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https://github.com/huggingface/datasets/issues/5243 | Download only split data | thank you for the answer but am not sure if this will not be helpful, as we
need maybe just 10% of the datasets for some experiment
can we get just a portion of the dataset with stream?
is there really no solution? :(
Am Di., 15. Nov. 2022 um 16:55 Uhr schrieb Polina Kazakova <
***@***.***>:
> Hi @capsabogdan <htt... | ### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test",
... | 208 | Download only split data
### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en",... |
https://github.com/huggingface/datasets/issues/5243 | Download only split data | maybe it would be nice if you guys ould do some sort of shard before
loading the dataset, so users can download just chunks of data :)
I think this would be very helpful
Am Di., 15. Nov. 2022 um 19:24 Uhr schrieb Bogdan Capsa <
***@***.***>:
> thank you for the answer but am not sure if this will not be helpful, as
... | ### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test",
... | 267 | Download only split data
### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en",... |
https://github.com/huggingface/datasets/issues/5243 | Download only split data | +1 on this feature request - I am running into the same problem, where I only need the test set for a dataset that has a huge training set | ### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test",
... | 29 | Download only split data
### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en",... |
https://github.com/huggingface/datasets/issues/5243 | Download only split data | Hey, I'm also interested in that as a feature. I'm having the same problem with Common Voice 13.0. The dataset is super big but I only want the test data to benchmark multilingual models, but I don't have much Terabytes to store all the dataset... | ### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test",
... | 45 | Download only split data
### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en",... |
https://github.com/huggingface/datasets/issues/5243 | Download only split data | Consider this approach: Download and save individual audio files by streaming each split, then compile a CSV file that contains the file names and corresponding text.
```python3
import os
import shutil
from pathlib import Path
import datasets
import pandas as pd
import soundfile
from datasets import Dataset... | ### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test",
... | 94 | Download only split data
### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en",... |
https://github.com/huggingface/datasets/issues/5243 | Download only split data | Faced this issue as well so wrote a short script that pulls a hub dataset, creates a small sample of it, and pushes the sample data to the hub as a new dataset.
```python
def create_sample_dataset(full_dataset_name, sample_count=100, username="my-username", cache_dir="./dataset"):
# Create a directory to save the ... | ### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test",
... | 136 | Download only split data
### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en",... |
https://github.com/huggingface/datasets/issues/3548 | Specify the feature types of a dataset on the Hub without needing a dataset script | After looking into this, discovered that this is already supported if the `dataset_infos.json` file is configured correctly! Here is a working example: https://huggingface.co/datasets/abidlabs/test-audio-13
This should be probably be documented, though. | **Is your feature request related to a problem? Please describe.**
Currently if I upload a CSV with paths to audio files, the column type is string instead of Audio.
**Describe the solution you'd like**
I'd like to be able to specify the types of the column, so that when loading the dataset I directly get the feat... | 30 | Specify the feature types of a dataset on the Hub without needing a dataset script
**Is your feature request related to a problem? Please describe.**
Currently if I upload a CSV with paths to audio files, the column type is string instead of Audio.
**Describe the solution you'd like**
I'd like to be able to specif... |
https://github.com/huggingface/datasets/issues/5419 | label_column='labels' in datasets.TextClassification and 'label' or 'label_ids' in transformers.DataColator | Hi! Thanks for pointing out this inconsistency. Changing the default value at this point is probably not worth it, considering we've started discussing the state of the task API internally - we will most likely deprecate the current one and replace it with a more robust solution that relies on the `train_eval_index` fi... | ### Describe the bug
When preparing a dataset for a task using `datasets.TextClassification`, the output feature is named `labels`. When preparing the trainer using the `transformers.DataCollator` the default column name is `label` if binary or `label_ids` if multi-class problem.
It is required to rename the column... | 62 | label_column='labels' in datasets.TextClassification and 'label' or 'label_ids' in transformers.DataColator
### Describe the bug
When preparing a dataset for a task using `datasets.TextClassification`, the output feature is named `labels`. When preparing the trainer using the `transformers.DataCollator` the default co... |
https://github.com/huggingface/datasets/issues/5419 | label_column='labels' in datasets.TextClassification and 'label' or 'label_ids' in transformers.DataColator | The task templates API has been deprecated (will be removed in version 3.0), so I'm closing this issue. | ### Describe the bug
When preparing a dataset for a task using `datasets.TextClassification`, the output feature is named `labels`. When preparing the trainer using the `transformers.DataCollator` the default column name is `label` if binary or `label_ids` if multi-class problem.
It is required to rename the column... | 18 | label_column='labels' in datasets.TextClassification and 'label' or 'label_ids' in transformers.DataColator
### Describe the bug
When preparing a dataset for a task using `datasets.TextClassification`, the output feature is named `labels`. When preparing the trainer using the `transformers.DataCollator` the default co... |
https://github.com/huggingface/datasets/issues/4917 | Keys mismatch: make error message more informative | Good idea ! I think this can be improved in `Features.reorder_fields_as()` indeed at
https://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/features/features.py#L1739-L1740
Is it something you would be interested in contributing ? | **Is your feature request related to a problem? Please describe.**
When loading a dataset from disk with a defect in its `dataset_info.json` describing its features (I don’t know when/why/how this happens but it deserves its own issue), you will get an error message like:
`ValueError: Keys mismatch: between {'bar': V... | 24 | Keys mismatch: make error message more informative
**Is your feature request related to a problem? Please describe.**
When loading a dataset from disk with a defect in its `dataset_info.json` describing its features (I don’t know when/why/how this happens but it deserves its own issue), you will get an error message l... |
https://github.com/huggingface/datasets/issues/4917 | Keys mismatch: make error message more informative | Is this open to work on? I'd love to take on this as my first issue. | **Is your feature request related to a problem? Please describe.**
When loading a dataset from disk with a defect in its `dataset_info.json` describing its features (I don’t know when/why/how this happens but it deserves its own issue), you will get an error message like:
`ValueError: Keys mismatch: between {'bar': V... | 16 | Keys mismatch: make error message more informative
**Is your feature request related to a problem? Please describe.**
When loading a dataset from disk with a defect in its `dataset_info.json` describing its features (I don’t know when/why/how this happens but it deserves its own issue), you will get an error message l... |
https://github.com/huggingface/datasets/issues/4717 | Dataset Viewer issue for LawalAfeez/englishreview-ds-mini | It's currently working, as far as I understand
https://huggingface.co/datasets/LawalAfeez/englishreview-ds-mini/viewer/LawalAfeez--englishreview-ds-mini/train
<img width="1556" alt="Capture d’écran 2022-07-19 à 09 24 01" src="https://user-images.githubusercontent.com/1676121/179761130-2d7980b9-c0f6-4093-8b1d-f0... | ### Link
_No response_
### Description
Unable to view the split data
### Owner
_No response_ | 24 | Dataset Viewer issue for LawalAfeez/englishreview-ds-mini
### Link
_No response_
### Description
Unable to view the split data
### Owner
_No response_
It's currently working, as far as I understand
https://huggingface.co/datasets/LawalAfeez/englishreview-ds-mini/viewer/LawalAfeez--englishreview-ds-mini/train
... |
https://github.com/huggingface/datasets/issues/7326 | Remove upper bound for fsspec | Unfortunately `fsspec` versioning allows breaking changes across version and there is no way we can keep it without constrains at the moment. It already broke `datasets` once in the past. Maybe one day once `fsspec` decides on a stable and future proof API but I don't think this will happen anytime soon
edit: bumped... | ### Describe the bug
As also raised by @cyyever in https://github.com/huggingface/datasets/pull/7296 and @NeilGirdhar in https://github.com/huggingface/datasets/commit/d5468836fe94e8be1ae093397dd43d4a2503b926#commitcomment-140952162 , `datasets` has a problematic version constraint on `fsspec`.
