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|---|---|---|---|---|---|---|
https://github.com/huggingface/datasets/issues/4575 | @winterfell2021 Hi, I wonder where the code you provided should be added. I tried to add them in the `datasets/table.py` in `array_cast` function, however, the 'zh' item is none. | Problem about wmt17 zh-en dataset | It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <module>
data = load_dataset(args.... | 29 | Problem about wmt17 zh-en dataset
It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <mo... | [
-0.33778464794158936,
-0.4670807719230652,
0.04239308834075928,
0.3219137191772461,
0.3430134654045105,
0.13319149613380432,
0.2732120454311371,
0.07307461649179459,
0.004815950989723206,
0.04988259822130203,
0.1188296377658844,
0.19630146026611328,
0.06979184597730637,
0.09495552629232407... |
https://github.com/huggingface/datasets/issues/4575 | I found some 'zh' item is none while 'c[hn]' is not.
So the code may change to:
```python
if 'c[hn]' in str(array.type):
py_array = array.to_pylist()
data_list = []
for vo in py_array:
tmp = {
'en': vo['en'],
}
if vo.get('zh'):
... | Problem about wmt17 zh-en dataset | It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <module>
data = load_dataset(args.... | 58 | Problem about wmt17 zh-en dataset
It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <mo... | [
-0.33778464794158936,
-0.4670807719230652,
0.04239308834075928,
0.3219137191772461,
0.3430134654045105,
0.13319149613380432,
0.2732120454311371,
0.07307461649179459,
0.004815950989723206,
0.04988259822130203,
0.1188296377658844,
0.19630146026611328,
0.06979184597730637,
0.09495552629232407... |
https://github.com/huggingface/datasets/issues/4575 | I just pushed a fix, we'll do a new release of `datasets` soon to include this fix. In the meantime you can use the fixed dataset by passing `revision="main"` to `load_dataset` | Problem about wmt17 zh-en dataset | It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <module>
data = load_dataset(args.... | 31 | Problem about wmt17 zh-en dataset
It seems that in subset casia2015, some samples are like `{'c[hn]':'xxx', 'en': 'aa'}`.
So when using `data = load_dataset('wmt17', "zh-en")` to load the wmt17 zh-en dataset, which will raise the exception:
```
Traceback (most recent call last):
File "train.py", line 78, in <mo... | [
-0.33778464794158936,
-0.4670807719230652,
0.04239308834075928,
0.3219137191772461,
0.3430134654045105,
0.13319149613380432,
0.2732120454311371,
0.07307461649179459,
0.004815950989723206,
0.04988259822130203,
0.1188296377658844,
0.19630146026611328,
0.06979184597730637,
0.09495552629232407... |
https://github.com/huggingface/datasets/issues/4572 | Thanks for reporting, @lewtun.
After investigation, it seems that the server https://gitlab.lip6.fr does not allow HTTP Range requests.
We are trying to find a workaround... | Dataset Viewer issue for mlsum | ### Link
https://huggingface.co/datasets/mlsum/viewer/de/train
### Description
There's seems to be a problem with the download / streaming of this dataset:
```
Server error
Status code: 400
Exception: BadZipFile
Message: File is not a zip file
```
### Owner
No | 25 | Dataset Viewer issue for mlsum
### Link
https://huggingface.co/datasets/mlsum/viewer/de/train
### Description
There's seems to be a problem with the download / streaming of this dataset:
```
Server error
Status code: 400
Exception: BadZipFile
Message: File is not a zip file
```
### Owner
No
... | [
-0.35809171199798584,
-0.025853827595710754,
0.009453006088733673,
0.3169581890106201,
0.44669580459594727,
-0.057465292513370514,
0.17195825278759003,
0.09538997709751129,
-0.32190221548080444,
0.042693816125392914,
-0.2626853585243225,
0.11927318572998047,
0.007780842483043671,
0.0432303... |
https://github.com/huggingface/datasets/issues/4571 | I'm just wondering why we don't have this dataset under:
- the `facebook` namespace
- or the canonical dataset `flores`: why does this only have 2 languages? | move under the facebook org? | ### Link
https://huggingface.co/datasets/gsarti/flores_101
### Description
It seems like streaming isn't supported for this dataset:
```
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://dl.fbaipublicfiles.com/flores101/dataset... | 27 | move under the facebook org?
### Link
https://huggingface.co/datasets/gsarti/flores_101
### Description
It seems like streaming isn't supported for this dataset:
```
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://dl.fbaipu... | [
-0.14406883716583252,
-0.047827690839767456,
-0.07658983021974564,
0.3923361897468567,
0.26025229692459106,
-0.10837756842374802,
-0.03830413147807121,
0.1519562304019928,
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0.355673223733902,
-0.13318674266338348,
-0.42659786343574524,
0.1303248405456543,
0.42473518848... |
https://github.com/huggingface/datasets/issues/4570 | This was silly; I was sure I'd looked for a `contiguous` argument, and was certain there wasn't one the first time I looked :smile:
Sorry about that. | Dataset sharding non-contiguous? | ## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggi... | 27 | Dataset sharding non-contiguous?
## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pul... | [
0.04632633924484253,
-0.24967244267463684,
0.015279565006494522,
0.20713451504707336,
0.06308206915855408,
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0.43037140369415283,
0.02758726477622986,
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-0.03165221959352493,
0.071589894592762,
-0.06453566253185272,
0.28934627771377563,
-0.10150067508... |
https://github.com/huggingface/datasets/issues/4570 | Hi! You can pass `contiguous=True` to `.shard()` get contiguous shards. More info on this and the default behavior can be found in the [docs](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.shard).
EDIT: Answered as you closed the thread 😄 | Dataset sharding non-contiguous? | ## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggi... | 32 | Dataset sharding non-contiguous?
## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pul... | [
0.039869144558906555,
-0.2767412066459656,
0.01883356273174286,
0.2221580147743225,
0.0832449421286583,
-0.04516328126192093,
0.41728323698043823,
0.029870927333831787,
-0.5351046323776245,
-0.012742266058921814,
0.03597652167081833,
-0.05814994126558304,
0.28019586205482483,
-0.0886441543... |
https://github.com/huggingface/datasets/issues/4570 | Hahaha I'm sorry; my excuse is: it's Sunday. (Which makes me all the more grateful for your response :smiley: | Dataset sharding non-contiguous? | ## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggi... | 19 | Dataset sharding non-contiguous?
## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pul... | [
0.04395988583564758,
-0.28850728273391724,
0.016205262392759323,
0.22780993580818176,
0.0785154402256012,
-0.04400923103094101,
0.4427950978279114,
0.02506529539823532,
-0.5470180511474609,
-0.026237331330776215,
0.018984287977218628,
-0.06162596493959427,
0.2754444181919098,
-0.0275835283... |
https://github.com/huggingface/datasets/issues/4570 | @mariosasko Sorry for reviving this, but I was curious as to why `contiguous=False` was the default. This might be a personal bias, but I feel that a user would expect the opposite to be the default. :thinking: | Dataset sharding non-contiguous? | ## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggi... | 37 | Dataset sharding non-contiguous?
## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pul... | [
0.015165053308010101,
-0.2807179093360901,
0.00930025428533554,
0.199540913105011,
0.06202265992760658,
-0.04880864918231964,
0.42980727553367615,
0.015108281746506691,
-0.5076963901519775,
-0.009925104677677155,
0.021955445408821106,
-0.09410621225833893,
0.328299343585968,
-0.13154400885... |
https://github.com/huggingface/datasets/issues/4569 | Hi @lewtun, thanks for reporting.
I have checked locally and refreshed the preview and it seems working smooth now:
```python
In [8]: ds
Out[8]:
DatasetDict({
train: Dataset({
features: ['idx', 'sentence', 'label'],
num_rows: 67349
})
validation: Dataset({
features: ['i... | Dataset Viewer issue for sst2 | ### Link
https://huggingface.co/datasets/sst2
### Description
Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):
```
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with Connectio... | 57 | Dataset Viewer issue for sst2
### Link
https://huggingface.co/datasets/sst2
### Description
Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):
```
Status code: 400
Exception: Exception
Message: Give up... | [
-0.33056479692459106,
0.08098079264163971,
-0.03653282672166824,
0.4152420461177826,
0.32362252473831177,
0.0548260435461998,
0.2566247582435608,
0.23660309612751007,
0.15832269191741943,
-0.027765631675720215,
-0.06476840376853943,
0.14777706563472748,
0.08745718747377396,
0.2538830935955... |
https://github.com/huggingface/datasets/issues/4569 | Thanks @albertvillanova - it is indeed working now (not sure what caused the error in the first place). Closing this :) | Dataset Viewer issue for sst2 | ### Link
https://huggingface.co/datasets/sst2
### Description
Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):
```
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with Connectio... | 21 | Dataset Viewer issue for sst2
### Link
https://huggingface.co/datasets/sst2
### Description
Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):
```
Status code: 400
Exception: Exception
Message: Give up... | [
-0.5275053977966309,
0.003739878535270691,
-0.06523801386356354,
0.36791011691093445,
0.20841868221759796,
-0.02098987251520157,
0.2145155966281891,
0.2552020251750946,
0.1403832584619522,
0.13036784529685974,
-0.060949161648750305,
0.09275022894144058,
0.17426444590091705,
0.2540132403373... |
https://github.com/huggingface/datasets/issues/4568 | Hi,
Could you tell us how you are running this code?
