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https://api.github.com/repos/huggingface/datasets/issues/2666 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2666/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2666/comments | https://api.github.com/repos/huggingface/datasets/issues/2666/events | https://github.com/huggingface/datasets/pull/2666 | 946,825,140 | MDExOlB1bGxSZXF1ZXN0NjkxOTMzMDM1 | 2,666 | Adds CodeClippy dataset [WIP] | {
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https://the-eye.eu/public/AI/training_data/code_clippy_data/ | {
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https://api.github.com/repos/huggingface/datasets/issues/2665 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2665/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2665/comments | https://api.github.com/repos/huggingface/datasets/issues/2665/events | https://github.com/huggingface/datasets/pull/2665 | 946,822,036 | MDExOlB1bGxSZXF1ZXN0NjkxOTMwNjky | 2,665 | Adds APPS dataset to the hub [WIP] | {
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https://api.github.com/repos/huggingface/datasets/issues/2663 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2663/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2663/comments | https://api.github.com/repos/huggingface/datasets/issues/2663/events | https://github.com/huggingface/datasets/issues/2663 | 946,552,273 | MDU6SXNzdWU5NDY1NTIyNzM= | 2,663 | [`to_json`] add multi-proc sharding support | {
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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 ... | 1,626,464,510,000 | 1,631,541,397,000 | 1,631,541,397,000 | MEMBER | 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 i... | {
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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... | 1,626,456,118,000 | 1,629,903,181,000 | 1,629,901,088,000 | MEMBER | null | ## 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. H... | {
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https://api.github.com/repos/huggingface/datasets/issues/2661 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2661/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2661/comments | https://api.github.com/repos/huggingface/datasets/issues/2661/events | https://github.com/huggingface/datasets/pull/2661 | 946,446,967 | MDExOlB1bGxSZXF1ZXN0NjkxNjE5MzAz | 2,661 | Add SD task for SUPERB | {
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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... | 1,626,453,801,000 | 1,628,096,633,000 | 1,628,096,633,000 | MEMBER | 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).
TODO:
- [x] Generate the LibriMix corpus
- [x] Prepare the corpus for diarization
- [x] Upl... | {
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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."
] | 1,626,443,613,000 | 1,630,937,543,000 | 1,630,937,543,000 | CONTRIBUTOR | null | 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 the... | {
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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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"following_url"... | [] | 1,626,431,138,000 | 1,626,439,567,000 | 1,626,439,567,000 | MEMBER | null | 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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https://api.github.com/repos/huggingface/datasets/issues/2658 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2658/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2658/comments | https://api.github.com/repos/huggingface/datasets/issues/2658/events | https://github.com/huggingface/datasets/issues/2658 | 946,139,532 | MDU6SXNzdWU5NDYxMzk1MzI= | 2,658 | Can't pass `sep=None` to load_dataset("csv", ...) to infer the separator via pandas.read_csv | {
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Related to https://github.com/huggingface/datasets/pull/2656
cc @SBrandeis | {
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"following_url"... | [] | 1,626,391,938,000 | 1,626,392,033,000 | null | MEMBER | 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... | {
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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 ... | 1,626,358,146,000 | 1,626,431,006,000 | 1,626,431,006,000 | CONTRIBUTOR | null | Passing `sep=None` to pandas's `read_csv` lets pandas guess the CSV file's separator
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... | 1,626,355,845,000 | 1,627,054,857,000 | null | CONTRIBUTOR | 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']]
```
**... | {
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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... | 1,626,340,047,000 | 1,627,902,201,000 | null | MEMBER | 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.... | {
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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... | 1,626,335,500,000 | 1,628,096,632,000 | 1,628,096,632,000 | MEMBER | 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] Up... | {
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"following_url"... | [] | 1,626,304,798,000 | 1,626,608,466,000 | 1,626,343,051,000 | CONTRIBUTOR | null | 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```",
"Thank you, it worked :)",
"See https://github.com/huggingface/datasets/issues/2528 for reference",
... | 1,626,296,811,000 | 1,639,533,564,000 | 1,626,320,495,000 | NONE | 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_VERBOS... | {
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"I need the same feature for distributed training"
