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https://api.github.com/repos/huggingface/datasets/issues/5310 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5310/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5310/comments | https://api.github.com/repos/huggingface/datasets/issues/5310/events | https://github.com/huggingface/datasets/pull/5310 | 1,467,719,635 | PR_kwDODunzps5D3rGw | 5,310 | Support xPath for Windows pathnames | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-29T09:20:47 | 2022-11-30T12:00:09 | 2022-11-30T11:57:16 | MEMBER | null | This PR implements a string representation of `xPath`, which is valid for local paths (also windows) and remote URLs.
Additionally, some `os.path` methods are fixed for remote URLs on Windows machines.
Now, on Windows machines:
```python
In [2]: str(xPath("C:\\dir\\file.txt"))
Out[2]: 'C:\\dir\\file.txt'
In [3]: str(xPath("http://domain.com/file.txt"))
Out[3]: 'http://domain.com/file.txt'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5309 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5309/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5309/comments | https://api.github.com/repos/huggingface/datasets/issues/5309/events | https://github.com/huggingface/datasets/pull/5309 | 1,466,758,987 | PR_kwDODunzps5D0g1y | 5,309 | Close stream in `ArrowWriter.finalize` before inference error | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-28T13:59:10 | 2022-11-28T15:30:37 | 2022-11-28T15:27:26 | CONTRIBUTOR | null | Use the correct dataset type in the `from_generator` docs (example with sharding). | {
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"Forgot to actually convert the feature dict to a Feature object. Closing."
] | 2022-11-28T07:55:44 | 2022-11-28T08:11:45 | 2022-11-28T08:11:44 | CONTRIBUTOR | null | ### Describe the bug
I have created a feature dictionary to describe my datasets' column types, to use when loading the dataset, following [the doc](https://huggingface.co/docs/datasets/main/en/about_dataset_features). It crashes at dataset load.
### Steps to reproduce the bug
```python
# Creating features
task_list = [f"motif_G{i}" for i in range(19, 53)]
features = {t: Sequence(feature=Value(dtype="float64")) for t in task_list}
for col_name in ["class_label"]:
features[col_name] = Sequence(feature=Value(dtype="int64"))
for col_name in ["num_nodes"]:
features[col_name] = Value(dtype="int64")
for col_name in ["num_bridges", "num_cycles", "avg_shortest_path_len"]:
features[col_name] = Sequence(feature=Value(dtype="float64"))
for col_name in ["edge_attr", "node_feat", "edge_index"]:
features[col_name] = Sequence(feature=Sequence(feature=Value(dtype="int64")))
print(features)
dataset = load_dataset(path=f"graphs-datasets/unbalanced-motifs-500K", split="train", features=features)
```
Last line will crash and say 'TypeError: argument of type 'Sequence' is not iterable'.
Full stack:
```
Traceback (most recent call last):
File "pretrain_tokengt.py", line 131, in <module>
main(output_folder = "../workspace/pretraining",
File "pretrain_tokengt.py", line 52, in main
dataset = load_dataset(path=f"graphs-datasets/{dataset_name}", split="train", features=features)
File "huggingface_env/lib/python3.8/site-packages/datasets/load.py", line 1718, in load_dataset
builder_instance = load_dataset_builder(
File "huggingface_env/lib/python3.8/site-packages/datasets/load.py", line 1514, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "huggingface_env/lib/python3.8/site-packages/datasets/builder.py", line 321, in __init__
info.update(self._info())
File "huggingface_env/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 62, in _info
return datasets.DatasetInfo(features=self.config.features)
File "<string>", line 20, in __init__
File "huggingface_env/lib/python3.8/site-packages/datasets/info.py", line 155, in __post_init__
self.features = Features.from_dict(self.features)
File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1599, in from_dict
obj = generate_from_dict(dic)
File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1282, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1282, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1281, in generate_from_dict
if "_type" not in obj or isinstance(obj["_type"], dict):
TypeError: argument of type 'Sequence' is not iterable
```
### Expected behavior
For it not to crash.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-5.14.0-1054-oem-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3 | {
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"Thanks for reporting @JoelNiklaus.\r\n\r\nPlease note that since we moved all dataset loading scripts to the Hub, the issues and pull requests relative to specific datasets are directly handled on the Hub, in their Community tab. I'm transferring this issue there: https://huggingface.co/datasets/joelito/mc4_legal/discussions\r\n\r\nI am also having a look at the bug in your script.",
"Issue transferred to: https://huggingface.co/datasets/joelito/mc4_legal/discussions/1"
] | 2022-11-28T00:16:16 | 2022-11-28T07:22:42 | 2022-11-28T07:22:42 | CONTRIBUTOR | null | ### Describe the bug
The dataset https://huggingface.co/datasets/joelito/mc4_legal works for languages like bg with a single data file, but not for languages with multiple files like de. It shows zero rows for the de dataset.
joelniklaus@Joels-MacBook-Pro ~/N/P/C/L/p/m/mc4_legal (main) [1]> python test_mc4_legal.py (debug)
Found cached dataset mc4_legal (/Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/de/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f)
Dataset({
features: ['index', 'url', 'timestamp', 'matches', 'text'],
num_rows: 0
})
joelniklaus@Joels-MacBook-Pro ~/N/P/C/L/p/m/mc4_legal (main)> python test_mc4_legal.py (debug)
Downloading and preparing dataset mc4_legal/bg to /Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/bg/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f...
Downloading data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 1240.55it/s]
Dataset mc4_legal downloaded and prepared to /Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/bg/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f. Subsequent calls will reuse this data.
Dataset({
features: ['index', 'url', 'timestamp', 'matches', 'text'],
num_rows: 204
})
### Steps to reproduce the bug
import datasets
from datasets import load_dataset, get_dataset_config_names
language = "bg"
test = load_dataset("joelito/mc4_legal", language, split='train')
### Expected behavior
It should display the correct number of rows for the de dataset which should be a large number (thousands or more).
### Environment info
Package Version
------------------------ --------------
absl-py 1.3.0
aiohttp 3.8.1
aiosignal 1.2.0
astunparse 1.6.3
async-timeout 4.0.2
attrs 22.1.0
beautifulsoup4 4.11.1
blinker 1.4
blis 0.7.8
Bottleneck 1.3.4
brotlipy 0.7.0
cachetools 5.2.0
catalogue 2.0.7
certifi 2022.5.18.1
cffi 1.15.1
chardet 4.0.0
charset-normalizer 2.1.0
click 8.0.4
conllu 4.5.2
cryptography 38.0.1
cymem 2.0.6
datasets 2.6.1
dill 0.3.5.1
docker-pycreds 0.4.0
fasttext 0.9.2
fasttext-langdetect 1.0.3
filelock 3.0.12
flatbuffers 20210226132247
frozenlist 1.3.0
fsspec 2022.5.0
gast 0.4.0
gcloud 0.18.3
gitdb 4.0.9
GitPython 3.1.27
google-auth 2.9.0
google-auth-oauthlib 0.4.6
google-pasta 0.2.0
googleapis-common-protos 1.57.0
grpcio 1.47.0
h5py 3.7.0
httplib2 0.21.0
huggingface-hub 0.8.1
idna 3.4
importlib-metadata 4.12.0
Jinja2 3.1.2
joblib 1.0.1
keras 2.9.0
Keras-Preprocessing 1.1.2
langcodes 3.3.0
lxml 4.9.1
Markdown 3.3.7
MarkupSafe 2.1.1
mkl-fft 1.3.1
mkl-random 1.2.2
mkl-service 2.4.0
multidict 6.0.2
multiprocess 0.70.13
murmurhash 1.0.7
numexpr 2.8.1
numpy 1.22.3
oauth2client 4.1.3
oauthlib 3.2.1
opt-einsum 3.3.0
packaging 21.3
pandas 1.4.2
pathtools 0.1.2
pathy 0.6.1
pip 21.1.2
preshed 3.0.6
promise 2.3
protobuf 4.21.9
psutil 5.9.1
pyarrow 8.0.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pybind11 2.9.2
pycountry 22.3.5
pycparser 2.21
pydantic 1.8.2
PyJWT 2.4.0
pylzma 0.5.0
pyOpenSSL 22.0.0
pyparsing 3.0.4
PySocks 1.7.1
python-dateutil 2.8.2
pytz 2021.3
PyYAML 6.0
regex 2021.4.4
requests 2.28.1
requests-oauthlib 1.3.1
responses 0.18.0
rsa 4.8
sacremoses 0.0.45
scikit-learn 1.1.1
scipy 1.8.1
sentencepiece 0.1.96
sentry-sdk 1.6.0
setproctitle 1.2.3
setuptools 65.5.0
shortuuid 1.0.9
six 1.16.0
smart-open 5.2.1
smmap 5.0.0
soupsieve 2.3.2.post1
spacy 3.3.1
spacy-legacy 3.0.9
spacy-loggers 1.0.2
srsly 2.4.3
tabulate 0.8.9
tensorboard 2.9.1
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.1
tensorflow 2.9.1
tensorflow-estimator 2.9.0
termcolor 2.1.0
thinc 8.0.17
threadpoolctl 3.1.0
tokenizers 0.12.1
torch 1.13.0
tqdm 4.64.0
transformers 4.20.1
typer 0.4.1
typing-extensions 4.3.0
Unidecode 1.3.6
urllib3 1.26.12
wandb 0.12.20
wasabi 0.9.1
web-anno-tsv 0.0.1
Werkzeug 2.1.2
wget 3.2
wheel 0.35.1
wrapt 1.14.1
xxhash 3.0.0
yarl 1.8.1
zipp 3.8.0
Python 3.8.10
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"The [timit_asr.py](https://huggingface.co/datasets/timit_asr/blob/main/timit_asr.py) script iterates over the WAV files per split directory using this:\r\n```python\r\nwav_paths = sorted(Path(data_dir).glob(f\"**/{split}/**/*.wav\"))\r\nwav_paths = wav_paths if wav_paths else sorted(Path(data_dir).glob(f\"**/{split.upper()}/**/*.WAV\"))\r\n```\r\n\r\nCan you check that there is a directory named \"test\" somewhere in your timit data directory ?"
] | 2022-11-26T10:18:22 | 2023-02-10T16:33:21 | 2023-02-10T16:33:21 | NONE | null | ### Describe the bug
When I use the function ```timit = load_dataset('timit_asr', data_dir=data_dir)```, it only loads train split, not test split.
I tried to change the directory and filename to lower case to upper case for the test split, but it does not work at all.
```python
DatasetDict({
train: Dataset({
features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
num_rows: 4620
})
test: Dataset({
features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
num_rows: 0
})
})
```
The directory structure of both splits are same. (DIALECT_REGION / SPEAKER_CODE / DATA_FILES)
### Steps to reproduce the bug
1. just use ```timit = load_dataset('timit_asr', data_dir=data_dir)```
### Expected behavior
```python
DatasetDict({
train: Dataset({
features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
num_rows: 4620
})
test: Dataset({
features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
num_rows: 1680
})
})
```
### Environment info
- ubuntu 20.04
- python 3.9.13
- datasets 2.7.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5303 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5303/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5303/comments | https://api.github.com/repos/huggingface/datasets/issues/5303/events | https://github.com/huggingface/datasets/pull/5303 | 1,464,837,251 | PR_kwDODunzps5DuVTa | 5,303 | Skip dataset verifications by default | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-25T18:39:09 | 2023-02-13T16:50:42 | 2023-02-13T16:43:47 | CONTRIBUTOR | null | Skip the dataset verifications (split and checksum verifications, duplicate keys check) by default unless a dataset is being tested (`datasets-cli test/run_beam`). The main goal is to avoid running the checksum check in the default case due to how expensive it can be for large datasets.
PS: Maybe we should deprecate `ignore_verifications`, which is `True` now by default, and give it a different name? | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-25T17:09:21 | 2022-12-09T14:20:15 | 2022-12-09T14:17:20 | CONTRIBUTOR | null | Clarify in the docstrings what happens when `use_auth_token` is `None` and deprecate the `use_auth_token` param in `download_and_prepare`. | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-25T16:35:54 | 2023-02-21T13:13:13 | 2023-02-21T13:13:13 | MEMBER | null | ...instead of a DatasetDict.
```python
# now supported
ds = load_dataset("squad")
ds[0]
for example in ds:
pass
# still works
ds["train"]
ds["validation"]
# new
ds.splits # Dict[str, Dataset] | None
# soon to be supported (not in this PR)
ds = load_dataset("dataset_with_no_splits")
ds[0]
for example in ds:
pass
```
I implemented `Dataset.__getitem__` and `IterableDataset.__getitem__` to be able to get a split from a dataset.
The splits are defined by the `ds.info.splits` dictionary.
Therefore a dataset is a table that optionally has some splits defined in the dataset info. And a split dataset is the concatenation of all its splits.
I made as little breaking changes as possible. Notable breaking changes:
- `load_dataset("potato").keys() / .items() / .values() /` don't work anymore, since we don't return a dict
- same for `for split_name in load_dataset("potato")`, since we now iterate on the examples
- ..
TODO:
- [x] Update push_to_hub
- [x] Update save_to_disk/load_from_disk
- [ ] check for other breaking changes
- [ ] fix existing tests
- [ ] add new tests
- [ ] docs
This is related to https://github.com/huggingface/datasets/issues/5189, to extend `load_dataset` to return datasets without splits | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-25T15:37:42 | 2022-12-07T12:55:39 | 2022-12-07T12:52:51 | CONTRIBUTOR | null | Use the same `num_proc` value for data download and generation. Additionally, do not set `num_proc` to 16 in `DownloadManager` by default (`num_proc` now has to be specified explicitly). | {
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Fix #5298. | {
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From its implementation:
```python
def xopen(file: str, mode="r", *args, **kwargs):
file = _as_posix(PurePath(file))
main_hop, *rest_hops = file.split("::")
if is_local_path(main_hop):
return open(file, mode, *args, **kwargs)
```
On a Windows machine, if we pass the argument:
```python
xopen("C:\\Users\\USERNAME\\filename.txt")
```
it returns
```python
open("C:/Users/USERNAME/filename.txt")
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Fix #5296. | {
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] | null | [] | 2022-11-25T13:29:33 | 2022-11-29T08:05:13 | 2022-11-29T08:05:13 | MEMBER | null | Currently, `xjoin` function has a bug with local Windows pathnames: instead of returning the OS-dependent join pathname, it always returns it in POSIX format.
```python
from datasets.download.streaming_download_manager import xjoin
path = xjoin("C:\\Users\\USERNAME", "filename.txt")
```
Join path should be:
```python
"C:\\Users\\USERNAME\\filename.txt"
```
However it is:
```python
"C:/Users/USERNAME/filename.txt"
``` | {
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Fix #5293. | {
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This feature will be useful e.g. for datasets containing text files and annotated files with the same name but different extension. | {
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"- Build docs for 2.6.2:\r\n - Commit: a6a5a1cf4cdf1e0be65168aed5a327f543001fe8\r\n - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539470622/jobs/5941404044\r\n- Build docs for 2.7.1:\r\n - Commit: 5ef1ab1cc06c2b7a574bf2df454cd9fcb071ccb2\r\n - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539574442/jobs/5941636792"
] | 2022-11-24T09:42:10 | 2022-11-24T10:10:02 | 2022-11-24T10:10:02 | MEMBER | null | After the patch releases [2.7.1](https://github.com/huggingface/datasets/releases/tag/2.7.1) and [2.6.2](https://github.com/huggingface/datasets/releases/tag/2.6.2), the online docs were not properly built (the build_documentation workflow was not triggered).
There was a fix by:
- #5291
However, both documentations were built from main branch, instead of their corresponding version branch.
