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When using the latest version of datasets (1.14.0), I cannot use the `bookcorpusopen` dataset. The process blocks always around `9924 examples [00:06, 1439.61 examples/s]` when preparing the dataset. I also noticed that after half an hour the process is automatically killed because of the RAM usage (the machine has 1TB of RAM...).
This did not happen with 1.4.1.
I tried also `rm -rf ~/.cache/huggingface` but did not help.
Changing python version between 3.7, 3.8 and 3.9 did not help too.
## Steps to reproduce the bug
```python
import datasets
d = datasets.load_dataset('bookcorpusopen')
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Linux-5.4.0-1054-aws-x86_64-with-glibc2.27
- Python version: 3.9.7
- PyArrow version: 4.0.1
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This PR adds a deprecation warning and removes it from all the library, using `dataset_module_factory` or `metric_module_factory` instead.
Fix #3165. | {
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Add deprecation warning and remove its usage from all the library. | {
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} | Adds the Image feature. This feature is heavily inspired by the recently added Audio feature (#2324). Currently, this PR is pretty simple.
Some considerations that need further discussion:
* I've decided to use `Pillow`/`PIL` as the image decoding library. Another candidate I considered is `torchvision`, mostly because of its `accimage` backend, which should be faster for loading `jpeg` images than `Pillow`. However, `torchvision`'s io module only supports png and jpeg images, has `torch` as a hard dependency, and requires magic to work with image bytes ( `torch.ByteTensor(torch.ByteStorage.from_buffer(image_bytes)))`).
* Currently, I'm converting `PIL`'s `Image` type to `np.ndarray`. The vision models in Transformers such as ViT prefer the raw `Image` type and not the decoded tensors, so there is a small overhead due to [this conversion](https://github.com/huggingface/transformers/blob/3e8761ab8077e3bb243fe2f78b2a682bd2257cf1/src/transformers/image_utils.py#L62-L73). IMO this is justified to keep this part aligned with the Audio feature, which also returns `np.ndarray`. What do you think?
* Still have to work on the channel decoding logic:
* PyTorch prefers the channel-first ordering (C, H, W); TF and Flax the channel-last ordering (H, W, C). One cool feature would be adjusting the channel order based on the selected formatter (`torch`, `tf`, `jax`).
* By default, `Image.open` returns images of shape (H, W, C). However, ViT's feature extractor expects the format (C, H, W) if the image is passed as an array (explained [here](https://huggingface.co/transformers/model_doc/vit.html#transformers.ViTFeatureExtractor.__call__)), so I'm more inclined to the format (C, H, W). Which one do you prefer, (C, H, W) or (H, W, C)?
* Are there any options you'd like to see? (the user could change those via `cast_column`, such as `sampling_rate` in the Audio feature)
TODOs:
* [x] tests
* in subsequent PRs:
* docs - a section in the docs, which gives some additional info on the Image and Audio feature and compares them to
`ArrayND`
* streaming (waiting for #3129 and #3133 to get merged first)
* update the image tasks and the datasets to use the new feature
* Image/Audio formatting
[Colab Notebook](https://colab.research.google.com/drive/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c?usp=sharing) where you can play with this feature.
I'm also adding a link to the [Image](https://github.com/tensorflow/datasets/blob/7ac7d506488d46038a5854961d068926b3f93c7f/tensorflow_datasets/core/features/image_feature.py#L155) feature in TFDS because one of our goals is to parse TFDS scripts eventually, so our Image feature has to (at least) support all the formats theirs does.
Feel free to cc anyone who might be interested.
P.S. Please ignore the changes in the `datasets/**/*.py` files 😄. | {
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] | open | false | null | [] | null | 5 | "2021-10-25T18:52:30Z" | "2021-11-25T16:04:29Z" | null | NONE | null | null | null | It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).
I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!
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https://api.github.com/repos/huggingface/datasets/issues/3160 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3160/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3160/comments | https://api.github.com/repos/huggingface/datasets/issues/3160/events | https://github.com/huggingface/datasets/pull/3160 | 1,035,274,640 | PR_kwDODunzps4tofO0 | 3,160 | Better error msg if `len(predictions)` doesn't match `len(references)` in metrics | {
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} | Improve the error message in `Metric.add_batch` if `len(predictions)` doesn't match `len(references)`.
cc: @BramVanroy (feel free to test this code on your examples and review this PR) | {
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https://api.github.com/repos/huggingface/datasets/issues/3159 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3159/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3159/comments | https://api.github.com/repos/huggingface/datasets/issues/3159/events | https://github.com/huggingface/datasets/pull/3159 | 1,035,174,560 | PR_kwDODunzps4toKD5 | 3,159 | Make inspect.get_dataset_config_names always return a non-empty list | {
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} | Make all named configs cases, so that no special unnamed config case needs to be handled differently.
Fix #3135. | {
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https://api.github.com/repos/huggingface/datasets/issues/3158 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3158/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3158/comments | https://api.github.com/repos/huggingface/datasets/issues/3158/events | https://github.com/huggingface/datasets/pull/3158 | 1,035,158,070 | PR_kwDODunzps4toGpe | 3,158 | Fix string encoding for Value type | {
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} | Some metrics have `string` features but currently it fails if users pass integers instead. Indeed feature encoding that handles the conversion of the user's objects to the right python type is missing a case for `string`, while it already works as expected for integers, floats and booleans
Here is an example code that didn't work previously, but that works with this fix:
```python
import datasets
# Note that 'id' is an integer while the SQuAD metric uses strings
predictions = [{'prediction_text': '1976', 'id': 5}]
references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': 5}]
squad_metric = datasets.load_metric("squad")
squad_metric.add_batch(predictions=predictions, references=references)
results = squad_metric.compute()
# {'exact_match': 100.0, 'f1': 100.0}
```
cc @sgugger @philschmid | {
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} | changing duplicate parameter `data_files` in `DatasetBuilder.__init__` to the missing parameter `data_dir` | {
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https://api.github.com/repos/huggingface/datasets/issues/3155 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3155/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3155/comments | https://api.github.com/repos/huggingface/datasets/issues/3155/events | https://github.com/huggingface/datasets/issues/3155 | 1,034,468,757 | I_kwDODunzps49qL2V | 3,155 | Illegal instruction (core dumped) at datasets import | {
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] | closed | false | null | [] | null | 1 | "2021-10-24T17:21:36Z" | "2021-11-18T19:07:04Z" | "2021-11-18T19:07:03Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
I install datasets using conda and when I import datasets I get: "Illegal instruction (core dumped)"
## Steps to reproduce the bug
```
conda create --prefix path/to/env
conda activate path/to/env
conda install -c huggingface -c conda-forge datasets
# exits with output "Illegal instruction (core dumped)"
python -m datasets
```
## Environment info
When I run "datasets-cli env", I also get "Illegal instruction (core dumped)"
If I run the following commands:
```
conda create --prefix path/to/another/new/env
conda activate path/to/another/new/env
conda install -c huggingface transformers
transformers-cli env
```
Then I get:
- `transformers` version: 4.11.3
- Platform: Linux-5.4.0-67-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
Let me know what additional information you need in order to debug this issue. Thanks in advance! | {
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] | closed | false | null | [] | null | 2 | "2021-10-24T08:55:33Z" | "2021-10-31T09:08:32Z" | "2021-10-31T09:08:31Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
When comparing with the original `sacrebleu` implementation, the `datasets` implementation does some strange things that I do not quite understand. This issue was triggered when I was trying to implement TER and found the datasets implementation of BLEU [here](https://github.com/huggingface/datasets/pull/3153).
In the below snippet, the original sacrebleu snippet works just fine whereas the datasets implementation throws an error.
## Steps to reproduce the bug
```python
import sacrebleu
import datasets
refs = [
['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],
['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],
]
hyps = ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.']
expected_bleu = 48.530827
ds_bleu = datasets.load_metric("sacrebleu")
bleu_score_sb = sacrebleu.corpus_bleu(hyps, refs).score
print(bleu_score_sb, expected_bleu)
# works: 48.5308...
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
print(bleu_score_ds, expected_bleu)
# ValueError: Predictions and/or references don't match the expected format.
```
This seems to be related to how datasets forces the features format here:
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L94-L99
and then manipulates the references during the compute stage here
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L119-L122
I do not quite understand why that is required since sacrebleu handles argument parsing quite well [by itself](https://github.com/mjpost/sacrebleu/blob/2787185dd0f8d224c72ee5a831d163c2ac711a47/sacrebleu/metrics/base.py#L229).
## Actual results
Traceback (most recent call last):
File "C:\Users\bramv\AppData\Roaming\JetBrains\PyCharm2020.3\scratches\scratch_23.py", line 23, in <module>
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
File "C:\dev\python\datasets\src\datasets\metric.py", line 392, in compute
self.add_batch(predictions=predictions, references=references)
File "C:\dev\python\datasets\src\datasets\metric.py", line 439, in add_batch
raise ValueError(
ValueError: Predictions and/or references don't match the expected format.
Expected format: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')},
Input predictions: ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'],
Input references: [['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.']]
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3153 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3153/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3153/comments | https://api.github.com/repos/huggingface/datasets/issues/3153/events | https://github.com/huggingface/datasets/pull/3153 | 1,034,179,198 | PR_kwDODunzps4tlEVE | 3,153 | Add TER (as implemented in sacrebleu) | {
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} | [] | closed | false | null | [] | null | 1 | "2021-10-23T14:26:45Z" | "2021-11-02T11:04:11Z" | "2021-11-02T11:04:11Z" | CONTRIBUTOR | null | 0 | {
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} | Implements TER (Translation Edit Rate) as per its implementation in sacrebleu. Sacrebleu for BLEU scores is already implemented in `datasets` so I thought this would be a nice addition.
I started from the sacrebleu implementation, as the two metrics have a lot in common.
Verified with sacrebleu's [testing suite](https://github.com/mjpost/sacrebleu/blob/078c440168c6adc89ba75fe6d63f0d922d42bcfe/test/test_ter.py) that this indeed works as intended.
