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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type int64 to null
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in cast_table_to_schema
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp>
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in cast_array_to_feature
                  arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in <listcomp>
                  arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
                  return func(array, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2116, in cast_array_to_feature
                  return array_cast(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
                  return func(array, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1962, in array_cast
                  raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
              TypeError: Couldn't cast array of type int64 to null
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1529, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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1,353,223,925
I_kwDODunzps5QqI71
4,906
Can't import datasets AttributeError: partially initialized module 'datasets' has no attribute 'utils' (most likely due to a circular import)
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## Describe the bug A clear and concise description of what the bug is. Not able to import datasets ## Steps to reproduce the bug ```python # Sample code to reproduce the bug import os os.environ["WANDB_API_KEY"] = "0" ## to silence warning import numpy as np import random import sklearn import matplotlib.pyplot as plt import pandas as pd import sys import tensorflow as tf import plotly.express as px import transformers import tokenizers import nlp as nlp import utils import datasets ``` ## Expected results A clear and concise description of the expected results. import should work normal ## Actual results Specify the actual results or traceback. --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-21-b3b5b0b62103> in <module> 13 import nlp as nlp 14 import utils ---> 15 import datasets ~\anaconda3\lib\site-packages\datasets\__init__.py in <module> 44 from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled 45 from .info import DatasetInfo, MetricInfo ---> 46 from .inspect import ( 47 get_dataset_config_info, 48 get_dataset_config_names, ~\anaconda3\lib\site-packages\datasets\inspect.py in <module> 28 from .download.streaming_download_manager import StreamingDownloadManager 29 from .info import DatasetInfo ---> 30 from .load import dataset_module_factory, import_main_class, load_dataset_builder, metric_module_factory 31 from .utils.file_utils import relative_to_absolute_path 32 from .utils.logging import get_logger ~\anaconda3\lib\site-packages\datasets\load.py in <module> 53 from .iterable_dataset import IterableDataset 54 from .metric import Metric ---> 55 from .packaged_modules import ( 56 _EXTENSION_TO_MODULE, 57 _MODULE_SUPPORTS_METADATA, ~\anaconda3\lib\site-packages\datasets\packaged_modules\__init__.py in <module> 4 from typing import List 5 ----> 6 from .csv import csv 7 from .imagefolder import imagefolder 8 from .json import json ~\anaconda3\lib\site-packages\datasets\packaged_modules\csv\csv.py in <module> 13 14 ---> 15 logger = datasets.utils.logging.get_logger(__name__) 16 17 _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS = ["names", "prefix"] AttributeError: partially initialized module 'datasets' has no attribute 'utils' (most likely due to a circular import) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.4.0 - Platform: Windows-10-10.0.22000-SP0 - Python version: 3.8.8 - PyArrow version: 9.0.0 - Pandas version: 1.2.4
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1,353,002,837
PR_kwDODunzps4959Ad
4,904
[LibriSpeech] Fix dev split local_extracted_archive for 'all' split
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4904). All of your documentation changes will be reflected on that endpoint." ]
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CONTRIBUTOR
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We define the keys for the `_DL_URLS` of the dev split as `dev.clean` and `dev.other`: https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L60-L61 These keys get forwarded to the `dl_manager` and thus the `local_extracted_archive`. However, when calling `SplitGenerator` for the dev sets, we query the `local_extracted_archive` keys `validation.clean` and `validation.other`: https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L212 https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L219 The consequence of this is that the `local_extracted_archive` arg passed to `_generate_examples` is always `None`, as the keys `validation.clean` and `validation.other` do not exists in the `local_extracted_archive`. When defining the `audio_file` in `_generate_examples`, since `local_extracted_archive` is always `None`, we always omit the `local_extracted_archive` path from the `audio_file` path, **even** if in non-streaming mode: https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L259-L263 Thus, `audio_file` will only ever be the streaming path (`audio_file`, not `os.path.join(local_extracted_archive, audio_file)`). This PR fixes the `.get()` keys for the `local_extracted_archive` for the dev splits.
