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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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"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 (`π€Datasets` -> `π€ 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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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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https://api.github.com/repos/huggingface/datasets/issues/2601 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2601/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2601/comments | https://api.github.com/repos/huggingface/datasets/issues/2601/events | https://github.com/huggingface/datasets/pull/2601 | 938,096,396 | MDExOlB1bGxSZXF1ZXN0Njg0NTQyNjY5 | 2,601 | Fix `filter` with multiprocessing in case all samples are discarded | {
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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 β> 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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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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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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"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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] | 1,625,064,499,000 | 1,625,198,897,000 | 1,625,198,897,000 | MEMBER | null | Close #2569. | {
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- link to README
- format of KWARGS_DESCRIPTION | {
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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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"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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"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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"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 π | {
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"following_url"... | [] | 1,624,957,181,000 | 1,624,957,181,000 | null | MEMBER | null | Currently `concatenate_datasets` only works for map-style `Dataset`.
It would be nice to have it work for `IterableDataset` objects as well.
It would simply chain the iterables of the iterable datasets. | {
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"following_url"... | [] | 1,624,957,044,000 | 1,625,132,013,000 | 1,625,132,013,000 | MEMBER | null | Currently the `interleave_datasets` functions only works for `IterableDataset`.
Let's make it work for map-style `Dataset` objects as well.
It would work the same way: either alternate between the datasets in order or randomly given probabilities specified by the user. | {
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"Hi ! I just tried to reproduce what you said:\r\n- create a local builder class\r\n- use `load_dataset`\r\n- update the builder class code\r\n- use `load_dataset` again (with or without `ignore_verifications=True`)\r\nAnd it creates a new cache, as expected.\r\n\r\nWhat modifications did you do to your builder's c... | 1,624,952,583,000 | 1,625,057,724,000 | null | MEMBER | null | ## Describe the bug
If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.
## Steps to reproduce th... | {
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"Hi ! Thanks for fixing this :)\r\n\r\nLooks like you have tons of changes due to code formatting.\r\nWe're using `black` for this, with a custom line length. To run our code formatting, you just need to run\r\n```\r\nmake style\r\n```\r\n\r\nThen for the windows error in the CI, I'm looking into it. It's probably ... | 1,624,933,451,000 | 1,624,978,818,000 | 1,624,978,411,000 | 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/2559 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2559/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2559/comments | https://api.github.com/repos/huggingface/datasets/issues/2559/events | https://github.com/huggingface/datasets/issues/2559 | 931,849,724 | MDU6SXNzdWU5MzE4NDk3MjQ= | 2,559 | Memory usage consistently increases when processing a dataset with `.map` | {
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"Hi ! Can you share the function you pass to `map` ?\r\nI know you mentioned it would be hard to share some code but this would really help to understand what happened"
] | 1,624,905,118,000 | 1,624,956,180,000 | null | MEMBER | null | ## Describe the bug
I have a HF dataset with image paths stored in it and I am trying to load those image paths using `.map` with `num_proc=80`. I am noticing that the memory usage consistently keeps on increasing with time. I tried using `DEFAULT_WRITER_BATCH_SIZE=10` in the builder to decrease arrow writer's batch... | {
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"following_url"... | [] | 1,624,896,997,000 | 1,624,900,997,000 | 1,624,900,996,000 | MEMBER | null | The master branch changed so I computed the new checksums.
I also pinned a specific revision so that it doesn't happen again in the future.
Fix https://github.com/huggingface/datasets/issues/2553 | {
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"following_url"... | [] | 1,624,890,422,000 | 1,624,896,690,000 | 1,624,896,689,000 | MEMBER | null | The keys has duplicates since they were reset to 0 after each file.
I fixed it by taking into account the file index as well. | {
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"following_url"... | [] | 1,624,888,257,000 | 1,624,888,257,000 | null | MEMBER | null | As mentioned in https://github.com/huggingface/datasets/issues/2552 it would be nice to improve the error message when a dataset fails to build because there are duplicate example keys.
