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https://api.github.com/repos/huggingface/datasets/issues/4799 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4799/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4799/comments | https://api.github.com/repos/huggingface/datasets/issues/4799/events | https://github.com/huggingface/datasets/issues/4799 | 1,330,889,854 | I_kwDODunzps5PU8R- | 4,799 | video dataset loader/parser | {
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] | open | false | null | [] | null | [] | 1,659,837,252,000 | 1,659,837,252,000 | null | CONTRIBUTOR | null | you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)?
could you please add functionality to load a video dataset? it would be really cool if i could point it to a bunch of video files and use pytorch to start looping through batches of videos. like if my batch size is 16, each sample in the batch is a frame from a video. i'm competing in the [minerl challenge](https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition), and it would be awesome to use the HF ecosystem. | {
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https://api.github.com/repos/huggingface/datasets/issues/4798 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4798/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4798/comments | https://api.github.com/repos/huggingface/datasets/issues/4798/events | https://github.com/huggingface/datasets/pull/4798 | 1,330,699,942 | PR_kwDODunzps48wVEG | 4,798 | Shard generator | {
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Example:
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="validation")
>>> ds
Dataset({
features: ['text', 'label'],
num_rows: 1066
})
>>> next(ds.shard_generator(300))
Dataset({
features: ['text', 'label'],
num_rows: 300
})
```
I hope it can be helpful to someone. Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/4797 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4797/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4797/comments | https://api.github.com/repos/huggingface/datasets/issues/4797/events | https://github.com/huggingface/datasets/pull/4797 | 1,330,000,998 | PR_kwDODunzps48uL-t | 4,797 | Torgo dataset creation | {
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https://api.github.com/repos/huggingface/datasets/issues/4796 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4796/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4796/comments | https://api.github.com/repos/huggingface/datasets/issues/4796/events | https://github.com/huggingface/datasets/issues/4796 | 1,329,887,810 | I_kwDODunzps5PRHpC | 4,796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | {
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] | open | false | null | [] | null | [] | 1,659,703,279,000 | 1,659,703,313,000 | null | CONTRIBUTOR | null | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
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https://api.github.com/repos/huggingface/datasets/issues/4795 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4795/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4795/comments | https://api.github.com/repos/huggingface/datasets/issues/4795/events | https://github.com/huggingface/datasets/issues/4795 | 1,329,525,732 | I_kwDODunzps5PPvPk | 4,795 | MBPP splits | {
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] | open | false | null | [] | null | [] | 1,659,682,261,000 | 1,659,688,432,000 | null | NONE | null | (@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later in the paper, we hold out 10 problems for **few-shot prompting**, another 500 as our **test** dataset (which is used to evaluate both few-shot inference and fine-tuned models), 374 problems for **fine-tuning**, and the rest for **validation**.
If the dataset on the Hub should reproduce most closely what the original authors use, I guess this four-way split should be reflected.
The paper doesn't explicitly state the task_id ranges of the splits, but the [GitHub readme](https://github.com/google-research/google-research/tree/master/mbpp) referenced in the paper specifies exact task_id ranges, although it misstates the total number of samples:
> We specify a train and test split to use for evaluation. Specifically:
>
> * Task IDs 11-510 are used for evaluation.
> * Task IDs 1-10 and 511-1000 are used for training and/or prompting. We typically used 1-10 for few-shot prompting, although you can feel free to use any of the training examples.
I.e. the few-shot, train and validation splits are combined into one split, with a soft suggestion of using the first ten for few-shot prompting. It is not explicitly stated whether the 374 fine-tuning samples mentioned in the paper have task_id 511 to 784 or 601 to 974 or are randomly sampled from task_id 511 to 974.
Regarding the "sanitized" split the paper states the following:
> For evaluations involving the edited dataset, we perform comparisons with 100 problems that appear in both the original and edited dataset, using the same held out 10 problems for few-shot prompting and 374 problems for fine-tuning.
