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https://api.github.com/repos/huggingface/datasets/issues/5514

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I_kwDODunzps5d9sbN

Improve inconsistency of `Dataset.map` interface for `load_from_cache_file`

open

2023-02-08T16:40:44Z

2023-02-08T23:58:29Z

CONTRIBUTOR

### Feature request 1. Replace the `load_from_cache_file` default value to `True`. 2. Remove or alter checks from `is_caching_enabled` logic. ### Motivation I stumbled over an inconsistency in the `Dataset.map` interface. The documentation (and source) states for the parameter `load_from_cache_file`: ``` load_from_cache_file (`bool`, defaults to `True` if caching is enabled): If a cache file storing the current computation from `function` can be identified, use it instead of recomputing. ``` 1. `load_from_cache_file` default value is `None`, while being annotated as `bool` 2. It is inconsistent with other method signatures like `filter`, that have the default value `True` 3. The logic is inconsistent, as the `map` method checks if caching is enabled through `is_caching_enabled`. This logic is not used for other similar methods. ### Your contribution I am not fully aware of the logic behind caching checks. If this is just a inconsistency that historically grew, I would suggest to remove the `is_caching_enabled` logic as the "default" logic. Maybe someone can give insights, if environment variables have a higher priority than local variables or vice versa. If this is clarified, I could adjust the source according to the "Feature request" section of this issue.

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I_kwDODunzps5d9HED

Some functions use a param named `type` shouldn't that be avoided since it's a Python reserved name?

open

2023-02-08T15:13:46Z

2023-02-08T16:01:07Z

CONTRIBUTOR

Hi @mariosasko, @lhoestq, or whoever reads this! :) After going through `ArrowDataset.set_format` I found out that the `type` param is actually named `type` which is a Python reserved name as you may already know, shouldn't that be renamed to `format_type` before the 3.0.0 is released? Just wanted to get your input, and if applicable, tackle this issue myself! Thanks 🤗

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PR_kwDODunzps5JhtQy

Speed up batched PyTorch DataLoader

open

2023-02-08T13:38:59Z

2023-02-08T16:16:04Z

MEMBER

I implemented `__getitems__` to speed up batched data loading in PyTorch close https://github.com/huggingface/datasets/issues/5505

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I_kwDODunzps5d7Zb4

Creating a dummy dataset from a bigger one

closed

2023-02-08T10:18:41Z

2023-02-08T10:35:48Z

MEMBER

### Describe the bug I often want to create a dummy dataset from a bigger dataset for fast iteration when training. However, I'm having a hard time doing this especially when trying to upload the dataset to the Hub. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("lambdalabs/pokemon-blip-captions") dataset["train"] = dataset["train"].select(range(20)) dataset.push_to_hub("patrickvonplaten/dummy_image_data") ``` gives: ``` ~/python_bin/datasets/arrow_dataset.py in _push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, embed_external_files) 4003 base_wait_time=2.0, 4004 max_retries=5, -> 4005 max_wait_time=20.0, 4006 ) 4007 return repo_id, split, uploaded_size, dataset_nbytes ~/python_bin/datasets/utils/file_utils.py in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time) 328 while True: 329 try: --> 330 return func(*func_args, **func_kwargs) 331 except exceptions as err: 332 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes): ~/hf/lib/python3.7/site-packages/huggingface_hub/utils/_validators.py in _inner_fn(*args, **kwargs) 122 ) 123 --> 124 return fn(*args, **kwargs) 125 126 return _inner_fn # type: ignore TypeError: upload_file() got an unexpected keyword argument 'identical_ok' In [2]: ``` ### Expected behavior I would have expected this to work. It's for me the most intuitive way of creating a dummy dataset. ### Environment info ``` - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13 - Python version: 3.7.3 - PyArrow version: 11.0.0 - Pandas version: 1.3.5 ```

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2023-02-08T10:35:48Z

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PR_kwDODunzps5JehbR

Milvus integration for search

open

2023-02-07T23:30:26Z

2023-02-08T18:35:53Z

NONE

Signed-off-by: Filip Haltmayer <filip.haltmayer@zilliz.com>

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PR_kwDODunzps5JbH-u

Add a static `__all__` to `__init__.py` for typecheckers

open

2023-02-07T11:42:40Z

2023-02-08T17:48:24Z

NONE

This adds a static `__all__` field to `__init__.py`, allowing typecheckers to know which symbols are accessible from `datasets` at runtime. In particular [Pyright](https://github.com/microsoft/pylance-release/issues/2328#issuecomment-1029381258) seems to rely on this. At this point I have added all (modulo oversight) the symbols mentioned in the Reference part of [the docs](https://huggingface.co/docs/datasets), but that could be adjusted. As a side effect, only these symbols will be imported by `from datasets import *`, which may or may not be a good thing (and if it isn't, that's easy to fix). Another option would be to add a pyi stub, but I think `__all__` should be the most pythonic solution. This should fix #3841.

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I_kwDODunzps5dxoF3

Saving a dataset after setting format to torch doesn't work, but only if filtering

open

2023-02-06T21:08:58Z

2023-02-08T14:22:26Z

NONE

### Describe the bug Saving a dataset after setting format to torch doesn't work, but only if filtering ### Steps to reproduce the bug ``` a = Dataset.from_dict({"b": [1, 2]}) a.set_format('torch') a.save_to_disk("test_save") # saves successfully a.filter(None).save_to_disk("test_save_filter") # does not >> [...] TypeError: Provided `function` which is applied to all elements of table returns a `dict` of types [<class 'torch.Tensor'>]. When using `batched=True`, make sure provided `function` returns a `dict` of types like `(<class 'list'>, <class 'numpy.ndarray'>)`. # note: skipping the format change to torch lets this work. ### Expected behavior Saving to work ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-6.1.9-arch1-1-x86_64-with-glibc2.36 - Python version: 3.10.9 - PyArrow version: 9.0.0 - Pandas version: 1.4.4

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I_kwDODunzps5dvP6c

Optimise behaviour in respect to indices mapping

open

2023-02-06T14:25:55Z

2023-02-06T14:25:55Z

CONTRIBUTOR

_Originally [posted](https://huggingface.slack.com/archives/C02V51Q3800/p1675443873878489?thread_ts=1675418893.373479&cid=C02V51Q3800) on Slack_ Considering all this, perhaps for Datasets 3.0, we can do the following: * have `continuous=True` by default in `.shard` (requested in the survey and makes more sense for us since it doesn't create an indices mapping) * allow calling `save_to_disk` on "unflattened" datasets * remove "hidden" expensive calls in `save_to_disk`, `unique`, `concatenate_datasets`, etc. For instance, instead of silently calling `flatten_indices` where it's needed, it's probably better to be explicit (considering how expensive these ops can be) and raise an error instead

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I_kwDODunzps5dsFqx

IterableDataset and Dataset return different batch sizes when using Trainer with multiple GPUs

closed

2023-02-06T03:26:03Z

2023-02-08T18:30:08Z

NONE

### Describe the bug I am training a Roberta model using 2 GPUs and the `Trainer` API with a batch size of 256. Initially I used a standard `Dataset`, but had issues with slow data loading. After reading [this issue](https://github.com/huggingface/datasets/issues/2252), I swapped to loading my dataset as contiguous shards and passing those to an `IterableDataset`. I observed an unexpected drop in GPU memory utilization, and found the batch size returned from the model had been cut in half. When using `Trainer` with 2 GPUs and a batch size of 256, `Dataset` returns a batch of size 512 (256 per GPU), while `IterableDataset` returns a batch size of 256 (256 total). My guess is `IterableDataset` isn't accounting for multiple cards. ### Steps to reproduce the bug ```python import datasets from datasets import IterableDataset from transformers import RobertaConfig from transformers import RobertaTokenizerFast from transformers import RobertaForMaskedLM from transformers import DataCollatorForLanguageModeling from transformers import Trainer, TrainingArguments use_iterable_dataset = True def gen_from_shards(shards): for shard in shards: for example in shard: yield example dataset = datasets.load_from_disk('my_dataset.hf') if use_iterable_dataset: n_shards = 100 shards = [dataset.shard(num_shards=n_shards, index=i) for i in range(n_shards)] dataset = IterableDataset.from_generator(gen_from_shards, gen_kwargs={"shards": shards}) tokenizer = RobertaTokenizerFast.from_pretrained("./my_tokenizer", max_len=160, use_fast=True) config = RobertaConfig( vocab_size=8248, max_position_embeddings=256, num_attention_heads=8, num_hidden_layers=6, type_vocab_size=1) model = RobertaForMaskedLM(config=config) data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True, mlm_probability=0.15) training_args = TrainingArguments( per_device_train_batch_size=256 # other args removed for brevity ) trainer = Trainer( model=model, args=training_args, data_collator=data_collator, train_dataset=dataset, ) trainer.train() ``` ### Expected behavior Expected `Dataset` and `IterableDataset` to have the same batch size behavior. If the current behavior is intentional, the batch size printout at the start of training should be updated. Currently, both dataset classes result in `Trainer` printing the same total batch size, even though the batch size sent to the GPUs are different. ### Environment info datasets 2.7.1 transformers 4.25.1

