html_url stringlengths 48 51 | title stringlengths 5 280 | comments stringlengths 63 51.8k | body stringlengths 0 36.2k ⌀ | comment_length int64 16 1.52k | text stringlengths 159 54.1k | embeddings listlengths 768 768 |
|---|---|---|---|---|---|---|
https://github.com/huggingface/datasets/issues/4863 | TFDS wiki_dialog dataset to Huggingface dataset | Nvm, I was able to port it to huggingface datasets, will upload to the hub soon | ## Adding a Dataset
- **Name:** *Wiki_dialog*
- **Description: https://github.com/google-research/dialog-inpainting#:~:text=JSON%20object%2C%20for-,example,-%3A
- **Paper: https://arxiv.org/abs/2205.09073
- **Data: https://github.com/google-research/dialog-inpainting
- **Motivation:** *Research and Development on ... | 16 | TFDS wiki_dialog dataset to Huggingface dataset
## Adding a Dataset
- **Name:** *Wiki_dialog*
- **Description: https://github.com/google-research/dialog-inpainting#:~:text=JSON%20object%2C%20for-,example,-%3A
- **Paper: https://arxiv.org/abs/2205.09073
- **Data: https://github.com/google-research/dialog-inpaintin... | [
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0.51248300075... |
https://github.com/huggingface/datasets/issues/4862 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code | Hi hi, instead of using `download_and_extract` function, I only use `download` function: `base_dir = Path(dl_manager.download(urls))`. It turns out that the code works for `datasets==2.2.2`, however, it doesn't work with `datasets==2.4.0`. | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""... | 30 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib impo... | [
-0.22395770251750946,
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0.0017217844724655151,
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0.40095254778862,
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0.11190696805715561,
... |
https://github.com/huggingface/datasets/issues/4862 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code | Hi @yana-xuyan, thanks for reporting.
Indeed you already found the answer: an Excel file should be just downloaded and not downloaded-and-extracted.
The reason why is that if you call also extract, our library will try to infer the compression format (and extract it). And Excel files are viewed as ZIP files and e... | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""... | 96 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib impo... | [
-0.22395770251750946,
0.1329294741153717,
0.0017217844724655151,
0.4737096428871155,
0.568895161151886,
0.08158870786428452,
0.35390034317970276,
0.3585244119167328,
0.5213257670402527,
0.40628087520599365,
-0.20903171598911285,
0.40095254778862,
-0.01900530233979225,
0.11190696805715561,
... |
https://github.com/huggingface/datasets/issues/4862 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code | Hi @albertvillanova, thank you for your reply! Do you have any clue on why the same error still exists with `datasets==2.4.0` even after I don't extract the downloaded file? FYI, if I downgrade to `datasets==2.2.2`, the code works fine. | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""... | 39 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib impo... | [
-0.22395770251750946,
0.1329294741153717,
0.0017217844724655151,
0.4737096428871155,
0.568895161151886,
0.08158870786428452,
0.35390034317970276,
0.3585244119167328,
0.5213257670402527,
0.40628087520599365,
-0.20903171598911285,
0.40095254778862,
-0.01900530233979225,
0.11190696805715561,
... |
https://github.com/huggingface/datasets/issues/4862 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code | I guess this has to do with the cache: you should remove the previously-wrongly generated directory from the cache; otherwise `datasets` tries to re-use it. | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""... | 25 | Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib impo... | [
-0.22395770251750946,
0.1329294741153717,
0.0017217844724655151,
0.4737096428871155,
0.568895161151886,
0.08158870786428452,
0.35390034317970276,
0.3585244119167328,
0.5213257670402527,
0.40628087520599365,
-0.20903171598911285,
0.40095254778862,
-0.01900530233979225,
0.11190696805715561,
... |
https://github.com/huggingface/datasets/issues/4861 | Using disk for memory with the method `from_dict` | This issue was also causing an OOM in @nateraw 's workflow and shows again that behavior is confusing - we should definitely switch to using the disk IMO | **Is your feature request related to a problem? Please describe.**
I start with an empty dataset. In a loop, at each iteration, I create a new dataset with the method `from_dict` (based on some data I load) and I concatenate this new dataset with the one at the previous iteration. After some iterations, I have an OOM ... | 28 | Using disk for memory with the method `from_dict`
**Is your feature request related to a problem? Please describe.**
I start with an empty dataset. In a loop, at each iteration, I create a new dataset with the method `from_dict` (based on some data I load) and I concatenate this new dataset with the one at the previ... | [
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0.14041100442409515,
0.09544262290000916,
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0.3349032700061798,
0.25292813777923584,
0.518291175365448,
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0.1335700154304... |
https://github.com/huggingface/datasets/issues/4858 | map() function removes columns when input_columns is not None | Hi! Thanks for reporting! This looks like a bug. I've just opened a PR with the fix. | ## Describe the bug
The map function, removes features from the dataset that are not present in the _input_columns_ list of columns, despite the columns being removed not mentioned in the _remove_columns_ argument.
## Steps to reproduce the bug
```python
from datasets import Dataset
ds = Dataset.from_dict({"a" : [... | 17 | map() function removes columns when input_columns is not None
## Describe the bug
The map function, removes features from the dataset that are not present in the _input_columns_ list of columns, despite the columns being removed not mentioned in the _remove_columns_ argument.
## Steps to reproduce the bug
```pytho... | [
0.2791581153869629,
-0.11062590032815933,
-0.06368225812911987,
0.1861584484577179,
0.16765299439430237,
0.16881519556045532,
0.5726051926612854,
0.39615511894226074,
0.3560894727706909,
0.1518978327512741,
0.006297950632870197,
0.5933223962783813,
-0.017512522637844086,
0.0155637245625257... |
https://github.com/huggingface/datasets/issues/4857 | No preprocessed wikipedia is working on huggingface/datasets | Thanks for reporting @aninrusimha.
Please, note that the preprocessed datasets are still available, as described in the dataset card, e.g.: https://huggingface.co/datasets/wikipedia
```python
ds = load_dataset("wikipedia", "20220301.en")
``` | ## Describe the bug
20220301 wikipedia dump has been deprecated, so now there is no working wikipedia dump on huggingface
https://huggingface.co/datasets/wikipedia
https://dumps.wikimedia.org/enwiki/
| 27 | No preprocessed wikipedia is working on huggingface/datasets
## Describe the bug
20220301 wikipedia dump has been deprecated, so now there is no working wikipedia dump on huggingface
https://huggingface.co/datasets/wikipedia
https://dumps.wikimedia.org/enwiki/
Thanks for reporting @aninrusimha.
Plea... | [
0.016649190336465836,
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0.023333508521318436,
0.005202166736125946,
0.1565481722354889,
0.1782524287700653,
0.22608761489391327,
0.3795302212238312,
0.11009877920150757,
0.008971981704235077,
-0.1580875962972641,
0.25768595933914185,
0.07001125812530518,
0.0686620175838... |
https://github.com/huggingface/datasets/issues/4857 | No preprocessed wikipedia is working on huggingface/datasets | This is working now, but I was getting an error a few days ago when running an existing script. Unfortunately I did not do a proper bug report, but for some reason I was unable to load the dataset due to a request being made to the wikimedia website. However, its working now. Thanks for the reply! | ## Describe the bug
20220301 wikipedia dump has been deprecated, so now there is no working wikipedia dump on huggingface
https://huggingface.co/datasets/wikipedia
https://dumps.wikimedia.org/enwiki/
| 57 | No preprocessed wikipedia is working on huggingface/datasets
## Describe the bug
20220301 wikipedia dump has been deprecated, so now there is no working wikipedia dump on huggingface
https://huggingface.co/datasets/wikipedia
https://dumps.wikimedia.org/enwiki/
This is working now, but I was getting an ... | [
-0.056750476360321045,
-0.25431889295578003,
0.014886856079101562,
-0.01529131829738617,
0.11912667751312256,
0.15966364741325378,
0.27118024230003357,
0.3747330605983734,
0.21714477241039276,
-0.006378106772899628,
-0.047890499234199524,
0.2112503945827484,
0.11526419967412949,
0.08615552... |
https://github.com/huggingface/datasets/issues/4856 | file missing when load_dataset with openwebtext on windows | I have tried to extract ```0015896-b1054262f7da52a0518521e29c8e352c.txt``` from ```17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz``` with 7-zip
and put the file into cache_path ```F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb2... | ## Describe the bug
0015896-b1054262f7da52a0518521e29c8e352c.txt is missing when I run run_mlm.py with openwebtext. I check the cache_path and can not find 0015896-b1054262f7da52a0518521e29c8e352c.txt. but I can find this file in the 17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_da... | 55 | file missing when load_dataset with openwebtext on windows
## Describe the bug
0015896-b1054262f7da52a0518521e29c8e352c.txt is missing when I run run_mlm.py with openwebtext. I check the cache_path and can not find 0015896-b1054262f7da52a0518521e29c8e352c.txt. but I can find this file in the 17ecf461bfccd469a1fbc264... | [
-0.2830095589160919,
0.1394357979297638,
0.03655129298567772,
0.36213862895965576,
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0.41025614738464355,
0.33363834023475647,
0.35540279746055603,
0.190585657954216,
-0.053093135356903076,
0.27123355865478516,
0.24614746868610382,
-0.1301415115594864,
-0.212688863277435... |
https://github.com/huggingface/datasets/issues/4855 | Dataset Viewer issue for super_glue | Thanks for reporting @wzsxxa.
