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[MINOR:TYPO] Fix typo in exception text
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Raise error for incorrect JSON serialization
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[ "PTAL @lhoestq @albertvillanova " ]
2024-11-01T11:54:35
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Raise error when `lines = False` and `batch_size < Dataset.num_rows` in `Dataset.to_json()`. Issue: #7037 Related PRs: #7039 #7181
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fix conda release worlflow
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Set dev version
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Release: 3.1.0
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Memory leak when streaming
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2024-10-31T13:33:52
2024-10-31T13:34:24
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### Describe the bug I try to use a dataset with streaming=True, the issue I have is that the RAM usage becomes higher and higher until it is no longer sustainable. I understand that huggingface store data in ram during the streaming, and more worker in dataloader there are, more a lot of shard will be stored in ram, but the issue I have is that the ram usage is not constant. So after each new shard loaded, the ram usage will be higher and higher. ### Steps to reproduce the bug You can run this code and see you ram usage, after each shard of 255 examples, your ram usage will be extended. ```py from datasets import load_dataset from torch.utils.data import DataLoader dataset = load_dataset("WaveGenAI/dataset", streaming=True) dataloader = DataLoader(dataset["train"], num_workers=3) for i, data in enumerate(dataloader): print(i, end="\r") ``` ### Expected behavior The Ram usage should be always the same (just 3 shards loaded in the ram). ### Environment info - `datasets` version: 3.0.1 - Platform: Linux-6.10.5-arch1-1-x86_64-with-glibc2.40 - Python version: 3.12.4 - `huggingface_hub` version: 0.26.0 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1
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load_from_disk
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[ "Hello, It's an interesting issue here. I have the same problem, I have a local dataset and I want to push the dataset to the hub but huggingface does a copy of it.\r\n\r\n```py\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"webdataset\", data_files=\"/media/works/data/*.tar\") # copy here\r\ndataset.push_to_hub(\"WaveGenAI/audios2\")\r\n```\r\n\r\nEdit: I can use HfApi for my use case\r\n" ]
2024-10-31T11:51:56
2024-10-31T14:43:47
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### Describe the bug I have data saved with save_to_disk. The data is big (700Gb). When I try loading it, the only option is load_from_disk, and this function copies the data to a tmp directory, causing me to run out of disk space. Is there an alternative solution to that? ### Steps to reproduce the bug when trying to load data using load_From_disk after being saved using save_to_disk ### Expected behavior run out of disk space ### Environment info lateest version
null
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2,626,490,029
I_kwDODunzps6cjQ6t
7,267
Source installation fails on Macintosh with python 3.10
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2024-10-31T10:18:45
2024-10-31T10:18:45
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### Describe the bug Hi, Decord is a dev dependency not maintained since couple years. It does not have an ARM package available rendering it uninstallable on non-intel based macs Suggestion is to move to eva-decord (https://github.com/georgia-tech-db/eva-decord) which doesnt have this problem. Happy to raise a PR ### Steps to reproduce the bug Source installation as mentioned in contributinog.md ### Expected behavior Installation without decord failing to be installed. ### Environment info python=3.10, M3 Mac
null
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I_kwDODunzps6ccTnn
7,266
The dataset viewer should be available soon. Please retry later.
