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https://api.github.com/repos/huggingface/datasets/issues/5170 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5170/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5170/comments | https://api.github.com/repos/huggingface/datasets/issues/5170/events | https://github.com/huggingface/datasets/issues/5170 | 1,425,301,835 | I_kwDODunzps5U9GFL | 5,170 | [Caching] Deterministic hashing of torch tensors | {
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] | closed | false | null | [] | null | [] | 2022-10-27T09:15:15Z | 2022-11-02T17:18:43Z | 2022-11-02T17:18:43Z | MEMBER | null | null | null | null | Currently this fails
```python
import torch
from datasets.fingerprint import Hasher
t = torch.tensor([1.])
def func(x):
return t + x
hash1 = Hasher.hash(func)
t = torch.tensor([1.])
hash2 = Hasher.hash(func)
assert hash1 == hash2
```
Also as noticed in https://discuss.huggingface.co/t/dataset-cant-cache-models-outputs/24945, using a model in a `map` function doesn't work well with caching. Indeed the `bert-base-uncased` model has a different hash every time you reload it. Supporting torch tensors may also help in this case.
This can be fixed by registering a custom pickling functions for torch tensors - as we did for other objects such as CodeType, FunctionType and Regex in `py_utils.py` | {
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https://api.github.com/repos/huggingface/datasets/issues/5165 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5165/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5165/comments | https://api.github.com/repos/huggingface/datasets/issues/5165/events | https://github.com/huggingface/datasets/issues/5165 | 1,423,616,677 | I_kwDODunzps5U2qql | 5,165 | Memory explosion when trying to access 4d tensors in datasets cast to torch or np | {
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} | [] | open | false | null | [] | null | [] | 2022-10-26T08:14:47Z | 2022-10-26T08:14:47Z | null | MEMBER | null | null | null | null | ### Describe the bug
When trying to access an item by index, in a datasets.Dataset cast to torch/np using `set_format` or `with_format`, we get a memory explosion if the item contains 4d (or above) tensors.
### Steps to reproduce the bug
MWE:
```python
from datasets import load_dataset
import numpy as np
def create_4d_tensor(item):
i = item["num_nodes"]
item["x_big"] = np.random.rand(i, 2*i, int(i/2), 1) + 1 # we create a big 4d tensor
return item
if __name__ == "__main__":
dataset = load_dataset(path=f"graphs-datasets/PROTEINS")
# This works
print(dataset["train"].format)
print(dataset["train"][0].keys())
dataset = dataset.map(
create_4d_tensor,
batched=False,
writer_batch_size=100,
)
# This works
print(dataset["train"].format)
print(dataset["train"][0].keys())
dataset.set_format("torch")
print(dataset["train"].format)
# This gets killed :(
print(dataset["train"][0].keys())
```
The problem likely comes from `format_table` [here](https://cs.github.com/huggingface/datasets/blob/f09f781be3278156ce3aa6ec90c1926b1846a78f/src/datasets/arrow_dataset.py#L2328)
### Expected behavior
No memory explosion when trying to access dataset items after cast.
### Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.14.0-1054-oem-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3 | null | {
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"Thanks for reporting, @Rijgersberg.\r\n\r\nWe were waiting for the release of `dill` 0.3.6, that happened 2 days ago (24 Oct 2022): https://github.com/uqfoundation/dill/releases/tag/dill-0.3.6\r\n- See comment: https://github.com/huggingface/datasets/pull/4397#discussion_r880629543\r\n\r\nAlso `multiprocess` 0.70.... | 2022-10-25T13:23:50Z | 2022-11-14T08:25:37Z | 2022-10-28T05:38:15Z | NONE | null | null | null | null | ### Describe the bug
When using `pip-compile` (part of `pip-tools`) to generate a pinned requirements file that includes `datasets`, a version conflict of `dill` appears.
It is caused by a transitive dependency conflict between `datasets` and `multiprocess`.
### Steps to reproduce the bug
```bash
$ echo "datasets" > requirements.in
$ pip install pip-tools
$ pip-compile requirements.in
Could not find a version that matches dill<0.3.6,>=0.3.6 (from datasets==2.6.1->-r requirements.in (line 1))
Tried: 0.2, 0.2, 0.2.1, 0.2.1, 0.2.2, 0.2.2, 0.2.3, 0.2.3, 0.2.4, 0.2.4, 0.2.5, 0.2.5, 0.2.6, 0.2.7, 0.2.7.1, 0.2.8, 0.2.8.1, 0.2.8.2, 0.2.9, 0.3.0, 0.3.1, 0.3.1.1, 0.3.2, 0.3.3, 0.3.3, 0.3.4, 0.3.4, 0.3.5, 0.3.5, 0.3.5.1, 0.3.5.1, 0.3.6, 0.3.6
Skipped pre-versions: 0.1a1, 0.2a1, 0.2a1, 0.2b1, 0.2b1
There are incompatible versions in the resolved dependencies:
dill<0.3.6 (from datasets==2.6.1->-r requirements.in (line 1))
dill>=0.3.6 (from multiprocess==0.70.14->datasets==2.6.1->-r requirements.in (line 1))
```
### Expected behavior
A correctly generated file `requirements.txt` with pinned dependencies
### Environment info
Tested with versions `2.6.1, 2.6.0, 2.5.2` on Python 3.8 and 3.10 on Ubuntu 20.04LTS and Python 3.10 on MacOS 12.6 (M1). | {
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"Addressed in https://github.com/huggingface/datasets/pull/5191 (torch.Tensor objects now produce deterministic hashes)"
] | 2022-10-25T12:19:00Z | 2022-11-03T16:12:52Z | 2022-11-03T16:12:51Z | NONE | null | null | null | null | ### Describe the bug
Hi,
I try to cache some outputs of teacher model( Knowledge Distillation ) by using map function of Dataset library, while every time I run my code, I still recompute all the sequences. I tested Bert Model like this, I got different hash every single run, so any idea to deal with this?
### Steps to reproduce the bug
1. run below code
2. get different hash
```
from transformers import BertModel
from transformers import AutoTokenizer
import torch
token = ['hello']
model = BertModel.from_pretrained("bert-base-uncased").eval()
tok = AutoTokenizer.from_pretrained("bert-base-uncased")
def abcd():
with torch.no_grad():
out = model(**tok(token,return_tensors='pt'))[0]
# out = tok(token)
return out
from datasets.fingerprint import Hasher
my_func = abcd
print(Hasher.hash(my_func))
print(abcd())
```
### Expected behavior
I wanna cache all the model output
### Environment info
datasets:2.5.0 | {
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"Also cc @anton-l ",
"BTW the exact same holds true for the audio folder",
"I'm fine with adding a new column with the file name personally. Not sure how breaking this is though",
"@patrickvonplaten do you mean just filename or full relative path inside the repo?\r\nI think it shouldn't be breaking, at least ... | 2022-10-25T09:56:49Z | 2022-10-26T16:51:46Z | null | CONTRIBUTOR | null | null | null | null | ### Feature request
When creating a custom audio of image dataset, it would be great to automatically have access to the filename. It should be both:
a) Automatically displayed in the viewer
b) Automatically added as a column to the dataset when doing `load_dataset`
In `diffusers` our test rely quite heavily on images and audio files now and it's a bit tedious at the moment to download specific images from a datasets repo.
E.g. we have a dataset of images for tests in `diffusers`: https://huggingface.co/datasets/hf-internal-testing/diffusers-images
where it would be extremely nice to have direct access to the filename both visually on the datasets page (@severo ) as well as via the `load_datasets` function. We currently have some akward functionality to download images by path name: https://github.com/huggingface/diffusers/blob/2fb8fafa4b761f6fc144cf75a6f6f0ea6af3a1c1/src/diffusers/utils/testing_utils.py#L131
It would be much nicer to just go over `load_dataset(...)`
### Motivation
Intuitively the filename is something people understand directly. E.g if you upload a folder of images online, it's nice if you recognize the image as well as the filename next to it directly and that you're able to use it right away.
The label on the other hand is less intuitive to understand as you haven't added it yourself.
### Your contribution
Not sure if I have the time to add it myself anytime soon, but it would help us a lot for `diffusers`. | null | {
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"There are currently 402 datasets with deprecated \"languages\" or \"licenses\".",
"hey @albertvillanova ,i would love to work on this issue if you like.",
"Hi @ayushthe1, thanks for your offer.\r\n\r\nBut as you can see, I self-assigned this issue.\r\n\r\nI have already fixed 200 out of the 402 datasets. My sc... | 2022-10-25T08:19:29Z | 2022-10-25T11:27:26Z | 2022-10-25T10:42:19Z | MEMBER | null | null | null | null | While working on this:
- #5137
we realized there are still many datasets with deprecated "languages" and "licenses" tag names (instead of "language" and "license").
This is a blocking issue: no subsequent PR can be opened to modify their metadata: a ValueError will be thrown.
We should fix the "language" and "license" tag names in all Hub datasets.
TODO:
- [x] Fix language and license tag names in 402 Hub datasets
CC: @julien-c | {
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"Hi ! Maybe it's possible to have a consistent hash for a function defined in `__main__` and a function define in a notebook.\r\n\r\nHowever for functions imported from another location, pickle uses the location to identify the code, so in that case we can't do much I believe.\r\n\r\nWould it be ok for you if we on... | 2022-10-25T01:34:33Z | 2022-11-02T15:43:22Z | 2022-11-02T15:43:22Z | CONTRIBUTOR | null | null | null | null | ### Feature request
I hope this is not my mistake, currently if I use `load_dataset` from a python session on a custom dataset to do the preprocessing, it will be saved in the cache and in other python sessions it will be loaded from the cache, however calling the same from a jupyter notebook does not work, meaning the preprocessing starts from scratch.
If adjusting the hashes is impossible, is there a way to manually set dataset fingerprint to "force" this behaviour?
### Motivation
If this is not already the case and I am doing something wrong, it would be useful to have the two fingerprints consistent so one can create the dataset once and then try small things on jupyter without preprocessing everything again.
### Your contribution
I am happy to try a PR if you give me some pointers where the changes should happen | {
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"Hi ! From the `adlfs` docs, there are two filesystems you can use:\r\n> To use the Gen1 filesystem:\r\n> - known_implementations[‘adl’] = {‘class’: ‘adlfs.AzureDatalakeFileSystem’}\r\n> \r\n> To use the Gen2 filesystem:\r\n> - known_implementations[‘abfs’] = {‘class’: ‘adlfs.AzureBlobFileSystem’}\r\n\r\nIf I'm no... | 2022-10-25T00:43:18Z | 2024-02-15T09:48:36Z | 2022-11-17T23:37:08Z | NONE | null | null | null | null | ### Describe the bug
When using the DatasetBuilder method with the credentials for the cloud storage Azure Data Lake (adl) Gen2, the following error is showed:
```
Traceback (most recent call last):
File "download_hf_dataset.py", line 143, in <module>
main()
File "download_hf_dataset.py", line 102, in main
builder.download_and_prepare(save_dir, storage_options=storage_options, max_shard_size="250MB", file_format="parquet")
File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/datasets/builder.py", line 671, in download_and_prepare
fs_token_paths = fsspec.get_fs_token_paths(output_dir, storage_options=storage_options)
File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/fsspec/core.py", line 639, in get_fs_token_paths
fs = cls(**options)
File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/fsspec/spec.py", line 76, in __call__
obj = super().__call__(*args, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'account_name'
```
If I don't pass the storage_options argument (leave it as None), it requires the credentials used in ADL Gen 1:
`TypeError: __init__() missing 3 required positional arguments: 'tenant_id', 'client_id', and 'client_secret'`
Thus, it is not possible to download a dataset from the cloud using Azure Data Lake (adl) Gen2.
### Steps to reproduce the bug
Assuming that you have an account on Azure and at Storage Account that can be used for reproduce:
1. Create a dict with the format to connect to Azure Data Lake Gen 2
```
storage_options = {"account_name": ACCOUNT_NAME, "account_key": ACCOUNT_KEY) # gen 2 filesystem
```
2. Create a dataset builder for any HF hosted dataset
```
builder = load_dataset_builder(dataset_name)
```
3. Try to download the dataset passing the storage_options as an argument
```
save_dir = 'adl://my_save_dir'
builder.download_and_prepare(save_dir, storage_options=storage_options, max_shard_size="250MB", file_format="parquet")
```
### Expected behavior
Not seeing the error mentioned above and being able to download the dataset to the provided path on ADL
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-46-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1 | {
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"Makes sense! For the last structure, we could count the path segments (delimited by \"/\" for URLs and `os.sep` for local paths) to ensure all inferred labels are on the same level. Otherwise, I think it's safe to assume they are meaningless and ignore them.\r\n"
] | 2022-10-24T13:28:18Z | 2022-11-15T16:31:10Z | 2022-11-15T16:31:09Z | CONTRIBUTOR | null | null | null | null | ### Describe the bug
By default FolderBasedBuilder infers labels if there is not metadata files, even if it's meaningless (for example, they are in a single directory or in the root folder, see this repo as an example: https://huggingface.co/datasets/patrickvonplaten/audios
As this is a corner case for quick exploration of images or audios on the Hub.
### Steps to reproduce the bug
If you have directory like this:
```
repo
image1.jpg
image2.jpg
image3.jpg
```
or
```
repo
data
image1.jpg
image2.jpg
image3.jpg
```
doing `ds = load_dataset(repo)` would create `label` feature:
```python
print(ds["train"][0])
>> {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x7FB5326468E0>, 'label': 0}
```
Also, if you have the following structure:
```
repo
data
image1.jpg
image2.jpg
image3.jpg
image4.jpg
image5.jpg
image6.jpg
```
it will infer two labels:
```python
print(ds["train"][0])
print(ds["train"][-1])
>> {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x7FB5326468E0>, 'label': 1}
>> {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x415 at 0x7FB5326555B0>, 'label': 0}
```
### Expected behavior
We should have only one base feature (Image/Audio) in such cases.
### Environment info
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"also asked in https://discuss.huggingface.co/t/create-multiple-dataset-configs-with-push-to-hub-method/25480"
] | 2022-10-24T12:59:18Z | 2022-11-04T14:55:20Z | null | CONTRIBUTOR | null | null | null | null | Now one can push only different splits within one default config of a dataset.
Would be nice to allow something like:
```
ds.push_to_hub(repo_name, config=config_name)
```
I'm not sure, but this will probably require changes in `data_files.py` patterns. If so, it would also allow to create different configs for packaged modules datasets.
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"Hi! I can't reproduce the error following these steps. Can you please provide a reproducible example?",
"I faced the same issue:\r\n\r\n### Repro\r\n```\r\n!pip install datasets==2.6.1\r\nimport datasets as Dataset\r\ndataset = Dataset.from_pandas(dataframe)\r\ndataset.save_to_disk(local)\r\n\r\n!pip install dat... | 2022-10-24T11:32:36Z | 2024-05-12T07:40:03Z | null | NONE | null | null | null | null | ### Describe the bug
Loading a dataset_dict from disk with `load_from_disk` is now creating a `KeyError "length"` that was not occurring in v2.5.2.
Context:
- Each individual dataset in the dict is created with `Dataset.from_pandas`
- The dataset_dict is create from a dict of `Dataset`s, e.g., `DatasetDict({"train": train_ds, "validation": val_ds})
- The pandas dataframe, besides text columns, has a column with a dictionary inside and potentially different keys in each row. Correctly the `Dataset.from_pandas` function adds `key: None` to all dictionaries in each row so that the schema can be correctly inferred.
### Steps to reproduce the bug
Steps to reproduce:
- Upgrade to datasets==2.6.1
- Create a dataset from pandas dataframe with `Dataset.from_pandas`
- Create a dataset_dict from a dict of `Dataset`s, e.g., `DatasetDict({"train": train_ds, "validation": val_ds})
- Save to disk with the `save` function
### Expected behavior
Same as in v2.5.2, that is load from disk without errors
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.4.209-129.367.amzn2int.x86_64-x86_64-with-glibc2.26
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1 | null | {
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"Hi, @santule.\r\n\r\nWe have transferred all dataset scripts from GitHub to the Hugging Face Hub: https://huggingface.co/datasets\r\n- Concretely, you have \"rvl_cdip\" here: https://huggingface.co/datasets/rvl_cdip\r\n\r\nTo be able to load them, you should update your `datasets` library:\r\n```\r\npip install -U... | 2022-10-24T04:57:42Z | 2022-10-24T12:23:47Z | 2022-10-24T06:25:28Z | NONE | null | null | null | null | Hi,
I am trying to use load_dataset to load the official "rvl_cdip" dataset but getting an error.
dataset = load_dataset("rvl_cdip")
Couldn't find 'rvl_cdip' on the Hugging Face Hub either: FileNotFoundError: Couldn't find the file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/rvl_cdip/rvl_cdip.py
Regards,
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"Hi ! In the `transformers` issue the object to not hash is a `Pool` - I think you can instantiate it inside your function instead of passing it as a parameter. It's good practice that your function and all its fn_kwargs are picklable, in case you want to parallelize `map` using `num_proc>1`\r\n\r\nFor the other ca... | 2022-10-22T21:46:38Z | 2022-11-01T22:19:07Z | null | NONE | null | null | null | null | ### Feature request
`dataset.map` accepts a `fn_kwargs` that is passed to `fn`. Currently, the whole `fn_kwargs` is used by `fingerprint_transform` to calculate the new fingerprint.
I'd like to be able to inform `fingerprint_transform` which `fn_kwargs` shoud/shouldn't be taken into account during hashing.
Of course, users should be aware to properly use this new feature, just like the internal usages of `fingerprint_transform` [does](https://github.com/huggingface/datasets/blob/2699593b33ee63d17aad2a2bfddedd38a8df57b8/src/datasets/arrow_dataset.py#L2700).
### Motivation
This is originally motivated by https://github.com/huggingface/transformers/pull/18351#issuecomment-1263588680.
Nonetheless, consider a more general processing function that accepts a kwarg that does not influence it's output:
```python
def fn(example, verbose=False):
...
```
Then `dataset.map(fn, verbose=True)` would not benefit from dataset caching.
I'm not sure if other methods in the `Dataset` API could benefit from this feature.
### Your contribution
Based on `fingerprint_transform `'s `wrapper` function [here](https://github.com/huggingface/datasets/blob/c59cc34fcd2a369d27b77cc678017f5976a926a9/src/datasets/fingerprint.py#L443), it seems to me that it should be possible to make `.map`/`._map_single` accept something like `fn_use_fingerprint_kwargs`/`fn_ignore_fingerprint_kwargs` (probably another arg name). This would then be used by `fingerprint_transform.wrapper` to better/more flexibly hash the transformation.
I could contribute with a PR if this feature and approach look good to you. | null | {
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"Thanks for reporting ! The issue doesn't come from shuffling, but from `beans` row order not being deterministic:\r\n\r\nhttps://huggingface.co/datasets/beans/blob/main/beans.py uses `dl_manager.iter_files` on ZIP archives and the file order doesn't seen to be deterministic and changes across machines",
"Thank y... | 2022-10-21T09:00:03Z | 2022-10-27T09:51:49Z | 2022-10-27T09:51:10Z | CONTRIBUTOR | null | null | null | null | ### Describe the bug
For the `beans` dataset (did not try on other), the order of samples is not the same on different machines. Tested on my local laptop, github actions machine, and ec2 instance. The three yield a different order.
### Steps to reproduce the bug
In a clean docker container or conda environment with datasets==2.6.1, run
```python
from datasets import load_dataset
from pprint import pprint
data = load_dataset("beans", split="validation")
pprint(data["image_file_path"])
```
### Expected behavior
The order of the images is the same on all machines.
### Environment info
On the EC2 instance:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.14.291-218.527.amzn2.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.7.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
- Numpy version: not checked
```
On my local laptop:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-50-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
- Numpy version: 1.23.1
```
On github actions:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-1022-azure-x86_64-with-glibc2.2.5
- Python version: 3.8.14
- PyArrow version: 9.0.0
- Pandas version: 1.5.1
- Numpy version: 1.23.4
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"Thanks for reporting, @zhaowei-wang-nlp.\r\n\r\nYou are right, the documentation is confusing on the behavior of `remove_columns`. We should better explain it. ",
"This is a duplicate of https://github.com/huggingface/datasets/issues/2343.",
"I'm closing this issue because as @mariosasko pointed out, it is a d... | 2022-10-21T08:37:53Z | 2022-11-15T14:15:10Z | 2022-11-15T14:15:10Z | NONE | null | null | null | null | ### Describe the bug
The page [process](https://huggingface.co/docs/datasets/process) says this about the parameter `remove_columns` of the function `map`:
When you remove a column, it is only removed after the example has been provided to the mapped function.
