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The error will still be printed at the end of the job. In this case the job also fails, and asks to manually update the datasets that failed. | {
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https://api.github.com/repos/huggingface/datasets/issues/4311 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4311/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4311/comments | https://api.github.com/repos/huggingface/datasets/issues/4311/events | https://github.com/huggingface/datasets/pull/4311 | 1,231,369,438 | PR_kwDODunzps43ln8- | 4,311 | [Imagefolder] Docs + Don't infer labels from file names when there are metadata + Error messages when metadata and images aren't linked correctly | {
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} | I updated the `docs/source/image_process.mdx` documentation and added an example for image captioning and object detection using `ImageFolder`.
While doing so I also improved a few aspects:
- we don't need to infer labels from file names when there are metadata - they can just be in the metadata if necessary
- raise informative error messages when metadata and images aren't linked correctly:
- when an image is missing a metadata file
- when a metadata file is missing an image
I added some tests for these changes as well
cc @mariosasko | {
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Loading a datasets with `load_dataset` and `streaming=True` returns `AttributeError: '_io.BufferedReader' object has no attribute 'loc'`. Notice that loading with `streaming=False` works fine.
In the following steps we load parquet files but the same happens with pickle files. The problem seems to come from `fsspec` lib, I put in the environment info also `s3fs` and `fsspec` versions since I'm loading from an s3 bucket.
## Steps to reproduce the bug
```python
from datasets import load_dataset
# path is the path to parquet files
data_files = {"train": path + "meta_train.parquet.gzip", "test": path + "meta_test.parquet.gzip"}
dataset = load_dataset("parquet", data_files=data_files, streaming=True)
```
## Expected results
A dataset object `datasets.dataset_dict.DatasetDict`
## Actual results
```
AttributeError Traceback (most recent call last)
<command-562086> in <module>
11
12 data_files = {"train": path + "meta_train.parquet.gzip", "test": path + "meta_test.parquet.gzip"}
---> 13 dataset = load_dataset("parquet", data_files=data_files, streaming=True)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1679 if streaming:
1680 extend_dataset_builder_for_streaming(builder_instance, use_auth_token=use_auth_token)
-> 1681 return builder_instance.as_streaming_dataset(
1682 split=split,
1683 use_auth_token=use_auth_token,
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/builder.py in as_streaming_dataset(self, split, base_path, use_auth_token)
904 )
905 self._check_manual_download(dl_manager)
--> 906 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
907 # By default, return all splits
908 if split is None:
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py in _split_generators(self, dl_manager)
30 if not self.config.data_files:
31 raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}")
---> 32 data_files = dl_manager.download_and_extract(self.config.data_files)
33 if isinstance(data_files, (str, list, tuple)):
34 files = data_files
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in download_and_extract(self, url_or_urls)
798
799 def download_and_extract(self, url_or_urls):
--> 800 return self.extract(self.download(url_or_urls))
801
802 def iter_archive(self, urlpath_or_buf: Union[str, io.BufferedReader]) -> Iterable[Tuple]:
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in extract(self, path_or_paths)
776
777 def extract(self, path_or_paths):
--> 778 urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)
779 return urlpaths
780
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
312 num_proc = 1
313 if num_proc <= 1 or len(iterable) <= num_proc:
--> 314 mapped = [
315 _single_map_nested((function, obj, types, None, True, None))
316 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0)
313 if num_proc <= 1 or len(iterable) <= num_proc:
314 mapped = [
--> 315 _single_map_nested((function, obj, types, None, True, None))
316 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
317 ]
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args)
267 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
268 else:
--> 269 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
270 if isinstance(data_struct, list):
271 return mapped
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0)
267 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
268 else:
--> 269 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
270 if isinstance(data_struct, list):
271 return mapped
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args)
249 # Singleton first to spare some computation
250 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 251 return function(data_struct)
252
253 # Reduce logging to keep things readable in multiprocessing with tqdm
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _extract(self, urlpath)
781 def _extract(self, urlpath: str) -> str:
782 urlpath = str(urlpath)
--> 783 protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)
784 if protocol is None:
785 # no extraction
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _get_extraction_protocol(urlpath, use_auth_token)
371 urlpath, kwargs = urlpath, {}
372 with fsspec.open(urlpath, **kwargs) as f:
--> 373 return _get_extraction_protocol_with_magic_number(f)
374
375
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _get_extraction_protocol_with_magic_number(f)
335 def _get_extraction_protocol_with_magic_number(f) -> Optional[str]:
336 """read the magic number from a file-like object and return the compression protocol"""
--> 337 prev_loc = f.loc
338 magic_number = f.read(MAGIC_NUMBER_MAX_LENGTH)
339 f.seek(prev_loc)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/fsspec/implementations/local.py in __getattr__(self, item)
337
338 def __getattr__(self, item):
--> 339 return getattr(self.f, item)
340
341 def __enter__(self):
AttributeError: '_io.BufferedReader' object has no attribute 'loc'
```
## Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-1071-aws-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
- `fsspec` version: 2021.08.1
- `s3fs` version: 2021.08.1 | {
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"color": "d93f0b",... | closed | false | null | [] | null | 11 | 2022-05-10 14:12:47+00:00 | 2022-06-17 12:54:40+00:00 | 2022-06-17 11:44:01+00:00 | CONTRIBUTOR | null | null | 0 | {
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} | Adds the TED-LIUM dataset https://www.tensorflow.org/datasets/catalog/tedlium#tedliumrelease3
TODO:
- [x] Port `tedium.py` from TF datasets using `convert_dataset.sh` script
- [x] Make `load_dataset` work
- [ ] ~~Run `datasets-cli` command to generate `dataset_infos.json`~~
- [ ] ~~Create dummy data for continuous testing~~
- [ ] ~~Dummy data tests~~
- [ ] ~~Real data tests~~
- [ ] Create the metadata JSON
- [ ] Close PR and add directly to the Hub under LIUM org | {
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"default": true,
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] | closed | false | null | [] | null | 1 | 2022-05-10 13:14:04+00:00 | 2022-05-10 18:58:36+00:00 | 2022-05-10 18:58:09+00:00 | NONE | null | null | null | null | ## Describe the bug
This is a weird bug that took me some time to find out.