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[ "Waiting is all you need. 10 hours later, it works." ]
2024-10-30T16:32:00
2024-10-31T03:48:11
2024-10-31T03:48:10
NONE
null
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### Describe the bug After waiting for 2 hours, it still presents ``The dataset viewer should be available soon. Please retry later.'' ### Steps to reproduce the bug dataset link: https://huggingface.co/datasets/BryanW/HI_EDIT ### Expected behavior Present the dataset viewer. ### Environment info NA
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7,265
Disallow video push_to_hub
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7265). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]
2024-10-30T13:21:55
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fix docs relative links
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Small addition to video docs
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Allow video with disabeld decoding without decord
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for the viewer, this way it can use Video(decode=False) and doesn't need decord (which causes segfaults)
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7,261
Cannot load the cache when mapping the dataset
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2024-10-29T08:29:40
2024-10-29T08:29:40
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### Describe the bug I'm training the flux controlnet. The train_dataset.map() takes long time to finish. However, when I killed one training process and want to restart a new training with the same dataset. I can't reuse the mapped result even I defined the cache dir for the dataset. with accelerator.main_process_first(): from datasets.fingerprint import Hasher # fingerprint used by the cache for the other processes to load the result # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401 new_fingerprint = Hasher.hash(args) train_dataset = train_dataset.map( compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint, batch_size=10, ) ### Steps to reproduce the bug train flux controlnet and start again ### Expected behavior will not map again ### Environment info latest diffusers
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7,260
cache can't cleaned or disabled
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2024-10-29T03:15:28
2024-10-29T03:18:22
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### Describe the bug I tried following ways, the cache can't be disabled. I got 2T data, but I also got more than 2T cache file. I got pressure on storage. I need to diable cache or cleaned immediately after processed. Following ways are all not working, please give some help! ```python from datasets import disable_caching from transformers import AutoTokenizer disable_caching() tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path) def tokenization_fn(examples): column_name = 'text' if 'text' in examples else 'data' tokenized_inputs = tokenizer( examples[column_name], return_special_tokens_mask=True, truncation=False, max_length=tokenizer.model_max_length ) return tokenized_inputs data = load_dataset('json', data_files=save_local_path, split='train', cache_dir=None) data.cleanup_cache_files() updated_dataset = data.map(tokenization_fn, load_from_cache_file=False) updated_dataset .cleanup_cache_files() ``` ### Expected behavior no cache file generated ### Environment info Ubuntu 20.04.6 LTS datasets 3.0.2
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7,259
Don't embed videos
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2024-10-28T16:25:10
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don't include video bytes when running download_and_prepare(format="parquet") this also affects push_to_hub which will just upload the local paths of the videos though
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Always set non-null writer batch size
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bug introduced in #7230, it was preventing the Viewer limit writes to work
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fix ci for pyarrow 18
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Retry all requests timeouts
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as reported in https://github.com/huggingface/datasets/issues/6843
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fix decord import
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delay the import until Video() is instantiated + also import duckdb first (otherwise importing duckdb later causes a segfault)
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2,616,174,996
I_kwDODunzps6b76mU
7,254
mismatch for datatypes when providing `Features` with `Array2D` and user specified `dtype` and using with_format("numpy")
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[ "It seems that https://github.com/huggingface/datasets/issues/5517 is exactly the same issue.\r\n\r\nIt was mentioned there that this would be fixed in version 3.x" ]
2024-10-26T22:06:27
2024-10-26T22:07:37
null