So it seems that the `remove_columns` parameter removes after the mapped functions.
However, another page, [the documentation of the function map](https://huggingface.co/docs/datasets/v2.6.1/en/package_reference/main_classes#datasets.Dataset.map.remove_columns) says:
Columns will be removed before updating the examples with the output of `function`, i.e. if `function` is adding columns with names in remove_columns, these columns will be kept.
So one page says "after the mapped function" and another says "before the mapped function."
Is there something wrong?
### Steps to reproduce the bug
Not about code.
### Expected behavior
consistent about the descriptions of the behavior of the parameter `remove_columns` in the function `map`.
### Environment info
datasets V2.6.0 | {
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"Hey ! Actually it works, just pass the right URL ;)\r\nThe URL must be the one with “/resolve/”\r\n\r\ne.g. https://huggingface.co/datasets/imagenet-1k/resolve/main/data/test_images.tar.gz\r\n\r\nYou can even pass a relative path to the dl_manager instead, like `dl_manager.download(\"data/test_images.tar.gz\")`",
... | 2022-10-20T15:29:29Z | 2022-10-20T17:17:10Z | 2022-10-20T17:17:10Z | CONTRIBUTOR | null | null | null | null | ### Feature request
Maybe I'm mistaken but the `DownloadManager` does not support extracting git lfs files out of the box right?
Using `dl_manager.download()` or `dl_manager.download_and_extract()` still returns lfs files afaict.
Is there a good way to write a dataset loading script for a repo with lfs files?
### Motivation
/
### Your contribution
/ | {
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"I removed all the invalid task_ids in datasts without namespace, based on the <s>(internal)</s> types.ts",
"(Types.ts is not internal it's public)",
"I have opened PRs to fix the task_ids in all datasets within a namespace as well.\r\n\r\nWorking on task_categories...",
"For future reference: this fix had so... | 2022-10-19T09:41:42Z | 2022-11-10T05:25:58Z | 2022-10-25T06:17:00Z | MEMBER | null | null | null | null | ## 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 datasets
- [x] task_ids: 556 datasets
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As discussed in:
- https://github.com/huggingface/hub-docs/pull/423#pullrequestreview-1146083701
we should update our docs once dataset scripts have been transferred to the Hub (and removed from GitHub):
- #4974
Concretely:
- [x] Datasets on GitHub (legacy): https://huggingface.co/docs/datasets/main/en/share#datasets-on-github-legacy
- [x] ADD_NEW_DATASET: https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md
- ...
This PR complements the work of:
- #5067
This PR is a follow-up of PRs:
- #3777
CC: @julien-c | {
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"hey ,i would like to work on this issue . Please assign it to me.",
"hey @mariosasko , i made a pr for this issue. Could you please review it.\r\nAlso i found multiple `OSError` in `extract.py` file which i thought could be replaced too but wasn't sure about them.\r\nPlease do tell if that also needs to be done.... | 2022-10-18T17:53:46Z | 2022-10-25T15:56:59Z | 2022-10-25T15:56:59Z | COLLABORATOR | null | null | null | null | According to the official Python docs, `OSError` should be thrown in the following situations:
> This exception is raised when a system function returns a system-related error, including I/O failures such as “file not found” or “disk full” (not for illegal argument types or other incidental errors).
Hence, it makes more sense to raise `ImportError` instead of `OSError` when the required extraction/decompression library is not installed. | {
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"Hi! The Torch ops in your snippet are not equivalent to the NumPy ones, hence the difference. You can get the same behavior by replacing the line `feature = torch.mean(feature, dim=1)` with `feature = feature.squeeze().mean(1)` .",
"> Hi! The Torch ops in your snippet are not equivalent to the NumPy ones, hence ... | 2022-10-18T17:53:35Z | 2022-10-19T04:15:45Z | 2022-10-19T04:15:44Z | NONE | null | null | null | null | ## Describe the bug
I'm doing a torch.mean() operation in data preprocessing, and it's not working.
## Steps to reproduce the bug
```
from transformers import pipeline
import torch
import numpy as np
from datasets import load_dataset
device = 'cuda:0'
raw_dataset = load_dataset("glue", "sst2")
feature_extraction = pipeline('feature-extraction', 'bert-base-uncased', device=device)
def extracted_data(examples):
# feature = torch.tensor(feature_extraction(examples['sentence'], batch_size=16), device=device)
# feature = torch.mean(feature, dim=1)
feature = np.asarray(feature_extraction(examples['sentence'], batch_size=16)).squeeze().mean(1)
print(feature.shape)
return {'feature': feature}
extracted_dataset = raw_dataset.map(extracted_data, batched=True, batch_size=16)
```
## Results
When running with torch.mean(), the shape printed out is [16, seq_len, 768], which is exactly the same before the operation. While numpy works just fine, which gives [16, 768].
## Environment info
- `datasets` version: 2.6.1
- Platform: Linux-4.4.0-142-generic-x86_64-with-glibc2.31
- Python version: 3.10.6
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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"I can take this! #self-assign",
"#self-assign",
"@lazarust i'm already working on this issue :smile: ",
"#self-assign",
"hey @mariosasko , i made a pr for this issue. Could you please review it."
] | 2022-10-18T17:41:05Z | 2022-10-25T15:56:46Z | 2022-10-25T15:56:46Z | COLLABORATOR | null | null | null | null | The `num_proc` parameter is only present in `DownloadManager.extract` but not in `StreamingDownloadManager.extract`, making it impossible to support streaming in the dataset scripts that use it (`openwebtext` and `the_pile_stack_exchange`). We can avoid this situation by deprecating this parameter and passing `DownloadConfig`'s `num_proc` to `map_nested` instead, as it's done in `DownloadManager.download`. | {
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https://api.github.com/repos/huggingface/datasets/issues/5131 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5131/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5131/comments | https://api.github.com/repos/huggingface/datasets/issues/5131/events | https://github.com/huggingface/datasets/issues/5131 | 1,413,534,863 | I_kwDODunzps5UQNSP | 5,131 | WikiText 103 tokenizer hangs | {
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"any updates on this? It happens to me on [OpenWikiText-20%](https://huggingface.co/datasets/Bingsu/openwebtext_20p) dataset, but not on [OpenWebText-10k](https://huggingface.co/datasets/stas/openwebtext-10k). This is really strange because I don't change anything else in my running script.\r\n\r\ntransformers vers... | 2022-10-18T16:44:00Z | 2023-08-08T08:42:40Z | 2023-07-21T14:41:51Z | NONE | null | null | null | null | See issue here: https://github.com/huggingface/transformers/issues/19702 | {
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https://api.github.com/repos/huggingface/datasets/issues/5129 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5129/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5129/comments | https://api.github.com/repos/huggingface/datasets/issues/5129/events | https://github.com/huggingface/datasets/issues/5129 | 1,413,031,664 | I_kwDODunzps5UOSbw | 5,129 | unexpected `cast` or `class_encode_column` result after `rename_column` | {
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"Hi! Unfortunately, I can't reproduce this issue locally (in Python 3.7/3.10) or in Colab. I would assume this is due to a bug we fixed in the latest release, but your version is up-to-date, so I'm not sure if there is something we can do to help...",
"Hi, 方子东. I tried running the code with exact the same configu... | 2022-10-18T11:15:24Z | 2022-10-19T03:02:26Z | 2022-10-19T03:02:26Z | NONE | null | null | null | null | ## Describe the bug
When invoke `cast` or `class_encode_column` to a colunm renamed by `rename_column` , it will convert all the variables in this column into one variable. I also run this script in version 2.5.2, this bug does not appear. So I switched to the older version.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("amazon_reviews_multi", "en")
data = dataset['train']
data = data.remove_columns(
[
"review_id",
"product_id",
"reviewer_id",
"review_title",
"language",
"product_category",
]
)
data = data.rename_column("review_body", "text")
data1 = data.class_encode_column("stars")
print(set(data1.data.columns[0]))
# output: {<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>}
data = data.rename_column("stars", "label")
print(set(data.data.columns[0]))
# output: {<pyarrow.Int32Scalar: 5>, <pyarrow.Int32Scalar: 4>, <pyarrow.Int32Scalar: 1>, <pyarrow.Int32Scalar: 3>, <pyarrow.Int32Scalar: 2>}
data2 = data.class_encode_column("label")
print(set(data2.data.columns[0]))
# output: {<pyarrow.Int64Scalar: 0>}
```
## Expected results
the last print should be:
{<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>}
## Actual results
but it output:
{<pyarrow.Int64Scalar: 0>}
## Environment info
- `datasets` version: 2.6.1
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.10.6
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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https://api.github.com/repos/huggingface/datasets/issues/5123 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5123/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5123/comments | https://api.github.com/repos/huggingface/datasets/issues/5123/events | https://github.com/huggingface/datasets/issues/5123 | 1,410,828,756 | I_kwDODunzps5UF4nU | 5,123 | datasets freezes with streaming mode in multiple-gpu | {
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"@lhoestq I tested the script without accelerator, and I confirm this is due to datasets part as this gets similar results without accelerator.",
"Hi ! You said it works on 1 GPU but doesn't wortk without accelerator - what's the difference between running on 1 GPU and running without accelerator in your case ?"... | 2022-10-17T03:28:16Z | 2023-05-14T06:55:20Z | null | NONE | null | null | null | null | ## Describe the bug
Hi. I am using this dataloader, which is for processing large datasets in streaming mode mentioned in one of examples of huggingface. I am using it to read c4: https://github.com/huggingface/transformers/blob/b48ac1a094e572d6076b46a9e4ed3e0ebe978afc/examples/research_projects/codeparrot/scripts/codeparrot_training.py#L22
During using multi-gpu in accelerator in one node, the code freezes, but works for 1 GPU:
```
10/16/2022 14:18:46 - INFO - datasets.info - Loading Dataset Infos from /home/jack/.cache/huggingface/modules/datasets_modules/datasets/c4/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01
Steps: 0%| | 0/400000 [00:00<?, ?it/s]10/16/2022 14:18:47 - INFO - torch.utils.data.dataloader - Shared seed (135290893754684706) sent to store on rank 0
```
# Code to reproduce
please run this code with `accelerate launch code.py`
```
from accelerate import Accelerator
from accelerate.logging import get_logger
from datasets import load_dataset
from torch.utils.data.dataloader import DataLoader
import torch
from datasets import load_dataset
from transformers import AutoTokenizer
import torch
from accelerate.logging import get_logger
from torch.utils.data import IterableDataset
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
logger = get_logger(__name__)
class ConstantLengthDataset(IterableDataset):
"""
Iterable dataset that returns constant length chunks of tokens from stream of text files.
Args:
tokenizer (Tokenizer): The processor used for proccessing the data.
dataset (dataset.Dataset): Dataset with text files.
infinite (bool): If True the iterator is reset after dataset reaches end else stops.
max_seq_length (int): Length of token sequences to return.
num_of_sequences (int): Number of token sequences to keep in buffer.
chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.
"""
def __init__(
self,
tokenizer,
dataset,
infinite=False,
max_seq_length=1024,
num_of_sequences=1024,
chars_per_token=3.6,
):
self.tokenizer = tokenizer
# self.concat_token_id = tokenizer.bos_token_id
self.dataset = dataset
self.max_seq_length = max_seq_length
self.epoch = 0
self.infinite = infinite
self.current_size = 0
self.max_buffer_size = max_seq_length * chars_per_token * num_of_sequences
self.content_field = "text"
def __iter__(self):
iterator = iter(self.dataset)
more_examples = True
while more_examples:
buffer, buffer_len = [], 0
while True:
if buffer_len >= self.max_buffer_size:
break
try:
buffer.append(next(iterator)[self.content_field])
buffer_len += len(buffer[-1])
except StopIteration:
if self.infinite:
iterator = iter(self.dataset)
self.epoch += 1
logger.info(f"Dataset epoch: {self.epoch}")
else:
more_examples = False
break
tokenized_inputs = self.tokenizer(buffer, truncation=False)["input_ids"]
all_token_ids = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input)
for i in range(0, len(all_token_ids), self.max_seq_length):
input_ids = all_token_ids[i : i + self.max_seq_length]
if len(input_ids) == self.max_seq_length:
self.current_size += 1
yield torch.tensor(input_ids)
def shuffle(self, buffer_size=1000):
return ShufflerIterDataPipe(self, buffer_size=buffer_size)
def create_dataloaders(tokenizer, accelerator):
ds_kwargs = {"streaming": True}
# In distributed training, the load_dataset function gaurantees that only one process
# can concurrently download the dataset.
datasets = load_dataset(
"c4",
"en",
cache_dir="cache_dir",
**ds_kwargs,
)
train_data, valid_data = datasets["train"], datasets["validation"]
with accelerator.main_process_first():
train_data = train_data.shuffle(buffer_size=10000, seed=None)
train_dataset = ConstantLengthDataset(
tokenizer,
train_data,
infinite=True,
max_seq_length=256,
)
valid_dataset = ConstantLengthDataset(
tokenizer,
valid_data,
infinite=False,
max_seq_length=256,
)
train_dataset = train_dataset.shuffle(buffer_size=10000)
train_dataloader = DataLoader(train_dataset, batch_size=160, shuffle=True)
eval_dataloader = DataLoader(valid_dataset, batch_size=160)
return train_dataloader, eval_dataloader
def main():
# Accelerator.
logging_dir = "data_save_dir/log"
accelerator = Accelerator(
gradient_accumulation_steps=1,
mixed_precision="bf16",
log_with="tensorboard",
logging_dir=logging_dir,
)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("test")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
# Load datasets and create dataloaders.
train_dataloader, _ = create_dataloaders(tokenizer, accelerator)
train_dataloader = accelerator.prepare(train_dataloader)
for step, batch in enumerate(train_dataloader, start=1):
print(step)
accelerator.end_training()
if __name__ == "__main__":
main()
```
## Results expected
Being able to run the code for streamining datasets with multi-gpu
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.2
- Platform: linux
- Python version: 3.9.12
- PyArrow version: 9.0.0
@lhoestq I do not have any idea why this freezing happens, and I removed the streaming mode and this was working fine, so I know this is caused by streaming mode of the dataloader part not working well with multi-gpu setting. Since datasets are large, I hope to keep the streamining mode. I very much appreciate your help.
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https://api.github.com/repos/huggingface/datasets/issues/5118 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5118/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5118/comments | https://api.github.com/repos/huggingface/datasets/issues/5118/events | https://github.com/huggingface/datasets/issues/5118 | 1,410,547,373 | I_kwDODunzps5UEz6t | 5,118 | Installing `datasets` on M1 computers | {
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"Thanks for reporting, @david1542."
] | 2022-10-16T16:50:08Z | 2022-10-19T09:10:08Z | 2022-10-19T09:10:08Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
I wanted to install `datasets` dependencies on my M1 (in order to start contributing to the project). However, I got an error regarding `tensorflow`.
On M1, `tensorflow-macos` needs to be installed instead. Can we add a conditional requirement, so that `tensorflow-macos` would be installed on M1?
## Steps to reproduce the bug
Fresh clone this project (on m1), create a virtualenv and run this:
```python
pip install -e ".[dev]"
```
## Expected results
Installation should be smooth, and all the dependencies should be installed on M1.
## Actual results
You should receive an error, saying pip couldn't find a version that matches this pattern:
```
tensorflow>=2.3,!=2.6.0,!=2.6.1
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.6.2.dev0
- Platform: macOS-12.6-arm64-arm-64bit
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.5.0
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https://api.github.com/repos/huggingface/datasets/issues/5117 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5117/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5117/comments | https://api.github.com/repos/huggingface/datasets/issues/5117/events | https://github.com/huggingface/datasets/issues/5117 | 1,409,571,346 | I_kwDODunzps5UBFoS | 5,117 | Progress bars have color red and never completed to 100% | {
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"Hi @echatzikyriakidis, thanks for submitting the issue.\r\nWhich shell are you using exactly? I tried to run the command you sent, but I don't see colors at all 🧐\r\n\r\nI tried from bash and zsh as well.",
"Hi @david1542 ,\r\n\r\nI use Google Colab.\r\n",
"Got it. I [created a PR](https://github.com/huggingf... | 2022-10-14T16:12:30Z | 2024-06-19T19:03:42Z | 2022-10-23T12:58:41Z | NONE | null | null | null | null | ## Describe the bug
Progress bars after transformative operations turn in red and never be completed to 100%
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset('rotten_tomatoes', split='test').filter(lambda o: True)
```
## Expected results
Progress bar should be 100% and green
## Actual results
Progress bar turn in red and never completed to 100%
## Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.14
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5114 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5114/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5114/comments | https://api.github.com/repos/huggingface/datasets/issues/5114/events | https://github.com/huggingface/datasets/issues/5114 | 1,409,236,738 | I_kwDODunzps5T_z8C | 5,114 | load_from_disk with remote filesystem fails due to a wrong temporary local folder path | {
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"Hi Hubert! Could you please probably create a publicly available `gs://` dataset link? I think this would be easier for others to directly start to debug.",
"What seems to work is to change the line to:\r\n```\r\nfs.download(src_dataset_path, dataset_path.parent.as_posix(), recursive=True)\r\n```"
] | 2022-10-14T11:54:53Z | 2022-11-19T07:13:10Z | null | CONTRIBUTOR | null | null | null | null | ## Describe the bug
The function load_from_disk fails when using a remote filesystem because of a wrong temporary path generation in the load_from_disk method of arrow_dataset.py:
```python
if is_remote_filesystem(fs):
src_dataset_path = extract_path_from_uri(dataset_path)
dataset_path = Dataset._build_local_temp_path(src_dataset_path)
fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True)
```
If _dataset_path_ is `gs://speech/mydataset/train`, then _src_dataset_path_ will be `speech/mydataset/train` and _dataset_path_ will be something like `/var/folders/9s/gf0b/T/tmp6t/speech/mydataset/train`
Then, after downloading the **folder** _src_dataset_path_, you will get a path like `/var/folders/9s/gf0b/T/tmp6t/speech/mydataset/train/train/state.json` (notice we have train twice)
Instead of downloading the remote folder we should be downloading all the files in the folder for the path to be right:
```python
fs.download(os.path.join(src_dataset_path,*), dataset_path.as_posix(), recursive=True)
```
## Steps to reproduce the bug
```python
fs = gcsfs.GCSFileSystem(**storage_options)
dataset = load_from_disk("common_voice_processed") # loading local dataset previously saved locally, works fine
dataset.save_to_disk(output_dir, fs=fs) #works fine
dataset = load_from_disk(output_dir, fs=fs) # crashes
```
## Expected results
The dataset is loaded
## Actual results
FileNotFoundError: [Errno 2] No such file or directory: '/var/folders/9s/gf0b9jz15d517yrf7m3nvlxr0000gn/T/tmp6t5e221_/speech/datasets/tests/common_voice_processed/train/state.json'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets-2.6.1.dev0
- Platform: mac os monterey 12.5.1
- Python version: 3.8.13
- PyArrow version:pyarrow==9.0.0
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https://api.github.com/repos/huggingface/datasets/issues/5112 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5112/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5112/comments | https://api.github.com/repos/huggingface/datasets/issues/5112/events | https://github.com/huggingface/datasets/issues/5112 | 1,409,143,409 | I_kwDODunzps5T_dJx | 5,112 | Bug with filtered indices | {
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"The issue is here:\r\nhttps://github.com/huggingface/datasets/blob/3ad9644b9a2e4558dd1d0f1e43c67658674e6228/src/datasets/arrow_dataset.py#L2964",
"@PartiallyTyped, @Muennighoff: the issue is fixed.\r\n\r\nWe are planning to make a patch release today.",
"Thanks a lot for the swift response! For a brief moment ... | 2022-10-14T10:35:47Z | 2022-10-14T13:55:03Z | 2022-10-14T12:11:45Z | MEMBER | null | null | null | null | ## Describe the bug
As reported by @PartiallyTyped (and by @Muennighoff):
- https://github.com/huggingface/datasets/issues/5111#issuecomment-1278652524
There is an issue with the indices of a filtered dataset.
