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https://api.github.com/repos/huggingface/datasets/issues/2718 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2718/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2718/comments | https://api.github.com/repos/huggingface/datasets/issues/2718/events | https://github.com/huggingface/datasets/pull/2718 | 953,360,663 | MDExOlB1bGxSZXF1ZXN0Njk3NDE0NTQy | 2,718 | New documentation structure | {
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"I just did some minor changes + added some content in these sections: share, about arrow, about cache\r\n\r\nFeel free to mark this PR as ready for review ! :)",
"I just separated the `Share` How-to page into three pages: share, dataset_script and dataset_card.\r\n\r\nThis way in the share page we can explain in... | 2021-07-26T23:15:13 | 2021-09-13T17:20:53 | 2021-09-13T17:20:52 | MEMBER | null | false | {
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} | Organize Datasets documentation into four documentation types to improve clarity and discoverability of content.
**Content to add in the very short term (feel free to add anything I'm missing):**
- A discussion on why Datasets uses Arrow that includes some context and background about why we use Arrow. Would also be great to talk about Datasets speed and performance here, and if you can share any benchmarking/tests you did, that would be awesome! Finally, a discussion about how memory-mapping frees the user from RAM constraints would be very helpful.
- Explain why you would want to disable or override verifications when loading a dataset.
- If possible, include a code sample of when the number of elements in the field of an output dictionary aren’t the same as the other fields in the output dictionary (taken from the [note](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset) here). | {
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} | Made a very minor change to fix the issue#2716. Added the missing argument in the constructor call.
As discussed in the bug report, the change is made to prevent the `shuffle` method call from resetting the value of `batched` attribute in `MappedExamplesIterable`
Fix #2716. | {
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"Hi :) Good catch ! Feel free to open a PR if you want to contribute, this would be very welcome ;)",
"Have raised the PR [here](https://github.com/huggingface/datasets/pull/2717)",
"Fixed by #2717."
] | 2021-07-26T13:24:59 | 2021-07-26T18:04:43 | 2021-07-26T18:04:43 | CONTRIBUTOR | null | null | null | When using dataset in streaming mode, if one applies `shuffle` method on the dataset and `map` method for which `batched=True` than the batching operation will not happen, instead `batched` will be set to `False`
I did RCA on the dataset codebase, the problem is emerging from [this line of code](https://github.com/huggingface/datasets/blob/d25a0bf94d9f9a9aa6cabdf5b450b9c327d19729/src/datasets/iterable_dataset.py#L197) here as it is
`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batch_size=self.batch_size`, as one can see it is missing batched argument, which means that the iterator fallsback to default constructor value, which in this case is `False`.
To remedy the problem we can change this line to
`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batched=self.batched, batch_size=self.batch_size`
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"Merging since the CI is just about missing infos in the dataset card"
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} | Related to #2710, #2691. | {
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"We already have this information in the dataset_infos.json files of each dataset.\r\nMaybe we can parse these files in the backend to return their content with the endpoint at huggingface.co/api/datasets\r\n\r\nFor now if you want to access this info you have to load the json for each dataset. For example:\r\n- fo... | 2021-07-26T07:11:03 | 2021-07-26T09:16:25 | null | NONE | null | null | null | For the import into ELG, we would like a more precise description of the size of the dataset, instead of the current size categories. The size can be expressed in bytes, or any other preferred size unit. As suggested in the slack channel, perhaps this could be computed with a regex for existing datasets. | {
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- Enumerate all ner_tags in dataset card Data Fields section
- Add all metadata tags to dataset card
Close #2709. | {
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"We have to update the URL in the XTREME benchmark as well:\r\n\r\nhttps://github.com/huggingface/datasets/blob/0dfc639cec450ed8762a997789a2ed63e63cdcf2/datasets/xtreme/xtreme.py#L411-L411\r\n\r\n"
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} | WikiANN data source URL is no longer accessible: 404 error from Dropbox.
We have decided to host it at Hugging Face. This PR updates the data source URL, the metadata JSON file and the dataset card.
Close #2691. | {
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"Hi @maxpel, thanks for reporting this issue.\r\n\r\nIndeed, the documentation in the dataset card is not complete. I’m opening a Pull Request to fix it.\r\n\r\nAs the paper explains, there are 6 entity types and we have ordered them alphabetically: `corporation`, `creative-work`, `group`, `location`, `person` and ... | 2021-07-23T12:25:32 | 2021-07-26T09:30:55 | 2021-07-26T09:30:55 | CONTRIBUTOR | null | null | null | On the info page of the wnut_17 data set (https://huggingface.co/datasets/wnut_17), the model output of ner-tags is only documented for these 5 cases:
`ner_tags: a list of classification labels, with possible values including O (0), B-corporation (1), I-corporation (2), B-creative-work (3), I-creative-work (4).`
I trained a model with the data and it gives me 13 classes:
```
"id2label": {
"0": 0,
"1": 1,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9,
"10": 10,
"11": 11,
"12": 12
}
"label2id": {
"0": 0,
"1": 1,
"10": 10,
"11": 11,
"12": 12,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9
}
```
The paper (https://www.aclweb.org/anthology/W17-4418.pdf) explains those 6 categories, but the ordering does not match:
```
1. person
2. location (including GPE, facility)
3. corporation
4. product (tangible goods, or well-defined
services)
5. creative-work (song, movie, book and
so on)
6. group (subsuming music band, sports team,
and non-corporate organisations)
```
I would be very helpful for me, if somebody could clarify the model ouputs and explain the "B-" and "I-" prefixes to me.
Really great work with that and the other packages, I couldn't believe that training the model with that data was basically a one-liner! | {
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https://api.github.com/repos/huggingface/datasets/issues/2708 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2708/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2708/comments | https://api.github.com/repos/huggingface/datasets/issues/2708/events | https://github.com/huggingface/datasets/issues/2708 | 951,092,660 | MDU6SXNzdWU5NTEwOTI2NjA= | 2,708 | QASC: incomplete training set | {
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"Hi @danyaljj, thanks for reporting.\r\n\r\nUnfortunately, I have not been able to reproduce your problem. My train split has 8134 examples:\r\n```ipython\r\nIn [10]: ds[\"train\"]\r\nOut[10]:\r\nDataset({\r\n features: ['id', 'question', 'choices', 'answerKey', 'fact1', 'fact2', 'combinedfact', 'formatted_quest... | 2021-07-22T21:59:44 | 2021-07-23T13:30:07 | 2021-07-23T13:30:07 | CONTRIBUTOR | null | null | null | ## Describe the bug
The training instances are not loaded properly.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("qasc", script_version='1.10.2')
def load_instances(split):
instances = dataset[split]
print(f"split: {split} - size: {len(instances)}")
for x in instances:
print(json.dumps(x))
load_instances('test')
load_instances('validation')
load_instances('train')
```
## results
For test and validation, we can see the examples in the output (which is good!):
```
split: test - size: 920
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Anthax", "under water", "uterus", "wombs", "two", "moles", "live", "embryo"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What type of birth do therian mammals have? (A) Anthax (B) under water (C) uterus (D) wombs (E) two (F) moles (G) live (H) embryo", "id": "3C44YUNSI1OBFBB8D36GODNOZN9DPA", "question": "What type of birth do therian mammals have?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Corvidae", "arthropods", "birds", "backbones", "keratin", "Jurassic", "front paws", "Parakeets."]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "By what time had mouse-sized viviparous mammals evolved? (A) Corvidae (B) arthropods (C) birds (D) backbones (E) keratin (F) Jurassic (G) front paws (H) Parakeets.", "id": "3B1NLC6UGZVERVLZFT7OUYQLD1SGPZ", "question": "By what time had mouse-sized viviparous mammals evolved?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Reduced friction", "causes infection", "vital to a good life", "prevents water loss", "camouflage from consumers", "Protection against predators", "spur the growth of the plant", "a smooth surface"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What does a plant's skin do? (A) Reduced friction (B) causes infection (C) vital to a good life (D) prevents water loss (E) camouflage from consumers (F) Protection against predators (G) spur the growth of the plant (H) a smooth surface", "id": "3QRYMNZ7FYGITFVSJET3PS0F4S0NT9", "question": "What does a plant's skin do?"}
...
```
However, only a few instances are loaded for the training split, which is not correct.
## Environment info
- `datasets` version: '1.10.2'
- Platform: MaxOS
- Python version:3.7
- PyArrow version: 3.0.0
| {
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https://api.github.com/repos/huggingface/datasets/issues/2707 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2707/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2707/comments | https://api.github.com/repos/huggingface/datasets/issues/2707/events | https://github.com/huggingface/datasets/issues/2707 | 950,812,945 | MDU6SXNzdWU5NTA4MTI5NDU= | 2,707 | 404 Not Found Error when loading LAMA dataset | {
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"Hi @dwil2444! I was able to reproduce your error when I downgraded to v1.1.2. Updating to the latest version of Datasets fixed the error for me :)",
"Hi @dwil2444, thanks for reporting.\r\n\r\nCould you please confirm which `datasets` version you were using and if the problem persists after you update it to the ... | 2021-07-22T15:52:33 | 2021-07-26T14:29:07 | 2021-07-26T14:29:07 | NONE | null | null | null | The [LAMA](https://huggingface.co/datasets/viewer/?dataset=lama) probing dataset is not available for download:
Steps to Reproduce:
1. `from datasets import load_dataset`
2. `dataset = load_dataset('lama', 'trex')`.