I tested on my machine (M1 Mac). And it is running fine both on and off internet.
<img width="1033" alt="Screen Shot 2022-07-03 at 1 32 25 AM" src="https://user-images.githubusercontent.com/8711912/177026364-4ad7cedb-e524-4513-97f7-7961bbb34c90.png">
Tested on ... | XNLI cache reload is very slow | ### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the ... | 46 | XNLI cache reload is very slow
### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only... | [
-0.14584565162658691,
0.058431074023246765,
0.006329033523797989,
-0.04914690554141998,
0.026816412806510925,
-0.23214510083198547,
0.19745686650276184,
0.24374711513519287,
0.18252922594547272,
-0.08453695476055145,
0.04058782011270523,
0.32581230998039246,
0.08739808201789856,
-0.1938985... |
https://github.com/huggingface/datasets/issues/4568 | Sure, I was running it on a Linux machine.
I found that if I turn the Internet off, it would still try to make a HTTPS call which would slow down the cache loading. If you can't reproduce then we can close the issue. | XNLI cache reload is very slow | ### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the ... | 44 | XNLI cache reload is very slow
### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only... | [
-0.14584565162658691,
0.058431074023246765,
0.006329033523797989,
-0.04914690554141998,
0.026816412806510925,
-0.23214510083198547,
0.19745686650276184,
0.24374711513519287,
0.18252922594547272,
-0.08453695476055145,
0.04058782011270523,
0.32581230998039246,
0.08739808201789856,
-0.1938985... |
https://github.com/huggingface/datasets/issues/4568 | Hi @Muennighoff! You can set the env variable `HF_DATASETS_OFFLINE` to `1` to avoid this behavior in offline mode. More info is available [here](https://huggingface.co/docs/datasets/master/en/loading#offline). | XNLI cache reload is very slow | ### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the ... | 23 | XNLI cache reload is very slow
### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only... | [
-0.14584565162658691,
0.058431074023246765,
0.006329033523797989,
-0.04914690554141998,
0.026816412806510925,
-0.23214510083198547,
0.19745686650276184,
0.24374711513519287,
0.18252922594547272,
-0.08453695476055145,
0.04058782011270523,
0.32581230998039246,
0.08739808201789856,
-0.1938985... |
https://github.com/huggingface/datasets/issues/4566 | Hi! This is indeed the link the docstring should point to. Are you interested in submitting a PR to fix this? | Document link #load_dataset_enhancing_performance points to nowhere | ## Describe the bug
A clear and concise description of what the bug is.
The [load_dataset_enhancing_performance](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#load_dat... | 21 | Document link #load_dataset_enhancing_performance points to nowhere
## Describe the bug
A clear and concise description of what the bug is.
The [load_dataset_enhancing_performance](https://huggingf... | [
-0.027187086641788483,
-0.4029795229434967,
-0.034978169947862625,
0.5097891688346863,
0.2210823893547058,
0.2674887180328369,
-0.04379716143012047,
0.2635038495063782,
0.029088273644447327,
0.06614872813224792,
0.18902194499969482,
0.17872260510921478,
0.14725756645202637,
-0.163204014301... |
https://github.com/huggingface/datasets/issues/4566 | https://github.com/huggingface/datasets/blame/master/docs/source/cache.mdx#L93
There seems already an anchor here. Somehow it doesn't work. I am not very familiar with how this online documentation works. | Document link #load_dataset_enhancing_performance points to nowhere | ## Describe the bug
A clear and concise description of what the bug is.
The [load_dataset_enhancing_performance](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#load_dat... | 22 | Document link #load_dataset_enhancing_performance points to nowhere
## Describe the bug
A clear and concise description of what the bug is.
The [load_dataset_enhancing_performance](https://huggingf... | [
0.13652057945728302,
-0.38280409574508667,
-0.017973225563764572,
0.2991184592247009,
0.16445040702819824,
0.22764651477336884,
0.12776067852973938,
0.10991460084915161,
-0.045570723712444305,
0.07462500780820847,
0.09591500461101532,
0.05238387733697891,
0.24965201318264008,
-0.1814276278... |
https://github.com/huggingface/datasets/issues/4562 | Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content *sequentially* (no random access). | Dataset Viewer issue for allocine | ### Link
https://huggingface.co/datasets/allocine
### Description
Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:
```
Status code: 400
Exception: AttributeError
Message: 'TarContainedFile' object has no attribute 'readable'
```
### Owner
No | 38 | Dataset Viewer issue for allocine
### Link
https://huggingface.co/datasets/allocine
### Description
Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:
```
Status code: 400
Exception: AttributeError
Message: 'TarContainedFile' object has no attrib... | [
-0.36794018745422363,
-0.06806251406669617,
-0.00010703131556510925,
0.7290312647819519,
0.3112786114215851,
0.09571222215890884,
-0.1255113035440445,
0.3293062746524811,
-0.08155225217342377,
0.2661767899990082,
-0.33151975274086,
0.096065953373909,
-0.31038469076156616,
0.042457863688468... |
https://github.com/huggingface/datasets/issues/4562 | > Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content _sequentially_ (no random access).
Ah thanks for the clarification! I'll look out for th... | Dataset Viewer issue for allocine | ### Link
https://huggingface.co/datasets/allocine
### Description
Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:
```
Status code: 400
Exception: AttributeError
Message: 'TarContainedFile' object has no attribute 'readable'
```
### Owner
No | 57 | Dataset Viewer issue for allocine
### Link
https://huggingface.co/datasets/allocine
### Description
Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:
```
Status code: 400
Exception: AttributeError
Message: 'TarContainedFile' object has no attrib... | [
-0.37044572830200195,
-0.0729556530714035,
0.011078320443630219,
0.6761921644210815,
0.3053101599216461,
0.07710179686546326,
-0.096096470952034,
0.3854776620864868,
-0.0936669260263443,
0.21664905548095703,
-0.31234055757522583,
0.11120909452438354,
-0.27702033519744873,
0.099453337490558... |
https://github.com/huggingface/datasets/issues/4550 | Thanks for reporting, @Muhtasham.
Indeed IMDB dataset is not accessible from yesterday, because the data is hosted on the data owners servers at Stanford (http://ai.stanford.edu/) and these are down due to a power outage originated by a fire: https://twitter.com/StanfordAILab/status/1539472302399623170?s=20&t=1HU1hr... | imdb source error | ## Describe the bug
imdb dataset not loading
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imdb")
```
## Expected results
## Actual results
```bash
06/23/2022 14:45:18 - INFO - datasets.builder - Dataset not on Hf google storage. Downloading and pr... | 58 | imdb source error
## Describe the bug
imdb dataset not loading
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imdb")
```
## Expected results
## Actual results
```bash
06/23/2022 14:45:18 - INFO - datasets.builder - Dataset not on Hf google storage... | [
-0.4599401354789734,
0.1556210219860077,
-0.12848228216171265,
0.22410422563552856,
0.09670615196228027,
0.1051635593175888,
0.3030327558517456,
0.24203070998191833,
0.2500801980495453,
0.16574536263942719,
-0.13026627898216248,
-0.16933149099349976,
0.2551254630088806,
0.08268379420042038... |
https://github.com/huggingface/datasets/issues/4548 | I agree it would be nice to support this. It doesn't fit really well in the current data_files.py, where files of each splits are separated in different folder though, maybe we have to modify a bit the logic here.
One idea would be to extend `get_patterns_in_dataset_repository` and `get_patterns_locally` to additio... | Metadata.jsonl for Imagefolder is ignored if it's in a parent directory to the splits directories/do not have "{split}_" prefix | If data contains a single `metadata.jsonl` file for several splits, it won't be included in a dataset's `data_files` and therefore ignored.
This happens when a directory is structured like as follows:
```
train/
file_1.jpg
file_2.jpg
test/
file_3.jpg
file_4.jpg
metadata.jsonl
```
or like as follows:... | 91 | Metadata.jsonl for Imagefolder is ignored if it's in a parent directory to the splits directories/do not have "{split}_" prefix
If data contains a single `metadata.jsonl` file for several splits, it won't be included in a dataset's `data_files` and therefore ignored.
