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I_kwDODunzps5VKCyn

Loading an external NER dataset

closed

2022-10-30T09:31:55Z

2022-11-01T13:15:49Z

2022-11-01T13:15:49Z

NONE

I need to use huggingface datasets to load a custom dataset similar to conll2003 but with more entities and each the files contain only two columns: word and ner tag. I tried this code snnipet that I found here as an answer to a similar issue: from datasets import Dataset INPUT_COLUMNS = "ID Text NER".split() def read_conll(file): example = {col: [] for col in INPUT_COLUMNS} idx = 0 with open(file) as f: for line in f: if line.startswith("-DOCSTART-") or line == "\n" or not line: if example[next(iter(example))]: yield idx, example idx += 1 example = {col: [] for col in INPUT_COLUMNS} else: row_cols = line.split() for i, col in enumerate(example): example[col] = row_cols[i].rstrip() train = Dataset.from_generator(read_conll, gen_kwargs={"file": "some_path"}) But the following error happened: ValueError: Please pass `features` or at least one example when writing data

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PR_kwDODunzps5Bv3rh

Preserve None in list type cast in PyArrow 10

closed

2022-10-28T12:48:30Z

2022-10-28T13:15:33Z

2022-10-28T13:13:18Z

CONTRIBUTOR

The `ListArray` type in PyArrow 10.0.0 supports the `mask` parameter, which allows us to preserve Nones in nested lists in `cast` instead of replacing them with empty lists. Fix https://github.com/huggingface/datasets/issues/3676

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PR_kwDODunzps5BreEm

Raise ffmpeg warnings only once

closed

2022-10-27T15:58:33Z

2022-10-28T16:03:05Z

2022-10-28T16:00:51Z

CONTRIBUTOR

Our warnings looks nice now. `librosa` warning that was raised at each decoding: ``` /usr/local/lib/python3.7/dist-packages/librosa/core/audio.py:165: UserWarning: PySoundFile failed. Trying audioread instead. warnings.warn("PySoundFile failed. Trying audioread instead.") ``` is suppressed with `filterwarnings("ignore")` in a context manager. That means the first warning is also ignored (setting `filterwarnings("once")` didn't work!), so I added info that audioread is used for decoding to our message. Hope it's enough. Tests failed at first because they used to check if the warning was raised at (each) decoding in `librosa` case but now we throw only one warning (at first decoding). I removed this check for warnings, do you think it's fine?

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I_kwDODunzps5U98Gq

Inconsistency behavior between handling local file protocol and other FS protocols

open

2022-10-27T12:03:20Z

2022-10-27T12:05:19Z

NONE

### Describe the bug These lines us used during load_from_disk: ``` if is_remote_filesystem(fs): dest_dataset_dict_path = extract_path_from_uri(dataset_dict_path) else: fs = fsspec.filesystem("file") dest_dataset_dict_path = dataset_dict_path ``` If a local FS is given, then it will the URL as the path name. If a remote Fs is given, then it will use the path of the URL. This is an inconsistent behavior when handling a file: when using remote FS, you must write a URL, but for local FS, even if you passed LocalFileSystem as `fs` you still can't use a `file://` URL. It will be recognized as a directory named `file:`. ### Steps to reproduce the bug ``` import fsspec.core url = "hdfs:///somewhere/MNIST" # url = "file:///somewhere/MNIST" fs, path = fsspec.core.url_to_fs(url) fs.ls(path) # this will always work load_from_disk(path, fs) # only works for local FS load_from_disk(url, fs) # only works for remote FS ``` ### Expected behavior one of `url` or `path` should always work I think we extract path from given URL by using `fsspec.core.url_to_fs` instead of using `is_remote_filesystem` and `extract_path_from_uri` will fix this, since: ``` fsspec.core.url_to_fs("/somewhere/MNIST") -> LocalFs, '/somewhere/MNIST' fsspec.core.url_to_fs("file:///somewhere/MNIST") -> LocalFs, '/somewhere/MNIST' fsspec.core.url_to_fs("hdfs:///somewhere/MNIST") -> HDFS, '/somewhere/MNIST' ``` and ``` fsspec.core.url_to_fs("file:///somewhere/MNIST") == fsspec.core.url_to_fs("/somewhere/MNIST") ``` In theory, this wouldn't break anything, since giving local path and remote uri still works. It will only affect local URI (make it works too) ### Environment info - `datasets` version: 2.5.1 - Platform: Linux-5.4.205.1**HIDDEN** - Python version: 3.7.10 - PyArrow version: 8.0.0 - Pandas version: 1.2.4

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PR_kwDODunzps5BpsXf

Add PB and TB in convert_file_size_to_int

closed

2022-10-27T09:50:31Z

2022-10-27T12:14:27Z

2022-10-27T12:12:30Z

MEMBER

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I_kwDODunzps5U9GFL

[Caching] Deterministic hashing of torch tensors

closed

2022-10-27T09:15:15Z

2022-11-02T17:18:43Z

2022-11-02T17:18:43Z

MEMBER

Currently this fails ```python import torch from datasets.fingerprint import Hasher t = torch.tensor([1.]) def func(x): return t + x hash1 = Hasher.hash(func) t = torch.tensor([1.]) hash2 = Hasher.hash(func) assert hash1 == hash2 ``` Also as noticed in https://discuss.huggingface.co/t/dataset-cant-cache-models-outputs/24945, using a model in a `map` function doesn't work well with caching. Indeed the `bert-base-uncased` model has a different hash every time you reload it. Supporting torch tensors may also help in this case. This can be fixed by registering a custom pickling functions for torch tensors - as we did for other objects such as CodeType, FunctionType and Regex in `py_utils.py`

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PR_kwDODunzps5Bow1Q

Add "ipykernel" to list of `co_filename`s to remove

closed

2022-10-27T05:56:17Z

2022-11-02T15:46:00Z

2022-11-02T15:43:20Z

CONTRIBUTOR

Should resolve #5157

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PR_kwDODunzps5BmYnq

Fix CI require beam

closed

2022-10-26T16:49:33Z

2022-10-27T09:25:19Z

2022-10-27T09:23:26Z

MEMBER

This PR: - Fixes the CI `require_beam`: before it was requiring PyTorch instead ```python def require_beam(test_case): if not config.TORCH_AVAILABLE: test_case = unittest.skip("test requires PyTorch")(test_case) return test_case ``` - Fixes a missing `require_beam` in `test_beam_based_builder_download_and_prepare_as_parquet` - Refactors `require_beam` to use `pytest` (`skipif`) instead

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PR_kwDODunzps5BljPw

Add ffmpeg4 installation instructions in warnings

closed

2022-10-26T14:21:14Z

2022-10-27T09:01:12Z

2022-10-27T08:58:58Z

CONTRIBUTOR

Adds instructions on how to install `ffmpeg=4` on Linux (relevant for Colab users). Looks pretty ugly because I didn't find a way to check `ffmpeg` version from python (without `subprocess.call()`; `ctypes.util.find_library` doesn't work`), so the warning is raised on each decoding. Any suggestions on how to make it look nice are welcome! This is how it looks on Colab: 

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PR_kwDODunzps5Bj5IQ

Support dill 0.3.6

closed

2022-10-26T08:24:59Z

2022-10-28T05:41:05Z

2022-10-28T05:38:14Z

MEMBER

This PR: - ~~Unpins dill to allow installing dill>=0.3.6~~ - ~~Removes the fix on dill for >=0.3.6 because they implemented a deterministic mode (to be confirmed by @anivegesana)~~ - Pins dill<0.3.7 to allow latest dill 0.3.6 - Implements a fix for dill `save_function` for dill 0.3.6 - Additionally had to implement a fix for dill `save_code` and `_save_regex` for dill 0.3.6 - Fixes the CI so that the latest dill version is tested (besides the minimum 0.3.1.1 required by apache-beam 2.42.0) Fix #5162.