I have a JSON dataset that I want to load with `load_dataset` like this:
```
data_files = dict(train="train.json.zip", val="val.json.zip")
dataset = load_dataset("json", data_files=data_files, field="data")
```
## Expected results
No error.
## Actual results
The val file is loaded as expected, but the train file throws JSON decoding error:
```
╭──────────────────────────── Traceback (most recent call last) ────────────────────────────╮
│ <ipython-input-74-97947e92c100>:5 in <module> │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/load.py:1687 in │
│ load_dataset │
│ │
│ 1684 │ try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES │
│ 1685 │ │
│ 1686 │ # Download and prepare data │
│ ❱ 1687 │ builder_instance.download_and_prepare( │
│ 1688 │ │ download_config=download_config, │
│ 1689 │ │ download_mode=download_mode, │
│ 1690 │ │ ignore_verifications=ignore_verifications, │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:605 in │
│ download_and_prepare │
│ │
│ 602 │ │ │ │ │ │ except ConnectionError: │
│ 603 │ │ │ │ │ │ │ logger.warning("HF google storage unreachable. Downloa │
│ 604 │ │ │ │ │ if not downloaded_from_gcs: │
│ ❱ 605 │ │ │ │ │ │ self._download_and_prepare( │
│ 606 │ │ │ │ │ │ │ dl_manager=dl_manager, verify_infos=verify_infos, **do │
│ 607 │ │ │ │ │ │ ) │
│ 608 │ │ │ │ │ # Sync info │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:694 in │
│ _download_and_prepare │
│ │
│ 691 │ │ │ │
│ 692 │ │ │ try: │
│ 693 │ │ │ │ # Prepare split will record examples associated to the split │
│ ❱ 694 │ │ │ │ self._prepare_split(split_generator, **prepare_split_kwargs) │
│ 695 │ │ │ except OSError as e: │
│ 696 │ │ │ │ raise OSError( │
│ 697 │ │ │ │ │ "Cannot find data file. " │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:1151 in │
│ _prepare_split │
│ │
│ 1148 │ │ │
│ 1149 │ │ generator = self._generate_tables(**split_generator.gen_kwargs) │
│ 1150 │ │ with ArrowWriter(features=self.info.features, path=fpath) as writer: │
│ ❱ 1151 │ │ │ for key, table in logging.tqdm( │
│ 1152 │ │ │ │ generator, unit=" tables", leave=False, disable=True # not loggin │
│ 1153 │ │ │ ): │
│ 1154 │ │ │ │ writer.write_table(table) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/notebook.py:257 in │
│ __iter__ │
│ │
│ 254 │ │
│ 255 │ def __iter__(self): │
│ 256 │ │ try: │
│ ❱ 257 │ │ │ for obj in super(tqdm_notebook, self).__iter__(): │
│ 258 │ │ │ │ # return super(tqdm...) will not catch exception │
│ 259 │ │ │ │ yield obj │
│ 260 │ │ # NB: except ... [ as ...] breaks IPython async KeyboardInterrupt │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/std.py:1183 in │
│ __iter__ │
│ │
│ 1180 │ │ # If the bar is disabled, then just walk the iterable │
│ 1181 │ │ # (note: keep this check outside the loop for performance) │
│ 1182 │ │ if self.disable: │
│ ❱ 1183 │ │ │ for obj in iterable: │
│ 1184 │ │ │ │ yield obj │
│ 1185 │ │ │ return │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/packaged_modules/j │
│ son/json.py:90 in _generate_tables │
│ │
│ 87 │ │ │ # If the file is one json object and if we need to look at the list of │
│ 88 │ │ │ if self.config.field is not None: │
│ 89 │ │ │ │ with open(file, encoding="utf-8") as f: │
│ ❱ 90 │ │ │ │ │ dataset = json.load(f) │
│ 91 │ │ │ │ │
│ 92 │ │ │ │ # We keep only the field we are interested in │
│ 93 │ │ │ │ dataset = dataset[self.config.field] │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:293 in load │
│ │
│ 290 │ To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` │
│ 291 │ kwarg; otherwise ``JSONDecoder`` is used. │
│ 292 │ """ │
│ ❱ 293 │ return loads(fp.read(), │
│ 294 │ │ cls=cls, object_hook=object_hook, │
│ 295 │ │ parse_float=parse_float, parse_int=parse_int, │
│ 296 │ │ parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:357 in loads │
│ │
│ 354 │ if (cls is None and object_hook is None and │
│ 355 │ │ │ parse_int is None and parse_float is None and │
│ 356 │ │ │ parse_constant is None and object_pairs_hook is None and not kw): │
│ ❱ 357 │ │ return _default_decoder.decode(s) │
│ 358 │ if cls is None: │
│ 359 │ │ cls = JSONDecoder │
│ 360 │ if object_hook is not None: │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:337 in decode │
│ │
│ 334 │ │ containing a JSON document). │
│ 335 │ │ │
│ 336 │ │ """ │
│ ❱ 337 │ │ obj, end = self.raw_decode(s, idx=_w(s, 0).end()) │