The statement doesn't appear to be very precise, as among the 10 few-shot problems, those with task_id 1, 5 and 10 are not even part of the sanitized variant, and many from the task_id range from 511 to 974 are missing (e.g. task_id 511 to 553). I suppose the idea the task_id ranges for each split remain the same, even if some of the task_ids are not present. That would result in 7 few-shot, 257 test, 141 train and 22 validation examples in the sanitized split. | {
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https://api.github.com/repos/huggingface/datasets/issues/4792 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4792/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4792/comments | https://api.github.com/repos/huggingface/datasets/issues/4792/events | https://github.com/huggingface/datasets/issues/4792 | 1,328,593,929 | I_kwDODunzps5PMLwJ | 4,792 | Add DocVQA | {
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] | open | false | null | [] | null | [] | 1,659,618,446,000 | 1,659,618,473,000 | null | CONTRIBUTOR | null | ## Adding a Dataset
- **Name:** DocVQA
- **Description:** Document Visual Question Answering (DocVQA) seeks to inspire a “purpose-driven” point of view in Document Analysis and Recognition research, where the document content is extracted and used to respond to high-level tasks defined by the human consumers of this information.
- **Paper:** https://arxiv.org/abs/2007.00398
- **Data:** https://www.docvqa.org/datasets/docvqa
- **Motivation:** Models like LayoutLM and Donut in the Transformers library are fine-tuned on DocVQA. Would be very handy to directly load this dataset from the hub.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
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"Thanks for reporting. It's a known issue that should be fixed soon. Meanwhile, I had to manually trigger the dataset viewer. It's OK now.\r\nNote that the extreme aspect ratio of the images generates another issue, that we're inspecting."
] | 1,659,617,356,000 | 1,659,620,596,000 | 1,659,620,596,000 | NONE | null | ### Link
https://huggingface.co/datasets/Team-PIXEL/rendered-wikipedia-english/viewer/rendered-wikipedia-en/train
### Description
The dataset can be loaded fine but the viewer shows this error:
```
Server Error
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
```
I'm guessing this is because I recently renamed the dataset. Based on related issues (e.g. https://github.com/huggingface/datasets/issues/4759) , is there something server-side that needs to be refreshed?
### Owner
Yes | {
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] | null | [] | 1,659,616,131,000 | 1,659,622,296,000 | null | MEMBER | null | ## Describe the bug
According to their paper, the TREC dataset contains 2 kinds of classes:
- 6 coarse classes: TREC-6
- 50 fine classes: TREC-50
However, our implementation only has 47 (instead of 50) fine classes. The reason for this is that we only considered the last segment of the label, which is repeated for several coarse classes:
- We have one `desc` fine label instead of 2:
- `DESC:desc`
- `HUM:desc`
- We have one `other` fine label instead of 3:
- `ENTY:other`
- `LOC:other`
- `NUM:other`
From their paper:
> We define a two-layered taxonomy, which represents a natural semantic classification for typical answers in the TREC task. The hierarchy contains 6 coarse classes and 50 fine classes,
> Each coarse class contains a non-overlapping set of fine classes.
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4789). All of your documentation changes will be reflected on that endpoint."
] | 1,659,608,640,000 | 1,659,609,064,000 | null | CONTRIBUTOR | null | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thank you for the quick response! Before noticing that you already had implemented the fix, I already had implemened my own version. I'd also suggest bumping the major version because the contents of the dataset changed, even if only slightly.\r\nI'll attach my version of the affected files: [mbpp-checksum-changes.zip](https://github.com/huggingface/datasets/files/9258161/mbpp-checksum-changes.zip).",
"Hi @stadlerb, thanks for your feedback.\r\n\r\nWe normally update the major version whenever there is a new dataset release, usually with a breaking change in schema. The patch version is updated whenever there is a small correction in the dataset that does not change its schema.\r\n\r\nAs a side note for future contributions, please note that this dataset is hosted in our library GitHub repository. Therefore, the PRs to GitHub-hosted datasets needs being done through GitHub.\r\n\r\nCurrently added datasets are hosted on the Hub and for them, PRs can be done through the Hub.",
"I just noticed another problem with the dataset: The [GitHub page](https://github.com/google-research/google-research/tree/master/mbpp) and the [paper](http://arxiv.org/abs/2108.07732) mention a train-test split, which is not reflected in the dataloader. I'll open a new issue regarding this later."
