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https://api.github.com/repos/huggingface/datasets/issues/1912/comments
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2021-02-24T13:44:53Z
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Update: WMT - use mirror links
|
https://api.github.com/repos/huggingface/datasets/issues/1912/events
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2021-02-19T13:42:34Z
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https://api.github.com/repos/huggingface/datasets/issues/1912
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[
"So much better - thank you for doing that, @lhoestq!",
"Also fixed the `uncorpus` urls for wmt19 ru-en and zh-en for https://github.com/huggingface/datasets/issues/1893",
"Thanks!\r\nCan this be merged sooner? \r\nI manually update it and it works well."
] |
https://api.github.com/repos/huggingface/datasets
|
MEMBER
|
As asked in #1892 I created mirrors of the data hosted on statmt.org and updated the wmt scripts.
Now downloading the wmt datasets is blazing fast :)
cc @stas00 @patrickvonplaten
|
2021-02-24T13:44:53Z
|
https://github.com/huggingface/datasets/pull/1912
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Saving processed dataset running infinitely
|
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2021-02-19T13:09:19Z
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https://api.github.com/repos/huggingface/datasets/issues/1911
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[
"@thomwolf @lhoestq can you guys please take a look and recommend some solution.",
"am suspicious of this thing? what's the purpose of this? pickling and unplickling\r\n`self = pickle.loads(pickle.dumps(self))`\r\n\r\n```\r\n def save_to_disk(self, dataset_path: str, fs=None):\r\n \"\"\"\r\n Saves a dataset to a dataset directory, or in a filesystem using either :class:`datasets.filesystem.S3FileSystem` or any implementation of ``fsspec.spec.AbstractFileSystem``.\r\n\r\n Args:\r\n dataset_path (``str``): path (e.g. ``dataset/train``) or remote uri (e.g. ``s3://my-bucket/dataset/train``) of the dataset directory where the dataset will be saved to\r\n fs (Optional[:class:`datasets.filesystem.S3FileSystem`,``fsspec.spec.AbstractFileSystem``], `optional`, defaults ``None``): instance of :class:`datasets.filesystem.S3FileSystem` or ``fsspec.spec.AbstractFileSystem`` used to download the files from remote filesystem.\r\n \"\"\"\r\n assert (\r\n not self.list_indexes()\r\n ), \"please remove all the indexes using `dataset.drop_index` before saving a dataset\"\r\n self = pickle.loads(pickle.dumps(self))\r\n ```",
"It's been 24 hours and sadly it's still running. With not a single byte written",
"Tried finding the root cause but was unsuccessful.\r\nI am using lazy tokenization with `dataset.set_transform()`, it works like a charm with almost same performance as pre-compute.",
"Hi ! This very probably comes from the hack you used.\r\n\r\nThe pickling line was added an a sanity check because save_to_disk uses the same assumptions as pickling for a dataset object. The main assumption is that memory mapped pyarrow tables must be reloadable from the disk. In your case it's not possible since you altered the pyarrow table.\r\nI would suggest you to rebuild a valid Dataset object from your new pyarrow table. To do so you must first save your new table to a file, and then make a new Dataset object from that arrow file.\r\n\r\nYou can save the raw arrow table (without all the `datasets.Datasets` metadata) by calling `map` with `cache_file_name=\"path/to/outut.arrow\"` and `function=None`. Having `function=None` makes the `map` write your dataset on disk with no data transformation.\r\n\r\nOnce you have your new arrow file, load it with `datasets.Dataset.from_file` to have a brand new Dataset object :)\r\n\r\nIn the future we'll have a better support for the fast filtering method from pyarrow so you don't have to do this very unpractical workaround. Since it breaks somes assumptions regarding the core behavior of Dataset objects, this is very discouraged.",
"Thanks, @lhoestq for your response. Will try your solution and let you know."
] |
https://api.github.com/repos/huggingface/datasets
|
NONE
|
I have a text dataset of size 220M.
For pre-processing, I need to tokenize this and filter rows with the large sequence.
My tokenization took roughly 3hrs. I used map() with batch size 1024 and multi-process with 96 processes.
filter() function was way to slow, so I used a hack to use pyarrow filter table function, which is damm fast. Mentioned [here](https://github.com/huggingface/datasets/issues/1796)
```dataset._data = dataset._data.filter(...)```
It took 1 hr for the filter.
Then i use `save_to_disk()` on processed dataset and it is running forever.
I have been waiting since 8 hrs, it has not written a single byte.
Infact it has actually read from disk more than 100GB, screenshot below shows the stats using `iotop`.
Second process is the one.
<img width="1672" alt="Screenshot 2021-02-19 at 6 36 53 PM" src="https://user-images.githubusercontent.com/20911334/108508197-7325d780-72e1-11eb-8369-7c057d137d81.png">
I am not able to figure out, whether this is some issue with dataset library or that it is due to my hack for filter() function.
|
2021-02-23T07:34:44Z
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https://github.com/huggingface/datasets/issues/1911
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2021-03-04T22:02:47Z
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Adding CoNLLpp dataset.
|
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2021-02-19T05:12:30Z
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https://api.github.com/repos/huggingface/datasets/issues/1910
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[
"It looks like this PR now includes changes to many other files than the ones for CoNLLpp.\r\n\r\nTo fix that feel free to create another branch and another PR.\r\n\r\nThis was probably caused by a git rebase. You can avoid this issue by using git merge if you've already pushed your branch."
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
2021-03-04T22:02:47Z
|
https://github.com/huggingface/datasets/pull/1910
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2021-02-22T23:22:04Z
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completed
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DBPedia14 Dataset Checksum bug?
|
https://api.github.com/repos/huggingface/datasets/issues/1907/events
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2021-02-18T22:25:48Z
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https://api.github.com/repos/huggingface/datasets/issues/1907
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[
"Hi ! :)\r\n\r\nThis looks like the same issue as https://github.com/huggingface/datasets/issues/1856 \r\nBasically google drive has quota issues that makes it inconvenient for downloading files.\r\n\r\nIf the quota of a file is exceeded, you have to wait 24h for the quota to reset (which is painful).\r\n\r\nThe error says that the checksum of the downloaded file doesn't match because google drive returns a text file with the \"Quota Exceeded\" error instead of the actual data file.",
"Thanks @lhoestq! Yes, it seems back to normal after a couple of days."
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
Hi there!!!
I've been using successfully the DBPedia dataset (https://huggingface.co/datasets/dbpedia_14) with my codebase in the last couple of weeks, but in the last couple of days now I get this error:
```
Traceback (most recent call last):
File "./conditional_classification/basic_pipeline.py", line 178, in <module>
main()
File "./conditional_classification/basic_pipeline.py", line 128, in main
corpus.load_data(limit_train_examples_per_class=args.data_args.train_examples_per_class,
File "/home/fp/dev/conditional_classification/conditional_classification/datasets_base.py", line 83, in load_data
datasets = load_dataset(self.name, split=dataset_split)
File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/load.py", line 609, in load_dataset
builder_instance.download_and_prepare(
File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/builder.py", line 526, in download_and_prepare
self._download_and_prepare(
File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/builder.py", line 586, in _download_and_prepare
verify_checksums(
File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 39, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k']
```
I've seen this has happened before in other datasets as reported in #537.
I've tried clearing my cache and call again `load_dataset` but still is not working. My same codebase is successfully downloading and using other datasets (e.g. AGNews) without any problem, so I guess something has happened specifically to the DBPedia dataset in the last few days.
Can you please check if there's a problem with the checksums?
Or this is related to any other stuff? I've seen that the path in the cache for the dataset is `/home/fp/.cache/huggingface/datasets/d_bpedia14/dbpedia_14/2.0.0/a70413e39e7a716afd0e90c9e53cb053691f56f9ef5fe317bd07f2c368e8e897...` and includes `d_bpedia14` instead maybe of `dbpedia_14`. Was this maybe a bug introduced recently?
Thanks!
|
2021-02-22T23:22:05Z
|
https://github.com/huggingface/datasets/issues/1907
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Feature Request: Support for Pandas `Categorical`
|
https://api.github.com/repos/huggingface/datasets/issues/1906/events
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2021-02-18T19:46:05Z
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[
"We already have a ClassLabel type that does this kind of mapping between the label ids (integers) and actual label values (strings).\r\n\r\nI wonder if actually we should use the DictionaryType from Arrow and the Categorical type from pandas for the `datasets` ClassLabel feature type.\r\nCurrently ClassLabel corresponds to `pa.int64()` in pyarrow and `dtype('int64')` in pandas (so the label names are lost during conversions).\r\n\r\nWhat do you think ?",
"Now that I've heard you explain ClassLabel, that makes a lot of sense! While DictionaryType for Arrow (I think) can have arbitrarily typed keys, so it won't cover all potential cases, pandas' Category is *probably* the most common use for that pyarrow type, and ClassLabel should match that perfectly?\r\n\r\nOther thoughts:\r\n\r\n- changing the resulting patype on ClassLabel might be backward-incompatible? I'm not totally sure if users of the `datasets` library tend to directly access the `patype` attribute (I don't think we really do, but we haven't been using it for very long yet).\r\n- would ClassLabel's dtype change to `dict[int64, string]`? It seems like in practice a ClassLabel (when not explicitly specified) would be constructed from the DictionaryType branch of `generate_from_arrow_type`, so it's not totally clear to me that anyone ever actually accesses/uses that dtype?\r\n- I don't quite know how `.int2str` and `.str2int` are used in practice - would those be kept? Perhaps the implementation might actually be substantially smaller if we can just delegate to pyarrow's dict methods?\r\n\r\nAnother idea that just occurred to me: add a branch in here to generate a ClassLabel if the dict key is int64 and the values are string: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L932 , and then don't touch anything else.\r\n\r\nIn practice, I don't think this would be backward-incompatible in a way anyone would care about since the current behavior just throws an exception, and this way, we could support *reading* a pandas Categorical into a `Dataset` as a ClassLabel. I *think* from there, while it would require some custom glue it wouldn't be too hard to convert the ClassLabel into a pandas Category if we want to go back - I think this would improve on the current behavior without risking changing the behavior of ClassLabel in a backward-incompat way.\r\n\r\nThoughts? I'm not sure if this is overly cautious. Whichever approach you think is better, I'd be happy to take it on!\r\n",
"I think we can first keep the int64 precision but with an arrow Dictionary for ClassLabel, and focus on the connection with arrow and pandas.\r\n\r\nIn this scope, I really like the idea of checking for the dictionary type:\r\n\r\n> Another idea that just occurred to me: add a branch in here to generate a ClassLabel if the dict key is int64 and the values are string: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L932 , and then don't touch anything else.\r\n\r\nThis looks like a great start.\r\n\r\nThen as you said we'd have to add the conversion from classlabel to the correct arrow dictionary type. Arrow is already able to convert from arrow Dictionary to pandas Categorical so it should be enough.\r\n\r\nI can see two things that we must take case of to make this change backward compatible:\r\n- first we must still be able to load an arrow file with arrow int64 dtype and `datasets` ClassLabel type without crashing. This can be fixed by casting the arrow int64 array to an arrow Dictionary array on-the-fly when loading the table in the ArrowReader.\r\n- then we still have to return integers when accessing examples from a ClassLabel column. Currently it would return the strings values since it's based on the pandas behavior for converting from pandas to python/numpy. To do so we just have to adapt the python/numpy extractors in formatting.py (it takes care of converting an arrow table to a dictionary of python objects by doing arrow table -> pandas dataframe -> python dictionary)\r\n\r\nAny help on this matter is very much welcome :)"
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
```
from datasets import Dataset
import pandas as pd
import pyarrow
df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category"))
pyarrow.Table.from_pandas(df)
Dataset.from_pandas(df)
# Throws NotImplementedError
# TODO(thom) this will need access to the dictionary as well (for labels). I.e. to the py_table
```
I'm curious if https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L796 could be built out in a way similar to `Sequence`?
e.g. a `Map` class (or whatever name the maintainers might prefer) that can accept:
```
index_type = generate_from_arrow_type(pa_type.index_type)
value_type = generate_from_arrow_type(pa_type.value_type)
```
and then additional code points to modify:
- FeatureType: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L694
- A branch to handle Map in get_nested_type: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L719
- I don't quite understand what `encode_nested_example` does but perhaps a branch there? https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L755
- Similarly, I don't quite understand why `Sequence` is used this way in `generate_from_dict`, but perhaps a branch here? https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L775
I couldn't find other usages of `Sequence` outside of defining specific datasets, so I'm not sure if that's a comprehensive set of touchpoints.
|
2021-02-23T14:38:50Z
|
https://github.com/huggingface/datasets/issues/1906
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Standardizing datasets.dtypes
|
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"Also - I took a stab at updating the docs, but I'm not sure how to actually check the outputs to see if it's formatted properly."
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
This PR was further branched off of jdy-str-to-pyarrow-parsing, so it depends on https://github.com/huggingface/datasets/pull/1900 going first for the diff to be up-to-date (I'm not sure if there's a way for me to use jdy-str-to-pyarrow-parsing as a base branch while having it appear in the pull requests here).
This moves away from `str(pyarrow.DataType)` as the method of choice for creating dtypes, favoring an explicit mapping to a list of supported Value dtypes.
I believe in practice this should be backward compatible, since anyone previously using Value() would only have been able to use dtypes that had an identically named pyarrow factory function, which are all explicitly supported here.
|
2021-02-20T22:01:30Z
|
https://github.com/huggingface/datasets/pull/1905
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Fix to_pandas for boolean ArrayXD
|
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[
"Thanks!"
] |
https://api.github.com/repos/huggingface/datasets
|
MEMBER
|
As noticed in #1887 the conversion of a dataset with a boolean ArrayXD feature types fails because of the underlying ListArray conversion to numpy requires `zero_copy_only=False`.
zero copy is available for all primitive types except booleans
see https://arrow.apache.org/docs/python/generated/pyarrow.Array.html#pyarrow.Array.to_numpy
and https://issues.apache.org/jira/browse/ARROW-2871?jql=text%20~%20%22boolean%20to_numpy%22
cc @SBrandeis
|
2021-02-18T17:10:03Z
|
https://github.com/huggingface/datasets/pull/1904
|
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Initial commit for the addition of TIMIT dataset
|
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[
"@patrickvonplaten could you please review and help me close this PR?",
"@lhoestq Thank you so much for your comments and for patiently reviewing the code. Have _hopefully_ included all the suggested changes. Let me know if any more changes are required.\r\n\r\nSorry the code had lots of silly errors from my side!:' Will be more careful from next time! :)\r\n\r\n\r\n"
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
Below points needs to be addressed:
- Creation of dummy dataset is failing
- Need to check on the data representation
- License is not creative commons. Copyright: Portions © 1993 Trustees of the University of Pennsylvania
Also the links (_except the download_) point to the ami corpus! ;-)
@patrickvonplaten Requesting your comments, will be happy to address them!
|
2021-03-01T09:39:12Z
|
https://github.com/huggingface/datasets/pull/1903
|
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Fix setimes_2 wmt urls
|
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MEMBER
|
Continuation of #1901
Some other urls were missing https
|
2021-02-18T09:55:41Z
|
https://github.com/huggingface/datasets/pull/1902
|
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Fix OPUS dataset download errors
|
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CONTRIBUTOR
|
Replace http to https.
https://github.com/huggingface/datasets/issues/854
https://discuss.huggingface.co/t/cannot-download-wmt16/2081
|
2021-02-18T15:07:20Z
|
https://github.com/huggingface/datasets/pull/1901
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2021-02-19T18:27:11Z
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Issue #1895: Bugfix for string_to_arrow timestamp[ns] support
|
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[
"OK! Thank you for the review - I will follow up with a separate PR for the comments here (https://github.com/huggingface/datasets/pull/1900#discussion_r578319725)!"
