number int64 2 7.91k | title stringlengths 1 290 | body stringlengths 0 228k | state stringclasses 2 values | created_at timestamp[s]date 2020-04-14 18:18:51 2025-12-16 10:45:02 | updated_at timestamp[s]date 2020-04-29 09:23:05 2025-12-16 19:34:46 | closed_at timestamp[s]date 2020-04-29 09:23:05 2025-12-16 14:20:48 β | url stringlengths 48 51 | author stringlengths 3 26 β | comments_count int64 0 70 | labels listlengths 0 4 |
|---|---|---|---|---|---|---|---|---|---|---|
4,143 | Unable to download `Wikepedia` 20220301.en version | ## Describe the bug
Unable to download `Wikepedia` dataset, 20220301.en version
## Steps to reproduce the bug
```python
!pip install apache_beam mwparserfromhell
dataset_wikipedia = load_dataset("wikipedia", "20220301.en")
```
## Actual results
```
ValueError: BuilderConfig 20220301.en not found.
Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Ubuntu
- Python version: 3.6
- PyArrow version: 6.0.1 | CLOSED | 2022-04-11T13:00:14 | 2022-08-17T00:37:55 | 2022-04-21T17:04:14 | https://github.com/huggingface/datasets/issues/4143 | beyondguo | 3 | [
"bug"
] |
4,142 | Add ObjectFolder 2.0 dataset | ## Adding a Dataset
- **Name:** ObjectFolder 2.0
- **Description:** ObjectFolder 2.0 is a dataset of 1,000 objects in the form of implicit representations. It contains 1,000 Object Files each containing the complete multisensory profile for an object instance.
- **Paper:** [*link to the dataset paper if available*](https://arxiv.org/abs/2204.02389)
- **Data:** https://github.com/rhgao/ObjectFolder
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| OPEN | 2022-04-11T10:57:51 | 2022-10-05T10:30:49 | null | https://github.com/huggingface/datasets/issues/4142 | osanseviero | 1 | [
"dataset request"
] |
4,141 | Why is the dataset not visible under the dataset preview section? | ## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
| CLOSED | 2022-04-11T08:36:42 | 2022-04-11T18:55:32 | 2022-04-11T17:09:49 | https://github.com/huggingface/datasets/issues/4141 | Nid989 | 0 | [
"dataset-viewer"
] |
4,140 | Error loading arxiv data set | ## Describe the bug
A clear and concise description of what the bug is.
I met the error below when loading arxiv dataset via `nlp.load_dataset('scientific_papers', 'arxiv',)`.
```
Traceback (most recent call last):
File "scripts/summarization.py", line 354, in <module>
main(args)
File "scripts/summarization.py", line 306, in main
model.hf_datasets = nlp.load_dataset('scientific_papers', 'arxiv')
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/load.py", line 549, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 463, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 522, in _download_and_prepare
self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 38, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
nlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download', 'https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download']
```
I then tried to ignore verification steps by `ignore_verifications=True` and there is another error.
```
Traceback (most recent call last):
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 537, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 810, in _prepare_split
for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False):
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/datasets/scientific_papers/9e4f2cfe3d8494e9f34a84ce49c3214605b4b52a3d8eb199104430d04c52cc12/scientific_papers.py", line 108, in _generate_examples
with open(path, encoding="utf-8") as f:
NotADirectoryError: [Errno 20] Not a directory: '/home/username/.cache/huggingface/datasets/downloads/c0deae7af7d9c87f25dfadf621f7126f708d7dcac6d353c7564883084a000076/arxiv-dataset/train.txt'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "scripts/summarization.py", line 354, in <module>
main(args)
File "scripts/summarization.py", line 306, in main
model.hf_datasets = nlp.load_dataset('scientific_papers', 'arxiv', ignore_verifications=True)
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/load.py", line 549, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 463, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/opt/conda/envs/longformer/lib/python3.7/site-packages/nlp/builder.py", line 539, in _download_and_prepare
raise OSError("Cannot find data file. " + (self.manual_download_instructions or ""))
OSError: Cannot find data file.
```
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
## 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:
- Platform:
- Python version:
- PyArrow version:
| CLOSED | 2022-04-11T07:06:34 | 2022-04-12T16:24:08 | 2022-04-12T16:24:08 | https://github.com/huggingface/datasets/issues/4140 | yjqiu | 3 | [
"bug"
] |
4,139 | Dataset viewer issue for Winoground | ## Dataset viewer issue for 'Winoground'
**Link:** [*link to the dataset viewer page*](https://huggingface.co/datasets/facebook/winoground/viewer/facebook--winoground/train)
*short description of the issue*
Getting 401, message='Unauthorized'
The dataset is subject to authorization, but I can access the files from the interface, so I assume I'm granted to access it. I'd assume the permission somehow doesn't propagate to the dataset viewer tool.
Am I the one who added this dataset ? No
| CLOSED | 2022-04-11T06:11:41 | 2022-06-21T16:43:58 | 2022-06-21T16:43:58 | https://github.com/huggingface/datasets/issues/4139 | alcinos | 11 | [
"dataset-viewer",
"dataset-viewer-gated"
] |
4,138 | Incorrect Russian filenames encoding after extraction by datasets.DownloadManager.download_and_extract() | ## Dataset viewer issue for 'MalakhovIlya/RuREBus'
**Link:** https://huggingface.co/datasets/MalakhovIlya/RuREBus
**Description**
Using os.walk(topdown=False) in DatasetBuilder causes following error:
Status code: 400
Exception: TypeError
Message: xwalk() got an unexpected keyword argument 'topdown'
Couldn't find where "xwalk" come from. How can I fix this?
Am I the one who added this dataset ? Yes
| CLOSED | 2022-04-11T02:07:13 | 2022-04-19T03:15:46 | 2022-04-16T15:46:29 | https://github.com/huggingface/datasets/issues/4138 | iluvvatar | 5 | [] |
4,134 | ELI5 supporting documents | if i am using dense search to create supporting documents for eli5 how much time it will take bcz i read somewhere that it takes about 18 hrs?? | OPEN | 2022-04-08T23:36:27 | 2022-04-13T13:52:46 | null | https://github.com/huggingface/datasets/issues/4134 | saurabh-0077 | 1 | [
"question"
] |
4,133 | HANS dataset preview broken | ## Dataset viewer issue for '*hans*'
**Link:** [https://huggingface.co/datasets/hans](https://huggingface.co/datasets/hans)
HANS dataset preview is broken with error 400
Am I the one who added this dataset ? No
| CLOSED | 2022-04-08T21:06:15 | 2022-04-13T11:57:34 | 2022-04-13T11:57:34 | https://github.com/huggingface/datasets/issues/4133 | pietrolesci | 3 | [
"streaming"
] |
4,129 | dataset metadata for reproducibility | When pulling a dataset from the hub, it would be useful to have some metadata about the specific dataset and version that is used. The metadata could then be passed to the `Trainer` which could then be saved to a model card. This is useful for people who run many experiments on different versions (commits/branches) of the same dataset.
The dataset could have a list of βsource datasetsβ metadata and ignore what happens to them before arriving in the Trainer (i.e. ignore mapping, filtering, etc.).
Here is a basic representation (made by @lhoestq )
```python
>>> from datasets import load_dataset
>>>
>>> my_dataset = load_dataset(...)["train"]
>>> my_dataset = my_dataset.map(...)
>>>
>>> my_dataset.sources
[HFHubDataset(repo_id=..., revision=..., arguments={...})]
``` | OPEN | 2022-04-08T14:17:28 | 2023-09-29T09:23:56 | null | https://github.com/huggingface/datasets/issues/4129 | nbroad1881 | 1 | [
"enhancement"
] |
4,126 | dataset viewer issue for common_voice | ## Dataset viewer issue for 'common_voice'
**Link:** https://huggingface.co/datasets/common_voice
Server Error
Status code: 400
Exception: TypeError
Message: __init__() got an unexpected keyword argument 'audio_column'
Am I the one who added this dataset ? No
| CLOSED | 2022-04-07T23:34:28 | 2022-04-25T13:42:17 | 2022-04-25T13:42:16 | https://github.com/huggingface/datasets/issues/4126 | laphang | 2 | [
"dataset-viewer",
"audio_column"
] |
4,124 | Image decoding often fails when transforming Image datasets | ## Describe the bug
When transforming/modifying images in an image dataset using the `map` function the PIL images often fail to decode in time for the image transforms, causing errors.
Using a debugger it is easy to see what the problem is, the Image decode invocation does not take place and the resulting image passed around is still raw bytes:
```
[{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00 \x00\x00\x00 \x08\x02\x00\x00\x00\xfc\x18\xed\xa3\x00\x00\x08\x02IDATx\x9cEVIs[\xc7\x11\xeemf\xde\x82\x8d\x80\x08\x89"\xb5V\\\xb6\x94(\xe5\x9f\x90\xca5\x7f$\xa7T\xe5\x9f&9\xd9\x8a\\.\xdb\xa4$J\xa4\x00\x02x\xc0{\xb3t\xe7\x00\xca\x99\xd3\\f\xba\xba\xbf\xa5?|\xfa\xf4\xa2\xeb\xba\xedv\xa3f^\xf8\xd5\x0bY\xb6\x10\xb3\xaaDq\xcd\x83\x87\xdf5\xf3gZ\x1a\x04\x0f\xa0fp\xfa\xe0\xd4\x07?\x9dN\xc4\xb1\x99\xfd\xf2\xcb/\x97\x97\x97H\xa2\xaaf\x16\x82\xaf\xeb\xca{\xbf\xd9l.\xdf\x7f\xfa\xcb_\xff&\x88\x08\x00\x80H\xc0\x80@.;\x0f\x8c@#v\xe3\xe5\xfc\xd1\x9f\xee6q\xbf\xdf\xa6\x14\'\x93\xf1\xc3\xe5\xe3\xd1x\x14c\x8c1\xa5\x1c\x9dsM\xd3\xb4\xed\x08\x89SJ)\xa5\xedv\xbb^\xafNO\x97D\x84Hf ....
```
## Steps to reproduce the bug
```python
from datasets import load_dataset, Dataset
import numpy as np
# seeded NumPy random number generator for reprodducinble results.
rng = np.random.default_rng(seed=0)
test_dataset = load_dataset('cifar100', split="test")
def preprocess_data(dataset):
"""
Helper function to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and
add is_flipped column
Args:
dataset: HuggingFace CIFAR-100 Dataset Object
Returns:
new_dataset: A Dataset object with "img" and "is_flipped" columns only
"""
# remove fine_label and coarse_label columns
new_dataset = dataset.remove_columns(['fine_label', 'coarse_label'])
# add the column for is_flipped
new_dataset = new_dataset.add_column(name="is_flipped", column=np.zeros((len(new_dataset)), dtype=np.uint8))
return new_dataset
def generate_flipped_data(example, p=0.5):
"""
A Dataset mapping function that transforms some of the images up-side-down.
If the probability value (p) is 0.5 approximately half the images will be flipped upside-down
Args:
example: An example from the dataset containing a Python dictionary with "img" and "is_flipped" key-value pair
p: the probability of flipping the image up-side-down, Default 0.5
Returns:
example: A Dataset object
"""
# example['img'] = example['img']
if rng.random() > p: # the flip the image and set is_flipped column to 1
example['img'] = example['img'].transpose(
1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM)
example['is_flipped'] = 1
return example
my_test = preprocess_data(test_dataset)
my_test = my_test.map(generate_flipped_data)
```
## Expected results
The dataset should be transformed without problems.
## Actual results
```
/home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py
Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142)
Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142)
20%|ββ | 1999/10000 [00:00<00:01, 5560.44ex/s]
Traceback (most recent call last):
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2326, in _map_single
writer.write(example)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 441, in write
self.write_examples_on_file()
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__
out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))
File "pyarrow/array.pxi", line 316, in pyarrow.lib.array
File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py", line 55, in <module>
my_test = my_test.map(generate_flipped_data)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1953, in map
return self._map_single(
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 519, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 486, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2360, in _map_single
writer.finalize()
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 522, in finalize
self.write_examples_on_file()
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__
out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))
File "pyarrow/array.pxi", line 316, in pyarrow.lib.array
File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type
Process finished with exit code 1
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux(Fedora 35)
- Python version: 3.10
- PyArrow version: 7.0.0
| CLOSED | 2022-04-07T19:17:25 | 2022-04-13T14:01:16 | 2022-04-13T14:01:16 | https://github.com/huggingface/datasets/issues/4124 | RafayAK | 7 | [
"bug"
] |
4,123 | Building C4 takes forever | ## Describe the bug
C4-en is a 300 GB dataset. However, when I try to download it through the hub it takes over _six hours_ to generate the train/test split from the downloaded files. This is an absurd amount of time and an unnecessary waste of resources.
## Steps to reproduce the bug
```python
c4 = datasets.load("c4", "en")
```
## Expected results
I would like to be able to download pre-split data.
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.13.0-35-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
| CLOSED | 2022-04-07T17:41:30 | 2023-06-26T22:01:29 | 2023-06-26T22:01:29 | https://github.com/huggingface/datasets/issues/4123 | StellaAthena | 1 | [
"bug"
] |
4,122 | medical_dialog zh has very slow _generate_examples | ## Describe the bug
After downloading the files from Google Drive, `load_dataset("medical_dialog", "zh", data_dir="./")` takes an unreasonable amount of time. Generating the train/test split for 33% of the dataset takes over 4.5 hours.
## Steps to reproduce the bug
The easiest way I've found to download files from Google Drive is to use `gdown` and use Google Colab because the download speeds will be very high due to the fact that they are both in Google Cloud.
```python
file_ids = [
"1AnKxGEuzjeQsDHHqL3NqI_aplq2hVL_E",
"1tt7weAT1SZknzRFyLXOT2fizceUUVRXX",
"1A64VBbsQ_z8wZ2LDox586JIyyO6mIwWc",
"1AKntx-ECnrxjB07B6BlVZcFRS4YPTB-J",
"1xUk8AAua_x27bHUr-vNoAuhEAjTxOvsu",
"1ezKTfe7BgqVN5o-8Vdtr9iAF0IueCSjP",
"1tA7bSOxR1RRNqZst8cShzhuNHnayUf7c",
"1pA3bCFA5nZDhsQutqsJcH3d712giFb0S",
"1pTLFMdN1A3ro-KYghk4w4sMz6aGaMOdU",
"1dUSnG0nUPq9TEQyHd6ZWvaxO0OpxVjXD",
"1UfCH05nuWiIPbDZxQzHHGAHyMh8dmPQH",
]
for i in file_ids:
url = f"https://drive.google.com/uc?id={i}"
!gdown $url
from datasets import load_dataset
ds = load_dataset("medical_dialog", "zh", data_dir="./")
```
## Expected results
Faster load time
## Actual results
`Generating train split: 33%: 625519/1921127 [4:31:03<31:39:20, 11.37 examples/s]`
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
@vrindaprabhu , could you take a look at this since you implemented it? I think the `_generate_examples` function might need to be rewritten | CLOSED | 2022-04-07T14:00:51 | 2022-04-08T16:20:51 | 2022-04-08T16:20:51 | https://github.com/huggingface/datasets/issues/4122 | nbroad1881 | 3 | [
"bug"
] |
4,121 | datasets.load_metric can not load a local metirc | ## Describe the bug
No matter how I hard try to tell load_metric that I want to load a local metric file, it still continues to fetch things on the Internet. And unfortunately it says 'ConnectionError: Couldn't reach'. However I can download this file without connectionerror and tell load_metric its local directory. And it comes back where it begins...
## Steps to reproduce the bug
```python
metric = load_metric(path=r'C:\Users\Gare\PycharmProjects\Gare\blue\bleu.py')
ConnectionError: Couldn't reach https://github.com/tensorflow/nmt/raw/master/nmt/scripts/bleu.py
metric = load_metric(path='bleu')
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.12.1/metrics/bleu/bleu.py
metric = load_metric(path='./blue/bleu.py')
ConnectionError: Couldn't reach https://github.com/tensorflow/nmt/raw/master/nmt/scripts/bleu.py
```
## Expected results
I do read the docs [here](https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_metric). There are no other parameters that help function to distinguish from local and online file but path. As what I code above, it should load from local.
## Actual results
> metric = load_metric(path=r'C:\Users\Gare\PycharmProjects\Gare\blue\bleu.py')
> ~\AppData\Local\Temp\ipykernel_19636\1855752034.py in <module>
----> 1 metric = load_metric(path=r'C:\Users\Gare\PycharmProjects\Gare\blue\bleu.py')
D:\Program Files\Anaconda\envs\Gare\lib\site-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)
817 if data_files is None and data_dir is not None:
818 data_files = os.path.join(data_dir, "**")
--> 819
820 self.name = name
821 self.revision = revision
D:\Program Files\Anaconda\envs\Gare\lib\site-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, return_associated_base_path, data_files, **download_kwargs)
639 self,
640 path: str,
--> 641 download_config: Optional[DownloadConfig] = None,
642 download_mode: Optional[DownloadMode] = None,
643 dynamic_modules_path: Optional[str] = None,
D:\Program Files\Anaconda\envs\Gare\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
297 token = hf_api.HfFolder.get_token()
298 if token:
--> 299 headers["authorization"] = f"Bearer {token}"
300 return headers
301
D:\Program Files\Anaconda\envs\Gare\lib\site-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)
604 def _resumable_file_manager():
605 with open(incomplete_path, "a+b") as f:
--> 606 yield f
607
608 temp_file_manager = _resumable_file_manager
ConnectionError: Couldn't reach https://github.com/tensorflow/nmt/raw/master/nmt/scripts/bleu.py
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Windows-10-10.0.22000-SP0
- Python version: 3.7.13
- PyArrow version: 7.0.0
- Pandas version: 1.3.4
Any advice would be appreciated. | CLOSED | 2022-04-07T12:48:56 | 2023-01-18T14:30:46 | 2022-04-07T13:53:27 | https://github.com/huggingface/datasets/issues/4121 | SadGare | 1 | [
"bug"
] |
4,120 | Representing dictionaries (json) objects as features | In the process of adding a new dataset to the hub, I stumbled upon the inability to represent dictionaries that contain different key names, unknown in advance (and may differ between samples), original asked in the [forum](https://discuss.huggingface.co/t/representing-nested-dictionary-with-different-keys/16442).