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Fix CI reporting
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
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Fix CI so that it reports defaults (failed and error) besides the custom (xfailed and xpassed) in the test summary. This PR fixes a regression introduced by: - #4845 This introduced the reporting of xfailed and xpassed, but wrongly removed the reporting of the defaults failed and error.
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4,902
Name the default config `default`
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CONTRIBUTOR
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Currently, if a dataset has no configuration, a default configuration is created from the dataset name. For example, for a dataset loaded from the hub repository, such as https://huggingface.co/datasets/user/dataset (repo id is `user/dataset`), the default configuration will be `user--dataset`. It might be easier to handle to set it to `default`, or another reserved word.
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Raise ManualDownloadError from get_dataset_config_info
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4901). All of your documentation changes will be reflected on that endpoint." ]
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This PRs raises a specific `ManualDownloadError` when `get_dataset_config_info` is called for a dataset that requires manual download. Related to: - #4898 CC: @severo
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I_kwDODunzps5QnBNf
4,900
Dataset Viewer issue for asaxena1990/Dummy_dataset
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[ "Seems to be linked to the use of the undocumented `_resolve_features` method in the dataset viewer backend:\r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"asaxena1990/Dummy_dataset\", name=\"asaxena1990--Dummy_dataset\", split=\"train\", streaming=True)\r\nUsing custom data con...
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### Link _No response_ ### Description _No response_ ### Owner _No response_
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Re-add code and und language tags
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This PR fixes the removal of 2 language tags done by: - #4882 The tags are: - "code": this is not a IANA tag but needed - "und": this is one of the special scoped tags removed by 0d53202b9abce6fd0358cb00d06fcfd904b875af - used in "mc4" and "udhr" datasets
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Dataset Viewer issue for timit_asr
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[ "Yes, the dataset viewer is based on `datasets`, and the following does not work:\r\n\r\n```\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names('timit_asr')\r\nDownloading builder script: 7.48kB [00:00, 6.69MB/s]\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/data...
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datasets generate large arrow file
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Checking the large file in disk, and found the large cache file in the cifar10 data directory: ![image](https://user-images.githubusercontent.com/18533904/186830449-ba96cdeb-0fe8-4543-994d-2abe7145933f.png) As we know, the size of cifar10 dataset is ~130MB, but the cache file has almost 30GB size, there may be some problems here.
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Fix missing tags in dataset cards
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Fix missing tags in dataset cards. This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task. Related to: - #4833 - #4891
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load_dataset method returns Unknown split "validation" even if this dir exists
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## Describe the bug The `datasets.load_dataset` returns a `ValueError: Unknown split "validation". Should be one of ['train', 'test'].` when running `load_dataset(local_data_dir_path, split="validation")` even if the `validation` sub-directory exists in the local data path. The data directories are as follows and attached to this issue: ``` test_data1 |_ train |_ 1012.png |_ metadata.jsonl ... |_ test ... |_ validation |_ 234.png |_ metadata.jsonl ... test_data2 |_ train |_ train_1012.png |_ metadata.jsonl ... |_ test ... |_ validation |_ val_234.png |_ metadata.jsonl ... ``` They contain the same image files and `metadata.jsonl` but the images in `test_data2` have the split names prepended i.e. `train_1012.png, val_234.png` and the images in `test_data1` do not have the split names prepended to the image names i.e. `1012.png, 234.png` I actually saw in another issue `val` was not recognized as a split name but here I would expect the files to take the split from the parent directory name i.e. val should become part of the validation split? ## Steps to reproduce the bug ```python import datasets datasets.logging.set_verbosity_error() from datasets import load_dataset, get_dataset_split_names # the following only finds train, validation and test splits correctly path = "./test_data1" print("######################", get_dataset_split_names(path), "######################") dataset_list = [] for spt in ["train", "test", "validation"]: dataset = load_dataset(path, split=spt) dataset_list.append(dataset) # the following only finds train and test splits path = "./test_data2" print("######################", get_dataset_split_names(path), "######################") dataset_list = [] for