] | 1,626,285,898,000 | 1,635,177,238,000 | null | MEMBER | 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 p... | {
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https://api.github.com/repos/huggingface/datasets/issues/2649 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2649/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2649/comments | https://api.github.com/repos/huggingface/datasets/issues/2649/events | https://github.com/huggingface/datasets/issues/2649 | 944,651,229 | MDU6SXNzdWU5NDQ2NTEyMjk= | 2,649 | adding progress bar / ETA for `load_dataset` | {
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"following_url"... | [] | 1,626,284,079,000 | 1,626,284,280,000 | null | MEMBER | 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 unre... | {
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https://api.github.com/repos/huggingface/datasets/issues/2648 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2648/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2648/comments | https://api.github.com/repos/huggingface/datasets/issues/2648/events | https://github.com/huggingface/datasets/issues/2648 | 944,484,522 | MDU6SXNzdWU5NDQ0ODQ1MjI= | 2,648 | Add web_split dataset for Paraphase and Rephrase benchmark | {
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"#take"
] | 1,626,272,676,000 | 1,626,272,772,000 | null | CONTRIBUTOR | 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 resu... | {
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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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https://api.github.com/repos/huggingface/datasets/issues/2646 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2646/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2646/comments | https://api.github.com/repos/huggingface/datasets/issues/2646/events | https://github.com/huggingface/datasets/issues/2646 | 944,379,954 | MDU6SXNzdWU5NDQzNzk5NTQ= | 2,646 | downloading of yahoo_answers_topics dataset failed | {
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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... | 1,626,265,865,000 | 1,626,340,516,000 | null | NONE | 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... | {
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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!"
] | 1,626,265,433,000 | 1,626,341,642,000 | 1,626,341,642,000 | NONE | 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')
```
... | {
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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... | 1,626,256,699,000 | 1,627,311,315,000 | 1,627,311,315,000 | CONTRIBUTOR | 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... | {
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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... | 1,626,254,168,000 | 1,635,846,671,000 | null | NONE | 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` ... | {
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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... | 1,626,250,978,000 | 1,626,341,854,000 | null | CONTRIBUTOR | 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`.
**D... | {
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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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"Hi! It's probably because this dataset is stored on google drive and it has a per day quota limit. It should work if you retry, I was able to initiate the download.\r\n\r\nSimilar issue [here](https://github.com/huggingface/datasets/issues/2646)",
"Hi ! Loading the dataset works on my side as well.\r\nFeel free ... | 1,626,211,309,000 | 1,626,701,170,000 | null | NONE | 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 financi... | {
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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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"following_url"... | [] | 1,626,188,925,000 | 1,626,195,169,000 | 1,626,195,169,000 | MEMBER | null | 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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"A note is that I think we should add a few indicator of status (as mentioned by @stas00 in #2649), probably at the (1) downloading, (2) extracting and (3) reading steps. In particular when loading many very large files it's interesting to know a bit where we are in the process.",
"I tested locally, and the built... | 1,626,187,026,000 | 1,626,451,172,000 | 1,626,451,171,000 | MEMBER | null | 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... | {
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] | 1,626,178,971,000 | 1,632,729,425,000 | null | NONE | null | Hi,
I find the api in https://huggingface.co/metrics quite useful.
I am playing around with video/image captioning task, where CIDEr is a popular metric.
Do you plan to add this into the HF ```datasets``` library?
Thanks. | {
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Indeed, when streaming, open is extended to support reading from remote files and handles authentication to the HF Hub | {
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"following_url"... | [] | 1,626,167,338,000 | 1,626,189,578,000 | 1,626,189,577,000 | MEMBER | null | 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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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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"title... | [] | 1,626,119,911,000 | 1,626,155,126,000 | 1,626,155,113,000 | MEMBER | null | 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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"Awesome!",
"Thanks for adding a new task template - great work @nateraw 🚀 !"
