python version : 3.9.12.final.0
virtual packages : __cuda=11.1=0
__win=0=0
__archspec=1=x86_64
base environment : G:\anaconda2022 (writable)
conda av data dir : G:\anaconda2022\etc\conda
conda av metadata url : None
channel URLs : https://conda.anaconda.org/pytorch/win-64
https://conda.anaconda.org/pytorch/noarch
https://conda.anaconda.org/huggingface/win-64
https://conda.anaconda.org/huggingface/noarch
https://conda.anaconda.org/conda-forge/win-64
https://conda.anaconda.org/conda-forge/noarch
https://conda.anaconda.org/anaconda-fusion/win-64
https://conda.anaconda.org/anaconda-fusion/noarch
https://repo.anaconda.com/pkgs/main/win-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/win-64
https://repo.anaconda.com/pkgs/r/noarch
https://repo.anaconda.com/pkgs/msys2/win-64
https://repo.anaconda.com/pkgs/msys2/noarch
package cache : G:\anaconda2022\pkgs
C:\Users\michael\.conda\pkgs
C:\Users\michael\AppData\Local\conda\conda\pkgs
envs directories : G:\anaconda2022\envs
C:\Users\michael\.conda\envs
C:\Users\michael\AppData\Local\conda\conda\envs
platform : win-64
user-agent : conda/4.13.0 requests/2.27.1 CPython/3.9.12 Windows/10 Windows/10.0.19044
administrator : False
netrc file : None
offline mode : False
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| 1,340,859,853
|
I_kwDODunzps5P6-XN
| 4,858
|
map() function removes columns when input_columns is not None
|
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[
"Hi! Thanks for reporting! This looks like a bug. I've just opened a PR with the fix.",
"Awesome! Thank you. I'll close the issue once the PR gets merged. :-)",
"I guess we should reopen after the revert by:\r\n- #5006"
] | 1,660,682,550,000
| 1,663,854,924,000
| 1,663,854,924,000
|
NONE
| null | null |
## Describe the bug
The map function, removes features from the dataset that are not present in the _input_columns_ list of columns, despite the columns being removed not mentioned in the _remove_columns_ argument.
## Steps to reproduce the bug
```python
from datasets import Dataset
ds = Dataset.from_dict({"a" : [1,2,3],"b" : [0,1,0], "c" : [2,4,5]})
def double(x,y):
x = x*2
y = y*2
return {"d" : x, "e" : y}
ds.map(double, input_columns=["a","c"])
```
## Expected results
```
Dataset({
features: ['a', 'b', 'c', 'd', 'e'],
num_rows: 3
})
```
## Actual results
```
Dataset({
features: ['a', 'c', 'd', 'e'],
num_rows: 3
})
```
In this specific example feature **b** should not be removed.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: linux (colab)
- Python version: 3.7.13
- PyArrow version: 6.0.1
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| 1,340,397,153
|
I_kwDODunzps5P5NZh
| 4,857
|
No preprocessed wikipedia is working on huggingface/datasets
|
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[] |
[
"Thanks for reporting @aninrusimha.\r\n\r\nPlease, note that the preprocessed datasets are still available, as described in the dataset card, e.g.: https://huggingface.co/datasets/wikipedia\r\n```python\r\nds = load_dataset(\"wikipedia\", \"20220301.en\")\r\n``` ",
"This is working now, but I was getting an error a few days ago when running an existing script. Unfortunately I did not do a proper bug report, but for some reason I was unable to load the dataset due to a request being made to the wikimedia website. However, its working now. Thanks for the reply!"
] | 1,660,658,133,000
| 1,660,743,308,000
| 1,660,743,308,000
|
NONE
| null | null |
## Describe the bug
20220301 wikipedia dump has been deprecated, so now there is no working wikipedia dump on huggingface
https://huggingface.co/datasets/wikipedia
https://dumps.wikimedia.org/enwiki/
|
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| 1,339,779,957
|
I_kwDODunzps5P22t1
| 4,856
|
file missing when load_dataset with openwebtext on windows
|
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[] |
[
"I have tried to extract ```0015896-b1054262f7da52a0518521e29c8e352c.txt``` from ```17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz``` with 7-zip\r\nand put the file into cache_path ```F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb21562d9b7486faa```\r\nthere is still raise the same error and I find the file was removed from cache_path after I run the run_mlm.py with ```python run_mlm.py --model_type roberta --tokenizer_name roberta-base --dataset_name openwebtext --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --output_dir F:/model/roberta-base```."
] | 1,660,622,662,000
| 1,672,803,552,000
| 1,672,803,552,000
|
NONE
| null | null |
## Describe the bug
0015896-b1054262f7da52a0518521e29c8e352c.txt is missing when I run run_mlm.py with openwebtext. I check the cache_path and can not find 0015896-b1054262f7da52a0518521e29c8e352c.txt. but I can find this file in the 17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz with 7-zip.
## Steps to reproduce the bug
```sh
python run_mlm.py --model_type roberta --tokenizer_name roberta-base --dataset_name openwebtext --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --output_dir F:/model/roberta-base
```
or
```python
from datasets import load_dataset
load_dataset("openwebtext", None, cache_dir=None, use_auth_token=None)
```
## Expected results
Loading is successful
## Actual results
Traceback (most recent call last):
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 795, in _download_and_prepare
raise OSError(
OSError: Cannot find data file.
Original error:
[Errno 22] Invalid argument: 'F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb21562d9b7486faa/0015896-b1054262f7da52a0518521e29c8e352c.txt'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: windows
- Python version: 3.8.5
- PyArrow version: 9.0.0
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| 1,339,699,975
|
I_kwDODunzps5P2jMH
| 4,855
|
Dataset Viewer issue for super_glue
|
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[] |
[
"Thanks for reporting @wzsxxa.\r\n\r\nHowever the \"super_glue\" dataset is rendered properly by the Dataset preview: https://huggingface.co/datasets/super_glue"
] | 1,660,613,696,000
| 1,661,162,881,000
| 1,661,162,865,000
|
NONE
| null | null |
### Link
https://huggingface.co/datasets/super_glue
### Description
can't view super_glue dataset on the web page
### Owner
_No response_
|
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| 1,339,450,991
|
I_kwDODunzps5P1mZv
| 4,852
|
Bug in multilingual_with_para config of exams dataset and checksums error
|
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[
"Hi @albertvillanova. Unfortunately I still get this error. Is this because the merge has yet to be released? Is there a way to track the release?",
"Hi @thesofakillers, yes you are right: the fix will be available after next release (it was planned for today; Monday at the latest).\r\n\r\nIn the meantime, you can use the version of the `exams` on our main branch by passing `revision` to `load_dataset`:\r\n```python\r\nds = load_dataset(\"exams\", revision=\"main\")\r\n```"
] | 1,660,594,492,000
| 1,663,321,855,000
| 1,660,631,347,000
|
MEMBER
| null | null |
## Describe the bug
There is a bug for "multilingual_with_para" config in exams dataset:
```python
ds = load_dataset("./datasets/exams", split="train")
```
raises:
```
KeyError: 'choices'
```
Moreover, there is a NonMatchingChecksumError:
```
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/train_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/dev_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_vi_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_vi_with_para.jsonl.tar.gz']
```
CC: @thesofakillers
|
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Dataset Viewer issue for darragh/demo_data_raw3
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[
"do you have an idea of why it can occur @huggingface/datasets? The dataset consists of a single parquet file.",
"Thanks for reporting @severo.\r\n\r\nI'm not able to reproduce that error. I get instead:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: 'orix/data/ChiSig/唐合乐-9-3.jpg'\r\n```\r\n\r\nWhich pyarrow version are you using? Mine is 6.0.1. ",
"OK, I get now your error when not streaming.",
"OK!\r\n\r\nIf it's useful, the pyarrow version is 7.0.0:\r\n\r\nhttps://github.com/huggingface/datasets-server/blob/487c39d87998f8d5a35972f1027d6c8e588e622d/services/worker/poetry.lock#L1537-L1543",
"Apparently, there is something weird with that Parquet file: its schema is:\r\n```\r\nimages: extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>>\r\n```\r\n\r\nI have forced a right schema:\r\n```python\r\nfrom datasets import Features, Image, load_dataset\r\n\r\nfeatures = Features({\"images\": Image()})\r\nds = datasets.load_dataset(\"parquet\", split=\"train\", data_files=\"train-00000-of-00001.parquet\", features=features)\r\n```\r\nand then recreated a new Parquet file:\r\n```python\r\nds.to_parquet(\"train.parquet\")\r\n```\r\n\r\nNow this Parquet file has the right schema:\r\n```\r\nimages: struct<bytes: binary, path: string>\r\n child 0, bytes: binary\r\n child 1, path: string\r\n```\r\nand can be loaded normally:\r\n```python\r\nIn [26]: ds = load_dataset(\"parquet\", split=\"train\", data_files=\"dataset.parquet\")\r\nn [27]: ds\r\nOut[27]: \r\nDataset({\r\n features: ['images'],\r\n num_rows: 20\r\n})\r\n```"
] | 1,660,317,778,000
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| null |
CONTRIBUTOR
| null | null |
### Link
https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by @NielsRogge
### Owner
No
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I_kwDODunzps5PtCZp
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ImageFolder dataset builder does not read the validation data set if it is named as "val"
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"#take"
] | 1,660,310,760,000
| 1,661,854,495,000
| 1,661,854,495,000
|
CONTRIBUTOR
| null | null |
**Is your feature request related to a problem? Please describe.**
Currently, the `'imagefolder'` data set builder in [`load_dataset()`](https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/load.py#L1541] ) only [supports](https://github.com/huggingface/datasets/blob/6c609a322da994de149b2c938f19439bca99408e/src/datasets/data_files.py#L31) the following names as the validation data set directory name: `["validation", "valid", "dev"]`. When the validation directory is named as `'val'`, the Data set will not have a validation split. I expected this to be a trivial task but ended up spending a lot of time before knowing that only the above names are supported.
Here's a minimal example of `val` not being recognized:
```python
import os
import numpy as np
import cv2
from datasets import load_dataset
# creating a dummy data set with the following structure:
# ROOT
# | -- train
# | ---- class_1
# | ---- class_2
# | -- val
# | ---- class_1
# | ---- class_2
ROOT = "data"
for which in ["train", "val"]:
for class_name in ["class_1", "class_2"]:
dir_name = os.path.join(ROOT, which, class_name)
if not os.path.exists(dir_name):
os.makedirs(dir_name)
for i in range(10):
cv2.imwrite(
os.path.join(dir_name, f"{i}.png"),
np.random.random((224, 224))
)
# trying to create a data set
dataset = load_dataset(
"imagefolder",
data_dir=ROOT
)
>> dataset
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 20
})
})
# ^ note how the dataset only has a 'train' subset
```
**Describe the solution you'd like**
The suggestion is to include `"val"` to [that list ](https://github.com/huggingface/datasets/blob/6c609a322da994de149b2c938f19439bca99408e/src/datasets/data_files.py#L31) as that's a commonly used phrase to name the validation directory.
Also, In the documentation, explicitly mention that only such directory names are supported as train/val/test directories to avoid confusion.
**Describe alternatives you've considered**
In the documentation, explicitly mention that only such directory names are supported as train/val/test directories without adding `val` to the above list.
**Additional context**
A question asked in the forum: [
Loading an imagenet-style image dataset with train/val directories](https://discuss.huggingface.co/t/loading-an-imagenet-style-image-dataset-with-train-val-directories/21554)
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I_kwDODunzps5Psghw
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Is it possible to pass multiple links to a split in load script?