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I_kwDODunzps5dro5u

PyTorch BatchSampler still loads from Dataset one-by-one

open

2023-02-06T01:14:55Z

2023-02-07T19:37:04Z

NONE

### Describe the bug In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue. I'm not sure if this is a mistake in the docs or the code, but it seems that the only way for a Dataset to be passed a list of indexes by PyTorch (instead of one index at a time) is to define a `__getitems__` method (note the plural) on the Dataset object, and since the HF Dataset doesn't have this, PyTorch executes [this line of code](https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/fetch.py#L58), reverting to fetching one-by-one. ### Steps to reproduce the bug You can put a breakpoint in `Dataset.__getitem__()` or just print the args from there and see that it's called multiple times for a single `next(iter(dataloader))`, even when using the code from the docs: ```py from torch.utils.data.sampler import BatchSampler, RandomSampler batch_sampler = BatchSampler(RandomSampler(ds), batch_size=32, drop_last=False) dataloader = DataLoader(ds, batch_sampler=batch_sampler) ``` ### Expected behavior The expected behaviour would be for it to fetch batches from the dataset, rather than one-by-one. To demonstrate that there is room for improvement: once I have a HF dataset `ds`, if I just add this line: ```py ds.__getitems__ = ds.__getitem__ ``` ...then the time taken to loop over the dataset improves considerably (for wikitext-103, from one minute to 13 seconds with batch size 32). Probably not a big deal in the grand scheme of things, but seems like an easy win. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.3

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PR_kwDODunzps5JPoWy

don't zero copy timestamps

closed

2023-02-03T23:39:04Z

2023-02-08T17:28:50Z

CONTRIBUTOR

Fixes https://github.com/huggingface/datasets/issues/5495 I'm not sure whether we prefer a test here or if timestamps are known to be unsupported (like booleans). The current test at least covers the bug

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2023-02-08T14:33:17Z

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PR_kwDODunzps5JN0aX

Added functionality: sort datasets by multiple keys

open

2023-02-03T16:17:00Z

2023-02-07T17:47:18Z

NONE

Added functionality implementation: sort datasets by multiple keys/columns as discussed in https://github.com/huggingface/datasets/issues/5425.

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PR_kwDODunzps5JMTn8

Speeding up file downloads

open

2023-02-03T10:50:10Z

2023-02-03T11:42:20Z

CONTRIBUTOR

Original fix: https://github.com/huggingface/huggingface_hub/pull/1267 Not sure this function is actually still called though. I haven't done benches on this. Is there a dataset where files are hosted on the hub through cloudfront so we can have the same setup as in `hf_hub` ?

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I_kwDODunzps5diPcY

WMT19 custom download checksum error

closed

2023-02-03T05:45:37Z

2023-02-03T05:52:56Z

NONE

### Describe the bug I use the following scripts to download data from WMT19: ```python import datasets from datasets import inspect_dataset, load_dataset_builder from wmt19.wmt_utils import _TRAIN_SUBSETS,_DEV_SUBSETS ## this is a must due to: https://discuss.huggingface.co/t/load-dataset-hangs-with-local-files/28034/3 if __name__ == '__main__': dev_subsets,train_subsets = [],[] for subset in _TRAIN_SUBSETS: if subset.target=='en' and 'de' in subset.sources: train_subsets.append(subset.name) for subset in _DEV_SUBSETS: if subset.target=='en' and 'de' in subset.sources: dev_subsets.append(subset.name) inspect_dataset("wmt19", "./wmt19") builder = load_dataset_builder( "./wmt19/wmt_utils.py", language_pair=("de", "en"), subsets={ datasets.Split.TRAIN: train_subsets, datasets.Split.VALIDATION: dev_subsets, }, ) builder.download_and_prepare() ds = builder.as_dataset() ds.to_json("../data/wmt19/ende/data.json") ``` And I got the following error: ``` Traceback (most recent call last): | 0/2 [00:00<?, ?obj/s] File "draft.py", line 26, in <module> builder.download_and_prepare() | 0/1 [00:00<?, ?obj/s] File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/builder.py", line 605, in download_and_prepare self._download_and_prepare(%| | 0/1 [00:00<?, ?obj/s] File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/builder.py", line 1104, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) | 0/1 [00:00<?, ?obj/s] File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/builder.py", line 676, in _download_and_prepare verify_checksums(s #13: 0%| | 0/1 [00:00<?, ?obj/s] File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 35, in verify_checksums raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) | 0/1 [00:00<?, ?obj/s] datasets.utils.info_utils.UnexpectedDownloadedFile: {'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-de.zipporah0-dedup-clean.tgz', 'https://huggingface.co/datasets/wmt/wmt13/resolve/main-zip/training-parallel-europarl-v7.zip', 'https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/rapid2016.zip', 'https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/training-parallel-nc-v13.zip', 'https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/training-parallel-nc-v12.zip', 'https://huggingface.co/datasets/wmt/wmt14/resolve/main-zip/training-parallel-nc-v9.zip', 'https://huggingface.co/datasets/wmt/wmt15/resolve/main-zip/training-parallel-nc-v10.zip', 'https://huggingface.co/datasets/wmt/wmt16/resolve/main-zip/translation-task/training-parallel-nc-v11.zip'} ``` ### Steps to reproduce the bug see above ### Expected behavior download data successfully ### Environment info datasets==2.1.0 python==3.8

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2023-02-03T05:52:56Z

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I_kwDODunzps5dhBRC

`load_dataset` has ~4 seconds of overhead for cached data

open

2023-02-02T23:34:50Z

2023-02-07T19:35:11Z

NONE

### Feature request When loading a dataset that has been cached locally, the `load_dataset` function takes a lot longer than it should take to fetch the dataset from disk (or memory). This is particularly noticeable for smaller datasets. For example, wikitext-2, comparing `load_data` (once cached) and `load_from_disk`, the `load_dataset` method takes 40 times longer. ⏱ 4.84s ⮜ load_dataset ⏱ 119ms ⮜ load_from_disk ### Motivation I assume this is doing something like checking for a newer version. If so, that's an age old problem: do you make the user wait _every single time they load from cache_ or do you do something like load from cache always, _then_ check for a newer version and alert if they have stale data. The decision usually revolves around what percentage of the time the data will have been updated, and how dangerous old data is. For most datasets it's extremely unlikely that there will be a newer version on any given run, so 99% of the time this is just wasted time. Maybe you don't want to make that decision for all users, but at least having the _option_ to not wait for checks would be an improvement. ### Your contribution .

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I_kwDODunzps5deLBB

TypeError: 'bool' object is not iterable when filtering a datasets.arrow_dataset.Dataset

closed

2023-02-02T14:46:49Z

2023-02-04T17:19:37Z

NONE

### Describe the bug Hi, Thanks for the amazing work on the library! **Describe the bug** I think I might have noticed a small bug in the filter method. Having loaded a dataset using `load_dataset`, when I try to filter out empty entries with `batched=True`, I get a TypeError. ### Steps to reproduce the bug ``` train_dataset = train_dataset.filter( function=lambda example: example["image"] is not None, batched=True, batch_size=10) ``` Error message: ``` File .../lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs) 476 validate_fingerprint(kwargs[fingerprint_name]) 478 # Call actual function --> 480 out = func(self, *args, **kwargs) ... -> 5666 indices_array = [i for i, to_keep in zip(indices, mask) if to_keep] 5667 if indices_mapping is not None: 5668 indices_array = pa.array(indices_array, type=pa.uint64()) TypeError: 'bool' object is not iterable ``` **Removing batched=True allows to bypass the issue.** ### Expected behavior According to the doc, "[batch_size corresponds to the] number of examples per batch provided to function if batched = True", so we shouldn't need to remove the batchd=True arg? source: https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.Dataset.filter ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31 - Python version: 3.9.10 - PyArrow version: 10.0.1 - Pandas version: 1.5.3

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PR_kwDODunzps5JFhvc

Improved error message for gated/private repos

closed

2023-02-02T08:56:15Z

2023-02-02T11:26:08Z

MEMBER

Using `use_auth_token=True` is not needed anymore. If a user logged in, the token will be automatically retrieved. Also include a mention for gated repos See https://github.com/huggingface/huggingface_hub/pull/1064

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I_kwDODunzps5dayCF

Add a `reduce` method

open

2023-02-02T04:30:22Z

2023-02-03T14:11:32Z

NONE

### Feature request Right now the `Dataset` class implements `map()` and `filter()`, but leaves out the third functional idiom popular among Python users: `reduce`. ### Motivation A `reduce` method is often useful when calculating dataset statistics, for example, the occurrence of a particular n-gram or the average line length of a code dataset. ### Your contribution I haven't contributed to `datasets` before, but I don't expect this will be too difficult, since the implementation will closely follow that of `map` and `filter`. I could have a crack over the weekend.

