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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,013,266,373
|
https://api.github.com/repos/huggingface/datasets/issues/2996
|
https://github.com/huggingface/datasets/pull/2996
| 2,996
|
Remove all query parameters when extracting protocol
|
closed
| 4
| 2021-10-01T12:05:34
| 2021-10-04T08:48:13
| 2021-10-04T08:48:13
|
albertvillanova
|
[] |
Fix `_get_extraction_protocol` to remove all query parameters, like `?raw=true`, `?dl=1`,...
| true
|
1,013,143,868
|
https://api.github.com/repos/huggingface/datasets/issues/2995
|
https://github.com/huggingface/datasets/pull/2995
| 2,995
|
Fix trivia_qa unfiltered
|
closed
| 1
| 2021-10-01T09:53:43
| 2021-10-01T10:04:11
| 2021-10-01T10:04:10
|
lhoestq
|
[] |
Fix https://github.com/huggingface/datasets/issues/2993
| true
|
1,013,000,475
|
https://api.github.com/repos/huggingface/datasets/issues/2994
|
https://github.com/huggingface/datasets/pull/2994
| 2,994
|
Fix loading compressed CSV without streaming
|
closed
| 0
| 2021-10-01T07:28:59
| 2021-10-01T15:53:16
| 2021-10-01T15:53:16
|
albertvillanova
|
[] |
When implementing support to stream CSV files (https://github.com/huggingface/datasets/commit/ad489d4597381fc2d12c77841642cbeaecf7a2e0#diff-6f60f8d0552b75be8b3bfd09994480fd60dcd4e7eb08d02f721218c3acdd2782), a regression was introduced preventing loading compressed CSV files in non-streaming mode.
This PR fixes it, allowing loading compressed/uncompressed CSV files in streaming/non-streaming mode.
Fix #2977.
| true
|
1,012,702,665
|
https://api.github.com/repos/huggingface/datasets/issues/2993
|
https://github.com/huggingface/datasets/issues/2993
| 2,993
|
Can't download `trivia_qa/unfiltered`
|
closed
| 3
| 2021-09-30T23:00:18
| 2021-10-01T19:07:23
| 2021-10-01T19:07:22
|
VictorSanh
|
[
"bug"
] |
## Describe the bug
For some reason, I can't download `trivia_qa/unfilted`. A file seems to be missing... I am able to see it fine though the viewer tough...
## Steps to reproduce the bug
```python
>>> from datasets import load_dataset
>>> load_dataset("trivia_qa", "unfiltered")
Downloading and preparing dataset trivia_qa/unfiltered (download: 3.07 GiB, generated: 27.23 GiB, post-processed: Unknown size, total: 30.30 GiB) to /gpfsscratch/rech/six/commun/datasets/trivia_qa/unfiltered/1.1.0/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6...
Traceback (most recent call last):
File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 251, in _add_context
with open(os.path.join(file_dir, fname), encoding="utf-8") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/gpfsscratch/rech/six/commun/datasets/downloads/extracted/9fcb7eddc6afd46fd074af3c5128931dfe4b548f933c925a23847faf4c1995ad/evidence/wikipedia/Peanuts.txt'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/load.py", line 852, in load_dataset
use_auth_token=use_auth_token,
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py", line 616, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py", line 1107, in _prepare_split
disable=bool(logging.get_verbosity() == logging.NOTSET),
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 303, in _generate_examples
example = parse_example(article)
File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 274, in parse_example
_add_context(article.get("EntityPages", []), "WikiContext", wiki_dir),
File "/gpfswork/rech/six/commun/modules/datasets_modules/datasets/trivia_qa/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6/trivia_qa.py", line 253, in _add_context
except (IOError, datasets.Value("errors").NotFoundError):
File "<string>", line 5, in __init__
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/features.py", line 265, in __post_init__
self.pa_type = string_to_arrow(self.dtype)
File "/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/features.py", line 134, in string_to_arrow
f"Neither {datasets_dtype} nor {datasets_dtype + '_'} seems to be a pyarrow data type. "
ValueError: Neither errors nor errors_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions
```
## Expected results
I am able to load another subset (`rc`), but unable to load.
I am not sure why the try/except doesn't catch it...
https://github.com/huggingface/datasets/blob/9675a5a1e7b99a86f9c250f6ea5fa5d1e6d5cc7d/datasets/trivia_qa/trivia_qa.py#L253
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-4.18.0-147.51.2.el8_1.x86_64-x86_64-with-redhat-8.1-Ootpa
- Python version: 3.7.10
- PyArrow version: 3.0.0
| false
|
1,012,325,594
|
https://api.github.com/repos/huggingface/datasets/issues/2992
|
https://github.com/huggingface/datasets/pull/2992
| 2,992
|
Fix f1 metric with None average
|
closed
| 0
| 2021-09-30T15:31:57
| 2021-10-01T14:17:39
| 2021-10-01T14:17:38
|
albertvillanova
|
[] |
Fix #2979.
| true
|
1,012,174,823
|
https://api.github.com/repos/huggingface/datasets/issues/2991
|
https://github.com/huggingface/datasets/issues/2991
| 2,991
|
add docmentation for the `Unix style pattern` matching feature that can be leverage for `data_files` into `load_dataset`
|
open
| 0
| 2021-09-30T13:22:01
| 2021-09-30T13:22:01
| null |
SaulLu
|
[
"enhancement"
] |
Unless I'm mistaken, it seems that in the new documentation it is no longer mentioned that you can use Unix style pattern matching in the `data_files` argument of the `load_dataset` method.
This feature was mentioned [here](https://huggingface.co/docs/datasets/loading_datasets.html#from-a-community-dataset-on-the-hugging-face-hub) in the previous documentation.
I'd love to hear your opinion @lhoestq , @albertvillanova and @stevhliu
| false
|
1,012,097,418
|
https://api.github.com/repos/huggingface/datasets/issues/2990
|
https://github.com/huggingface/datasets/pull/2990
| 2,990
|
Make Dataset.map accept list of np.array
|
closed
| 0
| 2021-09-30T12:08:54
| 2021-10-01T13:57:46
| 2021-10-01T13:57:46
|
albertvillanova
|
[] |
Fix #2987.
| true
|
1,011,220,375
|
https://api.github.com/repos/huggingface/datasets/issues/2989
|
https://github.com/huggingface/datasets/pull/2989
| 2,989
|
Add CommonLanguage
|
closed
| 0
| 2021-09-29T17:21:30
| 2021-10-01T17:36:39
| 2021-10-01T17:00:03
|
anton-l
|
[] |
This PR adds the Common Language dataset (https://zenodo.org/record/5036977)
The dataset is intended for language-identification speech classifiers and is already used by models on the Hub:
* https://huggingface.co/speechbrain/lang-id-commonlanguage_ecapa
* https://huggingface.co/anton-l/wav2vec2-base-langid
cc @patrickvonplaten
| true
|
1,011,148,017
|
https://api.github.com/repos/huggingface/datasets/issues/2988
|
https://github.com/huggingface/datasets/issues/2988
| 2,988
|
IndexError: Invalid key: 14 is out of bounds for size 0
|
closed
| 13
| 2021-09-29T16:04:24
| 2022-04-10T14:49:49
| 2022-04-10T14:49:49
|
dorost1234
|
[
"bug"
] |
## Describe the bug
A clear and concise description of what the bug is.
Hi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error, since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq
## Steps to reproduce the bug
```
Traceback (most recent call last):
File "run_clm.py", line 723, in <module>
main()
File "run_clm.py", line 669, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/transformers/trainer.py", line 1258, in train
for step, inputs in enumerate(epoch_iterator):
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
data = self._next_data()
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1530, in __getitem__
format_kwargs=self._format_kwargs,
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1517, in _getitem
pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/formatting/formatting.py", line 368, in query_table
_check_valid_index_key(key, size)
File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/formatting/formatting.py", line 311, in _check_valid_index_key
raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 14 is out of bounds for size 0
```
## Expected results
not getting the index error
## Actual results
Please see the above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets 1.12.1
- Platform: linux
- Python version: 3.7.11
- PyArrow version: 5.0.0
| false
|
1,011,026,141
|
https://api.github.com/repos/huggingface/datasets/issues/2987
|
https://github.com/huggingface/datasets/issues/2987
| 2,987
|
ArrowInvalid: Can only convert 1-dimensional array values
|
closed
| 1
| 2021-09-29T14:18:52
| 2021-10-01T13:57:45
| 2021-10-01T13:57:45
|
NielsRogge
|
[
"bug"
] |
## Describe the bug
For the ViT and LayoutLMv2 demo notebooks in my [Transformers-Tutorials repo](https://github.com/NielsRogge/Transformers-Tutorials), people reported an ArrowInvalid issue after applying the following function to a Dataset:
```
def preprocess_data(examples):
images = [Image.open(path).convert("RGB") for path in examples['image_path']]
words = examples['words']
boxes = examples['bboxes']
word_labels = examples['ner_tags']
encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels,
padding="max_length", truncation=True)
return encoded_inputs
```
```
Full trace:
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-8-0fc3efc6f0c2> in <module>()
27
28 train_dataset = datasets['train'].map(preprocess_data, batched=True, remove_columns=datasets['train'].column_names,
---> 29 features=features)
30 test_dataset = datasets['test'].map(preprocess_data, batched=True, remove_columns=datasets['test'].column_names,
31 features=features)
13 frames
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1701 new_fingerprint=new_fingerprint,
1702 disable_tqdm=disable_tqdm,
-> 1703 desc=desc,
1704 )
1705 else:
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
183 }
184 # apply actual function
--> 185 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
186 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
187 # re-apply format to the output
/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
396 # Call actual function
397
--> 398 out = func(self, *args, **kwargs)
399
400 # Update fingerprint of in-place transforms + update in-place history of transforms
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2063 writer.write_table(batch)
2064 else:
-> 2065 writer.write_batch(batch)
2066 if update_data and writer is not None:
2067 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
409 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
410 typed_sequence_examples[col] = typed_sequence
--> 411 pa_table = pa.Table.from_pydict(typed_sequence_examples)
412 self.write_table(pa_table, writer_batch_size)
413
/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in __arrow_array__(self, type)
106 storage = numpy_to_pyarrow_listarray(self.data, type=type.value_type)
107 else:
--> 108 storage = pa.array(self.data, type.storage_dtype)
109 out = pa.ExtensionArray.from_storage(type, storage)
110 elif isinstance(self.data, np.ndarray):
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Can only convert 1-dimensional array values
```
It can be fixed by adding the following line:
```diff
def preprocess_data(examples):
images = [Image.open(path).convert("RGB") for path in examples['image_path']]
words = examples['words']
boxes = examples['bboxes']
word_labels = examples['ner_tags']
encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels,
padding="max_length", truncation=True)
+ encoded_inputs["image"] = np.array(encoded_inputs["image"])
return encoded_inputs
```
However, would be great if this can be fixed within Datasets itself.
| false
|
1,010,792,783
|
https://api.github.com/repos/huggingface/datasets/issues/2986
|
https://github.com/huggingface/datasets/pull/2986
| 2,986
|
Refac module factory + avoid etag requests for hub datasets
|
closed
| 6
| 2021-09-29T10:42:00
| 2021-10-11T11:05:53
| 2021-10-11T11:05:52
|
lhoestq
|
[] |
## Refactor the module factory
When trying to extend the `data_files` logic to avoid doing unnecessary ETag requests, I noticed that the module preparation mechanism needed a refactor:
- the function was 600 lines long
- it was not readable
- it contained many different cases that made it complex to maintain
- it was hard to properly test it
- it was hard to extend without breaking anything
The module preparation mechanism is in charge of taking the name of a dataset or a metric given by the user (ex: "squad", "accuracy", "lhoestq/test", "path/to/my/script.py", "path/to/my/data/directory", "json", "csv") and return a module (possibly downloaded from the Hub) that contains the dataset builder or the metric class to use.
### Implementation details
I decided to separate all these use cases into different dataset/metric module factories.
First, the metric module factories:
- **CanonicalMetricModuleFactory**: "accuracy", "rouge", ...
- **LocalMetricModuleFactory**: "path/to/my/metric.py"
Then, the dataset module factories:
- **CanonicalDatasetModuleFactory**: "squad", "glue", ...
- **CommunityDatasetModuleFactoryWithScript**: "lhoestq/test"
- **CommunityDatasetModuleFactoryWithoutScript**: "lhoestq/demo1"
- **PackagedDatasetModuleFactory**: "json", "csv", ...
- **LocalDatasetModuleFactoryWithScript**: "path/to/my/script.py"
- **LocalDatasetModuleFactoryWithoutScript**: "path/to/my/data/directory"
And finally, additional factories when users have no internet:
- **CachedDatasetModuleFactory**
- **CachedMetricModuleFactory**
### Breaking changes
One thing is that I still don't know at what extent we want to keep backward compatibility for `prepare_module`. For now I just kept it (except I removed two parameters) just in case, but it's not used anywhere anymore.
## Avoid etag requests for hub datasets
To do this I added a class `DataFilesDict` that can be hashed to define the cache directory of the dataset.
It contains the usual data files formatted as `{"train": ["train.txt"]}` for example.
But each list of file is a `DataFilesList` that also has a `origin_metadata` attribute that contains metadata about the origin of each file:
- for URLs: it stores the ETags of the files
- for local files: it stores the last modification data
- for files from a Hugging Face repository on the Hub: it stores the pattern (`*`, `*.csv`, "train.txt", etc.) and the commit sha of the repository (so there're no ETag requests !)
This way if any file changes, the hash of the `DataFilesDict` changes too !
