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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 | "2024-07-15T18:24:52Z" | "2024-07-15T18:24:52Z" | 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 | "2024-07-15T11:36:11Z" | "2024-07-15T12:20:52Z" | 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 | "2024-07-15T11:21:04Z" | "2024-07-15T11:21:04Z" | 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 | "2024-07-12T23:47:51Z" | "2024-07-13T00:55:24Z" | 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 | "2024-07-12T12:42:47Z" | "2024-07-12T13:04:40Z" | "2024-07-12T12:58:17Z" | 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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https://api.github.com/repos/huggingface/datasets/issues/7045 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7045/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7045/comments | https://api.github.com/repos/huggingface/datasets/issues/7045/events | https://github.com/huggingface/datasets/pull/7045 | 2,405,447,858 | PR_kwDODunzps51Nsie | 7,045 | Fix tensorflow min version depending on Python version | {
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} | [] | closed | false | null | [] | null | 2 | "2024-07-12T12:20:23Z" | "2024-07-12T12:38:53Z" | "2024-07-12T12:33:00Z" | 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 | "2024-07-12T08:06:59Z" | "2024-07-12T09:06:32Z" | "2024-07-12T09:00:09Z" | 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 | "2024-07-12T07:35:23Z" | "2024-07-12T08:12:55Z" | "2024-07-12T08:07:10Z" | 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 | "2024-07-12T03:49:54Z" | "2024-07-12T03:49:54Z" | 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 | "2024-07-12T03:29:27Z" | "2024-07-12T03:29:27Z" | 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 | "2024-07-11T11:14:13Z" | "2024-07-11T14:11:56Z" | 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 | "2024-07-11T06:52:22Z" | "2024-07-11T07:27:58Z" | 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 | "2024-07-11T02:22:30Z" | "2024-07-11T05:28:39Z" | "2024-07-11T05:28:39Z" | 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 | "2024-07-10T09:11:22Z" | "2024-07-10T13:07:44Z" | 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 | "2024-07-10T07:58:46Z" | "2024-07-10T08:01:00Z" | 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 | "2024-07-10T07:51:24Z" | "2024-07-10T07:53:08Z" | 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 | "2024-07-09T07:47:58Z" | "2024-07-09T08:05:23Z" | 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 | "2024-07-08T12:39:50Z" | "2024-07-12T15:07:03Z" | "2024-07-12T15:07:03Z" | 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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] | closed | false | null | [] | null | 2 | "2024-07-06T05:43:37Z" | "2024-07-13T14:35:59Z" | "2024-07-13T14:35:59Z" | NONE | null | ### Feature request
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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} | [] | open | false | null | [] | null | 0 | "2024-07-04T19:15:16Z" | "2024-07-04T19:17:46Z" | null | NONE | null | ### Describe the bug
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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https://api.github.com/repos/huggingface/datasets/issues/7028 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7028/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7028/comments | https://api.github.com/repos/huggingface/datasets/issues/7028/events | https://github.com/huggingface/datasets/pull/7028 | 2,391,077,531 | PR_kwDODunzps50dQ1w | 7,028 | Fix ci | {
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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 | "2024-07-04T07:21:47Z" | "2024-07-04T07:21:47Z" | 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 | "2024-07-03T09:05:03Z" | "2024-07-03T09:24:46Z" | "2024-07-03T09:17:35Z" | 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 | "2024-07-03T08:52:57Z" | "2024-07-03T09:17:36Z" | "2024-07-03T09:17:36Z" | 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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https://api.github.com/repos/huggingface/datasets/issues/7021 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7021/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7021/comments | https://api.github.com/repos/huggingface/datasets/issues/7021/events | https://github.com/huggingface/datasets/pull/7021 | 2,387,948,935 | PR_kwDODunzps50SlKR | 7,021 | Fix casting list array to fixed size list | {
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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 | "2024-07-03T07:54:49Z" | "2024-07-03T08:41:56Z" | "2024-07-03T08:41:56Z" | 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 | "2024-07-01T12:19:19Z" | "2024-07-01T12:19:19Z" | 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 | "2024-07-01T09:01:06Z" | "2024-07-02T13:54:48Z" | 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 | "2024-07-01T08:09:25Z" | "2024-07-11T08:20:16Z" | 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 | "2024-06-28T05:03:28Z" | "2024-06-28T05:25:18Z" | "2024-06-28T05:25:18Z" | 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 | "2024-06-27T15:08:26Z" | "2024-06-28T09:56:19Z" | "2024-06-28T09:56:19Z" | 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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} | [] | closed | false | null | [] | null | 3 | "2024-06-26T18:58:05Z" | "2024-06-29T00:15:49Z" | "2024-06-28T09:30:12Z" | 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 | "2024-06-25T15:02:34Z" | "2024-06-25T15:21:19Z" | 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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https://api.github.com/repos/huggingface/datasets/issues/7000 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7000/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7000/comments | https://api.github.com/repos/huggingface/datasets/issues/7000/events | https://github.com/huggingface/datasets/issues/7000 | 2,372,887,585 | I_kwDODunzps6Nb2Qh | 7,000 | IterableDataset: Unsupported ScalarType BFloat16 | {
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} | [] | closed | false | null | [] | null | 3 | "2024-06-25T14:43:26Z" | "2024-06-25T16:04:00Z" | "2024-06-25T15:51:53Z" | 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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Fix #6997. | {
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```
FAILED tests/test_inspect.py::test_get_dataset_config_names[hf-internal-testing/librispeech_asr_dummy-expected4] - AssertionError: assert ['clean'] == ['clean', 'other']
Right contains one more item: 'other'
Full diff:
[
'clean',
- 'other',
]
FAILED tests/test_inspect.py::test_get_dataset_default_config_name[hf-internal-testing/librispeech_asr_dummy-None] - AssertionError: assert 'clean' is None
```
Note that repository was recently converted to Parquet: https://huggingface.co/datasets/hf-internal-testing/librispeech_asr_dummy/commit/5be91486e11a2d616f4ec5db8d3fd248585ac07a | {
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First merge:
- [x] #6983
- [x] #6987
- [ ] #6999 | {
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] | null | 3 | "2024-06-24T17:07:22Z" | "2024-06-25T07:17:25Z" | "2024-06-25T06:11:37Z" | NONE | null | ### Describe the bug
I encountered an ImportError while trying to import `load_dataset` from the `datasets` module in Hugging Face. The error message indicates a problem with importing 'CommitInfo' from 'huggingface_hub'.