spt in ["train", "test", "validation"]: dataset = load_dataset(path, split=spt) dataset_list.append(dataset) ``` ## Expected results ``` ###################### ['train', 'test', 'validation'] ###################### ###################### ['train', 'test', 'validation'] ###################### ``` ## Actual results ``` Traceback (most recent call last): File "test_data_loader.py", line 11, in <module> dataset = load_dataset(path, split=spt) File "/home/venv/lib/python3.8/site-packages/datasets/load.py", line 1758, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 893, in as_dataset datasets = map_nested( File "/home/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 385, in map_nested return function(data_struct) File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 924, in _build_single_dataset ds = self._as_dataset( File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 993, in _as_dataset dataset_kwargs = ArrowReader(self._cache_dir, self.info).read( File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 211, in read files = self.get_file_instructions(name, instructions, split_infos) File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 184, in get_file_instructions file_instructions = make_file_instructions( File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 107, in make_file_instructions absolute_instructions = instruction.to_absolute(name2len) File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 616, in to_absolute return [_rel_to_abs_instr(rel_instr, name2len) for rel_instr in self._relative_instructions] File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 616, in <listcomp> return [_rel_to_abs_instr(rel_instr, name2len) for rel_instr in self._relative_instructions] File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 433, in _rel_to_abs_instr raise ValueError(f'Unknown split "{split}". Should be one of {list(name2len)}.') ValueError: Unknown split "validation". Should be one of ['train', 'test']. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Linux Ubuntu 18.04 - Python version: 3.8.12 - PyArrow version: 9.0.0 Data files [test_data1.zip](https://github.com/huggingface/datasets/files/9424463/test_data1.zip) [test_data2.zip](https://github.com/huggingface/datasets/files/9424468/test_data2.zip)
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Add citation information to makhzan dataset
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This PR adds the citation information to `makhzan` datasets, once they have replied to our request for that information: - https://github.com/zeerakahmed/makhzan/issues/43
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Oversampling strategy for iterable datasets in `interleave_datasets`
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[ "Hi @lhoestq,\r\nI plunged into the code and it should be manageable for me to work on it!\r\n#take\r\n\r\nAlso, setting `d1`, `d2` and `d3` as you did raised a `SyntaxError: 'yield' inside list comprehension` for me, on Python 3.8.10.\r\nThe following snippet works for me though:\r\n```\r\nd1 = IterableDataset(Exa...
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In https://github.com/huggingface/datasets/pull/4831 @ylacombe added an oversampling strategy for `interleave_datasets`. However right now it doesn't work for datasets loaded using `load_dataset(..., streaming=True)`, which are `IterableDataset` objects. It would be nice to expand `interleave_datasets` for iterable datasets as well to support this oversampling strategy ```python >>> from datasets.iterable_dataset import IterableDataset, ExamplesIterable >>> d1 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [0, 1, 2]], {})) >>> d2 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [10, 11, 12, 13]], {})) >>> d3 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [20, 21, 22, 23, 24]], {})) >>> dataset = interleave_datasets([d1, d2, d3]) # is supported >>> [x["a"] for x in dataset] [0, 10, 20, 1, 11, 21, 2, 12, 22] >>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted") # is not supported yet >>> [x["a"] for x in dataset] [0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 0, 24] ``` This can be implemented by adding the strategy to both `CyclingMultiSourcesExamplesIterable` and `RandomlyCyclingMultiSourcesExamplesIterable` used in `_interleave_iterable_datasets` in `iterable_dataset.py` I would be happy to share some guidance if anyone would like to give it a shot :)
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Add citation to ro_sts and ro_sts_parallel datasets
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4892). All of your documentation changes will be reflected on that endpoint." ]
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This PR adds the citation information to `ro_sts_parallel` and `ro_sts_parallel` datasets, once they have replied our request for that information: - https://github.com/dumitrescustefan/RO-STS/issues/4
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Fix missing tags in dataset cards
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Fix missing tags in dataset cards. This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task. Related to: - #4833
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add Dataset.from_list
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4890). All of your documentation changes will be reflected on that endpoint.", "@albertvillanova it seems tests fail on pyarrow 6, perhaps from_pylist is a v7 method? How do you usually handle these version differences?\r\nAdde...