] | 1,626,111,663,000 | 1,626,191,068,000 | 1,626,190,096,000 | CONTRIBUTOR | null | 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_datase... | {
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"Sure @stas00, it is still a draft pull request. :)",
"Yes, I noticed it after reviewing - my apologies.",
"The problem with this approach is that it also deletes the downloaded files (if they need not be extracted). 😟 ",
"> The problem with this approach is that it also deletes the downloaded files (if they... | 1,626,107,973,000 | 1,626,685,699,000 | 1,626,685,699,000 | MEMBER | null | Close #2481, close #2604, close #2591.
cc: @stas00, @thomwolf, @BirgerMoell | {
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https://api.github.com/repos/huggingface/datasets/issues/2630 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2630/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2630/comments | https://api.github.com/repos/huggingface/datasets/issues/2630/events | https://github.com/huggingface/datasets/issues/2630 | 942,102,956 | MDU6SXNzdWU5NDIxMDI5NTY= | 2,630 | Progress bars are not properly rendered in Jupyter notebook | {
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"To add my experience when trying to debug this issue:\r\n\r\nSeems like previously the workaround given [here](https://github.com/tqdm/tqdm/issues/485#issuecomment-473338308) worked around this issue. But with the latest version of jupyter/tqdm I still get terminal warnings that IPython tried to send a message fro... | 1,626,098,833,000 | 1,643,903,733,000 | 1,643,903,733,000 | MEMBER | 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... | {
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https://api.github.com/repos/huggingface/datasets/issues/2629 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2629/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2629/comments | https://api.github.com/repos/huggingface/datasets/issues/2629/events | https://github.com/huggingface/datasets/issues/2629 | 941,819,205 | MDU6SXNzdWU5NDE4MTkyMDU= | 2,629 | Load datasets from the Hub without requiring a dataset script | {
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"This is so cool, let us know if we can help with anything on the hub side (@Pierrci @elishowk) 🎉 "
] | 1,626,079,517,000 | 1,629,901,088,000 | 1,629,901,088,000 | MEMBER | null | As a user I would like to be able to upload my csv/json/text/parquet/etc. files in a dataset repository on the Hugging Face Hub and be able to load this dataset with `load_dataset` without having to implement a dataset script.
Moreover I would like to be able to specify which file goes into which split using the `da... | {
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Related to #2616. | {
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https://api.github.com/repos/huggingface/datasets/issues/2624 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2624/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2624/comments | https://api.github.com/repos/huggingface/datasets/issues/2624/events | https://github.com/huggingface/datasets/issues/2624 | 941,318,247 | MDU6SXNzdWU5NDEzMTgyNDc= | 2,624 | can't set verbosity for `metric.py` | {
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"Thanks @thomas-happify for reporting and thanks @mariosasko for the fix."
] | 1,625,948,625,000 | 1,626,069,269,000 | 1,626,069,269,000 | NONE | null | ## Describe the bug
```
[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/huggingfa... | {
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"Looks all good to me thanks :)\r\nJust did some minor corrections in the docstring"
] | 1,625,928,710,000 | 1,626,272,893,000 | 1,626,129,271,000 | CONTRIBUTOR | null | Fixes: #2606
This pull request adds combine metrics for the wikisplit or English sentence split task
Reviewer: @patrickvonplaten | {
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"Hi,\r\n\r\nyou can define your own custom formatting with `Dataset.set_transform()` and then run the tokenizer with the batches of augmented data as follows:\r\n```python\r\ndset = load_dataset(\"imdb\", split=\"train\") # Let's say we are working with the IMDB dataset\r\ndset.set_transform(lambda ex: {\"text\": ... | 1,625,875,389,000 | 1,626,023,291,000 | null | NONE | 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 m... | {
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"Does this mean the `FileNotFoundError` that avoids infinite loop can be removed?",
"Yes, I think so...",
"Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?",
" > Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?\r\n\r\nWhat about converting relative p... | 1,625,848,793,000 | 1,626,449,292,000 | 1,626,449,291,000 | MEMBER | null | 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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"Are there any `task_categories:automatic-speech-recognition` dataset for which we should update the tags ?",
"> Are there any `task_categories:automatic-speech-recognition` dataset for which we should update the tags ?\r\n\r\nYes there's a few - I'll fix them tomorrow :)"
] | 1,625,846,849,000 | 1,626,114,779,000 | 1,626,111,122,000 | MEMBER | null | 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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"Wait until #2620 is merged before pushing the README tags in this PR",
"> Thanks!\r\n> \r\n> One question: aren't you adding `task_templates` to the `_info` method (and to the `dataset_infos.json`?\r\n\r\ngreat catch! i've now added the asr task template (along with a mapping from superb task -> template) and up... | 1,625,843,985,000 | 1,626,339,358,000 | 1,626,180,018,000 | MEMBER | null | 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