This happens when a directory is structured like... | [
-0.13286076486110687,
0.3551528751850128,
-0.035635434091091156,
0.39058658480644226,
-0.12059420347213745,
-0.20107102394104004,
0.4794278144836426,
0.270042359828949,
-0.11250650137662888,
0.16834184527397156,
0.014018326997756958,
0.23228810727596283,
-0.3527265787124634,
0.239828914403... |
https://github.com/huggingface/datasets/issues/4542 | This has so much potential to be great! Also I think you tagged some poor random dude on the internet whose name is also Joao, lol, edited that for you! | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 30 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.181320458650589,
-0.3511793315410614,
-0.211380273103714,
0.33765560388565063,
0.22188761830329895,
0.0864444449543953,
0.13927823305130005,
0.6314027905464172,
-0.27705419063568115,
0.034519847482442856,
-0.16364240646362305,
0.5301302075386047,
0.01765640825033188,
-0.158382385969162,... |
https://github.com/huggingface/datasets/issues/4542 | Noted and I will look into the thread in detail tomorrow once I log back in. | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 16 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.21949520707130432,
-0.3593599200248718,
-0.19776377081871033,
0.32190942764282227,
0.21467024087905884,
0.10271425545215607,
0.16388177871704102,
0.6277863383293152,
-0.2596609592437744,
0.027159586548805237,
-0.17609035968780518,
0.5258185863494873,
0.02515220269560814,
-0.151396602392... |
https://github.com/huggingface/datasets/issues/4542 | @lhoestq I have used TFRecords with `tf.data` for both vision and text and I can say that they are quite performant. I haven't worked with Feather yet as similarly as I have with TFRecords. If you haven't started the benchmarking script yet, I can prepare a Colab notebook that loads Feather files, converts them into a ... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 86 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.2573500871658325,
-0.35805952548980713,
-0.19969221949577332,
0.32994240522384644,
0.14493726193904877,
0.08801911771297455,
0.1933460235595703,
0.623721182346344,
-0.27848079800605774,
0.003372661769390106,
-0.17989248037338257,
0.471015989780426,
0.02251269668340683,
-0.23422750830650... |
https://github.com/huggingface/datasets/issues/4542 | > Not yet sure if it's good for modalities like images.
We store images pretty much the same way as tensorflow_datasets (i.e. storing the encoded image bytes, or a path to the local image, so that the image can be decoded on-the-fly), so as long as we use something similar as TFDS for image decoding it should be ok | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 59 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.19923509657382965,
-0.30785658955574036,
-0.22012367844581604,
0.3375192880630493,
0.210481196641922,
0.10459767282009125,
0.13910062611103058,
0.6205319166183472,
-0.28925424814224243,
0.033259835094213486,
-0.20853175222873688,
0.4609622359275818,
-0.030103834345936775,
-0.17199493944... |
https://github.com/huggingface/datasets/issues/4542 | So for image datasets, we could potentially store the paths in the feather format and decode and read them on the fly? But it introduces an I/O redundancy of having to read the images every time.
With caching it could be somewhat mitigated but it's not a good solution for bigger image datasets. | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 53 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.2225729376077652,
-0.2932808995246887,
-0.18375423550605774,
0.3646898865699768,
0.22176867723464966,
0.07951129972934723,
0.16813981533050537,
0.5981183648109436,
-0.28093236684799194,
0.04962286353111267,
-0.19159729778766632,
0.5340712666511536,
-0.1733432561159134,
-0.15028688311576... |
https://github.com/huggingface/datasets/issues/4542 | > So for image datasets, we could potentially store the paths in the feather format and decode and read them on the fly?
hopefully yes :)
I double-checked the TFDS source code and they always save the bytes actually, not the path. Anyway we'll see if we run into issues or not (as a first step we can require the ... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 67 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.20711156725883484,
-0.28940102458000183,
-0.19208842515945435,
0.39897215366363525,
0.2264750748872757,
0.07998555898666382,
0.11777082830667496,
0.5918861627578735,
-0.32529863715171814,
0.028287164866924286,
-0.18735405802726746,
0.5183922052383423,
-0.16292741894721985,
-0.1863040924... |
https://github.com/huggingface/datasets/issues/4542 | Yes. For images, TFDS actually prepares TFRecords first for encoding and then reuses them for every subsequent call. | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 18 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.23023487627506256,
-0.32609134912490845,
-0.20041289925575256,
0.345608115196228,
0.22998666763305664,
0.09835656732320786,
0.17120960354804993,
0.5812537670135498,
-0.3190217912197113,
0.059196434915065765,
-0.1856553703546524,
0.44048306345939636,
-0.00586073100566864,
-0.214825376868... |
https://github.com/huggingface/datasets/issues/4542 | @lhoestq @Rocketknight1 I worked on [this PoC](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59) that
* Creates Feather files from a medium resolution dataset (`tf_flowers`).
* Explores different options with TensorFlow IO to load the Feather files.
I haven't benchmarked those different option... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 61 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.21507534384727478,
-0.34239041805267334,
-0.19228717684745789,
0.37275904417037964,
0.12391859292984009,
0.09236167371273041,
0.11055280268192291,
0.6560561060905457,
-0.2998831868171692,
0.002716653048992157,
-0.15860393643379211,
0.537960946559906,
0.04900580272078514,
-0.216002523899... |
https://github.com/huggingface/datasets/issues/4542 | Cool thanks ! If I understand correctly in your PoC you store the flattened array of pixels in the feather file. This will take a lot of disk space.
Maybe we could just save the encoded bytes and let users apply a `map` to decode/transform them into the format they need for training ? Users can use tf.image to do so... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 63 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.16578984260559082,
-0.3481472432613373,
-0.16695038974285126,
0.35054653882980347,
0.2775440216064453,
0.07106626033782959,
0.16951113939285278,
0.5484033226966858,
-0.30505770444869995,
0.17293329536914825,
-0.13176760077476501,
0.3350340723991394,
-0.04377027228474617,
-0.025836635380... |
https://github.com/huggingface/datasets/issues/4542 | @lhoestq this is what I tried:
```py
def read_image(path):
with open(path, "rb") as f:
return f.read()
total_images_written = 0
for step in tqdm.tnrange(int(math.ceil(len(image_paths) / batch_size))):
batch_image_paths = image_paths[step * batch_size : (step + 1) * batch_size]
batch_... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 413 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.29168567061424255,
-0.34014660120010376,
-0.19338679313659668,
0.39395979046821594,
0.23133458197116852,
0.16601580381393433,
0.12491007149219513,
0.6766040921211243,
-0.3139682114124298,
-0.03397562354803085,
-0.20921389758586884,
0.5446645617485046,
-0.07021653652191162,
-0.2573133111... |
https://github.com/huggingface/datasets/issues/4542 | @lhoestq I think I was able to make it work in the way you were envisioning. Here's the PoC:
https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb
Some details:
* I am currently serializing the images as strings with `base64`). In comparison to the flattened arrays as... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 107 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.15111902356147766,
-0.25836998224258423,
-0.1787327378988266,
0.3322916030883789,
0.2542514204978943,
0.1207948550581932,
0.17362986505031586,
0.626450777053833,
-0.3042469024658203,
0.0829123705625534,
-0.29097700119018555,
0.42832648754119873,
-0.045313090085983276,
-0.210356667637825... |
https://github.com/huggingface/datasets/issues/4542 | Cool thanks ! Too bad the Arrow binary type doesn't seem to be supported in `arrow_io.ArrowFeatherDataset` :/ We would also need it to support Arrow struct type. Indeed images in `datasets` are represented using an Arrow type
```python
pa.struct({"path": pa.string(), "bytes": pa.binary()})
```
not sure yet how hard... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 78 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.22203563153743744,
-0.32139235734939575,
-0.18058302998542786,
0.3587228059768677,
0.23031304776668549,
0.08110860735177994,
0.1460738182067871,
0.6311076283454895,
-0.26082754135131836,
-0.05396735668182373,
-0.173746258020401,
0.5902267098426819,
-0.04628665745258331,
-0.1394915431737... |
https://github.com/huggingface/datasets/issues/4542 | If the ArrowFeatherDataset doesn't yet support it, I guess our hands are a bit tied at the moment.
IIUC, in my [latest PoC notebook](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb), you wanted to see each entry in the feather file to be represented like so?
```... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 52 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.2572591006755829,
-0.27029550075531006,
-0.19007013738155365,
0.3072044849395752,
0.25379297137260437,
0.10088390111923218,
0.10707826912403107,
0.6265484094619751,
-0.35871151089668274,
-0.04266532510519028,
-0.2065267413854599,
0.5051925778388977,
0.018076583743095398,
-0.131314203143... |
https://github.com/huggingface/datasets/issues/4542 | > IIUC, in my [latest PoC notebook](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb), you wanted to see each entry in the feather file to be represented like so?
>
> pa.struct({"path": pa.string(), "bytes": pa.binary()})
Yea because that's the data format we're u... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 108 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.25264880061149597,
-0.3275662660598755,
-0.18621893227100372,
0.32281360030174255,
0.2500064969062805,
0.11986039578914642,
0.11895262449979782,
0.6613970994949341,
-0.35216549038887024,
-0.03672151267528534,
-0.20221984386444092,
0.48952218890190125,
0.030787263065576553,
-0.1554342806... |
https://github.com/huggingface/datasets/issues/4542 | > Maybe it would take too much time to be worth exploring, but according to https://github.com/tensorflow/io/issues/1361#issuecomment-819029002 it's possible to add support for binary type in ArrowFeatherDataset.
