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} | [] | 2021-04-07T13:00:18 | 2021-04-20T14:20:12 | 2021-04-20T10:04:04 | MEMBER | null | Set a default value for `in_memory` depending on the size of the dataset to be loaded.
Close #2179.
TODO:
- [x] Add a section in the docs about this.
- ~Add a warning if someone tries to specify `cache_file_name=` in `map`, `filter` etc. on a dataset that is in memory, since the computation is not going to be cached in this case.~ | {
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https://api.github.com/repos/huggingface/datasets/issues/2181 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2181/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2181/comments | https://api.github.com/repos/huggingface/datasets/issues/2181/events | https://github.com/huggingface/datasets/issues/2181 | 852,261,607 | MDU6SXNzdWU4NTIyNjE2MDc= | 2,181 | Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries) | {
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"Hi ! Can you try to increase the block size ? For example\r\n```python\r\nblock_size_10MB = 10<<20\r\nload_dataset(\"json\", ..., block_size=block_size_10MB)\r\n```\r\nThe block size corresponds to how much bytes to process at a time from the input stream.\r\nThis will determine multi-threading granularity as well... | 2021-04-07T10:26:46 | 2021-04-12T07:15:55 | 2021-04-12T07:15:55 | NONE | null | Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.
When loading a huge json file of 500GB, pyarrow complains as follows:
```
Traceback (most recent call last):
File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 531, in incomplete_dir
yield tmp_dir
File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 573, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 650, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 1027, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/app/.cache/huggingface/modules/datasets_modules/datasets/json/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641/json.py", line 83, in _generate_tables
parse_options=self.config.pa_parse_options,
File "pyarrow/_json.pyx", line 247, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)
```
When using only a small portion of the sample file, say first 100 lines, it works perfectly well..
I see that it is the error from pyarrow, but could you give me a hint or possible solutions?
#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance! | {
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} | [] | closed | false | null | [] | null | [] | 2021-04-07T10:23:15 | 2021-04-07T15:50:35 | 2021-04-07T15:50:34 | MEMBER | null | This should fix issue #2149 | {
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However this might not be necessary for small datasets. If a dataset is small enough, then it can be loaded in-memory and:
- its memory footprint would be small so it's ok
- in-memory computations/queries would be faster
- the caching on-disk would be disabled, making computations even faster (no I/O bound because of the disk)
- but running the same computation a second time would recompute everything since there would be no cached results on-disk. But this is probably fine since computations would be fast anyway + users should be able to provide a cache filename if needed.
Therefore, maybe the default behavior of `load_dataset` should be to load small datasets in-memory and big datasets using memory mapping. | {
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} | [] | 2021-04-07T09:30:50 | 2021-04-20T14:20:44 | 2021-04-13T09:28:16 | MEMBER | null | The `cast` operation on a pyarrow Table may create new arrays in memory.
This is an issue since users expect memory mapped datasets to not fill up the RAM.
To fix that I used `map` to write a new arrow file on disk when cast is used.
To make things more convenient I introduced the `arrow` formatting of a dataset, to make it return pyarrow tables instead of python dicts. This way one can use pyarrow transforms directly when using `map`.
edit: we'll use the same mechanism for `filter` | {
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} | [] | closed | false | null | [] | null | [] | 2021-04-07T06:40:06 | 2021-04-07T08:16:01 | 2021-04-07T08:16:01 | MEMBER | null | # What does this PR do?
I added OpenGraph/ Twitter Card support to the docs to create nice social thumbnails.
To be able to add these I needed to install `sphinxext-opengraph`. I came across this [issue](https://github.com/readthedocs/readthedocs.org/issues/1758) on the readthedocs repo saying that since someone has built this plugin they are not integrating and providing documentation to it. That's why I added it for creating the documentation. The repository can be found [here](https://github.com/wpilibsuite/sphinxext-opengraph/tree/main).
P.S. It seemed that `make style` never ran for `docs/` i hope the changes are okay otherwise I'll revert it. | {
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"Hi @nelson-liu!\r\nHere is what I do to convert a string to class label:\r\n\r\n```python\r\nfrom datasets import load_dataset, features\r\n\r\n\r\ndset = load_dataset(...)\r\ncol_name = \"the string column name\"\r\n\r\nclass_names = dset.unique(col_name)\r\nclass_feature = features.ClassLabel(names=sorted(class... | 2021-04-06T22:54:16 | 2022-06-01T16:31:49 | 2022-06-01T16:31:49 | NONE | null | Hi!
In the docs for `cast`, it's noted that `For non-trivial conversion, e.g. string <-> ClassLabel you should use map() to update the Dataset.`
Would it be possible to have an example that demonstrates such a string <-> ClassLabel conversion using `map`? Thanks! | {
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"Actually, I found the answer [here](https://github.com/facebookresearch/faiss/wiki/FAQ#what-does-it-mean-when-a-search-returns--1-ids). \r\n\r\nSo we have to do some modifications to the code for instances where the index doesn't retrieve any IDs.",
"@lhoestq @patrickvonplaten \r\n\r\nI also found another short... | 2021-04-06T21:50:49 | 2021-04-16T12:21:16 | 2021-04-16T12:21:15 | NONE | null | I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, "IVF65536_HNSW32,Flat")**.
During the retrieval phase exactly in [this line of retrieval_rag.py](https://github.com/huggingface/transformers/blob/master/src/transformers/models/rag/retrieval_rag.py#L231) an error issue when all retrieved indices are -1. Please refer to the screenshot of a PID worker.
Here, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?
Is this a problem of the index, where the faiss can't find any similar vector?
Is there documentation on the output index being -1?
@lhoestq
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} | [] | closed | false | null | [] | null | [] | 2021-04-06T12:40:20 | 2021-04-06T12:55:53 | 2021-04-06T12:55:53 | MEMBER | null | The latest release of docutils make the navbar in the documentation weird and the Markdown wrongly interpreted:
We had the same problem in Transformers and solved it by pinning docutils (a dep of sphinx).
You can see the version after the change [here](https://32769-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html).
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} | [] | closed | false | null | [] | null | [] | 2021-04-06T12:08:34 | 2021-04-12T16:54:46 | 2021-04-12T16:54:46 | CONTRIBUTOR | null | OpenSLR (https://openslr.org/) is a site devoted to hosting speech and language resources, such as training corpora for speech recognition, and software related to speech recognition. There are around 80 speech datasets listed in OpenSLR, currently this PR includes only 9 speech datasets SLR41, SLR42, SLR43, SLR44, SLR63, SLR64, SLR65, SLR66 and SLR69 (Javanese, Khmer, Nepali and Sundanese, Malayalam, Marathi, Tamil, Telugu and Catalan). I can add other speech datasets gradually next time. | {
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} | [] | closed | false | null | [] | null | [] | 2021-04-06T09:19:09 | 2021-04-06T09:49:27 | 2021-04-06T09:49:26 | MEMBER | null | Today's release of `fsspec` 0.9.0 implied a new release of `s3fs` 0.6.0 but this version breaks the CI (see [here](https://app.circleci.com/pipelines/github/huggingface/datasets/5312/workflows/490f3240-cd1c-4dd1-bb60-b416771c5584/jobs/32734) for example)
I'm pinning `fsspec` until this has been resolved | {
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https://github.com/huggingface/datasets/issues/2078
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} | [] | closed | false | null | [] | null | [] | 2021-04-04T20:46:21 | 2021-04-19T10:57:05 | 2021-04-19T09:08:55 | CONTRIBUTOR | null | Fixes #2167
Using `eval` is not ideal for security reasons (in web apps I assume), but without it the code would be much more complex IMO.
In terms of style, instead of explicitly importing a private member (`_RelativeInstruction`), we can add these imports at the top of the module:
```python
from . import arrow_reader # gives us access to ReadInstruction and _RelativeInstruction
from . import splits # gives us access to NamedSplit
```
and then define the `eval` globals as follows:
```python
{**arrow_reader.__dict__, **splits.__dict__}
```
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} | [] | closed | false | null | [] | null | [] | 2021-04-04T19:29:54 | 2021-04-19T09:08:55 | 2021-04-19T09:08:55 | CONTRIBUTOR | null | A minimal reproducible example:
```python
>>> from datasets import load_dataset, Dataset
>>> dset = load_dataset("sst", split="train")
>>> dset.save_to_disk("sst")
>>> type(dset.split)
<class 'datasets.splits.NamedSplit'>
>>> dset = Dataset.load_from_disk("sst")
>>> type(dset.split) # NamedSplit expected
<class 'str'>
```
It seems like this bug was introduced in #2025. | {
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"Hi @vyraun ! The test references for CommonGen are not publicly available: you can reach out to the original dataset authors if you would like to ask for them, but we will not be releasing them as part of GEM (March 31st was the release date for the test set inputs, references are incidentally released for some of... | 2021-04-04T02:02:45 | 2021-04-06T08:13:12 | 2021-04-06T08:13:12 | NONE | null | @yjernite Hi, are the test sets for the GEM datasets scheduled to be [added soon](https://gem-benchmark.com/shared_task)?
e.g.
```
from datasets import load_dataset
DATASET_NAME="common_gen"
data = load_dataset("gem", DATASET_NAME)
```
The test set doesn't have the target or references.
```
data['test'][0]
{'concept_set_id': 0, 'concepts': ['drill', 'field', 'run', 'team'], 'gem_id': 'common_gen-test-0', 'gem_parent_id': 'common_gen-test-0', 'references': [], 'target': ''}
```
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"Hi,\r\n\r\na HF dataset can be converted to a Torch Dataset with a simple wrapper as follows:\r\n```python\r\nfrom torch.utils.data import Dataset\r\n \r\nclass HFDataset(Dataset):\r\n def __init__(self, dset):\r\n self.dset = dset\r\n\r\n def __getitem__(self, idx):\r\n return self.dset[idx]\r... | 2021-04-04T01:01:48 | 2021-08-24T15:55:35 | 2021-04-07T15:06:04 | NONE | null | Hi,
I'm trying to pretraine deep-speed model using HF arxiv dataset like:
```
train_ds = nlp.load_dataset('scientific_papers', 'arxiv')
train_ds.set_format(
type="torch",
columns=["input_ids", "attention_mask", "global_attention_mask", "labels"],
)
engine, _, _, _ = deepspeed.initialize(
args=args,
model=model,
model_parameters=[p for p in model.parameters() if p.requires_grad],
training_data=train_ds)
```
but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?