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"Hi @liyucheng09, thanks for reporting.\r\n\r\nApparently this issue has to do with your environment setup. One question: is your data in an NFS share? Some people have reported this error when using `fcntl` to write to an NFS share... If this is the case, then it might be that your NFS just may not be set up to pr... | 1,625,843,569,000 | 1,626,070,830,000 | null | NONE | 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)
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Therefore users could end up having two samples in the same line
Close https://github.com/huggingface/datasets/issues/2615 | {
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"@lhoestq maybe we could also use (if available) the ETag of the remote file in `create_config_id`?",
"> @lhoestq maybe we could also use (if available) the ETag of the remote file in `create_config_id`?\r\n\r\nSure ! We can get the ETag with\r\n```python\r\nheaders = get_authentication_headers_for_url(url, use_a... | 1,625,839,658,000 | 1,625,847,221,000 | 1,625,847,221,000 | MEMBER | null | 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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"Thanks for reporting @TevenLeScao! I'm having a look...",
"(not sure what just happened on the assignations sorry)",
"For some reason this happens (both `datasets` version are on master) only on Python 3.6 and not Python 3.8.",
"@TevenLeScao we are using `pandas` to serialize the dataset to JSON Lines. So it... | 1,625,839,325,000 | 1,625,844,547,000 | 1,625,844,513,000 | MEMBER | 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 wha... | {
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"title... | [] | 1,625,836,775,000 | 1,626,099,177,000 | 1,625,838,605,000 | MEMBER | null | This PR follows up on #2612 to use `numpy.ndarray.item` instead of `numpy.ndarray.tolist` as the latter is somewhat confusing to the developer (even though it works).
Judging from the `numpy` docs, `ndarray.item` is closer to what we want: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html#nump... | {
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"I opened an issue on the `sklearn` repo to understand why `numpy.float64` is the default: https://github.com/scikit-learn/scikit-learn/discussions/20490",
"It could be surprising at first to use `tolist()` on numpy scalars but it works ^^",
"did the same for Pearsonr here: https://github.com/huggingface/datase... | 1,625,824,089,000 | 1,626,099,173,000 | 1,625,835,834,000 | MEMBER | null | This PR converts the return type of all `sklearn` metrics to be Python `float` instead of `numpy.float64`.
The reason behind this is that our Hub evaluation framework relies on converting benchmark-specific metrics to YAML ([example](https://huggingface.co/datasets/autonlp/autonlp-benchmark-raft-neelalex__raft-test-... | {
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"following_url"... | [] | 1,625,789,357,000 | 1,626,196,399,000 | 1,626,192,510,000 | CONTRIBUTOR | null | As per @stas00's suggestion in #2500, this PR inserts a space between the logo and the lib name (`🤗Datasets` -> `🤗 Datasets`) for consistency with the Transformers lib. Additionally, more consistent names are used for Datasets Hub, etc. | {
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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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Close #2607. | {
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"Updating to pyarrow-4.0.1 didn't fix the issue",
"Here is an exemple dataset with 2 of these compressed JSON files: https://huggingface.co/datasets/thomwolf/github-python",
"Hi @thomwolf, thanks for reporting.\r\n\r\nIt seems this might be due to the fact that the JSON Dataset builder uses `pyarrow.json` (`paj... | 1,625,657,793,000 | 1,626,774,619,000 | 1,625,760,521,000 | MEMBER | null | ## Describe the bug
Using streaming to iterate on local gzip compressed JSON files raise a file not exist error
## Steps to reproduce the bug
```python
from datasets import load_dataset
streamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)
next(iter(streamed_dataset))... | {
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"#take"
] | 1,625,655,364,000 | 1,626,129,271,000 | 1,626,129,271,000 | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
While training the model on sentence split the task in English we require to evaluate the trained model on `Exact Match`, `SARI` and `BLEU` score
like this
![image](https://user-images.githubusercontent.com/26653468/124746876-ff5a3380-df3e-11eb-9a01... | {
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```python
aiohttp.client_exceptions.ClientOSError: [Errno 104] Connection reset by peer
```
This error must trigger a retry instead of directly crashing
Therefore I extended the error type that triggers the retry to be the base aiohttp er... | {
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"Hi !\r\nIf we want something more general, we could either\r\n1. delete the extracted files after the arrow data generation automatically, or \r\n2. delete each extracted file during the arrow generation right after it has been closed.\r\n\r\nSolution 2 is better to save disk space during the arrow generation. Is ... | 1,625,644,576,000 | 1,626,685,698,000 | 1,626,685,698,000 | MEMBER | null | I'm loading a dataset constituted of 44 GB of compressed JSON files.