Should be possible as per the comment but there hasn't been any progress and it's been more than a year.... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 117 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.17058001458644867,
-0.38737988471984863,
-0.1889614760875702,
0.3548504412174225,
0.22678042948246002,
0.13572296500205994,
0.14974047243595123,
0.6562884449958801,
-0.299713134765625,
-0.031844645738601685,
-0.14510977268218994,
0.4352554678916931,
-0.02686331607401371,
-0.212715178728... |
https://github.com/huggingface/datasets/issues/4542 | > I don't understand this. I would think TFRecords would also need something similar but I need the context you're coming from.
Users already have a copy of the dataset in Arrow format (we can change this to Feather). So to load the Arrow/feather files to a TF dataset we need TF IO or something like that. Otherwise ... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 116 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.11969158053398132,
-0.3496493399143219,
-0.19668906927108765,
0.4366620182991028,
0.2147144079208374,
0.18524810671806335,
0.1321239471435547,
0.646026611328125,
-0.34201717376708984,
-0.03586068004369736,
-0.22465968132019043,
0.3784806728363037,
0.031977880746126175,
-0.29962426424026... |
https://github.com/huggingface/datasets/issues/4542 | Yeah, it looks like in its current state the tfio support for `Feather` is incomplete, so we'd end up having to write a lot of it, or do a conversion that defeats the whole point (because if we're going to convert the whole dataset we might as well convert to `TFRecord`). | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 51 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
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0.03138425201177597,
-0.118345320224761... |
https://github.com/huggingface/datasets/issues/4542 | @lhoestq Although I think this is a dead-end for now unfortunately, because of the limitations at TF's end, we could still explore automatic conversion to TFRecord, or I could dive into refining `to_tf_dataset()` to yield unbatched samples and/or load samples with multiprocessing to improve throughput. Do you have any ... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 51 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
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0.5657948851585388,
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-0.20643191039562... |
https://github.com/huggingface/datasets/issues/4542 | > @lhoestq Although I think this is a dead-end for now unfortunately, because of the limitations at TF's end, we could still explore automatic conversion to TFRecord, or I could dive into refining `to_tf_dataset()` to yield unbatched samples and/or load samples with multiprocessing to improve throughput. Do you have an... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 58 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
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https://github.com/huggingface/datasets/issues/4542 | If `to_tf_dataset` can be unbatched, then it should be fairly easy for users to convert the TF dataset to TFRecords right ? | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 22 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
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https://github.com/huggingface/datasets/issues/4542 | > If to_tf_dataset can be unbatched, then it should be fairly easy for users to convert the TF dataset to TFRecords right ?
Sort of! A `tf.data.Dataset` is more like an iterator, and does not support sample indexing. `to_tf_dataset()` creates an iterator, but to convert that to `TFRecord`, the user would have to ite... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 67 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
-0.219713494181633,
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https://github.com/huggingface/datasets/issues/4542 | Someone would like to try to dive into tfio to fix this ? Sounds like a good opportunity to learn what are the best ways to load a dataset for TF, and also the connections between Arrow and TF.
If we can at least have the Arrow `binary` type working for TF that would be awesome already (issue https://github.com/tens... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 68 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
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https://github.com/huggingface/datasets/issues/4542 | > Sounds like a good opportunity to learn what are the best ways to load a dataset for TF
The recommended way would likely be a combination of TFRecords and `tf.data`.
Exploring the connection between Arrow and TensorFlow is definitely worth pursuing though. But I am not sure about the implications of storing im... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 91 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
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https://github.com/huggingface/datasets/issues/4542 | I am currently working on a fine-tuning notebook for the TFSegFormer model (Semantic Segmentation). The resolution is high for both the input images and the labels - (512, 512, 3). Here's the [Colab Notebook](https://colab.research.google.com/drive/1jAtR7Z0lYX6m6JsDI5VByh5vFaNhHIbP?usp=sharing) (it's a WIP so please be... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 62 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
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https://github.com/huggingface/datasets/issues/4542 | Here's a notebook showing the performance difference: https://colab.research.google.com/gist/sayakpaul/d7ca67c90beb47e354942c9d8c0bd8ef/scratchpad.ipynb.
Note that I acknowledge that it's not an apples-to-apples comparison in many aspects (the dataset isn't the same, data serialization format isn't the same, etc.) ... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 41 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
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https://github.com/huggingface/datasets/issues/4542 | Thanks ! I think the speed difference can be partly explained: you use ds.shuffle in your dataset, which is an exact shuffling (compared to TFDS which does buffer shuffling): it slows down query time by 2x to 10x since it has to play with data that are not contiguous.
The rest of the speed difference seems to be cau... | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 66 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
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0.5115911364555359,
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-0.25421509146690... |
https://github.com/huggingface/datasets/issues/4542 | Fair enough. Can do one without shuffling too. But it's an important one to consider I guess. | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ? | To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_... | 17 | [to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, a... | [
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https://github.com/huggingface/datasets/issues/4540 | Hi @espoirMur, thanks for reporting.
You are right: that code line could be improved and made more generically valid.
On the other hand, I would suggest using `os.path.splitext` instead.
Are you willing to open a PR? :) | Avoid splitting by` .py` for the file. | https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272
Hello,
Thanks you for this library .
I was using it and I had one edge case. my home folder name ends with `.py` it is `/home/espoir.py` so anytime I am running the code to load a local module thi... | 37 | Avoid splitting by` .py` for the file.
https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272
Hello,
Thanks you for this library .
I was using it and I had one edge case. my home folder name ends with `.py` it is `/home/espoir.py` so anytime I am ru... | [
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0.06515178084373474,
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0.2761284708976... |
https://github.com/huggingface/datasets/issues/4538 | Thanks so much for adding the docs. I was able to successfully hide the viewer using the
```
viewer: false
```
flag in the README.md of the dataset. I'm closing the issue because this is resolved. Thanks again! | Dataset Viewer issue for Pile of Law | ### Link
https://huggingface.co/datasets/pile-of-law/pile-of-law
### Description
Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines... | 38 | Dataset Viewer issue for Pile of Law
### Link
https://huggingface.co/datasets/pile-of-law/pile-of-law
### Description
Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that th... | [
0.005177460610866547,
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0.02856689691... |
https://github.com/huggingface/datasets/issues/4538 | Just for the record:
- the doc
<img width="1430" alt="Capture d’écran 2022-06-27 à 09 29 27" src="https://user-images.githubusercontent.com/1676121/175884089-bca6c0d5-6387-473e-98ca-86a910ede4bd.png">
- the dataset main page
<img width="1134" alt="Capture d’écran 2022-06-27 à 09 29 05" src="https://us... | Dataset Viewer issue for Pile of Law | ### Link
https://huggingface.co/datasets/pile-of-law/pile-of-law
### Description
Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines... | 47 | Dataset Viewer issue for Pile of Law
### Link
https://huggingface.co/datasets/pile-of-law/pile-of-law
### Description
Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that th... | [
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0.079562902... |
https://github.com/huggingface/datasets/issues/4531 | Confirmed, it's fixed now. Thanks for reporting, and thanks @coyotte508 for fixing it
<img width="1123" alt="Capture d’écran 2022-06-21 à 10 28 05" src="https://user-images.githubusercontent.com/1676121/174753833-1b453a5a-6a90-4717-bca1-1b5fc6b75e4a.png">
| Dataset Viewer issue for CSV datasets | ### Link
https://huggingface.co/datasets/scikit-learn/breast-cancer-wisconsin
### Description
I'm populating CSV datasets [here](https://huggingface.co/scikit-learn) but the viewer is not enabled and it looks for a dataset loading script, the datasets aren't on queue as well.