│ 338 │ │ end = _w(s, end).end() │
│ 339 │ │ if end != len(s): │
│ 340 │ │ │ raise JSONDecodeError("Extra data", s, end) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:353 in raw_decode │
│ │
│ 350 │ │ │
│ 351 │ │ """ │
│ 352 │ │ try: │
│ ❱ 353 │ │ │ obj, end = self.scan_once(s, idx) │
│ 354 │ │ except StopIteration as err: │
│ 355 │ │ │ raise JSONDecodeError("Expecting value", s, err.value) from None │
│ 356 │ │ return obj, end │
╰───────────────────────────────────────────────────────────────────────────────────────────╯
JSONDecodeError: Unterminated string starting at: line 85 column 20 (char 60051)
```
However, when I rename the `train.json.zip` to other names (like `training.json.zip`, or even to `train.json`), everything works fine; when I unzip the file to `train.json`, it works as well.
## Environment info
```
- `datasets` version: 2.1.0
- Platform: Linux-4.4.0-131-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4305 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4305/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4305/comments | https://api.github.com/repos/huggingface/datasets/issues/4305/events | https://github.com/huggingface/datasets/pull/4305 | 1,231,099,934 | PR_kwDODunzps43kt4P | 4,305 | Fixes FrugalScore | {
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"default": false,
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] | open | false | null | [] | null | 2 | 2022-05-10 12:44:06+00:00 | 2022-09-22 16:42:06+00:00 | NaT | CONTRIBUTOR | null | null | 0 | {
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} | There are two minor modifications in this PR:
1) `predictions` and `references` are swapped. Basically Frugalscore is commutative, however some tiny differences can occur if we swap the references and the predictions. I decided to swap them just to obtain the exact results as reported in the paper.
2) I switched to dynamic padding that was was used in the training, forcing the padding to `max_length` introduces errors for some reason that I ignore.
@lhoestq | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4304 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4304/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4304/comments | https://api.github.com/repos/huggingface/datasets/issues/4304/events | https://github.com/huggingface/datasets/issues/4304 | 1,231,047,051 | I_kwDODunzps5JYEmL | 4,304 | Language code search does direct matches | {
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] | open | false | null | [] | null | 1 | 2022-05-10 11:59:16+00:00 | 2022-05-10 12:38:42+00:00 | NaT | CONTRIBUTOR | null | null | null | null | ## Describe the bug
Hi. Searching for bcp47 tags that are just the language prefix (e.g. `sq` or `da`) excludes datasets that have added extra information in their language metadata (e.g. `sq-AL` or `da-bornholm`). The example codes given in the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging) encourages addition of the additional codes ("_expected format is BCP47 tags separated for ';' e.g. 'en-US;fr-FR'_") but this would lead to those datasets being hidden in datasets search.
## Steps to reproduce the bug
1. Add a dataset using a variant tag (e.g. [`sq-AL`](https://huggingface.co/datasets?languages=languages:sq-AL))
2. Look for datasets using the full code
3. Note that they're missing when just the language is searched for (e.g. [`sq`](https://huggingface.co/datasets?languages=languages:sq))
Some datasets are already affected by this - e.g. `AmazonScience/massive` is listed under `sq-AL` but not `sq`.
One workaround is for dataset creators to add an additional root language tag to dataset YAML metadata, but it's unclear how to communicate this. It might be possible to index the search on `languagecode.split('-')[0]` but I wanted to float this issue before trying to write any code :)
## Expected results
Datasets using longer bcp47 tags also appear under searches for just the language code; e.g. Quebecois datasets (`fr-CA`) would come up when looking for French datasets with no region specification (`fr`), or US English (`en-US`) datasets would come up when searching for English datasets (`en`).
## Actual results
The language codes seem to be directly string matched, excluding datasets with specific language tags from non-specific searches.
