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https://api.github.com/repos/huggingface/datasets/issues/3689 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3689/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3689/comments | https://api.github.com/repos/huggingface/datasets/issues/3689/events | https://github.com/huggingface/datasets/pull/3689 | 1,127,422,478 | PR_kwDODunzps4yPnp7 | 3,689 | Fix streaming for servers not supporting HTTP range requests | {
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~~This PR implements a workaround for those cases, by download the files locally in a temporary directory (cleaned up by the OS once the process is finished).~~
This PR raises custom error explaining that streaming is not possible because data host server does not support HTTP range requests.
Fix #3677. | {
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https://api.github.com/repos/huggingface/datasets/issues/3688 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3688/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3688/comments | https://api.github.com/repos/huggingface/datasets/issues/3688/events | https://github.com/huggingface/datasets/issues/3688 | 1,127,218,321 | I_kwDODunzps5DL_yR | 3,688 | Pyarrow version error | {
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"Hi @Zaker237, thanks for reporting.\r\n\r\nThis is weird: the error you get is only thrown if the installed pyarrow version is less than 3.0.0.\r\n\r\nCould you please check that you install pyarrow in the same Python virtual environment where you installed datasets?\r\n\r\nFrom the Python command line (or terminal) where you get the error, please type:\r\n```\r\nimport pyarrow\r\nprint(pyarrow.__version__)\r\nimport datasets\r\nprint(datasets.__version__)\r\n``` ",
"hi @albertvillanova i try yesterday to create a new python environement with python 7 and try it on the environement and it worked. so i think that the error was not the package but may be jupyter notebook on conda. still yet i'm not yet sure but it worked in an environment created with venv",
"OK, thanks @Zaker237 for your feedback.\r\n\r\nI close this issue then. Please, feel free to reopen it if the problem arises again."
] | "2022-02-08T12:53:59" | "2022-02-09T06:35:33" | "2022-02-09T06:35:32" | NONE | null | ## Describe the bug
I installed datasets(version 1.17.0, 1.18.0, 1.18.3) but i'm right now nor able to import it because of pyarrow. when i try to import it, i get the following error:
`To use datasets, the module pyarrow>=3.0.0 is required, and the current version of pyarrow doesn't match this condition`.
i tryed with all version of pyarrow execpt `4.0.0` but still get the same error.
## Steps to reproduce the bug
```python
import datasets
```
## Expected results
A clear and concise description of the expected results.
## Actual results
AttributeError Traceback (most recent call last)
<ipython-input-19-652e886d387f> in <module>
----> 1 import datasets
~\AppData\Local\Continuum\anaconda3\lib\site-packages\datasets\__init__.py in <module>
26
27
---> 28 if _version.parse(pyarrow.__version__).major < 3:
29 raise ImportWarning(
30 "To use `datasets`, the module `pyarrow>=3.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
AttributeError: 'Version' object has no attribute 'major'
## Environment info
Traceback (most recent call last):
File "c:\users\alex\appdata\local\continuum\anaconda3\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "c:\users\alex\appdata\local\continuum\anaconda3\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\Alex\AppData\Local\Continuum\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 5, in <module>
File "c:\users\alex\appdata\local\continuum\anaconda3\lib\site-packages\datasets\__init__.py", line 28, in <module>
if _version.parse(pyarrow.__version__).major < 3:
AttributeError: 'Version' object has no attribute 'major'
- `datasets` version:
- Platform: Linux(Ubuntu) and Windows: conda on the both
- Python version: 3.7
- PyArrow version: 7.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3687 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3687/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3687/comments | https://api.github.com/repos/huggingface/datasets/issues/3687/events | https://github.com/huggingface/datasets/issues/3687 | 1,127,154,766 | I_kwDODunzps5DLwRO | 3,687 | Can't get the text data when calling to_tf_dataset | {
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"cc @Rocketknight1 ",
"You are correct that `to_tf_dataset` only handles numerical columns right now, yes, though this is a limitation we might remove in future! The main reason we do this is that our models mostly do not include the tokenizer as a model layer, because it's very difficult to compile some of them in TF. So the \"normal\" Huggingface workflow is to first tokenize your dataset, and then pass tokenized tensors to the model.\r\n\r\nFor your use case, would you prefer to pass strings to the model, and use some text processing layers instead of the built-in tokenizers?",
"Also tagging @gante just so he's aware, but I can handle this one!",
"Thanks for the quick follow-up to my issue.\r\n\r\nFor my use-case, instead of the built-in tokenizers I wanted to use the `TextVectorization` layer to map from strings to integers. To achieve this, I came up with the following solution:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import DefaultDataCollator\r\nimport tensorflow as tf\r\nimport string\r\nimport re\r\nfrom tensorflow.keras.layers.experimental.preprocessing import TextVectorization\r\n\r\n#some hyper-parameters for the text-to-integer mapping\r\nmax_features = 20000\r\nembedding_dim = 128\r\nsequence_length = 210\r\n\r\ndata_collator = DefaultDataCollator(return_tensors=\"tf\")\r\ndataset = load_dataset(\"sst\", \"default\")\r\n\r\n#adapt the vectorization layer on train data only\r\nvectorize_layer.adapt(dataset[\"train\"].to_dict(batched=False)[\"sentence\"])\r\n\r\ndef prepare_features(text, label):\r\n text = tf.expand_dims(text, -1)\r\n return {\"vectorized_text\": vectorize_layer(text)[0], \"label\": tf.expand_dims(label, axis=-1)}\r\n\r\nencoded_dataset = dataset.map(lambda example: prepare_features(example[\"sentence\"], example[\"label\"]), batched=False)\r\n\r\n\r\ndef custom_standardization(input_data):\r\n lowercase = tf.strings.lower(input_data)\r\n return tf.strings.regex_replace(\r\n lowercase, f\"[{re.escape(string.punctuation)}]\", \"\"\r\n )\r\n\r\nvectorize_layer = TextVectorization(\r\n standardize=custom_standardization,\r\n max_tokens=max_features,\r\n output_mode=\"int\",\r\n output_sequence_length=sequence_length,\r\n)\r\n\r\ntrain_dataset = encoded_dataset[\"train\"].to_tf_dataset(columns=['vectorized_text'], label_cols=[\"label\"],\r\n shuffle=True, batch_size=1, collate_fn=data_collator).unbatch()\r\n#similar for the other sub-sets\r\n\r\n```\r\n\r\nSince the strings would have been mapped to integers or floats at some point, it's no drawback that this mapping is done early in the process. \r\n\r\nFor the future, however, it'd be more convenient to get the string data, since I am also inspecting the dataset (longest sentence, shortest sentence), which is more challenging when working with integer or float. For now, this can be done by calling `to_dict`.",
"> For the future, however, it'd be more convenient to get the string data, since I am also inspecting the dataset (longest sentence, shortest sentence), which is more challenging when working with integer or float.\r\n\r\nYes, I agree, so let's keep this issue open.",
"Going to close this now - methods like `to_tf_dataset` and `prepare_tf_dataset` now support string data, and have done for a while! If anyone sees this and is encountering issues with string data in those methods, please file a new issue!"
] | "2022-02-08T11:52:10" | "2023-01-19T14:55:18" | "2023-01-19T14:55:18" | NONE | null | I am working with the SST2 dataset, and am using TensorFlow 2.5
I'd like to convert it to a `tf.data.Dataset` by calling the `to_tf_dataset` method.
The following snippet is what I am using to achieve this:
```
from datasets import load_dataset
from transformers import DefaultDataCollator
data_collator = DefaultDataCollator(return_tensors="tf")
dataset = load_dataset("sst")
train_dataset = dataset["train"].to_tf_dataset(columns=['sentence'], label_cols="label", shuffle=True, batch_size=8,collate_fn=data_collator)
```
However, this only gets me the labels; the text--the most important part--is missing:
```
for s in train_dataset.take(1):
print(s) #prints something like: ({}, <tf.Tensor: shape=(8,), ...>)
```
As you can see, it only returns the label part, not the data, as indicated by the empty dictionary, `{}`. So far, I've played with various settings of the method arguments, but to no avail; I do not want to perform any text processing at this time. On my quest to achieve what I want ( a `tf.data.Dataset`), I've consulted these resources:
[https://www.philschmid.de/huggingface-transformers-keras-tf](https://www.philschmid.de/huggingface-transformers-keras-tf)
[https://huggingface.co/docs/datasets/use_dataset.html?highlight=tensorflow](https://huggingface.co/docs/datasets/use_dataset.html?highlight=tensorflow)
I was surprised to not find more extensive examples on how to transform a Hugginface dataset to one compatible with TensorFlow.
If you could point me to where I am going wrong, please do so.
Thanks in advance for your support.
---
Edit: In the [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset), I found the following description:
_In general, only columns that the model can use as input should be included here (numeric data only)._
Does this imply that no textual, i.e., `string` data can be loaded?
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"Thanks for reporting, @SBrandeis! Some additional feature types that don't behave as expected when flattened: `Audio`, `Image` and `TranslationVariableLanguages`"
] | "2022-02-08T11:33:48" | "2022-03-18T17:28:13" | "2022-03-18T17:28:13" | CONTRIBUTOR | null | ## Describe the bug
(`Dataset.flatten`)[https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L1265] fails for columns with feature (`Translation`)[https://github.com/huggingface/datasets/blob/3edbeb0ec6519b79f1119adc251a1a6b379a2c12/src/datasets/features/translation.py#L8]
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("europa_ecdc_tm", "en2fr", split="train[:10]")
print(dataset.features)
# {'translation': Translation(languages=['en', 'fr'], id=None)}
print(dataset[0])
# {'translation': {'en': 'Vaccination against hepatitis C is not yet available.', 'fr': 'Aucune vaccination contre l’hépatite C n’est encore disponible.'}}
dataset.flatten()
```
## Expected results
`dataset.flatten` should flatten the `Translation` column as if it were a dict of `Value("string")`
```python
dataset[0]
# {'translation.en': 'Vaccination against hepatitis C is not yet available.', 'translation.fr': 'Aucune vaccination contre l’hépatite C n’est encore disponible.' }
dataset.features
# {'translation.en': Value("string"), 'translation.fr': Value("string")}
```
## Actual results
```python
In [31]: dset.flatten()
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-31-bb88eb5276ee> in <module>
----> 1 dset.flatten()
[...]\site-packages\datasets\fingerprint.py in wrapper(*args, **kwargs)
411 # Call actual function
412
--> 413 out = func(self, *args, **kwargs)
414
415 # Update fingerprint of in-place transforms + update in-place history of transforms
[...]\site-packages\datasets\arrow_dataset.py in flatten(self, new_fingerprint, max_depth)
1294 break
1295 dataset.info.features = self.features.flatten(max_depth=max_depth)
-> 1296 dataset._data = update_metadata_with_features(dataset._data, dataset.features)
1297 logger.info(f'Flattened dataset from depth {depth} to depth {1 if depth + 1 < max_depth else "unknown"}.')
1298 dataset._fingerprint = new_fingerprint
[...]\site-packages\datasets\arrow_dataset.py in update_metadata_with_features(table, features)
534 def update_metadata_with_features(table: Table, features: Features):
535 """To be used in dataset transforms that modify the features of the dataset, in order to update the features stored in the metadata of its schema."""
--> 536 features = Features({col_name: features[col_name] for col_name in table.column_names})
537 if table.schema.metadata is None or b"huggingface" not in table.schema.metadata:
538 pa_metadata = ArrowWriter._build_metadata(DatasetInfo(features=features))
[...]\site-packages\datasets\arrow_dataset.py in <dictcomp>(.0)
534 def update_metadata_with_features(table: Table, features: Features):
535 """To be used in dataset transforms that modify the features of the dataset, in order to update the features stored in the metadata of its schema."""
--> 536 features = Features({col_name: features[col_name] for col_name in table.column_names})
537 if table.schema.metadata is None or b"huggingface" not in table.schema.metadata:
538 pa_metadata = ArrowWriter._build_metadata(DatasetInfo(features=features))
KeyError: 'translation.en'
```
## Environment info
- `datasets` version: 1.18.3
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3685 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3685/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3685/comments | https://api.github.com/repos/huggingface/datasets/issues/3685/events | https://github.com/huggingface/datasets/pull/3685 | 1,126,240,444 | PR_kwDODunzps4yLw3m | 3,685 | Add support for `Audio` and `Image` feature in `push_to_hub` | {
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} | [] | closed | false | null | [] | null | [] | "2022-02-07T16:47:16" | "2022-02-14T18:14:57" | "2022-02-14T18:04:58" | CONTRIBUTOR | null | Add support for the `Audio` and the `Image` feature in `push_to_hub`.
The idea is to remove local path information and store file content under "bytes" in the Arrow table before the push.
My initial approach (https://github.com/huggingface/datasets/commit/34c652afeff9686b6b8bf4e703c84d2205d670aa) was to use a map transform similar to [`decode_nested_example`](https://github.com/huggingface/datasets/blob/5e0f6068741464f833ff1802e24ecc2064aaea9f/src/datasets/features/features.py#L1023-L1056) while having decoding turned off, but I wasn't satisfied with the code quality, so I ended up using the `temporary_assignment` decorator to override `cast_storage`, which allows me to directly modify the underlying storage (the final op is similar to `Dataset.cast`) and results in a much simpler code.
Additionally, I added the `allow_cast` flag that can disable this behavior in the situations where it's not needed (e.g. the dataset is already in the correct format for the Hub, etc.)
EDIT:
`allow_cast` renamed to `embed_external_files` | {
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] | closed | false | null | [] | null | [] | "2022-02-06T07:56:55" | "2022-09-22T16:20:19" | "2022-09-22T16:20:18" | NONE | null | Fixes #2076. | {
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} | [] | closed | false | null | [] | null | [] | "2022-02-04T17:44:32" | "2022-02-07T21:14:52" | "2022-02-07T21:14:52" | CONTRIBUTOR | null | Hey,
Adding ToLD-Br. Feel free to ask for modifications.
Thanks!! | {
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I'm adding our dataset from our paper published at AACL 2020. Feel free to ask for modifications.
Thanks! | {
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CC: @lvwerra | {
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"For reference:\r\nhttps://github.com/huggingface/transformers/issues/15514\r\nhttps://github.com/huggingface/huggingface_hub/issues/650",
"Hi ! For information one can set the environment variable `HF_ENDPOINT` (default is `https://huggingface.co`) if they want to use a private hub.\r\n\r\nWe may need to coordinate with the other libraries to have a consistent way of changing the hub endpoint",
"Yes, I tested it successfully this morning. Thanks."
] | "2022-02-04T10:49:06" | "2022-02-22T11:08:07" | "2022-02-22T11:08:07" | NONE | null | In the context of a private hub deployment, customers would like to use load_dataset() to load datasets from their hub, not from the public hub. This doesn't seem to be configurable at the moment and it would be nice to add this feature.
The obvious workaround is to clone the repo first and then load it from local storage, but this adds an extra step. It'd be great to have the same experience regardless of where the hub is hosted.
The same issue exists with the transformers library and the CLI. I'm going to create issues there as well, and I'll reference them below. | {
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"Seems like a regression from https://github.com/huggingface/datasets/pull/2843\r\n\r\nOr maybe it's an issue with the hosting. I don't think so, though, because https://www.dropbox.com/s/aox84z90nyyuikz/discovery.zip seems to work as expected\r\n\r\n",
"Hi @severo, thanks for reporting.\r\n\r\nSome servers do not support HTTP range requests, and those are required to stream some file formats (like ZIP in this case).\r\n\r\nLet me try to propose a workaround. "
] | "2022-02-03T15:02:03" | "2022-02-10T16:51:24" | "2022-02-10T16:51:24" | CONTRIBUTOR | null | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset
iterable_dataset = load_dataset("discovery", name="discovery", split="train", streaming=True)
list(iterable_dataset.take(1))
```
## Expected results
The first row of the train split.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 365, in __iter__
for key, example in self._iter():
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 362, in _iter
yield from ex_iterable
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 272, in __iter__
yield from islice(self.ex_iterable, self.n)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 79, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/discovery/542fab7a9ddc1d9726160355f7baa06a1ccc44c40bc8e12c09e9bc743aca43a2/discovery.py", line 333, in _generate_examples
with open(data_file, encoding="utf8") as f:
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 64, in wrapper
return function(*args, use_auth_token=use_auth_token, **kwargs)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py", line 369, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/core.py", line 456, in open
return open_files(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/core.py", line 288, in open_files
fs, fs_token, paths = get_fs_token_paths(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/core.py", line 611, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 253, in filesystem
return cls(**storage_options)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 68, in __call__
obj = super().__call__(*args, **kwargs)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 57, in __init__
self.zip = zipfile.ZipFile(self.fo)
File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/zipfile.py", line 1257, in __init__
self._RealGetContents()
File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/zipfile.py", line 1320, in _RealGetContents
endrec = _EndRecData(fp)
File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/zipfile.py", line 263, in _EndRecData
fpin.seek(0, 2)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 676, in seek
raise ValueError("Cannot seek streaming HTTP file")
ValueError: Cannot seek streaming HTTP file
```
## Environment info
- `datasets` version: 1.18.3
- Platform: Linux-5.11.0-1027-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3676 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3676/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3676/comments | https://api.github.com/repos/huggingface/datasets/issues/3676/events | https://github.com/huggingface/datasets/issues/3676 | 1,123,096,362 | I_kwDODunzps5C8Rcq | 3,676 | `None` replaced by `[]` after first batch in map | {
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"It looks like this is because of this behavior in pyarrow:\r\n```python\r\nimport pyarrow as pa\r\n\r\narr = pa.array([None, [0]])\r\nreconstructed_arr = pa.ListArray.from_arrays(arr.offsets, arr.values)\r\nprint(reconstructed_arr.to_pylist())\r\n# [[], [0]]\r\n```\r\n\r\nIt seems that `arr.offsets` can reconstruct the array properly, but an offsets array with null values can:\r\n```python\r\nfixed_offsets = pa.array([None, 0, 1])\r\nfixed_arr = pa.ListArray.from_arrays(fixed_offsets, arr.values)\r\nprint(fixed_arr.to_pylist())\r\n# [None, [0]]\r\n\r\nprint(arr.offsets.to_pylist())\r\n# [0, 0, 1]\r\nprint(fixed_offsets.to_pylist())\r\n# [None, 0, 1]\r\n```\r\nEDIT: this is because `arr.offsets` is not enough to reconstruct the array, we also need the validity bitmap",
"The offsets don't have nulls because they don't include the validity bitmap from `arr.buffers()[0]`, which is used to say which values are null and which values are non-null.\r\n\r\nThough the validity bitmap also seems to be wrong:\r\n```python\r\nbin(int(arr.buffers()[0].hex(), 16))\r\n# '0b10'\r\n# it should be 0b110 - 1 corresponds to non-null and 0 corresponds to null, if you take the bits in reverse order\r\n```\r\n\r\nSo apparently I can't even create the fixed offsets array using this.\r\n\r\nIf I understand correctly it's always missing the 1 on the left, so I can add it manually as a hack to fix the issue until this is fixed in pyarrow EDIT: actually it may be more complicated than that\r\n\r\nEDIT2: actuall it's right, it corresponds to the validity bitmap of the array of logical length 2. So if we use the offsets array, the values array, and this validity bitmap it should be possible to reconstruct the array properly",
"I created an issue on Apache Arrow's JIRA: https://issues.apache.org/jira/browse/ARROW-15837",
"And another one: https://issues.apache.org/jira/browse/ARROW-15839",
"FYI the behavior is the same with:\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.8.0-50-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.11\r\n- PyArrow version: 6.0.1\r\n\r\n\r\nbut not with:\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-4.18.0-305.40.2.el8_4.x86_64-x86_64-with-redhat-8.4-Ootpa\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n\r\ni.e. it outputs:\r\n```py\r\n0 [None, [0]]\r\n1 [None, [0]]\r\n2 [None, [0]]\r\n3 [None, [0]]\r\n```\r\n",
"Thanks for the insights @PaulLerner !\r\n\r\nI found a way to workaround this issue for the code example presented in this issue.\r\n\r\nNote that empty lists will still appear when you explicitly `cast` a list of lists that contain None values like [None, [0]] to a new feature type (e.g. to change the integer precision). In this case it will show a warning that it happened. If you don't cast anything, then the None values will be kept as expected.\r\n\r\nLet me know what you think !",
"Hi! I feel like I’m missing something in your answer, *what* is the workaround? Is it fixed in some `datasets` version?",
"`pa.ListArray.from_arrays` returns empty lists instead of None values. The workaround I added inside `datasets` simply consists in not using `pa.ListArray.from_arrays` :)\r\n\r\nOnce this PR [here ](https://github.com/huggingface/datasets/pull/4282)is merged, we'll release a new version of `datasets` that currectly returns the None values in the case described in this issue\r\n\r\nEDIT: released :) but let's keep this issue open because it might happen again if users change the integer precision for example"
] | "2022-02-03T13:36:48" | "2022-10-28T13:13:20" | "2022-10-28T13:13:20" | MEMBER | null | Sometimes `None` can be replaced by `[]` when running map:
```python
from datasets import Dataset
ds = Dataset.from_dict({"a": range(4)})
ds = ds.map(lambda x: {"b": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=["a"])
print(ds.to_pandas())
# b
# 0 [None, [0]]
# 1 [[], [0]]
# 2 [[], [0]]
# 3 [[], [0]]
```
This issue has been experienced when running the `run_qa.py` example from `transformers` (see issue https://github.com/huggingface/transformers/issues/15401)
This can be due to a bug in when casting `None` in nested lists. Casting only happens after the first batch, since the first batch is used to infer the feature types.
cc @sgugger | {
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"@mariosasko Can I take this up?",
"This dataset is now available here: https://huggingface.co/datasets/deepmind/code_contests."