We are rebuilding them. | {
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https://api.github.com/repos/huggingface/datasets/issues/5287 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5287/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5287/comments | https://api.github.com/repos/huggingface/datasets/issues/5287/events | https://github.com/huggingface/datasets/pull/5287 | 1,461,971,889 | PR_kwDODunzps5Dkttf | 5,287 | Fix methods using `IterableDataset.map` that lead to `features=None` | {
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This PR is related to #3888, #5245, and #5284
## β
Current solution
The code in this PR is basically making sure that if the features were there since the beginning and a `rename_column`/`rename_columns` happens, those are kept and the rename is applied to the `Features` too. Also, if the features were not there before applying `rename_column`, `rename_columns` or `remove_columns`, a batch is prefetched and the features are being inferred (that could potentially be part of `IterableDataset.__init__` in case the `info.features` value is `None`).
## π‘ Ideas
Some ideas were proposed in https://github.com/huggingface/datasets/issues/3888, but probably the most consistent solution even though it may take some time is to actually do the type inferencing during the `IterableDataset.__init__` in case the provided `info.features` is `None`, otherwise, we can just use the provided features.
Additionally, as mentioned at https://github.com/huggingface/datasets/issues/3888, we could also include a `features` parameter to the `map` function, but that's probably more tedious.
Also thanks to @lhoestq for sharing some ideas in both https://github.com/huggingface/datasets/issues/3888 and https://github.com/huggingface/datasets/issues/5245 :hugs: | {
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https://api.github.com/repos/huggingface/datasets/issues/5286 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5286/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5286/comments | https://api.github.com/repos/huggingface/datasets/issues/5286/events | https://github.com/huggingface/datasets/issues/5286 | 1,461,908,087 | I_kwDODunzps5XIvJ3 | 5,286 | FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json | {
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"I found a solution \r\n\r\nIf you specifically install datasets==1.18 and then run\r\n\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.en')\r\nthen this should work (it worked for me.)"
] | 2022-11-23T14:54:15 | 2022-11-25T11:33:14 | 2022-11-25T11:33:14 | NONE | null | ### Describe the bug
I follow the steps provided on the website [https://huggingface.co/datasets/wikipedia](https://huggingface.co/datasets/wikipedia)
$ pip install apache_beam mwparserfromhell
>>> from datasets import load_dataset
>>> load_dataset("wikipedia", "20220301.en")
however this results in the following error:
raise MissingBeamOptions(
datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')`
If I then prompt the system with:
>>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
the following error occurs:
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
Here is the exact code:
Python 3.10.6 (main, Nov 2 2022, 18:53:38) [GCC 11.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> load_dataset('wikipedia', '20220301.en')
Downloading and preparing dataset wikipedia/20220301.en to /home/[EDITED]/.cache/huggingface/datasets/wikipedia/20220301.en/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 15.3k/15.3k [00:00<00:00, 22.2MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1879, in _download_and_prepare
raise MissingBeamOptions(
datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')`
>>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
Downloading and preparing dataset wikipedia/20220301.en to /home/[EDITED]/.cache/huggingface/datasets/wikipedia/20220301.en/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 15.3k/15.3k [00:00<00:00, 18.8MB/s]
Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1909, in _download_and_prepare
super()._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 891, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/rorytol/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559/wikipedia.py", line 945, in _split_generators
downloaded_files = dl_manager.download_and_extract({"info": info_url})
File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 447, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 311, in download
downloaded_path_or_paths = map_nested(
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 444, in map_nested
mapped = [
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 445, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 346, in _single_map_nested
return function(data_struct)
File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 338, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/file_utils.py", line 183, in cached_path
output_path = get_from_cache(
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/file_utils.py", line 530, in get_from_cache
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
### Steps to reproduce the bug
$ pip install apache_beam mwparserfromhell
>>> from datasets import load_dataset
>>> load_dataset("wikipedia", "20220301.en")
>>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
### Expected behavior
Download the dataset
### Environment info
Running linux on a remote workstation operated through a macbook terminal
Python 3.10.6
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Related to https://github.com/huggingface/datasets/issues/5276 | {
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"Related to https://github.com/huggingface/datasets/issues/5245",
"#self-assign",
"Thanks @lhoestq and @alvarobartt!\r\n\r\nThis would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!\r\n\r\n_c.f._ https://twitter.com/sanchitgandhi99/status/1592188332171493377",
"> Thanks @lhoestq and @alvarobartt!\n> \n> \n> \n> This would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!\n> \n> \n> \n> _c.f._ https://twitter.com/sanchitgandhi99/status/1592188332171493377\n\nI'm almost done with at least a temporary fix to `rename_column`, `rename_columns`, and `remove_columns`, just trying to figure out how to extend it to the `map` function itself!\n\nI'll probably open the PR for review either tomorrow or Sunday hopefully! Glad I can help you and HuggingFace π€ ",
"Awesome - thank you so much for this PR @alvarobartt! Is much appreciated!",
"@sanchit-gandhi PR is ready and open for review at #5287, but there's still one issue I may need @lhoestq's input :hugs:",
"Let us know @sanchit-gandhi if you need a new release of `datasets` soon with this fix included :)",
"Thanks for the fix guys! We can direct people to install `datasets` from main if that's easier!",
"Hey guys, any update around this? I'm facing the same issue with a streamable dataset. ",
"Hi @asennoussi so this was already fixed and released as part of https://github.com/huggingface/datasets/releases/tag/2.8.0, so you should be able to install it as `pip install datasets==2.8.0` or just to use `pip install datasets --upgrade` to get the latest version, as of now, the https://github.com/huggingface/datasets/releases/tag/2.9.0 released last week! π€",
"Still facing the same issue though: \r\n```\r\nfrom datasets import IterableDatasetDict, load_dataset\r\n\r\nraw_datasets = vectorized_datasets = IterableDatasetDict()\r\n\r\n\r\nraw_datasets[\"train\"] = load_dataset(\"asennoussi/private\", split=\"train\", use_auth_token=True, streaming=True)\r\nraw_datasets[\"test\"] = load_dataset(\"asennoussi/private\", split=\"test\", use_auth_token=True, streaming=True)\r\n\r\nprint(\"Original features: \", raw_datasets['train'].features.keys())\r\n\r\n...\r\n\r\ndef prepare_dataset(batch):\r\n\r\n # load and (possibly) resample audio datato 16kHz\r\n audio = batch[\"audio\"]\r\n\r\n # compute log-Mel input features from input audio array \r\n batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\r\n # compute input length of audio sample in seconds\r\n batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\r\n \r\n # optional pre-processing steps\r\n transcription = batch[\"sentence\"]\r\n \r\n # encode target text to label ids\r\n batch[\"labels\"] = processor.tokenizer(transcription).input_ids\r\n batch[\"labels_length\"] = len(batch[\"labels\"])\r\n return batch\r\n...\r\nvectorized_datasets = vectorized_datasets.remove_columns(['input_length', 'labels_length']+list(next(iter(raw_datasets.values())).features))\r\nprint(\"Processed features: \", vectorized_datasets['train'].features)\r\nprint(\"First sample:\", next(iter(vectorized_datasets['train'])))\r\n\r\n```\r\n\r\nOutput: \r\n```\r\nOriginal features: dict_keys(['path', 'audio', 'sentence'])\r\nProcessed features: None\r\n```",
"Hmm weird, could you try to print\r\n\r\n```python\r\nprint(\"Processed features: \", vectorized_datasets['train'].features)\r\n```\r\n\r\nagain after iterating over the `vectorized_datasets`? In the code above, should be last line :)",
"Didn't seem to fix it: \r\n```\r\nOriginal features: dict_keys(['path', 'audio', 'sentence'])\r\nProcessed features: None\r\nProcessed features: None\r\n```",
"Actually the culprit looks to be this one: \r\n`vectorized_datasets = raw_datasets.map(prepare_dataset).with_format(\"torch\")`\r\nWhen I remove this line: `vectorized_datasets = vectorized_datasets.remove_columns(['input_length', 'labels_length']+list(next(iter(raw_datasets.values())).features))`\r\n\r\nI still get \r\n```\r\nProcessed features: None\r\n```",
"The culprit is definitely `.map` \r\nJust validated it. \r\nAny idea please? ",
"> The culprit is definitely `.map` Just validated it. Any idea please?\r\n\r\nYes, indeed `.map` losses the features, because AFAIK pre-fetching the data to infer the features is expensive and not ideal, that's part of this issue https://github.com/huggingface/datasets/issues/3888\r\n\r\nAnyway, now you can pass the `features` as a param to `.map` as follows:\r\n\r\n```python\r\nfrom datasets import Features\r\nvectorized_datasets = raw_datasets.map(\r\n prepare_dataset,\r\n features=Features(\r\n {\"path\": raw_datasets[\"train\"].info.features[\"path\"], \"audio\": raw_datasets[\"train\"].info.features[\"audio\"], \"sentence\": raw_datasets[\"train\"].info.features[\"sentence\"]}\r\n ),\r\n).with_format(\"torch\")\r\n```\r\n\r\nAlso, to let you know, when calling `.remove_columns` over an `IterableDataset`, the `features` are not lost, as well as `.rename_column` and `rename_columns` :)\r\n\r\nMore information about the latter at https://github.com/huggingface/datasets/pull/5287",
"@asennoussi alternatively you can just call `._resolve_features()` from your `IterableDataset` and it will pre-fetch the data to resolve the features, but note that feature-inference is not as accurate as if you manually specify which features and feature-types the `IterableDataset` has, as mentioned in the comment above, the alternative is to provide `features` param to `.map` :hugs:",
"Got it thanks a lot! "
] | 2022-11-23T10:54:59 | 2023-02-02T09:05:51 | 2022-11-28T12:53:24 | CONTRIBUTOR | null | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
# check original features
print("Original features: ", dataset.features.keys())
# define features to remove: we KEEP audio and text
COLUMNS_TO_REMOVE = ['chapter_id', 'speaker_id', 'file', 'id']
dataset = dataset.remove_columns(COLUMNS_TO_REMOVE)
# check processed features, uh-oh!
print("Processed features: ", dataset.features)
# streaming the first audio sample still works
print("First sample:", next(iter(ds)))
```
**Print Output:**
```
Original features: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])
Processed features: None
First sample: {'audio': {'path': '2277-149896-0000.flac', 'array': array([ 0.00186157, 0.0005188 , 0.00024414, ..., -0.00097656,
-0.00109863, -0.00146484]), 'sampling_rate': 16000}, 'text': "HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE"}
```
### Expected behavior
The features should be those **not** removed by the `remove_column` method, i.e. audio and text.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
(Running on Google Colab for a blog post: https://colab.research.google.com/drive/1ySCQREPZEl4msLfxb79pYYOWjUZhkr9y#scrollTo=8pRDGiVmH2ml)
cc @polinaeterna @lhoestq | {
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"Hi ! Can you \r\n```python\r\nimport platform\r\nprint(platform.python_version())\r\n```\r\nto see that it returns ?",
"Hi,\n\n3.8.13\n\nGet Outlook for Android<https://aka.ms/AAb9ysg>\n________________________________\nFrom: Quentin Lhoest ***@***.***>\nSent: Tuesday, November 22, 2022 2:37:02 PM\nTo: huggingface/datasets ***@***.***>\nCc: feketedavid1012 ***@***.***>; Author ***@***.***>\nSubject: Re: [huggingface/datasets] Import error (Issue #5280)\n\n\nHi ! Can you\n\nimport platform\nprint(platform.python_version())\n\nto see that it returns ?\n\nβ\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/5280#issuecomment-1323691385>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AJW7F5YGG32W6WABYC25NJTWJTD75ANCNFSM6AAAAAASHZJ2AU>.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>\n",
"Then it should work as expected if you use the same python when using `datasets`\r\n\r\nPlease make sure you're running your code in the right environment",
"It's the right environment. But in if statement I have\n\"3.8.13\" < 3.7\nAnd in the error message is Python>=3.7 which is true in my case (3.8.13 is greater then 3.7), so I don't understand my python should be below the 3.7 which case the if statement is right, but the message is wrong, or above 3.7 which case if statement is wrong, error message is right.\n\nGet Outlook for Android<https://aka.ms/AAb9ysg>\n________________________________\nFrom: Quentin Lhoest ***@***.***>\nSent: Tuesday, November 22, 2022 2:41:43 PM\nTo: huggingface/datasets ***@***.***>\nCc: feketedavid1012 ***@***.***>; Author ***@***.***>\nSubject: Re: [huggingface/datasets] Import error (Issue #5280)\n\n\nThen it should work as expected if you use the same python when using datasets\n\nPlease make sure you're running your code in the right environment\n\nβ\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/5280#issuecomment-1323697094>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AJW7F54JURTAJJWWDO2QGI3WJTERPANCNFSM6AAAAAASHZJ2AU>.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>\n",
"If you're having an error then you're not running your code in the right environment."
] | 2022-11-22T12:56:43 | 2022-12-15T19:57:40 | 2022-12-15T19:57:40 | NONE | null | https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28
Hy,
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message wrong) | {
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cc @ola13 | {
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"Can you try to remove \"drop_labels=false\" ? It may force the loader to infer the labels instead of reading the metadata",
"Hi, thanks for responding. I tried that, but it does not change anything.",
"Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4",
"Probably the issue, will report back asap!",
"Okay, now it seems to actually load the metadata and create the train_split, but it still says only returns \"image\" and \"label\", which is always 0 since all images are from same folder",
"> Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4\r\n\r\nUpdate: This was the issue."
] | 2022-11-22T10:24:46 | 2023-02-14T14:48:16 | 2022-11-23T11:38:35 | NONE | null | ### Describe the bug
Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column. It just spits out "image" and "label" as features.
Below is code to reproduce my exact example/problem.
### Steps to reproduce the bug
```ruby
dataset_link="19Unu89Ih_kP6zsE7f9Mkw8dy3NwHopRF"
id = dataset_link
output = 'Godardv01.zip'
gdown.download(id=id, output=output, quiet=False)
ds = load_dataset("imagefolder", data_dir="/kaggle/working/Volumes/TOSHIBA/Godard_imgs/Volumes/TOSHIBA/Godard_imgs/Full/train", split="train", drop_labels=False)
print(ds)
```
### Expected behavior
I would expect that it returned "image" and "text" columns from the code above.