## Environment info
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https://api.github.com/repos/huggingface/datasets/issues/4303 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4303/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4303/comments | https://api.github.com/repos/huggingface/datasets/issues/4303/events | https://github.com/huggingface/datasets/pull/4303 | 1,230,867,728 | PR_kwDODunzps43j8cH | 4,303 | Fix: Add missing comma | {
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https://api.github.com/repos/huggingface/datasets/issues/4302 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4302/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4302/comments | https://api.github.com/repos/huggingface/datasets/issues/4302/events | https://github.com/huggingface/datasets/pull/4302 | 1,230,651,117 | PR_kwDODunzps43jPE5 | 4,302 | Remove hacking license tags when mirroring datasets on the Hub | {
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} | Currently, when mirroring datasets on the Hub, the license tags are hacked: removed of characters "." and "$". On the contrary, this hacking is not applied to community datasets on the Hub. This generates multiple variants of the same tag on the Hub.
I guess this hacking is no longer necessary:
- it is not applied to community datasets
- all canonical datasets are validated by maintainers before being merged: CI + maintainers make sure license tags are the right ones
Fix #4298. | {
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https://api.github.com/repos/huggingface/datasets/issues/4301 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4301/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4301/comments | https://api.github.com/repos/huggingface/datasets/issues/4301/events | https://github.com/huggingface/datasets/pull/4301 | 1,230,401,256 | PR_kwDODunzps43idlE | 4,301 | Add ImageNet-Sketch dataset | {
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} | This PR adds API code examples for loading methods, let me know if I've missed any important parameters we should showcase :)
I was a bit confused about `inspect_dataset` and `inspect_metric`. The `path` parameter says it will accept a dataset identifier from the Hub. But when I try the identifier `rotten_tomatoes`, it gives me:
```py
from datasets import inspect_dataset
inspect_dataset('rotten_tomatoes', local_path='/content/rotten_tomatoes')
FileNotFoundError: Couldn't find a dataset script at /content/rotten_tomatoes/rotten_tomatoes.py or any data file in the same directory.
```
Does the user need to have an existing copy of `rotten_tomatoes.py` on their local drive (in which case, it seems like the same option as the first option in `path`)? | {
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https://api.github.com/repos/huggingface/datasets/issues/4298 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4298/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4298/comments | https://api.github.com/repos/huggingface/datasets/issues/4298/events | https://github.com/huggingface/datasets/issues/4298 | 1,229,748,006 | I_kwDODunzps5JTHcm | 4,298 | Normalise license names | {
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] | closed | false | null | [] | null | 2 | 2022-05-09 13:51:32+00:00 | 2022-05-20 09:51:50+00:00 | 2022-05-20 09:51:50+00:00 | CONTRIBUTOR | null | null | null | null | **Is your feature request related to a problem? Please describe.**
When browsing datasets, the Licenses tag cloud (bottom left of e.g. https://huggingface.co/datasets) has multiple variants of the same license. This means the options exclude datasets arbitrarily, giving users artificially low recall. The cause of the dupes is probably due to a bit of variation in metadata.
**Describe the solution you'd like**
I'd like the licenses in metadata to follow the same standard as much as possible, to remove this problem. I'd like to go ahead and normalise the dataset metadata to follow the format & values given in [src/datasets/utils/resources/licenses.json](https://github.com/huggingface/datasets/blob/master/src/datasets/utils/resources/licenses.json) .
**Describe alternatives you've considered**
None
**Additional context**
None
**Priority**
Low
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https://api.github.com/repos/huggingface/datasets/issues/4297 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4297/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4297/comments | https://api.github.com/repos/huggingface/datasets/issues/4297/events | https://github.com/huggingface/datasets/issues/4297 | 1,229,735,498 | I_kwDODunzps5JTEZK | 4,297 | Datasets YAML tagging space is down | {
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"gists... | null | 3 | 2022-05-09 13:45:05+00:00 | 2022-05-09 14:44:25+00:00 | 2022-05-09 14:44:25+00:00 | CONTRIBUTOR | null | null | null | null | ## Describe the bug
The neat hf spaces app for generating YAML tags for dataset `README.md`s is down
## Steps to reproduce the bug
1. Visit https://huggingface.co/spaces/huggingface/datasets-tagging
## Expected results
There'll be a HF spaces web app for generating dataset metadata YAML
## Actual results
There's an error message; here's the step where it breaks:
```
Step 18/29 : RUN pip install -r requirements.txt
---> Running in e88bfe7e7e0c
Defaulting to user installation because normal site-packages is not writeable
Collecting git+https://github.com/huggingface/datasets.git@update-task-list (from -r requirements.txt (line 4))
Cloning https://github.com/huggingface/datasets.git (to revision update-task-list) to /tmp/pip-req-build-bm8t0r0k
Running command git clone --filter=blob:none --quiet https://github.com/huggingface/datasets.git /tmp/pip-req-build-bm8t0r0k
WARNING: Did not find branch or tag 'update-task-list', assuming revision or ref.
Running command git checkout -q update-task-list
error: pathspec 'update-task-list' did not match any file(s) known to git
error: subprocess-exited-with-error
× git checkout -q update-task-list did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× git checkout -q update-task-list did not run successfully.
│ exit code: 1
╰─> See above for output.
```
## Environment info
- Platform: Linux / Brave
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```
TypeError: _download_and_prepare() got an unexpected keyword argument 'save_infos'
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 2 | 2022-05-06 12:03:27+00:00 | 2022-05-09 08:25:58+00:00 | 2022-05-09 08:25:58+00:00 | CONTRIBUTOR | null | null | null | null | ### Link
https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train
### Description
The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss?
