html_url stringlengths 48 51 | title stringlengths 5 280 | comments stringlengths 63 51.8k | body stringlengths 0 36.2k ⌀ | comment_length int64 16 1.52k | text stringlengths 159 54.1k | embeddings listlengths 768 768 |
|---|---|---|---|---|---|---|
https://github.com/huggingface/datasets/issues/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 | 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... | [
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0.2229762226343155... |
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 | 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... | [
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0.12729084491729... |
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 | 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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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 | 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... | [
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0.10471632331... |
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 | 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... | [
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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 | 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... | [
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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 | 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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0.02641616947... |
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 | 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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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 | 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... | 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... | [
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0.03369786590337753,... |
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... | 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... | [
-0.3877435624599457,
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0.04365236684679985,
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0.7415615320205688,
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0.03649973869... |
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... | 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... | [
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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... | 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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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... | 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... | [
0.012716762721538544,
0.4021453857421875,
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0.38741418719291687,
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0.2276330441236496,
... |
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... | 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... | [
0.06664426624774933,
0.2528359889984131,
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0.033473189920186996,
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0.1846706122159958,
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0.322424352169036... |
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... | 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... | [
0.03763004019856453,
0.2596346139907837,
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0.4311474561691284,
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0.21859394013881683,
-0.33139386773109436,
0.33000004291534424... |
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... | 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... | [
0.05046708509325981,
0.11157144606113434,
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0.02393627166748047,
0.3661220073699951,
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0.27888646721839905,
0.18231338262557983,
0.22172625362873077,
0.39142513275146484,
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0.21928197145462036,
-0.3540952205657959,
0.289623260498046... |
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... | 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... | [
0.052459683269262314,
0.3182418942451477,
-0.07823818922042847,
0.009625442326068878,
0.16037358343601227,
0.07437034696340561,
0.2912677228450775,
0.1360299438238144,
0.11689557135105133,
0.47716212272644043,
-0.31609871983528137,
0.23639073967933655,
-0.3058529198169708,
0.23433209955692... |
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... | 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... | [
-0.07380013912916183,
0.3319534957408905,
-0.1016116589307785,
-0.03201451897621155,
0.31756943464279175,
0.09239640086889267,
0.3140403926372528,
0.22060765326023102,
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0.42897146940231323,
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0.27183184027671814,
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0.202827706933021... |
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... | 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... | [
0.025135602802038193,
0.26631906628608704,
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0.4302627444267273,
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0.24153397977352142,
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0.2463359385... |
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... | 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... | [
0.020111914724111557,
0.39976435899734497,
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0.27503466606140137,
0.06968855112791061,
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0.41292381286621094,
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0.3137819468975067,
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0.18966731429... |
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... | 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... | [
-0.05954696983098984,
0.2965109050273895,
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0.39920538663864136,
0.221605122089386,
0.12385514378547668,
0.43253225088119507,
-0.3550332188606262,
0.2529556155204773,
-0.3704719543457031,
0.2411772757768631,... |
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... | 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
... | [
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0.07972130179405212,
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0.2846687436103821,
0.13756658136844635,
-0.39467537403... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
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0.21562272310256958,
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0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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... | 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... | [
0.23812700808048248,
-0.6135581731796265,
-0.004315514117479324,
0.4543875455856323,
-0.08939257264137268,
-0.0826701894402504,
-0.0820554792881012,
0.21562272310256958,
-0.41769683361053467,
0.32503992319107056,
-0.31374332308769226,
0.16545580327510834,
0.2596942186355591,
-0.19970434904... |
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:... | 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]")`
... | [
-0.5406305193901062,
0.2308039665222168,
-0.012679360806941986,
0.05592242628335953,
0.21875785291194916,
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0.49410852789878845,
0.2683308720588684,
-0.26596638560295105,
0.1708059012889862,
-0.13953159749507904,
0.3741288483142853,
-0.13417577743530273,
0.311875969171... |
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... | 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... | [
-0.23342011868953705,
-0.3251979351043701,
-0.017481131479144096,
0.39176517724990845,
0.29942941665649414,
0.0018673017621040344,
0.04591747000813484,
-0.0035278350114822388,
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0.12349669635295868,
-0.19353342056274414,
-0.02383505180478096,
-0.14854159951210022,
0.121... |
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... | 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... | [
-0.23342011868953705,
-0.3251979351043701,
-0.017481131479144096,
0.39176517724990845,
0.29942941665649414,
0.0018673017621040344,
0.04591747000813484,
-0.0035278350114822388,
-0.07234817743301392,
0.12349669635295868,
-0.19353342056274414,
-0.02383505180478096,
-0.14854159951210022,
0.121... |
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... | 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 ... | [
-0.590391218662262,
0.12605814635753632,
0.008841346949338913,
0.09381023049354553,
0.035903751850128174,
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0.08397107571363449,
0.12456893920898438,
0.09366557002067566,
0.15948447585105896,
-0.04897703230381012,
0.31480634212493896,
-0.4971725642681122,
0.0750832185149... |
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... | 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 ... | [
-0.5778516530990601,
0.0768948346376419,
0.050096508115530014,
-0.0014432072639465332,
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0.17337970435619354,
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0.28644588589668274,
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0.37201949954... |
https://github.com/huggingface/datasets/issues/5454 | Save and resume the state of a DataLoader | Hi there! I think this is a critical issue and have an urgent need for it, in my attempt to train on a super large-scale dataset using `datasets`. It is impossible to resume a time-consuming (like one month) experiment by iterating all seen data again, which could possibly cost several days.