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| 1,660,302,371,000
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NONE
| null | null |
**Is your feature request related to a problem? Please describe.**
I wanted to use a python loading script in hugging face datasets that use different sources of text (it's somehow a compilation of multiple datasets + my own dataset) based on how `load_dataset` [works](https://huggingface.co/docs/datasets/loading) I assumed I could do something like bellow in my loading script:
```python
...
_URL = "MY_DATASET_URL/resolve/main/data/"
_URLS = {
"train": [
"FIRST_URL_TO.txt",
_URL + "train-00000-of-00001-676bfebbc8742592.parquet"
]
}
...
```
but when loading the dataset it raises the following error:
```python
File ~/.local/lib/python3.8/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
...
668 if isinstance(a, str):
669 # Force-cast str subclasses to str (issue #21127)
670 parts.append(str(a))
TypeError: expected str, bytes or os.PathLike object, not list
```
**Describe the solution you'd like**
I believe since it's possible for `load_dataset` to get list of URLs instead of just a URL for `train` split it can be possible here too.
**Describe alternatives you've considered**
An alternative solution would be to download all needed datasets locally and `push_to_hub` them all, but since the datasets I'm talking about are huge it's not among my options.
**Additional context**
I think loading `text` beside the `parquet` is completely a different issue but I believe I can figure it out by proposing a config for my dataset to load each entry of `_URLS['train']` separately either by `load_dataset("text", ...` or `load_dataset("parquet", ...`.
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I_kwDODunzps5Posfk
| 4,829
|
Misalignment between card tag validation and docs
|
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"(Note that the doc is aligned with the hub validation rules, and the \"ground truth\" is the hub validation rules given that they apply to all datasets, not just the canonical ones)"
] | 1,660,229,085,000
| 1,660,229,195,000
| null |
MEMBER
| null | null |
## Describe the bug
As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284
the validation of the dataset card tags is not aligned with its documentation: e.g.
- implementation: `license: List[str]`
- docs: `license: Union[str, List[str]]`
They should be aligned.
CC: @julien-c
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| 1,335,675,352
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I_kwDODunzps5PnMnY
| 4,822
|
Moving dataset between namespaces breaks dataset viewer
|
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[
"Let's keep open for now. We should try to reproduce"
] | 1,660,208,730,000
| 1,663,358,589,000
| null |
CONTRIBUTOR
| null | null |
## Describe the bug
I moved a dataset from my own namespace to an org and that broke the dataset viewer. To fix it I had to manually edit the `dataset_info.json` file and change the first key in the json from `username--datasetname` to `orgname--datasetname`
## Steps to reproduce the bug
What I did was:
1- Upload a dataset to my own namespace using `push_to_hub`
2- Move the dataset from my namespace to an org using the web interface.
## Expected results
For the file to be changed accordingly.
## Actual results
Broken dataset viewer.
## Environment info
- `datasets` version: 2.3.3.dev0
- Platform: Linux-4.15.0-189-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.5
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
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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"
] | 1,660,160,553,000
| 1,660,161,190,000
| 1,660,161,190,000
|
NONE
| null | 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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| 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. "
] | 1,660,135,545,000
| 1,660,210,672,000
| 1,660,210,672,000
|
NONE
| null | 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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MEMBER
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## Describe the bug
Our loading script for OPUS ParaCrawl loads its 7.1 version. Current existing version is 9.
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Support CSV as metadata file format in AudioFolder/ImageFolder
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CONTRIBUTOR
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Requested here: https://discuss.huggingface.co/t/how-to-structure-an-image-dataset-repo-using-the-image-folder-approach/21004. CSV is also used in AutoTrain for specifying metadata in image datasets.
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Bug in function validate_type for Python >= 3.9
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MEMBER
| null | null |
## Describe the bug
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]
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Add more information to the dataset card of mlqa dataset
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[
"#self-assign",
"Fixed by:\r\n- #4809"
] | 1,660,030,542,000
| 1,660,052,003,000
| 1,660,052,003,000
|
CONTRIBUTOR
| null | null | null |
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I_kwDODunzps5Pbq3b
| 4,805
|
Wrong example in opus_gnome dataset card
|
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[] | 1,660,015,287,000
| 1,660,045,925,000
| 1,660,045,925,000
|
CONTRIBUTOR
| null | 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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I_kwDODunzps5PblNW
| 4,804
|
streaming dataset with concatenating splits raises an error
|
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[
"Hi! Only the name of a particular split (\"train\", \"test\", ...) is supported as a split pattern if `streaming=True`. We plan to address this limitation soon."
] | 1,660,012,916,000
| 1,660,740,156,000
| null |
NONE
| null | null |
## Describe the bug
streaming dataset with concatenating splits raises an error
## Steps to reproduce the bug
```python
from datasets import load_dataset
# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation")
```
```python
from datasets import load_dataset
# error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation", streaming=True)
```
```sh
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-4-a6ae02d63899>](https://localhost:8080/#) in <module>()
3 # error
4 repo = "nateraw/ade20k-tiny"
----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True)
1 frames
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
1030 splits_generator = splits_generators[split]
1031 else:
-> 1032 raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
1033
1034 # Create a dataset for each of the given splits
ValueError: Bad split: train+validation. Available splits: ['validation', 'train']
```
[Colab](https://colab.research.google.com/drive/1wMj08_0bym9jnGgByib4lsBPu8NCZBG9?usp=sharing)
## Expected results
load successfully or throws an error saying it is not supported.
## Actual results
above
## Environment info
- `datasets` version: 2.4.0
- Platform: Windows-10-10.0.22000-SP0 (windows11 x64)
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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| 1,332,079,562
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I_kwDODunzps5PZevK
| 4,803
|
Support `pipeline` argument in inspect.py functions
|
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| null |
CONTRIBUTOR
| null | null |
**Is your feature request related to a problem? Please describe.**
The `wikipedia` dataset requires a `pipeline` argument to build the list of splits:
https://huggingface.co/datasets/wikipedia/blob/main/wikipedia.py#L937
But this is currently not supported in `get_dataset_config_info`:
https://github.com/huggingface/datasets/blob/main/src/datasets/inspect.py#L373-L375
which is called by other functions, e.g. `get_dataset_split_names`.
**Additional context**
The dataset viewer is not working out-of-the-box on `wikipedia` for this reason:
https://huggingface.co/datasets/wikipedia/viewer
<img width="637" alt="Capture d’écran 2022-08-08 à 12 01 16" src="https://user-images.githubusercontent.com/1676121/183461838-5330783b-0269-4ba7-a999-314cde2023d8.png">
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| 1,331,676,691
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I_kwDODunzps5PX8YT
| 4,802
|
`with_format` behavior is inconsistent on different datasets
|
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[
"Hi! You can get a `torch.Tensor` if you do the following:\r\n```python\r\nraw = load_dataset(\"beans\", split=\"train\")\r\nraw = raw.select(range(100))\r\n\r\npreprocessor = AutoFeatureExtractor.from_pretrained(\"nateraw/vit-base-beans\")\r\n\r\nfrom datasets import Array3D\r\nfeatures = raw.features.copy()\r\nfeatures[\"pixel_values\"] = datasets.Array3D(shape=(3, 224, 224), dtype=\"float32\")\r\n\r\ndef preprocess_func(examples):\r\n imgs = [img.convert(\"RGB\") for img in examples[\"image\"]]\r\n return preprocessor(imgs)\r\n\r\ndata = raw.map(preprocess_func, batched=True, features=features)\r\n\r\nprint(type(data[0][\"pixel_values\"]))\r\n\r\ndata = data.with_format(\"torch\", columns=[\"pixel_values\"])\r\n\r\nprint(type(data[0][\"pixel_values\"]))\r\n```\r\n\r\nThe reason for this \"inconsistency\" in the default case is the way PyArrow infers the type of multi-dim arrays (in this case, the `pixel_values` column). If the type is not specified manually, PyArrow assumes it is a dynamic-length sequence (it needs to know the type before writing the first batch to a cache file, and it can't be sure the array is fixed ahead of time; `ArrayXD` is our way of telling that the dims are fixed), so it already fails to convert the corresponding array to NumPy properly (you get an array of `np.object` arrays). And `with_format(\"torch\")` replaces NumPy arrays with Torch tensors, so this bad formatting propagates."
] | 1,659,955,294,000
| 1,660,063,749,000
| null |
CONTRIBUTOR
| null | null |
## Describe the bug
I found a case where `with_format` does not transform the dataset to the requested format.
## Steps to reproduce the bug
Run:
```python
from transformers import AutoTokenizer, AutoFeatureExtractor
from datasets import load_dataset
raw = load_dataset("glue", "sst2", split="train")
raw = raw.select(range(100))
tokenizer = AutoTokenizer.from_pretrained("philschmid/tiny-bert-sst2-distilled")
def preprocess_func(examples):
return tokenizer(examples["sentence"], padding=True, max_length=256, truncation=True)
data = raw.map(preprocess_func, batched=True)
print(type(data[0]["input_ids"]))
data = data.with_format("torch", columns=["input_ids"])
print(type(data[0]["input_ids"]))
```
printing as expected:
```python
<class 'list'>
<class 'torch.Tensor'>
```
Then run:
```python
raw = load_dataset("beans", split="train")
raw = raw.select(range(100))
preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans")
def preprocess_func(examples):
imgs = [img.convert("RGB") for img in examples["image"]]
return preprocessor(imgs)
data = raw.map(preprocess_func, batched=True)
print(type(data[0]["pixel_values"]))
data = data.with_format("torch", columns=["pixel_values"])
print(type(data[0]["pixel_values"]))
```
Printing, unexpectedly
```python
<class 'list'>
<class 'list'>
```
## Expected results
`with_format` should transform into the requested format; it's not the case.