Results:
`FileNotFoundError: Couldn't find file locally at lama/lama.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/lama/lama.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/lama/lama.py` | {
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https://api.github.com/repos/huggingface/datasets/issues/2706 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2706/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2706/comments | https://api.github.com/repos/huggingface/datasets/issues/2706/events | https://github.com/huggingface/datasets/pull/2706 | 950,606,561 | MDExOlB1bGxSZXF1ZXN0Njk1MTI3ODgz | 2,706 | Update BibTeX entry | {
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https://api.github.com/repos/huggingface/datasets/issues/2705 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2705/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2705/comments | https://api.github.com/repos/huggingface/datasets/issues/2705/events | https://github.com/huggingface/datasets/issues/2705 | 950,488,583 | MDU6SXNzdWU5NTA0ODg1ODM= | 2,705 | 404 not found error on loading WIKIANN dataset | {
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"Hi @ronbutan, thanks for reporting.\r\n\r\nYou are right: we have recently found that the link to the original PAN-X dataset (also called WikiANN), hosted at Dropbox, is no longer working.\r\n\r\nWe have opened an issue in the GitHub repository of the original dataset (afshinrahimi/mmner#4) and we have also contac... | 2021-07-22T09:55:50 | 2021-07-23T08:07:32 | 2021-07-23T08:07:32 | NONE | null | null | null | ## Describe the bug
Unable to retreive wikiann English dataset
## Steps to reproduce the bug
```python
from datasets import list_datasets, load_dataset, list_metrics, load_metric
WIKIANN = load_dataset("wikiann","en")
```
## Expected results
Colab notebook should display successful download status
## Actual results
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2704 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2704/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2704/comments | https://api.github.com/repos/huggingface/datasets/issues/2704/events | https://github.com/huggingface/datasets/pull/2704 | 950,483,980 | MDExOlB1bGxSZXF1ZXN0Njk1MDIzMTEz | 2,704 | Fix pick default config name message | {
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} | The error message to tell which config name to load is not displayed.
This is because in the code it was considering the config kwargs to be non-empty, which is a special case for custom configs created on the fly. It appears after this change: https://github.com/huggingface/datasets/pull/2659
I fixed that by making the config kwargs empty by default, even if default parameters are passed
Fix https://github.com/huggingface/datasets/issues/2703 | {
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https://api.github.com/repos/huggingface/datasets/issues/2703 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2703/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2703/comments | https://api.github.com/repos/huggingface/datasets/issues/2703/events | https://github.com/huggingface/datasets/issues/2703 | 950,482,284 | MDU6SXNzdWU5NTA0ODIyODQ= | 2,703 | Bad message when config name is missing | {
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"followers_url": "https://api.git... | [] | 2021-07-22T09:47:23 | 2021-07-22T10:02:40 | 2021-07-22T10:02:40 | MEMBER | null | null | null | When loading a dataset that have several configurations, we expect to see an error message if the user doesn't specify a config name.
However in `datasets` 1.10.0 and 1.10.1 it doesn't show the right message:
```python
import datasets
datasets.load_dataset("glue")
```
raises
```python
AttributeError: 'BuilderConfig' object has no attribute 'text_features'
```
instead of
```python
ValueError: Config name is missing.
Please pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']
Example of usage:
`load_dataset('glue', 'cola')`
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2701 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2701/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2701/comments | https://api.github.com/repos/huggingface/datasets/issues/2701/events | https://github.com/huggingface/datasets/pull/2701 | 950,422,403 | MDExOlB1bGxSZXF1ZXN0Njk0OTcxMzM3 | 2,701 | Fix download_mode docstrings | {
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https://api.github.com/repos/huggingface/datasets/issues/2700 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2700/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2700/comments | https://api.github.com/repos/huggingface/datasets/issues/2700/events | https://github.com/huggingface/datasets/issues/2700 | 950,276,325 | MDU6SXNzdWU5NTAyNzYzMjU= | 2,700 | from datasets import Dataset is failing | {
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"Hi @kswamy15, thanks for reporting.\r\n\r\nWe are fixing this critical issue and making an urgent patch release of the `datasets` library today.\r\n\r\nIn the meantime, you can circumvent this issue by updating the `tqdm` library: `!pip install -U tqdm`"
] | 2021-07-22T03:51:23 | 2021-07-22T07:23:45 | 2021-07-22T07:09:07 | NONE | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import Dataset
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: latest version as of 07/21/2021
- Platform: Google Colab
- Python version: 3.7
- PyArrow version:
| {
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https://api.github.com/repos/huggingface/datasets/issues/2699 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2699/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2699/comments | https://api.github.com/repos/huggingface/datasets/issues/2699/events | https://github.com/huggingface/datasets/issues/2699 | 950,221,226 | MDU6SXNzdWU5NTAyMjEyMjY= | 2,699 | cannot combine splits merging and streaming? | {
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"Hi ! That's missing indeed. We'll try to implement this for the next version :)\r\n\r\nI guess we just need to implement #2564 first, and then we should be able to add support for splits combinations"
] | 2021-07-22T01:13:25 | 2021-07-22T08:27:47 | null | NONE | null | null | null | this does not work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation',streaming=True)`
with error:
`ValueError: Bad split: train+validation. Available splits: ['train', 'validation']`
these work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation')`
`dataset = datasets.load_dataset('mc4','iw',split='train',streaming=True)`
`dataset = datasets.load_dataset('mc4','iw',split='validation',streaming=True)`
i could not find a reference to this in the documentation and the error message is confusing. also would be nice to allow streaming for the merged splits | {
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https://api.github.com/repos/huggingface/datasets/issues/2698 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2698/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2698/comments | https://api.github.com/repos/huggingface/datasets/issues/2698/events | https://github.com/huggingface/datasets/pull/2698 | 950,159,867 | MDExOlB1bGxSZXF1ZXN0Njk0NzUxMzMw | 2,698 | Ignore empty batch when writing | {
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} | This prevents an schema update with unknown column types, as reported in #2644.
This is my first attempt at fixing the issue. I tested the following:
- First batch returned by a batched map operation is empty.
- An intermediate batch is empty.
- `python -m unittest tests.test_arrow_writer` passes.
However, `arrow_writer` looks like a pretty generic interface, I'm not sure if there are other uses I may have overlooked. Let me know if that's the case, or if a better approach would be preferable. | {
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"@lhoestq @albertvillanova - It might be a good idea to have a patch release after this gets merged (presumably tomorrow morning when you're around). The Colab issue linked to this PR is a pretty big blocker. "
] | 2021-07-21T19:03:38 | 2021-07-22T07:09:08 | 2021-07-22T07:09:07 | CONTRIBUTOR | null | false | {
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} | Fix #2695, fix #2700. | {
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"The CI failure seems unrelated to this PR (probably has something to do with Transformers)."
] | 2021-07-21T16:34:53 | 2021-07-26T13:31:14 | 2021-07-26T09:38:37 | CONTRIBUTOR | null | false | {
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} | This PR is a continuation of #2667 and adds support for `utils.disable_progress_bar()` on Windows when using multiprocessing. This [answer](https://stackoverflow.com/a/6596695/14095927) on SO explains it nicely why the current approach (with calling `utils.is_progress_bar_enabled()` inside `Dataset._map_single`) would not work on Windows. | {
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https://api.github.com/repos/huggingface/datasets/issues/2695 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2695/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2695/comments | https://api.github.com/repos/huggingface/datasets/issues/2695/events | https://github.com/huggingface/datasets/issues/2695 | 949,864,823 | MDU6SXNzdWU5NDk4NjQ4MjM= | 2,695 | Cannot import load_dataset on Colab | {
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"I'm facing the same issue on Colab today too.\r\n\r\n```\r\nModuleNotFoundError Traceback (most recent call last)\r\n<ipython-input-4-5833ac0f5437> in <module>()\r\n 3 \r\n 4 from ray import tune\r\n----> 5 from datasets import DatasetDict, Dataset\r\n 6 from datasets import lo... | 2021-07-21T15:52:51 | 2021-07-22T07:26:25 | 2021-07-22T07:09:07 | NONE | null | null | null | ## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
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} | Before: copy/paste resulted in an error because the square bracket
characters `[]` are special characters in bash | {
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https://api.github.com/repos/huggingface/datasets/issues/2693 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2693/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2693/comments | https://api.github.com/repos/huggingface/datasets/issues/2693/events | https://github.com/huggingface/datasets/pull/2693 | 949,797,014 | MDExOlB1bGxSZXF1ZXN0Njk0NDQ1ODAz | 2,693 | Fix OSCAR Esperanto | {
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} | The Esperanto part (original) of OSCAR has the wrong number of examples:
```python
from datasets import load_dataset
raw_datasets = load_dataset("oscar", "unshuffled_original_eo")
```
raises
```python
NonMatchingSplitsSizesError:
[{'expected': SplitInfo(name='train', num_bytes=314188336, num_examples=121171, dataset_name='oscar'),
'recorded': SplitInfo(name='train', num_bytes=314064514, num_examples=121168, dataset_name='oscar')}]
```
I updated the number of expected examples in dataset_infos.json
cc @sgugger | {
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https://api.github.com/repos/huggingface/datasets/issues/2692 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2692/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2692/comments | https://api.github.com/repos/huggingface/datasets/issues/2692/events | https://github.com/huggingface/datasets/pull/2692 | 949,765,484 | MDExOlB1bGxSZXF1ZXN0Njk0NDE4MDg1 | 2,692 | Update BibTeX entry | {
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"Hi @severo, thanks for reporting.\r\n\r\nHowever I have not been able to reproduce this issue. Could you please confirm if the problem persists for you?\r\n\r\nMaybe Dropbox (where the data source is hosted) was temporarily unavailable when you tried.",
"Hmmm, the file (https://www.dropbox.com/s/dl/12h3qqog6q4bj... | 2021-07-21T14:18:05 | 2021-07-26T09:34:22 | 2021-07-26T09:34:22 | CONTRIBUTOR | null | null | null | ## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
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"Thanks for all the comments and for the corrections in the docs !\r\n\r\nAbout all the points you mentioned:\r\n\r\n> * the code samples assume the expected libraries have already been installed. Maybe add a section at start, or add it to every code sample. Something like `pip install datasets transformers torch ... | 2021-07-21T10:43:14 | 2021-07-27T18:40:54 | 2021-07-27T18:40:54 | CONTRIBUTOR | null | false | {
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"merged_at": "2021-07-27T18:40:53"
} | Some comments here:
- the code samples assume the expected libraries have already been installed. Maybe add a section at start, or add it to every code sample. Something like `pip install datasets transformers torch 'datasets[streaming]'` (maybe just link to https://huggingface.co/docs/datasets/installation.html + a one-liner that installs all the requirements / alternatively a requirements.txt file)
- "If you’d like to play with the examples, you must install it from source." in https://huggingface.co/docs/datasets/installation.html: it's not clear to me what this means (what are these "examples"?)
