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https://github.com/huggingface/datasets/pull/3712

PR_kwDODunzps4ynVYy

Fix the error of msr_sqa dataset

open

NONE

Fix the error of _load_table_data function in msr_sqa dataset, it is wrong to use comma to split each row.

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https://github.com/huggingface/datasets/pull/3711

PR_kwDODunzps4ymmlK

Fix the error of _load_table_data function in msr_sqa dataset

closed

NONE

The _load_table_data function from the last version is wrong, it is wrong to use comma to split each row.

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https://github.com/huggingface/datasets/pull/3710

PR_kwDODunzps4ymQMQ

Fix CI code quality issue

closed

MEMBER

Fix CI code quality issue introduced by #3695.

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https://github.com/huggingface/datasets/pull/3709

PR_kwDODunzps4yi0J4

Set base path to hub url for canonical datasets

open

MEMBER

This should allow canonical datasets to use relative paths to download data files from the Hub cc @polinaeterna this will be useful if we have audio datasets that are canonical and for which you'd like to host data files

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I_kwDODunzps5Dh7nS

Loading JSON gets stuck with many workers/threads

open

CONTRIBUTOR

## Describe the bug Loading a JSON dataset with `load_dataset` can get stuck when running on a machine with many CPUs. This is especially an issue when loading a large dataset on a large machine. ## Steps to reproduce the bug I originally created the following script to reproduce the issue: ```python from datasets import load_dataset from multiprocessing import Process from tqdm import tqdm import datasets from transformers import set_seed def run_tasks_in_parallel(tasks, ds_list): for _ in tqdm(range(1000)): print('new batch') running_tasks = [Process(target=task, args=(ds, i)) for i, (task, ds) in enumerate(zip(tasks, ds_list))] for running_task in running_tasks: running_task.start() for running_task in running_tasks: running_task.join() def get_dataset(): dataset_name = 'transformersbook/codeparrot' ds = load_dataset(dataset_name+'-train', split="train", streaming=True) ds = ds.shuffle(buffer_size=1000, seed=1) return iter(ds) def get_next_element(ds, process_id, N=10000): for _ in range(N): _ = next(ds)['content'] print(f'process {process_id} done') return set_seed(1) datasets.utils.logging.set_verbosity_debug() n_processes = 8 tasks = [get_next_element for _ in range(n_processes)] args = [get_dataset() for _ in range(n_processes)] run_tasks_in_parallel(tasks, args) ``` Today I noticed that it can happen when running it on a single process on a machine with many cores without streaming. So just `load_dataset("transformersbook/codeparrot-train")` alone might cause the issue after waiting long enough or trying many times. It's a slightly random process which makes it especially hard to track down. When I encountered it today it had already processed 17GB of data (the size of the cache folder when it got stuck) before getting stuck. Here's my current understanding of the error. As far as I can tell it happens in the following block: https://github.com/huggingface/datasets/blob/be701e9e89ab38022612c7263edc015bc7feaff9/src/datasets/packaged_modules/json/json.py#L119-L139 When the try on line 121 fails and the `block_size` is increased it can happen that it can't read the JSON again and gets stuck indefinitely. A hint that points in that direction is that increasing the `chunksize` argument decreases the chance of getting stuck and vice versa. Maybe it is an issue with a lock on the file that is not properly released. ## Expected results Read a JSON before the end of the universe. ## Actual results Read a JSON not before the end of the universe. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-glibc2.28 - Python version: 3.9.10 - PyArrow version: 7.0.0 @lhoestq we dicsussed this a while ago. @albertvillanova we discussed this today :)

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I_kwDODunzps5DhEUP

`.select`: unexpected behavior with `indices`

open

NONE

## Describe the bug The `.select` method will not throw when sending `indices` bigger than the dataset length; `indices` will be wrapped instead. This behavior is not documented anywhere, and is not intuitive. ## Steps to reproduce the bug ```python from datasets import Dataset ds = Dataset.from_dict({"text": ["d", "e", "f"], "label": [4, 5, 6]}) res1 = ds.select([1, 2, 3])['text'] res2 = ds.select([1000])['text'] ``` ## Expected results Both results should throw an `Error`. ## Actual results `res1` will give `['e', 'f', 'd']` `res2` will give `['e']` ## Environment info Bug found from this environment: - `datasets` version: 1.16.1 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.7 - PyArrow version: 6.0.1 It was also replicated on `master`.

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I_kwDODunzps5DfEn6

Unable to load dataset 'big_patent'

open

NONE

## Describe the bug Unable to load the "big_patent" dataset ## Steps to reproduce the bug ```python load_dataset('big_patent', 'd', 'validation') ``` ## Expected results Download big_patents' validation split from the 'd' subset ## Getting an error saying: {FileNotFoundError}Local file ..\huggingface\datasets\downloads\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\bigPatentData\train.tar.gz doesn't exist ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:1.18.3 - Platform: Windows - Python version:3.8 - PyArrow version:7.0.0

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https://github.com/huggingface/datasets/pull/3705

PR_kwDODunzps4yfhyj

Raise informative error when loading a save_to_disk dataset

closed

MEMBER

People recurrently report error when trying to load a dataset (using `load_dataset`) that was previously saved using `save_to_disk`. This PR raises an informative error message telling them they should use `load_from_disk` instead. Close #3700.

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I_kwDODunzps5DeZmH

OSCAR-2109 datasets are misaligned and truncated

open

NONE

## Describe the bug The `oscar-corpus/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines. ## Steps to reproduce the bug A few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations: ```python from datasets import load_dataset dataset = load_dataset("oscar-corpus/OSCAR-2109", "deduplicated_fi", split="train", use_auth_token=True) entry = dataset[0] # entry["text"] is from fi_part_3.txt.gz # entry["meta"] is from fi_meta_part_2.jsonl.gz dataset = load_dataset("oscar-corpus/OSCAR-2109", "deduplicated_no", split="train", use_auth_token=True) entry = dataset[900000] # entry["text"] is from no_part_3.txt.gz and contains a blank line # entry["meta"] is from no_meta_part_1.jsonl.gz dataset = load_dataset("oscar-corpus/OSCAR-2109", "deduplicated_mk", split="train", streaming=True, use_auth_token=True) # 9088 texts in the dataset are empty ``` For `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table. For `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens. For `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. I'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424. ## Issues * The meta / text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts). * The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped. * Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation. ## Expected results All texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata. ## Fixes Not necessarily the exact fixes/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues: ```diff diff --git a/OSCAR-2109.py b/OSCAR-2109.py index bbac1076..5eee8de7 100644 --- a/OSCAR-2109.py +++ b/OSCAR-2109.py @@ -20,6 +20,7 @@ import collections import gzip import json +import os import datasets @@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder): with open(checksum_file, encoding="utf-8") as f: data_filenames = [line.split()[1] for line in f if line] data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames] - text_files = dl_manager.download([url for url in data_urls if url.endswith(".txt.gz")]) - metadata_files = dl_manager.download([url for url in data_urls if url.endswith(".jsonl.gz")]) + # sort filenames so corresponding parts are aligned + text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(".txt.gz")])) + metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(".jsonl.gz")])) + assert len(text_files) == len(metadata_files) metadata_and_text_files = list(zip(metadata_files, text_files)) + for meta_path, text_path in metadata_and_text_files: + # check that meta/text part numbers are the same + if "part" in os.path.basename(text_path): + assert ( + os.path.basename(text_path).replace(".txt.gz", "").split("_")[-1] + == os.path.basename(meta_path).replace(".jsonl.gz", "").split("_")[-1] + ) + else: + assert len(metadata_and_text_files) == 1 return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"metadata_and_text_files": metadata_and_text_files}), ] @@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder): def _generate_examples(self, metadata_and_text_files): """This function returns the examples in the raw (text) form by iterating on all the files.""" id_ = 0 - offset = 0 for meta_path, text_path in metadata_and_text_files: + # line offsets are per text file + offset = 0 logger.info("generating examples from = %s", text_path) - with gzip.open(open(text_path, "rb"), "rt", encoding="utf-8") as text_f: + # some texts contain non-Unix newlines that should not be + # interpreted as line breaks for the line counts in the metadata + # with readline() + with gzip.open(open(text_path, "rb"), "rt", encoding="utf-8", newline="\n") as text_f: with gzip.open(open(meta_path, "rb"), "rt", encoding="utf-8") as meta_f: for line in meta_f: # read meta @@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder): offset += 1 text_f.readline() # read text - text = "".join([text_f.readline() for _ in range(meta["nb_sentences"])]).rstrip() + text_lines = [text_f.readline() for _ in range(meta["nb_sentences"])] + # all lines contain text (no blank lines or EOF) + assert all(text_lines) + assert "\n" not in text_lines offset += meta["nb_sentences"] + # only strip the trailing newline + text = "".join(text_lines).rstrip("\n") yield id_, {"id": id_, "text": text, "meta": meta} id_ += 1 ``` I've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data.

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I_kwDODunzps5DdykU

ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'

open

NONE

hi : I want to use the seqeval indicator because of direct load_ When metric ('seqeval '), it will prompt that the network connection fails. So I downloaded the seqeval Py to load locally. Loading code: metric = load_ metric(path='mymetric/seqeval/seqeval.py') But tips: Traceback (most recent call last): File "/home/ubuntu/Python3.6_project/zyf_project/transformers/examples/pytorch/token-classification/run_ner.py", line 604, in <module> main() File "/home/ubuntu/Python3.6_project/zyf_project/transformers/examples/pytorch/token-classification/run_ner.py", line 481, in main metric = load_metric(path='mymetric/seqeval/seqeval.py') File "/home/ubuntu/Python3.6_project/zyf_project/transformers_venv_0209/lib/python3.7/site-packages/datasets/load.py", line 610, in load_metric dataset=False, File "/home/ubuntu/Python3.6_project/zyf_project/transformers_venv_0209/lib/python3.7/site-packages/datasets/load.py", line 450, in prepare_module f"To be able to use this {module_type}, you need to install the following dependencies" ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance' **What should I do? Please help me, thank you**

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PR_kwDODunzps4yahKc

Update the address to use https

open

NONE

The http address doesn't work anymore

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PR_kwDODunzps4yZ8Dw

Pin ElasticSearch

closed

MEMBER

Until we manage to support ES 8.0, I'm setting the version to `<8.0.0` Currently we're getting this error on 8.0: ```python ValueError: Either 'hosts' or 'cloud_id' must be specified ``` When instantiating a `Elasticsearch()` object

