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I_kwDODunzps5zM4yw
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map() not recognizing "text"
|
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| 2023-10-09T10:27:30
| 2023-10-11T20:28:45
| 2023-10-11T20:28:45
|
NONE
| null | null | null |
### Describe the bug
The [map() documentation](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/main_classes#datasets.Dataset.map) reads:
`
ds = ds.map(lambda x: tokenizer(x['text'], truncation=True, padding=True), batched=True)`
I have been trying to reproduce it in my code as:
`tokenizedDataset = dataset.map(lambda x: tokenizer(x['text']), batched=True)`
But it doesn't work as it throws the error:
> KeyError: 'text'
Can you please guide me on how to fix it?
### Steps to reproduce the bug
1. `from datasets import load_dataset
dataset = load_dataset("amazon_reviews_multi")`
2. Then this code: `from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")`
3. The line I quoted above (which I have been trying)
### Expected behavior
As mentioned in the documentation, it should run without any error and map the tokenization on the whole dataset.
### Environment info
Python 3.10.2
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TypeError: expected str, bytes or os.PathLike object, not dict
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| 2023-10-09T04:56:26
| 2023-10-10T13:17:33
| null |
NONE
| null | null | null |
### Describe the bug
my dataset is in form : train- image /n -labels
and tried the code:
```
from datasets import load_dataset
data_files = {
"train": "/content/datasets/PotholeDetectionYOLOv8-1/train/",
"validation": "/content/datasets/PotholeDetectionYOLOv8-1/valid/",
"test": "/content/datasets/PotholeDetectionYOLOv8-1/test/"
}
dataset = load_dataset("imagefolder", data_dir=data_files)
dataset
```
got error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-29-2ef1926f73d9>](https://localhost:8080/#) in <cell line: 8>()
6 "test": "/content/datasets/PotholeDetectionYOLOv8-1/test/"
7 }
----> 8 dataset = load_dataset("imagefolder", data_dir=data_files)
9 dataset
6 frames
[/usr/lib/python3.10/pathlib.py](https://localhost:8080/#) in _parse_args(cls, args)
576 parts += a._parts
577 else:
--> 578 a = os.fspath(a)
579 if isinstance(a, str):
580 # Force-cast str subclasses to str (issue #21127)
TypeError: expected str, bytes or os.PathLike object, not dict
```
### Steps to reproduce the bug
as share above
### Expected behavior
load images and labels , but my dataset only uploads images
- https://huggingface.co/datasets/Andyrasika/potholes-dataset
### Environment info
colab pro
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|
I_kwDODunzps5zAp9g
| 6,284
|
Add Belebele multiple-choice machine reading comprehension (MRC) dataset
|
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| 2023-10-06T06:58:03
| 2023-10-06T13:26:51
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|
NONE
| null | null | null |
### Feature request
Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the [FLORES-200](https://github.com/facebookresearch/flores/tree/main/flores200) dataset. The human annotation procedure was carefully curated to create questions that discriminate between different levels of generalizable language comprehension and is reinforced by extensive quality checks. While all questions directly relate to the passage, the English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. Belebele opens up new avenues for evaluating and analyzing the multilingual abilities of language models and NLP systems.
Please refer to paper for more details, [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://arxiv.org/abs/2308.16884).
## Composition
- 900 questions per language variant
- 488 distinct passages, there are 1-2 associated questions for each.
- For each question, there is 4 multiple-choice answers, exactly 1 of which is correct.
- 122 language/language variants (including English).
- 900 x 122 = 109,800 total questions.
### Motivation
official repo https://github.com/facebookresearch/belebele
### Your contribution
-
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I_kwDODunzps5y7jru
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Couldn't cast array of type fixed_size_list to Sequence(Value(float64))
|
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| 2024-02-06T19:24:20
| 2024-02-06T19:24:20
|
NONE
| null | null | null |
### Describe the bug
I have a dataset with an embedding column, when I try to map that dataset I get the following exception:
```
Traceback (most recent call last):
File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3189, in map
for rank, done, content in iflatmap_unordered(
File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered
[async_result.get(timeout=0.05) for async_result in async_results]
File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1387, in <listcomp>
[async_result.get(timeout=0.05) for async_result in async_results]
File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/multiprocess/pool.py", line 774, in get
raise self._value
TypeError: Couldn't cast array of type
fixed_size_list<item: float>[2]
to
Sequence(feature=Value(dtype='float32', id=None), length=2, id=None)
```
### Steps to reproduce the bug
Here's a simple repro script:
```
from datasets import Features, Value, Sequence, ClassLabel, Dataset
dataset_features = Features({
'text': Value('string'),
'embedding': Sequence(Value('double'), length=2),
'categories': Sequence(ClassLabel(names=sorted([
'one',
'two',
'three'
]))),
})
dataset = Dataset.from_dict(
{
'text': ['A'] * 10000,
'embedding': [[0.0, 0.1]] * 10000,
'categories': [[0]] * 10000,
},
features=dataset_features
)
def test_mapper(r):
r['text'] = list(map(lambda t: t + ' b', r['text']))
return r
dataset = dataset.map(test_mapper, batched=True, batch_size=10, features=dataset_features, num_proc=2)
```
Removing the embedding column fixes the issue!
### Expected behavior
The mapping completes successfully.
### Environment info
- `datasets` version: 2.14.4
- Platform: macOS-14.0-arm64-arm-64bit
- Python version: 3.10.12
- Huggingface_hub version: 0.17.1
- PyArrow version: 13.0.0
- Pandas version: 2.0.3
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I_kwDODunzps5y62BC
| 6,279
|
Batched IterableDataset
|
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| 2023-10-05T11:12:49
| 2024-07-08T11:10:41
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NONE
| null | null | null |
### Feature request
Hi,
could you add an implementation of a batched `IterableDataset`. It already support an option to do batch iteration via `.iter(batch_size=...)` but this cannot be used in combination with a torch `DataLoader` since it just returns an iterator.
### Motivation
The current implementation loads each element of a batch individually which can be very slow in cases of a big batch_size. I did some experiments [here](https://discuss.huggingface.co/t/slow-dataloader-with-big-batch-size/57224) and using a batched iteration would speed up data loading significantly.
### Your contribution
N/A
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I_kwDODunzps5y3F3C
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FileNotFoundError: Couldn't find a module script at /content/paws-x/paws-x.py. Module 'paws-x' doesn't exist on the Hugging Face Hub either.
|
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| 2023-10-04T22:01:25
| 2023-10-08T17:05:46
| 2023-10-08T17:05:46
|
NONE
| null | null | null |
### Describe the bug
I'm encountering a "FileNotFoundError" while attempting to use the "paws-x" dataset to retrain the DistilRoBERTa-base model. The error message is as follows:
FileNotFoundError: Couldn't find a module script at /content/paws-x/paws-x.py. Module 'paws-x' doesn't exist on the Hugging Face Hub either.