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} | [] | closed | false | null | [] | null | [] | 2021-04-03T21:07:02 | 2021-04-06T14:41:09 | 2021-04-06T14:41:08 | CONTRIBUTOR | null | Replaces all the occurrences of the `assertTrue(isinstance(` pattern with `assertIsInstance`. | {
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Fixes #2103 | {
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"This looks like an issue with the cc100 dataset itself but not sure\r\nDid you try loading cc100 on your machine ?",
"Hi\nloading works fine, but the viewer only is broken\nthanks\n\nOn Wed, Apr 7, 2021 at 12:17 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> This looks like an issue with the cc100 dataset itself bu... | 2021-04-02T10:11:13 | 2022-10-05T13:20:24 | 2022-10-05T13:20:24 | NONE | null | Hi
visualization through dataset viewer for cc100 is broken
https://huggingface.co/datasets/viewer/
thanks a lot
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"Not yet but it’s on the short/mid-term roadmap (requested by many indeed).",
"oh, great, really awesome feature to have, thank you very much for the great, fabulous work",
"We'll work on dataset streaming soon. This should allow you to only load the examples you need ;)",
"thanks a lot Quentin, this would be... | 2021-04-02T10:06:46 | 2022-10-05T13:26:51 | 2022-10-05T13:26:51 | NONE | null | Hi
Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks | {
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"Hi.\r\nI cannot always reproduce this issue, and on later runs I did not see it so far. Sometimes also I set 8 processes but I see less being showed, is this normal, here only 5 are shown for 8 being set, thanks\r\n\r\n```\r\n#3: 11%|███████████████▊ ... | 2021-04-02T07:56:13 | 2021-04-02T10:14:32 | 2021-04-02T10:14:31 | NONE | null | Hi @lhoestq
I am running this code from huggingface transformers https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py
to speed up tokenization, since I am running on multiple datasets, I am using data_args.preprocessing_num_workers = 4 with opus100 corpus but this moves on till a point and then this freezes almost for sometime during tokenization steps and then this is back again, overall to me taking more time than normal case, I appreciate your advice on how I can use this option properly to speed up.
thanks | {
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"closing since I think this is cc100, just the name has been changed. thanks "
] | 2021-04-01T23:28:36 | 2021-04-02T10:05:19 | 2021-04-02T10:05:19 | NONE | null | ## Adding a Dataset
- **Name:** ccnet
- **Description:**
Common Crawl
- **Paper:**
https://arxiv.org/abs/1911.00359
- **Data:**
https://github.com/facebookresearch/cc_net
- **Motivation:**
this is one of the most comprehensive clean monolingual datasets across a variety of languages. Quite important for cross-lingual reseach
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
thanks | {
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"Thanks for reporting !\r\nThe viewer doesn't have all the dependencies of the datasets. We may add openpyxl to be able to show this dataset properly",
"This viewer tool is deprecated now and the new viewer at https://huggingface.co/datasets/fake_news_english works fine, so I'm closing this issue"
] | 2021-04-01T14:13:20 | 2022-10-05T13:22:02 | 2022-10-05T13:22:02 | NONE | null | When I visit the [Huggingface - viewer](https://huggingface.co/datasets/viewer/) web site, under the dataset "fake_news_english" I've got this error:
> ImportError: To be able to use this dataset, you need to install the following dependencies['openpyxl'] using 'pip install # noqa: requires this pandas optional dependency for reading xlsx files' for instance'
as well as the error Traceback.
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} | [] | closed | false | null | [] | null | [] | 2021-03-31T14:36:10 | 2021-04-01T16:46:30 | 2021-03-31T15:42:08 | MEMBER | null | Following #2025 , I added the table classes to the documentation
cc @albertvillanova | {
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See #1720. | {
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"Hi ! Thanks for reporting. I opened a PR to fix this issue: #2201",
"Nice question which helped me a lot! I have wasted a lot of time to the `DatasetDict` creation from a csv file. Hope the document of this module add some simple examples.",
"Hi :) We're indeed working on tutorials that we will add to the docs... | 2021-03-31T08:30:09 | 2022-10-05T13:29:12 | 2022-10-05T13:29:12 | NONE | null | First of all, I'm sorry if it is a repeated issue or the changes are already in master, I searched and I didn't find anything.
I'm using datasets 1.5.0
As you can see, when I load the dataset, the ClassLabels are ignored, I have to cast the dataset in order to make it work.
Code to reproduce:
```python
import datasets
data_location = "/data/prueba_multiclase"
features = datasets.Features(
{"texto": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["false", "true"])}
)
dataset = datasets.load_dataset(
"csv", data_files=data_location, delimiter="\t", features=features
)
```
Dataset I used:
[prueba_multiclase.zip](https://github.com/huggingface/datasets/files/6235022/prueba_multiclase.zip) (it has to be unzipped)
Thank you! ❤️
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} | [] | 2021-03-30T16:58:44 | 2021-06-23T17:41:02 | 2021-04-19T16:07:18 | MEMBER | null | Add support for `axis` (0 or 1) in `concatenate_datasets`.
Close #853. | {
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https://api.github.com/repos/huggingface/datasets/issues/2150 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2150/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2150/comments | https://api.github.com/repos/huggingface/datasets/issues/2150/events | https://github.com/huggingface/datasets/pull/2150 | 844,776,448 | MDExOlB1bGxSZXF1ZXN0NjAzOTg3OTcx | 2,150 | Allow pickling of big in-memory tables | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-30T15:51:56 | 2021-03-31T10:37:15 | 2021-03-31T10:37:14 | MEMBER | null | This should fix issue #2134
Pickling is limited to <4GiB objects, it's not possible to pickle a big arrow table (for multiprocessing for example).
For big tables, we have to write them on disk and only pickle the path to the table. | {
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https://api.github.com/repos/huggingface/datasets/issues/2149 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2149/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2149/comments | https://api.github.com/repos/huggingface/datasets/issues/2149/events | https://github.com/huggingface/datasets/issues/2149 | 844,734,076 | MDU6SXNzdWU4NDQ3MzQwNzY= | 2,149 | Telugu subset missing for xtreme tatoeba dataset | {
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"Good catch ! Thanks for reporting\r\n\r\nI just opened #2180 to fix this",
"Fixed in #2180"
] | 2021-03-30T15:26:34 | 2022-10-05T13:28:30 | 2022-10-05T13:28:30 | CONTRIBUTOR | null | from nlp import load_dataset
train_dataset = load_dataset('xtreme', 'tatoeba.tel')['validation']
ValueError: BuilderConfig tatoeba.tel not found.
but language tel is actually included in xtreme:
https://github.com/google-research/xtreme/blob/master/utils_preprocess.py
def tatoeba_preprocess(args):
lang3_dict = {
'afr':'af', 'ara':'ar', 'bul':'bg', 'ben':'bn',
'deu':'de', 'ell':'el', 'spa':'es', 'est':'et',
'eus':'eu', 'pes':'fa', 'fin':'fi', 'fra':'fr',
'heb':'he', 'hin':'hi', 'hun':'hu', 'ind':'id',
'ita':'it', 'jpn':'ja', 'jav':'jv', 'kat':'ka',
'kaz':'kk', 'kor':'ko', 'mal':'ml', 'mar':'mr',
'nld':'nl', 'por':'pt', 'rus':'ru', 'swh':'sw',
'tam':'ta', **_'tel':'te'_**, 'tha':'th', 'tgl':'tl', <----here
'tur':'tr', 'urd':'ur', 'vie':'vi', 'cmn':'zh',
'eng':'en',
} | {
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"Hi @marrodion. \r\n\r\nThanks for pointing this out. It would be great to incorporate this metric-specific enhancement.\r\n\r\nAnother possibility would be to require the user to input the scheme as a string `mode=\"strict\", scheme=\"IOB2\"` and then dynamically import the corresponding module using Python `impor... | 2021-03-30T15:04:06 | 2021-04-15T13:49:46 | 2021-04-15T13:49:46 | CONTRIBUTOR | null | Right now `load_metric("seqeval")` only works in the default mode of evaluation (equivalent to conll evaluation).
However, seqeval library [supports](https://github.com/chakki-works/seqeval#support-features) different evaluation schemes (IOB1, IOB2, etc.), which can be plugged in just by supporting additional kwargs in `Seqeval._compute`
https://github.com/huggingface/datasets/blob/85cf7ff920c90ca2e12bedca12b36d2a043c3da2/metrics/seqeval/seqeval.py#L109
Things that would be relevant are, for example, supporting `mode="strict", scheme=IOB2` to count only full entity match as a true positive and omit partial matches.
The only problem I see is that the spirit of `metrics` seems to not require additional imports from user. `seqeval` only supports schemes as objects, without any string aliases.
It can be solved naively with mapping like `{"IOB2": seqeval.scheme.IOB2}`. Or just left as is and require user to explicitly import scheme from `seqeval` if he wants to configure it past the default implementation.
If that makes sense, I am happy to implement the change. | {
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See e.g. current docs for `Dataset.to_csv`. | {
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} | [] | 2021-03-30T14:02:14 | 2021-04-29T14:50:44 | 2021-04-29T14:50:43 | MEMBER | null | Implement `Dataset.add_column`.
Close #1954. | {
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not satisfied with the API, it means as a dataset implementer I need to write a function with boilerplate and write classes for each `<dataset><task>` "facet". | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-29T23:47:02 | 2021-03-30T00:10:02 | 2021-03-30T00:10:02 | MEMBER | null | This branch updates the GEM benchmark to its 1.1 version which includes:
- challenge sets for most tasks
- detokenized TurkCorpus to match the rest of the text simplification subtasks
- fixed inputs for TurkCorpus and ASSET test sets
- 18 languages in WikiLingua
cc @sebastianGehrmann | {
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https://api.github.com/repos/huggingface/datasets/issues/2141 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2141/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2141/comments | https://api.github.com/repos/huggingface/datasets/issues/2141/events | https://github.com/huggingface/datasets/pull/2141 | 843,914,790 | MDExOlB1bGxSZXF1ZXN0NjAzMjM2MjUw | 2,141 | added spans field for the wikiann datasets | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-29T23:38:26 | 2021-03-31T13:27:50 | 2021-03-31T13:27:50 | CONTRIBUTOR | null | Hi @lhoestq
I tried to add spans to the wikiann datasets.
Thanks a lot for kindly having a look.
This addresses https://github.com/huggingface/datasets/issues/2130.
Best regards
Rabeeh | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-29T21:32:23 | 2021-04-09T09:32:18 | 2021-04-09T09:32:18 | CONTRIBUTOR | null | Intent classification/detection dataset from banking category with 77 unique intents. | {
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"Hi !\r\nI think this has been fixed recently on `master`.\r\nCan you try again by installing `datasets` from `master` ?\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```",
"Hi!\r\n\r\nUsing that version of the code solves the issue. Thanks!"
] | 2021-03-29T18:23:54 | 2021-03-30T09:12:53 | 2021-03-30T09:12:53 | NONE | null | Hi,
Loading a dataset with `load_dataset` using a split defined via `ReadInstruction` and then saving it to disk results in the following error: `TypeError: Object of type ReadInstruction is not JSON serializable`.
