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https://api.github.com/repos/huggingface/datasets/issues/2418 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2418/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2418/comments | https://api.github.com/repos/huggingface/datasets/issues/2418/events | https://github.com/huggingface/datasets/pull/2418 | 904,051,497 | MDExOlB1bGxSZXF1ZXN0NjU1MjM2OTEz | 2,418 | add utf-8 while reading README | {
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"Can you please add encoding to this line as well to fix the issue (and maybe replace `path.open(...)` with `open(path, ...)`)?\r\nhttps://github.com/huggingface/datasets/blob/7bee4be44706a59b084b9b69c4cd00f73ee72f76/src/datasets/utils/metadata.py#L58",
"Sure, in fact even I was thinking of adding this in order t... | 1,622,139,148,000 | 1,622,800,501,000 | 1,622,800,500,000 | CONTRIBUTOR | null | It was causing tests to fail in Windows (see #2416). In Windows, the default encoding is CP1252 which is unable to decode the character byte 0x9d | {
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https://api.github.com/repos/huggingface/datasets/issues/2417 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2417/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2417/comments | https://api.github.com/repos/huggingface/datasets/issues/2417/events | https://github.com/huggingface/datasets/pull/2417 | 903,956,071 | MDExOlB1bGxSZXF1ZXN0NjU1MTU3NTI4 | 2,417 | Make datasets PEP-561 compliant | {
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"This is super cool, I love that ❤️ "
] | 1,622,132,177,000 | 1,622,207,410,000 | 1,622,207,356,000 | CONTRIBUTOR | null | Allows to type-check datasets with `mypy` when imported as a third-party library
PEP-561: https://www.python.org/dev/peps/pep-0561
MyPy doc on the subject: https://mypy.readthedocs.io/en/stable/installed_packages.html
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https://api.github.com/repos/huggingface/datasets/issues/2416 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2416/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2416/comments | https://api.github.com/repos/huggingface/datasets/issues/2416/events | https://github.com/huggingface/datasets/pull/2416 | 903,932,299 | MDExOlB1bGxSZXF1ZXN0NjU1MTM3NDUy | 2,416 | Add KLUE dataset | {
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"I'm not sure why I got error like below when I auto-generate dummy data \"mrc\" \r\n```\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 0\r\nKeys should be unique and deterministic in nature\r\n```",
"> I'm not sure why I got error like below when I auto-generate du... | 1,622,130,591,000 | 1,623,250,802,000 | 1,622,828,715,000 | CONTRIBUTOR | null | Add `KLUE (Korean Language Understanding Evaluation)` dataset released recently from [paper](https://arxiv.org/abs/2105.09680), [github](https://github.com/KLUE-benchmark/KLUE) and [webpage](https://klue-benchmark.com/tasks).
Please let me know if there's anything missing in the code or README.
Thanks!
| {
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https://api.github.com/repos/huggingface/datasets/issues/2415 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2415/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2415/comments | https://api.github.com/repos/huggingface/datasets/issues/2415/events | https://github.com/huggingface/datasets/issues/2415 | 903,923,097 | MDU6SXNzdWU5MDM5MjMwOTc= | 2,415 | Cached dataset not loaded | {
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"It actually seems to happen all the time in above configuration:\r\n* the function `filter_by_duration` correctly loads cached processed dataset\r\n* the function `prepare_dataset` is always reexecuted\r\n\r\nI end up solving the issue by saving to disk my dataset at the end but I'm still wondering if it's a bug o... | 1,622,130,006,000 | 1,622,639,747,000 | 1,622,639,747,000 | CONTRIBUTOR | null | ## Describe the bug
I have a large dataset (common_voice, english) where I use several map and filter functions.
Sometimes my cached datasets after specific functions are not loaded.
I always use the same arguments, same functions, no seed…
## Steps to reproduce the bug
```python
def filter_by_duration(batch):
return (
batch["duration"] <= 10
and batch["duration"] >= 1
and len(batch["target_text"]) > 5
)
def prepare_dataset(batch):
batch["input_values"] = processor(
batch["speech"], sampling_rate=batch["sampling_rate"][0]
).input_values
with processor.as_target_processor():
batch["labels"] = processor(batch["target_text"]).input_ids
return batch
train_dataset = train_dataset.filter(
filter_by_duration,
remove_columns=["duration"],
num_proc=data_args.preprocessing_num_workers,
)
# PROBLEM HERE -> below function is reexecuted and cache is not loaded
train_dataset = train_dataset.map(
prepare_dataset,
remove_columns=train_dataset.column_names,
batch_size=training_args.per_device_train_batch_size,
batched=True,
num_proc=data_args.preprocessing_num_workers,
)
# Later in script
set_caching_enabled(False)
# apply map on trained model to eval/test sets
```
## Expected results
The cached dataset should always be reloaded.
## Actual results
The function is reexecuted.
I have access to cached files `cache-xxxxx.arrow`.
Is there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)?
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29
- Python version: 3.8.5
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No | {
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https://api.github.com/repos/huggingface/datasets/issues/2414 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2414/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2414/comments | https://api.github.com/repos/huggingface/datasets/issues/2414/events | https://github.com/huggingface/datasets/pull/2414 | 903,877,096 | MDExOlB1bGxSZXF1ZXN0NjU1MDg5OTIw | 2,414 | Update README.md | {
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"Merging since the CI error is unrelated to this PR and has been fixed on master",
"Thank you for taking a look at the CI error - I was a bit confused with that. Thanks!"
] | 1,622,127,199,000 | 1,624,887,974,000 | 1,624,885,496,000 | CONTRIBUTOR | null | Provides description of data instances and dataset features
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https://api.github.com/repos/huggingface/datasets/issues/2413 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2413/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2413/comments | https://api.github.com/repos/huggingface/datasets/issues/2413/events | https://github.com/huggingface/datasets/issues/2413 | 903,777,557 | MDU6SXNzdWU5MDM3Nzc1NTc= | 2,413 | AttributeError: 'DatasetInfo' object has no attribute 'task_templates' | {
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"Hi ! Can you try using a more up-to-date version ? We added the task_templates in `datasets` 1.7.0.\r\n\r\nIdeally when you're working on new datasets, you should install and use the local version of your fork of `datasets`. Here I think you tried to run the 1.7.0 tests with the 1.6.2 code"
] | 1,622,123,068,000 | 1,622,509,547,000 | 1,622,509,547,000 | CONTRIBUTOR | null | ## Describe the bug
Hello,
I'm trying to add dataset and contribute, but test keep fail with below cli.
` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<my_dataset>`
## Steps to reproduce the bug
It seems like a bug when I see an error with the existing dataset, not the dataset I'm trying to add.
` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<any_dataset>`
## Expected results
All test passed
## Actual results
```
# check that dataset is not empty
self.parent.assertListEqual(sorted(dataset_builder.info.splits.keys()), sorted(dataset))
for split in dataset_builder.info.splits.keys():
# check that loaded datset is not empty
self.parent.assertTrue(len(dataset[split]) > 0)
# check that we can cast features for each task template
> task_templates = dataset_builder.info.task_templates
E AttributeError: 'DatasetInfo' object has no attribute 'task_templates'
tests/test_dataset_common.py:175: AttributeError
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Darwin-20.4.0-x86_64-i386-64bit
- Python version: 3.7.7
- PyTorch version (GPU?): 1.7.0 (False)
- Tensorflow version (GPU?): 2.3.0 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
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https://api.github.com/repos/huggingface/datasets/issues/2412 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2412/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2412/comments | https://api.github.com/repos/huggingface/datasets/issues/2412/events | https://github.com/huggingface/datasets/issues/2412 | 903,769,151 | MDU6SXNzdWU5MDM3NjkxNTE= | 2,412 | Docstring mistake: dataset vs. metric | {
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"> I can provide a PR l8er...\r\n\r\nSee #2425 "
] | 1,622,122,751,000 | 1,622,535,484,000 | 1,622,535,484,000 | CONTRIBUTOR | null | This:
https://github.com/huggingface/datasets/blob/d95b95f8cf3cb0cff5f77a675139b584dcfcf719/src/datasets/load.py#L582
Should better be something like:
`a metric identifier on HuggingFace AWS bucket (list all available metrics and ids with ``datasets.list_metrics()``)`
I can provide a PR l8er... | {
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https://api.github.com/repos/huggingface/datasets/issues/2410 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2410/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2410/comments | https://api.github.com/repos/huggingface/datasets/issues/2410/events | https://github.com/huggingface/datasets/pull/2410 | 903,613,676 | MDExOlB1bGxSZXF1ZXN0NjU0ODUwMjY4 | 2,410 | fix #2391 add original answers in kilt-TriviaQA | {
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"LGTM, but I'm not sure what's going on with the Unix tests @lhoestq ",
"The CI error is unrelated to this PR, it's been fixed now on master.",
"Thanks @PaulLerner !",
"> #- [ ] - Hey@fr[fr_fr**fr~~... | 1,622,116,469,000 | 1,623,760,557,000 | 1,623,691,750,000 | CONTRIBUTOR | null | cc @yjernite is it ok like this? | {
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https://api.github.com/repos/huggingface/datasets/issues/2409 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2409/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2409/comments | https://api.github.com/repos/huggingface/datasets/issues/2409/events | https://github.com/huggingface/datasets/pull/2409 | 903,441,398 | MDExOlB1bGxSZXF1ZXN0NjU0Njk3NjA0 | 2,409 | Add HF_ prefix to env var MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES | {
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"I thought the renaming was suggested only for the env var, and not for the config variable... As you think is better! ;)",
"I think it's better if they match, so that users understand directly that they're directly connected",
"Well, if you're not concerned about back-compat here, perhaps it could be renamed a... | 1,622,106,420,000 | 1,623,168,055,000 | 1,622,108,021,000 | MEMBER | null | As mentioned in https://github.com/huggingface/datasets/pull/2399 the env var should be prefixed by HF_ | {
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https://api.github.com/repos/huggingface/datasets/issues/2408 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2408/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2408/comments | https://api.github.com/repos/huggingface/datasets/issues/2408/events | https://github.com/huggingface/datasets/pull/2408 | 903,422,648 | MDExOlB1bGxSZXF1ZXN0NjU0NjgxMzE4 | 2,408 | Fix head_qa keys | {
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https://api.github.com/repos/huggingface/datasets/issues/2407 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2407/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2407/comments | https://api.github.com/repos/huggingface/datasets/issues/2407/events | https://github.com/huggingface/datasets/issues/2407 | 903,111,755 | MDU6SXNzdWU5MDMxMTE3NTU= | 2,407 | .map() function got an unexpected keyword argument 'cache_file_name' | {
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"Hi @cindyxinyiwang,\r\nDid you try adding `.arrow` after `cache_file_name` argument? Here I think they're expecting something like that only for a cache file:\r\nhttps://github.com/huggingface/datasets/blob/e08362256fb157c0b3038437fc0d7a0bbb50de5c/src/datasets/arrow_dataset.py#L1556-L1558",
"Hi ! `cache_file_nam... | 1,622,080,466,000 | 1,622,123,200,000 | 1,622,123,200,000 | NONE | null | ## Describe the bug
I'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that ".map() function got an unexpected keyword argument 'cache_file_name'".