### Steps to reproduce the bug
https://colab.research.google.com/drive/11xUUFxloClpmqLvDy_Xxfmo3oUzjY5nx#scrollTo=kUn74FigzhHm
### Expected behavior
The the trained model
### Environment info
colab, "paws-x" dataset , DistilRoBERTa-base model
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| 1,925,961,878
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I_kwDODunzps5yy9iW
| 6,276
|
I'm trying to fine tune the openai/whisper model from huggingface using jupyter notebook and i keep getting this error
|
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| 2023-10-04T11:03:41
| 2023-11-27T10:39:16
| null |
NONE
| null | null | null |
### Describe the bug
I'm trying to fine tune the openai/whisper model from huggingface using jupyter notebook and i keep getting this error, i'm following the steps in this blog post
https://huggingface.co/blog/fine-tune-whisper
I tried google collab and it works but because I'm on the free version the training doesn't complete
the error comes in jupyter notebook when i run this line
`common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)`
here is the error message
```
Map (num_proc=4): 0% 0/2506 [00:52<?, ? examples/s]
The above exception was the direct cause of the following exception:
NameError Traceback (most recent call last) Cell In[19], line 1 ----> 1 common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)
File ~\anaconda\Lib\site-packages\datasets\dataset_dict.py:853, in DatasetDict.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc) 850 if cache_file_names is None: 851 cache_file_names = {k: None for k in self} 852 return DatasetDict( --> 853 { 854 k: dataset.map( 855 function=function, 856 with_indices=with_indices, 857 with_rank=with_rank, 858 input_columns=input_columns, 859 batched=batched, 860 batch_size=batch_size, 861 drop_last_batch=drop_last_batch, 862 remove_columns=remove_columns, 863 keep_in_memory=keep_in_memory, 864 load_from_cache_file=load_from_cache_file, 865 cache_file_name=cache_file_names[k], 866 writer_batch_size=writer_batch_size, 867 features=features, 868 disable_nullable=disable_nullable, 869 fn_kwargs=fn_kwargs, 870 num_proc=num_proc, 871 desc=desc, 872 ) 873 for k, dataset in self.items() 874 } 875 )
File ~\anaconda\Lib\site-packages\datasets\dataset_dict.py:854, in <dictcomp>(.0) 850 if cache_file_names is None: 851 cache_file_names = {k: None for k in self} 852 return DatasetDict( 853 { --> 854 k: dataset.map( 855 function=function, 856 with_indices=with_indices, 857 with_rank=with_rank, 858 input_columns=input_columns, 859 batched=batched, 860 batch_size=batch_size, 861 drop_last_batch=drop_last_batch, 862 remove_columns=remove_columns, 863 keep_in_memory=keep_in_memory, 864 load_from_cache_file=load_from_cache_file, 865 cache_file_name=cache_file_names[k], 866 writer_batch_size=writer_batch_size, 867 features=features, 868 disable_nullable=disable_nullable, 869 fn_kwargs=fn_kwargs, 870 num_proc=num_proc, 871 desc=desc, 872 ) 873 for k, dataset in self.items() 874 } 875 )
File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:592, in transmit_tasks.<locals>.wrapper(*args, **kwargs) 590 self: "Dataset" = kwargs.pop("self") 591 # apply actual function --> 592 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 593 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 594 for dataset in datasets: 595 # Remove task templates if a column mapping of the template is no longer valid
File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:557, in transmit_format.<locals>.wrapper(*args, **kwargs) 550 self_format = { 551 "type": self._format_type, 552 "format_kwargs": self._format_kwargs, 553 "columns": self._format_columns, 554 "output_all_columns": self._output_all_columns, 555 } 556 # apply actual function --> 557 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 558 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 559 # re-apply format to the output
File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:3189, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 3182 logger.info(f"Spawning {num_proc} processes") 3183 with logging.tqdm( 3184 disable=not logging.is_progress_bar_enabled(), 3185 unit=" examples", 3186 total=pbar_total, 3187 desc=(desc or "Map") + f" (num_proc={num_proc})", 3188 ) as pbar: -> 3189 for rank, done, content in iflatmap_unordered( 3190 pool, Dataset._map_single, kwargs_iterable=kwargs_per_job 3191 ): 3192 if done: 3193 shards_done += 1
File ~\anaconda\Lib\site-packages\datasets\utils\py_utils.py:1394, in iflatmap_unordered(pool, func, kwargs_iterable) 1391 finally: 1392 if not pool_changed: 1393 # we get the result in case there's an error to raise -> 1394 [async_result.get(timeout=0.05) for async_result in async_results]
File ~\anaconda\Lib\site-packages\datasets\utils\py_utils.py:1394, in <listcomp>(.0) 1391 finally: 1392 if not pool_changed: 1393 # we get the result in case there's an error to raise -> 1394 [async_result.get(timeout=0.05) for async_result in async_results]
File ~\anaconda\Lib\site-packages\multiprocess\pool.py:774, in ApplyResult.get(self, timeout) 772 return self._value 773 else: --> 774 raise self._value
NameError: name 'feature_extractor' is not defined
```
### Steps to reproduce the bug
1. follow the steps in this blog post
https://huggingface.co/blog/fine-tune-whisper
2. run this line of code
`common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)`
3. I'm using jupyter notebook from anaconda
### Expected behavior
No error message
### Environment info
datasets version: 2.8.0
Python version: 3.11
Windows 10
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| 1,921,354,680
|
I_kwDODunzps5yhYu4
| 6,275
|
Would like to Contribute a dataset
|
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[] | null | 1
| 2023-10-02T07:00:21
| 2023-10-10T16:27:54
| 2023-10-10T16:27:54
|
NONE
| null | null | null |
I have a dataset of 2500 images that can be used for color-blind machine-learning algorithms. Since , there was no dataset available online , I made this dataset myself and would like to contribute this now to community
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I_kwDODunzps5ygLAo
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FileNotFoundError for dataset with multiple builder config
|
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| 2023-10-01T23:45:56
| 2024-08-14T04:42:02
| 2023-10-02T20:09:38
|
NONE
| null | null | null |
### Describe the bug
When there is only one config and only the dataset name is entered when using datasets.load_dataset(), it works fine. But if I create a second builder_config for my dataset and enter the config name when using datasets.load_dataset(), the following error will happen.
FileNotFoundError: [Errno 2] No such file or directory: 'C:/Users/chenx/.cache/huggingface/datasets/my_dataset/0_shot_multiple_choice/1.0.0/97c3854a012cfd6b045e3be4c864739902af2d818bb9235b047baa94c302e9a2.incomplete/my_dataset-test-00000-00000-of-NNNNN.arrow'
The "XXX.incomplete folder" in the cache folder of my dataset will disappear before "generating test split", which does not happen when config name is not entered and the config name is "default"
C:\Users\chenx\.cache\huggingface\datasets\my_dataset\0_shot_multiple_choice\1.0.0
The folder that is supposed to remain under the above directory will disappear, and the data generator will not have a place to generate data into.
### Steps to reproduce the bug
test = load_dataset('my_dataset', '0_shot_multiple_choice')
### Expected behavior
FileNotFoundError: [Errno 2] No such file or directory: 'C:/Users/chenx/.cache/huggingface/datasets/my_dataset/0_shot_multiple_choice/1.0.0/97c3854a012cfd6b045e3be4c864739902af2d818bb9235b047baa94c302e9a2.incomplete/my_dataset-test-00000-00000-of-NNNNN.arrow'
### Environment info
datasets 2.14.5
python 3.8.18
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I_kwDODunzps5yfvKU
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|
Broken Link to PubMed Abstracts dataset .
|
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| 2023-10-01T19:08:48
| 2024-04-28T02:30:42
| null |
NONE
| null | null | null |
### Describe the bug
The link provided for the dataset is broken,
data_files =
[https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst](url)
The
### Steps to reproduce the bug
Steps to reproduce:
1) Head over to [https://huggingface.co/learn/nlp-course/chapter5/4?fw=pt#big-data-datasets-to-the-rescue](url)
2) In the Section "What is the Pile?", you can see a code snippet that contains the broken link.