You can instantiate a `DataFilesDict` by using patterns for local/remote files or files in a HF repository:
- for local/remote files: `DataFilesDict.from_local_or_remote(patterns)`
- for files in a HF repository: `DataFilesDict.from_hf_repo(patterns, dataset_info)`
Fix #2859
## TODO
Fix the latest test:
- [x] fix the call to dataset_info in offline mode (related to https://github.com/huggingface/huggingface_hub/issues/372)
Add some more tests:
- [x] test all the factories
- [x] test the new data files logic
Other:
- [x] docstrings
- [x] comments
| true
|
1,010,500,433
|
https://api.github.com/repos/huggingface/datasets/issues/2985
|
https://github.com/huggingface/datasets/pull/2985
| 2,985
|
add new dataset kan_hope
|
closed
| 0
| 2021-09-29T05:20:28
| 2021-10-01T16:55:19
| 2021-10-01T16:55:19
|
adeepH
|
[] |
## Adding a Dataset
- **Name:** *KanHope*
- **Description:** *A code-mixed English-Kannada dataset for Hope speech detection*
- **Task:** *Binary Text Classification*
- **Paper:** *https://arxiv.org/abs/2108.04616*
- **Data:** *https://github.com/adeepH/kan_hope/tree/main/dataset*
- **Motivation:** *The dataset is amongst the very few resources available for code-mixed low-resourced Dravidian languages of India*
| true
|
1,010,484,326
|
https://api.github.com/repos/huggingface/datasets/issues/2984
|
https://github.com/huggingface/datasets/issues/2984
| 2,984
|
Exceeded maximum rows when reading large files
|
closed
| 1
| 2021-09-29T04:49:22
| 2021-10-12T06:05:42
| 2021-10-12T06:05:42
|
zijwang
|
[
"bug"
] |
## Describe the bug
A clear and concise description of what the bug is.
When using `load_dataset` with json files, if the files are too large, there will be "Exceeded maximum rows" error.
## Steps to reproduce the bug
```python
dataset = load_dataset('json', data_files=data_files) # data files have 3M rows in a single file
```
## Expected results
No error
## Actual results
```
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
134 with open(file, encoding="utf-8") as f:
--> 135 dataset = json.load(f)
136 except json.JSONDecodeError:
~/anaconda3/envs/python/lib/python3.9/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
292 """
--> 293 return loads(fp.read(),
294 cls=cls, object_hook=object_hook,
~/anaconda3/envs/python/lib/python3.9/json/__init__.py in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
345 parse_constant is None and object_pairs_hook is None and not kw):
--> 346 return _default_decoder.decode(s)
347 if cls is None:
~/anaconda3/envs/python/lib/python3.9/json/decoder.py in decode(self, s, _w)
339 if end != len(s):
--> 340 raise JSONDecodeError("Extra data", s, end)
341 return obj
JSONDecodeError: Extra data: line 2 column 1 (char 20321)
During handling of the above exception, another exception occurred:
ArrowInvalid Traceback (most recent call last)
<ipython-input-20-ab3718a6482f> in <module>
----> 1 dataset = load_dataset('json', data_files=data_files)
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)
841
842 # Download and prepare data
--> 843 builder_instance.download_and_prepare(
844 download_config=download_config,
845 download_mode=download_mode,
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
606 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
607 if not downloaded_from_gcs:
--> 608 self._download_and_prepare(
609 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
610 )
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
684 try:
685 # Prepare split will record examples associated to the split
--> 686 self._prepare_split(split_generator, **prepare_split_kwargs)
687 except OSError as e:
688 raise OSError(
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _prepare_split(self, split_generator)
1153 generator = self._generate_tables(**split_generator.gen_kwargs)
1154 with ArrowWriter(features=self.info.features, path=fpath) as writer:
-> 1155 for key, table in utils.tqdm(
1156 generator, unit=" tables", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET)
1157 ):
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
135 dataset = json.load(f)
136 except json.JSONDecodeError:
--> 137 raise e
138 raise ValueError(
139 f"Not able to read records in the JSON file at {file}. "
~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
114 while True:
115 try:
--> 116 pa_table = paj.read_json(
117 BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
118 )
~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()
~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Exceeded maximum rows
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version: 3.9
- PyArrow version: 4.0.1
| false
|
1,010,263,058
|
https://api.github.com/repos/huggingface/datasets/issues/2983
|
https://github.com/huggingface/datasets/pull/2983
| 2,983
|
added SwissJudgmentPrediction dataset
|
closed
| 0
| 2021-09-28T22:17:56
| 2021-10-01T16:03:05
| 2021-10-01T16:03:05
|
JoelNiklaus
|
[] | null | true
|
1,010,118,418
|
https://api.github.com/repos/huggingface/datasets/issues/2982
|
https://github.com/huggingface/datasets/pull/2982
| 2,982
|
Add the Math Aptitude Test of Heuristics dataset.
|
closed
| 0
| 2021-09-28T19:18:37
| 2021-10-01T19:51:23
| 2021-10-01T12:21:00
|
hacobe
|
[] | null | true
|
1,009,969,310
|
https://api.github.com/repos/huggingface/datasets/issues/2981
|
https://github.com/huggingface/datasets/pull/2981
| 2,981
|
add wit dataset
|
closed
| 5
| 2021-09-28T16:34:49
| 2022-05-05T14:26:41
| 2022-05-05T14:26:41
|
nateraw
|
[] |
Resolves #2902 based on conversation there - would also close #2810. Open to suggestions/help 😀
CC @hassiahk @lhoestq @yjernite
| true
|
1,009,873,482
|
https://api.github.com/repos/huggingface/datasets/issues/2980
|
https://github.com/huggingface/datasets/issues/2980
| 2,980
|
OpenSLR 25: ASR data for Amharic, Swahili and Wolof
|
open
| 3
| 2021-09-28T15:04:36
| 2021-09-29T17:25:14
| null |
cdleong
|
[
"dataset request"
] |
## Adding a Dataset
- **Name:** *SLR25*
- **Description:** *Subset 25 from OpenSLR. Other subsets have been added to https://huggingface.co/datasets/openslr, 25 covers Amharic, Swahili and Wolof data*
- **Paper:** *https://www.openslr.org/25/ has citations for each of the three subsubsets. *
- **Data:** *Currently the three links to the .tar.bz2 files can be found a thttps://www.openslr.org/25/*
- **Motivation:** *Increase ASR data for underrepresented African languages. Also, other subsets of OpenSLR speech recognition have been uploaded, so this would be easy.*
https://github.com/huggingface/datasets/blob/master/datasets/openslr/openslr.py already has been created for various other OpenSLR subsets, this should be relatively straightforward to do.
| false
|
1,009,634,147
|
https://api.github.com/repos/huggingface/datasets/issues/2979
|
https://github.com/huggingface/datasets/issues/2979
| 2,979
|
ValueError when computing f1 metric with average None
|
closed
| 1
| 2021-09-28T11:34:53
| 2021-10-01T14:17:38
| 2021-10-01T14:17:38
|
asofiaoliveira
|
[
"bug"
] |
## Describe the bug
When I try to compute the f1 score for each class in a multiclass classification problem, I get a ValueError. The same happens with recall and precision. I traced the error to the `.item()` in these scripts, which is probably there for the other averages. E.g. from f1.py:
```python
return {
"f1": f1_score(
references,
predictions,
labels=labels,
pos_label=pos_label,
average=average,
sample_weight=sample_weight,
).item(),
}
```
Since the result is an array with more than one item, the `.item()` throws the error. I didn't submit a PR because this might be needed for the other averages, I'm not very familiar with the library
## Steps to reproduce the bug
```python
from datasets import load_metric
metric = load_metric("f1")
metric.add_batch(predictions=[2,34,1,34,1,2,3], references=[23,52,1,3,523,5,8])
metric.compute(average=None)
```
## Expected results
`array([0.66666667, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. ])`
## Actual results
ValueError: can only convert an array of size 1 to a Python scalar
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.5
- PyArrow version: 5.0.0
| false
|
1,009,521,419
|
https://api.github.com/repos/huggingface/datasets/issues/2978
|
https://github.com/huggingface/datasets/issues/2978
| 2,978
|
Run CI tests against non-production server
|
open
| 2
| 2021-09-28T09:41:26
| 2021-09-28T15:23:50
| null |
albertvillanova
|
[] |
Currently, the CI test suite performs requests to the HF production server.
As discussed with @elishowk, we should refactor our tests to use the HF staging server instead, like `huggingface_hub` and `transformers`.
| false
|
1,009,378,692
|
https://api.github.com/repos/huggingface/datasets/issues/2977
|
https://github.com/huggingface/datasets/issues/2977
| 2,977
|
Impossible to load compressed csv
|
closed
| 1
| 2021-09-28T07:18:54
| 2021-10-01T15:53:16
| 2021-10-01T15:53:15
|
Valahaar
|
[
"bug"
] |
## Describe the bug
It is not possible to load from a compressed csv anymore.
## Steps to reproduce the bug
```python
load_dataset('csv', data_files=['/path/to/csv.bz2'])
```
## Problem and possible solution
This used to work, but the commit that broke it is [this one](https://github.com/huggingface/datasets/commit/ad489d4597381fc2d12c77841642cbeaecf7a2e0#diff-6f60f8d0552b75be8b3bfd09994480fd60dcd4e7eb08d02f721218c3acdd2782).
`pandas` usually gets the compression information from the filename itself (which was previously directly passed). Now, since it gets a file descriptor, it might be good to auto-infer the compression or let the user pass the `compression` kwarg to `load_dataset` (or maybe warn the user if the file ends with a commonly known compression scheme?).
## Environment info
- `datasets` version: 1.10.0 (and over)
- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 3.0.0
| false
|
1,008,647,889
|
https://api.github.com/repos/huggingface/datasets/issues/2976
|
https://github.com/huggingface/datasets/issues/2976
| 2,976
|
Can't load dataset
|
closed
| 4
| 2021-09-27T21:38:14
| 2024-04-08T03:27:29
| 2021-09-28T06:53:01
|
mskovalova
|
[
"bug"
] |
I'm trying to load a wikitext dataset
```
from datasets import load_dataset
raw_datasets = load_dataset("wikitext")
```
ValueError: Config name is missing.
Please pick one among the available configs: ['wikitext-103-raw-v1', 'wikitext-2-raw-v1', 'wikitext-103-v1', 'wikitext-2-v1']
Example of usage:
`load_dataset('wikitext', 'wikitext-103-raw-v1')`.
If I try
```
from datasets import load_dataset
raw_datasets = load_dataset("wikitext-2-v1")
```
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.12.1/datasets/wikitext-2-v1/wikitext-2-v1.py
#### Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic (colab)
- Python version: 3.7.12
- PyArrow version: 3.0.0
| false
|
1,008,444,654
|
https://api.github.com/repos/huggingface/datasets/issues/2975
|
https://github.com/huggingface/datasets/pull/2975
| 2,975
|
ignore dummy folder and dataset_infos.json
|
closed
| 0
| 2021-09-27T18:09:03
| 2021-09-29T09:45:38
| 2021-09-29T09:05:38
|
Ishan-Kumar2
|
[] |
Fixes #2877
Added the `dataset_infos.json` to the ignored files list and also added check to ignore files which have parent directory as `dummy`.
Let me know if it is correct. Thanks :)
| true
|
1,008,247,787
|
https://api.github.com/repos/huggingface/datasets/issues/2974
|
https://github.com/huggingface/datasets/pull/2974
| 2,974
|
Actually disable dummy labels by default
|
closed
| 0
| 2021-09-27T14:50:20
| 2021-09-29T09:04:42
| 2021-09-29T09:04:41
|
Rocketknight1
|
[] |
So I might have just changed the docstring instead of the actual default argument value and not realized.
@lhoestq I'm sorry >.>
| true
|
1,007,894,592
|
https://api.github.com/repos/huggingface/datasets/issues/2973
|
https://github.com/huggingface/datasets/pull/2973
| 2,973
|
Fix JSON metadata of masakhaner dataset
|
closed
| 0
| 2021-09-27T09:09:08
| 2021-09-27T12:59:59
| 2021-09-27T12:59:59
|
albertvillanova
|
[] |
Fix #2971.
| true
|
1,007,808,714
|
https://api.github.com/repos/huggingface/datasets/issues/2972
|
https://github.com/huggingface/datasets/issues/2972
| 2,972
|
OSError: Not enough disk space.
|
closed
| 6
| 2021-09-27T07:41:22
| 2024-12-04T02:56:19
| 2021-09-28T06:43:15
|
qqaatw
|
[
"bug"
] |
## Describe the bug
I'm trying to download `natural_questions` dataset from the Internet, and I've specified the cache_dir which locates in a mounted disk and has enough disk space. However, even though the space is enough, the disk space checking function still reports the space of root `/` disk having no enough space.
The file system structure is like below. The root `/` has `115G` disk space available, and the `sda1` is mounted to `/mnt`, which has `1.2T` disk space available:
```
/
/mnt/sda1/path/to/args.dataset_cache_dir
```
## Steps to reproduce the bug
```python
dataset_config = DownloadConfig(
cache_dir=os.path.abspath(args.dataset_cache_dir),
resume_download=True,
)
dataset = load_dataset("natural_questions", download_config=dataset_config)
```
## Expected results
Can download the dataset without an error.