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I_kwDODunzps5DXkjQ

Unable to load a dataset

closed

NONE

## Describe the bug Unable to load a dataset from Huggingface that I have just saved. ## Steps to reproduce the bug On Google colab `! pip install datasets ` `from datasets import load_dataset` `my_path = "wiki_dataset"` `dataset = load_dataset('wikipedia', "20200501.fr")` `dataset.save_to_disk(my_path)` `dataset = load_dataset(my_path)` ## Expected results Loading the dataset ## Actual results ValueError: Couldn't cast _data_files: list<item: struct<filename: string>> child 0, item: struct<filename: string> child 0, filename: string _fingerprint: string _format_columns: null _format_kwargs: struct<> _format_type: null _indexes: struct<> _output_all_columns: bool _split: string to {'builder_name': Value(dtype='string', id=None), 'citation': Value(dtype='string', id=None), 'config_name': Value(dtype='string', id=None), 'dataset_size': Value(dtype='int64', id=None), 'description': Value(dtype='string', id=None), 'download_checksums': {}, 'download_size': Value(dtype='int64', id=None), 'features': {'title': {'dtype': Value(dtype='string', id=None), 'id': Value(dtype='null', id=None), '_type': Value(dtype='string', id=None)}, 'text': {'dtype': Value(dtype='string', id=None), 'id': Value(dtype='null', id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'post_processed': Value(dtype='null', id=None), 'post_processing_size': Value(dtype='null', id=None), 'size_in_bytes': Value(dtype='int64', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='string', id=None)}}, 'supervised_keys': Value(dtype='null', id=None), 'task_templates': Value(dtype='null', id=None), 'version': {'version_str': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'major': Value(dtype='int64', id=None), 'minor': Value(dtype='int64', id=None), 'patch': Value(dtype='int64', id=None)}} because column names don't match ## Environment info - `datasets` version: 1.18.3 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 6.0.1

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PR_kwDODunzps4yY49I

Add dev-only config to Natural Questions dataset

closed

MEMBER

As suggested by @lhoestq and @thomwolf, a new config has been added to Natural Questions dataset, so that only dev split can be downloaded. Fix #413.

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PR_kwDODunzps4yXtyQ

Add finetune-data CodeFill

open

NONE

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PR_kwDODunzps4yXeXo

Add code-fill datasets for pretraining/finetuning/evaluating

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NONE

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https://github.com/huggingface/datasets/pull/3696

PR_kwDODunzps4yXXgH

Force unique keys in newsqa dataset

open

MEMBER

Currently, it may raise `DuplicatedKeysError`. Fix #3630.

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PR_kwDODunzps4yXP44

Fix ClassLabel to/from dict when passed names_file

closed

MEMBER

Currently, `names_file` is a field of the data class `ClassLabel`, thus appearing when transforming it to dict (when saving infos). Afterwards, when trying to read it from infos, it conflicts with the other field `names`. This PR, removes `names_file` as a field of the data class `ClassLabel`. - it is only used at instantiation to generate the `labels` field Fix #3631.

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PR_kwDODunzps4yTTcQ

Standardize to `Example::`

open

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PR_kwDODunzps4yShiu

Update data URL in pubmed dataset

open

MEMBER

Fix #3655.

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PR_kwDODunzps4yQThV

Upgrade black to version ~=22.0

closed

MEMBER

Upgrades the `datasets` library quality tool `black` to use the first stable release of `black`, version 22.0.

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PR_kwDODunzps4yP2p5

WIP: update docs to new frontend/UI

open

NONE

### TLDR: Update `datasets` `docs` to the new syntax & frontend (as how it looks on [hf.co/transformers](https://huggingface.co/docs/transformers/index)) ## Checklist - [ ] update datasets docs to new syntax (should call `doc-builder convert`) (this PR) - [x] discuss `@property` methods frontend https://github.com/huggingface/doc-builder/pull/87 - [x] discuss `inject_arrow_table_documentation` (this PR) https://github.com/huggingface/datasets/pull/3690#discussion_r801847860 - [x] update datasets docs path on moon-landing https://github.com/huggingface/moon-landing/pull/2089 - [ ] update nginx `docs/datasets` to route to moon-landing (do similar to internal repo # 81) - [x] convert pyarrow docstring from Numpydoc style to groups style https://github.com/huggingface/doc-builder/pull/89(https://stackoverflow.com/a/24385103/6558628) - [x] handle `Raises` section on frontend and doc-builder https://github.com/huggingface/doc-builder/pull/86 - [x] check imgs path (this PR) (nothing to update here) - [ ] delete sphinx related files (this PR) - [ ] update github actions (doc quality check & PR doc) - [x] doc exaples block has to follow format `Examples::` https://github.com/huggingface/datasets/pull/3693 - [x] add `versions.yml` in doc-build https://github.com/huggingface/doc-build/pull/1 - [ ] add `versions.yml` in doc-build-dev

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PR_kwDODunzps4yPnp7

Fix streaming for servers not supporting HTTP range requests

closed

MEMBER

Some servers do not support HTTP range requests, whereas this is required to stream some file formats (like ZIP). ~~This PR implements a workaround for those cases, by download the files locally in a temporary directory (cleaned up by the OS once the process is finished).~~ This PR raises custom error explaining that streaming is not possible because data host server does not support HTTP range requests. Fix #3677.

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I_kwDODunzps5DL_yR

Pyarrow version error

closed

NONE

## Describe the bug I installed datasets(version 1.17.0, 1.18.0, 1.18.3) but i'm right now nor able to import it because of pyarrow. when i try to import it, i get the following error: `To use datasets, the module pyarrow>=3.0.0 is required, and the current version of pyarrow doesn't match this condition`. i tryed with all version of pyarrow execpt `4.0.0` but still get the same error. ## Steps to reproduce the bug ```python import datasets ``` ## Expected results A clear and concise description of the expected results. ## Actual results AttributeError Traceback (most recent call last) <ipython-input-19-652e886d387f> in <module> ----> 1 import datasets ~\AppData\Local\Continuum\anaconda3\lib\site-packages\datasets\__init__.py in <module> 26 27 ---> 28 if _version.parse(pyarrow.__version__).major < 3: 29 raise ImportWarning( 30 "To use `datasets`, the module `pyarrow>=3.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n" AttributeError: 'Version' object has no attribute 'major' ## Environment info Traceback (most recent call last): File "c:\users\alex\appdata\local\continuum\anaconda3\lib\runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) File "c:\users\alex\appdata\local\continuum\anaconda3\lib\runpy.py", line 85, in _run_code exec(code, run_globals) File "C:\Users\Alex\AppData\Local\Continuum\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 5, in <module> File "c:\users\alex\appdata\local\continuum\anaconda3\lib\site-packages\datasets\__init__.py", line 28, in <module> if _version.parse(pyarrow.__version__).major < 3: AttributeError: 'Version' object has no attribute 'major' - `datasets` version: - Platform: Linux(Ubuntu) and Windows: conda on the both - Python version: 3.7 - PyArrow version: 7.0.0

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I_kwDODunzps5DLwRO

Can't get the text data when calling to_tf_dataset

open

NONE

I am working with the SST2 dataset, and am using TensorFlow 2.5 I'd like to convert it to a `tf.data.Dataset` by calling the `to_tf_dataset` method. The following snippet is what I am using to achieve this: ``` from datasets import load_dataset from transformers import DefaultDataCollator data_collator = DefaultDataCollator(return_tensors="tf") dataset = load_dataset("sst") train_dataset = dataset["train"].to_tf_dataset(columns=['sentence'], label_cols="label", shuffle=True, batch_size=8,collate_fn=data_collator) ``` However, this only gets me the labels; the text--the most important part--is missing: ``` for s in train_dataset.take(1): print(s) #prints something like: ({}, <tf.Tensor: shape=(8,), ...>) ``` As you can see, it only returns the label part, not the data, as indicated by the empty dictionary, `{}`. So far, I've played with various settings of the method arguments, but to no avail; I do not want to perform any text processing at this time. On my quest to achieve what I want ( a `tf.data.Dataset`), I've consulted these resources: [https://www.philschmid.de/huggingface-transformers-keras-tf](https://www.philschmid.de/huggingface-transformers-keras-tf) [https://huggingface.co/docs/datasets/use_dataset.html?highlight=tensorflow](https://huggingface.co/docs/datasets/use_dataset.html?highlight=tensorflow) I was surprised to not find more extensive examples on how to transform a Hugginface dataset to one compatible with TensorFlow. If you could point me to where I am going wrong, please do so. Thanks in advance for your support. --- Edit: In the [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset), I found the following description: _In general, only columns that the model can use as input should be included here (numeric data only)._ Does this imply that no textual, i.e., `string` data can be loaded?

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I_kwDODunzps5DLsAK

`Translation` features cannot be `flatten`ed

open

CONTRIBUTOR

## Describe the bug (`Dataset.flatten`)[https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L1265] fails for columns with feature (`Translation`)[https://github.com/huggingface/datasets/blob/3edbeb0ec6519b79f1119adc251a1a6b379a2c12/src/datasets/features/translation.py#L8] ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("europa_ecdc_tm", "en2fr", split="train[:10]") print(dataset.features) # {'translation': Translation(languages=['en', 'fr'], id=None)} print(dataset[0]) # {'translation': {'en': 'Vaccination against hepatitis C is not yet available.', 'fr': 'Aucune vaccination contre l’hépatite C n’est encore disponible.'}} dataset.flatten() ``` ## Expected results `dataset.flatten` should flatten the `Translation` column as if it were a dict of `Value("string")` ```python dataset[0] # {'translation.en': 'Vaccination against hepatitis C is not yet available.', 'translation.fr': 'Aucune vaccination contre l’hépatite C n’est encore disponible.' } dataset.features # {'translation.en': Value("string"), 'translation.fr': Value("string")} ``` ## Actual results ```python In [31]: dset.flatten() --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-31-bb88eb5276ee> in <module> ----> 1 dset.flatten() [...]\site-packages\datasets\fingerprint.py in wrapper(*args, **kwargs) 411 # Call actual function 412 --> 413 out = func(self, *args, **kwargs) 414 415 # Update fingerprint of in-place transforms + update in-place history of transforms [...]\site-packages\datasets\arrow_dataset.py in flatten(self, new_fingerprint, max_depth) 1294 break 1295 dataset.info.features = self.features.flatten(max_depth=max_depth) -> 1296 dataset._data = update_metadata_with_features(dataset._data, dataset.features) 1297 logger.info(f'Flattened dataset from depth {depth} to depth {1 if depth + 1 < max_depth else "unknown"}.') 1298 dataset._fingerprint = new_fingerprint [...]\site-packages\datasets\arrow_dataset.py in update_metadata_with_features(table, features) 534 def update_metadata_with_features(table: Table, features: Features): 535 """To be used in dataset transforms that modify the features of the dataset, in order to update the features stored in the metadata of its schema.""" --> 536 features = Features({col_name: features[col_name] for col_name in table.column_names}) 537 if table.schema.metadata is None or b"huggingface" not in table.schema.metadata: 538 pa_metadata = ArrowWriter._build_metadata(DatasetInfo(features=features)) [...]\site-packages\datasets\arrow_dataset.py in <dictcomp>(.0) 534 def update_metadata_with_features(table: Table, features: Features): 535 """To be used in dataset transforms that modify the features of the dataset, in order to update the features stored in the metadata of its schema.""" --> 536 features = Features({col_name: features[col_name] for col_name in table.column_names}) 537 if table.schema.metadata is None or b"huggingface" not in table.schema.metadata: 538 pa_metadata = ArrowWriter._build_metadata(DatasetInfo(features=features)) KeyError: 'translation.en' ``` ## Environment info - `datasets` version: 1.18.3 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.7.10 - PyArrow version: 3.0.0

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PR_kwDODunzps4yLw3m

Add support for `Audio` and `Image` feature in `push_to_hub`

open

CONTRIBUTOR

Add support for the `Audio` and the `Image` feature in `push_to_hub`. The idea is to remove local path information and store file content under "bytes" in the Arrow table before the push. My initial approach (https://github.com/huggingface/datasets/commit/34c652afeff9686b6b8bf4e703c84d2205d670aa) was to use a map transform similar to [`decode_nested_example`](https://github.com/huggingface/datasets/blob/5e0f6068741464f833ff1802e24ecc2064aaea9f/src/datasets/features/features.py#L1023-L1056) while having decoding turned off, but I wasn't satisfied with the code quality, so I ended up using the `temporary_assignment` decorator to override `cast_storage`, which allows me to directly modify the underlying storage (the final op is similar to `Dataset.cast`) and results in a much simpler code. Additionally, I added the `allow_cast` flag that can disable this behavior in the situations where it's not needed (e.g. the dataset is already in the correct format for the Hub, etc.)