### Expected behavior
The link should Redirect to the "PubMed Abstracts dataset" as expected .
### Environment info
.
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I_kwDODunzps5yfY__
| 6,272
|
Duplicate `data_files` when named `<split>/<split>.parquet`
|
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| 2023-10-01T15:43:56
| 2024-03-15T15:22:05
| 2024-03-15T15:22:05
|
MEMBER
| null | null | null |
e.g. with `u23429/stock_1_minute_ticker`
```ipython
In [1]: from datasets import *
In [2]: b = load_dataset_builder("u23429/stock_1_minute_ticker")
Downloading readme: 100%|██████████████████████████| 627/627 [00:00<00:00, 246kB/s]
In [3]: b.config.data_files
Out[3]:
{NamedSplit('train'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet',
'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet'],
NamedSplit('validation'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet',
'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet'],
NamedSplit('test'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet',
'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet']}
```
This bug issue is present in the current `datasets` 2.14.5 and also on `main` even after https://github.com/huggingface/datasets/pull/6244 cc @mariosasko
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I_kwDODunzps5yd0nH
| 6,271
|
Overwriting Split overwrites data but not metadata, corrupting dataset
|
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| 2023-09-30T22:37:31
| 2023-10-16T13:30:50
| 2023-10-16T13:30:50
|
NONE
| null | null | null |
### Describe the bug
I want to be able to overwrite/update/delete splits in my dataset. Currently the only way to do is to manually go into the dataset and delete the split. If I try to overwrite programmatically I end up in an error state and (somewhat) corrupting the dataset. Read below.
**Current Behavior**
When I push to an existing split I get this error:
`ValueError: Split complexRoofLocation_01Apr2023_to_31May2023test already present`
This seems to suggest that the library doesn't support overwriting splits.
**Potential Bug**
What’s strange is that datasets, despite the operation erroring out with the ValueError above, does, in fact, overwrite the split:
`Pushing dataset shards to the dataset hub: 100% [.....................] 1/1 [00:00<00:00, 55.04it/s]`
Even though you got an error message and your code fails, your dataset is now changed. That seems like a bug. Either don't change the dataset, or don't throw the error and allow the script to proceed.
Additional Bug
While it overwrites the split, it doesn’t overwrite the split’s information. Because of this when you pull down the dataset you may end up getting a `NonMatchingSplitsSizesError` if the size of the dataset during the overwrite is different. For example, my original split had 5 rows, but on my overwrite, I only had 4. Then when I try to download the dataset, I get a `NonMatchingSplitsSizesError` because the dataset's data.json states there’s 5 but only 4 exist in the split.
Expected Behavior
This corrupts the dataset rendering it unusable (until you take manual intervention). Either the library should let the overwrite happen (which it does but should also update the metadata) or it shouldn’t do anything.
### Steps to reproduce the bug
[Colab Notebook](https://colab.research.google.com/drive/1bqVkD06Ngs9MQNdSk_ygCG6y1UqXA4pC?usp=sharing)
### Expected behavior
The split should be overwritten and I should be able to use the new version of the dataset without issue.
### Environment info
- `datasets` version: 2.14.5
- Platform: Linux-5.15.120+-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.17.3
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
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I_kwDODunzps5ydead
| 6,270
|
Dataset.from_generator raises with sharded gen_args
|
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| 2023-09-30T16:50:06
| 2023-10-11T20:29:12
| 2023-10-11T20:29:11
|
CONTRIBUTOR
| null | null | null |
### 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_kwargs was a list, then my generator would be called once for each element in the list with the dict in the list for that element.
It doesn't work that way though.
### Steps to reproduce the bug
```python
#!/usr/bin/python
from pathlib import Path
import datasets
def process_yaml(file):
yield dict(example=42)
if __name__ == '__main__':
import sys
dir = Path(sys.argv[0]).parent
ds = datasets.Dataset.from_generator(process_yaml, gen_kwargs=[{'file':f} for f in dir.glob('*.yml')],
)
ds.to_json('training.jsonl')
```
```
Generating train split: 0 examples [00:00, ? examples/s]
Traceback (most recent call last):
File "/tmp/dataset_bug.py", line 13, in <module>
ds = datasets.Dataset.from_generator(process_yaml, gen_kwargs=[{'file':f} for f in dir.glob('*.yml')],
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 1072, in from_generator
).read()
^^^^^^
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/io/generator.py", line 47, in read
self.builder.download_and_prepare(
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 954, in download_and_prepare
self._download_and_prepare(
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _download_and_prepare
super()._download_and_prepare(
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/hartmans/ai/venv/lib/python3.11/site-packages/datasets/builder.py", line 1656, in _prepare_split_single
generator = self._generate_examples(**gen_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: datasets.packaged_modules.generator.generator.Generator._generate_examples() argument after ** must be a ```
mapping, not list
### Expected behavior
I would expect that process_yaml would be called once for each yaml file in the directory where the script is run.
I also tried with the list being in gen_kwargs, but in that case process_yaml gets called with a list.
### Environment info
- `datasets` version: 2.14.6.dev0 (git commit 0cc77d7f45c7369; also tested with 2.14.0)
- Platform: Linux-6.1.0-10-amd64-x86_64-with-glibc2.36
- Python version: 3.11.2
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
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I_kwDODunzps5yOpp-
| 6,267
|
Multi label class encoding
|
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[] | null | 7
| 2023-09-27T22:48:08
| 2023-10-26T18:46:08
| null |
NONE
| null | null | null |
### Feature request
I have a multi label dataset and I'd like to be able to class encode the column and store the mapping directly in the features just as I can with a single label column. `class_encode_column` currently does not support multi labels.
Here's an example of what I'd like to encode:
```
data = {
'text': ['one', 'two', 'three', 'four'],
'labels': [['a', 'b'], ['b'], ['b', 'c'], ['a', 'd']]
}
dataset = Dataset.from_dict(data)
dataset = dataset.class_encode_column('labels')
```
I did some digging into the code base to evaluate the feasibility of this (note I'm very new to this code base) and from what I noticed the `ClassLabel` feature is still stored as an underlying raw data type of int so I thought a `MultiLabel` feature could similarly be stored as a Sequence of ints, thus not requiring significant serialization / conversion work to / from arrow.
I did a POC of this [here](https://github.com/huggingface/datasets/commit/15443098e9ce053943172f7ec6fce3769d7dff6e) and included a simple test case (please excuse all the commented out tests, going for speed of POC here and didn't want to fight IDE to debug a single test). In the test I just assert that `num_classes` is the same to show that things are properly serializing, but if you break after loading from disk you'll see the dataset correct and the dataset feature is as expected.
After digging more I did notice a few issues
- After loading from disk I noticed type of the `labels` class is `Sequence` not `MultiLabel` (though the added `feature` attribute came through). This doesn't happen for `ClassLabel` but I couldn't find the encode / decode code paths that handle this.