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As discussed in #4885 I initially added this bit at the end, thinking filling this field was necessary as it is done in from_dict. However, it seems the constructor takes care of filling info when it is empty. ``` if info.features is None: info.features = Features( { col: generate_from_arrow_type(coldata.type) for col, coldata in zip(pa_table.column_names, pa_table.columns) } ) ```
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torchaudio 11.0 yields different results than torchaudio 12.1 when loading MP3
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[ "Maybe we can just pass this along to torchaudio @lhoestq @albertvillanova ? It be great if you could investigate if the errors lies in datasets or in torchaudio.", "torchaudio did a change in [0.12](https://github.com/pytorch/audio/releases/tag/v0.12.0) on MP3 decoding (which affects common voice):\r\n> MP3 deco...
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## Describe the bug When loading Common Voice with torchaudio 0.11.0 the results are different to 0.12.1 which leads to problems in transformers see: https://github.com/huggingface/transformers/pull/18749 ## Steps to reproduce the bug If you run the following code once with `torchaudio==0.11.0+cu102` and `torchaudio==0.12.1+cu102` you can see that the tensors differ. This is a pretty big breaking change and makes some integration tests fail in Transformers. ```python #!/usr/bin/env python3 from datasets import load_dataset import datasets import numpy as np import torch import torchaudio print("torch vesion", torch.__version__) print("torchaudio vesion", torchaudio.__version__) save_audio = True load_audios = False if save_audio: ds = load_dataset("common_voice", "en", split="train", streaming=True) ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000)) ds_iter = iter(ds) sample = next(ds_iter) np.save(f"audio_sample_{torch.__version__}", sample["audio"]["array"]) print(sample["audio"]["array"]) if load_audios: array_torch_11 = np.load("/home/patrick/audio_sample_1.11.0+cu102.npy") print("Array 11 Shape", array_torch_11.shape) print("Array 11 abs sum", np.sum(np.abs(array_torch_11))) array_torch_12 = np.load("/home/patrick/audio_sample_1.12.1+cu102.npy") print("Array 12 Shape", array_torch_12.shape) print("Array 12 abs sum", np.sum(np.abs(array_torch_12))) ``` Having saved the tensors the print output yields: ``` torch vesion 1.12.1+cu102 torchaudio vesion 0.12.1+cu102 Array 11 Shape (122880,) Array 11 abs sum 1396.4988 Array 12 Shape (123264,) Array 12 abs sum 1396.5193 ``` ## Expected results torchaudio 11.0 and 12.1 should yield same results. ## Actual results See above. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.1.dev0 - Platform: Linux-5.18.10-76051810-generic-x86_64-with-glibc2.34 - Python version: 3.9.7 - PyArrow version: 6.0.1 - Pandas version: 1.4.2
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Dataset Viewer issue for subjqa
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[ "It's a bug in the viewer, thanks for reporting it. We're hoping to update to a new version in the next few days which should fix it." ]
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### Link https://huggingface.co/datasets/subjqa ### Description Getting the following error for this dataset: ``` Status code: 500 Exception: Status500Error Message: 2 or more items returned, instead of 1 ``` Not sure what's causing it though 🤔 ### Owner Yes
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Add "cc-by-nc-sa-2.0" to list of licenses
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Sorry for the issue @albertvillanova! I think it's now fixed! :heart: " ]
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Datasets side of https://github.com/huggingface/hub-docs/pull/285
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Loading huggan/CelebA-HQ throws pyarrow.lib.ArrowInvalid
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## Describe the bug Loading huggan/CelebA-HQ throws pyarrow.lib.ArrowInvalid ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('huggan/CelebA-HQ') ``` ## Expected results See https://colab.research.google.com/drive/141LJCcM2XyqprPY83nIQ-Zk3BbxWeahq?usp=sharing#scrollTo=N3ml_7f8kzDd ## Actual results ``` File "/home/jean/projects/cold_diffusion/celebA.py", line 4, in <module> dataset = load_dataset('huggan/CelebA-HQ') File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/load.py", line 1793, in load_dataset builder_instance.download_and_prepare( File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 1274, in _prepare_split for key, table in logging.tqdm( File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 67, in _generate_tables parquet_file = pq.ParquetFile(f) File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/pyarrow/parquet/__init__.py", line 286, in __init__ self.reader.open( File "pyarrow/_parquet.pyx", line 1227, in pyarrow._parquet.ParquetReader.open File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: datasets-2.4.1.dev0 - Platform: Ubuntu 18.04 - Python version: 3.10 - PyArrow version: pyarrow 9.0.0
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4,885
Create dataset from list of dicts
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[ "Hi @sanderland, thanks for your enhancement proposal.\r\n\r\nI agree with you that this would be useful.\r\n\r\nPlease note that under the hood, we use PyArrow tables as backend:\r\n- The implementation of `Dataset.from_dict` uses the PyArrow `Table.from_pydict`\r\n\r\nTherefore, I would suggest:\r\n- Implementin...