However the "super_glue" dataset is rendered properly by the Dataset preview: https://huggingface.co/datasets/super_glue | ### Link
https://huggingface.co/datasets/super_glue
### Description
can't view super_glue dataset on the web page
### Owner
_No response_ | 16 | Dataset Viewer issue for super_glue
### Link
https://huggingface.co/datasets/super_glue
### Description
can't view super_glue dataset on the web page
### Owner
_No response_
Thanks for reporting @wzsxxa.
However the "super_glue" dataset is rendered properly by the Dataset preview: https://huggingface.co/dat... | [
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0.3045909404754... |
https://github.com/huggingface/datasets/issues/4852 | Bug in multilingual_with_para config of exams dataset and checksums error | Hi @albertvillanova. Unfortunately I still get this error. Is this because the merge has yet to be released? Is there a way to track the release? | ## Describe the bug
There is a bug for "multilingual_with_para" config in exams dataset:
```python
ds = load_dataset("./datasets/exams", split="train")
```
raises:
```
KeyError: 'choices'
```
Moreover, there is a NonMatchingChecksumError:
```
NonMatchingChecksumError: Checksums didn't match for dataset so... | 26 | Bug in multilingual_with_para config of exams dataset and checksums error
## Describe the bug
There is a bug for "multilingual_with_para" config in exams dataset:
```python
ds = load_dataset("./datasets/exams", split="train")
```
raises:
```
KeyError: 'choices'
```
Moreover, there is a NonMatchingChecksum... | [
-0.2889246940612793,
-0.04891008883714676,
-0.08580703288316727,
0.430634081363678,
0.19367215037345886,
-0.07232353836297989,
0.11616993695497513,
0.4211183786392212,
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0.19296199083328247,
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0.12413590401411057,
-0.08251821994781... |
https://github.com/huggingface/datasets/issues/4852 | Bug in multilingual_with_para config of exams dataset and checksums error | Hi @thesofakillers, yes you are right: the fix will be available after next release (it was planned for today; Monday at the latest).
In the meantime, you can use the version of the `exams` on our main branch by passing `revision` to `load_dataset`:
```python
ds = load_dataset("exams", revision="main")
``` | ## Describe the bug
There is a bug for "multilingual_with_para" config in exams dataset:
```python
ds = load_dataset("./datasets/exams", split="train")
```
raises:
```
KeyError: 'choices'
```
Moreover, there is a NonMatchingChecksumError:
```
NonMatchingChecksumError: Checksums didn't match for dataset so... | 49 | Bug in multilingual_with_para config of exams dataset and checksums error
## Describe the bug
There is a bug for "multilingual_with_para" config in exams dataset:
```python
ds = load_dataset("./datasets/exams", split="train")
```
raises:
```
KeyError: 'choices'
```
Moreover, there is a NonMatchingChecksum... | [
-0.2889246940612793,
-0.04891008883714676,
-0.08580703288316727,
0.430634081363678,
0.19367215037345886,
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0.11616993695497513,
0.4211183786392212,
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0.20865973830223083,
0.19296199083328247,
0.3872624337673187,
0.12413590401411057,
-0.08251821994781... |
https://github.com/huggingface/datasets/issues/4840 | Dataset Viewer issue for darragh/demo_data_raw3 | do you have an idea of why it can occur @huggingface/datasets? The dataset consists of a single parquet file. | ### Link
https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by @NielsRogge
### Owner
No | 19 | Dataset Viewer issue for darragh/demo_data_raw3
### Link
https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by... | [
-0.35398557782173157,
-0.20391644537448883,
0.024462096393108368,
0.40717417001724243,
0.14356133341789246,
0.09458959847688675,
0.21502617001533508,
0.3621065020561218,
-0.18674451112747192,
0.11043444275856018,
-0.019130919128656387,
0.56197589635849,
-0.29584264755249023,
0.117787517607... |
https://github.com/huggingface/datasets/issues/4840 | Dataset Viewer issue for darragh/demo_data_raw3 | Thanks for reporting @severo.
I'm not able to reproduce that error. I get instead:
```
FileNotFoundError: [Errno 2] No such file or directory: 'orix/data/ChiSig/唐合乐-9-3.jpg'
```
Which pyarrow version are you using? Mine is 6.0.1. | ### Link
https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by @NielsRogge
### Owner
No | 34 | Dataset Viewer issue for darragh/demo_data_raw3
### Link
https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by... | [
-0.33789145946502686,
0.07463118433952332,
0.03789704665541649,
0.32135605812072754,
0.09047198295593262,
0.023588411509990692,
0.2501470148563385,
0.4774776101112366,
-0.4221305549144745,
0.08033768087625504,
-0.10341911762952805,
0.5430614948272705,
-0.2949056327342987,
-0.04329200088977... |
https://github.com/huggingface/datasets/issues/4840 | Dataset Viewer issue for darragh/demo_data_raw3 | Apparently, there is something weird with that Parquet file: its schema is:
```
images: extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>>
```
I have forced a right schema:
```python
from datasets import Features, Image, load_dataset
features = Features({"images": Image()})
ds = datasets.l... | ### Link
https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by @NielsRogge
### Owner
No | 97 | Dataset Viewer issue for darragh/demo_data_raw3
### Link
https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by... | [
-0.32083433866500854,
-0.062167443335056305,
0.02742553874850273,
0.42224323749542236,
0.2109047919511795,
0.03596245497465134,
0.1780756264925003,
0.33485084772109985,
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0.043284960091114044,
0.07184018939733505,
0.5532619953155518,
-0.306625097990036,
0.215770527720451... |
https://github.com/huggingface/datasets/issues/4829 | Misalignment between card tag validation and docs | (Note that the doc is aligned with the hub validation rules, and the "ground truth" is the hub validation rules given that they apply to all datasets, not just the canonical ones) | ## Describe the bug
As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284
the validation of the dataset card tags is not aligned with its documentation: e.g.
- implementation: `license: List[str]`
- docs: `license: Union[str, List[str]]`
They should be aligned.
... | 32 | Misalignment between card tag validation and docs
## Describe the bug
As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284
the validation of the dataset card tags is not aligned with its documentation: e.g.
- implementation: `license: List[str]`
- docs: `license: ... | [
-0.37480810284614563,
-0.17362239956855774,
-0.027834326028823853,
0.14412137866020203,
0.19238075613975525,
0.23069870471954346,
0.1749410778284073,
0.3382502496242523,
0.2947353720664978,
0.00824795663356781,
0.029535343870520592,
0.2788616418838501,
0.1573074609041214,
0.324702352285385... |
https://github.com/huggingface/datasets/issues/4829 | Misalignment between card tag validation and docs | Instead of our own implementation, we now use `huggingface_hub`'s `DatasetCardData`, which has the correct type hint, so I think we can close this issue. | ## Describe the bug
As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284
the validation of the dataset card tags is not aligned with its documentation: e.g.
- implementation: `license: List[str]`
- docs: `license: Union[str, List[str]]`
They should be aligned.
... | 24 | Misalignment between card tag validation and docs
## Describe the bug
As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284
the validation of the dataset card tags is not aligned with its documentation: e.g.
- implementation: `license: List[str]`
- docs: `license: ... | [
-0.30711981654167175,
-0.259937047958374,
-0.019689971581101418,
0.20848478376865387,
0.2689773142337799,
0.12550398707389832,
0.08933639526367188,
0.3309425711631775,
0.2765294313430786,
0.008246742188930511,
-0.1250099241733551,
0.3567868769168854,
0.009760169312357903,
0.403408110141754... |
https://github.com/huggingface/datasets/issues/4804 | streaming dataset with concatenating splits raises an error | Hi! Only the name of a particular split ("train", "test", ...) is supported as a split pattern if `streaming=True`. We plan to address this limitation soon. | ## Describe the bug
streaming dataset with concatenating splits raises an error
## Steps to reproduce the bug
```python
from datasets import load_dataset
# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation")
```
```python
from datasets import load_dataset
# er... | 26 | streaming dataset with concatenating splits raises an error
## Describe the bug
streaming dataset with concatenating splits raises an error
## Steps to reproduce the bug
```python
from datasets import load_dataset
# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation"... | [
-0.6321982145309448,
-0.14114195108413696,
-0.04174860566854477,
0.08145271986722946,
0.20941485464572906,
0.05047117918729782,
0.479400634765625,
0.28783243894577026,
-0.2424594759941101,
0.17925235629081726,
-0.017328202724456787,
0.1898089498281479,
-0.09531179815530777,
0.3461282551288... |
https://github.com/huggingface/datasets/issues/4804 | streaming dataset with concatenating splits raises an error | yes, same error occurs.