### Steps to reproduce the bug
1. pip install git+https://github.com/huggingface/datasets
2. from datasets import load_dataset
### Expected behavior
ImportError Traceback (most recent call last)
Cell In[7], [line 1](vscode-notebook-cell:?execution_count=7&line=1)
----> [1](vscode-notebook-cell:?execution_count=7&line=1) from datasets import load_dataset
[3](vscode-notebook-cell:?execution_count=7&line=3) train_set = load_dataset("mispeech/speechocean762", split="train")
[4](vscode-notebook-cell:?execution_count=7&line=4) test_set = load_dataset("mispeech/speechocean762", split="test")
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py:[1](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:1)7
1 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
[2](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:2) #
[3](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:3) # Licensed under the Apache License, Version 2.0 (the "License");
(...)
[12](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:12) # See the License for the specific language governing permissions and
[13](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:13) # limitations under the License.
[15](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:15) __version__ = "2.20.1.dev0"
---> [17](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:17) from .arrow_dataset import Dataset
[18](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:18) from .arrow_reader import ReadInstruction
[19](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:19) from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py:63
[61](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:61) import pyarrow.compute as pc
[62](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:62) from fsspec.core import url_to_fs
---> [63](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:63) from huggingface_hub import (
[64](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:64) CommitInfo,
[65](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:65) CommitOperationAdd,
...
[70](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:70) )
[71](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:71) from huggingface_hub.hf_api import RepoFile
[72](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:72) from multiprocess import Pool
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (d:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?580889ab-0f61-4f37-9214-eaa2b3807f85) or open in a [text editor](command:workbench.action.openLargeOutput?580889ab-0f61-4f37-9214-eaa2b3807f85). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
### Environment info
Leo@DESKTOP-9NHUAMI MSYS /d/Anaconda3/envs/CS224S/Lib/site-packages/huggingface_hub
$ datasets-cli env
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "D:\Anaconda3\envs\CS224S\Scripts\datasets-cli.exe\__main__.py", line 4, in <module>
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py", line 17, in <module>
from .arrow_dataset import Dataset
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py", line 63, in <module>
from huggingface_hub import (
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
(CS224S) | {
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} | [] | open | false | null | [] | null | 1 | "2024-06-22T16:12:08Z" | "2024-06-25T15:43:05Z" | null | NONE | null | ### Describe the bug
I'm currently trying to stream data using dataset since the dataset is too big but it hangs indefinitely without loading the first batch. I use AIMOS which is a supercomputer that uses proxy to connect to the internet. I assume it has to do with the network configurations. I've already set up both HTTP_PROXY and HTTPS_PROXY. streaming = False works fine.
### Steps to reproduce the bug
use load_dataset with streaming = True in AIMOS
### Expected behavior
does not hang indefinitely and loads batches to start training run
### Environment info
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
_pytorch_select 2.0 cuda_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
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zstd 1.5.5 h57e4825_0 | {
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} | [] | closed | false | null | [] | null | 1 | "2024-06-21T14:25:26Z" | "2024-06-25T16:19:19Z" | "2024-06-25T16:19:19Z" | CONTRIBUTOR | null | ### Describe the bug
I'm trying to split `IterableDataset` by `split_dataset_by_node`.
But when doing split on a already split dataset, the resulting `rank` is greater than `world_size`.
### Steps to reproduce the bug
Here is the minimal code for reproduction:
```py
>>> from datasets import load_dataset
>>> from datasets.distributed import split_dataset_by_node
>>> dataset = load_dataset('fla-hub/slimpajama-test', split='train', streaming=True)
>>> dataset = split_dataset_by_node(dataset, 1, 32)
>>> dataset._distributed
DistributedConfig(rank=1, world_size=32)
>>> dataset = split_dataset_by_node(dataset, 1, 15)
>>> dataset._distributed
DistributedConfig(rank=481, world_size=480)
```
As you can see, the second rank 481 > 480, which is problematic.