## Steps to reproduce the bug
```python
ds = Dataset.from_dict({"num": [0, 1, 2, 3]})
ds = ds.filter(lambda num: num % 2 == 0, input_columns="num", batch_size=2)
assert all(item["num"] % 2 == 0 for item in ds)
```
## Expected results
The indices of the filtered dataset should correspond to the examples with "language" equals to "english".
## Actual results
Indices to items with other languages are included in the filtered dataset indices
## Preliminar investigation
It seems a bug introduced by:
- #5030
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"Same bug exists with `num_proc=1` on colab. `3.7.14 (default, Sep 8 2022, 00:06:44) [GCC 7.5.0]` ",
"Thanks for reporting, @loubnabnl and for the additional information, @PartiallyTyped.\r\n\r\nHowever, I'm not able to reproduce this issue, neither locally nor on Colab:\r\n```\r\nDataset({\r\n features: ['re... | 2022-10-13T17:00:55Z | 2022-10-17T08:26:59Z | 2022-10-14T14:59:59Z | NONE | null | null | null | null | ## Describe the bug
When mapping is used on a dataset with more than one process, there is a weird behavior when trying to use `filter` , it's like only the samples from one worker are retrieved, one needs to specify the same `num_proc` in filter for it to work properly. This doesn't happen with `datasets` version 2.5.2
In the code below the data is filtered differently when we increase `num_proc` used in `map` although the datsets before and after mapping have identical elements.
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
def preprocess(example):
return example
ds = load_dataset("codeparrot/codeparrot-clean-valid", split="train").select([i for i in range(10)])
ds1 = ds.map(preprocess, num_proc=2)
ds2 = ds.map(preprocess)
# the datasets elements are the same
for i in range(len(ds1)):
assert ds1[i]==ds2[i]
print(f'Target column before filtering {ds1["autogenerated"]}')
print(f'Target column before filtering {ds2["autogenerated"]}')
print(f"datasets version {datasets.__version__}")
ds_filtered_1 = ds1.filter(lambda x: not x["autogenerated"])
ds_filtered_2 = ds2.filter(lambda x: not x["autogenerated"])
# all elements in Target column are false so they should all be kept, but for ds2 only the first 5=num_samples/num_proc are kept
print(ds_filtered_1)
print(ds_filtered_2)
```
```
Target column before filtering [False, False, False, False, False, False, False, False, False, False]
Target column before filtering [False, False, False, False, False, False, False, False, False, False]
Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 5
})
Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 10
})
```
## Expected results
Increasing `num_proc` in mapping shouldn't alter filtering. With the previous version 2.5.2 this doesn't happen
## Actual results
Filtering doesn't work properly when we increase `num_proc` in mapping but not when calling `filter`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.6.0
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
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"The hash used for caching is computed by pickling recursively the function passed to `map`. Maybe some objects don't have the same hash across sessions. In particular you can check the hash of your model using\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\nobj = AutoModel.from_config(config=config, ad... | 2022-10-13T09:12:58Z | 2022-10-17T10:38:45Z | 2022-10-17T10:38:45Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
The cache loading is not working as expected for some class methods with a model stored in an attribute.
The new fingerprint for `_map_single` is not the same at each run. The hasher generate a different hash for the class method.
This comes from `dumps` function in `datasets.utils.py_utils` which generates a different dump at each run.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import AutoConfig, AutoModel, AutoTokenizer
dataset = load_dataset("ethos", "binary")
BASE_MODELNAME = "sentence-transformers/all-MiniLM-L6-v2"
class Object:
def __init__(self):
config = AutoConfig.from_pretrained(BASE_MODELNAME)
self.bert = AutoModel.from_config(config=config, add_pooling_layer=False)
self.tok = AutoTokenizer.from_pretrained(BASE_MODELNAME)
def tokenize(self, examples):
tokenized_texts = self.tok(
examples["text"],
padding="max_length",
truncation=True,
max_length=256,
)
return tokenized_texts
instance = Object()
result = dict()
for phase in ["train"]:
result[phase] = dataset[phase].map(instance.tokenize, batched=True, load_from_cache_file=True, num_proc=2)
```
## Expected results
Load cache instead of recompute result.
## Actual results
Result recomputed from scratch at each run.
The cache works fine when deleting `bert` attribute.
## Environment info
- `datasets` version: 2.5.3.dev0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.13
- PyArrow version: 7.0.0
- Pandas version: 1.5.0
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"cc @lhoestq ",
"Thanks for reporting, @cakiki.\r\n\r\nI would say the deletion of the dir is an expected behavior though...",
"`dask.to_parquet` has an \"overwrite\" parameter and default is `False`, we could also have something similar",
"Thank you both for your feedback!\r\n\r\n@albertvillanova I think I m... | 2022-10-12T11:53:33Z | 2022-10-20T11:53:59Z | null | CONTRIBUTOR | null | null | null | null | ## Describe the bug
The builder correctly creates the `output_dir` folder if it doesn't exist, but if the folder exists everything within it is deleted. Specifying `"."` as the `output_dir` deletes everything in your current dir but also leads to **another bug** whose traceback is the following:
```
Traceback (most recent call last)
Input In [11], in <cell line: 1>()
----> 1 rotten_tomatoes_builder.download_and_prepare(output_dir=".", max_shard_size="200MB", file_format="parquet")
File ~/BIGSCIENCE/env/lib/python3.9/site-packages/datasets/builder.py:818, in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, storage_options, **download_and_prepare_kwargs)
File /usr/lib/python3.9/contextlib.py:124, in _GeneratorContextManager.__exit__(self, type, value, traceback)
122 if type is None:
123 try:
--> 124 next(self.gen)
125 except StopIteration:
126 return False
File ~/BIGSCIENCE/env/lib/python3.9/site-packages/datasets/builder.py:760, in incomplete_dir(dirname)
File /usr/lib/python3.9/shutil.py:722, in rmtree(path, ignore_errors, onerror)
720 os.rmdir(path)
721 except OSError:
--> 722 onerror(os.rmdir, path, sys.exc_info())
723 else:
724 try:
725 # symlinks to directories are forbidden, see bug #1669
File /usr/lib/python3.9/shutil.py:720, in rmtree(path, ignore_errors, onerror)
718 _rmtree_safe_fd(fd, path, onerror)
719 try:
--> 720 os.rmdir(path)
721 except OSError:
722 onerror(os.rmdir, path, sys.exc_info())
OSError: [Errno 22] Invalid argument: '/home/christopher/BIGSCIENCE/.'
```
## Steps to reproduce the bug
```python
rotten_tomatoes_builder = load_dataset_builder("rotten_tomatoes")
rotten_tomatoes_builder.download_and_prepare(output_dir="./test_folder", max_shard_size="200MB", file_format="parquet")
```
If `test_folder` contains any files they will all be deleted
## Expected results
Either a warning that all files will be deleted, but preferably that they not be deleted at all.
## Actual results
N/A
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.15.0-48-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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"Hi, thanks for reporting! The last line should be `dataset = Dataset.from_generator(my_gen)`.",
"Can I work on this one?"
] | 2022-10-11T14:28:58Z | 2022-10-12T11:31:56Z | 2022-10-12T11:31:56Z | NONE | null | null | null | null | ## Describe the bug
In HOW-TO-GUIDES > Load > [Python generator](https://huggingface.co/docs/datasets/v2.5.2/en/loading#python-generator), the code example defines the `my_gen` function, but when creating the dataset, an undefined `my_dict` is passed in.
```Python
>>> from datasets import Dataset
>>> def my_gen():
... for i in range(1, 4):
... yield {"a": i}
>>> dataset = Dataset.from_generator(my_dict)
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"`datasets` doesn't seem to urlencode the directory names here\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/utils/file_utils.py#L109-L111\r\n\r\nfor example we should have\r\n```python\r\nfrom datasets.utils.file_utils import hf_hub_url\r\n\r\nurl = hf_hu... | 2022-10-11T10:05:32Z | 2022-10-13T13:14:20Z | 2022-10-13T13:14:20Z | NONE | null | null | null | null | ## Describe the bug
dataset files with `#` symbol their paths aren't read correctly.
## Steps to reproduce the bug
The data in folder `c#`of this [dataset](https://huggingface.co/datasets/loubnabnl/bigcode_csharp) can't be loaded. While the folder `c_sharp` with the same data is loaded properly
```python
ds = load_dataset('loubnabnl/bigcode_csharp', split="train", data_files=["data/c#/*"])
```
```
FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/27a3166cff4bb18e11919cafa6f169c0f57483de/data/c#/data_0003.jsonl
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.2
- Platform: macOS-12.2.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
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"It can be solved temporarily by remove `resolve` in \r\nhttps://github.com/huggingface/datasets/blob/bef23be3d9543b1ca2da87ab2f05070201044ddc/src/datasets/data_files.py#L278",
"Hi, thanks for reporting and suggesting a fix! We still need to account for `.`/`..` in the file path, so a more robust fix would be `P... | 2022-10-11T06:10:58Z | 2022-11-14T14:40:20Z | 2022-11-14T14:40:20Z | NONE | null | null | null | null | **Is your feature request related to a problem? Please describe.**
Like this: #4015
When there are **symbolic links** to pictures in the data folder, the parent folder name of the **real file** will be used as the class name instead of the parent folder of the symbolic link itself. Can you give an option to decide whether to enable symbolic link tracking?
This is inconsistent with the `torchvision.datasets.ImageFolder` behavior.
For example:
It use `others` in green circle as class label but not `abnormal`, I wish `load_dataset` not use the real file parent as label.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context about the feature request here.
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"Thanks for reporting, @catalys1.\r\n\r\nThis seems a duplicate of:\r\n- #3310 \r\n\r\nThe source of the problem is in PyArrow:\r\n- [ARROW-15141: [C++] Fatal error condition occurred in aws_thread_launch](https://issues.apache.org/jira/browse/ARROW-15141)\r\n- [ARROW-17501: [C++] Fatal error condition occurred in ... | 2022-10-10T20:29:04Z | 2022-10-11T06:56:01Z | 2022-10-11T06:56:00Z | NONE | null | null | null | null | ## Describe the bug
When using datasets, at the very end of my jobs the program crashes (see trace below).
It doesn't seem to affect anything, as it appears to happen as the program is closing down. Just importing `datasets` is enough to cause the error.
## Steps to reproduce the bug
This is sufficient to reproduce the problem:
```bash
python -c "import datasets"
```
## Expected results
Program should run to completion without an error.
## Actual results
```bash
Fatal error condition occurred in /opt/vcpkg/buildtrees/aws-c-io/src/9e6648842a-364b708815.clean/source/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS
Exiting Application
################################################################################
Stack trace:
################################################################################
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x200af06) [0x150dff547f06]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x20028e5) [0x150dff53f8e5]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x1f27e09) [0x150dff464e09]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x150dff548a3d]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x1f25948) [0x150dff462948]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x150dff548a3d]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x1ee0b46) [0x150dff41db46]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x194546a) [0x150dfee8246a]
/lib64/libc.so.6(+0x39b0c) [0x150e15eadb0c]
/lib64/libc.so.6(on_exit+0) [0x150e15eadc40]
/u/user/miniconda3/envs/env/bin/python(+0x28db18) [0x560ae370eb18]
/u/user/miniconda3/envs/env/bin/python(+0x28db4b) [0x560ae370eb4b]
/u/user/miniconda3/envs/env/bin/python(+0x28db90) [0x560ae370eb90]
/u/user/miniconda3/envs/env/bin/python(_PyRun_SimpleFileObject+0x1e6) [0x560ae37123e6]
/u/user/miniconda3/envs/env/bin/python(_PyRun_AnyFileObject+0x44) [0x560ae37124c4]
/u/user/miniconda3/envs/env/bin/python(Py_RunMain+0x35d) [0x560ae37135bd]
/u/user/miniconda3/envs/env/bin/python(Py_BytesMain+0x39) [0x560ae37137d9]
/lib64/libc.so.6(__libc_start_main+0xf3) [0x150e15e97493]
/u/user/miniconda3/envs/env/bin/python(+0x2125d4) [0x560ae36935d4]
Aborted (core dumped)
```
## Environment info
- `datasets` version: 2.5.1
- Platform: Linux-4.18.0-348.23.1.el8_5.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.4
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
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"The transfer of the dummy dataset to the dummy org works as expected:\r\n```python\r\nIn [1]: from datasets import load_dataset; ds = load_dataset(\"dummy_canonical_dataset\", download_mode=\"force_redownload\"); ds\r\nDownloading builder script: 100%|███████████████████████████████████████████████████████████████... | 2022-10-10T15:44:31Z | 2024-06-24T06:06:28Z | 2024-06-24T06:02:45Z | MEMBER | null | null | null | null | As discussed during our @huggingface/datasets meeting, we are planning to move some "canonical" dataset scripts under their corresponding organization namespace (if this does not exist).
On the contrary, if the dataset already exists under the organization namespace, we are deprecating the canonical one (and eventually delete it).
First, we should test it using a dummy dataset/organization.
TODO:
- [x] Test with a dummy dataset
- [x] Create dummy canonical dataset: https://huggingface.co/datasets/dummy_canonical_dataset
- [x] Create dummy organization: https://huggingface.co/dummy-canonical-org
- [x] Transfer dummy canonical dataset to dummy organization
- [ ] Transfer datasets
- [x] babi_qa => facebook
- [x] blbooks => TheBritishLibrary/blbooks
- [x] blbooksgenre => TheBritishLibrary/blbooksgenre
- [x] common_gen => allenai
- [x] commonsense_qa => tau
- [x] competition_math => hendrycks/competition_math
- [x] cord19 => allenai
- [x] emotion => dair-ai
- [ ] gem => GEM
- [x] hellaswag => Rowan
- [x] hendrycks_test => cais/mmlu
- [x] indonlu => indonlp
- [ ] multilingual_librispeech => facebook
- It already exists "facebook/multilingual_librispeech"
- [ ] oscar => oscar-corpus
- [x] peer_read => allenai
- [x] qasper => allenai
- [x] reddit => webis/tldr-17
- [x] russian_super_glue => russiannlp
- [x] rvl_cdip => aharley
- [x] s2orc => allenai
- [x] scicite => allenai
- [x] scifact => allenai
- [x] scitldr => allenai
- [x] swiss_judgment_prediction => rcds
- [x] the_pile => EleutherAI
- [ ] wmt14, wmt15, wmt16, wmt17, wmt18, wmt19,... => wmt
- [ ] Deprecate (and eventually remove) datasets that cannot be transferred because they already exist
- [x] banking77 => PolyAI
- [x] common_voice => mozilla-foundation
- [x] german_legal_entity_recognition => elenanereiss
- ...
EDIT: the list above is continuously being updated | {
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"Hi ! Could it be an Out of Memory issue that could have killed one of the processes ? can you check your memory ?",
"Hi! I don't think it is a memory issue. I'm monitoring the main and spawn python processes and threads with `htop` and the memory does not peak. Besides, the example I've posted above should not b... | 2022-10-10T13:50:56Z | 2023-07-24T15:29:13Z | 2023-07-24T15:29:13Z | NONE | null | null | null | null | ## Describe the bug
There seems to be an issue with using multiprocessing with `datasets.Dataset.map` (i.e. setting `num_proc` to a value greater than one) combined with a function that uses `torch` under the hood. The subprocesses that `datasets.Dataset.map` spawns [a this step](https://github.com/huggingface/datasets/blob/1b935dab9d2f171a8c6294269421fe967eb55e34/src/datasets/arrow_dataset.py#L2663) go into wait mode forever.
## Steps to reproduce the bug
The below code goes into deadlock when `NUMBER_OF_PROCESSES` is greater than one.
```python
NUMBER_OF_PROCESSES = 2
from transformers import AutoTokenizer, AutoModel
from datasets import load_dataset
dataset = load_dataset("glue", "mrpc", split="train")
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model.to("cpu")
def cls_pooling(model_output):
return model_output.last_hidden_state[:, 0]
def generate_embeddings_batched(examples):
sentences_batch = list(examples['sentence1'])
encoded_input = tokenizer(
sentences_batch, padding=True, truncation=True, return_tensors="pt"
)
encoded_input = {k: v.to("cpu") for k, v in encoded_input.items()}
model_output = model(**encoded_input)
embeddings = cls_pooling(model_output)
examples['embeddings'] = embeddings.detach().cpu().numpy() # 64, 384
return examples
embeddings_dataset = dataset.map(
generate_embeddings_batched,
batched=True,
batch_size=10,
num_proc=NUMBER_OF_PROCESSES
)
```
While debugging it I've seen that it gets "stuck" when calling `torch.nn.Embedding.forward` but some testing shows that the same happens with other functions from `torch.nn`.
## Environment info
- Platform: Linux-5.14.0-1052-oem-x86_64-with-glibc2.31
- Python version: 3.9.14
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
Not sure if this is a HF problem, a PyTorch problem or something I'm doing wrong..
Thanks!
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"Hi, thanks for reporting! This line should be replaced with \r\n```python\r\ndataset = dataset.map(lambda examples: tokenizer(examples[\"text\"], return_tensors=\"np\"), batched=True)\r\n```\r\nfor it to work (the `return_tensors` part inside the `tokenizer` call).",
"Can I work on this?",
"Fixed in https://gi... | 2022-10-10T10:23:53Z | 2022-10-10T17:51:15Z | 2022-10-10T17:51:14Z | MEMBER | null | null | null | null | ## Describe the bug
In the "Process text data" tutorial, [`map` has `return_tensors` as kwarg](https://huggingface.co/docs/datasets/main/en/nlp_process#map). It does not seem to appear in the [function documentation](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.map), nor to work.
## Steps to reproduce the bug
MWE:
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
from datasets import load_dataset
dataset = load_dataset("lhoestq/demo1", split="train")
dataset = dataset.map(lambda examples: tokenizer(examples["review"]), batched=True, return_tensors="pt")
```
## Expected results
return_tensors to be a valid kwarg :smiley:
## Actual results
```python
>> TypeError: map() got an unexpected keyword argument 'return_tensors'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.14.0-1052-oem-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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"Nice!!"
] | 2022-10-07T12:31:56Z | 2022-10-08T07:07:36Z | 2022-10-08T07:07:36Z | MEMBER | null | null | null | null | ## Describe the bug
We have discovered that sometimes there were sync issues between GitHub and Hub datasets, after a merge commit to main branch.
For example:
- this merge commit: https://github.com/huggingface/datasets/commit/d74a9e8e4bfff1fed03a4cab99180a841d7caf4b
- was not properly synced with the Hub: https://github.com/huggingface/datasets/actions/runs/3002495269/jobs/4819769684
```
[main 9e641de] Add Papers with Code ID to scifact dataset (#4941)
Author: Albert Villanova del Moral <albertvillanova@users.noreply.huggingface.co>
1 file changed, 42 insertions(+), 14 deletions(-)
push failed !
GitCommandError(['git', 'push'], 1, b'remote: ---------------------------------------------------------- \nremote: Sorry, your push was rejected during YAML metadata verification: \nremote: - Error: "license" does not match any of the allowed types \nremote: ---------------------------------------------------------- \nremote: Please find the documentation at: \nremote: https://huggingface.co/docs/hub/models-cards#model-card-metadata \nremote: ---------------------------------------------------------- \nTo [https://huggingface.co/datasets/scifact.git\n](https://huggingface.co/datasets/scifact.git/n) ! [remote rejected] main -> main (pre-receive hook declined)\nerror: failed to push some refs to \'[https://huggingface.co/datasets/scifact.git\](https://huggingface.co/datasets/scifact.git/)'', b'')
```
We are reviewing sync issues in previous commits to recover them and repushing to the Hub.
TODO: Review
- [x] #4941
- scifact
- [x] #4931
- scifact
- [x] #4753
- wikipedia
- [x] #4554
- wmt17, wmt19, wmt_t2t
- Fixed with "Release 2.4.0" commit: https://github.com/huggingface/datasets/commit/401d4c4f9b9594cb6527c599c0e7a72ce1a0ea49
- https://huggingface.co/datasets/wmt17/commit/5c0afa83fbbd3508ff7627c07f1b27756d1379ea
- https://huggingface.co/datasets/wmt19/commit/b8ad5bf1960208a376a0ab20bc8eac9638f7b400
- https://huggingface.co/datasets/wmt_t2t/commit/b6d67191804dd0933476fede36754a436b48d1fc
- [x] #4607
- [x] #4416
- lccc
- Fixed with "Release 2.3.0" commit: https://huggingface.co/datasets/lccc/commit/8b1f8cf425b5653a0a4357a53205aac82ce038d1
- [x] #4367
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] | open | false | null | [] | null | [] | 2022-10-07T08:07:03Z | 2022-10-07T08:07:03Z | null | NONE | null | null | null | null | **Is your feature request related to a problem? Please describe.**
When a process (`map`, `filter`, etc.) crashes part-way through, you lose all progress.