The current one is
```python
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 48
Keys s... | {
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"Fix #2552."
] | 1,624,887,623,000 | 1,630,571,083,000 | 1,624,889,435,000 | MEMBER | null | There were duplicate keys in the `code_search_net` dataset, as reported in https://github.com/huggingface/datasets/issues/2552
I fixed the keys (it was an addition of the file and row indices, which was causing collisions)
Fix #2552. | {
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"Hi @GuillemGSubies, thanks for reporting.\r\n\r\nI have made a PR to fix this issue and allow metrics to be computed also for multilabel classification problems.",
"Looks nice, thank you very much! π ",
"Sorry for reopening but I just noticed that the `_compute` method for the F1 metric is still not good enou... | 1,624,878,586,000 | 1,634,128,153,000 | 1,625,733,615,000 | NONE | null | When I try to use a metric like F1 macro I get the following error:
```
TypeError: int() argument must be a string, a bytes-like object or a number, not 'list'
```
There is an explicit casting here:
https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/src/datasets/features.py#L... | {
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"Hi ! Thanks for reporting. This is due to the WebNLG repository that got updated today.\r\nI just pushed a fix at #2558 - this shouldn't happen anymore in the future.",
"This is fixed on `master` now :)\r\nWe'll do a new release soon !"
] | 1,624,872,406,000 | 1,624,901,019,000 | 1,624,900,996,000 | NONE | null | Hi! It seems the WebNLG dataset gives a NonMatchingChecksumError.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('web_nlg', name="release_v3.0_en", split="dev")
```
Gives
```
NonMatchingChecksumError: Checksums didn't match for dataset source files:
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"Two questions:\r\n- with `datasets-cli env` we don't have any information on the dataset script version used. Should we give access to this somehow? Either as a note in the Error message or as an argument with the name of the dataset to `datasets-cli env`?\r\n- I don't really understand why the id is duplicated in... | 1,624,871,720,000 | 1,630,937,310,000 | 1,630,571,129,000 | MEMBER | null | ## Describe the bug
Loading `code_search_net` seems not possible at the moment.
## Steps to reproduce the bug
```python
>>> load_dataset('code_search_net')
Downloading: 8.50kB [00:00, 3.09MB/s] ... | {
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"following_url"... | [] | 1,624,810,722,000 | 1,624,885,795,000 | 1,624,885,794,000 | CONTRIBUTOR | null | ### What this fixes:
This PR resolves several issues I discovered in the documentation on the `datasets.filesystems` module ([this page](https://huggingface.co/docs/datasets/filesystems.html)).
### What were the issues?
When I originally tried implementing the code examples I faced several bugs attributed to:
-... | {
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"following_url"... | [] | 1,624,806,058,000 | 1,632,663,759,000 | 1,632,663,759,000 | CONTRIBUTOR | null | Currently, using a metric allows for one of the following:
- Per example/batch metrics
- Cumulative metrics over the whole data
What I'd like is to have an efficient way to get cumulative metrics over the examples/batches added so far, in order to display it as part of the progress bar during training/evaluation.
... | {
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"Hi @nelson-liu,\r\n\r\nYou can pass the parameter `features` to `load_dataset`: https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset\r\n\r\nIf you look at the code of the MNLI script you referred in your question (https://github.com/huggingface/datasets/blob/master/datasets/multi_nli/multi... | 1,624,595,543,000 | 1,624,655,277,000 | 1,624,655,276,000 | NONE | null | Hi!
Is there a way for datasets to produce unlabeled instances (e.g., the `ClassLabel` can be nullable).