### Expected behavior
I think this error comes from this line @lhoestq
https://github.com/huggingface/datasets/blob/a6ccf944e42c1a84de81bf326accab9999b86c90/src/datasets/iterable_dataset.py#L2943-L2944
We may need to obtain the rank first. Then the above code gives
```py
>>> dataset._distributed
DistributedConfig(rank=16, world_size=480)
```
### Environment info
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} | [] | open | false | null | [] | null | 0 | "2024-06-21T02:09:22Z" | "2024-06-21T02:12:55Z" | null | NONE | null | ### Describe the bug
- When reading dataset, a cache will be generated to the ~/. cache/huggingface/datasets directory
- When using .map and .filter operations, runtime cache will be generated to the /tmp/hf_datasets-* directory
- The default is to use the path of tempfile.tempdir
- If I modify this path to the NFS disk, an error will be reported, but the program will continue to run
- https://github.com/huggingface/datasets/blob/main/src/datasets/config.py#L257
```
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap
self.run()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server
server.serve_forever()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever
sys.exit(0)
SystemExit: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers
finalizer()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd
onerror(os.unlink, fullname, sys.exc_info())
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd
os.unlink(entry.name, dir_fd=topfd)
OSError: [Errno 16] Device or resource busy: '.nfs000000038330a012000030b4'
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap
self.run()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server
server.serve_forever()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever
sys.exit(0)
SystemExit: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers
finalizer()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd
onerror(os.unlink, fullname, sys.exc_info())
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd
os.unlink(entry.name, dir_fd=topfd)
OSError: [Errno 16] Device or resource busy: '.nfs0000000400064d4a000030e5'
```
### Steps to reproduce the bug
```
import os
import time
import tempfile
from datasets import load_dataset
def add_column(sample):
# print(type(sample))
# time.sleep(0.1)
sample['__ds__stats__'] = {'data': 123}
return sample
def filt_column(sample):
# print(type(sample))
if len(sample['content']) > 10:
return True
else:
return False
if __name__ == '__main__':
input_dir = '/mnt/temp/CN/small' # some json dataset
dataset = load_dataset('json', data_dir=input_dir)
temp_dir = '/media/release/release/temp/temp' # a nfs folder
os.makedirs(temp_dir, exist_ok=True)
# change huggingface-datasets runtime cache in nfs（default in /tmp）
tempfile.tempdir = temp_dir
aa = dataset.map(add_column, num_proc=64)
aa = aa.filter(filt_column, num_proc=64)
print(aa)
```
### Expected behavior
no error occur
### Environment info
datasets==2.18.0
ubuntu 20.04 | {
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} | [] | open | false | null | [] | null | 6 | "2024-06-20T10:47:57Z" | "2024-06-21T16:04:58Z" | null | MEMBER | null | Hello!
## Pull Request overview
* Move dataset card creation to method for easier overriding
## Details
It's common for me to fully automatically download, reformat, and upload a dataset (e.g. see https://huggingface.co/datasets?other=sentence-transformers), but one aspect that I cannot easily automate is the dataset card generation. This is because during `push_to_hub`, the dataset card is created in 3 lines of code in a much larger method. To automatically generate a dataset card, I need to either:
1. Subclass `Dataset`/`DatasetDict`, copy the entire `push_to_hub` method to override the ~3 lines used to generate the dataset card. This is not viable as the method is likely to change over time.
2. Use `push_to_hub` normally, then separately download the pushed (but empty) dataset card, update it, and reupload the modified dataset. This works fine, but prevents me from being able to return a `Dataset` to my users which will automatically use a nice dataset card.
So, in this PR I'm proposing to move the dataset generation into another method so that it can be overridden more easily. For example, imagine the following use case:
````python
import json
from typing import Any, Dict, Optional
from datasets import Dataset, load_dataset
from datasets.info import DatasetInfosDict, DatasetInfo
from datasets.utils.metadata import MetadataConfigs
from huggingface_hub import DatasetCardData, DatasetCard
TEMPLATE = r"""---
{dataset_card_data}
---
# Dataset Card for {source_dataset_name} with mined hard negatives
This dataset is a collection of {column_one}-{column_two}-negative triplets from the {source_dataset_name} dataset. See [{source_dataset_name}](https://huggingface.co/datasets/{source_dataset_id}) for additional information. This dataset can be used directly with Sentence Transformers to train embedding models.
