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PR_kwDODunzps5WdL4K

Remove README link to deprecated Colab notebook

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I_kwDODunzps5soqFf

Iterating over DataLoader based on HF datasets is stuck forever

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### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64

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I_kwDODunzps5soSpg

resume_download with streaming=True

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### Describe the bug I used: ``` dataset = load_dataset( "oscar-corpus/OSCAR-2201", token=True, language="fr", streaming=True, split="train" ) ``` Unfortunately, the server had a problem during the training process. I saved the step my training stopped at. But how can I resume download from step 1_000_´000 without re-streaming all the first 1 million docs of the dataset? `download_config=DownloadConfig(resume_download=True)` seems to not work with streaming=True. ### Steps to reproduce the bug ``` from datasets import load_dataset, DownloadConfig dataset = load_dataset( "oscar-corpus/OSCAR-2201", token=True, language="fr", streaming=True, # optional split="train", download_config=DownloadConfig(resume_download=True) ) # interupt the run and try to relaunch it => this restart from scratch ``` ### Expected behavior I would expect a parameter to start streaming from a given index in the dataset. ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-5.19.0-45-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.0

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I_kwDODunzps5soPEa

Mapping gets stuck at 99%

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### Describe the bug Hi ! I'm currently working with a large (~150GB) unnormalized dataset at work. The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it. I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset. The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why. Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me. ### Steps to reproduce the bug I'm able to reproduce the problem using the following scripts: ```python # random_data.py import datasets import torch _VERSION = "1.0.0" class RandomDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( version=_VERSION, supervised_keys=None, features=datasets.Features( { "positions": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "normals": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "features": datasets.Array2D( shape=(30000, 6), dtype="float32", ), "scalars": datasets.Sequence( feature=datasets.Value("float32"), length=20, ), }, ), ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # type: ignore gen_kwargs={"nb_samples": 1000}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # type: ignore gen_kwargs={"nb_samples": 100}, ), ] def _generate_examples(self, nb_samples: int): for idx in range(nb_samples): yield idx, { "positions": torch.rand(30000, 3), "normals": torch.rand(30000, 3), "features": torch.rand(30000, 6), "scalars": torch.rand(20), } ``` ```python # main.py import datasets import torch def compute_mean_std( dataset: datasets.Dataset, ) -> dict[str, torch.Tensor]: """Compute the mean and standard deviation of each feature of the dataset. Args: dataset (`Dataset`): A huggingface dataset. Returns: dict: A dictionary containing the mean and standard deviation of each feature. """ result = {} for key in dataset: # extract data from dataset data: torch.Tensor = dataset[key] # type: ignore # reshape data, from (a, ..., b, c) -> (*, c) data = data.reshape(-1, data.shape[-1]) # compute mean and std mean = data.mean(dim=0) # (c) std = data.std(dim=0) # (c) # store in result result[key] = torch.stack((mean, std)) return result def apply_mean_std( dataset: datasets.Dataset, mean_std: datasets.Dataset, ) -> dict[str, torch.Tensor]: """Normalize the dataset using the mean and standard deviation of each feature. Args: dataset (`Dataset`): A huggingface dataset. mean_std (`Dataset`): A huggingface dataset containing the mean and standard deviation of each feature. Returns: dict: A dictionary containing the normalized dataset. """ result = {} for key in mean_std.column_names: # extract data from dataset data: torch.Tensor = dataset[key] # type: ignore # extract mean and std from dict mean = mean_std[key][0] # type: ignore std = mean_std[key][1] # type: ignore # normalize data normalized_data = (data - mean) / std result[key] = normalized_data return result # hack to force the map function to use the entire dataset MAX_MAP_BATCH_SIZE = 1_000_000_000 # get dataset ds = datasets.load_dataset( path="random_data.py", split="train", ).with_format("torch") # compute mean/std of each feature mean_std = ds.map( desc="Computing mean/std", # type: ignore remove_columns=ds.column_names, # type: ignore function=compute_mean_std, batch_size=MAX_MAP_BATCH_SIZE, batched=True, ) # normalize each feature of the dataset ds_normalized = ds.map( desc="Applying mean/std", # type: ignore function=apply_mean_std, batched=False, fn_kwargs={ "mean_std": mean_std, }, ) ``` ### Expected behavior Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2

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https://github.com/huggingface/datasets/pull/6076

PR_kwDODunzps5WcGVR

No gzip encoding from github

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Don't accept gzip encoding from github, otherwise some files are not streamable + seekable. fix https://huggingface.co/datasets/code_x_glue_cc_code_to_code_trans/discussions/2#64c0e0c1a04a514ba6303e84 and making sure https://github.com/huggingface/datasets/issues/2918 works as well

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I_kwDODunzps5snrkW

Error loading music files using `load_dataset`

closed

NONE

### Describe the bug I tried to load a music file using `datasets.load_dataset()` from the repository - https://huggingface.co/datasets/susnato/pop2piano_real_music_test I got the following error - ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2788, in _getitem formatted_output = format_table( File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 629, in format_table return formatter(pa_table, query_type=query_type) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 398, in __call__ return self.format_column(pa_table) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 442, in format_column column = self.python_features_decoder.decode_column(column, pa_table.column_names[0]) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 218, in decode_column return self.features.decode_column(column, column_name) if self.features else column File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in decode_column [decode_nested_example(self[column_name], value) if value is not None else None for value in column] File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in <listcomp> [decode_nested_example(self[column_name], value) if value is not None else None for value in column] File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1325, in decode_nested_example return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/audio.py", line 184, in decode_example array, sampling_rate = sf.read(f) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 372, in read with SoundFile(file, 'r', samplerate, channels, File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 740, in __init__ self._file = self._open(file, mode_int, closefd) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1264, in _open _error_check(_snd.sf_error(file_ptr), File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1455, in _error_check raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace')) RuntimeError: Error opening <_io.BufferedReader name='/home/susnato/.cache/huggingface/datasets/downloads/d2b09cb974b967b13f91553297c40c0f02f3c0d4c8356350743598ff48d6f29e'>: Format not recognised. ``` ### Steps to reproduce the bug Code to reproduce the error - ```python from datasets import load_dataset ds = load_dataset("susnato/pop2piano_real_music_test", split="test") print(ds[0]) ``` ### Expected behavior I should be able to read the music file without any error. ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-5.19.0-50-generic-x86_64-with-glibc2.35 - Python version: 3.9.16 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

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PR_kwDODunzps5Wb8O_

Misc doc improvements

open

CONTRIBUTOR

Removes the warning about requiring to write a dataset loading script to define multiple configurations, as the README YAML can be used instead (for simple cases). Also, deletes the section about using the `BatchSampler` in `torch<=1.12.1` to speed up loading, as `torch 1.12.1` is over a year old (and `torch 2.0` has been out for a while).