] | 1,659,601,060,000 | 1,659,634,440,000 | 1,659,633,661,000 | MEMBER | null | Fix issue reported on the Hub: https://huggingface.co/datasets/mbpp/discussions/1
Fix #4787. | {
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could you please add functionality to load a video dataset? it would be really cool if i could point it to a bunch of video files and use pytorch to start looping through batches of videos. like if my batch size is 16, each sample in the batch is a frame from a video. i'm competing in the [minerl challenge](https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition), and it would be awesome to use the HF ecosystem. | {
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Example:
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="validation")
>>> ds
Dataset({
features: ['text', 'label'],
num_rows: 1066
})
>>> next(ds.shard_generator(300))
Dataset({
features: ['text', 'label'],
num_rows: 300
})
```
I hope it can be helpful to someone. Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/4796 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4796/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4796/comments | https://api.github.com/repos/huggingface/datasets/issues/4796/events | https://github.com/huggingface/datasets/issues/4796 | 1,329,887,810 | I_kwDODunzps5PRHpC | 4,796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | {
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] | open | false | null | [] | null | [] | 1,659,703,279,000 | 1,659,703,313,000 | null | CONTRIBUTOR | null | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-internal-testing/example-documents")
# load any random Pillow image
image = Image.open("/content/cord_example.png").convert("RGB")
new_image = {'image': image}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Expected results
The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`:
```
import datasets
feature = datasets.Image(decode=False)
new_image = {'image': feature.encode_example(image)}
dataset['test'] = dataset['test'].add_item(new_image)
```
## Actual results
```
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type
```
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The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later in the paper, we hold out 10 problems for **few-shot prompting**, another 500 as our **test** dataset (which is used to evaluate both few-shot inference and fine-tuned models), 374 problems for **fine-tuning**, and the rest for **validation**.
If the dataset on the Hub should reproduce most closely what the original authors use, I guess this four-way split should be reflected.
The paper doesn't explicitly state the task_id ranges of the splits, but the [GitHub readme](https://github.com/google-research/google-research/tree/master/mbpp) referenced in the paper specifies exact task_id ranges, although it misstates the total number of samples:
> We specify a train and test split to use for evaluation. Specifically:
>
> * Task IDs 11-510 are used for evaluation.
> * Task IDs 1-10 and 511-1000 are used for training and/or prompting. We typically used 1-10 for few-shot prompting, although you can feel free to use any of the training examples.
I.e. the few-shot, train and validation splits are combined into one split, with a soft suggestion of using the first ten for few-shot prompting. It is not explicitly stated whether the 374 fine-tuning samples mentioned in the paper have task_id 511 to 784 or 601 to 974 or are randomly sampled from task_id 511 to 974.
Regarding the "sanitized" split the paper states the following:
> For evaluations involving the edited dataset, we perform comparisons with 100 problems that appear in both the original and edited dataset, using the same held out 10 problems for few-shot prompting and 374 problems for fine-tuning.
The statement doesn't appear to be very precise, as among the 10 few-shot problems, those with task_id 1, 5 and 10 are not even part of the sanitized variant, and many from the task_id range from 511 to 974 are missing (e.g. task_id 511 to 553). I suppose the idea the task_id ranges for each split remain the same, even if some of the task_ids are not present. That would result in 7 few-shot, 257 test, 141 train and 22 validation examples in the sanitized split. | {
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] | open | false | null | [] | null | [] | 1,659,618,446,000 | 1,659,618,473,000 | null | CONTRIBUTOR | null | ## Adding a Dataset
- **Name:** DocVQA
- **Description:** Document Visual Question Answering (DocVQA) seeks to inspire a “purpose-driven” point of view in Document Analysis and Recognition research, where the document content is extracted and used to respond to high-level tasks defined by the human consumers of this information.
- **Paper:** https://arxiv.org/abs/2007.00398
- **Data:** https://www.docvqa.org/datasets/docvqa
- **Motivation:** Models like LayoutLM and Donut in the Transformers library are fine-tuned on DocVQA. Would be very handy to directly load this dataset from the hub.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
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"Thanks for reporting. It's a known issue that should be fixed soon. Meanwhile, I had to manually trigger the dataset viewer. It's OK now.\r\nNote that the extreme aspect ratio of the images generates another issue, that we're inspecting."