```python
import datasets
test_cases = [
(['aaaa bbbb cccc dddd'], ['aaaa bbbb cccc dddd'], 0), # perfect match
(['dddd eeee ffff'], ['aaaa bbbb cccc'], 1), # no overlap
([''], ['a'], 1), # corner case, empty hypothesis
(['d e f g h a b c'], ['a b c d e f g h'], 1 / 8), # a single shift fixes MT
(
[
'wählen Sie " Bild neu berechnen , " um beim Ändern der Bildgröße Pixel hinzuzufügen oder zu entfernen , damit das Bild ungefähr dieselbe Größe aufweist wie die andere Größe .',
'wenn Sie alle Aufgaben im aktuellen Dokument aktualisieren möchten , wählen Sie im Menü des Aufgabenbedienfelds die Option " Alle Aufgaben aktualisieren . "',
'klicken Sie auf der Registerkarte " Optionen " auf die Schaltfläche " Benutzerdefiniert " und geben Sie Werte für " Fehlerkorrektur-Level " und " Y / X-Verhältnis " ein .',
'Sie können beispielsweise ein Dokument erstellen , das ein Auto über die Bühne enthält .',
'wählen Sie im Dialogfeld " Neu aus Vorlage " eine Vorlage aus und klicken Sie auf " Neu . "',
],
[
'wählen Sie " Bild neu berechnen , " um beim Ändern der Bildgröße Pixel hinzuzufügen oder zu entfernen , damit die Darstellung des Bildes in einer anderen Größe beibehalten wird .',
'wenn Sie alle Aufgaben im aktuellen Dokument aktualisieren möchten , wählen Sie im Menü des Aufgabenbedienfelds die Option " Alle Aufgaben aktualisieren . "',
'klicken Sie auf der Registerkarte " Optionen " auf die Schaltfläche " Benutzerdefiniert " und geben Sie für " Fehlerkorrektur-Level " und " Y / X-Verhältnis " niedrigere Werte ein .',
'Sie können beispielsweise ein Dokument erstellen , das ein Auto enthalt , das sich über die Bühne bewegt .',
'wählen Sie im Dialogfeld " Neu aus Vorlage " eine Vorlage aus und klicken Sie auf " Neu . "',
],
0.136 # realistic example from WMT dev data (2019)
),
]
ter = datasets.load_metric(r"path\to\datasets\metrics\ter")
predictions = ["hello there general kenobi", "foo bar foobar"]
references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]
print(ter.compute(predictions=predictions, references=references))
for hyp, ref, score in test_cases:
# Note the reference transformation which is different from scarebleu's input format
results = ter.compute(predictions=hyp, references=[[r] for r in ref])
assert 100*score == results["score"], f"expected {100*score}, got {results['score']}"
```
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https://api.github.com/repos/huggingface/datasets/issues/3152 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3152/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3152/comments | https://api.github.com/repos/huggingface/datasets/issues/3152/events | https://github.com/huggingface/datasets/pull/3152 | 1,034,039,379 | PR_kwDODunzps4tkqi- | 3,152 | Fix some typos in the documentation | {
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} | [] | closed | false | null | [] | null | 0 | "2021-10-23T01:38:35Z" | "2021-10-25T14:27:36Z" | "2021-10-25T14:03:48Z" | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/3151 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3151/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3151/comments | https://api.github.com/repos/huggingface/datasets/issues/3151/events | https://github.com/huggingface/datasets/pull/3151 | 1,033,890,501 | PR_kwDODunzps4tkL7t | 3,151 | Re-add faiss to windows testing suite | {
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} | [] | closed | false | null | [] | null | 0 | "2021-10-22T19:34:29Z" | "2021-11-02T10:47:34Z" | "2021-11-02T10:06:03Z" | CONTRIBUTOR | null | 0 | {
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} | In recent versions, `faiss-cpu` seems to be available for Windows as well. See the [PyPi page](https://pypi.org/project/faiss-cpu/#files) to confirm. We can therefore included it for Windows in the setup file.
At first tests didn't pass due to problems with permissions as caused by `NamedTemporaryFile` on Windows. This built-in library is notoriously poor in playing nice on Windows. The required change isn't pretty, but it works. First set `delete=False` to not automatically try to delete the file on `exit`. Then, manually delete the file with `unlink`. It's weird, I know, but it works.
```python
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
# do stuff
os.unlink(tmp_file.name)
```
closes #3150 | {
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} | [] | closed | false | null | [] | null | 1 | "2021-10-22T18:07:16Z" | "2021-11-02T10:06:03Z" | "2021-11-02T10:06:03Z" | CONTRIBUTOR | null | null | null | In the setup file, I find the following:
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/setup.py#L171
However, FAISS does install perfectly fine on Windows on my system. You can also confirm this on the [PyPi page](https://pypi.org/project/faiss-cpu/#files), where Windows wheels are available. Maybe this was true for older versions? For current versions, this can be removed I think.
(This isn't really a bug but didn't know how else to tag.)
If you agree I can do a quick PR and remove that line. | {
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} | [] | closed | false | null | [] | null | 2 | "2021-10-22T16:17:25Z" | "2021-11-15T11:36:42Z" | "2021-11-15T10:27:45Z" | CONTRIBUTOR | null | 0 | {
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} | Address part of #2841
Added the CMU Hinglish DoG Dataset as in GLUECoS. Added it as a seperate dataset as unlike other tasks of GLUE CoS this can't be evaluated for a BERT like model.
Consists of parallel dataset between Hinglish (Hindi-English) and English, can be used for Machine Translation between the two.
The data processing part is inspired from the GLUECoS repo [here](https://github.com/microsoft/GLUECoS/blob/7fdc51653e37a32aee17505c47b7d1da364fa77e/Data/Preprocess_Scripts/preprocess_mt_en_hi.py)
The dummy data part is not working properly, it shows
``` UnboundLocalError: local variable 'generator_splits' referenced before assignment ```
when I run without ``--auto_generate``.
Please let me know how I can fix that.
Thanks | {
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] | closed | false | null | [] | null | 4 | "2021-10-22T15:07:17Z" | "2022-07-04T12:14:58Z" | "2022-07-04T12:14:58Z" | NONE | null | null | null | ## Describe the bug
When using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.
The code owner is likely @lhoestq
## Steps to reproduce the bug
For your convenience, we've prepped a colab notebook that reproduces the bug
https://colab.research.google.com/drive/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing
```python
!pip install datasets==1.14.0
should_freeze_forever = True
# ^-- set this to True in order to freeze forever, set to False in order to work normally
import torch
from datasets import load_dataset
data = load_dataset("oscar", "unshuffled_deduplicated_bn", split="train", streaming=True)
data = data.map(lambda x: {"text": x["text"], "orig": f"oscar[{x['id']}]"}, batched=True)
data = data.shuffle(100, seed=1337)
data = data.with_format("torch")
loader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)
# v-- the code should freeze forever at this line
for i, row in enumerate(loader):
print(row)
if i > 10: break
print("DONE!")
```
## Expected results
The code should not freeze forever with num_workers=2
## Actual results
The code freezes forever with num_workers=2
## Environment info
- `datasets` version: 1.14.0 (also found in previous versions)
- Platform: google colab (also locally)
- Python version: 3.7, (also 3.8)
- PyArrow version: 3.0.0
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] | null | 0 | "2021-10-22T13:50:53Z" | "2021-10-27T08:01:49Z" | "2021-10-27T08:01:49Z" | MEMBER | null | null | null | When trying to generate a datset JSON metadata, a `NonMatchingSplitsSizesError` is thrown:
```
$ datasets-cli test datasets/arabic_billion_words --save_infos --all_configs
Testing builder 'Alittihad' (1/10)
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: Unknown size, post-processed: Unknown size, total: 332.13 MiB) to .cache\arabic_billion_words\Alittihad\1.1.0\8175ff1c9714c6d5d15b1141b6042e5edf048276bb81a9c14e35e149a7a62ae4...
Traceback (most recent call last):
File "path\huggingface\datasets\.venv\Scripts\datasets-cli-script.py", line 33, in <module>
sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
File "path\huggingface\datasets\src\datasets\commands\datasets_cli.py", line 33, in main
service.run()
File "path\huggingface\datasets\src\datasets\commands\test.py", line 144, in run
builder.download_and_prepare(
File "path\huggingface\datasets\src\datasets\builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "path\huggingface\datasets\src\datasets\builder.py", line 709, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "path\huggingface\datasets\src\datasets\utils\info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='arabic_billion_words'), 'recorded': SplitInfo(name='train', num_bytes=1601790302, num_examples=349342, dataset_name='arabic_billion_words')}]
```
This is due because a previous run generated a wrong `dataset_info.json`.
This error can be avoided by passing `--ignore_verifications`, but I think this should be assumed when passing `--save_infos`. | {
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] | closed | false | null | [] | null | 4 | "2021-10-22T13:23:49Z" | "2021-12-22T11:05:37Z" | "2021-12-22T11:05:36Z" | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
When a dataset cell contains a value of type Image (be it from a remote URL, an Array2D/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web.
Note: this issue would apply exactly the same for the `Audio` type.
**Describe the solution you'd like**
If a "cell" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as:
```python
filename: str
data: bytes
```
**Describe alternatives you've considered**
A way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.
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] | closed | false | null | [] | null | 1 | "2021-10-22T13:17:33Z" | "2022-09-08T08:23:10Z" | "2022-09-08T08:23:10Z" | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
Some datasets, in particular community datasets, have no info file, thus no features.
**Describe the solution you'd like**
If a dataset has no features, the first loaded data (5-10 rows) could be used to infer the type.
Related: `datasets` would provide a way to load the data, and get the rows AND the features as the result.
**Describe alternatives you've considered**
The HF hub could also provide some UI to help the dataset maintainers to explicit the types of their rows, or automatically infer them as an initial proposal. | {
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] | open | false | null | [] | null | 1 | "2021-10-22T13:13:36Z" | "2021-10-22T13:17:56Z" | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
I understand that currently the data loaded has not always the type described in the info features
**Describe the solution you'd like**
Provide a way to check if the rows have the type described by info features
**Describe alternatives you've considered**
Always check it, and raise an error when loading the data if their type doesn't match the features.
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] | open | false | null | [] | null | 2 | "2021-10-22T13:09:53Z" | "2024-01-12T07:26:43Z" | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
The streaming mode allows to get the 100 first rows of a dataset very quickly. But it does not cache the answer, so a posterior call to get the same 100 rows will send a request to the server again and again.
**Describe the solution you'd like**
Provide a way to write the streamed rows of a dataset on the disk, and to load from it later.
**Describe alternatives you've considered**
Provide a third mode: `lazy`, which would use the local cache for the data that have already been fetched previously, and use streaming to get the rest of the requested data.
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} | This PR fixes some caching bugs (most likely introduced in the latest refactor):
* remove ")" added by accident in the dataset dir name
* correctly pass the namespace kwargs in `CachedDatasetModuleFactory`
* improve the warning message if `HF_DATASETS_OFFLINE is `True`
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Examples:
- download a dataset, then force redownload it in the same session while keeping a reference to the downloaded dataset
```python
from datasets import load_dataset
dset = load_dataset("sst", split="train")
dset = load_dataset("sst", split="train", download_mode="force_redownload")
```
- try to clean up the cache files while keeping a reference to those files (via the mapped dataset):
```python
from datasets import load_dataset
dset = load_dataset("sst", split="train")
dset_mapped = dset.map(lambda _: {"dummy_col": 1})
dset.cleanup_cache_files()
```
We should fix those.