In our case this c... | 58 | Remove upper bound for fsspec
### Describe the bug
As also raised by @cyyever in https://github.com/huggingface/datasets/pull/7296 and @NeilGirdhar in https://github.com/huggingface/datasets/commit/d5468836fe94e8be1ae093397dd43d4a2503b926#commitcomment-140952162 , `datasets` has a problematic version constraint on `... |
https://github.com/huggingface/datasets/issues/6801 | got fileNotFound | Hi! I'll open a PR on the Hub to fix this, but please use the Hub's [Community tab](https://huggingface.co/datasets/nyanko7/danbooru2023/discussions) to report such issues in the future. | ### Describe the bug
When I use load_dataset to load the nyanko7/danbooru2023 data set, the cache is read in the form of a symlink. There may be a problem with the arrow_dataset initialization process and I get FileNotFoundError: [Errno 2] No such file or directory: '2945000.jpg'
### Steps to reproduce the bug
#code... | 25 | got fileNotFound
### Describe the bug
When I use load_dataset to load the nyanko7/danbooru2023 data set, the cache is read in the form of a symlink. There may be a problem with the arrow_dataset initialization process and I get FileNotFoundError: [Errno 2] No such file or directory: '2945000.jpg'
### Steps to reprodu... |
https://github.com/huggingface/datasets/issues/6270 | Dataset.from_generator raises with sharded gen_args | `gen_kwargs` should be a `dict`, as stated in the docstring, but you are passing a `list`.
So, to fix the error, replace the list of dicts with a dict of lists (and slightly modify the generator function):
```python
from pathlib import Path
import datasets
def process_yaml(files):
for f in files:
... | ### Describe the bug
According to the docs of Datasets.from_generator:
```
gen_kwargs(`dict`, *optional*):
Keyword arguments to be passed to the `generator` callable.
You can define a sharded dataset by passing the list of shards in `gen_kwargs`.
```
So I'd expect that if gen_kwar... | 74 | Dataset.from_generator raises with sharded gen_args
### Describe the bug
According to the docs of Datasets.from_generator:
```
gen_kwargs(`dict`, *optional*):
Keyword arguments to be passed to the `generator` callable.
You can define a sharded dataset by passing the list of shards i... |
https://github.com/huggingface/datasets/issues/6270 | Dataset.from_generator raises with sharded gen_args | That runs, and because my dataset is small, it's what I did to get past the problem.
However, it does not produce a sharded dataset. From the doc string I expect there ought to be a way to call from_generator such that num_shards in the resulting data set is equal to the number of items in the list.
The part of the ... | ### Describe the bug
According to the docs of Datasets.from_generator:
```
gen_kwargs(`dict`, *optional*):
Keyword arguments to be passed to the `generator` callable.
You can define a sharded dataset by passing the list of shards in `gen_kwargs`.
```
So I'd expect that if gen_kwar... | 108 | Dataset.from_generator raises with sharded gen_args
### Describe the bug
According to the docs of Datasets.from_generator:
```
gen_kwargs(`dict`, *optional*):
Keyword arguments to be passed to the `generator` callable.
You can define a sharded dataset by passing the list of shards i... |
https://github.com/huggingface/datasets/issues/6270 | Dataset.from_generator raises with sharded gen_args | The sharding mentioned here refers to using this function with `num_proc` (multiprocessing splits the `kwargs` into shards and passes them to the generator function)
> That runs, and because my dataset is small, it's what I did to get past the problem.
`from_generator` generates a memory-mapped dataset (can be la... | ### Describe the bug
According to the docs of Datasets.from_generator:
```
gen_kwargs(`dict`, *optional*):
Keyword arguments to be passed to the `generator` callable.
You can define a sharded dataset by passing the list of shards in `gen_kwargs`.
```
So I'd expect that if gen_kwar... | 72 | Dataset.from_generator raises with sharded gen_args
### Describe the bug
According to the docs of Datasets.from_generator:
```
gen_kwargs(`dict`, *optional*):
Keyword arguments to be passed to the `generator` callable.
You can define a sharded dataset by passing the list of shards i... |
https://github.com/huggingface/datasets/issues/6270 | Dataset.from_generator raises with sharded gen_args | It sounds like you are saying that num_proc affects the form of gen_kwargs.
Are you saying that for non-zero num_proc gen_kwargs should be a list whose length is the same as num_proc?
Or are you saying that for non-zero num_proc, gen_kwargs should be a dict whose elements are lists the length of num_proc?
| ### Describe the bug
According to the docs of Datasets.from_generator:
```
gen_kwargs(`dict`, *optional*):
Keyword arguments to be passed to the `generator` callable.
You can define a sharded dataset by passing the list of shards in `gen_kwargs`.
```
So I'd expect that if gen_kwar... | 53 | Dataset.from_generator raises with sharded gen_args
### Describe the bug
According to the docs of Datasets.from_generator:
```
gen_kwargs(`dict`, *optional*):
Keyword arguments to be passed to the `generator` callable.
You can define a sharded dataset by passing the list of shards i... |
https://github.com/huggingface/datasets/issues/6270 | Dataset.from_generator raises with sharded gen_args | I ran some tests. So, it looks like with num_proc greater than 1, gen_kwargs is expected to be a dict of lists. It calls the generator also with a dict of lists, but the lists are split.
I.E. if my original has `gen_kwargs=dict(a=[0,1,2])`, then my generator might get called with `gen_kwalrgs=dict([0])`.
That all m... | ### Describe the bug
According to the docs of Datasets.from_generator:
```
gen_kwargs(`dict`, *optional*):
Keyword arguments to be passed to the `generator` callable.
You can define a sharded dataset by passing the list of shards in `gen_kwargs`.
```
So I'd expect that if gen_kwar... | 139 | Dataset.from_generator raises with sharded gen_args
### Describe the bug
According to the docs of Datasets.from_generator:
```
gen_kwargs(`dict`, *optional*):
Keyword arguments to be passed to the `generator` callable.
You can define a sharded dataset by passing the list of shards i... |
https://github.com/huggingface/datasets/issues/6270 | Dataset.from_generator raises with sharded gen_args | Okay, that was fun; I took a dive through the dataset code and feel like I have a much better understanding.
Here is my understanding of the behavior:
* max_proc is an upper limit on the number of shards that `from_generator` produces
* If `max_proc` is greater than 1, then all lists in *gen_kwargs* must be the same... | ### Describe the bug
According to the docs of Datasets.from_generator:
```
gen_kwargs(`dict`, *optional*):
Keyword arguments to be passed to the `generator` callable.
You can define a sharded dataset by passing the list of shards in `gen_kwargs`.
```
So I'd expect that if gen_kwar... | 161 | Dataset.from_generator raises with sharded gen_args
### Describe the bug
According to the docs of Datasets.from_generator:
```
gen_kwargs(`dict`, *optional*):
Keyword arguments to be passed to the `generator` callable.
You can define a sharded dataset by passing the list of shards i... |
https://github.com/huggingface/datasets/issues/4276 | OpenBookQA has missing and inconsistent field names | Thanks for reporting, @vblagoje.
Indeed, I noticed some of these issues while reviewing this PR:
- #4259
This is in my TODO list. | ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanSc... | 23 | OpenBookQA has missing and inconsistent field names
## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fac... |
https://github.com/huggingface/datasets/issues/4276 | OpenBookQA has missing and inconsistent field names | On the other hand, I am not sure if we should always preserve the original nested structure. I think we should also consider other factors as convenience or consistency.
For example, other datasets also flatten "question.stem" into "question":
- ai2_arc:
```python
question = data["question"]["stem"]
choice... | ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanSc... | 132 | OpenBookQA has missing and inconsistent field names
## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fac... |
https://github.com/huggingface/datasets/issues/4276 | OpenBookQA has missing and inconsistent field names | @albertvillanova I agree that we should be consistent. In the last month, I have come across tons of code that deals with OpenBookQA and CommonSenseQA and all of that code relies on the original data format structure. We can't expect users to adopt HF Datasets if we arbitrarily change the structure of the format just b... | ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanSc... | 107 | OpenBookQA has missing and inconsistent field names
## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fac... |
https://github.com/huggingface/datasets/issues/4276 | OpenBookQA has missing and inconsistent field names | I'm opening a PR that adds the missing fields.