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PR_kwDODunzps4yIOer

[fix]: iwslt2017 download urls

open

NONE

Fixes #2076.

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PR_kwDODunzps4yGKoj

added told-br (brazilian hate speech) dataset

closed

CONTRIBUTOR

Hey, Adding ToLD-Br. Feel free to ask for modifications. Thanks!!

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PR_kwDODunzps4yGFml

adding told-br for toxic/abusive hatespeech detection

closed

CONTRIBUTOR

Hey, I'm adding our dataset from our paper published at AACL 2020. Feel free to ask for modifications. Thanks!

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PR_kwDODunzps4yFcpM

Fix TestCommand to move dataset_infos instead of copying

open

MEMBER

Why do we copy instead of moving the file? CC: @lhoestq @lvwerra

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PR_kwDODunzps4yFXm8

Fix TestCommand to copy dataset_infos to local dir with only data files

closed

MEMBER

Currently this case is missed. CC: @lvwerra

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I_kwDODunzps5C_9O1

Download datasets from a private hub

open

NONE

In the context of a private hub deployment, customers would like to use load_dataset() to load datasets from their hub, not from the public hub. This doesn't seem to be configurable at the moment and it would be nice to add this feature. The obvious workaround is to clone the repo first and then load it from local storage, but this adds an extra step. It'd be great to have the same experience regardless of where the hub is hosted. The same issue exists with the transformers library and the CLI. I'm going to create issues there as well, and I'll reference them below.

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PR_kwDODunzps4yCt91

Add code example in wikipedia card

closed

MEMBER

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I_kwDODunzps5C8pAi

Discovery cannot be streamed anymore

closed

CONTRIBUTOR

## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python from datasets import load_dataset iterable_dataset = load_dataset("discovery", name="discovery", split="train", streaming=True) list(iterable_dataset.take(1)) ``` ## Expected results The first row of the train split. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 365, in __iter__ for key, example in self._iter(): File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 362, in _iter yield from ex_iterable File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 272, in __iter__ yield from islice(self.ex_iterable, self.n) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 79, in __iter__ yield from self.generate_examples_fn(**self.kwargs) File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/discovery/542fab7a9ddc1d9726160355f7baa06a1ccc44c40bc8e12c09e9bc743aca43a2/discovery.py", line 333, in _generate_examples with open(data_file, encoding="utf8") as f: File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 64, in wrapper return function(*args, use_auth_token=use_auth_token, **kwargs) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py", line 369, in xopen file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/core.py", line 456, in open return open_files( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/core.py", line 288, in open_files fs, fs_token, paths = get_fs_token_paths( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/core.py", line 611, in get_fs_token_paths fs = filesystem(protocol, **inkwargs) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 253, in filesystem return cls(**storage_options) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 68, in __call__ obj = super().__call__(*args, **kwargs) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 57, in __init__ self.zip = zipfile.ZipFile(self.fo) File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/zipfile.py", line 1257, in __init__ self._RealGetContents() File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/zipfile.py", line 1320, in _RealGetContents endrec = _EndRecData(fp) File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/zipfile.py", line 263, in _EndRecData fpin.seek(0, 2) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 676, in seek raise ValueError("Cannot seek streaming HTTP file") ValueError: Cannot seek streaming HTTP file ``` ## Environment info - `datasets` version: 1.18.3 - Platform: Linux-5.11.0-1027-aws-x86_64-with-glibc2.31 - Python version: 3.9.6 - PyArrow version: 6.0.1

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I_kwDODunzps5C8Rcq

`None` replaced by `[]` after first batch in map

open

MEMBER

Sometimes `None` can be replaced by `[]` when running map: ```python from datasets import Dataset ds = Dataset.from_dict({"a": range(4)}) ds = ds.map(lambda x: {"b": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=["a"]) print(ds.to_pandas()) # b # 0 [None, [0]] # 1 [[], [0]] # 2 [[], [0]] # 3 [[], [0]] ``` This issue has been experienced when running the `run_qa.py` example from `transformers` (see issue https://github.com/huggingface/transformers/issues/15401) This can be due to a bug in when casting `None` in nested lists. Casting only happens after the first batch, since the first batch is used to infer the feature types. cc @sgugger

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I_kwDODunzps5C8NEI

Add CodeContests dataset

open

CONTRIBUTOR

## Adding a Dataset - **Name:** CodeContests - **Description:** CodeContests is a competitive programming dataset for machine-learning. - **Paper:** - **Data:** https://github.com/deepmind/code_contests - **Motivation:** This dataset was used when training [AlphaCode](https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode). 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_kwDODunzps4yBe17

Add FrugalScore metric

open

CONTRIBUTOR

This pull request add FrugalScore metric for NLG systems evaluation. FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance. Paper: https://arxiv.org/abs/2110.08559?context=cs Github: https://github.com/moussaKam/FrugalScore @lhoestq

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I_kwDODunzps5C78fY

`load_dataset("snli")` is different from dataset viewer

closed

NONE

## Describe the bug The dataset that is downloaded from the Hub via `load_dataset("snli")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., "neutral", "entailment", "contradiction"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2). Is this expected? ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Ubuntu 20.4 - Python version: 3.7

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PR_kwDODunzps4yBUrZ

Prioritize `module.builder_kwargs` over defaults in `TestCommand`

closed

CONTRIBUTOR

This fixes a bug in the `TestCommand` where multiple kwargs for `name` were passed if it was set in both default and `module.builder_kwargs`. Example error: ```Python Traceback (most recent call last): File "create_metadata.py", line 96, in <module> main(**vars(args)) File "create_metadata.py", line 86, in main metadata_command.run() File "/opt/conda/lib/python3.7/site-packages/datasets/commands/test.py", line 144, in run for j, builder in enumerate(get_builders()): File "/opt/conda/lib/python3.7/site-packages/datasets/commands/test.py", line 141, in get_builders name=name, cache_dir=self._cache_dir, data_dir=self._data_dir, **module.builder_kwargs TypeError: type object got multiple values for keyword argument 'name' ``` Let me know what you think.

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I_kwDODunzps5C7Yx9

Give an estimate of the dataset size in DatasetInfo

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** Currently, only part of the datasets provide `dataset_size`, `download_size`, `size_in_bytes` (and `num_bytes` and `num_examples` inside `splits`). I would want to get this information, or an estimation, for all the datasets. **Describe the solution you'd like** - get access to the git information for the dataset files hosted on the hub - look at the [`Content-Length`](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Length) for the files served by HTTP

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PR_kwDODunzps4x_kBx

feat: 🎸 generate info if dataset_infos.json does not exist

open

CONTRIBUTOR

in get_dataset_infos(). Also: add the `use_auth_token` parameter, and create get_dataset_config_info() ✅ Closes: #3013

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PR_kwDODunzps4x_OTI

Common voice validated partition

closed

CONTRIBUTOR

This patch adds access to the 'validated' partitions of CommonVoice datasets (provided by the dataset creators but not available in the HuggingFace interface yet). As 'validated' contains significantly more data than 'train' (although it contains both test and validation, so one needs to be careful there), it can be useful to train better models where no strict comparison with the previous work is intended.

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I_kwDODunzps5C5Fro

Couldn't cast array of type string error with cast_column

closed

NONE

## Describe the bug In OVH cloud during Huggingface Robust-speech-recognition event on a AI training notebook instance using jupyter lab and running jupyter notebook When using the dataset.cast_column("audio",Audio(sampling_rate=16_000)) method I get error  This was working with datasets version 1.17.1.dev0 but now with version 1.18.3 produces the error above. ## Steps to reproduce the bug load dataset:  remove columns:  run my fix_path function. This also creates the audio column that is referring to the absolute file path of the audio  Then I concatenate few other datasets and finally try the cast_column method  but get error:  ## Expected results A clear and concise description of the expected results. ## 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: 1.18.3 - Platform: OVH Cloud, AI Training section, container for Huggingface Robust Speech Recognition event image(baaastijn/ovh_huggingface)  - Python version: 3.8.8 - PyArrow version: 

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PR_kwDODunzps4x-Ujt

Process .opus files with torchaudio

closed

CONTRIBUTOR

@anton-l suggested to proccess .opus files with `torchaudio` instead of `soundfile` as it's faster:  (moreover, I didn't manage to load .opus files with `soundfile` / `librosa` locally on any my machine anyway for some reason, even with `ffmpeg` installed). For now my current changes work with locally stored file: ```python # download sample opus file (from MultilingualSpokenWords dataset) !wget https://huggingface.co/datasets/polinaeterna/test_opus/resolve/main/common_voice_tt_17737010.opus from datasets import Dataset, Audio audio_path = "common_voice_tt_17737010.opus" dataset = Dataset.from_dict({"audio": [audio_path]}).cast_column("audio", Audio(48000)) dataset[0] # {'audio': {'path': 'common_voice_tt_17737010.opus', # 'array': array([ 0.0000000e+00, 0.0000000e+00, 3.0517578e-05, ..., # -6.1035156e-05, 6.1035156e-05, 0.0000000e+00], dtype=float32), # 'sampling_rate': 48000}} ``` But it doesn't work when loading inside s dataset from bytes (I checked on [MultilingualSpokenWords](https://github.com/huggingface/datasets/pull/3666), the PR is a draft now, maybe the bug is somewhere there ) ```python import torchaudio with open(audio_path, "rb") as b: print(torchaudio.load(b)) # RuntimeError: Error loading audio file: failed to open file <in memory buffer> ```