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] | open | false | null | [] | null | 1 | "2021-10-22T09:35:29Z" | "2022-09-20T13:04:42Z" | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
Exceptions like `FileNotFoundError` can be raised by different parts of the code, and it's hard to detect which one did
**Describe the solution you'd like**
Give a specific exception type for every group of similar errors
**Describe alternatives you've considered**
Rely on the error message, using regex
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} | Numpy shows a deprecation warning when we call `np.array` on a list of ragged tensors without specifying the `dtype`. If their shapes match, the tensors can be collated together, otherwise the resulting array should have `dtype=np.object`.
Fix #3084
cc @Rocketknight1 | {
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} | The script has a bug and only parses and generates a portion of the entire dataset.
This PR fixes the loading script so that is properly parses the entire dataset.
Current implementation generates the same number of examples as reported in the [original paper](https://arxiv.org/abs/1611.04033) for all configurations except for one:
- For "Youm7" we generate more examples (1172136) than the ones reported by the paper (1025027)
| | Number of examples | Number of examples according to the source |
|:---------------|-------------------:|-----:|
| Alittihad | 349342 |349342 |
| Almasryalyoum | 291723 |291723 |
| Almustaqbal | 446873 |446873 |
| Alqabas | 817274 |817274 |
| Echoroukonline | 139732 |139732 |
| Ryiadh | 858188 | 858188 |
| Sabanews | 92149 |92149 |
| SaudiYoum | 888068 |888068 |
| Techreen | 314597 |314597 |
| Youm7 | 1172136 |1025027 |
Fix #3126. | {
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Currently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to
**Describe the solution you'd like**
In that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check/region_1`).
https://github.com/huggingface/datasets/blob/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af/src/datasets/inspect.py#L161
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`metric = datasets.load_metric('rouge')`
The error:
> ConnectionError Traceback (most recent call last)
> <ipython-input-3-dd10a0c5212f> in <module>
> ----> 1 metric = datasets.load_metric('rouge')
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)
> 613 download_config=download_config,
> 614 download_mode=download_mode,
> --> 615 dataset=False,
> 616 )
> 617 metric_cls = import_main_class(module_path, dataset=False)
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)
> 328 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)
> 329 try:
> --> 330 local_path = cached_path(file_path, download_config=download_config)
> 331 except FileNotFoundError:
> 332 if script_version is not None:
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
> 296 use_etag=download_config.use_etag,
> 297 max_retries=download_config.max_retries,
> --> 298 use_auth_token=download_config.use_auth_token,
> 299 )
> 300 elif os.path.exists(url_or_filename):
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
> 603 raise FileNotFoundError("Couldn't find file at {}".format(url))
> 604 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
> --> 605 raise ConnectionError("Couldn't reach {}".format(url))
> 606
> 607 # Try a second time
>
> ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
Is there any remedy to solve the connection issue ? | {
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Due to the large size of many speech datasets, we should also support Audio feature in streaming mode.
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] | closed | false | null | [] | null | 1 | "2021-10-21T10:13:09Z" | "2023-01-27T14:40:20Z" | "2023-01-27T14:40:20Z" | CONTRIBUTOR | null | null | null | ## Adding a Dataset
- **Name:** ADE20k (actually it's called the MIT Scene Parsing Benchmark, it's actually a subset of ADE20k but a lot of authors still call it ADE20k)
- **Description:** A semantic segmentation dataset, consisting of 150 classes.
- **Paper:** http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf
- **Data:** http://sceneparsing.csail.mit.edu/
- **Motivation:** I am currently adding Transformer-based semantic segmentation models that achieve SOTA on this dataset. It would be great to directly access this dataset using HuggingFace Datasets, in order to make example scripts in HuggingFace Transformers.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Fix #3128. | {
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However, streamed TAR archive files do not allow random access to their archived files.
Therefore, we should enhance the Audio feature to support TAR archived files in sequential access. | {
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<div type='discussions-op-text'>
<sup>Originally posted by **vitalyshalumov** October 14, 2021</sup>
I'm trying to convert a tfds dataset to a huggingface one.
I've tried:
1. datasets-cli convert --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/mnist/3.0.1/
2. datasets-cli convert --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/
and other permutations.
The script appears to be running and finishing without an error but when looking in the huggingface/datasets/ folder nothing is created.
</div> | {
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When running:
raw_dataset = load_dataset('arabic_billion_words','Alittihad')
the correct dataset file is pulled from the url.
But, the generated dataset includes just a small portion of the data included in the file.
This is true for all other portions of the "arabic_billion_words" dataset ('Almasryalyoum',.....)
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
raw_dataset = load_dataset('arabic_billion_words','Alittihad')
#The screen message
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 20.62 MiB, post-processed: Unknown size, total: 352.74 MiB)
## Expected results
over 100K sentences
## Actual results
only 11K sentences
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1
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} | Nested encoding of features wastes a lot of time on operations which are effectively doing nothing when lists are used.
For example, if in the input we have a list of integers, `encoded_nested_example` will iterate over it and apply `encoded_nested_example` on every element even though it just return the int as is.
A similar issue is handled at an earlier stage when casting pytorch/tensorflow/pandas objects to python lists/numpy arrays:
https://github.com/huggingface/datasets/blob/c98c23c4260edadab00f997d1a5d66b7f2e93ce9/src/datasets/features/features.py#L149-L156
https://github.com/huggingface/datasets/blob/c98c23c4260edadab00f997d1a5d66b7f2e93ce9/src/datasets/features/features.py#L212-L228
In this pull request I suggest to use the same approach in `encoded_nested_example`.
In my setup there was a major speedup with this change: loading the data was at least x4 faster. | {
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] | null | 2 | "2021-10-20T20:16:11Z" | "2021-11-02T14:57:07Z" | "2021-11-02T14:57:07Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
Custom dataset loading sometimes segfaults and kills the process if chunks contain a variety of features/
## Steps to reproduce the bug
Download an example file:
```
wget https://gist.githubusercontent.com/TevenLeScao/11e2184394b3fa47d693de2550942c6b/raw/4232704d08fbfcaf93e5b51def9e5051507651ad/tiny_kelm.jsonl
```
Then in Python:
```
import datasets
tiny_kelm = datasets.load_dataset("json", data_files="tiny_kelm.jsonl", chunksize=100000)
```
## Expected results
a `tiny_kelm` functional dataset
## Actual results
☠️ `Segmentation fault (core dumped)` ☠️
## Environment info
- `datasets` version: 1.14.0
- Platform: Linux-5.11.0-38-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 5.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/3122 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3122/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3122/comments | https://api.github.com/repos/huggingface/datasets/issues/3122/events | https://github.com/huggingface/datasets/issues/3122 | 1,031,787,509 | I_kwDODunzps49f9P1 | 3,122 | OSError with a custom dataset loading script | {
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] | closed | false | null | [] | null | 8 | "2021-10-20T20:08:39Z" | "2021-11-23T09:55:38Z" | "2021-11-23T09:55:38Z" | NONE | null | null | null | ## Describe the bug
I am getting an OS error when trying to load the newly uploaded dataset classla/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.
## Steps to reproduce the bug
```python
dataset = datasets.load_dataset('classla/janes_tag', split='validation')
```
## Expected results
Dataset correctly loaded.
## Actual results
Traceback (most recent call last):
File "C:/mypath/test.py", line 91, in <module>
load_and_print('janes_tag')
File "C:/mypath/test.py", line 32, in load_and_print
dataset = datasets.load_dataset('classla/{}'.format(ds_name), split='validation')
File "C:\mypath\venv\lib\site-packages\datasets\load.py", line 1632, in load_dataset
use_auth_token=use_auth_token,
File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 608, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 704, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
[Errno 2] No such file or directory: 'C:\\mypath\\.cache\\huggingface\\datasets\\downloads\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\data\\train_all.conllup'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.5
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3121 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3121/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3121/comments | https://api.github.com/repos/huggingface/datasets/issues/3121/events | https://github.com/huggingface/datasets/pull/3121 | 1,031,673,115 | PR_kwDODunzps4tc_6q | 3,121 | Use huggingface_hub.HfApi to list datasets/metrics | {
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} | Delete `datasets.inspect.HfApi` and use `huggingface_hub.HfApi` instead.
WIP until https://github.com/huggingface/huggingface_hub/pull/429 is merged, then wait for the new release of `huggingface_hub`, update the `huggingface_hub` version in `setup.py` and merge this PR.
cc: @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/3120 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3120/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3120/comments | https://api.github.com/repos/huggingface/datasets/issues/3120/events | https://github.com/huggingface/datasets/pull/3120 | 1,031,574,511 | PR_kwDODunzps4tcril | 3,120 | Correctly update metadata to preserve features when concatenating datasets with axis=1 | {
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} | This PR correctly updates metadata to preserve higher-level feature types (e.g. `ClassLabel`) in `datasets.concatenate_datasets` when `axis=1`. Previously, we would delete the feature metadata in `datasets.concatenate_datasets` if `axis=1` and restore the feature types from the arrow table schema in `Dataset.__init__`. However, this approach only works for simple feature types (e.g. `Value`).
Fixes #3111 | {
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- **Name:** *openslr**
- **Description:** *Data set which contains male and female recordings of English from various dialects of the UK and Ireland.*
- **Paper:** *https://www.openslr.org/resources/83/about.html*
- **Data:** *Eleven separate data files can be found via https://www.openslr.org/resources/83/*
- **Motivation:** *Increase english ASR data with UK and Irish dialects*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
The *openslr* dataset already exists, this will add additional subset, *SLR83*. | {
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} | Fix test_load_dataset_canonical at release commit.
Fix #3117. | {
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```
____________________ LoadTest.test_load_dataset_canonical _____________________
[gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe
self = <tests.test_load.LoadTest testMethod=test_load_dataset_canonical>
def test_load_dataset_canonical(self):
scripts_version = os.getenv("HF_SCRIPTS_VERSION", SCRIPTS_VERSION)
with self.assertRaises(FileNotFoundError) as context:
datasets.load_dataset("_dummy")
self.assertIn(
f"https://raw.githubusercontent.com/huggingface/datasets/{scripts_version}/datasets/_dummy/_dummy.py",
> str(context.exception),
)
E AssertionError: 'https://raw.githubusercontent.com/huggingface/datasets/1.14.0/datasets/_dummy/_dummy.py' not found in "Couldn't find a dataset script at C:\\Users\\circleci\\datasets\\_dummy\\_dummy.py or any data file in the same directory. Couldn't find '_dummy' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/_dummy/_dummy.py"
tests\test_load.py:358: AssertionError
```
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] | closed | false | null | [] | null | 0 | "2021-10-20T11:00:47Z" | "2021-10-22T08:29:28Z" | "2021-10-22T08:26:45Z" | CONTRIBUTOR | null | 0 | {
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* updates the README links and the ADD_NEW_DATASET template to point to the new docs (the new docs don't have a section with the list of all the possible features, so I added that info to the `Features` docstring, which is then referenced in the ADD_NEW_DATASET template)
* fixes some broken links in the `.rst` files (fixed with the `make linkcheck` tool) | {
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] | closed | false | null | [] | null | 2 | "2021-10-19T20:01:45Z" | "2022-02-14T14:00:28Z" | "2022-02-14T14:00:28Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
Passing a PyArrowHDFS implementation of fsspec.spec.AbstractFileSystem (in the `fs` param required by `load_from_disk` methods in `DatasetDict` (in datasets_dict.py) and `Dataset` (in arrow_dataset.py) results in an error when calling the download method in the `fs` parameter.