For instance:
```
sample1 = {"nps": {
"a": {"id": 0, "text": "text1"},
"b": {"id": 1, "text": "text2"},
}}
sample2 = {"nps": {
"a": {"id": 0, "text": "text1"},
"b": {"id": 1, "text": "text2"},
"c": {"id": 2, "text": "text3"},
}}
sample3 = {"nps": {
"a": {"id": 0, "text": "text1"},
"b": {"id": 1, "text": "text2"},
"c": {"id": 2, "text": "text3"},
"d": {"id": 3, "text": "text4"},
}}
```
the `nps` field cannot be represented as a Feature while maintaining its original structure.
@lhoestq suggested to add JSON as a new feature type, which will solve this problem.
It seems like an alternative solution would be to change the original data format, which isn't an optimal solution in my case. Moreover, JSON is a common structure, that will likely to be useful in future datasets as well. | OPEN | 2022-04-07T11:07:41 | 2022-04-07T11:07:41 | null | https://github.com/huggingface/datasets/issues/4120 | yanaiela | 0 | [
"enhancement"
] |
4,118 | Failing CI tests on Windows | ## Describe the bug
Our CI Windows tests are failing from yesterday: https://app.circleci.com/pipelines/github/huggingface/datasets/11092/workflows/9cfdb1dd-0fec-4fe0-8122-5f533192ebdc/jobs/67414
| CLOSED | 2022-04-07T07:36:25 | 2022-04-07T07:57:13 | 2022-04-07T07:57:13 | https://github.com/huggingface/datasets/issues/4118 | albertvillanova | 0 | [
"bug"
] |
4,117 | AttributeError: module 'huggingface_hub' has no attribute 'hf_api' | ## Describe the bug
Could you help me please. I got this following error.
AttributeError: module 'huggingface_hub' has no attribute 'hf_api'
## Steps to reproduce the bug
when I imported the datasets
# Sample code to reproduce the bug
from datasets import list_datasets, load_dataset, list_metrics, load_metric
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: macOS-12.3-x86_64-i386-64bit
- Python version: 3.8.9
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
- Huggingface-hub: 0.5.0
- Transformers: 4.18.0
Thank you in advance. | CLOSED | 2022-04-07T05:52:36 | 2024-05-07T09:24:35 | 2022-04-19T15:36:35 | https://github.com/huggingface/datasets/issues/4117 | arymbe | 13 | [
"bug"
] |
4,115 | ImageFolder add option to ignore some folders like '.ipynb_checkpoints' | **Is your feature request related to a problem? Please describe.**
I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large.
**Describe the solution you'd like**
maybe have an option `ignore` or something .gitignore style
`dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")`
**Describe alternatives you've considered**
Could filter out manually
| CLOSED | 2022-04-06T17:29:43 | 2022-06-01T13:04:16 | 2022-06-01T13:04:16 | https://github.com/huggingface/datasets/issues/4115 | cceyda | 5 | [
"enhancement"
] |
4,114 | Allow downloading just some columns of a dataset | **Is your feature request related to a problem? Please describe.**
Some people are interested in doing label analysis of a CV dataset without downloading all the images. Downloading the whole dataset does not always makes sense for this kind of use case
**Describe the solution you'd like**
Be able to just download some columns of a dataset, such as doing
```python
load_dataset("huggan/wikiart",columns=["artist", "genre"])
```
Although this might make things a bit complicated in terms of local caching of datasets. | OPEN | 2022-04-06T16:38:46 | 2025-02-17T15:10:56 | null | https://github.com/huggingface/datasets/issues/4114 | osanseviero | 14 | [
"enhancement"
] |
4,113 | Multiprocessing with FileLock fails in python 3.9 | On python 3.9, this code hangs:
```python
from multiprocessing import Pool
from filelock import FileLock
def run(i):
print(f"got the lock in multi process [{i}]")
with FileLock("tmp.lock"):
with Pool(2) as pool:
pool.map(run, range(2))
```
This is because the subprocesses try to acquire the lock from the main process for some reason. This is not the case in older versions of python.
This can cause many issues in python 3.9. In particular, we use multiprocessing to fetch data files when you load a dataset (as long as there are >16 data files). Therefore `imagefolder` hangs, and I expect any dataset that needs to download >16 files to hang as well.
Let's see if we can fix this and have a CI that runs on 3.9.
cc @mariosasko @julien-c | CLOSED | 2022-04-06T16:27:09 | 2022-11-28T11:49:14 | 2022-11-28T11:49:14 | https://github.com/huggingface/datasets/issues/4113 | lhoestq | 1 | [
"bug"
] |
4,112 | ImageFolder with Grayscale images dataset | Hi, I'm facing a problem with a grayscale images dataset I have uploaded [here](https://huggingface.co/datasets/ChainYo/rvl-cdip) (RVL-CDIP)
I'm getting an error while I want to use images for training a model with PyTorch DataLoader. Here is the full traceback:
```bash
AttributeError: Caught AttributeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1765, in __getitem__
return self._getitem(
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1750, in _getitem
formatted_output = format_table(
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 532, in format_table
return formatter(pa_table, query_type=query_type)
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 281, in __call__
return self.format_row(pa_table)
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 58, in format_row
return self.recursive_tensorize(row)
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 54, in recursive_tensorize
return map_nested(self._recursive_tensorize, data_struct, map_list=False)
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 314, in map_nested
mapped = [
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 315, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in _single_map_nested
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in <dictcomp>
return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 251, in _single_map_nested
return function(data_struct)
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 51, in _recursive_tensorize
return self._tensorize(data_struct)
File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 38, in _tensorize
if np.issubdtype(value.dtype, np.integer):
AttributeError: 'bytes' object has no attribute 'dtype'
```
I don't really understand why the image is still a bytes object while I used transformations on it. Here the code I used to upload the dataset (and it worked well):
```python
train_dataset = load_dataset("imagefolder", data_dir="data/train")
train_dataset = train_dataset["train"]
test_dataset = load_dataset("imagefolder", data_dir="data/test")
test_dataset = test_dataset["train"]
val_dataset = load_dataset("imagefolder", data_dir="data/val")
val_dataset = val_dataset["train"]
dataset = DatasetDict({
"train": train_dataset,
"val": val_dataset,
"test": test_dataset
})
dataset.push_to_hub("ChainYo/rvl-cdip")
```
Now here is the code I am using to get the dataset and prepare it for training:
```python
img_size = 512
batch_size = 128
normalize = [(0.5), (0.5)]
data_dir = "ChainYo/rvl-cdip"
dataset = load_dataset(data_dir, split="train")
transforms = transforms.Compose([
transforms.Resize(img_size),
transforms.CenterCrop(img_size),
transforms.ToTensor(),
transforms.Normalize(*normalize)
])
transformed_dataset = dataset.with_transform(transforms)
transformed_dataset.set_format(type="torch", device="cuda")
train_dataloader = torch.utils.data.DataLoader(
transformed_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True
)
```
But this get me the error above. I don't understand why it's doing this kind of weird thing?
Do I need to map something on the dataset? Something like this:
```python
labels = dataset.features["label"].names
num_labels = dataset.features["label"].num_classes
def preprocess_data(examples):
images = [ex.convert("RGB") for ex in examples["image"]]
labels = [ex for ex in examples["label"]]
return {"images": images, "labels": labels}
features = Features({
"images": Image(decode=True, id=None),
"labels": ClassLabel(num_classes=num_labels, names=labels)
})
decoded_dataset = dataset.map(preprocess_data, remove_columns=dataset.column_names, features=features, batched=True, batch_size=100)
```
| CLOSED | 2022-04-06T15:10:00 | 2022-04-22T10:21:53 | 2022-04-22T10:21:52 | https://github.com/huggingface/datasets/issues/4112 | chainyo | 3 | [] |
4,107 | Unable to view the dataset and loading the same dataset throws the error - ArrowInvalid: Exceeded maximum rows | ## Dataset viewer issue - -ArrowInvalid: Exceeded maximum rows
**Link:** *https://huggingface.co/datasets/Pavithree/explainLikeImFive*
*This is the subset of original eli5 dataset https://huggingface.co/datasets/vblagoje/lfqa. I just filtered the data samples which belongs to one particular subreddit thread. However, the dataset preview for train split returns the below mentioned error:
Status code: 400
Exception: ArrowInvalid
Message: Exceeded maximum rows
When I try to load the same dataset it returns ArrowInvalid: Exceeded maximum rows error*
Am I the one who added this dataset ? Yes
| CLOSED | 2022-04-06T11:37:15 | 2022-04-08T07:13:07 | 2022-04-06T14:39:55 | https://github.com/huggingface/datasets/issues/4107 | Pavithree | 5 | [
"bug"
] |
4,105 | push to hub fails with huggingface-hub 0.5.0 | ## Describe the bug
`ds.push_to_hub` is failing when updating a dataset in the form "org_id/repo_id"
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("rubrix/news_test")
ds.push_to_hub("<your-user>/news_test", token="<your-token>")
```
## Expected results
The dataset is successfully uploaded
## Actual results
An error validation is raised:
```bash
if repo_id and (name or organization):
> raise ValueError(
"Only pass `repo_id` and leave deprecated `name` and "
"`organization` to be None."
E ValueError: Only pass `repo_id` and leave deprecated `name` and `organization` to be None.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.1
- `huggingface-hub`: 0.5
- Platform: macOS
- Python version: 3.8.12
- PyArrow version: 6.0.0
cc @adrinjalali
| CLOSED | 2022-04-06T08:59:57 | 2022-04-13T14:30:47 | 2022-04-13T14:30:47 | https://github.com/huggingface/datasets/issues/4105 | frascuchon | 5 | [
"bug"
] |
4,104 | Add time series data - stock market | ## Adding a Time Series Dataset
- **Name:** 2min ticker data for stock market
- **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image
- **Data:** Collected by myself from investing.com
- **Motivation:** Test applicability of transformer based model on stock market / time series problem
 | OPEN | 2022-04-06T05:46:58 | 2024-07-21T16:54:30 | null | https://github.com/huggingface/datasets/issues/4104 | rozeappletree | 10 | [
"dataset request"
] |
4,101 | How can I download only the train and test split for full numbers using load_dataset()? | How can I download only the train and test split for full numbers using load_dataset()?
I do not need the extra split and it will take 40 mins just to download in Colab. I have very short time in hand. Please help. | OPEN | 2022-04-05T16:00:15 | 2022-04-06T13:09:01 | null | https://github.com/huggingface/datasets/issues/4101 | Nakkhatra | 1 | [
"enhancement"
] |
4,099 | UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128) | ## Describe the bug
Error "UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)" is thrown when downloading dataset.
## Steps to reproduce the bug
```python
from datasets import load_dataset
datasets = load_dataset("nielsr/XFUN", "xfun.ja")
```
## Expected results
Dataset should be downloaded without exceptions
## Actual results
Stack trace (for the second-time execution):
Downloading and preparing dataset xfun/xfun.ja to /root/.cache/huggingface/datasets/nielsr___xfun/xfun.ja/0.0.0/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477...
Downloading data files: 100%
2/2 [00:00<00:00, 88.48it/s]
Extracting data files: 100%
2/2 [00:00<00:00, 79.60it/s]
UnicodeDecodeErrorTraceback (most recent call last)
<ipython-input-31-79c26bd1109c> in <module>
1 from datasets import load_dataset
2
----> 3 datasets = load_dataset("nielsr/XFUN", "xfun.ja")
/usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 )
605
--> 606 # By default, return all splits
607 if split is None:
608 split = {s: s for s in self.info.splits}
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
692 Args:
693 split: `datasets.Split` which subset of the data to read.
--> 694
695 Returns:
696 `Dataset`
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys)
/usr/local/lib/python3.6/dist-packages/tqdm/notebook.py in __iter__(self)
252 if not self.disable:
253 self.display(check_delay=False)
--> 254
255 def __iter__(self):
256 try:
/usr/local/lib/python3.6/dist-packages/tqdm/std.py in __iter__(self)
1183 for obj in iterable:
1184 yield obj
-> 1185 return
1186
1187 mininterval = self.mininterval
~/.cache/huggingface/modules/datasets_modules/datasets/nielsr--XFUN/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477/XFUN.py in _generate_examples(self, filepaths)
140 logger.info("Generating examples from = %s", filepath)
141 with open(filepath[0], "r") as f:
--> 142 data = json.load(f)
143
144 for doc in data["documents"]:
/usr/lib/python3.6/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
294
295 """
--> 296 return loads(fp.read(),
297 cls=cls, object_hook=object_hook,
298 parse_float=parse_float, parse_int=parse_int,
/usr/lib/python3.6/encodings/ascii.py in decode(self, input, final)
24 class IncrementalDecoder(codecs.IncrementalDecoder):
25 def decode(self, input, final=False):
---> 26 return codecs.ascii_decode(input, self.errors)[0]
27
28 class StreamWriter(Codec,codecs.StreamWriter):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0 (but reproduced with many previous versions)
- Platform: Docker: Linux da5b74136d6b 5.3.0-1031-azure #32~18.04.1-Ubuntu SMP Mon Jun 22 15:27:23 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux ; Base docker image is : huggingface/transformers-pytorch-cpu
- Python version: 3.6.9
- PyArrow version: 6.0.1
| CLOSED | 2022-04-05T14:42:38 | 2022-04-06T06:37:44 | 2022-04-06T06:35:54 | https://github.com/huggingface/datasets/issues/4099 | andreybond | 3 | [
"bug"
] |
4,096 | Add support for streaming Zarr stores for hosted datasets | **Is your feature request related to a problem? Please describe.**
Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily.
**Describe the solution you'd like**
A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec.
**Describe alternatives you've considered**
Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users.
Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations.
| CLOSED | 2022-04-05T13:38:32 | 2023-12-07T09:01:49 | 2022-04-21T08:12:58 | https://github.com/huggingface/datasets/issues/4096 | jacobbieker | 11 | [
"enhancement"
] |
4,094 | Helo Mayfrends | ## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| CLOSED | 2022-04-05T02:42:57 | 2022-04-05T07:16:42 | 2022-04-05T07:16:42 | https://github.com/huggingface/datasets/issues/4094 | Budigming | 0 | [
"dataset request"
] |
4,093 | elena-soare/crawled-ecommerce: missing dataset | elena-soare/crawled-ecommerce
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
| CLOSED | 2022-04-05T02:25:19 | 2022-04-12T09:34:53 | 2022-04-12T09:34:53 | https://github.com/huggingface/datasets/issues/4093 | seevaratnam | 3 | [
"dataset-viewer"
] |
4,091 | Build a Dataset One Example at a Time Without Loading All Data Into Memory | **Is your feature request related to a problem? Please describe.**
I have a very large dataset stored on disk in a custom format. I have some custom code that reads one data example at a time and yields it in the form of a dictionary. I want to construct a `Dataset` with all examples, and then save it to disk. I later want to load the saved `Dataset` and use it like any other HuggingFace dataset, get splits, wrap it in a PyTorch `DataLoader`, etc. **Crucially, I do not ever want to materialize all the data in memory while building the dataset.**
**Describe the solution you'd like**
I would like to be able to do something like the following. Notice how each example is read and then immediately added to the dataset. We do not store all the data in memory when constructing the `Dataset`. If it helps, I will know the schema of my dataset before hand.
```
# Initialize an empty Dataset, possibly from a known schema.
dataset = Dataset()
# Read in examples one by one using a custom data streamer.
for example_dict in custom_example_dict_streamer("/path/to/raw/data"):
# Add this example to the dict but do not store it in memory.
dataset.add_item(example_dict)
# Save the final dataset to disk as an Arrow-backed dataset.
dataset.save_to_disk("/path/to/dataset")
...
# I'd like to be able to later `load_from_disk` and use the loaded Dataset
# just like any other memory-mapped pyarrow-backed HuggingFace dataset...
loaded_dataset = Dataset.load_from_disk("/path/to/dataset")
loaded_dataset.set_format(type="torch", columnns=["foo", "bar", "baz"])
dataloader = torch.utils.data.DataLoader(loaded_dataset, batch_size=16)
...