## Actual results
`type(data[0]["pixel_values"])` should be `torch.Tensor` in the example above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: dev version, commit 44af3fafb527302282f6b6507b952de7435f0979
- Platform: Linux
- Python version: 3.9.12
- PyArrow version: 7.0.0
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| 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 "
] | 1,659,837,252,000
| 1,660,063,371,000
| 1,660,063,371,000
|
CONTRIBUTOR
| null | 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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I_kwDODunzps5PRHpC
| 4,796
|
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
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"@mariosasko I'm getting a similar issue when creating a Dataset from a Pandas dataframe, like so:\r\n\r\n```\r\nfrom datasets import Dataset, Features, Image, Value\r\nimport pandas as pd\r\nimport requests\r\nimport PIL\r\n\r\n# we need to define the features ourselves\r\nfeatures = Features({\r\n 'a': Value(dtype='int32'),\r\n 'b': Image(),\r\n})\r\n\r\nurl = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\r\nimage = PIL.Image.open(requests.get(url, stream=True).raw)\r\n\r\ndf = pd.DataFrame({\"a\": [1, 2], \r\n \"b\": [image, image]})\r\n\r\ndataset = Dataset.from_pandas(df, features=features) \r\n```\r\nresults in \r\n\r\n```\r\nArrowInvalid: ('Could not convert <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F7991A15C10> with type JpegImageFile: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column b with type object')\r\n```\r\n\r\nWill the PR linked above also fix that?",
"I would expect this to work, but it doesn't. Shouldn't be too hard to fix tho (in a subsequent PR).",
"Hi @mariosasko just wanted to check in if there is a PR to follow for this. I was looking to create a demo app using this. If it's not working I can just use byte encoded images in the dataset which are not displayed. ",
"Hi @darraghdog! No PR yet, but I plan to fix this before the next release.",
"I was just pointed here by @mariosasko, meanwhile I found a workaround using `encode_example` like so:\r\n\r\n```\r\nfrom datasets import load_from_disk, Dataset\r\nDATASET_PATH = \"/hf/m4-master/data/cm4/cm4-10000-v0.1\"\r\nds1 = load_from_disk(DATASET_PATH)\r\nds2 = Dataset.from_dict(mapping={k: [] for k in ds1[99].keys()},\r\n features=ds1.features\r\n)\r\nfor i in range(2):\r\n # could add several representative items here\r\n row = ds1[99]\r\n row_encoded = ds2.features.encode_example(row)\r\n ds2 = ds2.add_item(row_encoded)\r\n```",
"Hmm, interesting. If I create the dataset on the fly:\r\n\r\n```\r\nfrom datasets import load_from_disk, Dataset\r\nDATASET_PATH = \"/hf/m4-master/data/cm4/cm4-10000-v0.1\"\r\nds1 = load_from_disk(DATASET_PATH)\r\nds2 = Dataset.from_dict(mapping={k: [v]*2 for k, v in ds1[99].items()},\r\n features=ds1.features)\r\n```\r\n\r\nit doesn't fail with the error in the OP, as `from_dict` performs `encode_batch`.\r\n\r\nHowever if I try to use this dataset it fails now with:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/multiprocess/pool.py\", line 125, in worker\r\n result = (True, func(*args, **kwds))\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 557, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 524, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/fingerprint.py\", line 480, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 2775, in _map_single\r\n batch = apply_function_on_filtered_inputs(\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 2655, in apply_function_on_filtered_inputs\r\n processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 2347, in decorated\r\n result = f(decorated_item, *args, **kwargs)\r\n File \"debug_leak2.py\", line 235, in split_pack_and_pad\r\n images.append(image_transform(image.convert(\"RGB\")))\r\nAttributeError: 'dict' object has no attribute 'convert'\r\n\"\"\"\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 \"debug_leak2.py\", line 418, in <module>\r\n train_loader, val_loader = get_dataloaders()\r\n File \"debug_leak2.py\", line 348, in get_dataloaders\r\n dataset = dataset.map(mapper, batch_size=32, batched=True, remove_columns=dataset.column_names, num_proc=4)\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 2500, in map\r\n transformed_shards[index] = async_result.get()\r\n File \"/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/multiprocess/pool.py\", line 771, in get\r\n raise self._value\r\nAttributeError: 'dict' object has no attribute 'convert'\r\n```\r\n\r\nbut if I create that same dataset one item at a time as in the previous comment's code snippet it doesn't fail.\r\n\r\nThe features of this dataset are set to:\r\n\r\n```\r\n{'texts': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), \r\n'images': Sequence(feature=Image(decode=True, id=None), length=-1, id=None)}\r\n```",
"> @mariosasko I'm getting a similar issue when creating a Dataset from a Pandas dataframe, like so:\r\n> \r\n> ```\r\n> from datasets import Dataset, Features, Image, Value\r\n> import pandas as pd\r\n> import requests\r\n> import PIL\r\n> \r\n> # we need to define the features ourselves\r\n> features = Features({\r\n> 'a': Value(dtype='int32'),\r\n> 'b': Image(),\r\n> })\r\n> \r\n> url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\r\n> image = PIL.Image.open(requests.get(url, stream=True).raw)\r\n> \r\n> df = pd.DataFrame({\"a\": [1, 2], \r\n> \"b\": [image, image]})\r\n> \r\n> dataset = Dataset.from_pandas(df, features=features) \r\n> ```\r\n> \r\n> results in\r\n> \r\n> ```\r\n> ArrowInvalid: ('Could not convert <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F7991A15C10> with type JpegImageFile: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column b with type object')\r\n> ```\r\n> \r\n> Will the PR linked above also fix that?\r\n\r\nIt looks like the problem still exists.\r\nAny news ? Any good workaround ?\r\n\r\nThank you",
"There is a workaround: \r\nCreate a loader python scrypt and upload the dataset to huggingface.\r\n\r\nHere is an example how to do that:\r\n\r\nhttps://huggingface.co/datasets/jamescalam/image-text-demo/tree/main\r\n\r\nand Here are videos with explanations:\r\n\r\nhttps://www.youtube.com/watch?v=lqK4ocAKveE and https://www.youtube.com/watch?v=ODdKC30dT8c",
"cc @mariosasko gentle ping for a fix :)",
"Any update on this? I'm still facing this issure. Any workaround?",
"I was facing the same issue. Downgrading datasets from 2.11.0 to 2.4.0 solved the issue. ",
"> Any update on this? I'm still facing this issure. Any workaround?\r\n\r\nI was able to resolve my issue with a quick workaround: \r\n\r\n```\r\nfrom collections import defaultdict\r\nfrom datasets import Dataset\r\n \r\ndata = defaultdict(list)\r\nfor idx in tqdm(range( len(dataloader)),desc=\"Captioning...\"):\r\n img = dataloader[idx]\r\n data['image'].append(img)\r\n data['text'].append(f\"{img_{idx}})\r\n \r\ndataset = Dataset.from_dict(data)\r\ndataset = dataset.filter(lambda example: example['image'] is not None)\r\ndataset = dataset.filter(lambda example: example['text'] is not None)\r\n \r\ndataset.push_to_hub(path-to-repo', private=False)\r\n```\r\n\r\nHope it helps!\r\nHappy coding",
"> > Any update on this? I'm still facing this issure. Any workaround?\r\n> \r\n> I was able to resolve my issue with a quick workaround:\r\n> \r\n> ```\r\n> from collections import defaultdict\r\n> from datasets import Dataset\r\n> \r\n> data = defaultdict(list)\r\n> for idx in tqdm(range( len(dataloader)),desc=\"Captioning...\"):\r\n> img = dataloader[idx]\r\n> data['image'].append(img)\r\n> data['text'].append(f\"{img_{idx}})\r\n> \r\n> dataset = Dataset.from_dict(data)\r\n> dataset = dataset.filter(lambda example: example['image'] is not None)\r\n> dataset = dataset.filter(lambda example: example['text'] is not None)\r\n> \r\n> dataset.push_to_hub(path-to-repo', private=False)\r\n> ```\r\n> \r\n> Hope it helps! Happy coding\r\n\r\nIt works!! "
] | 1,659,703,279,000
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CONTRIBUTOR
| null | null |
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
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| 4,795
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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"
] | 1,659,682,261,000
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NONE
| null | 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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Add DocVQA
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[
"Thanks for proposing, @NielsRogge.\r\n\r\nPlease, note this dataset requires registering in their website and their Terms and Conditions state we cannot distribute their URL:\r\n```\r\n1. You will NOT distribute the download URLs\r\n...\r\n```"
] | 1,659,618,446,000
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| null |
CONTRIBUTOR
| null | null |
## Adding a Dataset
- **Name:** DocVQA
- **Description:** Document Visual Question Answering (DocVQA) seeks to inspire a “purpose-driven” point of view in Document Analysis and Recognition research, where the document content is extracted and used to respond to high-level tasks defined by the human consumers of this information.
- **Paper:** https://arxiv.org/abs/2007.00398
- **Data:** https://www.docvqa.org/datasets/docvqa
- **Motivation:** Models like LayoutLM and Donut in the Transformers library are fine-tuned on DocVQA. Would be very handy to directly load this dataset from the hub.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
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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."
] | 1,659,617,356,000
| 1,659,620,596,000
| 1,659,620,596,000
|
NONE
| null | 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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I_kwDODunzps5PMARY
| 4,790
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Issue with fine classes in trec dataset
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MEMBER
| null | 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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I_kwDODunzps5PK2TH
| 4,787
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NonMatchingChecksumError in mbpp dataset
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| 1,659,633,661,000
| 1,659,633,661,000
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MEMBER
| null | 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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| 1,327,340,828
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I_kwDODunzps5PHZ0c
| 4,786
|
.save_to_disk('path', fs=s3) TypeError
|
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[] | 1,659,538,169,000
| 1,659,540,180,000
| 1,659,540,180,000
|
NONE
| null | null |
The following code:
```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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I_kwDODunzps5PDy-Q
| 4,784
|
Add Multiface dataset
|
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[
"Hi @osanseviero I would like to add this dataset.",
"Hey @nandwalritik! Thanks for offering to help!\r\n\r\nThis dataset might be somewhat complex and I'm concerned about it being 65 TB, which would be quite expensive to host. @lhoestq @mariosasko I would love your input if you think it's worth adding this dataset.",
"Thanks for proposing this interesting dataset, @osanseviero.\r\n\r\nPlease note that the data files are already hosted in a third-party server: e.g. the index of data files for entity \"6795937\" is at https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/index.html \r\n- audio.tar: https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/audio.tar\r\n- ...\r\n\r\nTherefore, in principle, we don't need to host them on our Hub: it would be enough to just implement a loading script in the corresponding Hub dataset repo, e.g. \"facebook/multiface\"..."
] | 1,659,474,022,000
| 1,659,969,756,000
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MEMBER
| null | null |
## Adding a Dataset
- **Name:** Multiface dataset
- **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capture rate at 30 fps
- **Data:** https://github.com/facebookresearch/multiface
The whole dataset is 65TB though, so I'm not sure
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
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I_kwDODunzps5PDOz2
| 4,782
|
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
|
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[
"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."
] | 1,659,465,365,000
| 1,661,161,588,000
| 1,660,961,513,000
|
NONE
| null | 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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I_kwDODunzps5PCRyp
| 4,779
|
Loading natural_questions requires apache_beam even with existing preprocessed data
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[] | 1,659,452,817,000
| 1,659,456,198,000
| 1,659,456,198,000
|
MEMBER
| null | 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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I_kwDODunzps5O8iwk
| 4,776
|
RuntimeError when using torchaudio 0.12.0 to load MP3 audio file
|
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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."
] | 1,659,363,083,000
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| 1,677,772,695,000
|
MEMBER
| null | 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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Streaming not supported in Theivaprakasham/wildreceipt
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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."
] | 1,659,347,177,000
| 1,659,349,829,000
| 1,659,349,829,000
|
NONE
| null | null |
### Link
_No response_
### Description
_No response_
### Owner
_No response_
|
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| 1,323,375,844
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I_kwDODunzps5O4Rzk
| 4,774
|
Training hangs at the end of epoch, with set_transform/with_transform+multiple workers
|
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[] | 1,659,249,148,000
| 1,659,249,403,000
| null |
NONE
| null | null |
## Describe the bug
I use load_dataset() (I tried with [wiki](https://huggingface.co/datasets/wikipedia) and my own json data) and use set_transform/with_transform for preprocessing. But it hangs at the end of the 1st epoch if dataloader_num_workers>=1. No problem with single worker.
## Steps to reproduce the bug
```python
train_dataset = datasets.load_dataset("wikipedia", "20220301.en",
split='train',
cache_dir=model_args.cache_dir,
streaming=False)
train_dataset.set_transform(psg_parse_fn)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.train_batch_size,
sampler=DistributedSampler(train_dataset),
collate_fn=data_collator,
drop_last=args.dataloader_drop_last,
num_workers=args.dataloader_num_workers,
)
```
## Expected results
## Actual results
It simply hangs. The ending step is num_example/batch_size (one epoch).
