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timestamp[s]date 2020-04-14 10:18:02
2025-08-05 09:28:51
| updated_at
timestamp[s]date 2020-04-27 16:04:17
2025-08-05 11:39:56
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2025-08-01 05:15:45
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,628,225,544
|
https://api.github.com/repos/huggingface/datasets/issues/5647
|
https://github.com/huggingface/datasets/issues/5647
| 5,647
|
Make all print statements optional
|
closed
| 2
| 2023-03-16T20:30:07
| 2023-07-21T14:20:25
| 2023-07-21T14:20:24
|
gagan3012
|
[
"enhancement"
] |
### Feature request
Make all print statements optional to speed up the development
### Motivation
Im loading multiple tiny datasets and all the print statements make the loading slower
### Your contribution
I can help contribute
| false
|
1,627,838,762
|
https://api.github.com/repos/huggingface/datasets/issues/5646
|
https://github.com/huggingface/datasets/pull/5646
| 5,646
|
Allow self as key in `Features`
|
closed
| 3
| 2023-03-16T16:17:03
| 2023-03-16T17:21:58
| 2023-03-16T17:14:50
|
mariosasko
|
[] |
Fix #5641
| true
|
1,627,108,278
|
https://api.github.com/repos/huggingface/datasets/issues/5645
|
https://github.com/huggingface/datasets/issues/5645
| 5,645
|
Datasets map and select(range()) is giving dill error
|
closed
| 2
| 2023-03-16T10:01:28
| 2023-03-17T04:24:51
| 2023-03-17T04:24:51
|
Tanya-11
|
[] |
### Describe the bug
I'm using Huggingface Datasets library to load the dataset in google colab
When I do,
> data = train_dataset.select(range(10))
or
> train_datasets = train_dataset.map(
> process_data_to_model_inputs,
> batched=True,
> batch_size=batch_size,
> remove_columns=["article", "abstract"],
> )
I get following error: `module 'dill._dill' has no attribute 'log'`
I've tried downgrading the dill version from latest to 0.2.8, but no luck.
Stack trace:
> ---------------------------------------------------------------------------
> ModuleNotFoundError Traceback (most recent call last)
> /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in _no_cache_fields(obj)
> 367 try:
> --> 368 import transformers as tr
> 369
>
> ModuleNotFoundError: No module named 'transformers'
>
> During handling of the above exception, another exception occurred:
>
> AttributeError Traceback (most recent call last)
> 17 frames
> <ipython-input-13-dd14813880a6> in <module>
> ----> 1 test = train_dataset.select(range(10))
>
> /usr/local/lib/python3.9/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
> 155 }
> 156 # apply actual function
> --> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
> 158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
> 159 # re-apply format to the output
>
> /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
> 155 if kwargs.get(fingerprint_name) is None:
> 156 kwargs_for_fingerprint["fingerprint_name"] = fingerprint_name
> --> 157 kwargs[fingerprint_name] = update_fingerprint(
> 158 self._fingerprint, transform, kwargs_for_fingerprint
> 159 )
>
> /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)
> 103 for key in sorted(transform_args):
> 104 hasher.update(key)
> --> 105 hasher.update(transform_args[key])
> 106 return hasher.hexdigest()
> 107
>
> /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in update(self, value)
> 55 def update(self, value):
> 56 self.m.update(f"=={type(value)}==".encode("utf8"))
> ---> 57 self.m.update(self.hash(value).encode("utf-8"))
> 58
> 59 def hexdigest(self):
>
> /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in hash(cls, value)
> 51 return cls.dispatch[type(value)](cls, value)
> 52 else:
> ---> 53 return cls.hash_default(value)
> 54
> 55 def update(self, value):
>
> /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in hash_default(cls, value)
> 44 @classmethod
> 45 def hash_default(cls, value):
> ---> 46 return cls.hash_bytes(dumps(value))
> 47
> 48 @classmethod
>
> /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in dumps(obj)
> 387 file = StringIO()
> 388 with _no_cache_fields(obj):
> --> 389 dump(obj, file)
> 390 return file.getvalue()
> 391
>
> /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in dump(obj, file)
> 359 def dump(obj, file):
> 360 """pickle an object to a file"""
> --> 361 Pickler(file, recurse=True).dump(obj)
> 362 return
> 363
>
> /usr/local/lib/python3.9/dist-packages/dill/_dill.py in dump(self, obj)
> 392 return
> 393
> --> 394 def load_session(filename='/tmp/session.pkl', main=None):
> 395 """update the __main__ module with the state from the session file"""
> 396 if main is None: main = _main_module
>
> /usr/lib/python3.9/pickle.py in dump(self, obj)
> 485 if self.proto >= 4:
> 486 self.framer.start_framing()
> --> 487 self.save(obj)
> 488 self.write(STOP)
> 489 self.framer.end_framing()
>
> /usr/local/lib/python3.9/dist-packages/dill/_dill.py in save(self, obj, save_persistent_id)
> 386 pickler._byref = False # disable pickling by name reference
> 387 pickler._recurse = False # disable pickling recursion for globals
> --> 388 pickler._session = True # is best indicator of when pickling a session
> 389 pickler.dump(main)
> 390 finally:
>
> /usr/lib/python3.9/pickle.py in save(self, obj, save_persistent_id)
> 558 f = self.dispatch.get(t)
> 559 if f is not None:
> --> 560 f(self, obj) # Call unbound method with explicit self
> 561 return
> 562
>
> /usr/local/lib/python3.9/dist-packages/dill/_dill.py in save_singleton(pickler, obj)
>
> /usr/lib/python3.9/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
> 689 write(NEWOBJ)
> 690 else:
> --> 691 save(func)
> 692 save(args)
> 693 write(REDUCE)
>
> /usr/local/lib/python3.9/dist-packages/dill/_dill.py in save(self, obj, save_persistent_id)
> 386 pickler._byref = False # disable pickling by name reference
> 387 pickler._recurse = False # disable pickling recursion for globals
> --> 388 pickler._session = True # is best indicator of when pickling a session
> 389 pickler.dump(main)
> 390 finally:
>
> /usr/lib/python3.9/pickle.py in save(self, obj, save_persistent_id)
> 558 f = self.dispatch.get(t)
> 559 if f is not None:
> --> 560 f(self, obj) # Call unbound method with explicit self
> 561 return
> 562
>
> /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in save_function(pickler, obj)
> 583 dill._dill.log.info("# F1")
> 584 else:
> --> 585 dill._dill.log.info("F2: %s" % obj)
> 586 name = getattr(obj, "__qualname__", getattr(obj, "__name__", None))
> 587 dill._dill.StockPickler.save_global(pickler, obj, name=name)
>
> AttributeError: module 'dill._dill' has no attribute 'log'
### Steps to reproduce the bug
After loading the dataset(eg: https://huggingface.co/datasets/scientific_papers) in google colab
do either
> data = train_dataset.select(range(10))
or
> train_datasets = train_dataset.map(
> process_data_to_model_inputs,
> batched=True,
> batch_size=batch_size,
> remove_columns=["article", "abstract"],
> )
### Expected behavior
The map and select function should work
### Environment info
dataset: https://huggingface.co/datasets/scientific_papers
dill = 0.3.6
python= 3.9.16
transformer = 4.2.0
| false
|
1,626,204,046
|
https://api.github.com/repos/huggingface/datasets/issues/5644
|
https://github.com/huggingface/datasets/pull/5644
| 5,644
|
Allow direct cast from binary to Audio/Image
|
closed
| 3
| 2023-03-15T20:02:54
| 2023-03-16T14:20:44
| 2023-03-16T14:12:55
|
mariosasko
|
[] |
To address https://github.com/huggingface/datasets/discussions/5593.
| true
|
1,626,160,220
|
https://api.github.com/repos/huggingface/datasets/issues/5643
|
https://github.com/huggingface/datasets/pull/5643
| 5,643
|
Support PyArrow arrays as column values in `from_dict`
|
closed
| 3
| 2023-03-15T19:32:40
| 2023-03-16T17:23:06
| 2023-03-16T17:15:40
|
mariosasko
|
[] |
For consistency with `pa.Table.from_pydict`, which supports both Python lists and PyArrow arrays as column values.
"Fixes" https://discuss.huggingface.co/t/pyarrow-lib-floatarray-did-not-recognize-python-value-type-when-inferring-an-arrow-data-type/33417
| true
|
1,626,043,177
|
https://api.github.com/repos/huggingface/datasets/issues/5642
|
https://github.com/huggingface/datasets/pull/5642
| 5,642
|
Bump hfh to 0.11.0
|
closed
| 6
| 2023-03-15T18:26:07
| 2023-03-20T12:34:09
| 2023-03-20T12:26:58
|
lhoestq
|
[] |
to fix errors like
```
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/...
```
(e.g. from this [failing CI](https://github.com/huggingface/datasets/actions/runs/4428956210/jobs/7769160997))
0.11.0 is the current minimum version in `transformers`
around 5% of users are currently using versions `<0.11.0`
| true
|
1,625,942,730
|
https://api.github.com/repos/huggingface/datasets/issues/5641
|
https://github.com/huggingface/datasets/issues/5641
| 5,641
|
Features cannot be named "self"
|
closed
| 0
| 2023-03-15T17:16:40
| 2023-03-16T17:14:51
| 2023-03-16T17:14:51
|
alialamiidrissi
|
[] |
### Describe the bug
Hi,
I noticed that we cannot create a HuggingFace dataset from Pandas DataFrame with a column named `self`.
The error seems to be coming from arguments validation in the `Features.from_dict` function.
### Steps to reproduce the bug
```python
import datasets
dummy_pandas = pd.DataFrame([0,1,2,3], columns = ["self"])
datasets.arrow_dataset.Dataset.from_pandas(dummy_pandas)
```
### Expected behavior
No error thrown
### Environment info
- `datasets` version: 2.8.0
- Python version: 3.9.5
- PyArrow version: 6.0.1
- Pandas version: 1.4.1
| false
|
1,625,896,057
|
https://api.github.com/repos/huggingface/datasets/issues/5640
|
https://github.com/huggingface/datasets/pull/5640
| 5,640
|
Less zip false positives
|
closed
| 6
| 2023-03-15T16:48:59
| 2023-03-16T13:47:37
| 2023-03-16T13:40:12
|
lhoestq
|
[] |
`zipfile.is_zipfile` return false positives for some Parquet files. It causes errors when loading certain parquet datasets, where some files are considered ZIP files by `zipfile.is_zipfile`
This is a known issue: https://github.com/python/cpython/issues/72680
At first I wanted to rely only on magic numbers, but then I found that someone contributed a [fix to is_zipfile](https://github.com/python/cpython/pull/5053) - do you think we should use it @albertvillanova or not ?
IMO it's ok to rely on magic numbers only for now, since in streaming mode we've had no issue checking only the magic number so far.
Close https://github.com/huggingface/datasets/issues/5639
| true
|
1,625,737,098
|
https://api.github.com/repos/huggingface/datasets/issues/5639
|
https://github.com/huggingface/datasets/issues/5639
| 5,639
|
Parquet file wrongly recognized as zip prevents loading a dataset
|
closed
| 0
| 2023-03-15T15:20:45
| 2023-03-16T13:40:14
| 2023-03-16T13:40:14
|
clefourrier
|
[] |
### Describe the bug
When trying to `load_dataset_builder` for `HuggingFaceGECLM/StackExchange_Mar2023`, extraction fails, because parquet file [devops-00000-of-00001-22fe902fd8702892.parquet](https://huggingface.co/datasets/HuggingFaceGECLM/StackExchange_Mar2023/resolve/1f8c9a2ab6f7d0f9ae904b8b922e4384592ae1a5/data/devops-00000-of-00001-22fe902fd8702892.parquet) is wrongly identified by python as being a zip not a parquet.
(Full thread on [Slack](https://huggingface.slack.com/archives/C02V51Q3800/p1678890880803599))
### Steps to reproduce the bug
```python
from datasets import load_dataset_builder
ds = load_dataset_builder("HuggingFaceGECLM/StackExchange_Mar2023")
```
### Expected behavior
Loading the file normally.
### Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.14.0-1058-oem-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| false
|
1,625,564,471
|
https://api.github.com/repos/huggingface/datasets/issues/5638
|
https://github.com/huggingface/datasets/issues/5638
| 5,638
|
xPath to implement all operations for Path
|
closed
| 5
| 2023-03-15T13:47:11
| 2023-03-17T13:21:12
| 2023-03-17T13:21:12
|
thomasw21
|
[
"enhancement"
] |
### Feature request
Current xPath implementation is a great extension of Path in order to work with remote objects. However some methods such as `mkdir` are not implemented correctly. It should instead rely on `fsspec` methods, instead of defaulting do `Path` methods which only work locally.
### Motivation
I'm using xPath to interact with remote objects.
### Your contribution
I could try to make a PR. I'm a bit unfamiliar with chaining right now.
| false
|
1,625,295,691
|
https://api.github.com/repos/huggingface/datasets/issues/5637
|
https://github.com/huggingface/datasets/issues/5637
| 5,637
|
IterableDataset with_format does not support 'device' keyword for jax
|
open
| 3
| 2023-03-15T11:04:12
| 2025-01-07T06:59:33
| null |
Lime-Cakes
|
[] |
### Describe the bug
As seen here: https://huggingface.co/docs/datasets/use_with_jax dataset.with_format() supports the keyword 'device', to put data on a specific device when loaded as jax. However, when called on an IterableDataset, I got the error `TypeError: with_format() got an unexpected keyword argument 'device'`
Looking over the code, it seems IterableDataset support only pytorch and no support for jax device keyword?
https://github.com/huggingface/datasets/blob/fc5c84f36684343bff3e424cb0fd1ac5ecdd66da/src/datasets/iterable_dataset.py#L1029
### Steps to reproduce the bug
1. Load an IterableDataset (tested in streaming mode)
2. Call with_format('jax',device=device)
### Expected behavior
I expect to call `with_format('jax', device=device)` as per [documentation](https://huggingface.co/docs/datasets/use_with_jax) without error
### Environment info
Tested with installing newest (dev) and also pip release (2.10.1).
- `datasets` version: 2.10.2.dev0
- Platform: Linux-5.15.89+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- Huggingface_hub version: 0.12.1
- PyArrow version: 11.0.0
- Pandas version: 1.3.5
| false
|
1,623,721,577
|
https://api.github.com/repos/huggingface/datasets/issues/5636
|
https://github.com/huggingface/datasets/pull/5636
| 5,636
|
Fix CI: ignore C901 ("some_func" is to complex) in `ruff`
|
closed
| 2
| 2023-03-14T15:29:11
| 2023-03-14T16:37:06
| 2023-03-14T16:29:52
|
polinaeterna
|
[] |
idk if I should have added this ignore to `ruff` too, but I added :)
| true
|
1,623,682,558
|
https://api.github.com/repos/huggingface/datasets/issues/5635
|
https://github.com/huggingface/datasets/pull/5635
| 5,635
|
Pass custom metadata filename to Image/Audio folders
|
open
| 4
| 2023-03-14T15:08:16
| 2023-03-22T17:50:31
| null |
polinaeterna
|
[] |
This is a quick fix.
Now it requires to pass data via `data_files` parameters and include a required metadata file there and pass its filename as `metadata_filename` parameter.
For example, with the structure like:
```
data
images_dir/
im1.jpg
im2.jpg
...
metadata_dir/
meta_file1.jsonl
meta_file2.jsonl
...
