id int64 599M 3.29B | url stringlengths 58 61 | html_url stringlengths 46 51 | number int64 1 7.72k | title stringlengths 1 290 | state stringclasses 2 values | comments int64 0 70 | created_at timestamp[s]date 2020-04-14 10:18:02 2025-08-05 09:28:51 | updated_at timestamp[s]date 2020-04-27 16:04:17 2025-08-05 11:39:56 | closed_at timestamp[s]date 2020-04-14 12:01:40 2025-08-01 05:15:45 ⌀ | user_login stringlengths 3 26 | labels listlengths 0 4 | body stringlengths 0 228k ⌀ | is_pull_request bool 2 classes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,274,010,628 | https://api.github.com/repos/huggingface/datasets/issues/4518 | https://github.com/huggingface/datasets/pull/4518 | 4,518 | Patch tests for hfh v0.8.0 | closed | 1 | 2022-06-16T19:45:32 | 2022-06-17T16:15:57 | 2022-06-17T16:06:07 | LysandreJik | [] | This PR patches testing utilities that would otherwise fail with hfh v0.8.0. | true |
1,273,960,476 | https://api.github.com/repos/huggingface/datasets/issues/4517 | https://github.com/huggingface/datasets/pull/4517 | 4,517 | Add tags for task_ids:summarization-* and task_categories:summarization* | closed | 2 | 2022-06-16T18:52:25 | 2022-07-08T15:14:23 | 2022-07-08T15:02:31 | hobson | [] | yaml header at top of README.md file was edited to add task tags because I couldn't find the existing tags in the json
separate Pull Request will modify dataset_infos.json to add these tags
The Enron dataset (dataset id aeslc) is only tagged with:
arxiv:1906.03497'
languages:en
pretty_name:AESLC
Using the email subject_line field as a label or target variable it possible to create models for the following task_ids (in order of relevance):
'task_ids:summarization'
'task_ids:summarization-other-conversations-summarization'
"task_ids:other-other-query-based-multi-document-summarization"
'task_ids:summarization-other-aspect-based-summarization'
'task_ids:summarization--other-headline-generation'
The subject might also be used for the task_category "task_categories:summarization"
E-mail chains might be used for the task category "task_categories:dialogue-system" | true |
1,273,825,640 | https://api.github.com/repos/huggingface/datasets/issues/4516 | https://github.com/huggingface/datasets/pull/4516 | 4,516 | Fix hashing for python 3.9 | closed | 2 | 2022-06-16T16:42:31 | 2022-06-28T13:33:46 | 2022-06-28T13:23:06 | lhoestq | [] | In python 3.9, pickle hashes the `glob_ids` dictionary in addition to the `globs` of a function.
Therefore the test at `tests/test_fingerprint.py::RecurseDumpTest::test_recurse_dump_for_function_with_shuffled_globals` is currently failing for python 3.9
To make hashing deterministic when the globals are not in the same order, we also need to make the order of `glob_ids` deterministic.
Right now we don't have a CI to test python 3.9 but we should definitely have one. For this PR in particular I ran the tests locally using python 3.9 and they're passing now.
Fix https://github.com/huggingface/datasets/issues/4506 | true |
1,273,626,131 | https://api.github.com/repos/huggingface/datasets/issues/4515 | https://github.com/huggingface/datasets/pull/4515 | 4,515 | Add uppercased versions of image file extensions for automatic module inference | closed | 1 | 2022-06-16T14:14:49 | 2022-06-16T17:21:53 | 2022-06-16T17:11:41 | mariosasko | [] | Adds the uppercased versions of the image file extensions to the supported extensions.
Another approach would be to call `.lower()` on extensions while resolving data files, but uppercased extensions are not something we want to encourage out of the box IMO unless they are commonly used (as they are in the vision domain)
Note that there is a slight discrepancy between the image file resolution and `imagefolder` as the latter calls `.lower()` on file extensions leading to some image file extensions being ignored by the resolution but not by the loader (e.g. `pNg`). Such extensions should also be discouraged, so I'm ignoring that case too.
Fix #4514. | true |
1,273,505,230 | https://api.github.com/repos/huggingface/datasets/issues/4514 | https://github.com/huggingface/datasets/issues/4514 | 4,514 | Allow .JPEG as a file extension | closed | 2 | 2022-06-16T12:36:20 | 2022-06-20T08:18:46 | 2022-06-16T17:11:40 | DiGyt | [
"bug"
] | ## Describe the bug
When loading image data, HF datasets seems to recognize `.jpg` and `.jpeg` file extensions, but not e.g. .JPEG. As the naming convention .JPEG is used in important datasets such as imagenet, I would welcome if according extensions like .JPEG or .JPG would be allowed.
## Steps to reproduce the bug
```python
# use bash to create 2 sham datasets with jpeg and JPEG ext
!mkdir dataset_a
!mkdir dataset_b
!wget https://upload.wikimedia.org/wikipedia/commons/7/71/Dsc_%28179253513%29.jpeg -O example_img.jpeg
!cp example_img.jpeg ./dataset_a/
!mv example_img.jpeg ./dataset_b/example_img.JPEG
from datasets import load_dataset
# working
df1 = load_dataset("./dataset_a", ignore_verifications=True)
#not working
df2 = load_dataset("./dataset_b", ignore_verifications=True)
# show
print(df1, df2)
```
## Expected results
```
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 1
})
}) DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 1
})
})
```
## Actual results
```
FileNotFoundError: Unable to resolve any data file that matches '['**']' at /..PATH../dataset_b with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```
I know that it can be annoying to allow seemingly arbitrary numbers of file extensions. But I think this one would be really welcome. | false |
1,273,450,338 | https://api.github.com/repos/huggingface/datasets/issues/4513 | https://github.com/huggingface/datasets/pull/4513 | 4,513 | Update Google Cloud Storage documentation and add Azure Blob Storage example | closed | 5 | 2022-06-16T11:46:09 | 2022-06-23T17:05:11 | 2022-06-23T16:54:59 | alvarobartt | [
"documentation"
] | While I was going through the 🤗 Datasets documentation of the Cloud storage filesystems at https://huggingface.co/docs/datasets/filesystems, I realized that the Google Cloud Storage documentation could be improved e.g. bullet point says "Load your dataset" when the actual call was to "Save your dataset", in-line code comment was mentioning "s3 bucket" instead of "gcs bucket", and some more in-line comments could be included.
Also, I think that mixing Google Cloud Storage documentation with AWS S3's one was a little bit confusing, so I moved all those to the end of the document under an h2 tab named "Other filesystems", with an h3 for "Google Cloud Storage".
Besides that, I was currently working with Azure Blob Storage and found out that the URL to [adlfs](https://github.com/fsspec/adlfs) was common for both filesystems Azure Blob Storage and Azure DataLake Storage, as well as the URL, which was updated even though the redirect was working fine, so I decided to group those under the same row in the column of supported filesystems.
And took also the change to add a small documentation entry as for Google Cloud Storage but for Azure Blob Storage, as I assume that AWS S3, GCP Cloud Storage, and Azure Blob Storage, are the most used cloud storage providers.
Let me know if you're OK with these changes, or whether you want me to roll back some of those! :hugs: | true |
1,273,378,129 | https://api.github.com/repos/huggingface/datasets/issues/4512 | https://github.com/huggingface/datasets/pull/4512 | 4,512 | Add links to vision tasks scripts in ADD_NEW_DATASET template | closed | 2 | 2022-06-16T10:35:35 | 2022-07-08T14:07:50 | 2022-07-08T13:56:23 | mariosasko | [] | Add links to vision dataset scripts in the ADD_NEW_DATASET template. | true |
1,273,336,874 | https://api.github.com/repos/huggingface/datasets/issues/4511 | https://github.com/huggingface/datasets/pull/4511 | 4,511 | Support all negative values in ClassLabel | closed | 4 | 2022-06-16T09:59:39 | 2025-07-23T18:38:15 | 2022-06-16T13:54:07 | lhoestq | [] | We usually use -1 to represent a missing label, but we should also support any negative values (some users use -100 for example). This is a regression from `datasets` 2.3
Fix https://github.com/huggingface/datasets/issues/4508 | true |
1,273,260,396 | https://api.github.com/repos/huggingface/datasets/issues/4510 | https://github.com/huggingface/datasets/pull/4510 | 4,510 | Add regression test for `ArrowWriter.write_batch` when batch is empty | closed | 2 | 2022-06-16T08:53:51 | 2022-06-16T12:38:02 | 2022-06-16T12:28:19 | alvarobartt | [] | As spotted by @cccntu in #4502, there's a logic bug in `ArrowWriter.write_batch` as the if-statement to handle the empty batches as detailed in the docstrings of the function ("Ignores the batch if it appears to be empty, preventing a potential schema update of unknown types."), the current if-statement is not handling properly `writer.write_batch({})` as an error is triggered.
Also, if we add a regression test in `test_arrow_writer.py::test_write_batch` before applying the fix, the test will fail as when trying to write an empty batch as follows:
```
=================================================================================== short test summary info ===================================================================================
FAILED tests/test_arrow_writer.py::test_write_batch[None-None] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[None-1] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[None-10] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields1-None] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields1-1] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields1-10] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields2-None] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields2-1] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields2-10] - ValueError: Schema and number of arrays unequal
======================================================================== 9 failed, 73 deselected, 7 warnings in 0.81s =========================================================================
```
So the batch is not ignored when empty, as `batch_examples={}` won't match the condition `if batch_examples: ...`. | true |
1,273,227,760 | https://api.github.com/repos/huggingface/datasets/issues/4509 | https://github.com/huggingface/datasets/pull/4509 | 4,509 | Support skipping Parquet to Arrow conversion when using Beam | closed | 3 | 2022-06-16T08:25:38 | 2022-11-07T16:22:41 | 2022-11-07T16:22:41 | albertvillanova | [] | null | true |
1,272,718,921 | https://api.github.com/repos/huggingface/datasets/issues/4508 | https://github.com/huggingface/datasets/issues/4508 | 4,508 | cast_storage method from datasets.features | closed | 2 | 2022-06-15T20:47:22 | 2022-06-16T13:54:07 | 2022-06-16T13:54:07 | romainremyb | [
"bug"
] | ## Describe the bug
A bug occurs when mapping a function to a dataset object. I ran the same code with the same data yesterday and it worked just fine. It works when i run locally on an old version of datasets.
## Steps to reproduce the bug
Steps are:
- load whatever datset
- write a preprocessing function such as "tokenize_and_align_labels" written in https://huggingface.co/docs/transformers/tasks/token_classification
- map the function on dataset and get "ValueError: Class label -100 less than -1" from cast_storage method from datasets.features
# Sample code to reproduce the bug
def tokenize_and_align_labels(examples):
tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True, max_length=38,padding="max_length")
labels = []
for i, label in enumerate(examples[f"labels"]):
word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word.
previous_word_idx = None
label_ids = []
for word_idx in word_ids: # Set the special tokens to -100.
if word_idx is None:
label_ids.append(-100)
elif word_idx != previous_word_idx: # Only label the first token of a given word.
label_ids.append(label[word_idx])
else:
label_ids.append(-100)
previous_word_idx = word_idx
labels.append(label_ids)
tokenized_inputs["labels"] = labels
return tokenized_inputs
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
dt = dataset.map(tokenize_and_align_labels, batched=True)
## Expected results
New dataset objects should load and do on older versions.
## Actual results
"ValueError: Class label -100 less than -1" from cast_storage method from datasets.features
## Environment info
everything works fine on older installations of datasets/transformers
Issue arises when installing datasets on google collab under python3.7
I can't manage to find the exact output you're requirering but version printed is datasets-2.3.2
| false |
1,272,615,932 | https://api.github.com/repos/huggingface/datasets/issues/4507 | https://github.com/huggingface/datasets/issues/4507 | 4,507 | How to let `load_dataset` return a `Dataset` instead of `DatasetDict` in customized loading script | closed | 2 | 2022-06-15T18:56:34 | 2022-06-16T10:40:08 | 2022-06-16T10:40:08 | liyucheng09 | [
"enhancement"
] | If the dataset does not need splits, i.e., no training and validation split, more like a table. How can I let the `load_dataset` function return a `Dataset` object directly rather than return a `DatasetDict` object with only one key-value pair.
