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artemis13fowl

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PR_kwDODunzps4tLbe-

add XCSR dataset

closed

CONTRIBUTOR

Hi, I wanted to add the [XCSR ](https://inklab.usc.edu//XCSR/xcsr_datasets) dataset to huggingface! :) I followed the instructions of adding new dataset to huggingface and have all the required files ready now! It would be super helpful if you can take a look and review them. Thanks in advance for your time and help. Look forward to hearing from you and can't wait to add XCSR to huggingface :D

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I_kwDODunzps49IzjF

Import error installing with ppc64le

closed

NONE

## Describe the bug Installing the datasets library with a computer running with ppc64le seems to cause an issue when importing the datasets library. ``` python Python 3.6.13 | packaged by conda-forge | (default, Sep 23 2021, 07:37:44) [GCC 9.4.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import datasets Illegal instruction (core dumped) ``` Error when importing `Illegal instruction (core dumped)` ## Steps to reproduce the bug I get this error when installing the library by using conda. I can't install with pip I believe because pyarrow only has the ppc64le library on conda forge ``` conda create --name transformers_py36_v2 python=3.6 conda activate transformers_py36_v2 conda install datasets ``` ## Tracebacks conda create --name transformers_py36_v2 python=3.6 ``` Collecting package metadata (current_repodata.json): done Solving environment: done ==> WARNING: A newer version of conda exists. <== current version: 4.9.2 latest version: 4.10.3 Please update conda by running $ conda update -n base -c defaults conda ## Package Plan ## environment location: /p/home/gerryc/.conda/envs/transformers_py36_v2 added / updated specs: - python=3.6 The following NEW packages will be INSTALLED: _libgcc_mutex conda-forge/linux-ppc64le::_libgcc_mutex-0.1-conda_forge _openmp_mutex conda-forge/linux-ppc64le::_openmp_mutex-4.5-1_gnu ca-certificates conda-forge/linux-ppc64le::ca-certificates-2021.10.8-h1084571_0 certifi pkgs/main/linux-ppc64le::certifi-2020.12.5-py36h6ffa863_0 ld_impl_linux-ppc~ conda-forge/linux-ppc64le::ld_impl_linux-ppc64le-2.36.1-ha35d02b_2 libffi conda-forge/linux-ppc64le::libffi-3.4.2-h3b9df90_4 libgcc-ng conda-forge/linux-ppc64le::libgcc-ng-11.2.0-h7698a5e_11 libgomp conda-forge/linux-ppc64le::libgomp-11.2.0-h7698a5e_11 libstdcxx-ng conda-forge/linux-ppc64le::libstdcxx-ng-11.2.0-habdf983_11 libzlib conda-forge/linux-ppc64le::libzlib-1.2.11-h339bb43_1013 ncurses conda-forge/linux-ppc64le::ncurses-6.2-hea85c5d_4 openssl conda-forge/linux-ppc64le::openssl-1.1.1l-h4e0d66e_0 pip conda-forge/noarch::pip-21.3-pyhd8ed1ab_0 python conda-forge/linux-ppc64le::python-3.6.13-h57873ef_2_cpython readline conda-forge/linux-ppc64le::readline-8.1-h5c45dff_0 setuptools pkgs/main/linux-ppc64le::setuptools-58.0.4-py36h6ffa863_0 sqlite conda-forge/linux-ppc64le::sqlite-3.36.0-h4e2196e_2 tk conda-forge/linux-ppc64le::tk-8.6.11-h41c6715_1 wheel conda-forge/noarch::wheel-0.37.0-pyhd8ed1ab_1 xz conda-forge/linux-ppc64le::xz-5.2.5-h6eb9509_1 zlib conda-forge/linux-ppc64le::zlib-1.2.11-h339bb43_1013 Proceed ([y]/n)? y Preparing transaction: done Verifying transaction: done Executing transaction: done # # To activate this environment, use # # $ conda activate transformers_py36_v2 # # To deactivate an active environment, use # # $ conda deactivate ``` conda activate transformers_py36_v2 conda install datasets ``` Collecting package metadata (current_repodata.json): done Solving environment: failed with initial frozen solve. Retrying with flexible solve. Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source. Collecting package metadata (repodata.json): done Solving environment: done ==> WARNING: A newer version of conda exists. <== current version: 4.9.2 latest version: 4.10.3 Please update conda by running $ conda update -n base -c defaults conda ## Package Plan ## environment location: /p/home/gerryc/.conda/envs/transformers_py36_v2 added / updated specs: - datasets The following NEW packages will be INSTALLED: abseil-cpp conda-forge/linux-ppc64le::abseil-cpp-20210324.2-h3b9df90_0 aiohttp conda-forge/linux-ppc64le::aiohttp-3.7.4.post0-py36hc33305d_0 arrow-cpp conda-forge/linux-ppc64le::arrow-cpp-5.0.0-py36hf9cf308_8_cpu async-timeout conda-forge/noarch::async-timeout-3.0.1-py_1000 attrs conda-forge/noarch::attrs-21.2.0-pyhd8ed1ab_0 aws-c-cal conda-forge/linux-ppc64le::aws-c-cal-0.5.11-hb3fac3d_0 aws-c-common conda-forge/linux-ppc64le::aws-c-common-0.6.2-h4e0d66e_0 aws-c-event-stream conda-forge/linux-ppc64le::aws-c-event-stream-0.2.7-h76da5f2_13 aws-c-io conda-forge/linux-ppc64le::aws-c-io-0.10.5-hf6a6c7c_0 aws-checksums conda-forge/linux-ppc64le::aws-checksums-0.1.11-hfe76d68_7 aws-sdk-cpp conda-forge/linux-ppc64le::aws-sdk-cpp-1.8.186-h90855e8_3 brotlipy conda-forge/linux-ppc64le::brotlipy-0.7.0-py36hc33305d_1001 bzip2 conda-forge/linux-ppc64le::bzip2-1.0.8-h4e0d66e_4 c-ares conda-forge/linux-ppc64le::c-ares-1.17.2-h4e0d66e_0 cffi conda-forge/linux-ppc64le::cffi-1.14.6-py36h021ab3c_1 chardet conda-forge/linux-ppc64le::chardet-4.0.0-py36h270354c_1 colorama conda-forge/noarch::colorama-0.4.4-pyh9f0ad1d_0 cryptography conda-forge/linux-ppc64le::cryptography-3.4.7-py36hc71b123_0 dataclasses conda-forge/noarch::dataclasses-0.8-pyh787bdff_2 datasets conda-forge/noarch::datasets-1.12.1-pyhd8ed1ab_1 dill conda-forge/noarch::dill-0.3.4-pyhd8ed1ab_0 filelock conda-forge/noarch::filelock-3.3.0-pyhd8ed1ab_0 fsspec conda-forge/noarch::fsspec-2021.10.0-pyhd8ed1ab_0 gflags conda-forge/linux-ppc64le::gflags-2.2.2-hb209c28_1004 glog conda-forge/linux-ppc64le::glog-0.5.0-h4040248_0 grpc-cpp conda-forge/linux-ppc64le::grpc-cpp-1.40.0-h2bf711c_2 huggingface_hub conda-forge/noarch::huggingface_hub-0.0.19-pyhd8ed1ab_0 idna conda-forge/noarch::idna-2.10-pyh9f0ad1d_0 idna_ssl conda-forge/noarch::idna_ssl-1.0.0-0 importlib-metadata conda-forge/linux-ppc64le::importlib-metadata-4.8.1-py36h270354c_0 importlib_metadata conda-forge/noarch::importlib_metadata-4.8.1-hd8ed1ab_0 krb5 conda-forge/linux-ppc64le::krb5-1.19.2-haf43566_2 libblas conda-forge/linux-ppc64le::libblas-3.9.0-11_linuxppc64le_openblas libbrotlicommon conda-forge/linux-ppc64le::libbrotlicommon-1.0.9-h4e0d66e_5 libbrotlidec conda-forge/linux-ppc64le::libbrotlidec-1.0.9-h4e0d66e_5 libbrotlienc conda-forge/linux-ppc64le::libbrotlienc-1.0.9-h4e0d66e_5 libcblas conda-forge/linux-ppc64le::libcblas-3.9.0-11_linuxppc64le_openblas libcurl conda-forge/linux-ppc64le::libcurl-7.79.1-he415e40_1 libedit conda-forge/linux-ppc64le::libedit-3.1.20191231-h41a240f_2 libev conda-forge/linux-ppc64le::libev-4.33-h6eb9509_1 libevent conda-forge/linux-ppc64le::libevent-2.1.10-h97db324_4 libgfortran-ng conda-forge/linux-ppc64le::libgfortran-ng-11.2.0-hfdc3801_11 libgfortran5 conda-forge/linux-ppc64le::libgfortran5-11.2.0-he58fbb4_11 liblapack conda-forge/linux-ppc64le::liblapack-3.9.0-11_linuxppc64le_openblas libnghttp2 conda-forge/linux-ppc64le::libnghttp2-1.43.0-h42039ad_1 libopenblas conda-forge/linux-ppc64le::libopenblas-0.3.17-pthreads_h486567c_1 libprotobuf conda-forge/linux-ppc64le::libprotobuf-3.18.1-h690f14c_0 libssh2 conda-forge/linux-ppc64le::libssh2-1.10.0-ha5a9321_2 libthrift conda-forge/linux-ppc64le::libthrift-0.15.0-h54f692e_1 libutf8proc conda-forge/linux-ppc64le::libutf8proc-2.6.1-h4e0d66e_0 lz4-c conda-forge/linux-ppc64le::lz4-c-1.9.3-h3b9df90_1 multidict conda-forge/linux-ppc64le::multidict-5.2.0-py36hc33305d_0 multiprocess conda-forge/linux-ppc64le::multiprocess-0.70.12.2-py36hc33305d_0 numpy conda-forge/linux-ppc64le::numpy-1.19.5-py36h86665d4_1 orc conda-forge/linux-ppc64le::orc-1.7.0-hae6b4bd_0 packaging conda-forge/noarch::packaging-21.0-pyhd8ed1ab_0 pandas conda-forge/linux-ppc64le::pandas-1.1.5-py36hab1a6e6_0 parquet-cpp conda-forge/noarch::parquet-cpp-1.5.1-2 pyarrow conda-forge/linux-ppc64le::pyarrow-5.0.0-py36h7a46c7e_8_cpu pycparser conda-forge/noarch::pycparser-2.20-pyh9f0ad1d_2 pyopenssl conda-forge/noarch::pyopenssl-21.0.0-pyhd8ed1ab_0 pyparsing conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0 pysocks conda-forge/linux-ppc64le::pysocks-1.7.1-py36h270354c_3 python-dateutil conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0 python-xxhash conda-forge/linux-ppc64le::python-xxhash-2.0.2-py36hc33305d_0 python_abi conda-forge/linux-ppc64le::python_abi-3.6-2_cp36m pytz conda-forge/noarch::pytz-2021.3-pyhd8ed1ab_0 pyyaml conda-forge/linux-ppc64le::pyyaml-5.4.1-py36hc33305d_1 re2 conda-forge/linux-ppc64le::re2-2021.09.01-h3b9df90_0 requests conda-forge/noarch::requests-2.25.1-pyhd3deb0d_0 s2n conda-forge/linux-ppc64le::s2n-1.0.10-h97db324_0 six conda-forge/noarch::six-1.16.0-pyh6c4a22f_0 snappy conda-forge/linux-ppc64le::snappy-1.1.8-hb209c28_3 tqdm conda-forge/noarch::tqdm-4.62.3-pyhd8ed1ab_0 typing-extensions conda-forge/noarch::typing-extensions-3.10.0.2-hd8ed1ab_0 typing_extensions conda-forge/noarch::typing_extensions-3.10.0.2-pyha770c72_0 urllib3 conda-forge/noarch::urllib3-1.26.7-pyhd8ed1ab_0 xxhash conda-forge/linux-ppc64le::xxhash-0.8.0-h4e0d66e_3 yaml conda-forge/linux-ppc64le::yaml-0.2.5-h6eb9509_0 yarl conda-forge/linux-ppc64le::yarl-1.6.3-py36hc33305d_2 zipp conda-forge/noarch::zipp-3.6.0-pyhd8ed1ab_0 zstd conda-forge/linux-ppc64le::zstd-1.5.0-h65c4b1a_0 The following packages will be UPDATED: certifi pkgs/main::certifi-2020.12.5-py36h6ff~ --> conda-forge::certifi-2021.5.30-py36h270354c_0 Proceed ([y]/n)? y Preparing transaction: done Verifying transaction: done Executing transaction: done ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Red Hat Enterprise Linux 8.2 (Ootpa) - Python version: 3.6 - PyArrow version: pyarrow - 5.0.0 - py36h7a46c7e_8_cpu - conda-forge Any help would be appreciated! I've been struggling on installing datasets on this machine.