I believe I'm using the latest dataset 1.6.2. Also seems like the document and the actual code indicates there is an argument 'cache_file_name' for the .map() function.
Here is the code I use
## Steps to reproduce the bug
```datasets = load_from_disk(dataset_path=my_path)
[...]
def tokenize_function(examples):
return tokenizer(examples[text_column_name])
logger.info("Mapping dataset to tokenized dataset.")
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=True,
cache_file_name="my_tokenized_file"
)
```
## Actual results
tokenized_datasets = datasets.map(
TypeError: map() got an unexpected keyword argument 'cache_file_name'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:1.6.2
- Platform:Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.10
- Python version:3.8.5
- PyArrow version:3.0.0
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"Thanks!"
] | 1,621,969,049,000 | 1,622,048,056,000 | 1,622,047,207,000 | CONTRIBUTOR | null | The dataset tags were provided by Peter Clark following the guide. | {
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https://api.github.com/repos/huggingface/datasets/issues/2404 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2404/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2404/comments | https://api.github.com/repos/huggingface/datasets/issues/2404/events | https://github.com/huggingface/datasets/pull/2404 | 901,179,832 | MDExOlB1bGxSZXF1ZXN0NjUyNjYzOTcz | 2,404 | Paperswithcode dataset mapping | {
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"messed up my branch, repushing",
"live mapping can be found at https://huggingface.co/api/pwc/datasets-mapping and will be kept up to date going forward"
] | 1,621,966,466,000 | 1,622,028,085,000 | 1,622,027,838,000 | MEMBER | null | This is a continuation of https://github.com/huggingface/huggingface_hub/pull/43, encoded directly inside dataset cards.
As discussed:
- `paperswithcode_id: null` when the dataset doesn't exist on paperswithcode's side.
- I've added this new key at the end of the yaml instead of ordering all keys alphabetically as pyyaml's default. No strong opinion on that one though
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https://api.github.com/repos/huggingface/datasets/issues/2403 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2403/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2403/comments | https://api.github.com/repos/huggingface/datasets/issues/2403/events | https://github.com/huggingface/datasets/pull/2403 | 900,059,014 | MDExOlB1bGxSZXF1ZXN0NjUxNjcxMTMw | 2,403 | Free datasets with cache file in temp dir on exit | {
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} | [] | closed | false | null | [] | null | [] | 1,621,894,511,000 | 1,622,049,919,000 | 1,622,047,169,000 | CONTRIBUTOR | null | This PR properly cleans up the memory-mapped tables that reference the cache files inside the temp dir.
Since the built-in `_finalizer` of `TemporaryDirectory` can't be modified, this PR defines its own `TemporaryDirectory` class that accepts a custom clean-up function.
Fixes #2402 | {
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] | closed | false | null | [] | null | [] | 1,621,891,379,000 | 1,622,047,169,000 | 1,622,047,169,000 | CONTRIBUTOR | null | Currently, the following code raises a PermissionError on master if working on Windows:
```python
# run as a script or call exit() in REPL to initiate the temp dir cleanup
from datasets import *
d = load_dataset("sst", split="train", keep_in_memory=False)
set_caching_enabled(False)
d.map(lambda ex: ex)
```
Error stack trace:
```
Traceback (most recent call last):
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 624, in _exitfunc
f()
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 548, in __call__
return info.func(*info.args, **(info.kwargs or {}))
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\tempfile.py", line 799, in _cleanup
_shutil.rmtree(name)
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 500, in rmtree
return _rmtree_unsafe(path, onerror)
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 395, in _rmtree_unsafe
onerror(os.unlink, fullname, sys.exc_info())
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 393, in _rmtree_unsafe
os.unlink(fullname)
PermissionError: [WinError 5] Access is denied: 'C:\\Users\\Mario\\AppData\\Local\\Temp\\tmp20epyhmq\\cache-87a87ffb5a956e68.arrow'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2401 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2401/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2401/comments | https://api.github.com/repos/huggingface/datasets/issues/2401/events | https://github.com/huggingface/datasets/issues/2401 | 899,910,521 | MDU6SXNzdWU4OTk5MTA1MjE= | 2,401 | load_dataset('natural_questions') fails with "ValueError: External features info don't match the dataset" | {
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"I faced the similar problem. Downgrading datasets to 1.5.0 fixed it.",
"Thanks for reporting, I'm looking into it",
"I just opened #2438 to fix this :)",
"Hi ! This has been fixed in the 1.8.0 release of `datasets`"
] | 1,621,881,533,000 | 1,623,229,645,000 | 1,623,229,645,000 | NONE | null | ## Describe the bug
load_dataset('natural_questions') throws ValueError
## Steps to reproduce the bug
```python
from datasets import load_dataset
datasets = load_dataset('natural_questions', split='validation[:10]')
```
## Expected results
Call to load_dataset returns data.
## Actual results
```
Using custom data configuration default
Reusing dataset natural_questions (/mnt/d/huggingface/datasets/natural_questions/default/0.0.2/19bc04755018a3ad02ee74f7045cde4ba9b4162cb64450a87030ab786b123b76)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-d55ab8a8cc1c> in <module>
----> 1 datasets = load_dataset('natural_questions', split='validation[:10]', cache_dir='/mnt/d/huggingface/datasets')
~/miniconda3/lib/python3.8/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, **config_kwargs)
756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
757 )
--> 758 ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
759 if save_infos:
760 builder_instance._save_infos()
~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in as_dataset(self, split, run_post_process, ignore_verifications, in_memory)
735
736 # Create a dataset for each of the given splits
--> 737 datasets = utils.map_nested(
738 partial(
739 self._build_single_dataset,
~/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)
193 # Singleton
194 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 195 return function(data_struct)
196
197 disable_tqdm = bool(logger.getEffectiveLevel() > INFO)
~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _build_single_dataset(self, split, run_post_process, ignore_verifications, in_memory)
762
763 # Build base dataset
--> 764 ds = self._as_dataset(
765 split=split,
766 in_memory=in_memory,
~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _as_dataset(self, split, in_memory)
838 in_memory=in_memory,
839 )
--> 840 return Dataset(**dataset_kwargs)
841
842 def _post_process(self, dataset: Dataset, resources_paths: Dict[str, str]) -> Optional[Dataset]:
~/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)
271 assert self._fingerprint is not None, "Fingerprint can't be None in a Dataset object"
272 if self.info.features.type != inferred_features.type:
--> 273 raise ValueError(
274 "External features info don't match the dataset:\nGot\n{}\nwith type\n{}\n\nbut expected something like\n{}\nwith type\n{}".format(
275 self.info.features, self.info.features.type, inferred_features, inferred_features.type
ValueError: External features info don't match the dataset:
Got
{'id': Value(dtype='string', id=None), 'document': {'title': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None), 'html': Value(dtype='string', id=None), 'tokens': Sequence(feature={'token': Value(dtype='string', id=None), 'is_html': Value(dtype='bool', id=None)}, length=-1, id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': Sequence(feature={'id': Value(dtype='string', id=None), 'long_answer': {'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None)}, 'short_answers': Sequence(feature={'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}, length=-1, id=None), 'yes_no_answer': ClassLabel(num_classes=2, names=['NO', 'YES'], names_file=None, id=None)}, length=-1, id=None)}
with type
struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<start_token: int64, end_token: int64, start_byte: int64, end_byte: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<title: string, url: string, html: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>>, id: string, question: struct<text: string, tokens: list<item: string>>>
but expected something like
{'id': Value(dtype='string', id=None), 'document': {'html': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'tokens': {'is_html': Sequence(feature=Value(dtype='bool', id=None), length=-1, id=None), 'token': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'url': Value(dtype='string', id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': {'id': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'long_answer': [{'end_byte': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'start_token': Value(dtype='int64', id=None)}], 'short_answers': [{'end_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'end_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}], 'yes_no_answer': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}}
with type
struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<end_byte: int64, end_token: int64, start_byte: int64, start_token: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<html: string, title: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>, url: string>, id: string, question: struct<text: string, tokens: list<item: string>>>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyTorch version (GPU?): 1.6.0 (False)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
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https://api.github.com/repos/huggingface/datasets/issues/2400 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2400/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2400/comments | https://api.github.com/repos/huggingface/datasets/issues/2400/events | https://github.com/huggingface/datasets/issues/2400 | 899,867,212 | MDU6SXNzdWU4OTk4NjcyMTI= | 2,400 | Concatenate several datasets with removed columns is not working. | {
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"Hi,\r\n\r\ndid you fill out the env info section manually or by copy-pasting the output of the `datasets-cli env` command?\r\n\r\nThis code should work without issues on 1.6.2 version (I'm working on master (1.6.2.dev0 version) and can't reproduce this error).",
"@mariosasko you are right I was still on `1.5.0`.... | 1,621,878,015,000 | 1,621,921,921,000 | 1,621,921,919,000 | MEMBER | null | ## Describe the bug
You can't concatenate datasets when you removed columns before.
## Steps to reproduce the bug
```python
from datasets import load_dataset, concatenate_datasets
wikiann= load_dataset("wikiann","en")
wikiann["train"] = wikiann["train"].remove_columns(["langs","spans"])
wikiann["test"] = wikiann["test"].remove_columns(["langs","spans"])
assert wikiann["train"].features.type == wikiann["test"].features.type
concate = concatenate_datasets([wikiann["train"],wikiann["test"]])
```
## Expected results
Merged dataset
## Actual results
```python
ValueError: External features info don't match the dataset:
Got
{'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ner_tags': Sequence(feature=ClassLabel(num_classes=7, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'], names_file=None, id=None), length=-1, id=None), 'langs': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'spans': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
with type
struct<langs: list<item: string>, ner_tags: list<item: int64>, spans: list<item: string>, tokens: list<item: string>>
but expected something like
{'ner_tags': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
with type
struct<ner_tags: list<item: int64>, tokens: list<item: string>>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: ~1.6.2~ 1.5.0
- Platform: macos
- Python version: 3.8.5
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2399 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2399/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2399/comments | https://api.github.com/repos/huggingface/datasets/issues/2399/events | https://github.com/huggingface/datasets/pull/2399 | 899,853,610 | MDExOlB1bGxSZXF1ZXN0NjUxNDk0OTc2 | 2,399 | Add env variable for MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES | {
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"Thank you for clarifying the precedence, @albertvillanova \r\n\r\nIsn't it typically the case where env vars have the highest precedence? \r\n\r\nIn my understanding the point of env vars is to be able to override software w/o needing to touch the code. \r\n\r\nPlease correct me if this is not so in the general ca... | 1,621,876,755,000 | 1,622,106,435,000 | 1,622,045,274,000 | MEMBER | null | Add env variable for `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES`.