] | "2022-02-03T13:20:00" | "2022-07-20T11:07:05" | "2022-07-20T11:07:05" | CONTRIBUTOR | null | ## Adding a Dataset
- **Name:** CodeContests
- **Description:** CodeContests is a competitive programming dataset for machine-learning.
- **Paper:**
- **Data:** https://github.com/deepmind/code_contests
- **Motivation:** This dataset was used when training [AlphaCode](https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode).
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
Paper: https://arxiv.org/abs/2110.08559?context=cs
Github: https://github.com/moussaKam/FrugalScore
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https://api.github.com/repos/huggingface/datasets/issues/3673 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3673/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3673/comments | https://api.github.com/repos/huggingface/datasets/issues/3673/events | https://github.com/huggingface/datasets/issues/3673 | 1,123,010,520 | I_kwDODunzps5C78fY | 3,673 | `load_dataset("snli")` is different from dataset viewer | {
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"Yes, we decided to replace the encoded label with the corresponding label when possible in the dataset viewer. But\r\n1. maybe it's the wrong default\r\n2. we could find a way to show both (with a switch, or showing both ie. `0 (neutral)`).\r\n",
"Hi @severo,\r\n\r\nThanks for clarifying. \r\n\r\nI think this default is a bit counterintuitive for the user. However, this is a personal opinion that might not be general. I think it is nice to have the actual (non-encoded) labels in the viewer. On the other hand, it would be nice to match what the user sees with what they get when they download a dataset. I don't know - I can see the difficulty of choosing a default :)\r\nMaybe having non-encoded labels as a default can be useful?\r\n\r\nAnyway, I think the issue has been addressed. Thanks a lot for your super-quick answer!\r\n\r\n ",
"Thanks for the 👍 in https://github.com/huggingface/datasets/issues/3673#issuecomment-1029008349 @mariosasko @gary149 @pietrolesci, but as I proposed various solutions, it's not clear to me which you prefer. Could you write your preferences as a comment?\r\n\r\n_(note for myself: one idea per comment in the future)_",
"As I am working with seq2seq, I prefer having the label in string form rather than numeric. So the viewer is fine and the underlying dataset should be \"decoded\" (from int to str). In this way, the user does not have to search for a mapping `int -> original name` (even though is trivial to find, I reckon). Also, encoding labels is rather easy.\r\n\r\nI hope this is useful",
"I like the idea of \"0 (neutral)\". The label name can even be greyed to make it clear that it's not part of the actual item in the dataset, it's just the meaning.",
"I like @lhoestq's idea of having grayed-out labels.",
"Proposals by @gary149. Which one do you prefer? Please vote with the thumbs\r\n\r\n- 👍 \r\n\r\n ![image](https://user-images.githubusercontent.com/1676121/152387949-883c7d7e-a9f3-48aa-bff9-11a691555e6e.png)\r\n\r\n- 👎 \r\n\r\n ![image (1)](https://user-images.githubusercontent.com/1676121/152388061-32d95e42-cade-4ae4-9a77-7365e7b72b8f.png)\r\n\r\n",
"I like Option 1 better as it shows clearly what the user is downloading",
"Thanks! ",
"It's [live](https://huggingface.co/datasets/glue/viewer/cola/train):\r\n\r\n<img width=\"1126\" alt=\"Capture d’écran 2022-02-14 à 10 26 03\" src=\"https://user-images.githubusercontent.com/1676121/153836716-25f6205b-96af-42d8-880a-7c09cb24c420.png\">\r\n\r\nThanks all for the help to improve the UI!",
"Love it ! thanks :)"
] | "2022-02-03T12:10:43" | "2022-02-16T11:22:31" | "2022-02-11T17:01:21" | NONE | null | ## Describe the bug
The dataset that is downloaded from the Hub via `load_dataset("snli")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., "neutral", "entailment", "contradiction"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).
Is this expected?
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Ubuntu 20.4
- Python version: 3.7
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https://api.github.com/repos/huggingface/datasets/issues/3672 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3672/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3672/comments | https://api.github.com/repos/huggingface/datasets/issues/3672/events | https://github.com/huggingface/datasets/pull/3672 | 1,122,980,556 | PR_kwDODunzps4yBUrZ | 3,672 | Prioritize `module.builder_kwargs` over defaults in `TestCommand` | {
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```Python
Traceback (most recent call last):
File "create_metadata.py", line 96, in <module>
main(**vars(args))
File "create_metadata.py", line 86, in main
metadata_command.run()
File "/opt/conda/lib/python3.7/site-packages/datasets/commands/test.py", line 144, in run
for j, builder in enumerate(get_builders()):
File "/opt/conda/lib/python3.7/site-packages/datasets/commands/test.py", line 141, in get_builders
name=name, cache_dir=self._cache_dir, data_dir=self._data_dir, **module.builder_kwargs
TypeError: type object got multiple values for keyword argument 'name'
```
Let me know what you think. | {
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https://api.github.com/repos/huggingface/datasets/issues/3670 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3670/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3670/comments | https://api.github.com/repos/huggingface/datasets/issues/3670/events | https://github.com/huggingface/datasets/pull/3670 | 1,122,439,827 | PR_kwDODunzps4x_kBx | 3,670 | feat: 🎸 generate info if dataset_infos.json does not exist | {
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✅ Closes: #3013 | {
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As 'validated' contains significantly more data than 'train' (although it contains both test and validation, so one needs to be careful there), it can be useful to train better models where no strict comparison with the previous work is intended. | {
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"Hi ! I wasn't able to reproduce the error, are you still experiencing this ? I tried calling `cast_column` on a string column containing paths.\r\n\r\nIf you manage to share a reproducible code example that would be perfect",
"Hi,\r\n\r\nI think my team mate got this solved. Clolsing it for now and will reopen if I experience this again.\r\nThanks :) ",
"Hi @R4ZZ3,\r\n\r\nIf it is not too much of a bother, can you please help me how to resolve this error? I am exactly getting the same error where I am going as per the documentation guideline:\r\n\r\n`my_audio_dataset = my_audio_dataset.cast_column(\"audio_paths\", Audio())`\r\n\r\nwhere `\"audio_paths\"` is a dataset column (feature) having strings of absolute paths to mp3 files of the dataset.\r\n\r\n",
"I was having the same issue with this code:\r\n\r\n```\r\ndataset = dataset.map(\r\n lambda batch: {\"full_path\" : os.path.join(self.data_path, batch[\"path\"])},\r\n num_procs = 4\r\n)\r\nmy_audio_dataset = dataset.cast_column(\"full_path\", Audio(sampling_rate=16_000))\r\n```\r\n\r\nRemoving the \"num_procs\" argument fixed it somehow.\r\nUsing a mac with m1 chip",
"Hi @Hubert-Bonisseur, I think this will be fixed by https://github.com/huggingface/datasets/pull/4614"
] | "2022-02-02T18:33:29" | "2022-07-19T13:36:24" | "2022-07-19T13:36:24" | NONE | null | ## Describe the bug
In OVH cloud during Huggingface Robust-speech-recognition event on a AI training notebook instance using jupyter lab and running jupyter notebook When using the dataset.cast_column("audio",Audio(sampling_rate=16_000))
method I get error
![image](https://user-images.githubusercontent.com/25264037/152214027-9c42a71a-dd24-463c-a346-57e0287e5a8f.png)
This was working with datasets version 1.17.1.dev0
but now with version 1.18.3 produces the error above.
## Steps to reproduce the bug
load dataset:
![image](https://user-images.githubusercontent.com/25264037/152216145-159553b6-cddc-4f0b-8607-7e76b600e22a.png)
remove columns:
![image](https://user-images.githubusercontent.com/25264037/152214707-7c7e89d1-87d8-4b4f-8cfc-5d7223d35644.png)
run my fix_path function.
This also creates the audio column that is referring to the absolute file path of the audio
![image](https://user-images.githubusercontent.com/25264037/152214773-51f71ccf-d31b-4449-b63a-1af56436e49f.png)
Then I concatenate few other datasets and finally try the cast_column method
![image](https://user-images.githubusercontent.com/25264037/152215032-f341ec86-9d6d-48c9-943b-e2efe37a4d98.png)
but get error:
![image](https://user-images.githubusercontent.com/25264037/152215073-b85bd057-98e8-413c-9b05-51e9805f2c24.png)
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform:
OVH Cloud, AI Training section, container for Huggingface Robust Speech Recognition event image(baaastijn/ovh_huggingface)
![image](https://user-images.githubusercontent.com/25264037/152215161-b4ff7bfb-2736-4afb-9223-761a3338d23c.png)
- Python version: 3.8.8
- PyArrow version:
![image](https://user-images.githubusercontent.com/25264037/152215936-4d365760-557e-456b-b5eb-ad1d15cf5073.png)
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![opus](https://user-images.githubusercontent.com/16348744/152177816-2df6076c-f28b-4aef-a08d-b499b921414d.png)
(moreover, I didn't manage to load .opus files with `soundfile` / `librosa` locally on any my machine anyway for some reason, even with `ffmpeg` installed).
For now my current changes work with locally stored file:
```python
# download sample opus file (from MultilingualSpokenWords dataset)
!wget https://huggingface.co/datasets/polinaeterna/test_opus/resolve/main/common_voice_tt_17737010.opus
from datasets import Dataset, Audio
audio_path = "common_voice_tt_17737010.opus"
dataset = Dataset.from_dict({"audio": [audio_path]}).cast_column("audio", Audio(48000))
dataset[0]
# {'audio': {'path': 'common_voice_tt_17737010.opus',
# 'array': array([ 0.0000000e+00, 0.0000000e+00, 3.0517578e-05, ...,
# -6.1035156e-05, 6.1035156e-05, 0.0000000e+00], dtype=float32),
# 'sampling_rate': 48000}}
```
But it doesn't work when loading inside s dataset from bytes (I checked on [MultilingualSpokenWords](https://github.com/huggingface/datasets/pull/3666), the PR is a draft now, maybe the bug is somewhere there )
```python
import torchaudio
with open(audio_path, "rb") as b:
print(torchaudio.load(b))
# RuntimeError: Error loading audio file: failed to open file <in memory buffer>
``` | {
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} | [] | closed | false | null | [] | null | [] | "2022-02-02T15:21:48" | "2022-02-22T10:04:03" | "2022-02-22T10:03:53" | CONTRIBUTOR | null | Opus files requires `libsndfile>=1.0.30`. Add check for this version and tests.
**outdated:**
Add [Multillingual Spoken Words dataset](https://mlcommons.org/en/multilingual-spoken-words/)
You can specify multiple languages for downloading 😌:
```python
ds = load_dataset("datasets/ml_spoken_words", languages=["ar", "tt"])
```
1. I didn't take into account that each time you pass a set of languages the data for a specific language is downloaded even if it was downloaded before (since these are custom configs like `ar+tt` and `ar+tt+br`. Maybe that wasn't a good idea?
2. The script will have to be slightly changed after merge of https://github.com/huggingface/datasets/pull/3664
2. Just can't figure out what wrong with dummy files... 😞 Maybe we should get rid of them at some point 😁 | {
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Fix https://github.com/huggingface/datasets/issues/3662 | {
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This is a proposed way of returning the old local file-based generator while keeping the new streaming generator intact.
TODO:
- [ ] brainstorm a bit more on https://github.com/huggingface/datasets/issues/3663 to see if we can do better
- [ ] refactor the heck out of this PR to avoid completely copying the logic between the two generators | {
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"Having talked to @lhoestq, I see that this feature is no longer supported. \r\n\r\nI really don't think this was a good idea. It is a major breaking change and one for which we don't even have a working solution at the moment, which is bad for PyTorch as we don't want to force people to have `datasets` decode audio files automatically, but **really** bad for Tensorflow and Flax where we **currently cannot** even use `datasets` to load `.mp3` files - e.g. `common_voice` doesn't work anymore in a TF training script. Note this worked perfectly fine before making the change (think it was done [here](https://github.com/huggingface/datasets/pull/3290) no?)\r\n\r\nIMO, it's really important to think about a solution here and I strongly favor to make a difference here between loading a dataset in streaming mode and in non-streaming mode, so that in non-streaming mode the actual downloaded file is displayed. It's really crucial for people to be able to analyse the original files IMO when the dataset is not downloaded in streaming mode. \r\n\r\nThere are the following reasons why it is paramount to have access to the **original** audio file in my opinion (in non-streaming mode):\r\n- There are a wide variety of different libraries to load audio data with varying support on different platforms. For me it was quite clear that there is simply to single good library to load audio files for all platforms - so we have to leave the option to the user to decide which loading to use.\r\n- We had support for audio datasets a long time before streaming audio was possible. There were quite some versions where we advertised **everywhere** to load the audio from the path name (and there are many places where we still do even though it's not possible anymore). To give some examples:\r\n - Official example of TF Wav2Vec2: https://github.com/huggingface/transformers/blob/f427e750490b486944cc9be3c99834ad5cf78b57/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py#L1423 Wav2Vec2 is as important for speech as BERT is for NLP - so it's **very** important. The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment. Same goes for Flax.\r\n - The most downloaded non-nlp checkpoint: https://huggingface.co/facebook/wav2vec2-base-960h#usage has a usage example which doesn't work anymore with the current datasets implementation. I'll update this now, but we have >1000 wav2vec2 checkpoints on the Hub and we can't update all the model cards.\r\n => This is a big breaking change with no current solution. For `transformers` breaking changes are one of the biggest complaints.\r\n- Similar to this we also shouldn't assume that there is only one resampling method for Audio. I think it's good to have one offered automatically by `datasets`, but we have to leave the user the freedom to choose her/his own resampling as well. Resampling can take very different filtering windows and other parameters which are currently somewhat hardcoded in `datasets`, which users might very well want to change.\r\n\r\n\r\n=> IMO, it's a **very** big priority to again have the correct absolute path in non-streaming mode. The other solution of providing a path-like object derived from the bytes stocked in the `.array` file is not nearly as user-friendly, but better than nothing. ",
"Agree that we need to have access to the original sound files. Few days ago I was looking for these original files because I suspected there is bug in the audio resampling (confirmed in https://github.com/huggingface/datasets/issues/3662) and I want to do my own resampling to workaround the bug, which is now not possible anymore due to the unavailability of the original files.",
"@patrickvonplaten \r\n> The other solution of providing a path-like object derived from the bytes stocked in the .array file is not nearly as user-friendly, but better than nothing\r\n\r\nJust to clarify, here you describe the approach that uses the `Audio.decode` attribute to access the underlying bytes?\r\n\r\n> The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment\r\n\r\nI'd assume this is because we use `sox_io` as a backend for decoding. However, soon we should be able to use `soundfile`, which supports path-like objects, for MP3 (https://github.com/huggingface/datasets/pull/3667#issuecomment-1030090627).\r\n\r\nYour concern is reasonable, but there are situations where we can only serve bytes (see https://github.com/huggingface/datasets/pull/3685 for instance). IMO it makes sense to fix the affected datasets for now, but I don't think we should care too much whether we rely on local paths or bytes after soundfile adds support for MP3 as long as our examples work (shouldn't be too hard to update the `map_to_array` functions) and we properly document how to access the underlying path/bytes for custom decoding (via `ds.cast_column(\"audio\", Audio(decode=False))`).\r\n",
"Related to this discussion: in https://github.com/huggingface/datasets/pull/3664#issuecomment-1031866858 I propose how we could change `iter_archive` to work for streaming and also return local paths (as it used too !). I'd love your opinions on this",
"> @patrickvonplaten\r\n> \r\n> > The other solution of providing a path-like object derived from the bytes stocked in the .array file is not nearly as user-friendly, but better than nothing\r\n> \r\n> Just to clarify, here you describe the approach that uses the `Audio.decode` attribute to access the underlying bytes?\r\n\r\nYes! \r\n\r\n> \r\n> > The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment\r\n> \r\n> I'd assume this is because we use `sox_io` as a backend for decoding. However, soon we should be able to use `soundfile`, which supports path-like objects, for MP3 ([#3667 (comment)](https://github.com/huggingface/datasets/pull/3667#issuecomment-1030090627)). \r\n> Your concern is reasonable, but there are situations where we can only serve bytes (see #3685 for instance). IMO it makes sense to fix the affected datasets for now, but I don't think we should care too much whether we rely on local paths or bytes after soundfile adds support for MP3 as long as our examples work (shouldn't be too hard to update the `map_to_array` functions) and we properly document how to access the underlying path/bytes for custom decoding (via `ds.cast_column(\"audio\", Audio(decode=False))`).\r\n\r\nYes this might be, but I highly doubt that `soundfile` is the go-to library for audio then. @anton-l and I have tried out a bunch of different audio loading libraries (`soundfile`, `librosa`, `torchaudio`, pure `ffmpeg`, `audioread`, ...). One thing that was pretty clear to me is that there is just no \"de-facto standard\" library and they all have pros and cons. None of the libraries really supports \"batch\"-ed audio loading. Some depend on PyTorch. `torchaudio` is 100x faster (really!) than `librosa's` fallback on MP3. `torchaudio` often has problems with multi-proessing, ... Also we should keep in mind that resampling is similarly not as simple as reading a text file. It's a pretty complex signal processing transform and people very well might want to use special filters, etc...at the moment we just hard-code `torchaudio's` or `librosa's` default filter when doing resampling.\r\n\r\n=> All this to say that we **should definitely** care about whether we rely on local paths or bytes IMO. We don't want to loose all users that are forced to use `datasets` decoding or resampling or have to built a very much not intuitive way of loading bytes into a numpy array. It's much more intuitive to be able to inspect a local file. I feel pretty strongly about this and am happy to also jump on a call. Keeping libraries flexible and lean as well as exposing internals is very important IMO (this philosophy has worked quite well so far with Transformers).\r\n\r\n",
"Thanks a lot for the very detailed explanation. Now everything makes much more sense.",
"From https://github.com/huggingface/datasets/pull/3736 the Common Voice dataset now gives access to the local audio files as before",
"I understand the argument that it is bad to have a breaking change. How to deal with the introduction of breaking changes is a topic of its own and not sure how you want to deal with that (or is the policy this is never allowed, and there must be a `load_dataset_v2` or so if you really want to introduce a breaking change?).\r\n\r\nRegardless of whether it is a breaking change, however, I don't see the other arguments.\r\n\r\n> but **really** bad for Tensorflow and Flax where we **currently cannot** even use `datasets` to load `.mp3` files\r\n\r\nI don't exactly understand this. Why not?\r\n\r\nWhy does the HF dataset on-the-fly decoding mechanism not work? Why is it anyway specific to PyTorch or TensorFlow? Isn't this independent?\r\n\r\nBut even if you just provide the raw bytes to TF, on TF you could just use sth like `tfio.audio.decode_mp3` or `tf.audio.decode_ogg` or `tfio.audio.decode_flac`?\r\n\r\n> There are the following reasons why it is paramount to have access to the original audio file in my opinion ...\r\n\r\nI don't really understand the arguments (despite that it maybe breaks existing code). You anyway have the original audio files but it is just embedded in the dataset? I don't really know about any library which cannot also load the audio from memory (i.e. from the dataset).\r\n\r\nBtw, on librosa being slow for decoding audio files, I saw that as well, so we have this comment RETURNN:\r\n\r\n> Don't use librosa.load which internally uses audioread which would use Gstreamer as a backend which has multiple issues:\r\n> https://github.com/beetbox/audioread/issues/62\r\n> https://github.com/beetbox/audioread/issues/63\r\n> Instead, use PySoundFile (soundfile), which is also faster. See here for discussions:\r\n> https://github.com/beetbox/audioread/issues/64\r\n> https://github.com/librosa/librosa/issues/681\r\n\r\nResampling is also a separate aspect, which is also less straightforward and with different compromises between speed and quality. So there the different tradeoffs and different implementations can make a difference.\r\n\r\nHowever, I don't see how this is related to the question whether there should be the raw bytes inside the dataset or as separate local files.\r\n",