@stas00 @thomasw21 @lhoe... | 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... | 63 | 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 ... | [
-0.5742830038070679,
0.283465713262558,
-0.016857344657182693,
0.12780483067035675,
-0.015207722783088684,
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0.22534172236919403,
0.06700689345598221,
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0.053413815796375275,
-0.08971792459487915,
0.1891605406999588,
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0.200499445199... |
https://github.com/huggingface/datasets/issues/5454 | Save and resume the state of a DataLoader | No update so far, I wonder if someone implemented a resumable pytorch Sampler somwhere.
Then regarding resuming a streaming dataset, we'd first like to have an efficient way to skip shards automatically but this is not implemented yet | 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... | 38 | 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 ... | [
-0.5450518131256104,
0.18089717626571655,
0.018949732184410095,
0.1065579354763031,
-0.029838714748620987,
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0.21635083854198456,
0.006563818082213402,
0.03224439173936844,
0.07169640064239502,
-0.06208067387342453,
0.20047461986541748,
-0.36095231771469116,
0.296282410... |
https://github.com/huggingface/datasets/issues/5454 | Save and resume the state of a DataLoader | I opened a draft here for IterableDataset: https://github.com/huggingface/datasets/pull/6658
```python
"""Requires https://github.com/huggingface/datasets/pull/6658 (WIP)"""
from datasets import load_dataset
from torch.utils.data import DataLoader
ds = load_dataset(..., streaming=True)
# ds = ds.map(token... | 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... | 72 | 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 ... | [
-0.6239463686943054,
0.08005508780479431,
0.00005788728594779968,
0.17961543798446655,
0.0208725742995739,
-0.10727176815271378,
0.25590506196022034,
0.05695898458361626,
-0.013182507827877998,
0.022553592920303345,
-0.1802491545677185,
0.2676003873348236,
-0.3988147974014282,
0.1267234534... |
https://github.com/huggingface/datasets/issues/5454 | Save and resume the state of a DataLoader | Hi @lhoestq - can you provide more information and how to implement on saving and restoring vanilla DataLoader states with map-style datasets?
| 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... | 22 | 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 ... | [
-0.5343466401100159,
0.19503113627433777,
0.00895291194319725,
0.20394235849380493,
-0.006507672369480133,
-0.1312706470489502,
0.21756188571453094,
0.11338263750076294,
0.18564753234386444,
-0.03387826681137085,
-0.11546783894300461,
0.30243730545043945,
-0.3291757106781006,
0.18333524465... |
https://github.com/huggingface/datasets/issues/5454 | Save and resume the state of a DataLoader | For now the easiest is probably to use the vanilla DataLoader only for batching and multiprocessing, and implement the resuming logic using a `Dataset` (it has `.select()` to skip examples) and a `dataset_state_dict`:
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
ds = loa... | 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... | 100 | 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 ... | [
-0.6678329706192017,
0.11887665092945099,
0.003964874893426895,
0.14554530382156372,
0.01231348142027855,
-0.1574954092502594,
0.26829859614372253,
0.04963402450084686,
-0.01073405146598816,
0.07346861809492111,
-0.12683165073394775,
0.27164962887763977,
-0.43771493434906006,
0.15645755827... |
https://github.com/huggingface/datasets/issues/5454 | Save and resume the state of a DataLoader | Hello, I found a similar implementation online that seems to solve your problem. https://github.com/facebookresearch/vissl/blob/main/vissl/data/data_helper.py#L93
it looks like we can set_start_iter in StatefulDistributedSampler to implement the stateful resume requirement we want.