```
**Describe alternatives you've considered**
I initially tried to read all the data into memory, construct a Pandas DataFrame and then call `Dataset.from_pandas`. This would not work as it requires storing all the data in memory. It seems that there is an `add_item` method already -- I tried to implement something like the desired API written above, but I've not been able to initialize an empty `Dataset` (this seems to require several layers of constructing `datasets.table.Table` which requires constructing a `pyarrow.lib.Table`, etc). I also considered writing my data to multiple sharded CSV files or JSON files and then using `from_csv` or `from_json`. I'd prefer not to do this because (1) I'd prefer to avoid the intermediate step of creating these temp CSV/JSON files and (2) I'm not sure if `from_csv` and `from_json` use memory-mapping.
Do you have any suggestions on how I'd be able to achieve this use case? Does something already exist to support this? Thank you very much in advance! | CLOSED | 2022-04-04T16:19:24 | 2022-04-20T14:31:00 | 2022-04-20T14:31:00 | https://github.com/huggingface/datasets/issues/4091 | aravind-tonita | 2 | [
"enhancement"
] |
4,086 | Dataset viewer issue for McGill-NLP/feedbackQA | ## Dataset viewer issue for '*McGill-NLP/feedbackQA*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/McGill-NLP/feedbackQA)*
*short description of the issue*
The dataset can be loaded correctly with `load_dataset` but the preview doesn't work. Error message:
```
Status code: 400
Exception: Status400Error
Message: Not found. Maybe the cache is missing, or maybe the dataset does not exist.
```
Am I the one who added this dataset ? Yes
| CLOSED | 2022-04-04T07:27:20 | 2022-04-04T22:29:53 | 2022-04-04T08:01:45 | https://github.com/huggingface/datasets/issues/4086 | cslizc | 2 | [
"dataset-viewer"
] |
4,085 | datasets.set_progress_bar_enabled(False) not working in datasets v2 | ## Describe the bug
datasets.set_progress_bar_enabled(False) not working in datasets v2
## Steps to reproduce the bug
```python
datasets.set_progress_bar_enabled(False)
```
## Expected results
datasets not using any progress bar
## Actual results
AttributeError: module 'datasets' has no attribute 'set_progress_bar_enabled
## Environment info
datasets version 2
| CLOSED | 2022-04-02T12:40:10 | 2022-09-17T02:18:03 | 2022-04-04T06:44:34 | https://github.com/huggingface/datasets/issues/4085 | virilo | 3 | [
"bug"
] |
4,084 | Errors in `Train with Datasets` Tensorflow code section on Huggingface.co | ## Describe the bug
Hi
### Error 1
Running the Tensforlow code on [Huggingface](https://huggingface.co/docs/datasets/use_dataset) gives a TypeError: __init__() got an unexpected keyword argument 'return_tensors'
### Error 2
`DataCollatorWithPadding` isn't imported
## Steps to reproduce the bug
```python
import tensorflow as tf
from datasets import load_dataset
from transformers import AutoTokenizer
dataset = load_dataset('glue', 'mrpc', split='train')
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
dataset = dataset.map(lambda e: tokenizer(e['sentence1'], truncation=True, padding='max_length'), batched=True)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
train_dataset = dataset["train"].to_tf_dataset(
columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'],
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
```
This is the same code on Huggingface.co
## Actual results
TypeError: __init__() got an unexpected keyword argument 'return_tensors'
## Environment info
- `datasets` version: 2.0.0
- Platform: Windows-10-10.0.19044-SP0
- Python version: 3.9.7
- PyArrow version: 6.0.0
- Pandas version: 1.4.1
> | CLOSED | 2022-04-01T17:02:47 | 2022-04-04T07:24:37 | 2022-04-04T07:21:31 | https://github.com/huggingface/datasets/issues/4084 | blackhat-coder | 1 | [
"bug"
] |
4,080 | NonMatchingChecksumError for downloading conll2012_ontonotesv5 dataset | ## Steps to reproduce the bug
```python
datasets.load_dataset("conll2012_ontonotesv5", "english_v12")
```
## Actual results
```
Downloading builder script: 32.2kB [00:00, 9.72MB/s]
Downloading metadata: 20.0kB [00:00, 10.4MB/s]
Downloading and preparing dataset conll2012_ontonotesv5/english_v12 (download: 174.83 MiB, generated: 204.29 MiB, post-processed: Unknown size
, total: 379.12 MiB) to ...
Traceback (most recent call last): [315/390]
File "/home/yisiang/lgtn/conll2012/run.py", line 86, in <module>
train()
File "/home/yisiang/lgtn/conll2012/run.py", line 65, in train
trainer.fit(model, datamodule=dm)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit
self._call_and_handle_interrupt(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_inte
rrupt
return trainer_fn(*args, **kwargs)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1131, in _run
self._data_connector.prepare_data()
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 154, in pre
pare_data
self.trainer.datamodule.prepare_data()
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py", line 474, in wrapped_fn
fn(*args, **kwargs)
File "/home/yisiang/lgtn/_abstract_task/data.py", line 43, in prepare_data
raw_dsets = datasets.load_dataset(**load_dataset_kwargs)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/load.py", line 1687, in load_dataset
builder_instance.download_and_prepare(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 605, in download_and_prepare
self._download_and_prepare(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 1104, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 676, in _download_and_prepare
verify_checksums(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0 | CLOSED | 2022-04-01T11:34:28 | 2022-04-01T13:59:10 | 2022-04-01T13:59:10 | https://github.com/huggingface/datasets/issues/4080 | richarddwang | 1 | [
"duplicate",
"dataset bug"
] |
4,077 | ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file. | ## Describe the bug
When uploading a relatively large image dataset of > 1GB, reloading doesn't work for me, even though pushing to the hub went just fine.
Basically, I do:
```
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_files="path_to_my_files")
dataset.push_to_hub("dataset_name") # works fine, no errors
reloaded_dataset = load_dataset("dataset_name")
```
and it returns:
```
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
I created a Colab notebook to reproduce my error: https://colab.research.google.com/drive/141LJCcM2XyqprPY83nIQ-Zk3BbxWeahq?usp=sharing
| CLOSED | 2022-04-01T08:49:13 | 2022-04-01T16:16:19 | 2022-04-01T16:16:19 | https://github.com/huggingface/datasets/issues/4077 | NielsRogge | 0 | [
"bug"
] |
4,075 | Add CCAgT dataset | ## Adding a Dataset
- **Name:** CCAgT dataset: Images of Cervical Cells with AgNOR Stain Technique
- **Description:** The dataset contains 2540 images (1600x1200 where each pixel is 0.111ΞΌmΓ0.111ΞΌm) from three different slides, having at least one nucleus per image. These images are from fields belonging to a sample cervical slide, colored with silver-stained, a method known as Argyrophilic Nucleolar Organizer Regions (AgNOR).
- **Paper:** https://doi.org/10.1109/cbms49503.2020.00110
- **Data:** https://arquivos.ufsc.br/d/373be2177a33426a9e6c/ or https://drive.google.com/drive/u/4/folders/1TBpYCv6S1ydASLauSzcsvO7Wc5O-WUw0
- **Motivation:** This is a unique dataset (because of the stain), for a major health problem, cervical cancer, with real data.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Hi, this is a public version of the dataset that I have been working on, soon we will have another version of this dataset. But until this new version goes out, I thought I would add this dataset here, if it makes sense for the repository. You can assign the task to me if possible | CLOSED | 2022-03-31T18:20:28 | 2022-07-06T19:03:42 | 2022-07-06T19:03:42 | https://github.com/huggingface/datasets/issues/4075 | johnnv1 | 4 | [
"dataset request",
"vision"
] |
4,074 | Error in google/xtreme_s dataset card | **Link:** https://huggingface.co/datasets/google/xtreme_s
Not a big deal but Hungarian is considered an Eastern European language, together with Serbian, Slovak, Slovenian (all correctly categorized; Slovenia is mostly to the West of Hungary, by the way).
| CLOSED | 2022-03-31T18:07:45 | 2022-04-01T08:12:56 | 2022-04-01T08:12:56 | https://github.com/huggingface/datasets/issues/4074 | wranai | 1 | [
"documentation",
"dataset bug"
] |
4,071 | Loading issue for xuyeliu/notebookCDG dataset | ## Dataset viewer issue for '*xuyeliu/notebookCDG*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/xuyeliu/notebookCDG)*
*Couldn't load the xuyeliu/notebookCDG with provided scripts: *
```
from datasets import load_dataset
dataset = load_dataset("xuyeliu/notebookCDG/dataset_notebook.pkl")
```
I get an error message as follows:
FileNotFoundError: Couldn't find a dataset script at /home/code_documentation/code/xuyeliu/notebookCDG/notebookCDG.py or any data file in the same directory. Couldn't find 'xuyeliu/notebookCDG' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['**train*'] in dataset repository xuyeliu/notebookCDG with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
Am I the one who added this dataset ? No
| CLOSED | 2022-03-31T06:36:29 | 2022-03-31T08:17:01 | 2022-03-31T08:16:16 | https://github.com/huggingface/datasets/issues/4071 | Jun-jie-Huang | 1 | [
"dataset bug"
] |
4,062 | Loading mozilla-foundation/common_voice_7_0 dataset failed | ## Describe the bug
I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN")
```
## Expected results
load `mozilla-foundation/common_voice_7_0` dataset succesfully
## Actual results
```
JSONDecodeError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
909 try:
--> 910 return complexjson.loads(self.text, **kwargs)
911 except JSONDecodeError as e:
/opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)
524 and not use_decimal and not kw):
--> 525 return _default_decoder.decode(s)
526 if cls is None:
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3)
369 s = str(s, self.encoding)
--> 370 obj, end = self.raw_decode(s)
371 end = _w(s, end).end()
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3)
399 idx += 3
--> 400 return self.scan_once(s, idx=_w(s, idx).end())
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
JSONDecodeError Traceback (most recent call last)
/tmp/ipykernel_358/370980805.py in <module>
1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split
----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True)
/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, revision, use_auth_token, task, streaming, **config_kwargs)
1690 ignore_verifications=ignore_verifications,
1691 try_from_hf_gcs=try_from_hf_gcs,
-> 1692 use_auth_token=use_auth_token,
1693 )
1694
/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)
604 if not downloaded_from_gcs:
605 self._download_and_prepare(
--> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
607 )
608 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1102
1103 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1105
1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
670 split_dict = SplitDict(dataset_name=self.name)
671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
673
674 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager)
151
152 self._log_download(self.config.name, bundle_version, hf_auth_token)
--> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
154
155 if self.config.version < datasets.Version("5.0.0"):
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template)
130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
--> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
133 return response["url"]
134
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
915 raise RequestsJSONDecodeError(e.message)
916 else:
--> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
918
919 @property
JSONDecodeError: [Errno Expecting value] Not Found: 0
```
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5
| CLOSED | 2022-03-30T11:39:41 | 2024-06-09T12:12:46 | 2022-03-31T08:18:04 | https://github.com/huggingface/datasets/issues/4062 | aapot | 10 | [
"dataset bug"
] |
4,061 | Loading cnn_dailymail dataset failed | ## Describe the bug
I wanted to load cnn_dailymail dataset from huggingface datasets on jupyter lab, but I am getting an error ` NotADirectoryError:[Errno20] Not a directory ` while loading it.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0')
```
## Expected results
load `cnn_dailymail` dataset succesfully
## Actual results
failed to load and get error
> NotADirectoryError: [Errno 20] Not a directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` 1.8.0:
- Platform: Ubuntu-20.04
- Python version: 3.9.10
- PyArrow version: 3.0.0
| CLOSED | 2022-03-30T11:29:02 | 2022-03-30T13:36:14 | 2022-03-30T13:36:14 | https://github.com/huggingface/datasets/issues/4061 | Arij-Aladel | 1 | [
"bug",
"duplicate"
] |
4,057 | `load_dataset` consumes too much memory for audio + tar archives |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | CLOSED | 2022-03-29T21:38:55 | 2022-08-16T10:22:55 | 2022-08-16T10:22:55 | https://github.com/huggingface/datasets/issues/4057 | JFCeron | 18 | [
"bug"
] |
4,056 | Unexpected behavior of _TempDirWithCustomCleanup | ## Describe the bug
This is not 100% a bug in `datasets`, but behavior that surprised me and I think this could be made more robust on the `datasets`side.
When using `datasets.disable_caching()`, cache files are written to a temporary directory. This directory should be based on the environment variable TMPDIR. I want to set TMPDIR at runtime using os.ENVIRON["TMPDIR"] = something, but depending on other imported modules this can fail to take effect.
## Steps to reproduce the bug
`_TempDirWithCustomCleanup` relies on `tempfile` to generate a path to a temporary directory. However, `tempfile` generates the path only once. This can be a problem when trying to set TMPDIR at runtime whenever other code imports `tempfile` first and does something unexpected.
For example (after too much trial and error) I found out that a different part of the code base I work with defines a class `PatchedDataCollatorForLanguageModeling(transformers.DataCollatorForLanguageModeling)` based on a `transformers` class. This import is enough to trigger `tempfile` to generate `tempfile` to generate a temporary path and leading to the wrong path being cached in `tempfile.tempdir`.
## Suggestion:
I could file this also as bug with `transformers`, but I think fixing this on the datasets would be much more robust:
Datasets could recompute the temporary path once (technically possible via `tempfile._get_default_tempdir` or resetting
the global variable `tempfile.tmpdir` to None) before setting its own global `_TEMP_DIR_FOR_TEMP_CACHE_FILES`.
| OPEN | 2022-03-29T16:58:22 | 2022-03-30T15:08:04 | null | https://github.com/huggingface/datasets/issues/4056 | JonasGeiping | 2 | [
"bug"
] |
4,053 | Modify datatype from `int32` to `float` for pearsonr, spearmanr. | **Is your feature request related to a problem? Please describe.**
- Now [Pearsonr](https://github.com/huggingface/datasets/blob/master/metrics/pearsonr/pearsonr.py) and [Spearmanr](https://github.com/huggingface/datasets/blob/master/metrics/spearmanr/spearmanr.py) both get input data as 'int32'.
**Describe the solution you'd like**
- Considering that those metrics are widely used for the STS task(labels are in 'float' data type),
it would be better to modify datatype from 'int32' to 'float' for getting exact values of similarity. | CLOSED | 2022-03-29T08:27:41 | 2022-03-29T14:02:20 | 2022-03-29T14:02:20 | https://github.com/huggingface/datasets/issues/4053 | woodywarhol9 | 1 | [
"enhancement"
] |
4,052 | metric = metric_cls( TypeError: 'NoneType' object is not callable | Hi, friend. I meet a problem.
When I run the code:
`metric = load_metric('glue', 'rte')`
There is a problem raising:
`metric = metric_cls(
TypeError: 'NoneType' object is not callable `
I don't know why. Thanks for your help!
| CLOSED | 2022-03-29T07:43:08 | 2022-03-29T14:06:01 | 2022-03-29T14:06:01 | https://github.com/huggingface/datasets/issues/4052 | klyuhang9 | 1 | [] |
4,051 | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py | Hi, I meet a problem.
When I run the code:
`dataset = load_dataset('glue','sst2')`
There is a issue raising:
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
I don't know why; it is ok when I use Google Chrome to view this url.
Thanks for your help! | CLOSED | 2022-03-29T07:00:31 | 2022-05-08T07:27:32 | 2022-03-29T08:29:25 | https://github.com/huggingface/datasets/issues/4051 | klyuhang9 | 5 | [] |
4,048 | Split size error on `amazon_us_reviews` / `PC_v1_00` dataset | ## Describe the bug
When downloading this subset as of 3-28-2022 you will encounter a split size error after the dataset is extracted. The extracted dataset has roughly ~6m rows while the split expects <1m.
Upon digging a little deeper, I downloaded the raw files from `https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_PC_v1_00.tsv.gz` and extracted them. A line count via `wc -l` confirms the ~6m number that we see and the data looks valid at a glance (I did not check for duplicate rows). My guess is this file has either been updated in place or there is a bug in the dataset metadata.
Happy to submit a PR and fix this up if turns out to be a metadata issue but wanted to get some other :eyes: on it first.
## Steps to reproduce the bug
```python
load_dataset('amazon_us_reviews', 'PC_v1_00')
```
## Expected results
Dataset is downloaded and extracted successfully.
## Actual results
An split size exception is thrown.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
| CLOSED | 2022-03-28T18:12:04 | 2022-04-08T12:29:30 | 2022-04-08T12:29:30 | https://github.com/huggingface/datasets/issues/4048 | trentonstrong | 3 | [
"bug",
"good first issue"
] |
4,047 | Dataset.unique(column: str) -> ArrowNotImplementedError | ## Describe the bug
I'm trying to use `unique()` function, but it fails
## Steps to reproduce the bug
1. Get dataset
2. Call `unique`
3. Error
# Sample code to reproduce the bug
```python
!pip show datasets
from datasets import load_dataset
dataset = load_dataset('wikiann', 'en')
dataset['train'].column_names
dataset['train'].unique(dataset['train'].column_names[0])
```
## Expected results
It would be nice to actually see unique items
## Actual results
Error:
```python
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
[<ipython-input-10-5e0de07ed42c>](https://s0qyv2vjaji-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20220324-060046-RC00_436956229#) in <module>()
6
7 dataset['train'].column_names
----> 8 dataset['train'].unique(dataset['train'].column_names[0])
5 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: Function unique has no kernel matching input types (array[list<item: string>])
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Google Collab
- Python version: 3.7.13
- PyArrow version: 6.0.1
| CLOSED | 2022-03-28T17:59:32 | 2022-04-01T18:24:57 | 2022-04-01T18:24:57 | https://github.com/huggingface/datasets/issues/4047 | orkenstein | 3 | [
"bug"
] |
4,044 | CLI dummy data generation is broken | ## Describe the bug
We get a TypeError when running CLI dummy data generation:
```shell
datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate
```
gives:
```
File ".../huggingface/datasets/src/datasets/commands/dummy_data.py", line 361, in _autogenerate_dummy_data
dataset_builder._prepare_split(split_generator)
TypeError: _prepare_split() missing 1 required positional argument: 'check_duplicate_keys'
```
| CLOSED | 2022-03-28T16:07:37 | 2022-03-31T14:59:06 | 2022-03-31T14:59:06 | https://github.com/huggingface/datasets/issues/4044 | albertvillanova | 0 | [
"bug"
] |
4,041 | Add support for IIIF in datasets | This is a feature request for support for IIIF in `datasets`. Apologies for the long issue. I have also used a different format to the usual feature request since I think that makes more sense but happy to use the standard template if preferred.