You can replicate the problem by sim... | 23 | Dataset Viewer issue for CSV datasets
### Link
https://huggingface.co/datasets/scikit-learn/breast-cancer-wisconsin
### Description
I'm populating CSV datasets [here](https://huggingface.co/scikit-learn) but the viewer is not enabled and it looks for a dataset loading script, the datasets aren't on queue as well. ... | [
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https://github.com/huggingface/datasets/issues/4529 | Hi! Very cool dataset! I answered your questions on the forum. Also, feel free to comment `#self-assign` on this issue to self-assign it. | Ecoset | ## Adding a Dataset
- **Name:** *Ecoset*
- **Description:** *https://www.kietzmannlab.org/ecoset/*
- **Paper:** *https://doi.org/10.1073/pnas.2011417118*
- **Data:** *https://codeocean.com/capsule/9570390/tree/v1*
- **Motivation:**
**Ecoset** was created as a clean and ecologically valid alternative to **Imagen... | 23 | Ecoset
## Adding a Dataset
- **Name:** *Ecoset*
- **Description:** *https://www.kietzmannlab.org/ecoset/*
- **Paper:** *https://doi.org/10.1073/pnas.2011417118*
- **Data:** *https://codeocean.com/capsule/9570390/tree/v1*
- **Motivation:**
**Ecoset** was created as a clean and ecologically valid alternative to... | [
-0.1667053997516632,
-0.11438918858766556,
-0.1641036868095398,
0.23576496541500092,
0.18277901411056519,
0.05192042887210846,
0.004891797434538603,
0.02119314670562744,
0.19682422280311584,
0.031190715730190277,
-0.2643851935863495,
0.29552119970321655,
-0.3128069341182709,
0.497958630323... |
https://github.com/huggingface/datasets/issues/4529 | Hey There, thanks for closing 🤗
Forgot the issue existed, so I didn't close it after implementing the downloader :) | Ecoset | ## Adding a Dataset
- **Name:** *Ecoset*
- **Description:** *https://www.kietzmannlab.org/ecoset/*
- **Paper:** *https://doi.org/10.1073/pnas.2011417118*
- **Data:** *https://codeocean.com/capsule/9570390/tree/v1*
- **Motivation:**
**Ecoset** was created as a clean and ecologically valid alternative to **Imagen... | 20 | Ecoset
## Adding a Dataset
- **Name:** *Ecoset*
- **Description:** *https://www.kietzmannlab.org/ecoset/*
- **Paper:** *https://doi.org/10.1073/pnas.2011417118*
- **Data:** *https://codeocean.com/capsule/9570390/tree/v1*
- **Motivation:**
**Ecoset** was created as a clean and ecologically valid alternative to... | [
-0.3009144067764282,
-0.1027955412864685,
-0.14716821908950806,
0.3084723651409149,
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0.0714859813451767,
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0.2522229850292206,
-0.32661503553390503,
0.466395497322082... |
https://github.com/huggingface/datasets/issues/4528 | Here is a modified repro example that makes it easier to see the leak:
```
$ cat ds2.py
import gc, sys
import time
from datasets import load_dataset
import os, psutil
process = psutil.Process(os.getpid())
print(process.memory_info().rss/2**20)
corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_mem... | Memory leak when iterating a Dataset | e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil
logging.ba... | 160 | Memory leak when iterating a Dataset
e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import ... | [
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-0.14323680102825165,
-0.215461... |
https://github.com/huggingface/datasets/issues/4528 | @NouamaneTazi, please check my analysis here https://github.com/huggingface/datasets/issues/4883#issuecomment-1242599722 so if you agree with my research this Issue can be closed as well.
I also made a suggestion at how to proceed to hunt for a real leak here https://github.com/huggingface/datasets/issues/4883#issue... | Memory leak when iterating a Dataset | e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil
logging.ba... | 49 | Memory leak when iterating a Dataset
e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import ... | [
-0.15567344427108765,
-0.16558007895946503,
-0.015643302351236343,
0.3840184509754181,
0.13620814681053162,
-0.02972259372472763,
-0.06453166902065277,
0.019132312387228012,
-0.13223889470100403,
-0.10357401520013809,
0.16322138905525208,
0.3858797252178192,
-0.14323680102825165,
-0.215461... |
https://github.com/huggingface/datasets/issues/4528 | Amazing job! Thanks for taking time to debug this 🤗
For my side, I tried to do some more research as well, but to no avail. https://github.com/huggingface/datasets/issues/4883#issuecomment-1243415957 | Memory leak when iterating a Dataset | e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil
logging.ba... | 27 | Memory leak when iterating a Dataset
e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import ... | [
-0.15567344427108765,
-0.16558007895946503,
-0.015643302351236343,
0.3840184509754181,
0.13620814681053162,
-0.02972259372472763,
-0.06453166902065277,
0.019132312387228012,
-0.13223889470100403,
-0.10357401520013809,
0.16322138905525208,
0.3858797252178192,
-0.14323680102825165,
-0.215461... |
https://github.com/huggingface/datasets/issues/4526 | I was not able to reproduce this behavior (I tried without using pytorch lightning though, since I don't know what code you ran in pytorch lightning to get this).
If you can provide a MWE that would be perfect ! :) | split cache used when processing different split | ## Describe the bug`
```
ds1 = load_dataset('squad', split='validation')
ds2 = load_dataset('squad', split='train')
ds1 = ds1.map(some_function)
ds2 = ds2.map(some_function)
assert ds1 == ds2
```
This happens when ds1 and ds2 are created in `pytorch_lightning.DataModule` through
```
class myDataModule:
... | 41 | split cache used when processing different split
## Describe the bug`
```
ds1 = load_dataset('squad', split='validation')
ds2 = load_dataset('squad', split='train')
ds1 = ds1.map(some_function)
ds2 = ds2.map(some_function)
assert ds1 == ds2
```
This happens when ds1 and ds2 are created in `pytorch_lightning.D... | [
-0.10032157599925995,
-0.33055487275123596,
0.00468047708272934,
0.449718177318573,
0.16972173750400543,
-0.17919030785560608,
0.5268458724021912,
0.22238361835479736,
0.144949808716774,
0.14330196380615234,
-0.1651817262172699,
0.34816986322402954,
0.0782676562666893,
-0.08422871679067612... |
https://github.com/huggingface/datasets/issues/4526 | Hi, I think the issue happened because I was loading datasets under an `if` ... `else` statement and the condition would change the dataset I would need to load but instead the cached one was always returned. However, I believe that is expected behaviour, if so I'll close the issue.
Otherwise I will try to provide a... | split cache used when processing different split | ## Describe the bug`
```
ds1 = load_dataset('squad', split='validation')
ds2 = load_dataset('squad', split='train')
ds1 = ds1.map(some_function)
ds2 = ds2.map(some_function)
assert ds1 == ds2
```
This happens when ds1 and ds2 are created in `pytorch_lightning.DataModule` through
```
class myDataModule:
... | 58 | split cache used when processing different split
## Describe the bug`
```
ds1 = load_dataset('squad', split='validation')
ds2 = load_dataset('squad', split='train')
ds1 = ds1.map(some_function)
ds2 = ds2.map(some_function)
assert ds1 == ds2
```
This happens when ds1 and ds2 are created in `pytorch_lightning.D... | [
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-0.32965248823165894,
0.01821368932723999,
0.5120731592178345,
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0.4617888033390045,
0.24098673462867737,
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0.014833297580480576,
-0.18508730828762054,
0.303051620721817,
0.07645097374916077,
-0.11425362527370... |
https://github.com/huggingface/datasets/issues/4525 | Some naive ideas to cope with this:
- enable more RAM on each worker
- force the spanning of more workers
- others? | Out of memory error on workers while running Beam+Dataflow | ## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently worker... | 23 | Out of memory error on workers while running Beam+Dataflow
## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Tr... | [
-0.3179209232330322,
-0.12448757886886597,
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0.32580500841140747,
0.3414509892463684,
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0.436259925365448,
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0.3672865033149719,
0.2916370630264282,
0.17966371774673462,
-0.2072913944721222,
0.2970902323722839... |
https://github.com/huggingface/datasets/issues/4525 | @albertvillanova We were finally able to process the full NQ dataset on our machines using 600 gb with 5 workers. Maybe these numbers will work for you as well. | Out of memory error on workers while running Beam+Dataflow | ## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently worker... | 29 | Out of memory error on workers while running Beam+Dataflow
## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Tr... | [
-0.3179209232330322,
-0.12448757886886597,
-0.04219033569097519,
0.32580500841140747,
0.3414509892463684,
-0.1422143131494522,
-0.09138494729995728,
0.436259925365448,
-0.17715445160865784,
0.3672865033149719,
0.2916370630264282,
0.17966371774673462,
-0.2072913944721222,
0.2970902323722839... |
https://github.com/huggingface/datasets/issues/4525 | Thanks a lot for the hint, @seirasto.
I have one question: what runner did you use? Direct, Apache Flink/Nemo/Samza/Spark, Google Dataflow...? Thank you. | Out of memory error on workers while running Beam+Dataflow | ## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently worker... | 23 | Out of memory error on workers while running Beam+Dataflow
## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Tr... | [
-0.3179209232330322,
-0.12448757886886597,
-0.04219033569097519,
0.32580500841140747,
0.3414509892463684,
-0.1422143131494522,
-0.09138494729995728,
0.436259925365448,
-0.17715445160865784,
0.3672865033149719,
0.2916370630264282,
0.17966371774673462,
-0.2072913944721222,
0.2970902323722839... |
https://github.com/huggingface/datasets/issues/4525 | @albertvillanova Since we have already processed the NQ dataset on our machines can we upload it to datasets so the NQ PR can be merged? | Out of memory error on workers while running Beam+Dataflow | ## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently worker... | 25 | Out of memory error on workers while running Beam+Dataflow
## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Tr... | [
-0.3179209232330322,
-0.12448757886886597,
-0.04219033569097519,
0.32580500841140747,
0.3414509892463684,
-0.1422143131494522,
-0.09138494729995728,
0.436259925365448,
-0.17715445160865784,
0.3672865033149719,
0.2916370630264282,
0.17966371774673462,
-0.2072913944721222,
0.2970902323722839... |
https://github.com/huggingface/datasets/issues/4525 | Maybe @lhoestq can give a more accurate answer as I am not sure about the authentication requirements to upload those files to our cloud bucket.
