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PR_kwDODunzps46QSHV

Tell users to upload on the hub directly

closed

MEMBER

As noted in https://github.com/huggingface/datasets/pull/4534, it is still not clear that it is recommended to add datasets on the Hugging Face Hub directly instead of GitHub, so I updated some docs. Moreover since users won't be able to get reviews from us on the Hub, I added a paragraph to tell users that they can open a discussion and tag `datasets` maintainers for reviews. Finally I removed the _previous good reasons_ to add a dataset on GitHub to only keep this one: > In some rare cases it makes more sense to open a PR on GitHub. For example when you are not the author of the dataset and there is no clear organization / namespace that you can put the dataset under. Does it sound good to you @albertvillanova @julien-c ?

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PR_kwDODunzps46QAV-

Perform hidden file check on relative data file path

closed

COLLABORATOR

Fix #4549

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I_kwDODunzps5Mb3sp

imdb source error

closed

NONE

## Describe the bug imdb dataset not loading ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("imdb") ``` ## Expected results ## Actual results ```bash 06/23/2022 14:45:18 - INFO - datasets.builder - Dataset not on Hf google storage. Downloading and preparing it from source 06/23/2022 14:46:34 - INFO - datasets.utils.file_utils - HEAD request to http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz timed out, retrying... [1.0] ..... ConnectionError: Couldn't reach http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz (ConnectTimeout(MaxRetryError("HTTPConnectionPool(host='ai.stanford.edu', port=80): Max retries exceeded with url: /~amaas/data/sentiment/aclImdb_v1.tar.gz (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x7f2d750cf690>, 'Connection to ai.stanford.edu timed out. (connect timeout=100)'))"))) ``` ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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I_kwDODunzps5MbosP

FileNotFoundError when passing a data_file inside a directory starting with double underscores

closed

MEMBER

Bug experienced in the `accelerate` CI: https://github.com/huggingface/accelerate/runs/7016055148?check_suite_focus=true This is related to https://github.com/huggingface/datasets/pull/4505 and the changes from https://github.com/huggingface/datasets/pull/4412

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I_kwDODunzps5MbRhw

Metadata.jsonl for Imagefolder is ignored if it's in a parent directory to the splits directories/do not have "{split}_" prefix

closed

CONTRIBUTOR

If data contains a single `metadata.jsonl` file for several splits, it won't be included in a dataset's `data_files` and therefore ignored. This happens when a directory is structured like as follows: ``` train/ file_1.jpg file_2.jpg test/ file_3.jpg file_4.jpg metadata.jsonl ``` or like as follows: ``` train_file_1.jpg train_file_2.jpg test_file_3.jpg test_file_4.jpg metadata.jsonl ``` The same for HF repos. because it's ignored by the patterns [here](https://github.com/huggingface/datasets/blob/master/src/datasets/data_files.py#L29) @lhoestq @mariosasko Do you think it's better to add this functionality in `data_files.py` or just specifically in imagefolder/audiofolder code? In `data_files.py` would me more general but I don't know if there are any other cases when that might be needed.

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PR_kwDODunzps46Ot5u

[CI] Fix some warnings

closed

MEMBER

There are some warnings in the CI that are annoying, I tried to remove most of them

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PR_kwDODunzps46Oe_K

[CI] fixing seqeval install in ci by pinning setuptools-scm

closed

MEMBER

The latest setuptools-scm version supported on 3.6 is 6.4.2. However for some reason circleci has version 7, which doesn't work. I fixed this by pinning the version of setuptools-scm in the circleci job Fix https://github.com/huggingface/datasets/issues/4544

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PR_kwDODunzps46KV-y

Make DuplicateKeysError more user friendly [For Issue #2556]

closed

CONTRIBUTOR

# What does this PR do? ## Summary *DuplicateKeysError error does not provide any information regarding the examples which have the same the key.* *This information is very helpful for debugging the dataset generator script.* ## Additions - ## Changes - Changed `DuplicateKeysError Class` in `src/datasets/keyhash.py` to add current index and duplicate_key_indices to error message. - Changed `check_duplicate_keys` function in `src/datasets/arrow_writer.py` to find indices of examples with duplicate hash if duplicate keys are found. ## Deletions - ## To do : - [x] Find way to find and print path `<Path to Dataset>` in Error message ## Issues Addressed : Fixes #2556

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I_kwDODunzps5MUuJ0

[CI] seqeval installation fails sometimes on python 3.6

closed

MEMBER

The CI sometimes fails to install seqeval, which cause the `seqeval` metric tests to fail. The installation fails because of this error: ``` Collecting seqeval Downloading seqeval-1.2.2.tar.gz (43 kB) |███████▌ | 10 kB 42.1 MB/s eta 0:00:01 |███████████████ | 20 kB 53.3 MB/s eta 0:00:01 |██████████████████████▌ | 30 kB 67.2 MB/s eta 0:00:01 |██████████████████████████████ | 40 kB 76.1 MB/s eta 0:00:01 |████████████████████████████████| 43 kB 10.0 MB/s Preparing metadata (setup.py) ... - error ERROR: Command errored out with exit status 1: command: /home/circleci/.pyenv/versions/3.6.15/bin/python3.6 -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py'"'"'; __file__='"'"'/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-pip-egg-info-pf54_vqy cwd: /tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/ Complete output (22 lines): Traceback (most recent call last): File "<string>", line 1, in <module> File "/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py", line 56, in <module> 'Programming Language :: Python :: Implementation :: PyPy' File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/__init__.py", line 143, in setup return distutils.core.setup(**attrs) File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/distutils/core.py", line 108, in setup _setup_distribution = dist = klass(attrs) File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/dist.py", line 442, in __init__ k: v for k, v in attrs.items() File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/distutils/dist.py", line 281, in __init__ self.finalize_options() File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/dist.py", line 601, in finalize_options ep.load()(self, ep.name, value) File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/pkg_resources/__init__.py", line 2346, in load return self.resolve() File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/pkg_resources/__init__.py", line 2352, in resolve module = __import__(self.module_name, fromlist=['__name__'], level=0) File "/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/.eggs/setuptools_scm-7.0.2-py3.6.egg/setuptools_scm/__init__.py", line 5 from __future__ import annotations ^ SyntaxError: future feature annotations is not defined ---------------------------------------- WARNING: Discarding https://files.pythonhosted.org/packages/9d/2d/233c79d5b4e5ab1dbf111242299153f3caddddbb691219f363ad55ce783d/seqeval-1.2.2.tar.gz#sha256=f28e97c3ab96d6fcd32b648f6438ff2e09cfba87f05939da9b3970713ec56e6f (from https://pypi.org/simple/seqeval/). Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output. ``` for example in https://app.circleci.com/pipelines/github/huggingface/datasets/12665/workflows/93878eb9-a923-4b35-b2e7-c5e9b22f10ad/jobs/75300 Here is a diff of the pip install logs until the error is reached: https://www.diffchecker.com/VkQDLeQT This could be caused by the latest updates of setuptools-scm

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PR_kwDODunzps46IiEp

[CI] Fix upstream hub test url

closed

MEMBER

Some tests were still using moon-stagign instead of hub-ci. I also updated the token to use one dedicated to `datasets`

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I_kwDODunzps5MT1yF

[to_tf_dataset] Use Feather for better compatibility with TensorFlow ?