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I_kwDODunzps5U2qql

Memory explosion when trying to access 4d tensors in datasets cast to torch or np

open

2022-10-26T08:14:47Z

2022-10-26T08:14:47Z

CONTRIBUTOR

### Describe the bug When trying to access an item by index, in a datasets.Dataset cast to torch/np using `set_format` or `with_format`, we get a memory explosion if the item contains 4d (or above) tensors. ### Steps to reproduce the bug MWE: ```python from datasets import load_dataset import numpy as np def create_4d_tensor(item): i = item["num_nodes"] item["x_big"] = np.random.rand(i, 2*i, int(i/2), 1) + 1 # we create a big 4d tensor return item if __name__ == "__main__": dataset = load_dataset(path=f"graphs-datasets/PROTEINS") # This works print(dataset["train"].format) print(dataset["train"][0].keys()) dataset = dataset.map( create_4d_tensor, batched=False, writer_batch_size=100, ) # This works print(dataset["train"].format) print(dataset["train"][0].keys()) dataset.set_format("torch") print(dataset["train"].format) # This gets killed :( print(dataset["train"][0].keys()) ``` The problem likely comes from `format_table` [here](https://cs.github.com/huggingface/datasets/blob/f09f781be3278156ce3aa6ec90c1926b1846a78f/src/datasets/arrow_dataset.py#L2328) ### Expected behavior No memory explosion when trying to access dataset items after cast. ### Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.14.0-1054-oem-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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PR_kwDODunzps5BhL4J

WIP: drop labels in Image and Audio folders by default

closed

2022-10-25T17:21:49Z

2022-11-16T14:21:16Z

2022-11-02T14:03:02Z

CONTRIBUTOR

will fix https://github.com/huggingface/datasets/issues/5153 and redundant labels displaying for most of the images datasets on the Hub (which are used just to store files) TODO: discuss adding `drop_labels` (and `drop_metadata`) params to yaml

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PR_kwDODunzps5BgQxp

Reduce default max `writer_batch_size`

closed

2022-10-25T14:14:52Z

2022-10-27T12:19:27Z

2022-10-27T12:16:47Z

CONTRIBUTOR

Reduce the default writer_batch_size from 10k to 1k examples. Additionally, align the default values of `batch_size` and `writer_batch_size` in `Dataset.cast` with the values from the corresponding docstring.

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I_kwDODunzps5UyQi4

Pip-compile: Could not find a version that matches dill<0.3.6,>=0.3.6

closed

2022-10-25T13:23:50Z

2022-11-14T08:25:37Z

2022-10-28T05:38:15Z

NONE

### Describe the bug When using `pip-compile` (part of `pip-tools`) to generate a pinned requirements file that includes `datasets`, a version conflict of `dill` appears. It is caused by a transitive dependency conflict between `datasets` and `multiprocess`. ### Steps to reproduce the bug ```bash $ echo "datasets" > requirements.in $ pip install pip-tools $ pip-compile requirements.in Could not find a version that matches dill<0.3.6,>=0.3.6 (from datasets==2.6.1->-r requirements.in (line 1)) Tried: 0.2, 0.2, 0.2.1, 0.2.1, 0.2.2, 0.2.2, 0.2.3, 0.2.3, 0.2.4, 0.2.4, 0.2.5, 0.2.5, 0.2.6, 0.2.7, 0.2.7.1, 0.2.8, 0.2.8.1, 0.2.8.2, 0.2.9, 0.3.0, 0.3.1, 0.3.1.1, 0.3.2, 0.3.3, 0.3.3, 0.3.4, 0.3.4, 0.3.5, 0.3.5, 0.3.5.1, 0.3.5.1, 0.3.6, 0.3.6 Skipped pre-versions: 0.1a1, 0.2a1, 0.2a1, 0.2b1, 0.2b1 There are incompatible versions in the resolved dependencies: dill<0.3.6 (from datasets==2.6.1->-r requirements.in (line 1)) dill>=0.3.6 (from multiprocess==0.70.14->datasets==2.6.1->-r requirements.in (line 1)) ``` ### Expected behavior A correctly generated file `requirements.txt` with pinned dependencies ### Environment info Tested with versions `2.6.1, 2.6.0, 2.5.2` on Python 3.8 and 3.10 on Ubuntu 20.04LTS and Python 3.10 on MacOS 12.6 (M1).

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I_kwDODunzps5Ux6uk

Dataset can’t cache model’s outputs

closed

2022-10-25T12:19:00Z

2022-11-03T16:12:52Z

2022-11-03T16:12:51Z

NONE

### Describe the bug Hi, I try to cache some outputs of teacher model( Knowledge Distillation ) by using map function of Dataset library, while every time I run my code, I still recompute all the sequences. I tested Bert Model like this, I got different hash every single run, so any idea to deal with this? ### Steps to reproduce the bug 1. run below code 2. get different hash ``` from transformers import BertModel from transformers import AutoTokenizer import torch token = ['hello'] model = BertModel.from_pretrained("bert-base-uncased").eval() tok = AutoTokenizer.from_pretrained("bert-base-uncased") def abcd(): with torch.no_grad(): out = model(**tok(token,return_tensors='pt'))[0] # out = tok(token) return out from datasets.fingerprint import Hasher my_func = abcd print(Hasher.hash(my_func)) print(abcd()) ``` ### Expected behavior I wanna cache all the model output ### Environment info datasets:2.5.0

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I_kwDODunzps5UxPUS

Automatically add filename for image/audio folder

open

2022-10-25T09:56:49Z

2022-10-26T16:51:46Z

MEMBER

### Feature request When creating a custom audio of image dataset, it would be great to automatically have access to the filename. It should be both: a) Automatically displayed in the viewer b) Automatically added as a column to the dataset when doing `load_dataset` In `diffusers` our test rely quite heavily on images and audio files now and it's a bit tedious at the moment to download specific images from a datasets repo. E.g. we have a dataset of images for tests in `diffusers`: https://huggingface.co/datasets/hf-internal-testing/diffusers-images where it would be extremely nice to have direct access to the filename both visually on the datasets page (@severo ) as well as via the `load_datasets` function. We currently have some akward functionality to download images by path name: https://github.com/huggingface/diffusers/blob/2fb8fafa4b761f6fc144cf75a6f6f0ea6af3a1c1/src/diffusers/utils/testing_utils.py#L131 It would be much nicer to just go over `load_dataset(...)` ### Motivation Intuitively the filename is something people understand directly. E.g if you upload a folder of images online, it's nice if you recognize the image as well as the filename next to it directly and that you're able to use it right away. The label on the other hand is less intuitive to understand as you haven't added it yourself. ### Your contribution Not sure if I have the time to add it myself anytime soon, but it would help us a lot for `diffusers`.