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PR_kwDODunzps4x-ULz

Multilingual Spoken Words

open

CONTRIBUTOR

Add [Multillingual Spoken Words dataset](https://mlcommons.org/en/multilingual-spoken-words/) You can specify multiple languages for downloading 😌: ```python ds = load_dataset("datasets/ml_spoken_words", languages=["ar", "tt"]) ``` 1. I didn't take into account that each time you pass a set of languages the data for a specific language is downloaded even if it was downloaded before (since these are custom configs like `ar+tt` and `ar+tt+br`. Maybe that wasn't a good idea? 2. The script will have to be slightly changed after merge of https://github.com/huggingface/datasets/pull/3664 2. Just can't figure out what wrong with dummy files... 😞 Maybe we should get rid of them at some point 😁

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PR_kwDODunzps4x9TnU

Fix MP3 resampling when a dataset's audio files have different sampling rates

closed

MEMBER

The resampler needs to be updated if the `orig_freq` doesn't match the audio file sampling rate Fix https://github.com/huggingface/datasets/issues/3662

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PR_kwDODunzps4x7mg_

[WIP] Return local paths to Common Voice

open

MEMBER

Fixes https://github.com/huggingface/datasets/issues/3663 This is a proposed way of returning the old local file-based generator while keeping the new streaming generator intact. TODO: - [ ] brainstorm a bit more on https://github.com/huggingface/datasets/issues/3663 to see if we can do better - [ ] refactor the heck out of this PR to avoid completely copying the logic between the two generators

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I_kwDODunzps5C0iJ_

[Audio] Path of Common Voice cannot be used for audio loading anymore

open

MEMBER

## Describe the bug ## Steps to reproduce the bug ```python from datasets import load_dataset from torchaudio import load ds = load_dataset("common_voice", "ab", split="train") # both of the following commands fail at the moment load(ds[0]["audio"]["path"]) load(ds[0]["path"]) ``` ## Expected results The path should be the complete absolute path to the downloaded audio file not some relative path. ## Actual results ```bash ~/hugging_face/venv_3.9/lib/python3.9/site-packages/torchaudio/backend/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format) 150 filepath, frame_offset, num_frames, normalize, channels_first, format) 151 filepath = os.fspath(filepath) --> 152 return torch.ops.torchaudio.sox_io_load_audio_file( 153 filepath, frame_offset, num_frames, normalize, channels_first, format) 154 RuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11/ab/clips/common_voice_ab_19904194.mp3 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3.dev0 - Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27 - Python version: 3.9.1 - PyArrow version: 3.0.0

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I_kwDODunzps5C0XmT

[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates

closed

MEMBER

The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect. Here is a code to reproduce the issue: Let's first consider two audio files with different sampling rates 32000 and 16000: ```python # first download a mp3 file with sampling_rate=32000 !wget https://file-examples-com.github.io/uploads/2017/11/file_example_MP3_700KB.mp3 import torchaudio audio_path = "file_example_MP3_700KB.mp3" audio_path2 = audio_path.replace(".mp3", "_resampled.mp3") resample = torchaudio.transforms.Resample(32000, 16000) # create a new file with sampling_rate=16000 torchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000) ``` Then we can see an issue here when decoding: ```python from datasets import Dataset, Audio dataset = Dataset.from_dict({"audio": [audio_path, audio_path2]}).cast_column("audio", Audio(48000)) dataset[0] # decode the first audio file sets the resampler orig_freq to 32000 print(dataset .features["audio"]._resampler.orig_freq) # 32000 print(dataset[0]["audio"]["array"].shape) # here decoding is fine # (1308096,) dataset = Dataset.from_dict({"audio": [audio_path, audio_path2]}).cast_column("audio", Audio(48000)) dataset[1] # decode the second audio file sets the resampler orig_freq to 16000 print(dataset .features["audio"]._resampler.orig_freq) # 16000 print(dataset[0]["audio"]["array"].shape) # here decoding uses orig_freq=16000 instead of 32000 # (2616192,) ``` The value of `orig_freq` doesn't change no matter what file needs to be decoded cc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova The issue seems to be here in `Audio.decode_mp3`: https://github.com/huggingface/datasets/blob/4c417d52def6e20359ca16c6723e0a2855e5c3fd/src/datasets/features/audio.py#L176-L180

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PR_kwDODunzps4x61ad

Remove unnecessary 'r' arg in

closed

CONTRIBUTOR

Originally from #3489

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PR_kwDODunzps4x6xr8

Change HTTP links to HTTPS

open

CONTRIBUTOR

I tested the links. I also fixed some typos. Originally from #3489

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I_kwDODunzps5Cz8kI

push_to_hub but preview not working

closed

NONE

## Dataset viewer issue for '*happifyhealth/twitter_pnn*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/happifyhealth/twitter_pnn)* I used ``` dataset.push_to_hub("happifyhealth/twitter_pnn") ``` but the preview is not working. Am I the one who added this dataset ? Yes

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I_kwDODunzps5Cz0cL

Dataset viewer issue for *P3*

open

NONE

## Dataset viewer issue for '*P3*' **Link: https://huggingface.co/datasets/bigscience/P3** ``` Status code: 400 Exception: SplitsNotFoundError Message: The split names could not be parsed from the dataset config. ``` Am I the one who added this dataset ? No

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PR_kwDODunzps4x5f1I

Extend dataset builder for streaming in `get_dataset_split_names`

closed

CONTRIBUTOR

Currently, `get_dataset_split_names` doesn't extend a builder module to support streaming, even though it uses `StreamingDownloadManager` to download data. This PR fixes that. To test the change, run the following: ```bash pip install git+https://github.com/huggingface/datasets.git@fix-get_dataset_split_names-streaming python -c "from datasets import get_dataset_split_names; print(get_dataset_split_names('facebook/multilingual_librispeech', 'german', download_mode='force_redownload', revision='137923f945552c6afdd8b60e4a7b43e3088972c1'))" ```

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I_kwDODunzps5CyaNn

checksum error subjqa dataset

closed

NONE

## Describe the bug I get a checksum error when loading the `subjqa` dataset (used in the transformers book). ## Steps to reproduce the bug ```python from datasets import load_dataset subjqa = load_dataset("subjqa","electronics") ``` ## Expected results Loading the dataset ## Actual results ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-2-d2857d460155> in <module>() 2 from datasets import load_dataset 3 ----> 4 subjqa = load_dataset("subjqa","electronics") 3 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/lewtun/SubjQA/archive/refs/heads/master.zip'] ``` ## Environment info Google colab - `datasets` version: 1.18.2 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0

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I_kwDODunzps5Cvs71

Pubmed dataset not reachable

open

NONE

## Describe the bug Trying to use the `pubmed` dataset fails to reach / download the source files. ## Steps to reproduce the bug ```python pubmed_train = datasets.load_dataset('pubmed', split='train') ``` ## Expected results Should begin downloading the pubmed dataset. ## Actual results ``` ConnectionError: Couldn't reach ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0865.xml.gz (InvalidSchema("No connection adapters were found for 'ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0865.xml.gz'")) ``` ## Environment info - `datasets` version: 1.18.2 - Platform: macOS-11.4-x86_64-i386-64bit - Python version: 3.8.2 - PyArrow version: 6.0.0

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PR_kwDODunzps4x2kiX

Better TQDM output

closed

CONTRIBUTOR

This PR does the following: * if `dataset_infos.json` exists for a dataset, uses `num_examples` to print the total number of examples that needs to be generated (in `builder.py`) * fixes `tqdm` + multiprocessing in Jupyter Notebook/Colab (the issue stems from this commit in the `tqdm` repo: https://github.com/tqdm/tqdm/commit/f7722edecc3010cb35cc1c923ac4850a76336f82) * adds the missing `drop_last_batch` and `with_ranks` params to `DatasetDict.map` * correctly computes the number of iterations in `map` and the CSV/JSON loader when `batched=True` to fix `tqdm` progress bars * removes the `bool(logging.get_verbosity() == logging.NOTSET)` (or simplifies `bool(logging.get_verbosity() == logging.NOTSET) or not utils.is_progress_bar_enabled()` to `not utils.is_progress_bar_enabled()`) condition and uses `utils.is_progress_bar_enabled` to check if `tqdm` output is enabled (this comment from @stas00 explains why the `bool(logging.get_verbosity() == logging.NOTSET)` check is problematic: https://github.com/huggingface/transformers/issues/14889#issue-1087318463) Fix #2630

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I_kwDODunzps5CtXAI

`to_json` in multiprocessing fashion sometimes deadlock

open

MEMBER

## Describe the bug `to_json` in multiprocessing fashion sometimes deadlock, instead of raising exceptions. Temporary solution is to see that it deadlocks, and then reduce the number of processes or batch size in order to reduce the memory footprint. As @lhoestq pointed out, this might be related to https://bugs.python.org/issue22393#msg315684 where `multiprocessing` fails to raise the OOM exception. One suggested alternative is not use `concurrent.futures` instead. ## Steps to reproduce the bug ## Expected results Script fails when one worker hits OOM, and raise appropriate error. ## Actual results Deadlock ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.1 - Platform: Linux - Python version: 3.8 - PyArrow version: 6.0.1

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PR_kwDODunzps4xzinr

sp. Columbia => Colombia

closed

CONTRIBUTOR

"Columbia" is various places in North America. The country is "Colombia".

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PR_kwDODunzps4xy3De

Update link in wiki_bio dataset

closed

CONTRIBUTOR

Fixes #3580 and makes the wiki_bio dataset work again. I changed the link and some documentation, and all the tests pass. Thanks @lhoestq for uploading the dataset to the HuggingFace data bucket. @lhoestq -- all the tests pass, but I'm still not able to import the dataset, as the old Google Drive link is cached somewhere: ```python >>> from datasets import load_dataset load_dataset("wiki_bio>>> load_dataset("wiki_bio") Using custom data configuration default Downloading and preparing dataset wiki_bio/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to /home/jxm3/.cache/huggingface/datasets/wiki_bio/default/1.1.0/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9... Traceback (most recent call last): ... File "/home/jxm3/random/datasets/src/datasets/utils/file_utils.py", line 612, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://drive.google.com/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil ``` what do I have to do to invalidate the cache and actually import the dataset? It's clearly set up correctly, since the data is downloaded and processed by the tests. As an aside, this caching-loading-scripts behavior makes for a really bad developer experience. I just wasted an hour trying to figure out where the caching was happening and how to disable it, and I don't know. All I wanted to do was update the link and submit a pull request! I recommend that you all either change this behavior (i.e. updating the link to a dataset should "just work") or document it, since I couldn't find any information about this in the contributing.md or readme or anywhere else! Thanks!