open

MEMBER

To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory. It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library. Here are a few points to explore - [ ] check the performance of ArrowFeatherDataset in tf.data - [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc. We would also need to implement sharding when loading a dataset (this will be done anyway for #546) cc @Rocketknight1 @gante feel free to comment in case I missed anything ! I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data

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PR_kwDODunzps46HyPK

Fix timestamp conversion from Pandas to Python datetime in streaming mode

closed

MEMBER

Arrow accepts both pd.Timestamp and datetime.datetime objects to create timestamp arrays. However a timestamp array is always converted to datetime.datetime objects. This created an inconsistency between streaming in non-streaming. e.g. the `ett` dataset outputs datetime.datetime objects in non-streaming but pd.timestamp in streaming. I fixed this by always converting pd.Timestamp to datetime.datetime during the example encoding step. I fixed the same issue for pd.Timedelta as well. Finally I added an extra step of conversion for Series and DataFrame to take this into account in case such data are passed as Series or DataFrame. Fix https://github.com/huggingface/datasets/issues/4533 Related to https://github.com/huggingface/datasets-server/issues/397

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I_kwDODunzps5MTW5e

Avoid splitting by` .py` for the file.

closed

NONE

https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272 Hello, Thanks you for this library . I was using it and I had one edge case. my home folder name ends with `.py` it is `/home/espoir.py` so anytime I am running the code to load a local module this code here it is failing because after splitting it is trying to save the code to my home directory. Step to reproduce. - If you have a home folder which ends with `.py` - load a module with a local folder `qa_dataset = load_dataset("src/data/build_qa_dataset.py")` it is failed A possible workaround would be to use pathlib at the mentioned line ` meta_path = Path(importable_local_file).parent.joinpath("metadata.json")` this can alivate the issue . Let me what are your thought on this and I can try to fix it by A PR.

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PR_kwDODunzps46GfWv

Replace deprecated logging.warn with logging.warning

closed

CONTRIBUTOR

Replace `logging.warn` (deprecated in [Python 2.7, 2011](https://github.com/python/cpython/commit/04d5bc00a219860c69ea17eaa633d3ab9917409f)) with `logging.warning` (added in [Python 2.3, 2003](https://github.com/python/cpython/commit/6fa635df7aa88ae9fd8b41ae42743341316c90f7)). * https://docs.python.org/3/library/logging.html#logging.Logger.warning * https://github.com/python/cpython/issues/57444

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I_kwDODunzps5MQj56

Dataset Viewer issue for Pile of Law

closed

NONE

### Link https://huggingface.co/datasets/pile-of-law/pile-of-law ### Description Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines and so would like to turn off dataset previews. But we do not want to collect user emails because it would violate single blind review, allowing us to deduce potential reviewers' identities. Is there a way that we can turn off the dataset viewer without collecting identity information? Thanks so much! ### Owner Yes

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PR_kwDODunzps46ESJn

Fix WMT dataset loading issue and docs update

closed

CONTRIBUTOR

This PR is a fix for #4354 Changes are made for `wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t`. And READMEs are updated for the corresponding datasets. As I am on a M1 Mac, I am not able to create a virtual `dev` environment using `pip install -e ".[dev]"`. Issue is with `tensorflow-text` not supported on M1s and there is no supporting repo by Apple or Google. So, if I was needed to perform local testing, I am not able to do that. Let me know, if any additional changes are required. Thanks

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PR_kwDODunzps46C2z6

Properly raise FileNotFound even if the dataset is private

closed

MEMBER

`tests/test_load.py::test_load_streaming_private_dataset` was failing because the hub now returns 401 when getting the HfApi.dataset_info of a dataset without authentication. `load_dataset` was raising ConnectionError, while it should be FileNoteFoundError since it first checks for local files before checking the Hub. Moreover when use_auth_token is not set (default is False), we should not pass `token=None` to HfApi.dataset_info, or it will use the local token by default - instead it should use no token. It's currently not possible to ask for no token to be used, so as a workaround I simply set token="no-token"

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PR_kwDODunzps46BnXq

Add `batch_size` parameter when calling `add_faiss_index` and `add_faiss_index_from_external_arrays`

closed

MEMBER

Currently, even though the `batch_size` when adding vectors to the FAISS index can be tweaked in `FaissIndex.add_vectors()`, the function `ArrowDataset.add_faiss_index` doesn't have either the parameter `batch_size` to be propagated to the nested `FaissIndex.add_vectors` function or `*args, **kwargs`, so on, this PR adds the `batch_size` parameter to both `ArrowDataset.add_faiss_index` and `ArrowDataset.add_faiss_index_from_external_arrays`. This is useful so as to tweak the `batch_size` according to the VM specifications.

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PR_kwDODunzps46AFK_

Add `tldr_news` dataset

closed

NONE

This PR aims at adding support for a news dataset: `tldr news`. This dataset is based on the daily [tldr tech newsletter](https://tldr.tech/newsletter) and contains a `headline` as well as a `content` for every piece of news contained in a newsletter.

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I_kwDODunzps5MILNi

Timestamp not returned as datetime objects in streaming mode

closed

MEMBER

As reported in (internal) https://github.com/huggingface/datasets-server/issues/397 ```python >>> from datasets import load_dataset >>> dataset = load_dataset("ett", name="h2", split="test", streaming=True) >>> d = next(iter(dataset)) >>> d['start'] Timestamp('2016-07-01 00:00:00') ``` while loading in non-streaming mode it returns `datetime.datetime(2016, 7, 1, 0, 0)`

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PR_kwDODunzps459kB7

Add Video feature

closed

CONTRIBUTOR

The following adds a `Video` feature for encoding/decoding videos on the fly from in memory bytes. It uses my own `encoded-video` library which is basically `pytorchvideo`'s encoded video but with all the `torch` specific stuff stripped out. Because of that, and because the tool I used under the hood is not very mature, I leave this as a draft idea that we can use to build off of.

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I_kwDODunzps5MHkzc

Dataset Viewer issue for CSV datasets

closed

CONTRIBUTOR

### Link https://huggingface.co/datasets/scikit-learn/breast-cancer-wisconsin ### Description I'm populating CSV datasets [here](https://huggingface.co/scikit-learn) but the viewer is not enabled and it looks for a dataset loading script, the datasets aren't on queue as well. You can replicate the problem by simply uploading any CSV dataset. ### Owner Yes