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PR_kwDODunzps5BfBN9

fsspec lock reset in multiprocessing

closed

2022-10-25T09:41:59Z

2022-11-03T20:51:15Z

2022-11-03T20:48:53Z

MEMBER

`fsspec` added a clean way of resetting its lock - instead of doing it manually

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I_kwDODunzps5UwucX

Fix language and license tag names in all Hub datasets

closed

2022-10-25T08:19:29Z

2022-10-25T11:27:26Z

2022-10-25T10:42:19Z

MEMBER

While working on this: - #5137 we realized there are still many datasets with deprecated "languages" and "licenses" tag names (instead of "language" and "license"). This is a blocking issue: no subsequent PR can be opened to modify their metadata: a ValueError will be thrown. We should fix the "language" and "license" tag names in all Hub datasets. TODO: - [x] Fix language and license tag names in 402 Hub datasets CC: @julien-c

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I_kwDODunzps5UvXmZ

Consistent caching between python and jupyter

closed

2022-10-25T01:34:33Z

2022-11-02T15:43:22Z

2022-11-02T15:43:22Z

CONTRIBUTOR

### Feature request I hope this is not my mistake, currently if I use `load_dataset` from a python session on a custom dataset to do the preprocessing, it will be saved in the cache and in other python sessions it will be loaded from the cache, however calling the same from a jupyter notebook does not work, meaning the preprocessing starts from scratch. If adjusting the hashes is impossible, is there a way to manually set dataset fingerprint to "force" this behaviour? ### Motivation If this is not already the case and I am doing something wrong, it would be useful to have the two fingerprints consistent so one can create the dataset once and then try small things on jupyter without preprocessing everything again. ### Your contribution I am happy to try a PR if you give me some pointers where the changes should happen

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I_kwDODunzps5UvOs1

Unable to download dataset using Azure Data Lake Gen 2

closed

2022-10-25T00:43:18Z

2022-11-17T23:37:09Z

2022-11-17T23:37:08Z

NONE

### Describe the bug When using the DatasetBuilder method with the credentials for the cloud storage Azure Data Lake (adl) Gen2, the following error is showed: ``` Traceback (most recent call last): File "download_hf_dataset.py", line 143, in <module> main() File "download_hf_dataset.py", line 102, in main builder.download_and_prepare(save_dir, storage_options=storage_options, max_shard_size="250MB", file_format="parquet") File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/datasets/builder.py", line 671, in download_and_prepare fs_token_paths = fsspec.get_fs_token_paths(output_dir, storage_options=storage_options) File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/fsspec/core.py", line 639, in get_fs_token_paths fs = cls(**options) File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/fsspec/spec.py", line 76, in __call__ obj = super().__call__(*args, **kwargs) TypeError: __init__() got an unexpected keyword argument 'account_name' ``` If I don't pass the storage_options argument (leave it as None), it requires the credentials used in ADL Gen 1: `TypeError: __init__() missing 3 required positional arguments: 'tenant_id', 'client_id', and 'client_secret'` Thus, it is not possible to download a dataset from the cloud using Azure Data Lake (adl) Gen2. ### Steps to reproduce the bug Assuming that you have an account on Azure and at Storage Account that can be used for reproduce: 1. Create a dict with the format to connect to Azure Data Lake Gen 2 ``` storage_options = {"account_name": ACCOUNT_NAME, "account_key": ACCOUNT_KEY) # gen 2 filesystem ``` 2. Create a dataset builder for any HF hosted dataset ``` builder = load_dataset_builder(dataset_name) ``` 3. Try to download the dataset passing the storage_options as an argument ``` save_dir = 'adl://my_save_dir' builder.download_and_prepare(save_dir, storage_options=storage_options, max_shard_size="250MB", file_format="parquet") ``` ### Expected behavior Not seeing the error mentioned above and being able to download the dataset to the provided path on ADL ### Environment info - `datasets` version: 2.6.1 - Platform: Linux-5.15.0-46-generic-x86_64-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 9.0.0 - Pandas version: 1.5.1

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PR_kwDODunzps5BcCYr

TextConfig: added "errors"

closed

2022-10-24T18:56:52Z

2022-11-03T13:38:13Z

2022-11-03T13:35:35Z

CONTRIBUTOR

This patch adds the ability to set the `errors` option of `open` for loading text datasets. I needed it because some data I had scraped had bad bytes in it, so I needed `errors='ignore'`.

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PR_kwDODunzps5BbpQZ

Test latest fsspec in CI

closed

2022-10-24T17:18:13Z

2022-10-25T09:32:51Z

2022-10-25T09:30:45Z

MEMBER

Following the discussion in https://discuss.huggingface.co/t/attributeerror-module-fsspec-has-no-attribute-asyn/19255 I think we need to test the latest fsspec in the CI

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I_kwDODunzps5UsDKx

default Image/AudioFolder infers labels when there is no metadata files even if there is only one dir

closed

2022-10-24T13:28:18Z

2022-11-15T16:31:10Z

2022-11-15T16:31:09Z

CONTRIBUTOR

### Describe the bug By default FolderBasedBuilder infers labels if there is not metadata files, even if it's meaningless (for example, they are in a single directory or in the root folder, see this repo as an example: https://huggingface.co/datasets/patrickvonplaten/audios As this is a corner case for quick exploration of images or audios on the Hub. ### Steps to reproduce the bug If you have directory like this: ``` repo image1.jpg image2.jpg image3.jpg ``` or ``` repo data image1.jpg image2.jpg image3.jpg ``` doing `ds = load_dataset(repo)` would create `label` feature: ```python print(ds["train"][0]) >> {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x7FB5326468E0>, 'label': 0} ``` Also, if you have the following structure: ``` repo data image1.jpg image2.jpg image3.jpg image4.jpg image5.jpg image6.jpg ``` it will infer two labels: ```python print(ds["train"][0]) print(ds["train"][-1]) >> {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x7FB5326468E0>, 'label': 1} >> {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x415 at 0x7FB5326555B0>, 'label': 0} ``` ### Expected behavior We should have only one base feature (Image/Audio) in such cases. ### Environment info all versions of `datasets`

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I_kwDODunzps5Ur9LX

refactor FolderBasedBuilder and Image/AudioFolder tests

open

2022-10-24T13:11:52Z

2022-10-24T13:11:52Z

CONTRIBUTOR

Tests for FolderBasedBuilder, ImageFolder and AudioFolder are mostly duplicating each other. They need to be refactored and Audio/ImageFolder should have only tests specific to the loader.

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I_kwDODunzps5Ur417

Add support to create different configs with `push_to_hub` (+ inferring configs from directories with package managers?)

open

2022-10-24T12:59:18Z

2022-11-04T14:55:20Z

CONTRIBUTOR

Now one can push only different splits within one default config of a dataset. Would be nice to allow something like: ``` ds.push_to_hub(repo_name, config=config_name) ``` I'm not sure, but this will probably require changes in `data_files.py` patterns. If so, it would also allow to create different configs for packaged modules datasets.