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Linux-5.4.170+-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 8.0.0
- Pandas version: 1.4.1
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|
I_kwDODunzps5O1rID
| 4,772
|
AssertionError when using label_cols in to_tf_dataset
|
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[
"cc @Rocketknight1 ",
"Hi @lehrig, this is caused by the data collator renaming \"label\" to \"labels\". If you set `label_cols=[\"labels\"]` in the call it will work correctly. However, I agree that the cause of the bug is not obvious, so I'll see if I can make a PR to clarify things when the collator renames columns.",
"Thanks - and wow, that appears like a strange side-effect of the data collator. Is that really needed?\r\n\r\nWhy not make it more explicit? For example, extend `DefaultDataCollator` with an optional property `label_col_name` to be used as label column; only when it is not provided default to `labels` (and document that this happens) for backwards-compatibility? ",
"Haha, I honestly have no idea why our data collators rename `\"label\"` (the standard label column name in our datasets) to `\"labels\"` (the standard label column name input to our models). It's been a pain point when I design TF data pipelines, though, because I don't want to hardcode things like that - especially in `datasets`, because the renaming is something that happens purely at the `transformers` end. I don't think I could make the change in the data collators themselves at this point, because it would break backward compatibility for everything in PyTorch as well as TF.\r\n\r\nIn the most recent version of `transformers` we added a [prepare_tf_dataset](https://huggingface.co/docs/transformers/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset) method to our models which takes care of these details for you, and even chooses appropriate columns and labels for the model you're using. In future we might make that the officially recommended way to convert HF datasets to `tf.data.Dataset`.",
"Interesting, that'd be great especially for clarity. https://huggingface.co/docs/datasets/use_with_tensorflow#data-loading already improved clarity, yet, all those options will still confuse people. Looking forward to those advances in the hope there'll be only 1 way in the future ;)\r\n\r\nAnyways, I am happy for the time being with the work-around you provided. Thank you!"
] | 1,659,130,332,000
| 1,662,981,886,000
| 1,662,981,886,000
|
NONE
| null | 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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| 1,322,121,554
|
I_kwDODunzps5OzflS
| 4,769
|
Fail to process SQuADv1.1 datasets with max_seq_length=128, doc_stride=96.
|
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[] | 1,659,093,504,000
| 1,659,093,504,000
| null |
NONE
| null | null |
## Describe the bug
datasets fail to process SQuADv1.1 with max_seq_length=128, doc_stride=96 when calling datasets["train"].train_dataset.map().
## Steps to reproduce the bug
I used huggingface[ TF2 question-answering examples](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering). And my scripts are as follows:
```
python run_qa.py \
--model_name_or_path $BERT_DIR \
--dataset_name $SQUAD_DIR \
--do_train \
--do_eval \
--per_device_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 128 \
--doc_stride 96 \
--output_dir $OUTPUT \
--save_steps 10000 \
--overwrite_cache \
--overwrite_output_dir \
```
## Expected results
Normally process SQuADv1.1 datasets with max_seq_length=128, doc_stride=96.
## Actual results
```
INFO:__main__:Padding all batches to max length because argument was set or we're on TPU.
WARNING:datasets.fingerprint:Parameter 'function'=<function main.<locals>.prepare_train_features at 0x7f15bc2d07a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
0%| | 0/88 [00:00<?, ?ba/s]thread '<unnamed>' panicked at 'assertion failed: stride < max_len', /__w/tokenizers/tokenizers/tokenizers/src/tokenizer/encoding.rs:311:9
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
0%| | 0/88 [00:00<?, ?ba/s]
Traceback (most recent call last):
File "run_qa.py", line 743, in <module>
main()
File "run_qa.py", line 485, in main
load_from_cache_file=not data_args.overwrite_cache,
File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2394, in map
desc=desc,
File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 551, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 518, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2768, in _map_single
offset=offset,
File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2644, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2336, in decorated
result = f(decorated_item, *args, **kwargs)
File "run_qa.py", line 410, in prepare_train_features
padding=padding,
File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2512, in __call__
**kwargs,
File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2703, in batch_encode_plus
**kwargs,
File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py", line 429, in _batch_encode_plus
is_pretokenized=is_split_into_words,
pyo3_runtime.PanicException: assertion failed: stride < max_len
Traceback (most recent call last):
File "./data/SQuADv1.1/evaluate-v1.1.py", line 92, in <module>
with open(args.prediction_file) as prediction_file:
FileNotFoundError: [Errno 2] No such file or directory: './output/bert_base_squadv1.1_tf2/eval_predictions.json'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Ubuntu, pytorch=1.11.0, tensorflow-gpu=2.9.1
- Python version: 2.7
- PyArrow version: 8.0.0
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I_kwDODunzps5OyTXk
| 4,766
|
Dataset Viewer issue for openclimatefix/goes-mrms
|
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[
"Thanks for reporting, @cheaterHy.\r\n\r\nThe cause of this issue is a misalignment between the names of the repo (`goes-mrms`, with hyphen) and its Python loading scrip file (`goes_mrms.py`, with underscore).\r\n\r\nI've opened an Issue discussion in their repo: https://huggingface.co/datasets/openclimatefix/goes-mrms/discussions/1"
] | 1,659,075,434,000
| 1,659,084,238,000
| null |
NONE
| null | null |
### Link
_No response_
### Description
_No response_
### Owner
_No response_
|
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|
I_kwDODunzps5Oved7
| 4,761
|
parallel searching in multi-gpu setting using faiss
|
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"And I don't see any speed up when increasing the number of GPUs while calling `get_nearest_examples_batch`.",
"Hi ! Yes search_batch uses FAISS search which happens in parallel across the GPUs\r\n\r\n> And I don't see any speed up when increasing the number of GPUs while calling get_nearest_examples_batch.\r\n\r\nThat's unexpected, can you share the code you're running ?",
"here is the code snippet\r\n\r\n```python\r\n\r\n# add faiss index\r\nsource_dataset = load_dataset(source_path)\r\nqueries = load_dataset(query_path)\r\ngpu = [0,1,2,3]\r\nsource_dataset.add_faiss_index(\r\n \"embedding\",\r\n device=gpu,\r\n )\r\n\r\n\r\n# batch query\r\nbatch_size = 32\r\nfor i in tqdm(range(0, len(queries), batch_size)):\r\n if i + batch_size >= len(queries):\r\n batched_queries = queries[i:]\r\n else:\r\n batched_queries = queries[i:i+batch_size]\r\n\r\n batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)\r\n scores, candidates = source_dataset.get_nearest_examples_batch(\r\n \"embedding\",\r\n batched_query_embeddings,\r\n k=5\r\n )\r\n```",
"My version of datasets is `2.4.1.dev0`.",
"The code looks all good to me, do you see all the GPUs being utilized ? What version of faiss are you using ?",
"I can see the memory usage of all the GPUs.\r\nMy version of `faiss-gpu` is `1.7.2`",
"It looks all good to me then ^^ though you said you didn't experienced speed improvements by adding more GPUs ? What size is your source dataset and what time differences did you experience ?",
"query set: 1e6\r\nsource dataset: 1e6\r\nembedding size: 768\r\nindex: Flat\r\ntopk: 20\r\nGPU: V100\r\n\r\nThe time taken to traverse the query set once is about 1.5h, which is almost not influenced by the value of query batch size or the number of GPUs according to my experiments.",
"Hmmm the number of GPUs should divide the time, something is going wrong. Can you check that adding more GPU does divide the memory used per GPU ? Maybe it can be worth looking at similar issues in the FAISS repository or create a noew issue over there to understand what's going on",
"> Can you check that adding more GPU does divide the memory used per GPU \r\n\r\nThe memory used per GPU is unchanged while adding more GPU. Is this unexpected?\r\n\r\nI used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆 ",
"> I used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆\r\n\r\nOh indeed that's possible, I wasn't sure. Anyway you can check that calling get_nearest_examples_batch simply calls search under the hood: \r\n\r\nhttps://github.com/huggingface/datasets/blob/f90f71fbbb33889fe75a3ffc101cdf16a88a3453/src/datasets/search.py#L375",
"Here is a runnable script. \r\nMulti-GPU searching still does not work in my experiments.\r\n\r\n\r\n```python\r\nimport os\r\nfrom tqdm import tqdm\r\nimport numpy as np\r\nimport datasets\r\nfrom datasets import Dataset\r\n\r\nclass DPRSelector:\r\n\r\n def __init__(self, source, target, index_name, gpu=None):\r\n self.source = source\r\n self.target = target\r\n self.index_name = index_name\r\n\r\n cache_path = 'embedding.faiss'\r\n\r\n if not os.path.exists(cache_path):\r\n self.source.add_faiss_index(\r\n column=\"embedding\",\r\n index_name=index_name,\r\n device=gpu,\r\n )\r\n self.source.save_faiss_index(index_name, cache_path)\r\n else:\r\n self.source.load_faiss_index(\r\n index_name,\r\n cache_path,\r\n device=gpu\r\n )\r\n print('index builded!')\r\n\r\n def build_dataset(self, top_k, batch_size):\r\n print('start search')\r\n\r\n for i in tqdm(range(0, len(self.target), batch_size)):\r\n if i + batch_size >= len(self.target):\r\n batched_queries = self.target[i:]\r\n else:\r\n batched_queries = self.target[i:i+batch_size]\r\n\r\n\r\n batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)\r\n search_res = self.source.get_nearest_examples_batch(\r\n self.index_name,\r\n batched_query_embeddings,\r\n k=top_k\r\n )\r\n \r\n print('finish search')\r\n\r\n\r\ndef get_pseudo_dataset():\r\n pseudo_dict = {\"embedding\": np.zeros((1000000, 768), dtype=np.float32)}\r\n print('generate pseudo data')\r\n\r\n dataset = Dataset.from_dict(pseudo_dict)\r\n def list_to_array(data):\r\n return {\"embedding\": [np.array(vector, dtype=np.float32) for vector in data[\"embedding\"]]} \r\n dataset.set_transform(list_to_array, columns='embedding', output_all_columns=True)\r\n\r\n print('build dataset')\r\n return dataset\r\n\r\n\r\n\r\nif __name__==\"__main__\":\r\n\r\n np.random.seed(42)\r\n\r\n\r\n source_dataset = get_pseudo_dataset()\r\n target_dataset = get_pseudo_dataset()\r\n\r\n gpu = [0,1,2,3,4,5,6,7]\r\n selector = DPRSelector(source_dataset, target_dataset, \"embedding\", gpu=gpu)\r\n\r\n selector.build_dataset(top_k=20, batch_size=32)\r\n```",
"@lhoestq Hi, could you please test the code above if you have time? 😄 ",
"Maybe @albertvillanova you can take a look ? I won't be available in the following days",
"@albertvillanova Hi, can you help with this issue?",
"Hi @xwwwwww I'm investigating it, but I'm not an expert in Faiss. In principle, it is weird that your code does not work properly because it seems right...",
"Have you tried passing `gpu=-1` and check if there is a speedup?",
