Datasets:

Ioana23
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github-issues-with-embeddings

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train · 3.1k rows

html_url stringlengths 51 51	title stringlengths 6 280	comments stringlengths 67 24.7k	body stringlengths 51 36.2k	__index_level_0__ int64 1 1.17k	comment_length int64 16 1.45k	text stringlengths 190 38.3k	embeddings sequence
https://github.com/huggingface/datasets/issues/4386	Bug for wiki_auto_asset_turk from GEM	Hi again @StevenTang1998, I don't see any problem when loading `totto` dataset: ```python In [4]: import datasets ...: ds = datasets.load_dataset("totto") Downloading builder script: 5.58kB [00:00, 5.33MB/s] Downloading metadata: 2.78kB [00:00, 2.96MB/s] Using custom data configuration default Downloading and preparing dataset totto/default (download: 179.03 MiB, generated: 706.59 MiB, post-processed: Unknown size, total: 885.62 MiB) to .../.cache/huggingface/datasets/totto/default/1.0.0/263c85871e5451bc892c65ca0306c0629eb7beb161e0eb998f56231562335dd2... Downloading data: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 188M/188M [00:32<00:00, 5.77MB/s] Dataset totto downloaded and prepared to .../.cache/huggingface/datasets/totto/default/1.0.0/263c85871e5451bc892c65ca0306c0629eb7beb161e0eb998f56231562335dd2. Subsequent calls will reuse this data. 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 147.95it/s] In [5]: ds Out[5]: DatasetDict({ train: Dataset({ features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'], num_rows: 120761 }) validation: Dataset({ features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'], num_rows: 7700 }) test: Dataset({ features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'], num_rows: 7700 }) }) ```	## Describe the bug The script of wiki_auto_asset_turk for GEM may be out of date. ## Steps to reproduce the bug ```python import datasets datasets.load_dataset('gem', 'wiki_auto_asset_turk') ``` ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/load.py", line 1731, in load_dataset builder_instance.download_and_prepare( File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 640, in download_and_prepare self._download_and_prepare( File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 1158, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 707, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/tangtianyi/.cache/huggingface/modules/datasets_modules/datasets/gem/982a54473b12c6a6e40d4356e025fb7172a5bb2065e655e2c1af51f2b3cf4ca1/gem.py", line 538, in _split_generators dl_dir = dl_manager.download_and_extract(_URLs[self.config.name]) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 416, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 294, in download downloaded_path_or_paths = map_nested( File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 351, in map_nested mapped = [ File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 352, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 288, in _single_map_nested return function(data_struct) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 320, in _download return cached_path(url_or_filename, download_config=download_config) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 234, in cached_path output_path = get_from_cache( File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 579, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://github.com/facebookresearch/asset/raw/master/dataset/asset.test.orig ```	749	134	Bug for wiki_auto_asset_turk from GEM ## Describe the bug The script of wiki_auto_asset_turk for GEM may be out of date. ## Steps to reproduce the bug ```python import datasets datasets.load_dataset('gem', 'wiki_auto_asset_turk') ``` ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/load.py", line 1731, in load_dataset builder_instance.download_and_prepare( File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 640, in download_and_prepare self._download_and_prepare( File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 1158, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 707, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/tangtianyi/.cache/huggingface/modules/datasets_modules/datasets/gem/982a54473b12c6a6e40d4356e025fb7172a5bb2065e655e2c1af51f2b3cf4ca1/gem.py", line 538, in _split_generators dl_dir = dl_manager.download_and_extract(_URLs[self.config.name]) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 416, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 294, in download downloaded_path_or_paths = map_nested( File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 351, in map_nested mapped = [ File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 352, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 288, in _single_map_nested return function(data_struct) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 320, in _download return cached_path(url_or_filename, download_config=download_config) File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 234, in cached_path output_path = get_from_cache( File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 579, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://github.com/facebookresearch/asset/raw/master/dataset/asset.test.orig ``` Hi again @StevenTang1998, I don't see any problem when loading `totto` dataset: ```python In [4]: import datasets ...: ds = datasets.load_dataset("totto") Downloading builder script: 5.58kB [00:00, 5.33MB/s] Downloading metadata: 2.78kB [00:00, 2.96MB/s] Using custom data configuration default Downloading and preparing dataset totto/default (download: 179.03 MiB, generated: 706.59 MiB, post-processed: Unknown size, total: 885.62 MiB) to .../.cache/huggingface/datasets/totto/default/1.0.0/263c85871e5451bc892c65ca0306c0629eb7beb161e0eb998f56231562335dd2... Downloading data: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 188M/188M [00:32<00:00, 5.77MB/s] Dataset totto downloaded and prepared to .../.cache/huggingface/datasets/totto/default/1.0.0/263c85871e5451bc892c65ca0306c0629eb7beb161e0eb998f56231562335dd2. Subsequent calls will reuse this data. 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 147.95it/s] In [5]: ds Out[5]: DatasetDict({ train: Dataset({ features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'], num_rows: 120761 }) validation: Dataset({ features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'], num_rows: 7700 }) test: Dataset({ features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'], num_rows: 7700 }) }) ```	[ -1.2292745113372803, 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https://github.com/huggingface/datasets/issues/4381	Bug in caching 2 datasets both with the same builder class name	Hi @NouamaneTazi, thanks for reporting. Please note that both datasets are cached in the same directory because their loading builder classes have the same name: `class MTOP(datasets.GeneratorBasedBuilder)`. You should name their builder classes differently, e.g.: - `MtopDomain` - `MtopIntent`	## Describe the bug The two datasets `mteb/mtop_intent` and `mteb/mtop_domain `use both the same cache folder `.cache/huggingface/datasets/mteb___mtop`. So if you first load `mteb/mtop_intent` then datasets will not load `mteb/mtop_domain`. If you delete this cache folder and flip the order how you load the two datasets , you will get the opposite datasets loaded (difference is here in terms of the label and label_text). ## Steps to reproduce the bug ```python import datasets dataset = datasets.load_dataset("mteb/mtop_intent", "en") print(dataset['train'][0]) dataset = datasets.load_dataset("mteb/mtop_domain", "en") print(dataset['train'][0]) ``` ## Expected results ``` Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_intent/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 920.14it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'} Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_domain/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1307.59it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 0, 'label_text': 'messaging'} ``` ## Actual results ``` Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 920.14it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'} Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1307.59it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.1 - Platform: macOS-12.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 8.0.0 - Pandas version: 1.4.2	750	39	Bug in caching 2 datasets both with the same builder class name ## Describe the bug The two datasets `mteb/mtop_intent` and `mteb/mtop_domain `use both the same cache folder `.cache/huggingface/datasets/mteb___mtop`. So if you first load `mteb/mtop_intent` then datasets will not load `mteb/mtop_domain`. If you delete this cache folder and flip the order how you load the two datasets , you will get the opposite datasets loaded (difference is here in terms of the label and label_text). ## Steps to reproduce the bug ```python import datasets dataset = datasets.load_dataset("mteb/mtop_intent", "en") print(dataset['train'][0]) dataset = datasets.load_dataset("mteb/mtop_domain", "en") print(dataset['train'][0]) ``` ## Expected results ``` Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_intent/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 920.14it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'} Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_domain/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1307.59it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 0, 'label_text': 'messaging'} ``` ## Actual results ``` Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 920.14it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'} Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1307.59it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.1 - Platform: macOS-12.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 8.0.0 - Pandas version: 1.4.2 Hi @NouamaneTazi, thanks for reporting. Please note that both datasets are cached in the same directory because their loading builder classes have the same name: `class MTOP(datasets.GeneratorBasedBuilder)`. You should name their builder classes differently, e.g.: - `MtopDomain` - `MtopIntent`	[ -1.2074164152145386, -0.9168906211853027, -0.7638037204742432, 1.519376277923584, -0.13338908553123474, -1.1608984470367432, 0.12146271765232086, -1.021623969078064, 1.5816986560821533, -0.7711011171340942, 0.235040545463562, -1.7120236158370972, -0.08406396210193634, -0.5840659141540527, -0.7569152116775513, -0.8672776222229004, -0.30276551842689514, -0.8156992197036743, 1.017450213432312, 2.495422840118408, 1.2440500259399414, -1.33834707736969, 2.7351346015930176, 0.583246111869812, -0.1762780398130417, -1.0479936599731445, 0.575314998626709, -0.06301495432853699, -1.2384964227676392, -0.4441158175468445, -0.9786556959152222, 0.04336153343319893, -0.5761247873306274, -0.520456075668335, 0.01205524057149887, 0.4517882764339447, -0.3422555923461914, -0.35481682419776917, -0.6211875677108765, -0.7645813226699829, 0.44438862800598145, -0.3279893100261688, 0.9055328369140625, -0.330657035112381, 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https://github.com/huggingface/datasets/issues/4381	Bug in caching 2 datasets both with the same builder class name	Hi @NouamaneTazi, please note that after our fix: - #4388 we do not consider the class name anymore, but the name of the file where the loading builder class is implemented.	## Describe the bug The two datasets `mteb/mtop_intent` and `mteb/mtop_domain `use both the same cache folder `.cache/huggingface/datasets/mteb___mtop`. So if you first load `mteb/mtop_intent` then datasets will not load `mteb/mtop_domain`. If you delete this cache folder and flip the order how you load the two datasets , you will get the opposite datasets loaded (difference is here in terms of the label and label_text). ## Steps to reproduce the bug ```python import datasets dataset = datasets.load_dataset("mteb/mtop_intent", "en") print(dataset['train'][0]) dataset = datasets.load_dataset("mteb/mtop_domain", "en") print(dataset['train'][0]) ``` ## Expected results ``` Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_intent/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 920.14it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'} Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_domain/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1307.59it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 0, 'label_text': 'messaging'} ``` ## Actual results ``` Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 920.14it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'} Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1307.59it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.1 - Platform: macOS-12.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 8.0.0 - Pandas version: 1.4.2	750	31	Bug in caching 2 datasets both with the same builder class name ## Describe the bug The two datasets `mteb/mtop_intent` and `mteb/mtop_domain `use both the same cache folder `.cache/huggingface/datasets/mteb___mtop`. So if you first load `mteb/mtop_intent` then datasets will not load `mteb/mtop_domain`. If you delete this cache folder and flip the order how you load the two datasets , you will get the opposite datasets loaded (difference is here in terms of the label and label_text). ## Steps to reproduce the bug ```python import datasets dataset = datasets.load_dataset("mteb/mtop_intent", "en") print(dataset['train'][0]) dataset = datasets.load_dataset("mteb/mtop_domain", "en") print(dataset['train'][0]) ``` ## Expected results ``` Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_intent/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 920.14it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'} Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_domain/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1307.59it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 0, 'label_text': 'messaging'} ``` ## Actual results ``` Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 920.14it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'} Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35) 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1307.59it/s] {'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.1 - Platform: macOS-12.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 8.0.0 - Pandas version: 1.4.2 Hi @NouamaneTazi, please note that after our fix: - #4388 we do not consider the class name anymore, but the name of the file where the loading builder class is implemented.	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https://github.com/huggingface/datasets/issues/4379	Latest dill release raises exception	Just an additional insight, the latest dill (either 0.3.5 or 0.3.5.1) also broke the hashing/fingerprinting of any mapping function. For example: ``` from datasets import load_dataset d = load_dataset("rotten_tomatoes") d.map(lambda x: x) ``` Returns the standard non-dillable error: ``` Parameter 'function'=<function <lambda> at 0x7fe7d18c9560> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly.... ```	## Describe the bug As reported by @sgugger, latest dill release is breaking things with Datasets. ``` ______________ ExamplesTests.test_run_speech_recognition_seq2seq _______________ self = <multiprocess.pool.ApplyResult object at 0x7fa5981a1cd0>, timeout = None def get(self, timeout=None): self.wait(timeout) if not self.ready(): raise TimeoutError if self._success: return self._value else: > raise self._value E TypeError: '>' not supported between instances of 'NoneType' and 'float' ```	751	53	Latest dill release raises exception ## Describe the bug As reported by @sgugger, latest dill release is breaking things with Datasets. ``` ______________ ExamplesTests.test_run_speech_recognition_seq2seq _______________ self = <multiprocess.pool.ApplyResult object at 0x7fa5981a1cd0>, timeout = None def get(self, timeout=None): self.wait(timeout) if not self.ready(): raise TimeoutError if self._success: return self._value else: > raise self._value E TypeError: '>' not supported between instances of 'NoneType' and 'float' ``` Just an additional insight, the latest dill (either 0.3.5 or 0.3.5.1) also broke the hashing/fingerprinting of any mapping function. For example: ``` from datasets import load_dataset d = load_dataset("rotten_tomatoes") d.map(lambda x: x) ``` Returns the standard non-dillable error: ``` Parameter 'function'=<function <lambda> at 0x7fe7d18c9560> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly.... ```	[ -1.2098135948181152, -0.9242041110992432, -0.6457740664482117, 1.6308683156967163, -0.1237775906920433, -1.1937587261199951, 0.23423385620117188, -1.0522080659866333, 1.6262333393096924, -0.8332138657569885, 0.3194727301597595, -1.5127794742584229, 0.04049406200647354, -0.6783664226531982, -0.7132858633995056, -0.8685106039047241, -0.340608686208725, -0.7053385972976685, 1.1343685388565063, 2.383326530456543, 1.2348077297210693, -1.3244270086288452, 2.6450653076171875, 0.6613112092018127, -0.24110719561576843, -1.0719962120056152, 0.43626868724823, -0.005988291930407286, -1.2314287424087524, -0.4797288477420807, -0.8522950410842896, -0.062212467193603516, -0.6664986610412598, 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https://github.com/huggingface/datasets/issues/4379	Latest dill release raises exception	Thanks a lot @gugarosa for the insight: we will incorporate it in our CI as regression testing for future dill releases.	## Describe the bug As reported by @sgugger, latest dill release is breaking things with Datasets. ``` ______________ ExamplesTests.test_run_speech_recognition_seq2seq _______________ self = <multiprocess.pool.ApplyResult object at 0x7fa5981a1cd0>, timeout = None def get(self, timeout=None): self.wait(timeout) if not self.ready(): raise TimeoutError if self._success: return self._value else: > raise self._value E TypeError: '>' not supported between instances of 'NoneType' and 'float' ```	751	21	Latest dill release raises exception ## Describe the bug As reported by @sgugger, latest dill release is breaking things with Datasets. ``` ______________ ExamplesTests.test_run_speech_recognition_seq2seq _______________ self = <multiprocess.pool.ApplyResult object at 0x7fa5981a1cd0>, timeout = None def get(self, timeout=None): self.wait(timeout) if not self.ready(): raise TimeoutError if self._success: return self._value else: > raise self._value E TypeError: '>' not supported between instances of 'NoneType' and 'float' ``` Thanks a lot @gugarosa for the insight: we will incorporate it in our CI as regression testing for future dill releases.	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https://github.com/huggingface/datasets/issues/4379	Latest dill release raises exception	@albertvillanova I did a deep dive into @gugarosa's problem and found the issue and it might be related to the one @sgugger discovered. In dill 0.3.5(.1), I created a new `save_function` that fixes a bug in dill that prevented the pickling of recursive inner functions. It was a more complete solution to the problem that `dill._dill.stack` tried to solve in the internal API of dill. Since `dill._dill.stack` was no longer needed, I removed it. Since datasets copies the `save_function` directly from the dill API, it stops working with the new dill version since `dill._dill.stack` is no longer present and the `save_function` has been updated with new code. https://github.com/huggingface/datasets/blob/95193ae61e92aa537d0c65d37a1fd9d2393aae89/src/datasets/utils/py_utils.py#L607-L678 ~If the dill version is below 0.3.5, you should keep this function. If it is after, you would need to update your copy of `save_function` to use the code I introduced, or manually add a `stack` variable to `dill._dill` if it doesn't exist. Fortunately, in any version of Python 3.7+, dictionaries are always in insertion order and dill no longer supports Python 3.6 or older. So, any globals dictionary saved by dill 0.3.5+ will be deterministic given that the version of dill is held constant and this save_function is unnecessary for newer versions of dill.~ Ah. I see what is happening. I guess a different copy of the function code is needed that sorts the global variables by name. ```py if dill.__version__.split('.') < ['0', '3', '5']: # current save_function code inside here else: # new save_function code inside here with the following line inserted after creating the globals globs = {k: globs[k] for k in sorted(globs.keys())} ``` Will look into the test case @sgugger pointed out after that and verify if this is causing the problem. I am actually looking into rewriting the global variables code in uqfoundation/dill#466 and will keep this in mind and will try to create an easy way to modify the global variables in dill 0.3.6 (for example, sort them by key like datasets does).	## Describe the bug As reported by @sgugger, latest dill release is breaking things with Datasets. ``` ______________ ExamplesTests.test_run_speech_recognition_seq2seq _______________ self = <multiprocess.pool.ApplyResult object at 0x7fa5981a1cd0>, timeout = None def get(self, timeout=None): self.wait(timeout) if not self.ready(): raise TimeoutError if self._success: return self._value else: > raise self._value E TypeError: '>' not supported between instances of 'NoneType' and 'float' ```	751	327	Latest dill release raises exception ## Describe the bug As reported by @sgugger, latest dill release is breaking things with Datasets. ``` ______________ ExamplesTests.test_run_speech_recognition_seq2seq _______________ self = <multiprocess.pool.ApplyResult object at 0x7fa5981a1cd0>, timeout = None def get(self, timeout=None): self.wait(timeout) if not self.ready(): raise TimeoutError if self._success: return self._value else: > raise self._value E TypeError: '>' not supported between instances of 'NoneType' and 'float' ``` @albertvillanova I did a deep dive into @gugarosa's problem and found the issue and it might be related to the one @sgugger discovered. In dill 0.3.5(.1), I created a new `save_function` that fixes a bug in dill that prevented the pickling of recursive inner functions. It was a more complete solution to the problem that `dill._dill.stack` tried to solve in the internal API of dill. Since `dill._dill.stack` was no longer needed, I removed it. Since datasets copies the `save_function` directly from the dill API, it stops working with the new dill version since `dill._dill.stack` is no longer present and the `save_function` has been updated with new code. https://github.com/huggingface/datasets/blob/95193ae61e92aa537d0c65d37a1fd9d2393aae89/src/datasets/utils/py_utils.py#L607-L678 ~If the dill version is below 0.3.5, you should keep this function. If it is after, you would need to update your copy of `save_function` to use the code I introduced, or manually add a `stack` variable to `dill._dill` if it doesn't exist. Fortunately, in any version of Python 3.7+, dictionaries are always in insertion order and dill no longer supports Python 3.6 or older. So, any globals dictionary saved by dill 0.3.5+ will be deterministic given that the version of dill is held constant and this save_function is unnecessary for newer versions of dill.~ Ah. I see what is happening. I guess a different copy of the function code is needed that sorts the global variables by name. ```py if dill.__version__.split('.') < ['0', '3', '5']: # current save_function code inside here else: # new save_function code inside here with the following line inserted after creating the globals globs = {k: globs[k] for k in sorted(globs.keys())} ``` Will look into the test case @sgugger pointed out after that and verify if this is causing the problem. I am actually looking into rewriting the global variables code in uqfoundation/dill#466 and will keep this in mind and will try to create an easy way to modify the global variables in dill 0.3.6 (for example, sort them by key like datasets does).	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https://github.com/huggingface/datasets/issues/4379	Latest dill release raises exception	Thanks a lot for your investigation @anivegesana. Yes, we copied-pasted the old `save_function` function from `dill`, just adding a line to make deterministic the order of global variables `globs`. However, this function has changed a lot from version 0.3.5, after your PR (thank you for the fix in recursiveness, indeed): - uqfoundation/dill#443 We have to address this change. If finally your PR to sort global variables is merged into dill 0.3.6, that will make our life easier, as the tweak will no longer be necessary. ;) I have included a regression test so that we are sure future releases of dill do not break `datasets`: - #4385	## Describe the bug As reported by @sgugger, latest dill release is breaking things with Datasets. ``` ______________ ExamplesTests.test_run_speech_recognition_seq2seq _______________ self = <multiprocess.pool.ApplyResult object at 0x7fa5981a1cd0>, timeout = None def get(self, timeout=None): self.wait(timeout) if not self.ready(): raise TimeoutError if self._success: return self._value else: > raise self._value E TypeError: '>' not supported between instances of 'NoneType' and 'float' ```	751	107	Latest dill release raises exception ## Describe the bug As reported by @sgugger, latest dill release is breaking things with Datasets. ``` ______________ ExamplesTests.test_run_speech_recognition_seq2seq _______________ self = <multiprocess.pool.ApplyResult object at 0x7fa5981a1cd0>, timeout = None def get(self, timeout=None): self.wait(timeout) if not self.ready(): raise TimeoutError if self._success: return self._value else: > raise self._value E TypeError: '>' not supported between instances of 'NoneType' and 'float' ``` Thanks a lot for your investigation @anivegesana. Yes, we copied-pasted the old `save_function` function from `dill`, just adding a line to make deterministic the order of global variables `globs`. However, this function has changed a lot from version 0.3.5, after your PR (thank you for the fix in recursiveness, indeed): - uqfoundation/dill#443 We have to address this change. If finally your PR to sort global variables is merged into dill 0.3.6, that will make our life easier, as the tweak will no longer be necessary. ;) I have included a regression test so that we are sure future releases of dill do not break `datasets`: - #4385	[ -1.1867324113845825, -0.9131833910942078, -0.6666879653930664, 1.604217529296875, -0.13851337134838104, -1.2304669618606567, 0.16067668795585632, -1.0760917663574219, 1.5788607597351074, -0.8221644163131714, 0.3662334680557251, -1.5388507843017578, 0.12920497357845306, -0.7196709513664246, -0.7086942195892334, -0.8365586400032043, -0.3835884928703308, -0.7954094409942627, 1.1482301950454712, 2.470864772796631, 1.2667396068572998, -1.375248908996582, 2.622871160507202, 0.7194170355796814, -0.2093680053949356, -1.011407494544983, 0.4116246700286865, -0.03709118068218231, -1.2034529447555542, -0.5151874423027039, -0.8562967777252197, -0.12458702176809311, -0.6601662039756775, -0.3600744903087616, -0.0682341679930687, 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https://github.com/huggingface/datasets/issues/4379	Latest dill release raises exception	I should note that because Python 3.6 and older are now deprecated and Python 3.7 has insertion order dictionaries, the globals in dill will have a deterministic order, just not sorted. I would still keep it sorted like you have it to help with stability (for example, if someone reorders variables in a file, then sorting the globals would not invalidate the cache.) It seems that the order is not quite deterministic in IPython. Huggingface datasets seems to do well in Jupyter regardless, so it is not a good idea to remove the sorting. uqfoundation/dill#19	## Describe the bug As reported by @sgugger, latest dill release is breaking things with Datasets. ``` ______________ ExamplesTests.test_run_speech_recognition_seq2seq _______________ self = <multiprocess.pool.ApplyResult object at 0x7fa5981a1cd0>, timeout = None def get(self, timeout=None): self.wait(timeout) if not self.ready(): raise TimeoutError if self._success: return self._value else: > raise self._value E TypeError: '>' not supported between instances of 'NoneType' and 'float' ```	751	95	Latest dill release raises exception ## Describe the bug As reported by @sgugger, latest dill release is breaking things with Datasets. ``` ______________ ExamplesTests.test_run_speech_recognition_seq2seq _______________ self = <multiprocess.pool.ApplyResult object at 0x7fa5981a1cd0>, timeout = None def get(self, timeout=None): self.wait(timeout) if not self.ready(): raise TimeoutError if self._success: return self._value else: > raise self._value E TypeError: '>' not supported between instances of 'NoneType' and 'float' ``` I should note that because Python 3.6 and older are now deprecated and Python 3.7 has insertion order dictionaries, the globals in dill will have a deterministic order, just not sorted. I would still keep it sorted like you have it to help with stability (for example, if someone reorders variables in a file, then sorting the globals would not invalidate the cache.) It seems that the order is not quite deterministic in IPython. Huggingface datasets seems to do well in Jupyter regardless, so it is not a good idea to remove the sorting. uqfoundation/dill#19	[ -1.2067763805389404, -0.936922013759613, -0.6720626354217529, 1.5780889987945557, -0.11557842046022415, -1.2240122556686401, 0.10953758656978607, -1.0938847064971924, 1.6134039163589478, -0.8471161127090454, 0.36419278383255005, -1.4899840354919434, 0.09714096784591675, -0.7082810997962952, -0.7234145998954773, -0.8502175807952881, -0.3746436834335327, -0.8606133460998535, 1.1009764671325684, 2.4371626377105713, 1.2561813592910767, -1.404029369354248, 2.648690938949585, 0.6383852362632751, -0.19078567624092102, -1.0435746908187866, 0.4489925801753998, -0.04889228940010071, -1.1989182233810425, -0.5278873443603516, -0.9448556303977966, -0.027629677206277847, -0.6732011437416077, -0.37271326780319214, -0.032664988189935684, 0.366573303937912, -0.3367380201816559, -0.31056225299835205, -0.5449031591415405, -0.8739578127861023, 0.4152489900588989, 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https://github.com/huggingface/datasets/issues/4376	irc_disentagle viewer error	DUPLICATED comment from https://github.com/huggingface/datasets/issues/3807: my code: ``` from datasets import load_dataset dataset = load_dataset("irc_disentangle", download_mode="force_redownload") ``` however, it produces the same error ``` [38](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=37) if len(bad_urls) > 0: [39](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=38) error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> [40](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=39) raise NonMatchingChecksumError(error_msg + str(bad_urls)) [41](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=40) logger.info("All the checksums matched successfully" + for_verification_name) NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/jkkummerfeld/irc-disentanglement/tarball/master'] ``` I attempted to use the `ignore_verifications' as such: ``` ds = datasets.load_dataset('irc_disentangle', download_mode="force_redownload", ignore_verifications=True) Downloading builder script: 12.0kB [00:00, 5.92MB/s] Downloading metadata: 7.58kB [00:00, 3.48MB/s] No config specified, defaulting to: irc_disentangle/ubuntu Downloading and preparing dataset irc_disentangle/ubuntu (download: 112.98 MiB, generated: 60.05 MiB, post-processed: Unknown size, total: 173.03 MiB) to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5... Downloading data: 118MB [00:09, 12.1MB/s] Dataset irc_disentangle downloaded and prepared to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5. Subsequent calls will reuse this data. 100%\|██████████\| 3/3 [00:00<00:00, 675.38it/s] ``` but, this returns an empty set? ``` DatasetDict({ train: Dataset({ features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'], num_rows: 0 }) test: Dataset({ features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'], num_rows: 0 }) validation: Dataset({ features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'], num_rows: 0 }) }) ``` not sure what else to try at this point? Thanks in advanced🤗	the dataviewer shows this message for "ubuntu" - "train", "test", and "validation" splits: ``` Server error Status code: 400 Exception: ValueError Message: Cannot seek streaming HTTP file ``` it appears to give the same message for the "channel_two" data as well. I get a Checksums error when using `load_data()` with this dataset. Even with the `download_mode` and `ignore_verifications` options set. i referenced the issue here: https://github.com/huggingface/datasets/issues/3807	752	193	irc_disentagle viewer error the dataviewer shows this message for "ubuntu" - "train", "test", and "validation" splits: ``` Server error Status code: 400 Exception: ValueError Message: Cannot seek streaming HTTP file ``` it appears to give the same message for the "channel_two" data as well. I get a Checksums error when using `load_data()` with this dataset. Even with the `download_mode` and `ignore_verifications` options set. i referenced the issue here: https://github.com/huggingface/datasets/issues/3807 DUPLICATED comment from https://github.com/huggingface/datasets/issues/3807: my code: ``` from datasets import load_dataset dataset = load_dataset("irc_disentangle", download_mode="force_redownload") ``` however, it produces the same error ``` [38](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=37) if len(bad_urls) > 0: [39](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=38) error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> [40](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=39) raise NonMatchingChecksumError(error_msg + str(bad_urls)) [41](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=40) logger.info("All the checksums matched successfully" + for_verification_name) NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/jkkummerfeld/irc-disentanglement/tarball/master'] ``` I attempted to use the `ignore_verifications' as such: ``` ds = datasets.load_dataset('irc_disentangle', download_mode="force_redownload", ignore_verifications=True) Downloading builder script: 12.0kB [00:00, 5.92MB/s] Downloading metadata: 7.58kB [00:00, 3.48MB/s] No config specified, defaulting to: irc_disentangle/ubuntu Downloading and preparing dataset irc_disentangle/ubuntu (download: 112.98 MiB, generated: 60.05 MiB, post-processed: Unknown size, total: 173.03 MiB) to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5... Downloading data: 118MB [00:09, 12.1MB/s] Dataset irc_disentangle downloaded and prepared to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5. Subsequent calls will reuse this data. 100%\|██████████\| 3/3 [00:00<00:00, 675.38it/s] ``` but, this returns an empty set? ``` DatasetDict({ train: Dataset({ features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'], num_rows: 0 }) test: Dataset({ features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'], num_rows: 0 }) validation: Dataset({ features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'], num_rows: 0 }) }) ``` not sure what else to try at this point? 