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https://api.github.com/repos/huggingface/datasets/issues/7051 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7051/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7051/comments | https://api.github.com/repos/huggingface/datasets/issues/7051/events | https://github.com/huggingface/datasets/issues/7051 | 2,409,353,929 | I_kwDODunzps6Pm9LJ | 7,051 | How to set_epoch with interleave_datasets? | {
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} | [] | open | false | null | [] | null | 0 | "2024-07-15T18:24:52" | "2024-07-15T18:24:52" | null | NONE | null | Let's say I have dataset A which has 100k examples, and dataset B which has 100m examples.
I want to train on an interleaved dataset of A+B, with stopping_strategy='all_exhausted' so dataset B doesn't repeat any examples. But every time A is exhausted I want it to be reshuffled (eg. calling set_epoch)
Of course I want to interleave as IterableDatasets / streaming mode so B doesn't have to get tokenized completely at the start.
How could I achieve this? I was thinking something like, if I wrap dataset A in some new IterableDataset with from_generator() and manually call set_epoch before interleaving it? But I'm not sure how to keep the number of shards in that dataset...
Something like
```
dataset_a = load_dataset(...)
dataset_b = load_dataset(...)
def epoch_shuffled_dataset(ds):
# How to make this maintain the number of shards in ds??
for epoch in itertools.count():
ds.set_epoch(epoch)
yield from iter(ds)
shuffled_dataset_a = IterableDataset.from_generator(epoch_shuffled_dataset, gen_kwargs={'ds': dataset_a})
interleaved = interleave_datasets([shuffled_dataset_a, dataset_b], probs, stopping_strategy='all_exhausted')
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7050 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7050/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7050/comments | https://api.github.com/repos/huggingface/datasets/issues/7050/events | https://github.com/huggingface/datasets/pull/7050 | 2,409,048,733 | PR_kwDODunzps51Z1Yp | 7,050 | add checkpoint and resume title in docs | {
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https://api.github.com/repos/huggingface/datasets/issues/7049 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7049/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7049/comments | https://api.github.com/repos/huggingface/datasets/issues/7049/events | https://github.com/huggingface/datasets/issues/7049 | 2,408,514,366 | I_kwDODunzps6PjwM- | 7,049 | Save nparray as list | {
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} | [] | open | false | null | [] | null | 1 | "2024-07-15T11:36:11" | "2024-07-15T12:20:52" | null | NONE | null | ### Describe the bug
When I use the `map` function to convert images into features, datasets saves nparray as a list. Some people use the `set_format` function to convert the column back, but doesn't this lose precision?
### Steps to reproduce the bug
the map function
```python
def convert_image_to_features(inst, processor, image_dir):
image_file = inst["image_url"]
file = image_file.split("/")[-1]
image_path = os.path.join(image_dir, file)
image = Image.open(image_path)
image = image.convert("RGBA")
inst["pixel_values"] = processor(images=image, return_tensors="np")["pixel_values"]
return inst
```
main function
```python
map_fun = partial(
convert_image_to_features, processor=processor, image_dir=image_dir
)
ds = ds.map(map_fun, batched=False, num_proc=20)
print(type(ds[0]["pixel_values"])
```
### Expected behavior
(type < list>)
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-4.19.91-009.ali4000.alios7.x86_64-x86_64-with-glibc2.35
- Python version: 3.11.5
- `huggingface_hub` version: 0.23.4
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7048 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7048/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7048/comments | https://api.github.com/repos/huggingface/datasets/issues/7048/events | https://github.com/huggingface/datasets/issues/7048 | 2,408,487,547 | I_kwDODunzps6Pjpp7 | 7,048 | ImportError: numpy.core.multiarray when using `filter` | {
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} | [] | open | false | null | [] | null | 0 | "2024-07-15T11:21:04" | "2024-07-15T11:21:04" | null | NONE | null | ### Describe the bug
I can't apply the filter method on my dataset.
### Steps to reproduce the bug
The following snippet generates a bug:
```python
from datasets import load_dataset
ami = load_dataset('kamilakesbi/ami', 'ihm')
ami['train'].filter(
lambda example: example["file_name"] == 'EN2001a'
)
```
I get the following error:
`ImportError: numpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it).`
### Expected behavior
It should work properly!
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7047 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7047/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7047/comments | https://api.github.com/repos/huggingface/datasets/issues/7047/events | https://github.com/huggingface/datasets/issues/7047 | 2,406,495,084 | I_kwDODunzps6PcDNs | 7,047 | Save Dataset as Sharded Parquet | {
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] | open | false | null | [] | null | 1 | "2024-07-12T23:47:51" | "2024-07-13T00:55:24" | null | NONE | null | ### Feature request
`to_parquet` currently saves the dataset as one massive, monolithic parquet file, rather than as several small parquet files. It should shard large datasets automatically.
### Motivation
This default behavior makes me very sad because a program I ran for 6 hours saved its results using `to_parquet`, putting the entire billion+ row dataset into a 171 GB *single shard parquet file* which pyarrow, apache spark, etc. all cannot work with without completely exhausting the memory of my system. I was previously able to work with larger-than-memory parquet files, but not this one. I *assume* the reason why this is happening is because it is a single shard. Making sharding the default behavior puts datasets in parity with other frameworks, such as spark, which automatically shard when a large dataset is saved as parquet.