"> Have you tried passing `gpu=-1` and check if there is a speedup?\r\n\r\nyes, there is a speed up using GPU compared with CPU. ",
"When passing `device=-1`, ALL existing GPUs are used (multi GPU): this is the maximum speedup you can get. To know the number of total GPUs:\r\n```\r\nimport faiss\r\n\r\nngpus = faiss.get_num_gpus()\r\nprint(ngpus)\r\n```\r\n\r\nWhen passing a list of integers to `device`, then only that number of GPUs are used (multi GPU as well)\r\n- the speedup should be proportional (more or less) to the ratio of the number of elements passed to `device` over `ngpus`\r\n- if this is not the case, then there is an issue in the implementation of this use case (however, I have reviewed the code and in principle I can't find any evident bug)\r\n\r\nWhen passing a positive integer to `device`, then only a single GPU is used.\r\n- this time should be more or less proportional to the time when passing `device=-1` over `ngpus`",
"Thanks for your help!\r\nHave you run the code and replicated the same experimental results (i.e., no speedup while increasing the number of GPUs)?",
"@albertvillanova @lhoestq Sorry for the bother, is there any progress on this issue? 😃 ",
"I can confirm `add_faiss_index` calls `index = faiss.index_cpu_to_gpus_list(index, gpus=list(device))`.\r\n\r\nCould this be an issue with your environment ? Could you try running with 1 and 8 GPUs with a code similar to[ this one from the FAISS examples](https://github.com/facebookresearch/faiss/blob/main/tutorial/python/5-Multiple-GPUs.py) but using `gpu_index = faiss.index_cpu_to_gpus_list(cpu_index, gpus=list(device))`, and see if the speed changes ?",
"Hi, I test the FAISS example and the speed indeed changes. I set `nb=1000000`, `nq=1000000` and `d=64`\r\n\r\n| num GPUS | time cost |\r\n| -------- | --------- |\r\n| 1 | 28.53 |\r\n| 5 | 7.16 |\r\n\r\n\r\n\r\n",
"Ok the benchmark is great, not sure why it doesn't speed up the index in your case though. You can try running the benchmark with the same settings as your actual dataset\r\n```\r\nquery set: 1e6\r\nsource dataset: 1e6\r\nembedding size: 768\r\nindex: Flat\r\ntopk: 20\r\nGPU: V100\r\n```\r\n\r\nNote that you can still pass a FAISS index you built yourself to a dataset using https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index_from_external_arrays",
"> Here is a runnable script. Multi-GPU searching still does not work in my experiments.\r\n> \r\n> ```python\r\n> import os\r\n> from tqdm import tqdm\r\n> import numpy as np\r\n> import datasets\r\n> from datasets import Dataset\r\n> \r\n> class DPRSelector:\r\n> \r\n> def __init__(self, source, target, index_name, gpu=None):\r\n> self.source = source\r\n> self.target = target\r\n> self.index_name = index_name\r\n> \r\n> cache_path = 'embedding.faiss'\r\n> \r\n> if not os.path.exists(cache_path):\r\n> self.source.add_faiss_index(\r\n> column=\"embedding\",\r\n> index_name=index_name,\r\n> device=gpu,\r\n> )\r\n> self.source.save_faiss_index(index_name, cache_path)\r\n> else:\r\n> self.source.load_faiss_index(\r\n> index_name,\r\n> cache_path,\r\n> device=gpu\r\n> )\r\n> print('index builded!')\r\n> \r\n> def build_dataset(self, top_k, batch_size):\r\n> print('start search')\r\n> \r\n> for i in tqdm(range(0, len(self.target), batch_size)):\r\n> if i + batch_size >= len(self.target):\r\n> batched_queries = self.target[i:]\r\n> else:\r\n> batched_queries = self.target[i:i+batch_size]\r\n> \r\n> \r\n> batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)\r\n> search_res = self.source.get_nearest_examples_batch(\r\n> self.index_name,\r\n> batched_query_embeddings,\r\n> k=top_k\r\n> )\r\n> \r\n> print('finish search')\r\n> \r\n> \r\n> def get_pseudo_dataset():\r\n> pseudo_dict = {\"embedding\": np.zeros((1000000, 768), dtype=np.float32)}\r\n> print('generate pseudo data')\r\n> \r\n> dataset = Dataset.from_dict(pseudo_dict)\r\n> def list_to_array(data):\r\n> return {\"embedding\": [np.array(vector, dtype=np.float32) for vector in data[\"embedding\"]]} \r\n> dataset.set_transform(list_to_array, columns='embedding', output_all_columns=True)\r\n> \r\n> print('build dataset')\r\n> return dataset\r\n> \r\n> \r\n> \r\n> if __name__==\"__main__\":\r\n> \r\n> np.random.seed(42)\r\n> \r\n> \r\n> source_dataset = get_pseudo_dataset()\r\n> target_dataset = get_pseudo_dataset()\r\n> \r\n> gpu = [0,1,2,3,4,5,6,7]\r\n> selector = DPRSelector(source_dataset, target_dataset, \"embedding\", gpu=gpu)\r\n> \r\n> selector.build_dataset(top_k=20, batch_size=32)\r\n> ```\r\n\r\nBy the way, have you run this toy example and replicated my experiment results? I think it is a more direct way to figure this out :)"
] | 1,659,020,223,000
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CONTRIBUTOR
| null | null |
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360
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Issue with offline mode
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[
"Hi @SaulLu, thanks for reporting.\r\n\r\nI think offline mode is not supported for datasets containing only data files (without any loading script). I'm having a look into this...",
"Thanks for your feedback! \r\n\r\nTo give you a little more info, if you don't set the offline mode flag, the script will load the cache. I first noticed this behavior with the `evaluate` library, and while trying to understand the downloading flow I realized that I had a similar error with datasets.",
"This is an issue we have to fix.",
"This is related to https://github.com/huggingface/datasets/issues/3547"
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CONTRIBUTOR
| null | null |
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
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Dataset Viewer issue for Toygar/turkish-offensive-language-detection
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[
"I refreshed the dataset viewer manually, it's fixed now. Sorry for the inconvenience.\r\n<img width=\"1557\" alt=\"Capture d’écran 2022-07-28 à 09 17 39\" src=\"https://user-images.githubusercontent.com/1676121/181514666-92d7f8e1-ddc1-4769-84f3-f1edfdb902e8.png\">\r\n\r\n"
] | 1,659,007,303,000
| 1,659,014,276,000
| 1,659,014,268,000
|
NONE
| null | 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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I_kwDODunzps5Otsuk
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|
Document better when relative paths are transformed to URLs
|
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| 1,661,452,464,000
| 1,661,452,464,000
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MEMBER
| null | 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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I_kwDODunzps5OqNOE
| 4,755
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Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size
|
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[
"I've built a minimal example that shows this bug without `n_proc`. It seems like it's a problem any way of using **tokenizers, `overflow_to_sample_mapping`, and Dataset.map, with a small batch size**:\r\n\r\n```\r\nimport datasets\r\nimport transformers\r\npretrained = 'deepset/tinyroberta-squad2'\r\ntokenizer = transformers.AutoTokenizer.from_pretrained(pretrained)\r\n\r\nquestions = ['Can you tell me why?', 'What time is it?']\r\ncontexts = ['This is context zero', 'Another paragraph goes here'] \r\n\r\ndef tok(questions, contexts):\r\n return tokenizer(text=questions,\r\n text_pair=contexts,\r\n truncation='only_second',\r\n return_overflowing_tokens=True,\r\n )\r\nprint(tok(questions, contexts)['overflow_to_sample_mapping'])\r\nassert tok(questions, contexts)['overflow_to_sample_mapping'] == [0, 1] # PASSES\r\n\r\ndef tok2(d):\r\n return tok(d['question'], d['context'])\r\n\r\ndef tok2(d):\r\n return tok(d['question'], d['context'])\r\n\r\nds = datasets.Dataset.from_dict({'question': questions, 'context': contexts})\r\ntokens = ds.map(tok2, batched=True, batch_size=1)\r\nprint(tokens['overflow_to_sample_mapping'])\r\nassert tokens['overflow_to_sample_mapping'] == [0, 1] # FAILS produces [0,0]\r\n```\r\n\r\nNote that even if the batch size would be larger, there will be instances where we will not have a lot of data, and end up using small batches. This can occur e.g. if `n_proc` causes batches to be underfill. I imagine it can also occur in other ways, e.g. the final leftover batch at the end.",
"A larger batch size does _not_ have this behavior:\r\n\r\n```\r\ndef tok2(d):\r\n return tok(d['question'], d['context'])\r\n\r\nds = datasets.Dataset.from_dict({'question': questions, 'context': contexts})\r\ntokens = ds.map(tok2, batched=True, batch_size=2)\r\nprint(tokens['overflow_to_sample_mapping'])\r\nassert tokens['overflow_to_sample_mapping'] == [0, 1] # PASSES\r\n```"
] | 1,658,933,651,000
| 1,658,944,648,000
| null |
NONE
| null | null |
## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because each tokenizer only looks at its share of the samples, and maps to the index _within its share_, but then `Dataset.map` collates them together.
## Steps to reproduce the bug
1. Make a dataset of 3 strings.
2. Tokenize via Dataset.map with n_proc = 8
3. Inspect the `overflow_to_sample_mapping` field
## Expected results
`[0, 1, 2]`
## Actual results
`[0, 0, 0]`
Notes:
1. I have not yet extracted a minimal example, but the above works reliably
2. If the dataset is large, I've yet to determine if this bug still happens a. not at all b. always c. on the small, leftover batch at the end.
|
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I_kwDODunzps5OpW3Z
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|
DatasetInfo issue when testing multiple configs: mixed task_templates
|
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[
"I've narrowed down the issue to the `dataset_module_factory` which already creates a `dataset_infos.json` file down in the `.cache/modules/dataset_modules/..` folder. That JSON file already contains the wrong task_templates for `unfiltered`.",
"Ugh. Found the issue: apparently `datasets` was reusing the already existing `dataset_infos.json` that is inside `datasets/datasets/hebban-reviews`! Is this desired behavior?\r\n\r\nPerhaps when `--save_infos` and `--all_configs` are given, an existing `dataset_infos.json` file should first be deleted before continuing with the test? Because that would assume that the user wants to create a new infos file for all configs anyway.",
"Hi! I think this is a reasonable solution. Would you be interested in submitting a PR?"
] | 1,658,923,494,000
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| null |
CONTRIBUTOR
| null | null |
## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is create three configs:
- unfiltered: no classlabel, no tasks. Gets data from unfiltered.json.gz (I'd want this without splits, just one chunk of data, but that does not seem possible?)
- filtered_sentiment: `review_sentiment` as ClassLabel, TextClassification task with `review_sentiment` as label. Gets train/test split from respective json.gz files
- filtered_rating: `review_rating0` as ClassLabel, TextClassification task with `review_rating0` as label. Gets train/test split from respective json.gz files
This might be a bit tedious to reproduce, so I am sorry, but these are the steps:
- Clone datasets -> `datasets/` and install it
- Clone `https://huggingface.co/datasets/BramVanroy/hebban-reviews` into `datasets/datasets` so that you have a new folder `datasets/datasets/hebban-reviews/`.
- Replace the HebbanReviews class with this new one:
```python
class HebbanReviews(datasets.GeneratorBasedBuilder):
"""The Hebban book reviews dataset."""