"Thanks for your comments here @albertz - cool to get your input! \r\n\r\nAnswering a bit here between the lines:\r\n\r\n> I understand the argument that it is bad to have a breaking change. How to deal with the introduction of breaking changes is a topic of its own and not sure how you want to deal with that (or is the policy this is never allowed, and there must be a `load_dataset_v2` or so if you really want to introduce a breaking change?).\r\n> \r\n> Regardless of whether it is a breaking change, however, I don't see the other arguments.\r\n> \r\n> > but **really** bad for Tensorflow and Flax where we **currently cannot** even use `datasets` to load `.mp3` files\r\n> \r\n> I don't exactly understand this. Why not?\r\n\r\n> Why does the HF dataset on-the-fly decoding mechanism not work? Why is it anyway specific to PyTorch or TensorFlow? Isn't this independent?\r\n\r\nThe problem with decoding on the fly is that we currently rely on `torchaudio` for this now which relies on `torch` which is not necessarily something people would like to install when using `tensorflow` or `flax`. Therefore we cannot just rely on people using the decoding on the fly method. We just didn't find a library that is ML framework independent and fast enough for all formats. `torchaudio` is currently in our opinion by far the best here.\r\n\r\nSo for TF and Flax it's important that users can load audio files or bytes they way the want to - this might become less important if we find (or make) a good library with few dependencies that is fast for all kinds of platforms / use cases.\r\n\r\n\r\nNow the question is whether it's better to store audio data as a path to a file or as raw bytes I guess.\\\r\nMy main arguments for storing the audio data as a path to a file is pretty much all about users experience - I don't really expect our users to understand the inner workings of datasets:\r\n\r\n- 1. It's not straightforward to know which function to use to decode it - not all `load_audio(...)` or `read_audio(...)` work on raw bytes. E.g. Looking at https://pytorch.org/audio/stable/torchaudio.html?highlight=load#torchaudio.load one would not see directly how to load raw bytes . There are also some functions of other libraries which only work on files which would require the user to save the bytes as a file first before being able to load it.\r\n- 2. It's difficult to see which format the bytes are coming from (mp3, ogg, ...) - guess this could be remedied by adding the format to each sample though\r\n- 3. It is a bit scary IMO to see raw bytes for users. Overall, I think it's better to leave the data in it's raw form as this way it's much easier for people to play around with the audio files, less need to read docs because people don't worry about what happened to the audio files (are the bytes already resampled?)\r\n\r\nBut the argument that the audio should be loadable directly from memory is good - haven't thought about this too much. \r\nI guess it's still very much possible for the user to do this:\r\n\r\n```python\r\ndef save_as_bytes:\r\n batch[\"bytes\"] = read_in_bytes_from_file(batch[\"file\"])\\\r\n os.remove(batch[\"file\"])\r\n\r\nds = ds.map(save_as_bytes)\r\n\r\nds.save_to_disk(...)\r\n```\r\n\r\nGuess the question is more a bit about what should be the default case?",
"> The problem with decoding on the fly is that we currently rely on `torchaudio` for this now which relies on `torch` which is not necessarily something people would like to install when using `tensorflow` or `flax`. Therefore we cannot just rely on people using the decoding on the fly method. We just didn't find a library that is ML framework independent and fast enough for all formats. `torchaudio` is currently in our opinion by far the best here.\r\n\r\nBut how is this relevant for this issue here? I thought this issue here is about having the (correct) path in the dataset or having raw bytes in the dataset.\r\n\r\nHow did TF users use it at all then? Or they just do not use on-the-fly decoding? I did not even notice this problem (maybe because I had `torchaudio` installed). But what do they use instead?\r\n\r\nBut as I outlined before, they could just use `tfio.audio.decode_flac` and co, where it would be more natural if you already provide the raw bytes.\r\n\r\n> Looking at https://pytorch.org/audio/stable/torchaudio.html?highlight=load#torchaudio.load one would not see directly how to load raw bytes\r\n\r\nI was not really familiar with `torchaudio`. It seems that they really don't provide an easy/direct API to operate on raw bytes. Which is very strange and unfortunate because as far as I can see, all the underlying backend libraries (e.g. soundfile) easily allow that. So I would say that this is the fault of `torchaudio` then. But despite, if you anyway use `torchaudio` with `soundfile` backend, why not just use `soundfile` directly. It's very simple to use and crossplatform.\r\n\r\nBut ok, now we are just discussing how to handle the on-the-fly decoding. I still think this is a separate issue and having raw bytes in the dataset instead of local files should just be fine as well.\r\n\r\n\r\n> It is a bit scary IMO to see raw bytes for users.\r\n\r\nI think nobody who writes code is scared by seeing the raw bytes content of a binary file. :)\r\n\r\n\r\n> I guess it's still very much possible for the user to do this:\r\n> \r\n> ```python\r\n> def save_as_bytes:\r\n> batch[\"bytes\"] = read_in_bytes_from_file(batch[\"file\"])\\\r\n> os.remove(batch[\"file\"])\r\n> \r\n> ds = ds.map(save_as_bytes)\r\n> \r\n> ds.save_to_disk(...)\r\n> ```\r\n\r\nIn https://github.com/huggingface/datasets/pull/4184#issuecomment-1105191639, you said/proposed that this `map` is not needed anymore and `save_to_disk` could do it automatically (maybe via some option)?\r\n\r\n> Guess the question is more a bit about what should be the default case?\r\n\r\nYea this is up to you. I'm happy as long as we can get it the way we want easily and this is a well supported use case. :)\r\n",
"> In https://github.com/huggingface/datasets/pull/4184#issuecomment-1105191639, you said/proposed that this map is not needed anymore and save_to_disk could do it automatically (maybe via some option)?\r\n\r\nYes! Should be super easy now see discussion here: https://github.com/rwth-i6/i6_core/issues/257#issuecomment-1105494468\r\n\r\nThanks for the super useful input :-)",
"Despite the comments that this has been fixed, I am finding the exact same problem is occurring again (with datasets version 2.3.2)",
"> Despite the comments that this has been fixed, I am finding the exact same problem is occurring again (with datasets version 2.3.2)\r\n\r\nIt appears downgrading to torchaudio 0.11.0 fixed this problem.",
"@DCNemesis, sorry which problem exactly is occuring again? Also cc @lhoestq @polinaeterna here",
"@patrickvonplaten @lhoestq @polinaeterna I was unable to load audio from Common Voice using 🤗 with the current version of torchaudio, but downgrading to torchaudio 0.11.0 fixed it. This is probably more of a torch problem than a Hugging Face problem.",
"@DCNemesis that's interesting, could you please share the error message if you still can access it? ",
"@polinaeterna I believe it is the same exact error as above. It occurs on other .mp3 sources as well, but the problem is with torchaudio > 0.11.0. I've created a short colab notebook that reproduces the error, and the fix here: https://colab.research.google.com/drive/18wsuwdHwBPN3JkcnhEtk8MUYqF9swuWZ?usp=sharing",
"Hi @DCNemesis,\r\n\r\nYour issue was slightly different from the original one in this issue page. Yours seems related to a change in the backend used by `torchaudio` (`ffmpeg` instead of `sox`). Refer to the issue page here:\r\n- #4776\r\n\r\nNormally, it should be circumvented with the patch made by @polinaeterna in:\r\n- #4923",
"I think the original issue reported here was already fixed by:\r\n- #3736\r\n\r\nOtherwise, feel free to reopen."
] | "2022-02-01T18:40:10" | "2022-09-21T15:03:09" | "2022-09-21T14:56:22" | MEMBER | null | ## Describe the bug
## Steps to reproduce the bug
```python
from datasets import load_dataset
from torchaudio import load
ds = load_dataset("common_voice", "ab", split="train")
# both of the following commands fail at the moment
load(ds[0]["audio"]["path"])
load(ds[0]["path"])
```
## Expected results
The path should be the complete absolute path to the downloaded audio file not some relative path.
## Actual results
```bash
~/hugging_face/venv_3.9/lib/python3.9/site-packages/torchaudio/backend/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)
150 filepath, frame_offset, num_frames, normalize, channels_first, format)
151 filepath = os.fspath(filepath)
--> 152 return torch.ops.torchaudio.sox_io_load_audio_file(
153 filepath, frame_offset, num_frames, normalize, channels_first, format)
154
RuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11/ab/clips/common_voice_ab_19904194.mp3
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3.dev0
- Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27
- Python version: 3.9.1
- PyArrow version: 3.0.0
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"Thanks @lhoestq for finding the reason of incorrect resampling. This issue affects all languages which have sound files with different sampling rates such as Turkish and Luganda.",
"@cahya-wirawan - do you know how many languages have different sampling rates in Common Voice? I'm quite surprised to see this for multiple languages actually",
"@cahya-wirawan, I can reproduce the problem for Common Voice 7 for Turkish. Here a script you can use:\r\n\r\n\r\n```python\r\n#!/usr/bin/env python3\r\nfrom datasets import load_dataset\r\nimport torchaudio\r\nfrom io import BytesIO\r\nfrom datasets import Audio\r\nfrom collections import Counter\r\nimport sys\r\n\r\nds_name = str(sys.argv[1])\r\nlang = str(sys.argv[2])\r\n\r\nds = load_dataset(ds_name, lang, split=\"train\", use_auth_token=True)\r\nds = ds.cast_column(\"audio\", Audio(decode=False))\r\n\r\nall_sampling_rates = []\r\n\r\n\r\ndef print_sampling_rate(x):\r\n x, sr = torchaudio.load(BytesIO(x[\"audio\"][\"bytes\"]), format=\"mp3\")\r\n all_sampling_rates.append(sr)\r\n\r\nds.map(print_sampling_rate)\r\n\r\n\r\nprint(Counter(all_sampling_rates))\r\n```\r\n\r\ncan be run with:\r\n\r\n```bash\r\npython run.py mozilla-foundation/common_voice_7_0 tr\r\n```\r\n\r\nFor CV 6.1 all samples seem to have the same audio",
"It actually shows that many more samples are in 32kHz format than it 48kHz which is unexpected. Thanks a lot for flagging! Will contact Common Voice about this as well",
"I only checked the CV 7.0 for Turkish, Luganda and Indonesian, they have audio files with difference sampling rates, and all of them are affected by this issue. Percentage of incorrect resampling as follow, Turkish: 9.1%, Luganda: 88.2% and Indonesian: 64.1%.\r\nI checked it using the original CV files. I check the original sampling rates and the length of audio array of each files and compare it with the length of audio array (and the sampling rate which is always 48kHz) from mozilla-foundation/common_voice_7_0 datasets. if the length of audio array from dataset is not equal to 48kHz/original sampling rate * length of audio array of the original audio file then it is affected,",
"Ok wow, thanks a lot for checking this - you've found a pretty big bug :sweat_smile: It seems like **a lot** more datasets are actually affected than I original thought. We'll try to solve this as soon as possible and make an announcement tomorrow."
] | "2022-02-01T17:55:04" | "2022-02-02T10:52:25" | "2022-02-02T10:52:25" | MEMBER | null | The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect.
Here is a code to reproduce the issue:
Let's first consider two audio files with different sampling rates 32000 and 16000:
```python
# first download a mp3 file with sampling_rate=32000
!wget https://file-examples-com.github.io/uploads/2017/11/file_example_MP3_700KB.mp3
import torchaudio
audio_path = "file_example_MP3_700KB.mp3"
audio_path2 = audio_path.replace(".mp3", "_resampled.mp3")
resample = torchaudio.transforms.Resample(32000, 16000) # create a new file with sampling_rate=16000
torchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000)
```
Then we can see an issue here when decoding:
```python
from datasets import Dataset, Audio
dataset = Dataset.from_dict({"audio": [audio_path, audio_path2]}).cast_column("audio", Audio(48000))
dataset[0] # decode the first audio file sets the resampler orig_freq to 32000
print(dataset .features["audio"]._resampler.orig_freq)
# 32000
print(dataset[0]["audio"]["array"].shape) # here decoding is fine
# (1308096,)
dataset = Dataset.from_dict({"audio": [audio_path, audio_path2]}).cast_column("audio", Audio(48000))
dataset[1] # decode the second audio file sets the resampler orig_freq to 16000
print(dataset .features["audio"]._resampler.orig_freq)
# 16000
print(dataset[0]["audio"]["array"].shape) # here decoding uses orig_freq=16000 instead of 32000
# (2616192,)
```
The value of `orig_freq` doesn't change no matter what file needs to be decoded
cc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova
The issue seems to be here in `Audio.decode_mp3`:
https://github.com/huggingface/datasets/blob/4c417d52def6e20359ca16c6723e0a2855e5c3fd/src/datasets/features/audio.py#L176-L180 | {
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https://api.github.com/repos/huggingface/datasets/issues/3661 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3661/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3661/comments | https://api.github.com/repos/huggingface/datasets/issues/3661/events | https://github.com/huggingface/datasets/pull/3661 | 1,121,000,251 | PR_kwDODunzps4x61ad | 3,661 | Remove unnecessary 'r' arg in | {
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https://api.github.com/repos/huggingface/datasets/issues/3659 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3659/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3659/comments | https://api.github.com/repos/huggingface/datasets/issues/3659/events | https://github.com/huggingface/datasets/issues/3659 | 1,120,913,672 | I_kwDODunzps5Cz8kI | 3,659 | push_to_hub but preview not working | {
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"Hi @thomas-happify, please note that the preview may take some time before rendering the data.\r\n\r\nI've seen it is already working.\r\n\r\nI close this issue. Please feel free to reopen it if the problem arises again."
] | "2022-02-01T16:23:57" | "2022-02-09T08:00:37" | "2022-02-09T08:00:37" | NONE | null | ## Dataset viewer issue for '*happifyhealth/twitter_pnn*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/happifyhealth/twitter_pnn)*
I used
```
dataset.push_to_hub("happifyhealth/twitter_pnn")
```
but the preview is not working.
Am I the one who added this dataset ? Yes
| {
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https://api.github.com/repos/huggingface/datasets/issues/3657 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3657/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3657/comments | https://api.github.com/repos/huggingface/datasets/issues/3657/events | https://github.com/huggingface/datasets/pull/3657 | 1,120,602,620 | PR_kwDODunzps4x5f1I | 3,657 | Extend dataset builder for streaming in `get_dataset_split_names` | {
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} | [] | closed | false | null | [] | null | [] | "2022-02-01T12:21:24" | "2022-02-03T22:49:06" | "2022-02-02T11:22:01" | CONTRIBUTOR | null | Currently, `get_dataset_split_names` doesn't extend a builder module to support streaming, even though it uses `StreamingDownloadManager` to download data. This PR fixes that.
To test the change, run the following:
```bash
pip install git+https://github.com/huggingface/datasets.git@fix-get_dataset_split_names-streaming
python -c "from datasets import get_dataset_split_names; print(get_dataset_split_names('facebook/multilingual_librispeech', 'german', download_mode='force_redownload', revision='137923f945552c6afdd8b60e4a7b43e3088972c1'))"
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3656 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3656/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3656/comments | https://api.github.com/repos/huggingface/datasets/issues/3656/events | https://github.com/huggingface/datasets/issues/3656 | 1,120,510,823 | I_kwDODunzps5CyaNn | 3,656 | checksum error subjqa dataset | {
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"Hi @RensDimmendaal, \r\n\r\nI'm sorry but I can't reproduce your bug:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: ds = load_dataset(\"subjqa\", \"electronics\")\r\nDownloading builder script: 9.15kB [00:00, 4.10MB/s] \r\nDownloading metadata: 17.7kB [00:00, 8.51MB/s] \r\nDownloading and preparing dataset subjqa/electronics (download: 10.86 MiB, generated: 3.01 MiB, post-processed: Unknown size, total: 13.86 MiB) to .../.cache/huggingface/datasets/subjqa/electronics/1.1.0/e5588f9298ff2d70686a00cc377e4bdccf4e32287459e3c6baf2dc5ab57fe7fd...\r\nDownloading data: 11.4MB [00:03, 3.50MB/s]\r\nDataset subjqa downloaded and prepared to .../.cache/huggingface/datasets/subjqa/electronics/1.1.0/e5588f9298ff2d70686a00cc377e4bdccf4e32287459e3c6baf2dc5ab57fe7fd. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 605.09it/s]\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['domain', 'nn_mod', 'nn_asp', 'query_mod', 'query_asp', 'q_reviews_id', 'question_subj_level', 'ques_subj_score', 'is_ques_subjective', 'review_id', 'id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 1295\r\n })\r\n test: Dataset({\r\n features: ['domain', 'nn_mod', 'nn_asp', 'query_mod', 'query_asp', 'q_reviews_id', 'question_subj_level', 'ques_subj_score', 'is_ques_subjective', 'review_id', 'id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 358\r\n })\r\n validation: Dataset({\r\n features: ['domain', 'nn_mod', 'nn_asp', 'query_mod', 'query_asp', 'q_reviews_id', 'question_subj_level', 'ques_subj_score', 'is_ques_subjective', 'review_id', 'id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 255\r\n })\r\n})\r\n```\r\n\r\nCould you please try again and see if the problem persists?\r\n\r\nIf that is the case, you can circumvent the issue by passing `ignore_verifications`:\r\n```python\r\nds = load_dataset(\"subjqa\", \"electronics\", ignore_verifications=True)",
"Thanks checking!\r\n\r\nYou're totally right. I don't know what's changed, but I'm glad it's working now!\r\n\r\n"
] | "2022-02-01T10:53:33" | "2022-02-10T10:56:59" | "2022-02-10T10:56:38" | NONE | null | ## Describe the bug
I get a checksum error when loading the `subjqa` dataset (used in the transformers book).
## Steps to reproduce the bug
```python
from datasets import load_dataset
subjqa = load_dataset("subjqa","electronics")
```
## Expected results
Loading the dataset
## Actual results
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-2-d2857d460155> in <module>()
2 from datasets import load_dataset
3
----> 4 subjqa = load_dataset("subjqa","electronics")
3 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/lewtun/SubjQA/archive/refs/heads/master.zip']
```
## Environment info
Google colab
- `datasets` version: 1.18.2
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
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"Hi @abhi-mosaic, thanks for reporting.\r\n\r\nI'm looking at it... ",
"also hitting this issue",
"Hey @albertvillanova, sorry to reopen this... I can confirm that on `master` branch the dataset is downloadable now but it is still broken in streaming mode:\r\n\r\n```python\r\n >>> import datasets\r\n >>> pubmed_train = datasets.load_dataset('pubmed', split='train', streaming=True)\r\n >>> next(iter(pubmed_train))\r\n```\r\n```\r\n No such file or directory: 'gzip://pubmed22n0001.xml::ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed22n0001.xml.gz'\r\n```\r\n",
"Hi @abhi-mosaic, would you mind opening another issue for this new problem?\r\n\r\nFirst issue (already solved) was a ConnectionError due to the yearly update release of PubMed: we fixed it by updating the URLs from year 2021 to year 2022.\r\n\r\nHowever this is another problem: to make pubmed streamable. Please note that NOT all our datastes are streamable: we are making streamable more and more of them... but this is an on-going process...\r\n\r\nThanks.",
"@albertvillanova \r\nWhen I tried below codes, I got the similar error\r\n\r\n```\r\n\r\ndataset=load_dataset(\"pubmed\",split=\"train\")\r\n\r\nCouldn't reach ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0601.xml.gz\r\n```",
"@y-rok you need to update `datasets`:\r\n```shell\r\npip install -U datasets\r\n```"
] | "2022-01-31T18:45:47" | "2022-12-19T19:18:10" | "2022-02-14T14:15:41" | CONTRIBUTOR | null | ## Describe the bug
Trying to use the `pubmed` dataset fails to reach / download the source files.
## Steps to reproduce the bug
```python
pubmed_train = datasets.load_dataset('pubmed', split='train')
```
## Expected results
Should begin downloading the pubmed dataset.