For example, I want to use the MNLI dataset reader ( https://github.com/huggingface/datasets/blob/master/datasets/multi_nli/multi_nli.py ) on a file that doesn't have the `gold_label` field. I tried setting `"... | {
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"Hi @luyug, thanks for reporting.\r\n\r\nThe good news is that we fixed this issue only 9 days ago: #2507.\r\n\r\nThe patch is already in the master branch of our repository and it will be included in our next `datasets` release version 1.9.0.\r\n\r\nFeel free to reopen the issue if the problem persists."
] | 1,624,541,393,000 | 1,624,545,403,000 | 1,624,545,245,000 | NONE | null | ## Describe the bug
The `load_dataset` function expects columns in alphabetical order when loading json files.
Similar bug was previously reported for csv in #623 and fixed in #684.
## Steps to reproduce the bug
For a json file `j.json`,
```
{"c":321, "a": 1, "b": 2}
```
Running the following,
```
f= data... | {
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"Hi ! It looks like an issue with the virtual disk you are using.\r\n\r\nWe load datasets using memory mapping. In general it makes it possible to load very big files instantaneously since it doesn't have to read the file (it just assigns virtual memory to the file on disk).\r\nHowever there happens to be issues wi... | 1,624,538,744,000 | 1,624,632,998,000 | null | NONE | null | @lhoestq
## Describe the bug
It's not normal that I have to wait 7-8 hours for a dataset to be loaded from disk, as there are no preprocessing steps, it's only loading it with load_from_disk. I have 96 cpus, however only 1 is used for this, which is inefficient. Moreover, its usage is at 1%... This is happening in t... | {
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https://api.github.com/repos/huggingface/datasets/issues/2546 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2546/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2546/comments | https://api.github.com/repos/huggingface/datasets/issues/2546/events | https://github.com/huggingface/datasets/pull/2546 | 929,091,689 | MDExOlB1bGxSZXF1ZXN0Njc2OTk2MjQ0 | 2,546 | Add license to the Cambridge English Write & Improve + LOCNESS dataset card | {
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I added it and I also filled a few other empty sections. | {
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https://api.github.com/repos/huggingface/datasets/issues/2545 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2545/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2545/comments | https://api.github.com/repos/huggingface/datasets/issues/2545/events | https://github.com/huggingface/datasets/pull/2545 | 929,016,580 | MDExOlB1bGxSZXF1ZXN0Njc2OTMxOTYw | 2,545 | Fix DuplicatedKeysError in drop dataset | {
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cc: @VictorSanh. | {
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https://api.github.com/repos/huggingface/datasets/issues/2544 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2544/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2544/comments | https://api.github.com/repos/huggingface/datasets/issues/2544/events | https://github.com/huggingface/datasets/pull/2544 | 928,900,827 | MDExOlB1bGxSZXF1ZXN0Njc2ODM1MjYz | 2,544 | Fix logging levels | {
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"following_url"... | [] | 1,624,516,896,000 | 1,624,628,419,000 | 1,624,628,419,000 | MEMBER | null | Sometimes default `datasets` logging can be too verbose. One approach could be reducing some logging levels, from info to debug, or from warning to info.
Close #2543.
cc: @stas00 | {
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https://api.github.com/repos/huggingface/datasets/issues/2543 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2543/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2543/comments | https://api.github.com/repos/huggingface/datasets/issues/2543/events | https://github.com/huggingface/datasets/issues/2543 | 928,571,915 | MDU6SXNzdWU5Mjg1NzE5MTU= | 2,543 | switching some low-level log.info's to log.debug? | {
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"Hi @stas00, thanks for pointing out this issue with logging.\r\n\r\nI agree that `datasets` can sometimes be too verbose... I can create a PR and we could discuss there the choice of the log levels for different parts of the code."
] | 1,624,476,415,000 | 1,624,628,419,000 | 1,624,628,419,000 | MEMBER | null | In https://github.com/huggingface/transformers/pull/12276 we are now changing the examples to have `datasets` on the same log level as `transformers`, so that one setting can do a consistent logging across all involved components.
