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|---|---|---|---|---|---|---|---|
https://github.com/huggingface/datasets/issues/5513 | Some functions use a param named `type` shouldn't that be avoided since it's a Python reserved name? | Hi @mariosasko, ok it makes sense. Anyway, don't you think it's worth it at some point to start a deprecation cycle e.g. `fs` in `load_from_disk`? It doesn't affect user experience but it's for sure a bad practice IMO, but's up to you 😄 Feel free to close this issue otherwise! | Hi @mariosasko, @lhoestq, or whoever reads this! :)
After going through `ArrowDataset.set_format` I found out that the `type` param is actually named `type` which is a Python reserved name as you may already know, shouldn't that be renamed to `format_type` before the 3.0.0 is released?
Just wanted to get your inp... | 304 | 50 | Some functions use a param named `type` shouldn't that be avoided since it's a Python reserved name?
Hi @mariosasko, @lhoestq, or whoever reads this! :)
After going through `ArrowDataset.set_format` I found out that the `type` param is actually named `type` which is a Python reserved name as you may already know, ... | [
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... |
https://github.com/huggingface/datasets/issues/5513 | Some functions use a param named `type` shouldn't that be avoided since it's a Python reserved name? | I don't think deprecating a param name in this particular instance is worth the hassle, so I'm closing the issue 🙂. | Hi @mariosasko, @lhoestq, or whoever reads this! :)
After going through `ArrowDataset.set_format` I found out that the `type` param is actually named `type` which is a Python reserved name as you may already know, shouldn't that be renamed to `format_type` before the 3.0.0 is released?
Just wanted to get your inp... | 304 | 21 | Some functions use a param named `type` shouldn't that be avoided since it's a Python reserved name?
Hi @mariosasko, @lhoestq, or whoever reads this! :)
After going through `ArrowDataset.set_format` I found out that the `type` param is actually named `type` which is a Python reserved name as you may already know, ... | [
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... |
https://github.com/huggingface/datasets/issues/5511 | Creating a dummy dataset from a bigger one | Update `datasets` or downgrade `huggingface-hub` ;)
The `huggingface-hub` lib did a breaking change a few months ago, and you're using an old version of `datasets` that does't support it | ### Describe the bug
I often want to create a dummy dataset from a bigger dataset for fast iteration when training. However, I'm having a hard time doing this especially when trying to upload the dataset to the Hub.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset... | 305 | 29 | Creating a dummy dataset from a bigger one
### Describe the bug
I often want to create a dummy dataset from a bigger dataset for fast iteration when training. However, I'm having a hard time doing this especially when trying to upload the dataset to the Hub.
### Steps to reproduce the bug
```python
from datasets ... | [
-1.2701120376586914,
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1.4185889959335327,
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0.2889370322227478,
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0.009617511183023453,
-0.6312329769134521,
-0.... |
https://github.com/huggingface/datasets/issues/5511 | Creating a dummy dataset from a bigger one | Getting same error with latest versions.
```shell
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[99], line 1
----> 1 dataset.push_to_hub("mirfan899/kids_phoneme_asr")
File /opt/conda/lib/python3.... | ### Describe the bug
I often want to create a dummy dataset from a bigger dataset for fast iteration when training. However, I'm having a hard time doing this especially when trying to upload the dataset to the Hub.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset... | 305 | 237 | Creating a dummy dataset from a bigger one
### Describe the bug
I often want to create a dummy dataset from a bigger dataset for fast iteration when training. However, I'm having a hard time doing this especially when trying to upload the dataset to the Hub.
### Steps to reproduce the bug
```python
from datasets ... | [
-1.2701120376586914,
-0.9678511023521423,
-0.6017754077911377,
1.4185889959335327,
-0.156211256980896,
-1.290795922279358,
0.181573748588562,
-1.0857007503509521,
1.5226128101348877,
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0.2889370322227478,
-1.6111980676651,
0.009617511183023453,
-0.6312329769134521,
-0.... |
https://github.com/huggingface/datasets/issues/5508 | Saving a dataset after setting format to torch doesn't work, but only if filtering | Hey, I'm a research engineer working on language modelling wanting to contribute to open source. I was wondering if I could give it a shot? | ### Describe the bug
Saving a dataset after setting format to torch doesn't work, but only if filtering
### Steps to reproduce the bug
```
a = Dataset.from_dict({"b": [1, 2]})
a.set_format('torch')
a.save_to_disk("test_save") # saves successfully
a.filter(None).save_to_disk("test_save_filter") # does not
>> [..... | 306 | 25 | Saving a dataset after setting format to torch doesn't work, but only if filtering
### Describe the bug
Saving a dataset after setting format to torch doesn't work, but only if filtering
### Steps to reproduce the bug
```
a = Dataset.from_dict({"b": [1, 2]})
a.set_format('torch')
a.save_to_disk("test_save") # s... | [
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0.31460586190223694,
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0.09192617982625961,
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... |
https://github.com/huggingface/datasets/issues/5506 | IterableDataset and Dataset return different batch sizes when using Trainer with multiple GPUs | Hi ! `datasets` doesn't do batching - the PyTorch DataLoader does and is created by the `Trainer`. Do you pass other arguments to training_args with respect to data loading ?
Also we recently released `.to_iterable_dataset` that does pretty much what you implemented, but using contiguous shards to get a better speed... | ### Describe the bug
I am training a Roberta model using 2 GPUs and the `Trainer` API with a batch size of 256.
Initially I used a standard `Dataset`, but had issues with slow data loading. After reading [this issue](https://github.com/huggingface/datasets/issues/2252), I swapped to loading my dataset as contiguous... | 307 | 61 | IterableDataset and Dataset return different batch sizes when using Trainer with multiple GPUs
### Describe the bug
I am training a Roberta model using 2 GPUs and the `Trainer` API with a batch size of 256.
Initially I used a standard `Dataset`, but had issues with slow data loading. After reading [this issue](ht... | [
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... |
https://github.com/huggingface/datasets/issues/5506 | IterableDataset and Dataset return different batch sizes when using Trainer with multiple GPUs | This is the full set of training args passed. No training args were changed when switching dataset types.
```python
training_args = TrainingArguments(
output_dir="./checkpoints",
overwrite_output_dir=True,
num_train_epochs=1,
per_device_train_batch_size=256,
save_steps=2000,
save_total... | ### Describe the bug
I am training a Roberta model using 2 GPUs and the `Trainer` API with a batch size of 256.
Initially I used a standard `Dataset`, but had issues with slow data loading. After reading [this issue](https://github.com/huggingface/datasets/issues/2252), I swapped to loading my dataset as contiguous... | 307 | 43 | IterableDataset and Dataset return different batch sizes when using Trainer with multiple GPUs
### Describe the bug
I am training a Roberta model using 2 GPUs and the `Trainer` API with a batch size of 256.
Initially I used a standard `Dataset`, but had issues with slow data loading. After reading [this issue](ht... | [
-1.330300211906433,
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1.541914939880371,
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... |
https://github.com/huggingface/datasets/issues/5506 | IterableDataset and Dataset return different batch sizes when using Trainer with multiple GPUs | Makes sense. Given that it's a `transformers` issue and already being tracked, I'll close this out. | ### Describe the bug
I am training a Roberta model using 2 GPUs and the `Trainer` API with a batch size of 256.
Initially I used a standard `Dataset`, but had issues with slow data loading. After reading [this issue](https://github.com/huggingface/datasets/issues/2252), I swapped to loading my dataset as contiguous... | 307 | 16 | IterableDataset and Dataset return different batch sizes when using Trainer with multiple GPUs
### Describe the bug
I am training a Roberta model using 2 GPUs and the `Trainer` API with a batch size of 256.
Initially I used a standard `Dataset`, but had issues with slow data loading. After reading [this issue](ht... | [
-1.330300211906433,
-1.0070868730545044,
-0.7057075500488281,
1.541914939880371,
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0.18128500878810883,
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0.10278196632862091,
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... |
https://github.com/huggingface/datasets/issues/5505 | PyTorch BatchSampler still loads from Dataset one-by-one | This change seems to come from a few months ago in the PyTorch side. That's good news and it means we may not need to pass a batch_sampler as soon as we add `Dataset.__getitems__` to get the optimal speed :)
Thanks for reporting ! Would you like to open a PR to add `__getitems__` and remove this outdated documentati... | ### Describe the bug
In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue.
I'm not sure if this is a mistake in the docs or the code, but it seems that the on... | 308 | 60 | PyTorch BatchSampler still loads from Dataset one-by-one
### Describe the bug
In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue.
I'm not sure if this is ... | [
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https://github.com/huggingface/datasets/issues/5505 | PyTorch BatchSampler still loads from Dataset one-by-one | Yeah I figured this was the sort of thing that probably once worked. I can confirm that you no longer need the batch sampler, just `batch_size=n` in the `DataLoader`.
I'll pass on the PR, I'm flat out right now, sorry. | ### Describe the bug
In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue.
I'm not sure if this is a mistake in the docs or the code, but it seems that the on... | 308 | 40 | PyTorch BatchSampler still loads from Dataset one-by-one
### Describe the bug
In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue.
I'm not sure if this is ... | [
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https://github.com/huggingface/datasets/issues/5499 | `load_dataset` has ~4 seconds of overhead for cached data | Hi ! To skip the verification step that checks if newer data exist, you can enable offline mode with `HF_DATASETS_OFFLINE=1`.
Although I agree this step should be much faster for datasets hosted on the HF Hub - we could just compare the commit hash from the local data and the remote git repository. We're not been le... | ### Feature request
When loading a dataset that has been cached locally, the `load_dataset` function takes a lot longer than it should take to fetch the dataset from disk (or memory).