```
to load data with `metadata_file1.jsonl` do:
```python
ds = load_dataset("imagefolder", data_files=["data/images_dir/**", "data/metadata_dir/meta_file1.jsonl"], metadata_filename="meta_file1.jsonl")
```
Note that if you have multiple splits, metadata file should be specified in each of them in `data_files`, smth like:
```python
data_files={
"train": ["data/train/**", "data/metadata_dir/meta_file1.jsonl"],
"test": ["data/train/**", "data/metadata_dir/meta_file1.jsonl"]
}
```
| true
|
1,622,424,174
|
https://api.github.com/repos/huggingface/datasets/issues/5634
|
https://github.com/huggingface/datasets/issues/5634
| 5,634
|
Not all progress bars are showing up when they should for downloading dataset
|
closed
| 2
| 2023-03-13T23:04:18
| 2023-10-11T16:30:16
| 2023-10-11T16:30:16
|
garlandz-db
|
[] |
### Describe the bug
During downloading the rotten tomatoes dataset, not all progress bars are displayed properly. This might be related to [this ticket](https://github.com/huggingface/datasets/issues/5117) as it raised the same concern but its not clear if the fix solves this issue too.
ipywidgets
<img width="1243" alt="image" src="https://user-images.githubusercontent.com/110427462/224851138-13fee5b7-ab51-4883-b96f-1b9808782e3b.png">
tqdm
<img width="1251" alt="Screen Shot 2023-03-13 at 3 58 59 PM" src="https://user-images.githubusercontent.com/110427462/224851180-5feb7825-9250-4b1e-ad0c-f3172ac1eb78.png">
### Steps to reproduce the bug
1. Run this line
```
from datasets import load_dataset
rotten_tomatoes = load_dataset("rotten_tomatoes", split="train")
```
### Expected behavior
all progress bars for builder script, metadata, readme, training, validation, and test set
### Environment info
requirements.txt
```
aiofiles==22.1.0
aiohttp==3.8.4
aiosignal==1.3.1
aiosqlite==0.18.0
anyio==3.6.2
appnope==0.1.3
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
arrow==1.2.3
asttokens==2.2.1
async-generator==1.10
async-timeout==4.0.2
attrs==22.2.0
Babel==2.12.1
backcall==0.2.0
beautifulsoup4==4.11.2
bleach==6.0.0
brotlipy @ file:///Users/runner/miniforge3/conda-bld/brotlipy_1666764961872/work
certifi==2022.12.7
cffi @ file:///Users/runner/miniforge3/conda-bld/cffi_1671179414629/work
cfgv==3.3.1
charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1661170624537/work
comm==0.1.2
conda==22.9.0
conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1669907009957/work
conda_package_streaming @ file:///home/conda/feedstock_root/build_artifacts/conda-package-streaming_1669733752472/work
coverage==7.2.1
cryptography @ file:///Users/runner/miniforge3/conda-bld/cryptography_1669592251328/work
datasets==2.1.0
debugpy==1.6.6
decorator==5.1.1
defusedxml==0.7.1
dill==0.3.6
distlib==0.3.6
distro==1.4.0
entrypoints==0.4
exceptiongroup==1.1.0
executing==1.2.0
fastjsonschema==2.16.3
filelock==3.9.0
flaky==3.7.0
fqdn==1.5.1
frozenlist==1.3.3
fsspec==2023.3.0
huggingface-hub==0.10.1
identify==2.5.18
idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work
iniconfig==2.0.0
ipykernel==6.12.1
ipyparallel==8.4.1
ipython==7.32.0
ipython-genutils==0.2.0
ipywidgets==8.0.4
isoduration==20.11.0
jedi==0.18.2
Jinja2==3.1.2
json5==0.9.11
jsonpointer==2.3
jsonschema==4.17.3
jupyter-events==0.6.3
jupyter-ydoc==0.2.2
jupyter_client==8.0.3
jupyter_core==5.2.0
jupyter_server==2.4.0
jupyter_server_fileid==0.8.0
jupyter_server_terminals==0.4.4
jupyter_server_ydoc==0.6.1
jupyterlab==3.6.1
jupyterlab-pygments==0.2.2
jupyterlab-widgets==3.0.5
jupyterlab_server==2.20.0
libmambapy @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/libmambapy
mamba @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/mamba
MarkupSafe==2.1.2
matplotlib-inline==0.1.6
mistune==2.0.5
multidict==6.0.4
multiprocess==0.70.14
nbclassic==0.5.3
nbclient==0.7.2
nbconvert==7.2.9
nbformat==5.7.3
nest-asyncio==1.5.6
nodeenv==1.7.0
notebook==6.5.3
notebook_shim==0.2.2
numpy==1.24.2
outcome==1.2.0
packaging==23.0
pandas==1.5.3
pandocfilters==1.5.0
parso==0.8.3
pexpect==4.8.0
pickleshare==0.7.5
platformdirs==3.0.0
plotly==5.13.1
pluggy==1.0.0
pre-commit==3.1.0
prometheus-client==0.16.0
prompt-toolkit==3.0.38
psutil==5.9.4
ptyprocess==0.7.0
pure-eval==0.2.2
pyarrow==11.0.0
pycosat @ file:///Users/runner/miniforge3/conda-bld/pycosat_1666836580084/work
pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work
Pygments==2.14.0
pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1665350324128/work
pyrsistent==0.19.3
PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work
pytest==7.2.1
pytest-asyncio==0.20.3
pytest-cov==4.0.0
pytest-timeout==2.1.0
python-dateutil==2.8.2
python-json-logger==2.0.7
pytz==2022.7.1
PyYAML==6.0
pyzmq==25.0.0
requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1661872987712/work
responses==0.18.0
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
ruamel-yaml-conda @ file:///Users/runner/miniforge3/conda-bld/ruamel_yaml_1666819760545/work
Send2Trash==1.8.0
simplegeneric==0.8.1
six==1.16.0
sniffio==1.3.0
sortedcontainers==2.4.0
soupsieve==2.4
stack-data==0.6.2
tenacity==8.2.2
terminado==0.17.1
tinycss2==1.2.1
tomli==2.0.1
toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1657485559105/work
tornado==6.2
tqdm==4.64.1
traitlets==5.8.1
trio==0.22.0
typing_extensions==4.5.0
uri-template==1.2.0
urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1669259737463/work
virtualenv==20.19.0
wcwidth==0.2.6
webcolors==1.12
webencodings==0.5.1
websocket-client==1.5.1
widgetsnbextension==4.0.5
xxhash==3.2.0
y-py==0.5.9
yarl==1.8.2
ypy-websocket==0.8.2
zstandard==0.19.0
```
| false
|
1,621,469,970
|
https://api.github.com/repos/huggingface/datasets/issues/5633
|
https://github.com/huggingface/datasets/issues/5633
| 5,633
|
Cannot import datasets
|
closed
| 1
| 2023-03-13T13:14:44
| 2023-03-13T17:54:19
| 2023-03-13T17:54:19
|
ruplet
|
[] |
### Describe the bug
Hi,
I cannot even import the library :( I installed it by running:
```
$ conda install datasets
```
Then I realized I should maybe use the huggingface channel, because I encountered the error below, so I ran:
```
$ conda remove datasets
$ conda install -c huggingface datasets
```
Please see 'steps to reproduce the bug' for the specific error, as steps to reproduce is just importing the library
### Steps to reproduce the bug
```
$ python3
Python 3.8.15 (default, Nov 24 2022, 15:19:38)
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import datasets
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/datasets/__init__.py", line 33, in <module>
from .arrow_dataset import Dataset, concatenate_datasets
File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 59, in <module>
from .arrow_reader import ArrowReader
File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/datasets/arrow_reader.py", line 27, in <module>
import pyarrow.parquet as pq
File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/pyarrow/parquet/__init__.py", line 20, in <module>
from .core import *
File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/pyarrow/parquet/core.py", line 37, in <module>
from pyarrow._parquet import (ParquetReader, Statistics, # noqa
ImportError: cannot import name 'FileEncryptionProperties' from 'pyarrow._parquet' (/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/pyarrow/_parquet.cpython-38-x86_64-linux-gnu.so)
```
### Expected behavior
I would expect for the statement `import datasets` to cause no error
### Environment info
Output of `conda list`:
```
# packages in environment at /home/jack/.conda/envs/pbalawender_zpp:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main
_openmp_mutex 5.1 1_gnu
abseil-cpp 20210324.2 h2531618_0
advertools 0.13.2 pypi_0 pypi
aiofiles 0.8.0 pypi_0 pypi
aiohttp 3.8.3 py38h5eee18b_0
aiosignal 1.2.0 pyhd3eb1b0_0
aiosqlite 0.17.0 pypi_0 pypi
anyio 3.6.2 pypi_0 pypi
aquirdturtle-collapsible-headings 3.1.0 pypi_0 pypi
argon2-cffi 21.3.0 pypi_0 pypi
argon2-cffi-bindings 21.2.0 pypi_0 pypi
arrow 1.2.3 pypi_0 pypi
arrow-cpp 3.0.0 py38h6b21186_4
asttokens 2.2.0 pypi_0 pypi
async-timeout 4.0.2 py38h06a4308_0
attrs 22.1.0 py38h06a4308_0
automat 22.10.0 pypi_0 pypi
aws-c-common 0.4.57 he6710b0_1
aws-c-event-stream 0.1.6 h2531618_5
aws-checksums 0.1.9 he6710b0_0
aws-sdk-cpp 1.8.185 hce553d0_0
babel 2.11.0 pypi_0 pypi
backcall 0.2.0 pyhd3eb1b0_0
beautifulsoup4 4.11.1 pypi_0 pypi
blas 1.0 mkl
bleach 5.0.1 pypi_0 pypi
boost-cpp 1.73.0 h27cfd23_11
bottleneck 1.3.5 py38h7deecbd_0
brotli 1.0.9 h5eee18b_7
brotli-bin 1.0.9 h5eee18b_7
brotlipy 0.7.0 py38h27cfd23_1003
bzip2 1.0.8 h7b6447c_0
c-ares 1.18.1 h7f8727e_0
ca-certificates 2023.01.10 h06a4308_0
certifi 2022.9.24 pypi_0 pypi
cffi 1.15.1 py38h5eee18b_3
charset-normalizer 2.1.1 pypi_0 pypi
click 8.1.3 pypi_0 pypi
constantly 15.1.0 pypi_0 pypi
contourpy 1.0.6 pypi_0 pypi
cryptography 38.0.4 pypi_0 pypi
cssselect 1.2.0 pypi_0 pypi
cudatoolkit 10.1.243 h8cb64d8_10 conda-forge
cycler 0.11.0 pypi_0 pypi
dacite 1.6.0 pypi_0 pypi
dataclasses 0.8 pyh6d0b6a4_7
datasets 1.18.4 py_0 huggingface
datetime 4.7 pypi_0 pypi
debugpy 1.6.4 pypi_0 pypi
decorator 5.1.1 pyhd3eb1b0_0
defusedxml 0.7.1 pypi_0 pypi
dill 0.3.6 py38h06a4308_0
docker-pycreds 0.4.0 pypi_0 pypi
double-conversion 3.1.5 he6710b0_1
entrypoints 0.4 py38h06a4308_0
executing 0.8.3 pyhd3eb1b0_0
filelock 3.8.0 pypi_0 pypi
flake8 6.0.0 pypi_0 pypi
flask 2.1.3 py38h06a4308_0
flit-core 3.6.0 pyhd3eb1b0_0
fonttools 4.38.0 pypi_0 pypi
fqdn 1.5.1 pypi_0 pypi
freetype 2.12.1 h4a9f257_0
frozenlist 1.3.3 py38h5eee18b_0
fsspec 2022.11.0 py38h06a4308_0
gensim 4.2.0 pypi_0 pypi
gflags 2.2.2 he6710b0_0
giflib 5.2.1 h5eee18b_3
gitdb 4.0.10 pypi_0 pypi
gitpython 3.1.30 pypi_0 pypi
glog 0.5.0 h2531618_0
grpc-cpp 1.39.0 hae934f6_5
huggingface-hub 0.11.1 pypi_0 pypi
huggingface_hub 0.13.1 py_0 huggingface
hyperlink 21.0.0 pypi_0 pypi
icu 58.2 he6710b0_3
idna 3.4 py38h06a4308_0
importlib-metadata 5.1.0 pypi_0 pypi
importlib_metadata 4.11.3 hd3eb1b0_0
importlib_resources 5.2.0 pyhd3eb1b0_1
incremental 22.10.0 pypi_0 pypi
intel-openmp 2021.4.0 h06a4308_3561
ipykernel 6.17.1 pyh210e3f2_0 conda-forge
ipython 8.7.0 pypi_0 pypi
ipython-genutils 0.2.0 pypi_0 pypi
ipywidgets 8.0.2 pyhd8ed1ab_1 conda-forge
isoduration 20.11.0 pypi_0 pypi
itemadapter 0.7.0 pypi_0 pypi
itemloaders 1.0.6 pypi_0 pypi
itsdangerous 2.0.1 pyhd3eb1b0_0
jedi 0.18.2 pypi_0 pypi
jinja2 3.1.2 py38h06a4308_0
jmespath 1.0.1 pypi_0 pypi
joblib 1.2.0 pypi_0 pypi
jpeg 9b h024ee3a_2
json5 0.9.10 pypi_0 pypi
jsonpickle 3.0.0 pypi_0 pypi
jsonpointer 2.3 pypi_0 pypi
jsonschema 4.17.3 py38h06a4308_0
jupyter-core 5.1.0 pypi_0 pypi
jupyter-events 0.5.0 pypi_0 pypi
jupyter-server 1.23.3 pypi_0 pypi
jupyter-server-fileid 0.6.0 pypi_0 pypi
jupyter-server-ydoc 0.4.0 pypi_0 pypi
jupyter-ydoc 0.2.2 pypi_0 pypi
jupyter_client 7.4.9 py38h06a4308_0
jupyter_core 5.2.0 py38h06a4308_0
jupyterlab 3.6.0a4 pypi_0 pypi
jupyterlab-pygments 0.2.2 pypi_0 pypi
jupyterlab-server 2.16.3 pypi_0 pypi
jupyterlab_widgets 3.0.3 pyhd8ed1ab_0 conda-forge
kiwisolver 1.4.4 pypi_0 pypi
krb5 1.19.4 h568e23c_0
lcms2 2.12 h3be6417_0
ld_impl_linux-64 2.38 h1181459_1
libboost 1.73.0 h3ff78a5_11
libbrotlicommon 1.0.9 h5eee18b_7
libbrotlidec 1.0.9 h5eee18b_7
libbrotlienc 1.0.9 h5eee18b_7
libcurl 7.88.1 h91b91d3_0
libedit 3.1.20221030 h5eee18b_0
libev 4.33 h7f8727e_1
libevent 2.1.12 h8f2d780_0
libffi 3.4.2 h6a678d5_6
libgcc-ng 11.2.0 h1234567_1
libgomp 11.2.0 h1234567_1
libnghttp2 1.46.0 hce63b2e_0
libpng 1.6.39 h5eee18b_0
libprotobuf 3.17.2 h4ff587b_1
libsodium 1.0.18 h7b6447c_0
libssh2 1.10.0 h8f2d780_0
libstdcxx-ng 11.2.0 h1234567_1
libthrift 0.14.2 hcc01f38_0
libtiff 4.1.0 h2733197_1
libuv 1.44.2 h5eee18b_0
libwebp 1.2.0 h89dd481_0
lz4-c 1.9.4 h6a678d5_0
markupsafe 2.1.1 py38h7f8727e_0
matplotlib 3.6.2 pypi_0 pypi
matplotlib-inline 0.1.6 py38h06a4308_0
mccabe 0.7.0 pypi_0 pypi
mistune 2.0.4 pypi_0 pypi
mkl 2021.4.0 h06a4308_640
mkl-service 2.4.0 py38h7f8727e_0
mkl_fft 1.3.1 py38hd3c417c_0
mkl_random 1.2.2 py38h51133e4_0
morfeusz2 1.99.6 pypi_0 pypi
multidict 6.0.2 py38h5eee18b_0
multiprocess 0.70.14 py38h06a4308_0
nbclassic 0.4.8 pypi_0 pypi
nbclient 0.7.2 pypi_0 pypi
nbconvert 7.2.5 pypi_0 pypi
nbformat 5.7.0 py38h06a4308_0
ncurses 6.4 h6a678d5_0
nest-asyncio 1.5.6 py38h06a4308_0
ninja 1.10.2 h06a4308_5
ninja-base 1.10.2 hd09550d_5
notebook 6.5.2 pypi_0 pypi
notebook-shim 0.2.2 pypi_0 pypi
numexpr 2.8.4 py38he184ba9_0
numpy 1.23.5 py38h14f4228_0
numpy-base 1.23.5 py38h31eccc5_0
oauthlib 3.2.2 pypi_0 pypi
opencv-python 4.6.0.66 pypi_0 pypi
openssl 1.1.1t h7f8727e_0
orc 1.6.9 ha97a36c_3
packaging 22.0 py38h06a4308_0
pandas 1.5.2 pypi_0 pypi
pandocfilters 1.5.0 pypi_0 pypi
parsel 1.7.0 pypi_0 pypi
parso 0.8.3 pyhd3eb1b0_0
pathlib 1.0.1 pypi_0 pypi
pathtools 0.1.2 pypi_0 pypi
pexpect 4.8.0 pyhd3eb1b0_3
pickleshare 0.7.5 pyhd3eb1b0_1003
pillow 9.3.0 pypi_0 pypi
pip 22.2.2 py38h06a4308_0
pkgutil-resolve-name 1.3.10 py38h06a4308_0
platformdirs 2.5.4 pypi_0 pypi
prometheus-client 0.15.0 pypi_0 pypi
promise 2.3 pypi_0 pypi
prompt-toolkit 3.0.33 pypi_0 pypi
protego 0.2.1 pypi_0 pypi