## Actual results
The following error raised:
```
OSError: Not enough disk space. Needed: 134.92 GiB (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Ubuntu 18.04
- Python version: 3.8.10
- PyArrow version:
| false
|
1,007,696,522
|
https://api.github.com/repos/huggingface/datasets/issues/2971
|
https://github.com/huggingface/datasets/issues/2971
| 2,971
|
masakhaner dataset load problem
|
closed
| 1
| 2021-09-27T04:59:07
| 2021-09-27T12:59:59
| 2021-09-27T12:59:59
|
huu4ontocord
|
[
"bug"
] |
## Describe the bug
Masakhaner dataset is not loading
## Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset("masakhaner",'amh')
```
## Expected results
Expected the return of a dataset
## Actual results
```
NonMatchingSplitsSizesError Traceback (most recent call last)
<ipython-input-3-a6abc1161d4c> in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("masakhaner",'amh')
3 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py
in verify_splits(expected_splits, recorded_splits)
72 ]
73 if len(bad_splits) > 0:
---> 74 raise NonMatchingSplitsSizesError(str(bad_splits))
75 logger.info("All the splits matched successfully.")
76
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=639927, num_examples=1751, dataset_name='masakhaner'), 'recorded': SplitInfo(name='train', num_bytes=639911, num_examples=1750, dataset_name='masakhaner')}, {'expected': SplitInfo(name='validation', num_bytes=92768, num_examples=251, dataset_name='masakhaner'), 'recorded': SplitInfo(name='validation', num_bytes=92753, num_examples=250, dataset_name='masakhaner')}, {'expected': SplitInfo(name='test', num_bytes=184286, num_examples=501, dataset_name='masakhaner'), 'recorded': SplitInfo(name='test', num_bytes=184271, num_examples=500, dataset_name='masakhaner')}]
```
## Environment info
Google Colab
| false
|
1,007,340,089
|
https://api.github.com/repos/huggingface/datasets/issues/2970
|
https://github.com/huggingface/datasets/issues/2970
| 2,970
|
Magnet’s
|
closed
| 0
| 2021-09-26T09:50:29
| 2021-09-26T10:38:59
| 2021-09-26T10:38:59
|
rcacho172
|
[
"dataset request"
] |
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| false
|
1,007,217,867
|
https://api.github.com/repos/huggingface/datasets/issues/2969
|
https://github.com/huggingface/datasets/issues/2969
| 2,969
|
medical-dialog error
|
closed
| 3
| 2021-09-25T23:08:44
| 2024-01-08T09:55:12
| 2021-10-11T07:46:42
|
smeyerhot
|
[
"bug"
] |
## Describe the bug
A clear and concise description of what the bug is.
When I attempt to download the huggingface datatset medical_dialog it errors out midway through
## Steps to reproduce the bug
```python
raw_datasets = load_dataset("medical_dialog", "en", split="train", download_mode="force_redownload", data_dir="./Medical-Dialogue-Dataset-English")
```
## Expected results
A clear and concise description of the expected results.
No error
## Actual results
```
3 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_splits(expected_splits, recorded_splits)
72 ]
73 if len(bad_splits) > 0:
---> 74 raise NonMatchingSplitsSizesError(str(bad_splits))
75 logger.info("All the splits matched successfully.")
76
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='medical_dialog'), 'recorded': SplitInfo(name='train', num_bytes=295097913, num_examples=229674, dataset_name='medical_dialog')}]
```
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.21.1
- Platform: colab
- Python version: colab 3.7
- PyArrow version: N/A
| false
|
1,007,209,488
|
https://api.github.com/repos/huggingface/datasets/issues/2968
|
https://github.com/huggingface/datasets/issues/2968
| 2,968
|
`DatasetDict` cannot be exported to parquet if the splits have different features
|
closed
| 9
| 2021-09-25T22:18:39
| 2021-10-07T22:47:42
| 2021-10-07T22:47:26
|
LysandreJik
|
[
"bug"
] |
## Describe the bug
I'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly.
For `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.
## Steps to reproduce the bug
The following works as expected:
```python
from datasets import load_dataset
ds = load_dataset("lhoestq/custom_squad")
ds['train'].to_parquet("./ds/train/split.parquet")
ds['validation'].to_parquet("./ds/validation/split.parquet")
brand_new_dataset = load_dataset("ds")
```
Modifying a single split to add a new feature ends up in a crash:
```python
from datasets import load_dataset
ds = load_dataset("lhoestq/custom_squad")
def identical_answers(e):
e['identical_answers'] = len(set(e['answers']['text'])) == 1
return e
ds['validation'] = ds['validation'].map(identical_answers)
ds['train'].to_parquet("./ds/train/split.parquet")
ds['validation'].to_parquet("./ds/validation/split.parquet")
brand_new_dataset = load_dataset("ds")
```
```
File "/home/lysandre/.config/JetBrains/PyCharm2021.2/scratches/datasets/upload_dataset.py", line 26, in <module>
brand_new_dataset = load_dataset("ds")
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/load.py", line 1151, in load_dataset
builder_instance.download_and_prepare(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 642, in download_and_prepare
self._download_and_prepare(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 732, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 1194, in _prepare_split
writer.write_table(table)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_writer.py", line 428, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_writer.py", line 428, in <listcomp>
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1257, in pyarrow.lib.Table.__getitem__
File "pyarrow/table.pxi", line 1833, in pyarrow.lib.Table.column
File "pyarrow/table.pxi", line 1808, in pyarrow.lib.Table._ensure_integer_index
KeyError: 'Field "identical_answers" does not exist in table schema'
```
It does work, however, to use the `save_to_disk` and `load_from_disk` methods:
```py
from datasets import load_from_disk
ds = load_dataset("lhoestq/custom_squad")
def identical_answers(e):
e['identical_answers'] = len(set(e['answers']['text'])) == 1
return e
ds['validation'] = ds['validation'].map(identical_answers)
ds.save_to_disk("local_path")
brand_new_dataset = load_from_disk("local_path")
```
## Expected results
The saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.
If it's helpful, I've traced a possible patch to the `write_table` method here:
https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/arrow_writer.py#L424-L425
The writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:
https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/arrow_writer.py#L254-L255
but the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:
https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/builder.py#L1190
Updating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:
```
File "/home/lysandre/.config/JetBrains/PyCharm2021.2/scratches/datasets/upload_dataset.py", line 27, in <module>
brand_new_dataset = load_dataset("ds")
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/load.py", line 1163, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 819, in as_dataset
datasets = utils.map_nested(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 207, in map_nested
mapped = [
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 208, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 143, in _single_map_nested
return function(data_struct)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 850, in _build_single_dataset
ds = self._as_dataset(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 920, in _as_dataset
dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 217, in read
return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 238, in read_files
pa_table = self._read_files(files, in_memory=in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 173, in _read_files
pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 308, in _get_table_from_filename
table = ArrowReader.read_table(filename, in_memory=in_memory)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 327, in read_table
return table_cls.from_file(filename)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/table.py", line 458, in from_file
table = _memory_mapped_arrow_table_from_file(filename)
File "/home/lysandre/Workspaces/Python/datasets/src/datasets/table.py", line 45, in _memory_mapped_arrow_table_from_file
pa_table = opened_stream.read_all()
File "pyarrow/ipc.pxi", line 563, in pyarrow.lib.RecordBatchReader.read_all
File "pyarrow/error.pxi", line 114, in pyarrow.lib.check_status
OSError: Header-type of flatbuffer-encoded Message is not RecordBatch.
```
## Environment info
- `datasets` version: 1.12.2.dev0
- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 5.0.0
| false
|
1,007,194,837
|
https://api.github.com/repos/huggingface/datasets/issues/2967
|
https://github.com/huggingface/datasets/issues/2967
| 2,967
|
Adding vision-and-language datasets (e.g., VQA, VCR) to Datasets
|
closed
| 0
| 2021-09-25T20:58:15
| 2021-10-03T20:34:22
| 2021-10-03T20:34:22
|
WadeYin9712
|
[
"enhancement"
] |
**Is your feature request related to a problem? Please describe.**
Would you like to add any vision-and-language datasets (e.g., VQA, VCR) to Huggingface Datasets?
**Describe the solution you'd like**
N/A
**Describe alternatives you've considered**
N/A
**Additional context**
This is Da Yin at UCLA. Recently, we have published an EMNLP 2021 paper about geo-diverse visual commonsense reasoning (https://arxiv.org/abs/2109.06860). We propose a new dataset called GD-VCR, a vision-and-language dataset to evaluate how well V&L models perform on scenarios involving geo-location-specific commonsense. We hope to have our V&L dataset incorporated into Huggingface to further promote our project, but I haven't seen much V&L datasets in the current package. Is it possible to add V&L datasets, and if so, how should we prepare for the loading? Thank you very much!
| false
|
1,007,142,233
|
https://api.github.com/repos/huggingface/datasets/issues/2966
|
https://github.com/huggingface/datasets/pull/2966
| 2,966
|
Upload greek-legal-code dataset
|
closed
| 1
| 2021-09-25T16:52:15
| 2021-10-13T13:37:30
| 2021-10-13T13:37:30
|
christospi
|
[] | null | true
|
1,007,084,153
|
https://api.github.com/repos/huggingface/datasets/issues/2965
|
https://github.com/huggingface/datasets/issues/2965
| 2,965
|
Invalid download URL of WMT17 `zh-en` data
|
closed
| 1
| 2021-09-25T13:17:32
| 2022-08-31T06:47:11
| 2022-08-31T06:47:10
|
Ririkoo
|
[
"bug",
"dataset bug"
] |
## Describe the bug
Partial data (wmt17 zh-en) cannot be downloaded due to an invalid URL.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('wmt17','zh-en')
```
## Expected results
ConnectionError: Couldn't reach ftp://cwmt-wmt:cwmt-wmt@datasets.nju.edu.cn/parallel/casia2015.zip
| false
|
1,006,605,904
|
https://api.github.com/repos/huggingface/datasets/issues/2964
|
https://github.com/huggingface/datasets/issues/2964
| 2,964
|
Error when calculating Matthews Correlation Coefficient loaded with `load_metric`
|
closed
| 1
| 2021-09-24T15:55:21
| 2024-02-16T10:14:35
| 2021-09-25T08:06:07
|
alvarobartt
|
[
"bug"
] |
## Describe the bug
After loading the metric named "[Matthews Correlation Coefficient](https://huggingface.co/metrics/matthews_correlation)" from `🤗datasets`, the `.compute` method fails with the following exception `AttributeError: 'float' object has no attribute 'item'` (complete stack trace can be provided if required).
## Steps to reproduce the bug
```python
import torch
predictions = torch.ones((10,))
references = torch.zeros((10,))
from datasets import load_metric
METRIC = load_metric("matthews_correlation")
result = METRIC.compute(predictions=predictions, references=references)
```
## Expected results
We should expect a Python `dict` as it follows:
```
{
"matthews_correlation": float()
}
```
as defined in https://github.com/huggingface/datasets/blob/master/metrics/matthews_correlation/matthews_correlation.py, so the fix will imply removing `.item()`, since the value returned by the `scikit-learn` function is not a `torch.Tensor` but a `float`, which means that the `.item()` will fail.
## Actual results
```
Traceback (most recent call last):
File "/home/alvaro.bartolome/XXX/xxx/cli.py", line 59, in main
app()
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/typer/main.py", line 214, in __call__
return get_command(self)(*args, **kwargs)
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1137, in __call__
return self.main(*args, **kwargs)
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1062, in main
rv = self.invoke(ctx)
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1668, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 763, in invoke
return __callback(*args, **kwargs)
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/typer/main.py", line 500, in wrapper
return callback(**use_params) # type: ignore
File "/home/alvaro.bartolome/XXX/xxx/cli.py", line 43, in train
metrics = trainer.evaluate()
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/transformers/trainer.py", line 2051, in evaluate
output = eval_loop(
File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/transformers/trainer.py", line 2292, in evaluation_loop
metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))
File "/home/alvaro.bartolome/XXX/xxx/metrics.py", line 20, in compute_metrics
res = METRIC.compute(predictions=predictions, references=eval_preds.label_ids)
File "/home/alvaro.bartolome/miniconda3/envs/lang/lib/python3.9/site-packages/datasets/metric.py", line 402, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "/home/alvaro.bartolome/.cache/huggingface/modules/datasets_modules/metrics/matthews_correlation/0275f1e9a4d318e3ea8cdd87547ee0d58d894966616052e3d18444ac8ddd2357/matthews_correlation.py", line 88, in _compute
"matthews_correlation": matthews_corrcoef(references, predictions, sample_weight=sample_weight).item(),
AttributeError: 'float' object has no attribute 'item'
```
## Environment info
- `datasets` version: 1.12.1
- Platform: Linux-4.15.0-1113-azure-x86_64-with-glibc2.23
- Python version: 3.9.7
- PyArrow version: 5.0.0
| false
|
1,006,588,605
|
https://api.github.com/repos/huggingface/datasets/issues/2963
|
https://github.com/huggingface/datasets/issues/2963
| 2,963
|
raise TypeError( TypeError: Provided `function` which is applied to all elements of table returns a variable of type <class 'list'>. Make sure provided `function` returns a variable of type `dict` to update the dataset or `None` if you are only interested in side effects.
|
closed
| 0
| 2021-09-24T15:35:11
| 2021-09-24T15:38:24
| 2021-09-24T15:38:24
|
keloemma
|
[
"bug"
] |
## Describe the bug
A clear and concise description of what the bug is.
I am trying to use Dataset to load my file in order to use Bert embeddings model baut when I finished loading using dataset and I want to pass to the tokenizer using the function map; I get the following error : raise TypeError(
TypeError: Provided `function` which is applied to all elements of table returns a variable of type <class 'list'>. Make sure provided `function` returns a variable of type `dict` to update the dataset or `None` if you are only interested in side effects.
I was able to load my file using dataset before but since this morning , I keep getting this erreor.