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PR_kwDODunzps458n_S

Add AudioFolder packaged loader

closed

CONTRIBUTOR

will close #3964 AudioFolder is almost identical to ImageFolder except for inferring labels is not the default behavior (`drop_labels` is set to True in config), the option of inferring them is preserved though. The weird thing is happening with the `test_data_files_with_metadata_and_archives` when `streaming` is `True`. Here is the log from the CI: ``` ../.pyenv/versions/3.6.15/lib/python3.6/site-packages/datasets/features/audio.py:237: in _decode_non_mp3_path_like array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) ../.pyenv/versions/3.6.15/lib/python3.6/site-packages/librosa/util/decorators.py:88: in inner_f return f(*args, **kwargs) ../.pyenv/versions/3.6.15/lib/python3.6/site-packages/librosa/core/audio.py:176: in load raise (exc) ../.pyenv/versions/3.6.15/lib/python3.6/site-packages/librosa/core/audio.py:155: in load context = sf.SoundFile(path) ../.pyenv/versions/3.6.15/lib/python3.6/site-packages/soundfile.py:629: in __init__ self._file = self._open(file, mode_int, closefd) ../.pyenv/versions/3.6.15/lib/python3.6/site-packages/soundfile.py:1184: in _open "Error opening {0!r}: ".format(self.name)) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ err = 72 prefix = "Error opening <zipfile.ZipExtFile name='audio_file.wav' mode='r' compress_type=deflate>: " def _error_check(err, prefix=""): """Pretty-print a numerical error code if there is an error.""" if err != 0: err_str = _snd.sf_error_number(err) > raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace')) E RuntimeError: Error opening <zipfile.ZipExtFile name='audio_file.wav' mode='r' compress_type=deflate>: Error in WAV file. No 'data' chunk marker. ``` I hadn't been able to reproduce this locally until I created the same test environment (I mean with `pip install .[tests]`) with python3.6. The same env but with python3.8 passes the test! I didn't manage to figure out what's wrong, I also tried simply to replace the test wav file and still got the same error. Versions of `soundfile`, `librosa` and `libsndfile` are identical. Might it be something with zip compression? Sounds weird but I don't have any other ideas... TODO: - [x] align with #4622 - [x] documentation - [x] tests for AutoFolder?

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I_kwDODunzps5MGVfX

Ecoset

closed

NONE

## Adding a Dataset - **Name:** *Ecoset* - **Description:** *https://www.kietzmannlab.org/ecoset/* - **Paper:** *https://doi.org/10.1073/pnas.2011417118* - **Data:** *https://codeocean.com/capsule/9570390/tree/v1* - **Motivation:** **Ecoset** was created as a clean and ecologically valid alternative to **Imagenet**. It is a large image recognition dataset, similar to Imagenet in size and structure. However, the authors of ecoset claim several improvements over Imagenet, like: - more ecologically valid classes (e.g. not over-focussed on distinguishing different dog breeds) - less NSFW content - 'pre-packed image recognition models' that come with the dataset and can be used for validation of other models. I am working for one of the authors of the paper with the aim of bringing Ecoset to huggingface datasets. Therefore I can work on this issue personally, but could use some help from devs and experienced users if the dataset is of interest to them. I phrased some of my questions on [discuss.huggingface](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhandling-large-image-datasets%2F19373).

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I_kwDODunzps5MGJPz

Memory leak when iterating a Dataset

closed

MEMBER

e## Describe the bug It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop) ## Steps to reproduce the bug ```python import gc import logging import time import pyarrow from datasets import load_dataset from tqdm import trange import os, psutil logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) process = psutil.Process(os.getpid()) print(process.memory_info().rss) # output: 633507840 bytes corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus'] # or "BeIR/trec-covid" for a smaller dataset print(process.memory_info().rss) # output: 698601472 bytes logger.info("Applying method to all examples in all splits") for i in trange(0, len(corpus), 1000): batch = corpus[i:i+1000] data = pyarrow.total_allocated_bytes() if data > 0: logger.info(f"{i}/{len(corpus)}: {data}") print(process.memory_info().rss) # output: 3788247040 bytes del batch gc.collect() print(process.memory_info().rss) # output: 3788247040 bytes logger.info("Done...") time.sleep(100) ``` ## Expected results Limited memory usage, and memory to be freed after processing ## Actual results Memory leak  You can see how the memory allocation keeps increasing until it reaches a steady state when we hit the `time.sleep(100)`, which showcases that even the garbage collector couldn't free the allocated memory ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 8.0.0 - Pandas version: 1.4.2

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I_kwDODunzps5MFx5w

Dataset Viewer issue for vadis/sv-ident

closed

MEMBER

### Link https://huggingface.co/datasets/vadis/sv-ident ### Description The dataset preview does not work: ``` Server Error Status code: 400 Exception: Status400Error Message: The dataset does not exist. ``` However, the dataset is streamable and works locally: ```python In [1]: from datasets import load_dataset; ds = load_dataset("sv-ident.py", split="train", streaming=True); item = next(iter(ds)); item Using custom data configuration default Out[1]: {'sentence': 'Our point, however, is that so long as downward (favorable) comparisons overwhelm the potential for unfavorable comparisons, system justification should be a likely outcome amongst the disadvantaged.', 'is_variable': 1, 'variable': ['exploredata-ZA5400_VarV66', 'exploredata-ZA5400_VarV53'], 'research_data': ['ZA5400'], 'doc_id': '73106', 'uuid': 'b9fbb80f-3492-4b42-b9d5-0254cc33ac10', 'lang': 'en'} ``` CC: @e-tornike ### Owner No

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I_kwDODunzps5MFxFZ

split cache used when processing different split

open

CONTRIBUTOR

## Describe the bug` ``` ds1 = load_dataset('squad', split='validation') ds2 = load_dataset('squad', split='train') ds1 = ds1.map(some_function) ds2 = ds2.map(some_function) assert ds1 == ds2 ``` This happens when ds1 and ds2 are created in `pytorch_lightning.DataModule` through ``` class myDataModule: def train_dataloader(self): ds = load_dataset('squad', split='train') ds = ds.map(some_function) return [ds] def val_dataloader(self): ds = load_dataset('squad', split="validation") ds = ds.map(some_function) return [ds] ``` I don't know if it depends on `pytorch_lightning` or `datasets` but setting `ds.map(some_function, load_from_cache_file=False)` fixes the issue. If this is not enough to replicate I will try and provide and MWE, I don't have time now so I thought I wuld open the issue first!

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I_kwDODunzps5MFbZ6

Out of memory error on workers while running Beam+Dataflow

closed

MEMBER

## Describe the bug While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files). Previously we ran the preprocessing for the "dev" config (only dev files) with success. Train data files are larger than dev ones and apparently workers run out of memory while processing them. Any help/hint is welcome! Error message: ``` Data channel closed, unable to receive additional data from SDK sdk-0-0 ``` Info from the Diagnostics tab: ``` Out of memory: Killed process 1882 (python) total-vm:6041764kB, anon-rss:3290928kB, file-rss:0kB, shmem-rss:0kB, UID:0 pgtables:9520kB oom_score_adj:900 The worker VM had to shut down one or more processes due to lack of memory. ``` ## Additional information ### Stack trace ``` Traceback (most recent call last): File "/home/albert_huggingface_co/natural_questions/venv/bin/datasets-cli", line 8, in <module> sys.exit(main()) File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main service.run() File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/run_beam.py", line 127, in run builder.download_and_prepare( File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 1389, in _download_and_prepare pipeline_results.wait_until_finish() File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/apache_beam/runners/dataflow/dataflow_runner.py", line 1667, in wait_until_finish raise DataflowRuntimeException( apache_beam.runners.dataflow.dataflow_runner.DataflowRuntimeException: Dataflow pipeline failed. State: FAILED, Error: Data channel closed, unable to receive additional data from SDK sdk-0-0 ``` ### Logs ``` Error message from worker: Data channel closed, unable to receive additional data from SDK sdk-0-0 Workflow failed. Causes: S30:train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/Read+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/GroupByWindow+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/FlatMap(restore_timestamps)+train/ReadAllFromText/ReadAllFiles/Reshard/RemoveRandomKeys+train/ReadAllFromText/ReadAllFiles/ReadRange+train/Map(_parse_example)+train/Encode+train/Count N. Examples+train/Get values/Values+train/Save to parquet/Write/WriteImpl/WindowInto(WindowIntoFn)+train/Save to parquet/Write/WriteImpl/WriteBundles+train/Save to parquet/Write/WriteImpl/Pair+train/Save to parquet/Write/WriteImpl/GroupByKey/Write failed., The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors. The work item was attempted on these workers: beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: Data channel closed, unable to receive additional data from SDK sdk-0-0, beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-bwsj Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-5052 Root cause: The worker lost contact with the service. ```