The trouble is that now we get a ton of these:
```
06/23/2021 12:15:31 - INFO - da... | {
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"very much related: https://github.com/huggingface/datasets/pull/2333",
"Hi @VictorSanh, thank you for reporting this issue with duplicated keys.\r\n\r\n- The issue with \"adversarial_qa\" was fixed 23 days ago: #2433. Current version of `datasets` (1.8.0) includes the patch.\r\n- I am investigating the issue wit... | 1,624,473,676,000 | 1,624,657,805,000 | 1,624,546,628,000 | MEMBER | null | ## Describe the bug
Failure to generate the datasets (`drop` and subset `adversarialQA` from `adversarial_qa`) because of duplicate keys.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("drop")
load_dataset("adversarial_qa", "adversarialQA")
```
## Expected results
Th... | {
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"The CI is failing because the dataset tags for `discofuse` are missing. I'm merging this PR since this is unrelated to this PR, but feel free to open another PR to add the tags here if you have some time:\r\n\r\nhttps://github.com/huggingface/datasets/blob/19408f9fab85c79b966085574cd2da3b90959179/datasets/discofus... | 1,624,472,698,000 | 1,624,890,891,000 | 1,624,890,890,000 | MEMBER | null | Updating the discofuse link: https://github.com/google-research-datasets/discofuse/commit/fd4b120cb3dd19a417e7f3b5432010b574b5eeee | {
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https://api.github.com/repos/huggingface/datasets/issues/2540 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2540/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2540/comments | https://api.github.com/repos/huggingface/datasets/issues/2540/events | https://github.com/huggingface/datasets/pull/2540 | 928,433,892 | MDExOlB1bGxSZXF1ZXN0Njc2NDM5NTM1 | 2,540 | Remove task templates if required features are removed during `Dataset.map` | {
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"following_url"... | [] | 1,624,465,225,000 | 1,624,545,675,000 | 1,624,541,643,000 | MEMBER | null | This PR fixes a bug reported by @craffel where removing a dataset's columns during `Dataset.map` triggered a `KeyError` because the `TextClassification` template tried to access the removed columns during `DatasetInfo.__post_init__`:
```python
from datasets import load_dataset
# `yelp_polarity` comes with a `Tex... | {
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"Hi ! I'm sorry to hear that.\r\nThough we are not redistributing the dataset, we just provide a python script that downloads and process the dataset from its original source hosted at https://www.cl.cam.ac.uk\r\n\r\nTherefore I'm not sure what's the issue with licensing. What do you mean exactly ?",
"I think tha... | 1,624,433,732,000 | 1,624,632,762,000 | 1,624,632,762,000 | CONTRIBUTOR | null | It was brought to my attention that this dataset's license is not only missing, but also prohibits redistribution. I contacted the original author to apologize for this oversight and asked if we could still use it, but unfortunately we can't and the author kindly asked to take down this dataset. | {
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"Hi ! `load_dataset` downloads the full dataset once and caches it, so that subsequent calls to `load_dataset` just reloads the dataset from your disk.\r\nThen when you specify a `split` in `load_dataset`, it will just load the requested split from the disk. If your specified split is a sliced split (e.g. `\"train[... | 1,624,432,792,000 | 1,627,567,833,000 | null | NONE | null | I am using PyTorch Lightning along with datasets (thanks for so many datasets already prepared and the great splits).
Every time I execute load_dataset for the imdb dataset it takes some time even if I specify a split involving very few samples. I guess this due to hashing as per the other issues.