] | 1,659,617,356,000 | 1,659,620,596,000 | 1,659,620,596,000 | NONE | null | ### Link
https://huggingface.co/datasets/Team-PIXEL/rendered-wikipedia-english/viewer/rendered-wikipedia-en/train
### Description
The dataset can be loaded fine but the viewer shows this error:
```
Server Error
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
```
I'm guessing this is because I recently renamed the dataset. Based on related issues (e.g. https://github.com/huggingface/datasets/issues/4759) , is there something server-side that needs to be refreshed?
### Owner
Yes | {
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According to their paper, the TREC dataset contains 2 kinds of classes:
- 6 coarse classes: TREC-6
- 50 fine classes: TREC-50
However, our implementation only has 47 (instead of 50) fine classes. The reason for this is that we only considered the last segment of the label, which is repeated for several coarse classes:
- We have one `desc` fine label instead of 2:
- `DESC:desc`
- `HUM:desc`
- We have one `other` fine label instead of 3:
- `ENTY:other`
- `LOC:other`
- `NUM:other`
From their paper:
> We define a two-layered taxonomy, which represents a natural semantic classification for typical answers in the TREC task. The hierarchy contains 6 coarse classes and 50 fine classes,
> Each coarse class contains a non-overlapping set of fine classes.
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4789). All of your documentation changes will be reflected on that endpoint."
] | 1,659,608,640,000 | 1,659,609,064,000 | null | CONTRIBUTOR | null | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thank you for the quick response! Before noticing that you already had implemented the fix, I already had implemened my own version. I'd also suggest bumping the major version because the contents of the dataset changed, even if only slightly.\r\nI'll attach my version of the affected files: [mbpp-checksum-changes.zip](https://github.com/huggingface/datasets/files/9258161/mbpp-checksum-changes.zip).",
"Hi @stadlerb, thanks for your feedback.\r\n\r\nWe normally update the major version whenever there is a new dataset release, usually with a breaking change in schema. The patch version is updated whenever there is a small correction in the dataset that does not change its schema.\r\n\r\nAs a side note for future contributions, please note that this dataset is hosted in our library GitHub repository. Therefore, the PRs to GitHub-hosted datasets needs being done through GitHub.\r\n\r\nCurrently added datasets are hosted on the Hub and for them, PRs can be done through the Hub.",
"I just noticed another problem with the dataset: The [GitHub page](https://github.com/google-research/google-research/tree/master/mbpp) and the [paper](http://arxiv.org/abs/2108.07732) mention a train-test split, which is not reflected in the dataloader. I'll open a new issue regarding this later."
] | 1,659,601,060,000 | 1,659,634,440,000 | 1,659,633,661,000 | MEMBER | null | Fix issue reported on the Hub: https://huggingface.co/datasets/mbpp/discussions/1
Fix #4787. | {
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] | null | [] | 1,659,600,951,000 | 1,659,633,661,000 | 1,659,633,661,000 | MEMBER | null | ## Describe the bug
As reported on the Hub [Fix Checksum Mismatch](https://huggingface.co/datasets/mbpp/discussions/1), there is a `NonMatchingChecksumError` when loading mbpp dataset
## Steps to reproduce the bug
```python
ds = load_dataset("mbpp", "full")
```
## Expected results
Loading of the dataset without any exception raised.