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### Describe the bug If the user provides a `Features` type value to `datasets.Dataset` with members having `Array2D` with a value for `dtype`, it is not respected during `with_format("numpy")` which should return a `np.array` with `dtype` that the user provided for `Array2D`. It seems for floats, it will be set to `float32` and for ints it will be set to `int64` ### Steps to reproduce the bug ```python import numpy as np import datasets from datasets import Dataset, Features, Array2D print(f"datasets version: {datasets.__version__}") data_info = { "arr_float" : "float64", "arr_int" : "int32" } sample = {key : [np.zeros([4, 5], dtype=dtype)] for key, dtype in data_info.items()} features = {key : Array2D(shape=(None, 5), dtype=dtype) for key, dtype in data_info.items()} features = Features(features) dataset = Dataset.from_dict(sample, features=features) ds = dataset.with_format("numpy") for key in features: print(f"{key} feature dtype: ", ds.features[key].dtype) print(f"{key} dtype:", ds[key].dtype) ``` Output: ```bash datasets version: 3.0.2 arr_float feature dtype: float64 arr_float dtype: float32 arr_int feature dtype: int32 arr_int dtype: int64 ``` ### Expected behavior It should return a `np.array` with `dtype` that the user provided for the corresponding member in the `Features` type value ### Environment info - `datasets` version: 3.0.2 - Platform: Linux-6.11.5-arch1-1-x86_64-with-glibc2.40 - Python version: 3.12.7 - `huggingface_hub` version: 0.26.1 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.5.0
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2,615,862,202
I_kwDODunzps6b6uO6
7,253
Unable to upload a large dataset zip either from command line or UI
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2024-10-26T13:17:06
2024-10-26T13:17:06
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### Describe the bug Unable to upload a large dataset zip from command line or UI. UI simply says error. I am trying to a upload a tar.gz file of 17GB. <img width="550" alt="image" src="https://github.com/user-attachments/assets/f9d29024-06c8-49c4-a109-0492cff79d34"> <img width="755" alt="image" src="https://github.com/user-attachments/assets/a8d4acda-7f02-4279-9c2d-b2e0282b4faa"> ### Steps to reproduce the bug Upload a large file ### Expected behavior The file should upload without any issue. ### Environment info None
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PR_kwDODunzps5_41s7
7,252
Add IterableDataset.shard()
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7252). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]
2024-10-25T11:07:12
2024-10-25T15:45:24
2024-10-25T15:45:22
MEMBER
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Will be useful to distribute a dataset across workers (other than pytorch) like spark I also renamed `.n_shards` -> `.num_shards` for consistency and kept the old name for backward compatibility. And a few changes in internal functions for consistency as well (rank, world_size -> num_shards, index) Breaking change: the new default for `contiguous` in `Dataset.shard()` is `True`, but imo not a big deal since I couldn't find any usage of `contiguous=False` internally (we always do contiguous=True for map-style datasets since its more optimized) or in the wild
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2,612,097,435
PR_kwDODunzps5_zPTt
7,251
Missing video docs
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7251). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]
2024-10-24T16:45:12
2024-10-24T16:48:29
2024-10-24T16:48:27
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Basic XML support (mostly copy pasted from text)
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2024-10-24T16:14:50
2024-10-24T16:19:18
2024-10-24T16:19:16
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enable the viewer for datasets like https://huggingface.co/datasets/FrancophonIA/e-calm (there will be more and more apparently)
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How to debugging
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2024-10-24T01:03:51
2024-10-24T01:03:51
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### Describe the bug I wanted to use my own script to handle the processing, and followed the tutorial documentation by rewriting the MyDatasetConfig and MyDatasetBuilder (which contains the _info,_split_generators and _generate_examples methods) classes. Testing with simple data was able to output the results of the processing, but when I wished to do more complex processing, I found that I was unable to debug (even the simple samples were inaccessible). There are no errors reported, and I am able to print the _info,_split_generators and _generate_examples messages, but I am unable to access the breakpoints. ### Steps to reproduce the bug # my_dataset.py import json import datasets class MyDatasetConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(MyDatasetConfig, self).__init__(**kwargs) class MyDataset(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ MyDatasetConfig( name="default", version=VERSION, description="myDATASET" ), ] def _info(self): print("info") # breakpoints return datasets.DatasetInfo( description="myDATASET", features=datasets.Features( { "id": datasets.Value("int32"), "text": datasets.Value("string"), "label": datasets.ClassLabel(names=["negative", "positive"]), } ), supervised_keys=("text", "label"), ) def _split_generators(self, dl_manager): print("generate") # breakpoints data_file = "data.json" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_file} ), ] def _generate_examples(self, filepath): print("example") # breakpoints with open(filepath, encoding="utf-8") as f: data = json.load(f) for idx, sample in enumerate(data): yield idx, { "id": sample["id"], "text": sample["text"], "label": sample["label"], } #main.py import os os.environ["TRANSFORMERS_NO_MULTIPROCESSING"] = "1" from datasets import load_dataset dataset = load_dataset("my_dataset.py", split="train", cache_dir=None) print(dataset[:5]) ### Expected behavior Pause at breakpoints while running debugging ### Environment info pycharm
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ModuleNotFoundError: No module named 'datasets.tasks'