BUILDER_CONFIGS = [
HebbanReviewsConfig(
name="unfiltered",
description=_HEBBAN_REVIEWS_UNFILTERED_DESCRIPTION,
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_sentiment",
description=f"This config has the negative, neutral, and positive sentiment scores as ClassLabel in the 'review_sentiment' column.\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_rating",
description=f"This config has the 5-class ratings as ClassLabel in the 'review_rating0' column (which is a variant of 'review_rating' that starts counting from 0 instead of 1).\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
)
]
DEFAULT_CONFIG_NAME = "filtered_sentiment"
_URLS = {
"train": "train.jsonl.gz",
"test": "test.jsonl.gz",
"unfiltered": "unfiltered.jsonl.gz",
}
def _info(self):
features = {
"review_title": datasets.Value("string"),
"review_text": datasets.Value("string"),
"review_text_without_quotes": datasets.Value("string"),
"review_n_quotes": datasets.Value("int32"),
"review_n_tokens": datasets.Value("int32"),
"review_rating": datasets.Value("int32"),
"review_rating0": datasets.Value("int32"),
"review_author_url": datasets.Value("string"),
"review_author_type": datasets.Value("string"),
"review_n_likes": datasets.Value("int32"),
"review_n_comments": datasets.Value("int32"),
"review_url": datasets.Value("string"),
"review_published_date": datasets.Value("string"),
"review_crawl_date": datasets.Value("string"),
"lid": datasets.Value("string"),
"lid_probability": datasets.Value("float32"),
"review_sentiment": datasets.features.ClassLabel(names=["negative", "neutral", "positive"]),
"review_sentiment_label": datasets.Value("string"),
"book_id": datasets.Value("int32"),
}
if self.config.name == "filtered_sentiment":
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_sentiment")]
elif self.config.name == "filtered_rating":
# For CrossEntropy, our classes need to start at index 0 -- not 1
features["review_rating0"] = datasets.features.ClassLabel(names=["1", "2", "3", "4", "5"])
features["review_sentiment"] = datasets.Value("int32")
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_rating0")]
elif self.config.name == "unfiltered": # no ClassLabels in unfiltered
features["review_sentiment"] = datasets.Value("int32")
task_templates = None
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
print("AT INFO", self.config.name, task_templates)
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(features),
homepage="https://huggingface.co/datasets/BramVanroy/hebban-reviews",
citation=_HEBBAN_REVIEWS_CITATION,
task_templates=task_templates,
license="cc-by-4.0"
)
def _split_generators(self, dl_manager):
if self.config.name.startswith("filtered"):
files = dl_manager.download_and_extract({"train": "train.jsonl.gz",
"test": "test.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": files["test"]
},
),
]
elif self.config.name == "unfiltered":
files = dl_manager.download_and_extract({"train": "unfiltered.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
]
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
def _generate_examples(self, data_file):
lines = Path(data_file).open(encoding="utf-8").readlines()
for line_idx, line in enumerate(lines):
row = json.loads(line)
yield line_idx, row
```
- finally, run `datasets-cli test ./datasets/hebban-reviews/ --save_infos --all_configs` from within the topmost `datasets` directory
## Expected results
Succeeding tests for three different configs.
## Actual results
I printed out the values that are given to `DatasetInfo` for config name and task_templates, as you can see. There, as expected, I get `unfiltered None`. I also modified datasets/info.py and added this line [at L.170](https://github.com/huggingface/datasets/blob/f5847a304aa1b38b3a3c54a8318b4df60f1299bc/src/datasets/info.py#L170):
```python
print("INTERNALLY AT INFO.PY", self.config_name, self.task_templates)
```
to my surprise, here I get `unfiltered [TextClassification(task='text-classification', text_column='review_text_without_quotes', label_column='review_sentiment')]`. So one way or another, here I suddenly see that `unfiltered` now does have a task_template -- even though that is not what is written in the data loading script, as the first print statement correctly shows.
I do not quite understand how, but it seems that the config name and task_templates get mixed.
This ultimately leads to the following error, but this trace may not be very useful in itself:
```
Traceback (most recent call last):
File "C:\Users\bramv\.virtualenvs\hebban-U6poXNQd\Scripts\datasets-cli-script.py", line 33, in <module>
sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
File "c:\dev\python\hebban\datasets\src\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\dev\python\hebban\datasets\src\datasets\commands\test.py", line 144, in run
builder.as_dataset()
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 899, in as_dataset
datasets = map_nested(
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 393, in map_nested
mapped = [
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 394, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 330, in _single_map_nested
return function(data_struct)
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 930, in _build_single_dataset
ds = self._as_dataset(
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 1006, in _as_dataset
return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File "c:\dev\python\hebban\datasets\src\datasets\arrow_dataset.py", line 661, in __init__
info = info.copy() if info is not None else DatasetInfo()
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 286, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File "<string>", line 20, in __init__
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 176, in __post_init__
self.task_templates = [
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 177, in <listcomp>
template.align_with_features(self.features) for template in (self.task_templates)
File "c:\dev\python\hebban\datasets\src\datasets\tasks\text_classification.py", line 22, in align_with_features
raise ValueError(f"Column {self.label_column} is not a ClassLabel.")
ValueError: Column review_sentiment is not a ClassLabel.
```
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
|
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I_kwDODunzps5Oo28N
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|
Easily create loading script for benchmark comprising multiple huggingface datasets
|
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"Hi ! I think the simplest is to copy paste the `_split_generators` code from the other datasets and do a bunch of if-else, as in the glue dataset: https://huggingface.co/datasets/glue/blob/main/glue.py#L467",
"Ok, I see. Thank you"
] | 1,658,916,818,000
| 1,658,930,287,000
| 1,658,930,287,000
|
CONTRIBUTOR
| null | 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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Dataset Viewer issue for yanekyuk/wikikey
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"The dataset is empty, as far as I can tell: there are no files in the repository at https://huggingface.co/datasets/yanekyuk/wikikey/tree/main\r\n\r\nMaybe the viewer can display a better message for empty datasets",
"OK. Closing as it's not an error. We will work on making the error message a lot clearer."
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NONE
| null | null |
### Link
_No response_
### Description
_No response_
### Owner
_No response_
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I_kwDODunzps5Oj1aP
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Allow `list_datasets` to include private datasets
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"Thanks for opening this issue :)\r\n\r\nIf it can help, I think you can already use `huggingface_hub` to achieve this:\r\n```python\r\n>>> from huggingface_hub import HfApi\r\n>>> [ds_info.id for ds_info in HfApi().list_datasets(use_auth_token=token) if ds_info.private]\r\n['bigscience/xxxx', 'bigscience-catalogue-data/xxxxxxx', ... ]\r\n```\r\n\r\n---------\r\n\r\nThough the latest versions of `huggingface_hub` that contain this feature are not available on python 3.6, so maybe we should first drop support for python 3.6 (see #4460) to update `list_datasets` in `datasets` as well (or we would have to copy/paste some `huggingface_hub` code)",
"Great, thanks @lhoestq the workaround works! I think it would be intuitive to have the support directly in `datasets` but it makes sense to wait given that the workaround exists :)",
"i also think that going forward we should replace more and more implementations inside datasets with the corresponding ones from `huggingface_hub` (same as we're doing in `transformers`)"
] | 1,658,830,568,000
| 1,658,836,765,000
| null |
NONE
| null | null |
I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the list of all datasets I have permissions to view, including private ones. The only current alternative I see is to use the hub website to manually obtain the list of dataset names - this is in the context of BigScience where respective private spaces contain hundreds of datasets, so not very convenient to list manually.
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I_kwDODunzps5OjF-J
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Remove instructions to generate dummy data from our docs
|
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[
"Note that for me personally, conceptually all the dummy data (even for \"canonical\" datasets) should be superseded by `datasets-server`, which performs some kind of CI/CD of datasets (including the canonical ones)",
"I totally agree: next step should be rethinking if dummy data makes sense for canonical datasets (once we have datasets-server) and eventually remove it.\r\n\r\nBut for now, we could at least start by removing the indication to generate dummy data from our docs."
] | 1,658,820,778,000
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| 1,659,484,230,000
|
MEMBER
| null | 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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I_kwDODunzps5Og813
| 4,742
|
Dummy data nowhere to be found
|
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[
"Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst of all, please note that you do not need the dummy data: this was the case when we were adding datasets to the `datasets` library (on this GitHub repo), so that we could test the correct loading of all datasets with our CI. However, this is no longer the case for datasets on the Hub.\r\n- We should definitely update our docs.\r\n\r\nSecond, the dummy data is generated locally:\r\n- in your case, the dummy data will be generated inside the directory: `./datasets/hebban-reviews/dummy`\r\n- please note the preceding `./datasets` directory: the reason for this is that the command to generate the dummy data was specifically created for our `datasets` library, and therefore assumes our directory structure: commands are run from the root directory of our GitHub repo, and datasets scripts are under `./datasets` \r\n\r\n\r\n ",
"I have opened an Issue to update the instructions on dummy data generation:\r\n- #4744",
"Dummy data generation is deprecated now, so I think we can close this issue."
] | 1,658,776,722,000
| 1,667,570,664,000
| 1,667,570,650,000
|
CONTRIBUTOR
| null | 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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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."
] | 1,658,496,508,000
| 1,662,046,631,000
| 1,662,046,631,000
|
NONE
| null | 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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I_kwDODunzps5OYEUc
| 4,736
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Dataset Viewer issue for deepklarity/huggingface-spaces-dataset
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[
"Thanks for reporting. You're right, workers were under-provisioned due to a manual error, and the job queue was full. It's fixed now."
] | 1,658,492,058,000
| 1,658,497,598,000
| 1,658,497,598,000
|
NONE
| null | 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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I_kwDODunzps5OWZuW
| 4,734
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Package rouge-score cannot be imported
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[
"We have added a comment on an existing issue opened in their repo: https://github.com/google-research/google-research/issues/1212#issuecomment-1192267130\r\n- https://github.com/google-research/google-research/issues/1212"
] | 1,658,474,105,000
| 1,658,475,919,000
| 1,658,475,918,000
|
MEMBER
| null | 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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I_kwDODunzps5OWV4A
| 4,733
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rouge metric
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[
"Fixed by:\r\n- #4735"
] | 1,658,473,611,000
| 1,658,480,882,000
| 1,658,480,735,000
|
NONE
| null | 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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I_kwDODunzps5OV7fu
| 4,732
|
Document better that loading a dataset passing its name does not use the local script
|
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[
"Thanks for the feedback!\r\n\r\nI think since this issue is closely related to loading, I can add a clearer explanation under [Load > local loading script](https://huggingface.co/docs/datasets/main/en/loading#local-loading-script).",
"That makes sense but I think having a line about it under https://huggingface.co/docs/datasets/installation#source the \"source\" header here would be useful. My mental model of `pip install -e .` does not include the fact that the source files aren't actually being used. ",
"Thanks for sharing your perspective. I think the `load_dataset` function is the only one that pulls from GitHub, and since this use-case is very specific, I don't think we need to include such a broad clarification in the Installation section.\r\n\r\nFeel free to check out the linked PR and let me know if it needs any additional explanation 😊"
] | 1,658,470,051,000
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|
MEMBER
| null | 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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I_kwDODunzps5OSTfP
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Loading imagenet-1k validation split takes much more RAM than expected
|
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[
"My bad, `482 * 418 * 50000 * 3 / 1000000 = 30221 MB` ( https://stackoverflow.com/a/42979315 ).\r\n\r\nMeanwhile `256 * 256 * 50000 * 3 / 1000000 = 9830 MB`. We are loading the non-cropped images and that is why we take so much RAM."
] | 1,658,416,446,000
| 1,658,421,664,000
| 1,658,421,664,000
|
CONTRIBUTOR
| null | 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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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%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 682k/682k [00:00<00:00, 7.66MB/s]\r\nDataset financial_phrasebank downloaded and prepared to .../.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 918.80it/s]\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label'],\r\n num_rows: 2264\r\n })\r\n})\r\n```\r\n\r\nAre you able to access the link? https://www.researchgate.net/profile/Pekka-Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip",
"Yes was able to download from the link manually. But still, get the same error when I use load_dataset.",
"Fixed once data files are hosted on the Hub:\r\n- #4598"
] | 1,658,393,012,000
| 1,659,601,955,000
| 1,659,601,955,000
|
NONE
| null | 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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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"
] | 1,658,381,088,000
| 1,658,389,916,000
| 1,658,389,501,000
|
NONE
| null | 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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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."