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
Should resolve https://github.com/huggingface/datasets/issues/1895
The main part of this PR adds additional parsing in `string_to_arrow` to convert the timestamp dtypes that result from `str(pa_type)` back into the pa.DataType TimestampType.
While adding unit-testing, I noticed that support for the double/float types also don't invert correctly, so I added them, which I believe would hypothetically make this section of `Value` redundant:
```
def __post_init__(self):
if self.dtype == "double": # fix inferred type
self.dtype = "float64"
if self.dtype == "float": # fix inferred type
self.dtype = "float32"
```
However, since I think Value.dtype is part of the public interface, removing that would result in a backward-incompatible change, so I didn't muck with that.
The rest of the PR consists of docstrings that I added while developing locally so I could keep track of which functions were supposed to be inverses of each other, and thought I'd include them initially in case you want to keep them around, but I'm happy to delete or remove any of them at your request!
|
2021-02-19T18:27:11Z
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Fix: ALT - fix duplicated examples in alt-parallel
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MEMBER
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As noticed in #1898 by @10-zin the examples of the `alt-paralel` configurations have all the same values for the `translation` field.
This was due to a bad copy of a python dict.
This PR fixes that.
|
2021-02-17T17:20:49Z
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https://github.com/huggingface/datasets/pull/1899
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ALT dataset has repeating instances in all splits
|
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[
"Thanks for reporting. This looks like a very bad issue. I'm looking into it",
"I just merged a fix, we'll do a patch release soon. Thanks again for reporting, and sorry for the inconvenience.\r\nIn the meantime you can load `ALT` using `datasets` from the master branch",
"Thanks!!! works perfectly in the bleading edge master version",
"Closed by #1899"
] |
https://api.github.com/repos/huggingface/datasets
|
NONE
|
The [ALT](https://huggingface.co/datasets/alt) dataset has all the same instances within each split :/
Seemed like a great dataset for some experiments I wanted to carry out, especially since its medium-sized, and has all splits.
Would be great if this could be fixed :)
Added a snapshot of the contents from `explore-datset` feature, for quick reference.
|
2021-02-19T06:18:46Z
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Fix PandasArrayExtensionArray conversion to native type
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MEMBER
|
To make the conversion to csv work in #1887 , we need PandasArrayExtensionArray used for multidimensional numpy arrays to be converted to pandas native types.
However previously pandas.core.internals.ExtensionBlock.to_native_types would fail with an PandasExtensionArray because
1. the PandasExtensionArray.isna method was wrong
2. the conversion of a PandasExtensionArray to a numpy array with dtype=object was returning a multidimensional array while pandas excepts a 1D array in this case (more info [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.api.extensions.ExtensionArray.html#pandas.api.extensions.ExtensionArray))
I fixed these two issues and now the conversion to native types works, and so is the export to csv.
cc @SBrandeis
|
2021-02-17T13:15:16Z
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https://github.com/huggingface/datasets/pull/1897
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Bug Report: timestamp[ns] not recognized
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[
"Thanks for reporting !\r\n\r\nYou're right, `string_to_arrow` should be able to take `\"timestamp[ns]\"` as input and return the right pyarrow timestamp type.\r\nFeel free to suggest a fix for `string_to_arrow` and open a PR if you want to contribute ! This would be very appreciated :)\r\n\r\nTo give you more context:\r\n\r\nAs you may know we define the features types of a dataset using the `Features` object in combination with feature types like `Value`. For example\r\n```python\r\nfeatures = Features({\r\n \"age\": Value(\"int32\")\r\n})\r\n```\r\nHowever under the hood we are actually using pyarrow to store the data, and so we have a mapping between the feature types of `datasets` and the types of pyarrow.\r\n\r\nFor example, the `Value` feature types are created from a pyarrow type with `Value(str(pa_type))`.\r\nHowever it looks like the conversion back to a pyarrow type doesn't work with `\"timestamp[ns]\"`.\r\nThis is the `string_to_arrow` function you highlighted that does this conversion, so we should fix that.\r\n\r\n",
"Thanks for the clarification @lhoestq !\r\n\r\nThis may be a little bit of a stupid question, but I wanted to clarify one more thing before I took a stab at this:\r\n\r\nWhen the features get inferred, I believe they already have a pyarrow schema (https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L234).\r\n\r\nWe then convert it to a string (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L778) only to convert it back into the arrow type (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L143, and https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L35). Is there a reason for this round-trip?\r\n\r\nI'll open a PR later to add `timestamp` support to `string_to_arrow`, but I'd be curious to understand since it feels like there may be some opportunities to simplify!",
"The objective in terms of design is to make it easy to create Features in a pythonic way. So for example we use a string to define a Value type.\r\nThat's why when inferring the Features from an arrow schema we have to find the right string definitions for Value types. I guess we could also have a constructor `Value.from_arrow_type` to avoid recreating the arrow type, but this could create silent errors if the pyarrow type doesn't have a valid mapping with the string definition. The \"round-trip\" is used to enforce that the ground truth is the string definition, not the pyarrow type, and also as a sanity check.\r\n\r\nLet me know if that makes sense ",
"OK I think I understand now:\r\n\r\nFeatures are datasets' internal representation of a schema type, distinct from pyarrow's schema.\r\nValue() corresponds to pyarrow's \"primitive\" types (e.g. `int` or `string`, but not things like `list` or `dict`).\r\n`get_nested_type()` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L698) and `generate_from_arrow_type()` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L778) *should* be inverses of each other, and similarly, for the primitive values, `string_to_arrow()` and `Value.__call__` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L146) should be inverses of each other?\r\n\r\nThanks for taking the time to answer - I just wanted to make sure I understood before opening a PR so I'm not disrupting anything about how the codebase is expected to work!",
"Yes you're totally right :)"
] |
https://api.github.com/repos/huggingface/datasets
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CONTRIBUTOR
|
Repro:
```
from datasets import Dataset
import pandas as pd
import pyarrow
df = pd.DataFrame(pd.date_range("2018-01-01", periods=3, freq="H"))
pyarrow.Table.from_pandas(df)
Dataset.from_pandas(df)
# Throws ValueError: Neither timestamp[ns] nor timestamp[ns]_ seems to be a pyarrow data type.
```
The factory function seems to be just "timestamp": https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html#pyarrow.timestamp
It seems like https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L36-L43 could have a little bit of additional structure for handling these cases? I'd be happy to take a shot at opening a PR if I could receive some guidance on whether parsing something like `timestamp[ns]` and resolving it to timestamp('ns') is the goal of this method.
Alternatively, if I'm using this incorrectly (e.g. is the expectation that we always provide a schema when timestamps are involved?), that would be very helpful to know as well!
```
$ pip list # only the relevant libraries/versions
datasets 1.2.1
pandas 1.0.3
pyarrow 3.0.0
```
|
2021-02-19T18:27:11Z
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benchmarking against MMapIndexedDataset
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[
"Hi sam !\r\nIndeed we can expect the performances to be very close since both MMapIndexedDataset and the `datasets` implem use memory mapping. With memory mapping what determines the I/O performance is the speed of your hard drive/SSD.\r\n\r\nIn terms of performance we're pretty close to the optimal speed for reading text, even though I found recently that we could still slightly improve speed for big datasets (see [here](https://github.com/huggingface/datasets/issues/1803)).\r\n\r\nIn terms of number of examples and example sizes, the only limit is the available disk space you have.\r\n\r\nI haven't used `psrecord` yet but it seems to be a very interesting tool for benchmarking. Currently for benchmarks we only have github actions to avoid regressions in terms of speed. But it would be cool to have benchmarks with comparisons with other dataset tools ! This would be useful to many people",
"Also I would be interested to know what data types `MMapIndexedDataset` supports. Is there some documentation somewhere ?",
"no docs haha, it's written to support integer numpy arrays.\r\n\r\nYou can build one in fairseq with, roughly:\r\n```bash\r\n\r\nwget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip\r\nunzip wikitext-103-raw-v1.zip\r\nexport dd=$HOME/fairseq-py/wikitext-103-raw\r\n\r\nexport mm_dir=$HOME/mmap_wikitext2\r\nmkdir -p gpt2_bpe\r\nwget -O gpt2_bpe/encoder.json https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json\r\nwget -O gpt2_bpe/vocab.bpe https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe\r\nwget -O gpt2_bpe/dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt\r\nfor SPLIT in train valid; do \\\r\n python -m examples.roberta.multiprocessing_bpe_encoder \\\r\n --encoder-json gpt2_bpe/encoder.json \\\r\n --vocab-bpe gpt2_bpe/vocab.bpe \\\r\n --inputs /scratch/stories_small/${SPLIT}.txt \\\r\n --outputs /scratch/stories_small/${SPLIT}.bpe \\\r\n --keep-empty \\\r\n --workers 60; \\\r\ndone\r\n\r\nmkdir -p $mm_dir\r\nfairseq-preprocess \\\r\n --only-source \\\r\n --srcdict gpt2_bpe/dict.txt \\\r\n --trainpref $dd/wiki.train.bpe \\\r\n --validpref $dd/wiki.valid.bpe \\\r\n --destdir $mm_dir \\\r\n --workers 60 \\\r\n --dataset-impl mmap\r\n```\r\n\r\nI'm noticing in my benchmarking that it's much smaller on disk than arrow (200mb vs 900mb), and that both incur significant cost by increasing the number of data loader workers. \r\nThis somewhat old [post](https://ray-project.github.io/2017/10/15/fast-python-serialization-with-ray-and-arrow.html) suggests there are some gains to be had from using `pyarrow.serialize(array).tobuffer()`. I haven't yet figured out how much of this stuff `pa.Table` does under the hood.\r\n\r\nThe `MMapIndexedDataset` bottlenecks we are working on improving (by using arrow) are:\r\n1) `MMapIndexedDataset`'s index, which stores offsets, basically gets read in its entirety by each dataloading process.\r\n2) we have separate, identical, `MMapIndexedDatasets` on each dataloading worker, so there's redundancy there; we wonder if there is a way that arrow can somehow dedupe these in shared memory.\r\n\r\nIt will take me a few hours to get `MMapIndexedDataset` benchmarks out of `fairseq`/onto a branch in this repo, but I'm happy to invest the time if you're interested in collaborating on some performance hacking."
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
I am trying to benchmark my datasets based implementation against fairseq's [`MMapIndexedDataset`](https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L365) and finding that, according to psrecord, my `datasets` implem uses about 3% more CPU memory and runs 1% slower for `wikitext103` (~1GB of tokens).
Questions:
1) Is this (basically identical) performance expected?
2) Is there a scenario where this library will outperform `MMapIndexedDataset`? (maybe more examples/larger examples?)
3) Should I be using different benchmarking tools than `psrecord`/how do you guys do benchmarks?
Thanks in advance! Sam
|
2021-02-17T18:52:28Z
|
https://github.com/huggingface/datasets/issues/1894
|
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2021-03-03T17:42:02Z
| null |
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MDU6SXNzdWU4MDk1NTY1MDM=
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closed
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wmt19 is broken
|
https://api.github.com/repos/huggingface/datasets/issues/1893/events
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2021-02-16T18:39:58Z
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|
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"default": false,
"description": "A bug in a dataset script provided in the library",
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"name": "dataset bug",
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"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug"
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https://api.github.com/repos/huggingface/datasets/issues/1893
|
[
"This was also mentioned in https://github.com/huggingface/datasets/issues/488 \r\n\r\nThe bucket where is data was stored seems to be unavailable now. Maybe we can change the URL to the ones in https://conferences.unite.un.org/uncorpus/en/downloadoverview ?",
"Closing since this has been fixed by #1912"
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
1. Check which lang pairs we have: `--dataset_name wmt19`:
Please pick one among the available configs: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de']
2. OK, let's pick `ru-en`:
`--dataset_name wmt19 --dataset_config "ru-en"`
no cookies:
```
Traceback (most recent call last):
File "./run_seq2seq.py", line 661, in <module>
main()
File "./run_seq2seq.py", line 317, in main
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 740, in load_dataset
builder_instance.download_and_prepare(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 572, in download_and_prepare
self._download_and_prepare(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 628, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt19/436092de5f3faaf0fc28bc84875475b384e90a5470fa6afaee11039ceddc5052/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 276, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 191, in download
downloaded_path_or_paths = map_nested(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 233, in map_nested
mapped = [
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 234, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 190, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 190, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 172, in _single_map_nested
return function(data_struct)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 211, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path
output_path = get_from_cache(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-ru.tar.gz
```
|
2021-03-03T17:42:02Z
|
https://github.com/huggingface/datasets/issues/1893
|
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2021-03-25T11:53:23Z
| null |
completed
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MDU6SXNzdWU4MDk1NTQxNzQ=
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closed
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request to mirror wmt datasets, as they are really slow to download
|
https://api.github.com/repos/huggingface/datasets/issues/1892/events
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2021-02-16T18:36:11Z
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|
[] |
https://api.github.com/repos/huggingface/datasets/issues/1892
|
[
"Yes that would be awesome. Not only the download speeds are awful, but also some files are missing.\r\nWe list all the URLs in the datasets/wmt19/wmt_utils.py so we can make a script to download them all and host on S3.\r\nAlso I think most of the materials are under the CC BY-NC-SA 3.0 license (must double check) so it should be possible to redistribute the data with no issues.\r\n\r\ncc @patrickvonplaten who knows more about the wmt scripts",
"Yeah, the scripts are pretty ugly! A big refactor would make sense here...and I also remember that the datasets were veeery slow to download",
"I'm downloading them.\r\nI'm starting with the ones hosted on http://data.statmt.org which are the slowest ones",
"@lhoestq better to use our new git-based system than just raw S3, no? (that way we have built-in CDN etc.)",
"Closing since the urls were changed to mirror urls in #1912 ",
"Hi there! What about mirroring other datasets like [CCAligned](http://www.statmt.org/cc-aligned/) as well? All of them are really slow to download..."
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download.
Thank you!
|
2021-10-26T06:55:42Z
|
https://github.com/huggingface/datasets/issues/1892
|
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2022-10-05T12:48:38Z
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closed
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[] | null | 1,891
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suggestion to improve a missing dataset error
|
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2021-02-16T18:29:13Z
| null | false
| null | null | 809,550,001
|
[] |
https://api.github.com/repos/huggingface/datasets/issues/1891
|
[
"This is the current error thrown for missing datasets:\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at C:\\Users\\Mario\\Desktop\\projects\\datasets\\missing_dataset\\missing_dataset.py or any data file in the same directory. Couldn't find 'missing_dataset' on the Hugging Face Hub either: FileNotFoundError: Dataset 'missing_dataset' doesn't exist on the Hub. If the repo is private, make sure you are authenticated with `use_auth_token=True` after logging in with `huggingface-cli login`.\r\n```\r\n\r\nSeems much more informative, so I think we can close this issue."