When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables
Having a simple way to delete the extracted files after usage (or even better, to strea... | {
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"title... | [] | 1,625,641,098,000 | 1,626,099,022,000 | 1,625,646,531,000 | MEMBER | null | When pickling a tokenizer within multiprocessing, check that is instance of transformers PreTrainedTokenizerBase without importing transformers.
Related to huggingface/transformers#12549 and #502. | {
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"title... | [] | 1,625,591,188,000 | 1,626,099,035,000 | 1,625,662,231,000 | CONTRIBUTOR | null | Fixes #2600
Also I moved the check for `num_proc` larger than dataset size added in #2566 up so that multiprocessing is not used with one process. | {
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"following_url"... | [] | 1,625,590,405,000 | 1,625,662,231,000 | 1,625,662,231,000 | CONTRIBUTOR | null | ## Describe the bug
If `filter` is applied to a dataset using multiprocessing (`num_proc` > 1) and all sharded datasets are empty afterwards (due to all samples being discarded), the program crashes.
## Steps to reproduce the bug
```python
from datasets import Dataset
data = Dataset.from_dict({'id': [0,1]})
dat... | {
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https://api.github.com/repos/huggingface/datasets/issues/2599 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2599/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2599/comments | https://api.github.com/repos/huggingface/datasets/issues/2599/events | https://github.com/huggingface/datasets/pull/2599 | 937,980,229 | MDExOlB1bGxSZXF1ZXN0Njg0NDQ2MTYx | 2,599 | Update processing.rst with other export formats | {
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"Hi @erikadistefano , thanks for reporting the issue.\r\n\r\nI have created a Pull Request that should fix it. \r\n\r\nOnce merged into master, feel free to update your installed `datasets` library (either by installing it from our GitHub master branch or waiting until our next release) to be able to load omp datas... | 1,625,580,052,000 | 1,625,662,595,000 | 1,625,662,595,000 | NONE | null | ## Describe the bug
The omp dataset cannot be downloaded because of a DuplicatedKeysError
## Steps to reproduce the bug
from datasets import load_dataset
omp = load_dataset('omp', 'posts_labeled')
print(omp)
## Expected results
This code should download the omp dataset and print the dictionary
## Actual r... | {
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"Hi ! Do you have an example in mind that shows how this could be useful ?",
"Example:\n\nMerge 2 datasets into one datasets\n\nLabel extraction from dataset\n\ndataset(text, label)\n —> dataset(text, newlabel)\n\nTextCleaning.\n\n\nFor image dataset, \nTransformation are easier (ie linear algebra).\n\n\n\n\n\n... | 1,625,556,435,000 | 1,625,732,525,000 | null | NONE | null | Just wondering if you have intenttion to create
TransformerClass :
dataset --> dataset
and make determnistic transformation (ie not fit).