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[ "tasks was removed in v3: #6999 \r\n\r\nI also don't see why TextClassification is imported, since it's not used after. So the fix is simple: delete this line.", "I opened https://huggingface.co/datasets/knowledgator/events_classification_biotech/discussions/7 to remove the line, hopefully the dataset owner will merge it soon" ]
2024-10-23T21:58:25
2024-10-24T17:00:19
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### Describe the bug --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) [<ipython-input-9-13b5f31bd391>](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in <cell line: 1>() ----> 1 dataset = load_dataset('knowledgator/events_classification_biotech') 11 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs) 2130 2131 # Create a dataset builder -> 2132 builder_instance = load_dataset_builder( 2133 path=path, 2134 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs) 1886 raise ValueError(error_msg) 1887 -> 1888 builder_cls = get_dataset_builder_class(dataset_module, dataset_name=dataset_name) 1889 # Instantiate the dataset builder 1890 builder_instance: DatasetBuilder = builder_cls( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in get_dataset_builder_class(dataset_module, dataset_name) 246 dataset_module.importable_file_path 247 ) if dataset_module.importable_file_path else nullcontext(): --> 248 builder_cls = import_main_class(dataset_module.module_path) 249 if dataset_module.builder_configs_parameters.builder_configs: 250 dataset_name = dataset_name or dataset_module.builder_kwargs.get("dataset_name") [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in import_main_class(module_path) 167 def import_main_class(module_path) -> Optional[Type[DatasetBuilder]]: 168 """Import a module at module_path and return its main class: a DatasetBuilder""" --> 169 module = importlib.import_module(module_path) 170 # Find the main class in our imported module 171 module_main_cls = None [/usr/lib/python3.10/importlib/__init__.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in import_module(name, package) 124 break 125 level += 1 --> 126 return _bootstrap._gcd_import(name[level:], package, level) 127 128 /usr/lib/python3.10/importlib/_bootstrap.py in _gcd_import(name, package, level) /usr/lib/python3.10/importlib/_bootstrap.py in _find_and_load(name, import_) /usr/lib/python3.10/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_) /usr/lib/python3.10/importlib/_bootstrap.py in _load_unlocked(spec) /usr/lib/python3.10/importlib/_bootstrap_external.py in exec_module(self, module) /usr/lib/python3.10/importlib/_bootstrap.py in _call_with_frames_removed(f, *args, **kwds) [~/.cache/huggingface/modules/datasets_modules/datasets/knowledgator--events_classification_biotech/9c8086d498c3104de3a3c5b6640837e18ccd829dcaca49f1cdffe3eb5c4a6361/events_classification_biotech.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in <module> 1 import datasets 2 from datasets import load_dataset ----> 3 from datasets.tasks import TextClassification 4 5 DESCRIPTION = """ ModuleNotFoundError: No module named 'datasets.tasks' --------------------------------------------------------------------------- NOTE: If your import is failing due to a missing package, you can manually install dependencies using either !pip or !apt. To view examples of installing some common dependencies, click the "Open Examples" button below. --------------------------------------------------------------------------- ### Steps to reproduce the bug !pip install datasets from datasets import load_dataset dataset = load_dataset('knowledgator/events_classification_biotech') ### Expected behavior no ModuleNotFoundError ### Environment info google colab
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Adding column with dict struction when mapping lead to wrong order
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2024-10-22T18:55:11
2024-10-22T18:55:23
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### Describe the bug in `map()` function, I want to add a new column with a dict structure. ``` def map_fn(example): example['text'] = {'user': ..., 'assistant': ...} return example ``` However this leads to a wrong order `{'assistant':..., 'user':...}` in the dataset. Thus I can't concatenate two datasets due to the different feature structures. [Here](https://colab.research.google.com/drive/1zeaWq9Ith4DKWP_EiBNyLfc8S8I68LyY?usp=sharing) is a minimal reproducible example This seems an issue in low level pyarrow library instead of datasets, however, I think datasets should allow concatenate two datasets actually in the same structure. ### Steps to reproduce the bug [Here](https://colab.research.google.com/drive/1zeaWq9Ith4DKWP_EiBNyLfc8S8I68LyY?usp=sharing) is a minimal reproducible example ### Expected behavior two datasets could be concatenated. ### Environment info N/A
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Set dev version
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2024-10-22T15:04:47
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use huggingface_hub offline mode
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2024-10-22T13:27:16
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and better handling of LocalEntryNotfoundError cc @Wauplin follow up to #7234
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7,243
ArrayXD with None as leading dim incompatible with DatasetCardData