| 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... | 30 | 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 ... | [
-0.5830392241477966,
-0.028724588453769684,
0.0343194417655468,
0.03829655051231384,
0.1457652449607849,
-0.21221542358398438,
0.20966994762420654,
0.0201973095536232,
0.1514657884836197,
0.11498825997114182,
-0.0759880319237709,
0.3399212956428528,
-0.20448316633701324,
0.1302765756845474... |
https://github.com/huggingface/datasets/issues/5454 | Save and resume the state of a DataLoader | Hi y'all, @lhoestq I wanted to flag that we currently have a StatefulDataLoader in `pytorch/data/torchdata` that has state_dict/load_state_dict methods, which will call a dataset's state_dict/load_state_dict methods but also handle multiprocessing under the hood. Any chance we can collaborate on this and try to get the... | 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... | 59 | 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 ... | [
-0.5166980624198914,
0.2312217652797699,
-0.020020106807351112,
0.2962813675403595,
-0.012600567191839218,
-0.140153706073761,
0.3636290729045868,
0.05864112079143524,
-0.020938070490956306,
0.024620257318019867,
-0.19342146813869476,
0.21055634319782257,
-0.27893418073654175,
0.3259393870... |
https://github.com/huggingface/datasets/issues/5454 | Save and resume the state of a DataLoader | Fantastic ! This will help pushing our IterableDataset state_dict implementation at https://github.com/huggingface/datasets/pull/6658 :) I'll check if there is anything missing to maker them work together, and add tests and some docs referring to the StatefulDataLoader :) | 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... | 36 | 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 ... | [
-0.6076581478118896,
0.16840311884880066,
-0.009039124473929405,
0.15778236091136932,
0.01520393043756485,
-0.12502631545066833,
0.26245567202568054,
0.06707271933555603,
0.034385547041893005,
-0.001626625657081604,
-0.1298682689666748,
0.23947398364543915,
-0.3492177724838257,
0.344280689... |
https://github.com/huggingface/datasets/issues/5454 | Save and resume the state of a DataLoader | Ah I just saw this disclaimer in the torchdata README and it feels like people should not rely on it. Should the StatefulDataLoader live elsewhere @andrewkho ?
> ⚠️ As of July 2023, we have paused active development on TorchData and have paused new releases. We have learnt a lot from building it and hearing from use... | 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... | 108 | 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 ... | [
-0.4684315621852875,
0.1632944941520691,
-0.044503167271614075,
0.2255735844373703,
0.06937481462955475,
-0.1226317510008812,
0.2561340034008026,
0.037055276334285736,
0.04639343544840813,
0.0850113034248352,
-0.11960995197296143,
0.23862218856811523,
-0.2816274166107178,
0.073475271463394... |
https://github.com/huggingface/datasets/issues/5454 | Save and resume the state of a DataLoader | @lhoestq Good find, we are in the midst of updating this disclaimer as we're re-starting development and regular releases, though our approach will be to iterate on DL V1 (ie StatefulDataLoader) instead of continuing development on datapipes+DLV2. Let's discuss on a call at some point to figure out the best path forwar... | 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... | 52 | 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 ... | [
-0.6144964098930359,
0.26708850264549255,
0.0007030051201581955,
0.11657968908548355,
-0.0054785385727882385,
-0.13829156756401062,
0.24171565473079681,
0.08379434049129486,
0.004760442301630974,
0.03440595418214798,
-0.054624512791633606,
0.2859041094779968,
-0.3432391583919525,
0.2249539... |
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 ... | 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... | [
-0.30214881896972656,
-0.1288907378911972,
0.00013344548642635345,
0.5274219512939453,
0.3718428611755371,
0.04527028650045395,
-0.03887492045760155,
0.13735799491405487,
-0.2835582494735718,
0.11153984814882278,
0.09652269631624222,
0.02506321109831333,
-0.05475345253944397,
-0.1096777841... |
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 ... | 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... | [
-0.3858197331428528,
-0.01046067476272583,
-0.012387007474899292,
0.5257979035377502,
0.27683910727500916,
0.06778963655233383,
-0.04070879891514778,
0.1476408839225769,
-0.32300660014152527,
0.1038580983877182,
0.12979844212532043,
0.0709238052368164,
-0.04522546008229256,
-0.227389559149... |
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 ... | 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... | [
-0.3408025801181793,
0.04916226863861084,
-0.01547899842262268,
0.4781758785247803,
0.3053165078163147,
0.02558182179927826,
-0.046696070581674576,
0.18121616542339325,
-0.3135695457458496,
0.09213079512119293,
0.10882571339607239,
0.04875483736395836,
-0.05684623867273331,
-0.220491915941... |
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... | 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... | [
-0.23047439754009247,
-0.06307906657457352,
0.056947726756334305,
0.1785079538822174,
0.2604309320449829,
0.053447164595127106,
0.2793619930744171,
0.18530496954917908,
-0.3290843367576599,
0.28478318452835083,
-0.12698425352573395,
0.4587993919849396,
-0.0764392614364624,
-0.0096908845007... |
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... | 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... | [
-0.25836968421936035,
-0.07155779004096985,
0.07093456387519836,
0.15415999293327332,
0.2888794541358948,
0.023602105677127838,
0.2706558108329773,
0.17421673238277435,
-0.4144011437892914,
0.31717103719711304,
-0.1040775403380394,
0.4773680567741394,
-0.07838501036167145,
0.02827969752252... |