Let's agree on the feature structure: @lhoestq @mariosasko @polinaeterna | ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanSc... | 18 | OpenBookQA has missing and inconsistent field names
## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fac... |
https://github.com/huggingface/datasets/issues/4276 | OpenBookQA has missing and inconsistent field names | IMO we should always try to preserve the original structure unless there is a good reason not to (and I don't see one in this case). | ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanSc... | 26 | OpenBookQA has missing and inconsistent field names
## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fac... |
https://github.com/huggingface/datasets/issues/4276 | OpenBookQA has missing and inconsistent field names | I agree with @mariosasko . The transition to the original format could be done in one PR for the next minor release, clearly documenting all dataset changes just as @albertvillanova outlined them above and perhaps even providing a per dataset util method to convert the new valid format to the old for backward compatibi... | ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanSc... | 81 | OpenBookQA has missing and inconsistent field names
## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fac... |
https://github.com/huggingface/datasets/issues/4276 | OpenBookQA has missing and inconsistent field names | I don't have a strong opinion on this, besides the fact that whatever decision we agree on, should be applied to all datasets.
There is always the tension between:
- preserving each dataset original structure (which has the advantage of not forcing users to learn other structure for the same dataset),
- and on th... | ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanSc... | 161 | OpenBookQA has missing and inconsistent field names
## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fac... |
https://github.com/huggingface/datasets/issues/4276 | OpenBookQA has missing and inconsistent field names | @albertvillanova, thanks for the detailed answer and the new perspectives. I understand the friction for the best design approach much better now. Ultimately, it is essential to include all the missing fields and the correct data first. Whatever approach is determined to be optimal is important but not as crucial once... | ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanSc... | 69 | OpenBookQA has missing and inconsistent field names
## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fac... |
https://github.com/huggingface/datasets/issues/4276 | OpenBookQA has missing and inconsistent field names | Datasets are not tracked in this repository anymore. I think we must move this thread to the [discussions tab of the dataset](https://huggingface.co/datasets/openbookqa/discussions) | ## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanSc... | 22 | OpenBookQA has missing and inconsistent field names
## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fac... |
https://github.com/huggingface/datasets/issues/7551 | Issue with offline mode and partial dataset cached | It seems the problem comes from builder.py / create_config_id()
On the first call, when the cache is empty we have
```
config_kwargs = {'data_files': {'train': ['hf://datasets/uonlp/CulturaX@6a8734bc69fefcbb7735f4f9250f43e4cd7a442e/fr/fr_part_00038.parquet']}}
```
leading to config_id beeing 'default-2935e8cdcc21c613'... | ### Describe the bug
Hi,
a issue related to #4760 here when loading a single file from a dataset, unable to access it in offline mode afterwards
### Steps to reproduce the bug
```python
import os
# os.environ["HF_HUB_OFFLINE"] = "1"
os.environ["HF_TOKEN"] = "xxxxxxxxxxxxxx"
import datasets
dataset_name = "uonlp/... | 62 | Issue with offline mode and partial dataset cached
### Describe the bug
Hi,
a issue related to #4760 here when loading a single file from a dataset, unable to access it in offline mode afterwards
### Steps to reproduce the bug
```python
import os
# os.environ["HF_HUB_OFFLINE"] = "1"
os.environ["HF_TOKEN"] = "xxxxx... |
https://github.com/huggingface/datasets/issues/7551 | Issue with offline mode and partial dataset cached |
> It seems the problem comes from builder.py / create_config_id()
>
> On the first call, when the cache is empty we have
>
> ```
> config_kwargs = {'data_files': {'train': ['hf://datasets/uonlp/CulturaX@6a8734bc69fefcbb7735f4f9250f43e4cd7a442e/fr/fr_part_00038.parquet']}}
> ```
>
> leading to config_id beeing 'de... | ### Describe the bug
Hi,
a issue related to #4760 here when loading a single file from a dataset, unable to access it in offline mode afterwards
### Steps to reproduce the bug
```python
import os
# os.environ["HF_HUB_OFFLINE"] = "1"
os.environ["HF_TOKEN"] = "xxxxxxxxxxxxxx"
import datasets
dataset_name = "uonlp/... | 141 | Issue with offline mode and partial dataset cached
### Describe the bug
Hi,
a issue related to #4760 here when loading a single file from a dataset, unable to access it in offline mode afterwards
### Steps to reproduce the bug
```python
import os
# os.environ["HF_HUB_OFFLINE"] = "1"
os.environ["HF_TOKEN"] = "xxxxx... |
https://github.com/huggingface/datasets/issues/6597 | Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace | Also note the information in the docstring: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/dataset_dict.py#L1582-L1585
> Also accepts `<dataset_name>`, which will default to the namespace of the logged-in user.
This behavior was "reverted" by the PR:
- #6519
... | While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace.
## Steps to reproduce the bug
The command:
```python
commit_info = ds.push_to_hub(
"caner",
config_name="default",
commit_message="Convert dataset to Parquet",
commit_descriptio... | 103 | Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace
While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace.
## Steps to reproduce the bug
The command:
```python
commit_info = ds.push_to_hub(
"caner",
... |
https://github.com/huggingface/datasets/issues/6597 | Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace | IIUC, this could also be "fixed" by `create_repo("dataset_name")` not defaulting to `create_repo("user/dataset_name")` (when the user's token is available), which would be consistent with the rest of the `HfApi` ops used in the `push_to_hub` implementation. This is a (small) breaking change for `huggingface_hub`, but j... | While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace.
## Steps to reproduce the bug
The command:
```python
commit_info = ds.push_to_hub(
"caner",
config_name="default",
commit_message="Convert dataset to Parquet",
commit_descriptio... | 50 | Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace
While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace.
## Steps to reproduce the bug
The command:
```python
commit_info = ds.push_to_hub(
"caner",
... |
https://github.com/huggingface/datasets/issues/6597 | Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace | Hmm, creating repo with implicit namespace (e.g. `create_repo("dataset_name")`) is a convenient feature used in a lot of integrations. It is not consistent with other HfApi methods specifically because it is the method to create repos. Once the repo is created, the return value provides the explicit repo_id (`namespace... | While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace.
## Steps to reproduce the bug
The command:
```python
commit_info = ds.push_to_hub(
"caner",
config_name="default",
commit_message="Convert dataset to Parquet",
commit_descriptio... | 162 | Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace
While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace.
## Steps to reproduce the bug
The command:
```python
commit_info = ds.push_to_hub(
"caner",
... |
https://github.com/huggingface/datasets/issues/6597 | Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace | As canonical datasets are going to disappear in the following couple of months, I would not make any effort on their support.
I propose reverting #6519, so that the behavior of `push_to_hub` is aligned with the one described in its dosctring: "Also accepts `<dataset_name>`, which will default to the namespace of the... | While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace.
## Steps to reproduce the bug
The command:
```python
commit_info = ds.push_to_hub(
"caner",
config_name="default",
commit_message="Convert dataset to Parquet",
commit_descriptio... | 58 | Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace
While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace.
## Steps to reproduce the bug
The command:
```python
commit_info = ds.push_to_hub(
"caner",
... |
https://github.com/huggingface/datasets/issues/7600 | `push_to_hub` is not concurrency safe (dataset schema corruption) | @lhoestq can you please take a look? I've submitted a PR that fixes this issue. Thanks. | ### Describe the bug
Concurrent processes modifying and pushing a dataset can overwrite each others' dataset card, leaving the dataset unusable.
Consider this scenario:
- we have an Arrow dataset
- there are `N` configs of the dataset
- there are `N` independent processes operating on each of the individual configs (... | 16 | `push_to_hub` is not concurrency safe (dataset schema corruption)
### Describe the bug
Concurrent processes modifying and pushing a dataset can overwrite each others' dataset card, leaving the dataset unusable.
