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|---|---|---|---|---|---|---|
https://github.com/huggingface/datasets/issues/5345 | Wrong dtype for array in audio features | After some more investigation, this is due to [this line of code](https://github.com/huggingface/datasets/blob/main/src/datasets/features/audio.py#L279). The function `sf.read(file)` should be updated to `sf.read(file, dtype="float32")`
Indeed, the default value in soundfile is `float64` ([see here](https://pysoundf... | ### Describe the bug
When concatenating/interleaving different datasets, I stumble into an error because the features can't be aligned. After some investigation, I understood that the audio arrays had different dtypes, namely `float32` and `float64`. Consequently, the datasets cannot be merged.
### Steps to repro... | 31 | Wrong dtype for array in audio features
### Describe the bug
When concatenating/interleaving different datasets, I stumble into an error because the features can't be aligned. After some investigation, I understood that the audio arrays had different dtypes, namely `float32` and `float64`. Consequently, the datase... | [
-0.12291823327541351,
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0.221769526600... |
https://github.com/huggingface/datasets/issues/5345 | Wrong dtype for array in audio features | @qmeeus I agree, decoding of different audio formats should return the same dtypes indeed!
But note that here you are concatenating datasets with different sampling rates: 48000 for CommonVoice and 16000 for Voxpopuli. So you should cast them to the same sampling rate value before interleaving, for example:
```
cv... | ### Describe the bug
When concatenating/interleaving different datasets, I stumble into an error because the features can't be aligned. After some investigation, I understood that the audio arrays had different dtypes, namely `float32` and `float64`. Consequently, the datasets cannot be merged.
### Steps to repro... | 115 | Wrong dtype for array in audio features
### Describe the bug
When concatenating/interleaving different datasets, I stumble into an error because the features can't be aligned. After some investigation, I understood that the audio arrays had different dtypes, namely `float32` and `float64`. Consequently, the datase... | [
-0.12291823327541351,
-0.4239892363548279,
-0.13561224937438965,
0.3905717134475708,
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0.07607387751340866,
0.3804249167442322,
0.0684790313243866,
-0.2117391973733902,
0.051939159631729126,
-0.31208425760269165,
0.08473389595746994,
-0.049408070743083954,
0.221769526600... |
https://github.com/huggingface/datasets/issues/5345 | Wrong dtype for array in audio features | Hi Polina,
Sorry for the late answer
It is possible that the issue was due to a bug that is now fixed. I installed an editable version of datasets from github, but I don't recall whether I had updated it at the time of the issue. My research led me to other directions so I did not follow through on the interleave dat... | ### Describe the bug
When concatenating/interleaving different datasets, I stumble into an error because the features can't be aligned. After some investigation, I understood that the audio arrays had different dtypes, namely `float32` and `float64`. Consequently, the datasets cannot be merged.
### Steps to repro... | 63 | Wrong dtype for array in audio features
### Describe the bug
When concatenating/interleaving different datasets, I stumble into an error because the features can't be aligned. After some investigation, I understood that the audio arrays had different dtypes, namely `float32` and `float64`. Consequently, the datase... | [
-0.12291823327541351,
-0.4239892363548279,
-0.13561224937438965,
0.3905717134475708,
0.23831605911254883,
0.07607387751340866,
0.3804249167442322,
0.0684790313243866,
-0.2117391973733902,
0.051939159631729126,
-0.31208425760269165,
0.08473389595746994,
-0.049408070743083954,
0.221769526600... |
https://github.com/huggingface/datasets/issues/5342 | Emotion dataset cannot be downloaded | Hi @cbarond there's already an open issue at https://github.com/dair-ai/emotion_dataset/issues/5, as the data seems to be missing now, so check that issue instead 👍🏻 | ### Describe the bug
The emotion dataset gives a FileNotFoundError. The full error is: `FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1`.
It was working yesterday (December 7, 2022), but stopped working today (December 8, 2022).
### Steps to reproduce the bug
... | 23 | Emotion dataset cannot be downloaded
### Describe the bug
The emotion dataset gives a FileNotFoundError. The full error is: `FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1`.
It was working yesterday (December 7, 2022), but stopped working today (December 8, 202... | [
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https://github.com/huggingface/datasets/issues/5342 | Emotion dataset cannot be downloaded | Thanks @cbarond for reporting and @alvarobartt for pointing to the issue we opened in the author's repo.
Indeed, this issue was first raised in the "emotion" dataset Community tab: https://huggingface.co/datasets/emotion/discussions/3
I'm closing this issue and leave the issue above for the subsequent updates.
... | ### Describe the bug
The emotion dataset gives a FileNotFoundError. The full error is: `FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1`.
It was working yesterday (December 7, 2022), but stopped working today (December 8, 2022).
### Steps to reproduce the bug
... | 46 | Emotion dataset cannot be downloaded
### Describe the bug
The emotion dataset gives a FileNotFoundError. The full error is: `FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1`.
It was working yesterday (December 7, 2022), but stopped working today (December 8, 202... | [
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https://github.com/huggingface/datasets/issues/5342 | Emotion dataset cannot be downloaded | > try using "SetFit/emotion" instead
I' replaced "emotion" with "SetFit/Emotion", but the code is getting stuck at
`emotions = load_dataset("SetFit/emotion")`
I pause execution using the debugger, and it takes me to filelock.py:226
`with self._thread_lock:`
Do you know a way to get past this issue? | ### Describe the bug
The emotion dataset gives a FileNotFoundError. The full error is: `FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1`.
It was working yesterday (December 7, 2022), but stopped working today (December 8, 2022).
### Steps to reproduce the bug
... | 44 | Emotion dataset cannot be downloaded
### Describe the bug
The emotion dataset gives a FileNotFoundError. The full error is: `FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1`.
It was working yesterday (December 7, 2022), but stopped working today (December 8, 202... | [
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https://github.com/huggingface/datasets/issues/5338 | `map()` stops every 1000 steps | Hi !
> It starts using all the cores (I am not sure why because I did not pass num_proc)
The tokenizer uses Rust code that is multithreaded. And maybe the `feature_extractor` might run some things in parallel as well - but I'm not super familiar with its internals.
> then progress bar stops at every 1k steps. ... | ### Describe the bug
I am passing the following `prepare_dataset` function to `Dataset.map` (code is inspired from [here](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py#L454))
```python3
def prepare_dataset(batch):
# load and res... | 108 | `map()` stops every 1000 steps
### Describe the bug
I am passing the following `prepare_dataset` function to `Dataset.map` (code is inspired from [here](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py#L454))
```python3
def prepare_da... | [
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https://github.com/huggingface/datasets/issues/5338 | `map()` stops every 1000 steps | Hi @lhoestq
Thanks for the explanation! it was so helpful! Let me check why `feature_extractor` is running on multiple cpus. | ### Describe the bug
I am passing the following `prepare_dataset` function to `Dataset.map` (code is inspired from [here](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py#L454))
```python3
def prepare_dataset(batch):
# load and res... | 20 | `map()` stops every 1000 steps
### Describe the bug
I am passing the following `prepare_dataset` function to `Dataset.map` (code is inspired from [here](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py#L454))
```python3
def prepare_da... | [
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https://github.com/huggingface/datasets/issues/5337 | Support webdataset format | I like the idea of having `webdataset` as an optional dependency to ensure our loader generates web datasets the same way as the main project. | Webdataset is an efficient format for iterable datasets. It would be nice to support it in `datasets`, as discussed in https://github.com/rom1504/img2dataset/issues/234.
In particular it would be awesome to be able to load one using `load_dataset` in streaming mode (either from a local directory, or from a dataset o... | 25 | Support webdataset format
Webdataset is an efficient format for iterable datasets. It would be nice to support it in `datasets`, as discussed in https://github.com/rom1504/img2dataset/issues/234.
In particular it would be awesome to be able to load one using `load_dataset` in streaming mode (either from a local di... | [
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https://github.com/huggingface/datasets/issues/5337 | Support webdataset format | Webdataset is the one of the most popular dataset formats for large scale computer vision tasks. Upvote for this issue. | Webdataset is an efficient format for iterable datasets. It would be nice to support it in `datasets`, as discussed in https://github.com/rom1504/img2dataset/issues/234.
In particular it would be awesome to be able to load one using `load_dataset` in streaming mode (either from a local directory, or from a dataset o... | 20 | Support webdataset format
Webdataset is an efficient format for iterable datasets. It would be nice to support it in `datasets`, as discussed in https://github.com/rom1504/img2dataset/issues/234.