- I subclass `Sequence` in `MultiLabel` to leverage existing serialization, but this does miss the custom encode logic that `ClassLabel` has. I'm not sure of the best way to approach this as I haven't fully understood the encode / decode flow for datasets. I suspect my simple implementation will need some improvement as it'll require a significant amount of repeated logic to mimic `ClassLabel` behavior.
### Motivation
See above - would like to support multi label class encodings.
### Your contribution
This would be a big help for us and we're open to contributing but I'll likely need some guidance on how to implement to fit the encode / decode flow. Some suggestions on tests / would be great too, I'm guessing in addition to the class encode tests (that I'll need to expand) we'll need encode / decode tests.
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I_kwDODunzps5yI9WD
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|
CI is broken: ImportError: cannot import name 'context' from 'tensorflow.python'
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| 2023-09-27T08:12:05
| 2023-09-27T08:36:40
| 2023-09-27T08:36:40
|
MEMBER
| null | null | null |
Python 3.10 CI is broken for `test_py310`.
See: https://github.com/huggingface/datasets/actions/runs/6322990957/job/17169678812?pr=6262
```
FAILED tests/test_py_utils.py::TempSeedTest::test_tensorflow - ImportError: cannot import name 'context' from 'tensorflow.python' (/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/tensorflow/python/__init__.py)
```
```
_________________________ TempSeedTest.test_tensorflow _________________________
[gw1] linux -- Python 3.10.13 /opt/hostedtoolcache/Python/3.10.13/x64/bin/python
self = <tests.test_py_utils.TempSeedTest testMethod=test_tensorflow>
@require_tf
def test_tensorflow(self):
import tensorflow as tf
from tensorflow.keras import layers
model = layers.Dense(2)
def gen_random_output():
x = tf.random.uniform((1, 3))
return model(x).numpy()
> with temp_seed(42, set_tensorflow=True):
tests/test_py_utils.py:155:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/contextlib.py:135: in __enter__
return next(self.gen)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
seed = 42, set_pytorch = False, set_tensorflow = True
@contextmanager
def temp_seed(seed: int, set_pytorch=False, set_tensorflow=False):
"""Temporarily set the random seed. This works for python numpy, pytorch and tensorflow."""
np_state = np.random.get_state()
np.random.seed(seed)
if set_pytorch and config.TORCH_AVAILABLE:
import torch
torch_state = torch.random.get_rng_state()
torch.random.manual_seed(seed)
if torch.cuda.is_available():
torch_cuda_states = torch.cuda.get_rng_state_all()
torch.cuda.manual_seed_all(seed)
if set_tensorflow and config.TF_AVAILABLE:
import tensorflow as tf
> from tensorflow.python import context as tfpycontext
E ImportError: cannot import name 'context' from 'tensorflow.python' (/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/tensorflow/python/__init__.py)
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/datasets/utils/py_utils.py:257: ImportError
```
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I_kwDODunzps5yEni6
| 6,261
|
Can't load a dataset
|
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| 2023-09-26T15:46:25
| 2023-10-05T10:23:23
| 2023-10-05T10:23:22
|
NONE
| null | null | null |
### 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/private, make sure you have run huggingface-cli login
----> 2 dataset = load_dataset("JourneyDB/JourneyDB", data_files="data", use_auth_token=True)
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1664, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1661 ignore_verifications = ignore_verifications or save_infos
1663 # Create a dataset builder
-> 1664 builder_instance = load_dataset_builder(
1665 path=path,
1666 name=name,
1667 data_dir=data_dir,
1668 data_files=data_files,
1669 cache_dir=cache_dir,
1670 features=features,
1671 download_config=download_config,
1672 download_mode=download_mode,
1673 revision=revision,
1674 use_auth_token=use_auth_token,
1675 **config_kwargs,
1676 )
1678 # Return iterable dataset in case of streaming
1679 if streaming:
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1490, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1488 download_config = download_config.copy() if download_config else DownloadConfig()
1489 download_config.use_auth_token = use_auth_token
-> 1490 dataset_module = dataset_module_factory(
1491 path,
1492 revision=revision,
1493 download_config=download_config,
1494 download_mode=download_mode,
1495 data_dir=data_dir,
1496 data_files=data_files,
1497 )
1499 # Get dataset builder class from the processing script
1500 builder_cls = import_main_class(dataset_module.module_path)
File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1238, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
1237 if isinstance(e1, FileNotFoundError):
-> 1238 raise FileNotFoundError(
1239 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
1240 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1241 ) from None
1242 raise e1 from None
1243 else:
FileNotFoundError: Couldn't find a dataset script at /kaggle/working/JourneyDB/JourneyDB/JourneyDB.py or any data file in the same directory. Couldn't find 'JourneyDB/JourneyDB' on the Hugging Face Hub either: FileNotFoundError: Unable to find data in dataset repository JourneyDB/JourneyDB with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```
### Steps to reproduce the bug
1)
```
from huggingface_hub import notebook_login
notebook_login()
```
2)
```
!pip install -q datasets
from datasets import load_dataset
```
3)
`dataset = load_dataset("JourneyDB/JourneyDB", data_files="data", use_auth_token=True)`
### Expected behavior
Load the dataset
### Environment info
Notebook
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| 1,912,593,466
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I_kwDODunzps5x_9w6
| 6,260
|
REUSE_DATASET_IF_EXISTS don't work
|
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| 2023-09-26T03:02:16
| 2023-09-28T18:23:36
| 2023-09-28T18:23:36
|
NONE
| null | null | null |
### Describe the bug
I use the following code to download natural_question dataset. Even though I have completely download it, the next time I run this code, the new download procedure will start and cover the original /data/lxy/NQ
config=datasets.DownloadConfig(resume_download=True,max_retries=100,cache_dir=r'/data/lxy/NQ',download_desc='NQ')
data=datasets.load_dataset('natural_questions',cache_dir=r'/data/lxy/NQ',download_config=config,download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)
---
Since I don't have apache_beam installed, it throw a exception. After I pip install apache_beam ,the download restart..
### Steps to reproduce the bug
run this two line code
config=datasets.DownloadConfig(resume_download=True,max_retries=100,cache_dir=r'/data/lxy/NQ',download_desc='NQ')
data=datasets.load_dataset('natural_questions',cache_dir=r'/data/lxy/NQ',download_config=config,download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)
### Expected behavior
Download behavior can be correctly follow DownloadMode
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-3.10.0-1160.88.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.17
- Huggingface_hub version: 0.16.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.3
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I_kwDODunzps5x9kg-
| 6,259
|
Duplicated Rows When Loading Parquet Files from Root Directory with Subdirectories
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| 2024-03-15T15:22:04
| 2024-03-15T15:22:04
|
NONE
| null | null | null |
### Describe the bug
When parquet files are saved in "train" and "val" subdirectories under a root directory, and datasets are then loaded using `load_dataset("parquet", data_dir="root_directory")`, the resulting dataset has duplicated rows for both the training and validation sets.
