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https://api.github.com/repos/huggingface/datasets/issues/7051

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I_kwDODunzps6Pm9LJ

How to set_epoch with interleave_datasets?

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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://github.com/huggingface/datasets/pull/7050

PR_kwDODunzps51Z1Yp

add checkpoint and resume title in docs

closed

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(minor) just to make it more prominent in the docs page for the soon-to-be-released new torchdata

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https://github.com/huggingface/datasets/issues/7049

I_kwDODunzps6PjwM-

Save nparray as list

open

NONE

### 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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I_kwDODunzps6Pjpp7

ImportError: numpy.core.multiarray when using `filter`

open

NONE

### 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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I_kwDODunzps6PcDNs

Save Dataset as Sharded Parquet

open

NONE

### 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://github.com/huggingface/datasets/pull/7046

PR_kwDODunzps51N05n

Support librosa and numpy 2.0 for Python 3.10

closed

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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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PR_kwDODunzps51Nsie

Fix tensorflow min version depending on Python version

closed

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Fix tensorflow min version depending on Python version. Related to: - #6991

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https://github.com/huggingface/datasets/pull/7044

PR_kwDODunzps51MLbh

Mark tests that require librosa

closed

MEMBER

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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PR_kwDODunzps51MAN0

Add decorator as explicit test dependency

closed

MEMBER

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://github.com/huggingface/datasets/pull/7042

PR_kwDODunzps51K8CM

Improved the tutorial by adding a link for loading datasets

open

NONE

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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I_kwDODunzps6PUusm

`sort` after `filter` unreasonably slow

open

NONE

### 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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I_kwDODunzps6POZ-_

load `streaming=True` dataset with downloaded cache

open

NONE

### 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://github.com/huggingface/datasets/pull/7039

PR_kwDODunzps51DgCY

Fix export to JSON when dataset larger than batch size

open

MEMBER

Fix export to JSON (`files=False`) when dataset larger than batch size. Fix #7037.

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I_kwDODunzps6PLNnL

Yes, can definitely elaborate:

closed

NONE

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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I_kwDODunzps6PEAej

A bug of Dataset.to_json() function

open

NONE

### 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://github.com/huggingface/datasets/pull/7036

PR_kwDODunzps507bZk

Fix doc generation when NamedSplit is used as parameter default value

open

MEMBER

Fix doc generation when `NamedSplit` is used as parameter default value. Fix #7035.

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https://github.com/huggingface/datasets/issues/7035

I_kwDODunzps6PDWrp

Docs are not generated when a parameter defaults to a NamedSplit value

open

MEMBER

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://github.com/huggingface/datasets/pull/7034

PR_kwDODunzps50y-ya

chore: fix typos in docs

open

NONE

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https://github.com/huggingface/datasets/issues/7033

I_kwDODunzps6O5bj4

`from_generator` does not allow to specify the split name

open

CONTRIBUTOR

### 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://github.com/huggingface/datasets/pull/7032

PR_kwDODunzps50sJTq

Register `.zstd` extension for zstd-compressed files

closed

CONTRIBUTOR

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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I_kwDODunzps6Oxu3c

CI quality is broken: use ruff check instead

closed

MEMBER

CI quality is broken: https://github.com/huggingface/datasets/actions/runs/9838873879/job/27159697027 ``` error: `ruff <path>` has been removed. Use `ruff check <path>` instead. ```

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I_kwDODunzps6OqJAv

Add option to disable progress bar when reading a dataset ("Loading dataset from disk")

closed

NONE

### 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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I_kwDODunzps6OiVwo

load_dataset on AWS lambda throws OSError(30, 'Read-only file system') error

open

NONE

### 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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PR_kwDODunzps50dQ1w

Fix ci

closed

MEMBER

...after last pr errors

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PR_kwDODunzps50dCsE

Missing line from previous pr

closed

MEMBER

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PR_kwDODunzps50c8Mf

Fix check_library_imports

closed

MEMBER

move it to after the `trust_remote_code` check Note that it only affects local datasets that already exist on disk, not datasets loaded from HF directly

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PR_kwDODunzps50bSyD

feat: support non streamable arrow file binary format

open

NONE

Support Arrow files (`.arrow`) that are in non streamable binary file formats.