Consider this scenario:
- we have an Arrow dataset
- there are `N` configs of the dataset
- there are `N` i... |
https://github.com/huggingface/datasets/issues/7600 | `push_to_hub` is not concurrency safe (dataset schema corruption) | Thanks for the ping ! As I said in https://github.com/huggingface/datasets/pull/7605 there is maybe a more general approach using retries :) | ### Describe the bug
Concurrent processes modifying and pushing a dataset can overwrite each others' dataset card, leaving the dataset unusable.
Consider this scenario:
- we have an Arrow dataset
- there are `N` configs of the dataset
- there are `N` independent processes operating on each of the individual configs (... | 20 | `push_to_hub` is not concurrency safe (dataset schema corruption)
### Describe the bug
Concurrent processes modifying and pushing a dataset can overwrite each others' dataset card, leaving the dataset unusable.
Consider this scenario:
- we have an Arrow dataset
- there are `N` configs of the dataset
- there are `N` i... |
https://github.com/huggingface/datasets/issues/7600 | `push_to_hub` is not concurrency safe (dataset schema corruption) | Dropping this due to inactivity; we've implemented push_to_hub outside of HF datasets that's concurrency safe. Feel free to use the code I provided as a starting point if there's still interest in addressing this issue. | ### Describe the bug
Concurrent processes modifying and pushing a dataset can overwrite each others' dataset card, leaving the dataset unusable.
Consider this scenario:
- we have an Arrow dataset
- there are `N` configs of the dataset
- there are `N` independent processes operating on each of the individual configs (... | 35 | `push_to_hub` is not concurrency safe (dataset schema corruption)
### Describe the bug
Concurrent processes modifying and pushing a dataset can overwrite each others' dataset card, leaving the dataset unusable.
Consider this scenario:
- we have an Arrow dataset
- there are `N` configs of the dataset
- there are `N` i... |
https://github.com/huggingface/datasets/issues/6475 | laion2B-en failed to load on Windows with PrefetchVirtualMemory failed | ~~You will see this error if the cache dir filepath contains relative `..` paths. Use `os.path.realpath(_CACHE_DIR)` before passing it to the `load_dataset` function.~~ | ### Describe the bug
I have downloaded laion2B-en, and I'm receiving the following error trying to load it:
```
Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s]
Traceback (most recent call last):
File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 31, in <mo... | 23 | laion2B-en failed to load on Windows with PrefetchVirtualMemory failed
### Describe the bug
I have downloaded laion2B-en, and I'm receiving the following error trying to load it:
```
Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s]
Traceback (most recent call last):
File "D:\Art-Worksp... |
https://github.com/huggingface/datasets/issues/6475 | laion2B-en failed to load on Windows with PrefetchVirtualMemory failed | Based on the StackOverflow answer, this causes the error to go away:
```diff
diff --git a/table.py b/table.py
--- a/table.py
+++ b/table.py (date 1701824849806)
@@ -47,7 +47,7 @@
def _memory_mapped_record_batch_reader_from_file(filename: str) -> pa.RecordBatchStreamReader:
- memory_mapped_stream = pa.... | ### Describe the bug
I have downloaded laion2B-en, and I'm receiving the following error trying to load it:
```
Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s]
Traceback (most recent call last):
File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 31, in <mo... | 56 | laion2B-en failed to load on Windows with PrefetchVirtualMemory failed
### Describe the bug
I have downloaded laion2B-en, and I'm receiving the following error trying to load it:
```
Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s]
Traceback (most recent call last):
File "D:\Art-Worksp... |
https://github.com/huggingface/datasets/issues/6475 | laion2B-en failed to load on Windows with PrefetchVirtualMemory failed | I don't really comprehend what it is that `datasets` gave me when it downloaded the laion2B-en dataset, because nothing can seemingly read these 1024 .arrow files it is retrieving. Not `polars`, not `pyarrow`, it's not an `ipc` file, it's not a `parquet` file... | ### Describe the bug
I have downloaded laion2B-en, and I'm receiving the following error trying to load it:
```
Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s]
Traceback (most recent call last):
File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 31, in <mo... | 43 | laion2B-en failed to load on Windows with PrefetchVirtualMemory failed
### Describe the bug
I have downloaded laion2B-en, and I'm receiving the following error trying to load it:
```
Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s]
Traceback (most recent call last):
File "D:\Art-Worksp... |
https://github.com/huggingface/datasets/issues/6475 | laion2B-en failed to load on Windows with PrefetchVirtualMemory failed | Hi!
Instead of generating one (potentially large) Arrow file, we shard the generated data into 500 MB shards because memory-mapping large Arrow files can be problematic on some systems. Maybe deleting the dataset's cache and increasing the shard size (controlled with the `datasets.config.MAX_SHARD_SIZE` variable; e... | ### Describe the bug
I have downloaded laion2B-en, and I'm receiving the following error trying to load it:
```
Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s]
Traceback (most recent call last):
File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 31, in <mo... | 130 | laion2B-en failed to load on Windows with PrefetchVirtualMemory failed
### Describe the bug
I have downloaded laion2B-en, and I'm receiving the following error trying to load it:
```
Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s]
Traceback (most recent call last):
File "D:\Art-Worksp... |
https://github.com/huggingface/datasets/issues/6475 | laion2B-en failed to load on Windows with PrefetchVirtualMemory failed | Hmm, I have a feeling this works fine on Linux, and is a real bug for however `datasets` is doing the sharding on Windows. I will follow up, but I think this is a real bug. | ### Describe the bug
I have downloaded laion2B-en, and I'm receiving the following error trying to load it:
```
Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s]
Traceback (most recent call last):
File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 31, in <mo... | 36 | laion2B-en failed to load on Windows with PrefetchVirtualMemory failed
### Describe the bug
I have downloaded laion2B-en, and I'm receiving the following error trying to load it:
```
Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s]
Traceback (most recent call last):
File "D:\Art-Worksp... |
https://github.com/huggingface/datasets/issues/6261 | Can't load a dataset | `JourneyDB/JourneyDB` is a gated dataset, so this error means you are not authenticated to access it, either by using an invalid token or by not agreeing to the terms in the dialog on the dataset page.
> I believe is due to the fact that doesn't work with .tgz files.
Indeed, the dataset's data files structure is ... | ### Describe the bug
Can't seem to load the JourneyDB dataset.
It throws the following error:
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[15], line 2
1 # If the dataset is gated/priv... | 84 | Can't load a dataset
### Describe the bug
Can't seem to load the JourneyDB dataset.
It throws the following error:
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[15], line 2
1 # If the ... |
https://github.com/huggingface/datasets/issues/6261 | Can't load a dataset | > JourneyDB/JourneyDB is a gated dataset, so this error means you are not authenticated to access it, either by using an invalid token or by not agreeing to the terms in the dialog on the dataset page.´
I did authentication with:
```
from huggingface_hub import notebook_login
notebook_login()
```
Isn't that... | ### Describe the bug
Can't seem to load the JourneyDB dataset.
It throws the following error:
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[15], line 2
1 # If the dataset is gated/priv... | 99 | Can't load a dataset
### Describe the bug
Can't seem to load the JourneyDB dataset.
It throws the following error:
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[15], line 2
1 # If the ... |
https://github.com/huggingface/datasets/issues/6261 | Can't load a dataset | Have you accepted the terms in the dialog [here](https://huggingface.co/datasets/JourneyDB/JourneyDB)?
IIRC Kaggle preinstalls an outdated `datasets` version, so it's also a good idea to update it before importing `datasets` (and do the same for `huggingface_hub`) | ### Describe the bug
Can't seem to load the JourneyDB dataset.
It throws the following error:
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[15], line 2
1 # If the dataset is gated/priv... | 34 | Can't load a dataset
### Describe the bug
Can't seem to load the JourneyDB dataset.