In particular it would be awesome to be able to load one using `load_dataset` in streaming mode (either from a local di... | [
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https://github.com/huggingface/datasets/issues/5337 | Support webdataset format | We haven't had the bandwidth to implement it so far, but if someone wants to give it a shot please don't hesitate ^^ | Webdataset is an efficient format for iterable datasets. It would be nice to support it in `datasets`, as discussed in https://github.com/rom1504/img2dataset/issues/234.
In particular it would be awesome to be able to load one using `load_dataset` in streaming mode (either from a local directory, or from a dataset o... | 23 | Support webdataset format
Webdataset is an efficient format for iterable datasets. It would be nice to support it in `datasets`, as discussed in https://github.com/rom1504/img2dataset/issues/234.
In particular it would be awesome to be able to load one using `load_dataset` in streaming mode (either from a local di... | [
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https://github.com/huggingface/datasets/issues/5332 | Passing numpy array to ClassLabel names causes ValueError | Should `datasets` allow `ClassLabel` input parameter to be an `np.array` even though internally we need to cast it to a Python list? @lhoestq @mariosasko | ### Describe the bug
If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX
TLDR:
If I define my classes as:
```
my_classes = np.array(['on... | 24 | Passing numpy array to ClassLabel names causes ValueError
### Describe the bug
If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX
TLDR:
... | [
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https://github.com/huggingface/datasets/issues/5332 | Passing numpy array to ClassLabel names causes ValueError | Hi! No, I don't think so. The `names` parameter is [annotated](https://github.com/huggingface/datasets/blob/582236640b9109988e5f7a16a8353696ffa09a16/src/datasets/features/features.py#L892) as `List[str]` (**NumPy arrays are not lists**), and considering that type checking is not a common practice in Python, I think we ... | ### Describe the bug
If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX
TLDR:
If I define my classes as:
```
my_classes = np.array(['on... | 38 | Passing numpy array to ClassLabel names causes ValueError
### Describe the bug
If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX
TLDR:
... | [
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0.7399469614028931,
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-0.0428033657371... |
https://github.com/huggingface/datasets/issues/5332 | Passing numpy array to ClassLabel names causes ValueError | I appreciate it is the wrong type, and that type checking is not common, but I think there's a few circumstances that make it a good idea from a usability perspective.
It's quite a difficult error to debug because it comes from a utility function (so it's not immediately obvious which parameter caused it). What make... | ### Describe the bug
If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX
TLDR:
If I define my classes as:
```
my_classes = np.array(['on... | 234 | Passing numpy array to ClassLabel names causes ValueError
### Describe the bug
If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX
TLDR:
... | [
0.21883399784564972,
0.1317862570285797,
-0.08366705477237701,
0.43438130617141724,
0.32979291677474976,
0.023139841854572296,
0.724523663520813,
0.2451750487089157,
0.061613429337739944,
0.22844676673412323,
-0.0062066735699772835,
0.7399469614028931,
-0.1995663046836853,
-0.0428033657371... |
https://github.com/huggingface/datasets/issues/5332 | Passing numpy array to ClassLabel names causes ValueError | I agree that it's not easy to debug this issue, so perhaps we could add some basic type checking (e.g. `not isinstance(names, list)` -> error) to make debugging easier. Feel free to submit a PR.
> Additionally, passing in a set instead of the list (again, relatively easy because people may run set(classes) to genera... | ### Describe the bug
If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX
TLDR:
If I define my classes as:
```
my_classes = np.array(['on... | 100 | Passing numpy array to ClassLabel names causes ValueError
### Describe the bug
If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX
TLDR:
... | [
0.21883399784564972,
0.1317862570285797,
-0.08366705477237701,
0.43438130617141724,
0.32979291677474976,
0.023139841854572296,
0.724523663520813,
0.2451750487089157,
0.061613429337739944,
0.22844676673412323,
-0.0062066735699772835,
0.7399469614028931,
-0.1995663046836853,
-0.0428033657371... |
https://github.com/huggingface/datasets/issues/5332 | Passing numpy array to ClassLabel names causes ValueError | What about checking for `Sequence` instead? I think users can pass a list or a tuple as well. | ### Describe the bug
If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX
TLDR:
If I define my classes as:
```
my_classes = np.array(['on... | 18 | Passing numpy array to ClassLabel names causes ValueError
### Describe the bug
If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX
TLDR:
... | [
0.21883399784564972,
0.1317862570285797,
-0.08366705477237701,
0.43438130617141724,
0.32979291677474976,
0.023139841854572296,
0.724523663520813,
0.2451750487089157,
0.061613429337739944,
0.22844676673412323,
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0.7399469614028931,
-0.1995663046836853,
-0.0428033657371... |
https://github.com/huggingface/datasets/issues/5325 | map(...batch_size=None) for IterableDataset | Hi! I agree it makes sense for `IterableDataset.map` to support the `batch_size=None` case. This should be super easy to fix. | ### Feature request
Dataset.map(...) allows batch_size to be None. It would be nice if IterableDataset did too.
### Motivation
Although it may seem a bit of a spurious request given that `IterableDataset` is meant for larger than memory datasets, but there are a couple of reasons why this might be nice.
One is th... | 20 | map(...batch_size=None) for IterableDataset
### Feature request
Dataset.map(...) allows batch_size to be None. It would be nice if IterableDataset did too.
### Motivation
Although it may seem a bit of a spurious request given that `IterableDataset` is meant for larger than memory datasets, but there are a couple o... | [
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https://github.com/huggingface/datasets/issues/5325 | map(...batch_size=None) for IterableDataset | @mariosasko as this is something simple maybe I can include it as part of https://github.com/huggingface/datasets/pull/5311? Let me know :+1: | ### Feature request
Dataset.map(...) allows batch_size to be None. It would be nice if IterableDataset did too.
### Motivation
Although it may seem a bit of a spurious request given that `IterableDataset` is meant for larger than memory datasets, but there are a couple of reasons why this might be nice.
One is th... | 19 | map(...batch_size=None) for IterableDataset
### Feature request
Dataset.map(...) allows batch_size to be None. It would be nice if IterableDataset did too.
### Motivation
Although it may seem a bit of a spurious request given that `IterableDataset` is meant for larger than memory datasets, but there are a couple o... | [
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https://github.com/huggingface/datasets/issues/5325 | map(...batch_size=None) for IterableDataset | Thanks again :)
> For practical usages, an alternative to this would be to convert from an iterable dataset to a map-style dataset, but it is not obvious how to do this.
This is interesting as well, if anyone wants to explore | ### Feature request
Dataset.map(...) allows batch_size to be None. It would be nice if IterableDataset did too.
### Motivation
Although it may seem a bit of a spurious request given that `IterableDataset` is meant for larger than memory datasets, but there are a couple of reasons why this might be nice.
One is th... | 42 | map(...batch_size=None) for IterableDataset
### Feature request
Dataset.map(...) allows batch_size to be None. It would be nice if IterableDataset did too.
### Motivation
Although it may seem a bit of a spurious request given that `IterableDataset` is meant for larger than memory datasets, but there are a couple o... | [
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https://github.com/huggingface/datasets/issues/5324 | Fix docstrings and types in documentation that appears on the website | Ok, I believe we've cleaned up most of the old syntax we were using for the user-facing docs! There are still a couple of `:obj:`'s and `:class:` floating around in the docstrings we don't expose that I'll track down :) | While I was working on https://github.com/huggingface/datasets/pull/5313 I've noticed that we have a mess in how we annotate types and format args and return values in the code. And some of it is displayed in the [Reference section](https://huggingface.co/docs/datasets/package_reference/builder_classes) of the document... | 40 | Fix docstrings and types in documentation that appears on the website
While I was working on https://github.com/huggingface/datasets/pull/5313 I've noticed that we have a mess in how we annotate types and format args and return values in the code. And some of it is displayed in the [Reference section](https://hugging... | [
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https://github.com/huggingface/datasets/issues/5324 | Fix docstrings and types in documentation that appears on the website | Hi @polinaeterna @albertvillanova @stevhliu, I hope you all are doing well.