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I_kwDODunzps5dY4X8

to_tf_dataset fails with datetime UTC columns even if not included in columns argument

closed

2023-02-01T20:47:33Z

2023-02-08T14:33:19Z

CONTRIBUTOR

### Describe the bug There appears to be some eager behavior in `to_tf_dataset` that runs against every column in a dataset even if they aren't included in the columns argument. This is problematic with datetime UTC columns due to them not working with zero copy. If I don't have UTC information in my datetime column, then everything works as expected. ### Steps to reproduce the bug ```python import numpy as np import pandas as pd from datasets import Dataset df = pd.DataFrame(np.random.rand(2, 1), columns=["x"]) # df["dt"] = pd.to_datetime(["2023-01-01", "2023-01-01"]) # works fine df["dt"] = pd.to_datetime(["2023-01-01 00:00:00.00000+00:00", "2023-01-01 00:00:00.00000+00:00"]) df.to_parquet("test.pq") ds = Dataset.from_parquet("test.pq") tf_ds = ds.to_tf_dataset(columns=["x"], batch_size=2, shuffle=True) ``` ``` ArrowInvalid Traceback (most recent call last) Cell In[1], line 12 8 df.to_parquet("test.pq") 11 ds = Dataset.from_parquet("test.pq") ---> 12 tf_ds = ds.to_tf_dataset(columns=["r"], batch_size=2, shuffle=True) File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:411, in TensorflowDatasetMixin.to_tf_dataset(self, batch_size, columns, shuffle, collate_fn, drop_remainder, collate_fn_args, label_cols, prefetch, num_workers) 407 dataset = self 409 # TODO(Matt, QL): deprecate the retention of label_ids and label --> 411 output_signature, columns_to_np_types = dataset._get_output_signature( 412 dataset, 413 collate_fn=collate_fn, 414 collate_fn_args=collate_fn_args, 415 cols_to_retain=cols_to_retain, 416 batch_size=batch_size if drop_remainder else None, 417 ) 419 if "labels" in output_signature: 420 if ("label_ids" in columns or "label" in columns) and "labels" not in columns: File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:254, in TensorflowDatasetMixin._get_output_signature(dataset, collate_fn, collate_fn_args, cols_to_retain, batch_size, num_test_batches) 252 for _ in range(num_test_batches): 253 indices = sample(range(len(dataset)), test_batch_size) --> 254 test_batch = dataset[indices] 255 if cols_to_retain is not None: 256 test_batch = {key: value for key, value in test_batch.items() if key in cols_to_retain} File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2590, in Dataset.__getitem__(self, key) 2588 def __getitem__(self, key): # noqa: F811 2589 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 2590 return self._getitem( 2591 key, 2592 ) File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2575, in Dataset._getitem(self, key, **kwargs) 2573 formatter = get_formatter(format_type, features=self.features, **format_kwargs) 2574 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 2575 formatted_output = format_table( 2576 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 2577 ) 2578 return formatted_output File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:634, in format_table(table, key, formatter, format_columns, output_all_columns) 632 python_formatter = PythonFormatter(features=None) 633 if format_columns is None: --> 634 return formatter(pa_table, query_type=query_type) 635 elif query_type == "column": 636 if key in format_columns: File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:410, in Formatter.__call__(self, pa_table, query_type) 408 return self.format_column(pa_table) 409 elif query_type == "batch": --> 410 return self.format_batch(pa_table) File ~/venv/lib/python3.8/site-packages/datasets/formatting/np_formatter.py:78, in NumpyFormatter.format_batch(self, pa_table) 77 def format_batch(self, pa_table: pa.Table) -> Mapping: ---> 78 batch = self.numpy_arrow_extractor().extract_batch(pa_table) 79 batch = self.python_features_decoder.decode_batch(batch) 80 batch = self.recursive_tensorize(batch) File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:164, in NumpyArrowExtractor.extract_batch(self, pa_table) 163 def extract_batch(self, pa_table: pa.Table) -> dict: --> 164 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:164, in <dictcomp>(.0) 163 def extract_batch(self, pa_table: pa.Table) -> dict: --> 164 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:185, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array) 181 else: 182 zero_copy_only = _is_zero_copy_only(pa_array.type) and all( 183 not _is_array_with_nulls(chunk) for chunk in pa_array.chunks 184 ) --> 185 array: List = [ 186 row for chunk in pa_array.chunks for row in chunk.to_numpy(zero_copy_only=zero_copy_only) 187 ] 188 else: 189 if isinstance(pa_array.type, _ArrayXDExtensionType): 190 # don't call to_pylist() to preserve dtype of the fixed-size array File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:186, in <listcomp>(.0) 181 else: 182 zero_copy_only = _is_zero_copy_only(pa_array.type) and all( 183 not _is_array_with_nulls(chunk) for chunk in pa_array.chunks 184 ) 185 array: List = [ --> 186 row for chunk in pa_array.chunks for row in chunk.to_numpy(zero_copy_only=zero_copy_only) 187 ] 188 else: 189 if isinstance(pa_array.type, _ArrayXDExtensionType): 190 # don't call to_pylist() to preserve dtype of the fixed-size array File ~/venv/lib/python3.8/site-packages/pyarrow/array.pxi:1475, in pyarrow.lib.Array.to_numpy() File ~/venv/lib/python3.8/site-packages/pyarrow/error.pxi:100, in pyarrow.lib.check_status() ArrowInvalid: Needed to copy 1 chunks with 0 nulls, but zero_copy_only was True ``` ### Expected behavior I think there are two potential issues/fixes 1. Proper handling of datetime UTC columns (perhaps there is something incorrect with zero copy handling here) 2. Not eagerly running against every column in a dataset when the columns argument of `to_tf_dataset` specifies a subset of columns (although I'm not sure if this is unavoidable) ### Environment info - `datasets` version: 2.9.0 - Platform: macOS-13.2-x86_64-i386-64bit - Python version: 3.8.12 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

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I_kwDODunzps5dYUN0

Update audio installation doc page

open

2023-02-01T19:07:50Z

2023-02-02T13:11:58Z

CONTRIBUTOR

Our [installation documentation page](https://huggingface.co/docs/datasets/installation#audio) says that one can use Datasets for mp3 only with `torchaudio<0.12`. `torchaudio>0.12` is actually supported too but requires a specific version of ffmpeg which is not easily installed on all linux versions but there is a custom ubuntu repo for it, we have insctructions in the code: https://github.com/huggingface/datasets/blob/main/src/datasets/features/audio.py#L327 So we should update the doc page. But first investigate [this issue](5488).

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PR_kwDODunzps5JCSAZ

Remove unused `load_from_cache_file` arg from `Dataset.shard()` docstring

closed

2023-02-01T18:57:48Z

2023-02-08T15:10:46Z

CONTRIBUTOR

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I_kwDODunzps5dYHu4

Push_to_hub in a pull request

open

2023-02-01T18:32:14Z

2023-02-01T18:40:46Z

MEMBER

Right now `ds.push_to_hub()` can push a dataset on `main` or on a new branch with `branch=`, but there is no way to open a pull request. Even passing `branch=refs/pr/x` doesn't seem to work: it tries to create a branch with that name cc @nateraw It should be possible to tweak the use of `huggingface_hub` in `push_to_hub` to make it open a PR or push to an existing PR

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PR_kwDODunzps5JA9OD

[MINOR] Typo

closed

2023-02-01T14:39:39Z

2023-02-02T07:42:28Z

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PR_kwDODunzps5I_nz-

Do not add index column by default when exporting to CSV

open

2023-02-01T10:20:55Z

2023-02-01T10:25:21Z

MEMBER

As pointed out by @merveenoyan, default behavior of `Dataset.to_csv` adds the index as an additional column without name. This PR changes the default behavior, so that now the index column is not written. To add the index column, now you need to pass `index=True` and also `index_label=<name of the index colum>` to name that column. CC: @merveenoyan

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PR_kwDODunzps5I_WPH

Pin dill lower version

closed

2023-02-01T09:33:42Z

2023-02-02T07:48:09Z

MEMBER

Pin `dill` lower version compatible with `datasets`. Related to: - #5487 - #288 Note that the required `dill._dill` module was introduced in dill-2.8.0, however we have heuristically tested that datasets can only be installed with dill>=3.0.0 (otherwise pip hangs indefinitely while preparing metadata for multiprocess-0.70.7)

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I_kwDODunzps5dSGPu

Error loading MP3 files from CommonVoice

open

2023-01-31T21:25:33Z

2023-02-01T15:28:56Z

NONE

### Describe the bug When loading a CommonVoice dataset with `datasets==2.9.0` and `torchaudio>=0.12.0`, I get an error reading the audio arrays: ```python --------------------------------------------------------------------------- LibsndfileError Traceback (most recent call last) ~/.local/lib/python3.8/site-packages/datasets/features/audio.py in _decode_mp3(self, path_or_file) 310 try: # try torchaudio anyway because sometimes it works (depending on the os and os packages installed) --> 311 array, sampling_rate = self._decode_mp3_torchaudio(path_or_file) 312 except RuntimeError: ~/.local/lib/python3.8/site-packages/datasets/features/audio.py in _decode_mp3_torchaudio(self, path_or_file) 351 --> 352 array, sampling_rate = torchaudio.load(path_or_file, format="mp3") 353 if self.sampling_rate and self.sampling_rate != sampling_rate: ~/.local/lib/python3.8/site-packages/torchaudio/backend/soundfile_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format) 204 """ --> 205 with soundfile.SoundFile(filepath, "r") as file_: 206 if file_.format != "WAV" or normalize: ~/.local/lib/python3.8/site-packages/soundfile.py in __init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd) 654 format, subtype, endian) --> 655 self._file = self._open(file, mode_int, closefd) 656 if set(mode).issuperset('r+') and self.seekable(): ~/.local/lib/python3.8/site-packages/soundfile.py in _open(self, file, mode_int, closefd) 1212 err = _snd.sf_error(file_ptr) -> 1213 raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name)) 1214 if mode_int == _snd.SFM_WRITE: LibsndfileError: Error opening <_io.BytesIO object at 0x7fa539462090>: File contains data in an unknown format. ``` I assume this is because there's some issue with the mp3 decoding process. I've verified that I have `ffmpeg>=4` (on a Linux distro), which appears to be the fallback backend for `torchaudio,` (at least according to #4889). ### Steps to reproduce the bug ```python dataset = load_dataset("mozilla-foundation/common_voice_11_0", "be", split="train") dataset[0] ``` ### Expected behavior Similar behavior to `torchaudio<0.12.0`, which doesn't result in a `LibsndfileError` ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 10.0.1 - Pandas version: 1.5.1