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5 | {
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"@huggingface/datasets if you agree, I can make the bulk edit on the Hub to fix integer keys into strings.",
"Ok for me, and we can merge (internal) https://github.com/huggingface/moon-landing/pull/4609",
"FYI there are still 2k+ weekly users on `datasets` 2.6.1 which doesn't support the string label format for class labels. And among those, some are using datasets with class labels like imdb (60 users), conllpp (40), msra_ner (40), peoples_daily_enr (40), weibo_ner (30), conll2003 (20), etc. And renaming to string would break these users code.",
"but isn't `datasets 2.6.1` downloading files from the Hub with the corresponding tag? I thought we had something like this before",
"We're using `main` as models do. Some datasets need to be updated from time to time, e.g. when a link to download the data is dead.\r\n\r\nBut yea a year ago we had those tags, we just ended up not using them",
"I opened https://github.com/huggingface/datasets/issues/5406 to communicate on this. Let me know what you think, and if it sounds good to you I can pin this issue",
"So, is it OK to make the bulk edit on the Hub now or should we wait longer? If the latter, how long?",
"I think we can do it. If you want to be extra cautious you can do it for all datasets except imdb and conllpp for now which are actively used by 2.6.1 users. For those two we can keep the YAML like this for some more time, or alternatively use the old dataset_infos.json file",
"The bulk edit of canonical datasets (except imdb and conllpp) is running. \r\n\r\nSee e.g.: https://huggingface.co/datasets/acronym_identification/discussions/3\r\n\r\nEDITED: \r\nDone, except for \"universal_morphologies\", where I get\r\n```\r\nHTTPError: 413 Client Error: Payload Too Large for url: https://huggingface.co/api/validate-yaml\r\n```\r\n\r\nAlso not done for the datasets missing matadata \"dataset_info\":\r\n- mc4: https://huggingface.co/datasets/mc4/discussions/3\r\n- the_pile: https://huggingface.co/datasets/the_pile/discussions/6\r\n- timit_asr: https://huggingface.co/datasets/timit_asr/discussions/1",
"Thank you !",
"@lhoestq, there are 6 community datasets with YAML integer keys in their `dataset_info` `class_label`:\r\n- indonlp/indonlu\r\n- rcds/swiss_judgment_prediction\r\n- Jean-Baptiste/wikiner_fr\r\n- Bingsu/Cat_and_Dog\r\n- taskydata/tasky_or_not\r\n- RCC-MSU/collection3\r\n\r\nMaybe we could open a PR on them as well?",
"Let's do this then:\r\n\r\n- [x] [indonlp/indonlu](https://huggingface.co/datasets/indonlp/indonlu/discussions/3)\r\n- [x] rcds/swiss_judgment_prediction\r\n- [x] Jean-Baptiste/wikiner_fr\r\n- [x] Bingsu/Cat_and_Dog -> merged\r\n- [x] taskydata/tasky_or_not (was already using quotes)\r\n- [x] RCC-MSU/collection3\r\n\r\nEDIT: all done :)",
"@lhoestq I was not asking you to do it, but asking if you agree me to do it... :man_facepalming: \r\nAs I self-assigned this issue... :sweat_smile: "
] | 2022-11-22T08:14:47 | 2023-01-26T10:52:35 | 2023-01-26T10:40:21 | MEMBER | null | After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets | {
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"@BradleyHsu",
"Btw, thanks very much for finding the hub rollback temporary fix and bringing the issue to our attention @KhoomeiK!",
"I see the same issue when calling `load_dataset('poloclub/diffusiondb', 'large_random_1k')` with `datasets==2.7.1` and `huggingface-hub=0.11.0`. No issue with `datasets=2.6.1` and `huggingface_hub==0.10.1`.\r\n\r\nhttps://github.com/poloclub/diffusiondb/issues/7",
"I fixed my issue by specifying `repo_type` in `hf_hub_url()`. https://github.com/poloclub/diffusiondb/commit/9eb91c79aaca98b0515a0ce45778b8af65b84652\r\n\r\nI opened a PR on the Winoground's repo: https://huggingface.co/datasets/facebook/winoground/discussions/2",
"This is a bug in the script, indeed. The most robust fix is to use a relative path instead of `hf_hub_url`, which does not depend on `huggingface_hub`'s version π. I've opened a PR here: https://huggingface.co/datasets/facebook/winoground/discussions/3.",
"Awesome, big thanks to both @xiaohk and @mariosasko!"
] | 2022-11-21T21:59:53 | 2022-11-28T02:50:42 | 2022-11-28T02:50:42 | MEMBER | null | ### Describe the bug
When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:
```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
165 if repo_id.count("/") > 1:
166 raise HFValidationError(
--> 167 "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
168 f" '{repo_id}'. Use `repo_type` argument if needed."
169 )
HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```
### Steps to reproduce the bug
Install requirements:
```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```
Then:
```
from datasets import load_dataset
auth_token = "" # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```
### Expected behavior
Downloading of the datset
### Environment info
Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing | {
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https://api.github.com/repos/huggingface/datasets/issues/5271 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5271/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5271/comments | https://api.github.com/repos/huggingface/datasets/issues/5271/events | https://github.com/huggingface/datasets/pull/5271 | 1,456,807,738 | PR_kwDODunzps5DTDX1 | 5,271 | Fix #5269 | {
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$ datasets-cli convert --datasets_directory <TAB>
datasets_directory
benchmarks/ docs/ metrics/ notebooks/ src/ templates/ tests/ utils/
```
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"I don't think we need completion on the datasets-cli, since we're mainly developing huggingface-cli",
"I see."
] | 2022-11-19T13:48:59 | 2022-11-21T15:06:15 | 2022-11-21T15:06:14 | NONE | null | ### Feature request
Like <https://github.com/huggingface/huggingface_hub/issues/1197>, datasets-cli maybe need it, too.
### Motivation
See above.
### Your contribution
Maybe. | {
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https://api.github.com/repos/huggingface/datasets/issues/5268 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5268/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5268/comments | https://api.github.com/repos/huggingface/datasets/issues/5268/events | https://github.com/huggingface/datasets/pull/5268 | 1,455,633,978 | PR_kwDODunzps5DPIsp | 5,268 | Sharded save_to_disk + multiprocessing | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-18T18:50:01 | 2022-12-14T18:25:52 | 2022-12-14T18:22:58 | MEMBER | null | Added `num_shards=` and `num_proc=` to `save_to_disk()`
EDIT: also added `max_shard_size=` to `save_to_disk()`, and also `num_shards=` to `push_to_hub`
I also:
- deprecated the fs parameter in favor of storage_options (for consistency with the rest of the lib) in save_to_disk and load_from_disk
- always embed the image/audio data in arrow when doing `save_to_disk`
- added a tqdm bar in `save_to_disk`
- Use the MockFileSystem in tests for `save_to_disk` and `load_from_disk`
- removed the unused integration tests with S3, since we can now test with `mockfs` instead of `s3fs`
TODO:
- [x] implem save_to_disk for dataset dict
- [x] save_to_disk for dataset dict tests
- [x] deprecate fs in dataset dict load_from_disk as well
- [x] update docs
Close #5263
Close https://github.com/huggingface/datasets/issues/4196
Close https://github.com/huggingface/datasets/issues/4351 | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-18T14:58:47 | 2022-11-21T15:45:02 | 2022-11-21T15:41:57 | CONTRIBUTOR | null | Fixes a warning and future deprecation from `librosa.reshape`:
```
FutureWarning: Pass orig_sr=16000, target_sr=48000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
``` | {
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"I think `stream` could be misleading since the data is not being streamed from remote endpoints (one could think that's the case when they see `load_dataset` followed by `stream`). Hence, I prefer the second option.\r\n\r\nPS: When we resolve https://github.com/huggingface/datasets/issues/4542, we could add `as_tf_dataset` to the API for consistency and deprecate `to_tf_dataset`."
] | 2022-11-18T14:54:40 | 2023-02-01T16:36:03 | 2023-02-01T16:36:03 | MEMBER | null | This is useful to leverage iterable datasets specific features like:
- fast approximate shuffling
- lazy map, filter etc.
Iterating over the resulting iterable dataset should be at least as fast at iterating over the map-style dataset.
Here are some ideas regarding the API:
```python
# 1.
# - consistency with load_dataset(..., streaming=True)
# - gives intuition that map/filter/etc. are done on-the-fly
ids = ds.stream()
# 2.
# - more explicit on the output type
# - but maybe sounds like a conversion tool rather than a step in a processing pipeline
ids = ds.as_iterable_dataset()
``` | {
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"Could you share the full stack trace please ?\r\n\r\n\r\nCan you also try running this code ? It can be useful to determine if the issue comes from `datasets` or `fsspec` (streaming) or `pyarrow` (parquet reading):\r\n```python\r\nds = load_dataset(\"parquet\", data_files=a_parquet_file_url, use_auth_token=True)\r\n```",
"Here's the full trace\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/loubna_huggingface_co/load.py\", line 15, in <module>\r\n ds_all = load_dataset(\"bigcode/the-stack-dedup-pjj\", data_dir=\"data/java\",use_auth_token=True, split=\"train\", revision=\"v1.1.a1\")\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1742, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 814, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 905, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 1502, in _prepare_split\r\n for key, table in logging.tqdm(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py\", line 1195, in __iter__\r\n for obj in iterable:\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py\", line 67, in _generate_tables\r\n parquet_file = pq.ParquetFile(f)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/parquet/__init__.py\", line 286, in __init__\r\n self.reader.open(\r\n File \"pyarrow/_parquet.pyx\", line 1227, in pyarrow._parquet.ParquetReader.open\r\n File \"pyarrow/error.pxi\", line 100, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\r\n```\r\n\r\nwhen running\r\n```python\r\nds = load_dataset(\"parquet\", data_files=\"https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/blob/v1.1.a1/data/java/data_0000.parquet\", use_auth_token=True)\r\n```\r\nI get 401 error, but that's the case for the python subset too which I can load properly\r\n```\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1719, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1497, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1134, in dataset_module_factory\r\n return PackagedDatasetModuleFactory(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 707, in get_module\r\n data_files = DataFilesDict.from_local_or_remote(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py\", line 795, in from_local_or_remote\r\n DataFilesList.from_local_or_remote(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py\", line 764, in from_local_or_remote\r\n origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py\", line 710, in _get_origin_metadata_locally_or_by_urls\r\n return thread_map(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py\", line 94, in thread_map\r\n return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py\", line 76, in _executor_map\r\n return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py\", line 1183, in __iter__\r\n for obj in iterable:\r\n File \"/opt/conda/envs/venv/lib/python3.9/concurrent/futures/_base.py\", line 609, in result_iterator\r\n yield fs.pop().result()\r\n File \"/opt/conda/envs/venv/lib/python3.9/concurrent/futures/_base.py\", line 446, in result\r\n return self.__get_result()\r\n File \"/opt/conda/envs/venv/lib/python3.9/concurrent/futures/_base.py\", line 391, in __get_result\r\n raise self._exception\r\n File \"/opt/conda/envs/venv/lib/python3.9/concurrent/futures/thread.py\", line 58, in run\r\n result = self.fn(*self.args, **self.kwargs)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py\", line 701, in _get_single_origin_metadata_locally_or_by_urls\r\n return (request_etag(data_file, use_auth_token=use_auth_token),)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 411, in request_etag\r\n response.raise_for_status()\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/requests/models.py\", line 960, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/blob/v1.1.a1/data/python/data_0000.parquet```",
"Can you check you used the right token ? You shouldn't get a 401 using your token",
"I checked itβs the right token, when loading the full dataset I get the error after data extraction so I can access the files. \r\n```\r\nDownloading and preparing dataset parquet/bigcode--the-stack-dedup-pjj to /home/loubna_huggingface_co/.cache/huggingface/datasets/bigcode___parquet/bigcode--the-stack-dedup-pjj-872ffac7f4bb46ca/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\r\nDownloading data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 22.38it/s]\r\nExtracting data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 49.91it/s]\r\nTraceback (most recent call last):\r\n File \"/home/loubna_huggingface_co/load_ds.py\", line 5, in <module>\r\n ds = load_dataset(\"bigcode/the-stack-dedup-pjj\", data_dir=\"data/java\", use_auth_token=True,split=\"train\", revision=\"v1.1.a1\")\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1742, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 814, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 905, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 1502, in _prepare_split\r\n for key, table in logging.tqdm(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py\", line 1195, in __iter__\r\n for obj in iterable:\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py\", line 67, in _generate_tables\r\n parquet_file = pq.ParquetFile(f)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/parquet/__init__.py\", line 286, in __init__\r\n self.reader.open(\r\n File \"pyarrow/_parquet.pyx\", line 1227, in pyarrow._parquet.ParquetReader.open\r\n File \"pyarrow/error.pxi\", line 100, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\r\n```\r\nCould it be that I'm using a wrong url, I just copied it from the address bar",
"The URL is wrong indeed, the right one is the one with \"resolve\" (the one you get when clicking on \"download\")- otherwise you try to download an html page ;)\r\n```\r\nhttps://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/resolve/v1.1.a1/data/java/data_0000.parquet\r\n```",
"Ah thanks! So I tried it with the first parquet file and it works, is there a way to know which parquet file was causing the issue since there are a lot of shards?",
"I think you have to try them all :/\r\n\r\nAlternatively you can add a try/catch in `parquet.py` in `datasets` to raise the name of the file that fails at doing `parquet_file = pq.ParquetFile(f)` when you run your initial code\r\n```python\r\nload_dataset(\"bigcode/the-stack-dedup-pjj\", data_dir=\"data/java\", split=\"train\", revision=\"v1.1.a1\", use_auth_token=True)\r\n```\r\nbut it will still iterate on all the files until it fails",
"Ok I will do that",
"I did find the file, and I get the same error as before \r\n```\r\nDownloading data files: 100%|βββββββββββββββββββ| 1/1 [00:00<00:00, 8160.12it/s]\r\nExtracting data files: 100%|ββββββββββββββββββββ| 1/1 [00:00<00:00, 1447.81it/s]\r\n \r\n---------------------------------------------------------------------------\r\nArrowInvalid Traceback (most recent call last)\r\nInput In [22], in <cell line: 7>()\r\n 4 data_features = (data[\"train\"].features)\r\n 6 url = \"/home/loubna_huggingface_co/.cache/huggingface/datasets/downloads/93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7\"\r\n----> 7 data = load_dataset(\"parquet\", \r\n 8 data_files=url,\r\n 9 split=\"train\",\r\n 10 features=data_features,\r\n 11 use_auth_token=True)\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py:1742, 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)\r\n 1739 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n 1741 # Download and prepare data\r\n-> 1742 builder_instance.download_and_prepare(\r\n 1743 download_config=download_config,\r\n 1744 download_mode=download_mode,\r\n 1745 ignore_verifications=ignore_verifications,\r\n 1746 try_from_hf_gcs=try_from_hf_gcs,\r\n 1747 use_auth_token=use_auth_token,\r\n 1748 )\r\n 1750 # Build dataset for splits\r\n 1751 keep_in_memory = (\r\n 1752 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1753 )\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py:814, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, storage_options, **download_and_prepare_kwargs)\r\n 808 if not downloaded_from_gcs:\r\n 809 prepare_split_kwargs = {\r\n 810 \"file_format\": file_format,\r\n 811 \"max_shard_size\": max_shard_size,\r\n 812 **download_and_prepare_kwargs,\r\n 813 }\r\n--> 814 self._download_and_prepare(\r\n 815 dl_manager=dl_manager,\r\n 816 verify_infos=verify_infos,\r\n 817 **prepare_split_kwargs,\r\n 818 **download_and_prepare_kwargs,\r\n 819 )\r\n 820 # Sync info\r\n 821 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py:905, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 901 split_dict.add(split_generator.split_info)\r\n 903 try:\r\n 904 # Prepare split will record examples associated to the split\r\n--> 905 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n 906 except OSError as e:\r\n 907 raise OSError(\r\n 908 \"Cannot find data file. \"\r\n 909 + (self.manual_download_instructions or \"\")\r\n 910 + \"\\nOriginal error:\\n\"\r\n 911 + str(e)\r\n 912 ) from None\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py:1502, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, max_shard_size)\r\n 1500 total_num_examples, total_num_bytes = 0, 0\r\n 1501 try:\r\n-> 1502 for key, table in logging.tqdm(\r\n 1503 generator,\r\n 1504 unit=\" tables\",\r\n 1505 leave=False,\r\n 1506 disable=not logging.is_progress_bar_enabled(),\r\n 1507 ):\r\n 1508 if max_shard_size is not None and writer._num_bytes > max_shard_size:\r\n 1509 num_examples, num_bytes = writer.finalize()\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py:1195, in tqdm.__iter__(self)\r\n 1192 time = self._time\r\n 1194 try:\r\n-> 1195 for obj in iterable:\r\n 1196 yield obj\r\n 1197 # Update and possibly print the progressbar.\r\n 1198 # Note: does not call self.update(1) for speed optimisation.\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py:67, in Parquet._generate_tables(self, files)\r\n 65 for file_idx, file in enumerate(itertools.chain.from_iterable(files)):\r\n 66 with open(file, \"rb\") as f:\r\n---> 67 parquet_file = pq.ParquetFile(f)\r\n 68 try:\r\n 69 for batch_idx, record_batch in enumerate(\r\n 70 parquet_file.iter_batches(batch_size=self.config.batch_size, columns=self.config.columns)\r\n 71 ):\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/parquet/__init__.py:286, in ParquetFile.__init__(self, source, metadata, common_metadata, read_dictionary, memory_map, buffer_size, pre_buffer, coerce_int96_timestamp_unit, decryption_properties, thrift_string_size_limit, thrift_container_size_limit)\r\n 280 def __init__(self, source, *, metadata=None, common_metadata=None,\r\n 281 read_dictionary=None, memory_map=False, buffer_size=0,\r\n 282 pre_buffer=False, coerce_int96_timestamp_unit=None,\r\n 283 decryption_properties=None, thrift_string_size_limit=None,\r\n 284 thrift_container_size_limit=None):\r\n 285 self.reader = ParquetReader()\r\n--> 286 self.reader.open(\r\n 287 source, use_memory_map=memory_map,\r\n 288 buffer_size=buffer_size, pre_buffer=pre_buffer,\r\n 289 read_dictionary=read_dictionary, metadata=metadata,\r\n 290 coerce_int96_timestamp_unit=coerce_int96_timestamp_unit,\r\n 291 decryption_properties=decryption_properties,\r\n 292 thrift_string_size_limit=thrift_string_size_limit,\r\n 293 thrift_container_size_limit=thrift_container_size_limit,\r\n 294 )\r\n 295 self.common_metadata = common_metadata\r\n 296 self._nested_paths_by_prefix = self._build_nested_paths()\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/_parquet.pyx:1227, in pyarrow._parquet.ParquetReader.open()\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/error.pxi:100, in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\r\n```",
"Can you check the JSON file associated to `/home/loubna_huggingface_co/.cache/huggingface/datasets/downloads/93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7` ? In the JSON file we can know from where it was downloaded\r\n\r\nYou can find it at `/home/loubna_huggingface_co/.cache/huggingface/datasets/downloads/93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7.json`",
"It's this file `https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/resolve/f48656daa9f3a3607dacf8b57a65810a6a7a7f73/data/java/data_0022.parquet` loading it gives the same error",
"I'm able to load it properly using\r\n```python\r\nds = load_dataset(\"parquet\", data_files=a_parquet_file_url, use_auth_token=token)\r\n```\r\n\r\nMy guess is that your download was corrupted. Please delete `93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7` and `93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7.json` locally and try again",
"That worked, thanks! But I thought if something went wrong with a download `datasets` creates new cache for all the files, that's not the case? (at some point I even changed dataset versions so it was still using that cache?)",
"Cool !\r\n\r\n> But I thought if something went wrong with a download datasets creates new cache for all the files\r\n\r\nWe don't perform integrity verifications if we don't know in advance the hash of the file to download.\r\n\r\n> at some point I even changed dataset versions so it was still using that cache?\r\n\r\n`datasets` caches the files by URL and ETag. If the content of a file changes, then the ETag changes and so it redownloads the file",
"I see, thank you!\r\n"
] | 2022-11-18T14:44:01 | 2022-11-22T11:18:08 | 2022-11-22T11:18:08 | NONE | null | ### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
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https://api.github.com/repos/huggingface/datasets/issues/5263 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5263/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5263/comments | https://api.github.com/repos/huggingface/datasets/issues/5263/events | https://github.com/huggingface/datasets/issues/5263 | 1,455,252,626 | I_kwDODunzps5WvWSS | 5,263 | Save a dataset in a determined number of shards | {
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] | null | [] | 2022-11-18T14:43:54 | 2022-12-14T18:22:59 | 2022-12-14T18:22:59 | MEMBER | null | This is useful to distribute the shards to training nodes.