## Steps to reproduce the bug
```python
# Xtrain, ytrain, filename, len_labels = read_file_2(fic)
# Xtrain, lge_size = get_flaubert_layer(Xtrain, path_to_model_lge)
data_preprocessed = make_new_traindata(Xtrain)
my_dict = {"verbatim": data_preprocessed[1], "label": ytrain} # lemme avec conjonction
dataset = Dataset.from_dict(my_dict)
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
| false
|
1,006,557,666
|
https://api.github.com/repos/huggingface/datasets/issues/2962
|
https://github.com/huggingface/datasets/issues/2962
| 2,962
|
Enable splits during streaming the dataset
|
open
| 1
| 2021-09-24T15:01:29
| 2025-07-17T04:53:20
| null |
merveenoyan
|
[
"enhancement"
] |
## Describe the Problem
I'd like to stream only a specific percentage or part of the dataset.
I want to do splitting when I'm streaming dataset as well.
## Solution
Enabling splits when `streaming = True` as well.
`e.g. dataset = load_dataset('dataset', split='train[:100]', streaming = True)`
## Alternatives
Below is the alternative of doing it.
`dataset = load_dataset("dataset", split='train', streaming = True).take(100)`
| false
|
1,006,453,781
|
https://api.github.com/repos/huggingface/datasets/issues/2961
|
https://github.com/huggingface/datasets/pull/2961
| 2,961
|
Fix CI doc build
|
closed
| 0
| 2021-09-24T13:13:28
| 2021-09-24T13:18:07
| 2021-09-24T13:18:07
|
albertvillanova
|
[] |
Pin `fsspec`.
Before the issue: 'fsspec-2021.8.1', 's3fs-2021.8.1'
Generating the issue: 'fsspec-2021.9.0', 's3fs-0.5.1'
| true
|
1,006,222,850
|
https://api.github.com/repos/huggingface/datasets/issues/2960
|
https://github.com/huggingface/datasets/pull/2960
| 2,960
|
Support pandas 1.3 new `read_csv` parameters
|
closed
| 0
| 2021-09-24T08:37:24
| 2021-09-24T11:22:31
| 2021-09-24T11:22:30
|
SBrandeis
|
[] |
Support two new arguments introduced in pandas v1.3.0:
- `encoding_errors`
- `on_bad_lines`
`read_csv` reference: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html
| true
|
1,005,547,632
|
https://api.github.com/repos/huggingface/datasets/issues/2959
|
https://github.com/huggingface/datasets/pull/2959
| 2,959
|
Added computer vision tasks
|
closed
| 5
| 2021-09-23T15:07:27
| 2022-03-01T17:41:51
| 2022-03-01T17:41:51
|
merveenoyan
|
[] |
Added various image processing/computer vision tasks.
| true
|
1,005,144,601
|
https://api.github.com/repos/huggingface/datasets/issues/2958
|
https://github.com/huggingface/datasets/pull/2958
| 2,958
|
Add security policy to the project
|
closed
| 0
| 2021-09-23T08:20:55
| 2021-10-21T15:16:44
| 2021-10-21T15:16:43
|
albertvillanova
|
[] |
Add security policy to the project, as recommended by GitHub: https://docs.github.com/en/code-security/getting-started/adding-a-security-policy-to-your-repository
Close #2953.
| true
|
1,004,868,337
|
https://api.github.com/repos/huggingface/datasets/issues/2957
|
https://github.com/huggingface/datasets/issues/2957
| 2,957
|
MultiWOZ Dataset NonMatchingChecksumError
|
closed
| 1
| 2021-09-22T23:45:00
| 2022-03-15T16:07:02
| 2022-03-15T16:07:02
|
bradyneal
|
[
"bug"
] |
## Describe the bug
The checksums for the downloaded MultiWOZ dataset and source MultiWOZ dataset aren't matching.
## Steps to reproduce the bug
Both of the below dataset versions yield the checksum error:
```python
from datasets import load_dataset
dataset = load_dataset('multi_woz_v22', 'v2.2')
dataset = load_dataset('multi_woz_v22', 'v2.2_active_only')
```
## Expected results
For the above calls to `load_dataset` to work.
## Actual results
NonMatchingChecksumError. Traceback:
> Traceback (most recent call last):
File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-15-4e91280e112e>", line 1, in <module>
dataset = load_dataset('multi_woz_v22', 'v2.2')
File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/load.py", line 847, in load_dataset
builder_instance.download_and_prepare(
File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/builder.py", line 615, in download_and_prepare
self._download_and_prepare(
File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
verify_checksums(
File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json']
## Environment info
- `datasets` version: 1.11.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.10
- PyArrow version: 5.0.0
| false
|
1,004,306,367
|
https://api.github.com/repos/huggingface/datasets/issues/2956
|
https://github.com/huggingface/datasets/issues/2956
| 2,956
|
Cache problem in the `load_dataset` method for local compressed file(s)
|
open
| 1
| 2021-09-22T13:34:32
| 2023-08-31T16:49:01
| null |
SaulLu
|
[
"bug"
] |
## Describe the bug
Cache problem in the `load_dataset` method: when modifying a compressed file in a local folder `load_dataset` doesn't detect the change and load the previous version.
## Steps to reproduce the bug
To test it directly, I have prepared a [Google Colaboratory notebook](https://colab.research.google.com/drive/11Em_Amoc-aPGhSBIkSHU2AvEh24nVayy?usp=sharing) that shows this behavior.
For this example, I have created a toy dataset at: https://huggingface.co/datasets/SaulLu/toy_struc_dataset
This dataset is composed of two versions:
- v1 on commit `a6beb46` which has a single example `{'id': 1, 'value': {'tag': 'a', 'value': 1}}` in file `train.jsonl.gz`
- v2 on commit `e7935f4` (`main` head) which has a single example `{'attr': 1, 'id': 1, 'value': 'a'}` in file `train.jsonl.gz`
With a terminal, we can start to get the v1 version of the dataset
```bash
git lfs install
git clone https://huggingface.co/datasets/SaulLu/toy_struc_dataset
cd toy_struc_dataset
git checkout a6beb46
```
Then we can load it with python and look at the content:
```python
from datasets import load_dataset
path = "/content/toy_struc_dataset"
dataset = load_dataset(path, data_files={"train": "*.jsonl.gz"})
print(dataset["train"][0])
```
Output
```
{'id': 1, 'value': {'tag': 'a', 'value': 1}} # This is the example in v1
```
With a terminal, we can now start to get the v1 version of the dataset
```bash
git checkout main
```
Then we can load it with python and look at the content:
```python
from datasets import load_dataset
path = "/content/toy_struc_dataset"
dataset = load_dataset(path, data_files={"train": "*.jsonl.gz"})
print(dataset["train"][0])
```
Output
```
{'id': 1, 'value': {'tag': 'a', 'value': 1}} # This is the example in v1 (not v2)
```
## Expected results
The last output should have been
```
{"id":1, "value": "a", "attr": 1} # This is the example in v2
```
## Ideas
As discussed offline with Quentin, if the cache hash was ever sensitive to changes in a compressed file we would probably not have the problem anymore.
This situation leads me to suggest 2 other features:
- to also have an `load_from_cache_file` argument in the "load_dataset" method
- to reorganize the cache so that we can delete the caches related to a dataset (cf issue #ToBeFilledSoon)
And thanks again for this great library :hugs:
## Environment info
- `datasets` version: 1.12.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
| false
|
1,003,999,469
|
https://api.github.com/repos/huggingface/datasets/issues/2955
|
https://github.com/huggingface/datasets/pull/2955
| 2,955
|
Update legacy Python image for CI tests in Linux
|
closed
| 1
| 2021-09-22T08:25:27
| 2021-09-24T10:36:05
| 2021-09-24T10:36:05
|
albertvillanova
|
[] |
Instead of legacy, use next-generation convenience images, built from the ground up with CI, efficiency, and determinism in mind. Here are some of the highlights:
- Faster spin-up time - In Docker terminology, these next-gen images will generally have fewer and smaller layers. Using these new images will lead to faster image downloads when a build starts, and a higher likelihood that the image is already cached on the host.
- Improved reliability and stability - The existing legacy convenience images are rebuilt practically every day with potential changes from upstream that we cannot always test fast enough. This leads to frequent breaking changes, which is not the best environment for stable, deterministic builds. Next-gen images will only be rebuilt for security and critical-bugs, leading to more stable and deterministic images.
More info: https://circleci.com/docs/2.0/circleci-images
| true
|
1,003,904,803
|
https://api.github.com/repos/huggingface/datasets/issues/2954
|
https://github.com/huggingface/datasets/pull/2954
| 2,954
|
Run tests in parallel
|
closed
| 2
| 2021-09-22T07:00:44
| 2021-09-28T06:55:51
| 2021-09-28T06:55:51
|
albertvillanova
|
[] |
Run CI tests in parallel to speed up the test suite.
Speed up results:
- Linux: from `7m 30s` to `5m 32s`
- Windows: from `13m 52s` to `11m 10s`
| true
|
1,002,766,517
|
https://api.github.com/repos/huggingface/datasets/issues/2953
|
https://github.com/huggingface/datasets/issues/2953
| 2,953
|
Trying to get in touch regarding a security issue
|
closed
| 1
| 2021-09-21T15:58:13
| 2021-10-21T15:16:43
| 2021-10-21T15:16:43
|
JamieSlome
|
[] |
Hey there!
I'd like to report a security issue but cannot find contact instructions on your repository.
If not a hassle, might you kindly add a `SECURITY.md` file with an email, or another contact method? GitHub [recommends](https://docs.github.com/en/code-security/getting-started/adding-a-security-policy-to-your-repository) this best practice to ensure security issues are responsibly disclosed, and it would serve as a simple instruction for security researchers in the future.
Thank you for your consideration, and I look forward to hearing from you!
(cc @huntr-helper)
| false
|
1,002,704,096
|
https://api.github.com/repos/huggingface/datasets/issues/2952
|
https://github.com/huggingface/datasets/pull/2952
| 2,952
|
Fix missing conda deps
|
closed
| 0
| 2021-09-21T15:23:01
| 2021-09-22T04:39:59
| 2021-09-21T15:30:44
|
lhoestq
|
[] |
`aiohttp` was added as a dependency in #2662 but was missing for the conda build, which causes the 1.12.0 and 1.12.1 to fail.
Fix #2932.
| true
|
1,001,267,888
|
https://api.github.com/repos/huggingface/datasets/issues/2951
|
https://github.com/huggingface/datasets/pull/2951
| 2,951
|
Dummy labels no longer on by default in `to_tf_dataset`
|
closed
| 2
| 2021-09-20T18:26:59
| 2021-09-21T14:00:57
| 2021-09-21T10:14:32
|
Rocketknight1
|
[] |
After more experimentation, I think I have a way to do things that doesn't depend on adding `dummy_labels` - they were quite a hacky solution anyway!
| true
|
1,001,085,353
|
https://api.github.com/repos/huggingface/datasets/issues/2950
|
https://github.com/huggingface/datasets/pull/2950
| 2,950
|
Fix fn kwargs in filter
|
closed
| 0
| 2021-09-20T15:10:26
| 2021-09-20T16:22:59
| 2021-09-20T15:28:01
|
lhoestq
|
[] |
#2836 broke the `fn_kwargs` parameter of `filter`, as mentioned in https://github.com/huggingface/datasets/issues/2927
I fixed that and added a test to make sure it doesn't happen again (for either map or filter)
Fix #2927
| true
|
1,001,026,680
|
https://api.github.com/repos/huggingface/datasets/issues/2949
|
https://github.com/huggingface/datasets/pull/2949
| 2,949
|
Introduce web and wiki config in triviaqa dataset
|
closed
| 3
| 2021-09-20T14:17:23
| 2021-10-05T13:20:52
| 2021-10-01T15:39:29
|
shirte
|
[] |
The TriviaQA paper suggests that the two subsets (Wikipedia and Web)
should be treated differently. There are also different leaderboards
for the two sets on CodaLab. For that reason, introduce additional
builder configs in the trivia_qa dataset.
| true
|
1,000,844,077
|
https://api.github.com/repos/huggingface/datasets/issues/2948
|
https://github.com/huggingface/datasets/pull/2948
| 2,948
|
Fix minor URL format in scitldr dataset
|
closed
| 0
| 2021-09-20T11:11:32
| 2021-09-20T13:18:28
| 2021-09-20T13:18:28
|
albertvillanova
|
[] |
While investigating issue #2918, I found this minor format issues in the URLs (if runned in a Windows machine).
| true
|
1,000,798,338
|
https://api.github.com/repos/huggingface/datasets/issues/2947
|
https://github.com/huggingface/datasets/pull/2947
| 2,947
|
Don't use old, incompatible cache for the new `filter`
|
closed
| 0
| 2021-09-20T10:18:59
| 2021-09-20T16:25:09
| 2021-09-20T13:43:02
|
lhoestq
|
[] |
#2836 changed `Dataset.filter` and the resulting data that are stored in the cache are different and incompatible with the ones of the previous `filter` implementation.
However the caching mechanism wasn't able to differentiate between the old and the new implementation of filter (only the method name was taken into account).
This is an issue because anyone that update `datasets` and re-runs some code that uses `filter` would see an error, because the cache would try to load an incompatible `filter` result.
To fix this I added the notion of versioning for dataset transform in the caching mechanism, and bumped the version of the `filter` implementation to 2.0.0
This way the new `filter` outputs are now considered different from the old ones from the caching point of view.