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I often find myself with data from a variety of sources, and a list of dicts is very common among these. However, converting this to a Dataset is a little awkward, requiring either ```Dataset.from_pandas(pd.DataFrame(formatted_training_data))``` Which can error out on some more exotic values as 2-d arrays for reasons that are not entirely clear > ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column labels with type object') Alternatively: ```Dataset.from_dict({k: [s[k] for s in formatted_training_data] for k in formatted_training_data[0].keys()})``` Which works, but is a little ugly. **Describe the solution you'd like** Either `.from_dict` accepting a list of dicts, or a `.from_records` function accepting such. I am happy to PR this, just wanted to check you are happy to accept this I haven't missed something obvious, and which of the solutions would be prefered.
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Fix documentation card of math_qa dataset
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4884). All of your documentation changes will be reflected on that endpoint." ]
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Fix documentation card of math_qa dataset.
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With dataloader RSS memory consumed by HF datasets monotonically increases
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## Describe the bug When the HF datasets is used in conjunction with PyTorch Dataloader, the RSS memory of the process keeps on increasing when it should stay constant. ## Steps to reproduce the bug Run and observe the output of this snippet which logs RSS memory. ```python import psutil import os from transformers import BertTokenizer from datasets import load_dataset from torch.utils.data import DataLoader BATCH_SIZE = 32 NUM_TRIES = 10 tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") def transform(x): x.update(tokenizer(x["text"], return_tensors="pt", max_length=64, padding="max_length", truncation=True)) x.pop("text") x.pop("label") return x dataset = load_dataset("imdb", split="train") dataset.set_transform(transform) train_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4) mem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024) count = 0 while count < NUM_TRIES: for idx, batch in enumerate(train_loader): mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024) print(count, idx, mem_after - mem_before) count += 1 ``` ## Expected results Memory should not increase after initial setup and loading of the dataset ## Actual results Memory continuously increases as can be seen in the log. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0
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Fix language tags resource file
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4882). All of your documentation changes will be reflected on that endpoint." ]
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This PR fixes/updates/adds ALL language tags from IANA (as of 2022-08-08). This PR also removes all BCP47 suffixes (the languages file only contains language subtags, i.e. ISO 639 1 or 2 codes; no script/region/variant suffixes). See: - #4753
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Language names and language codes: connecting to a big database (rather than slow enrichment of custom list)
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[ "Thanks for opening this discussion, @alexis-michaud.\r\n\r\nAs the language validation procedure is shared with other Hugging Face projects, I'm tagging them as well.\r\n\r\nCC: @huggingface/moon-landing ", "on the Hub side, there is not fine grained validation we just check that `language:` contains an array of...