- in https://huggingface.co/docs/datasets/loading_datasets.html: "or AWS bucket if it’s not already stored in the library". It's the only place in the doc (aside from the docstring https://huggingface.co/docs/datasets/package_reference/loading_methods.html?highlight=aws bucket#datasets.list_datasets) where the "AWS bucket" is mentioned. It's not easy to understand what this means. Maybe explain more, and link to https://s3.amazonaws.com/datasets.huggingface.co and/or https://huggingface.co/docs/datasets/filesystems.html.
- example in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files is obsoleted by https://github.com/huggingface/datasets/pull/2326. Also: see https://github.com/huggingface/datasets/issues/2691 for a bug on this specific dataset.
- in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files the doc says "After you’ve downloaded the files, you can point to the folder hosting them locally with the data_dir argument as follows:", but the following example does not show how to use `data_dir`
- in https://huggingface.co/docs/datasets/loading_datasets.html#csv-files, it would be nice to have an URL to the csv loader reference (but I'm not sure there is one in the API reference). This comment applies in many places in the doc: I would want the API reference to contain doc for all the code/functions/classes... and I would want a lot more links inside the doc pointing to the API entries.
- in the API reference (docstrings) I would prefer "SOURCE" to link to github instead of a copy of the code inside the docs site (eg. https://github.com/huggingface/datasets/blob/master/src/datasets/load.py#L711 instead of https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset)
- it seems like not all the API is exposed in the doc. For example, there is no doc for [`disable_progress_bar`](https://github.com/huggingface/datasets/search?q=disable_progress_bar), see https://huggingface.co/docs/datasets/search.html?q=disable_progress_bar, even if the code contains docstrings. Does it mean that the function is not officially supported? (otherwise, maybe it also deserves a mention in https://huggingface.co/docs/datasets/package_reference/logging_methods.html)
- in https://huggingface.co/docs/datasets/loading_datasets.html?highlight=most%20efficient%20format%20have%20json%20files%20consisting%20multiple%20json%20objects#json-files, "The most efficient format is to have JSON files consisting of multiple JSON objects, one per line, representing individual data rows:", maybe link to https://en.wikipedia.org/wiki/JSON_streaming#Line-delimited_JSON and give it a name ("line-delimited JSON"? "JSON Lines" as in https://huggingface.co/docs/datasets/processing.html#exporting-a-dataset-to-csv-json-parquet-or-to-python-objects ?)
- in https://huggingface.co/docs/datasets/loading_datasets.html, for the local files sections, it would be nice to provide sample csv / json / text files to download, so that it's easier for the reader to try to load them (instead: they won't try)
- the doc explains how to shard a dataset, but does not explain why and when a dataset should be sharded (I have no idea... for [parallelizing](https://huggingface.co/docs/datasets/processing.html#multiprocessing)?). It does neither give an idea of the number of shards a dataset typically should have and why.
- the code example in https://huggingface.co/docs/datasets/processing.html#mapping-in-a-distributed-setting does not work, because `training_args` has not been defined before in the doc. | {
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https://api.github.com/repos/huggingface/datasets/issues/2689 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2689/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2689/comments | https://api.github.com/repos/huggingface/datasets/issues/2689/events | https://github.com/huggingface/datasets/issues/2689 | 949,447,104 | MDU6SXNzdWU5NDk0NDcxMDQ= | 2,689 | cannot save the dataset to disk after rename_column | {
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"Hi ! That's because you are trying to overwrite a file that is already open and being used.\r\nIndeed `foo/dataset.arrow` is open and used by your `dataset` object.\r\n\r\nWhen you do `rename_column`, the resulting dataset reads the data from the same arrow file.\r\nIn other cases like when using `map` on the othe... | 2021-07-21T08:13:40 | 2023-11-02T14:54:00 | 2021-07-21T13:11:04 | CONTRIBUTOR | null | null | null | ## Describe the bug
If you use `rename_column` and do no other modification, you will be unable to save the dataset using `save_to_disk`
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
In [1]: from datasets import Dataset, load_from_disk
In [5]: dataset=Dataset.from_dict({'foo': [0]})
In [7]: dataset.save_to_disk('foo')
In [8]: dataset=load_from_disk('foo')
In [10]: dataset=dataset.rename_column('foo', 'bar')
In [11]: dataset.save_to_disk('foo')
---------------------------------------------------------------------------
PermissionError Traceback (most recent call last)
<ipython-input-11-a3bc0d4fc339> in <module>
----> 1 dataset.save_to_disk('foo')
/mnt/beegfs/projects/meerqat/anaconda3/envs/meerqat/lib/python3.7/site-packages/datasets/arrow_dataset.py in save_to_disk(self, dataset_path
, fs)
597 if Path(dataset_path, config.DATASET_ARROW_FILENAME) in cache_files_paths:
598 raise PermissionError(
--> 599 f"Tried to overwrite {Path(dataset_path, config.DATASET_ARROW_FILENAME)} but a dataset can't overwrite itself."
600 )
601 if Path(dataset_path, config.DATASET_INDICES_FILENAME) in cache_files_paths:
PermissionError: Tried to overwrite foo/dataset.arrow but a dataset can't overwrite itself.
```
N. B. I created the dataset from dict to enable easy reproduction but the same happens if you load an existing dataset (e.g. starting from `In [8]`)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-3.10.0-1160.11.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core
- Python version: 3.7.10
- PyArrow version: 3.0.0
| {
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https://api.github.com/repos/huggingface/datasets/issues/2688 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2688/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2688/comments | https://api.github.com/repos/huggingface/datasets/issues/2688/events | https://github.com/huggingface/datasets/issues/2688 | 949,182,074 | MDU6SXNzdWU5NDkxODIwNzQ= | 2,688 | hebrew language codes he and iw should be treated as aliases | {
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"Hi @eyaler, thanks for reporting.\r\n\r\nWhile you are true with respect the Hebrew language tag (\"iw\" is deprecated and \"he\" is the preferred value), in the \"mc4\" dataset (which is a derived dataset) we have kept the language tags present in the original dataset: [Google C4](https://www.tensorflow.org/datas... | 2021-07-20T23:13:52 | 2021-07-21T16:34:53 | 2021-07-21T16:34:53 | NONE | null | null | null | https://huggingface.co/datasets/mc4 not listed when searching for hebrew datasets (he) as it uses the older language code iw, preventing discoverability. | {
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} | Currently, [Writing a dataset loading script](https://huggingface.co/docs/datasets/add_dataset.html) page has a small error. A link to `matinf` dataset in [_Dataset scripts of reference_](https://huggingface.co/docs/datasets/add_dataset.html#dataset-scripts-of-reference) section actually leads to `xsquad`, instead. This PR fixes that. | {
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} | `data_dir=None` was considered a dataset config parameter, hence creating a special config_id for all dataset being loaded.
Since the config_id is used to name the cache directories, this leaded to datasets being regenerated for users.
I fixed this by ignoring the value of `data_dir` when it's `None` when computing the config_id.
I also added a test to make sure the cache directories are not unexpectedly renamed in the future.
Fix https://github.com/huggingface/datasets/issues/2683 | {
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"Normally, I'm expecting errors from the validation of the README file... 😅 ",
"That is:\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_dataset_cards.py::test_changed_dataset_card[blog_authorship_corpus]\r\n==== 1 failed, 3182 passed, 2763 skipped,... | 2021-07-20T15:44:50 | 2021-07-21T13:11:58 | 2021-07-21T13:11:58 | MEMBER | null | false | {
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} | This PR:
- Update the JSON metadata file, which previously was raising a `NonMatchingSplitsSizesError`
- Fix the codec of the data files (`latin_1` instead of `utf-8`), which previously was raising ` UnicodeDecodeError` for some files
Close #2679. | {
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} | Use absolute local paths in the error messages of `load_dataset` as per @stas00's suggestion in https://github.com/huggingface/datasets/pull/2500#issuecomment-874891223 | {
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```python
from datasets import load_dataset_builder
c4_builder = load_dataset_builder("c4", "en")
print(c4_builder.cache_dir)