**Describe the solution you'd like**
It would be good if the cache reflected the partial progress, so that after we restart the script, the process can restart where it left off.
**Describe alternatives you've considered**
Doing processing outside of `datasets`, by writing the dataset to json files and building a restart mechanism myself.
**Additional context**
N/A
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"Hi @junwang-wish, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce the bug. Which version of `datasets` are you using? Does the problem persist if you update `datasets`?\r\n```shell\r\npip install -U datasets\r\n``` ",
"Thanks @albertvillanova I updated `datasets` from `2.5.1` to `2.5.2` and... | 2022-10-07T02:16:58Z | 2022-10-07T14:43:16Z | null | NONE | null | null | null | null | ## Describe the bug
I have a local file `*.json.gz` and it can be read by `pandas.read_json(lines=True)`, but cannot be read by `load_datasets("json")` (resulting in 0 lines)
## Steps to reproduce the bug
```python
fpath = '/data/junwang/.cache/general/57b6f2314cbe0bc45dda5b78f0871df2/test.json.gz'
ds_panda = DatasetDict(
test=Dataset.from_pandas(
pd.read_json(fpath, lines=True)
)
)
ds_direct = load_dataset(
'json', data_files={
'test': fpath
}, features=Features(
text_input=Value(dtype="string", id=None),
text_output=Value(dtype="string", id=None)
)
)
len(ds_panda['test']), len(ds_direct['test'])
```
## Expected results
Lines of `ds_panda['test']` and `ds_direct['test']` should match.
## Actual results
```
Using custom data configuration default-c0ef2598760968aa
Downloading and preparing dataset json/default to /data/junwang/.cache/huggingface/datasets/json/default-c0ef2598760968aa/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab...
Dataset json downloaded and prepared to /data/junwang/.cache/huggingface/datasets/json/default-c0ef2598760968aa/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab. Subsequent calls will reuse this data.
(62087, 0)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Ubuntu 18.04.4 LTS
- Python version: 3.8.13
- PyArrow version: 9.0.0
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"FYI @lewtun ",
"Hi @km5ar, thanks for reporting.\r\n\r\nThis should be fixed in the notebook:\r\n- the filename `datasets-issues-with-hf-doc-builder.jsonl` no longer exists on the repo; instead, current filename is `datasets-issues-with-comments.jsonl`\r\n- see: https://huggingface.co/datasets/lewtun/github-issu... | 2022-10-06T19:48:58Z | 2022-10-07T15:12:01Z | 2022-10-07T15:12:01Z | NONE | null | null | null | null | ## Describe the bug
I was following chap 5 from huggingface course: https://huggingface.co/course/chapter5/6?fw=tf
However, I'm not able to download the datasets, with a 404 erros
<img width="1160" alt="iShot2022-10-06_15 54 50" src="https://user-images.githubusercontent.com/54015474/194406327-ae62c2f3-1da5-4686-8631-13d879a0edee.png">
## Steps to reproduce the bug
```python
from huggingface_hub import hf_hub_url
data_files = hf_hub_url(
repo_id="lewtun/github-issues",
filename="datasets-issues-with-hf-doc-builder.jsonl",
repo_type="dataset",
)
from datasets import load_dataset
issues_dataset = load_dataset("json", data_files=data_files, split="train")
issues_dataset
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.2
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.12
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
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https://api.github.com/repos/huggingface/datasets/issues/5085 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5085/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5085/comments | https://api.github.com/repos/huggingface/datasets/issues/5085/events | https://github.com/huggingface/datasets/issues/5085 | 1,400,113,569 | I_kwDODunzps5TdAmh | 5,085 | Filtering on an empty dataset returns a corrupted dataset. | {
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"~~It seems like #5043 fix (merged recently) is the root cause of such behaviour. When we empty indices mapping (because the dataset length equals to zero), we can no longer get column item like: `ds_filter_2['sentence']` which uses\r\n`ds_filter_1._indices.column(0)`~~\r\n\r\n**UPDATE:**\r\nEmpty datasets are retu... | 2022-10-06T18:18:49Z | 2022-10-07T19:06:02Z | 2022-10-07T18:40:26Z | NONE | null | null | null | null | ## Describe the bug
When filtering a dataset twice, where the first result is an empty dataset, the second dataset seems corrupted.
## Steps to reproduce the bug
```python
datasets = load_dataset("glue", "sst2")
dataset_split = datasets['validation']
ds_filter_1 = dataset_split.filter(lambda x: False) # Some filtering condition that leads to an empty dataset
assert ds_filter_1.num_rows == 0
sentences = ds_filter_1['sentence']
assert len(sentences) == 0
ds_filter_2 = ds_filter_1.filter(lambda x: False) # Some other filtering condition
assert ds_filter_2.num_rows == 0
assert 'sentence' in ds_filter_2.column_names
sentences = ds_filter_2['sentence']
```
## Expected results
The last line should be returning an empty list, same as 4 lines above.
## Actual results
The last line currently raises `IndexError: index out of bounds`.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.2
- Platform: macOS-11.6.6-x86_64-i386-64bit
- Python version: 3.9.11
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
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https://api.github.com/repos/huggingface/datasets/issues/5083 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5083/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5083/comments | https://api.github.com/repos/huggingface/datasets/issues/5083/events | https://github.com/huggingface/datasets/issues/5083 | 1,399,842,514 | I_kwDODunzps5Tb-bS | 5,083 | Support numpy/torch/tf/jax formatting for IterableDataset | {
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"hii @lhoestq, can you assign this issue to me? Though i am new to open source still I would love to put my best foot forward. I can see there isn't anyone right now assigned to this issue.",
"Hi @zutarich ! This issue was fixed by #5852 - sorry I forgot to close it\r\n\r\nFeel free to look for other issues and p... | 2022-10-06T15:14:58Z | 2023-10-09T12:42:15Z | 2023-10-09T12:42:15Z | MEMBER | null | null | null | null | Right now `IterableDataset` doesn't do any formatting.
In particular this code should return a numpy array:
```python
from datasets import load_dataset
ds = load_dataset("imagenet-1k", split="train", streaming=True).with_format("np")
print(next(iter(ds))["image"])
```
Right now it returns a PIL.Image.
Setting `streaming=False` does return a numpy array after #5072 | {
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https://api.github.com/repos/huggingface/datasets/issues/5081 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5081/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5081/comments | https://api.github.com/repos/huggingface/datasets/issues/5081/events | https://github.com/huggingface/datasets/issues/5081 | 1,399,340,050 | I_kwDODunzps5TaDwS | 5,081 | Bug loading `sentence-transformers/parallel-sentences` | {
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"tagging @nreimers ",
"The dataset is sadly not really compatible to be loaded with `load_dataset`. So far it is better to git clone it and to use the files directly.\r\n\r\nA data loading script would be needed to be added to this dataset. But this was too much overhead / not really intuitive how to create it.",... | 2022-10-06T10:47:51Z | 2022-10-11T10:00:48Z | null | CONTRIBUTOR | null | null | null | null | ## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("sentence-transformers/parallel-sentences")
```
raises this:
```
/home/phmay/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:697: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'
return pd.read_csv(xopen(filepath_or_buffer, "rb", use_auth_token=use_auth_token), **kwargs)
/home/phmay/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:697: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'
return pd.read_csv(xopen(filepath_or_buffer, "rb", use_auth_token=use_auth_token), **kwargs)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In [4], line 1
----> 1 dataset = load_dataset("sentence-transformers/parallel-sentences", split="train")
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/load.py:1693, 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)
1690 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1692 # Download and prepare data
-> 1693 builder_instance.download_and_prepare(
1694 download_config=download_config,
1695 download_mode=download_mode,
1696 ignore_verifications=ignore_verifications,
1697 try_from_hf_gcs=try_from_hf_gcs,
1698 use_auth_token=use_auth_token,
1699 )
1701 # Build dataset for splits
1702 keep_in_memory = (
1703 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1704 )
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/builder.py:807, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, storage_options, **download_and_prepare_kwargs)
801 if not downloaded_from_gcs:
802 prepare_split_kwargs = {
803 "file_format": file_format,
804 "max_shard_size": max_shard_size,
805 **download_and_prepare_kwargs,
806 }
--> 807 self._download_and_prepare(
808 dl_manager=dl_manager,
809 verify_infos=verify_infos,
810 **prepare_split_kwargs,
811 **download_and_prepare_kwargs,
812 )
813 # Sync info
814 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/builder.py:898, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
894 split_dict.add(split_generator.split_info)
896 try:
897 # Prepare split will record examples associated to the split
--> 898 self._prepare_split(split_generator, **prepare_split_kwargs)
899 except OSError as e:
900 raise OSError(
901 "Cannot find data file. "
902 + (self.manual_download_instructions or "")
903 + "\nOriginal error:\n"
904 + str(e)
905 ) from None
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/builder.py:1513, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, max_shard_size)
1506 shard_id += 1
1507 writer = writer_class(
1508 features=writer._features,
1509 path=fpath.replace("SSSSS", f"{shard_id:05d}"),
1510 storage_options=self._fs.storage_options,
1511 embed_local_files=embed_local_files,
1512 )
-> 1513 writer.write_table(table)
1514 finally:
1515 num_shards = shard_id + 1
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/arrow_writer.py:540, in ArrowWriter.write_table(self, pa_table, writer_batch_size)
538 if self.pa_writer is None:
539 self._build_writer(inferred_schema=pa_table.schema)
--> 540 pa_table = table_cast(pa_table, self._schema)
541 if self.embed_local_files:
542 pa_table = embed_table_storage(pa_table)
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/table.py:2044, in table_cast(table, schema)
2032 """Improved version of pa.Table.cast.
2033
2034 It supports casting to feature types stored in the schema metadata.
(...)
2041 table (:obj:`pyarrow.Table`): the casted table
2042 """
2043 if table.schema != schema:
-> 2044 return cast_table_to_schema(table, schema)
2045 elif table.schema.metadata != schema.metadata:
2046 return table.replace_schema_metadata(schema.metadata)
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/table.py:2005, in cast_table_to_schema(table, schema)
2003 features = Features.from_arrow_schema(schema)
2004 if sorted(table.column_names) != sorted(features):
-> 2005 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match")
2006 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
2007 return pa.Table.from_arrays(arrays, schema=schema)
ValueError: Couldn't cast
Action taken on Parliament's resolutions: see Minutes: string
Následný postup na základě usnesení Parlamentu: viz zápis: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 742
to
{'Membership of Parliament: see Minutes': Value(dtype='string', id=None), 'Състав на Парламента: вж. протоколи': Value(dtype='string', id=None)}
because column names don't match
```
## Expected results
no error
## Actual results
error
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version: Python 3.9.13
- PyArrow version: pyarrow 9.0.0
- transformers 4.22.2
- datasets 2.5.2 | null | {
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"There is some discussion in https://github.com/huggingface/huggingface_hub/pull/1088 if it can help :)"
] | 2022-10-06T05:51:58Z | 2022-10-06T14:26:05Z | null | MEMBER | null | null | null | null | ## Is your feature request related to a problem?
As previously discussed in our meeting with @Wauplin and agreed on our last datasets team sync meeting, I'm investigating how `datasets` can use `hfh` for caching.
## Describe the solution you'd like
Due to the peculiarities of the `datasets` cache, I would propose adopting `hfh` caching system in stages.
First, we could easily start using `hfh` caching for:
- dataset Python scripts
- dataset READMEs
- dataset infos JSON files (now deprecated)
Second, we could also use `hfh` caching for data files downloaded from the Hub.
Further investigation is needed for:
- files downloaded from non-Hub hosts
- extracted files from downloaded archive/compressed files
- generated Arrow files
## Additional context
Docs about the `hfh` caching system:
- [Manage huggingface_hub cache-system](https://huggingface.co/docs/huggingface_hub/main/en/how-to-cache)
- [Cache-system reference](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/cache)
The `transformers` library has already adopted `hfh` for caching. See:
- huggingface/transformers#18438
- huggingface/transformers#18857
- huggingface/transformers#18966
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"@mariosasko I've raised a PR #5076 against this issue. Please help to review. Thanks."
] | 2022-10-05T14:14:18Z | 2022-10-07T14:33:28Z | 2022-10-07T14:33:28Z | COLLABORATOR | null | null | null | null | Throw EnvironmentError instead of OSError ([link](https://github.com/huggingface/datasets/blob/6ad430ba0cdeeb601170f732d4bd977f5c04594d/src/datasets/arrow_dataset.py#L4306) to the line) in `push_to_hub` when the Hub token is not present. | {
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"Hi, can I pick up this issue?",
"#self-assign",
"Looks like the top-level `datasource` directory was removed when https://github.com/huggingface/datasets/pull/4974 was merged, so there are 3 source files to fix."
] | 2022-10-05T14:03:55Z | 2022-10-07T14:33:11Z | 2022-10-07T14:33:11Z | COLLABORATOR | null | null | null | null | Replace the AssertionErrors with more meaningful errors such as ValueError, TypeError, etc.
The files with AssertionErrors that need to be replaced:
```
src/datasets/arrow_reader.py
src/datasets/builder.py
src/datasets/utils/version.py
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"Thank you for creating this feature request, Albert.\r\n\r\nFor context this is the datatest where Albert has been helping me to switch to on-the-fly split config https://huggingface.co/datasets/HuggingFaceM4/cm4-synthetic-testing\r\n\r\nand the attempt to switch on-the-fly splits was here: https://huggingface.co/... | 2022-10-04T19:49:35Z | 2022-10-06T14:40:26Z | 2022-10-06T14:40:26Z | MEMBER | null | null | null | null | **Is your feature request related to a problem? Please describe.**
As discussed with @stas00, we could support defining a default config name, even if no predefined allowed config names are set. That is, support `DEFAULT_CONFIG_NAME`, even when `BUILDER_CONFIGS` is not defined.
**Additional context**
In order to support creating configs on the fly **by name** (not using kwargs), the list of allowed builder configs `BUILDER_CONFIGS` must not be set.
However, if so, then `DEFAULT_CONFIG_NAME` is not supported.
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"This is maybe related to python 3.10, do you think you could try on 3.8 ?\r\n\r\nIn the meantime we'll keep improving the support for 3.10. Let me add a dedicated CI",
"I did some binary search and seems like the root cause is either `multiprocess` or `dill`. python 3.10 is fine. Specifically:\r\n- `multiprocess... | 2022-10-03T23:51:38Z | 2023-07-21T14:43:35Z | 2023-07-21T14:43:34Z | NONE | null | null | null | null | ## Describe the bug
When I `map` with multiple processes, this error occurs. The `.name` of the `logger` that fails to pickle in the final line is `datasets.fingerprint`.
```
File "~/project/dataset.py", line 204, in <dictcomp>
split: dataset.map(
File ".../site-packages/datasets/arrow_dataset.py", line 2489, in map
transformed_shards[index] = async_result.get()
File ".../site-packages/multiprocess/pool.py", line 771, in get
raise self._value
File ".../site-packages/multiprocess/pool.py", line 537, in _handle_tasks
put(task)
File ".../site-packages/multiprocess/connection.py", line 214, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File ".../site-packages/multiprocess/reduction.py", line 54, in dumps
cls(buf, protocol, *args, **kwds).dump(obj)
File ".../site-packages/dill/_dill.py", line 620, in dump
StockPickler.dump(self, obj)
File ".../pickle.py", line 487, in dump
self.save(obj)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../pickle.py", line 902, in save_tuple
save(element)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../site-packages/dill/_dill.py", line 1963, in save_function
_save_with_postproc(pickler, (_create_function, (
File ".../site-packages/dill/_dill.py", line 1140, in _save_with_postproc
pickler.save_reduce(*reduction, obj=obj)
File ".../pickle.py", line 717, in save_reduce
save(state)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../pickle.py", line 887, in save_tuple
save(element)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../site-packages/dill/_dill.py", line 1251, in save_module_dict
StockPickler.save_dict(pickler, obj)
File ".../pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File ".../pickle.py", line 998, in _batch_setitems
save(v)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../site-packages/dill/_dill.py", line 1963, in save_function
_save_with_postproc(pickler, (_create_function, (
File ".../site-packages/dill/_dill.py", line 1140, in _save_with_postproc
pickler.save_reduce(*reduction, obj=obj)
File ".../pickle.py", line 717, in save_reduce
save(state)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../pickle.py", line 887, in save_tuple
save(element)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../site-packages/dill/_dill.py", line 1251, in save_module_dict
StockPickler.save_dict(pickler, obj)
File ".../pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File ".../pickle.py", line 998, in _batch_setitems
save(v)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../site-packages/dill/_dill.py", line 1963, in save_function
_save_with_postproc(pickler, (_create_function, (
File ".../site-packages/dill/_dill.py", line 1154, in _save_with_postproc
pickler._batch_setitems(iter(source.items()))
File ".../pickle.py", line 998, in _batch_setitems
save(v)
File ".../pickle.py", line 578, in save
rv = reduce(self.proto)
File ".../logging/__init__.py", line 1774, in __reduce__
raise pickle.PicklingError('logger cannot be pickled')
_pickle.PicklingError: logger cannot be pickled
```
## Steps to reproduce the bug
Sorry I failed to have a minimal reproducible example, but the offending line on my end is
```python
dataset.map(
lambda examples: self.tokenize(examples), # this doesn't matter, lambda e: [1] * len(...) also breaks. In fact I'm pretty sure it breaks before executing this lambda
batched=True,
num_proc=4,
)
```
This does work when `num_proc=1`, so it's likely a multiprocessing thing.
## Expected results
`map` succeeds
## Actual results
The error trace above.
## Environment info
- `datasets` version: 1.16.1 and 2.5.1 both failed
- Platform: Ubuntu 20.04.4 LTS
- Python version: 3.10.4
- PyArrow version: 9.0.0
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https://api.github.com/repos/huggingface/datasets/issues/5060 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5060/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5060/comments | https://api.github.com/repos/huggingface/datasets/issues/5060/events | https://github.com/huggingface/datasets/issues/5060 | 1,395,382,940 | I_kwDODunzps5TK9qc | 5,060 | Unable to Use Custom Dataset Locally | {
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"Hi ! I opened a PR in your repo to fix this :)\r\nhttps://huggingface.co/datasets/zpn/pubchem_selfies/discussions/7\r\n\r\nbasically you need to use `open` for streaming to work properly",
"Thank you so much for this! Naive question, is this a feature of `open` or have you all overloaded it to be able to read fr... | 2022-10-03T21:55:16Z | 2022-10-06T14:29:18Z | 2022-10-06T14:29:17Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
I have uploaded a [dataset](https://huggingface.co/datasets/zpn/pubchem_selfies) and followed the instructions from the [dataset_loader](https://huggingface.co/docs/datasets/dataset_script#download-data-files-and-organize-splits) tutorial. In that tutorial, it says
```
If the data files live in the same folder or repository of the dataset script,
you can just pass the relative paths to the files instead of URLs.