This will allow to turn off default behavior: loading in memory (and not caching) small datasets.
Fix #2387. | {
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https://api.github.com/repos/huggingface/datasets/issues/2397 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2397/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2397/comments | https://api.github.com/repos/huggingface/datasets/issues/2397/events | https://github.com/huggingface/datasets/pull/2397 | 899,427,378 | MDExOlB1bGxSZXF1ZXN0NjUxMTMxMTY0 | 2,397 | Fix number of classes in indic_glue sna.bn dataset | {
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"@lhoestq there are many things missing in the README.md file, but this correction is right despite not passing the validation tests...",
"Yes indeed. We run the validation in all modified readme because we think that it is the time when contributors are the most likely to fix a dataset card - or it will never be... | 1,621,844,335,000 | 1,621,960,336,000 | 1,621,960,336,000 | MEMBER | null | As read in the [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.445.pdf), Table 11. | {
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https://api.github.com/repos/huggingface/datasets/issues/2392 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2392/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2392/comments | https://api.github.com/repos/huggingface/datasets/issues/2392/events | https://github.com/huggingface/datasets/pull/2392 | 898,156,795 | MDExOlB1bGxSZXF1ZXN0NjUwMDYxOTE3 | 2,392 | Update text classification template labels in DatasetInfo __post_init__ | {
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"If I'm not mistaken, one way to fix this would be to drop the task templates when copying the info by inserting `dataset.info.task_templates = None` before the `Dataset.cast` call in `Dataset.prepare_for_task`. Moreover, we should do this change independently of the KeyError being raised because currently the foll... | 1,621,610,981,000 | 1,622,201,855,000 | 1,622,201,852,000 | MEMBER | null | This PR implements the idea discussed in #2389 to update the `labels` of the `TextClassification` template in the `DatasetInfo.__post_init__`. The main reason for doing so is so avoid duplicating the label definitions in both `DatasetInfo.features` and `DatasetInfo.task_templates`.
To avoid storing state in `DatasetInfo.__post_init__`, the current implementation flushes `DatasetInfo.task_templates` before the features are cast in `Dataset.prepare_for_task` (thanks to @mariosasko for this idea!).
Here is an example of the current workflow:
```python
ds1 = load_dataset("./datasets/emotion/")
# cast features and flush templates
ds2 = ds1.prepare_for_task("text-classification")
assert ds2.info.task_templates is None
```
Note that if users want to pass a `TextClassification` template to `prepare_for_task`, we require them to set `TextClassification.labels` to match the dataset's features corresponding to `label_column`:
```python
ds1 = load_dataset("./datasets/emotion/")
# TextClassification.labels is None by default => invalid template
task = TextClassification(text_column="text", label_column="label")
# Raises ValueError
ds1.prepare_for_task(task)
# Specifying the labels => valid template
task = TextClassification(text_column="text", label_column="label", labels=['anger', 'fear', 'joy', 'love', 'sadness', 'surprise'])
ds1.prepare_for_task(task)
```
This PR also adds:
* New tests + fixed some old tests that weren't testing `assertRaises` properly
* A decorator to share docstrings across common functions. This allows us to document `DatasetDict.prepare_for_task` and `Dataset.prepare_for_task` in one place.
* Fixes to avoid side-effects from in-place replacements of `DatasetInfo.task_templates` in `DatasetInfo.__post_init__`. Thanks to @lhoestq for figuring this out!
* Removal of `FeaturesWithLazyClassLabel` since we now create a new instance of `TextClassification` in `DatasetInfo.__post_init__` and avoid the side-effects first pointed out by @mariosasko
### PR Description from original WIP
Hi @yjernite and @lhoestq, here's a first stab at the suggestion discussed in #2389 to update the `labels` of the `TextClassification` template in the `DatasetInfo.__post_init__`.
One problem I've spotted is that my current implementation introduces state into the `__post_init__`:
* When we call `load_dataset`, `DatasetInfo.features` are the "raw" features without any casting so we can access the column names by the `label_column` specified in `TextClassification`
* When we call `Dataset.prepare_for_task` we run into a problem because the `DatasetInfo.features` are first cast into the new schema which triggers a `KeyError` when we update the infos [here](https://github.com/huggingface/datasets/blob/8b2a78520828e0cc13c14a31f413a5395ef25110/src/datasets/arrow_dataset.py#L1959).
Here's an explicit example of what I mean with the stack trace appended below:
```python
from datasets import load_dataset
# this works
ds = load_dataset("emotion")
# we can verify the task template is correctly set
ds["train"].info.task_templates # returns [TextClassification(labels=('sadness', 'joy', 'love', 'anger', 'fear', 'surprise'), text_column='text', label_column='label')]
# but this fails because the _post_init__ is looking for the original column names
ds.prepare_for_task("text-classification")
```
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-4-54a43019b319> in <module>
----> 1 ds.prepare_for_task("text-classification")
~/git/datasets/src/datasets/dataset_dict.py in prepare_for_task(self, task)
807 """
808 self._check_values_type()
--> 809 return DatasetDict({k: dataset.prepare_for_task(task=task) for k, dataset in self.items()})
~/git/datasets/src/datasets/dataset_dict.py in <dictcomp>(.0)
807 """
808 self._check_values_type()
--> 809 return DatasetDict({k: dataset.prepare_for_task(task=task) for k, dataset in self.items()})
~/git/datasets/src/datasets/arrow_dataset.py in prepare_for_task(self, task)
1421 dataset = self.remove_columns(columns_to_drop)
1422 dataset = dataset.rename_columns(column_mapping)
-> 1423 dataset = dataset.cast(features=template.features)
1424 return dataset
1425
~/git/datasets/src/datasets/arrow_dataset.py in cast(self, features, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, num_proc)
970 format = self.format
971 dataset = self.with_format("arrow")
--> 972 dataset = dataset.map(
973 lambda t: t.cast(schema),
974 batched=True,
~/git/datasets/src/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)
1583
1584 if num_proc is None or num_proc == 1:
-> 1585 return self._map_single(
1586 function=function,
1587 with_indices=with_indices,
~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
173 }
174 # apply actual function
--> 175 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
176 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
177 # re-apply format to the output
~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs)
338 # Call actual function
339
--> 340 out = func(self, *args, **kwargs)
341
342 # Update fingerprint of in-place transforms + update in-place history of transforms
~/git/datasets/src/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)
1959 if update_data:
1960 # Create new Dataset from buffer or file
-> 1961 info = self.info.copy()
1962 info.features = writer._features
1963 if buf_writer is None:
~/git/datasets/src/datasets/info.py in copy(self)
274
275 def copy(self) -> "DatasetInfo":
--> 276 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
277
278
~/git/datasets/src/datasets/info.py in __init__(self, description, citation, homepage, license, features, post_processed, supervised_keys, task_templates, builder_name, config_name, version, splits, download_checksums, download_size, post_processing_size, dataset_size, size_in_bytes)
~/git/datasets/src/datasets/info.py in __post_init__(self)
174 # The reason is that Dataset.prepare_for_task calls Dataset.cast which converts the
175 # DatasetInfo.features to the new schema and thus template.label_column is no longer a valid key
--> 176 object.__setattr__(template, "labels", tuple(self.features[template.label_column].names))
177 template.label_schema["labels"] = ClassLabel(names=template.labels)
178 self.task_templates[idx] = template
KeyError: 'label'
```
What do you think? I did this a bit quickly, so maybe I'm overlooking something obvious :) One thing would be to only update the labels of the task template on load, but this seems a bit hacky IMO | {
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https://api.github.com/repos/huggingface/datasets/issues/2391 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2391/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2391/comments | https://api.github.com/repos/huggingface/datasets/issues/2391/events | https://github.com/huggingface/datasets/issues/2391 | 898,128,099 | MDU6SXNzdWU4OTgxMjgwOTk= | 2,391 | Missing original answers in kilt-TriviaQA | {
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"That could be useful indeed! Feel free to open a PR on the dataset card if you already have some code that runs, otherwise we'll take care of it soon :) ",
"I can open a PR but there is 2 details to fix:\r\n- the name for the corresponding key (e.g. `original_answer`)\r\n- how to implement it: I’m not sure what ... | 1,621,609,027,000 | 1,623,691,751,000 | 1,623,691,751,000 | CONTRIBUTOR | null | I previously opened an issue at https://github.com/facebookresearch/KILT/issues/42 but from the answer of @fabiopetroni it seems that the problem comes from HF-datasets
## Describe the bug
The `answer` field in kilt-TriviaQA, e.g. `kilt_tasks['train_triviaqa'][0]['output']['answer']` contains a list of alternative answer which are accepted for the question.
However it'd be nice to know the original answer to the question (the only fields in `output` are `'answer', 'meta', 'provenance'`)
## How to fix
It can be fixed by retrieving the original answer from the original TriviaQA (e.g. `trivia_qa['train'][0]['answer']['value']`), perhaps at the same place as here where one retrieves the questions https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md#loading-the-kilt-knowledge-source-and-task-data
cc @yjernite who previously answered to an issue about KILT and TriviaQA :)
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https://api.github.com/repos/huggingface/datasets/issues/2390 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2390/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2390/comments | https://api.github.com/repos/huggingface/datasets/issues/2390/events | https://github.com/huggingface/datasets/pull/2390 | 897,903,642 | MDExOlB1bGxSZXF1ZXN0NjQ5ODQ0NjQ2 | 2,390 | Add check for task templates on dataset load | {
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"LGTM now, thank you =)"
] | 1,621,592,217,000 | 1,621,612,149,000 | 1,621,612,146,000 | MEMBER | null | This PR adds a check that the features of a dataset match the schema of each compatible task template. | {
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"Update: found a few datasets that slipped through the net. Adding them shortly!",
"You might have thought about this already, but would it make sense to use the `datasets.features.ClassLabel` values when possible instead of declaring the list once for the `feature` and once for the `template`?",
"> You might h... | 1,621,586,186,000 | 1,622,215,738,000 | 1,622,215,588,000 | MEMBER | null | This PR inserts text-classification templates for datasets with the following properties:
* Only one config
* At most two features of `(Value, ClassLabel)` type
Note that this misses datasets like `sentiment140` which only has `Value` type features - these will be handled in a separate PR | {
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"followers_url": "https://api.github... | null | [] | 1,621,581,755,000 | 1,622,828,385,000 | 1,622,828,385,000 | MEMBER | null | ## Describe the bug
It seems that the URLs for the following datasets are invalid:
- [ ] `bn_hate_speech` has been renamed: https://github.com/rezacsedu/Bengali-Hate-Speech-Dataset/commit/c67ecfc4184911e12814f6b36901f9828df8a63a
- [ ] `covid_tweets_japanese` has been renamed: http://www.db.info.gifu-u.ac.jp/covid-19-twitter-dataset/
As a result we can no longer load these datasets using `load_dataset`. The simple fix is to rename the URL in the dataset script - will do this asap.