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I_kwDODunzps5snBL8

version2.3.2 load_dataset()data_files can't include .xxxx in path

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### Describe the bug First, I cd workdir. Then, I just use load_dataset("json", data_file={"train":"/a/b/c/.d/train/train.json", "test":"/a/b/c/.d/train/test.json"}) that couldn't work and <FileNotFoundError: Unable to find '/a/b/c/.d/train/train.jsonl' at /a/b/c/.d/> And I debug, it is fine in version2.1.2 So there maybe a bug in path join. Here is the whole bug report: /x/datasets/loa │ │ d.py:1656 in load_dataset │ │ │ │ 1653 │ ignore_verifications = ignore_verifications or save_infos │ │ 1654 │ │ │ 1655 │ # Create a dataset builder │ │ ❱ 1656 │ builder_instance = load_dataset_builder( │ │ 1657 │ │ path=path, │ │ 1658 │ │ name=name, │ │ 1659 │ │ data_dir=data_dir, │ │ │ │ x/datasets/loa │ │ d.py:1439 in load_dataset_builder │ │ │ │ 1436 │ if use_auth_token is not None: │ │ 1437 │ │ download_config = download_config.copy() if download_config e │ │ 1438 │ │ download_config.use_auth_token = use_auth_token │ │ ❱ 1439 │ dataset_module = dataset_module_factory( │ │ 1440 │ │ path, │ │ 1441 │ │ revision=revision, │ │ 1442 │ │ download_config=download_config, │ │ │ │ x/datasets/loa │ │ d.py:1097 in dataset_module_factory │ │ │ │ 1094 │ │ │ 1095 │ # Try packaged │ │ 1096 │ if path in _PACKAGED_DATASETS_MODULES: │ │ ❱ 1097 │ │ return PackagedDatasetModuleFactory( │ │ 1098 │ │ │ path, │ │ 1099 │ │ │ data_dir=data_dir, │ │ 1100 │ │ │ data_files=data_files, │ │ │ │x/datasets/loa │ │ d.py:743 in get_module │ │ │ │ 740 │ │ │ if self.data_dir is not None │ │ 741 │ │ │ else get_patterns_locally(str(Path().resolve())) │ │ 742 │ │ ) │ │ ❱ 743 │ │ data_files = DataFilesDict.from_local_or_remote( │ │ 744 │ │ │ patterns, │ │ 745 │ │ │ use_auth_token=self.download_config.use_auth_token, │ │ 746 │ │ │ base_path=str(Path(self.data_dir).resolve()) if self.data │ │ │ │ x/datasets/dat │ │ a_files.py:590 in from_local_or_remote │ │ │ │ 587 │ │ out = cls() │ │ 588 │ │ for key, patterns_for_key in patterns.items(): │ │ 589 │ │ │ out[key] = ( │ │ ❱ 590 │ │ │ │ DataFilesList.from_local_or_remote( │ │ 591 │ │ │ │ │ patterns_for_key, │ │ 592 │ │ │ │ │ base_path=base_path, │ │ 593 │ │ │ │ │ allowed_extensions=allowed_extensions, │ │ │ │ /x/datasets/dat │ │ a_files.py:558 in from_local_or_remote │ │ │ │ 555 │ │ use_auth_token: Optional[Union[bool, str]] = None, │ │ 556 │ ) -> "DataFilesList": │ │ 557 │ │ base_path = base_path if base_path is not None else str(Path() │ │ ❱ 558 │ │ data_files = resolve_patterns_locally_or_by_urls(base_path, pa │ │ 559 │ │ origin_metadata = _get_origin_metadata_locally_or_by_urls(data │ │ 560 │ │ return cls(data_files, origin_metadata) │ │ 561 │ │ │ │ /x/datasets/dat │ │ a_files.py:195 in resolve_patterns_locally_or_by_urls │ │ │ │ 192 │ │ if is_remote_url(pattern): │ │ 193 │ │ │ data_files.append(Url(pattern)) │ │ 194 │ │ else: │ │ ❱ 195 │ │ │ for path in _resolve_single_pattern_locally(base_path, pat │ │ 196 │ │ │ │ data_files.append(path) │ │ 197 │ │ │ 198 │ if not data_files: │ │ │ │ /x/datasets/dat │ │ a_files.py:145 in _resolve_single_pattern_locally │ │ │ │ 142 │ │ error_msg = f"Unable to find '{pattern}' at {Path(base_path).r │ │ 143 │ │ if allowed_extensions is not None: │ │ 144 │ │ │ error_msg += f" with any supported extension {list(allowed │ │ ❱ 145 │ │ raise FileNotFoundError(error_msg) │ │ 146 │ return sorted(out) │ │ 147 ### Steps to reproduce the bug 1. Version=2.3.2 2. In shell, cd workdir.(cd /a/b/c/.d/) 3. load_dataset("json", data_file={"train":"/a/b/c/.d/train/train.json", "test":"/a/b/c/.d/train/test.json"}) ### Expected behavior fix it please~ ### Environment info 2.3.2

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PR_kwDODunzps5WbWFN

Fix fsspec storage_options from load_dataset

open

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close https://github.com/huggingface/datasets/issues/6071

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I_kwDODunzps5smV9d

storage_options provided to load_dataset not fully piping through since datasets 2.14.0

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### Describe the bug Since the latest release of `datasets` (`2.14.0`), custom filesystem `storage_options` passed to `load_dataset()` do not seem to propagate through all the way - leading to problems if loading data files that need those options to be set. I think this is because of the new `_prepare_path_and_storage_options()` (https://github.com/huggingface/datasets/pull/6028), which returns the right `storage_options` to use given a path and a `DownloadConfig` - but which might not be taking into account the extra `storage_options` explicitly provided e.g. through `load_dataset()` ### Steps to reproduce the bug ```python import fsspec import pandas as pd import datasets # Generate mock parquet file data_files = "demo.parquet" pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}).to_parquet(data_files) _storage_options = {"x": 1, "y": 2} fs = fsspec.filesystem("file", **_storage_options) dataset = datasets.load_dataset( "parquet", data_files=data_files, storage_options=fs.storage_options ) ``` Looking at the `storage_options` resolved here: https://github.com/huggingface/datasets/blob/b0177910b32712f28d147879395e511207e39958/src/datasets/data_files.py#L331 they end up being `{}`, instead of propagating through the `storage_options` that were provided to `load_dataset` (`fs.storage_options`). As these then get used for the filesystem operation a few lines below https://github.com/huggingface/datasets/blob/b0177910b32712f28d147879395e511207e39958/src/datasets/data_files.py#L339 the call will fail if the user-provided `storage_options` were needed. --- A temporary workaround that seemed to work locally to bypass the problem was to bundle a duplicate of the `storage_options` into the `download_config`, so that they make their way all the way to `_prepare_path_and_storage_options()` and get extracted correctly: ```python dataset = datasets.load_dataset( "parquet", data_files=data_files, storage_options=fs.storage_options, download_config=datasets.DownloadConfig(storage_options={fs.protocol: fs.storage_options}), ) ``` ### Expected behavior `storage_options` provided to `load_dataset` take effect in all backend filesystem operations. ### Environment info datasets==2.14.0

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https://github.com/huggingface/datasets/pull/6070

PR_kwDODunzps5WXDLc

Fix Quickstart notebook link

closed

CONTRIBUTOR

Reported in https://github.com/huggingface/datasets/pull/5902#issuecomment-1649885621 (cc @alvarobartt)

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I_kwDODunzps5sh68v

KeyError: dataset has no key "image"

open

NONE

### Describe the bug I've loaded a local image dataset with: `ds = laod_dataset("imagefolder", data_dir=path-to-data)` And defined a transform to process the data, following the Datasets docs. However, I get a keyError error, indicating there's no "image" key in my dataset. When I printed out the example_batch sent to the transformation function, it shows only the labels are being sent to the function. For some reason, the images are not in the example batches. ### Steps to reproduce the bug I'm using the latest stable version of datasets ### Expected behavior I expect the example_batches to contain both images and labels ### Environment info I'm using the latest stable version of datasets

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PR_kwDODunzps5WUkZi

fix tqdm lock deletion

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PR_kwDODunzps5WT7EQ

fix tqdm lock

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close https://github.com/huggingface/datasets/issues/6066

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I_kwDODunzps5sdq-m

AttributeError: '_tqdm_cls' object has no attribute '_lock'