## Actual results
```
ConnectionError: Couldn't reach ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0865.xml.gz (InvalidSchema("No connection adapters were found for 'ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0865.xml.gz'"))
```
## Environment info
- `datasets` version: 1.18.2
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.2
- PyArrow version: 6.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3654 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3654/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3654/comments | https://api.github.com/repos/huggingface/datasets/issues/3654/events | https://github.com/huggingface/datasets/pull/3654 | 1,119,717,475 | PR_kwDODunzps4x2kiX | 3,654 | Better TQDM output | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-31T17:22:43" | "2022-02-03T15:55:34" | "2022-02-03T15:55:33" | CONTRIBUTOR | null | This PR does the following:
* if `dataset_infos.json` exists for a dataset, uses `num_examples` to print the total number of examples that needs to be generated (in `builder.py`)
* fixes `tqdm` + multiprocessing in Jupyter Notebook/Colab (the issue stems from this commit in the `tqdm` repo: https://github.com/tqdm/tqdm/commit/f7722edecc3010cb35cc1c923ac4850a76336f82)
* adds the missing `drop_last_batch` and `with_ranks` params to `DatasetDict.map`
* correctly computes the number of iterations in `map` and the CSV/JSON loader when `batched=True` to fix `tqdm` progress bars
* removes the `bool(logging.get_verbosity() == logging.NOTSET)` (or simplifies `bool(logging.get_verbosity() == logging.NOTSET) or not utils.is_progress_bar_enabled()` to `not utils.is_progress_bar_enabled()`) condition and uses `utils.is_progress_bar_enabled` to check if `tqdm` output is enabled (this comment from @stas00 explains why the `bool(logging.get_verbosity() == logging.NOTSET)` check is problematic: https://github.com/huggingface/transformers/issues/14889#issue-1087318463)
Fix #2630 | {
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https://api.github.com/repos/huggingface/datasets/issues/3652 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3652/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3652/comments | https://api.github.com/repos/huggingface/datasets/issues/3652/events | https://github.com/huggingface/datasets/pull/3652 | 1,118,808,738 | PR_kwDODunzps4xzinr | 3,652 | sp. Columbia => Colombia | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-31T00:41:03" | "2022-02-09T16:55:25" | "2022-01-31T08:29:07" | CONTRIBUTOR | null | "Columbia" is various places in North America. The country is "Colombia". | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-30T16:28:54" | "2022-01-31T14:50:48" | "2022-01-31T08:38:09" | CONTRIBUTOR | null | Fixes #3580 and makes the wiki_bio dataset work again. I changed the link and some documentation, and all the tests pass. Thanks @lhoestq for uploading the dataset to the HuggingFace data bucket.
@lhoestq -- all the tests pass, but I'm still not able to import the dataset, as the old Google Drive link is cached somewhere:
```python
>>> from datasets import load_dataset
load_dataset("wiki_bio>>> load_dataset("wiki_bio")
Using custom data configuration default
Downloading and preparing dataset wiki_bio/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to /home/jxm3/.cache/huggingface/datasets/wiki_bio/default/1.1.0/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...
Traceback (most recent call last):
...
File "/home/jxm3/random/datasets/src/datasets/utils/file_utils.py", line 612, in get_from_cache
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://drive.google.com/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil
```
what do I have to do to invalidate the cache and actually import the dataset? It's clearly set up correctly, since the data is downloaded and processed by the tests.
As an aside, this caching-loading-scripts behavior makes for a really bad developer experience. I just wasted an hour trying to figure out where the caching was happening and how to disable it, and I don't know. All I wanted to do was update the link and submit a pull request! I recommend that you all either change this behavior (i.e. updating the link to a dataset should "just work") or document it, since I couldn't find any information about this in the contributing.md or readme or anywhere else! Thanks! | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-28T13:06:29" | "2022-01-28T15:35:58" | "2022-01-28T15:35:58" | CONTRIBUTOR | null | My initial idea was to avoid the `flatten_indices` call and reorder a new column instead, but in the end I decided to follow `concatenate_datasets` and use `flatten_indices` to avoid padding when `dataset._indices.num_rows != dataset._data.num_rows`.
Fix #3599 | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-27T17:21:02" | "2022-01-28T16:34:29" | "2022-01-28T16:34:28" | MEMBER | null | Streaming datasets that use `StreamingDownloadManager.iter_archive` and `StreamingDownloadManager.iter_files` had some issues. Indeed if you try to iterate over such dataset twice, then the second time it will be empty.
This is because the two methods above are generator functions. I fixed this by making them return iterables that are reset properly instead.
Close https://github.com/huggingface/datasets/issues/3645
cc @anton-l | {
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] | null | [] | "2022-01-27T17:17:41" | "2022-01-28T16:34:28" | "2022-01-28T16:34:28" | MEMBER | null | Hi ! When iterating over a streaming dataset once, it's not reset correctly because of some issues with `dl_manager.iter_archive` and `dl_manager.iter_files`. Indeed they are generator functions (so the iterator that is returned can be exhausted). They should be iterables instead, and be reset if we do a for loop again:
```python
from datasets import load_dataset
d = load_dataset("common_voice", "ab", split="test", streaming=True)
i = 0
for i, _ in enumerate(d):
pass
print(i) # 8
# let's do it again
i = 0
for i, _ in enumerate(d):
pass
print(i) # 0
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3643 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3643/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3643/comments | https://api.github.com/repos/huggingface/datasets/issues/3643/events | https://github.com/huggingface/datasets/pull/3643 | 1,116,417,428 | PR_kwDODunzps4xr8mX | 3,643 | Fix sem_eval_2018_task_1 download location | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-27T15:45:00" | "2022-02-04T15:15:26" | "2022-02-04T15:15:26" | CONTRIBUTOR | null | As discussed with @lhoestq in https://github.com/huggingface/datasets/issues/3549#issuecomment-1020176931_ this is the new pull request to fix the download location. | {
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https://api.github.com/repos/huggingface/datasets/issues/3642 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3642/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3642/comments | https://api.github.com/repos/huggingface/datasets/issues/3642/events | https://github.com/huggingface/datasets/pull/3642 | 1,116,306,986 | PR_kwDODunzps4xrj2S | 3,642 | Fix dataset slicing with negative bounds when indices mapping is not `None` | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-27T14:45:53" | "2022-01-27T18:16:23" | "2022-01-27T18:16:22" | CONTRIBUTOR | null | Fix #3611 | {
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https://api.github.com/repos/huggingface/datasets/issues/3641 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3641/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3641/comments | https://api.github.com/repos/huggingface/datasets/issues/3641/events | https://github.com/huggingface/datasets/pull/3641 | 1,116,284,268 | PR_kwDODunzps4xre7C | 3,641 | Fix numpy rngs when seed is None | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-27T14:29:09" | "2022-01-27T18:16:08" | "2022-01-27T18:16:07" | CONTRIBUTOR | null | Fixes the NumPy RNG when `seed` is `None`.
The problem becomes obvious after reading the NumPy notes on RNG (returned by `np.random.get_state()`):
> The MT19937 state vector consists of a 624-element array of 32-bit unsigned integers plus a single integer value between 0 and 624 that indexes the current position within the main array.
`The MT19937 state vector`: the seed which we currently index, but this value stays the same for multiple rounds.
`plus a single integer value`: the `pos` value in this PR (is 624 if `seed` is set to a fixed value with `np.random.seed`, so we take the first value in the `seed` array returned by `np.random.get_state()`: https://stackoverflow.com/questions/32172054/how-can-i-retrieve-the-current-seed-of-numpys-random-number-generator)
NumPy notes: https://numpy.org/doc/stable/reference/random/bit_generators/mt19937.html
Fix #3634 | {
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https://api.github.com/repos/huggingface/datasets/issues/3640 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3640/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3640/comments | https://api.github.com/repos/huggingface/datasets/issues/3640/events | https://github.com/huggingface/datasets/issues/3640 | 1,116,133,769 | I_kwDODunzps5ChtmJ | 3,640 | Issues with custom dataset in Wav2Vec2 | {
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"Closed and moved to transformers."
] | "2022-01-27T12:09:05" | "2022-01-27T12:29:48" | "2022-01-27T12:29:48" | NONE | null | We are training Vav2Vec using the run_speech_recognition_ctc_bnb.py-script.
This is working fine with Common Voice, however using our custom dataset and data loader at [NbAiLab/NPSC]( https://huggingface.co/datasets/NbAiLab/NPSC) it crashes after roughly 1 epoch with the following stack trace:
![image](https://user-images.githubusercontent.com/9079808/151355893-6d5887cc-ca19-4b12-948a-124eb6dac372.png)
We are able to work around the issue, for instance by adding this check in line#222 in transformers/models/wav2vec2/modeling_wav2vec2.py:
```python
if input_length - (mask_length - 1) < num_masked_span:
num_masked_span = input_length - (mask_length - 1)
```
Interestingly, these are the variable values before the adjustment:
```
input_length=10
mask_length=10
num_masked_span=2
````
After adjusting num_masked_spin to 1, the training script runs. The issue is also fixed by setting “replace=True” in the same function.
Do you have any idea what is causing this, and how to fix this error permanently? If you do not think this is an Datasets issue, feel free to move the issue.
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"Hi @Dhanachandra, \r\n\r\nWe have tests for all our metrics and they work as expected: under the hood, we use scikit-learn implementations.\r\n\r\nMaybe the cause is somewhere else. For example:\r\n- Is it a binary or a multiclass or a multilabel classification? Default computation of these metrics is for binary classification; if you would like multiclass or multilabel, you should pass the corresponding parameters; see their documentation (e.g.: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) or code below:\r\n\r\nhttps://huggingface.co/docs/datasets/using_metrics.html#computing-the-metric-scores\r\n\r\n```python\r\nIn [1]: from datasets import load_metric\r\n\r\nIn [2]: precision = load_metric(\"precision\")\r\n\r\nIn [3]: print(precision.inputs_description)\r\n\r\nArgs:\r\n predictions: Predicted labels, as returned by a model.\r\n references: Ground truth labels.\r\n labels: The set of labels to include when average != 'binary', and\r\n their order if average is None. Labels present in the data can\r\n be excluded, for example to calculate a multiclass average ignoring\r\n a majority negative class, while labels not present in the data will\r\n result in 0 components in a macro average. For multilabel targets,\r\n labels are column indices. By default, all labels in y_true and\r\n y_pred are used in sorted order.\r\n average: This parameter is required for multiclass/multilabel targets.\r\n If None, the scores for each class are returned. Otherwise, this\r\n determines the type of averaging performed on the data:\r\n binary: Only report results for the class specified by pos_label.\r\n This is applicable only if targets (y_{true,pred}) are binary.\r\n micro: Calculate metrics globally by counting the total true positives,\r\n false negatives and false positives.\r\n macro: Calculate metrics for each label, and find their unweighted mean.\r\n This does not take label imbalance into account.\r\n weighted: Calculate metrics for each label, and find their average\r\n weighted by support (the number of true instances for each label).\r\n This alters ‘macro’ to account for label imbalance; it can result\r\n in an F-score that is not between precision and recall.\r\n samples: Calculate metrics for each instance, and find their average\r\n (only meaningful for multilabel classification).\r\n sample_weight: Sample weights.\r\n\r\nReturns:\r\n precision: Precision score.\r\n\r\nExamples:\r\n\r\n >>> precision_metric = datasets.load_metric(\"precision\")\r\n >>> results = precision_metric.compute(references=[0, 1], predictions=[0, 1])\r\n >>> print(results)\r\n {'precision': 1.0}\r\n\r\n >>> predictions = [0, 2, 1, 0, 0, 1]\r\n >>> references = [0, 1, 2, 0, 1, 2]\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average='macro')\r\n >>> print(results)\r\n {'precision': 0.2222222222222222}\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average='micro')\r\n >>> print(results)\r\n {'precision': 0.3333333333333333}\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average='weighted')\r\n >>> print(results)\r\n {'precision': 0.2222222222222222}\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\r\n >>> print(results)\r\n {'precision': array([0.66666667, 0. , 0. ])}\r\n```\r\n"
] | "2022-01-27T10:14:16" | "2022-02-24T09:02:18" | "2022-02-24T09:02:17" | NONE | null | **1st Epoch:**
1/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.59it/s]
01/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow
01/27/2022 09:30:49 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow
PRECISION: {'precision': 0.7612903225806451}
RECALL: {'recall': 0.7612903225806451}
F1: {'f1': 0.7612903225806451}
{'eval_loss': 1.4658324718475342, 'eval_accuracy': 0.7612903118133545, 'eval_runtime': 30.0054, 'eval_samples_per_second': 46.492, 'eval_steps_per_second': 46.492, 'epoch': 3.0}
**4th Epoch:**
1/27/2022 09:56:55 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.92it/s]
01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow
01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow
PRECISION: {'precision': 0.7698924731182796}
RECALL: {'recall': 0.7698924731182796}
F1: {'f1': 0.7698924731182796}
## Environment info
!git clone https://github.com/huggingface/transformers
%cd transformers
!pip install .
!pip install -r /content/transformers/examples/pytorch/token-classification/requirements.txt
!pip install datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/3637 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3637/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3637/comments | https://api.github.com/repos/huggingface/datasets/issues/3637/events | https://github.com/huggingface/datasets/issues/3637 | 1,115,526,438 | I_kwDODunzps5CfZUm | 3,637 | [TypeError: Couldn't cast array of type] Cannot load dataset in v1.18 | {
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"Hi @lewtun!\r\n \r\nThis one was tricky to debug. Initially, I tought there is a bug in the recently-added (by @lhoestq ) `cast_array_to_feature` function because `git bisect` points to the https://github.com/huggingface/datasets/commit/6ca96c707502e0689f9b58d94f46d871fa5a3c9c commit. Then, I noticed that the feature tpye of the `dialogue` field is `list`, which explains why you didn't get an error in earlier versions. Is there a specific reason why you use `list` instead of `Sequence` in the script? Maybe to avoid turning list of dicts to dicts of lists as it's done by `Sequence` for compatibility with TFDS or for performance reasons? If the field was `Sequence`, you would get an error in `encode_nested_example` because **the scripts yields some additional (nested) columns which are not specified in the `features` dictionary**. Previously, these additional columns would've been ignored by PyArrow (1), but now we have a check for them (2).\r\n(1) See PyArrow behavior:\r\n```\r\n>>> pa.array([{\"a\": 2, \"b\": 3}], type=pa.struct({\"a\": pa.int32()})) # pyarrow ignores the extra column\r\n-- is_valid: all not null\r\n-- child 0 type: int32\r\n [\r\n 2\r\n ]\r\n ```\r\n\r\n(2) Check:\r\nhttps://github.com/huggingface/datasets/blob/4c417d52def6e20359ca16c6723e0a2855e5c3fd/src/datasets/table.py#L1059\r\n\r\nThe fix is very simple: just add the missing columns to the _EMPTY_BELIEF_STATE list:\r\n```python\r\n_EMPTY_BELIEF_STATE.extend(['通用-产品类别', '火车-舱位档次', '通用-系列', '通用-价格区间', '通用-品牌'])\r\n```",
"Hey @mariosasko, thank you so much for figuring this one out - it certainly looks like a tricky bug 😱 ! I don't think there's a specific reason to use `list` instead of `Sequence` with the script, but I'll let the dataset creators know to see if your suggestion is acceptable.\r\n\r\nThank you again!",
"Thanks, this was indeed the fix! Would it make sense to produce a more informative error message in such cases? \r\n\r\nThe issue can be closed. \r\n\r\n"
] | "2022-01-26T21:38:02" | "2022-02-09T16:15:53" | "2022-02-09T16:15:53" | MEMBER | null | ## Describe the bug
I am trying to load the [`GEM/RiSAWOZ` dataset](https://huggingface.co/datasets/GEM/RiSAWOZ) in `datasets` v1.18.1 and am running into a type error when casting the features. The strange thing is that I can load the dataset with v1.17.0. Note that the error is also present if I install from `master` too.
As far as I can tell, the dataset loading script is correct and the problematic features [here](https://huggingface.co/datasets/GEM/RiSAWOZ/blob/main/RiSAWOZ.py#L237) also look fine to me.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dset = load_dataset("GEM/RiSAWOZ")
```
## Expected results
I can load the dataset without error.
## Actual results
<details><summary>Traceback</summary>
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator)
1083 example = self.info.features.encode_example(record)
-> 1084 writer.write(example, key)
1085 finally:
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write(self, example, key, writer_batch_size)
445
--> 446 self.write_examples_on_file()
447
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_examples_on_file(self)
403 batch_examples[col] = [row[0][col] for row in self.current_examples]
--> 404 self.write_batch(batch_examples=batch_examples)
405 self.current_examples = []
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
496 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
--> 497 arrays.append(pa.array(typed_sequence))
498 inferred_features[col] = typed_sequence.get_inferred_type()
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array()
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type)
204 # We only do it if trying_type is False - since this is what the user asks for.