Or I can paraphrase the question in the following way: how to skip `_split_generators` step in `DatasetBuilder` to let `as_dataset` gives a single `Dataset` rather than a list`[Dataset]`?
Many thanks for any help. | false |
1,272,516,895 | https://api.github.com/repos/huggingface/datasets/issues/4506 | https://github.com/huggingface/datasets/issues/4506 | 4,506 | Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results | closed | 5 | 2022-06-15T17:11:31 | 2023-02-16T03:14:32 | 2022-06-28T13:23:05 | DrMatters | [
"bug"
] | ## Describe the bug
Sometimes I get messages about not being able to hash a method:
`Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform datasets.arrow_dataset.Dataset.
_map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.`
Whilst the function looks like this:
```python
@staticmethod
def _separate_speaker_id_from_dialogue(example: arrow_dataset.Example):
speaker_id, dialogue = tuple(zip(*(example["dialogue"])))
example["speaker_id"] = speaker_id
example["dialogue"] = dialogue
return example
```
This is the first step in my preprocessing pipeline, but sometimes the message about failure to hash is not appearing on the first step, but then appears on a later step.
This error is sometimes causing a failure to use cached data, instead of re-running all steps again.
## Steps to reproduce the bug
```python
import copy
import datasets
from datasets import arrow_dataset
def main():
dataset = datasets.load_dataset("blended_skill_talk")
res = dataset.map(method)
print(res)
def method(example: arrow_dataset.Example):
example['previous_utterance_copy'] = copy.deepcopy(example['previous_utterance'])
return example
if __name__ == '__main__':
main()
```
Run with:
```
python -m reproduce_error
```
## Expected results
Dataset is mapped and cached correctly.
## Actual results
The code outputs this at some point:
`Parameter 'function'=<function method at 0x7faa83d2a160> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Ubuntu 20.04.3
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Datasets version: 2.3.1
| false |
1,272,477,226 | https://api.github.com/repos/huggingface/datasets/issues/4505 | https://github.com/huggingface/datasets/pull/4505 | 4,505 | Fix double dots in data files | closed | 2 | 2022-06-15T16:31:04 | 2022-06-15T17:15:58 | 2022-06-15T17:05:53 | lhoestq | [] | As mentioned in https://github.com/huggingface/transformers/pull/17715 `data_files` can't find a file if the path contains double dots `/../`. This has been introduced in https://github.com/huggingface/datasets/pull/4412, by trying to ignore hidden files and directories (i.e. if they start with a dot)
I fixed this and added a test
cc @sgugger @ydshieh | true |
1,272,418,480 | https://api.github.com/repos/huggingface/datasets/issues/4504 | https://github.com/huggingface/datasets/issues/4504 | 4,504 | Can you please add the Stanford dog dataset? | closed | 16 | 2022-06-15T15:39:35 | 2024-12-09T15:44:11 | 2023-10-18T18:55:30 | dgrnd4 | [
"good first issue",
"dataset request"
] | ## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/ImageNetDogs/)*
- **Motivation:** *The dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It is useful for fine-grain purpose *
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| false |
1,272,367,055 | https://api.github.com/repos/huggingface/datasets/issues/4503 | https://github.com/huggingface/datasets/pull/4503 | 4,503 | Refactor and add metadata to fever dataset | closed | 5 | 2022-06-15T14:59:47 | 2022-07-06T11:54:15 | 2022-07-06T11:41:30 | albertvillanova | [] | Related to: #4452 and #3792. | true |
1,272,353,700 | https://api.github.com/repos/huggingface/datasets/issues/4502 | https://github.com/huggingface/datasets/issues/4502 | 4,502 | Logic bug in arrow_writer? | closed | 10 | 2022-06-15T14:50:00 | 2022-06-18T15:15:51 | 2022-06-18T15:15:51 | changjonathanc | [] | https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(iter(batch_examples.values()))) == 0:
+ if not batch_examples or len(next(iter(batch_examples.values()))) == 0:
return
```
@lhoestq | false |
1,272,300,646 | https://api.github.com/repos/huggingface/datasets/issues/4501 | https://github.com/huggingface/datasets/pull/4501 | 4,501 | Corrected broken links in doc | closed | 1 | 2022-06-15T14:12:17 | 2022-06-15T15:11:05 | 2022-06-15T15:00:56 | clefourrier | [] | null | true |
1,272,281,992 | https://api.github.com/repos/huggingface/datasets/issues/4500 | https://github.com/huggingface/datasets/pull/4500 | 4,500 | Add `concatenate_datasets` for iterable datasets | closed | 3 | 2022-06-15T13:58:50 | 2022-06-28T21:25:39 | 2022-06-28T21:15:04 | lhoestq | [] | `concatenate_datasets` currently only supports lists of `datasets.Dataset`, not lists of `datasets.IterableDataset` like `interleave_datasets`
Fix https://github.com/huggingface/datasets/issues/2564
I also moved `_interleave_map_style_datasets` from combine.py to arrow_dataset.py, since the logic depends a lot on the `Dataset` object internals
And I moved `concatenate_datasets` from arrow_dataset.py to combine.py to have it with `interleave_datasets` (though it's also copied in arrow_dataset module for backward compatibility for now) | true |
1,272,118,162 | https://api.github.com/repos/huggingface/datasets/issues/4499 | https://github.com/huggingface/datasets/pull/4499 | 4,499 | fix ETT m1/m2 test/val dataset | closed | 3 | 2022-06-15T11:51:02 | 2022-06-15T14:55:56 | 2022-06-15T14:45:13 | kashif | [] | https://huggingface.co/datasets/ett/discussions/1 | true |
1,272,100,549 | https://api.github.com/repos/huggingface/datasets/issues/4498 | https://github.com/huggingface/datasets/issues/4498 | 4,498 | WER and CER > 1 | closed | 1 | 2022-06-15T11:35:12 | 2022-06-15T16:38:05 | 2022-06-15T16:38:05 | sadrasabouri | [
"bug"
] | ## Describe the bug
It seems that in some cases in which the `prediction` is longer than the `reference` we may have word/character error rate higher than 1 which is a bit odd.
If it's a real bug I think I can solve it with a PR changing [this](https://github.com/huggingface/datasets/blob/master/metrics/wer/wer.py#L105) line to
```python
return min(incorrect / total, 1.0)
```
## Steps to reproduce the bug
```python
from datasets import load_metric
wer = load_metric("wer")
wer_value = wer.compute(predictions=["Hi World vka"], references=["Hello"])
print(wer_value)
```
## Expected results
```
1.0
```
## Actual results
```
3.0
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | false |
1,271,964,338 | https://api.github.com/repos/huggingface/datasets/issues/4497 | https://github.com/huggingface/datasets/pull/4497 | 4,497 | Re-add download_manager module in utils | closed | 5 | 2022-06-15T09:44:33 | 2022-06-15T10:33:28 | 2022-06-15T10:23:44 | lhoestq | [] | https://github.com/huggingface/datasets/pull/4384 moved `datasets.utils.download_manager` to `datasets.download.download_manager`
This breaks `evaluate` which imports `DownloadMode` from `datasets.utils.download_manager`
This PR re-adds `datasets.utils.download_manager` without circular imports.
We could also show a message that says that accessing it is deprecated, but I think we can do this in a subsequent PR, and just focus on doing a patch release for now | true |
1,271,945,704 | https://api.github.com/repos/huggingface/datasets/issues/4496 | https://github.com/huggingface/datasets/pull/4496 | 4,496 | Replace `assertEqual` with `assertTupleEqual` in unit tests for verbosity | closed | 2 | 2022-06-15T09:29:16 | 2022-07-07T17:06:51 | 2022-07-07T16:55:48 | alvarobartt | [] | As detailed in #4419 and as suggested by @mariosasko, we could replace the `assertEqual` assertions with `assertTupleEqual` when the assertion is between Tuples, in order to make the tests more verbose. | true |
1,271,851,025 | https://api.github.com/repos/huggingface/datasets/issues/4495 | https://github.com/huggingface/datasets/pull/4495 | 4,495 | Fix patching module that doesn't exist | closed | 1 | 2022-06-15T08:17:50 | 2022-06-15T16:40:49 | 2022-06-15T08:54:09 | lhoestq | [] | Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true
When trying to patch `scipy.io.loadmat`:
```python
ModuleNotFoundError: No module named 'scipy'
```
Instead it shouldn't raise an error and do nothing
Bug introduced by #4375
Fix https://github.com/huggingface/datasets/issues/4494 | true |
1,271,850,599 | https://api.github.com/repos/huggingface/datasets/issues/4494 | https://github.com/huggingface/datasets/issues/4494 | 4,494 | Patching fails for modules that are not installed or don't exist | closed | 0 | 2022-06-15T08:17:29 | 2022-06-15T08:54:09 | 2022-06-15T08:54:09 | lhoestq | [] | Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true
When trying to patch `scipy.io.loadmat`:
```python
ModuleNotFoundError: No module named 'scipy'
```
Instead it shouldn't raise an error and do nothing
We use patching to extend such functions to support remote URLs and work in streaming mode | false |
1,271,306,385 | https://api.github.com/repos/huggingface/datasets/issues/4493 | https://github.com/huggingface/datasets/pull/4493 | 4,493 | Add `@transmit_format` in `flatten` | closed | 4 | 2022-06-14T20:09:09 | 2022-09-27T11:37:25 | 2022-09-27T10:48:54 | alvarobartt | [] | As suggested by @mariosasko in https://github.com/huggingface/datasets/pull/4411, we should include the `@transmit_format` decorator to `flatten`, `rename_column`, and `rename_columns` so as to ensure that the value of `_format_columns` in an `ArrowDataset` is properly updated.
**Edit**: according to @mariosasko comment below, the decorator `@transmit_format` doesn't handle column renaming, so it's done manually for those instead. | true |
1,271,112,497 | https://api.github.com/repos/huggingface/datasets/issues/4492 | https://github.com/huggingface/datasets/pull/4492 | 4,492 | Pin the revision in imagenet download links | closed | 1 | 2022-06-14T17:15:17 | 2022-06-14T17:35:13 | 2022-06-14T17:25:45 | lhoestq | [] | Use the commit sha in the data files URLs of the imagenet-1k download script, in case we want to restructure the data files in the future. For example we may split it into many more shards for better paralellism.
cc @mariosasko | true |
1,270,803,822 | https://api.github.com/repos/huggingface/datasets/issues/4491 | https://github.com/huggingface/datasets/issues/4491 | 4,491 | Dataset Viewer issue for Pavithree/test | closed | 1 | 2022-06-14T13:23:10 | 2022-06-14T14:37:21 | 2022-06-14T14:34:33 | Pavithree | [
"dataset-viewer"
] | ### Link
https://huggingface.co/datasets/Pavithree/test
### Description
I have extracted the subset of original eli5 dataset found at hugging face. However, while loading the dataset It throws ArrowNotImplementedError: Unsupported cast from string to null using function cast_null error. Is there anything missing from my end? Kindly help.
### Owner
_No response_ | false |
1,270,719,074 | https://api.github.com/repos/huggingface/datasets/issues/4490 | https://github.com/huggingface/datasets/issues/4490 | 4,490 | Use `torch.nested_tensor` for arrays of varying length in torch formatter | open | 2 | 2022-06-14T12:19:40 | 2023-07-07T13:02:58 | null | mariosasko | [
"enhancement"
] | Use `torch.nested_tensor` for arrays of varying length in `TorchFormatter`.
The PyTorch API of nested tensors is in the prototype stage, so wait for it to become more mature. | false |
1,270,706,195 | https://api.github.com/repos/huggingface/datasets/issues/4489 | https://github.com/huggingface/datasets/pull/4489 | 4,489 | Add SV-Ident dataset | closed | 5 | 2022-06-14T12:09:00 | 2022-06-20T08:48:26 | 2022-06-20T08:37:27 | e-tornike | [] | null | true |
1,270,613,857 | https://api.github.com/repos/huggingface/datasets/issues/4488 | https://github.com/huggingface/datasets/pull/4488 | 4,488 | Update PASS dataset version | closed | 1 | 2022-06-14T10:47:14 | 2022-06-14T16:41:55 | 2022-06-14T16:32:28 | mariosasko | [] | Update the PASS dataset to version v3 (the newest one) from the [version history](https://github.com/yukimasano/PASS/blob/main/version_history.txt).