Anyway I propose to continue this discussion on the dedicated PR for Natural questions dataset:
- #4368 | Out of memory error on workers while running Beam+Dataflow | ## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently worker... | 42 | Out of memory error on workers while running Beam+Dataflow
## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Tr... | [
-0.3179209232330322,
-0.12448757886886597,
-0.04219033569097519,
0.32580500841140747,
0.3414509892463684,
-0.1422143131494522,
-0.09138494729995728,
0.436259925365448,
-0.17715445160865784,
0.3672865033149719,
0.2916370630264282,
0.17966371774673462,
-0.2072913944721222,
0.2970902323722839... |
https://github.com/huggingface/datasets/issues/4525 | > I asked my colleague who ran the code and he said apache beam.
He looked into it further and he just used DirectRunner. @albertvillanova | Out of memory error on workers while running Beam+Dataflow | ## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently worker... | 25 | Out of memory error on workers while running Beam+Dataflow
## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Tr... | [
-0.3179209232330322,
-0.12448757886886597,
-0.04219033569097519,
0.32580500841140747,
0.3414509892463684,
-0.1422143131494522,
-0.09138494729995728,
0.436259925365448,
-0.17715445160865784,
0.3672865033149719,
0.2916370630264282,
0.17966371774673462,
-0.2072913944721222,
0.2970902323722839... |
https://github.com/huggingface/datasets/issues/4525 | OK, thank you @seirasto for your hint.
That explains why you did not encounter the out of memory error: this only appears when the processing is distributed (on workers memory) and DirectRunner does not distribute the processing (all is done in a single machine). | Out of memory error on workers while running Beam+Dataflow | ## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently worker... | 44 | Out of memory error on workers while running Beam+Dataflow
## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Tr... | [
-0.3179209232330322,
-0.12448757886886597,
-0.04219033569097519,
0.32580500841140747,
0.3414509892463684,
-0.1422143131494522,
-0.09138494729995728,
0.436259925365448,
-0.17715445160865784,
0.3672865033149719,
0.2916370630264282,
0.17966371774673462,
-0.2072913944721222,
0.2970902323722839... |
https://github.com/huggingface/datasets/issues/4524 | Hi @dan-the-meme-man, thanks for reporting.
We are investigating a similar issue but with Beam+Dataflow (instead of Beam+Flink):
- #4525
In order to go deeper into the root cause, we need as much information as possible: logs from the main process + logs from the workers are very informative.
In the case of ... | Downloading via Apache Pipeline, client cancelled (org.apache.beam.vendor.grpc.v1p43p2.io.grpc.StatusRuntimeException) | ## Describe the bug
When downloading some `wikipedia` languages (in particular, I'm having a hard time with Spanish, Cebuano, and Russian) via FlinkRunner, I encounter the exception in the title. I have been playing with package versions a lot, because unfortunately, the different dependencies required by these packag... | 67 | Downloading via Apache Pipeline, client cancelled (org.apache.beam.vendor.grpc.v1p43p2.io.grpc.StatusRuntimeException)
## Describe the bug
When downloading some `wikipedia` languages (in particular, I'm having a hard time with Spanish, Cebuano, and Russian) via FlinkRunner, I encounter the exception in the title. I ... | [
-0.11411868035793304,
0.04956531524658203,
-0.06658175587654114,
0.27392804622650146,
-0.010553060099482536,
0.06644286215305328,
0.25469547510147095,
0.23666128516197205,
0.049875110387802124,
0.20283350348472595,
0.2942866086959839,
-0.005584418773651123,
-0.030992958694696426,
-0.128714... |
https://github.com/huggingface/datasets/issues/4524 | As I continued working on this today, I came to suspect that it is in fact an out of memory issue - I have a few more notebooks that I've left running, and if they produce the same error, I will try to get the logs. In the meantime, if there's any chance that there is a repo out there with those three languages already... | Downloading via Apache Pipeline, client cancelled (org.apache.beam.vendor.grpc.v1p43p2.io.grpc.StatusRuntimeException) | ## Describe the bug
When downloading some `wikipedia` languages (in particular, I'm having a hard time with Spanish, Cebuano, and Russian) via FlinkRunner, I encounter the exception in the title. I have been playing with package versions a lot, because unfortunately, the different dependencies required by these packag... | 88 | Downloading via Apache Pipeline, client cancelled (org.apache.beam.vendor.grpc.v1p43p2.io.grpc.StatusRuntimeException)
## Describe the bug
When downloading some `wikipedia` languages (in particular, I'm having a hard time with Spanish, Cebuano, and Russian) via FlinkRunner, I encounter the exception in the title. I ... | [
-0.11411868035793304,
0.04956531524658203,
-0.06658175587654114,
0.27392804622650146,
-0.010553060099482536,
0.06644286215305328,
0.25469547510147095,
0.23666128516197205,
0.049875110387802124,
0.20283350348472595,
0.2942866086959839,
-0.005584418773651123,
-0.030992958694696426,
-0.128714... |
https://github.com/huggingface/datasets/issues/4521 | Didn't realize it's a bug when I asked the question yesterday! Free free to post an answer if you are sure the cause has been addressed.
https://stackoverflow.com/questions/72664827/can-pickle-dill-foo-but-not-lambda-x-foox | Datasets method `.map` not hashing | ## Describe the bug
Datasets method `.map` not hashing, even with an empty no-op function
## Steps to reproduce the bug
```python
from datasets import load_dataset
# download 9MB dummy dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")
def prepare_dataset(batch):
return(b... | 27 | Datasets method `.map` not hashing
## Describe the bug
Datasets method `.map` not hashing, even with an empty no-op function
## Steps to reproduce the bug
```python
from datasets import load_dataset
# download 9MB dummy dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")
def p... | [
-0.20823633670806885,
-0.014491185545921326,
0.05189558118581772,
-0.06907932460308075,
0.21113207936286926,
0.06441684067249298,
0.46527954936027527,
0.33714091777801514,
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0.09930916875600815,
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0.6498111486434937,
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-0.2695939242... |
https://github.com/huggingface/datasets/issues/4520 | I think this has been fixed by #4516, let me know if you encounter this again :)
I re-ran your code in 3.7 and 3.9 and it works fine | Failure to hash `dataclasses` - results in functions that cannot be hashed or cached in `.map` | Dataclasses cannot be hashed. As a result, they cannot be hashed or cached if used in the `.map` method. Dataclasses are used extensively in Transformers examples scripts: (c.f. [CTC example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py)). Since... | 29 | Failure to hash `dataclasses` - results in functions that cannot be hashed or cached in `.map`
Dataclasses cannot be hashed. As a result, they cannot be hashed or cached if used in the `.map` method. Dataclasses are used extensively in Transformers examples scripts: (c.f. [CTC example](https://github.com/huggingface... | [
0.01322636753320694,
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0.28255510330200195,
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-0.154488205909729... |
https://github.com/huggingface/datasets/issues/4508 | Hi! We've recently added a check to the `ClassLabel` type to ensure the values are in the valid label range `-1, 0, ..., num_classes-1` (-1 is used for missing values). The error in your case happens only if the `labels` column is of type `Sequence(ClassLabel(...))` before the `map` call and can be avoided by calling `... | cast_storage method from datasets.features | ## Describe the bug
A bug occurs when mapping a function to a dataset object. I ran the same code with the same data yesterday and it worked just fine. It works when i run locally on an old version of datasets.
## Steps to reproduce the bug
Steps are:
- load whatever datset
- write a preprocessing function such ... | 86 | cast_storage method from datasets.features
## Describe the bug
A bug occurs when mapping a function to a dataset object. I ran the same code with the same data yesterday and it worked just fine. It works when i run locally on an old version of datasets.
## Steps to reproduce the bug
Steps are:
- load whatever d... | [
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0.11094888299703598,
0.05474093183875084,
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0.358804315328598,
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0.057177625596523... |
https://github.com/huggingface/datasets/issues/4507 | Hi @liyucheng09.
Users can pass the `split` parameter to `load_dataset`. For example, if your split name is "train",
```python
ds = load_dataset("dataset_name", split="train")
```
will return a `Dataset` instance. | How to let `load_dataset` return a `Dataset` instead of `DatasetDict` in customized loading script | If the dataset does not need splits, i.e., no training and validation split, more like a table. How can I let the `load_dataset` function return a `Dataset` object directly rather than return a `DatasetDict` object with only one key-value pair.
Or I can paraphrase the question in the following way: how to skip `_spl... | 29 | How to let `load_dataset` return a `Dataset` instead of `DatasetDict` in customized loading script
If the dataset does not need splits, i.e., no training and validation split, more like a table. How can I let the `load_dataset` function return a `Dataset` object directly rather than return a `DatasetDict` object with... | [
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0.30113357305526733,
0.04661174118518829,
0.5615159273147583,
-0.18064871430397034,
0.5828616619110... |
https://github.com/huggingface/datasets/issues/4506 | Important info:
As hashes are generated randomly for functions, it leads to **false identifying some results as already hashed** (mapping function is not executed after a method update) when there's a `pytorch_lightning.seed_everything(123)` | Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results | ## Describe the bug
Sometimes I get messages about not being able to hash a method:
`Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform datasets.arrow_dataset.Dataset.