## Actual results
```
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-1-a3fbdd3ed82e> in <module>
----> 1 ds = load_dataset("mbpp", "full")
.../huggingface/datasets/src/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1791
1792 # Download and prepare data
-> 1793 builder_instance.download_and_prepare(
1794 download_config=download_config,
1795 download_mode=download_mode,
.../huggingface/datasets/src/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
.../huggingface/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1225
1226 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1228
1229 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
.../huggingface/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
--> 775 verify_checksums(
776 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
777 )
.../huggingface/datasets/src/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://raw.githubusercontent.com/google-research/google-research/master/mbpp/mbpp.jsonl']
```
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```python
import datasets
train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"])
s3 = datasets.filesystems.S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key)
train_dataset.save_to_disk("s3://datasets/", fs=s3)
```
produces following traceback:
```shell
File "C:\Users\Hong Knop\AppData\Local\Programs\Python\Python310\lib\site-packages\botocore\auth.py", line 374, in scope
return '/'.join(scope)
```
I invoke print(scope) in <auth.py> (line 373) and find this:
```python
[('4VA08VLL3VTKQJKCAI8M',), '20220803', 'us-east-1', 's3', 'aws4_request']
``` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,659,533,520,000 | 1,659,539,263,000 | 1,659,538,336,000 | MEMBER | null | This PR adds to docs the requirement of torchaudio<0.12.0 to avoid RuntimeError.
Subsequent to PR:
- #4777 | {
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"Hi @osanseviero I would like to add this dataset.",
"Hey @nandwalritik! Thanks for offering to help!\r\n\r\nThis dataset might be somewhat complex and I'm concerned about it being 65 TB, which would be quite expensive to host. @lhoestq @mariosasko I would love your input if you think it's worth adding this dataset.",
"Thanks for proposing this interesting dataset, @osanseviero.\r\n\r\nPlease note that the data files are already hosted in a third-party server: e.g. the index of data files for entity \"6795937\" is at https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/index.html \r\n- audio.tar: https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/audio.tar\r\n- ...\r\n\r\nTherefore, in principle, we don't need to host them on our Hub: it would be enough to just implement a loading script in the corresponding Hub dataset repo, e.g. \"facebook/multiface\"..."
] | 1,659,474,022,000 | 1,659,596,096,000 | null | MEMBER | null | ## Adding a Dataset
- **Name:** Multiface dataset
- **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capture rate at 30 fps
- **Data:** https://github.com/facebookresearch/multiface
The whole dataset is 65TB though, so I'm not sure
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4783). All of your documentation changes will be reflected on that endpoint."
] | 1,659,472,563,000 | 1,659,560,623,000 | null | MEMBER | null | This PR is a first draft of creating a loading script for image datasets. Feel free to let me know if there are any specificities I'm missing for this. 🙂
To do:
- [x] Document how to create different configurations. | {
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https://api.github.com/repos/huggingface/datasets/issues/4782 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4782/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4782/comments | https://api.github.com/repos/huggingface/datasets/issues/4782/events | https://github.com/huggingface/datasets/issues/4782 | 1,326,247,158 | I_kwDODunzps5PDOz2 | 4,782 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 | {
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"Thanks for reporting @conceptofmind.\r\n\r\nCould you please give details about your environment? \r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n```",
"Hi @albertvillanova ,\r\n\r\nHere is the environment information:\r\n```\r\n- `datasets` version: 2.3.2\r\n- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.12\r\n- PyArrow version: 7.0.0\r\n- Pandas version: 1.4.2\r\n```\r\nThanks,\r\n\r\nEnrico"
] | 1,659,465,365,000 | 1,659,545,056,000 | null | NONE | null | ## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.unique("hash"))
```
Gists for minimum reproducible example:
https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a
https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2
## Expected results
Chunking and writing out a deduplicated dataset.
## Actual results
```
return dataset._data.column(column).unique().to_pylist()
File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique
File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4781 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4781/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4781/comments | https://api.github.com/repos/huggingface/datasets/issues/4781/events | https://github.com/huggingface/datasets/pull/4781 | 1,326,114,161 | PR_kwDODunzps48hOie | 4,781 | Fix label renaming and add a battery of tests | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4781). All of your documentation changes will be reflected on that endpoint."
] | 1,659,458,527,000 | 1,659,459,455,000 | null | MEMBER | null | This PR makes some changes to label renaming in `to_tf_dataset()`, both to fix some issues when users input something we weren't expecting, and also to make it easier to deprecate label renaming in future, if/when we want to move this special-casing logic to a function in `transformers`.
The main changes are:
- Label renaming now only happens when the `auto_rename_labels` argument is set. For backward compatibility, this defaults to `True` for now.
- If the user requests "label" but the data collator renames that column to "labels", the label renaming logic will now handle that case correctly.