This can be implemented in `save_to_disk` and can also leverage multiprocessing to speed up the process | {
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https://api.github.com/repos/huggingface/datasets/issues/5262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5262/comments | https://api.github.com/repos/huggingface/datasets/issues/5262/events | https://github.com/huggingface/datasets/issues/5262 | 1,455,171,100 | I_kwDODunzps5WvCYc | 5,262 | AttributeError: 'Value' object has no attribute 'names' | {
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"Hi ! It looks like your \"isDif\" column is a Sequence of Value(\"string\"), not a Sequence of ClassLabel.\r\n\r\nYou can convert your Value(\"string\") feature type to a ClassLabel feature type this way:\r\n```python\r\nfrom datasets import ClassLabel, Sequence\r\n\r\n# provide the label_names yourself\r\nlabel_names = [...]\r\n# OR get them from the dataset\r\nlabel_names = sorted(set(label for labels in raw_datasets[\"train\"][\"isDif\"] for label in labels))\r\n\r\n# Cast to ClassLabel\r\nraw_datasets = raw_datasets.cast_column(\"isDif\", Sequence(ClassLabel(names=label_names)))\r\n```\r\n",
"thank you \r\nit works π― "
] | 2022-11-18T13:58:42 | 2022-11-22T10:09:24 | 2022-11-22T10:09:23 | NONE | null | Hello
I'm trying to build a model for custom token classification
I already followed the token classification course on huggingface
while adapting the code to my work, this message occures :
'Value' object has no attribute 'names'
Here's my code:
`raw_datasets`
generates
DatasetDict({
train: Dataset({
features: ['isDisf', 'pos', 'tokens', 'id'],
num_rows: 14
})
})
`raw_datasets["train"][3]["isDisf"]`
generates
['B_RM', 'I_RM', 'I_RM', 'B_RP', 'I_RP', 'O', 'O']
`dis_feature = raw_datasets["train"].features["isDisf"]
dis_feature`
generates
Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)
and
`label_names = dis_feature.feature.names
label_names`
generates
AttributeError Traceback (most recent call last)
[<ipython-input-28-972fd54a869a>](https://localhost:8080/#) in <module>
----> 1 label_names = dis_feature.feature.names
2 label_names
AttributeError: 'Value' object has
AttributeError: 'Value' object has no attribute 'names'
Thank you for your help | {
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https://api.github.com/repos/huggingface/datasets/issues/5259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5259/comments | https://api.github.com/repos/huggingface/datasets/issues/5259/events | https://github.com/huggingface/datasets/issues/5259 | 1,453,555,923 | I_kwDODunzps5Wo4DT | 5,259 | datasets 2.7 introduces sharding error | {
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"I notice a comment in the code says:\r\n`Having lists of different sizes makes sharding ambigious, raise an error in this case until we decide how to define sharding without ambiguity for users` \r\n \r\n ... which suggests this update was pushed knowing that it might break some things. But, it didn't seem to have a useful error message of an argument that could be passed to avoid the error.",
"Sorry for the inconvenience, I opened a PR in your repo to fix this: https://huggingface.co/datasets/sil-ai/bloom-speech/discussions/2\r\n\r\nBasically we've always considered lists in `gen_kwargs` to be a shard list that we can split and pass into different workers to generate the dataset (e.g. if you pass `num_proc=` in `load_dataset()` to generate the dataset in parallel), but it was documented only recently",
"@lhoestq Thanks for the help. It looks like that took care of it."
] | 2022-11-17T15:36:52 | 2022-12-24T01:44:02 | 2022-11-18T12:52:05 | NONE | null | ### Describe the bug
dataset fails to load with runtime error
`RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.`
### Steps to reproduce the bug
With datasets[audio] 2.7 loaded, and logged into hugging face,
`data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)`
creates the error.
Full stack trace:
```---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
[<ipython-input-7-8cb9ca0f79f0>](https://localhost:8080/#) in <module>
----> 1 data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)
5 frames
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)
1745 try_from_hf_gcs=try_from_hf_gcs,
1746 use_auth_token=use_auth_token,
-> 1747 num_proc=num_proc,
1748 )
1749
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
824 verify_infos=verify_infos,
825 **prepare_split_kwargs,
--> 826 **download_and_prepare_kwargs,
827 )
828 # Sync info
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs)
1554 def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs):
1555 super()._download_and_prepare(
-> 1556 dl_manager, verify_infos, check_duplicate_keys=verify_infos, **prepare_splits_kwargs
1557 )
1558
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
911 try:
912 # Prepare split will record examples associated to the split
--> 913 self._prepare_split(split_generator, **prepare_split_kwargs)
914 except OSError as e:
915 raise OSError(
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)
1362 fpath = path_join(self._output_dir, fname)
1363
-> 1364 num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs)
1365 if num_input_shards <= 1 and num_proc is not None:
1366 logger.warning(
[/usr/local/lib/python3.7/dist-packages/datasets/utils/sharding.py](https://localhost:8080/#) in _number_of_shards_in_gen_kwargs(gen_kwargs)
16 + "\n".join(f"\t- key {key} has length {length}" for key, length in lists_lengths.items())
17 + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
---> 18 + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
19 )
20 )
RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.```
### Expected behavior
the dataset loads in datasets version 2.6.1 and should load with datasets 2.7
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | {
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"The bulk edit is running...\r\n\r\nSee for example: \r\n- A single config: https://huggingface.co/datasets/acronym_identification/discussions/2\r\n- Multiple configs: https://huggingface.co/datasets/babi_qa/discussions/1",
"TODO: Add \"dataset_info\" YAML metadata to:\r\n- [x] \"chr_en\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n - Fixing PR: https://huggingface.co/datasets/chr_en/discussions/1 \r\n- [x] \"conll2000\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"crime_and_punish\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"dart\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"iwslt2017\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [ ] \"mc4\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n- [ ] \"the_pile\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n- [ ] \"timit_asr\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card",
"The bulk edit is finished."
] | 2022-11-17T15:13:15 | 2023-02-16T09:49:05 | 2022-11-19T06:51:37 | MEMBER | null | After a bulk edit of canonical datasets to create the YAML `dataset_info` metadata, the split names were accidentally sorted alphabetically. See for example:
- https://huggingface.co/datasets/bc2gm_corpus/commit/2384629484401ecf4bb77cd808816719c424e57c
Note that this order is the one appearing in the preview of the datasets.
I'm making a bulk edit to align the order of the splits appearing in the metadata info with the order appearing in the loading script.
Related to:
- #5202 | {
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"Also cc @mariosasko and @lhoestq ",
"Cool ! Let us know if you have questions or if we can help :)\r\n\r\nI guess we'll also have to create the NYU CS Department on the Hub ?",
"> I guess we'll also have to create the NYU CS Department on the Hub ?\r\n\r\nYes, you're right! Let me add it to my profile first, and then we can transfer. Meanwhile, if it's recommended to loop the dataset author in here, let me know. \r\n\r\nAlso, the NYU Depth dataset seems big. Any example scripts for creating image datasets that I could refer? ",
"You can check the imagenet-1k one.\r\n\r\nPS: If the licenses allows it, it'b be nice to host the dataset as sharded TAR archives (like imagenet-1k) instead of the ZIP format they use:\r\n- it will make streaming much faster\r\n- ZIP compression is not well suited for images\r\n- it will allow parallel processing of the dataset (you can pass a subset of shards to each worker)\r\n\r\n> if it's recommended to loop the dataset author in here, let me know.\r\n\r\nIt's recommended indeed, you can send them an email once you have the dataset ready and invite them to the org on the Hub",
"> You can check the imagenet-1k one.\r\n\r\nWhere can I find the script? Are you referring to https://huggingface.co/docs/datasets/image_process ? Or is there anything more specific? ",
"You can find it here: https://huggingface.co/datasets/imagenet-1k/blob/main/imagenet-1k.py",
"Update: started working on it here: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2. \r\n\r\nI am facing an issue and I have detailed it here: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/discussions/1\r\n\r\nEdit: The issue is gone. \r\n\r\nHowever, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive? \r\n\r\n@lhoestq \r\n\r\n",
"A Colab Notebook demonstrating the dataset loading part: \r\n\r\nhttps://colab.research.google.com/gist/sayakpaul/aa0958c8d4ad8518d52a78f28044d871/scratchpad.ipynb\r\n\r\n@osanseviero @lhoestq \r\n\r\nI will work on a notebook to work with the dataset including data visualization.",
"@osanseviero @lhoestq things seem to work fine with the current version of the dataset [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2). Here's a notebook I developed to help with visualization: https://colab.research.google.com/drive/1K3ZU8XUPRDOYD38MQS9nreQXJYitlKSW?usp=sharing. \r\n\r\n@lhoestq I need your help with the following:\r\n\r\n> However, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive?\r\n\r\n@osanseviero @lhoestq question for you:\r\n\r\nWhere should we host the dataset? I think hosting it under hf.co/datasets (that is HF is the org) is fine as we have ImageNet-1k hosted similarly. We could then reach out to Diana Wofk (author of [Fast Depth](https://github.com/dwofk/fast-depth) and the owner of the repo on which TFDS NYU Depth V2 is based) for a review. WDYT? ",
"> However, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive?\r\n\r\nFirst you can separate the train data and the validation data.\r\n\r\nThen since the dataset is quite big, you can even shard the train split and the validation split in multiple TAR archives. Something around 16 archives for train and 4 for validation would be fine for example.\r\n\r\nAlso no need to gzip the TAR archives, the images are already compressed in png or jpeg.",
"> Then since the dataset is quite big, you can even shard the train split and the validation split in multiple TAR archives. Something around 16 archives for train and 4 for validation would be fine for example.\r\n\r\nYes, I got you. But this process seems to be manual and should be tailored for the given dataset. Do you have any script that you used to create the ImageNet-1k shards? \r\n\r\n> Also no need to gzip the TAR archives, the images are already compressed in png or jpeg.\r\n\r\nI was not going to do that. Not sure what brought it up. ",
"> Yes, I got you. But this process seems to be manual and should be tailored for the given dataset. Do you have any script that you used to create the ImageNet-1k shards?\r\n\r\nI don't, but I agree it'd be nice to have a script for that !\r\n\r\n> I was not going to do that. Not sure what brought it up.\r\n\r\nThe original dataset is gzipped for some reason",
"Oh, I am using this URL for the download: https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py#L24. ",
"> Where should we host the dataset? I think hosting it under hf.co/datasets (that is HF is the org) is fine as we have ImageNet-1k hosted similarly.\r\n\r\nMaybe you can create an org for NYU Courant (this is the institute of the lab of the main author of the dataset if I'm not mistaken), and invite the authors to join.\r\n\r\nWe don't add datasets without namespace anymore",
"Updates: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/discussions/5\r\n\r\nThe entire process (preparing multiple archives, preparing data loading script, etc.) was fun and engaging, thanks to the documentation. I believe we could work on a small blog post that would work as a reference for the future contributors following this path. What say? \r\n\r\nCc: @lhoestq @osanseviero ",
"> I believe we could work on a small blog post that would work as a reference for the future contributors following this path. What say?\r\n\r\n@polinaeterna already mentioned it would be nice to present this process for audio (it's exactly the same), I believe it can be useful to many people",
"Cool. Let's work on that after the NYU Depth Dataset is fully in on Hub (under the appropriate org). π€",
"@lhoestq need to discuss something while I am adding the dataset card to https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/. \r\n\r\nAs per [Papers With Code](https://paperswithcode.com/dataset/nyuv2), NYU Depth v2 is used for many different tasks:\r\n\r\n* Monocular depth estimation\r\n* Depth estimation \r\n* Semantic segmentation\r\n* Plane instance segmentation \r\n* ...\r\n\r\nSo, while writing the supported task part of the dataset card, should we focus on all these? IMO, we could focus on just depth estimation and semantic segmentation for now since we have supported models for these two. WDYT?\r\n\r\nAlso, I am getting: \r\n\r\n\r\n```\r\nremote: Your push was accepted, but with warnings:\r\nremote: - Warning: The task_ids \"depth-estimation\" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering\r\nremote: ----------------------------------------------------------\r\nremote: Please find the documentation at:\r\nremote: https://huggingface.co/docs/hub/model-cards#model-card-metadata\r\n```\r\n\r\nWhat should be the plan of action for this?\r\n\r\nCc: @osanseviero \r\n\r\n",
"> What should be the plan of action for this?\r\n\r\nWhen you merged https://github.com/huggingface/hub-docs/pull/488, there is a JS Interfaces GitHub Actions workflow that runs https://github.com/huggingface/hub-docs/actions/workflows/js-interfaces-tests.yml. It has a step called [export-task scripts](https://github.com/huggingface/hub-docs/actions/runs/3622479064/jobs/6107238948) which exports an interface you can use in `dataset`. If you look at the logs, it prints out a map. This map can replace https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/tasks.json (tasks.json was generated with this script), which should add depth estimation\r\n",
"Thanks @osanseviero. \r\n\r\nhttps://github.com/huggingface/datasets/pull/5335",
"Closing the issue as the dataset has been successfully added: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2"
] | 2022-11-17T03:22:22 | 2022-12-17T12:20:38 | 2022-12-17T12:20:37 | MEMBER | null | ### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it. | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-16T15:02:13 | 2022-12-13T17:01:54 | 2022-12-13T16:59:04 | CONTRIBUTOR | null | Add support for decoding the `Image`/`Audio` types in `map` for the formats (Numpy, TF, Jax, PyTorch) other than the default one (Python).