## What is [IIIF](https://iiif.io/)?
IIIF (International Image Interoperability Framework)
> is a set of open standards for delivering high-quality, attributed digital objects online at scale. Itβs also an international community developing and implementing the IIIF APIs. IIIF is backed by a consortium of leading cultural institutions.
The tl;dr is that IIIF provides various specifications for implementing useful functionality for:
- Institutions to make available images for various use cases
- Users to have a consistent way of interacting/requesting these images
- For developers to have a common standard for developing tools for working with IIIF images that will work across all institutions that implement a particular IIIF standard (for example the image viewer for the BNF can also work for the Library of Congress if they both use IIIF).
Some institutions that various levels of support IIF include: The British Library, Internet Archive, Library of Congress, Wikidata. There are also many smaller institutions that have IIIF support. An incomplete list can be found here: https://iiif.io/guides/finding_resources/
## IIIF APIs
IIIF consists of a number of APIs which could be integrated with datasets. I think the most obvious candidate for inclusion would be the [Image API](https://iiif.io/api/image/3.0/)
### IIIF Image API
The Image API https://iiif.io/api/image/3.0/ is likely the most suitable first candidate for integration with datasets. The Image API offers a consistent protocol for requesting images via a URL:
```{scheme}://{server}{/prefix}/{identifier}/{region}/{size}/{rotation}/{quality}.{format}```
A concrete example of this:
```https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/full/0/default.jpg```
As you can see the scheme offers a number of options that can be specified in the URL, for example, size. Using the example URL we return:
We can change the size to request a size of 250 by 250, this is done by changing the size from `full` to `250,250` i.e. switching the URL to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/250,250/0/default.jpg`
We can also request the image with max width 250, max height 250 whilst maintaining the aspect ratio using `!w,h`. i.e. change the url to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg`
A full overview of the options for size can be found here: https://iiif.io/api/image/3.0/#42-size
## Why would/could this be useful for datasets?
There are a few reasons why support for the IIIF Image API could be useful. Broadly the ability to have more control over how an image is returned from a server is useful for many ML workflows:
- images can be requested in the right size, this prevents having to download/stream large images when the actual desired size is much smaller
- can select a subset of an image: it is possible to select a sub-region of an image, this could be useful for example when you already have a bounding box for a subset of an image and then want to use this subset of an image for another task. For example, https://github.com/Living-with-machines/nnanno uses IIIF to request parts of a newspaper image that have been detected as 'photograph', 'illustration' etc for downstream use.
- options for quality, rotation, the format can all be encoded in the URL request.
These may become particularly useful when pre-training models on large image datasets where the cost of downloading images with 1600 pixel width when you actually want 240 has a larger impact.
## What could this look like in datasets?
I think there are various ways in which support for IIIF could potentially be included in `datasets`. These suggestions aren't fully fleshed out but hopefully, give a sense of possible approaches that match existing `datasets` methods in their approach.
### Use through datasets scripts
Loading images via URL is already supported. There are a few possible 'extras' that could be included when using IIIF. One option is to leverage the IIIF protocol in datasets scripts, i.e. the dataset script can expose the IIIF options via the dataset script:
```python
ds = load_dataset("iiif_dataset", image_size="250,250", fmt="jpg")
```
This is already possible. The approach to parsing the IIIF URLs would be left to the person creating the dataset script.
### Support through dataset scripts (with some datasets support)
This is similar to the above but `datasets` would offer some way of saying this is a iiif URL and then expose the options associated with IIIF images automatically. i.e. if you did something like:
```python
features = {"label": ClassLabel(names=['dog','cat']),
"url": datasets.IIIFURL()}
```
inside your loading script, you would automatically have exposed `size`, `fmt` etc. options when loading the dataset.
### Other possible integrations
Some other possible pseudocode ways that a user could interact with IIIF URLs:
The ability to cast to an `IIIFImage` feature type:
```
ds.cast_column('url', IIIFImage, download=False)
```
The ability to specify some options associated with IIIF urls.
```
ds = ds.set_iiif_options(column='url', size="250,250")
```
I think all of these would rely on having an `IIIFImage` feature type - this would be a little bit of a Frankenstein between a `string` and `datasets.Image`. I think most of the actual image behaviour would be exactly the same as `datasets.Image`, the difference would be that the underlying URL could be modified in various ways.
## prerequisite requirements
There are a few pre-requisites that I can anticipate. This doesn't cover a full implementation of IIIF support which would have different requirements depending on the approach taken to implementing IIIF. Some of these features would be useful independently of adding IIIF support:
### support for handling failed images loaded via a URL (or a specific IIIFImage feature).
Working with images via web requests will inevitably return the odd failed request. If these images are then requests and don't return it would be useful to have a `None` returned instead of an error. For example, when using `push_to_hub` `datasets` will try and include the image but currently fails with bad URLs.
```python
from datasets import Dataset
import datasets
urls = ['https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg']*3
urls.append("badurl.com/image.jpg")
data = {"url":urls}
ds = Dataset.from_dict(data)
ds = ds.cast_column('url', datasets.Image())
ds[3]['url']
```
returns a `FileNotFoundError`, for streaming large datasets of images using their URLs it could be useful to have `None` returned instead. This has implications for the actual training loop i.e. you now need to somehow skip those examples because of this it might not be desirable to support this.
### Caching support
Since IIIF requests images via a URL it would be great to have a way of not requesting the images multiple times. This is tracked in https://github.com/huggingface/datasets/issues/3142 and I think this would also be very desirable to have here particularly as one of the primary use cases of IIIF may be to do unsupervised pre-training on large datasets of IIIF URLs.
### Support for Parsing IIIF URLs
This gets closer to the actual implementation. Here the requirement would be some way for `datasets` to parse a URL that the users specify is an IIIF URL. An example of a Python library that does this: https://github.com/Princeton-CDH/piffle. I also have a rough version that uses `dataclasses` which I can share.
## Why it might not be worthwhile/suitable for datasets
There are some reasons that this might not be worth implementing:
- currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models.
- It may end up being better to leave this to the user. It would for example be possible for someone to write map functions to change an IIIF URL to the correct size etc. Adding direct support for IIIF in datasets may potentially not be worth the trouble.
- The impact of different approaches to doing image scaling can impact the downstream model's performance, see: https://twitter.com/wightmanr/status/1479528581466243073?s=20. Since different IIIF image servers may implement different approaches to resizing images this could have a downstream impact on model performance. think this is something that could be flagged to the end-user in the documentation. This probably also falls into general "gotchas" that probably aren't the `datasets` libraries' role to protect users from.
Some of the requirements outlined above would be useful for images anyway. These could be implemented prior to a final decision about whether IIIF support could/should be added to datasets.
## Suggested next steps:
I realise this is a long and slightly open-ended issue. I am happy to clarify/answer questions on IIIF and possible integrations. If the prerequisite requirements seem worth exploring/are better explored in their own issues let me know and I can open new issues for those.
| OPEN | 2022-03-28T15:19:25 | 2022-04-05T18:20:53 | null | https://github.com/huggingface/datasets/issues/4041 | davanstrien | 1 | [
"enhancement"
] |
4,037 | Error while building documentation | ## Describe the bug
Documentation building is failing:
- https://github.com/huggingface/datasets/runs/5716300989?check_suite_focus=true
```
ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format.
Unable to find datasets.filesystems.S3FileSystem in datasets. Make sure the path to that object is correct.
```
| CLOSED | 2022-03-28T09:22:44 | 2022-03-28T10:01:52 | 2022-03-28T10:00:48 | https://github.com/huggingface/datasets/issues/4037 | albertvillanova | 2 | [
"bug"
] |
4,032 | can't download cats_vs_dogs dataset | ## Describe the bug
can't download cats_vs_dogs dataset. error: Checksums didn't match for dataset source files
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("cats_vs_dogs")
```
## Expected results
loaded successfully.
## Actual results
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip']
## Environment info
fresh google colab notebook
| CLOSED | 2022-03-27T17:05:39 | 2022-03-28T07:44:24 | 2022-03-28T07:44:24 | https://github.com/huggingface/datasets/issues/4032 | RRaphaell | 1 | [
"bug"
] |
4,031 | Cannot load the dataset conll2012_ontonotesv5 | ## Describe the bug
Cannot load the dataset conll2012_ontonotesv5
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
dataset = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test")
print(dataset)
```
## Expected results
The datasets should be downloaded successfully
## Actual results
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip']
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-5.4.0-88-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 7.0.0
| CLOSED | 2022-03-27T07:38:23 | 2022-03-28T06:58:31 | 2022-03-28T06:31:18 | https://github.com/huggingface/datasets/issues/4031 | cathyxl | 1 | [
"bug"
] |
4,029 | Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold | **Is your feature request related to a problem? Please describe.**
I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly.
**Describe the solution you'd like**
dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold.
It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold.
see details here: https://github.com/facebookresearch/faiss/issues/1273
**Describe alternatives you've considered**
I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets
**Additional context**
The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts.
| CLOSED | 2022-03-25T17:31:33 | 2022-05-06T08:35:52 | 2022-05-06T08:35:52 | https://github.com/huggingface/datasets/issues/4029 | MoritzLaurer | 4 | [
"enhancement"
] |
4,027 | ElasticSearch Indexing example: TypeError: __init__() missing 1 required positional argument: 'scheme' | ## Describe the bug
I am following the example in the documentation for elastic search step by step (on google colab): https://huggingface.co/docs/datasets/faiss_es#elasticsearch
```
from datasets import load_dataset
squad = load_dataset('crime_and_punish', split='train[:1000]')
```
When I run the line:
`squad.add_elasticsearch_index("context", host="localhost", port="9200")`
I get the error:
`TypeError: __init__() missing 1 required positional argument: 'scheme'`
## Expected results
No error message
## Actual results
```
TypeError Traceback (most recent call last)
[<ipython-input-23-9205593edef3>](https://localhost:8080/#) in <module>()
1 import elasticsearch
----> 2 squad.add_elasticsearch_index("text", host="localhost", port="9200")
6 frames
[/usr/local/lib/python3.7/dist-packages/elasticsearch/_sync/client/utils.py](https://localhost:8080/#) in host_mapping_to_node_config(host)
209 options["path_prefix"] = options.pop("url_prefix")
210
--> 211 return NodeConfig(**options) # type: ignore
212
213
TypeError: __init__() missing 1 required positional argument: 'scheme'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.0
- Platform: Linux, Google Colab
- Python version: Google Colab (probably 3.7)
- PyArrow version: ?
| CLOSED | 2022-03-25T16:22:28 | 2022-04-07T10:29:52 | 2022-03-28T07:58:56 | https://github.com/huggingface/datasets/issues/4027 | MoritzLaurer | 2 | [
"bug",
"duplicate"
] |
4,025 | Missing argument in precision/recall | **Is your feature request related to a problem? Please describe.**
[`sklearn.metrics.precision_score`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) accepts an argument `zero_division`, but it is not available in [precision Metric](https://github.com/huggingface/datasets/blob/master/metrics/precision/precision.py#L117)
Same issue is present for Recall.
**Describe the solution you'd like**
Support for **kwargs or adding a new field for `zero_division`.
**Describe alternatives you've considered**
I could filter the warnings myself, but that is not ideal.
**Additional context**
I can make the requested changes if this is approved. | CLOSED | 2022-03-25T15:55:52 | 2022-03-28T09:53:06 | 2022-03-28T09:53:06 | https://github.com/huggingface/datasets/issues/4025 | Dref360 | 1 | [
"enhancement"
] |
4,015 | Can not correctly parse the classes with imagefolder | ## Describe the bug
I try to load my own image dataset with imagefolder, but the parsing of classes is incorrect.
## Steps to reproduce the bug
I organized my dataset (ImageNet) in the following structure:
```
- imagenet/
- train/
- n01440764/
- ILSVRC2012_val_00000293.jpg
- ......
- n01695060/
- ......
- val/
- n01440764/
- n01695060/
- ......
```
At first, I followed the instructions from the Huggingface [example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification#using-your-own-data) to load my data as:
```
from datasets import load_dataset
data_files = {'train': 'imagenet/train', 'val': 'imagenet/val'}
ds = load_dataset("nateraw/image-folder", data_files=data_files, task="image-classification")
```
but it resulted following error (I mask my personal path as <PERSONAL_PATH>):
```
FileNotFoundError: Unable to find 'https://huggingface.co/datasets/nateraw/image-folder/resolve/main/imagenet/train' at <PERSONAL_PATH>/ImageNet/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main
```
Next, I followed a recent issue #3960 to load data as:
```
from datasets import load_dataset
data_files = {'train': ['imagenet/train/**'], 'val': ['imagenet/val/**']}
ds = load_dataset("imagefolder", data_files=data_files, task="image-classification")
```
and the data can be loaded without error as: (I copy val folder to train folder for illustration)
```
>>> ds
DatasetDict({
train: Dataset({
features: ['image', 'labels'],
num_rows: 50000
})
val: Dataset({
features: ['image', 'labels'],
num_rows: 50000
})
})
```
However, the parsed classes is wrong (should be 1000 classes):
```
>>> ds["train"].features
{'image': Image(decode=True, id=None), 'labels': ClassLabel(num_classes=1, names=['val'], id=None)}
```
## Expected results
I expect that the "labels" in ds["train"].features should contain 1000 classes.
## Actual results
The "labels" in ds["train"].features contains only 1 wrong class.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Ubuntu 18.04
- Python version: Python 3.7.12
- PyArrow version: 7.0.0
| CLOSED | 2022-03-25T08:51:17 | 2022-03-28T01:02:03 | 2022-03-25T09:27:56 | https://github.com/huggingface/datasets/issues/4015 | YiSyuanChen | 2 | [
"bug"
] |
4,013 | Cannot preview "hazal/Turkish-Biomedical-corpus-trM" | ## Dataset viewer issue for '*hazal/Turkish-Biomedical-corpus-trM'
**Link:** *https://huggingface.co/datasets/hazal/Turkish-Biomedical-corpus-trM*
*I cannot see the dataset preview.*
```
Server Error
Status code: 400
Exception: HTTPError
Message: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/hazal/Turkish-Biomedical-corpus-trM?full=true
```
Am I the one who added this dataset ? Yes
| CLOSED | 2022-03-25T07:12:02 | 2022-04-04T08:05:01 | 2022-03-25T14:16:11 | https://github.com/huggingface/datasets/issues/4013 | hazalturkmen | 2 | [] |
4,009 | AMI load_dataset error: sndfile library not found | ## Describe the bug
Getting error message when loading AMI dataset.
## Steps to reproduce the bug
`python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
`
## Expected results
A clear and concise description of the expected results.
## Actual results
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.3
- PyArrow version: 7.0.0
| CLOSED | 2022-03-24T15:13:38 | 2022-03-24T15:46:38 | 2022-03-24T15:17:29 | https://github.com/huggingface/datasets/issues/4009 | i-am-neo | 1 | [
"bug"
] |
4,007 | set_format does not work with multi dimension tensor | ## Describe the bug
set_format only transforms the last dimension of a multi-dimension list to tensor
## Steps to reproduce the bug
```python
import torch
from datasets import Dataset
ds = Dataset.from_dict({"A": [torch.rand((2, 2))]})
# ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result
ds = ds.with_format("torch")
print(ds[0])
```
## Expected results
```
{'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]}
```
## Actual results
```
{'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- datasets version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0 | CLOSED | 2022-03-24T11:27:43 | 2022-03-30T07:28:57 | 2022-03-24T14:39:29 | https://github.com/huggingface/datasets/issues/4007 | phihung | 4 | [
"bug"
] |
4,005 | Yelp not working | ## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
| CLOSED | 2022-03-24T11:14:00 | 2022-03-25T14:59:57 | 2022-03-25T14:56:10 | https://github.com/huggingface/datasets/issues/4005 | patrickvonplaten | 6 | [] |
4,003 | ASSIN2 dataset checksum bug | ## Describe the bug
Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2).
`NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`.
Similar to #3952 , #3942 , #3941 , etc.
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
[<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>()
----> 1 load_dataset('assin2')
4 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
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://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download']
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("assin2")
```
## Expected results
Load the dataset.