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PR_kwDODunzps4xyr2o

Allow 'to_json' to run in unordered fashion in order to lower memory footprint

open

MEMBER

I'm using `to_json(..., num_proc=num_proc, compressiong='gzip')` with `num_proc>1`. I'm having an issue where things seem to deadlock at some point. Eventually I see OOM. I'm guessing it's an issue where one process starts to take a long time for a specific batch, and so other process keep accumulating their results in memory. In order to flush memory, I propose we use optional `imap_unordered`. This will prevent one process to block the other ones. The logical thinking is that index are rarily relevant, and in one wants to keep an index, one can still create another column and reconstruct from there.

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I_kwDODunzps5Cm7sq

Add IGLUE dataset

open

MEMBER

## Adding a Dataset - **Name:** IGLUE - **Description:** IGLUE brings together 4 vision-and-language tasks across 20 languages (Twitter [thread](https://twitter.com/ebugliarello/status/1487045497583976455?s=20&t=SB4LZGDhhkUW83ugcX_m5w)) - **Paper:** https://arxiv.org/abs/2201.11732 - **Data:** https://github.com/e-bug/iglue - **Motivation:** This dataset would provide a nice example of combining the text and image features of `datasets` together for multimodal applications. Note: the data / code are not yet visible on the GitHub repo, so I've pinged the authors for more information. 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_kwDODunzps4xvXig

Fix Windows CI: bump python to 3.7

closed

MEMBER

Python>=3.7 is needed to install `tokenizers` 0.11

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PR_kwDODunzps4xvGDQ

Fix `add_column` on datasets with indices mapping

closed

CONTRIBUTOR

My initial idea was to avoid the `flatten_indices` call and reorder a new column instead, but in the end I decided to follow `concatenate_datasets` and use `flatten_indices` to avoid padding when `dataset._indices.num_rows != dataset._data.num_rows`. Fix #3599

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PR_kwDODunzps4xsX66

Fix streaming datasets that are not reset correctly

closed

MEMBER

Streaming datasets that use `StreamingDownloadManager.iter_archive` and `StreamingDownloadManager.iter_files` had some issues. Indeed if you try to iterate over such dataset twice, then the second time it will be empty. This is because the two methods above are generator functions. I fixed this by making them return iterables that are reset properly instead. Close https://github.com/huggingface/datasets/issues/3645 cc @anton-l

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I_kwDODunzps5CjRFy

Streaming dataset based on dl_manager.iter_archive/iter_files are not reset correctly

closed

MEMBER

Hi ! When iterating over a streaming dataset once, it's not reset correctly because of some issues with `dl_manager.iter_archive` and `dl_manager.iter_files`. Indeed they are generator functions (so the iterator that is returned can be exhausted). They should be iterables instead, and be reset if we do a for loop again: ```python from datasets import load_dataset d = load_dataset("common_voice", "ab", split="test", streaming=True) i = 0 for i, _ in enumerate(d): pass print(i) # 8 # let's do it again i = 0 for i, _ in enumerate(d): pass print(i) # 0 ```

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I_kwDODunzps5CjLz2

Add a GROUP BY operator

open

NONE

**Is your feature request related to a problem? Please describe.** Using batch mapping, we can easily split examples. However, we lack an appropriate option for merging them back together by some key. Consider this example: ```python # features: # { # "example_id": datasets.Value("int32"), # "text": datasets.Value("string") # } ds = datasets.Dataset() def split(examples): sentences = [text.split(".") for text in examples["text"]] return { "example_id": [ example_id for example_id, sents in zip(examples["example_id"], sentences) for _ in sents ], "sentence": [sent for sents in sentences for sent in sents], "sentence_id": [i for sents in sentences for i in range(len(sents))], } split_ds = ds.map(split, batched=True) def process(examples): outputs = some_neural_network_that_works_on_sentences(examples["sentence"]) return {"outputs": outputs} split_ds = split_ds.map(process, batched=True) ``` I have a dataset consisting of texts that I would like to process sentence by sentence in a batched way. Afterwards, I would like to put it back together as it was, merging the outputs together. **Describe the solution you'd like** Ideally, it would look something like this: ```python def join(examples): order = np.argsort(examples["sentence_id"]) text = ".".join(examples["text"][i] for i in order) outputs = [examples["outputs"][i] for i in order] return {"text": text, "outputs": outputs} ds = split_ds.group_by("example_id", join) ``` **Describe alternatives you've considered** Right now, we can do this: ```python def merge(example): meeting_id = example["example_id"] parts = split_ds.filter(lambda x: x["example_id"] == meeting_id).sort("segment_no") return {"outputs": list(parts["outputs"])} ds = ds.map(merge) ``` Of course, we could process the dataset like this: ```python def process(example): outputs = some_neural_network_that_works_on_sentences(example["text"].split(".")) return {"outputs": outputs} ds = ds.map(process, batched=True) ``` However, that does not allow using an arbitrary batch size and may lead to very inefficient use of resources if the batch size is much larger than the number of sentences in one example. I would very much appreciate some kind of group by operator to merge examples based on the value of one column.

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PR_kwDODunzps4xr8mX

Fix sem_eval_2018_task_1 download location

closed

CONTRIBUTOR

As discussed with @lhoestq in https://github.com/huggingface/datasets/issues/3549#issuecomment-1020176931_ this is the new pull request to fix the download location.

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PR_kwDODunzps4xrj2S

Fix dataset slicing with negative bounds when indices mapping is not `None`

closed

CONTRIBUTOR

Fix #3611

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PR_kwDODunzps4xre7C

Fix numpy rngs when seed is None

closed

CONTRIBUTOR

Fixes the NumPy RNG when `seed` is `None`. The problem becomes obvious after reading the NumPy notes on RNG (returned by `np.random.get_state()`): > The MT19937 state vector consists of a 624-element array of 32-bit unsigned integers plus a single integer value between 0 and 624 that indexes the current position within the main array. `The MT19937 state vector`: the seed which we currently index, but this value stays the same for multiple rounds. `plus a single integer value`: the `pos` value in this PR (is 624 if `seed` is set to a fixed value with `np.random.seed`, so we take the first value in the `seed` array returned by `np.random.get_state()`: https://stackoverflow.com/questions/32172054/how-can-i-retrieve-the-current-seed-of-numpys-random-number-generator) NumPy notes: https://numpy.org/doc/stable/reference/random/bit_generators/mt19937.html Fix #3634

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I_kwDODunzps5ChtmJ

Issues with custom dataset in Wav2Vec2

closed

NONE

We are training Vav2Vec using the run_speech_recognition_ctc_bnb.py-script. This is working fine with Common Voice, however using our custom dataset and data loader at [NbAiLab/NPSC]( https://huggingface.co/datasets/NbAiLab/NPSC) it crashes after roughly 1 epoch with the following stack trace:  We are able to work around the issue, for instance by adding this check in line#222 in transformers/models/wav2vec2/modeling_wav2vec2.py: ```python if input_length - (mask_length - 1) < num_masked_span: num_masked_span = input_length - (mask_length - 1) ``` Interestingly, these are the variable values before the adjustment: ``` input_length=10 mask_length=10 num_masked_span=2 ```` After adjusting num_masked_spin to 1, the training script runs. The issue is also fixed by setting “replace=True” in the same function. Do you have any idea what is causing this, and how to fix this error permanently? If you do not think this is an Datasets issue, feel free to move the issue.

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I_kwDODunzps5ChSKs

same value of precision, recall, f1 score at each epoch for classification task.

open

NONE

**1st Epoch:** 1/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.59it/s] 01/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow 01/27/2022 09:30:49 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow PRECISION: {'precision': 0.7612903225806451} RECALL: {'recall': 0.7612903225806451} F1: {'f1': 0.7612903225806451} {'eval_loss': 1.4658324718475342, 'eval_accuracy': 0.7612903118133545, 'eval_runtime': 30.0054, 'eval_samples_per_second': 46.492, 'eval_steps_per_second': 46.492, 'epoch': 3.0} **4th Epoch:** 1/27/2022 09:56:55 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.92it/s] 01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow 01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow PRECISION: {'precision': 0.7698924731182796} RECALL: {'recall': 0.7698924731182796} F1: {'f1': 0.7698924731182796} ## Environment info !git clone https://github.com/huggingface/transformers %cd transformers !pip install . !pip install -r /content/transformers/examples/pytorch/token-classification/requirements.txt !pip install datasets

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I_kwDODunzps5CgJ-H

AutoTokenizer hash value got change after datasets.map

open

NONE

## Describe the bug AutoTokenizer hash value got change after datasets.map ## Steps to reproduce the bug 1. trash huggingface datasets cache 2. run the following code: ```python from transformers import AutoTokenizer, BertTokenizer from datasets import load_dataset from datasets.fingerprint import Hasher tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') def tokenize_function(example): return tokenizer(example["sentence1"], example["sentence2"], truncation=True) raw_datasets = load_dataset("glue", "mrpc") print(Hasher.hash(tokenize_function)) print(Hasher.hash(tokenizer)) tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) print(Hasher.hash(tokenize_function)) print(Hasher.hash(tokenizer)) ``` got ``` Reusing dataset glue (/home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad) 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1112.35it/s] f4976bb4694ebc51 3fca35a1fd4a1251 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 6.96ba/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15.25ba/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 5.81ba/s] d32837619b7d7d01 5fd925c82edd62b6 ``` 3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache. ## Expected results `AutoTokenizer` work like specific Tokenizer (The hash value don't change after map): ```python from transformers import AutoTokenizer, BertTokenizer from datasets import load_dataset from datasets.fingerprint import Hasher tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') def tokenize_function(example): return tokenizer(example["sentence1"], example["sentence2"], truncation=True) raw_datasets = load_dataset("glue", "mrpc") print(Hasher.hash(tokenize_function)) print(Hasher.hash(tokenizer)) tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) print(Hasher.hash(tokenize_function)) print(Hasher.hash(tokenizer)) ``` ``` Reusing dataset glue (/home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad) 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1091.22it/s] 46d4b31f54153fc7 5b8771afd8d43888 Loading cached processed dataset at /home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6b07ff82ae9d5c51.arrow Loading cached processed dataset at /home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-af738a6d84f3864b.arrow Loading cached processed dataset at /home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-531d2a603ba713c1.arrow 46d4b31f54153fc7 5b8771afd8d43888 ``` ## Environment info - `datasets` version: 1.18.0 - Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27 - Python version: 3.9.7 - PyArrow version: 6.0.1

