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https://api.github.com/repos/huggingface/datasets/issues/3659 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3659/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3659/comments | https://api.github.com/repos/huggingface/datasets/issues/3659/events | https://github.com/huggingface/datasets/issues/3659 | 1,120,913,672 | I_kwDODunzps5Cz8kI | 3,659 | push_to_hub but preview not working | {
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"following_url"... | [] | 1,643,732,637,000 | 1,643,732,637,000 | null | NONE | null | ## Dataset viewer issue for '*happifyhealth/twitter_pnn*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/happifyhealth/twitter_pnn)*
I used
```
dataset.push_to_hub("happifyhealth/twitter_pnn")
```
but the preview is not working.
Am I the one who added this dataset ? Yes
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"following_url"... | [] | 1,643,731,076,000 | 1,643,731,076,000 | null | NONE | null | ## Dataset viewer issue for '*P3*'
**Link: https://huggingface.co/datasets/bigscience/P3**
```
Status code: 400
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
```
Am I the one who added this dataset ? No
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"I'm impatient to see if it has an impact on the number of valid datasets for the dataset viewer. For the record, today:\r\n\r\n<img width=\"660\" alt=\"Capture d’écran 2022-02-01 à 14 32 19\" src=\"https://user-images.githubusercontent.com/1676121/151977579-b5a239d9-6662-4aeb-bfd1-eef6b8249991.png\">\r\n"
] | 1,643,718,084,000 | 1,643,722,350,000 | null | CONTRIBUTOR | null | Currently, `get_dataset_split_names` doesn't extend a builder module to support streaming, even though it uses `StreamingDownloadManager` to download data. This PR fixes that.
To test the change, run the following:
```bash
pip install git+https://github.com/huggingface/datasets.git@fix-get_dataset_split_names-stre... | {
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"following_url"... | [] | 1,643,712,813,000 | 1,643,712,813,000 | null | NONE | null | ## Describe the bug
I get a checksum error when loading the `subjqa` dataset (used in the transformers book).
## Steps to reproduce the bug
```python
from datasets import load_dataset
subjqa = load_dataset("subjqa","electronics")
```
## Expected results
Loading the dataset
## Actual results
```
---... | {
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"following_url"... | [] | 1,643,654,747,000 | 1,643,654,747,000 | null | NONE | null | ## Describe the bug
Trying to use the `pubmed` dataset fails to reach / download the source files.
## Steps to reproduce the bug
```python
pubmed_train = datasets.load_dataset('pubmed', split='train')
```
## Expected results
Should begin downloading the pubmed dataset.
## Actual results
```
ConnectionEr... | {
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"following_url"... | [] | 1,643,649,763,000 | 1,643,723,173,000 | null | CONTRIBUTOR | null | This PR does the following:
* if `dataset_infos.json` exists for a dataset, uses `num_examples` to print the total number of examples that needs to be generated (in `builder.py`)
* fixes `tqdm` + multiprocessing in Jupyter Notebook/Colab (the issue stems from this commit in the `tqdm` repo: https://github.com/tqdm/tq... | {
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"following_url"... | [] | 1,643,621,707,000 | 1,643,621,707,000 | null | CONTRIBUTOR | null | ## Describe the bug
`to_json` in multiprocessing fashion sometimes deadlock, instead of raising exceptions. Temporary solution is to see that it deadlocks, and then reduce the number of processes or batch size in order to reduce the memory footprint.
As @lhoestq pointed out, this might be related to https://bugs.... | {
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"> all the tests pass, but I'm still not able to import the dataset\r\n\r\nSince it's not merged on `master` yet, you have to provide the path to your local `wiki_bio.py` to use it.\r\nIndeed the library downloads the dataset files from `master` if you have a dev installation of the library.\r\n\r\nI agree it would... | 1,643,560,134,000 | 1,643,640,648,000 | null | CONTRIBUTOR | null | Fixes #3580 and makes the wiki_bio dataset work again. I changed the link and some documentation, and all the tests pass. Thanks @lhoestq for uploading the dataset to the HuggingFace data bucket.
@lhoestq -- all the tests pass, but I'm still not able to import the dataset, as the old Google Drive link is cached some... | {
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"Hi @thomasw21, I remember suggesting `imap_unordered` to @lhoestq at that time to speed up `to_json` further but after trying `pool_imap` on multiple datasets (>9GB) , memory utilisation was almost constant and we decided to go ahead with that only. \r\n\r\n1. Did you try this without `gzip`? Because `gzip` featu... | 1,643,548,999,000 | 1,643,711,949,000 | null | CONTRIBUTOR | null | I'm using `to_json(..., num_proc=num_proc, compressiong='gzip')` with `num_proc>1`. I'm having an issue where things seem to deadlock at some point. Eventually I see OOM. I'm guessing it's an issue where one process starts to take a long time for a specific batch, and so other process keep accumulating their results in... | {
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"following_url"... | [] | 1,643,381,981,000 | 1,643,382,155,000 | null | MEMBER | null | ## Adding a Dataset
- **Name:** IGLUE
- **Description:** IGLUE brings together 4 vision-and-language tasks across 20 languages (Twitter [thread](https://twitter.com/ebugliarello/status/1487045497583976455?s=20&t=SB4LZGDhhkUW83ugcX_m5w))
- **Paper:** https://arxiv.org/abs/2201.11732
- **Data:** https://github.com/e-... | {
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"Sure, let's include this in today's release.",
"Cool ! The windows CI should be fixed on master now, feel free to merge :)"
] | 1,643,375,189,000 | 1,643,384,158,000 | null | CONTRIBUTOR | null | My initial idea was to avoid the `flatten_indices` call and reorder a new column instead, but in the end I decided to follow `concatenate_datasets` and use `flatten_indices` to avoid padding when `dataset._indices.num_rows != dataset._data.num_rows`.