### Your contribution
I could change the logic here https://github.com/huggingface/datasets/blob/bf6f41e94d9b2f1c620cf937a2e85e5754a8b960/src/datasets/io/parquet.py#L109-L158
to use `pyarrow.dataset.write_dataset`, which seems to support sharding, or periodically open new files. We would only shard if the user passed in a path rather than file handle. | {
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https://api.github.com/repos/huggingface/datasets/issues/7046 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7046/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7046/comments | https://api.github.com/repos/huggingface/datasets/issues/7046/events | https://github.com/huggingface/datasets/pull/7046 | 2,405,485,582 | PR_kwDODunzps51N05n | 7,046 | Support librosa and numpy 2.0 for Python 3.10 | {
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} | [] | closed | false | null | [] | null | 2 | "2024-07-12T12:42:47" | "2024-07-12T13:04:40" | "2024-07-12T12:58:17" | MEMBER | null | Support librosa and numpy 2.0 for Python 3.10 by installing soxr 0.4.0b1 pre-release:
- https://github.com/dofuuz/python-soxr/releases/tag/v0.4.0b1
- https://github.com/dofuuz/python-soxr/issues/28 | {
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} | [] | closed | false | null | [] | null | 2 | "2024-07-12T12:20:23" | "2024-07-12T12:38:53" | "2024-07-12T12:33:00" | MEMBER | null | Fix tensorflow min version depending on Python version.
Related to:
- #6991 | {
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https://api.github.com/repos/huggingface/datasets/issues/7044 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7044/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7044/comments | https://api.github.com/repos/huggingface/datasets/issues/7044/events | https://github.com/huggingface/datasets/pull/7044 | 2,405,002,987 | PR_kwDODunzps51MLbh | 7,044 | Mark tests that require librosa | {
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} | [] | closed | false | null | [] | null | 2 | "2024-07-12T08:06:59" | "2024-07-12T09:06:32" | "2024-07-12T09:00:09" | MEMBER | null | Mark tests that require `librosa`.
Note that `librosa` is an optional dependency (installed with `audio` option) and we should be able to test environments without that library installed. This is the case if we want to test Numpy 2.0, which is currently incompatible with `librosa` due to its dependency on `soxr`:
- https://github.com/dofuuz/python-soxr/issues/28 | {
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https://api.github.com/repos/huggingface/datasets/issues/7043 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7043/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7043/comments | https://api.github.com/repos/huggingface/datasets/issues/7043/events | https://github.com/huggingface/datasets/pull/7043 | 2,404,951,714 | PR_kwDODunzps51MAN0 | 7,043 | Add decorator as explicit test dependency | {
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} | [] | closed | false | null | [] | null | 2 | "2024-07-12T07:35:23" | "2024-07-12T08:12:55" | "2024-07-12T08:07:10" | MEMBER | null | Add decorator as explicit test dependency.
We use `decorator` library in our CI test since PR:
- #4845
However we did not add it as an explicit test requirement, and we depended on it indirectly through other libraries' dependencies.
I discovered this while testing Numpy 2.0 and removing incompatible libraries. | {
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https://api.github.com/repos/huggingface/datasets/issues/7042 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7042/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7042/comments | https://api.github.com/repos/huggingface/datasets/issues/7042/events | https://github.com/huggingface/datasets/pull/7042 | 2,404,605,836 | PR_kwDODunzps51K8CM | 7,042 | Improved the tutorial by adding a link for loading datasets | {
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} | [] | open | false | null | [] | null | 0 | "2024-07-12T03:49:54" | "2024-07-12T03:49:54" | null | NONE | null | Improved the tutorial by letting readers know about loading datasets with common files and including a link. I left the local files section alone because the methods were already listed with code snippets. | {
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https://api.github.com/repos/huggingface/datasets/issues/7041 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7041/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7041/comments | https://api.github.com/repos/huggingface/datasets/issues/7041/events | https://github.com/huggingface/datasets/issues/7041 | 2,404,576,038 | I_kwDODunzps6PUusm | 7,041 | `sort` after `filter` unreasonably slow | {
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} | [] | open | false | null | [] | null | 0 | "2024-07-12T03:29:27" | "2024-07-12T03:29:27" | null | NONE | null | ### Describe the bug
as the tittle says ...
### Steps to reproduce the bug
`sort` seems to be normal.
```python
from datasets import Dataset
import random
nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]
ds = Dataset.from_list(nums)
print("start sort")
ds = ds.sort("k")
print("finish sort")
```
but `sort` after `filter` is extremely slow.
```python
from datasets import Dataset
import random
nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]
ds = Dataset.from_list(nums)
ds = ds.filter(lambda x:x > 100, input_columns="k")
print("start sort")
ds = ds.sort("k")
print("finish sort")
```
### Expected behavior
Is this a bug, or is it a misuse of the `sort` function?