### Owner
Yes | {
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When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception.
All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
import torch
torch.set_grad_enabled(False)
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
from datasets import load_dataset
ds = load_dataset('crime_and_punish', split='train[:100]')
ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()})
ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None`
```
## Expected results
A new column named `embeddings` in the dataset that we're adding the index to.
## Actual results
An exception is triggered with the following message `NameError: name 'faiss' is not defined`.
## Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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"color": "d73a4a",
"default": true,
"description": "Something isn't working",
"id": 1935892857,
"name": "bug",
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] | closed | false | null | [] | null | 2 | 2022-05-05 05:01:09+00:00 | 2023-07-25 15:12:38+00:00 | 2023-07-25 15:12:38+00:00 | NONE | null | null | null | null | ## Describe the bug
I'm trying to add a feature called "subgraph" to CNN/DM dataset (modifications on run_summarization.py of Huggingface Transformers script) --- I'm not quite sure if I'm doing it the right way, though--- but the main problem appears when the training starts where the error ` [OSError: [Errno 12] Cannot allocate memory]` appears. I suppose this problem roots in RAM issues and how the dataset is loaded during training, but I have no clue of what I can do to fix it. Observing the dataset's cache directory, I see that it takes ~600GB of memory and that's why I believe special care is needed when loading it into the memory.
Here are my modifications to `run_summarization.py` code.
```
# loading pre-computed dictionary where keys are 'id' of article and values are corresponding subgraph
graph_data_train = get_graph_data('train')
graph_data_validation = get_graph_data('val')
...
...
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function_train,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
```
And here is the modified preprocessed function:
```
def preprocess_function_train(examples):
inputs, targets, sub_graphs, ids = [], [], [], []
for i in range(len(examples[text_column])):
if examples[text_column][i] is not None and examples[summary_column][i] is not None:
# if examples['doc_id'][i] in graph_data.keys():
inputs.append(examples[text_column][i])
targets.append(examples[summary_column][i])
sub_graphs.append(graph_data_train[examples['id'][i]])
ids.append(examples['id'][i])
inputs = [prefix + inp for inp in inputs]
model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True,
sub_graphs=sub_graphs, ids=ids)
# Setup the tokenizer for targets
with tokenizer.as_target_tokenizer():
labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
# padding in the loss.
if padding == "max_length" and data_args.ignore_pad_token_for_loss:
labels["input_ids"] = [
[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
]
model_inputs["labels"] = labels["input_ids"]
return model_inputs
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux Ubuntu
- Python version: 3.6
- PyArrow version: 6.0.1
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} | [] | closed | false | null | [] | null | 1 | 2022-05-04 17:42:42+00:00 | 2022-05-06 16:32:02+00:00 | 2022-05-06 06:22:17+00:00 | MEMBER | null | null | 0 | {
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} | This PR untangles the `S3FileSystem` docstring so the [parameters](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes#parameters) are properly displayed. | {
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} | In certain cases, `None` values are replaced by empty lists when casting feature types.
It happens every time you cast an array of nested lists like [None, [0, 1, 2, 3]] to a different type (to change the integer precision for example). In this case you'd get [[], [0, 1, 2, 3]] for example. This issue comes from PyArrow, see the discussion in https://github.com/huggingface/datasets/issues/3676
This issue also happens when no type casting is needed, because casting is supposed to be a no-op in this case. But as https://github.com/huggingface/datasets/issues/3676 shown, it's not the case and `None` are replaced by empty lists even if we cast to the exact same type.
In this PR I just workaround this bug in the case where no type casting is needed. In particular, I only call `pa.ListArray.from_arrays` only when necessary.
I also added a warning when some `None` are effectively replaced by empty lists. I wanted to raise an error in this case, but maybe we should wait a major update to do so
This PR fixes this particular case, that is occurring in `run_qa.py` in `transformers`:
```python
from datasets import Dataset
ds = Dataset.from_dict({"a": range(4)})
ds = ds.map(lambda x: {"b": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=["a"])
print(ds.to_pandas())
# before:
# b
# 0 [None, [0]]
# 1 [[], [0]]
# 2 [[], [0]]
# 3 [[], [0]]
#
# now:
# b
# 0 [None, [0]]
# 1 [None, [0]]
# 2 [None, [0]]
# 3 [None, [0]]
```
cc @sgugger | {
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} | Remove the following copy-paste sentence from dataset cards:
```
We show detailed information for up to 5 configurations of the dataset.
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4278 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4278/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4278/comments | https://api.github.com/repos/huggingface/datasets/issues/4278/events | https://github.com/huggingface/datasets/pull/4278 | 1,225,122,123 | PR_kwDODunzps43RyTs | 4,278 | Add missing features to openbookqa dataset for additional config | {
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https://api.github.com/repos/huggingface/datasets/issues/4277 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4277/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4277/comments | https://api.github.com/repos/huggingface/datasets/issues/4277/events | https://github.com/huggingface/datasets/pull/4277 | 1,225,002,286 | PR_kwDODunzps43RZV9 | 4,277 | Enable label alignment for token classification datasets | {
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} | This PR extends the `Dataset.align_labels_with_mapping()` method to support alignment of label mappings between datasets and models for token classification (e.g. NER).