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I_kwDODunzps5MDNRC

Downloading via Apache Pipeline, client cancelled (org.apache.beam.vendor.grpc.v1p43p2.io.grpc.StatusRuntimeException)

open

NONE

## Describe the bug When downloading some `wikipedia` languages (in particular, I'm having a hard time with Spanish, Cebuano, and Russian) via FlinkRunner, I encounter the exception in the title. I have been playing with package versions a lot, because unfortunately, the different dependencies required by these packages seem to be incompatible in terms of versions (dill and requests, for instance). It should be noted that the following code runs for several hours without issue, executing the `load_dataset()` function, before the exception occurs. ## Steps to reproduce the bug ```python # bash commands !pip install datasets !pip install apache-beam[interactive] !pip install mwparserfromhell !pip install dill==0.3.5.1 !pip install requests==2.23.0 # imports import os from datasets import load_dataset import apache_beam as beam import mwparserfromhell from google.colab import drive import dill import requests # mount drive drive_dir = os.path.join(os.getcwd(), 'drive') drive.mount(drive_dir) # confirming the versions of these two packages are the ones that are suggested by the outputs from the bash commands print(dill.__version__) print(requests.__version__) lang = 'es' # or 'ru' or 'ceb' - these are the ones causing the issue lang_dir = os.path.join(drive_dir, 'path/to/my/folder', lang) if not os.path.exists(lang_dir): x = None x = load_dataset('wikipedia', '20220301.' + lang, beam_runner='Flink', split='train') x.save_to_disk(lang_dir) ``` ## Expected results Although some warnings are generally produced by this code (run in Colab Notebook), most languages I've tried have been successfully downloaded. It should simply go through without issue, but for these languages, I am continually encountering this error. ## Actual results Traceback below: ``` Exception in thread run_worker_3-1: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 234, in run for work_request in self._control_stub.Control(get_responses()): File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 426, in __next__ return self._next() File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 826, in _next raise self grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with: status = StatusCode.UNAVAILABLE details = "Socket closed" debug_error_string = "{"created":"@1655593643.871830638","description":"Error received from peer ipv4:127.0.0.1:44441","file":"src/core/lib/surface/call.cc","file_line":952,"grpc_message":"Socket closed","grpc_status":14}" > Traceback (most recent call last): File "apache_beam/runners/common.py", line 1198, in apache_beam.runners.common.DoFnRunner.process File "apache_beam/runners/common.py", line 718, in apache_beam.runners.common.PerWindowInvoker.invoke_process File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 426, in __getitem__ self._cache[target_window] = self._side_input_data.view_fn(raw_view) File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 391, in <lambda> lambda iterable: from_runtime_iterable(iterable, view_options)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 512, in _from_runtime_iterable head = list(itertools.islice(it, 2)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1228, in _lazy_iterator self._underlying.get_raw(state_key, continuation_token)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1019, in get_raw continuation_token=continuation_token))) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1060, in _blocking_request raise RuntimeError(response.error) RuntimeError: Unknown process bundle instruction id '26' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 267, in _execute response = task() File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 340, in <lambda> lambda: self.create_worker().do_instruction(request), request) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 581, in do_instruction getattr(request, request_type), request.instruction_id) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 618, in process_bundle bundle_processor.process_bundle(instruction_id)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 996, in process_bundle element.data) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 221, in process_encoded self.output(decoded_value) File "apache_beam/runners/worker/operations.py", line 346, in apache_beam.runners.worker.operations.Operation.output File "apache_beam/runners/worker/operations.py", line 348, in apache_beam.runners.worker.operations.Operation.output File "apache_beam/runners/worker/operations.py", line 215, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive File "apache_beam/runners/worker/operations.py", line 707, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam/runners/worker/operations.py", line 708, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam/runners/common.py", line 1200, in apache_beam.runners.common.DoFnRunner.process File "apache_beam/runners/common.py", line 1281, in apache_beam.runners.common.DoFnRunner._reraise_augmented File "apache_beam/runners/common.py", line 1198, in apache_beam.runners.common.DoFnRunner.process File "apache_beam/runners/common.py", line 718, in apache_beam.runners.common.PerWindowInvoker.invoke_process File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 426, in __getitem__ self._cache[target_window] = self._side_input_data.view_fn(raw_view) File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 391, in <lambda> lambda iterable: from_runtime_iterable(iterable, view_options)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 512, in _from_runtime_iterable head = list(itertools.islice(it, 2)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1228, in _lazy_iterator self._underlying.get_raw(state_key, continuation_token)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1019, in get_raw continuation_token=continuation_token))) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1060, in _blocking_request raise RuntimeError(response.error) RuntimeError: Unknown process bundle instruction id '26' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles'] ERROR:apache_beam.runners.worker.sdk_worker:Error processing instruction 26. Original traceback is Traceback (most recent call last): File "apache_beam/runners/common.py", line 1198, in apache_beam.runners.common.DoFnRunner.process File "apache_beam/runners/common.py", line 718, in apache_beam.runners.common.PerWindowInvoker.invoke_process File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 426, in __getitem__ self._cache[target_window] = self._side_input_data.view_fn(raw_view) File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 391, in <lambda> lambda iterable: from_runtime_iterable(iterable, view_options)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 512, in _from_runtime_iterable head = list(itertools.islice(it, 2)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1228, in _lazy_iterator self._underlying.get_raw(state_key, continuation_token)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1019, in get_raw continuation_token=continuation_token))) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1060, in _blocking_request raise RuntimeError(response.error) RuntimeError: Unknown process bundle instruction id '26' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 267, in _execute response = task() File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 340, in <lambda> lambda: self.create_worker().do_instruction(request), request) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 581, in do_instruction getattr(request, request_type), request.instruction_id) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 618, in process_bundle bundle_processor.process_bundle(instruction_id)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 996, in process_bundle element.data) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 221, in process_encoded self.output(decoded_value) File "apache_beam/runners/worker/operations.py", line 346, in apache_beam.runners.worker.operations.Operation.output File "apache_beam/runners/worker/operations.py", line 348, in apache_beam.runners.worker.operations.Operation.output File "apache_beam/runners/worker/operations.py", line 215, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive File "apache_beam/runners/worker/operations.py", line 707, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam/runners/worker/operations.py", line 708, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam/runners/common.py", line 1200, in apache_beam.runners.common.DoFnRunner.process File "apache_beam/runners/common.py", line 1281, in apache_beam.runners.common.DoFnRunner._reraise_augmented File "apache_beam/runners/common.py", line 1198, in apache_beam.runners.common.DoFnRunner.process File "apache_beam/runners/common.py", line 718, in apache_beam.runners.common.PerWindowInvoker.invoke_process File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 426, in __getitem__ self._cache[target_window] = self._side_input_data.view_fn(raw_view) File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 391, in <lambda> lambda iterable: from_runtime_iterable(iterable, view_options)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 512, in _from_runtime_iterable head = list(itertools.islice(it, 2)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1228, in _lazy_iterator self._underlying.get_raw(state_key, continuation_token)) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1019, in get_raw continuation_token=continuation_token))) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1060, in _blocking_request raise RuntimeError(response.error) RuntimeError: Unknown process bundle instruction id '26' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles'] ERROR:root:org.apache.beam.vendor.grpc.v1p43p2.io.grpc.StatusRuntimeException: CANCELLED: client cancelled ERROR:apache_beam.runners.worker.data_plane:Failed to read inputs in the data plane. Traceback (most recent call last): File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/data_plane.py", line 634, in _read_inputs for elements in elements_iterator: File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 426, in __next__ return self._next() File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 826, in _next raise self grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with: status = StatusCode.CANCELLED details = "Multiplexer hanging up" debug_error_string = "{"created":"@1655593654.436885887","description":"Error received from peer ipv4:127.0.0.1:43263","file":"src/core/lib/surface/call.cc","file_line":952,"grpc_message":"Multiplexer hanging up","grpc_status":1}" > Exception in thread read_grpc_client_inputs: Traceback (most recent call last): File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner self.run() File "/usr/lib/python3.7/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/data_plane.py", line 651, in <lambda> target=lambda: self._read_inputs(elements_iterator), File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/data_plane.py", line 634, in _read_inputs for elements in elements_iterator: File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 426, in __next__ return self._next() File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 826, in _next raise self grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with: status = StatusCode.CANCELLED details = "Multiplexer hanging up" debug_error_string = "{"created":"@1655593654.436885887","description":"Error received from peer ipv4:127.0.0.1:43263","file":"src/core/lib/surface/call.cc","file_line":952,"grpc_message":"Multiplexer hanging up","grpc_status":1}" > --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) [/tmp/ipykernel_219/3869142325.py](https://localhost:8080/#) in <module> 18 x = None 19 x = load_dataset('wikipedia', '20220301.' + lang, beam_runner='Flink', ---> 20 split='train') 21 x.save_to_disk(lang_dir) 3 frames [/usr/local/lib/python3.7/dist-packages/apache_beam/runners/portability/portable_runner.py](https://localhost:8080/#) in wait_until_finish(self, duration) 604 605 if self._runtime_exception: --> 606 raise self._runtime_exception 607 608 return self._state RuntimeError: Pipeline BeamApp-root-0618220708-b3b59a0e_d8efcf67-9119-4f76-b013-70de7b29b54d failed in state FAILED: org.apache.beam.vendor.grpc.v1p43p2.io.grpc.StatusRuntimeException: CANCELLED: client cancelled ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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PR_kwDODunzps452hgh