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PR_kwDODunzps4tJNnD

Fix pathlib patches for streaming

closed

MEMBER

Fix issue https://github.com/huggingface/datasets/issues/2866 (for good this time) `counter` now works in both streaming and non-streaming mode. And the `AttributeError: 'str' object has no attribute 'as_posix'` related to the patch of Path.open is fixed as well Note : the patches should only affect the datasets module, not the user's ones ! That's why we should probably use something else than patch.object to patch the Path class' methods. cc @severo @albertvillanova

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I_kwDODunzps49FqI1

Custom plain text dataset, plain json dataset and plain csv dataset are remove from datasets template folder

closed

NONE

## Adding a Dataset - **Name:** text, json, csv - **Description:** I am developing a customized dataset loading script. The problem is mainly about my custom dataset is seperate into many files and I only find a dataset loading template in [https://github.com/huggingface/datasets/blob/1.2.1/datasets/json/json.py](https://github.com/huggingface/datasets/blob/1.2.1/datasets/json/json.py) that can handle my circumstance. I'm afraid these templates are too old to use. Could you re-add these three templates to current master branch?

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PR_kwDODunzps4tIBRp

Fix Windows CI with FileNotFoundError when stting up s3_base fixture

closed

MEMBER

Fix #3069.

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I_kwDODunzps49FX34

CI fails on Windows with FileNotFoundError when stting up s3_base fixture

closed

MEMBER

## Describe the bug After commit 9353fc863d0c99ab0427f83cc5a4f04fcf52f1df, the CI fails on Windows with FileNotFoundError when stting up s3_base fixture. See: https://app.circleci.com/pipelines/github/huggingface/datasets/8151/workflows/5db8d154-badd-4d3d-b202-ca7a318997a2/jobs/50321 Error summary: ``` ERROR tests/test_arrow_dataset.py::test_dummy_dataset_serialize_s3 - FileNotF... ERROR tests/test_dataset_dict.py::test_dummy_dataset_serialize_s3 - FileNotFo... ``` Stack trace: ``` ______________ ERROR at setup of test_dummy_dataset_serialize_s3 ______________ [gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe @pytest.fixture() def s3_base(): # writable local S3 system import shlex import subprocess # Mocked AWS Credentials for moto. old_environ = os.environ.copy() os.environ.update(S3_FAKE_ENV_VARS) > proc = subprocess.Popen(shlex.split("moto_server s3 -p %s" % s3_port)) tests\s3_fixtures.py:32: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ C:\tools\miniconda3\lib\subprocess.py:729: in __init__ restore_signals, start_new_session) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <subprocess.Popen object at 0x0000012BB8A4B908> args = 'moto_server s3 -p 5555', executable = None, preexec_fn = None close_fds = True, pass_fds = (), cwd = None, env = None startupinfo = <subprocess.STARTUPINFO object at 0x0000012BB8177630> creationflags = 0, shell = False, p2cread = -1, p2cwrite = -1, c2pread = -1 c2pwrite = -1, errread = -1, errwrite = -1, unused_restore_signals = True unused_start_new_session = False def _execute_child(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_start_new_session): """Execute program (MS Windows version)""" assert not pass_fds, "pass_fds not supported on Windows." if not isinstance(args, str): args = list2cmdline(args) # Process startup details if startupinfo is None: startupinfo = STARTUPINFO() if -1 not in (p2cread, c2pwrite, errwrite): startupinfo.dwFlags |= _winapi.STARTF_USESTDHANDLES startupinfo.hStdInput = p2cread startupinfo.hStdOutput = c2pwrite startupinfo.hStdError = errwrite if shell: startupinfo.dwFlags |= _winapi.STARTF_USESHOWWINDOW startupinfo.wShowWindow = _winapi.SW_HIDE comspec = os.environ.get("COMSPEC", "cmd.exe") args = '{} /c "{}"'.format (comspec, args) # Start the process try: hp, ht, pid, tid = _winapi.CreateProcess(executable, args, # no special security None, None, int(not close_fds), creationflags, env, os.fspath(cwd) if cwd is not None else None, > startupinfo) E FileNotFoundError: [WinError 2] The system cannot find the file specified C:\tools\miniconda3\lib\subprocess.py:1017: FileNotFoundError ```

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PR_kwDODunzps4tHhOC

feat: increase streaming retry config

closed

CONTRIBUTOR

Increase streaming config parameters: * retry interval set to 5 seconds * max retries set to 20 (so 1mn 40s)