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[ "It looks like `CardData` in `huggingface_hub` removes None values where it shouldn't. Indeed it calls `_remove_none` on the return of `to_dict()`:\r\n\r\n```python\r\n def to_dict(self) -> Dict[str, Any]:\r\n \"\"\"Converts CardData to a dict.\r\n\r\n Returns:\r\n `dict`: CardData represented as a dictionary ready to be dumped to a YAML\r\n block for inclusion in a README.md file.\r\n \"\"\"\r\n\r\n data_dict = copy.deepcopy(self.__dict__)\r\n self._to_dict(data_dict)\r\n return _remove_none(data_dict)\r\n```\r\n\r\nWould it be ok to remove `list()` from being scanned in `_remove_none` ? it could also be a specific behavior to DatasetCardData if necessary @Wauplin ", "I have actually no idea why none values are removed in model and dataset card data... :see_no_evil:\r\nLooks like `_remove_none` has been introduced at the same time as the entire repocard module (see https://github.com/huggingface/huggingface_hub/pull/940). I would be tempted to remove `_remove_none` entirely actually and only remove \"top-level\" None values (i.e. if something like `pipeline_tag=None` due to a default value in kwargs => we remove it). Hard to tell what could be the side effects but I'm not against trying.\r\n\r\n\r\nHowever, I'm not really in favor in making an exception only for lists. It would mean that tuples, sets and dicts are filtered but not lists, which is pretty inconsistent.", "let's do it for top level attributes yes", "I opened https://github.com/huggingface/huggingface_hub/pull/2626 to address it :)", "thanks !" ]
2024-10-21T15:08:13
2024-10-22T14:18:10
null
CONTRIBUTOR
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### Describe the bug Creating a dataset with ArrayXD features leads to errors when downloading from hub due to DatasetCardData removing the Nones @lhoestq ### Steps to reproduce the bug ```python import numpy as np from datasets import Array2D, Dataset, Features, load_dataset def examples_generator(): for i in range(4): yield { "array_1d": np.zeros((10,1), dtype="uint16"), "array_2d": np.zeros((10, 1), dtype="uint16"), } features = Features(array_1d=Array2D((None,1), "uint16"), array_2d=Array2D((None, 1), "uint16")) dataset = Dataset.from_generator(examples_generator, features=features) dataset.push_to_hub("alex-hh/test_array_1d2d") ds = load_dataset("alex-hh/test_array_1d2d") ``` Source of error appears to be DatasetCardData.to_dict invoking DatasetCardData._remove_none ```python from huggingface_hub import DatasetCardData from datasets.info import DatasetInfosDict dataset_card_data = DatasetCardData() DatasetInfosDict({"default": dataset.info.copy()}).to_dataset_card_data(dataset_card_data) print(dataset_card_data.to_dict()) # removes Nones in shape ``` ### Expected behavior Should be possible to load datasets saved with shape None in leading dimension ### Environment info 3.0.2 and latest huggingface_hub
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`push_to_hub` overwrite argument
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[ "Hi ! Do you mean deleting all the files ? or erasing the repository git history before push_to_hub ?", "Hi! I meant the latter.", "I don't think there is a `huggingface_hub` utility to erase the git history, cc @Wauplin maybe ?", "What is the goal exactly of deleting all the git history without deleting the repo? ", "You can use [`super_squash_commit`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/hf_api#huggingface_hub.HfApi.super_squash_history) to squash all the commits into a single one, hence deleting the git history. This is not exactly what you asked for since it squashes the commits for a specific revision (example: \"all commits on main\"). This means that if other branches exists, they are kept the same. Also if some PRs are already opened on the repo, they will become unmergeable since the commits will have diverted.", "So the solution is:\r\n\r\n```python\r\nfrom huggingface_hub import HfApi\r\nrepo_id = \"username/dataset_name\"\r\nds.push_to_hub(repo_id)\r\nHfApi().super_squash_commit(repo_id)\r\n```\r\n\r\nThis way you erase previous git history to end up with only 1 commit containing your dataset.\r\nStill, I'd be curious why it's important in your case. Is it to save storage space ? or to disallow loading old versions of the data ?", "Thanks, everyone! I am building a new dataset and playing around with column names, splits, etc. Sometimes I push to the hub to share it with other teammates, I don't want those variations to be part of the repo. Deleting the repo from the website takes a little time, but it also loses repo settings that I have set, since I always set it to public with manually approved requests.\r\n\r\nBTW, I had to write `HfApi().super_squash_history(repo_id, repo_type=\"dataset\")`, but otherwise it works.", "@ceferisbarov just to let you know, recreating a gated repo + granting access to your teammates is something that you can automate with something like this (not fully tested but should work):\r\n\r\n```py\r\nfrom huggingface_hub import HfApi\r\n\r\napi = HfApi()\r\napi.delete_repo(repo_id, repo_type=\"dataset\", missing_ok=True)\r\napi.create_repo(repo_id, repo_type=\"dataset\", private=False)\r\napi.update_repo_settings(repo_id, repo_type=\"dataset\", gated=\"manual\")\r\nfor user in [\"user1\", \"user2\"] # list of teammates\r\n api.grant_access(repo_id, user, repo_type=\"dataset\")\r\n```\r\n\r\nI think it'd be a better solution than squashing commits (which is more of a hack), typically if you are using the dataset viewer.", "This is great, @Wauplin. If we can achieve this with HfApi, then we probably don't need to add another parameter to push_to_hub. I am closing the issue." ]
2024-10-20T03:23:26
2024-10-24T17:39:08
2024-10-24T17:39:08
NONE
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### Feature request Add an `overwrite` argument to the `push_to_hub` method. ### Motivation I want to overwrite a repo without deleting it on Hugging Face. Is this possible? I couldn't find anything in the documentation or tutorials. ### Your contribution I can create a PR.