```
Accordingly, I put the [relative path](https://huggingface.co/datasets/zpn/pubchem_selfies/blob/main/pubchem_selfies.py#L76) to the data to be used. I was able to test the dataset and generate the metadata locally with `datasets-cli test path/to/<your-dataset-loading-script> --save_infos --all_configs`
However, if I try to load the data using `load_dataset`, I get the following error
```
with gzip.open(filepath, mode="rt") as f:
File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 58, in open
binary_file = GzipFile(filename, gz_mode, compresslevel)
File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 173, in __init__
fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
FileNotFoundError: [Errno 2] No such file or directory: 'https://huggingface.co/datasets/zpn/pubchem_selfies/resolve/main/data/Compound_021000001_021500000/Compound_021000001_021500000_SELFIES.jsonl.gz'
```
## Steps to reproduce the bug
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("zpn/pubchem_selfies", streaming=True)
>>> t = dataset["train"]
>>> for item in t:
...... print(item)
...... break
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zachnussbaum/env/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 723, in __iter__
for key, example in self._iter():
File "/Users/zachnussbaum/env/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 713, in _iter
yield from ex_iterable
File "/Users/zachnussbaum/env/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 113, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/Users/zachnussbaum/.cache/huggingface/modules/datasets_modules/datasets/zpn--pubchem_selfies/d2571f35996765aea70fd3f3f8e3882d59c401fb738615c79282e2eb1d9f7a25/pubchem_selfies.py", line 475, in _generate_examples
with gzip.open(filepath, mode="rt") as f:
File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 58, in open
binary_file = GzipFile(filename, gz_mode, compresslevel)
File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 173, in __init__
fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
FileNotFoundError: [Errno 2] No such file or directory: 'https://huggingface.co/datasets/zpn/pubchem_selfies/resolve/main/data/Compound_021000001_021500000/Compound_021000001_021500000_SELFIES.jsonl.gz'
````
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.1
- Platform: macOS-12.5.1-x86_64-i386-64bit
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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https://api.github.com/repos/huggingface/datasets/issues/5053 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5053/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5053/comments | https://api.github.com/repos/huggingface/datasets/issues/5053/events | https://github.com/huggingface/datasets/issues/5053 | 1,393,739,882 | I_kwDODunzps5TEshq | 5,053 | Intermittent JSON parse error when streaming the Pile | {
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"Maybe #2838 can help. In this PR we allow to skip bad chunks of JSON data to not crash the training\r\n\r\nDid you have warning messages before the error ?\r\n\r\nsomething like this maybe ?\r\n```\r\n03/24/2022 02:19:46 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host... | 2022-10-02T11:56:46Z | 2022-10-04T17:59:03Z | null | NONE | null | null | null | null | ## Describe the bug
I have an intermittent error when streaming the Pile, where I get a JSON parse error which causes my program to crash.
This is intermittent - when I rerun the program with the same random seed it does not crash in the same way. The exact point this happens also varied - it happened to me 11B tokens and 4 days into a training run, and now just happened 2 minutes into one, but I can't reliably reproduce it.
I'm using a remote machine with 8 A6000 GPUs via runpod.io
## Expected results
I have a DataLoader which can iterate through the whole Pile
## Actual results
Stack trace:
```
Failed to read file 'zstd://12.jsonl::https://the-eye.eu/public/AI/pile/train/12.jsonl.zst' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Invalid value. in row 0
```
I'm currently using HuggingFace accelerate, which also gave me the following stack trace, but I've also experienced this problem intermittently when using DataParallel, so I don't think it's to do with parallelisation
```
Traceback (most recent call last):
File "ddp_script.py", line 1258, in <module>
main()
File "ddp_script.py", line 1143, in main
for c, batch in tqdm.tqdm(enumerate(data_iter)):
File "/opt/conda/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 503, in __iter__
next_batch, next_batch_info, next_skip = self._fetch_batches(main_iterator)
File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 454, in _fetch_batches
broadcast_object_list(batch_info)
File "/opt/conda/lib/python3.7/site-packages/accelerate/utils/operations.py", line 333, in broadcast_object_list
torch.distributed.broadcast_object_list(object_list, src=from_process)
File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1900, in broadcast_object_list
object_list[i] = _tensor_to_object(obj_view, obj_size)
File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1571, in _tensor_to_object
return _unpickler(io.BytesIO(buf)).load()
_pickle.UnpicklingError: invalid load key, '@'.
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset(
cfg["dataset_name"], streaming=True, split="train")
dataset = dataset.remove_columns("meta")
dataset = dataset.map(tokenize_and_concatenate, batched=True)
dataset = dataset.with_format(type="torch")
train_data_loader = DataLoader(
dataset, batch_size=cfg["batch_size"], num_workers=3)
for batch in train_data_loader:
continue
```
`tokenize_and_concatenate` is a custom tokenization function I defined on the GPT-NeoX tokenizer to tokenize the text, separated by endoftext tokens, and reshape to have length batch_size, I don't think this is related to tokenization:
```
import numpy as np
import einops
import torch
def tokenize_and_concatenate(examples):
texts = examples["text"]
full_text = tokenizer.eos_token.join(texts)
div = 20
length = len(full_text) // div
text_list = [full_text[i * length: (i + 1) * length]
for i in range(div)]
tokens = tokenizer(text_list, return_tensors="np", padding=True)[
"input_ids"
].flatten()
tokens = tokens[tokens != tokenizer.pad_token_id]
n = len(tokens)
curr_batch_size = n // (seq_len - 1)
tokens = tokens[: (seq_len - 1) * curr_batch_size]
tokens = einops.rearrange(
tokens,
"(batch_size seq) -> batch_size seq",
batch_size=curr_batch_size,
seq=seq_len - 1,
)
prefix = np.ones((curr_batch_size, 1), dtype=np.int64) * \
tokenizer.bos_token_id
return {
"text": np.concatenate([prefix, tokens], axis=1)
}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-5.4.0-105-generic-x86_64-with-debian-buster-sid
- Python version: 3.7.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
ZStandard data:
Version: 0.18.0
Summary: Zstandard bindings for Python
Home-page: https://github.com/indygreg/python-zstandard
Author: Gregory Szorc
Author-email: gregory.szorc@gmail.com
License: BSD
Location: /opt/conda/lib/python3.7/site-packages
Requires:
Required-by: | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5050 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5050/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5050/comments | https://api.github.com/repos/huggingface/datasets/issues/5050/events | https://github.com/huggingface/datasets/issues/5050 | 1,392,381,882 | I_kwDODunzps5S_g-6 | 5,050 | Restore saved format state in `load_from_disk` | {
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"Hi, can I work on this?",
"Hi, sure! Let us know if you need some pointers/help."
] | 2022-09-30T12:40:07Z | 2022-10-11T16:49:24Z | 2022-10-11T16:49:24Z | COLLABORATOR | null | null | null | null | Even though we save the `format` state in `save_to_disk`, we don't restore it in `load_from_disk`. We should fix that.
Reported here: https://discuss.huggingface.co/t/save-to-disk-loses-formatting-information/23815 | {
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"Hi! Unfortunately, I can't reproduce this behavior. Instead, I get `ValueError: audio at 2063_fe9936e7-62b2-4e62-a276-acbd344480ce_1.wav doesn't have metadata in /audio-data/metadata.csv`, which can be fixed by removing the `./` from the file name.\r\n\r\n(Link to a Colab that tries to reproduce this behavior: htt... | 2022-09-29T19:17:23Z | 2022-10-28T13:05:07Z | 2022-10-28T13:05:07Z | NONE | null | null | null | null | ## Describe the bug
When audio files are at the same level as the metadata (`metadata.csv` or `metadata.jsonl` ), the `load_dataset` returns a `DatasetDict` with no rows but the correct columns.
https://github.com/huggingface/datasets/blob/1ea4d091b7a4b83a85b2eeb8df65115d39af3766/docs/source/audio_dataset.mdx?plain=1#L88
## Steps to reproduce the bug
`metadata.csv`:
```csv
file_name,duration,transcription
./2063_fe9936e7-62b2-4e62-a276-acbd344480ce_1.wav,10.768,hello
```
```python
>>> audio_dataset = load_dataset("audiofolder", data_dir="/audio-data/")
>>> audio_dataset
DatasetDict({
train: Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 0
})
validation: Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 0
})
})
```
I've tried, with no success,:
- setting `split` to something else so I don't get a `DatasetDict`,
- removing the `./`,
- using `.jsonl`.
## Expected results
```
Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 1
})
```
## Actual results
```
DatasetDict({
train: Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 0
})
validation: Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 0
})
})
```
## Environment info
- `datasets` version: 2.5.1
- Platform: Linux-5.13.0-1025-aws-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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"Could you share the error you got please ? Maybe the full stack trace if you have it ?\r\n\r\nMaybe `push_to_hub` be implemented as a single commit @Wauplin ? This way if it fails, the repo is still at the previous (valid) state instead of ending-up in an invalid/incimplete state.",
"> Maybe push_to_hub be imple... | 2022-09-29T18:08:12Z | 2023-10-16T13:30:49Z | 2023-10-16T13:30:49Z | NONE | null | null | null | null | **Is your feature request related to a problem? Please describe.**
I pushed a modification of a large dataset (remove a column) to the hub. The push was interrupted after some files were committed to the repo. This left the dataset to raise an error on load_dataset() (ValueError couldn’t cast … because column names don’t match). Only by specifying the previous (complete) commit as revision=commit_hash in load_data(), I was able to repair this and after a successful, complete push, the dataset loads without error again.
**Describe the solution you'd like**
Would it make sense to detect an incomplete push_to_hub() and automatically revert to the previous commit/revision?
**Describe alternatives you've considered**
Leave everything as is, the revision parameter in load_dataset() allows to manually fix this problem.
**Additional context**
Provide useful defaults
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"I agree the situation is not ideal and it would be awesome to use `load_dataset` to reload a dataset saved locally !\r\n\r\nFor context:\r\n\r\n- `load_dataset` works in three steps: download the dataset, then prepare it as an arrow dataset, and finally return a memory mapped arrow dataset. In particular it create... | 2022-09-29T17:37:12Z | 2025-06-28T09:00:44Z | null | CONTRIBUTOR | null | null | null | null | **Is your feature request related to a problem? Please describe.**
Is it possible to make `load_dataset` more universal similar to `from_pretrained` in `transformers` so that it can handle the hub, and the local path datasets of all supported types?
Currently one has to choose a different loader depending on how the dataset has been created.
e.g. this won't work:
```
$ git clone https://huggingface.co/datasets/severo/test-parquet
$ python -c 'from datasets import load_dataset; ds=load_dataset("test-parquet"); \
ds.save_to_disk("my_dataset"); load_dataset("my_dataset")'
[...]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/load.py", line 1746, in load_dataset
builder_instance.download_and_prepare(
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/builder.py", line 1277, in _prepare_split
writer.write_table(table)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_writer.py", line 524, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/table.py", line 2005, in table_cast
return cast_table_to_schema(table, schema)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/table.py", line 1968, in cast_table_to_schema
raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match")
ValueError: Couldn't cast
_data_files: list<item: struct<filename: string>>
child 0, item: struct<filename: string>
child 0, filename: string
```
both times the dataset is being loaded from disk. Why does it fail the second time?
Why can't `save_to_disk` generate a dataset that can be immediately loaded by `load_dataset`?
e.g. the simplest hack would be to have `save_to_disk` add some flag to the saved dataset, that tells `load_dataset` to internally call `load_from_disk`. like having `save_to_disk` create a `load_me_with_load_from_disk.txt` file ;) and `load_dataset` will support that feature from saved datasets from new `datasets` versions. The old ones will still need to use `load_from_disk` explicitly. Unless the flag is not needed and one can immediately tell by looking at the saved dataset that it was saved via `save_to_disk` and thus use `load_from_disk` internally.
The use-case is defining a simple API where the user only ever needs to pass a `dataset_name_or_path` and it will always just work. Currently one needs to manually add additional switches telling the system whether to use one loading method or the other which works but it's not smooth.
Thank you! | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5039 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5039/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5039/comments | https://api.github.com/repos/huggingface/datasets/issues/5039/events | https://github.com/huggingface/datasets/issues/5039 | 1,390,353,315 | I_kwDODunzps5S3xuj | 5,039 | Hendrycks Checksum | {
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"Thanks for reporting, @DanielHesslow. We are fixing it. ",
"@albertvillanova thanks for taking care of this so quickly!",
"The dataset metadata is fixed. You can download it normally."
] | 2022-09-29T06:56:20Z | 2022-09-29T10:23:30Z | 2022-09-29T10:04:20Z | NONE | null | null | null | null | Hi,
The checksum for [hendrycks_test](https://huggingface.co/datasets/hendrycks_test) does not compare correctly, I guess it has been updated on the remote.
```
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://people.eecs.berkeley.edu/~hendrycks/data.tar']
```
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https://api.github.com/repos/huggingface/datasets/issues/5038 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5038/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5038/comments | https://api.github.com/repos/huggingface/datasets/issues/5038/events | https://github.com/huggingface/datasets/issues/5038 | 1,389,631,122 | I_kwDODunzps5S1BaS | 5,038 | `Dataset.unique` showing wrong output after filtering | {
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"Hi! It seems like `flatten_indices` (called in `unique`) doesn't know how to handle empty indices mappings. I'm working on the fix.",
"Thanks, that was fast!"
] | 2022-09-28T16:20:35Z | 2022-09-30T15:44:25Z | 2022-09-30T15:44:25Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
After filtering a dataset, and if no samples remain, `Dataset.unique` will return the unique values of the unfiltered dataset.
## Steps to reproduce the bug
```python
from datasets import Dataset
dataset = Dataset.from_dict({'id': [0]})
dataset = dataset.filter(lambda _: False)
print(dataset.unique('id'))
```
## Expected results
The above code should return an empty list since the dataset is empty.
## Actual results
```bash
[0]
```
## Environment info
- `datasets` version: 2.5.1
- Platform: Linux-5.18.19-100.fc35.x86_64-x86_64-with-glibc2.34
- Python version: 3.9.14
- PyArrow version: 7.0.0
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5032 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5032/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5032/comments | https://api.github.com/repos/huggingface/datasets/issues/5032/events | https://github.com/huggingface/datasets/issues/5032 | 1,388,270,935 | I_kwDODunzps5Sv1VX | 5,032 | new dataset type: single-label and multi-label video classification | {
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"Hi ! You can in the `features` folder how we implemented the audio and image feature types.\r\n\r\nWe can have something similar to videos. What we need to decide:\r\n- the video loading library to use\r\n- the output format when a user accesses a video type object\r\n- what parameters a `Video()` feature type nee... | 2022-09-27T19:40:11Z | 2022-11-02T19:10:13Z | null | NONE | null | null | null | null | **Is your feature request related to a problem? Please describe.**
In my research, I am dealing with multi-modal (audio+text+frame sequence) video classification. It would be great if the datasets library supported generating multi-modal batches from a video dataset.
**Describe the solution you'd like**
Assume I have video files having single/multiple labels. I want to train a single/multi-label video classification model. I want datasets to support generating multi-modal batches (audio+frame sequence) from video files. Audio waveform and frame sequence can be extracted from each video clip then I can use any audio, image and video model from transformers library to extract features which will be fed into my model.
**Describe alternatives you've considered**
Currently, I am using https://github.com/facebookresearch/pytorchvideo dataloaders. There seems to be not much alternative.
**Additional context**
I am wiling to open a PR but don't know where to start.
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/5028 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5028/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5028/comments | https://api.github.com/repos/huggingface/datasets/issues/5028/events | https://github.com/huggingface/datasets/issues/5028 | 1,386,272,533 | I_kwDODunzps5SoNcV | 5,028 | passing parameters to the method passed to Dataset.from_generator() | {
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"Hi! Yes, you can either use the `gen_kwargs` param in `Dataset.from_generator` (`ds = Dataset.from_generator(gen, gen_kwargs={\"param1\": val})`) or wrap the generator function with `functools.partial`\r\n(`ds = Dataset.from_generator(functools.partial(gen, param1=\"val\"))`) to pass custom parameters to it.\r\n"
... | 2022-09-26T15:20:06Z | 2022-10-03T13:00:00Z | 2022-10-03T13:00:00Z | NONE | null | null | null | null | Big thanks for providing dataset creation via a generator.
I want to ask whether there is any way that parameters can be passed to the method Dataset.from_generator() method, like as follows.
```
from datasets import Dataset
def gen(param1):
for idx in len(custom_dataset):
yield custom_dataset[idx] + param1
ds = Dataset.from_generator(gen(param1))
```
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https://api.github.com/repos/huggingface/datasets/issues/5025 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5025/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5025/comments | https://api.github.com/repos/huggingface/datasets/issues/5025/events | https://github.com/huggingface/datasets/issues/5025 | 1,386,011,239 | I_kwDODunzps5SnNpn | 5,025 | Custom Json Dataset Throwing Error when batch is False | {
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"Hi! Our processors are meant to be used in `batched` mode, so if `batched` is `False`, you need to drop the batch dimension (the error message warns you that the array has an extra dimension meaning it's 4D instead of 3D) to avoid the error:\r\n```python\r\ndef prepare_examples(examples):\r\n #Some preporcessin... | 2022-09-26T12:38:39Z | 2022-09-27T19:50:00Z | 2022-09-27T19:50:00Z | NONE | null | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
I tried to create my custom dataset using below code
```
from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D
from torchvision import transforms
from transformers import AutoProcessor
# we'll use the Auto API here - it will load LayoutLMv3Processor behind the scenes,
# based on the checkpoint we provide from the hub
from datasets import load_dataset
def prepare_examples(examples):
#Some preporcessing for each image and text as all my data saved in cloud
#For this reason I couldn't set the batch to True.
encoding = processor(img_as_tensor, words, boxes=boxes, word_labels=labels,
truncation=True, padding="max_length")
# encoding['pixel_values']=np.array(encoding['pixel_values'])
return encoding
dataset = load_dataset("json", data_files='issues.jsonl')
processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
features = dataset["train"].features
column_names = dataset["train"].column_names
# we need to define custom features for `set_format` (used later on) to work properly
features = Features({
'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)),
'input_ids': Sequence(feature=Value(dtype='int64')),
'attention_mask': Sequence(Value(dtype='int64')),
'bbox': Array2D(dtype="int64", shape=(512, 4)),
'labels': Sequence(feature=Value(dtype='int64')),
})
train_dataset = dataset["train"].map(
prepare_examples,
batched=False,
remove_columns=column_names,
features=features
)
```
It throws below error.
```
/opt/conda/lib/python3.7/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type)
172 storage = to_pyarrow_listarray(data, pa_type)
--> 173 return pa.ExtensionArray.from_storage(pa_type, storage)
174
/opt/conda/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.ExtensionArray.from_storage()
TypeError: Incompatible storage type list<item: list<item: list<item: list<item: float>>>> for extension type extension<arrow.py_extension_type<Array3DExtensionType>>
```
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
rom datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D
from torchvision import transforms
from transformers import AutoProcessor
# we'll use the Auto API here - it will load LayoutLMv3Processor behind the scenes,
# based on the checkpoint we provide from the hub
from datasets import load_dataset
def prepare_examples(examples):
#Some preporcessing for each image and text as all my data saved in cloud
encoding = processor(img_as_tensor, words, boxes=boxes, word_labels=labels,
truncation=True, padding="max_length")
# encoding['pixel_values']=np.array(encoding['pixel_values'])
return encoding
dataset = load_dataset("json", data_files='issues.jsonl')
processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
features = dataset["train"].features
column_names = dataset["train"].column_names
# we need to define custom features for `set_format` (used later on) to work properly
features = Features({
'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)),
'input_ids': Sequence(feature=Value(dtype='int64')),
'attention_mask': Sequence(Value(dtype='int64')),
'bbox': Array2D(dtype="int64", shape=(512, 4)),
'labels': Sequence(feature=Value(dtype='int64')),
})
train_dataset = dataset["train"].map(
prepare_examples,
batched=False,
remove_columns=column_names,
features=features
)
## Expected results
A clear and concise description of the expected results.
Expected would be similar to all the otherdatasets with no error.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Unix
- Python version: 3.9
- PyArrow version: 9.0.0
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | [] | 2022-09-26T11:11:50Z | 2022-09-28T07:54:20Z | 2022-09-28T07:54:20Z | MEMBER | null | null | null | null | ## Describe the bug
Text strings are split into lists of characters.