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
I was using `--dataset_name wmt19` all was good. Then thought perhaps wmt20 is out, so I tried to use `--dataset_name wmt20`, got 3 different errors (1 repeated twice), none telling me the real issue - that `wmt20` isn't in the `datasets`:
```
True, predict_with_generate=True)
Traceback (most recent call last):
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 323, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path
output_path = get_from_cache(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/wmt20/wmt20.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 335, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path
output_path = get_from_cache(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/wmt20/wmt20.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "./run_seq2seq.py", line 661, in <module>
main()
File "./run_seq2seq.py", line 317, in main
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 706, in load_dataset
module_path, hash, resolved_file_path = prepare_module(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 343, in prepare_module
raise FileNotFoundError(
FileNotFoundError: Couldn't find file locally at wmt20/wmt20.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/wmt20/wmt20.py.
The file is also not present on the master branch on github.
```
Suggestion: if it is not in a local path, check that there is an actual `https://github.com/huggingface/datasets/tree/master/datasets/wmt20` first and assert "dataset `wmt20` doesn't exist in datasets", rather than trying to find a load script - since the whole repo is not there.
The error occured when running:
```
cd examples/seq2seq
export BS=16; rm -r output_dir; PYTHONPATH=../../src USE_TF=0 CUDA_VISIBLE_DEVICES=0 python ./run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --evaluation_strategy=steps --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --predict_with_generate --eval_steps 25000 --sortish_sampler --task translation_en_to_ro --val_max_target_length 128 --warmup_steps 500 --max_val_samples 500 --dataset_name wmt20 --dataset_config "ro-en" --source_prefix "translate English to Romanian: "
```
Thanks.
|
2022-10-05T12:48:38Z
|
https://github.com/huggingface/datasets/issues/1891
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Reformat dataset cards section titles
|
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MEMBER
|
Titles are formatted like [Foo](#foo) instead of just Foo
|
2021-02-16T15:12:34Z
|
https://github.com/huggingface/datasets/pull/1890
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2021-02-18T18:42:34Z
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Implement to_dict and to_pandas for Dataset
|
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2021-02-16T12:38:19Z
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[
"Next step is going to add these two in the documentation ^^"
] |
https://api.github.com/repos/huggingface/datasets
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CONTRIBUTOR
|
With options to return a generator or the full dataset
|
2021-02-18T18:42:37Z
|
https://github.com/huggingface/datasets/pull/1889
|
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2021-02-16T11:58:57Z
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Docs for adding new column on formatted dataset
|
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MEMBER
|
As mentioned in #1872 we should add in the documentation how the format gets updated when new columns are added
Close #1872
|
2021-03-30T14:01:03Z
|
https://github.com/huggingface/datasets/pull/1888
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Implement to_csv for Dataset
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[
"@lhoestq I stumbled upon an interesting failure when adding tests for CSV serialization of `ArrayXD` features (see the failing unit tests in the CI)\r\n\r\nIt's due to the fact that booleans cannot be converted from arrow format to numpy without copy: https://arrow.apache.org/docs/python/generated/pyarrow.Array.html#pyarrow.Array.to_numpy",
"Good catch ! I must be able to fix that one by allowing copies for this kind of arrays.\r\nThis is the kind of surprise you get sometimes when playing with arrow x)",
"Raising this error for booleans was introduced in https://issues.apache.org/jira/browse/ARROW-2871?jql=text%20~%20%22boolean%20to_numpy%22 without much explanations unfortunately.\r\nSo \"no copy\" only works for primitive types - except booleans.\r\nThis is confirmed in the source code at https://github.com/wesm/arrow/blob/c07b9b48cf3e0bbbab493992a492ae47e5b04cad/python/pyarrow/array.pxi#L621\r\n\r\nI'm opening a PR to allow copies for booleans...",
"I just merged the fix for boolean ArrayXD, feel free to merge from master to see if it fixes the ci :)",
"@lhoestq unfirtunately, arrays of strings (or any other non-primitive type) require a copy too\r\n\r\nA list of primitive types can be found here: https://github.com/wesm/arrow/blob/c07b9b48cf3e0bbbab493992a492ae47e5b04cad/python/pyarrow/types.pxi#L821\r\n\r\npyarrow provides a `is_primitive` function to check whether a type is primitive , I used it to set `zero_copy_only`\r\n\r\nAlso, `PandasArrayExtensionArray.isna` was using `numpy.isnan` which fails for arrays of strings. I replaced it with `pandas.isna`. Let me know what you think! :) "
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory
|
2021-02-19T09:41:59Z
|
https://github.com/huggingface/datasets/pull/1887
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Common voice
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[
"Does it make sense to make the domains as the different languages?\r\nA problem is that you need to download the datasets from the browser.\r\nOne idea would be to either contact Mozilla regarding API access to the dataset or make use of a headless browser for downloading the datasets (might be hard since we have to figure out how to host them). An even more creative idea would be to host the dataset inside a torrent and figure out a way to download specific datasets from within that torrent.\r\n\r\nHere is some information about the download authorization. They are hosting the data on S3.\r\n\r\nhttps://docs.aws.amazon.com/AmazonS3/latest/API/sigv4-auth-using-authorization-header.html\r\n\r\nHere is an example of how a download link looks.\r\n\r\nhttps://mozilla-common-voice-datasets.s3.dualstack.us-west-2.amazonaws.com/cv-corpus-6.1-2020-12-11/nl.tar.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAQ3GQRTO3ND4UAQXB%2F20210217%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20210217T080740Z&X-Amz-Expires=43200&X-Amz-Security-Token=FwoGZXIvYXdzEGIaDCC6ALh%2FwIK9ovvRdCKSBCs5WaSJNsZ2h0SnhpnWFv4yiAJHJTe%2BY6pBcCqadRMs0RABHeQ2n1QDACJ5V9WOqIHfMfT0AI%2Bfe6iFkTGLgRrJOMYpgV%2FmIBcXCjeb72r4ZvudMA8tprkSxZsEh53bJkIDQx1tXqfpz0yoefM0geD3461suEGhHnLIyiwffrUpRg%2BkNZN9%2FLZZXpF5F2pogieKKV533Jetkd1xlWOR%2Bem9R2bENu2RV563XX3JvbWxSYN9IHkVT1xwd4ZiOpUtX7%2F2RoluJUKw%2BUPpyml3J%2FOPPGdr7CyPLjqNxdq9ceRi8lRybty64XvNYZGt45VNTQ3pkTTz4VpUCJAGkgxq95Ve%2BOwW%2Fsc8JtblTFKrH11vej62NB7C0n7JPPS4SLKXHKW%2B7ZbybcNf3BnsAVouPdsGTMslcgkD81b9trnjyXJdOZkzdHUf2KcWVXVceEsZnMhcCZQ1cJpI7qXPEk8QrKCQcNByPLHmPIEdHpj9IrIBKDkl2qO7VX7CCB65WDt2eZRltOcNHXWVFXFktMdQOQztI1j0XSZz2iOX4jPKKaqz193VEytlAqmehNi8pePOnxkP9Z1SP7d3I6rayuBF3phmpHxw499tY3ECYYgoCnJ6QSFa3KxMjFmEpQlmjxuwEMHd4CDL2FJYGcCiIxbCcL1r8ZE3%2BbGdcu7PRsVCHX3Huh%2FqGIaF4h40FgteN6teyKCHKOebs4EGMipb9xmEMZ9ZbVopz4bkhLdMTrjKon9w624Xem0MTPqN7XY%2BB6lRgrW8rd4%3D&X-Amz-Signature=28eabdfce72a472a70b0f9e1e2c37fe1471b5ec8ed60614fbe900bfa97ae1ac8&X-Amz-SignedHeaders=host\r\n\r\nIt could be that we simply need to make a http-request with the right parameters and we can download the datasets.",
"> Wow, this looks great already! It's really a difficult dataset so thanks a lot for opening a PR.\r\n> I think the tagging tool is not too important for now and we can take a look at that later!\r\n> \r\n> At the moment, it would be very good to correctly generate some dummy data for all the possible languages. I think the structure of the `.tsv` file as you've noted in the PR is the one we want to use as the structure for `features = datasets.Features(`\r\n> \r\n> The splits `'Train\"`, `\"Test\"`, `\"Validation\"` look great to me! Because this is a special dataset that also has files called `\"Invalidated\"` I think the best option is to also add those as splits, _i.e._ `\"other\"`, `\"invalidated\"`, `\"reported\"`, `\"validated\"` . Those split names can be gives as shown here for example:\r\n> \r\n> https://github.com/huggingface/datasets/blob/28be129db862ec89a87ac9349c64df6b6118aff4/datasets/librispeech_asr/librispeech_asr.py#L124\r\n> \r\n> Also putting @lhoestq in cc here to hear his opinion on the different splits. @lhoestq Common Voicie is a crowd collected dataset where if a collected data sample did not receive enough \"up_votes\" from the community -> then it is (If I understood it correctly) marked as invalid -> hence the file `\"invalidated.tsv\"`. I think this is still useful data, so I would include it what do you think?\r\n> \r\n> @BirgerMoell let me know if you have any more questions :-)\r\n\r\nI think reporting is a separate feature. People can help annotate the data and then they can report things while annotating.\r\nhttps://commonvoice.mozilla.org/sv-SE/listen\r\n\r\nHere is the interface that shows reporting and the thumbs up and down which gives upvotes and downvotes.\r\n<img src=\"https://i.imgur.com/utWjszt.png\" height=\"800px\">\r\n",
"I added splits and features. I'm not sure how you want me to generate dummy data for all the languages?",
"Hey @BirgerMoell,\r\n\r\nI tweaked your dataset file a bit to have a first working version. To test this dataset downloading script, you can do the following:\r\n\r\n- 1) Download the Common Voice Georgian dataset from https://commonvoice.mozilla.org/en/datasets (It's pretty small which is why I chose it)\r\n- 2) Run the following command using this branch: \r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"./../datasets/datasets/common_voice\", \"Georgian\", data_dir=\"./cv-corpus-6.1-2020-12-11/ka/\", split=\"train\")\r\n```\r\n\r\nNote that I'm loading a local version of the dataset script (`\"./../datasets/datasets/common_voice/\"` points to the folder in your branch) and that I also insert the downloaded data with the `data_dir` arg.\r\n\r\n-> You'll see that the data is correctly loaded and that `ds` contains all the information we need.\r\n\r\nNow there are a lot of different datasets on Common Voice, so it probably takes too much time to test all of those, but maybe you can test whether the current script works as well *e.g.* for Swedish, 3,4 other languages.\r\n\r\nIt would be very nice if we can use the exact same structure for all languages, meaning that we don't have to change the `datasets.Features(...)` structure depending on the language, but can use the exact same one for every language.\r\n\r\nIf everything works as expected we can then go over to cleaning the script and seeing how to add dummy data tests for it."
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
Started filling out information about the dataset and a dataset card.
To do
Create tagging file
Update the common_voice.py file with more information
|
2021-03-09T18:51:31Z
|
https://github.com/huggingface/datasets/pull/1886
|
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add missing info on how to add large files
|
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CONTRIBUTOR
|
Thanks to @lhoestq's instructions I was able to add data files to a custom dataset repo. This PR is attempting to tell others how to do the same if they need to.
@lhoestq
|
2021-02-16T16:22:19Z
|
https://github.com/huggingface/datasets/pull/1885
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dtype fix when using numpy arrays
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CONTRIBUTOR
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As discussed in #625 this fix lets the user preserve the dtype of numpy array to pyarrow array which was getting lost due to conversion of numpy array -> list -> pyarrow array
|
2021-07-30T11:01:18Z
|
https://github.com/huggingface/datasets/pull/1884
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Add not-in-place implementations for several dataset transforms
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[
"@lhoestq I am not sure how to test `dictionary_encode_column` (in-place version was not tested before)",
"I can take a look at dictionary_encode_column tomorrow.\r\nAlthough it's likely that it doesn't work then. It was added at the beginning of the lib and never tested nor used afaik.",
"Now let's update the documentation to use the new methods x)"
] |
https://api.github.com/repos/huggingface/datasets
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CONTRIBUTOR
|
Should we deprecate in-place versions of such methods?
|
2021-02-24T14:54:49Z
|
https://github.com/huggingface/datasets/pull/1883
|
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Create Remote Manager
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[
"@lhoestq I have refactorized the logic. Instead of the previous hierarchy call (local temp file opening -> remote call -> use again temp local file logic but from within the remote caller scope), now it is flattened. Schematically:\r\n```python\r\nwith src.open() as src_file, dst.open() as dst_file:\r\n src_file.fetch(dst_file)\r\n```\r\n\r\nI have created `RemotePath` (analogue to Path) with method `.open()` that returns `FtpFile`/`HttpFile` (analogue to file-like).\r\n\r\nNow I am going to implement `RemotePath.exists()` method (analogue to the Path's method) to check if remote resource is accessible, using `Ftp/Http.head()`.",
"Quick update on this one:\r\nwe discussed offline with @albertvillanova on this PR and I think using `fsspec` can help a lot, since it already implements many parts of the abstraction we need to have nice download tools for both http and ftp (and others !)"
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MEMBER
|
Refactoring to separate the concern of remote (HTTP/FTP requests) management.
|
2022-07-06T15:19:47Z
|
https://github.com/huggingface/datasets/pull/1882
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2021-02-15T15:09:48Z
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`list_datasets()` returns a list of strings, not objects
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CONTRIBUTOR
|
Here and there in the docs there is still stuff like this:
```python
>>> datasets_list = list_datasets()
>>> print(', '.join(dataset.id for dataset in datasets_list))
```
However, my understanding is that `list_datasets()` returns a list of strings rather than a list of objects.
|
2021-02-15T15:09:49Z
|
https://github.com/huggingface/datasets/pull/1881
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Update multi_woz_v22 checksums
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MEMBER
|
As noticed in #1876 the checksums of this dataset are outdated.
I updated them in this PR
|
2021-02-15T14:18:19Z
|
https://github.com/huggingface/datasets/pull/1880
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Replace flatten_nested
|
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[] |
https://api.github.com/repos/huggingface/datasets/issues/1879
|
[
"Hi @lhoestq. If you agree to merge this, I will start separating the logic for NestedDataStructure.map ;)"
] |
https://api.github.com/repos/huggingface/datasets
|
MEMBER
|
Replace `flatten_nested` with `NestedDataStructure.flatten`.
This is a first step towards having all NestedDataStructure logic as a separated concern, independent of the caller/user of the data structure.
Eventually, all checks (whether the underlying data is list, dict, etc.) will be only inside this class.
I have also generalized the flattening, and now it handles multiple levels of nesting.
|
2021-02-19T18:35:14Z
|
https://github.com/huggingface/datasets/pull/1879
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2021-02-15T14:18:09Z
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Add LJ Speech dataset
|
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https://api.github.com/repos/huggingface/datasets/issues/1878
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[
"Hey @anton-l,\r\n\r\nThanks a lot for the very clean integration!\r\n\r\n1) I think we should now start having \"automatic-speech-recognition\" as a label in the dataset tagger (@yjernite is it easy to add?). But we can surely add this dataset with the tag you've added and then later change the label to `asr` \r\n\r\n2) That's perfect! Yeah good question - we're currently thinking about a better design with @lhoestq \r\n\r\n3) Again tagging @yjernite & @lhoestq here - guess we should add this license though!",
"Thanks @anton-l for adding this one :)\r\nAbout the points you mentioned:\r\n1. Sure as soon as we've updated the tag sets in https://github.com/huggingface/datasets-tagging/blob/main/task_set.json, we can update the tags in this dataset card and also in the other audio dataset card.\r\n2. For now we just try to have them as small as possible but we may switch to S3/LFS at one point indeed\r\n3. If it's not part of the license set at https://github.com/huggingface/datasets-tagging/blob/main/license_set.json we can add it to this license set\r\n\r\nFor now it's ok to have the other-* tags but we'll update them very soon",
"Let's merge this one and then we'll update the tags for the audio datasets. We'll probably also add something like this:\r\n```\r\ntype:\r\n- text\r\n- audio\r\n```\r\n\r\nThank you so much for adding this one, good job !"