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I_kwDODunzps5dQBJ5

Incorrect filepath for dill module

open

2023-01-31T15:01:08Z

2023-02-02T07:07:55Z

NONE

### Describe the bug I installed the `datasets` package and when I try to `import` it, I get the following error: ``` Traceback (most recent call last): File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module> import datasets File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/__init__.py", line 43, in <module> from .arrow_dataset import Dataset File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 66, in <module> from .arrow_writer import ArrowWriter, OptimizedTypedSequence File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/arrow_writer.py", line 27, in <module> from .features import Features, Image, Value File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/features/__init__.py", line 17, in <module> from .audio import Audio File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/features/audio.py", line 12, in <module> from ..download.streaming_download_manager import xopen File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/download/__init__.py", line 9, in <module> from .download_manager import DownloadManager, DownloadMode File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/download/download_manager.py", line 36, in <module> from ..utils.py_utils import NestedDataStructure, map_nested, size_str File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 602, in <module> class Pickler(dill.Pickler): File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 605, in Pickler dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy()) AttributeError: module 'dill' has no attribute '_dill' ``` Looking at the github source code for dill, it appears that `datasets` has a bug or is not compatible with the latest `dill`. Specifically, rather than `dill._dill.XXXX` it should be `dill.dill._dill.XXXX`. But given the popularity of `datasets` I feel confused about me being the first person to have this issue, so it makes me wonder if I'm misdiagnosing the issue. ### Steps to reproduce the bug Install `dill` and `datasets` packages and then `import datasets` ### Expected behavior I expect `datasets` to import. ### Environment info - `datasets` version: 2.9.0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.9.13 - PyArrow version: 11.0.0 - Pandas version: 1.4.4

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I_kwDODunzps5dOahl

Adding `sep` to TextConfig

open

2023-01-31T10:39:53Z

2023-01-31T14:50:18Z

NONE

I have a local a `.txt` file that follows the `CONLL2003` format which I need to load using `load_script`. However, by using `sample_by='line'`, one can only split the dataset into lines without splitting each line into columns. Would it be reasonable to add a `sep` argument in combination with `sample_by='paragraph'` to parse a paragraph into an array for each column ? If so, I am happy to contribute! ## Environment * `python 3.8.10` * `datasets 2.9.0` ## Snippet of `train.txt` ```txt Distribution NN O O and NN O O dynamics NN O O of NN O O electron NN O B-RP complexes NN O I-RP in NN O O cyanobacterial NN O B-R membranes NN O I-R The NN O O occurrence NN O O of NN O O prostaglandin NN O B-R F2α NN O I-R in NN O O Pharbitis NN O B-R seedlings NN O I-R grown NN O O under NN O O short NN O B-P days NN O I-P or NN O I-P days NN O I-P ``` ## Current Behaviour ```python # defining 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'] here would fail with `ValueError: Length of names (4) does not match length of arrays (1)` dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='line') dataset['train']['tokens'][0] >>> 'Distribution\tNN\tO\tO' ``` ## Expected Behaviour / Suggestion ```python # suppose we defined 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'] dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='paragraph', sep='\t') dataset['train']['tokens'][0] >>> ['Distribution', 'and', 'dynamics', ... ] dataset['train']['ner_tags'][0] >>> ['O', 'O', 'O', ... ] ```

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PR_kwDODunzps5I2ER2

Add section in tutorial for IterableDataset

closed

2023-01-30T18:43:04Z

2023-02-01T18:15:38Z

MEMBER

Introduces an `IterableDataset` and how to access it in the tutorial section. It also adds a brief next step section at the end to provide a path for users who want more explanation and a path for users who want something more practical and learn how to preprocess these dataset types. It'll complement the awesome new doc introduced in: - #5410

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PR_kwDODunzps5I1oaq

Update docs for `nyu_depth_v2` dataset

closed

2023-01-30T17:37:08Z

2023-02-05T14:22:10Z

CONTRIBUTOR

This PR will fix the issue mentioned in #5461. cc: @sayakpaul @lhoestq

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I_kwDODunzps5dCVzi

Unable to upload dataset

closed

2023-01-28T15:18:26Z

2023-01-29T08:09:49Z

NONE

### Describe the bug Uploading a simple dataset ends with an exception ### Steps to reproduce the bug I created a new conda env with python 3.10, pip installed datasets and: ```python >>> from datasets import load_dataset, load_from_disk, Dataset >>> d = Dataset.from_dict({"text": ["hello"] * 2}) >>> d.push_to_hub("ttt111") /home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_hf_folder.py:92: UserWarning: A token has been found in `/a/home/cc/students/cs/kirstain/.huggingface/token`. This is the old path where tokens were stored. The new location is `/home/olab/kirstain/.cache/huggingface/token` which is configurable using `HF_HOME` environment variable. Your token has been copied to this new location. You can now safely delete the old token file manually or use `huggingface-cli logout`. warnings.warn( Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 279.94ba/s] Upload 1 LFS files: 0%| | 0/1 [00:02<?, ?it/s] Pushing dataset shards to the dataset hub: 0%| | 0/1 [00:04<?, ?it/s] Traceback (most recent call last): File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 264, in hf_raise_for_status response.raise_for_status() File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/requests/models.py", line 1021, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://s3.us-east-1.amazonaws.com/lfs.huggingface.co/repos/cf/0c/cf0c5ab8a3f729e5f57a8b79a36ecea64a31126f13218591c27ed9a1c7bd9b41/ece885a4bb6bbc8c1bb51b45542b805283d74590f72cd4c45d3ba76628570386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230128%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230128T151640Z&X-Amz-Expires=900&X-Amz-Signature=89e78e9a9d70add7ed93d453334f4f93c6f29d889d46750a1f2da04af73978db&X-Amz-SignedHeaders=host&x-amz-storage-class=INTELLIGENT_TIERING&x-id=PutObject The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/_commit_api.py", line 334, in _inner_upload_lfs_object return _upload_lfs_object( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/_commit_api.py", line 391, in _upload_lfs_object lfs_upload( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/lfs.py", line 273, in lfs_upload _upload_single_part( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/lfs.py", line 305, in _upload_single_part hf_raise_for_status(upload_res) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 318, in hf_raise_for_status raise HfHubHTTPError(str(e), response=response) from e huggingface_hub.utils._errors.HfHubHTTPError: 403 Client Error: Forbidden for url: https://s3.us-east-1.amazonaws.com/lfs.huggingface.co/repos/cf/0c/cf0c5ab8a3f729e5f57a8b79a36ecea64a31126f13218591c27ed9a1c7bd9b41/ece885a4bb6bbc8c1bb51b45542b805283d74590f72cd4c45d3ba76628570386?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230128%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230128T151640Z&X-Amz-Expires=900&X-Amz-Signature=89e78e9a9d70add7ed93d453334f4f93c6f29d889d46750a1f2da04af73978db&X-Amz-SignedHeaders=host&x-amz-storage-class=INTELLIGENT_TIERING&x-id=PutObject The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 4909, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes, repo_files, deleted_size = self._push_parquet_shards_to_hub( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 4804, in _push_parquet_shards_to_hub _retry( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 281, in _retry return func(*func_args, **func_kwargs) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 124, in _inner_fn return fn(*args, **kwargs) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2537, in upload_file commit_info = self.create_commit( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 124, in _inner_fn return fn(*args, **kwargs) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2346, in create_commit upload_lfs_files( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 124, in _inner_fn return fn(*args, **kwargs) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/_commit_api.py", line 346, in upload_lfs_files thread_map( File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/tqdm/contrib/concurrent.py", line 94, in thread_map return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/tqdm/contrib/concurrent.py", line 76, in _executor_map return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs)) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/_base.py", line 621, in result_iterator yield _result_or_cancel(fs.pop()) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/_base.py", line 319, in _result_or_cancel return fut.result(timeout) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/_base.py", line 458, in result return self.__get_result() File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result raise self._exception File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/concurrent/futures/thread.py", line 58, in run result = self.fn(*self.args, **self.kwargs) File "/home/olab/kirstain/anaconda3/envs/datasets/lib/python3.10/site-packages/huggingface_hub/_commit_api.py", line 338, in _inner_upload_lfs_object raise RuntimeError( RuntimeError: Error while uploading 'data/train-00000-of-00001-6df93048e66df326.parquet' to the Hub. ``` ### Expected behavior The dataset should be uploaded without any exceptions ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-4.15.0-65-generic-x86_64-with-glibc2.27 - Python version: 3.10.9 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

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I_kwDODunzps5dCLqR

Reload features from Parquet metadata

open

2023-01-28T13:12:31Z

2023-02-05T18:09:54Z

MEMBER

The idea would be to allow this : ```python ds.to_parquet("my_dataset/ds.parquet") reloaded = load_dataset("my_dataset") assert ds.features == reloaded.features ``` And it should also work with Image and Audio types (right now they're reloaded as a dict type) This can be implemented by storing and reading the feature types in the parquet metadata, as we do for arrow files.