This should fix #2943
cc @anton-l
| true
|
1,000,754,824
|
https://api.github.com/repos/huggingface/datasets/issues/2946
|
https://github.com/huggingface/datasets/pull/2946
| 2,946
|
Update meteor score from nltk update
|
closed
| 0
| 2021-09-20T09:28:46
| 2021-09-20T09:35:59
| 2021-09-20T09:35:59
|
lhoestq
|
[] |
It looks like there were issues in NLTK on the way the METEOR score was computed.
A fix was added in NLTK at https://github.com/nltk/nltk/pull/2763, and therefore the scoring function no longer returns the same values.
I updated the score of the example in the docs
| true
|
1,000,624,883
|
https://api.github.com/repos/huggingface/datasets/issues/2945
|
https://github.com/huggingface/datasets/issues/2945
| 2,945
|
Protect master branch
|
closed
| 2
| 2021-09-20T06:47:01
| 2021-09-20T12:01:27
| 2021-09-20T12:00:16
|
albertvillanova
|
[
"enhancement"
] |
After accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.:
- 00cc036fea7c7745cfe722360036ed306796a3f2
- 13ae8c98602bbad8197de3b9b425f4c78f582af1
- ...
I propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future:
- [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch
- Currently, simple merge commits are already disabled
- I propose to disable rebase merging as well
- ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~
- ~~This protection would reject direct pushes to master branch~~
- ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~
- [x] Protect the master branch only from direct pushing of **merge commits**
- GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch).
- No need to disable/re-enable this protection on each release
This purpose of this Issue is to open a discussion about this problem and to agree in a solution.
| false
|
1,000,544,370
|
https://api.github.com/repos/huggingface/datasets/issues/2944
|
https://github.com/huggingface/datasets/issues/2944
| 2,944
|
Add `remove_columns` to `IterableDataset `
|
closed
| 1
| 2021-09-20T04:01:00
| 2021-10-08T15:31:53
| 2021-10-08T15:31:53
|
changjonathanc
|
[
"enhancement",
"good first issue"
] |
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
```python
from datasets import load_dataset
dataset = load_dataset("c4", 'realnewslike', streaming =True, split='train')
dataset = dataset.remove_columns('url')
```
```
AttributeError: 'IterableDataset' object has no attribute 'remove_columns'
```
**Describe the solution you'd like**
It would be nice to have `.remove_columns()` to match the `Datasets` api.
**Describe alternatives you've considered**
This can be done with a single call to `.map()`,
I can try to help add this. 🤗
| false
|
1,000,355,115
|
https://api.github.com/repos/huggingface/datasets/issues/2943
|
https://github.com/huggingface/datasets/issues/2943
| 2,943
|
Backwards compatibility broken for cached datasets that use `.filter()`
|
closed
| 6
| 2021-09-19T16:16:37
| 2021-09-20T16:25:43
| 2021-09-20T16:25:42
|
anton-l
|
[
"bug"
] |
## Describe the bug
After upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with
`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`
Related feature: https://github.com/huggingface/datasets/pull/2836
:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :)
## Workaround
Remove the cache for the given dataset, e.g. `rm -rf ~/.cache/huggingface/datasets/librispeech_asr`.
## Steps to reproduce the bug
1. Delete `~/.cache/huggingface/datasets/librispeech_asr` if it exists.
2. `pip install datasets==1.11.0` and run the following snippet:
```python
from datasets import load_dataset
ids = ["1272-141231-0000"]
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
ds = ds.filter(lambda x: x["id"] in ids)
```
3. `pip install datasets==1.12.1` and re-run the code again
## Expected results
Same result as with the previous `datasets` version.
## Actual results
```bash
Reusing dataset librispeech_asr (./.cache/huggingface/datasets/librispeech_asr/clean/2.1.0/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)
Loading cached processed dataset at ./.cache/huggingface/datasets/librispeech_asr/clean/2.1.0/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1/cache-cd1c29844fdbc87a.arrow
Traceback (most recent call last):
File "./repos/transformers/src/transformers/models/wav2vec2/try_dataset.py", line 5, in <module>
ds = ds.filter(lambda x: x["id"] in ids)
File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "./envs/transformers/lib/python3.8/site-packages/datasets/fingerprint.py", line 398, in wrapper
out = func(self, *args, **kwargs)
File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2169, in filter
indices = self.map(
File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1686, in map
return self._map_single(
File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "./envs/transformers/lib/python3.8/site-packages/datasets/fingerprint.py", line 398, in wrapper
out = func(self, *args, **kwargs)
File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1896, in _map_single
return Dataset.from_file(cache_file_name, info=info, split=self.split)
File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 343, in from_file
return cls(
File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 282, in __init__
self.info.features = self.info.features.reorder_fields_as(inferred_features)
File "./envs/transformers/lib/python3.8/site-packages/datasets/features.py", line 1151, in reorder_fields_as
return Features(recursive_reorder(self, other))
File "./envs/transformers/lib/python3.8/site-packages/datasets/features.py", line 1140, in recursive_reorder
raise ValueError(f"Keys mismatch: between {source} and {target}" + stack_position)
ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}
Process finished with exit code 1
```
## Environment info
- `datasets` version: 1.12.1
- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 5.0.0
| false
|
1,000,309,765
|
https://api.github.com/repos/huggingface/datasets/issues/2942
|
https://github.com/huggingface/datasets/pull/2942
| 2,942
|
Add SEDE dataset
|
closed
| 4
| 2021-09-19T13:11:24
| 2021-09-24T10:39:55
| 2021-09-24T10:39:54
|
Hazoom
|
[] |
This PR adds the SEDE dataset for the task of realistic Text-to-SQL, following the instructions of how to add a database and a dataset card.
Please see our paper for more details: https://arxiv.org/abs/2106.05006
| true
|
1,000,000,711
|
https://api.github.com/repos/huggingface/datasets/issues/2941
|
https://github.com/huggingface/datasets/issues/2941
| 2,941
|
OSCAR unshuffled_original_ko: NonMatchingSplitsSizesError
|
open
| 1
| 2021-09-18T10:39:13
| 2022-01-19T14:10:07
| null |
ayaka14732
|
[
"bug",
"dataset bug"
] |
## Describe the bug
Cannot download OSCAR `unshuffled_original_ko` due to `NonMatchingSplitsSizesError`.
## Steps to reproduce the bug
```python
>>> dataset = datasets.load_dataset('oscar', 'unshuffled_original_ko')
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=25292102197, num_examples=7345075, dataset_name='oscar'), 'recorded': SplitInfo(name='train', num_bytes=25284578514, num_examples=7344907, dataset_name='oscar')}]
```
## Expected results
Loading is successful.
## Actual results
Loading throws above error.
## Environment info
- `datasets` version: 1.12.1
- Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 5.0.0
| false
|
999,680,796
|
https://api.github.com/repos/huggingface/datasets/issues/2940
|
https://github.com/huggingface/datasets/pull/2940
| 2,940
|
add swedish_medical_ner dataset
|
closed
| 0
| 2021-09-17T20:03:05
| 2021-10-05T12:13:34
| 2021-10-05T12:13:33
|
bwang482
|
[] |
Adding the Swedish Medical NER dataset, listed in "Biomedical Datasets - BigScience Workshop 2021"
| true
|
999,639,630
|
https://api.github.com/repos/huggingface/datasets/issues/2939
|
https://github.com/huggingface/datasets/pull/2939
| 2,939
|
MENYO-20k repo has moved, updating URL
|
closed
| 0
| 2021-09-17T19:01:54
| 2021-09-21T15:31:37
| 2021-09-21T15:31:36
|
cdleong
|
[] |
Dataset repo moved to https://github.com/uds-lsv/menyo-20k_MT, now editing URL to match.
https://github.com/uds-lsv/menyo-20k_MT/blob/master/data/train.tsv is the file we're looking for
| true
|
999,552,263
|
https://api.github.com/repos/huggingface/datasets/issues/2938
|
https://github.com/huggingface/datasets/pull/2938
| 2,938
|
Take namespace into account in caching
|
closed
| 7
| 2021-09-17T16:57:33
| 2021-12-17T10:52:18
| 2021-09-29T13:01:31
|
lhoestq
|
[] |
Loading a dataset "username/dataset_name" hosted by a user on the hub used to cache the dataset only taking into account the dataset name, and ignorign the username. Because of this, if a user later loads "dataset_name" without specifying the username, it would reload the dataset from the cache instead of failing.
I changed the dataset cache and module cache mechanism to include the username in the name of the cache directory that is used:
<s>
`~/.cache/huggingface/datasets/username/dataset_name` for the data
`~/.cache/huggingface/modules/datasets_modules/datasets/username/dataset_name` for the python files
</s>
EDIT: actually using three underscores:
`~/.cache/huggingface/datasets/username___dataset_name` for the data
`~/.cache/huggingface/modules/datasets_modules/datasets/username___dataset_name` for the python files
This PR should fix the issue https://github.com/huggingface/datasets/issues/2842
cc @stas00
| true
|
999,548,277
|
https://api.github.com/repos/huggingface/datasets/issues/2937
|
https://github.com/huggingface/datasets/issues/2937
| 2,937
|
load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied
|
closed
| 4
| 2021-09-17T16:52:10
| 2022-08-24T13:09:08
| 2022-08-24T13:09:08
|
daqieq
|
[
"bug"
] |
## Describe the bug
Standard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('wiki_bio')
```
## Expected results
It is expected that the dataset downloads without any errors.
## Actual results
PermissionError see trace below:
```
Using custom data configuration default
Downloading and preparing dataset wiki_bio/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\Users\username\.cache\huggingface\datasets\wiki_bio\default\1.1.0\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\username\.conda\envs\hf\lib\site-packages\datasets\load.py", line 1112, in load_dataset
builder_instance.download_and_prepare(
File "C:\Users\username\.conda\envs\hf\lib\site-packages\datasets\builder.py", line 644, in download_and_prepare
self._save_info()
File "C:\Users\username\.conda\envs\hf\lib\contextlib.py", line 120, in __exit__
next(self.gen)
File "C:\Users\username\.conda\envs\hf\lib\site-packages\datasets\builder.py", line 598, in incomplete_dir
os.rename(tmp_dir, dirname)
PermissionError: [WinError 5] Access is denied: 'C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'
```
By commenting out the os.rename() [L604](https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L604) and the shutil.rmtree() [L607](https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.
It seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https://github.com/conan-io/conan/issues/6560) with similar issue with os.rename() if it helps debug this issue.
## Environment info
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.22449-SP0
- Python version: 3.8.12
- PyArrow version: 5.0.0
| false
|
999,521,647
|
https://api.github.com/repos/huggingface/datasets/issues/2936
|
https://github.com/huggingface/datasets/pull/2936
| 2,936
|
Check that array is not Float as nan != nan
|
closed
| 0
| 2021-09-17T16:16:41
| 2021-09-21T09:39:05
| 2021-09-21T09:39:04
|
Iwontbecreative
|
[] |
The Exception wants to check for issues with StructArrays/ListArrays but catches FloatArrays with value nan as nan != nan.
Pass on FloatArrays as we should not raise an Exception for them.
| true
|
999,518,469
|
https://api.github.com/repos/huggingface/datasets/issues/2935
|
https://github.com/huggingface/datasets/pull/2935
| 2,935
|
Add Jigsaw unintended Bias
|
closed
| 3
| 2021-09-17T16:12:31
| 2021-09-24T10:41:52
| 2021-09-24T10:41:52
|
Iwontbecreative
|
[] |
Hi,
Here's a first attempt at this dataset. Would be great if it could be merged relatively quickly as it is needed for Bigscience-related stuff.
This requires manual download, and I had some trouble generating dummy_data in this setting, so welcoming feedback there.
| true
|
999,477,413
|
https://api.github.com/repos/huggingface/datasets/issues/2934
|
https://github.com/huggingface/datasets/issues/2934
| 2,934
|
to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows
|
closed
| 2
| 2021-09-17T15:26:53
| 2021-10-13T09:03:23
| 2021-10-13T09:03:23
|
lhoestq
|
[
"bug"
] |
To reproduce:
```python
import datasets as ds
import weakref
import gc
d = ds.load_dataset("mnist", split="train")
ref = weakref.ref(d._data.table)
tfd = d.to_tf_dataset("image", batch_size=1, shuffle=False, label_cols="label")
del tfd, d
gc.collect()
assert ref() is None, "Error: there is at least one reference left"
```
This causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards.
Moreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this.
cc @Rocketknight1
| false
|
999,392,566
|
https://api.github.com/repos/huggingface/datasets/issues/2933
|
https://github.com/huggingface/datasets/pull/2933
| 2,933
|
Replace script_version with revision
|
closed
| 1
| 2021-09-17T14:04:39
| 2021-09-20T09:52:10
| 2021-09-20T09:52:10
|
albertvillanova
|
[] |
As discussed in https://github.com/huggingface/datasets/pull/2718#discussion_r707013278, the parameter name `script_version` is no longer applicable to datasets without loading script (i.e., datasets only with raw data files).
This PR replaces the parameter name `script_version` with `revision`.
This way, we are also aligned with:
- Transformers: `AutoTokenizer.from_pretrained(..., revision=...)`
- Hub: `HfApi.dataset_info(..., revision=...)`, `HfApi.upload_file(..., revision=...)`
| true
|
999,317,750
|
https://api.github.com/repos/huggingface/datasets/issues/2932
|
https://github.com/huggingface/datasets/issues/2932
| 2,932
|
Conda build fails
|
closed
| 2
| 2021-09-17T12:49:22
| 2021-09-21T15:31:10
| 2021-09-21T15:31:10
|
albertvillanova
|
[
"bug"
] |
## Describe the bug
Current `datasets` version in conda is 1.9 instead of 1.12.