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-3.10.0-1127.19.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2023.10.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7040 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7040/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7040/comments | https://api.github.com/repos/huggingface/datasets/issues/7040/events | https://github.com/huggingface/datasets/issues/7040 | 2,402,918,335 | I_kwDODunzps6POZ-_ | 7,040 | load `streaming=True` dataset with downloaded cache | {
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} | [] | open | false | null | [] | null | 2 | "2024-07-11T11:14:13" | "2024-07-11T14:11:56" | null | NONE | null | ### Describe the bug
We build a dataset which contains several hdf5 files and write a script using `h5py` to generate the dataset. The hdf5 files are large and the processed dataset cache takes more disk space. So we hope to try streaming iterable dataset. Unfortunately, `h5py` can't convert a remote URL into a hdf5 file descriptor. So we use `fsspec` as an interface like below:
```python
def _generate_examples(self, filepath, split):
for file in filepath:
with fsspec.open(file, "rb") as fs:
with h5py.File(fs, "r") as fp:
# for event_id in sorted(list(fp.keys())):
event_ids = list(fp.keys())
......
```
### Steps to reproduce the bug
The `fsspec` works, but it takes 10+ min to print the first 10 examples, which is even longer than the downloading time. I'm not sure if it just caches the whole hdf5 file and generates the examples.
### Expected behavior
So does the following make sense so far?
1. download the files
```python
dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True)
```
2. load the iterable dataset faster (using the raw file cache at path `.cache/huggingface/datasets/downloads`)
```python
dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True, streaming=true)
```
I made some tests, but the code above can't get the expected result. I'm not sure if this is supported. I also find the issue #6327 . It seemed similar to mine, but I couldn't find a solution.
### Environment info
- `datasets` = 2.18.0
- `h5py` = 3.10.0
- `fsspec` = 2023.10.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7039 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7039/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7039/comments | https://api.github.com/repos/huggingface/datasets/issues/7039/events | https://github.com/huggingface/datasets/pull/7039 | 2,402,403,390 | PR_kwDODunzps51DgCY | 7,039 | Fix export to JSON when dataset larger than batch size | {
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} | [] | open | false | null | [] | null | 2 | "2024-07-11T06:52:22" | "2024-07-11T07:27:58" | null | MEMBER | null | Fix export to JSON (`files=False`) when dataset larger than batch size.
Fix #7037. | {
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https://api.github.com/repos/huggingface/datasets/issues/7038 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7038/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7038/comments | https://api.github.com/repos/huggingface/datasets/issues/7038/events | https://github.com/huggingface/datasets/issues/7038 | 2,402,081,227 | I_kwDODunzps6PLNnL | 7,038 | Yes, can definitely elaborate: | {
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} | [] | closed | false | null | [] | null | 1 | "2024-07-11T02:22:30" | "2024-07-11T05:28:39" | "2024-07-11T05:28:39" | NONE | null | Yes, can definitely elaborate:
Say I want to use HF Trainer with an arbitrary PyTorch optimizer (`AdamW` here just as an example). Then I should intuitively extend `Trainer` like:
```python
class CustomOptimizerTrainer(Trainer):
@staticmethod
def get_optimizer_cls_and_kwargs(args: HfTrainingArguments, model=None) -> tuple[type[torch.optim.Optimizer], dict[str, Any]]:
optimizer = torch.optim.AdamW
optimizer_kwargs = {
"lr": 4e-3,
"betas": (0.9, 0.999),
"weight_decay": 0.05,
}
return optimizer, optimizer_kwargs
```
However, this won't take effect, because `Trainer.create_optimizer` hardcodes the `Trainer` class name when calling `get_optimizer_cls_and_kwargs`:
https://github.com/huggingface/transformers/blob/6c1d0b069de22d7ed8aa83f733c25045eea0585d/src/transformers/trainer.py#L1076
`CustomOptimizerTrainer.get_optimizer_cls_and_kwargs` will never be called.
So I could either:
- also override the entire `create_optimizer` and rewrite `Trainer.get_optimizer_cls_and_kwargs` to `self.get_optimizer_cls_and_kwargs` (overkill)
- or monkey-patch (not ideal):
```python
class CustomOptimizerTrainer(Trainer):
# def get_optimizer_cls_and_kwargs ...
def create_optimizer(self):
trainer_get_optimizer_fn = Trainer.get_optimizer_cls_and_kwargs
Trainer.get_optimizer_cls_and_kwargs = self.get_optimizer_cls_and_kwargs
optimizer = super().create_optimizer()
Trainer.get_optimizer_cls_and_kwargs = trainer_get_optimizer_fn
return optimizer
```
But I think the best fix is to change `Trainer.get_optimizer_cls_and_kwargs` to `self.get_optimizer_cls_and_kwargs` in the original source of `Trainer.create_optimizer`.
I also made `get_optimizer_cls_and_kwargs` an instance method instead of a static method, but that probably doesn't matter as much and can be reverted. It breaks the syntax of the tests.
Please let me know if that's clearer and if you agree! Thanks!
_Originally posted by @apoorvkh in https://github.com/huggingface/transformers/issues/31875#issuecomment-2221491647_
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https://api.github.com/repos/huggingface/datasets/issues/7037 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7037/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7037/comments | https://api.github.com/repos/huggingface/datasets/issues/7037/events | https://github.com/huggingface/datasets/issues/7037 | 2,400,192,419 | I_kwDODunzps6PEAej | 7,037 | A bug of Dataset.to_json() function | {
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] | null | 1 | "2024-07-10T09:11:22" | "2024-07-10T13:07:44" | null | NONE | null | ### Describe the bug
When using the Dataset.to_json() function, an unexpected error occurs if the parameter is set to lines=False. The stored data should be in the form of a list, but it actually turns into multiple lists, which causes an error when reading the data again.
