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PR_kwDODunzps58WlX5

Support JSON lines with empty struct

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Support JSON lines with empty struct. Fix #7161. Related to: - #7160

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I_kwDODunzps6Xg2nb

JSON lines with empty struct raise ArrowTypeError

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JSON lines with empty struct raise ArrowTypeError: struct fields don't match or are in the wrong order See example: https://huggingface.co./datasets/wikimedia/structured-wikipedia/discussions/5 > ArrowTypeError: struct fields don't match or are in the wrong order: Input fields: struct<> output fields: struct<pov_count: int64, update_count: int64, citation_needed_count: int64> Related to: - #7159

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PR_kwDODunzps58VDhq

Support JSON lines with missing struct fields

closed

MEMBER

Support JSON lines with missing struct fields. Fix #7159. The implemented test raised: ``` TypeError: Couldn't cast array of type struct<age: int64> to {'age': Value(dtype='int32', id=None), 'name': Value(dtype='string', id=None)} ```

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I_kwDODunzps6XgcqN

JSON lines with missing struct fields raise TypeError: Couldn't cast array

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JSON lines with missing struct fields raise TypeError: Couldn't cast array of type. See example: https://huggingface.co./datasets/wikimedia/structured-wikipedia/discussions/5 One would expect that the struct missing fields are added with null values.

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PR_kwDODunzps58Tuw9

google colab ex

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PR_kwDODunzps58P-1R

Fix zero proba interleave datasets

closed

MEMBER

fix https://github.com/huggingface/datasets/issues/7147

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I_kwDODunzps6XW5Fp

interleave_datasets resets shuffle state

open

NONE

### Describe the bug ``` import datasets import torch.utils.data def gen(shards): yield {"shards": shards} def main(): dataset = datasets.IterableDataset.from_generator( gen, gen_kwargs={'shards': list(range(25))} ) dataset = dataset.shuffle(buffer_size=1) dataset = datasets.interleave_datasets( [dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted" ) dataloader = torch.utils.data.DataLoader( dataset, batch_size=8, num_workers=8, ) for i, batch in enumerate(dataloader): print(batch) if i >= 10: break if __name__ == "__main__": main() ``` ### Steps to reproduce the bug Run the script, it will output ``` {'shards': [tensor([ 0, 8, 16, 24, 0, 8, 16, 24])]} {'shards': [tensor([ 1, 9, 17, 1, 9, 17, 1, 9])]} {'shards': [tensor([ 2, 10, 18, 2, 10, 18, 2, 10])]} {'shards': [tensor([ 3, 11, 19, 3, 11, 19, 3, 11])]} {'shards': [tensor([ 4, 12, 20, 4, 12, 20, 4, 12])]} {'shards': [tensor([ 5, 13, 21, 5, 13, 21, 5, 13])]} {'shards': [tensor([ 6, 14, 22, 6, 14, 22, 6, 14])]} {'shards': [tensor([ 7, 15, 23, 7, 15, 23, 7, 15])]} {'shards': [tensor([ 0, 8, 16, 24, 0, 8, 16, 24])]} {'shards': [tensor([17, 1, 9, 17, 1, 9, 17, 1])]} {'shards': [tensor([18, 2, 10, 18, 2, 10, 18, 2])]} ``` ### Expected behavior The shards should be shuffled. ### Environment info - `datasets` version: 3.0.0 - Platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.25.0 - PyArrow version: 17.0.0 - Pandas version: 2.0.3 - `fsspec` version: 2023.6.0

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I_kwDODunzps6XBE6O

Dataset viewer not working! Failure due to more than 32 splits.

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NONE

Hello guys, I have a dataset and I didn't know I couldn't upload more than 32 splits. Now, my dataset viewer is not working. I don't have the dataset locally on my node anymore and recreating would take a week. And I have to publish the dataset coming Monday. I read about the practice, how I can resolve it and avoid this issue in the future. But, at the moment I need a hard fix for two of my datasets. And I don't want to mess or change anything and allow everyone in public to see the dataset and interact with it. Can you please help me? https://huggingface.co./datasets/laion/Wikipedia-X https://huggingface.co./datasets/laion/Wikipedia-X-Full

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PR_kwDODunzps572hBd

Support ndjson data files

closed

MEMBER

Support `ndjson` (Newline Delimited JSON) data files. Fix #7153.

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I_kwDODunzps6W90lL

Support data files with .ndjson extension

closed

MEMBER

### Feature request Support data files with `.ndjson` extension. ### Motivation We already support data files with `.jsonl` extension. ### Your contribution I am opening a PR.

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PR_kwDODunzps57kyY4

Align filename prefix splitting with WebDataset library

closed

MEMBER

Align filename prefix splitting with WebDataset library. This PR uses the same `base_plus_ext` function as the one used by the `webdataset` library. Fix #7150. Related to #7144.

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I_kwDODunzps6Wp6zn

WebDataset loader splits keys differently than WebDataset library

closed

MEMBER

As reported by @ragavsachdeva (see discussion here: https://github.com/huggingface/datasets/pull/7144#issuecomment-2348307792), our webdataset loader is not aligned with the `webdataset` library when splitting keys from filenames. For example, we get a different key splitting for filename `/some/path/22.0/1.1.png`: - datasets library: `/some/path/22` and `0/1.1.png` - webdataset library: `/some/path/22.0/1`, `1.png` ```python import webdataset as wds wds.tariterators.base_plus_ext("/some/path/22.0/1.1.png") # ('/some/path/22.0/1', '1.png') ```

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I_kwDODunzps6WeMYo

Datasets Unknown Keyword Argument Error - task_templates

closed

NONE

### Describe the bug Issue ```python from datasets import load_dataset examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>) ``` Gives error ``` TypeError: DatasetInfo.__init__() got an unexpected keyword argument 'task_templates' ``` A simple downgrade to lower `datasets v 2.21.0` solves it. ### Steps to reproduce the bug 1. `pip install datsets` 2. ```python from datasets import load_dataset examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>) ``` ### Expected behavior Should load the dataset correctly. ### Environment info - Datasets version `3.0.0` - `transformers` version: 4.45.0.dev0 - Platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.35 - Python version: 3.12.4 - Huggingface_hub version: 0.24.6 - Safetensors version: 0.4.5 - Accelerate version: 0.35.0.dev0 - Accelerate config: not found - PyTorch version (GPU?): 2.4.1+cu121 (True) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: Yes

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I_kwDODunzps6WbqRF

Bug: Error when downloading mteb/mtop_domain

closed

NONE

### Describe the bug When downloading the dataset "mteb/mtop_domain", ran into the following error: ``` Traceback (most recent call last): File "/share/project/xzy/test/test_download.py", line 3, in <module> data = load_dataset("mteb/mtop_domain", "en", trust_remote_code=True) File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2606, in load_dataset builder_instance = load_dataset_builder( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2277, in load_dataset_builder dataset_module = dataset_module_factory( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1923, in dataset_module_factory raise e1 from None File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1896, in dataset_module_factory ).get_module() File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1507, in get_module local_path = self.download_loading_script() File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1467, in download_loading_script return cached_path(file_path, download_config=download_config) File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 211, in cached_path output_path = get_from_cache( File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 689, in get_from_cache fsspec_get( File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 395, in fsspec_get fs.get_file(path, temp_file.name, callback=callback) File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 648, in get_file http_get( File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 578, in http_get raise EnvironmentError( OSError: Consistency check failed: file should be of size 2191 but has size 2190 ((…)ets/mteb/mtop_domain@main/mtop_domain.py). We are sorry for the inconvenience. Please retry with `force_download=True`. If the issue persists, please let us know by opening an issue on https://github.com/huggingface/huggingface_hub. ``` Try to download through HF datasets directly but got the same error as above. ```python from datasets import load_dataset data = load_dataset("mteb/mtop_domain", "en") ``` ### Steps to reproduce the bug ```python from datasets import load_dataset data = load_dataset("mteb/mtop_domain", "en", force_download=True) ``` With and without `force_download=True` both ran into the same error. ### Expected behavior Should download the dataset successfully. ### Environment info - datasets version: 2.21.0 - huggingface-hub version: 0.24.6