Update download url and improve card of `cats_vs_dogs` dataset

closed

COLLABORATOR

Improve the download URL (reported here: https://huggingface.co/datasets/cats_vs_dogs/discussions/1), remove the `image_file_path` column (not used in Transformers, so it should be safe) and add more info to the card.

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I_kwDODunzps5L_eCw

Try to reduce the number of datasets that require manual download

open

COLLABORATOR

> Currently, 41 canonical datasets require manual download. I checked their scripts and I'm pretty sure this number can be reduced to ≈ 30 by not relying on bash scripts to download data, hosting data directly on the Hub when the license permits, etc. Then, we will mostly be left with datasets with restricted access, which we can ignore from https://github.com/huggingface/datasets-server/issues/12#issuecomment-1026920432

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I_kwDODunzps5L_boN

Datasets method `.map` not hashing

closed

CONTRIBUTOR

## Describe the bug Datasets method `.map` not hashing, even with an empty no-op function ## Steps to reproduce the bug ```python from datasets import load_dataset # download 9MB dummy dataset ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean") def prepare_dataset(batch): return(batch) ds = ds.map( prepare_dataset, num_proc=1, desc="preprocess train dataset", ) ``` ## Expected results Hashed and cached dataset preprocessing ## Actual results Does not hash properly: ``` Parameter 'function'=<function prepare_dataset at 0x7fccb68e9280> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.3.dev0 - Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 8.0.0 - Pandas version: 1.4.2 cc @lhoestq

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I_kwDODunzps5L_RzM

Failure to hash `dataclasses` - results in functions that cannot be hashed or cached in `.map`