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### Describe the bug ```python File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/load.py", line 1034, in get_module data_files = DataFilesDict.from_patterns( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/data_files.py", line 671, in from_patterns DataFilesList.from_patterns( File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/data_files.py", line 586, in from_patterns origin_metadata = _get_origin_metadata(data_files, download_config=download_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/data_files.py", line 502, in _get_origin_metadata return thread_map( ^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 70, in thread_map return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 48, in _executor_map with ensure_lock(tqdm_class, lock_name=lock_name) as lk: File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/contextlib.py", line 144, in __exit__ next(self.gen) File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 25, in ensure_lock del tqdm_class._lock ^^^^^^^^^^^^^^^^ AttributeError: '_tqdm_cls' object has no attribute '_lock' ``` ### Steps to reproduce the bug Happens ocasionally. ### Expected behavior I added a print in tqdm `ensure_lock()`, got a `ensure_lock <datasets.utils.logging._tqdm_cls object at 0x16dddead0> ` print. According to the code in https://github.com/tqdm/tqdm/blob/master/tqdm/contrib/concurrent.py#L24 ```python @contextmanager def ensure_lock(tqdm_class, lock_name=""): """get (create if necessary) and then restore `tqdm_class`'s lock""" print("ensure_lock", tqdm_class, lock_name) old_lock = getattr(tqdm_class, '_lock', None) # don't create a new lock lock = old_lock or tqdm_class.get_lock() # maybe create a new lock lock = getattr(lock, lock_name, lock) # maybe subtype tqdm_class.set_lock(lock) yield lock if old_lock is None: del tqdm_class._lock # <-- It tries to del the `_lock` attribute from tqdm_class. else: tqdm_class.set_lock(old_lock) ``` But, huggingface datasets `datasets.utils.logging._tqdm_cls` does not have the field `_lock`: https://github.com/huggingface/datasets/blob/main/src/datasets/utils/logging.py#L205 ```python class _tqdm_cls: def __call__(self, *args, disable=False, **kwargs): if _tqdm_active and not disable: return tqdm_lib.tqdm(*args, **kwargs) else: return EmptyTqdm(*args, **kwargs) def set_lock(self, *args, **kwargs): self._lock = None if _tqdm_active: return tqdm_lib.tqdm.set_lock(*args, **kwargs) def get_lock(self): if _tqdm_active: return tqdm_lib.tqdm.get_lock() ``` ### Environment info Python 3.11.4 tqdm '4.65.0' datasets master

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https://github.com/huggingface/datasets/pull/6065

PR_kwDODunzps5WR8jI

Add column type guessing from map return function

closed

NONE

As discussed [here](https://github.com/huggingface/datasets/issues/5965), there are some cases where datasets is unable to automatically promote columns during mapping. The fix is to explicitly provide a `features` definition so pyarrow can configure itself with the right column types from the outset. This PR provides an alternative approach, which is functionally equivalent to specifying features but a bit cleaner within a larger mapping pipeline. It allows clients to typehint the return variable coming from the mapper function - if we find one of these type annotations specified, and no explicit features have been passed in, we'll try to convert it into a Features map. If the map function runs and casting is unable to succeed, it will raise a DatasetTransformationNotAllowedError that indicates the typehint may be to blame. It works for batched and non-batched mapping functions. Currently supported column types: - builtins primitives: string, int, float, bool - dictionaries, lists (nested and one-deep) - Optional types and None-Unions (synonymous with optional types) It's used like: ```python class DatasetTyped(TypedDict): texts: list[str] def dataset_typed_map(batch) -> DatasetTyped: return {"texts": [text.split() for text in batch["raw_text"]]} dataset = {"raw_text": ["", "This is a test", "This is another test"]} with Dataset.from_dict(dataset) as dset: new_dataset = dset.map( dataset_typed_map, batched=True, batch_size=1, num_proc=1, ) ``` Open questions: - Should logging indicate we have automatically guessed these types? Or proceed quietly until we hit an error (as is the current implementation).

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PR_kwDODunzps5WPzAv

set dev version

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PR_kwDODunzps5WPtxi

Release: 2.14.0

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PR_kwDODunzps5WOj62

Improve `Dataset.from_list` docstring

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PR_kwDODunzps5WOi79

Dill 3.7 support

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Adds support for dill 3.7.

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I_kwDODunzps5sR1To

Dataset.map() execute twice when in PyTorch DDP mode

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### Describe the bug I use `torchrun --standalone --nproc_per_node=2 train.py` to start training. And write the code following the [docs](https://huggingface.co/docs/datasets/process#distributed-usage). The trick about using `torch.distributed.barrier()` to only execute map at the main process doesn't always work. When I am training model, it will map twice. When I am running a test for dataset and dataloader (just print the batches), it can work. Their code about loading dataset are same. And on another server with 30 CPU cores, I use 2 GPUs and it can't work neither. I have tried to use `rank` and `local_rank` to check, they all didn't make sense. ### Steps to reproduce the bug use `torchrun --standalone --nproc_per_node=2 train.py` or `torchrun --standalone train.py` to run This is my code: ```python if args.distributed and world_size > 1: if args.local_rank > 0: print(f"Rank {args.rank}: Gpu {args.gpu} waiting for main process to perform the mapping", force=True) torch.distributed.barrier() print("Mapping dataset") dataset = dataset.map(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True), num_proc=8, desc="cut_reorder_keys") dataset = dataset.map(lambda x: random_shift(x, shift_range=(-160, 0), feature_scale=16), num_proc=8, desc="random_shift") dataset_test = dataset_test.map(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=False), num_proc=8, desc="cut_reorder_keys") if args.local_rank == 0: print("Mapping finished, loading results from main process") torch.distributed.barrier() ``` ### Expected behavior Only the main process will execute `map`, while the sub process will load cache from disk. ### Environment info server with 64 CPU cores (AMD Ryzen Threadripper PRO 5995WX 64-Cores) and 2 RTX 4090 - `python==3.9.16` - `datasets==2.13.1` - `torch==2.0.1+cu117` - `22.04.1-Ubuntu` server with 30 CPU cores (Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz) and 2 RTX 4090 - `python==3.9.0` - `datasets==2.13.1` - `torch==2.0.1+cu117` - `Ubuntu 20.04`

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I_kwDODunzps5sRihY

Provide ability to load label mappings from file

open

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### Feature request My task is classification of a dataset containing a large label set that includes a hierarchy. Even ignoring the hierarchy I'm not able to find an example using `datasets` where the label names aren't hard-coded. This works find for classification of a handful of labels but ideally there would be a way of loading the name/id mappings required for `datasets.features.ClassLabel` from a file. It is possible to pass a file to ClassLabel but I cannot see an easy way of using this with `GeneratorBasedBuilder` since `self._info` is called before the `dl_manager` is constructed so even if my dataset contains say `label_mappings.json` there's no way of loading it in order to construct the `datasets.DatasetInfo` I can see other uses to accessing the `download_manager` from `self._info` - i.e. if the files contain a schema (i.e. `arrow` or `parquet` files) the `datasets.DatasetInfo` could be inferred. The workaround that was suggested in the forum is to generate a `.py` file from the `label_mappings.json` and import it. ``` class TestDatasetBuilder(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["label_1", "label_2"]), } ), task_templates=[TextClassification(text_column="text", label_column="label")], ) def _split_generators(self, dl_manager): train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL) test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}), ] def _generate_examples(self, filepath): """Generate AG News examples.""" with open(filepath, encoding="utf-8") as csv_file: csv_reader = csv.DictReader(csv_file) for id_, row in enumerate(csv_reader): yield id_, row ``` ### Motivation Allow `datasets.DatasetInfo` to be generated based on the contents of the dataset. ### Your contribution I'm willing to work on a PR with guidence.