The reason is that to_json() writes to the file in several segments based on the batch size. This is not a problem when lines=True, but it is incorrect when lines=False, because writing in several times will produce multiple lists(when len(dataset) > batch_size).
### Steps to reproduce the bug
try this code:
```python
from datasets import load_dataset
import json
train_dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")["train"]
output_path = "./harmless-base_hftojs.json"
print(len(train_dataset))
train_dataset.to_json(output_path, lines=False, force_ascii=False, indent=2)
with open(output_path, encoding="utf-8") as f:
data = json.loads(f.read())
```
it raise error: json.decoder.JSONDecodeError: Extra data: line 4003 column 1 (char 1373709)
Extra square brackets have appeared here:
<img width="265" alt="image" src="https://github.com/huggingface/datasets/assets/26499566/81492332-386d-42e8-88d1-b6d4ae3682cc">
### Expected behavior
The code runs normally.
### Environment info
datasets=2.20.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7036 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7036/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7036/comments | https://api.github.com/repos/huggingface/datasets/issues/7036/events | https://github.com/huggingface/datasets/pull/7036 | 2,400,035,672 | PR_kwDODunzps507bZk | 7,036 | Fix doc generation when NamedSplit is used as parameter default value | {
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} | [] | open | false | null | [] | null | 1 | "2024-07-10T07:58:46" | "2024-07-10T08:01:00" | null | MEMBER | null | Fix doc generation when `NamedSplit` is used as parameter default value.
Fix #7035. | {
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https://api.github.com/repos/huggingface/datasets/issues/7035 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7035/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7035/comments | https://api.github.com/repos/huggingface/datasets/issues/7035/events | https://github.com/huggingface/datasets/issues/7035 | 2,400,021,225 | I_kwDODunzps6PDWrp | 7,035 | Docs are not generated when a parameter defaults to a NamedSplit value | {
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] | null | 0 | "2024-07-10T07:51:24" | "2024-07-10T07:53:08" | null | MEMBER | null | While generating the docs, we get an error when some parameter defaults to a `NamedSplit` value, like:
```python
def call_function(split=Split.TRAIN):
...
```
The error is: ValueError: Equality not supported between split train and <class 'inspect._empty'>
See: https://github.com/huggingface/datasets/actions/runs/9869660902/job/27254359863?pr=7015
```
Building the MDX files: 97%|█████████▋| 58/60 [00:00<00:00, 91.94it/s]
Traceback (most recent call last):
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 197, in build_mdx_files
content, new_anchors, source_files, errors = resolve_autodoc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 123, in resolve_autodoc
doc = autodoc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 499, in autodoc
method_doc, check = document_object(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 395, in document_object
signature = format_signature(obj)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 126, in format_signature
if param.default != inspect._empty:
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 136, in __ne__
return not self.__eq__(other)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 379, in __eq__
raise ValueError(f"Equality not supported between split {self} and {other}")
ValueError: Equality not supported between split train and <class 'inspect._empty'>
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/datasets/datasets/.venv/bin/doc-builder", line 8, in <module>
sys.exit(main())
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/doc_builder_cli.py", line 47, in main
args.func(args)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/build.py", line 102, in build_command
build_doc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 367, in build_doc
anchors_mapping, source_files_mapping = build_mdx_files(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 230, in build_mdx_files
raise type(e)(f"There was an error when converting {file} to the MDX format.\n" + e.args[0]) from e
ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format.
Equality not supported between split train and <class 'inspect._empty'>
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7034 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7034/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7034/comments | https://api.github.com/repos/huggingface/datasets/issues/7034/events | https://github.com/huggingface/datasets/pull/7034 | 2,397,525,974 | PR_kwDODunzps50y-ya | 7,034 | chore: fix typos in docs | {
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https://api.github.com/repos/huggingface/datasets/issues/7033 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7033/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7033/comments | https://api.github.com/repos/huggingface/datasets/issues/7033/events | https://github.com/huggingface/datasets/issues/7033 | 2,397,419,768 | I_kwDODunzps6O5bj4 | 7,033 | `from_generator` does not allow to specify the split name | {
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} | [] | open | false | null | [] | null | 1 | "2024-07-09T07:47:58" | "2024-07-09T08:05:23" | null | CONTRIBUTOR | null | ### Describe the bug
I'm building train, dev, and test using `from_generator`; however, in all three cases, the logger prints `Generating train split:`
It's not possible to change the split name since it seems to be hardcoded: https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/generator/generator.py
### Steps to reproduce the bug
```
In [1]: from datasets import Dataset
In [2]: def gen():
...: yield {"pokemon": "bulbasaur", "type": "grass"}
...:
In [3]: ds = Dataset.from_generator(gen)
Generating train split: 1 examples [00:00, 133.89 examples/s]
```
### Expected behavior
It should be possible to specify any split name
### Environment info
- `datasets` version: 2.19.2
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.5
- `huggingface_hub` version: 0.23.3
- PyArrow version: 15.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2023.10.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7032 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7032/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7032/comments | https://api.github.com/repos/huggingface/datasets/issues/7032/events | https://github.com/huggingface/datasets/pull/7032 | 2,395,531,699 | PR_kwDODunzps50sJTq | 7,032 | Register `.zstd` extension for zstd-compressed files | {
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} | [] | closed | false | null | [] | null | 8 | "2024-07-08T12:39:50" | "2024-07-12T15:07:03" | "2024-07-12T15:07:03" | CONTRIBUTOR | null | For example, https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0 dataset files have `.zstd` extension which is currently ignored (only `.zst` is registered). | {
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```
error: `ruff <path>` has been removed. Use `ruff check <path>` instead.