This is particularly noticeable for smaller datasets. For example, wikitext-2, comparing `load_data` (once cached) and `load_from_disk... | 310 | 77 | `load_dataset` has ~4 seconds of overhead for cached data
### Feature request
When loading a dataset that has been cached locally, the `load_dataset` function takes a lot longer than it should take to fetch the dataset from disk (or memory).
This is particularly noticeable for smaller datasets. For example, wikitex... | [
-1.2485748529434204,
-0.9309433698654175,
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1.4467313289642334,
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0.14992734789848328,
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-0.5667169690132141... |
https://github.com/huggingface/datasets/issues/5498 | TypeError: 'bool' object is not iterable when filtering a datasets.arrow_dataset.Dataset | Hi! Instead of a single boolean, your filter function should return an iterable (of booleans) in the batched mode like so:
```python
train_dataset = train_dataset.filter(
function=lambda batch: [image is not None for image in batch["image"]],
batched=True,
batch_size=10)
```
... | ### Describe the bug
Hi,
Thanks for the amazing work on the library!
**Describe the bug**
I think I might have noticed a small bug in the filter method.
Having loaded a dataset using `load_dataset`, when I try to filter out empty entries with `batched=True`, I get a TypeError.
### Steps to reproduce the ... | 311 | 74 | TypeError: 'bool' object is not iterable when filtering a datasets.arrow_dataset.Dataset
### Describe the bug
Hi,
Thanks for the amazing work on the library!
**Describe the bug**
I think I might have noticed a small bug in the filter method.
Having loaded a dataset using `load_dataset`, when I try to fi... | [
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https://github.com/huggingface/datasets/issues/5496 | Add a `reduce` method | Hi! Sure, feel free to open a PR, so we can see the API you have in mind. | ### Feature request
Right now the `Dataset` class implements `map()` and `filter()`, but leaves out the third functional idiom popular among Python users: `reduce`.
### Motivation
A `reduce` method is often useful when calculating dataset statistics, for example, the occurrence of a particular n-gram or the average... | 312 | 18 | Add a `reduce` method
### Feature request
Right now the `Dataset` class implements `map()` and `filter()`, but leaves out the third functional idiom popular among Python users: `reduce`.
### Motivation
A `reduce` method is often useful when calculating dataset statistics, for example, the occurrence of a particul... | [
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https://github.com/huggingface/datasets/issues/5495 | to_tf_dataset fails with datetime UTC columns even if not included in columns argument | Hi! This is indeed a bug in our zero-copy logic.
To fix it, instead of the line:
https://github.com/huggingface/datasets/blob/7cfac43b980ab9e4a69c2328f085770996323005/src/datasets/features/features.py#L702
we should have:
```python
return pa.types.is_primitive(pa_type) and not (pa.types.is_boolean(pa_type) or ... | ### Describe the bug
There appears to be some eager behavior in `to_tf_dataset` that runs against every column in a dataset even if they aren't included in the columns argument. This is problematic with datetime UTC columns due to them not working with zero copy. If I don't have UTC information in my datetime column... | 313 | 30 | to_tf_dataset fails with datetime UTC columns even if not included in columns argument
### Describe the bug
There appears to be some eager behavior in `to_tf_dataset` that runs against every column in a dataset even if they aren't included in the columns argument. This is problematic with datetime UTC columns due ... | [
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https://github.com/huggingface/datasets/issues/5494 | Update audio installation doc page | Totally agree, the docs should be in sync with our code.
Indeed to avoid confusing users, I think we should have updated the docs at the same time as this PR:
- #5167 | Our [installation documentation page](https://huggingface.co/docs/datasets/installation#audio) says that one can use Datasets for mp3 only with `torchaudio<0.12`. `torchaudio>0.12` is actually supported too but requires a specific version of ffmpeg which is not easily installed on all linux versions but there is a cust... | 314 | 33 | Update audio installation doc page
Our [installation documentation page](https://huggingface.co/docs/datasets/installation#audio) says that one can use Datasets for mp3 only with `torchaudio<0.12`. `torchaudio>0.12` is actually supported too but requires a specific version of ffmpeg which is not easily installed on a... | [
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https://github.com/huggingface/datasets/issues/5494 | Update audio installation doc page | No, @polinaeterna, nothing to be sorry about.
My comment was for all of us datasets team, as a reminder: when making a PR, but also when reviewing some other's PR, we should not forget to update the corresponding docstring and doc pages. It is something we can improve if we help each other in reminding about it... :... | Our [installation documentation page](https://huggingface.co/docs/datasets/installation#audio) says that one can use Datasets for mp3 only with `torchaudio<0.12`. `torchaudio>0.12` is actually supported too but requires a specific version of ffmpeg which is not easily installed on all linux versions but there is a cust... | 314 | 58 | Update audio installation doc page
Our [installation documentation page](https://huggingface.co/docs/datasets/installation#audio) says that one can use Datasets for mp3 only with `torchaudio<0.12`. `torchaudio>0.12` is actually supported too but requires a specific version of ffmpeg which is not easily installed on a... | [
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https://github.com/huggingface/datasets/issues/5494 | Update audio installation doc page | @polinaeterna I think we can close this issue now as we no longer use `torchaudio` for decoding. | Our [installation documentation page](https://huggingface.co/docs/datasets/installation#audio) says that one can use Datasets for mp3 only with `torchaudio<0.12`. `torchaudio>0.12` is actually supported too but requires a specific version of ffmpeg which is not easily installed on all linux versions but there is a cust... | 314 | 17 | Update audio installation doc page
Our [installation documentation page](https://huggingface.co/docs/datasets/installation#audio) says that one can use Datasets for mp3 only with `torchaudio<0.12`. `torchaudio>0.12` is actually supported too but requires a specific version of ffmpeg which is not easily installed on a... | [
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https://github.com/huggingface/datasets/issues/5492 | Push_to_hub in a pull request | Assigned to myself and will get to it in the next week, but if someone finds this issue annoying and wants to submit a PR before I do, just ping me here and I'll reassign :). | Right now `ds.push_to_hub()` can push a dataset on `main` or on a new branch with `branch=`, but there is no way to open a pull request. Even passing `branch=refs/pr/x` doesn't seem to work: it tries to create a branch with that name
cc @nateraw
It should be possible to tweak the use of `huggingface_hub` in `pus... | 315 | 36 | Push_to_hub in a pull request
Right now `ds.push_to_hub()` can push a dataset on `main` or on a new branch with `branch=`, but there is no way to open a pull request. Even passing `branch=refs/pr/x` doesn't seem to work: it tries to create a branch with that name
cc @nateraw
It should be possible to tweak the ... | [
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-1.1027363538742065,
1.681578516960144,
-0.9570409655570984,
0.4714111387729645,
-1.5528804063796997,
0.0343179851770401,
-0.6533536314964294,
... |
https://github.com/huggingface/datasets/issues/5488 | Error loading MP3 files from CommonVoice | Hi @kradonneoh, thanks for reporting.
Please note that to work with audio datasets (and specifically with MP3 files) we have detailed installation instructions in our docs: https://huggingface.co/docs/datasets/installation#audio
- one of the requirements is torchaudio<0.12.0
Let us know if the problem persists a... | ### Describe the bug
When loading a CommonVoice dataset with `datasets==2.9.0` and `torchaudio>=0.12.0`, I get an error reading the audio arrays:
```python
---------------------------------------------------------------------------
LibsndfileError Traceback (most recent call last)
~/.l... | 316 | 45 | Error loading MP3 files from CommonVoice
### Describe the bug
When loading a CommonVoice dataset with `datasets==2.9.0` and `torchaudio>=0.12.0`, I get an error reading the audio arrays:
```python
---------------------------------------------------------------------------
LibsndfileError ... | [
-1.2167824506759644,
-0.7806340456008911,
-0.4928833842277527,
1.4399757385253906,
-0.05507618933916092,
-1.3829612731933594,
0.13091428577899933,
-0.8968741297721863,
1.5224993228912354,
-0.8785139322280884,
0.40658533573150635,
-1.6223372220993042,
0.05925789102911949,
-0.610502600669860... |
https://github.com/huggingface/datasets/issues/5488 | Error loading MP3 files from CommonVoice | I saw that and have followed it (hence the Expected Behavior section of the bug report).
Is there no intention of updating to the latest version? It does limit the version of `torch` I can use, which isn’t ideal. | ### Describe the bug
When loading a CommonVoice dataset with `datasets==2.9.0` and `torchaudio>=0.12.0`, I get an error reading the audio arrays:
```python
---------------------------------------------------------------------------
LibsndfileError Traceback (most recent call last)
~/.l... | 316 | 39 | Error loading MP3 files from CommonVoice
### Describe the bug
When loading a CommonVoice dataset with `datasets==2.9.0` and `torchaudio>=0.12.0`, I get an error reading the audio arrays:
```python
---------------------------------------------------------------------------
LibsndfileError ... | [
-1.2167824506759644,
-0.7806340456008911,
-0.4928833842277527,
1.4399757385253906,
-0.05507618933916092,
-1.3829612731933594,
0.13091428577899933,
-0.8968741297721863,
1.5224993228912354,
-0.8785139322280884,
0.40658533573150635,
-1.6223372220993042,
0.05925789102911949,
-0.610502600669860... |
https://github.com/huggingface/datasets/issues/5488 | Error loading MP3 files from CommonVoice | @kradonneoh hey! actually with `ffmpeg4` loading of mp3 files should work, so this is a not expected behavior and we need to investigate it. It works on my side with `torchaudio==0.13` and `ffmpeg==4.2.7`. Which `torchaudio` version do you use?