## Mining Parameters
The negative samples have been mined using the following parameters:
- `range_min`: {range_min}, i.e. we skip the {range_min} most similar samples
- `range_max`: {range_max}, i.e. we only look at the top {range_max} most similar samples
- `margin`: {margin}, i.e. we require negative similarity + margin < positive similarity, so negative samples can't be more similar than the known true answer
- `sampling_strategy`: {sampling_strategy}, i.e. whether to randomly sample from the candidate negatives or take the "top" negatives
- `num_negatives`: {num_negatives}, i.e. we mine {num_negatives} negatives per question-answer pair
## Dataset Format
- Columns: {column_one}, {column_two}, negative
- Column types: str, str, str
- Example:
```python
{example}
```
"""
class HNMDataset(Dataset):
@classmethod
def from_dict(cls, *args, mining_kwargs: Dict[str, Any], **kwargs) -> "HNMDataset":
dataset = super().from_dict(*args, **kwargs)
dataset.mining_kwargs = mining_kwargs
return dataset
def _create_dataset_card(
self,
dataset_card_data: DatasetCardData,
dataset_card: Optional[DatasetCard],
config_name: str,
info_to_dump: DatasetInfo,
metadata_config_to_dump: MetadataConfigs,
) -> DatasetCard:
if dataset_card:
return dataset_card
DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data)
MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data)
dataset_card_data.tags = ["sentence-transformers"]
dataset_name = self.mining_kwargs["source_dataset"].info.dataset_name
# Very messy, just as an example:
dataset_id = list(self.mining_kwargs["source_dataset"].info.download_checksums.keys())[0].removeprefix("hf://datasets/").split("@")[0]
content = TEMPLATE.format(**{
"dataset_card_data": str(dataset_card_data),
"source_dataset_name": dataset_name,
"source_dataset_id": dataset_id,
"range_min": self.mining_kwargs["range_min"],
"range_max": self.mining_kwargs["range_max"],
"margin": self.mining_kwargs["margin"],
"sampling_strategy": self.mining_kwargs["sampling_strategy"],
"num_negatives": self.mining_kwargs["num_negatives"],
"column_one": self.column_names[0],
"column_two": self.column_names[1],
"example": json.dumps(self[0], indent=4),
})
return DatasetCard(content)
source_dataset = load_dataset("sentence-transformers/gooaq", split="train[:100]")
dataset = HNMDataset.from_dict({
"query": source_dataset["question"],
"answer": source_dataset["answer"],
# "negative": ... <- In my case, this column would be 'mined' automatically with these parameters
}, mining_kwargs={
"range_min": 10,
"range_max": 20,
"max_score": 0.9,
"margin": 0.1,
"sampling_strategy": "random",
"num_negatives": 3,
"source_dataset": source_dataset,
})
dataset.push_to_hub("tomaarsen/mining_demo", private=True)
````
In this script, I've created a subclass which stores some additional information about how the dataset was generated. It's a bit hacky (e.g. setting a `mining_kwargs` parameter in `from_dict` that wasn't created in `__init__`, but that's just a consequence of how the `from_...` methods don't accept kwargs), but it allows me to create a "hard negatives mining" function that returns a dataset which people can use locally like normal, but if they choose to upload it, then it'll automatically include some information, e.g.: https://huggingface.co/datasets/tomaarsen/mining_demo
This allows others to actually find this dataset (e.g. via the `sentence-transformers` tag) and get an idea of the quality, source, etc. by looking at the model card.
## Note
I'm not fixed on this solution whatsoever: I am also completely fine with other solutions, e.g. a `dataset.set_dataset_card_creator` method that allows me to provide a function without even having to subclass anything. I'm open to all ideas :)
cc @albertvillanova @lhoestq
cc @LysandreJik
- Tom Aarsen | {
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Fix #6984
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I have been struggling with this for two days, any help would be appreciated. Python 3.10
```
from setfit import SetFitModel
from huggingface_hub import login
access_token_read = "cccxxxccc"
# Authenticate with the Hugging Face Hub
login(token=access_token_read)
# Load the models from the Hugging Face Hub
trainer_relv = SetFitModel.from_pretrained("snowdere/trainer_relevance")
trainer_trust = SetFitModel.from_pretrained("snowdere/trainer_trust")
trainer_sent = SetFitModel.from_pretrained("snowdere/trainer_sent")
trainer_topic = SetFitModel.from_pretrained("snowdere/trainer_topic")
```
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 from setfit import SetFitModel
2 from huggingface_hub import login
4 access_token_read = "ccsddsds"
File /opt/conda/lib/python3.10/site-packages/setfit/__init__.py:7
4 import os
5 import warnings
----> 7 from .data import get_templated_dataset, sample_dataset
8 from .model_card import SetFitModelCardData
9 from .modeling import SetFitHead, SetFitModel
File /opt/conda/lib/python3.10/site-packages/setfit/data.py:5
3 import pandas as pd
4 import torch
----> 5 from datasets import Dataset, DatasetDict, load_dataset
6 from torch.utils.data import Dataset as TorchDataset
8 from . import logging
File /opt/conda/lib/python3.10/site-packages/datasets/__init__.py:18
1 # ruff: noqa
2 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
3 #
(...)
13 # See the License for the specific language governing permissions and
14 # limitations under the License.
16 __version__ = "2.19.0"
---> 18 from .arrow_dataset import Dataset
19 from .arrow_reader import ReadInstruction
20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:76
73 from tqdm.contrib.concurrent import thread_map
75 from . import config
---> 76 from .arrow_reader import ArrowReader
77 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
78 from .data_files import sanitize_patterns
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_reader.py:29
26 from typing import TYPE_CHECKING, List, Optional, Union
28 import pyarrow as pa
---> 29 import pyarrow.parquet as pq
30 from tqdm.contrib.concurrent import thread_map
32 from .download.download_config import DownloadConfig
File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20
1 # Licensed to the Apache Software Foundation (ASF) under one
2 # or more contributor license agreements. See the NOTICE file
3 # distributed with this work for additional information
(...)