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**The problem:** Language diversity is an important dimension of the diversity of datasets. To find one's way around datasets, being able to search by language name and by standardized codes appears crucial. Currently the list of language codes is [here](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/languages.json), right? At about 1,500 entries, it is roughly at 1/4th of the world's diversity of extant languages. (Probably less, as the list of 1,418 contains variants that are linguistically very close: 108 varieties of English, for instance.) Looking forward to ever increasing coverage, how will the list of language names and language codes improve over time? Enrichment of the custom list by HFT contributors (like [here](https://github.com/huggingface/datasets/pull/4880)) has several issues: * progress is likely to be slow: ![image](https://user-images.githubusercontent.com/6072524/186253353-62f42168-3d31-4105-be1c-5eb1f818d528.png) (input required from reviewers, etc.) * the more contributors, the less consistency can be expected among contributions. No need to elaborate on how much confusion is likely to ensue as datasets accumulate. * there is no information on which language relates with which: no encoding of the special closeness between the languages of the Northwestern Germanic branch (English+Dutch+German etc.), for instance. Information on phylogenetic closeness can be relevant to run experiments on transfer of technology from one language to its close relatives. **A solution that seems desirable:** Connecting to an established database that (i) aims at full coverage of the world's languages and (ii) has information on higher-level groupings, alternative names, etc. It takes a lot of hard work to do such databases. Two important initiatives are [Ethnologue](https://www.ethnologue.com/) (ISO standard) and [Glottolog](https://glottolog.org/). Both have pros and cons. Glottolog contains references to Ethnologue identifiers, so adopting Glottolog entails getting the advantages of both sets of language codes. Both seem technically accessible & 'developer-friendly'. Glottolog has a [GitHub repo](https://github.com/glottolog/glottolog). For Ethnologue, harvesting tools have been devised (see [here](https://github.com/lyy1994/ethnologue); I did not try it out). In case a conversation with linguists seemed in order here, I'd be happy to participate ('pro bono', of course), & to rustle up more colleagues as useful, to help this useful development happen. With appreciation of HFT,
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Added names of less-studied languages
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[ "OK, I removed Glottolog codes and only added ISO 639-3 ones. The former are for the moment in corpus card description, language details, and in subcorpora names.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4880). All of your documentation changes will be reflected on ...
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CONTRIBUTOR
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Added names of less-studied languages (nru – Narua and jya – Japhug) for existing datasets.
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Fix Citation Information section in dataset cards
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4879). All of your documentation changes will be reflected on that endpoint." ]
1,661,278,003,000
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MEMBER
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Fix Citation Information section in dataset cards. This PR partially fixes the Citation Information section in dataset cards. Subsequent PRs will follow to complete this task.
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I_kwDODunzps5QXPg9
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[not really a bug] `identical_ok` is deprecated in huggingface-hub's `upload_file`
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In the huggingface-hub dependency, the `identical_ok` argument has no effect in `upload_file` (and it will be removed soon) See https://github.com/huggingface/huggingface_hub/blob/43499582b19df1ed081a5b2bd7a364e9cacdc91d/src/huggingface_hub/hf_api.py#L2164-L2169 It's used here: https://github.com/huggingface/datasets/blob/fcfcc951a73efbc677f9def9a8707d0af93d5890/src/datasets/dataset_dict.py#L1373-L1381 https://github.com/huggingface/datasets/blob/fdcb8b144ce3ef241410281e125bd03e87b8caa1/src/datasets/arrow_dataset.py#L4354-L4362 https://github.com/huggingface/datasets/blob/fdcb8b144ce3ef241410281e125bd03e87b8caa1/src/datasets/arrow_dataset.py#L4197-L4213 We should remove it. Maybe the third code sample has an unexpected behavior since it uses the non-default value `identical_ok = False`, but the argument is ignored.
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Fix documentation card of covid_qa_castorini dataset
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4877). All of your documentation changes will be reflected on that endpoint." ]
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Fix documentation card of covid_qa_castorini dataset.