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"Hi @profsatwinder.\r\n\r\nIt looks like you are using an old version of `datasets`. Please update it with `pip install -U datasets` and indicate if the problem persists.",
"@albertvillanova Thanks for the information. I updated it to 1.9.0 and the issue is resolved. Thanks again. "
] | 1,625,541,655,000 | 1,625,551,189,000 | 1,625,551,189,000 | NONE | null | Error traceback:
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-8-a7b592d3bca0> in <module>()
1 from datasets import load_dataset, load_metric
2
----> 3 common_voice_train = load_da... | {
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"following_url"... | [] | 1,625,503,204,000 | 1,625,505,254,000 | 1,625,505,254,000 | MEMBER | null | Workaround for this issue in pandas 1.3.0 : https://github.com/pandas-dev/pandas/issues/42387
The csv reader raises an error:
```python
/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py in _refine_defaults_read(dialect, delimiter, delim_whitespace, engine, sep, error_bad_lines, warn_bad_lines, on... | {
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"Hi! I'm transferring this issue over to `datasets`",
"I'm using the datasets concatenate dataset to combine the datasets and then train.\r\ntrain_dataset = concatenate_datasets([dataset1, dataset2, common_voice_train])\r\n\r\n",
"Hi @BirgerMoell.\r\n\r\nYou have several options:\r\n- to set caching to be store... | 1,625,481,799,000 | 1,626,685,699,000 | 1,626,685,699,000 | CONTRIBUTOR | null | I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).
The cache folder is 500gb (and now my disk space is full).
Is there a way to toggle caching or set the caching to b... | {
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"Hi ! Thanks for the fix :)\r\n\r\nIf I understand correctly, `OptionalSequence` doesn't have an associated arrow type that we know in advance unlike the other feature types, because it depends on the type of the examples.\r\n\r\nFor example, I tested this and it raises an error:\r\n```python\r\nimport datasets as ... | 1,625,473,165,000 | 1,626,099,130,000 | 1,625,733,615,000 | MEMBER | null | Currently, multilabel metrics are not supported because `predictions` and `references` are defined as `Value("int32")`.
This PR creates a new feature type `OptionalSequence` which can act as either `Value("int32")` or `Sequence(Value("int32"))`, depending on the data passed.
Close #2554. | {
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"title... | [] | 1,625,471,186,000 | 1,626,099,011,000 | 1,625,591,370,000 | MEMBER | null | Remove environment variable fixture `env_max_in_memory_dataset_size`. This fixture does not work because env variable is read in datasets.config when first loading datasets, and it is never reread during tests. | {
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Our CI test suite should be exhaustive and test all the library functionalities. | {
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"title... | [] | 1,625,467,340,000 | 1,626,099,070,000 | 1,625,663,931,000 | MEMBER | null | Fix misalignment between:
- the answer text and
- the answer_start within the context
by keeping original leading blank spaces in the context.
Fix #2585. | {
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"Hi @mmajurski, thanks for reporting this issue.\r\n\r\nIndeed this misalignment arises because the source dataset context field contains leading blank spaces (and these are counted within the answer_start), while our datasets loading script removes these leading blank spaces.\r\n\r\nI'm going to fix our script so ... | 1,625,413,189,000 | 1,625,663,931,000 | 1,625,663,931,000 | NONE | null | ## Describe the bug
The built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start'].
For example:
id = '56d1f453e7d4791d009025bd'
answers = {'text': ['P... | {
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"Hi ! This is because you first need to format the dataset for pytorch:\r\n\r\n```python\r\n>>> import torch\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n>>> torch_iterable_dataset = dataset.with_format(\"torch\")\r... | 1,625,255,758,000 | 1,626,771,885,000 | 1,625,528,903,000 | NONE | null | ## Describe the bug
I have an IterableDataset (created using streaming=True) and I am trying to create batches using Torch DataLoader class by passing this IterableDataset to it. This throws error which is pasted below. I can do the same by using Torch IterableDataset. One thing I noticed is that in the former case wh... | {
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"@lhoestq looks good. I tried with https://huggingface.co/datasets/vblagoje/wikipedia_snippets_streamed and it worked nicely. I would add more unit tests for edge cases. What happens if the n is larger than the total number of samples? Just to make sure these cases are handled properly. ",
"Yup I'll add the tests... | 1,625,238,619,000 | 1,625,501,200,000 | 1,625,501,199,000 | MEMBER | null | As discussed in https://github.com/huggingface/datasets/pull/2375#discussion_r657084544 I added the `IterableDataset.skip` and `IterableDataset.take` methods that allows to do basic splitting of iterable datasets.