17
18 # flake8: noqa
---> 20 from .core import *
File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33
30 import pyarrow as pa
32 try:
---> 33 import pyarrow._parquet as _parquet
34 except ImportError as exc:
35 raise ImportError(
36 "The pyarrow installation is not built with support "
37 f"for the Parquet file format ({str(exc)})"
38 ) from None
File /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet()
AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'
```
setfit: 1.0.3
transformers: 4.41.2
lingua-language-detector: 2.0.2
polars: 0.20.31
lightning: None
google-cloud-bigquery: 3.24.0
shapely: 2.0.4
pyarrow: 16.0.0
### Steps to reproduce the bug
I have tried all version combinations for Dataset and Pyarrow, the all have the same error since a few days ago. This is accross multiple scripts I have.
### Expected behavior
Just ron normally.
### Environment info
3.10 | {
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This returns error.
```python
from datasets import Dataset
dsdf = Dataset.from_dict({"x": [[1, 2], [3, 4, 5]], "y": ["a", "b"]})
Dataset.from_polars(dsdf.to_polars())
```
ValueError: Arrow type large_list<item: int64> does not have a datasets dtype equivalent.
### Motivation
When datasets contain Sequence data type, it will be converted to Arrow type large_list. However, the reverse (from large_list to Sequence) does not work.
### Your contribution
No | {
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Note they are deprecated since 2.5.0 version. | {
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https://api.github.com/repos/huggingface/datasets/issues/6982 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6982/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6982/comments | https://api.github.com/repos/huggingface/datasets/issues/6982/events | https://github.com/huggingface/datasets/issues/6982 | 2,361,661,469 | I_kwDODunzps6MxBgd | 6,982 | cannot split dataset when using load_dataset | {
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when I use load_dataset methods to load mozilla-foundation/common_voice_7_0, it can successfully download and extracted the dataset but It cannot generating the arrow document,
This bug happened in my server, my laptop, so as #6906 , but it won't happen in the google colab. I work for it for days, even I load the datasets from local path, it can Generating train split and validation split but bug happen again in test split.
### Steps to reproduce the bug
from datasets import load_dataset, load_metric, Audio
common_voice_train = load_dataset("mozilla-foundation/common_voice_7_0", "ja", split="train", token=selftoken, trust_remote_code=True)
### Expected behavior
```
{
"name": "ValueError",
"message": "Instruction \"train\" corresponds to no data!",
"stack": "---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[2], line 3
1 from datasets import load_dataset, load_metric, Audio
----> 3 common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"train\",token='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')#,trust_remote_code=True)#,streaming=True)
4 common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"test\",token='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')#,trust_remote_code=True)#,streaming=True)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\load.py:2626, 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)
2622 # Build dataset for splits
2623 keep_in_memory = (
2624 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2625 )
-> 2626 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
2627 # Rename and cast features to match task schema
2628 if task is not None:
2629 # To avoid issuing the same warning twice
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1266, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)
1263 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS)
1265 # Create a dataset for each of the given splits
-> 1266 datasets = map_nested(
1267 partial(
1268 self._build_single_dataset,
1269 run_post_process=run_post_process,
1270 verification_mode=verification_mode,
1271 in_memory=in_memory,
1272 ),
1273 split,
1274 map_tuple=True,
1275 disable_tqdm=True,
1276 )
1277 if isinstance(datasets, dict):
1278 datasets = DatasetDict(datasets)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\utils\\py_utils.py:484, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, batched, batch_size, types, disable_tqdm, desc)
482 if batched:
483 data_struct = [data_struct]
--> 484 mapped = function(data_struct)
485 if batched:
486 mapped = mapped[0]
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1296, in DatasetBuilder._build_single_dataset(self, split, run_post_process, verification_mode, in_memory)
1293 split = Split(split)
1295 # Build base dataset
-> 1296 ds = self._as_dataset(
1297 split=split,
1298 in_memory=in_memory,
1299 )
1300 if run_post_process:
1301 for resource_file_name in self._post_processing_resources(split).values():
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1370, in DatasetBuilder._as_dataset(self, split, in_memory)
1368 if self._check_legacy_cache():
1369 dataset_name = self.name
-> 1370 dataset_kwargs = ArrowReader(cache_dir, self.info).read(
1371 name=dataset_name,
1372 instructions=split,
1373 split_infos=self.info.splits.values(),
1374 in_memory=in_memory,
1375 )
1376 fingerprint = self._get_dataset_fingerprint(split)
1377 return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\arrow_reader.py:256, in BaseReader.read(self, name, instructions, split_infos, in_memory)
254 msg = f'Instruction \"{instructions}\" corresponds to no data!'
255 #msg = f'Instruction \"{self._path}\",\"{name}\",\"{instructions}\",\"{split_infos}\" corresponds to no data!'
--> 256 raise ValueError(msg)
257 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
ValueError: Instruction \"train\" corresponds to no data!"