Here is the minimal reproducible example:
```python
from datasets import load_dataset
from datasets import ReadInstruction
data_1 = load_dataset(
"wikiann",
"en",
split="validation",
)
data_1.save_to_disk("temporary_path_1")
print("Save with regular split works.")
data_2 = load_dataset(
"wikiann",
"en",
split=ReadInstruction("validation", to=50, unit="%"),
)
data_2.save_to_disk("temporary_path_2")
```
and the corresponding output:
```
Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9)
Save with regular split works.
Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9)
Traceback (most recent call last):
File "bug.py", line 20, in <module>
data_2.save_to_disk("temporary_path_2")
File "/xxxxx/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 645, in save_to_disk
json.dump(state, state_file, indent=2, sort_keys=True)
File "/usr/lib/python3.7/json/__init__.py", line 179, in dump
for chunk in iterable:
File "/usr/lib/python3.7/json/encoder.py", line 431, in _iterencode
yield from _iterencode_dict(o, _current_indent_level)
File "/usr/lib/python3.7/json/encoder.py", line 405, in _iterencode_dict
yield from chunks
File "/usr/lib/python3.7/json/encoder.py", line 438, in _iterencode
o = _default(o)
File "/usr/lib/python3.7/json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type ReadInstruction is not JSON serializable
```
Let me know if there is some misuse from my end.
Thanks in advance.
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} | [] | closed | false | null | [] | null | [] | 2021-03-29T15:52:27 | 2021-04-06T16:16:11 | 2021-04-06T07:14:38 | CONTRIBUTOR | null | Add Character Error Rate (CER) metric that is used in evaluation in ASR. I also have written unittests (hopefully thorough enough) but I'm not sure how to integrate them into the existed codebase.
```python
from cer import CER
cer = CER()
class TestCER(unittest.TestCase):
def test_cer_case_senstive(self):
refs = ['White House']
preds = ['white house']
# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
char_error_rate = cer.compute(predictions=preds, references=refs)
self.assertTrue(abs(char_error_rate - 0.1818181818) < 1e-6)
def test_cer_whitespace(self):
refs = ['were wolf']
preds = ['werewolf']
# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
char_error_rate = cer.compute(predictions=preds, references=refs)
self.assertTrue(abs(char_error_rate - 0.1111111) < 1e-6)
refs = ['werewolf']
preds = ['weae wolf']
# S = 1, D = 1, I = 0, N = 8, CER = 0.25
char_error_rate = cer.compute(predictions=preds, references=refs)
self.assertTrue(abs(char_error_rate - 0.25) < 1e-6)
# consecutive whitespaces case 1
refs = ['were wolf']
preds = ['were wolf']
# S = 0, D = 0, I = 0, N = 9, CER = 0
char_error_rate = cer.compute(predictions=preds, references=refs)
self.assertTrue(abs(char_error_rate - 0.0) < 1e-6)
# consecutive whitespaces case 2
refs = ['were wolf']
preds = ['were wolf']
# S = 0, D = 0, I = 0, N = 9, CER = 0
char_error_rate = cer.compute(predictions=preds, references=refs)
self.assertTrue(abs(char_error_rate - 0.0) < 1e-6)
def test_cer_sub(self):
refs = ['werewolf']
preds = ['weaewolf']
# S = 1, D = 0, I = 0, N = 8, CER = 0.125
char_error_rate = cer.compute(predictions=preds, references=refs)
self.assertTrue(abs(char_error_rate - 0.125) < 1e-6)
def test_cer_del(self):
refs = ['werewolf']
preds = ['wereawolf']
# S = 0, D = 1, I = 0, N = 8, CER = 0.125
char_error_rate = cer.compute(predictions=preds, references=refs)
self.assertTrue(abs(char_error_rate - 0.125) < 1e-6)
def test_cer_insert(self):
refs = ['werewolf']
preds = ['wereolf']
# S = 0, D = 0, I = 1, N = 8, CER = 0.125
char_error_rate = cer.compute(predictions=preds, references=refs)
self.assertTrue(abs(char_error_rate - 0.125) < 1e-6)
def test_cer_equal(self):
refs = ['werewolf']
char_error_rate = cer.compute(predictions=refs, references=refs)
self.assertEqual(char_error_rate, 0.0)
def test_cer_list_of_seqs(self):
refs = ['werewolf', 'I am your father']
char_error_rate = cer.compute(predictions=refs, references=refs)
self.assertEqual(char_error_rate, 0.0)
refs = ['werewolf', 'I am your father', 'doge']
preds = ['werxwolf', 'I am your father', 'doge']
# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
char_error_rate = cer.compute(predictions=preds, references=refs)
self.assertTrue(abs(char_error_rate - 0.03571428) < 1e-6)
def test_cer_unicode(self):
ref = [u'我能吞下玻璃而不伤身体']
pred = [u' 能吞虾玻璃而 不霜身体啦']
# S = 3, D = 2, I = 0, N = 11
# CER = 5 / 11
char_error_rate = cer.compute(predictions=pred, references=ref)
self.assertTrue(abs(char_error_rate - 0.4545454545) < 1e-6)
ref = [u'我能吞', u'下玻璃而不伤身体']
pred = [u'我 能 吞 下 玻 璃', u'而不伤身体']
# S = 0, D = 5, I = 0, N = 11
# CER = 5 / 11
char_error_rate = cer.compute(predictions=pred, references=ref)
self.assertTrue(abs(char_error_rate - 0.454545454545) < 1e-6)
ref = [u'我能吞下玻璃而不伤身体']
char_error_rate = cer.compute(predictions=ref, references=ref)
self.assertFalse(char_error_rate, 0.0)
def test_cer_empty(self):
ref = ''
pred = 'Hypothesis'
with self.assertRaises(ValueError):
char_error_rate = cer.compute(predictions=pred, references=ref)
if __name__ == '__main__':
unittest.main()
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2137 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2137/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2137/comments | https://api.github.com/repos/huggingface/datasets/issues/2137/events | https://github.com/huggingface/datasets/pull/2137 | 843,502,835 | MDExOlB1bGxSZXF1ZXN0NjAyODc0MDYw | 2,137 | Fix missing infos from concurrent dataset loading | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-29T15:46:12 | 2021-03-31T10:35:56 | 2021-03-31T10:35:55 | MEMBER | null | This should fix issue #2131
When calling `load_dataset` at the same time from 2 workers, one of the worker could have missing split infos when reloading the dataset from the cache.
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https://api.github.com/repos/huggingface/datasets/issues/2136 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2136/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2136/comments | https://api.github.com/repos/huggingface/datasets/issues/2136/events | https://github.com/huggingface/datasets/pull/2136 | 843,492,015 | MDExOlB1bGxSZXF1ZXN0NjAyODY0ODY5 | 2,136 | fix dialogue action slot name and value | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-29T15:34:13 | 2021-03-31T12:48:02 | 2021-03-31T12:48:01 | CONTRIBUTOR | null | fix #2128 | {
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https://api.github.com/repos/huggingface/datasets/issues/2135 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2135/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2135/comments | https://api.github.com/repos/huggingface/datasets/issues/2135/events | https://github.com/huggingface/datasets/issues/2135 | 843,246,344 | MDU6SXNzdWU4NDMyNDYzNDQ= | 2,135 | en language data from MLQA dataset is missing | {
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"Hi ! Indeed only the languages of the `translate-train` data are included...\r\nI can't find a link to download the english train set on https://github.com/facebookresearch/MLQA though, do you know where we can download it ?",
"Hi @lhoestq \r\nthank you very much for coming back to me, now I see, you are right, ... | 2021-03-29T10:47:50 | 2021-03-30T10:20:23 | 2021-03-30T10:20:23 | CONTRIBUTOR | null | Hi
I need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq thank you for your help to fix this issue. | {
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https://api.github.com/repos/huggingface/datasets/issues/2134 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2134/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2134/comments | https://api.github.com/repos/huggingface/datasets/issues/2134/events | https://github.com/huggingface/datasets/issues/2134 | 843,242,849 | MDU6SXNzdWU4NDMyNDI4NDk= | 2,134 | Saving large in-memory datasets with save_to_disk crashes because of pickling | {
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"Hi !\r\nIndeed `save_to_disk` doesn't call pickle anymore. Though the `OverflowError` can still appear for in-memory datasets bigger than 4GB. This happens when doing this for example:\r\n```python\r\nimport pyarrow as pa\r\nimport pickle\r\n\r\narr = pa.array([0] * ((4 * 8 << 30) // 64))\r\ntable = pa.Table.from_... | 2021-03-29T10:43:15 | 2021-05-03T17:59:21 | 2021-05-03T17:59:21 | NONE | null | Using Datasets 1.5.0 on Python 3.7.
Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.
So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method.
When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).
```
Traceback (most recent call last):
File "./tokenize_and_chunkify_in_memory.py", line 80, in <module>
main()
File "./tokenize_and_chunkify_in_memory.py", line 75, in main
tokenize_and_chunkify(config)
File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify
contexts_dataset.save_to_disk(chunked_path)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk
self = pickle.loads(pickle.dumps(self))
OverflowError: cannot serialize a bytes object larger than 4 GiB
```
From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.
To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.
Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.