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Move DatasetInfo from `datasets_infos.json` to the YAML tags in `README.md`
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[ "also @osanseviero @Pierrci @SBrandeis potentially", "Love this in principle 🚀 \r\n\r\nLet's keep in mind users might rely on `dataset_infos.json` already.\r\n\r\nI'm not convinced by the two-syntax solution, wouldn't it be simpler to have only one syntax with a `default` config for datasets with only one config...
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Currently there are two places to find metadata for datasets: - datasets_infos.json, which contains **per dataset config** - description - citation - license - splits and sizes - checksums of the data files - feature types - and more - YAML tags, which contain - license - language - train-eval-index - and more It would be nice to have a single place instead. We can rely on the YAML tags more than the JSON file for consistency with models. And it would all be indexed by our back-end directly, which is nice to have. One way would be to move everything to the YAML tags except the checksums (there can be tens of thousands of them). The description/citation is already in the dataset card so we probably don't need to have them in the YAML card, it would be redundant. Here is an example for SQuAD ```yaml download_size: 35142551 dataset_size: 89789763 version: 1.0.0 splits: - name: train num_examples: 87599 num_bytes: 79317110 - name: validation num_examples: 10570 num_bytes: 10472653 features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: text list: dtype: string - name: answer_start list: dtype: int32 ``` Since there is only one configuration for SQuAD, this structure is ok. For datasets with several configs we can see in a second step, but IMO it would be ok to have these fields per config using another syntax ```yaml configs: - config: unlabeled splits: - name: train num_examples: 10000 features: - name: text dtype: string - config: labeled splits: - name: train num_examples: 100 features: - name: text dtype: string - name: label dtype: ClassLabel names: - negative - positive ``` So in the end you could specify a YAML tag either at the top level (for all configs) or per config in the `configs` field Alternatively we could keep config specific stuff in the `dataset_infos.json` as it it today Not sure yet what's the best approach here but cc @julien-c @mariosasko @albertvillanova @polinaeterna for feedback :)
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`_resolve_features` ignores the token
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[ "Hi ! Your HF_ENDPOINT seems wrong because of the extra \"/\"\r\n```diff\r\n- os.environ[\"HF_ENDPOINT\"] = \"https://hub-ci.huggingface.co/\"\r\n+ os.environ[\"HF_ENDPOINT\"] = \"https://hub-ci.huggingface.co\"\r\n```\r\n\r\ncan you try again without the extra \"/\" ?", "Oh, yes, sorry, but it's not the issue.\r...
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CONTRIBUTOR
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## Describe the bug When calling [`_resolve_features()`](https://github.com/huggingface/datasets/blob/54b532a8a2f5353fdb0207578162153f7b2da2ec/src/datasets/iterable_dataset.py#L1255) on a gated dataset, ie. a dataset which requires a token to be loaded, the token seems to be ignored even if it has been provided to `load_dataset` before. ## Steps to reproduce the bug ```python import os os.environ["HF_ENDPOINT"] = "https://hub-ci.huggingface.co/" hf_token = "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" from datasets import load_dataset # public dataset_name = "__DUMMY_DATASETS_SERVER_USER__/repo_csv_data-16612654226756" config_name = "__DUMMY_DATASETS_SERVER_USER__--repo_csv_data-16612654226756" split_name = "train" iterable_dataset = load_dataset( dataset_name, name=config_name, split=split_name, streaming=True, use_auth_token=hf_token, ) iterable_dataset = iterable_dataset._resolve_features() print(iterable_dataset.features) # gated dataset_name = "__DUMMY_DATASETS_SERVER_USER__/repo_csv_data-16612654317644" config_name = "__DUMMY_DATASETS_SERVER_USER__--repo_csv_data-16612654317644" split_name = "train" iterable_dataset = load_dataset( dataset_name, name=config_name, split=split_name, streaming=True, use_auth_token=hf_token, ) try: iterable_dataset = iterable_dataset._resolve_features() except FileNotFoundError as e: print("FAILS") ``` ## Expected results I expect to have the same result on a public dataset and on a gated (or private) dataset, if the token has been provided. ## Actual results An exception is thrown on gated datasets. ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.15.0-1017-aws-x86_64-with-glibc2.35 - Python version: 3.9.6 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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[docs] Some tiny doc tweaks
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4874). All of your documentation changes will be reflected on that endpoint." ]
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Multiple dataloader memory error