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PR_kwDODunzps4tFSCy

add story_cloze

closed

CONTRIBUTOR

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PR_kwDODunzps4tFObl

Add iter_archive

closed

MEMBER

Added the `iter_archive` method for the StreamingDownloadManager. It was already implemented in the regular DownloadManager. Now it can be used to stream from TAR archives as mentioned in https://github.com/huggingface/datasets/issues/2829 I also updated the `food101` dataset as an example. Any image/audio dataset using TAR archives can be updated to use `iter_archive` in order to be streamable :) cc @severo

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PR_kwDODunzps4tFDjk

Fix test command after refac

closed

MEMBER

Fix the `datasets-cli` test command after the `prepare_module` change in #2986

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I_kwDODunzps49B3mr

Make `interleave_datasets` more robust

open

NONE

**Is your feature request related to a problem? Please describe.** Right now there are few hiccups using `interleave_datasets`. Interleaved dataset iterates until the smallest dataset completes it's iterator. In this way larger datasets may not complete full epoch of iteration. It creates new problems in calculation of epoch since there are no way to track which dataset from `interleave_datasets` completes how many epoch. **Describe the solution you'd like** For `interleave_datasets` module, - [ ] Add a boolean argument `--stop-iter` in `interleave_datasets` that enables dataset to either iterate infinite amount of time or not. That means it should not return `StopIterator` exception in case `--stop-iter=False`. - [ ] Internal list variable `iter_cnt` that explains how many times (in steps/epochs) each dataset iterates at a given point. - [ ] Add an argument `--max-iter` (list type) that explain maximum amount of time each of the dataset can iterate. After complete `--max-iter` of one dataset, other dataset should continue sampling and when all the dataset finish their respective `--max-iter`, only then return `StopIterator` Note: I'm new to `datasets` api. May be these features are already there in the datasets. Since multitask training is the latest trends, I believe this feature would make the `datasets` api more popular. @lhoestq

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I_kwDODunzps49ArfJ

Windows CI is unable to test streaming properly because of SSL issues

open

MEMBER

In https://github.com/huggingface/datasets/pull/3041 the windows tests were skipped because of SSL issues with moon-staging.huggingface.co:443 The issue appears only on windows with asyncio. On Linux it works. With requests it works as well. And with the production environment huggingface.co it also works. to reproduce on windows: ```python import fsspec # use any URL to a file in a dataset repo url = "https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes" fsspec.open(url).open() ``` raises ```python FileNotFoundError: https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes ``` because of ```python aiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host moon-staging.huggingface.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)')] ```

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PR_kwDODunzps4tCxfK

Update summary on PyPi beyond NLP

closed

MEMBER

More than just NLP now

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I_kwDODunzps48-1CP

Feature request : add leave=True to dataset.map to enable tqdm nested bars (and whilst we're at it couldn't we get a way to access directly tqdm underneath?)

open

NONE

**A clear and concise description of what you want to happen.** It would be so nice to be able to nest HuggingFace `Datasets.map() ` progress bars in the grander scheme of things and whilst we're at it why not other functions. **Describe alternatives you've considered** By the way is there not a way to directly interact with underlying tqdm module ? **kwargs-ish? **Additional context** Furthering tqdm integration #2374 and huggingface/transformers#11797 solutioned by huggingface/transformers#12226 provided with tqdm description as `desc=` @sgugger @bhavitvyamalik

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I_kwDODunzps48-MVM

load_dataset('openwebtext') yields "Compressed file ended before the end-of-stream marker was reached"

closed

NONE

## Describe the bug When I try `load_dataset('openwebtext')`, I receive a "EOFError: Compressed file ended before the end-of-stream marker was reached" error. ## Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset('openwebtext') ``` ## Expected results I expect the `dataset` variable to be properly constructed. ## Actual results ``` File "/home/rschaef/CoCoSci-Language-Distillation/distillation_v2/ratchet_learning/tasks/base.py", line 37, in create_dataset dataset_str, File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/load.py", line 1117, in load_dataset use_auth_token=use_auth_token, File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/builder.py", line 637, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/builder.py", line 704, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/rschaef/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/85b3ae7051d2d72e7c5fdf6dfb462603aaa26e9ed506202bf3a24d261c6c40a1/openwebtext.py", line 61, in _split_generators dl_dir = dl_manager.download_and_extract(_URL) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 261, in extract partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 197, in map_nested return function(data_struct) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 316, in cached_path output_path, force_extract=download_config.force_extract File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 40, in extract self.extractor.extract(input_path, output_path, extractor=extractor) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 179, in extract return extractor.extract(input_path, output_path) File "/home/rschaef/CoCoSci-Language-Distillation/cocosci/lib/python3.6/site-packages/datasets/utils/extract.py", line 53, in extract tar_file.extractall(output_path) File "/usr/lib/python3.6/tarfile.py", line 2010, in extractall numeric_owner=numeric_owner) File "/usr/lib/python3.6/tarfile.py", line 2052, in extract numeric_owner=numeric_owner) File "/usr/lib/python3.6/tarfile.py", line 2122, in _extract_member self.makefile(tarinfo, targetpath) File "/usr/lib/python3.6/tarfile.py", line 2171, in makefile copyfileobj(source, target, tarinfo.size, ReadError, bufsize) File "/usr/lib/python3.6/tarfile.py", line 249, in copyfileobj buf = src.read(bufsize) File "/usr/lib/python3.6/lzma.py", line 200, in read return self._buffer.read(size) File "/usr/lib/python3.6/_compression.py", line 68, in readinto data = self.read(len(byte_view)) File "/usr/lib/python3.6/_compression.py", line 99, in read raise EOFError("Compressed file ended before the " python-BaseException EOFError: Compressed file ended before the end-of-stream marker was reached ``` ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial - Python version: 3.6.10 - PyArrow version: 5.0.0

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PR_kwDODunzps4tA54w

Fix task reloading from cache

closed

MEMBER

When reloading a dataset from the cache when doing `map`, the tasks templates were kept instead of being updated regarding the output of the `map` function. This is an issue because we drop the tasks templates that are not compatible anymore after `map`, for example if a column of the template was removed. This PR fixes this and for convenience introduces a decorator `@transmit_tasks` that takes care of doing this verification, similar to the `@transmit_format` decorator. This should fix issue https://github.com/huggingface/datasets/issues/3047 cc @sgugger

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I_kwDODunzps4889S4

Dataset wikipedia and Bookcorpusopen cannot be fetched from dataloader.

open

NONE

## Describe the bug I have used the previous version of `transformers` and `datasets`. The dataset `wikipedia` can be successfully used. Recently, I upgrade them to the newest version and find it raises errors. I also tried other datasets. The `wikitext` works and the `bookcorpusopen` raises the same errors as `wikipedia`. ## Steps to reproduce the bug Run the `run_mlm_no_trainer.py` and the given script on this [link](https://github.com/huggingface/transformers/tree/master/examples/pytorch/language-modeling). Change the dataset from wikitext to wikipedia or bookcorpusopen. BTW, the library transformers is of version 4.11.3. ## Expected results The data batchs are fetched from the data loader and train. ## Actual results The first time to fetch data batch occurs error. `Traceback (most recent call last): File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 705, in convert_to_tensors tensor = as_tensor(value) ValueError: too many dimensions 'str' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "src/original_run_mlm_no_trainer.py", line 528, in <module> main() File "src/original_run_mlm_no_trainer.py", line 488, in main for step, batch in enumerate(train_dataloader): File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/accelerate/data_loader.py", line 303, in __iter__ for batch in super().__iter__(): File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 517, in __next__ data = self._next_data() File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 557, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch return self.collate_fn(data) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 41, in __call__ return self.torch_call(features) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/data/data_collator.py", line 671, in torch_call batch = self.tokenizer.pad(examples, return_tensors="pt", pad_to_multiple_of=self.pad_to_multiple_of) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2774, in pad return BatchEncoding(batch_outputs, tensor_type=return_tensors) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 210, in __init__ self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis) File "/home/zyli/anaconda3/envs/LatestStacking/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 722, in convert_to_tensors "Unable to create tensor, you should probably activate truncation and/or padding " ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. ` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Linux-5.8.0-59-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.6 - PyArrow version: 5.0.0

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I_kwDODunzps488j0b

Error in per class precision computation

closed

NONE

## Describe the bug When trying to get the per class precision values by providing `average=None`, following error is thrown `ValueError: can only convert an array of size 1 to a Python scalar` ## Steps to reproduce the bug ```python from datasets import load_dataset, load_metric precision_metric = load_metric("precision") predictions = [0, 2, 1, 0, 0, 1] references = [0, 1, 2, 0, 1, 2] results = precision_metric.compute(predictions=predictions, references=references, average=None) ``` ## Expected results ` {'precision': array([0.66666667, 0. , 0. ])}` as per https://github.com/huggingface/datasets/blob/master/metrics/precision/precision.py ## Actual results ``` output = self._compute(predictions=predictions, references=references, **kwargs) File "~/.cache/huggingface/modules/datasets_modules/metrics/precision/94709a71c6fe37171ef49d3466fec24dee9a79846c9f176dff66a649e9811690/precision.py", line 110, in _compute sample_weight=sample_weight, ValueError: can only convert an array of size 1 to a Python scalar ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: linux - Python version: 3.6.9 - PyArrow version: 5.0.0

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PR_kwDODunzps4tAB9h

Fix meteor metric for version >= 3.6.4

closed

MEMBER

After `nltk` update, the meteor metric expects pre-tokenized inputs (breaking change). This PR fixes this issue, while maintaining compatibility with older versions.