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I_kwDODunzps5Ure7H

Problems after upgrading to 2.6.1

open

2022-10-24T11:32:36Z

2022-12-16T15:59:54Z

NONE

### Describe the bug Loading a dataset_dict from disk with `load_from_disk` is now creating a `KeyError "length"` that was not occurring in v2.5.2. Context: - Each individual dataset in the dict is created with `Dataset.from_pandas` - The dataset_dict is create from a dict of `Dataset`s, e.g., `DatasetDict({"train": train_ds, "validation": val_ds}) - The pandas dataframe, besides text columns, has a column with a dictionary inside and potentially different keys in each row. Correctly the `Dataset.from_pandas` function adds `key: None` to all dictionaries in each row so that the schema can be correctly inferred. ### Steps to reproduce the bug Steps to reproduce: - Upgrade to datasets==2.6.1 - Create a dataset from pandas dataframe with `Dataset.from_pandas` - Create a dataset_dict from a dict of `Dataset`s, e.g., `DatasetDict({"train": train_ds, "validation": val_ds}) - Save to disk with the `save` function ### Expected behavior Same as in v2.5.2, that is load from disk without errors ### Environment info - `datasets` version: 2.6.1 - Platform: Linux-5.4.209-129.367.amzn2int.x86_64-x86_64-with-glibc2.26 - Python version: 3.9.13 - PyArrow version: 9.0.0 - Pandas version: 1.5.1

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PR_kwDODunzps5BZJab

Make iter_files deterministic

closed

2022-10-24T08:16:27Z

2022-10-27T09:53:23Z

2022-10-27T09:51:09Z

MEMBER

Fix #5145.

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I_kwDODunzps5UptNW

Cannot find the rvl_cdip dataset

closed

2022-10-24T04:57:42Z

2022-10-24T12:23:47Z

2022-10-24T06:25:28Z

NONE

Hi, I am trying to use load_dataset to load the official "rvl_cdip" dataset but getting an error. dataset = load_dataset("rvl_cdip") Couldn't find 'rvl_cdip' on the Hugging Face Hub either: FileNotFoundError: Couldn't find the file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/rvl_cdip/rvl_cdip.py Regards,

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I_kwDODunzps5UnDDj

Allow ignoring kwargs inside fn_kwargs during dataset.map's fingerprinting

open

2022-10-22T21:46:38Z

2022-11-01T22:19:07Z

NONE

### Feature request `dataset.map` accepts a `fn_kwargs` that is passed to `fn`. Currently, the whole `fn_kwargs` is used by `fingerprint_transform` to calculate the new fingerprint. I'd like to be able to inform `fingerprint_transform` which `fn_kwargs` shoud/shouldn't be taken into account during hashing. Of course, users should be aware to properly use this new feature, just like the internal usages of `fingerprint_transform` [does](https://github.com/huggingface/datasets/blob/2699593b33ee63d17aad2a2bfddedd38a8df57b8/src/datasets/arrow_dataset.py#L2700). ### Motivation This is originally motivated by https://github.com/huggingface/transformers/pull/18351#issuecomment-1263588680. Nonetheless, consider a more general processing function that accepts a kwarg that does not influence it's output: ```python def fn(example, verbose=False): ... ``` Then `dataset.map(fn, verbose=True)` would not benefit from dataset caching. I'm not sure if other methods in the `Dataset` API could benefit from this feature. ### Your contribution Based on `fingerprint_transform `'s `wrapper` function [here](https://github.com/huggingface/datasets/blob/c59cc34fcd2a369d27b77cc678017f5976a926a9/src/datasets/fingerprint.py#L443), it seems to me that it should be possible to make `.map`/`._map_single` accept something like `fn_use_fingerprint_kwargs`/`fn_ignore_fingerprint_kwargs` (probably another arg name). This would then be used by `fingerprint_transform.wrapper` to better/more flexibly hash the transformation. I could contribute with a PR if this feature and approach look good to you.

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PR_kwDODunzps5BSUWW

Delete duplicate issue template file

closed

2022-10-21T13:18:46Z

2022-10-21T13:52:30Z

2022-10-21T13:50:04Z

MEMBER

A conflict between two PRs: - #5116 - #5136 was not properly resolved, resulting in a duplicate issue template. This PR removes the duplicate template.

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I_kwDODunzps5UhQvM

Dataset order is not deterministic with ZIP archives and `iter_files`

closed

2022-10-21T09:00:03Z

2022-10-27T09:51:49Z

2022-10-27T09:51:10Z

CONTRIBUTOR

### Describe the bug For the `beans` dataset (did not try on other), the order of samples is not the same on different machines. Tested on my local laptop, github actions machine, and ec2 instance. The three yield a different order. ### Steps to reproduce the bug In a clean docker container or conda environment with datasets==2.6.1, run ```python from datasets import load_dataset from pprint import pprint data = load_dataset("beans", split="validation") pprint(data["image_file_path"]) ``` ### Expected behavior The order of the images is the same on all machines. ### Environment info On the EC2 instance: ``` - `datasets` version: 2.6.1 - Platform: Linux-4.14.291-218.527.amzn2.x86_64-x86_64-with-glibc2.2.5 - Python version: 3.7.10 - PyArrow version: 9.0.0 - Pandas version: 1.3.5 - Numpy version: not checked ``` On my local laptop: ``` - `datasets` version: 2.6.1 - Platform: Linux-5.15.0-50-generic-x86_64-with-glibc2.35 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - Numpy version: 1.23.1 ``` On github actions: ``` - `datasets` version: 2.6.1 - Platform: Linux-5.15.0-1022-azure-x86_64-with-glibc2.2.5 - Python version: 3.8.14 - PyArrow version: 9.0.0 - Pandas version: 1.5.1 - Numpy version: 1.23.4 ```

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I_kwDODunzps5UhJPL

Inconsistent documentation on map remove_columns

closed

2022-10-21T08:37:53Z

2022-11-15T14:15:10Z

2022-11-15T14:15:10Z

NONE

### Describe the bug The page [process](https://huggingface.co/docs/datasets/process) says this about the parameter `remove_columns` of the function `map`: When you remove a column, it is only removed after the example has been provided to the mapped function. So it seems that the `remove_columns` parameter removes after the mapped functions. However, another page, [the documentation of the function map](https://huggingface.co/docs/datasets/v2.6.1/en/package_reference/main_classes#datasets.Dataset.map.remove_columns) says: Columns will be removed before updating the examples with the output of `function`, i.e. if `function` is adding columns with names in remove_columns, these columns will be kept. So one page says "after the mapped function" and another says "before the mapped function." Is there something wrong? ### Steps to reproduce the bug Not about code. ### Expected behavior consistent about the descriptions of the behavior of the parameter `remove_columns` in the function `map`. ### Environment info datasets V2.6.0

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I_kwDODunzps5UczhC

DownloadManager Git LFS support

closed

2022-10-20T15:29:29Z

2022-10-20T17:17:10Z

2022-10-20T17:17:10Z

CONTRIBUTOR

### Feature request Maybe I'm mistaken but the `DownloadManager` does not support extracting git lfs files out of the box right? Using `dl_manager.download()` or `dl_manager.download_and_extract()` still returns lfs files afaict. Is there a good way to write a dataset loading script for a repo with lfs files? ### Motivation / ### Your contribution /

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PR_kwDODunzps5BLd90

Deprecate num_proc parameter in DownloadManager.extract

closed

2022-10-20T09:52:52Z

2022-10-25T18:06:56Z

2022-10-25T15:56:45Z

CONTRIBUTOR

fixes #5132 : Deprecated the `num_proc` parameter in `DownloadManager.extract` by passing `num_proc` parameter to `map_nested` .

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PR_kwDODunzps5BIp1l

Raise ImportError instead of OSError

closed

2022-10-19T19:30:05Z

2022-10-25T15:59:25Z

2022-10-25T15:56:58Z

CONTRIBUTOR

fixes #5134 : Replaced OSError with ImportError if required extraction library is not installed.