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https://github.com/huggingface/datasets/issues/4376	irc_disentagle viewer error	The issue with checksum and empty dataset has been fixed by: - #4377 To load the dataset, you should force the re-generation of the dataset from the downloaded file by passing `download_mode="reuse_cache_if_exists"` to `load_dataset`. In relation with the issue with the dataset viewer, first the dataset should be refactored to support streaming.	the dataviewer shows this message for "ubuntu" - "train", "test", and "validation" splits: ``` Server error Status code: 400 Exception: ValueError Message: Cannot seek streaming HTTP file ``` it appears to give the same message for the "channel_two" data as well. I get a Checksums error when using `load_data()` with this dataset. Even with the `download_mode` and `ignore_verifications` options set. i referenced the issue here: https://github.com/huggingface/datasets/issues/3807	752	52	irc_disentagle viewer error the dataviewer shows this message for "ubuntu" - "train", "test", and "validation" splits: ``` Server error Status code: 400 Exception: ValueError Message: Cannot seek streaming HTTP file ``` it appears to give the same message for the "channel_two" data as well. I get a Checksums error when using `load_data()` with this dataset. Even with the `download_mode` and `ignore_verifications` options set. i referenced the issue here: https://github.com/huggingface/datasets/issues/3807 The issue with checksum and empty dataset has been fixed by: - #4377 To load the dataset, you should force the re-generation of the dataset from the downloaded file by passing `download_mode="reuse_cache_if_exists"` to `load_dataset`. In relation with the issue with the dataset viewer, first the dataset should be refactored to support streaming.	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https://github.com/huggingface/datasets/issues/4376	irc_disentagle viewer error	Hi there, I see this issue is closed, but I am wondering if there is any chance the source files have been moved since this fix? I am stumbling into the same NonMatchingChecksumError noted by lebouz's second post once 118MB of data has been downloaded, and have tried the solutions noted in the various fix checksum posts linked here and in other posts regarding passing in "reuse_cache_if_exists" to download_mode. Any suggestions? Thank you!	the dataviewer shows this message for "ubuntu" - "train", "test", and "validation" splits: ``` Server error Status code: 400 Exception: ValueError Message: Cannot seek streaming HTTP file ``` it appears to give the same message for the "channel_two" data as well. I get a Checksums error when using `load_data()` with this dataset. Even with the `download_mode` and `ignore_verifications` options set. i referenced the issue here: https://github.com/huggingface/datasets/issues/3807	752	73	irc_disentagle viewer error the dataviewer shows this message for "ubuntu" - "train", "test", and "validation" splits: ``` Server error Status code: 400 Exception: ValueError Message: Cannot seek streaming HTTP file ``` it appears to give the same message for the "channel_two" data as well. I get a Checksums error when using `load_data()` with this dataset. Even with the `download_mode` and `ignore_verifications` options set. i referenced the issue here: https://github.com/huggingface/datasets/issues/3807 Hi there, I see this issue is closed, but I am wondering if there is any chance the source files have been moved since this fix? I am stumbling into the same NonMatchingChecksumError noted by lebouz's second post once 118MB of data has been downloaded, and have tried the solutions noted in the various fix checksum posts linked here and in other posts regarding passing in "reuse_cache_if_exists" to download_mode. Any suggestions? Thank you!	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https://github.com/huggingface/datasets/issues/4374	extremely slow processing when using a custom dataset	Hi ! My guess is that some examples in your dataset are bigger than your RAM, and therefore loading them in RAM to pass them to `remove_non_indic_sentences` takes forever because it might use SWAP memory. Maybe several examples in your dataset are grouped together, can you check `len(lang_dataset["train"])` and `lang_dataset["train"].data.nbytes` of both datasets please ? It can also be helpful to check the distribution of lengths of each examples in your dataset.	## processing a custom dataset loaded as .txt file is extremely slow, compared to a dataset of similar volume from the hub I have a large .txt file of 22 GB which i load into HF dataset `lang_dataset = datasets.load_dataset("text", data_files="hi.txt")` further i use a pre-processing function to clean the dataset `lang_dataset["train"] = lang_dataset["train"].map( remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)` the following processing takes astronomical time to process, while hoging all the ram. similar dataset of same size that's available in the huggingface hub works completely fine. which runs the same processing function and has the same amount of data. `lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", use_auth_token=True)` the hours predicted to preprocess are as follows: huggingface hub dataset: 6.5 hrs custom loaded dataset: 7000 hrs note: both the datasets are almost actually same, just provided by different sources with has +/- some samples, only one is hosted on the HF hub and the other is downloaded in a text format. ## Steps to reproduce the bug ``` import datasets import psutil import sys import glob from fastcore.utils import listify import re import gc def remove_non_indic_sentences(example): tmp_ls = [] eng_regex = r'[. a-zA-Z0-9ÖÄÅöäå _.,!"\'\/$]' for e in listify(example['text']): matches = re.findall(eng_regex, e) for match in (str(match).strip() for match in matches if match not in [""," ", " ", ",", " ,", ", ", " , "]): if len(list(match.split(" "))) > 2: e = re.sub(match," ",e,count=1) tmp_ls.append(e) gc.collect() example['clean_text'] = tmp_ls return example lang_dataset = datasets.load_dataset("text", data_files="hi.txt") lang_dataset["train"] = lang_dataset["train"].map( remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64) ## same thing work much faster when loading similar dataset from hub lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", split="train", use_auth_token=True) lang_dataset["train"] = lang_dataset["train"].map( remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64) ``` ## Actual results similar dataset of same size that's available in the huggingface hub works completely fine. which runs the same processing function and has the same amount of data. `lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", use_auth_token=True) the hours predicted to preprocess are as follows:* huggingface hub dataset: 6.5 hrs custom loaded dataset: 7000 hrs i even tried the following: - sharding the large 22gb text files into smaller files and loading - saving the file to disk and then loading - using lesser num_proc - using smaller batch size - processing without batches ie : without `batched=True` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.2.dev0 - Platform: Ubuntu 20.04 LTS - Python version: 3.9.7 - PyArrow version:8.0.0	753	72	extremely slow processing when using a custom dataset ## processing a custom dataset loaded as .txt file is extremely slow, compared to a dataset of similar volume from the hub I have a large .txt file of 22 GB which i load into HF dataset `lang_dataset = datasets.load_dataset("text", data_files="hi.txt")` further i use a pre-processing function to clean the dataset `lang_dataset["train"] = lang_dataset["train"].map( remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)` the following processing takes astronomical time to process, while hoging all the ram. similar dataset of same size that's available in the huggingface hub works completely fine. which runs the same processing function and has the same amount of data. `lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", use_auth_token=True)` the hours predicted to preprocess are as follows: huggingface hub dataset: 6.5 hrs custom loaded dataset: 7000 hrs note: both the datasets are almost actually same, just provided by different sources with has +/- some samples, only one is hosted on the HF hub and the other is downloaded in a text format. ## Steps to reproduce the bug ``` import datasets import psutil import sys import glob from fastcore.utils import listify import re import gc def remove_non_indic_sentences(example): tmp_ls = [] eng_regex = r'[. a-zA-Z0-9ÖÄÅöäå _.,!"\'\/$]' for e in listify(example['text']): matches = re.findall(eng_regex, e) for match in (str(match).strip() for match in matches if match not in [""," ", " ", ",", " ,", ", ", " , "]): if len(list(match.split(" "))) > 2: e = re.sub(match," ",e,count=1) tmp_ls.append(e) gc.collect() example['clean_text'] = tmp_ls return example lang_dataset = datasets.load_dataset("text", data_files="hi.txt") lang_dataset["train"] = lang_dataset["train"].map( remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64) ## same thing work much faster when loading similar dataset from hub lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", split="train", use_auth_token=True) lang_dataset["train"] = lang_dataset["train"].map( remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64) ``` ## Actual results similar dataset of same size that's available in the huggingface hub works completely fine. which runs the same processing function and has the same amount of data. `lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", use_auth_token=True) the hours predicted to preprocess are as follows:* huggingface hub dataset: 6.5 hrs custom loaded dataset: 7000 hrs i even tried the following: - sharding the large 22gb text files into smaller files and loading - saving the file to disk and then loading - using lesser num_proc - using smaller batch size - processing without batches ie : without `batched=True` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.2.dev0 - Platform: Ubuntu 20.04 LTS - Python version: 3.9.7 - PyArrow version:8.0.0 Hi ! My guess is that some examples in your dataset are bigger than your RAM, and therefore loading them in RAM to pass them to `remove_non_indic_sentences` takes forever because it might use SWAP memory. Maybe several examples in your dataset are grouped together, can you check `len(lang_dataset["train"])` and `lang_dataset["train"].data.nbytes` of both datasets please ? It can also be helpful to check the distribution of lengths of each examples in your dataset.	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https://github.com/huggingface/datasets/issues/4363	The dataset preview is not available for this split.	Hi! A dataset has to be streamable to work with the viewer. I did a quick test, and yours is, so this might be a bug in the viewer. cc @severo	I have uploaded the corpus developed by our lab in the speech domain to huggingface [datasets](https://huggingface.co/datasets/Roh/ryanspeech). You can read about the companion paper accepted in interspeech 2021 [here](https://arxiv.org/abs/2106.08468). The dataset works fine but I can't make the dataset preview work. It gives me the following error that I don't understand. Can you help me to begin debugging it? ``` Status code: 400 Exception: AttributeError Message: 'NoneType' object has no attribute 'split' ```	755	31	The dataset preview is not available for this split. I have uploaded the corpus developed by our lab in the speech domain to huggingface [datasets](https://huggingface.co/datasets/Roh/ryanspeech). You can read about the companion paper accepted in interspeech 2021 [here](https://arxiv.org/abs/2106.08468). The dataset works fine but I can't make the dataset preview work. It gives me the following error that I don't understand. Can you help me to begin debugging it? ``` Status code: 400 Exception: AttributeError Message: 'NoneType' object has no attribute 'split' ``` Hi! A dataset has to be streamable to work with the viewer. I did a quick test, and yours is, so this might be a bug in the viewer. cc @severo	[ -1.2063881158828735, -0.9290589690208435, -0.9087710380554199, 1.4220242500305176, -0.22107140719890594, -1.2269936800003052, 0.03576553612947464, -1.0450767278671265, 1.7291114330291748, -0.7768911123275757, 0.2798609733581543, -1.6541661024093628, -0.007488706614822149, -0.5147876143455505, -0.8037127256393433, -0.8887601494789124, -0.44398343563079834, -0.8575811982154846, 0.9504746198654175, 2.5597264766693115, 1.1099175214767456, -1.4442914724349976, 2.7487986087799072, 0.6006159782409668, -0.07370762526988983, -1.1779870986938477, 0.6158691048622131, 0.014632482081651688, -1.2588980197906494, -0.3985868990421295, -0.9403941631317139, 0.015635527670383453, -0.5946388840675354, -0.5278047323226929, 0.0386614054441452, 0.3602423369884491, -0.20891377329826355, -0.3313697576522827, -0.4281861186027527, -0.7842872738838196, 0.4343157410621643, -0.39680784940719604, 0.92726731300354, 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https://github.com/huggingface/datasets/issues/4363	The dataset preview is not available for this split.	Looking at it. The message is now: ``` Message: cannot cache function '__shear_dense': no locator available for file '/src/services/worker/.venv/lib/python3.9/site-packages/librosa/util/utils.py' ``` so possibly it's related to the libraries versions?	I have uploaded the corpus developed by our lab in the speech domain to huggingface [datasets](https://huggingface.co/datasets/Roh/ryanspeech). You can read about the companion paper accepted in interspeech 2021 [here](https://arxiv.org/abs/2106.08468). The dataset works fine but I can't make the dataset preview work. It gives me the following error that I don't understand. Can you help me to begin debugging it? ``` Status code: 400 Exception: AttributeError Message: 'NoneType' object has no attribute 'split' ```	755	28	The dataset preview is not available for this split. I have uploaded the corpus developed by our lab in the speech domain to huggingface [datasets](https://huggingface.co/datasets/Roh/ryanspeech). You can read about the companion paper accepted in interspeech 2021 [here](https://arxiv.org/abs/2106.08468). The dataset works fine but I can't make the dataset preview work. It gives me the following error that I don't understand. Can you help me to begin debugging it? ``` Status code: 400 Exception: AttributeError Message: 'NoneType' object has no attribute 'split' ``` Looking at it. The message is now: ``` Message: cannot cache function '__shear_dense': no locator available for file '/src/services/worker/.venv/lib/python3.9/site-packages/librosa/util/utils.py' ``` so possibly it's related to the libraries versions?	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https://github.com/huggingface/datasets/issues/4358	Missing dataset tags and sections in some dataset cards	@lhoestq I can take this issue. Please can you point out to me where I can find the other positional arguments?	Summary of CircleCI errors for different dataset metadata: - BoolQ: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - Conllpp: expected some content in section `Citation Information` but it is empty. - GLUE: 'annotations_creators', 'language_creators', 'source_datasets' :['unknown'] are not registered tags - ConLL2003: field 'task_ids': ['part-of-speech-tagging'] are not registered tags for 'task_ids' - Hate_speech18: Expected some content in section `Data Instances` but it is empty, Expected some content in section `Data Splits` but it is empty - Jjigsaw_toxicity_pred: `Citation Information` but it is empty. - LIAR : `Data Instances`,`Data Fields`, `Data Splits`, `Citation Information` are empty. - MSRA NER : Dataset Summary`, `Data Instances`, `Data Fields`, `Data Splits`, `Citation Information` are empty. - sem_eval_2010_task_8: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - sms_spam: `Data Instances` and`Data Splits` are empty. - Quora : Expected some content in section `Citation Information` but it is empty, missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - sentiment140: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'	756	21	Missing dataset tags and sections in some dataset cards Summary of CircleCI errors for different dataset metadata: - BoolQ: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - Conllpp: expected some content in section `Citation Information` but it is empty. - GLUE: 'annotations_creators', 'language_creators', 'source_datasets' :['unknown'] are not registered tags - ConLL2003: field 'task_ids': ['part-of-speech-tagging'] are not registered tags for 'task_ids' - Hate_speech18: Expected some content in section `Data Instances` but it is empty, Expected some content in section `Data Splits` but it is empty - Jjigsaw_toxicity_pred: `Citation Information` but it is empty. - LIAR : `Data Instances`,`Data Fields`, `Data Splits`, `Citation Information` are empty. - MSRA NER : Dataset Summary`, `Data Instances`, `Data Fields`, `Data Splits`, `Citation Information` are empty. - sem_eval_2010_task_8: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - sms_spam: `Data Instances` and`Data Splits` are empty. - Quora : Expected some content in section `Citation Information` but it is empty, missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - sentiment140: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' @lhoestq I can take this issue. Please can you point out to me where I can find the other positional arguments?	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https://github.com/huggingface/datasets/issues/4358	Missing dataset tags and sections in some dataset cards	Hi @RohitRathore1 :) You can find all the YAML tags in the tagging app here: https://hf.co/spaces/huggingface/datasets-tagging). They're all passed as arguments to a DatasetMetadata object used to validate the tags.	Summary of CircleCI errors for different dataset metadata: - BoolQ: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - Conllpp: expected some content in section `Citation Information` but it is empty. - GLUE: 'annotations_creators', 'language_creators', 'source_datasets' :['unknown'] are not registered tags - ConLL2003: field 'task_ids': ['part-of-speech-tagging'] are not registered tags for 'task_ids' - Hate_speech18: Expected some content in section `Data Instances` but it is empty, Expected some content in section `Data Splits` but it is empty - Jjigsaw_toxicity_pred: `Citation Information` but it is empty. - LIAR : `Data Instances`,`Data Fields`, `Data Splits`, `Citation Information` are empty. - MSRA NER : Dataset Summary`, `Data Instances`, `Data Fields`, `Data Splits`, `Citation Information` are empty. - sem_eval_2010_task_8: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - sms_spam: `Data Instances` and`Data Splits` are empty. - Quora : Expected some content in section `Citation Information` but it is empty, missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - sentiment140: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'	756	30	Missing dataset tags and sections in some dataset cards Summary of CircleCI errors for different dataset metadata: - BoolQ: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - Conllpp: expected some content in section `Citation Information` but it is empty. - GLUE: 'annotations_creators', 'language_creators', 'source_datasets' :['unknown'] are not registered tags - ConLL2003: field 'task_ids': ['part-of-speech-tagging'] are not registered tags for 'task_ids' - Hate_speech18: Expected some content in section `Data Instances` but it is empty, Expected some content in section `Data Splits` but it is empty - Jjigsaw_toxicity_pred: `Citation Information` but it is empty. - LIAR : `Data Instances`,`Data Fields`, `Data Splits`, `Citation Information` are empty. - MSRA NER : Dataset Summary`, `Data Instances`, `Data Fields`, `Data Splits`, `Citation Information` are empty. - sem_eval_2010_task_8: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - sms_spam: `Data Instances` and`Data Splits` are empty. - Quora : Expected some content in section `Citation Information` but it is empty, missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' - sentiment140: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' Hi @RohitRathore1 :) You can find all the YAML tags in the tagging app here: https://hf.co/spaces/huggingface/datasets-tagging). They're all passed as arguments to a DatasetMetadata object used to validate the tags.	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https://github.com/huggingface/datasets/issues/4354	Problems with WMT dataset	Hi! Yes, the docs are outdated. Expect this to be fixed soon. In the meantime, you can try to fix the issue yourself. These are the configs/language pairs supported by `wmt15` from which you can choose: * `cs-en` (Czech - English) * `de-en` (German - English) * `fi-en` (Finnish- English) * `fr-en` (French - English) * `ru-en` (Russian - English) And the current implementation always uses all the subsets available for a language, so to define custom subsets, you'll have to clone the repo from the Hub and replace the line https://huggingface.co/datasets/wmt15/blob/main/wmt_utils.py#L688 with: `for split, ss_names in (self._subsets if self.config.subsets is None else self.config.subsets).items()` Then, you can load the dataset as follows: ```python from datasets import load_dataset dset = load_dataset("path/to/local/wmt15_folder", "<one of 5 available configs>", subsets=...)	## Describe the bug I am trying to load WMT15 dataset and to define which data-sources to use for train/validation/test splits, but unfortunately it seems that the official documentation at [https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)](https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)) doesn't work anymore. ## Steps to reproduce the bug ```shell >>> import datasets >>> a = datasets.translate.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'translate' >>> a = datasets.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'wmt' ``` ## Expected results To load WMT15 with given data-sources. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.0-10-amd64-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1	757	126	Problems with WMT dataset ## Describe the bug I am trying to load WMT15 dataset and to define which data-sources to use for train/validation/test splits, but unfortunately it seems that the official documentation at [https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)](https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)) doesn't work anymore. ## Steps to reproduce the bug ```shell >>> import datasets >>> a = datasets.translate.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'translate' >>> a = datasets.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'wmt' ``` ## Expected results To load WMT15 with given data-sources. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.0-10-amd64-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1 Hi! Yes, the docs are outdated. Expect this to be fixed soon. In the meantime, you can try to fix the issue yourself. These are the configs/language pairs supported by `wmt15` from which you can choose: * `cs-en` (Czech - English) * `de-en` (German - English) * `fi-en` (Finnish- English) * `fr-en` (French - English) * `ru-en` (Russian - English) And the current implementation always uses all the subsets available for a language, so to define custom subsets, you'll have to clone the repo from the Hub and replace the line https://huggingface.co/datasets/wmt15/blob/main/wmt_utils.py#L688 with: `for split, ss_names in (self._subsets if self.config.subsets is None else self.config.subsets).items()` Then, you can load the dataset as follows: ```python from datasets import load_dataset dset = load_dataset("path/to/local/wmt15_folder", "<one of 5 available configs>", subsets=...)	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https://github.com/huggingface/datasets/issues/4354	Problems with WMT dataset	Hi @mariosasko Are the docs updated? If not, I would like to get on it. I am new around here, would we helpful, if you can guide. Thanks	## Describe the bug I am trying to load WMT15 dataset and to define which data-sources to use for train/validation/test splits, but unfortunately it seems that the official documentation at [https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)](https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)) doesn't work anymore. ## Steps to reproduce the bug ```shell >>> import datasets >>> a = datasets.translate.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'translate' >>> a = datasets.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'wmt' ``` ## Expected results To load WMT15 with given data-sources. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.0-10-amd64-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1	757	28	Problems with WMT dataset ## Describe the bug I am trying to load WMT15 dataset and to define which data-sources to use for train/validation/test splits, but unfortunately it seems that the official documentation at [https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)](https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)) doesn't work anymore. ## Steps to reproduce the bug ```shell >>> import datasets >>> a = datasets.translate.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'translate' >>> a = datasets.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'wmt' ``` ## Expected results To load WMT15 with given data-sources. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.0-10-amd64-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1 Hi @mariosasko Are the docs updated? If not, I would like to get on it. I am new around here, would we helpful, if you can guide. 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https://github.com/huggingface/datasets/issues/4354	Problems with WMT dataset	Hi @khushmeeet! The docs haven't been updated, so feel free to work on this issue. This is a tricky issue, so I'll give the steps you can follow to fix this: First, this code: https://github.com/huggingface/datasets/blob/7cff5b9726a223509dbd6224de3f5f452c8d924f/src/datasets/load.py#L113-L118 needs to be replaced with (makes the dataset builder search more robust and allows us to remove the ABC stuff from `wmt_utils.py`): ```python for name, obj in module.__dict__.items(): if inspect.isclass(obj) and issubclass(obj, main_cls_type): if inspect.isabstract(obj): continue module_main_cls = obj obj_module = inspect.getmodule(obj) if obj_module is not None and module == obj_module: break ``` Then, all the `wmt_utils.py` scripts need to be updated as follows (these are the diffs with the requiered changes): ````diff import os import re import xml.etree.cElementTree as ElementTree -from abc import ABC, abstractmethod import datasets ```` ````diff logger = datasets.logging.get_logger(__name__) _DESCRIPTION = """\ -Translate dataset based on the data from statmt.org. +Translation dataset based on the data from statmt.org. -Versions exists for the different years using a combination of multiple data -sources. The base `wmt_translate` allows you to create your own config to choose -your own data/language pair by creating a custom `datasets.translate.wmt.WmtConfig`. +Versions exist for different years using a combination of data +sources. The base `wmt` allows you to create a custom dataset by choosing +your own data/language pair. This can be done as follows: ``` -config = datasets.wmt.WmtConfig( - version="0.0.1", +from datasets import inspect_dataset, load_dataset_builder + +inspect_dataset("<insert the dataset name", "path/to/scripts") +builder = load_dataset_builder( + "path/to/scripts/wmt_utils.py", language_pair=("fr", "de"), subsets={ datasets.Split.TRAIN: ["commoncrawl_frde"], datasets.Split.VALIDATION: ["euelections_dev2019"], }, ) -builder = datasets.builder("wmt_translate", config=config) -``` +# Standard version +builder.download_and_prepare() +ds = builder.as_dataset() + +# Streamable version +ds = builder.as_streaming_dataset() +``` """ ```` ````diff +class Wmt(datasets.GeneratorBasedBuilder): """WMT translation dataset.""" + + BUILDER_CONFIG_CLASS = WmtConfig def __init__(self, args, kwargs): - if type(self) == Wmt and "config" not in kwargs: # pylint: disable=unidiomatic-typecheck - raise ValueError( - "The raw `wmt_translate` can only be instantiated with the config " - "kwargs. You may want to use one of the `wmtYY_translate` " - "implementation instead to get the WMT dataset for a specific year." - ) super(Wmt, self).__init__(args, **kwargs) @property - @abstractmethod def _subsets(self): """Subsets that make up each split of the dataset.""" ```` ```diff """Subsets that make up each split of the dataset for the language pair.""" source, target = self.config.language_pair filtered_subsets = {} - for split, ss_names in self._subsets.items(): + subsets = self._subsets if self.config.subsets is None else self.config.subsets + for split, ss_names in subsets.items(): filtered_subsets[split] = [] for ss_name in ss_names: dataset = DATASET_MAP[ss_name] ``` `wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t` have this script, so all of them need to be updated. Also, the dataset summaries from the READMEs of these datasets need to be updated to match the new `_DESCRIPTION` string. And that's it! Let me know if you need additional help.	## Describe the bug I am trying to load WMT15 dataset and to define which data-sources to use for train/validation/test splits, but unfortunately it seems that the official documentation at [https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)](https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)) doesn't work anymore. ## Steps to reproduce the bug ```shell >>> import datasets >>> a = datasets.translate.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'translate' >>> a = datasets.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'wmt' ``` ## Expected results To load WMT15 with given data-sources. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.0-10-amd64-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1	757	458	Problems with WMT dataset ## Describe the bug I am trying to load WMT15 dataset and to define which data-sources to use for train/validation/test splits, but unfortunately it seems that the official documentation at [https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)](https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)) doesn't work anymore. ## Steps to reproduce the bug ```shell >>> import datasets >>> a = datasets.translate.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'translate' >>> a = datasets.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'wmt' ``` ## Expected results To load WMT15 with given data-sources. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.0-10-amd64-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1 Hi @khushmeeet! The docs haven't been updated, so feel free to work on this issue. This is a tricky issue, so I'll give the steps you can follow to fix this: First, this code: https://github.com/huggingface/datasets/blob/7cff5b9726a223509dbd6224de3f5f452c8d924f/src/datasets/load.py#L113-L118 needs to be replaced with (makes the dataset builder search more robust and allows us to remove the ABC stuff from `wmt_utils.py`): ```python for name, obj in module.__dict__.items(): if inspect.isclass(obj) and issubclass(obj, main_cls_type): if inspect.isabstract(obj): continue module_main_cls = obj obj_module = inspect.getmodule(obj) if obj_module is not None and module == obj_module: break ``` Then, all the `wmt_utils.py` scripts need to be updated as follows (these are the diffs with the requiered changes): ````diff import os import re import xml.etree.cElementTree as ElementTree -from abc import ABC, abstractmethod import datasets ```` ````diff logger = datasets.logging.get_logger(__name__) _DESCRIPTION = """\ -Translate dataset based on the data from statmt.org. +Translation dataset based on the data from statmt.org. -Versions exists for the different years using a combination of multiple data -sources. The base `wmt_translate` allows you to create your own config to choose -your own data/language pair by creating a custom `datasets.translate.wmt.WmtConfig`. +Versions exist for different years using a combination of data +sources. The base `wmt` allows you to create a custom dataset by choosing +your own data/language pair. This can be done as follows: ``` -config = datasets.wmt.WmtConfig( - version="0.0.1", +from datasets import inspect_dataset, load_dataset_builder + +inspect_dataset("<insert the dataset name", "path/to/scripts") +builder = load_dataset_builder( + "path/to/scripts/wmt_utils.py", language_pair=("fr", "de"), subsets={ datasets.Split.TRAIN: ["commoncrawl_frde"], datasets.Split.VALIDATION: ["euelections_dev2019"], }, ) -builder = datasets.builder("wmt_translate", config=config) -``` +# Standard version +builder.download_and_prepare() +ds = builder.as_dataset() + +# Streamable version +ds = builder.as_streaming_dataset() +``` """ ```` ````diff +class Wmt(datasets.GeneratorBasedBuilder): """WMT translation dataset.""" + + BUILDER_CONFIG_CLASS = WmtConfig def __init__(self, args, kwargs): - if type(self) == Wmt and "config" not in kwargs: # pylint: disable=unidiomatic-typecheck - raise ValueError( - "The raw `wmt_translate` can only be instantiated with the config " - "kwargs. You may want to use one of the `wmtYY_translate` " - "implementation instead to get the WMT dataset for a specific year." - ) super(Wmt, self).__init__(args, **kwargs) @property - @abstractmethod def _subsets(self): """Subsets that make up each split of the dataset.""" ```` ```diff """Subsets that make up each split of the dataset for the language pair.""" source, target = self.config.language_pair filtered_subsets = {} - for split, ss_names in self._subsets.items(): + subsets = self._subsets if self.config.subsets is None else self.config.subsets + for split, ss_names in subsets.items(): filtered_subsets[split] = [] for ss_name in ss_names: dataset = DATASET_MAP[ss_name] ``` `wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t` have this script, so all of them need to be updated. Also, the dataset summaries from the READMEs of these datasets need to be updated to match the new `_DESCRIPTION` string. And that's it! Let me know if you need additional help.	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https://github.com/huggingface/datasets/issues/4354	Problems with WMT dataset	Hi @mariosasko , I have made the changes as suggested by you and have opened a PR #4537. Thanks	## Describe the bug I am trying to load WMT15 dataset and to define which data-sources to use for train/validation/test splits, but unfortunately it seems that the official documentation at [https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)](https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)) doesn't work anymore. ## Steps to reproduce the bug ```shell >>> import datasets >>> a = datasets.translate.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'translate' >>> a = datasets.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'wmt' ``` ## Expected results To load WMT15 with given data-sources. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.0-10-amd64-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1	757	19	Problems with WMT dataset ## Describe the bug I am trying to load WMT15 dataset and to define which data-sources to use for train/validation/test splits, but unfortunately it seems that the official documentation at [https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)](https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)) doesn't work anymore. ## Steps to reproduce the bug ```shell >>> import datasets >>> a = datasets.translate.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'translate' >>> a = datasets.wmt.WmtConfig() Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'datasets' has no attribute 'wmt' ``` ## Expected results To load WMT15 with given data-sources. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.0-10-amd64-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1 Hi @mariosasko , I have made the changes as suggested by you and have opened a PR #4537. 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https://github.com/huggingface/datasets/issues/4352	When using `dataset.map()` if passed `Features` types do not match what is returned from the mapped function, execution does not except in an obvious way	Hi ! Thanks for reporting :) `datasets` usually returns a `pa.lib.ArrowInvalid` error if the feature types don't match. It would be awesome if we had a way to reproduce the `OverflowError` in this case, to better understand what happened and be able to provide the best error message	## Describe the bug Recently I was trying to using `.map()` to preprocess a dataset. I defined the expected Features and passed them into `.map()` like `dataset.map(preprocess_data, features=features)`. My expected `Features` keys matched what came out of `preprocess_data`, but the types i had defined for them did not match the types that came back. Because of this, i ended up in tracebacks deep inside arrow_dataset.py and arrow_writer.py with exceptions that [did not make clear what the problem was](https://github.com/huggingface/datasets/issues/4349). In short i ended up with overflows and the OS killing processes when Arrow was attempting to write. It wasn't until I dug into `def write_batch` and the loop that loops over cols that I figured out what was going on. It seems like `.map()` could set a boolean that it's checked that for at least 1 instance from the dataset, the returned data's types match the types provided by the `features` param and error out with a clear exception if they don't. This would make the cause of the issue much more understandable and save people time. This could be construed as a feature but it feels more like a bug to me. ## Steps to reproduce the bug I don't have explicit code to repro the bug, but ill show an example Code prior to the fix: ```python def preprocess(examples): # returns an encoded data dict with keys that match the features, but the types do not match ... def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['audit_type'].unique().tolist() features = Features({ 'image': Array3D(dtype="uint8", shape=(3, 224, 224))), 'input_ids': Sequence(feature=Value(dtype='int64'))), 'attention_mask': Sequence(Value(dtype='int64'))), 'token_type_ids': Sequence(Value(dtype='int64'))), 'bbox': Array2D(dtype="int64", shape=(512, 4))), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names) ``` The Features set that fixed it: ```python features = Features({ 'image': Sequence(Array3D(dtype="uint8", shape=(3, 224, 224))), 'input_ids': Sequence(Sequence(feature=Value(dtype='int64'))), 'attention_mask': Sequence(Sequence(Value(dtype='int64'))), 'token_type_ids': Sequence(Sequence(Value(dtype='int64'))), 'bbox': Sequence(Array2D(dtype="int64", shape=(512, 4))), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) ``` The difference between my original code (which was based on documentation) and the working code is the addition of the `Sequence(...)` to 4/5 features as I am working with paginated data and the doc examples are not. ## Expected results Dataset.map() attempts to validate the data types for each Feature on the first iteration and errors out if they are not validated. ## Actual results Specify the actual results or traceback. Based on the value of `writer_batch_size`, execution errors out when Arrow attempts to write because the types do not match, though its error messages dont make this obvious Example errors: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> datasets version: 2.1.0 Platform: macOS-12.2.1-arm64-arm-64bit Python version: 3.9.12 PyArrow version: 6.0.1 Pandas version: 1.4.2	758	48	When using `dataset.map()` if passed `Features` types do not match what is returned from the mapped function, execution does not except in an obvious way ## Describe the bug Recently I was trying to using `.map()` to preprocess a dataset. I defined the expected Features and passed them into `.map()` like `dataset.map(preprocess_data, features=features)`. My expected `Features` keys matched what came out of `preprocess_data`, but the types i had defined for them did not match the types that came back. Because of this, i ended up in tracebacks deep inside arrow_dataset.py and arrow_writer.py with exceptions that [did not make clear what the problem was](https://github.com/huggingface/datasets/issues/4349). In short i ended up with overflows and the OS killing processes when Arrow was attempting to write. It wasn't until I dug into `def write_batch` and the loop that loops over cols that I figured out what was going on. It seems like `.map()` could set a boolean that it's checked that for at least 1 instance from the dataset, the returned data's types match the types provided by the `features` param and error out with a clear exception if they don't. This would make the cause of the issue much more understandable and save people time. This could be construed as a feature but it feels more like a bug to me. ## Steps to reproduce the bug I don't have explicit code to repro the bug, but ill show an example Code prior to the fix: ```python def preprocess(examples): # returns an encoded data dict with keys that match the features, but the types do not match ... def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['audit_type'].unique().tolist() features = Features({ 'image': Array3D(dtype="uint8", shape=(3, 224, 224))), 'input_ids': Sequence(feature=Value(dtype='int64'))), 'attention_mask': Sequence(Value(dtype='int64'))), 'token_type_ids': Sequence(Value(dtype='int64'))), 'bbox': Array2D(dtype="int64", shape=(512, 4))), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names) ``` The Features set that fixed it: ```python features = Features({ 'image': Sequence(Array3D(dtype="uint8", shape=(3, 224, 224))), 'input_ids': Sequence(Sequence(feature=Value(dtype='int64'))), 'attention_mask': Sequence(Sequence(Value(dtype='int64'))), 'token_type_ids': Sequence(Sequence(Value(dtype='int64'))), 'bbox': Sequence(Array2D(dtype="int64", shape=(512, 4))), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) ``` The difference between my original code (which was based on documentation) and the working code is the addition of the `Sequence(...)` to 4/5 features as I am working with paginated data and the doc examples are not. ## Expected results Dataset.map() attempts to validate the data types for each Feature on the first iteration and errors out if they are not validated. ## Actual results Specify the actual results or traceback. Based on the value of `writer_batch_size`, execution errors out when Arrow attempts to write because the types do not match, though its error messages dont make this obvious Example errors: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> datasets version: 2.1.0 Platform: macOS-12.2.1-arm64-arm-64bit Python version: 3.9.12 PyArrow version: 6.0.1 Pandas version: 1.4.2 Hi ! Thanks for reporting :) `datasets` usually returns a `pa.lib.ArrowInvalid` error if the feature types don't match. 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https://github.com/huggingface/datasets/issues/4351	Add optional progress bar for .save_to_disk(..) and .load_from_disk(..) when working with remote filesystems	Hi! I like this idea. For consistency with `load_dataset`, we can use `fsspec`'s `TqdmCallback` in `.load_from_disk` to monitor the number of bytes downloaded, and in `.save_to_disk`, we can track the number of saved shards for consistency with `push_to_hub` (after we implement https://github.com/huggingface/datasets/issues/4196).	Is your feature request related to a problem? Please describe. When working with large datasets stored on remote filesystems(such as s3), the process of uploading a dataset could take really long time. For instance: I was uploading a re-processed version of wmt17 en-ru to my s3 bucket and it took like 35 minutes(and that's given that I have a fiber optic connection). The only output during that process was a progress bar for flattening indices and then ~35 minutes of complete silence. Describe the solution you'd like I want to be able to enable a progress bar when calling .save_to_disk(..) and .load_from_disk(..), it would track either amount of bytes sent/received or amount of records written/loaded, and will give some ETA. Basically just tqdm. Describe alternatives you've considered - Save dataset to tmp folder at the disk and then upload it using custom wrapper over botocore, which will work with progress bar, like [this](https://alexwlchan.net/2021/04/s3-progress-bars/).	759	42	Add optional progress bar for .save_to_disk(..) and .load_from_disk(..) when working with remote filesystems Is your feature request related to a problem? Please describe. When working with large datasets stored on remote filesystems(such as s3), the process of uploading a dataset could take really long time. For instance: I was uploading a re-processed version of wmt17 en-ru to my s3 bucket and it took like 35 minutes(and that's given that I have a fiber optic connection). The only output during that process was a progress bar for flattening indices and then ~35 minutes of complete silence. Describe the solution you'd like I want to be able to enable a progress bar when calling .save_to_disk(..) and .load_from_disk(..), it would track either amount of bytes sent/received or amount of records written/loaded, and will give some ETA. Basically just tqdm. Describe alternatives you've considered - Save dataset to tmp folder at the disk and then upload it using custom wrapper over botocore, which will work with progress bar, like [this](https://alexwlchan.net/2021/04/s3-progress-bars/). Hi! I like this idea. For consistency with `load_dataset`, we can use `fsspec`'s `TqdmCallback` in `.load_from_disk` to monitor the number of bytes downloaded, and in `.save_to_disk`, we can track the number of saved shards for consistency with `push_to_hub` (after we implement https://github.com/huggingface/datasets/issues/4196).	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https://github.com/huggingface/datasets/issues/4349	Dataset.map()'s fails at any value of parameter writer_batch_size	Note that this same issue occurs even if i preprocess with the more default way of tokenizing that uses LayoutLMv2Processor's internal OCR: ```python feature_extractor = LayoutLMv2FeatureExtractor() tokenizer = LayoutLMv2Tokenizer.from_pretrained("microsoft/layoutlmv2-base-uncased") processor = LayoutLMv2Processor(feature_extractor, tokenizer) encoded_inputs = processor(images, padding="max_length", truncation=True) encoded_inputs["image"] = np.array(encoded_inputs["image"]) encoded_inputs["label"] = examples['label_id'] ```	## Describe the bug If the the value of `writer_batch_size` is less than the total number of instances in the dataset it will fail at that same number of instances. If it is greater than the total number of instances, it fails on the last instance. Context: I am attempting to fine-tune a pre-trained HuggingFace transformers model called LayoutLMv2. This model takes three inputs: document images, words and word bounding boxes. [The Processor for this model has two options](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor), the default is passing a document to the Processor and allowing it to create images of the document and use PyTesseract to perform OCR and generate words/bounding boxes. The other option is to provide `revision="no_ocr"` to the pre-trained model which allows you to use your own OCR results (in my case, Amazon Textract) so you have to provide the image, words and bounding boxes yourself. I am using this second option which might be good context for the bug. I am using the Dataset.map() paradigm to create these three inputs, encode them and save the dataset. Note that my documents (data instances) on average are fairly large and can range from 1 page up to 300 pages. Code I am using is provided below ## Steps to reproduce the bug I do not have explicit sample code, but I will paste the code I'm using in case reading it helps. When `.map()` is called, the dataset has 2933 rows, many of which represent large pdf documents. ```python def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['label'].unique() features = Features({ 'image': Array3D(dtype="int64", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'token_type_ids': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names, writer_batch_size=dataset.num_rows+1) encoded_dataset.save_to_disk(TRAINING_DATA_PATH + ENCODED_DATASET_NAME) encoded_dataset.set_format(type="torch") return encoded_dataset ``` ```python PROCESSOR = LayoutLMv2Processor.from_pretrained(MODEL_PATH, revision="no_ocr", use_fast=False) def preprocess_data(examples): directory = os.path.join(FILES_PATH, examples['file_location']) images_dir = os.path.join(directory, PDF_IMAGE_DIR) textract_response_path = os.path.join(directory, 'textract.json') doc_meta_path = os.path.join(directory, 'doc_meta.json') textract_document = get_textract_document(textract_response_path, doc_meta_path) images, words, bboxes = get_doc_training_data(images_dir, textract_document) encoded_inputs = PROCESSOR(images, words, boxes=bboxes, padding="max_length", truncation=True) # https://github.com/NielsRogge/Transformers-Tutorials/issues/36 encoded_inputs["image"] = np.array(encoded_inputs["image"]) encoded_inputs["label"] = examples['label_id'] return encoded_inputs ``` ## Expected results My expectation is that `writer_batch_size` allows one to simply trade off performance and memory requirements, not that it must be a specific number for `.map()` to function correctly. ## Actual results If writer_batch_size is set to a value less than the number of rows, I get either: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` or simply ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` If it is greater than the number of rows, i get the `zsh: killed` error above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.2	760	45	Dataset.map()'s fails at any value of parameter writer_batch_size ## Describe the bug If the the value of `writer_batch_size` is less than the total number of instances in the dataset it will fail at that same number of instances. If it is greater than the total number of instances, it fails on the last instance. Context: I am attempting to fine-tune a pre-trained HuggingFace transformers model called LayoutLMv2. This model takes three inputs: document images, words and word bounding boxes. [The Processor for this model has two options](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor), the default is passing a document to the Processor and allowing it to create images of the document and use PyTesseract to perform OCR and generate words/bounding boxes. The other option is to provide `revision="no_ocr"` to the pre-trained model which allows you to use your own OCR results (in my case, Amazon Textract) so you have to provide the image, words and bounding boxes yourself. I am using this second option which might be good context for the bug. I am using the Dataset.map() paradigm to create these three inputs, encode them and save the dataset. Note that my documents (data instances) on average are fairly large and can range from 1 page up to 300 pages. Code I am using is provided below ## Steps to reproduce the bug I do not have explicit sample code, but I will paste the code I'm using in case reading it helps. When `.map()` is called, the dataset has 2933 rows, many of which represent large pdf documents. ```python def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['label'].unique() features = Features({ 'image': Array3D(dtype="int64", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'token_type_ids': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names, writer_batch_size=dataset.num_rows+1) encoded_dataset.save_to_disk(TRAINING_DATA_PATH + ENCODED_DATASET_NAME) encoded_dataset.set_format(type="torch") return encoded_dataset ``` ```python PROCESSOR = LayoutLMv2Processor.from_pretrained(MODEL_PATH, revision="no_ocr", use_fast=False) def preprocess_data(examples): directory = os.path.join(FILES_PATH, examples['file_location']) images_dir = os.path.join(directory, PDF_IMAGE_DIR) textract_response_path = os.path.join(directory, 'textract.json') doc_meta_path = os.path.join(directory, 'doc_meta.json') textract_document = get_textract_document(textract_response_path, doc_meta_path) images, words, bboxes = get_doc_training_data(images_dir, textract_document) encoded_inputs = PROCESSOR(images, words, boxes=bboxes, padding="max_length", truncation=True) # https://github.com/NielsRogge/Transformers-Tutorials/issues/36 encoded_inputs["image"] = np.array(encoded_inputs["image"]) encoded_inputs["label"] = examples['label_id'] return encoded_inputs ``` ## Expected results My expectation is that `writer_batch_size` allows one to simply trade off performance and memory requirements, not that it must be a specific number for `.map()` to function correctly. ## Actual results If writer_batch_size is set to a value less than the number of rows, I get either: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` or simply ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` If it is greater than the number of rows, i get the `zsh: killed` error above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.2 Note that this same issue occurs even if i preprocess with the more default way of tokenizing that uses LayoutLMv2Processor's internal OCR: ```python feature_extractor = LayoutLMv2FeatureExtractor() tokenizer = LayoutLMv2Tokenizer.from_pretrained("microsoft/layoutlmv2-base-uncased") processor = LayoutLMv2Processor(feature_extractor, tokenizer) encoded_inputs = processor(images, padding="max_length", truncation=True) encoded_inputs["image"] = 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https://github.com/huggingface/datasets/issues/4349	Dataset.map()'s fails at any value of parameter writer_batch_size	Did you close it because you found that it was due to the incorrect Feature types ?	## Describe the bug If the the value of `writer_batch_size` is less than the total number of instances in the dataset it will fail at that same number of instances. If it is greater than the total number of instances, it fails on the last instance. Context: I am attempting to fine-tune a pre-trained HuggingFace transformers model called LayoutLMv2. This model takes three inputs: document images, words and word bounding boxes. [The Processor for this model has two options](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor), the default is passing a document to the Processor and allowing it to create images of the document and use PyTesseract to perform OCR and generate words/bounding boxes. The other option is to provide `revision="no_ocr"` to the pre-trained model which allows you to use your own OCR results (in my case, Amazon Textract) so you have to provide the image, words and bounding boxes yourself. I am using this second option which might be good context for the bug. I am using the Dataset.map() paradigm to create these three inputs, encode them and save the dataset. Note that my documents (data instances) on average are fairly large and can range from 1 page up to 300 pages. Code I am using is provided below ## Steps to reproduce the bug I do not have explicit sample code, but I will paste the code I'm using in case reading it helps. When `.map()` is called, the dataset has 2933 rows, many of which represent large pdf documents. ```python def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['label'].unique() features = Features({ 'image': Array3D(dtype="int64", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'token_type_ids': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names, writer_batch_size=dataset.num_rows+1) encoded_dataset.save_to_disk(TRAINING_DATA_PATH + ENCODED_DATASET_NAME) encoded_dataset.set_format(type="torch") return encoded_dataset ``` ```python PROCESSOR = LayoutLMv2Processor.from_pretrained(MODEL_PATH, revision="no_ocr", use_fast=False) def preprocess_data(examples): directory = os.path.join(FILES_PATH, examples['file_location']) images_dir = os.path.join(directory, PDF_IMAGE_DIR) textract_response_path = os.path.join(directory, 'textract.json') doc_meta_path = os.path.join(directory, 'doc_meta.json') textract_document = get_textract_document(textract_response_path, doc_meta_path) images, words, bboxes = get_doc_training_data(images_dir, textract_document) encoded_inputs = PROCESSOR(images, words, boxes=bboxes, padding="max_length", truncation=True) # https://github.com/NielsRogge/Transformers-Tutorials/issues/36 encoded_inputs["image"] = np.array(encoded_inputs["image"]) encoded_inputs["label"] = examples['label_id'] return encoded_inputs ``` ## Expected results My expectation is that `writer_batch_size` allows one to simply trade off performance and memory requirements, not that it must be a specific number for `.map()` to function correctly. ## Actual results If writer_batch_size is set to a value less than the number of rows, I get either: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` or simply ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` If it is greater than the number of rows, i get the `zsh: killed` error above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.2	760	17	Dataset.map()'s fails at any value of parameter writer_batch_size ## Describe the bug If the the value of `writer_batch_size` is less than the total number of instances in the dataset it will fail at that same number of instances. If it is greater than the total number of instances, it fails on the last instance. Context: I am attempting to fine-tune a pre-trained HuggingFace transformers model called LayoutLMv2. This model takes three inputs: document images, words and word bounding boxes. [The Processor for this model has two options](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor), the default is passing a document to the Processor and allowing it to create images of the document and use PyTesseract to perform OCR and generate words/bounding boxes. The other option is to provide `revision="no_ocr"` to the pre-trained model which allows you to use your own OCR results (in my case, Amazon Textract) so you have to provide the image, words and bounding boxes yourself. I am using this second option which might be good context for the bug. I am using the Dataset.map() paradigm to create these three inputs, encode them and save the dataset. Note that my documents (data instances) on average are fairly large and can range from 1 page up to 300 pages. Code I am using is provided below ## Steps to reproduce the bug I do not have explicit sample code, but I will paste the code I'm using in case reading it helps. When `.map()` is called, the dataset has 2933 rows, many of which represent large pdf documents. ```python def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['label'].unique() features = Features({ 'image': Array3D(dtype="int64", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'token_type_ids': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names, writer_batch_size=dataset.num_rows+1) encoded_dataset.save_to_disk(TRAINING_DATA_PATH + ENCODED_DATASET_NAME) encoded_dataset.set_format(type="torch") return encoded_dataset ``` ```python PROCESSOR = LayoutLMv2Processor.from_pretrained(MODEL_PATH, revision="no_ocr", use_fast=False) def preprocess_data(examples): directory = os.path.join(FILES_PATH, examples['file_location']) images_dir = os.path.join(directory, PDF_IMAGE_DIR) textract_response_path = os.path.join(directory, 'textract.json') doc_meta_path = os.path.join(directory, 'doc_meta.json') textract_document = get_textract_document(textract_response_path, doc_meta_path) images, words, bboxes = get_doc_training_data(images_dir, textract_document) encoded_inputs = PROCESSOR(images, words, boxes=bboxes, padding="max_length", truncation=True) # https://github.com/NielsRogge/Transformers-Tutorials/issues/36 encoded_inputs["image"] = np.array(encoded_inputs["image"]) encoded_inputs["label"] = examples['label_id'] return encoded_inputs ``` ## Expected results My expectation is that `writer_batch_size` allows one to simply trade off performance and memory requirements, not that it must be a specific number for `.map()` to function correctly. ## Actual results If writer_batch_size is set to a value less than the number of rows, I get either: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` or simply ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` If it is greater than the number of rows, i get the `zsh: killed` error above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.2 Did you close it because you found that it was due to the incorrect Feature types ?	