### Steps to reproduce the bug
1. Create a root directory, e.g., "testing123".
2. Under "testing123", create two subdirectories: "train" and "val".
3. Create and save a parquet file with 3 unique rows in the "train" subdirectory.
4. Create and save a parquet file with 4 unique rows in the "val" subdirectory.
5. Load the datasets from the root directory using `load_dataset("parquet", data_dir="testing123")`
6. Iterate through the datasets and print the rows
Here's a collab reproducing these steps:
https://colab.research.google.com/drive/11NEdImnQ3OqJlwKSHRMhr7jCBesNdLY4?usp=sharing
### Expected behavior
- Training set should contain 3 unique rows.
- Validation set should contain 4 unique rows.
### Environment info
- `datasets` version: 2.14.5
- Platform: Linux-5.15.120+-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.17.2
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
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I_kwDODunzps5x45g0
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HfHubHTTPError - exceeded our hourly quotas for action: commit
|
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| 2023-09-25T06:11:43
| 2023-10-16T13:30:49
| 2023-10-16T13:30:48
|
NONE
| null | null | null |
### Describe the bug
I try to upload a very large dataset of images, and get the following error:
```
File /fsx-multigen/yuvalkirstain/miniconda/envs/pickapic/lib/python3.10/site-packages/huggingface_hub/hf_api.py:2712, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads, parent_commit, run_as_future)
2710 try:
2711 commit_resp = get_session().post(url=commit_url, headers=headers, data=data, params=params)
-> 2712 hf_raise_for_status(commit_resp, endpoint_name="commit")
2713 except RepositoryNotFoundError as e:
2714 e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE)
File /fsx-multigen/yuvalkirstain/miniconda/envs/pickapic/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py:301, in hf_raise_for_status(response, endpoint_name)
297 raise BadRequestError(message, response=response) from e
299 # Convert `HTTPError` into a `HfHubHTTPError` to display request information
300 # as well (request id and/or server error message)
--> 301 raise HfHubHTTPError(str(e), response=response) from e
HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/yuvalkirstain/pickapic_v2/commit/main (Request ID: Root=1-65112399-12d63f7d7f28bfa40a36a0fd)
You have exceeded our hourly quotas for action: commit. We invite you to retry later.
```
this makes it much less convenient to host large datasets on HF hub.
### Steps to reproduce the bug
Upload a very large dataset of images
### Expected behavior
the upload to work well
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-5.15.0-1033-aws-x86_64-with-glibc2.31
- Python version: 3.10.11
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 1.5.3
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I_kwDODunzps5x3Hx_
| 6,256
|
load_dataset() function's cache_dir does not seems to work
|
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| 2023-09-24T15:34:06
| 2023-09-27T13:40:45
| null |
NONE
| null | null | null |
### Describe the bug
datasets version: 2.14.5
when trying to run the following command
trec = load_dataset('trec', split='train[:1000]', cache_dir='/path/to/my/dir')
I keep getting error saying the command does not have permission to the default cache directory on my macbook pro machine.
It seems the cache_dir parameter cannot change the dataset saving directory from the default
what ever explained in the https://huggingface.co/docs/datasets/cache does not seem to work
### Steps to reproduce the bug
datasets version: 2.14.5
when trying to run the following command
trec = load_dataset('trec', split='train[:1000]', cache_dir='/path/to/my/dir')
I keep getting error saying the command does not have permission to the default cache directory on my macbook pro machine.
It seems the cache_dir parameter cannot change the dataset saving directory from the default
what ever explained in the https://huggingface.co/docs/datasets/cache does not seem to work
### Expected behavior
the dataset should be saved to the cache_dir points to
### Environment info
datasets version: 2.14.5
macos X: Ventura 13.4.1 (c)
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I_kwDODunzps5x00io
| 6,254
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Dataset.from_generator() cost much more time in vscode debugging mode then running mode
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| 2023-09-23T02:07:26
| 2023-10-03T14:42:53
| 2023-10-03T14:42:53
|
NONE
| null | null | null |
### Describe the bug
Hey there,
I’m using Dataset.from_generator() to convert a torch_dataset to the Huggingface Dataset.
However, when I debug my code on vscode, I find that it runs really slow on Dataset.from_generator() which may even 20 times longer then run the script on terminal.
### Steps to reproduce the bug
I write a simple test code :
```python
import os
from functools import partial
from typing import Callable
import torch
import time
from torch.utils.data import Dataset as TorchDataset
from datasets import load_from_disk, Dataset as HFDataset
import torch
from torch.utils.data import Dataset
class SimpleDataset(Dataset):
def __init__(self, data):
self.data = data
self.keys = list(data[0].keys())
def __len__(self):
return len(self.data)
def __getitem__(self, index):
sample = self.data[index]
return {key: sample[key] for key in self.keys}
def TorchDataset2HuggingfaceDataset(torch_dataset: TorchDataset, cache_dir: str = None
) -> HFDataset:
"""
convert torch dataset to huggingface dataset
"""
generator : Callable[[], TorchDataset] = lambda: (sample for sample in torch_dataset)
return HFDataset.from_generator(generator, cache_dir=cache_dir)
if __name__ == '__main__':
data = [
{'id': 1, 'name': 'Alice'},
{'id': 2, 'name': 'Bob'},
{'id': 3, 'name': 'Charlie'}
]
torch_dataset = SimpleDataset(data)
start_time = time.time()
huggingface_dataset = TorchDataset2HuggingfaceDataset(torch_dataset)
end_time = time.time()
print("time: ", end_time - start_time)
print(huggingface_dataset)
```
### Expected behavior
this test on my machine report that the running time on terminal is 0.086,
however the running time in debugging mode on vscode is 0.25, which I think is much longer than expected.
I’d like to know is the anything wrong in the code or just because of debugging?
I have traced the code and I find is this func which I get stuck.
```python
def create_config_id(
self,
config_kwargs: dict,
custom_features: Optional[Features] = None,
) -> str:
...
# stuck in this line
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
```
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31
- Python version: 3.11.3
- Huggingface_hub version: 0.17.2
- PyArrow version: 11.0.0
- Pandas version: 2.0.1
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I_kwDODunzps5xoPrS
| 6,252
|
exif_transpose not done to Image (PIL problem)
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| 2024-03-19T15:29:43
| 2024-03-19T15:29:43
|
NONE
| null | null | null |
### Feature request
I noticed that some of my images loaded using PIL have some metadata related to exif that can rotate them when loading.
Since the dataset.features.Image uses PIL for loading, the loaded image may be rotated (width and height will be inverted) thus for tasks as object detection and layoutLM this can create some inconsistencies (between input bboxes and input images).
For now there is no option in datasets.features.Image to specify that. We need to do the following when preparing examples (when preparing images for training, test or inference):
```
from PIL import Image, ImageOps
pil = ImageOps.exif_transpose(pil)
```
reference: https://stackoverflow.com/a/63950647/5720150
Is it possible to add this by default to the datasets.feature.Image ? or to add the option to do the ImageOps.exif_transpose?
Thank you
### Motivation
Prevent having inverted data related to exif metadata that may affect object detection tasks
### Your contribution
Changing in datasets.featrues.Image I can help with that.