It throws the following error:
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[15], line 2
1 # If the ... |
https://github.com/huggingface/datasets/issues/7694 | Dataset.to_json consumes excessive memory, appears to not be a streaming operation | Hi ! to_json is memory efficient and writes the data by batch:
https://github.com/huggingface/datasets/blob/d9861d86be222884dabbd534a2db770c70c9b558/src/datasets/io/json.py#L153-L159
What memory are you mesuring ? If you are mesuring RSS, it is likely that it counts the memory mapped data of the dataset. Memory mappe... | ### Describe the bug
When exporting a Dataset object to a JSON Lines file using the .to_json(lines=True) method, the process consumes a very large amount of memory. The memory usage is proportional to the size of the entire Dataset object being saved, rather than being a low, constant memory operation.
This behavior ... | 61 | Dataset.to_json consumes excessive memory, appears to not be a streaming operation
### Describe the bug
When exporting a Dataset object to a JSON Lines file using the .to_json(lines=True) method, the process consumes a very large amount of memory. The memory usage is proportional to the size of the entire Dataset obje... |
https://github.com/huggingface/datasets/issues/7373 | Excessive RAM Usage After Dataset Concatenation concatenate_datasets | I'm having the same issue where concatenation seems to use a huge amount of RAM.
```python
# Load all chunks and concatenate them into a final dataset.
chunk_datasets = [
Dataset.load_from_disk(file, keep_in_memory=False)
for file in tqdm(chunk_files, desc="Loading chunk datasets")
... | ### Describe the bug
When loading a dataset from disk, concatenating it, and starting the training process, the RAM usage progressively increases until the kernel terminates the process due to excessive memory consumption.
https://github.com/huggingface/datasets/issues/2276
### Steps to reproduce the bug
```python
... | 74 | Excessive RAM Usage After Dataset Concatenation concatenate_datasets
### Describe the bug
When loading a dataset from disk, concatenating it, and starting the training process, the RAM usage progressively increases until the kernel terminates the process due to excessive memory consumption.
https://github.com/hugging... |
https://github.com/huggingface/datasets/issues/7373 | Excessive RAM Usage After Dataset Concatenation concatenate_datasets | I did have a short look, the error seems to be from `memory_map` and the stream not being closed.
https://github.com/huggingface/datasets/blob/5f8d2ad9a1b0bccfd962d998987228addfd5be9f/src/datasets/table.py#L48-L50
Did not have the time to test jet: https://github.com/sam-hey/datasets/tree/fix/concatenate_datasets
... | ### Describe the bug
When loading a dataset from disk, concatenating it, and starting the training process, the RAM usage progressively increases until the kernel terminates the process due to excessive memory consumption.
https://github.com/huggingface/datasets/issues/2276
### Steps to reproduce the bug
```python
... | 41 | Excessive RAM Usage After Dataset Concatenation concatenate_datasets
### Describe the bug
When loading a dataset from disk, concatenating it, and starting the training process, the RAM usage progressively increases until the kernel terminates the process due to excessive memory consumption.
https://github.com/hugging... |
https://github.com/huggingface/datasets/issues/6359 | Stuck in "Resolving data files..." | Most likely, the data file inference logic is the problem here.
You can run the following code to verify this:
```python
import time
from datasets.data_files import get_data_patterns
start_time = time.time()
get_data_patterns("/path/to/img_dir")
end_time = time.time()
print(f"Elapsed time: {end_time - start_t... | ### Describe the bug
I have an image dataset with 300k images, the size of image is 768 * 768.
When I run `dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')` in second time, it takes 50 minutes to finish "Resolving data files" part, what's going on in this part?
From my understa... | 74 | Stuck in "Resolving data files..."
### Describe the bug
I have an image dataset with 300k images, the size of image is 768 * 768.
When I run `dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')` in second time, it takes 50 minutes to finish "Resolving data files" part, what's going o... |
https://github.com/huggingface/datasets/issues/6359 | Stuck in "Resolving data files..." | Hi, @mariosasko, you are right; data file inference logic is extremely slow.
I have done a similar test, that is I modify the source code of datasets/load.py to measure the cost of two suspicious operations:
```python
def get_module(self) -> DatasetModule:
base_path = Path(self.data_dir or "").expanduser(... | ### Describe the bug
I have an image dataset with 300k images, the size of image is 768 * 768.
When I run `dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')` in second time, it takes 50 minutes to finish "Resolving data files" part, what's going on in this part?
From my understa... | 99 | Stuck in "Resolving data files..."
### Describe the bug
I have an image dataset with 300k images, the size of image is 768 * 768.
When I run `dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')` in second time, it takes 50 minutes to finish "Resolving data files" part, what's going o... |
https://github.com/huggingface/datasets/issues/6359 | Stuck in "Resolving data files..." | Furthermore, what's my current workaround about this problem? Should I save it by `save_to_disk()` and load dataset through `load_from_disk`? | ### Describe the bug
I have an image dataset with 300k images, the size of image is 768 * 768.
When I run `dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')` in second time, it takes 50 minutes to finish "Resolving data files" part, what's going on in this part?
From my understa... | 19 | Stuck in "Resolving data files..."
### Describe the bug
I have an image dataset with 300k images, the size of image is 768 * 768.
When I run `dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')` in second time, it takes 50 minutes to finish "Resolving data files" part, what's going o... |
https://github.com/huggingface/datasets/issues/6359 | Stuck in "Resolving data files..." | Still suffering from this issue. For me, I cannot download Emilia dataset, and stucked at 'Resolving data files' forever.
``` e = load_dataset('amphion/Emilia-Dataset', token=my_token)
Resolving data files: 0%| | 1/4343 [00:00<11:27, 6.... | ### Describe the bug
I have an image dataset with 300k images, the size of image is 768 * 768.
When I run `dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')` in second time, it takes 50 minutes to finish "Resolving data files" part, what's going on in this part?
From my understa... | 40 | Stuck in "Resolving data files..."
### Describe the bug
I have an image dataset with 300k images, the size of image is 768 * 768.
When I run `dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')` in second time, it takes 50 minutes to finish "Resolving data files" part, what's going o... |
https://github.com/huggingface/datasets/issues/8116 | `to_json()` serializes `Json()` feature as escaped string instead of native JSON object | Hi! I ran into this issue as well, I think the root cause was that under `_batch_json()` of `JsonDatasetWriter` it was directly exporting the encoded string without decoding it first. I will try to add a PR with a small fix. | ### Describe the bug
When a dataset column uses the `Json()` feature type, calling `to_json()` writes the value as an escaped JSON string (e.g., `"{\"cat_a\":1,\"cat_b\":2}"`) instead of a native JSON object (e.g., `{"cat_a":1,"cat_b":2}`).
This causes two problems:
1. The output JSON file is not interoperable with ... | 41 | `to_json()` serializes `Json()` feature as escaped string instead of native JSON object
### Describe the bug
When a dataset column uses the `Json()` feature type, calling `to_json()` writes the value as an escaped JSON string (e.g., `"{\"cat_a\":1,\"cat_b\":2}"`) instead of a native JSON object (e.g., `{"cat_a":1,"cat... |
https://github.com/huggingface/datasets/issues/4352 | When using `dataset.map()` if passed `Features` types do not match what is returned from the mapped function, execution does not except in an obvious way | Hi ! Thanks for reporting :) `datasets` usually returns a `pa.lib.ArrowInvalid` error if the feature types don't match.
It would be awesome if we had a way to reproduce the `OverflowError` in this case, to better understand what happened and be able to provide the best error message | ## Describe the bug
Recently I was trying to using `.map()` to preprocess a dataset. I defined the expected Features and passed them into `.map()` like `dataset.map(preprocess_data, features=features)`. My expected `Features` keys matched what came out of `preprocess_data`, but the types i had defined for them did not... | 48 | When using `dataset.map()` if passed `Features` types do not match what is returned from the mapped function, execution does not except in an obvious way
## Describe the bug
Recently I was trying to using `.map()` to preprocess a dataset. I defined the expected Features and passed them into `.map()` like `dataset.map(... |
https://github.com/huggingface/datasets/issues/7818 | train_test_split and stratify breaks with Numpy 2.0 | I can't reproduce this. Could you pls provide an example with a public dataset/artificial dataset and show how you loaded that?