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https://github.com/huggingface/datasets/issues/4349	Dataset.map()'s fails at any value of parameter writer_batch_size	Yeah-- my analysis of the issue was wrong in this one so I just closed it while linking to the new issue	## Describe the bug If the the value of `writer_batch_size` is less than the total number of instances in the dataset it will fail at that same number of instances. If it is greater than the total number of instances, it fails on the last instance. Context: I am attempting to fine-tune a pre-trained HuggingFace transformers model called LayoutLMv2. This model takes three inputs: document images, words and word bounding boxes. [The Processor for this model has two options](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor), the default is passing a document to the Processor and allowing it to create images of the document and use PyTesseract to perform OCR and generate words/bounding boxes. The other option is to provide `revision="no_ocr"` to the pre-trained model which allows you to use your own OCR results (in my case, Amazon Textract) so you have to provide the image, words and bounding boxes yourself. I am using this second option which might be good context for the bug. I am using the Dataset.map() paradigm to create these three inputs, encode them and save the dataset. Note that my documents (data instances) on average are fairly large and can range from 1 page up to 300 pages. Code I am using is provided below ## Steps to reproduce the bug I do not have explicit sample code, but I will paste the code I'm using in case reading it helps. When `.map()` is called, the dataset has 2933 rows, many of which represent large pdf documents. ```python def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['label'].unique() features = Features({ 'image': Array3D(dtype="int64", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'token_type_ids': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names, writer_batch_size=dataset.num_rows+1) encoded_dataset.save_to_disk(TRAINING_DATA_PATH + ENCODED_DATASET_NAME) encoded_dataset.set_format(type="torch") return encoded_dataset ``` ```python PROCESSOR = LayoutLMv2Processor.from_pretrained(MODEL_PATH, revision="no_ocr", use_fast=False) def preprocess_data(examples): directory = os.path.join(FILES_PATH, examples['file_location']) images_dir = os.path.join(directory, PDF_IMAGE_DIR) textract_response_path = os.path.join(directory, 'textract.json') doc_meta_path = os.path.join(directory, 'doc_meta.json') textract_document = get_textract_document(textract_response_path, doc_meta_path) images, words, bboxes = get_doc_training_data(images_dir, textract_document) encoded_inputs = PROCESSOR(images, words, boxes=bboxes, padding="max_length", truncation=True) # https://github.com/NielsRogge/Transformers-Tutorials/issues/36 encoded_inputs["image"] = np.array(encoded_inputs["image"]) encoded_inputs["label"] = examples['label_id'] return encoded_inputs ``` ## Expected results My expectation is that `writer_batch_size` allows one to simply trade off performance and memory requirements, not that it must be a specific number for `.map()` to function correctly. ## Actual results If writer_batch_size is set to a value less than the number of rows, I get either: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` or simply ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` If it is greater than the number of rows, i get the `zsh: killed` error above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.2	760	22	Dataset.map()'s fails at any value of parameter writer_batch_size ## Describe the bug If the the value of `writer_batch_size` is less than the total number of instances in the dataset it will fail at that same number of instances. If it is greater than the total number of instances, it fails on the last instance. Context: I am attempting to fine-tune a pre-trained HuggingFace transformers model called LayoutLMv2. This model takes three inputs: document images, words and word bounding boxes. [The Processor for this model has two options](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor), the default is passing a document to the Processor and allowing it to create images of the document and use PyTesseract to perform OCR and generate words/bounding boxes. The other option is to provide `revision="no_ocr"` to the pre-trained model which allows you to use your own OCR results (in my case, Amazon Textract) so you have to provide the image, words and bounding boxes yourself. I am using this second option which might be good context for the bug. I am using the Dataset.map() paradigm to create these three inputs, encode them and save the dataset. Note that my documents (data instances) on average are fairly large and can range from 1 page up to 300 pages. Code I am using is provided below ## Steps to reproduce the bug I do not have explicit sample code, but I will paste the code I'm using in case reading it helps. When `.map()` is called, the dataset has 2933 rows, many of which represent large pdf documents. ```python def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['label'].unique() features = Features({ 'image': Array3D(dtype="int64", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'token_type_ids': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names, writer_batch_size=dataset.num_rows+1) encoded_dataset.save_to_disk(TRAINING_DATA_PATH + ENCODED_DATASET_NAME) encoded_dataset.set_format(type="torch") return encoded_dataset ``` ```python PROCESSOR = LayoutLMv2Processor.from_pretrained(MODEL_PATH, revision="no_ocr", use_fast=False) def preprocess_data(examples): directory = os.path.join(FILES_PATH, examples['file_location']) images_dir = os.path.join(directory, PDF_IMAGE_DIR) textract_response_path = os.path.join(directory, 'textract.json') doc_meta_path = os.path.join(directory, 'doc_meta.json') textract_document = get_textract_document(textract_response_path, doc_meta_path) images, words, bboxes = get_doc_training_data(images_dir, textract_document) encoded_inputs = PROCESSOR(images, words, boxes=bboxes, padding="max_length", truncation=True) # https://github.com/NielsRogge/Transformers-Tutorials/issues/36 encoded_inputs["image"] = np.array(encoded_inputs["image"]) encoded_inputs["label"] = examples['label_id'] return encoded_inputs ``` ## Expected results My expectation is that `writer_batch_size` allows one to simply trade off performance and memory requirements, not that it must be a specific number for `.map()` to function correctly. ## Actual results If writer_batch_size is set to a value less than the number of rows, I get either: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` or simply ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` If it is greater than the number of rows, i get the `zsh: killed` error above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.2 Yeah-- my analysis of the issue was wrong in this one so I just closed it while linking to the new issue	[ -1.25897216796875, -0.9736562371253967, -0.7583965063095093, 1.5511164665222168, -0.21435335278511047, -1.256626844406128, 0.06937089562416077, -1.0330222845077515, 1.6731901168823242, -0.8561477065086365, 0.33457037806510925, -1.6195642948150635, 0.09062574803829193, -0.5467575192451477, -0.7664734721183777, -0.9078165292739868, -0.4079038202762604, 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https://github.com/huggingface/datasets/issues/4349	Dataset.map()'s fails at any value of parameter writer_batch_size	I met with the same problem when doing some experiments about layoutlm. I tried to set the writer_batch_size to 1, and the error still exists. Is there any solutions to this problem?	## Describe the bug If the the value of `writer_batch_size` is less than the total number of instances in the dataset it will fail at that same number of instances. If it is greater than the total number of instances, it fails on the last instance. Context: I am attempting to fine-tune a pre-trained HuggingFace transformers model called LayoutLMv2. This model takes three inputs: document images, words and word bounding boxes. [The Processor for this model has two options](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor), the default is passing a document to the Processor and allowing it to create images of the document and use PyTesseract to perform OCR and generate words/bounding boxes. The other option is to provide `revision="no_ocr"` to the pre-trained model which allows you to use your own OCR results (in my case, Amazon Textract) so you have to provide the image, words and bounding boxes yourself. I am using this second option which might be good context for the bug. I am using the Dataset.map() paradigm to create these three inputs, encode them and save the dataset. Note that my documents (data instances) on average are fairly large and can range from 1 page up to 300 pages. Code I am using is provided below ## Steps to reproduce the bug I do not have explicit sample code, but I will paste the code I'm using in case reading it helps. When `.map()` is called, the dataset has 2933 rows, many of which represent large pdf documents. ```python def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['label'].unique() features = Features({ 'image': Array3D(dtype="int64", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'token_type_ids': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names, writer_batch_size=dataset.num_rows+1) encoded_dataset.save_to_disk(TRAINING_DATA_PATH + ENCODED_DATASET_NAME) encoded_dataset.set_format(type="torch") return encoded_dataset ``` ```python PROCESSOR = LayoutLMv2Processor.from_pretrained(MODEL_PATH, revision="no_ocr", use_fast=False) def preprocess_data(examples): directory = os.path.join(FILES_PATH, examples['file_location']) images_dir = os.path.join(directory, PDF_IMAGE_DIR) textract_response_path = os.path.join(directory, 'textract.json') doc_meta_path = os.path.join(directory, 'doc_meta.json') textract_document = get_textract_document(textract_response_path, doc_meta_path) images, words, bboxes = get_doc_training_data(images_dir, textract_document) encoded_inputs = PROCESSOR(images, words, boxes=bboxes, padding="max_length", truncation=True) # https://github.com/NielsRogge/Transformers-Tutorials/issues/36 encoded_inputs["image"] = np.array(encoded_inputs["image"]) encoded_inputs["label"] = examples['label_id'] return encoded_inputs ``` ## Expected results My expectation is that `writer_batch_size` allows one to simply trade off performance and memory requirements, not that it must be a specific number for `.map()` to function correctly. ## Actual results If writer_batch_size is set to a value less than the number of rows, I get either: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` or simply ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` If it is greater than the number of rows, i get the `zsh: killed` error above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.2	760	32	Dataset.map()'s fails at any value of parameter writer_batch_size ## Describe the bug If the the value of `writer_batch_size` is less than the total number of instances in the dataset it will fail at that same number of instances. If it is greater than the total number of instances, it fails on the last instance. Context: I am attempting to fine-tune a pre-trained HuggingFace transformers model called LayoutLMv2. This model takes three inputs: document images, words and word bounding boxes. [The Processor for this model has two options](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor), the default is passing a document to the Processor and allowing it to create images of the document and use PyTesseract to perform OCR and generate words/bounding boxes. The other option is to provide `revision="no_ocr"` to the pre-trained model which allows you to use your own OCR results (in my case, Amazon Textract) so you have to provide the image, words and bounding boxes yourself. I am using this second option which might be good context for the bug. I am using the Dataset.map() paradigm to create these three inputs, encode them and save the dataset. Note that my documents (data instances) on average are fairly large and can range from 1 page up to 300 pages. Code I am using is provided below ## Steps to reproduce the bug I do not have explicit sample code, but I will paste the code I'm using in case reading it helps. When `.map()` is called, the dataset has 2933 rows, many of which represent large pdf documents. ```python def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['label'].unique() features = Features({ 'image': Array3D(dtype="int64", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'token_type_ids': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names, writer_batch_size=dataset.num_rows+1) encoded_dataset.save_to_disk(TRAINING_DATA_PATH + ENCODED_DATASET_NAME) encoded_dataset.set_format(type="torch") return encoded_dataset ``` ```python PROCESSOR = LayoutLMv2Processor.from_pretrained(MODEL_PATH, revision="no_ocr", use_fast=False) def preprocess_data(examples): directory = os.path.join(FILES_PATH, examples['file_location']) images_dir = os.path.join(directory, PDF_IMAGE_DIR) textract_response_path = os.path.join(directory, 'textract.json') doc_meta_path = os.path.join(directory, 'doc_meta.json') textract_document = get_textract_document(textract_response_path, doc_meta_path) images, words, bboxes = get_doc_training_data(images_dir, textract_document) encoded_inputs = PROCESSOR(images, words, boxes=bboxes, padding="max_length", truncation=True) # https://github.com/NielsRogge/Transformers-Tutorials/issues/36 encoded_inputs["image"] = np.array(encoded_inputs["image"]) encoded_inputs["label"] = examples['label_id'] return encoded_inputs ``` ## Expected results My expectation is that `writer_batch_size` allows one to simply trade off performance and memory requirements, not that it must be a specific number for `.map()` to function correctly. ## Actual results If writer_batch_size is set to a value less than the number of rows, I get either: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` or simply ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` If it is greater than the number of rows, i get the `zsh: killed` error above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.2 I met with the same problem when doing some experiments about layoutlm. I tried to set the writer_batch_size to 1, and the error still exists. Is there any solutions to this problem?	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https://github.com/huggingface/datasets/issues/4349	Dataset.map()'s fails at any value of parameter writer_batch_size	The problem lies in how your Features are defined. It's erroring out when it actually goes to write them to disk	## Describe the bug If the the value of `writer_batch_size` is less than the total number of instances in the dataset it will fail at that same number of instances. If it is greater than the total number of instances, it fails on the last instance. Context: I am attempting to fine-tune a pre-trained HuggingFace transformers model called LayoutLMv2. This model takes three inputs: document images, words and word bounding boxes. [The Processor for this model has two options](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor), the default is passing a document to the Processor and allowing it to create images of the document and use PyTesseract to perform OCR and generate words/bounding boxes. The other option is to provide `revision="no_ocr"` to the pre-trained model which allows you to use your own OCR results (in my case, Amazon Textract) so you have to provide the image, words and bounding boxes yourself. I am using this second option which might be good context for the bug. I am using the Dataset.map() paradigm to create these three inputs, encode them and save the dataset. Note that my documents (data instances) on average are fairly large and can range from 1 page up to 300 pages. Code I am using is provided below ## Steps to reproduce the bug I do not have explicit sample code, but I will paste the code I'm using in case reading it helps. When `.map()` is called, the dataset has 2933 rows, many of which represent large pdf documents. ```python def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['label'].unique() features = Features({ 'image': Array3D(dtype="int64", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'token_type_ids': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names, writer_batch_size=dataset.num_rows+1) encoded_dataset.save_to_disk(TRAINING_DATA_PATH + ENCODED_DATASET_NAME) encoded_dataset.set_format(type="torch") return encoded_dataset ``` ```python PROCESSOR = LayoutLMv2Processor.from_pretrained(MODEL_PATH, revision="no_ocr", use_fast=False) def preprocess_data(examples): directory = os.path.join(FILES_PATH, examples['file_location']) images_dir = os.path.join(directory, PDF_IMAGE_DIR) textract_response_path = os.path.join(directory, 'textract.json') doc_meta_path = os.path.join(directory, 'doc_meta.json') textract_document = get_textract_document(textract_response_path, doc_meta_path) images, words, bboxes = get_doc_training_data(images_dir, textract_document) encoded_inputs = PROCESSOR(images, words, boxes=bboxes, padding="max_length", truncation=True) # https://github.com/NielsRogge/Transformers-Tutorials/issues/36 encoded_inputs["image"] = np.array(encoded_inputs["image"]) encoded_inputs["label"] = examples['label_id'] return encoded_inputs ``` ## Expected results My expectation is that `writer_batch_size` allows one to simply trade off performance and memory requirements, not that it must be a specific number for `.map()` to function correctly. ## Actual results If writer_batch_size is set to a value less than the number of rows, I get either: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` or simply ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` If it is greater than the number of rows, i get the `zsh: killed` error above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.2	760	21	Dataset.map()'s fails at any value of parameter writer_batch_size ## Describe the bug If the the value of `writer_batch_size` is less than the total number of instances in the dataset it will fail at that same number of instances. If it is greater than the total number of instances, it fails on the last instance. Context: I am attempting to fine-tune a pre-trained HuggingFace transformers model called LayoutLMv2. This model takes three inputs: document images, words and word bounding boxes. [The Processor for this model has two options](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor), the default is passing a document to the Processor and allowing it to create images of the document and use PyTesseract to perform OCR and generate words/bounding boxes. The other option is to provide `revision="no_ocr"` to the pre-trained model which allows you to use your own OCR results (in my case, Amazon Textract) so you have to provide the image, words and bounding boxes yourself. I am using this second option which might be good context for the bug. I am using the Dataset.map() paradigm to create these three inputs, encode them and save the dataset. Note that my documents (data instances) on average are fairly large and can range from 1 page up to 300 pages. Code I am using is provided below ## Steps to reproduce the bug I do not have explicit sample code, but I will paste the code I'm using in case reading it helps. When `.map()` is called, the dataset has 2933 rows, many of which represent large pdf documents. ```python def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['label'].unique() features = Features({ 'image': Array3D(dtype="int64", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'token_type_ids': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names, writer_batch_size=dataset.num_rows+1) encoded_dataset.save_to_disk(TRAINING_DATA_PATH + ENCODED_DATASET_NAME) encoded_dataset.set_format(type="torch") return encoded_dataset ``` ```python PROCESSOR = LayoutLMv2Processor.from_pretrained(MODEL_PATH, revision="no_ocr", use_fast=False) def preprocess_data(examples): directory = os.path.join(FILES_PATH, examples['file_location']) images_dir = os.path.join(directory, PDF_IMAGE_DIR) textract_response_path = os.path.join(directory, 'textract.json') doc_meta_path = os.path.join(directory, 'doc_meta.json') textract_document = get_textract_document(textract_response_path, doc_meta_path) images, words, bboxes = get_doc_training_data(images_dir, textract_document) encoded_inputs = PROCESSOR(images, words, boxes=bboxes, padding="max_length", truncation=True) # https://github.com/NielsRogge/Transformers-Tutorials/issues/36 encoded_inputs["image"] = np.array(encoded_inputs["image"]) encoded_inputs["label"] = examples['label_id'] return encoded_inputs ``` ## Expected results My expectation is that `writer_batch_size` allows one to simply trade off performance and memory requirements, not that it must be a specific number for `.map()` to function correctly. ## Actual results If writer_batch_size is set to a value less than the number of rows, I get either: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` or simply ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` If it is greater than the number of rows, i get the `zsh: killed` error above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 6.0.1 - Pandas version: 1.4.2 The problem lies in how your Features are defined. 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https://github.com/huggingface/datasets/issues/4348	`inspect` functions can't fetch dataset script from the Hub	Hi, thanks for reporting! `git bisect` points to #2986 as the PR that introduced the bug. Since then, there have been some additional changes to the loading logic, and in the current state, `force_local_path` (set via `local_path`) forbids pulling a script from the internet instead of downloading it: https://github.com/huggingface/datasets/blob/cfae0545b2ba05452e16136cacc7d370b4b186a1/src/datasets/inspect.py#L89-L91 cc @lhoestq: `force_local_path` is only used in `inspect_dataset` and `inspect_metric`. Is it OK if we revert the behavior to match the old one?	The `inspect_dataset` and `inspect_metric` functions are unable to retrieve a dataset or metric script from the Hub and store it locally at the specified `local_path`: ```py >>> from datasets import inspect_dataset >>> inspect_dataset('rotten_tomatoes', local_path='path/to/my/local/folder') FileNotFoundError: Couldn't find a dataset script at /content/rotten_tomatoes/rotten_tomatoes.py or any data file in the same directory. ```	761	72	`inspect` functions can't fetch dataset script from the Hub The `inspect_dataset` and `inspect_metric` functions are unable to retrieve a dataset or metric script from the Hub and store it locally at the specified `local_path`: ```py >>> from datasets import inspect_dataset >>> inspect_dataset('rotten_tomatoes', local_path='path/to/my/local/folder') FileNotFoundError: Couldn't find a dataset script at /content/rotten_tomatoes/rotten_tomatoes.py or any data file in the same directory. ``` Hi, thanks for reporting! `git bisect` points to #2986 as the PR that introduced the bug. Since then, there have been some additional changes to the loading logic, and in the current state, `force_local_path` (set via `local_path`) forbids pulling a script from the internet instead of downloading it: https://github.com/huggingface/datasets/blob/cfae0545b2ba05452e16136cacc7d370b4b186a1/src/datasets/inspect.py#L89-L91 cc @lhoestq: `force_local_path` is only used in `inspect_dataset` and `inspect_metric`. Is it OK if we revert the behavior to match the old one?	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https://github.com/huggingface/datasets/issues/4343	Metrics documentation is not accessible in the datasets doc UI	Hey @fxmarty :) Yes we are working on showing the docs of all the metrics on the Hugging face website. If you want to follow the advancements you can check the [evaluate](https://github.com/huggingface/evaluate) repository cc @lvwerra @sashavor	Is your feature request related to a problem? Please describe. Search for a metric name like "seqeval" yields no results on https://huggingface.co/docs/datasets/master/en/index . One needs to go look in `datasets/metrics/README.md` to find the doc. Even in the `README.md`, it can be hard to understand what the metric expects as an input, for example for `squad` there is a [key `id`](https://github.com/huggingface/datasets/blob/1a4c185663a6958f48ec69624473fdc154a36a9d/metrics/squad/squad.py#L42) documented only in the function doc but not in the `README.md`, and one needs to go look into the code to understand what the metric expects. Describe the solution you'd like Have the documentation for metrics appear as well in the doc UI, e.g. this https://github.com/huggingface/datasets/blob/1a4c185663a6958f48ec69624473fdc154a36a9d/metrics/squad/squad.py#L21-L63 I know there are plans to migrate metrics to the evaluate library, but just pointing this out.	762	36	Metrics documentation is not accessible in the datasets doc UI Is your feature request related to a problem? Please describe. Search for a metric name like "seqeval" yields no results on https://huggingface.co/docs/datasets/master/en/index . One needs to go look in `datasets/metrics/README.md` to find the doc. Even in the `README.md`, it can be hard to understand what the metric expects as an input, for example for `squad` there is a [key `id`](https://github.com/huggingface/datasets/blob/1a4c185663a6958f48ec69624473fdc154a36a9d/metrics/squad/squad.py#L42) documented only in the function doc but not in the `README.md`, and one needs to go look into the code to understand what the metric expects. Describe the solution you'd like Have the documentation for metrics appear as well in the doc UI, e.g. this https://github.com/huggingface/datasets/blob/1a4c185663a6958f48ec69624473fdc154a36a9d/metrics/squad/squad.py#L21-L63 I know there are plans to migrate metrics to the evaluate library, but just pointing this out. Hey @fxmarty :) Yes we are working on showing the docs of all the metrics on the Hugging face website. 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https://github.com/huggingface/datasets/issues/4327	`wikipedia` pre-processed datasets	Hi @vpj, thanks for reporting. I'm sorry, but I can't reproduce your bug: I load "20220301.simple"in 9 seconds: ```shell time python -c "from datasets import load_dataset; load_dataset('wikipedia', '20220301.simple')" Downloading and preparing dataset wikipedia/20220301.simple (download: 228.58 MiB, generated: 224.18 MiB, post-processed: Unknown size, total: 452.76 MiB) to .../.cache/huggingface/datasets/wikipedia/20220301.simple/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559... Downloading: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1.66k/1.66k [00:00<00:00, 1.02MB/s] Downloading: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 235M/235M [00:02<00:00, 82.8MB/s] Dataset wikipedia downloaded and prepared to .../.cache/huggingface/datasets/wikipedia/20220301.simple/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559. Subsequent calls will reuse this data. 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:00<00:00, 290.75it/s] real 0m9.693s user 0m6.002s sys 0m3.260s ``` Could you please check your environment info, as requested when opening this issue? ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version: ``` Maybe you are using an old version of `datasets`...	## Describe the bug [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset readme says that certain subsets are preprocessed. However it seems like they are not available. When I try to load them it takes a really long time, and it seems like it's processing them. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("wikipedia", "20220301.en") ``` ## Expected results To load the dataset ## Actual results Takes a very long time to load (after downloading) After `Downloading data files: 100%`. It takes hours and gets killed. Tried `wikipedia.simple` and it got processed after ~30mins.	763	133	`wikipedia` pre-processed datasets ## Describe the bug [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset readme says that certain subsets are preprocessed. However it seems like they are not available. When I try to load them it takes a really long time, and it seems like it's processing them. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("wikipedia", "20220301.en") ``` ## Expected results To load the dataset ## Actual results Takes a very long time to load (after downloading) After `Downloading data files: 100%`. It takes hours and gets killed. Tried `wikipedia.simple` and it got processed after ~30mins. Hi @vpj, thanks for reporting. I'm sorry, but I can't reproduce your bug: I load "20220301.simple"in 9 seconds: ```shell time python -c "from datasets import load_dataset; load_dataset('wikipedia', '20220301.simple')" Downloading and preparing dataset wikipedia/20220301.simple (download: 228.58 MiB, generated: 224.18 MiB, post-processed: Unknown size, total: 452.76 MiB) to .../.cache/huggingface/datasets/wikipedia/20220301.simple/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559... Downloading: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1.66k/1.66k [00:00<00:00, 1.02MB/s] Downloading: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 235M/235M [00:02<00:00, 82.8MB/s] Dataset wikipedia downloaded and prepared to .../.cache/huggingface/datasets/wikipedia/20220301.simple/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559. Subsequent calls will reuse this data. 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:00<00:00, 290.75it/s] real 0m9.693s user 0m6.002s sys 0m3.260s ``` Could you please check your environment info, as requested when opening this issue? ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version: ``` Maybe you are using an old version of `datasets`...	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https://github.com/huggingface/datasets/issues/4327	`wikipedia` pre-processed datasets	Downloading and processing `wikipedia simple` dataset completed in under 11sec on M1 Mac. Could you please check `dataset` version as mentioned by @albertvillanova? Also check system specs, if system is under load processing could take some time I guess.	## Describe the bug [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset readme says that certain subsets are preprocessed. However it seems like they are not available. When I try to load them it takes a really long time, and it seems like it's processing them. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("wikipedia", "20220301.en") ``` ## Expected results To load the dataset ## Actual results Takes a very long time to load (after downloading) After `Downloading data files: 100%`. It takes hours and gets killed. Tried `wikipedia.simple` and it got processed after ~30mins.	763	39	`wikipedia` pre-processed datasets ## Describe the bug [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset readme says that certain subsets are preprocessed. However it seems like they are not available. When I try to load them it takes a really long time, and it seems like it's processing them. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("wikipedia", "20220301.en") ``` ## Expected results To load the dataset ## Actual results Takes a very long time to load (after downloading) After `Downloading data files: 100%`. It takes hours and gets killed. Tried `wikipedia.simple` and it got processed after ~30mins. Downloading and processing `wikipedia simple` dataset completed in under 11sec on M1 Mac. Could you please check `dataset` version as mentioned by @albertvillanova? Also check system specs, if system is under load processing could take some time I guess.	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https://github.com/huggingface/datasets/issues/4325	Dataset Viewer issue for strombergnlp/offenseval_2020, strombergnlp/polstance	Not sure if it's related... I was going to raise an issue for https://huggingface.co/datasets/domenicrosati/TruthfulQA which also has the same issue... https://huggingface.co/datasets/domenicrosati/TruthfulQA/viewer/domenicrosati--TruthfulQA/train	### Link https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train ### Description The viewer isn't running for these two datasets. I left it overnight because a wait sometimes helps things get loaded, and the error messages have all gone, but the datasets are still turning up blank in viewer. Maybe it needs a bit more time. * https://huggingface.co/datasets/strombergnlp/polstance/viewer/PolStance/train * https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train While offenseval_2020 is gated w. prompt, the other gated previews I have run fine in Viewer, e.g. https://huggingface.co/datasets/strombergnlp/shaj , so I'm a bit stumped! ### Owner Yes	764	21	Dataset Viewer issue for strombergnlp/offenseval_2020, strombergnlp/polstance ### Link https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train ### Description The viewer isn't running for these two datasets. I left it overnight because a wait sometimes helps things get loaded, and the error messages have all gone, but the datasets are still turning up blank in viewer. Maybe it needs a bit more time. * https://huggingface.co/datasets/strombergnlp/polstance/viewer/PolStance/train * https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train While offenseval_2020 is gated w. prompt, the other gated previews I have run fine in Viewer, e.g. https://huggingface.co/datasets/strombergnlp/shaj , so I'm a bit stumped! ### Owner Yes Not sure if it's related... I was going to raise an issue for https://huggingface.co/datasets/domenicrosati/TruthfulQA which also has the same issue... https://huggingface.co/datasets/domenicrosati/TruthfulQA/viewer/domenicrosati--TruthfulQA/train	[ -1.1779078245162964, -0.9166066646575928, -0.9029459357261658, 1.378021001815796, -0.10964472591876984, -1.3168209791183472, 0.05087197944521904, -0.9338369369506836, 1.5611786842346191, -0.6602224111557007, 0.25161123275756836, -1.6102300882339478, -0.12926965951919556, -0.5099684596061707, -0.7283740043640137, -0.9870007634162903, -0.2993662357330322, -0.9791164398193359, 0.9381110668182373, 2.5987637042999268, 1.1784231662750244, -1.3847805261611938, 2.688868999481201, 0.6135810613632202, -0.26870259642601013, -1.0807157754898071, 0.6005756258964539, -0.024381518363952637, -1.2511703968048096, -0.46551182866096497, -0.9534335136413574, -0.02470478042960167, -0.5337433218955994, -0.45492586493492126, 0.21699638664722443, 0.4685477018356323, -0.14971402287483215, 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https://github.com/huggingface/datasets/issues/4325	Dataset Viewer issue for strombergnlp/offenseval_2020, strombergnlp/polstance	Yes, it's related. The backend behind the dataset viewer is currently under too much load, and these datasets are still in the jobs queue. We're actively working on this issue, and we expect to fix the issue permanently soon. Thanks for your patience 🙏	### Link https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train ### Description The viewer isn't running for these two datasets. I left it overnight because a wait sometimes helps things get loaded, and the error messages have all gone, but the datasets are still turning up blank in viewer. Maybe it needs a bit more time. * https://huggingface.co/datasets/strombergnlp/polstance/viewer/PolStance/train * https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train While offenseval_2020 is gated w. prompt, the other gated previews I have run fine in Viewer, e.g. https://huggingface.co/datasets/strombergnlp/shaj , so I'm a bit stumped! ### Owner Yes	764	44	Dataset Viewer issue for strombergnlp/offenseval_2020, strombergnlp/polstance ### Link https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train ### Description The viewer isn't running for these two datasets. I left it overnight because a wait sometimes helps things get loaded, and the error messages have all gone, but the datasets are still turning up blank in viewer. Maybe it needs a bit more time. * https://huggingface.co/datasets/strombergnlp/polstance/viewer/PolStance/train * https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train While offenseval_2020 is gated w. prompt, the other gated previews I have run fine in Viewer, e.g. https://huggingface.co/datasets/strombergnlp/shaj , so I'm a bit stumped! ### Owner Yes Yes, it's related. The backend behind the dataset viewer is currently under too much load, and these datasets are still in the jobs queue. We're actively working on this issue, and we expect to fix the issue permanently soon. 