```
Traceback (most recent call last):
File "./tokenize_and_chunkify_in_memory.py", line 94, in <module>
main()
File "./tokenize_and_chunkify_in_memory.py", line 89, in main
tokenize_and_chunkify(config)
File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify
contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map
transformed_shards = [r.get() for r in results]
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get
raise self._value
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks
put(task)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps
cls(buf, protocol, *args, **kwds).dump(obj)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump
StockPickler.dump(self, obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump
self.save(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict
self._batch_setitems(obj.items())
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems
save(v)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce
save(state)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict
self._batch_setitems(obj.items())
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems
save(v)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends
save(x)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
save(tmp[0])
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
save(tmp[0])
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
save(tmp[0])
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends
save(x)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes
self._write_large_bytes(BINBYTES + pack("<I", n), obj)
struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):
File "./tokenize_and_chunkify_in_memory.py", line 94, in <module>
main()
File "./tokenize_and_chunkify_in_memory.py", line 89, in main
tokenize_and_chunkify(config)
File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify
contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map
transformed_shards = [r.get() for r in results]
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get
raise self._value
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks
put(task)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps
cls(buf, protocol, *args, **kwds).dump(obj)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump
StockPickler.dump(self, obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump
self.save(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict
self._batch_setitems(obj.items())
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems
save(v)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce
save(state)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict
self._batch_setitems(obj.items())
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems
save(v)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends
save(x)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
save(tmp[0])
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
save(tmp[0])
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
save(tmp[0])
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends
save(x)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes
self._write_large_bytes(BINBYTES + pack("<I", n), obj)
struct.error: 'I' format requires 0 <= number <= 4294967295
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2133 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2133/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2133/comments | https://api.github.com/repos/huggingface/datasets/issues/2133/events | https://github.com/huggingface/datasets/issues/2133 | 843,149,680 | MDU6SXNzdWU4NDMxNDk2ODA= | 2,133 | bug in mlqa dataset | {
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"If you print those questions, you get readable texts:\r\n```python\r\n>>> questions = [\r\n... \"\\u0645\\u062a\\u0649 \\u0628\\u062f\\u0627\\u062a \\u0627\\u0644\\u0645\\u062c\\u0644\\u0629 \\u0627\\u0644\\u0645\\u062f\\u0631\\u0633\\u064a\\u0629 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u064... | 2021-03-29T09:03:09 | 2021-03-30T17:40:57 | 2021-03-30T17:40:57 | NONE | null | Hi
Looking into MLQA dataset for langauge "ar":
```
"question": [
"\u0645\u062a\u0649 \u0628\u062f\u0627\u062a \u0627\u0644\u0645\u062c\u0644\u0629 \u0627\u0644\u0645\u062f\u0631\u0633\u064a\u0629 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645 \u0628\u0627\u0644\u0646\u0634\u0631?",
"\u0643\u0645 \u0645\u0631\u0629 \u064a\u062a\u0645 \u0646\u0634\u0631\u0647\u0627 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?",
"\u0645\u0627 \u0647\u064a \u0627\u0644\u0648\u0631\u0642\u0629 \u0627\u0644\u064a\u0648\u0645\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?",
"\u0643\u0645 \u0639\u062f\u062f \u0627\u0644\u0627\u0648\u0631\u0627\u0642 \u0627\u0644\u0627\u062e\u0628\u0627\u0631\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0627\u0644\u062a\u064a \u0648\u062c\u062f\u062a \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?",
"\u0641\u064a \u0627\u064a \u0633\u0646\u0629 \u0628\u062f\u0627\u062a \u0648\u0631\u0642\u0629 \u0627\u0644\u0637\u0627\u0644\u0628 \u0627\u0644\u062d\u0633 \u0627\u0644\u0633\u0644\u064a\u0645 \u0628\u0627\u0644\u0646\u0634\u0631 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?"
]
```
the questions are in the wrong format, and not readable, could you please have a look? thanks @lhoestq
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https://api.github.com/repos/huggingface/datasets/issues/2131 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2131/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2131/comments | https://api.github.com/repos/huggingface/datasets/issues/2131/events | https://github.com/huggingface/datasets/issues/2131 | 843,133,112 | MDU6SXNzdWU4NDMxMzMxMTI= | 2,131 | When training with Multi-Node Multi-GPU the worker 2 has TypeError: 'NoneType' object | {
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"Hi ! Thanks for reporting\r\nI was able to reproduce this issue. This was caused by missing split infos if a worker reloads the cache of the other worker.\r\n\r\nI just opened https://github.com/huggingface/datasets/pull/2137 to fix this issue",
"The PR got merged :)\r\nFeel free to try it out on the `master` br... | 2021-03-29T08:45:58 | 2021-04-10T11:08:55 | 2021-04-10T11:08:55 | NONE | null | version: 1.5.0
met a very strange error, I am training large scale language model, and need train on 2 machines(workers).
And sometimes I will get this error `TypeError: 'NoneType' object is not iterable`
This is traceback
```
71 | | Traceback (most recent call last):
-- | -- | --
72 | | File "run_gpt.py", line 316, in <module>
73 | | main()
74 | | File "run_gpt.py", line 222, in main
75 | | delimiter="\t", column_names=["input_ids", "attention_mask", "chinese_ref"])
76 | | File "/data/miniconda3/lib/python3.7/site-packages/datasets/load.py", line 747, in load_dataset
77 | | use_auth_token=use_auth_token,
78 | | File "/data/miniconda3/lib/python3.7/site-packages/datasets/builder.py", line 513, in download_and_prepare
79 | | self.download_post_processing_resources(dl_manager)
80 | | File "/data/miniconda3/lib/python3.7/site-packages/datasets/builder.py", line 673, in download_post_processing_resources
81 | | for split in self.info.splits:
82 | | TypeError: 'NoneType' object is not iterable
83 | | WARNING:datasets.builder:Reusing dataset csv (/usr/local/app/.cache/huggingface/datasets/csv/default-1c257ebd48e225e7/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2)
84 | | Traceback (most recent call last):
85 | | File "/data/miniconda3/lib/python3.7/runpy.py", line 193, in _run_module_as_main
86 | | "__main__", mod_spec)
87 | | File "/data/miniconda3/lib/python3.7/runpy.py", line 85, in _run_code
88 | | exec(code, run_globals)
89 | | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 340, in <module>
90 | | main()
91 | | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 326, in main
92 | | sigkill_handler(signal.SIGTERM, None) # not coming back
93 | | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler
94 | | raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd)
```
On worker 1 it loads the dataset well, however on worker 2 will get this error.
And I will meet this error from time to time, sometimes it just goes well. | {
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"Here please find TFDS format of this dataset: https://www.tensorflow.org/datasets/catalog/wikiann\r\nwhere there is a span column, this is really necessary to be able to use the data, and I appreciate your help @lhoestq ",
"Hi !\r\nApparently you can get the spans from the NER tags using `tags_to_spans` defined ... | 2021-03-29T08:23:00 | 2021-08-27T14:44:18 | 2021-08-27T14:44:18 | NONE | null | Hi
Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq | {
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"Hi !\r\nWe're not using `TextDatasetForNextSentencePrediction` in `datasets`.\r\nAlthough you can probably use the `TextDatasetForNextSentencePrediction.create_examples_from_document` on a dataset to prepare it for next sentence prediction.",
"Thanks.\r\n\r\nDo you mean that `TextDatasetForNextSentencePrediction... | 2021-03-29T06:48:03 | 2021-04-01T04:58:40 | 2021-04-01T04:58:40 | NONE | null | Hello.
I'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction
like ` TextDatasetForNextSentencePrediction` of `huggingface/transformers` ?
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"Hi\r\nGood catch ! Thanks for reporting\r\n\r\nIf you are interested in contributing, feel free to open a PR to fix this :) "
] | 2021-03-29T06:34:02 | 2021-03-31T12:48:01 | 2021-03-31T12:48:01 | CONTRIBUTOR | null | Hi @yjernite, thank you for adding MultiWoZ 2.2 in the huggingface datasets platform. It is beneficial!
I spot an error that the order of Dialogue action slot names and values are reversed.
https://github.com/huggingface/datasets/blob/649b2c469779bc4221e1b6969aa2496d63eb5953/datasets/multi_woz_v22/multi_woz_v22.py#L251-L262 | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-29T06:24:06 | 2021-03-29T12:16:24 | 2021-03-29T12:16:24 | MEMBER | null | This PR extends the cloud storage documentation. To show you can use a different `fsspec` implementation. | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-28T16:57:30 | 2021-03-29T09:27:14 | 2021-03-29T09:27:13 | CONTRIBUTOR | null | The title says it all (motivated by [this issue](https://github.com/pytorch/pytorch/issues/53146) in the pytorch repo). | {
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"Hi,\r\n\r\nthanks for the report, but this is a duplicate of #2052. ",
"@mariosasko \r\nThank you for your quick response! Following #2052, I've fixed the problem."
] | 2021-03-28T08:30:18 | 2021-03-28T12:29:25 | 2021-03-28T12:29:25 | NONE | null | Using `timit_asr` dataset, I saw all records are the same.
``` python
from datasets import load_dataset, load_metric
timit = load_dataset("timit_asr")
from datasets import ClassLabel
import random
import pandas as pd
from IPython.display import display, HTML
def show_random_elements(dataset, num_examples=10):
assert num_examples <= len(dataset), "Can't pick more elements than there are in the dataset."
picks = []
for _ in range(num_examples):
pick = random.randint(0, len(dataset)-1)
while pick in picks:
pick = random.randint(0, len(dataset)-1)
picks.append(pick)
df = pd.DataFrame(dataset[picks])
display(HTML(df.to_html()))
show_random_elements(timit['train'].remove_columns(["file", "phonetic_detail", "word_detail", "dialect_region", "id",
"sentence_type", "speaker_id"]), num_examples=20)
```
`output`
<img width="312" alt="Screen Shot 2021-03-28 at 17 29 04" src="https://user-images.githubusercontent.com/42398050/112746646-21acee80-8feb-11eb-84f3-dbb5d4269724.png">
I double-checked it [here](https://huggingface.co/datasets/viewer/), and met the same problem.
<img width="1374" alt="Screen Shot 2021-03-28 at 17 32 07" src="https://user-images.githubusercontent.com/42398050/112746698-9bdd7300-8feb-11eb-97ed-5babead385f4.png">
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https://api.github.com/repos/huggingface/datasets/issues/2123 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2123/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2123/comments | https://api.github.com/repos/huggingface/datasets/issues/2123/events | https://github.com/huggingface/datasets/issues/2123 | 842,577,285 | MDU6SXNzdWU4NDI1NzcyODU= | 2,123 | Problem downloading GEM wiki_auto_asset_turk dataset | {
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"Hi,\r\n\r\nsadly I can't replicate the problem on my Windows machine. Try to update the library to the newest version with:\r\n```bash\r\npip install git+https://github.com/huggingface/datasets\r\n``` ",
"Thanks for the answer! I updated the library but unfortunately it didn't solve the problem.",
"Is there an... | 2021-03-27T18:41:28 | 2021-05-12T16:15:18 | 2021-05-12T16:15:17 | NONE | null | @yjernite
### Summary
I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code.
### Steps to reproduce
Code snippet:
from datasets import load_dataset
#dataset = load_dataset('gem', 'web_nlg_en')
dataset = load_dataset('gem', 'wiki_auto_asset_turk')
```
**Expected behavior:**
I expect the dataset to start downloading (download bar appears and progresses toward 100%)
**Actual behavior:**
Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more:
Downloading: 36.6kB [00:00, 37.2MB/s]
Downloading: 41.7kB [00:00, ?B/s]
Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d...
### Is this a regression?
No, it was the first time I was trying to download this dataset (same for the other ones).
### Debug info
- Python version: Python 3.8.2
- OS version: Windows 10 Family | {
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https://api.github.com/repos/huggingface/datasets/issues/2122 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2122/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2122/comments | https://api.github.com/repos/huggingface/datasets/issues/2122/events | https://github.com/huggingface/datasets/pull/2122 | 842,194,588 | MDExOlB1bGxSZXF1ZXN0NjAxODE3MjI0 | 2,122 | Fast table queries with interpolation search | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-26T18:09:20 | 2021-08-04T18:11:59 | 2021-04-06T14:33:01 | MEMBER | null | ## Intro
This should fix issue #1803
Currently querying examples in a dataset is O(n) because of the underlying pyarrow ChunkedArrays implementation.