Is there a wa... | {
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https://api.github.com/repos/huggingface/datasets/issues/2537 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2537/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2537/comments | https://api.github.com/repos/huggingface/datasets/issues/2537/events | https://github.com/huggingface/datasets/pull/2537 | 927,472,659 | MDExOlB1bGxSZXF1ZXN0Njc1NjI1OTY3 | 2,537 | Add Parquet loader + from_parquet and to_parquet | {
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"`pyarrow` 1.0.0 doesn't support some types in parquet, we'll have to bump its minimum version.\r\n\r\nAlso I still need to add dummy data to test the parquet builder.",
"I had to bump the minimum pyarrow version to 3.0.0 to properly support parquet.\r\n\r\nEverything is ready for review now :)\r\nI reused pretty... | 1,624,382,903,000 | 1,625,070,663,000 | 1,625,070,658,000 | MEMBER | null | Continuation of #2247
I added a "parquet" dataset builder, as well as the methods `Dataset.from_parquet` and `Dataset.to_parquet`.
As usual, the data are converted to arrow in a batched way to avoid loading everything in memory. | {
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https://api.github.com/repos/huggingface/datasets/issues/2536 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2536/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2536/comments | https://api.github.com/repos/huggingface/datasets/issues/2536/events | https://github.com/huggingface/datasets/issues/2536 | 927,338,639 | MDU6SXNzdWU5MjczMzg2Mzk= | 2,536 | Use `Audio` features for `AutomaticSpeechRecognition` task template | {
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"I'm just retaking and working on #2324. π "
] | 1,624,374,441,000 | 1,624,375,011,000 | null | MEMBER | null | In #2533 we added a task template for speech recognition that relies on the file paths to the audio files. As pointed out by @SBrandeis this is brittle as it doesn't port easily across different OS'.
The solution is to use dedicated `Audio` features when casting the dataset. These features are not yet available in ... | {
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"following_url"... | [] | 1,624,374,207,000 | 1,624,455,643,000 | 1,624,455,643,000 | MEMBER | null | - Fix rendering and cross-references in Features docs
- Add docstrings to Features methods | {
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https://api.github.com/repos/huggingface/datasets/issues/2534 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2534/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2534/comments | https://api.github.com/repos/huggingface/datasets/issues/2534/events | https://github.com/huggingface/datasets/pull/2534 | 927,201,435 | MDExOlB1bGxSZXF1ZXN0Njc1MzkzODg0 | 2,534 | Sync with transformers disabling NOTSET | {
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"Nice thanks ! I think there are other places with\r\n```python\r\nnot_verbose = bool(logger.getEffectiveLevel() > WARNING)\r\n```\r\n\r\nCould you replace them as well ?",
"Sure @lhoestq! I was not sure if this change should only be circumscribed to `http_get`..."
] | 1,624,366,461,000 | 1,624,545,767,000 | 1,624,545,767,000 | MEMBER | null | Close #2528. | {
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https://api.github.com/repos/huggingface/datasets/issues/2533 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2533/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2533/comments | https://api.github.com/repos/huggingface/datasets/issues/2533/events | https://github.com/huggingface/datasets/pull/2533 | 927,193,264 | MDExOlB1bGxSZXF1ZXN0Njc1Mzg2OTMw | 2,533 | Add task template for automatic speech recognition | {
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"@SBrandeis @lhoestq i've integrated your suggestions, so this is ready for another review :)",
"Merging if it's good for you @lewtun :)"
] | 1,624,365,902,000 | 1,624,464,886,000 | 1,624,463,817,000 | MEMBER | null | This PR adds a task template for automatic speech recognition. In this task, the input is a path to an audio file which the model consumes to produce a transcription.
Usage:
```python
from datasets import load_dataset
from datasets.tasks import AutomaticSpeechRecognition
ds = load_dataset("timit_asr", split=... | {
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https://api.github.com/repos/huggingface/datasets/issues/2532 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2532/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2532/comments | https://api.github.com/repos/huggingface/datasets/issues/2532/events | https://github.com/huggingface/datasets/issues/2532 | 927,063,196 | MDU6SXNzdWU5MjcwNjMxOTY= | 2,532 | Tokenizer's normalization preprocessor cause misalignment in return_offsets_mapping for tokenizer classification task | {
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"Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?",
"> Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?\r\n\r\nOh, I am sorry\r\nI would reopen the post on huggingface/transformers"
] | 1,624,356,498,000 | 1,624,425,445,000 | 1,624,425,445,000 | CONTRIBUTOR | null | [This colab notebook](https://colab.research.google.com/drive/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) implements a token classification input pipeline extending the logic from [this hugging example](https://huggingface.co/transformers/custom_datasets.html#tok-ner).