## Steps to reproduce the bug
```python
from datasets import load_dataset
# pick one of the datasets from the list above
ds = load_dataset("bn_hate_speech")
```
## Expected results
Dataset loads without error.
## Actual results
```
Downloading: 3.36kB [00:00, 1.07MB/s]
Downloading: 2.03kB [00:00, 678kB/s]
Using custom data configuration default
Downloading and preparing dataset bn_hate_speech/default (download: 951.48 KiB, generated: 949.84 KiB, post-processed: Unknown size, total: 1.86 MiB) to /Users/lewtun/.cache/huggingface/datasets/bn_hate_speech/default/0.0.0/a2dc726e511a2177523301bcad196af05d4d8a2cff30d2769ba8aacc1f5fdb5c...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/load.py", line 744, in load_dataset
builder_instance.download_and_prepare(
File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/builder.py", line 574, in download_and_prepare
self._download_and_prepare(
File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/builder.py", line 630, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/lewtun/.cache/huggingface/modules/datasets_modules/datasets/bn_hate_speech/a2dc726e511a2177523301bcad196af05d4d8a2cff30d2769ba8aacc1f5fdb5c/bn_hate_speech.py", line 76, in _split_generators
train_path = dl_manager.download_and_extract(_URL)
File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 287, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 195, in download
downloaded_path_or_paths = map_nested(
File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 195, in map_nested
return function(data_struct)
File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 218, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 281, in cached_path
output_path = get_from_cache(
File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/rezacsedu/Bengali-Hate-Speech-Dataset/main/Bengali_%20Hate_Speech_Dataset_Subset.csv
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2.dev0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2387 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2387/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2387/comments | https://api.github.com/repos/huggingface/datasets/issues/2387/events | https://github.com/huggingface/datasets/issues/2387 | 897,566,666 | MDU6SXNzdWU4OTc1NjY2NjY= | 2,387 | datasets 1.6 ignores cache | {
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"Looks like there are multiple issues regarding this (#2386, #2322) and it's a WIP #2329. Currently these datasets are being loaded in-memory which is causing this issue. Quoting @mariosasko here for a quick fix:\r\n\r\n> set `keep_in_memory` to `False` when loading a dataset (`sst = load_dataset(\"sst\", keep_in_m... | 1,621,555,978,000 | 1,622,045,274,000 | 1,622,045,274,000 | CONTRIBUTOR | null | Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b/cache-97cf4c813e6469c6.arrow'}]}`
>
> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:
> > `{'train': [], 'validation': []}`
>
I also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.
to reproduce:
```
USE_TF=0 python examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path gpt2 \
--dataset_name "stas/openwebtext-10k" \
--output_dir output_dir \
--overwrite_output_dir \
--do_train \
--do_eval \
--max_train_samples 1000 \
--max_eval_samples 200 \
--per_device_train_batch_size 4 \
--per_device_eval_batch_size 4 \
--num_train_epochs 1 \
--warmup_steps 8 \
--block_size 64 \
--fp16 \
--report_to none
```
the first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.
@lhoestq
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"Thanks @bhavitvyamalik for referencing the workaround. Setting `keep_in_memory=False` is working."
] | 1,621,555,063,000 | 1,621,624,683,000 | 1,621,624,683,000 | NONE | null | ## Describe the bug
In datasets 1.5.0 the following code snippet would have printed the cache_files:
```
train_data = load_dataset('conll2003', split='train', cache_dir='data')
print(train_data.cache_files[0]['filename'])
```
However, in the newest release (1.6.1), it prints an empty list.
I also tried loading the dataset with `keep_in_memory=True` argument but still `cache_files` is empty.
Was wondering if this is a bug or I need to pass additional arguments so I can access the cache_files.
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https://api.github.com/repos/huggingface/datasets/issues/2384 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2384/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2384/comments | https://api.github.com/repos/huggingface/datasets/issues/2384/events | https://github.com/huggingface/datasets/pull/2384 | 896,866,461 | MDExOlB1bGxSZXF1ZXN0NjQ4OTI4NTQ0 | 2,384 | Add args description to DatasetInfo | {
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"Thanks for the suggestions! I've included them and made a few minor tweaks along the way",
"Please merge master into this branch to fix the CI, I just fixed metadata validation tests."
] | 1,621,518,790,000 | 1,621,675,576,000 | 1,621,675,574,000 | MEMBER | null | Closes #2354
I am not sure what `post_processed` and `post_processing_size` correspond to, so have left them empty for now. I also took a guess at some of the other fields like `dataset_size` vs `size_in_bytes`, so might have misunderstood their meaning. | {
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https://api.github.com/repos/huggingface/datasets/issues/2383 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2383/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2383/comments | https://api.github.com/repos/huggingface/datasets/issues/2383/events | https://github.com/huggingface/datasets/pull/2383 | 895,779,723 | MDExOlB1bGxSZXF1ZXN0NjQ3OTU4MTQ0 | 2,383 | Improve example in rounding docs | {
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} | [] | closed | false | null | [] | null | [] | 1,621,450,763,000 | 1,621,601,602,000 | 1,621,600,589,000 | CONTRIBUTOR | null | Improves the example in the rounding subsection of the Split API docs. With this change, it should more clear what's the difference between the `closest` and the `pct1_dropremainder` rounding. | {
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https://api.github.com/repos/huggingface/datasets/issues/2382 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2382/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2382/comments | https://api.github.com/repos/huggingface/datasets/issues/2382/events | https://github.com/huggingface/datasets/issues/2382 | 895,610,216 | MDU6SXNzdWU4OTU2MTAyMTY= | 2,382 | DuplicatedKeysError: FAILURE TO GENERATE DATASET ! load_dataset('head_qa', 'en') | {
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} | [] | closed | false | null | [] | null | [] | 1,621,439,388,000 | 1,622,381,176,000 | 1,622,381,176,000 | NONE | null | Hello everyone,
I try to use head_qa dataset in [https://huggingface.co/datasets/viewer/?dataset=head_qa&config=en](url)
```
!pip install datasets
from datasets import load_dataset
dataset = load_dataset(
'head_qa', 'en')
```
When I write above load_dataset(.), it throws the following:
```
DuplicatedKeysError Traceback (most recent call last)
<ipython-input-6-ea87002d32f0> in <module>()
2 from datasets import load_dataset
3 dataset = load_dataset(
----> 4 'head_qa', 'en')
5 frames
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self)
347 for hash, key in self.hkey_record:
348 if hash in tmp_record:
--> 349 raise DuplicatedKeysError(key)
350 else:
351 tmp_record.add(hash)
DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 1
Keys should be unique and deterministic in nature
```
How can I fix the error? Thanks
| {
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https://api.github.com/repos/huggingface/datasets/issues/2381 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2381/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2381/comments | https://api.github.com/repos/huggingface/datasets/issues/2381/events | https://github.com/huggingface/datasets/pull/2381 | 895,588,844 | MDExOlB1bGxSZXF1ZXN0NjQ3NzkyNDcw | 2,381 | add dataset card title | {
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} | [] | closed | false | null | [] | null | [] | 1,621,438,203,000 | 1,621,536,700,000 | 1,621,536,700,000 | CONTRIBUTOR | null | few of them were missed by me earlier which I've added now | {
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https://api.github.com/repos/huggingface/datasets/issues/2380 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2380/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2380/comments | https://api.github.com/repos/huggingface/datasets/issues/2380/events | https://github.com/huggingface/datasets/pull/2380 | 895,367,201 | MDExOlB1bGxSZXF1ZXN0NjQ3NTk3NTc3 | 2,380 | maintain YAML structure reading from README | {
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} | [] | closed | false | null | [] | null | [] | 1,621,426,327,000 | 1,621,429,718,000 | 1,621,429,718,000 | CONTRIBUTOR | null | How YAML used be loaded earlier in the string (structure of YAML was affected because of this and YAML for datasets with multiple configs was not being loaded correctly):
```
annotations_creators:
labeled_final:
- expert-generated
labeled_swap:
- expert-generated
unlabeled_final:
- machine-generated
language_creators:
- machine-generated
languages:
- en
licenses:
- other
multilinguality:
- monolingual
size_categories:
labeled_final:
- 10K<n<100K
labeled_swap:
- 10K<n<100K
unlabeled_final:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
- text-scoring
task_ids:
- semantic-similarity-classification
- semantic-similarity-scoring
- text-scoring-other-paraphrase-identification
```
How YAML is loaded in string now:
```
annotations_creators:
labeled_final:
- expert-generated
labeled_swap:
- expert-generated
unlabeled_final:
- machine-generated
language_creators:
- machine-generated
languages:
- en
licenses:
- other
multilinguality:
- monolingual
size_categories:
labeled_final:
- 10K<n<100K
labeled_swap:
- 10K<n<100K
unlabeled_final:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
- text-scoring
task_ids:
- semantic-similarity-classification
- semantic-similarity-scoring
- text-scoring-other-paraphrase-identification
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2379 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2379/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2379/comments | https://api.github.com/repos/huggingface/datasets/issues/2379/events | https://github.com/huggingface/datasets/pull/2379 | 895,252,597 | MDExOlB1bGxSZXF1ZXN0NjQ3NDk2ODUx | 2,379 | Disallow duplicate keys in yaml tags | {
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} | [] | closed | false | null | [] | null | [] | 1,621,419,007,000 | 1,621,421,132,000 | 1,621,421,131,000 | MEMBER | null | Make sure that there's no duplidate keys in yaml tags.