Example of usage:
```python
from datasets import load_dataset
ner_ds = load_dataset("conll2003", split="train")
# returns [3, 0, 7, 0, 0, 0, 7, 0, 0]
ner_ds[0]["ner_tags"]
# hypothetical model mapping with O <--> B-LOC
label2id = {
"B-LOC": "0",
"B-MISC": "7",
"B-ORG": "3",
"B-PER": "1",
"I-LOC": "6",
"I-MISC": "8",
"I-ORG": "4",
"I-PER": "2",
"O": "5"
}
ner_aligned_ds = ner_ds.align_labels_with_mapping(label2id, "ner_tags")
# returns [3, 5, 7, 5, 5, 5, 7, 5, 5]
ner_aligned_ds[0]["ner_tags"]
```
Context: we need this in AutoTrain to automatically align datasets / models during evaluation. cc @abhishekkrthakur | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 11 | 2022-05-04 05:51:52+00:00 | 2022-10-11 17:11:53+00:00 | 2022-10-05 13:50:03+00:00 | CONTRIBUTOR | null | null | null | null | ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanScore'],
- 'clarity': row['clarity'],
- 'turkIdAnonymized': row['turkIdAnonymized']
3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Expected results
The structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2 | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 1 | 2022-05-04 05:38:59+00:00 | 2022-05-04 11:41:18+00:00 | NaT | CONTRIBUTOR | null | null | null | null | ## Describe the bug
In short, CommonSenseQA implementation is inconsistent with the original dataset.
More precisely, we need to:
1. Add the dataset matching "id" field. The current dataset, instead, regenerates monotonically increasing id.
2. The [“question”][“stem”] field is flattened into "question". We should match the original dataset and unflatten it
3. Add the missing "question_concept" field in the question tree node
4. Anything else? Go over the data structure of the newly repaired CommonSenseQA and make sure it matches the original
## Expected results
Every data item of the CommonSenseQA should structurally and data-wise match the original CommonSenseQA dataset.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
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in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable". | {
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- #4259
was green, but after merging it to master, a code quality error appeared. | {
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- license
- point of contact | {
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https://api.github.com/repos/huggingface/datasets/issues/4268 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4268/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4268/comments | https://api.github.com/repos/huggingface/datasets/issues/4268/events | https://github.com/huggingface/datasets/issues/4268 | 1,223,331,964 | I_kwDODunzps5I6pB8 | 4,268 | error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered | {
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{
"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library",
"id": 2067388877,
"name": "dataset bug",
"node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug"
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] | closed | false | null | [] | null | 10 | 2022-05-02 20:34:25+00:00 | 2022-05-06 15:53:30+00:00 | 2022-05-03 11:23:48+00:00 | NONE | null | null | null | null | ## Describe the bug
Error generated when attempting to download dataset
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
```
## Expected results
A clear and concise description of the expected results.
## Actual results
```
ExpectedMoreDownloadedFiles Traceback (most recent call last)
[<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
3 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
31 return
32 if len(set(expected_checksums) - set(recorded_checksums)) > 0:
---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
34 if len(set(recorded_checksums) - set(expected_checksums)) > 0:
35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
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} | Adds the HF Speech Bench to Librispeech Dataset Card in place of the Papers With Code Leaderboard. Should improve usage and visibility of this leaderboard! Wondering whether this can also be done for [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) and [8](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) through someone with permissions?
cc @patrickvonplaten: more leaderboard promotion! | {
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} | On the Hugging Face Hub, users refer to imagenet2012 (from #4178 ) as imagenet-1k in their model tags.
To correctly link models to imagenet, we should rename this dataset `imagenet-1k`.
Later we can add `imagenet-21k` as a new dataset if we want.
Once this one is merged we can delete the `imagenet2012` dataset repository on the Hub.
EDIT: to complete the rationale on why we should name it `imagenet-1k`:
If users specifically added the tag `imagenet-1k` , then it could be for two reasons (not sure which one is predominant), either they
- wanted to make it explicit that it’s not 21k -> the distinction is important for the community
- or they have been following this convention from other models -> the convention implicitly exists already | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 5 | 2022-04-30 17:43:37+00:00 | 2022-05-03 06:04:26+00:00 | 2022-05-03 06:04:26+00:00 | NONE | null | null | null | null | ## Describe the bug
Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results.
Furthermore, all splits contain duplicate samples.
## Steps to reproduce the bug
```python
from datasets import load_dataset
training = load_dataset("webis/conclugen", "base", split="train")
validation = load_dataset("webis/conclugen", "base", split="validation")
testing = load_dataset("webis/conclugen", "base", split="test")
# collect which sample id's are present in the training split
ids_validation = list()
ids_testing = list()
for train_sample in training:
train_argument = train_sample["argument"]
train_conclusion = train_sample["conclusion"]
train_id = train_sample["id"]
# test if current sample is in validation split
if train_argument in validation["argument"]:
for validation_sample in validation:
validation_argument = validation_sample["argument"]
validation_conclusion = validation_sample["conclusion"]
validation_id = validation_sample["id"]
if train_argument == validation_argument and train_conclusion == validation_conclusion:
ids_validation.append(validation_id)
# test if current sample is in test split
if train_argument in testing["argument"]:
for testing_sample in testing:
testing_argument = testing_sample["argument"]
testing_conclusion = testing_sample["conclusion"]
testing_id = testing_sample["id"]
if train_argument == testing_argument and train_conclusion == testing_conclusion:
ids_testing.append(testing_id)
```
## Expected results
Length of both lists `ids_validation` and `ids_testing` should be zero.