protobuf 4.21.12 pypi_0 pypi
psutil 5.9.0 py38h5eee18b_0
ptyprocess 0.7.0 pyhd3eb1b0_2
pure_eval 0.2.2 pyhd3eb1b0_0
pyarrow 10.0.1 pypi_0 pypi
pyasn1 0.4.8 pypi_0 pypi
pyasn1-modules 0.2.8 pypi_0 pypi
pycodestyle 2.10.0 pypi_0 pypi
pycparser 2.21 pyhd3eb1b0_0
pydispatcher 2.0.6 pypi_0 pypi
pyflakes 3.0.1 pypi_0 pypi
pygments 2.11.2 pyhd3eb1b0_0
pyopenssl 22.1.0 pypi_0 pypi
pyrsistent 0.18.0 py38heee7806_0
pysocks 1.7.1 py38h06a4308_0
python 3.8.15 h7a1cb2a_2
python-dateutil 2.8.2 pyhd3eb1b0_0
python-dotenv 0.21.0 pypi_0 pypi
python-fastjsonschema 2.16.2 py38h06a4308_0
python-json-logger 2.0.4 pypi_0 pypi
python-xxhash 2.0.2 py38h5eee18b_1
pytorch 1.7.1 py3.8_cuda10.1.243_cudnn7.6.3_0 pytorch
pytz 2022.6 pypi_0 pypi
pyyaml 6.0 py38h5eee18b_1
pyzmq 23.2.0 py38h6a678d5_0
queuelib 1.6.2 pypi_0 pypi
re2 2022.04.01 h295c915_0
readline 8.2 h5eee18b_0
regex 2022.10.31 pypi_0 pypi
requests 2.28.1 py38h06a4308_0
requests-file 1.5.1 pypi_0 pypi
requests-oauthlib 1.3.1 pypi_0 pypi
rfc3339-validator 0.1.4 pypi_0 pypi
rfc3986-validator 0.1.1 pypi_0 pypi
scikit-learn 1.1.3 pypi_0 pypi
scipy 1.9.3 pypi_0 pypi
scrapy 2.7.1 pypi_0 pypi
seaborn 0.12.1 pypi_0 pypi
send2trash 1.8.0 pypi_0 pypi
sentry-sdk 1.12.1 pypi_0 pypi
service-identity 21.1.0 pypi_0 pypi
setproctitle 1.3.2 pypi_0 pypi
setuptools 65.6.3 pypi_0 pypi
shortuuid 1.0.11 pypi_0 pypi
six 1.16.0 pyhd3eb1b0_1
smart-open 6.2.0 pypi_0 pypi
smmap 5.0.0 pypi_0 pypi
snappy 1.1.9 h295c915_0
sniffio 1.3.0 pypi_0 pypi
soupsieve 2.3.2.post1 pypi_0 pypi
sqlite 3.40.1 h5082296_0
stack-data 0.6.2 pypi_0 pypi
stack_data 0.2.0 pyhd3eb1b0_0
terminado 0.17.0 pypi_0 pypi
threadpoolctl 3.1.0 pypi_0 pypi
tinycss2 1.2.1 pypi_0 pypi
tk 8.6.12 h1ccaba5_0
tldextract 3.4.0 pypi_0 pypi
tokenizers 0.13.2 pypi_0 pypi
tomli 2.0.1 pypi_0 pypi
torchvision 0.8.2 py38_cu101 pytorch
tornado 6.2 py38h5eee18b_0
tqdm 4.64.1 py38h06a4308_0
traitlets 5.6.0 pypi_0 pypi
transformers 4.25.1 pypi_0 pypi
tweepy 4.12.1 pypi_0 pypi
twisted 22.10.0 pypi_0 pypi
twython 3.9.1 pypi_0 pypi
typing-extensions 4.4.0 py38h06a4308_0
typing_extensions 4.4.0 py38h06a4308_0
uri-template 1.2.0 pypi_0 pypi
uriparser 0.9.3 he6710b0_1
urllib3 1.26.13 pypi_0 pypi
utf8proc 2.6.1 h27cfd23_0
w3lib 2.1.0 pypi_0 pypi
wandb 0.13.7 pypi_0 pypi
wcwidth 0.2.5 pyhd3eb1b0_0
webcolors 1.12 pypi_0 pypi
webencodings 0.5.1 pypi_0 pypi
websocket-client 1.4.2 pypi_0 pypi
werkzeug 2.2.2 py38h06a4308_0
wheel 0.38.4 py38h06a4308_0
widgetsnbextension 4.0.3 py38h06a4308_0
xxhash 0.8.0 h7f8727e_3
xz 5.2.10 h5eee18b_1
y-py 0.5.4 pypi_0 pypi
yaml 0.2.5 h7b6447c_0
yarl 1.8.1 py38h5eee18b_0
ypy-websocket 0.5.0 pypi_0 pypi
zeromq 4.3.4 h2531618_0
zipp 3.11.0 py38h06a4308_0
zlib 1.2.13 h5eee18b_0
zope-interface 5.5.2 pypi_0 pypi
zstd 1.4.9 haebb681_0
```
| false
|
1,621,177,391
|
https://api.github.com/repos/huggingface/datasets/issues/5632
|
https://github.com/huggingface/datasets/issues/5632
| 5,632
|
Dataset cannot convert too large dictionnary
|
open
| 1
| 2023-03-13T10:14:40
| 2023-03-16T15:28:57
| null |
MaraLac
|
[] |
### Describe the bug
Hello everyone!
I tried to build a new dataset with the command "dict_valid = datasets.Dataset.from_dict({'input_values': values_array})".
However, I have a very large dataset (~400Go) and it seems that dataset cannot handle this.
Indeed, I can create the dataset until a certain size of my dictionnary, and then I have the error "OverflowError: Python int too large to convert to C long".
Do you know how to solve this problem?
Unfortunately I cannot give a reproductible code because I cannot share a so large file, but you can find the code below (it's a test on only a part of the validation data ~10Go, but it's already the case).
Thank you!
### Steps to reproduce the bug
SAVE_DIR = './data/'
features = h5py.File(SAVE_DIR+'features.hdf5','r')
valid_data = features["validation"]["data/features"]
v_array_values = [np.float32(item[()]) for item in valid_data.values()]
for i in range(len(v_array_values)):
v_array_values[i] = v_array_values[i].round(decimals=5)
dict_valid = datasets.Dataset.from_dict({'input_values': v_array_values})
### Expected behavior
The code is expected to give me a Huggingface dataset.
### Environment info
python: 3.8.15
numpy: 1.22.3
datasets: 2.3.2
pyarrow: 8.0.0
| false
|
1,620,442,854
|
https://api.github.com/repos/huggingface/datasets/issues/5631
|
https://github.com/huggingface/datasets/issues/5631
| 5,631
|
Custom split names
|
closed
| 1
| 2023-03-12T17:21:43
| 2023-03-24T14:13:00
| 2023-03-24T14:13:00
|
ErfanMoosaviMonazzah
|
[
"enhancement"
] |
### Feature request
Hi,
I participated in multiple NLP tasks where there are more than just train, test, validation splits, there could be multiple validation sets or test sets. But it seems currently only those mentioned three splits supported. It would be nice to have the support for more splits on the hub. (currently i can have more splits when I am loading datasets from urls, but not hub)
### Motivation
Easier access to more splits
### Your contribution
No
| false
|
1,620,327,510
|
https://api.github.com/repos/huggingface/datasets/issues/5630
|
https://github.com/huggingface/datasets/pull/5630
| 5,630
|
adds early exit if url is `PathLike`
|
open
| 1
| 2023-03-12T11:23:28
| 2023-03-15T11:58:38
| null |
vvvm23
|
[] |
Closes #4864
Should fix errors thrown when attempting to load `json` dataset using `pathlib.Path` in `data_files` argument.
| true
|
1,619,921,247
|
https://api.github.com/repos/huggingface/datasets/issues/5629
|
https://github.com/huggingface/datasets/issues/5629
| 5,629
|
load_dataset gives "403" error when using Financial phrasebank
|
open
| 1
| 2023-03-11T07:46:39
| 2023-03-13T18:27:26
| null |
Jimchoo91
|
[] |
When I try to load this dataset, I receive the following error:
ConnectionError: Couldn't reach https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip (error 403)
Has this been seen before? Thanks. The website loads when I try to access it manually.
| false
|
1,619,641,810
|
https://api.github.com/repos/huggingface/datasets/issues/5628
|
https://github.com/huggingface/datasets/pull/5628
| 5,628
|
add kwargs to index search
|
closed
| 1
| 2023-03-10T21:24:58
| 2023-03-15T14:48:47
| 2023-03-15T14:46:04
|
SaulLu
|
[] |
This PR proposes to add kwargs to index search methods.
This is particularly useful for setting the timeout of a query on elasticsearch.
A typical use case would be:
```python
dset.add_elasticsearch_index("filename", es_client=es_client)
scores, examples = dset.get_nearest_examples("filename", "my_name-train_29", request_timeout=60)
```
| true
|
1,619,336,609
|
https://api.github.com/repos/huggingface/datasets/issues/5627
|
https://github.com/huggingface/datasets/issues/5627
| 5,627
|
Unable to load AutoTrain-generated dataset from the hub
|
open
| 2
| 2023-03-10T17:25:58
| 2023-03-11T15:44:42
| null |
ijmiller2
|
[] |
### Describe the bug
DatasetGenerationError: An error occurred while generating the dataset -> ValueError: Couldn't cast ... because column names don't match
```
ValueError: Couldn't cast
_data_files: list<item: struct<filename: string>>
child 0, item: struct<filename: string>
child 0, filename: string
_fingerprint: string
_format_columns: list<item: string>
child 0, item: string
_format_kwargs: struct<>
_format_type: null
_indexes: struct<>
_output_all_columns: bool
_split: null
to
{'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}}
because column names don't match
```
### Steps to reproduce the bug
Steps to reproduce:
1. `pip install datasets==2.10.1`
2. Attempt to load (private dataset). Note that I'm authenticated via ` huggingface-cli login`
```
from datasets import load_dataset
# load dataset
dataset = "ijmiller2/autotrain-data-betterbin-vision-10000"
dataset = load_dataset(dataset)
```
Here's the full traceback:
```Downloading and preparing dataset json/ijmiller2--autotrain-data-betterbin-vision-10000 to /Users/ian/.cache/huggingface/datasets/ijmiller2___json/ijmiller2--autotrain-data-betterbin-vision-10000-2eae034a9ff8a1a9/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2383.80it/s]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 505.95it/s]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1874, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1868 writer = writer_class(
1869 features=writer._features,
1870 path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
1871 storage_options=self._fs.storage_options,
1872 embed_local_files=embed_local_files,
1873 )
-> 1874 writer.write_table(table)
1875 num_examples_progress_update += len(table)
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/arrow_writer.py:568, in ArrowWriter.write_table(self, pa_table, writer_batch_size)
567 pa_table = pa_table.combine_chunks()
--> 568 pa_table = table_cast(pa_table, self._schema)
569 if self.embed_local_files:
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2312, in table_cast(table, schema)
2311 if table.schema != schema:
-> 2312 return cast_table_to_schema(table, schema)
2313 elif table.schema.metadata != schema.metadata:
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2270, in cast_table_to_schema(table, schema)
2269 if sorted(table.column_names) != sorted(features):
-> 2270 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match")
2271 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
ValueError: Couldn't cast
_data_files: list<item: struct<filename: string>>
child 0, item: struct<filename: string>
child 0, filename: string
_fingerprint: string
_format_columns: list<item: string>
child 0, item: string
_format_kwargs: struct<>
_format_type: null
_indexes: struct<>
_output_all_columns: bool
_split: null
to
{'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}}
because column names don't match
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
Input In [8], in <cell line: 6>()
4 # load dataset
5 dataset = "ijmiller2/autotrain-data-betterbin-vision-10000"
----> 6 dataset = load_dataset(dataset)
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/load.py:1782, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)
1779 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1781 # Download and prepare data
-> 1782 builder_instance.download_and_prepare(
1783 download_config=download_config,
1784 download_mode=download_mode,
1785 verification_mode=verification_mode,
1786 try_from_hf_gcs=try_from_hf_gcs,
1787 num_proc=num_proc,
1788 )
1790 # Build dataset for splits
1791 keep_in_memory = (
1792 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1793 )
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:872, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_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)
870 if num_proc is not None:
871 prepare_split_kwargs["num_proc"] = num_proc
--> 872 self._download_and_prepare(
873 dl_manager=dl_manager,
874 verification_mode=verification_mode,
875 **prepare_split_kwargs,
876 **download_and_prepare_kwargs,
877 )
878 # Sync info
879 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:967, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
963 split_dict.add(split_generator.split_info)
965 try:
966 # Prepare split will record examples associated to the split
--> 967 self._prepare_split(split_generator, **prepare_split_kwargs)
968 except OSError as e:
969 raise OSError(
970 "Cannot find data file. "
971 + (self.manual_download_instructions or "")
972 + "\nOriginal error:\n"
973 + str(e)
974 ) from None
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1749, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1747 job_id = 0
1748 with pbar:
-> 1749 for job_id, done, content in self._prepare_split_single(
1750 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1751 ):
1752 if done:
1753 result = content
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1892, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1891 e = e.__context__
-> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1894 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
I'm ultimately trying to generate my own performance metrics on validation data (before putting an endpoint into production) and so was hoping to load all or at least the validation subset from the hub.
I'm expecting the `load_dataset()` function to work as shown in the documentation [here](https://huggingface.co/docs/datasets/loading#hugging-face-hub):
```python
dataset = load_dataset(
"lhoestq/custom_squad",
revision="main" # tag name, or branch name, or commit hash
)
```
### Environment info
- `datasets` version: 2.10.1
- Platform: macOS-13.2.1-arm64-arm-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
| false
|
1,619,252,984
|
https://api.github.com/repos/huggingface/datasets/issues/5626
|
https://github.com/huggingface/datasets/pull/5626
| 5,626
|
Support streaming datasets with numpy.load
|
closed
| 2
| 2023-03-10T16:33:39
| 2023-03-21T06:36:05
| 2023-03-21T06:28:54
|
albertvillanova
|
[] |
Support streaming datasets with `numpy.load`.