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I_kwDODunzps5sMLUF

laion-coco download error

closed

NONE

### Describe the bug The full trace: ``` /home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/load.py:1744: FutureWarning: 'ignore_verifications' was de precated in favor of 'verification_mode' in version 2.9.1 and will be removed in 3.0.0. You can remove this warning by passing 'verification_mode=no_checks' instead. warnings.warn( Downloading and preparing dataset parquet/laion--laion-coco to /home/bian/.cache/huggingface/datasets/laion___parquet/laion-- laion-coco-cb4205d7f1863066/0.0.0/bcacc8bdaa0614a5d73d0344c813275e590940c6ea8bc569da462847103a1afd... Downloading data: 100%|█| 1.89G/1.89G [04:57<00:00, Downloading data files: 100%|█| 1/1 [04:59<00:00, 2 Extracting data files: 100%|█| 1/1 [00:00<00:00, 13 Generating train split: 0 examples [00:00, ? examples/s]<_io.BufferedReader name='/home/bian/.cache/huggingface/datasets/downlo ads/26d7a016d25bbd9443115cfa3092136e8eb2f1f5bcd4154 0cb9234572927f04c'> Traceback (most recent call last): File "/home/bian/data/ZOC/download_laion_coco.py", line 4, in <module> dataset = load_dataset("laion/laion-coco", ignore_verifications=True) File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/load.py", line 1791, in load_dataset builder_instance.download_and_prepare( File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 891, in download_and_prepare self._download_and_prepare( File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 986, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 1748, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 1842, in _prepare_split_single generator = self._generate_tables(**gen_kwargs) File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 67, in _generate_tables parquet_file = pq.ParquetFile(f) File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/pyarrow/parquet/core.py", line 323, in __init__ self.reader.open( File "pyarrow/_parquet.pyx", line 1227, in pyarrow._parquet.ParquetReader.open File "pyarrow/error.pxi", line 100, 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 . ``` I have carefully followed the instructions in #5264 but still get the same error. Other helpful information: ``` ds = load_dataset("parquet", data_files= ...: "https://huggingface.co/datasets/laion/l ...: aion-coco/resolve/d22869de3ccd39dfec1507 ...: f7ded32e4a518dad24/part-00000-2256f782-1 ...: 26f-4dc6-b9c6-e6757637749d-c000.snappy.p ...: arquet") Found cached dataset parquet (/home/bian/.cache/huggingface/datasets/parquet/default-a02eea00aeb08b0e/0.0.0/bb8ccf89d9ee38581ff5e51506d721a9b37f14df8090dc9b2d8fb4a40957833f) 100%|██████████████| 1/1 [00:00<00:00, 4.55it/s] ``` ### Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset("laion/laion-coco", ignore_verifications=True/False) ``` ### Expected behavior Properly load Laion-coco dataset ### Environment info datasets==2.11.0 torch==1.12.1 python 3.10

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I_kwDODunzps5sMDr3

Why is the speed difference of gen example so big?

open

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```python def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir): with open(metadata_path, 'r') as file: metadata = json.load(file) for idx, item in enumerate(metadata): image_path = item.get('image_path') text_content = item.get('text_content') image_data = open(image_path, "rb").read() yield idx, { "text": text_content, "image": { "path": image_path, "bytes": image_data, }, "conditioning_image": { "path": image_path, "bytes": image_data, }, } ``` Hello, I use the above function to deal with my local data set, but I am very surprised that the speed at which I generate example is very different. When I start a training task, **sometimes 1000examples/s, sometimes only 10examples/s.**  I'm not saying that speed is changing all the time. I mean, the reading speed is different in different training, which will cause me to start training over and over again until the speed of this generation of examples is normal.

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PR_kwDODunzps5WD4RY

Implement proper checkpointing for dataset uploading with resume function that does not require remapping shards that have already been uploaded

open

NONE

Context: issue #5990 In order to implement the checkpointing, I introduce a metadata folder that keeps one yaml file for each set that one is uploading. This yaml keeps track of what shards have already been uploaded, and which one the idx of the latest one was. Using this information I am then able to easily get the push_to_hub function to retrieve on demand past history of uploads and continue mapping and uploading from where it was left off.

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I_kwDODunzps5sGC6x

Fix host URL in The Pile datasets

open

NONE

### Describe the bug In #3627 and #5543, you tried to fix the host URL in The Pile datasets. But both URLs are not working now: `HTTPError: 404 Client Error: Not Found for URL: https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst` And `ConnectTimeout: HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(, 'Connection to mystic.the-eye.eu timed out. (connect timeout=10.0)'))` ### Steps to reproduce the bug ``` from datasets import load_dataset # This takes a few minutes to run, so go grab a tea or coffee while you wait :) data_files = "https://mystic.the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst" pubmed_dataset = load_dataset("json", data_files=data_files, split="train") pubmed_dataset ``` Result: `ConnectTimeout: HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(, 'Connection to mystic.the-eye.eu timed out. (connect timeout=10.0)'))` And ``` from datasets import load_dataset # This takes a few minutes to run, so go grab a tea or coffee while you wait :) data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst" pubmed_dataset = load_dataset("json", data_files=data_files, split="train") pubmed_dataset ``` Result: `HTTPError: 404 Client Error: Not Found for URL: https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst` ### Expected behavior Downloading as normal. ### Environment info Environment info `datasets` version: 2.9.0 Platform: Windows Python version: 3.9.13

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I_kwDODunzps5sFFMI

Multi-processed `Dataset.map` slows down a lot when `import torch`

closed

NONE

### Describe the bug When using `Dataset.map` with `num_proc > 1`, the speed slows down much if I add `import torch` to the start of the script even though I don't use it. I'm not sure if it's `torch` only or if any other package that is "large" will also cause the same result. BTW, `import lightning` also slows it down. Below are the progress bars of `Dataset.map`, the only difference between them is with or without `import torch`, but the speed varies by 6-7 times. - without `import torch`  - with `import torch`  ### Steps to reproduce the bug Below is the code I used, but I don't think the dataset and the mapping function have much to do with the phenomenon. ```python3 from datasets import load_from_disk, disable_caching from transformers import AutoTokenizer # import torch # import lightning def rearrange_datapoints( batch, tokenizer, sequence_length, ): datapoints = [] input_ids = [] for x in batch['input_ids']: input_ids += x while len(input_ids) >= sequence_length: datapoint = input_ids[:sequence_length] datapoints.append(datapoint) input_ids[:sequence_length] = [] if input_ids: paddings = [-1] * (sequence_length - len(input_ids)) datapoint = paddings + input_ids if tokenizer.padding_side == 'left' else input_ids + paddings datapoints.append(datapoint) batch['input_ids'] = datapoints return batch if __name__ == '__main__': disable_caching() tokenizer = AutoTokenizer.from_pretrained('...', use_fast=False) dataset = load_from_disk('...') dataset = dataset.map( rearrange_datapoints, fn_kwargs=dict( tokenizer=tokenizer, sequence_length=2048, ), batched=True, num_proc=8, ) ``` ### Expected behavior The multi-processed `Dataset.map` function speed between with and without `import torch` should be the same. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1127.el7.x86_64-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1

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I_kwDODunzps5sCqD-

Change package name from "datasets" to something less generic

open

NONE

### Feature request I'm repeatedly finding myself in situations where I want to have a package called `datasets.py` or `evaluate.py` in my code and can't because those names are being taken up by Huggingface packages. While I can understand how (even from the user's perspective) it's aesthetically pleasing to have nice terse library names, ultimately a library hogging simple names like this is something I find short-sighted, impractical and at my most irritable, frankly rude. My preference would be a pattern like what you get with all the other big libraries like numpy or pandas: ``` import huggingface as hf # hf.transformers, hf.datasets, hf.evaluate ``` or things like ``` import huggingface.transformers as tf # tf.load_model(), etc ``` If this isn't possible for some technical reason, at least just call the packages something like `hf_transformers` and so on. I realize this is a very big change that's probably been discussed internally already, but I'm making this issue and sister issues on each huggingface project just to start the conversation and begin tracking community feeling on the matter, since I suspect I'm not the only one who feels like this. Sorry if this has been requested already on this issue tracker, I couldn't find anything looking for terms like "package name". Sister issues: - [transformers](https://github.com/huggingface/transformers/issues/24934) - **datasets** - [evaluate](https://github.com/huggingface/evaluate/issues/476) ### Motivation Not taking up package names the user is likely to want to use. ### Your contribution No - more a matter of internal discussion among core library authors.