This works for me:
```python
import numpy as np
from datasets import Dataset, Features, ClassLabel, Value
data = {"text": [f"sample_{i}" for i in range(100)], "label": [i % 3 for i in range... | ### Describe the bug
As stated in the title, since Numpy changed in version >2.0 with copy, the stratify parameters break.
e.g. `all_dataset.train_test_split(test_size=0.2,stratify_by_column="label")` returns a Numpy error.
It works if you downgrade Numpy to a version lower than 2.0.
### Steps to reproduce the bug
... | 80 | train_test_split and stratify breaks with Numpy 2.0
### Describe the bug
As stated in the title, since Numpy changed in version >2.0 with copy, the stratify parameters break.
e.g. `all_dataset.train_test_split(test_size=0.2,stratify_by_column="label")` returns a Numpy error.
It works if you downgrade Numpy to a vers... |
https://github.com/huggingface/datasets/issues/7818 | train_test_split and stratify breaks with Numpy 2.0 | @davebulaval tried with numpy 2.3.4, and maybe i have successfully reproduced the bug!
```
ValueError: Unable to avoid copy while creating an array as requested.
If using `np.array(obj, copy=False)` replace it with `np.asarray(obj)` to allow a copy when needed (no behavior change in NumPy 1.x).
For more details, see ht... | ### Describe the bug
As stated in the title, since Numpy changed in version >2.0 with copy, the stratify parameters break.
e.g. `all_dataset.train_test_split(test_size=0.2,stratify_by_column="label")` returns a Numpy error.
It works if you downgrade Numpy to a version lower than 2.0.
### Steps to reproduce the bug
... | 81 | train_test_split and stratify breaks with Numpy 2.0
### Describe the bug
As stated in the title, since Numpy changed in version >2.0 with copy, the stratify parameters break.
e.g. `all_dataset.train_test_split(test_size=0.2,stratify_by_column="label")` returns a Numpy error.
It works if you downgrade Numpy to a vers... |
https://github.com/huggingface/datasets/issues/7818 | train_test_split and stratify breaks with Numpy 2.0 | Also @CloseChoice The bug only appears in cases where the Arrow array cannot be represented as a contiguous NumPy array without copying.
So closing the discussion loop here -
The error occurs because `train_test_split(..., stratify_by_column=...)` attempts to convert
an Arrow column to a NumPy array using `np.array(... | ### Describe the bug
As stated in the title, since Numpy changed in version >2.0 with copy, the stratify parameters break.
e.g. `all_dataset.train_test_split(test_size=0.2,stratify_by_column="label")` returns a Numpy error.
It works if you downgrade Numpy to a version lower than 2.0.
### Steps to reproduce the bug
... | 97 | train_test_split and stratify breaks with Numpy 2.0
### Describe the bug
As stated in the title, since Numpy changed in version >2.0 with copy, the stratify parameters break.
e.g. `all_dataset.train_test_split(test_size=0.2,stratify_by_column="label")` returns a Numpy error.
It works if you downgrade Numpy to a vers... |
https://github.com/huggingface/datasets/issues/5908 | Unbearably slow sorting on big mapped datasets | Hi ! `shard` currently returns a slow dataset by default, with examples evenly distributed in the dataset.
You can get a fast dataset using `contiguous=True` (which should be the default imo):
```python
dataset = dataset.shard(10, 0, contiguous=True)
```
This way you don't need to flatten_indices() and sort ... | ### Describe the bug
For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially with bigger datasets (wasn't able to sort 700k lin... | 52 | Unbearably slow sorting on big mapped datasets
### Describe the bug
For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially wit... |
https://github.com/huggingface/datasets/issues/5908 | Unbearably slow sorting on big mapped datasets | @lhoestq
> contiguous=True (which should be the default imo)
For `IterableDataset`, it's not possible to implement contiguous sharding without knowing the number of examples in advance, so setting the default value to `contiguous=True` would result in an inconsistency between `Dataset` and `IterableDataset` (whe... | ### Describe the bug
For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially with bigger datasets (wasn't able to sort 700k lin... | 46 | Unbearably slow sorting on big mapped datasets
### Describe the bug
For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially wit... |
https://github.com/huggingface/datasets/issues/5908 | Unbearably slow sorting on big mapped datasets | Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.
If the dataset is made of one shard it's indeed not possible to shard it contiguously though | ### Describe the bug
For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially with bigger datasets (wasn't able to sort 700k lin... | 45 | Unbearably slow sorting on big mapped datasets
### Describe the bug
For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially wit... |
https://github.com/huggingface/datasets/issues/5908 | Unbearably slow sorting on big mapped datasets | > Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.
But sharding an iterable dataset by sharding its `gen_kwargs` would still yield approximate shards(not equal to `Dataset.shard`), no? | ### Describe the bug
For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially with bigger datasets (wasn't able to sort 700k lin... | 47 | Unbearably slow sorting on big mapped datasets
### Describe the bug
For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially wit... |
https://github.com/huggingface/datasets/issues/5908 | Unbearably slow sorting on big mapped datasets | I understand the issue doesn't exist with non-mapped datasets, but if flattening is so much more efficient than sorting the indices, that's an issue in itself.
There are plenty of issues people posted for which the root cause turns out to be the same. It seems like mapped datasets are terribly inefficient. I think I s... | ### Describe the bug
For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially with bigger datasets (wasn't able to sort 700k lin... | 82 | Unbearably slow sorting on big mapped datasets
### Describe the bug
For me, with ~40k lines, sorting took 3.5 seconds on a flattened dataset (including the flatten operation) and 22.7 seconds on a mapped dataset (right after sharding), which is about x5 slowdown. Moreover, it seems like it slows down exponentially wit... |
https://github.com/huggingface/datasets/issues/5606 | Add `Dataset.to_list` to the API | Hello, I have an interest in this issue.
Is the `Dataset.to_dict` you are describing correct in the code here?
https://github.com/huggingface/datasets/blob/35b789e8f6826b6b5a6b48fcc2416c890a1f326a/src/datasets/arrow_dataset.py#L4633-L4667 | Since there is `Dataset.from_list` in the API, we should also add `Dataset.to_list` to be consistent.
Regarding the implementation, we can re-use `Dataset.to_dict`'s code and replace the `to_pydict` calls with `to_pylist`. | 20 | Add `Dataset.to_list` to the API
Since there is `Dataset.from_list` in the API, we should also add `Dataset.to_list` to be consistent.
Regarding the implementation, we can re-use `Dataset.to_dict`'s code and replace the `to_pydict` calls with `to_pylist`.
Hello, I have an interest in this issue.
Is the `Dataset.to... |
https://github.com/huggingface/datasets/issues/7668 | Broken EXIF crash the whole program | There are other discussions about error handling for images decoding here : https://github.com/huggingface/datasets/issues/7632 https://github.com/huggingface/datasets/issues/7612
and a PR here: https://github.com/huggingface/datasets/pull/7638 (would love your input on the proposed solution !) | ### Describe the bug
When parsing this image in the ImageNet1K dataset, the `datasets` crashs whole training process just because unable to parse an invalid EXIF tag.
### Steps to reproduce the bug
Use the `datasets.Image.decod... | 28 | Broken EXIF crash the whole program
### Describe the bug
When parsing this image in the ImageNet1K dataset, the `datasets` crashs whole training process just because unable to parse an invalid EXIF tag.
### Steps to reproduce th... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | When the process starts to hang, can you interrupt it with CTRL + C and paste the error stack trace here? | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 21 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | Thanks @mariosasko for your prompt response, here's the stack trace:
```
KeyboardInterrupt Traceback (most recent call last)
Cell In[12], line 4
2 t = time.time()
3 iter_ = 0
----> 4 for batch in train_dataloader:
5 #batch_proc = streaming_obj.collect_streaming_dat... | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 308 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | Update: If i let it run, it eventually fails with:
```
RuntimeError Traceback (most recent call last)
Cell In[16], line 4
2 t = time.time()
3 iter_ = 0
----> 4 for batch in train_dataloader:
5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch)
... | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 416 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | PyTorch tensors cannot store `Decimal` objects. Casting the column with decimals to `float` should fix the issue. | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 17 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | I already have cast in collate_fn, in which I perform .astype(float) for each numerical field.