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7030 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7030/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7030/comments | https://api.github.com/repos/huggingface/datasets/issues/7030/events | https://github.com/huggingface/datasets/issues/7030 | 2,393,411,631 | I_kwDODunzps6OqJAv | 7,030 | Add option to disable progress bar when reading a dataset ("Loading dataset from disk") | {
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Add an option in load_from_disk to disable the progress bar even if the number of files is larger than 16.
### Motivation
I am reading a lot of datasets that it creates lots of logs.
<img width="1432" alt="image" src="https://github.com/huggingface/datasets/assets/57996478/8d4bbf03-6b89-44b6-937c-932f01b4eb2a">
### Your contribution
Seems like an easy fix to make. I can create a PR if necessary. | {
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https://api.github.com/repos/huggingface/datasets/issues/7029 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7029/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7029/comments | https://api.github.com/repos/huggingface/datasets/issues/7029/events | https://github.com/huggingface/datasets/issues/7029 | 2,391,366,696 | I_kwDODunzps6OiVwo | 7,029 | load_dataset on AWS lambda throws OSError(30, 'Read-only file system') error | {
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I'm using AWS lambda to run a python application. I run the `load_dataset` function with cache_dir="/tmp" and is still throws the OSError(30, 'Read-only file system') error. Is even updated all the HF envs to point to /tmp dir but the issue still persists. I can confirm that the I can write to /tmp directory.
### Steps to reproduce the bug
```python
d = load_dataset(
path=hugging_face_link,
split=split,
token=token,
cache_dir="/tmp/hugging_face_cache",
)
```
### Expected behavior
Everything written to the file system as part of the load_datasets function should be in the /tmp directory.
### Environment info
datasets version: 2.16.1
Platform: Linux-5.10.216-225.855.amzn2.x86_64-x86_64-with-glibc2.26
Python version: 3.11.9
huggingface_hub version: 0.19.4
PyArrow version: 16.1.0
Pandas version: 2.2.2
fsspec version: 2023.10.0 | {
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Note that it only affects local datasets that already exist on disk, not datasets loaded from HF directly | {
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} | [] | open | false | null | [] | null | 0 | "2024-07-04T07:21:47" | "2024-07-04T07:21:47" | null | NONE | null | ### Describe the bug
I'm deciding to post here because I'm still not sure what the issue is, or if I am using IterableDatasets wrongly.
I'm following the guide on here https://huggingface.co/learn/cookbook/en/fine_tuning_code_llm_on_single_gpu pretty much to a tee and have verified that it works when I'm fine-tuning on the provided dataset.
However, I'm doing some data preprocessing steps (filtering out entries), when I try to swap out the dataset for mine, it fails to train. However, I eventually fixed this by simply setting `stream=False` in `load_dataset`.
Coud this be some sort of network / firewall issue I'm facing?
### Steps to reproduce the bug
I made a post with greater description about how I reproduced this problem before I found my workaround: https://discuss.huggingface.co/t/problem-with-custom-iterator-of-streaming-dataset-not-returning-anything/94551
Here is the problematic dataset snippet, which works when streaming=False (and with buffer keyword removed from shuffle)
```
commitpackft = load_dataset(
"chargoddard/commitpack-ft-instruct", split="train", streaming=True
).filter(lambda example: example["language"] == "Python")
def form_template(example):
"""Forms a template for each example following the alpaca format for CommitPack"""
example["content"] = (
"### Human: " + example["instruction"] + " " + example["input"] + " ### Assistant: " + example["output"]
)
return example
dataset = commitpackft.map(
form_template,
remove_columns=["id", "language", "license", "instruction", "input", "output"],
).shuffle(
seed=42, buffer_size=10000
) # remove everything since its all inside "content" now
validation_data = dataset.take(4000)
train_data = dataset.skip(4000)
```
The annoying part about this is that it only fails during training and I don't know when it will fail, except that it always fails during evaluation.
### Expected behavior
The expected behavior is that I should be able to get something from the iterator when called instead of getting nothing / stuck in a loop somewhere.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.31
- Python version: 3.11.7
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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https://api.github.com/repos/huggingface/datasets/issues/7023 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7023/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7023/comments | https://api.github.com/repos/huggingface/datasets/issues/7023/events | https://github.com/huggingface/datasets/pull/7023 | 2,388,090,424 | PR_kwDODunzps50TDot | 7,023 | Remove dead code for pyarrow < 15.0.0 | {
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} | [] | closed | false | null | [] | null | 2 | "2024-07-03T09:05:03" | "2024-07-03T09:24:46" | "2024-07-03T09:17:35" | MEMBER | null | Remove dead code for pyarrow < 15.0.0.
Code is dead since the merge of:
- #6892
Fix #7022. | {
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] | null | 0 | "2024-07-03T08:52:57" | "2024-07-03T09:17:36" | "2024-07-03T09:17:36" | MEMBER | null | There are code lines specific for pyarrow versions < 15.0.0.