--> 205 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
206 return out
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
919 else:
--> 920 return func(array, *args, **kwargs)
921
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1064 if isinstance(feature, list):
-> 1065 return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))
1066 elif isinstance(feature, Sequence):
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
919 else:
--> 920 return func(array, *args, **kwargs)
921
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0)
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
919 else:
--> 920 return func(array, *args, **kwargs)
921
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0)
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
919 else:
--> 920 return func(array, *args, **kwargs)
921
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1086 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
-> 1087 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
1088
TypeError: Couldn't cast array of type
struct<医院-3.0T MRI: string, 医院-CT: string, 医院-DSA: string, 医院-公交线路: string, 医院-区域: string, 医院-名称: string, 医院-地址: string, 医院-地铁可达: string, 医院-地铁线路: string, 医院-性质: string, 医院-挂号时间: string, 医院-电话: string, 医院-等级: string, 医院-类别: string, 医院-重点科室: string, 医院-门诊时间: string, 天气-城市: string, 天气-天气: string, 天气-日期: string, 天气-温度: string, 天气-紫外线强度: string, 天气-风力风向: string, 旅游景点-区域: string, 旅游景点-名称: string, 旅游景点-地址: string, 旅游景点-开放时间: string, 旅游景点-是否地铁直达: string, 旅游景点-景点类型: string, 旅游景点-最适合人群: string, 旅游景点-消费: string, 旅游景点-特点: string, 旅游景点-电话号码: string, 旅游景点-评分: string, 旅游景点-门票价格: string, 汽车-价格(万元): string, 汽车-倒车影像: string, 汽车-动力水平: string, 汽车-厂商: string, 汽车-发动机排量(L): string, 汽车-发动机马力(Ps): string, 汽车-名称: string, 汽车-定速巡航: string, 汽车-巡航系统: string, 汽车-座位数: string, 汽车-座椅加热: string, 汽车-座椅通风: string, 汽车-所属价格区间: string, 汽车-油耗水平: string, 汽车-环保标准: string, 汽车-级别: string, 汽车-综合油耗(L/100km): string, 汽车-能源类型: string, 汽车-车型: string, 汽车-车系: string, 汽车-车身尺寸(mm): string, 汽车-驱动方式: string, 汽车-驾驶辅助影像: string, 火车-出发地: string, 火车-出发时间: string, 火车-到达时间: string, 火车-坐席: string, 火车-日期: string, 火车-时长: string, 火车-目的地: string, 火车-票价: string, 火车-舱位档次: string, 火车-车型: string, 火车-车次信息: string, 电影-主演: string, 电影-主演名单: string, 电影-具体上映时间: string, 电影-制片国家/地区: string, 电影-导演: string, 电影-年代: string, 电影-片名: string, 电影-片长: string, 电影-类型: string, 电影-豆瓣评分: string, 电脑-CPU: string, 电脑-CPU型号: string, 电脑-产品类别: string, 电脑-价格: string, 电脑-价格区间: string, 电脑-内存容量: string, 电脑-分类: string, 电脑-品牌: string, 电脑-商品名称: string, 电脑-屏幕尺寸: string, 电脑-待机时长: string, 电脑-显卡型号: string, 电脑-显卡类别: string, 电脑-游戏性能: string, 电脑-特性: string, 电脑-硬盘容量: string, 电脑-系列: string, 电脑-系统: string, 电脑-色系: string, 电脑-裸机重量: string, 电视剧-主演: string, 电视剧-主演名单: string, 电视剧-制片国家/地区: string, 电视剧-单集片长: string, 电视剧-导演: string, 电视剧-年代: string, 电视剧-片名: string, 电视剧-类型: string, 电视剧-豆瓣评分: string, 电视剧-集数: string, 电视剧-首播时间: string, 辅导班-上课方式: string, 辅导班-上课时间: string, 辅导班-下课时间: string, 辅导班-价格: string, 辅导班-区域: string, 辅导班-年级: string, 辅导班-开始日期: string, 辅导班-教室地点: string, 辅导班-教师: string, 辅导班-教师网址: string, 辅导班-时段: string, 辅导班-校区: string, 辅导班-每周: string, 辅导班-班号: string, 辅导班-科目: string, 辅导班-结束日期: string, 辅导班-课时: string, 辅导班-课次: string, 辅导班-课程网址: string, 辅导班-难度: string, 通用-产品类别: string, 通用-价格区间: string, 通用-品牌: string, 通用-系列: string, 酒店-价位: string, 酒店-停车场: string, 酒店-区域: string, 酒店-名称: string, 酒店-地址: string, 酒店-房型: string, 酒店-房费: string, 酒店-星级: string, 酒店-电话号码: string, 酒店-评分: string, 酒店-酒店类型: string, 飞机-准点率: string, 飞机-出发地: string, 飞机-到达时间: string, 飞机-日期: string, 飞机-目的地: string, 飞机-票价: string, 飞机-航班信息: string, 飞机-舱位档次: string, 飞机-起飞时间: string, 餐厅-人均消费: string, 餐厅-价位: string, 餐厅-区域: string, 餐厅-名称: string, 餐厅-地址: string, 餐厅-推荐菜: string, 餐厅-是否地铁直达: string, 餐厅-电话号码: string, 餐厅-菜系: string, 餐厅-营业时间: string, 餐厅-评分: string>
to
{'旅游景点-名称': Value(dtype='string', id=None), '旅游景点-区域': Value(dtype='string', id=None), '旅游景点-景点类型': Value(dtype='string', id=None), '旅游景点-最适合人群': Value(dtype='string', id=None), '旅游景点-消费': Value(dtype='string', id=None), '旅游景点-是否地铁直达': Value(dtype='string', id=None), '旅游景点-门票价格': Value(dtype='string', id=None), '旅游景点-电话号码': Value(dtype='string', id=None), '旅游景点-地址': Value(dtype='string', id=None), '旅游景点-评分': Value(dtype='string', id=None), '旅游景点-开放时间': Value(dtype='string', id=None), '旅游景点-特点': Value(dtype='string', id=None), '餐厅-名称': Value(dtype='string', id=None), '餐厅-区域': Value(dtype='string', id=None), '餐厅-菜系': Value(dtype='string', id=None), '餐厅-价位': Value(dtype='string', id=None), '餐厅-是否地铁直达': Value(dtype='string', id=None), '餐厅-人均消费': Value(dtype='string', id=None), '餐厅-地址': Value(dtype='string', id=None), '餐厅-电话号码': Value(dtype='string', id=None), '餐厅-评分': Value(dtype='string', id=None), '餐厅-营业时间': Value(dtype='string', id=None), '餐厅-推荐菜': Value(dtype='string', id=None), '酒店-名称': Value(dtype='string', id=None), '酒店-区域': Value(dtype='string', id=None), '酒店-星级': Value(dtype='string', id=None), '酒店-价位': Value(dtype='string', id=None), '酒店-酒店类型': Value(dtype='string', id=None), '酒店-房型': Value(dtype='string', id=None), '酒店-停车场': Value(dtype='string', id=None), '酒店-房费': Value(dtype='string', id=None), '酒店-地址': Value(dtype='string', id=None), '酒店-电话号码': Value(dtype='string', id=None), '酒店-评分': Value(dtype='string', id=None), '电脑-品牌': Value(dtype='string', id=None), '电脑-产品类别': Value(dtype='string', id=None), '电脑-分类': Value(dtype='string', id=None), '电脑-内存容量': Value(dtype='string', id=None), '电脑-屏幕尺寸': Value(dtype='string', id=None), '电脑-CPU': Value(dtype='string', id=None), '电脑-价格区间': Value(dtype='string', id=None), '电脑-系列': Value(dtype='string', id=None), '电脑-商品名称': Value(dtype='string', id=None), '电脑-系统': Value(dtype='string', id=None), '电脑-游戏性能': Value(dtype='string', id=None), '电脑-CPU型号': Value(dtype='string', id=None), '电脑-裸机重量': Value(dtype='string', id=None), '电脑-显卡类别': Value(dtype='string', id=None), '电脑-显卡型号': Value(dtype='string', id=None), '电脑-特性': Value(dtype='string', id=None), '电脑-色系': Value(dtype='string', id=None), '电脑-待机时长': Value(dtype='string', id=None), '电脑-硬盘容量': Value(dtype='string', id=None), '电脑-价格': Value(dtype='string', id=None), '火车-出发地': Value(dtype='string', id=None), '火车-目的地': Value(dtype='string', id=None), '火车-日期': Value(dtype='string', id=None), '火车-车型': Value(dtype='string', id=None), '火车-坐席': Value(dtype='string', id=None), '火车-车次信息': Value(dtype='string', id=None), '火车-时长': Value(dtype='string', id=None), '火车-出发时间': Value(dtype='string', id=None), '火车-到达时间': Value(dtype='string', id=None), '火车-票价': Value(dtype='string', id=None), '飞机-出发地': Value(dtype='string', id=None), '飞机-目的地': Value(dtype='string', id=None), '飞机-日期': Value(dtype='string', id=None), '飞机-舱位档次': Value(dtype='string', id=None), '飞机-航班信息': Value(dtype='string', id=None), '飞机-起飞时间': Value(dtype='string', id=None), '飞机-到达时间': Value(dtype='string', id=None), '飞机-票价': Value(dtype='string', id=None), '飞机-准点率': Value(dtype='string', id=None), '天气-城市': Value(dtype='string', id=None), '天气-日期': Value(dtype='string', id=None), '天气-天气': Value(dtype='string', id=None), '天气-温度': Value(dtype='string', id=None), '天气-风力风向': Value(dtype='string', id=None), '天气-紫外线强度': Value(dtype='string', id=None), '电影-制片国家/地区': Value(dtype='string', id=None), '电影-类型': Value(dtype='string', id=None), '电影-年代': Value(dtype='string', id=None), '电影-主演': Value(dtype='string', id=None), '电影-导演': Value(dtype='string', id=None), '电影-片名': Value(dtype='string', id=None), '电影-主演名单': Value(dtype='string', id=None), '电影-具体上映时间': Value(dtype='string', id=None), '电影-片长': Value(dtype='string', id=None), '电影-豆瓣评分': Value(dtype='string', id=None), '电视剧-制片国家/地区': Value(dtype='string', id=None), '电视剧-类型': Value(dtype='string', id=None), '电视剧-年代': Value(dtype='string', id=None), '电视剧-主演': Value(dtype='string', id=None), '电视剧-导演': Value(dtype='string', id=None), '电视剧-片名': Value(dtype='string', id=None), '电视剧-主演名单': Value(dtype='string', id=None), '电视剧-首播时间': Value(dtype='string', id=None), '电视剧-集数': Value(dtype='string', id=None), '电视剧-单集片长': Value(dtype='string', id=None), '电视剧-豆瓣评分': Value(dtype='string', id=None), '辅导班-班号': Value(dtype='string', id=None), '辅导班-难度': Value(dtype='string', id=None), '辅导班-科目': Value(dtype='string', id=None), '辅导班-年级': Value(dtype='string', id=None), '辅导班-区域': Value(dtype='string', id=None), '辅导班-校区': Value(dtype='string', id=None), '辅导班-上课方式': Value(dtype='string', id=None), '辅导班-开始日期': Value(dtype='string', id=None), '辅导班-结束日期': Value(dtype='string', id=None), '辅导班-每周': Value(dtype='string', id=None), '辅导班-上课时间': Value(dtype='string', id=None), '辅导班-下课时间': Value(dtype='string', id=None), '辅导班-时段': Value(dtype='string', id=None), '辅导班-课次': Value(dtype='string', id=None), '辅导班-课时': Value(dtype='string', id=None), '辅导班-教室地点': Value(dtype='string', id=None), '辅导班-教师': Value(dtype='string', id=None), '辅导班-价格': Value(dtype='string', id=None), '辅导班-课程网址': Value(dtype='string', id=None), '辅导班-教师网址': Value(dtype='string', id=None), '汽车-名称': Value(dtype='string', id=None), '汽车-车型': Value(dtype='string', id=None), '汽车-级别': Value(dtype='string', id=None), '汽车-座位数': Value(dtype='string', id=None), '汽车-车身尺寸(mm)': Value(dtype='string', id=None), '汽车-厂商': Value(dtype='string', id=None), '汽车-能源类型': Value(dtype='string', id=None), '汽车-发动机排量(L)': Value(dtype='string', id=None), '汽车-发动机马力(Ps)': Value(dtype='string', id=None), '汽车-驱动方式': Value(dtype='string', id=None), '汽车-综合油耗(L/100km)': Value(dtype='string', id=None), '汽车-环保标准': Value(dtype='string', id=None), '汽车-驾驶辅助影像': Value(dtype='string', id=None), '汽车-巡航系统': Value(dtype='string', id=None), '汽车-价格(万元)': Value(dtype='string', id=None), '汽车-车系': Value(dtype='string', id=None), '汽车-动力水平': Value(dtype='string', id=None), '汽车-油耗水平': Value(dtype='string', id=None), '汽车-倒车影像': Value(dtype='string', id=None), '汽车-定速巡航': Value(dtype='string', id=None), '汽车-座椅加热': Value(dtype='string', id=None), '汽车-座椅通风': Value(dtype='string', id=None), '汽车-所属价格区间': Value(dtype='string', id=None), '医院-名称': Value(dtype='string', id=None), '医院-等级': Value(dtype='string', id=None), '医院-类别': Value(dtype='string', id=None), '医院-性质': Value(dtype='string', id=None), '医院-区域': Value(dtype='string', id=None), '医院-地址': Value(dtype='string', id=None), '医院-电话': Value(dtype='string', id=None), '医院-挂号时间': Value(dtype='string', id=None), '医院-门诊时间': Value(dtype='string', id=None), '医院-公交线路': Value(dtype='string', id=None), '医院-地铁可达': Value(dtype='string', id=None), '医院-地铁线路': Value(dtype='string', id=None), '医院-重点科室': Value(dtype='string', id=None), '医院-CT': Value(dtype='string', id=None), '医院-3.0T MRI': Value(dtype='string', id=None), '医院-DSA': Value(dtype='string', id=None)}
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
/var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_44306/2896005239.py in <module>
----> 1 dset = load_dataset("GEM/RiSAWOZ")
2 dset
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)
1692
1693 # Download and prepare data
-> 1694 builder_instance.download_and_prepare(
1695 download_config=download_config,
1696 download_mode=download_mode,
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
593 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
594 if not downloaded_from_gcs:
--> 595 self._download_and_prepare(
596 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
597 )
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
682 try:
683 # Prepare split will record examples associated to the split
--> 684 self._prepare_split(split_generator, **prepare_split_kwargs)
685 except OSError as e:
686 raise OSError(
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator)
1084 writer.write(example, key)
1085 finally:
-> 1086 num_examples, num_bytes = writer.finalize()
1087
1088 split_generator.split_info.num_examples = num_examples
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in finalize(self, close_stream)
525 # Re-intializing to empty list for next batch
526 self.hkey_record = []
--> 527 self.write_examples_on_file()
528 if self.pa_writer is None:
529 if self.schema:
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_examples_on_file(self)
402 # Since current_examples contains (example, key) tuples
403 batch_examples[col] = [row[0][col] for row in self.current_examples]
--> 404 self.write_batch(batch_examples=batch_examples)
405 self.current_examples = []
406
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
495 col_try_type = try_features[col] if try_features is not None and col in try_features else None
496 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
--> 497 arrays.append(pa.array(typed_sequence))
498 inferred_features[col] = typed_sequence.get_inferred_type()
499 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array()
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type)
203 # Also, when trying type "string", we don't want to convert integers or floats to "string".
204 # We only do it if trying_type is False - since this is what the user asks for.
--> 205 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
206 return out
207 except (TypeError, pa.lib.ArrowInvalid) as e: # handle type errors and overflows
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"):
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
946 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
919 else:
--> 920 return func(array, *args, **kwargs)
921
922 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1063 # feature must be either [subfeature] or Sequence(subfeature)
1064 if isinstance(feature, list):
-> 1065 return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))
1066 elif isinstance(feature, Sequence):
1067 if feature.length > -1:
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"):
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
946 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
919 else:
--> 920 return func(array, *args, **kwargs)
921
922 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1058 }
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
1062 elif pa.types.is_list(array.type):
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0)
1058 }
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
1062 elif pa.types.is_list(array.type):
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"):
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
946 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
919 else:
--> 920 return func(array, *args, **kwargs)
921
922 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1058 }
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
1062 elif pa.types.is_list(array.type):
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0)
1058 }
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
1062 elif pa.types.is_list(array.type):
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"):
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
946 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
919 else:
--> 920 return func(array, *args, **kwargs)
921
922 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1085 elif not isinstance(feature, (Sequence, dict, list, tuple)):
1086 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
-> 1087 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
1088
1089
TypeError: Couldn't cast array of type
struct<医院-3.0T MRI: string, 医院-CT: string, 医院-DSA: string, 医院-公交线路: string, 医院-区域: string, 医院-名称: string, 医院-地址: string, 医院-地铁可达: string, 医院-地铁线路: string, 医院-性质: string, 医院-挂号时间: string, 医院-电话: string, 医院-等级: string, 医院-类别: string, 医院-重点科室: string, 医院-门诊时间: string, 天气-城市: string, 天气-天气: string, 天气-日期: string, 天气-温度: string, 天气-紫外线强度: string, 天气-风力风向: string, 旅游景点-区域: string, 旅游景点-名称: string, 旅游景点-地址: string, 旅游景点-开放时间: string, 旅游景点-是否地铁直达: string, 旅游景点-景点类型: string, 旅游景点-最适合人群: string, 旅游景点-消费: string, 旅游景点-特点: string, 旅游景点-电话号码: string, 旅游景点-评分: string, 旅游景点-门票价格: string, 汽车-价格(万元): string, 汽车-倒车影像: string, 汽车-动力水平: string, 汽车-厂商: string, 汽车-发动机排量(L): string, 汽车-发动机马力(Ps): string, 汽车-名称: string, 汽车-定速巡航: string, 汽车-巡航系统: string, 汽车-座位数: string, 汽车-座椅加热: string, 汽车-座椅通风: string, 汽车-所属价格区间: string, 汽车-油耗水平: string, 汽车-环保标准: string, 汽车-级别: string, 汽车-综合油耗(L/100km): string, 汽车-能源类型: string, 汽车-车型: string, 汽车-车系: string, 汽车-车身尺寸(mm): string, 汽车-驱动方式: string, 汽车-驾驶辅助影像: string, 火车-出发地: string, 火车-出发时间: string, 火车-到达时间: string, 火车-坐席: string, 火车-日期: string, 火车-时长: string, 火车-目的地: string, 火车-票价: string, 火车-舱位档次: string, 火车-车型: string, 火车-车次信息: string, 电影-主演: string, 电影-主演名单: string, 电影-具体上映时间: string, 电影-制片国家/地区: string, 电影-导演: string, 电影-年代: string, 电影-片名: string, 电影-片长: string, 电影-类型: string, 电影-豆瓣评分: string, 电脑-CPU: string, 电脑-CPU型号: string, 电脑-产品类别: string, 电脑-价格: string, 电脑-价格区间: string, 电脑-内存容量: string, 电脑-分类: string, 电脑-品牌: string, 电脑-商品名称: string, 电脑-屏幕尺寸: string, 电脑-待机时长: string, 电脑-显卡型号: string, 电脑-显卡类别: string, 电脑-游戏性能: string, 电脑-特性: string, 电脑-硬盘容量: string, 电脑-系列: string, 电脑-系统: string, 电脑-色系: string, 电脑-裸机重量: string, 电视剧-主演: string, 电视剧-主演名单: string, 电视剧-制片国家/地区: string, 电视剧-单集片长: string, 电视剧-导演: string, 电视剧-年代: string, 电视剧-片名: string, 电视剧-类型: string, 电视剧-豆瓣评分: string, 电视剧-集数: string, 电视剧-首播时间: string, 辅导班-上课方式: string, 辅导班-上课时间: string, 辅导班-下课时间: string, 辅导班-价格: string, 辅导班-区域: string, 辅导班-年级: string, 辅导班-开始日期: string, 辅导班-教室地点: string, 辅导班-教师: string, 辅导班-教师网址: string, 辅导班-时段: string, 辅导班-校区: string, 辅导班-每周: string, 辅导班-班号: string, 辅导班-科目: string, 辅导班-结束日期: string, 辅导班-课时: string, 辅导班-课次: string, 辅导班-课程网址: string, 辅导班-难度: string, 通用-产品类别: string, 通用-价格区间: string, 通用-品牌: string, 通用-系列: string, 酒店-价位: string, 酒店-停车场: string, 酒店-区域: string, 酒店-名称: string, 酒店-地址: string, 酒店-房型: string, 酒店-房费: string, 酒店-星级: string, 酒店-电话号码: string, 酒店-评分: string, 酒店-酒店类型: string, 飞机-准点率: string, 飞机-出发地: string, 飞机-到达时间: string, 飞机-日期: string, 飞机-目的地: string, 飞机-票价: string, 飞机-航班信息: string, 飞机-舱位档次: string, 飞机-起飞时间: string, 餐厅-人均消费: string, 餐厅-价位: string, 餐厅-区域: string, 餐厅-名称: string, 餐厅-地址: string, 餐厅-推荐菜: string, 餐厅-是否地铁直达: string, 餐厅-电话号码: string, 餐厅-菜系: string, 餐厅-营业时间: string, 餐厅-评分: string>
to
{'旅游景点-名称': Value(dtype='string', id=None), '旅游景点-区域': Value(dtype='string', id=None), '旅游景点-景点类型': Value(dtype='string', id=None), '旅游景点-最适合人群': Value(dtype='string', id=None), '旅游景点-消费': Value(dtype='string', id=None), '旅游景点-是否地铁直达': Value(dtype='string', id=None), '旅游景点-门票价格': Value(dtype='string', id=None), '旅游景点-电话号码': Value(dtype='string', id=None), '旅游景点-地址': Value(dtype='string', id=None), '旅游景点-评分': Value(dtype='string', id=None), '旅游景点-开放时间': Value(dtype='string', id=None), '旅游景点-特点': Value(dtype='string', id=None), '餐厅-名称': Value(dtype='string', id=None), '餐厅-区域': Value(dtype='string', id=None), '餐厅-菜系': Value(dtype='string', id=None), '餐厅-价位': Value(dtype='string', id=None), '餐厅-是否地铁直达': Value(dtype='string', id=None), '餐厅-人均消费': Value(dtype='string', id=None), '餐厅-地址': Value(dtype='string', id=None), '餐厅-电话号码': Value(dtype='string', id=None), '餐厅-评分': Value(dtype='string', id=None), '餐厅-营业时间': Value(dtype='string', id=None), '餐厅-推荐菜': Value(dtype='string', id=None), '酒店-名称': Value(dtype='string', id=None), '酒店-区域': Value(dtype='string', id=None), '酒店-星级': Value(dtype='string', id=None), '酒店-价位': Value(dtype='string', id=None), '酒店-酒店类型': Value(dtype='string', id=None), '酒店-房型': Value(dtype='string', id=None), '酒店-停车场': Value(dtype='string', id=None), '酒店-房费': Value(dtype='string', id=None), '酒店-地址': Value(dtype='string', id=None), '酒店-电话号码': Value(dtype='string', id=None), '酒店-评分': Value(dtype='string', id=None), '电脑-品牌': Value(dtype='string', id=None), '电脑-产品类别': Value(dtype='string', id=None), '电脑-分类': Value(dtype='string', id=None), '电脑-内存容量': Value(dtype='string', id=None), '电脑-屏幕尺寸': Value(dtype='string', id=None), '电脑-CPU': Value(dtype='string', id=None), '电脑-价格区间': Value(dtype='string', id=None), '电脑-系列': Value(dtype='string', id=None), '电脑-商品名称': Value(dtype='string', id=None), '电脑-系统': Value(dtype='string', id=None), '电脑-游戏性能': Value(dtype='string', id=None), '电脑-CPU型号': Value(dtype='string', id=None), '电脑-裸机重量': Value(dtype='string', id=None), '电脑-显卡类别': Value(dtype='string', id=None), '电脑-显卡型号': Value(dtype='string', id=None), '电脑-特性': Value(dtype='string', id=None), '电脑-色系': Value(dtype='string', id=None), '电脑-待机时长': Value(dtype='string', id=None), '电脑-硬盘容量': Value(dtype='string', id=None), '电脑-价格': Value(dtype='string', id=None), '火车-出发地': Value(dtype='string', id=None), '火车-目的地': Value(dtype='string', id=None), '火车-日期': Value(dtype='string', id=None), '火车-车型': Value(dtype='string', id=None), '火车-坐席': Value(dtype='string', id=None), '火车-车次信息': Value(dtype='string', id=None), '火车-时长': Value(dtype='string', id=None), '火车-出发时间': Value(dtype='string', id=None), '火车-到达时间': Value(dtype='string', id=None), '火车-票价': Value(dtype='string', id=None), '飞机-出发地': Value(dtype='string', id=None), '飞机-目的地': Value(dtype='string', id=None), '飞机-日期': Value(dtype='string', id=None), '飞机-舱位档次': Value(dtype='string', id=None), '飞机-航班信息': Value(dtype='string', id=None), '飞机-起飞时间': Value(dtype='string', id=None), '飞机-到达时间': Value(dtype='string', id=None), '飞机-票价': Value(dtype='string', id=None), '飞机-准点率': Value(dtype='string', id=None), '天气-城市': Value(dtype='string', id=None), '天气-日期': Value(dtype='string', id=None), '天气-天气': Value(dtype='string', id=None), '天气-温度': Value(dtype='string', id=None), '天气-风力风向': Value(dtype='string', id=None), '天气-紫外线强度': Value(dtype='string', id=None), '电影-制片国家/地区': Value(dtype='string', id=None), '电影-类型': Value(dtype='string', id=None), '电影-年代': Value(dtype='string', id=None), '电影-主演': Value(dtype='string', id=None), '电影-导演': Value(dtype='string', id=None), '电影-片名': Value(dtype='string', id=None), '电影-主演名单': Value(dtype='string', id=None), '电影-具体上映时间': Value(dtype='string', id=None), '电影-片长': Value(dtype='string', id=None), '电影-豆瓣评分': Value(dtype='string', id=None), '电视剧-制片国家/地区': Value(dtype='string', id=None), '电视剧-类型': Value(dtype='string', id=None), '电视剧-年代': Value(dtype='string', id=None), '电视剧-主演': Value(dtype='string', id=None), '电视剧-导演': Value(dtype='string', id=None), '电视剧-片名': Value(dtype='string', id=None), '电视剧-主演名单': Value(dtype='string', id=None), '电视剧-首播时间': Value(dtype='string', id=None), '电视剧-集数': Value(dtype='string', id=None), '电视剧-单集片长': Value(dtype='string', id=None), '电视剧-豆瓣评分': Value(dtype='string', id=None), '辅导班-班号': Value(dtype='string', id=None), '辅导班-难度': Value(dtype='string', id=None), '辅导班-科目': Value(dtype='string', id=None), '辅导班-年级': Value(dtype='string', id=None), '辅导班-区域': Value(dtype='string', id=None), '辅导班-校区': Value(dtype='string', id=None), '辅导班-上课方式': Value(dtype='string', id=None), '辅导班-开始日期': Value(dtype='string', id=None), '辅导班-结束日期': Value(dtype='string', id=None), '辅导班-每周': Value(dtype='string', id=None), '辅导班-上课时间': Value(dtype='string', id=None), '辅导班-下课时间': Value(dtype='string', id=None), '辅导班-时段': Value(dtype='string', id=None), '辅导班-课次': Value(dtype='string', id=None), '辅导班-课时': Value(dtype='string', id=None), '辅导班-教室地点': Value(dtype='string', id=None), '辅导班-教师': Value(dtype='string', id=None), '辅导班-价格': Value(dtype='string', id=None), '辅导班-课程网址': Value(dtype='string', id=None), '辅导班-教师网址': Value(dtype='string', id=None), '汽车-名称': Value(dtype='string', id=None), '汽车-车型': Value(dtype='string', id=None), '汽车-级别': Value(dtype='string', id=None), '汽车-座位数': Value(dtype='string', id=None), '汽车-车身尺寸(mm)': Value(dtype='string', id=None), '汽车-厂商': Value(dtype='string', id=None), '汽车-能源类型': Value(dtype='string', id=None), '汽车-发动机排量(L)': Value(dtype='string', id=None), '汽车-发动机马力(Ps)': Value(dtype='string', id=None), '汽车-驱动方式': Value(dtype='string', id=None), '汽车-综合油耗(L/100km)': Value(dtype='string', id=None), '汽车-环保标准': Value(dtype='string', id=None), '汽车-驾驶辅助影像': Value(dtype='string', id=None), '汽车-巡航系统': Value(dtype='string', id=None), '汽车-价格(万元)': Value(dtype='string', id=None), '汽车-车系': Value(dtype='string', id=None), '汽车-动力水平': Value(dtype='string', id=None), '汽车-油耗水平': Value(dtype='string', id=None), '汽车-倒车影像': Value(dtype='string', id=None), '汽车-定速巡航': Value(dtype='string', id=None), '汽车-座椅加热': Value(dtype='string', id=None), '汽车-座椅通风': Value(dtype='string', id=None), '汽车-所属价格区间': Value(dtype='string', id=None), '医院-名称': Value(dtype='string', id=None), '医院-等级': Value(dtype='string', id=None), '医院-类别': Value(dtype='string', id=None), '医院-性质': Value(dtype='string', id=None), '医院-区域': Value(dtype='string', id=None), '医院-地址': Value(dtype='string', id=None), '医院-电话': Value(dtype='string', id=None), '医院-挂号时间': Value(dtype='string', id=None), '医院-门诊时间': Value(dtype='string', id=None), '医院-公交线路': Value(dtype='string', id=None), '医院-地铁可达': Value(dtype='string', id=None), '医院-地铁线路': Value(dtype='string', id=None), '医院-重点科室': Value(dtype='string', id=None), '医院-CT': Value(dtype='string', id=None), '医院-3.0T MRI': Value(dtype='string', id=None), '医院-DSA': Value(dtype='string', id=None)}
```
</details>
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.10
- PyArrow version: 3.0.0
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"I'm not sure if this is expected behavior.\r\n\r\nAm I supposed to work with a copy of the dataset, i.e. `shuffled_dataset = data.shuffle(seed=None)`?\r\n\r\n```diff\r\nimport datasets\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n+shuffled_data = data.shuffle(seed=None)\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n+ shuffled_data = shuffled_data.shuffle(seed=None)\r\n+ print(shuffled_data[:])\r\n- print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n df = data.to_pandas().sample(frac=1.0)\r\n print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```\r\n\r\nor provide a `generator` instead?\r\n\r\n```diff\r\nimport datasets\r\n+from numpy.random import default_rng\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n+rng = default_rng()\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n+ print(data.shuffle(generator=rng)[:])\r\n- print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n df = data.to_pandas().sample(frac=1.0)\r\n print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```",
"Hi! Thanks for reporting! Yes, this is not expected behavior. I've opened a PR with the fix."