See: https://huggingface.co/datasets/qgallouedec/gia_dataset/discussions/1
| true
|
1,618,971,855
|
https://api.github.com/repos/huggingface/datasets/issues/5625
|
https://github.com/huggingface/datasets/issues/5625
| 5,625
|
Allow "jsonl" data type signifier
|
open
| 2
| 2023-03-10T13:21:48
| 2023-03-11T10:35:39
| null |
BramVanroy
|
[
"enhancement"
] |
### Feature request
`load_dataset` currently does not accept `jsonl` as type but only `json`.
### Motivation
I was working with one of the `run_translation` scripts and used my own datasets (`.jsonl`) as train_dataset. But the default code did not work because
```
FileNotFoundError: Couldn't find a dataset script at jsonl\jsonl.py or any data file in the same directory. Couldn't find 'jsonl' on the Hugging Face Hub either: FileNotFoundError: Dataset 'jsonl' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.
```
The reason is because the script has these lines to extract the data type by its extension. Therefore, the derived type is `jsonl` which is not recognized by datasets as the error above shows.
https://github.com/huggingface/transformers/blob/ade26bf9912f69e2110137443e4406d7dbe253e7/examples/pytorch/translation/run_translation.py#L342-L356
I suppose you could argue that this is the script's fault (in which case I'll do a PR over at `transformers`) but it makes sense to me to add `jsonl` as an alias to `json` in `datasets`.
### Your contribution
At the moment I cannot work on this. I think it can be as "easy" as having an alias for json, namely jsonl.
| false
|
1,617,400,192
|
https://api.github.com/repos/huggingface/datasets/issues/5624
|
https://github.com/huggingface/datasets/issues/5624
| 5,624
|
glue datasets returning -1 for test split
|
closed
| 1
| 2023-03-09T14:47:18
| 2023-03-09T16:49:29
| 2023-03-09T16:49:29
|
lithafnium
|
[] |
### Describe the bug
Downloading any dataset from GLUE has -1 as class labels for test split. Train and validation have regular 0/1 class labels. This is also present in the dataset card online.
### Steps to reproduce the bug
```
dataset = load_dataset("glue", "sst2")
for d in dataset:
# prints out -1
print(d["label"]
```
### Expected behavior
Expected behavior should be 0/1 instead of -1.
### Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.15.0-46-generic-x86_64-with-glibc2.17
- Python version: 3.8.16
- PyArrow version: 8.0.0
- Pandas version: 1.5.3
| false
|
1,616,712,665
|
https://api.github.com/repos/huggingface/datasets/issues/5623
|
https://github.com/huggingface/datasets/pull/5623
| 5,623
|
Remove set_access_token usage + fail tests if FutureWarning
|
closed
| 6
| 2023-03-09T08:46:01
| 2023-03-09T15:39:00
| 2023-03-09T15:31:59
|
Wauplin
|
[] |
`set_access_token` is deprecated and will be removed in `huggingface_hub>=0.14`.
This PR removes it from the tests (it was not used in `datasets` source code itself). FYI, it was not needed since `set_access_token` was just setting git credentials and `datasets` doesn't seem to use git anywhere.
In the future, use `set_git_credential` if needed. It is a git-credential-agnostic helper, i.e. you can store your git token in `git-credential-cache`, `git-credential-store`, `osxkeychain`, etc. The legacy `set_access_token` could only set in `git-credential-store` no matter the user preference.
(for context, I found out about this while working on https://github.com/huggingface/huggingface_hub/pull/1381)
---
In addition to this, I have added
```
filterwarnings =
error::FutureWarning:huggingface_hub*
```
to the `setup.cfg` config file to fail on future warnings from `huggingface_hub`. In `hfh`'s CI we trigger on FutureWarning from any package but it's less robust (any package update leads can lead to a failure). No obligation to keep it like that (I can remove it if you prefer) but I think it's a good idea in order to track future FutureWarnings.
FYI, in `huggingface_hub` tests we use `-Werror::FutureWarning --log-cli-level=INFO -sv --durations=0`
- FutureWarning are processed as error
- verbose mode / INFO logs (and above) are captured for easier debugging in github report
- track each test duration, just to see where we can improve. We have a quite long CI (~10min) so it helped improve that.
| true
|
1,615,190,942
|
https://api.github.com/repos/huggingface/datasets/issues/5622
|
https://github.com/huggingface/datasets/pull/5622
| 5,622
|
Update README template to better template
|
closed
| 3
| 2023-03-08T12:30:23
| 2023-03-11T05:07:38
| 2023-03-11T05:07:38
|
emiltj
|
[] | null | true
|
1,615,029,615
|
https://api.github.com/repos/huggingface/datasets/issues/5621
|
https://github.com/huggingface/datasets/pull/5621
| 5,621
|
Adding Oracle Cloud to docs
|
closed
| 2
| 2023-03-08T10:22:50
| 2023-03-11T00:57:18
| 2023-03-11T00:49:56
|
ahosler
|
[] |
Adding Oracle Cloud's fsspec implementation to the list of supported cloud storage providers.
| true
|
1,613,460,520
|
https://api.github.com/repos/huggingface/datasets/issues/5620
|
https://github.com/huggingface/datasets/pull/5620
| 5,620
|
Bump pyarrow to 8.0.0
|
closed
| 12
| 2023-03-07T13:31:53
| 2023-03-08T14:01:27
| 2023-03-08T13:54:22
|
lhoestq
|
[] |
Fix those for Pandas 2.0 (tested [here](https://github.com/huggingface/datasets/actions/runs/4346221280/jobs/7592010397) with pandas==2.0.0.rc0):
```python
=========================== short test summary info ============================
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_in_memory - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
- Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
- Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_on_disk - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
- Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
- Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
===== 2 failed, 2137 passed, 18 skipped, 32 warnings in 212.76s (0:03:32) ======
```
EDIT: also for performance - with 8.0 we can use `.to_reader()`
| true
|
1,613,439,709
|
https://api.github.com/repos/huggingface/datasets/issues/5619
|
https://github.com/huggingface/datasets/pull/5619
| 5,619
|
unpin fsspec
|
closed
| 3
| 2023-03-07T13:22:41
| 2023-03-07T13:47:01
| 2023-03-07T13:39:02
|
lhoestq
|
[] |
close https://github.com/huggingface/datasets/issues/5618
| true
|
1,612,977,934
|
https://api.github.com/repos/huggingface/datasets/issues/5618
|
https://github.com/huggingface/datasets/issues/5618
| 5,618
|
Unpin fsspec < 2023.3.0 once issue fixed
|
closed
| 0
| 2023-03-07T08:41:51
| 2023-03-07T13:39:03
| 2023-03-07T13:39:03
|
albertvillanova
|
[] |
Unpin `fsspec` upper version once root cause of our CI break is fixed.
See:
- #5614
| false
|
1,612,947,422
|
https://api.github.com/repos/huggingface/datasets/issues/5617
|
https://github.com/huggingface/datasets/pull/5617
| 5,617
|
Fix CI by temporarily pinning fsspec < 2023.3.0
|
closed
| 2
| 2023-03-07T08:18:20
| 2023-03-07T08:44:55
| 2023-03-07T08:37:28
|
albertvillanova
|
[] |
As a hotfix for our CI, temporarily pin `fsspec`:
Fix #5616.
Until root cause is fixed, see:
- #5614
| true
|
1,612,932,508
|
https://api.github.com/repos/huggingface/datasets/issues/5616
|
https://github.com/huggingface/datasets/issues/5616
| 5,616
|
CI is broken after fsspec-2023.3.0 release
|
closed
| 0
| 2023-03-07T08:06:39
| 2023-03-07T08:37:29
| 2023-03-07T08:37:29
|
albertvillanova
|
[
"bug"
] |
As reported by @lhoestq, our CI is broken after `fsspec` 2023.3.0 release:
```
FAILED tests/test_filesystem.py::test_compression_filesystems[Bz2FileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
At index 0 diff: {'name': 'file.txt', 'size': 70, 'type': 'file', 'created': 1678175677.1887748, 'islink': False, 'mode': 33188, 'uid': 1001, 'gid': 123, 'mtime': 1678175677.1887748, 'ino': 286957, 'nlink': 1} != 'file.txt'
Full diff:
[
- 'file.txt',
+ {'created': 1678175677.1887748,
+ 'gid': 123,
+ 'ino': 286957,
+ 'islink': False,
+ 'mode': 33188,
+ 'mtime': 1678175677.1887748,
+ 'name': 'file.txt',
+ 'nlink': 1,
+ 'size': 70,
+ 'type': 'file',
+ 'uid': 1001},
]
```
Also:
```
FAILED tests/test_filesystem.py::test_compression_filesystems[GzipFileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
FAILED tests/test_filesystem.py::test_compression_filesystems[Lz4FileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
FAILED tests/test_filesystem.py::test_compression_filesystems[XzFileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
FAILED tests/test_filesystem.py::test_compression_filesystems[ZstdFileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
===== 5 failed, 2134 passed, 18 skipped, 38 warnings in 157.21s (0:02:37) ======
```
See:
- fsspec/filesystem_spec#1205
| false
|
1,612,552,653
|
https://api.github.com/repos/huggingface/datasets/issues/5615
|
https://github.com/huggingface/datasets/issues/5615
| 5,615
|
IterableDataset.add_column is unable to accept another IterableDataset as a parameter.
|
closed
| 1
| 2023-03-07T01:52:00
| 2023-03-09T15:24:05
| 2023-03-09T15:23:54
|
zsaladin
|
[
"wontfix"
] |
### Describe the bug
`IterableDataset.add_column` occurs an exception when passing another `IterableDataset` as a parameter.
The method seems to accept only eager evaluated values.
https://github.com/huggingface/datasets/blob/35b789e8f6826b6b5a6b48fcc2416c890a1f326a/src/datasets/iterable_dataset.py#L1388-L1391
I wrote codes below to make it.
```py
def add_column(dataset: IterableDataset, name: str, add_dataset: IterableDataset, key: str) -> IterableDataset:
iter_add_dataset = iter(add_dataset)
def add_column_fn(example):
if name in example:
raise ValueError(f"Error when adding {name}: column {name} is already in the dataset.")
return {name: next(iter_add_dataset)[key]}
return dataset.map(add_column_fn)
```
Is there other way to do it? Or is it intended?
### Steps to reproduce the bug
Thie codes below occurs `NotImplementedError`
```py
from datasets import IterableDataset
def gen(num):
yield {f"col{num}": 1}
yield {f"col{num}": 2}
yield {f"col{num}": 3}
ids1 = IterableDataset.from_generator(gen, gen_kwargs={"num": 1})
ids2 = IterableDataset.from_generator(gen, gen_kwargs={"num": 2})
new_ids = ids1.add_column("new_col", ids1)
for row in new_ids:
print(row)
```
### Expected behavior
`IterableDataset.add_column` is able to task `IterableDataset` and lazy evaluated values as a parameter since IterableDataset is lazy evalued.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-3.10.0-1160.36.2.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.7
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
| false
|
1,611,896,357
|
https://api.github.com/repos/huggingface/datasets/issues/5614
|
https://github.com/huggingface/datasets/pull/5614
| 5,614
|
Fix archive fs test
|
closed
| 4
| 2023-03-06T17:28:09
| 2023-03-07T13:27:50
| 2023-03-07T13:20:57
|
lhoestq
|
[] | null | true
|
1,611,875,473
|
https://api.github.com/repos/huggingface/datasets/issues/5613
|
https://github.com/huggingface/datasets/issues/5613
| 5,613
|
Version mismatch with multiprocess and dill on Python 3.10
|
open
| 6
| 2023-03-06T17:14:41
| 2024-04-05T20:13:52
| null |
adampauls
|
[] |
### Describe the bug
Grabbing the latest version of `datasets` and `apache-beam` with `poetry` using Python 3.10 gives a crash at runtime. The crash is
```
File "/Users/adpauls/sc/git/DSI-transformers/data/NQ/create_NQ_train_vali.py", line 1, in <module>
import datasets
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/__init__.py", line 43, in <module>
from .arrow_dataset import Dataset
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 65, in <module>
from .arrow_reader import ArrowReader
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 30, in <module>
from .download.download_config import DownloadConfig
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/__init__.py", line 9, in <module>
from .download_manager import DownloadManager, DownloadMode
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/download_manager.py", line 35, in <module>
from ..utils.py_utils import NestedDataStructure, map_nested, size_str
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 40, in <module>
import multiprocess.pool
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 609, in <module>
class ThreadPool(Pool):
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 611, in ThreadPool
from .dummy import Process
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/dummy/__init__.py", line 87, in <module>
class Condition(threading._Condition):
AttributeError: module 'threading' has no attribute '_Condition'. Did you mean: 'Condition'?
```
I think this is a bad interaction of versions from `dill`, `multiprocess`, `apache-beam`, and `threading` from the Python (3.10) standard lib. Upgrading `multiprocess` to a version that does not crash like this is not possible because `apache-beam` pins `dill` to and old version:
```
Because multiprocess (0.70.10) depends on dill (>=0.3.2)
and apache-beam (2.45.0) depends on dill (>=0.3.1.1,<0.3.2), multiprocess (0.70.10) is incompatible with apache-beam (2.45.0).
And because no versions of apache-beam match >2.45.0,<3.0.0, multiprocess (0.70.10) is incompatible with apache-beam (>=2.45.0,<3.0.0).
So, because yyy depends on both apache-beam (^2.45.0) and multiprocess (0.70.10), version solving failed.
```
Perhaps it is not right to file a bug here, but I'm not totally sure whose fault it is. And in any case, this is an immediate blocker to using `datasets` out of the box.
Possibly related to https://github.com/huggingface/datasets/issues/5232.
### Steps to reproduce the bug
Steps to reproduce:
1. Make a poetry project with this configuration
```
[tool.poetry]
name = "yyy"
version = "0.1.0"
description = ""
authors = ["Adam Pauls <adpauls@gmail.com>"]
readme = "README.md"
packages = [{ include = "xxx" }]
[tool.poetry.dependencies]
python = ">=3.10,<3.11"
datasets = "^2.10.1"
apache-beam = "^2.45.0"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
```
2. `poetry install`.
3. `poetry run python -c "import datasets"`.
### Expected behavior
Script runs.