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I_kwDODunzps6Odqq6

Streaming dataset not returning data

open

NONE

### 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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PR_kwDODunzps50TDot

Remove dead code for pyarrow < 15.0.0

closed

MEMBER

Remove dead code for pyarrow < 15.0.0. Code is dead since the merge of: - #6892 Fix #7022.

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I_kwDODunzps6OVvmK

There is dead code after we require pyarrow >= 15.0.0

closed

MEMBER

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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PR_kwDODunzps50SlKR

Fix casting list array to fixed size list

closed

MEMBER

Fix casting list array to fixed size list. This bug was introduced in [datasets-2.17.0](https://github.com/huggingface/datasets/releases/tag/2.17.0) by PR: https://github.com/huggingface/datasets/pull/6283/files#diff-1cb2b66aa9311d729cfd83013dad56cf5afcda35b39dfd0bfe9c3813a049eab0R1899 - #6283 Fix #7020.

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I_kwDODunzps6OVRZ-

Casting list array to fixed size list raises error

closed

MEMBER

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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PR_kwDODunzps50LMjW

Allow Polars round trip by supporting pyarrow large list

open

MEMBER

Allow Polars round trip by supporting pyarrow large list. Fix #6834, fix #6984. Supersede and close #4800, close #6835, close #6986.

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I_kwDODunzps6OFGE-

`load_dataset` fails to load dataset saved by `save_to_disk`

open

NONE

### 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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PR_kwDODunzps50D3gi

Support fsspec 2024.6.1

closed

MEMBER

Support fsspec 2024.6.1.

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I_kwDODunzps6ODbOQ

`drop_duplicates` method

open

NONE

### 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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PR_kwDODunzps50CJuE

add split argument to Generator

open

NONE

## 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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PR_kwDODunzps50BlwV

Skip faiss tests on Windows to avoid running CI for 360 minutes

closed

MEMBER

Skip faiss tests on Windows to avoid running CI for 360 minutes. Fix #7013. Revert once the underlying issue is fixed.

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I_kwDODunzps6OCVbi

CI is broken for faiss tests on Windows: node down: Not properly terminated

closed

MEMBER

Faiss tests on Windows make the CI run indefinitely until maximum execution time (360 minutes) is reached. 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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PR_kwDODunzps5z61A3

Raise an error when a nested object is expected to be a mapping that displays the object

closed

NONE

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PR_kwDODunzps5z27Fs

Re-enable raising error from huggingface-hub FutureWarning in CI

closed

MEMBER

Re-enable raising error from huggingface-hub FutureWarning in tests, once that the fix in transformers - 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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I_kwDODunzps6N2IXI

Re-enable raising error from huggingface-hub FutureWarning in CI

closed

MEMBER

Re-enable raising error from huggingface-hub FutureWarning in CI, which was disabled by PR: - #6876 Note that this can only be done once transformers releases the fix: - https://github.com/huggingface/transformers/pull/31007

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PR_kwDODunzps5z2Xe6

Support ruff 0.5.0 in CI

closed

MEMBER

Support ruff 0.5.0 in CI and revert: - #7007 Fix #7008.

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I_kwDODunzps6N1a3l

Support ruff 0.5.0 in CI

closed

MEMBER

Support ruff 0.5.0 in CI. Also revert: - #7007

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PR_kwDODunzps5z2Q68

Fix CI by temporarily pinning ruff < 0.5.0

closed

MEMBER

As a hotfix for CI, temporarily pin ruff upper version < 0.5.0. Fix #7006. Revert once root cause is fixed.

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I_kwDODunzps6N1Yhn

CI is broken after ruff-0.5.0: E721

closed

MEMBER

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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I_kwDODunzps6Nw-Ad

EmptyDatasetError: The directory at /metadata.jsonl doesn't contain any data files

closed

NONE

### 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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PR_kwDODunzps5zrIYR

Fix WebDatasets KeyError for user-defined Features when a field is missing in an example

closed

CONTRIBUTOR

Fixes: https://github.com/huggingface/datasets/issues/6900 Not sure if this needs any addition stuff before merging

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PR_kwDODunzps5zhRAK

minor fix for bfloat16

closed

MEMBER

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PR_kwDODunzps5zhBld

Fix dump of bfloat16 torch tensor

closed

MEMBER

close https://github.com/huggingface/datasets/issues/7000

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I_kwDODunzps6NcA0_

Datasetbuilder Local Download FileNotFoundError

open

NONE

### 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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I_kwDODunzps6Nb2Qh

IterableDataset: Unsupported ScalarType BFloat16

closed

NONE

### 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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PR_kwDODunzps5zd-ak

Remove tasks

open

MEMBER

Remove tasks, as part of the 3.0 release.