To fix this I implemented interpolation search that is pretty effective since datasets usually verifies the condition of evenly distributed chunks (the default chunk size is fixed).
## Benchmark
Here is a [benchmark](https://pastebin.com/utEXUqsR) I did on bookcorpus (74M rows):
for the current implementation
```python
>>> python speed.py
Loaded dataset 'bookcorpus', len=74004228, nbytes=4835358766
========================= Querying unshuffled bookcorpus =========================
Avg access time key=1 : 0.018ms
Avg access time key=74004227 : 0.215ms
Avg access time key=range(74003204, 74004228) : 1.416ms
Avg access time key=RandIter(low=0, high=74004228, size=1024, seed=42): 92.532ms
========================== Querying shuffled bookcorpus ==========================
Avg access time key=1 : 0.187ms
Avg access time key=74004227 : 6.642ms
Avg access time key=range(74003204, 74004228) : 90.941ms
Avg access time key=RandIter(low=0, high=74004228, size=1024, seed=42): 3448.456ms
```
for the new one using interpolation search:
```python
>>> python speed.py
Loaded dataset 'bookcorpus', len=74004228, nbytes=4835358766
========================= Querying unshuffled bookcorpus =========================
Avg access time key=1 : 0.076ms
Avg access time key=74004227 : 0.056ms
Avg access time key=range(74003204, 74004228) : 1.807ms
Avg access time key=RandIter(low=0, high=74004228, size=1024, seed=42): 24.028ms
========================== Querying shuffled bookcorpus ==========================
Avg access time key=1 : 0.061ms
Avg access time key=74004227 : 0.058ms
Avg access time key=range(74003204, 74004228) : 22.166ms
Avg access time key=RandIter(low=0, high=74004228, size=1024, seed=42): 42.757ms
```
The RandIter class is just an iterable of 1024 random indices from 0 to 74004228.
Here is also a plot showing the speed improvement depending on the dataset size:
## Implementation details:
- `datasets.table.Table` objects implement interpolation search for the `slice` method
- The interpolation search requires to store the offsets of all the chunks of a table. The offsets are stored when the `Table` is initialized.
- `datasets.table.Table.slice` returns a `datasets.table.Table` using interpolation search
- `datasets.table.Table.fast_slice` returns a `pyarrow.Table` object using interpolation search. This is useful to get a part of a dataset if we don't need the indexing structure for future computations. For example it's used when querying an example as a dictionary.
- Now a `Dataset` object is always backed by a `datasets.table.Table` object. If one passes a `pyarrow.Table` to initialize a `Dataset`, then it's converted to a `datasets.table.Table`
## Checklist:
- [x] implement interpolation search
- [x] use `datasets.table.Table` in `Dataset` objects
- [x] update current tests
- [x] add tests for interpolation search
- [x] comments and docstring
- [x] add the benchmark to the CI
Fix #1803. | {
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https://api.github.com/repos/huggingface/datasets/issues/2121 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2121/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2121/comments | https://api.github.com/repos/huggingface/datasets/issues/2121/events | https://github.com/huggingface/datasets/pull/2121 | 842,148,633 | MDExOlB1bGxSZXF1ZXN0NjAxNzc4NDc4 | 2,121 | Add Validation For README | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-26T17:02:17 | 2021-05-10T13:17:18 | 2021-05-10T09:41:41 | CONTRIBUTOR | null | Hi @lhoestq, @yjernite
This is a simple Readme parser. All classes specific to different sections can inherit `Section` class, and we can define more attributes in each.
Let me know if this is going in the right direction :)
Currently the output looks like this, for `to_dict()` on `FashionMNIST` `README.md`:
```json
{
"name": "./datasets/fashion_mnist/README.md",
"attributes": "",
"subsections": [
{
"name": "Dataset Card for FashionMNIST",
"attributes": "",
"subsections": [
{
"name": "Table of Contents",
"attributes": "- [Dataset Description](#dataset-description)\n - [Dataset Summary](#dataset-summary)\n - [Supported Tasks](#supported-tasks-and-leaderboards)\n - [Languages](#languages)\n- [Dataset Structure](#dataset-structure)\n - [Data Instances](#data-instances)\n - [Data Fields](#data-instances)\n - [Data Splits](#data-instances)\n- [Dataset Creation](#dataset-creation)\n - [Curation Rationale](#curation-rationale)\n - [Source Data](#source-data)\n - [Annotations](#annotations)\n - [Personal and Sensitive Information](#personal-and-sensitive-information)\n- [Considerations for Using the Data](#considerations-for-using-the-data)\n - [Social Impact of Dataset](#social-impact-of-dataset)\n - [Discussion of Biases](#discussion-of-biases)\n - [Other Known Limitations](#other-known-limitations)\n- [Additional Information](#additional-information)\n - [Dataset Curators](#dataset-curators)\n - [Licensing Information](#licensing-information)\n - [Citation Information](#citation-information)\n - [Contributions](#contributions)",
"subsections": []
},
{
"name": "Dataset Description",
"attributes": "- **Homepage:** [GitHub](https://github.com/zalandoresearch/fashion-mnist)\n- **Repository:** [GitHub](https://github.com/zalandoresearch/fashion-mnist)\n- **Paper:** [arXiv](https://arxiv.org/pdf/1708.07747.pdf)\n- **Leaderboard:**\n- **Point of Contact:**",
"subsections": [
{
"name": "Dataset Summary",
"attributes": "Fashion-MNIST is a dataset of Zalando's article images\u2014consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.",
"subsections": []
},
{
"name": "Supported Tasks and Leaderboards",
"attributes": "[More Information Needed]",
"subsections": []
},
{
"name": "Languages",
"attributes": "[More Information Needed]",
"subsections": []
}
]
},
{
"name": "Dataset Structure",
"attributes": "",
"subsections": [
{
"name": "Data Instances",
"attributes": "A data point comprises an image and its label.",
"subsections": []
},
{
"name": "Data Fields",
"attributes": "- `image`: a 2d array of integers representing the 28x28 image.\n- `label`: an integer between 0 and 9 representing the classes with the following mapping:\n | Label | Description |\n | --- | --- |\n | 0 | T-shirt/top |\n | 1 | Trouser |\n | 2 | Pullover |\n | 3 | Dress |\n | 4 | Coat |\n | 5 | Sandal |\n | 6 | Shirt |\n | 7 | Sneaker |\n | 8 | Bag |\n | 9 | Ankle boot |",
"subsections": []
},
{
"name": "Data Splits",
"attributes": "The data is split into training and test set. The training set contains 60,000 images and the test set 10,000 images.",
"subsections": []
}
]
},
{
"name": "Dataset Creation",
"attributes": "",
"subsections": [
{
"name": "Curation Rationale",
"attributes": "**From the arXiv paper:**\nThe original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. \"If it doesn't work on MNIST, it won't work at all\", they said. \"Well, if it does work on MNIST, it may still fail on others.\"\nHere are some good reasons:\n- MNIST is too easy. Convolutional nets can achieve 99.7% on MNIST. Classic machine learning algorithms can also achieve 97% easily. Check out our side-by-side benchmark for Fashion-MNIST vs. MNIST, and read \"Most pairs of MNIST digits can be distinguished pretty well by just one pixel.\"\n- MNIST is overused. In this April 2017 Twitter thread, Google Brain research scientist and deep learning expert Ian Goodfellow calls for people to move away from MNIST.\n- MNIST can not represent modern CV tasks, as noted in this April 2017 Twitter thread, deep learning expert/Keras author Fran\u00e7ois Chollet.",
"subsections": []
},
{
"name": "Source Data",
"attributes": "",
"subsections": [
{
"name": "Initial Data Collection and Normalization",
"attributes": "**From the arXiv paper:**\nFashion-MNIST is based on the assortment on Zalando\u2019s website. Every fashion product on Zalando has a set of pictures shot by professional photographers, demonstrating different aspects of the product, i.e. front and back looks, details, looks with model and in an outfit. The original picture has a light-gray background (hexadecimal color: #fdfdfd) and stored in 762 \u00d7 1000 JPEG format. For efficiently serving different frontend components, the original picture is resampled with multiple resolutions, e.g. large, medium, small, thumbnail and tiny.\nWe use the front look thumbnail images of 70,000 unique products to build Fashion-MNIST. Those products come from different gender groups: men, women, kids and neutral. In particular, whitecolor products are not included in the dataset as they have low contrast to the background. The thumbnails (51 \u00d7 73) are then fed into the following conversion pipeline:\n1. Converting the input to a PNG image.\n2. Trimming any edges that are close to the color of the corner pixels. The \u201ccloseness\u201d is defined by the distance within 5% of the maximum possible intensity in RGB space.\n3. Resizing the longest edge of the image to 28 by subsampling the pixels, i.e. some rows and columns are skipped over.\n4. Sharpening pixels using a Gaussian operator of the radius and standard deviation of 1.0, with increasing effect near outlines.\n5. Extending the shortest edge to 28 and put the image to the center of the canvas.\n6. Negating the intensities of the image.\n7. Converting the image to 8-bit grayscale pixels.",
"subsections": []
},
{
"name": "Who are the source image producers?",
"attributes": "**From the arXiv paper:**\nEvery fashion product on Zalando has a set of pictures shot by professional photographers, demonstrating different aspects of the product, i.e. front and back looks, details, looks with model and in an outfit.",
"subsections": []
}
]
},
{
"name": "Annotations",
"attributes": "",
"subsections": [
{
"name": "Annotation process",
"attributes": "**From the arXiv paper:**\nFor the class labels, they use the silhouette code of the product. The silhouette code is manually labeled by the in-house fashion experts and reviewed by a separate team at Zalando. Each product Zalando is the Europe\u2019s largest online fashion platform. Each product contains only one silhouette code.",
"subsections": []
},
{
"name": "Who are the annotators?",
"attributes": "**From the arXiv paper:**\nThe silhouette code is manually labeled by the in-house fashion experts and reviewed by a separate team at Zalando.",
"subsections": []
}
]
},
{
"name": "Personal and Sensitive Information",
"attributes": "[More Information Needed]",
"subsections": []
}
]
},
{
"name": "Considerations for Using the Data",
"attributes": "",
"subsections": [
{
"name": "Social Impact of Dataset",
"attributes": "[More Information Needed]",
"subsections": []
},
{
"name": "Discussion of Biases",
"attributes": "[More Information Needed]",
"subsections": []
},
{
"name": "Other Known Limitations",
"attributes": "[More Information Needed]",
"subsections": []
}
]
},
{
"name": "Additional Information",
"attributes": "",
"subsections": [
{
"name": "Dataset Curators",
"attributes": "Han Xiao and Kashif Rasul and Roland Vollgraf",
"subsections": []
},
{
"name": "Licensing Information",
"attributes": "MIT Licence",
"subsections": []
},
{
"name": "Citation Information",
"attributes": "@article{DBLP:journals/corr/abs-1708-07747,\n author = {Han Xiao and\n Kashif Rasul and\n Roland Vollgraf},\n title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning\n Algorithms},\n journal = {CoRR},\n volume = {abs/1708.07747},\n year = {2017},\n url = {http://arxiv.org/abs/1708.07747},\n archivePrefix = {arXiv},\n eprint = {1708.07747},\n timestamp = {Mon, 13 Aug 2018 16:47:27 +0200},\n biburl = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}",
"subsections": []
},
{
"name": "Contributions",
"attributes": "Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset.",
"subsections": []
}
]
}
]
}
]
}
```
Thanks,
Gunjan | {
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"Thanks for reporting :) We're looking into it",
"Back up. "
] | 2021-03-26T13:22:13 | 2021-03-26T15:52:22 | 2021-03-26T15:52:22 | NONE | null | Hi
I normally use this link to see all datasets and how I can load them
https://huggingface.co/datasets/viewer/
Now I am getting
502 Bad Gateway
nginx/1.18.0 (Ubuntu)
could you bring this webpage back ? this was very helpful @lhoestq
thanks for your help | {
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https://api.github.com/repos/huggingface/datasets/issues/2119 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2119/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2119/comments | https://api.github.com/repos/huggingface/datasets/issues/2119/events | https://github.com/huggingface/datasets/pull/2119 | 841,567,199 | MDExOlB1bGxSZXF1ZXN0NjAxMjg2MjIy | 2,119 | copy.deepcopy os.environ instead of copy | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-26T03:58:38 | 2021-03-26T15:13:52 | 2021-03-26T15:13:52 | CONTRIBUTOR | null | Fixes: https://github.com/huggingface/datasets/issues/2115
- bug fix: using envrion.copy() returns a dict.