### Environment info
Python 3.10. Here are the versions installed by `poetry`:
```
•• Installing frozenlist (1.3.3)
• Installing idna (3.4)
• Installing multidict (6.0.4)
• Installing aiosignal (1.3.1)
• Installing async-timeout (4.0.2)
• Installing attrs (22.2.0)
• Installing certifi (2022.12.7)
• Installing charset-normalizer (3.1.0)
• Installing six (1.16.0)
• Installing urllib3 (1.26.14)
• Installing yarl (1.8.2)
• Installing aiohttp (3.8.4)
• Installing dill (0.3.1.1)
• Installing docopt (0.6.2)
• Installing filelock (3.9.0)
• Installing numpy (1.22.4)
• Installing pyparsing (3.0.9)
• Installing protobuf (3.19.4)
• Installing packaging (23.0)
• Installing python-dateutil (2.8.2)
• Installing pytz (2022.7.1)
• Installing pyyaml (6.0)
• Installing requests (2.28.2)
• Installing tqdm (4.65.0)
• Installing typing-extensions (4.5.0)
• Installing cloudpickle (2.2.1)
• Installing crcmod (1.7)
• Installing fastavro (1.7.2)
• Installing fasteners (0.18)
• Installing fsspec (2023.3.0)
• Installing grpcio (1.51.3)
• Installing hdfs (2.7.0)
• Installing httplib2 (0.20.4)
• Installing huggingface-hub (0.12.1)
• Installing multiprocess (0.70.9)
• Installing objsize (0.6.1)
• Installing orjson (3.8.7)
• Installing pandas (1.5.3)
• Installing proto-plus (1.22.2)
• Installing pyarrow (9.0.0)
• Installing pydot (1.4.2)
• Installing pymongo (3.13.0)
• Installing regex (2022.10.31)
• Installing responses (0.18.0)
• Installing xxhash (3.2.0)
• Installing zstandard (0.20.0)
• Installing apache-beam (2.45.0)
• Installing datasets (2.10.1)
```
| false
|
1,611,262,510
|
https://api.github.com/repos/huggingface/datasets/issues/5612
|
https://github.com/huggingface/datasets/issues/5612
| 5,612
|
Arrow map type in parquet files unsupported
|
open
| 4
| 2023-03-06T12:03:24
| 2024-03-15T18:56:12
| null |
TevenLeScao
|
[] |
### Describe the bug
When I try to load parquet files that were processed with Spark, I get the following issue:
`ValueError: Arrow type map<string, string ('warc_headers')> does not have a datasets dtype equivalent.`
Strangely, loading the dataset with `streaming=True` solves the issue.
### Steps to reproduce the bug
The dataset is private, but this can be reproduced with any dataset that has Arrow maps.
### Expected behavior
Loading the dataset no matter whether streaming is True or not.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-5.15.0-1029-gcp-x86_64-with-glibc2.31
- Python version: 3.10.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
| false
|
1,611,197,906
|
https://api.github.com/repos/huggingface/datasets/issues/5611
|
https://github.com/huggingface/datasets/pull/5611
| 5,611
|
add Dataset.to_list
|
closed
| 3
| 2023-03-06T11:21:57
| 2023-03-27T13:34:19
| 2023-03-27T13:26:38
|
kyoto7250
|
[] |
close https://github.com/huggingface/datasets/issues/5606
This PR is for adding the `Dataset.to_list` method.
Thank you in advance.
| true
|
1,610,698,006
|
https://api.github.com/repos/huggingface/datasets/issues/5610
|
https://github.com/huggingface/datasets/issues/5610
| 5,610
|
use datasets streaming mode in trainer ddp mode cause memory leak
|
open
| 3
| 2023-03-06T05:26:49
| 2024-03-07T01:11:32
| null |
gromzhu
|
[] |
### Describe the bug
use datasets streaming mode in trainer ddp mode cause memory leak
### Steps to reproduce the bug
import os
import time
import datetime
import sys
import numpy as np
import random
import torch
from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler,DistributedSampler,BatchSampler
torch.manual_seed(42)
from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, GPT2Model,DataCollatorForLanguageModeling,AutoModelForCausalLM
from transformers import AdamW, get_linear_schedule_with_warmup
hf_model_path ='./Wenzhong-GPT2-110M'
tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path)
tokenizer.add_special_tokens({'pad_token': '<|pad|>'})
from datasets import load_dataset
gpus=8
max_len = 576
batch_size_node = 17
save_step = 5000
gradient_accumulation = 2
dataloader_num = 4
max_step = 351000*1000//batch_size_node//gradient_accumulation//gpus
#max_step = -1
print("total_step:%d"%(max_step))
import datasets
datasets.version
dataset = load_dataset("text", data_files="./gpt_data_v1/*",split='train',cache_dir='./dataset_cache',streaming=True)
print('load over')
shuffled_dataset = dataset.shuffle(seed=42)
print('shuffle over')
def dataset_tokener(example,max_lenth=max_len):
example['text'] = list(map(lambda x : x.strip()+'<|endoftext|>',example['text'] ))
return tokenizer(example['text'], truncation=True, max_length=max_lenth, padding="longest")
new_new_dataset = shuffled_dataset.map(dataset_tokener, batched=True, remove_columns=["text"])
print('map over')
configuration = GPT2Config.from_pretrained(hf_model_path, output_hidden_states=False)
model = AutoModelForCausalLM.from_pretrained(hf_model_path)
model.resize_token_embeddings(len(tokenizer))
seed_val = 42
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
from transformers import Trainer,TrainingArguments
import os
print("strat train")
training_args = TrainingArguments(output_dir="./test_trainer",
num_train_epochs=1.0,
report_to="none",
do_train=True,
dataloader_num_workers=dataloader_num,
local_rank=int(os.environ.get('LOCAL_RANK', -1)),
overwrite_output_dir=True,
logging_strategy='steps',
logging_first_step=True,
logging_dir="./logs",
log_on_each_node=False,
per_device_train_batch_size=batch_size_node,
warmup_ratio=0.03,
save_steps=save_step,
save_total_limit=5,
gradient_accumulation_steps=gradient_accumulation,
max_steps=max_step,
disable_tqdm=False,
data_seed=42
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=new_new_dataset,
eval_dataset=None,
tokenizer=tokenizer,
data_collator=DataCollatorForLanguageModeling(tokenizer,mlm=False),
#compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
#preprocess_logits_for_metrics=preprocess_logits_for_metrics
#if training_args.do_eval and not is_torch_tpu_available()
#else None,
)
trainer.train(resume_from_checkpoint=True)
### Expected behavior
use the train code uppper
my dataset ./gpt_data_v1 have 1000 files, each file size is 120mb
start cmd is : python -m torch.distributed.launch --nproc_per_node=8 my_train.py
here is result:
here is memory usage monitor in 12 hours
every dataloader work allocate over 24gb cpu memory
according to memory usage monitor in 12 hours,sometime small memory releases, but total memory usage is increase.
i think datasets streaming mode should not used so much memery,so maybe somewhere has memory leak.
### Environment info
pytorch 1.11.0
py 3.8
cuda 11.3
transformers 4.26.1
datasets 2.9.0
| false
|
1,610,062,862
|
https://api.github.com/repos/huggingface/datasets/issues/5609
|
https://github.com/huggingface/datasets/issues/5609
| 5,609
|
`load_from_disk` vs `load_dataset` performance.
|
open
| 4
| 2023-03-05T05:27:15
| 2023-07-13T18:48:05
| null |
davidgilbertson
|
[] |
### Describe the bug
I have downloaded `openwebtext` (~12GB) and filtered out a small amount of junk (it's still huge). Now, I would like to use this filtered version for future work. It seems I have two choices:
1. Use `load_dataset` each time, relying on the cache mechanism, and re-run my filtering.
2. `save_to_disk` and then use `load_from_disk` to load the filtered version.
The performance of these two approaches is wildly different:
* Using `load_dataset` takes about 20 seconds to load the dataset, and a few seconds to re-filter (thanks to the brilliant filter/map caching)
* Using `load_from_disk` takes 14 minutes! And the second time I tried, the session just crashed (on a machine with 32GB of RAM)
I don't know if you'd call this a bug, but it seems like there shouldn't need to be two methods to load from disk, or that they should not take such wildly different amounts of time, or that one should not crash. Or maybe that the docs could offer some guidance about when to pick which method and why two methods exist, or just how do most people do it?
Something I couldn't work out from reading the docs was this: can I modify a dataset from the hub, save it (locally) and use `load_dataset` to load it? This [post seemed to suggest that the answer is no](https://discuss.huggingface.co/t/save-and-load-datasets/9260).
### Steps to reproduce the bug
See above
### Expected behavior
Load times should be about the same.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
| false
|
1,609,996,563
|
https://api.github.com/repos/huggingface/datasets/issues/5608
|
https://github.com/huggingface/datasets/issues/5608
| 5,608
|
audiofolder only creates dataset of 13 rows (files) when the data folder it's reading from has 20,000 mp3 files.
|
closed
| 2
| 2023-03-05T00:14:45
| 2023-03-12T00:02:57
| 2023-03-12T00:02:57
|
jcho19
|
[] |
### Describe the bug
x = load_dataset("audiofolder", data_dir="x")
When running this, x is a dataset of 13 rows (files) when it should be 20,000 rows (files) as the data_dir "x" has 20,000 mp3 files. Does anyone know what could possibly cause this (naming convention of mp3 files, etc.)
### Steps to reproduce the bug
x = load_dataset("audiofolder", data_dir="x")
### Expected behavior
x = load_dataset("audiofolder", data_dir="x") should create a dataset of 20,000 rows (files).
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
| false
|
1,609,166,035
|
https://api.github.com/repos/huggingface/datasets/issues/5607
|
https://github.com/huggingface/datasets/pull/5607
| 5,607
|
Fix outdated `verification_mode` values
|
closed
| 2
| 2023-03-03T19:50:29
| 2023-03-09T17:34:13
| 2023-03-09T17:27:07
|
polinaeterna
|
[] |
~I think it makes sense not to save `dataset_info.json` file to a dataset cache directory when loading dataset with `verification_mode="no_checks"` because otherwise when next time the dataset is loaded **without** `verification_mode="no_checks"`, it will be loaded successfully, despite some values in info might not correspond to the ones in the repo which was the reason for using `verification_mode="no_checks"` first.~
Updated values of `verification_mode` to the current ones in some places ("none" -> "no_checks", "all" -> "all_checks")
| true
|
1,608,911,632
|
https://api.github.com/repos/huggingface/datasets/issues/5606
|
https://github.com/huggingface/datasets/issues/5606
| 5,606
|
Add `Dataset.to_list` to the API
|
closed
| 3
| 2023-03-03T16:17:10
| 2023-03-27T13:26:40
| 2023-03-27T13:26:40
|
mariosasko
|
[
"enhancement",
"good first issue"
] |
Since there is `Dataset.from_list` in the API, we should also add `Dataset.to_list` to be consistent.
Regarding the implementation, we can re-use `Dataset.to_dict`'s code and replace the `to_pydict` calls with `to_pylist`.
| false
|
1,608,865,460
|
https://api.github.com/repos/huggingface/datasets/issues/5605
|
https://github.com/huggingface/datasets/pull/5605
| 5,605
|
Update README logo
|
closed
| 3
| 2023-03-03T15:46:31
| 2023-03-03T21:57:18
| 2023-03-03T21:50:17
|
gary149
|
[] | null | true
|
1,608,304,775
|
https://api.github.com/repos/huggingface/datasets/issues/5604
|
https://github.com/huggingface/datasets/issues/5604
| 5,604
|
Problems with downloading The Pile
|
closed
| 7
| 2023-03-03T09:52:08
| 2023-10-14T02:15:52
| 2023-03-24T12:44:25
|
sentialx
|
[] |
### Describe the bug
The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.
Here are the downloaded files:
They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/).
Alternatively, can I somehow download the files by myself and use the datasets preparing script?
### Steps to reproduce the bug
dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets')
### Expected behavior
The files should be downloaded correctly.
### Environment info
- `datasets` version: 2.10.1
- Platform: Windows-10-10.0.22623-SP0
- Python version: 3.10.5
- PyArrow version: 9.0.0
- Pandas version: 1.4.2
| false
|
1,607,143,509
|
https://api.github.com/repos/huggingface/datasets/issues/5603
|
https://github.com/huggingface/datasets/pull/5603
| 5,603
|
Don't compute checksums if not necessary in `datasets-cli test`
|
closed
| 3
| 2023-03-02T16:42:39
| 2023-03-03T15:45:32
| 2023-03-03T15:38:28
|
lhoestq
|
[] |
we only need them if there exists a `dataset_infos.json`
| true
|
1,607,054,110
|
https://api.github.com/repos/huggingface/datasets/issues/5602
|
https://github.com/huggingface/datasets/pull/5602
| 5,602
|
Return dict structure if columns are lists - to_tf_dataset
|
open
| 19
| 2023-03-02T15:51:12
| 2023-04-12T15:54:53
| null |
amyeroberts
|
[] |
This PR introduces new logic to `to_tf_dataset` affecting the returned data structure, enabling a dictionary structure to be returned, even if only one feature column is selected.
If the passed in `columns` or `label_cols` to `to_tf_dataset` are a list, they are returned as a dictionary, respectively. If they are a string, the tensor is returned.
An outline of the behaviour:
```
dataset,to_tf_dataset(columns=["col_1"], label_cols="col_2")
# ({'col_1': col_1}, col_2}
dataset,to_tf_dataset(columns="col1", label_cols="col_2")
# (col1, col2)
dataset,to_tf_dataset(columns="col1")
# col1
dataset,to_tf_dataset(columns=["col_1"], labels=["col_2"])
# ({'col1': tensor}, {'col2': tensor}}
dataset,to_tf_dataset(columns="col_1", labels=["col_2"])
# (col1, {'col2': tensor}}
```
## Motivation
Currently, when calling `to_tf_dataset`, the returned dataset will always return a tuple structure if a single feature column is used. This can cause issues when calling `model.fit` on models which train without labels e.g. [TFVitMAEForPreTraining](https://github.com/huggingface/transformers/blob/b6f47b539377ac1fd845c7adb4ccaa5eb514e126/src/transformers/models/vit_mae/modeling_vit_mae.py#L849). Specifically, [this line](https://github.com/huggingface/transformers/blob/d9e28d91a8b2d09b51a33155d3a03ad9fcfcbd1f/src/transformers/modeling_tf_utils.py#L1521) where it's assumed the input `x` is a dictionary if there is no label.
## Example
Previous behaviour
```python
In [1]: import tensorflow as tf
...: from datasets import load_dataset
...:
...:
...: def transform(batch):
...: def _transform_img(img):
...: img = img.convert("RGB")
...: img = tf.keras.utils.img_to_array(img)
...: img = tf.image.resize(img, (224, 224))
...: img /= 255.0
...: img = tf.transpose(img, perm=[2, 0, 1])
...: return img
...: batch['pixel_values'] = [_transform_img(pil_img) for pil_img in batch['img']]
...: return batch
...:
...:
...: def collate_fn(examples):
...: pixel_values = tf.stack([example["pixel_values"] for example in examples])
...: return {"pixel_values": pixel_values}
...:
...:
...: dataset = load_dataset('cifar10')['train']
...: dataset = dataset.with_transform(transform)
...: dataset.to_tf_dataset(batch_size=8, columns=['pixel_values'], collate_fn=collate_fn)
Out[1]: <PrefetchDataset element_spec=TensorSpec(shape=(None, 3, 224, 224), dtype=tf.float32, name=None)>
```
New behaviour
```python
In [1]: import tensorflow as tf
...: from datasets import load_dataset
...:
...:
...: def transform(batch):
...: def _transform_img(img):
...: img = img.convert("RGB")
...: img = tf.keras.utils.img_to_array(img)
...: img = tf.image.resize(img, (224, 224))
...: img /= 255.0
...: img = tf.transpose(img, perm=[2, 0, 1])
...: return img
...: batch['pixel_values'] = [_transform_img(pil_img) for pil_img in batch['img']]
...: return batch
...:
...:
...: def collate_fn(examples):
...: pixel_values = tf.stack([example["pixel_values"] for example in examples])
...: return {"pixel_values": pixel_values}
...:
...:
...: dataset = load_dataset('cifar10')['train']
...: dataset = dataset.with_transform(transform)
...: dataset.to_tf_dataset(batch_size=8, columns=['pixel_values'], collate_fn=collate_fn)
Out[1]: <PrefetchDataset element_spec={'pixel_values': TensorSpec(shape=(None, 3, 224, 224), dtype=tf.float32, name=None)}>
```
| true
|
1,606,685,976
|
https://api.github.com/repos/huggingface/datasets/issues/5601
|
https://github.com/huggingface/datasets/issues/5601
| 5,601
|
Authorization error
|
closed
| 2
| 2023-03-02T12:08:39
| 2023-03-14T16:55:35
| 2023-03-14T16:55:34
|
OleksandrKorovii
|
[] |
### Describe the bug
Get `Authorization error` when try to push data into hugginface datasets hub.