However, we require pyarrow >= 15.0.0 since the merge of PR:
- #6892
Those code lines are now dead code and should be removed. | {
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} | [] | closed | false | null | [] | null | 2 | "2024-07-03T07:58:57" | "2024-07-03T08:47:49" | "2024-07-03T08:41:55" | MEMBER | null | Fix casting list array to fixed size list.
This bug was introduced in [datasets-2.17.0](https://github.com/huggingface/datasets/releases/tag/2.17.0) by PR: https://github.com/huggingface/datasets/pull/6283/files#diff-1cb2b66aa9311d729cfd83013dad56cf5afcda35b39dfd0bfe9c3813a049eab0R1899
- #6283
Fix #7020. | {
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] | null | 0 | "2024-07-03T07:54:49" | "2024-07-03T08:41:56" | "2024-07-03T08:41:56" | MEMBER | null | When trying to cast a list array to fixed size list, an AttributeError is raised:
> AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length'
Steps to reproduce the bug:
```python
import pyarrow as pa
from datasets.table import array_cast
arr = pa.array([[0, 1]])
array_cast(arr, pa.list_(pa.int64(), 2))
```
Stack trace:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-12-6cb90a1d8216> in <module>
3
4 arr = pa.array([[0, 1]])
----> 5 array_cast(arr, pa.list_(pa.int64(), 2))
~/huggingface/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
1802 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1803 else:
-> 1804 return func(array, *args, **kwargs)
1805
1806 return wrapper
~/huggingface/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_primitive_to_str, allow_decimal_to_str)
1920 else:
1921 array_values = array.values[
-> 1922 array.offset * pa_type.length : (array.offset + len(array)) * pa_type.length
1923 ]
1924 return pa.FixedSizeListArray.from_arrays(_c(array_values, pa_type.value_type), pa_type.list_size)
AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7019 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7019/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7019/comments | https://api.github.com/repos/huggingface/datasets/issues/7019/events | https://github.com/huggingface/datasets/pull/7019 | 2,385,793,897 | PR_kwDODunzps50LMjW | 7,019 | Allow Polars round trip by supporting pyarrow large list | {
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Fix #6834, fix #6984.
Supersede and close #4800, close #6835, close #6986. | {
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https://api.github.com/repos/huggingface/datasets/issues/7018 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7018/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7018/comments | https://api.github.com/repos/huggingface/datasets/issues/7018/events | https://github.com/huggingface/datasets/issues/7018 | 2,383,700,286 | I_kwDODunzps6OFGE- | 7,018 | `load_dataset` fails to load dataset saved by `save_to_disk` | {
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} | [] | open | false | null | [] | null | 0 | "2024-07-01T12:19:19" | "2024-07-01T12:19:19" | null | NONE | null | ### Describe the bug
This code fails to load the dataset it just saved:
```python
from datasets import load_dataset
from transformers import AutoTokenizer
MODEL = "google-bert/bert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
dataset = load_dataset("yelp_review_full")
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
tokenized_datasets.save_to_disk("dataset")
tokenized_datasets = load_dataset("dataset/") # raises
```
It raises `ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('test'): ('json', {})}`.
I believe this bug is caused by the [logic that tries to infer dataset format](https://github.com/huggingface/datasets/blob/9af8dd3de7626183a9a9ec8973cebc672d690400/src/datasets/load.py#L556). It counts the most common file extension. However, a small dataset can fit in a single `.arrow` file and have two JSON metadata files, causing the format to be inferred as JSON:
```shell
$ ls -l dataset/test
-rw-r--r-- 1 sliedes sliedes 191498784 Jul 1 13:55 data-00000-of-00001.arrow
-rw-r--r-- 1 sliedes sliedes 1730 Jul 1 13:55 dataset_info.json
-rw-r--r-- 1 sliedes sliedes 249 Jul 1 13:55 state.json
```
### Steps to reproduce the bug
Execute the code above.
### Expected behavior
The dataset is loaded successfully.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.9.3-arch1-1-x86_64-with-glibc2.39
- Python version: 3.12.4
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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] | open | false | null | [] | null | 1 | "2024-07-01T09:01:06" | "2024-07-02T13:54:48" | null | NONE | null | ### Feature request
`drop_duplicates` method for huggingface datasets (similiar in simplicity to the `pandas` one)
### Motivation
Ease of use
### Your contribution
I don't think i am good enough to help | {
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} | [] | open | false | null | [] | null | 3 | "2024-07-01T08:09:25" | "2024-07-11T08:20:16" | null | NONE | null | ## Actual
When creating a multi-split dataset using generators like
```python
datasets.DatasetDict({
"val": datasets.Dataset.from_generator(
generator=generator_val,
features=features
),
"test": datasets.Dataset.from_generator(
generator=generator_test,
features=features,
)
})
```
It displays (for both test and val)
```
Generating train split
```
## Expected
I would like to be able to improve this behavior by doing
```python
datasets.DatasetDict({
"val": datasets.Dataset.from_generator(
generator=generator_val,
features=features,
split="val"
),
"test": datasets.Dataset.from_generator(
generator=generator_test,
features=features,
split="test"
)
})
```
It would display
```
Generating val split
```
and
```
Generating test split
```
## Proposal
Current PR is adding an explicit `split` argument and replace the implicit "train" split in the following classes/function :
* Generator
* from_generator
* AbstractDatasetInputStream
* GeneratorDatasetInputStream
Please share your feedbacks | {
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Fix #7013.