`datasets` should support decoding of mp3 files with `torchaudio` when i... | ### Describe the bug
When loading a CommonVoice dataset with `datasets==2.9.0` and `torchaudio>=0.12.0`, I get an error reading the audio arrays:
```python
---------------------------------------------------------------------------
LibsndfileError Traceback (most recent call last)
~/.l... | 316 | 137 | Error loading MP3 files from CommonVoice
### Describe the bug
When loading a CommonVoice dataset with `datasets==2.9.0` and `torchaudio>=0.12.0`, I get an error reading the audio arrays:
```python
---------------------------------------------------------------------------
LibsndfileError ... | [
-1.2167824506759644,
-0.7806340456008911,
-0.4928833842277527,
1.4399757385253906,
-0.05507618933916092,
-1.3829612731933594,
0.13091428577899933,
-0.8968741297721863,
1.5224993228912354,
-0.8785139322280884,
0.40658533573150635,
-1.6223372220993042,
0.05925789102911949,
-0.610502600669860... |
https://github.com/huggingface/datasets/issues/5487 | Incorrect filepath for dill module | Hi! The correct path is still `dill._dill.XXXX` in the latest release. What do you get when you run `python -c "import dill; print(dill.__version__)"` in your environment? | ### Describe the bug
I installed the `datasets` package and when I try to `import` it, I get the following error:
```
Traceback (most recent call last):
File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module>
import datasets
File "/Users/avivbrokman/... | 317 | 26 | Incorrect filepath for dill module
### Describe the bug
I installed the `datasets` package and when I try to `import` it, I get the following error:
```
Traceback (most recent call last):
File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module>
import da... | [
-1.1599488258361816,
-0.8837870359420776,
-0.7099396586418152,
1.3716564178466797,
-0.08292015641927719,
-1.3277747631072998,
0.0947943702340126,
-1.1010955572128296,
1.5574061870574951,
-0.6909691095352173,
0.2057599425315857,
-1.688206672668457,
-0.19245323538780212,
-0.4060768485069275,... |
https://github.com/huggingface/datasets/issues/5487 | Incorrect filepath for dill module | `0.3.6` I feel like that's bad news, because it's probably not the issue.
My mistake, about the wrong path guess. I think I didn't notice that the first `dill` in the path isn't supposed to be included in the path specification in python.
<img width="146" alt="Screen Shot 2023-01-31 at 12 58 32 PM" src="https://us... | ### Describe the bug
I installed the `datasets` package and when I try to `import` it, I get the following error:
```
Traceback (most recent call last):
File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module>
import datasets
File "/Users/avivbrokman/... | 317 | 54 | Incorrect filepath for dill module
### Describe the bug
I installed the `datasets` package and when I try to `import` it, I get the following error:
```
Traceback (most recent call last):
File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module>
import da... | [
-1.1599488258361816,
-0.8837870359420776,
-0.7099396586418152,
1.3716564178466797,
-0.08292015641927719,
-1.3277747631072998,
0.0947943702340126,
-1.1010955572128296,
1.5574061870574951,
-0.6909691095352173,
0.2057599425315857,
-1.688206672668457,
-0.19245323538780212,
-0.4060768485069275,... |
https://github.com/huggingface/datasets/issues/5487 | Incorrect filepath for dill module | Hi, @avivbrokman, this issue you report appeared only with old versions of dill. See:
- #288
Are you sure you are in the right Python environment?
- Please note that Jupyter (where I guess you get the error) may have multiple execution backends (IPython kernels) that might be different from the Python environment ... | ### Describe the bug
I installed the `datasets` package and when I try to `import` it, I get the following error:
```
Traceback (most recent call last):
File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module>
import datasets
File "/Users/avivbrokman/... | 317 | 85 | Incorrect filepath for dill module
### Describe the bug
I installed the `datasets` package and when I try to `import` it, I get the following error:
```
Traceback (most recent call last):
File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module>
import da... | [
-1.1599488258361816,
-0.8837870359420776,
-0.7099396586418152,
1.3716564178466797,
-0.08292015641927719,
-1.3277747631072998,
0.0947943702340126,
-1.1010955572128296,
1.5574061870574951,
-0.6909691095352173,
0.2057599425315857,
-1.688206672668457,
-0.19245323538780212,
-0.4060768485069275,... |
https://github.com/huggingface/datasets/issues/5487 | Incorrect filepath for dill module | I'm using spyder, and I am still getting `0.3.6` for `dill`, so unfortunately #288 isn't applicable, I think. However, I found something odd that I believe is a clue:
```
import inspect
import dill
inspect.getfile(dill)
>>> '/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/dill/__init__.py'
```
... | ### Describe the bug
I installed the `datasets` package and when I try to `import` it, I get the following error:
```
Traceback (most recent call last):
File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module>
import datasets
File "/Users/avivbrokman/... | 317 | 152 | Incorrect filepath for dill module
### Describe the bug
I installed the `datasets` package and when I try to `import` it, I get the following error:
```
Traceback (most recent call last):
File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module>
import da... | [
-1.1599488258361816,
-0.8837870359420776,
-0.7099396586418152,
1.3716564178466797,
-0.08292015641927719,
-1.3277747631072998,
0.0947943702340126,
-1.1010955572128296,
1.5574061870574951,
-0.6909691095352173,
0.2057599425315857,
-1.688206672668457,
-0.19245323538780212,
-0.4060768485069275,... |
https://github.com/huggingface/datasets/issues/5487 | Incorrect filepath for dill module | As I said above, I guess this is not a problem with `datasets`. I think you have different Python environments: one with the new dill version (the one you get while using pip) and other with the old dill version (the one where you get the AttributeError).
You should update `dill` in the Python environment you are us... | ### Describe the bug
I installed the `datasets` package and when I try to `import` it, I get the following error:
```
Traceback (most recent call last):
File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module>
import datasets
File "/Users/avivbrokman/... | 317 | 76 | Incorrect filepath for dill module
### Describe the bug
I installed the `datasets` package and when I try to `import` it, I get the following error:
```
Traceback (most recent call last):
File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module>
import da... | [
-1.1599488258361816,
-0.8837870359420776,
-0.7099396586418152,
1.3716564178466797,
-0.08292015641927719,
-1.3277747631072998,
0.0947943702340126,
-1.1010955572128296,
1.5574061870574951,
-0.6909691095352173,
0.2057599425315857,
-1.688206672668457,
-0.19245323538780212,
-0.4060768485069275,... |
https://github.com/huggingface/datasets/issues/5486 | Adding `sep` to TextConfig | Hi @omar-araboghli, thanks for your proposal.
Have you tried to use "csv" loader instead of "text"? That already has a `sep` argument. | I have a local a `.txt` file that follows the `CONLL2003` format which I need to load using `load_script`. However, by using `sample_by='line'`, one can only split the dataset into lines without splitting each line into columns. Would it be reasonable to add a `sep` argument in combination with `sample_by='paragraph'` ... | 318 | 22 | Adding `sep` to TextConfig
I have a local a `.txt` file that follows the `CONLL2003` format which I need to load using `load_script`. However, by using `sample_by='line'`, one can only split the dataset into lines without splitting each line into columns. Would it be reasonable to add a `sep` argument in combination... | [
-1.1810396909713745,
-0.8709943294525146,
-0.7613259553909302,
1.5773694515228271,
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0.13493561744689941,
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1.7744081020355225,
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0.3840358853340149,
-1.6877012252807617,
0.06789879500865936,
-0.7430889010429382,
... |
https://github.com/huggingface/datasets/issues/5486 | Adding `sep` to TextConfig | Hi @albertvillanova, thanks for the quick response!
Indeed, I have been trying to use `csv` instead of `text`. However I am still not able to define range of rows as one sequence, that is achievable with passing `sample_by='paragraph'` to the `TextConfig`
For instance, the below code
```python
import datasets... | I have a local a `.txt` file that follows the `CONLL2003` format which I need to load using `load_script`. However, by using `sample_by='line'`, one can only split the dataset into lines without splitting each line into columns. Would it be reasonable to add a `sep` argument in combination with `sample_by='paragraph'` ... | 318 | 116 | Adding `sep` to TextConfig
I have a local a `.txt` file that follows the `CONLL2003` format which I need to load using `load_script`. However, by using `sample_by='line'`, one can only split the dataset into lines without splitting each line into columns. Would it be reasonable to add a `sep` argument in combination... | [
-1.1810396909713745,
-0.8709943294525146,
-0.7613259553909302,
1.5773694515228271,
-0.22678525745868683,
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0.13493561744689941,
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0.3840358853340149,
-1.6877012252807617,
0.06789879500865936,
-0.7430889010429382,
... |
https://github.com/huggingface/datasets/issues/5482 | Reload features from Parquet metadata | I'd be happy to have a look, if nobody else has started working on this yet @lhoestq.