] | "2022-01-26T15:13:08" | "2022-01-27T18:16:07" | "2022-01-27T18:16:07" | NONE | null | ## Describe the bug
Repeated attempts to `shuffle` a dataset without specifying a seed give the same results.
## Steps to reproduce the bug
```python
import datasets
# Some toy example
data = datasets.Dataset.from_dict(
{"feature": [1, 2, 3, 4, 5], "label": ["a", "b", "c", "d", "e"]}
)
# Doesn't work as expected
print("Shuffle dataset")
for _ in range(3):
print(data.shuffle(seed=None)[:])
# This seems to work with pandas
print("\nShuffle via pandas")
for _ in range(3):
df = data.to_pandas().sample(frac=1.0)
print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])
```
## Expected results
I assumed that the default setting would initialize a new/random state of a `np.random.BitGenerator` (see [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=shuffle#datasets.Dataset.shuffle)).
Wouldn't that reshuffle the rows each time I call `data.shuffle()`?
## Actual results
```bash
Shuffle dataset
{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}
{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}
{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}
Shuffle via pandas
{'feature': [4, 2, 3, 1, 5], 'label': ['d', 'b', 'c', 'a', 'e']}
{'feature': [2, 5, 3, 4, 1], 'label': ['b', 'e', 'c', 'd', 'a']}
{'feature': [5, 2, 3, 1, 4], 'label': ['e', 'b', 'c', 'a', 'd']}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.0
- Platform: Linux-5.13.0-27-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3633 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3633/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3633/comments | https://api.github.com/repos/huggingface/datasets/issues/3633/events | https://github.com/huggingface/datasets/pull/3633 | 1,115,040,174 | PR_kwDODunzps4xng6E | 3,633 | Mirror canonical datasets in prod | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-26T13:49:37" | "2022-01-26T13:56:21" | "2022-01-26T13:56:21" | MEMBER | null | Push the datasets changes to the Hub in production by setting `HF_USE_PROD=1`
I also added a fix that makes the script ignore the json, csv, text, parquet and pandas dataset builders.
cc @SBrandeis | {
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"Hi @AnzorGozalishvili,\r\n\r\nMaybe their site was temporarily down, but it seems to work fine now.\r\n\r\nCould you please try again and confirm if the problem persists? ",
"Hi @albertvillanova \r\nI checked and it works. \r\nIt seems that it was really temporarily down.\r\nThanks!"
] | "2022-01-26T13:35:37" | "2022-02-10T06:58:11" | "2022-02-10T06:58:11" | CONTRIBUTOR | null | ## Describe the bug
The dataset links are no longer valid for CC-100. It seems that the website which was keeping these files are no longer accessible and therefore this dataset became unusable.
Check out the dataset [homepage](http://data.statmt.org/cc-100/) which isn't accessible.
Also the URLs for dataset file per language isn't accessible: http://data.statmt.org/cc-100/<language code here>.txt.xz (language codes: am, sr, ka, etc.)
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("cc100", "ka")
```
It throws 503 error.
## Expected results
It should successfully download and load dataset but it throws an exception because the dataset files are no longer accessible.
## Environment info
Run from google colab. Just installed the library using pip:
```!pip install -U datasets```
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"Hi @pichljan, thanks for reporting.\r\n\r\nThis should be fixed. I'm looking at it. "
] | "2022-01-26T10:00:33" | "2022-02-11T23:02:31" | "2022-02-11T23:02:31" | NONE | null | ## Describe the bug
I am trying to load a local CSV file with a separate file containing label names. It is successfully loaded for the first time, but when I try to load it again, there is a conflict between provided labels and the cached dataset info. Disabling caching globally and/or using `download_mode="force_redownload"` did not help.
## Steps to reproduce the bug
```python
load_dataset('csv', data_files='data/my_data.csv',
features=Features(text=Value(dtype='string'),
label=ClassLabel(names_file='data/my_data_labels.txt')))
```
`my_data.csv` file has the following structure:
```
text,label
"example1",0
"example2",1
...
```
and the `my_data_labels.txt` looks like this:
```
label1
label2
...
```
## Expected results
Successfully loaded dataset.
## Actual results
```python
File "/usr/local/lib/python3.8/site-packages/datasets/load.py", line 1706, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 766, in as_dataset
datasets = utils.map_nested(
File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 261, in map_nested
mapped = [
File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 262, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 797, in _build_single_dataset
ds = self._as_dataset(
File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 872, in _as_dataset
return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File "/usr/local/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 638, in __init__
inferred_features = Features.from_arrow_schema(arrow_table.schema)
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1242, in from_arrow_schema
return Features.from_dict(metadata["info"]["features"])
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1271, in from_dict
obj = generate_from_dict(dic)
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1076, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1076, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1083, in generate_from_dict
return class_type(**{k: v for k, v in obj.items() if k in field_names})
File "<string>", line 7, in __init__
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 776, in __post_init__
raise ValueError("Please provide either names or names_file but not both.")
ValueError: Please provide either names or names_file but not both.
```
## Environment info
- `datasets` version: 1.18.0
- Python version: 3.8.2
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https://api.github.com/repos/huggingface/datasets/issues/3630 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3630/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3630/comments | https://api.github.com/repos/huggingface/datasets/issues/3630/events | https://github.com/huggingface/datasets/issues/3630 | 1,114,578,625 | I_kwDODunzps5Cbx7B | 3,630 | DuplicatedKeysError of NewsQA dataset | {
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"Thanks for reporting, @StevenTang1998.\r\n\r\nI'm fixing it. "
] | "2022-01-26T03:05:49" | "2022-02-14T08:37:19" | "2022-02-14T08:37:19" | NONE | null | After processing the dataset following official [NewsQA](https://github.com/Maluuba/newsqa), I used datasets to load it:
```
a = load_dataset('newsqa', data_dir='news')
```
and the following error occurred:
```
Using custom data configuration default-data_dir=news
Downloading and preparing dataset newsqa/default to /root/.cache/huggingface/datasets/newsqa/default-data_dir=news/1.0.0/b0b23e22d94a3d352ad9d75aff2b71375264a122fae301463079ee8595e05ab9...
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1084, in _prepare_split
writer.write(example, key)
File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 442, in write
self.check_duplicate_keys()
File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 453, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: ./cnn/stories/6a0f9c8a5d0c6e8949b37924163c92923fe5770d.story
Keys should be unique and deterministic in nature
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 1694, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 595, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 684, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1086, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 524, in finalize
self.check_duplicate_keys()
File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 453, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: ./cnn/stories/6a0f9c8a5d0c6e8949b37924163c92923fe5770d.story
Keys should be unique and deterministic in nature
``` | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-25T14:39:45" | "2022-01-25T14:55:46" | "2022-01-25T14:55:46" | MEMBER | null | It was not listing the full list of datasets correctly
cc @SBrandeis this is why it failed for 1.18.0
We should be good now ! | {
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https://api.github.com/repos/huggingface/datasets/issues/3627 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3627/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3627/comments | https://api.github.com/repos/huggingface/datasets/issues/3627/events | https://github.com/huggingface/datasets/pull/3627 | 1,113,556,837 | PR_kwDODunzps4xitGe | 3,627 | Fix host URL in The Pile datasets | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-25T08:11:28" | "2022-07-20T20:54:42" | "2022-02-14T08:40:58" | MEMBER | null | This PR fixes the host URL in The Pile datasets, once they have mirrored their data in another server.
Fix #3626. | {
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The Pile had issues with their previous host server and have mirrored its content to another server.
The new URL server should be updated.
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https://api.github.com/repos/huggingface/datasets/issues/3623 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3623/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3623/comments | https://api.github.com/repos/huggingface/datasets/issues/3623/events | https://github.com/huggingface/datasets/pull/3623 | 1,112,835,239 | PR_kwDODunzps4xgWig | 3,623 | Extend support for streaming datasets that use os.path.relpath | {
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This feature will also be useful to yield the relative path of audio or image files, within an archive or parent dir.
Close #3622. | {
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This feature will also be useful to yield the relative path of audio or image files.
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"Hi! We use `tqdm` to display progress bars, so I suggest you open this issue in their repo.",
"It depends on how you use `tqdm`, no? \r\n\r\nDoesn't this library import via; \r\n\r\n```\r\nfrom tqdm.notebook import tqdm\r\n```",
"Hi! Sorry for the late reply. We import `tqdm` as `from tqdm.auto import tqdm`, which should be equal to `from tqdm.notebook import tqdm` in Jupyter.",
"Any objection if I make a PR that checks if the widgets library is installed beforehand? "
] | "2022-01-24T14:27:11" | "2022-02-24T09:04:36" | "2022-02-24T09:04:36" | NONE | null | When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error.
```
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
```
It's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here? | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-24T12:54:38" | "2022-01-24T20:53:22" | "2022-01-24T20:53:22" | CONTRIBUTOR | null | `monolingual` value of `m ultilinguality` param in yaml meta was changed to `multilingual` :) | {
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- I've used `iter_files`
Close #3615 | {
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"@albertvillanova let me know if there is anything I can do to help with this. I had a quick look at the code again and though I could try the following changes:\r\n- use `download` instead of `download_and_extract`\r\nhttps://github.com/huggingface/datasets/blob/d3d339fb86d378f4cb3c5d1de423315c07a466c6/datasets/bnl_newspapers/bnl_newspapers.py#L136\r\n- swith to using `iter_archive` to loop through downloaded data to replace\r\nhttps://github.com/huggingface/datasets/blob/d3d339fb86d378f4cb3c5d1de423315c07a466c6/datasets/bnl_newspapers/bnl_newspapers.py#L159\r\n\r\nLet me know if it's useful for me to try and make those changes. ",
"Thanks @davanstrien.\r\n\r\nI have already been working on it so that it can be used in the BigScience workshop.\r\n\r\nI agree that the `rglob()` is not efficient in this case.\r\n\r\nI tried different solutions without success:\r\n- `iter_archive` cannot be used in this case because it does not support ZIP files yet\r\n\r\nFinally I have used `iter_files()`.",
"I see this is fixed now 🙂. I also picked up a few other tips from your redactors so hopefully my next attempts will support streaming from the start. "
] | "2022-01-22T14:12:59" | "2022-02-04T14:05:21" | "2022-02-04T14:05:21" | MEMBER | null | ## Describe the bug
When trying to load in streaming mode, it "hangs"...
## Steps to reproduce the bug
```python
ds = load_dataset("bnl_newspapers", split="train", streaming=True)
```
## Expected results
The code should be optimized, so that it works fast in streaming mode.
CC: @davanstrien
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https://api.github.com/repos/huggingface/datasets/issues/3614 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3614/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3614/comments | https://api.github.com/repos/huggingface/datasets/issues/3614/events | https://github.com/huggingface/datasets/pull/3614 | 1,110,736,657 | PR_kwDODunzps4xZdCe | 3,614 | Minor fixes | {
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* adds "desc" to the `ignore_kwargs` list in `Dataset.filter`
* fixes the default value of `id` in `DatasetDict.prepare_for_task` | {
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"Yes. The jobs queue is full right now, following an upgrade... Back to normality in the next hours hopefully. I'll look at your datasets to be sure the dataset viewer works as expected on them.",
"Should have been fixed now."
] | "2022-01-21T16:47:20" | "2022-01-22T08:13:13" | "2022-01-22T08:13:13" | MEMBER | null | ## Dataset viewer issue for '*name of the dataset*'
**Link:**
Some examples:
* https://huggingface.co/datasets/abidlabs/crowdsourced-speech4
* https://huggingface.co/datasets/abidlabs/test-audio-13
*short description of the issue*
It seems that the dataset viewer is reading a cached version of the dataset and it is not updating to reflect new files that are added to the dataset. I get this error:
![image](https://user-images.githubusercontent.com/1778297/150566660-30dc0dcd-18fd-4471-b70c-7c4bdc6a23c6.png)
Am I the one who added this dataset? Yes | {
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https://api.github.com/repos/huggingface/datasets/issues/3612 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3612/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3612/comments | https://api.github.com/repos/huggingface/datasets/issues/3612/events | https://github.com/huggingface/datasets/pull/3612 | 1,110,506,466 | PR_kwDODunzps4xYsvS | 3,612 | wikifix | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-21T14:05:11" | "2022-02-03T17:58:16" | "2022-02-03T17:58:16" | CONTRIBUTOR | null | This should get the wikipedia dataloading script back up and running - at least I hope so (tested with language ff and ii) | {
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"Hi! Thanks for reporting! I've opened a PR with the fix."
] | "2022-01-21T12:09:30" | "2022-01-27T18:16:22" | "2022-01-27T18:16:22" | NONE | null | ## Describe the bug
A clear and concise description of what the bug is.
Dataset indexing is not working as expected after `dataset.select(range(100))`
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import datasets
task_to_keys = {
"cola": ("sentence", None),
"mnli": ("premise", "hypothesis"),
"mrpc": ("sentence1", "sentence2"),
"qnli": ("question", "sentence"),
"qqp": ("question1", "question2"),
"rte": ("sentence1", "sentence2"),
"sst2": ("sentence", None),
"stsb": ("sentence1", "sentence2"),
"wnli": ("sentence1", "sentence2"),
}
task_name = "sst2"
raw_datasets = datasets.load_dataset("glue", task_name)
train_dataset = raw_datasets["train"]
print("before select: ",train_dataset[-2:])
# before select: {'sentence': ['a patient viewer ', 'this new jangle of noise , mayhem and stupidity must be a serious contender for the title . '], 'label': [1, 0], 'idx': [67347, 67348]}
train_dataset = train_dataset.select(range(100))
print("after select: ",train_dataset[-2:])
# after select: {'sentence': [], 'label': [], 'idx': []}
```
link to colab: https://colab.research.google.com/drive/1LngeRC9f0jE7eSQ4Kh1cIeb411lRXQD-?usp=sharing
## Expected results
A clear and concise description of the expected results.
showing 98, 99 index data
## Actual results
Specify the actual results or traceback.
empty
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3610 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3610/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3610/comments | https://api.github.com/repos/huggingface/datasets/issues/3610/events | https://github.com/huggingface/datasets/issues/3610 | 1,109,777,314 | I_kwDODunzps5CJdui | 3,610 | Checksum error when trying to load amazon_review dataset | {
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"It is solved now"
] | "2022-01-20T21:20:32" | "2022-01-21T13:22:31" | "2022-01-21T13:22:31" | NONE | null | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
I am getting the issue when trying to load dataset using
```
dataset = load_dataset("amazon_polarity")
```
## Expected results
dataset loaded
## Actual results
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-3-b4758ba980ae> in <module>()
----> 1 dataset = load_dataset("amazon_polarity")
2 dataset.set_format(type='pandas')
3 content_series = dataset['train']['content']
4 label_series = dataset['train']['label']
5 df = pd.concat([content_series, label_series], axis=1)
3 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: Google colab
- Python version: 3.7.12 | {
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https://api.github.com/repos/huggingface/datasets/issues/3609 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3609/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3609/comments | https://api.github.com/repos/huggingface/datasets/issues/3609/events | https://github.com/huggingface/datasets/pull/3609 | 1,109,579,112 | PR_kwDODunzps4xVrsG | 3,609 | Fixes to pubmed dataset download function | {
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https://api.github.com/repos/huggingface/datasets/issues/3608 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3608/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3608/comments | https://api.github.com/repos/huggingface/datasets/issues/3608/events | https://github.com/huggingface/datasets/issues/3608 | 1,109,310,981 | I_kwDODunzps5CHr4F | 3,608 | Add support for continuous metrics (RMSE, MAE) | {
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"Hey @ck37 \r\n\r\nYou can always use a custom metric as explained [in this guide from HF](https://huggingface.co/docs/datasets/master/loading_metrics.html#using-a-custom-metric-script).\r\n\r\nIf this issue needs to be contributed to (for enhancing the metric API) I think [this link](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html) would be helpful for the `MAE` metric.",
"You can use a local metric script just by providing its path instead of the usual shortcut name ",
"#self-assign I have starting working on this issue to enhance the metric API."