] | 1,658,350,650,000
| 1,658,470,186,000
| 1,658,389,099,000
|
NONE
| null | 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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| 1,310,253,552
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I_kwDODunzps5OGOHw
| 4,721
|
PyArrow Dataset error when calling `load_dataset`
|
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[
"Hi ! It looks like a bug in `pyarrow`. If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nTo achieve that you can try to lower the value of `max_shard_size` and also don't use `map` before `push_to_hub`.\r\n\r\nDo you have a minimum reproducible example that we can share with the Arrow team for further debugging ?",
"> If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nYup, I did not encounter this bug when I was testing my script with a slice of <1000 samples for my dataset.\r\n\r\n> Do you have a minimum reproducible example...\r\n\r\nNot sure if I can get more minimal than the script I shared above. Are you asking for a sample json file?\r\nJust generate a random manifest list, I can add that to the above script if that's what you mean?\r\n",
"Actually this is probably linked to this open issue: https://issues.apache.org/jira/browse/ARROW-5030.\r\n\r\nsetting `max_shard_size=\"2GB\"` should do the job (or `max_shard_size=\"1GB\"` if you want to be on the safe side, especially given that there can be some variance in the shard sizes if the dataset is not evenly distributed)"
] | 1,658,279,763,000
| 1,658,499,107,000
| null |
NONE
| null | null |
## Describe the bug
I am fine tuning a wav2vec2 model following the script here using my own dataset: https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
Loading my Audio dataset from the hub which was originally generated from disk results in the following PyArrow error:
```sh
File "/home/ubuntu/w2v2/run_speech_recognition_ctc.py", line 227, in main
raw_datasets = load_dataset(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/load.py", line 1679, in load_dataset
builder_instance.download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 1268, in _prepare_split
for key, table in logging.tqdm(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables
for batch_idx, record_batch in enumerate(
File "pyarrow/_parquet.pyx", line 1309, in iter_batches
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
```
## Steps to reproduce the bug
I created a dataset from a JSON lines manifest of `audio_filepath`, `text`, and `duration`.
When creating the dataset, I do something like this:
```python
import json
from datasets import Dataset, Audio
# manifest_lines is a list of dicts w/ "audio_filepath", "duration", and "text
for line in manifest_lines:
line = line.strip()
if line:
line_dict = json.loads(line)
manifest_dict["audio"].append(f"{root_path}/{line_dict['audio_filepath']}")
manifest_dict["duration"].append(line_dict["duration"])
manifest_dict["transcription"].append(line_dict["text"])
# Create a HF dataset
dataset = Dataset.from_dict(manifest_dict).cast_column(
"audio", Audio(sampling_rate=16_000),
)
# From the docs for saving to disk
# https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.save_to_disk
def read_audio_file(example):
with open(example["audio"]["path"], "rb") as f:
return {"audio": {"bytes": f.read()}}
dataset = dataset.map(read_audio_file, num_proc=70)
dataset.save_to_disk(f"/audio-data/hf/{artifact_name}")
dataset.push_to_hub(f"{org-name}/{artifact_name}", max_shard_size="5GB", private=True)
```
Then when I call `load_dataset()` in my training script, with the same dataset I generated above, and download from the huggingface hub I get the above stack trace.
I am able to load the dataset fine if I use `load_from_disk()`.
## Expected results
`load_dataset()` should behave just like `load_from_disk()` and not cause any errors.
## Actual results
See above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
I am using the `huggingface/transformers-pytorch-gpu:latest` image
- `datasets` version: 2.3.0
- Platform: Docker/Ubuntu 20.04
- Python version: 3.8
- PyArrow version: 8.0.0
|
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I_kwDODunzps5OFLYj
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Dataset Viewer issue for shamikbose89/lancaster_newsbooks
|
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[
"It seems like the list of splits could not be obtained:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"shamikbose89/lancaster_newsbooks\", \"default\")\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 354, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/shamikbose89--lancaster_newsbooks/2d1c63d269bf7b9342accce0a95960b1710ab4bc774248878bd80eb96c1afaf7/lancaster_newsbooks.py\", line 73, in _split_generators\r\n data_dir = dl_manager.download_and_extract(_URL)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 916, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 879, in extract\r\n urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 348, in map_nested\r\n return function(data_struct)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 884, in _extract\r\n protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 388, in _get_extraction_protocol\r\n return _get_extraction_protocol_with_magic_number(f)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 354, in _get_extraction_protocol_with_magic_number\r\n f.seek(0)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 684, in seek\r\n raise ValueError(\"Cannot seek streaming HTTP file\")\r\nValueError: Cannot seek streaming HTTP file\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nping @huggingface/datasets ",
"Oh, I removed the 'split' key from `kwargs`. I put it back in, but there's still the same error",
"It looks like the data host doesn't support http range requests, which is necessary to glob inside a ZIP archive in streaming mode. Can you try hosting the dataset elsewhere ? Or download each file separately from https://ota.bodleian.ox.ac.uk/repository/xmlui/handle/20.500.12024/2531 ?",
"@lhoestq Thanks! That seems to have solved it. I can get the splits with the `get_dataset_split_names()` function. The dataset viewer is still not loading properly, though. The new error is\r\n```\r\nStatus code: 400\r\nException: BadZipFile\r\nMessage: File is not a zip file\r\n```\r\n\r\nPS. The dataset loads properly and can be accessed"
] | 1,658,260,807,000
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| 1,662,655,641,000
|
NONE
| null | 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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I_kwDODunzps5OEssc
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Issue loading TheNoob3131/mosquito-data dataset
|
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[
"I am also getting a ValueError: 'Couldn't cast' at the bottom. Is this because of some delimiter issue? My dataset is on the Huggingface Hub. If you could look at it, that would be greatly appreciated.",
"Hi @thenerd31, thanks for reporting.\r\n\r\nPlease note that your issue is not caused by the Hugging Face Datasets library, but it has to do with the specific implementation of your dataset on the Hub.\r\n\r\nTherefore, I'm transferring this discussion to your own dataset Community tab: https://huggingface.co/datasets/TheNoob3131/mosquito-data/discussions/1"
] | 1,658,252,857,000
| 1,658,299,617,000
| 1,658,299,562,000
|
NONE
| null | 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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I_kwDODunzps5ODZMj
| 4,717
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Dataset Viewer issue for LawalAfeez/englishreview-ds-mini
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[
"It's currently working, as far as I understand\r\n\r\nhttps://huggingface.co/datasets/LawalAfeez/englishreview-ds-mini/viewer/LawalAfeez--englishreview-ds-mini/train\r\n\r\n<img width=\"1556\" alt=\"Capture d’écran 2022-07-19 à 09 24 01\" src=\"https://user-images.githubusercontent.com/1676121/179761130-2d7980b9-c0f6-4093-8b1d-f0a3872fef3f.png\">\r\n\r\n---\r\n\r\nWhat was your issue?"
] | 1,658,236,779,000
| 1,658,305,977,000
| 1,658,305,977,000
|
NONE
| null | null |
### Link
_No response_
### Description
Unable to view the split data
### Owner
_No response_
|
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I_kwDODunzps5OB96K
| 4,711
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Document how to create a dataset loading script for audio/vision
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MEMBER
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Currently, in our docs for Audio/Vision/Text, we explain how to:
- Load data
- Process data
However we only explain how to *Create a dataset loading script* for text data.
I think it would be useful that we add the same for Audio/Vision as these have some specificities different from Text.
See, for example:
- #4697
- and comment there: https://github.com/huggingface/datasets/issues/4697#issuecomment-1191502492
CC: @stevhliu
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WMT21 & WMT22
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"Hi ! That would be awesome to have them indeed, thanks for opening this issue\r\n\r\nI just added you to the WMT org on the HF Hub if you're interested in adding those datasets.\r\n\r\nFeel free to create a dataset repository for each dataset and upload the data files there :) preferably in ZIP archives instead of TAR archives (the current WMT scripts don't support streaming TAR archives, so it would break the dataset preview). We've also had issues with the `statmt.org` host (data unavailable, slow download speed), that's why I think it's better if we re-host the files on the Hub.\r\n\r\n`wmt21` (and wmt22) can be added <s>in this GitHub repository I think</s> on the HF Hub under the `WMT` org (we'll move the previous ones to this org soon as well).\r\nTo add it, you can copy paste the code of the previous one (e.g. wmt19), and add the new data:\r\n- in wmt_utils.py, add the new data subsets. You need to provide the download URLs, as well as the target and source languages\r\n- in wmt21.py (renamed from wmt19.py), you can specify the subsets that WMT21 uses (i.e. the one you just added)\r\n- in wmt_utils.py, define the python function that must be used to parse the subsets you added. To do so, you must go in `_generate_examples` and chose the proper `sub_generator` based on the subset name. For example, the `paracrawl_v3` subset uses the `_parse_tmx` function:\r\n\r\nhttps://github.com/huggingface/datasets/blob/ede72d3f9796339701ec59899c7c31d2427046fb/datasets/wmt19/wmt_utils.py#L834-L835\r\n\r\nHopefully the data is in a format that is already supported and there's no need to write a new `_parse_*` function for the new subsets. Let me know if you have questions or if I can help :)",
"@Muennighoff , @lhoestq let me know if you want me to look into this. Happy to help bring WMT21 & WMT22 datasets into 🤗 ! ",
"Hi @srhrshr :) Sure, feel free to create a dataset repository on the Hub and start from the implementation of WMT19 if you want. Then we can move the dataset under the WMT org (we'll move the other ones there as well).\r\n\r\nLet me know if you have questions or if I can help",
"#self-assign"
] | 1,658,178,333,000
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CONTRIBUTOR
| null | null |
## Adding a Dataset
- **Name:** WMT21 & WMT22
- **Description:** We are going to have three tracks: two small tasks and a large task.
The small tracks evaluate translation between fairly related languages and English (all pairs). The large track uses 101 languages.
- **Paper:** /
- **Data:** https://statmt.org/wmt21/large-scale-multilingual-translation-task.html https://statmt.org/wmt22/large-scale-multilingual-translation-task.html
- **Motivation:** Many more languages than previous WMT versions - Could be very high impact
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
I could also tackle this. I saw the existing logic for WMT models is a bit complex (datasets are stored on the wmt account & retrieved in separate wmt datasets afaict). How long do you think it would take me? @lhoestq
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I_kwDODunzps5N-lUN
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Dataset Viewer issue for TheNoob3131/mosquito-data
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"Thanks for reporting. I refreshed the dataset viewer and it now works as expected.\r\n\r\nhttps://huggingface.co/datasets/TheNoob3131/mosquito-data\r\n\r\n<img width=\"1135\" alt=\"Capture d’écran 2022-07-18 à 13 15 22\" src=\"https://user-images.githubusercontent.com/1676121/179566497-e47f1a27-fd84-4a8d-9d7f-2e0f2da803df.png\">\r\n\r\nWe will investigate why it occurred in the first place\r\n",
"By chance, could you provide some details about the operations done on the dataset: was it private? gated?",
"Yes, it was a private dataset, and when I made it public, the Dataset Preview did not work. \r\n\r\nHowever, now when I make the dataset private, it says that the Dataset Preview has been disabled. Why is this?",
"Thanks for the details. For now, the dataset viewer is always disabled on private datasets (see https://huggingface.co/docs/hub/datasets-viewer for more details)",
"Hi, it was working fine for a few hours, but then I can't see the dataset viewer again (public dataset). Why is this still happening?\r\nIt's the same error too:\r\n\r\n",
"OK? This is a bug, thanks for help spotting and reproducing it (it occurs when a dataset is switched to private, then to public). We will be working on it, meanwhile, I've restored the dataset viewer manually again."