_map_single couldn't be hashed properly, a random hash was used instead. Make sur... | 32 | Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results
## Describe the bug
Sometimes I get messages about not being able to hash a method:
`Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform data... | [
0.10672198235988617,
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0.33440566062927246,
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0.43940913677215576,
-0.22675369679927826,
-0.48845267295... |
https://github.com/huggingface/datasets/issues/4506 | Hi ! Thanks for reporting. This bug seems to appear in python 3.9 using dill 3.5.1
As a workaround you can use an older version of dill:
```
pip install "dill<0.3.5"
``` | Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results | ## Describe the bug
Sometimes I get messages about not being able to hash a method:
`Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform datasets.arrow_dataset.Dataset.
_map_single couldn't be hashed properly, a random hash was used instead. Make sur... | 32 | Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results
## Describe the bug
Sometimes I get messages about not being able to hash a method:
`Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform data... | [
0.10672198235988617,
0.023449376225471497,
0.008314590901136398,
-0.041031911969184875,
0.2454579770565033,
0.06037791073322296,
0.33440566062927246,
0.3241561949253082,
-0.10414817929267883,
0.04988355189561844,
0.2782006859779358,
0.43940913677215576,
-0.22675369679927826,
-0.48845267295... |
https://github.com/huggingface/datasets/issues/4506 | installing `dill<0.3.5` after installing `datasets` by pip results in dependency conflict with the version required for `multiprocess`. It can be solved by installing `pip install datasets "dill<0.3.5"` (simultaneously) on a clean environment | Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results | ## Describe the bug
Sometimes I get messages about not being able to hash a method:
`Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform datasets.arrow_dataset.Dataset.
_map_single couldn't be hashed properly, a random hash was used instead. Make sur... | 32 | Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results
## Describe the bug
Sometimes I get messages about not being able to hash a method:
`Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform data... | [
0.10672198235988617,
0.023449376225471497,
0.008314590901136398,
-0.041031911969184875,
0.2454579770565033,
0.06037791073322296,
0.33440566062927246,
0.3241561949253082,
-0.10414817929267883,
0.04988355189561844,
0.2782006859779358,
0.43940913677215576,
-0.22675369679927826,
-0.48845267295... |
https://github.com/huggingface/datasets/issues/4506 | This has been fixed in https://github.com/huggingface/datasets/pull/4516, we will do a new release soon to include the fix :) | Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results | ## Describe the bug
Sometimes I get messages about not being able to hash a method:
`Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform datasets.arrow_dataset.Dataset.
_map_single couldn't be hashed properly, a random hash was used instead. Make sur... | 18 | Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results
## Describe the bug
Sometimes I get messages about not being able to hash a method:
`Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform data... | [
0.10672198235988617,
0.023449376225471497,
0.008314590901136398,
-0.041031911969184875,
0.2454579770565033,
0.06037791073322296,
0.33440566062927246,
0.3241561949253082,
-0.10414817929267883,
0.04988355189561844,
0.2782006859779358,
0.43940913677215576,
-0.22675369679927826,
-0.48845267295... |
https://github.com/huggingface/datasets/issues/4504 | would you like to give it a try, @dgrnd4? (maybe with the help of the dataset author?) | Can you please add the Stanford dog dataset? | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github... | 17 | Can you please add the Stanford dog dataset?
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/I... | [
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https://github.com/huggingface/datasets/issues/4504 | @julien-c i am sorry but I have no idea about how it works: can I add the dataset by myself, following "instructions to add a new dataset"?
Can I add a dataset even if it's not mine? (it's public in the link that I wrote on the post)
| Can you please add the Stanford dog dataset? | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github... | 48 | Can you please add the Stanford dog dataset?
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/I... | [
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https://github.com/huggingface/datasets/issues/4504 | Hi! The [ADD NEW DATASET](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) instructions are indeed the best place to start. It's also perfectly fine to add a dataset if it's public, even if it's not yours. Let me know if you need some additional pointers. | Can you please add the Stanford dog dataset? | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github... | 38 | Can you please add the Stanford dog dataset?
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/I... | [
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https://github.com/huggingface/datasets/issues/4504 | @khushmeeet this is the [link](https://huggingface.co/datasets/dgrnd4/stanford_dog_dataset) where I added the dataset already. If you can I would ask you to do this:
1) The dataset it's all in TRAINING SET: can you please divide it in Training,Test and Validation Set? If you can for each class, take the 80% for the Tr... | Can you please add the Stanford dog dataset? | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github... | 90 | Can you please add the Stanford dog dataset?
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/I... | [
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https://github.com/huggingface/datasets/issues/4504 | Hi @khushmeeet! Thanks for the interest. You can self-assign the issue by commenting `#self-assign` on it.
Also, I think we can skip @dgrnd4's steps as we try to avoid any custom processing on top of raw data. One can later copy the script and override `_post_process` in it to perform such processing on the generat... | Can you please add the Stanford dog dataset? | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github... | 56 | Can you please add the Stanford dog dataset?
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/I... | [
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https://github.com/huggingface/datasets/issues/4504 | Thanks @mariosasko
@dgrnd4 As dataset is there on Hub, and preprocessing is not recommended. I am not sure if there is any other task to do. However, I can't seem to find relevant `.py` files for this dataset in GitHub repo. | Can you please add the Stanford dog dataset? | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github... | 41 | Can you please add the Stanford dog dataset?
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/I... | [
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https://github.com/huggingface/datasets/issues/4504 | @khushmeeet @mariosasko The point is that the images must be processed and must have the same size in order to can be used for things for example "Training". | Can you please add the Stanford dog dataset? | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github... | 28 | Can you please add the Stanford dog dataset?
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/I... | [
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https://github.com/huggingface/datasets/issues/4504 | @dgrnd4 Yes, but this can be done after loading (`map` to resize images and `train_test_split` to create extra splits)
@khushmeeet The linked version is implemented as a no-code dataset and is generated directly from the ZIP archive, but our "GitHub" datasets (these are datasets without a user/org namespace on the H... | Can you please add the Stanford dog dataset? | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github... | 81 | Can you please add the Stanford dog dataset?
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/I... | [
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https://github.com/huggingface/datasets/issues/4504 | @mariosasko The point is that if I use something like this:
x_train, x_test = train_test_split(dataset, test_size=0.1)
to get Train 90% and Test 10%, and then to get the Validation Set (10% of the whole 100%):
```
train_ratio = 0.80
validation_ratio = 0.10
test_ratio = 0.10
x_train, x_test, y_train, y_tes... | Can you please add the Stanford dog dataset? | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github... | 188 | Can you please add the Stanford dog dataset?
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/I... | [
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https://github.com/huggingface/datasets/issues/4504 | @mariosasko in order to resize images I'm trying this method:
```
for i in range(0,len(dataset['train'])): #len(dataset['train'])
ex = dataset['train'][i] #i
image = ex['image']
image = image.convert("RGB") # <class 'PIL.Image.Image'> <PIL.Image.Image image mode=RGB size=500x333 at 0x7F84F1948150>
imag... | Can you please add the Stanford dog dataset? | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github... | 82 | Can you please add the Stanford dog dataset?
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/I... | [
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https://github.com/huggingface/datasets/issues/4504 | I have raised PR for adding stanford-dog dataset. I have not added any data preprocessing code. Only dataset generation script is there. Let me know any changes required, or anything to add to README. | Can you please add the Stanford dog dataset? | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github... | 34 | Can you please add the Stanford dog dataset?
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/I... | [
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https://github.com/huggingface/datasets/issues/4504 | Is this issue still open, i am new to open source thus want to take this one as my start. | Can you please add the Stanford dog dataset? | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github... | 20 | Can you please add the Stanford dog dataset?
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/I... | [
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https://github.com/huggingface/datasets/issues/4504 | @zutarich This issue should have been closed since the dataset in question is available on the Hub [here](https://huggingface.co/datasets/dgrnd4/stanford_dog_dataset). | Can you please add the Stanford dog dataset? | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github... | 18 | Can you please add the Stanford dog dataset?
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/I... | [
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https://github.com/huggingface/datasets/issues/4502 | Hi @cccntu you're right, as when `batch_examples={}` the current if-statement won't be triggered as the condition won't be satisfied, I'll prepare a PR to address it as well as add the regression tests so that this issue is handled properly. | Logic bug in arrow_writer? | https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(iter(batch_examples.values())... | 40 | Logic bug in arrow_writer?
https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(... | [
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-0.0042255967855453... |
https://github.com/huggingface/datasets/issues/4502 | Hi @alvarobartt ,
Thanks for answering. Do you know when and why an empty batch is passed to this function? This only happened to me when processing with multiple workers, while chunking examples, I think. | Logic bug in arrow_writer? | https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(iter(batch_examples.values())... | 35 | Logic bug in arrow_writer?
https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(... | [
-0.09734899550676346,
-0.15007999539375305,
-0.06967014819383621,
0.26273244619369507,
-0.4199850559234619,
-0.38796794414520264,
0.4252558946609497,
0.2011992335319519,
-0.16761314868927002,
0.35478323698043823,
0.8012014031410217,
0.1710001528263092,
-0.25645288825035095,
-0.123260058462... |
https://github.com/huggingface/datasets/issues/4502 | > Hi @alvarobartt , Thanks for answering. Do you know when and why an empty batch is passed to this function? This only happened to me when processing with multiple workers, while chunking examples, I think.