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| 1,899,848,414
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I_kwDODunzps5xPWLe
| 6,246
|
Add new column to dataset
|
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| 2023-09-17T16:59:48
| 2023-09-18T16:20:09
| 2023-09-18T16:20:09
|
NONE
| null | null | null |
### Describe the bug
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-9-bd197b36b6a0>](https://localhost:8080/#) in <cell line: 1>()
----> 1 dataset['train']['/workspace/data']
3 frames
[/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_column_key(key, columns)
518 def _check_valid_column_key(key: str, columns: List[str]) -> None:
519 if key not in columns:
--> 520 raise KeyError(f"Column {key} not in the dataset. Current columns in the dataset: {columns}")
521
522
KeyError: "Column train not in the dataset. Current columns in the dataset: ['image', '/workspace/data']"
```
### Steps to reproduce the bug
please find the notebook for reference: https://colab.research.google.com/drive/10lZ_zLtU4itYVmIVTvIEVbjfOtCZaAZy?usp=sharing
### Expected behavior
add column to the dataset
### Environment info
colab pro
|
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| 1,896,899,123
|
I_kwDODunzps5xEGIz
| 6,242
|
Data alteration when loading dataset with unspecified inner sequence length
|
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| 2023-09-14T16:12:45
| 2023-09-19T17:53:18
| 2023-09-19T17:53:18
|
MEMBER
| null | null | null |
### Describe the bug
When a dataset saved with a specified inner sequence length is loaded without specifying that length, the original data is altered and becomes inconsistent.
### Steps to reproduce the bug
```python
from datasets import Dataset, Features, Value, Sequence, load_dataset
# Repository ID
repo_id = "my_repo_id"
# Define features with a specific length of 3 for each inner sequence
specified_features = Features({"key": Sequence(Sequence(Value("float32"), length=3))})
# Create a dataset with the specified features
data = [
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
[[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]],
]
dataset = Dataset.from_dict({"key": data}, features=specified_features)
# Push the dataset to the hub
dataset.push_to_hub(repo_id)
# Define features without specifying the length
unspecified_features = Features({"key": Sequence(Sequence(Value("float32")))})
# Load the dataset from the hub with this new feature definition
dataset = load_dataset(f"qgallouedec/{repo_id}", split="train", features=unspecified_features)
# The obtained data is altered
print(dataset.to_dict()) # {'key': [[[1.0], [2.0]], [[3.0], [4.0]]]}
```
### Expected behavior
```python
print(dataset.to_dict()) # {'key': [[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]]}
```
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-6.2.0-32-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
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| 1,895,723,888
|
I_kwDODunzps5w_nNw
| 6,240
|
Dataloader stuck on multiple GPUs
|
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| 2023-09-14T05:30:30
| 2023-09-14T23:54:42
| 2023-09-14T23:54:42
|
NONE
| null | null | null |
### Describe the bug
I am trying to get CLIP to fine-tuning with my code.
When I tried to run it on multiple GPUs using accelerate, I encountered the following phenomenon.
- Validation dataloader stuck in 2nd epoch only on multi-GPU
Specifically, when the "for inputs in valid_loader:" process is finished, it does not proceed to the next step. train_loader process is completed. Also, both train and valid are working correctly in the first epoch.
The accelerate command at that time is as follows.
`accelerate launch --multi_gpu --num_processes=2 {script_name.py} {--arg1} {--arg2} ...`
- This will not happen when single GPU is used.
`CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...`
- Setting num_workers=0 in dataloader did not change the result.
### Steps to reproduce the bug
1. The codes for fine-tuning the regular CLIP were updated for accelerate.
2. Run the code with the accelerate command as `accelerate launch --multi_gpu --num_processes=2 {script_name.py} {--arg1} {--arg2} ...` and the above problem will occur.
3. CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...` , it works fine.
### Expected behavior
It Should end normally as if it was run on a single GPU.
### Environment info
Since `datasets-cli env` did not work, the environment is described below.
- OS: Ubuntu 22.04 with Docker
- Docker: 24.0.5, build ced0996
- Python: 3.10.12
- torch==2.0.1
- accelerate==0.21.0
- transformers==4.33.1
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| 1,895,349,382
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I_kwDODunzps5w-LyG
| 6,239
|
Load local audio data doesn't work
|
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| 2023-09-13T22:30:01
| 2023-09-15T14:32:10
| 2023-09-15T14:32:10
|
NONE
| null | null | null |
### Describe the bug
I get a RuntimeError from the following code:
```python
audio_dataset = Dataset.from_dict({"audio": ["/kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3"]}).cast_column("audio", Audio())
audio_dataset[0]
```
### Traceback
<details>
```python
RuntimeError Traceback (most recent call last)
Cell In[33], line 1
----> 1 train_dataset[0]
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key)
1762 def __getitem__(self, key): # noqa: F811
1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 1764 return self._getitem(
1765 key,
1766 )
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs)
1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 1749 formatted_output = format_table(
1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
1751 )
1752 return formatted_output
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
--> 532 return formatter(pa_table, query_type=query_type)
533 elif query_type == "column":
534 if key in format_columns:
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type)
279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
280 if query_type == "row":
--> 281 return self.format_row(pa_table)
282 elif query_type == "column":
283 return self.format_column(pa_table)
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:312, in PythonFormatter.format_row(self, pa_table)
310 row = self.python_arrow_extractor().extract_row(pa_table)
311 if self.decoded:
--> 312 row = self.python_features_decoder.decode_row(row)
313 return row
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:221, in PythonFeaturesDecoder.decode_row(self, row)
220 def decode_row(self, row: dict) -> dict:
--> 221 return self.features.decode_example(row) if self.features else row
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1386, in Features.decode_example(self, example)
1376 def decode_example(self, example: dict):
1377 """Decode example with custom feature decoding.
1378
1379 Args:
(...)
1383 :obj:`dict[str, Any]`
1384 """
-> 1386 return {
1387 column_name: decode_nested_example(feature, value)
1388 if self._column_requires_decoding[column_name]
1389 else value
1390 for column_name, (feature, value) in zip_dict(
1391 {key: value for key, value in self.items() if key in example}, example
1392 )
1393 }
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1387, in <dictcomp>(.0)
1376 def decode_example(self, example: dict):
1377 """Decode example with custom feature decoding.
1378
1379 Args:
(...)
1383 :obj:`dict[str, Any]`
1384 """
1386 return {
-> 1387 column_name: decode_nested_example(feature, value)
1388 if self._column_requires_decoding[column_name]
1389 else value
1390 for column_name, (feature, value) in zip_dict(
1391 {key: value for key, value in self.items() if key in example}, example
1392 )
1393 }
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1087, in decode_nested_example(schema, obj)
1085 # Object with special decoding:
1086 elif isinstance(schema, (Audio, Image)):
-> 1087 return schema.decode_example(obj) if obj is not None else None
1088 return obj
File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:103, in Audio.decode_example(self, value)
101 raise ValueError(f"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.")
102 elif path is not None and path.endswith("mp3"):
--> 103 array, sampling_rate = self._decode_mp3(file if file else path)
104 elif path is not None and path.endswith("opus"):
105 if file:
File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:241, in Audio._decode_mp3(self, path_or_file)
238 except RuntimeError as err:
239 raise ImportError("To support decoding 'mp3' audio files, please install 'sox'.") from err
--> 241 array, sampling_rate = torchaudio.load(path_or_file, format="mp3")
242 if self.sampling_rate and self.sampling_rate != sampling_rate:
243 if not hasattr(self, "_resampler") or self._resampler.orig_freq != sampling_rate:
File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:256, in load(filepath, frame_offset, num_frames, normalize, channels_first, format)
254 if ret is not None:
255 return ret
--> 256 return _fallback_load(filepath, frame_offset, num_frames, normalize, channels_first, format)
File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:30, in _fail_load(filepath, frame_offset, num_frames, normalize, channels_first, format)
22 def _fail_load(
23 filepath: str,
24 frame_offset: int = 0,
(...)