## Actual results
The dataset won't load.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Google Colab
- Python version: 3.7.12
- PyArrow version: 6.0.1
| CLOSED | 2022-03-24T10:08:50 | 2022-04-27T14:14:45 | 2022-03-28T13:56:39 | https://github.com/huggingface/datasets/issues/4003 | ruanchaves | 6 | [
"bug"
] |
4,001 | How to use generate this multitask dataset for SQUAD? I am getting a value error. | ## Dataset viewer issue for 'squad_multitask*'
**Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask
*short description of the issue*
I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine.
I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format')
Error:
Status code: 400
Exception: TypeError
Message: argument of type 'Value' is not iterable
Kindly advice.
| CLOSED | 2022-03-24T09:21:51 | 2022-03-26T09:48:21 | 2022-03-26T03:35:43 | https://github.com/huggingface/datasets/issues/4001 | gsk1692 | 4 | [] |
4,000 | load_dataset error: sndfile library not found | ## Describe the bug
Can't load ami dataset
## Steps to reproduce the bug
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
## Expected results
## Actual results
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 136/136 [00:00<00:00, 36004.88it/s]
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 136/136 [00:01<00:00, 79.10it/s]
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 18/18 [00:00<00:00, 25343.23it/s]
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 18/18 [00:00<00:00, 2874.78it/s]
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 16/16 [00:00<00:00, 27950.38it/s]
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 16/16 [00:00<00:00, 2892.25it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.3
- PyArrow version: 7.0.0
| CLOSED | 2022-03-24T01:52:32 | 2022-03-25T17:53:33 | 2022-03-25T17:53:33 | https://github.com/huggingface/datasets/issues/4000 | i-am-neo | 4 | [
"bug"
] |
3,996 | Audio.encode_example() throws an error when writing example from array | ## Describe the bug
When trying to do `Audio().encode_example()` with preexisting array (see [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L73)), `sf.write()` throws you an error:
`TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7f4218c0db30>`
## Steps to reproduce the bug
### Sample code to reproduce the bug
```python
# download sample file
!wget https://huggingface.co/datasets/polinaeterna/test_encode_example/resolve/main/common_voice_vi_21824030.mp3
arr, sr = librosa.load("common_voice_vi_21824030.mp3")
Audio().encode_example({
"path": "common_voice_vi_21824030.mp3",
"array": arr,
"sampling_rate":sr
})
```
## Expected results
An encoded example (`{"bytes": b'....', "path": 'path'}`)
## Actual results
```python
TypeError Traceback (most recent call last)
Input In [3], in <module>
1 arr, sr = librosa.load("common_voice_vi_21824030.mp3")
----> 3 Audio().encode_example({
4 "path": "common_voice_vi_21824030.mp3",
5 "array": arr,
6 "sampling_rate":sr
7 })
File ~/workspace/datasets/src/datasets/features/audio.py:75, in Audio.encode_example(self, value)
73 elif isinstance(value, dict) and "array" in value:
74 buffer = BytesIO()
---> 75 sf.write(buffer, value["array"], value["sampling_rate"])
76 return {"bytes": buffer.getvalue(), "path": value.get("path")}
77 elif value.get("bytes") is not None or value.get("path") is not None:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:314, in write(file, data, samplerate, subtype, endian, format, closefd)
312 else:
313 channels = data.shape[1]
--> 314 with SoundFile(file, 'w', samplerate, channels,
315 subtype, endian, format, closefd) as f:
316 f.write(data)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:627, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)
625 mode_int = _check_mode(mode)
626 self._mode = mode
--> 627 self._info = _create_info_struct(file, mode, samplerate, channels,
628 format, subtype, endian)
629 self._file = self._open(file, mode_int, closefd)
630 if set(mode).issuperset('r+') and self.seekable():
631 # Move write position to 0 (like in Python file objects)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1416, in _create_info_struct(file, mode, samplerate, channels, format, subtype, endian)
1414 original_format = format
1415 if format is None:
-> 1416 format = _get_format_from_filename(file, mode)
1417 assert isinstance(format, (_unicode, str))
1418 else:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1457, in _get_format_from_filename(file, mode)
1455 pass
1456 if format.upper() not in _formats and 'r' not in mode:
-> 1457 raise TypeError("No format specified and unable to get format from "
1458 "file extension: {0!r}".format(file))
1459 return format
TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7fd8daf88180>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets master
- Platform: Ubuntu 20.04
- Python version: python 3.8.12
- PyArrow version: 6.0.1
## Solution
I guess we just need to add `format` arg in [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L75) like this:
```python
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
```
BTW discovered this when trying to decode audio in mp3 format without torchaudio (would be useful for TensorFlow users), like this:
```python
from datasets import load_dataset, Features, Audio
ds = load_dataset("common_voice", "vi", split="test")
ds = ds.remove_columns("audio")
ds.select(range(3)) # 3 samples just for testing
def load_mp3_with_librosa(example):
arr, sr = librosa.load(example["path"])
example["audio"] = {
"path": example["path"],
"array": arr,
"sampling_rate": sr
}
return example
updated_dataset = ds.map(lambda example: load_mp3_with_librosa(example),
features=Features(
{"audio": Audio(decode=False)}
))
```
@lhoestq @mariosasko @albertvillanova am I right in my logic? do we agree that we can set wav as the format? π€ | CLOSED | 2022-03-23T17:11:47 | 2022-03-29T14:16:13 | 2022-03-29T14:16:13 | https://github.com/huggingface/datasets/issues/3996 | polinaeterna | 3 | [
"bug"
] |
3,993 | Streaming dataset + interleave + DataLoader hangs with multiple workers | ## Describe the bug
Interleaving multiple iterable datasets that use `load_dataset` on streaming mode hangs when passed to `torch.utils.data.DataLoader` with multiple workers.
## Steps to reproduce the bug
```python
from datasets import interleave_datasets, load_dataset
from torch.utils.data import DataLoader
en_dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
fr_dataset = load_dataset('oscar', "unshuffled_deduplicated_fr", split='train', streaming=True)
it_dataset = load_dataset('oscar', "unshuffled_deduplicated_it", split='train', streaming=True)
de_dataset = load_dataset('oscar', "unshuffled_deduplicated_de", split='train', streaming=True)
multilingual_dataset = interleave_datasets([en_dataset, fr_dataset, de_dataset, it_dataset])
multilingual_dataset = multilingual_dataset.with_format('torch')
next(iter(multilingual_dataset)) # works fairly fast
dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=4)
for batch in dataloader:
print(len(batch)) # prints nothing after 30 min of waiting
dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=0)
for batch in dataloader:
print(len(batch)) # prints right away
```
## Expected results
It should be able to iterate the dataset with multiple workers.
## Actual results
Prints with results with `next(iter(multilingual_dataset)) ` and `num_workers=0` but it prints nothing with `num_workers=4` or any number above 0.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.1.dev0
- `pytorch` version: 1.10.0+cu113
- Python version: 3.7
- PyArrow version: 6.0.1
| OPEN | 2022-03-23T14:27:29 | 2023-02-28T14:14:24 | null | https://github.com/huggingface/datasets/issues/3993 | jpilaul | 5 | [
"bug"
] |
3,992 | Image column is not decoded in map when using with with_transform | ## Describe the bug
Image column is not _decoded_ in **map** when using with `with_transform`
## Steps to reproduce the bug
```python
from datasets import Image, Dataset
def add_C(batch):
batch["C"] = batch["A"]
return batch
ds = Dataset.from_dict({"A": ["image.png"]}).cast_column("A", Image())
ds = ds.with_transform(lambda x: x) # <= This line causes the problem
ds = ds.map(add_C, batched=True)
print(ds[0])
```
## Expected results
```
{'C': <PIL.PngImagePlugin.PngImageFile>, ...}
```
## Actual results
```
{'C': {'bytes': None, 'path': 'image.png'}, ...}
```
If we remove the `with_transform` line, we get the expected result.
## Environment info
- `datasets` version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0
| CLOSED | 2022-03-23T10:51:13 | 2022-12-13T16:59:06 | 2022-12-13T16:59:06 | https://github.com/huggingface/datasets/issues/3992 | phihung | 1 | [
"bug"
] |
3,991 | Add Lung Image Database Consortium image collection (LIDC-IDRI) dataset | ## Adding a Dataset
- **Name:** *Lung Image Database Consortium image collection (LIDC-IDRI)*
- **Description:** *Consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.*
- **Data:** *[link to the Github repository or current dataset location](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)*
- **Motivation:** *Key dataset in the healthcare community*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
FYI @osanseviero @abidlabs | OPEN | 2022-03-22T22:16:05 | 2022-03-23T12:57:16 | null | https://github.com/huggingface/datasets/issues/3991 | omarespejel | 0 | [
"dataset request",
"vision"
] |
3,990 | Improve AutomaticSpeechRecognition task template | **Is your feature request related to a problem? Please describe.**
[AutomaticSpeechRecognition task template](https://github.com/huggingface/datasets/blob/master/src/datasets/tasks/automatic_speech_recognition.py) is outdated as it uses path to audiofile as an audio column instead of a Audio feature itself (I guess it's because Audio feature didn't exist at the time this template was created).
**Describe the solution you'd like**
Change audio columns from string path to Audio feature.
| CLOSED | 2022-03-22T15:41:08 | 2022-03-23T17:12:40 | 2022-03-23T17:12:40 | https://github.com/huggingface/datasets/issues/3990 | polinaeterna | 2 | [
"enhancement"
] |
3,986 | Dataset loads indefinitely after modifying default cache path (~/.cache/huggingface) | ## Describe the bug
Dataset loads indefinitely after modifying cache path (~/.cache/huggingface)
If none of the environment variables are set, this custom dataset loads fine ( json-based dataset with custom dataset load script)
** Update: Transformer modules faces the same issue as well during loading
## A clear and concise description of what the bug is.
Issue:
- Dataset loading stalls / freezes indefinitely when HF_HOME is changed to a custom directory
- No error code, had to terminate the process
- There are some files created in the cache directory:
```
custom_cache_dir
| -- modules
| -- __init__.py
| -- datasets_modules
| -- __init__.py
| -- datasets
| -- __init__.py
| -- script.py (Dataset loading script)
| -- script.lock
```
There's no error nor any logs thrown so I'm out of ideas of how to to debug this. The custom dataset works fine if the default ~/.cache dir is used, but unfortunately it's out of space and we do not have permissions to modify the disk.
## Steps to reproduce the bug
What I've tried:
- Modifying HF_HOME (https://github.com/huggingface/transformers/issues/8703)
- Modifying HF_DATASETS_CACHE (https://huggingface.co/docs/datasets/v1.12.0/cache.html)
- Modifying cache_dir param during runtime
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('test_dataset', cache_dir='/path/to/new/cache')
```
- Disabling dataset cache
```python
>>> from datasets import set_caching_enabled
>>> set_caching_enabled(False)
```
## Expected results
Datasets should load / cache as usual with the only exception that cache directory is different
## Actual results
Any actions taken above to change the cache directory results in loading indefinitely without terminating.
## Environment info
- `transformers` version: 4.18.0.dev0
- Platform: Linux-4.15.0-54-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.4.0
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
| OPEN | 2022-03-22T08:23:21 | 2023-03-06T16:55:04 | null | https://github.com/huggingface/datasets/issues/3986 | kelvinAI | 5 | [
"bug"
] |
3,985 | [image feature] Too many files open error when image feature is returned as a path | ## Describe the bug
PR in context: #3967. If I load the dataset in this PR (TextVQA), and do a simple list comprehension on the dataset, I get `Too many open files error`. This is happening due to the way we are loading the image feature when a str path is returned from the `_generate_examples`. Specifically at https://github.com/huggingface/datasets/blob/508eb4ab5d52f590baa677b4f64b1cc069139f7b/src/datasets/features/image.py#L110, we are open the file handle to the image but never closing it. This in my understanding is causing the issue.
## Steps to reproduce the bug
Pull the PR locally and run the following code
```python
from datasets import load_dataset
dataset = load_dataset("./datasets/textvqa")["train"]
data = [item for item in dataset]
# Error happens
```
## Expected results
List comprehension should work smoothly
## Actual results
`Too many open files error`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.1.dev0
- Platform: macOS-12.2-arm64-arm-64bit
- Python version: 3.10.0
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
| CLOSED | 2022-03-21T21:54:05 | 2022-03-23T18:19:27 | 2022-03-23T18:19:27 | https://github.com/huggingface/datasets/issues/3985 | apsdehal | 0 | [
"bug"
] |
3,984 | Local and automatic tests fail | ## Describe the bug
Running the tests from CircleCI on a PR or locally fails, even with no changes. Tests seem to fail on `test_metric_common.py`
## Steps to reproduce the bug
```shell
git clone https://huggingface/datasets.git
cd datasets
```
```python
python -m pip install -e .
pytest
```
## Expected results
All tests passing
## Actual results
```
tests/test_metric_common.py:91:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../.pyenv/versions/3.8.5/lib/python3.8/doctest.py:1336: in __run
exec(compile(example.source, filename, "single",
<doctest datasets_modules.metrics.ter.c0cfb5adedac7eb15ffa47bba6a70fabd80f3eb906ee508abf5e1906285d1155.ter.Ter[3]>:1: in <module>
???
../datasets/src/datasets/metric.py:430: in compute
output = self._compute(**inputs, **compute_kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = Metric(name: "ter", features: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Val...ences=references)
>>> print(results)
{'score': 0.0, 'num_edits': 0, 'ref_length': 6.5}
""", stored examples: 0)
predictions = ['hello there general kenobi', 'foo bar foobar']
references = [['hello there general kenobi', 'hello there !'], ['foo bar foobar', 'foo bar foobar']]
normalized = False, no_punct = False, asian_support = False, case_sensitive = False
def _compute(
self,
predictions,
references,
normalized: bool = False,
no_punct: bool = False,
asian_support: bool = False,
case_sensitive: bool = False,
):
references_per_prediction = len(references[0])
if any(len(refs) != references_per_prediction for refs in references):
raise ValueError("Sacrebleu requires the same number of references for each prediction")
transformed_references = [[refs[i] for refs in references] for i in range(references_per_prediction)]
> sb_ter = TER(normalized, no_punct, asian_support, case_sensitive)
E TypeError: __init__() takes 2 positional arguments but 5 were given
/tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/metrics/ter/c0cfb5adedac7eb15ffa47bba6a70fabd80f3eb906ee508abf5e1906285d1155/ter.py:130: TypeError
------------------------------ Captured stdout call -------------------------------
Trying:
predictions = ["hello there general kenobi", "foo bar foobar"]
Expecting nothing
ok
Trying:
references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]
Expecting nothing
ok
Trying:
ter = datasets.load_metric("ter")
Expecting nothing
ok
Trying:
results = ter.compute(predictions=predictions, references=references)
Expecting nothing
================================ warnings summary =================================
../.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/hdfs/config.py:15
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/hdfs/config.py:15: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
from imp import load_source
../datasets/src/datasets/commands/test.py:35
/home/markussagen/datasets/src/datasets/commands/test.py:35: PytestCollectionWarning: cannot collect test class 'TestCommand' because it has a __init__ constructor (from: tests/commands/test_test.py)
class TestCommand(BaseDatasetsCLICommand):
tests/commands/test_test.py:33
/home/markussagen/mydataset/tests/commands/test_test.py:33: PytestCollectionWarning: cannot collect test class 'TestCommandArgs' because it has a __new__ constructor (from: tests/commands/test_test.py)
class TestCommandArgs:
tests/test_arrow_dataset.py: 760 warnings
tests/test_formatting.py: 60 warnings
tests/test_search.py: 31 warnings
tests/features/test_array_xd.py: 117 warnings
/home/markussagen/datasets/src/datasets/formatting/formatting.py:197: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
(isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape))
tests/test_arrow_dataset.py: 154 warnings
tests/features/test_array_xd.py: 1 warning
/home/markussagen/datasets/src/datasets/formatting/formatting.py:201: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object})
tests/test_arrow_dataset.py: 60 warnings
/home/markussagen/datasets/src/datasets/arrow_dataset.py:3105: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
elif np.issubdtype(values.dtype, np.str):
tests/test_arrow_dataset.py: 138 warnings
tests/test_formatting.py: 21 warnings
/home/markussagen/datasets/src/datasets/formatting/tf_formatter.py:69: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
data_struct.dtype == np.object
tests/test_arrow_dataset.py: 240 warnings
tests/test_formatting.py: 20 warnings
/home/markussagen/datasets/src/datasets/formatting/torch_formatter.py:49: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if data_struct.dtype == np.object: # pytorch tensors cannot be instantied from an array of objects
tests/test_arrow_dataset.py: 12 warnings
tests/test_search.py: 2 warnings
tests/features/test_array_xd.py: 6 warnings
tests/features/test_image.py: 4 warnings
/home/markussagen/datasets/src/datasets/features/features.py:1129: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
[0] + [len(arr) for arr in l_arr], dtype=np.object
tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_banking77
/tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/datasets/banking77/aec0289529599d4572d76ab00c8944cb84f88410ad0c9e7da26189d31f62a55b/banking77.py:24: DeprecationWarning: invalid escape sequence \~
_CITATION = """\
tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_universal_dependencies
/tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/datasets/universal_dependencies/065e728dfe9a8371434a6e87132c2386a6eacab1a076d3a12aa417b994e6ef7d/universal_dependencies.py:6: DeprecationWarning: invalid escape sequence \=
_CITATION = """\
tests/test_filesystem.py: 105 warnings
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/responses/__init__.py:398: DeprecationWarning: stream argument is deprecated. Use stream parameter in request directly
warn(
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
/home/markussagen/datasets/src/datasets/formatting/jax_formatter.py:57: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if data_struct.dtype == np.object: # jax arrays cannot be instantied from an array of objects
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/jax/_src/numpy/lax_numpy.py:3567: UserWarning: Explicitly requested dtype <class 'jax._src.numpy.lax_numpy.int64'> requested in array is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.
lax._check_user_dtype_supported(dtype, "array")
tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/apscheduler/util.py:95: PytzUsageWarning: The zone attribute is specific to pytz's interface; please migrate to a new time zone provider. For more details on how to do so, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html
if obj.zone == 'local':
tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_custom_features
_audio
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/librosa/core/constantq.py:1059: DeprecationWarning: `np.complex` is a deprecated alias for the builtin `complex`. To silence this warning, use `complex` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.complex128` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
dtype=np.complex,
tests/features/test_array_xd.py::test_array_xd_with_none
/home/markussagen/mydataset/tests/features/test_array_xd.py:338: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
assert isinstance(arr, np.ndarray) and arr.dtype == np.object and arr.shape == (3,)
-- Docs: https://docs.pytest.org/en/stable/warnings.html
============================= short test summary info =============================
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_bleurt - I...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_chrf - Att...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_code_eval
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_comet - Im...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_competition_math
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_coval - Im...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_perplexity
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_ter - Type...