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I_kwDODunzps5CfZUm

[TypeError: Couldn't cast array of type] Cannot load dataset in v1.18

closed

MEMBER

## Describe the bug I am trying to load the [`GEM/RiSAWOZ` dataset](https://huggingface.co/datasets/GEM/RiSAWOZ) in `datasets` v1.18.1 and am running into a type error when casting the features. The strange thing is that I can load the dataset with v1.17.0. Note that the error is also present if I install from `master` too. As far as I can tell, the dataset loading script is correct and the problematic features [here](https://huggingface.co/datasets/GEM/RiSAWOZ/blob/main/RiSAWOZ.py#L237) also look fine to me. ## Steps to reproduce the bug ```python from datasets import load_dataset dset = load_dataset("GEM/RiSAWOZ") ``` ## Expected results I can load the dataset without error. ## Actual results <details><summary>Traceback</summary> ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator) 1083 example = self.info.features.encode_example(record) -> 1084 writer.write(example, key) 1085 finally: ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write(self, example, key, writer_batch_size) 445 --> 446 self.write_examples_on_file() 447 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_examples_on_file(self) 403 batch_examples[col] = [row[0][col] for row in self.current_examples] --> 404 self.write_batch(batch_examples=batch_examples) 405 self.current_examples = [] ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 496 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 497 arrays.append(pa.array(typed_sequence)) 498 inferred_features[col] = typed_sequence.get_inferred_type() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 204 # We only do it if trying_type is False - since this is what the user asks for. --> 205 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) 206 return out ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1064 if isinstance(feature, list): -> 1065 return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0])) 1066 elif isinstance(feature, Sequence): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1086 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) -> 1087 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") 1088 TypeError: Couldn't cast array of type struct<医院-3.0T MRI: string, 医院-CT: string, 医院-DSA: string, 医院-公交线路: string, 医院-区域: string, 医院-名称: string, 医院-地址: string, 医院-地铁可达: string, 医院-地铁线路: string, 医院-性质: string, 医院-挂号时间: string, 医院-电话: string, 医院-等级: string, 医院-类别: string, 医院-重点科室: string, 医院-门诊时间: string, 天气-城市: string, 天气-天气: string, 天气-日期: string, 天气-温度: string, 天气-紫外线强度: string, 天气-风力风向: string, 旅游景点-区域: string, 旅游景点-名称: string, 旅游景点-地址: string, 旅游景点-开放时间: string, 旅游景点-是否地铁直达: string, 旅游景点-景点类型: string, 旅游景点-最适合人群: string, 旅游景点-消费: string, 旅游景点-特点: string, 旅游景点-电话号码: string, 旅游景点-评分: string, 旅游景点-门票价格: string, 汽车-价格(万元): string, 汽车-倒车影像: string, 汽车-动力水平: string, 汽车-厂商: string, 汽车-发动机排量(L): string, 汽车-发动机马力(Ps): string, 汽车-名称: string, 汽车-定速巡航: string, 汽车-巡航系统: string, 汽车-座位数: string, 汽车-座椅加热: string, 汽车-座椅通风: string, 汽车-所属价格区间: string, 汽车-油耗水平: string, 汽车-环保标准: string, 汽车-级别: string, 汽车-综合油耗(L/100km): string, 汽车-能源类型: string, 汽车-车型: string, 汽车-车系: string, 汽车-车身尺寸(mm): string, 汽车-驱动方式: string, 汽车-驾驶辅助影像: string, 火车-出发地: string, 火车-出发时间: string, 火车-到达时间: string, 火车-坐席: string, 火车-日期: string, 火车-时长: string, 火车-目的地: string, 火车-票价: string, 火车-舱位档次: string, 火车-车型: string, 火车-车次信息: string, 电影-主演: string, 电影-主演名单: string, 电影-具体上映时间: string, 电影-制片国家/地区: string, 电影-导演: string, 电影-年代: string, 电影-片名: string, 电影-片长: string, 电影-类型: string, 电影-豆瓣评分: string, 电脑-CPU: string, 电脑-CPU型号: string, 电脑-产品类别: string, 电脑-价格: string, 电脑-价格区间: string, 电脑-内存容量: string, 电脑-分类: string, 电脑-品牌: string, 电脑-商品名称: string, 电脑-屏幕尺寸: string, 电脑-待机时长: string, 电脑-显卡型号: string, 电脑-显卡类别: string, 电脑-游戏性能: string, 电脑-特性: string, 电脑-硬盘容量: string, 电脑-系列: string, 电脑-系统: string, 电脑-色系: string, 电脑-裸机重量: string, 电视剧-主演: string, 电视剧-主演名单: string, 电视剧-制片国家/地区: string, 电视剧-单集片长: string, 电视剧-导演: string, 电视剧-年代: string, 电视剧-片名: string, 电视剧-类型: string, 电视剧-豆瓣评分: string, 电视剧-集数: string, 电视剧-首播时间: string, 辅导班-上课方式: string, 辅导班-上课时间: string, 辅导班-下课时间: string, 辅导班-价格: string, 辅导班-区域: string, 辅导班-年级: string, 辅导班-开始日期: string, 辅导班-教室地点: string, 辅导班-教师: string, 辅导班-教师网址: string, 辅导班-时段: string, 辅导班-校区: string, 辅导班-每周: string, 辅导班-班号: string, 辅导班-科目: string, 辅导班-结束日期: string, 辅导班-课时: string, 辅导班-课次: string, 辅导班-课程网址: string, 辅导班-难度: string, 通用-产品类别: string, 通用-价格区间: string, 通用-品牌: string, 通用-系列: string, 酒店-价位: string, 酒店-停车场: string, 酒店-区域: string, 酒店-名称: string, 酒店-地址: string, 酒店-房型: string, 酒店-房费: string, 酒店-星级: string, 酒店-电话号码: string, 酒店-评分: string, 酒店-酒店类型: string, 飞机-准点率: string, 飞机-出发地: string, 飞机-到达时间: string, 飞机-日期: string, 飞机-目的地: string, 飞机-票价: string, 飞机-航班信息: string, 飞机-舱位档次: string, 飞机-起飞时间: string, 餐厅-人均消费: string, 餐厅-价位: string, 餐厅-区域: string, 餐厅-名称: string, 餐厅-地址: string, 餐厅-推荐菜: string, 餐厅-是否地铁直达: string, 餐厅-电话号码: string, 餐厅-菜系: string, 餐厅-营业时间: string, 餐厅-评分: string> to {'旅游景点-名称': Value(dtype='string', id=None), '旅游景点-区域': Value(dtype='string', id=None), '旅游景点-景点类型': Value(dtype='string', id=None), '旅游景点-最适合人群': Value(dtype='string', id=None), '旅游景点-消费': Value(dtype='string', id=None), '旅游景点-是否地铁直达': Value(dtype='string', id=None), '旅游景点-门票价格': Value(dtype='string', id=None), '旅游景点-电话号码': Value(dtype='string', id=None), '旅游景点-地址': Value(dtype='string', id=None), '旅游景点-评分': Value(dtype='string', id=None), '旅游景点-开放时间': Value(dtype='string', id=None), '旅游景点-特点': Value(dtype='string', id=None), '餐厅-名称': Value(dtype='string', id=None), '餐厅-区域': Value(dtype='string', id=None), '餐厅-菜系': Value(dtype='string', id=None), '餐厅-价位': Value(dtype='string', id=None), '餐厅-是否地铁直达': Value(dtype='string', id=None), '餐厅-人均消费': Value(dtype='string', id=None), '餐厅-地址': Value(dtype='string', id=None), '餐厅-电话号码': Value(dtype='string', id=None), '餐厅-评分': Value(dtype='string', id=None), '餐厅-营业时间': Value(dtype='string', id=None), '餐厅-推荐菜': Value(dtype='string', id=None), '酒店-名称': Value(dtype='string', id=None), '酒店-区域': Value(dtype='string', id=None), '酒店-星级': Value(dtype='string', id=None), '酒店-价位': Value(dtype='string', id=None), '酒店-酒店类型': Value(dtype='string', id=None), '酒店-房型': Value(dtype='string', id=None), '酒店-停车场': Value(dtype='string', id=None), '酒店-房费': Value(dtype='string', id=None), '酒店-地址': Value(dtype='string', id=None), '酒店-电话号码': Value(dtype='string', id=None), '酒店-评分': Value(dtype='string', id=None), '电脑-品牌': Value(dtype='string', id=None), '电脑-产品类别': Value(dtype='string', id=None), '电脑-分类': Value(dtype='string', id=None), '电脑-内存容量': Value(dtype='string', id=None), '电脑-屏幕尺寸': Value(dtype='string', id=None), '电脑-CPU': Value(dtype='string', id=None), '电脑-价格区间': Value(dtype='string', id=None), '电脑-系列': Value(dtype='string', id=None), '电脑-商品名称': Value(dtype='string', id=None), '电脑-系统': Value(dtype='string', id=None), '电脑-游戏性能': Value(dtype='string', id=None), '电脑-CPU型号': Value(dtype='string', id=None), '电脑-裸机重量': Value(dtype='string', id=None), '电脑-显卡类别': Value(dtype='string', id=None), '电脑-显卡型号': Value(dtype='string', id=None), '电脑-特性': Value(dtype='string', id=None), '电脑-色系': Value(dtype='string', id=None), '电脑-待机时长': Value(dtype='string', id=None), '电脑-硬盘容量': Value(dtype='string', id=None), '电脑-价格': Value(dtype='string', id=None), '火车-出发地': Value(dtype='string', id=None), '火车-目的地': Value(dtype='string', id=None), '火车-日期': Value(dtype='string', id=None), '火车-车型': Value(dtype='string', id=None), '火车-坐席': Value(dtype='string', id=None), '火车-车次信息': Value(dtype='string', id=None), '火车-时长': Value(dtype='string', id=None), '火车-出发时间': Value(dtype='string', id=None), '火车-到达时间': Value(dtype='string', id=None), '火车-票价': Value(dtype='string', id=None), '飞机-出发地': Value(dtype='string', id=None), '飞机-目的地': Value(dtype='string', id=None), '飞机-日期': Value(dtype='string', id=None), '飞机-舱位档次': Value(dtype='string', id=None), '飞机-航班信息': Value(dtype='string', id=None), '飞机-起飞时间': Value(dtype='string', id=None), '飞机-到达时间': Value(dtype='string', id=None), '飞机-票价': Value(dtype='string', id=None), '飞机-准点率': Value(dtype='string', id=None), '天气-城市': Value(dtype='string', id=None), '天气-日期': Value(dtype='string', id=None), '天气-天气': Value(dtype='string', id=None), '天气-温度': Value(dtype='string', id=None), '天气-风力风向': Value(dtype='string', id=None), '天气-紫外线强度': Value(dtype='string', id=None), '电影-制片国家/地区': Value(dtype='string', id=None), '电影-类型': Value(dtype='string', id=None), '电影-年代': Value(dtype='string', id=None), '电影-主演': Value(dtype='string', id=None), '电影-导演': Value(dtype='string', id=None), '电影-片名': Value(dtype='string', id=None), '电影-主演名单': Value(dtype='string', id=None), '电影-具体上映时间': Value(dtype='string', id=None), '电影-片长': Value(dtype='string', id=None), '电影-豆瓣评分': Value(dtype='string', id=None), '电视剧-制片国家/地区': Value(dtype='string', id=None), '电视剧-类型': Value(dtype='string', id=None), '电视剧-年代': Value(dtype='string', id=None), '电视剧-主演': Value(dtype='string', id=None), '电视剧-导演': Value(dtype='string', id=None), '电视剧-片名': Value(dtype='string', id=None), '电视剧-主演名单': Value(dtype='string', id=None), '电视剧-首播时间': Value(dtype='string', id=None), '电视剧-集数': Value(dtype='string', id=None), '电视剧-单集片长': Value(dtype='string', id=None), '电视剧-豆瓣评分': Value(dtype='string', id=None), '辅导班-班号': Value(dtype='string', id=None), '辅导班-难度': Value(dtype='string', id=None), '辅导班-科目': Value(dtype='string', id=None), '辅导班-年级': Value(dtype='string', id=None), '辅导班-区域': Value(dtype='string', id=None), '辅导班-校区': Value(dtype='string', id=None), '辅导班-上课方式': Value(dtype='string', id=None), '辅导班-开始日期': Value(dtype='string', id=None), '辅导班-结束日期': Value(dtype='string', id=None), '辅导班-每周': Value(dtype='string', id=None), '辅导班-上课时间': Value(dtype='string', id=None), '辅导班-下课时间': Value(dtype='string', id=None), '辅导班-时段': Value(dtype='string', id=None), '辅导班-课次': Value(dtype='string', id=None), '辅导班-课时': Value(dtype='string', id=None), '辅导班-教室地点': Value(dtype='string', id=None), '辅导班-教师': Value(dtype='string', id=None), '辅导班-价格': Value(dtype='string', id=None), '辅导班-课程网址': Value(dtype='string', id=None), '辅导班-教师网址': Value(dtype='string', id=None), '汽车-名称': Value(dtype='string', id=None), '汽车-车型': Value(dtype='string', id=None), '汽车-级别': Value(dtype='string', id=None), '汽车-座位数': Value(dtype='string', id=None), '汽车-车身尺寸(mm)': Value(dtype='string', id=None), '汽车-厂商': Value(dtype='string', id=None), '汽车-能源类型': Value(dtype='string', id=None), '汽车-发动机排量(L)': Value(dtype='string', id=None), '汽车-发动机马力(Ps)': Value(dtype='string', id=None), '汽车-驱动方式': Value(dtype='string', id=None), '汽车-综合油耗(L/100km)': Value(dtype='string', id=None), '汽车-环保标准': Value(dtype='string', id=None), '汽车-驾驶辅助影像': Value(dtype='string', id=None), '汽车-巡航系统': Value(dtype='string', id=None), '汽车-价格(万元)': Value(dtype='string', id=None), '汽车-车系': Value(dtype='string', id=None), '汽车-动力水平': Value(dtype='string', id=None), '汽车-油耗水平': Value(dtype='string', id=None), '汽车-倒车影像': Value(dtype='string', id=None), '汽车-定速巡航': Value(dtype='string', id=None), '汽车-座椅加热': Value(dtype='string', id=None), '汽车-座椅通风': Value(dtype='string', id=None), '汽车-所属价格区间': Value(dtype='string', id=None), '医院-名称': Value(dtype='string', id=None), '医院-等级': Value(dtype='string', id=None), '医院-类别': Value(dtype='string', id=None), '医院-性质': Value(dtype='string', id=None), '医院-区域': Value(dtype='string', id=None), '医院-地址': Value(dtype='string', id=None), '医院-电话': Value(dtype='string', id=None), '医院-挂号时间': Value(dtype='string', id=None), '医院-门诊时间': Value(dtype='string', id=None), '医院-公交线路': Value(dtype='string', id=None), '医院-地铁可达': Value(dtype='string', id=None), '医院-地铁线路': Value(dtype='string', id=None), '医院-重点科室': Value(dtype='string', id=None), '医院-CT': Value(dtype='string', id=None), '医院-3.0T MRI': Value(dtype='string', id=None), '医院-DSA': Value(dtype='string', id=None)} During handling of the above exception, another exception occurred: TypeError Traceback (most recent call last) /var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_44306/2896005239.py in <module> ----> 1 dset = load_dataset("GEM/RiSAWOZ") 2 dset ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1692 1693 # Download and prepare data -> 1694 builder_instance.download_and_prepare( 1695 download_config=download_config, 1696 download_mode=download_mode, ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 593 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 594 if not downloaded_from_gcs: --> 595 self._download_and_prepare( 596 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 597 ) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 682 try: 683 # Prepare split will record examples associated to the split --> 684 self._prepare_split(split_generator, **prepare_split_kwargs) 685 except OSError as e: 686 raise OSError( ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator) 1084 writer.write(example, key) 1085 finally: -> 1086 num_examples, num_bytes = writer.finalize() 1087 1088 split_generator.split_info.num_examples = num_examples ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in finalize(self, close_stream) 525 # Re-intializing to empty list for next batch 526 self.hkey_record = [] --> 527 self.write_examples_on_file() 528 if self.pa_writer is None: 529 if self.schema: ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_examples_on_file(self) 402 # Since current_examples contains (example, key) tuples 403 batch_examples[col] = [row[0][col] for row in self.current_examples] --> 404 self.write_batch(batch_examples=batch_examples) 405 self.current_examples = [] 406 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 495 col_try_type = try_features[col] if try_features is not None and col in try_features else None 496 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 497 arrays.append(pa.array(typed_sequence)) 498 inferred_features[col] = typed_sequence.get_inferred_type() 499 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 203 # Also, when trying type "string", we don't want to convert integers or floats to "string". 