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I_kwDODunzps4871qG

CI test suite fails after meteor metric update

closed

MEMBER

## Describe the bug CI test suite fails: https://app.circleci.com/pipelines/github/huggingface/datasets/8110/workflows/f059ba43-9154-4632-bebb-82318447ddc9/jobs/50010 Stack trace: ``` ___________________ LocalMetricTest.test_load_metric_meteor ____________________ [gw1] linux -- Python 3.6.15 /home/circleci/.pyenv/versions/3.6.15/bin/python3.6 self = <tests.test_metric_common.LocalMetricTest testMethod=test_load_metric_meteor> metric_name = 'meteor' def test_load_metric(self, metric_name): doctest.ELLIPSIS_MARKER = "[...]" metric_module = importlib.import_module(datasets.load.prepare_module(os.path.join("metrics", metric_name))[0]) metric = datasets.load.import_main_class(metric_module.__name__, dataset=False) # check parameters parameters = inspect.signature(metric._compute).parameters self.assertTrue("predictions" in parameters) self.assertTrue("references" in parameters) self.assertTrue(all([p.kind != p.VAR_KEYWORD for p in parameters.values()])) # no **kwargs # run doctest with self.patch_intensive_calls(metric_name, metric_module.__name__): with self.use_local_metrics(): > results = doctest.testmod(metric_module, verbose=True, raise_on_error=True) tests/test_metric_common.py:75: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1951: in testmod runner.run(test) ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1839: in run r = DocTestRunner.run(self, test, compileflags, out, False) ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1476: in run return self.__run(test, compileflags, out) ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1382: in __run exception) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <doctest.DebugRunner object at 0x7f4c26bd3da0> out = <built-in method write of _io.TextIOWrapper object at 0x7f51a21852d0> test = <DocTest datasets_modules.datasets.meteor.6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7.meteor.Mete...ets_modules/datasets/meteor/6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7/meteor.py:87 (5 examples)> example = <doctest.Example object at 0x7f4c26bd3eb8> exc_info = (<class 'TypeError'>, TypeError('"hypothesis" expects pre-tokenized hypothesis (Iterable[str]): It is a guide to action which ensures that the military always obeys the commands of the party',), <traceback object at 0x7f4cd01afec8>) def report_unexpected_exception(self, out, test, example, exc_info): > raise UnexpectedException(test, example, exc_info) E doctest.UnexpectedException: <DocTest datasets_modules.datasets.meteor.6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7.meteor.Meteor from /tmp/pytest-of-circleci/pytest-0/popen-gw1/cache/modules/datasets_modules/datasets/meteor/6201bb45d2c144ea7963680949d20f523d74a741fa0f8a806f836e6caa5245d7/meteor.py:87 (5 examples)> ../.pyenv/versions/3.6.15/lib/python3.6/doctest.py:1845: UnexpectedException ```

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PR_kwDODunzps4s_TmE

Update Biosses

closed

CONTRIBUTOR

Fix variable naming

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I_kwDODunzps4866fp

load_dataset('the_pile_openwebtext2') produces ArrowInvalid, value too large to fit in C integer type

open

NONE

## Describe the bug When loading `the_pile_openwebtext2`, we get the error `pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type` ## Steps to reproduce the bug ```python import datasets ds = datasets.load_dataset('the_pile_openwebtext2') ``` ## Expected results Should download the dataset, convert it to an arrow file, and return a working Dataset object. ## Actual results The download works, but conversion to the arrow file fails as follows: ``` >>> ds = datasets.load_dataset('the_pile_openwebtext2') Downloading and preparing dataset openwebtext2/plain_text (download: 27.33 GiB, generated: 63.86 GiB , post-processed: Unknown size, total: 91.19 GiB) to /home/davidbau/.cache/huggingface/datasets/open webtext2/plain_text/1.0.0/c48ec73ba3483bac673463f48f67e9a4fd8cb49a9d6ec4fb957f0b424b97cf25... Traceback (most recent call last): File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/builder.py", line 1133, in _prepare_split writer.write(example, key) File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 366, in write self.write_examples_on_file() File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 311, in write_examples_on_file pa_array = pa.array(typed_sequence) File "pyarrow/array.pxi", line 222, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 115, in __arrow_array__ out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type) File "pyarrow/array.pxi", line 305, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: ``` - Platform: Ubuntu 20.04 - Python version: python 3.9 - PyArrow version: 3.0.0

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I_kwDODunzps486aJz

load_dataset cannot download the data and hangs on forever if cache dir specified

closed

NONE

## Describe the bug After updating datasets, a code that ran just fine for ages began to fail. Specifying _datasets.load_dataset_'s _cache_dir_ optional argument on Windows 10 machine results in data download to hang on forever. Same call without cache_dir works just fine. Surprisingly exact same code just runs perfectly fine on Linux docker instance running in cloud. Unfortunately I updated Windows also at the same time and I can't remember which version of datasets was running in my conda environment prior to the update otherwise I would have tried both to check this out. :( ## Steps to reproduce the bug ```python # Sample code to reproduce the bug ``` cache_dir = 'c:/data/datasets' dataset = load_dataset('wikipedia', '20200501.en', split='train',cache_dir=cache_dir) ``` Note that exact same code without specifying _cache_dir_ argument works perfectly fine. ``` cache_dir = 'c:/data/datasets' dataset = load_dataset('wikipedia', '20200501.en', split='train') ``` ## Expected results Downloads the dataset and cache is handled in the _cache_dir_ directory ## Actual results Data download keeps hanging on forever, **NO TRACEBACK**! ## Environment info - `datasets` version: 1.12.1 - Platform: Windows-10-10.0.19042-SP0 - Python version: 3.8.11 - PyArrow version: 3.0.0

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I_kwDODunzps486DpK

Non-Matching Checksum Error with crd3 dataset

open

NONE

## Describe the bug When I try loading the crd3 dataset (https://huggingface.co/datasets/crd3), an error is thrown. ## Steps to reproduce the bug ```python dataset = load_dataset('crd3', split='train') ``` ## Expected results I expect no error to be thrown. ## Actual results A non-matching checksum error is thrown. ``` datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/RevanthRameshkumar/CRD3/archive/master.zip'] ``` ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial - Python version: 3.6.10 - PyArrow version: 5.0.0

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PR_kwDODunzps4s-anK

Fix streaming: catch Timeout error

closed

CONTRIBUTOR

Catches Timeout error during streaming. fix #3049

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I_kwDODunzps485vkY

TimeoutError during streaming

closed

CONTRIBUTOR

## Describe the bug I got a TimeoutError after streaming for about 10h. ## Steps to reproduce the bug Very long code but we could do a test of streaming indefinitely data, though error may take a while to appear. ## Expected results This error was not expected in the code which considers only `ClientError` but not `TimeoutError`. See [this line](https://github.com/huggingface/datasets/blob/2814fbd0e18150be409f10804670e98d9ecb87d4/src/datasets/utils/streaming_download_manager.py#L129). Based on the traceback, it looks like the `TimeoutError` was not captured. ## Actual results ``` File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/asyn.py", line 25, in _runner result[0] = await coro File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/implementations/http.py", line 614, in async_fetch_range out = await r.read() File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/client_reqrep.py", line 1032, in read self._body = await self.content.read() File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/streams.py", line 370, in read block = await self.readany() File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/streams.py", line 392, in readany await self._wait("readany") File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/streams.py", line 306, in _wait await waiter File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/aiohttp/helpers.py", line 656, in __exit__ raise asyncio.TimeoutError from None asyncio.exceptions.TimeoutError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py", line 1027, in <module> main() File "/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py", line 991, in main for batch in tqdm( File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/tqdm/std.py", line 1180, in __iter__ for obj in iterable: File "/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py", line 376, in data_loader_streaming for item in dataset: File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 341, in __iter__ for key, example in self._iter(): File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 338, in _iter yield from ex_iterable File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 179, in __iter__ key_examples_list = [(key, example)] + [ File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 179, in <listcomp> key_examples_list = [(key, example)] + [ File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 176, in __iter__ for key, example in iterator: File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 225, in __iter__ for x in self.ex_iterable: File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 99, in __iter__ for key, example in self.generate_examples_fn(**kwargs_with_shuffled_shards): File "/home/koush/datasets/src/datasets/iterable_dataset.py", line 287, in wrapper for key, table in generate_tables_fn(**kwargs): File "/home/koush/datasets/src/datasets/packaged_modules/json/json.py", line 107, in _generate_tables batch = f.read(self.config.chunksize) File "/home/koush/datasets/src/datasets/utils/streaming_download_manager.py", line 126, in read_with_retries out = read(*args, **kwargs) File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/implementations/http.py", line 572, in read return super().read(length) File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/spec.py", line 1533, in read out = self.cache._fetch(self.loc, self.loc + length) File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/caching.py", line 390, in _fetch self.cache = self.fetcher(start, bend) File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/asyn.py", line 91, in wrapper return sync(self.loop, func, *args, **kwargs) File "/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/fsspec/asyn.py", line 69, in sync raise FSTimeoutError from return_result fsspec.exceptions.FSTimeoutError ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.2.dev0 - Platform: Linux-5.4.0-1043-gcp-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 5.0.0