Revert once the underlying issue is fixed. | {
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See: https://github.com/huggingface/datasets/actions/runs/9712659783
```
test (integration, windows-latest, deps-minimum)
The job running on runner GitHub Actions 60 has exceeded the maximum execution time of 360 minutes.
test (integration, windows-latest, deps-latest)
The job running on runner GitHub Actions 238 has exceeded the maximum execution time of 360 minutes.
```
```
____________________________ tests/test_search.py _____________________________
[gw1] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw1' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
____________________________ tests/test_search.py _____________________________
[gw2] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw2' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
```
```
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw0] node down: Not properly terminated
[gw0] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw0
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw1] node down: Not properly terminated
[gw1] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw1
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw2] node down: Not properly terminated
[gw2] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw2
``` | {
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- https://github.com/huggingface/transformers/pull/31007
was just released yesterday in transformers-4.42.0: https://github.com/huggingface/transformers/releases/tag/v4.42.0
Fix #7010. | {
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- #6876
Note that this can only be done once transformers releases the fix:
- https://github.com/huggingface/transformers/pull/31007 | {
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- #7007
Fix #7008. | {
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Also revert:
- #7007 | {
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Fix #7006.
Revert once root cause is fixed. | {
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] | null | 0 | "2024-06-28T05:03:28" | "2024-06-28T05:25:18" | "2024-06-28T05:25:18" | MEMBER | null | After ruff-0.5.0 release (https://github.com/astral-sh/ruff/releases/tag/0.5.0), our CI is broken due to E721 rule.
See: https://github.com/huggingface/datasets/actions/runs/9707641618/job/26793170961?pr=6983
> src/datasets/features/features.py:844:12: E721 Use `is` and `is not` for type comparisons, or `isinstance()` for isinstance checks | {
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https://api.github.com/repos/huggingface/datasets/issues/7005 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7005/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7005/comments | https://api.github.com/repos/huggingface/datasets/issues/7005/events | https://github.com/huggingface/datasets/issues/7005 | 2,378,424,349 | I_kwDODunzps6Nw-Ad | 7,005 | EmptyDatasetError: The directory at /metadata.jsonl doesn't contain any data files | {
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} | [] | closed | false | null | [] | null | 3 | "2024-06-27T15:08:26" | "2024-06-28T09:56:19" | "2024-06-28T09:56:19" | NONE | null | ### Describe the bug
while trying to load custom dataset from jsonl file, I get the error: "metadata.jsonl doesn't contain any data files"
### Steps to reproduce the bug
This is my [metadata_v2.jsonl](https://github.com/user-attachments/files/16016011/metadata_v2.json) file. I have this file in the folder with all images mentioned in that json(l) file.
Through below mentioned command I am trying to load_dataset so that I can upload it as mentioned here on the [official website](https://huggingface.co/docs/datasets/en/image_dataset#upload-dataset-to-the-hub).
````
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="path/to/jsonl/metadata.jsonl")
````
error:
````
EmptyDatasetError Traceback (most recent call last)
Cell In[18], line 3
1 from datasets import load_dataset
----> 3 dataset = load_dataset("imagefolder",
4 data_dir="path/to/jsonl/file/metadata.jsonl")
5 dataset[0]["objects"]
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2594, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2589 verification_mode = VerificationMode(
2590 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
2591 )
2593 # Create a dataset builder
-> 2594 builder_instance = load_dataset_builder(
2595 path=path,
2596 name=name,
2597 data_dir=data_dir,
2598 data_files=data_files,
2599 cache_dir=cache_dir,
2600 features=features,
2601 download_config=download_config,
2602 download_mode=download_mode,
2603 revision=revision,
2604 token=token,
2605 storage_options=storage_options,
2606 trust_remote_code=trust_remote_code,
2607 _require_default_config_name=name is None,
2608 **config_kwargs,
2609 )
2611 # Return iterable dataset in case of streaming
2612 if streaming:
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2266, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
2264 download_config = download_config.copy() if download_config else DownloadConfig()
2265 download_config.storage_options.update(storage_options)
-> 2266 dataset_module = dataset_module_factory(
2267 path,
2268 revision=revision,
2269 download_config=download_config,
2270 download_mode=download_mode,
2271 data_dir=data_dir,
2272 data_files=data_files,
2273 cache_dir=cache_dir,
2274 trust_remote_code=trust_remote_code,
2275 _require_default_config_name=_require_default_config_name,
2276 _require_custom_configs=bool(config_kwargs),
2277 )
2278 # Get dataset builder class from the processing script
2279 builder_kwargs = dataset_module.builder_kwargs
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1805, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
1782 # We have several ways to get a dataset builder:
1783 #
1784 # - if path is the name of a packaged dataset module
(...)