The build of the conda package fails.
| false
|
998,326,359
|
https://api.github.com/repos/huggingface/datasets/issues/2931
|
https://github.com/huggingface/datasets/pull/2931
| 2,931
|
Fix bug in to_tf_dataset
|
closed
| 1
| 2021-09-16T15:08:03
| 2021-09-16T17:01:38
| 2021-09-16T17:01:37
|
Rocketknight1
|
[] |
Replace `set_format()` to `with_format()` so that we don't alter the original dataset in `to_tf_dataset()`
| true
|
998,154,311
|
https://api.github.com/repos/huggingface/datasets/issues/2930
|
https://github.com/huggingface/datasets/issues/2930
| 2,930
|
Mutable columns argument breaks set_format
|
closed
| 1
| 2021-09-16T12:27:22
| 2021-09-16T13:50:53
| 2021-09-16T13:50:53
|
Rocketknight1
|
[
"bug"
] |
## Describe the bug
If you pass a mutable list to the `columns` argument of `set_format` and then change the list afterwards, the returned columns also change.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("glue", "cola")
column_list = ["idx", "label"]
dataset.set_format("python", columns=column_list)
column_list[1] = "foo" # Change the list after we call `set_format`
dataset['train'][:4].keys()
```
## Expected results
```python
dict_keys(['idx', 'label'])
```
## Actual results
```python
dict_keys(['idx'])
```
| false
|
997,960,024
|
https://api.github.com/repos/huggingface/datasets/issues/2929
|
https://github.com/huggingface/datasets/pull/2929
| 2,929
|
Add regression test for null Sequence
|
closed
| 0
| 2021-09-16T08:58:33
| 2021-09-17T08:23:59
| 2021-09-17T08:23:59
|
albertvillanova
|
[] |
Relates to #2892 and #2900.
| true
|
997,941,506
|
https://api.github.com/repos/huggingface/datasets/issues/2928
|
https://github.com/huggingface/datasets/pull/2928
| 2,928
|
Update BibTeX entry
|
closed
| 0
| 2021-09-16T08:39:20
| 2021-09-16T12:35:34
| 2021-09-16T12:35:34
|
albertvillanova
|
[] |
Update BibTeX entry.
| true
|
997,654,680
|
https://api.github.com/repos/huggingface/datasets/issues/2927
|
https://github.com/huggingface/datasets/issues/2927
| 2,927
|
Datasets 1.12 dataset.filter TypeError: get_indices_from_mask_function() got an unexpected keyword argument
|
closed
| 2
| 2021-09-16T01:14:02
| 2021-09-20T16:23:22
| 2021-09-20T16:23:21
|
timothyjlaurent
|
[
"bug"
] |
## Describe the bug
Upgrading to 1.12 caused `dataset.filter` call to fail with
> get_indices_from_mask_function() got an unexpected keyword argument valid_rel_labels
## Steps to reproduce the bug
```pythondef
filter_good_rows(
ex: Dict,
valid_rel_labels: Set[str],
valid_ner_labels: Set[str],
tokenizer: PreTrainedTokenizerFast,
) -> bool:
"""Get the good rows"""
encoding = get_encoding_for_text(text=ex["text"], tokenizer=tokenizer)
ex["encoding"] = encoding
for relation in ex["relations"]:
if not is_valid_relation(relation, valid_rel_labels):
return False
for span in ex["spans"]:
if not is_valid_span(span, valid_ner_labels, encoding):
return False
return True
def get_dataset():
loader_path = str(Path(__file__).parent / "prodigy_dataset_builder.py")
ds = load_dataset(
loader_path,
name="prodigy-dataset",
data_files=sorted(file_paths),
cache_dir=cache_dir,
)["train"]
valid_ner_labels = set(vocab.ner_category)
valid_relations = set(vocab.relation_types.keys())
ds = ds.filter(
filter_good_rows,
fn_kwargs=dict(
valid_rel_labels=valid_relations,
valid_ner_labels=valid_ner_labels,
tokenizer=vocab.tokenizer,
),
keep_in_memory=True,
num_proc=num_proc,
)
```
`ds` is a `DatasetDict` produced by a jsonl dataset.
This runs fine on 1.11 but fails on 1.12
**Stack Trace**
## Expected results
I expect 1.12 datasets filter to filter the dataset without raising as it does on 1.11
## Actual results
```
tf_ner_rel_lib/dataset.py:695: in load_prodigy_arrow_datasets_from_jsonl
ds = ds.filter(
../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:185: in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/fingerprint.py:398: in wrapper
out = func(self, *args, **kwargs)
../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:2169: in filter
indices = self.map(
../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:1686: in map
return self._map_single(
../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:185: in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/fingerprint.py:398: in wrapper
out = func(self, *args, **kwargs)
../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:2048: in _map_single
batch = apply_function_on_filtered_inputs(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
inputs = {'_input_hash': [2108817714, 1477695082, -1021597032, 2130671338, -1260483858, -1203431639, ...], '_task_hash': [18070...ons', 'relations', 'relations', ...], 'answer': ['accept', 'accept', 'accept', 'accept', 'accept', 'accept', ...], ...}
indices = [0, 1, 2, 3, 4, 5, ...], check_same_num_examples = False, offset = 0
def apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples=False, offset=0):
"""Utility to apply the function on a selection of columns."""
nonlocal update_data
fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]
if offset == 0:
effective_indices = indices
else:
effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset
processed_inputs = (
> function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
)
E TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'valid_rel_labels'
../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:1939: TypeError
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Mac
- Python version: 3.8.9
- PyArrow version: pyarrow==5.0.0
| false
|
997,463,277
|
https://api.github.com/repos/huggingface/datasets/issues/2926
|
https://github.com/huggingface/datasets/issues/2926
| 2,926
|
Error when downloading datasets to non-traditional cache directories
|
open
| 1
| 2021-09-15T19:59:46
| 2021-11-24T21:42:31
| null |
dar-tau
|
[
"bug"
] |
## Describe the bug
When the cache directory is linked (soft link) to a directory on a NetApp device, the download fails.
## Steps to reproduce the bug
```bash
ln -s /path/to/netapp/.cache ~/.cache
```
```python
load_dataset("imdb")
```
## Expected results
Successfully loading IMDB dataset
## Actual results
```
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=33432835,
num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0,
dataset_name='imdb')}, {'expected': SplitInfo(name='test', num_bytes=32650697, num_examples=25000, dataset_name='imdb'),
'recorded': SplitInfo(name='test', num_bytes=659932, num_examples=503, dataset_name='imdb')}, {'expected':
SplitInfo(name='unsupervised', num_bytes=67106814, num_examples=50000, dataset_name='imdb'), 'recorded':
SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.1.2
- Platform: Ubuntu
- Python version: 3.8
## Extra notes
Stranger yet, trying to debug the phenomenon, I found the range of results to vary a lot without clear direction:
- With `cache_dir="/path/to/netapp/.cache"` the same thing happens.
- However, when linking `~/netapp/` to `/path/to/netapp` *and* setting `cache_dir="~/netapp/.cache/huggingface/datasets"` - it does work
- On the other hand, when linking `~/.cache` to `~/netapp/.cache` without using `cache_dir`, it does work anymore.
While I could test it only for a NetApp device, it might have to do with any other mounted FS.
Thanks :)
| false
|
997,407,034
|
https://api.github.com/repos/huggingface/datasets/issues/2925
|
https://github.com/huggingface/datasets/pull/2925
| 2,925
|
Add tutorial for no-code dataset upload
|
closed
| 3
| 2021-09-15T18:54:42
| 2021-09-27T17:51:55
| 2021-09-27T17:51:55
|
stevhliu
|
[
"documentation"
] |
This PR is for a tutorial for uploading a dataset to the Hub. It relies on the Hub UI elements to upload a dataset, introduces the online tagging tool for creating tags, and the Dataset card template to get a head start on filling it out. The addition of this tutorial should make it easier for beginners to upload a dataset without accessing the terminal or knowing Git.
| true
|
997,378,113
|
https://api.github.com/repos/huggingface/datasets/issues/2924
|
https://github.com/huggingface/datasets/issues/2924
| 2,924
|
"File name too long" error for file locks
|
closed
| 12
| 2021-09-15T18:16:50
| 2023-12-08T13:39:51
| 2021-10-29T09:42:24
|
gar1t
|
[
"bug"
] |
## Describe the bug
Getting the following error when calling `load_dataset("gar1t/test")`:
```
OSError: [Errno 36] File name too long: '<user>/.cache/huggingface/datasets/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'
```
## Steps to reproduce the bug
Where the user cache dir (e.g. `~/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):
```python
from datasets import load_dataset
load_dataset("gar1t/test")
```
## Expected results
Expect the function to return without an error.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<python_venv>/lib/python3.9/site-packages/datasets/load.py", line 1112, in load_dataset
builder_instance.download_and_prepare(
File "<python_venv>/lib/python3.9/site-packages/datasets/builder.py", line 644, in download_and_prepare
self._save_info()
File "<python_venv>/lib/python3.9/site-packages/datasets/builder.py", line 765, in _save_info
with FileLock(lock_path):
File "<python_venv>/lib/python3.9/site-packages/datasets/utils/filelock.py", line 323, in __enter__
self.acquire()
File "<python_venv>/lib/python3.9/site-packages/datasets/utils/filelock.py", line 272, in acquire
self._acquire()
File "<python_venv>/lib/python3.9/site-packages/datasets/utils/filelock.py", line 403, in _acquire
fd = os.open(self._lock_file, open_mode)
OSError: [Errno 36] File name too long: '<user>/.cache/huggingface/datasets/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'
```
## Environment info
- `datasets` version: 1.12.1
- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 5.0.0
| false
|
997,351,590
|
https://api.github.com/repos/huggingface/datasets/issues/2923
|
https://github.com/huggingface/datasets/issues/2923
| 2,923
|
Loading an autonlp dataset raises in normal mode but not in streaming mode
|
closed
| 1
| 2021-09-15T17:44:38
| 2022-04-12T10:09:40
| 2022-04-12T10:09:39
|
severo
|
[
"bug",
"dataset-viewer"
] |
## Describe the bug
The same dataset (from autonlp) raises an error in normal mode, but does not raise in streaming mode
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("severo/autonlp-data-sentiment_detection-3c8bcd36", split="train", streaming=False)
## raises an error
load_dataset("severo/autonlp-data-sentiment_detection-3c8bcd36", split="train", streaming=True)
## does not raise an error
```
## Expected results
Both calls should raise the same error
## Actual results
Call with streaming=False:
```
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 5825.42it/s]
Using custom data configuration autonlp-data-sentiment_detection-3c8bcd36-fe30267462d1d42b
Downloading and preparing dataset json/autonlp-data-sentiment_detection-3c8bcd36 to /home/slesage/.cache/huggingface/datasets/json/autonlp-data-sentiment_detection-3c8bcd36-fe30267462d1d42b/0.0.0/d75ead8d5cfcbe67495df0f89bd262f0023257fbbbd94a730313295f3d756d50...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 15923.71it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 3346.88it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/load.py", line 1112, in load_dataset
builder_instance.download_and_prepare(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/builder.py", line 636, in download_and_prepare
self._download_and_prepare(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/builder.py", line 726, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/builder.py", line 1187, in _prepare_split
writer.write_table(table)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/arrow_writer.py", line 418, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/arrow_writer.py", line 418, in <listcomp>
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1249, in pyarrow.lib.Table.__getitem__
File "pyarrow/table.pxi", line 1825, in pyarrow.lib.Table.column
File "pyarrow/table.pxi", line 1800, in pyarrow.lib.Table._ensure_integer_index
KeyError: 'Field "splits" does not exist in table schema'
```
Call with `streaming=False`:
```
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 6000.43it/s]
Using custom data configuration autonlp-data-sentiment_detection-3c8bcd36-fe30267462d1d42b
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 46916.15it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 148734.18it/s]
```
## Environment info
- `datasets` version: 1.12.1.dev0
- Platform: Linux-5.11.0-1017-aws-x86_64-with-glibc2.29
- Python version: 3.8.11
- PyArrow version: 4.0.1
| false
|
997,332,662
|
https://api.github.com/repos/huggingface/datasets/issues/2922
|
https://github.com/huggingface/datasets/pull/2922
| 2,922
|
Fix conversion of multidim arrays in list to arrow
|
closed
| 0
| 2021-09-15T17:21:36
| 2021-09-15T17:22:52
| 2021-09-15T17:21:45
|
lhoestq
|
[] |
Arrow only supports 1-dim arrays. Previously we were converting all the numpy arrays to python list before instantiating arrow arrays to workaround this limitation.
However in #2361 we started to keep numpy arrays in order to keep their dtypes.
It works when we pass any multi-dim numpy array (the conversion to arrow has been added on our side), but not for lists of multi-dim numpy arrays.
In this PR I added two strategies:
- one that takes a list of multi-dim numpy arrays on returns an arrow array in an optimized way (more common case)
- one that takes a list of possibly very nested data (lists, dicts, tuples) containing multi-dim arrays. This one is less optimized since it converts all the multi-dim numpy arrays into lists of 1-d arrays for compatibility with arrow. This strategy is simpler that just trying to create the arrow array from a possibly very nested data structure, but in the future we can improve it if needed.