On the same instance, I installed a conda env with python 3.6, and this works well.
Sample:
```
def streaming_data_collate_fn(batch):
df = pd.DataFrame.from_dict(batch)
feat_vals = torch.FloatTensor(np.nan_t... | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 42 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | `collate_fn` is applied after the `torch` formatting step, so I think the only option when working with an `IterableDataset` is to remove the `with_format` call and perform the conversion from Python values to PyTorch tensors in `collate_fn`. The standard `Dataset` supports `with_format("numpy")`, which should make thi... | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 48 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | Thanks!
Python 3.10 conda-env: After replacing with_format("torch") with with_format("numpy"), the error went away. However, it was still taking over 2 minutes to load a very small batch of 64 samples with num_workers set to 32. Once I removed with_format call altogether, it is finishing in 11 seconds.
Python 3.6 ... | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 83 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | Can you share the `datasets` and `torch` versions installed in these conda envs?
> Once I removed with_format call altogether, it is finishing in 11 seconds.
Hmm, that's surprising. What are your dataset's `.features`? | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 34 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | Python 3.6:
datasets.__version__ 2.4.0
torch.__version__ 1.10.1+cu102
Python 3.10:
datasets.__version__ 2.14.0
torch.__version__ 2.0.0
Anonymized features are of the form (subset shown here):
{
'string_feature_i': Value(dtype='string', id=None),
'numerical_feature_i': Value(dtype='decimal128(38, 0)', id=N... | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 46 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | One more thing, in python 3.10 based kernel, interestingly increasing num_workers seem to be increasing the runtime of iterating I was trying out. In python 3.10 kernel execution, I do not even see multiple CPU cores spiking unlike in 3.6.
512 batch size on 32 workers executes in 2.4 seconds on python 3.6 kernel, wh... | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 61 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | **Update**: It seems the latency part is more of a multiprocessing issue with torch and some host specific issue, and I had to scourge through relevant pytorch issues, when I stumbled across these threads:
1. https://github.com/pytorch/pytorch/issues/102494
2. https://github.com/pytorch/pytorch/issues/102269
3. http... | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 87 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | I'm facing a similar situation in the local VS Code.
Datasets version 2.14.4
Torch 2.0.1+cu118
Same code runs without issues in Colab
```
from datasets import load_dataset
dataset = load_dataset("Supermaxman/esa-hubble", streaming=True)
sample = next(iter(dataset["train"]))
```
is stuck for minutes.... | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 155 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | @osanseviero I assume the `self._sslobj.read(len, buffer)` line comes from the built-in `ssl` module, so this probably has something to do with your network. Please open a new issue with the full stack trace in case you haven't resolved this yet. | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 40 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/6079 | Iterating over DataLoader based on HF datasets is stuck forever | Ran into same issue after upgrading to pytorch-2.0. Disabling KMP_AFFINITY as mentioned above worked for me. Thanks!
| ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What shou... | 17 | Iterating over DataLoader based on HF datasets is stuck forever
### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is work... |
https://github.com/huggingface/datasets/issues/5137 | Align task tags in dataset metadata | I have opened PRs to fix the task_ids in all datasets within a namespace as well.
Working on task_categories... | ## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets
- [x] task_categories: 451 datas... | 19 | Align task tags in dataset metadata
## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets... |
https://github.com/huggingface/datasets/issues/5137 | Align task tags in dataset metadata | For future reference: this fix had some complications
When trying to open a PR to fix the task tags, an exception was thrown if:
- the metadata contained "languages" or "licenses" (instead of "language" or "license")
- the metadata contained a non-valid language: `en-US` (instead of `en`), `no` (instead of `'no'`)... | ## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets
- [x] task_categories: 451 datas... | 169 | Align task tags in dataset metadata
## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets... |
https://github.com/huggingface/datasets/issues/5137 | Align task tags in dataset metadata | Yes, @julien-c. These are some of the feedbacks:
- Most people just thank for the fix: [cahya/librivox-indonesia](https://huggingface.co/datasets/cahya/librivox-indonesia/discussions/1#6357cd8a292a050ebd705f84), [TurkuNLP/xlsum-fi](https://huggingface.co/datasets/TurkuNLP/xlsum-fi/discussions/1#6357828aa1f8ad1c31bcbe4... | ## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets
- [x] task_categories: 451 datas... | 189 | Align task tags in dataset metadata
## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets... |
https://github.com/huggingface/datasets/issues/5137 | Align task tags in dataset metadata | As someone with feedback on the updates (which I highly appreciate seeing included here :D), a few comments from a "user perspective":
* I think the general confusion for me was also surrounding the hierarchy; it doesn't really become super clear (even when using the tagger space) that one is a subset of the other... | ## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets
- [x] task_categories: 451 datas... | 216 | Align task tags in dataset metadata
## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets... |
https://github.com/huggingface/datasets/issues/5137 | Align task tags in dataset metadata | Thanks to you both for your feedback! super useful! cc'ing @osanseviero too 🙂
> The datasets explorer still shows tags that are no longer valid
wait which explorer is that? is it https://huggingface.co/datasets/viewer/ ?
| ## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets
- [x] task_categories: 451 datas... | 34 | Align task tags in dataset metadata
## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets... |
https://github.com/huggingface/datasets/issues/5137 | Align task tags in dataset metadata | - Some people do not agree about current "hierarchy":
- symbolic-regression: [yoshitomo-matsubara/srsd-feynman_hard](https://huggingface.co/datasets/yoshitomo-matsubara/srsd-feynman_hard/discussions/2#63614194c12a09b8a31457cc), [yoshitomo-matsubara/srsd-feynman_medium](https://huggingface.co/datasets/yoshitomo-matsu... | ## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets
- [x] task_categories: 451 datas... | 21 | Align task tags in dataset metadata
## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets... |
https://github.com/huggingface/datasets/issues/5137 | Align task tags in dataset metadata | @albertvillanova
Thank you for sharing our voice here!
Yes, we want `symbolic-regression` to be listed as a task. This task has been attracting attention from the machine learning/deep learning community, and unfortunately existing symbolic regression datasets are de-centralized in the community (hosted at individ... | ## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets
- [x] task_categories: 451 datas... | 64 | Align task tags in dataset metadata
## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets... |
https://github.com/huggingface/datasets/issues/7196 | concatenate_datasets does not preserve shuffling state | It also does preserve `split_by_node`, so in the meantime you should call `shuffle` or `split_by_node` AFTER `interleave_datasets` or `concatenate_datasets` | ### Describe the bug
After concatenate datasets on an iterable dataset, the shuffling state is destroyed, similar to #7156
This means concatenation cant be used for resolving uneven numbers of samples across devices when using iterable datasets in a distributed setting as discussed in #6623
I also noticed th... | 19 | concatenate_datasets does not preserve shuffling state
### Describe the bug
After concatenate datasets on an iterable dataset, the shuffling state is destroyed, similar to #7156
This means concatenation cant be used for resolving uneven numbers of samples across devices when using iterable datasets in a distribu... |
https://github.com/huggingface/datasets/issues/6840 | Delete uploaded files from the UI | This is super late, but if you click on any directory, you can delete the directory using a "Delete Directory" button in the top right of the interface, and similarly, if you click on any file, you can delete the file using a "Delete File" button in the top right. | ### Feature request
Once a file is uploaded and the commit is made, I am unable to delete individual files without completely deleting the whole dataset via the website UI.
### Motivation
Would be a useful addition
### Your contribution
Would love to help out with some guidance | 50 | Delete uploaded files from the UI
### Feature request
Once a file is uploaded and the commit is made, I am unable to delete individual files without completely deleting the whole dataset via the website UI.