Is this issue still unresolved as I am interested in it? | While I was working on https://github.com/huggingface/datasets/pull/5313 I've noticed that we have a mess in how we annotate types and format args and return values in the code. And some of it is displayed in the [Reference section](https://huggingface.co/docs/datasets/package_reference/builder_classes) of the document... | 22 | Fix docstrings and types in documentation that appears on the website
While I was working on https://github.com/huggingface/datasets/pull/5313 I've noticed that we have a mess in how we annotate types and format args and return values in the code. And some of it is displayed in the [Reference section](https://hugging... | [
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https://github.com/huggingface/datasets/issues/5324 | Fix docstrings and types in documentation that appears on the website | It should be mostly fixed for the user-facing APIs, but there may be some Sphinx syntax still lurking around in the non-public APIs. Feel free to open a PR to fix those if you catch any! 🤗 | While I was working on https://github.com/huggingface/datasets/pull/5313 I've noticed that we have a mess in how we annotate types and format args and return values in the code. And some of it is displayed in the [Reference section](https://huggingface.co/docs/datasets/package_reference/builder_classes) of the document... | 37 | Fix docstrings and types in documentation that appears on the website
While I was working on https://github.com/huggingface/datasets/pull/5313 I've noticed that we have a mess in how we annotate types and format args and return values in the code. And some of it is displayed in the [Reference section](https://hugging... | [
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https://github.com/huggingface/datasets/issues/5324 | Fix docstrings and types in documentation that appears on the website | Thanks for your reply @stevhliu :)
Sure, I will try to find out the remaining and fix that.
| While I was working on https://github.com/huggingface/datasets/pull/5313 I've noticed that we have a mess in how we annotate types and format args and return values in the code. And some of it is displayed in the [Reference section](https://huggingface.co/docs/datasets/package_reference/builder_classes) of the document... | 18 | Fix docstrings and types in documentation that appears on the website
While I was working on https://github.com/huggingface/datasets/pull/5313 I've noticed that we have a mess in how we annotate types and format args and return values in the code. And some of it is displayed in the [Reference section](https://hugging... | [
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https://github.com/huggingface/datasets/issues/5317 | `ImageFolder` performs poorly with large datasets | Hi ! ImageFolder is made for small scale datasets indeed. For large scale image datasets you better group your images in TAR archives or Arrow/Parquet files. This is true not just for ImageFolder loading performance, but also because having millions of files is not ideal for your filesystem or when moving the data arou... | ### Describe the bug
While testing image dataset creation, I'm seeing significant performance bottlenecks with imagefolders when scanning a directory structure with large number of images.
## Setup
* Nested directories (5 levels deep)
* 3M+ images
* 1 `metadata.jsonl` file
## Performance Degradation Point... | 186 | `ImageFolder` performs poorly with large datasets
### Describe the bug
While testing image dataset creation, I'm seeing significant performance bottlenecks with imagefolders when scanning a directory structure with large number of images.
## Setup
* Nested directories (5 levels deep)
* 3M+ images
* 1 `metada... | [
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https://github.com/huggingface/datasets/issues/5317 | `ImageFolder` performs poorly with large datasets | @lhoestq Thanks!
Perhaps it'd be worth adding a note on the documentation that `ImageFolder` is not intended for large datasets? This limitation is not intuitively obvious to someone who has not used it before, I think. | ### Describe the bug
While testing image dataset creation, I'm seeing significant performance bottlenecks with imagefolders when scanning a directory structure with large number of images.
## Setup
* Nested directories (5 levels deep)
* 3M+ images
* 1 `metadata.jsonl` file
## Performance Degradation Point... | 36 | `ImageFolder` performs poorly with large datasets
### Describe the bug
While testing image dataset creation, I'm seeing significant performance bottlenecks with imagefolders when scanning a directory structure with large number of images.
## Setup
* Nested directories (5 levels deep)
* 3M+ images
* 1 `metada... | [
-0.3610551953315735,
0.17454558610916138,
-0.12663346529006958,
0.4763832092285156,
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0.0810229554772377,
0.21064667403697968,
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0.016278676688671112,
0.13247263431549072,
0.15416306257247925,
-0.42717069387435913,
0.13595110177... |
https://github.com/huggingface/datasets/issues/5317 | `ImageFolder` performs poorly with large datasets | Thanks for the feedback @salieri! I opened #5329 to make it clear `ImageFolder` is not intended for large datasets. Please feel free to comment if you have any other feedback! 🙂 | ### Describe the bug
While testing image dataset creation, I'm seeing significant performance bottlenecks with imagefolders when scanning a directory structure with large number of images.
## Setup
* Nested directories (5 levels deep)
* 3M+ images
* 1 `metadata.jsonl` file
## Performance Degradation Point... | 31 | `ImageFolder` performs poorly with large datasets
### Describe the bug
While testing image dataset creation, I'm seeing significant performance bottlenecks with imagefolders when scanning a directory structure with large number of images.
## Setup
* Nested directories (5 levels deep)
* 3M+ images
* 1 `metada... | [
-0.3610551953315735,
0.17454558610916138,
-0.12663346529006958,
0.4763832092285156,
-0.022155042737722397,
-0.15415185689926147,
0.0810229554772377,
0.21064667403697968,
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0.016278676688671112,
0.13247263431549072,
0.15416306257247925,
-0.42717069387435913,
0.13595110177... |
https://github.com/huggingface/datasets/issues/5315 | Adding new splits to a dataset script with existing old splits info in metadata's `dataset_info` fails | EDIT:
I think in this case, the metadata files (either README or JSON) should not be read (i.e. `self.info.splits` should be None).
One idea:
- I think ideally we should set this behavior when we pass `--save_info` to the CLI `test`
- However, currently, the builder is unaware of this: `save_info` arg is not pas... | ### Describe the bug
If you first create a custom dataset with a specific set of splits, generate metadata with `datasets-cli test ... --save_info`, then change your script to include more splits, it fails.
That's what happened in https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/discussions/2#6385f... | 57 | Adding new splits to a dataset script with existing old splits info in metadata's `dataset_info` fails
### Describe the bug
If you first create a custom dataset with a specific set of splits, generate metadata with `datasets-cli test ... --save_info`, then change your script to include more splits, it fails.
That... | [
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0.27656653523... |
https://github.com/huggingface/datasets/issues/5315 | Adding new splits to a dataset script with existing old splits info in metadata's `dataset_info` fails | > I think in this case
@albertvillanova You mean in cases when the script was changed?
I suggest that we:
* add a check on the slice (like 'split_name[n%]) kind of format here: https://github.com/huggingface/datasets/blob/main/src/datasets/splits.py#L523 to catch things like this.
* Error here happens before... | ### Describe the bug
If you first create a custom dataset with a specific set of splits, generate metadata with `datasets-cli test ... --save_info`, then change your script to include more splits, it fails.
That's what happened in https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/discussions/2#6385f... | 142 | Adding new splits to a dataset script with existing old splits info in metadata's `dataset_info` fails
### Describe the bug
If you first create a custom dataset with a specific set of splits, generate metadata with `datasets-cli test ... --save_info`, then change your script to include more splits, it fails.
That... | [
-0.24759887158870697,
0.12104375660419464,
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0.38429567217826843,
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0.02560550719499588,
-0.011631143279373646,
0.3385595381259918,
-0.1291259229183197,
0.27656653523... |
https://github.com/huggingface/datasets/issues/5315 | Adding new splits to a dataset script with existing old splits info in metadata's `dataset_info` fails | I edited my previous comment:
- First I proposed setting `self.info.splits` to None when `ignore_verifications=True`
- I thought it was the easiest implementation because `ignore_verifications` is passed to `DatasetBuilder.download_and_prepare`
- However, afterwards, I realized this might not be a good idea for ... | ### Describe the bug
If you first create a custom dataset with a specific set of splits, generate metadata with `datasets-cli test ... --save_info`, then change your script to include more splits, it fails.
That's what happened in https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/discussions/2#6385f... | 201 | Adding new splits to a dataset script with existing old splits info in metadata's `dataset_info` fails
### Describe the bug
If you first create a custom dataset with a specific set of splits, generate metadata with `datasets-cli test ... --save_info`, then change your script to include more splits, it fails.
That... | [
-0.24759887158870697,
0.12104375660419464,
0.013389259576797485,
0.18365581333637238,
0.02445199154317379,
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0.42855992913246155,
0.38429567217826843,
-0.08495767414569855,
0.02560550719499588,
-0.011631143279373646,
0.3385595381259918,
-0.1291259229183197,
0.27656653523... |
https://github.com/huggingface/datasets/issues/5314 | Datasets: classification_report() got an unexpected keyword argument 'suffix' | @JonathanAlis also note that the metrics are deprecated in our `datasets` library.