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I_kwDODunzps485ugd

Identify which shard data belongs to

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** I'm training on a large dataset made of multiple sub-datasets. During training I can observe some jumps in loss which may correspond to different shards.  My suspicion is that either: * some of the sub-datasets are harder for the model than others * some of the sub-datasets are not formatted properly I'd like to identify which shards correspond to those jumps. **Describe the solution you'd like** It would be nice to have a key associated to each data sample or data batch containing details on where the data comes from (shard idx + item idx within the shard). This should be supported both in local and streaming mode. **Describe alternatives you've considered** A fix would be for me to add myself details (shard id, sample id) as part of each data sample. The inconvenient is that it requires users to process/reupload every dataset when they need this feature.

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I_kwDODunzps484Lno

Loading from cache a dataset for LM built from a text classification dataset sometimes errors

closed

MEMBER

## Describe the bug Yes, I know, that description sucks. So the problem is arising in the course when we build a masked language modeling dataset using the IMDB dataset. To reproduce (or try since it's a bit fickle). Create a dataset for masled-language modeling from the IMDB dataset. ```python from datasets import load_dataset from transformers import Autotokenizer tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased) imdb_dataset = load_dataset("imdb", split="train") def tokenize_function(examples): return tokenizer(examples["text"]) tokenized_dataset = imdb_dataset.map( tokenize_function, batched=True, remove_columns=["text", "label"] ) chunk_size = 128 def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()} # Compute length of concatenated texts total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the last chunk if it's smaller than chunk_size total_length = (total_length // chunk_size) * chunk_size # Split by chunks of max_len. result = { k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)] for k, t in concatenated_examples.items() } # Create a new labels column result["labels"] = result["input_ids"].copy() return result lm_dataset = tokenized_dataset.map(group_texts, batched=True) ``` Until now, all is well. The problem comes when you re-execute that code, more specifically: ```python tokenized_dataset = imdb_dataset.map( tokenize_function, batched=True, remove_columns=["text", "label"] ) lm_dataset = tokenized_dataset.map(group_texts, batched=True) ``` Try several times if the bug doesn't appear instantly, or just each line at a time, ideally in a notebook/Colab and you should get at some point: ```python --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-40-357a56ee3d53> in <module> ----> 1 lm_dataset = tokenized_dataset.map(group_texts, batched=True) ~/git/datasets/src/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 1947 new_fingerprint=new_fingerprint, 1948 disable_tqdm=disable_tqdm, -> 1949 desc=desc, 1950 ) 1951 else: ~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 424 } 425 # apply actual function --> 426 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 427 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 428 # re-apply format to the output ~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs) 404 # Call actual function 405 --> 406 out = func(self, *args, **kwargs) 407 408 # Update fingerprint of in-place transforms + update in-place history of transforms ~/git/datasets/src/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2138 if os.path.exists(cache_file_name) and load_from_cache_file: 2139 logger.warning("Loading cached processed dataset at %s", cache_file_name) -> 2140 info = self.info.copy() 2141 info.features = features 2142 return Dataset.from_file(cache_file_name, info=info, split=self.split) ~/git/datasets/src/datasets/info.py in copy(self) 278 279 def copy(self) -> "DatasetInfo": --> 280 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) 281 282 ~/git/datasets/src/datasets/info.py in __init__(self, description, citation, homepage, license, features, post_processed, supervised_keys, task_templates, builder_name, config_name, version, splits, download_checksums, download_size, post_processing_size, dataset_size, size_in_bytes) ~/git/datasets/src/datasets/info.py in __post_init__(self) 177 for idx, template in enumerate(self.task_templates): 178 if isinstance(template, TextClassification): --> 179 labels = self.features[template.label_column].names 180 self.task_templates[idx] = TextClassification( 181 text_column=template.text_column, label_column=template.label_column, labels=labels KeyError: 'label' ``` It seems that when loading the cache, the dataset tries to access some kind of text classification template (which I imagine comes from the original dataset) and to look at a key that has since been removed.

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PR_kwDODunzps4s8MjS

Fix MedDialog metadata JSON

closed

MEMBER

Fix #2969.

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PR_kwDODunzps4s8B2b

Fix inconsistent caching behaviour in Dataset.map() with multiprocessing #3044

closed

NONE

Fix #3044 1. A rough unit test that fails without the fix. It probably doesn't comply with your code standards, but that just to draft the idea. 2. A one liner fix

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I_kwDODunzps482TyS

Inconsistent caching behaviour when using `Dataset.map()` with a `new_fingerprint` and `num_proc>1`

open

NONE

## Describe the bug Caching does not work when using `Dataset.map()` with: 1. a function that cannot be deterministically fingerprinted 2. `num_proc>1` 3. using a custom fingerprint set with the argument `new_fingerprint`. This means that the dataset will be mapped with the function for each and every call, which does not happen if `num_proc==1`. In that case (`num_proc==1`) subsequent calls will load the transformed dataset from the cache, which is the expected behaviour. The example can easily be translated into a unit test. I have a fix and will submit a pull request asap. ## Steps to reproduce the bug ```python import hashlib import json import os from typing import Dict, Any import numpy as np from datasets import load_dataset, Dataset Batch = Dict[str, Any] filename = 'example.json' class Transformation(): """A transformation with a random state that cannot be fingerprinted""" def __init__(self): self.state = np.random.random() def __call__(self, batch: Batch) -> Batch: batch['x'] = [np.random.random() for _ in batch['x']] return batch def generate_dataset(): """generate a simple dataset""" rgn = np.random.RandomState(24) data = { 'data': [{'x': float(y), 'y': -float(y)} for y in rgn.random(size=(1000,))]} if not os.path.exists(filename): with open(filename, 'w') as f: f.write(json.dumps(data)) return filename def process_dataset_with_cache(num_proc=1, remove_cache=False, cache_expected_to_exist=False): # load the generated dataset dset: Dataset = next( iter(load_dataset('json', data_files=filename, field='data').values())) new_fingerprint = hashlib.md5("static-id".encode("utf8")).hexdigest() # get the expected cached path cache_path = dset._get_cache_file_path(new_fingerprint) if remove_cache and os.path.exists(cache_path): os.remove(cache_path) # check that the cache exists, and print a statement # if was actually expected to exist cache_exist = os.path.exists(cache_path) print(f"> cache file exists={cache_exist}") if cache_expected_to_exist and not cache_exist: print("=== Cache does not exist! ====") # apply the transformation with the new fingerprint dset = dset.map( Transformation(), batched=True, num_proc=num_proc, new_fingerprint=new_fingerprint, desc="mapping dataset with transformation") generate_dataset() for num_proc in [1, 2]: print(f"# num_proc={num_proc}, first pass") # first pass to generate the cache (always create a new cache here) process_dataset_with_cache(remove_cache=True, num_proc=num_proc, cache_expected_to_exist=False) print(f"# num_proc={num_proc}, second pass") # second pass, expects the cache to exist process_dataset_with_cache(remove_cache=False, num_proc=num_proc, cache_expected_to_exist=True) os.remove(filename) ``` ## Expected results In the above python example, with `num_proc=2`, the **cache file should exist in the second call** of `process_dataset_with_cache` ("=== Cache does not exist! ====" should not be printed). When the cache is successfully created, `map()` is called only one time. ## Actual results In the above python example, with `num_proc=2`, the **cache does not exist in the second call** of `process_dataset_with_cache` (this results in printing "=== Cache does not exist! ===="). Because the cache doesn't exist, the `map()` method is executed a second time and the dataset is not loaded from the cache. ## Environment info - `datasets` version: 1.12.1 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.8 - PyArrow version: 5.0.0