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PR_kwDODunzps5BHTNq

Make the KeyHasher FIPS compliant

closed

2022-10-19T14:25:52Z

2022-11-07T16:20:43Z

2022-11-07T16:20:43Z

NONE

MD5 is not FIPS compliant thus I am proposing this minimal change to make datasets package FIPS compliant

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I_kwDODunzps5UUbwj

Align task tags in dataset metadata

closed

2022-10-19T09:41:42Z

2022-11-10T05:25:58Z

2022-10-25T06:17:00Z

MEMBER

## Describe Once we have agreed on a common naming for task tags for all open source projects, we should align on them. ## Steps - [x] Align task tags in canonical datasets - [x] task_categories: 4 datasets - [x] task_ids (by @lhoestq) - [x] Open PRs in community datasets - [x] task_categories: 451 datasets - [x] task_ids: 556 datasets

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PR_kwDODunzps5BFWMG

Update docs once dataset scripts transferred to the Hub

closed

2022-10-19T07:58:27Z

2022-10-20T08:12:21Z

2022-10-20T08:10:00Z

MEMBER

Todo: - [x] Update docs: - [x] Datasets on GitHub (legacy) - [x] Load: offline - [x] About dataset load: - [x] Maintaining integrity - [x] Security - [x] Update docstrings: - [x] Inspect: - [x] get_dataset_config_info - [x] get_dataset_split_names - [x] Load: - [x] dataset_module_factory - [x] load_dataset_builder - [x] load_dataset - [x] Remove `ADD_NEW_DATASET.md` - [x] Update `.github/ISSUE_TEMPLATE/config.yml` Fix #5135.

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I_kwDODunzps5UTjzP

Update docs once dataset scripts transferred to the Hub

closed

2022-10-19T06:58:19Z

2022-10-20T08:10:01Z

2022-10-20T08:10:01Z

MEMBER

## Describe the bug As discussed in: - https://github.com/huggingface/hub-docs/pull/423#pullrequestreview-1146083701 we should update our docs once dataset scripts have been transferred to the Hub (and removed from GitHub): - #4974 Concretely: - [x] Datasets on GitHub (legacy): https://huggingface.co/docs/datasets/main/en/share#datasets-on-github-legacy - [x] ADD_NEW_DATASET: https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md - ... This PR complements the work of: - #5067 This PR is a follow-up of PRs: - #3777 CC: @julien-c

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I_kwDODunzps5UQi-H

Raise ImportError instead of OSError if required extraction library is not installed

closed

2022-10-18T17:53:46Z

2022-10-25T15:56:59Z

2022-10-25T15:56:59Z

CONTRIBUTOR

According to the official Python docs, `OSError` should be thrown in the following situations: > This exception is raised when a system function returns a system-related error, including I/O failures such as “file not found” or “disk full” (not for illegal argument types or other incidental errors). Hence, it makes more sense to raise `ImportError` instead of `OSError` when the required extraction/decompression library is not installed.

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I_kwDODunzps5UQi6m

Tensor operation not functioning in dataset mapping

closed

2022-10-18T17:53:35Z

2022-10-19T04:15:45Z

2022-10-19T04:15:44Z

NONE

## Describe the bug I'm doing a torch.mean() operation in data preprocessing, and it's not working. ## Steps to reproduce the bug ``` from transformers import pipeline import torch import numpy as np from datasets import load_dataset device = 'cuda:0' raw_dataset = load_dataset("glue", "sst2") feature_extraction = pipeline('feature-extraction', 'bert-base-uncased', device=device) def extracted_data(examples): # feature = torch.tensor(feature_extraction(examples['sentence'], batch_size=16), device=device) # feature = torch.mean(feature, dim=1) feature = np.asarray(feature_extraction(examples['sentence'], batch_size=16)).squeeze().mean(1) print(feature.shape) return {'feature': feature} extracted_dataset = raw_dataset.map(extracted_data, batched=True, batch_size=16) ``` ## Results When running with torch.mean(), the shape printed out is [16, seq_len, 768], which is exactly the same before the operation. While numpy works just fine, which gives [16, 768]. ## Environment info - `datasets` version: 2.6.1 - Platform: Linux-4.4.0-142-generic-x86_64-with-glibc2.31 - Python version: 3.10.6 - PyArrow version: 9.0.0 - Pandas version: 1.5.0

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I_kwDODunzps5UQe-K

Depracate `num_proc` parameter in `DownloadManager.extract`

closed

2022-10-18T17:41:05Z

2022-10-25T15:56:46Z

2022-10-25T15:56:46Z

CONTRIBUTOR

The `num_proc` parameter is only present in `DownloadManager.extract` but not in `StreamingDownloadManager.extract`, making it impossible to support streaming in the dataset scripts that use it (`openwebtext` and `the_pile_stack_exchange`). We can avoid this situation by deprecating this parameter and passing `DownloadConfig`'s `num_proc` to `map_nested` instead, as it's done in `DownloadManager.download`.

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I_kwDODunzps5UQNSP

WikiText 103 tokenizer hangs

open

2022-10-18T16:44:00Z

2022-10-18T16:44:00Z

NONE

See issue here: https://github.com/huggingface/transformers/issues/19702

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PR_kwDODunzps5BBxXX

Avoid extra cast in `class_encode_column`

closed

2022-10-18T15:31:24Z

2022-10-19T11:53:02Z

2022-10-19T11:50:46Z

CONTRIBUTOR

Pass the updated features to `map` to avoid the `cast` in `class_encode_column`.

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I_kwDODunzps5UOSbw

unexpected `cast` or `class_encode_column` result after `rename_column`

closed

2022-10-18T11:15:24Z

2022-10-19T03:02:26Z

2022-10-19T03:02:26Z

NONE

## Describe the bug When invoke `cast` or `class_encode_column` to a colunm renamed by `rename_column` , it will convert all the variables in this column into one variable. I also run this script in version 2.5.2, this bug does not appear. So I switched to the older version. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("amazon_reviews_multi", "en") data = dataset['train'] data = data.remove_columns( [ "review_id", "product_id", "reviewer_id", "review_title", "language", "product_category", ] ) data = data.rename_column("review_body", "text") data1 = data.class_encode_column("stars") print(set(data1.data.columns[0])) # output: {<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>} data = data.rename_column("stars", "label") print(set(data.data.columns[0])) # output: {<pyarrow.Int32Scalar: 5>, <pyarrow.Int32Scalar: 4>, <pyarrow.Int32Scalar: 1>, <pyarrow.Int32Scalar: 3>, <pyarrow.Int32Scalar: 2>} data2 = data.class_encode_column("label") print(set(data2.data.columns[0])) # output: {<pyarrow.Int64Scalar: 0>} ``` ## Expected results the last print should be: {<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>} ## Actual results but it output: {<pyarrow.Int64Scalar: 0>} ## Environment info - `datasets` version: 2.6.1 - Platform: macOS-12.5.1-arm64-arm-64bit - Python version: 3.10.6 - PyArrow version: 9.0.0 - Pandas version: 1.5.0