closed

CONTRIBUTOR

Dataclasses cannot be hashed. As a result, they cannot be hashed or cached if used in the `.map` method. Dataclasses are used extensively in Transformers examples scripts: (c.f. [CTC example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py)). Since dataclasses cannot be hashed, one has to define separate variables prior to passing dataclass attributes to the `.map` method: ```python phoneme_language = data_args.phoneme_language ``` in the example https://github.com/huggingface/transformers/blob/3c7e56fbb11f401de2528c1dcf0e282febc031cd/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L603-L630 ## Steps to reproduce the bug ```python from dataclasses import dataclass, field from datasets.fingerprint import Hasher @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ phoneme_language: str = field( default=None, metadata={"help": "The name of the phoneme language to use."} ) data_args = DataTrainingArguments(phoneme_language ="foo") Hasher.hash(data_args) phoneme_language = data_args.phoneme_language Hasher.hash(phoneme_language) ``` ## Expected results A hash. ## Actual results <details> <summary> Traceback </summary> ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) Input In [1], in <cell line: 16>() 10 phoneme_language: str = field( 11 default=None, metadata={"help": "The name of the phoneme language to use."} 12 ) 14 data_args = DataTrainingArguments(phoneme_language ="foo") ---> 16 Hasher.hash(data_args) 18 phoneme_language = data_args. phoneme_language 20 Hasher.hash(phoneme_language) File ~/datasets/src/datasets/fingerprint.py:237, in Hasher.hash(cls, value) 235 return cls.dispatch[type(value)](cls, value) 236 else: --> 237 return cls.hash_default(value) File ~/datasets/src/datasets/fingerprint.py:230, in Hasher.hash_default(cls, value) 228 @classmethod 229 def hash_default(cls, value: Any) -> str: --> 230 return cls.hash_bytes(dumps(value)) File ~/datasets/src/datasets/utils/py_utils.py:564, in dumps(obj) 562 file = StringIO() 563 with _no_cache_fields(obj): --> 564 dump(obj, file) 565 return file.getvalue() File ~/datasets/src/datasets/utils/py_utils.py:539, in dump(obj, file) 537 def dump(obj, file): 538 """pickle an object to a file""" --> 539 Pickler(file, recurse=True).dump(obj) 540 return File ~/hf/lib/python3.8/site-packages/dill/_dill.py:620, in Pickler.dump(self, obj) 618 raise PicklingError(msg) 619 else: --> 620 StockPickler.dump(self, obj) 621 return File /usr/lib/python3.8/pickle.py:487, in _Pickler.dump(self, obj) 485 if self.proto >= 4: 486 self.framer.start_framing() --> 487 self.save(obj) 488 self.write(STOP) 489 self.framer.end_framing() File /usr/lib/python3.8/pickle.py:603, in _Pickler.save(self, obj, save_persistent_id) 599 raise PicklingError("Tuple returned by %s must have " 600 "two to six elements" % reduce) 602 # Save the reduce() output and finally memoize the object --> 603 self.save_reduce(obj=obj, *rv) File /usr/lib/python3.8/pickle.py:687, in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj) 684 raise PicklingError( 685 "args[0] from __newobj__ args has the wrong class") 686 args = args[1:] --> 687 save(cls) 688 save(args) 689 write(NEWOBJ) File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id) 558 f = self.dispatch.get(t) 559 if f is not None: --> 560 f(self, obj) # Call unbound method with explicit self 561 return 563 # Check private dispatch table if any, or else 564 # copyreg.dispatch_table File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1838, in save_type(pickler, obj, postproc_list) 1836 postproc_list = [] 1837 postproc_list.append((setattr, (obj, '__qualname__', obj_name))) -> 1838 _save_with_postproc(pickler, (_create_type, ( 1839 type(obj), obj.__name__, obj.__bases__, _dict 1840 )), obj=obj, postproc_list=postproc_list) 1841 log.info("# %s" % _t) 1842 else: File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1140, in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list) 1137 pickler._postproc[id(obj)] = postproc_list 1139 # TODO: Use state_setter in Python 3.8 to allow for faster cPickle implementations -> 1140 pickler.save_reduce(*reduction, obj=obj) 1142 if is_pickler_dill: 1143 # pickler.x -= 1 1144 # print(pickler.x*' ', 'pop', obj, id(obj)) 1145 postproc = pickler._postproc.pop(id(obj)) File /usr/lib/python3.8/pickle.py:692, in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj) 690 else: 691 save(func) --> 692 save(args) 693 write(REDUCE) 695 if obj is not None: 696 # If the object is already in the memo, this means it is 697 # recursive. In this case, throw away everything we put on the 698 # stack, and fetch the object back from the memo. File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id) 558 f = self.dispatch.get(t) 559 if f is not None: --> 560 f(self, obj) # Call unbound method with explicit self 561 return 563 # Check private dispatch table if any, or else 564 # copyreg.dispatch_table File /usr/lib/python3.8/pickle.py:901, in _Pickler.save_tuple(self, obj) 899 write(MARK) 900 for element in obj: --> 901 save(element) 903 if id(obj) in memo: 904 # Subtle. d was not in memo when we entered save_tuple(), so 905 # the process of saving the tuple's elements must have saved (...) 909 # could have been done in the "for element" loop instead, but 910 # recursive tuples are a rare thing. 911 get = self.get(memo[id(obj)][0]) File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id) 558 f = self.dispatch.get(t) 559 if f is not None: --> 560 f(self, obj) # Call unbound method with explicit self 561 return 563 # Check private dispatch table if any, or else 564 # copyreg.dispatch_table File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1251, in save_module_dict(pickler, obj) 1248 if is_dill(pickler, child=False) and pickler._session: 1249 # we only care about session the first pass thru 1250 pickler._first_pass = False -> 1251 StockPickler.save_dict(pickler, obj) 1252 log.info("# D2") 1253 return File /usr/lib/python3.8/pickle.py:971, in _Pickler.save_dict(self, obj) 968 self.write(MARK + DICT) 970 self.memoize(obj) --> 971 self._batch_setitems(obj.items()) File /usr/lib/python3.8/pickle.py:997, in _Pickler._batch_setitems(self, items) 995 for k, v in tmp: 996 save(k) --> 997 save(v) 998 write(SETITEMS) 999 elif n: File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id) 558 f = self.dispatch.get(t) 559 if f is not None: --> 560 f(self, obj) # Call unbound method with explicit self 561 return 563 # Check private dispatch table if any, or else 564 # copyreg.dispatch_table File ~/datasets/src/datasets/utils/py_utils.py:862, in save_function(pickler, obj) 859 if state_dict: 860 state = state, state_dict --> 862 dill._dill._save_with_postproc( 863 pickler, 864 ( 865 dill._dill._create_function, 866 (obj.__code__, globs, obj.__name__, obj.__defaults__, closure), 867 state, 868 ), 869 obj=obj, 870 postproc_list=postproc_list, 871 ) 872 else: 873 closure = obj.func_closure File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1153, in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list) 1151 dest, source = reduction[1] 1152 if source: -> 1153 pickler.write(pickler.get(pickler.memo[id(dest)][0])) 1154 pickler._batch_setitems(iter(source.items())) 1155 else: 1156 # Updating with an empty dictionary. Same as doing nothing. KeyError: 140434581781568 ``` </details> ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.3.dev0 - Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.2 cc @lhoestq

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PR_kwDODunzps45zhqa

Create new sections for audio and vision in guides

closed

MEMBER

This PR creates separate sections in the guides for audio, vision, text, and general usage so it is easier for users to find loading, processing, or sharing guides specific to the dataset type they're working with. It'll also allow us to scale the docs to additional dataset types - like time series, tabular, etc. - while keeping our docs information architecture. Some other changes include: - ~Experimented with decorating text with some CSS to highlight guides specific to each modality. Hopefully, it'll be easier for users to find and realize that these different docs exist!~ Will experiment with this in a different PR. - Added deprecation warning for Metrics and redirect to Evaluate. - Updated `set_format` section to recommend using the new `to_tf_dataset` function if you need to convert to a TensorFlow dataset. - Reorganized `toctree` to nest general usage, audio, vision, and text sections under the how-to guides. - A quick review and edit to the Load and Process docs for clarity.

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PR_kwDODunzps45zMnB

Patch tests for hfh v0.8.0

closed

MEMBER

This PR patches testing utilities that would otherwise fail with hfh v0.8.0.

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PR_kwDODunzps45zBl0

Add tags for task_ids:summarization-* and task_categories:summarization*

closed

CONTRIBUTOR

yaml header at top of README.md file was edited to add task tags because I couldn't find the existing tags in the json separate Pull Request will modify dataset_infos.json to add these tags The Enron dataset (dataset id aeslc) is only tagged with: arxiv:1906.03497' languages:en pretty_name:AESLC Using the email subject_line field as a label or target variable it possible to create models for the following task_ids (in order of relevance): 'task_ids:summarization' 'task_ids:summarization-other-conversations-summarization' "task_ids:other-other-query-based-multi-document-summarization" 'task_ids:summarization-other-aspect-based-summarization' 'task_ids:summarization--other-headline-generation' The subject might also be used for the task_category "task_categories:summarization" E-mail chains might be used for the task category "task_categories:dialogue-system"

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PR_kwDODunzps45ykYX

Fix hashing for python 3.9

closed

MEMBER

In python 3.9, pickle hashes the `glob_ids` dictionary in addition to the `globs` of a function. Therefore the test at `tests/test_fingerprint.py::RecurseDumpTest::test_recurse_dump_for_function_with_shuffled_globals` is currently failing for python 3.9 To make hashing deterministic when the globals are not in the same order, we also need to make the order of `glob_ids` deterministic. Right now we don't have a CI to test python 3.9 but we should definitely have one. For this PR in particular I ran the tests locally using python 3.9 and they're passing now. Fix https://github.com/huggingface/datasets/issues/4506

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PR_kwDODunzps45x5mB

Add uppercased versions of image file extensions for automatic module inference

closed

COLLABORATOR

Adds the uppercased versions of the image file extensions to the supported extensions. Another approach would be to call `.lower()` on extensions while resolving data files, but uppercased extensions are not something we want to encourage out of the box IMO unless they are commonly used (as they are in the vision domain) Note that there is a slight discrepancy between the image file resolution and `imagefolder` as the latter calls `.lower()` on file extensions leading to some image file extensions being ignored by the resolution but not by the loader (e.g. `pNg`). Such extensions should also be discouraged, so I'm ignoring that case too. Fix #4514.