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https://github.com/huggingface/datasets/issues/4325	Dataset Viewer issue for strombergnlp/offenseval_2020, strombergnlp/polstance	Thanks @severo and no worries! - a suggestion for a UI usability thing maybe is to indicate that the dataset processing is in the job queue (rather than no data?)	### Link https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train ### Description The viewer isn't running for these two datasets. I left it overnight because a wait sometimes helps things get loaded, and the error messages have all gone, but the datasets are still turning up blank in viewer. Maybe it needs a bit more time. * https://huggingface.co/datasets/strombergnlp/polstance/viewer/PolStance/train * https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train While offenseval_2020 is gated w. prompt, the other gated previews I have run fine in Viewer, e.g. https://huggingface.co/datasets/strombergnlp/shaj , so I'm a bit stumped! ### Owner Yes	764	30	Dataset Viewer issue for strombergnlp/offenseval_2020, strombergnlp/polstance ### Link https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train ### Description The viewer isn't running for these two datasets. I left it overnight because a wait sometimes helps things get loaded, and the error messages have all gone, but the datasets are still turning up blank in viewer. Maybe it needs a bit more time. * https://huggingface.co/datasets/strombergnlp/polstance/viewer/PolStance/train * https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train While offenseval_2020 is gated w. prompt, the other gated previews I have run fine in Viewer, e.g. https://huggingface.co/datasets/strombergnlp/shaj , so I'm a bit stumped! ### Owner Yes Thanks @severo and no worries! - a suggestion for a UI usability thing maybe is to indicate that the dataset processing is in the job queue (rather than no data?)	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https://github.com/huggingface/datasets/issues/4323	Audio can not find value["bytes"]	![image](https://user-images.githubusercontent.com/34292279/168063684-fff5c12a-8b1e-4c65-b18b-36100ab8a1af.png) that is reason my bytes`s empty but i have some confused why path prior is higher than bytes? if you can make bytes in _generate_examples , you don`t have to make bytes to path? because we have path and bytes already	## Describe the bug I wrote down _generate_examples like: ![image](https://user-images.githubusercontent.com/34292279/168027186-2fe8b255-2cd8-4b9b-ab1e-8d5a7182979b.png) but where is the bytes? ![image](https://user-images.githubusercontent.com/34292279/168027330-f2496dd0-1d99-464c-b15c-bc57eee0415a.png) ## Expected results value["bytes"] is not None, so i can make datasets with bytes, not path ## bytes looks like: blah blah~~ \xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff\|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03 blah blah~~ that function not return None ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:2.2.1 - Platform:ubuntu 18.04 - Python version:3.6.9 - PyArrow version:6.0.1	766	42	Audio can not find value["bytes"] ## Describe the bug I wrote down _generate_examples like: ![image](https://user-images.githubusercontent.com/34292279/168027186-2fe8b255-2cd8-4b9b-ab1e-8d5a7182979b.png) but where is the bytes? ![image](https://user-images.githubusercontent.com/34292279/168027330-f2496dd0-1d99-464c-b15c-bc57eee0415a.png) ## Expected results value["bytes"] is not None, so i can make datasets with bytes, not path ## bytes looks like: blah blah~~ \xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff\|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03 blah blah~~ that function not return None ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:2.2.1 - Platform:ubuntu 18.04 - Python version:3.6.9 - PyArrow version:6.0.1 ![image](https://user-images.githubusercontent.com/34292279/168063684-fff5c12a-8b1e-4c65-b18b-36100ab8a1af.png) that is reason my bytes`s empty but i have some confused why path prior is higher than bytes? if you can make bytes in _generate_examples , you don`t have to make bytes to path? because we have path and bytes already	[ -1.454211711883545, -0.9173529148101807, -0.8072428107261658, 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https://github.com/huggingface/datasets/issues/4323	Audio can not find value["bytes"]	> but i have some confused why path prior is higher than bytes? If the audio file is already available locally, we don't need to store the bytes again. If you don't specify a "path" to a local file, then the bytes are stored. You can set "path" to None for example. > if you can make bytes in _generate_examples , you don`t have to make bytes to path? > because we have path and bytes already It's useful to pass both "path" and "bytes" in `_generate_examples`: - when the dataset has been downloaded, then the "path" to the audio files are stored and we can ignore "bytes" in order to save disk space. - when the dataset is loaded in streaming mode, the audio files are not available on your disk and therefore we use the "bytes"	## Describe the bug I wrote down _generate_examples like: ![image](https://user-images.githubusercontent.com/34292279/168027186-2fe8b255-2cd8-4b9b-ab1e-8d5a7182979b.png) but where is the bytes? ![image](https://user-images.githubusercontent.com/34292279/168027330-f2496dd0-1d99-464c-b15c-bc57eee0415a.png) ## Expected results value["bytes"] is not None, so i can make datasets with bytes, not path ## bytes looks like: blah blah~~ \xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff\|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03 blah blah~~ that function not return None ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:2.2.1 - Platform:ubuntu 18.04 - Python version:3.6.9 - PyArrow version:6.0.1	766	138	Audio can not find value["bytes"] ## Describe the bug I wrote down _generate_examples like: ![image](https://user-images.githubusercontent.com/34292279/168027186-2fe8b255-2cd8-4b9b-ab1e-8d5a7182979b.png) but where is the bytes? ![image](https://user-images.githubusercontent.com/34292279/168027330-f2496dd0-1d99-464c-b15c-bc57eee0415a.png) ## Expected results value["bytes"] is not None, so i can make datasets with bytes, not path ## bytes looks like: blah blah~~ \xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff\|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03 blah blah~~ that function not return None ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:2.2.1 - Platform:ubuntu 18.04 - Python version:3.6.9 - PyArrow version:6.0.1 > but i have some confused why path prior is higher than bytes? If the audio file is already available locally, we don't need to store the bytes again. If you don't specify a "path" to a local file, then the bytes are stored. You can set "path" to None for example. > if you can make bytes in _generate_examples , you don`t have to make bytes to path? > because we have path and bytes already It's useful to pass both "path" and "bytes" in `_generate_examples`: - when the dataset has been downloaded, then the "path" to the audio files are stored and we can ignore "bytes" in order to save disk space. - when the dataset is loaded in streaming mode, the audio files are not available on your disk and therefore we use the "bytes"	[ -1.454211711883545, -0.9173529148101807, -0.8072428107261658, 1.1904082298278809, -0.06571447849273682, -0.9581543207168579, 0.2057676911354065, -0.8597610592842102, 1.9477181434631348, -0.7501056790351868, 0.15708839893341064, -1.8205370903015137, 0.10042087733745575, -0.8151751756668091, -0.7079520225524902, -0.9018890857696533, -0.204398512840271, -0.7166482210159302, 1.0508997440338135, 2.629455089569092, 1.2388880252838135, -1.581251859664917, 2.822287082672119, 0.8462175130844116, 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https://github.com/huggingface/datasets/issues/4323	Audio can not find value["bytes"]	@lhoestq First of all, thx for reply but, if i put in "bytes" and "path" ex) {"bytes":"blah blah~", "path":"blah blah~"} that source working that my bytes to empty first, and then, re-calculate my bytes! ![image](https://user-images.githubusercontent.com/34292279/168534687-1fb60d8c-d369-47d2-a4bb-db68f95194b4.png) if you have some pcm file, pcm is can read bytes. so, i put in bytes and paths. but bytes is been None why encode_example func make None and then, on decode_example func, we no have bytes. so, calculate bytes to path. pcm is not support librosa or soundfile, error occured! the most important thing is not announced anywhere this situation can be reproduced is that truly right process flow?	## Describe the bug I wrote down _generate_examples like: ![image](https://user-images.githubusercontent.com/34292279/168027186-2fe8b255-2cd8-4b9b-ab1e-8d5a7182979b.png) but where is the bytes? ![image](https://user-images.githubusercontent.com/34292279/168027330-f2496dd0-1d99-464c-b15c-bc57eee0415a.png) ## Expected results value["bytes"] is not None, so i can make datasets with bytes, not path ## bytes looks like: blah blah~~ \xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff\|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03 blah blah~~ that function not return None ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:2.2.1 - Platform:ubuntu 18.04 - Python version:3.6.9 - PyArrow version:6.0.1	766	105	Audio can not find value["bytes"] ## Describe the bug I wrote down _generate_examples like: ![image](https://user-images.githubusercontent.com/34292279/168027186-2fe8b255-2cd8-4b9b-ab1e-8d5a7182979b.png) but where is the bytes? ![image](https://user-images.githubusercontent.com/34292279/168027330-f2496dd0-1d99-464c-b15c-bc57eee0415a.png) ## Expected results value["bytes"] is not None, so i can make datasets with bytes, not path ## bytes looks like: blah blah~~ \xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff\|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03 blah blah~~ that function not return None ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:2.2.1 - Platform:ubuntu 18.04 - Python version:3.6.9 - PyArrow version:6.0.1 @lhoestq First of all, thx for reply but, if i put in "bytes" and "path" ex) {"bytes":"blah blah~", "path":"blah blah~"} that source working that my bytes to empty first, and then, re-calculate my bytes! ![image](https://user-images.githubusercontent.com/34292279/168534687-1fb60d8c-d369-47d2-a4bb-db68f95194b4.png) if you have some pcm file, pcm is can read bytes. so, i put in bytes and paths. but bytes is been None why encode_example func make None and then, on decode_example func, we no have bytes. so, calculate bytes to path. pcm is not support librosa or soundfile, error occured! the most important thing is not announced anywhere this situation can be reproduced is that truly right process flow?	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https://github.com/huggingface/datasets/issues/4323	Audio can not find value["bytes"]	I don't think we support PCM files, feel free to convert your data to WAV for now. It would be awesome to support PCM files though, let me know if you'd like to contribute this feature, I'd be happy to help	## Describe the bug I wrote down _generate_examples like: ![image](https://user-images.githubusercontent.com/34292279/168027186-2fe8b255-2cd8-4b9b-ab1e-8d5a7182979b.png) but where is the bytes? ![image](https://user-images.githubusercontent.com/34292279/168027330-f2496dd0-1d99-464c-b15c-bc57eee0415a.png) ## Expected results value["bytes"] is not None, so i can make datasets with bytes, not path ## bytes looks like: blah blah~~ \xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff\|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03 blah blah~~ that function not return None ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:2.2.1 - Platform:ubuntu 18.04 - Python version:3.6.9 - PyArrow version:6.0.1	766	41	Audio can not find value["bytes"] ## Describe the bug I wrote down _generate_examples like: ![image](https://user-images.githubusercontent.com/34292279/168027186-2fe8b255-2cd8-4b9b-ab1e-8d5a7182979b.png) but where is the bytes? ![image](https://user-images.githubusercontent.com/34292279/168027330-f2496dd0-1d99-464c-b15c-bc57eee0415a.png) ## Expected results value["bytes"] is not None, so i can make datasets with bytes, not path ## bytes looks like: blah blah~~ \xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff\|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03 blah blah~~ that function not return None ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:2.2.1 - Platform:ubuntu 18.04 - Python version:3.6.9 - PyArrow version:6.0.1 I don't think we support PCM files, feel free to convert your data to WAV for now. It would be awesome to support PCM files though, let me know if you'd like to contribute this feature, I'd be happy to help	[ -1.454211711883545, -0.9173529148101807, -0.8072428107261658, 1.1904082298278809, -0.06571447849273682, -0.9581543207168579, 0.2057676911354065, -0.8597610592842102, 1.9477181434631348, -0.7501056790351868, 0.15708839893341064, -1.8205370903015137, 0.10042087733745575, -0.8151751756668091, -0.7079520225524902, -0.9018890857696533, -0.204398512840271, -0.7166482210159302, 1.0508997440338135, 2.629455089569092, 1.2388880252838135, -1.581251859664917, 2.822287082672119, 0.8462175130844116, -0.39456433057785034, -0.7795448899269104, 0.31538641452789307, 0.029245436191558838, -1.157312035560608, -0.4035544693470001, -0.8650192618370056, 0.23765259981155396, -0.5370672345161438, -0.5623040199279785, -0.17387402057647705, 0.27389776706695557, -0.3334064185619354, -0.1442139893770218, -0.46417155861854553, -0.5230646729469299, 0.37036842107772827, -0.5501229763031006, 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https://github.com/huggingface/datasets/issues/4323	Audio can not find value["bytes"]	You can clone the repository (see the guide on [how to contribute](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-create-a-pull-request)) and see how we can make the `Image.encode_example` method work with PCM data. There might be other ways to approach this problem, but here is what I think is a reasonable one: I think `Image.encode_example` should be able to take PCM bytes as input and the sampling rate, and return the WAV bytes (built by combining the PCM bytes and the sampling rate info), so that `Image.decode_example` can read it. To check if the input bytes are PCM data, you can just check if the extension of the `path` is ".pcm".	## Describe the bug I wrote down _generate_examples like: ![image](https://user-images.githubusercontent.com/34292279/168027186-2fe8b255-2cd8-4b9b-ab1e-8d5a7182979b.png) but where is the bytes? ![image](https://user-images.githubusercontent.com/34292279/168027330-f2496dd0-1d99-464c-b15c-bc57eee0415a.png) ## Expected results value["bytes"] is not None, so i can make datasets with bytes, not path ## bytes looks like: blah blah~~ \xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff\|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03 blah blah~~ that function not return None ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:2.2.1 - Platform:ubuntu 18.04 - Python version:3.6.9 - PyArrow version:6.0.1	766	103	Audio can not find value["bytes"] ## Describe the bug I wrote down _generate_examples like: ![image](https://user-images.githubusercontent.com/34292279/168027186-2fe8b255-2cd8-4b9b-ab1e-8d5a7182979b.png) but where is the bytes? ![image](https://user-images.githubusercontent.com/34292279/168027330-f2496dd0-1d99-464c-b15c-bc57eee0415a.png) ## Expected results value["bytes"] is not None, so i can make datasets with bytes, not path ## bytes looks like: blah blah~~ \xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff\|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03 blah blah~~ that function not return None ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:2.2.1 - Platform:ubuntu 18.04 - Python version:3.6.9 - PyArrow version:6.0.1 You can clone the repository (see the guide on [how to contribute](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-create-a-pull-request)) and see how we can make the `Image.encode_example` method work with PCM data. There might be other ways to approach this problem, but here is what I think is a reasonable one: I think `Image.encode_example` should be able to take PCM bytes as input and the sampling rate, and return the WAV bytes (built by combining the PCM bytes and the sampling rate info), so that `Image.decode_example` can read it. To check if the input bytes are PCM data, you can just check if the extension of the `path` is ".pcm".	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https://github.com/huggingface/datasets/issues/4320	Multi-news dataset loader attempts to strip wrong character from beginning of summaries	Hi ! Thanks for reporting :) This dataset was simply converted from [tensorflow datasets](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/summarization/multi_news.py) I think we can just remove the `.strip("- ")` and keep this character	## Describe the bug The `multi_news.py` data loader has [a line which attempts to strip `"- "` from the beginning of summaries](https://github.com/huggingface/datasets/blob/aa743886221d76afb409d263e1b136e7a71fe2b4/datasets/multi_news/multi_news.py#L97). The actual character in the multi-news dataset, however, is `"– "`, which is different, e.g. `"– " != "- "`. I would have just opened a PR to fix the mistake, but I am wondering what the motivation for stripping this character is? AFAICT most approaches just leave it in, e.g. the current SOTA on this dataset, [PRIMERA](https://huggingface.co/allenai/PRIMERA-multinews) (you can see its in the generated summaries of the model in their [example notebook](https://github.com/allenai/PRIMER/blob/main/Evaluation_Example.ipynb)). ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.0 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5	767	27	Multi-news dataset loader attempts to strip wrong character from beginning of summaries ## Describe the bug The `multi_news.py` data loader has [a line which attempts to strip `"- "` from the beginning of summaries](https://github.com/huggingface/datasets/blob/aa743886221d76afb409d263e1b136e7a71fe2b4/datasets/multi_news/multi_news.py#L97). The actual character in the multi-news dataset, however, is `"– "`, which is different, e.g. `"– " != "- "`. I would have just opened a PR to fix the mistake, but I am wondering what the motivation for stripping this character is? AFAICT most approaches just leave it in, e.g. the current SOTA on this dataset, [PRIMERA](https://huggingface.co/allenai/PRIMERA-multinews) (you can see its in the generated summaries of the model in their [example notebook](https://github.com/allenai/PRIMER/blob/main/Evaluation_Example.ipynb)). ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.0 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 Hi ! Thanks for reporting :) This dataset was simply converted from [tensorflow datasets](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/summarization/multi_news.py) I think we can just remove the `.strip("- ")` and keep this character	[ -1.203878402709961, -0.8592821955680847, -0.7119171023368835, 1.4162696599960327, -0.11170239001512527, -1.33296799659729, 0.1161591187119484, -1.0097342729568481, 1.644713282585144, -0.7169384956359863, 0.22928708791732788, -1.6407580375671387, 0.011146500706672668, -0.4852910339832306, -0.7399172186851501, -0.8314548134803772, -0.3542686104774475, -0.7714124321937561, 0.9884060621261597, 2.500643014907837, 1.236729621887207, -1.3735371828079224, 2.7092690467834473, 0.6314456462860107, -0.2575626075267792, -0.9548446536064148, 0.5223934054374695, 0.03254225477576256, -1.3727611303329468, -0.3517318665981293, -0.9579203724861145, -0.01934007555246353, -0.546845555305481, -0.5387033224105835, 0.048118267208337784, 0.39633768796920776, 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https://github.com/huggingface/datasets/issues/4304	Language code search does direct matches	Thanks for reporting ! I forwarded the issue to the front-end team :) Will keep you posted ! I also changed the tagging app to suggest two letters code for now.	## Describe the bug Hi. Searching for bcp47 tags that are just the language prefix (e.g. `sq` or `da`) excludes datasets that have added extra information in their language metadata (e.g. `sq-AL` or `da-bornholm`). The example codes given in the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging) encourages addition of the additional codes ("_expected format is BCP47 tags separated for ';' e.g. 'en-US;fr-FR'_") but this would lead to those datasets being hidden in datasets search. ## Steps to reproduce the bug 1. Add a dataset using a variant tag (e.g. [`sq-AL`](https://huggingface.co/datasets?languages=languages:sq-AL)) 2. Look for datasets using the full code 3. Note that they're missing when just the language is searched for (e.g. [`sq`](https://huggingface.co/datasets?languages=languages:sq)) Some datasets are already affected by this - e.g. `AmazonScience/massive` is listed under `sq-AL` but not `sq`. One workaround is for dataset creators to add an additional root language tag to dataset YAML metadata, but it's unclear how to communicate this. It might be possible to index the search on `languagecode.split('-')[0]` but I wanted to float this issue before trying to write any code :) ## Expected results Datasets using longer bcp47 tags also appear under searches for just the language code; e.g. Quebecois datasets (`fr-CA`) would come up when looking for French datasets with no region specification (`fr`), or US English (`en-US`) datasets would come up when searching for English datasets (`en`). ## Actual results The language codes seem to be directly string matched, excluding datasets with specific language tags from non-specific searches. ## Environment info (web app)	769	31	Language code search does direct matches ## Describe the bug Hi. Searching for bcp47 tags that are just the language prefix (e.g. `sq` or `da`) excludes datasets that have added extra information in their language metadata (e.g. `sq-AL` or `da-bornholm`). The example codes given in the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging) encourages addition of the additional codes ("_expected format is BCP47 tags separated for ';' e.g. 'en-US;fr-FR'_") but this would lead to those datasets being hidden in datasets search. ## Steps to reproduce the bug 1. Add a dataset using a variant tag (e.g. [`sq-AL`](https://huggingface.co/datasets?languages=languages:sq-AL)) 2. Look for datasets using the full code 3. Note that they're missing when just the language is searched for (e.g. [`sq`](https://huggingface.co/datasets?languages=languages:sq)) Some datasets are already affected by this - e.g. `AmazonScience/massive` is listed under `sq-AL` but not `sq`. One workaround is for dataset creators to add an additional root language tag to dataset YAML metadata, but it's unclear how to communicate this. It might be possible to index the search on `languagecode.split('-')[0]` but I wanted to float this issue before trying to write any code :) ## Expected results Datasets using longer bcp47 tags also appear under searches for just the language code; e.g. Quebecois datasets (`fr-CA`) would come up when looking for French datasets with no region specification (`fr`), or US English (`en-US`) datasets would come up when searching for English datasets (`en`). ## Actual results The language codes seem to be directly string matched, excluding datasets with specific language tags from non-specific searches. ## Environment info (web app) Thanks for reporting ! I forwarded the issue to the front-end team :) Will keep you posted ! I also changed the tagging app to suggest two letters code for now.	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https://github.com/huggingface/datasets/issues/4298	Normalise license names	we'll add the same server-side metadata validation system as for hf.co/models soon-ish (you can check on hf.co/models that licenses are "clean")	Is your feature request related to a problem? Please describe. When browsing datasets, the Licenses tag cloud (bottom left of e.g. https://huggingface.co/datasets) has multiple variants of the same license. This means the options exclude datasets arbitrarily, giving users artificially low recall. The cause of the dupes is probably due to a bit of variation in metadata. Describe the solution you'd like I'd like the licenses in metadata to follow the same standard as much as possible, to remove this problem. I'd like to go ahead and normalise the dataset metadata to follow the format & values given in [src/datasets/utils/resources/licenses.json](https://github.com/huggingface/datasets/blob/master/src/datasets/utils/resources/licenses.json) . Describe alternatives you've considered None Additional context None Priority Low	770	21	Normalise license names Is your feature request related to a problem? Please describe. When browsing datasets, the Licenses tag cloud (bottom left of e.g. https://huggingface.co/datasets) has multiple variants of the same license. This means the options exclude datasets arbitrarily, giving users artificially low recall. The cause of the dupes is probably due to a bit of variation in metadata. Describe the solution you'd like I'd like the licenses in metadata to follow the same standard as much as possible, to remove this problem. I'd like to go ahead and normalise the dataset metadata to follow the format & values given in [src/datasets/utils/resources/licenses.json](https://github.com/huggingface/datasets/blob/master/src/datasets/utils/resources/licenses.json) . 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https://github.com/huggingface/datasets/issues/4291	Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message	Hi @leondz, thanks for reporting. Indeed, the dataset viewer relies on the dataset being streamable (passing `streaming=True` to `load_dataset`). Whereas most of the datastes are streamable out of the box (thanks to our implementation of streaming), there are still some exceptions. In particular, in your case, that is due to the data file being TAR. This format is not streamable out of the box (it does not allow random access to the archived files), but we use a trick to allow streaming: using `dl_manager.iter_archive`. Let me know if you need some help: I could push a commit to your repo with the fix.	### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes	772	103	Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message ### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes Hi @leondz, thanks for reporting. Indeed, the dataset viewer relies on the dataset being streamable (passing `streaming=True` to `load_dataset`). Whereas most of the datastes are streamable out of the box (thanks to our implementation of streaming), there are still some exceptions. In particular, in your case, that is due to the data file being TAR. This format is not streamable out of the box (it does not allow random access to the archived files), but we use a trick to allow streaming: using `dl_manager.iter_archive`. Let me know if you need some help: I could push a commit to your repo with the fix.	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https://github.com/huggingface/datasets/issues/4291	Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message	Ah, right! The preview is working now, but this explanation is good to know, thank you. I'll prefer formats with random file access supported in datasets.utils.extract in future, and try out this fix for the tarfiles :)	### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes	772	37	Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message ### Link https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train ### Description The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss? ### Owner Yes Ah, right! The preview is working now, but this explanation is good to know, thank you. I'll prefer formats with random file access supported in datasets.utils.extract in future, and try out this fix for the tarfiles :)	[ -1.127845287322998, -0.9890932440757751, -0.895614504814148, 1.430274486541748, -0.14873585104942322, -1.2830994129180908, 0.1291768103837967, -0.9686226844787598, 1.58632493019104, -0.8037676215171814, 0.23225714266300201, -1.674389123916626, -0.10080511122941971, -0.49987199902534485, -0.6487389802932739, -0.8732990622520447, -0.3729706406593323, -0.8928044438362122, 0.963624894618988, 2.6223840713500977, 1.118980050086975, -1.365286946296692, 2.802037477493286, 0.684302031993866, -0.29683560132980347, -1.1686484813690186, 0.580420970916748, 0.029527656733989716, -1.1729413270950317, -0.48969316482543945, -0.9819064140319824, 0.020939886569976807, -0.4322405755519867, -0.3737213909626007, -0.014016654342412949, 0.45060983300209045, -0.16003301739692688, -0.3494393229484558, -0.43921589851379395, -0.766993522644043, 0.5159110426902771, 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https://github.com/huggingface/datasets/issues/4287	"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None	So I managed to solve this by adding a missing `import faiss` in the `@staticmethod` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L305, triggered from https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L249 when trying to `ds_with_embeddings.add_faiss_index(column='embeddings', device=0)` with the code above. As it seems that the `@staticmethod` doesn't recognize the `import faiss` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L261, so whenever the value of `device` is not None in https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L438, that exception is triggered. So on, adding `import faiss` inside https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L305 right after the check of `device`'s value, solves the issue and lets you calculate the indices in GPU. I'll add the code in a PR linked to this issue in case you want to merge it!	## Describe the bug When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception. All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from transformers import DPRContextEncoder, DPRContextEncoderTokenizer import torch torch.set_grad_enabled(False) ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") from datasets import load_dataset ds = load_dataset('crime_and_punish', split='train[:100]') ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()}) ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` ``` ## Expected results A new column named `embeddings` in the dataset that we're adding the index to. ## Actual results An exception is triggered with the following message `NameError: name 'faiss' is not defined`. ## Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.2	773	102	"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None ## Describe the bug When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception. All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from transformers import DPRContextEncoder, DPRContextEncoderTokenizer import torch torch.set_grad_enabled(False) ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") from datasets import load_dataset ds = load_dataset('crime_and_punish', split='train[:100]') ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()}) ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` ``` ## Expected results A new column named `embeddings` in the dataset that we're adding the index to. ## Actual results An exception is triggered with the following message `NameError: name 'faiss' is not defined`. ## Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 So I managed to solve this by adding a missing `import faiss` in the `@staticmethod` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L305, triggered from https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L249 when trying to `ds_with_embeddings.add_faiss_index(column='embeddings', device=0)` with the code above. As it seems that the `@staticmethod` doesn't recognize the `import faiss` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L261, so whenever the value of `device` is not None in https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L438, that exception is triggered. So on, adding `import faiss` inside https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L305 right after the check of `device`'s value, solves the issue and lets you calculate the indices in GPU. I'll add the code in a PR linked to this issue in case you want to merge it!	