```python
from datasets import load_dataset
# error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation", streaming=True)
```
```python
---------------------------------------------------------------------------
ValueError ... | ## Describe the bug
streaming dataset with concatenating splits raises an error
## Steps to reproduce the bug
```python
from datasets import load_dataset
# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation")
```
```python
from datasets import load_dataset
# er... | 201 | streaming dataset with concatenating splits raises an error
## Describe the bug
streaming dataset with concatenating splits raises an error
## Steps to reproduce the bug
```python
from datasets import load_dataset
# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation"... | [
-0.6321982145309448,
-0.14114195108413696,
-0.04174860566854477,
0.08145271986722946,
0.20941485464572906,
0.05047117918729782,
0.479400634765625,
0.28783243894577026,
-0.2424594759941101,
0.17925235629081726,
-0.017328202724456787,
0.1898089498281479,
-0.09531179815530777,
0.3461282551288... |
https://github.com/huggingface/datasets/issues/4804 | streaming dataset with concatenating splits raises an error | Hi!, still not fixed this, the truth is that it is an important update for what we want to train the entire dataset because we want to train fast, also should be enabled the function "[train:18%]" for streaming | ## Describe the bug
streaming dataset with concatenating splits raises an error
## Steps to reproduce the bug
```python
from datasets import load_dataset
# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation")
```
```python
from datasets import load_dataset
# er... | 38 | streaming dataset with concatenating splits raises an error
## Describe the bug
streaming dataset with concatenating splits raises an error
## Steps to reproduce the bug
```python
from datasets import load_dataset
# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation"... | [
-0.6321982145309448,
-0.14114195108413696,
-0.04174860566854477,
0.08145271986722946,
0.20941485464572906,
0.05047117918729782,
0.479400634765625,
0.28783243894577026,
-0.2424594759941101,
0.17925235629081726,
-0.017328202724456787,
0.1898089498281479,
-0.09531179815530777,
0.3461282551288... |
https://github.com/huggingface/datasets/issues/4803 | Support `pipeline` argument in inspect.py functions | Now: the preview (first-rows) works, but not the conversion to parquet. See https://huggingface.co/datasets/wikipedia/viewer/20220301.de/train
```
_split_generators() missing 1 required positional argument: 'pipeline'
Error code: UnexpectedError
``` | **Is your feature request related to a problem? Please describe.**
The `wikipedia` dataset requires a `pipeline` argument to build the list of splits:
https://huggingface.co/datasets/wikipedia/blob/main/wikipedia.py#L937
But this is currently not supported in `get_dataset_config_info`:
https://github.com/hu... | 25 | Support `pipeline` argument in inspect.py functions
**Is your feature request related to a problem? Please describe.**
The `wikipedia` dataset requires a `pipeline` argument to build the list of splits:
https://huggingface.co/datasets/wikipedia/blob/main/wikipedia.py#L937
But this is currently not supported ... | [
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0.06105368584394455,
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0.02691756933927536,
0.012311053462326527,
0.3654104769229889,
-0.10572706162929535,
0.208480700850... |
https://github.com/huggingface/datasets/issues/4802 | `with_format` behavior is inconsistent on different datasets | Hi! You can get a `torch.Tensor` if you do the following:
```python
raw = load_dataset("beans", split="train")
raw = raw.select(range(100))
preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans")
from datasets import Array3D
features = raw.features.copy()
features["pixel_values"] = data... | ## Describe the bug
I found a case where `with_format` does not transform the dataset to the requested format.
## Steps to reproduce the bug
Run:
```python
from transformers import AutoTokenizer, AutoFeatureExtractor
from datasets import load_dataset
raw = load_dataset("glue", "sst2", split="train")
raw =... | 167 | `with_format` behavior is inconsistent on different datasets
## Describe the bug
I found a case where `with_format` does not transform the dataset to the requested format.
## Steps to reproduce the bug
Run:
```python
from transformers import AutoTokenizer, AutoFeatureExtractor
from datasets import load_data... | [
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-0.291262686252594,
0.0034207236021757126,
0.3133130967617035,
0.44153374433517456,
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0.5510598421096802,
0.47599294781684875,
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-0.020153358578681946,
-0.009704445488750935,
0.5724509358406067,
-0.03235768526792526,
0.026017503... |
https://github.com/huggingface/datasets/issues/4799 | video dataset loader/parser | Hi! We've just started discussing the video support in `datasets` (decoding backends, video feature type, etc.), so I believe we should have something tangible by the end of this year.
Also, if you have additional video features in mind that you would like to see, feel free to let us know | you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)?
could you please add functionality to load a video dataset? it would be really... | 51 | video dataset loader/parser
you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)?
could you please add functionality to load a vid... | [
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0.04512060806155205,
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0.562821626... |
https://github.com/huggingface/datasets/issues/4799 | video dataset loader/parser | Hey @mariosasko, I was wondering if there's a way to load video data currently in the library?
Alternatively is there a way I could hack it through the dataset.from_dict() method? I tried to hack it, but the issue I run into is that earlier I was doing a `cast_column()` call for the `Image` feature, but now I'm not s... | you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)?
could you please add functionality to load a video dataset? it would be really... | 111 | video dataset loader/parser
you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)?
could you please add functionality to load a vid... | [
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0.0951714962720871,
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0.4927778840065... |
https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | @mariosasko I'm getting a similar issue when creating a Dataset from a Pandas dataframe, like so:
```
from datasets import Dataset, Features, Image, Value
import pandas as pd
import requests
import PIL
# we need to define the features ourselves
features = Features({
'a': Value(dtype='int32'),
'b': ... | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-inte... | 113 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```pyth... | [
-0.16127412021160126,
-0.40690404176712036,
-0.0032085739076137543,
0.25208738446235657,
0.09850870817899704,
0.09969864040613174,
0.329359769821167,
0.29973095655441284,
-0.21165382862091064,
0.0772438794374466,
0.133023202419281,
0.6992092728614807,
-0.03194902464747429,
-0.0508978962898... |
https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | I would expect this to work, but it doesn't. Shouldn't be too hard to fix tho (in a subsequent PR). | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-inte... | 20 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```pyth... | [
-0.2117660790681839,
-0.5029460191726685,
-0.013351897709071636,
0.2395038604736328,
-0.024524599313735962,
0.12014967948198318,
0.2301861196756363,
0.2871049642562866,
-0.1885230839252472,
0.07185448706150055,
0.15346845984458923,
0.6264151930809021,
-0.030290737748146057,
-0.120998039841... |
https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | Hi @mariosasko just wanted to check in if there is a PR to follow for this. I was looking to create a demo app using this. If it's not working I can just use byte encoded images in the dataset which are not displayed. | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-inte... | 44 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```pyth... | [
-0.19783848524093628,
-0.503277599811554,
-0.025165170431137085,
0.23628848791122437,
0.07377518713474274,
0.11654048413038254,
0.25312894582748413,
0.28388524055480957,
-0.2029329240322113,
0.07516364753246307,
0.25091004371643066,
0.6167120337486267,
-0.08183755725622177,
-0.048006493598... |
https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | I was just pointed here by @mariosasko, meanwhile I found a workaround using `encode_example` like so:
```
from datasets import load_from_disk, Dataset
DATASET_PATH = "/hf/m4-master/data/cm4/cm4-10000-v0.1"
ds1 = load_from_disk(DATASET_PATH)
ds2 = Dataset.from_dict(mapping={k: [] for k in ds1[99].keys()},
... | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-inte... | 59 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```pyth... | [
-0.22391314804553986,
-0.4254264533519745,
-0.014295726083219051,
0.30249708890914917,
0.04177479445934296,
0.09449950605630875,
0.24505077302455902,
0.33873170614242554,
-0.21961276233196259,
0.15128777921199799,
0.1216961219906807,
0.6982002258300781,
-0.10216367989778519,
-0.00687600113... |
https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | Hmm, interesting. If I create the dataset on the fly:
```
from datasets import load_from_disk, Dataset
DATASET_PATH = "/hf/m4-master/data/cm4/cm4-10000-v0.1"
ds1 = load_from_disk(DATASET_PATH)
ds2 = Dataset.from_dict(mapping={k: [v]*2 for k, v in ds1[99].items()},
features=ds1.features)
... | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-inte... | 264 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```pyth... | [
-0.3208317160606384,
-0.3864695131778717,
0.014370642602443695,
0.26698410511016846,
0.10654259473085403,
0.08001408725976944,
0.3989081084728241,
0.18045632541179657,
-0.1433137059211731,
0.1974937617778778,
0.21877415478229523,
0.6241245269775391,
-0.10098988562822342,
0.0569922700524330... |
https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | > @mariosasko I'm getting a similar issue when creating a Dataset from a Pandas dataframe, like so:
>
> ```
> from datasets import Dataset, Features, Image, Value
> import pandas as pd
> import requests
> import PIL
>
> # we need to define the features ourselves
> features = Features({
> 'a': Value(dtyp... | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-inte... | 159 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```pyth... | [
-0.17800284922122955,
-0.4027806520462036,
-0.0016966573894023895,
0.2543632686138153,
0.07100436091423035,
0.08462772518396378,
0.30354931950569153,
0.28193244338035583,
-0.22124244272708893,
0.06873466074466705,
0.1500566601753235,
0.6716852188110352,
-0.03752507269382477,
-0.08095704019... |
https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | There is a workaround:
Create a loader python scrypt and upload the dataset to huggingface.
Here is an example how to do that:
https://huggingface.co/datasets/jamescalam/image-text-demo/tree/main
and Here are videos with explanations:
https://www.youtube.com/watch?v=lqK4ocAKveE and https://www.youtube.com... | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-inte... | 33 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```pyth... | [
-0.17007660865783691,
-0.634972333908081,
0.0006580948829650879,
0.31334173679351807,
-0.040805377066135406,
0.10565967112779617,
0.10329051315784454,
0.22061453759670258,
-0.11078351736068726,
0.10120533406734467,
0.03682214766740799,
0.5722434520721436,
-0.05125032365322113,
-0.001174241... |
https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | > Any update on this? I'm still facing this issure. Any workaround?