Fix https://github.com/huggingface/datasets/issues/2921
| true
|
997,325,424
|
https://api.github.com/repos/huggingface/datasets/issues/2921
|
https://github.com/huggingface/datasets/issues/2921
| 2,921
|
Using a list of multi-dim numpy arrays raises an error "can only convert 1-dimensional array values"
|
closed
| 0
| 2021-09-15T17:12:11
| 2021-09-15T17:21:45
| 2021-09-15T17:21:45
|
lhoestq
|
[] |
This error has been introduced in https://github.com/huggingface/datasets/pull/2361
To reproduce:
```python
import numpy as np
from datasets import Dataset
d = Dataset.from_dict({"a": [np.zeros((2, 2))]})
```
raises
```python
Traceback (most recent call last):
File "playground/ttest.py", line 5, in <module>
d = Dataset.from_dict({"a": [np.zeros((2, 2))]}).with_format("torch")
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/arrow_dataset.py", line 458, in from_dict
pa_table = InMemoryTable.from_pydict(mapping=mapping)
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/table.py", line 365, in from_pydict
return cls(pa.Table.from_pydict(*args, **kwargs))
File "pyarrow/table.pxi", line 1639, in pyarrow.lib.Table.from_pydict
File "pyarrow/array.pxi", line 332, in pyarrow.lib.asarray
File "pyarrow/array.pxi", line 223, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/arrow_writer.py", line 107, in __arrow_array__
out = pa.array(self.data, type=type)
File "pyarrow/array.pxi", line 306, in pyarrow.lib.array
File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Can only convert 1-dimensional array values
| false
|
997,323,014
|
https://api.github.com/repos/huggingface/datasets/issues/2920
|
https://github.com/huggingface/datasets/pull/2920
| 2,920
|
Fix unwanted tqdm bar when accessing examples
|
closed
| 0
| 2021-09-15T17:09:11
| 2021-09-15T17:18:24
| 2021-09-15T17:18:24
|
lhoestq
|
[] |
A change in #2814 added bad progress bars in `map_nested`. Now they're disabled by default
Fix #2919
| true
|
997,127,487
|
https://api.github.com/repos/huggingface/datasets/issues/2919
|
https://github.com/huggingface/datasets/issues/2919
| 2,919
|
Unwanted progress bars when accessing examples
|
closed
| 1
| 2021-09-15T14:05:10
| 2021-09-15T17:21:49
| 2021-09-15T17:18:23
|
lhoestq
|
[] |
When accessing examples from a dataset formatted for pytorch, some progress bars appear when accessing examples:
```python
In [1]: import datasets as ds
In [2]: d = ds.Dataset.from_dict({"a": [0, 1, 2]}).with_format("torch")
In [3]: d[0]
100%|████████████████████████████████| 1/1 [00:00<00:00, 3172.70it/s]
Out[3]: {'a': tensor(0)}
```
This is because the pytorch formatter calls `map_nested` that uses progress bars
cc @sgugger
| false
|
997,063,347
|
https://api.github.com/repos/huggingface/datasets/issues/2918
|
https://github.com/huggingface/datasets/issues/2918
| 2,918
|
`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming
|
closed
| 3
| 2021-09-15T13:06:07
| 2021-12-01T08:15:00
| 2021-12-01T08:15:00
|
SBrandeis
|
[
"bug",
"streaming"
] |
## Describe the bug
Trying to load the `"FullText"` config of the `"scitldr"` dataset with `streaming=True` raises an error from `aiohttp`:
```python
ClientPayloadError: 400, message='Can not decode content-encoding: gzip'
```
cc @lhoestq
## Steps to reproduce the bug
```python
from datasets import load_dataset
iter_dset = iter(
load_dataset("scitldr", name="FullText", split="test", streaming=True)
)
next(iter_dset)
```
## Expected results
Returns the first sample of the dataset
## Actual results
Calling `__next__` crashes with the following Traceback:
```python
----> 1 next(dset_iter)
~\miniconda3\envs\datasets\lib\site-packages\datasets\iterable_dataset.py in __iter__(self)
339
340 def __iter__(self):
--> 341 for key, example in self._iter():
342 if self.features:
343 # we encode the example for ClassLabel feature types for example
~\miniconda3\envs\datasets\lib\site-packages\datasets\iterable_dataset.py in _iter(self)
336 else:
337 ex_iterable = self._ex_iterable
--> 338 yield from ex_iterable
339
340 def __iter__(self):
~\miniconda3\envs\datasets\lib\site-packages\datasets\iterable_dataset.py in __iter__(self)
76
77 def __iter__(self):
---> 78 for key, example in self.generate_examples_fn(**self.kwargs):
79 yield key, example
80
~\.cache\huggingface\modules\datasets_modules\datasets\scitldr\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\scitldr.py in _generate_examples(self, filepath, split)
162
163 with open(filepath, encoding="utf-8") as f:
--> 164 for id_, row in enumerate(f):
165 data = json.loads(row)
166 if self.config.name == "AIC":
~\miniconda3\envs\datasets\lib\site-packages\fsspec\implementations\http.py in read(self, length)
496 else:
497 length = min(self.size - self.loc, length)
--> 498 return super().read(length)
499
500 async def async_fetch_all(self):
~\miniconda3\envs\datasets\lib\site-packages\fsspec\spec.py in read(self, length)
1481 # don't even bother calling fetch
1482 return b""
-> 1483 out = self.cache._fetch(self.loc, self.loc + length)
1484 self.loc += len(out)
1485 return out
~\miniconda3\envs\datasets\lib\site-packages\fsspec\caching.py in _fetch(self, start, end)
378 elif start < self.start:
379 if self.end - end > self.blocksize:
--> 380 self.cache = self.fetcher(start, bend)
381 self.start = start
382 else:
~\miniconda3\envs\datasets\lib\site-packages\fsspec\asyn.py in wrapper(*args, **kwargs)
86 def wrapper(*args, **kwargs):
87 self = obj or args[0]
---> 88 return sync(self.loop, func, *args, **kwargs)
89
90 return wrapper
~\miniconda3\envs\datasets\lib\site-packages\fsspec\asyn.py in sync(loop, func, timeout, *args, **kwargs)
67 raise FSTimeoutError
68 if isinstance(result[0], BaseException):
---> 69 raise result[0]
70 return result[0]
71
~\miniconda3\envs\datasets\lib\site-packages\fsspec\asyn.py in _runner(event, coro, result, timeout)
23 coro = asyncio.wait_for(coro, timeout=timeout)
24 try:
---> 25 result[0] = await coro
26 except Exception as ex:
27 result[0] = ex
~\miniconda3\envs\datasets\lib\site-packages\fsspec\implementations\http.py in async_fetch_range(self, start, end)
538 if r.status == 206:
539 # partial content, as expected
--> 540 out = await r.read()
541 elif "Content-Length" in r.headers:
542 cl = int(r.headers["Content-Length"])
~\miniconda3\envs\datasets\lib\site-packages\aiohttp\client_reqrep.py in read(self)
1030 if self._body is None:
1031 try:
-> 1032 self._body = await self.content.read()
1033 for trace in self._traces:
1034 await trace.send_response_chunk_received(
~\miniconda3\envs\datasets\lib\site-packages\aiohttp\streams.py in read(self, n)
342 async def read(self, n: int = -1) -> bytes:
343 if self._exception is not None:
--> 344 raise self._exception
345
346 # migration problem; with DataQueue you have to catch
ClientPayloadError: 400, message='Can not decode content-encoding: gzip'
```
## Environment info
- `datasets` version: 1.12.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.5
- PyArrow version: 2.0.0
- aiohttp version: 3.7.4.post0
| false
|
997,041,658
|
https://api.github.com/repos/huggingface/datasets/issues/2917
|
https://github.com/huggingface/datasets/issues/2917
| 2,917
|
windows download abnormal
|
closed
| 3
| 2021-09-15T12:45:35
| 2021-09-16T17:17:48
| 2021-09-16T17:17:48
|
wei1826676931
|
[
"bug"
] |
## Describe the bug
The script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??
## Steps to reproduce the bug
```python3.7 + windows
# Sample code to reproduce the bug
```
## Expected results
It can be downloaded normally.
## Actual results
it cann't
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:1.11.0
- Platform:windows
- Python version:3.7
- PyArrow version:
| false
|
997,003,661
|
https://api.github.com/repos/huggingface/datasets/issues/2916
|
https://github.com/huggingface/datasets/pull/2916
| 2,916
|
Add OpenAI's pass@k code evaluation metric
|
closed
| 4
| 2021-09-15T12:05:43
| 2021-11-12T14:19:51
| 2021-11-12T14:19:50
|
lvwerra
|
[] |
This PR introduces the `code_eval` metric which implements [OpenAI's code evaluation harness](https://github.com/openai/human-eval) introduced in the [Codex paper](https://arxiv.org/abs/2107.03374). It is heavily based on the original implementation and just adapts the interface to follow the `predictions`/`references` convention.
The addition of this metric should enable the evaluation against the code evaluation datasets added in #2897 and #2893.
A few open questions:
- The implementation makes heavy use of multiprocessing which this PR does not touch. Is this conflicting with multiprocessing natively integrated in `datasets`?
- This metric executes generated Python code and as such it poses dangers of executing malicious code. OpenAI addresses this issue by 1) commenting the `exec` call in the code so the user has to actively uncomment it and read the warning and 2) suggests using a sandbox environment (gVisor container). Should we add a similar safeguard? E.g. a prompt that needs to be answered when initialising the metric? Or at least a warning message?
- Naming: the implementation sticks to the `predictions`/`references` naming, however, the references are not reference solutions but unittest to test the solution. While reference solutions are also available they are not used. Should the naming be adapted?
| true
|
996,870,071
|
https://api.github.com/repos/huggingface/datasets/issues/2915
|
https://github.com/huggingface/datasets/pull/2915
| 2,915
|
Fix fsspec AbstractFileSystem access
|
closed
| 0
| 2021-09-15T09:39:20
| 2021-09-15T11:35:24
| 2021-09-15T11:35:24
|
pierre-godard
|
[] |
This addresses the issue from #2914 by changing the way fsspec's AbstractFileSystem is accessed.
| true
|
996,770,168
|
https://api.github.com/repos/huggingface/datasets/issues/2914
|
https://github.com/huggingface/datasets/issues/2914
| 2,914
|
Having a dependency defining fsspec entrypoint raises an AttributeError when importing datasets
|
closed
| 1
| 2021-09-15T07:54:06
| 2021-09-15T16:49:17
| 2021-09-15T16:49:16
|
pierre-godard
|
[
"bug"
] |
## Describe the bug
In one of my project, I defined a custom fsspec filesystem with an entrypoint.
My guess is that by doing so, a variable named `spec` is created in the module `fsspec` (created by entering a for loop as there are entrypoints defined, see the loop in question [here](https://github.com/intake/filesystem_spec/blob/0589358d8a029ed6b60d031018f52be2eb721291/fsspec/__init__.py#L55)).
So that `fsspec.spec`, that was previously referring to the `spec` submodule, is now referring to that `spec` variable.
This make the import of datasets failing as it is using that `fsspec.spec`.
## Steps to reproduce the bug
I could reproduce the bug with a dummy poetry project.
Here is the pyproject.toml:
```toml
[tool.poetry]
name = "debug-datasets"
version = "0.1.0"
description = ""
authors = ["Pierre Godard"]
[tool.poetry.dependencies]
python = "^3.8"
datasets = "^1.11.0"
[tool.poetry.dev-dependencies]
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.poetry.plugins."fsspec.specs"]
"file2" = "fsspec.implementations.local.LocalFileSystem"
```
The only other file being a `debug_datasets/__init__.py` empty file.
The overall structure of the project is as follows:
```
.
├── pyproject.toml
└── debug_datasets
└── __init__.py
```
Then, within the project folder run:
```
poetry install
poetry run python
```
And in the python interpreter, try to import `datasets`:
```
import datasets
```
## Expected results
The import should run successfully.
## Actual results
Here is the trace of the error I get:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/godarpi/.cache/pypoetry/virtualenvs/debug-datasets-JuFzTKL--py3.8/lib/python3.8/site-packages/datasets/__init__.py", line 33, in <module>
from .arrow_dataset import Dataset, concatenate_datasets
File "/home/godarpi/.cache/pypoetry/virtualenvs/debug-datasets-JuFzTKL--py3.8/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 48, in <module>
from .filesystems import extract_path_from_uri, is_remote_filesystem
File "/home/godarpi/.cache/pypoetry/virtualenvs/debug-datasets-JuFzTKL--py3.8/lib/python3.8/site-packages/datasets/filesystems/__init__.py", line 30, in <module>
def is_remote_filesystem(fs: fsspec.spec.AbstractFileSystem) -> bool:
AttributeError: 'EntryPoint' object has no attribute 'AbstractFileSystem'
```
## Suggested fix
`datasets/filesystems/__init__.py`, line 30, replace:
```
def is_remote_filesystem(fs: fsspec.spec.AbstractFileSystem) -> bool:
```
by:
```
def is_remote_filesystem(fs: fsspec.AbstractFileSystem) -> bool:
```
I will come up with a PR soon if this effectively solves the issue.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: WSL2 (Ubuntu 20.04.1 LTS)
- Python version: 3.8.5
- PyArrow version: 5.0.0
- `fsspec` version: 2021.8.1
| false
|
996,436,368
|
https://api.github.com/repos/huggingface/datasets/issues/2913
|
https://github.com/huggingface/datasets/issues/2913
| 2,913
|
timit_asr dataset only includes one text phrase
|
closed
| 2
| 2021-09-14T21:06:07
| 2021-09-15T08:05:19
| 2021-09-15T08:05:18
|
margotwagner
|
[
"bug"
] |
## Describe the bug
The dataset 'timit_asr' only includes one text phrase. It only includes the transcription "Would such an act of refusal be useful?" multiple times rather than different phrases.