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I_kwDODunzps48z8_S

Add PASS dataset

open

NONE

## Adding a Dataset - **Name:** PASS - **Description:** An ImageNet replacement for self-supervised pretraining without humans - **Data:** https://www.robots.ox.ac.uk/~vgg/research/pass/ https://github.com/yukimasano/PASS Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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PR_kwDODunzps4s5Lxo

Improving elasticsearch integration

open

CONTRIBUTOR

- adding murmurhash signature to sample in index - adding optional credentials for remote elasticsearch server - enabling sample update in index - upgrade the elasticsearch 7.10.1 python client - adding ElasticsearchBulider to instantiate a dataset from an index and a filtering query

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PR_kwDODunzps4s1ZAc

Load private data files + use glob on ZIP archives for json/csv/etc. module inference

closed

MEMBER

As mentioned in https://github.com/huggingface/datasets/issues/3032 loading data files from private repository isn't working correctly because of the data files resolved. #2986 did a refactor of the data files resolver. I added authentication to it. I also improved it to glob inside ZIP archives to look for json/csv/etc. files and infer which dataset builder (json/csv/etc.) to use. Fix https://github.com/huggingface/datasets/issues/3032 Note that #2986 needs to get merged first

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I_kwDODunzps48uWML

[save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset

closed

MEMBER

## Describe the bug When only keeping a dummy size of a dataset (say the first 100 samples), and then saving it to disk to upload it in the following to the hub for easy demo/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big. ## Steps to reproduce the bug E.g. run the following: ```python from datasets import load_dataset, save_to_disk nlp = load_dataset("glue", "mnli", split="train") nlp.save_to_disk("full") nlp = nlp.select(range(100)) nlp.save_to_disk("dummy") ``` Now one can see that both `"dummy"` and `"full"` have the same size. This shouldn't be the case IMO. ## Expected results IMO `"dummy"` should be much smaller so that one can easily play around with the dataset on the hub. ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.2.dev0 - Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 5.0.0

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PR_kwDODunzps4sy_J-

Add sberquad dataset

closed

CONTRIBUTOR

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PR_kwDODunzps4syno_

add sberquad dataset

closed

CONTRIBUTOR

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PR_kwDODunzps4syi15

SberQuad

closed

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I_kwDODunzps48qK-I

Protect master branch to force contributions via Pull Requests

closed

MEMBER

In order to have a clearer Git history in the master branch, I propose to protect it so that all contributions must be done through a Pull Request and no direct commits to master are allowed. - The Pull Request allows to give context, discuss any potential issues and improve the quality of the contribution - The Pull Request will eventually be squashed and merged into master with a single commit that links to the Pull Request page (with all the context/discussions) Note that we already implemented a protection in the master branch to avoid *merge* commits and ensure a linear history. This proposal goes one step further by avoiding all kind of direct commits and forcing contributions **only** through Pull Requests. Please note that we can temporarily deactivate this protection if we need to make a direct commit, e.g. at each new version release. The only way GitHub allows this kind or protection is by requiring a minimal number (at least one) of approvals of the Pull Request. The inconvenient is that the PR creator cannot approve their own PR: another person must approve it before it can be merged into master. To circumvent this, we could eventually disable this protection in the master branch when an urgent commit is needed (e.g. for a hotfix) and there is no other person available at that time to approve the PR.

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I_kwDODunzps48mq4X

`load_dataset` does not work with uploaded arrow file

open

MEMBER

## Describe the bug I've preprocessed and uploaded a dataset here: https://huggingface.co/datasets/ami-wav2vec2/ami_headset_single_preprocessed . The dataset is in `.arrow` format. The dataset can correctly be loaded when doing: ```bash git lfs install git clone https://huggingface.co/datasets/ami-wav2vec2/ami_headset_single_preprocessed ``` followed by ```python from datasets import load_from_disk ds = load_from_disk("./ami_headset_single_preprocessed") ``` However when I try to directly download the dataset as follows: ```python from datasets import load_dataset ds = load_dataset("ami-wav2vec2/ami_headset_single_preprocessed") ``` the following error occurs: ```bash /usr/local/lib/python3.7/dist-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, script_version, use_auth_token, task, streaming, **config_kwargs) 1115 ignore_verifications=ignore_verifications, 1116 try_from_hf_gcs=try_from_hf_gcs, -> 1117 use_auth_token=use_auth_token, 1118 ) 1119 /usr/local/lib/python3.7/dist-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) 635 if not downloaded_from_gcs: 636 self._download_and_prepare( --> 637 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 638 ) 639 # Sync info /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 724 try: 725 # Prepare split will record examples associated to the split --> 726 self._prepare_split(split_generator, **prepare_split_kwargs) 727 except OSError as e: 728 raise OSError( /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator) 1186 generator, unit=" tables", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET) 1187 ): -> 1188 writer.write_table(table) 1189 num_examples, num_bytes = writer.finalize() 1190 /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in write_table(self, pa_table, writer_batch_size) 424 # reorder the arrays if necessary + cast to self._schema 425 # we can't simply use .cast here because we may need to change the order of the columns --> 426 pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) 427 batches: List[pa.RecordBatch] = pa_table.to_batches(max_chunksize=writer_batch_size) 428 self._num_bytes += sum(batch.nbytes for batch in batches) /usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays() /usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib._sanitize_arrays() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray() /usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.ChunkedArray.cast() /usr/local/lib/python3.7/dist-packages/pyarrow/compute.py in cast(arr, target_type, safe) 279 else: 280 options = CastOptions.unsafe(target_type) --> 281 return call_function("cast", [arr], options) 282 283 /usr/local/lib/python3.7/dist-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function() /usr/local/lib/python3.7/dist-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowNotImplementedError: Unsupported cast from struct<train: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, validation: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, test: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>> to list using function cast_list ``` ## Expected results The dataset should be correctly loaded with `load_dataset` IMO. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.2.dev0 - Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 5.0.0

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I_kwDODunzps48moOi

Errors loading dataset using fs = a gcsfs.GCSFileSystem

open

NONE

## Describe the bug Cannot load dataset using a `gcsfs.GCSFileSystem`. I'm not sure if this should be a bug in `gcsfs` or here... Basically what seems to be happening is that since datasets saves datasets as folders and folders aren't "real objects" in gcs, gcsfs raises a 404 error. There are workarounds if you use gcsfs directly to download the file, but as is I can't get `load_from_disk` to work. ## Steps to reproduce the bug ```python from datasets import load_dataset # load some dataset dataset = load_dataset("squad", split="train") # save it to gcs import gcsfs fs = gcsfs.GCSFileSystem(project="my-gs-project") dataset.save_to_disk("gs://my-bucket/squad", fs=fs) # try to load it from gcs from datasets import load_from_disk dataset2 = load_from_disk("my-bucket/squad", fs=fs) ``` ## Expected results `dataset2` would be a copy of `dataset` but loaded from my bucket. ## Actual results Long traceback but essentially it's a 404 error from gcsfs saying the object `my-bucket/squad` doesn't exist when this is called: https://github.com/huggingface/datasets/blob/9c81b7d2e6d9feae69a084a3abda265a4ca07fb5/src/datasets/arrow_dataset.py#L977 This is because there is no actual object called `my-bucket/squad`, there are objects called `my-bucket/squad/dataset.arrow`, etc. Note that *this* works fine, since it's explicitly saying "download all the objects with this prefix": ```python fs.download(src_dataset_path + "/*", dataset_path.as_posix(), recursive=True) ``` For example, I can do a workaround this way: ```python import tempfile with tempfile.TemporaryDirectory() as temppath: fs.download("gs://my-bucket/squad/*", temppath) dataset2 = load_from_disk(temppath) ``` It's unclear to me if it's `gcsfs`'s responsibility to say "hey that's folder not a file, I should try to get objects inside of it not the object itself", or if that's `datasets`'s responsibility... I'm leaning towards the latter since you're never loading a dataset from one file using this function/method, only a dataset folder? Another minor thing that should maybe should be rolled into this bug... https://github.com/huggingface/datasets/blob/9c81b7d2e6d9feae69a084a3abda265a4ca07fb5/src/datasets/arrow_dataset.py#L968 These fail if you pass in a `gs://` path, e.g. ```python dataset2 = load_from_disk("gs://my-bucket/squad", fs=fs) ``` Because at this point, `dataset_info_path` is `gs:/my-bucket/squad/dataset_info.json`, gcsfs throws a: ``` Invalid bucket name: 'gs:' ``` error ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: macOS Big Sur 11.6 - Python version: 3.7.12 - PyArrow version: 5.0.0