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I_kwDODunzps6WY-Z5

IterableDataset strange deadlock

closed

NONE

### Describe the bug ``` import datasets import torch.utils.data num_shards = 1024 def gen(shards): for shard in shards: if shard < 25: yield {"shard": shard} def main(): dataset = datasets.IterableDataset.from_generator( gen, gen_kwargs={"shards": list(range(num_shards))}, ) dataset = dataset.shuffle(buffer_size=1) dataset = datasets.interleave_datasets( [dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted" ) dataset = dataset.shuffle(buffer_size=1) dataloader = torch.utils.data.DataLoader( dataset, batch_size=8, num_workers=8, ) for i, batch in enumerate(dataloader): print(batch) if i >= 10: break print() if __name__ == "__main__": for _ in range(100): main() ``` ### Steps to reproduce the bug Running the script above, at some point it will freeze. - Changing `num_shards` from 1024 to 25 avoids the issue - Commenting out the final shuffle avoids the issue - Commenting out the interleave_datasets call avoids the issue As an aside, if you comment out just the final shuffle, the output from interleave_datasets is not shuffled at all even though there's the shuffle before it. So something about that shuffle config is not being propagated to interleave_datasets. ### Expected behavior The script should not freeze. ### Environment info - `datasets` version: 3.0.0 - Platform: macOS-14.6.1-arm64-arm-64bit - Python version: 3.12.5 - `huggingface_hub` version: 0.24.7 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.6.1 I observed this with 2.21.0 initially, then tried upgrading to 3.0.0 and could still repro.

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PR_kwDODunzps57KqRV

Set dev version

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MEMBER

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PR_kwDODunzps57Kjjc

Release: 3.0.0

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PR_kwDODunzps57JLmb

Fix key error in webdataset

closed

NONE

I was running into ``` example[field_name] = {"path": example["__key__"] + "." + field_name, "bytes": example[field_name]} KeyError: 'png' ``` The issue is that a filename may have multiple "." e.g. `22.05.png`. Changing `split` to `rsplit` fixes it. Related https://github.com/huggingface/datasets/issues/6880

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PR_kwDODunzps56xCm6

Modify add_column() to optionally accept a FeatureType as param

closed

CONTRIBUTOR

Fix #7142. **Before (Add + Cast)**: ``` from datasets import load_dataset, Value ds = load_dataset("rotten_tomatoes", split="test") lst = [i for i in range(len(ds))] ds = ds.add_column("new_col", lst) # Assigns int64 to new_col by default print(ds.features) ds = ds.cast_column("new_col", Value(dtype="uint16", id=None)) print(ds.features) ``` **Before (Numpy Workaround)**: ``` from datasets import load_dataset import numpy as np ds = load_dataset("rotten_tomatoes", split="test") lst = [i for i in range(len(ds))] ds = ds.add_column("new_col", np.array(lst, dtype=np.uint16)) print(ds.features) ``` **After**: ``` from datasets import load_dataset, Value ds = load_dataset("rotten_tomatoes", split="test") lst = [i for i in range(len(ds))] val = Value(dtype="uint16", id=None)) ds = ds.add_column("new_col", lst, feature=val) print(ds.features) ```

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I_kwDODunzps6VvdDK

Specifying datatype when adding a column to a dataset.

closed

CONTRIBUTOR

### Feature request There should be a way to specify the datatype of a column in `datasets.add_column()`. ### Motivation To specify a custom datatype, we have to use `datasets.add_column()` followed by `datasets.cast_column()` which is slow for large datasets. Another workaround is to pass a `numpy.array()` of desired type to the `datasets.add_column()` function. IMO this functionality should be natively supported. https://discuss.huggingface.co/t/add-column-with-a-particular-type-in-datasets/95674 ### Your contribution I can submit a PR for this.

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I_kwDODunzps6Vp7tV

Older datasets throwing safety errors with 2.21.0

closed

NONE

### Describe the bug The dataset loading was throwing some safety errors for this popular dataset `wmt14`. [in]: ``` import datasets # train_data = datasets.load_dataset("wmt14", "de-en", split="train") train_data = datasets.load_dataset("wmt14", "de-en", split="train") val_data = datasets.load_dataset("wmt14", "de-en", split="validation[:10%]") ``` [out]: ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-9-445f0ecc4817>](https://localhost:8080/#) in <cell line: 4>() 2 3 # train_data = datasets.load_dataset("wmt14", "de-en", split="train") ----> 4 train_data = datasets.load_dataset("wmt14", "de-en", split="train") 5 val_data = datasets.load_dataset("wmt14", "de-en", split="validation[:10%]") 12 frames [/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py](https://localhost:8080/#) in __init__(self, **kwargs) 636 if security is not None: 637 security = BlobSecurityInfo( --> 638 safe=security["safe"], av_scan=security["avScan"], pickle_import_scan=security["pickleImportScan"] 639 ) 640 self.security = security KeyError: 'safe' ``` ### Steps to reproduce the bug See above. ### Expected behavior Dataset properly loaded. ### Environment info version: 2.21.0

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I_kwDODunzps6Vfj8K

Use load_dataset to load imagenet-1K But find a empty dataset

open

NONE

### Describe the bug ```python def get_dataset(data_path, train_folder="train", val_folder="val"): traindir = os.path.join(data_path, train_folder) valdir = os.path.join(data_path, val_folder) def transform_val_examples(examples): transform = Compose([ Resize(256), CenterCrop(224), ToTensor(), ]) examples["image"] = [transform(image.convert("RGB")) for image in examples["image"]] return examples def transform_train_examples(examples): transform = Compose([ RandomResizedCrop(224), RandomHorizontalFlip(), ToTensor(), ]) examples["image"] = [transform(image.convert("RGB")) for image in examples["image"]] return examples # @fengsicheng: This way is very slow for big dataset like ImageNet-1K (but can pass the network problem using local dataset) # train_set = load_dataset("imagefolder", data_dir=traindir, num_proc=4) # test_set = load_dataset("imagefolder", data_dir=valdir, num_proc=4) train_set = load_dataset("imagenet-1K", split="train", trust_remote_code=True) test_set = load_dataset("imagenet-1K", split="test", trust_remote_code=True) print(train_set["label"]) train_set.set_transform(transform_train_examples) test_set.set_transform(transform_val_examples) return train_set, test_set ``` above the code, but output of the print is a list of None: <img width="952" alt="image" src="https://github.com/user-attachments/assets/c4e2fdd8-3b8f-481e-8f86-9bbeb49d79fb"> ### Steps to reproduce the bug 1. just ran the code 2. see the print ### Expected behavior I do not know how to fix this, can anyone provide help or something? It is hurry for me ### Environment info - `datasets` version: 2.21.0 - Platform: Linux-5.4.0-190-generic-x86_64-with-glibc2.31 - Python version: 3.10.14 - `huggingface_hub` version: 0.24.6 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.6.1

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I_kwDODunzps6VeQzE

Cache only changed columns?

open

CONTRIBUTOR

### Feature request Cache only the actual changes to the dataset i.e. changed columns. ### Motivation I realized that caching actually saves the complete dataset again. This is especially problematic for image datasets if one wants to only change another column e.g. some metadata and then has to save 5 TB again. ### Your contribution Is this even viable in the current architecture of the package? I quickly looked into it and it seems it would require significant changes. I would spend some time looking into this but maybe somebody could help with the feasibility and some plan to implement before spending too much time on it?