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PR_kwDODunzps5V5yOi

Remove `HfFileSystem` and deprecate `S3FileSystem`

closed

CONTRIBUTOR

Remove the legacy `HfFileSystem` and deprecate `S3FileSystem` cc @philschmid for the SageMaker scripts/notebooks that still use `datasets`' `S3FileSystem`

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I_kwDODunzps5r-g3S

Skipping shard in the remote repo and resume upload

closed

NONE

### Describe the bug For some reason when I try to resume the upload of my dataset, it is very slow to reach the index of the shard from which to resume the uploading. From my understanding, the problem is in this part of the code: arrow_dataset.py ```python for index, shard in logging.tqdm( enumerate(itertools.chain([first_shard], shards_iter)), desc="Pushing dataset shards to the dataset hub", total=num_shards, disable=not logging.is_progress_bar_enabled(), ): shard_path_in_repo = path_in_repo(index, shard) # Upload a shard only if it doesn't already exist in the repository if shard_path_in_repo not in data_files: ``` In particular, iterating the generator is slow during the call: ```python self._select_contiguous(start, length, new_fingerprint=new_fingerprint) ``` I wonder if it is possible to avoid calling this function for shards that are already uploaded and just start from the correct shard index. ### Steps to reproduce the bug 1. Start the upload ```python dataset = load_dataset("imagefolder", data_dir=DATA_DIR, split="train", drop_labels=True) dataset.push_to_hub("repo/name") ``` 2. Stop and restart the upload after hundreds of shards ### Expected behavior Skip the uploaded shards faster. ### Environment info - `datasets` version: 2.5.1 - Platform: Linux-4.18.0-193.el8.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.16 - PyArrow version: 12.0.1 - Pandas version: 2.0.2

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PR_kwDODunzps5Vz1pd

Update `ruff` version in pre-commit config

open

CONTRIBUTOR

so that it corresponds to the one that is being run in CI

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I_kwDODunzps5r3MCi

when i use datasets.load_dataset, i encounter the http connect error!

closed

NONE

### Describe the bug `common_voice_test = load_dataset("audiofolder", data_dir="./dataset/",cache_dir="./cache",split=datasets.Split.TEST)` when i run the code above, i got the error as below: -------------------------------------------- ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f299ed082e0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))"))) -------------------------------------------------- My all data is on local machine, why does it need to connect the internet? how can i fix it, because my machine cannot connect the internet. ### Steps to reproduce the bug 1 ### Expected behavior no error when i use the load_dataset func ### Environment info python=3.8.15

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PR_kwDODunzps5VxRLA

Bump dev version

closed

MEMBER

workaround to fix an issue with transformers CI https://github.com/huggingface/transformers/pull/24867#discussion_r1266519626

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I_kwDODunzps5rxj8u

Support proxy and user-agent in fsspec calls

open

MEMBER

Since we switched to the new HfFileSystem we no longer apply user's proxy and user-agent. Using the HTTP_PROXY and HTTPS_PROXY environment variables works though since we use aiohttp to call the HF Hub. This can be implemented in `_prepare_single_hop_path_and_storage_options`. Though ideally the `HfFileSystem` could support passing at least the proxies

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PR_kwDODunzps5Vr-r1

Check if column names match in Parquet loader only when config `features` are specified

closed

CONTRIBUTOR

Fix #6039

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PR_kwDODunzps5Vr7jr

Rename "pattern" to "path" in YAML data_files configs

closed

MEMBER

To make it easier to understand for users. They can use "path" to specify a single path, <s>or "paths" to use a list of paths.</s> Glob patterns are still supported though

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I_kwDODunzps5rwGhm

Compression kwargs have no effect when saving datasets as csv

open

NONE

### Describe the bug Attempting to save a dataset as a compressed csv file, the compression kwargs provided to `.to_csv()` that get piped to panda's `pandas.DataFrame.to_csv` do not have any effect - resulting in the dataset not getting compressed. A warning is raised if explicitly providing a `compression` kwarg, but no warnings are raised if relying on the defaults. This can lead to datasets secretly not getting compressed for users expecting the behaviour to match panda's `.to_csv()`, where the compression format is automatically inferred from the destination path suffix. ### Steps to reproduce the bug ```python # dataset is not compressed (but at least a warning is emitted) import datasets dataset = datasets.load_dataset("rotten_tomatoes", split="train") dataset.to_csv("uncompressed.csv") print(os.path.getsize("uncompressed.csv")) # 1008607 dataset.to_csv("compressed.csv.gz", compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}) print(os.path.getsize("compressed.csv.gz")) # 1008607 ``` ```shell >>> RuntimeWarning: compression has no effect when passing a non-binary object as input. csv_str = batch.to_pandas().to_csv( ``` ```python # dataset is not compressed and no warnings are emitted dataset.to_csv("compressed.csv.gz") print(os.path.getsize("compressed.csv.gz")) # 1008607 # compare with dataset.to_pandas().to_csv("pandas.csv.gz") print(os.path.getsize("pandas.csv.gz")) # 418561 ``` --- I think that this is because behind the scenes `pandas.DataFrame.to_csv` is always called with a buf-like `path_or_buf`, but users that are providing a path-like to `datasets.Dataset.to_csv` are likely not to expect / know that - leading to a mismatch in their understanding of the expected behaviour of the `compression` kwarg. ### Expected behavior The dataset to be saved as a compressed csv file when providing a `compression` kwarg, or when relying on the default `compression='infer'` ### Environment info `datasets == 2.13.1`

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PR_kwDODunzps5VqEyb

Fix unused DatasetInfosDict code in push_to_hub

closed

MEMBER

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PR_kwDODunzps5Vp0GX

Flatten repository_structure docs on yaml

closed

MEMBER

To have Splits, Configurations and Builder parameters at the same doc level

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PR_kwDODunzps5VptVf

Fix legacy_dataset_infos

closed

MEMBER

was causing transformers CI to fail https://circleci.com/gh/huggingface/transformers/855105

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I_kwDODunzps5rrSGj

Loading column subset from parquet file produces error since version 2.13

closed

NONE

### Describe the bug `load_dataset` allows loading a subset of columns from a parquet file with the `columns` argument. Since version 2.13, this produces the following error: ``` Traceback (most recent call last): File "/usr/lib/python3.10/site-packages/datasets/builder.py", line 1879, in _prepare_split_single for _, table in generator: File "/usr/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables raise ValueError( ValueError: Tried to load parquet data with columns '['sepal_length']' with mismatching features '{'sepal_length': Value(dtype='float64', id=None), 'sepal_width': Value(dtype='float64', id=None), 'petal_length': Value(dtype='float64', id=None), 'petal_width': Value(dtype='float64', id=None), 'species': Value(dtype='string', id=None)}' ``` This seems to occur because `datasets` is checking whether the columns in the schema exactly match the provided list of columns, instead of whether they are a subset. ### Steps to reproduce the bug ```python # Prepare some sample data import pandas as pd iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv') iris.to_parquet('iris.parquet') # ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species'] print(iris.columns) # Load data with datasets from datasets import load_dataset # Load full parquet file dataset = load_dataset('parquet', data_files='iris.parquet') # Load column subset; throws error for datasets>=2.13 dataset = load_dataset('parquet', data_files='iris.parquet', columns=['sepal_length']) ``` ### Expected behavior No error should be thrown and the given column subset should be loaded. ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35 - Python version: 3.10.9 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3

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I_kwDODunzps5rpMQ0

File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 992, in _download_and_prepare if str(split_generator.split_info.name).lower() == "all": AttributeError: 'str' object has no attribute 'split_info'. Did you mean: 'splitlines'?

closed

NONE

Hi, I use the code below to load local file ``` def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive # urls = _URLS[self.config.name] data_dir = dl_manager.download_and_extract(_URLs) print(data_dir) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir["train"]), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir["dev"]), "split": "dev", }, ), ] ``` and error occured ``` Traceback (most recent call last): File "/home/zhizhou/data1/zhanghao/huggingface/FineTuning_Transformer/load_local_dataset.py", line 2, in <module> dataset = load_dataset("./QA_script.py",data_files='/home/zhizhou/.cache/huggingface/datasets/conversatiom_corps/part_file.json') File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/load.py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 1670, in _download_and_prepare super()._download_and_prepare( File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 992, in _download_and_prepare if str(split_generator.split_info.name).lower() == "all": AttributeError: 'str' object has no attribute 'split_info'. Did you mean: 'splitlines'? ``` Could you help me?