```
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-5.16.11-76051611-generic-x86_64-with-glibc2.33
- Python version: 3.8.5
- PyArrow version: 5.0.0
| CLOSED | 2022-03-21T19:07:37 | 2023-07-25T15:18:40 | 2023-07-25T15:18:40 | https://github.com/huggingface/datasets/issues/3984 | MarkusSagen | 1 | [
"bug"
] |
3,983 | Infinitely attempting lock | I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`.
Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS).
```
%sh
python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /dbfs/transformers/tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--log_level debug \
--cache_dir /dbfs/transformers/cache
```
All goes well until acquiring a lock --
```
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
```
and so on.
I imagine this has to do with DBFS -- is there a way to tackle this? | CLOSED | 2022-03-21T18:11:57 | 2024-05-09T08:24:34 | 2022-05-06T16:12:18 | https://github.com/huggingface/datasets/issues/3983 | jyrr | 4 | [] |
3,978 | I can't view HFcallback dataset for ASR Space | ## Dataset viewer issue for '*Urdu-ASR-flags*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/kingabzpro/Urdu-ASR-flags)*
*I think dataset should show some thing and if you want me to add script, please show me the documentation. I thought this was suppose to be automatic task.*
Am I the one who added this dataset ? Yes
| OPEN | 2022-03-21T11:07:49 | 2023-09-25T12:19:53 | null | https://github.com/huggingface/datasets/issues/3978 | kingabzpro | 4 | [] |
3,977 | Adapt `docs/README.md` for datasets | ## Describe the bug
Currently `docs/README.md` is a direct copy from `transformers`, we should probably adapt this file for `datasets`. | CLOSED | 2022-03-21T08:26:49 | 2023-02-27T10:32:37 | 2023-02-27T10:32:37 | https://github.com/huggingface/datasets/issues/3977 | qqaatw | 1 | [
"documentation"
] |
3,973 | ConnectionError and SSLError | code
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
```
bug report
```
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module>
----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1658
1659 # Create a dataset builder
-> 1660 builder_instance = load_dataset_builder(
1661 path=path,
1662 name=name,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1484 download_config = download_config.copy() if download_config else DownloadConfig()
1485 download_config.use_auth_token = use_auth_token
-> 1486 dataset_module = dataset_module_factory(
1487 path,
1488 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1237 ) from None
-> 1238 raise e1 from None
1239 else:
1240 raise FileNotFoundError(
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now
1174 # TODO(QL): use a Hub dataset module factory instead of GitHub
-> 1175 return GithubDatasetModuleFactory(
1176 path,
1177 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self)
531 revision = self.revision
532 try:
--> 533 local_path = self.download_loading_script(revision)
534 except FileNotFoundError:
535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision)
511 if download_config.download_desc is None:
512 download_config.download_desc = "Downloading builder script"
--> 513 return cached_path(file_path, download_config=download_config)
514
515 def download_dataset_infos_file(self, revision: Optional[str]) -> str:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
232 if is_remote_url(url_or_filename):
233 # URL, so get it from the cache (downloading if necessary)
--> 234 output_path = get_from_cache(
235 url_or_filename,
236 cache_dir=cache_dir,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
581 if head_error is not None:
--> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
583 elif response is not None:
584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
It may be caused by Caused by SSLError(in China?) because it works well on google colab.
So how can I download this dataset manually?
| CLOSED | 2022-03-20T06:45:37 | 2022-03-30T08:13:32 | 2022-03-30T08:13:32 | https://github.com/huggingface/datasets/issues/3973 | yanyu2015 | 6 | [
"bug"
] |
3,969 | Cannot preview cnn_dailymail dataset | ## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
| CLOSED | 2022-03-19T14:08:57 | 2022-04-20T15:52:49 | 2022-04-20T15:52:49 | https://github.com/huggingface/datasets/issues/3969 | hasan-besh | 10 | [] |
3,968 | Cannot preview 'indonesian-nlp/eli5_id' dataset | ## Dataset viewer issue for '*indonesian-nlp/eli5_id*'
**Link:** https://huggingface.co/datasets/indonesian-nlp/eli5_id
I can not see the dataset preview.
```
Server Error
Status code: 400
Exception: Status400Error
Message: Not found. Maybe the cache is missing, or maybe the dataset does not exist.
```
Am I the one who added this dataset ? Yes
| CLOSED | 2022-03-19T06:54:09 | 2022-03-24T16:34:24 | 2022-03-24T16:34:24 | https://github.com/huggingface/datasets/issues/3968 | cahya-wirawan | 5 | [
"dataset-viewer"
] |
3,965 | TypeError: Couldn't cast array of type for JSONLines dataset | ## Describe the bug
One of the [course participants](https://discuss.huggingface.co/t/chapter-5-questions/11744/20?u=lewtun) is having trouble loading a JSONLines dataset that's composed of the GitHub issues from `spacy` (see stack trace below).
This reminds me a bit of #2799 where one can load the dataset in `pandas` but not in `datasets` and perhaps increasing the `block_size` is needed again.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns 'https://huggingface.co/datasets/Evan/spaCy-github-issues/resolve/main/spacy-issues.jsonl'
data_files = hf_hub_url(repo_id="Evan/spaCy-github-issues", filename="spacy-issues.jsonl", repo_type="dataset")
# throws TypeError: Couldn't cast array of type
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas - note this take a while as the file is >2GB
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to pandas.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset
builder_instance.download_and_prepare(
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare
self._download_and_prepare(
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/builder.py", line 683, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/builder.py", line 1136, in _prepare_split
writer.write_table(table)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/arrow_writer.py", line 511, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1121, in table_cast
return cast_table_to_features(table, Features.from_arrow_schema(schema))
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1102, in cast_table_to_features
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1102, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 944, in wrapper
return func(array, *args, **kwargs)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 918, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 918, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1086, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 944, in wrapper
return func(array, *args, **kwargs)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 920, in wrapper
return func(array, *args, **kwargs)
File "/Users/lewtun/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/table.py", line 1019, in array_cast
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
TypeError: Couldn't cast array of type
struct<url: string, html_url: string, labels_url: string, id: int64, node_id: string, number: int64, title: string, description: string, creator: struct<login: string, id: int64, node_id: string, avatar_url: string, gravatar_id: string, url: string, html_url: string, followers_url: string, following_url: string, gists_url: string, starred_url: string, subscriptions_url: string, organizations_url: string, repos_url: string, events_url: string, received_events_url: string, type: string, site_admin: bool>, open_issues: int64, closed_issues: int64, state: string, created_at: timestamp[s], updated_at: timestamp[s], due_on: null, closed_at: timestamp[s]>
to
null
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.7
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
| CLOSED | 2022-03-18T15:17:53 | 2022-05-06T16:13:51 | 2022-05-06T16:13:51 | https://github.com/huggingface/datasets/issues/3965 | lewtun | 1 | [
"bug"
] |
3,964 | Add default Audio Loader | **Is your feature request related to a problem? Please describe.**
Writing a custom loading dataset script might be a bit challenging for users.
**Describe the solution you'd like**
Add default Audio loader (analogous to ImageFolder) for small datasets with standard directory structure.
**Describe alternatives you've considered**
Create a custom loading script? that's what users doing now.
| CLOSED | 2022-03-18T12:58:55 | 2022-08-22T14:20:46 | 2022-08-22T14:20:46 | https://github.com/huggingface/datasets/issues/3964 | polinaeterna | 0 | [
"enhancement"
] |
3,961 | Scores from Index at extra positions are not filtered out | If a FAISS index has fewer records than the requested number of top results (k), then it returns -1 in indices for the additional positions. The get_nearest_examples method only filters out the extra results from the dataset samples. It would be better to filter out extra scores too.
Reference: https://github.com/huggingface/datasets/blob/2.0.0/src/datasets/search.py#L693
| CLOSED | 2022-03-18T06:13:23 | 2022-04-12T14:41:58 | 2022-04-12T14:41:58 | https://github.com/huggingface/datasets/issues/3961 | vishalsrao | 2 | [
"bug"
] |
3,960 | Load local dataset error | When i used the datasets==1.11.0οΌ it's all right. Util update the latest version, it get the error like this:
```
>>> from datasets import load_dataset
>>> data_files={'train': ['/ssd/datasets/imagenet/pytorch/train'], 'validation': ['/ssd/datasets/imagenet/pytorch/val']}
>>> ds = load_dataset('nateraw/image-folder', data_files=data_files, cache_dir='./', task='image-classification')
[] https://huggingface.co/datasets/nateraw/image-folder/resolve/main/ /dat/txacs/git/txacs/examples/image-classification/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/load.py", line 1671, in load_dataset
**config_kwargs,
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/load.py", line 1521, in load_dataset_builder
**config_kwargs,
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/builder.py", line 1031, in __init__
super().__init__(*args, **kwargs)
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/builder.py", line 255, in __init__
sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 584, in from_local_or_remote
if not isinstance(patterns_for_key, DataFilesList)
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 546, in from_local_or_remote
data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 196, in resolve_patterns_locally_or_by_urls
for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):
File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 146, in _resolve_single_pattern_locally
raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to find '/ssd/datasets/imagenet/pytorch/train' at /dat/txacs/git/txacs/examples/image-classification/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main
```
I need some help to solve the problem, thanks! | OPEN | 2022-03-18T03:32:49 | 2023-08-02T17:12:20 | null | https://github.com/huggingface/datasets/issues/3960 | TXacs | 13 | [
"bug",
"dataset bug"
] |
3,959 | Medium-sized dataset conversion from pandas causes a crash | Hi, I am suffering from the following issue:
## Describe the bug
Conversion to arrow dataset from pandas dataframe of a certain size deterministically causes the following crash:
```
File "/home/datasets_crash.py", line 7, in <module>
arrow=datasets.Dataset.from_pandas(d)
File "/home/.conda/envs/tools/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 783, in from_pandas
table = InMemoryTable.from_pandas(
File "/home/.conda/envs/tools/lib/python3.9/site-packages/datasets/table.py", line 379, in from_pandas
return cls(pa.Table.from_pandas(*args, **kwargs))
File "pyarrow/table.pxi", line 1487, in pyarrow.lib.Table.from_pandas
File "pyarrow/table.pxi", line 1532, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 1181, in pyarrow.lib.Table.validate
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 1: In chunk 0: Invalid: List child array invalid: Invalid: Struct child array #1 has length smaller than expected for struct array (1192457 < 1192458)
```
## Steps to reproduce the bug
I have a dataset made from replicated single example mocking a dict representation of a publication.
I copy over this example 140k times and create a pandas frame.
I use 'Dataset.from_pandas' and boom
```python
# Sample code to reproduce the bug
import copy
import datasets
import pandas
# serialized dict is quite long to be realistic representation of a publication content
paper_as_dict=eval("{'article_id': '2020-11-05T14:25:05.321Z02bc3286-91b7-486a-9c74-4f457fbc586a', 'sections': [{'section_id': 'body.0', 'paragraphs': [{'sentences': ['11010111001000000011010011110011101110111011000100001010011100101001111010110111101011101111101010101110001111011110111010111', '1101100110110010010101010100110011000111001100100000011100010111010000011100001101111000000011010111001111001010101111110011010010111011000110100110010', '101011011000010100000010011001011011000000110011011110000101001110110000010001100110111100011100110101010010110000101', '1101101110101010101000000010101011111001111000101000110001110100111000100000011001110100110000110100111011001010110011101001001110']}]}, {'section_id': 'body.1', 'paragraphs': [{'sentences': ['11111100100100111000101001011110100110011001011011001001100110100111011010000110011000010001010100101110001001101011110111110101111100001001001000011110110010110011100110110111110011100011111000101010111010101011001110000100000001001010010010011101111100011010', '10101000110000110111110011101111000101010010001001010000001111001100000010001000001110111110010011101000000111011', '111010011111101111110011111110110001000111100101001000100110101111110000111000111111110000101001101000110011010111011101001010110110001000100000001110001111100110110001110001001100011010100110100010100111000110110100010010100101011110000110000101010010001110101100000']}, {'sentences': ['111110011110110110001111001101011110010110100011101010110101011001101110110111100000111101010110011110111101001111000101110001001010010101100111111001001000011101000100110000101', '011101101101111101001100101010000010111101100101110100101000001100010100110011010010100001101001110111100011010011011111000111111101110001010111010011010110001000010101100110000100010110101110110011001010011001100111101100001001', '1110001011011010101001100001110001110001000111111111101110100001011101101001110100000110000011010001101010101110101110101101001010100100010000000010110010010010', '11101111000111111100111110010000111101110010010101001111011001111110011000011100110001010010000100101010', '111000110110110010101100010010100001100100110010101000001000011101000100101011011010000011001011011111001101100001110010100001111110111001001010101100100110001011011100000101010010000000001100010000101100110110111101110010100010011101110110111010011011000011001010111011100000000010101001011000100000011010100011101001011001010010011110100100']}, {'sentences': ['001101111100001101001001001110000110010101011101001001111111011000111001111011101011110111000000100001110110101110001010001111110100010', '0000110010110101001100011011000011001101001110001000000110010101000011101011110110000000100111000001010000101011111011110001001100001110101010101110101011111000000011001111011110001010010111010000100100000001111001011100101111010101111001001101100101001101111000111011010110010001010010010111010000001101101111100101000111101011001000101', '00000101100101100111101010000101011100101100001100011001100100001100001010001010010011001001111001000010100010000110100111110000001000101000111100010111110011000100000111100010000100010111100010101', '111100110010100110000010010101010101110011110100000101110000000111010101111001011110010101001110000001001000010110010010011110111110010110100101110011001101110111001111100011100100011110010010100101011111111']}, {'sentences': ['1100001110101111000001011001100110001011100011110110010011001000101000011110010101010011011000111010000101010011010000000111011001000010100101000011111101000000000101111000', '1110101000100110001111000011000101110111001100101010011001100011010011111111111010101011010101010011000101001100100000110010100110110110110001101100', '00010001100100101100100111111110111111101000100110101111101111110101110001010001011100000000000011010101101001111010001110101101110011001011111101110100010000111101', '011100011101011001000110010110100100000010100010010110011000000010101110011111111101010010010001100110101010010001100010110011110001011011101010111111100100110110010111101001100101010111001', '10111000011010101111110110011010101011111001000001010010111111010010111111100100010100110100101101110100110011001000110100000111000100110000001000111010', '0010011111111011100111010001111001011101001010000010110000010111000101001101000011101110100100000000100100010010101010100011100101001000100110110000010111111110000011011101111000111010']}]}, {'section_id': 'body.2.0', 