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... | 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... | [
-0.2688671052455902,
-0.08593864738941193,
0.07341070473194122,
0.15628845989704132,
0.2705063223838806,
0.028361022472381592,
0.30375584959983826,
0.14333941042423248,
-0.38831591606140137,
0.3228151798248291,
-0.1225116178393364,
0.4677031338214874,
-0.06810709834098816,
0.03653506934642... |
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... | 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... | [
-0.19386005401611328,
0.03455595672130585,
0.02873828262090683,
0.18287555873394012,
0.2498314082622528,
0.09574023634195328,
0.2958502769470215,
0.24304035305976868,
-0.3283993899822235,
0.32617223262786865,
-0.12927180528640747,
0.4373798966407776,
-0.08544529974460602,
-0.03806677088141... |
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... | 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 ... | [
0.14982615411281586,
-0.21058619022369385,
-0.05635552108287811,
0.3685871958732605,
0.20154303312301636,
0.3165709674358368,
0.3913244903087616,
0.2663443982601166,
0.14894641935825348,
-0.18562708795070648,
-0.005769385024905205,
0.2793087959289551,
-0.3714497983455658,
0.156526997685432... |
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... | 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 ... | [
0.016657602041959763,
-0.07572902739048004,
-0.08646491169929504,
0.2924633026123047,
0.27877113223075867,
0.2673483192920685,
0.24067941308021545,
0.36863699555397034,
0.33353886008262634,
-0.11565123498439789,
-0.040144287049770355,
0.31386545300483704,
-0.30156391859054565,
-0.130674734... |
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... | 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 ... | [
0.14408552646636963,
-0.2291892170906067,
-0.0032132714986801147,
0.32334697246551514,
0.37744367122650146,
0.3381856679916382,
0.31798070669174194,
0.2857082188129425,
0.30939778685569763,
-0.12606394290924072,
-0.0030651530250906944,
0.003642665222287178,
-0.2675057649612427,
0.070504263... |
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... | 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 ... | [
0.11643534898757935,
-0.19251158833503723,
-0.03884235396981239,
0.3318280577659607,
0.26427432894706726,
0.35502636432647705,
0.3619454801082611,
0.3249266445636749,
0.4101949632167816,
-0.18531031906604767,
-0.011977544054389,
0.4151666462421417,
-0.21757540106773376,
-0.0451037138700485... |
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... | 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 ... | [
0.14952674508094788,
0.012142419815063477,
-0.04143369197845459,
0.3016056418418884,
0.1850384920835495,
0.2751148045063019,
0.3156786561012268,
0.17848330736160278,
0.13307008147239685,
-0.17578820884227753,
0.12940077483654022,
0.1620422601699829,
-0.4320485293865204,
-0.1107400432229042... |
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... | 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 ... | [
-0.07937045395374298,
0.07965441048145294,
0.029848266392946243,
0.05610685423016548,
0.19800172746181488,
0.17779475450515747,
0.4890155792236328,
0.2522006630897522,
0.006849374622106552,
0.16207557916641235,
0.0032052656169980764,
0.36870697140693665,
-0.2772739827632904,
0.081741556525... |
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... | 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 ... | [
0.06931596994400024,
-0.15233156085014343,
-0.06864544749259949,
0.2598941922187805,
0.29669278860092163,
0.17542406916618347,
0.36649343371391296,
0.2837667465209961,
0.07602934539318085,
-0.08946344256401062,
0.04998654127120972,
0.308676540851593,
-0.3139187693595886,
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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*... | 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... | [
-0.22796830534934998,
0.09350568056106567,
-0.12087270617485046,
0.06623005121946335,
0.09376987814903259,
0.19753435254096985,
0.19331802427768707,
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0.22741268575191498,
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0.07262565940618515,
0.59712129831314... |
https://github.com/huggingface/datasets/issues/5437 | Can't load png dataset with 4 channel (RGBA) | Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.
| I try to create dataset which contains about 9000 png images 64x64 in size, and they are all 4-channel (RGBA). When trying to use load_dataset() then a dataset is created from only 2 images. What exactly interferes I can not understand.
I try to create dataset which contains about 9000 png images 64x64 in size, and they are all 4-channel (RGBA). When trying to use load_dataset() then a dataset is created from only 2 images. What exactly interferes I can not understand. | > Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.
>
>
I have only 1 folder that I use in the load_dataset function with the name "IMGDATA" and all my 9000 images are located... | I try to create dataset which contains about 9000 png images 64x64 in size, and they are all 4-channel (RGBA). When trying to use load_dataset() then a dataset is created from only 2 images. What exactly interferes I can not understand.
I try to create dataset which contains about 9000 png images 64x64 in size, and they are all 4-channel (RGBA). When trying to use load_dataset() then a dataset is created from only 2 images. What exactly interferes I can not understand. | Okay, I figured out what was wrong. When uploading my dataset via Google Drive, the images broke and Pillow couldn't open them. As a result, I solved the problem by downloading the ZIP archive | I try to create dataset which contains about 9000 png images 64x64 in size, and they are all 4-channel (RGBA). When trying to use load_dataset() then a dataset is created from only 2 images. What exactly interferes I can not understand.