### Motivation
Would be a useful addition
### Your contribution
Would love to help out with some guidance
Th... |
https://github.com/huggingface/datasets/issues/5505 | PyTorch BatchSampler still loads from Dataset one-by-one | This change seems to come from a few months ago in the PyTorch side. That's good news and it means we may not need to pass a batch_sampler as soon as we add `Dataset.__getitems__` to get the optimal speed :)
Thanks for reporting ! Would you like to open a PR to add `__getitems__` and remove this outdated documentati... | ### Describe the bug
In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue.
I'm not sure if this is a mistake in the docs or the code, but it seems that the on... | 60 | PyTorch BatchSampler still loads from Dataset one-by-one
### Describe the bug
In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue.
I'm not sure if this is a ... |
https://github.com/huggingface/datasets/issues/5505 | PyTorch BatchSampler still loads from Dataset one-by-one | Yeah I figured this was the sort of thing that probably once worked. I can confirm that you no longer need the batch sampler, just `batch_size=n` in the `DataLoader`.
I'll pass on the PR, I'm flat out right now, sorry. | ### Describe the bug
In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue.
I'm not sure if this is a mistake in the docs or the code, but it seems that the on... | 40 | PyTorch BatchSampler still loads from Dataset one-by-one
### Describe the bug
In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue.
I'm not sure if this is a ... |
https://github.com/huggingface/datasets/issues/8078 | Inconsistent loading of LFS-hosted files in Dangindev/viet-cultural-vqa dataset | This should be fixed, thanks for reporting ! let us know if you ever encounter other issues in the future | ### Describe the bug
Describe the bug
Some files in the Dangindev/viet-cultural-vqa dataset fail to load via the Hugging Face datasets library.
xet-hosted files load fine
LFS-hosted files sometimes fail
Example:
Fails: https://huggingface.co/datasets/Dangindev/viet-cultural-vqa/blob/main/images/am_thuc/banh_chung_T... | 20 | Inconsistent loading of LFS-hosted files in Dangindev/viet-cultural-vqa dataset
### Describe the bug
Describe the bug
Some files in the Dangindev/viet-cultural-vqa dataset fail to load via the Hugging Face datasets library.
xet-hosted files load fine
LFS-hosted files sometimes fail
Example:
Fails: https://huggingfa... |
https://github.com/huggingface/datasets/issues/5498 | TypeError: 'bool' object is not iterable when filtering a datasets.arrow_dataset.Dataset | Hi! Instead of a single boolean, your filter function should return an iterable (of booleans) in the batched mode like so:
```python
train_dataset = train_dataset.filter(
function=lambda batch: [image is not None for image in batch["image"]],
batched=True,
batch_size=10)
```
... | ### Describe the bug
Hi,
Thanks for the amazing work on the library!
**Describe the bug**
I think I might have noticed a small bug in the filter method.
Having loaded a dataset using `load_dataset`, when I try to filter out empty entries with `batched=True`, I get a TypeError.
### Steps to reproduce the ... | 74 | TypeError: 'bool' object is not iterable when filtering a datasets.arrow_dataset.Dataset
### Describe the bug
Hi,
Thanks for the amazing work on the library!
**Describe the bug**
I think I might have noticed a small bug in the filter method.
Having loaded a dataset using `load_dataset`, when I try to filt... |
https://github.com/huggingface/datasets/issues/5498 | TypeError: 'bool' object is not iterable when filtering a datasets.arrow_dataset.Dataset | I hit the same issue and the error message isn't really clear on what's going wrong. It might be helpful to update the docs with a batched example. | ### Describe the bug
Hi,
Thanks for the amazing work on the library!
**Describe the bug**
I think I might have noticed a small bug in the filter method.
Having loaded a dataset using `load_dataset`, when I try to filter out empty entries with `batched=True`, I get a TypeError.
### Steps to reproduce the ... | 28 | TypeError: 'bool' object is not iterable when filtering a datasets.arrow_dataset.Dataset
### Describe the bug
Hi,
Thanks for the amazing work on the library!
**Describe the bug**
I think I might have noticed a small bug in the filter method.
Having loaded a dataset using `load_dataset`, when I try to filt... |
https://github.com/huggingface/datasets/issues/4570 | Dataset sharding non-contiguous? | This was silly; I was sure I'd looked for a `contiguous` argument, and was certain there wasn't one the first time I looked :smile:
Sorry about that. | ## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggi... | 27 | Dataset sharding non-contiguous?
## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull ... |
https://github.com/huggingface/datasets/issues/4570 | Dataset sharding non-contiguous? | Hi! You can pass `contiguous=True` to `.shard()` get contiguous shards. More info on this and the default behavior can be found in the [docs](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.shard).
EDIT: Answered as you closed the thread 😄 | ## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggi... | 32 | Dataset sharding non-contiguous?
## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull ... |
https://github.com/huggingface/datasets/issues/4570 | Dataset sharding non-contiguous? | Hahaha I'm sorry; my excuse is: it's Sunday. (Which makes me all the more grateful for your response :smiley: | ## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggi... | 19 | Dataset sharding non-contiguous?
## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull ... |
https://github.com/huggingface/datasets/issues/4570 | Dataset sharding non-contiguous? | @mariosasko Sorry for reviving this, but I was curious as to why `contiguous=False` was the default. This might be a personal bias, but I feel that a user would expect the opposite to be the default. :thinking: | ## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggi... | 37 | Dataset sharding non-contiguous?
## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull ... |
https://github.com/huggingface/datasets/issues/3950 | Streaming Datasets don't work with Transformers Trainer when dataloader_num_workers>1 | Hi, thanks for reporting. This could be related to https://github.com/huggingface/datasets/issues/3148 too
We should definitely make `TorchIterableDataset` picklable by moving it in the main code instead of inside a function. If you'd like to contribute, feel free to open a Pull Request :)
I'm also taking a look ... | ## Describe the bug
Streaming Datasets can't be pickled, so any interaction between them and multiprocessing results in a crash.
## Steps to reproduce the bug
```python
import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets
ds = datasets.load_dataset('os... | 55 | Streaming Datasets don't work with Transformers Trainer when dataloader_num_workers>1
## Describe the bug
Streaming Datasets can't be pickled, so any interaction between them and multiprocessing results in a crash.
## Steps to reproduce the bug
```python
import transformers
from transformers import Trainer, Auto... |
https://github.com/huggingface/datasets/issues/7107 | load_dataset broken in 2.21.0 | There seems to be a PR related to the load_dataset path that went into 2.21.0 -- https://github.com/huggingface/datasets/pull/6862/files
Taking a look at it now | ### Describe the bug
`eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval_gpt4_baseline", trust_remote_code=True)`
used to work till 2.20.0 but doesn't work in 2.21.0
In 2.20.0:
![Screenshot 2024-08-16 at 3 57 10 PM](https://github.com/user-attachments/assets/0516489b-8187-486d-bee8-88af3381de... | 23 | load_dataset broken in 2.21.0
### Describe the bug
`eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval_gpt4_baseline", trust_remote_code=True)`
used to work till 2.20.0 but doesn't work in 2.21.0
In 2.20.0:
![Screenshot 2024-08-16 at 3 57 10 PM](https://github.com/user-attachments/assets/0516... |
https://github.com/huggingface/datasets/issues/7107 | load_dataset broken in 2.21.0 | I tried adding a simple test to `test_load.py` with the alpaca eval dataset but the test didn't fail :(.
So looks like this might have something to do with the environment? | ### Describe the bug
`eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval_gpt4_baseline", trust_remote_code=True)`
used to work till 2.20.0 but doesn't work in 2.21.0
In 2.20.0:
![Screenshot 2024-08-16 at 3 57 10 PM](https://github.com/user-attachments/assets/0516489b-8187-486d-bee8-88af3381de... | 31 | load_dataset broken in 2.21.0
### Describe the bug
`eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval_gpt4_baseline", trust_remote_code=True)`
used to work till 2.20.0 but doesn't work in 2.21.0
In 2.20.0:
![Screenshot 2024-08-16 at 3 57 10 PM](https://github.com/user-attachments/assets/0516... |
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