- Added a battery of tests to make this more reliable in future.
- Adds an optimization to loading in `to_tf_dataset()` for unshuffled datasets (uses slicing instead of a list of indices)
Fixes #4772 | {
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https://api.github.com/repos/huggingface/datasets/issues/4780 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4780/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4780/comments | https://api.github.com/repos/huggingface/datasets/issues/4780/events | https://github.com/huggingface/datasets/pull/4780 | 1,326,034,767 | PR_kwDODunzps48g9oA | 4,780 | Remove apache_beam import from module level in natural_questions dataset | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,659,454,494,000 | 1,659,456,993,000 | 1,659,456,197,000 | MEMBER | null | Instead of importing `apache_beam` at the module level, import it in the method `_build_pcollection`.
Fix #4779. | {
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https://api.github.com/repos/huggingface/datasets/issues/4779 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4779/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4779/comments | https://api.github.com/repos/huggingface/datasets/issues/4779/events | https://github.com/huggingface/datasets/issues/4779 | 1,325,997,225 | I_kwDODunzps5PCRyp | 4,779 | Loading natural_questions requires apache_beam even with existing preprocessed data | {
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] | closed | false | null | [] | null | [] | 1,659,452,817,000 | 1,659,456,198,000 | 1,659,456,198,000 | MEMBER | null | ## Describe the bug
When loading "natural_questions", the package "apache_beam" is required:
```
ImportError: To be able to use natural_questions, you need to install the following dependency: apache_beam.
Please install it using 'pip install apache_beam' for instance'
```
This requirement is unnecessary, once there exists preprocessed data and the script just needs to download it.
## Steps to reproduce the bug
```python
load_dataset("natural_questions", "dev", split="validation", revision="main")
```
## Expected results
No ImportError raised.
## Actual results
```
ImportError Traceback (most recent call last)
[<ipython-input-3-c938e7c05d02>](https://localhost:8080/#) in <module>()
----> 1 from datasets import load_dataset; ds = load_dataset("natural_questions", "dev", split="validation", revision="main")
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1732 revision=revision,
1733 use_auth_token=use_auth_token,
-> 1734 **config_kwargs,
1735 )
1736
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1504 download_mode=download_mode,
1505 data_dir=data_dir,
-> 1506 data_files=data_files,
1507 )
1508
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1245 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1246 ) from None
-> 1247 raise e1 from None
1248 else:
1249 raise FileNotFoundError(
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1180 download_config=download_config,
1181 download_mode=download_mode,
-> 1182 dynamic_modules_path=dynamic_modules_path,
1183 ).get_module()
1184 elif path.count("/") == 1: # community dataset on the Hub
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self)
490 base_path=hf_github_url(path=self.name, name="", revision=revision),
491 imports=imports,
--> 492 download_config=self.download_config,
493 )
494 additional_files = [(config.DATASETDICT_INFOS_FILENAME, dataset_infos_path)] if dataset_infos_path else []
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in _download_additional_modules(name, base_path, imports, download_config)
214 _them_str = "them" if len(needs_to_be_installed) > 1 else "it"
215 raise ImportError(
--> 216 f"To be able to use {name}, you need to install the following {_depencencies_str}: "
217 f"{', '.join(needs_to_be_installed)}.\nPlease install {_them_str} using 'pip install "
218 f"{' '.join(needs_to_be_installed.values())}' for instance'"
ImportError: To be able to use natural_questions, you need to install the following dependency: apache_beam.
Please install it using 'pip install apache_beam' for instance'
```
## Environment info
Colab notebook.
| {
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https://api.github.com/repos/huggingface/datasets/issues/4778 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4778/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4778/comments | https://api.github.com/repos/huggingface/datasets/issues/4778/events | https://github.com/huggingface/datasets/pull/4778 | 1,324,928,750 | PR_kwDODunzps48dRPh | 4,778 | Update local loading script docs | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4778). All of your documentation changes will be reflected on that endpoint."
] | 1,659,385,267,000 | 1,659,385,665,000 | null | MEMBER | null | This PR clarifies the local loading script section to include how to load a dataset after you've modified the local loading script (closes #4732). | {
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