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I_kwDODunzps5dAtrj

Load a cached dataset as iterable

open

2023-01-27T21:43:51Z

2023-02-07T15:58:15Z

MEMBER

The idea would be to allow something like ```python ds = load_dataset("c4", "en", as_iterable=True) ``` To be used to train models. It would load an IterableDataset from the cached Arrow files. Cc @stas00 Edit : from the discussions we may load from cache when streaming=True

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PR_kwDODunzps5ItY2y

Select columns of Dataset or DatasetDict

open

2023-01-27T20:06:16Z

2023-02-08T19:12:22Z

NONE

Close #5474 and #5468.

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I_kwDODunzps5dASrW

audiofolder works on local env, but creates empty dataset in a remote one, what dependencies could I be missing/outdated

closed

2023-01-27T20:01:22Z

2023-01-29T05:23:14Z

NONE

### Describe the bug I'm using a custom audio dataset (400+ audio files) in the correct format for audiofolder. Although loading the dataset with audiofolder works in one local setup, it doesn't in a remote one (it just creates an empty dataset). I have both ffmpeg and libndfile installed on both computers, what could be missing/need to be updated in the one that doesn't work? On the remote env, libsndfile is 1.0.28 and ffmpeg is 4.2.1. from datasets import load_dataset ds = load_dataset("audiofolder", data_dir="...") Here is the output (should be generating 400+ rows): Downloading and preparing dataset audiofolder/default to ... Downloading data files: 0%| | 0/2 [00:00<?, ?it/s] Downloading data files: 0it [00:00, ?it/s] Extracting data files: 0it [00:00, ?it/s] Generating train split: 0 examples [00:00, ? examples/s] Dataset audiofolder downloaded and prepared to ... Subsequent calls will reuse this data. 0%| | 0/1 [00:00<?, ?it/s] DatasetDict({ train: Dataset({ features: ['audio', 'transcription'], num_rows: 1 }) }) Here is my pip environment in the one that doesn't work (uses torch 1.11.a0 from shared env): Package Version ------------------- ------------------- aiofiles 22.1.0 aiohttp 3.8.3 aiosignal 1.3.1 altair 4.2.1 anyio 3.6.2 appdirs 1.4.4 argcomplete 2.0.0 argon2-cffi 20.1.0 astunparse 1.6.3 async-timeout 4.0.2 attrs 21.2.0 audioread 3.0.0 backcall 0.2.0 bleach 4.0.0 certifi 2021.10.8 cffi 1.14.6 charset-normalizer 2.0.12 click 8.1.3 contourpy 1.0.7 cycler 0.11.0 datasets 2.9.0 debugpy 1.4.1 decorator 5.0.9 defusedxml 0.7.1 dill 0.3.6 distlib 0.3.4 entrypoints 0.3 evaluate 0.4.0 expecttest 0.1.3 fastapi 0.89.1 ffmpy 0.3.0 filelock 3.6.0 fonttools 4.38.0 frozenlist 1.3.3 fsspec 2023.1.0 future 0.18.2 gradio 3.16.2 h11 0.14.0 httpcore 0.16.3 httpx 0.23.3 huggingface-hub 0.12.0 idna 3.3 ipykernel 6.2.0 ipython 7.26.0 ipython-genutils 0.2.0 ipywidgets 7.6.3 jedi 0.18.0 Jinja2 3.0.1 jiwer 2.5.1 joblib 1.2.0 jsonschema 3.2.0 jupyter 1.0.0 jupyter-client 6.1.12 jupyter-console 6.4.0 jupyter-core 4.7.1 jupyterlab-pygments 0.1.2 jupyterlab-widgets 1.0.0 kiwisolver 1.4.4 Levenshtein 0.20.2 librosa 0.9.2 linkify-it-py 1.0.3 llvmlite 0.39.1 markdown-it-py 2.1.0 MarkupSafe 2.0.1 matplotlib 3.6.3 matplotlib-inline 0.1.2 mdit-py-plugins 0.3.3 mdurl 0.1.2 mistune 0.8.4 multidict 6.0.4 multiprocess 0.70.14 nbclient 0.5.4 nbconvert 6.1.0 nbformat 5.1.3 nest-asyncio 1.5.1 notebook 6.4.3 numba 0.56.4 numpy 1.20.3 orjson 3.8.5 packaging 21.0 pandas 1.5.3 pandocfilters 1.4.3 parso 0.8.2 pexpect 4.8.0 pickleshare 0.7.5 Pillow 9.4.0 pip 22.3.1 pipx 1.1.0 platformdirs 2.5.2 pooch 1.6.0 prometheus-client 0.11.0 prompt-toolkit 3.0.19 psutil 5.9.0 ptyprocess 0.7.0 pyarrow 10.0.1 pycparser 2.20 pycryptodome 3.16.0 pydantic 1.10.4 pydub 0.25.1 Pygments 2.10.0 pyparsing 2.4.7 pyrsistent 0.18.0 python-dateutil 2.8.2 python-multipart 0.0.5 pytz 2022.7.1 PyYAML 6.0 pyzmq 22.2.1 qtconsole 5.1.1 QtPy 1.10.0 rapidfuzz 2.13.7 regex 2022.10.31 requests 2.27.1 resampy 0.4.2 responses 0.18.0 rfc3986 1.5.0 scikit-learn 1.2.1 scipy 1.6.3 Send2Trash 1.8.0 setuptools 65.5.1 shiboken6 6.3.1 shiboken6-generator 6.3.1 six 1.16.0 sniffio 1.3.0 soundfile 0.11.0 starlette 0.22.0 terminado 0.11.0 testpath 0.5.0 threadpoolctl 3.1.0 tokenizers 0.13.2 toolz 0.12.0 torch 1.11.0a0+gitunknown tornado 6.1 tqdm 4.64.1 traitlets 5.0.5 transformers 4.27.0.dev0 types-dataclasses 0.6.4 typing_extensions 4.1.1 uc-micro-py 1.0.1 urllib3 1.26.9 userpath 1.8.0 uvicorn 0.20.0 virtualenv 20.14.1 wcwidth 0.2.5 webencodings 0.5.1 websockets 10.4 wheel 0.37.1 widgetsnbextension 3.5.1 xxhash 3.2.0 yarl 1.8.2 ### Steps to reproduce the bug Create a pip environment with the packages listed above (make sure ffmpeg and libsndfile is installed with same versions listed above). Create a custom audio dataset and load it in with load_dataset("audiofolder", ...) ### Expected behavior load_dataset should create a dataset with 400+ rows. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.9.0 - PyArrow version: 10.0.1 - Pandas version: 1.5.3

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PR_kwDODunzps5ItXQG

Tip for recomputing metadata

closed

2023-01-27T20:01:22Z

2023-01-30T19:22:21Z

MEMBER

From this [feedback](https://discuss.huggingface.co/t/nonmatchingsplitssizeserror/30033) on the forum, thought I'd include a tip for recomputing the metadata numbers if it is your own dataset.

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I_kwDODunzps5c-lYE

Unpin sqlalchemy once issue is fixed

open

2023-01-27T15:01:55Z

2023-01-27T15:01:55Z

MEMBER

Once the source issue is fixed: - pandas-dev/pandas#51015 we should revert the pin introduced in: - #5476

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PR_kwDODunzps5IqwC_

Pin sqlalchemy

closed

2023-01-27T11:26:38Z

2023-01-27T12:06:51Z

MEMBER

since sqlalchemy update to 2.0.0 the CI started to fail: https://github.com/huggingface/datasets/actions/runs/4023742457/jobs/6914976514 the error comes from pandas: https://github.com/pandas-dev/pandas/issues/51015

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I_kwDODunzps5c7OmF

Dataset scan time is much slower than using native arrow

closed

2023-01-27T01:32:25Z

2023-01-30T16:17:11Z

CONTRIBUTOR

### Describe the bug I'm basically running the same scanning experiment from the tutorials https://huggingface.co/course/chapter5/4?fw=pt except now I'm comparing to a native pyarrow version. I'm finding that the native pyarrow approach is much faster (2 orders of magnitude). Is there something I'm missing that explains this phenomenon? ### Steps to reproduce the bug https://colab.research.google.com/drive/11EtHDaGAf1DKCpvYnAPJUW-LFfAcDzHY?usp=sharing ### Expected behavior I expect scan times to be on par with using pyarrow directly. ### Environment info standard colab environment

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2023-01-30T16:17:11Z

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I_kwDODunzps5c6dCT

Column project operation on `datasets.Dataset`

open

2023-01-26T21:47:53Z

2023-02-01T16:44:09Z

NONE

### Feature request There is no operation to select a subset of columns of original dataset. Expected API follows. ```python a = Dataset.from_dict({ 'int': [0, 1, 2] 'char': ['a', 'b', 'c'], 'none': [None] * 3, }) b = a.project('int', 'char') # usually, .select() print(a.column_names) # stdout: ['int', 'char', 'none'] print(b.column_names) # stdout: ['int', 'char'] ``` Method project can easily accept not only column names (as a `str)` but univariant function applied to corresponding column as an example. Or keyword arguments can be used in order to rename columns in advance (see `pandas`, `pyspark`, `pyarrow`, and SQL).. ### Motivation Projection is a typical operation in every data processing library. And it is a basic block of a well-known data manipulation language like SQL. Without this operation `datasets.Dataset` interface is not complete. ### Your contribution Not sure. Some of my PRs are still open and some do not have any discussions.