I try to create dataset which contains about 9000 png images 64x64 in size, and they are all 4-channel (RGBA). When trying to use load_dataset() then a dataset is created from only 2 images. What exactly interferes I can not understand.![Screenshot_20230117_212213.jpg](htt... | [
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0.0012568158563226461,
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0.18264588713645935,
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https://github.com/huggingface/datasets/issues/5435 | Wrong statement in "Load a Dataset in Streaming mode" leads to data leakage | Thanks for reporting, @HaoyuYang59.
Please note that these are different "dataset" objects: our docs refer to Hugging Face `datasets.Dataset` and not to TensorFlow `tf.data.Dataset`.
Our `datasets.Dataset.shuffle` method does not have a `reshuffle_each_iteration` argument. Therefore, I would say the statement in ... | ### Describe the bug
In the [Split your dataset with take and skip](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#split-your-dataset-with-take-and-skip), it states:
> Using take (or skip) prevents future calls to shuffle from shuffling the dataset shards order, otherwise the taken examples cou... | 77 | Wrong statement in "Load a Dataset in Streaming mode" leads to data leakage
### Describe the bug
In the [Split your dataset with take and skip](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#split-your-dataset-with-take-and-skip), it states:
> Using take (or skip) prevents future calls to shu... | [
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0.288862794... |
https://github.com/huggingface/datasets/issues/5435 | Wrong statement in "Load a Dataset in Streaming mode" leads to data leakage | Also note that you are referring to an outdated documentation page: datasets 1.10.2 version
Current datasets version is 2.8.0 and the corresponding documentation page is: https://huggingface.co/docs/datasets/stream#split-dataset | ### Describe the bug
In the [Split your dataset with take and skip](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#split-your-dataset-with-take-and-skip), it states:
> Using take (or skip) prevents future calls to shuffle from shuffling the dataset shards order, otherwise the taken examples cou... | 26 | Wrong statement in "Load a Dataset in Streaming mode" leads to data leakage
### Describe the bug
In the [Split your dataset with take and skip](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#split-your-dataset-with-take-and-skip), it states:
> Using take (or skip) prevents future calls to shu... | [
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0.1533891260623932,
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0.22939811646938324,
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0.31560957431793213,
-0.16571290791034698,
0.2511996626853... |
https://github.com/huggingface/datasets/issues/5435 | Wrong statement in "Load a Dataset in Streaming mode" leads to data leakage | Hi @albertvillanova thanks for your reply and your explaination here.
Sorry for the confusion as I'm not actually a user of your repo and I just happen to find the thread by Google (and didn't read carefully).
Great to know that and you made everything very clear now.
Thanks for your time and sorry for the co... | ### Describe the bug
In the [Split your dataset with take and skip](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#split-your-dataset-with-take-and-skip), it states:
> Using take (or skip) prevents future calls to shuffle from shuffling the dataset shards order, otherwise the taken examples cou... | 63 | Wrong statement in "Load a Dataset in Streaming mode" leads to data leakage
### Describe the bug
In the [Split your dataset with take and skip](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#split-your-dataset-with-take-and-skip), it states:
> Using take (or skip) prevents future calls to shu... | [
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0.2710457444... |
https://github.com/huggingface/datasets/issues/5434 | sample_dataset module not found | working on the setfit example script
from setfit import SetFitModel, SetFitTrainer, sample_dataset
ImportError: cannot import name 'sample_dataset' from 'setfit' (C:\Python\Python38\lib\site-packages\setfit\__init__.py)
apart from that, I also had to hack these loads to import thses modules:
from data... | null | 46 | sample_dataset module not found
working on the setfit example script
from setfit import SetFitModel, SetFitTrainer, sample_dataset
ImportError: cannot import name 'sample_dataset' from 'setfit' (C:\Python\Python38\lib\site-packages\setfit\__init__.py)
apart from that, I also had to hack these loads to... | [
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0.50126200914... |
https://github.com/huggingface/datasets/issues/5434 | sample_dataset module not found | Hi! This issue is related to the [SetFit](https://github.com/huggingface/setfit) project, so can you please open it there? | null | 16 | sample_dataset module not found
Hi! This issue is related to the [SetFit](https://github.com/huggingface/setfit) project, so can you please open it there? | [
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0.7102677822... |
https://github.com/huggingface/datasets/issues/5433 | Support latest Docker image in CI benchmarks | Sorry, it was us:[^1] https://github.com/iterative/cml/pull/1317 & https://github.com/iterative/cml/issues/1319#issuecomment-1385599559; should be fixed with [v0.18.17](https://github.com/iterative/cml/releases/tag/v0.18.17).
[^1]: More or less, see https://github.com/yargs/yargs/issues/873. | Once we find out the root cause of:
- #5431
we should revert the temporary pin on the Docker image version introduced by:
- #5432 | 18 | Support latest Docker image in CI benchmarks
Once we find out the root cause of:
- #5431
we should revert the temporary pin on the Docker image version introduced by:
- #5432
Sorry, it was us:[^1] https://github.com/iterative/cml/pull/1317 & https://github.com/iterative/cml/issues/1319#issuecomment-1385599559... | [
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0.21651791036128998,
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https://github.com/huggingface/datasets/issues/5433 | Support latest Docker image in CI benchmarks | Hi @0x2b3bfa0, thanks a lot for the investigation, the context about the the root cause and for fixing it!!