I was able to resolve my issue with a quick workaround:
```
from collections import defaultdict
from datasets import Dataset
data = defaultdict(list)
for idx in tqdm(range( len(dataloader)),desc="Captioning..."):
img = dataloader[id... | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-inte... | 72 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```pyth... | [
-0.21556289494037628,
-0.37681540846824646,
-0.003592759370803833,
0.17942631244659424,
0.04033011943101883,
0.045936353504657745,
0.24427945911884308,
0.30551138520240784,
-0.3000451326370239,
0.07468070089817047,
0.11448895186185837,
0.7439548969268799,
-0.05820801109075546,
-0.119697399... |
https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | > > Any update on this? I'm still facing this issure. Any workaround?
>
> I was able to resolve my issue with a quick workaround:
>
> ```
> from collections import defaultdict
> from datasets import Dataset
>
> data = defaultdict(list)
> for idx in tqdm(range( len(dataloader)),desc="Captioning..."):
> ... | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-inte... | 96 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```pyth... | [
-0.20946869254112244,
-0.38411784172058105,
-0.006247101351618767,
0.20440155267715454,
0.028693852946162224,
0.0638066753745079,
0.25298359990119934,
0.3120417594909668,
-0.2905447781085968,
0.0909748375415802,
0.13077376782894135,
0.7444420456886292,
-0.06099429726600647,
-0.123890921473... |
https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | I had a similar issue to @NielsRogge where I was unable to create a dataset from a Pandas DataFrame containing PIL.Images.
I found another workaround that works in this case which involves converting the DataFrame to a python dictionary, and then creating a dataset from said python dictionary.
This is a generic e... | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-inte... | 94 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```pyth... | [
-0.253017783164978,
-0.33836400508880615,
-0.015728265047073364,
0.2290961593389511,
0.028430582955479622,
0.13585340976715088,
0.26774609088897705,
0.35649606585502625,
-0.15451519191265106,
0.052685461938381195,
0.02554387040436268,
0.6401681303977966,
0.008616359904408455,
-0.0767744481... |
https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | cc @mariosasko this issue has been open for 2 years, would be great to resolve it :) | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-inte... | 17 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```pyth... | [
-0.24003681540489197,
-0.5063414573669434,
-0.017784323543310165,
0.26419374346733093,
-0.03489115834236145,
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0.0991419181227684,
0.15779849886894226,
0.6225147843360901,
-0.02979535609483719,
-0.11810520291... |
https://github.com/huggingface/datasets/issues/4796 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset | I have the same issue, my current workaround is saving the dataframe to a csv and then loading the dataset from the csv. Would also appreciate it a fix :) | ## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from PIL import Image
dataset = load_dataset("hf-inte... | 30 | ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
## Describe the bug
When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature.
## Steps to reproduce the bug
```pyth... | [
-0.25443893671035767,
-0.4661736786365509,
-0.0017221234738826752,
0.24875161051750183,
0.006114210933446884,
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0.34891921281814575,
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0.07658447325229645,
0.10348154604434967,
0.62934410572052,
-0.0017570210620760918,
-0.068387798... |
https://github.com/huggingface/datasets/issues/4795 | Missing MBPP splits | Thanks for reporting this as well, @stadlerb.
I suggest waiting for the answer of the data owners... | (@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later in the paper, we hold... | 17 | Missing MBPP splits
(@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later... | [
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0.232895746827125... |
https://github.com/huggingface/datasets/issues/4795 | Missing MBPP splits | @albertvillanova The first author of the paper responded to the upstream issue:
> Task IDs 11-510 are the 500 test problems. We use 90 problems (511-600) for validation and then remaining 374 for fine-tuning (601-974). The other problems can be used as desired, either for training or few-shot prompting (although this ... | (@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later in the paper, we hold... | 54 | Missing MBPP splits
(@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later... | [
-0.10014323890209198,
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0.059523578733205795,
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0.182694211602211,
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0.232895746827125... |
https://github.com/huggingface/datasets/issues/4795 | Missing MBPP splits | Thanks for the follow-up, @stadlerb.
Would you be willing to open a Pull Request to address this issue? :wink: | (@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later in the paper, we hold... | 19 | Missing MBPP splits
(@albertvillanova)
The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split:
> In the experiments described later... | [
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0.182694211602211,
-0.3087799847126007,
0.4114336371421814,
-0.1424715220928192,
0.232895746827125... |
https://github.com/huggingface/datasets/issues/4792 | Add DocVQA | Thanks for proposing, @NielsRogge.
Please, note this dataset requires registering in their website and their Terms and Conditions state we cannot distribute their URL:
```
1. You will NOT distribute the download URLs
...
``` | ## Adding a Dataset
- **Name:** DocVQA
- **Description:** Document Visual Question Answering (DocVQA) seeks to inspire a “purpose-driven” point of view in Document Analysis and Recognition research, where the document content is extracted and used to respond to high-level tasks defined by the human consumers of this ... | 35 | Add DocVQA
## Adding a Dataset
- **Name:** DocVQA
- **Description:** Document Visual Question Answering (DocVQA) seeks to inspire a “purpose-driven” point of view in Document Analysis and Recognition research, where the document content is extracted and used to respond to high-level tasks defined by the human consu... | [
-0.21714720129966736,
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0.059842828661203384,
0.03232497721910477,
0.23950688540935516,
-0.27115482091903687,
0.330279111862... |
https://github.com/huggingface/datasets/issues/4791 | Dataset Viewer issue for Team-PIXEL/rendered-wikipedia-english | Thanks for reporting. It's a known issue that should be fixed soon. Meanwhile, I had to manually trigger the dataset viewer. It's OK now.
Note that the extreme aspect ratio of the images generates another issue, that we're inspecting. | ### Link
https://huggingface.co/datasets/Team-PIXEL/rendered-wikipedia-english/viewer/rendered-wikipedia-en/train
### Description
The dataset can be loaded fine but the viewer shows this error:
```
Server Error
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
```
... | 39 | Dataset Viewer issue for Team-PIXEL/rendered-wikipedia-english
### Link
https://huggingface.co/datasets/Team-PIXEL/rendered-wikipedia-english/viewer/rendered-wikipedia-en/train
### Description
The dataset can be loaded fine but the viewer shows this error:
```
Server Error
Status code: 400
Exception: S... | [
-0.16521969437599182,
-0.020517565310001373,
0.11151394248008728,
0.37648099660873413,
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0.061710599809885025,
0.031158167868852615,
-0.07475249469... |
https://github.com/huggingface/datasets/issues/4784 | Add Multiface dataset | Hey @nandwalritik! Thanks for offering to help!
This dataset might be somewhat complex and I'm concerned about it being 65 TB, which would be quite expensive to host. @lhoestq @mariosasko I would love your input if you think it's worth adding this dataset. | ## Adding a Dataset
- **Name:** Multiface dataset
- **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capture rate at 30 fps
- **... | 43 | Add Multiface dataset
## Adding a Dataset
- **Name:** Multiface dataset
- **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capt... | [
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0.1296854317188263,
0.3332798480987549,
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0.257561057806015,
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-0.47519344091415405,
-0.20964425802230835,
0.722880780... |
https://github.com/huggingface/datasets/issues/4784 | Add Multiface dataset | Thanks for proposing this interesting dataset, @osanseviero.
Please note that the data files are already hosted in a third-party server: e.g. the index of data files for entity "6795937" is at https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/index.html
... | ## Adding a Dataset
- **Name:** Multiface dataset
- **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capture rate at 30 fps
- **... | 67 | Add Multiface dataset
## Adding a Dataset
- **Name:** Multiface dataset
- **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capt... | [
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0.08893826603889465,
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-0.29963943362236023,
-0.17815382778644562,
0.6216028332... |
https://github.com/huggingface/datasets/issues/4782 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 | Thanks for reporting @conceptofmind.
Could you please give details about your environment?
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
``` | ## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.u... | 42 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_data... | [
-0.1611001342535019,
0.047698602080345154,
-0.050540030002593994,
0.3585401773452759,
0.16585665941238403,
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0.2806347906589508,
0.31342485547065735,
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0.2098603993654251,
0.08780544996261597,
0.5031485557556152,
-0.06874771416187286,
0.047830529510... |
https://github.com/huggingface/datasets/issues/4782 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 | Hi @albertvillanova ,
Here is the environment information:
```
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.27
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
```
Thanks,
Enrico | ## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.u... | 31 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_data... | [
-0.12226594984531403,
0.07334493100643158,
-0.04566896706819534,
0.3679642081260681,
0.1909770667552948,
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0.3065476715564728,
0.29477015137672424,
-0.17574293911457062,
0.18962258100509644,
0.07779772579669952,
0.49761074781417847,
-0.055663131177425385,
0.026189396157... |
https://github.com/huggingface/datasets/issues/4782 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 | I think this issue is solved here https://discuss.huggingface.co/t/minhash-deduplication/19992/12?u=loubnabnl, this only happens for very large datasets we will update it in CodeParrot code | ## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.u... | 22 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_data... | [
-0.06517191976308823,
0.03707914054393768,
-0.054094262421131134,
0.3319741487503052,
0.15671859681606293,
-0.037929169833660126,
0.2668175995349884,
0.29228436946868896,
-0.18059410154819489,
0.21585214138031006,
0.07281449437141418,
0.5009046792984009,
0.0007561733946204185,
-0.016357794... |
https://github.com/huggingface/datasets/issues/4782 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 | Hi @loubnabnl,
Yes, the issue is solved in the discussion thread.