### Steps to reproduce the bug
I did all steps in the [tutorial](https://huggingface.co/docs/datasets/share),
1. `huggingface-cli login` with WRITE token
2. `git lfs install`
3. `git clone https://huggingface.co/datasets/namespace/your_dataset_name`
4.
```
cp /somewhere/data/*.json .
git lfs track *.json
git add .gitattributes
git add *.json
git commit -m "add json files"
```
but when I execute `git push` I got the error:
```
Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done.
batch response: Authorization error.
error: failed to push some refs to 'https://huggingface.co/datasets/zeusfsx/ukrainian-news'
```
Size of data ~100Gb. I have five json files - different parts.
### Expected behavior
All my data pushed into hub
### Environment info
- `datasets` version: 2.10.1
- Platform: macOS-13.2.1-arm64-arm-64bit
- Python version: 3.10.10
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
| false
|
1,606,585,596
|
https://api.github.com/repos/huggingface/datasets/issues/5600
|
https://github.com/huggingface/datasets/issues/5600
| 5,600
|
Dataloader getitem not working for DreamboothDatasets
|
closed
| 1
| 2023-03-02T11:00:27
| 2023-03-13T17:59:35
| 2023-03-13T17:59:35
|
salahiguiliz
|
[] |
### Describe the bug
Dataloader getitem is not working as before (see example of [DreamboothDatasets](https://github.com/huggingface/peft/blob/main/examples/lora_dreambooth/train_dreambooth.py#L451C14-L529))
moving Datasets to 2.8.0 solved the issue.
### Steps to reproduce the bug
1- using DreamBoothDataset to load some images
2- error after loading when trying to visualise the images
### Expected behavior
I was expecting a numpy array of the image
### Environment info
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
| false
|
1,605,018,478
|
https://api.github.com/repos/huggingface/datasets/issues/5598
|
https://github.com/huggingface/datasets/pull/5598
| 5,598
|
Fix push_to_hub with no dataset_infos
|
closed
| 2
| 2023-03-01T13:54:06
| 2023-03-02T13:47:13
| 2023-03-02T13:40:17
|
lhoestq
|
[] |
As reported in https://github.com/vijaydwivedi75/lrgb/issues/10, `push_to_hub` fails if the remote repository already exists and has a README.md without `dataset_info` in the YAML tags
cc @clefourrier
| true
|
1,604,928,721
|
https://api.github.com/repos/huggingface/datasets/issues/5597
|
https://github.com/huggingface/datasets/issues/5597
| 5,597
|
in-place dataset update
|
closed
| 3
| 2023-03-01T12:58:18
| 2023-03-02T13:30:41
| 2023-03-02T03:47:00
|
speedcell4
|
[
"wontfix"
] |
### Motivation
For the circumstance that I creat an empty `Dataset` and keep appending new rows into it, I found that it leads to creating a new dataset at each call. It looks quite memory-consuming. I just wonder if there is any more efficient way to do this.
```python
from datasets import Dataset
ds = Dataset.from_list([])
ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: [],
>>> num_rows: 0
>>> })
ds = ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: ['a', 'b'],
>>> num_rows: 1
>>> })
```
### Feature request
Call for in-place dataset update functions, that update the existing `Dataset` in place without creating a new copy. The interface is supposed to keep the same style as PyTorch, such as the in-place version of a `function` is named `function_`. For example, the in-pace version of `add_item`, i.e., `add_item_`, immediately updates the `Dataset`.
```python
from datasets import Dataset
ds = Dataset.from_list([])
ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: [],
>>> num_rows: 0
>>> })
ds.add_item_({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: ['a', 'b'],
>>> num_rows: 1
>>> })
```
### Related Functions
* `.map`
* `.filter`
* `.add_item`
| false
|
1,604,919,993
|
https://api.github.com/repos/huggingface/datasets/issues/5596
|
https://github.com/huggingface/datasets/issues/5596
| 5,596
|
[TypeError: Couldn't cast array of type] Can only load a subset of the dataset
|
closed
| 5
| 2023-03-01T12:53:08
| 2023-12-05T03:22:00
| 2023-03-02T11:12:11
|
loubnabnl
|
[] |
### Describe the bug
I'm trying to load this [dataset](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues) which consists of jsonl files and I get the following error:
```
casted_values = _c(array.values, feature[0])
File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 1839, in wrapper
return func(array, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 2132, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
struct<type: string, action: string, datetime: timestamp[s], author: string, title: string, description: string, comment_id: int64, comment: string, labels: list<item: string>>
to
{'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)}
```
But I can succesfully load a subset of the dataset, for example this works:
```python
ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train", data_files=[f"data/data-{x}.jsonl" for x in range(10)])
```
and `ds.features` returns:
```
{'repo': Value(dtype='string', id=None),
'org': Value(dtype='string', id=None),
'issue_id': Value(dtype='int64', id=None),
'issue_number': Value(dtype='int64', id=None),
'pull_request': {'user_login': Value(dtype='string', id=None),
'repo': Value(dtype='string', id=None),
'number': Value(dtype='int64', id=None)},
'events': [{'type': Value(dtype='string', id=None),
'action': Value(dtype='string', id=None),
'datetime': Value(dtype='timestamp[s]', id=None),
'author': Value(dtype='string', id=None),
'title': Value(dtype='string', id=None),
'description': Value(dtype='string', id=None),
'comment_id': Value(dtype='int64', id=None),
'comment': Value(dtype='string', id=None)}]}
```
So I'm not sure if there's an issue with just some of the files. Grateful if you have any suggestions to fix the issue.
Side note:
I saw this related [issue](https://github.com/huggingface/datasets/issues/3637) and tried to write a loading script to have `events` as a `Sequence` and not `list` [here](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/blob/main/loading.py) (the script was renamed). It worked with a subset locally but doesn't for the remote dataset it can't find https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/resolve/main/data.
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train")
```
### Expected behavior
Load the entire dataset succesfully.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
| false
|
1,604,070,629
|
https://api.github.com/repos/huggingface/datasets/issues/5595
|
https://github.com/huggingface/datasets/pull/5595
| 5,595
|
Unpins sqlAlchemy
|
closed
| 3
| 2023-03-01T01:33:45
| 2023-04-04T08:20:19
| 2023-04-04T08:19:14
|
lazarust
|
[] |
Closes #5477
| true
|
1,603,980,995
|
https://api.github.com/repos/huggingface/datasets/issues/5594
|
https://github.com/huggingface/datasets/issues/5594
| 5,594
|
Error while downloading the xtreme udpos dataset
|
closed
| 21
| 2023-02-28T23:40:53
| 2023-11-04T20:45:56
| 2023-07-24T14:22:18
|
simran-khanuja
|
[] |
### Describe the bug
Hi,
I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed
```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4...
Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s]
Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last):
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single
for key, record in generator:
File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples
yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs)
File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples
for path, file in filepath:
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__
yield from self.generator(*self.args, **self.kwargs)
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path
yield from cls._iter_tar(f)
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar
for tarinfo in stream:
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__
tarinfo = self.next()
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next
raise ReadError("unexpected end of data")
tarfile.ReadError: unexpected end of data
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module>
main()
File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main
train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload")
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset
builder_instance.download_and_prepare(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare
self._download_and_prepare(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare
super()._download_and_prepare(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
```
train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload")
```
### Expected behavior
Download the udpos dataset
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
| false
|
1,603,619,124
|
https://api.github.com/repos/huggingface/datasets/issues/5592
|
https://github.com/huggingface/datasets/pull/5592
| 5,592
|
Fix docstring example
|
closed
| 2
| 2023-02-28T18:42:37
| 2023-02-28T19:26:33
| 2023-02-28T19:19:15
|
stevhliu
|
[] |
Fixes #5581 to use the correct output for the `set_format` method.
| true
|
1,603,571,407
|
https://api.github.com/repos/huggingface/datasets/issues/5591
|
https://github.com/huggingface/datasets/pull/5591
| 5,591
|
set dev version
|
closed
| 3
| 2023-02-28T18:09:05
| 2023-02-28T18:16:31
| 2023-02-28T18:09:15
|
lhoestq
|
[] | null | true
|
1,603,549,504
|
https://api.github.com/repos/huggingface/datasets/issues/5590
|
https://github.com/huggingface/datasets/pull/5590
| 5,590
|
Release: 2.10.1
|
closed
| 5
| 2023-02-28T17:58:11
| 2023-02-28T18:16:27
| 2023-02-28T18:06:08
|
lhoestq
|
[] | null | true
|
1,603,535,704
|
https://api.github.com/repos/huggingface/datasets/issues/5589
|
https://github.com/huggingface/datasets/pull/5589
| 5,589
|
Revert "pass the dataset features to the IterableDataset.from_generator"
|
closed
| 5
| 2023-02-28T17:52:04
| 2023-09-24T10:07:33
| 2023-03-21T14:18:18
|
lhoestq
|
[] |
This reverts commit b91070b9c09673e2e148eec458036ab6a62ac042 (temporarily)
It hurts iterable dataset performance a lot (e.g. x4 slower because it encodes+decodes images unnecessarily). I think we need to fix this before re-adding it
cc @mariosasko @Hubert-Bonisseur
| true
|
1,603,304,766
|
https://api.github.com/repos/huggingface/datasets/issues/5588
|
https://github.com/huggingface/datasets/pull/5588
| 5,588
|
Flatten dataset on the fly in `save_to_disk`
|
closed
| 3
| 2023-02-28T15:37:46
| 2023-02-28T17:28:35
| 2023-02-28T17:21:17
|
mariosasko
|
[] |
Flatten a dataset on the fly in `save_to_disk` instead of doing it with `flatten_indices` to avoid creating an additional cache file.
(this is one of the sub-tasks in https://github.com/huggingface/datasets/issues/5507)
| true
|
1,603,139,420
|
https://api.github.com/repos/huggingface/datasets/issues/5587
|
https://github.com/huggingface/datasets/pull/5587
| 5,587
|
Fix `sort` with indices mapping
|
closed
| 3
| 2023-02-28T14:05:08
| 2023-02-28T17:28:57
| 2023-02-28T17:21:58
|
mariosasko
|
[] |
Fixes the `key` range in the `query_table` call in `sort` to account for an indices mapping
Fix #5586
| true
|
1,602,961,544
|
https://api.github.com/repos/huggingface/datasets/issues/5586
|
https://github.com/huggingface/datasets/issues/5586
| 5,586
|
.sort() is broken when used after .filter(), only in 2.10.0
|
closed
| 1
| 2023-02-28T12:18:09
| 2023-02-28T18:17:26
| 2023-02-28T17:21:59
|
MattYoon
|
[
"bug"
] |
### Describe the bug
Hi, thank you for your support!
It seems like the addition of multiple key sort (#5502) in 2.10.0 broke the `.sort()` method.
After filtering a dataset with `.filter()`, the `.sort()` seems to refer to the query_table index of the previous unfiltered dataset, resulting in an IndexError.
This only happens with the 2.10.0 release.
### Steps to reproduce the bug
```Python
from datasets import load_dataset
# dataset with length of 1104
ds = load_dataset('glue', 'ax')['test']
ds = ds.filter(lambda x: x['idx'] > 1100)
ds.sort('premise')
print('Done')
```
File "/home/dongkeun/datasets_test/test.py", line 5, in <module>
ds.sort('premise')
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 528, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 511, in wrapper
out = func(dataset, *args, **kwargs)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3959, in sort
sort_table = query_table(
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 588, in query_table
_check_valid_index_key(key, size)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 537, in _check_valid_index_key
_check_valid_index_key(max(key), size=size)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 531, in _check_valid_index_key
raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 1103 is out of bounds for size 3
### Expected behavior
It should sort the dataset and print "Done". Which it does on 2.9.0.
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
| false
|
1,602,190,030
|
https://api.github.com/repos/huggingface/datasets/issues/5585
|
https://github.com/huggingface/datasets/issues/5585
| 5,585
|
Cache is not transportable
|
closed
| 2
| 2023-02-28T00:53:06
| 2023-02-28T21:26:52
| 2023-02-28T21:26:52
|
davidgilbertson
|
[] |
### Describe the bug
I would like to share cache between two machines (a Windows host machine and a WSL instance).
I run most my code in WSL. I have just run out of space in the virtual drive. Rather than expand the drive size, I plan to move to cache to the host Windows machine, thereby sharing the downloads.
I'm hoping that I can just copy/paste the cache files, but I notice that a lot of the file names start with the path name, e.g. `_home_davidg_.cache_huggingface_datasets_conll2003_default-451...98.lock` where `home/davidg` is where the cache is in WSL.
This seems to suggest that the cache is not portable/cannot be centralised or shared. Is this the case, or are the files that start with path names not integral to the caching mechanism? Because copying the cache files _seems_ to work, but I'm not filled with confidence that something isn't going to break.
A related issue, when trying to load a dataset that should come from cache (running in WSL, pointing to cache on the Windows host) it seemed to work fine, but it still uses a WSL directory for `.cache\huggingface\modules\datasets_modules`. I see nothing in the docs about this, or how to point it to a different place.
I have asked a related question on the forum: https://discuss.huggingface.co/t/is-datasets-cache-operating-system-agnostic/32656
### Steps to reproduce the bug
View the cache directory in WSL/Windows.
### Expected behavior
Cache can be shared between (virtual) machines and be transportable.
It would be nice to have a simple way to say "Dear Hugging Face packages, please put ALL your cache in `blah/de/blah`" and have all the Hugging Face packages respect that single location.
### Environment info
```
- `datasets` version: 2.9.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- ```
| false
|
1,601,821,808
|
https://api.github.com/repos/huggingface/datasets/issues/5584
|
https://github.com/huggingface/datasets/issues/5584
| 5,584
|
Unable to load coyo700M dataset
|
closed
| 1
| 2023-02-27T19:35:03
| 2023-02-28T07:27:59
| 2023-02-28T07:27:58
|
manuaero
|
[] |
### Describe the bug
Seeing this error when downloading https://huggingface.co/datasets/kakaobrain/coyo-700m:
```ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.```
Full stack trace
```Downloading and preparing dataset parquet/kakaobrain--coyo-700m to /root/.cache/huggingface/datasets/kakaobrain___parquet/kakaobrain--coyo-700m-ae729692ae3e0073/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...
Downloading data files: 100%
1/1 [00:00<00:00, 63.35it/s]
Extracting data files: 100%
1/1 [00:00<00:00, 5.00it/s]
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
[/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1859 _time = time.time()
-> 1860 for _, table in generator:
1861 if max_shard_size is not None and writer._num_bytes > max_shard_size:
9 frames
ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
[/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1891 e = e.__context__
-> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1893
1894 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset```
### Steps to reproduce the bug
```
from datasets import load_dataset
hf_dataset = load_dataset("kakaobrain/coyo-700m")
```
### Expected behavior
The above commands load the dataset successfully. Or handles exception and continue loading the remainder.