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I_kwDODunzps5ro6bQ

Documentation links to examples are broken

closed

NONE

### Describe the bug The links at the bottom of [add_dataset](https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html) to examples of specific datasets are all broken, for example - text classification: [ag_news](https://github.com/huggingface/datasets/blob/master/datasets/ag_news/ag_news.py) (original data are in csv files) ### Steps to reproduce the bug Click on links to examples from latest documentation ### Expected behavior Links should be up to date - it might be more stable to link to https://huggingface.co/datasets/ag_news/blob/main/ag_news.py ### Environment info dataset v1.2.1

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PR_kwDODunzps5ViKc4

Deprecate search API

open

CONTRIBUTOR

The Search API only supports Faiss and ElasticSearch as vector stores, is somewhat difficult to maintain (e.g., it still doesn't support ElasticSeach 8.0, difficult testing, ...), does not have the best design (adds a bunch of methods to the `Dataset` class that are only useful after creating an index), the usage doesn't seem to be significant and is not integrated with the Hub. Since we have no plans/bandwidth to improve it and better alternatives such as `langchain` and `docarray` exist, I think it should be deprecated (and eventually removed). If we decide to deprecate/remove it, the following usage instances need to be addressed: * [Course](https://github.com/huggingface/course/blob/0018bb434204d9750a03592cb0d4e846093218d8/chapters/en/chapter5/6.mdx#L342 ) and [Blog](https://github.com/huggingface/blog/blob/4897c6f73d4492a0955ade503281711d01840e09/image-search-datasets.md?plain=1#L252) - calling the FAISS API directly should be OK in these instances as it's pretty simple to use for basic scenarios. Alternatively, we can use `langchain`, but this adds an extra dependency * [Transformers](https://github.com/huggingface/transformers/blob/50726f9ea7afc6113da617f8f4ca1ab264a5e28a/src/transformers/models/rag/retrieval_rag.py#L183) - we can use the FAISS API directly and store the index as a separate attribute (and instead of building the `wiki_dpr` index each time the dataset is generated, we can generate it once and push it to the Hub repo, and then read it from there cc @huggingface/datasets @LysandreJik for the opinion

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PR_kwDODunzps5Vh_QR

Dataset representation

open

NONE

__repr__ and _repr_html_ now both are similar to that of Polars

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I_kwDODunzps5rjoFx

load_dataset hangs on WSL

closed

NONE

### Describe the bug load_dataset simply hangs. It happens once every ~5 times, and interestingly hangs for a multiple of 5 minutes (hangs for 5/10/15 minutes). Using the profiler in PyCharm shows that it spends the time at <method 'connect' of '_socket.socket' objects>. However, a local cache is available so I am not sure why socket is needed. ([profiler result](https://ibb.co/0Btbbp8)) It only happens on WSL for me. It works for native Windows and my MacBook. (cache quickly recognized and loaded within a second). ### Steps to reproduce the bug I am using Ubuntu 22.04.2 LTS (GNU/Linux 5.15.90.1-microsoft-standard-WSL2 x86_64) Python 3.10.10 (main, Mar 21 2023, 18:45:11) [GCC 11.2.0] on linux >>> import datasets >>> datasets.load_dataset('ai2_arc', 'ARC-Challenge') # hangs for 5/10/15 minutes ### Expected behavior cache quickly recognized and loaded within a second ### Environment info Please let me know if I should provide more environment information.

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I_kwDODunzps5rjjYD

`map` function doesn't fully utilize `input_columns`.

closed

NONE

### Describe the bug I wanted to select only some columns of data. And I thought that's why the argument `input_columns` exists. What I expected is like this: If there are ["a", "b", "c", "d"] columns, and if I set `input_columns=["a", "d"]`, the data will have only ["a", "d"] columns. But it doesn't select columns. It preserves existing columns. The main cause is `update` function of `dictionary` type `transformed_batch`. https://github.com/huggingface/datasets/blob/682d21e94ab1e64c11b583de39dc4c93f0101c5a/src/datasets/iterable_dataset.py#L687-L691 `transformed_batch` gets all the columns by `transformed_batch = dict(batch)`. Even `function_args` selects `input_columns`, `update` preserves columns other than `input_columns`. I think it should take a new dictionary with columns in `input_columns` like this: ``` # transformed_batch = dict(batch) # transformed_batch.update(self.function(*function_args, **self.fn_kwargs) # This is what I think correct. transformed_batch = self.function(*function_args, **self.fn_kwargs) ``` Let me know how to use `input_columns`. ### Steps to reproduce the bug Described all above. ### Expected behavior Described all above. ### Environment info datasets: 2.12 python: 3.8

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I_kwDODunzps5rjFQX

DownloadConfig.proxies not work when load_dataset_builder calling HfApi.dataset_info

open

NONE

### Describe the bug ```python download_config = DownloadConfig(proxies={'https': '<my proxy>'}) builder = load_dataset_builder(..., download_config=download_config) ``` But, when getting the dataset_info from HfApi, the http requests not using the proxies. ### Steps to reproduce the bug 1. Setup proxies in DownloadConfig. 2. Call `load_dataset_build` with download_config. 3. Inspect the call stack in HfApi.dataset_info.  ### Expected behavior DownloadConfig.proxies works for getting dataset_info. ### Environment info https://github.com/huggingface/datasets/commit/406b2212263c0d33f267e35b917f410ff6b3bc00 Python 3.11.4

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I_kwDODunzps5riaky

Argument type for map function changes when using `input_columns` for `IterableDataset`

closed

NONE

### Describe the bug I wrote `tokenize(examples)` function as an argument for `map` function for `IterableDataset`. It process dictionary type `examples` as a parameter. It is used in `train_dataset = train_dataset.map(tokenize, batched=True)` No error is raised. And then, I found some unnecessary keys and values in `examples` so I added `input_columns` argument to `map` function to select keys and values. It gives me an error saying ``` TypeError: tokenize() takes 1 positional argument but 3 were given. ``` The code below matters. https://github.com/huggingface/datasets/blob/406b2212263c0d33f267e35b917f410ff6b3bc00/src/datasets/iterable_dataset.py#L687 For example, `inputs = {"a":1, "b":2, "c":3}`. If `self.input_coluns` is `None`, `inputs` is a dictionary type variable and `function_args` becomes a `list` of a single `dict` variable. `function_args` becomes `[{"a":1, "b":2, "c":3}]` Otherwise, lets say `self.input_columns = ["a", "c"]` `[inputs[col] for col in self.input_columns]` results in `[1, 3]`. I think it should be `[{"a":1, "c":3}]`. I want to ask if the resulting format is intended. Maybe I can modify `tokenize()` to have 2 parameters in this case instead of having 1 dictionary. But this is confusing to me. Or it should be fixed as `[{col:inputs[col] for col in self.input_columns}]` ### Steps to reproduce the bug Run `map` function of `IterableDataset` with `input_columns` argument. ### Expected behavior `function_args` looks better to have same format. I think it should be `[{"a":1, "c":3}]`. ### Environment info dataset version: 2.12 python: 3.8

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PR_kwDODunzps5Vd0ZG

fixed typo in comment

closed

CONTRIBUTOR

This mistake was a bit confusing, so I thought it was worth sending a PR over.

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PR_kwDODunzps5VcbPW

[docs] Fix link

closed

MEMBER

Fixes link to the builder classes :)

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PR_kwDODunzps5Vb3LJ

Use new hffs

closed

MEMBER

Thanks to @janineguo 's work in https://github.com/huggingface/datasets/pull/5919 which was needed to support HfFileSystem. Switching to `HfFileSystem` will help implementing optimization in data files resolution ## Implementation details I replaced all the from_hf_repo and from_local_or_remote in data_files.py to only use a new `from_patterns` which works for any fsspec path, including hf:// paths, https:// URLs and local paths. This simplifies the codebase since there is no logic duplication anymore when it comes to data files resolution. I added `_prepare_path_and_storage_options` which returns the right storage_options to use given a path and a `DownloadConfig`. This is the only place where the logic depends on the filesystem type that must be used. I also removed the `get_metadata_data_files_list ` and `get_patterns_and_data_files` functions added recently, since data files resolution is now handled using a common interface. ## New features hf:// paths are now supported in data_files ## Breaking changes DataFilesList and DataFilesDict: - use `str` paths instead of `Union[Path, Url]` - require posix paths for windows paths close https://github.com/huggingface/datasets/issues/6017

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PR_kwDODunzps5Va4g3

Delete `task_templates` in `IterableDataset` when they are no longer valid

closed

CONTRIBUTOR

Fix #6025

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PR_kwDODunzps5VanI8

Fix style with ruff 0.0.278

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I_kwDODunzps5rZha5

Using a dataset for a use other than it was intended for.