# /Users/quentinlhoest/.cache/huggingface/datasets/c4/en-174d3b7155eb68db/0.0.0/...
# instead of
# /Users/quentinlhoest/.cache/huggingface/datasets/c4/en/0.0.0/...
```
This issue could be annoying since it would simply ignore old cache directories for users, and regenerate datasets
cc @stas00 this is what you experienced a few days ago
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https://api.github.com/repos/huggingface/datasets/issues/2682 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2682/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2682/comments | https://api.github.com/repos/huggingface/datasets/issues/2682/events | https://github.com/huggingface/datasets/pull/2682 | 948,713,137 | MDExOlB1bGxSZXF1ZXN0NjkzNTE2NjU2 | 2,682 | Fix c4 expected files | {
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} | Some files were not registered in the list of expected files to download
Fix https://github.com/huggingface/datasets/issues/2677 | {
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"Yes this was documented in the PR that added this hf->paperswithcode mapping (https://github.com/huggingface/datasets/pull/2404) and AFAICT those are slightly distinct datasets so I think it's a wontfix\r\n\r\nFor context on the paperswithcode mapping you can also refer to https://github.com/huggingface/huggingfac... | 2021-07-20T14:25:00 | 2021-07-20T15:44:17 | 2021-07-20T15:44:17 | CONTRIBUTOR | null | null | null | ## Describe the bug
In 5 cases, I could find a dataset on Paperswithcode which references two Hugging Face datasets as dataset loaders. They are:
- https://paperswithcode.com/dataset/multinli -> https://huggingface.co/datasets/multi_nli and https://huggingface.co/datasets/multi_nli_mismatch
<img width="838" alt="Capture d’écran 2021-07-20 à 16 33 58" src="https://user-images.githubusercontent.com/1676121/126342757-4625522a-f788-41a3-bd1f-2a8b9817bbf5.png">
- https://paperswithcode.com/dataset/squad -> https://huggingface.co/datasets/squad and https://huggingface.co/datasets/squad_v2
- https://paperswithcode.com/dataset/narrativeqa -> https://huggingface.co/datasets/narrativeqa and https://huggingface.co/datasets/narrativeqa_manual
- https://paperswithcode.com/dataset/hate-speech-and-offensive-language -> https://huggingface.co/datasets/hate_offensive and https://huggingface.co/datasets/hate_speech_offensive
- https://paperswithcode.com/dataset/newsph-nli -> https://huggingface.co/datasets/newsph and https://huggingface.co/datasets/newsph_nli
Possible solutions:
- don't fix (it works)
- for each pair of duplicate datasets, remove one, and create an alias to the other.
## Steps to reproduce the bug
Visit the Paperswithcode links, and look at the "Dataset Loaders" section
## Expected results
There should only be one reference to a Hugging Face dataset loader
## Actual results
Two Hugging Face dataset loaders
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} | The reverse reference exists on paperswithcode:
https://paperswithcode.com/dataset/qasper | {
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"Hi @izaskr, thanks for reporting.\r\n\r\nHowever the traceback you joined does not correspond to the codec error message: it is about other error `NonMatchingSplitsSizesError`. Maybe you missed some important part of your traceback...\r\n\r\nI'm going to have a look at the dataset anyway...",
"Hi @izaskr, thanks... | 2021-07-20T10:13:20 | 2021-07-21T17:02:21 | 2021-07-21T13:11:58 | NONE | null | null | null | ## Describe the bug
A codec error is raised while loading the blog_authorship_corpus.
## Steps to reproduce the bug
```
from datasets import load_dataset
raw_datasets = load_dataset("blog_authorship_corpus")
```
## Expected results
Loading the dataset without errors.
## Actual results
An error similar to the one below was raised for (what seems like) every XML file.
/home/izaskr/.cache/huggingface/datasets/downloads/extracted/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a/blogs/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/load.py", line 856, in load_dataset
builder_instance.download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 583, in download_and_prepare
self._download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 671, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 4.0.1
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"This looks like an issue with PyArrow. Did you try reinstalling it ?",
"@lhoestq I did, and then let pip handle the installation in `pip import datasets`. I also tried using conda but it gives the same error.\r\n\r\nEdit: pyarrow version on kaggle is 4.0.0, it gets replaced with 4.0.1. So, I don't think uninstal... | 2021-07-20T09:28:38 | 2021-07-21T13:59:26 | 2021-07-21T13:03:02 | NONE | null | null | null | ## Describe the bug
Not able to import datasets library in kaggle notebooks
## Steps to reproduce the bug
```python
!pip install datasets
import datasets
```
## Expected results
No such error
## Actual results
```
ImportError Traceback (most recent call last)
<ipython-input-9-652e886d387f> in <module>
----> 1 import datasets
/opt/conda/lib/python3.7/site-packages/datasets/__init__.py in <module>
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in <module>
36 import pandas as pd
37 import pyarrow as pa
---> 38 import pyarrow.compute as pc
39 from multiprocess import Pool, RLock
40 from tqdm.auto import tqdm
/opt/conda/lib/python3.7/site-packages/pyarrow/compute.py in <module>
16 # under the License.
17
---> 18 from pyarrow._compute import ( # noqa
19 Function,
20 FunctionOptions,
ImportError: /opt/conda/lib/python3.7/site-packages/pyarrow/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Kaggle
- Python version: 3.7.10
- PyArrow version: 4.0.1
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"Hi Thanks for reporting !\r\nIt looks like these files are not correctly reported in the list of expected files to download, let me fix that ;)",
"Alright this is fixed now. We'll do a new release soon to make the fix available.\r\n\r\nIn the meantime feel free to simply pass `ignore_verifications=True` to `load... | 2021-07-20T08:37:30 | 2021-07-20T14:41:31 | 2021-07-20T14:38:10 | NONE | null | null | null | Hi,
I am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive:
`datasets.load_dataset('c4', 'en')`
Is this a bug or do I have some configurations missing on my server?
Thanks!
<img width="1014" alt="Снимок экрана 2021-07-20 в 11 37 17" src="https://user-images.githubusercontent.com/36672861/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png"> | {
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https://api.github.com/repos/huggingface/datasets/issues/2676 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2676/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2676/comments | https://api.github.com/repos/huggingface/datasets/issues/2676/events | https://github.com/huggingface/datasets/pull/2676 | 947,734,909 | MDExOlB1bGxSZXF1ZXN0NjkyNjc2NTg5 | 2,676 | Increase json reader block_size automatically | {
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} | Currently some files can't be read with the default parameters of the JSON lines reader.
For example this one:
https://huggingface.co/datasets/thomwolf/codeparrot/resolve/main/file-000000000006.json.gz
raises a pyarrow error:
```python
ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)
```
The block size that is used is the default one by pyarrow (related to this [jira issue](https://issues.apache.org/jira/browse/ARROW-9612)).
To fix this issue I changed the block_size to increase automatically if there is a straddling issue when parsing a batch of json lines.
By default the value is `chunksize // 32` in order to leverage multithreading, and it doubles every time a straddling issue occurs. The block_size is then reset for each file.
cc @thomwolf @albertvillanova | {
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} | Since https://github.com/huggingface/datasets/pull/2628 we use the ETag or the remote data files to compute the directory in the cache where a dataset is saved. This is useful in order to reload the dataset from the cache only if the remote files haven't changed.
In this I made the ETag requests parallel using multithreading. There is also a tqdm progress bar that shows up if there are more than 16 data files. | {
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} | DONE:
- Fix parameter name: `smooth` to `smooth_method`.
- Improve kwargs description.
- Align docs on using a metric.
- Add example of passing additional arguments in using metrics.
Related to #2669. | {
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https://api.github.com/repos/huggingface/datasets/issues/2673 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2673/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2673/comments | https://api.github.com/repos/huggingface/datasets/issues/2673/events | https://github.com/huggingface/datasets/pull/2673 | 947,300,008 | MDExOlB1bGxSZXF1ZXN0NjkyMzAxMTgw | 2,673 | Fix potential DuplicatedKeysError in SQuAD | {
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} | DONE:
- Fix potential DiplicatedKeysError by ensuring keys are unique.
- Align examples in the docs with SQuAD code.
We should promote as a good practice, that the keys should be programmatically generated as unique, instead of read from data (which might be not unique). | {
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} | DONE:
- Fix unnecessary path join.
- Fix potential DiplicatedKeysError by ensuring keys are unique.
We should promote as a good practice, that the keys should be programmatically generated as unique, instead of read from data (which might be not unique). | {
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"It'll be new pull request with new commits."
] | 2021-07-19T05:14:38 | 2021-07-19T07:32:28 | 2021-07-19T06:45:50 | NONE | null | false | {
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} | https://zenodo.org/search?page=1&size=20&q=mesinesp, Mesinesp has Medical Semantic Indexed records in Spanish. Indexing is done using DeCS codes, a sort of Spanish equivalent to MeSH terms.
The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) development set has a total of 750 records.
The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) training set has a total of 369,368 records.
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https://api.github.com/repos/huggingface/datasets/issues/2670 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2670/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2670/comments | https://api.github.com/repos/huggingface/datasets/issues/2670/events | https://github.com/huggingface/datasets/issues/2670 | 947,120,709 | MDU6SXNzdWU5NDcxMjA3MDk= | 2,670 | Using sharding to parallelize indexing | {
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] | open | false | null | [] | [] | 2021-07-18T21:26:26 | 2021-10-07T13:33:25 | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
Creating an elasticsearch index on large dataset could be quite long and cannot be parallelized on shard (the index creation is colliding)
**Describe the solution you'd like**
When working on dataset shards, if an index already exists, its mapping should be checked and if compatible, the indexing process should continue with the shard data.
Additionally, at the end of the process, the `_indexes` dict should be send back to the original dataset object (from which the shards have been created) to allow to use the index for later filtering on the whole dataset.
**Describe alternatives you've considered**
Each dataset shard could created independent partial indices. then on the whole dataset level, indices should be all referred in `_indexes` dict and be used in querying through `get_nearest_examples()`. The drawback is that the scores will be computed independently on the partial indices leading to inconsistent values for most scoring based on corpus level statistics (tf/idf, BM25).
**Additional context**
The objectives is to parallelize the index creation to speed-up the process (ie surcharging the ES server which is fine to handle large load) while later enabling search on the whole dataset. | {
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"Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst, note that `\"min\"` is not an allowed value for `average`. According to scikit-learn [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html), `average` can only take the values: `{\"micro\", \"macro\", \"samples\", \"weigh... | 2021-07-18T08:32:31 | 2021-07-18T18:36:05 | 2021-07-18T11:19:04 | CONTRIBUTOR | null | null | null | ## Describe the bug
When I want to use F1 score with average="min", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) throws an error telling me so.