We are reviewing your PR to unpin the container image. | Once we find out the root cause of:
- #5431
we should revert the temporary pin on the Docker image version introduced by:
- #5432 | 29 | Support latest Docker image in CI benchmarks
Once we find out the root cause of:
- #5431
we should revert the temporary pin on the Docker image version introduced by:
- #5432
Hi @0x2b3bfa0, thanks a lot for the investigation, the context about the the root cause and for fixing it!!
We are reviewing your PR... | [
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... |
https://github.com/huggingface/datasets/issues/5430 | Support Apache Beam >= 2.44.0 | Some of the shard files now have 0 number of rows.
We have opened an issue in the Apache Beam repo:
- https://github.com/apache/beam/issues/25041 | Once we find out the root cause of:
- #5426
we should revert the temporary pin on apache-beam introduced by:
- #5429 | 23 | Support Apache Beam >= 2.44.0
Once we find out the root cause of:
- #5426
we should revert the temporary pin on apache-beam introduced by:
- #5429
Some of the shard files now have 0 number of rows.
We have opened an issue in the Apache Beam repo:
- https://github.com/apache/beam/issues/25041 | [
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https://github.com/huggingface/datasets/issues/5428 | Load/Save FAISS index using fsspec | Hi! Sure, feel free to submit a PR. Maybe if we want to be consistent with the existing API, it would be cleaner to directly add support for `fsspec` paths in `Dataset.load_faiss_index`/`Dataset.save_faiss_index` in the same manner as it was done in `Dataset.load_from_disk`/`Dataset.save_to_disk`. | ### Feature request
From what I understand `faiss` already support this [link](https://github.com/facebookresearch/faiss/wiki/Index-IO,-cloning-and-hyper-parameter-tuning#generic-io-support)
I would like to use a stream as input to `Dataset.load_faiss_index` and `Dataset.save_faiss_index`.
### Motivation
In... | 42 | Load/Save FAISS index using fsspec
### Feature request
From what I understand `faiss` already support this [link](https://github.com/facebookresearch/faiss/wiki/Index-IO,-cloning-and-hyper-parameter-tuning#generic-io-support)
I would like to use a stream as input to `Dataset.load_faiss_index` and `Dataset.save_... | [
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0.1213657334446907,
0.58028531074... |
https://github.com/huggingface/datasets/issues/5427 | Unable to download dataset id_clickbait | Thanks for reporting, @ilos-vigil.
We have transferred this issue to the corresponding dataset on the Hugging Face Hub: https://huggingface.co/datasets/id_clickbait/discussions/1 | ### Describe the bug
I tried to download dataset `id_clickbait`, but receive this error message.
```
FileNotFoundError: Couldn't find file at https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/k42j7x2kpn-1.zip
```
When i open the link using browser, i got this XML data.
```xml
<?xml versi... | 19 | Unable to download dataset id_clickbait
### Describe the bug
I tried to download dataset `id_clickbait`, but receive this error message.
```
FileNotFoundError: Couldn't find file at https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/k42j7x2kpn-1.zip
```
When i open the link using browser, i... | [
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0.007942423224449158,
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0.3713671565055847,
0.2508448362350464,
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0.1588071584701538,
0.15100619196891785,
0.288879692554... |
https://github.com/huggingface/datasets/issues/5425 | Sort on multiple keys with datasets.Dataset.sort() | Hi!
`Dataset.sort` calls `df.sort_values` internally, and `df.sort_values` brings all the "sort" columns in memory, so sorting on multiple keys could be very expensive. This makes me think that maybe we can replace `df.sort_values` with `pyarrow.compute.sort_indices` - the latter can also sort on multiple keys and ... | ### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggested solution:
> ... having something similar to panda... | 109 | Sort on multiple keys with datasets.Dataset.sort()
### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggeste... | [
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-0.17726768553256989,
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0.13469411432743073,
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0.1555032730102539,
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0.4349202811717987,
-0.001666518859565258,
0.1752358227... |
https://github.com/huggingface/datasets/issues/5425 | Sort on multiple keys with datasets.Dataset.sort() | @mariosasko If I understand the code right, using `pyarrow.compute.sort_indices` would also require changes to the `select` method if it is meant to sort multiple keys. That's because `select` only accepts 1D input for `indices`, not an iterable or similar which would be required for multiple keys unless you want some ... | ### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggested solution:
> ... having something similar to panda... | 64 | Sort on multiple keys with datasets.Dataset.sort()
### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggeste... | [
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0.14701536297798157,
-0.16095459461212158,
0.0437161922454834,
0.3155261278152466,
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0.20779173076152802,
0.21705663204193115,
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0.16988101601600647,
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0.42477181553840637,
0.021939245983958244,
0.2009476721... |
https://github.com/huggingface/datasets/issues/5425 | Sort on multiple keys with datasets.Dataset.sort() | @MichlF No, it doesn't require modifying select because sorting on multiple keys also returns a 1D array.