] | "2022-01-20T13:35:36" | "2022-03-09T17:18:20" | "2022-03-09T17:18:20" | NONE | null | **Is your feature request related to a problem? Please describe.**
I am uploading our dataset and models for the "Constructing interval measures" method we've developed, which uses item response theory to convert multiple discrete labels into a continuous spectrum for hate speech. Once we have this outcome our NLP models conduct regression rather than classification, so binary metrics are not relevant. The only continuous metrics available at https://huggingface.co/metrics are pearson & spearman correlation, which don't ensure that the prediction is on the same scale as the outcome.
**Describe the solution you'd like**
I would like to be able to tag our models on the Hub with the following metrics:
- RMSE
- MAE
**Describe alternatives you've considered**
I don't know if there are any alternatives.
**Additional context**
Our preprint is available here: https://arxiv.org/abs/2009.10277 . We are making it available for use in Jigsaw's Toxic Severity Rating Kaggle competition: https://www.kaggle.com/c/jigsaw-toxic-severity-rating/overview . I have our first model uploaded to the Hub at https://huggingface.co/ucberkeley-dlab/hate-measure-roberta-large
Thanks,
Chris
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https://api.github.com/repos/huggingface/datasets/issues/3607 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3607/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3607/comments | https://api.github.com/repos/huggingface/datasets/issues/3607/events | https://github.com/huggingface/datasets/pull/3607 | 1,109,218,370 | PR_kwDODunzps4xUgrR | 3,607 | Add MIT Scene Parsing Benchmark | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-20T12:03:07" | "2022-02-18T12:51:01" | "2022-02-18T12:51:00" | CONTRIBUTOR | null | Add MIT Scene Parsing Benchmark (a subset of ADE20k).
TODOs:
* [x] add dummy data
* [x] add dataset card
* [x] generate `dataset_info.json`
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"Hi ! We just released a new version of `datasets` that should fix this.\r\n\r\nI tested resampling and using save/load_from_disk afterwards and it seems to be fixed now",
"Hi @lhoestq, \r\n\r\nJust tested the latest datasets version, and confirming that this is fixed for me. \r\n\r\nThanks!",
"Also, just an FYI, data that I had saved (with save_to_disk) previously from common voice using datasets==1.17.0 now give the error below when loading (with load_from disk) using datasets==1.18.0. \r\n\r\nHowever, when starting fresh using load_dataset, then doing the resampling, the save/load_from disk worked fine. \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n<timed exec> in <module>\r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_from_disk(dataset_path, fs, keep_in_memory)\r\n 1747 return Dataset.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n 1748 elif fs.isfile(Path(dest_dataset_path, config.DATASETDICT_JSON_FILENAME).as_posix()):\r\n-> 1749 return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n 1750 else:\r\n 1751 raise FileNotFoundError(\r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/dataset_dict.py in load_from_disk(dataset_dict_path, fs, keep_in_memory)\r\n 769 else Path(dest_dataset_dict_path, k).as_posix()\r\n 770 )\r\n--> 771 dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n 772 return dataset_dict\r\n 773 \r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in load_from_disk(dataset_path, fs, keep_in_memory)\r\n 1118 info=dataset_info,\r\n 1119 split=split,\r\n-> 1120 fingerprint=state[\"_fingerprint\"],\r\n 1121 )\r\n 1122 \r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n 655 if self.info.features.type != inferred_features.type:\r\n 656 raise ValueError(\r\n--> 657 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n 658 )\r\n 659 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=48000, mono=True, id=None), 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<accent: string, age: string, audio: struct<bytes: binary, path: string>, client_id: string, down_votes: int64, gender: string, locale: string, path: string, segment: string, sentence: string, up_votes: int64>\r\n\r\nbut expected something like\r\n{'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': {'path': Value(dtype='string', id=None), 'bytes': Value(dtype='binary', id=None)}, 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<accent: string, age: string, audio: struct<path: string, bytes: binary>, client_id: string, down_votes: int64, gender: string, locale: string, path: string, segment: string, sentence: string, up_votes: int64> \r\n```"
] | "2022-01-20T06:37:10" | "2022-01-23T01:41:01" | "2022-01-23T01:24:14" | NONE | null | ## Describe the bug
After resampling the audio column, saving with save_to_disk doesn't seem to save with the correct type.
## Steps to reproduce the bug
- load a subset of common voice dataset (48Khz)
- resample audio column to 16Khz
- save with save_to_disk()
- load with load_from_disk()
## Expected results
I expected that after saving the data, and then loading it back in, the audio column has the correct dataset.Audio type (i.e. same as before saving it)
{'accent': Value(dtype='string', id=None),
'age': Value(dtype='string', id=None),
'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None),
'client_id': Value(dtype='string', id=None),
'down_votes': Value(dtype='int64', id=None),
'gender': Value(dtype='string', id=None),
'locale': Value(dtype='string', id=None),
'path': Value(dtype='string', id=None),
'segment': Value(dtype='string', id=None),
'sentence': Value(dtype='string', id=None),
'up_votes': Value(dtype='int64', id=None)}
## Actual results
Audio column does not have the right type
{'accent': Value(dtype='string', id=None),
'age': Value(dtype='string', id=None),
'audio': {'bytes': Value(dtype='binary', id=None),
'path': Value(dtype='string', id=None)},
'client_id': Value(dtype='string', id=None),
'down_votes': Value(dtype='int64', id=None),
'gender': Value(dtype='string', id=None),
'locale': Value(dtype='string', id=None),
'path': Value(dtype='string', id=None),
'segment': Value(dtype='string', id=None),
'sentence': Value(dtype='string', id=None),
'up_votes': Value(dtype='int64', id=None)}
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: linux
- Python version:
- PyArrow version:
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https://api.github.com/repos/huggingface/datasets/issues/3605 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3605/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3605/comments | https://api.github.com/repos/huggingface/datasets/issues/3605/events | https://github.com/huggingface/datasets/pull/3605 | 1,108,738,561 | PR_kwDODunzps4xS9rX | 3,605 | Adding Turkic X-WMT evaluation set for machine translation | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-20T01:40:29" | "2022-01-31T09:50:57" | "2022-01-31T09:50:57" | CONTRIBUTOR | null | This dataset is a human-translated evaluation set for MT crowdsourced and provided by the [Turkic Interlingua ](turkic-interlingua.org) community. It contains eval sets for 8 Turkic languages covering 88 language directions. Languages being covered are:
Azerbaijani (az)
Bashkir (ba)
English (en)
Karakalpak (kaa)
Kazakh (kk)
Kirghiz (ky)
Russian (ru)
Turkish (tr)
Sakha (sah)
Uzbek (uz)
More info about the corpus is here: [https://github.com/turkic-interlingua/til-mt/tree/master/xwmt](https://github.com/turkic-interlingua/til-mt/tree/master/xwmt)
A paper describing the test set is here: [https://arxiv.org/abs/2109.04593](https://arxiv.org/abs/2109.04593)
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https://api.github.com/repos/huggingface/datasets/issues/3604 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3604/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3604/comments | https://api.github.com/repos/huggingface/datasets/issues/3604/events | https://github.com/huggingface/datasets/issues/3604 | 1,108,477,316 | I_kwDODunzps5CEgWE | 3,604 | Dataset Viewer not showing Previews for Private Datasets | {
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"Sure, it's on the roadmap.",
"Closing in favor of https://github.com/huggingface/datasets-server/issues/39."
] | "2022-01-19T19:29:26" | "2022-09-26T08:04:43" | "2022-09-26T08:04:43" | MEMBER | null | ## Dataset viewer issue for 'abidlabs/test-audio-13'
It seems that the dataset viewer does not show previews for `private` datasets, even for the user who's private dataset it is. See [1] for example. If I change the visibility to public, then it does show, but it would be useful to have the viewer even for private datasets.
![image](https://user-images.githubusercontent.com/1778297/150200515-93ff1545-11fd-4793-be64-6bed3cd895e2.png)
**Link:**
[1] https://huggingface.co/datasets/abidlabs/test-audio-13
**Am I the one who added this dataset?**
Yes
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https://api.github.com/repos/huggingface/datasets/issues/3603 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3603/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3603/comments | https://api.github.com/repos/huggingface/datasets/issues/3603/events | https://github.com/huggingface/datasets/pull/3603 | 1,108,392,141 | PR_kwDODunzps4xR1ih | 3,603 | Add British Library books dataset | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-19T17:53:05" | "2022-01-31T17:22:51" | "2022-01-31T17:01:49" | MEMBER | null | This pull request adds a dataset of text from digitised (primarily 19th Century) books from the British Library. This collection has previously been used for training language models, e.g. https://github.com/dbmdz/clef-hipe/blob/main/hlms.md. It would be nice to make this dataset more accessible for others to use through datasets.
This is still a WIP but I wanted to get some initial feedback in particular; I wanted to check:
- I am handling the use of `iter_archive` correctly - I intend to ensure that `dl_manager.download` gets the complete list of URLs to download upfront, so the progress bar knows how much is left to download and then to pass through the `gen_kwargs` a list of downloaded zip archives wrapped in `iter_archive`. I am unsure if there is a more elegant approach for this?
- the number of configs: I have aimed to keep this limited - there are a lot of URLs covering the entire dataset, but I have tried to base the configs on what I believe the majority of people will want to they are not presented with too many options - I am happy to hear suggestions for changing this
If there are other glaring omissions or mistakes, I'd be happy to hear them. If this approach seems sensible in general, I will finish all the remaining TODOs, generate dummy_data, etc.
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https://api.github.com/repos/huggingface/datasets/issues/3602 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3602/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3602/comments | https://api.github.com/repos/huggingface/datasets/issues/3602/events | https://github.com/huggingface/datasets/pull/3602 | 1,108,247,870 | PR_kwDODunzps4xRXVm | 3,602 | Update url for conll2003 | {
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https://api.github.com/repos/huggingface/datasets/issues/3601 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3601/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3601/comments | https://api.github.com/repos/huggingface/datasets/issues/3601/events | https://github.com/huggingface/datasets/pull/3601 | 1,108,207,131 | PR_kwDODunzps4xROtF | 3,601 | Add conll2003 licensing | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-19T15:00:41" | "2022-01-19T17:17:28" | "2022-01-19T17:17:28" | MEMBER | null | Following https://github.com/huggingface/datasets/issues/3582, this PR updates the licensing section of the CoNLL2003 dataset. | {
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https://api.github.com/repos/huggingface/datasets/issues/3600 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3600/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3600/comments | https://api.github.com/repos/huggingface/datasets/issues/3600/events | https://github.com/huggingface/datasets/pull/3600 | 1,108,131,878 | PR_kwDODunzps4xQ-vt | 3,600 | Use old url for conll2003 | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-19T13:56:49" | "2022-01-19T14:16:28" | "2022-01-19T14:16:28" | MEMBER | null | As reported in https://github.com/huggingface/datasets/issues/3582 the CoNLL2003 data files are not available in the master branch of the repo that used to host them.
For now we can use the URL from an older commit to access the data files | {
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https://api.github.com/repos/huggingface/datasets/issues/3599 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3599/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3599/comments | https://api.github.com/repos/huggingface/datasets/issues/3599/events | https://github.com/huggingface/datasets/issues/3599 | 1,108,111,607 | I_kwDODunzps5CDHD3 | 3,599 | The `add_column()` method does not work if used on dataset sliced with `select()` | {
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"similar #3611 "
] | "2022-01-19T13:36:50" | "2022-01-28T15:35:57" | "2022-01-28T15:35:57" | NONE | null | Hello, I posted this as a question on the forums ([here](https://discuss.huggingface.co/t/add-column-does-not-work-if-used-on-dataset-sliced-with-select/13893)):
I have a dataset with 2000 entries
> dataset = Dataset.from_dict({'colA': list(range(2000))})
and from which I want to extract the first one thousand rows, create a new dataset with these and also add a new column to it:
> dataset2 = dataset.select(list(range(1000)))
> final_dataset = dataset2.add_column('colB', list(range(1000)))
This gives an error
>ArrowInvalid: Added column's length must match table's length. Expected length 2000 but got length 1000
So it looks like even though it is a dataset with 1000 rows, it "remembers" the shape of the one it was sliced from.
## Actual results
```
ArrowInvalid Traceback (most recent call last)
<ipython-input-138-e806860f3ce3> in <module>
----> 1 final_dataset = dataset2.add_column('colB', list(range(1000)))
~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
468 }
469 # apply actual function
--> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
472 # re-apply format to the output
~/.local/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
404 # Call actual function
405
--> 406 out = func(self, *args, **kwargs)
407
408 # Update fingerprint of in-place transforms + update in-place history of transforms
~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in add_column(self, name, column, new_fingerprint)
3343 column_table = InMemoryTable.from_pydict({name: column})
3344 # Concatenate tables horizontally
-> 3345 table = ConcatenationTable.from_tables([self._data, column_table], axis=1)
3346 # Update features
3347 info = self.info.copy()
~/.local/lib/python3.8/site-packages/datasets/table.py in from_tables(cls, tables, axis)
729 table_blocks = to_blocks(table)
730 blocks = _extend_blocks(blocks, table_blocks, axis=axis)
--> 731 return cls.from_blocks(blocks)
732
733 @property
~/.local/lib/python3.8/site-packages/datasets/table.py in from_blocks(cls, blocks)
668 @classmethod
669 def from_blocks(cls, blocks: TableBlockContainer) -> "ConcatenationTable":
--> 670 blocks = cls._consolidate_blocks(blocks)
671 if isinstance(blocks, TableBlock):
672 table = blocks
~/.local/lib/python3.8/site-packages/datasets/table.py in _consolidate_blocks(cls, blocks)
664 return cls._merge_blocks(blocks, axis=0)
665 else:
--> 666 return cls._merge_blocks(blocks)
667
668 @classmethod
~/.local/lib/python3.8/site-packages/datasets/table.py in _merge_blocks(cls, blocks, axis)
650 merged_blocks += list(block_group)
651 else: # both
--> 652 merged_blocks = [cls._merge_blocks(row_block, axis=1) for row_block in blocks]
653 if all(len(row_block) == 1 for row_block in merged_blocks):
654 merged_blocks = cls._merge_blocks(
~/.local/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0)
650 merged_blocks += list(block_group)
651 else: # both
--> 652 merged_blocks = [cls._merge_blocks(row_block, axis=1) for row_block in blocks]
653 if all(len(row_block) == 1 for row_block in merged_blocks):
654 merged_blocks = cls._merge_blocks(
~/.local/lib/python3.8/site-packages/datasets/table.py in _merge_blocks(cls, blocks, axis)
647 for is_in_memory, block_group in groupby(blocks, key=lambda x: isinstance(x, InMemoryTable)):
648 if is_in_memory:
--> 649 block_group = [InMemoryTable(cls._concat_blocks(list(block_group), axis=axis))]
650 merged_blocks += list(block_group)
651 else: # both
~/.local/lib/python3.8/site-packages/datasets/table.py in _concat_blocks(blocks, axis)
626 else:
627 for name, col in zip(table.column_names, table.columns):
--> 628 pa_table = pa_table.append_column(name, col)
629 return pa_table
630 else:
~/.local/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.append_column()
~/.local/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.add_column()
~/.local/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/.local/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Added column's length must match table's length. Expected length 2000 but got length 1000
```
A solution provided by @mariosasko is to use `dataset2.flatten_indices()` after the `select()` and before attempting to add the new column:
> dataset = Dataset.from_dict({'colA': list(range(2000))})
> dataset2 = dataset.select(list(range(1000)))
> dataset2 = dataset2.flatten_indices()
> final_dataset = dataset2.add_column('colB', list(range(1000)))
which works.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.13.2 (note: also checked with version 1.17.0, still the same error)
- Platform: Ubuntu 20.04.3
- Python version: 3.8.10
- PyArrow version: 6.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3598 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3598/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3598/comments | https://api.github.com/repos/huggingface/datasets/issues/3598/events | https://github.com/huggingface/datasets/issues/3598 | 1,108,107,199 | I_kwDODunzps5CDF-_ | 3,598 | Readme info not being parsed to show on Dataset card page | {
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"i suspect a markdown parsing error, @severo do you want to take a quick look at it when you have some time?",
"# Problem\r\nThe issue seems to coming from the front matter of the README\r\n```---\r\nannotations_creators:\r\n- no-annotation\r\nlanguage_creators:\r\n- machine-generated\r\nlanguages:\r\n- 'ca'\r\n- 'de'\r\nlicenses:\r\n- cc-by-4.0\r\nmultilinguality:\r\n- translation\r\npretty_name: Catalan-German aligned corpora to train NMT systems.\r\nsize_categories:\r\n- \"1M<n<10M\" \r\nsource_datasets:\r\n- extended|tilde_model\r\ntask_categories:\r\n- machine-translation\r\ntask_ids:\r\n- machine-translation\r\n---\r\n``` \r\n# Solution\r\nThe fix is to correctly style the README as explained [here](https://huggingface.co/docs/datasets/v1.12.0/dataset_card.html). I have also correctly parsed the font matter as shown below:\r\n```\r\n---\r\nannotations_creators: []\r\nlanguage_creators: [machine-generated]\r\nlanguages: ['ca', 'de']\r\nlicenses: []\r\nmultilinguality:\r\n- multilingual\r\npretty_name: 'Catalan-German aligned corpora to train NMT systems.'\r\nsize_categories: \r\n- 1M<n<10M\r\nsource_datasets: ['extended|tilde_model']\r\ntask_categories: ['machine-translation']\r\ntask_ids: ['machine-translation']\r\n---\r\n```\r\nYou can find the README for a sample dataset [here](https://huggingface.co/datasets/ritwikraha/Test)",
"Thank you. It finally worked implementing your changes and leaving a white line between title and text in the description.",
"Thanks, if this solves your issue, can you please close it?"
] | "2022-01-19T13:32:29" | "2022-01-21T10:20:01" | "2022-01-21T10:20:01" | NONE | null | ## Describe the bug
The info contained in the README.md file is not being shown in the dataset main page. Basic info and table of contents are properly formatted in the README.
## Steps to reproduce the bug
# Sample code to reproduce the bug
The README file is this one: https://huggingface.co/datasets/softcatala/Tilde-MODEL-Catalan/blob/main/README.md
## Expected results
README info should appear in the Dataset card page.
## Actual results
Nothing is shown. However, labels are parsed and shown successfully.
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https://api.github.com/repos/huggingface/datasets/issues/3597 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3597/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3597/comments | https://api.github.com/repos/huggingface/datasets/issues/3597/events | https://github.com/huggingface/datasets/issues/3597 | 1,108,092,864 | I_kwDODunzps5CDCfA | 3,597 | ERROR: File "setup.py" or "setup.cfg" not found. Directory cannot be installed in editable mode: /content | {
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"Hi! The `cd` command in Jupyer/Colab needs to start with `%`, so this should work:\r\n```\r\n!git clone https://github.com/huggingface/datasets.git\r\n%cd datasets\r\n!pip install -e \".[streaming]\"\r\n```",
"thanks @mariosasko i had the same mistake and your solution is what was needed"
] | "2022-01-19T13:19:28" | "2022-08-05T12:35:51" | "2022-02-14T08:46:34" | NONE | null | ## Bug
The install of streaming dataset is giving following error.
## Steps to reproduce the bug
```python
! git clone https://github.com/huggingface/datasets.git
! cd datasets
! pip install -e ".[streaming]"
```
## Actual results
Cloning into 'datasets'...
remote: Enumerating objects: 50816, done.
remote: Counting objects: 100% (2356/2356), done.
remote: Compressing objects: 100% (1606/1606), done.
remote: Total 50816 (delta 834), reused 1741 (delta 525), pack-reused 48460
Receiving objects: 100% (50816/50816), 72.47 MiB | 27.68 MiB/s, done.