## Steps to reproduce the bug
```python
import datasets
f1 = datasets.load_metric("f1", keep_in_memory=True, average="min")
f1.add_batch(predictions=[0,2,3], references=[1, 2, 3])
f1.compute()
```
## Expected results
No error, because `average="min"` should be passed correctly to f1_score in sklearn.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\datasets\metric.py", line 402, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "C:\Users\bramv\.cache\huggingface\modules\datasets_modules\metrics\f1\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\f1.py", line 97, in _compute
"f1": f1_score(
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1071, in f1_score
return fbeta_score(y_true, y_pred, beta=1, labels=labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1195, in fbeta_score
_, _, f, _ = precision_recall_fscore_support(y_true, y_pred,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1464, in precision_recall_fscore_support
labels = _check_set_wise_labels(y_true, y_pred, average, labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1294, in _check_set_wise_labels
raise ValueError("Target is %s but average='binary'. Please "
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1 | {
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"Added the missing label classes and their explanations (to the best of my understanding)",
"Thanks a lot ! Once the last comment about the label names is addressed we can merge :)"
] | 2021-07-17T17:41:28 | 2021-07-29T11:50:31 | 2021-07-29T11:50:31 | CONTRIBUTOR | null | false | {
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} | Hi,
This adds the [Russian SuperGLUE](https://russiansuperglue.com/) dataset. For the most part I reused the code for the original SuperGLUE, although there are some relatively minor differences in the structure that I accounted for. | {
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"The current CI failure is due to modifications in the dataset script.",
"Merging since the CI is only failing because of dataset card issues, which is unrelated to this PR"
] | 2021-07-17T17:06:35 | 2021-07-19T17:39:10 | 2021-07-19T17:32:00 | CONTRIBUTOR | null | false | {
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} | This PR replaces `tqdm` from the `tqdm` lib with `tqdm` from `datasets.utils.tqdm_utils`. With this change, it's possible to disable progress bars just by calling `disable_progress_bar`. Note this doesn't work on Windows when using multiprocessing due to how global variables are shared between processes. Currently, there is no easy way to disable progress bars in a multiprocess setting on Windows (patching logging with `datasets.utils.logging.get_verbosity = lambda: datasets.utils.logging.NOTSET` doesn't seem to work as well), so adding support for this is a future goal. Additionally, this PR adds a unit ("ba" for batches) to the bar printed by `Dataset.to_json` (this change is motivated by https://github.com/huggingface/datasets/issues/2657). | {
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"Thanks for your contribution, @arampacha. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if... | 2021-07-17T13:32:04 | 2023-07-26T23:06:01 | 2022-10-03T09:37:35 | NONE | null | true | {
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https://the-eye.eu/public/AI/training_data/code_clippy_data/ | {
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"Hi @stas00, \r\nI want to work on this issue and I was thinking why don't we use `imap` [in this loop](https://github.com/huggingface/datasets/blob/440b14d0dd428ae1b25881aa72ba7bbb8ad9ff84/src/datasets/io/json.py#L99)? This way, using offset (which is being used to slice the pyarrow table) we can convert pyarrow ... | 2021-07-16T19:41:50 | 2021-09-13T13:56:37 | 2021-09-13T13:56:37 | CONTRIBUTOR | null | null | null | As discussed on slack it appears that `to_json` is quite slow on huge datasets like OSCAR.
I implemented sharded saving, which is much much faster - but the tqdm bars all overwrite each other, so it's hard to make sense of the progress, so if possible ideally this multi-proc support could be implemented internally in `to_json` via `num_proc` argument. I guess `num_proc` will be the number of shards?
I think the user will need to use this feature wisely, since too many processes writing to say normal style HD is likely to be slower than one process.
I'm not sure whether the user should be responsible to concatenate the shards at the end or `datasets`, either way works for my needs.
The code I was using:
```
from multiprocessing import cpu_count, Process, Queue
[...]
filtered_dataset = concat_dataset.map(filter_short_documents, batched=True, batch_size=256, num_proc=cpu_count())
DATASET_NAME = "oscar"
SHARDS = 10
def process_shard(idx):
print(f"Sharding {idx}")
ds_shard = filtered_dataset.shard(SHARDS, idx, contiguous=True)
# ds_shard = ds_shard.shuffle() # remove contiguous=True above if shuffling
print(f"Saving {DATASET_NAME}-{idx}.jsonl")
ds_shard.to_json(f"{DATASET_NAME}-{idx}.jsonl", orient="records", lines=True, force_ascii=False)
queue = Queue()
processes = [Process(target=process_shard, args=(idx,)) for idx in range(SHARDS)]
for p in processes:
p.start()
for p in processes:
p.join()
```
Thank you!
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https://api.github.com/repos/huggingface/datasets/issues/2662 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2662/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2662/comments | https://api.github.com/repos/huggingface/datasets/issues/2662/events | https://github.com/huggingface/datasets/pull/2662 | 946,470,815 | MDExOlB1bGxSZXF1ZXN0NjkxNjM5MjU5 | 2,662 | Load Dataset from the Hub (NO DATASET SCRIPT) | {
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"This is ready for review now :)\r\n\r\nI would love to have some feedback on the changes in load.py @albertvillanova. There are many changes so if you have questions let me know, especially on the `resolve_data_files` functions and on the changes in `prepare_module`.\r\n\r\nAnd @thomwolf if you want to take a look... | 2021-07-16T17:21:58 | 2021-08-25T14:53:01 | 2021-08-25T14:18:08 | MEMBER | null | false | {
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} | ## Load the data from any Dataset repository on the Hub
This PR adds support for loading datasets from any dataset repository on the hub, without requiring any dataset script.
As a user it's now possible to create a repo and upload some csv/json/text/parquet files, and then be able to load the data in one line. Here is an example with the `allenai/c4` repository that contains a lot of compressed json lines files:
```python
from datasets import load_dataset
data_files = {"train": "en/c4-train.*.json.gz"}
c4 = load_dataset("allenai/c4", data_files=data_files, split="train", streaming=True)
print(c4.n_shards)
# 1024
print(next(iter(c4)))
# {'text': 'Beginners BBQ Class Takin...'}
```
By default it loads all the files, but as shown in the example you can choose the ones you want with unix style patterns.
Of course it's still possible to use dataset scripts since they offer the most flexibility.
## Implementation details
It uses `huggingface_hub` to list the files in a dataset repository.
If you provide a path to a local directory instead of a repository name, it works the same way but it uses `glob`.
Depending on the data files available, or passed in the `data_files` parameter, one of the available builders will be used among the csv, json, text and parquet builders.
Because of this, it's not possible to load both csv and json files at once. In this case you have to load them separately and then concatenate the two datasets for example.
## TODO
- [x] tests
- [x] docs
- [x] when huggingface_hub gets a new release, update the CI and the setup.py
Close https://github.com/huggingface/datasets/issues/2629 | {
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"I make a summary about our discussion with @lewtun and @Narsil on the agreed schema for this dataset and the additional steps required to generate the 2D array labels:\r\n- The labels for this dataset are a 2D array:\r\n Given an example:\r\n ```python\r\n {\"record_id\": record_id, \"file\": file, \"start\": s... | 2021-07-16T16:43:21 | 2021-08-04T17:03:53 | 2021-08-04T17:03:53 | MEMBER | null | false | {
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} | Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization).
TODO:
- [x] Generate the LibriMix corpus
- [x] Prepare the corpus for diarization
- [x] Upload these files to the superb-data repo
- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script
- [x] README: tags + description sections
- ~~Add DER metric~~ (we leave the DER metric for a follow-up PR)
Related to #2619.
Close #2653.
cc: @lewtun | {
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https://api.github.com/repos/huggingface/datasets/issues/2660 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2660/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2660/comments | https://api.github.com/repos/huggingface/datasets/issues/2660/events | https://github.com/huggingface/datasets/pull/2660 | 946,316,180 | MDExOlB1bGxSZXF1ZXN0NjkxNTA4NzE0 | 2,660 | Move checks from _map_single to map | {
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"@lhoestq This one has been open for a while. Could you please take a look?",
"@lhoestq Ready for the final review!",
"I forgot to update the signature of `DatasetDict.map`, so did that now."
] | 2021-07-16T13:53:33 | 2021-09-06T14:12:23 | 2021-09-06T14:12:23 | CONTRIBUTOR | null | false | {
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} | The goal of this PR is to remove duplicated checks in the `map` logic to execute them only once whenever possible (`fn_kwargs`, `input_columns`, ...). Additionally, this PR improves the consistency (to align it with `input_columns`) of the `remove_columns` check by adding support for a single string value, which is then wrapped into a list. | {
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https://api.github.com/repos/huggingface/datasets/issues/2659 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2659/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2659/comments | https://api.github.com/repos/huggingface/datasets/issues/2659/events | https://github.com/huggingface/datasets/pull/2659 | 946,155,407 | MDExOlB1bGxSZXF1ZXN0NjkxMzcwNzU3 | 2,659 | Allow dataset config kwargs to be None | {
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} | Close https://github.com/huggingface/datasets/issues/2658
The dataset config kwargs that were set to None we simply ignored.
This was an issue when None has some meaning for certain parameters of certain builders, like the `sep` parameter of the "csv" builder that allows to infer to separator.
cc @SBrandeis | {
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Related to https://github.com/huggingface/datasets/pull/2656
cc @SBrandeis | {
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] | open | false | null | [] | [] | 2021-07-15T23:32:18 | 2021-07-15T23:33:53 | null | CONTRIBUTOR | null | null | null | While using `to_json` 2 things came to mind that would have made the experience easier on the user:
1. Could we have a `desc` arg for the tqdm use and a fallback to just `to_json` so that it'd be clear to the user what's happening? Surely, one can just print the description before calling json, but I thought perhaps it'd help to have it self-identify like you did for other progress bars recently.