## Steps to reproduce the bug
The documentation for the `fs` parameter states:
```
fs (:class:`~filesystems.S3FileSystem` or ``fsspec.spec.AbstractFileSystem``, optional, default ``None``):
Instance of the remote filesystem used to download the files from.
```
`PyArrowHDFS` from [fsspec](https://filesystem-spec.readthedocs.io/en/latest/_modules/fsspec/implementations/hdfs.html) implements `fsspec.spec.AbstractFileSystem`. However, when using it as shown below, I get an error.
```python
from fsspec.implementations.hdfs import PyArrowHDFS
...
transformed_corpus_path = "/user/my_user/clickbait/transformed_ds/"
fs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)
dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)
```
## Expected results
Previous to load from disk, I have managed to successfully store in HDFS the data and meta-information of a DatasetDict by doing:
```python
transformed_corpus_path = "/user/my_user/clickbait/transformed_ds/"
fs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)
my_datasets.save_to_disk(transformed_corpus_path, fs=fs)
```
As I have 3 datasets in the DatasetDict named `my_datasets`, the previous Python code creates the following contents in HDFS:
```sh
$ hadoop fs -ls "/user/my_user/clickbait/transformed_ds/"
Found 4 items
-rw------- 3 my_user users 43 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/dataset_dict.json
drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/test
drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/train
drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/validation
```
I would expect to recover on `dss` the Arrow-backed datasets I previously saved in HDFS calling the `save_to_disk` method on the `DatasetDict` object when invoking `DatasetDict.load_from_disk(...)` as described above.
## Actual results
However, when trying to recover the saved datasets, I get this error:
```
...
File "/home/fperez/dev/neuromancer/neuromancer/corpus.py", line 186, in load_transformed_corpus_from_disk
dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)
File "/home/fperez/anaconda3/envs/neuromancer/lib/python3.9/site-packages/datasets/dataset_dict.py", line 748, in load_from_disk
dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)
File "/home/fperez/anaconda3/envs/neuromancer/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1048, in load_from_disk
fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True)
File "pyarrow/_hdfsio.pyx", line 438, in pyarrow._hdfsio.HadoopFileSystem.download
TypeError: download() got an unexpected keyword argument 'recursive'
```
Examining the [signature of the download method in pyarrow 5.0.0](https://github.com/apache/arrow/blob/54d2bd89c99df72fa091b025452f85dd5d88e3cf/python/pyarrow/_hdfsio.pyx#L438) we can see that there's no download parameter:
```python
def download(self, path, stream, buffer_size=None):
with self.open(path, 'rb') as f:
f.download(stream, buffer_size=buffer_size)
```
## Environment info
- `datasets` version: 1.13.3
- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 5.0.0
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] | open | false | null | [] | null | 7 | "2021-10-19T19:26:46Z" | "2023-12-27T20:49:51Z" | null | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
More often than not I come along big HDF datasets, and currently there is no straight forward way to feed them to a dataset.
**Describe the solution you'd like**
I would love to see a `from_h5` method that gets an interface implemented by the user on how items are extracted from dataset (in case of multiple datasets containing elements like arrays and metadata and etc).
**Describe alternatives you've considered**
Currently I manually load hdf files using `h5py` and implement PyTorch dataset interface. For small h5 files I load them into a pandas dataframe and use `from_pandas` function in the `datasets` package to load them, but for big datasets this is not feasible.
**Additional context**
HDF files are widespread throughout different domains and are one of the go to's for many researchers/scientists/engineers who work with numerical data. Given `datasets`' usecases have outgrown NLP use cases, it will make a lot of sense focusing on things like supporting HDF files.
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] | open | false | null | [] | null | 4 | "2021-10-19T18:21:41Z" | "2021-10-19T18:52:29Z" | null | NONE | null | null | null | ## Describe the bug
Despite having batches way under 2Gb when running `datasets.map()`, after processing correctly the data of the first batch without fuss and irrespective of writer_batch_size (say 2,4,8,16,32,64 and 128 in my case), it returns the following error :
> OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB
Note that I always run `batch_size=writer_batch_size` :
## Steps to reproduce the bug
```python
datasets.map(lambda example : {"column_name" : function(arguments)}, batched=False, remove_columns = datasets.column_names, batch_size=batch_size, writer_batch_size=batch_size, disable_nullable=True, num_proc=None, desc="blablabla")
```
## Introspecting CUDA memory during bug
Placed within `function(arguments)` the following statement to introspect memory usage, merely a little over 1/4 of 2Gb
`print(torch.cuda.memory_summary(device=device, abbreviated=False))`
> |===========================================================================|
| PyTorch CUDA memory summary, device ID 0 |
|---------------------------------------------------------------------------|
| CUDA OOMs: 0 | cudaMalloc retries: 0 |
|===========================================================================|
| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |
|---------------------------------------------------------------------------|
| Allocated memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB |
| from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB |
| from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB |
|---------------------------------------------------------------------------|
| Active memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB |
| from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB |
| from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB |
|---------------------------------------------------------------------------|
| GPU reserved memory | 598016 KB | 598016 KB | 598016 KB | 0 B |
| from large pool | 595968 KB | 595968 KB | 595968 KB | 0 B |
| from small pool | 2048 KB | 2048 KB | 2048 KB | 0 B |
|---------------------------------------------------------------------------|
| Non-releasable memory | 36117 KB | 52292 KB | 274275 KB | 238158 KB |
| from large pool | 34816 KB | 51537 KB | 261713 KB | 226897 KB |
| from small pool | 1301 KB | 2045 KB | 12562 KB | 11261 KB |
|---------------------------------------------------------------------------|
| Allocations | 198 | 224 | 478 | 280 |
| from large pool | 74 | 75 | 75 | 1 |
| from small pool | 124 | 150 | 403 | 279 |
|---------------------------------------------------------------------------|
| Active allocs | 198 | 224 | 478 | 280 |
| from large pool | 74 | 75 | 75 | 1 |
| from small pool | 124 | 150 | 403 | 279 |
|---------------------------------------------------------------------------|
| GPU reserved segments | 21 | 21 | 21 | 0 |
| from large pool | 20 | 20 | 20 | 0 |
| from small pool | 1 | 1 | 1 | 0 |
|---------------------------------------------------------------------------|
| Non-releasable allocs | 18 | 23 | 166 | 148 |
| from large pool | 17 | 18 | 19 | 2 |
| from small pool | 1 | 6 | 147 | 146 |
|===========================================================================|
## Expected results
Efficiently process the datasets and write it down to disk.
## Actual results
--------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2390 else:
-> 2391 writer.write(example)
2392 else:
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write(self, example, key, writer_batch_size)
367
--> 368 self.write_examples_on_file()
369
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self)
316 if not isinstance(pa_array[0], pa.lib.FloatScalar):
--> 317 raise OverflowError(
318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format(
OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB
During handling of the above exception, another exception occurred:
OverflowError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_16268/2456940807.py in <module>
3 #tracker = OfflineEmissionsTracker(country_iso_code="FRA", project_name='xxx'+time_stamp,output_dir='./codecarbon')
4 #tracker.start()
----> 5 process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection=['wikipedia'], from_scratch=True,
6 clean_sentences=False, negative_sampling=False, translate=False, tokenize=False, generate_embeddings=True, concatenate_embeddings=False,
7 max_sample=10000, padding='do_not_pad', truncation=True, cpu_batch_size=1000, gpu_batch_size=2, cpu_writer_batch_size=1000, gpu_writer_batch_size=2, disable_nullable=True, num_proc=None) #
~\xxx\xxx.py in process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection, from_scratch, clean_sentences, translate, negative_sampling, tokenize, generate_embeddings, concatenate_embeddings, max_sample, padding, truncation, cpu_batch_size, gpu_batch_size, cpu_writer_batch_size, gpu_writer_batch_size, disable_nullable, num_proc)
481 for column in tqdm(dataset.column_names, desc=f'Processing column', leave=False):
482 if "xxx_" in column:
--> 483 dataset = dataset.map(lambda example :
484 {"embeddings_"+str(column).replace("translated_",""):function(input_ids=example[column],
485 token_type_ids=example[column.replace("input_ids","token_type_ids")],
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
2034
2035 if num_proc is None or num_proc == 1:
-> 2036 return self._map_single(
2037 function=function,
2038 with_indices=with_indices,
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs)
501 self: "Dataset" = kwargs.pop("self")
502 # apply actual function
--> 503 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
504 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
505 for dataset in datasets:
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs)
468 }
469 # apply actual function
--> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
472 # re-apply format to the output
~\anaconda3\envs\xxx\lib\site-packages\datasets\fingerprint.py in wrapper(*args, **kwargs)
404 # Call actual function
405
--> 406 out = func(self, *args, **kwargs)
407
408 # Update fingerprint of in-place transforms + update in-place history of transforms
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2425 if update_data:
2426 if writer is not None:
-> 2427 writer.finalize()
2428 if tmp_file is not None:
2429 tmp_file.close()
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in finalize(self, close_stream)
440 # Re-intializing to empty list for next batch
441 self.hkey_record = []
--> 442 self.write_examples_on_file()
443 if self.pa_writer is None:
444 if self._schema is not None:
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self)
315 # This check fails with FloatArrays with nans, which is not what we want, so account for that:
316 if not isinstance(pa_array[0], pa.lib.FloatScalar):
--> 317 raise OverflowError(
318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format(
319 type(pa_array)
OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.13.3
- Platform: Windows-10-10.0.19042-SP0
- Python version: 3.8.11
- PyArrow version: 3.0.0
##Next steps
Testing on Linux.
@albertvillanova
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https://api.github.com/repos/huggingface/datasets/issues/3111 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3111/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3111/comments | https://api.github.com/repos/huggingface/datasets/issues/3111/events | https://github.com/huggingface/datasets/issues/3111 | 1,030,598,983 | I_kwDODunzps49bbFH | 3,111 | concatenate_datasets removes ClassLabel typing. | {
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] | null | 1 | "2021-10-19T18:05:31Z" | "2021-10-21T14:50:21Z" | "2021-10-21T14:50:21Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
When concatenating two datasets, we lose typing of ClassLabel columns.