PS: The older versions are not exposed as configs in the script because v1 was removed from Zenodo, and the same thing will probably happen to v2. | true |
1,270,525,163 | https://api.github.com/repos/huggingface/datasets/issues/4487 | https://github.com/huggingface/datasets/pull/4487 | 4,487 | Support streaming UDHR dataset | closed | 1 | 2022-06-14T09:33:33 | 2022-06-15T05:09:22 | 2022-06-15T04:59:49 | albertvillanova | [] | This PR:
- Adds support for streaming UDHR dataset
- Adds the BCP 47 language code as feature | true |
1,269,518,084 | https://api.github.com/repos/huggingface/datasets/issues/4486 | https://github.com/huggingface/datasets/pull/4486 | 4,486 | Add CCAgT dataset | closed | 8 | 2022-06-13T14:20:19 | 2022-07-04T14:37:03 | 2022-07-04T14:25:45 | johnnv1 | [] | As described in #4075
I could not generate the dummy data. Also, on the data repository isn't provided the split IDs, but I copy the functions that provide the correct data split. In summary, to have a better distribution, the data in this dataset should be separated based on the amount of NORs in each image. | true |
1,269,463,054 | https://api.github.com/repos/huggingface/datasets/issues/4485 | https://github.com/huggingface/datasets/pull/4485 | 4,485 | Fix cast to null | closed | 1 | 2022-06-13T13:44:32 | 2022-06-14T13:43:54 | 2022-06-14T13:34:14 | lhoestq | [] | It currently fails with `ArrowNotImplementedError` instead of `TypeError` when one tries to cast integer to null type.
Because if this, type inference breaks when one replaces null values with integers in `map` (it first tries to cast to the previous type before inferring the new type).
Fix https://github.com/huggingface/datasets/issues/4483 | true |
1,269,383,811 | https://api.github.com/repos/huggingface/datasets/issues/4484 | https://github.com/huggingface/datasets/pull/4484 | 4,484 | Better ImportError message when a dataset script dependency is missing | closed | 4 | 2022-06-13T12:44:37 | 2022-07-08T14:30:44 | 2022-06-13T13:50:47 | lhoestq | [] | When a depenency is missing for a dataset script, an ImportError message is shown, with a tip to install the missing dependencies. This message is not ideal at the moment: it may show duplicate dependencies, and is not very readable.
I improved it from
```
ImportError: To be able to use bigbench, you need to install the following dependencies['bigbench', 'bigbench', 'bigbench', 'bigbench'] using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz" bigbench bigbench bigbench' for instance'
```
to
```
ImportError: To be able to use bigbench, you need to install the following dependency: bigbench.
Please install it using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz"' for instance'
``` | true |
1,269,253,840 | https://api.github.com/repos/huggingface/datasets/issues/4483 | https://github.com/huggingface/datasets/issues/4483 | 4,483 | Dataset.map throws pyarrow.lib.ArrowNotImplementedError when converting from list of empty lists | closed | 1 | 2022-06-13T10:47:52 | 2022-06-14T13:34:14 | 2022-06-14T13:34:14 | sanderland | [
"bug"
] | ## Describe the bug
Dataset.map throws pyarrow.lib.ArrowNotImplementedError: Unsupported cast from int64 to null using function cast_null when converting from a type of 'empty lists' to 'lists with some type'.
This appears to be due to the interaction of arrow internals and some assumptions made by datasets.
The bug appeared when binarizing some labels, and then adding a dataset which had all these labels absent (to force the model to not label empty strings such with anything)
Particularly the fact that this only happens in batched mode is strange.
## Steps to reproduce the bug
```python
import numpy as np
ds = Dataset.from_dict(
{
"text": ["the lazy dog jumps over the quick fox", "another sentence"],
"label": [[], []],
}
)
def mapper(features):
features['label'] = [
[0,0,0] for l in features['label']
]
return features
ds_mapped = ds.map(mapper,batched=True)
```
## Expected results
Not crashing
## Actual results
```
../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2346: in map
return self._map_single(
../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:532: in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:499: in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
../.venv/lib/python3.8/site-packages/datasets/fingerprint.py:458: in wrapper
out = func(self, *args, **kwargs)
../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2751: in _map_single
writer.write_batch(batch)
../.venv/lib/python3.8/site-packages/datasets/arrow_writer.py:503: in write_batch
arrays.append(pa.array(typed_sequence))
pyarrow/array.pxi:230: in pyarrow.lib.array
???
pyarrow/array.pxi:110: in pyarrow.lib._handle_arrow_array_protocol
???
../.venv/lib/python3.8/site-packages/datasets/arrow_writer.py:198: in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
../.venv/lib/python3.8/site-packages/datasets/table.py:1675: in wrapper
return func(array, *args, **kwargs)
../.venv/lib/python3.8/site-packages/datasets/table.py:1812: in cast_array_to_feature
casted_values = _c(array.values, feature.feature)
../.venv/lib/python3.8/site-packages/datasets/table.py:1675: in wrapper
return func(array, *args, **kwargs)
../.venv/lib/python3.8/site-packages/datasets/table.py:1843: in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
../.venv/lib/python3.8/site-packages/datasets/table.py:1675: in wrapper
return func(array, *args, **kwargs)
../.venv/lib/python3.8/site-packages/datasets/table.py:1752: in array_cast
return array.cast(pa_type)
pyarrow/array.pxi:915: in pyarrow.lib.Array.cast
???
../.venv/lib/python3.8/site-packages/pyarrow/compute.py:376: in cast
return call_function("cast", [arr], options)
pyarrow/_compute.pyx:542: in pyarrow._compute.call_function
???
pyarrow/_compute.pyx:341: in pyarrow._compute.Function.call
???
pyarrow/error.pxi:144: in pyarrow.lib.pyarrow_internal_check_status
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
E pyarrow.lib.ArrowNotImplementedError: Unsupported cast from int64 to null using function cast_null
pyarrow/error.pxi:121: ArrowNotImplementedError
```
## Workarounds
* Not using batched=True
* Using an np.array([],dtype=float) or similar instead of [] in the input
* Naming the output column differently from the input column
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2
- Platform: Ubuntu
- Python version: 3.8
- PyArrow version: 8.0.0
| false |
1,269,237,447 | https://api.github.com/repos/huggingface/datasets/issues/4482 | https://github.com/huggingface/datasets/pull/4482 | 4,482 | Test that TensorFlow is not imported on startup | closed | 3 | 2022-06-13T10:33:49 | 2023-10-12T06:31:39 | 2023-10-11T09:11:56 | lhoestq | [] | TF takes some time to be imported, and also uses some GPU memory.
I just added a test to make sure that in the future it's never imported by default when
```python
import datasets
```
is called.
Right now this fails because `huggingface_hub` does import tensorflow (though this is fixed now on their `main` branch)
I'll mark this PR as ready for review once `huggingface_hub` has a new release | true |
1,269,187,792 | https://api.github.com/repos/huggingface/datasets/issues/4481 | https://github.com/huggingface/datasets/pull/4481 | 4,481 | Fix iwslt2017 | closed | 4 | 2022-06-13T09:51:21 | 2022-10-26T09:09:31 | 2022-06-13T10:40:18 | lhoestq | [] | The files were moved to google drive, I hosted them on the Hub instead (ok according to the license)
I also updated the `datasets_infos.json` | true |
1,268,921,567 | https://api.github.com/repos/huggingface/datasets/issues/4480 | https://github.com/huggingface/datasets/issues/4480 | 4,480 | Bigbench tensorflow GPU dependency | closed | 3 | 2022-06-13T05:24:06 | 2022-06-14T19:45:24 | 2022-06-14T19:45:23 | cceyda | [
"bug"
] | ## Describe the bug
Loading bigbech
```py
from datasets import load_dataset
dataset = load_dataset("bigbench","swedish_to_german_proverbs")
```
tries to use gpu and fails with OOM with the following error
```
Downloading and preparing dataset bigbench/swedish_to_german_proverbs (download: Unknown size, generated: 68.92 KiB, post-processed: Unknown size, total: 68.92 KiB) to /home/ceyda/.cache/huggingface/datasets/bigbench/swedish_to_german_proverbs/1.0.0/7d2f6e537fa937dfaac8b1c1df782f2055071d3fd8e4f4ae93d28012a354ced0...
Generating default split: 0%| | 0/72 [00:00<?, ? examples/s]2022-06-13 14:11:04.154469: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-06-13 14:11:05.133600: F tensorflow/core/platform/statusor.cc:33] Attempting to fetch value instead of handling error INTERNAL: failed initializing StreamExecutor for CUDA device ordinal 3: INTERNAL: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 25396838400
Aborted (core dumped)
```
I think this is because bigbench dependency (below) installs tensorflow (GPU version) and dataloading tries to use GPU as default.
`pip install bigbench@https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz`
while just doing 'pip install bigbench' results in following error
```
File "/home/ceyda/.local/lib/python3.7/site-packages/datasets/load.py", line 109, in import_main_class
module = importlib.import_module(module_path)
File "/usr/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/ceyda/.cache/huggingface/modules/datasets_modules/datasets/bigbench/7d2f6e537fa937dfaac8b1c1df782f2055071d3fd8e4f4ae93d28012a354ced0/bigbench.py", line 118, in <module>
class Bigbench(datasets.GeneratorBasedBuilder):
File "/home/ceyda/.cache/huggingface/modules/datasets_modules/datasets/bigbench/7d2f6e537fa937dfaac8b1c1df782f2055071d3fd8e4f4ae93d28012a354ced0/bigbench.py", line 127, in Bigbench
BigBenchConfig(name=name, version=datasets.Version("1.0.0")) for name in bb_utils.get_all_json_task_names()
AttributeError: module 'bigbench.api.util' has no attribute 'get_all_json_task_names'
```
## Steps to avoid the bug
Not ideal but can solve with (since I don't really use tensorflow elsewhere)
`pip uninstall tensorflow`
`pip install tensorflow-cpu`
## Environment info
- datasets @ master
- Python version: 3.7
| false |
1,268,558,237 | https://api.github.com/repos/huggingface/datasets/issues/4479 | https://github.com/huggingface/datasets/pull/4479 | 4,479 | Include entity positions as feature in ReCoRD | closed | 3 | 2022-06-12T11:56:28 | 2022-08-19T23:23:02 | 2022-08-19T13:23:48 | richarddwang | [] | https://huggingface.co/datasets/super_glue/viewer/record/validation
TLDR: We need to record entity positions, which are included in the source data but excluded by the loading script, to enable efficient and effective training for ReCoRD.
Currently, the loading script ignores the entity positions ("entity_start", "entity_end") and only records entity text. This might be because the training method of the official baseline is to make n training instance from a datapoint by replacing \"\@ placeholder\" in query with each entity individually.
But it increases the already heavy computation by multiple folds. So DeBERTa uses a method that take entity embeddings by their positions in the passage, and thus makes one training instance from one data point. It is way more efficient and proved effective for the ReCoRD task.
Can anybody help me with the dataset card rendering error? Maybe @lhoestq ? | true |
1,268,358,213 | https://api.github.com/repos/huggingface/datasets/issues/4478 | https://github.com/huggingface/datasets/issues/4478 | 4,478 | Dataset slow during model training | open | 5 | 2022-06-11T19:40:19 | 2022-06-14T12:04:31 | null | lehrig | [
"bug"
] | ## Describe the bug
While migrating towards 🤗 Datasets, I encountered an odd performance degradation: training suddenly slows down dramatically. I train with an image dataset using Keras and execute a `to_tf_dataset` just before training.
First, I have optimized my dataset following https://discuss.huggingface.co/t/solved-image-dataset-seems-slow-for-larger-image-size/10960/6, which actually improved the situation from what I had before but did not completely solve it.
Second, I saved and loaded my dataset using `tf.data.experimental.save` and `tf.data.experimental.load` before training (for which I would have expected no performance change). However, I ended up with the performance I had before tinkering with 🤗 Datasets.
Any idea what's the reason for this and how to speed-up training with 🤗 Datasets?