Example for "X-CSQA-en":
```
{'id': 'd3845adc08414fda',
'lang': 'en',
'question': {'stem': ['T',
'h',
'e',
' ',
'd',
'e',
'n',
't',
'a',
'l',
' ',
'o',
'f',
'f',
'i',
'c',
'e',
' ',
'h',
'a',
'n',
'd',
'l',
'e',
'd',
' ',
'a',
' ',
'l',
'o',
't',
' ',
'o',
'f',
' ',
'p',
'a',
't',
'i',
'e',
'n',
't',
's',
' ',
'w',
'h',
'o',
' ',
'e',
'x',
'p',
'e',
'r',
'i',
'e',
'n',
'c',
'e',
'd',
' ',
't',
'r',
'a',
'u',
'm',
'a',
't',
'i',
'c',
' ',
'm',
'o',
'u',
't',
'h',
' ',
'i',
'n',
'j',
'u',
'r',
'y',
',',
' ',
'w',
'h',
'e',
'r',
'e',
' ',
'w',
'e',
'r',
'e',
' ',
't',
'h',
'e',
's',
'e',
' ',
'p',
'a',
't',
'i',
'e',
'n',
't',
's',
' ',
'c',
'o',
'm',
'i',
'n',
'g',
' ',
'f',
'r',
'o',
'm',
'?'],
'choices': [{'label': ['A'], 'text': ['t', 'o', 'w', 'n']},
{'label': ['B'], 'text': ['m', 'i', 'c', 'h', 'i', 'g', 'a', 'n']},
{'label': ['C'], 'text': ['h', 'o', 's', 'p', 'i', 't', 'a', 'l']},
{'label': ['D'], 'text': ['s', 'c', 'h', 'o', 'o', 'l', 's']},
{'label': ['E'],
'text': ['o',
'f',
'f',
'i',
'c',
'e',
' ',
'b',
'u',
'i',
'l',
'd',
'i',
'n',
'g']}]},
'answerKey': 'C'}
## Steps to reproduce the bug
```python
ds = load_dataset("datasets/xcsr", "X-CSQA-en", split="validation", streaming=True)
item = next(iter(ds))
item
```
## Expected results
```
{'id': 'd3845adc08414fda',
'lang': 'en',
'question': {'stem': 'The dental office handled a lot of patients who experienced traumatic mouth injury, where were these patients coming from?',
'choices': {'label': ['A', 'B', 'C', 'D', 'E'],
'text': ['town', 'michigan', 'hospital', 'schools', 'office building']}},
'answerKey': 'C'}
```
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"Hi! What's the structure of your image folder? `datasets` by default tries to infer to what split each file belongs based on directory/file names. If it's OK to load all the images inside the `dataset` folder in the `train` split, you can do the following:\r\n```python\r\ndataset = load_dataset(\"imagefolder\", da... | 2022-09-26T03:22:14Z | 2022-09-29T08:07:50Z | 2022-09-29T08:07:50Z | NONE | null | null | null | null | ## Describe the bug
Including the strings "test" or "train" anywhere in a filename causes `datasets` to infer the split and silently ignore all other files.
This behavior is documented for directory names but not filenames: https://huggingface.co/docs/datasets/image_dataset#imagefolder
## Steps to reproduce the bug
`metadata.jsonl`
```json
{"file_name": "photo of a cat.jpg", "text": "a photo of a cat"}
{"file_name": "photo of a dog.jpg", "text": "a photo of a dog"}
{"file_name": "photo of a train.jpg", "text": "a photo of a train"}
{"file_name": "photo of test tubes.jpg", "text": "a photo of test tubes"}
```
`bug.py`
```python
from datasets import load_dataset
dataset = load_dataset("dataset")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['image', 'text'],
# num_rows: 1
# })
# test: Dataset({
# features: ['image', 'text'],
# num_rows: 1
# })
# })
for split in dataset:
for n in dataset[split]:
print(n['text'])
# a photo of a train
# a photo of test tubes
```
## Expected results
One single dataset with all four images / a warning for unused files / documentation of this behavior
## Actual results
Only the images with "test" or "train" in the name are loaded
## Environment info
- `datasets` version: 2.5.1
- Platform: macOS-12.5.1-x86_64-i386-64bit
- Python version: 3.10.4
- PyArrow version: 9.0.0
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"Thanks for reporting, @thesofakillers. Good catch. We are fixing this. "
] | 2022-09-23T16:11:54Z | 2022-09-26T10:57:31Z | 2022-09-26T10:57:31Z | NONE | null | null | null | null | ## Describe the bug
All the alleged non-english subcollections for the X-CSQA task in the [xcsr benchmark dataset ](https://huggingface.co/datasets/xcsr) seem to be copies of the english subcollection, rather than translations. This is in contrast to the data description:
> we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR
## Steps to reproduce the bug
```python
# let's say you want to load the french X-CSQA subcollection
french = datasets.load_dataset("xcsr", "X-CSQA-fr")
# for good measure, let's load english too
english = datasets.load_dataset("xcsr", "X-CSQA-en")
# let's inspect
"".join(english['test'][0]['question']['stem'])
# output: 'The people wanted to stop the parade, so what did they set up to thwart it?'
"".join(french['test'][0]['question']['stem'])
# output: 'The people wanted to stop the parade, so what did they set up to thwart it?'
# what? Why are they both in english?
# I've checked this for validation and train splits too, across many datapoints. It's all the same english dataset
# maybe i need to look better?
french['test'].unique('lang')
# output: ['en']
# no, it's all english
```
## Expected results
Accessing a subcollection in language X should return a subcollection containg samples in language X
## Actual results
Accessing a subcollection in language X returns a subcollection containing samples in English.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.1
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
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"Sounds good ! Can I help with anything ?"
] | 2022-09-23T08:48:10Z | 2022-10-05T07:15:57Z | 2022-10-05T07:15:57Z | MEMBER | null | null | null | null | Before merging:
- #4974
TODO:
- [x] Create label: ["dataset contribution"](https://github.com/huggingface/datasets/pulls?q=label%3A%22dataset+contribution%22)
- [x] Create project: [Datasets: Transfer datasets to Hub](https://github.com/orgs/huggingface/projects/22/)
- [x] PRs:
- [x] Add dataset: we should recommend transfer all additions of datasets to the Hub, under the appropriate namespace; no more additions of datasets on GitHub
- [x] Update dataset: in general, we should merge bug fixes; enhancements should be considered on a case-by-case basis, depending on whether there is a more suitable namespace on the Hub
- [ ] Issues
Finally:
- [x] #4974
Let me know what you think! :hugs: | {
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"Hi! We don't currently have a builder for parsing custom `conll` datasets, but I guess we could add one as a packaged module (similarly to what [TFDS](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/core/dataset_builders/conll/conll_dataset_builder.py) did). @lhoestq @albertvillanova WDYT?\r... | 2022-09-23T07:49:42Z | 2022-11-02T11:57:15Z | null | NONE | null | null | null | null | I need to read the custom dataset in conll format
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"Hi ! Can you share more information about your use case ? How could it help you to have cpp bindings versus using the python libraries ?",
"> Hi ! Can you share more information about your use case ? How could it help you to have cpp bindings versus using the python libraries ?\r\n\r\nfor example ,the huggingfac... | 2022-09-23T07:42:49Z | 2023-02-24T16:20:57Z | 2023-02-24T16:20:57Z | NONE | null | null | null | null | HI:
I use cpp env libtorch, I like use hugggingface ,but huggingface not cpp binding, would you like publish cpp binding for it.
thanks | {
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"Hi ! Support for URIs like `s3://...` is not implemented yet in `data_files=`. You can use the HTTP URL instead if your data is public in the meantime",
"Hi,\r\nI want to make sure I understand this response. I have a set of files on S3 that are private for security reasons. Because they are not public files I ... | 2022-09-22T18:28:15Z | 2023-08-16T09:58:36Z | 2023-08-16T09:58:36Z | NONE | null | null | null | null | I have a file on S3 created by Data Version Control, it looks like `s3://dvc/ac/badff5b134382a0f25248f1b45d7b2` but contains a json file. If I run
```python
dataset = load_dataset(
"json",
data_files='s3://dvc/ac/badff5b134382a0f25248f1b45d7b2'
)
```
It gives me
```
InvalidSchema: No connection adapters were found for 's3://dvc/ac/badff5b134382a0f25248f1b45d7b2'
```
However, I cannot go ahead and change the names of the s3 file. Is there a way to "force" load a S3 url with certain decoder (JSON, CSV, etc.) regardless of s3 URL naming? | {
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"Sorry bug on my part 😅 Closing "
] | 2022-09-22T15:07:27Z | 2022-09-23T09:05:18Z | 2022-09-23T09:05:18Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
Loading the dataset [earnings-22](https://huggingface.co/datasets/sanchit-gandhi/earnings22_split) from the Hub yields an Index Error. I created this dataset locally and then pushed to hub at the specified URL. Thus, I expect the dataset should work out-of-the-box! Indeed, the dataset viewer functions correctly, and there were no issues when I had the dataset locally.
Don't think it's a sound file bug as the version matches what worked previously.
Update: the bug appeared for me on a GPU, mysteriously on a TPU I can't repro and it downloads correctly...
## Steps to reproduce the bug
```python
from datasets import load_dataset
earnings22 = load_dataset("sanchit-gandhi/earnings22_split")
```
## Expected results
```
>>> earnings22
DatasetDict({
validation: Dataset({
features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'],
num_rows: 2650
})
train: Dataset({
features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'],
num_rows: 52006
})
test: Dataset({
features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'],
num_rows: 2735
})
})
```
## Actual results
```
Traceback (most recent call last):
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2764, in _map_single
writer.write(example)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 451, in write
self.write_examples_on_file()
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 409, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 508, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 231, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 197, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/table.py", line 1683, in wrapper
return func(array, *args, **kwargs)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/table.py", line 1795, in cast_array_to_feature
return feature.cast_storage(array)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 190, in cast_storage
storage = pa.array([Audio().encode_example(x) if x is not None else None for x in storage.to_pylist()])
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 190, in <listcomp>
storage = pa.array([Audio().encode_example(x) if x is not None else None for x in storage.to_pylist()])
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 92, in encode_example
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
File "/opt/conda/envs/hf/lib/python3.8/site-packages/soundfile.py", line 313, in write
channels = data.shape[1]
IndexError: tuple index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
Plus:
- SoundFile version: 0.10.3.post1
cc @lhoestq @polinaeterna | {
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https://api.github.com/repos/huggingface/datasets/issues/5009 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5009/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5009/comments | https://api.github.com/repos/huggingface/datasets/issues/5009/events | https://github.com/huggingface/datasets/issues/5009 | 1,381,194,067 | I_kwDODunzps5SU1lT | 5,009 | Error loading StonyBrookNLP/tellmewhy dataset from hub even though local copy loads correctly | {
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"I think this is because some columns are mostly empty lists. In particular the train and validation splits only have empty lists for `val_ann`. Therefore the type inference doesn't know which type is inside (or it would have to scan the other splits first before knowing).\r\n\r\nYou can fix that by specifying the ... | 2022-09-21T16:23:06Z | 2022-09-29T13:07:29Z | 2022-09-29T13:07:29Z | NONE | null | null | null | null | ## Describe the bug
I have added a new dataset with the identifier `StonyBrookNLP/tellmewhy` to the hub. When I load the individual files using my local copy using `dataset = datasets.load_dataset("json", data_files="data/train.jsonl")`, it loads the dataset correctly. However, when I try to load it from the hub, I get an error (pasted below). Additionally, `dataset = datasets.load_dataset("json", data_dir="data/")` throws the same error.
## Steps to reproduce the bug
```python
dataset = datasets.load_dataset('StonyBrookNLP/tellmewhy')
```
## Expected results
Successfully load the `StonyBrookNLP/tellmewhy` dataset.
## Actual results
```
Using custom data configuration StonyBrookNLP--tellmewhy-82712924092694ff
Downloading and preparing dataset json/StonyBrookNLP--tellmewhy to /home/yklal95/.cache/huggingface/datasets/StonyBrookNLP___json/StonyBrookNLP--tellmewhy-82712924092694ff/0.0.0/a3e658c4731e59120d44081ac10bf85dc7e1388126b92338344ce9661907f253...
Downloading data files: 100%|██████████████████████████████| 3/3 [00:00<00:00, 957.46it/s]
Extracting data files: 100%|███████████████████████████████| 3/3 [00:00<00:00, 299.14it/s]
Traceback (most recent call last):
File "/home/yklal95/tmw-generalization/src/load_datasets.py", line 17, in <module>
main(args)
File "/home/yklal95/tmw-generalization/src/load_datasets.py", line 11, in main
dataset = datasets.load_dataset(args.dataset_name)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/load.py", line 1746, in load_dataset
builder_instance.download_and_prepare(
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 1277, in _prepare_split
writer.write_table(table)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/arrow_writer.py", line 524, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 2005, in table_cast
return cast_table_to_schema(table, schema)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1969, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1969, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1681, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1681, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1822, in cast_array_to_feature
casted_values = _c(array.values, feature.feature)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1683, in wrapper
return func(array, *args, **kwargs)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1853, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1683, in wrapper
return func(array, *args, **kwargs)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1761, in array_cast
raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
TypeError: Couldn't cast array of type int64 to null
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-121-generic-x86_64-with-glibc2.27
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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"Shall we revert https://github.com/huggingface/datasets/pull/4971 @mariosasko ?\r\n\r\nAnd for consistency we can update IterableDataset.map later"
] | 2022-09-21T13:39:19Z | 2022-09-21T14:11:57Z | 2022-09-21T14:11:57Z | MEMBER | null | null | null | null | ## Describe the bug
As reported by @lhoestq:
> see https://app.circleci.com/pipelines/github/huggingface/transformers/47634/workflows/b491886b-e66e-4edb-af96-8b459e72aa25/jobs/564563
this is used here: [https://github.com/huggingface/transformers/blob/3b19c0317b6909e2d7f11b5053895ac55[…]torch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py](https://github.com/huggingface/transformers/blob/3b19c0317b6909e2d7f11b5053895ac55250e7da/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py#L482-L488)
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"It's a bug! Thanks for reporting, I'm looking at it",
"Fixed."
] | 2022-09-21T09:06:17Z | 2022-09-21T11:49:49Z | 2022-09-21T11:49:49Z | NONE | null | null | null | null | ### Link
https://huggingface.co/datasets/loubnabnl/humaneval-x/viewer/
### Description
The dataset has subsets but the viewer gets stuck in the default subset even when I select another one (the data loading of the subsets works fine)
### Owner
Yes | {
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"<img width=\"519\" alt=\"Capture d’écran 2022-09-20 à 22 33 47\" src=\"https://user-images.githubusercontent.com/1676121/191358952-1220cb7d-745a-4203-a66b-3c707b25038f.png\">\r\n\r\n```\r\nNot found.\r\n\r\nError code: SplitsResponseNotFound\r\n```\r\n\r\nhttps://datasets-server.huggingface.co/splits?dataset=a... | 2022-09-20T16:45:45Z | 2022-09-27T07:04:03Z | 2022-09-21T07:24:07Z | NONE | null | null | null | null | ### Link
https://huggingface.co/datasets/asapp/slue/viewer/
### Description
Hi,
I wonder how to get the dataset viewer of our slue dataset to work.
Best,
Felix
### Owner
Yes | {
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"The script uses `Dataset.load_from_disk`, which as you can expect, doesn't work in streaming mode.\r\n\r\nIt would probably be more practical to load the dataset locally using `Dataset.load_from_disk` first and then `push_to_hub` to upload it in Parquet on the Hub",
"I've transferred this issue to the Hub repo: ... | 2022-09-20T12:32:07Z | 2022-09-27T12:35:44Z | 2022-09-27T12:35:44Z | COLLABORATOR | null | null | null | null | ### Link
https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr
### Description
```
Error code: StreamingRowsError
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: 'zip:/data/train::https:/huggingface.co/datasets/Jean-Baptiste/wikiner_fr/resolve/main/data.zip/state.json'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/responses/first_rows.py", line 337, in get_first_rows_response
rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token)
File "/src/services/worker/src/worker/utils.py", line 123, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/responses/first_rows.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 718, in __iter__
for key, example in self._iter():
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 708, in _iter
yield from ex_iterable
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 112, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/tmp/modules-cache/datasets_modules/datasets/Jean-Baptiste--wikiner_fr/683a580ba6ec769d508f7dfc603a651667b0ed3817b1ae5bfd45f97cc024923f/wikiner_fr.py", line 165, in _generate_examples
dataset = Dataset.load_from_disk(filepath)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1210, in load_from_disk
with open(Path(dataset_path, config.DATASET_STATE_JSON_FILENAME).as_posix(), encoding="utf-8") as state_file:
FileNotFoundError: [Errno 2] No such file or directory: 'zip:/data/train::https:/huggingface.co/datasets/Jean-Baptiste/wikiner_fr/resolve/main/data.zip/state.json'
```
Is it an error with the dataset script, or the data itself, @huggingface/datasets?
https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr/tree/main
### Owner
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"gists... | null | [] | 2022-09-20T09:31:59Z | 2022-09-21T12:21:25Z | 2022-09-21T12:21:25Z | COLLABORATOR | null | null | null | null | In the dataset viewer on the Hub (https://huggingface.co/datasets/glue/viewer), we would like (https://github.com/huggingface/moon-landing/issues/3882) to show a specific message when the repository lacks any data files.
In that case, instead of showing a complex traceback, we want to show a call to action to help the user upload data.
To do that, it would be very helpful to know for sure that the repository is missing any (supported) data files.
It could be done by raising a custom exception, for example, `NoDataError`. | {
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>
> Also FYI in #4926 I also removed all the validation steps anyway (language, license, types etc.)
_Originally posted by @lhoestq in https://github.com/huggingface/datasets/issues/4930#issuecomment-1238401662_
> [...], in my opinion we can just delete this file from `datasets`, the validation is happening hub-side anyways now?
_Originally posted by @julien-c in https://github.com/huggingface/datasets/issues/4930#issuecomment-1238390659_ | {
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https://api.github.com/repos/huggingface/datasets/issues/4990 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4990/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4990/comments | https://api.github.com/repos/huggingface/datasets/issues/4990/events | https://github.com/huggingface/datasets/issues/4990 | 1,378,120,806 | I_kwDODunzps5SJHRm | 4,990 | "no-token" is passed to `huggingface_hub` when token is `None` | {
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"Hi @Wauplin, thanks for raising this potential issue.\r\n\r\nThe choice of passing `\"no-token\"` instead of `None` was made in this PR:\r\n- #4536 \r\n\r\nAccording to the PR description, the reason why it is passed is to avoid that `HfApi.dataset_info` uses the local token when no token should be used.",
"Hi @... | 2022-09-19T15:14:40Z | 2022-09-30T09:16:00Z | 2022-09-30T09:16:00Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
In the 2 lines listed below, a token is passed to `huggingface_hub` to get information from a dataset. If no token is provided, a "no-token" string is passed. What is the purpose of it ? If no real, I would prefer if the `None` value could be sent directly to be handle by `huggingface_hub`. I feel that here it is working because we assume the token will never be validated.
https://github.com/huggingface/datasets/blob/5b23f58535f14cc4dd7649485bce1ccc836e7bca/src/datasets/load.py#L753
https://github.com/huggingface/datasets/blob/5b23f58535f14cc4dd7649485bce1ccc836e7bca/src/datasets/load.py#L1121
## Expected results
Pass `token=None` to `huggingface_hub`.
## Actual results
`token="no-token"` is passed.
## Environment info
`huggingface_hub v0.10.0dev` | {
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https://api.github.com/repos/huggingface/datasets/issues/4989 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4989/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4989/comments | https://api.github.com/repos/huggingface/datasets/issues/4989/events | https://github.com/huggingface/datasets/issues/4989 | 1,376,832,233 | I_kwDODunzps5SEMrp | 4,989 | Running add_column() seems to corrupt existing sequence-type column info | {
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"Nevermind, I was incorrect."
] | 2022-09-17T17:42:05Z | 2022-09-19T12:54:54Z | 2022-09-19T12:54:54Z | NONE | null | null | null | null | I have a dataset that contains a column ("foo") that is a sequence type of length 4. So when I run .to_pandas() on it, the resulting dataframe correctly contains 4 columns - foo_0, foo_1, foo_2, foo_3. So the 1st row of the dataframe might look like:
ds = load_dataset(...)
df = ds.to_pandas()
df:
foo_0 | foo_1 | foo_2 | foo_3
0.0 | 1.0 | 2.0 | 3.0
If I run .add_column("new_col", data) on the dataset, and then .to_pandas() on the resulting new dataset, the resulting dataframe contains only 2 columns - foo, new_col. The values in column foo are lists of length 4, the 4 elements that should have been split into separate columns. Dataframe 1st row would be:
ds = load_dataset(...)
new_ds = ds.add_column("new_col", data)
df = new_ds.to_pandas()
df:
foo | new_col
[0.0, 1.0, 2.0, 3.0] | new_val
I've explored the 2 datasets in a debugger and haven't noticed any changes to any attributes related to the foo column, but I can't determine why the dataframes are so different. | {
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https://api.github.com/repos/huggingface/datasets/issues/4988 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4988/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4988/comments | https://api.github.com/repos/huggingface/datasets/issues/4988/events | https://github.com/huggingface/datasets/issues/4988 | 1,376,096,584 | I_kwDODunzps5SBZFI | 4,988 | Add `IterableDataset.from_generator` to the API | {
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"#take",
"Thanks @hamid-vakilzadeh ! Let us know if you have some questions or if we can help",
"Thank you! I certainly will reach out if I need any help."