I will close this issue.
Thank you again for all of your help.
Enrico | ## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.u... | 25 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_data... | [
-0.10459886491298676,
0.0047159940004348755,
-0.05382324755191803,
0.38536959886550903,
0.16545060276985168,
-0.02828454226255417,
0.2812250256538391,
0.3220089375972748,
-0.16077037155628204,
0.21095740795135498,
0.10079135000705719,
0.4835177958011627,
-0.07366732507944107,
0.06690037995... |
https://github.com/huggingface/datasets/issues/4776 | RuntimeError when using torchaudio 0.12.0 to load MP3 audio file | Requiring torchaudio<0.12.0 isn't really a viable solution because that implies torch<0.12.0 which means no sm_86 CUDA support which means no RTX 3090 support in PyTorch.
But in my case, the error only occurs if `_fallback_load` resolves to `_fail_load` inside torchaudio 0.12.0 which is only the case if FFMPEG initi... | Current version of `torchaudio` (0.12.0) raises a RuntimeError when trying to use `sox_io` backend but non-Python dependency `sox` is not installed:
https://github.com/pytorch/audio/blob/2e1388401c434011e9f044b40bc8374f2ddfc414/torchaudio/backend/sox_io_backend.py#L21-L29
```python
def _fail_load(
filepath: str... | 156 | RuntimeError when using torchaudio 0.12.0 to load MP3 audio file
Current version of `torchaudio` (0.12.0) raises a RuntimeError when trying to use `sox_io` backend but non-Python dependency `sox` is not installed:
https://github.com/pytorch/audio/blob/2e1388401c434011e9f044b40bc8374f2ddfc414/torchaudio/backend/sox_i... | [
-0.4879215359687805,
-0.2076202630996704,
0.079262375831604,
0.11577180027961731,
0.05602937191724777,
-0.14573261141777039,
0.44489559531211853,
0.143636092543602,
-0.10682991147041321,
0.35547006130218506,
0.007712907157838345,
0.6597689986228943,
-0.2022406905889511,
-0.5119498372077942... |
https://github.com/huggingface/datasets/issues/4775 | Streaming not supported in Theivaprakasham/wildreceipt | Thanks for reporting @NitishkKarra.
The root source of the issue is that streaming mode is not supported out-of-the-box for that dataset, because it contains a TAR file.
We have opened a discussion in the corresponding Hub dataset page, pointing out this issue: https://huggingface.co/datasets/Theivaprakasham/wild... | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | 55 | Streaming not supported in Theivaprakasham/wildreceipt
### Link
_No response_
### Description
_No response_
### Owner
_No response_
Thanks for reporting @NitishkKarra.
The root source of the issue is that streaming mode is not supported out-of-the-box for that dataset, because it contains a TAR file.
We ... | [
-0.727268636226654,
-0.49570825695991516,
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0.2204316... |
https://github.com/huggingface/datasets/issues/4772 | AssertionError when using label_cols in to_tf_dataset | Hi @lehrig, this is caused by the data collator renaming "label" to "labels". If you set `label_cols=["labels"]` in the call it will work correctly. However, I agree that the cause of the bug is not obvious, so I'll see if I can make a PR to clarify things when the collator renames columns. | ## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets... | 53 | AssertionError when using label_cols in to_tf_dataset
## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
... | [
0.09022931754589081,
0.05867615342140198,
0.07491619884967804,
0.2682400643825531,
0.3147228956222534,
0.22435206174850464,
0.7236326932907104,
0.27399417757987976,
-0.16925710439682007,
0.21495488286018372,
-0.006875986233353615,
0.4143609404563904,
-0.1195538118481636,
-0.068256206810474... |
https://github.com/huggingface/datasets/issues/4772 | AssertionError when using label_cols in to_tf_dataset | Thanks - and wow, that appears like a strange side-effect of the data collator. Is that really needed?
Why not make it more explicit? For example, extend `DefaultDataCollator` with an optional property `label_col_name` to be used as label column; only when it is not provided default to `labels` (and document that th... | ## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets... | 55 | AssertionError when using label_cols in to_tf_dataset
## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
... | [
0.08114537596702576,
0.1701888144016266,
0.09567870199680328,
0.22990332543849945,
0.2721518874168396,
0.1384037733078003,
0.7091570496559143,
0.15918731689453125,
-0.23340512812137604,
0.29326513409614563,
0.11378087103366852,
0.36120152473449707,
-0.1787453591823578,
-0.11207465082406998... |
https://github.com/huggingface/datasets/issues/4772 | AssertionError when using label_cols in to_tf_dataset | Haha, I honestly have no idea why our data collators rename `"label"` (the standard label column name in our datasets) to `"labels"` (the standard label column name input to our models). It's been a pain point when I design TF data pipelines, though, because I don't want to hardcode things like that - especially in `da... | ## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets... | 149 | AssertionError when using label_cols in to_tf_dataset
## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
... | [
0.19720184803009033,
0.13782697916030884,
0.06203187629580498,
0.21990785002708435,
0.3263667821884155,
0.23189198970794678,
0.7796977162361145,
0.21121565997600555,
-0.18252956867218018,
0.2845122218132019,
0.010953271761536598,
0.3895106613636017,
-0.11095575243234634,
-0.078154392540454... |
https://github.com/huggingface/datasets/issues/4772 | AssertionError when using label_cols in to_tf_dataset | Interesting, that'd be great especially for clarity. https://huggingface.co/docs/datasets/use_with_tensorflow#data-loading already improved clarity, yet, all those options will still confuse people. Looking forward to those advances in the hope there'll be only 1 way in the future ;)
Anyways, I am happy for the time... | ## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets... | 51 | AssertionError when using label_cols in to_tf_dataset
## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
... | [
0.0172334723174572,
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0.2765328288078308,
0.27082011103630066,
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0.6481465697288513,
0.2578235864639282,
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0.2621188461780548,
-0.06051237881183624,
0.3850279152393341,
-0.14136449992656708,
-0.03756694495677948... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | And I don't see any speed up when increasing the number of GPUs while calling `get_nearest_examples_batch`. | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 16 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.180458202958107,
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0.09218783676624298,
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0.11597269773483276,
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0.5140131115913391,
-0.214628338813... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Hi ! Yes search_batch uses FAISS search which happens in parallel across the GPUs
> And I don't see any speed up when increasing the number of GPUs while calling get_nearest_examples_batch.
That's unexpected, can you share the code you're running ? | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 41 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.15467223525047302,
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0.043653860688209534,
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0.5126692056655884,
-0.1853103935... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | here is the code snippet
```python
# add faiss index
source_dataset = load_dataset(source_path)
queries = load_dataset(query_path)
gpu = [0,1,2,3]
source_dataset.add_faiss_index(
"embedding",
device=gpu,
)
# batch query
batch_size = 32
for i in tqdm(range(... | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 65 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.20685411989688873,
-0.6374272108078003,
-0.16138027608394623,
0.07671567797660828,
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0.06628788262605667,
0.12914201617240906,
0.10195738077163696,
0.6137714982032776,
0.07456554472446442,
-0.20291566848754883,
-0.17052660882472992,
0.5054278373718262,
-0.136249363422... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | The code looks all good to me, do you see all the GPUs being utilized ? What version of faiss are you using ? | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 24 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.10110102593898773,
-0.6452791690826416,
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0.12754398584365845,
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0.030707240104675293,
-0.241022527217865,
-0.2427918165922165,
0.5606644749641418,
-0.1748718321323... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | I can see the memory usage of all the GPUs.
My version of `faiss-gpu` is `1.7.2` | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 16 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.10703255236148834,
-0.6442115306854248,
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0.17503702640533447,
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0.0413370355963707,
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0.5348204970359802,
0.07835784554481506,
-0.22934526205062866,
-0.2848074436187744,
0.5310181975364685,
-0.2142222970724... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | It looks all good to me then ^^ though you said you didn't experienced speed improvements by adding more GPUs ? What size is your source dataset and what time differences did you experience ? | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 35 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.1954076737165451,
-0.5493392944335938,
-0.19500628113746643,
0.0717109739780426,
-0.3401581645011902,
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0.11930117011070251,
0.09034474939107895,
0.500456690788269,
0.04198215901851654,
-0.2722722291946411,
-0.14196830987930298,
0.4037453234195709,
-0.151715725660324... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | query set: 1e6
source dataset: 1e6
embedding size: 768
index: Flat
topk: 20
GPU: V100
The time taken to traverse the query set once is about 1.5h, which is almost not influenced by the value of query batch size or the number of GPUs according to my experiments. | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 48 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.2177337259054184,
-0.6628204584121704,
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0.07346982508897781,
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0.008454367518424988,
0.13630062341690063,
0.025091467425227165,
0.545770525932312,
0.03036227822303772,
-0.23599079251289368,
-0.2463901787996292,
0.5220271944999695,
-0.145321041345... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Hmmm the number of GPUs should divide the time, something is going wrong. Can you check that adding more GPU does divide the memory used per GPU ? Maybe it can be worth looking at similar issues in the FAISS repository or create a noew issue over there to understand what's going on | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 53 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.04531923308968544,
-0.66455078125,
-0.16315530240535736,
0.21673570573329926,
-0.3153473734855652,
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0.09823170304298401,
0.013767112046480179,
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0.10069864243268967,
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0.5046354532241821,
-0.231651738286018... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | > Can you check that adding more GPU does divide the memory used per GPU
The memory used per GPU is unchanged while adding more GPU. Is this unexpected?