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For the use of multiple datasets and tasks, we use around more than 200+ dataloaders, then pass it into `dataloader1, dataloader2, ..., dataloader200=accelerate.prepare(dataloader1, dataloader2, ..., dataloader200)` It causes the memory error when generating batches. Any solutions to it? ```bash File "/home/xxx/my_code/src/utils/data_utils.py", line 54, in generate_batch x = next(iterator) File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/accelerate/data_loader.py", line 301, in __iter__ for batch in super().__iter__(): File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__ data = self._next_data() File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 28, in fetch data.append(next(self.dataset_iter)) File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/accelerate/data_loader.py", line 249, in __iter__ for element in self.dataset: File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 503, in __iter__ for key, example in self._iter(): File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 500, in _iter yield from ex_iterable File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 231, in __iter__ new_key = "_".join(str(key) for key in keys) MemoryError ```
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[WIP] Docs for creating an audio dataset
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4872). All of your documentation changes will be reflected on that endpoint.", "Awesome thanks ! I think we can also encourage TAR archives as for image dataset scripts (feel free to copy paste some parts from there lol)" ]
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This PR is a first draft of how to create audio datasets (`AudioFolder` and loading script). Feel free to let me know if there are any specificities I'm missing for this. 🙂
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Fix: wmt datasets - fix CWMT zh subsets
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Fix https://github.com/huggingface/datasets/issues/4575 TODO: run `datasets-cli test`: - [x] wmt17 - [x] wmt18 - [x] wmt19
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audio folder check CI
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Fix typos in documentation
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adding mafand to datasets
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I'm addding the MAFAND dataset by Masakhane based on the paper/repository below: Paper: https://aclanthology.org/2022.naacl-main.223/ Code: https://github.com/masakhane-io/lafand-mt Please, help merge this Everything works except for creating dummy data file
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Complete tags of superglue dataset card
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Related to #4479 .
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amend docstring for dunder
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display dunder method in docsting with underlines an not bold markdown.
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Dataset Viewer issue for MoritzLaurer/multilingual_nli
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[ "Thanks for reporting @MoritzLaurer.\r\n\r\nCurrently, the dataset preview is working properly: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli\r\n\r\nPlease note that when a dataset is modified, it might take some time until the preview is completely updated.\r\n\r\n@severo might it be worth adding ...
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### Link _No response_ ### Description I've just uploaded a new dataset to the hub and the viewer does not work for some reason, see here: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli It displays the error: ``` Status code: 400 Exception: Status400Error Message: The dataset does not exist. ``` Weirdly enough the dataviewer works for an earlier version of the same dataset. The only difference is that it is smaller, but I'm not aware of other changes I have made: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli_test Do you know why the dataviewer is not working? ### Owner _No response_
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Allow pathlib PoxisPath in Dataset.read_json
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CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.** ``` from pathlib import Path from datasets import Dataset ds = Dataset.read_json(Path('data.json')) ``` causes an error ``` AttributeError: 'PosixPath' object has no attribute 'decode' ``` **Describe the solution you'd like** It should be able to accept PosixPath and read the json from inside.
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TFDS wiki_dialog dataset to Huggingface dataset
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[ "@albertvillanova any help ? The linked dataset is in beam format which is similar to wikipedia dataset in huggingface that you scripted..", "Nvm, I was able to port it to huggingface datasets, will upload to the hub soon", "https://huggingface.co/datasets/djaym7/wiki_dialog", "Thanks for the addition, @djay...
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## Adding a Dataset - **Name:** *Wiki_dialog* - **Description: https://github.com/google-research/dialog-inpainting#:~:text=JSON%20object%2C%20for-,example,-%3A - **Paper: https://arxiv.org/abs/2205.09073 - **Data: https://github.com/google-research/dialog-inpainting - **Motivation:** *Research and Development on biggest corpus of dialog data* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
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