'paragraphs': [{'sentences': ['110010010011001110100100011001111100010011110111101011011011001010010010010011101011', '000110101110011011101011000000100011111000001100011011110101101011000110011010001010001101101100000111100101001011111001001101111', '1000011100100000100100100010010000111011000100110010000011110111100110110001101001010100011111010100101000111', '11110111111000110010000000000100010010110001100010001010000111011000101100011010010101110110011010110101001101110011101011101100000001000100101011010110110100101011101010010101101000011110000010101011001011000001000000001010110000100010000100011110101001111100001000100000111000001010011111111110101010100011011000010000111000110', '1001000111011000111110001111111001100001000000101000111011101101100101010110001101000000001111010111100011111000000100001001110', '100110010111010101111010100000010001110101111001010010001100001110100100100101110011010101001000100101000100100011001110001100111000010010011011000010011010010000110001000000100011110010110110011010001100111010111110011']}, {'sentences': ['10010101011100010111011111001001001010100011001001111101101001000000001111101110000111101011000001001011101110101001100010010001101111001110000100010010001001101111011111110010011011110011', '110001110010110000101111000000110010010010100000010100001111101101000101100000000110000000011111011001111000010110110001011010011011101100100110011000100110101010111010111111000111001111010110010001001110100001011011000110000000111101110000001111011011101110100000100010000110001000000110100000', '101010000000010000110110111000110000100111000001110100101101101010001010010010101010100111010110001001000101011110010011001001001110111001101101100100011110011011110101100010110111001010000001000110100000001010011111111110111010011110001001110100011011000101011000110110011011010110100100011111111011100111110110000110011011110110110011101010101111001101010110101000000001100101111010000101110', '1010100110111111111000110110111110010100000100001110101110111001011000010001110110001111111110000101001001110010001110000111010101111010111111011100100011100111111101101111000010001100101000010001100110110100110111111100100011001011000001111110010100110111000010011110111011001101100000101011111110101000011000010', '00000001110000101001110101110011101001110011000111111101111101111000010011100000101000001011001110', '101000111010010000011010011010011010010010100010110100011100100111011101010100101110100111010001000000', '01101000110001101011001101100010100011011010000000001010101000010101000110100010000000110001110001010010000000101101000011000100000110011101100001010100011111101010010110001101110101010111101100001110000011001101', '0010010111000011110010011110001010100000111100001011010100100010101010010011101101100110001001111001000110000111011110010000110101010110111111010110100000011010001001010001000110001101101000101110001011110000101101110000110010110010111001100010011011100011', '00110111110000000100110111101011000100100110001000001001101011001000010100100001100111100110000110110101111010000010101000000101000011001011101001', '0100100001000111001110110110000001000100111001101101110100100111010111110001110010110111100110011111001001000011101110100101111011000110100000111010011101']}, {'sentences': ['100001001011101111111100110111011110001101111101100001000110110000100101011000000100000', '10101001001111110101001010100110011110101101001']}]}, {'section_id': 'body.2.0.0', 'paragraphs': [{'sentences': ['1110101100001100011000101000010000100010101101010110101011100101110110110111010101001100100000000111011001000100011110101011111010100101001010000010001001101010100011110010101110011001100010000100110011000011101010001000111001000001100', '101000000011001001110101000100101010000111000111100010010001111111100110001100000100011010011010010101101111010101010000110011101001111001111011111001110001010000110101101011101111010000001100', '01100001011110010100000101001101111101010011100010011001011110110010010011100101000', '0011100111000101111000010001111100000111000101110001111010001100001000111010000101100001110101100111111', '00001100000011110001011010010110000000111110110001111000110000011011001110000000100011001010110000010000010001101010101100000010011011000101011111100010010', '1011101011101111000001100100111000011000010010011110011000110111010010111100111101100110011010000110000111000110111110101111000001000010011101111000110000100011110101101101001101000110010000001000010011011010101100', '1000010011100011100000010011011111111110101101111011101010010111000000101011000000110101111000010011', '01100000110011001110101111101101011001011101000010001100101010100011010101010100111011011110100010100111', '011011010100011011110010101000110001111110110']}]}, {'section_id': 'body.2.0.1', 'paragraphs': [{'sentences': ['00111011011101000100100111000001101001011000111100100010101001010011001011000010011111001100000100010001100101110011001000110001101011010111011111011000010011010010111010011111101000110111011100010011100111111110110111011', '011011010101101101010000001011010110011111011110100111010101010110001101000010011111000011100', '110001000110010000000111101110111110101110111000101000010001110101000101001000111000010001011101010000110001010001101001001110111110111010111010011101000101101010000', '001000111110100110000001111100000111001110111001110111001000111010001001100111001101000001001001010111000111011100001111011001111110001011000111110011111101011101000100101001111011100001000110101010101111111110011111111011000101110001000000000100111011111011001100111', '11010101100010010100010010010101001011001011000001100010101111111101001101110011001010010100000111010101', '01110000110011111000110010011010000011100000010010001111100010010100100001011011111110001100', '011101111100011101100111110101111001101010010001001110101100001101000000111000']}]}, {'section_id': 'body.2.0.2', 'paragraphs': [{'sentences': ['0111011000110100110000001011001110111000011110100111011000000001000010001111111001101111011100101110101101000111000101000010000111011010110000011101111110111110100111000111000011', '00100110111000110101100111000110100010011010010101001010011000000101000110100110011010011111000100000011000000010001010000100111101011111111101010001111010000001011100001110100000101001101101010011011101000', '000001110001010010100101010100010101001100011001001101101101110111011111101010010111010110110111011110101100001000011110111011001', '0001110010111110100110110011000001111100100100110101011010010101010100101000010101000100101000011011', '1000010010010101001100101110010111010100000110101110000000111001111111001011111010000011110001011001001001000101', '0001111100111010010100010111010110011011000000001111010010110001000011010001100111101110001110000011010101111100001000011010110100000100100001111011110110000000101000010001111001010010110101110111101101110111000100', '1000101100001000100001101110111110000100000001000010101111010011010010010111011010100011001000100100001010001100110']}]}, {'section_id': 'body.2.0.3', 'paragraphs': [{'sentences': ['1010100111100011110110101011100001011010011010100100010011000110111000001010010110111001001101111000010100100110101001010001010001000110010000001', '100010101010100111000011111101010100101110011000100011100100100111000010000011001010010111011010000101010011011110111001010110', '0110000110110110110011011000011010010000001010011000010001011110110010000100011111010100110111111010010111000101111', '10100100000011100010110110011111011011101101111000001001010100001001011010000011001010101100000', '1011111111100001001100000010000100110010101000010100111111110010110011101110000101101011101', '10001111110000011100100000101100000000010000100000011100110000011110111010011101010111101001111000100000000110000011010010001100110111100001001011101011001111110010100111001001010001010011010010010111001101110101110000101011', '101101111111101101010010000110111110000110000111001001010011111101011001011010101100010100110101101011100111100100110010001011110001110010000011101100100100001001110010000010011111100110101']}]}, {'section_id': 'body.2.1', 'paragraphs': [{'sentences': ['1010010011010011001111111001000110010001101111101011001011011000101001010101010001000110100011110101110001110110111010010010100100111000101100100101111110100000011111001101010111101010100101011011110111111110', '000010101101111100000110010110011001111100001101011101000100010001001001000000101101000001110000011010111100000010010000010101110101100010011000101110110111111001000101000111000110100001001100001010101010100011', '0000000011101110111100100010111100101010110001111101110110010000100100010000101001101111001111001001100110010011010000101001110010000000100101011101001010100100011101101001011000010111110100101010110110011001110000110010010111110110101100001011101001100111010001000010111010001010000100010010011110111100110011100011111101101000011100111110101010100110001100100000100011011010111000111110010110100010111101001001101000001100100010000111110000011101111100111101000000000']}, {'sentences': ['01011000010110011000000101101000110101011010100111011001001001100001101101111101111001101111100101111001101011011001011110110110110100001100111111010100101110111111101000101100101010110011111011100101101010100110111001111100100011001110011101000110100000001100001100110001110101001000011010000110101011010000001111100100000100101110011000001001010011011101100011000001100000011', '1001100000101000000011110100110001100001101001100011010000111111010110101111001000100111000011010100100000110110001', '10010011000110110111010110000010010000000111101000100101100111101101001100111110101001001111100001110011110000010101000001000000010100011011110011000100110101001100110111111001101000011010100110000000011110001000101010101000110010010']}]}, {'section_id': 'body.2.2', 'paragraphs': [{'sentences': ['000011000000010011000001101111000101000111111111111010001011110000011001010111010101010110001111110000010', '10101001101011101010001111011000110100000100011110010001100111111101101100010010111110110101101011000011000001101110010111011111100111110000000101110010111', '100001011110010111010110001101101001100000000001000010110101011001111100101101101111010010111111000000111001111010011111000100010001111011110001010000110010101010111110100101011011100001010101000001011011111111101', '1000110111111011101000110101001111111111000100011001000011010100001010011110001111010011011111000111011100101001011111001000010101110110101000111011111111010010001101001010110111000011110101011000010000110', '1011100000100000010101101111001001100110111000010001011010111111000000001010101001111011101011010101101001111101101100101001011101000011011010001001101100100111101111111100010011010101111011100001100001000100101100100110101000010000011000000011001100000110000001', '0001001101111001111111010000001101010110110110100110110100000100110101101010010101011000010010111011000010111110000001110101110111000010011000100110111001000111011000100101110111111', '0110010010011000011010001111001100101001100001001000010100101100010110000000101010110001001010001100111101010001110010010000111011100101101010111111101001100010001011100110010100110111010101000100001110000101110011111011111000010101010110101100010010111100100010010100111110111100101010100011101001110110010000011110001010101010000100010000100111001111011101', '000001010000010001100000101011000000110101000100010111111100101111111000110111001001110110101111110011100001001000011001010000011011', '0101101001010101001101010100011000111011001000100001110100110011100000001001010110001101010110011100111111100101101111101111011001111111110010111010011011011111011011110000101011010', '11000001110111000001100100001110000111001010000101011011101010111001011100010010010111111111000011111110010111100011100110001001100011111010100111110111001110010', '0100010110100001010101110111100011100100010111111011101001100101111110101011010010101111001000101001111000001110001100011001110010100110101100110100100000001010101101011110011001000101100111001001001110100', '100000100010011111001101010000100110011110001100000010010110110100000111111011010100101111010111001110101000100001111101001110000011010110000010100', '00100110000011100101000110110001000011101000011010101000010001111011100001111111001011100111101000001000000110110001000101111010010010001100111', '0110110100011001110011001111100010101001011111011001011001101101010010101101110101010100001000100100000111101110001001110111000110011101101010100000101', '0011111010010011011101010110100110000011000011100100101011011001110110001110001111000011010111011000110100111111011101110111000010010000011011010011011100000011101100110110100100000010110101110100110101001100111011101001010111011011110100110101110010011011010001010111110011001000010100010101010010110010010110000100110001000011010011000100101011010100100111010']}]}]}")
d=pandas.DataFrame.from_records(copy.deepcopy(paper_as_dict) for _ in range(140_100))
arrow=datasets.Dataset.from_pandas(d)
```
## Expected results
The dataset should be converted without error.
## Actual results
Error `pyarrow.lib.ArrowInvalid: Column 1: In chunk 0: Invalid: List child array invalid: Invalid: Struct child array #1 has length smaller than expected for struct array (1192457 < 1192458)`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets==1.18.4 pandas==1.3.5
- Platform: macOS 11.6 or CentOS Linux 7 (Core)
- Python version: Python 3.9.7
- PyArrow version: pyarrow==3.0.0
| CLOSED | 2022-03-17T20:20:35 | 2022-12-12T17:14:06 | 2022-04-20T12:35:37 | https://github.com/huggingface/datasets/issues/3959 | Antymon | 3 | [
"bug"
] |
3,956 | TypeError: __init__() missing 1 required positional argument: 'scheme' | ## Describe the bug
Based on [this tutorial](https://huggingface.co/docs/datasets/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
squad = load_dataset('squad', split='validation')
squad.add_elasticsearch_index("context", host="localhost", port="9200")
```
## Expected results
[Creating an elastic index based on the provided tutorial](https://huggingface.co/docs/datasets/faiss_es#elasticsearch)
## Actual results
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-8fb51aa33961> in <module>
1 from datasets import load_dataset
2 squad = load_dataset('squad', split='validation')
----> 3 squad.add_elasticsearch_index("context", host="localhost", port="9200")
~/opt/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)
3777 """
3778 with self.formatted_as(type=None, columns=[column]):
-> 3779 super().add_elasticsearch_index(
3780 column=column,
3781 index_name=index_name,
~/opt/anaconda3/lib/python3.8/site-packages/datasets/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)
587 """
588 index_name = index_name if index_name is not None else column
--> 589 es_index = ElasticSearchIndex(
590 host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config
591 )
~/opt/anaconda3/lib/python3.8/site-packages/datasets/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)
123 from elasticsearch import Elasticsearch # noqa: F811
124
--> 125 self.es_client = es_client if es_client is not None else Elasticsearch([{"host": host, "port": str(port)}])
126 self.es_index_name = (
127 es_index_name
~/opt/anaconda3/lib/python3.8/site-packages/elasticsearch/_sync/client/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)
310
311 if _transport is None:
--> 312 node_configs = client_node_configs(
313 hosts,
314 cloud_id=cloud_id,
~/opt/anaconda3/lib/python3.8/site-packages/elasticsearch/_sync/client/utils.py in client_node_configs(hosts, cloud_id, **kwargs)
99 else:
100 assert hosts is not None
--> 101 node_configs = hosts_to_node_configs(hosts)
102
103 # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.
~/opt/anaconda3/lib/python3.8/site-packages/elasticsearch/_sync/client/utils.py in hosts_to_node_configs(hosts)
142
143 elif isinstance(host, Mapping):
--> 144 node_configs.append(host_mapping_to_node_config(host))
145 else:
146 raise ValueError(
~/opt/anaconda3/lib/python3.8/site-packages/elasticsearch/_sync/client/utils.py in host_mapping_to_node_config(host)
209 options["path_prefix"] = options.pop("url_prefix")
210
--> 211 return NodeConfig(**options) # type: ignore
212
213
TypeError: __init__() missing 1 required positional argument: 'scheme'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Mac
- Python version: 3.8.0
- PyArrow version: 7.0.0
- ElaticSearch Info:
{
"name" : "byname",
"cluster_name" : "elasticsearch_brew",
"cluster_uuid" : "9xkjrltiQIG0J95ciWhqRA",
"version" : {
"number" : "7.10.2-SNAPSHOT",
"build_flavor" : "oss",
"build_type" : "tar",
"build_hash" : "unknown",
"build_date" : "2021-01-16T01:41:27.115673Z",
"build_snapshot" : true,
"lucene_version" : "8.7.0",
"minimum_wire_compatibility_version" : "6.8.0",
"minimum_index_compatibility_version" : "6.0.0-beta1"
},
"tagline" : "You Know, for Search"
}
| CLOSED | 2022-03-17T11:43:13 | 2023-11-21T04:26:20 | 2022-03-28T08:00:01 | https://github.com/huggingface/datasets/issues/3956 | amirj | 8 | [
"bug"
] |
3,954 | The dataset preview is not available for tdklab/Hebrew_Squad_v1.1 dataset | ## Dataset viewer issue for 'tdklab/Hebrew_Squad_v1.1'
**Link:** https://huggingface.co/api/datasets/tdklab/Hebrew_Squad_v1.1?full=true
The dataset preview is not available for this dataset.
Am I the one who added this dataset ? Yes | CLOSED | 2022-03-17T09:38:11 | 2022-04-20T12:39:07 | 2022-04-20T12:39:07 | https://github.com/huggingface/datasets/issues/3954 | MatanBenChorin | 6 | [] |
3,953 | Add ImageNet Sketch | ## Adding a Dataset
- **Name:** ImageNet Sketch
- **Description:** ImageNet-Sketch is a dataset consisting of sketch-like images, that matches the ImageNet classification validation set in categories and scale.
- **Paper:** [Learning Robust Global Representations by Penalizing Local Predictive Power](https://arxiv.org/abs/1905.13549)
- **Data:** https://github.com/HaohanWang/ImageNet-Sketch
- **Motivation:** Allows for evaluating the robustness of vision models.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| CLOSED | 2022-03-17T09:20:31 | 2022-05-23T18:05:29 | 2022-05-23T18:05:29 | https://github.com/huggingface/datasets/issues/3953 | NielsRogge | 2 | [
"dataset request",
"vision"
] |
3,952 | Checksum error for glue sst2, stsb, rte etc datasets | ## Describe the bug
Checksum error for glue sst2, stsb, rte etc datasets
## Steps to reproduce the bug
```python
>>> nlp.load_dataset('glue', 'sst2')
Downloading and preparing dataset glue/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown sizetotal: 11.90 MiB) to
Downloading: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 73.0/73.0 [00:00<00:00, 18.2kB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/3.8/lib/python/site-packages/nlp/load.py", line 548, in load_dataset
builder_instance.download_and_prepare(
File "/Library/Python/3.8/lib/python/site-packages/nlp/builder.py", line 462, in download_and_prepare
self._download_and_prepare(
File "/Library/Python/3.8/lib/python/site-packages/nlp/builder.py", line 521, in _download_and_prepare
verify_checksums(
File "/Library/Python/3.8/lib/python/site-packages/nlp/utils/info_utils.py", line 38, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
nlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8']
```
## Expected results
dataset load should succeed without checksum error.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/3.8/lib/python/site-packages/nlp/load.py", line 548, in load_dataset
builder_instance.download_and_prepare(
File "/Library/Python/3.8/lib/python/site-packages/nlp/builder.py", line 462, in download_and_prepare
self._download_and_prepare(
File "/Library/Python/3.8/lib/python/site-packages/nlp/builder.py", line 521, in _download_and_prepare
verify_checksums(
File "/Library/Python/3.8/lib/python/site-packages/nlp/utils/info_utils.py", line 38, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
nlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8']
```
## Environment info
- `datasets` version: '1.18.3'
- Platform: Mac OS
- Python version: Python 3.8.9
- PyArrow version: '7.0.0'
| CLOSED | 2022-03-17T03:45:47 | 2022-03-17T07:10:15 | 2022-03-17T07:10:14 | https://github.com/huggingface/datasets/issues/3952 | ravindra-ut | 1 | [
"bug"
] |
3,951 | Forked streaming datasets try to `open` data urls rather than use network | ## Describe the bug
Building on #3950, if you bypass the pickling problem you still can't use the dataset. Somehow something gets confused and the forked processes try to `open` urls rather than anything else.