The pipeline works fine with most instance i... | {
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However it happens that `1.8.0 > 1.8.0.dev0` for example.
Therefore we need to use `1.8.1.dev0` for example in this case.
I updated the dev version to use `1.8.1.dev0`, and I also added a comment in the setup.py in the release steps a... | {
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https://api.github.com/repos/huggingface/datasets/issues/2530 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2530/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2530/comments | https://api.github.com/repos/huggingface/datasets/issues/2530/events | https://github.com/huggingface/datasets/pull/2530 | 927,013,773 | MDExOlB1bGxSZXF1ZXN0Njc1MjMyNDk0 | 2,530 | Fixed label parsing in the ProductReviews dataset | {
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"@lhoestq, can you please review this PR?\r\nWhat exactly is the problem in the test case? Should it matter?",
"Hi ! Thanks for fixing this :)\r\n\r\nThe CI fails for two reasons:\r\n- the `pretty_name` tag is missing in yaml tags in ./datasets/turkish_product_reviews/README.md. You can fix that by adding this in... | 1,624,353,165,000 | 1,624,366,520,000 | 1,624,366,360,000 | CONTRIBUTOR | null | Fixed issue with parsing dataset labels. | {
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https://api.github.com/repos/huggingface/datasets/issues/2529 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2529/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2529/comments | https://api.github.com/repos/huggingface/datasets/issues/2529/events | https://github.com/huggingface/datasets/pull/2529 | 926,378,812 | MDExOlB1bGxSZXF1ZXN0Njc0NjkxNjA5 | 2,529 | Add summarization template | {
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"> Nice thanks !\r\n> Could you just move the test outside of the BaseDatasetTest class please ? Otherwise it will unnecessarily be run twice.\r\n\r\nsure, on it! thanks for the explanations about the `self._to` method :)",
"@lhoestq i've moved all the task template tests outside of `BaseDatasetTest` and collecte... | 1,624,291,711,000 | 1,624,458,131,000 | 1,624,455,010,000 | MEMBER | null | This PR adds a task template for text summarization. As far as I can tell, we do not need to distinguish between "extractive" or "abstractive" summarization - both can be handled with this template.
Usage:
```python
from datasets import load_dataset
from datasets.tasks import Summarization
ds = load_dataset(... | {
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https://api.github.com/repos/huggingface/datasets/issues/2528 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2528/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2528/comments | https://api.github.com/repos/huggingface/datasets/issues/2528/events | https://github.com/huggingface/datasets/issues/2528 | 926,314,656 | MDU6SXNzdWU5MjYzMTQ2NTY= | 2,528 | Logging cannot be set to NOTSET similar to transformers | {
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"Hi @joshzwiebel, thanks for reporting. We are going to align with `transformers`."