Fix #3599 | {
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"Works smoothly with the `transformers.Trainer` class now, thank you!"
] | 1,643,304,062,000 | 1,643,387,669,000 | null | MEMBER | null | Streaming datasets that use `StreamingDownloadManager.iter_archive` and `StreamingDownloadManager.iter_files` had some issues. Indeed if you try to iterate over such dataset twice, then the second time it will be empty.
This is because the two methods above are generator functions. I fixed this by making them return... | {
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"following_url"... | [] | 1,643,303,861,000 | 1,643,387,668,000 | null | MEMBER | null | Hi ! When iterating over a streaming dataset once, it's not reset correctly because of some issues with `dl_manager.iter_archive` and `dl_manager.iter_files`. Indeed they are generator functions (so the iterator that is returned can be exhausted). They should be iterables instead, and be reset if we do a for loop again... | {
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https://api.github.com/repos/huggingface/datasets/issues/3644 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3644/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3644/comments | https://api.github.com/repos/huggingface/datasets/issues/3644/events | https://github.com/huggingface/datasets/issues/3644 | 1,116,519,670 | I_kwDODunzps5CjLz2 | 3,644 | Add a GROUP BY operator | {
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Using batch mapping, we can easily split examples. However, we lack an appropriate option for merging them back together by some key. Consider this example:
```python
# features:
# {
# "example_id": datasets.Value("int32"),
# "text": datas... | {
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"I fixed those two things, the two remaining failing checks seem to be due to some dependency missing in the tests."
] | 1,643,298,300,000 | 1,643,666,802,000 | null | CONTRIBUTOR | null | As discussed with @lhoestq in https://github.com/huggingface/datasets/issues/3549#issuecomment-1020176931_ this is the new pull request to fix the download location. | {
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"following_url"... | [] | 1,643,293,749,000 | 1,643,307,368,000 | null | CONTRIBUTOR | null | Fixes the NumPy RNG when `seed` is `None`.
The problem becomes obvious after reading the NumPy notes on RNG (returned by `np.random.get_state()`):
> The MT19937 state vector consists of a 624-element array of 32-bit unsigned integers plus a single integer value between 0 and 624 that indexes the current position wi... | {
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"Closed and moved to transformers."
] | 1,643,285,345,000 | 1,643,286,588,000 | null | NONE | null | We are training Vav2Vec using the run_speech_recognition_ctc_bnb.py-script.
This is working fine with Common Voice, however using our custom dataset and data loader at [NbAiLab/NPSC]( https://huggingface.co/datasets/NbAiLab/NPSC) it crashes after roughly 1 epoch with the following stack trace:
:\r\n```\r\n- ... | 1,643,253,543,000 | 1,643,340,038,000 | null | NONE | null | ## Describe the bug
AutoTokenizer hash value got change after datasets.map
## Steps to reproduce the bug
1. trash huggingface datasets cache
2. run the following code:
```python
from transformers import AutoTokenizer, BertTokenizer
from datasets import load_dataset
from datasets.fingerprint import Hasher
tok... | {
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"following_url"... | [] | 1,643,233,082,000 | 1,643,233,112,000 | null | MEMBER | null | ## Describe the bug
I am trying to load the [`GEM/RiSAWOZ` dataset](https://huggingface.co/datasets/GEM/RiSAWOZ) in `datasets` v1.18.1 and am running into a type error when casting the features. The strange thing is that I can load the dataset with v1.17.0. Note that the error is also present if I install from `master... | {
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"Thanks for adding this @mariosasko! It worked for me when running it with a local data file, however, when using the file on Google Drive I get the following error:\r\n```Python\r\nds = load_dataset(\"./ted_talks_iwslt\",\"eu_ca_2014\", streaming=True, split=\"train\", use_auth_token=True)\r\nnext(iter(ds))\r\n```... | 1,643,220,476,000 | 1,643,290,855,000 | null | CONTRIBUTOR | null | null | {
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"I'm not sure if this is expected behavior.\r\n\r\nAm I supposed to work with a copy of the dataset, i.e. `shuffled_dataset = data.shuffle(seed=None)`?\r\n\r\n```diff\r\nimport datasets\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"... | 1,643,209,988,000 | 1,643,307,367,000 | null | NONE | null | ## Describe the bug
Repeated attempts to `shuffle` a dataset without specifying a seed give the same results.
## Steps to reproduce the bug
```python
import datasets
# Some toy example
data = datasets.Dataset.from_dict(
{"feature": [1, 2, 3, 4, 5], "label": ["a", "b", "c", "d", "e"]}
)
# Doesn't work... | {
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I also added a fix that makes the script ignore the json, csv, text, parquet and pandas dataset builders.
cc @SBrandeis | {
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"following_url"... | [] | 1,643,204,137,000 | 1,643,204,137,000 | null | CONTRIBUTOR | null | ## Describe the bug
The dataset links are no longer valid for CC-100. It seems that the website which was keeping these files are no longer accessible and therefore this dataset became unusable.
Check out the dataset [homepage](http://data.statmt.org/cc-100/) which isn't accessible.
Also the URLs for dataset file ... | {
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I am trying to load a local CSV file with a separate file containing label names. It is successfully loaded for the first time, but when I try to load it again, there is a conflict between provided labels and the cached dataset info. Disabling caching globally and/or using `download_mode="force_red... | {
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