] |
https://api.github.com/repos/huggingface/datasets
|
MEMBER
|
This PR adds the LJ Speech dataset (https://keithito.com/LJ-Speech-Dataset/)
As requested by #1841
The ASR format is based on #1767
There are a couple of quirks that should be addressed:
- I tagged this dataset as `other-other-automatic-speech-recognition` and `other-other-text-to-speech` (as classified by paperswithcode). Since the number of speech datasets is about to grow, maybe these categories should be added to the main list?
- Similarly to #1767 this dataset uses only a single dummy sample to reduce the zip size (`wav`s are quite heavy). Is there a plan to allow LFS or S3 usage for dummy data in the repo?
- The dataset is distributed under the Public Domain license, which is not used anywhere else in the repo, AFAIK. Do you think Public Domain is worth adding to the tagger app as well?
Pinging @patrickvonplaten to review
|
2021-02-15T19:39:41Z
|
https://github.com/huggingface/datasets/pull/1878
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2021-03-26T16:51:58Z
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Allow concatenation of both in-memory and on-disk datasets
|
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https://api.github.com/repos/huggingface/datasets/issues/1877
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[
"I started working on this. My idea is to first add the pyarrow Table wrappers InMemoryTable and MemoryMappedTable that both implement what's necessary regarding copy/pickle. Then have another wrapper that takes the concatenation of InMemoryTable/MemoryMappedTable objects.\r\n\r\nWhat's important here is that concatenating two tables into one doesn't double the memory used (`total_allocated_bytes()` stays the same).",
"Hi @lhoestq @albertvillanova,\r\n\r\nI checked the linked issues and PR, this seems like a great idea. Would you mind elaborating on the in-memory and memory-mapped datasets? \r\nBased on my understanding, it is something like this, please correct me if I am wrong:\r\n1. For in-memory datasets, we don't have any dataset files so the entire dataset is pickled to the cache during loading, and then whenever required it is unpickled .\r\n2. For on-disk/memory-mapped datasets, we have the data files provided, so they can be re-loaded from the paths, and only the file-paths are stored while pickling.\r\n\r\nIf this is correct, will the feature also handle pickling/unpickling of a concatenated dataset? Will this be cached?\r\n\r\nThis also leads me to ask whether datasets are chunked during pickling? \r\n\r\nThanks,\r\nGunjan",
"Hi ! Yes you're totally right about your two points :)\r\n\r\nAnd in the case of a concatenated dataset, then we should reload each sub-table depending on whether it's in-memory or memory mapped. That means the dataset will be made of several blocks in order to keep track of what's from memory and what's memory mapped. This allows to pickle/unpickle concatenated datasets",
"Hi @lhoestq\r\n\r\nThanks, that sounds nice. Can you explain where the issue of the double memory may arise? Also, why is the existing `concatenate_datasets` not sufficient for this purpose?",
"Hi @lhoestq,\r\n\r\nWill the `add_item` feature also help with lazy writing (or no caching) during `map`/`filter`?",
"> Can you explain where the issue of the double memory may arise?\r\n\r\nWe have to keep each block (in-memory vs memory mapped) separated in order to be able to reload them with pickle.\r\nOn the other hand we also need to have the full table from mixed in-memory and memory mapped data in order to iterate or extract data conveniently. That means that each block is accessible twice: once in the full table, and once in the separated blocks. But since pyarrow tables concatenation doesn't double the memory, then building the full table doesn't cost memory which is what we want :)\r\n\r\n> Also, why is the existing concatenate_datasets not sufficient for this purpose?\r\n\r\nThe existing `concatenate_datasets` doesn't support having both in-memory and memory mapped data together (there's no fancy block separation logic). It works for datasets fully in-memory or fully memory mapped but not a mix of the two.\r\n\r\n> Will the add_item feature also help with lazy writing (or no caching) during map/filter?\r\n\r\nIt will enable the implementation of the fast, masked filter from this discussion: https://github.com/huggingface/datasets/issues/1949\r\nHowever I don't think this will affect map."
] |
https://api.github.com/repos/huggingface/datasets
|
MEMBER
|
This is a prerequisite for the addition of the `add_item` feature (see #1870).
Currently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).
This assumption is used for pickling for example:
- in-memory dataset can just be pickled/unpickled in-memory
- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling
Maybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future
One idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.
Then the dataset would be the concatenation of all these tables.
Depending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.
If you have some ideas you would like to share about the design/API feel free to do so :)
cc @albertvillanova
|
2021-03-26T16:51:58Z
|
https://github.com/huggingface/datasets/issues/1877
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load_dataset("multi_woz_v22") NonMatchingChecksumError
|
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| null | null | 808,025,859
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https://api.github.com/repos/huggingface/datasets/issues/1876
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[
"Thanks for reporting !\r\nThis is due to the changes made in the data files in the multiwoz repo: https://github.com/budzianowski/multiwoz/pull/59\r\nI'm opening a PR to update the checksums of the data files.",
"I just merged the fix. It will be available in the new release of `datasets` later today.\r\nYou'll be able to get the new version with\r\n```\r\npip install --upgrade datasets\r\n```",
"Hi, I still meet the error when loading the datasets after upgradeing datasets.\r\n\r\nraise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json']",
"This must be related to https://github.com/budzianowski/multiwoz/pull/72\r\nThose files have changed, let me update the checksums for this dataset.\r\n\r\nFor now you can use `ignore_verifications=True` in `load_dataset` to skip the checksum verification."
] |
https://api.github.com/repos/huggingface/datasets
|
NONE
|
Hi, it seems that loading the multi_woz_v22 dataset gives a NonMatchingChecksumError.
To reproduce:
`dataset = load_dataset('multi_woz_v22','v2.2_active_only',split='train')`
This will give the following error:
```
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_003.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_004.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_005.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_006.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_007.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_008.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_009.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_010.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_012.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_013.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_014.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_015.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_016.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_017.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dev/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dev/dialogues_002.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_002.json']
```
|
2021-08-04T18:08:00Z
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https://github.com/huggingface/datasets/issues/1876
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Adding sari metric
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CONTRIBUTOR
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Adding SARI metric that is used in evaluation of text simplification. This is required as part of the GEM benchmark.
|
2021-02-17T15:56:27Z
|
https://github.com/huggingface/datasets/pull/1875
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Adding Europarl Bilingual dataset
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[
"is there a way to check errors without subscribing to CircleCI? Because they want access to private repositories when logging.",
"I think you need to be logged in to check the errors unfortunately. Feel free to create an account with bitbucket maybe if you don't want it to access your private github repos",
"I've resolved some requirements, but I cannot create dummy data. The dataset works as follows: for each language pair `<lang1>-<lang2>` 3 files are downloaded:\r\n- dataset for `<lang1>`\r\n- dataset for `<lang2>`\r\n- alignments between `<lang1>` and `<lang2>`\r\n\r\nSuppose we work with the `bg-cs` language pair. Then, the dataset will download three `gzip` files which should be decompressed. I do not understand the relation between the folders created by the script to create dummy data and the original data provided by the download manager.",
"Hi ! Indeed the data files structure of this dataset looks very specific.\r\nThe command `datasets-cli dummy_data ./datasets/europarl_bilingual` shows some instructions for each split but let me add more details.\r\n\r\nFirst things to know is that the dummy data files need to be uncompressed data, so for example for the file `bg.zip` you should actually have one folder with all the xml files in it instead. In the same way, `bg-cs.xml.gz` must be replaced by an actual uncompressed xml file.\r\n\r\nLet's take the bg-cs config as an example. To make the dummy data you need to:\r\n- go to `./datasets/europarl_bilingual/dummy/bg-cs/8.0.0` and create a folder named `dummy_data`. Then go inside this folder\r\n- create a text file named `bg-cs.xml.gz` containing xml content (so without .gz compression). The xml content must have the same structure as the original `bg-cs.zml` but only include 1 `linkGrp` entry. You can pick one entry from the original `bg-cs.xml` file. Let's say this entry is about this file: `ep-06-01-16-003.xml`\r\n- create a folder named `bg.zip` and inside this folder add one file Europarl/raw/bg/ep-06-01-16-003.xml. You can pick the xml file from the original `bg.zip` archive.\r\n- create a folder named `cs.zip` and inside this folder add one file Europarl/raw/cs/ep-06-01-16-003.xml. You can pick the xml file from the original `cs.zip` archive.\r\n- zip the `dummy_data` into `dummy_data.zip`\r\n\r\nAt this point you have dummy data files to generate 1 example which is what we want to be able to test the dataset script `europarl_bilingual.py` with pytest. \r\n\r\nIn particular this will make this test pass:\r\n```\r\npytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_europarl_bilingual\r\n```\r\n\r\nIdeally it would be awesome to have dummy data for all the different configs so if we manage to make a script that generates all of it automatically that would be perfect. However since the structure is not trivial, another option would be to only have the dummy data for only 1 or 2 configs, like what we do for [bible_para](https://github.com/huggingface/datasets/blob/master/datasets/bible_para/bible_para.py) for example. In `bible_para` only a few configurations are tested. As you can see there is only 6 configs in the `BUILDER_CONFIGS` attribute. All the other configs can still be used, here is what is said inside the dataset card of bible_para:\r\n```\r\nTo load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.\r\nYou can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/bible-uedin.php\r\nE.g.\r\n\r\n`dataset = load_dataset(\"bible_para\", lang1=\"fi\", lang2=\"hi\")`\r\n```\r\nIn this case the configuration \"fi-hi\" is simply created on the fly, instead of being picked from the `BUILDER_CONFIGS` list.\r\n\r\nI hope this helps, let me know if you have questions or if I can help",
"I already created the scripts to create reduced versions of the data. What I didn't understand was how to put files in the dummy_data folder because, as you noticed, some file decompress to a nested tree structure. I will now try again with your suggestions!",
"Is there something else I should do? If not can this be integrated?",
"Thanks a lot !!\r\nSince the set of all the dummy data files is quite big I only kept a few of them. If we had kept them all the size of the `datasets` repo would have increased too much :/\r\nSo I did the same as for `bible_para`: only keep a few configurations in BUILDER_CONFIGS and have all the other pairs loadable with the lang1 and lang2 parameters like this:\r\n\r\n`dataset = load_dataset(\"europarl_bilingual\", lang1=\"fi\", lang2=\"fr\")`"
] |
https://api.github.com/repos/huggingface/datasets
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CONTRIBUTOR
|
Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
|
2021-03-04T10:38:22Z
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https://github.com/huggingface/datasets/pull/1874
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2021-02-16T14:21:58Z
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add iapp_wiki_qa_squad
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CONTRIBUTOR
|
`iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles.
It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset)
to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in
5761/742/739 questions from 1529/191/192 articles.
|
2021-02-16T14:21:58Z
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https://github.com/huggingface/datasets/pull/1873
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2021-03-30T14:01:45Z
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Adding a new column to the dataset after set_format was called
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[
"Hi ! Indeed if you add a column to a formatted dataset, then the new dataset gets a new formatting in which:\r\n```\r\nnew formatted columns = (all columns - previously unformatted columns)\r\n```\r\nTherefore the new column is going to be formatted using the `torch` formatting.\r\n\r\nIf you want your new column to be unformatted you can re-run this line:\r\n```python\r\ndata.set_format(\"torch\", columns=[\"some_integer_column1\", \"some_integer_column2\"], output_all_columns=True)\r\n```",
"Hi, thanks that solved my problem. Maybe mention that in the documentation. ",
"Ok cool :) \r\nAlso I just did a PR to mention this behavior in the documentation",
"Closed by #1888"
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https://api.github.com/repos/huggingface/datasets
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NONE
|
Hi,
thanks for the nice library. I'm in the process of creating a custom dataset, which has a mix of tensors and lists of strings. I stumbled upon an error and want to know if its a problem on my side.
I load some lists of strings and integers, then call `data.set_format("torch", columns=["some_integer_column1", "some_integer_column2"], output_all_columns=True)`. This converts the integer columns into tensors, but keeps the lists of strings as they are. I then call `map` to add a new column to my dataset, which is a **list of strings**. Once I iterate through my dataset, I get an error that the new column can't be converted into a tensor (which is probably caused by `set_format`).
Below some pseudo code:
```python
def augment_func(sample: Dict) -> Dict:
# do something
return {
"some_integer_column1" : augmented_data["some_integer_column1"], # <-- tensor
"some_integer_column2" : augmented_data["some_integer_column2"], # <-- tensor
"NEW_COLUMN": targets, # <-- list of strings
}
data = datasets.load_dataset(__file__, data_dir="...", split="train")
data.set_format("torch", columns=["some_integer_column1", "some_integer_column2"], output_all_columns=True)
augmented_dataset = data.map(augment_func, batched=False)
for sample in augmented_dataset:
print(sample) # fails
```
and the exception:
```python
Traceback (most recent call last):
File "dataset.py", line 487, in <module>
main()
File "dataset.py", line 471, in main
for sample in augmented_dataset:
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 697, in __iter__
yield self._getitem(
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1069, in _getitem
outputs = self._convert_outputs(
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 890, in _convert_outputs
v = map_nested(command, v, **map_nested_kwargs)
File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
return function(data_struct)
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in command
return [map_nested(command, i, **map_nested_kwargs) for i in x]
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in <listcomp>
return [map_nested(command, i, **map_nested_kwargs) for i in x]
File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
return function(data_struct)
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in command
return [map_nested(command, i, **map_nested_kwargs) for i in x]
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in <listcomp>
return [map_nested(command, i, **map_nested_kwargs) for i in x]
File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
return function(data_struct)
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 851, in command
return torch.tensor(x, **format_kwargs)
TypeError: new(): invalid data type 'str'
```
Thanks!
|
2021-03-30T14:01:45Z
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https://github.com/huggingface/datasets/issues/1872
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2021-03-08T10:12:45Z
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Add newspop dataset
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"Thanks for the changes :)\r\nmerging"
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CONTRIBUTOR
|
2021-03-08T10:12:45Z
|
https://github.com/huggingface/datasets/pull/1871
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Implement Dataset add_item
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[
"Thanks @lhoestq for your remarks. Yes, I agree there are still many issues to be tackled... This PR is just a starting point, so that we can discuss how Dataset should be generalized.",
"Sure ! I opened an issue #1877 so we can discuss this specific aspect :)",
"I am going to implement this consolidation step in #2151.",
"Sounds good !",
"I retake this PR once the consolidation step is already implemented by #2151."
] |
https://api.github.com/repos/huggingface/datasets
|
MEMBER
|
Implement `Dataset.add_item`.