Please, use the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate | https://github.com/huggingface/datasets/blob/main/metrics/seqeval/seqeval.py
> import datasets
predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
seqeval = datasets.load_metri... | 21 | Datasets: classification_report() got an unexpected keyword argument 'suffix'
https://github.com/huggingface/datasets/blob/main/metrics/seqeval/seqeval.py
> import datasets
predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
references = [['O', 'O', 'O', 'B-MISC', '... | [
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https://github.com/huggingface/datasets/issues/5305 | Dataset joelito/mc4_legal does not work with multiple files | Thanks for reporting @JoelNiklaus.
Please note that since we moved all dataset loading scripts to the Hub, the issues and pull requests relative to specific datasets are directly handled on the Hub, in their Community tab. I'm transferring this issue there: https://huggingface.co/datasets/joelito/mc4_legal/discussio... | ### Describe the bug
The dataset https://huggingface.co/datasets/joelito/mc4_legal works for languages like bg with a single data file, but not for languages with multiple files like de. It shows zero rows for the de dataset.
joelniklaus@Joels-MacBook-Pro ~/N/P/C/L/p/m/mc4_legal (main) [1]> python test_mc4_legal.... | 54 | Dataset joelito/mc4_legal does not work with multiple files
### Describe the bug
The dataset https://huggingface.co/datasets/joelito/mc4_legal works for languages like bg with a single data file, but not for languages with multiple files like de. It shows zero rows for the de dataset.
joelniklaus@Joels-MacBook-... | [
-0.18840166926383972,
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0.021941691637039185,
0.08973878622055054,
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0.137567773461... |
https://github.com/huggingface/datasets/issues/5304 | timit_asr doesn't load the test split. | The [timit_asr.py](https://huggingface.co/datasets/timit_asr/blob/main/timit_asr.py) script iterates over the WAV files per split directory using this:
```python
wav_paths = sorted(Path(data_dir).glob(f"**/{split}/**/*.wav"))
wav_paths = wav_paths if wav_paths else sorted(Path(data_dir).glob(f"**/{split.upper()}/**/... | ### Describe the bug
When I use the function ```timit = load_dataset('timit_asr', data_dir=data_dir)```, it only loads train split, not test split.
I tried to change the directory and filename to lower case to upper case for the test split, but it does not work at all.
```python
DatasetDict({
train: Datase... | 42 | timit_asr doesn't load the test split.
### Describe the bug
When I use the function ```timit = load_dataset('timit_asr', data_dir=data_dir)```, it only loads train split, not test split.
I tried to change the directory and filename to lower case to upper case for the test split, but it does not work at all.
``... | [
-0.24493838846683502,
-0.11158958077430725,
-0.020474180579185486,
0.3899768590927124,
0.1943710893392563,
0.033279068768024445,
0.4489305913448334,
0.22222363948822021,
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0.05952685326337814,
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0.2974373400211334,
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0.360800117254... |
https://github.com/huggingface/datasets/issues/5295 | Extractions failed when .zip file located on read-only path (e.g., SageMaker FastFile mode) | Hi ! Thanks for reporting. Indeed the lock file should be placed in a directory with write permission (e.g. in the directory where the archive is extracted). | ### Describe the bug
Hi,
`load_dataset()` does not work .zip files located on a read-only directory. Looks like it's because Dataset creates a lock file in the [same directory](https://github.com/huggingface/datasets/blob/df4bdd365f2abb695f113cbf8856a925bc70901b/src/datasets/utils/extract.py) as the .zip file.
... | 27 | Extractions failed when .zip file located on read-only path (e.g., SageMaker FastFile mode)
### Describe the bug
Hi,
`load_dataset()` does not work .zip files located on a read-only directory. Looks like it's because Dataset creates a lock file in the [same directory](https://github.com/huggingface/datasets/blo... | [
-0.1946299523115158,
-0.12467759847640991,
0.037194933742284775,
0.46573561429977417,
0.0893811285495758,
-0.10493116080760956,
0.30083128809928894,
0.1528676450252533,
0.026789184659719467,
0.14810168743133545,
-0.43420031666755676,
0.4216318130493164,
-0.0977354422211647,
0.3973970115184... |
https://github.com/huggingface/datasets/issues/5295 | Extractions failed when .zip file located on read-only path (e.g., SageMaker FastFile mode) | I opened https://github.com/huggingface/datasets/pull/5320 to fix this - it places the lock file in the cache directory instead of trying to put in next to the ZIP where it's read-only | ### Describe the bug
Hi,
`load_dataset()` does not work .zip files located on a read-only directory. Looks like it's because Dataset creates a lock file in the [same directory](https://github.com/huggingface/datasets/blob/df4bdd365f2abb695f113cbf8856a925bc70901b/src/datasets/utils/extract.py) as the .zip file.
... | 29 | Extractions failed when .zip file located on read-only path (e.g., SageMaker FastFile mode)
### Describe the bug
Hi,
`load_dataset()` does not work .zip files located on a read-only directory. Looks like it's because Dataset creates a lock file in the [same directory](https://github.com/huggingface/datasets/blo... | [
-0.1946299523115158,
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0.30083128809928894,
0.1528676450252533,
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0.14810168743133545,
-0.43420031666755676,
0.4216318130493164,
-0.0977354422211647,
0.3973970115184... |
https://github.com/huggingface/datasets/issues/5292 | Missing documentation build for versions 2.7.1 and 2.6.2 | - Build docs for 2.6.2:
- Commit: a6a5a1cf4cdf1e0be65168aed5a327f543001fe8
- Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539470622/jobs/5941404044
- Build docs for 2.7.1:
- Commit: 5ef1ab1cc06c2b7a574bf2df454cd9fcb071ccb2
- Build docs GH Action: https://github.com/huggingfac... | After the patch releases [2.7.1](https://github.com/huggingface/datasets/releases/tag/2.7.1) and [2.6.2](https://github.com/huggingface/datasets/releases/tag/2.6.2), the online docs were not properly built (the build_documentation workflow was not triggered).
There was a fix by:
- #5291
However, both documentati... | 28 | Missing documentation build for versions 2.7.1 and 2.6.2
After the patch releases [2.7.1](https://github.com/huggingface/datasets/releases/tag/2.7.1) and [2.6.2](https://github.com/huggingface/datasets/releases/tag/2.6.2), the online docs were not properly built (the build_documentation workflow was not triggered).
... | [
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0.015760108828544617,
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0.03283867985010147,
0.3588838279247284,
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0.015483394265174866,
0.04930455982685089,
0.2143089771270752,
0.14360961318016052,
0.21212178468... |
https://github.com/huggingface/datasets/issues/5288 | Lossy json serialization - deserialization of dataset info | Hi ! JSON is a lossy format indeed. If you want to keep the feature types or other metadata I'd encourage you to store them as well. For example you can use `dataset.info.write_to_directory` and `DatasetInfo.from_directory` to store the feature types, split info, description, license etc. | ### Describe the bug
Saving a dataset to disk as json (using `to_json`) and then loading it again (using `load_dataset`) results in features whose labels are not type-cast correctly. In the code snippet below, `features.label` should have a label of type `ClassLabel` but has type `Value` instead.
### Steps to re... | 45 | Lossy json serialization - deserialization of dataset info
### Describe the bug
Saving a dataset to disk as json (using `to_json`) and then loading it again (using `load_dataset`) results in features whose labels are not type-cast correctly. In the code snippet below, `features.label` should have a label of type ... | [
0.0051988884806632996,
0.21891960501670837,
-0.008309511467814445,
0.42117345333099365,
0.520891547203064,
0.14566725492477417,
0.4419677257537842,
0.2881326675415039,
-0.20148856937885284,
-0.11444404721260071,
-0.14278297126293182,
0.7578492760658264,
0.030339397490024567,
0.128287360072... |
https://github.com/huggingface/datasets/issues/5286 | FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json | I found a solution
If you specifically install datasets==1.18 and then run
import datasets
wiki = datasets.load_dataset('wikipedia', '20200501.en')
then this should work (it worked for me.) | ### Describe the bug
I follow the steps provided on the website [https://huggingface.co/datasets/wikipedia](https://huggingface.co/datasets/wikipedia)
$ pip install apache_beam mwparserfromhell
>>> from datasets import load_dataset
>>> load_dataset("wikipedia", "20220301.en")
however this results in the follo... | 26 | FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
### Describe the bug
I follow the steps provided on the website [https://huggingface.co/datasets/wikipedia](https://huggingface.co/datasets/wikipedia)
$ pip install apache_beam mwparserfromhell
>>> from datasets... | [
-0.23028507828712463,
0.18276682496070862,
-0.009024469181895256,
0.26874202489852905,
0.10095739364624023,
0.13449454307556152,
0.10901649296283722,
0.34828802943229675,
0.15581688284873962,
0.21776749193668365,
0.2789643108844757,
0.14793366193771362,
-0.1085590198636055,
-0.358284473419... |
https://github.com/huggingface/datasets/issues/5284 | Features of IterableDataset set to None by remove column | Thanks @lhoestq and @alvarobartt!