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NONE
| null | 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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Domain specific dataset discovery on the Hugging Face hub
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"Hi! I added a link to this issue in our internal request for adding keywords/topics to the Hub, which is identical to the `topic tags` solution. The `collections` solution seems too complex (as you point out). Regarding the `domain tags` solution, we primarily focus on machine learning, so I'm not sure if it's a good idea to make our current taxonomy more complex.",
"> Hi! I added a link to this issue in our internal request for adding keywords/topics to the Hub, which is identical to the `topic tags` solution. The `collections` solution seems too complex (as you point out). Regarding the `domain tags` solution, we primarily focus on machine learning, so I'm not sure if it's a good idea to make our current taxonomy more complex.\r\n\r\nThanks, for letting me know. Will you allow the topic tags to be user-generated or only chosen from a list?",
"Thanks for opening this issue @davanstrien.\r\n\r\nAs we discussed last week, the tag approach would be in principle the simpler to be implemented, either the domain tag (with closed vocabulary: more reliable but also more rigid), or the topic tag (with open vocabulary: more flexible for user needs)",
"Hi @davanstrien If i remember correctly this was also discussed inside a hf.co Discussion, would you be able to link it here too?\r\n\r\n(where i suggested using `tags: - foo - bar` IIRC.\r\n\r\nThanks a ton!",
"> Hi @davanstrien If i remember correctly this was also discussed inside a hf.co Discussion, would you be able to link it here too?\r\n> \r\n> (where i suggested using `tags: - foo - bar` IIRC.\r\n> \r\n> Thanks a ton!\r\n\r\nThis doesn't ring a bell - I did a quick search of https://discuss.huggingface.co but didn't find anything. \r\n\r\nThe `tags: ` approach sounds like a good option for this. It would be especially nice if these could suggest existing tags, but this probably won't be easily possible through the current interface. \r\n",
"I opened a PR to add \"tags\" to the YAML validator:\r\nhttps://github.com/huggingface/datasets/pull/4716\r\n\r\nI also added \"tags\" to the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging), with suggestions like \"bio\" or \"newspapers\"",
"Thanks @lhoestq for the initiative.\r\n \r\nJust one question: are \"tags\" already supported on the Hub? \r\n\r\nI think they aren't. Thus, the Hub should support them so that they are properly displayed.",
"I think they're not displayed, but at least it should enable users to filter by tag in using `huggingface_hub` or using the appropriate query params on the website (not sure if it's possible yet though)",
"> I think they're not displayed, but at least it should enable users to filter by tag in using `huggingface_hub` or using the appropriate query params on the website (not sure if it's possible yet though)\r\n\r\nI think this would already be a helpful start. I'm happy to try this out with the datasets added to https://huggingface.co/organizations/biglam and use the `huggingface_hub` to filter those datasets using the tags. "
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MEMBER
| null | null |
**Is your feature request related to a problem? Please describe.**
## The problem
The datasets hub currently has `8,239` datasets. These datasets span a wide range of different modalities and tasks (currently with a bias towards textual data).
There are various ways of identifying datasets that may be relevant for a particular use case:
- searching
- various filters
Currently, however, there isn't an easy way to identify datasets belonging to a specific domain. For example, I want to browse machine learning datasets related to 'social science' or 'climate change research'.
The ability to identify datasets relating to a specific domain has come up in discussions around the [BigLA](https://github.com/bigscience-workshop/lam/) datasets hackathon https://github.com/bigscience-workshop/lam/discussions/31#discussioncomment-3123610. As part of the hackathon, we're currently collecting datasets related to Libraries, Archives and Museums and making them available via the hub. We currently do this under a Hugging Face organization (https://huggingface.co/biglam). However, going forward, I can see some of these datasets being migrated to sit under an organization that is the custodian of the dataset (for example, a national library the data was originally from). At this point, it becomes more difficult to quickly identify datasets from this domain without relying on search.
This is also related to some existing issues on Github related to metadata on the hub:
- https://github.com/huggingface/datasets/issues/3625
- https://github.com/huggingface/datasets/issues/3877
**Describe the solution you'd like**
### Some possible solutions that may help with this:
#### Enable domain tags (from a controlled vocabulary)
- This would add metadata field to the YAML for the domain a dataset relates to
- Advantages:
- the list is controlled, allowing it to be more easily integrated into the datasets tag app (https://huggingface.co/space/huggingface/datasets-tagging)
- the controlled vocabulary could align with an existing controlled vocabulary
- this additional metadata can be used to perform filtering by domain
- disadvantages
- choosing the best controlled vocab may be difficult
- there are many datasets that are likely to fit into the 'machine learning' domain (i.e. there is a long tail of datasets that aren't in more 'generic' machine learning domain
#### Enable topic tags (user-generated)
Enable 'free form' topic tags for datasets and models. This would be closer to GitHub's repository topics which can be chosen from a controlled list (https://github.com/topics/) but can also be more user/org specific. This could potentially be useful for organizations to also manage their own models and datasets as the number they hold in their org grows. For example, they may create 'topic tags' for a specific project, so it's clearer which datasets /models are related to that project.
#### Collections
This solution would likely be the biggest shift and may require significant changes in the hub fronted. Collections could work in several different ways but would include:
Users can curate particular datasets, models, spaces, etc., into a collection. For example, they may create a collection of 'historic newspapers suitable for training language models'. These collections would not be mutually exclusive, i.e. a dataset can belong to zero, one or many collections. Collections can also potentially be nested under other collections.
This is fairly common on other data reposotiores for example the following collections:
<img width="293" alt="Screenshot 2022-07-18 at 11 50 44" src="https://user-images.githubusercontent.com/8995957/179496445-963ed122-5e26-4574-96e8-41081bce3e2b.png">
all belong under a higher level collection (https://bl.iro.bl.uk/collections/353c908d-b495-4413-b047-87236d2573e3?locale=en).
There are different models one could use for how these collections could be created:
- only within an org
- for any dataset/model
- the owner or a dataset/model has to agree to be added to a collection
- a collection owner can have people suggest additions to their collection
- other models....
These collections could be thematic, related to particular training approaches, curate models with particular inference properties etc. Whilst some of these features may duplicate current/or future tag filters on the hub, they offer the advantage of being flexible and not having to predict what users will want to do upfront.
There is also potential for automating the creation of these collections based on existing metadata. For example, one could collect models trained on a collection of datasets so for example, if we had a collection of 'historic newspapers suitable for training language models' that contained 30 datasets, we could create another collection 'historic newspaper language models' that takes any model on the hub whose metadata says it used one or more of those 30 datasets.
There is also the option of exploring ML approaches to suggest models/datasets may be relevant to a particular collection.
This approach is likely to be quite difficult to implement well and would require significant thought. There is also likely to be a benefit in doing quite a bit of upfront work in curating useful collections to demonstrate the benefits of collections.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
It is possible to collate this information externally, i.e. one could link back to the relevant models/datasets from an external platform.
**Additional context**
Add any other context about the feature request here.
I'm cc'ing others involved in the BigLAM hackathon who may also have thoughts @cakiki @clancyoftheoverflow @albertvillanova
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Trouble with streaming frgfm/imagenette vision dataset with TAR archive
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[
"Hi @frgfm, thanks for reporting.\r\n\r\nAs the error message says, streaming mode is not supported out of the box when the dataset contains TAR archive files.\r\n\r\nTo make the dataset streamable, you have to use `dl_manager.iter_archive`.\r\n\r\nThere are several examples in other datasets, e.g. food101: https://huggingface.co/datasets/food101/blob/main/food101.py\r\n\r\nAnd yes, as the link you pointed out, for the streaming to be possible, the metadata file must be loaded before all of the images:\r\n- either this is the case when iterating the archive (and you get the metadata file before the images)\r\n- or you have to extract the metadata file by hand and upload it separately to the Hub",
"Hi @albertvillanova :wave:\r\n\r\nThanks! Yeah I saw that but since I didn't have any metadata, I wasn't sure whether I should create them myself.\r\n\r\nSo one last question:\r\nWhat is the metadata supposed to be for archives? The relative path of all files in it?\r\n_(Sorry I'm a bit confused since it's quite hard to debug using the single error message from the data preview :sweat_smile: )_",
"Hi @frgfm, streaming a dataset that contains a TAR file requires some tweaks because (contrary to ZIP files), tha TAR archive does not allow random access to any of the contained member files. Instead they have to be accessed sequentially (in the order in which they were put into the TAR file when created) and yielded.\r\n\r\nSo when iterating over the TAR file content, when an image file is found, we need to yield it (and not keeping it in memory, which will require huge RAM memory for large datasets). But when yielding an image file, we also need to yield with it what we call \"metadata\": the class label, and other textual information (for example, for audio files, sometimes we also add info such as the speaker ID, their sex, their age,...).\r\n\r\nAll this information usually is stored in what we call the metadata file: either a JSON or a CSV/TSV file.\r\n\r\nBut if this is also inside the TAR archive, we need to find this file in the first place when iterating the TAR archive, so that we already have this information when we find an image file and we can yield the image file and its metadata info.\r\n\r\nTherefore:\r\n- either the TAR archive contains the metadata file as the first member when iterating it (something we cannot change as it is done at the creation of the TAR file)\r\n- or if not, then we need to have the metadata file elsewhere\r\n - in these cases, what we do (if the dataset license allows it) is:\r\n - we download the TAR file locally, we extract the metadata file and we host the metadata on the Hub\r\n - we modify the dataset loading script so that it first downloads the metadata file (and reads it) and only then starts iterating the content of the TAR archive file\r\n\r\nSee an example of this process we recently did for \"google/fleurs\" (their metadata files for \"train\" were at the end of the TAR archives, after all audio files): https://huggingface.co/datasets/google/fleurs/discussions/4\r\n- we uploaded the metadata file to the Hub\r\n- we adapted the loading script to use it",
"Hi @albertvillanova :wave: \r\n\r\nThanks, since my last message, I went through the repo of https://huggingface.co/datasets/food101/blob/main/food101.py and managed to get it to work in the end :pray: \r\n\r\nHere it is: https://huggingface.co/datasets/frgfm/imagenette\r\n\r\nI appreciate you opening an issue to document the process, it might help a few!",
"Great to see that you manage to make your dataset streamable. :rocket: \r\n\r\nI'm closing this issue, as for the docs update there is another issue opened:\r\n- #4711"
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### Link
https://huggingface.co/datasets/frgfm/imagenette
### Description
Hello there :wave:
Thanks for the amazing work you've done with HF Datasets! I've just started playing with it, and managed to upload my first dataset. But for the second one, I'm having trouble with the preview since there is some archive extraction involved :sweat_smile:
Basically, I get a:
```
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
I've tried several things and checked this issue https://github.com/huggingface/datasets/issues/4181 as well, but no luck so far!
Could you point me in the right direction please? :pray:
### Owner
Yes
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