Close #1854.
|
2021-04-23T10:01:31Z
|
https://github.com/huggingface/datasets/pull/1870
|
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2021-02-12T16:13:08Z
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MDExOlB1bGxSZXF1ZXN0NTcyNDU0NTMy
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Remove outdated commands in favor of huggingface-cli
|
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2021-02-12T11:28:10Z
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https://api.github.com/repos/huggingface/datasets
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MEMBER
|
Removing the old user commands since `huggingface_hub` is going to be used instead.
cc @julien-c
|
2021-02-12T16:13:09Z
|
https://github.com/huggingface/datasets/pull/1869
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2021-02-12T11:03:06Z
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Update oscar sizes
|
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MEMBER
|
This commit https://github.com/huggingface/datasets/commit/837a152e4724adc5308e2c4481908c00a8d93383 removed empty lines from the oscar deduplicated datasets. This PR updates the size of each deduplicated dataset to fix possible `NonMatchingSplitsSizesError` errors. cc @cahya-wirawan
|
2021-02-12T11:03:07Z
|
https://github.com/huggingface/datasets/pull/1868
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2021-02-24T12:00:43Z
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completed
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MDU6SXNzdWU4MDcxMjcxODE=
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ERROR WHEN USING SET_TRANSFORM()
|
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2021-02-12T10:38:31Z
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[
"Hi @alejandrocros it looks like an incompatibility with the current Trainer @sgugger \r\nIndeed currently the Trainer of `transformers` doesn't support a dataset with a transform\r\n\r\nIt looks like it comes from this line: https://github.com/huggingface/transformers/blob/f51188cbe74195c14c5b3e2e8f10c2f435f9751a/src/transformers/trainer.py#L442\r\n\r\nThis line sets the format to not return certain unused columns. But this has two issues:\r\n1. it forgets to also set the format_kwargs (this causes the error you got):\r\n```python\r\ndataset.set_format(type=dataset.format[\"type\"], columns=columns, format_kwargs=dataset.format[\"format_kwargs\"])\r\n```\r\n2. the Trainer wants to keep only the fields that are used as input for a model. However for a dataset with a transform, the output fields are often different from the columns fields. For example from a column \"text\" in the dataset, the strings can be transformed on-the-fly into \"input_ids\". If you want your dataset to only output certain fields and not other you must change your transform function.\r\n",
"FYI that option can be removed with `remove_unused_columns = False` in your `TrainingArguments`, so there is a workaround @alexvaca0 while the fix in `Trainer` is underway.\r\n\r\n@lhoestq I think I will just use the line you suggested and if someone is using the columns that are removed in their transform they will need to change `remove_unused_columns` to `False`. We might switch the default of that argument in the next version if that proves too bug-proof.",
"I've tried your solutions @sgugger @lhoestq and the good news is that it throws no error. However, TPU training is taking forever, in 1 hour it has only trained 1 batch of 8192 elements, which doesn't make much sense... Is it possible that \"on the fly\" tokenization of batches is slowing down TPU training to that extent?",
"I'm pretty sure this is because of padding but @sgugger might know better",
"I don't know what the value of `padding` is in your lines of code pasted above so I can't say for sure. The first batch will be very slow on TPU since it compiles everything, so that's normal (1 hour is long but 8192 elements is also large). Then if your batches are not of the same lengths, it will recompile everything at each step instead of using the same graph, which will be very slow, so you should double check you are using padding to make everything the exact same shape. ",
"I have tried now on a GPU and it goes smooth! Amazing feature .set_transform() instead of .map()! Now I can pre-train my model without the hard disk limitation. Thanks for your work all HuggingFace team!! :clap: ",
"In the end, to make it work I turned to A-100 gpus instead of TPUS, among other changes. Set_transform doesn't work as expected and slows down training very much even in GPUs, and applying map destroys the disk, as it multiplies by 100 the size of the data passed to it (due to inefficient implementation converting strings to int64 floats I guess). For that reason, I chose to use datasets to load the data as text, and then edit the Collator from Transformers to tokenize every batch it receives before processing it. That way, I'm being able to train fast, without memory breaks, without the disk being unnecessarily filled, while making use of GPUs almost all the time I'm paying for them (the map function over the whole dataset took ~15hrs, in which you're not training at all). I hope this info helps others that are looking for training a language model from scratch cheaply, I'm going to close the issue as the optimal solution I found after many experiments to the problem posted in it is explained above. ",
"Great comment @alexvaca0 . I think that we could re-open the issue as a reformulation of why it takes so much space to save the arrow. Saving a 1% of oscar corpus takes more thank 600 GB (it breaks when it pass 600GB because it is the free memory that I have at this moment) when the full dataset is 1,3 TB. I have a 1TB M.2 NVMe disk that I can not train on because the saved .arrow files goes crazily big. If you can share your Collator I will be grateful. "
] |
https://api.github.com/repos/huggingface/datasets
|
NONE
|
Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https://github.com/huggingface/datasets/issues/1825#issuecomment-774202797
However, when I try to use Trainer from transformers with such dataset, it throws an error:
```
TypeError: __init__() missing 1 required positional argument: 'transform'
[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.
Exception in device=TPU:0: __init__() missing 1 required positional argument: 'transform'
Traceback (most recent call last):
File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 330, in _mp_start_fn
_start_fn(index, pf_cfg, fn, args)
File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 324, in _start_fn
fn(gindex, *args)
File "/home/alejandro_vaca/transformers/examples/language-modeling/run_mlm_wwm.py", line 368, in _mp_fn
main()
File "/home/alejandro_vaca/transformers/examples/language-modeling/run_mlm_wwm.py", line 332, in main
data_collator=data_collator,
File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/transformers/trainer.py", line 286, in __init__
self._remove_unused_columns(self.train_dataset, description="training")
File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/transformers/trainer.py", line 359, in _remove_unused_columns
dataset.set_format(type=dataset.format["type"], columns=columns)
File "/home/alejandro_vaca/datasets/src/datasets/fingerprint.py", line 312, in wrapper
out = func(self, *args, **kwargs)
File "/home/alejandro_vaca/datasets/src/datasets/arrow_dataset.py", line 818, in set_format
_ = get_formatter(type, **format_kwargs)
File "/home/alejandro_vaca/datasets/src/datasets/formatting/__init__.py", line 112, in get_formatter
return _FORMAT_TYPES[format_type](**format_kwargs)
TypeError: __init__() missing 1 required positional argument: 'transform'
```
The code I'm using:
```{python}
def tokenize_function(examples):
# Remove empty lines
examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
return tokenizer(examples["text"], padding=padding, truncation=True, max_length=data_args.max_seq_length)
datasets.set_transform(tokenize_function)
data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=datasets["train"] if training_args.do_train else None,
eval_dataset=datasets["val"] if training_args.do_eval else None,
tokenizer=tokenizer,
data_collator=data_collator,
)
```
I've installed from source, master branch.
|
2021-03-01T14:04:24Z
|
https://github.com/huggingface/datasets/issues/1867
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2021-02-17T14:22:36Z
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Add dataset for Financial PhraseBank
|
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[
"Thanks for the feedback. All accepted and metadata regenerated."
] |
https://api.github.com/repos/huggingface/datasets
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CONTRIBUTOR
|
2021-02-17T14:22:36Z
|
https://github.com/huggingface/datasets/pull/1866
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Updated OPUS Open Subtitles Dataset with metadata information
|
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[
"Hi !\r\nAbout the problems you mentioned:\r\n- Saving the infos is only done for the configurations inside the BUILDER_CONFIGS. Otherwise you would need to run the scripts on ALL language pairs, which is not what we want.\r\n- Moreover when you're on your branch, please specify the path to your local version of the dataset script, like \"./datasets/open_subtitles\". Otherwise the dataset is loaded from the master branch on github.\r\nHope that clarifies things a bit\r\n\r\nAnd of course feel free to add methods or classmethods to your builder.\r\n",
"Great! Thank you :)\r\nI'll close the issue as well."
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
Close #1844
Problems:
- I ran `python datasets-cli test datasets/open_subtitles --save_infos --all_configs`, hence the change in `dataset_infos.json`, but it appears that the metadata features have not been added for all pairs. Any idea why that might be?
- Possibly related to the above, I tried doing `pip uninstall datasets && pip install -e ".[dev]"` after the changes, and loading the dataset via `load_dataset("open_subtitles", lang1='hi', lang2='it')` to check if the update worked, but the loaded dataset did not contain the metadata fields (neither in the features nor doing `next(iter(dataset['train']))`). What step(s) did I miss?
Questions:
- Is it ok to have a `classmethod` in there? I have not seen any in the few other datasets I have checked. I could make it a local method of the `_generate_examples` method, but I'd rather not duplicate the logic...
|
2021-02-19T12:38:09Z
|
https://github.com/huggingface/datasets/pull/1865
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2021-02-11T08:19:51Z
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Add Winogender Schemas
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2021-02-11T08:18:38Z
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https://api.github.com/repos/huggingface/datasets/issues/1864
|
[
"Nevermind, this one is already available on the hub under the name `'wino_bias'`: https://huggingface.co/datasets/wino_bias"
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
## Adding a Dataset
- **Name:** Winogender Schemas
- **Description:** Winogender Schemas (inspired by Winograd Schemas) are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to test for the presence of gender bias in automated coreference resolution systems.
- **Paper:** https://arxiv.org/abs/1804.09301
- **Data:** https://github.com/rudinger/winogender-schemas (see data directory)
- **Motivation:** Testing gender bias in automated coreference resolution systems, improve coreference resolution in general.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
2021-02-11T08:19:51Z
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https://github.com/huggingface/datasets/issues/1864
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Add WikiCREM
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2021-02-11T08:16:00Z
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[
"Hi @NielsRogge I would like to work on this dataset.\r\n\r\nThanks!",
"Hi @udapy, are you working on this?"
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
## Adding a Dataset
- **Name:** WikiCREM
- **Description:** A large unsupervised corpus for coreference resolution.
- **Paper:** https://arxiv.org/abs/1905.06290
- **Github repo:**: https://github.com/vid-koci/bert-commonsense
- **Data:** https://ora.ox.ac.uk/objects/uuid:c83e94bb-7584-41a1-aef9-85b0e764d9e3
- **Motivation:** Coreference resolution, common sense reasoning
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
2021-03-07T07:27:13Z
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https://github.com/huggingface/datasets/issues/1863
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2021-02-10T18:17:47Z
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|
Fix writing GPU Faiss index
|
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2021-02-10T17:32:03Z
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|
MEMBER
|
As reported in by @corticalstack there is currently an error when we try to save a faiss index on GPU.
I fixed that by checking the index `getDevice()` method before calling `index_gpu_to_cpu`
Close #1859
|
2021-02-10T18:17:48Z
|
https://github.com/huggingface/datasets/pull/1862
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2021-02-10T16:14:59Z
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Fix Limit url
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2021-02-10T15:44:56Z
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|
MEMBER
|
The test.json file of the Literal-Motion-in-Text (LiMiT) dataset was removed recently on the master branch of the repo at https://github.com/ilmgut/limit_dataset
This PR uses the previous commit sha to download the file instead, as suggested by @Paethon
Close #1836
|
2021-02-10T16:15:00Z
|
https://github.com/huggingface/datasets/pull/1861
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2021-02-12T19:13:29Z
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Add loading from the Datasets Hub + add relative paths in download manager
|
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2021-02-10T13:24:11Z
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https://api.github.com/repos/huggingface/datasets/issues/1860
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[
"I just added the steps to share a dataset on the datasets hub. It's highly inspired by the steps to share a model in the `transformers` doc.\r\n\r\nMoreover once the new huggingface_hub is released we can update the version in the setup.py. We also need to update the command to create a dataset repo in the documentation\r\n\r\nI added a few more tests with the \"lhoestq/test\" dataset I added on the hub and it works fine :) ",
"Here is the PR adding support for datasets repos in `huggingface_hub`: https://github.com/huggingface/huggingface_hub/pull/14"
] |
https://api.github.com/repos/huggingface/datasets
|
MEMBER
|
With the new Datasets Hub on huggingface.co it's now possible to have a dataset repo with your own script and data.
For example: https://huggingface.co/datasets/lhoestq/custom_squad/tree/main contains one script and two json files.
You can load it using
```python
from datasets import load_dataset
d = load_dataset("lhoestq/custom_squad")
```
To be able to use the data files that live right next to the dataset script on the repo in the hub, I added relative paths support for the DownloadManager. For example in the repo mentioned above, there are two json files that can be downloaded via
```python
_URLS = {
"train": "train-v1.1.json",
"dev": "dev-v1.1.json",
}
downloaded_files = dl_manager.download_and_extract(_URLS)
```
To make it work, I set the `base_path` of the DownloadManager to be the parent path of the dataset script (which comes from either a local path or a remote url).
I also had to add the auth header of the requests to huggingface.co for private datasets repos. The token is fetched from [huggingface_hub](https://github.com/huggingface/huggingface_hub).
|
2021-02-12T19:13:30Z
|
https://github.com/huggingface/datasets/pull/1860
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2021-02-10T18:17:47Z
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Error "in void don't know how to serialize this type of index" when saving index to disk when device=0 (GPU)
|
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2021-02-10T12:41:00Z
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[] |
https://api.github.com/repos/huggingface/datasets/issues/1859
|
[
"Hi @corticalstack ! Thanks for reporting. Indeed in the recent versions of Faiss we must use `getDevice` to check if the index in on GPU.\r\n\r\nI'm opening a PR",
"I fixed this issue. It should work fine now.\r\nFeel free to try it out by installing `datasets` from source.\r\nOtherwise you can wait for the next release of `datasets` (in a few days)",
"Thanks for such a quick fix and merge to master, pip installed git master, tested all OK"
] |
https://api.github.com/repos/huggingface/datasets
|
NONE
|
Error serializing faiss index. Error as follows:
`Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /home/conda/feedstock_root/build_artifacts/faiss-split_1612472484670/work/faiss/impl/index_write.cpp:453: don't know how to serialize this type of index`
Note:
`torch.cuda.is_available()` reports:
```
Cuda is available
cuda:0
```
Adding index, device=0 for GPU.
`dataset.add_faiss_index(column='embeddings', index_name='idx_embeddings', device=0)`
However, during a quick debug, self.faiss_index has no attr "device" when checked in` search.py, method save`, so fails to transform gpu index to cpu index. If I add index without device, index is saved OK.
```
def save(self, file: str):
"""Serialize the FaissIndex on disk"""
import faiss # noqa: F811
if (
hasattr(self.faiss_index, "device")
and self.faiss_index.device is not None
and self.faiss_index.device > -1
):
index = faiss.index_gpu_to_cpu(self.faiss_index)
else:
index = self.faiss_index
faiss.write_index(index, file)
```
|
2021-02-10T18:32:12Z
|
https://github.com/huggingface/datasets/issues/1859
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Clean config getenvs
|
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[] |
https://api.github.com/repos/huggingface/datasets/issues/1858
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[] |
https://api.github.com/repos/huggingface/datasets
|
MEMBER
|
Following #1848
Remove double getenv calls and fix one issue with rarfile
cc @albertvillanova
|
2021-02-10T15:52:30Z
|
https://github.com/huggingface/datasets/pull/1858
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Unable to upload "community provided" dataset - 400 Client Error
|
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https://api.github.com/repos/huggingface/datasets/issues/1857
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[
"Hi ! We're in the process of switching the community datasets to git repos, exactly like what we're doing for models.\r\nYou can find an example here:\r\nhttps://huggingface.co/datasets/lhoestq/custom_squad/tree/main\r\n\r\nWe'll update the CLI in the coming days and do a new release :)\r\n\r\nAlso cc @julien-c maybe we can make improve the error message ?"