It seems to me that for the `arrow` format features are currently attached as metadata [in `datasets.arrow_writer`](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/arrow_writer.py... | The idea would be to allow this :
```python
ds.to_parquet("my_dataset/ds.parquet")
reloaded = load_dataset("my_dataset")
assert ds.features == reloaded.features
```
And it should also work with Image and Audio types (right now they're reloaded as a dict type)
This can be implemented by storing and reading th... | 320 | 66 | Reload features from Parquet metadata
The idea would be to allow this :
```python
ds.to_parquet("my_dataset/ds.parquet")
reloaded = load_dataset("my_dataset")
assert ds.features == reloaded.features
```
And it should also work with Image and Audio types (right now they're reloaded as a dict type)
This can ... | [
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1.5049726963043213,
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0.29063931107521057,
-1.6855441331863403,
0.01580018177628517,
-0.5152422189712524... |
https://github.com/huggingface/datasets/issues/5482 | Reload features from Parquet metadata | Thanks @MFreidank ! That's correct :)
Reading the metadata to infer the features can be ideally done in the `parquet.py` file in `packaged_builder` when a parquet file is read. You can cast the arrow table to the schema you get from the features.arrow_schema | The idea would be to allow this :
```python
ds.to_parquet("my_dataset/ds.parquet")
reloaded = load_dataset("my_dataset")
assert ds.features == reloaded.features
```
And it should also work with Image and Audio types (right now they're reloaded as a dict type)
This can be implemented by storing and reading th... | 320 | 43 | Reload features from Parquet metadata
The idea would be to allow this :
```python
ds.to_parquet("my_dataset/ds.parquet")
reloaded = load_dataset("my_dataset")
assert ds.features == reloaded.features
```
And it should also work with Image and Audio types (right now they're reloaded as a dict type)
This can ... | [
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-0.8986155390739441,
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1.652525544166565,
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0.11700111627578735,
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0.4493255317211151,
-1.6518818140029907,
0.18065370619297028,
-0.5315032005310059,
... |
https://github.com/huggingface/datasets/issues/5481 | Load a cached dataset as iterable | Hi ! Sure :) you can comment `#self-assign` to assign yourself to this issue.
I can give you some pointers to get started:
`load_dataset` works roughly this way:
1. it instantiate a dataset builder using `load_dataset_builder()`
2. the builder download and prepare the dataset as Arrow files in the cache using `... | The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when streaming=True | 321 | 146 | Load a cached dataset as iterable
The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when stream... | [
-1.1753803491592407,
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1.5490326881408691,
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0.1041148230433464,
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-... |
https://github.com/huggingface/datasets/issues/5481 | Load a cached dataset as iterable | This use-case is a bit specific, and `load_dataset` already has enough parameters (plus, `streaming=True` also returns an iterable dataset, so we would have to explain the difference), so I think it would be better to add `IterableDataset.from_file` to the API (more flexible and aligned with the goal from https://githu... | The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when streaming=True | 321 | 50 | Load a cached dataset as iterable
The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when stream... | [
-1.2134650945663452,
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1.5663814544677734,
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-... |
https://github.com/huggingface/datasets/issues/5481 | Load a cached dataset as iterable | > This use-case is a bit specific
This allows to use `datasets` for large scale training where map-style datasets are too slow and use too much memory in PyTorch. So I would still consider adding it.
Alternatively we could add this feature one level bellow:
```python
builder = load_dataset_builder(...)
builder... | The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when streaming=True | 321 | 54 | Load a cached dataset as iterable
The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when stream... | [
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0.07562471926212311,
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... |
https://github.com/huggingface/datasets/issues/5481 | Load a cached dataset as iterable | Yes, I see how this can be useful. Still, I think `Dataset.to_iterable` + `IterableDataset.from_file` would be much cleaner in terms of the API design (and more flexible since `load_dataset` can only access the "initial" (unprocessed) version of a dataset).
And since it can be tricky to manually find the "initial" v... | The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when streaming=True | 321 | 75 | Load a cached dataset as iterable
The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when stream... | [
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0.44232386350631714,
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0.05562882125377655,
-0.508151650428772,
... |
https://github.com/huggingface/datasets/issues/5481 | Load a cached dataset as iterable | > This allows to use datasets for large scale training where map-style datasets are too slow and use too much memory in PyTorch.
I second that. e.g. In my last experiment Oscar-en uses 16GB RSS RAM per process and when using multiple processes the host quickly runs out cpu memory. | The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when streaming=True | 321 | 50 | Load a cached dataset as iterable
The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when stream... | [
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0.3990180492401123,
-1.6514431238174438,
0.05712546035647392,
-0.41546377539634705,
... |
https://github.com/huggingface/datasets/issues/5481 | Load a cached dataset as iterable | >And since it can be tricky to manually find the "initial" version of a dataset in the cache, maybe load_dataset could return an iterable dataset streamed from the cache if streaming=True and the cache is up-to-date.
This is exactly the need on JeanZay (HPC) - I have the dataset cache ready, but the compute node is ... | The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when streaming=True | 321 | 81 | Load a cached dataset as iterable
The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when stream... | [
-1.2290716171264648,
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0.11137185990810394,
-0.4876655042171478,
... |
https://github.com/huggingface/datasets/issues/5481 | Load a cached dataset as iterable | > Yes, I see how this can be useful. Still, I think Dataset.to_iterable + IterableDataset.from_file would be much cleaner in terms of the API design (and more flexible since load_dataset can only access the "initial" (unprocessed) version of a dataset).
I like `IterableDataset.from_file` as well. On the other hand `... | The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when streaming=True | 321 | 172 | Load a cached dataset as iterable
The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when stream... | [
-1.2615983486175537,
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0.38723519444465637,
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0.015965620055794716,
-0.4623053073883056... |
https://github.com/huggingface/datasets/issues/5481 | Load a cached dataset as iterable | > I was suggesting a different parameter just to make explicit the difference between
MosaicML's `streaming` library does the same (tries to stream from the local cache if possible), so logging a message should be explicit enough :). | The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when streaming=True | 321 | 38 | Load a cached dataset as iterable
The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when stream... | [
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-0.4802227318286896... |
https://github.com/huggingface/datasets/issues/5481 | Load a cached dataset as iterable | Hi Both! It has been a while since my first issue so I am gonna go for this one ! #self-assign | The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when streaming=True | 321 | 21 | Load a cached dataset as iterable
The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when stream... | [
-1.2416939735412598,
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https://github.com/huggingface/datasets/issues/5481 | Load a cached dataset as iterable | https://github.com/huggingface/datasets/pull/5821 should be helpful to implement `IterableDataset.from_file`, since it defines a new ArrowExamplesIterable that takes an Arrow tables generator function (e.g. from a file) and can be used in an IterableDataset | The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when streaming=True | 321 | 31 | Load a cached dataset as iterable
The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when stream... | [
-1.205568790435791,
-0.9849755764007568,
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0.0354824997484684,
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... |
https://github.com/huggingface/datasets/issues/5481 | Load a cached dataset as iterable | So what's recommanded usage of `IterableDataset.from_file` and `load_dataset`? How about I have multiple arrow files and `load_dataset` is often convenient to handle that. | The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when streaming=True | 321 | 23 | Load a cached dataset as iterable
The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when stream... | [
-1.1906013488769531,
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0.492199182510376,
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-0.005908402614295483,
-0.503328204154968... |
https://github.com/huggingface/datasets/issues/5481 | Load a cached dataset as iterable | If you have multiple Arrow files you can load them using
```python
from datasets import load_dataset
data_files = {"train": ["path/to/0.arrow", "path/to/1.arrow", ..., "path/to/n.arrow"]}
ds = load_dataset("arrow", data_files=data_files, streaming=True)
```
This is equivalent to calling `IterableDataset.f... | The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when streaming=True | 321 | 37 | Load a cached dataset as iterable
The idea would be to allow something like
```python
ds = load_dataset("c4", "en", as_iterable=True)
```
To be used to train models. It would load an IterableDataset from the cached Arrow files.
Cc @stas00
Edit : from the discussions we may load from cache when stream... | [
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0.4649083614349365,
-1.70931077003479,
0.02902182564139366,
-0.5327165722846985,
... |
https://github.com/huggingface/datasets/issues/5477 | Unpin sqlalchemy once issue is fixed | @albertvillanova It looks like that issue has been fixed so I made a PR to unpin sqlalchemy! | Once the source issue is fixed:
- pandas-dev/pandas#51015
we should revert the pin introduced in:
- #5476 | 322 | 17 | Unpin sqlalchemy once issue is fixed
Once the source issue is fixed:
- pandas-dev/pandas#51015
we should revert the pin introduced in:
- #5476
@albertvillanova It looks like that issue has been fixed so I made a PR to unpin sqlalchemy! | [
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0.4368641674518585,
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-0.07652505487203598,
-0.6610858440399... |
https://github.com/huggingface/datasets/issues/5477 | Unpin sqlalchemy once issue is fixed | The source issue:
- https://github.com/pandas-dev/pandas/issues/40686
has been fixed:
- https://github.com/pandas-dev/pandas/pull/48576
The fix was released yesterday (2023-04-03) only in `pandas-2.0.0`:
- https://github.com/pandas-dev/pandas/releases/tag/v2.0.0
but it will not be back-ported to `pandas-1`:... | Once the source issue is fixed:
- pandas-dev/pandas#51015
we should revert the pin introduced in:
- #5476 | 322 | 62 | Unpin sqlalchemy once issue is fixed
Once the source issue is fixed:
- pandas-dev/pandas#51015
we should revert the pin introduced in:
- #5476
The source issue:
- https://github.com/pandas-dev/pandas/issues/40686
has been fixed:
- https://github.com/pandas-dev/pandas/pull/48576
The fix was released yes... | [
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https://github.com/huggingface/datasets/issues/5475 | Dataset scan time is much slower than using native arrow | Hi ! In your code you only iterate on the Arrow buffers - you don't actually load the data as python objects. For a fair comparison, you can modify your code using:
```diff
- for _ in range(0, len(table), bsz):
- _ = {k:table[k][_ : _ + bsz] for k in cols}
+ for _ in range(0, len(table), bsz):
+ ... | ### Describe the bug
I'm basically running the same scanning experiment from the tutorials https://huggingface.co/course/chapter5/4?fw=pt except now I'm comparing to a native pyarrow version.