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
import os
dataset_dir = "./dataset"
prep_dataset_dir = "./prepdataset"
model_dir = "./model"
# Load Data
dataset = load_dataset("Lehrig/Monkey-Species-Collection", "downsized")
def read_image_file(example):
with open(example["image"].filename, "rb") as f:
example["image"] = {"bytes": f.read()}
return example
dataset = dataset.map(read_image_file)
dataset.save_to_disk(dataset_dir)
# Preprocess
from datasets import (
Array3D,
DatasetDict,
Features,
load_from_disk,
Sequence,
Value
)
import numpy as np
from transformers import ImageFeatureExtractionMixin
dataset = load_from_disk(dataset_dir)
num_classes = dataset["train"].features["label"].num_classes
one_hot_matrix = np.eye(num_classes)
feature_extractor = ImageFeatureExtractionMixin()
def to_pixels(image):
image = feature_extractor.resize(image, size=size)
image = feature_extractor.to_numpy_array(image, channel_first=False)
image = image / 255.0
return image
def process(examples):
examples["pixel_values"] = [
to_pixels(image) for image in examples["image"]
]
examples["label"] = [
one_hot_matrix[label] for label in examples["label"]
]
return examples
features = Features({
"pixel_values": Array3D(dtype="float32", shape=(size, size, 3)),
"label": Sequence(feature=Value(dtype="int32"), length=num_classes)
})
prep_dataset = dataset.map(
process,
remove_columns=["image"],
batched=True,
batch_size=batch_size,
num_proc=2,
features=features,
)
prep_dataset = prep_dataset.with_format("numpy")
# Split
train_dev_dataset = prep_dataset['test'].train_test_split(
test_size=test_size,
shuffle=True,
seed=seed
)
train_dev_test_dataset = DatasetDict({
'train': train_dev_dataset['train'],
'dev': train_dev_dataset['test'],
'test': prep_dataset['test'],
})
train_dev_test_dataset.save_to_disk(prep_dataset_dir)
# Train Model
import datetime
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.applications import InceptionV3
from tensorflow.keras.layers import Dense, Dropout, GlobalAveragePooling2D, BatchNormalization
from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping
from transformers import DefaultDataCollator
dataset = load_from_disk(prep_data_dir)
data_collator = DefaultDataCollator(return_tensors="tf")
train_dataset = dataset["train"].to_tf_dataset(
columns=['pixel_values'],
label_cols=['label'],
shuffle=True,
batch_size=batch_size,
collate_fn=data_collator
)
validation_dataset = dataset["dev"].to_tf_dataset(
columns=['pixel_values'],
label_cols=['label'],
shuffle=False,
batch_size=batch_size,
collate_fn=data_collator
)
print(f'{datetime.datetime.now()} - Saving Data')
tf.data.experimental.save(train_dataset, model_dir+"/train")
tf.data.experimental.save(validation_dataset, model_dir+"/val")
print(f'{datetime.datetime.now()} - Loading Data')
train_dataset = tf.data.experimental.load(model_dir+"/train")
validation_dataset = tf.data.experimental.load(model_dir+"/val")
shape = np.shape(dataset["train"][0]["pixel_values"])
backbone = InceptionV3(
include_top=False,
weights='imagenet',
input_shape=shape
)
for layer in backbone.layers:
layer.trainable = False
model = Sequential()
model.add(backbone)
model.add(GlobalAveragePooling2D())
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(64, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(10, activation='softmax'))
model.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy']
)
print(model.summary())
earlyStopping = EarlyStopping(
monitor='val_loss',
patience=10,
verbose=0,
mode='min'
)
mcp_save = ModelCheckpoint(
f'{model_dir}/best_model.hdf5',
save_best_only=True,
monitor='val_loss',
mode='min'
)
reduce_lr_loss = ReduceLROnPlateau(
monitor='val_loss',
factor=0.1,
patience=7,
verbose=1,
min_delta=0.0001,
mode='min'
)
hist = model.fit(
train_dataset,
epochs=epochs,
validation_data=validation_dataset,
callbacks=[earlyStopping, mcp_save, reduce_lr_loss]
)
```
## Expected results
Same performance when training without my "save/load hack" or a good explanation/recommendation about the issue.
## Actual results
Performance slower without my "save/load hack".
**Epoch Breakdown (without my "save/load hack"):**
- Epoch 1/10
41s 2s/step - loss: 1.6302 - accuracy: 0.5048 - val_loss: 1.4713 - val_accuracy: 0.3273 - lr: 0.0010
- Epoch 2/10
32s 2s/step - loss: 0.5357 - accuracy: 0.8510 - val_loss: 1.0447 - val_accuracy: 0.5818 - lr: 0.0010
- Epoch 3/10
36s 3s/step - loss: 0.3547 - accuracy: 0.9231 - val_loss: 0.6245 - val_accuracy: 0.7091 - lr: 0.0010
- Epoch 4/10
36s 3s/step - loss: 0.2721 - accuracy: 0.9231 - val_loss: 0.3395 - val_accuracy: 0.9091 - lr: 0.0010
- Epoch 5/10
32s 2s/step - loss: 0.1676 - accuracy: 0.9856 - val_loss: 0.2187 - val_accuracy: 0.9636 - lr: 0.0010
- Epoch 6/10
42s 3s/step - loss: 0.2066 - accuracy: 0.9615 - val_loss: 0.1635 - val_accuracy: 0.9636 - lr: 0.0010
- Epoch 7/10
32s 2s/step - loss: 0.1814 - accuracy: 0.9423 - val_loss: 0.1418 - val_accuracy: 0.9636 - lr: 0.0010
- Epoch 8/10
32s 2s/step - loss: 0.1301 - accuracy: 0.9856 - val_loss: 0.1388 - val_accuracy: 0.9818 - lr: 0.0010
- Epoch 9/10
loss: 0.1102 - accuracy: 0.9856 - val_loss: 0.1185 - val_accuracy: 0.9818 - lr: 0.0010
- Epoch 10/10
32s 2s/step - loss: 0.1013 - accuracy: 0.9808 - val_loss: 0.0978 - val_accuracy: 0.9818 - lr: 0.0010
**Epoch Breakdown (with my "save/load hack"):**
- Epoch 1/10
13s 625ms/step - loss: 3.0478 - accuracy: 0.1146 - val_loss: 2.3061 - val_accuracy: 0.0727 - lr: 0.0010
- Epoch 2/10
0s 80ms/step - loss: 2.3105 - accuracy: 0.2656 - val_loss: 2.3085 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 3/10
0s 77ms/step - loss: 1.8608 - accuracy: 0.3542 - val_loss: 2.3130 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 4/10
1s 98ms/step - loss: 1.8677 - accuracy: 0.3750 - val_loss: 2.3157 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 5/10
1s 204ms/step - loss: 1.5561 - accuracy: 0.4583 - val_loss: 2.3049 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 6/10
1s 210ms/step - loss: 1.4657 - accuracy: 0.4896 - val_loss: 2.2944 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 7/10
1s 205ms/step - loss: 1.4018 - accuracy: 0.5312 - val_loss: 2.2917 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 8/10
1s 207ms/step - loss: 1.2370 - accuracy: 0.5729 - val_loss: 2.2814 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 9/10
1s 214ms/step - loss: 1.1190 - accuracy: 0.6250 - val_loss: 2.2733 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 10/10
1s 207ms/step - loss: 1.1484 - accuracy: 0.6302 - val_loss: 2.2624 - val_accuracy: 0.0909 - lr: 0.0010
## Environment info
- `datasets` version: 2.2.2
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
- TensorFlow: 2.8.0
- GPU (used during training): Tesla V100-SXM2-32GB
| false |
1,268,308,986 | https://api.github.com/repos/huggingface/datasets/issues/4477 | https://github.com/huggingface/datasets/issues/4477 | 4,477 | Dataset Viewer issue for fgrezes/WIESP2022-NER | closed | 2 | 2022-06-11T15:49:17 | 2022-07-18T13:07:33 | 2022-07-18T13:07:33 | AshTayade | [] | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | false |
1,267,987,499 | https://api.github.com/repos/huggingface/datasets/issues/4476 | https://github.com/huggingface/datasets/issues/4476 | 4,476 | `to_pandas` doesn't take into account format. | closed | 4 | 2022-06-10T20:25:31 | 2022-06-15T17:41:41 | 2022-06-15T17:41:41 | Dref360 | [
"enhancement"
] | **Is your feature request related to a problem? Please describe.**
I have a large dataset that I need to convert part of to pandas to do some further analysis. Calling `to_pandas` directly on it is expensive. So I thought I could simply select the columns that I want and then call `to_pandas`.
**Describe the solution you'd like**
```python
from datasets import Dataset
ds = Dataset.from_dict({'a': [1,2,3], 'b': [5,6,7], 'c': [8,9,10]})
pandas_df = ds.with_format(columns=['a', 'b']).to_pandas()
# I would expect `pandas_df` to only include a,b as column.
```
**Describe alternatives you've considered**
I could remove all columns that I don't want? But I don't know all of them in advance.
**Additional context**
I can probably make a PR with some pointers.
| false |
1,267,798,451 | https://api.github.com/repos/huggingface/datasets/issues/4475 | https://github.com/huggingface/datasets/pull/4475 | 4,475 | Improve error message for missing pacakges from inside dataset script | closed | 3 | 2022-06-10T16:59:36 | 2022-10-06T13:46:26 | 2022-06-13T13:16:43 | mariosasko | [] | Improve the error message for missing packages from inside a dataset script:
With this change, the error message for missing packages for `bigbench` looks as follows:
```
ImportError: To be able to use bigbench, you need to install the following dependencies:
- 'bigbench' using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz"'
```
And this is how it looked before:
```
ImportError: To be able to use bigbench, you need to install the following dependencies['bigbench', 'bigbench', 'bigbench', 'bigbench'] using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz" bigbench bigbench bigbench' for instance'
``` | true |
1,267,767,541 | https://api.github.com/repos/huggingface/datasets/issues/4474 | https://github.com/huggingface/datasets/pull/4474 | 4,474 | [Docs] How to use with PyTorch page | closed | 1 | 2022-06-10T16:25:49 | 2022-06-14T14:40:32 | 2022-06-14T14:04:33 | lhoestq | [
"documentation"
] | Currently the docs about PyTorch are scattered around different pages, and we were missing a place to explain more in depth how to use and optimize a dataset for PyTorch. This PR is related to #4457 which is the TF counterpart :)
cc @Rocketknight1 we can try to align both documentations contents now I think
cc @stevhliu let me know what you think ! | true |
1,267,555,994 | https://api.github.com/repos/huggingface/datasets/issues/4473 | https://github.com/huggingface/datasets/pull/4473 | 4,473 | Add SST-2 dataset | closed | 5 | 2022-06-10T13:37:26 | 2022-06-13T14:11:34 | 2022-06-13T14:01:09 | albertvillanova | [] | Add SST-2 dataset.
Currently it is part of GLUE benchmark.
This PR adds it as a standalone dataset.
CC: @julien-c | true |
1,267,488,523 | https://api.github.com/repos/huggingface/datasets/issues/4472 | https://github.com/huggingface/datasets/pull/4472 | 4,472 | Fix 401 error for unauthticated requests to non-existing repos | closed | 1 | 2022-06-10T12:38:11 | 2022-06-10T13:05:11 | 2022-06-10T12:55:57 | lhoestq | [] | The hub now returns 401 instead of 404 for unauthenticated requests to non-existing repos.