closed

NONE

### Describe the bug Hi, I want to use the rotten tomatoes dataset but for a task other than classification, but when I interleave the dataset, it throws ```'ValueError: Column label is not present in features.'```. It seems that the label_col must be there in the dataset for some reason? Here is the full stacktrace ``` File "/home/suryahari/Vornoi/tryage-handoff-other-datasets.py", line 276, in create_dataloaders dataset = interleave_datasets(dsfold, stopping_strategy="all_exhausted") File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py", line 134, in interleave_datasets return _interleave_iterable_datasets( File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1833, in _interleave_iterable_datasets info = DatasetInfo.from_merge([d.info for d in datasets]) File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in from_merge dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None] File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in <listcomp> dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None] File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 378, in copy return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File "<string>", line 20, in __init__ File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 208, in __post_init__ self.task_templates = [ File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 209, in <listcomp> template.align_with_features(self.features) for template in (self.task_templates) File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/tasks/text_classification.py", line 20, in align_with_features raise ValueError(f"Column {self.label_column} is not present in features.") ValueError: Column label is not present in features. ``` ### Steps to reproduce the bug Delete the column `labels` from the `rotten_tomatoes` dataset. Try to interleave it with other datasets. ### Expected behavior Should let me use the dataset with just the `text` field ### Environment info latest datasets library? I don't think this was an issue in earlier versions.

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PR_kwDODunzps5VWbGe

Don't reference self in Spark._validate_cache_dir

closed

CONTRIBUTOR

Fix for https://github.com/huggingface/datasets/issues/5963

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PR_kwDODunzps5VU7EG

Fix `ClassLabel` min max check for `None` values

closed

CONTRIBUTOR

Fix #6022

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I_kwDODunzps5rSzut

Batch map raises TypeError: '>=' not supported between instances of 'NoneType' and 'int'

closed

NONE

### Describe the bug When mapping some datasets with `batched=True`, datasets may raise an exeception: ```python Traceback (most recent call last): File "/Users/codingl2k1/Work/datasets/venv/lib/python3.11/site-packages/multiprocess/pool.py", line 125, in worker result = (True, func(*args, **kwds)) ^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1328, in _write_generator_to_queue for i, result in enumerate(func(**kwargs)): File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 3483, in _map_single writer.write_batch(batch) File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_writer.py", line 549, in write_batch array = cast_array_to_feature(col_values, col_type) if col_type is not None else col_values ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 1831, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 1831, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 2063, in cast_array_to_feature return feature.cast_storage(array) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/features/features.py", line 1098, in cast_storage if min_max["max"] >= self.num_classes: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: '>=' not supported between instances of 'NoneType' and 'int' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/Users/codingl2k1/Work/datasets/t1.py", line 33, in <module> ds = ds.map(transforms, num_proc=14, batched=True, batch_size=5) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/dataset_dict.py", line 850, in map { File "/Users/codingl2k1/Work/datasets/src/datasets/dataset_dict.py", line 851, in <dictcomp> k: dataset.map( ^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 577, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 542, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 3179, in map for rank, done, content in iflatmap_unordered( File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1368, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1368, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/venv/lib/python3.11/site-packages/multiprocess/pool.py", line 774, in get raise self._value TypeError: '>=' not supported between instances of 'NoneType' and 'int' ``` ### Steps to reproduce the bug 1. Checkout the latest main of datasets. 2. Run the code: ```python from datasets import load_dataset def transforms(examples): # examples["pixel_values"] = [image.convert("RGB").resize((100, 100)) for image in examples["image"]] return examples ds = load_dataset("scene_parse_150") ds = ds.map(transforms, num_proc=14, batched=True, batch_size=5) print(ds) ``` ### Expected behavior map without exception. ### Environment info Datasets: https://github.com/huggingface/datasets/commit/b8067c0262073891180869f700ebef5ac3dc5cce Python: 3.11.4 System: Macos

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PR_kwDODunzps5VP11Q

[docs] Update return statement of index search

closed

MEMBER

Clarifies in the return statement of the docstring that the retrieval score is `IndexFlatL2` by default (see [PR](https://github.com/huggingface/transformers/issues/24739) and internal Slack [convo](https://huggingface.slack.com/archives/C01229B19EX/p1689105179711689)), and fixes the formatting because multiple return values are not supported.

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I_kwDODunzps5rRY5Y

Inconsistent "The features can't be aligned" error when combining map, multiprocessing, and variable length outputs

open

NONE

### Describe the bug I'm using a dataset with map and multiprocessing to run a function that returned a variable length list of outputs. This output list may be empty. Normally this is handled fine, but there is an edge case that crops up when using multiprocessing. In some cases, an empty list result ends up in a dataset shard consisting of a single item. This results in a `The features can't be aligned` error that is difficult to debug because it depends on the number of processes/shards used. I've reproduced a minimal example below. My current workaround is to fill empty results with a dummy value that I filter after, but this was a weird error that took a while to track down. ### Steps to reproduce the bug ```python import datasets dataset = datasets.Dataset.from_list([{'idx':i} for i in range(60)]) def test_func(row, idx): if idx==58: return {'output': []} else: return {'output' : [{'test':1}, {'test':2}]} # this works fine test1 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=4) # this fails test2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32) >ValueError: The features can't be aligned because the key output of features {'idx': Value(dtype='int64', id=None), 'output': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None)} has unexpected type - Sequence(feature=Value(dtype='null', id=None), length=-1, id=None) (expected either [{'test': Value(dtype='int64', id=None)}] or Value("null"). ``` The error occurs during the check ```python _check_if_features_can_be_aligned([dset.features for dset in dsets]) ``` When the multiprocessing splitting lines up just right with the empty return value, one of the `dset` in `dsets` will have a single item with an empty list value, causing the error. ### Expected behavior Expected behavior is the result would be the same regardless of the `num_proc` value used. ### Environment info Datasets version 2.11.0 Python 3.9.16

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PR_kwDODunzps5VPAlD

Improve logging

closed

CONTRIBUTOR

Adds the StreamHandler (as `hfh` and `transformers` do) to the library's logger to log INFO messages and logs the messages about "loading a cached result" (and some other warnings) as INFO (Also removes the `leave=False` arg in the progress bars to be consistent with `hfh` and `transformers` - progress bars serve as an indicator that a result is not cached, so it makes more sense not to delete them) Fix #2832, fix https://github.com/huggingface/datasets/issues/1948, fix https://github.com/huggingface/datasets/issues/5444

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PR_kwDODunzps5VOmKY

test1

closed

NONE

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I_kwDODunzps5rP0dM

Switch to huggingface_hub's HfFileSystem

closed

MEMBER

instead of the current datasets.filesystems.hffilesystem.HfFileSystem which can be slow in some cases related to https://github.com/huggingface/datasets/issues/5846 and https://github.com/huggingface/datasets/pull/5919

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PR_kwDODunzps5VNEvn

Dataset string representation enhancement

open

NONE

my attempt at #6010 not sure if this is the right way to go about it, I will wait for your feedback

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PR_kwDODunzps5VMhgB

Add metadata ui screenshot in docs

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I_kwDODunzps5rLpC4

Request to Share/Update Dataset Viewer Code

open

NONE

Overview: The repository (huggingface/datasets-viewer) was recently archived and when I tried to run the code, there was the error message "AttributeError: module 'datasets.load' has no attribute 'prepare_module'". I could not resolve the issue myself due to lack of documentation of that attribute. Request: I kindly request the sharing of the code responsible for the dataset preview functionality or help with resolving the error. The dataset viewer on the Hugging Face website is incredibly useful since it is compatible with different types of inputs. It allows users to find datasets that meet their needs more efficiently. If needed, I am willing to contribute to the project by testing, documenting, and providing feedback on the dataset viewer code. Thank you for considering this request, and I look forward to your response.