## Steps to reproduce the bug
```python
from multiprocessing import freeze_support
import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets
import torch.utils.data
# work around #3950
class TorchIterableDataset(datasets.IterableDataset, torch.utils.data.IterableDataset):
pass
def _ensure_format(v: datasets.IterableDataset) -> datasets.IterableDataset:
return TorchIterableDataset(v._ex_iterable, v.info, v.split, "torch", v._shuffling)
if __name__ == '__main__':
freeze_support()
ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
ds = _ensure_format(ds)
model = AutoModelForCausalLM.from_pretrained("distilgpt2")
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
```
## Expected results
I'd expect the dataset to load the url correctly and produce examples.
## Actual results
```
warnings.warn(
***** Running training *****
Num examples = 8000
Num Epochs = 9223372036854775807
Instantaneous batch size per device = 8
Total train batch size (w. parallel, distributed & accumulation) = 8
Gradient Accumulation steps = 1
Total optimization steps = 1000
0%| | 0/1000 [00:00<?, ?it/s]Traceback (most recent call last):
File "/Users/dlwh/src/mistral/src/stream_fork_crash.py", line 22, in <module>
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/transformers/trainer.py", line 1339, in train
for step, inputs in enumerate(epoch_iterator):
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 521, in __next__
data = self._next_data()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
data.reraise()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/_utils.py", line 434, in reraise
raise exception
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
data.append(next(self.dataset_iter))
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 497, in __iter__
for key, example in self._iter():
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 494, in _iter
yield from ex_iterable
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 87, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/Users/dlwh/.cache/huggingface/modules/datasets_modules/datasets/oscar/84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2/oscar.py", line 358, in _generate_examples
with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
FileNotFoundError: [Errno 2] No such file or directory: 'https://s3.amazonaws.com/datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/en/en_part_1.txt.gz'
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_fork.py", line 27, in poll
pid, sts = os.waitpid(self.pid, flag)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 6932) is killed by signal: Terminated: 15.
0%| | 0/1000 [00:02<?, ?it/s]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: macOS-12.2-arm64-arm-64bit
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
| CLOSED | 2022-03-16T21:21:02 | 2022-06-10T20:47:26 | 2022-06-10T20:47:26 | https://github.com/huggingface/datasets/issues/3951 | dlwh | 1 | [
"bug"
] |
3,950 | Streaming Datasets don't work with Transformers Trainer when dataloader_num_workers>1 | ## Describe the bug
Streaming Datasets can't be pickled, so any interaction between them and multiprocessing results in a crash.
## Steps to reproduce the bug
```python
import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets
ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True).with_format("torch")
model = AutoModelForCausalLM.from_pretrained("distilgpt2")
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
```
## Expected results
For this code I'd expect a crash related to not having preprocessed the data, but instead we get a pickling error.
## Actual results
```
0%| | 0/1000 [00:00<?, ?it/s]Traceback (most recent call last):
File "/Users/dlwh/src/mistral/src/stream_fork_crash.py", line 7, in <module>
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/transformers/trainer.py", line 1339, in train
for step, inputs in enumerate(epoch_iterator):
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 359, in __iter__
return self._get_iterator()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 305, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 918, in __init__
w.start()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/process.py", line 121, in start
self._popen = self._Popen(self)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 32, in __init__
super().__init__(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_fork.py", line 19, in __init__
self._launch(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 47, in _launch
reduction.dump(process_obj, fp)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object 'iterable_dataset.<locals>.TorchIterableDataset'
0%| | 0/1000 [00:00<?, ?it/s]
```
This immediate crash can be fixed by not using a local class to make the `TorchIterableDataset` (Note that you have to do with_format("torch") or you get an exception because the dataset has no len) However, any lambdas etc used as maps will also trigger this crash. A more permanent fix would be to move away from multiprocessing and instead use something like pathos or multiprocessing_on_dill (https://stackoverflow.com/questions/19984152/what-can-multiprocessing-and-dill-do-together)
Note that if you bypass this crash you get another crash. (I'll file a separate bug).
## Environment info
- `datasets` version: 2.0.0
- Platform: macOS-12.2-arm64-arm-64bit
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
| CLOSED | 2022-03-16T21:14:11 | 2022-06-10T20:47:26 | 2022-06-10T20:47:26 | https://github.com/huggingface/datasets/issues/3950 | dlwh | 1 | [
"bug",
"good first issue"
] |
3,942 | reddit_tifu dataset: Checksums didn't match for dataset source files | ## Describe the bug
When loading the reddit_tifu dataset, it throws the exception "Checksums didn't match for dataset source files"
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
print(datasets.__version__)
# load_dataset('billsum')
load_dataset('reddit_tifu', 'short')
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: mac os
- Python version: Python 3.7.6
- PyArrow version: 3.0.0
| CLOSED | 2022-03-16T15:23:30 | 2022-03-16T15:57:43 | 2022-03-16T15:39:25 | https://github.com/huggingface/datasets/issues/3942 | XingxingZhang | 3 | [
"bug",
"duplicate"
] |
3,941 | billsum dataset: Checksums didn't match for dataset source files: | ## Describe the bug
When loading the `billsum` dataset, it throws the exception "Checksums didn't match for dataset source files"
```
File "virtualenv_projects/codex/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1g89WgFHMRbr4QrvA0ngh26PY081Nv3lx']
```
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
print(datasets.__version__)
load_dataset('billsum')
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: mac os
- Python version: Python 3.7.6
- PyArrow version: 3.0.0
| CLOSED | 2022-03-16T14:52:08 | 2024-03-13T12:11:35 | 2022-03-16T15:46:44 | https://github.com/huggingface/datasets/issues/3941 | XingxingZhang | 3 | [
"bug"
] |
3,939 | Source links broken | ## Describe the bug
The source links of v2.0.0 docs are broken:
For exmaple, clicking the source button of this [class](https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.ClassLabel) will direct users to `https://github.com/huggingface/datasets/blob/v2.0.0/src/datasets/features/features.py#L747`
here, the `v2.0.0` should be `2.0.0`.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
## Expected results
Redirecting to this link: `https://github.com/huggingface/datasets/blob/2.0.0/src/datasets/features/features.py#L747`
## Actual results
Described above.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
| CLOSED | 2022-03-16T11:17:47 | 2022-03-19T04:41:32 | 2022-03-19T04:41:32 | https://github.com/huggingface/datasets/issues/3939 | qqaatw | 8 | [
"bug"
] |
3,937 | Missing languages in lvwerra/github-code dataset | Hi,
I'm working with the github-code dataset. First of all, thank you for creating this amazing dataset!
I've noticed that two languages are missing from the dataset: TypeScript and Scala.
Looks like they're also omitted from the query you used to get the original code.
Are there any plans to add them in the future?
Thanks! | CLOSED | 2022-03-16T10:32:03 | 2022-03-22T07:09:23 | 2022-03-21T14:50:47 | https://github.com/huggingface/datasets/issues/3937 | Eytan-S | 5 | [
"Dataset discussion"
] |
3,929 | Load a local dataset twice | ## Describe the bug
Load a local "dataset" composed of two csv files twice.
## Steps to reproduce the bug
Put the two joined files in a repository named "Data".
Then in python:
import datasets as ds
ds.load_dataset('Data', data_files = {'file1.csv', 'file2.csv'})
## Expected results
Should give something like (because files have only one data row):
Title, clicks
Truc et astuce, 123
Machin, 12
## Actual results
Gives
Title, clicks
Truc et astuce, 123
Machin, 12
Truc et astuce, 123
Machin, 12
## Environment info
[file1.csv](https://github.com/huggingface/datasets/files/8256322/file1.csv)
[file2.csv](https://github.com/huggingface/datasets/files/8256323/file2.csv)
- `datasets` version: 2.0.0
- Platform: Linux-5.4.0-65-generic-x86_64-with-glibc2.10
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1 | CLOSED | 2022-03-15T18:59:26 | 2022-03-16T09:55:09 | 2022-03-16T09:54:06 | https://github.com/huggingface/datasets/issues/3929 | caush | 1 | [
"bug"
] |
3,928 | Frugal score deprecations | ## Describe the bug
The frugal score returns a really verbose output with warnings that can be easily changed.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets.load import load_metric
frugal = load_metric("frugalscore")
frugal.compute(predictions=["Do you like spinachis"], references=["Do you like spinach"])
```
## Expected results
A clear and concise description of the expected results.
```
{'scores': [0.9946]}
```
## Actual results
Specify the actual results or traceback.
```
PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 864.09ba/s]
Using amp half precision backend
The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, sentence1. If sentence2, sentence1 are not expected by `BertForSequenceClassification.forward`, you can safely ignore this message.
***** Running Prediction *****
Num examples = 1
Batch size = 64
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 4644.85it/s]
{'scores': [0.9946]}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0
| CLOSED | 2022-03-15T18:10:42 | 2022-03-17T08:37:24 | 2022-03-17T08:37:24 | https://github.com/huggingface/datasets/issues/3928 | ierezell | 1 | [
"bug"
] |
3,920 | 'datasets.features' is not a package | @albertvillanova
python 3.9
os: ubuntu 20.04
In conda environment
torch installed by
```/env/bin/pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html```
datasets package is installed by
```
/env/bin/pip install datasets==1.8.0
```
During runing the code I have this error
```
[6]<stderr>: File "/home/arij/Memory-transformer-with-hierarchical-attention_MLM/env/lib/python3.9/site-packages/torch/serialization.py", line 875, in find_class
[6]<stderr>: return super().find_class(mod_name, name)
[6]<stderr>:ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package
```
precisely this error appears when
torch.load('data_file.pt')
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/arij/Memory-transformer-with-hierarchical-attention_MLM/env/lib/python3.9/site-packages/torch/serialization.py", line 607, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "/home/arij/Memory-transformer-with-hierarchical-attention_MLM/env/lib/python3.9/site-packages/torch/serialization.py", line 882, in _load
result = unpickler.load()
File "/home/arij/Memory-transformer-with-hierarchical-attention_MLM/env/lib/python3.9/site-packages/torch/serialization.py", line 875, in find_class
return super().find_class(mod_name, name)
ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package
```
Why I am getting this error?
| CLOSED | 2022-03-15T11:14:23 | 2022-03-16T09:17:12 | 2022-03-16T09:17:12 | https://github.com/huggingface/datasets/issues/3920 | Arij-Aladel | 2 | [] |
3,919 | AttributeError: 'DatasetDict' object has no attribute 'features' | ## Describe the bug
Receiving the error when trying to check for Dataset features
## Steps to reproduce the bug
from datasets import Dataset
dataset = Dataset.from_pandas(df[['id', 'words', 'bboxes', 'ner_tags', 'image_path']])
dataset.features
## Expected results
A clear and concise description of the expected results.
## Actual results
Getting the following errror
AttributeError: 'DatasetDict' object has no attribute 'features'
## Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 1.18.4
- Platform: Linux-4.14.252-131.483.amzn1.x86_64-x86_64-with-glibc2.9
- Python version: 3.6.13
- PyArrow version: 6.0.1
| CLOSED | 2022-03-15T10:46:59 | 2022-03-17T04:16:14 | 2022-03-17T04:16:14 | https://github.com/huggingface/datasets/issues/3919 | jswapnil10 | 2 | [
"bug"
] |
3,918 | datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files | ## Describe the bug
Can't load the dataset
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset('multi_news')
dataset_2=load_dataset("reddit_tifu", "long")
## Actual results
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']
## Environment info
- `datasets` version: 1.18.4
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.0
- PyArrow version: 6.0.1
| CLOSED | 2022-03-15T08:53:45 | 2022-03-16T15:36:58 | 2022-03-15T14:01:25 | https://github.com/huggingface/datasets/issues/3918 | willowdong | 3 | [
"bug",
"duplicate"
] |
3,909 | Error loading file audio when downloading the Common Voice dataset directly from the Hub | ## Describe the bug
When loading the Common_Voice dataset, by downloading it directly from the Hugging Face hub, some files can not be opened.
## Steps to reproduce the bug
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "it", split="test")
#test_dataset = load_dataset('csv', data_files = {'test': '/workspace/Dataset/Common_Voice/cv-corpus80/it/test.csv'})
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-italian")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-italian")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\β\'\οΏ½]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
```
## Expected results
The common voice dataset downloaded and correctly loaded whit the use of the hugging face datasets library.
## Actual results
The error is:
```python
0ex [00:00, ?ex/s]
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-48-ef87f4129e6e> in <module>
7 return batch
8
----> 9 test_dataset = test_dataset.map(speech_file_to_array_fn)
/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, with_rank, 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)
2107
2108 if num_proc is None or num_proc == 1:
-> 2109 return self._map_single(
2110 function=function,
2111 with_indices=with_indices,
/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
516 self: "Dataset" = kwargs.pop("self")
517 # apply actual function
--> 518 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
519 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
520 for dataset in datasets:
/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
483 }
484 # apply actual function
--> 485 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
486 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
487 # re-apply format to the output
/opt/conda/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
411 # Call actual function
412
--> 413 out = func(self, *args, **kwargs)
414
415 # Update fingerprint of in-place transforms + update in-place history of transforms
/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, with_rank, 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)
2465 if not batched:
2466 for i, example in enumerate(pbar):
-> 2467 example = apply_function_on_filtered_inputs(example, i, offset=offset)
2468 if update_data:
2469 if i == 0:
/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)
2372 if with_rank:
2373 additional_args += (rank,)
-> 2374 processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
2375 if update_data is None:
2376 # Check if the function returns updated examples
/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in decorated(item, *args, **kwargs)
2067 )
2068 # Use the LazyDict internally, while mapping the function
-> 2069 result = f(decorated_item, *args, **kwargs)
2070 # Return a standard dict
2071 return result.data if isinstance(result, LazyDict) else result
<ipython-input-48-ef87f4129e6e> in speech_file_to_array_fn(batch)
3 def speech_file_to_array_fn(batch):
4 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
----> 5 speech_array, sampling_rate = torchaudio.load(batch["path"])
6 batch["speech"] = resampler(speech_array).squeeze().numpy()
7 return batch
/opt/conda/lib/python3.8/site-packages/torchaudio/backend/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)
150 filepath, frame_offset, num_frames, normalize, channels_first, format)
151 filepath = os.fspath(filepath)
--> 152 return torch.ops.torchaudio.sox_io_load_audio_file(
153 filepath, frame_offset, num_frames, normalize, channels_first, format)
154
RuntimeError: Error loading audio file: failed to open file common_voice_it_17415776.mp3 ```
## Environment info
- `datasets` version: 1.18.4
- Platform: Linux-5.4.0-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 7.0.0 | CLOSED | 2022-03-14T15:53:50 | 2023-03-02T15:31:27 | 2023-03-02T15:31:26 | https://github.com/huggingface/datasets/issues/3909 | aliceinland | 8 | [
"bug"
] |
3,906 | NonMatchingChecksumError on Spider dataset | ## Describe the bug
Failure to generate dataset ```spider``` because of checksums error for dataset source files.
## Steps to reproduce the bug
```
from datasets import load_dataset
spider = load_dataset("spider")
```
## Expected results
Checksums should match for files from url ['https://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']
## Actual results
```
>>> load_dataset("spider")
load_dataset("spider")
Downloading and preparing dataset spider/spider (download: 95.12 MiB, generated: 5.17 MiB, post-processed: Unknown size, total: 100.29 MiB) to /home/user/.cache/huggingface/datasets/spider/spider/1.0.0/79778ebea87c59b19411f1eb3eda317e9dd5f7788a556d837ef25c3ae6e5e8b7...
Traceback (most recent call last):
File "/home/user/py3_env/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-5-d4cb54197348>", line 1, in <module>
load_dataset("spider")
File "/home/user/py3_env/lib/python3.8/site-packages/datasets/load.py", line 1702, in load_dataset
builder_instance.download_and_prepare(
File "/home/user/py3_env/lib/python3.8/site-packages/datasets/builder.py", line 594, in download_and_prepare
self._download_and_prepare(
File "/home/user/py3_env/lib/python3.8/site-packages/datasets/builder.py", line 665, in _download_and_prepare
verify_checksums(
File "/home/user/py3_env/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']
```
## Environment info
datasets version: 1.18.3
Platform: Ubuntu 20 LTS
Python version: 3.8.10
PyArrow version: 6.0.1
| CLOSED | 2022-03-14T14:54:53 | 2022-03-15T07:09:51 | 2022-03-15T07:09:51 | https://github.com/huggingface/datasets/issues/3906 | kolk | 1 | [
"bug"
] |
3,904 | CONLL2003 Dataset not available | ## Describe the bug
[CONLL2003](https://huggingface.co/datasets/conll2003) Dataset can no longer reach 'https://data.deepai.org/conll2003.zip'
## Steps to reproduce the bug
```python
from datasets import load_dataset
datasets = load_dataset("conll2003")
```
## Expected results
Download the conll2003 dataset.
## Actual results
Error: `ConnectionError: Couldn't reach https://data.deepai.org/conll2003.zip (error 502)`
| CLOSED | 2022-03-13T23:46:15 | 2023-06-28T18:08:16 | 2022-03-17T08:21:32 | https://github.com/huggingface/datasets/issues/3904 | omarespejel | 4 | [
"dataset bug"
] |
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