Resolving deltas: 100% (22541/22541), done.
Checking out files: 100% (6722/6722), done.
ERROR: File "setup.py" or "setup.cfg" not found. Directory cannot be installed in editable mode: /content
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https://api.github.com/repos/huggingface/datasets/issues/3596 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3596/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3596/comments | https://api.github.com/repos/huggingface/datasets/issues/3596/events | https://github.com/huggingface/datasets/issues/3596 | 1,107,345,338 | I_kwDODunzps5CAL-6 | 3,596 | Loss of cast `Image` feature on certain dataset method | {
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"Hi! Thanks for reporting! The issue with `cast_column` should be fixed by #3575 and after we merge that PR I'll start working on the `push_to_hub` support for the `Image`/`Audio` feature.",
"> Hi! Thanks for reporting! The issue with `cast_column` should be fixed by #3575 and after we merge that PR I'll start working on the `push_to_hub` support for the `Image`/`Audio` feature.\r\n\r\nThanks, I'll keep an eye out for #3575 getting merged. I managed to use `push_to_hub` sucesfully with images when they were loaded via `map` - something like `ds.map(lambda example: {\"img\": load_image_function(example['fname']})`, this only pushed the images to the hub if the `load_image_function` return a PIL Image without the filename attribute though. I guess this might often be the prefered behaviour though. \r\n",
"Hi ! We merged the PR and did a release of `datasets` that includes the changes. Can you try updating `datasets` and try again ?",
"> Hi ! We merged the PR and did a release of `datasets` that includes the changes. Can you try updating `datasets` and try again ?\r\n\r\nThanks for checking. There is no longer an error when calling `select` but it appears the cast value isn't preserved. Before `select`\r\n\r\n```python\r\ndataset.features\r\n{'url': Image(id=None)}\r\n```\r\n\r\nafter select:\r\n```\r\n{'url': Value(dtype='string', id=None)}\r\n```\r\n\r\nUpdated Colab example [here](https://colab.research.google.com/gist/davanstrien/4e88f55a3675c279b5c2f64299ae5c6f/potential_casting_bug.ipynb) ",
"Hmmm, if I re-run your google colab I'm getting the right type at the end:\r\n```\r\nsample.features\r\n# {'url': Image(id=None)}\r\n```",
"Appolgies - I've just run again and also got this output. I have also sucesfully used the `push_to_hub` method. I think this is fixed now so will close this issue. ",
"Fixed in #3575 "
] | "2022-01-18T20:44:01" | "2022-01-21T18:07:28" | "2022-01-21T18:07:28" | MEMBER | null | ## Describe the bug
When an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.
I suspect this might be related to https://github.com/huggingface/datasets/pull/3556 but I don't believe that pull request fixes this issue.
## Steps to reproduce the bug
An example of casting a url to an image followed by using the `select` method:
```python
from datasets import Dataset
from datasets import features
url = "https://cf.ltkcdn.net/cats/images/std-lg/246866-1200x816-grey-white-kitten.webp"
data_dict = {"url": [url]*2}
dataset = Dataset.from_dict(data_dict)
dataset = dataset.cast_column('url',features.Image())
sample = dataset.select([1])
```
[example notebook](https://gist.github.com/davanstrien/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)
## Expected results
The cast value is maintained when further methods are applied to the dataset.
## Actual results
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-12-47f393bc2d0d> in <module>()
----> 1 sample = dataset.select([1])
4 frames
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
487 }
488 # apply actual function
--> 489 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
490 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
491 # re-apply format to the output
/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
409 # Call actual function
410
--> 411 out = func(self, *args, **kwargs)
412
413 # Update fingerprint of in-place transforms + update in-place history of transforms
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
2772 )
2773 else:
-> 2774 return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)
2775
2776 @transmit_format
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)
2688 split=self.split,
2689 indices_table=indices_table,
-> 2690 fingerprint=fingerprint,
2691 )
2692
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)
664 if self.info.features.type != inferred_features.type:
665 raise ValueError(
--> 666 f"External features info don't match the dataset:\nGot\n{self.info.features}\nwith type\n{self.info.features.type}\n\nbut expected something like\n{inferred_features}\nwith type\n{inferred_features.type}"
667 )
668
ValueError: External features info don't match the dataset:
Got
{'url': Image(id=None)}
with type
struct<url: extension<arrow.py_extension_type<ImageExtensionType>>>
but expected something like
{'url': Value(dtype='string', id=None)}
with type
struct<url: string>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.1.dev0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/3595 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3595/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3595/comments | https://api.github.com/repos/huggingface/datasets/issues/3595/events | https://github.com/huggingface/datasets/pull/3595 | 1,107,260,527 | PR_kwDODunzps4xOIxH | 3,595 | Add ImageNet toy datasets from fastai | {
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] | closed | false | null | [] | null | [] | "2022-01-18T19:03:35" | "2022-09-30T14:39:35" | "2022-09-30T14:39:35" | CONTRIBUTOR | null | Adds the ImageNet toy datasets from FastAI: Imagenette, Imagewoof and Imagewang.
TODOs:
* [ ] add dummy data
* [ ] add dataset card
* [ ] generate `dataset_info.json` | {
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https://api.github.com/repos/huggingface/datasets/issues/3594 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3594/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3594/comments | https://api.github.com/repos/huggingface/datasets/issues/3594/events | https://github.com/huggingface/datasets/pull/3594 | 1,107,174,619 | PR_kwDODunzps4xN3Kk | 3,594 | fix multiple language downloading in mC4 | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-18T17:25:19" | "2022-01-19T11:22:57" | "2022-01-18T19:10:22" | CONTRIBUTOR | null | If we try to access multiple languages of the [mC4 dataset](https://github.com/huggingface/datasets/tree/master/datasets/mc4), it will throw an error. For example, if we do
```python
mc4_subset_two_langs = load_dataset("mc4", languages=["st", "su"])
```
we got
```
FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/multilingual/c4-st+su.tfrecord-00000-of-00002.json.gz
```
Now it should work. Check it (from the root dir of a project):
```python
mc4_subset_two_langs = load_dataset("./datasets/mc4/", languages=["st", "su"])
``` | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-18T15:52:16" | "2022-01-20T17:14:53" | "2022-01-20T17:14:53" | CONTRIBUTOR | null | Towards license of Tweet Eval parts | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-18T15:13:39" | "2022-06-09T10:04:54" | "2022-06-09T09:56:13" | CONTRIBUTOR | null | Add the QuickDraw dataset.
TODOs:
* [x] add dummy data
* [x] add dataset card
* [x] generate `dataset_info.json` | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-18T13:46:05" | "2022-01-31T18:29:34" | "2022-01-20T17:37:33" | CONTRIBUTOR | null | Add support for the pyarrow time (maps to `datetime.time` in python), date (maps to `datetime.time` in python), duration (maps to `datetime.timedelta` in python), and decimal (maps to `decimal.decimal` in python) dtypes. This should be helpful when writing scripts for time-series datasets. | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-18T11:22:53" | "2022-01-20T16:58:41" | "2022-01-20T16:58:41" | CONTRIBUTOR | null | Update license and little things concerning ANLI | {
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https://api.github.com/repos/huggingface/datasets/issues/3589 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3589/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3589/comments | https://api.github.com/repos/huggingface/datasets/issues/3589/events | https://github.com/huggingface/datasets/pull/3589 | 1,106,766,114 | PR_kwDODunzps4xMhGp | 3,589 | Pin torchmetrics to fix the COMET test | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-18T11:03:49" | "2022-01-18T11:04:56" | "2022-01-18T11:04:55" | MEMBER | null | Torchmetrics 0.7.0 got released and has issues with `transformers` (see https://github.com/PyTorchLightning/metrics/issues/770)
I'm pinning it to 0.6.0 in the CI, since 0.7.0 makes the COMET metric test fail. COMET requires torchmetrics==0.6.0 anyway. | {
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] | closed | false | null | [] | null | [] | "2022-01-18T10:17:01" | "2022-08-11T09:57:54" | "2022-01-18T10:33:10" | NONE | null | ## Describe the bug
Cannot import datasets after installation.
## Steps to reproduce the bug
```shell
$ python
Python 3.9.7 (default, Sep 16 2021, 13:09:58)
[GCC 7.5.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import datasets
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/__init__.py", line 34, in <module>
from .arrow_dataset import Dataset, concatenate_datasets
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 61, in <module>
from .arrow_writer import ArrowWriter, OptimizedTypedSequence
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/arrow_writer.py", line 28, in <module>
from .features import (
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/features/__init__.py", line 2, in <module>
from .audio import Audio
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/features/audio.py", line 7, in <module>
from ..utils.streaming_download_manager import xopen
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py", line 18, in <module>
from ..filesystems import COMPRESSION_FILESYSTEMS
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/filesystems/__init__.py", line 6, in <module>
from . import compression
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/filesystems/compression.py", line 5, in <module>
from fsspec.archive import AbstractArchiveFileSystem
ModuleNotFoundError: No module named 'fsspec.archive'
```
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- De-deprecating `disable_progress_bar()`
- Adding `enable_progress_bar()`
- On the caching side, adding `enable_caching` and `disable_caching`
Additional decisions have to be made with regards to the existing `set_enabled_X` functions; that is, whether to keep them as is or deprecate them in favor of the aforementioned functions.
cc @mariosasko @lhoestq | {
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"This seems related to https://github.com/huggingface/datasets/issues/3505."
] | "2022-01-17T12:55:42" | "2022-01-20T13:28:00" | "2022-01-20T13:28:00" | MEMBER | null | ## Describe the bug
When using audio datasets in streaming mode, applying a `map(...)` before iterating leads to an error as the key `array` does not exist anymore.
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("common_voice", "en", streaming=True, split="train")
def map_fn(batch):
print("audio keys", batch["audio"].keys())
batch["audio"] = batch["audio"]["array"][:100]
return batch
ds = ds.map(map_fn)
sample = next(iter(ds))
```
I think the audio is somehow decoded before `.map(...)` is actually called.
## Expected results
IMO, the above code snippet should work.
## Actual results
```bash
audio keys dict_keys(['path', 'bytes'])
Traceback (most recent call last):
File "./run_audio.py", line 15, in <module>
sample = next(iter(ds))
File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 341, in __iter__
for key, example in self._iter():
File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 338, in _iter
yield from ex_iterable
File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 192, in __iter__
yield key, self.function(example)
File "./run_audio.py", line 9, in map_fn
batch["input"] = batch["audio"]["array"][:100]
KeyError: 'array'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.1.dev0
- Platform: Linux-5.3.0-64-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.1
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**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
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"I came to open the same issue.",
"Thanks for reporting !\r\n\r\nI pushed a temporary fix on `master` that uses an URL from a previous commit to access the dataset for now, until we have a better solution",
"I changed the URL again to use another host, the fix is available on `master` and we'll probably do a new release of `datasets` tomorrow.\r\n\r\nIn the meantime, feel free to do `load_dataset(..., revision=\"master\")` to use the fixed script",
"We just released a new version of `datasets` with a working URL. Feel free to update `datasets` and try again :)",
"Hello! Unfortunately, this URL does not work for me. \r\nCould you please tell me how I can solve the problem?\r\n\r\n`>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"conll2003\")\r\nDownloading and preparing dataset conll2003/conll2003 (download: 4.63 MiB, generated: 9.78 MiB, post-processed: Unknown size, total: 14.41 MiB) to /home/dafedo/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/load.py\", line 745, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/builder.py\", line 574, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/builder.py\", line 630, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/dafedo/.cache/huggingface/modules/datasets_modules/datasets/conll2003/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6/conll2003.py\", line 196, in _split_generators\r\n downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 287, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 195, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 203, in map_nested\r\n mapped = [\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 204, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 142, in _single_map_nested\r\n return function(data_struct)\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 218, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 281, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 621, in get_from_cache\r\n raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt\r\n`\r\n\r\nI receive the same error when I run \"itrain run_configs/conll2003.json\" from https://github.com/adapter-hub/efficient-task-transfer\r\n\r\nThank you very much in advance!\r\n\r\nRegards, \r\nDaria\r\n",
"Can you try updating `datasets` and try again ?\r\n```\r\npip install -U datasets\r\n```",
"@lhoestq Thank you very much for your answer! \r\n\r\nIt works this way, but for my research I need datasets==1.6.3 or closest to it because otherwise the other package would not work as it is built on this version.\r\nDo you have any other suggestion? I would really appreciate it. Maybe which version of the datasets is without hard-coded link but closest to 1.6.3\r\n",
"No problem, I have solved it. \r\nThank you anyway.",
"Out of curiosity, which package has the `datasets==1.6.3` requirement ?"
] | "2022-01-15T23:04:17" | "2022-07-20T13:06:40" | "2022-01-21T16:57:32" | NONE | null | ## Describe the bug
Loading `conll2003` dataset fails because it was removed (just yesterday 1/14/2022) from the location it is looking for.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("conll2003")
```
## Expected results
The dataset should load.
## Actual results
It is looking for the dataset at `https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt` but it was removed from there yesterday (see [commit](https://github.com/davidsbatista/NER-datasets/commit/9d8f45cc7331569af8eb3422bbe1c97cbebd5690) that removed the file and related [issue](https://github.com/davidsbatista/NER-datasets/issues/8)).
- We should replace this with an alternate valid location.
- this is being referenced in the huggingface course chapter 7 [colab notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/chapter7/section2_pt.ipynb), which is also broken.
```python
FileNotFoundError Traceback (most recent call last)
<ipython-input-4-27c956bec93c> in <module>()
1 from datasets import load_dataset
2
----> 3 raw_datasets = load_dataset("conll2003")
11 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params)
610 )
611 elif response is not None and response.status_code == 404:
--> 612 raise FileNotFoundError(f"Couldn't find file at {url}")
613 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
614 if head_error is not None:
FileNotFoundError: Couldn't find file at https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
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https://api.github.com/repos/huggingface/datasets/issues/3580 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3580/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3580/comments | https://api.github.com/repos/huggingface/datasets/issues/3580/events | https://github.com/huggingface/datasets/issues/3580 | 1,104,663,242 | I_kwDODunzps5B19LK | 3,580 | Bug in wiki bio load | {
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"+1, here's the error I got: \r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>>\r\n>>> load_dataset(\"wiki_bio\")\r\nDownloading: 7.58kB [00:00, 4.42MB/s]\r\nDownloading: 2.71kB [00:00, 1.30MB/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to /home/jxm3/.cache/huggingface/datasets/wiki_bio/default/1.1.0/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/load.py\", line 1694, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/builder.py\", line 595, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/builder.py\", line 662, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/jxm3/.cache/huggingface/modules/datasets_modules/datasets/wiki_bio/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9/wiki_bio.py\", line 125, in _split_generators\r\n data_dir = dl_manager.download_and_extract(my_urls)\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 308, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 196, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 251, in map_nested\r\n return function(data_struct)\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 217, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 298, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 612, in get_from_cache\r\n raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\nFileNotFoundError: Couldn't find file at https://drive.google.com/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil\r\n>>>\r\n```\r\n",
"@alejandrocros and @lhoestq - you added the wiki_bio dataset in #1173. It doesn't work anymore. Can you take a look at this?",
"And if something is wrong with Google Drive, you could try to download (and collate and unzip) from here: https://github.com/DavidGrangier/wikipedia-biography-dataset",
"Hi ! Thanks for reporting. I've downloaded the data and concatenated them into a zip file available here: https://huggingface.co/datasets/wiki_bio/tree/main/data\r\n\r\nI guess we can update the dataset script to use this zip file now :)"
] | "2022-01-15T10:04:33" | "2022-01-31T08:38:09" | "2022-01-31T08:38:09" | NONE | null |
wiki_bio is failing to load because of a failing drive link . Can someone fix this ?
![7E90023B-A3B1-4930-BA25-45CCCB4E1710](https://user-images.githubusercontent.com/3104771/149617870-5a32a2da-2c78-483b-bff6-d7534215a423.png)
![653C1C76-C725-4A04-A0D8-084373BA612F](https://user-images.githubusercontent.com/3104771/149617875-ef0e30b0-b76e-48cf-b3eb-93ba8e6e5465.png)
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} | [] | closed | false | null | [] | null | [] | "2022-01-14T10:45:01" | "2022-01-20T17:09:44" | "2022-01-20T17:09:44" | CONTRIBUTOR | null | Adding the text2log dataset used for training FOL sentence translating models | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-13T17:16:07" | "2022-01-20T16:50:48" | "2022-01-20T16:50:47" | CONTRIBUTOR | null | This PR adds the PASS dataset.
Closes #3043 | {
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https://api.github.com/repos/huggingface/datasets/issues/3575 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3575/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3575/comments | https://api.github.com/repos/huggingface/datasets/issues/3575/events | https://github.com/huggingface/datasets/pull/3575 | 1,101,947,955 | PR_kwDODunzps4w8Usm | 3,575 | Add Arrow type casting to struct for Image and Audio + Support nested casting | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-13T15:36:59" | "2022-11-29T11:14:16" | "2022-01-21T13:22:27" | MEMBER | null | ## Intro
1. Currently, it's not possible to have nested features containing Audio or Image.
2. Moreover one can keep an Arrow array as a StringArray to store paths to images, but such arrays can't be directly concatenated to another image array if it's stored an another Arrow type (typically, a StructType).
3. Allowing several Arrow types for a single HF feature type also leads to bugs like this one #3497
4. Issues like #3247 are quite frequent and happen when Arrow fails to reorder StructArrays.
5. Casting Audio feature type is blocking preparation for the ASR task template: https://github.com/huggingface/datasets/pull/3364
All those issues are linked together by the fact that:
- we are limited by the Arrow type casting which is lacking features for nested types.
- and especially for Audio and Image: they are not robust enough for concatenation and feature inference.
## Proposed solution
To fix 1 and 4 I implemented nested array type casting (which is missing in PyArrow).
To fix 2, 3 and 5 while having a simple implementation for nested array type casting, I changed the storage type of Audio and Image to always be a StructType. Also casting from StringType is directly implemented via a new function `cast_storage` that is defined individually for Audio and Image. I also added nested decoding.
## Implementation details
### I. Better Arrow data type casting for nested data structures
I implemented new functions `array_cast` and `table_cast` that do the exact same as `pyarrow.Array.cast` or `pyarrow.Table.cast` but support nested struct casting and array re-ordering.
These functions can be used on PyArrow objects, and are already integrated in our own `datasets.table.Table.cast` functions. So one can do `my_dataset.data.cast(pyarrow_schema_with_custom_hf_types)` directly.
### II. New image and audio extension types with custom casting
I used PyArrow extension types to be able to define what casting is allowed or not. For example both StringType->ImageExtensionType and StructType->ImageExtensionType are allowed, via the `cast_storage` method.
I factorized all the PyArrow + Pandas extension stuff in the `base_extension.py` file. This aims at separating the front-facing API code of `datasets` from the Arrow back-end which requires advanced knowledge.
### III. Nested feature decoding
I added a new function `decode_nested_example` to decode image and audio data in nested data structures. For optimization's sake, this function is only called if a column has at least one feature that requires decoding.
## Alternative considered
The casting to struct type could have been done directly with python objects using some Audio and Image methods, but bringing arrow data to python objects is expensive. The Audio and Image types could also have been able to convert the arrow data directly, but this is not convenient to use when casting a full Arrow Table with nested fields. Therefore I decided to keep the Arrow data casting logic in Arrow extension types.
## Future work
This work can be used to allow the ArrayND feature types to be nested too (see issue #887)
## TODO
- [x] fix current tests
- [x] add new tests
- [x] docstrings/comments | {
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https://api.github.com/repos/huggingface/datasets/issues/3574 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3574/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3574/comments | https://api.github.com/repos/huggingface/datasets/issues/3574/events | https://github.com/huggingface/datasets/pull/3574 | 1,101,781,401 | PR_kwDODunzps4w7vu6 | 3,574 | Fix qa4mre tags | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-13T13:56:59" | "2022-01-13T14:03:02" | "2022-01-13T14:03:01" | MEMBER | null | The YAML tags were invalid. I also fixed the dataset mirroring logging that failed because of this issue [here](https://github.com/huggingface/datasets/actions/runs/1690109581) | {
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} | [] | closed | false | null | [] | null | [] | "2022-01-13T03:52:48" | "2022-01-20T15:00:08" | "2022-01-20T15:00:08" | CONTRIBUTOR | null | Add support for the [Mauve](https://github.com/krishnap25/mauve) metric introduced in this [paper](https://arxiv.org/pdf/2102.01454.pdf) (Neurips, 2021). | {
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