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PR_kwDODunzps5A_k9s

Make filename matching more robust

closed

2022-10-18T08:22:48Z

2022-10-28T13:07:38Z

2022-10-28T13:05:06Z

CONTRIBUTOR

Fix #5046

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PR_kwDODunzps5A8m-Q

[WIP] WebDataset export

open

2022-10-17T16:50:22Z

2022-10-17T17:06:04Z

MEMBER

I added a first draft of the `IterableDataset.to_wds` method. You can use it to savea dataset loaded in streamign mode as a webdataset locally. The API can be further improved to allow to export to a cloud storage like the HF Hub. I also included sharding with a default max shard size of 500MB (uncompressed), and it is single-processed fo rnow. Choosing the number of shards is not implemented yet - though if we know the size of the `IterableDataset` this is probably doable`. For example ```python >>> from datasets import load_dataset >>> ds = load_dataset("rotten_tomatoes", split="train", streaming=True) >>> ds.to_wds("output_dir", compress=True) >>> import webdataset as wds >>> ds = wds.WebDataset("output_dir/rotten_tomatoes-train-000000.tar.gz").decode() >>> next(iter(ds)) {'__key__': '0', '__url__': 'output_dir/rotten_tomatoes-train-000000.tar.gz', 'label.cls': 1, 'text.txt': 'the rock is destined to be the 21st century\'s new ..., jean-claud van damme or steven segal .'} ``` ### Implementation details The WebDataset format is made of TAR archives containing a series of files per example. For example one pair of `image.jpg` and `label.cls` for image classification. WebDataset automatically decodes serialized data based on the extension of the files, and output a dictionary. For example `{"image.png": np.array(...), "label.cls": 0}` if you choose the numpy decoding. To use the automatic decoding, I store each field of each example as a file with its corresponding extension (jpg, json, cls, etc.) While this is useful to end up with a dictionary with one key per column and appropriate decoding, it can create huge TAR archives if the dataset is made of small samples of text - probably because of useless TAR metadata for each file. This also makes loading super slow: iterating on SQuAD takes 50sec vs 7sec using `datasets` in streaming mode. I haven't taken a look at alternatives for text datasets made out of small samples, but for image datasets this can already be used to run some benchmarks.

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PR_kwDODunzps5A8Iw3

Fix class name of symbolic link

closed

2022-10-17T15:11:02Z

2022-11-14T14:40:18Z

2022-11-14T14:40:18Z

CONTRIBUTOR

Fix #5098

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PR_kwDODunzps5A7nr8

Add `pyproject.toml` for `black`

closed

2022-10-17T13:38:47Z

2022-10-17T14:23:27Z

2022-10-17T14:21:09Z

CONTRIBUTOR

Add `pyproject.toml` as a config file for the `black` tool to support VS Code's auto-formatting on save (and to be more consistent with the other HF projects).

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PR_kwDODunzps5A6HeL

Install tensorflow-macos dependency conditionally

closed

2022-10-17T08:45:08Z

2022-10-19T09:12:17Z

2022-10-19T09:10:06Z

MEMBER

Fix #5118.

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I_kwDODunzps5UF4nU

datasets freezes with streaming mode in multiple-gpu

open

2022-10-17T03:28:16Z

2022-10-24T16:13:43Z

NONE

## Describe the bug Hi. I am using this dataloader, which is for processing large datasets in streaming mode mentioned in one of examples of huggingface. I am using it to read c4: https://github.com/huggingface/transformers/blob/b48ac1a094e572d6076b46a9e4ed3e0ebe978afc/examples/research_projects/codeparrot/scripts/codeparrot_training.py#L22 During using multi-gpu in accelerator in one node, the code freezes, but works for 1 GPU: ``` 10/16/2022 14:18:46 - INFO - datasets.info - Loading Dataset Infos from /home/jack/.cache/huggingface/modules/datasets_modules/datasets/c4/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01 Steps: 0%| | 0/400000 [00:00<?, ?it/s]10/16/2022 14:18:47 - INFO - torch.utils.data.dataloader - Shared seed (135290893754684706) sent to store on rank 0 ``` # Code to reproduce please run this code with `accelerate launch code.py` ``` from accelerate import Accelerator from accelerate.logging import get_logger from datasets import load_dataset from torch.utils.data.dataloader import DataLoader import torch from datasets import load_dataset from transformers import AutoTokenizer import torch from accelerate.logging import get_logger from torch.utils.data import IterableDataset from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe logger = get_logger(__name__) class ConstantLengthDataset(IterableDataset): """ Iterable dataset that returns constant length chunks of tokens from stream of text files. Args: tokenizer (Tokenizer): The processor used for proccessing the data. dataset (dataset.Dataset): Dataset with text files. infinite (bool): If True the iterator is reset after dataset reaches end else stops. max_seq_length (int): Length of token sequences to return. num_of_sequences (int): Number of token sequences to keep in buffer. chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer. """ def __init__( self, tokenizer, dataset, infinite=False, max_seq_length=1024, num_of_sequences=1024, chars_per_token=3.6, ): self.tokenizer = tokenizer # self.concat_token_id = tokenizer.bos_token_id self.dataset = dataset self.max_seq_length = max_seq_length self.epoch = 0 self.infinite = infinite self.current_size = 0 self.max_buffer_size = max_seq_length * chars_per_token * num_of_sequences self.content_field = "text" def __iter__(self): iterator = iter(self.dataset) more_examples = True while more_examples: buffer, buffer_len = [], 0 while True: if buffer_len >= self.max_buffer_size: break try: buffer.append(next(iterator)[self.content_field]) buffer_len += len(buffer[-1]) except StopIteration: if self.infinite: iterator = iter(self.dataset) self.epoch += 1 logger.info(f"Dataset epoch: {self.epoch}") else: more_examples = False break tokenized_inputs = self.tokenizer(buffer, truncation=False)["input_ids"] all_token_ids = [] for tokenized_input in tokenized_inputs: all_token_ids.extend(tokenized_input) for i in range(0, len(all_token_ids), self.max_seq_length): input_ids = all_token_ids[i : i + self.max_seq_length] if len(input_ids) == self.max_seq_length: self.current_size += 1 yield torch.tensor(input_ids) def shuffle(self, buffer_size=1000): return ShufflerIterDataPipe(self, buffer_size=buffer_size) def create_dataloaders(tokenizer, accelerator): ds_kwargs = {"streaming": True} # In distributed training, the load_dataset function gaurantees that only one process # can concurrently download the dataset. datasets = load_dataset( "c4", "en", cache_dir="cache_dir", **ds_kwargs, ) train_data, valid_data = datasets["train"], datasets["validation"] with accelerator.main_process_first(): train_data = train_data.shuffle(buffer_size=10000, seed=None) train_dataset = ConstantLengthDataset( tokenizer, train_data, infinite=True, max_seq_length=256, ) valid_dataset = ConstantLengthDataset( tokenizer, valid_data, infinite=False, max_seq_length=256, ) train_dataset = train_dataset.shuffle(buffer_size=10000) train_dataloader = DataLoader(train_dataset, batch_size=160, shuffle=True) eval_dataloader = DataLoader(valid_dataset, batch_size=160) return train_dataloader, eval_dataloader def main(): # Accelerator. logging_dir = "data_save_dir/log" accelerator = Accelerator( gradient_accumulation_steps=1, mixed_precision="bf16", log_with="tensorboard", logging_dir=logging_dir, ) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if accelerator.is_main_process: accelerator.init_trackers("test") tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") # Load datasets and create dataloaders. train_dataloader, _ = create_dataloaders(tokenizer, accelerator) train_dataloader = accelerator.prepare(train_dataloader) for step, batch in enumerate(train_dataloader, start=1): print(step) accelerator.end_training() if __name__ == "__main__": main() ``` ## Results expected Being able to run the code for streamining datasets with multi-gpu ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.5.2 - Platform: linux - Python version: 3.9.12 - PyArrow version: 9.0.0 @lhoestq I do not have any idea why this freezing happens, and I removed the streaming mode and this was working fine, so I know this is caused by streaming mode of the dataloader part not working well with multi-gpu setting. Since datasets are large, I hope to keep the streamining mode. I very much appreciate your help.