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https://github.com/huggingface/datasets/issues/4287	"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None	Adding here the complete error traceback! ``` Traceback (most recent call last): File "/home/alvarobartt/lol.py", line 12, in <module> ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3656, in add_faiss_index super().add_faiss_index( File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 478, in add_faiss_index faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=True) File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 281, in add_vectors self.faiss_index = self._faiss_index_to_device(index, self.device) File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 327, in _faiss_index_to_device faiss_res = faiss.StandardGpuResources() NameError: name 'faiss' is not defined ```	## Describe the bug When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception. All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from transformers import DPRContextEncoder, DPRContextEncoderTokenizer import torch torch.set_grad_enabled(False) ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") from datasets import load_dataset ds = load_dataset('crime_and_punish', split='train[:100]') ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()}) ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` ``` ## Expected results A new column named `embeddings` in the dataset that we're adding the index to. ## Actual results An exception is triggered with the following message `NameError: name 'faiss' is not defined`. ## Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.2	773	66	"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None ## Describe the bug When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception. All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from transformers import DPRContextEncoder, DPRContextEncoderTokenizer import torch torch.set_grad_enabled(False) ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") from datasets import load_dataset ds = load_dataset('crime_and_punish', split='train[:100]') ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()}) ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` ``` ## Expected results A new column named `embeddings` in the dataset that we're adding the index to. ## Actual results An exception is triggered with the following message `NameError: name 'faiss' is not defined`. ## Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 Adding here the complete error traceback! ``` Traceback (most recent call last): File "/home/alvarobartt/lol.py", line 12, in <module> ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None` File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3656, in add_faiss_index super().add_faiss_index( File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 478, in add_faiss_index faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=True) File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 281, in add_vectors self.faiss_index = self._faiss_index_to_device(index, self.device) File "/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py", line 327, in _faiss_index_to_device faiss_res = faiss.StandardGpuResources() NameError: name 'faiss' is not defined ```	[ -1.2124148607254028, -0.9491466283798218, -0.7390639185905457, 1.592803716659546, -0.12548525631427765, -1.1911717653274536, 0.09714372456073761, -1.063795804977417, 1.6753393411636353, -0.853920578956604, 0.302219420671463, -1.6499511003494263, 0.05493294820189476, -0.5489169359207153, -0.769882321357727, -0.8686373233795166, -0.3376951217651367, -0.7621251344680786, 1.1999812126159668, 2.4599411487579346, 1.1796239614486694, -1.3977150917053223, 2.7379236221313477, 0.7068011164665222, -0.22986216843128204, -1.0249508619308472, 0.45971646904945374, -0.11724960803985596, -1.3515340089797974, -0.49299535155296326, -1.008345365524292, -0.06531260907649994, -0.6237419843673706, -0.5240451693534851, 0.041773054748773575, 0.46222981810569763, -0.35599324107170105, -0.5677943825721741, 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https://github.com/huggingface/datasets/issues/4284	Issues in processing very large datasets	Hi ! `datasets` doesn't load the dataset in memory. Instead it uses memory mapping to load your dataset from your disk (it is stored as arrow files). Do you know at what point you have RAM issues exactly ? How big are your graph_data_train dictionaries btw ?	## Describe the bug I'm trying to add a feature called "subgraph" to CNN/DM dataset (modifications on run_summarization.py of Huggingface Transformers script) --- I'm not quite sure if I'm doing it the right way, though--- but the main problem appears when the training starts where the error ` [OSError: [Errno 12] Cannot allocate memory]` appears. I suppose this problem roots in RAM issues and how the dataset is loaded during training, but I have no clue of what I can do to fix it. Observing the dataset's cache directory, I see that it takes ~600GB of memory and that's why I believe special care is needed when loading it into the memory. Here are my modifications to `run_summarization.py` code. ``` # loading pre-computed dictionary where keys are 'id' of article and values are corresponding subgraph graph_data_train = get_graph_data('train') graph_data_validation = get_graph_data('val') ... ... with training_args.main_process_first(desc="train dataset map pre-processing"): train_dataset = train_dataset.map( preprocess_function_train, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on train dataset", ) ``` And here is the modified preprocessed function: ``` def preprocess_function_train(examples): inputs, targets, sub_graphs, ids = [], [], [], [] for i in range(len(examples[text_column])): if examples[text_column][i] is not None and examples[summary_column][i] is not None: # if examples['doc_id'][i] in graph_data.keys(): inputs.append(examples[text_column][i]) targets.append(examples[summary_column][i]) sub_graphs.append(graph_data_train[examples['id'][i]]) ids.append(examples['id'][i]) inputs = [prefix + inp for inp in inputs] model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True, sub_graphs=sub_graphs, ids=ids) # Setup the tokenizer for targets with tokenizer.as_target_tokenizer(): labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True) # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore # padding in the loss. if padding == "max_length" and data_args.ignore_pad_token_for_loss: labels["input_ids"] = [ [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"] ] model_inputs["labels"] = labels["input_ids"] return model_inputs ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: Linux Ubuntu - Python version: 3.6 - PyArrow version: 6.0.1	774	47	Issues in processing very large datasets ## Describe the bug I'm trying to add a feature called "subgraph" to CNN/DM dataset (modifications on run_summarization.py of Huggingface Transformers script) --- I'm not quite sure if I'm doing it the right way, though--- but the main problem appears when the training starts where the error ` [OSError: [Errno 12] Cannot allocate memory]` appears. I suppose this problem roots in RAM issues and how the dataset is loaded during training, but I have no clue of what I can do to fix it. Observing the dataset's cache directory, I see that it takes ~600GB of memory and that's why I believe special care is needed when loading it into the memory. Here are my modifications to `run_summarization.py` code. ``` # loading pre-computed dictionary where keys are 'id' of article and values are corresponding subgraph graph_data_train = get_graph_data('train') graph_data_validation = get_graph_data('val') ... ... with training_args.main_process_first(desc="train dataset map pre-processing"): train_dataset = train_dataset.map( preprocess_function_train, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on train dataset", ) ``` And here is the modified preprocessed function: ``` def preprocess_function_train(examples): inputs, targets, sub_graphs, ids = [], [], [], [] for i in range(len(examples[text_column])): if examples[text_column][i] is not None and examples[summary_column][i] is not None: # if examples['doc_id'][i] in graph_data.keys(): inputs.append(examples[text_column][i]) targets.append(examples[summary_column][i]) sub_graphs.append(graph_data_train[examples['id'][i]]) ids.append(examples['id'][i]) inputs = [prefix + inp for inp in inputs] model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True, sub_graphs=sub_graphs, ids=ids) # Setup the tokenizer for targets with tokenizer.as_target_tokenizer(): labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True) # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore # padding in the loss. if padding == "max_length" and data_args.ignore_pad_token_for_loss: labels["input_ids"] = [ [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"] ] model_inputs["labels"] = labels["input_ids"] return model_inputs ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.0 - Platform: Linux Ubuntu - Python version: 3.6 - PyArrow version: 6.0.1 Hi ! `datasets` doesn't load the dataset in memory. Instead it uses memory mapping to load your dataset from your disk (it is stored as arrow files). Do you know at what point you have RAM issues exactly ? How big are your graph_data_train dictionaries btw ?	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https://github.com/huggingface/datasets/issues/4276	OpenBookQA has missing and inconsistent field names	Thanks for reporting, @vblagoje. Indeed, I noticed some of these issues while reviewing this PR: - #4259 This is in my TODO list.	## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2	775	23	OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 Thanks for reporting, @vblagoje. Indeed, I noticed some of these issues while reviewing this PR: - #4259 This is in my TODO list.	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https://github.com/huggingface/datasets/issues/4276	OpenBookQA has missing and inconsistent field names	On the other hand, I am not sure if we should always preserve the original nested structure. I think we should also consider other factors as convenience or consistency. For example, other datasets also flatten "question.stem" into "question": - ai2_arc: ```python question = data["question"]["stem"] choices = data["question"]["choices"] text_choices = [choice["text"] for choice in choices] label_choices = [choice["label"] for choice in choices] yield id_, { "id": id_, "answerKey": answerkey, "question": question, "choices": {"text": text_choices, "label": label_choices}, } ``` - commonsense_qa: ```python question = data["question"] stem = question["stem"] yield id_, { "answerKey": answerkey, "question": stem, "choices": {"label": labels, "text": texts}, } ``` - cos_e: ```python "question": cqa["question"]["stem"], ``` - qasc - quartz - wiqa Exceptions: - exams I think we should agree on a CONVENIENT format for QA and use always CONSISTENTLY the same.	## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2	775	132	OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 On the other hand, I am not sure if we should always preserve the original nested structure. I think we should also consider other factors as convenience or consistency. For example, other datasets also flatten "question.stem" into "question": - ai2_arc: ```python question = data["question"]["stem"] choices = data["question"]["choices"] text_choices = [choice["text"] for choice in choices] label_choices = [choice["label"] for choice in choices] yield id_, { "id": id_, "answerKey": answerkey, "question": question, "choices": {"text": text_choices, "label": label_choices}, } ``` - commonsense_qa: ```python question = data["question"] stem = question["stem"] yield id_, { "answerKey": answerkey, "question": stem, "choices": {"label": labels, "text": texts}, } ``` - cos_e: ```python "question": cqa["question"]["stem"], ``` - qasc - quartz - wiqa Exceptions: - exams I think we should agree on a CONVENIENT format for QA and use always CONSISTENTLY the same.	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https://github.com/huggingface/datasets/issues/4276	OpenBookQA has missing and inconsistent field names	@albertvillanova I agree that we should be consistent. In the last month, I have come across tons of code that deals with OpenBookQA and CommonSenseQA and all of that code relies on the original data format structure. We can't expect users to adopt HF Datasets if we arbitrarily change the structure of the format just because we think something makes more sense. I am in that position now (downloading original data rather than using HF Datasets) and undoubtedly it hinders HF Datasets' widespread use and adoption. Missing fields like in the case of #4275 is definitely bad and not even up for a discussion IMHO! cc @lhoestq	## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2	775	107	OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 @albertvillanova I agree that we should be consistent. In the last month, I have come across tons of code that deals with OpenBookQA and CommonSenseQA and all of that code relies on the original data format structure. We can't expect users to adopt HF Datasets if we arbitrarily change the structure of the format just because we think something makes more sense. I am in that position now (downloading original data rather than using HF Datasets) and undoubtedly it hinders HF Datasets' widespread use and adoption. Missing fields like in the case of #4275 is definitely bad and not even up for a discussion IMHO! cc @lhoestq	[ -1.2248849868774414, -0.8888794183731079, -0.7284150719642639, 1.4283013343811035, -0.1619182974100113, -1.2589123249053955, 0.0202217698097229, -1.1168133020401, 1.6764713525772095, -0.8145091533660889, 0.1790311336517334, -1.6472833156585693, 0.03348078951239586, -0.551364004611969, -0.7610780000686646, -0.9007698893547058, -0.40561649203300476, -0.7474669218063354, 1.0913095474243164, 2.4841504096984863, 1.131537675857544, -1.4815216064453125, 2.764204263687134, 0.6712855696678162, -0.15248529613018036, -1.0297564268112183, 0.5762649774551392, 0.0797523781657219, -1.3705177307128906, -0.43041256070137024, -0.9561245441436768, -0.016808513551950455, -0.5412758588790894, -0.5324642062187195, 0.001918761059641838, 0.26611796021461487, -0.2172473520040512, -0.5264074206352234, -0.4304588735103607, -0.7556474208831787, 0.3483940362930298, -0.3474535644054413, 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https://github.com/huggingface/datasets/issues/4276	OpenBookQA has missing and inconsistent field names	I'm opening a PR that adds the missing fields. Let's agree on the feature structure: @lhoestq @mariosasko @polinaeterna	## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2	775	18	OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 I'm opening a PR that adds the missing fields. Let's agree on the feature structure: @lhoestq @mariosasko @polinaeterna	[ -1.1906375885009766, -0.8371402025222778, -0.7898828983306885, 1.5117089748382568, -0.18207137286663055, -1.264879822731018, -0.008320402354001999, -1.057259202003479, 1.6450623273849487, -0.7752101421356201, 0.19048185646533966, -1.6376582384109497, 0.06893451511859894, -0.5813731551170349, -0.7984316945075989, -0.8814036250114441, -0.39410364627838135, -0.6672260761260986, 1.0828286409378052, 2.429485559463501, 1.0943766832351685, -1.4388824701309204, 2.771198272705078, 0.6767571568489075, -0.0933266282081604, -1.030583381652832, 0.6257411241531372, 0.03938674181699753, -1.3822070360183716, -0.4097200036048889, -1.0302447080612183, -0.030616287142038345, -0.5578322410583496, -0.514598548412323, 0.05524306371808052, 0.23202835023403168, -0.20626850426197052, -0.5469199419021606, -0.3996446132659912, -0.7637374401092529, 0.3144068121910095, -0.36627453565597534, 0.8331934213638306, -0.3200829029083252, 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https://github.com/huggingface/datasets/issues/4276	OpenBookQA has missing and inconsistent field names	IMO we should always try to preserve the original structure unless there is a good reason not to (and I don't see one in this case).	## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2	775	26	OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 IMO we should always try to preserve the original structure unless there is a good reason not to (and I don't see one in this case).	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https://github.com/huggingface/datasets/issues/4276	OpenBookQA has missing and inconsistent field names	I agree with @mariosasko . The transition to the original format could be done in one PR for the next minor release, clearly documenting all dataset changes just as @albertvillanova outlined them above and perhaps even providing a per dataset util method to convert the new valid format to the old for backward compatibility. Users who relied on the old format will update their code with either the util method for a quick fix or slightly more elaborate for the new.	## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2	775	81	OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 I agree with @mariosasko . The transition to the original format could be done in one PR for the next minor release, clearly documenting all dataset changes just as @albertvillanova outlined them above and perhaps even providing a per dataset util method to convert the new valid format to the old for backward compatibility. Users who relied on the old format will update their code with either the util method for a quick fix or slightly more elaborate for the new.	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https://github.com/huggingface/datasets/issues/4276	OpenBookQA has missing and inconsistent field names	I don't have a strong opinion on this, besides the fact that whatever decision we agree on, should be applied to all datasets. There is always the tension between: - preserving each dataset original structure (which has the advantage of not forcing users to learn other structure for the same dataset), - and on the other hand performing some kind of standardization/harmonization depending on the task (this has the advantage that once learnt, the same structure applies to all datasets; this has been done for e.g. POS tagging: all datasets have been adapted to a certain "standard" structure). - Another advantage: datasets can easily be interchanged (or joined) to be used by the same model Recently, in the BigScience BioMedical hackathon, they adopted a different approach: - they implement a "source" config, respecting the original structure as much as possible - they implement additional config for each task, with a "standard" nested structure per task, which is most useful for users.	## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2	775	161	OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 I don't have a strong opinion on this, besides the fact that whatever decision we agree on, should be applied to all datasets. There is always the tension between: - preserving each dataset original structure (which has the advantage of not forcing users to learn other structure for the same dataset), - and on the other hand performing some kind of standardization/harmonization depending on the task (this has the advantage that once learnt, the same structure applies to all datasets; this has been done for e.g. POS tagging: all datasets have been adapted to a certain "standard" structure). - Another advantage: datasets can easily be interchanged (or joined) to be used by the same model Recently, in the BigScience BioMedical hackathon, they adopted a different approach: - they implement a "source" config, respecting the original structure as much as possible - they implement additional config for each task, with a "standard" nested structure per task, which is most useful for users.	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https://github.com/huggingface/datasets/issues/4276	OpenBookQA has missing and inconsistent field names	@albertvillanova, thanks for the detailed answer and the new perspectives. I understand the friction for the best design approach much better now. Ultimately, it is essential to include all the missing fields and the correct data first. Whatever approach is determined to be optimal is important but not as crucial once all the data is there, and users can create lambda functions to create whatever structure serves them best.	## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2	775	69	OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 @albertvillanova, thanks for the detailed answer and the new perspectives. I understand the friction for the best design approach much better now. Ultimately, it is essential to include all the missing fields and the correct data first. Whatever approach is determined to be optimal is important but not as crucial once all the data is there, and users can create lambda functions to create whatever structure serves them best.	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https://github.com/huggingface/datasets/issues/4276	OpenBookQA has missing and inconsistent field names	Datasets are not tracked in this repository anymore. I think we must move this thread to the [discussions tab of the dataset](https://huggingface.co/datasets/openbookqa/discussions)	## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2	775	22	OpenBookQA has missing and inconsistent field names ## Describe the bug OpenBookQA implementation is inconsistent with the original dataset. We need to: 1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format. 2. Add missing additional fields: - 'fact1': row['fact1'], - 'humanScore': row['humanScore'], - 'clarity': row['clarity'], - 'turkIdAnonymized': row['turkIdAnonymized'] 3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Expected results The structure and every data item in the original OpenBookQA matches our OpenBookQA version. ## Actual results TBD ## Environment info - `datasets` version: 2.1.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 Datasets are not tracked in this repository anymore. I think we must move this thread to the [discussions tab of the dataset](https://huggingface.co/datasets/openbookqa/discussions)	[ -1.1745386123657227, -0.8170990943908691, -0.7875366806983948, 1.475058674812317, -0.1232234388589859, -1.299286961555481, -0.01610148884356022, -1.0619772672653198, 1.6374751329421997, -0.7768942713737488, 0.18954959511756897, -1.6499474048614502, 0.04717228561639786, -0.5778653025627136, -0.7946587800979614, -0.8957010507583618, -0.38867807388305664, -0.6469797492027283, 1.0366674661636353, 2.459909200668335, 1.1122210025787354, -1.4599566459655762, 2.7267565727233887, 0.6709302067756653, -0.06831134110689163, -1.0537165403366089, 0.6485459804534912, 0.06254416704177856, -1.388223648071289, -0.4207996428012848, -0.9784543514251709, -0.009355236776173115, -0.5587805509567261, -0.5082058906555176, 0.047129955142736435, 0.25115665793418884, -0.2184881865978241, -0.5085332989692688, -0.41675207018852234, -0.7638015151023865, 0.3107009828090668, 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https://github.com/huggingface/datasets/issues/4271	A typo in docs of datasets.disable_progress_bar	Hi! Thanks for catching and reporting the typo, a PR has been opened to fix it :)	## Describe the bug in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable".	777	17	A typo in docs of datasets.disable_progress_bar ## Describe the bug in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable". Hi! Thanks for catching and reporting the typo, a PR has been opened to fix it :)	[ -1.2477343082427979, -0.9352830648422241, -0.8799794316291809, 1.4343416690826416, -0.18829888105392456, -1.3034683465957642, 0.20818637311458588, -1.1650056838989258, 1.6266173124313354, -0.9553149938583374, 0.24564479291439056, -1.6569467782974243, 0.09685659408569336, -0.5144250392913818, -0.5792786478996277, -0.9795315265655518, -0.49016696214675903, -0.47560185194015503, 1.0612908601760864, 2.5210652351379395, 1.0583559274673462, -1.1368614435195923, 2.6944029331207275, 0.923111081123352, -0.20831286907196045, -0.9469980001449585, 0.750334620475769, 0.03760113567113876, -1.2164188623428345, -0.3846104145050049, -1.0901962518692017, -0.017334213480353355, -0.37023547291755676, -0.4786961078643799, -0.13742277026176453, 0.22620056569576263, -0.30985522270202637, -0.5172290205955505, -0.5953006744384766, -0.8940209746360779, 0.27060338854789734, -0.5741587281227112, 0.8865729570388794, -0.546697735786438, 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https://github.com/huggingface/datasets/issues/4268	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered	It would help a lot to be able to preview the dataset - I'd like to see if the pronunciations are in the dataset, eg. for ["word"](https://en.wiktionary.org/wiki/word), Pronunciation ([Received Pronunciation](https://en.wikipedia.org/wiki/Received_Pronunciation)) [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɜːd/ ([General American](https://en.wikipedia.org/wiki/General_American)) [enPR](https://en.wiktionary.org/wiki/Appendix:English_pronunciation): wûrd, [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɝd/	## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1	778	38	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 It would help a lot to be able to preview the dataset - I'd like to see if the pronunciations are in the dataset, eg. for ["word"](https://en.wiktionary.org/wiki/word), Pronunciation ([Received Pronunciation](https://en.wikipedia.org/wiki/Received_Pronunciation)) [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɜːd/ ([General American](https://en.wikipedia.org/wiki/General_American)) [enPR](https://en.wiktionary.org/wiki/Appendix:English_pronunciation): wûrd, [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɝd/	[ -1.1990233659744263, -0.8957808613777161, -0.5553922057151794, 1.5229593515396118, -0.015718523412942886, -1.2148336172103882, 0.15458449721336365, -0.8494728207588196, 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https://github.com/huggingface/datasets/issues/4268	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered	Hi @i-am-neo, thanks for reporting. Normally this dataset should be private and not accessible for public use. @cakiki, @lvwerra, any reason why is it public? I see many other Wikimedia datasets are also public. Also note that last commit "Add metadata" (https://huggingface.co/datasets/bigscience-catalogue-lm-data/lm_en_wiktionary_filtered/commit/dc2f458dab50e00f35c94efb3cd4009996858609) introduced buggy data files (`data/file-01.jsonl.gz.lock`, `data/file-01.jsonl.gz.lock.lock`). The same bug appears in other datasets as well. @i-am-neo, please note that in the near future we are planning to make public all datasets used for the BigScience project (at least all of them whose license allows to do that). Once public, they will be accessible for all the NLP community.	## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1	778	100	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Hi @i-am-neo, thanks for reporting. Normally this dataset should be private and not accessible for public use. @cakiki, @lvwerra, any reason why is it public? I see many other Wikimedia datasets are also public. Also note that last commit "Add metadata" (https://huggingface.co/datasets/bigscience-catalogue-lm-data/lm_en_wiktionary_filtered/commit/dc2f458dab50e00f35c94efb3cd4009996858609) introduced buggy data files (`data/file-01.jsonl.gz.lock`, `data/file-01.jsonl.gz.lock.lock`). The same bug appears in other datasets as well. @i-am-neo, please note that in the near future we are planning to make public all datasets used for the BigScience project (at least all of them whose license allows to do that). Once public, they will be accessible for all the NLP community.	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https://github.com/huggingface/datasets/issues/4268	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered	Ah this must be a bug introduced at creation time since the repos were created programmatically; I'll go ahead and make them private; sorry about that!	## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1	778	26	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Ah this must be a bug introduced at creation time since the repos were created programmatically; I'll go ahead and make them private; sorry about that!	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https://github.com/huggingface/datasets/issues/4268	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered	All datasets are private now. Re:that bug I think we're currently avoiding it by avoiding verifications. (i.e. `ignore_verifications=True`)	## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1	778	18	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 All datasets are private now. Re:that bug I think we're currently avoiding it by avoiding verifications. 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https://github.com/huggingface/datasets/issues/4268	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered	Thanks a lot, @cakiki. @i-am-neo, I'm closing this issue for now because the dataset is not publicly available yet. Just stay tuned, as we will soon release all the BigScience open-license datasets.	## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1	778	32	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Thanks a lot, @cakiki. @i-am-neo, I'm closing this issue for now because the dataset is not publicly available yet. Just stay tuned, as we will soon release all the BigScience open-license datasets.	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https://github.com/huggingface/datasets/issues/4268	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered	Thanks for letting me know, @albertvillanova @cakiki. Any chance of having a subset alpha version in the meantime? I only need two dicts out of wiktionary: 1) phoneme(as key): word, and 2) word(as key): its phonemes. Would like to use it for a mini-poc [Robust ASR](https://github.com/huggingface/transformers/issues/13162#issuecomment-1096881290) decoding, cc @patrickvonplaten. (Patrick, possible to email you so as not to litter github with comments? I have some observations after experiments training hubert on some YT AMI-like data (11.44% wer). Also wonder if a robust ASR is on your/HG's roadmap). Thanks!	## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1	778	88	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Thanks for letting me know, @albertvillanova @cakiki. Any chance of having a subset alpha version in the meantime? I only need two dicts out of wiktionary: 1) phoneme(as key): word, and 2) word(as key): its phonemes. Would like to use it for a mini-poc [Robust ASR](https://github.com/huggingface/transformers/issues/13162#issuecomment-1096881290) decoding, cc @patrickvonplaten. (Patrick, possible to email you so as not to litter github with comments? I have some observations after experiments training hubert on some YT AMI-like data (11.44% wer). Also wonder if a robust ASR is on your/HG's roadmap). Thanks!	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https://github.com/huggingface/datasets/issues/4268	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered	Hey @i-am-neo, Cool to hear that you're working on Robust ASR! Feel free to drop me a mail :-)	## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1	778	19	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 Hey @i-am-neo, Cool to hear that you're working on Robust ASR! 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https://github.com/huggingface/datasets/issues/4268	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered	@i-am-neo This particular subset of the dataset was taken from the [CirrusSearch dumps](https://dumps.wikimedia.org/other/cirrussearch/current/) You're specifically after the [enwiktionary-20220425-cirrussearch-content.json.gz](https://dumps.wikimedia.org/other/cirrussearch/current/enwiktionary-20220425-cirrussearch-content.json.gz) file	## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1	778	19	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 @i-am-neo This particular subset of the dataset was taken from the [CirrusSearch dumps](https://dumps.wikimedia.org/other/cirrussearch/current/) You're specifically after the 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https://github.com/huggingface/datasets/issues/4268	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered	thanks @cakiki ! <del>I could access the gz file yesterday (but neglected to tuck it away somewhere safe), and today the link is throwing a 404. Can you help? </del> Never mind, got it!	## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1	778	34	error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered ## Describe the bug Error generated when attempting to download dataset ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` ExpectedMoreDownloadedFiles Traceback (most recent call last) [<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered") 3 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 thanks @cakiki ! <del>I could access the gz file yesterday (but neglected to tuck it away somewhere safe), and today the link is throwing a 404. Can you help? </del> Never mind, got it!	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https://github.com/huggingface/datasets/issues/4261	data leakage in `webis/conclugen` dataset	Hi @xflashxx, thanks for reporting. Please note that this dataset was generated and shared by Webis Group: https://huggingface.co/webis We are contacting the dataset owners to inform them about the issue you found. We'll keep you updated of their reply.	## Describe the bug Some samples (argument-conclusion pairs) in the training split of the `webis/conclugen` dataset are present in both the validation and test splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the training split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the validation and test split seems to be present in the training split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0	779	39	data leakage in `webis/conclugen` dataset ## Describe the bug Some samples (argument-conclusion pairs) in the training split of the `webis/conclugen` dataset are present in both the validation and test splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the training split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the validation and test split seems to be present in the training split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 Hi @xflashxx, thanks for reporting. Please note that this dataset was generated and shared by Webis Group: https://huggingface.co/webis We are contacting the dataset owners to inform them about the issue you found. We'll keep you updated of their reply.	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https://github.com/huggingface/datasets/issues/4261	data leakage in `webis/conclugen` dataset	Thanks for reporting this @xflashxx. I'll have a look and get back to you on this.	## Describe the bug Some samples (argument-conclusion pairs) in the training split of the `webis/conclugen` dataset are present in both the validation and test splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the training split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the validation and test split seems to be present in the training split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0	779	16	data leakage in `webis/conclugen` dataset ## Describe the bug Some samples (argument-conclusion pairs) in the training split of the `webis/conclugen` dataset are present in both the validation and test splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the training split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the validation and test split seems to be present in the training split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 Thanks for reporting this @xflashxx. I'll have a look and get back to you on this.	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https://github.com/huggingface/datasets/issues/4261	data leakage in `webis/conclugen` dataset	Hi @xflashxx and @albertvillanova, I have updated the files with de-duplicated splits. Apparently the debate portals from which part of the examples were sourced had unique timestamps for some examples (up to 6%; updated counts in the README) without any actual content updated that lead to "new" items. The length of `ids_validation` and `ids_testing` is zero. Regarding impact on scores: 1. We employed automatic evaluation (on a separate set of 1000 examples) only to justify the exclusion of the smaller models for manual evaluation (due to budget constraints). I am confident the ranking still stands (unsurprisingly, the bigger models doing better than those trained on the smaller splits). We also highlight this in the paper. 2. The examples used for manual evaluation have no overlap with any splits (also because they do not have any ground truth as we applied the trained models on an unlabeled sample to test its practical usage). I've added these two files to the dataset repository. Hope this helps!	## Describe the bug Some samples (argument-conclusion pairs) in the training split of the `webis/conclugen` dataset are present in both the validation and test splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the training split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the validation and test split seems to be present in the training split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0	779	164	data leakage in `webis/conclugen` dataset ## Describe the bug Some samples (argument-conclusion pairs) in the training split of the `webis/conclugen` dataset are present in both the validation and test splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the training split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the validation and test split seems to be present in the training split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 Hi @xflashxx and @albertvillanova, I have updated the files with de-duplicated splits. Apparently the debate portals from which part of the examples were sourced had unique timestamps for some examples (up to 6%; updated counts in the README) without any actual content updated that lead to "new" items. The length of `ids_validation` and `ids_testing` is zero. Regarding impact on scores: 1. We employed automatic evaluation (on a separate set of 1000 examples) only to justify the exclusion of the smaller models for manual evaluation (due to budget constraints). I am confident the ranking still stands (unsurprisingly, the bigger models doing better than those trained on the smaller splits). We also highlight this in the paper. 2. The examples used for manual evaluation have no overlap with any splits (also because they do not have any ground truth as we applied the trained models on an unlabeled sample to test its practical usage). I've added these two files to the dataset repository. Hope this helps!	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https://github.com/huggingface/datasets/issues/4261	data leakage in `webis/conclugen` dataset	Thanks @shahbazsyed for your fast fix. As a side note: - Your email appearing as Point of Contact in the dataset README has a typo: @uni.leipzig.de instead of @uni-leipzig.de - Your commits on the Hub are not linked to your profile on the Hub: this is because we use the email address to make this link; the email address used in your commit author and the email address set on your Hub account settings.	## Describe the bug Some samples (argument-conclusion pairs) in the training split of the `webis/conclugen` dataset are present in both the validation and test splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the training split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the validation and test split seems to be present in the training split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0	779	74	data leakage in `webis/conclugen` dataset ## Describe the bug Some samples (argument-conclusion pairs) in the training split of the `webis/conclugen` dataset are present in both the validation and test splits, creating data leakage and distorting model results. Furthermore, all splits contain duplicate samples. ## Steps to reproduce the bug ```python from datasets import load_dataset training = load_dataset("webis/conclugen", "base", split="train") validation = load_dataset("webis/conclugen", "base", split="validation") testing = load_dataset("webis/conclugen", "base", split="test") # collect which sample id's are present in the training split ids_validation = list() ids_testing = list() for train_sample in training: train_argument = train_sample["argument"] train_conclusion = train_sample["conclusion"] train_id = train_sample["id"] # test if current sample is in validation split if train_argument in validation["argument"]: for validation_sample in validation: validation_argument = validation_sample["argument"] validation_conclusion = validation_sample["conclusion"] validation_id = validation_sample["id"] if train_argument == validation_argument and train_conclusion == validation_conclusion: ids_validation.append(validation_id) # test if current sample is in test split if train_argument in testing["argument"]: for testing_sample in testing: testing_argument = testing_sample["argument"] testing_conclusion = testing_sample["conclusion"] testing_id = testing_sample["id"] if train_argument == testing_argument and train_conclusion == testing_conclusion: ids_testing.append(testing_id) ``` ## Expected results Length of both lists `ids_validation` and `ids_testing` should be zero. ## Actual results Length of `ids_validation` = `2556` Length of `ids_testing` = `287` Furthermore, there seems to be duplicate samples in (at least) the training split, since: `print(len(set(ids_validation)))` = `950` `print(len(set(ids_testing)))` = `101` All in all, around 7% of the samples of each the validation and test split seems to be present in the training split. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: macOS-12.3.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 Thanks @shahbazsyed for your fast fix. As a side note: - Your email appearing as Point of Contact in the dataset README has a typo: @uni.leipzig.de instead of @uni-leipzig.de - Your commits on the Hub are not linked to your profile on the Hub: this is because we use the email address to make this link; the email address used in your commit author and the email address set on your Hub account settings.	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https://github.com/huggingface/datasets/issues/4248	conll2003 dataset loads original data.	Thanks for reporting @sue99. Unfortunately. I'm not able to reproduce your problem: ```python In [1]: import datasets ...: from datasets import load_dataset ...: dataset = load_dataset("conll2003") In [2]: dataset Out[2]: DatasetDict({ train: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 14042 }) validation: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 3251 }) test: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 3454 }) }) In [3]: dataset["train"][0] Out[3]: {'id': '0', 'tokens': ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'], 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7], 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0], 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]} ``` Just guessing: might be the case that you are calling `load_dataset` from a working directory that contains a local folder named `conll2003` (containing the raw data files)? If that is the case, `datasets` library gives precedence to the local folder over the dataset on the Hub.	## Describe the bug I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text. Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ? ## Steps to reproduce the bug ```python import datasets from datasets import load_dataset dataset = load_dataset("conll2003") ``` ## Expected results { "chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0], "id": "0", "ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7], "tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."] } ## Actual results ```python print(dataset) DatasetDict({ train: Dataset({ features: ['text'], num_rows: 219554 }) test: Dataset({ features: ['text'], num_rows: 50350 }) validation: Dataset({ features: ['text'], num_rows: 55044 }) }) ``` ```python for i in range(20): print(dataset['train'][i]) {'text': '-DOCSTART- -X- -X- O'} {'text': ''} {'text': 'EU NNP B-NP B-ORG'} {'text': 'rejects VBZ B-VP O'} {'text': 'German JJ B-NP B-MISC'} {'text': 'call NN I-NP O'} {'text': 'to TO B-VP O'} {'text': 'boycott VB I-VP O'} {'text': 'British JJ B-NP B-MISC'} {'text': 'lamb NN I-NP O'} {'text': '. . O O'} {'text': ''} {'text': 'Peter NNP B-NP B-PER'} {'text': 'Blackburn NNP I-NP I-PER'} {'text': ''} {'text': 'BRUSSELS NNP B-NP B-LOC'} {'text': '1996-08-22 CD I-NP O'} {'text': ''} {'text': 'The DT B-NP O'} {'text': 'European NNP I-NP B-ORG'} ```	780	158	conll2003 dataset loads original data. ## Describe the bug I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text. Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ? ## Steps to reproduce the bug ```python import datasets from datasets import load_dataset dataset = load_dataset("conll2003") ``` ## Expected results { "chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0], "id": "0", "ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7], "tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."] } ## Actual results ```python print(dataset) DatasetDict({ train: Dataset({ features: ['text'], num_rows: 219554 }) test: Dataset({ features: ['text'], num_rows: 50350 }) validation: Dataset({ features: ['text'], num_rows: 55044 }) }) ``` ```python for i in range(20): print(dataset['train'][i]) {'text': '-DOCSTART- -X- -X- O'} {'text': ''} {'text': 'EU NNP B-NP B-ORG'} {'text': 'rejects VBZ B-VP O'} {'text': 'German JJ B-NP B-MISC'} {'text': 'call NN I-NP O'} {'text': 'to TO B-VP O'} {'text': 'boycott VB I-VP O'} {'text': 'British JJ B-NP B-MISC'} {'text': 'lamb NN I-NP O'} {'text': '. . O O'} {'text': ''} {'text': 'Peter NNP B-NP B-PER'} {'text': 'Blackburn NNP I-NP I-PER'} {'text': ''} {'text': 'BRUSSELS NNP B-NP B-LOC'} {'text': '1996-08-22 CD I-NP O'} {'text': ''} {'text': 'The DT B-NP O'} {'text': 'European NNP I-NP B-ORG'} ``` Thanks for reporting @sue99. Unfortunately. I'm not able to reproduce your problem: ```python In [1]: import datasets ...: from datasets import load_dataset ...: dataset = load_dataset("conll2003") In [2]: dataset Out[2]: DatasetDict({ train: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 14042 }) validation: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 3251 }) test: Dataset({ features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'], num_rows: 3454 }) }) In [3]: dataset["train"][0] Out[3]: {'id': '0', 'tokens': ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'], 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7], 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0], 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]} ``` Just guessing: might be the case that you are calling `load_dataset` from a working directory that contains a local folder named `conll2003` (containing the raw data files)? If that is the case, `datasets` library gives precedence to the local folder over the dataset on the Hub.	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https://github.com/huggingface/datasets/issues/4247	The data preview of XGLUE	Thanks for reporting @czq1999. Note that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet. That is the case for XGLUE dataset (as the error message points out): this must be refactored to support streaming.	It seems that something wrong with the data previvew of XGLUE	781	43	The data preview of XGLUE It seems that something wrong with the data previvew of XGLUE Thanks for reporting @czq1999. Note that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet. That is the case for XGLUE dataset (as the error message points out): this must be refactored to support streaming.	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https://github.com/huggingface/datasets/issues/4241	NonMatchingChecksumError when attempting to download GLUE	Hi :) I think your issue may be related to the older `nlp` library. I was able to download `glue` with the latest version of `datasets`. Can you try updating with: ```py pip install -U datasets ``` Then you can download: ```py from datasets import load_dataset ds = load_dataset("glue", "rte") ```	## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%\|██████████\| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5	782	51	NonMatchingChecksumError when attempting to download GLUE ## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%\|██████████\| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5 Hi :) I think your issue may be related to the older `nlp` library. I was able to download `glue` with the latest version of `datasets`. Can you try updating with: ```py pip install -U datasets ``` Then you can download: ```py from datasets import load_dataset ds = load_dataset("glue", "rte") ```	[ -1.167021632194519, -0.9371128082275391, -0.7908138632774353, 1.319181203842163, -0.17419584095478058, -1.2544281482696533, 0.11773422360420227, -1.0371555089950562, 1.5875951051712036, -0.6915667653083801, 0.20951901376247406, -1.6552684307098389, -0.09963834285736084, -0.5304948091506958, -0.635225772857666, -0.9485264420509338, -0.3367857038974762, -0.8876282572746277, 1.0266799926757812, 2.5640296936035156, 1.289549469947815, -1.3324342966079712, 2.7262613773345947, 0.6812225580215454, -0.2886331081390381, -0.9991445541381836, 0.458454430103302, -0.017439240589737892, -1.254330039024353, -0.45752257108688354, -0.9489942193031311, 0.021682221442461014, -0.5863613486289978, -0.539829671382904, 0.15368781983852386, 0.45881354808807373, -0.32249966263771057, -0.3003857135772705, -0.5711013078689575, -0.643170952796936, 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https://github.com/huggingface/datasets/issues/4241	NonMatchingChecksumError when attempting to download GLUE	This appears to work. Thank you! On Wed, Apr 27, 2022, 1:18 PM Steven Liu *@.> wrote: > Hi :) > > I think your issue may be related to the older nlp library. I was able to > download glue with the latest version of datasets. Can you try updating > with: > > pip install -U datasets > > Then you can download: > > from datasets import load_datasetds = load_dataset("glue", "rte") > > — > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/4241#issuecomment-1111267650>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/ACJUEKLUP2EL7ES3RRWJRPTVHFZHBANCNFSM5UPJBYXA> > . > You are receiving this because you authored the thread.Message ID: > @.*> >	## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%\|██████████\| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5	782	110	NonMatchingChecksumError when attempting to download GLUE ## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%\|██████████\| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5 This appears to work. Thank you! On Wed, Apr 27, 2022, 1:18 PM Steven Liu *@.> wrote: > Hi :) > > I think your issue may be related to the older nlp library. I was able to > download glue with the latest version of datasets. Can you try updating > with: > > pip install -U datasets > > Then you can download: > > from datasets import load_datasetds = load_dataset("glue", "rte") > > — > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/4241#issuecomment-1111267650>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/ACJUEKLUP2EL7ES3RRWJRPTVHFZHBANCNFSM5UPJBYXA> > . > You are receiving this because you authored the thread.Message ID: > @.*> >	[ -1.167021632194519, -0.9371128082275391, -0.7908138632774353, 1.319181203842163, -0.17419584095478058, -1.2544281482696533, 0.11773422360420227, -1.0371555089950562, 1.5875951051712036, -0.6915667653083801, 0.20951901376247406, -1.6552684307098389, -0.09963834285736084, -0.5304948091506958, -0.635225772857666, -0.9485264420509338, -0.3367857038974762, -0.8876282572746277, 1.0266799926757812, 2.5640296936035156, 1.289549469947815, -1.3324342966079712, 2.7262613773345947, 0.6812225580215454, 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https://github.com/huggingface/datasets/issues/4238	Dataset caching policy	Hi @loretoparisi, thanks for reporting. There is an option to force the redownload of the data files (and thus not using previously download and cached data files): `load_dataset(..., download_mode="force_redownload")`. Please, let me know if this fixes your problem. I can confirm you that your dataset loads without any problem for me: ```python In [2]: ds = load_dataset("loretoparisi/tatoeba-sentences", data_files={"train": "train.csv", "test": "test.csv"}, delimiter="\t", column_names=['label', 'text']) In [3]: ds Out[3]: DatasetDict({ train: Dataset({ features: ['label', 'text'], num_rows: 8256449 }) test: Dataset({ features: ['label', 'text'], num_rows: 2061204 }) }) ```	## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ```	783	87	Dataset caching policy ## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ``` Hi @loretoparisi, thanks for reporting. There is an option to force the redownload of the data files (and thus not using previously download and cached data files): `load_dataset(..., download_mode="force_redownload")`. Please, let me know if this fixes your problem. I can confirm you that your dataset loads without any problem for me: ```python In [2]: ds = load_dataset("loretoparisi/tatoeba-sentences", data_files={"train": "train.csv", "test": "test.csv"}, delimiter="\t", column_names=['label', 'text']) In [3]: ds Out[3]: DatasetDict({ train: Dataset({ features: ['label', 'text'], num_rows: 8256449 }) test: Dataset({ features: ['label', 'text'], num_rows: 2061204 }) }) ```	[ -1.184346318244934, -0.8846843242645264, -0.778140664100647, 1.4549767971038818, -0.08425062894821167, -1.2840534448623657, 0.09011413902044296, -0.9552542567253113, 1.6598578691482544, -0.7701401114463806, 0.3181559145450592, -1.6752535104751587, 0.020424887537956238, -0.5375863909721375, -0.7423824667930603, -0.7933813333511353, -0.3734804093837738, -0.8531193733215332, 1.0393558740615845, 2.4701120853424072, 1.2401710748672485, -1.363675594329834, 2.7785043716430664, 0.6777925491333008, -0.25129231810569763, -1.0293110609054565, 0.5641726851463318, -0.03513217717409134, 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https://github.com/huggingface/datasets/issues/4238	Dataset caching policy	@albertvillanova thank you, it seems it still does not work using: ```python sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], download_mode="force_redownload" ) ``` [This](https://colab.research.google.com/drive/1EA6FWo5pHxU8rPHHRn24NlHqRPiOlPTr?usp=sharing) is my notebook! The problem is that the download file's revision for `test.csv` is not correctly parsed ![Schermata 2022-04-27 alle 18 09 41](https://user-images.githubusercontent.com/163333/165563507-0be53eb6-8f61-49b0-b959-306e59281de3.png) If you download that file `test.csv` from the repo, the line `\\N` is not there anymore (it was there at the first file upload). My impression is that the Apache Arrow file is still cached - so server side, despite of enabling a forced download. For what I can see I get those two arrow files, but I cannot grep the bad line (`\\N`) since are binary files: ``` !ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 !ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/csv-test.arrow !head /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/dataset_info.json ```	## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ```	783	125	Dataset caching policy ## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ``` @albertvillanova thank you, it seems it still does not work using: ```python sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], download_mode="force_redownload" ) ``` [This](https://colab.research.google.com/drive/1EA6FWo5pHxU8rPHHRn24NlHqRPiOlPTr?usp=sharing) is my notebook! The problem is that the download file's revision for `test.csv` is not correctly parsed ![Schermata 2022-04-27 alle 18 09 41](https://user-images.githubusercontent.com/163333/165563507-0be53eb6-8f61-49b0-b959-306e59281de3.png) If you download that file `test.csv` from the repo, the line `\\N` is not there anymore (it was there at the first file upload). My impression is that the Apache Arrow file is still cached - so server side, despite of enabling a forced download. For what I can see I get those two arrow files, but I cannot grep the bad line (`\\N`) since are binary files: ``` !ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 !ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/csv-test.arrow !head /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/dataset_info.json ```	[ -1.184346318244934, -0.8846843242645264, -0.778140664100647, 1.4549767971038818, -0.08425062894821167, -1.2840534448623657, 0.09011413902044296, -0.9552542567253113, 1.6598578691482544, -0.7701401114463806, 0.3181559145450592, -1.6752535104751587, 0.020424887537956238, -0.5375863909721375, -0.7423824667930603, -0.7933813333511353, 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-0.7019999027252197, 0.8318225741386414, -0.23974482715129852, 1.3330409526824951, 0.4968165159225464, -1.0738321542739868, 1.6286059617996216, -0.3639591634273529, 0.5396745204925537, -0.6848441958427429, -0.4838365614414215, -0.7403388619422913, 0.2934403419494629, 1.9469071626663208, -0.35453957319259644, 1.5793509483337402, -0.3451177477836609, -1.5941039323806763, -1.5144506692886353, 0.7503666281700134, 0.5931270718574524, -0.8082752227783203, 0.08200778067111969, -0.4252634644508362, 0.1196863129734993, 0.020707203075289726, 1.1147209405899048, 1.219312310218811, 0.7038719654083252, -0.26481491327285767, -0.8886616230010986, 0.23318709433078766, 0.02493336983025074, -0.6297130584716797, -1.8299795389175415, -0.2784639894962311, 0.18537011742591858, 0.6846004724502563, -1.2221136093139648, 1.7315901517868042, 0.8923154473304749, 2.02626895904541, 1.0631940364837646, -0.2903980016708374, 1.5534228086471558, 0.03944510966539383, 1.8289355039596558, -0.44210439920425415, 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https://github.com/huggingface/datasets/issues/4238	Dataset caching policy	SOLVED! The problem was the with the file itself, using caching parameter helped indeed. Thanks for helping!	## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ```	783	17	Dataset caching policy ## Describe the bug I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error ``` [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally: ```python from datasets import load_dataset_builder dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences") print(dataset_builder.cache_dir) print(dataset_builder.info.features) print(dataset_builder.info.splits) ``` ``` Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519 None None ``` and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`. Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it? Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last. Thank you. ## Steps to reproduce the bug ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset( "loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], ) # You can make this part faster with num_proc=<some int> sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) sentences = sentences.shuffle() ``` ## Expected results Properly tokenize dataset file `test.csv` without issues. ## Actual results Specify the actual results or traceback. ``` Downloading data files: 100% 2/2 [00:16<00:00, 7.34s/it] Downloading data: 100% 391M/391M [00:12<00:00, 36.6MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 40.0MB/s] Extracting data files: 100% 2/2 [00:00<00:00, 47.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 2/2 [00:00<00:00, 25.94it/s] 11% 942339/8256449 [01:55<13:11, 9245.85ex/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>() 12 ) 13 # You can make this part faster with num_proc=<some int> ---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features) 15 sentences = sentences.shuffle() 10 frames [/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values) 852 if value not in self._str2int: 853 value = str(value).strip() --> 854 output.append(self._str2int[str(value)]) 855 else: 856 # No names provided, try to integerize KeyError: '\\N' ``` ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 - ``` ``` - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.11.0+cu113 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> - ``` SOLVED! The problem was the with the file itself, using caching parameter helped indeed. Thanks for helping!	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https://github.com/huggingface/datasets/issues/4237	Common Voice 8 doesn't show datasets viewer	Thanks for reporting. I understand it's an error in the dataset script. To reproduce: ```python >>> import datasets as ds >>> split_names = ds.get_dataset_split_names("mozilla-foundation/common_voice_8_0", use_auth_token="**********") Downloading builder script: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 10.9k/10.9k [00:00<00:00, 10.9MB/s] Downloading extra modules: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2.98k/2.98k [00:00<00:00, 3.36MB/s] Downloading extra modules: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 53.1k/53.1k [00:00<00:00, 650kB/s] No config specified, defaulting to: common_voice/en Traceback (most recent call last): File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 280, in get_dataset_config_info for split_generator in builder._split_generators( File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 153, in _split_generators self._log_download(self.config.name, bundle_version, hf_auth_token) File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 139, in _log_download email = HfApi().whoami(auth_token)["email"] KeyError: 'email' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 323, in get_dataset_split_names info = get_dataset_config_info( File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 285, in get_dataset_config_info raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config. ```	https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0	784	151	Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 Thanks for reporting. I understand it's an error in the dataset script. To reproduce: ```python >>> import datasets as ds >>> split_names = ds.get_dataset_split_names("mozilla-foundation/common_voice_8_0", use_auth_token="**********") Downloading builder script: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 10.9k/10.9k [00:00<00:00, 10.9MB/s] Downloading extra modules: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2.98k/2.98k [00:00<00:00, 3.36MB/s] Downloading extra modules: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 53.1k/53.1k [00:00<00:00, 650kB/s] No config specified, defaulting to: common_voice/en Traceback (most recent call last): File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 280, in get_dataset_config_info for split_generator in builder._split_generators( File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 153, in _split_generators self._log_download(self.config.name, bundle_version, hf_auth_token) File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py", line 139, in _log_download email = HfApi().whoami(auth_token)["email"] KeyError: 'email' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 323, in get_dataset_split_names info = get_dataset_config_info( File "/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 285, in get_dataset_config_info raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config. ```	[ -1.210037112236023, -0.8946057558059692, -0.6435888409614563, 1.329050898551941, -0.14699207246303558, -1.2846145629882812, 0.15607203543186188, -1.0925593376159668, 1.524633526802063, -0.6602165699005127, 0.23302631080150604, -1.6079670190811157, -0.07557850331068039, -0.5002406239509583, -0.7416560053825378, -0.8483732342720032, -0.39945635199546814, -0.8411332964897156, 0.9581155180931091, 2.5173914432525635, 1.3004271984100342, -1.3437061309814453, 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https://github.com/huggingface/datasets/issues/4237	Common Voice 8 doesn't show datasets viewer	Thanks for reporting @patrickvonplaten and thanks for the investigation @severo. Unfortunately I'm not able to reproduce the error. I think the error has to do with authentication with `huggingface_hub`, because the exception is thrown from these code lines: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/blob/main/common_voice_8_0.py#L137-L139 ```python from huggingface_hub import HfApi, HfFolder if isinstance(auth_token, bool): email = HfApi().whoami(auth_token) email = HfApi().whoami(auth_token)["email"] ``` Could you please verify the previous code with the `auth_token` you pass to `load_dataset(..., use_auth_token=auth_token,...`?	https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0	784	70	Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 Thanks for reporting @patrickvonplaten and thanks for the investigation @severo. Unfortunately I'm not able to reproduce the error. I think the error has to do with authentication with `huggingface_hub`, because the exception is thrown from these code lines: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/blob/main/common_voice_8_0.py#L137-L139 ```python from huggingface_hub import HfApi, HfFolder if isinstance(auth_token, bool): email = HfApi().whoami(auth_token) email = HfApi().whoami(auth_token)["email"] ``` Could you please verify the previous code with the `auth_token` you pass to `load_dataset(..., use_auth_token=auth_token,...`?	