] | 1,624,287,894,000 | 1,624,545,767,000 | 1,624,545,767,000 | NONE | null | ## Describe the bug
In the transformers library you can set the verbosity level to logging.NOTSET to work around the usage of tqdm and IPywidgets, however in Datasets this is no longer possible. This is because transformers set the verbosity level of tqdm with [this](https://github.com/huggingface/transformers/blob/b5... | {
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https://api.github.com/repos/huggingface/datasets/issues/2527 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2527/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2527/comments | https://api.github.com/repos/huggingface/datasets/issues/2527/events | https://github.com/huggingface/datasets/pull/2527 | 926,031,525 | MDExOlB1bGxSZXF1ZXN0Njc0MzkzNjQ5 | 2,527 | Replace bad `n>1M` size tag | {
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This resulted in unexpected results when searching for datasets bigger than 1M on the hub, since it was only showing the ones with the tag `n>1M`. | {
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https://api.github.com/repos/huggingface/datasets/issues/2526 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2526/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2526/comments | https://api.github.com/repos/huggingface/datasets/issues/2526/events | https://github.com/huggingface/datasets/issues/2526 | 925,929,228 | MDU6SXNzdWU5MjU5MjkyMjg= | 2,526 | Add COCO datasets | {
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"I'm currently adding it, the entire dataset is quite big around 30 GB so I add splits separately. You can take a look here https://huggingface.co/datasets/merve/coco",
"I talked to @lhoestq and it's best if I download this dataset through TensorFlow datasets instead, so I'll be implementing that one really soon.... | 1,624,261,712,000 | 1,640,007,218,000 | null | CONTRIBUTOR | null | ## Adding a Dataset
- **Name:** COCO
- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.
- **Paper + website:** https://cocodataset.org/#home
- **Data:** https://cocodataset.org/#download
- **Motivation:** It would be great to have COCO available in HuggingFace datasets... | {
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https://api.github.com/repos/huggingface/datasets/issues/2525 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2525/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2525/comments | https://api.github.com/repos/huggingface/datasets/issues/2525/events | https://github.com/huggingface/datasets/pull/2525 | 925,896,358 | MDExOlB1bGxSZXF1ZXN0Njc0Mjc5MTgy | 2,525 | Use scikit-learn package rather than sklearn in setup.py | {
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"following_url"... | [] | 1,624,259,065,000 | 1,624,269,673,000 | 1,624,265,853,000 | CONTRIBUTOR | null | The sklearn package is an historical thing and should probably not be used by anyone, see https://github.com/scikit-learn/scikit-learn/issues/8215#issuecomment-344679114 for some caveats.
Note: this affects only TESTS_REQUIRE so I guess only developers not end users. | {
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https://api.github.com/repos/huggingface/datasets/issues/2524 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2524/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2524/comments | https://api.github.com/repos/huggingface/datasets/issues/2524/events | https://github.com/huggingface/datasets/pull/2524 | 925,610,934 | MDExOlB1bGxSZXF1ZXN0Njc0MDQzNzk1 | 2,524 | Raise FileNotFoundError in WindowsFileLock | {
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"Hi ! Could you clarify what it fixes exactly and give more details please ? Especially why this is related to the windows hanging error ?",
"This has already been merged, but I'll clarify the idea of this PR. Before this merge, FileLock was the only component affected by the max path limit on Windows (that came ... | 1,624,199,111,000 | 1,624,874,182,000 | 1,624,870,059,000 | CONTRIBUTOR | null | Closes #2443 | {
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https://api.github.com/repos/huggingface/datasets/issues/2523 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2523/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2523/comments | https://api.github.com/repos/huggingface/datasets/issues/2523/events | https://github.com/huggingface/datasets/issues/2523 | 925,421,008 | MDU6SXNzdWU5MjU0MjEwMDg= | 2,523 | Fr | {
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https://api.github.com/repos/huggingface/datasets/issues/2522 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2522/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2522/comments | https://api.github.com/repos/huggingface/datasets/issues/2522/events | https://github.com/huggingface/datasets/issues/2522 | 925,334,379 | MDU6SXNzdWU5MjUzMzQzNzk= | 2,522 | Documentation Mistakes in Dataset: emotion | {