I added the check in the yaml tree constructor's method, so that the verification is done at every level in the yaml structure.
cc @julien-c | {
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https://api.github.com/repos/huggingface/datasets/issues/2376 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2376/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2376/comments | https://api.github.com/repos/huggingface/datasets/issues/2376/events | https://github.com/huggingface/datasets/pull/2376 | 894,852,264 | MDExOlB1bGxSZXF1ZXN0NjQ3MTU1NDE4 | 2,376 | Improve task api code quality | {
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} | [] | closed | false | null | [] | null | [
"Looks good thanks, what do you think @lewtun ?",
"thanks for including the lazy `ClassLabel` class @mariosasko ! from my side this LGTM!"
] | 1,621,379,620,000 | 1,622,666,397,000 | 1,621,956,654,000 | CONTRIBUTOR | null | Improves the code quality of the `TaskTemplate` dataclasses.
Changes:
* replaces `return NotImplemented` with raise `NotImplementedError`
* replaces `sorted` with `len` in the uniqueness check
* defines `label2id` and `id2label` in the `TextClassification` template as properties
* replaces the `object.__setattr__(self, attr, value)` syntax with (IMO nicer) `self.__dict__[attr] = value` | {
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https://api.github.com/repos/huggingface/datasets/issues/2375 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2375/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2375/comments | https://api.github.com/repos/huggingface/datasets/issues/2375/events | https://github.com/huggingface/datasets/pull/2375 | 894,655,157 | MDExOlB1bGxSZXF1ZXN0NjQ2OTg2NTcw | 2,375 | Dataset Streaming | {
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} | [] | closed | false | null | [] | null | [] | 1,621,362,000,000 | 1,624,466,102,000 | 1,624,466,101,000 | MEMBER | null | # Dataset Streaming
## API
Current API is
```python
from datasets import load_dataset
# Load an IterableDataset without downloading data
snli = load_dataset("snli", streaming=True)
# Access examples by streaming data
print(next(iter(snli["train"])))
# {'premise': 'A person on a horse jumps over a broken down airplane.',
# 'hypothesis': 'A person is training his horse for a competition.',
# 'label': 1}
```
I already implemented a few methods:
- IterableDataset.map: apply transforms on-the-fly to the examples
- IterableDataset.shuffle: shuffle the data _a la_ TFDS, i.e. with a shuffling buffer
- IterableDataset.with_format: set the format to `"torch"` to get a `torch.utils.data.IterableDataset`
- merge_datasets: merge two iterable datasets by alternating one or the other (you can specify the probabilities)
I would love to have your opinion on the API design :)
## Implementation details
### Streaming
Data streaming is done using `fsspec` which has nice caching features.
To make dataset streaming work I extend the `open` function of dataset scripts to support opening remote files without downloading them entirely. It also works with remote compressed archives (currently only zip is supported):
```python
# Get a file-like object by streaming data from a remote file
open("https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt")
# Get a file-like object by streaming data from a remote compressed archive by using the hop separator "::"
open("zip://snli_1.0_train.txt::https://nlp.stanford.edu/projects/snli/snli_1.0.zip")
```
I also extend the `os.path.join` function to support navigation in remote compressed archives, since it has to deal with the `"::"` separator. This separator is used by `fsspec`.
Finally I also added a retry mechanism in case the connection fails during data streaming.
### Transforms
An IterableDataset wraps an ExamplesIterable instance. There are different subclasses depending on the transforms we want to apply:
- ExamplesIterable: the basic one
- MappedExamplesIterable: an iterable with a `map` function applied on the fly
- BufferShuffledExamplesIterable: an iterable with a shuffling buffer
- CyclingMultiSourcesExamplesIterable: alternates between several ExamplesIterable
- RandomlyCyclingMultiSourcesExamplesIterable: randomly alternates between several ExamplesIterable
### DatasetBuilder
I use the same builders as usual. I just added a new method `_get_examples_iterable_for_split` to get an ExamplesIterable for a given split. Currently only the GeneratorBasedBuilder and the ArrowBasedBuilder implement it.
The BeamBasedBuilder doesn't implement it yet.
It means that datasets like wikipedia and natural_questions can't be loaded as IterableDataset for now.
## Other details
<S>I may have to do some changes in many dataset script to use `download` instead of `download_and_extract` when extraction is not needed. This will avoid errors for streaming.</s>
EDIT: Actually I just check for the extension of the file to do extraction only if needed.
EDIT2: It's not possible to stream from .tar.gz files without downloading the file completely. For now I raise an error if one want to get a streaming dataset based on .tar.gz files.
## TODO
usual stuff:
- [x] make streaming dependency "aiohttp" optional: `pip install datasets[streaming]`
- [x] tests
- [x] docs | {
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https://api.github.com/repos/huggingface/datasets/issues/2374 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2374/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2374/comments | https://api.github.com/repos/huggingface/datasets/issues/2374/events | https://github.com/huggingface/datasets/pull/2374 | 894,579,364 | MDExOlB1bGxSZXF1ZXN0NjQ2OTIyMjkw | 2,374 | add `desc` to `tqdm` in `Dataset.map()` | {
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"Once this is merged, let's update `transformers` examples to use this new code. As currently all those tqdm bars are who knows what they are....\r\n\r\nhttps://github.com/huggingface/transformers/issues/11797",
"Sure @stas00! Once this is merged let's discuss what all changes can be done on `transformers` side",... | 1,621,356,269,000 | 1,622,130,244,000 | 1,622,041,161,000 | CONTRIBUTOR | null | Fixes #2330. Please let me know if anything is also required in this | {
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"Version below works, checked again in the docs, and data_files should be a path.\r\n```\r\nds = datasets.load_dataset('my_script.py', \r\n data_files='/data/dir/corpus.txt', \r\n cache_dir='.')\r\n```"
] | 1,621,351,250,000 | 1,621,352,196,000 | 1,621,352,195,000 | NONE | null | I'm trying to load a local dataset with the code below
```
ds = datasets.load_dataset('my_script.py',
data_files='corpus.txt',
data_dir='/data/dir',
cache_dir='.')
```
But internally a BuilderConfig is created, which tries to use getmtime on the data_files string, without using data_dir. Is this a bug or am I not using the load_dataset correctly?
https://github.com/huggingface/datasets/blob/bc61954083f74e6460688202e9f77dde2475319c/src/datasets/builder.py#L153 | {
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https://api.github.com/repos/huggingface/datasets/issues/2372 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2372/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2372/comments | https://api.github.com/repos/huggingface/datasets/issues/2372/events | https://github.com/huggingface/datasets/pull/2372 | 894,496,064 | MDExOlB1bGxSZXF1ZXN0NjQ2ODUxODc2 | 2,372 | ConvQuestions benchmark added | {
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"Thanks for your helpful comments and suggestions! :)\r\nI integrated the additional fields, and extended some of the README/dataset card.\r\nAnd I actually realized that we had the cc-by-4.0 for the dataset, so this was also changed.",
"I added the answers to the test set actually :)",
"Oh great ! Let me rever... | 1,621,351,010,000 | 1,622,025,105,000 | 1,622,025,105,000 | CONTRIBUTOR | null | Hello,
I would like to integrate our dataset on conversational QA. The answers are grounded in the KG.
The work was published in CIKM 2019 (https://dl.acm.org/doi/10.1145/3357384.3358016).
We hope for further research on how to deal with the challenges of factoid conversational QA.
Thanks! :) | {
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"@lhoestq Thank you for the review. I've made the suggested changes. There still might be some problems with dummy data though due to some csv loading issues (which I haven't found the cause to).",
"I took a look at the dummy data and some csv lines were cropped. I fixed them :)"
] | 1,621,277,585,000 | 1,622,479,033,000 | 1,622,479,033,000 | CONTRIBUTOR | null | Adding Hendrycks test from https://arxiv.org/abs/2009.03300.
I'm having a bit of trouble with dummy data creation because some lines in the csv files aren't being loaded properly (only the first entry loaded in a row of length 6). The dataset is loading just fine. Hope you can kindly help!
Thank you! | {
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https://api.github.com/repos/huggingface/datasets/issues/2369 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2369/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2369/comments | https://api.github.com/repos/huggingface/datasets/issues/2369/events | https://github.com/huggingface/datasets/pull/2369 | 893,554,153 | MDExOlB1bGxSZXF1ZXN0NjQ2MDQ5NDM1 | 2,369 | correct labels of conll2003 | {
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} | [] | closed | false | null | [] | null | [] | 1,621,273,074,000 | 1,621,326,462,000 | 1,621,326,462,000 | MEMBER | null | # What does this PR
It fixes/extends the `ner_tags` for conll2003 to include all.
Paper reference https://arxiv.org/pdf/cs/0306050v1.pdf
Model reference https://huggingface.co/elastic/distilbert-base-cased-finetuned-conll03-english/blob/main/config.json
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@lhoestq | {
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https://bugs.python.org/issue29209 | {
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```python
dataset = load_dataset("json", data_files=data_files, features=features)
```
Then depending on the order of the features in the json data field it fails:
```python
[...]
~/Desktop/hf/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
94 if self.config.schema:
95 # Cast allows str <-> int/float, while parse_option explicit_schema does NOT
---> 96 pa_table = pa_table.cast(self.config.schema)
97 yield i, pa_table
[...]
ValueError: Target schema's field names are not matching the table's field names: ['tokens', 'ner_tags'], ['ner_tags', 'tokens']
```
This is because one must first re-order the columns of the table to match the `self.config.schema` before calling cast.
One way to fix the `cast` would be to replace it with:
```python
# reorder the arrays if necessary + cast to schema
# we can't simply use .cast here because we may need to change the order of the columns
pa_table = pa.Table.from_arrays([pa_table[name] for name in schema.names], schema=schema)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2365 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2365/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2365/comments | https://api.github.com/repos/huggingface/datasets/issues/2365/events | https://github.com/huggingface/datasets/issues/2365 | 893,179,697 | MDU6SXNzdWU4OTMxNzk2OTc= | 2,365 | Missing ClassLabel encoding in Json loader | {
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} | [] | 1,621,246,750,000 | 1,624,892,734,000 | 1,624,892,734,000 | MEMBER | null | Currently if you want to load a json dataset this way
```python
dataset = load_dataset("json", data_files=data_files, features=features)
```
Then if your features has ClassLabel types and if your json data needs class label encoding (i.e. if the labels in the json files are strings and not integers), then it would fail:
```python
[...]
~/Desktop/hf/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
94 if self.config.schema:
95 # Cast allows str <-> int/float, while parse_option explicit_schema does NOT
---> 96 pa_table = pa_table.cast(self.config.schema)
97 yield i, pa_table
[...]