204 # We only do it if trying_type is False - since this is what the user asks for. --> 205 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) 206 return out 207 except (TypeError, pa.lib.ArrowInvalid) as e: # handle type errors and overflows ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1063 # feature must be either [subfeature] or Sequence(subfeature) 1064 if isinstance(feature, list): -> 1065 return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0])) 1066 elif isinstance(feature, Sequence): 1067 if feature.length > -1: ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1085 elif not isinstance(feature, (Sequence, dict, list, tuple)): 1086 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) -> 1087 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") 1088 1089 TypeError: Couldn't cast array of type struct<医院-3.0T MRI: string, 医院-CT: string, 医院-DSA: string, 医院-公交线路: string, 医院-区域: string, 医院-名称: string, 医院-地址: string, 医院-地铁可达: string, 医院-地铁线路: string, 医院-性质: string, 医院-挂号时间: string, 医院-电话: string, 医院-等级: string, 医院-类别: string, 医院-重点科室: string, 医院-门诊时间: string, 天气-城市: string, 天气-天气: string, 天气-日期: string, 天气-温度: string, 天气-紫外线强度: string, 天气-风力风向: string, 旅游景点-区域: string, 旅游景点-名称: string, 旅游景点-地址: string, 旅游景点-开放时间: string, 旅游景点-是否地铁直达: string, 旅游景点-景点类型: string, 旅游景点-最适合人群: string, 旅游景点-消费: string, 旅游景点-特点: string, 旅游景点-电话号码: string, 旅游景点-评分: string, 旅游景点-门票价格: string, 汽车-价格(万元): string, 汽车-倒车影像: string, 汽车-动力水平: string, 汽车-厂商: string, 汽车-发动机排量(L): string, 汽车-发动机马力(Ps): string, 汽车-名称: string, 汽车-定速巡航: string, 汽车-巡航系统: string, 汽车-座位数: string, 汽车-座椅加热: string, 汽车-座椅通风: string, 汽车-所属价格区间: string, 汽车-油耗水平: string, 汽车-环保标准: string, 汽车-级别: string, 汽车-综合油耗(L/100km): string, 汽车-能源类型: string, 汽车-车型: string, 汽车-车系: string, 汽车-车身尺寸(mm): string, 汽车-驱动方式: string, 汽车-驾驶辅助影像: string, 火车-出发地: string, 火车-出发时间: string, 火车-到达时间: string, 火车-坐席: string, 火车-日期: string, 火车-时长: string, 火车-目的地: string, 火车-票价: string, 火车-舱位档次: string, 火车-车型: string, 火车-车次信息: string, 电影-主演: string, 电影-主演名单: string, 电影-具体上映时间: string, 电影-制片国家/地区: string, 电影-导演: string, 电影-年代: string, 电影-片名: string, 电影-片长: string, 电影-类型: string, 电影-豆瓣评分: string, 电脑-CPU: string, 电脑-CPU型号: string, 电脑-产品类别: string, 电脑-价格: string, 电脑-价格区间: string, 电脑-内存容量: string, 电脑-分类: string, 电脑-品牌: string, 电脑-商品名称: string, 电脑-屏幕尺寸: string, 电脑-待机时长: string, 电脑-显卡型号: string, 电脑-显卡类别: string, 电脑-游戏性能: string, 电脑-特性: string, 电脑-硬盘容量: string, 电脑-系列: string, 电脑-系统: string, 电脑-色系: string, 电脑-裸机重量: string, 电视剧-主演: string, 电视剧-主演名单: string, 电视剧-制片国家/地区: string, 电视剧-单集片长: string, 电视剧-导演: string, 电视剧-年代: string, 电视剧-片名: string, 电视剧-类型: string, 电视剧-豆瓣评分: string, 电视剧-集数: string, 电视剧-首播时间: string, 辅导班-上课方式: string, 辅导班-上课时间: string, 辅导班-下课时间: string, 辅导班-价格: string, 辅导班-区域: string, 辅导班-年级: string, 辅导班-开始日期: string, 辅导班-教室地点: string, 辅导班-教师: string, 辅导班-教师网址: string, 辅导班-时段: string, 辅导班-校区: string, 辅导班-每周: string, 辅导班-班号: string, 辅导班-科目: string, 辅导班-结束日期: string, 辅导班-课时: string, 辅导班-课次: string, 辅导班-课程网址: string, 辅导班-难度: string, 通用-产品类别: string, 通用-价格区间: string, 通用-品牌: string, 通用-系列: string, 酒店-价位: string, 酒店-停车场: string, 酒店-区域: string, 酒店-名称: string, 酒店-地址: string, 酒店-房型: string, 酒店-房费: string, 酒店-星级: string, 酒店-电话号码: string, 酒店-评分: string, 酒店-酒店类型: string, 飞机-准点率: string, 飞机-出发地: string, 飞机-到达时间: string, 飞机-日期: string, 飞机-目的地: string, 飞机-票价: string, 飞机-航班信息: string, 飞机-舱位档次: string, 飞机-起飞时间: string, 餐厅-人均消费: string, 餐厅-价位: string, 餐厅-区域: string, 餐厅-名称: string, 餐厅-地址: string, 餐厅-推荐菜: string, 餐厅-是否地铁直达: string, 餐厅-电话号码: string, 餐厅-菜系: string, 餐厅-营业时间: string, 餐厅-评分: string> to {'旅游景点-名称': Value(dtype='string', id=None), '旅游景点-区域': Value(dtype='string', id=None), '旅游景点-景点类型': Value(dtype='string', id=None), '旅游景点-最适合人群': Value(dtype='string', id=None), '旅游景点-消费': Value(dtype='string', id=None), '旅游景点-是否地铁直达': Value(dtype='string', id=None), '旅游景点-门票价格': Value(dtype='string', id=None), '旅游景点-电话号码': Value(dtype='string', id=None), '旅游景点-地址': Value(dtype='string', id=None), '旅游景点-评分': Value(dtype='string', id=None), '旅游景点-开放时间': Value(dtype='string', id=None), '旅游景点-特点': Value(dtype='string', id=None), '餐厅-名称': Value(dtype='string', id=None), '餐厅-区域': Value(dtype='string', id=None), '餐厅-菜系': Value(dtype='string', id=None), '餐厅-价位': Value(dtype='string', id=None), '餐厅-是否地铁直达': Value(dtype='string', id=None), '餐厅-人均消费': Value(dtype='string', id=None), '餐厅-地址': Value(dtype='string', id=None), '餐厅-电话号码': Value(dtype='string', id=None), '餐厅-评分': Value(dtype='string', id=None), '餐厅-营业时间': Value(dtype='string', id=None), '餐厅-推荐菜': Value(dtype='string', id=None), '酒店-名称': Value(dtype='string', id=None), '酒店-区域': Value(dtype='string', id=None), '酒店-星级': Value(dtype='string', id=None), '酒店-价位': Value(dtype='string', id=None), '酒店-酒店类型': Value(dtype='string', id=None), '酒店-房型': Value(dtype='string', id=None), '酒店-停车场': Value(dtype='string', id=None), '酒店-房费': Value(dtype='string', id=None), '酒店-地址': Value(dtype='string', id=None), '酒店-电话号码': Value(dtype='string', id=None), '酒店-评分': Value(dtype='string', id=None), '电脑-品牌': Value(dtype='string', id=None), '电脑-产品类别': Value(dtype='string', id=None), '电脑-分类': Value(dtype='string', id=None), '电脑-内存容量': Value(dtype='string', id=None), '电脑-屏幕尺寸': Value(dtype='string', id=None), '电脑-CPU': Value(dtype='string', id=None), '电脑-价格区间': Value(dtype='string', id=None), '电脑-系列': Value(dtype='string', id=None), '电脑-商品名称': Value(dtype='string', id=None), '电脑-系统': Value(dtype='string', id=None), '电脑-游戏性能': Value(dtype='string', id=None), '电脑-CPU型号': Value(dtype='string', id=None), '电脑-裸机重量': Value(dtype='string', id=None), '电脑-显卡类别': Value(dtype='string', id=None), '电脑-显卡型号': Value(dtype='string', id=None), '电脑-特性': Value(dtype='string', id=None), '电脑-色系': Value(dtype='string', id=None), '电脑-待机时长': Value(dtype='string', id=None), '电脑-硬盘容量': Value(dtype='string', id=None), '电脑-价格': Value(dtype='string', id=None), '火车-出发地': Value(dtype='string', id=None), '火车-目的地': Value(dtype='string', id=None), '火车-日期': Value(dtype='string', id=None), '火车-车型': Value(dtype='string', id=None), '火车-坐席': Value(dtype='string', id=None), '火车-车次信息': Value(dtype='string', id=None), '火车-时长': Value(dtype='string', id=None), '火车-出发时间': Value(dtype='string', id=None), '火车-到达时间': Value(dtype='string', id=None), '火车-票价': Value(dtype='string', id=None), '飞机-出发地': Value(dtype='string', id=None), '飞机-目的地': Value(dtype='string', id=None), '飞机-日期': Value(dtype='string', id=None), '飞机-舱位档次': Value(dtype='string', id=None), '飞机-航班信息': Value(dtype='string', id=None), '飞机-起飞时间': Value(dtype='string', id=None), '飞机-到达时间': Value(dtype='string', id=None), '飞机-票价': Value(dtype='string', id=None), '飞机-准点率': Value(dtype='string', id=None), '天气-城市': Value(dtype='string', id=None), '天气-日期': Value(dtype='string', id=None), '天气-天气': Value(dtype='string', id=None), '天气-温度': Value(dtype='string', id=None), '天气-风力风向': Value(dtype='string', id=None), '天气-紫外线强度': Value(dtype='string', id=None), '电影-制片国家/地区': Value(dtype='string', id=None), '电影-类型': Value(dtype='string', id=None), '电影-年代': Value(dtype='string', id=None), '电影-主演': Value(dtype='string', id=None), '电影-导演': Value(dtype='string', id=None), '电影-片名': Value(dtype='string', id=None), '电影-主演名单': Value(dtype='string', id=None), '电影-具体上映时间': Value(dtype='string', id=None), '电影-片长': Value(dtype='string', id=None), '电影-豆瓣评分': Value(dtype='string', id=None), '电视剧-制片国家/地区': Value(dtype='string', id=None), '电视剧-类型': Value(dtype='string', id=None), '电视剧-年代': Value(dtype='string', id=None), '电视剧-主演': Value(dtype='string', id=None), '电视剧-导演': Value(dtype='string', id=None), '电视剧-片名': Value(dtype='string', id=None), '电视剧-主演名单': Value(dtype='string', id=None), '电视剧-首播时间': Value(dtype='string', id=None), '电视剧-集数': Value(dtype='string', id=None), '电视剧-单集片长': Value(dtype='string', id=None), '电视剧-豆瓣评分': Value(dtype='string', id=None), '辅导班-班号': Value(dtype='string', id=None), '辅导班-难度': Value(dtype='string', id=None), '辅导班-科目': Value(dtype='string', id=None), '辅导班-年级': Value(dtype='string', id=None), '辅导班-区域': Value(dtype='string', id=None), '辅导班-校区': Value(dtype='string', id=None), '辅导班-上课方式': Value(dtype='string', id=None), '辅导班-开始日期': Value(dtype='string', id=None), '辅导班-结束日期': Value(dtype='string', id=None), '辅导班-每周': Value(dtype='string', id=None), '辅导班-上课时间': Value(dtype='string', id=None), '辅导班-下课时间': Value(dtype='string', id=None), '辅导班-时段': Value(dtype='string', id=None), '辅导班-课次': Value(dtype='string', id=None), '辅导班-课时': Value(dtype='string', id=None), '辅导班-教室地点': Value(dtype='string', id=None), '辅导班-教师': Value(dtype='string', id=None), '辅导班-价格': Value(dtype='string', id=None), '辅导班-课程网址': Value(dtype='string', id=None), '辅导班-教师网址': Value(dtype='string', id=None), '汽车-名称': Value(dtype='string', id=None), '汽车-车型': Value(dtype='string', id=None), '汽车-级别': Value(dtype='string', id=None), '汽车-座位数': Value(dtype='string', id=None), '汽车-车身尺寸(mm)': Value(dtype='string', id=None), '汽车-厂商': Value(dtype='string', id=None), '汽车-能源类型': Value(dtype='string', id=None), '汽车-发动机排量(L)': Value(dtype='string', id=None), '汽车-发动机马力(Ps)': Value(dtype='string', id=None), '汽车-驱动方式': Value(dtype='string', id=None), '汽车-综合油耗(L/100km)': Value(dtype='string', id=None), '汽车-环保标准': Value(dtype='string', id=None), '汽车-驾驶辅助影像': Value(dtype='string', id=None), '汽车-巡航系统': Value(dtype='string', id=None), '汽车-价格(万元)': Value(dtype='string', id=None), '汽车-车系': Value(dtype='string', id=None), '汽车-动力水平': Value(dtype='string', id=None), '汽车-油耗水平': Value(dtype='string', id=None), '汽车-倒车影像': Value(dtype='string', id=None), '汽车-定速巡航': Value(dtype='string', id=None), '汽车-座椅加热': Value(dtype='string', id=None), '汽车-座椅通风': Value(dtype='string', id=None), '汽车-所属价格区间': Value(dtype='string', id=None), '医院-名称': Value(dtype='string', id=None), '医院-等级': Value(dtype='string', id=None), '医院-类别': Value(dtype='string', id=None), '医院-性质': Value(dtype='string', id=None), '医院-区域': Value(dtype='string', id=None), '医院-地址': Value(dtype='string', id=None), '医院-电话': Value(dtype='string', id=None), '医院-挂号时间': Value(dtype='string', id=None), '医院-门诊时间': Value(dtype='string', id=None), '医院-公交线路': Value(dtype='string', id=None), '医院-地铁可达': Value(dtype='string', id=None), '医院-地铁线路': Value(dtype='string', id=None), '医院-重点科室': Value(dtype='string', id=None), '医院-CT': Value(dtype='string', id=None), '医院-3.0T MRI': Value(dtype='string', id=None), '医院-DSA': Value(dtype='string', id=None)} ``` </details> ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.1 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.10 - PyArrow version: 3.0.0