2. It took me a while to make sense of the reported numbers:
```
22%|██▏ | 1536/7076 [12:30:57<44:09:42, 28.70s/it]
```
So iteration here happens to be 10K samples, and the total is 70M records. But the user does't know that, so the progress bar is perfect, but the numbers it reports are meaningless until one discovers that 1it=10K samples. And one still has to convert these in the head - so it's not quick. Not exactly sure what's the best way to approach this, perhaps it can be part of `desc`? or report M or K, so it'd be built-in if it were to print, e.g.:
```
22%|██▏ | 15360K/70760K [12:30:57<44:09:42, 28.70s/it]
```
or
```
22%|██▏ | 15.36M/70.76M [12:30:57<44:09:42, 28.70s/it]
```
(while of course remaining friendly to small datasets)
I forget if tqdm lets you add a magnitude identifier to the running count.
Thank you! | {
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"This is not the default in pandas right ?\r\nWe try to align our CSV loader with the pandas API.\r\n\r\nMoreover according to their documentation, the python parser is used when sep is None, which might not be the fastest one.\r\n\r\nMaybe users could just specify `sep=None` themselves ?\r\nIn this case we should ... | 2021-07-15T14:09:06 | 2023-09-24T09:56:44 | 2021-07-16T10:23:26 | CONTRIBUTOR | null | false | {
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This PR allows users to use this pandas's feature by passing `sep=None` to `Dataset.from_csv`:
```python
Dataset.from_csv(
...,
sep=None
)
``` | {
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"Hi! I was looking into this and hope you can clarify a point. Your my_dataset variable would be of type DatasetDict which means the alternative you've described (dict comprehension) is what makes sense. \r\nIs there a reason why you wouldn't want to convert my_dataset to a pandas df if you'd like to use it like on... | 2021-07-15T13:30:45 | 2021-07-23T15:40:57 | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
Similar to pandas, it would be great if we could select multiple columns at once.
**Describe the solution you'd like**
```python
my_dataset = ... # Has columns ['idx', 'sentence', 'label']
idx, label = my_dataset[['idx', 'label']]
```
**Describe alternatives you've considered**
we can do `[dataset[col] for col in ('idx', 'label')]`
**Additional context**
This is of course very minor.
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"#self-assign",
"I understand it already raises a `NotImplementedError` exception, eg:\r\n\r\n```\r\n>>> dataset = load_dataset(\"journalists_questions\", name=\"plain_text\", split=\"train\", streaming=True)\r\n\r\n[...]\r\nNotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=d... | 2021-07-15T09:07:27 | 2021-08-02T11:03:21 | null | MEMBER | null | null | null | **Is your feature request related to a problem? Please describe.**
I would love to know if a `dataset` is with the current implementation streamable or not.
**Describe the solution you'd like**
We could show a warning when a dataset is loaded with `load_dataset('...',streaming=True)` when its lot streamable, e.g. if it is an archive.
**Describe alternatives you've considered**
Add a new metadata tag for "streaming"
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"Note that this subset requires us to:\r\n\r\n* generate the LibriMix corpus from LibriSpeech\r\n* prepare the corpus for diarization\r\n\r\nAs suggested by @lhoestq we should perform these steps locally and add the prepared data to this public repo on the Hub: https://huggingface.co/datasets/superb/superb-data\r\n... | 2021-07-15T07:51:40 | 2021-08-04T17:03:52 | 2021-08-04T17:03:52 | MEMBER | null | null | null | Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization).
Steps:
- [x] Generate the LibriMix corpus
- [x] Prepare the corpus for diarization
- [x] Upload these files to the superb-data repo
- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script
- [ ] README: tags + description sections
Related to #2619.
cc: @lewtun
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} | Remove "no tqdm bars" from the docstring in the logging module to align it with the changes introduced in #2534. | {
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"Hi,\r\n\r\nyou can suppress progress bars by patching logging as follows:\r\n```python\r\nimport datasets\r\nimport logging\r\ndatasets.logging.get_verbosity = lambda: logging.NOTSET\r\n# map call ...\r\n```\r\nEDIT: now you have to use `disable_progress_bar `",
"Thank you, it worked :)",
"See https://github.c... | 2021-07-14T21:06:51 | 2022-07-08T14:51:57 | 2021-07-15T03:41:35 | NONE | null | null | null | ## Describe the bug
I would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well).
According to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0.
I also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work.
## Steps to reproduce the bug
```python
import datasets
from datasets.utils.logging import set_verbosity_error
set_verbosity_error()
def dummy_map(batch):
return batch
common_voice_train = datasets.load_dataset("common_voice", "de", split="train")
common_voice_test = datasets.load_dataset("common_voice", "de", split="test")
common_voice_train.map(dummy_map)
```
## Expected results
- The progress bar for `.map` call won't be shown
## Actual results
- The progress bar for `.map` is still shown
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.5
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2650 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2650/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2650/comments | https://api.github.com/repos/huggingface/datasets/issues/2650/events | https://github.com/huggingface/datasets/issues/2650 | 944,672,565 | MDU6SXNzdWU5NDQ2NzI1NjU= | 2,650 | [load_dataset] shard and parallelize the process | {
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"I need the same feature for distributed training",
"I think @TevenLeScao is exploring adding multiprocessing in `GeneratorBasedBuilder._prepare_split` - feel free to post updates here :)",
"Posted a PR to address the building side, still needs something to load sharded arrow files + tests",
"Closing as this ... | 2021-07-14T18:04:58 | 2023-11-28T19:11:41 | 2023-11-28T19:11:40 | CONTRIBUTOR | null | null | null | - Some huge datasets take forever to build the first time. (e.g. oscar/en) as it's done in a single cpu core.
- If the build crashes, everything done up to that point gets lost
Request: Shard the build over multiple arrow files, which would enable:
- much faster build by parallelizing the build process
- if the process crashed, the completed arrow files don't need to be re-built again
Thank you!
@lhoestq | {
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"Is this done now? I see progress bars when using `load_dataset`.",
"There are progress bars when downloading data and when preparing them as Arrow files.\r\n\r\nThe \"total silence\" part mentioned in OP refer to checksums verifications which have had some changes in the latest release 2.10:\r\n- they're disable... | 2021-07-14T17:34:39 | 2023-03-27T10:32:49 | null | CONTRIBUTOR | null | null | null | Please consider:
```
Downloading and preparing dataset oscar/unshuffled_deduplicated_en (download: 462.40 GiB, generated: 1.18 TiB, post-processed: Unknown size, total: 1.63 TiB) to cache/oscar/unshuffled_deduplicated_en/1.0.0/84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2...
HF google storage unreachable. Downloading and preparing it from source
```
and no indication whatsoever of whether things work well or when it'll be done. It's important to have an estimated completion time for when doing slurm jobs since some instances have a cap on run-time.
I think for this particular job it sat for 30min in total silence and then after 30min it started generating:
```
897850 examples [07:24, 10286.71 examples/s]
```
which is already great!
Request:
1. ETA - knowing how many hours to allocate for a slurm job
2. progress bar - helps to know things are working and aren't stuck and where we are at.
Thank you!
@lhoestq
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"#take"
] | 2021-07-14T14:24:36 | 2021-07-14T14:26:12 | null | CONTRIBUTOR | null | null | null | ## Describe:
For getting simple sentences from complex sentence there are dataset and task like wiki_split that is available in hugging face datasets. This web_split is a very similar dataset. There some research paper which states that by combining these two datasets we if we train the model it will yield better results on both tests data.
This dataset is made from web NLG data.
All the dataset related details are provided in the below repository
Github link: https://github.com/shashiongithub/Split-and-Rephrase
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https://api.github.com/repos/huggingface/datasets/issues/2647 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2647/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2647/comments | https://api.github.com/repos/huggingface/datasets/issues/2647/events | https://github.com/huggingface/datasets/pull/2647 | 944,424,941 | MDExOlB1bGxSZXF1ZXN0Njg5OTExMzky | 2,647 | Fix anchor in README | {
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} | I forgot to push this fix in #2611, so I'm sending it now. | {
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"Hi ! I just tested and it worked fine today for me.\r\n\r\nI think this is because the dataset is stored on Google Drive which has a quota limit for the number of downloads per day, see this similar issue https://github.com/huggingface/datasets/issues/996 \r\n\r\nFeel free to try again today, now that the quota wa... | 2021-07-14T12:31:05 | 2022-08-04T08:28:24 | 2022-08-04T08:28:24 | NONE | null | null | null | ## Describe the bug
I get an error datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files when I try to download the yahoo_answers_topics dataset
## Steps to reproduce the bug
self.dataset = load_dataset(
'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]')
# Sample code to reproduce the bug
self.dataset = load_dataset(
'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]')
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files
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https://api.github.com/repos/huggingface/datasets/issues/2645 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2645/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2645/comments | https://api.github.com/repos/huggingface/datasets/issues/2645/events | https://github.com/huggingface/datasets/issues/2645 | 944,374,284 | MDU6SXNzdWU5NDQzNzQyODQ= | 2,645 | load_dataset processing failed with OS error after downloading a dataset | {
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"Hi ! It looks like an issue with pytorch.\r\n\r\nCould you try to run `import torch` and see if it raises an error ?",
"> Hi ! It looks like an issue with pytorch.\r\n> \r\n> Could you try to run `import torch` and see if it raises an error ?\r\n\r\nIt works. Thank you!"