ArrowInvalid: Failed to parse string: 'O' as a scalar of type int64
```
This is because it just tries to cast the string data to integers, without applying the mapping str->int first
The current workaround is to do instead
```python
dataset = load_dataset("json", data_files=data_files)
dataset = dataset.map(features.encode_example, features=features)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2364 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2364/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2364/comments | https://api.github.com/repos/huggingface/datasets/issues/2364/events | https://github.com/huggingface/datasets/pull/2364 | 892,420,500 | MDExOlB1bGxSZXF1ZXN0NjQ1MTI4MDYx | 2,364 | README updated for SNLI, MNLI | {
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"Regarding the license issue, I think we should allow it since it starts with `other-`. Cc @gchhablani what do you think ?",
"@lhoestq I agree, I'll look into it."
] | 1,621,078,679,000 | 1,621,260,867,000 | 1,621,258,459,000 | CONTRIBUTOR | null | Closes #2275. Mentioned about -1 labels in MNLI, SNLI and how they should be removed before training. @lhoestq `check_code_quality` test might fail for MNLI as the license name `other-Open Portion of the American National Corpus` is not a registered tag for 'licenses' | {
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https://api.github.com/repos/huggingface/datasets/issues/2362 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2362/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2362/comments | https://api.github.com/repos/huggingface/datasets/issues/2362/events | https://github.com/huggingface/datasets/pull/2362 | 892,100,749 | MDExOlB1bGxSZXF1ZXN0NjQ0ODYzOTQw | 2,362 | Fix web_nlg metadata | {
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"Hi ! `release_v2.1` and the others are dataset configuration names.\r\n\r\nThe configuration names are used to show the right code snippet in the UI to load the dataset.\r\nFor example if the parsing of the web_nlg tags worked correctly we would have:\r\n![image](https://user-images.githubusercontent.com/42851186/... | 1,621,012,507,000 | 1,621,259,057,000 | 1,621,258,948,000 | MEMBER | null | Our metadata storage system does not support `.` inside keys. cc @Pierrci
| {
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https://api.github.com/repos/huggingface/datasets/issues/2361 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2361/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2361/comments | https://api.github.com/repos/huggingface/datasets/issues/2361/events | https://github.com/huggingface/datasets/pull/2361 | 891,982,808 | MDExOlB1bGxSZXF1ZXN0NjQ0NzYzNTU4 | 2,361 | Preserve dtype for numpy/torch/tf/jax arrays | {
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"Hi @lhoestq, \r\nIt turns out that pyarrow `ListArray` are not recognized as list-like when we get output from `numpy_to_pyarrow_listarray`. This might cause tests to fail. If possible can we convert that `ListArray` output to list inorder for tests to pass? Under the hood it'll maintain the dtype as that of numpy... | 1,621,003,523,000 | 1,629,189,004,000 | 1,629,189,004,000 | CONTRIBUTOR | null | Fixes #625. This lets the user preserve the dtype of numpy array to pyarrow array which was getting lost due to conversion of numpy array -> list -> pyarrow array. | {
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https://api.github.com/repos/huggingface/datasets/issues/2358 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2358/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2358/comments | https://api.github.com/repos/huggingface/datasets/issues/2358/events | https://github.com/huggingface/datasets/pull/2358 | 891,269,577 | MDExOlB1bGxSZXF1ZXN0NjQ0MTYyOTY2 | 2,358 | Roman Urdu Stopwords List | {
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"Hi ! Thanks for sharing :)\r\nI think the best place to share this is probably the `Languages at Hugging Face` section of the forum:\r\nhttps://discuss.huggingface.co/c/languages-at-hugging-face/15\r\n\r\nSince this is not a dataset, I'm closing this PR if you don't mind",
"Thank you I will look into the link th... | 1,620,930,567,000 | 1,621,414,243,000 | 1,621,260,310,000 | NONE | null | A list of most frequently used Roman Urdu words with different spellings and usages.
This is a very basic effort to collect some basic stopwords for Roman Urdu to help efforts of analyzing text data in roman Urdu which makes up a huge part of daily internet interaction of Roman-Urdu users. | {
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https://api.github.com/repos/huggingface/datasets/issues/2357 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2357/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2357/comments | https://api.github.com/repos/huggingface/datasets/issues/2357/events | https://github.com/huggingface/datasets/pull/2357 | 890,595,693 | MDExOlB1bGxSZXF1ZXN0NjQzNTk0NDcz | 2,357 | Adding Microsoft CodeXGlue Datasets | {
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"Oh one other thing. Mentioned in the PR was that I would need to regenerate the dataset_infos.json once the camel casing was done. However, I am unsure why this is the case since there is no reference to any object names in the dataset_infos.json file.\r\n\r\nIf it needs to be reran, I can try it do it on my own m... | 1,620,866,581,000 | 1,623,144,597,000 | 1,623,144,597,000 | CONTRIBUTOR | null | Hi there, this is a new pull request to get the CodeXGlue datasets into the awesome HF datasets lib. Most of the work has been done in this PR #997 by the awesome @madlag. However, that PR has been stale for a while now and so I spoke with @lhoestq about finishing up the final mile and so he told me to open a new PR with the final changes :smile:.
I believe I've met all of the changes still left in the old PR to do, except for the change to the languages. I believe the READMEs should include the different programming languages used rather than just using the tag "code" as when searching for datasets, SE researchers may specifically be looking only for what type of programming language and so being able to quickly filter will be very valuable. Let me know what you think of that or if you still believe it should be the "code" tag @lhoestq. | {
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https://api.github.com/repos/huggingface/datasets/issues/2355 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2355/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2355/comments | https://api.github.com/repos/huggingface/datasets/issues/2355/events | https://github.com/huggingface/datasets/pull/2355 | 890,484,408 | MDExOlB1bGxSZXF1ZXN0NjQzNDk5NTIz | 2,355 | normalized TOCs and titles in data cards | {
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"Oh right! I'd be in favor of still having the same TOC across the board, we can either leave it as is or add a `[More Info Needed]` `Contributions` Section wherever it's currently missing, wdyt?",
"(I thought those were programmatically updated based on git history :D )",
"Merging for now to avoid conflict sin... | 1,620,853,199,000 | 1,620,998,592,000 | 1,620,998,592,000 | MEMBER | null | I started fixing some of the READMEs that were failing the tests introduced by @gchhablani but then realized that there were some consistent differences between earlier and newer versions of some of the titles (e.g. Data Splits vs Data Splits Sample Size, Supported Tasks vs Supported Tasks and Leaderboards). We also had different versions of the Table of Content
This PR normalizes all of them to the newer version | {
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"followers_url": "https://api.github... | null | [] | 1,620,849,689,000 | 1,621,675,574,000 | 1,621,675,574,000 | MEMBER | null | **Is your feature request related to a problem? Please describe.**
As noted in PR #2255, the attributes of `DatasetInfo` are not documented in the [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=datasetinfo#datasetinfo). It would be nice to do so :)
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@lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/2352 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2352/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2352/comments | https://api.github.com/repos/huggingface/datasets/issues/2352/events | https://github.com/huggingface/datasets/pull/2352 | 889,810,100 | MDExOlB1bGxSZXF1ZXN0NjQyOTI4NTgz | 2,352 | Set to_json default to JSON lines | {
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"This is perfect, @albertvillanova - thank you! Tested it to work.\r\n\r\nMight it be a good idea to document the args to `to_json`?\r\n\r\nand also even a very basic progress bar? took 10min for 8M large records for `openwebtext` so perhaps some indication of it's being alive every min or so?",
"@lhoestq I added... | 1,620,807,565,000 | 1,621,587,674,000 | 1,621,587,673,000 | MEMBER | null | With this PR, the method `Dataset.to_json`:
- is added to the docs
- defaults to JSON lines | {
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} | [] | closed | false | null | [] | null | [] | 1,620,791,650,000 | 1,621,258,901,000 | 1,621,258,901,000 | CONTRIBUTOR | null | Fixes #2350
In some cases, Faiss GPU index objects do not have neither "device" nor "getDevice". Possibly this happens when some part of the index is computed on CPU.
In particular, this would happen with the index `OPQ16_128,IVF512,PQ32` (issue #2350). I did check it, but it is likely that `OPQ` or `PQ` transforms cause it.
I propose, instead of using the index object to get the device, to infer it form the `FaissIndex.device` field as it is done in `.add_vectors`. Here we assume that `.device` always corresponds to the index placement and it seems reasonable. | {
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"Just in case, this is a workaround that I use in my code and it seems to do the job.\r\n\r\n```python\r\nif use_gpu_index:\r\n data[\"train\"]._indexes[\"text_emb\"].faiss_index = faiss.index_gpu_to_cpu(data[\"train\"]._indexes[\"text_emb\"].faiss_index)\r\n```"
] | 1,620,790,916,000 | 1,621,258,901,000 | 1,621,258,901,000 | CONTRIBUTOR | null | ## Describe the bug
After training an index with a factory string `OPQ16_128,IVF512,PQ32` on GPU, `.save_faiss_index` throws this error.
```
File "index_wikipedia.py", line 119, in <module>
data["train"].save_faiss_index("text_emb", index_save_path)
File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/datasets/search.py", line 470, in save_faiss_index
index.save(file)
File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/datasets/search.py", line 334, in save
faiss.write_index(index, str(file))
File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/faiss/swigfaiss_avx2.py", line 5654, in write_index
return _swigfaiss.write_index(*args)
RuntimeError: Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /root/miniconda3/conda-bld/faiss-pkg_1613235005464/work/faiss/impl/index_write.cpp:453: don't know how to serialize this type of index
```
## Steps to reproduce the bug
Any dataset will do, I just selected a familiar one.
```python
import numpy as np
import datasets
INDEX_STR = "OPQ16_128,IVF512,PQ32"
INDEX_SAVE_PATH = "will_not_save.faiss"
data = datasets.load_dataset("Fraser/news-category-dataset", split=f"train[:10000]")
def encode(item):
return {"text_emb": np.random.randn(768).astype(np.float32)}
data = data.map(encode)
data.add_faiss_index(column="text_emb", string_factory=INDEX_STR, train_size=10_000, device=0)
data.save_faiss_index("text_emb", INDEX_SAVE_PATH)
```
## Expected results
Saving the index
## Actual results
Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) ... don't know how to serialize this type of index
## Environment info
- `datasets` version: 1.6.2
- Platform: Linux-4.15.0-142-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyTorch version (GPU?): 1.8.1+cu111 (True)
- Tensorflow version (GPU?): 2.2.0 (False)
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
I will be proposing a fix in a couple of minutes | {
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https://api.github.com/repos/huggingface/datasets/issues/2348 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2348/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2348/comments | https://api.github.com/repos/huggingface/datasets/issues/2348/events | https://github.com/huggingface/datasets/pull/2348 | 887,927,737 | MDExOlB1bGxSZXF1ZXN0NjQxMTMwOTM4 | 2,348 | Add tests for dataset cards | {
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"@lhoestq\r\n\r\nShould I remove the scripts? or atleast remove running them from the CircleCI config?\r\n\r\nAlso, I hope it is okay that the combined method (metadata+content) is only a slow test, and for the Circle CI, I assume only non-slow tests are run? If yes, this would mean separate tests for content and m... | 1,620,753,267,000 | 1,621,599,047,000 | 1,621,599,047,000 | CONTRIBUTOR | null | Adding tests for dataset cards
This PR will potentially remove the scripts being used for dataset tags and readme validation.