Additional improvements:
* make `Dataset`'s "iter" API cleaner by removing `_iter` and replacing `_iter_batches` with `iter(batch_size)` (also implemented for `IterableDataset`)
* iterate over arrow tables in `map` to avoid `_getitem` calls, which are much slower than `__iter__`/`iter(batch_size)`, when the `format_type` is not Python
* fix `_iter_batches` (now named `iter`) when `drop_last_batch=True` and `pyarrow<=8.0.0` is installed
* lazily extract and decode arrow data in the default format
TODO:
* [x] update the `iter` benchmark in the docs (the `BeamBuilder` cannot load the preprocessed datasets from our bucket, so wait for this to be fixed (cc @lhoestq))
Fix https://github.com/huggingface/datasets/issues/3992, fix https://github.com/huggingface/datasets/issues/3756 | {
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"After a discussion with @mishig25:\r\n- He said that this action should be triggered if we call our release branch according to the regex `v*-release`, as transformers does\r\n- I said that our procedure is different: our release branch is *temporary* and it is deleted just after the release PR is merged to main\r\n - Indeed the release tag is not yet created when we make the release PR (not event when this is merged to main), but when we make the Release itself.\r\n\r\nI was thinking that maybe we could change the triggering event: use `release` instead of `push`.\r\n\r\nWhat do you think, @huggingface/datasets?",
"Why is it an issue if our branch is temporary ?",
"He says not; but the branch has no tag yet; does the doc building require the tag? Or just the version number in `__init__.py` or setup.py?",
"It uses `module.__version__` (i.e. the one defined in `__init__.py`) - no need to have a tag\r\n\r\nhttps://github.com/huggingface/doc-builder/blob/81575cf081964c30ea5fd39450f4820db963f18e/src/doc_builder/commands/build.py#L69",
"Thanks, @lhoestq.\r\n\r\n@mishig25 has manually forced the generation of the docs, that are live for 2.7.0 version: https://huggingface.co/docs/datasets/v2.7.0/en/index ",
"Cool ! this can be closed then ?",
"I was waiting for #5250 to be merged to close this.",
"just to confirm, is there anything I need to do from my side ? Or is everything good here ?"
] | 2022-11-16T14:59:31 | 2022-11-22T16:27:50 | 2022-11-22T16:27:50 | MEMBER | null | After the latest `datasets` release version 0.7.0, the docs were not generated.
As we have changed the release procedure (so that now we do not push directly to main branch), maybe we should also change the corresponding GitHub action:
https://github.com/huggingface/datasets/blob/edf1902f954c5568daadebcd8754bdad44b02a85/.github/workflows/build_documentation.yml#L3-L8
Related to:
- #5250
CC: @mishig25 | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-16T14:35:32 | 2022-11-22T16:30:58 | 2022-11-22T16:27:48 | MEMBER | null | This PR changes the release procedure so that:
- it only make changes to main branch via pull requests
- it is no longer necessary to directly commit/push to main branch
Close #5251.
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} | [] | closed | false | null | [] | null | [] | 2022-11-16T10:41:04 | 2022-11-16T15:06:50 | 2022-11-16T10:41:10 | CONTRIBUTOR | null | Reverts huggingface/datasets#5214
Everything is handled on the doc-builder side now π | {
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"Hi @peregilk this bug is directly related to https://github.com/huggingface/datasets/issues/3888, and still not fixed... But I'll try to have a look!",
"Thanks @alvarobartt. It is great if you are able to fix it, but when reading the explanation it seems like it is possible to work around it.\r\n\r\nWe also tried keeping the 'info.features' and then adding a modified version back after the remove/rename. Unforutunately that leads to a dataset that is not possible to iterate over.",
"So if you iterate over the `IterableDataset` as `next(iter(ds))` and then run `rename_columns` when checking that data it will work, but in the end, it's just renaming the column one example/batch at a time, not renaming the column name for all the entries in the dataset, which is the ideal.",
"@alvarobartt Thanks. My use case was that I wanted to do multiple things, ie removing all unnecessary columns, renaming some valid columns, and then using cast (in my case checking if the audio is not 16K and casting it). It is just convenient to look into the info.features between each of these operations. Alternatively, I will just plan ahead...;) To me it seems like all the operations are working.\r\n\r\nThanks for the advice. It was very useful.",
"If we know the features before renaming, then we know the features after renaming, so we can pass the new features to the returned dataset in `rename_column` indeed ! If anyone is interested in contributing, feel free to open a PR and I'd be happy to help / give some pointers :)",
"Sure @lhoestq thanks! Iβll try to work on that",
"#self-assign"
] | 2022-11-15T21:04:41 | 2022-11-28T12:53:24 | 2022-11-28T12:53:24 | NONE | null | ### Describe the bug
Trying to rename column in a streaming datasets, destroys the features object.
### Steps to reproduce the bug
The following code illustrates the error:
```
from datasets import load_dataset
dataset = load_dataset('mc4', 'en', streaming=True, split='train')
dataset.info.features
# {'text': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)}
dataset = dataset.rename_column("text", "content")
dataset.info.features
# This returned object is now None!
```
### Expected behavior
This should just alter the renamed column.
### Environment info
datasets 2.6.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5241 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5241/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5241/comments | https://api.github.com/repos/huggingface/datasets/issues/5241/events | https://github.com/huggingface/datasets/pull/5241 | 1,448,510,407 | PR_kwDODunzps5C3MTG | 5,241 | Support hfh rc version | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-14T18:05:47 | 2022-11-15T16:11:30 | 2022-11-15T16:09:31 | MEMBER | null | otherwise the code doesn't work for hfh 0.11.0rc0
following #5237 | {
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https://api.github.com/repos/huggingface/datasets/issues/5240 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5240/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5240/comments | https://api.github.com/repos/huggingface/datasets/issues/5240/events | https://github.com/huggingface/datasets/pull/5240 | 1,448,478,617 | PR_kwDODunzps5C3Fe6 | 5,240 | Cleaner error tracebacks for dataset script errors | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-14T17:42:02 | 2022-11-15T18:26:48 | 2022-11-15T18:24:38 | CONTRIBUTOR | null | Make the traceback of the errors raised in `_generate_examples` cleaner for easier debugging. Additionally, initialize the `writer` in the for-loop to avoid the `ValueError` from `ArrowWriter.finalize` raised in the `finally` block when no examples are yielded before the `_generate_examples` error.
<details>
<summary>
The full traceback of the "SQLAlchemy ImportError" error that gets printed with these changes:
</summary>
```bash
ImportError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split_single(self, arg)
1759 _time = time.time()
-> 1760 for _, table in generator:
1761 # Only initialize the writer when we have the first record (to avoid having to do the clean-up if an error occurs before that)
9 frames
/usr/local/lib/python3.7/dist-packages/datasets/packaged_modules/sql/sql.py in _generate_tables(self)
112 sql_reader = pd.read_sql(
--> 113 self.config.sql, self.config.con, chunksize=chunksize, **self.config.pd_read_sql_kwargs
114 )
/usr/local/lib/python3.7/dist-packages/pandas/io/sql.py in read_sql(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize)
598 """
--> 599 pandas_sql = pandasSQL_builder(con)
600
/usr/local/lib/python3.7/dist-packages/pandas/io/sql.py in pandasSQL_builder(con, schema, meta, is_cursor)
789 elif isinstance(con, str):
--> 790 raise ImportError("Using URI string without sqlalchemy installed.")
791 else:
ImportError: Using URI string without sqlalchemy installed.
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
<ipython-input-4-5af11af4737b> in <module>
----> 1 ds = Dataset.from_sql('''SELECT * from states WHERE state=="New York";''', "sqlite:///us_covid_data.db")
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in from_sql(sql, con, features, cache_dir, keep_in_memory, **kwargs)
1152 cache_dir=cache_dir,
1153 keep_in_memory=keep_in_memory,
-> 1154 **kwargs,
1155 ).read()
1156
/usr/local/lib/python3.7/dist-packages/datasets/io/sql.py in read(self)
47 # try_from_hf_gcs=try_from_hf_gcs,
48 base_path=base_path,
---> 49 use_auth_token=use_auth_token,
50 )
51
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
825 verify_infos=verify_infos,
826 **prepare_split_kwargs,
--> 827 **download_and_prepare_kwargs,
828 )
829 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
912 try:
913 # Prepare split will record examples associated to the split
--> 914 self._prepare_split(split_generator, **prepare_split_kwargs)
915 except OSError as e:
916 raise OSError(
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1652 job_id = 0
1653 for job_id, done, content in self._prepare_split_single(
-> 1654 {"gen_kwargs": gen_kwargs, "job_id": job_id, **_prepare_split_args}
1655 ):
1656 if done:
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split_single(self, arg)
1789 raise DatasetGenerationError(
1790 f"An error occured while generating the dataset"
-> 1791 ) from e
1792 finally:
1793 yield job_id, False, num_examples_progress_update
DatasetGenerationError: An error occurred while generating the dataset
```
</details>
PS: I've also considered raising the error as follows:
```python
tb = sys.exc_info()[2]
raise DatasetGenerationError(f"An error occurred while generating the dataset: {type(e).__name__}: {e}").with_traceback(tb) from None # this raises the DatasetGenerationError with "e"'s traceback
```
But it seems like "from e" is now the [preferred](https://docs.python.org/3/library/exceptions.html#BaseException.with_traceback) way to chain exceptions.
Fix https://github.com/huggingface/datasets/issues/5186
cc @nateraw
| {
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https://api.github.com/repos/huggingface/datasets/issues/5239 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5239/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5239/comments | https://api.github.com/repos/huggingface/datasets/issues/5239/events | https://github.com/huggingface/datasets/pull/5239 | 1,448,211,373 | PR_kwDODunzps5C2L_P | 5,239 | Add num_proc to from_csv/generator/json/parquet/text | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-14T14:53:00 | 2022-12-06T15:39:10 | 2022-12-06T15:39:09 | MEMBER | null | Allow multiprocessing to from_* methods | {
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https://api.github.com/repos/huggingface/datasets/issues/5238 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5238/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5238/comments | https://api.github.com/repos/huggingface/datasets/issues/5238/events | https://github.com/huggingface/datasets/pull/5238 | 1,448,211,251 | PR_kwDODunzps5C2L9h | 5,238 | Make `Version` hashable | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-14T14:52:55 | 2022-11-14T15:30:02 | 2022-11-14T15:27:35 | CONTRIBUTOR | null | Add `__hash__` to the `Version` class to make it hashable (and remove the unneeded methods), as `Version("0.0.0")` is the default value of `BuilderConfig.version` and the default fields of a dataclass need to be hashable in Python 3.11.
Fix https://github.com/huggingface/datasets/issues/5230 | {
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https://api.github.com/repos/huggingface/datasets/issues/5237 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5237/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5237/comments | https://api.github.com/repos/huggingface/datasets/issues/5237/events | https://github.com/huggingface/datasets/pull/5237 | 1,448,202,491 | PR_kwDODunzps5C2KGz | 5,237 | Encode path only for old versions of hfh | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-14T14:46:57 | 2022-11-14T17:38:18 | 2022-11-14T17:35:59 | MEMBER | null | Next version of `huggingface-hub` 0.11 does encode the `path`, and we don't want to encode twice | {
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https://api.github.com/repos/huggingface/datasets/issues/5236 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5236/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5236/comments | https://api.github.com/repos/huggingface/datasets/issues/5236/events | https://github.com/huggingface/datasets/pull/5236 | 1,448,190,801 | PR_kwDODunzps5C2Hnj | 5,236 | Handle ArrowNotImplementedError caused by try_type being Image or Audio in cast | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-14T14:38:59 | 2022-11-14T16:04:29 | 2022-11-14T16:01:48 | CONTRIBUTOR | null | Handle the `ArrowNotImplementedError` thrown when `try_type` is `Image` or `Audio` and the input array cannot be converted to their storage formats.
Reproducer:
```python
from datasets import Dataset
from PIL import Image
import requests
ds = Dataset.from_dict({"image": [Image.open(requests.get("https://upload.wikimedia.org/wikipedia/commons/e/e9/Felis_silvestris_silvestris_small_gradual_decrease_of_quality.png", stream=True).raw)]})
ds.map(lambda x: {"image": True}) # ArrowNotImplementedError
```
PS: This could also be fixed by raising `TypeError` in `{Image, Audio}.cast_storage` for unsupported types instead of passing the array to `array_cast.` | {
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https://api.github.com/repos/huggingface/datasets/issues/5235 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5235/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5235/comments | https://api.github.com/repos/huggingface/datasets/issues/5235/events | https://github.com/huggingface/datasets/pull/5235 | 1,448,052,660 | PR_kwDODunzps5C1pjc | 5,235 | Pin `typer` version in tests to <0.5 to fix Windows CI | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-14T13:17:02 | 2022-11-14T15:43:01 | 2022-11-14T13:41:12 | CONTRIBUTOR | null | Otherwise `click` fails on Windows:
```
Traceback (most recent call last):
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\spacy\__main__.py", line 4, in <module>
setup_cli()
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\spacy\cli\_util.py", line 71, in setup_cli
command(prog_name=COMMAND)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\click\core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\typer\core.py", line 785, in main
**extra,
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\typer\core.py", line 190, in _main
args = click.utils._expand_args(args)
AttributeError: module 'click.utils' has no attribute '_expand_args'
```
See https://github.com/tiangolo/typer/issues/427 | {
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https://api.github.com/repos/huggingface/datasets/issues/5234 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5234/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5234/comments | https://api.github.com/repos/huggingface/datasets/issues/5234/events | https://github.com/huggingface/datasets/pull/5234 | 1,447,999,062 | PR_kwDODunzps5C1diq | 5,234 | fix: dataset path should be absolute | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-14T12:47:40 | 2022-12-07T23:49:22 | 2022-12-07T23:46:34 | CONTRIBUTOR | null | cache_file_name depends on dataset's path.