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PR_kwDODunzps5Inm9h

Set dev version

closed

2023-01-26T19:34:44Z

2023-01-26T19:47:34Z

MEMBER

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PR_kwDODunzps5Inlp8

Release: 2.9.0

closed

2023-01-26T19:29:42Z

2023-01-26T19:40:44Z

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PR_kwDODunzps5InPA7

Add num_test_batches option

closed

2023-01-26T18:09:40Z

2023-01-27T18:16:45Z

CONTRIBUTOR

`to_tf_dataset` calls can be very costly because of the number of test batches drawn during `_get_output_signature`. The test batches are draw in order to estimate the shapes when creating the tensorflow dataset. This is necessary when the shapes can be irregular, but not in cases when the tensor shapes are the same across all samples. This PR adds an option to change the number of batches drawn, so the user can speed this conversion up. Running the following, and modifying `num_test_batches` ``` import time from datasets import load_dataset from transformers import DefaultDataCollator data_collator = DefaultDataCollator() dataset = load_dataset("beans") dataset = dataset["train"].with_format("np") start = time.time() dataset = dataset.to_tf_dataset( columns=["image"], label_cols=["label"], batch_size=8, collate_fn=data_collator, num_test_batches=NUM_TEST_BATCHES, ) end = time.time() print(end - start) ``` NUM_TEST_BATCHES=200: 0.8197s NUM_TEST_BATCHES=50: 0.3070s NUM_TEST_BATCHES=2: 0.1417s NUM_TEST_BATCHES=1: 0.1352s

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PR_kwDODunzps5InLw9

Update dataset card creation

closed

2023-01-26T17:57:51Z

2023-01-27T16:27:00Z

MEMBER

Encourages users to create a dataset card on the Hub directly with the new metadata ui + import dataset card template instead of telling users to manually create and upload one.

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PR_kwDODunzps5Imhk2

Remove deprecated `shard_size` arg from `.push_to_hub()`

closed

2023-01-26T15:40:56Z

2023-01-26T17:37:51Z

CONTRIBUTOR

The docstrings say that it was supposed to be deprecated since version 2.4.0, can we remove it?

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2023-01-26T17:30:59Z

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I_kwDODunzps5c3jXB

Allow opposite of remove_columns on Dataset and DatasetDict

open

2023-01-26T12:28:09Z

2023-01-31T10:48:56Z

NONE

### Feature request In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code: ```python COLUMNS_TO_KEEP = ["text", "audio"] all_columns = gigaspeech["train"].column_names columns_to_remove = set(all_columns) - set(COLUMNS_TO_KEEP) gigaspeech = gigaspeech.remove_columns(columns_to_remove) ``` This kind of thing happens a lot when you don't need to keep all columns from the dataset. It would be more convenient (and less error prone) if you could just write: ```python gigaspeech = gigaspeech.keep_columns(["text", "audio"]) ``` Internally, `keep_columns` could still call `remove_columns`, but it expresses more clearly what the user's intent is. ### Motivation Less code to write for the user of the dataset. ### Your contribution -

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PR_kwDODunzps5IlAlk

Fix conda command in readme

closed

2023-01-26T10:03:01Z

2023-01-26T18:32:16Z

MEMBER

The [conda forge channel](https://anaconda.org/conda-forge/datasets) is lagging behind (as of right now, only 2.7.1 is available), we should recommend using the [Hugging face channel](https://anaconda.org/HuggingFace/datasets) that we are maintaining ``` conda install -c huggingface datasets ```

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PR_kwDODunzps5Ij-z1

remove pathlib.Path with URIs

closed

2023-01-26T03:25:45Z

2023-01-26T17:08:57Z

CONTRIBUTOR

Pathlib will convert "//" to "/" which causes retry errors when downloading from cloud storage

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2023-01-26T16:59:11Z

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I_kwDODunzps5c1bna

audiofolder creates empty dataset even though the dataset passed in follows the correct structure

closed

2023-01-26T01:45:45Z

2023-01-26T08:48:45Z

NONE

### Describe the bug The structure of my dataset folder called "my_dataset" is : data metadata.csv The data folder consists of all mp3 files and metadata.csv consist of file locations like 'data/...mp3 and transcriptions. There's 400+ mp3 files and corresponding transcriptions for my dataset. When I run the following: ds = load_dataset("audiofolder", data_dir="my_dataset") I get: Using custom data configuration default-... Downloading and preparing dataset audiofolder/default to /... Downloading data files: 0%| | 0/2 [00:00<?, ?it/s] Downloading data files: 0it [00:00, ?it/s] Extracting data files: 0it [00:00, ?it/s] Generating train split: 0 examples [00:00, ? examples/s] Dataset audiofolder downloaded and prepared to /.... Subsequent calls will reuse this data. 0%| | 0/1 [00:00<?, ?it/s] DatasetDict({ train: Dataset({ features: ['audio', 'transcription'], num_rows: 1 }) }) ### Steps to reproduce the bug Create a dataset folder called 'my_dataset' with a subfolder called 'data' that has mp3 files. Also, create metadata.csv that has file locations like 'data/...mp3' and their corresponding transcription. Run: ds = load_dataset("audiofolder", data_dir="my_dataset") ### Expected behavior It should generate a dataset with numerous rows. ### Environment info Run on Jupyter notebook

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I_kwDODunzps5c1PxY

NonMatchingChecksumError for hendrycks_test

closed

2023-01-26T00:43:23Z

2023-01-27T05:44:31Z

NONE

### Describe the bug The checksum of the file has likely changed on the remote host. ### Steps to reproduce the bug `dataset = nlp.load_dataset("hendrycks_test", "anatomy")` ### Expected behavior no error thrown ### Environment info - `datasets` version: 2.2.1 - Platform: macOS-13.1-arm64-arm-64bit - Python version: 3.9.13 - PyArrow version: 9.0.0 - Pandas version: 1.5.1

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PR_kwDODunzps5IiGWb

Imagefolder docs: mention support of CSV and ZIP

closed

2023-01-25T17:24:01Z

2023-01-25T18:33:35Z

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PR_kwDODunzps5Iglqu

Concatenate on axis=1 with misaligned blocks

closed

2023-01-25T12:33:22Z

2023-01-26T09:37:00Z

MEMBER

Allow to concatenate on axis 1 two tables made of misaligned blocks. For example if the first table has 2 row blocks of 3 rows each, and the second table has 3 row blocks or 2 rows each. To do that, I slice the row blocks to re-align the blocks. Fix https://github.com/huggingface/datasets/issues/5413

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2023-01-26T09:27:19Z

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I_kwDODunzps5ct4uv

Discrepancy in `nyu_depth_v2` dataset

open

2023-01-24T19:15:46Z

2023-02-06T20:52:00Z

CONTRIBUTOR

### Describe the bug I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-side comparison,  I tried to find the origin of this issue but sadly as I mentioned in tensorflow/datasets/issues/4674, the download link from `fast-depth` doesn't work anymore hence couldn't verify if the error originated there or during porting data from there to HF. Hi @sayakpaul, as you worked on huggingface/datasets/issues/5255, if you still have access to that data could you please share the data or perhaps checkout this issue? ### Steps to reproduce the bug This [notebook](https://colab.research.google.com/drive/1K3ZU8XUPRDOYD38MQS9nreQXJYitlKSW?usp=sharing#scrollTo=UEW7QSh0jf0i) from @sayakpaul could be used to generate depth maps and actual ground truths could be checked from this [dataset](https://www.kaggle.com/datasets/awsaf49/nyuv2-bts-dataset) from BTS repo. > Note: BTS dataset has only 36K data compared to the train-test 50K. They sampled the data as adjacent frames look quite the same ### Expected behavior Expected depth maps should be smooth rather than discrete/clipped. ### Environment info - `datasets` version: 2.8.1.dev0 - Platform: Linux-5.10.147+-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.3.5

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PR_kwDODunzps5Icn9C

Document that removing all the columns returns an empty document and the num_row is lost

closed

2023-01-24T17:33:38Z

2023-01-25T16:11:10Z

MEMBER

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2023-01-25T16:04:03Z

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PR_kwDODunzps5Icjwe

Disable aiohttp requoting of redirection URL

closed

2023-01-24T17:18:59Z

2023-02-01T08:45:33Z

MEMBER

The library `aiohttp` performs a requoting of redirection URLs that unquotes the single quotation mark character: `%27` => `'` This is a problem for our Hugging Face Hub, which requires exact URL from location header. Specifically, in the query component of the URL (`https://netloc/path?query`), the value for `response-content-disposition` contains `%27`: ``` response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27sample.jsonl.gz%3B+filename%3D%22sample.jsonl.gz%22%3B ``` and after the requoting, the `%27` characters get unquoted to `'`: ``` response-content-disposition=attachment%3B+filename*%3DUTF-8''sample.jsonl.gz%3B+filename%3D%22sample.jsonl.gz%22%3B ``` This PR disables the `aiohttp` requoting of redirection URLs.