] | 2022-09-16T15:19:41Z | 2022-10-05T12:10:49Z | 2022-10-05T12:10:49Z | COLLABORATOR | null | null | null | null | We've just added `Dataset.from_generator` to the API. It would also be cool to add `IterableDataset.from_generator` to support creating an iterable dataset from a generator.
cc @lhoestq | {
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"Hi! I think you can use the newly-added `from_generator` method for that:\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndef gen():\r\n for idx in len(torch_dataset):\r\n yield torch_dataset[idx] # this has to be a dictionary\r\n ## or if it's an IterableDataset\r\n # for ex in torch_dataset:... | 2022-09-16T09:15:10Z | 2023-12-14T20:54:15Z | 2022-09-20T11:23:43Z | NONE | null | null | null | null | I look through the huggingface dataset docs, and it seems that there is no offical support function to convert `torch.utils.data.Dataset` to huggingface dataset. However, there is a way to convert huggingface dataset to `torch.utils.data.Dataset`, like below:
```python
from datasets import Dataset
data = [[1, 2],[3, 4]]
ds = Dataset.from_dict({"data": data})
ds = ds.with_format("torch")
ds[0]
ds[:2]
```
So is there something I miss, or there IS no function to convert `torch.utils.data.Dataset` to huggingface dataset. If so, is there any way to do this convert?
Thanks. | {
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https://api.github.com/repos/huggingface/datasets/issues/4982 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4982/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4982/comments | https://api.github.com/repos/huggingface/datasets/issues/4982/events | https://github.com/huggingface/datasets/issues/4982 | 1,375,604,693 | I_kwDODunzps5R_g_V | 4,982 | Create dataset_infos.json with VALIDATION and TEST splits | {
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"@mariosasko could you help me with this issue? we've started the discussion from [here](https://github.com/huggingface/datasets/issues/4895#issuecomment-1248227130)",
"Hi again! Can you please pass the directory name containing the dataset script instead of the script name to `datasets-cli test`?",
"Yes, it wo... | 2022-09-16T08:21:19Z | 2022-09-28T07:59:39Z | 2022-09-28T07:59:39Z | NONE | null | null | null | null | The problem is described in that [issue](https://github.com/huggingface/datasets/issues/4895#issuecomment-1247975569).
> When I try to create data_infos.json using datasets-cli test Peter.py --save_infos --all_configs I get an error:
> ValueError: Unknown split "test". Should be one of ['train'].
>
> The data_infos.json is created perfectly fine when I use only one split - datasets.Split.TRAIN
>
> You can find the code here: https://huggingface.co/datasets/sberbank-ai/Peter/tree/add_splits (add_splits branch)
I tried to clear the cache folder, than I got an another error. I run:
```
git clone https://huggingface.co/datasets/sberbank-ai/Peter
cd Peter
git checkout add_splits # switch to a add_splits branch
rm dataset_infos.json # remove local dataset_infos.json
rm -r ~/.cache/huggingface # remove cached dataset_infos.json
datasets-cli test Peter.py --save_infos --all_configs # trying to create new dataset_infos.json
```
The error message:
```
Using custom data configuration default
Testing builder 'default' (1/1)
Downloading and preparing dataset peter/default to /Users/kalinin/.cache/huggingface/datasets/peter/default/0.0.0/ef579519e140d6a40df2555996f26165f04c47557d7373709c8d7e7b4fd7465d...
Downloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 5160.63it/s]
Extracting data files: 0%| | 0/4 [00:00<?, ?it/s]Traceback (most recent call last):
File "/usr/local/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/usr/local/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/usr/local/lib/python3.9/site-packages/datasets/commands/test.py", line 137, in run
builder.download_and_prepare(
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 771, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/kalinin/.cache/huggingface/modules/datasets_modules/datasets/Peter/ef579519e140d6a40df2555996f26165f04c47557d7373709c8d7e7b4fd7465d/Peter.py", line 23, in _split_generators
data_files = dl_manager.download_and_extract(_URLS)
File "/usr/local/lib/python3.9/site-packages/datasets/download/download_manager.py", line 431, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/usr/local/lib/python3.9/site-packages/datasets/download/download_manager.py", line 403, in extract
extracted_paths = map_nested(
File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 393, in map_nested
mapped = [
File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 394, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 330, in _single_map_nested
return function(data_struct)
File "/usr/local/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 213, in cached_path
output_path = ExtractManager(cache_dir=download_config.cache_dir).extract(
File "/usr/local/lib/python3.9/site-packages/datasets/utils/extract.py", line 46, in extract
self.extractor.extract(input_path, output_path, extractor_format)
File "/usr/local/lib/python3.9/site-packages/datasets/utils/extract.py", line 263, in extract
with FileLock(lock_path):
File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 399, in __init__
max_filename_length = os.statvfs(os.path.dirname(lock_file)).f_namemax
FileNotFoundError: [Errno 2] No such file or directory: ''
Exception ignored in: <function BaseFileLock.__del__ at 0x11caeec10>
Traceback (most recent call last):
File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 328, in __del__
self.release(force=True)
File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 303, in release
with self._thread_lock:
AttributeError: 'UnixFileLock' object has no attribute '_thread_lock'
Extracting data files: 0%| | 0/4 [00:00<?, ?it/s]
```
Can you help me please?
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-12.5.1-x86_64-i386-64bit
- Python version: 3.9.5
- PyArrow version: 9.0.0
- Pandas version: 1.2.4
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https://api.github.com/repos/huggingface/datasets/issues/4981 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4981/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4981/comments | https://api.github.com/repos/huggingface/datasets/issues/4981/events | https://github.com/huggingface/datasets/issues/4981 | 1,375,086,773 | I_kwDODunzps5R9ii1 | 4,981 | Can't create a dataset with `float16` features | {
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"Hi @dconathan, thanks for reporting.\r\n\r\nWe rely on Arrow as a backend, and as far as I know currently support for `float16` in Arrow is not fully implemented in Python (C++), hence the `ArrowNotImplementedError` you get.\r\n\r\nSee, e.g.: https://arrow.apache.org/docs/status.html?highlight=float16#data-types",... | 2022-09-15T21:03:24Z | 2025-06-12T11:47:42Z | null | CONTRIBUTOR | null | null | null | null | ## Describe the bug
I can't create a dataset with `float16` features.
I understand from the traceback that this is a `pyarrow` error, but I don't see anywhere in the `datasets` documentation about how to successfully do this. Is it actually supported? I've tried older versions of `pyarrow` as well with the same exact error.
The bug seems to arise from `datasets` casting the values to `double` and then `pyarrow` doesn't know how to convert those back to `float16`... does that sound right? Is there a way to bypass this since it's not necessary in the `numpy` and `torch` cases?
Thanks!
## Steps to reproduce the bug
All of the following raise the following error with the same exact (as far as I can tell) traceback:
```python
ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float
```
```python
from datasets import Dataset, Features, Value
Dataset.from_dict({"x": [0.0, 1.0, 2.0]}, features=Features(x=Value("float16")))
import numpy as np
Dataset.from_dict({"x": np.arange(3, dtype=np.float16)}, features=Features(x=Value("float16")))
import torch
Dataset.from_dict({"x": torch.arange(3).to(torch.float16)}, features=Features(x=Value("float16")))
```
## Expected results
A dataset with `float16` features is successfully created.
## Actual results
```python
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
Cell In [14], line 1
----> 1 Dataset.from_dict({"x": [1.0, 2.0, 3.0]}, features=Features(x=Value("float16")))
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py:870, in Dataset.from_dict(cls, mapping, features, info, split)
865 mapping = features.encode_batch(mapping)
866 mapping = {
867 col: OptimizedTypedSequence(data, type=features[col] if features is not None else None, col=col)
868 for col, data in mapping.items()
869 }
--> 870 pa_table = InMemoryTable.from_pydict(mapping=mapping)
871 if info.features is None:
872 info.features = Features({col: ts.get_inferred_type() for col, ts in mapping.items()})
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:750, in InMemoryTable.from_pydict(cls, *args, **kwargs)
734 @classmethod
735 def from_pydict(cls, *args, **kwargs):
736 """
737 Construct a Table from Arrow arrays or columns
738
(...)
748 :class:`datasets.table.Table`:
749 """
--> 750 return cls(pa.Table.from_pydict(*args, **kwargs))
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:3648, in pyarrow.lib.Table.from_pydict()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:5174, in pyarrow.lib._from_pydict()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:343, in pyarrow.lib.asarray()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:231, in pyarrow.lib.array()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py:197, in TypedSequence.__arrow_array__(self, type)
192 # otherwise we can finally use the user's type
193 elif type is not None:
194 # We use cast_array_to_feature to support casting to custom types like Audio and Image
195 # Also, when trying type "string", we don't want to convert integers or floats to "string".
196 # We only do it if trying_type is False - since this is what the user asks for.
--> 197 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
198 return out
199 except (TypeError, pa.lib.ArrowInvalid) as e: # handle type errors and overflows
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)
1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1682 else:
-> 1683 return func(array, *args, **kwargs)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1853, in cast_array_to_feature(array, feature, allow_number_to_str)
1851 return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str)
1852 elif not isinstance(feature, (Sequence, dict, list, tuple)):
-> 1853 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
1854 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)
1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1682 else:
-> 1683 return func(array, *args, **kwargs)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1762, in array_cast(array, pa_type, allow_number_to_str)
1760 if pa.types.is_null(pa_type) and not pa.types.is_null(array.type):
1761 raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
-> 1762 return array.cast(pa_type)
1763 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:919, in pyarrow.lib.Array.cast()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/compute.py:389, in cast(arr, target_type, safe, options)
387 else:
388 options = CastOptions.safe(target_type)
--> 389 return call_function("cast", [arr], options)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:560, in pyarrow._compute.call_function()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:355, in pyarrow._compute.Function.call()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:121, in pyarrow.lib.check_status()
ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float
```
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
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"The whole datasets library is pretty much a wrapper to pyarrow (just take a look at some of the source for a Dataset) https://github.com/huggingface/datasets/blob/51aef08ad7053c0bfe8f9a961207b26df15850d3/src/datasets/arrow_dataset.py#L639 \r\n\r\nI think removing the pyarrow dependency would involve a complete rew... | 2022-09-15T17:38:03Z | 2022-09-16T17:23:47Z | 2022-09-16T17:23:47Z | NONE | null | null | null | null | **Is your feature request related to a problem? Please describe.**
Is `pyarrow` really needed for every dataset?
**Describe the solution you'd like**
It is made optional.
**Describe alternatives you've considered**
Likely, no.
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https://api.github.com/repos/huggingface/datasets/issues/4977 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4977/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4977/comments | https://api.github.com/repos/huggingface/datasets/issues/4977/events | https://github.com/huggingface/datasets/issues/4977 | 1,372,962,157 | I_kwDODunzps5R1b1t | 4,977 | Providing dataset size | {
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] | open | false | null | [] | null | [
"Hi @sashavor, thanks for your suggestion.\r\n\r\nUntil now we have the CLI command \r\n```\r\ndatasets-cli test datasets/<your-dataset-folder> --save_infos --all_configs\r\n```\r\nthat generates the `dataset_infos.json` with the size of the downloaded dataset, among other information.\r\n\r\nWe are currently in th... | 2022-09-14T13:09:27Z | 2022-09-15T16:03:58Z | null | CONTRIBUTOR | null | null | null | null | **Is your feature request related to a problem? Please describe.**
Especially for big datasets like [LAION](https://huggingface.co/datasets/laion/laion2B-en/), it's hard to know exactly the downloaded size (because there are many files and you don't have their exact size when downloaded).
**Describe the solution you'd like**
Auto-populating the downloaded dataset size on the dataset page would be really useful, including that of each split (when there are some).
**Describe alternatives you've considered**
People should be adding this to dataset cards, but I don't think that is systematically the case :slightly_smiling_face:
**Additional context**
Mentioned to @lhoestq
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/4976 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4976/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4976/comments | https://api.github.com/repos/huggingface/datasets/issues/4976/events | https://github.com/huggingface/datasets/issues/4976 | 1,372,322,382 | I_kwDODunzps5Ry_pO | 4,976 | Hope to adapt Python3.9 as soon as possible | {
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"Hi! `datasets` should work in Python 3.9. What kind of issue have you encountered?",
"There is this related issue already: https://github.com/huggingface/datasets/issues/4113\r\nAnd I guess we need a CI job for 3.9 ^^",
"Perhaps we should report this issue in the `filelock` repo?"
] | 2022-09-14T04:42:22Z | 2022-09-26T16:32:35Z | null | NONE | null | null | null | null | **Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context about the feature request here.
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/4965 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4965/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4965/comments | https://api.github.com/repos/huggingface/datasets/issues/4965/events | https://github.com/huggingface/datasets/issues/4965 | 1,368,661,002 | I_kwDODunzps5RlBwK | 4,965 | [Apple M1] MemoryError: Cannot allocate write+execute memory for ffi.callback() | {
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"Hi! This seems like a bug in `soundfile`. Could you please open an issue in their repo? `soundfile` works without any issues on my M1, so I'm not sure we can help.",
"Hi @mariosasko, can you share how you installed `soundfile` on your mac M1?",
"Hi @hoangtnm - I upgraded to python 3.10 and it fixed the proble... | 2022-09-10T15:55:49Z | 2024-03-21T17:25:53Z | 2023-07-21T14:45:50Z | NONE | null | null | null | null | ## Describe the bug
I'm trying to run `cast_column("audio", Audio())` on Apple M1 Pro, but it seems that it doesn't work.
## Steps to reproduce the bug
```python
import datasets
dataset = load_dataset("csv", data_files="./train.csv")["train"]
dataset = dataset.map(lambda x: {"audio": str(DATA_DIR / "audio" / x["audio"])})
dataset = dataset.cast_column("audio", Audio())
dataset[0]
```
## Expected results
```
{'audio': {'bytes': None,
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav'},
'english_transcription': 'I would like to set up a joint account with my partner',
'intent_class': 11,
'lang_id': 4,
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'transcription': 'I would like to set up a joint account with my partner'}
```
## Actual results
````---------------------------------------------------------------------------
MemoryError Traceback (most recent call last)
Input In [6], in <cell line: 1>()
----> 1 dataset[0]
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/arrow_dataset.py:2165, in Dataset.__getitem__(self, key)
2163 def __getitem__(self, key): # noqa: F811
2164 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2165 return self._getitem(
2166 key,
2167 )
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/arrow_dataset.py:2150, in Dataset._getitem(self, key, decoded, **kwargs)
2148 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
2149 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 2150 formatted_output = format_table(
2151 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
2152 )
2153 return formatted_output
File ~/miniconda3/envs/rodan/lib/python3.8/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 ~/miniconda3/envs/rodan/lib/python3.8/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 ~/miniconda3/envs/rodan/lib/python3.8/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 ~/miniconda3/envs/rodan/lib/python3.8/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 ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/features.py:1647, in Features.decode_example(self, example, token_per_repo_id)
1634 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1635 """Decode example with custom feature decoding.
1636
1637 Args:
(...)
1644 :obj:`dict[str, Any]`
1645 """
-> 1647 return {
1648 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1649 if self._column_requires_decoding[column_name]
1650 else value
1651 for column_name, (feature, value) in zip_dict(
1652 {key: value for key, value in self.items() if key in example}, example
1653 )
1654 }
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/features.py:1648, in <dictcomp>(.0)
1634 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1635 """Decode example with custom feature decoding.
1636
1637 Args:
(...)
1644 :obj:`dict[str, Any]`
1645 """
1647 return {
-> 1648 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1649 if self._column_requires_decoding[column_name]
1650 else value
1651 for column_name, (feature, value) in zip_dict(
1652 {key: value for key, value in self.items() if key in example}, example
1653 )
1654 }
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/features.py:1260, in decode_nested_example(schema, obj, token_per_repo_id)
1257 # Object with special decoding:
1258 elif isinstance(schema, (Audio, Image)):
1259 # we pass the token to read and decode files from private repositories in streaming mode
-> 1260 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
1261 return obj
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/audio.py:156, in Audio.decode_example(self, value, token_per_repo_id)
154 array, sampling_rate = self._decode_non_mp3_file_like(file)
155 else:
--> 156 array, sampling_rate = self._decode_non_mp3_path_like(path, token_per_repo_id=token_per_repo_id)
157 return {"path": path, "array": array, "sampling_rate": sampling_rate}
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/audio.py:257, in Audio._decode_non_mp3_path_like(self, path, format, token_per_repo_id)
254 use_auth_token = None
256 with xopen(path, "rb", use_auth_token=use_auth_token) as f:
--> 257 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)
258 return array, sampling_rate
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/librosa/util/decorators.py:88, in deprecate_positional_args.<locals>._inner_deprecate_positional_args.<locals>.inner_f(*args, **kwargs)
86 extra_args = len(args) - len(all_args)
87 if extra_args <= 0:
---> 88 return f(*args, **kwargs)
90 # extra_args > 0
91 args_msg = [
92 "{}={}".format(name, arg)
93 for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:])
94 ]
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/librosa/core/audio.py:164, in load(path, sr, mono, offset, duration, dtype, res_type)
161 else:
162 # Otherwise try soundfile first, and then fall back if necessary
163 try:
--> 164 y, sr_native = __soundfile_load(path, offset, duration, dtype)
166 except RuntimeError as exc:
167 # If soundfile failed, try audioread instead
168 if isinstance(path, (str, pathlib.PurePath)):
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/librosa/core/audio.py:195, in __soundfile_load(path, offset, duration, dtype)
192 context = path
193 else:
194 # Otherwise, create the soundfile object
--> 195 context = sf.SoundFile(path)
197 with context as sf_desc:
198 sr_native = sf_desc.samplerate
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/soundfile.py:629, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)
626 self._mode = mode
627 self._info = _create_info_struct(file, mode, samplerate, channels,
628 format, subtype, endian)
--> 629 self._file = self._open(file, mode_int, closefd)
630 if set(mode).issuperset('r+') and self.seekable():
631 # Move write position to 0 (like in Python file objects)
632 self.seek(0)
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/soundfile.py:1179, in SoundFile._open(self, file, mode_int, closefd)
1177 file_ptr = _snd.sf_open_fd(file, mode_int, self._info, closefd)
1178 elif _has_virtual_io_attrs(file, mode_int):
-> 1179 file_ptr = _snd.sf_open_virtual(self._init_virtual_io(file),
1180 mode_int, self._info, _ffi.NULL)
1181 else:
1182 raise TypeError("Invalid file: {0!r}".format(self.name))
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/soundfile.py:1197, in SoundFile._init_virtual_io(self, file)
1194 def _init_virtual_io(self, file):
1195 """Initialize callback functions for sf_open_virtual()."""
1196 @_ffi.callback("sf_vio_get_filelen")
-> 1197 def vio_get_filelen(user_data):
1198 curr = file.tell()
1199 file.seek(0, SEEK_END)
MemoryError: Cannot allocate write+execute memory for ffi.callback(). You might be running on a system that prevents this. For more information, see https://cffi.readthedocs.io/en/latest/using.html#callbacks
```
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/4964 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4964/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4964/comments | https://api.github.com/repos/huggingface/datasets/issues/4964/events | https://github.com/huggingface/datasets/issues/4964 | 1,368,617,322 | I_kwDODunzps5Rk3Fq | 4,964 | Column of arrays (2D+) are using unreasonably high memory | {
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"note i have tried the same code with `datasets` version 2.4.0, the outcome is the very same as described above.",
"Seems related to issues #4623 and #4802 so it would appear this issue has been around for a few months.",
"Hi ! `Dataset.from_dict` keeps the data in memory. You can write on disk and reload them ... | 2022-09-10T13:07:22Z | 2022-09-22T18:29:22Z | null | CONTRIBUTOR | null | null | null | null | ## Describe the bug
When trying to store `Array2D, Array3D, etc` as column values in a dataset, accessing that column (or creating depending on how you create it, see code below) will cause more than 10 fold of memory usage.
## Steps to reproduce the bug
```python
from datasets import Dataset, Features, Array2D, Array3D
import numpy as np
column_name = "a"
array_shape = (64, 64, 3)
data = np.random.random((10000,) + array_shape)
dataset = Dataset.from_dict({column_name: data}, features=Features({column_name: Array3D(shape=array_shape, dtype="float64")}))
```
the code above will use about 10Gb of RAM while constructing the `dataset` object.