A simple way where this could cause a problem:
```
import os
import datasets
def add_prefix(example):
example["text"] = "Review: " + example["text"]
return example
ds = datasets.load_from_disk("a/relative/path")
os.chdir("/tmp")
ds_1 = ds.map(add_prefix)
```
while it may feel that the `chdir` is quite constructed, there are many scenarios when the current working dir can/will change... | {
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https://api.github.com/repos/huggingface/datasets/issues/5233 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5233/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5233/comments | https://api.github.com/repos/huggingface/datasets/issues/5233/events | https://github.com/huggingface/datasets/pull/5233 | 1,447,906,868 | PR_kwDODunzps5C1JVh | 5,233 | Fix shards in IterableDataset.from_generator | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-14T11:42:09 | 2022-11-14T14:16:03 | 2022-11-14T14:13:22 | MEMBER | null | Allow to define a sharded iterable dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/5232 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5232/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5232/comments | https://api.github.com/repos/huggingface/datasets/issues/5232/events | https://github.com/huggingface/datasets/issues/5232 | 1,446,294,165 | I_kwDODunzps5WNLKV | 5,232 | Incompatible dill versions in datasets 2.6.1 | {
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"Thanks for reporting, @vinaykakade.\r\n\r\nWe are discussing about making a release early this week.\r\n\r\nPlease note that in the meantime, in your specific case (as we also pointed out here: https://github.com/huggingface/datasets/issues/5162#issuecomment-1291720293), you can circumvent the issue by pinning `multiprocess` to 0.70.13 version (instead of using latest 0.70.14).\r\n\r\nDuplicate of:\r\n- https://github.com/huggingface/datasets/issues/5162",
"You can also make `pip-compile` work by using the backtracking resolver (instead of the legacy one): https://pip-tools.readthedocs.io/en/latest/#a-note-on-resolvers\r\n```\r\npip-compile --resolver=backtracking requirements.in\r\n```\r\nThis resolver will automatically use `multiprocess` 0.70.13 version.\r\n"
] | 2022-11-12T06:46:23 | 2022-11-14T08:24:43 | 2022-11-14T08:07:59 | NONE | null | ### Describe the bug
datasets version 2.6.1 has a dependency on dill<0.3.6. This causes a conflict with dill>=0.3.6 used by multiprocess dependency in datasets 2.6.1
This issue is already fixed in https://github.com/huggingface/datasets/pull/5166/files, but not yet been released. Please release a new version of the datasets library to fix this.
### Steps to reproduce the bug
1. Create requirements.in with only dependency being datasets (or datasets[s3])
2. Run pip-compile
3. The output is as follows:
```
Could not find a version that matches dill<0.3.6,>=0.3.6 (from datasets[s3]==2.6.1->-r requirements.in (line 1))
Tried: 0.2, 0.2, 0.2.1, 0.2.1, 0.2.2, 0.2.2, 0.2.3, 0.2.3, 0.2.4, 0.2.4, 0.2.5, 0.2.5, 0.2.6, 0.2.7, 0.2.7.1, 0.2.8, 0.2.8.1, 0.2.8.2, 0.2.9, 0.3.0, 0.3.1, 0.3.1.1, 0.3.2, 0.3.3, 0.3.3, 0.3.4, 0.3.4, 0.3.5, 0.3.5, 0.3.5.1, 0.3.5.1, 0.3.6, 0.3.6
Skipped pre-versions: 0.1a1, 0.2a1, 0.2a1, 0.2b1, 0.2b1
There are incompatible versions in the resolved dependencies:
dill<0.3.6 (from datasets[s3]==2.6.1->-r requirements.in (line 1))
dill>=0.3.6 (from multiprocess==0.70.14->datasets[s3]==2.6.1->-r requirements.in (line 1))
```
### Expected behavior
pip-compile produces requirements.txt without any conflicts
### Environment info
datasets version 2.6.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5231 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5231/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5231/comments | https://api.github.com/repos/huggingface/datasets/issues/5231/events | https://github.com/huggingface/datasets/issues/5231 | 1,445,883,267 | I_kwDODunzps5WLm2D | 5,231 | Using `set_format(type='torch', columns=columns)` makes Array2D/3D columns stop formatting correctly | {
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"In case others find this, the problem was not with set_format, but my usages of `to_pandas()` and `from_pandas()` which I was using during dataset splitting; somewhere in the chain of converting to and from pandas the `Array2D/Array3D` types get converted to series of `Sequence()` types"
] | 2022-11-11T18:54:36 | 2022-11-11T20:42:29 | 2022-11-11T18:59:50 | NONE | null | I have a Dataset with two Features defined as follows:
```
'image': Array3D(dtype="int64", shape=(3, 224, 224)),
'bbox': Array2D(dtype="int64", shape=(512, 4)),
```
On said dataset, if I `dataset.set_format(type='torch')` and then use the dataset in a dataloader, these columns are correctly cast to Tensors of (batch_size, 3, 224, 244) for example.
However, if I `dataset.set_format(type='torch', columns=['image', 'bbox'])` these columns are cast to Lists of tensors and miss the batch size completely (the 3 dimension is the list length).
I'm currently digging through datasets formatting code to try and find out why, but was curious if someone knew an immediate solution for this. | {
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https://api.github.com/repos/huggingface/datasets/issues/5230 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5230/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5230/comments | https://api.github.com/repos/huggingface/datasets/issues/5230/events | https://github.com/huggingface/datasets/issues/5230 | 1,445,507,580 | I_kwDODunzps5WKLH8 | 5,230 | dataclasses error when importing the library in python 3.11 | {
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"I opened [this issue](https://github.com/python/cpython/issues/99401).\r\nPython's maintainers say that the issue is caused by [this change](https://docs.python.org/3.11/whatsnew/3.11.html#dataclasses).\r\nI believe adding a `__hash__` method to `datasets.utils.version.Version` should solve (at least partially) this issue.",
"Has this been fixed? I am running into this issue now. \r\n\r\nIf this has been fixed, could have a new release with this?\r\n"
] | 2022-11-11T13:53:49 | 2022-11-14T20:51:44 | 2022-11-14T15:27:37 | NONE | null | ### Describe the bug
When I import datasets using python 3.11 the dataclasses standard library raises the following error:
`ValueError: mutable default <class 'datasets.utils.version.Version'> for field version is not allowed: use default_factory`
When I tried to import the library using the following jupyter notebook:
```
%%bash
# create python 3.11 conda env
conda create --yes --quiet -n myenv -c conda-forge python=3.11
# activate is
source activate myenv
# install pyarrow
/opt/conda/envs/myenv/bin/python -m pip install --quiet --extra-index-url https://pypi.fury.io/arrow-nightlies/ \
--prefer-binary --pre pyarrow
# install datasets
/opt/conda/envs/myenv/bin/python -m pip install --quiet datasets
```
```
# create a python file that only imports datasets
with open("import_datasets.py", 'w') as f:
f.write("import datasets")
# run it with the env
!/opt/conda/envs/myenv/bin/python import_datasets.py
```
I get the following error:
```
Traceback (most recent call last):
File "/kaggle/working/import_datasets.py", line 1, in <module>
import datasets
File "/opt/conda/envs/myenv/lib/python3.11/site-packages/datasets/__init__.py", line 45, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/opt/conda/envs/myenv/lib/python3.11/site-packages/datasets/builder.py", line 91, in <module>
@dataclass
^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 1221, in dataclass
return wrap(cls)
^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 1211, in wrap
return _process_class(cls, init, repr, eq, order, unsafe_hash,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 959, in _process_class
cls_fields.append(_get_field(cls, name, type, kw_only))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 816, in _get_field
raise ValueError(f'mutable default {type(f.default)} for field '
ValueError: mutable default <class 'datasets.utils.version.Version'> for field version is not allowed: use default_factory
```
This is probably due to one of the following changes in the [dataclasses standard library](https://docs.python.org/3/library/dataclasses.html) in version 3.11:
1. Changed in version 3.11: Instead of looking for and disallowing objects of type list, dict, or set, unhashable objects are now not allowed as default values. Unhashability is used to approximate mutability.
2. fields may optionally specify a default value, using normal Python syntax:
```
@dataclass
class C:
a: int # 'a' has no default value
b: int = 0 # assign a default value for 'b'
In this example, both a and b will be included in the added __init__() method, which will be defined as:
def __init__(self, a: int, b: int = 0):
```
3. Changed in version 3.11: If a field name is already included in the __slots__ of a base class, it will not be included in the generated __slots__ to prevent [overriding them](https://docs.python.org/3/reference/datamodel.html#datamodel-note-slots). Therefore, do not use __slots__ to retrieve the field names of a dataclass. Use [fields()](https://docs.python.org/3/library/dataclasses.html#dataclasses.fields) instead. To be able to determine inherited slots, base class __slots__ may be any iterable, but not an iterator.
4. weakref_slot: If true (the default is False), add a slot named β__weakref__β, which is required to make an instance weakref-able. It is an error to specify weakref_slot=True without also specifying slots=True.
[TypeError](https://docs.python.org/3/library/exceptions.html#TypeError) will be raised if a field without a default value follows a field with a default value. This is true whether this occurs in a single class, or as a result of class inheritance.
### Steps to reproduce the bug
Steps to reproduce the behavior:
1. go to [the notebook in kaggle](https://www.kaggle.com/yonikremer/repreducing-issue)
2. rub both of the cells
### Expected behavior
I'm expecting no issues.
This error should not occur.
### Environment info
kaggle kernels, with default settings:
pin to original environment, no accelerator. | {
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https://api.github.com/repos/huggingface/datasets/issues/5229 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5229/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5229/comments | https://api.github.com/repos/huggingface/datasets/issues/5229/events | https://github.com/huggingface/datasets/issues/5229 | 1,445,121,028 | I_kwDODunzps5WIswE | 5,229 | Type error when calling `map` over dataset containing 0-d tensors | {
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"Hi! \r\n\r\nWe could address this by calling `.item()` on such tensors to extract the value, but this would lose us the type, which could lead to storing the generated dataset in a suboptimal format. Considering this, I think the only proper fix would be implementing support for 0-D tensors on Apache Arrow's side (Arrow is the underlying format we use to store datasets on disk/in memory). WDYT @lhoestq?",
"I think we can just convert the item to a numpy typed scalar using `.numpy()` ?\r\n\r\nFor example this works:\r\n```python\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\nassert pa.array([np.float64(1.0)]).type == pa.float64()\r\nassert pa.array([np.float32(1.0)]).type == pa.float32()\r\nassert pa.array([np.int32(1)]).type == pa.int32()\r\nassert pa.array([np.int64(1)]).type == pa.int64()\r\n```\r\n\r\nAnd therefore it would work the same as for PyTorch N-D Tensors: convert to Numpy Array to keep the type in `_cast_to_python_objects`, then convert to Arrow"
] | 2022-11-11T08:27:28 | 2023-01-13T16:00:53 | 2023-01-13T16:00:53 | NONE | null | ### Describe the bug
0-dimensional tensors in a dataset lead to `TypeError: iteration over a 0-d array` when calling `map`. It is easy to generate such tensors by using `.with_format("...")` on the whole dataset.
### Steps to reproduce the bug
```
ds = datasets.Dataset.from_list([{"a": 1}, {"a": 1}]).with_format("torch")
ds.map(None)
```
### Expected behavior
Getting back `ds` without errors.
### Environment info
Python 3.10.8
datasets 2.6.
torch 1.13.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5227 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5227/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5227/comments | https://api.github.com/repos/huggingface/datasets/issues/5227/events | https://github.com/huggingface/datasets/issues/5227 | 1,444,620,094 | I_kwDODunzps5WGyc- | 5,227 | datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files | {
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"Fixed. Please close."
] | 2022-11-10T21:57:06 | 2022-11-10T22:05:43 | 2022-11-10T22:05:43 | NONE | null | ### Describe the bug
From these lines:
from datasets import list_datasets, load_dataset
dataset = load_dataset("wikisql","binary")
I get error message:
datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files
And yet the 'wikisql' is reported to exist via the list_datasets().
Any help appreciated.
### Steps to reproduce the bug
From these lines:
from datasets import list_datasets, load_dataset
dataset = load_dataset("wikisql","binary")
I get error message:
datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files
And yet the 'wikisql' is reported to exist via the list_datasets().
Any help appreciated.
### Expected behavior
Dataset should load. This same code used to work.
### Environment info
Mac OS | {
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https://api.github.com/repos/huggingface/datasets/issues/5226 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5226/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5226/comments | https://api.github.com/repos/huggingface/datasets/issues/5226/events | https://github.com/huggingface/datasets/issues/5226 | 1,444,385,148 | I_kwDODunzps5WF5F8 | 5,226 | Q: Memory release when removing the column? | {
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"Hi ! Datasets are memory mapped from your disk, i.e. they're not loaded in RAM. This is possible thanks to the Arrow data format.\r\n\r\nTherefore the column you remove is not in RAM, so removing it doesn't cause the RAM to decrease.",
"Thanks for the explanation! @lhoestq \r\nI wonder since it is memory mapped, can we reduce or remove this memory map?",
"Yes you can `del common_voice` for example or wait for it to be garbage collected"
] | 2022-11-10T18:35:27 | 2022-11-29T15:10:10 | 2022-11-29T15:10:10 | NONE | null | ### Describe the bug
How do I release memory when I use methods like `.remove_columns()` or `clear()` in notebooks?
```python
from datasets import load_dataset
common_voice = load_dataset("mozilla-foundation/common_voice_11_0", "ja", use_auth_token=True)
# check memory -> RAM Used (GB): 0.704 / Total (GB) 33.670
common_voice = common_voice.remove_columns(column_names=common_voice.column_names['train'])
common_voice.clear()
# check memory -> RAM Used (GB): 0.705 / Total (GB) 33.670
```
I tried `gc.collect()` but did not help
### Steps to reproduce the bug
1. load dataset
2. remove all the columns
3. check memory is reduced or not
[link to reproduce](https://www.kaggle.com/code/bayartsogtya/huggingface-dataset-memory-issue/notebook?scriptVersionId=110630567)
### Expected behavior
Memory released when I remove the column
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5224 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5224/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5224/comments | https://api.github.com/repos/huggingface/datasets/issues/5224/events | https://github.com/huggingface/datasets/issues/5224 | 1,443,640,867 | I_kwDODunzps5WDDYj | 5,224 | Seems to freeze when loading audio dataset with wav files from local folder | {
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"I just tried to do the same but changing the `.wav` files to `.mp3` files and that doesn't fix it.",
"I don't know if anyone will ever read this but I've tried to upload the same dataset with google colab and the output seems more clarifying. I didn't specify the train/test split so the dataset wasn't fully uploaded (or that is what I understood, might be wrong!!).\r\n\r\nNow, including the `drop_metadata` flag I can load the dataset normally (at least with colab notebook):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"audiofolder\", data_dir=\"../archive/Dataset\", , drop_metadata=True)\r\n```\r\n\r\nI'll close the issue."
] | 2022-11-10T10:29:31 | 2022-11-22T11:24:19 | 2022-11-22T11:24:19 | NONE | null | ### Describe the bug
I'm following the instructions in [https://huggingface.co/docs/datasets/audio_load#audiofolder-with-metadata](url) to be able to load a dataset from a local folder.
I have everything into a folder, into a train folder and then the audios and csv. When I try to load the dataset and run from terminal, seems to work but then freezes with no apparent reason.
The metadata.csv file contains a few columns but the important ones, `file_name` with the filename and `transcription` with the transcription are okay.