This would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!
_c.f._ https://twitter.com/sanchitgandhi99/status/15921... | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
... | 43 | Features of IterableDataset set to None by remove column
### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librisp... | [
-0.2610943019390106,
-0.29308420419692993,
-0.019567813724279404,
0.13754193484783173,
0.29093313217163086,
-0.026312723755836487,
0.49931764602661133,
0.15073545277118683,
-0.12898750603199005,
0.1906326711177826,
-0.24397888779640198,
0.44379934668540955,
-0.07863331586122513,
-0.0467832... |
https://github.com/huggingface/datasets/issues/5284 | Features of IterableDataset set to None by remove column | > Thanks @lhoestq and @alvarobartt!
>
>
>
> This would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!
>
>
>
> _c.f._ https://twitter.com/sanchitgan... | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
... | 100 | Features of IterableDataset set to None by remove column
### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librisp... | [
-0.2610943019390106,
-0.29308420419692993,
-0.019567813724279404,
0.13754193484783173,
0.29093313217163086,
-0.026312723755836487,
0.49931764602661133,
0.15073545277118683,
-0.12898750603199005,
0.1906326711177826,
-0.24397888779640198,
0.44379934668540955,
-0.07863331586122513,
-0.0467832... |
https://github.com/huggingface/datasets/issues/5284 | Features of IterableDataset set to None by remove column | @sanchit-gandhi PR is ready and open for review at #5287, but there's still one issue I may need @lhoestq's input :hugs: | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
... | 21 | Features of IterableDataset set to None by remove column
### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librisp... | [
-0.2610943019390106,
-0.29308420419692993,
-0.019567813724279404,
0.13754193484783173,
0.29093313217163086,
-0.026312723755836487,
0.49931764602661133,
0.15073545277118683,
-0.12898750603199005,
0.1906326711177826,
-0.24397888779640198,
0.44379934668540955,
-0.07863331586122513,
-0.0467832... |
https://github.com/huggingface/datasets/issues/5284 | Features of IterableDataset set to None by remove column | Let us know @sanchit-gandhi if you need a new release of `datasets` soon with this fix included :) | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
... | 18 | Features of IterableDataset set to None by remove column
### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librisp... | [
-0.2610943019390106,
-0.29308420419692993,
-0.019567813724279404,
0.13754193484783173,
0.29093313217163086,
-0.026312723755836487,
0.49931764602661133,
0.15073545277118683,
-0.12898750603199005,
0.1906326711177826,
-0.24397888779640198,
0.44379934668540955,
-0.07863331586122513,
-0.0467832... |
https://github.com/huggingface/datasets/issues/5284 | Features of IterableDataset set to None by remove column | Thanks for the fix guys! We can direct people to install `datasets` from main if that's easier! | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
... | 17 | Features of IterableDataset set to None by remove column
### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librisp... | [
-0.2610943019390106,
-0.29308420419692993,
-0.019567813724279404,
0.13754193484783173,
0.29093313217163086,
-0.026312723755836487,
0.49931764602661133,
0.15073545277118683,
-0.12898750603199005,
0.1906326711177826,
-0.24397888779640198,
0.44379934668540955,
-0.07863331586122513,
-0.0467832... |
https://github.com/huggingface/datasets/issues/5284 | Features of IterableDataset set to None by remove column | Hi @asennoussi so this was already fixed and released as part of https://github.com/huggingface/datasets/releases/tag/2.8.0, so you should be able to install it as `pip install datasets==2.8.0` or just to use `pip install datasets --upgrade` to get the latest version, as of now, the https://github.com/huggingface/datas... | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
... | 47 | Features of IterableDataset set to None by remove column
### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librisp... | [
-0.2610943019390106,
-0.29308420419692993,
-0.019567813724279404,
0.13754193484783173,
0.29093313217163086,
-0.026312723755836487,
0.49931764602661133,
0.15073545277118683,
-0.12898750603199005,
0.1906326711177826,
-0.24397888779640198,
0.44379934668540955,
-0.07863331586122513,
-0.0467832... |
https://github.com/huggingface/datasets/issues/5284 | Features of IterableDataset set to None by remove column | Still facing the same issue though:
```
from datasets import IterableDatasetDict, load_dataset
raw_datasets = vectorized_datasets = IterableDatasetDict()
raw_datasets["train"] = load_dataset("asennoussi/private", split="train", use_auth_token=True, streaming=True)
raw_datasets["test"] = load_dataset("asenno... | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
... | 120 | Features of IterableDataset set to None by remove column
### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librisp... | [
-0.2610943019390106,
-0.29308420419692993,
-0.019567813724279404,
0.13754193484783173,
0.29093313217163086,
-0.026312723755836487,
0.49931764602661133,
0.15073545277118683,
-0.12898750603199005,
0.1906326711177826,
-0.24397888779640198,
0.44379934668540955,
-0.07863331586122513,
-0.0467832... |
https://github.com/huggingface/datasets/issues/5284 | Features of IterableDataset set to None by remove column | Hmm weird, could you try to print
```python
print("Processed features: ", vectorized_datasets['train'].features)
```
again after iterating over the `vectorized_datasets`? In the code above, should be last line :) | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
... | 28 | Features of IterableDataset set to None by remove column
### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librisp... | [
-0.2610943019390106,
-0.29308420419692993,
-0.019567813724279404,
0.13754193484783173,
0.29093313217163086,
-0.026312723755836487,
0.49931764602661133,
0.15073545277118683,
-0.12898750603199005,
0.1906326711177826,
-0.24397888779640198,
0.44379934668540955,
-0.07863331586122513,
-0.0467832... |
https://github.com/huggingface/datasets/issues/5284 | Features of IterableDataset set to None by remove column | Didn't seem to fix it:
```
Original features: dict_keys(['path', 'audio', 'sentence'])
Processed features: None
Processed features: None
``` | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
... | 18 | Features of IterableDataset set to None by remove column
### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librisp... | [
-0.2610943019390106,
-0.29308420419692993,
-0.019567813724279404,
0.13754193484783173,
0.29093313217163086,
-0.026312723755836487,
0.49931764602661133,
0.15073545277118683,
-0.12898750603199005,
0.1906326711177826,
-0.24397888779640198,
0.44379934668540955,
-0.07863331586122513,
-0.0467832... |
https://github.com/huggingface/datasets/issues/5284 | Features of IterableDataset set to None by remove column | Actually the culprit looks to be this one:
`vectorized_datasets = raw_datasets.map(prepare_dataset).with_format("torch")`
When I remove this line: `vectorized_datasets = vectorized_datasets.remove_columns(['input_length', 'labels_length']+list(next(iter(raw_datasets.values())).features))`
I still get
```
Proce... | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
... | 28 | Features of IterableDataset set to None by remove column
### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librisp... | [
-0.2610943019390106,
-0.29308420419692993,
-0.019567813724279404,
0.13754193484783173,
0.29093313217163086,
-0.026312723755836487,
0.49931764602661133,
0.15073545277118683,
-0.12898750603199005,
0.1906326711177826,
-0.24397888779640198,
0.44379934668540955,
-0.07863331586122513,
-0.0467832... |
https://github.com/huggingface/datasets/issues/5284 | Features of IterableDataset set to None by remove column | > The culprit is definitely `.map` Just validated it. Any idea please?