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7,240
Feature Request: Add functionality to pass split types like train, test in DatasetDict.map
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2024-10-19T09:59:12
2024-10-19T09:59:12
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Hello datasets! We often encounter situations where we need to preprocess data differently depending on split types such as train, valid, and test. However, while DatasetDict.map has features to pass rank or index, there's no functionality to pass split types. Therefore, I propose adding a 'with_splits' parameter to DatasetDict, which would allow passing the split type through fn_kwargs.
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7,238
incompatibily issue when using load_dataset with datasets==3.0.1
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2024-10-18T21:25:23
2024-10-18T21:25:23
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### Describe the bug There is a bug when using load_dataset with dataset version at 3.0.1 . Please see below in the "steps to reproduce the bug". To resolve the bug, I had to downgrade to version 2.21.0 OS: Ubuntu 24 (AWS instance) Python: same bug under 3.12 and 3.10 The error I had was: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/load.py", line 2096, in load_dataset builder_instance.download_and_prepare( File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 924, in download_and_prepare self._download_and_prepare( File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 1647, in _download_and_prepare super()._download_and_prepare( File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 977, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_6_0/cb17afd34f5799f97e8f48398748f83006335b702bd785f9880797838d541b81/common_voice_6_0.py", line 159, in _split_generators archive_path = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template)) File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/download/download_manager.py", line 150, in download download_config = self.download_config.copy() File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/download/download_config.py", line 73, in copy return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) TypeError: DownloadConfig.__init__() got an unexpected keyword argument 'ignore_url_params' ### Steps to reproduce the bug 1. install dataset with ```pip install datasets --upgrade``` 2. launch python; from datasets import loaad_dataset 3. run load_dataset("mozilla-foundation/common_voice_6_0") 4. exit python 5. uninstall datasets; then ```pip install datasets==2.21.0``` 6. launch python; from datasets import loaad_dataset 7. run load_dataset("mozilla-foundation/common_voice_6_0") 8. Everything runs great now ### Expected behavior Be able to download a dataset without error ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 3.0.1 - Platform: Linux-6.8.0-1017-aws-x86_64-with-glibc2.39 - Python version: 3.12.4 - `huggingface_hub` version: 0.26.0 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1
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[MINOR:TYPO] Update arrow_dataset.py
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2024-10-18T12:10:03
2024-10-24T15:06:43
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Fix wrong link. csv kwargs docstring link was pointing to pandas json docs.
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No need for dataset_info
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7234). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "merging this one for now, let me know if you'd like to see additional changes for error handling (I'll take care of them before doing a release)" ]
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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/7234", "html_url": "https://github.com/huggingface/datasets/pull/7234", "diff_url": "https://github.com/huggingface/datasets/pull/7234.diff", "patch_url": "https://github.com/huggingface/datasets/pull/7234.patch", "merged_at": "2024-10-21T16:44:34" }
save a useless call to /api/datasets/repo_id
{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "user_view_type": "public", "site_admin": false }
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https://api.github.com/repos/huggingface/datasets/issues/7234/timeline
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README.md exists but content is empty.
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