28 format: Optional[str] = None,
29 ) -> Tuple[torch.Tensor, int]:
---> 30 raise RuntimeError("Failed to load audio from {}".format(filepath))
RuntimeError: Failed to load audio from /kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3
```
</details>
### Steps to reproduce the bug
1. - Create a custom dataset using Local files of type mp3.
3. - Try to read the first audio item.
### Expected behavior
Expected output
```python
audio_dataset[0]["audio"]
{'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414,
0. , 0. ], dtype=float32),
'path': 'path/to/audio_1',
'sampling_rate': 16000}
```
### Environment info
N/A
|
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| 1,895,207,828
|
I_kwDODunzps5w9pOU
| 6,238
|
`dataset.filter` ALWAYS removes the first item from the dataset when using batched=True
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| 2023-09-13T20:20:37
| 2023-09-17T07:05:07
| 2023-09-17T07:05:07
|
NONE
| null | null | null |
### Describe the bug
If you call batched=True when calling `filter`, the first item is _always_ filtered out, regardless of the filter condition.
### Steps to reproduce the bug
Here's a minimal example:
```python
def filter_batch_always_true(batch, indices):
print("First index being passed into this filter function: ", indices[0])
return indices # Keep all indices
data = {"value": list(range(10))}
dataset = Dataset.from_dict(data)
filtered_dataset = dataset.filter(filter_batch_always_true, with_indices=True, batched=True)
print("Length of original dataset: ", len(dataset))
print("Length of filtered_dataset: ", len(filtered_dataset))
print("Is equal to original? ", len(filtered_dataset) == len(dataset))
print("First item of filtered dataset: ", filtered_dataset[0])
print("Last item of filtered dataset: ", filtered_dataset[-1])
```
prints:
```
First index being passed into this filter function: 0
Length of original dataset: 10
Length of filtered_dataset: 9
Is equal to original? False
First item of filtered dataset: {'value': 1}
Last item of filtered dataset: {'value': 9}
```
### Expected behavior
Filter should respect the filter condition.
### Environment info
- `datasets` version: 2.14.4
- Platform: macOS-13.5-arm64-arm-64bit
- Python version: 3.9.18
- Huggingface_hub version: 0.17.1
- PyArrow version: 10.0.1
- Pandas version: 2.0.2
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| 1,893,822,321
|
I_kwDODunzps5w4W9x
| 6,237
|
Tokenization with multiple workers is too slow
|
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| 2023-09-13T06:18:34
| 2023-09-19T21:54:58
| 2023-09-19T21:54:58
|
NONE
| null | null | null |
I am trying to tokenize a few million documents with multiple workers but the tokenization process is taking forever.
Code snippet:
```
raw_datasets.map(
encode_function,
batched=False,
num_proc=args.preprocessing_num_workers,
load_from_cache_file=not args.overwrite_cache,
remove_columns=[name for name in raw_datasets["train"].column_names if name not in ["input_ids", "labels", "attention_mask"]],
desc="Tokenizing data",
)
```
Details:
```
transformers==4.28.0.dev0
datasets==4.28.0.dev0
preprocessing_num_workers==48
```
tokenizer == decapoda-research/llama-7b-hf
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| 1,893,648,480
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I_kwDODunzps5w3shg
| 6,236
|
Support buffer shuffle for to_tf_dataset
|
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open
| false
| null |
[] | null | 3
| 2023-09-13T03:19:44
| 2023-09-18T01:11:21
| null |
NONE
| null | null | null |
### Feature request
I'm using to_tf_dataset to convert a large dataset to tf.data.Dataset and use Keras fit to train model.
Currently, to_tf_dataset only supports full size shuffle, which can be very slow on large dataset.
tf.data.Dataset support buffer shuffle by default.
shuffle(
buffer_size, seed=None, reshuffle_each_iteration=None, name=None
)
### Motivation
I'm very frustrated to find the loading with shuffling large dataset is very slow. It seems impossible to shuffle before training Keras with big dataset.
### Your contribution
NA
| null |
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| 1,893,337,083
|
I_kwDODunzps5w2gf7
| 6,235
|
Support multiprocessing for download/extract nestedly
|
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| 2023-09-12T21:51:08
| 2023-09-12T21:51:08
| null |
NONE
| null | null | null |
### Feature request
Current multiprocessing for download/extract is not done nestedly. For example, when processing SlimPajama, there is only 3 processes (for train/test/val), while there are many files inside these 3 folders
```
Downloading data files #0: 0%| | 0/1 [00:00<?, ?obj/s]
Downloading data files #1: 0%| | 0/1 [00:00<?, ?obj/s]
Downloading data files #2: 0%| | 0/1 [00:00<?, ?obj/s]
Extracting data files #0: 0%| | 0/1 [00:00<?, ?obj/s]
Extracting data files #1: 0%| | 0/1 [00:00<?, ?obj/s][A
Extracting data files #2: 0%| | 0/1 [00:00<?, ?obj/s][A[A
```
### Motivation
speedup dataset loading
### Your contribution
I can help test the feature
| null |
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| 1,889,050,954
|
I_kwDODunzps5wmKFK
| 6,229
|
Apply inference on all images in the dataset
|
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| 2023-09-20T16:11:53
| 2023-09-20T16:11:52
|
NONE
| null | null | null |
### Describe the bug
```
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
Cell In[14], line 11
9 for idx, example in enumerate(dataset['train']):
10 image_path = example['image']
---> 11 mask_image = process_image(image_path)
12 mask_image.save(f"mask_{idx}.png")
Cell In[14], line 4, in process_image(image_path)
2 def process_image(image_path):
3 print("Processing image:", image_path)
----> 4 result = inferencer(image_path)['predictions']
5 mask = np.where(result == 12, 255, 0).astype('uint8')
6 return Image.fromarray(mask)
File /usr/local/lib/python3.10/dist-packages/mmseg/apis/mmseg_inferencer.py:183, in MMSegInferencer.__call__(self, inputs, return_datasamples, batch_size, show, wait_time, out_dir, img_out_dir, pred_out_dir, **kwargs)
180 pred_out_dir = ''
181 img_out_dir = ''
--> 183 return super().__call__(
184 inputs=inputs,
185 return_datasamples=return_datasamples,
186 batch_size=batch_size,
187 show=show,
188 wait_time=wait_time,
189 img_out_dir=img_out_dir,
190 pred_out_dir=pred_out_dir,
191 **kwargs)
File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:221, in BaseInferencer.__call__(self, inputs, return_datasamples, batch_size, **kwargs)
218 inputs = self.preprocess(
219 ori_inputs, batch_size=batch_size, **preprocess_kwargs)
220 preds = []
--> 221 for data in (track(inputs, description='Inference')
222 if self.show_progress else inputs):
223 preds.extend(self.forward(data, **forward_kwargs))
224 visualization = self.visualize(
225 ori_inputs, preds,
226 **visualize_kwargs) # type: ignore # noqa: E501
File /usr/local/lib/python3.10/dist-packages/rich/progress.py:168, in track(sequence, description, total, auto_refresh, console, transient, get_time, refresh_per_second, style, complete_style, finished_style, pulse_style, update_period, disable, show_speed)
157 progress = Progress(
158 *columns,
159 auto_refresh=auto_refresh,
(...)