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PR_kwDODunzps4std7u

Actual "proper" install of ruamel.yaml in the windows CI

closed

MEMBER

It was impossible to update the package directly with `pip`. Indeed it was installed with `distutils` which prevents `pip` or `conda` to uninstall it. I had to `rm` a directory from the `site-packages` python directory, and then do `pip install ruamel.yaml` It's not that "proper" but I couldn't find better solutions

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I_kwDODunzps48lmIb

Error when loading private dataset with "data_files" arg

closed

CONTRIBUTOR

## Describe the bug A clear and concise description of what the bug is. Private datasets with no loading script can't be loaded using `data_files` parameter. ## Steps to reproduce the bug ```python from datasets import load_dataset data_files = {"train": "**/train/*/*.jsonl", "valid": "**/valid/*/*.jsonl"} dataset = load_dataset('dalle-mini/encoded', data_files=data_files, use_auth_token=True, streaming=True) ``` Same error happens in non-streaming mode. ## Expected results Files should be loaded (whether in streaming or not). ## Actual results Error: ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, return_associated_base_path, data_files, **download_kwargs) 539 try: --> 540 local_path = cached_path(file_path, download_config=download_config) 541 except FileNotFoundError: 8 frames FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/dalle-mini/encoded/resolve/main/encoded.py During handling of the above exception, another exception occurred: HTTPError Traceback (most recent call last) HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/datasets/dalle-mini/encoded?full=true During handling of the above exception, another exception occurred: FileNotFoundError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, return_associated_base_path, data_files, **download_kwargs) 547 except Exception: 548 raise FileNotFoundError( --> 549 f"Couldn't find a directory or a {resource_type} named '{path}'. " 550 f"It doesn't exist locally at {expected_dir_for_combined_path_abs} or remotely on {hf_api.endpoint}/datasets" 551 ) FileNotFoundError: Couldn't find a directory or a dataset named 'dalle-mini/encoded'. It doesn't exist locally at /content/dalle-mini/encoded or remotely on https://huggingface.co/datasets ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0 @lhoestq

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PR_kwDODunzps4ss9jn

Align tqdm control with cache control

closed

CONTRIBUTOR

Currently, once disabled with `disable_progress_bar`, progress bars cannot be re-enabled again. To overcome this limitation, this PR introduces the `set_progress_bar_enabled` function that accepts a boolean indicating whether to display progress bars. The goal is to provide a similar API to the existing cache control API. Following the Zen of Python (😄), there should be one and preferably only one obvious way to do it, so I'm also deprecating the aforementioned `disable_progress_bar` function. Additionally, I justify the deprecation with the fact that this function has never been in the docs. Moreover, similar API changes have recently been introduced to [`tfds`](https://github.com/tensorflow/datasets/blob/a1e8b98f45b0214082b546cc967c67c43fffda55/tensorflow_datasets/core/utils/tqdm_utils.py#L98-L112). Considering the popularity of the [comment](https://github.com/huggingface/datasets/issues/1627#issuecomment-751383559) I made a while ago, this API (`set_progress_bar_enabled` and `is_progress_bar_enabled`) should be mentioned in the docs, but I'm not sure where to put it exactly. Maybe we can replace the `logging_methods` page under `package_reference` with `utility_methods` and then introduce two subsections on that page: `Logging methods` and `tqdm control`. Additionally, this PR: * adds the `disable_tqdm` keyword arg of `Dataset._map_single` to the `ignore_kwargs` list to ignore it when computing the fingerprint (forgot to add it in #2696) * deletes the unused components in `tqdm_utils.py`, which seem to be inherited from `tfds` * disables the tqdm output in the test suite. As I see it, this output doesn't seem informative, but let me know if this is not a good idea

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PR_kwDODunzps4ss41W

Add `remove_columns` to `IterableDataset`

closed

CONTRIBUTOR

Fixes #2944 WIP * Not tested yet. * We might want to allow batched remove for efficiency. @lhoestq Do you think it should have `batched=` and `batch_size=`?

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PR_kwDODunzps4ssvkr

Use standard open-domain validation split in nq_open

closed

CONTRIBUTOR

The nq_open dataset originally drew the validation set from this file: https://github.com/google-research-datasets/natural-questions/blob/master/nq_open/NQ-open.efficientqa.dev.1.1.sample.jsonl However, that's the dev set used specifically and only for the efficientqa competition, and it's not the same dev set as is used in every open-domain question answering paper (including the Lee et al paper that introduced the open-domain variant of NQ, cited at the top of the dataset file). This PR changes nq_open to use the standard validation split and bumps the version to 2.0.0 since this is a breaking change.

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PR_kwDODunzps4ssO4s

Properly install ruamel-yaml for windows CI

closed

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I_kwDODunzps48kThl

Resolve data_files by split name

closed

MEMBER

This issue is about discussing the default behavior when someone loads a dataset that consists in data files. For example: ```python load_dataset("lhoestq/demo1") ``` should return two splits "train" and "test" since the dataset repostiory is like ``` data/ ├── train.csv └── test.csv ``` Currently it returns only one split "train" which contains the data of both files I started playing with this idea on this branch btw: `resolve-data_files-by-split-name` Basically the idea is that if you named you data files after split names then the default pattern is ```python { "train": ["*train*"], "test": ["*test*"], "validation": ["*dev*", "valid"], } ``` otherwise it's ```python { "train": ["*"] } ``` Let me know what you think ! cc @albertvillanova @LysandreJik @vblagoje

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PR_kwDODunzps4srtyc

added arxiv paper inswiss_judgment_prediction dataset card

closed

CONTRIBUTOR

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PR_kwDODunzps4srsgG

Fix Windows test suite

closed

MEMBER

Try a hotfix to restore Windows test suite. Fix #3024.

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I_kwDODunzps48j7yv

Windows test suite fails

closed

MEMBER

## Describe the bug There is an error during installation of tests dependencies for Windows: https://app.circleci.com/pipelines/github/huggingface/datasets/7981/workflows/9b6a0114-2b8e-4069-94e5-e844dbbdba4e/jobs/49206 ``` ERROR: Cannot uninstall 'ruamel-yaml'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall. ```

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PR_kwDODunzps4srQ4i

Fix typo

closed

CONTRIBUTOR

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PR_kwDODunzps4sqve6

MeDAL dataset: Add further description and update download URL

closed

CONTRIBUTOR

Added more details in the following sections: * Dataset Structure * Data Instances * Data Splits * Source Data * Annotations * Discussions of Biases * LIcensing Information

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PR_kwDODunzps4spzJU

Support loading dataset from multiple zipped CSV data files

closed

MEMBER

Fix partially #3018. CC: @lewtun

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PR_kwDODunzps4sprfa

Add a metric for the MATH dataset (competition_math).

closed

CONTRIBUTOR

This metric computes accuracy for the MATH dataset (https://arxiv.org/abs/2103.03874) after canonicalizing the prediction and the reference (e.g., converting "1/2" to "\\\\frac{1}{2}").

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PR_kwDODunzps4speOB

Fix filter leaking

closed

MEMBER

If filter is called after using a first transform `shuffle`, `select`, `shard`, `train_test_split`, or `filter`, then it could not work as expected and return examples from before the first transform. This is because the indices mapping was not taken into account when saving the indices to keep when doing the filtering Affected versions: 1.12.0 and 1.12.1 This should fix issue https://github.com/huggingface/datasets/issues/3010

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I_kwDODunzps48hG4F

Support multiple zipped CSV data files

open

MEMBER

As requested by @lewtun, support loading multiple zipped CSV data files. ```python from datasets import load_dataset url = "https://domain.org/filename.zip" data_files = {"train": "train_filename.csv", "test": "test_filename.csv"} dataset = load_dataset("csv", data_dir=url, data_files=data_files) ```

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PR_kwDODunzps4spE9m

Remove unused parameter in xdirname

closed

MEMBER

Minor fix to remove unused args `*p` in `xdirname`.

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PR_kwDODunzps4spDlX

Fix Windows paths in LJ Speech dataset

closed

MEMBER

Minor fix in LJ Speech dataset for Windows pathname component separator. Related to #1878.

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PR_kwDODunzps4so0GX

Extend support for streaming datasets that use glob.glob

closed

MEMBER

This PR extends the support in streaming mode for datasets that use `glob`, by patching the function `glob.glob`. Related to #2880, #2876, #2874

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PR_kwDODunzps4son8A

Fix Windows path in MATH dataset

closed

MEMBER

Minor fix in MATH dataset for Windows pathname component separator. Related to #2982.