I used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆 | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 51 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.03190736100077629,
-0.653843343257904,
-0.15973937511444092,
0.3106303811073303,
-0.3666626214981079,
0.01485617458820343,
0.09648585319519043,
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0.4460489749908447,
0.0431780144572258,
-0.23672442138195038,
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0.4890957474708557,
-0.2042381167411... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | > I used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆
Oh indeed that's possible, I wasn't sure. Anyway you can check that calling get_nearest_examples_batch simply calls search under the hood:
https://github.com/huggingface/datasets/blob/f90f71fbbb33889f... | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 44 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.13199757039546967,
-0.6725637912750244,
-0.11344027519226074,
0.12774920463562012,
-0.42620331048965454,
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0.14609083533287048,
0.050825826823711395,
0.4054100513458252,
0.08167822659015656,
-0.3057650029659271,
-0.23161597549915314,
0.5747027397155762,
-0.187802031... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Here is a runnable script.
Multi-GPU searching still does not work in my experiments.
```python
import os
from tqdm import tqdm
import numpy as np
import datasets
from datasets import Dataset
class DPRSelector:
def __init__(self, source, target, index_name, gpu=None):
self.source = source... | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 163 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.28333064913749695,
-0.5681073665618896,
-0.11883765459060669,
0.016156505793333054,
-0.40221691131591797,
0.05902078002691269,
0.22499704360961914,
0.12042197585105896,
0.5553640127182007,
0.01924268901348114,
-0.22989924252033234,
-0.09577411413192749,
0.48070430755615234,
-0.211476489... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Maybe @albertvillanova you can take a look ? I won't be available in the following days | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 16 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.1451474279165268,
-0.6219744682312012,
-0.1648423820734024,
0.08024608343839645,
-0.40835070610046387,
0.04980514198541641,
0.1447599083185196,
0.013119002804160118,
0.542361319065094,
0.025429323315620422,
-0.27304017543792725,
-0.24332985281944275,
0.5346524715423584,
-0.1012987345457... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Hi @xwwwwww I'm investigating it, but I'm not an expert in Faiss. In principle, it is weird that your code does not work properly because it seems right... | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 28 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.14981277287006378,
-0.6301243901252747,
-0.16075704991817474,
0.1275802105665207,
-0.31355810165405273,
0.027824722230434418,
0.16284237802028656,
0.07424211502075195,
0.4965071678161621,
0.0691094845533371,
-0.2820940613746643,
-0.2224423587322235,
0.5053197145462036,
-0.15431375801563... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | > Have you tried passing `gpu=-1` and check if there is a speedup?
yes, there is a speed up using GPU compared with CPU. | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 24 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.20230837166309357,
-0.5759716033935547,
-0.18073216080665588,
0.09722200036048889,
-0.2973669171333313,
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0.09971855580806732,
0.17794236540794373,
0.5709666609764099,
0.04531220346689224,
-0.3375532627105713,
-0.08671995252370834,
0.49778708815574646,
-0.1023655086... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | When passing `device=-1`, ALL existing GPUs are used (multi GPU): this is the maximum speedup you can get. To know the number of total GPUs:
```
import faiss
ngpus = faiss.get_num_gpus()
print(ngpus)
```
When passing a list of integers to `device`, then only that number of GPUs are used (multi GPU as well)
-... | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 140 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.13285492360591888,
-0.6349003911018372,
-0.15890169143676758,
0.10762965679168701,
-0.38746023178100586,
-0.0066644251346588135,
0.14906999468803406,
-0.014693211764097214,
0.5617420077323914,
0.03745792806148529,
-0.1784658432006836,
-0.2431056946516037,
0.5187183022499084,
-0.18129047... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Thanks for your help!
Have you run the code and replicated the same experimental results (i.e., no speedup while increasing the number of GPUs)? | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 24 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.164279967546463,
-0.6130456924438477,
-0.15808863937854767,
0.07875458896160126,
-0.4297664165496826,
0.02876034379005432,
0.14976513385772705,
0.01764865592122078,
0.5717771649360657,
0.04981115460395813,
-0.22750075161457062,
-0.20043428242206573,
0.48780810832977295,
-0.1717614084482... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | I can confirm `add_faiss_index` calls `index = faiss.index_cpu_to_gpus_list(index, gpus=list(device))`.
Could this be an issue with your environment ? Could you try running with 1 and 8 GPUs with a code similar to[ this one from the FAISS examples](https://github.com/facebookresearch/faiss/blob/main/tutorial/python/... | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 51 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.21215417981147766,
-0.5152871012687683,
-0.16691350936889648,
0.032151661813259125,
-0.2920154333114624,
0.018033139407634735,
0.11776268482208252,
0.11870433390140533,
0.6410746574401855,
0.03626525402069092,
-0.27248191833496094,
-0.19213053584098816,
0.5188185572624207,
-0.2209095060... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Hi, I test the FAISS example and the speed indeed changes. I set `nb=1000000`, `nq=1000000` and `d=64`
| num GPUS | time cost |
| -------- | --------- |
| 1 | 28.53 |
| 5 | 7.16 |
| While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 39 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.19229860603809357,
-0.5928233861923218,
-0.15238569676876068,
0.09184186905622482,
-0.46605974435806274,
0.0241340771317482,
0.11798329651355743,
0.0171394944190979,
0.5296295881271362,
0.024866022169589996,
-0.25367045402526855,
-0.22022292017936707,
0.5533517599105835,
-0.138790383934... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Ok the benchmark is great, not sure why it doesn't speed up the index in your case though. You can try running the benchmark with the same settings as your actual dataset
```
query set: 1e6
source dataset: 1e6
embedding size: 768
index: Flat
topk: 20
GPU: V100
```
Note that you can still pass a FAISS index y... | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 66 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.3129778802394867,
-0.5995798110961914,
-0.12577636539936066,
0.08850856870412827,
-0.21280419826507568,
0.01707787811756134,
0.11629132926464081,
0.24929934740066528,
0.5388679504394531,
0.15496665239334106,
-0.37405896186828613,
-0.06922652572393417,
0.4282967448234558,
0.0227259863168... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | > Here is a runnable script. Multi-GPU searching still does not work in my experiments.
>
> ```python
> import os
> from tqdm import tqdm
> import numpy as np
> import datasets
> from datasets import Dataset
>
> class DPRSelector:
>
> def __init__(self, source, target, index_name, gpu=None):
> ... | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 270 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.2644190788269043,
-0.5796562433242798,
-0.12350831925868988,
0.022541087120771408,
-0.39932119846343994,
0.061722882091999054,
0.24263951182365417,
0.09434978663921356,
0.5489586591720581,
0.029109321534633636,
-0.22996775805950165,
-0.09751355648040771,
0.46716755628585815,
-0.21397936... |
https://github.com/huggingface/datasets/issues/4761 | parallel searching in multi-gpu setting using faiss | Hi,
I have a similar question and would like to know if there's any progress in this issue.
`dataset.add_faiss_index(column="embedding")`, this takes around 5minutes to add the index.
`dataset.add_faiss_index(column="embedding", device=-1)`, this ran for more than 10minutes and still didn't complete execution... | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/sea... | 58 | parallel searching in multi-gpu setting using faiss
While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76... | [
-0.1596042364835739,
-0.5895235538482666,
-0.1664852499961853,
0.062093283981084824,
-0.40339672565460205,
0.08989090472459793,
0.20352447032928467,
0.0684148296713829,
0.6028568148612976,
0.01861383020877838,
-0.19229143857955933,
-0.2063247263431549,
0.542914867401123,
-0.114626057446002... |
https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | Hi @SaulLu, thanks for reporting.
I think offline mode is not supported for datasets containing only data files (without any loading script). I'm having a look into this... | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_i... | 28 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datase... | [
-0.24646364152431488,
-0.04167885333299637,
-0.07200251519680023,
0.2695726752281189,
0.4369027614593506,
0.05344054847955704,
0.4702564775943756,
0.04334656894207001,
0.1487271785736084,
0.14899706840515137,
-0.12468088418245316,
0.0070837922394275665,
-0.07967312633991241,
-0.05512129887... |
https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | Thanks for your feedback!