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I_kwDODunzps6Va4Lo

[BUG] dataset_info sequence unexpected behavior in README.md YAML

open

NONE

### Describe the bug When working on `dataset_info` yaml, I find my data column with format `list[dict[str, str]]` cannot be coded correctly. My data looks like ``` {"answers":[{"text": "ADDRESS", "label": "abc"}]} ``` My `dataset_info` in README.md is: ``` dataset_info: - config_name: default features: - name: answers sequence: - name: text dtype: string - name: label dtype: string ``` **Error log**: ``` pyarrow.lib.ArrowNotImplementedError: Unsupported cast from list<item: struct<text: string, label: string>> to struct using function cast_struct ``` ## Potential Reason After some analysis, it turns out that my yaml config is requiring `dict[str, list[str]]` instead of `list[dict[str, str]]`. It would work if I change my data to ``` {"answers":{"text": ["ADDRESS"], "label": ["abc", "def"]}} ``` These following 2 different `dataset_info` are actually equivalent. ``` dataset_info: - config_name: default features: - name: answers dtype: - name: text sequence: string - name: label sequence: string dataset_info: - config_name: default features: - name: answers sequence: - name: text dtype: string - name: label dtype: string ``` ### Steps to reproduce the bug ``` # README.md --- dataset_info: - config_name: default features: - name: answers sequence: - name: text dtype: string - name: label dtype: string configs: - config_name: default default: true data_files: - split: train path: - "test.jsonl" --- # test.jsonl # expected but not working {"answers":[{"text": "ADDRESS", "label": "abc"}]} # unexpected but working {"answers":{"text": ["ADDRESS"], "label": ["abc", "def"]}} ``` ### Expected behavior ``` dataset_info: - config_name: default features: - name: answers sequence: - name: text dtype: string - name: label dtype: string ``` Should work on following data format: ``` {"answers":[{"text":"ADDRESS", "label": "abc"}]} ``` ### Environment info - `datasets` version: 2.21.0 - Platform: macOS-14.6.1-arm64-arm-64bit - Python version: 3.12.4 - `huggingface_hub` version: 0.24.5 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.6.1

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PR_kwDODunzps56b9R-

Do not consume unnecessary memory during sharding

open

NONE

When sharding `IterableDataset`s, a temporary list is created that is then indexed. There is no need to create a temporary list of a potentially very large step/world size, with standard `islice` functionality, so we avoid it. ```shell pytest tests/test_distributed.py -k iterable ``` Runs successfully.

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I_kwDODunzps6VNZs4

Bug: Type Mismatch in Dataset Mapping

open

NONE

# Issue: Type Mismatch in Dataset Mapping ## Description There is an issue with the `map` function in the `datasets` library where the mapped output does not reflect the expected type change. After applying a mapping function to convert an integer label to a string, the resulting type remains an integer instead of a string. ## Reproduction Code Below is a Python script that demonstrates the problem: ```python from datasets import Dataset # Original data data = { 'text': ['Hello', 'world', 'this', 'is', 'a', 'test'], 'label': [0, 1, 0, 1, 1, 0] } # Creating a Dataset object dataset = Dataset.from_dict(data) # Mapping function to convert label to string def add_one(example): example['label'] = str(example['label']) return example # Applying the mapping function dataset = dataset.map(add_one) # Iterating over the dataset to show results for item in dataset: print(item) print(type(item['label'])) ``` ## Expected Output After applying the mapping function, the expected output should have the `label` field as strings: ```plaintext {'text': 'Hello', 'label': '0'} <class 'str'> {'text': 'world', 'label': '1'} <class 'str'> {'text': 'this', 'label': '0'} <class 'str'> {'text': 'is', 'label': '1'} <class 'str'> {'text': 'a', 'label': '1'} <class 'str'> {'text': 'test', 'label': '0'} <class 'str'> ``` ## Actual Output The actual output still shows the `label` field values as integers: ```plaintext {'text': 'Hello', 'label': 0} <class 'int'> {'text': 'world', 'label': 1} <class 'int'> {'text': 'this', 'label': 0} <class 'int'> {'text': 'is', 'label': 1} <class 'int'> {'text': 'a', 'label': 1} <class 'int'> {'text': 'test', 'label': 0} <class 'int'> ``` ## Why necessary In the case of Image process we often need to convert PIL to tensor with same column name. Thank for every dev who review this issue. 🤗

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I_kwDODunzps6U-xmJ

Attempting to return a rank 3 grayscale image from dataset.map results in extreme slowdown

open

NONE

### Describe the bug Background: Digital images are often represented as a (Height, Width, Channel) tensor. This is the same for huggingface datasets that contain images. These images are loaded in Pillow containers which offer, for example, the `.convert` method. I can convert an image from a (H,W,3) shape to a grayscale (H,W) image and I have no problems with this. But when attempting to return a (H,W,1) shaped matrix from a map function, it never completes and sometimes even results in an OOM from the OS. I've used various methods to expand a (H,W) shaped array to a (H,W,1) array. But they all resulted in extremely long map operations consuming a lot of CPU and RAM. ### Steps to reproduce the bug Below is a minimal example using two methods to get the desired output. Both of which don't work ```py import tensorflow as tf import datasets import numpy as np ds = datasets.load_dataset("project-sloth/captcha-images") to_gray_pillow = lambda sample: {'image': np.expand_dims(sample['image'].convert("L"), axis=-1)} ds_gray = ds.map(to_gray_pillow) # Alternatively ds = datasets.load_dataset("project-sloth/captcha-images").with_format("tensorflow") to_gray_tf = lambda sample: {'image': tf.expand_dims(tf.image.rgb_to_grayscale(sample['image']), axis=-1)} ds_gray = ds.map(to_gray_tf) ``` ### Expected behavior I expect the map operation to complete and return a new dataset containing grayscale images in a (H,W,1) shape. ### Environment info datasets 2.21.0 python tested with both 3.11 and 3.12 host os : linux

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PR_kwDODunzps557zng

remove filecheck to enable symlinks

closed

CONTRIBUTOR

Enables streaming from local symlinks #7083 @lhoestq

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PR_kwDODunzps551k1C

Fix data file module inference

open

NONE

I saved a dataset with two splits to disk with `DatasetDict.save_to_disk`. The train is bigger and ended up in 10 shards, whereas the test split only resulted in 1 split. Now when trying to load the dataset, an error is raised that not all splits have the same data format: > ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('test'): ('json', {})} This is not expected because both splits are saved as arrow files. I did some debugging and found that this is the case because the list of data_files includes a `state.json` file. Now this means for train split I get 10 ".arrow" and 1 ".json" file. Since datasets picks based on the most common extension this is correctly inferred as "arrow". In the test split, there is 1 .arrow and 1 .json file. Given the function description: > It picks the module based on the most common file extension. In case of a draw ".parquet" is the favorite, and then alphabetical order. This is not quite true though, because in a tie the extensions are actually based on reverse-alphabetical order: ``` for (ext, _), _ in sorted(extensions_counter.items(), key=sort_key, *reverse=True*): ``` Which thus leads to the module wrongly inferred as "json", whereas it should be "arrow", matching the train split. I first thought about adding "state.json" in the list of excluded files for the inference: https://github.com/huggingface/datasets/blob/main/src/datasets/load.py#L513. However, I think from digging into the code it looks like the right thing to do is to exclude it in the list of `data_files` to start with, because it is more of a metadata than a data file.