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I_kwDODunzps5L6CXO

Allow .JPEG as a file extension

closed

NONE

## Describe the bug When loading image data, HF datasets seems to recognize `.jpg` and `.jpeg` file extensions, but not e.g. .JPEG. As the naming convention .JPEG is used in important datasets such as imagenet, I would welcome if according extensions like .JPEG or .JPG would be allowed. ## Steps to reproduce the bug ```python # use bash to create 2 sham datasets with jpeg and JPEG ext !mkdir dataset_a !mkdir dataset_b !wget https://upload.wikimedia.org/wikipedia/commons/7/71/Dsc_%28179253513%29.jpeg -O example_img.jpeg !cp example_img.jpeg ./dataset_a/ !mv example_img.jpeg ./dataset_b/example_img.JPEG from datasets import load_dataset # working df1 = load_dataset("./dataset_a", ignore_verifications=True) #not working df2 = load_dataset("./dataset_b", ignore_verifications=True) # show print(df1, df2) ``` ## Expected results ``` DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 1 }) }) DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 1 }) }) ``` ## Actual results ``` FileNotFoundError: Unable to resolve any data file that matches '['**']' at /..PATH../dataset_b with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] ``` I know that it can be annoying to allow seemingly arbitrary numbers of file extensions. But I think this one would be really welcome.

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PR_kwDODunzps45xTqv

Update Google Cloud Storage documentation and add Azure Blob Storage example

closed

MEMBER

While I was going through the 🤗 Datasets documentation of the Cloud storage filesystems at https://huggingface.co/docs/datasets/filesystems, I realized that the Google Cloud Storage documentation could be improved e.g. bullet point says "Load your dataset" when the actual call was to "Save your dataset", in-line code comment was mentioning "s3 bucket" instead of "gcs bucket", and some more in-line comments could be included. Also, I think that mixing Google Cloud Storage documentation with AWS S3's one was a little bit confusing, so I moved all those to the end of the document under an h2 tab named "Other filesystems", with an h3 for "Google Cloud Storage". Besides that, I was currently working with Azure Blob Storage and found out that the URL to [adlfs](https://github.com/fsspec/adlfs) was common for both filesystems Azure Blob Storage and Azure DataLake Storage, as well as the URL, which was updated even though the redirect was working fine, so I decided to group those under the same row in the column of supported filesystems. And took also the change to add a small documentation entry as for Google Cloud Storage but for Azure Blob Storage, as I assume that AWS S3, GCP Cloud Storage, and Azure Blob Storage, are the most used cloud storage providers. Let me know if you're OK with these changes, or whether you want me to roll back some of those! :hugs:

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PR_kwDODunzps45xEDN

Add links to vision tasks scripts in ADD_NEW_DATASET template

closed

COLLABORATOR

Add links to vision dataset scripts in the ADD_NEW_DATASET template.

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PR_kwDODunzps45w7RN

Support all negative values in ClassLabel

closed

MEMBER

We usually use -1 to represent a missing label, but we should also support any negative values (some users use -100 for example). This is a regression from `datasets` 2.3 Fix https://github.com/huggingface/datasets/issues/4508

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PR_kwDODunzps45wq6o

Add regression test for `ArrowWriter.write_batch` when batch is empty

closed

MEMBER

As spotted by @cccntu in #4502, there's a logic bug in `ArrowWriter.write_batch` as the if-statement to handle the empty batches as detailed in the docstrings of the function ("Ignores the batch if it appears to be empty, preventing a potential schema update of unknown types."), the current if-statement is not handling properly `writer.write_batch({})` as an error is triggered. Also, if we add a regression test in `test_arrow_writer.py::test_write_batch` before applying the fix, the test will fail as when trying to write an empty batch as follows: ``` =================================================================================== short test summary info =================================================================================== FAILED tests/test_arrow_writer.py::test_write_batch[None-None] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[None-1] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[None-10] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields1-None] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields1-1] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields1-10] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields2-None] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields2-1] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields2-10] - ValueError: Schema and number of arrays unequal ======================================================================== 9 failed, 73 deselected, 7 warnings in 0.81s ========================================================================= ``` So the batch is not ignored when empty, as `batch_examples={}` won't match the condition `if batch_examples: ...`.

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PR_kwDODunzps45wkDl

Support skipping Parquet to Arrow conversion when using Beam

closed

MEMBER

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I_kwDODunzps5L3CZJ

cast_storage method from datasets.features

closed

NONE

## Describe the bug A bug occurs when mapping a function to a dataset object. I ran the same code with the same data yesterday and it worked just fine. It works when i run locally on an old version of datasets. ## Steps to reproduce the bug Steps are: - load whatever datset - write a preprocessing function such as "tokenize_and_align_labels" written in https://huggingface.co/docs/transformers/tasks/token_classification - map the function on dataset and get "ValueError: Class label -100 less than -1" from cast_storage method from datasets.features # Sample code to reproduce the bug def tokenize_and_align_labels(examples): tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True, max_length=38,padding="max_length") labels = [] for i, label in enumerate(examples[f"labels"]): word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word. previous_word_idx = None label_ids = [] for word_idx in word_ids: # Set the special tokens to -100. if word_idx is None: label_ids.append(-100) elif word_idx != previous_word_idx: # Only label the first token of a given word. label_ids.append(label[word_idx]) else: label_ids.append(-100) previous_word_idx = word_idx labels.append(label_ids) tokenized_inputs["labels"] = labels return tokenized_inputs tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") dt = dataset.map(tokenize_and_align_labels, batched=True) ## Expected results New dataset objects should load and do on older versions. ## Actual results "ValueError: Class label -100 less than -1" from cast_storage method from datasets.features ## Environment info everything works fine on older installations of datasets/transformers Issue arises when installing datasets on google collab under python3.7 I can't manage to find the exact output you're requirering but version printed is datasets-2.3.2

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I_kwDODunzps5L2pP8

How to let `load_dataset` return a `Dataset` instead of `DatasetDict` in customized loading script

closed

NONE

If the dataset does not need splits, i.e., no training and validation split, more like a table. How can I let the `load_dataset` function return a `Dataset` object directly rather than return a `DatasetDict` object with only one key-value pair. Or I can paraphrase the question in the following way: how to skip `_split_generators` step in `DatasetBuilder` to let `as_dataset` gives a single `Dataset` rather than a list`[Dataset]`? Many thanks for any help.