To give you a little more info, if you don't set the offline mode flag, the script will load the cache. I first noticed this behavior with the `evaluate` library, and while trying to understand the downloading flow I realized that I had a similar error with datasets. | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_i... | 52 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datase... | [
-0.24646364152431488,
-0.04167885333299637,
-0.07200251519680023,
0.2695726752281189,
0.4369027614593506,
0.05344054847955704,
0.4702564775943756,
0.04334656894207001,
0.1487271785736084,
0.14899706840515137,
-0.12468088418245316,
0.0070837922394275665,
-0.07967312633991241,
-0.05512129887... |
https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | #5331 will be helpful to fix this, as it updates the cache directory template to be aligned with the other datasets | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_i... | 21 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datase... | [
-0.24646364152431488,
-0.04167885333299637,
-0.07200251519680023,
0.2695726752281189,
0.4369027614593506,
0.05344054847955704,
0.4702564775943756,
0.04334656894207001,
0.1487271785736084,
0.14899706840515137,
-0.12468088418245316,
0.0070837922394275665,
-0.07967312633991241,
-0.05512129887... |
https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | This issue has been fixed in `datasets` 2.16 by https://github.com/huggingface/datasets/pull/6493. The cache is now working properly :)
You just have to update `datasets`:
```
pip install -U datasets
``` | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_i... | 29 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datase... | [
-0.24646364152431488,
-0.04167885333299637,
-0.07200251519680023,
0.2695726752281189,
0.4369027614593506,
0.05344054847955704,
0.4702564775943756,
0.04334656894207001,
0.1487271785736084,
0.14899706840515137,
-0.12468088418245316,
0.0070837922394275665,
-0.07967312633991241,
-0.05512129887... |
https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | Can you share some code to reproduce your issue ?
Also make sure your cache was populated with recent versions of `datasets`. Datasets cached with old versions may not be reloadable in offline mode, though we did our best to keep as much backward compatibility as possible. | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_i... | 47 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datase... | [
-0.24646364152431488,
-0.04167885333299637,
-0.07200251519680023,
0.2695726752281189,
0.4369027614593506,
0.05344054847955704,
0.4702564775943756,
0.04334656894207001,
0.1487271785736084,
0.14899706840515137,
-0.12468088418245316,
0.0070837922394275665,
-0.07967312633991241,
-0.05512129887... |
https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | I'm not sure if this is related @lhoestq but I am experiencing a similar issue when using offline mode:
```bash
$ python -c "from datasets import load_dataset; load_dataset('openai_humaneval', split='test')"
$ HF_DATASETS_OFFLINE=1 python -c "from datasets import load_dataset; load_dataset('openai_humaneval', spli... | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_i... | 126 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datase... | [
-0.24646364152431488,
-0.04167885333299637,
-0.07200251519680023,
0.2695726752281189,
0.4369027614593506,
0.05344054847955704,
0.4702564775943756,
0.04334656894207001,
0.1487271785736084,
0.14899706840515137,
-0.12468088418245316,
0.0070837922394275665,
-0.07967312633991241,
-0.05512129887... |
https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | > Thanks for reporting @BramVanroy, I managed to reproduce and I opened a fix here: #6741
Thanks a lot！ I have faced the same problem. Can I use your fix code to directly replace the existing version code? I noticed that this fix has not been merged yet. Will it affect other functionalities?
| ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_i... | 53 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datase... | [
-0.24646364152431488,
-0.04167885333299637,
-0.07200251519680023,
0.2695726752281189,
0.4369027614593506,
0.05344054847955704,
0.4702564775943756,
0.04334656894207001,
0.1487271785736084,
0.14899706840515137,
-0.12468088418245316,
0.0070837922394275665,
-0.07967312633991241,
-0.05512129887... |
https://github.com/huggingface/datasets/issues/4760 | Issue with offline mode | I just merged the fix, you can install `datasets` from source or wait for the patch release which will be out in the coming days | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_i... | 25 | Issue with offline mode
## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datase... | [
-0.24646364152431488,
-0.04167885333299637,
-0.07200251519680023,
0.2695726752281189,
0.4369027614593506,
0.05344054847955704,
0.4702564775943756,
0.04334656894207001,
0.1487271785736084,
0.14899706840515137,
-0.12468088418245316,
0.0070837922394275665,
-0.07967312633991241,
-0.05512129887... |
https://github.com/huggingface/datasets/issues/4759 | Dataset Viewer issue for Toygar/turkish-offensive-language-detection | I refreshed the dataset viewer manually, it's fixed now. Sorry for the inconvenience.
<img width="1557" alt="Capture d’écran 2022-07-28 à 09 17 39" src="https://user-images.githubusercontent.com/1676121/181514666-92d7f8e1-ddc1-4769-84f3-f1edfdb902e8.png">
| ### Link
https://huggingface.co/datasets/Toygar/turkish-offensive-language-detection
### Description
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
Hi, I provided train.csv, test.csv and valid.csv files. However, viewer says dataset does not exist.
Should I n... | 23 | Dataset Viewer issue for Toygar/turkish-offensive-language-detection
### Link
https://huggingface.co/datasets/Toygar/turkish-offensive-language-detection
### Description
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
Hi, I provided train.csv, test.csv and val... | [
-0.33209851384162903,
-0.1849137842655182,
-0.033416323363780975,
0.32832127809524536,
0.31721216440200806,
0.14756479859352112,
0.3913799822330475,
0.2583248019218445,
-0.10462626814842224,
-0.08844418823719025,
0.07775677740573883,
-0.2634667456150055,
-0.17928782105445862,
0.15133619308... |
https://github.com/huggingface/datasets/issues/4755 | Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size | I've built a minimal example that shows this bug without `n_proc`. It seems like it's a problem any way of using **tokenizers, `overflow_to_sample_mapping`, and Dataset.map, with a small batch size**:
```
import datasets
import transformers
pretrained = 'deepset/tinyroberta-squad2'
tokenizer = transformers.AutoT... | ## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because ea... | 170 | Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size
## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Data... | [
-0.35161277651786804,
-0.22139035165309906,
0.07477585971355438,
0.09442124515771866,
0.06536842882633209,
-0.18519334495067596,
0.2197558730840683,
0.15348996222019196,
-0.24925515055656433,
0.10310765355825424,
0.20500028133392334,
0.37535014748573303,
-0.012278138659894466,
-0.289909988... |
https://github.com/huggingface/datasets/issues/4755 | Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size | A larger batch size does _not_ have this behavior:
```
def tok2(d):
return tok(d['question'], d['context'])
ds = datasets.Dataset.from_dict({'question': questions, 'context': contexts})
tokens = ds.map(tok2, batched=True, batch_size=2)
print(tokens['overflow_to_sample_mapping'])
assert tokens['overflow_t... | ## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because ea... | 35 | Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size
## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Data... | [
-0.38004496693611145,
-0.1890600472688675,
0.032079022377729416,
0.09150373935699463,
-0.028541024774312973,
-0.1576000154018402,
0.15276341140270233,
0.16440533101558685,
-0.021619120612740517,
0.14799197018146515,
0.2348051220178604,
0.3651667833328247,
-0.030411724001169205,
-0.14136043... |
https://github.com/huggingface/datasets/issues/4755 | Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size | I was trying the [Question answering](https://huggingface.co/learn/nlp-course/chapter7/7#question-answering) tutorial on Hugging face when i faced the same problem. The preprocessing step is [here](https://huggingface.co/learn/nlp-course/chapter7/7#processing-the-validation-data). i have changed ```max_length=200, str... | ## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because ea... | 139 | Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size
## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Data... | [
-0.26761865615844727,
-0.2340690791606903,
0.07868227362632751,
0.06765780597925186,
0.029236195608973503,
-0.1428496241569519,
0.04256002604961395,
0.07200469076633453,
-0.04074838012456894,
0.06729298830032349,
0.22891736030578613,
0.2538045644760132,
-0.04525277763605118,
-0.12501609325... |
https://github.com/huggingface/datasets/issues/4752 | DatasetInfo issue when testing multiple configs: mixed task_templates | I've narrowed down the issue to the `dataset_module_factory` which already creates a `dataset_infos.json` file down in the `.cache/modules/dataset_modules/..` folder. That JSON file already contains the wrong task_templates for `unfiltered`. | ## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is create three configs:
- unfiltered: no classlabel, no tasks. Gets data fr... | 29 | DatasetInfo issue when testing multiple configs: mixed task_templates
## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is cr... | [
-0.30294740200042725,
-0.02238108217716217,
-0.007900869473814964,
0.27841848134994507,
0.04747239127755165,
0.0910637304186821,
0.4126910865306854,
0.2915855348110199,
-0.2092212736606598,
-0.03551861643791199,
-0.2731928825378418,
0.08178993314504623,
-0.011083702556788921,
0.13023871183... |
https://github.com/huggingface/datasets/issues/4752 | DatasetInfo issue when testing multiple configs: mixed task_templates | Ugh. Found the issue: apparently `datasets` was reusing the already existing `dataset_infos.json` that is inside `datasets/datasets/hebban-reviews`! Is this desired behavior?