I can work on this if this is a legitimate bug,
## Steps to reproduce the bug
```python
import datasets
from datasets import Dataset, ClassLabel, Value, concatenate_datasets
DS_LEN = 100
my_dataset = Dataset.from_dict(
{
"sentence": [f"{chr(i % 10)}" for i in range(DS_LEN)],
"label": [i % 2 for i in range(DS_LEN)]
}
)
my_predictions = Dataset.from_dict(
{
"pred": [(i + 1) % 2 for i in range(DS_LEN)]
}
)
my_dataset = my_dataset.cast(datasets.Features({"sentence": Value("string"), "label": ClassLabel(2, names=["POS", "NEG"])}))
print("Original")
print(my_dataset)
print(my_dataset.features)
concat_ds = concatenate_datasets([my_dataset, my_predictions], axis=1)
print("Concatenated")
print(concat_ds)
print(concat_ds.features)
```
## Expected results
The features of `concat_ds` should contain ClassLabel.
## Actual results
On master, I get:
```
{'sentence': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None), 'pred': Value(dtype='int64', id=None)}
```
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3110 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3110/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3110/comments | https://api.github.com/repos/huggingface/datasets/issues/3110/events | https://github.com/huggingface/datasets/pull/3110 | 1,030,558,484 | PR_kwDODunzps4tZakS | 3,110 | Stream TAR-based dataset using iter_archive | {
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} | I converted all the dataset based on TAR archive to use iter_archive instead, so that they can be streamable.
It means that around 80 datasets become streamable :) | {
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} | This PR adds the NLTK implementation of Google BLEU metric. This is also a part of an effort to resolve an unfortunate naming collision between GLEU for machine translation and GLEU for grammatical error correction.
I used [this page](https://huggingface.co/docs/datasets/add_metric.html) for reference. Please, point me to the right direction if I missed anything. | {
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- the old URLs are no longer valid
- there are alternative host URLs
Fix #3091. | {
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] | open | false | null | [] | null | 0 | "2021-10-18T13:12:38Z" | "2021-10-22T09:36:10Z" | null | CONTRIBUTOR | null | null | null | ## Describe the bug
If a cached dataset is removed from the library, I don't see how to delete it programmatically. I thought that using `force_redownload` would try to refresh the cache, then raise an exception, but it reuses the cache instead.
## Steps to reproduce the bug
_requires to already have `wit` in the cache_: see https://github.com/huggingface/datasets/pull/2981
```python
import datasets as ds
dataset = ds.load_dataset("wit", split="train", download_mode='force_redownload')
```
## Expected results
It should raise an exception, since the dataset does not exist anymore.
## Actual results
It uses the cached result
```
Using the latest cached version of the module from /home/slesage/.cache/huggingface/modules/datasets_modules/datasets/wit/107afbffd48e058b19101bddc47fbee25fa68eb6d50a733e262875f1285a5171 (last modified on Wed Sep 29 08:21:10 2021) since it couldn't be found locally at wit, or remotely on the Hugging Face Hub.
```
## Environment info
- `datasets` version: 1.13.4.dev0
- Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1 | {
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TODO:
- [x] Contact Zenodo support
- [x] Check it is fixed | {
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Currently, `datasets` project description appearing in PyPI shows the release instructions addressed to core maintainers: https://pypi.org/project/datasets/1.13.3/
Fix #3102. | {
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cc @patrickvonplaten | {
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https://api.github.com/repos/huggingface/datasets/issues/3100 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3100/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3100/comments | https://api.github.com/repos/huggingface/datasets/issues/3100/events | https://github.com/huggingface/datasets/pull/3100 | 1,028,738,180 | PR_kwDODunzps4tTwpn | 3,100 | Replace FSTimeoutError with parent TimeoutError | {
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} | PR #3050 introduced a dependency on `fsspec.FSTiemoutError`. Note that this error only exists from `fsspec` version `2021.06.0` (June 2021).
To fix #3097, there are 2 alternatives:
- Either pinning `fsspec` to versions newer or equal to `2021.06.0`
- Or replacing `fsspec.FSTimeoutError` wth its parent `asyncio.TimeoutError`, which exists from Python 3.8.0 (Sep 2018).
This PR implements the second approach.
Fix #3097. | {
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When using `pip install datasets`
or use `conda install -c huggingface -c conda-forge datasets`
cannot install datasets
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("sst", "default")
```
## Actual results
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-fbe7981e6e21> in <module>
1 import torch
2 import transformers
----> 3 from datasets import load_dataset
4
5 dataset = load_dataset("sst", "default")
~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/__init__.py in <module>
35 from .arrow_reader import ArrowReader, ReadInstruction
36 from .arrow_writer import ArrowWriter
---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
38 from .combine import interleave_datasets
39 from .dataset_dict import DatasetDict, IterableDatasetDict
~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/builder.py in <module>
42 )
43 from .arrow_writer import ArrowWriter, BeamWriter
---> 44 from .data_files import DataFilesDict, _sanitize_patterns
45 from .dataset_dict import DatasetDict, IterableDatasetDict
46 from .fingerprint import Hasher
~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/data_files.py in <module>
118
119 def _exec_patterns_in_dataset_repository(
--> 120 dataset_info: huggingface_hub.hf_api.DatasetInfo,
121 patterns: List[str],
122 allowed_extensions: Optional[list] = None,
AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.13.3
- Platform: macOS-11.3.1-arm64-arm-64bit
- Python version: 3.8.10
- PyArrow version: 5.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3098 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3098/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3098/comments | https://api.github.com/repos/huggingface/datasets/issues/3098/events | https://github.com/huggingface/datasets/pull/3098 | 1,028,210,790 | PR_kwDODunzps4tSRSZ | 3,098 | Push to hub capabilities for `Dataset` and `DatasetDict` | {
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} | This PR implements a `push_to_hub` method on `Dataset` and `DatasetDict`. This does not currently work in `IterableDatasetDict` nor `IterableDataset` as those are simple dicts and I would like your opinion on how you would like to implement this before going ahead and doing it.
This implementation needs to be used with the following `huggingface_hub` branch in order to work correctly: https://github.com/huggingface/huggingface_hub/pull/415
### Implementation
The `push_to_hub` API is entirely based on HTTP requests rather than a git-based workflow:
- This allows pushing changes without firstly cloning the repository, which reduces the time in half for the `push_to_hub` method.
- Collaboration, as well as the system of branches/merges/rebases is IMO less straightforward than for models and spaces. In the situation where such collaboration is needed, I would *heavily* advocate for the `Repository` helper of the `huggingface_hub` to be used instead of the `push_to_hub` method which will always be, by design, limiting in that regard (even if based on a git-workflow instead of HTTP requests)
In order to overcome the limit of 5GB files set by the HTTP requests, dataset sharding is used.
### Testing
The test suite implemented here makes use of the moon-staging instead of the production setup. As several repositories are created and deleted, it is better to use the staging.
It does not require setting an environment variable or any kind of special attention but introduces a new decorator `with_staging_testing` which patches global variables to use the staging endpoint instead of the production endpoint.
### Examples
The tests cover a lot of examples and behavior. | {
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https://api.github.com/repos/huggingface/datasets/issues/3097 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3097/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3097/comments | https://api.github.com/repos/huggingface/datasets/issues/3097/events | https://github.com/huggingface/datasets/issues/3097 | 1,027,750,811 | I_kwDODunzps49Qjub | 3,097 | `ModuleNotFoundError: No module named 'fsspec.exceptions'` | {
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] | null | 1 | "2021-10-15T19:34:38Z" | "2021-10-18T07:51:54Z" | "2021-10-18T07:51:54Z" | MEMBER | null | null | null | ## Describe the bug
I keep runnig into a fsspec ModuleNotFound error
## Steps to reproduce the bug
```python
>>> from datasets import get_dataset_infos
2021-10-15 15:25:37.863206: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-10-15 15:25:37.863252: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/__init__.py", line 37, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 56, in <module>
from .utils.streaming_download_manager import StreamingDownloadManager
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/streaming_download_manager.py", line 11, in <module>
from fsspec.exceptions import FSTimeoutError
ModuleNotFoundError: No module named 'fsspec.exceptions'
```
Yet, I do have `fsspec`:
```bash
hf@victor-scale:~/dev/promptsource$ pip show fsspec
Name: fsspec
Version: 2021.5.0
Summary: File-system specification
Home-page: http://github.com/intake/filesystem_spec
Author: None
Author-email: None
License: BSD
Location: /home/hf/dev/promptsource/.venv/lib/python3.7/site-packages
Requires:
Required-by: datasets
```
With the same version of fsspec and `datasets==1.9.0`, I don't see this problem....
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
I can't even run `datasets-cli env` actually.., but here's my env:
- `datasets` version: 1.13.3
- Platform: Ubuntu 18.04
- Python version: 3.7.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3096 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3096/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3096/comments | https://api.github.com/repos/huggingface/datasets/issues/3096/events | https://github.com/huggingface/datasets/pull/3096 | 1,027,535,685 | PR_kwDODunzps4tQblQ | 3,096 | Fix Audio feature mp3 resampling | {
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} | Issue #3095 is related to mp3 resampling, not to `cast_column`.
This PR fixes Audio feature mp3 resampling.
Fix #3095. | {
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] | null | 2 | "2021-10-15T13:36:58Z" | "2023-04-07T09:43:20Z" | "2021-10-15T15:38:30Z" | MEMBER | null | null | null | ## Describe the bug
After changing the sampling rate automatic decoding fails.
## Steps to reproduce the bug
```python
from datasets import load_dataset
import datasets
ds = load_dataset("common_voice", "ab", split="train")
ds = ds.cast_column("audio", datasets.features.Audio(sampling_rate=16_000))
print(ds[0]["audio"]) # <- this fails currently
```
yields:
```
TypeError: forward() takes 2 positional arguments but 4 were given
```
## Expected results
no failure
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 1.13.2 (master)
- Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 5.0.0
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] | closed | false | null | [] | null | 2 | "2021-10-15T09:33:25Z" | "2022-04-10T14:06:29Z" | "2022-04-10T14:06:29Z" | NONE | null | null | null | ## Describe the bug
Loading a json dataset with multiple splits that have nested dicts with keys in different order results in the error below.
If the keys in the nested dicts always have the same order or even if you just load a single split in which the nested dicts don't have the same order, everything works fine.
## Steps to reproduce the bug
Create two json files:
train.json
```
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
```
test.json
```
{"a": {"b": 1, "c": 2}}
{"a": {"b": 3, "c": 4}}
```
```python
from datasets import load_dataset
# Loading the files individually works (even though the keys in train.json don't have the same order)
load_dataset('json', data_files={"test": "test.json"})
load_dataset('json', data_files={"train": "train.json"})
# Loading both splits fails
load_dataset('json', data_files={"train": "train.json", "test": "test.json"})
```
## Expected results
Loading both splits should not give an error whether the nested dicts are have the same order or not.