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PR_kwDODunzps5zddYG

Fix tests using hf-internal-testing/librispeech_asr_dummy

closed

MEMBER

Fix tests using hf-internal-testing/librispeech_asr_dummy once that dataset has been converted to Parquet. Fix #6997.

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I_kwDODunzps6NYVSv

CI is broken for tests using hf-internal-testing/librispeech_asr_dummy

closed

MEMBER

CI is broken: https://github.com/huggingface/datasets/actions/runs/9657882317/job/26637998686?pr=6996 ``` 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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PR_kwDODunzps5zdAg0

Remove deprecated code

open

MEMBER

Remove deprecated code, as part of the 3.0 release. First merge: - [x] #6983 - [x] #6987 - [ ] #6999

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I_kwDODunzps6NTjeD

ImportError when importing datasets.load_dataset

closed

NONE

### 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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PR_kwDODunzps5zYYXr

Fix incorrect rank value in data splitting

closed

CONTRIBUTOR

Fix #6990.

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PR_kwDODunzps5zYN7P

less script docs

closed

MEMBER

+ mark as legacy in some parts of the docs since we'll not build new features for script datasets

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I_kwDODunzps6NIyS-

Dataset with streaming doesn't work with proxy

open

NONE

### 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 abseil-cpp 20220623.0 h9888cd1_6 conda-forge absl-py 1.0.0 py311h399429b_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 aiofiles 23.2.1 pyhd8ed1ab_0 conda-forge aiohttp 3.8.6 py311hf118e41_0 aiosignal 1.2.0 pyhd3eb1b0_0 archspec 0.2.3 pyhd8ed1ab_0 conda-forge arrow-cpp 11.0.0 ha3edaa6_5_cpu conda-forge async-timeout 4.0.2 py311h6ffa863_0 attrs 23.1.0 py311h6ffa863_0 av 10.0.0 py311he6153ed_2 https://ftp.osuosl.org/pub/open-ce/1.10.0 aws-c-auth 0.6.24 hb81f6d7_5 conda-forge aws-c-cal 0.5.20 h3c2b4d9_6 conda-forge aws-c-common 0.8.11 h4194056_0 conda-forge aws-c-compression 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ha17a0cc_1 conda-forge libxml2 2.10.4 h18e3229_1 libzlib 1.2.13 h1f2b957_6 conda-forge llvm-openmp 14.0.6 hc028133_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 lmdb 0.9.31 ha17a0cc_1 conda-forge lz4-c 1.9.4 h4a02239_0 markdown 3.4.4 pyhd8ed1ab_0 conda-forge markupsafe 2.1.5 py311h32d8acf_0 conda-forge matplotlib 3.8.0 py311h6ffa863_0 matplotlib-base 3.8.0 py311h52e1fcc_0 menuinst 2.1.1 py311h1af927a_0 conda-forge mpc 1.3.1 heaf1863_0 conda-forge mpfr 4.2.1 haad2271_1 conda-forge mpmath 1.3.0 pyhd8ed1ab_0 conda-forge multidict 6.0.2 py311hf118e41_0 multiprocess 0.70.14 py311h6ffa863_0 munkres 1.1.4 py_0 mypy_extensions 1.0.0 pyha770c72_0 conda-forge nccl 2.18.3 cuda11.8_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 ncurses 6.4 h4a02239_0 nest-asyncio 1.6.0 pyhd8ed1ab_0 conda-forge networkx 2.8.8 pyhd8ed1ab_0 conda-forge nomkl 3.0 0 https://ftp.osuosl.org/pub/open-ce/1.10.0 numactl 2.0.16 hba61f60_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 numexpr 2.8.7 py311hc46fc55_0 numpy 1.24.3 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pyhd3eb1b0_0 python-tzdata 2023.3 pyhd3eb1b0_0 python-xxhash 2.0.2 py311hf118e41_1 python_abi 3.11 4_cp311 conda-forge pytorch 2.0.1 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 pytorch-base 2.0.1 cuda11.8_py311_pb4.21.12_4 https://ftp.osuosl.org/pub/open-ce/1.10.0 pytz 2023.3.post1 py311h6ffa863_0 pyu2f 0.1.5 pyhd8ed1ab_0 conda-forge pyyaml 6.0.1 py311hf118e41_0 re2 2023.02.01 h883269e_0 conda-forge readline 8.2 