Perhaps when `--save_infos` and `--all_configs` are given, an existing `dataset_infos.json` file should first be deleted before continuing wit... | ## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is create three configs:
- unfiltered: no classlabel, no tasks. Gets data fr... | 58 | DatasetInfo issue when testing multiple configs: mixed task_templates
## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is cr... | [
-0.30294740200042725,
-0.02238108217716217,
-0.007900869473814964,
0.27841848134994507,
0.04747239127755165,
0.0910637304186821,
0.4126910865306854,
0.2915855348110199,
-0.2092212736606598,
-0.03551861643791199,
-0.2731928825378418,
0.08178993314504623,
-0.011083702556788921,
0.13023871183... |
https://github.com/huggingface/datasets/issues/4752 | DatasetInfo issue when testing multiple configs: mixed task_templates | Hi! I think this is a reasonable solution. Would you be interested in submitting a PR? | ## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is create three configs:
- unfiltered: no classlabel, no tasks. Gets data fr... | 16 | DatasetInfo issue when testing multiple configs: mixed task_templates
## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is cr... | [
-0.30294740200042725,
-0.02238108217716217,
-0.007900869473814964,
0.27841848134994507,
0.04747239127755165,
0.0910637304186821,
0.4126910865306854,
0.2915855348110199,
-0.2092212736606598,
-0.03551861643791199,
-0.2731928825378418,
0.08178993314504623,
-0.011083702556788921,
0.13023871183... |
https://github.com/huggingface/datasets/issues/4750 | Easily create loading script for benchmark comprising multiple huggingface datasets | Hi ! I think the simplest is to copy paste the `_split_generators` code from the other datasets and do a bunch of if-else, as in the glue dataset: https://huggingface.co/datasets/glue/blob/main/glue.py#L467 | Hi,
I would like to create a loading script for a benchmark comprising multiple huggingface datasets.
The function _split_generators needs to return the files for the respective dataset. However, the files are not always in the same location for each dataset. I want to just make a wrapper dataset that provides a si... | 29 | Easily create loading script for benchmark comprising multiple huggingface datasets
Hi,
I would like to create a loading script for a benchmark comprising multiple huggingface datasets.
The function _split_generators needs to return the files for the respective dataset. However, the files are not always in the sa... | [
-0.28242307901382446,
-0.36613157391548157,
-0.012129168026149273,
0.3533135652542114,
-0.1201755553483963,
-0.058816585689783096,
0.2566535770893097,
0.38626936078071594,
0.6584388613700867,
0.150325208902359,
-0.1860688030719757,
-0.11190982162952423,
-0.12338083237409592,
0.550899446010... |
https://github.com/huggingface/datasets/issues/4746 | Dataset Viewer issue for yanekyuk/wikikey | The dataset is empty, as far as I can tell: there are no files in the repository at https://huggingface.co/datasets/yanekyuk/wikikey/tree/main
Maybe the viewer can display a better message for empty datasets | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | 30 | Dataset Viewer issue for yanekyuk/wikikey
### Link
_No response_
### Description
_No response_
### Owner
_No response_
The dataset is empty, as far as I can tell: there are no files in the repository at https://huggingface.co/datasets/yanekyuk/wikikey/tree/main
Maybe the viewer can display a better message ... | [
-0.030399218201637268,
0.03358881175518036,
-0.027283184230327606,
0.2718106508255005,
0.08809173107147217,
0.10236532241106033,
0.18875958025455475,
0.31571274995803833,
-0.2368868738412857,
0.2281379997730255,
0.20766262710094452,
0.17036490142345428,
-0.012126083485782146,
0.18291975557... |
https://github.com/huggingface/datasets/issues/4746 | Dataset Viewer issue for yanekyuk/wikikey | OK. Closing as it's not an error. We will work on making the error message a lot clearer. | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | 18 | Dataset Viewer issue for yanekyuk/wikikey
### Link
_No response_
### Description
_No response_
### Owner
_No response_
OK. Closing as it's not an error. We will work on making the error message a lot clearer. | [
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0.08109290897846222,
-0.05684897303581238,
0.1138695627450943,
0.175531268119812,
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0.42363032698631287,
0.2916473150253296,
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0.207700714468956,
0.08009187877178192,
0.2805224657058716,
0.10691715031862259,
0.23086152970790863,... |
https://github.com/huggingface/datasets/issues/4745 | Allow `list_datasets` to include private datasets | Thanks for opening this issue :)
If it can help, I think you can already use `huggingface_hub` to achieve this:
```python
>>> from huggingface_hub import HfApi
>>> [ds_info.id for ds_info in HfApi().list_datasets(use_auth_token=token) if ds_info.private]
['bigscience/xxxx', 'bigscience-catalogue-data/xxxxxxx', .... | I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the li... | 84 | Allow `list_datasets` to include private datasets
I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(us... | [
-0.2407407909631729,
0.09022775292396545,
-0.1712995171546936,
0.26900479197502136,
-0.0063143037259578705,
0.22895395755767822,
0.39418959617614746,
0.42811140418052673,
0.48214811086654663,
0.11412980407476425,
-0.5409525632858276,
0.2599234879016876,
-0.2146034687757492,
0.1799088269472... |
https://github.com/huggingface/datasets/issues/4745 | Allow `list_datasets` to include private datasets | Great, thanks @lhoestq the workaround works! I think it would be intuitive to have the support directly in `datasets` but it makes sense to wait given that the workaround exists :) | I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the li... | 31 | Allow `list_datasets` to include private datasets
I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(us... | [
-0.36706212162971497,
0.2388341724872589,
-0.21399037539958954,
0.16870927810668945,
-0.03367892652750015,
0.21034163236618042,
0.4328232705593109,
0.3512451946735382,
0.42124444246292114,
0.17374423146247864,
-0.3727339804172516,
0.3447163701057434,
-0.16999661922454834,
0.113786481320858... |
https://github.com/huggingface/datasets/issues/4745 | Allow `list_datasets` to include private datasets | i also think that going forward we should replace more and more implementations inside datasets with the corresponding ones from `huggingface_hub` (same as we're doing in `transformers`) | I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the li... | 27 | Allow `list_datasets` to include private datasets
I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(us... | [
-0.31781333684921265,
0.045434728264808655,
-0.18101336061954498,
0.2624085247516632,
-0.08682224899530411,
0.20398208498954773,
0.4568547010421753,
0.338810533285141,
0.45900219678878784,
0.16035446524620056,
-0.4918583631515503,
0.2582712173461914,
-0.12066314369440079,
0.204289987683296... |
https://github.com/huggingface/datasets/issues/4745 | Allow `list_datasets` to include private datasets | `datasets.list_datasets` is now deprecated in favor of `huggingface_hub.list_datasets` (returns private datasets when `token` is present), so I'm closing this issue. | I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the li... | 20 | Allow `list_datasets` to include private datasets
I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(us... | [
-0.35511213541030884,
0.15006837248802185,
-0.20737211406230927,
0.19087514281272888,
-0.07998719066381454,
0.20234817266464233,
0.4435308277606964,
0.3617459535598755,
0.4644303619861603,
0.11035358160734177,
-0.40475502610206604,
0.3337220847606659,
-0.1913793832063675,
0.092517837882041... |
https://github.com/huggingface/datasets/issues/4744 | Remove instructions to generate dummy data from our docs | Note that for me personally, conceptually all the dummy data (even for "canonical" datasets) should be superseded by `datasets-server`, which performs some kind of CI/CD of datasets (including the canonical ones) | In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata
However:
- dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI
- for datasets on the Hub:
- they do not pass any CI t... | 31 | Remove instructions to generate dummy data from our docs
In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata
However:
- dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI... | [
-0.02785693109035492,
-0.2410700023174286,
-0.039112888276576996,
0.199554905295372,
0.11549055576324463,
0.10173336416482925,
0.383828341960907,
0.11605188250541687,
0.2553574740886688,
0.33947861194610596,
-0.04621175676584244,
0.009783570654690266,
-0.06736115366220474,
0.39556631445884... |
https://github.com/huggingface/datasets/issues/4744 | Remove instructions to generate dummy data from our docs | I totally agree: next step should be rethinking if dummy data makes sense for canonical datasets (once we have datasets-server) and eventually remove it.
But for now, we could at least start by removing the indication to generate dummy data from our docs. | In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata
However:
- dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI
- for datasets on the Hub:
- they do not pass any CI t... | 43 | Remove instructions to generate dummy data from our docs
In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata
However:
- dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI... | [
-0.011543713510036469,
-0.19179721176624298,
-0.018710779026150703,
0.1561170518398285,
0.20634526014328003,
0.020942427217960358,
0.3873959481716156,
0.268899142742157,
0.2308344542980194,
0.37518537044525146,
-0.00498806731775403,
0.04575367644429207,
-0.040706735104322433,
0.36946091055... |
https://github.com/huggingface/datasets/issues/4742 | Dummy data nowhere to be found | Hi @BramVanroy, thanks for reporting.
First of all, please note that you do not need the dummy data: this was the case when we were adding datasets to the `datasets` library (on this GitHub repo), so that we could test the correct loading of all datasets with our CI. However, this is no longer the case for datasets ... | ## Describe the bug
To finalize my dataset, I wanted to create dummy data as per the guide and I ran
```shell
datasets-cli dummy_data datasets/hebban-reviews --auto_generate
```
where hebban-reviews is [this repo](https://huggingface.co/datasets/BramVanroy/hebban-reviews). And even though the scripts runs an... | 139 | Dummy data nowhere to be found
## Describe the bug
To finalize my dataset, I wanted to create dummy data as per the guide and I ran
```shell
datasets-cli dummy_data datasets/hebban-reviews --auto_generate
```
where hebban-reviews is [this repo](https://huggingface.co/datasets/BramVanroy/hebban-reviews). An... | [
-0.08476535975933075,
0.04323665797710419,
0.020455893129110336,
0.2214217633008957,
0.06339038908481598,
0.16641145944595337,
0.5988914370536804,
-0.014363080263137817,
0.08097237348556519,
0.1957332044839859,
-0.02974100597202778,
-0.029453717172145844,
0.10871540009975433,
-0.0814531669... |
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