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"Hi,\r\n\r\nthis issue has been already reported in the dataset repo (https://github.com/dair-ai/emotion_dataset/issues/2), so this is a bug on their side.",
"The documentation has another bug in the dataset card [here](https://huggingface.co/datasets/emotion). \r\n\r\nIn the dataset summary **six** emotions are ... | 1,624,086,537,000 | 1,643,109,239,000 | null | NONE | null | As per documentation,
Dataset: emotion
Homepage: https://github.com/dair-ai/emotion_dataset
Dataset: https://github.com/huggingface/datasets/blob/master/datasets/emotion/emotion.py
Permalink: https://huggingface.co/datasets/viewer/?dataset=emotion
Emotion is a dataset of English Twitter messages with eight b... | {
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https://api.github.com/repos/huggingface/datasets/issues/2521 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2521/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2521/comments | https://api.github.com/repos/huggingface/datasets/issues/2521/events | https://github.com/huggingface/datasets/pull/2521 | 925,030,685 | MDExOlB1bGxSZXF1ZXN0NjczNTgxNzQ4 | 2,521 | Insert text classification template for Emotion dataset | {
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"following_url"... | [] | 1,624,031,779,000 | 1,624,267,351,000 | 1,624,267,351,000 | MEMBER | null | This PR includes a template and updated `dataset_infos.json` for the `emotion` dataset. | {
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https://api.github.com/repos/huggingface/datasets/issues/2520 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2520/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2520/comments | https://api.github.com/repos/huggingface/datasets/issues/2520/events | https://github.com/huggingface/datasets/issues/2520 | 925,015,004 | MDU6SXNzdWU5MjUwMTUwMDQ= | 2,520 | Datasets with tricky task templates | {
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"following_url"... | [] | 1,624,030,437,000 | 1,624,031,186,000 | null | MEMBER | null | I'm collecting a list of datasets here that don't follow the "standard" taxonomy and require further investigation to implement task templates for.
## Text classification
* [hatexplain](https://huggingface.co/datasets/hatexplain): ostensibly a form of text classification, but not in the standard `(text, target)` ... | {
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https://api.github.com/repos/huggingface/datasets/issues/2519 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2519/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2519/comments | https://api.github.com/repos/huggingface/datasets/issues/2519/events | https://github.com/huggingface/datasets/pull/2519 | 924,903,240 | MDExOlB1bGxSZXF1ZXN0NjczNDcyMzYy | 2,519 | Improve performance of pandas arrow extractor | {
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"Looks like this change\r\n```\r\npa_table[pa_table.column_names[0]].to_pandas(types_mapper=pandas_types_mapper)\r\n```\r\ndoesn't return a Series with the correct type.\r\nThis is related to https://issues.apache.org/jira/browse/ARROW-9664\r\n\r\nSince the types_mapper isn't taken into account, the ArrayXD types a... | 1,624,022,681,000 | 1,624,266,366,000 | 1,624,266,366,000 | MEMBER | null | While reviewing PR #2505, I noticed that pandas arrow extractor could be refactored to be faster. | {
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https://api.github.com/repos/huggingface/datasets/issues/2518 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2518/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2518/comments | https://api.github.com/repos/huggingface/datasets/issues/2518/events | https://github.com/huggingface/datasets/pull/2518 | 924,654,100 | MDExOlB1bGxSZXF1ZXN0NjczMjU5Nzg1 | 2,518 | Add task templates for tydiqa and xquad | {
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"Just tested TydiQA and it works fine :)"
] | 1,624,003,594,000 | 1,624,028,477,000 | 1,624,027,833,000 | MEMBER | null | This PR adds question-answering templates to the remaining datasets that are linked to a model on the Hub.
Notes:
* I could not test the tydiqa implementation since I don't have enough disk space π’ . But I am confident the template works :)
* there exist other datasets like `fquad` and `mlqa` which are candida... | {
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https://api.github.com/repos/huggingface/datasets/issues/2517 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2517/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2517/comments | https://api.github.com/repos/huggingface/datasets/issues/2517/events | https://github.com/huggingface/datasets/pull/2517 | 924,643,345 | MDExOlB1bGxSZXF1ZXN0NjczMjUwODk1 | 2,517 | Fix typo in MatthewsCorrelation class name | {
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