## Steps to reproduce the bug
Note: I am following the tutorial https://huggingface.co/blog/fine-tune-wav2vec2-english
1. Install the dataset and other packages
```python
!pip install datasets>=1.5.0
!pip install transformers==4.4.0
!pip install soundfile
!pip install jiwer
```
2. Load the dataset
```python
from datasets import load_dataset, load_metric
timit = load_dataset("timit_asr")
```
3. Remove columns that we don't want
```python
timit = timit.remove_columns(["phonetic_detail", "word_detail", "dialect_region", "id", "sentence_type", "speaker_id"])
```
4. Write a short function to display some random samples of the dataset.
```python
from datasets import ClassLabel
import random
import pandas as pd
from IPython.display import display, HTML
def show_random_elements(dataset, num_examples=10):
assert num_examples <= len(dataset), "Can't pick more elements than there are in the dataset."
picks = []
for _ in range(num_examples):
pick = random.randint(0, len(dataset)-1)
while pick in picks:
pick = random.randint(0, len(dataset)-1)
picks.append(pick)
df = pd.DataFrame(dataset[picks])
display(HTML(df.to_html()))
show_random_elements(timit["train"].remove_columns(["file"]))
```
## Expected results
10 random different transcription phrases.
## Actual results
10 of the same transcription phrase "Would such an act of refusal be useful?"
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.4.1
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: not listed
| false
|
996,256,005
|
https://api.github.com/repos/huggingface/datasets/issues/2912
|
https://github.com/huggingface/datasets/pull/2912
| 2,912
|
Update link to Blog in docs footer
|
closed
| 0
| 2021-09-14T17:23:14
| 2021-09-15T07:59:23
| 2021-09-15T07:59:23
|
albertvillanova
|
[] |
Update link.
| true
|
996,202,598
|
https://api.github.com/repos/huggingface/datasets/issues/2911
|
https://github.com/huggingface/datasets/pull/2911
| 2,911
|
Fix exception chaining
|
closed
| 0
| 2021-09-14T16:19:29
| 2021-09-16T15:04:44
| 2021-09-16T15:04:44
|
albertvillanova
|
[] |
Fix exception chaining to avoid tracebacks with message: `During handling of the above exception, another exception occurred:`
| true
|
996,149,632
|
https://api.github.com/repos/huggingface/datasets/issues/2910
|
https://github.com/huggingface/datasets/pull/2910
| 2,910
|
feat: 🎸 pass additional arguments to get private configs + info
|
closed
| 1
| 2021-09-14T15:24:19
| 2021-09-15T16:19:09
| 2021-09-15T16:19:06
|
severo
|
[] |
`use_auth_token` can now be passed to the functions to get the configs
or infos of private datasets on the hub
| true
|
996,002,180
|
https://api.github.com/repos/huggingface/datasets/issues/2909
|
https://github.com/huggingface/datasets/pull/2909
| 2,909
|
fix anli splits
|
closed
| 0
| 2021-09-14T13:10:35
| 2021-10-13T11:27:49
| 2021-10-13T11:27:49
|
zaidalyafeai
|
[] |
I can't run the tests for dummy data, facing this error
`ImportError while loading conftest '/home/zaid/tmp/fix_anli_splits/datasets/tests/conftest.py'.
tests/conftest.py:10: in <module>
from datasets import config
E ImportError: cannot import name 'config' from 'datasets' (unknown location)`
| true
|
995,970,612
|
https://api.github.com/repos/huggingface/datasets/issues/2908
|
https://github.com/huggingface/datasets/pull/2908
| 2,908
|
Update Zenodo metadata with creator names and affiliation
|
closed
| 0
| 2021-09-14T12:39:37
| 2021-09-14T14:29:25
| 2021-09-14T14:29:25
|
albertvillanova
|
[] |
This PR helps in prefilling author data when automatically generating the DOI after each release.
| true
|
995,968,152
|
https://api.github.com/repos/huggingface/datasets/issues/2907
|
https://github.com/huggingface/datasets/pull/2907
| 2,907
|
add story_cloze dataset
|
closed
| 1
| 2021-09-14T12:36:53
| 2021-10-08T21:41:42
| 2021-10-08T21:41:41
|
zaidalyafeai
|
[] |
@lhoestq I have spent some time but I still I can't succeed in correctly testing the dummy_data.
| true
|
995,962,905
|
https://api.github.com/repos/huggingface/datasets/issues/2906
|
https://github.com/huggingface/datasets/pull/2906
| 2,906
|
feat: 🎸 add a function to get a dataset config's split names
|
closed
| 1
| 2021-09-14T12:31:22
| 2021-10-04T09:55:38
| 2021-10-04T09:55:37
|
severo
|
[] |
Also: pass additional arguments (use_auth_token) to get private configs + info of private datasets on the hub
Questions:
- [x] I'm not sure how the versions work: I changed 1.12.1.dev0 to 1.12.1.dev1, was it correct?
-> no: reverted
- [x] Should I add a section in https://github.com/huggingface/datasets/blob/master/docs/source/load_hub.rst? (there is no section for get_dataset_infos)
-> yes: added
| true
|
995,843,964
|
https://api.github.com/repos/huggingface/datasets/issues/2905
|
https://github.com/huggingface/datasets/pull/2905
| 2,905
|
Update BibTeX entry
|
closed
| 0
| 2021-09-14T10:16:17
| 2021-09-14T12:25:37
| 2021-09-14T12:25:37
|
albertvillanova
|
[] |
Update BibTeX entry.
| true
|
995,814,222
|
https://api.github.com/repos/huggingface/datasets/issues/2904
|
https://github.com/huggingface/datasets/issues/2904
| 2,904
|
FORCE_REDOWNLOAD does not work
|
open
| 3
| 2021-09-14T09:45:26
| 2021-10-06T09:37:19
| null |
anoopkatti
|
[
"bug"
] |
## Describe the bug
With GenerateMode.FORCE_REDOWNLOAD, the documentation says
+------------------------------------+-----------+---------+
| | Downloads | Dataset |
+====================================+===========+=========+
| `REUSE_DATASET_IF_EXISTS` (default)| Reuse | Reuse |
+------------------------------------+-----------+---------+
| `REUSE_CACHE_IF_EXISTS` | Reuse | Fresh |
+------------------------------------+-----------+---------+
| `FORCE_REDOWNLOAD` | Fresh | Fresh |
+------------------------------------+-----------+---------+
However, the old dataset is loaded even when FORCE_REDOWNLOAD is chosen.
## Steps to reproduce the bug
```python
import pandas as pd
from datasets import load_dataset, GenerateMode
pd.DataFrame(range(5), columns=['numbers']).to_csv('/tmp/test.tsv.gz', index=False)
ee = load_dataset('csv', data_files=['/tmp/test.tsv.gz'], delimiter='\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)
print(ee)
pd.DataFrame(range(10), columns=['numerals']).to_csv('/tmp/test.tsv.gz', index=False)
ee = load_dataset('csv', data_files=['/tmp/test.tsv.gz'], delimiter='\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)
print(ee)
```
## Expected results
Dataset({
features: ['numbers'],
num_rows: 5
})
Dataset({
features: ['numerals'],
num_rows: 10
})
## Actual results
Dataset({
features: ['numbers'],
num_rows: 5
})
Dataset({
features: ['numbers'],
num_rows: 5
})
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-4.14.181-108.257.amzn1.x86_64-x86_64-with-glibc2.10
- Python version: 3.7.10
- PyArrow version: 3.0.0
| false
|
995,715,191
|
https://api.github.com/repos/huggingface/datasets/issues/2903
|
https://github.com/huggingface/datasets/pull/2903
| 2,903
|
Fix xpathopen to accept positional arguments
|
closed
| 1
| 2021-09-14T08:02:50
| 2021-09-14T08:51:21
| 2021-09-14T08:40:47
|
albertvillanova
|
[] |
Fix `xpathopen()` so that it also accepts positional arguments.
Fix #2901.
| true
|
995,254,216
|
https://api.github.com/repos/huggingface/datasets/issues/2902
|
https://github.com/huggingface/datasets/issues/2902
| 2,902
|
Add WIT Dataset
|
closed
| 6
| 2021-09-13T19:38:49
| 2024-10-02T15:37:48
| 2022-06-01T17:28:40
|
nateraw
|
[
"dataset request"
] |
## Adding a Dataset
- **Name:** *WIT*
- **Description:** *Wikipedia-based Image Text Dataset*
- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
](https://arxiv.org/abs/2103.01913)*
- **Data:** *https://github.com/google-research-datasets/wit*
- **Motivation:** (excerpt from their Github README.md)
> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.
> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.
> - A collection of diverse set of concepts and real world entities.
> - Brings forth challenging real-world test sets.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| false
|
995,232,844
|
https://api.github.com/repos/huggingface/datasets/issues/2901
|
https://github.com/huggingface/datasets/issues/2901
| 2,901
|
Incompatibility with pytest
|
closed
| 1
| 2021-09-13T19:12:17
| 2021-09-14T08:40:47
| 2021-09-14T08:40:47
|
severo
|
[
"bug"
] |
## Describe the bug
pytest complains about xpathopen / path.open("w")
## Steps to reproduce the bug
Create a test file, `test.py`:
```python
import datasets as ds
def load_dataset():
ds.load_dataset("counter", split="train", streaming=True)
```
And launch it with pytest:
```bash
python -m pytest test.py
```
## Expected results
It should give something like:
```
collected 1 item
test.py . [100%]
======= 1 passed in 3.15s =======
```
## Actual results
```
============================================================================================================================= test session starts ==============================================================================================================================
platform linux -- Python 3.8.11, pytest-6.2.5, py-1.10.0, pluggy-1.0.0
rootdir: /home/slesage/hf/datasets-preview-backend, configfile: pyproject.toml
plugins: anyio-3.3.1
collected 1 item
tests/queries/test_rows.py . [100%]Traceback (most recent call last):
File "/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/pytest/__main__.py", line 5, in <module>
raise SystemExit(pytest.console_main())
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/_pytest/config/__init__.py", line 185, in console_main
code = main()
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/_pytest/config/__init__.py", line 162, in main
ret: Union[ExitCode, int] = config.hook.pytest_cmdline_main(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/pluggy/_hooks.py", line 265, in __call__
return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/pluggy/_manager.py", line 80, in _hookexec
return self._inner_hookexec(hook_name, methods, kwargs, firstresult)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/pluggy/_callers.py", line 60, in _multicall
return outcome.get_result()
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/pluggy/_result.py", line 60, in get_result
raise ex[1].with_traceback(ex[2])
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/pluggy/_callers.py", line 39, in _multicall
res = hook_impl.function(*args)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/_pytest/main.py", line 316, in pytest_cmdline_main
return wrap_session(config, _main)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/_pytest/main.py", line 304, in wrap_session
config.hook.pytest_sessionfinish(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/pluggy/_hooks.py", line 265, in __call__
return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/pluggy/_manager.py", line 80, in _hookexec
return self._inner_hookexec(hook_name, methods, kwargs, firstresult)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/pluggy/_callers.py", line 55, in _multicall
gen.send(outcome)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/_pytest/terminal.py", line 803, in pytest_sessionfinish
outcome.get_result()
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/pluggy/_result.py", line 60, in get_result
raise ex[1].with_traceback(ex[2])
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/pluggy/_callers.py", line 39, in _multicall
res = hook_impl.function(*args)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/_pytest/cacheprovider.py", line 428, in pytest_sessionfinish
config.cache.set("cache/nodeids", sorted(self.cached_nodeids))
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/_pytest/cacheprovider.py", line 188, in set
f = path.open("w")
TypeError: xpathopen() takes 1 positional argument but 2 were given
```
## Environment info
- `datasets` version: 1.12.0
- Platform: Linux-5.11.0-1017-aws-x86_64-with-glibc2.29
- Python version: 3.8.11
- PyArrow version: 4.0.1
| false
|
994,922,580
|
https://api.github.com/repos/huggingface/datasets/issues/2900
|
https://github.com/huggingface/datasets/pull/2900
| 2,900
|
Fix null sequence encoding
|
closed
| 0
| 2021-09-13T13:55:08
| 2021-09-13T14:17:43
| 2021-09-13T14:17:42
|
lhoestq
|
[] |
The Sequence feature encoding was failing when a `None` sequence was used in a dataset.
Fix https://github.com/huggingface/datasets/issues/2892
| true
|
994,082,432
|
https://api.github.com/repos/huggingface/datasets/issues/2899
|
https://github.com/huggingface/datasets/issues/2899
| 2,899
|
Dataset
|
closed
| 0
| 2021-09-12T07:38:53
| 2021-09-12T16:12:15
| 2021-09-12T16:12:15
|
rcacho172
|
[
"dataset request"
] |
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| false
|
994,032,814
|
https://api.github.com/repos/huggingface/datasets/issues/2898
|
https://github.com/huggingface/datasets/issues/2898
| 2,898
|
Hug emoji
|
closed
| 0
| 2021-09-12T03:27:51
| 2021-09-12T16:13:13
| 2021-09-12T16:13:13
|
Jackg-08
|
[
"dataset request"
] |
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| false
|
993,798,386
|
https://api.github.com/repos/huggingface/datasets/issues/2897
|
https://github.com/huggingface/datasets/pull/2897
| 2,897
|
Add OpenAI's HumanEval dataset
|
closed
| 1
| 2021-09-11T09:37:47
| 2021-09-16T15:02:11
| 2021-09-16T15:02:11
|
lvwerra
|
[] |
This PR adds OpenAI's [HumanEval](https://github.com/openai/human-eval) dataset. The dataset consists of 164 handcrafted programming problems with solutions and unittests to verify solution. This dataset is useful to evaluate code generation models.
| true
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.