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
Hi,
i'm trying to a upload a dataset as described [here](https://huggingface.co/docs/datasets/v1.2.0/share_dataset.html#sharing-a-community-provided-dataset). This is what happens:
```
$ datasets-cli login
$ datasets-cli upload_dataset my_dataset
About to upload file /path/to/my_dataset/dataset_infos.json to S3 under filename my_dataset/dataset_infos.json and namespace username
About to upload file /path/to/my_dataset/my_dataset.py to S3 under filename my_dataset/my_dataset.py and namespace username
Proceed? [Y/n] Y
Uploading... This might take a while if files are large
400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/presign
huggingface.co migrated to a new model hosting system.
You need to upgrade to transformers v3.5+ to upload new models.
More info at https://discuss.hugginface.co or https://twitter.com/julien_c. Thank you!
```
I'm using the latest releases of datasets and transformers.
|
2021-08-03T05:06:13Z
|
https://github.com/huggingface/datasets/issues/1857
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load_dataset("amazon_polarity") NonMatchingChecksumError
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[
"Hi ! This issue may be related to #996 \r\nThis comes probably from the Quota Exceeded error from Google Drive.\r\nCan you try again tomorrow and see if you still have the error ?\r\n\r\nOn my side I didn't get any error today with `load_dataset(\"amazon_polarity\")`",
"+1 encountering this issue as well",
"@lhoestq Hi! I encounter the same error when loading `yelp_review_full`.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset_yp = load_dataset(\"yelp_review_full\")\r\n```\r\n\r\nWhen you say the \"Quota Exceeded from Google drive\". Is this a quota from the dataset owner? or the quota from our (the runner) Google Drive?",
"+1 Also encountering this issue",
"> When you say the \"Quota Exceeded from Google drive\". Is this a quota from the dataset owner? or the quota from our (the runner) Google Drive?\r\n\r\nEach file on Google Drive can be downloaded only a certain amount of times per day because of a quota. The quota is reset every day. So if too many people download the dataset the same day, then the quota is likely to exceed.\r\nThat's a really bad limitations of Google Drive and we should definitely find another host for these dataset than Google Drive.\r\nFor now I would suggest to wait and try again later..\r\n\r\nSo far the issue happened with CNN DailyMail, Amazon Polarity and Yelp Reviews. \r\nAre you experiencing the issue with other datasets ? @calebchiam @dtch1997 ",
"@lhoestq Gotcha, that is quite problematic...for what it's worth, I've had no issues with the other datasets I tried, such as `yelp_reviews_full` and `amazon_reviews_multi`.",
"Same issue today with \"big_patent\", though the symptoms are slightly different.\r\n\r\nWhen running\r\n\r\n```py\r\nfrom datasets import load_dataset\r\nload_dataset(\"big_patent\", split=\"validation\")\r\n```\r\n\r\nI get the following\r\n`FileNotFoundError: Local file \\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist`\r\n\r\nI had to look into `6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5` (which is a file instead of a folder) and got the following:\r\n\r\n`<!DOCTYPE html><html><head><title>Google Drive - Quota exceeded</title><meta http-equiv=\"content-type\" content=\"text/html; charset=utf-8\"/><link href=/static/doclist/client/css/4033072956-untrustedcontent.css rel=\"stylesheet\" nonce=\"JV0t61Smks2TEKdFCGAUFA\"><link rel=\"icon\" href=\"//ssl.gstatic.com/images/branding/product/1x/drive_2020q4_32dp.png\"/><style nonce=\"JV0t61Smks2TEKdFCGAUFA\">#gbar,#guser{font-size:13px;padding-top:0px !important;}#gbar{height:22px}#guser{padding-bottom:7px !important;text-align:right}.gbh,.gbd{border-top:1px solid #c9d7f1;font-size:1px}.gbh{height:0;position:absolute;top:24px;width:100%}@media all{.gb1{height:22px;margin-right:.5em;vertical-align:top}#gbar{float:left}}a.gb1,a.gb4{text-decoration:underline !important}a.gb1,a.gb4{color:#00c !important}.gbi .gb4{color:#dd8e27 !important}.gbf .gb4{color:#900 !important}\r\n</style><script nonce=\"iNUHigT+ENVQ3UZrLkFtRw\"></script></head><body><div id=gbar><nobr><a target=_blank class=gb1 href=\"https://www.google.fr/webhp?tab=ow\">Search</a> <a target=_blank class=gb1 href=\"http://www.google.fr/imghp?hl=en&tab=oi\">Images</a> <a target=_blank class=gb1 href=\"https://maps.google.fr/maps?hl=en&tab=ol\">Maps</a> <a target=_blank class=gb1 href=\"https://play.google.com/?hl=en&tab=o8\">Play</a> <a target=_blank class=gb1 href=\"https://www.youtube.com/?gl=FR&tab=o1\">YouTube</a> <a target=_blank class=gb1 href=\"https://news.google.com/?tab=on\">News</a> <a target=_blank class=gb1 href=\"https://mail.google.com/mail/?tab=om\">Gmail</a> <b class=gb1>Drive</b> <a target=_blank class=gb1 style=\"text-decoration:none\" href=\"https://www.google.fr/intl/en/about/products?tab=oh\"><u>More</u> »</a></nobr></div><div id=guser width=100%><nobr><span id=gbn class=gbi></span><span id=gbf class=gbf></span><span id=gbe></span><a target=\"_self\" href=\"/settings?hl=en_US\" class=gb4>Settings</a> | <a target=_blank href=\"//support.google.com/drive/?p=web_home&hl=en_US\" class=gb4>Help</a> | <a target=_top id=gb_70 href=\"https://accounts.google.com/ServiceLogin?hl=en&passive=true&continue=https://drive.google.com/uc%3Fexport%3Ddownload%26id%3D1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa&service=writely&ec=GAZAMQ\" class=gb4>Sign in</a></nobr></div><div class=gbh style=left:0></div><div class=gbh style=right:0></div><div class=\"uc-main\"><div id=\"uc-text\"><p class=\"uc-error-caption\">Sorry, you can't view or download this file at this time.</p><p class=\"uc-error-subcaption\">Too many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.</p></div></div><div class=\"uc-footer\"><hr class=\"uc-footer-divider\">© 2021 Google - <a class=\"goog-link\" href=\"//support.google.com/drive/?p=web_home\">Help</a> - <a class=\"goog-link\" href=\"//support.google.com/drive/bin/answer.py?hl=en_US&answer=2450387\">Privacy & Terms</a></div></body></html>`",
"A similar issue arises when trying to stream the dataset\r\n\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> iter_dset = load_dataset(\"amazon_polarity\", split=\"test\", streaming=True)\r\n>>> iter(iter_dset).__next__()\r\n\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n~\\lib\\tarfile.py in nti(s)\r\n 186 s = nts(s, \"ascii\", \"strict\")\r\n--> 187 n = int(s.strip() or \"0\", 8)\r\n 188 except ValueError:\r\n\r\nValueError: invalid literal for int() with base 8: 'e nonce='\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nInvalidHeaderError Traceback (most recent call last)\r\n~\\lib\\tarfile.py in next(self)\r\n 2288 try:\r\n-> 2289 tarinfo = self.tarinfo.fromtarfile(self)\r\n 2290 except EOFHeaderError as e:\r\n\r\n~\\lib\\tarfile.py in fromtarfile(cls, tarfile)\r\n 1094 buf = tarfile.fileobj.read(BLOCKSIZE)\r\n-> 1095 obj = cls.frombuf(buf, tarfile.encoding, tarfile.errors)\r\n 1096 obj.offset = tarfile.fileobj.tell() - BLOCKSIZE\r\n\r\n~\\lib\\tarfile.py in frombuf(cls, buf, encoding, errors)\r\n 1036\r\n-> 1037 chksum = nti(buf[148:156])\r\n 1038 if chksum not in calc_chksums(buf):\r\n\r\n~\\lib\\tarfile.py in nti(s)\r\n 188 except ValueError:\r\n--> 189 raise InvalidHeaderError(\"invalid header\")\r\n 190 return n\r\n\r\nInvalidHeaderError: invalid header\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nReadError Traceback (most recent call last)\r\n<ipython-input-5-6b9058341b2b> in <module>\r\n----> 1 iter(iter_dset).__next__()\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 363\r\n 364 def __iter__(self):\r\n--> 365 for key, example in self._iter():\r\n 366 if self.features:\r\n 367 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 360 else:\r\n 361 ex_iterable = self._ex_iterable\r\n--> 362 yield from ex_iterable\r\n 363\r\n 364 def __iter__(self):\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 77\r\n 78 def __iter__(self):\r\n---> 79 yield from self.generate_examples_fn(**self.kwargs)\r\n 80\r\n 81 def shuffle_data_sources(self, seed: Optional[int]) -> \"ExamplesIterable\":\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\amazon_polarity\\56923eeb72030cb6c4ea30c8a4e1162c26b25973475ac1f44340f0ec0f2936f4\\amazon_polarity.py in _generate_examples(self, filepath, files)\r\n 114 def _generate_examples(self, filepath, files):\r\n 115 \"\"\"Yields examples.\"\"\"\r\n--> 116 for path, f in files:\r\n 117 if path == filepath:\r\n 118 lines = (line.decode(\"utf-8\") for line in f)\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in __iter__(self)\r\n 616\r\n 617 def __iter__(self):\r\n--> 618 yield from self.generator(*self.args, **self.kwargs)\r\n 619\r\n 620\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in _iter_from_urlpath(cls, urlpath, use_auth_token)\r\n 644 ) -> Generator[Tuple, None, None]:\r\n 645 with xopen(urlpath, \"rb\", use_auth_token=use_auth_token) as f:\r\n--> 646 yield from cls._iter_from_fileobj(f)\r\n 647\r\n 648 @classmethod\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in _iter_from_fileobj(cls, f)\r\n 624 @classmethod\r\n 625 def _iter_from_fileobj(cls, f) -> Generator[Tuple, None, None]:\r\n--> 626 stream = tarfile.open(fileobj=f, mode=\"r|*\")\r\n 627 for tarinfo in stream:\r\n 628 file_path = tarinfo.name\r\n\r\n~\\lib\\tarfile.py in open(cls, name, mode, fileobj, bufsize, **kwargs)\r\n 1603 stream = _Stream(name, filemode, comptype, fileobj, bufsize)\r\n 1604 try:\r\n-> 1605 t = cls(name, filemode, stream, **kwargs)\r\n 1606 except:\r\n 1607 stream.close()\r\n\r\n~\\lib\\tarfile.py in __init__(self, name, mode, fileobj, format, tarinfo, dereference, ignore_zeros, encoding, errors, pax_headers, debug, errorlevel, copybufsize)\r\n 1484 if self.mode == \"r\":\r\n 1485 self.firstmember = None\r\n-> 1486 self.firstmember = self.next()\r\n 1487\r\n 1488 if self.mode == \"a\":\r\n\r\n~\\lib\\tarfile.py in next(self)\r\n 2299 continue\r\n 2300 elif self.offset == 0:\r\n-> 2301 raise ReadError(str(e))\r\n 2302 except EmptyHeaderError:\r\n 2303 if self.offset == 0:\r\n\r\nReadError: invalid header\r\n\r\n```",
"This error still happens, but for a different reason now: Google Drive returns a warning instead of the dataset.",
"Met the same issue +1",
"Hi ! Thanks for reporting. Google Drive changed the way to bypass the warning message recently.\r\n\r\nThe latest release `1.18.4` fixes this for datasets loaded in a regular way.\r\n\r\nWe opened a PR to fix this recently for streaming mode at #3843 - we'll do a new release once the fix is merged :)",
"Fixed by:\r\n- #3787 \r\n- #3843"
] |
https://api.github.com/repos/huggingface/datasets
|
NONE
|
Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.
To reproduce:
```
load_dataset("amazon_polarity")
```
This will give the following error:
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-3-8559a03fe0f8> in <module>()
----> 1 dataset = load_dataset("amazon_polarity")
3 frames
/usr/local/lib/python3.6/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
37 if len(bad_urls) > 0:
38 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 39 raise NonMatchingChecksumError(error_msg + str(bad_urls))
40 logger.info("All the checksums matched successfully" + for_verification_name)
41
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']
```
|
2022-03-15T13:55:24Z
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https://github.com/huggingface/datasets/issues/1856
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Minor fix in the docs
|
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MEMBER
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2021-02-10T12:33:09Z
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https://github.com/huggingface/datasets/pull/1855
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Feature Request: Dataset.add_item
|
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[
"Hi @sshleifer.\r\n\r\nI am not sure of understanding the need of the `add_item` approach...\r\n\r\nBy just reading your \"Desired API\" section, I would say you could (nearly) get it with a 1-column Dataset:\r\n```python\r\ndata = {\"input_ids\": [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])]}\r\nds = Dataset.from_dict(data)\r\nassert (ds[\"input_ids\"][0] == np.array([4,4,2])).all()\r\n```",
"Hi @sshleifer :) \r\n\r\nWe don't have methods like `Dataset.add_batch` or `Dataset.add_entry/add_item` yet.\r\nBut that's something we'll add pretty soon. Would an API that looks roughly like this help ? Do you have suggestions ?\r\n```python\r\nimport numpy as np\r\nfrom datasets import Dataset\r\n\r\ntokenized = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])\r\n\r\n# API suggestion (not available yet)\r\nd = Dataset()\r\nfor input_ids in tokenized:\r\n d.add_item({\"input_ids\": input_ids})\r\n\r\nprint(d[0][\"input_ids\"])\r\n# [4, 4, 2]\r\n```\r\n\r\nCurrently you can define a dataset with what @albertvillanova suggest, or via a generator using dataset builders. It's also possible to [concatenate datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets).",
"Your API looks perfect @lhoestq, thanks!"
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
I'm trying to integrate `huggingface/datasets` functionality into `fairseq`, which requires (afaict) being able to build a dataset through an `add_item` method, such as https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L318, as opposed to loading all the text into arrow, and then `dataset.map(binarizer)`.
Is this possible at the moment? Is there an example? I'm happy to use raw `pa.Table` but not sure whether it will support uneven length entries.
### Desired API
```python
import numpy as np
tokenized: List[np.NDArray[np.int64]] = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])
def build_dataset_from_tokenized(tokenized: List[np.NDArray[int]]) -> Dataset:
"""FIXME"""
dataset = EmptyDataset()
for t in tokenized: dataset.append(t)
return dataset
ds = build_dataset_from_tokenized(tokenized)
assert (ds[0] == np.array([4,4,2])).all()
```
### What I tried
grep, google for "add one entry at a time", "datasets.append"
### Current Code
This code achieves the same result but doesn't fit into the `add_item` abstraction.
```python
dataset = load_dataset('text', data_files={'train': 'train.txt'})
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length=4096)
def tokenize_function(examples):
ids = tokenizer(examples['text'], return_attention_mask=False)['input_ids']
return {'input_ids': [x[1:] for x in ids]}
ds = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=['text'], load_from_cache_file=not overwrite_cache)
print(ds['train'][0]) => np array
```
Thanks in advance!
|
2021-04-23T10:01:30Z
|
https://github.com/huggingface/datasets/issues/1854
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Configure library root logger at the module level
|
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MEMBER
|
Configure library root logger at the datasets.logging module level (singleton-like).