I'm finding that the native pyarrow approach is much faster (2 orders of magnitude). Is there something I'm missing that exp... | 323 | 85 | Dataset scan time is much slower than using native arrow
### Describe the bug
I'm basically running the same scanning experiment from the tutorials https://huggingface.co/course/chapter5/4?fw=pt except now I'm comparing to a native pyarrow version.
I'm finding that the native pyarrow approach is much faster (2 or... | [
-1.2639212608337402,
-0.9404871463775635,
-0.7692417502403259,
1.4909127950668335,
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0.34533968567848206,
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0.10426580905914307,
-0.5708794593811035... |
https://github.com/huggingface/datasets/issues/5475 | Dataset scan time is much slower than using native arrow | Ah I see, datasets is implicitly making this conversion. Thanks for pointing that out!
If it's not too much, I would also suggest updating some of your docs with the same `.to_pylist()` conversion in the code snippet that follows [here](https://huggingface.co/course/chapter5/4?fw=pt#:~:text=let%E2%80%99s%20run%20a%2... | ### Describe the bug
I'm basically running the same scanning experiment from the tutorials https://huggingface.co/course/chapter5/4?fw=pt except now I'm comparing to a native pyarrow version.
I'm finding that the native pyarrow approach is much faster (2 orders of magnitude). Is there something I'm missing that exp... | 323 | 40 | Dataset scan time is much slower than using native arrow
### Describe the bug
I'm basically running the same scanning experiment from the tutorials https://huggingface.co/course/chapter5/4?fw=pt except now I'm comparing to a native pyarrow version.
I'm finding that the native pyarrow approach is much faster (2 or... | [
-1.24544095993042,
-0.9433806538581848,
-0.7787543535232544,
1.4426261186599731,
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0.03638889268040657,
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0.3589247167110443,
-1.6049509048461914,
0.019263528287410736,
-0.4325990080833435,
... |
https://github.com/huggingface/datasets/issues/5475 | Dataset scan time is much slower than using native arrow | This code snippet shows `datasets` code that reads the Arrow data as python objects already, there is no need to add to_pylist. Or were you thinking about something else ? | ### Describe the bug
I'm basically running the same scanning experiment from the tutorials https://huggingface.co/course/chapter5/4?fw=pt except now I'm comparing to a native pyarrow version.
I'm finding that the native pyarrow approach is much faster (2 orders of magnitude). Is there something I'm missing that exp... | 323 | 30 | Dataset scan time is much slower than using native arrow
### Describe the bug
I'm basically running the same scanning experiment from the tutorials https://huggingface.co/course/chapter5/4?fw=pt except now I'm comparing to a native pyarrow version.
I'm finding that the native pyarrow approach is much faster (2 or... | [
-1.2313355207443237,
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1.4891432523727417,
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0.05242260918021202,
-0.5046954154968262... |
https://github.com/huggingface/datasets/issues/5474 | Column project operation on `datasets.Dataset` | Hi ! This would be a nice addition indeed :) This sounds like a duplicate of https://github.com/huggingface/datasets/issues/5468
> Not sure. Some of my PRs are still open and some do not have any discussions.
Sorry to hear that, feel free to ping me on those PRs | ### Feature request
There is no operation to select a subset of columns of original dataset. Expected API follows.
```python
a = Dataset.from_dict({
'int': [0, 1, 2]
'char': ['a', 'b', 'c'],
'none': [None] * 3,
})
b = a.project('int', 'char') # usually, .select()
print(a.column_names) # std... | 324 | 46 | Column project operation on `datasets.Dataset`
### Feature request
There is no operation to select a subset of columns of original dataset. Expected API follows.
```python
a = Dataset.from_dict({
'int': [0, 1, 2]
'char': ['a', 'b', 'c'],
'none': [None] * 3,
})
b = a.project('int', 'char') # u... | [
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1.6048171520233154,
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0.025476552546024323,
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0.2529119849205017,
-1.71471107006073,
-0.05852080509066582,
-0.7319914698600769,
... |
https://github.com/huggingface/datasets/issues/5468 | Allow opposite of remove_columns on Dataset and DatasetDict | Hi! I agree it would be nice to have a method like that. Instead of `keep_columns`, we can name it `select_columns` to be more aligned with PyArrow's naming convention (`pa.Table.select`). | ### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COLUMNS_TO_KEEP)
gigaspeech = gigaspeech.remove_columns(column... | 325 | 30 | Allow opposite of remove_columns on Dataset and DatasetDict
### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COL... | [
-1.1291453838348389,
-0.8903515338897705,
-0.7809796929359436,
1.5449533462524414,
-0.03898512199521065,
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0.24319131672382355,
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1.6578035354614258,
-0.8081666231155396,
0.3863639533519745,
-1.6128418445587158,
0.09286542981863022,
-0.7118729948997498,... |
https://github.com/huggingface/datasets/issues/5468 | Allow opposite of remove_columns on Dataset and DatasetDict | Hi, I am a newbie to open source and would like to contribute. @mariosasko can I take up this issue ? | ### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COLUMNS_TO_KEEP)
gigaspeech = gigaspeech.remove_columns(column... | 325 | 21 | Allow opposite of remove_columns on Dataset and DatasetDict
### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COL... | [
-1.154079556465149,
-0.9099525809288025,
-0.8128042817115784,
1.554242491722107,
-0.06166444718837738,
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0.19262157380580902,
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1.5956411361694336,
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0.38709521293640137,
-1.5716898441314697,
0.083161361515522,
-0.7508717775344849,
... |
https://github.com/huggingface/datasets/issues/5468 | Allow opposite of remove_columns on Dataset and DatasetDict | Hey, I also want to work on this issue I am a newbie to open source. | ### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COLUMNS_TO_KEEP)
gigaspeech = gigaspeech.remove_columns(column... | 325 | 16 | Allow opposite of remove_columns on Dataset and DatasetDict
### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COL... | [
-1.1514225006103516,
-0.914588987827301,
-0.8264305591583252,
1.5600435733795166,
-0.06846963614225388,
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0.19848120212554932,
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1.606711506843567,
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0.3895787000656128,
-1.5868126153945923,
0.08885469287633896,
-0.7562308311462402,
... |
https://github.com/huggingface/datasets/issues/5468 | Allow opposite of remove_columns on Dataset and DatasetDict | This sounds related to https://github.com/huggingface/datasets/issues/5474
I'm fine with `select_columns`, or we could also override `select` to also accept a list of columns maybe ? | ### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COLUMNS_TO_KEEP)
gigaspeech = gigaspeech.remove_columns(column... | 325 | 24 | Allow opposite of remove_columns on Dataset and DatasetDict
### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COL... | [
-1.1184251308441162,
-0.908778727054596,
-0.8135619759559631,
1.5733529329299927,
-0.02999010682106018,
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0.2329113781452179,
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1.6241936683654785,
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0.370052695274353,
-1.6105045080184937,
0.04719815403223038,
-0.7533395886421204,
... |
https://github.com/huggingface/datasets/issues/5468 | Allow opposite of remove_columns on Dataset and DatasetDict | @lhoestq, I am planning to add a member function to the dataset class to perform the selection operation. Do you think its the right way to proceed? or there is a better option ? | ### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COLUMNS_TO_KEEP)
gigaspeech = gigaspeech.remove_columns(column... | 325 | 34 | Allow opposite of remove_columns on Dataset and DatasetDict
### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COL... | [
-1.1540052890777588,
-0.9093753695487976,
-0.8194781541824341,
1.5442306995391846,
-0.05323059856891632,
-1.2564054727554321,
0.1866016536951065,
-1.096221685409546,
1.6075315475463867,
-0.7959123253822327,
0.37741926312446594,
-1.6004903316497803,
0.09928056597709656,
-0.7280531525611877,... |
https://github.com/huggingface/datasets/issues/5468 | Allow opposite of remove_columns on Dataset and DatasetDict | Unless @mariosasko thinks otherwise, I think it can go in `Dataset.select()` :)
Though some parameters like keep_in_memory, indices_cache_file_name or writer_batch_size wouldn't when selecting columns, so we would need to update the docstring as well | ### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COLUMNS_TO_KEEP)
gigaspeech = gigaspeech.remove_columns(column... | 325 | 34 | Allow opposite of remove_columns on Dataset and DatasetDict
### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COL... | [
-1.1602294445037842,
-0.8891732096672058,
-0.7769017219543457,
1.5607486963272095,
-0.04427981376647949,
-1.2542165517807007,
0.21812741458415985,
-1.0332354307174683,
1.5975195169448853,
-0.8014072179794312,
0.37465348839759827,
-1.5837072134017944,
0.07537930458784103,
-0.724290728569030... |
https://github.com/huggingface/datasets/issues/5468 | Allow opposite of remove_columns on Dataset and DatasetDict | If someone wants to give it a shot, feel free to comment `#self-assign` and it will assign the issue to you.