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I_kwDODunzps5L2REf

Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results

closed

NONE

## Describe the bug Sometimes I get messages about not being able to hash a method: `Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform datasets.arrow_dataset.Dataset. _map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.` Whilst the function looks like this: ```python @staticmethod def _separate_speaker_id_from_dialogue(example: arrow_dataset.Example): speaker_id, dialogue = tuple(zip(*(example["dialogue"]))) example["speaker_id"] = speaker_id example["dialogue"] = dialogue return example ``` This is the first step in my preprocessing pipeline, but sometimes the message about failure to hash is not appearing on the first step, but then appears on a later step. This error is sometimes causing a failure to use cached data, instead of re-running all steps again. ## Steps to reproduce the bug ```python import copy import datasets from datasets import arrow_dataset def main(): dataset = datasets.load_dataset("blended_skill_talk") res = dataset.map(method) print(res) def method(example: arrow_dataset.Example): example['previous_utterance_copy'] = copy.deepcopy(example['previous_utterance']) return example if __name__ == '__main__': main() ``` Run with: ``` python -m reproduce_error ``` ## Expected results Dataset is mapped and cached correctly. ## Actual results The code outputs this at some point: `Parameter 'function'=<function method at 0x7faa83d2a160> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Ubuntu 20.04.3 - Python version: 3.9.12 - PyArrow version: 8.0.0 - Datasets version: 2.3.1

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PR_kwDODunzps45uH-o

Fix double dots in data files

closed

MEMBER

As mentioned in https://github.com/huggingface/transformers/pull/17715 `data_files` can't find a file if the path contains double dots `/../`. This has been introduced in https://github.com/huggingface/datasets/pull/4412, by trying to ignore hidden files and directories (i.e. if they start with a dot) I fixed this and added a test cc @sgugger @ydshieh

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I_kwDODunzps5L15Cw

Can you please add the Stanford dog dataset?

closed

NONE

## Adding a Dataset - **Name:** *Stanford dog dataset* - **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/* - **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/* - **Data:** *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/ImageNetDogs/)* - **Motivation:** *The dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It is useful for fine-grain purpose * 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_kwDODunzps45twLR

Refactor and add metadata to fever dataset

closed

MEMBER

Related to: #4452 and #3792.

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I_kwDODunzps5L1pOk

Logic bug in arrow_writer?

closed

CONTRIBUTOR

https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488 I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows: ``` - if batch_examples and len(next(iter(batch_examples.values()))) == 0: + if not batch_examples or len(next(iter(batch_examples.values()))) == 0: return ``` @lhoestq

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PR_kwDODunzps45th2M

Corrected broken links in doc

closed

MEMBER

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PR_kwDODunzps45tdxk

Add `concatenate_datasets` for iterable datasets

closed

MEMBER

`concatenate_datasets` currently only supports lists of `datasets.Dataset`, not lists of `datasets.IterableDataset` like `interleave_datasets` Fix https://github.com/huggingface/datasets/issues/2564 I also moved `_interleave_map_style_datasets` from combine.py to arrow_dataset.py, since the logic depends a lot on the `Dataset` object internals And I moved `concatenate_datasets` from arrow_dataset.py to combine.py to have it with `interleave_datasets` (though it's also copied in arrow_dataset module for backward compatibility for now)

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PR_kwDODunzps45s6Jh

fix ETT m1/m2 test/val dataset

closed

CONTRIBUTOR

https://huggingface.co/datasets/ett/discussions/1

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I_kwDODunzps5L0rbF

WER and CER > 1

closed

NONE

## Describe the bug It seems that in some cases in which the `prediction` is longer than the `reference` we may have word/character error rate higher than 1 which is a bit odd. If it's a real bug I think I can solve it with a PR changing [this](https://github.com/huggingface/datasets/blob/master/metrics/wer/wer.py#L105) line to ```python return min(incorrect / total, 1.0) ``` ## Steps to reproduce the bug ```python from datasets import load_metric wer = load_metric("wer") wer_value = wer.compute(predictions=["Hi World vka"], references=["Hello"]) print(wer_value) ``` ## Expected results ``` 1.0 ``` ## Actual results ``` 3.0 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.0 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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PR_kwDODunzps45sYns

Re-add download_manager module in utils

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https://github.com/huggingface/datasets/pull/4384 moved `datasets.utils.download_manager` to `datasets.download.download_manager` This breaks `evaluate` which imports `DownloadMode` from `datasets.utils.download_manager` This PR re-adds `datasets.utils.download_manager` without circular imports. We could also show a message that says that accessing it is deprecated, but I think we can do this in a subsequent PR, and just focus on doing a patch release for now

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PR_kwDODunzps45sUnW

Replace `assertEqual` with `assertTupleEqual` in unit tests for verbosity

closed

MEMBER

As detailed in #4419 and as suggested by @mariosasko, we could replace the `assertEqual` assertions with `assertTupleEqual` when the assertion is between Tuples, in order to make the tests more verbose.

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PR_kwDODunzps45sAgO

Fix patching module that doesn't exist

closed

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Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true When trying to patch `scipy.io.loadmat`: ```python ModuleNotFoundError: No module named 'scipy' ``` Instead it shouldn't raise an error and do nothing Bug introduced by #4375 Fix https://github.com/huggingface/datasets/issues/4494

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I_kwDODunzps5LzuZn

Patching fails for modules that are not installed or don't exist

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Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true When trying to patch `scipy.io.loadmat`: ```python ModuleNotFoundError: No module named 'scipy' ``` Instead it shouldn't raise an error and do nothing We use patching to extend such functions to support remote URLs and work in streaming mode

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PR_kwDODunzps45qL7J

Add `@transmit_format` in `flatten`

closed

MEMBER

As suggested by @mariosasko in https://github.com/huggingface/datasets/pull/4411, we should include the `@transmit_format` decorator to `flatten`, `rename_column`, and `rename_columns` so as to ensure that the value of `_format_columns` in an `ArrowDataset` is properly updated. **Edit**: according to @mariosasko comment below, the decorator `@transmit_format` doesn't handle column renaming, so it's done manually for those instead.

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PR_kwDODunzps45pktu

Pin the revision in imagenet download links

closed

MEMBER

Use the commit sha in the data files URLs of the imagenet-1k download script, in case we want to restructure the data files in the future. For example we may split it into many more shards for better paralellism. cc @mariosasko

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I_kwDODunzps5Lvu1u

Dataset Viewer issue for Pavithree/test

closed

NONE

### Link https://huggingface.co/datasets/Pavithree/test ### Description I have extracted the subset of original eli5 dataset found at hugging face. However, while loading the dataset It throws ArrowNotImplementedError: Unsupported cast from string to null using function cast_null error. Is there anything missing from my end? Kindly help. ### Owner _No response_

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I_kwDODunzps5LvaJi

Use `torch.nested_tensor` for arrays of varying length in torch formatter

open

COLLABORATOR

Use `torch.nested_tensor` for arrays of varying length in `TorchFormatter`. The PyTorch API of nested tensors is in the prototype stage, so wait for it to become more mature.

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PR_kwDODunzps45oONF

Add SV-Ident dataset

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NONE

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PR_kwDODunzps45n6Ja

Update PASS dataset version

closed

COLLABORATOR

Update the PASS dataset to version v3 (the newest one) from the [version history](https://github.com/yukimasano/PASS/blob/main/version_history.txt). PS: The older versions are not exposed as configs in the script because v1 was removed from Zenodo, and the same thing will probably happen to v2.

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PR_kwDODunzps45nm5J

Support streaming UDHR dataset

closed

MEMBER

This PR: - Adds support for streaming UDHR dataset - Adds the BCP 47 language code as feature

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PR_kwDODunzps45kP88

Add CCAgT dataset

closed

NONE

As described in #4075 I could not generate the dummy data. Also, on the data repository isn't provided the split IDs, but I copy the functions that provide the correct data split. In summary, to have a better distribution, the data in this dataset should be separated based on the amount of NORs in each image.

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PR_kwDODunzps45kD7A

Fix cast to null

closed

MEMBER

It currently fails with `ArrowNotImplementedError` instead of `TypeError` when one tries to cast integer to null type. Because if this, type inference breaks when one replaces null values with integers in `map` (it first tries to cast to the previous type before inferring the new type). Fix https://github.com/huggingface/datasets/issues/4483

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PR_kwDODunzps45jywZ

Better ImportError message when a dataset script dependency is missing

closed