- using deepcopy(environ) returns an `_environ` object
- Changing the datatype of the _environ object can break code, if subsequent libraries perform operations using apis exclusive to the environ object, like `environ.getenv()` for example.
Testing:
Tested the change on my terminal:
```
>>> import os
>>> x = deepcopy(os.environ)
>>> y = os.environ
>>> x is y
False
>>> isinstance(x, type(os.environ))
True
>>> z = os.environ.copy()
>>> isinstance(z, type(os.environ))
False
>>> isinstance(z, dict)
True
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2118 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2118/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2118/comments | https://api.github.com/repos/huggingface/datasets/issues/2118/events | https://github.com/huggingface/datasets/pull/2118 | 841,563,329 | MDExOlB1bGxSZXF1ZXN0NjAxMjgzMDUx | 2,118 | Remove os.environ.copy in Dataset.map | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-26T03:48:17 | 2021-03-26T12:03:23 | 2021-03-26T12:00:05 | CONTRIBUTOR | null | Replace `os.environ.copy` with in-place modification
Fixes #2115 | {
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https://api.github.com/repos/huggingface/datasets/issues/2117 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2117/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2117/comments | https://api.github.com/repos/huggingface/datasets/issues/2117/events | https://github.com/huggingface/datasets/issues/2117 | 841,535,283 | MDU6SXNzdWU4NDE1MzUyODM= | 2,117 | load_metric from local "glue.py" meet error 'NoneType' object is not callable | {
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"@Frankie123421 what was the resolution to this?",
"> @Frankie123421 what was the resolution to this?\r\n\r\nuse glue_metric.py instead of glue.py in load_metric",
"thank you!"
] | 2021-03-26T02:35:22 | 2021-08-25T21:44:05 | 2021-03-26T02:40:26 | NONE | null | actual_task = "mnli" if task == "mnli-mm" else task
dataset = load_dataset(path='/home/glue.py', name=actual_task)
metric = load_metric(path='/home/glue.py', name=actual_task)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-7ab77a465d81> in <module>
1 actual_task = "mnli" if task == "mnli-mm" else task
2 dataset = load_dataset(path='/home/jcli/glue.py', name=actual_task)
----> 3 metric = load_metric(path='/home/jcli/glue.py', name=actual_task)
~/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)
508 keep_in_memory=keep_in_memory,
509 experiment_id=experiment_id,
--> 510 **metric_init_kwargs,
511 )
512
TypeError: 'NoneType' object is not callable
Please help | {
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https://api.github.com/repos/huggingface/datasets/issues/2116 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2116/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2116/comments | https://api.github.com/repos/huggingface/datasets/issues/2116/events | https://github.com/huggingface/datasets/issues/2116 | 841,481,292 | MDU6SXNzdWU4NDE0ODEyOTI= | 2,116 | Creating custom dataset results in error while calling the map() function | {
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"Hi,\r\n\r\nthe `_data` attribute is missing due to `MyDataset.__init__` not calling the parent `__init__`. However, I don't think it's a good idea to subclass the `datasets.Dataset` class (e.g. it's kind of dangerous to override `datasets.Dataset.__getitem__`). Instead, it's better to follow the \"association over... | 2021-03-26T00:37:46 | 2021-03-31T14:30:32 | 2021-03-31T14:30:32 | NONE | null | calling `map()` of `datasets` library results into an error while defining a Custom dataset.
Reproducible example:
```
import datasets
class MyDataset(datasets.Dataset):
def __init__(self, sentences):
"Initialization"
self.samples = sentences
def __len__(self):
"Denotes the total number of samples"
return len(self.samples)
def __getitem__(self, index):
"Generates one sample of data"
# Select sample
# Load data and get label
samples = self.samples[index]
return samples
def preprocess_function_train(examples):
inputs = examples
labels = [example+tokenizer.eos_token for example in examples ]
inputs = tokenizer(inputs, max_length=30, padding=True, truncation=True)
labels = tokenizer(labels, max_length=30, padding=True, truncation=True)
model_inputs = inputs
model_inputs["labels"] = labels["input_ids"]
print("about to return")
return model_inputs
##train["sentence"] is dataframe column
train_dataset = MyDataset(train['sentence'].values.tolist())
train_dataset = train_dataset.map(
preprocess_function,
batched = True,
batch_size=32
)
```
Stack trace of error:
```
Traceback (most recent call last):
File "dir/train_generate.py", line 362, in <module>
main()
File "dir/train_generate.py", line 245, in main
train_dataset = train_dataset.map(
File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1244, in map
return self._map_single(
File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 149, in wrapper
unformatted_columns = set(self.column_names) - set(self._format_columns or [])
File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 526, in column_names
return self._data.column_names
AttributeError: 'MyDataset' object has no attribute '_data'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2115 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2115/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2115/comments | https://api.github.com/repos/huggingface/datasets/issues/2115/events | https://github.com/huggingface/datasets/issues/2115 | 841,283,974 | MDU6SXNzdWU4NDEyODM5NzQ= | 2,115 | The datasets.map() implementation modifies the datatype of os.environ object | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-25T20:29:19 | 2021-03-26T15:13:52 | 2021-03-26T15:13:52 | NONE | null | In our testing, we noticed that the datasets.map() implementation is modifying the datatype of python os.environ object from '_Environ' to 'dict'.
This causes following function calls to fail as follows:
`
x = os.environ.get("TEST_ENV_VARIABLE_AFTER_dataset_map", default=None)
TypeError: get() takes no keyword arguments
`
It looks like the following line in datasets.map implementation introduced this functionality.
https://github.com/huggingface/datasets/blob/0cb1ac06acb0df44a1cf4128d03a01865faa2504/src/datasets/arrow_dataset.py#L1421
Here is the test script to reproduce this error.
```
from datasets import load_dataset
from transformers import AutoTokenizer
import os
def test_train():
model_checkpoint = "distilgpt2"
datasets = load_dataset('wikitext', 'wikitext-2-raw-v1')
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)
tokenizer.pad_token = tokenizer.eos_token
def tokenize_function(examples):
y = tokenizer(examples['text'], truncation=True, max_length=64)
return y
x = os.environ.get("TEST_ENV_VARIABLE_BEFORE_dataset_map", default=None)
print(f"Testing environment variable: TEST_ENV_VARIABLE_BEFORE_dataset_map {x}")
print(f"Data type of os.environ before datasets.map = {os.environ.__class__.__name__}")
datasets.map(tokenize_function, batched=True, num_proc=2, remove_columns=["text"])
print(f"Data type of os.environ after datasets.map = {os.environ.__class__.__name__}")
x = os.environ.get("TEST_ENV_VARIABLE_AFTER_dataset_map", default=None)
print(f"Testing environment variable: TEST_ENV_VARIABLE_AFTER_dataset_map {x}")
if __name__ == "__main__":
test_train()
```
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} | [] | closed | false | null | [] | null | [] | 2021-03-25T18:40:17 | 2021-03-31T10:38:50 | 2021-03-31T10:38:50 | CONTRIBUTOR | null | Add support for two legal NLP datasets:
- EURLEX (https://www.aclweb.org/anthology/P19-1636/)
- ECtHR cases (https://arxiv.org/abs/2103.13084)
- EU-REG-IR (https://arxiv.org/abs/2101.10726) | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-25T18:18:30 | 2021-03-31T11:30:14 | 2021-03-31T08:30:11 | MEMBER | null | When used as context manager, it would be safely deleted if some exception is raised.
This will avoid
> During handling of the above exception, another exception occurred: | {
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- EURLEX (https://www.aclweb.org/anthology/P19-1636/)
- ECtHR cases (https://arxiv.org/abs/2103.13084) | {
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https://api.github.com/repos/huggingface/datasets/issues/2111 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2111/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2111/comments | https://api.github.com/repos/huggingface/datasets/issues/2111/events | https://github.com/huggingface/datasets/pull/2111 | 841,082,087 | MDExOlB1bGxSZXF1ZXN0NjAwODY4OTg5 | 2,111 | Compute WER metric iteratively | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-25T16:06:48 | 2021-04-06T07:20:43 | 2021-04-06T07:20:43 | MEMBER | null | Compute WER metric iteratively to avoid MemoryError.
Fix #2078. | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-25T09:41:53 | 2021-04-19T06:20:11 | 2021-04-19T06:20:11 | MEMBER | null | When opening an issue, it is not evident for the users how to choose a blank issue template. There is a link at the bottom of all the other issue templates (`Don’t see your issue here? Open a blank issue.`), but this is not very visible for users. This is the reason why many users finally chose the `add-dataset` template instead (this is more visible) for issues that indeed are not requesting the addition of a new dataset.