By doing it this way:
- we are sure configuration is done only once: module level code is only runned once
- no need of global variable
- no need of threading lock
|
2021-02-10T12:32:34Z
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https://github.com/huggingface/datasets/pull/1853
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Add Arabic Speech Corpus
|
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2021-02-11T10:18:55Z
|
https://github.com/huggingface/datasets/pull/1852
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2021-02-09T14:21:48Z
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set bert_score version dependency
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CONTRIBUTOR
|
Set the bert_score version in requirements since previous versions of bert_score will fail with datasets (closes #843)
|
2021-02-09T14:21:48Z
|
https://github.com/huggingface/datasets/pull/1851
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Add cord 19 dataset
|
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"",
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] |
https://api.github.com/repos/huggingface/datasets
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CONTRIBUTOR
|
Initial version only reading the metadata in CSV.
### Checklist:
- [x] Create the dataset script /datasets/my_dataset/my_dataset.py using the template
- [x] Fill the _DESCRIPTION and _CITATION variables
- [x] Implement _infos(), _split_generators() and _generate_examples()
- [x] Make sure that the BUILDER_CONFIGS class attribute is filled with the different configurations of the dataset and that the BUILDER_CONFIG_CLASS is specified if there is a custom config class.
- [x] Generate the metadata file dataset_infos.json for all configurations
- [x] Generate the dummy data dummy_data.zip files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card README.md using the template and at least fill the tags
- [x] Both tests for the real data and the dummy data pass.
### Extras:
- [x] add more metadata
- [x] add full text
- [x] add pre-computed document embedding
|
2021-02-09T15:16:26Z
|
https://github.com/huggingface/datasets/pull/1850
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2021-03-15T05:59:37Z
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Add TIMIT
|
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https://api.github.com/repos/huggingface/datasets
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CONTRIBUTOR
|
## Adding a Dataset
- **Name:** *TIMIT*
- **Description:** *The TIMIT corpus of read speech has been designed to provide speech data for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition systems*
- **Paper:** *Homepage*: http://groups.inf.ed.ac.uk/ami/corpus/ / *Wikipedia*: https://en.wikipedia.org/wiki/TIMIT
- **Data:** *https://deepai.org/dataset/timit*
- **Motivation:** Important speech dataset
If interested in tackling this issue, feel free to tag @patrickvonplaten
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
2021-03-15T05:59:37Z
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https://github.com/huggingface/datasets/issues/1849
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Refactoring: Create config module
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Refactorize configuration settings into their own module.
This could be seen as a Pythonic singleton-like approach. Eventually a config instance class might be created.
|
2021-02-10T12:29:35Z
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https://github.com/huggingface/datasets/pull/1848
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[Metrics] Add word error metric metric
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CONTRIBUTOR
|
This PR adds the word error rate metric to datasets.
WER: https://en.wikipedia.org/wiki/Word_error_rate
for speech recognition. WER is the main metric used in ASR.
`jiwer` seems to be a solid library (see https://github.com/asteroid-team/asteroid/pull/329#discussion_r525158939)
|
2021-02-09T17:53:21Z
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https://github.com/huggingface/datasets/pull/1847
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2021-02-25T14:10:18Z
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Make DownloadManager downloaded/extracted paths accessible
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MEMBER
|
Make accessible the file paths downloaded/extracted by DownloadManager.
Close #1831.
The approach:
- I set these paths as DownloadManager attributes: these are DownloadManager's concerns
- To access to these from DatasetBuilder, I set the DownloadManager instance as DatasetBuilder attribute: object composition
|
2021-02-25T14:10:18Z
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https://github.com/huggingface/datasets/pull/1846
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Enable logging propagation and remove logging handler
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MEMBER
|
We used to have logging propagation disabled because of this issue: https://github.com/tensorflow/tensorflow/issues/26691
But since it's now fixed we should re-enable it. This is important to keep the default logging behavior for users, and propagation is also needed for pytest fixtures as asked in #1826
I also removed the handler that was added since, according to the logging [documentation](https://docs.python.org/3/howto/logging.html#configuring-logging-for-a-library):
> It is strongly advised that you do not add any handlers other than NullHandler to your library’s loggers. This is because the configuration of handlers is the prerogative of the application developer who uses your library. The application developer knows their target audience and what handlers are most appropriate for their application: if you add handlers ‘under the hood’, you might well interfere with their ability to carry out unit tests and deliver logs which suit their requirements.
It could have been useful if we wanted to have a custom formatter for the logging but I think it's more important to keep the logging as default to not interfere with the users' logging management.
Therefore I also removed the two methods `datasets.logging.enable_default_handler` and `datasets.logging.disable_default_handler`.
cc @albertvillanova this should let you use capsys/caplog in pytest
cc @LysandreJik @sgugger if you want to do the same in `transformers`
|
2021-02-09T14:22:38Z
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https://github.com/huggingface/datasets/pull/1845
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Update Open Subtitles corpus with original sentence IDs
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CONTRIBUTOR
|
Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https://huggingface.co/datasets/open_subtitles).
I can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts.
I think I should tag @abhishekkrthakur as he's the one who added it in the first place.
Thanks!
|
2021-02-12T17:38:58Z
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https://github.com/huggingface/datasets/issues/1844
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MustC Speech Translation
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CONTRIBUTOR
|
## Adding a Dataset
- **Name:** *IWSLT19*
- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*
- **Hompage:** *https://sites.google.com/view/iwslt-evaluation-2019/speech-translation*
- **Data:** *https://sites.google.com/view/iwslt-evaluation-2019/speech-translation* - all data under "Allowed Training Data" and "Development and Evalutaion Data for TED/How2"
- **Motivation:** Important speech dataset
If interested in tackling this issue, feel free to tag @patrickvonplaten
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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2021-05-14T14:53:34Z
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Add AMI Corpus
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}
] |
https://api.github.com/repos/huggingface/datasets/issues/1842
|
[
"",
""
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
## Adding a Dataset
- **Name:** *AMI*
- **Description:** *The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting recordings. For a gentle introduction to the corpus, see the corpus overview. To access the data, follow the directions given there. Around two-thirds of the data has been elicited using a scenario in which the participants play different roles in a design team, taking a design project from kick-off to completion over the course of a day. The rest consists of naturally occurring meetings in a range of domains. Detailed information can be found in the documentation section.*
- **Paper:** *Homepage*: http://groups.inf.ed.ac.uk/ami/corpus/
- **Data:** *http://groups.inf.ed.ac.uk/ami/download/* - Select all cases in 1) and select "Individual Headsets" & "Microphone array" for 2)
- **Motivation:** Important speech dataset
If interested in tackling this issue, feel free to tag @patrickvonplaten
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
2023-02-28T16:29:22Z
|
https://github.com/huggingface/datasets/issues/1842
|
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2021-03-15T05:59:02Z
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completed
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MDU6SXNzdWU4MDM1NjExMjM=
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closed
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[] | null | 1,841
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Add ljspeech
|
https://api.github.com/repos/huggingface/datasets/issues/1841/events
| null |
https://api.github.com/repos/huggingface/datasets/issues/1841/labels{/name}
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2021-02-08T13:22:26Z
| null | false
| null | null | 803,561,123
|
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https://api.github.com/repos/huggingface/datasets/issues/1841
|
[
"",
""
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
## Adding a Dataset
- **Name:** *ljspeech*
- **Description:** *This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.
The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.)*
- **Paper:** *Homepage*: https://keithito.com/LJ-Speech-Dataset/
- **Data:** *https://keithito.com/LJ-Speech-Dataset/*
- **Motivation:** Important speech dataset
- **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/ljspeech
If interested in tackling this issue, feel free to tag @patrickvonplaten
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
2021-03-15T05:59:02Z
|
https://github.com/huggingface/datasets/issues/1841
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2021-03-15T05:56:21Z
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completed
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closed
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Add common voice
|
https://api.github.com/repos/huggingface/datasets/issues/1840/events
| null |
https://api.github.com/repos/huggingface/datasets/issues/1840/labels{/name}
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2021-02-08T13:21:05Z
| null | false
| null | null | 803,560,039
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https://api.github.com/repos/huggingface/datasets/issues/1840
|
[
"",
""
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
## Adding a Dataset
- **Name:** *common voice*
- **Description:** *Mozilla Common Voice Dataset*
- **Paper:** Homepage: https://voice.mozilla.org/en/datasets
- **Data:** https://voice.mozilla.org/en/datasets
- **Motivation:** Important speech dataset
- **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/common_voice
If interested in tackling this issue, feel free to tag @patrickvonplaten
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
2022-03-20T15:23:40Z
|
https://github.com/huggingface/datasets/issues/1840
|
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open
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Add Voxforge
|
https://api.github.com/repos/huggingface/datasets/issues/1839/events
| null |
https://api.github.com/repos/huggingface/datasets/issues/1839/labels{/name}
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2021-02-08T13:19:56Z
| null | false
| null | null | 803,559,164
|
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https://api.github.com/repos/huggingface/datasets/issues/1839
|
[
"",
""
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
## Adding a Dataset
- **Name:** *voxforge*
- **Description:** *VoxForge is a language classification dataset. It consists of user submitted audio clips submitted to the website. In this release, data from 6 languages is collected - English, Spanish, French, German, Russian, and Italian. Since the website is constantly updated, and for the sake of reproducibility, this release contains only recordings submitted prior to 2020-01-01. The samples are splitted between train, validation and testing so that samples from each speaker belongs to exactly one split.*
- **Paper:** *Homepage*: http://www.voxforge.org/
- **Data:** *http://www.voxforge.org/home/downloads*
- **Motivation:** Important speech dataset
- **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/voxforge
If interested in tackling this issue, feel free to tag @patrickvonplaten
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
2021-02-08T13:28:31Z
|
https://github.com/huggingface/datasets/issues/1839
|
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2022-10-04T14:34:12Z
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completed
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closed
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Add tedlium
|
https://api.github.com/repos/huggingface/datasets/issues/1838/events
| null |
https://api.github.com/repos/huggingface/datasets/issues/1838/labels{/name}
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2021-02-08T13:17:52Z
| null | false
| null | null | 803,557,521
|
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] |
https://api.github.com/repos/huggingface/datasets/issues/1838
|
[
"",
""
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
## Adding a Dataset
- **Name:** *tedlium*
- **Description:** *The TED-LIUM 1-3 corpus is English-language TED talks, with transcriptions, sampled at 16kHz. It contains about 118 hours of speech.*
- **Paper:** Homepage: http://www.openslr.org/7/, https://lium.univ-lemans.fr/en/ted-lium2/ &, https://www.openslr.org/51/
- **Data:** http://www.openslr.org/7/
- **Motivation:** Important speech dataset
- **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/tedlium
If interested in tackling this issue, feel free to tag @patrickvonplaten
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
2022-10-04T14:34:12Z
|
https://github.com/huggingface/datasets/issues/1838
|
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2021-12-28T15:05:08Z
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Add VCTK
|
https://api.github.com/repos/huggingface/datasets/issues/1837/events
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https://api.github.com/repos/huggingface/datasets/issues/1837/labels{/name}
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2021-02-08T13:15:28Z
| null | false
| null | null | 803,555,650
|
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https://api.github.com/repos/huggingface/datasets/issues/1837
|
[
"",
""
] |
https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
## Adding a Dataset
- **Name:** *VCTK*
- **Description:** *This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.*
- **Paper:** Homepage: https://datashare.ed.ac.uk/handle/10283/3443
- **Data:** https://datashare.ed.ac.uk/handle/10283/3443
- **Motivation:** Important speech dataset
- **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/vctk
If interested in tackling this issue, feel free to tag @patrickvonplaten
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
2021-12-28T15:05:08Z
|
https://github.com/huggingface/datasets/issues/1837
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2021-02-10T16:14:58Z
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test.json has been removed from the limit dataset repo (breaks dataset)
|
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2021-02-08T12:45:53Z
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https://api.github.com/repos/huggingface/datasets
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NONE
|
https://github.com/huggingface/datasets/blob/16042b233dbff2a7585110134e969204c69322c3/datasets/limit/limit.py#L51
The URL is not valid anymore since test.json has been removed in master for some reason. Directly referencing the last commit works:
`https://raw.githubusercontent.com/ilmgut/limit_dataset/0707d3989cd8848f0f11527c77dcf168fefd2b23/data`
|
2021-02-10T16:14:58Z
|
https://github.com/huggingface/datasets/issues/1836
|
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Add CHiME4 dataset
|
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2021-02-08T12:36:38Z
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https://api.github.com/repos/huggingface/datasets
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CONTRIBUTOR
|
## Adding a Dataset
- **Name:** Chime4
- **Description:** Chime4 is a dataset for automatic speech recognition. It is especially useful for evaluating models in a noisy environment and for multi-channel ASR
- **Paper:** Dataset comes from a channel: http://spandh.dcs.shef.ac.uk/chime_challenge/CHiME4/ . Results paper:
- **Data:** http://spandh.dcs.shef.ac.uk/chime_challenge/CHiME4/download.html
- **Motivation:** So far there are very little datasets for speech in `datasets`. Only `lbirispeech_asr` so far.
If interested in tackling this issue, feel free to tag @patrickvonplaten
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
2024-02-01T10:25:03Z
|
https://github.com/huggingface/datasets/issues/1835
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2021-02-08T12:42:50Z
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Fixes base_url of limit dataset
|
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2021-02-08T12:26:35Z
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|
`test.json` is not available in the master branch of the repository anymore. Linking to a specific commit.
|
2021-02-08T12:42:50Z
|
https://github.com/huggingface/datasets/pull/1834
|
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2021-02-12T14:08:24Z
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closed
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Add OSCAR dataset card
|
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2021-02-08T01:39:49Z
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CONTRIBUTOR
|
I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824).
|
2021-02-12T14:09:25Z
|
https://github.com/huggingface/datasets/pull/1833
|
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2021-02-08T17:27:29Z
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completed
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MDU6SXNzdWU4MDI4ODA4OTc=
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closed
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Looks like nokogumbo is up-to-date now, so this is no longer needed.
|
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2021-02-07T06:52:07Z
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NONE
|
Looks like nokogumbo is up-to-date now, so this is no longer needed.
__Originally posted by @dependabot in https://github.com/discourse/discourse/pull/11373#issuecomment-738993432__
|
2021-02-08T17:27:29Z
|
https://github.com/huggingface/datasets/issues/1832
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2021-02-25T14:10:18Z
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Some question about raw dataset download info in the project .
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Hi , i review the code in
https://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py
in the _split_generators function is the truly logic of download raw datasets with dl_manager
and use Conll2003 cls by use import_main_class in load_dataset function
My question is that , with this logic it seems that i can not have the raw dataset download location
in variable in downloaded_files in _split_generators.
If someone also want use huggingface datasets as raw dataset downloader,
how can he retrieve the raw dataset download path from attributes in
datasets.dataset_dict.DatasetDict ?
|
2021-02-25T14:10:18Z
|
https://github.com/huggingface/datasets/issues/1831
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using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?
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2021-02-06T21:00:26Z
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NONE
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This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower:
````
def save_tokenizer(original_tokenizer,text,path="simpledata/tokenizer"):
words_unique = set(text.split(" "))
for i in words_unique:
original_tokenizer.add_tokens(i)
original_tokenizer.save_pretrained(path)
tokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,"tokenizer_squad"))
train_set_baby=Dataset.from_dict({"text":[train_set["text"][0][0:50]]})
````
I then applied the dataset map function on a fairly small set of text:
```
%%time
train_set_baby = train_set_baby.map(lambda d:tokenizer2(d["text"]),batched=True)
```
The run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds
**100% 1/1 [00:02<00:00, 2.60s/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**
In comparison using (even after adding additional tokens):
`
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")`
```
%%time
train_set_baby = train_set_baby.map(lambda d:tokenizer2(d["text"]),batched=True)
```
The time is
**100% 1/1 [00:00<00:00, 34.09ba/s] CPU times: user 68.1 ms, sys: 16 µs, total: 68.1 ms Wall time: 62.9 ms**
It seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer to the map function.