## Actual results
```
>>> load_dataset('json', data_files={"train": "train.json", "test": "test.json"})
Using custom data configuration default-f1bc76fd07398c4c
Downloading and preparing dataset json/default to /home/dthulke/.cache/huggingface/datasets/json/default-f1bc76fd07398c4c/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 8839.42it/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 477.82it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/load.py", line 1632, in load_dataset
use_auth_token=use_auth_token,
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 608, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 1159, in _prepare_split
writer.write_table(table)
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/arrow_writer.py", line 428, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1596, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 592, in pyarrow.lib._sanitize_arrays
File "pyarrow/array.pxi", line 329, in pyarrow.lib.asarray
File "pyarrow/table.pxi", line 277, in pyarrow.lib.ChunkedArray.cast
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/pyarrow/compute.py", line 297, in cast
return call_function("cast", [arr], options)
File "pyarrow/_compute.pyx", line 527, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 337, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct
```
## Environment info
- `datasets` version: 1.13.2
- Platform: Linux-4.15.0-147-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyArrow version: 5.0.0
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I'm having problem with generating dummy data as `datasets-cli dummy_data ./datasets/jnlpba --auto_generate --match_text_files "*.iob2"` is giving `datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
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] | null | 3 | "2021-10-15T09:20:40Z" | "2021-10-19T13:06:10Z" | "2021-10-19T12:50:39Z" | NONE | null | null | null | ## Describe the bug
The dataset `blog_authorship_corpus` is broken.
By bypassing the checksum checks, the loading does not return any error but the resulting dataset is empty.
I suspect it is because the data download url is broken (http://www.cs.biu.ac.il/~koppel/blogs/blogs.zip).
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("blog_authorship_corpus", split="train", download_mode='force_redownload')
```
## Expected results
No error.
## Actual results
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
/tmp/ipykernel_5237/1729238701.py in <module>
2 ds = load_dataset(
3 "blog_authorship_corpus", split="train",
----> 4 download_mode='force_redownload'
5 )
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)
1115 ignore_verifications=ignore_verifications,
1116 try_from_hf_gcs=try_from_hf_gcs,
-> 1117 use_auth_token=use_auth_token,
1118 )
1119
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
635 if not downloaded_from_gcs:
636 self._download_and_prepare(
--> 637 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
638 )
639 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
707 if verify_infos:
708 verify_checksums(
--> 709 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
710 )
711
/opt/conda/lib/python3.7/site-packages/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:
['http://www.cs.biu.ac.il/~koppel/blogs/blogs.zip']
```
## Environment info
- `datasets` version: 1.13.2
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.10
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https://api.github.com/repos/huggingface/datasets/issues/3090 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3090/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3090/comments | https://api.github.com/repos/huggingface/datasets/issues/3090/events | https://github.com/huggingface/datasets/pull/3090 | 1,027,100,371 | PR_kwDODunzps4tPEtH | 3,090 | Update BibTeX entry | {
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https://api.github.com/repos/huggingface/datasets/issues/3089 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3089/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3089/comments | https://api.github.com/repos/huggingface/datasets/issues/3089/events | https://github.com/huggingface/datasets/issues/3089 | 1,026,973,360 | I_kwDODunzps49Nl6w | 3,089 | JNLPBA Dataset | {
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] | closed | false | null | [] | null | 2 | "2021-10-15T01:16:02Z" | "2021-10-22T08:23:57Z" | "2021-10-22T08:23:57Z" | NONE | null | 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
```
## Expected results
The dataset loading script for this dataset is incorrect. This is a biomedical dataset used for named entity recognition. The entities in the [script](https://github.com/huggingface/datasets/blob/master/datasets/jnlpba/jnlpba.py#L81-L83) are: O, B, and I. The correct entities from the original data file are:
['O',
'B-DNA',
'I-DNA',
'B-RNA',
'I-RNA',
'B-cell_line',
'I-cell_line',
'B-cell_type',
'I-cell_type',
'B-protein',
'I-protein']
## Actual results
The dataset loader script needs to include the following NER names:
['O',
'B-DNA',
'I-DNA',
'B-RNA',
'I-RNA',
'B-cell_line',
'I-cell_line',
'B-cell_type',
'I-cell_type',
'B-protein',
'I-protein']
And the [data](https://github.com/huggingface/datasets/blob/master/datasets/jnlpba/jnlpba.py#L46) that is being pulled has been modified from the original dataset and does not include the original NER tags.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
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https://api.github.com/repos/huggingface/datasets/issues/3088 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3088/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3088/comments | https://api.github.com/repos/huggingface/datasets/issues/3088/events | https://github.com/huggingface/datasets/pull/3088 | 1,026,920,369 | PR_kwDODunzps4tOhRx | 3,088 | Use template column_mapping to transmit_format instead of template features | {
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} | Use `template.column_mapping` to check for modified columns since `template.features` represent a generic template/column mapping.
Fix #3087
TODO:
- [x] Add a test | {
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https://api.github.com/repos/huggingface/datasets/issues/3087 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3087/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3087/comments | https://api.github.com/repos/huggingface/datasets/issues/3087/events | https://github.com/huggingface/datasets/issues/3087 | 1,026,780,469 | I_kwDODunzps49M201 | 3,087 | Removing label column in a text classification dataset yields to errors | {
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] | closed | false | null | [] | null | 0 | "2021-10-14T20:12:50Z" | "2021-10-15T10:11:04Z" | "2021-10-15T10:11:04Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
This looks like #3059 but it's not linked to the cache this time. Removing the `label` column from a text classification dataset and then performing any processing will result in an error.
To reproduce:
```py
from datasets import load_dataset
from transformers import AutoTokenizer
raw_datasets = load_dataset("imdb")
raw_datasets = raw_datasets.remove_columns("label")
model_checkpoint = "distilbert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
context_length = 128
def tokenize_pad_and_truncate(texts):
return tokenizer(texts["text"], truncation=True, padding="max_length", max_length=context_length)
tokenized_datasets = raw_datasets.map(tokenize_pad_and_truncate, batched=True)
```
Traceback:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-1-ba61bb32f786> in <module>
12 return tokenizer(texts["text"], truncation=True, padding="max_length", max_length=context_length)
13
---> 14 tokenized_datasets = raw_datasets.map(tokenize_pad_and_truncate, batched=True)
~/git/datasets/src/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)
500 desc=desc,
501 )
--> 502 for k, dataset in self.items()
503 }
504 )
~/git/datasets/src/datasets/dataset_dict.py in <dictcomp>(.0)
500 desc=desc,
501 )
--> 502 for k, dataset in self.items()
503 }
504 )
~/git/datasets/src/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
2051 new_fingerprint=new_fingerprint,
2052 disable_tqdm=disable_tqdm,
-> 2053 desc=desc,
2054 )
2055 else:
~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
501 self: "Dataset" = kwargs.pop("self")
502 # apply actual function
--> 503 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
504 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
505 for dataset in datasets:
~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
468 }
469 # apply actual function
--> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
472 # re-apply format to the output
~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs)
404 # Call actual function
405
--> 406 out = func(self, *args, **kwargs)
407
408 # Update fingerprint of in-place transforms + update in-place history of transforms
~/git/datasets/src/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2243 if os.path.exists(cache_file_name) and load_from_cache_file:
2244 logger.warning("Loading cached processed dataset at %s", cache_file_name)
-> 2245 info = self.info.copy()
2246 info.features = features
2247 info.task_templates = None
~/git/datasets/src/datasets/info.py in copy(self)
278
279 def copy(self) -> "DatasetInfo":
--> 280 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
281
282
~/git/datasets/src/datasets/info.py in __init__(self, description, citation, homepage, license, features, post_processed, supervised_keys, task_templates, builder_name, config_name, version, splits, download_checksums, download_size, post_processing_size, dataset_size, size_in_bytes)
~/git/datasets/src/datasets/info.py in __post_init__(self)
177 for idx, template in enumerate(self.task_templates):
178 if isinstance(template, TextClassification):
--> 179 labels = self.features[template.label_column].names
180 self.task_templates[idx] = TextClassification(
181 text_column=template.text_column, label_column=template.label_column, labels=labels
KeyError: 'label'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3086 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3086/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3086/comments | https://api.github.com/repos/huggingface/datasets/issues/3086/events | https://github.com/huggingface/datasets/pull/3086 | 1,026,481,905 | PR_kwDODunzps4tNIvp | 3,086 | Remove _resampler from Audio fields | {
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} | The `_resampler` Audio attribute was implemented to optimize audio resampling, but it should not be cached.
This PR removes `_resampler` from Audio fields, so that it is not returned by `fields()` or `asdict()`.
Fix #3083. | {
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https://api.github.com/repos/huggingface/datasets/issues/3085 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3085/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3085/comments | https://api.github.com/repos/huggingface/datasets/issues/3085/events | https://github.com/huggingface/datasets/pull/3085 | 1,026,467,384 | PR_kwDODunzps4tNFza | 3,085 | Fixes to `to_tf_dataset` | {
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```
from datasets import load_dataset
dataset = load_dataset("glue", "mnli")
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-cased')
def tokenize_function(dataset):
return tokenizer(dataset['premise'])
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset['train'].features)
tokenized_datasets.set_format("numpy")
tokenized_datasets['train'][5:8]
```
Outputs:
```
python3.9/site-packages/datasets/formatting/formatting.py:167: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
return np.array(array, copy=False, **self.np_array_kwargs)
``` | {
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] | null | 0 | "2021-10-14T13:23:53Z" | "2021-10-14T15:13:40Z" | "2021-10-14T15:13:40Z" | MEMBER | null | null | null | ## Describe the bug
As reported by @patrickvonplaten, when loaded from the cache, datasets containing the Audio feature raise TypeError.
## Steps to reproduce the bug
```python
from datasets import load_dataset
# load first time works
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean")
# load from cache breaks
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean")
```
## Actual results
```
TypeError: __init__() got an unexpected keyword argument '_resampler'
```
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} | The `huggingface_hub` package added the parameter `timeout` from version 0.0.19.
This PR bumps this minimal version.
Fix #3080. | {
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} | This PR adds the new `Audio(...)` features - see: https://github.com/huggingface/datasets/pull/2324 to the most important audio datasets:
- Librispeech
- Timit
- Common Voice
- AMI
- ... (others I'm forgetting now)
The PR is curently blocked because the following leads to a problem:
```python
from datasets import load_dataset
# load first time works
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean")
# load from cache breaks
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean")
```
As soon as it's unblocked, I'll adapt the other audio datasets as well. | {
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] | null | 0 | "2021-10-14T13:10:58Z" | "2021-10-14T14:39:51Z" | "2021-10-14T14:39:51Z" | MEMBER | null | null | null | ## Describe the bug
As reported by @patrickvonplaten, a TypeError is raised when trying to load a dataset.
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean")
```
## Actual results
```
TypeError: dataset_info() got an unexpected keyword argument 'timeout'
```
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https://api.github.com/repos/huggingface/datasets/issues/3077 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3077/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3077/comments | https://api.github.com/repos/huggingface/datasets/issues/3077/events | https://github.com/huggingface/datasets/pull/3077 | 1,026,150,362 | PR_kwDODunzps4tMFPG | 3,077 | Fix loading a metric with internal import | {
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} | After refactoring the module factory (#2986), a bug was introduced when loading metrics with internal imports.