### Environment info
colab. any
| false
|
1,601,583,625
|
https://api.github.com/repos/huggingface/datasets/issues/5583
|
https://github.com/huggingface/datasets/pull/5583
| 5,583
|
Do no write index by default when exporting a dataset
|
closed
| 3
| 2023-02-27T17:04:46
| 2023-02-28T13:52:15
| 2023-02-28T13:44:04
|
mariosasko
|
[] |
Ensures all the writers that use Pandas for conversion (JSON, CSV, SQL) do not export `index` by default (https://github.com/huggingface/datasets/pull/5490 only did this for CSV)
| true
|
1,600,932,092
|
https://api.github.com/repos/huggingface/datasets/issues/5582
|
https://github.com/huggingface/datasets/pull/5582
| 5,582
|
Add column_names to IterableDataset
|
closed
| 2
| 2023-02-27T10:50:07
| 2023-03-13T19:10:22
| 2023-03-13T19:03:32
|
patrickloeber
|
[] |
This PR closes #5383
* Add column_names property to IterableDataset
* Add multiple tests for this new property
| true
|
1,600,675,489
|
https://api.github.com/repos/huggingface/datasets/issues/5581
|
https://github.com/huggingface/datasets/issues/5581
| 5,581
|
[DOC] Mistaken docs on set_format
|
closed
| 1
| 2023-02-27T08:03:09
| 2023-02-28T19:19:17
| 2023-02-28T19:19:17
|
NightMachinery
|
[
"good first issue"
] |
### Describe the bug
https://huggingface.co/docs/datasets/v2.10.0/en/package_reference/main_classes#datasets.Dataset.set_format
<img width="700" alt="image" src="https://user-images.githubusercontent.com/36224762/221506973-ae2e3991-60a7-4d4e-99f8-965c6eb61e59.png">
While actually running it will result in:
<img width="1094" alt="image" src="https://user-images.githubusercontent.com/36224762/221507032-007dab82-8781-4319-b21a-e6e4d40d97b3.png">
### Steps to reproduce the bug
_
### Expected behavior
_
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
| false
|
1,600,431,792
|
https://api.github.com/repos/huggingface/datasets/issues/5580
|
https://github.com/huggingface/datasets/pull/5580
| 5,580
|
Support cloud storage in load_dataset via fsspec
|
closed
| 8
| 2023-02-27T04:06:05
| 2024-11-27T01:25:39
| 2023-03-11T00:55:40
|
dwyatte
|
[] |
Closes https://github.com/huggingface/datasets/issues/5281
This PR uses fsspec to support datasets on cloud storage (tested manually with GCS). ETags are currently unsupported for cloud storage. In general, a much larger refactor could be done to just use fsspec for all schemes (ftp, http/s, s3, gcs) to unify the interfaces here, but I ultimately opted to leave that out of this PR
I didn't create a GCS filesystem class in `datasets.filesystems` since the S3 one appears to be a wrapper around `s3fs.S3FileSystem` and mainly used to generate docs.
| true
|
1,599,732,211
|
https://api.github.com/repos/huggingface/datasets/issues/5579
|
https://github.com/huggingface/datasets/pull/5579
| 5,579
|
Add instructions to create `DataLoader` from augmented dataset in object detection guide
|
closed
| 3
| 2023-02-25T14:53:17
| 2023-03-23T19:24:59
| 2023-03-23T19:24:50
|
Laurent2916
|
[] |
The following adds instructions on how to create a `DataLoader` from the guide on how to use object detection with augmentations (#4710). I am open to hearing any suggestions for improvement !
| true
|
1,598,863,119
|
https://api.github.com/repos/huggingface/datasets/issues/5578
|
https://github.com/huggingface/datasets/pull/5578
| 5,578
|
Add `huggingface_hub` version to env cli command
|
closed
| 4
| 2023-02-24T15:37:43
| 2023-02-27T17:28:25
| 2023-02-27T17:21:09
|
mariosasko
|
[] |
Add the `huggingface_hub` version to the `env` command's output.
| true
|
1,598,587,665
|
https://api.github.com/repos/huggingface/datasets/issues/5577
|
https://github.com/huggingface/datasets/issues/5577
| 5,577
|
Cannot load `the_pile_openwebtext2`
|
closed
| 1
| 2023-02-24T13:01:48
| 2023-02-24T14:01:09
| 2023-02-24T14:01:09
|
wjfwzzc
|
[] |
### Describe the bug
I met the same bug mentioned in #3053 which is never fixed. Because several `reddit_scores` are larger than `int8` even `int16`. https://huggingface.co/datasets/the_pile_openwebtext2/blob/main/the_pile_openwebtext2.py#L62
### Steps to reproduce the bug
```python3
from datasets import load_dataset
dataset = load_dataset("the_pile_openwebtext2")
```
### Expected behavior
load as normal.
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
| false
|
1,598,582,744
|
https://api.github.com/repos/huggingface/datasets/issues/5576
|
https://github.com/huggingface/datasets/issues/5576
| 5,576
|
I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value("int8"))`, but the actual values can be well outside the max range for 8-bit integers.
|
closed
| 1
| 2023-02-24T12:57:49
| 2023-02-24T12:58:31
| 2023-02-24T12:58:18
|
wjfwzzc
|
[] |
I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value("int8"))`, but the actual values can be well outside the max range for 8-bit integers.
I worked around this by downloading the `the_pile_openwebtext2.py` and editing it to use local files and drop reddit scores as a column (not needed for my purposes).
_Originally posted by @tc-wolf in https://github.com/huggingface/datasets/issues/3053#issuecomment-1281392422_
| false
|
1,598,396,552
|
https://api.github.com/repos/huggingface/datasets/issues/5575
|
https://github.com/huggingface/datasets/issues/5575
| 5,575
|
Metadata for each column
|
open
| 5
| 2023-02-24T10:53:44
| 2024-01-05T21:48:35
| null |
parsa-ra
|
[
"enhancement"
] |
### Feature request
Being able to put some metadata for each column as a string or any other type.
### Motivation
I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata
### Your contribution
Maybe we could map another relational like database as the metadata?
| false
|
1,598,104,691
|
https://api.github.com/repos/huggingface/datasets/issues/5574
|
https://github.com/huggingface/datasets/issues/5574
| 5,574
|
c4 dataset streaming fails with `FileNotFoundError`
|
closed
| 12
| 2023-02-24T07:57:32
| 2023-12-18T07:32:32
| 2023-02-27T04:03:38
|
krasserm
|
[] |
### Describe the bug
Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("c4", "en", split="train", streaming=True)
next(iter(dataset))
```
causes a
```
FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz
```
I can download this file manually though e.g. by entering this URL in a browser.
There is an underlying HTTP 403 status code:
```
aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX')
```
### Expected behavior
This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
| false
|
1,597,400,836
|
https://api.github.com/repos/huggingface/datasets/issues/5573
|
https://github.com/huggingface/datasets/pull/5573
| 5,573
|
Use soundfile for mp3 decoding instead of torchaudio
|
closed
| 7
| 2023-02-23T19:19:44
| 2023-02-28T20:25:14
| 2023-02-28T20:16:02
|
polinaeterna
|
[] |
I've removed `torchaudio` completely and switched to use `soundfile` for everything. With the new version of `soundfile` package this should work smoothly because the `libsndfile` C library is bundled, in Linux wheels too.
Let me know if you think it's too harsh and we should continue to support `torchaudio` decoding.
I decided that we can drop it completely because:
1. it's always something wrong with `torchaudio` (for example recently https://github.com/huggingface/datasets/issues/5488 )
2. the results of mp3 decoding are different depending on `torchaudio` version
3. `soundfile` is slightly faster then the latest `torchaudio`
4. anyway users can pass any custom decoding function with any library they want if needed (worth putting a snippet in the docs).
cc @sanchit-gandhi @vaibhavad
| true
|
1,597,257,624
|
https://api.github.com/repos/huggingface/datasets/issues/5572
|
https://github.com/huggingface/datasets/issues/5572
| 5,572
|
Datasets 2.10.0 does not reuse the dataset cache
|
closed
| 0
| 2023-02-23T17:28:11
| 2023-02-23T18:03:55
| 2023-02-23T18:03:55
|
lsb
|
[] |
### Describe the bug
download_mode="reuse_dataset_if_exists" will always consider that a dataset doesn't exist.
Specifically, upon losing an internet connection trying to load a dataset for a second time in ten seconds, a connection error results, showing a breakpoint of:
```
File ~/jupyterlab/.direnv/python-3.9.6/lib/python3.9/site-packages/datasets/load.py:1174, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1165 except Exception as e: # noqa: catch any exception of hf_hub and consider that the dataset doesn't exist
1166 if isinstance(
1167 e,
1168 (
(...)
1172 ),
1173 ):
-> 1174 raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})")
1175 elif "404" in str(e):
1176 msg = f"Dataset '{path}' doesn't exist on the Hub"
ConnectionError: Couldn't reach 'lsb/tenk' on the Hub (ConnectionError)
```
This has been around since at least v2.0.
### Steps to reproduce the bug
```
from datasets import load_dataset
import numpy as np
tenk = load_dataset("lsb/tenk") # ten thousand integers
print(np.average(tenk['train']['a'])) # prints 4999.5
### now disconnect your internet
tenk_too = load_dataset("lsb/tenk", download_mode="reuse_dataset_if_exists")
# Raises ConnectionError: Couldn't reach 'lsb/tenk' on the Hub (ConnectionError)
```
### Expected behavior
I expected that I would be able to reuse the dataset I just downloaded.
### Environment info
- `datasets` version: 2.10.0
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.5.2
| false
|
1,597,198,953
|
https://api.github.com/repos/huggingface/datasets/issues/5571
|
https://github.com/huggingface/datasets/issues/5571
| 5,571
|
load_dataset fails for JSON in windows
|
closed
| 2
| 2023-02-23T16:50:11
| 2023-02-24T13:21:47
| 2023-02-24T13:21:47
|
abinashsahu
|
[] |
### Describe the bug
Steps:
1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method.
2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json"
3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON.
4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py.
raise InvalidConfigName(
f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. "
f"They could create issues when creating a directory for this config on Windows filesystem."
6. When I bring the data to the current directory, it works fine.
### Steps to reproduce the bug
Steps:
1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method.
2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json"
3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON.
4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py.
raise InvalidConfigName(
f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. "
f"They could create issues when creating a directory for this config on Windows filesystem."
6. When I bring the data to the current directory, it works fine.
### Expected behavior
Should be able to read from a path different than current directory in Windows machine.
### Environment info
datasets version: 2.3.1
python version: 3.8
Windows OS
| false
|
1,597,190,926
|
https://api.github.com/repos/huggingface/datasets/issues/5570
|
https://github.com/huggingface/datasets/issues/5570
| 5,570
|
load_dataset gives FileNotFoundError on imagenet-1k if license is not accepted on the hub
|
closed
| 2
| 2023-02-23T16:44:32
| 2023-07-24T15:18:50
| 2023-07-24T15:18:50
|
buoi
|
[] |
### Describe the bug
When calling ```load_dataset('imagenet-1k')``` FileNotFoundError is raised, if not logged in and if logged in with huggingface-cli but not having accepted the licence on the hub. There is no error once accepting.
### Steps to reproduce the bug
```
from datasets import load_dataset
imagenet = load_dataset("imagenet-1k", split="train", streaming=True)
FileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub
```
tested on a colab notebook.
### Expected behavior
I would expect a specific error indicating that I have to login then accept the dataset licence.
I find this bug very relevant as this code is on a guide on the [Huggingface documentation for Datasets](https://huggingface.co/docs/datasets/about_mapstyle_vs_iterable)
### Environment info
google colab cpu-only instance
| false
|
1,597,132,383
|
https://api.github.com/repos/huggingface/datasets/issues/5569
|
https://github.com/huggingface/datasets/pull/5569
| 5,569
|
pass the dataset features to the IterableDataset.from_generator function
|
closed
| 3
| 2023-02-23T16:06:04
| 2023-02-24T14:06:37
| 2023-02-23T18:15:16
|
bruno-hays
|
[] |
[5558](https://github.com/huggingface/datasets/issues/5568)
| true
|
1,596,900,532
|
https://api.github.com/repos/huggingface/datasets/issues/5568
|
https://github.com/huggingface/datasets/issues/5568
| 5,568
|
dataset.to_iterable_dataset() loses useful info like dataset features
|
closed
| 3
| 2023-02-23T13:45:33
| 2023-02-24T13:22:36
| 2023-02-24T13:22:36
|
bruno-hays
|
[
"enhancement",
"good first issue"
] |
### Describe the bug
Hello,
I like the new `to_iterable_dataset` feature but I noticed something that seems to be missing.
When using `to_iterable_dataset` to transform your map style dataset into iterable dataset, you lose valuable metadata like the features.
These metadata are useful if you want to interleave iterable datasets, cast columns etc.
### Steps to reproduce the bug
```python
dataset = load_dataset("lhoestq/demo1")["train"]
print(dataset.features)
# {'id': Value(dtype='string', id=None), 'package_name': Value(dtype='string', id=None), 'review': Value(dtype='string', id=None), 'date': Value(dtype='string', id=None), 'star': Value(dtype='int64', id=None), 'version_id': Value(dtype='int64', id=None)}
dataset = dataset.to_iterable_dataset()
print(dataset.features)
# None
```
### Expected behavior
Keep the relevant information
### Environment info
datasets==2.10.0
| false
|
1,595,916,674
|
https://api.github.com/repos/huggingface/datasets/issues/5566
|
https://github.com/huggingface/datasets/issues/5566
| 5,566
|
Directly reading parquet files in a s3 bucket from the load_dataset method
|
open
| 1
| 2023-02-22T22:13:40
| 2023-02-23T11:03:29
| null |
shamanez
|
[
"duplicate",
"enhancement"
] |
### Feature request
Right now, we have to read the get the parquet file to the local storage. So having ability to read given the bucket directly address would be benificial
### Motivation
In a production set up, this feature can help us a lot. So we do not need move training datafiles in between storage.
### Your contribution
I am willing to help if there's anyway.
| false
|
1,595,281,752
|
https://api.github.com/repos/huggingface/datasets/issues/5565
|
https://github.com/huggingface/datasets/pull/5565
| 5,565
|
Add writer_batch_size for ArrowBasedBuilder
|
closed
| 6
| 2023-02-22T15:09:30
| 2023-03-10T13:53:03
| 2023-03-10T13:45:43
|
lhoestq
|
[] |
This way we can control the size of the record_batches/row_groups of arrow/parquet files.
This can be useful for `datasets-server` to keep control of the row groups size which can affect random access performance for audio/image/video datasets
Right now having 1,000 examples per row group cause some image datasets to be pretty slow for random access (e.g. 4seconds for `beans` to get 20 rows)
| true
|
1,595,064,698
|
https://api.github.com/repos/huggingface/datasets/issues/5564
|
https://github.com/huggingface/datasets/pull/5564
| 5,564
|
Set dev version
|
closed
| 3
| 2023-02-22T13:00:09
| 2023-02-22T13:09:26
| 2023-02-22T13:00:25
|
lhoestq
|
[] | null | true
|
1,595,049,025
|
https://api.github.com/repos/huggingface/datasets/issues/5563
|
https://github.com/huggingface/datasets/pull/5563
| 5,563
|
Release: 2.10.0
|
closed
| 4
| 2023-02-22T12:48:52
| 2023-02-22T13:05:55
| 2023-02-22T12:56:48
|
lhoestq
|
[] | null | true
|
1,594,625,539
|
https://api.github.com/repos/huggingface/datasets/issues/5562
|
https://github.com/huggingface/datasets/pull/5562
| 5,562
|
Update csv.py
|
closed
| 4
| 2023-02-22T07:56:10
| 2023-02-23T11:07:49
| 2023-02-23T11:00:58
|
xdoubleu
|
[] |
Removed mangle_dup_cols=True from BuilderConfig.