The code below will use roughly the same amount of memory (and time) when trying to actually access the data itself of that column.
```python
from datasets import Dataset
import numpy as np
column_name = "a"
array_shape = (64, 64, 3)
data = np.random.random((10000,) + array_shape)
dataset = Dataset.from_dict({column_name: data})
dataset[column_name]
```
## Expected results
Some memory overhead, but not like as it is now and certainly not an overhead of such runtime that is currently happening.
## Actual results
Enormous memory- and runtime overhead.
## Environment info
- `datasets` version: 2.3.2
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4963 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4963/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4963/comments | https://api.github.com/repos/huggingface/datasets/issues/4963/events | https://github.com/huggingface/datasets/issues/4963 | 1,368,201,188 | I_kwDODunzps5RjRfk | 4,963 | Dataset without script does not support regular JSON data file | {
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"Hi @julien-c,\r\n\r\nOut of the box, we only support JSON lines (NDJSON) data files, but your data file is a regular JSON file. The reason is we use `pyarrow.json.read_json` and this only supports line-delimited JSON. "
] | 2022-09-09T18:45:33Z | 2022-09-20T15:40:07Z | 2022-09-20T15:40:07Z | MEMBER | null | null | null | null | ### Link
https://huggingface.co/datasets/julien-c/label-studio-my-dogs
### Description
<img width="1115" alt="image" src="https://user-images.githubusercontent.com/326577/189422048-7e9c390f-bea7-4521-a232-43f049ccbd1f.png">
### Owner
Yes | {
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https://api.github.com/repos/huggingface/datasets/issues/4961 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4961/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4961/comments | https://api.github.com/repos/huggingface/datasets/issues/4961/events | https://github.com/huggingface/datasets/issues/4961 | 1,368,124,033 | I_kwDODunzps5Ri-qB | 4,961 | fsspec 2022.8.2 breaks xopen in streaming mode | {
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"loading `fsspec==2022.7.1` fixes this issue, setup.py would need to be changed to prevent users from using the latest version of fsspec.",
"Opened [PR](https://github.com/huggingface/datasets/pull/4962) to address this.",
"Hi @DCNemesis, thanks for reporting.\r\n\r\nThat was a temporary issue in `fsspec` relea... | 2022-09-09T17:26:55Z | 2022-09-12T17:45:50Z | 2022-09-12T14:32:05Z | NONE | null | null | null | null | ## Describe the bug
When fsspec 2022.8.2 is installed in your environment, xopen will prematurely close files, making streaming mode inoperable.
## Steps to reproduce the bug
```python
import datasets
data = datasets.load_dataset('MLCommons/ml_spoken_words', 'id_wav', split='train', streaming=True)
```
## Expected results
Dataset should load as iterator.
## Actual results
```
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) 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)
1737 # Return iterable dataset in case of streaming
1738 if streaming:
-> 1739 return builder_instance.as_streaming_dataset(split=split)
1740
1741 # Some datasets are already processed on the HF google storage
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
1023 )
1024 self._check_manual_download(dl_manager)
-> 1025 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
1026 # By default, return all splits
1027 if split is None:
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _split_generators(self, dl_manager)
182 name=datasets.Split.TRAIN,
183 gen_kwargs={
--> 184 "audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages],
185 "local_audio_archives_paths": [download_extract_audio(split="train", lang=lang) for lang in
186 self.config.languages] if not dl_manager.is_streaming else None,
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in <listcomp>(.0)
182 name=datasets.Split.TRAIN,
183 gen_kwargs={
--> 184 "audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages],
185 "local_audio_archives_paths": [download_extract_audio(split="train", lang=lang) for lang in
186 self.config.languages] if not dl_manager.is_streaming else None,
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _download_audio_archives(dl_manager, lang, format, split)
267 # for streaming case
268 def _download_audio_archives(dl_manager, lang, format, split):
--> 269 archives_paths = _download_audio_archives_paths(dl_manager, lang, format, split)
270 return [dl_manager.iter_archive(archive_path) for archive_path in archives_paths]
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _download_audio_archives_paths(dl_manager, lang, format, split)
251 n_files_path = dl_manager.download(n_files_url)
252
--> 253 with open(n_files_path, "r", encoding="utf-8") as file:
254 n_files = int(file.read().strip()) # the file contains a number of archives
255
ValueError: I/O operation on closed file.
```
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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https://api.github.com/repos/huggingface/datasets/issues/4960 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4960/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4960/comments | https://api.github.com/repos/huggingface/datasets/issues/4960/events | https://github.com/huggingface/datasets/issues/4960 | 1,368,035,159 | I_kwDODunzps5Rio9X | 4,960 | BioASQ AttributeError: 'BuilderConfig' object has no attribute 'schema' | {
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"id": 2067388877,
"name": "dataset bug",
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"Following worked:\r\n\r\n```\r\ndata_dir = \"/Users/dlituiev/repos/datasets/bioasq/\"\r\nbioasq_task_b = load_dataset(\"aps/bioasq_task_b\", data_dir=data_dir, name=\"bioasq_9b_source\")\r\n```\r\n\r\nWould maintainers be open to one of the following:\r\n- automating this with a latest default config (e.g. `bioas... | 2022-09-09T16:06:43Z | 2022-09-13T08:51:03Z | null | NONE | null | null | null | null | ## Describe the bug
I am trying to load a dataset from drive and running into an error.
## Steps to reproduce the bug
```python
data_dir = "/Users/dlituiev/repos/datasets/bioasq/BioASQ-training9b"
bioasq_task_b = load_dataset("aps/bioasq_task_b", data_dir=data_dir)
```
## Actual results
`AttributeError: 'BuilderConfig' object has no attribute 'schema'`
<details>
```
Using custom data configuration default-a1ca3e05be5abf2f
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Input In [8], in <cell line: 2>()
1 data_dir = "/Users/dlituiev/repos/datasets/bioasq/BioASQ-training9b"
----> 2 bioasq_task_b = load_dataset("aps/bioasq_task_b", data_dir=data_dir)
File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/load.py:1723, 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)
1720 ignore_verifications = ignore_verifications or save_infos
1722 # Create a dataset builder
-> 1723 builder_instance = load_dataset_builder(
1724 path=path,
1725 name=name,
1726 data_dir=data_dir,
1727 data_files=data_files,
1728 cache_dir=cache_dir,
1729 features=features,
1730 download_config=download_config,
1731 download_mode=download_mode,
1732 revision=revision,
1733 use_auth_token=use_auth_token,
1734 **config_kwargs,
1735 )
1737 # Return iterable dataset in case of streaming
1738 if streaming:
File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/load.py:1526, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1523 raise ValueError(error_msg)
1525 # Instantiate the dataset builder
-> 1526 builder_instance: DatasetBuilder = builder_cls(
1527 cache_dir=cache_dir,
1528 config_name=config_name,
1529 data_dir=data_dir,
1530 data_files=data_files,
1531 hash=hash,
1532 features=features,
1533 use_auth_token=use_auth_token,
1534 **builder_kwargs,
1535 **config_kwargs,
1536 )
1538 return builder_instance
File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/builder.py:1154, in GeneratorBasedBuilder.__init__(self, writer_batch_size, *args, **kwargs)
1153 def __init__(self, *args, writer_batch_size=None, **kwargs):
-> 1154 super().__init__(*args, **kwargs)
1155 # Batch size used by the ArrowWriter
1156 # It defines the number of samples that are kept in memory before writing them
1157 # and also the length of the arrow chunks
1158 # None means that the ArrowWriter will use its default value
1159 self._writer_batch_size = writer_batch_size or self.DEFAULT_WRITER_BATCH_SIZE
File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/builder.py:307, in DatasetBuilder.__init__(self, cache_dir, config_name, hash, base_path, info, features, use_auth_token, repo_id, data_files, data_dir, name, **config_kwargs)
305 if info is None:
306 info = self.get_exported_dataset_info()
--> 307 info.update(self._info())
308 info.builder_name = self.name
309 info.config_name = self.config.name
File ~/.cache/huggingface/modules/datasets_modules/datasets/aps--bioasq_task_b/3d54b1213f7e8001eef755af92877f9efa44161ee83c2a70d5d649defa95759e/bioasq_task_b.py:477, in BioasqTaskBDataset._info(self)
474 def _info(self):
475
476 # BioASQ Task B source schema
--> 477 if self.config.schema == "source":
478 features = datasets.Features(
479 {
480 "id": datasets.Value("string"),
(...)
504 }
505 )
506 # simplified schema for QA tasks
AttributeError: 'BuilderConfig' object has no attribute 'schema'
```
</details>
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.10.4
- PyArrow version: 9.0.0
- Pandas version: 1.4.3 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4958 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4958/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4958/comments | https://api.github.com/repos/huggingface/datasets/issues/4958/events | https://github.com/huggingface/datasets/issues/4958 | 1,367,695,376 | I_kwDODunzps5RhWAQ | 4,958 | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.4.0/datasets/jsonl/jsonl.py | {
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"I have solved this problem... The extension of the file should be `.json` not `.jsonl`"
] | 2022-09-09T11:29:55Z | 2022-09-09T11:38:44Z | 2022-09-09T11:38:44Z | NONE | null | null | null | null | Hi,
When I use load_dataset from local jsonl files, below error happens, and I type the link into the browser prompting me `404: Not Found`. I download the other `.py` files using the same method and it works. It seems that the server is missing the appropriate file, or it is a problem with the code version.
```
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.3.0/datasets/jsonl/jsonl.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.3.0/datasets/jsonl/jsonl.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x2b08342004c0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))")))
```
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https://api.github.com/repos/huggingface/datasets/issues/4955 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4955/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4955/comments | https://api.github.com/repos/huggingface/datasets/issues/4955/events | https://github.com/huggingface/datasets/issues/4955 | 1,366,382,314 | I_kwDODunzps5RcVbq | 4,955 | Raise a more precise error when the URL is unreachable in streaming mode | {
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] | open | false | null | [] | null | [] | 2022-09-08T13:52:37Z | 2022-09-08T13:53:36Z | null | COLLABORATOR | null | null | null | null | See for example:
- https://github.com/huggingface/datasets/issues/3191
- https://github.com/huggingface/datasets/issues/3186
It would help provide clearer information on the Hub and help the dataset maintainer solve the issue by themselves quicker. Currently:
- https://huggingface.co/datasets/compguesswhat
<img width="1029" alt="Capture d’écran 2022-09-08 à 15 51 37" src="https://user-images.githubusercontent.com/1676121/189139946-6deffb91-f21b-4281-8825-a98026c69740.png">
- https://huggingface.co/datasets/nli_tr
<img width="1032" alt="Capture d’écran 2022-09-08 à 15 51 44" src="https://user-images.githubusercontent.com/1676121/189139963-d26490ed-ad23-48ea-9cfc-1ab9c4d08d0c.png">
cc @albertvillanova | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4953 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4953/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4953/comments | https://api.github.com/repos/huggingface/datasets/issues/4953/events | https://github.com/huggingface/datasets/issues/4953 | 1,366,356,514 | I_kwDODunzps5RcPIi | 4,953 | CI test of TensorFlow is failing | {
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] | closed | false | null | [] | null | [] | 2022-09-08T13:39:29Z | 2022-09-08T15:14:45Z | 2022-09-08T15:14:45Z | MEMBER | null | null | null | null | ## Describe the bug
The following CI test fails: https://github.com/huggingface/datasets/runs/8246722693?check_suite_focus=true
```
FAILED tests/test_py_utils.py::TempSeedTest::test_tensorflow - AssertionError:
```
Details:
```
_________________________ TempSeedTest.test_tensorflow _________________________
[gw0] linux -- Python 3.7.13 /opt/hostedtoolcache/Python/3.7.13/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
def gen_random_output():
model = layers.Dense(2)
x = tf.random.uniform((1, 3))
return model(x).numpy()
with temp_seed(42, set_tensorflow=True):
out1 = gen_random_output()
with temp_seed(42, set_tensorflow=True):
out2 = gen_random_output()
out3 = gen_random_output()
> np.testing.assert_equal(out1, out2)
E AssertionError:
E Arrays are not equal
E
E Mismatched elements: 2 / 2 (100%)
E Max absolute difference: 0.84619296
E Max relative difference: 16.083529
E x: array([[-0.793581, 0.333286]], dtype=float32)
E y: array([[0.052612, 0.539708]], dtype=float32)
tests/test_py_utils.py:149: AssertionError
```
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https://api.github.com/repos/huggingface/datasets/issues/4945 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4945/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4945/comments | https://api.github.com/repos/huggingface/datasets/issues/4945/events | https://github.com/huggingface/datasets/issues/4945 | 1,364,691,096 | I_kwDODunzps5RV4iY | 4,945 | Push to hub can push splits that do not respect the regex | {
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] | closed | false | null | [] | null | [] | 2022-09-07T13:45:17Z | 2022-09-13T10:16:35Z | 2022-09-13T10:16:35Z | MEMBER | null | null | null | null | ## Describe the bug
The `push_to_hub` method can push splits that do not respect the regex check that is used for downloads. Therefore, splits may be pushed but never re-used, which can be painful if the split was done after runtime preprocessing.
## Steps to reproduce the bug
```python
>>> from datasets import Dataset, DatasetDict, load_dataset
>>> d = Dataset.from_dict({'x': [1,2,3], 'y': [1,2,3]})
>>> di = DatasetDict()
>>> di['identifier-with-column'] = d
>>> di.push_to_hub('open-source-metrics/test')
Pushing split identifier-with-column to the Hub.
Pushing dataset shards to the dataset hub: 100%|██████████| 1/1 [00:04<00:00, 4.40s/it]
```
Loading it afterwards:
```python
>>> load_dataset('open-source-metrics/test')
Downloading: 100%|██████████| 610/610 [00:00<00:00, 432kB/s]
Using custom data configuration open-source-metrics--test-28b63ec7cde80488
Downloading and preparing dataset None/None (download: 950 bytes, generated: 48 bytes, post-processed: Unknown size, total: 998 bytes) to /home/lysandre/.cache/huggingface/datasets/open-source-metrics___parquet/open-source-metrics--test-28b63ec7cde80488/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...
Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]
Downloading data: 100%|██████████| 950/950 [00:00<00:00, 1.01MB/s]
Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.48s/it]
Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 2291.97it/s]
Traceback (most recent call last):
File "/home/lysandre/.pyenv/versions/3.10.6/lib/python3.10/code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 1, in <module>
File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/load.py", line 1746, in load_dataset
builder_instance.download_and_prepare(
File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/builder.py", line 771, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 48, in _split_generators
splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={"files": files}))
File "<string>", line 5, in __init__
File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/splits.py", line 599, in __post_init__
NamedSplit(self.name) # check that it's a valid split name
File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/splits.py", line 346, in __init__
raise ValueError(f"Split name should match '{_split_re}' but got '{split_name}'.")
ValueError: Split name should match '^\w+(\.\w+)*$' but got 'identifier-with-column'.
```
## Expected results
I would expect `push_to_hub` to stop me in my tracks if trying to upload a split that will not be working afterwards.
## Actual results
See above
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.15.64-1-lts-x86_64-with-glibc2.36
- Python version: 3.10.6
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
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https://api.github.com/repos/huggingface/datasets/issues/4944 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4944/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4944/comments | https://api.github.com/repos/huggingface/datasets/issues/4944/events | https://github.com/huggingface/datasets/issues/4944 | 1,364,313,569 | I_kwDODunzps5RUcXh | 4,944 | larger dataset, larger GPU memory in the training phase? Is that correct? | {
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"does the trainer save it in GPU? sooo curious... how to fix it",
"It's my bad. didn't limit the input length"
] | 2022-09-07T08:46:30Z | 2022-09-07T12:34:58Z | 2022-09-07T12:34:58Z | NONE | null | null | null | null | from datasets import set_caching_enabled
set_caching_enabled(False)
for ds_name in ["squad","newsqa","nqopen","narrativeqa"]:
train_ds = load_from_disk("../../../dall/downstream/processedproqa/{}-train.hf".format(ds_name))
break
train_ds = concatenate_datasets([train_ds,train_ds,train_ds,train_ds]) #operation 1
trainer = QuestionAnsweringTrainer( #huggingface trainer
model=model,
args=training_args,
train_dataset=train_ds,
eval_dataset= None,
eval_examples=None,
answer_column_name=answer_column,
dataset_name="squad",
tokenizer=tokenizer,
data_collator=data_collator,
compute_metrics=compute_metrics if training_args.predict_with_generate else None,
)
with operation 1, the GPU memory increases from 16G to 23G | {
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https://api.github.com/repos/huggingface/datasets/issues/4942 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4942/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4942/comments | https://api.github.com/repos/huggingface/datasets/issues/4942/events | https://github.com/huggingface/datasets/issues/4942 | 1,363,869,421 | I_kwDODunzps5RSv7t | 4,942 | Trec Dataset has incorrect labels | {
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"Thanks for reporting, @wmpauli. \r\n\r\nIndeed we recently fixed this issue:\r\n- #4801 \r\n\r\nThe fix will be accessible after our next library release. In the meantime, you can have it by passing `revision=\"main\"` to `load_dataset`."
] | 2022-09-06T22:13:40Z | 2022-09-08T11:12:03Z | 2022-09-08T11:12:03Z | NONE | null | null | null | null | ## Describe the bug
Both coarse and fine labels seem to be out of line.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = "trec"
raw_datasets = load_dataset(dataset)
df = pd.DataFrame(raw_datasets["test"])
df.head()
```
## Expected results
text (string) | coarse_label (class label) | fine_label (class label)
-- | -- | --
How far is it from Denver to Aspen ? | 5 (NUM) | 40 (NUM:dist)
What county is Modesto , California in ? | 4 (LOC) | 32 (LOC:city)
Who was Galileo ? | 3 (HUM) | 31 (HUM:desc)
What is an atom ? | 2 (DESC) | 24 (DESC:def)
When did Hawaii become a state ? | 5 (NUM) | 39 (NUM:date)
## Actual results
index | label-coarse |label-fine | text
-- |-- | -- | --
0 | 4 | 40 | How far is it from Denver to Aspen ?
1 | 5 | 21 | What county is Modesto , California in ?
2 | 3 | 12 | Who was Galileo ?
3 | 0 | 7 | What is an atom ?
4 | 4 | 8 | When did Hawaii become a state ?
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.4.0-1086-azure-x86_64-with-glibc2.27
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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https://api.github.com/repos/huggingface/datasets/issues/4936 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4936/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4936/comments | https://api.github.com/repos/huggingface/datasets/issues/4936/events | https://github.com/huggingface/datasets/issues/4936 | 1,363,274,907 | I_kwDODunzps5RQeyb | 4,936 | vivos (Vietnamese speech corpus) dataset not accessible | {
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"If you need an example of a small audio datasets, I just created few hours ago a speech dataset with only 300MB of compressed audio files https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia. It works also with streaming (@albertvillanova helped me adding this functionality) :-)",
"@cahya-wirawan om... | 2022-09-06T13:17:55Z | 2022-09-21T06:06:02Z | 2022-09-12T07:14:20Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
VIVOS data is not accessible anymore, neither of these links work (at least from France):
* https://ailab.hcmus.edu.vn/assets/vivos.tar.gz (data)
* https://ailab.hcmus.edu.vn/vivos (dataset page)
Therefore `load_dataset` doesn't work.
## Steps to reproduce the bug
```python
ds = load_dataset("vivos")
```
## Expected results
dataset loaded
## Actual results
```
ConnectionError: Couldn't reach https://ailab.hcmus.edu.vn/assets/vivos.tar.gz (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='ailab.hcmus.edu.vn', port=443): Max retries exceeded with url: /assets/vivos.tar.gz (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f9d8a27d190>: Failed to establish a new connection: [Errno -5] No address associated with hostname'))")))
```
Will try to contact the authors, as we wanted to use Vivos as an example in documentation on how to create scripts for audio datasets (https://github.com/huggingface/datasets/pull/4872), because it's small and straightforward and uses tar archives. | {
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"The dataset maintainers (https://huggingface.co/datasets/ubuntu_dialogs_corpus) decided to forbid the dataset from being downloaded automatically (https://huggingface.co/docs/datasets/v2.4.0/en/loading#manual-download), and the dataset viewer respects this.\r\nWe will try to improve the error display though. Thank... | 2022-09-06T12:41:50Z | 2022-09-06T12:51:25Z | 2022-09-06T12:51:25Z | NONE | null | null | null | null | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | {
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