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I_kwDODunzps5rDg7t

[FR] `map` should reuse unchanged columns from the previous dataset to avoid disk usage

open

CONTRIBUTOR

### Feature request Currently adding a new column with `map` will cause all the data in the dataset to be duplicated and stored/cached on the disk again. It should reuse unchanged columns. ### Motivation This allows having datasets with different columns but sharing some basic columns. Currently, these datasets would become too expensive to store and one would need some kind of on-the-fly join; which also doesn't seem implemented. ### Your contribution _

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I_kwDODunzps5rBk6I

[FR] Transform Chaining, Lazy Mapping

open

CONTRIBUTOR

### Feature request Currently using a `map` call processes and duplicates the whole dataset, which takes both time and disk space. The solution is to allow lazy mapping, which is essentially a saved chain of transforms that are applied on the fly whenever a slice of the dataset is requested. The API should look like `map`, as `set_transform` changes the current dataset while `map` returns another dataset. ### Motivation Lazy processing allows lower disk usage and faster experimentation. ### Your contribution _

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I_kwDODunzps5rAg04

Documentation: wiki_dpr Dataset has no metric_type for Faiss Index

closed

NONE

### Describe the bug After loading `wiki_dpr` using: ```py ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train') print(ds.get_index("embeddings").metric_type) # prints nothing because the value is None ``` the index does not have a defined `metric_type`. This is an issue because I do not know how the `scores` are being computed for `get_nearest_examples()`. ### Steps to reproduce the bug System: Python 3.9.16, Transformers 4.30.2, WSL After loading `wiki_dpr` using: ```py ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train') print(ds.get_index("embeddings").metric_type) # prints nothing because the value is None ``` the index does not have a defined `metric_type`. This is an issue because I do not know how the `scores` are being computed for `get_nearest_examples()`. ```py from transformers import DPRQuestionEncoder, DPRContextEncoder, DPRQuestionEncoderTokenizer, DPRContextEncoderTokenizer tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-multiset-base") encoder = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-multiset-base") def encode_question(query, tokenizer=tokenizer, encoder=encoder): inputs = tokenizer(query, return_tensors='pt') question_embedding = encoder(**inputs)[0].detach().numpy() return question_embedding def get_knn(query, k=5, tokenizer=tokenizer, encoder=encoder, verbose=False): enc_question = encode_question(query, tokenizer, encoder) topk_results = ds.get_nearest_examples(index_name='embeddings', query=enc_question, k=k) a = torch.tensor(enc_question[0]).reshape(768) b = torch.tensor(topk_results.examples['embeddings'][0]) print(a.shape, b.shape) print(torch.dot(a, b)) print((a-b).pow(2).sum()) return topk_results ``` The [FAISS documentation](https://github.com/facebookresearch/faiss/wiki/MetricType-and-distances) suggests the metric is usually L2 distance (without the square root) or the inner product. I compute both for the sample query: ```py query = """ it catapulted into popular culture along with a line of action figures and other toys by Bandai.[2] By 2001, the media franchise had generated over $6 billion in toy sales. Despite initial criticism that its action violence targeted child audiences, the franchise has been commercially successful.""" get_knn(query,k=5) ``` Here, I get dot product of 80.6020 and L2 distance of 77.6616 and ```py NearestExamplesResults(scores=array([76.20431 , 75.312416, 74.945404, 74.866394, 74.68506 ], dtype=float32), examples={'id': ['3081096', '2004811', '8908258', '9594124', '286575'], 'text': ['actors, resulting in the "Power Rangers" franchise which has continued since then into sequel TV series (with "Power Rangers Beast Morphers" set to premiere in 2019), comic books, video games, and three feature films, with a further cinematic universe planned. Following from the success of "Power Rangers", Saban acquired the rights to more of Toei\'s library, creating "VR Troopers" and "Big Bad Beetleborgs" from several Metal Hero Series shows and "Masked Rider" from Kamen Rider Series footage. DIC Entertainment joined this boom by acquiring the rights to "Gridman the Hyper Agent" and turning it into "Superhuman Samurai Syber-Squad". In 2002,', ``` Doing `k=1` indicates the higher the outputted number, the better the match, so the metric should not be L2 distance. However, my manually computed inner product (80.6) has a discrepancy with the reported (76.2). Perhaps, this has to do with me using the `compressed` embeddings? ### Expected behavior ```py ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train') print(ds.get_index("embeddings").metric_type) # METRIC_INNER_PRODUCT ``` ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-4.18.0-477.13.1.el8_8.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1

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I_kwDODunzps5q68xI

Improve `Dataset`'s string representation

open

CONTRIBUTOR

Currently, `Dataset.__repr__` outputs a dataset's column names and the number of rows. We could improve it by printing its features and the first few rows. We should also implement `_repr_html_` to have a rich HTML representation in notebooks/Streamlit.

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PR_kwDODunzps5U1mus

Fix cast for dictionaries with no keys

closed

CONTRIBUTOR

Fix #5677

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I_kwDODunzps5qrz0g

Dataset.from_generator consistently freezes at ~1000 rows

closed

NONE

### Describe the bug Whenever I try to create a dataset which contains images using `Dataset.from_generator`, it freezes around 996 rows. I suppose it has something to do with memory consumption, but there's more memory available. I Somehow it worked a few times but mostly this makes the datasets library much more cumbersome to work with because generators are the easiest way to turn an existing dataset into a Hugging Face dataset. I've let it run in the frozen state for way longer than it can possibly take to load the actual dataset. Let me know if you have ideas how to resolve it! ### Steps to reproduce the bug ```python from datasets import Dataset import numpy as np def gen(): for row in range(10000): yield {"i": np.random.rand(512, 512, 3)} Dataset.from_generator(gen) # -> 90% of the time gets stuck around 1000 rows ``` ### Expected behavior Should continue and go through all the examples yielded by the generator, or at least throw an error or somehow communicate what's going on. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 12.0.1 - Pandas version: 1.5.1

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I_kwDODunzps5qreql

Get an error "OverflowError: Python int too large to convert to C long" when loading a large dataset

open

CONTRIBUTOR

### Describe the bug When load a large dataset with the following code ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train') ``` We encountered the error: "OverflowError: Python int too large to convert to C long" The error look something like: ``` OverflowError: Python int too large to convert to C long During handling of the above exception, another exception occurred: OverflowError Traceback (most recent call last) <ipython-input-7-0ed8700e662d> in <module> ----> 1 dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', cache_dir='/sfs/MNBVC/.cache/') /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1749 ignore_verifications=ignore_verifications, 1750 try_from_hf_gcs=try_from_hf_gcs, -> 1751 use_auth_token=use_auth_token, 1752 ) 1753 /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 703 if not downloaded_from_gcs: 704 self._download_and_prepare( --> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1225 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1228 1229 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 791 try: 792 # Prepare split will record examples associated to the split --> 793 self._prepare_split(split_generator, **prepare_split_kwargs) 794 except OSError as e: 795 raise OSError( /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys) 1219 writer.write(example, key) 1220 finally: -> 1221 num_examples, num_bytes = writer.finalize() 1222 1223 split_generator.split_info.num_examples = num_examples /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in finalize(self, close_stream) 536 # Re-intializing to empty list for next batch 537 self.hkey_record = [] --> 538 self.write_examples_on_file() 539 if self.pa_writer is None: 540 if self.schema: /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_examples_on_file(self) 407 # Since current_examples contains (example, key) tuples 408 batch_examples[col] = [row[0][col] for row in self.current_examples] --> 409 self.write_batch(batch_examples=batch_examples) 410 self.current_examples = [] 411 /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 506 col_try_type = try_features[col] if try_features is not None and col in try_features else None 507 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 508 arrays.append(pa.array(typed_sequence)) 509 inferred_features[col] = typed_sequence.get_inferred_type() 510 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 180 else: 181 trying_cast_to_python_objects = True --> 182 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) 183 # use smaller integer precisions if possible 184 if self.trying_int_optimization: /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() OverflowError: Python int too large to convert to C long ``` However, that dataset can be loaded in a streaming manner: ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', streaming=True) for i in dataset: pass # it work well ``` Another issue is reported in our dataset hub: https://huggingface.co/datasets/liwu/MNBVC/discussions/2 ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train') ### Expected behavior the dataset can be safely loaded ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-3.10.0-1160.an7.x86_64-x86_64-with-centos-7.9 - Python version: 3.6.8 - PyArrow version: 6.0.1 - Pandas version: 1.1.5

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