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I_kwDODunzps5csECR

slice split while streaming

closed

2023-01-24T14:08:17Z

2023-01-24T15:11:47Z

NONE

### Describe the bug When using the `load_dataset` function with streaming set to True, slicing splits is apparently not supported. Did I miss this in the documentation? ### Steps to reproduce the bug `load_dataset("lhoestq/demo1",revision=None, streaming=True, split="train[:3]")` causes ValueError: Bad split: train[:3]. Available splits: ['train', 'test'] in builder.py, line 1213, in as_streaming_dataset ### Expected behavior The first 3 entries of the dataset as a stream ### Environment info - `datasets` version: 2.8.0 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.10.9 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

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I_kwDODunzps5cosWA

prebuilt dataset relies on `downloads/extracted`

open

2023-01-24T02:09:32Z

2023-01-24T18:14:10Z

MEMBER

### Describe the bug I pre-built the dataset: ``` python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing ``` and it can be used just fine. now I wipe out `downloads/extracted` and it no longer works. ``` rm -r ~/.cache/huggingface/datasets/downloads ``` That is I can still load it: ``` python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing No config specified, defaulting to: general-pmd-synthetic-testing/100.unique Found cached dataset general-pmd-synthetic-testing (/home/stas/.cache/huggingface/datasets/HuggingFaceM4___general-pmd-synthetic-testing/100.unique/1.1.1/86bc445e3e48cb5ef79de109eb4e54ff85b318cd55c3835c4ee8f86eae33d9d2) ``` but if I try to use it: ``` E stderr: Traceback (most recent call last): E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/main.py", line 116, in <module> E stderr: train_loader, val_loader = get_dataloaders( E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/dataset.py", line 170, in get_dataloaders E stderr: train_loader = get_dataloader_from_config( E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/dataset.py", line 443, in get_dataloader_from_config E stderr: dataloader = get_dataloader( E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/dataset.py", line 264, in get_dataloader E stderr: is_pmd = "meta" in hf_dataset[0] and "source" in hf_dataset[0] E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/arrow_dataset.py", line 2601, in __getitem__ E stderr: return self._getitem( E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/arrow_dataset.py", line 2586, in _getitem E stderr: formatted_output = format_table( E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 634, in format_table E stderr: return formatter(pa_table, query_type=query_type) E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 406, in __call__ E stderr: return self.format_row(pa_table) E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 442, in format_row E stderr: row = self.python_features_decoder.decode_row(row) E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 225, in decode_row E stderr: return self.features.decode_example(row) if self.features else row E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1846, in decode_example E stderr: return { E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1847, in <dictcomp> E stderr: column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id) E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1304, in decode_nested_example E stderr: return decode_nested_example([schema.feature], obj) E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1296, in decode_nested_example E stderr: if decode_nested_example(sub_schema, first_elmt) != first_elmt: E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1309, in decode_nested_example E stderr: return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/image.py", line 144, in decode_example E stderr: image = PIL.Image.open(path) E stderr: File "/home/stas/anaconda3/envs/py38-pt113/lib/python3.8/site-packages/PIL/Image.py", line 3092, in open E stderr: fp = builtins.open(filename, "rb") E stderr: FileNotFoundError: [Errno 2] No such file or directory: '/mnt/nvme0/code/data/cache/huggingface/datasets/downloads/extracted/134227b9b94c4eccf19b205bf3021d4492d0227b9be6c2ddb6bf517d8d55a8cb/data/101/images_01.jpg' ``` Only if I wipe out the cached dir and rebuild then it starts working as `download/extracted` is back again with extracted files. ``` rm -r ~/.cache/huggingface/datasets/HuggingFaceM4___general-pmd-synthetic-testing python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing ``` I think there are 2 issues here: 1. why does it still rely on extracted files after `arrow` files were printed - did I do something incorrectly when creating this dataset? 2. why doesn't the dataset know that it has been gutted and loads just fine? If it has a dependency on `download/extracted` then `load_dataset` should check if it's there and fail or force rebuilding. I am sure this could be a very expensive operation, so probably really solving #1 will not require this check. and this second item is probably an overkill. Other than perhaps if it had an optional `check_consistency` flag to do that. ### Environment info datasets@main

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PR_kwDODunzps5IXq92

feat: tqdm for `to_parquet`

closed

2023-01-23T22:05:38Z

2023-01-24T11:26:47Z

CONTRIBUTOR

As described in #5418 I noticed also that the `to_json` function supports multi-workers whereas `to_parquet`, is that not possible/not needed with Parquet or something that hasn't been implemented yet?

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2023-01-24T11:17:12Z

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PR_kwDODunzps5IUvAZ

Single TQDM bar in multi-proc map

open

2023-01-23T12:49:40Z

2023-02-07T15:50:43Z

CONTRIBUTOR

Use the "shard generator approach with periodic progress updates" (used in `save_to_disk` and multi-proc `load_dataset`) in `Dataset.map` to enable having a single TQDM progress bar in the multi-proc mode. Closes https://github.com/huggingface/datasets/issues/771, closes https://github.com/huggingface/datasets/issues/3177 TODO: - [x] cleaner refactor of the `_map_single` decorators now that they also have to wrap generator functions (decorate `map` instead of `map_single` with the `transmit_` decorators and predict the shards' fingerprint in `map`)

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I_kwDODunzps5cjzoz

Save and resume the state of a DataLoader

open

2023-01-23T10:58:54Z

2023-01-24T01:45:48Z

MEMBER

It would be nice when using `datasets` with a PyTorch DataLoader to be able to resume a training from a DataLoader state (e.g. to resume a training that crashed) What I have in mind (but lmk if you have other ideas or comments): For map-style datasets, this requires to have a PyTorch Sampler state that can be saved and reloaded per node and worker. For iterable datasets, this requires to save the state of the dataset iterator, which includes: - the current shard idx and row position in the current shard - the epoch number - the rng state - the shuffle buffer Right now you can already resume the data loading of an iterable dataset by using `IterableDataset.skip` but it takes a lot of time because it re-iterates on all the past data until it reaches the resuming point. cc @stas00 @sgugger

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PR_kwDODunzps5ITraa

Fix base directory while extracting insecure TAR files

closed

2023-01-23T08:57:40Z

2023-01-24T01:34:20Z

MEMBER

This PR fixes the extraction of insecure TAR files by changing the base path against which TAR members are compared: - from: "." - to: `output_path` This PR also adds tests for extracting insecure TAR files. Related to: - #5441 - #5452 @stas00 please note this PR addresses just one of the issues you pointed out: the use of the cwd by the extractor. The other issues (actionable error messages, raise instead of log error) should be addressed in other PRs.

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PR_kwDODunzps5ITcA3

Swap log messages for symbolic/hard links in tar extractor

closed

2023-01-23T07:53:38Z

2023-01-23T09:40:55Z

MEMBER

The log messages do not match their if-condition. This PR swaps them. Found while investigating: - #5441 CC: @lhoestq

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I_kwDODunzps5chsWs

ImageFolder BadZipFile: Bad offset for central directory

open

2023-01-22T23:50:12Z

2023-01-23T17:25:56Z

NONE

### Describe the bug I'm getting the following exception: ``` lib/python3.10/zipfile.py:1353 in _RealGetContents │ │ │ │ 1350 │ │ # self.start_dir: Position of start of central directory │ │ 1351 │ │ self.start_dir = offset_cd + concat │ │ 1352 │ │ if self.start_dir < 0: │ │ ❱ 1353 │ │ │ raise BadZipFile("Bad offset for central directory") │ │ 1354 │ │ fp.seek(self.start_dir, 0) │ │ 1355 │ │ data = fp.read(size_cd) │ │ 1356 │ │ fp = io.BytesIO(data) │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ BadZipFile: Bad offset for central directory Extracting data files: 35%|█████████████████▊ | 38572/110812 [00:10<00:20, 3576.26it/s] ``` ### Steps to reproduce the bug ``` load_dataset( args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, ), ``` ### Expected behavior loads the dataset ### Environment info datasets==2.8.0 Python 3.10.8 Linux 129-146-3-202 5.15.0-52-generic #58~20.04.1-Ubuntu SMP Thu Oct 13 13:09:46 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux

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I_kwDODunzps5cdAz1

to_tf_dataset with a TF collator causes bizarrely persistent slowdown

open

2023-01-20T16:08:37Z

2023-01-23T18:54:09Z

MEMBER

### Describe the bug This will make more sense if you take a look at [a Colab notebook that reproduces this issue.](https://colab.research.google.com/drive/1rxyeciQFWJTI0WrZ5aojp4Ls1ut18fNH?usp=sharing) Briefly, there are several datasets that, when you iterate over them with `to_tf_dataset` **and** a data collator that returns `tf` tensors, become very slow. We haven't been able to figure this one out - it can be intermittent, and we have no idea what could possibly cause it. The weirdest thing is that **the slowdown affects other attempts to access the underlying dataset**. If you try to iterate over the `tf.data.Dataset`, then interrupt execution, and then try to iterate over the original dataset, the original dataset is now also very slow! This is true even if the dataset format is not set to `tf` - the iteration is slow even though it's not calling TF at all! There is a simple workaround for this - we can simply get our data collators to return `np` tensors. When we do this, the bug is never triggered and everything is fine. In general, `np` is preferred for this kind of preprocessing work anyway, when the preprocessing is not going to be compiled into a pure `tf.data` pipeline! However, the issue is fascinating, and the TF team were wondering if anyone in datasets (cc @lhoestq @mariosasko) might have an idea of what could cause this. ### Steps to reproduce the bug Run the attached Colab. ### Expected behavior The slowdown should go away, or at least not persist after we stop iterating over the `tf.data.Dataset` ### Environment info The issue occurs on multiple versions of Python and TF, both on local machines and on Colab. All testing was done using the latest versions of `transformers` and `datasets` from `main`

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PR_kwDODunzps5INgD9

Support fsspec 2023.1.0 in CI

closed

2023-01-20T12:53:17Z

2023-01-20T13:32:50Z

MEMBER

Support fsspec 2023.1.0 in CI. In the 2023.1.0 fsspec release, they replaced the type of `fsspec.registry`: - from `ReadOnlyRegistry`, with an attribute called `target` - to `MappingProxyType`, without that attribute Consequently, we need to change our `mock_fsspec` fixtures, that were using the `target` attribute. Fix #5448.