Feel free to ping us here if you have questions or if we can help :) | ### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COLUMNS_TO_KEEP)
gigaspeech = gigaspeech.remove_columns(column... | 325 | 37 | Allow opposite of remove_columns on Dataset and DatasetDict
### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COL... | [
-1.160651683807373,
-0.895520031452179,
-0.8101415038108826,
1.591101050376892,
-0.07814791053533554,
-1.243514895439148,
0.18331453204154968,
-1.0582562685012817,
1.6232577562332153,
-0.8004816770553589,
0.3806670308113098,
-1.590840458869934,
0.13563409447669983,
-0.7386615872383118,
-... |
https://github.com/huggingface/datasets/issues/5468 | Allow opposite of remove_columns on Dataset and DatasetDict | I would rather have this functionality as a separate method. IMO it's always better to be explicit than to have an API where a single method can do different/uncorrelated things (somewhat reminds me of Pandas, and there is probably a good reason why PyArrow is more rigid in this aspect). | ### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COLUMNS_TO_KEEP)
gigaspeech = gigaspeech.remove_columns(column... | 325 | 50 | Allow opposite of remove_columns on Dataset and DatasetDict
### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COL... | [
-1.1706547737121582,
-0.9165715575218201,
-0.7993765473365784,
1.5321871042251587,
-0.061782486736774445,
-1.2707600593566895,
0.18224802613258362,
-1.0692819356918335,
1.6468626260757446,
-0.820591151714325,
0.3929552733898163,
-1.61613929271698,
0.0563240572810173,
-0.7124407887458801,
... |
https://github.com/huggingface/datasets/issues/5468 | Allow opposite of remove_columns on Dataset and DatasetDict | In the end I also think it would be nice to have it as a separate method, this way we can also have it for `IterableDataset` (which can't have `select` for indices) | ### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COLUMNS_TO_KEEP)
gigaspeech = gigaspeech.remove_columns(column... | 325 | 32 | Allow opposite of remove_columns on Dataset and DatasetDict
### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COL... | [
-1.1840059757232666,
-0.908944845199585,
-0.8097745180130005,
1.5823670625686646,
-0.048173509538173676,
-1.2653944492340088,
0.17485898733139038,
-1.0706654787063599,
1.6354033946990967,
-0.8036683797836304,
0.397324800491333,
-1.597825288772583,
0.09538523852825165,
-0.7207145690917969,
... |
https://github.com/huggingface/datasets/issues/5464 | NonMatchingChecksumError for hendrycks_test | Thanks for reporting, @sarahwie.
Please note this issue was already fixed in `datasets` 2.6.0 version:
- #5040
If you update your `datasets` version, you will be able to load the dataset:
```
pip install -U datasets
``` | ### Describe the bug
The checksum of the file has likely changed on the remote host.
### Steps to reproduce the bug
`dataset = nlp.load_dataset("hendrycks_test", "anatomy")`
### Expected behavior
no error thrown
### Environment info
- `datasets` version: 2.2.1
- Platform: macOS-13.1-arm64-arm-64bit
- Pyt... | 326 | 37 | NonMatchingChecksumError for hendrycks_test
### Describe the bug
The checksum of the file has likely changed on the remote host.
### Steps to reproduce the bug
`dataset = nlp.load_dataset("hendrycks_test", "anatomy")`
### Expected behavior
no error thrown
### Environment info
- `datasets` version: 2.2.1
... | [
-1.1270794868469238,
-0.8291107416152954,
-0.736801028251648,
1.4307700395584106,
-0.1305014193058014,
-1.2828882932662964,
0.15632571280002594,
-1.0467504262924194,
1.7284445762634277,
-0.8059728145599365,
0.35828888416290283,
-1.7001981735229492,
0.025911666452884674,
-0.604555070400238,... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | Ccing @dwofk (the author of `fast-depth`).
Thanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed.
If you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) f... | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 56 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | Good catch ! Ideally it would be nice to have the datasets in the raw form, this way users can choose whatever processing they want to apply | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 27 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | > Ccing @dwofk (the author of `fast-depth`).
>
> Thanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed.
>
> If you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_dep... | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 155 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | I think BTS. Repositories like https://github.com/vinvino02/GLPDepth usually use BTS. Also, just for clarity, the PR will be to https://huggingface.co/datasets/sayakpaul/nyu_depth_v2. Once we have worked it out, we can update the following things:
* https://github.com/huggingface/blog/pull/718
* https://huggingface... | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 55 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | @sayakpaul If I get this right I have to,
1. Create a PR on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2
2. Create a PR on https://github.com/huggingface/blog
3. Create a PR on https://github.com/huggingface/datasets to update https://github.com/huggingface/datasets/blob/main/docs/source/depth_estimation.m... | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 30 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | Yup opening a PR to use BTS on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2 sounds good :) Thanks for the help ! | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 17 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | Finally, I have found the origin of the **discretized depth map**. When I first loaded the datasets from HF I noticed it was 30GB but in DenseDepth data is only 4GB with dtype=uint8. This means data from fast-depth (before loading to HF) must have high precision. So when I tried to dig deeper by directly loading depth_... | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 270 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | Thanks so much for digging into this.
Since the second solution entails changes to core datatypes in `datasets`, I think it's better to go with the first solution.
@lhoestq WDYT? | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 30 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | @sayakpaul Yes, Solution 1 requires minimal change and provides no precision loss. But I think support for `uint16` image would be a great addition as many datasets come with `uint16` image. For example [UW-Madison GI Tract Image Segmentation](https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation) ... | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 77 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | Using Array2D makes it not practical to use to train a model - in `transformers` we expect an image type.
There is a pull request to support more precision than uint8 in Image() here: https://github.com/huggingface/datasets/pull/5365/files
we can probably merge it today and do a release right away | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 47 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | Fantastic, @lhoestq!
@awsaf49 then let's wait for the PR to get merged and then take the next steps? | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 18 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | The PR adds support for uint16 which is ok for BTS if I understand correctly, would it be ok for you ? | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 22 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | If the main issue with the current version of NYU we have on the Hub is related to the precision loss stemming from `Image()`, I'd prefer if `Image()` supported float32 as well. | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 32 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | I also prefer `float32` as it offers more precision. But I'm not sure if we'll be able to visualize image with `float32` precision. | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 23 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | We could have a separate loading for the float32 one using Array2D, but I feel like it's less convenient to use due to the amount of disk space and because it's not an Image() type. That's why I think uint16 is a better solution for users | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 46 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | Yep. I just tested from the PR and it works:
```python
>>> train_dataset = load_dataset("sayakpaul/nyu_depth_v2", split="train", streaming=True)
Downloading readme: 100%|██████████████████| 8.71k/8.71k [00:00<00:00, 3.60MB/s]
>>> next(iter(train_dataset))
{'... | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 120 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | Great! the case is closed! This issue has been solved and I have to say, it was quite the thrill ride. I felt like Sherlock Holmes, solving a mystery and finding the bug🕵️♂️. But in all seriousness, it was a pleasure working on this issue and I'm glad we could get to the bottom of it.
On another note, should I con... | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 77 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | Haha thanks Mr Holmes :p
maybe let's close this issue when we're done updating the blog post and the documentation | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 20 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | @awsaf49 thank you for your hard work!
I am a little unsure why the other links need to be updated, though. They all rely on datasets internally. | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 27 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | I think depth_map still shows discretized version. It would be nice to have corrected one.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/depth_est_target_viz.png" width = 300> | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 20 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | Also, I think we need to make some changes in the code to visualize depth_map as it is `float32` . `plot.imshow()` supports either [0, 1] + float32 or [0. 255] + uint8 | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 32 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5461 | Discrepancy in `nyu_depth_v2` dataset | Oh yes! Do you want to start with the fixes? Please feel free to say no but I wanted to make sure your contributions are reflected properly in our doc and the blog :) | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-sid... | 327 | 34 | Discrepancy in `nyu_depth_v2` dataset
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different... | [
-1.2323219776153564,
-0.9139350652694702,
-0.7461334466934204,
1.4001511335372925,
-0.17456850409507751,
-1.2431707382202148,
0.08405475318431854,
-1.0615496635437012,
1.6305798292160034,
-0.7169316411018372,
0.2860521078109741,
-1.66029691696167,
-0.011840286664664745,
-0.5992560386657715... |
https://github.com/huggingface/datasets/issues/5458 | slice split while streaming | Hi! Yes, that's correct. When `streaming` is `True`, only split names can be specified as `split`, and for slicing, you have to use `.skip`/`.take` instead.
E.g.
`load_dataset("lhoestq/demo1",revision=None, streaming=True, split="train[:3]")`
rewritten with `.skip`/`.take`:
`load_dataset("lhoestq/demo1",revisi... | ### Describe the bug
When using the `load_dataset` function with streaming set to True, slicing splits is apparently not supported.
Did I miss this in the documentation?
### Steps to reproduce the bug
`load_dataset("lhoestq/demo1",revision=None, streaming=True, split="train[:3]")`
causes ValueError: Bad split:... | 328 | 35 | slice split while streaming
### Describe the bug
When using the `load_dataset` function with streaming set to True, slicing splits is apparently not supported.
Did I miss this in the documentation?
### Steps to reproduce the bug
`load_dataset("lhoestq/demo1",revision=None, streaming=True, split="train[:3]")`
... | [
-1.167305827140808,
-0.9285175800323486,
-0.6649534702301025,
1.4969956874847412,
-0.1945435255765915,
-1.1529728174209595,
0.1905038207769394,
-1.076942801475525,
1.6675045490264893,
-0.7395195960998535,
0.3413146436214447,
-1.6742693185806274,
-0.03626634180545807,
-0.6227717399597168,
... |
https://github.com/huggingface/datasets/issues/5457 | prebuilt dataset relies on `downloads/extracted` | Hi!
This issue is due to our audio/image datasets not being self-contained. This allows us to save disk space (files are written only once) but also leads to the issues like this one. We plan to make all our datasets self-contained in Datasets 3.0.
In the meantime, you can run the following map to ensure your da... | ### Describe the bug
I pre-built the dataset:
```
python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
```
and it can be used just fine.
now I wipe out `downloads/extracted` and it no longer works.