I should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory appears to impact the time it takes to run the map function.
|
2021-02-24T21:56:14Z
|
https://github.com/huggingface/datasets/issues/1830
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Add Tweet Eval Dataset
|
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CONTRIBUTOR
|
Closes Draft PR #1407.
Notes:
1. I have excluded `mapping.txt` from the dataset at it only contained the name mappings, which are already present in the ClassLabels.
2. I have also exluded the textual names for the emojis mentioned in the [mapping](https://github.com/cardiffnlp/tweeteval/blob/main/datasets/emoji/mapping.txt).
3. I do not understand @abhishekkrthakur's example generator on #1407. Maybe he was trying to build up on code from some other dataset.
Requesting @lhoestq to review.
|
2021-02-08T13:17:54Z
|
https://github.com/huggingface/datasets/pull/1829
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Add CelebA Dataset
|
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CONTRIBUTOR
|
Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10).
|
2021-02-18T14:17:07Z
|
https://github.com/huggingface/datasets/pull/1828
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2021-02-18T13:55:16Z
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Regarding On-the-fly Data Loading
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CONTRIBUTOR
|
Hi,
I was wondering if it is possible to load images/texts as a batch during the training process, without loading the entire dataset on the RAM at any given point.
Thanks,
Gunjan
|
2021-02-18T13:55:16Z
|
https://github.com/huggingface/datasets/issues/1827
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2021-02-09T17:39:27Z
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Print error message with filename when malformed CSV
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MEMBER
|
Print error message specifying filename when malformed CSV file.
Close #1821
|
2021-02-09T17:39:27Z
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https://github.com/huggingface/datasets/pull/1826
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Datasets library not suitable for huge text datasets.
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NONE
|
Hi,
I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training.
Moreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).
Any suggestions??
|
2021-03-30T14:04:01Z
|
https://github.com/huggingface/datasets/issues/1825
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Add OSCAR dataset card
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MEMBER
|
I started adding the dataset card for OSCAR !
For now it's just basic info for all the different configurations in `Dataset Structure`.
In particular the Data Splits section tells how may samples there are for each config. The Data Instances section show an example for each config, and it also shows the size in MB. Since the Data Instances section is very long the user has to click to expand the info. I was able to generate it thanks to the tools made by @madlag and @yjernite :D
Cc @pjox could you help me with the other sections ? (Dataset Description, Dataset Creation, Considerations for Using the Data, Additional Information)
|
2021-05-05T18:24:14Z
|
https://github.com/huggingface/datasets/pull/1824
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2021-03-01T10:21:39Z
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Add FewRel Dataset
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https://api.github.com/repos/huggingface/datasets
|
CONTRIBUTOR
|
Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan
|
2021-03-01T11:56:20Z
|
https://github.com/huggingface/datasets/pull/1823
|
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2021-02-15T09:57:39Z
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Add Hindi Discourse Analysis Natural Language Inference Dataset
|
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CONTRIBUTOR
|
# Dataset Card for Hindi Discourse Analysis Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- HomePage : https://github.com/midas-research/hindi-nli-data
- Paper : https://www.aclweb.org/anthology/2020.aacl-main.71
- Point of Contact : https://github.com/midas-research/hindi-nli-data
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. Hindi Discourse Analysis (HDA) Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Premise and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Entailed means that hypotheis can be inferred from premise and not-entailed means vice versa
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
- Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- train, test and dev files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह एक वर्णनात्मक कथन है।', 'label': 1, 'premise': 'जैसे उस का सारा चेहरा अपना हो और आँखें किसी दूसरे की जो चेहरे पर पपोटों के पीछे महसूर कर दी गईं।', 'topic': 1}
```
### Data Fields
- Each row contatins 4 columns - premise, hypothesis, label and topic.
### Data Splits
- Train : 31892
- Valid : 9460
- Test : 9970
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available Hindi Discourse Analysis classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper https://www.aclweb.org/anthology/2020.aacl-main.71
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- Initial Data was collected by members of MIDAS Lab from Hindi Websites. They crowd sourced the data annotation process and selected two random stories from our corpus and had the three annotators work on them independently and classify each sentence based on the discourse mode.
- Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
- The Discourse is further classified into "Argumentative" , "Descriptive" , "Dialogic" , "Informative" and "Narrative" - 5 Clases.
#### Who are the source language producers?
Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically by machine and corresponding recasting process.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
No known bias exist in the dataset.
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations . Size of data may not be enough to train large models
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/midas-research/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
### Contributions
Thanks to [@avinsit123](https://github.com/avinsit123) for adding this dataset.
|
2021-02-15T09:57:39Z
|
https://github.com/huggingface/datasets/pull/1822
|
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2021-02-09T17:39:27Z
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completed
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MDU6SXNzdWU4MDE3NDc2NDc=
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closed
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Provide better exception message when one of many files results in an exception
|
https://api.github.com/repos/huggingface/datasets/issues/1821/events
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2021-02-05T00:49:03Z
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NONE
|
I find when I process many files, i.e.
```
train_files = glob.glob('rain*.csv')
validation_files = glob.glob(validation*.csv')
datasets = load_dataset("csv", data_files=dict(train=train_files, validation=validation_files))
```
I sometimes encounter an error due to one of the files being misformed (i.e. no data, or a comma in a field that isn't quoted, etc).
For example, this is the tail of an exception which I suspect is due to a stray comma.
> File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read
> File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory
> File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows
> File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows
> File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error
> pandas.errors.ParserError: Error tokenizing data. C error: Expected 2 fields in line 559, saw 3
It would be nice if the exception trace contained the name of the file being processed (I have 250 separate files!)
|
2021-02-09T17:39:27Z
|
https://github.com/huggingface/datasets/issues/1821
|
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2021-02-05T14:00:00Z
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closed
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Add metrics usage examples and tests
|
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MEMBER
|
All metrics finally have usage examples and proper fast + slow tests :)
I added examples of usage for every metric, and I use doctest to make sure they all work as expected.
For "slow" metrics such as bert_score or bleurt which require to download + run a transformer model, the download + forward pass are only done in the slow test.
In the fast test on the other hand, the download + forward pass are monkey patched.
Metrics that need to be installed from github are not added to setup.py because it prevents uploading the `datasets` package to pypi.
An additional-test-requirements.txt file is used instead. This file also include `comet` in order to not have to resolve its *impossible* dependencies.
Also `comet` is not tested on windows because one of its dependencies (fairseq) can't be installed in the CI for some reason.
|
2021-02-05T14:00:01Z
|
https://github.com/huggingface/datasets/pull/1820
|
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2021-02-04T16:52:26Z
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Fixed spelling `S3Fileystem` to `S3FileSystem`
|
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MEMBER
|
Fixed documentation spelling errors.
Wrong `S3Fileystem`
Right `S3FileSystem`
|
2021-02-04T16:52:27Z
|
https://github.com/huggingface/datasets/pull/1819
|
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2022-06-01T15:38:42Z
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Loading local dataset raise requests.exceptions.ConnectTimeout
|
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2021-02-04T05:55:23Z
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NONE
|
Load local dataset:
```
dataset = load_dataset('json', data_files=["../../data/json.json"])
train = dataset["train"]
print(train.features)
train1 = train.map(lambda x: {"labels": 1})
print(train1[:2])
```
but it raised requests.exceptions.ConnectTimeout:
```
/Users/littlely/myvirtual/tf2/bin/python3.7 /Users/littlely/projects/python_projects/pytorch_learning/nlp/dataset/transformers_datasets.py
Traceback (most recent call last):
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py", line 160, in _new_conn
(self._dns_host, self.port), self.timeout, **extra_kw
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/connection.py", line 84, in create_connection
raise err
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/connection.py", line 74, in create_connection
sock.connect(sa)
socket.timeout: timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 677, in urlopen
chunked=chunked,
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 381, in _make_request
self._validate_conn(conn)
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 978, in _validate_conn
conn.connect()
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py", line 309, in connect
conn = self._new_conn()
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py", line 167, in _new_conn
% (self.host, self.timeout),
urllib3.exceptions.ConnectTimeoutError: (<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)')
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/adapters.py", line 449, in send
timeout=timeout
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 727, in urlopen
method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/retry.py", line 439, in increment
raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/json/json.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/littlely/projects/python_projects/pytorch_learning/nlp/dataset/transformers_datasets.py", line 12, in <module>
dataset = load_dataset('json', data_files=["../../data/json.json"])
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/load.py", line 591, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/load.py", line 263, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset, max_retries=download_config.max_retries)
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 232, in head_hf_s3
max_retries=max_retries,
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 523, in http_head
max_retries=max_retries,
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 458, in _request_with_retry
raise err
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 454, in _request_with_retry
response = requests.request(verb.upper(), url, **params)
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/sessions.py", line 530, in request
resp = self.send(prep, **send_kwargs)
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/sessions.py", line 643, in send
r = adapter.send(request, **kwargs)
File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/adapters.py", line 504, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/json/json.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
Process finished with exit code 1
```
Why it want to connect a remote url when I load local datasets, and how can I fix it?
|
2022-06-01T15:38:42Z
|
https://github.com/huggingface/datasets/issues/1818
|
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pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 599 but got length 1500
|
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NONE
|
I am trying to preprocess any dataset in this package with GPT-2 tokenizer, so I need to structure the datasets as long sequences of text without padding. I've been following a couple of your tutorials and here you can find the script that is failing right at the end
https://github.com/LuCeHe/GenericTools/blob/master/KerasTools/lm_preprocessing.py
In the last iteration of the last dset.map, it gives the error that I copied in the title. Another issue that I have, if I leave the batch_size set as 1000 in the last .map, I'm afraid it's going to lose most text, so I'm considering setting both writer_batch_size and batch_size to 300 K, but I'm not sure it's the best way to go.
Can you help me?
Thanks!
|
2022-10-05T12:42:57Z
|
https://github.com/huggingface/datasets/issues/1817
|
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2021-02-15T15:04:33Z
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closed
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Doc2dial rc update to latest version
|
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2021-02-03T20:08:54Z
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CONTRIBUTOR
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2021-02-15T15:15:24Z
|
https://github.com/huggingface/datasets/pull/1816
|
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2021-03-01T10:36:21Z
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Add CCAligned Multilingual Dataset
|
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2021-02-03T18:59:52Z
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CONTRIBUTOR
|
Hello,
I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756.
This dataset has two types - Document-Pairs, and Sentence-Pairs.
The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted.
I'm expecting the usage to be something like -
`load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`.
It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition.
Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments.
A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests.
Thanks,
Gunjan
Requesting @lhoestq / @yjernite to review.
|
2021-03-01T12:33:03Z
|
https://github.com/huggingface/datasets/pull/1815
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2021-02-04T16:21:48Z
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Add Freebase QA Dataset
|
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CONTRIBUTOR
|
Closes PR #1435. Fixed issues with PR #1809.
Requesting @lhoestq to review.
|
2021-02-04T19:47:51Z
|
https://github.com/huggingface/datasets/pull/1814
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2021-02-05T10:33:47Z
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Support future datasets
|
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MEMBER
|
If a dataset is available at the version of the local installation of `datasets` (e.g. 1.2.0), then loading this dataset means loading the script at this version.
However when trying to load a dataset that is only available on master, currently users have to specify `script_version="master"` in `load_dataset` to make it work.
However we could automatically get the dataset from master instead in this case.
I added this feature in this PR.
I also added a warning if a dataset is not available at the version of the local installation of `datasets` but is loaded from master:
```python
>>> load_dataset("silicone", "dyda_da")
Couldn't find file locally at silicone/silicone.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/silicone/silicone.py.
The file was picked from the master branch on github instead at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/silicone/silicone.py.
Downloading and preparing dataset silicone/dyda_da (download: 8.46 MiB, generated: 9.39 MiB, post-processed: Unknown size, total: 17.86 MiB) to /Users/quentinlhoest/.cache/huggingface/datasets/silicone/dyda_da/1.0.0/d41d8c0b73c6df035b1369c45774418f0051163ea689b5502b8bda783adf6342...
...
```
|
2021-02-05T10:33:48Z
|
https://github.com/huggingface/datasets/pull/1813
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Add CIFAR-100 Dataset
|
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CONTRIBUTOR
|
Adding CIFAR-100 Dataset.
|
2021-02-08T11:10:18Z
|
https://github.com/huggingface/datasets/pull/1812
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Unable to add Multi-label Datasets
|
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CONTRIBUTOR
|
I am trying to add [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as
`supervised_keys=("img", "fine_label")` raises no issue. But trying `supervised_keys=("img", "fine_label","coarse_label")` leads to this error :
```python
Traceback (most recent call last):
File "test_script.py", line 2, in <module>
d = load_dataset('./datasets/cifar100')
File "~/datasets/src/datasets/load.py", line 668, in load_dataset
**config_kwargs,
File "~/datasets/src/datasets/builder.py", line 896, in __init__
super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
File "~/datasets/src/datasets/builder.py", line 247, in __init__
info.update(self._info())
File "~/.cache/huggingface/modules/datasets_modules/datasets/cifar100/61d2489b2d4a4abc34201432541b7380984ec714e290817d9a1ee318e4b74e0f/cifar100.py", line 79, in _info
citation=_CITATION,
File "<string>", line 19, in __init__
File "~/datasets/src/datasets/info.py", line 136, in __post_init__
self.supervised_keys = SupervisedKeysData(*self.supervised_keys)
TypeError: __init__() takes from 1 to 3 positional arguments but 4 were given
```
Is there a way I can fix this?
Also, what does adding `supervised_keys` do? Is it necessary? How would I specify `supervised_keys` for a multi-input, multi-label dataset?
Thanks,
Gunjan
|
2021-02-18T14:16:31Z
|
https://github.com/huggingface/datasets/issues/1811
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Add Hateful Memes Dataset
|
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CONTRIBUTOR
|
## Add Hateful Memes Dataset
- **Name:** Hateful Memes
- **Description:** [https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set]( https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set)
- **Paper:** [https://arxiv.org/pdf/2005.04790.pdf](https://arxiv.org/pdf/2005.04790.pdf)
- **Data:** [This link](https://drivendata-competition-fb-hateful-memes-data.s3.amazonaws.com/XjiOc5ycDBRRNwbhRlgH.zip?AWSAccessKeyId=AKIARVBOBDCY4MWEDJKS&Signature=DaUuGgZWUgDHzEPPbyJ2PhSJ56Q%3D&Expires=1612816874)
- **Motivation:** Including multi-modal datasets to 🤗 datasets.
I will be adding this dataset. It requires the user to sign an agreement on DrivenData. So, it will be used with a manual download.
The issue with this dataset is that the images are of different sizes. The image datasets added so far (CIFAR-10 and MNIST) have a uniform shape throughout.
So something like
```python
datasets.Array2D(shape=(28, 28), dtype="uint8")
```
won't work for the images. How would I add image features then? I checked `datasets/features.py` but couldn't figure out the appropriate class for this. I'm assuming I would want to avoid re-sizing at all since we want the user to be able to access the original images.
Also, in case I want to load only a subset of the data, since the actual data is around 8.8GB, how would that be possible?
Thanks,
Gunjan
|
2021-12-08T12:03:59Z
|
https://github.com/huggingface/datasets/issues/1810
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