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I_kwDODunzps6UiAb6

Inconsistent output in documentation example: `num_classes` not displayed in `ClassLabel` output

closed

CONTRIBUTOR

In the documentation for [ClassLabel](https://huggingface.co./docs/datasets/v2.21.0/en/package_reference/main_classes#datasets.ClassLabel), there is an example of usage with the following code: ```` from datasets import Features features = Features({'label': ClassLabel(num_classes=3, names=['bad', 'ok', 'good'])}) features ```` which expects to output (as stated in the documentation): ```` {'label': ClassLabel(num_classes=3, names=['bad', 'ok', 'good'], id=None)} ```` but it generates the following ```` {'label': ClassLabel(names=['bad', 'ok', 'good'], id=None)} ```` If my understanding is correct, this happens because although num_classes is used during the init of the object, it is afterward ignored: https://github.com/huggingface/datasets/blob/be5cff059a2a5b89d7a97bc04739c4919ab8089f/src/datasets/features/features.py#L975 I would like to work on this issue if this is something needed 😄

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I_kwDODunzps6UbpPX

Filter Large Dataset Entry by Entry

open

NONE

### Feature request I am not sure if this is a new feature, but I wanted to post this problem here, and hear if others have ways of optimizing and speeding up this process. Let's say I have a really large dataset that I cannot load into memory. At this point, I am only aware of `streaming=True` to load the dataset. Now, the dataset consists of many tables. Ideally, I would want to have some simple filtering criterion, such that I only see the "good" tables. Here is an example of what the code might look like: ``` dataset = load_dataset( "really-large-dataset", streaming=True ) # And let's say we process the dataset bit by bit because we want intermediate results dataset = islice(dataset, 10000) # Define a function to filter the data def filter_function(table): if some_condition: return True else: return False # Use the filter function on your dataset filtered_dataset = (ex for ex in dataset if filter_function(ex)) ``` And then I work on the processed dataset, which would be magnitudes faster than working on the original. I would love to hear if the problem setup + solution makes sense to people, and if anyone has suggestions! ### Motivation See description above ### Your contribution Happy to make PR if this is a new feature

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I_kwDODunzps6UNVwm

Caching shuffles by np.random.Generator results in unintiutive behavior

open

NONE

### Describe the bug Create a dataset. Save it to disk. Load from disk. Shuffle, usning a `np.random.Generator`. Iterate. Shuffle again. Iterate. The iterates are different since the supplied np.random.Generator has progressed between the shuffles. Load dataset from disk again. Shuffle and Iterate. See same result as before. Shuffle and iterate, and this time it does not have the same shuffling as ion previous run. The motivation is I have a deep learning loop with ``` for epoch in range(10): for batch in dataset.shuffle(generator=generator).iter(batch_size=32): .... # do stuff ``` where I want a new shuffling at every epoch. Instead I get the same shuffling. ### Steps to reproduce the bug Run the code below two times. ```python import datasets import numpy as np generator = np.random.default_rng(0) ds = datasets.Dataset.from_dict(mapping={"X":range(1000)}) ds.save_to_disk("tmp") print("First loop: ", end="") for _ in range(10): print(next(ds.shuffle(generator=generator).iter(batch_size=1))['X'], end=", ") print("") print("Second loop: ", end="") ds = datasets.Dataset.load_from_disk("tmp") for _ in range(10): print(next(ds.shuffle(generator=generator).iter(batch_size=1))['X'], end=", ") print("") ``` The output is: ``` $ python main.py Saving the dataset (1/1 shards): 100%|███████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 495019.95 examples/s] First loop: 459, 739, 72, 943, 241, 181, 845, 830, 896, 334, Second loop: 741, 847, 944, 795, 483, 842, 717, 865, 231, 840, $ python main.py Saving the dataset (1/1 shards): 100%|████████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 22243.40 examples/s] First loop: 459, 739, 72, 943, 241, 181, 845, 830, 896, 334, Second loop: 741, 741, 741, 741, 741, 741, 741, 741, 741, 741, ``` The second loop, on the second run, only spits out "741, 741, 741...." which is *not* the desired output ### Expected behavior I want the dataset to shuffle at every epoch since I provide it with a generator for shuffling. ### Environment info Datasets version 2.21.0 Ubuntu linux.

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PR_kwDODunzps55Y-Ws

Disable implicit token in CI

closed

MEMBER

Disable implicit token in CI. This PR allows running CI tests locally without implicitly using the local user HF token. For example, run locally the tests in: - #7124

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PR_kwDODunzps55Y4TM

Fix wrong SHA in CI tests of HubDatasetModuleFactoryWithParquetExport

closed

MEMBER

Fix wrong SHA in CI tests of HubDatasetModuleFactoryWithParquetExport.

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PR_kwDODunzps55YzWr

Test get_dataset_config_info with non-existing/gated/private dataset

closed

MEMBER

Test get_dataset_config_info with non-existing/gated/private dataset. Related to: - #7109 See also: - https://github.com/huggingface/dataset-viewer/pull/3037: https://github.com/huggingface/dataset-viewer/pull/3037/commits/bb1a7e00c53c242088597cab6572e4fd57797ecb

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I_kwDODunzps6UDuRh

Make dataset viewer more flexible in displaying metadata alongside images

open

NONE

### Feature request To display images with their associated metadata in the dataset viewer, a `metadata.csv` file is required. In the case of a dataset with multiple subsets, this would require the CSVs to be contained in the same folder as the images since they all need to be named `metadata.csv`. The request is that this be made more flexible for datasets with multiple subsets to avoid the need to put a `metadata.csv` into each image directory where they are not as easily accessed. ### Motivation When creating datasets with multiple subsets I can't get the images to display alongside their associated metadata (it's usually one or the other that will show up). Since this requires a file specifically named `metadata.csv`, I then have to place that file within the image directory, which makes it much more difficult to access. Additionally, it still doesn't necessarily display the images alongside their metadata correctly (see, for instance, [this discussion](https://huggingface.co./datasets/imageomics/2018-NEON-beetles/discussions/8)). It was suggested I bring this discussion to GitHub on another dataset struggling with a similar issue ([discussion](https://huggingface.co./datasets/imageomics/fish-vista/discussions/4)). In that case, it's a mix of data subsets, where some just reference the image URLs, while others actually have the images uploaded. The ones with images uploaded are not displaying images, but renaming that file to just `metadata.csv` would diminish the clarity of the construction of the dataset itself (and I'm not entirely convinced it would solve the issue). ### Your contribution I can make a suggestion for one approach to address the issue: For instance, even if it could just end in `_metadata.csv` or `-metadata.csv`, that would be very helpful to allow for more flexibility of dataset structure without impacting clarity. I would think that the functionality on the backend looking for `metadata.csv` could reasonably be adapted to look for such an ending on a filename (maybe also check that it has a `file_name` column?). Presumably, requiring the `configs` in a setup like on [this dataset](https://huggingface.co./datasets/imageomics/rare-species/blob/main/README.md) could also help in figuring out how it should work? ``` configs: - config_name: <image subset> data_files: - <image-metadata>.csv - <path/to/images>/*.jpg ``` I'd also be happy to look at whatever solution is decided upon and contribute to the ideation. Thanks for your time and consideration! The dataset viewer really is fabulous when it works :)

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I_kwDODunzps6T9896

[interleave_dataset] sample batches from a single source at a time

open

NONE

### Feature request interleave_dataset and [RandomlyCyclingMultiSourcesExamplesIterable](https://github.com/huggingface/datasets/blob/3813ce846e52824b38e53895810682f0a496a2e3/src/datasets/iterable_dataset.py#L816) enable us to sample data examples from different sources. But can we also sample batches in a similar manner (each batch only contains data from a single source)? ### Motivation Some recent research [[1](https://blog.salesforceairesearch.com/sfr-embedded-mistral/), [2](https://arxiv.org/pdf/2310.07554)] shows that source homogenous batching can be helpful for contrastive learning. Can we add a function called `RandomlyCyclingMultiSourcesBatchesIterable` to support this functionality? ### Your contribution I can contribute a PR. But I wonder what the best way is to test its correctness and robustness.