```
rm -r ~/.cache/huggingface... | 329 | 76 | prebuilt dataset relies on `downloads/extracted`
### Describe the bug
I pre-built the dataset:
```
python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
```
and it can be used just fine.
now I wipe out `downloads/extracted` and it... | [
-1.157984972000122,
-0.9018886685371399,
-0.7691069841384888,
1.5294994115829468,
-0.02931012213230133,
-1.293380618095398,
0.09620700776576996,
-0.9256891012191772,
1.568138599395752,
-0.7634966373443604,
0.3926277160644531,
-1.7433313131332397,
-0.05582356080412865,
-0.556559681892395,
... |
https://github.com/huggingface/datasets/issues/5457 | prebuilt dataset relies on `downloads/extracted` | Understood. Thank you, Mario.
Perhaps the solution could be very simple - move the extracted files into the directory of the cached dataset? Which would make it self-contained already and won't require waiting for a new major release. Unless I'm missing some back-compat nuance.
But regardless if X relies on Y - i... | ### Describe the bug
I pre-built the dataset:
```
python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
```
and it can be used just fine.
now I wipe out `downloads/extracted` and it no longer works.
```
rm -r ~/.cache/huggingface... | 329 | 76 | prebuilt dataset relies on `downloads/extracted`
### Describe the bug
I pre-built the dataset:
```
python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
```
and it can be used just fine.
now I wipe out `downloads/extracted` and it... | [
-1.157984972000122,
-0.9018886685371399,
-0.7691069841384888,
1.5294994115829468,
-0.02931012213230133,
-1.293380618095398,
0.09620700776576996,
-0.9256891012191772,
1.568138599395752,
-0.7634966373443604,
0.3926277160644531,
-1.7433313131332397,
-0.05582356080412865,
-0.556559681892395,
... |
https://github.com/huggingface/datasets/issues/5454 | Save and resume the state of a DataLoader | Something that'd be nice to have is "manual update of state". One of the learning from training LLMs is the ability to skip some batches whenever we notice huge spike might be handy. | It would be nice when using `datasets` with a PyTorch DataLoader to be able to resume a training from a DataLoader state (e.g. to resume a training that crashed)
What I have in mind (but lmk if you have other ideas or comments):
For map-style datasets, this requires to have a PyTorch Sampler state that can be sav... | 330 | 33 | Save and resume the state of a DataLoader
It would be nice when using `datasets` with a PyTorch DataLoader to be able to resume a training from a DataLoader state (e.g. to resume a training that crashed)
What I have in mind (but lmk if you have other ideas or comments):
For map-style datasets, this requires to ... | [
-1.210391640663147,
-0.9462154507637024,
-0.8508016467094421,
1.3852672576904297,
-0.17072966694831848,
-1.3206204175949097,
0.04050850868225098,
-1.0508242845535278,
1.6814910173416138,
-0.917292058467865,
0.3468030095100403,
-1.7072945833206177,
0.19839245080947876,
-0.5438280701637268,
... |
https://github.com/huggingface/datasets/issues/5454 | Save and resume the state of a DataLoader | Your outline spec is very sound and clear, @lhoestq - thank you!
@thomasw21, indeed that would be a wonderful extra feature. In Megatron-Deepspeed we manually drained the dataloader for the range we wanted. I wasn't very satisfied with the way we did it, since its behavior would change if you were to do multiple ran... | It would be nice when using `datasets` with a PyTorch DataLoader to be able to resume a training from a DataLoader state (e.g. to resume a training that crashed)
What I have in mind (but lmk if you have other ideas or comments):
For map-style datasets, this requires to have a PyTorch Sampler state that can be sav... | 330 | 97 | Save and resume the state of a DataLoader
It would be nice when using `datasets` with a PyTorch DataLoader to be able to resume a training from a DataLoader state (e.g. to resume a training that crashed)
What I have in mind (but lmk if you have other ideas or comments):
For map-style datasets, this requires to ... | [
-1.2777979373931885,
-0.9837959408760071,
-0.755339503288269,
1.356204867362976,
-0.17536111176013947,
-1.2910062074661255,
0.03848791867494583,
-1.072117805480957,
1.6808425188064575,
-0.8867015242576599,
0.33993273973464966,
-1.6807563304901123,
0.16393020749092102,
-0.521294355392456,
... |
https://github.com/huggingface/datasets/issues/5451 | ImageFolder BadZipFile: Bad offset for central directory | Hi ! Could you share the full stack trace ? Which dataset did you try to load ?
it may be related to https://github.com/huggingface/datasets/pull/5640 | ### Describe the bug
I'm getting the following exception:
```
lib/python3.10/zipfile.py:1353 in _RealGetContents │
│ │
│ 1350 │ │ # self.start_dir: Position of start of central directory ... | 331 | 24 | ImageFolder BadZipFile: Bad offset for central directory
### Describe the bug
I'm getting the following exception:
```
lib/python3.10/zipfile.py:1353 in _RealGetContents │
│ │
│ 1350 │ │ # self.start_dir... | [
-1.1801573038101196,
-0.8495473861694336,
-0.6627269387245178,
1.5185000896453857,
-0.19597958028316498,
-1.228810429573059,
0.24361631274223328,
-1.032873272895813,
1.5680439472198486,
-0.7590497732162476,
0.3073175847530365,
-1.6794732809066772,
0.13305480778217316,
-0.5226603746414185,
... |
https://github.com/huggingface/datasets/issues/5451 | ImageFolder BadZipFile: Bad offset for central directory | The `BadZipFile` error means the ZIP file is corrupted, so I'm closing this issue as it's not directly related to `datasets`. | ### Describe the bug
I'm getting the following exception:
```
lib/python3.10/zipfile.py:1353 in _RealGetContents │
│ │
│ 1350 │ │ # self.start_dir: Position of start of central directory ... | 331 | 21 | ImageFolder BadZipFile: Bad offset for central directory
### Describe the bug
I'm getting the following exception:
```
lib/python3.10/zipfile.py:1353 in _RealGetContents │
│ │
│ 1350 │ │ # self.start_dir... | [
-1.1939665079116821,
-0.8490809202194214,
-0.6932085752487183,
1.5191311836242676,
-0.2111867517232895,
-1.2388697862625122,
0.26149576902389526,
-1.0506815910339355,
1.5851229429244995,
-0.7903262376785278,
0.3210504353046417,
-1.6974228620529175,
0.1194610595703125,
-0.546713650226593,
... |
https://github.com/huggingface/datasets/issues/5451 | ImageFolder BadZipFile: Bad offset for central directory | For others that find this issue following a `BadZipFile` error, I had the same problem because I had a file in a folder dataset `my-image.target` and the datasets library was incorrectly determining that the (PNG) file was a zip archive. When it tried to extract the file, this error occurred.
Updating to `datasets=... | ### Describe the bug
I'm getting the following exception:
```
lib/python3.10/zipfile.py:1353 in _RealGetContents │
│ │
│ 1350 │ │ # self.start_dir: Position of start of central directory ... | 331 | 58 | ImageFolder BadZipFile: Bad offset for central directory
### Describe the bug
I'm getting the following exception:
```
lib/python3.10/zipfile.py:1353 in _RealGetContents │
│ │
│ 1350 │ │ # self.start_dir... | [
-1.2359187602996826,
-0.8569865822792053,
-0.6782492399215698,
1.4818766117095947,
-0.21162763237953186,
-1.261195182800293,
0.23142798244953156,
-1.0930302143096924,
1.6386640071868896,
-0.8028919100761414,
0.26819145679473877,
-1.6773107051849365,
0.13818736374378204,
-0.5428324937820435... |
https://github.com/huggingface/datasets/issues/5450 | to_tf_dataset with a TF collator causes bizarrely persistent slowdown | A possible hint: The function it seems to be spending a lot of time in (when iterating over the original dataset) is `_get_mp` in the PIL JPEG decoder:
| ### Describe the bug
This will make more sense if you take a look at [a Colab notebook that reproduces this issue.](https://colab.research.google.com/drive/1rxyeciQFWJTI0WrZ5aojp4Ls1ut18fNH?usp=sharing)
Briefly, there are several datasets that, when you iterate over them with `to_tf_dataset` **and** a data colla... | 332 | 29 | to_tf_dataset with a TF collator causes bizarrely persistent slowdown
### Describe the bug
This will make more sense if you take a look at [a Colab notebook that reproduces this issue.](https://colab.research.google.com/drive/1rxyeciQFWJTI0WrZ5aojp4Ls1ut18fNH?usp=sharing)
Briefly, there are several datasets th... | [
-1.1414843797683716,
-0.9540003538131714,
-0.714849054813385,
1.382982850074768,
-0.2000240981578827,
-1.2782437801361084,
0.1743442714214325,
-1.1151078939437866,
1.7982470989227295,
-0.8330002427101135,
0.24018649756908417,
-1.7073076963424683,
0.051127463579177856,
-0.6064121723175049,
... |
https://github.com/huggingface/datasets/issues/5450 | to_tf_dataset with a TF collator causes bizarrely persistent slowdown | If "mp" is multiprocessing, this might suggest some kind of negative interaction between the JPEG decoder and TF's handling of processes/threads. Note that we haven't merged the parallel `to_tf_dataset` PR yet, so it's not caused by that PR! | ### Describe the bug
This will make more sense if you take a look at [a Colab notebook that reproduces this issue.](https://colab.research.google.com/drive/1rxyeciQFWJTI0WrZ5aojp4Ls1ut18fNH?usp=sharing)
Briefly, there are several datasets that, when you iterate over them with `to_tf_dataset` **and** a data colla... | 332 | 38 | to_tf_dataset with a TF collator causes bizarrely persistent slowdown
### Describe the bug
This will make more sense if you take a look at [a Colab notebook that reproduces this issue.](https://colab.research.google.com/drive/1rxyeciQFWJTI0WrZ5aojp4Ls1ut18fNH?usp=sharing)
Briefly, there are several datasets th... | [
-1.1403965950012207,
-0.9472631216049194,
-0.7174069881439209,
1.387075424194336,
-0.2061592936515808,
-1.2699196338653564,
0.17962321639060974,
-1.1113897562026978,
1.7824658155441284,
-0.8306373357772827,
0.23737767338752747,
-1.7016518115997314,
0.050736263394355774,
-0.6071605086326599... |
https://github.com/huggingface/datasets/issues/5450 | to_tf_dataset with a TF collator causes bizarrely persistent slowdown | Update: MP isn't multiprocessing at all, it's an internal PIL method for loading metadata from JPEG files. No idea why that would be a bottleneck, but I'll see if a Python profiler can't figure out where the time is actually being spent. | ### Describe the bug
This will make more sense if you take a look at [a Colab notebook that reproduces this issue.](https://colab.research.google.com/drive/1rxyeciQFWJTI0WrZ5aojp4Ls1ut18fNH?usp=sharing)
Briefly, there are several datasets that, when you iterate over them with `to_tf_dataset` **and** a data colla... | 332 | 42 | to_tf_dataset with a TF collator causes bizarrely persistent slowdown
### Describe the bug
This will make more sense if you take a look at [a Colab notebook that reproduces this issue.](https://colab.research.google.com/drive/1rxyeciQFWJTI0WrZ5aojp4Ls1ut18fNH?usp=sharing)
Briefly, there are several datasets th... | [
-1.141693353652954,
-0.9571100473403931,
-0.7233511209487915,
1.385690450668335,
-0.20012611150741577,
-1.2680176496505737,
0.1750892698764801,
-1.1131263971328735,
1.7796988487243652,
-0.8293800950050354,
0.22731220722198486,
-1.697354793548584,
0.05005267634987831,
-0.6047587394714355,
... |
https://github.com/huggingface/datasets/issues/5450 | to_tf_dataset with a TF collator causes bizarrely persistent slowdown | After further profiling, the slowdown is in the C methods for JPEG decoding that are included as part of PIL. Because Python profilers can't inspect inside that, I don't have any further information on which lines exactly are responsible for the slowdown or why.