~~With this PR, the default blank issue template would be as visible as the other templates (as the `add-dataset` template), thus making easier for the users to choose it.~~
With this PR:
- more issue templates, besides `add-dataset`, are added: `bug-report` and `feature-request`
- the issue template chooser is customized, so that it now includes a link to `Discussions` for questions | {
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"followers_url": "https://a... | null | [] | 2021-03-24T08:52:41 | 2021-04-26T08:27:14 | 2021-04-26T08:27:13 | CONTRIBUTOR | null | - `pydantic` metadata schema with dedicated validators against our taxonomy
- ci script to validate new changes against this schema and start a vertuous loop
- soft validation on tasks ids since we expect the taxonomy to undergo some changes in the near future
for reference with the current validation we have ~365~ 378 datasets with invalid metadata! full error report [_here_.](https://gist.github.com/theo-m/61b3c0c47fc6121d08d3174bd4c2a26b) | {
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"Hi ! `wiki_movies` was added in `datasets==1.2.0`. However it looks like you have `datasets==1.1.2`.\r\n\r\nTo use `wiki_movies`, please update `datasets` with\r\n```\r\npip install --upgrade datasets\r\n```",
"Thanks a lot! That solved it and I was able to upload a model trained on it as well :)"
] | 2021-03-23T18:59:54 | 2022-03-30T08:22:58 | 2022-03-30T08:22:58 | NONE | null | Hello,
I am trying to load_dataset("wiki_movies") and it gives me this error -
`FileNotFoundError: Couldn't find file locally at wiki_movies/wiki_movies.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/wiki_movies/wiki_movies.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/wiki_movies/wiki_movies.py`
Trying to do `python run_mlm.py \
--model_name_or_path roberta-base \
--dataset_name wiki_movies \` also gives the same error.
Is this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago.
Thank you! | {
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"Thanks for reporting :)\r\nMaybe we can concatenate fields only if they are different.\r\n\r\nCurrently this is done here:\r\n\r\nhttps://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/src/datasets/info.py#L180-L196\r\n\r\nThis can be a good first contribution to the library.\r\nPlease co... | 2021-03-23T17:18:09 | 2021-04-06T14:39:59 | 2021-04-06T14:39:59 | NONE | null | This happens after a `map` operation when `num_proc` is set to `>1`. I tested this by cleaning up the json before running the `map` op on the dataset so it's unlikely it's coming from an earlier concatenation.
Example result:
```
"citation": "@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n
```
@lhoestq and I believe this is happening due to the fields being concatenated `num_proc` times. | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-23T10:41:23 | 2021-03-23T18:08:10 | 2021-03-23T18:08:10 | CONTRIBUTOR | null | Hi @lhoestq, I have updated the citations to reference an OpenReview preprint. | {
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] | closed | false | null | [] | null | [] | 2021-03-23T10:27:52 | 2021-03-24T08:19:41 | 2021-03-23T18:03:49 | MEMBER | null | Fix deprecated warnings:
- Use deprecated Sphinx directive in docstring
- Fix format of deprecated message
- Raise FutureWarning | {
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"Hi !\r\nCan you share more information about the features of your dataset ? You can get them by printing `my_dataset.features`\r\nCan you also share the code of your `map` function ?",
"It is actually just the tokenized `wikipedia` dataset with `input_ids`, `attention_mask`, etc, with one extra column which is a... | 2021-03-23T09:28:37 | 2021-03-23T17:12:16 | 2021-03-23T17:12:16 | NONE | null | I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).
Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?
Tagging @lhoestq since you seem to be working on these issues and PRs :) | {
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"Hi ! This is 1.1 as specified by the download urls here:\r\n\r\nhttps://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/datasets/squad/squad.py#L50-L55",
"Got it. Thank you~"
] | 2021-03-23T07:47:54 | 2021-03-26T09:48:54 | 2021-03-26T09:48:54 | NONE | null | Hi~
I want train on squad dataset. What's the version of the squad? Is it 1.1 or 1.0? I'm new in QA, I don't find some descriptions about it. | {
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It's a simple fix, but i am not sure if it works for all possible types of `obj`. Let me know what you think @lhoestq | {
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"Hi ! We plan to add streaming features in the future.\r\n\r\nThis should allow to load a dataset instantaneously without generating the arrow table. The trade-off is that accessing examples from a streaming dataset must be done in an iterative way, and with an additional (but hopefully minor) overhead.\r\nWhat do ... | 2021-03-21T04:50:07 | 2022-06-01T16:49:52 | 2022-06-01T16:49:52 | NONE | null | Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? | {
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IMO when using BibTex Author should be used instead of Authors.
See here: http://www.bibtex.org/Using/de/
Thanks
Philip | {
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Fixes #2083
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"+1 on this request"
] | 2021-03-19T09:27:43 | 2021-04-16T08:50:44 | 2021-04-16T08:50:44 | CONTRIBUTOR | null | ## Adding a Dataset
- **Name:** CUAD - Contract Understanding Atticus Dataset
- **Description:** As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community.
- **Paper:** https://arxiv.org/abs/2103.06268
- **Data:** https://github.com/TheAtticusProject/cuad/
- **Motivation:** good domain specific datasets are valuable
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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"Hi,\r\n\r\nthis bug is related to `Dataset.{remove_columns, rename_column, flatten}` not propagating the change to the schema metadata when the info features are updated, so this line is the culprit:\r\n```python\r\ncommon_voice_train = common_voice_train.remove_columns(['client_id', 'up_votes', 'down_votes', 'age... | 2021-03-19T08:29:48 | 2021-04-09T09:25:33 | 2021-04-09T09:25:33 | MEMBER | null | Hey,
I played around with the `concatenate_datasets(...)` function: https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate_datasets#datasets.concatenate_datasets
and noticed that when the order in which the datasets are concatenated changes an error is thrown where it should not IMO.
Here is a google colab to reproduce the error: https://colab.research.google.com/drive/17VTFU4KQ735-waWZJjeOHS6yDTfV5ekK?usp=sharing | {
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https://api.github.com/repos/huggingface/datasets/issues/2082 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2082/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2082/comments | https://api.github.com/repos/huggingface/datasets/issues/2082/events | https://github.com/huggingface/datasets/pull/2082 | 835,401,555 | MDExOlB1bGxSZXF1ZXN0NTk2MDY1NTM0 | 2,082 | Updated card using information from data statement and datasheet | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-19T00:39:38 | 2021-03-19T14:29:09 | 2021-03-19T14:29:09 | CONTRIBUTOR | null | I updated and clarified the REFreSD [data card](https://github.com/mcmillanmajora/datasets/blob/refresd_card/datasets/refresd/README.md) with information from the Eleftheria's [website](https://elbria.github.io/post/refresd/). I added brief descriptions where the initial card referred to the paper, and I also recreated some of the tables in the paper to show relevant dataset statistics.
I'll email Eleftheria to see if she has any comments on the card. | {
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https://api.github.com/repos/huggingface/datasets/issues/2081 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2081/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2081/comments | https://api.github.com/repos/huggingface/datasets/issues/2081/events | https://github.com/huggingface/datasets/pull/2081 | 835,112,968 | MDExOlB1bGxSZXF1ZXN0NTk1ODE3OTM4 | 2,081 | Fix docstrings issues | {
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https://api.github.com/repos/huggingface/datasets/issues/2080 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2080/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2080/comments | https://api.github.com/repos/huggingface/datasets/issues/2080/events | https://github.com/huggingface/datasets/issues/2080 | 835,023,000 | MDU6SXNzdWU4MzUwMjMwMDA= | 2,080 | Multidimensional arrays in a Dataset | {
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"Hi !\r\n\r\nThis is actually supported ! but not yet in `from_pandas`.\r\nYou can use `from_dict` for now instead:\r\n```python\r\nfrom datasets import Dataset, Array2D, Features, Value\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = {\r\n 'bbox': [\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,... | 2021-03-18T16:29:14 | 2021-03-25T12:46:53 | 2021-03-25T12:46:53 | NONE | null | Hi,
I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row.
The following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`)
```
from datasets import Dataset
import pandas as pd
import numpy as np
dataset = pd.DataFrame({
'bbox': [
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])
],
'input_ids': [1, 2, 3, 4]
})
dataset = Dataset.from_pandas(dataset)
```
Since I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists.
```
import torch
from datasets import Dataset
import pandas as pd
dataset = pd.DataFrame({
'bbox': [
[[1,2,3,4],[1,2,3,4],[1,2,3,4]],
[[1,2,3,4],[1,2,3,4],[1,2,3,4]],
[[1,2,3,4],[1,2,3,4],[1,2,3,4]],
[[1,2,3,4],[1,2,3,4],[1,2,3,4]]
],
'input_ids': [1, 2, 3, 4]
})
dataset = Dataset.from_pandas(dataset)
def test(examples):
return {'bbbox': torch.Tensor(examples['bbox'])}
dataset = dataset.map(test)
print(dataset[0]['bbox'])
print(dataset[0]['bbbox'])
dataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True)
print(dataset[0]['bbox'])
print(dataset[0]['bbbox'])
def test2(examples):
return {'bbbox': torch.stack(examples['bbox'])}
dataset = dataset.map(test2)
print(dataset[0]['bbox'])
print(dataset[0]['bbbox'])
```
Is is possible to support n-D arrays/tensors in datasets?