This PR add support for the 401 error and fixes the CI fails on `master` | true |
1,267,475,268 | https://api.github.com/repos/huggingface/datasets/issues/4471 | https://github.com/huggingface/datasets/issues/4471 | 4,471 | CI error with repo lhoestq/_dummy | closed | 1 | 2022-06-10T12:26:06 | 2022-06-10T13:24:53 | 2022-06-10T13:24:53 | albertvillanova | [
"bug"
] | ## Describe the bug
CI is failing because of repo "lhoestq/_dummy". See: https://app.circleci.com/pipelines/github/huggingface/datasets/12461/workflows/1b040b45-9578-4ab9-8c44-c643c4eb8691/jobs/74269
```
requests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/api/datasets/lhoestq/_dummy?full=true
```
The repo seems to no longer exist: https://huggingface.co/api/datasets/lhoestq/_dummy
```
error: "Repository not found"
```
CC: @lhoestq | false |
1,267,470,051 | https://api.github.com/repos/huggingface/datasets/issues/4470 | https://github.com/huggingface/datasets/pull/4470 | 4,470 | Reorder returned validation/test splits in script template | closed | 1 | 2022-06-10T12:21:13 | 2022-06-10T18:04:10 | 2022-06-10T17:54:50 | albertvillanova | [] | null | true |
1,267,213,849 | https://api.github.com/repos/huggingface/datasets/issues/4469 | https://github.com/huggingface/datasets/pull/4469 | 4,469 | Replace data URLs in wider_face dataset once hosted on the Hub | closed | 1 | 2022-06-10T08:13:25 | 2022-06-10T16:42:08 | 2022-06-10T16:32:46 | albertvillanova | [] | This PR replaces the URLs of data files in Google Drive with our Hub ones, once the data owners have approved to host their data on the Hub.
They also informed us that their dataset is licensed under CC BY-NC-ND. | true |
1,266,715,742 | https://api.github.com/repos/huggingface/datasets/issues/4468 | https://github.com/huggingface/datasets/pull/4468 | 4,468 | Generalize tutorials for audio and vision | closed | 1 | 2022-06-09T22:00:44 | 2022-06-14T16:22:02 | 2022-06-14T16:12:00 | stevhliu | [
"documentation"
] | This PR updates the tutorials to be more generalizable to all modalities. After reading the tutorials, a user should be able to load any type of dataset, know how to index into and slice a dataset, and do the most basic/common type of preprocessing (tokenization, resampling, applying transforms) depending on their dataset.
Other changes include:
- Removed the sections about a dataset's metadata, features, and columns because we cover this in an earlier tutorial about inspecting the `DatasetInfo` through the dataset builder.
- Separate the sharing dataset tutorial into two sections: (1) uploading via the web interface and (2) using the `huggingface_hub` library.
- Renamed some tutorials in the TOC to be more clear and specific.
- Added more text to nudge users towards joining the community and asking questions on the forums.
- If it's okay with everyone, I'd also like to remove the section about loading and using metrics since we have the `evaluate` docs now.
| true |
1,266,218,358 | https://api.github.com/repos/huggingface/datasets/issues/4467 | https://github.com/huggingface/datasets/issues/4467 | 4,467 | Transcript string 'null' converted to [None] by load_dataset() | closed | 3 | 2022-06-09T14:26:00 | 2023-07-04T02:18:39 | 2022-06-09T16:29:02 | mbarnig | [
"bug"
] | ## Issue
I am training a luxembourgish speech-recognition model in Colab with a custom dataset, including a dictionary of luxembourgish words, for example the speaken numbers 0 to 9. When preparing the dataset with the script
`ds_train1 = mydataset.map(prepare_dataset)`
the following error was issued:
```
ValueError Traceback (most recent call last)
<ipython-input-69-1e8f2b37f5bc> in <module>()
----> 1 ds_train = mydataset_train.map(prepare_dataset)
11 frames
/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py in __call__(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)
2450 if not _is_valid_text_input(text):
2451 raise ValueError(
-> 2452 "text input must of type str (single example), List[str] (batch or single pretokenized example) "
2453 "or List[List[str]] (batch of pretokenized examples)."
2454 )
ValueError: text input must of type str (single example), List[str] (batch or single pretokenized example) or List[List[str]] (batch of pretokenized examples).
```
Debugging this problem was not easy, all transcriptions in the dataset are correct strings. Finally I discovered that the transcription string 'null' is interpreted as [None] by the `load_dataset()` script. By deleting this row in the dataset the training worked fine.
## Expected result:
transcription 'null' interpreted as 'str' instead of 'None'.
## Reproduction
Here is the code to reproduce the error with a one-row-dataset.
```
with open("null-test.csv") as f:
reader = csv.reader(f)
for row in reader:
print(row)
```
['wav_filename', 'wav_filesize', 'transcript']
['wavs/female/NULL1.wav', '17530', 'null']
```
dataset = load_dataset('csv', data_files={'train': 'null-test.csv'})
```
Using custom data configuration default-81ac0c0e27af3514
Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-81ac0c0e27af3514/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...
Downloading data files: 100%
1/1 [00:00<00:00, 29.55it/s]
Extracting data files: 100%
1/1 [00:00<00:00, 23.66it/s]
Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-81ac0c0e27af3514/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.
100%
1/1 [00:00<00:00, 25.84it/s]
```
print(dataset['train']['transcript'])
```
[None]
## Environment info
```
!pip install datasets==2.2.2
!pip install transformers==4.19.2
``` | false |
1,266,159,920 | https://api.github.com/repos/huggingface/datasets/issues/4466 | https://github.com/huggingface/datasets/pull/4466 | 4,466 | Optimize contiguous shard and select | closed | 3 | 2022-06-09T13:45:39 | 2022-06-14T16:04:30 | 2022-06-14T15:54:45 | lhoestq | [] | Currently `.shard()` and `.select()` always create an indices mapping. However if the requested data are contiguous, it's much more optimized to simply slice the Arrow table instead of building an indices mapping. In particular:
- the shard/select operation will be much faster
- reading speed will be much faster in the resulting dataset, since it won't have to do a lookup step in the indices mapping
Since `.shard()` is also used for `.map()` with `num_proc>1`, it will also significantly improve the reading speed of multiprocessed `.map()` operations
Here is an example of speed-up:
```python
>>> import io
>>> import numpy as np
>>> from datasets import Dataset
>>> ds = Dataset.from_dict({"a": np.random.rand(10_000_000)})
>>> shard = ds.shard(num_shards=4, index=0, contiguous=True) # this calls `.select(range(2_500_000))`
>>> buf = io.BytesIO()
>>> %time dd.to_json(buf)
Creating json from Arrow format: 100%|██████████████████| 100/100 [00:00<00:00, 376.17ba/s]
CPU times: user 258 ms, sys: 9.06 ms, total: 267 ms
Wall time: 266 ms
```
while previously it was
```python
Creating json from Arrow format: 100%|███████████████████| 100/100 [00:03<00:00, 29.41ba/s]
CPU times: user 3.33 s, sys: 69.1 ms, total: 3.39 s
Wall time: 3.4 s
```
In this simple case the speed-up is x10, but @sayakpaul experienced a x100 speed-up on its data when exporting to JSON.
## Implementation details
I mostly improved `.select()`: it now checks if the input corresponds to a contiguous chunk of data and then it slices the main Arrow table (or the indices mapping table if it exists). To check if the input indices are contiguous it checks two possibilities:
- if the indices is of type `range`, it checks that start >= 0 and step = 1
- otherwise in the general case, it iterates over the indices. If all the indices are contiguous then we're good, otherwise we have to build an indices mapping.
Having to iterate over the indices doesn't cause performance issues IMO because:
- either they are contiguous and in this case the cost of iterating over the indices is much less than the cost of creating an indices mapping
- or they are not contiguous, and then iterating generally stops quickly when it first encounters the first indice that is not contiguous. | true |
1,265,754,479 | https://api.github.com/repos/huggingface/datasets/issues/4465 | https://github.com/huggingface/datasets/pull/4465 | 4,465 | Fix bigbench config names | closed | 1 | 2022-06-09T08:06:19 | 2022-06-09T14:38:36 | 2022-06-09T14:29:19 | lhoestq | [] | Fix https://github.com/huggingface/datasets/issues/4462 in the case of bigbench | true |
1,265,682,931 | https://api.github.com/repos/huggingface/datasets/issues/4464 | https://github.com/huggingface/datasets/pull/4464 | 4,464 | Extend support for streaming datasets that use xml.dom.minidom.parse | closed | 1 | 2022-06-09T06:58:25 | 2022-06-09T08:43:24 | 2022-06-09T08:34:16 | albertvillanova | [] | This PR extends the support in streaming mode for datasets that use `xml.dom.minidom.parse`, by patching that function.
This PR adds support for streaming datasets like "Yaxin/SemEval2015".
Fix #4453. | true |
1,265,093,211 | https://api.github.com/repos/huggingface/datasets/issues/4463 | https://github.com/huggingface/datasets/pull/4463 | 4,463 | Use config_id to check split sizes instead of config name | closed | 2 | 2022-06-08T17:45:24 | 2023-09-24T10:03:00 | 2022-06-09T08:06:37 | lhoestq | [] | Fix https://github.com/huggingface/datasets/issues/4462 | true |
1,265,079,347 | https://api.github.com/repos/huggingface/datasets/issues/4462 | https://github.com/huggingface/datasets/issues/4462 | 4,462 | BigBench: NonMatchingSplitsSizesError when passing a dataset configuration parameter | open | 3 | 2022-06-08T17:31:24 | 2022-07-05T07:39:55 | null | lhoestq | [
"bug"
] | As noticed in https://github.com/huggingface/datasets/pull/4125 when a dataset config class has a parameter that reduces the number of examples (e.g. named `max_examples`), then loading the dataset and passing `max_examples` raises `NonMatchingSplitsSizesError`.
This is because it will check for expected the number of examples of the config with the same name without taking into account the `max_examples` parameter. This can be fixed by checking the expected number of examples using the **config id** instead of name. Indeed the config id corresponds to the config name + an optional suffix that depends on the config parameters | false |
1,264,800,451 | https://api.github.com/repos/huggingface/datasets/issues/4461 | https://github.com/huggingface/datasets/issues/4461 | 4,461 | AttributeError: module 'datasets' has no attribute 'load_dataset' | closed | 4 | 2022-06-08T13:59:20 | 2024-03-25T12:58:29 | 2022-06-08T14:41:00 | AlexNLP | [
"bug"
] | ## Describe the bug
I have piped install datasets, but this package doesn't have these attributes: load_dataset, load_metric.
## Environment info
- `datasets` version: 1.9.0
- Platform: Linux-5.13.0-44-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.6.13
- PyArrow version: 6.0.1
| false |
1,264,644,205 | https://api.github.com/repos/huggingface/datasets/issues/4460 | https://github.com/huggingface/datasets/pull/4460 | 4,460 | Drop Python 3.6 support | closed | 5 | 2022-06-08T12:10:18 | 2022-07-26T19:16:39 | 2022-07-26T19:04:21 | mariosasko | [] | Remove the fallback imports/checks in the code needed for Python 3.6 and update the requirements/CI files. Also, use Python types for the NumPy dtype wherever possible to avoid deprecation warnings in newer NumPy versions.
| true |
1,264,636,481 | https://api.github.com/repos/huggingface/datasets/issues/4459 | https://github.com/huggingface/datasets/pull/4459 | 4,459 | Add and fix language tags for udhr dataset | closed | 1 | 2022-06-08T12:03:42 | 2022-06-08T12:36:24 | 2022-06-08T12:27:13 | albertvillanova | [] | Related to #4362. | true |
1,263,531,911 | https://api.github.com/repos/huggingface/datasets/issues/4457 | https://github.com/huggingface/datasets/pull/4457 | 4,457 | First draft of the docs for TF + Datasets | closed | 4 | 2022-06-07T16:06:48 | 2022-06-14T16:08:41 | 2022-06-14T15:59:08 | Rocketknight1 | [
"documentation"
] | I might cc a few of the other TF people to take a look when this is closer to being finished, but it's still a draft for now. | true |
1,263,241,449 | https://api.github.com/repos/huggingface/datasets/issues/4456 | https://github.com/huggingface/datasets/issues/4456 | 4,456 | Workflow for Tabular data | open | 8 | 2022-06-07T12:48:22 | 2023-03-06T08:53:55 | null | lhoestq | [
"enhancement",
"generic discussion"
] | Tabular data are treated very differently than data for NLP, audio, vision, etc. and therefore the worflow for tabular data in `datasets` is not ideal.
For example for tabular data, it is common to use pandas/spark/dask to process the data, and then load the data into X and y (X is an array of features and y an array of labels), then train_test_split and finally feed the data to a machine learning model.
In `datasets` the workflow is different: we use load_dataset, then map, then train_test_split (if we only have a train split) and we end up with columnar dataset splits, not formatted as X and y.