It triggered following deprecation warning:
/usr/local/lib/python3.8/dist-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'
return pd.read_csv(xopen(filepath_or_buffer, "rb", use_auth_token=use_auth_token), **kwargs)
Further documentation of pandas: https://pandas.pydata.org/docs/whatsnew/v1.4.0.html#mangle-dupe-cols-in-read-csv-no-longer-renames-unique-columns-conflicting-with-target-names
At first sight it seems like this flag is resolved internally, it might need some more research.
| true
|
1,593,862,388
|
https://api.github.com/repos/huggingface/datasets/issues/5561
|
https://github.com/huggingface/datasets/pull/5561
| 5,561
|
Add pre-commit config yaml file to enable automatic code formatting
|
closed
| 6
| 2023-02-21T17:35:07
| 2023-02-28T15:37:22
| 2023-02-23T18:23:29
|
polinaeterna
|
[] |
@huggingface/datasets do you think it would be useful? Motivation - sometimes PRs are like 30% "fix: style" commits :)
If so - I need to double check the config but for me locally it works as expected.
| true
|
1,593,809,978
|
https://api.github.com/repos/huggingface/datasets/issues/5560
|
https://github.com/huggingface/datasets/pull/5560
| 5,560
|
Ensure last tqdm update in `map`
|
closed
| 10
| 2023-02-21T16:56:17
| 2023-02-21T18:26:23
| 2023-02-21T18:19:09
|
mariosasko
|
[] |
This PR modifies `map` to:
* ensure the TQDM bar gets the last progress update
* when a map function fails, avoid throwing a chained exception in the single-proc mode
| true
|
1,593,676,489
|
https://api.github.com/repos/huggingface/datasets/issues/5559
|
https://github.com/huggingface/datasets/pull/5559
| 5,559
|
Fix map suffix_template
|
closed
| 4
| 2023-02-21T15:26:26
| 2023-02-21T17:21:37
| 2023-02-21T17:14:29
|
lhoestq
|
[] |
#5455 introduced a small bug that lead `map` to ignore the `suffix_template` argument and not put suffixes to cached files in multiprocessing.
I fixed this and also improved a few things:
- regarding logging: "Loading cached processed dataset" is now logged only once even in multiprocessing (it used to be logged `num_proc` times)
- regarding new_fingerprint: I made sure that the returned dataset satisfies `ds._fingerprint==new_fingerprint` if `new_fingerprint` is passed to `map`
| true
|
1,593,655,815
|
https://api.github.com/repos/huggingface/datasets/issues/5558
|
https://github.com/huggingface/datasets/pull/5558
| 5,558
|
Remove instructions for `ffmpeg` system package installation on Colab
|
closed
| 2
| 2023-02-21T15:13:36
| 2023-03-01T13:46:04
| 2023-02-23T13:50:27
|
polinaeterna
|
[] |
Colab now has Ubuntu 20.04 which already has `ffmpeg` of required (>4) version.
| true
|
1,593,545,324
|
https://api.github.com/repos/huggingface/datasets/issues/5557
|
https://github.com/huggingface/datasets/pull/5557
| 5,557
|
Add filter desc
|
closed
| 3
| 2023-02-21T14:04:42
| 2023-02-21T14:19:54
| 2023-02-21T14:12:39
|
lhoestq
|
[] |
Otherwise it would show a `Map` progress bar, since it uses `map` under the hood
| true
|
1,593,246,936
|
https://api.github.com/repos/huggingface/datasets/issues/5556
|
https://github.com/huggingface/datasets/pull/5556
| 5,556
|
Use default audio resampling type
|
closed
| 5
| 2023-02-21T10:45:50
| 2023-02-21T12:49:50
| 2023-02-21T12:42:52
|
lhoestq
|
[] |
...instead of relying on the optional librosa dependency `resampy`.
It was only used for `_decode_non_mp3_file_like` anyway and not for the other ones - removing it fixes consistency between decoding methods (except torchaudio decoding)
Therefore I think it is a better solution than adding `resampy` as a dependency in https://github.com/huggingface/datasets/pull/5554
cc @polinaeterna
| true
|
1,592,469,938
|
https://api.github.com/repos/huggingface/datasets/issues/5555
|
https://github.com/huggingface/datasets/issues/5555
| 5,555
|
`.shuffle` throwing error `ValueError: Protocol not known: parent`
|
open
| 4
| 2023-02-20T21:33:45
| 2023-02-27T09:23:34
| null |
prabhakar267
|
[] |
### Describe the bug
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In [16], line 1
----> 1 train_dataset = train_dataset.shuffle()
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs)
544 self_format = {
545 "type": self._format_type,
546 "format_kwargs": self._format_kwargs,
547 "columns": self._format_columns,
548 "output_all_columns": self._output_all_columns,
549 }
550 # apply actual function
--> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
553 # re-apply format to the output
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
482 # Update fingerprint of in-place transforms + update in-place history of transforms
484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3616, in Dataset.shuffle(self, seed, generator, keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint)
3610 return self._new_dataset_with_indices(
3611 fingerprint=new_fingerprint, indices_cache_file_name=indices_cache_file_name
3612 )
3614 permutation = generator.permutation(len(self))
-> 3616 return self.select(
3617 indices=permutation,
3618 keep_in_memory=keep_in_memory,
3619 indices_cache_file_name=indices_cache_file_name if not keep_in_memory else None,
3620 writer_batch_size=writer_batch_size,
3621 new_fingerprint=new_fingerprint,
3622 )
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs)
544 self_format = {
545 "type": self._format_type,
546 "format_kwargs": self._format_kwargs,
547 "columns": self._format_columns,
548 "output_all_columns": self._output_all_columns,
549 }
550 # apply actual function
--> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
553 # re-apply format to the output
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
482 # Update fingerprint of in-place transforms + update in-place history of transforms
484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3266, in Dataset.select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
3263 return self._select_contiguous(start, length, new_fingerprint=new_fingerprint)
3265 # If not contiguous, we need to create a new indices mapping
-> 3266 return self._select_with_indices_mapping(
3267 indices,
3268 keep_in_memory=keep_in_memory,
3269 indices_cache_file_name=indices_cache_file_name,
3270 writer_batch_size=writer_batch_size,
3271 new_fingerprint=new_fingerprint,
3272 )
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs)
544 self_format = {
545 "type": self._format_type,
546 "format_kwargs": self._format_kwargs,
547 "columns": self._format_columns,
548 "output_all_columns": self._output_all_columns,
549 }
550 # apply actual function
--> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
553 # re-apply format to the output
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
482 # Update fingerprint of in-place transforms + update in-place history of transforms
484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3389, in Dataset._select_with_indices_mapping(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
3387 logger.info(f"Caching indices mapping at {indices_cache_file_name}")
3388 tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False)
-> 3389 writer = ArrowWriter(
3390 path=tmp_file.name, writer_batch_size=writer_batch_size, fingerprint=new_fingerprint, unit="indices"
3391 )
3393 indices = indices if isinstance(indices, list) else list(indices)
3395 size = len(self)
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_writer.py:315, in ArrowWriter.__init__(self, schema, features, path, stream, fingerprint, writer_batch_size, hash_salt, check_duplicates, disable_nullable, update_features, with_metadata, unit, embed_local_files, storage_options)
312 self._disable_nullable = disable_nullable
314 if stream is None:
--> 315 fs_token_paths = fsspec.get_fs_token_paths(path, storage_options=storage_options)
316 self._fs: fsspec.AbstractFileSystem = fs_token_paths[0]
317 self._path = (
318 fs_token_paths[2][0]
319 if not is_remote_filesystem(self._fs)
320 else self._fs.unstrip_protocol(fs_token_paths[2][0])
321 )
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:593, in get_fs_token_paths(urlpath, mode, num, name_function, storage_options, protocol, expand)
591 else:
592 urlpath = stringify_path(urlpath)
--> 593 chain = _un_chain(urlpath, storage_options or {})
594 if len(chain) > 1:
595 inkwargs = {}
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:330, in _un_chain(path, kwargs)
328 for bit in reversed(bits):
329 protocol = split_protocol(bit)[0] or "file"
--> 330 cls = get_filesystem_class(protocol)
331 extra_kwargs = cls._get_kwargs_from_urls(bit)
332 kws = kwargs.get(protocol, {})
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/registry.py:240, in get_filesystem_class(protocol)
238 if protocol not in registry:
239 if protocol not in known_implementations:
--> 240 raise ValueError("Protocol not known: %s" % protocol)
241 bit = known_implementations[protocol]
242 try:
ValueError: Protocol not known: parent
```
This is what the `train_dataset` object looks like
```
Dataset({
features: ['label', 'input_ids', 'attention_mask'],
num_rows: 364166
})
```
### Steps to reproduce the bug
The `train_dataset` obj is created by concatenating two datasets
And then shuffle is called, but it throws the mentioned error.
### Expected behavior
Should shuffle the dataset properly.
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-1022-aws-x86_64-with-glibc2.31
- Python version: 3.9.13
- PyArrow version: 10.0.0
- Pandas version: 1.4.4
| false
|
1,592,285,062
|
https://api.github.com/repos/huggingface/datasets/issues/5554
|
https://github.com/huggingface/datasets/pull/5554
| 5,554
|
Add resampy dep
|
closed
| 5
| 2023-02-20T18:15:43
| 2023-09-24T10:07:29
| 2023-02-21T12:43:38
|
lhoestq
|
[] |
In librosa 0.10 they removed the `resmpy` dependency and set it to optional.
However it is necessary for resampling. I added it to the "audio" extra dependencies.
| true
|
1,592,236,998
|
https://api.github.com/repos/huggingface/datasets/issues/5553
|
https://github.com/huggingface/datasets/pull/5553
| 5,553
|
improved message error row formatting
|
closed
| 2
| 2023-02-20T17:29:14
| 2023-02-21T13:08:25
| 2023-02-21T12:58:12
|
Plutone11011
|
[] |
Solves #5539
| true
|
1,592,186,703
|
https://api.github.com/repos/huggingface/datasets/issues/5552
|
https://github.com/huggingface/datasets/pull/5552
| 5,552
|
Make tiktoken tokenizers hashable
|
closed
| 4
| 2023-02-20T16:50:09
| 2023-02-21T13:20:42
| 2023-02-21T13:13:05
|
mariosasko
|
[] |
Fix for https://discord.com/channels/879548962464493619/1075729627546406912/1075729627546406912
| true
|
1,592,140,836
|
https://api.github.com/repos/huggingface/datasets/issues/5551
|
https://github.com/huggingface/datasets/pull/5551
| 5,551
|
Suggest scikit-learn instead of sklearn
|
closed
| 4
| 2023-02-20T16:16:57
| 2023-02-21T13:27:57
| 2023-02-21T13:21:07
|
osbm
|
[] |
This is kinda unimportant fix but, the suggested `pip install sklearn` does not work.
The current error message if sklearn is not installed:
```
ImportError: To be able to use [dataset name], you need to install the following dependency: sklearn.
Please install it using 'pip install sklearn' for instance.
```
| true
|
1,591,409,475
|
https://api.github.com/repos/huggingface/datasets/issues/5550
|
https://github.com/huggingface/datasets/pull/5550
| 5,550
|
Resolve four broken refs in the docs
|
closed
| 3
| 2023-02-20T08:52:11
| 2023-02-20T15:16:13
| 2023-02-20T15:09:13
|
tomaarsen
|
[] |
Hello!
## Pull Request overview
* Resolve 4 broken references in the docs
## The problems
Two broken references [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.class_encode_column):
---
One broken reference [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.unique):
---
One missing reference [here](https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.DatasetDict.class_encode_column):
- Tom Aarsen
| true
|
1,590,836,848
|
https://api.github.com/repos/huggingface/datasets/issues/5549
|
https://github.com/huggingface/datasets/pull/5549
| 5,549
|
Apply ruff flake8-comprehension checks
|
closed
| 2
| 2023-02-19T20:09:28
| 2023-02-23T14:06:39
| 2023-02-23T13:59:39
|
Skylion007
|
[] |
Fix #5548
Apply ruff's flake8-comprehension checks for better performance, and more readable code.
| true
|
1,590,835,479
|
https://api.github.com/repos/huggingface/datasets/issues/5548
|
https://github.com/huggingface/datasets/issues/5548
| 5,548
|
Apply flake8-comprehensions to codebase
|
closed
| 0
| 2023-02-19T20:05:38
| 2023-02-23T13:59:41
| 2023-02-23T13:59:41
|
Skylion007
|
[
"enhancement"
] |
### Feature request
Apply ruff flake8 comprehension checks to codebase.
### Motivation
This should strictly improve the performance / readability of the codebase by removing unnecessary iteration, function calls, etc. This should generate better Python bytecode which should strictly improve performance.
I already applied this fixes to PyTorch and Sympy with little issue and have opened PRs to diffusers and transformers todo this as well.
### Your contribution
Making a PR.
| false
|
1,590,468,200
|
https://api.github.com/repos/huggingface/datasets/issues/5547
|
https://github.com/huggingface/datasets/pull/5547
| 5,547
|
Add JAX device selection when formatting
|
closed
| 9
| 2023-02-18T20:57:40
| 2023-02-21T16:10:55
| 2023-02-21T16:04:03
|
alvarobartt
|
[] |
## What's in this PR?
After exploring for a while the JAX integration in 🤗`datasets`, I found out that, even though JAX prioritizes the TPU and GPU as the default device when available, the `JaxFormatter` doesn't let you specify the device where you want to place the `jax.Array`s in case you don't want to rely on JAX's default array placement.
So on, I've included the `device` param in `JaxFormatter` but there are some things to take into consideration:
* A formatted `Dataset` is copied with `copy.deepcopy` which means that if one adds the param `device` in `JaxFormatter` as a `jaxlib.xla_extension.Device`, it "fails" because that object cannot be serialized (instead of serializing the param adds a random hash instead). That's the reason why I added a function `_map_devices_to_str` to basically create a mapping of strings to `jaxlib.xla_extension.Device`s so that `self.device` is a string and not a `jaxlib.xla_extension.Device`.
* To create a `jax.Array` in a device you need to either create it in the default device and then move it to the desired device with `jax.device_put` or directly create it in the device you want with `jax.default_device()` context manager.
* JAX will create an array by default in `jax.devices()[0]`
More information on JAX device management is available at https://jax.readthedocs.io/en/latest/faq.html#controlling-data-and-computation-placement-on-devices
## What's missing in this PR?
I've tested it both locally in CPU (Mac M2 and Mac M1, as no GPU support for Mac yet), and in GPU and TPU in Google Colab, let me know if you want me to provide you the Notebook for the latter.
But I did not implement any integration test as I wanted to get your feedback first.
| true
|
1,590,346,349
|
https://api.github.com/repos/huggingface/datasets/issues/5546
|
https://github.com/huggingface/datasets/issues/5546
| 5,546
|
Downloaded datasets do not cache at $HF_HOME
|
closed
| 1
| 2023-02-18T13:30:35
| 2023-07-24T14:22:43
| 2023-07-24T14:22:43
|
ErfanMoosaviMonazzah
|
[] |
### Describe the bug
In the huggingface course (https://huggingface.co/course/chapter3/2?fw=pt) it said that if we set HF_HOME, downloaded datasets would be cached at specified address but it does not. downloaded models from checkpoint names are downloaded and cached at HF_HOME but this is not the case for datasets, they are still cached at ~/.cache/huggingface/datasets.
### Steps to reproduce the bug
Run the following code
```
from datasets import load_dataset
raw_datasets = load_dataset("glue", "mrpc")
raw_datasets
```
it downloads and store dataset at ~/.cache/huggingface/datasets
### Expected behavior
to cache dataset at HF_HOME.
### Environment info
python 3.10.6
Kubuntu 22.04
HF_HOME located on a separate partition
| false
|
1,590,315,972
|
https://api.github.com/repos/huggingface/datasets/issues/5545
|
https://github.com/huggingface/datasets/pull/5545
| 5,545
|
Added return methods for URL-references to the pushed dataset
|
open
| 6
| 2023-02-18T11:26:25
| 2023-12-18T16:57:56
| null |
davidberenstein1957
|
[] |
Hi,
I was missing the ability to easily open the pushed dataset and it seemed like a quick fix.
Maybe we also want to log this info somewhere, but let me know if I need to add that too.
Cheers,
David
| true
|
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