You can create new dataset with the first `n` examples using `IterableDataset.take()`, or you can get a... | {
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I think it makes sense to perform search_batch threaded, so ES can perform search in parallel.
Cheers! | {
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https://api.github.com/repos/huggingface/datasets/issues/2580 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2580/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2580/comments | https://api.github.com/repos/huggingface/datasets/issues/2580/events | https://github.com/huggingface/datasets/pull/2580 | 935,767,421 | MDExOlB1bGxSZXF1ZXN0NjgyNjI2MTkz | 2,580 | Fix Counter import | {
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https://api.github.com/repos/huggingface/datasets/issues/2579 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2579/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2579/comments | https://api.github.com/repos/huggingface/datasets/issues/2579/events | https://github.com/huggingface/datasets/pull/2579 | 935,486,894 | MDExOlB1bGxSZXF1ZXN0NjgyMzkyNjYx | 2,579 | Fix BibTeX entry | {
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"following_url"... | [] | 1,625,209,840,000 | 1,625,211,224,000 | 1,625,211,224,000 | MEMBER | null | Add missing contributor to BibTeX entry.
cc: @abhishekkrthakur @thomwolf | {
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"> What if people want to run some tests without having zstandard ?\r\n> Usually what we do is add a decorator @require_zstandard for example\r\n\r\n@lhoestq I think I'm missing something here...\r\n\r\nTests are a *development* tool (to ensure we deliver a good quality lib), not something we offer to the end users... | 1,625,170,954,000 | 1,628,693,184,000 | 1,625,482,227,000 | MEMBER | null | Close #2572.
cc: @thomwolf | {
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https://api.github.com/repos/huggingface/datasets/issues/2576 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2576/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2576/comments | https://api.github.com/repos/huggingface/datasets/issues/2576/events | https://github.com/huggingface/datasets/pull/2576 | 934,986,761 | MDExOlB1bGxSZXF1ZXN0NjgxOTc5MTA1 | 2,576 | Add mC4 | {
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"following_url"... | [] | 1,625,154,685,000 | 1,625,237,456,000 | 1,625,237,455,000 | MEMBER | null | AllenAI is now hosting the processed C4 and mC4 dataset in this repo: https://huggingface.co/datasets/allenai/c4
Thanks a lot to them !
In this PR I added the mC4 dataset builder. It supports 108 languages
You can load it with
```python
from datasets import load_dataset
en_mc4 = load_dataset("mc4", "en")
f... | {
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"following_url"... | [] | 1,625,147,888,000 | 1,625,237,423,000 | 1,625,237,423,000 | MEMBER | null | The old code for the C4 dataset was to generate the C4 with Apache Beam, as in Tensorflow Datasets.
However AllenAI is now hosting the processed C4 dataset in this repo: https://huggingface.co/datasets/allenai/c4
Thanks a lot to them for their amazing work !
In this PR I changed the script to download and prepare ... | {
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"This was actually a second error arising from a too small block-size in the json reader.\r\n\r\nFinding the right block size is difficult for the layman user"
] | 1,625,129,315,000 | 1,625,166,653,000 | null | MEMBER | null | As reported by @thomwolf, while loading a JSON Lines file with "json" loading script, he gets
> json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 383)
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https://api.github.com/repos/huggingface/datasets/issues/2571 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2571/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2571/comments | https://api.github.com/repos/huggingface/datasets/issues/2571/events | https://github.com/huggingface/datasets/pull/2571 | 933,791,018 | MDExOlB1bGxSZXF1ZXN0NjgwOTQ2NzQ1 | 2,571 | Filter expected warning log from transformers | {
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"I think the failing test has nothing to do with my PR..."