Yes, indeed `.map` losses the features, because AFAIK pre-fetching the data to infer the features is expensive and not ideal, that's part of this issue https://github.com/huggingface/datasets/issues/3888
Anyway, now you can pass the `features`... | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
... | 101 | Features of IterableDataset set to None by remove column
### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librisp... | [
-0.2610943019390106,
-0.29308420419692993,
-0.019567813724279404,
0.13754193484783173,
0.29093313217163086,
-0.026312723755836487,
0.49931764602661133,
0.15073545277118683,
-0.12898750603199005,
0.1906326711177826,
-0.24397888779640198,
0.44379934668540955,
-0.07863331586122513,
-0.0467832... |
https://github.com/huggingface/datasets/issues/5284 | Features of IterableDataset set to None by remove column | @asennoussi alternatively you can just call `._resolve_features()` from your `IterableDataset` and it will pre-fetch the data to resolve the features, but note that feature-inference is not as accurate as if you manually specify which features and feature-types the `IterableDataset` has, as mentioned in the comment abo... | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
... | 56 | Features of IterableDataset set to None by remove column
### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librisp... | [
-0.2610943019390106,
-0.29308420419692993,
-0.019567813724279404,
0.13754193484783173,
0.29093313217163086,
-0.026312723755836487,
0.49931764602661133,
0.15073545277118683,
-0.12898750603199005,
0.1906326711177826,
-0.24397888779640198,
0.44379934668540955,
-0.07863331586122513,
-0.0467832... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | Or for example an archive on GitHub releases! Before I added support for JXL (locally only, PR still pending) I was considering hosting my files on GitHub instead... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 28 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.1793348789215088,
0.11834008991718292,
-0.13391616940498352,
0.07898153364658356,
0.12148791551589966,
-0.06233639270067215,
0.28353244066238403,
0.1423346847295761,
0.2978156805038452,
0.134517639875412,
-0.06672046333551407,
0.1702692061662674,
-0.22052747011184692,
0.8026942014694214... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | +1 to this. I would like to use 'audiofolder' with a data_dir that's on S3, for example. I don't want to upload my dataset to the Hub, but I would find all the fingerprinting/caching features useful. | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 36 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.27272891998291016,
-0.034030482172966,
-0.1325058937072754,
0.08983884751796722,
0.22382508218288422,
0.015936121344566345,
0.2426188588142395,
-0.08133552968502045,
0.16180968284606934,
0.07210321724414825,
-0.1999158412218094,
0.2966148853302002,
-0.38912639021873474,
0.82465410232543... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | Adding to the conversation, Dask also uses `fsspec` for this feature.
[Dask: How to connect to remote data](https://docs.dask.org/en/stable/how-to/connect-to-remote-data.html)
Happy to help on this feature :D | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 25 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.23842088878154755,
-0.11731865257024765,
-0.1280565857887268,
0.03234294056892395,
0.1728338897228241,
-0.046507179737091064,
0.1993662714958191,
0.04815254360437393,
0.21960368752479553,
0.08897943049669266,
-0.08867606520652771,
0.19696977734565735,
-0.16965462267398834,
0.88736963272... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | +1 to this feature request since I think it also tackles my use-case. I am collaborating with a team, working with a loading script which takes some time to generate the dataset artifacts. It would be very handy to use this as a cloud cache to avoid duplicating the effort.
Currently we could use `builder.download_a... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 78 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.32151010632514954,
0.11817887425422668,
-0.10677774250507355,
0.13897544145584106,
0.15476307272911072,
0.09225202351808548,
0.18456116318702698,
0.014310130849480629,
0.21039386093616486,
-0.0009162053465843201,
0.0446501299738884,
0.19866110384464264,
-0.2637076675891876,
0.6508303284... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | Makes sense ! If you want to load locally a dataset that you download_and_prepared on a cloud storage, you would use `load_dataset(path_to_cloud_storage)` indeed. It would download the data from the cloud storage, cache them locally, and return a `Dataset`. | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 39 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.20289891958236694,
-0.1459718942642212,
-0.1402789056301117,
0.05473892390727997,
0.205915167927742,
-0.039676565676927567,
0.23502153158187866,
0.02785985916852951,
0.2646523416042328,
0.07531080394983292,
-0.15618759393692017,
0.24585048854351044,
-0.19070763885974884,
0.8310504555702... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | It seems currently the `cached_path` function handles all URLs by `get_from_cache` that only supports `ftp` and `http(s)` here:
https://github.com/huggingface/datasets/blob/b5672a956d5de864e6f5550e493527d962d6ae55/src/datasets/utils/file_utils.py#L181
I guess one can add another condition that handles `s3://` or `g... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 35 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.11678887903690338,
0.031523630023002625,
-0.11542966961860657,
0.0976506918668747,
0.17531050741672516,
-0.05701031535863876,
0.08391141146421432,
0.07078946381807327,
0.24949780106544495,
-0.13037139177322388,
-0.059850625693798065,
0.1062265932559967,
-0.27721452713012695,
0.634497165... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | I could use this functionality, so I put together a PR using @kyamagu's suggestion to use `fsspec` in `datasets.utils.file_utils`
https://github.com/huggingface/datasets/pull/5580 | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 20 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.15794737637043,
-0.09473367035388947,
-0.14925868809223175,
0.10644632577896118,
0.2577477693557739,
0.0018483921885490417,
0.1547192931175232,
-0.026336800307035446,
0.17803868651390076,
0.01903793215751648,
-0.05524948984384537,
0.23475636541843414,
-0.2650807499885559,
0.803022623062... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | Thanks @dwyatte for adding support for fsspec urls
Let me just reopen this since the original issue is not resolved | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 20 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.1257811188697815,
-0.09776660799980164,
-0.12361013889312744,
0.10396527498960495,
0.21622490882873535,
-0.03363719582557678,
0.15729880332946777,
-0.06592392921447754,
0.15778641402721405,
0.009724751114845276,
-0.07180558890104294,
0.1858242154121399,
-0.2063719481229782,
0.8174085617... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | I'm not yet understanding how to use https://github.com/huggingface/datasets/pull/5580 in order to use `load_dataset(data_files="s3://...")`. Any help/example would be much appreciated :) thanks! | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 21 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.09307734668254852,
-0.14620983600616455,
-0.12993286550045013,
0.1842309981584549,
0.19940678775310516,
0.01147424429655075,
0.21903063356876373,
-0.11655628681182861,
0.13301406800746918,
-0.01619960367679596,
-0.012743466533720493,
0.14910444617271423,
-0.2301374077796936,
0.858941078... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | It's still not officially supported x) But you can try to update `request_etag` in `file_utils.py` to use `fsspec_head` instead of `http_head`. It is responsible of getting the ETags of the remote files for caching. This change may do the trick for S3 urls | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 43 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.10912948101758957,
-0.17285196483135223,
-0.08976604789495468,
0.053350772708654404,
0.22264890372753143,
-0.09287692606449127,
0.2767224907875061,
0.06577999889850616,
0.1796683967113495,
0.0005551055073738098,
-0.2392396777868271,
0.2122480869293213,
-0.14371651411056519,
0.6675627231... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | Thank you for your guys help on this and merging in #5580. I manually pulled the changes to my local datasets package (datasets.utils.file_utils.py) since it only seemed to be this file that was changed in the PR and I'm getting the error:
InvalidSchema: No connection adapters were found for 's3://bucket/folder/'. I'... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 92 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.2100406289100647,
0.0872044563293457,
-0.05265483632683754,
0.14210334420204163,
0.11030708253383636,
-0.05429212749004364,
0.27416685223579407,
-0.006058700382709503,
0.0784444808959961,
-0.024943619966506958,
-0.19915413856506348,
0.19848473370075226,
-0.01397672202438116,
0.808444678... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | The current implementation depends on gcsfs/s3fs being able to authenticate through some other means e.g., environmental variables. For AWS, it looks like you can set `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN`
Note that while testing this just now, I did note a discrepancy between gcsfs an... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 130 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.22709102928638458,
0.1061171293258667,
-0.09425002336502075,
0.04079984873533249,
0.20343028008937836,
-0.008658364415168762,
0.43176454305648804,
-0.10082876682281494,
0.18912360072135925,
0.0013835504651069641,
-0.11103865504264832,
0.2538585066795349,
-0.08419501781463623,
0.72529500... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | > Note that while testing this just now, I did note a discrepancy between gcsfs and s3fs that we might want to address where gcsfs passes the timeout from storage_options [here](https://github.com/huggingface/datasets/blob/3e6269979fc80ae8939294d26298897f0db5b84d/src/datasets/utils/file_utils.py#L333) down into the aio... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 109 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.11338302493095398,
-0.004142701625823975,
-0.12318012118339539,
-0.020559631288051605,
0.18401814997196198,
-0.1016637459397316,
0.46522918343544006,
-0.13734637200832367,
0.13873884081840515,
0.11933711916208267,
0.0024719564244151115,
0.19910310208797455,
-0.03207060694694519,
0.71764... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | @lhoestq sorry for being a little dense here but I am very keen to use fsspec / adlfs for for a larger image dataset I have for object detection. I have to keep it on Azure storage and would also like to avoid a full download or zipping (so use `load_dataset(..., streaming=True)`. So this development is godsend :) only... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 274 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.20576947927474976,
-0.1730315387248993,
-0.12662512063980103,
0.02529376745223999,
0.10306788980960846,
-0.10649223625659943,
0.3078852891921997,
-0.11788056790828705,
0.09713080525398254,
-0.04736436903476715,
-0.16153837740421295,
0.07935129851102829,
-0.285468190908432,
0.80796432495... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | @lhoestq
hello, i still have problem with loading json from S3:
storage_options = {
"key": xxxx,
"secret": xxx,
"endpoint_url": xxxx
}
path = 's3://xxx/xxxxxxx.json'
dataset = load_dataset("json", data_files=path, storage_options=storage_options)
and it throws an error:
TypeError: AioSessio... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 49 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.07479588687419891,
-0.10796868801116943,
-0.08909515291452408,
0.1721281260251999,
0.16700851917266846,
-0.03813119977712631,
0.1875777393579483,
-0.007055513560771942,
0.20047776401042938,
-0.0032003596425056458,
-0.08756592124700546,
0.25158393383026123,
-0.1180756464600563,
0.7572133... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | Hi @lhoestq, thanks for getting back to me :) you have been busy over the summer I see... I was on `2.12.0`. I have updated to `2.14.4`.