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PR_kwDODunzps5A4rWn

Add warning

closed

2022-10-17T01:30:37Z

2022-11-05T12:23:53Z

2022-11-05T12:23:53Z

NONE

Fixes: #5105 I think removing the directory with warning is a better solution for this issue. Because if we decide to keep existing files in directory, then we should deal with the case providing same directory for several datasets! Which we know is not possible since `dataset_info.json` exists in that directory.

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PR_kwDODunzps5A4gUB

Bugfix ignore function when creating new_fingerprint for caching

closed

2022-10-17T00:03:43Z

2022-10-17T12:39:36Z

2022-10-17T12:39:36Z

NONE

maybe fixes: #5109

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PR_kwDODunzps5A4X10

Fix `tqdm` zip bug

closed

2022-10-16T22:19:18Z

2022-10-23T10:27:53Z

2022-10-19T08:53:17Z

CONTRIBUTOR

This PR solves #5117, by wrapping the entire `zip` clause in tqdm. For more information, please checkout this Stack Overflow thread: https://stackoverflow.com/questions/41171191/tqdm-progressbar-and-zip-built-in-do-not-work-together

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PR_kwDODunzps5A4IQp

[TYPO] Update new_dataset_script.py

closed

2022-10-16T17:36:49Z

2022-10-19T09:48:19Z

2022-10-19T09:45:59Z

CONTRIBUTOR

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I_kwDODunzps5UEz6t

Installing `datasets` on M1 computers

closed

2022-10-16T16:50:08Z

2022-10-19T09:10:08Z

2022-10-19T09:10:08Z

CONTRIBUTOR

## Describe the bug I wanted to install `datasets` dependencies on my M1 (in order to start contributing to the project). However, I got an error regarding `tensorflow`. On M1, `tensorflow-macos` needs to be installed instead. Can we add a conditional requirement, so that `tensorflow-macos` would be installed on M1? ## Steps to reproduce the bug Fresh clone this project (on m1), create a virtualenv and run this: ```python pip install -e ".[dev]" ``` ## Expected results Installation should be smooth, and all the dependencies should be installed on M1. ## Actual results You should receive an error, saying pip couldn't find a version that matches this pattern: ``` tensorflow>=2.3,!=2.6.0,!=2.6.1 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.6.2.dev0 - Platform: macOS-12.6-arm64-arm-64bit - Python version: 3.9.6 - PyArrow version: 7.0.0 - Pandas version: 1.5.0

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I_kwDODunzps5UBFoS

Progress bars have color red and never completed to 100%

closed

2022-10-14T16:12:30Z

2022-10-23T12:58:41Z

2022-10-23T12:58:41Z

NONE

## Describe the bug Progress bars after transformative operations turn in red and never be completed to 100% ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset('rotten_tomatoes', split='test').filter(lambda o: True) ``` ## Expected results Progress bar should be 100% and green ## Actual results Progress bar turn in red and never completed to 100% ## Environment info - `datasets` version: 2.6.1 - Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.14 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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PR_kwDODunzps5A09sk

Use yaml for issue templates + revamp

closed

2022-10-14T15:53:13Z

2022-10-19T13:05:49Z

2022-10-19T13:03:22Z

CONTRIBUTOR

Use YAML instead of markdown (more expressive) for the issue templates. In addition, update their structure/fields to be more aligned with Transformers. PS: also removes the "add_dataset" PR template, as we no longer accept such PRs.

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PR_kwDODunzps5Az9Pm

Fix iter_batches

closed

2022-10-14T12:06:14Z

2022-10-14T15:02:15Z

2022-10-14T14:59:58Z

MEMBER

The `pa.Table.to_reader()` method available in `pyarrow>=8.0.0` may return chunks of size < `max_chunksize`, therefore `iter_batches` can return batches smaller than the `batch_size` specified by the user Therefore batched `map` couldn't always use batches of the right size, e.g. this fails because it runs only on one batch of one element: ```python from datasets import Dataset, concatenate_datasets ds = concatenate_datasets([Dataset.from_dict({"a": [i]}) for i in range(10)]) ds2 = ds.map(lambda _: {}, batched=True) assert list(ds2) == list(ds) ``` This was introduced in https://github.com/huggingface/datasets/pull/5030 Close https://github.com/huggingface/datasets/issues/5111 This will require a patch release along with https://github.com/huggingface/datasets/pull/5113 TODO: - [x] fix tests - [x] add more tests

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I_kwDODunzps5T_z8C

load_from_disk with remote filesystem fails due to a wrong temporary local folder path

open

2022-10-14T11:54:53Z

2022-11-19T07:13:10Z

NONE

## Describe the bug The function load_from_disk fails when using a remote filesystem because of a wrong temporary path generation in the load_from_disk method of arrow_dataset.py: ```python if is_remote_filesystem(fs): src_dataset_path = extract_path_from_uri(dataset_path) dataset_path = Dataset._build_local_temp_path(src_dataset_path) fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True) ``` If _dataset_path_ is `gs://speech/mydataset/train`, then _src_dataset_path_ will be `speech/mydataset/train` and _dataset_path_ will be something like `/var/folders/9s/gf0b/T/tmp6t/speech/mydataset/train` Then, after downloading the **folder** _src_dataset_path_, you will get a path like `/var/folders/9s/gf0b/T/tmp6t/speech/mydataset/train/train/state.json` (notice we have train twice) Instead of downloading the remote folder we should be downloading all the files in the folder for the path to be right: ```python fs.download(os.path.join(src_dataset_path,*), dataset_path.as_posix(), recursive=True) ``` ## Steps to reproduce the bug ```python fs = gcsfs.GCSFileSystem(**storage_options) dataset = load_from_disk("common_voice_processed") # loading local dataset previously saved locally, works fine dataset.save_to_disk(output_dir, fs=fs) #works fine dataset = load_from_disk(output_dir, fs=fs) # crashes ``` ## Expected results The dataset is loaded ## Actual results FileNotFoundError: [Errno 2] No such file or directory: '/var/folders/9s/gf0b9jz15d517yrf7m3nvlxr0000gn/T/tmp6t5e221_/speech/datasets/tests/common_voice_processed/train/state.json' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: datasets-2.6.1.dev0 - Platform: mac os monterey 12.5.1 - Python version: 3.8.13 - PyArrow version:pyarrow==9.0.0

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PR_kwDODunzps5Az0Ei

Fix filter indices when batched

closed

2022-10-14T11:30:03Z

2022-10-24T06:21:09Z

2022-10-14T12:11:44Z

MEMBER

This PR fixes a bug introduced by: - #5030 Fix #5112.