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PR_kwDODunzps55Iukl

Fix typed examples iterable state dict

closed

MEMBER

fix https://github.com/huggingface/datasets/issues/7085 as noted by @VeryLazyBoy and reported by @AjayP13

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PR_kwDODunzps55HrBy

don't mention the script if trust_remote_code=False

closed

COLLABORATOR

See https://huggingface.co./datasets/Omega02gdfdd/bioclip-demo-zero-shot-mistakes for example. The error is: ``` FileNotFoundError: Couldn't find a dataset script at /src/services/worker/Omega02gdfdd/bioclip-demo-zero-shot-mistakes/bioclip-demo-zero-shot-mistakes.py or any data file in the same directory. Couldn't find 'Omega02gdfdd/bioclip-demo-zero-shot-mistakes' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/Omega02gdfdd/bioclip-demo-zero-shot-mistakes@12b0313ba4c3189ee5a24cb76200959e9bf7492e/data.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.geoparquet', '.gpq', '.arrow', '.txt', '.tar', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] ``` The issue there is that a `configs` parameter is set in the README, while the mentioned data file (`data.csv`) does not exist.

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PR_kwDODunzps54-GjY

Install transformers with numpy-2 CI

closed

MEMBER

Install transformers with numpy-2 CI. Note that transformers no longer pins numpy < 2 since transformers-4.43.0: - https://github.com/huggingface/transformers/pull/32018 - https://github.com/huggingface/transformers/releases/tag/v4.43.0

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PR_kwDODunzps549yu4

Allow numpy-2.1 and test it without audio extra

closed

MEMBER

Allow numpy-2.1 and test it without audio extra. This PR reverts: - #7114 Note that audio extra tests can be included again with numpy-2.1 once next numba-0.61.0 version is released.

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I_kwDODunzps6TnT2L

Audio dataset load everything in RAM and is very slow

open

NONE

Hello, I'm working with an audio dataset. I want to transcribe the audio that the dataset contain, and for that I use whisper. My issue is that the dataset load everything in the RAM when I map the dataset, obviously, when RAM usage is too high, the program crashes. To fix this issue, I'm using writer_batch_size that I set to 10, but in this case, the mapping of the dataset is extremely slow. To illustrate this, on 50 examples, with `writer_batch_size` set to 10, it takes 123.24 seconds to process the dataset, but without `writer_batch_size` set to 10, it takes about ten seconds to process the dataset, but then the process remains blocked (I assume that it is writing the dataset and therefore suffers from the same problem as `writer_batch_size`) ### Steps to reproduce the bug Hug ram usage but fast (but actually slow when saving the dataset): ```py from datasets import load_dataset import time ds = load_dataset("WaveGenAI/audios2", split="train[:50]") # map the dataset def transcribe_audio(row): audio = row["audio"] # get the audio but do nothing with it row["transcribed"] = True return row time1 = time.time() ds = ds.map( transcribe_audio ) for row in ds: pass # do nothing, just iterate to trigger the map function print(f"Time taken: {time.time() - time1:.2f} seconds") ``` Low ram usage but very very slow: ```py from datasets import load_dataset import time ds = load_dataset("WaveGenAI/audios2", split="train[:50]") # map the dataset def transcribe_audio(row): audio = row["audio"] # get the audio but do nothing with it row["transcribed"] = True return row time1 = time.time() ds = ds.map( transcribe_audio, writer_batch_size=10 ) # set low writer_batch_size to avoid memory issues for row in ds: pass # do nothing, just iterate to trigger the map function print(f"Time taken: {time.time() - time1:.2f} seconds") ``` ### Expected behavior I think the processing should be much faster, on only 50 audio examples, the mapping takes several minutes while nothing is done (just loading the audio). ### Environment info - `datasets` version: 2.21.0 - Platform: Linux-6.10.5-arch1-1-x86_64-with-glibc2.40 - Python version: 3.10.4 - `huggingface_hub` version: 0.24.5 - PyArrow version: 17.0.0 - Pandas version: 1.5.3 - `fsspec` version: 2024.6.1 # Extra The dataset has been generated by using audio folder, so I don't think anything specific in my code is causing this problem. ```py import argparse from datasets import load_dataset parser = argparse.ArgumentParser() parser.add_argument("--folder", help="folder path", default="/media/works/test/") args = parser.parse_args() dataset = load_dataset("audiofolder", data_dir=args.folder) # push the dataset to hub dataset.push_to_hub("WaveGenAI/audios") ``` Also, it's the combination of `audio = row["audio"]` and `row["transcribed"] = True` which causes problems, `row["transcribed"] = True `alone does nothing and `audio = row["audio"]` alone sometimes causes problems, sometimes not.

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I_kwDODunzps6TjXqh

datasets cannot handle nested json if features is given.

closed

NONE

### Describe the bug I have a json named temp.json. ```json {"ref1": "ABC", "ref2": "DEF", "cuts":[{"cut1": 3, "cut2": 5}]} ``` I want to load it. ```python ds = datasets.load_dataset('json', data_files="./temp.json", features=datasets.Features({ 'ref1': datasets.Value('string'), 'ref2': datasets.Value('string'), 'cuts': datasets.Sequence({ "cut1": datasets.Value("uint16"), "cut2": datasets.Value("uint16") }) })) ``` The above code does not work. However, I can load it without giving features. ```python ds = datasets.load_dataset('json', data_files="./temp.json") ``` Is it possible to load integers as uint16 to save some memory? ### Steps to reproduce the bug As in the bug description. ### Expected behavior The data are loaded and integers are uint16. ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.21.0 - Platform: Linux-5.15.0-118-generic-x86_64-with-glibc2.35 - Python version: 3.11.9 - `huggingface_hub` version: 0.24.5 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.5.0