It seems that it can also be useful for this [feature request](https://github.com/huggingface/datasets/issues/263). | {
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https://api.github.com/repos/huggingface/datasets/issues/2079 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2079/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2079/comments | https://api.github.com/repos/huggingface/datasets/issues/2079/events | https://github.com/huggingface/datasets/pull/2079 | 834,920,493 | MDExOlB1bGxSZXF1ZXN0NTk1NjU2MDQ5 | 2,079 | Refactorize Metric.compute signature to force keyword arguments only | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-18T15:05:50 | 2021-03-23T15:31:44 | 2021-03-23T15:31:44 | MEMBER | null | Minor refactoring of Metric.compute signature to force the use of keyword arguments, by using the single star syntax. | {
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https://api.github.com/repos/huggingface/datasets/issues/2078 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2078/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2078/comments | https://api.github.com/repos/huggingface/datasets/issues/2078/events | https://github.com/huggingface/datasets/issues/2078 | 834,694,819 | MDU6SXNzdWU4MzQ2OTQ4MTk= | 2,078 | MemoryError when computing WER metric | {
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"Hi ! Thanks for reporting.\r\nWe're indeed using `jiwer` to compute the WER.\r\n\r\nMaybe instead of calling `jiwer.wer` once for all the preditions/references we can compute the WER iteratively to avoid memory issues ? I'm not too familial with `jiwer` but this must be possible.\r\n\r\nCurrently the code to compu... | 2021-03-18T11:30:05 | 2021-05-01T08:31:49 | 2021-04-06T07:20:43 | NONE | null | Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:
```
wer = load_metric("wer")
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
However, I receive the following exception:
`Traceback (most recent call last):
File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module>
print(wer.compute(predictions=result["predicted"], references=result["target"]))
File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute
return wer(references, predictions)
File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer
truth, hypothesis, truth_transform, hypothesis_transform, **kwargs
File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures
H, S, D, I = _get_operation_counts(truth, hypothesis)
File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts
editops = Levenshtein.editops(source_string, destination_string)
MemoryError`
My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
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https://api.github.com/repos/huggingface/datasets/issues/2077 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2077/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2077/comments | https://api.github.com/repos/huggingface/datasets/issues/2077/events | https://github.com/huggingface/datasets/pull/2077 | 834,649,536 | MDExOlB1bGxSZXF1ZXN0NTk1NDI0MTYw | 2,077 | Bump huggingface_hub version | {
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https://api.github.com/repos/huggingface/datasets/issues/2075 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2075/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2075/comments | https://api.github.com/repos/huggingface/datasets/issues/2075/events | https://github.com/huggingface/datasets/issues/2075 | 834,301,246 | MDU6SXNzdWU4MzQzMDEyNDY= | 2,075 | ConnectionError: Couldn't reach common_voice.py | {
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"Hi @LifaSun, thanks for reporting this issue.\r\n\r\nSometimes, GitHub has some connectivity problems. Could you confirm that the problem persists?",
"@albertvillanova Thanks! It works well now. "
] | 2021-03-18T01:19:06 | 2021-03-20T10:29:41 | 2021-03-20T10:29:41 | NONE | null | When I run:
from datasets import load_dataset, load_metric
common_voice_train = load_dataset("common_voice", "zh-CN", split="train+validation")
common_voice_test = load_dataset("common_voice", "zh-CN", split="test")
Got:
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/master/datasets/common_voice/common_voice.py
Version:
1.4.1
Thanks! @lhoestq @LysandreJik @thomwolf | {
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https://api.github.com/repos/huggingface/datasets/issues/2074 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2074/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2074/comments | https://api.github.com/repos/huggingface/datasets/issues/2074/events | https://github.com/huggingface/datasets/pull/2074 | 834,268,463 | MDExOlB1bGxSZXF1ZXN0NTk1MTIzMjYw | 2,074 | Fix size categories in YAML Tags | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-18T00:02:36 | 2021-03-23T17:11:10 | 2021-03-23T17:11:10 | CONTRIBUTOR | null | This PR fixes several `size_categories` in YAML tags and makes them consistent. Additionally, I have added a few more categories after `1M`, up to `1T`. I would like to add that to the streamlit app also.
This PR also adds a couple of infos that I found missing.
The code for generating this:
```python
for dataset in sorted(os.listdir('./datasets/')):
if '.' not in dataset and dataset not in ['c4', 'csv', 'downloads', 'cc100', 'ccaligned_multilingual', 'celeb_a', 'chr_en', 'emea', 'glue']:
infos = {}
stats = {}
st = ''
with open(f'datasets/{dataset}/README.md') as f:
d = f.read()
start_dash = d.find('---') + 3
end_dash = d[start_dash:].find('---') + 3
rest_text = d[end_dash + 3:]
try:
full_yaml = OmegaConf.create(d[start_dash:end_dash])
readme = OmegaConf.to_container(full_yaml['size_categories'], resolve=True)
except Exception as e:
print(e)
continue
try:
with open(f'datasets/{dataset}/dataset_infos.json') as f:
data = json.load(f)
except Exception as e:
print(e)
continue # Skip those without infos.
done_set = set([])
num_keys = len(data.keys())
for keys in data:
# dataset = load_dataset('opus100', f'{dirs}')
total = 0
for split in data[keys]['splits']:
total = total + data[keys]['splits'][split]['num_examples']
if total < 1000:
st += "- n<1K" + '\n'
infos[keys] = ["n<1K"]
elif total >= 1000 and total < 10000:
infos[keys] = ["1K<n<10K"]
elif total >= 10000 and total < 100000:
infos[keys] = ["10K<n<100K"]
elif total >= 100000 and total < 1000000:
infos[keys] = ["100K<n<1M"]
elif total >= 1000000 and total < 10000000:
infos[keys] = ["1M<n<10M"]
elif total >= 10000000 and total < 100000000:
infos[keys] = ["10M<n<100M"]
elif total >= 100000000 and total < 1000000000:
infos[keys] = ["100M<n<1B"]
elif total >= 1000000000 and total < 10000000000:
infos[keys] = ["1B<n<10B"]
elif total >= 10000000000 and total < 100000000000:
infos[keys] = ["10B<n<100B"]
elif total >= 100000000000 and total < 1000000000000:
infos[keys] = ["100B<n<1T"]
else:
infos[keys] = ["n>1T"]
done_set = done_set.union(infos[keys])
if (isinstance(readme, list) and list(infos.values())[0] != readme) or (isinstance(readme, dict) and readme != infos):
print('-' * 30)
print(done_set)
print(f"Changing Full YAML for {dataset}")
print(OmegaConf.to_yaml(full_yaml))
if len(done_set) == 1:
full_yaml['size_categories'] = list(done_set)
else:
full_yaml['size_categories'] = dict([(k, v) for k, v in sorted(infos.items(), key=lambda x: x[0])])
full_yaml_string = OmegaConf.to_yaml(full_yaml)
print('-' * 30)
print(full_yaml_string)
inp = input('Do you wish to continue?(Y/N)')
if inp == 'Y':
with open(f'./datasets/{dataset}/README.md', 'w') as f:
f.write('---\n')
f.write(full_yaml_string)
f.write('---')
f.write(rest_text)
else:
break
```
Note that the lower-bound is inclusive. I'm unsure if this is how it is done in the tagging app.
EDIT:
It would be great if there was a way to make the task categories consistent too. For this, the streamlit app can look into all the datasets and check for existing categories and show them in the list. This may add some consistency.
EDIT:
I understand this will not work for cases where only the infos for some of the configs are present, for example: `ccaligned_multingual` has only 5 out of several configs present, and infos has only information about them. Hence, I have skipped a few datasets in the code, if there are more such datasets, then I'll ignore them too. | {
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https://api.github.com/repos/huggingface/datasets/issues/2073 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2073/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2073/comments | https://api.github.com/repos/huggingface/datasets/issues/2073/events | https://github.com/huggingface/datasets/pull/2073 | 834,192,501 | MDExOlB1bGxSZXF1ZXN0NTk1MDYyMzQ2 | 2,073 | Fixes check of TF_AVAILABLE and TORCH_AVAILABLE | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-17T21:28:53 | 2021-03-18T09:09:25 | 2021-03-18T09:09:24 | MEMBER | null | # What is this PR doing
This PR implements the checks if `Tensorflow` and `Pytorch` are available the same way as `transformers` does it. I added the additional checks for the different `Tensorflow` and `torch` versions. #2068 | {
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https://api.github.com/repos/huggingface/datasets/issues/2072 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2072/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2072/comments | https://api.github.com/repos/huggingface/datasets/issues/2072/events | https://github.com/huggingface/datasets/pull/2072 | 834,054,837 | MDExOlB1bGxSZXF1ZXN0NTk0OTQ5NjA4 | 2,072 | Fix docstring issues | {
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https://api.github.com/repos/huggingface/datasets/issues/2071 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2071/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2071/comments | https://api.github.com/repos/huggingface/datasets/issues/2071/events | https://github.com/huggingface/datasets/issues/2071 | 833,950,824 | MDU6SXNzdWU4MzM5NTA4MjQ= | 2,071 | Multiprocessing is slower than single process | {
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"dupe of #1992"
] | 2021-03-17T16:08:58 | 2021-03-18T09:10:23 | 2021-03-18T09:10:23 | CONTRIBUTOR | null | ```python
# benchmark_filter.py
import logging
import sys
import time
from datasets import load_dataset, set_caching_enabled
if __name__ == "__main__":
set_caching_enabled(False)
logging.basicConfig(level=logging.DEBUG)
bc = load_dataset("bookcorpus")
now = time.time()
try:
bc["train"].filter(lambda x: len(x["text"]) < 64, num_proc=int(sys.argv[1]))
except Exception as e:
print(f"cancelled: {e}")
elapsed = time.time() - now
print(elapsed)
```
Running `python benchmark_filter.py 1` (20min+) is faster than `python benchmark_filter.py 2` (2hrs+) | {
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https://api.github.com/repos/huggingface/datasets/issues/2070 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2070/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2070/comments | https://api.github.com/repos/huggingface/datasets/issues/2070/events | https://github.com/huggingface/datasets/issues/2070 | 833,799,035 | MDU6SXNzdWU4MzM3OTkwMzU= | 2,070 | ArrowInvalid issue for squad v2 dataset | {
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"Hi ! This error happens when you use `map` in batched mode and then your function doesn't return the same number of values per column.\r\n\r\nIndeed since you're using `map` in batched mode, `prepare_validation_features` must take a batch as input (i.e. a dictionary of multiple rows of the dataset), and return a b... | 2021-03-17T13:51:49 | 2021-08-04T17:57:16 | 2021-08-04T17:57:16 | NONE | null | Hello, I am using the huggingface official question answering example notebook (https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb).
In the prepare_validation_features function, I made some modifications to tokenize a new set of quesions with the original contexts and save them in three different list called candidate_input_dis, candidate_attetion_mask and candidate_token_type_ids. When I try to run the next cell for dataset.map, I got the following error:
`ArrowInvalid: Column 1 named candidate_attention_mask expected length 1180 but got length 1178`
My code is as follows:
```
def generate_candidate_questions(examples):
val_questions = examples["question"]
candididate_questions = random.sample(datasets["train"]["question"], len(val_questions))
candididate_questions = [x[:max_length] for x in candididate_questions]
return candididate_questions
def prepare_validation_features(examples, use_mixing=False):
pad_on_right = tokenizer.padding_side == "right"
tokenized_examples = tokenizer(
examples["question" if pad_on_right else "context"],
examples["context" if pad_on_right else "question"],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
if use_mixing:
candidate_questions = generate_candidate_questions(examples)
tokenized_candidates = tokenizer(
candidate_questions if pad_on_right else examples["context"],
examples["context"] if pad_on_right else candidate_questions,
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
tokenized_examples["example_id"] = []
if use_mixing:
tokenized_examples["candidate_input_ids"] = tokenized_candidates["input_ids"]
tokenized_examples["candidate_attention_mask"] = tokenized_candidates["attention_mask"]
tokenized_examples["candidate_token_type_ids"] = tokenized_candidates["token_type_ids"]
for i in range(len(tokenized_examples["input_ids"])):
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
tokenized_examples["offset_mapping"][i] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
validation_features = datasets["validation"].map(
lambda xs: prepare_validation_features(xs, True),
batched=True,
remove_columns=datasets["validation"].column_names
)
```
I guess this might happen because of the batched=True. I see similar issues in this repo related to arrow table length mismatch error, but in their cases, the numbers vary a lot. In my case, this error always happens when the expected length and unexpected length are very close. Thanks for the help! | {
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