164 disable=disable,
165 )
167 with progress:
--> 168 yield from progress.track(
169 sequence, total=total, description=description, update_period=update_period
170 )
File /usr/local/lib/python3.10/dist-packages/rich/progress.py:1210, in Progress.track(self, sequence, total, task_id, description, update_period)
1208 if self.live.auto_refresh:
1209 with _TrackThread(self, task_id, update_period) as track_thread:
-> 1210 for value in sequence:
1211 yield value
1212 track_thread.completed += 1
File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:291, in BaseInferencer.preprocess(self, inputs, batch_size, **kwargs)
266 """Process the inputs into a model-feedable format.
267
268 Customize your preprocess by overriding this method. Preprocess should
(...)
287 Any: Data processed by the ``pipeline`` and ``collate_fn``.
288 """
289 chunked_data = self._get_chunk_data(
290 map(self.pipeline, inputs), batch_size)
--> 291 yield from map(self.collate_fn, chunked_data)
File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:588, in BaseInferencer._get_chunk_data(self, inputs, chunk_size)
586 chunk_data = []
587 for _ in range(chunk_size):
--> 588 processed_data = next(inputs_iter)
589 chunk_data.append(processed_data)
590 yield chunk_data
File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/base.py:12, in BaseTransform.__call__(self, results)
9 def __call__(self,
10 results: Dict) -> Optional[Union[Dict, Tuple[List, List]]]:
---> 12 return self.transform(results)
File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/wrappers.py:88, in Compose.transform(self, results)
79 """Call function to apply transforms sequentially.
80
81 Args:
(...)
85 dict or None: Transformed results.
86 """
87 for t in self.transforms:
---> 88 results = t(results) # type: ignore
89 if results is None:
90 return None
File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/base.py:12, in BaseTransform.__call__(self, results)
9 def __call__(self,
10 results: Dict) -> Optional[Union[Dict, Tuple[List, List]]]:
---> 12 return self.transform(results)
File /usr/local/lib/python3.10/dist-packages/mmseg/datasets/transforms/loading.py:496, in InferencerLoader.transform(self, single_input)
494 inputs = single_input
495 else:
--> 496 raise NotImplementedError
498 if 'img' in inputs:
499 return self.from_ndarray(inputs)
NotImplementedError:
````
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset('Andyrasika/cat_kingdom')
dataset
from mmseg.apis import MMSegInferencer
checkpoint_name = 'segformer_mit-b5_8xb2-160k_ade20k-640x640'
inferencer = MMSegInferencer(model=checkpoint_name)
# Define a function to apply the code to each image in the dataset
def process_image(image_path):
print("Processing image:", image_path)
result = inferencer(image_path)['predictions']
mask = np.where(result == 12, 255, 0).astype('uint8')
return Image.fromarray(mask)
# Process and save masks for each image in the dataset
for idx, example in enumerate(dataset['train']):
image_path = example['image']
mask_image = process_image(image_path)
mask_image.save(f"mask_{idx}.png")
```
### Expected behavior
create a separate column with masks in the dataset and further shows as a separate column in hub
### Environment info
jupyter notebook RTX 3090
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| 1,887,054,320
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I_kwDODunzps5weinw
| 6,225
|
Conversion from RGB to BGR in Object Detection tutorial
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[] | null | 1
| 2023-09-08T06:49:19
| 2023-09-08T17:52:18
| 2023-09-08T17:52:17
|
NONE
| null | null | null |
The [tutorial](https://huggingface.co/docs/datasets/main/en/object_detection) mentions the necessity of conversion the input image from BGR to RGB
> albumentations expects the image to be in BGR format, not RGB, so you’ll have to convert the image before applying the transform.
[Link to tutorial](https://github.com/huggingface/datasets/blob/0a068dbf3b446417ffd89d32857608394ec699e6/docs/source/object_detection.mdx#L77)
However, relevant albumentations' tutorials [on channels conversion](https://albumentations.ai/docs/examples/example/#read-the-image-from-the-disk-and-convert-it-from-the-bgr-color-space-to-the-rgb-color-space) and [on boxes](https://albumentations.ai/docs/examples/example_bboxes/) imply that it's not really true no more.
I suggest removing this outdated conversion from the tutorial.
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| 1,884,324,631
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I_kwDODunzps5wUIMX
| 6,221
|
Support saving datasets with custom formatting
|
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[] | null | 1
| 2023-09-06T16:03:32
| 2023-09-06T18:32:07
| null |
COLLABORATOR
| null | null | null |
Requested in https://discuss.huggingface.co/t/using-set-transform-on-a-dataset-leads-to-an-exception/53036.
I am not sure if supporting this is the best idea for the following reasons:
>For this to work, we would have to pickle a custom transform, which means the transform and the objects it references need to be serializable. Also, deserializing these bytes would make `load_from_disk` unsafe, so I'm not sure this is a good idea.
@lhoestq WDYT?
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I_kwDODunzps5wRa2P
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`Dataset.to_dict()` ignore `decode=True` with Image feature
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[] | null | 1
| 2023-09-06T09:26:16
| 2023-09-08T17:08:52
| null |
MEMBER
| null | null | null |
### Describe the bug
`Dataset.to_dict` seems to ignore the decoding instruction passed in features.
### Steps to reproduce the bug
```python
import datasets
import numpy as np
from PIL import Image
img = np.random.randint(0, 256, (5, 5, 3), dtype=np.uint8)
img = Image.fromarray(img)
features = datasets.Features({"image": datasets.Image(decode=True)})
dataset = datasets.Dataset.from_dict({"image": [img]}, features=features)
print({key: dataset[key] for key in dataset.column_names})
# {'image': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=5x5 at 0x7EFBC80E15B0>]}
print(dataset.to_dict())
# {'image': [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x05\x00\x00\x00\x05\x08\x02\x00\x00\x00\x02\r\xb1\xb2\x00\x00\x00[IDATx\x9c\x01P\x00\xaf\xff\x01\x13\x1b<7\xe7\xe0\xdc^6\xed\x04\xc7M\xd2\x9f\x00X\x1b\xb0?\x1ba\x15\xc5 o\xd0\x80\xbe\x19/\x01\xec\x95\x1f\x9f\xffj\xfa1\xa7\xc4X\xea\xbe\xa4g\x00\xc4\x15\xdeC\xc7 \xbbaqe\xc8\xb9\xa9q\xe7\x00,?M\xc0)\xdaD`}\xb1\xdci\x1e\xafC\xa9]%.@\xa6\xf0\xb3\x00\x00\x00\x00IEND\xaeB`\x82', 'path': None}]}
```
### Expected behavior
I would expect `{key: dataset[key] for key in dataset.column_names}` and `dataset.to_dict()` to be equivalent. If the previous behavior is expected, then it should be stated [in the doc](https://huggingface.co/docs/datasets/v2.14.4/en/package_reference/main_classes#datasets.Dataset.to_dict).
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-6.2.0-31-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
- Pillow 9.5.0
- numpy 1.25.2
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