This PR adds a new test case and fixes this bug.
Fix #3076.
CC: @sgugger @merveenoyan | {
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] | null | 0 | "2021-10-14T08:29:27Z" | "2021-10-14T09:14:55Z" | "2021-10-14T09:14:55Z" | MEMBER | null | null | null | ## Describe the bug
As reported by @sgugger, after last release, exception is thrown when loading a metric.
## Steps to reproduce the bug
```python
from datasets import load_metric
metric = load_metric("squad_v2")
```
## Actual results
```
FileNotFoundError Traceback (most recent call last)
<ipython-input-1-e612a8cab787> in <module>
1 from datasets import load_metric
----> 2 metric = load_metric("squad_v2")
d:\projects\huggingface\datasets\src\datasets\load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, revision, script_version, **metric_init_kwargs)
1336 )
1337 revision = script_version
-> 1338 metric_module = metric_module_factory(
1339 path, revision=revision, download_config=download_config, download_mode=download_mode
1340 ).module_path
d:\projects\huggingface\datasets\src\datasets\load.py in metric_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, **download_kwargs)
1237 if not isinstance(e1, FileNotFoundError):
1238 raise e1 from None
-> 1239 raise FileNotFoundError(
1240 f"Couldn't find a metric script at {relative_to_absolute_path(combined_path)}. "
1241 f"Metric '{path}' doesn't exist on the Hugging Face Hub either."
FileNotFoundError: Couldn't find a metric script at D:\projects\huggingface\datasets\squad_v2\squad_v2.py. Metric 'squad_v2' doesn't exist on the Hugging Face Hub either.
```
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https://api.github.com/repos/huggingface/datasets/issues/3075 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3075/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3075/comments | https://api.github.com/repos/huggingface/datasets/issues/3075/events | https://github.com/huggingface/datasets/pull/3075 | 1,026,103,388 | PR_kwDODunzps4tL75E | 3,075 | Updates LexGLUE and MultiEURLEX README.md files | {
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} | Updates LexGLUE and MultiEURLEX README.md files
- Fix leaderboard in LexGLUE.
- Fix an error in the CaseHOLD data example.
- Turn MultiEURLEX dataset statistics table into HTML to nicely render in HF website. | {
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https://api.github.com/repos/huggingface/datasets/issues/3074 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3074/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3074/comments | https://api.github.com/repos/huggingface/datasets/issues/3074/events | https://github.com/huggingface/datasets/pull/3074 | 1,025,940,085 | PR_kwDODunzps4tLbe- | 3,074 | add XCSR dataset | {
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} | Hi,
I wanted to add the [XCSR ](https://inklab.usc.edu//XCSR/xcsr_datasets) dataset to huggingface! :)
I followed the instructions of adding new dataset to huggingface and have all the required files ready now! It would be super helpful if you can take a look and review them. Thanks in advance for your time and help. Look forward to hearing from you and can't wait to add XCSR to huggingface :D | {
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https://api.github.com/repos/huggingface/datasets/issues/3073 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3073/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3073/comments | https://api.github.com/repos/huggingface/datasets/issues/3073/events | https://github.com/huggingface/datasets/issues/3073 | 1,025,718,469 | I_kwDODunzps49IzjF | 3,073 | Import error installing with ppc64le | {
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Installing the datasets library with a computer running with ppc64le seems to cause an issue when importing the datasets library.
```
python
Python 3.6.13 | packaged by conda-forge | (default, Sep 23 2021, 07:37:44)
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import datasets
Illegal instruction (core dumped)
```
Error when importing
`Illegal instruction (core dumped)`
## Steps to reproduce the bug
I get this error when installing the library by using conda. I can't install with pip I believe because pyarrow only has the ppc64le library on conda forge
```
conda create --name transformers_py36_v2 python=3.6
conda activate transformers_py36_v2
conda install datasets
```
## Tracebacks
conda create --name transformers_py36_v2 python=3.6
```
Collecting package metadata (current_repodata.json): done
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.9.2
latest version: 4.10.3
Please update conda by running
$ conda update -n base -c defaults conda
## Package Plan ##
environment location: /p/home/gerryc/.conda/envs/transformers_py36_v2
added / updated specs:
- python=3.6
The following NEW packages will be INSTALLED:
_libgcc_mutex conda-forge/linux-ppc64le::_libgcc_mutex-0.1-conda_forge
_openmp_mutex conda-forge/linux-ppc64le::_openmp_mutex-4.5-1_gnu
ca-certificates conda-forge/linux-ppc64le::ca-certificates-2021.10.8-h1084571_0
certifi pkgs/main/linux-ppc64le::certifi-2020.12.5-py36h6ffa863_0
ld_impl_linux-ppc~ conda-forge/linux-ppc64le::ld_impl_linux-ppc64le-2.36.1-ha35d02b_2
libffi conda-forge/linux-ppc64le::libffi-3.4.2-h3b9df90_4
libgcc-ng conda-forge/linux-ppc64le::libgcc-ng-11.2.0-h7698a5e_11
libgomp conda-forge/linux-ppc64le::libgomp-11.2.0-h7698a5e_11
libstdcxx-ng conda-forge/linux-ppc64le::libstdcxx-ng-11.2.0-habdf983_11
libzlib conda-forge/linux-ppc64le::libzlib-1.2.11-h339bb43_1013
ncurses conda-forge/linux-ppc64le::ncurses-6.2-hea85c5d_4
openssl conda-forge/linux-ppc64le::openssl-1.1.1l-h4e0d66e_0
pip conda-forge/noarch::pip-21.3-pyhd8ed1ab_0
python conda-forge/linux-ppc64le::python-3.6.13-h57873ef_2_cpython
readline conda-forge/linux-ppc64le::readline-8.1-h5c45dff_0
setuptools pkgs/main/linux-ppc64le::setuptools-58.0.4-py36h6ffa863_0
sqlite conda-forge/linux-ppc64le::sqlite-3.36.0-h4e2196e_2
tk conda-forge/linux-ppc64le::tk-8.6.11-h41c6715_1
wheel conda-forge/noarch::wheel-0.37.0-pyhd8ed1ab_1
xz conda-forge/linux-ppc64le::xz-5.2.5-h6eb9509_1
zlib conda-forge/linux-ppc64le::zlib-1.2.11-h339bb43_1013
Proceed ([y]/n)? y
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate transformers_py36_v2
#
# To deactivate an active environment, use
#
# $ conda deactivate
```
conda activate transformers_py36_v2
conda install datasets
```
Collecting package metadata (current_repodata.json): done
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.
Collecting package metadata (repodata.json): done
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.9.2
latest version: 4.10.3
Please update conda by running
$ conda update -n base -c defaults conda
## Package Plan ##
environment location: /p/home/gerryc/.conda/envs/transformers_py36_v2
added / updated specs:
- datasets
The following NEW packages will be INSTALLED:
abseil-cpp conda-forge/linux-ppc64le::abseil-cpp-20210324.2-h3b9df90_0
aiohttp conda-forge/linux-ppc64le::aiohttp-3.7.4.post0-py36hc33305d_0
arrow-cpp conda-forge/linux-ppc64le::arrow-cpp-5.0.0-py36hf9cf308_8_cpu
async-timeout conda-forge/noarch::async-timeout-3.0.1-py_1000
attrs conda-forge/noarch::attrs-21.2.0-pyhd8ed1ab_0
aws-c-cal conda-forge/linux-ppc64le::aws-c-cal-0.5.11-hb3fac3d_0
aws-c-common conda-forge/linux-ppc64le::aws-c-common-0.6.2-h4e0d66e_0
aws-c-event-stream conda-forge/linux-ppc64le::aws-c-event-stream-0.2.7-h76da5f2_13
aws-c-io conda-forge/linux-ppc64le::aws-c-io-0.10.5-hf6a6c7c_0
aws-checksums conda-forge/linux-ppc64le::aws-checksums-0.1.11-hfe76d68_7
aws-sdk-cpp conda-forge/linux-ppc64le::aws-sdk-cpp-1.8.186-h90855e8_3
brotlipy conda-forge/linux-ppc64le::brotlipy-0.7.0-py36hc33305d_1001
bzip2 conda-forge/linux-ppc64le::bzip2-1.0.8-h4e0d66e_4
c-ares conda-forge/linux-ppc64le::c-ares-1.17.2-h4e0d66e_0
cffi conda-forge/linux-ppc64le::cffi-1.14.6-py36h021ab3c_1
chardet conda-forge/linux-ppc64le::chardet-4.0.0-py36h270354c_1
colorama conda-forge/noarch::colorama-0.4.4-pyh9f0ad1d_0
cryptography conda-forge/linux-ppc64le::cryptography-3.4.7-py36hc71b123_0
dataclasses conda-forge/noarch::dataclasses-0.8-pyh787bdff_2
datasets conda-forge/noarch::datasets-1.12.1-pyhd8ed1ab_1
dill conda-forge/noarch::dill-0.3.4-pyhd8ed1ab_0
filelock conda-forge/noarch::filelock-3.3.0-pyhd8ed1ab_0
fsspec conda-forge/noarch::fsspec-2021.10.0-pyhd8ed1ab_0
gflags conda-forge/linux-ppc64le::gflags-2.2.2-hb209c28_1004
glog conda-forge/linux-ppc64le::glog-0.5.0-h4040248_0
grpc-cpp conda-forge/linux-ppc64le::grpc-cpp-1.40.0-h2bf711c_2
huggingface_hub conda-forge/noarch::huggingface_hub-0.0.19-pyhd8ed1ab_0
idna conda-forge/noarch::idna-2.10-pyh9f0ad1d_0
idna_ssl conda-forge/noarch::idna_ssl-1.0.0-0
importlib-metadata conda-forge/linux-ppc64le::importlib-metadata-4.8.1-py36h270354c_0
importlib_metadata conda-forge/noarch::importlib_metadata-4.8.1-hd8ed1ab_0
krb5 conda-forge/linux-ppc64le::krb5-1.19.2-haf43566_2
libblas conda-forge/linux-ppc64le::libblas-3.9.0-11_linuxppc64le_openblas
libbrotlicommon conda-forge/linux-ppc64le::libbrotlicommon-1.0.9-h4e0d66e_5
libbrotlidec conda-forge/linux-ppc64le::libbrotlidec-1.0.9-h4e0d66e_5
libbrotlienc conda-forge/linux-ppc64le::libbrotlienc-1.0.9-h4e0d66e_5
libcblas conda-forge/linux-ppc64le::libcblas-3.9.0-11_linuxppc64le_openblas
libcurl conda-forge/linux-ppc64le::libcurl-7.79.1-he415e40_1
libedit conda-forge/linux-ppc64le::libedit-3.1.20191231-h41a240f_2
libev conda-forge/linux-ppc64le::libev-4.33-h6eb9509_1
libevent conda-forge/linux-ppc64le::libevent-2.1.10-h97db324_4
libgfortran-ng conda-forge/linux-ppc64le::libgfortran-ng-11.2.0-hfdc3801_11
libgfortran5 conda-forge/linux-ppc64le::libgfortran5-11.2.0-he58fbb4_11
liblapack conda-forge/linux-ppc64le::liblapack-3.9.0-11_linuxppc64le_openblas
libnghttp2 conda-forge/linux-ppc64le::libnghttp2-1.43.0-h42039ad_1
libopenblas conda-forge/linux-ppc64le::libopenblas-0.3.17-pthreads_h486567c_1