Now `d = load_dataset("new_dataset.py", storage_options=storage_options, split="train", streaming=True)` works for Azure blob storage (with a local data loader script) when I expl... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 140 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.16544894874095917,
0.19363835453987122,
-0.14471273124217987,
0.1706695258617401,
0.19591417908668518,
-0.038629885762929916,
0.33907458186149597,
0.0681251510977745,
0.06344939768314362,
0.051087960600852966,
-0.07675474137067795,
0.15149839222431183,
-0.33743005990982056,
0.7472612857... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | > Any chance that it could work out-of-the-box by supplying just the image folder, not the full list of image filenames? It seems that dl_manager.download(_URL) always wants one or more (possibly archived) files. In my situation, where I don't want to archive or download, it would be great to just supply the folder (se... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 104 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.12195324897766113,
0.12658937275409698,
-0.14086255431175232,
0.26538458466529846,
0.28087082505226135,
0.06839855760335922,
0.22423100471496582,
0.013140462338924408,
0.2562103569507599,
0.12566734850406647,
-0.03907793015241623,
0.22973865270614624,
-0.34713298082351685,
0.61140507459... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | I'm seeing same problem as @hjq133 with following versions:
```
datasets==2.15.0
(venv) ➜ finetuning-llama2 git:(main) ✗ pip freeze | grep s3fs
s3fs==2023.10.0
(venv) ➜ finetuning-llama2 git:(main) ✗ pip freeze | grep fsspec
fsspec==2023.10.0
``` | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 34 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.18196022510528564,
-0.02283729612827301,
-0.11067049205303192,
0.12068100273609161,
0.24369290471076965,
-0.06892726570367813,
0.21550054848194122,
0.01004670187830925,
0.16540159285068512,
0.09179404377937317,
-0.08870052546262741,
0.3211118280887604,
-0.25414687395095825,
0.7336252331... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | > @lhoestq hello, i still have problem with loading json from S3:
>
> storage_options = { "key": xxxx, "secret": xxx, "endpoint_url": xxxx } path = 's3://xxx/xxxxxxx.json' dataset = load_dataset("json", data_files=path, storage_options=storage_options)
>
> and it throws an error: TypeError: AioSession.**init**() ... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 83 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
0.000038076192140579224,
-0.01902710646390915,
-0.07329662144184113,
0.2648736834526062,
0.07412987947463989,
-0.0986940860748291,
0.2839660346508026,
-0.09188660979270935,
0.1530684232711792,
0.05079592019319534,
-0.1496574729681015,
0.23283538222312927,
-0.05071883648633957,
0.6841094493... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | Hi ! S3 support is still experimental. It seems like there is an extra `hf` field passed to the `s3fs` storage_options that causes this error. I just check the source code of `_prepare_single_hop_path_and_storage_options` and I think you can try passing explicitly your own `storage_options={"s3": {...}}`. Also note tha... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 66 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.24600493907928467,
-0.1542782485485077,
-0.04496544599533081,
0.038176171481609344,
0.33450716733932495,
0.0012636259198188782,
0.22305093705654144,
0.08378458023071289,
0.19387361407279968,
0.058323830366134644,
-0.18344491720199585,
0.17693760991096497,
-0.21696114540100098,
0.6514803... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | @lhoestq Would this work either with an Azure Blob Storage Container or its respective Azure Machine Learning Datastore? If yes, what would that look like in code? I've tried a couple of combinations but no success so far, on the latest version of `datasets`. I need to migrate a dataset to the Azure cloud, `load_datase... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 92 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.15654821693897247,
0.009563177824020386,
-0.11528204381465912,
0.011856067925691605,
0.04420924931764603,
-0.08809709548950195,
0.2413681000471115,
-0.06805340945720673,
0.08408209681510925,
-0.008036300539970398,
-0.05647017061710358,
0.2682783603668213,
-0.2457059770822525,
0.80053001... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | @lhoestq I've been using this feature for the last year on GCS without problem, but I think we need to fix an issue with S3 and then document the supported calling patterns to reduce confusion
It looks like `datasets` uses a default `DownloadConfig` which is where some potentially unintended storage options are gett... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 157 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.04143977165222168,
-0.10600677132606506,
-0.07206203043460846,
0.10603194683790207,
0.23607735335826874,
-0.17956870794296265,
0.24351844191551208,
-0.1231687068939209,
0.058073025196790695,
-0.008839413523674011,
0.0329861119389534,
0.2888803482055664,
-0.2181895524263382,
0.7569690942... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | Hmm not sure, maybe it has to do with `_prepare_single_hop_path_and_storage_options` returning the "hf" storage options when it shouldn't | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 18 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.1713862121105194,
-0.1215299442410469,
-0.13658471405506134,
0.06388726830482483,
0.21852919459342957,
-0.003932259976863861,
0.19721341133117676,
-0.009359832853078842,
0.22520862519741058,
0.05114622414112091,
-0.1244749054312706,
0.19488286972045898,
-0.20609353482723236,
0.806364595... |
https://github.com/huggingface/datasets/issues/5281 | Support cloud storage in load_dataset | Also running into this issue downloading a parquet dataset from S3 (Upload worked fine using [current main branch](https://github.com/huggingface/datasets/commit/90b896193a0a315789022580eb4c80305d168d4d)).
`dataset = Dataset.from_parquet('s3://path-to-file')`
raises
`TypeError: AioSession.__init__() got an unexpect... | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been reque... | 54 | Support cloud storage in load_dataset
Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` an... | [
-0.0993228331208229,
-0.09167934954166412,
0.005515158176422119,
0.15895980596542358,
0.2513149380683899,
-0.027243029326200485,
0.1024014949798584,
-0.07242538034915924,
0.15689998865127563,
-0.022419676184654236,
-0.030340461060404778,
0.20717450976371765,
-0.09602717310190201,
0.7470210... |
https://github.com/huggingface/datasets/issues/5280 | Import error | Hi,
3.8.13
Get Outlook for Android<https://aka.ms/AAb9ysg>
________________________________
From: Quentin Lhoest ***@***.***>
Sent: Tuesday, November 22, 2022 2:37:02 PM
To: huggingface/datasets ***@***.***>
Cc: feketedavid1012 ***@***.***>; Author ***@***.***>
Subject: Re: [huggingface/datasets] Import error (Issue ... | https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28
Hy,
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message wrong) | 69 | Import error
https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28
Hy,
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message w... | [
-0.09654326736927032,
-0.7863262295722961,
-0.11001822352409363,
0.4207393527030945,
0.2069006711244583,
0.1251608431339264,
-0.06425079703330994,
0.2611984610557556,
0.2554006576538086,
0.0638369545340538,
0.08255617320537567,
0.010273336432874203,
-0.18947285413742065,
0.1399582475423813... |
https://github.com/huggingface/datasets/issues/5280 | Import error | Then it should work as expected if you use the same python when using `datasets`
Please make sure you're running your code in the right environment | https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28
Hy,
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message wrong) | 26 | Import error
https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28
Hy,
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message w... | [
-0.2534981369972229,
-0.4373350143432617,
-0.18320977687835693,
0.3004246950149536,
0.2493928223848343,
0.22554424405097961,
0.03226657956838608,
0.4504213333129883,
0.23203414678573608,
0.024222426116466522,
-0.04509653151035309,
-0.061574000865221024,
-0.1873689889907837,
0.3363009095191... |
https://github.com/huggingface/datasets/issues/5280 | Import error | It's the right environment. But in if statement I have
"3.8.13" < 3.7
And in the error message is Python>=3.7 which is true in my case (3.8.13 is greater then 3.7), so I don't understand my python should be below the 3.7 which case the if statement is right, but the message is wrong, or above 3.7 which case if statemen... | https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28
Hy,
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message wrong) | 147 | Import error
https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28
Hy,
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message w... | [
-0.04788173735141754,
-0.7668336629867554,
-0.12649737298488617,
0.33807075023651123,
0.16299687325954437,
0.03349868953227997,
-0.06797128915786743,
0.2466147094964981,
0.29136043787002563,
-0.024381011724472046,
-0.0549495592713356,
-0.1026594266295433,
-0.0633687824010849,
0.21890458464... |
https://github.com/huggingface/datasets/issues/5278 | load_dataset does not read jsonl metadata file properly | Can you try to remove "drop_labels=false" ? It may force the loader to infer the labels instead of reading the metadata | ### Describe the bug
Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column. It just spits out "image" and "label" as features.