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I_kwDODunzps6TiwtG

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

closed

NONE

### Describe the bug Code: `!pipuninstall -y pyarrow !pip install --no-cache-dir pyarrow !pip uninstall -y pyarrow !pip install pyarrow --no-cache-dir !pip install --upgrade datasets transformers pyarrow !pip install pyarrow.parquet ! pip install pyarrow-core libparquet !pip install pyarrow --no-cache-dir !pip install pyarrow !pip install transformers !pip install --upgrade datasets !pip install datasets ! pip install pyarrow ! pip install pyarrow.lib ! pip install pyarrow.parquet !pip install transformers import pyarrow as pa print(pa.__version__) from datasets import load_dataset import pyarrow.parquet as pq import pyarrow.lib as lib import pandas as pd from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments from datasets import load_dataset from transformers import AutoTokenizer ! pip install pyarrow-core libparquet # Load the dataset for content moderation dataset = load_dataset("PolyAI/banking77") # Example dataset for customer support # Initialize the tokenizer tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") # Tokenize the dataset def tokenize_function(examples): return tokenizer(examples['text'], padding="max_length", truncation=True) # Apply tokenization to the entire dataset tokenized_datasets = dataset.map(tokenize_function, batched=True) # Check the first few tokenized samples print(tokenized_datasets['train'][0]) from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments # Load the model model = AutoModelForSequenceClassification.from_pretrained("facebook/opt-350m", num_labels=77) # Define training arguments training_args = TrainingArguments( output_dir="./results", per_device_train_batch_size=16, per_device_eval_batch_size=16, num_train_epochs=3, eval_strategy="epoch", # save_strategy="epoch", logging_dir="./logs", learning_rate=2e-5, ) # Initialize the Trainer trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets["train"], eval_dataset=tokenized_datasets["test"], ) # Train the model trainer.train() # Evaluate the model trainer.evaluate() ` AttributeError Traceback (most recent call last) [<ipython-input-23-60bed3143a93>](https://localhost:8080/#) in <cell line: 22>() 20 21 ---> 22 from datasets import load_dataset 23 import pyarrow.parquet as pq 24 import pyarrow.lib as lib 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 15 __version__ = "2.21.0" 16 ---> 17 from .arrow_dataset import Dataset 18 from .arrow_reader import ReadInstruction 19 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 74 75 from . import config ---> 76 from .arrow_reader import ArrowReader 77 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 78 from .data_files import sanitize_patterns [/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module> 27 28 import pyarrow as pa ---> 29 import pyarrow.parquet as pq 30 from tqdm.contrib.concurrent import thread_map 31 [/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/__init__.py](https://localhost:8080/#) in <module> 18 # flake8: noqa 19 ---> 20 from .core import * [/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/core.py](https://localhost:8080/#) in <module> 31 32 try: ---> 33 import pyarrow._parquet as _parquet 34 except ImportError as exc: 35 raise ImportError( /usr/local/lib/python3.10/dist-packages/pyarrow/_parquet.pyx in init pyarrow._parquet() AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType' ### Steps to reproduce the bug https://colab.research.google.com/drive/1HNbsg3tHxUJOHVtYIaRnNGY4T2PnLn4a?usp=sharing ### Expected behavior Looks like there is an issue with datasets and pyarrow ### Environment info google colab python huggingface Found existing installation: pyarrow 17.0.0 Uninstalling pyarrow-17.0.0: Successfully uninstalled pyarrow-17.0.0 Collecting pyarrow Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB) Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.10/dist-packages (from pyarrow) (1.26.4) Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl (39.9 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 39.9/39.9 MB 188.9 MB/s eta 0:00:00 Installing collected packages: pyarrow ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible. ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible. Successfully installed pyarrow-17.0.0 WARNING: The following packages were previously imported in this runtime: [pyarrow] You must restart the runtime in order to use newly installed versions.

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PR_kwDODunzps5404mO

Temporarily pin numpy<2.1 to fix CI

closed

MEMBER

Temporarily pin numpy<2.1 to fix CI. Fix #7111.

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I_kwDODunzps6ThfSI

Stream dataset does not iterate if the batch size is larger than the dataset size (related to drop_last_batch)

closed

NONE

### Describe the bug Hi there, I use streaming and interleaving to combine multiple datasets saved in jsonl files. The size of dataset can vary (from 100ish to 100k-ish). I use dataset.map() and a big batch size to reduce the IO cost. It was working fine with datasets-2.16.1 but this problem shows up after I upgraded to datasets-2.19.2. With 2.21.0 the problem remains. Please see the code below to reproduce the problem. The dataset can iterate correctly if we set either streaming=False or drop_last_batch=False. I have to use drop_last_batch=True since it's for distributed training. ### Steps to reproduce the bug ```python # datasets==2.21.0 import datasets def data_prepare(examples): print(examples["sentence1"][0]) return examples batch_size = 101 # the size of the dataset is 100 # the dataset iterates correctly if we set either streaming=False or drop_last_batch=False dataset = datasets.load_dataset("mteb/biosses-sts", split="test", streaming=True) dataset = dataset.map(lambda x: data_prepare(x), drop_last_batch=True, batched=True, batch_size=batch_size) for ex in dataset: print(ex) pass ``` ### Expected behavior The dataset iterates regardless of the batch size. ### Environment info - `datasets` version: 2.21.0 - Platform: Linux-6.1.58+-x86_64-with-glibc2.35 - Python version: 3.10.14 - `huggingface_hub` version: 0.24.5 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.2.0

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I_kwDODunzps6ThZLk

cudf-cu12 24.4.1, ibis-framework 8.0.0 requires pyarrow<15.0.0a0,>=14.0.1,pyarrow<16,>=2 and datasets 2.21.0 requires pyarrow>=15.0.0

open

NONE

### Describe the bug !pip install accelerate>=0.16.0 torchvision transformers>=4.25.1 datasets>=2.19.1 ftfy tensorboard Jinja2 peft==0.7.0 ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible. ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible. to solve above error !pip install pyarrow==14.0.1 ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. datasets 2.21.0 requires pyarrow>=15.0.0, but you have pyarrow 14.0.1 which is incompatible. ### Steps to reproduce the bug !pip install datasets>=2.19.1 ### Expected behavior run without dependency error ### Environment info Diffusers version: 0.31.0.dev0 Platform: Linux-6.1.85+-x86_64-with-glibc2.35 Running on Google Colab?: Yes Python version: 3.10.12 PyTorch version (GPU?): 2.3.1+cu121 (True) Flax version (CPU?/GPU?/TPU?): 0.8.4 (gpu) Jax version: 0.4.26 JaxLib version: 0.4.26 Huggingface_hub version: 0.23.5 Transformers version: 4.42.4 Accelerate version: 0.32.1 PEFT version: 0.7.0 Bitsandbytes version: not installed Safetensors version: 0.4.4 xFormers version: not installed Accelerator: Tesla T4, 15360 MiB Using GPU in script?: Using distributed or parallel set-up in script?:

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I_kwDODunzps6ThDgF

CI is broken for numpy-2: Failed to fetch wheel: llvmlite==0.34.0

closed

MEMBER

Ci is broken with error `Failed to fetch wheel: llvmlite==0.34.0`: https://github.com/huggingface/datasets/actions/runs/10466825281/job/28984414269 ``` Run uv pip install --system "datasets[tests_numpy2] @ ." Resolved 150 packages in 4.42s error: Failed to prepare distributions Caused by: Failed to fetch wheel: llvmlite==0.34.0 Caused by: Build backend failed to build wheel through `build_wheel()` with exit status: 1 --- stdout: running bdist_wheel /home/runner/.cache/uv/builds-v0/.tmpcyKh8S/bin/python /home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py LLVM version... --- stderr: Traceback (most recent call last): File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 105, in main_posix out = subprocess.check_output([llvm_config, '--version']) File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 421, in check_output return run(*popenargs, stdout=PIPE, timeout=timeout, check=True, File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 503, in run with Popen(*popenargs, **kwargs) as process: File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 971, in __init__ self._execute_child(args, executable, preexec_fn, close_fds, File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 1863, in _execute_child raise child_exception_type(errno_num, err_msg, err_filename) FileNotFoundError: [Errno 2] No such file or directory: 'llvm-config' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 191, in <module> main() File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 181, in main main_posix('linux', '.so') File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 107, in main_posix raise RuntimeError("%s failed executing, please point LLVM_CONFIG " RuntimeError: llvm-config failed executing, please point LLVM_CONFIG to the path for llvm-config error: command '/home/runner/.cache/uv/builds-v0/.tmpcyKh8S/bin/python' failed with exit code 1 ```