The audios are `.wav` files, I don't know if that might be the problem (I will proceed to try to change them all to `.mp3` and try again).
### Steps to reproduce the bug
The code I'm using:
```python
from datasets import load_dataset
dataset = load_dataset("audiofolder", data_dir="../archive/Dataset")
dataset[0]["audio"]
```
The output I obtain:
```
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 311135.43it/s]
Using custom data configuration default-38d4546ffd010f3e
Downloading and preparing dataset audiofolder/default to /Users/mine/.cache/huggingface/datasets/audiofolder/default-38d4546ffd010f3e/0.0.0/6cbdd16f8688354c63b4e2a36e1585d05de285023ee6443ffd71c4182055c0fc...
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 166467.72it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 187772.74it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 59623.71it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 138090.55it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 106065.64it/s]
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 56036.38it/s]
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 74004.24it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 162343.45it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 101881.23it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 60145.67it/s]
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 80890.02it/s]
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 54036.67it/s]
Resolving data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 95851.09it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 155897.00it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 137656.96it/s]
Resolving data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 439/439 [00:00<00:00, 131230.81it/s]
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
```
And then here it just freezes and nothing more happens.
### Expected behavior
Load the dataset.
### Environment info
Datasets version:
datasets 2.6.1 pypi_0 pypi
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https://api.github.com/repos/huggingface/datasets/issues/5223 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5223/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5223/comments | https://api.github.com/repos/huggingface/datasets/issues/5223/events | https://github.com/huggingface/datasets/pull/5223 | 1,442,610,658 | PR_kwDODunzps5CjT9Z | 5,223 | Add SQL guide | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-09T19:10:27 | 2022-11-15T17:40:25 | 2022-11-15T17:40:21 | MEMBER | null | This PR adapts @nateraw's awesome SQL notebook as a guide for the docs! | {
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"cc @McPatate maybe you know what's happening ?",
"Yes I think I know what is happening. We check in zips for pickles, and the UI must display the pickle jar when a scan has an associated list of imports, even when empty.\r\n~I'll fix ASAP !~",
"> I'll fix ASAP !\r\n\r\nActually I'd rather leave it like that for now, as it indicates that we checked for pickles and nothing dangerous appeared :)",
"Closing the issue with the typical \"feature not a bug\" "
] | 2022-11-09T16:41:16 | 2022-11-09T18:10:46 | 2022-11-09T18:06:57 | NONE | null | ### Describe the bug
HuggingFace is incorrectly reporting that my datasets are pickled. They are not picked, they are simple ZIP files containing PNG images.
Hopefully this is the right location to report this bug.
### Steps to reproduce the bug
Inspect my dataset respository here: https://huggingface.co/datasets/ProGamerGov/StableDiffusion-v1-5-Regularization-Images
### Expected behavior
They should not be reported as being pickled.
### Environment info
N/A | {
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"Did you run `huggingface-cli lfs-enable-largefiles` before committing or before adding ? Maybe you can try before adding\r\n\r\nAnyway I'd encourage you to split your data into several TAR archives if possible, this way the dataset can loaded faster using multiprocessing (by giving each process a subset of shards to process)",
"@lhoestq \r\nThanks for the help!\r\n> Maybe you can try before adding\r\n\r\nIt did not help\r\n\r\nBut I totally got your point about split into multiple TAR archives. It really helped!"
] | 2022-11-09T15:32:05 | 2022-11-10T18:11:21 | 2022-11-10T18:11:11 | NONE | null | ### Describe the bug
I am facing the issue when I try to push the tar.gz file around 11G to HUB.
```
(venv) ββlaptop@laptop ~/PersonalProjects/data/ulaanbal_v0 βΉmainββΊ
β°β$ du -sh *
4.0K README.md
13G data
516K test.jsonl
18M train.jsonl
4.0K ulaanbal_v0.py
11G ulaanbal_v0.tar.gz
452K validation.jsonl
(venv) ββlaptop@laptop~/PersonalProjects/data/ulaanbal_v0 βΉmainββΊ
β°β$ git add ulaanbal_v0.tar.gz && git commit -m 'large version'
(venv) ββlaptop@laptop ~/PersonalProjects/data/ulaanbal_v0 βΉmainββΊ
β°β$ git push
EOFoading LFS objects: 0% (0/1), 0 B | 0 B/s
Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done.
error: failed to push some refs to 'https://huggingface.co/datasets/bayartsogt/ulaanbal_v0'
```
I have already tried pushing a small version of this and it was working fine. So my guess it is probably because of the big file.
Following I run before the commit:
```
β°β$ git lfs install
β°β$ huggingface-cli lfs-enable-largefiles .
```
### Steps to reproduce the bug
Create a private dataset on huggingface and push 12G tar.gz file
### Expected behavior
To be pushed with no issue
### Environment info
- `datasets` version: 2.6.1
- Platform: Darwin-21.6.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 10.0.0
- Pandas version: 1.3.5
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"I think this behavior comes from PyArrow:\r\n```python\r\nimport pyarrow as pa\r\nt = pa.table({\"a\": [[0]]})\r\nt.to_pandas().a.values[0]\r\n# array([0])\r\n```\r\n\r\nI believe this has to do with zero-copy: you can get a pandas DataFrame without copying the buffers from arrow, and therefore end up with numpy arrays.",
"That's interesting, I guess not much to do here then."
] | 2022-11-09T08:40:18 | 2022-11-10T16:12:26 | 2022-11-10T16:12:26 | CONTRIBUTOR | null | ### Describe the bug
```
ds = Dataset.from_list([{'a':[1,2,3]}])
ds.to_pandas().a.values[0]
```
Results in `array([1, 2, 3])` -- a rather unexpected conversion of types which made downstream tools expecting lists not happy.
### Steps to reproduce the bug
See snippet
### Expected behavior
Keep the original type
### Environment info
datasets 2.6.1
python 3.8.10 | {
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https://api.github.com/repos/huggingface/datasets/issues/5218 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5218/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5218/comments | https://api.github.com/repos/huggingface/datasets/issues/5218/events | https://github.com/huggingface/datasets/issues/5218 | 1,441,254,194 | I_kwDODunzps5V58sy | 5,218 | Delta Tables usage using Datasets Library | {
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] | closed | false | null | [] | null | [] | 2022-11-09T02:42:18 | 2022-11-09T02:42:36 | 2022-11-09T02:42:36 | NONE | null | ### Feature request
Adding compatibility of Datasets library with Delta Format. Elevating the utilities of Datasets library from Machine Learning Scope to Data Engineering Scope as well.
### Motivation
We know datasets library can absorb csv, json, parquet, etc. file formats but it would be great if Datasets library could work with Delta Tables (with delta format) as it has different features such as time travelling, layout optimization, query performance, aids in Data Engineering.
This will help and enhance Datasets library from Machine Learning utility to Data Engineering utilities and expand horizons thereafter. I am totally using Datasets library in all my usecases and as my role expands so does the work, compatibility with Datasets library is something I don't want to lose.
### Your contribution
Would love to work on this feature, even if this has to picked up from scratch, including design paradigms and patterns.
I have basic idea about Delta Live Tables, would brush it easily for this feature. | {
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@lhoestq @albertvillanova @mariosasko
This is still a super draft so please ignore code issues but I want to discuss some conceptually important things.
I suggest a way to do `.push_to_hub("repo_id", "config_name")` with pushing parquet files to directories named as `config_name` (inside `data/` dir as it is now), for example:
```
data
|__config-v1
train-00000-00002-...-.parquet
train-00001-00002-...-.parquet
...
|__config-v2
....
```
When loading a dataset, I parse these configs from repository data files (only for `"data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*"` pattern that is used for parquet datasets pushed with `.push_to_hub`).
Therefore,
- when user tries to load a dataset that has configs parsed from data files dir names without providing a config (like `load_dataset("repo")` instead of `load_dataset("repo", "config-v1")`) - raise error and asks for config - to be aligned with how it works in datasets with scripts.
- for backward compatibility: if user tries to `.push_to_hub(""repo", "config_name")` to an existing parquet repo with no configurations (all parquet files are directly in `data/` dir) - raise error. My initial idea was to raise a warning and move these files to another dir with name (config) like "default" or smth but in a PR and suggest user to merge it on the Hub. But there is no support for renaming (moving) files via `HfApi` yet so it would require deleting and pushing again if I understand it right.
This parsing approach can be extended to other Hub packaged modules, and to local packaged modules and other data files patterns
(except for cases when splits are in dir names `KEYWORDS_IN_DIR_NAME_BASE_PATTERNS` because we allow for arbitrary depth of directory hierarchy).
Do you think it's reasonable? Not sure how to provide flexibility (and backward compatibility) to not parsing configs and load all the data in a single config as it is now.
I also thought about getting information about configs from Readme.md `dataset_info` ([example](https://huggingface.co/datasets/polinaeterna/test_push_two_configs/blob/main/README.md)). But that way we
are dependent on if it exists. It is created automatically with `.push_to_hub` but what if it is
accidentally deleted or smth).
Also, what I don't like is that this parsing is a part of Module/DataFiles logic, not Builder's one, which is not aligned with datasets with custom scripts. But I don't know to implement the second approach in current library's logic.
What do you think about this all? Am I missing smth?
TODO:
- [ ] save cache in the same dir for configs of the same datasets
- [ ] fix verification errors
- [ ] correctly update `dataset_infos.json` too
- [ ] ...
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} | [] | closed | false | null | [] | null | [] | 2022-11-08T07:30:01 | 2022-11-15T06:32:27 | 2022-11-15T06:32:26 | MEMBER | null | A previous commit to main branch introduced an additional requirement on maximum compatible `dill` version with `apache-beam` in our CI `require_beam`:
- d7c942228b8dcf4de64b00a3053dce59b335f618
- ec222b220b79f10c8d7b015769f0999b15959feb
This PR fixes the maximum compatible `dill` version with `apache-beam`, which is <0.3.2 (and not 0.3.6): https://github.com/apache/beam/blob/v2.42.0/sdks/python/setup.py#L219 | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-07T15:23:52 | 2022-11-29T15:59:48 | 2022-11-29T15:54:17 | MEMBER | null | - removed metrics stuff
- added image example
- added audio example (with ffmpeg instructions)
- updated the "add a new dataset" section | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-07T14:51:23 | 2022-11-24T11:35:07 | 2022-11-24T11:26:16 | MEMBER | null | Tweaked some paragraphs mentioning the modalities we support + added a paragraph on security | {
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"Actually upon closer inspection, it is documented in the code that this behavior is intentional, so I'll close this."
] | 2022-11-05T23:48:02 | 2022-11-06T00:06:00 | 2022-11-06T00:05:59 | NONE | null | ### Describe the bug
Some logs ([here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L778), [here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L786), and [here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L830)) generated by the `DatasetBuilder` are printed to the console instead of passed to `datasets` logger.
### Steps to reproduce the bug
```python
>> import datasets
>> datasets.load_dataset("some-dataset")
Downloading and preparing dataset csv/data to <path>...
Downloading data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 7729.06it/s]
Extracting data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 527.23it/s]
Dataset csv downloaded and prepared to <path>. Subsequent calls will reuse this data.
```
### Expected behavior
The logs should not be printed to the console directly but passed to the logger so that the user can redirect them wherever he wants.
### Environment info
- `datasets` version: 2.6.1
- Platform: macOS-13.0-x86_64-i386-64bit
- Python version: 3.9.15
- PyArrow version: 10.0.0
- Pandas version: 1.5.1 | {
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Now the `token` is propagated so that `DownloadConfig.use_auth_token` value is set before trying to download private files from existing datasets in the Hub. | {
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"#self-assign",
"@lhoestq can you close this issue as part of the recent #5205 merge? Thanks π€ ",
"Thank you :)"
] | 2022-11-05T23:32:20 | 2022-11-08T10:12:09 | 2022-11-08T10:12:08 | CONTRIBUTOR | null | ### Describe the bug
When trying to upload a new π€ Dataset to the Hub via Python, and providing the `token` as a parameter to the `Dataset.push_to_hub` function, it just works for the first time, assuming that the dataset didn't exist before.
But when trying to run `Dataset.push_to_hub` again over the same dataset, instead of updating it, it throws a `ConnectionError` when trying to retrieve the `README.md` that may contain some metadata about the dataset, so as to also update it, but since the `token` is not propagated, the `DownloadConfig` provided to the `datasets.utils.file_utils.get_from_cache` function doesn't contain the `use_auth_token` value set to `token`, it's just using the default one which is None/False.
So on, when uploading a dataset via Python with `push_to_hub` with the `token` as a parameter with the HuggingFace API Token as value, it can just be uploaded when the dataset is new, otherwise it fails with to `ConnectionError` due to the `token` not being propagated as `use_auth_token`.
### Steps to reproduce the bug
Let's create a new dataset in our HF account via Python as:
```python
from datasets import Dataset
data = {"a": [1, 2, 3], "b": [4, 5, 6]}
ds = Dataset.from_dict(data)
ds.push_to_hub(repo_id=<HF_USERNAME>/<HF_DATASET>, private=private, token=<HF_TOKEN_HERE>)
```
When we create the `Dataset` for the first time it works and there are no issues, but when trying to actually upload a new version of the same dataset (same name under the same username), we encounter the following issue:
```python
from datasets import Dataset
data = {"a": [1, 2, 3], "b": [4, 5, 6]}
ds = Dataset.from_dict(data)
ds.push_to_hub(repo_id=<HF_USERNAME>/<HF_DATASET>, private=private, token=<HF_TOKEN_HERE>)
>>> ConnectionError: Couldn't reach https://huggingface.co/datasets/alvarobartt/demo/resolve/main/README.md (ConnectionError('Unauthorized for URL https://huggingface.co/datasets/<HF_USERNAME>/<HF_DATASET>/resolve/main/README.md. Please use the parameter `use_auth_token=True` after logging in with `huggingface-cli login`'))
```
### Expected behavior
Ideally, the `token` parameter provided to `push_to_hub` should be propagated and used to download the `README.md` when trying to update a `Dataset`, instead of throwing that exception, so that the authentication can be done directly through code without running `huggingface-cli login`as mentioned at https://huggingface.co/docs/datasets/upload_dataset#upload-with-python.