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I_kwDODunzps48fxEj

Improve `get_dataset_infos`?

open

CONTRIBUTOR

Using the dedicated function `get_dataset_infos` on a dataset that has no dataset-info.json file returns an empty info: ``` >>> from datasets import get_dataset_infos >>> get_dataset_infos('wit') {} ``` While it's totally possible to get it (regenerate it) with: ``` >>> from datasets import load_dataset_builder >>> builder = load_dataset_builder('wit') >>> builder.info DatasetInfo(description='Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set\n of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its\n size enables WIT to be used as a pretraining dataset for multimodal machine learning models.\n', citation='@article{srinivasan2021wit,\n title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},\n author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},\n journal={arXiv preprint arXiv:2103.01913},\n year={2021}\n}\n', homepage='https://github.com/google-research-datasets/wit', license='', features={'b64_bytes': Value(dtype='string', id=None), 'embedding': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'image_url': Value(dtype='string', id=None), 'metadata_url': Value(dtype='string', id=None), 'original_height': Value(dtype='int32', id=None), 'original_width': Value(dtype='int32', id=None), 'mime_type': Value(dtype='string', id=None), 'caption_attribution_description': Value(dtype='string', id=None), 'wit_features': Sequence(feature={'language': Value(dtype='string', id=None), 'page_url': Value(dtype='string', id=None), 'attribution_passes_lang_id': Value(dtype='string', id=None), 'caption_alt_text_description': Value(dtype='string', id=None), 'caption_reference_description': Value(dtype='string', id=None), 'caption_title_and_reference_description': Value(dtype='string', id=None), 'context_page_description': Value(dtype='string', id=None), 'context_section_description': Value(dtype='string', id=None), 'hierarchical_section_title': Value(dtype='string', id=None), 'is_main_image': Value(dtype='string', id=None), 'page_changed_recently': Value(dtype='string', id=None), 'page_title': Value(dtype='string', id=None), 'section_title': Value(dtype='string', id=None)}, length=-1, id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='wit', config_name='default', version=0.0.0, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None) ``` Should we test if info is empty, and in that case regenerate it? Or always generate it?

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PR_kwDODunzps4soRTu

Replace item with float in metrics

closed

MEMBER

As pointed out by @mariosasko in #3001, calling `float()` instad of `.item()` is faster. Moreover, it might avoid potential issues if any of the third-party functions eventually returns a `float` instead of an `np.float64`. Related to #3001.

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I_kwDODunzps48frCh

load_dataset_builder should error if "name" does not exist?

open

CONTRIBUTOR

``` import datasets as ds builder = ds.load_dataset_builder('sent_comp', name="doesnotexist") builder.info.config_name ``` returns ``` 'doesnotexist' ``` Shouldn't it raise an error instead? For this dataset, the only valid values for `name` should be: `"default"` or `None` (ie. argument not passed)

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I_kwDODunzps48fm1G

Chain filtering is leaking

open

NONE

## Describe the bug As there's no support for lists within dataset fields, I convert my lists to json-string format. However, the bug described is occurring even when the data format is 'string'. These samples show that filtering behavior diverges from what's expected when chaining filterings. On sample 2 the second filtering leads to "leaking" of data that should've been filtered on the first filtering into the results. ## Steps to reproduce the bug Sample 1: ```python import datasets import json items = [[1, 2], [3], [4]] jsoned_items = map(json.dumps, [[1, 2], [3], [4]]) ds = datasets.Dataset.from_dict({'a': jsoned_items}) print(list(ds)) # > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}] as expected filtered = ds # get all lists that are shorter than 2 filtered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False) print(list(filtered)) # > Prints: [{'a': '[3]'}, {'a': '[4]'}] as expected # get all lists, which have a value bigger than 3 on its zero index filtered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False) print(list(filtered)) # > Should be: [{'a': [4]}] # > Prints: [{'a': [3]}] ``` Sample 2: ```python import datasets import json items = [[1, 2], [3], [4]] jsoned_items = map(json.dumps, [[1, 2], [3], [4]]) ds = datasets.Dataset.from_dict({'a': jsoned_items}) print(list(ds)) # > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}] filtered = ds # get all lists, which have a value bigger than 3 on its zero index filtered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False) print(list(filtered)) # > Prints: [{'a': '[4]'}] as expected # get all lists that are shorter than 2 filtered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False) print(list(filtered)) # > Prints: [{'a': '[1, 2]'}] # > Should be: [{'a': '[4]'}] (remain intact) ``` ## Expected results Expected and actual results are attached to the code snippets. ## Actual results Expected and actual results are attached to the code snippets. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Windows-10-10.0.19042-SP0 - Python version: 3.9.7 - PyArrow version: 5.0.0

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PR_kwDODunzps4sn_YG

Fix Windows paths in SUPERB benchmark datasets

closed

MEMBER

Minor fix in SUPERB benchmark datasets for Windows pathname component separator. Related to #2884, #2783 and #2619.

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PR_kwDODunzps4sn7iU

Fix precision/recall metrics with None average

closed

MEMBER

Related to issue #2979 and PR #2992.

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PR_kwDODunzps4sns-n

Correct a typo

closed

CONTRIBUTOR

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PR_kwDODunzps4snsBm

Fix Windows paths in CommonLanguage dataset

closed

MEMBER

Minor fix in CommonLanguage dataset for Windows pathname component separator. Related to #2989.

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I_kwDODunzps48ec18

DatasetDict.filter and Dataset.filter crashes with any "fn_kwargs" argument

closed

NONE

## Describe the bug The ".filter" method of DatasetDict or Dataset objects fails when passing any "fn_kwargs" argument ## Steps to reproduce the bug ```python import datasets example_dataset = datasets.Dataset.from_dict({"a": {1, 2, 3, 4}}) def filter_value(example, value): return example['a'] == value filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3}) ``` ## Expected results `filtered` is a dataset containing {"a": {3}} ## Actual results > Traceback (most recent call last): > File "C:\Users\qsemi\Documents\git\nlp_experiments\gpt_celebrity\src\test_faulty_filter.py", line 8, in <module> > filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3}) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 185, in wrapper > out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\fingerprint.py", line 398, in wrapper > out = func(self, *args, **kwargs) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 2169, in filter > indices = self.map( > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 1686, in map > return self._map_single( > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 185, in wrapper > out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\fingerprint.py", line 398, in wrapper > out = func(self, *args, **kwargs) > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 2048, in _map_single > batch = apply_function_on_filtered_inputs( > File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 1939, in apply_function_on_filtered_inputs > function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) > TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'value' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Windows-10-10.0.19042-SP0 - Python version: 3.9.7 - PyArrow version: 5.0.0

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PR_kwDODunzps4smfPF

LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.

closed

CONTRIBUTOR

Inspired by the recent widespread use of the GLUE multi-task benchmark NLP dataset (Wang et al., 2018), the subsequent more difficult SuperGLUE (Wang et al., 2019), other previous multi-task NLP benchmarks (Conneau and Kiela, 2018; McCann et al., 2018), and similar initiatives in other domains (Peng et al., 2019), we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a benchmark dataset to evaluate the performance of NLP methods in legal tasks. LexGLUE is based on seven existing legal NLP datasets, selected using criteria largely from SuperGLUE. As in GLUE and SuperGLUE (Wang et al., 2019b,a), one of our goals is to push towards generic (or ‘foundation’) models that can cope with multiple NLP tasks, in our case legal NLP tasks possibly with limited task-specific fine-tuning. Another goal is to provide a convenient and informative entry point for NLP researchers and practitioners wishing to explore or develop methods for legalNLP. Having these goals in mind, the datasets we include in LexGLUE and the tasks they address have been simplified in several ways to make it easier for newcomers and generic models to address all tasks. LexGLUE benchmark is accompanied by experimental infrastructure that relies on Hugging Face Transformers library and resides at: https://github.com/coastalcph/lex-glue.

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PR_kwDODunzps4smExP

common_language: Fix license in README.md

closed

CONTRIBUTOR

...it's correct elsewhere

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PR_kwDODunzps4smCNO

Remove a reference to the open Arrow file when deleting a TF dataset created with to_tf_dataset

closed

CONTRIBUTOR

This [comment](https://github.com/huggingface/datasets/issues/2934#issuecomment-922970919) explains the issue. This PR fixes that with a `weakref` callback, and additionally: * renames `TensorflowDatasetMixIn` to `TensorflowDatasetMixin` for consistency * correctly indents `TensorflowDatasetMixin`'s docstring * replaces `tf.data.AUTOTUNE` with `tf.data.experimental.AUTOTUNE` (we support TF>=2.2 according to the [setup.py](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/setup.py#L188) and `AUTOTUNE` has been moved to the experimental part of `tf.data` in 1.X if I'm not mistaken) Fixes #2934

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