Right now, it is already possible to convert a dataset from and to pandas, but there are still many things that could improve the workflow for tabular data:
- be able to load the data into X and y
- be able to load a dataset from the output of spark or dask (as far as I know it's usually csv or parquet files on S3/GCS/HDFS etc.)
- support "unsplit" datasets explicitly, instead of putting everything in "train" by default
cc @adrinjalali @merveenoyan feel free to complete/correct this :)
Feel free to also share ideas of APIs that would be super intuitive in your opinion ! | false |
1,263,089,067 | https://api.github.com/repos/huggingface/datasets/issues/4455 | https://github.com/huggingface/datasets/pull/4455 | 4,455 | Update data URLs in fever dataset | closed | 1 | 2022-06-07T10:40:54 | 2022-06-08T07:24:54 | 2022-06-08T07:16:17 | albertvillanova | [] | As stated in their website, data owners updated their URLs on 28/04/2022.
This PR updates the data URLs.
Fix #4452. | true |
1,262,674,973 | https://api.github.com/repos/huggingface/datasets/issues/4454 | https://github.com/huggingface/datasets/issues/4454 | 4,454 | Dataset Viewer issue for Yaxin/SemEval2015 | closed | 1 | 2022-06-07T03:31:46 | 2022-06-07T11:53:11 | 2022-06-07T11:53:11 | WithYouTo | [
"duplicate",
"dataset-viewer"
] | ### Link
_No response_
### Description
the link could not visit
### Owner
_No response_ | false |
1,262,674,105 | https://api.github.com/repos/huggingface/datasets/issues/4453 | https://github.com/huggingface/datasets/issues/4453 | 4,453 | Dataset Viewer issue for Yaxin/SemEval2015 | closed | 3 | 2022-06-07T03:30:08 | 2022-06-09T08:34:16 | 2022-06-09T08:34:16 | WithYouTo | [] | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | false |
1,262,529,654 | https://api.github.com/repos/huggingface/datasets/issues/4452 | https://github.com/huggingface/datasets/issues/4452 | 4,452 | Trying to load FEVER dataset results in NonMatchingChecksumError | closed | 2 | 2022-06-06T23:13:15 | 2022-12-15T13:36:40 | 2022-06-08T07:16:16 | santhnm2 | [
"bug"
] | ## Describe the bug
Trying to load the `fever` dataset fails with `datasets.utils.info_utils.NonMatchingChecksumError`.
I tried with `download_mode="force_redownload"` but that did not fix the error. I also tried with `ignore_verification=True` but then that raised a `json.decoder.JSONDecodeError`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('fever', 'v1.0') # Fails with NonMatchingChecksumError
dataset = load_dataset('fever', 'v1.0', download_mode="force_redownload") # Fails with NonMatchingChecksumError
dataset = load_dataset('fever', 'v1.0', ignore_verification=True)` # Fails with JSONDecodeError
```
## Expected results
I expect this call to return with no error raised.
## Actual results
With `ignore_verification=False`:
```
*** datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://s3-eu-west-1.amazonaws.com/fever.public/train.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev_public.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_test.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_test.jsonl']
```
With `ignore_verification=True`:
```
*** json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.3.dev0
- Platform: Linux-4.15.0-50-generic-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
| false |
1,262,103,323 | https://api.github.com/repos/huggingface/datasets/issues/4451 | https://github.com/huggingface/datasets/pull/4451 | 4,451 | Use newer version of multi-news with fixes | closed | 2 | 2022-06-06T16:57:08 | 2022-06-07T17:40:01 | 2022-06-07T17:14:44 | JohnGiorgi | [] | Closes #4430. | true |
1,261,878,324 | https://api.github.com/repos/huggingface/datasets/issues/4450 | https://github.com/huggingface/datasets/pull/4450 | 4,450 | Update README.md of fquad | closed | 1 | 2022-06-06T13:52:41 | 2022-06-06T14:51:49 | 2022-06-06T14:43:03 | lhoestq | [] | null | true |
1,261,262,326 | https://api.github.com/repos/huggingface/datasets/issues/4449 | https://github.com/huggingface/datasets/issues/4449 | 4,449 | Rj | closed | 0 | 2022-06-06T02:24:32 | 2022-06-06T15:44:50 | 2022-06-06T15:44:50 | Aeckard45 | [] | import android.content.DialogInterface;
import android.database.Cursor;
import android.os.Bundle;
import android.view.View;
import android.widget.ArrayAdapter;
import android.widget.Button;
import android.widget.EditText;
import android.widget.Toast;
import androidx.appcompat.app.AlertDialog;
import androidx.appcompat.app.AppCompatActivity;
public class MainActivity extends AppCompatActivity {
private EditText editTextID;
private EditText editTextName;
private EditText editTextNum;
private String name;
private int number;
private String ID;
private dbHelper db;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
db = new dbHelper(this);
editTextID = findViewById(R.id.editText1);
editTextName = findViewById(R.id.editText2);
editTextNum = findViewById(R.id.editText3);
Button buttonSave = findViewById(R.id.button);
Button buttonRead = findViewById(R.id.button2);
Button buttonUpdate = findViewById(R.id.button3);
Button buttonDelete = findViewById(R.id.button4);
Button buttonSearch = findViewById(R.id.button5);
Button buttonDeleteAll = findViewById(R.id.button6);
buttonSave.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
name = editTextName.getText().toString();
String num = editTextNum.getText().toString();
if (name.isEmpty() || num.isEmpty()) {
Toast.makeText(MainActivity.this, "Cannot Submit Empty Fields", Toast.LENGTH_SHORT).show();
} else {
number = Integer.parseInt(num);
try {
// Insert Data
db.insertData(name, number);
// Clear the fields
editTextID.getText().clear();
editTextName.getText().clear();
editTextNum.getText().clear();
} catch (Exception e) {
e.printStackTrace();
}
}
}
});
buttonRead.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
final ArrayAdapter<String> adapter = new ArrayAdapter<>(MainActivity.this, android.R.layout.simple_list_item_1);
String name;
String num;
String id;
try {
Cursor cursor = db.readData();
if (cursor != null && cursor.getCount() > 0) {
while (cursor.moveToNext()) {
id = cursor.getString(0); // get data in column index 0
name = cursor.getString(1); // get data in column index 1
num = cursor.getString(2); // get data in column index 2
// Add SQLite data to listView
adapter.add("ID :- " + id + "\n" +
"Name :- " + name + "\n" +
"Number :- " + num + "\n\n");
}
} else {
adapter.add("No Data");
}
cursor.close();
} catch (Exception e) {
e.printStackTrace();
}
// show the saved data in alertDialog
AlertDialog.Builder builder = new AlertDialog.Builder(MainActivity.this);
builder.setTitle("SQLite saved data");
builder.setIcon(R.mipmap.app_icon_foreground);
builder.setAdapter(adapter, new DialogInterface.OnClickListener() {
@Override
public void onClick(DialogInterface dialog, int which) {
}
});
builder.setPositiveButton("OK", new DialogInterface.OnClickListener() {
@Override
public void onClick(DialogInterface dialog, int which) {
dialog.cancel();
}
});
AlertDialog dialog = builder.create();
dialog.show();
}
});
buttonUpdate.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
name = editTextName.getText().toString();
String num = editTextNum.getText().toString();
ID = editTextID.getText().toString();
if (name.isEmpty() || num.isEmpty() || ID.isEmpty()) {
Toast.makeText(MainActivity.this, "Cannot Submit Empty Fields", Toast.LENGTH_SHORT).show();
} else {
number = Integer.parseInt(num);
try {
// Update Data
db.updateData(ID, name, number);
// Clear the fields
editTextID.getText().clear();
editTextName.getText().clear();
editTextNum.getText().clear();
} catch (Exception e) {
e.printStackTrace();
}
}
}
});
buttonDelete.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
ID = editTextID.getText().toString();
if (ID.isEmpty()) {
Toast.makeText(MainActivity.this, "Please enter the ID", Toast.LENGTH_SHORT).show();
} else {
try {
// Delete Data
db.deleteData(ID);
// Clear the fields
editTextID.getText().clear();
editTextName.getText().clear();
editTextNum.getText().clear();
} catch (Exception e) {
e.printStackTrace();
}
}
}
});
buttonDeleteAll.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
// Delete all data
// You can simply delete all the data by calling this method --> db.deleteAllData();
// You can try this also
AlertDialog.Builder builder = new AlertDialog.Builder(MainActivity.this);
builder.setIcon(R.mipmap.app_icon_foreground);
builder.setTitle("Delete All Data");
builder.setCancelable(false);
builder.setMessage("Do you really need to delete your all data ?");
builder.setPositiveButton("Yes", new DialogInterface.OnClickListener() {
@Override
public void onClick(DialogInterface dialog, int which) {
// User confirmed , now you can delete the data
db.deleteAllData();
// Clear the fields
editTextID.getText().clear();
editTextName.getText().clear();
editTextNum.getText().clear();
}
});
builder.setNegativeButton("No", new DialogInterface.OnClickListener() {
@Override
public void onClick(DialogInterface dialog, int which) {
// user not confirmed
dialog.cancel();
}
});
AlertDialog dialog = builder.create();
dialog.show();
}
});
buttonSearch.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
ID = editTextID.getText().toString();
if (ID.isEmpty()) {
Toast.makeText(MainActivity.this, "Please enter the ID", Toast.LENGTH_SHORT).show();
} else {
try {
// Search data
Cursor cursor = db.searchData(ID);
if (cursor.moveToFirst()) {
editTextName.setText(cursor.getString(1));
editTextNum.setText(cursor.getString(2));
Toast.makeText(MainActivity.this, "Data successfully searched", Toast.LENGTH_SHORT).show();
} else {
Toast.makeText(MainActivity.this, "ID not found", Toast.LENGTH_SHORT).show();
editTextNum.setText("ID Not found");
editTextName.setText("ID not found");
}
cursor.close();
} catch (Exception e) {
e.printStackTrace();
}
}
}
});
}
} | false |
1,260,966,129 | https://api.github.com/repos/huggingface/datasets/issues/4448 | https://github.com/huggingface/datasets/issues/4448 | 4,448 | New Preprocessing Feature - Deduplication [Request] | open | 2 | 2022-06-05T05:32:56 | 2023-12-12T07:52:40 | null | yuvalkirstain | [
"duplicate",
"enhancement"
] | **Is your feature request related to a problem? Please describe.**
Many large datasets are full of duplications and it has been shown that deduplicating datasets can lead to better performance while training, and more truthful evaluation at test-time.
A feature that allows one to easily deduplicate a dataset can be cool!
**Describe the solution you'd like**
We can define a function and keep only the first/last data-point that yields the value according to this function.
**Describe alternatives you've considered**
The clear alternative is to repeat a clear boilerplate every time someone want to deduplicate a dataset.
| false |
1,260,041,805 | https://api.github.com/repos/huggingface/datasets/issues/4447 | https://github.com/huggingface/datasets/pull/4447 | 4,447 | Minor fixes/improvements in `scene_parse_150` card | closed | 1 | 2022-06-03T15:22:34 | 2022-06-06T15:50:25 | 2022-06-06T15:41:37 | mariosasko | [] | Add `paperswithcode_id` and fix some links in the `scene_parse_150` card. | true |
1,260,028,995 | https://api.github.com/repos/huggingface/datasets/issues/4446 | https://github.com/huggingface/datasets/pull/4446 | 4,446 | Add missing kwargs to docstrings | closed | 1 | 2022-06-03T15:10:27 | 2022-06-03T16:10:09 | 2022-06-03T16:01:29 | albertvillanova | [] | null | true |
1,259,947,568 | https://api.github.com/repos/huggingface/datasets/issues/4445 | https://github.com/huggingface/datasets/pull/4445 | 4,445 | Fix missing args in docstring of load_dataset_builder | closed | 1 | 2022-06-03T13:55:50 | 2022-06-03T14:35:32 | 2022-06-03T14:27:09 | albertvillanova | [] | Currently, the docstring of `load_dataset_builder` only contains the first parameter `path` (no other):
- https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/loading_methods#datasets.load_dataset_builder.path | true |
1,259,738,209 | https://api.github.com/repos/huggingface/datasets/issues/4444 | https://github.com/huggingface/datasets/pull/4444 | 4,444 | Fix kwargs in docstrings | closed | 1 | 2022-06-03T10:29:02 | 2022-06-03T11:01:28 | 2022-06-03T10:52:46 | albertvillanova | [] | To fix the rendering of `**kwargs` in docstrings, a parentheses must be added afterwards.