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PR_kwDODunzps4xohMB

Update index.rst

closed

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PR_kwDODunzps4xobAe

Make `ted_talks_iwslt` dataset streamable

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https://github.com/huggingface/datasets/issues/3634

I_kwDODunzps5Cd5Vf

Dataset.shuffle(seed=None) gives fixed row permutation

closed

NONE

## Describe the bug Repeated attempts to `shuffle` a dataset without specifying a seed give the same results. ## Steps to reproduce the bug ```python import datasets # Some toy example data = datasets.Dataset.from_dict( {"feature": [1, 2, 3, 4, 5], "label": ["a", "b", "c", "d", "e"]} ) # Doesn't work as expected print("Shuffle dataset") for _ in range(3): print(data.shuffle(seed=None)[:]) # This seems to work with pandas print("\nShuffle via pandas") for _ in range(3): df = data.to_pandas().sample(frac=1.0) print(datasets.Dataset.from_pandas(df, preserve_index=False)[:]) ``` ## Expected results I assumed that the default setting would initialize a new/random state of a `np.random.BitGenerator` (see [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=shuffle#datasets.Dataset.shuffle)). Wouldn't that reshuffle the rows each time I call `data.shuffle()`? ## Actual results ```bash Shuffle dataset {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} Shuffle via pandas {'feature': [4, 2, 3, 1, 5], 'label': ['d', 'b', 'c', 'a', 'e']} {'feature': [2, 5, 3, 4, 1], 'label': ['b', 'e', 'c', 'd', 'a']} {'feature': [5, 2, 3, 1, 4], 'label': ['e', 'b', 'c', 'a', 'd']} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.0 - Platform: Linux-5.13.0-27-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.1

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PR_kwDODunzps4xng6E

Mirror canonical datasets in prod

closed

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