## Actual results
Length of `ids_validation` = `2556`
Length of `ids_testing` = `287`
Furthermore, there seems to be duplicate samples in (at least) the *training* split, since:
`print(len(set(ids_validation)))` = `950`
`print(len(set(ids_testing)))` = `101`
All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: macOS-12.3.1-arm64-arm-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0 | {
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} | Add the MR (Movie Review) dataset. The original dataset contains sentences from Rotten Tomatoes labeled as either "positive" or "negative".
Homepage: [https://www.cs.cornell.edu/people/pabo/movie-review-data/](https://www.cs.cornell.edu/people/pabo/movie-review-data/)
paperswithcode: [https://paperswithcode.com/dataset/mr](https://paperswithcode.com/dataset/mr)
- [ ] I was not able to generate dummy data, the original dataset files have ".pos" and ".neg" as file extensions so the auto-generator does not work. Is it fine like this or should dummy data be added?
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} | This PR fixes the Bug in the openbookqa dataset as mentioned in this issue #3550.
Fix #3550.
cc. @lhoestq @mariosasko | {
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1) the perplexity was calculated with a 0 in the attention mask for the start token, which was causing high perplexity scores that were not correct
2) the documentation was not updated | {
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cc @stas00 | {
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- original host (arxiv.org) does not allow HTTP Range requests
- we have hosted the data on the Hub (license: CC BY-NC-ND 4.0)
Moreover, it implements support for streaming.
Fix #4146.
Related to: #4236.
CC: @severo | {
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sorry for spamming you with review requests, @albertvillanova ! :hugs: | {
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This PR updates the PyArrow version to 6 in setup.py, CI job files .circleci/config.yaml and .github/workflows/benchmarks.yaml files.
This will fix ArrayND error which exists in pyarrow 5. | {
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Fix #4247.
CC: @severo | {
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I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text.
Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ?
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
dataset = load_dataset("conll2003")
```
## Expected results
{
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
"id": "0",
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
}
## Actual results
```python
print(dataset)
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 219554
})
test: Dataset({
features: ['text'],
num_rows: 50350
})
validation: Dataset({
features: ['text'],
num_rows: 55044
})
})
```
```python
for i in range(20):
print(dataset['train'][i])
{'text': '-DOCSTART- -X- -X- O'}
{'text': ''}
{'text': 'EU NNP B-NP B-ORG'}
{'text': 'rejects VBZ B-VP O'}
{'text': 'German JJ B-NP B-MISC'}
{'text': 'call NN I-NP O'}
{'text': 'to TO B-VP O'}
{'text': 'boycott VB I-VP O'}
{'text': 'British JJ B-NP B-MISC'}
{'text': 'lamb NN I-NP O'}
{'text': '. . O O'}
{'text': ''}
{'text': 'Peter NNP B-NP B-PER'}
{'text': 'Blackburn NNP I-NP I-PER'}
{'text': ''}
{'text': 'BRUSSELS NNP B-NP B-LOC'}
{'text': '1996-08-22 CD I-NP O'}
{'text': ''}
{'text': 'The DT B-NP O'}
{'text': 'European NNP I-NP B-ORG'}
```
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ds = load_dataset("dataset/name")
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The refactoring allows for future builder kwargs customizations based on file extension.
Related to #4238. | {
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"color": "d73a4a",
"default": true,
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] | closed | false | null | [] | null | 2 | 2022-04-27 14:14:21+00:00 | 2022-04-28 07:45:27+00:00 | 2022-04-28 07:45:27+00:00 | NONE | null | null | null | null | ## Describe the bug
I am trying to download the GLUE dataset from the NLP module but get an error (see below).
## Steps to reproduce the bug
```python
import nlp
nlp.__version__ # '0.2.0'
nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
```
## Expected results
I expect the dataset to download without an error.
## Actual results
```
INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports.
INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue
INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py
INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json
INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json
INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0)
INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source
INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb
INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805
Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0...
Downloading: 100%|██████████| 73.0/73.0 [00:00<00:00, 73.9kB/s]
INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-7-669a8343dcc1> in <module>
----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
418 verify_infos = not save_infos and not ignore_verifications
419 self._download_and_prepare(
--> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
421 )
422 # Sync info
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
458 # Checksums verification
459 if verify_infos:
--> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
461 for split_generator in split_generators:
462 if str(split_generator.split_info.name).lower() == "all":
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums)
34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
35 if len(bad_urls) > 0:
---> 36 raise NonMatchingChecksumError(str(bad_urls))
37 logger.info("All the checksums matched successfully.")