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PR_kwDODunzps54zz3r

Fix ConnectionError for gated datasets and unauthenticated users

closed

MEMBER

Fix `ConnectionError` for gated datasets and unauthenticated users. See: - https://github.com/huggingface/dataset-viewer/issues/3025 Note that a recent change in the Hub returns dataset info for gated datasets and unauthenticated users, instead of raising a `GatedRepoError` as before. See: - https://github.com/huggingface/huggingface_hub/issues/2457 This PR adds an additional check (/auth-check) for gated datasets and raises `DatasetNotFoundError` for unauthenticated users, as it was the case before the change in the Hub. - Fix suggested by @Pierrci (thanks @Wauplin for pointing it out). Fix #7109.

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I_kwDODunzps6TbJko

ConnectionError for gated datasets and unauthenticated users

closed

MEMBER

Since the Hub returns dataset info for gated datasets and unauthenticated users, there is dead code: https://github.com/huggingface/datasets/blob/98fdc9e78e6d057ca66e58a37f49d6618aab8130/src/datasets/load.py#L1846-L1852 We should remove the dead code and properly handle this case: currently we are raising a `ConnectionError` instead of a `DatasetNotFoundError` (as before). See: - https://github.com/huggingface/dataset-viewer/issues/3025 - https://github.com/huggingface/huggingface_hub/issues/2457

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I_kwDODunzps6TQ1xv

website broken: Create a new dataset repository, doesn't create a new repo in Firefox

closed

NONE

### Describe the bug This issue is also reported here: https://discuss.huggingface.co/t/create-a-new-dataset-repository-broken-page/102644 This page is broken. https://huggingface.co./new-dataset I fill in the form with my text, and click `Create Dataset`.  Then the form gets wiped. And no repo got created. No error message visible in the developer console.  # Idea for improvement For better UX, if the repo cannot be created, then show an error message, that something went wrong. # Work around, that works for me ```python from huggingface_hub import HfApi, HfFolder repo_id = 'simon-arc-solve-fractal-v3' api = HfApi() username = api.whoami()['name'] repo_url = api.create_repo(repo_id=repo_id, exist_ok=True, private=True, repo_type="dataset") ``` ### Steps to reproduce the bug Go https://huggingface.co./new-dataset Fill in the form. Click `Create dataset`. Now the form is cleared. And the page doesn't jump anywhere. ### Expected behavior The moment the user clicks `Create dataset`, the repo gets created and the page jumps to the created repo. ### Environment info Firefox 128.0.3 (64-bit) macOS Sonoma 14.5

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I_kwDODunzps6TP_68

load_dataset broken in 2.21.0

closed

NONE

### Describe the bug `eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval_gpt4_baseline", trust_remote_code=True)` used to work till 2.20.0 but doesn't work in 2.21.0 In 2.20.0:  in 2.21.0:  ### Steps to reproduce the bug 1. Spin up a new google collab 2. `pip install datasets==2.21.0` 3. `import datasets` 4. `eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval_gpt4_baseline", trust_remote_code=True)` 5. Will throw an error. ### Expected behavior Try steps 1-5 again but replace datasets version with 2.20.0, it will work ### Environment info - `datasets` version: 2.21.0 - Platform: Linux-6.1.85+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.23.5 - PyArrow version: 17.0.0 - Pandas version: 2.1.4 - `fsspec` version: 2024.5.0

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PR_kwDODunzps54jntM

Rename LargeList.dtype to LargeList.feature

closed

MEMBER

Rename `LargeList.dtype` to `LargeList.feature`. Note that `dtype` is usually used for NumPy data types ("int64", "float32",...): see `Value.dtype`. However, `LargeList` attribute (like `Sequence.feature`) expects a `FeatureType` instead. With this renaming: - we avoid confusion about the expected type and - we also align `LargeList` with `Sequence`.

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PR_kwDODunzps54eZ0D

Use `huggingface_hub` cache

closed

MEMBER

- use `hf_hub_download()` from `huggingface_hub` for HF files - `datasets` cache_dir is still used for: - caching datasets as Arrow files (that back `Dataset` objects) - extracted archives, uncompressed files - files downloaded via http (datasets with scripts) - I removed code that were made for http files (and also the dummy_data / mock_download_manager stuff that happened to rely on them and have been legacy for a while now)

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PR_kwDODunzps54dAhE

remove more script docs

closed

MEMBER

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PR_kwDODunzps54clrp

Fix args of feature docstrings

closed

MEMBER

Fix Args section of feature docstrings. Currently, some args do not appear in the docs because they are not properly parsed due to the lack of their type (between parentheses).

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I_kwDODunzps6TCc0y

Slow iteration speeds when using IterableDataset.shuffle with load_dataset(data_files=..., streaming=True)

open

NONE

### Describe the bug When I load a dataset from a number of arrow files, as in: ``` random_dataset = load_dataset( "arrow", data_files={split: shard_filepaths}, streaming=True, split=split, ) ``` I'm able to get fast iteration speeds when iterating over the dataset without shuffling. When I shuffle the dataset, the iteration speed is reduced by ~1000x. It's very possible the way I'm loading dataset shards is not appropriate; if so please advise! Thanks for the help ### Steps to reproduce the bug Here's full code to reproduce the issue: - Generate a random dataset - Create shards of data independently using Dataset.save_to_disk() - The below will generate 16 shards (arrow files), of 512 examples each ``` import time from pathlib import Path from multiprocessing import Pool, cpu_count import torch from datasets import Dataset, load_dataset split = "train" split_save_dir = "/tmp/random_split" def generate_random_example(): return { 'inputs': torch.randn(128).tolist(), 'indices': torch.randint(0, 10000, (2, 20000)).tolist(), 'values': torch.randn(20000).tolist(), } def generate_shard_dataset(examples_per_shard: int = 512): dataset_dict = { 'inputs': [], 'indices': [], 'values': [] } for _ in range(examples_per_shard): example = generate_random_example() dataset_dict['inputs'].append(example['inputs']) dataset_dict['indices'].append(example['indices']) dataset_dict['values'].append(example['values']) return Dataset.from_dict(dataset_dict) def save_shard(shard_idx, save_dir, examples_per_shard): shard_dataset = generate_shard_dataset(examples_per_shard) shard_write_path = Path(save_dir) / f"shard_{shard_idx}" shard_dataset.save_to_disk(shard_write_path) return str(Path(shard_write_path) / "data-00000-of-00001.arrow") def generate_split_shards(save_dir, num_shards: int = 16, examples_per_shard: int = 512): with Pool(cpu_count()) as pool: args = [(m, save_dir, examples_per_shard) for m in range(num_shards)] shard_filepaths = pool.starmap(save_shard, args) return shard_filepaths shard_filepaths = generate_split_shards(split_save_dir) ``` Load the dataset as IterableDataset: ``` random_dataset = load_dataset( "arrow", data_files={split: shard_filepaths}, streaming=True, split=split, ) random_dataset = random_dataset.with_format("numpy") ``` Observe the iterations/second when iterating over the dataset directly, and applying shuffling before iterating: Without shuffling, this gives ~1500 iterations/second ``` start_time = time.time() for count, item in enumerate(random_dataset): if count > 0 and count % 100 == 0: elapsed_time = time.time() - start_time iterations_per_second = count / elapsed_time print(f"Processed {count} items at an average of {iterations_per_second:.2f} iterations/second") ``` ``` Processed 100 items at an average of 705.74 iterations/second Processed 200 items at an average of 1169.68 iterations/second Processed 300 items at an average of 1497.97 iterations/second Processed 400 items at an average of 1739.62 iterations/second Processed 500 items at an average of 1931.11 iterations/second` ``` When shuffling, this gives ~3 iterations/second: ``` random_dataset = random_dataset.shuffle(buffer_size=100,seed=42) start_time = time.time() for count, item in enumerate(random_dataset): if count > 0 and count % 100 == 0: elapsed_time = time.time() - start_time iterations_per_second = count / elapsed_time print(f"Processed {count} items at an average of {iterations_per_second:.2f} iterations/second") ``` ``` Processed 100 items at an average of 3.75 iterations/second Processed 200 items at an average of 3.93 iterations/second ``` ### Expected behavior Iterations per second should be barely affected by shuffling, especially with a small buffer size ### Environment info Datasets version: 2.21.0 Python 3.10 Ubuntu 22.04