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I_kwDODunzps5cbI-S

Support fsspec 2023.1.0 in CI

closed

2023-01-20T10:26:31Z

2023-01-20T13:26:05Z

MEMBER

Once we find out the root cause of: - #5445 we should revert the temporary pin on fsspec introduced by: - #5447

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PR_kwDODunzps5IM0Nu

Fix CI by temporarily pinning fsspec < 2023.1.0

closed

2023-01-20T10:11:02Z

2023-01-20T10:38:13Z

MEMBER

Temporarily pin fsspec < 2023.1.0 Fix #5445.

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PR_kwDODunzps5IMyka

test v0.12.0.rc0

closed

2023-01-20T10:05:19Z

2023-01-20T10:43:22Z

CONTRIBUTOR

DO NOT MERGE. Only to test the CI. cc @lhoestq @albertvillanova

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I_kwDODunzps5cbBsf

CI tests are broken: AttributeError: 'mappingproxy' object has no attribute 'target'

closed

2023-01-20T10:03:10Z

2023-01-20T10:28:44Z

MEMBER

CI tests are broken, raising `AttributeError: 'mappingproxy' object has no attribute 'target'`. See: https://github.com/huggingface/datasets/actions/runs/3966497597/jobs/6797384185 ``` ... ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://top_level-date=2019-10-0[1-4]/*-expected_paths4] - AttributeError: 'mappingproxy' object has no attribute 'target' ===== 2076 passed, 19 skipped, 15 warnings, 47 errors in 115.54s (0:01:55) ===== ```

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I_kwDODunzps5cZfJv

info messages logged as warnings

open

2023-01-20T01:19:18Z

2023-01-25T15:43:22Z

NONE

### Describe the bug Code in `datasets` is using `logger.warning` when it should be using `logger.info`. Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category. Definitions from the Python docs for reference: * INFO: Confirmation that things are working as expected. * WARNING: An indication that something unexpected happened, or indicative of some problem in the near future (e.g. ‘disk space low’). The software is still working as expected. In theory, a user should be able to resolve things such that there are no warnings. ### Steps to reproduce the bug Load any dataset that's already cached. ### Expected behavior No output when log level is at the default WARNING level. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 9.0.0 - Pandas version: 1.5.2

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PR_kwDODunzps5ILbk8

Update share tutorial

closed

2023-01-20T01:09:14Z

2023-01-20T15:44:45Z

MEMBER

Based on feedback from discussion #5423, this PR updates the sharing tutorial with a mention of writing your own dataset loading script to support more advanced dataset creation options like multiple configs. I'll open a separate PR to update the *Create a Dataset card* with the new Hub metadata UI update 😄

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I_kwDODunzps5cZGli

OneDrive Integrations with HF Datasets

open

2023-01-19T23:12:08Z

2023-01-20T18:05:52Z

NONE

### Feature request First of all , I would like to thank all community who are developed DataSet storage and make it free available How to integrate our Onedrive account or any other possible storage clouds (like google drive,...) with the **HF** datasets section. For example, if I have **50GB** on my **Onedrive** account and I want to move between drive and Hugging face repo or vis versa ### Motivation make the dataset section more flexible with other possible storage like the integration between Google Collab and Google drive the storage ### Your contribution Can be done using Hugging face CLI

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PR_kwDODunzps5IFeCW

resolving a weird tar extract issue

open

2023-01-19T02:17:21Z

2023-01-20T16:49:22Z

MEMBER

ok, every so often, I have been getting a strange failure on dataset install: ``` $ python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing No config specified, defaulting to: general-pmd-synthetic-testing/100.unique Downloading and preparing dataset general-pmd-synthetic-testing/100.unique (download: 3.21 KiB, generated: 16.01 MiB, post-processed: Unknown size, total: 16.02 MiB) to /home/stas/.cache/huggingface/datasets/HuggingFaceM4___general-pmd-synthetic-testing/100.unique/1.1.1/86bc445e3e48cb5ef79de109eb4e54ff85b318cd55c3835c4ee8f86eae33d9d2... Extraction of data is blocked (illegal path) Extraction of data/1 is blocked (illegal path) Extraction of data/1/text.null is blocked (illegal path) [...] ``` I had no idea what to do with that - what in the world does **illegal path** mean? I started looking at the code in `TarExtractor` and added a debug print of `base` so that told me that there was a problem with the current directory - which was a clone of one of the hf repos. This particular dataset extracts into a directory `data` and the current dir I was running the tests from already had `data` in it which was a symbolic link to another partition and somehow all that `badpath` code was blowing up there. https://github.com/huggingface/datasets/blob/80eb8db74f49b7ee9c0f73a819c22177fabd61db/src/datasets/utils/extract.py#L113-L114 I tried hard to come up with a repro, but no matter what I tried it only fails in that particular clone directory that has a `data` symlink and not anywhere else. In any case, in this PR I'm proposing to at least give a user a hint of what seems to be an issue. I'm not at all happy with the info I got with this proposed change, but at least it gave me a hint that `TarExtractor` tries to extract into the current directory without any respect to pre-existing files. Say what? https://github.com/huggingface/datasets/blob/80eb8db74f49b7ee9c0f73a819c22177fabd61db/src/datasets/utils/extract.py#L110 why won't it use the `datasets` designated directory for that? There would never be a problem if it were to do that. I had to look at all those `resolved`, `badpath` calls and see what it did and why it failed, since it was far from obvious. It appeared like it resolved a symlink and compared it to the original path which of course wasn't matching. So perhaps you have a better solution than what I proposed in this PR. I think that code line I quoted is the one that should be fixed instead. But if you can't think of a better solution let's merge this at least so that the user will have a clue that the current dir is somehow involved. p.s. I double checked that if I remove the pre-existing `data` symlink in the current dir I'm running the dataset install command from, the problem goes away too. Thanks.

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https://github.com/huggingface/datasets/pull/5440

PR_kwDODunzps5HpRbF

Fix documentation about batch samplers

closed

2023-01-18T17:04:27Z

2023-01-18T17:57:29Z

MEMBER

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2023-01-18T17:50:04Z

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I_kwDODunzps5bq508

[dataset request] Add Common Voice 12.0

open

2023-01-18T13:07:05Z

2023-01-25T18:38:53Z

NONE

### Feature request Please add the common voice 12_0 datasets. Apart from English, a significant amount of audio-data has been added to the other minor-language datasets. ### Motivation The dataset link: https://commonvoice.mozilla.org/en/datasets

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PR_kwDODunzps5HmWA8

Update actions/checkout in CD Conda release

closed

2023-01-18T06:53:15Z

2023-01-18T13:49:51Z

MEMBER

This PR updates the "checkout" GitHub Action to its latest version, as previous ones are deprecated: https://github.blog/changelog/2022-09-22-github-actions-all-actions-will-begin-running-on-node16-instead-of-node12/

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2023-01-18T13:42:49Z

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I_kwDODunzps5bmkYY

Can't load png dataset with 4 channel (RGBA)

closed

2023-01-17T18:22:27Z

2023-01-18T20:20:15Z

NONE

I try to create dataset which contains about 9000 png images 64x64 in size, and they are all 4-channel (RGBA). When trying to use load_dataset() then a dataset is created from only 2 images. What exactly interferes I can not understand.

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2023-01-18T20:20:15Z

completed

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https://github.com/huggingface/datasets/pull/5436

PR_kwDODunzps5Hjh4v

Revert container image pin in CI benchmarks

closed

2023-01-17T15:59:50Z

2023-01-18T09:05:49Z

CONTRIBUTOR

Closes #5433, reverts #5432, and also: * Uses [ghcr.io container images](https://cml.dev/doc/self-hosted-runners/#docker-images) for extra speed * Updates `actions/checkout` to `v3` (note that `v2` is [deprecated](https://github.blog/changelog/2022-09-22-github-actions-all-actions-will-begin-running-on-node16-instead-of-node12/)) * Follows the new naming convention for environment variables introduced with [iterative/cml#1272](https://github.com/iterative/cml/pull/1272)

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2023-01-18T06:29:06Z

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