] | 2021-07-14T12:23:53 | 2021-07-15T09:34:02 | 2021-07-15T09:34:02 | NONE | null | null | null | ## Describe the bug
After downloading a dataset like opus100, there is a bug that
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Steps to reproduce the bug
```python
from datasets import load_dataset
this_dataset = load_dataset('opus100', 'af-en')
```
## Expected results
there is no error when running load_dataset.
## Actual results
Specify the actual results or traceback.
Traceback (most recent call last):
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prep
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 989, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 952, in encode_example
example = cast_to_python_objects(example)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 219, in cast_to_python_ob
return _cast_to_python_objects(obj)[0]
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 165, in _cast_to_python_o
import torch
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 188, in <module>
_load_global_deps()
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 141, in _load_global_deps
ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
File "/home/anaconda3/lib/python3.6/ctypes/__init__.py", line 348, in __init__
self._handle = _dlopen(self._name, mode)
OSError: dlopen: cannot load any more object with static TLS
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "download_hub_opus100.py", line 9, in <module>
this_dataset = load_dataset('opus100', language_pair)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepa
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 658, in _download_and_prep
+ str(e)
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid
- Python version: 3.6.6
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2644 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2644/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2644/comments | https://api.github.com/repos/huggingface/datasets/issues/2644/events | https://github.com/huggingface/datasets/issues/2644 | 944,254,748 | MDU6SXNzdWU5NDQyNTQ3NDg= | 2,644 | Batched `map` not allowed to return 0 items | {
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"Hi ! Thanks for reporting. Indeed it looks like type inference makes it fail. We should probably just ignore this step until a non-empty batch is passed.",
"Sounds good! Do you want me to propose a PR? I'm quite busy right now, but if it's not too urgent I could take a look next week.",
"Sure if you're interes... | 2021-07-14T09:58:19 | 2021-07-26T14:55:15 | 2021-07-26T14:55:15 | MEMBER | null | null | null | ## Describe the bug
I'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.
However, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L100), but no elements were returned in this case.
For this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.
## Steps to reproduce the bug
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
# The following line simulates no matches found, so we return an empty batch
result = {'key': []}
return result
filtered_dataset = dataset.map(
select_rows,
remove_columns = dataset.column_names,
batched = True,
num_proc = 1,
desc = "Selecting rows with images that exist"
)
```
The code above immediately triggers the exception. If we use the following instead:
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
result = {'key': []} # or defaultdict or whatever
# code to check for condition and append elements to result
# some_items_found will be set to True if there were any matching elements in the batch
return result if some_items_found else {}
```
Then it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.
In my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.
## Expected results
The dataset would be filtered and only the matching fields would be returned.
## Actual results
An exception is encountered, as described. Using a workaround makes it fail further along the line.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2643 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2643/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2643/comments | https://api.github.com/repos/huggingface/datasets/issues/2643/events | https://github.com/huggingface/datasets/issues/2643 | 944,220,273 | MDU6SXNzdWU5NDQyMjAyNzM= | 2,643 | Enum used in map functions will raise a RecursionError with dill. | {
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"I'm running into this as well. (Thank you so much for reporting @jorgeecardona — was staring at this massive stack trace and unsure what exactly was wrong!)",
"Hi ! Thanks for reporting :)\r\n\r\nUntil this is fixed on `dill`'s side, we could implement a custom saving in our Pickler indefined in utils.py_utils.p... | 2021-07-14T09:16:08 | 2021-11-02T09:51:11 | null | NONE | null | null | null | ## Describe the bug
Enums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https://github.com/uqfoundation/dill/issues/250#issuecomment-852566284
In my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from enum import Enum
class A(Enum):
a = 'a'
def main():
a = A.a
def f(x):
return {} if a == a.a else x
ds = load_dataset('cnn_dailymail', '3.0.0')['test']
ds = ds.map(f, num_proc=15)
if __name__ == "__main__":
main()
```
## Expected results
The known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood.
## Actual results
```python
File "/home/xxxx/miniconda3/lib/python3.8/site-packages/dill/_dill.py", line 1373, in save_type
pickler.save_reduce(_create_type, (type(obj), obj.__name__,
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 690, in save_reduce
save(args)
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 899, in save_tuple
save(element)
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 534, in save
self.framer.commit_frame()
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 220, in commit_frame
if f.tell() >= self._FRAME_SIZE_TARGET or force:
RecursionError: maximum recursion depth exceeded while calling a Python object
```
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2642 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2642/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2642/comments | https://api.github.com/repos/huggingface/datasets/issues/2642/events | https://github.com/huggingface/datasets/issues/2642 | 944,175,697 | MDU6SXNzdWU5NDQxNzU2OTc= | 2,642 | Support multi-worker with streaming dataset (IterableDataset). | {
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"Hi ! This is a great idea :)\r\nI think we could have something similar to what we have in `datasets.Dataset.map`, i.e. a `num_proc` parameter that tells how many processes to spawn to parallelize the data processing. \r\n\r\nRegarding AUTOTUNE, this could be a nice feature as well, we could see how to add it in a... | 2021-07-14T08:22:58 | 2021-07-15T09:37:34 | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
The current `.map` does not support multi-process, CPU can become bottleneck if the pre-processing is complex (e.g. t5 span masking).
**Describe the solution you'd like**
Ideally `.map` should support multi-worker like tfds, with `AUTOTUNE`.
**Describe alternatives you've considered**
A simpler solution is to shard the dataset and process it in parallel with pytorch dataloader. The shard does not need to be of equal size.
* https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset
**Additional context**
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https://api.github.com/repos/huggingface/datasets/issues/2641 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2641/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2641/comments | https://api.github.com/repos/huggingface/datasets/issues/2641/events | https://github.com/huggingface/datasets/issues/2641 | 943,838,085 | MDU6SXNzdWU5NDM4MzgwODU= | 2,641 | load_dataset("financial_phrasebank") NonMatchingChecksumError | {
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"message",
"documentation_url"
] | 2021-07-13T21:21:49 | 2022-08-04T08:30:08 | 2022-08-04T08:30:08 | NONE | null | null | null | ## Describe the bug
Attempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("financial_phrasebank", 'sentences_allagree')
```
## Expected results
I expect to see the financial_phrasebank dataset downloaded successfully
## Actual results
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip']
## Environment info
- `datasets` version: 1.9.0
- Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6
- Python version: 3.7.10
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2640 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2640/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2640/comments | https://api.github.com/repos/huggingface/datasets/issues/2640/events | https://github.com/huggingface/datasets/pull/2640 | 943,591,055 | MDExOlB1bGxSZXF1ZXN0Njg5MjAxMDkw | 2,640 | Fix docstrings | {
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https://api.github.com/repos/huggingface/datasets/issues/2639 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2639/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2639/comments | https://api.github.com/repos/huggingface/datasets/issues/2639/events | https://github.com/huggingface/datasets/pull/2639 | 943,527,463 | MDExOlB1bGxSZXF1ZXN0Njg5MTQ3NDE5 | 2,639 | Refactor patching to specific submodule | {
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} | Minor reorganization of the code, so that additional patching functions (not related to streaming) might be created.
In relation with the initial approach followed in #2631. | {
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https://api.github.com/repos/huggingface/datasets/issues/2638 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2638/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2638/comments | https://api.github.com/repos/huggingface/datasets/issues/2638/events | https://github.com/huggingface/datasets/pull/2638 | 943,484,913 | MDExOlB1bGxSZXF1ZXN0Njg5MTA5NTg1 | 2,638 | Streaming for the Json loader | {
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} | It was not using `open` in the builder. Therefore `pyarrow.json.read_json` was downloading the full file to start yielding rows.
Moreover, it appeared that `pyarrow.json.read_json` was not really suited for streaming as it was downloading too much data and failing if `block_size` was not properly configured (related to #2573).
So I switched to using `open` which is extended to support reading from remote file progressively, and I removed the pyarrow json reader which was not practical.
Instead, I'm using the classical `json.loads` from the standard library. | {
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} | It was not using open in the builder. Therefore pd.read_pickle could fail when streaming from a private repo for example.
Indeed, when streaming, open is extended to support reading from remote files and handles authentication to the HF Hub | {
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} | It was not using `open` in the builder. Therefore `pd.read_csv` was downloading the full file to start yielding rows.
Indeed, when streaming, `open` is extended to support reading from remote file progressively. | {
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https://api.github.com/repos/huggingface/datasets/issues/2634 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2634/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2634/comments | https://api.github.com/repos/huggingface/datasets/issues/2634/events | https://github.com/huggingface/datasets/pull/2634 | 942,805,621 | MDExOlB1bGxSZXF1ZXN0Njg4NDk2Mzc2 | 2,634 | Inject ASR template for lj_speech dataset | {
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} | Related to: #2565, #2633.
cc: @lewtun | {
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https://api.github.com/repos/huggingface/datasets/issues/2633 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2633/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2633/comments | https://api.github.com/repos/huggingface/datasets/issues/2633/events | https://github.com/huggingface/datasets/pull/2633 | 942,396,414 | MDExOlB1bGxSZXF1ZXN0Njg4MTMwOTA5 | 2,633 | Update ASR tags | {
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} | This PR updates the ASR tags of the 5 datasets added in #2565 following the change of task categories in #2620 | {
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https://api.github.com/repos/huggingface/datasets/issues/2632 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2632/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2632/comments | https://api.github.com/repos/huggingface/datasets/issues/2632/events | https://github.com/huggingface/datasets/pull/2632 | 942,293,727 | MDExOlB1bGxSZXF1ZXN0Njg4MDQyMjcw | 2,632 | add image-classification task template | {
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} | Snippet below is the tl;dr, but you can try it out directly here:
[](https://colab.research.google.com/gist/nateraw/005c025d41f0e48ae3d4ee61c0f20b70/image-classification-task-template-demo.ipynb)
```python
from datasets import load_dataset
ds = load_dataset('nateraw/image-folder', data_files='PetImages/')
# DatasetDict({
# train: Dataset({
# features: ['file', 'labels'],
# num_rows: 23410
# })
# })
ds = ds.prepare_for_task('image-classification')
# DatasetDict({
# train: Dataset({
# features: ['image_file_path', 'labels'],
# num_rows: 23410
# })
# })
``` | {
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} | Close #2481, close #2604, close #2591.
cc: @stas00, @thomwolf, @BirgerMoell | {
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] | 2021-07-12T14:07:13 | 2022-02-03T15:55:33 | 2022-02-03T15:55:33 | MEMBER | null | null | null | ## Describe the bug
The progress bars are not Jupyter widgets; regular progress bars appear (like in a terminal).