In the meantime, I'm going to suggest switching from `... | ### Describe the bug
This will make more sense if you take a look at [a Colab notebook that reproduces this issue.](https://colab.research.google.com/drive/1rxyeciQFWJTI0WrZ5aojp4Ls1ut18fNH?usp=sharing)
Briefly, there are several datasets that, when you iterate over them with `to_tf_dataset` **and** a data colla... | 332 | 76 | to_tf_dataset with a TF collator causes bizarrely persistent slowdown
### Describe the bug
This will make more sense if you take a look at [a Colab notebook that reproduces this issue.](https://colab.research.google.com/drive/1rxyeciQFWJTI0WrZ5aojp4Ls1ut18fNH?usp=sharing)
Briefly, there are several datasets th... | [
-1.1419216394424438,
-0.9476639628410339,
-0.7116085886955261,
1.3707692623138428,
-0.20300760865211487,
-1.2872434854507446,
0.18772634863853455,
-1.1250361204147339,
1.7960673570632935,
-0.837628960609436,
0.22950974106788635,
-1.699198603630066,
0.06453695893287659,
-0.5998414754867554,... |
https://github.com/huggingface/datasets/issues/5444 | info messages logged as warnings | Looks like a duplicate of https://github.com/huggingface/datasets/issues/1948.
I also think these should be logged as INFO messages, but let's see what @lhoestq thinks. | ### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from the Python docs for reference:
* I... | 333 | 22 | info messages logged as warnings
### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from ... | [
-1.2012889385223389,
-0.9273018836975098,
-0.7546572685241699,
1.4767590761184692,
-0.167137011885643,
-1.2878497838974,
0.1407080888748169,
-1.0096759796142578,
1.6865419149398804,
-0.8163502812385559,
0.3636291027069092,
-1.626015067100525,
-0.005010148044675589,
-0.5913500189781189,
-... |
https://github.com/huggingface/datasets/issues/5444 | info messages logged as warnings | It can be considered unexpected to see a `map` function return instantaneously. The warning is here to explain this case by mentioning that the cache was used. I don't expect first time users (only seeing warnings) to guess that the cache works this way | ### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from the Python docs for reference:
* I... | 333 | 44 | info messages logged as warnings
### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from ... | [
-1.236562967300415,
-0.916245698928833,
-0.7499101161956787,
1.4740647077560425,
-0.1550338864326477,
-1.2958605289459229,
0.12919111549854279,
-1.0116636753082275,
1.7336289882659912,
-0.8567910194396973,
0.3752639889717102,
-1.6075327396392822,
0.01064293272793293,
-0.584657609462738,
... |
https://github.com/huggingface/datasets/issues/5444 | info messages logged as warnings | Oh, so it's intentional? Do all Hugging Face packages use `warning` when using cache?
I guess feel free to close this issue then. | ### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from the Python docs for reference:
* I... | 333 | 23 | info messages logged as warnings
### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from ... | [
-1.2241021394729614,
-0.9066925644874573,
-0.7336789965629578,
1.4768046140670776,
-0.1610291451215744,
-1.2774314880371094,
0.1545158475637436,
-1.0086294412612915,
1.7311090230941772,
-0.8399935364723206,
0.3464798033237457,
-1.6371490955352783,
0.03219063580036163,
-0.602618932723999,
... |
https://github.com/huggingface/datasets/issues/5444 | info messages logged as warnings | Yes it's intentional for `map`. For `load_dataset` it's also intentional but for a different reason: it shows where in the cache the dataset is located, in case the user wants to clear the cache. | ### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from the Python docs for reference:
* I... | 333 | 34 | info messages logged as warnings
### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from ... | [
-1.2135945558547974,
-0.9044673442840576,
-0.756445050239563,
1.4845976829528809,
-0.15900501608848572,
-1.2960442304611206,
0.15157468616962433,
-1.0147960186004639,
1.7531806230545044,
-0.8709537982940674,
0.3316876292228699,
-1.6485707759857178,
0.00995037704706192,
-0.5834614038467407,... |
https://github.com/huggingface/datasets/issues/5444 | info messages logged as warnings | OK I see. It's surprising to me that these are considered "something unexpected happened", the concept of cache is pretty common.
Has a user every actually complained that they ran their code once, and it took a minute while the data downloaded, then ran their code again and it ran really fast (and completed success... | ### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from the Python docs for reference:
* I... | 333 | 183 | info messages logged as warnings
### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from ... | [
-1.255374789237976,
-0.9473716616630554,
-0.7523675560951233,
1.4561004638671875,
-0.1649414300918579,
-1.2520487308502197,
0.0686570405960083,
-1.0906296968460083,
1.6898003816604614,
-0.8039632439613342,
0.305169939994812,
-1.6514534950256348,
0.04116402938961983,
-0.6184441447257996,
... |
https://github.com/huggingface/datasets/issues/5444 | info messages logged as warnings | I see your point and I'm not closed to switching to INFO, but I think those logs are important to make the library less opaque. I also just checked `transformers` scripts and they default to INFO which is nice. However for colab users the default is still WARNING iirc, and it counts as one of the main env where `datase... | ### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from the Python docs for reference:
* I... | 333 | 94 | info messages logged as warnings
### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from ... | [
-1.202043056488037,
-0.9279480576515198,
-0.7376196980476379,
1.464721441268921,
-0.15710747241973877,
-1.2811208963394165,
0.1379225254058838,
-1.106611967086792,
1.726864218711853,
-0.8411441445350647,
0.34654518961906433,
-1.6311951875686646,
0.00867342110723257,
-0.5844743251800537,
... |
https://github.com/huggingface/datasets/issues/5444 | info messages logged as warnings | These kinds of messages are logged as INFO in Transformers, so we should probably be consistent with them | ### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from the Python docs for reference:
* I... | 333 | 18 | info messages logged as warnings
### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from ... | [
-1.2303543090820312,
-0.8941532373428345,
-0.7463688254356384,
1.490765929222107,
-0.19838981330394745,
-1.3130637407302856,
0.13688313961029053,
-1.0077579021453857,
1.7194478511810303,
-0.8827599287033081,
0.3706198036670685,
-1.6408991813659668,
0.030980480834841728,
-0.6354483962059021... |
https://github.com/huggingface/datasets/issues/5442 | OneDrive Integrations with HF Datasets | Hi!
We use [`fsspec`](https://github.com/fsspec/filesystem_spec) to integrate with storage providers. You can find more info (and the usage examples) in [our docs](https://huggingface.co/docs/datasets/v2.8.0/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage).
[`gdrivefs`](https://github.com/fsspec/... | ### Feature request
First of all , I would like to thank all community who are developed DataSet storage and make it free available
How to integrate our Onedrive account or any other possible storage clouds (like google drive,...) with the **HF** datasets section.
For example, if I have **50GB** on my **Onedrive*... | 334 | 67 | OneDrive Integrations with HF Datasets
### Feature request
First of all , I would like to thank all community who are developed DataSet storage and make it free available
How to integrate our Onedrive account or any other possible storage clouds (like google drive,...) with the **HF** datasets section.
For exa... | [
-1.24271821975708,
-0.959071159362793,
-0.7232421040534973,
1.3471896648406982,
-0.11604730039834976,
-1.284589171409607,
0.12122423946857452,
-1.082366943359375,
1.6358044147491455,
-0.7559685111045837,
0.28111669421195984,
-1.7193418741226196,
-0.004981605801731348,
-0.5995687246322632,
... |
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