}
```
### Environment info
Environment:
python 3.9
windows 11 pro
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https://api.github.com/repos/huggingface/datasets/issues/6981 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6981/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6981/comments | https://api.github.com/repos/huggingface/datasets/issues/6981/events | https://github.com/huggingface/datasets/pull/6981 | 2,361,520,022 | PR_kwDODunzps5y6tnN | 6,981 | Update docs on trust_remote_code defaults to False | {
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The docs needed to be updated due to this PR:
- #6954 | {
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https://api.github.com/repos/huggingface/datasets/issues/6980 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6980/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6980/comments | https://api.github.com/repos/huggingface/datasets/issues/6980/events | https://github.com/huggingface/datasets/issues/6980 | 2,360,909,930 | I_kwDODunzps6MuKBq | 6,980 | Support NumPy 2.0 | {
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] | closed | false | null | [] | null | 0 | "2024-06-18T23:30:22Z" | "2024-07-12T12:04:54Z" | "2024-07-12T12:04:53Z" | CONTRIBUTOR | null | ### Feature request
Support NumPy 2.0.
### Motivation
NumPy introduces the Array API, which bridges the gap between machine learning libraries. Many clients of HuggingFace are eager to start using the Array API.
Besides that, NumPy 2 provides a cleaner interface than NumPy 1.
### Tasks
NumPy 2.0 was released for testing so that libraries could ensure compatibility [since mid-March](https://github.com/numpy/numpy/issues/24300#issuecomment-1986815755). What needs to be done for HuggingFace to support Numpy 2?
- [x] Fix use of `array`: https://github.com/huggingface/datasets/pull/6976
- [ ] Remove [NumPy version limit](https://github.com/huggingface/datasets/pull/6975): https://github.com/huggingface/datasets/pull/6991 | {
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https://api.github.com/repos/huggingface/datasets/issues/6979 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6979/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6979/comments | https://api.github.com/repos/huggingface/datasets/issues/6979/events | https://github.com/huggingface/datasets/issues/6979 | 2,360,175,363 | I_kwDODunzps6MrWsD | 6,979 | How can I load partial parquet files only? | {
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dataset = load_dataset("xx/", data_files="data/train-001*-of-00314.parquet")
How can I just using 000 - 100 from a 00314 from all partially?
I search whole net didn't found a solution, **this is stupid if they didn't support it, and I swear I wont using stupid parquet any more**
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https://api.github.com/repos/huggingface/datasets/issues/6978 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6978/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6978/comments | https://api.github.com/repos/huggingface/datasets/issues/6978/events | https://github.com/huggingface/datasets/pull/6978 | 2,359,511,469 | PR_kwDODunzps5yz0h6 | 6,978 | Fix regression for pandas < 2.0.0 in JSON loader | {
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- #6914
As described in pandas docs, the `dtype_backend` parameter was first added in pandas 2.0.0: https://pandas.pydata.org/docs/reference/api/pandas.read_json.html
This PR fixes the regression by passing (or not) the `dtype_backend` parameter depending on pandas version.
Maybe, in a future 3.0 `datasets` release, we could just require pandas > 2.0.
Reported by:
- #6977 | {
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] | null | 2 | "2024-06-18T08:41:01Z" | "2024-06-18T10:06:10Z" | "2024-06-18T10:06:09Z" | NONE | null | ### Describe the bug
```
load_dataset(path="json", data_files="./test.json")
```
```
Generating train split: 0 examples [00:00, ? examples/s]
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 132, in _generate_tables
pa_table = paj.read_json(
File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1997, in _prepare_split_single
for _, table in generator:
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 155, in _generate_tables
df = pd.read_json(f, dtype_backend="pyarrow")
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper
return func(*args, **kwargs)
TypeError: read_json() got an unexpected keyword argument 'dtype_backend'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/app/t1.py", line 11, in <module>
load_dataset(path=data_path, data_files="./t2.json")
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2616, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1124, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1884, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 2040, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
```
```
import pandas as pd
with open("./test.json", "r") as f:
df = pd.read_json(f, dtype_backend="pyarrow")
```
```
Traceback (most recent call last):
File "/app/t3.py", line 3, in <module>
df = pd.read_json(f, dtype_backend="pyarrow")
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper
return func(*args, **kwargs)
TypeError: read_json() got an unexpected keyword argument 'dtype_backend'
```
### Steps to reproduce the bug
.
### Expected behavior
.
### Environment info
```
datasets 2.20.0
pandas 1.5.3
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} | [] | closed | false | null | [] | null | 2 | "2024-06-17T11:29:22Z" | "2024-06-19T14:30:32Z" | "2024-06-19T14:04:34Z" | CONTRIBUTOR | null | Following the conversion guide, copy=False is no longer required and will result in an error: https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword.
The following fix should resolve the issue.
error found during testing on the MTEB repository e.g. [here](https://github.com/embeddings-benchmark/mteb/pull/938) | {
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https://api.github.com/repos/huggingface/datasets/issues/6975 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6975/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6975/comments | https://api.github.com/repos/huggingface/datasets/issues/6975/events | https://github.com/huggingface/datasets/pull/6975 | 2,357,003,959 | PR_kwDODunzps5yrPct | 6,975 | Set temporary numpy upper version < 2.0.0 to fix CI | {
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} | [] | closed | false | null | [] | null | 2 | "2024-06-17T10:36:54Z" | "2024-06-17T12:49:53Z" | "2024-06-17T12:43:56Z" | MEMBER | null | Set temporary numpy upper version < 2.0.0 to fix CI. See: https://github.com/huggingface/datasets/actions/runs/9546031216/job/26308072017
```
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.