] | 1,625,064,499,000 | 1,625,198,897,000 | 1,625,198,897,000 | MEMBER | null | Close #2569. | {
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https://api.github.com/repos/huggingface/datasets/issues/2570 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2570/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2570/comments | https://api.github.com/repos/huggingface/datasets/issues/2570/events | https://github.com/huggingface/datasets/pull/2570 | 933,402,521 | MDExOlB1bGxSZXF1ZXN0NjgwNjEzNzc0 | 2,570 | Minor fix docs format for bertscore | {
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"following_url"... | [] | 1,625,038,932,000 | 1,625,067,061,000 | 1,625,067,061,000 | MEMBER | null | Minor fix docs format for bertscore:
- link to README
- format of KWARGS_DESCRIPTION | {
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https://api.github.com/repos/huggingface/datasets/issues/2569 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2569/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2569/comments | https://api.github.com/repos/huggingface/datasets/issues/2569/events | https://github.com/huggingface/datasets/issues/2569 | 933,015,797 | MDU6SXNzdWU5MzMwMTU3OTc= | 2,569 | Weights of model checkpoint not initialized for RobertaModel for Bertscore | {
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"Hi @suzyahyah, thanks for reporting.\r\n\r\nThe message you get is indeed not an error message, but a warning coming from Hugging Face `transformers`. The complete warning message is:\r\n```\r\nSome weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.wei... | 1,624,992,923,000 | 1,625,123,339,000 | 1,625,038,549,000 | NONE | null | When applying bertscore out of the box,
```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']```
Following the typical ... | {
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"following_url"... | [] | 1,624,987,164,000 | 1,625,132,014,000 | 1,625,132,013,000 | MEMBER | null | ### Add interleave_datasets for map-style datasets
Add support for map-style datasets (i.e. `Dataset` objects) in `interleave_datasets`.
It was only supporting iterable datasets (i.e. `IterableDataset` objects).
### Implementation details
It works by concatenating the datasets and then re-order the indices to... | {
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https://api.github.com/repos/huggingface/datasets/issues/2567 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2567/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2567/comments | https://api.github.com/repos/huggingface/datasets/issues/2567/events | https://github.com/huggingface/datasets/pull/2567 | 932,933,536 | MDExOlB1bGxSZXF1ZXN0NjgwMjE3OTY3 | 2,567 | Add ASR task and new languages to resources | {
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"following_url"... | [] | 1,624,987,081,000 | 1,625,132,543,000 | 1,625,132,529,000 | MEMBER | null | This PR adds a new `automatic-speech-recognition` task to the list of supported tasks in `tasks.json` and also includes a few new languages missing from `common_voice`.
Note: I used the [Papers with Code list](https://www.paperswithcode.com/area/speech/speech-recognition) as inspiration for the ASR subtasks | {
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https://api.github.com/repos/huggingface/datasets/issues/2566 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2566/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2566/comments | https://api.github.com/repos/huggingface/datasets/issues/2566/events | https://github.com/huggingface/datasets/pull/2566 | 932,804,725 | MDExOlB1bGxSZXF1ZXN0NjgwMTA2NzM0 | 2,566 | fix Dataset.map when num_procs > num rows | {
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"following_url"... | [] | 1,624,979,227,000 | 1,625,130,673,000 | 1,625,130,673,000 | CONTRIBUTOR | null | closes #2470
## Testing notes
To run updated tests:
```sh
pytest tests/test_arrow_dataset.py -k "BaseDatasetTest and test_map_multiprocessing" -s
```
With Python code (to view warning):
```python
from datasets import Dataset
dataset = Dataset.from_dict({"x": ["sample"]})
print(len(dataset))
dataset.map... | {
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https://api.github.com/repos/huggingface/datasets/issues/2565 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2565/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2565/comments | https://api.github.com/repos/huggingface/datasets/issues/2565/events | https://github.com/huggingface/datasets/pull/2565 | 932,445,439 | MDExOlB1bGxSZXF1ZXN0Njc5Nzg3NTI4 | 2,565 | Inject templates for ASR datasets | {
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"Wait until #2567 is merged so we can benefit from the tagger :)",
"thanks for the feedback @lhoestq! i've added the new language codes and this PR should be ready for a merge :)"
] | 1,624,960,921,000 | 1,625,495,186,000 | 1,625,495,186,000 | MEMBER | null | This PR adds ASR templates for 5 of the most common speech datasets on the Hub, where "common" is defined by the number of models trained on them.
I also fixed a bunch of the tags in the READMEs 😎 | {
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