See:
- huggingface/doc-builder/issues/235 | true |
1,259,606,334 | https://api.github.com/repos/huggingface/datasets/issues/4443 | https://github.com/huggingface/datasets/issues/4443 | 4,443 | Dataset Viewer issue for openclimatefix/nimrod-uk-1km | open | 7 | 2022-06-03T08:17:16 | 2023-09-25T12:15:08 | null | ZYMXIXI | [] | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | false |
1,258,589,276 | https://api.github.com/repos/huggingface/datasets/issues/4442 | https://github.com/huggingface/datasets/issues/4442 | 4,442 | Dataset Viewer issue for amazon_polarity | closed | 2 | 2022-06-02T19:18:38 | 2022-06-07T18:50:37 | 2022-06-07T18:50:37 | lewtun | [
"dataset-viewer"
] | ### Link
https://huggingface.co/datasets/amazon_polarity/viewer/amazon_polarity/test
### Description
For some reason the train split is OK but the test split is not for this dataset:
```
Server error
Status code: 400
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/amazon_polarity/__init__.py'
```
### Owner
No | false |
1,258,568,656 | https://api.github.com/repos/huggingface/datasets/issues/4441 | https://github.com/huggingface/datasets/issues/4441 | 4,441 | Dataset Viewer issue for aeslc | closed | 1 | 2022-06-02T18:57:12 | 2022-06-07T18:50:55 | 2022-06-07T18:50:55 | lewtun | [
"dataset-viewer"
] | ### Link
https://huggingface.co/datasets/aeslc
### Description
The dataset viewer can't find `dataset_infos.json` in it's cache:
```
Server error
Status code: 400
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/aeslc/eb8e30234cf984a58ebe9f205674597ac1db2ec91e7321cd7f36864f7e3671b8/dataset_infos.json'
```
### Owner
No | false |
1,258,494,469 | https://api.github.com/repos/huggingface/datasets/issues/4440 | https://github.com/huggingface/datasets/pull/4440 | 4,440 | Update docs around audio and vision | closed | 2 | 2022-06-02T17:42:03 | 2022-06-23T16:33:19 | 2022-06-23T16:23:02 | stevhliu | [
"documentation"
] | As part of the strategy to center the docs around the different modalities, this PR updates the quickstart to include audio and vision examples. This improves the developer experience by making audio and vision content more discoverable, enabling users working in these modalities to also quickly get started without digging too deeply into the docs.
Other changes include:
- Moved the installation guide to the Get Started section because it should be part of a user's onboarding to the library before exploring tutorials or how-to's.
- Updated the native TF code at creating a `tf.data.Dataset` because it was throwing an error. The `to_tensor()` bit was redundant and removing it fixed the error (please double-check me here!).
- Added some UI components to the quickstart so it's easier for users to navigate directly to the relevant section with context about what to expect.
- Reverted to the code tabs for content that don't have any framework-specific text. I think this saves space compared to the code blocks. We'll still use the code blocks if the `torch` text is different from the `tf` text.
Let me know what you think, especially if we should include some code samples for training a model in the audio/vision sections. I left this out since we already showed it in the NLP section. I want to keep the focus on using Datasets to load and process a dataset, and not so much the training part. Maybe we can add links to the Transformers docs instead? | true |
1,258,434,111 | https://api.github.com/repos/huggingface/datasets/issues/4439 | https://github.com/huggingface/datasets/issues/4439 | 4,439 | TIMIT won't load after manual download: Errors about files that don't exist | closed | 3 | 2022-06-02T16:35:56 | 2022-06-03T08:44:17 | 2022-06-03T08:44:16 | drscotthawley | [
"bug"
] | ## Describe the bug
I get the message from HuggingFace that it must be downloaded manually. From the URL provided in the message, I got to UPenn page for manual download. (UPenn apparently want $250? for the dataset??) ...So, ok, I obtained a copy from a friend and also a smaller version from Kaggle. But in both cases the HF dataloader fails; it is looking for files that don't exist anywhere in the dataset: it is looking for files with lower-case letters like "**test*" (all the filenames in both my copies are uppercase) and certain file extensions that exclude the .DOC which is provided in TIMIT:
## Steps to reproduce the bug
```python
data = load_dataset('timit_asr', 'clean')['train']
```
## Expected results
The dataset should load with no errors.
## Actual results
This error message:
```
File "/home/ubuntu/envs/data2vec/lib/python3.9/site-packages/datasets/data_files.py", line 201, in resolve_patterns_locally_or_by_urls
raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to resolve any data file that matches '['**test*', '**eval*']' at /home/ubuntu/datasets/timit with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```
But this is a strange sort of error: why is it looking for lower-case file names when all the TIMIT dataset filenames are uppercase? Why does it exclude .DOC files when the only parts of the TIMIT data set with "TEST" in them have ".DOC" extensions? ...I wonder, how was anyone able to get this to work in the first place?
The files in the dataset look like the following:
```
³ PHONCODE.DOC
³ PROMPTS.TXT
³ SPKRINFO.TXT
³ SPKRSENT.TXT
³ TESTSET.DOC
```
...so why are these being excluded by the dataset loader?
## Environment info
- `datasets` version: 2.2.2
- Platform: Linux-5.4.0-1060-aws-x86_64-with-glibc2.27
- Python version: 3.9.9
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
| false |
1,258,255,394 | https://api.github.com/repos/huggingface/datasets/issues/4438 | https://github.com/huggingface/datasets/pull/4438 | 4,438 | Fix docstring of inspect_dataset | closed | 1 | 2022-06-02T14:21:10 | 2022-06-02T16:40:55 | 2022-06-02T16:32:27 | albertvillanova | [] | As pointed out by @sgugger:
- huggingface/doc-builder/issues/235 | true |
1,258,249,582 | https://api.github.com/repos/huggingface/datasets/issues/4437 | https://github.com/huggingface/datasets/pull/4437 | 4,437 | Add missing columns to `blended_skill_talk` | closed | 1 | 2022-06-02T14:16:26 | 2022-06-06T15:49:56 | 2022-06-06T15:41:25 | mariosasko | [] | Adds the missing columns to `blended_skill_talk` to align the loading logic with [ParlAI](https://github.com/facebookresearch/ParlAI/blob/main/parlai/tasks/blended_skill_talk/build.py).
Fix #4426 | true |
1,257,758,834 | https://api.github.com/repos/huggingface/datasets/issues/4436 | https://github.com/huggingface/datasets/pull/4436 | 4,436 | Fix directory names for LDC data in timit_asr dataset | closed | 1 | 2022-06-02T06:45:04 | 2022-06-02T09:32:56 | 2022-06-02T09:24:27 | albertvillanova | [] | Related to:
- #4422 | true |
1,257,496,552 | https://api.github.com/repos/huggingface/datasets/issues/4435 | https://github.com/huggingface/datasets/issues/4435 | 4,435 | Load a local cached dataset that has been modified | closed | 2 | 2022-06-02T01:51:49 | 2022-06-02T23:59:26 | 2022-06-02T23:59:18 | mihail911 | [
"bug"
] | ## Describe the bug
I have loaded a dataset as follows:
```
d = load_dataset("emotion", split="validation")
```
Afterwards I make some modifications to the dataset via a `map` call:
```
d.map(some_update_func, cache_file_name=modified_dataset)
```
This generates a cached version of the dataset on my local system in the same directory as the original download of the data (/path/to/cache). Running an `ls` returns:
```
modified_dataset
dataset_info.json
emotion-test.arrow
emotion-train.arrow
emotion-validation.arrow
```
as expected. However, when I try to load up the modified cached dataset via a call to
```
modified = load_dataset("emotion", split="validation", data_files="/path/to/cache/modified_dataset")
```
it simply redownloads a new version of the dataset and dumps to a new cache rather than loading up the original modified dataset:
```
Using custom data configuration validation-cdbf51685638421b
Downloading and preparing dataset emotion/validation to ...
```
How am I supposed to load the original modified local cache copy of the dataset?
## Environment info
- `datasets` version: 2.2.2
- Platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
| false |
1,256,207,321 | https://api.github.com/repos/huggingface/datasets/issues/4434 | https://github.com/huggingface/datasets/pull/4434 | 4,434 | Fix dummy dataset generation script for handling nested types of _URLs | closed | 0 | 2022-06-01T14:53:15 | 2022-06-07T12:08:28 | 2022-06-07T09:24:09 | silverriver | [] | It seems that when user specify nested _URLs structures in their dataset script. An error will be raised when generating dummy dataset.
I think the types of all elements in `dummy_data_dict.values()` should be checked because they may have different types.
Linked to issue #4428
PS: I am not sure whether my code fix this issue in a proper way. | true |
1,255,830,758 | https://api.github.com/repos/huggingface/datasets/issues/4433 | https://github.com/huggingface/datasets/pull/4433 | 4,433 | Fix script fetching and local path handling in `inspect_dataset` and `inspect_metric` | closed | 2 | 2022-06-01T12:09:56 | 2022-06-09T10:34:54 | 2022-06-09T10:26:07 | mariosasko | [] | Fix #4348 | true |
1,255,523,720 | https://api.github.com/repos/huggingface/datasets/issues/4432 | https://github.com/huggingface/datasets/pull/4432 | 4,432 | Fix builder docstring | closed | 1 | 2022-06-01T09:45:30 | 2022-06-02T17:43:47 | 2022-06-02T17:35:15 | albertvillanova | [] | Currently, the args of `DatasetBuilder` do not appear in the docs: https://huggingface.co/docs/datasets/v2.1.0/en/package_reference/builder_classes#datasets.DatasetBuilder | true |
1,254,618,948 | https://api.github.com/repos/huggingface/datasets/issues/4431 | https://github.com/huggingface/datasets/pull/4431 | 4,431 | Add personaldialog datasets | closed | 5 | 2022-06-01T01:20:40 | 2022-06-11T12:40:23 | 2022-06-11T12:31:16 | silverriver | [] | It seems that all tests are passed | true |
1,254,412,591 | https://api.github.com/repos/huggingface/datasets/issues/4430 | https://github.com/huggingface/datasets/issues/4430 | 4,430 | Add ability to load newer, cleaner version of Multi-News | closed | 6 | 2022-05-31T21:00:44 | 2022-06-07T17:14:44 | 2022-06-07T17:14:44 | JohnGiorgi | [
"enhancement"
] | **Is your feature request related to a problem? Please describe.**
The [Multi-News dataloader points to the original version of the Multi-News dataset](https://github.com/huggingface/datasets/blob/12540dd75015678ec6019f258d811ee107439a73/datasets/multi_news/multi_news.py#L47), but this has [known errors in it](https://github.com/Alex-Fabbri/Multi-News/issues/11). There exists a [newer version which fixes some of these issues](https://drive.google.com/open?id=1jwBzXBVv8sfnFrlzPnSUBHEEAbpIUnFq).
Unfortunately I don't think you can just replace this old URL with the new one, otherwise this could lead to issues with reproducibility.
**Describe the solution you'd like**
Add a new version to the Multi-News dataloader that points to the updated dataset which has fixes for some known issues.
**Describe alternatives you've considered**
Replace the current URL to the original version to the dataset with the URL to the version with fixes.
**Additional context**
Would be happy to make a PR for this, could someone maybe point me to another dataloader that has multiple versions so I can see how this is handled in `datasets`?
| false |
1,254,184,358 | https://api.github.com/repos/huggingface/datasets/issues/4429 | https://github.com/huggingface/datasets/pull/4429 | 4,429 | Update builder docstring for deprecated/added arguments | closed | 5 | 2022-05-31T17:37:25 | 2022-06-08T11:40:18 | 2022-06-08T11:31:45 | albertvillanova | [] | This PR updates the builder docstring with deprecated/added directives for arguments name/config_name.