### Environment info
- `datasets` version: 2.6.1
- Platform: macOS-13.0-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.0
- Pandas version: 1.5.1 | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-04T22:50:50 | 2022-11-07T18:43:05 | 2022-11-07T18:40:19 | MEMBER | null | This PR updates some of the canonical dataset links to their corresponding links on the Hub; closes #5200. | {
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"Fixed by: https://huggingface.co/datasets/paws/discussions/1"
] | 2022-11-04T10:51:20 | 2023-02-16T09:11:10 | 2023-02-16T09:11:10 | MEMBER | null | ```
______ test_get_dataset_config_info[paws-labeled_final-expected_splits2] _______
[gw0] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
path = 'paws', config_name = 'labeled_final'
expected_splits = ['train', 'test', 'validation']
@pytest.mark.parametrize(
"path, config_name, expected_splits",
[
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
],
)
def test_get_dataset_config_info(path, config_name, expected_splits):
info = get_dataset_config_info(path, config_name=config_name)
assert info.config_name == config_name
> assert list(info.splits.keys()) == expected_splits
E AssertionError: assert ['test', 'tra... 'validation'] == ['train', 'te... 'validation']
E At index 0 diff: 'test' != 'train'
E Full diff:
E - ['train', 'test', 'validation']
E + ['test', 'train', 'validation']
tests/test_inspect.py:45: AssertionError
_ test_get_dataset_info[paws-expected_configs2-expected_splits_in_first_config2] _
[gw0] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
path = 'paws'
expected_configs = ['labeled_final', 'labeled_swap', 'unlabeled_final']
expected_splits_in_first_config = ['train', 'test', 'validation']
@pytest.mark.parametrize(
"path, expected_configs, expected_splits_in_first_config",
[
("squad", ["plain_text"], ["train", "validation"]),
("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
],
)
def test_get_dataset_info(path, expected_configs, expected_splits_in_first_config):
infos = get_dataset_infos(path)
assert list(infos.keys()) == expected_configs
expected_config = expected_configs[0]
assert expected_config in infos
info = infos[expected_config]
assert info.config_name == expected_config
> assert list(info.splits.keys()) == expected_splits_in_first_config
E AssertionError: assert ['test', 'tra... 'validation'] == ['train', 'te... 'validation']
E At index 0 diff: 'test' != 'train'
E Full diff:
E - ['train', 'test', 'validation']
E + ['test', 'train', 'validation']
tests/test_inspect.py:90: AssertionError
______ test_get_dataset_split_names[paws-labeled_final-expected_splits2] _______
[gw0] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
path = 'paws', expected_config = 'labeled_final'
expected_splits = ['train', 'test', 'validation']
@pytest.mark.parametrize(
"path, expected_config, expected_splits",
[
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
],
)
def test_get_dataset_split_names(path, expected_config, expected_splits):
infos = get_dataset_infos(path)
assert expected_config in infos
info = infos[expected_config]
assert info.config_name == expected_config
> assert list(info.splits.keys()) == expected_splits
E AssertionError: assert ['test', 'tra... 'validation'] == ['train', 'te... 'validation']
E At index 0 diff: 'test' != 'train'
E Full diff:
E - ['train', 'test', 'validation']
E + ['test', 'train', 'validation']
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5201 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5201/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5201/comments | https://api.github.com/repos/huggingface/datasets/issues/5201/events | https://github.com/huggingface/datasets/pull/5201 | 1,435,881,554 | PR_kwDODunzps5CM0zn | 5,201 | Do not sort splits in dataset info | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-04T10:47:21 | 2022-11-04T14:47:37 | 2022-11-04T14:45:09 | CONTRIBUTOR | null | I suggest not to sort splits by their names in dataset_info in README so that they are displayed in the order specified in the loading script. Otherwise `test` split is displayed first, see this repo: https://huggingface.co/datasets/paws
What do you think?
But I added sorting in tests to fix CI (for the same dataset). | {
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"Thanks for catching this, I can go through the docs and replace the links to their corresponding datasets on the Hub!"
] | 2022-11-04T10:06:21 | 2022-11-07T18:40:20 | 2022-11-07T18:40:20 | CONTRIBUTOR | null | As we don't have canonical datasets in the github repo anymore, some old links to them doesn't work. I don't know how many of them are there, I found link to SuperGlue here: https://huggingface.co/docs/datasets/dataset_script#multiple-configurations, probably there are more of them. These links should be replaced by links to the corresponding datasets on the Hub. | {
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also fixed two minor issues in audio docs (broken links) | {
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https://api.github.com/repos/huggingface/datasets/issues/5196 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5196/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5196/comments | https://api.github.com/repos/huggingface/datasets/issues/5196/events | https://github.com/huggingface/datasets/pull/5196 | 1,434,401,646 | PR_kwDODunzps5CH439 | 5,196 | Use hfh hf_hub_url function | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-03T10:08:09 | 2022-12-06T11:38:17 | 2022-11-09T07:15:12 | MEMBER | null | Small refactoring to use `hf_hub_url` function from `huggingface_hub`.
This PR also creates the `hub` module that will contain all `huggingface_hub` functionalities relevant to `datasets`.
This is a necessary stage before implementing the use of the `hfh` caching system (which uses its `hf_hub_url` under the hood).
EDIT:
~~Finally, we use our `config.HUB_DATASETS_URL` when using `hfh.hf_hub_url`~~
There is a breaking change: the `hfh` `hf_hub_url` function uses
- `hfh` `HUGGINGFACE_CO_URL_TEMPLATE` URL template, different from the `datasets` `config.HUB_DATASETS_URL`
- also, `hfh` `DEFAULT_REVISION`, instead of `datasets` `config.HUB_DEFAULT_VERSION` | {
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- #4926
Related to:
- #5193 | {
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"Also unrelated but still: https://huggingface.co/docs/datasets/image_dataset#generate-the-dataset\r\n```If your loading script passed the test, you should now have a dataset_infos.json file in your dataset folder.```\r\nIt's not the case anymore as it's now in the readme.md, it was confusing to me",
"And here is my data loader script: https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data/blob/main/SDH_16k.py\r\nI have one file archive to download that contains the images for all splits and one `metadata.jsonl` to download that contains the informations about what image goes into what split.",
"Hi @lambda-science! It seems that your repo is recognized as a packaged module [ImageFolder](https://huggingface.co/docs/datasets/main/en/image_dataset#imagefolder), not as a dataset with the custom loading script, because loader looks for a script that has the same name as the dataset repo. So please try to rename your script to `MyoQuant-SDH-Data.py`, this should help.",
"> Hi @lambda-science! It seems that your repo is recognized as a packaged module [ImageFolder](https://huggingface.co/docs/datasets/main/en/image_dataset#imagefolder), not as a dataset with the custom loading script, because loader looks for a script that has the same name as the dataset repo. So please try to rename your script to `MyoQuant-SDH-Data.py`, this should help.\r\n\r\nHi !\r\n\r\nThank you for your answer. That was... embarrassingly easy, sorry for this issue, everything is fixed now ! \r\n\r\nHave a nice day ! :)",
"@lambda-science that's not embarrassing at all! it's actually not clear from the documentation that the script should have the same name, so thank you for the issue, we'll add this information to the docs :) "
] | 2022-11-02T22:46:25 | 2022-11-03T13:39:16 | 2022-11-03T13:35:44 | NONE | null | ### Describe the bug
When loading my own dataset, on loading it I get an error.
Here is my dataset link: https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data
And the error after loading with:
```python
from datasets import load_dataset
load_dataset("corentinm7/MyoQuant-SDH-Data")
```
```python
Downloading readme: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3.34k/3.34k [00:00<00:00, 4.45MB/s]
Using custom data configuration SDH_16k-53e7301a92ab0025
Downloading and preparing dataset None/SDH_16k to /home/corentin/.cache/huggingface/datasets/corentinm7___imagefolder/SDH_16k-53e7301a92ab0025/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f...
Downloading data: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3.28M/3.28M [00:00<00:00, 4.31MB/s]
Downloading data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:01<00:00, 1.75s/it]
Downloading data: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1.13G/1.13G [00:15<00:00, 74.3MB/s]
Downloading data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:16<00:00, 16.09s/it]
Extracting data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:13<00:00, 13.16s/it]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/load.py", line 1742, in load_dataset
builder_instance.download_and_prepare(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 814, in download_and_prepare
self._download_and_prepare(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1423, in _download_and_prepare
super()._download_and_prepare(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 905, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1374, in _prepare_split
for key, record in logging.tqdm(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 394, in _generate_examples
raise ValueError(
ValueError: One or several metadata. were found, but not in the same directory or in a parent directory of /home/corentin/.cache/huggingface/datasets/downloads/extracted/60c4aa8d4da3065bb3d310de4373dffd73bd4dc331aedcb4ee867febe4fdb7cd/validation/sick/2_CG_SDH_TAM_Bin1cKO_ko_pla_4_1640.tif.
```
However the test command is working fine. ```datasets-cli test hugging_face_play/ds_test/SDH_16k.py --save_info --all_configs --force_redownload```
```
Using custom data configuration SDH_16k
Testing builder 'SDH_16k' (1/1)
Downloading and preparing dataset sdh_16k/SDH_16k to /home/corentin/.cache/huggingface/datasets/sdh_16k/SDH_16k/1.0.0/21b584239a638aeeda33cba1ac2ca4869d48e4b4f20fb22274d5a5ddc487659d...
Downloading data: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1.13G/1.13G [00:14<00:00, 76.5MB/s]
Downloading data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:15<00:00, 15.66s/it]
Downloading data: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3.28M/3.28M [00:02<00:00, 1.44MB/s]
Downloading data files: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:03<00:00, 3.21s/it]
Downloading data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 11586.48it/s]
Extracting data files: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:13<00:00, 13.42s/it]
Dataset sdh_16k downloaded and prepared to /home/corentin/.cache/huggingface/datasets/sdh_16k/SDH_16k/1.0.0/21b584239a638aeeda33cba1ac2ca4869d48e4b4f20fb22274d5a5ddc487659d. Subsequent calls will reuse this data.
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 605.27it/s]
Dataset card saved at hugging_face_play/ds_test/README.md
Test successful.
```
### Steps to reproduce the bug
Simply run on python
```python
from datasets import load_dataset
load_dataset("corentinm7/MyoQuant-SDH-Data")
```
### Expected behavior
As the test command worked, this error should not appear
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.6
- PyArrow version: 10.0.0
- Pandas version: 1.5.1
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https://api.github.com/repos/huggingface/datasets/issues/5192 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5192/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5192/comments | https://api.github.com/repos/huggingface/datasets/issues/5192/events | https://github.com/huggingface/datasets/pull/5192 | 1,433,199,790 | PR_kwDODunzps5CD2BQ | 5,192 | Drop labels in Image and Audio folders if files are on different levels in directory or if there is only one label | {
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] | null | [] | 2022-11-02T14:01:41 | 2022-11-15T16:32:53 | 2022-11-15T16:31:07 | CONTRIBUTOR | null | Will close https://github.com/huggingface/datasets/issues/5153
Drop labels by default (`drop_labels=None`) when:
* there are files on different levels of directory hierarchy by checking their path depth
* all files are in the same directory (=only one label was inferred)
First one fixes cases like this:
```
repo
image3.jpg
image4.jpg
data
image1.jpg
image2.jpg
```
Second one fixes cases like this:
```
repo
image1.jpg
image2.jpg
image3.jpg
```
This is mostly to fix the viewer for people who just drop images in the Hub interface into the root dir.
I added tests for both of the cases on local and remote files. **I also changed data files for old test on drop_labels** (`test_generate_examples_drop_labels`). The files I provide to `test_generate_examples_drop_labels` now has "canonical" classification structure (two dirs) in order not to change the logic of the test (=not to check these two cases addressed in the PR).
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} | [] | closed | false | null | [] | null | [] | 2022-11-02T13:56:18 | 2022-11-02T17:20:48 | 2022-11-02T17:18:42 | CONTRIBUTOR | null | Override `Pickler.save` to implement deterministic reduction (lazily registered; inspired by https://github.com/uqfoundation/dill/blob/master/dill/_dill.py#L343) functions for `torch.Tensor` and spaCy models.
Fix https://github.com/huggingface/datasets/issues/5170, fix https://github.com/huggingface/datasets/issues/3178
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"Hi! Yes, this is expected behavior - we do this as a security measure to not leak local paths (this info would be useless on other users' machines anyways) and only push audio bytes. \r\n"
] | 2022-11-02T11:51:25 | 2022-11-02T12:55:02 | 2022-11-02T12:55:02 | MEMBER | null | ### Describe the bug
I've created an [audio dataset](https://huggingface.co/datasets/lewtun/audio-test-push) using the `audiofolder` feature desribed in the [docs](https://huggingface.co/docs/datasets/audio_dataset#audiofolder) and then pushed it to the Hub.
Locally, I can see the `audio.path` feature is of the expected form `path/to/data_dir`, but when I download the dataset from the Hub, I see `audio.path` is `None`
Here's an example:
```python
from datasets import load_dataset
ds = load_dataset("lewtun/audio-test-push")
ds["train"][0]
# {
# "audio": {
# "path": None, <-- Is this expected?
# "array": array(
# [
# 3.97140226e-07,
# 7.30310290e-07,
# 7.56406735e-07,
# ...,
# -1.19636677e-01,
# -1.16811886e-01,
# -1.12441722e-01,
# ]
# ),
# "sampling_rate": 44100,
# },
# "song_id": 0,
# "genre_id": 0,
# "genre": "Electronic",
# }
```
Is this expected behaviour? If yes, feel free to close this issue as it's not a true bug then :)
### Steps to reproduce the bug
1. Create an audio dataset with the `audiofolder` feature
2. Push the dataset to the Hub with `push_to_hub()`
3. Download the Hub dataset and inspect the `audio.path` feature
### Expected behavior
`audio.path` points to the file associated with the audio data
### Environment info
- `datasets` version: 2.6.2.dev0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
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] | null | [] | 2022-11-02T04:34:36 | 2022-11-04T18:25:57 | 2022-11-04T18:23:34 | MEMBER | null | Closes #5181
I have opened a PR on Hub (https://huggingface.co/datasets/huggingface/documentation-images/discussions/5) to include the images in our central Hub repository. Once the PR is merged I will edit the image links.
I have also prepared a [Colab Notebook](https://colab.research.google.com/drive/1BMDCfOTBnyshoME5RSxn5iQy-TWeFbOA?usp=sharing) in case anyone wants to play.
- [x] Replace the image links | {
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] | null | [] | 2022-11-02T02:30:09 | 2022-11-03T01:52:24 | 2022-11-03T01:49:56 | MEMBER | null | Closes https://github.com/huggingface/datasets/issues/5182 | {
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"Hi! The first `Dataset.from_sql` call also outputs the \"ImportError: Using URI string without sqlalchemy installed.\" message, but you also get \"During handling of the above exception another exception occurred: ...\" after which the ValueError is printed. I agree that this behavior makes it easy to miss the original error. \r\n\r\nI think we can improve this by not throwing the writer's ValueError if the error from a dataset script is already being handled to make debugging easier. @lhoestq @albertvillanova wdyt?",
"Yup ! Alternatively the error can be raised in sql.py before generating the examples ? In `_info` for example",
"yea @lhoestq that would probably be good. The 2nd error is useless if the 1st error is the real reason it failed. "
] | 2022-11-01T20:25:51 | 2022-11-15T18:24:39 | 2022-11-15T18:24:39 | CONTRIBUTOR | null | ### Describe the bug
When calling `Dataset.from_sql` (in my case, with sqlite3), it fails with a message ```ValueError: Please pass `features` or at least one example when writing data``` when I don't have `sqlalchemy` installed.
### Steps to reproduce the bug
Make a new sqlite db with `sqlite3` and `pandas` from a remote [URL](https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv).
```python
import sqlite3
import pandas as pd
from datasets import Dataset
conn = sqlite3.connect('us_covid_data.db')
df = pd.read_csv('https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv')
df.to_sql('states', conn, if_exists='replace')
```
Then if you try to query this DB like this:
```python
ds = Dataset.from_sql('''SELECT * from states WHERE state=="New York";''', "sqlite:///us_covid_data.db")
```
You run into the error I described above:
```ValueError: Please pass `features` or at least one example when writing data```
However, if you try to pass features, as the error suggests, then you get an error that tells you the underlying problem...
```python
from datasets import Dataset, Features, Value
features = Features({
'date': Value('date32'),
'label': Value('string'),
'fips': Value('int32'),
'cases': Value('int32'),
'deaths': Value('int32')
})
ds = Dataset.from_sql(
'''SELECT * from states WHERE state=="New York";''',
"sqlite:///us_covid_data.db",
features=features
)
```
Which results in the actual underlying error: `ImportError: Using URI string without sqlalchemy installed.`
### Expected behavior
Instead of `ValueError` about needing to pass features, we should provide the actual underlying error about not having SQLAlchemy installed when it isn't found in the environment.
### Environment info
- `datasets` version: 2.6.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.10
- PyArrow version: 10.0.0
- Pandas version: 1.2.5 | {
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