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I_kwDODunzps5T_dJx

Bug with filtered indices

closed

2022-10-14T10:35:47Z

2022-10-14T13:55:03Z

2022-10-14T12:11:45Z

MEMBER

## Describe the bug As reported by @PartiallyTyped (and by @Muennighoff): - https://github.com/huggingface/datasets/issues/5111#issuecomment-1278652524 There is an issue with the indices of a filtered dataset. ## Steps to reproduce the bug ```python ds = Dataset.from_dict({"num": [0, 1, 2, 3]}) ds = ds.filter(lambda num: num % 2 == 0, input_columns="num", batch_size=2) assert all(item["num"] % 2 == 0 for item in ds) ``` ## Expected results The indices of the filtered dataset should correspond to the examples with "language" equals to "english". ## Actual results Indices to items with other languages are included in the filtered dataset indices ## Preliminar investigation It seems a bug introduced by: - #5030

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I_kwDODunzps5T7o9C

map and filter not working properly in multiprocessing with the new release 2.6.0

closed

2022-10-13T17:00:55Z

2022-10-17T08:26:59Z

2022-10-14T14:59:59Z

NONE

## Describe the bug When mapping is used on a dataset with more than one process, there is a weird behavior when trying to use `filter` , it's like only the samples from one worker are retrieved, one needs to specify the same `num_proc` in filter for it to work properly. This doesn't happen with `datasets` version 2.5.2 In the code below the data is filtered differently when we increase `num_proc` used in `map` although the datsets before and after mapping have identical elements. ## Steps to reproduce the bug ```python import datasets from datasets import load_dataset def preprocess(example): return example ds = load_dataset("codeparrot/codeparrot-clean-valid", split="train").select([i for i in range(10)]) ds1 = ds.map(preprocess, num_proc=2) ds2 = ds.map(preprocess) # the datasets elements are the same for i in range(len(ds1)): assert ds1[i]==ds2[i] print(f'Target column before filtering {ds1["autogenerated"]}') print(f'Target column before filtering {ds2["autogenerated"]}') print(f"datasets version {datasets.__version__}") ds_filtered_1 = ds1.filter(lambda x: not x["autogenerated"]) ds_filtered_2 = ds2.filter(lambda x: not x["autogenerated"]) # all elements in Target column are false so they should all be kept, but for ds2 only the first 5=num_samples/num_proc are kept print(ds_filtered_1) print(ds_filtered_2) ``` ``` Target column before filtering [False, False, False, False, False, False, False, False, False, False] Target column before filtering [False, False, False, False, False, False, False, False, False, False] Dataset({ features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'], num_rows: 5 }) Dataset({ features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'], num_rows: 10 }) ``` ## Expected results Increasing `num_proc` in mapping shouldn't alter filtering. With the previous version 2.5.2 this doesn't happen ## Actual results Filtering doesn't work properly when we increase `num_proc` in mapping but not when calling `filter` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.6.0 - Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28 - Python version: 3.9.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.2

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I_kwDODunzps5T47_S

Map caching not working for some class methods

closed

2022-10-13T09:12:58Z

2022-10-17T10:38:45Z

2022-10-17T10:38:45Z

CONTRIBUTOR

## Describe the bug The cache loading is not working as expected for some class methods with a model stored in an attribute. The new fingerprint for `_map_single` is not the same at each run. The hasher generate a different hash for the class method. This comes from `dumps` function in `datasets.utils.py_utils` which generates a different dump at each run. ## Steps to reproduce the bug ```python from datasets import load_dataset from transformers import AutoConfig, AutoModel, AutoTokenizer dataset = load_dataset("ethos", "binary") BASE_MODELNAME = "sentence-transformers/all-MiniLM-L6-v2" class Object: def __init__(self): config = AutoConfig.from_pretrained(BASE_MODELNAME) self.bert = AutoModel.from_config(config=config, add_pooling_layer=False) self.tok = AutoTokenizer.from_pretrained(BASE_MODELNAME) def tokenize(self, examples): tokenized_texts = self.tok( examples["text"], padding="max_length", truncation=True, max_length=256, ) return tokenized_texts instance = Object() result = dict() for phase in ["train"]: result[phase] = dataset[phase].map(instance.tokenize, batched=True, load_from_cache_file=True, num_proc=2) ``` ## Expected results Load cache instead of recompute result. ## Actual results Result recomputed from scratch at each run. The cache works fine when deleting `bert` attribute. ## Environment info - `datasets` version: 2.5.3.dev0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.9.13 - PyArrow version: 7.0.0 - Pandas version: 1.5.0

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PR_kwDODunzps5AskeK

Fix a typo in arrow_dataset.py

closed

2022-10-13T02:33:55Z

2022-10-14T09:47:28Z

2022-10-14T09:47:27Z

CONTRIBUTOR

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PR_kwDODunzps5ArjCZ

Multiprocessed dataset builder

closed

2022-10-12T19:59:17Z

2022-12-01T15:37:09Z

2022-11-09T17:11:43Z

MEMBER

This PR adds the multiprocessing part of #2650 (but not the caching of already-computed arrow files). On the other side, loading of sharded arrow files still needs to be implemented (sharded parquet files can already be loaded).

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PR_kwDODunzps5ArM6G

Fix task template reload from dict

closed

2022-10-12T18:33:49Z

2022-10-13T09:59:07Z

2022-10-13T09:56:51Z

MEMBER

Since #4926 the JSON dumps are simplified and it made task template dicts empty by default. I fixed this by always including the task name which is needed to reload a task from a dict

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I_kwDODunzps5Tzw2V

Specifying an exisiting folder in download_and_prepare deletes everything in it

open

2022-10-12T11:53:33Z

2022-10-20T11:53:59Z

CONTRIBUTOR

## Describe the bug The builder correctly creates the `output_dir` folder if it doesn't exist, but if the folder exists everything within it is deleted. Specifying `"."` as the `output_dir` deletes everything in your current dir but also leads to **another bug** whose traceback is the following: ``` Traceback (most recent call last) Input In [11], in <cell line: 1>() ----> 1 rotten_tomatoes_builder.download_and_prepare(output_dir=".", max_shard_size="200MB", file_format="parquet") File ~/BIGSCIENCE/env/lib/python3.9/site-packages/datasets/builder.py:818, in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, storage_options, **download_and_prepare_kwargs) File /usr/lib/python3.9/contextlib.py:124, in _GeneratorContextManager.__exit__(self, type, value, traceback) 122 if type is None: 123 try: --> 124 next(self.gen) 125 except StopIteration: 126 return False File ~/BIGSCIENCE/env/lib/python3.9/site-packages/datasets/builder.py:760, in incomplete_dir(dirname) File /usr/lib/python3.9/shutil.py:722, in rmtree(path, ignore_errors, onerror) 720 os.rmdir(path) 721 except OSError: --> 722 onerror(os.rmdir, path, sys.exc_info()) 723 else: 724 try: 725 # symlinks to directories are forbidden, see bug #1669 File /usr/lib/python3.9/shutil.py:720, in rmtree(path, ignore_errors, onerror) 718 _rmtree_safe_fd(fd, path, onerror) 719 try: --> 720 os.rmdir(path) 721 except OSError: 722 onerror(os.rmdir, path, sys.exc_info()) OSError: [Errno 22] Invalid argument: '/home/christopher/BIGSCIENCE/.' ``` ## Steps to reproduce the bug ```python rotten_tomatoes_builder = load_dataset_builder("rotten_tomatoes") rotten_tomatoes_builder.download_and_prepare(output_dir="./test_folder", max_shard_size="200MB", file_format="parquet") ``` If `test_folder` contains any files they will all be deleted ## Expected results Either a warning that all files will be deleted, but preferably that they not be deleted at all. ## Actual results N/A ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-5.15.0-48-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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PR_kwDODunzps5Ao9Mq

Fix loading how to guide (#5102)

closed

2022-10-12T10:34:42Z

2022-10-12T11:34:07Z

2022-10-12T11:31:55Z

CONTRIBUTOR

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PR_kwDODunzps5Ao5gI

url encode hub url (#5099)

closed

2022-10-12T10:22:12Z

2022-10-12T15:27:24Z

2022-10-12T15:24:47Z

CONTRIBUTOR

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I_kwDODunzps5Turs6

Error in create a dataset from a Python generator

closed