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I_kwDODunzps6TA_e_

`load_dataset` from Hub with `name` to specify `config` using incorrect builder type when multiple data formats are present

open

NONE

Following [documentation](https://huggingface.co./docs/datasets/repository_structure#define-your-splits-and-subsets-in-yaml) I had defined different configs for [`Dataception`](https://huggingface.co./datasets/bigdata-pw/Dataception), a dataset of datasets: ```yaml configs: - config_name: dataception data_files: - path: dataception.parquet split: train default: true - config_name: dataset_5423 data_files: - path: datasets/5423.tar split: train ... - config_name: dataset_721736 data_files: - path: datasets/721736.tar split: train ``` The intent was for metadata to be browsable via Dataset Viewer, in addition to each individual dataset, and to allow datasets to be loaded by specifying the config/name to `load_dataset`. While testing `load_dataset` I encountered the following error: ```python >>> dataset = load_dataset("bigdata-pw/Dataception", "dataset_7691") Downloading readme: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 467k/467k [00:00<00:00, 1.99MB/s] Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 71.0M/71.0M [00:02<00:00, 26.8MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "datasets\load.py", line 2145, in load_dataset builder_instance.download_and_prepare( File "datasets\builder.py", line 1027, in download_and_prepare self._download_and_prepare( File "datasets\builder.py", line 1100, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "datasets\packaged_modules\parquet\parquet.py", line 58, in _split_generators self.info.features = datasets.Features.from_arrow_schema(pq.read_schema(f)) ^^^^^^^^^^^^^^^^^ File "pyarrow\parquet\core.py", line 2325, in read_schema file = ParquetFile( ^^^^^^^^^^^^ File "pyarrow\parquet\core.py", line 318, in __init__ self.reader.open( File "pyarrow\_parquet.pyx", line 1470, in pyarrow._parquet.ParquetReader.open File "pyarrow\error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file. ``` The correct file is downloaded, however the incorrect builder type is detected; `parquet` due to other content of the repository. It would appear that the config needs to be taken into account. Note that I have removed the additional configs from the repository because of this issue and there is a limit of 3000 configs anyway so the Dataset Viewer doesn't work as I intended. I'll add them back in if it assists with testing.

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I_kwDODunzps6S9P5G

IterableDataset: cannot resolve features from list of numpy arrays

open

NONE

### Describe the bug when resolve features of `IterableDataset`, got `pyarrow.lib.ArrowInvalid: Can only convert 1-dimensional array values` error. ``` Traceback (most recent call last): File "test.py", line 6 iter_ds = iter_ds._resolve_features() File "lib/python3.10/site-packages/datasets/iterable_dataset.py", line 2876, in _resolve_features features = _infer_features_from_batch(self.with_format(None)._head()) File "lib/python3.10/site-packages/datasets/iterable_dataset.py", line 63, in _infer_features_from_batch pa_table = pa.Table.from_pydict(batch) File "pyarrow/table.pxi", line 1813, in pyarrow.lib._Tabular.from_pydict File "pyarrow/table.pxi", line 5339, in pyarrow.lib._from_pydict File "pyarrow/array.pxi", line 374, in pyarrow.lib.asarray File "pyarrow/array.pxi", line 344, in pyarrow.lib.array File "pyarrow/array.pxi", line 42, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Can only convert 1-dimensional array values ``` ### Steps to reproduce the bug ```python from datasets import Dataset import numpy as np # create list of numpy iter_ds = Dataset.from_dict({'a': [[[1, 2, 3], [1, 2, 3]]]}).to_iterable_dataset().map(lambda x: {'a': [np.array(x['a'])]}) iter_ds = iter_ds._resolve_features() # errors here ``` ### Expected behavior features can be successfully resolved ### Environment info - `datasets` version: 2.21.0 - Platform: Linux-5.15.0-94-generic-x86_64-with-glibc2.35 - Python version: 3.10.13 - `huggingface_hub` version: 0.23.4 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0

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PR_kwDODunzps54U7s4

Set dev version

closed

MEMBER

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PR_kwDODunzps54UPMS

Release: 2.21.0

closed

MEMBER

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I_kwDODunzps6SiQ3B

Some of DownloadConfig's properties are always being overridden in load.py

open

NONE

### Describe the bug The `extract_compressed_file` and `force_extract` properties of DownloadConfig are always being set to True in the function `dataset_module_factory` in the `load.py` file. This behavior is very annoying because data extracted will just be ignored the next time the dataset is loaded. See this image below:  ### Steps to reproduce the bug 1. Have a local dataset that contains archived files (zip, tar.gz, etc) 2. Build a dataset loading script to download and extract these files 3. Run the load_dataset function with a DownloadConfig that specifically set `force_extract` to False 4. The extraction process will start no matter if the archives was extracted previously ### Expected behavior The extraction process should not run when the archives were previously extracted and `force_extract` is set to False. ### Environment info datasets==2.20.0 python3.9

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PR_kwDODunzps535Xkr

Automatically create `cache_dir` from `cache_file_name`

closed

CONTRIBUTOR

You get a pretty unhelpful error message when specifying a `cache_file_name` in a directory that doesn't exist, e.g. `cache_file_name="./cache/data.map"` ```python import datasets cache_file_name="./cache/train.map" dataset = datasets.load_dataset("ylecun/mnist") dataset["train"].map(lambda x: x, cache_file_name=cache_file_name) ``` ``` FileNotFoundError: [Errno 2] No such file or directory: '/.../cache/tmp48r61siw' ``` It is simple enough to create and I was expecting that this would have been the case. cc: @albertvillanova @lhoestq

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https://github.com/huggingface/datasets/pull/7094

PR_kwDODunzps53w2b7

Add Arabic Docs to Datasets

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NONE

Translate Docs into Arabic issue-number : #7093 [Arabic Docs](https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx) [English Docs](https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/en/index.mdx) @stevhliu

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I_kwDODunzps6SS18S

Add Arabic Docs to datasets

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### Feature request Add Arabic Docs to datasets [Datasets Arabic](https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx) ### Motivation @AhmedAlmaghz https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx ### Your contribution @AhmedAlmaghz https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx

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I_kwDODunzps6SHUx6

load_dataset with multiple jsonlines files interprets datastructure too early

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