hf118e41_0 regex 2023.10.3 py311hf118e41_0 reproc 14.2.4 h29c3540_1 reproc-cpp 14.2.4 h29c3540_1 requests 2.31.0 py311h6ffa863_0 requests-oauthlib 2.0.0 pyhd8ed1ab_0 conda-forge responses 0.13.3 pyhd3eb1b0_0 rsa 4.9 pyhd8ed1ab_0 conda-forge ruamel.yaml 0.17.21 py311hf118e41_0 s2n 1.3.37 h5e47323_0 conda-forge safetensors 0.4.0 py311hda16d9e_0 scipy 1.11.1 py311hd69e9bb_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 sentencepiece 0.1.97 h1e74c73_py311_pb4.21.12_2 https://ftp.osuosl.org/pub/open-ce/1.10.0 setuptools 68.0.0 py311h6ffa863_0 six 1.16.0 pyhd3eb1b0_1 snappy 1.1.9 h29c3540_0 sqlite 3.41.2 hf118e41_0 sympy 1.12.1 pypyh2585a3b_103 conda-forge tabulate 0.8.10 pyhd8ed1ab_0 conda-forge tensorboard 2.13.0 pyhab0730d_pb4.21.12_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 tensorboard-data-server 0.7.0 pyh6f84499_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 tensorboard-plugin-wit 1.6.0 pyh9f0ad1d_0 conda-forge tk 8.6.13 hd4bbf49_0 conda-forge tokenizers 0.13.3 py311h3d4f45a_0 torchdata 0.6.0 py311_2 https://ftp.osuosl.org/pub/open-ce/1.10.0 torchsnapshot 0.1.0 pyhd8ed1ab_0 conda-forge torchtext-base 0.15.2 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 torchtnt 0.2.4 pyhd8ed1ab_0 conda-forge torchvision-base 0.15.2 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 tornado 6.3.3 py311hf118e41_0 tqdm 4.65.0 py311h7837921_0 transformers 4.32.1 py311h6ffa863_0 truststore 0.8.0 py311h6ffa863_0 typing-extensions 4.7.1 py311h6ffa863_0 typing_extensions 4.7.1 py311h6ffa863_0 typing_inspect 0.9.0 pyhd8ed1ab_0 conda-forge tzdata 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PR_kwDODunzps5zPoQs

Unblock NumPy 2.0

closed

CONTRIBUTOR

Fixes https://github.com/huggingface/datasets/issues/6980

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I_kwDODunzps6NEGCx

Problematic rank after calling `split_dataset_by_node` twice

closed

CONTRIBUTOR

### 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 datasets==2.20.0

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I_kwDODunzps6M_4bh

cache in nfs error

open

NONE

### 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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PR_kwDODunzps5zDpXX

[`feat`] Move dataset card creation to method for easier overriding

open

MEMBER

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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PR_kwDODunzps5zCRH6

Remove beam

closed

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Remove beam, as part of the 3.0 release.

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PR_kwDODunzps5y-Zi0

Add large_list type support in string_to_arrow

open

NONE

add large_list type support in string_to_arrow() and _arrow_to_datasets_dtype() in features.py Fix #6984

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I_kwDODunzps6Mzwgk

AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'

closed

NONE

### Describe the bug 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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I_kwDODunzps6My3NC

Convert polars DataFrame back to datasets

open

NONE

### Feature request 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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PR_kwDODunzps5y7tK7

Remove metrics

closed

MEMBER

Remove all metrics, as part of the 3.0 release. Note they are deprecated since 2.5.0 version.

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I_kwDODunzps6MxBgd

cannot split dataset when using load_dataset

closed

NONE

### Describe the bug 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 VScode+jupyter

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