38
NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa
- Python version: 3.6.13
- PyArrow version: 6.0.1
- Pandas version: 1.1.5
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} | Modified the `_generate_examples` function to consider all the turns for a chunk id as a single example
Modified the features accordingly
```
"turns": [
{
"names": datasets.features.Sequence(datasets.Value("string")),
"utterances": datasets.features.Sequence(datasets.Value("string")),
"number": datasets.Value("int32"),
}
],
}
```
I wasn't able to run `datasets-cli dummy_data datasets` command. Is there a workaround for this? | {
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Additionally, some typo's we're fixed. | {
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I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error
```
[/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values)
852 if value not in self._str2int:
853 value = str(value).strip()
--> 854 output.append(self._str2int[str(value)])
855 else:
856 # No names provided, try to integerize
KeyError: '\\N'
```
The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally:
```python
from datasets import load_dataset_builder
dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences")
print(dataset_builder.cache_dir)
print(dataset_builder.info.features)
print(dataset_builder.info.splits)
```
```
Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd
/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519
None
None
```
and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`.
Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it?
Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last.
Thank you.
## Steps to reproduce the bug
```python
from datasets import load_dataset,Features,Value,ClassLabel
class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"]
features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')})
num_labels = features['label'].num_classes
data_files = { "train": "train.csv", "test": "test.csv" }
sentences = load_dataset(
"loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text'],
)
# You can make this part faster with num_proc=<some int>
sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)
sentences = sentences.shuffle()
```
## Expected results
Properly tokenize dataset file `test.csv` without issues.
## Actual results
Specify the actual results or traceback.
```
Downloading data files: 100%
2/2 [00:16<00:00, 7.34s/it]
Downloading data: 100%
391M/391M [00:12<00:00, 36.6MB/s]
Downloading data: 100%
92.4M/92.4M [00:02<00:00, 40.0MB/s]
Extracting data files: 100%
2/2 [00:00<00:00, 47.66it/s]
Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.
100%
2/2 [00:00<00:00, 25.94it/s]
11%
942339/8256449 [01:55<13:11, 9245.85ex/s]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>()
12 )
13 # You can make this part faster with num_proc=<some int>
---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)
15 sentences = sentences.shuffle()
10 frames
[/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values)
852 if value not in self._str2int:
853 value = str(value).strip()
--> 854 output.append(self._str2int[str(value)])
855 else:
856 # No names provided, try to integerize
KeyError: '\\N'
```
## Environment info
```
- `datasets` version: 2.1.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
- ```
```
- `transformers` version: 4.18.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- Huggingface_hub version: 0.5.1
- PyTorch version (GPU?): 1.11.0+cu113 (True)
- Tensorflow version (GPU?): 2.8.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
- ```
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Moreover, this PR makes the dataset streamable.
Fix #4217. | {
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```shell
I am using transformer trainer while meeting the issue.
The trainer requests torch.utils.data.Dataset as input, which loads the whole dataset into the memory at once. Therefore, when the dataset is too large to load, there's nothing I can do except using IterDataset, which loads samples of data seperately, and results in low efficiency.
I wonder if there are any tricks like Sharding in huggingface trainer.
Looking forward to your reply.
```
### Who can help?
Trainer: @sgugger
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
None
### Expected behavior
```shell
I wonder if there are any tricks like fairseq Sharding very large datasets https://fairseq.readthedocs.io/en/latest/getting_started.html.
Thanks a lot!
```
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But on huggingface datasets:
Where is the German data? | {
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- p-value was mentioned in the original docs as a return value, but there was no option to return it. I updated the _compute function slightly to have an option to return the p-value. | {
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- updates docstring in accuracy.py
- adds .json file with metric card and docstring information | {
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} | I noticed many links were not properly displayed (only text, no link) on the Hub because of wrong syntax, e.g.: https://huggingface.co/datasets/big_patent
This PR fixes all description links in dataset cards. | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | 2 | 2022-04-26 12:50:18+00:00 | 2022-04-29 14:52:19+00:00 | 2022-04-28 17:04:58+00:00 | NONE | null | null | null | null | Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something?
Thank you in advance. | {
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**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)*
*Unable to view because it says FileNotFound, also cannot download it through the python API*
Am I the one who added this dataset ? No
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} | I noticed this bug while answering [this question](https://discuss.huggingface.co/t/correct-way-to-create-a-dataset-from-a-csv-file/15686/11?u=mariosasko).
This code replicates the bug:
```python
from datasets import Dataset
dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]})
dset.map(lambda ex: {"translation": ex})
```
and this is the fix for it (before this PR):
```python
from datasets import Dataset
dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]})
dset.map(lambda ex: {"translation": dict(ex)})
```
Internally, this can be fixed by merging two dicts via dict unpacking (instead of `dict.update) `in `Dataset.map`, which avoids creating recursive dictionaries.
P.S. `{**a, **b}` is slightly more performant than `a.update(b)` in my bencmarks. | {
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The user can still pass `DownloadConfig(record_checksums=True)` to not skip this part. | {
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} | [] | closed | false | null | [] | null | 2 | 2022-04-25 13:18:26+00:00 | 2022-04-26 22:54:28+00:00 | 2022-04-26 22:48:27+00:00 | CONTRIBUTOR | null | null | 0 | {
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