1796
1797 # Try packaged
1798 if path in _PACKAGED_DATASETS_MODULES:
1799 return PackagedDatasetModuleFactory(
1800 path,
1801 data_dir=data_dir,
1802 data_files=data_files,
1803 download_config=download_config,
1804 download_mode=download_mode,
-> 1805 ).get_module()
1806 # Try locally
1807 elif path.endswith(filename):
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1140, in PackagedDatasetModuleFactory.get_module(self)
1135 def get_module(self) -> DatasetModule:
1136 base_path = Path(self.data_dir or "").expanduser().resolve().as_posix()
1137 patterns = (
1138 sanitize_patterns(self.data_files)
1139 if self.data_files is not None
-> 1140 else get_data_patterns(base_path, download_config=self.download_config)
1141 )
1142 data_files = DataFilesDict.from_patterns(
1143 patterns,
1144 download_config=self.download_config,
1145 base_path=base_path,
1146 )
1147 supports_metadata = self.name in _MODULE_SUPPORTS_METADATA
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/data_files.py:503, in get_data_patterns(base_path, download_config)
501 return _get_data_files_patterns(resolver)
502 except FileNotFoundError:
--> 503 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None
EmptyDatasetError: The directory at path/to/jsonl/file/metadata.jsonl doesn't contain any data files`
```
### Expected behavior
It should be able load the whole file in a format of "dataset" inside the dataset variable. But it gives error "The directory at "path/to/jsonl/metadata.jsonl" doesn't contain any data files."
### Environment info
I am using conda environment. | {
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https://api.github.com/repos/huggingface/datasets/issues/7004 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7004/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7004/comments | https://api.github.com/repos/huggingface/datasets/issues/7004/events | https://github.com/huggingface/datasets/pull/7004 | 2,376,064,264 | PR_kwDODunzps5zrIYR | 7,004 | Fix WebDatasets KeyError for user-defined Features when a field is missing in an example | {
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} | [] | closed | false | null | [] | null | 3 | "2024-06-26T18:58:05" | "2024-06-29T00:15:49" | "2024-06-28T09:30:12" | CONTRIBUTOR | null | Fixes: https://github.com/huggingface/datasets/issues/6900
Not sure if this needs any addition stuff before merging | {
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https://api.github.com/repos/huggingface/datasets/issues/7003 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7003/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7003/comments | https://api.github.com/repos/huggingface/datasets/issues/7003/events | https://github.com/huggingface/datasets/pull/7003 | 2,373,084,132 | PR_kwDODunzps5zhRAK | 7,003 | minor fix for bfloat16 | {
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https://api.github.com/repos/huggingface/datasets/issues/7002 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7002/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7002/comments | https://api.github.com/repos/huggingface/datasets/issues/7002/events | https://github.com/huggingface/datasets/pull/7002 | 2,373,010,351 | PR_kwDODunzps5zhBld | 7,002 | Fix dump of bfloat16 torch tensor | {
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https://api.github.com/repos/huggingface/datasets/issues/7001 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7001/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7001/comments | https://api.github.com/repos/huggingface/datasets/issues/7001/events | https://github.com/huggingface/datasets/issues/7001 | 2,372,930,879 | I_kwDODunzps6NcA0_ | 7,001 | Datasetbuilder Local Download FileNotFoundError | {
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} | [] | open | false | null | [] | null | 1 | "2024-06-25T15:02:34" | "2024-06-25T15:21:19" | null | NONE | null | ### Describe the bug
So I was trying to download a dataset and save it as parquet and I follow the [tutorial](https://huggingface.co/docs/datasets/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage) of Huggingface. However, during the excution I face a FileNotFoundError.
I debug the code and it seems there is a bug there:
So first it creates a .incomplete folder and before moving its contents the following code deletes the directory
[Code](https://github.com/huggingface/datasets/blob/98fdc9e78e6d057ca66e58a37f49d6618aab8130/src/datasets/builder.py#L984)
hence as a result I face with:
``` FileNotFoundError: [Errno 2] No such file or directory: '~/data/Parquet/.incomplete '```
### Steps to reproduce the bug
```
from datasets import load_dataset_builder
from pathlib import Path
parquet_dir = "~/data/Parquet/"
Path(parquet_dir).mkdir(parents=True, exist_ok=True)
builder = load_dataset_builder(
"rotten_tomatoes",
)
builder.download_and_prepare(parquet_dir, file_format="parquet")
```
### Expected behavior
Downloads the files and saves as parquet
### Environment info
Ubuntu,
Python 3.10
```
datasets 2.19.1
``` | {
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} | [] | closed | false | null | [] | null | 3 | "2024-06-25T14:43:26" | "2024-06-25T16:04:00" | "2024-06-25T15:51:53" | NONE | null | ### Describe the bug
`IterableDataset.from_generator` crashes when using BFloat16:
```
File "/usr/local/lib/python3.11/site-packages/datasets/utils/_dill.py", line 169, in _save_torchTensor
args = (obj.detach().cpu().numpy(),)
^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: Got unsupported ScalarType BFloat16
```
### Steps to reproduce the bug
```python
import torch
from datasets import IterableDataset
def demo(x):
yield {"x": x}
x = torch.tensor([1.], dtype=torch.bfloat16)
dataset = IterableDataset.from_generator(
demo,
gen_kwargs=dict(x=x),
)
example = next(iter(dataset))
print(example)
```
### Expected behavior
Code sample should print:
```python
{'x': tensor([1.], dtype=torch.bfloat16)}
```
### Environment info
```
datasets==2.20.0
torch==2.2.2
``` | {
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