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} | [] | closed | false | null | [] | null | 2 | "2024-06-16T07:53:54Z" | "2024-07-01T11:25:40Z" | "2024-07-01T11:25:40Z" | NONE | null | ### Describe the bug
I am encountering an IndexError while training a T5-small model on the Squad dataset using the transformers and datasets libraries. The error occurs even with a minimal reproducible example, suggesting a potential bug or incompatibility.
### Steps to reproduce the bug
1.Install the required libraries: !pip install transformers datasets
2.Run the following code:
!pip install transformers datasets
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments, Trainer, DataCollatorWithPadding
# Load a small, publicly available dataset
from datasets import load_dataset
dataset = load_dataset("squad", split="train[:100]") # Use a small subset for testing
# Load a pre-trained model and tokenizer
model_name = "t5-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Define a basic data collator
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
per_device_train_batch_size=2,
num_train_epochs=1,
)
# Create a trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset,
data_collator=data_collator,
)
# Train the model
trainer.train()
### Expected behavior
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
[<ipython-input-23-f13a4b23c001>](https://localhost:8080/#) in <cell line: 34>()
32
33 # Train the model
---> 34 trainer.train()
10 frames
[/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_index_key(key, size)
427 if isinstance(key, int):
428 if (key < 0 and key + size < 0) or (key >= size):
--> 429 raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
430 return
431 elif isinstance(key, slice):
IndexError: Invalid key: 42 is out of bounds for size 0
### Environment info
transformers version:4.41.2
datasets version:1.18.4
Python version:3.10.12
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This is important to make streaming datasets compatible with multiprocessing e.g. for parallel data loading | {
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The changes are backward-compatible meaning that:
- `HF_DATASETS_OFFLINE` environment is still taken into account, though not documented
- `datasets.config.HF_DATASETS_OFFLINE` still exists, though it is not used anymore (in favor of `datasets.config.HF_HUB_OFFLINE`)
**Note:** it might break things in downstream libraries if they were monkeypatching `datasets.config.HF_DATASETS_OFFLINE` in their CI tests (for instance). Not much of a problem IMO. | {
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] | open | false | null | [] | null | 0 | "2024-06-12T16:04:04Z" | "2024-06-12T16:04:04Z" | null | NONE | null | ### Feature request
Large datasets like Laion400m are provided as embeddings. The provided methods in load_dataset are not straightforward for loading embedding files, i.e. img_emb_XX.npy ; XX = 0 to 99
### Motivation
The trial and experimentation is the key pivot of HF. It would be great if HF can load embeddings files s,ealessly.
### Your contribution
I cam write the loader with some help. | {
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https://api.github.com/repos/huggingface/datasets/issues/6966 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6966/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6966/comments | https://api.github.com/repos/huggingface/datasets/issues/6966/events | https://github.com/huggingface/datasets/pull/6966 | 2,348,934,466 | PR_kwDODunzps5yPwL4 | 6,966 | Remove underlines between badges | {
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<img width="935" alt="image" src="https://github.com/huggingface/datasets/assets/35881688/93666e72-059b-4180-9e1d-ff176a3d9dac">
## After:
<img width="956" alt="image" src="https://github.com/huggingface/datasets/assets/35881688/75df7c3e-f473-44f0-a872-eeecf6a85fe2">
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https://api.github.com/repos/huggingface/datasets/issues/6965 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6965/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6965/comments | https://api.github.com/repos/huggingface/datasets/issues/6965/events | https://github.com/huggingface/datasets/pull/6965 | 2,348,653,895 | PR_kwDODunzps5yOyNG | 6,965 | Improve skip take shuffling and distributed | {
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cc @Wauplin it looks like the retries from `hfh` are not always enough. In this PR I let `datasets` do additional retries (that users can configure in `datasets.config`) since I couldn't find an easy way to increase the max_retries for `hfh` users in general. | {
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Remove unnecessary permissions granted to the actions workflow.
Sorry for the mishap. | {
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] | open | false | null | [] | null | 1 | "2024-06-09T04:52:06Z" | "2024-06-13T16:05:00Z" | null | NONE | null | ### Feature request
I would like to request that manual downloads of data files from Hugging Face dataset repositories count as downloads of a dataset. According to the documentation for the Hugging Face Hub, that is currently not the case: https://huggingface.co/docs/hub/en/datasets-download-stats
### Motivation
This would ensure that downloads are accurately reported to end users.
### Your contribution
N/A | {
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https://api.github.com/repos/huggingface/datasets/issues/6960 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6960/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6960/comments | https://api.github.com/repos/huggingface/datasets/issues/6960/events | https://github.com/huggingface/datasets/pull/6960 | 2,340,791,685 | PR_kwDODunzps5x0R3T | 6,960 | feat(ci): add trufflehog secrets detection | {
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Adding a GH action to scan for leaked secrets on each commit.