So it depends on how you're actually chunking the data as if you're not handling empty chunks `batch_examples... | Logic bug in arrow_writer? | https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(iter(batch_examples.values())... | 94 | Logic bug in arrow_writer?
https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(... | [
-0.17684590816497803,
-0.07852344214916229,
-0.07085531949996948,
0.22444674372673035,
-0.24380692839622498,
-0.29227906465530396,
0.40107008814811707,
0.3219429850578308,
-0.16090717911720276,
0.1594412624835968,
0.6263853311538696,
0.23006664216518402,
-0.25662967562675476,
0.05985462665... |
https://github.com/huggingface/datasets/issues/4502 | Thanks, I added a if-print and I found it does return an empty examples in the chunking function that is passed to `.map()`. | Logic bug in arrow_writer? | https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(iter(batch_examples.values())... | 23 | Logic bug in arrow_writer?
https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(... | [
-0.028830021619796753,
-0.23289914429187775,
-0.07871432602405548,
0.3852706551551819,
-0.3537974953651428,
-0.27569329738616943,
0.2524559199810028,
0.35920581221580505,
-0.10528847575187683,
0.22964051365852356,
0.5770527124404907,
0.29913321137428284,
-0.34472769498825073,
-0.0568806491... |
https://github.com/huggingface/datasets/issues/4502 | Hi ! We consider an empty batch to look like this:
```python
empty_batch = {
"column_1": [],
"column_2": [],
...
}
```
While `{}` corresponds to a batch with no columns.
Therefore calling this code should fail, because the two batches don't have the same columns:
```python
writer.write_batch(... | Logic bug in arrow_writer? | https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(iter(batch_examples.values())... | 74 | Logic bug in arrow_writer?
https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(... | [
-0.07702662795782089,
-0.06066294014453888,
-0.11207467317581177,
0.21322540938854218,
-0.2605230510234833,
-0.0949762687087059,
0.438445508480072,
0.23383907973766327,
-0.013233080506324768,
0.17256689071655273,
0.5886090993881226,
0.1397274285554886,
-0.27847397327423096,
-0.041127774864... |
https://github.com/huggingface/datasets/issues/4502 | Makes sense, then the if-statement should remain the same or is it better to handle both cases separately using `if not batch_examples or len(next(iter(batch_examples.values()))) == 0: ...`?
Updating the regressions tests with an empty batch formatted as `{"col_1": [], "col_2": []}` instead of `{}` works fine with t... | Logic bug in arrow_writer? | https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(iter(batch_examples.values())... | 58 | Logic bug in arrow_writer?
https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(... | [
-0.08192867040634155,
-0.09121575206518173,
-0.10583413392305374,
0.107330821454525,
-0.520084798336029,
-0.29115286469459534,
0.4126380980014801,
0.2771487832069397,
-0.05077877268195152,
0.09768598526716232,
0.6894097328186035,
0.2009698748588562,
-0.2975049614906311,
-0.0531533397734165... |
https://github.com/huggingface/datasets/issues/4502 | > Makes sense, then the if-statement should remain the same or is it better to handle both cases separately using if not batch_examples or len(next(iter(batch_examples.values()))) == 0: ...?
There's a check later in the code that makes sure that the columns are the right ones, so I don't think we need to check for `... | Logic bug in arrow_writer? | https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(iter(batch_examples.values())... | 141 | Logic bug in arrow_writer?
https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(... | [
-0.10326507687568665,
-0.06579664349555969,
-0.11863885819911957,
0.10876264423131943,
-0.39075177907943726,
-0.27570080757141113,
0.4332880973815918,
0.2744448781013489,
0.02154691517353058,
0.09205935895442963,
0.6777889728546143,
0.19794505834579468,
-0.29002153873443604,
-0.00334179401... |
https://github.com/huggingface/datasets/issues/4502 | Great thanks for the response! So I'll just add that regression test and remove the current if-statement. | Logic bug in arrow_writer? | https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(iter(batch_examples.values())... | 17 | Logic bug in arrow_writer?
https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(... | [
-0.0973808541893959,
-0.20760293304920197,
-0.1353929340839386,
0.19127239286899567,
-0.37433692812919617,
-0.2949839234352112,
0.37354859709739685,
0.2602039575576782,
0.0011965706944465637,
0.10691025853157043,
0.7534393072128296,
0.2963051199913025,
-0.3206690549850464,
-0.0292248800396... |
https://github.com/huggingface/datasets/issues/4502 | Hi @lhoestq ,
Thanks for your explanation. Now I get it that `{}` means the columns are different. But wouldn't it be nice if the code can ignore it, like it ignores `{"a": []}`?
---
BTW,
> There's a check later in the code that makes sure that the columns are the right ones, so I don't think we need to che... | Logic bug in arrow_writer? | https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(iter(batch_examples.values())... | 130 | Logic bug in arrow_writer?
https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(... | [
-0.04479769244790077,
-0.03950589895248413,
-0.12906381487846375,
0.3717552721500397,
-0.12446074932813644,
-0.13376332819461823,
0.3163696527481079,
0.23021110892295837,
-0.1128930151462555,
0.15248212218284607,
0.5265916585922241,
0.264665424823761,
-0.13810276985168457,
0.00214847922325... |
https://github.com/huggingface/datasets/issues/4502 | > But wouldn't it be nice if the code can ignore it, like it ignores {"a": []}?
I think it would make things confusing because it doesn't follow our definition of a batch: "the columns of a batch = the keys of the dict". It would probably break certain behaviors as well. For example if you remove all the columns of ... | Logic bug in arrow_writer? | https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(iter(batch_examples.values())... | 154 | Logic bug in arrow_writer?
https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(... | [
-0.16384321451187134,
0.0012410134077072144,
-0.11076246201992035,
0.25900906324386597,
-0.1594432294368744,
-0.14531828463077545,
0.23462381958961487,
0.17750047147274017,
0.06671136617660522,
0.09348024427890778,
0.643965482711792,
0.21049562096595764,
-0.3761606216430664,
0.109192863106... |
https://github.com/huggingface/datasets/issues/4498 | WER can have values bigger than 1.0, this is expected when there are too many insertions
From [wikipedia](https://en.wikipedia.org/wiki/Word_error_rate):
> Note that since N is the number of words in the reference, the word error rate can be larger than 1.0 | WER and CER > 1 | ## Describe the bug
It seems that in some cases in which the `prediction` is longer than the `reference` we may have word/character error rate higher than 1 which is a bit odd.
If it's a real bug I think I can solve it with a PR changing [this](https://github.com/huggingface/datasets/blob/master/metrics/wer/wer.py#... | 40 | WER and CER > 1
## Describe the bug
It seems that in some cases in which the `prediction` is longer than the `reference` we may have word/character error rate higher than 1 which is a bit odd.
If it's a real bug I think I can solve it with a PR changing [this](https://github.com/huggingface/datasets/blob/master/m... | [
0.08050629496574402,
-0.2461024671792984,
0.013707712292671204,
0.11002643406391144,
0.3432493805885315,
-0.03220328688621521,
-0.007794834673404694,
0.29825058579444885,
-0.20607371628284454,
0.41888171434402466,
0.07657525688409805,
-0.18652546405792236,
0.18944992125034332,
-0.089545555... |
https://github.com/huggingface/datasets/issues/4491 | This issue can be resolved according to this post https://stackoverflow.com/questions/70566660/parquet-with-null-columns-on-pyarrow. It looks like first data entry in the json file must not have any null values as pyarrow uses this first file to infer schema for entire dataset. | Dataset Viewer issue for Pavithree/test | ### Link
https://huggingface.co/datasets/Pavithree/test
### Description
I have extracted the subset of original eli5 dataset found at hugging face. However, while loading the dataset It throws ArrowNotImplementedError: Unsupported cast from string to null using function cast_null error. Is there anything missi... | 38 | Dataset Viewer issue for Pavithree/test
### Link
https://huggingface.co/datasets/Pavithree/test
### Description
I have extracted the subset of original eli5 dataset found at hugging face. However, while loading the dataset It throws ArrowNotImplementedError: Unsupported cast from string to null using functio... | [
-0.20974215865135193,
-0.01985529065132141,
0.07286099344491959,
0.5056537985801697,
0.26237067580223083,
0.07652749121189117,
-0.14816847443580627,
0.0921078473329544,
-0.2811112105846405,
0.016643214970827103,
0.03859499841928482,
0.39983171224594116,
0.05336074158549309,
-0.037922650575... |
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