It's easier to understand with an example:
```python
>>> import pyarrow as pa
>>> import pyarrow.compute as pc
>>> table = pa.table({
... "name": ["John", "Eve", "Peter", "John"],
... "surname": ["... | ### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggested solution:
> ... having something similar to panda... | 76 | Sort on multiple keys with datasets.Dataset.sort()
### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggeste... | [
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0.10141076147556305,
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-0.04086395353078842,
0.2572186589241028,
0.016521252691745758,
0.13104653358459473,
0.1736159473657608,
-0.13526873290538788,
0.12119153887033463,
-0.09460056573152542,
0.4578700065612793,
-0.03541899845004082,
0.19050554931... |
https://github.com/huggingface/datasets/issues/5425 | Sort on multiple keys with datasets.Dataset.sort() | Thanks for clarifying.
I can prepare a PR to address this issue. This would be my first PR here so I have a few maybe silly questions but:
- What is the preferred input type of `sort_keys` for the sort method? A sequence with name, order tuples like pyarrow's `sort_indices` requires?
- What about backwards compatabi... | ### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggested solution:
> ... having something similar to panda... | 112 | Sort on multiple keys with datasets.Dataset.sort()
### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggeste... | [
0.11508440971374512,
0.1414814293384552,
-0.10742218792438507,
0.019289661198854446,
0.3736000657081604,
-0.035536449402570724,
0.1930682510137558,
0.1085553765296936,
-0.16140493750572205,
0.07203352451324463,
-0.02655572071671486,
0.329670786857605,
-0.05473189055919647,
0.26411339640617... |
https://github.com/huggingface/datasets/issues/5425 | Sort on multiple keys with datasets.Dataset.sort() | I think we can have the following signature:
```python
def sort(
self,
column_names: Union[str, Sequence[str]],
reverse: Union[bool, Sequence[bool]] = False,
kind="deprecated",
null_placement: str = "last",
keep_in_memory: bool = False,
load_from_cache_fi... | ### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggested solution:
> ... having something similar to panda... | 127 | Sort on multiple keys with datasets.Dataset.sort()
### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggeste... | [
0.06601080298423767,
0.13722801208496094,
-0.16857987642288208,
-0.07267683744430542,
0.35078465938568115,
0.057556502521038055,
0.17645317316055298,
0.23617905378341675,
-0.028036050498485565,
0.040990330278873444,
-0.07484530657529831,
0.34253013134002686,
-0.10433489829301834,
0.2484688... |
https://github.com/huggingface/datasets/issues/5425 | Sort on multiple keys with datasets.Dataset.sort() | I am pretty much done with the PR. Just one clarification: `Sequence` in `arrow_dataset.py` is a custom dataclass from `features.py` instead of the `type.hinting` class `Sequence` from Python. Do you suggest using that custom `Sequence` class somehow ? Otherwise signature currently reads instead:
```Python
def so... | ### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggested solution:
> ... having something similar to panda... | 119 | Sort on multiple keys with datasets.Dataset.sort()
### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggeste... | [
0.05497751012444496,
0.10999909043312073,
-0.16999320685863495,
0.010580737143754959,
0.25775980949401855,
-0.014488905668258667,
0.15814895927906036,
0.20468533039093018,
-0.06443106383085251,
-0.013975679874420166,
0.06072346121072769,
0.4184730350971222,
-0.13011512160301208,
0.26022803... |
https://github.com/huggingface/datasets/issues/5425 | Sort on multiple keys with datasets.Dataset.sort() | I meant `typing.Sequence` (`datasets.Sequence` is a feature type).
Regarding `null_placement`, I think we can support both `at_start` and `at_end`, and `last` and `first` (for backward compatibility; convert internally to `at_end` and `at_start` respectively). | ### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggested solution:
> ... having something similar to panda... | 33 | Sort on multiple keys with datasets.Dataset.sort()
### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggeste... | [
0.02541567012667656,
0.0421115905046463,
-0.15855026245117188,
0.08235688507556915,
0.2454751431941986,
0.004766426980495453,
0.21588291227817535,
0.14735998213291168,
-0.11042693257331848,
0.054425954818725586,
0.0009615541202947497,
0.24596276879310608,
0.010052447207272053,
0.3148943483... |
https://github.com/huggingface/datasets/issues/5425 | Sort on multiple keys with datasets.Dataset.sort() | > I meant typing.Sequence (datasets.Sequence is a feature type).