libprotobuf conda-forge/linux-ppc64le::libprotobuf-3.18.1-h690f14c_0
libssh2 conda-forge/linux-ppc64le::libssh2-1.10.0-ha5a9321_2
libthrift conda-forge/linux-ppc64le::libthrift-0.15.0-h54f692e_1
libutf8proc conda-forge/linux-ppc64le::libutf8proc-2.6.1-h4e0d66e_0
lz4-c conda-forge/linux-ppc64le::lz4-c-1.9.3-h3b9df90_1
multidict conda-forge/linux-ppc64le::multidict-5.2.0-py36hc33305d_0
multiprocess conda-forge/linux-ppc64le::multiprocess-0.70.12.2-py36hc33305d_0
numpy conda-forge/linux-ppc64le::numpy-1.19.5-py36h86665d4_1
orc conda-forge/linux-ppc64le::orc-1.7.0-hae6b4bd_0
packaging conda-forge/noarch::packaging-21.0-pyhd8ed1ab_0
pandas conda-forge/linux-ppc64le::pandas-1.1.5-py36hab1a6e6_0
parquet-cpp conda-forge/noarch::parquet-cpp-1.5.1-2
pyarrow conda-forge/linux-ppc64le::pyarrow-5.0.0-py36h7a46c7e_8_cpu
pycparser conda-forge/noarch::pycparser-2.20-pyh9f0ad1d_2
pyopenssl conda-forge/noarch::pyopenssl-21.0.0-pyhd8ed1ab_0
pyparsing conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0
pysocks conda-forge/linux-ppc64le::pysocks-1.7.1-py36h270354c_3
python-dateutil conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0
python-xxhash conda-forge/linux-ppc64le::python-xxhash-2.0.2-py36hc33305d_0
python_abi conda-forge/linux-ppc64le::python_abi-3.6-2_cp36m
pytz conda-forge/noarch::pytz-2021.3-pyhd8ed1ab_0
pyyaml conda-forge/linux-ppc64le::pyyaml-5.4.1-py36hc33305d_1
re2 conda-forge/linux-ppc64le::re2-2021.09.01-h3b9df90_0
requests conda-forge/noarch::requests-2.25.1-pyhd3deb0d_0
s2n conda-forge/linux-ppc64le::s2n-1.0.10-h97db324_0
six conda-forge/noarch::six-1.16.0-pyh6c4a22f_0
snappy conda-forge/linux-ppc64le::snappy-1.1.8-hb209c28_3
tqdm conda-forge/noarch::tqdm-4.62.3-pyhd8ed1ab_0
typing-extensions conda-forge/noarch::typing-extensions-3.10.0.2-hd8ed1ab_0
typing_extensions conda-forge/noarch::typing_extensions-3.10.0.2-pyha770c72_0
urllib3 conda-forge/noarch::urllib3-1.26.7-pyhd8ed1ab_0
xxhash conda-forge/linux-ppc64le::xxhash-0.8.0-h4e0d66e_3
yaml conda-forge/linux-ppc64le::yaml-0.2.5-h6eb9509_0
yarl conda-forge/linux-ppc64le::yarl-1.6.3-py36hc33305d_2
zipp conda-forge/noarch::zipp-3.6.0-pyhd8ed1ab_0
zstd conda-forge/linux-ppc64le::zstd-1.5.0-h65c4b1a_0
The following packages will be UPDATED:
certifi pkgs/main::certifi-2020.12.5-py36h6ff~ --> conda-forge::certifi-2021.5.30-py36h270354c_0
Proceed ([y]/n)? y
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Red Hat Enterprise Linux 8.2 (Ootpa)
- Python version: 3.6
- PyArrow version: pyarrow - 5.0.0 - py36h7a46c7e_8_cpu - conda-forge
Any help would be appreciated! I've been struggling on installing datasets on this machine.
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} | Fix issue https://github.com/huggingface/datasets/issues/2866 (for good this time)
`counter` now works in both streaming and non-streaming mode.
And the `AttributeError: 'str' object has no attribute 'as_posix'` related to the patch of Path.open is fixed as well
Note : the patches should only affect the datasets module, not the user's ones ! That's why we should probably use something else than patch.object to patch the Path class' methods.
cc @severo @albertvillanova | {
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- **Name:** text, json, csv
- **Description:** I am developing a customized dataset loading script. The problem is mainly about my custom dataset is seperate into many files and I only find a dataset loading template in [https://github.com/huggingface/datasets/blob/1.2.1/datasets/json/json.py](https://github.com/huggingface/datasets/blob/1.2.1/datasets/json/json.py) that can handle my circumstance. I'm afraid these templates are too old to use. Could you re-add these three templates to current master branch?
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] | null | 0 | "2021-10-13T05:52:26Z" | "2021-10-13T08:05:49Z" | "2021-10-13T06:49:48Z" | MEMBER | null | null | null | ## Describe the bug
After commit 9353fc863d0c99ab0427f83cc5a4f04fcf52f1df, the CI fails on Windows with FileNotFoundError when stting up s3_base fixture. See: https://app.circleci.com/pipelines/github/huggingface/datasets/8151/workflows/5db8d154-badd-4d3d-b202-ca7a318997a2/jobs/50321
Error summary:
```
ERROR tests/test_arrow_dataset.py::test_dummy_dataset_serialize_s3 - FileNotF...
ERROR tests/test_dataset_dict.py::test_dummy_dataset_serialize_s3 - FileNotFo...
```
Stack trace:
```
______________ ERROR at setup of test_dummy_dataset_serialize_s3 ______________
[gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe
@pytest.fixture()
def s3_base():
# writable local S3 system
import shlex
import subprocess
# Mocked AWS Credentials for moto.
old_environ = os.environ.copy()
os.environ.update(S3_FAKE_ENV_VARS)
> proc = subprocess.Popen(shlex.split("moto_server s3 -p %s" % s3_port))
tests\s3_fixtures.py:32:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
C:\tools\miniconda3\lib\subprocess.py:729: in __init__
restore_signals, start_new_session)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <subprocess.Popen object at 0x0000012BB8A4B908>
args = 'moto_server s3 -p 5555', executable = None, preexec_fn = None
close_fds = True, pass_fds = (), cwd = None, env = None
startupinfo = <subprocess.STARTUPINFO object at 0x0000012BB8177630>
creationflags = 0, shell = False, p2cread = -1, p2cwrite = -1, c2pread = -1
c2pwrite = -1, errread = -1, errwrite = -1, unused_restore_signals = True
unused_start_new_session = False
def _execute_child(self, args, executable, preexec_fn, close_fds,
pass_fds, cwd, env,
startupinfo, creationflags, shell,
p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite,
unused_restore_signals, unused_start_new_session):
"""Execute program (MS Windows version)"""
assert not pass_fds, "pass_fds not supported on Windows."
if not isinstance(args, str):
args = list2cmdline(args)
# Process startup details
if startupinfo is None:
startupinfo = STARTUPINFO()
if -1 not in (p2cread, c2pwrite, errwrite):
startupinfo.dwFlags |= _winapi.STARTF_USESTDHANDLES
startupinfo.hStdInput = p2cread
startupinfo.hStdOutput = c2pwrite
startupinfo.hStdError = errwrite
if shell:
startupinfo.dwFlags |= _winapi.STARTF_USESHOWWINDOW
startupinfo.wShowWindow = _winapi.SW_HIDE
comspec = os.environ.get("COMSPEC", "cmd.exe")
args = '{} /c "{}"'.format (comspec, args)
# Start the process
try:
hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
# no special security
None, None,
int(not close_fds),
creationflags,
env,
os.fspath(cwd) if cwd is not None else None,
> startupinfo)
E FileNotFoundError: [WinError 2] The system cannot find the file specified
C:\tools\miniconda3\lib\subprocess.py:1017: FileNotFoundError
``` | {
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} | Increase streaming config parameters:
* retry interval set to 5 seconds
* max retries set to 20 (so 1mn 40s) | {
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https://api.github.com/repos/huggingface/datasets/issues/3067 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3067/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3067/comments | https://api.github.com/repos/huggingface/datasets/issues/3067/events | https://github.com/huggingface/datasets/pull/3067 | 1,024,023,185 | PR_kwDODunzps4tFSCy | 3,067 | add story_cloze | {
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https://api.github.com/repos/huggingface/datasets/issues/3066 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3066/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3066/comments | https://api.github.com/repos/huggingface/datasets/issues/3066/events | https://github.com/huggingface/datasets/pull/3066 | 1,024,005,311 | PR_kwDODunzps4tFObl | 3,066 | Add iter_archive | {
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} | Added the `iter_archive` method for the StreamingDownloadManager.
It was already implemented in the regular DownloadManager.
Now it can be used to stream from TAR archives as mentioned in https://github.com/huggingface/datasets/issues/2829
I also updated the `food101` dataset as an example.
Any image/audio dataset using TAR archives can be updated to use `iter_archive` in order to be streamable :)
cc @severo
Fix #2829. | {
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} | Fix the `datasets-cli` test command after the `prepare_module` change in #2986 | {
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] | open | false | null | [] | null | 3 | "2021-10-12T14:34:53Z" | "2022-07-30T08:47:26Z" | null | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
Right now there are few hiccups using `interleave_datasets`. Interleaved dataset iterates until the smallest dataset completes it's iterator. In this way larger datasets may not complete full epoch of iteration.
It creates new problems in calculation of epoch since there are no way to track which dataset from `interleave_datasets` completes how many epoch.
**Describe the solution you'd like**
For `interleave_datasets` module,
- [ ] Add a boolean argument `--stop-iter` in `interleave_datasets` that enables dataset to either iterate infinite amount of time or not. That means it should not return `StopIterator` exception in case `--stop-iter=False`.
- [ ] Internal list variable `iter_cnt` that explains how many times (in steps/epochs) each dataset iterates at a given point.
- [ ] Add an argument `--max-iter` (list type) that explain maximum amount of time each of the dataset can iterate. After complete `--max-iter` of one dataset, other dataset should continue sampling and when all the dataset finish their respective `--max-iter`, only then return `StopIterator`
Note: I'm new to `datasets` api. May be these features are already there in the datasets.
Since multitask training is the latest trends, I believe this feature would make the `datasets` api more popular.
@lhoestq | {
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