Additionally, this will allow testing dataset readmes by providing the name as follows:
```bash
pytest tests/test_dataset_cards.py::test_dataset_tags[fashion_mnist]
```
and
```bash
pytest tests/test_dataset_cards.py::test_readme_content[fashion_mnist]
```
or a combined test as:
```bash
pytest tests/test_dataset_cards.py::test_dataset_card[fashion_mnist]
```
@lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/2346 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2346/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2346/comments | https://api.github.com/repos/huggingface/datasets/issues/2346/events | https://github.com/huggingface/datasets/pull/2346 | 886,632,114 | MDExOlB1bGxSZXF1ZXN0NjM5OTAzMjk3 | 2,346 | Add Qasper Dataset | {
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"I saw that the README [template](https://github.com/huggingface/datasets/blob/master/templates/README.md) changed while I was working on this 😅 Some TOC titles may be different but I filled it to the best of my knowledge & readme quality check passes now.\r\nready for review @lhoestq "
] | 1,620,725,144,000 | 1,621,340,908,000 | 1,621,340,908,000 | CONTRIBUTOR | null | [Question Answering on Scientific Research Papers](https://allenai.org/project/qasper/home)
Doing NLP on NLP papers to do NLP ♻️ I had to add it~
- [x] Add README (just gotta fill out some more )
- [x] Dataloader code
- [x] Make dummy dataset
- [x] generate dataset infos
- [x] Tests
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"@lhoestq @LysandreJik",
"<s>Hi :) Can you share with us the code you used ?</s>\r\n\r\nEDIT: from https://github.com/huggingface/transformers/issues/11665#issuecomment-838348291 I understand you're using the run_clm.py script. Can you share your logs ?\r\n",
"Also note that for the caching to work, you must re... | 1,620,724,157,000 | 1,623,386,351,000 | 1,623,386,351,000 | NONE | null | Hi, I am training a gpt-2 from scratch using run_clm.py.
I want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),
I tried to :
copy path_to_cache_dir/datasets to new_cache_dir/datasets
set export HF_DATASETS_CACHE="new_cache_dir/"
but the program still re-preprocess the whole dataset without loading cache.
I also tried to torch.save(lm_datasets, fw), but the saved file is only 14M.
What is the proper way to do this? | {
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https://api.github.com/repos/huggingface/datasets/issues/2342 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2342/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2342/comments | https://api.github.com/repos/huggingface/datasets/issues/2342/events | https://github.com/huggingface/datasets/pull/2342 | 882,981,420 | MDExOlB1bGxSZXF1ZXN0NjM2NDg0MzM3 | 2,342 | Docs - CER above 1 | {
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"Thanks for approving it!"
] | 1,620,569,859,000 | 1,620,724,619,000 | 1,620,724,619,000 | CONTRIBUTOR | null | Added the Ascent Commonsense KB of 8.9M assertions.
- Paper: [Advanced Semantics for Commonsense Knowledge Extraction (WWW'21)](https://arxiv.org/abs/2011.00905)
- Website: https://ascent.mpi-inf.mpg.de/
(I am the author of the dataset) | {
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`Features.copy` now creates a deep copy. | {
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`dataset = load_dataset("web_of_science", "WOS11967", ignore_verifications=True)` | {
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"I've raised a PR for this. Should work with `dataset = load_dataset(\"web_of_science\", \"WOS11967\", ignore_verifications=True)`once it gets merged into the main branch. Thanks for reporting this! "
] | 1,620,525,722,000 | 1,620,653,753,000 | 1,620,653,753,000 | NONE | null | NonMatchingChecksumError when trying to download the web_of_science dataset.
>NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://data.mendeley.com/datasets/9rw3vkcfy4/6/files/c9ea673d-5542-44c0-ab7b-f1311f7d61df/WebOfScience.zip?dl=1']
Setting `ignore_verfications=True` results in OSError.
>OSError: Cannot find data file.
Original error:
[Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/37ab2c42f50d553c1d0ea432baca3e9e11fedea4aeec63a81e6b7e25dd10d4e7/WOS5736/X.txt'
```python
dataset = load_dataset('web_of_science', 'WOS5736')
```
There are 3 data instances and they all don't work. 'WOS5736', 'WOS11967', 'WOS46985'
datasets 1.6.2
python 3.7.10
Ubuntu 18.04.5 LTS | {
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https://api.github.com/repos/huggingface/datasets/issues/2336 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2336/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2336/comments | https://api.github.com/repos/huggingface/datasets/issues/2336/events | https://github.com/huggingface/datasets/pull/2336 | 881,298,783 | MDExOlB1bGxSZXF1ZXN0NjM0ODk1OTU5 | 2,336 | Fix overflow issue in interpolation search | {
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"~~Seems like the CI failure is unrelated to this PR~~ (fixed with the merge). \r\n\r\n@lhoestq Can you please verify that everything is OK in terms of speed? Another solution is to change the offsets array dtype to np.int64 (but this doesn't scale in theory compared to Python integer which is unbound). I'm not sur... | 1,620,507,096,000 | 1,620,653,347,000 | 1,620,653,172,000 | CONTRIBUTOR | null | Fixes #2335
More info about this error can be found [here](https://stackoverflow.com/questions/53239890/why-do-i-keep-getting-this-error-runtimewarning-overflow-encountered-in-int-sc/53240100). | {
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https://api.github.com/repos/huggingface/datasets/issues/2335 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2335/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2335/comments | https://api.github.com/repos/huggingface/datasets/issues/2335/events | https://github.com/huggingface/datasets/issues/2335 | 881,291,887 | MDU6SXNzdWU4ODEyOTE4ODc= | 2,335 | Index error in Dataset.map | {
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] | closed | false | null | [] | null | [] | 1,620,506,697,000 | 1,620,653,172,000 | 1,620,653,172,000 | CONTRIBUTOR | null | The following code, if executed on master, raises an IndexError (due to overflow):
```python
>>> from datasets import *
>>> d = load_dataset("bookcorpus", split="train")
Reusing dataset bookcorpus (C:\Users\Mario\.cache\huggingface\datasets\bookcorpus\plain_text\1.0.0\44662c4a114441c35200992bea923b170e6f13f2f0beb7c14e43759cec498700)
2021-05-08 21:23:46.859818: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
>>> d.map(lambda ex: ex)
0%|▎ | 289430/74004228 [00:13<58:41, 20935.33ex/s]c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py:84: RuntimeWarning: overflow encountered in int_scalars
k = i + ((j - i) * (x - arr[i]) // (arr[j] - arr[i]))
0%|▎ | 290162/74004228 [00:13<59:11, 20757.23ex/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1498, in map
new_fingerprint=new_fingerprint,
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 174, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\fingerprint.py", line 340, in wrapper
out = func(self, *args, **kwargs)
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1799, in _map_single
for i, example in enumerate(pbar):
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\site-packages\tqdm\std.py", line 1133, in __iter__
for obj in iterable:
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1145, in __iter__
format_kwargs=format_kwargs,
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1337, in _getitem
pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\formatting\formatting.py", line 368, in query_table
pa_subtable = _query_table(table, key)
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\formatting\formatting.py", line 79, in _query_table
return table.fast_slice(key % table.num_rows, 1)
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py", line 128, in fast_slice
i = _interpolation_search(self._offsets, offset)
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py", line 91, in _interpolation_search
raise IndexError(f"Invalid query '{x}' for size {arr[-1] if len(arr) else 'none'}.")
IndexError: Invalid query '290162' for size 74004228.
```
Tested on Windows, can run on Linux if needed.
EDIT:
It seems like for this to happen, the default NumPy dtype has to be np.int32. | {
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https://api.github.com/repos/huggingface/datasets/issues/2334 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2334/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2334/comments | https://api.github.com/repos/huggingface/datasets/issues/2334/events | https://github.com/huggingface/datasets/pull/2334 | 879,810,107 | MDExOlB1bGxSZXF1ZXN0NjMzNTAzNTEw | 2,334 | Updating the DART file checksums in GEM | {
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"@sebastianGehrmann "
] | 1,620,424,424,000 | 1,620,425,890,000 | 1,620,425,890,000 | MEMBER | null | The DART files were just updated on the source GitHub
https://github.com/Yale-LILY/dart/commit/34b3c872da4811523e334f1631e54ca8105dffab | {
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https://api.github.com/repos/huggingface/datasets/issues/2333 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2333/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2333/comments | https://api.github.com/repos/huggingface/datasets/issues/2333/events | https://github.com/huggingface/datasets/pull/2333 | 879,214,067 | MDExOlB1bGxSZXF1ZXN0NjMyOTUwNzIy | 2,333 | Fix duplicate keys | {
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"- @jplu "
] | 1,620,401,288,000 | 1,620,510,451,000 | 1,620,403,028,000 | MEMBER | null | As noticed in https://github.com/huggingface/datasets/pull/2245, many datasets yield duplicate keys.
Most of the time it was because the counter used for ids were reset at each new data file. | {
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https://api.github.com/repos/huggingface/datasets/issues/2332 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2332/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2332/comments | https://api.github.com/repos/huggingface/datasets/issues/2332/events | https://github.com/huggingface/datasets/pull/2332 | 879,041,608 | MDExOlB1bGxSZXF1ZXN0NjMyNzk1NDE4 | 2,332 | Add note about indices mapping in save_to_disk docstring | {
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https://api.github.com/repos/huggingface/datasets/issues/2330 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2330/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2330/comments | https://api.github.com/repos/huggingface/datasets/issues/2330/events | https://github.com/huggingface/datasets/issues/2330 | 878,490,927 | MDU6SXNzdWU4Nzg0OTA5Mjc= | 2,330 | Allow passing `desc` to `tqdm` in `Dataset.map()` | {
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"Hi @lhoestq,\r\nShould we change `desc` in [pbar](https://github.com/huggingface/datasets/blob/81fcf88172ed5e3026ef68aed4c0ec6980372333/src/datasets/arrow_dataset.py#L1860) to something meaningful?",
"I think the user could pass the `desc` parameter to `map` so that it can be displayed in the tqdm progress bar, ... | 1,620,366,774,000 | 1,622,041,161,000 | 1,622,041,161,000 | CONTRIBUTOR | null | It's normal to have many `map()` calls, and some of them can take a few minutes,
it would be nice to have a description on the progress bar.