Follow up of:
- #4414
- huggingface/doc-builder#233
First merge:
- #4432 | true |
1,254,092,818 | https://api.github.com/repos/huggingface/datasets/issues/4428 | https://github.com/huggingface/datasets/issues/4428 | 4,428 | Errors when building dummy data if you use nested _URLS | closed | 0 | 2022-05-31T16:10:57 | 2022-06-07T09:24:09 | 2022-06-07T09:24:09 | silverriver | [
"bug"
] | ## Describe the bug
When making dummy data with the `datasets-cli dummy_data` tool,
an error will be raised if you use a nested _URLS in your dataset script.
Traceback (most recent call last):
File "/home/name/LCCC/datasets/src/datasets/commands/datasets_cli.py", line 43, in <module>
main()
File "/home/name/LCCC/datasets/src/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/home/name/LCCC/datasets/src/datasets/commands/dummy_data.py", line 311, in run
self._autogenerate_dummy_data(
File "/home/name/LCCC/datasets/src/datasets/commands/dummy_data.py", line 337, in _autogenerate_dummy_data
dataset_builder._split_generators(dl_manager)
File "/home/name/.cache/huggingface/modules/datasets_modules/datasets/personal_dialog/559332bced5eeafa7f7efc2a7c10ce02cee2a8116bbab4611c35a50ba2715b77/personal_dialog.py", line 108, in _split_generators
data_dir = dl_manager.download_and_extract(urls)
File "/home/name/LCCC/datasets/src/datasets/commands/dummy_data.py", line 56, in download_and_extract
dummy_output = self.mock_download_manager.download(url_or_urls)
File "/home/name/LCCC/datasets/src/datasets/download/mock_download_manager.py", line 130, in download
return self.download_and_extract(data_url)
File "/home/name/LCCC/datasets/src/datasets/download/mock_download_manager.py", line 122, in download_and_extract
return self.create_dummy_data_dict(dummy_file, data_url)
File "/home/name/LCCC/datasets/src/datasets/download/mock_download_manager.py", line 165, in create_dummy_data_dict
if isinstance(first_value, str) and len(set(dummy_data_dict.values())) < len(dummy_data_dict.values()):
TypeError: unhashable type: 'list'
## Steps to reproduce the bug
You can use my dataset script implemented here:
https://github.com/silverriver/datasets/blob/2ecd36760c40b8e29b1137cd19b5bad0e19c76fd/datasets/personal_dialog/personal_dialog.py
```python
datasets_cli dummy_data datasets/personal_dialog --auto_generate
```
You can change https://github.com/silverriver/datasets/blob/2ecd36760c40b8e29b1137cd19b5bad0e19c76fd/datasets/personal_dialog/personal_dialog.py#L54
to
```
"train": "https://huggingface.co/datasets/silver/personal_dialog/resolve/main/dev_random.jsonl.gz"
```
before runing the above script to avoid downloading a large training data.
## Expected results
The dummy data should be generated
## Actual results
An error is raised.
It seems that in https://github.com/huggingface/datasets/blob/12540dd75015678ec6019f258d811ee107439a73/src/datasets/download/mock_download_manager.py#L165
We only check if the first item of dummy_data_dict.values() is str.
However, dummy_data_dict.values() may have the type of [str, list, list].
A simple fix would be changing https://github.com/huggingface/datasets/blob/12540dd75015678ec6019f258d811ee107439a73/src/datasets/download/mock_download_manager.py#L165 to
```python
if all([isinstance(value, str) for value in dummy_data_dict.values()]) and len(set(dummy_data_dict.values())) < len(dummy_data_dict.values()):
```
But I don't know if this kinds of change may bring any side effect since I am not sure about the detail logic here.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version: Python 3.9.10
- PyArrow version: 7.0.0
| false |
1,253,959,313 | https://api.github.com/repos/huggingface/datasets/issues/4427 | https://github.com/huggingface/datasets/pull/4427 | 4,427 | Add HF.co for PRs/Issues for specific datasets | closed | 1 | 2022-05-31T14:31:21 | 2022-06-01T12:37:42 | 2022-06-01T12:29:02 | lhoestq | [] | As in https://github.com/huggingface/transformers/pull/17485, issues and PR for datasets under a namespace have to be on the HF Hub | true |
1,253,887,311 | https://api.github.com/repos/huggingface/datasets/issues/4426 | https://github.com/huggingface/datasets/issues/4426 | 4,426 | Add loading variable number of columns for different splits | closed | 1 | 2022-05-31T13:40:16 | 2022-06-03T16:25:25 | 2022-06-03T16:25:25 | DrMatters | [
"enhancement"
] | **Is your feature request related to a problem? Please describe.**
The original dataset `blended_skill_talk` consists of different sets of columns for the different splits: (test/valid) splits have additional data column `label_candidates` that the (train) doesn't have.
When loading such data, an exception occurs at table.py:cast_table_to_schema, because of mismatched columns. | false |
1,253,641,604 | https://api.github.com/repos/huggingface/datasets/issues/4425 | https://github.com/huggingface/datasets/pull/4425 | 4,425 | Make extensions case-insensitive in timit_asr dataset | closed | 1 | 2022-05-31T10:10:04 | 2022-06-01T14:15:30 | 2022-06-01T14:06:51 | albertvillanova | [] | Related to #4422. | true |
1,253,542,488 | https://api.github.com/repos/huggingface/datasets/issues/4424 | https://github.com/huggingface/datasets/pull/4424 | 4,424 | Fix DuplicatedKeysError in timit_asr dataset | closed | 1 | 2022-05-31T08:47:45 | 2022-05-31T13:50:50 | 2022-05-31T13:42:31 | albertvillanova | [] | Fix #4422. | true |
1,253,326,023 | https://api.github.com/repos/huggingface/datasets/issues/4423 | https://github.com/huggingface/datasets/pull/4423 | 4,423 | Add new dataset MMChat | closed | 2 | 2022-05-31T04:45:07 | 2022-06-11T12:40:52 | 2022-06-11T12:31:42 | silverriver | [] | Hi, I am adding a new dataset MMChat.
It seems that all tests are passed | true |
1,253,146,511 | https://api.github.com/repos/huggingface/datasets/issues/4422 | https://github.com/huggingface/datasets/issues/4422 | 4,422 | Cannot load timit_asr data set | closed | 6 | 2022-05-30T22:00:22 | 2022-06-02T06:34:05 | 2022-05-31T13:42:31 | bhaddow | [
"bug"
] | ## Describe the bug
I am trying to load the timit_asr data set. I have tried with a copy from the LDC, and a copy from deepai. In both cases they fail with a "duplicate key" error. With the LDC version I have to convert the file extensions all to upper-case before I can load it at all.
## Steps to reproduce the bug
```python
timit = datasets.load_dataset("timit_asr", data_dir = "/path/to/dataset")
# Sample code to reproduce the bug
```
## Expected results
The data set should load without error. It worked for me before the LDC url change.
## Actual results
```
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: SA1
Keys should be unique and deterministic in nature
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- `datasets` version: 2.2.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
| false |
1,253,059,467 | https://api.github.com/repos/huggingface/datasets/issues/4421 | https://github.com/huggingface/datasets/pull/4421 | 4,421 | Add extractor for bzip2-compressed files | closed | 0 | 2022-05-30T19:19:40 | 2022-06-06T15:22:50 | 2022-06-06T15:22:50 | osyvokon | [] | This change enables loading bzipped datasets, just like any other compressed dataset. | true |
1,252,739,239 | https://api.github.com/repos/huggingface/datasets/issues/4420 | https://github.com/huggingface/datasets/issues/4420 | 4,420 | Metric evaluation problems in multi-node, shared file system | closed | 6 | 2022-05-30T13:24:05 | 2023-07-11T09:33:18 | 2023-07-11T09:33:17 | gullabi | [
"bug"
] | ## Describe the bug
Metric evaluation fails in multi-node within a shared file system, because the master process cannot find the lock files from other nodes. (This issue was originally mentioned in the transformers repo https://github.com/huggingface/transformers/issues/17412)
## Steps to reproduce the bug
1. clone [this huggingface model](https://huggingface.co/PereLluis13/wav2vec2-xls-r-300m-ca-lm) and replace the `run_speech_recognition_ctc.py` script with the version in the gist [here](https://gist.github.com/gullabi/3f66094caa8db1c1e615dd35bd67ec71#file-run_speech_recognition_ctc-py).
2. Setup the `venv` according to the requirements of the model file plus `datasets==2.0.0`, `transformers==4.18.0` and `torch==1.9.0`
3. Launch the runner in a distributed environment which has a shared file system for two nodes, preferably with SLURM. Example [here](https://gist.github.com/gullabi/3f66094caa8db1c1e615dd35bd67ec71)
Specifically for the datasets, for the distributed setup the `load_metric` is called as:
```
process_id=int(os.environ["RANK"])
num_process=int(os.environ["WORLD_SIZE"])
eval_metrics = {metric: load_metric(metric,
process_id=process_id,
num_process=num_process,
experiment_id="slurm")
for metric in data_args.eval_metrics}
```
## Expected results
The training should not fail, due to the failure of the `Metric.compute()` step.
## Actual results
For the test I am executing the world size is 4, with 2 GPUs in 2 nodes. However the process is not finding the necessary lock files
```
File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 841, in <module>
main()
File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 792, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 1497, in train
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 1624, in _maybe_log_save_evaluate
metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 2291, in evaluate
metric_key_prefix=metric_key_prefix,
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 2535, in evaluation_loop
metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))
File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 742, in compute_metrics
metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 742, in <dictcomp>
metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 419, in compute
self.add_batch(**inputs)
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 465, in add_batch
self._init_writer()
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 552, in _init_writer
self._check_rendez_vous() # wait for master to be ready and to let everyone go
File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 342, in _check_rendez_vous
) from None
ValueError: Expected to find locked file /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-0.arrow.lock from process 3 but it doesn't exist.
```
When I look at the cache directory, I can see all the lock files in principle:
```
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-0.arrow
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-0.arrow.lock
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-1.arrow
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-1.arrow.lock
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-2.arrow
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-2.arrow.lock
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-3.arrow
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-3.arrow.lock
/home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-rdv.lock
```
I see that there was another related issue here https://github.com/huggingface/datasets/issues/1942, but it seems to have resolved via https://github.com/huggingface/datasets/pull/1966. Let me know if there is problem with how I am calling the `load_metric` or whether I need to make changes to the `.compute()` steps.
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-4.18.0-147.8.1.el8_1.x86_64-x86_64-with-centos-8.1.1911-Core
- Python version: 3.7.4
- PyArrow version: 7.0.0
- Pandas version: 1.3.0
| false |
1,252,652,896 | https://api.github.com/repos/huggingface/datasets/issues/4419 | https://github.com/huggingface/datasets/issues/4419 | 4,419 | Update `unittest` assertions over tuples from `assertEqual` to `assertTupleEqual` | closed | 3 | 2022-05-30T12:13:18 | 2022-09-30T16:01:37 | 2022-09-30T16:01:37 | alvarobartt | [
"enhancement"
] | **Is your feature request related to a problem? Please describe.**
So this is more a readability improvement rather than a proposal, wouldn't it be better to use `assertTupleEqual` over the tuples rather than `assertEqual`? As `unittest` added that function in `v3.1`, as detailed at https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertTupleEqual, so maybe it's worth updating.
Find an example of an `assertEqual` over a tuple in 🤗 `datasets` unit tests over an `ArrowDataset` at https://github.com/huggingface/datasets/blob/0bb47271910c8a0b628dba157988372307fca1d2/tests/test_arrow_dataset.py#L570
**Describe the solution you'd like**
Start slowly replacing all the `assertEqual` statements with `assertTupleEqual` if the assertion is done over a Python tuple, as we're doing with the Python lists using `assertListEqual` rather than `assertEqual`.
**Additional context**
If so, please let me know and I'll try to go over the tests and create a PR if applicable, otherwise, if you consider this should stay as `assertEqual` rather than `assertSequenceEqual` feel free to close this issue! Thanks 🤗
| false |
1,252,506,268 | https://api.github.com/repos/huggingface/datasets/issues/4418 | https://github.com/huggingface/datasets/pull/4418 | 4,418 | Add dataset MMChat | closed | 0 | 2022-05-30T10:10:40 | 2022-05-30T14:58:18 | 2022-05-30T14:58:18 | silverriver | [] | null | true |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.