B... | 21 | load_dataset does not read jsonl metadata file properly
### Describe the bug
Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column... | [
0.008629031479358673,
0.2975691556930542,
0.0013801846653223038,
0.8008166551589966,
0.3934635519981384,
0.05997196584939957,
0.39974090456962585,
0.21833297610282898,
0.06595310568809509,
-0.09075051546096802,
-0.08651738613843918,
0.5651577711105347,
-0.14891023933887482,
0.1204215660691... |
https://github.com/huggingface/datasets/issues/5278 | load_dataset does not read jsonl metadata file properly | Okay, now it seems to actually load the metadata and create the train_split, but it still says only returns "image" and "label", which is always 0 since all images are from same folder | ### Describe the bug
Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column. It just spits out "image" and "label" as features.
B... | 33 | load_dataset does not read jsonl metadata file properly
### Describe the bug
Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column... | [
0.009905148297548294,
0.3267071843147278,
0.018782731145620346,
0.821053147315979,
0.37091660499572754,
0.02410472184419632,
0.4215976297855377,
0.23792299628257751,
0.018688693642616272,
-0.12908156216144562,
-0.09945625811815262,
0.5441843271255493,
-0.15521135926246643,
0.10858703404664... |
https://github.com/huggingface/datasets/issues/5278 | load_dataset does not read jsonl metadata file properly | > Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4
Update: This was the issue. | ### Describe the bug
Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column. It just spits out "image" and "label" as features.
B... | 19 | load_dataset does not read jsonl metadata file properly
### Describe the bug
Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column... | [
-0.00625208392739296,
0.3133273422718048,
0.008775461465120316,
0.7784017324447632,
0.385869562625885,
0.06175412982702255,
0.37528473138809204,
0.24541564285755157,
0.0358828566968441,
-0.13153040409088135,
-0.09185982495546341,
0.5690751075744629,
-0.15033259987831116,
0.1018590852618217... |
https://github.com/huggingface/datasets/issues/5276 | Bug in downloading common_voice data and snall chunk of it to one's own hub | Sounds like one of the file is not a valid one, can you make sure you uploaded valid mp3 files ? | ### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
| ### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
:
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
... | 19 | YAML integer keys are not preserved Hub server-side
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discu... | [
-0.026737648993730545,
-0.3303128480911255,
0.08248203992843628,
0.08531197905540466,
0.39381879568099976,
-0.08466369658708572,
0.458366721868515,
0.06925182044506073,
0.2985337972640991,
-0.023020446300506592,
-0.12341997772455215,
0.2475965917110443,
-0.23985113203525543,
0.163886666297... |
https://github.com/huggingface/datasets/issues/5275 | YAML integer keys are not preserved Hub server-side | FYI there are still 2k+ weekly users on `datasets` 2.6.1 which doesn't support the string label format for class labels. And among those, some are using datasets with class labels like imdb (60 users), conllpp (40), msra_ner (40), peoples_daily_enr (40), weibo_ner (30), conll2003 (20), etc. And renaming to string would... | After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
... | 54 | YAML integer keys are not preserved Hub server-side
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discu... | [
-0.026737648993730545,
-0.3303128480911255,
0.08248203992843628,
0.08531197905540466,
0.39381879568099976,
-0.08466369658708572,
0.458366721868515,
0.06925182044506073,
0.2985337972640991,
-0.023020446300506592,
-0.12341997772455215,
0.2475965917110443,
-0.23985113203525543,
0.163886666297... |
https://github.com/huggingface/datasets/issues/5275 | YAML integer keys are not preserved Hub server-side | but isn't `datasets 2.6.1` downloading files from the Hub with the corresponding tag? I thought we had something like this before | After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
... | 21 | YAML integer keys are not preserved Hub server-side
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discu... | [
-0.026737648993730545,
-0.3303128480911255,
0.08248203992843628,
0.08531197905540466,
0.39381879568099976,
-0.08466369658708572,
0.458366721868515,
0.06925182044506073,
0.2985337972640991,
-0.023020446300506592,
-0.12341997772455215,
0.2475965917110443,
-0.23985113203525543,
0.163886666297... |
https://github.com/huggingface/datasets/issues/5275 | YAML integer keys are not preserved Hub server-side | We're using `main` as models do. Some datasets need to be updated from time to time, e.g. when a link to download the data is dead.
But yea a year ago we had those tags, we just ended up not using them | After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
... | 42 | YAML integer keys are not preserved Hub server-side
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discu... | [
-0.026737648993730545,
-0.3303128480911255,
0.08248203992843628,
0.08531197905540466,
0.39381879568099976,
-0.08466369658708572,
0.458366721868515,
0.06925182044506073,
0.2985337972640991,
-0.023020446300506592,
-0.12341997772455215,
0.2475965917110443,
-0.23985113203525543,
0.163886666297... |
https://github.com/huggingface/datasets/issues/5275 | YAML integer keys are not preserved Hub server-side | I opened https://github.com/huggingface/datasets/issues/5406 to communicate on this. Let me know what you think, and if it sounds good to you I can pin this issue | After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
... | 25 | YAML integer keys are not preserved Hub server-side
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discu... | [
-0.026737648993730545,
-0.3303128480911255,
0.08248203992843628,
0.08531197905540466,
0.39381879568099976,
-0.08466369658708572,
0.458366721868515,
0.06925182044506073,
0.2985337972640991,
-0.023020446300506592,
-0.12341997772455215,
0.2475965917110443,
-0.23985113203525543,
0.163886666297... |
https://github.com/huggingface/datasets/issues/5275 | YAML integer keys are not preserved Hub server-side | So, is it OK to make the bulk edit on the Hub now or should we wait longer? If the latter, how long? | After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
... | 23 | YAML integer keys are not preserved Hub server-side
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discu... | [
-0.026737648993730545,
-0.3303128480911255,
0.08248203992843628,
0.08531197905540466,
0.39381879568099976,
-0.08466369658708572,
0.458366721868515,
0.06925182044506073,
0.2985337972640991,
-0.023020446300506592,
-0.12341997772455215,
0.2475965917110443,
-0.23985113203525543,
0.163886666297... |
https://github.com/huggingface/datasets/issues/5275 | YAML integer keys are not preserved Hub server-side | I think we can do it. If you want to be extra cautious you can do it for all datasets except imdb and conllpp for now which are actively used by 2.6.1 users. For those two we can keep the YAML like this for some more time, or alternatively use the old dataset_infos.json file | After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
... | 54 | YAML integer keys are not preserved Hub server-side
After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discu... | [
-0.026737648993730545,
-0.3303128480911255,
0.08248203992843628,
0.08531197905540466,
0.39381879568099976,
-0.08466369658708572,
0.458366721868515,
0.06925182044506073,
0.2985337972640991,
-0.023020446300506592,
-0.12341997772455215,
0.2475965917110443,
-0.23985113203525543,
0.163886666297... |
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