Sorry, I actually meant `typing.Sequence` and not `type.hinting`. However, the issue is still that `dataset.Sequence` is imported in `arrow_dataset.py` so I cannot import and use `typing.Sequence` for the `sort`'s signature without overwriting the `dat... | ### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggested solution:
> ... having something similar to panda... | 119 | Sort on multiple keys with datasets.Dataset.sort()
### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggeste... | [
0.0753103494644165,
0.08347347378730774,
-0.10561510920524597,
0.025613337755203247,
0.3077031373977661,
0.03900536894798279,
0.2000204622745514,
0.20026621222496033,
0.0951324850320816,
-0.0224170982837677,
0.030367432162165642,
0.3968810737133026,
-0.14236189424991608,
0.3277457952499389... |
https://github.com/huggingface/datasets/issues/5425 | Sort on multiple keys with datasets.Dataset.sort() | You can avoid the name collision by renaming `typing.Sequence` to `Sequence_` when importing:
```python
from typing import Sequence as Sequence_
``` | ### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggested solution:
> ... having something similar to panda... | 21 | Sort on multiple keys with datasets.Dataset.sort()
### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggeste... | [
-0.00019793584942817688,
-0.030909940600395203,
-0.21666118502616882,
-0.004868805408477783,
0.23341327905654907,
0.065778948366642,
0.14013485610485077,
0.21374519169330597,
-0.002911064773797989,
0.09664606302976608,
-0.058280348777770996,
0.2569771707057953,
-0.04670356214046478,
0.3306... |
https://github.com/huggingface/datasets/issues/5424 | When applying `ReadInstruction` to custom load it's not DatasetDict but list of Dataset? | Hi! You can get a `DatasetDict` if you pass a dictionary with read instructions as follows:
```python
instructions = [
ReadInstruction(split_name="train", from_=0, to=10, unit='%', rounding='closest'),
ReadInstruction(split_name="dev", from_=0, to=10, unit='%', rounding='closest'),
ReadInstruction(spli... | ### Describe the bug
I am loading datasets from custom `tsv` files stored locally and applying split instructions for each split. Although the ReadInstruction is being applied correctly and I was expecting it to be `DatasetDict` but instead it is a list of `Dataset`.
### Steps to reproduce the bug
Steps to reproduc... | 51 | When applying `ReadInstruction` to custom load it's not DatasetDict but list of Dataset?
### Describe the bug
I am loading datasets from custom `tsv` files stored locally and applying split instructions for each split. Although the ReadInstruction is being applied correctly and I was expecting it to be `DatasetDict`... | [
-0.018154535442590714,
-0.21285168826580048,
-0.08146777749061584,
0.4585762023925781,
0.15075650811195374,
0.18857413530349731,
0.3720724880695343,
0.2850267291069031,
0.6990030407905579,
0.2172621637582779,
-0.38054004311561584,
0.26292750239372253,
-0.13858652114868164,
0.11160587519407... |
https://github.com/huggingface/datasets/issues/5422 | Datasets load error for saved github issues | I can confirm that the error exists!
I'm trying to read 3 parquet files locally:
```python
from datasets import load_dataset, Features, Value, ClassLabel
review_dataset = load_dataset(
"parquet",
data_files={
"train": os.path.join(sentiment_analysis_data_path, "train.parquet"),
"valida... | ### Describe the bug
Loading a previously downloaded & saved dataset as described in the HuggingFace course:
issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train")
Gives this error:
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset... | 95 | Datasets load error for saved github issues
### Describe the bug
Loading a previously downloaded & saved dataset as described in the HuggingFace course:
issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train")
Gives this error:
datasets.builder.DatasetGenerationError: ... | [
-0.4710150957107544,
0.07906512916088104,
0.059535037726163864,
0.4664856791496277,
0.22032460570335388,
0.09920280426740646,
0.3799690008163452,
0.2986510694026947,
0.09120248258113861,
-0.014517247676849365,
0.041823916137218475,
0.09861104935407639,
-0.28378358483314514,
0.1802484840154... |
https://github.com/huggingface/datasets/issues/5422 | Datasets load error for saved github issues | @Extremesarova I think this is a different issue, but understand using features could be a work-around.
It seems the field `closed_at` is `null` in many cases.
I've not found a way to specify only a single feature without (succesfully) specifiying the full and quite detailed set of expected features. Using this fea... | ### Describe the bug
Loading a previously downloaded & saved dataset as described in the HuggingFace course:
issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train")
Gives this error:
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset... | 66 | Datasets load error for saved github issues
### Describe the bug
Loading a previously downloaded & saved dataset as described in the HuggingFace course:
issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train")
Gives this error:
datasets.builder.DatasetGenerationError: ... | [
-0.4710150957107544,
0.07906512916088104,
0.059535037726163864,
0.4664856791496277,
0.22032460570335388,
0.09920280426740646,
0.3799690008163452,
0.2986510694026947,
0.09120248258113861,
-0.014517247676849365,
0.041823916137218475,
0.09861104935407639,
-0.28378358483314514,
0.1802484840154... |
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