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https://github.com/huggingface/datasets/issues/4237	Common Voice 8 doesn't show datasets viewer	OK, thanks for digging a bit into it. Indeed, the error occurs with the dataset-viewer, but not with a normal user token, because we use an app token, and it does not have a related email! ```python >>> from huggingface_hub import HfApi, HfFolder >>> auth_token = "hf_app_******" >>> t = HfApi().whoami(auth_token) >>> t {'type': 'app', 'name': 'dataset-preview-backend'} >>> t["email"] Traceback (most recent call last): File "<stdin>", line 1, in <module> KeyError: 'email' ``` Note also that the doc (https://huggingface.co/docs/huggingface_hub/package_reference/hf_api#huggingface_hub.HfApi.whoami) does not state that `whoami` should return an `email` key. @SBrandeis @julien-c: do you think the app token should have an email associated, like the users?	https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0	784	105	Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 OK, thanks for digging a bit into it. Indeed, the error occurs with the dataset-viewer, but not with a normal user token, because we use an app token, and it does not have a related email! ```python >>> from huggingface_hub import HfApi, HfFolder >>> auth_token = "hf_app_******" >>> t = HfApi().whoami(auth_token) >>> t {'type': 'app', 'name': 'dataset-preview-backend'} >>> t["email"] Traceback (most recent call last): File "<stdin>", line 1, in <module> KeyError: 'email' ``` Note also that the doc (https://huggingface.co/docs/huggingface_hub/package_reference/hf_api#huggingface_hub.HfApi.whoami) does not state that `whoami` should return an `email` key. @SBrandeis @julien-c: do you think the app token should have an email associated, like the users?	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https://github.com/huggingface/datasets/issues/4237	Common Voice 8 doesn't show datasets viewer	We can workaround this with ```python email = HfApi().whoami(auth_token).get("email", "[email protected]") ``` in the common voice scripts	https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0	784	16	Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 We can workaround this with ```python email = HfApi().whoami(auth_token).get("email", "[email protected]") ``` in the common voice scripts	[ -1.1277536153793335, -0.7831279039382935, -0.6548466086387634, 1.606382131576538, -0.3687717616558075, -1.2496014833450317, 0.38551342487335205, -0.9539969563484192, 1.6816378831863403, -0.8163290619850159, 0.3934832811355591, -1.6862012147903442, -0.07145816832780838, -0.4579418897628784, -0.7826219797134399, -0.8186318874359131, -0.42392298579216003, -0.5461307168006897, 1.0310114622116089, 2.4984354972839355, 1.265077829360962, 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https://github.com/huggingface/datasets/issues/4237	Common Voice 8 doesn't show datasets viewer	Hmmm, does this mean that any person who downloads the common voice dataset will be logged as "[email protected]"? If so, it would defeat the purpose of sending the user's email to the commonvoice API, right?	https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0	784	35	Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 Hmmm, does this mean that any person who downloads the common voice dataset will be logged as "[email protected]"? If so, it would defeat the purpose of sending the user's email to the commonvoice API, right?	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https://github.com/huggingface/datasets/issues/4237	Common Voice 8 doesn't show datasets viewer	I agree with @severo: we cannot set our system email as default, allowing anybody not authenticated to by-pass the Common Voice usage policy. Additionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead. CC: @patrickvonplaten @lhoestq @SBrandeis @julien-c	https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0	784	61	Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 I agree with @severo: we cannot set our system email as default, allowing anybody not authenticated to by-pass the Common Voice usage policy. Additionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead. 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https://github.com/huggingface/datasets/issues/4237	Common Voice 8 doesn't show datasets viewer	Hmm I don't agree here. Anybody can always just bypass the system by setting whatever email. As soon as someone has access to the downloading script it's trivial to tweak the code to not send the "correct" email but to just whatever and it would work. Note that someone only has visibility on the code after having "signed" the access-mechanism so I think we can expect the users to have agreed to not do anything malicious. I'm fine with both @lhoestq's solution or we find a way that forces the user to be logged in + being able to load the data for the datasets viewer. Wdyt @lhoestq @severo @albertvillanova ?	https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0	784	111	Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 Hmm I don't agree here. Anybody can always just bypass the system by setting whatever email. As soon as someone has access to the downloading script it's trivial to tweak the code to not send the "correct" email but to just whatever and it would work. Note that someone only has visibility on the code after having "signed" the access-mechanism so I think we can expect the users to have agreed to not do anything malicious. I'm fine with both @lhoestq's solution or we find a way that forces the user to be logged in + being able to load the data for the datasets viewer. Wdyt @lhoestq @severo @albertvillanova ?	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https://github.com/huggingface/datasets/issues/4237	Common Voice 8 doesn't show datasets viewer	> Additionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead. Yes, I agree we can forget about this @patrickvonplaten. After having had a look at Common Voice website, I've seen they only require sending an email (no auth is inplace on their side, contrary to what I had previously thought). Therefore, currently we impose stronger requirements than them: we require the user having logged in and accepted the access mechanism. Currently the script as it is already requires the user being logged in: ```python HfApi().whoami(auth_token) ``` throws an exception if None/invalid auth_token is passed. On the other hand, we should agree on the way to allow the viewer to stream the data.	https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0	784	136	Common Voice 8 doesn't show datasets viewer https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 > Additionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead. Yes, I agree we can forget about this @patrickvonplaten. After having had a look at Common Voice website, I've seen they only require sending an email (no auth is inplace on their side, contrary to what I had previously thought). Therefore, currently we impose stronger requirements than them: we require the user having logged in and accepted the access mechanism. Currently the script as it is already requires the user being logged in: ```python HfApi().whoami(auth_token) ``` throws an exception if None/invalid auth_token is passed. On the other hand, we should agree on the way to allow the viewer to stream the data.	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https://github.com/huggingface/datasets/issues/4230	Why the `conll2003` dataset on huggingface only contains the `en` subset? Where is the German data?	Thanks for reporting @beyondguo. Indeed, we generate this dataset from this raw data file URL: https://data.deepai.org/conll2003.zip And that URL only contains the English version.	![image](https://user-images.githubusercontent.com/37113676/165416606-96b5db18-b16c-4b6b-928c-de8620fd943e.png) But on huggingface datasets: ![image](https://user-images.githubusercontent.com/37113676/165416649-8fd77980-ca0d-43f0-935e-f398ba8323a4.png) Where is the German data?	786	24	Why the `conll2003` dataset on huggingface only contains the `en` subset? Where is the German data? ![image](https://user-images.githubusercontent.com/37113676/165416606-96b5db18-b16c-4b6b-928c-de8620fd943e.png) But on huggingface datasets: ![image](https://user-images.githubusercontent.com/37113676/165416649-8fd77980-ca0d-43f0-935e-f398ba8323a4.png) Where is the German data? Thanks for reporting @beyondguo. Indeed, we generate this dataset from this raw data file URL: https://data.deepai.org/conll2003.zip And that URL only contains the English version.	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https://github.com/huggingface/datasets/issues/4230	Why the `conll2003` dataset on huggingface only contains the `en` subset? Where is the German data?	The German data requires payment The [original task page](https://www.clips.uantwerpen.be/conll2003/ner/) states "The German data is a collection of articles from the Frankfurter Rundschau. The named entities have been annotated by people of the University of Antwerp. Only the annotations are available here. In order to build these data sets you need access to the ECI Multilingual Text Corpus. It can be ordered from the Linguistic Data Consortium (2003 non-member price: US$ 35.00)." Inflation since 2003 has also affected LDC's prices, and today the dataset [LDC94T5](https://catalog.ldc.upenn.edu/LDC94T5) is available under license for $75 a copy. The [license](https://catalog.ldc.upenn.edu/license/eci-slash-mci-user-agreement.pdf) includes a non-distribution condition, which is probably why the data has not turned up openly. The ACL hold copyright of this data; I'll mail them and anyone I can find at ECI to see if they'll open this up now. After all, it worked with Microsoft 3DMM, why not here too, after 28 years? :)	![image](https://user-images.githubusercontent.com/37113676/165416606-96b5db18-b16c-4b6b-928c-de8620fd943e.png) But on huggingface datasets: ![image](https://user-images.githubusercontent.com/37113676/165416649-8fd77980-ca0d-43f0-935e-f398ba8323a4.png) Where is the German data?	786	149	Why the `conll2003` dataset on huggingface only contains the `en` subset? Where is the German data? ![image](https://user-images.githubusercontent.com/37113676/165416606-96b5db18-b16c-4b6b-928c-de8620fd943e.png) But on huggingface datasets: ![image](https://user-images.githubusercontent.com/37113676/165416649-8fd77980-ca0d-43f0-935e-f398ba8323a4.png) Where is the German data? The German data requires payment The [original task page](https://www.clips.uantwerpen.be/conll2003/ner/) states "The German data is a collection of articles from the Frankfurter Rundschau. The named entities have been annotated by people of the University of Antwerp. Only the annotations are available here. In order to build these data sets you need access to the ECI Multilingual Text Corpus. It can be ordered from the Linguistic Data Consortium (2003 non-member price: US$ 35.00)." Inflation since 2003 has also affected LDC's prices, and today the dataset [LDC94T5](https://catalog.ldc.upenn.edu/LDC94T5) is available under license for $75 a copy. The [license](https://catalog.ldc.upenn.edu/license/eci-slash-mci-user-agreement.pdf) includes a non-distribution condition, which is probably why the data has not turned up openly. The ACL hold copyright of this data; I'll mail them and anyone I can find at ECI to see if they'll open this up now. 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https://github.com/huggingface/datasets/issues/4221	Dictionary Feature	Hi @jordiae, Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`: ```python "list_of_dict_feature": [ { "key1_in_dict": datasets.Value("string"), "key2_in_dict": datasets.Value("int32"), ... } ], ``` Feel free to re-open this issue if that does not work for your use case.	Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something? Thank you in advance.	787	48	Dictionary Feature Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something? Thank you in advance. Hi @jordiae, Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`: ```python "list_of_dict_feature": [ { "key1_in_dict": datasets.Value("string"), "key2_in_dict": datasets.Value("int32"), ... } ], ``` Feel free to re-open this issue if that does not work for your use case.	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https://github.com/huggingface/datasets/issues/4221	Dictionary Feature	> Hi @jordiae, > > Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`: > > ```python > "list_of_dict_feature": [ > { > "key1_in_dict": datasets.Value("string"), > "key2_in_dict": datasets.Value("int32"), > ... > } > ], > ``` > > Feel free to re-open this issue if that does not work for your use case. Thank you	Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something? Thank you in advance.	787	65	Dictionary Feature Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something? Thank you in advance. > Hi @jordiae, > > Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`: > > ```python > "list_of_dict_feature": [ > { > "key1_in_dict": datasets.Value("string"), > "key2_in_dict": datasets.Value("int32"), > ... > } > ], > ``` > > Feel free to re-open this issue if that does not work for your use case. 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https://github.com/huggingface/datasets/issues/4217	Big_Patent dataset broken	Thanks for reporting. The issue seems not to be directly related to the dataset viewer or the `datasets` library, but instead to it being hosted on Google Drive. See related issues: https://github.com/huggingface/datasets/issues?q=is%3Aissue+is%3Aopen+drive.google.com To quote [@lhoestq](https://github.com/huggingface/datasets/issues/4075#issuecomment-1087362551): > PS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable.	## Dataset viewer issue for 'big_patent' Link: [link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train) Unable to view because it says FileNotFound, also cannot download it through the python API Am I the one who added this dataset ? No	788	62	Big_Patent dataset broken ## Dataset viewer issue for 'big_patent' Link: [link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train) Unable to view because it says FileNotFound, also cannot download it through the python API Am I the one who added this dataset ? No Thanks for reporting. The issue seems not to be directly related to the dataset viewer or the `datasets` library, but instead to it being hosted on Google Drive. See related issues: https://github.com/huggingface/datasets/issues?q=is%3Aissue+is%3Aopen+drive.google.com To quote [@lhoestq](https://github.com/huggingface/datasets/issues/4075#issuecomment-1087362551): > PS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable.	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https://github.com/huggingface/datasets/issues/4217	Big_Patent dataset broken	We should find out if the dataset license allows redistribution and contact the data owners to propose them to host their data on our Hub.	## Dataset viewer issue for 'big_patent' Link: [link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train) Unable to view because it says FileNotFound, also cannot download it through the python API Am I the one who added this dataset ? No	788	25	Big_Patent dataset broken ## Dataset viewer issue for 'big_patent' Link: [link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train) Unable to view because it says FileNotFound, also cannot download it through the python API Am I the one who added this dataset ? No We should find out if the dataset license allows redistribution and contact the data owners to propose them to host their data on our Hub.	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https://github.com/huggingface/datasets/issues/4211	DatasetDict containing Datasets with different features when pushed to hub gets remapped features	Hi @pietrolesci, thanks for reporting. Please note that this is a design purpose: a `DatasetDict` has the same features for all its datasets. Normally, a `DatasetDict` is composed of several sub-datasets each corresponding to a different split. To handle sub-datasets with different features, we use another approach: use different configurations instead of splits. However, for the moment `push_to_hub` does not support specifying different configurations. IMHO, we should implement this.	Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```	789	69	DatasetDict containing Datasets with different features when pushed to hub gets remapped features Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ``` Hi @pietrolesci, thanks for reporting. Please note that this is a design purpose: a `DatasetDict` has the same features for all its datasets. Normally, a `DatasetDict` is composed of several sub-datasets each corresponding to a different split. To handle sub-datasets with different features, we use another approach: use different configurations instead of splits. However, for the moment `push_to_hub` does not support specifying different configurations. IMHO, we should implement this.	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https://github.com/huggingface/datasets/issues/4211	DatasetDict containing Datasets with different features when pushed to hub gets remapped features	Hi @albertvillanova, Thanks a lot for your reply! I got it now. The strange thing for me was to have it correctly working (i.e., DatasetDict with different features in some datasets) locally and not on the Hub. It would be great to have configuration supported by `push_to_hub`. Personally, this latter functionality allowed me to iterate rather quickly on dataset curation. Again, thanks for your time @albertvillanova! Best, Pietro	Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```	789	68	DatasetDict containing Datasets with different features when pushed to hub gets remapped features Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ``` Hi @albertvillanova, Thanks a lot for your reply! I got it now. The strange thing for me was to have it correctly working (i.e., DatasetDict with different features in some datasets) locally and not on the Hub. It would be great to have configuration supported by `push_to_hub`. Personally, this latter functionality allowed me to iterate rather quickly on dataset curation. Again, thanks for your time @albertvillanova! 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https://github.com/huggingface/datasets/issues/4211	DatasetDict containing Datasets with different features when pushed to hub gets remapped features	Hi! Yes, we should override `DatasetDict.__setitem__` and throw an error if features dictionaries are different. `DatasetDict` is a subclass of `dict`, so `DatasetDict.{update/setdefault}` need to be overridden as well. We could avoid this by subclassing `UserDict`, but then we would get the name collision - `DatasetDict.data` vs. `UserDict.data`. This makes me think we should rename the `data` attribute of `DatasetDict`/`Dataset` for easier dict subclassing (would also simplify https://github.com/huggingface/datasets/pull/3997) and to follow good Python practices. Another option is to have a custom `UserDict` class in `py_utils`, but it can be hard to keep this class consistent with the built-in `UserDict`. @albertvillanova @lhoestq wdyt?	Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```	789	102	DatasetDict containing Datasets with different features when pushed to hub gets remapped features Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ``` Hi! Yes, we should override `DatasetDict.__setitem__` and throw an error if features dictionaries are different. `DatasetDict` is a subclass of `dict`, so `DatasetDict.{update/setdefault}` need to be overridden as well. We could avoid this by subclassing `UserDict`, but then we would get the name collision - `DatasetDict.data` vs. `UserDict.data`. This makes me think we should rename the `data` attribute of `DatasetDict`/`Dataset` for easier dict subclassing (would also simplify https://github.com/huggingface/datasets/pull/3997) and to follow good Python practices. Another option is to have a custom `UserDict` class in `py_utils`, but it can be hard to keep this class consistent with the built-in `UserDict`. @albertvillanova @lhoestq wdyt?	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https://github.com/huggingface/datasets/issues/4211	DatasetDict containing Datasets with different features when pushed to hub gets remapped features	I would keep things simple and keep subclassing dict. Regarding the features check, I guess this can be done only for `push_to_hub` right ? It is the only function right now that requires the underlying datasets to be splits (e.g. train/test) and have the same features. Note that later you will be able to push datasets with different features as different dataset configurations (similarly to the [GLUE subsets](https://huggingface.co/datasets/glue) for example). We will work on this soon	Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```	789	76	DatasetDict containing Datasets with different features when pushed to hub gets remapped features Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ``` I would keep things simple and keep subclassing dict. Regarding the features check, I guess this can be done only for `push_to_hub` right ? It is the only function right now that requires the underlying datasets to be splits (e.g. train/test) and have the same features. Note that later you will be able to push datasets with different features as different dataset configurations (similarly to the [GLUE subsets](https://huggingface.co/datasets/glue) for example). 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https://github.com/huggingface/datasets/issues/4211	DatasetDict containing Datasets with different features when pushed to hub gets remapped features	Hi @lhoestq, Returning to this thread to ask whether the possibility to create `DatasetDict` with different configurations will be supported in the future. Best, Pietro	Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```	789	25	DatasetDict containing Datasets with different features when pushed to hub gets remapped features Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ``` Hi @lhoestq, Returning to this thread to ask whether the possibility to create `DatasetDict` with different configurations will be supported in the future. 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https://github.com/huggingface/datasets/issues/4211	DatasetDict containing Datasets with different features when pushed to hub gets remapped features	DatasetDict is likely to always require the datasets to have the same columns and types, while different configurations may have different columns and types. Why would you like to see that ? If it's related to push_to_hub, we plan to allow pushing several configs, but not using DatasetDict	Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```	789	48	DatasetDict containing Datasets with different features when pushed to hub gets remapped features Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ``` DatasetDict is likely to always require the datasets to have the same columns and types, while different configurations may have different columns and types. Why would you like to see that ? 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https://github.com/huggingface/datasets/issues/4211	DatasetDict containing Datasets with different features when pushed to hub gets remapped features	Hi @lhoestq and @pietrolesci, I have been curious about this question as well. I don't have experience working with different configurations, but I can give a bit more detail on the work flow that I have been using with `Dataset_dict`. As @pietrolesci mentions, I have been using `push_to_hub` to quickly iterate on dataset curation for different ML experiments - locally I create a set of dataset splits e.g. `train/val/test/inference`, then convert them to `HF_Datasets` and finally a to `Dataset_Dict` to `push_to_hub`. Where I have run into issues is when I want to include different metadata for different splits. For example, I have situations where I only have meta-data for one of the splits (e.g. test) or situations where I am working with `inference` data that does not have labels. Currently I use a rather hacky work around by adding "dummy" columns for missing columns to avoid the error: ``` ValueError: All datasets in `DatasetDict` should have the same features ``` I am curious why `DatasetDict` will likely not support this functionality? I don't know much about working with different configurations, but allowing for different columns between datasets / splits would be a very helpful use-case for me. Are there any docs for using different configuration OR a more info about incorporating it with `push_to_hub`. Best wishes, Jonathan	Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```	789	217	DatasetDict containing Datasets with different features when pushed to hub gets remapped features Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ``` Hi @lhoestq and @pietrolesci, I have been curious about this question as well. I don't have experience working with different configurations, but I can give a bit more detail on the work flow that I have been using with `Dataset_dict`. As @pietrolesci mentions, I have been using `push_to_hub` to quickly iterate on dataset curation for different ML experiments - locally I create a set of dataset splits e.g. `train/val/test/inference`, then convert them to `HF_Datasets` and finally a to `Dataset_Dict` to `push_to_hub`. Where I have run into issues is when I want to include different metadata for different splits. For example, I have situations where I only have meta-data for one of the splits (e.g. test) or situations where I am working with `inference` data that does not have labels. Currently I use a rather hacky work around by adding "dummy" columns for missing columns to avoid the error: ``` ValueError: All datasets in `DatasetDict` should have the same features ``` I am curious why `DatasetDict` will likely not support this functionality? I don't know much about working with different configurations, but allowing for different columns between datasets / splits would be a very helpful use-case for me. Are there any docs for using different configuration OR a more info about incorporating it with `push_to_hub`. Best wishes, Jonathan	[ -1.2451794147491455, -0.8649232387542725, -0.7281451225280762, 1.553868055343628, -0.22664426267147064, -0.9993091821670532, 0.2305677980184555, -1.1509039402008057, 1.659450888633728, -0.6978124380111694, 0.4650605022907257, -1.6354515552520752, -0.01876036450266838, -0.5618094801902771, -0.7158192992210388, -0.9354511499404907, -0.43358394503593445, -0.647844135761261, 1.0594372749328613, 2.504539966583252, 1.3537321090698242, -1.3717018365859985, 2.678347110748291, 0.7667494416236877, -0.07971303164958954, -0.941303014755249, 0.6392586827278137, 0.026954039931297302, -1.1907784938812256, -0.4691286087036133, -1.0424803495407104, -0.2747510075569153, -0.4182811975479126, -0.5155866146087646, -0.12353280186653137, 0.4750727117061615, -0.41198456287384033, -0.5022237300872803, -0.7168172597885132, -0.8658886551856995, 0.5331714153289795, -0.3562619090080261, 0.9353601336479187, -0.39720025658607483, 1.8053985834121704, -0.5535914897918701, 0.518395721912384, 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https://github.com/huggingface/datasets/issues/4211	DatasetDict containing Datasets with different features when pushed to hub gets remapped features	> I am curious why DatasetDict will likely not support this functionality? There's a possibility we may merge the Dataset and DatasetDict classes. The DatasetDict purpose was to define a way to get the train/test splits of a dataset. see the discussions at https://github.com/huggingface/datasets/issues/5189 > Are there any docs for using different configuration OR a more info about incorporating it with push_to_hub. There's a PR open to allow to upload a dataset with a certain configuration name. Then later you can reload this specific configuration using `load_dataset(ds_name, config_name)` see the PR at https://github.com/huggingface/datasets/pull/5213	Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```	789	93	DatasetDict containing Datasets with different features when pushed to hub gets remapped features Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ``` > I am curious why DatasetDict will likely not support this functionality? There's a possibility we may merge the Dataset and DatasetDict classes. The DatasetDict purpose was to define a way to get the train/test splits of a dataset. see the discussions at https://github.com/huggingface/datasets/issues/5189 > Are there any docs for using different configuration OR a more info about incorporating it with push_to_hub. There's a PR open to allow to upload a dataset with a certain configuration name. 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https://github.com/huggingface/datasets/issues/4211	DatasetDict containing Datasets with different features when pushed to hub gets remapped features	Hi, regarding the following information: > Please note that this is a design purpose: a `DatasetDict` has the same features for all its datasets. Normally, a `DatasetDict` is composed of several sub-datasets each corresponding to a different split. > > To handle sub-datasets with different features, we use another approach: use different configurations instead of splits. Altough this is often implied (such as how else would `DatasetDict` be able to process multiple splits in the same way?), I would expect it to be written somewhere in the docs plainly and maybe even in bold. Also I would expect to see it in multiple places such as: - in docstring of `DatasetDict` - in nlp/image/audio guides on how to create a dataset - [in conceptual guide on how to create a loading script](https://huggingface.co/docs/datasets/main/en/about_dataset_load) I think this addition would benefit the docs, especially when you guide a newbie (such as me) through the process of creating a dataset. As I said, you somehow suspect that this is in fact the case, but without reading it in the docs you cannot be sure.	Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ```	789	180	DatasetDict containing Datasets with different features when pushed to hub gets remapped features Hi there, I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same. Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli). In short: I have 3 feature mapping ```python Tri_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]), } ) Ent_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]), } ) Con_features = Features( { "idx": Value(dtype="int64"), "premise": Value(dtype="string"), "hypothesis": Value(dtype="string"), "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]), } ) ``` Then I create different datasets ```python dataset_splits = {} for split in df["split"].unique(): print(split) df_split = df.loc[df["split"] == split].copy() if split in Tri_dataset: df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2}) ds = Dataset.from_pandas(df_split, features=Tri_features) elif split in Ent_bin_dataset: df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1}) ds = Dataset.from_pandas(df_split, features=Ent_features) elif split in Con_bin_dataset: df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1}) ds = Dataset.from_pandas(df_split, features=Con_features) else: print("ERROR:", split) dataset_splits[split] = ds datasets = DatasetDict(dataset_splits) ``` I then push to hub ```python datasets.push_to_hub("pietrolesci/robust_nli", token="<token>") ``` Finally, I load it from the hub ```python datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli") ``` And I get that ```python datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features ``` since ```python "label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]) ``` gets remapped to ```python "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]) ``` Hi, regarding the following information: > Please note that this is a design purpose: a `DatasetDict` has the same features for all its datasets. Normally, a `DatasetDict` is composed of several sub-datasets each corresponding to a different split. > > To handle sub-datasets with different features, we use another approach: use different configurations instead of splits. Altough this is often implied (such as how else would `DatasetDict` be able to process multiple splits in the same way?), I would expect it to be written somewhere in the docs plainly and maybe even in bold. Also I would expect to see it in multiple places such as: - in docstring of `DatasetDict` - in nlp/image/audio guides on how to create a dataset - [in conceptual guide on how to create a loading script](https://huggingface.co/docs/datasets/main/en/about_dataset_load) I think this addition would benefit the docs, especially when you guide a newbie (such as me) through the process of creating a dataset. As I said, you somehow suspect that this is in fact the case, but without reading it in the docs you cannot be sure.	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