## Steps to reproduce the bug
```python
ds.map(tokenize, num_proc=10)
```
## Expected results
Jupyter widgets displaying the progress bars.
## Actual results
Simple plane progress bars.
cc: Reported by @thomwolf | {
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Moreover I would like to be able to specify which file goes into which split using the `data_files` argument.
This feature should be compatible with private repositories and dataset streaming.
This can be implemented by checking the extension of the files in the dataset repository and then by using the right dataset builder that is already packaged in the library (csv/json/text/parquet/etc.) | {
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Related to #2616. | {
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```
[2021-07-10 20:13:11,528][datasets.utils.filelock][INFO] - Lock 139705371374976 acquired on /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.lock
[2021-07-10 20:13:11,529][datasets.arrow_writer][INFO] - Done writing 32 examples in 6100 bytes /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.
[2021-07-10 20:13:11,531][datasets.arrow_dataset][INFO] - Set __getitem__(key) output type to python objects for no columns (when key is int or slice) and don't output other (un-formatted) columns.
[2021-07-10 20:13:11,543][/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric.py][INFO] - Removing /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow
```
As you can see, `datasets` logging come from different places.
`filelock`, `arrow_writer` & `arrow_dataset` comes from `datasets.*` which are expected
However, `metric.py` logging comes from `/conda/envs/myenv/lib/python3.8/site-packages/datasets/`
So when setting `datasets.utils.logging.set_verbosity_error()`, it still logs the last message which is annoying during evaluation.
I had to do
```
logging.getLogger("/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric").setLevel(logging.ERROR)
```
to fully mute these messages
## Expected results
it shouldn't log these messages when setting `datasets.utils.logging.set_verbosity_error()`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: tried both 1.8.0 & 1.9.0
- Platform: Ubuntu 18.04.5 LTS
- Python version: 3.8.10
- PyArrow version: 3.0.0
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] | 2021-07-10T14:51:50 | 2021-07-14T14:28:13 | 2021-07-12T22:34:31 | CONTRIBUTOR | null | false | {
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} | Fixes: #2606
This pull request adds combine metrics for the wikisplit or English sentence split task
Reviewer: @patrickvonplaten | {
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https://api.github.com/repos/huggingface/datasets/issues/2622 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2622/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2622/comments | https://api.github.com/repos/huggingface/datasets/issues/2622/events | https://github.com/huggingface/datasets/issues/2622 | 941,127,785 | MDU6SXNzdWU5NDExMjc3ODU= | 2,622 | Integration with AugLy | {
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] | 2021-07-10T00:03:09 | 2023-07-20T13:18:48 | 2023-07-20T13:18:47 | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
Facebook recently launched a library, [AugLy](https://github.com/facebookresearch/AugLy) , that has a unified API for augmentations for image, video and text.
It would be pretty exciting to have it hooked up to HF libraries so that we can make NLP models robust to misspellings or to punctuation, or emojis etc. Plus, with Transformers supporting more CV use cases, having augmentations support becomes crucial.
**Describe the solution you'd like**
The biggest difference between augmentations and preprocessing is that preprocessing happens only once, but you are running augmentations once per epoch. AugLy operates on text directly, so this breaks the typical workflow where we would run the tokenizer once, set format to pt tensors and be ready for the Dataloader.
**Describe alternatives you've considered**
One possible way of implementing these is to make a custom Dataset class where getitem(i) runs the augmentation and the tokenizer every time, though this would slow training down considerably given we wouldn't even run the tokenizer in batches.
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https://api.github.com/repos/huggingface/datasets/issues/2621 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2621/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2621/comments | https://api.github.com/repos/huggingface/datasets/issues/2621/events | https://github.com/huggingface/datasets/pull/2621 | 940,916,446 | MDExOlB1bGxSZXF1ZXN0Njg2OTE1Mzcw | 2,621 | Use prefix to allow exceed Windows MAX_PATH | {
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} | By using this prefix, you can exceed the Windows MAX_PATH limit.
See: https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces
Related to #2524, #2220. | {
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https://api.github.com/repos/huggingface/datasets/issues/2620 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2620/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2620/comments | https://api.github.com/repos/huggingface/datasets/issues/2620/events | https://github.com/huggingface/datasets/pull/2620 | 940,893,389 | MDExOlB1bGxSZXF1ZXN0Njg2ODk3MDky | 2,620 | Add speech processing tasks | {
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} | This PR replaces the `automatic-speech-recognition` task category with a broader `speech-processing` category.
The tasks associated with this category are derived from the [SUPERB benchmark](https://arxiv.org/abs/2105.01051), and ASR is included in this set. | {
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} | This PR starts building up the SUPERB benchmark by including the ASR task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/v0.2.0/downstream#asr-automatic-speech-recognition).
Usage:
```python
from datasets import load_dataset
asr = load_dataset("superb", "asr")
# DatasetDict({
# train: Dataset({
# features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
# num_rows: 28539
# })
# validation: Dataset({
# features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
# num_rows: 2703
# })
# test: Dataset({
# features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
# num_rows: 2620
# })
# })
```
I've used the GLUE benchmark as a guide for filling out the README.
To move fast during the evaluation PoC I propose to merge one task at a time, so we can continue building the training / evaluation framework in parallel.
Note: codewise this PR is ready for review - I'll add the missing YAML tags once #2620 is merged :) | {
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https://api.github.com/repos/huggingface/datasets/issues/2618 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2618/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2618/comments | https://api.github.com/repos/huggingface/datasets/issues/2618/events | https://github.com/huggingface/datasets/issues/2618 | 940,852,640 | MDU6SXNzdWU5NDA4NTI2NDA= | 2,618 | `filelock.py` Error | {
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"message",
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] | 2021-07-09T15:12:49 | 2023-11-23T19:06:20 | 2023-11-23T19:06:19 | NONE | null | null | null | ## Describe the bug
It seems that the `filelock.py` went error.
```
>>> ds=load_dataset('xsum')
^CTraceback (most recent call last):
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
OSError: [Errno 37] No locks available
```
According to error log, it is OSError, but there is an `except` in the `_acquire` function.
```
def _acquire(self):
open_mode = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
fd = os.open(self._lock_file, open_mode)
except (IOError, OSError):
pass
else:
self._lock_file_fd = fd
return None
```
I don't know why it stucked rather than `pass` directly.
I am not quite familiar with filelock operation, so any help is highly appriciated.
## Steps to reproduce the bug
```python
ds = load_dataset('xsum')
```
## Expected results
A clear and concise description of the expected results.
## Actual results
```
>>> ds=load_dataset('xsum')
^CTraceback (most recent call last):
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
OSError: [Errno 37] No locks available
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 818, in load_dataset
use_auth_token=use_auth_token,
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 470, in prepare_module
with FileLock(lock_path):
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 323, in __enter__
self.acquire()
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 272, in acquire
self._acquire()
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
KeyboardInterrupt
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-135-generic-x86_64-with-debian-buster-sid
- Python version: 3.6.13
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2617 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2617/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2617/comments | https://api.github.com/repos/huggingface/datasets/issues/2617/events | https://github.com/huggingface/datasets/pull/2617 | 940,846,847 | MDExOlB1bGxSZXF1ZXN0Njg2ODU3NzQz | 2,617 | Fix missing EOL issue in to_json for old versions of pandas | {
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} | Some versions of pandas don't add an EOL at the end of the output of `to_json`.
Therefore users could end up having two samples in the same line
Close https://github.com/huggingface/datasets/issues/2615 | {
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"message",
"documentation_url"
] | 2021-07-09T14:07:38 | 2021-07-09T16:13:41 | 2021-07-09T16:13:41 | MEMBER | null | false | {
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} | Add support for (streaming) remote data files:
```python
data_files = f"https://huggingface.co/datasets/{repo_id}/resolve/main/{relative_file_path}"
ds = load_dataset("json", split="train", data_files=data_files, streaming=True)
```
cc: @thomwolf | {
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https://api.github.com/repos/huggingface/datasets/issues/2615 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2615/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2615/comments | https://api.github.com/repos/huggingface/datasets/issues/2615/events | https://github.com/huggingface/datasets/issues/2615 | 940,794,339 | MDU6SXNzdWU5NDA3OTQzMzk= | 2,615 | Jsonlines export error | {
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] | 2021-07-09T14:02:05 | 2021-07-09T15:29:07 | 2021-07-09T15:28:33 | CONTRIBUTOR | null | null | null | ## Describe the bug
When exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default
## Steps to reproduce the bug
This what I'm running:
in python:
```
from datasets import load_dataset
ptb = load_dataset("ptb_text_only")
ptb["train"].to_json("ptb.jsonl")
```
then out of python:
```
head -10000 ptb.jsonl
```
## Expected results
Properly separated lines
## Actual results
The last line is a concatenation of two lines
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyArrow version: 4.0.1 | {
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