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https://api.github.com/repos/huggingface/datasets/issues/6959 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6959/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6959/comments | https://api.github.com/repos/huggingface/datasets/issues/6959/events | https://github.com/huggingface/datasets/pull/6959 | 2,340,229,908 | PR_kwDODunzps5xyVt6 | 6,959 | Better error handling in `dataset_module_factory` | {
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This PR updates how errors are handled in `dataset_module_factory` when the `dataset_info` cannot be accessed:
1. Use multiple `except ... as e` instead of using `isinstance(e, ...)`
2. Always raise `DatasetNotFoundError` with `from e` so that the initial error is explicitly logged in the stacktrace.
3. Differentiate `RepoNotFoundError` / `GatedRepoError` / `RevisionNotFoundError` cases | {
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https://api.github.com/repos/huggingface/datasets/issues/6958 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6958/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6958/comments | https://api.github.com/repos/huggingface/datasets/issues/6958/events | https://github.com/huggingface/datasets/issues/6958 | 2,337,476,383 | I_kwDODunzps6LUw8f | 6,958 | My Private Dataset doesn't exist on the Hub or cannot be accessed | {
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} | [] | closed | false | null | [] | null | 8 | "2024-06-06T06:52:19Z" | "2024-07-01T11:27:46Z" | "2024-07-01T11:27:46Z" | NONE | null | ### Describe the bug
```
File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 1852, in dataset_module_factory
raise DatasetNotFoundError(msg + f" at revision '{revision}'" if revision else msg)
datasets.exceptions.DatasetNotFoundError: Dataset 'xxx' doesn't exist on the Hub or cannot be accessed
>>> dataset = load_dataset("xxxx", token=True)
404 error 404 Client Error. (Request ID: Root=xxxx)
Repository Not Found for url: https://huggingface.co/api/datasets/xxx/xxx.
Please make sure you specified the correct `repo_id` and `repo_type`.
If you are trying to access a private or gated repo, make sure you are authenticated.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 2593, in load_dataset
builder_instance = load_dataset_builder(
File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 2265, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 1910, in dataset_module_factory
raise e1 from None
File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 1852, in dataset_module_factory
raise DatasetNotFoundError(msg + f" at revision '{revision}'" if revision else msg)
datasets.exceptions.DatasetNotFoundError: Dataset 'xxx' doesn't exist on the Hub or cannot be accessed
```
### Steps to reproduce the bug
123
### Expected behavior
123
### Environment info
123 | {
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- #6956
Typos:
- `comparisions` => `comparisons`
- two consecutive sentences both ending in colon
- split one sentence into two
Sorry, I did not have time to review that PR.
CC: @lhoestq | {
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- [x] fix tests | {
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https://api.github.com/repos/huggingface/datasets/issues/6952 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6952/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6952/comments | https://api.github.com/repos/huggingface/datasets/issues/6952/events | https://github.com/huggingface/datasets/pull/6952 | 2,333,320,411 | PR_kwDODunzps5xaosH | 6,952 | Move info_utils errors to exceptions module | {
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Additionally rename some of them, deprecate the former ones, and make the deprecation backward compatible (by making the new errors inherit from the former ones). | {
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https://api.github.com/repos/huggingface/datasets/issues/6951 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6951/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6951/comments | https://api.github.com/repos/huggingface/datasets/issues/6951/events | https://github.com/huggingface/datasets/issues/6951 | 2,333,231,042 | I_kwDODunzps6LEkfC | 6,951 | load_dataset() should load all subsets, if no specific subset is specified | {
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] | closed | false | null | [] | null | 4 | "2024-06-04T11:02:33Z" | "2024-07-01T11:33:10Z" | "2024-07-01T11:33:10Z" | NONE | null | ### Feature request
Currently load_dataset() is forcing users to specify a subset. Example
`from datasets import load_dataset
dataset = load_dataset("m-a-p/COIG-CQIA")`
```---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-10-c0cb49385da6>](https://localhost:8080/#) in <cell line: 2>()
1 from datasets import load_dataset
----> 2 dataset = load_dataset("m-a-p/COIG-CQIA")
3 frames
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _create_builder_config(self, config_name, custom_features, **config_kwargs)
582 if not config_kwargs:
583 example_of_usage = f"load_dataset('{self.dataset_name}', '{self.BUILDER_CONFIGS[0].name}')"
--> 584 raise ValueError(
585 "Config name is missing."
586 f"\nPlease pick one among the available configs: {list(self.builder_configs.keys())}"
ValueError: Config name is missing.
Please pick one among the available configs: ['chinese_traditional', 'coig_pc', 'exam', 'finance', 'douban', 'human_value', 'logi_qa', 'ruozhiba', 'segmentfault', 'wiki', 'wikihow', 'xhs', 'zhihu']
Example of usage:
`load_dataset('coig-cqia', 'chinese_traditional')`
```
This means a dataset cannot contain all the subsets at the same time. I guess one workaround is to manually specify the subset files like in [here](https://huggingface.co/datasets/m-a-p/COIG-CQIA/discussions/1#658698b44bb41498f75c5622), which is clumsy.
### Motivation
Ideally, if not subset is specified, the API should just try to load all subsets. This makes it much easier to handle datasets w/ subsets.
### Your contribution
Not sure since I'm not familiar w/ the lib src. | {
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