Alternative solution:
Print the description before/after the `map()` call. | {
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https://api.github.com/repos/huggingface/datasets/issues/2329 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2329/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2329/comments | https://api.github.com/repos/huggingface/datasets/issues/2329/events | https://github.com/huggingface/datasets/pull/2329 | 877,924,198 | MDExOlB1bGxSZXF1ZXN0NjMxODA3MTk0 | 2,329 | Add cache dir for in-memory datasets | {
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"Yes, having `cache_dir` as an attribute looks cleaner.\r\n\r\n\r\n\r\n",
"Good job! Looking forward to this new feature! 🥂",
"@lhoestq Sorry for the late reply. Yes, I'll start working on tests. Thanks for the detailed explanation of the current issues with caching (like the idea of adding the `use_caching` p... | 1,620,329,732,000 | 1,623,181,608,000 | 1,623,179,206,000 | CONTRIBUTOR | null | Adds the cache dir attribute to DatasetInfo as suggested by @lhoestq.
Should fix #2322 | {
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https://api.github.com/repos/huggingface/datasets/issues/2328 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2328/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2328/comments | https://api.github.com/repos/huggingface/datasets/issues/2328/events | https://github.com/huggingface/datasets/pull/2328 | 877,673,896 | MDExOlB1bGxSZXF1ZXN0NjMxNTg2MzU2 | 2,328 | Add Matthews/Pearson/Spearman correlation metrics | {
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} | [] | closed | false | null | [] | null | [] | 1,620,317,367,000 | 1,620,320,290,000 | 1,620,320,290,000 | MEMBER | null | Added three metrics:
- The Matthews correlation coefficient (from sklearn)
- The Pearson correlation coefficient (from scipy)
- The Spearman correlation coefficient (from scipy)
cc @sgugger | {
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https://api.github.com/repos/huggingface/datasets/issues/2327 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2327/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2327/comments | https://api.github.com/repos/huggingface/datasets/issues/2327/events | https://github.com/huggingface/datasets/issues/2327 | 877,565,831 | MDU6SXNzdWU4Nzc1NjU4MzE= | 2,327 | A syntax error in example | {
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"cc @beurkinger but I think this has been fixed internally and will soon be updated right ?",
"This issue has been fixed."
] | 1,620,311,684,000 | 1,621,479,859,000 | 1,621,479,859,000 | NONE | null | 
Sorry to report with an image, I can't find the template source code of this snippet. | {
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https://api.github.com/repos/huggingface/datasets/issues/2326 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2326/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2326/comments | https://api.github.com/repos/huggingface/datasets/issues/2326/events | https://github.com/huggingface/datasets/pull/2326 | 876,829,254 | MDExOlB1bGxSZXF1ZXN0NjMwODk3MjI4 | 2,326 | Enable auto-download for PAN-X / Wikiann domain in XTREME | {
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While re-generating `dataset_infos.json` I ran into a `KeyError` in the `udpos.Arabic` domain so have included a fix for this as well. | {
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https://api.github.com/repos/huggingface/datasets/issues/2325 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2325/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2325/comments | https://api.github.com/repos/huggingface/datasets/issues/2325/events | https://github.com/huggingface/datasets/pull/2325 | 876,653,121 | MDExOlB1bGxSZXF1ZXN0NjMwNzU1MzIx | 2,325 | Added the HLGD dataset | {
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"Is there anything else needed from my end?",
"Thanks Bhavitvya and Quentin, this was very streamlined!"
] | 1,620,233,609,000 | 1,620,831,313,000 | 1,620,828,998,000 | CONTRIBUTOR | null | Added the Headline Grouping Dataset (HLGD), from the NAACL2021 paper: News Headline Grouping as a Challenging NLU Task
Dataset Link: https://github.com/tingofurro/headline_grouping
Paper link: https://people.eecs.berkeley.edu/~phillab/pdfs/NAACL2021_HLG.pdf | {
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https://api.github.com/repos/huggingface/datasets/issues/2324 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2324/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2324/comments | https://api.github.com/repos/huggingface/datasets/issues/2324/events | https://github.com/huggingface/datasets/pull/2324 | 876,602,064 | MDExOlB1bGxSZXF1ZXN0NjMwNzE1NTQz | 2,324 | Create Audio feature | {
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"For optimal storage, it would be better to:\r\n- store only the audio file path in the cache Arrow file\r\n- perform decoding of the audio file (into audio array and sample rate) on the fly, while loading the dataset from cache (or by adding a convenient `load_audio` function)",
"Thanks a lot @lhoestq for your h... | 1,620,230,122,000 | 1,634,120,793,000 | 1,634,120,793,000 | MEMBER | null | Create `Audio` feature to handle raw audio files.
Some decisions to be further discussed:
- I have chosen `soundfile` as the audio library; another interesting library is `librosa`, but this requires `soundfile` (see [here](https://github.com/librosa/librosa/blob/main/setup.cfg#L53)). If we require some more advanced functionalities, we could eventually switch the library.
- I have implemented the audio feature as an extra: `pip install datasets[audio]`. For the moment, the typical datasets user uses only text datasets, and there is no need for them for additional package requirements for audio/image if they do not need them.
- For tests, I require audio dependencies (so that all audio functionalities are checked with our CI test suite); I exclude Linux platforms, which require an additional library to be installed with the distribution package manager
- I also require `pytest-datadir`, which allow to have (audio) data files for tests
- The audio data contain: array and sample_rate.
- The array is reshaped as 1D array (expected input for `Wav2Vec2`).
Note that to install `soundfile` on Linux, you need to install `libsndfile` using your distribution’s package manager, for example `sudo apt-get install libsndfile1`.
## Requirements Specification
- Access example with audio loading and resampling:
```python
ds[0]["audio"]
```
- Map with audio loading & resampling:
```python
def preprocess(batch):
batch["input_values"] = processor(batch["audio"]).input_values
return batch
ds = ds.map(preprocess)
```
- Map without audio loading and resampling:
```python
def preprocess(batch):
batch["labels"] = processor(batch["target_text"]).input_values
return batch
ds = ds.map(preprocess)
```
- Additional requirement specification (see https://github.com/huggingface/datasets/pull/2324#pullrequestreview-768864998): Cast audio column to change sampling sate:
```python
ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2323 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2323/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2323/comments | https://api.github.com/repos/huggingface/datasets/issues/2323/events | https://github.com/huggingface/datasets/issues/2323 | 876,438,507 | MDU6SXNzdWU4NzY0Mzg1MDc= | 2,323 | load_dataset("timit_asr") gives back duplicates of just one sample text | {
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"Upgrading datasets to version 1.6 fixes the issue",
"This bug was fixed in #1995. Upgrading the `datasets` should work! ",
"Thanks @ekeleshian for having reported.\r\n\r\nI am closing this issue once that you updated `datasets`. Feel free to reopen it if the problem persists."
] | 1,620,220,488,000 | 1,620,383,550,000 | 1,620,383,550,000 | NONE | null | ## Describe the bug
When you look up on key ["train"] and then ['text'], you get back a list with just one sentence duplicated 4620 times. Namely, the sentence "Would such an act of refusal be useful?". Similarly when you look up ['test'] and then ['text'], the list is one sentence repeated "The bungalow was pleasantly situated near the shore." 1680 times.
I tried to work around the issue by downgrading to datasets version 1.3.0, inspired by [this post](https://www.gitmemory.com/issue/huggingface/datasets/2052/798904836) and removing the entire huggingface directory from ~/.cache, but I still get the same issue.
## Steps to reproduce the bug
```python
from datasets import load_dataset
timit = load_dataset("timit_asr")
print(timit['train']['text'])
print(timit['test']['text'])
```
## Expected Result
Rows of diverse text, like how it is shown in the [wav2vec2.0 tutorial](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb)
<img width="485" alt="Screen Shot 2021-05-05 at 9 09 57 AM" src="https://user-images.githubusercontent.com/33647474/117146094-d9b77f00-ad81-11eb-8306-f281850c127a.png">
## Actual results
Rows of repeated text.
<img width="319" alt="Screen Shot 2021-05-05 at 9 11 53 AM" src="https://user-images.githubusercontent.com/33647474/117146231-f8b61100-ad81-11eb-834a-fc10410b0c9c.png">
## Versions
- Datasets: 1.3.0
- Python: 3.9.1
- Platform: macOS-11.2.1-x86_64-i386-64bit}
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https://api.github.com/repos/huggingface/datasets/issues/2322 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2322/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2322/comments | https://api.github.com/repos/huggingface/datasets/issues/2322/events | https://github.com/huggingface/datasets/issues/2322 | 876,383,853 | MDU6SXNzdWU4NzYzODM4NTM= | 2,322 | Calls to map are not cached. | {
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"I tried upgrading to `datasets==1.6.2` and downgrading to `1.6.0`. Both versions produce the same output.\r\n\r\nDowngrading to `1.5.0` works and produces the following output for me:\r\n\r\n```bash\r\nDownloading: 9.20kB [00:00, 3.94MB/s] \r\nDownloading: 5.99kB [00:00, 3.29MB/s] ... | 1,620,216,687,000 | 1,623,179,402,000 | 1,623,179,301,000 | NONE | null | ## Describe the bug
Somehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?
## Steps to reproduce the bug
```python
import datasets
datasets.set_caching_enabled(True)
sst = datasets.load_dataset("sst")
def foo(samples, i):
print("executed", i[:10])
return samples
# first call
x = sst.map(foo, batched=True, with_indices=True, num_proc=2)
print('\n'*3, "#" * 30, '\n'*3)
# second call
y = sst.map(foo, batched=True, with_indices=True, num_proc=2)
# print version
import sys
import platform
print(f"""
- Datasets: {datasets.__version__}
- Python: {sys.version}
- Platform: {platform.platform()}
""")
```
## Actual results
This code prints the following output for me:
```bash
No config specified, defaulting to: sst/default
Reusing dataset sst (/home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/b8a7889ef01c5d3ae8c379b84cc4080f8aad3ac2bc538701cbe0ac6416fb76ff)
#0: 0%| | 0/5 [00:00<?, ?ba/s]
#1: 0%| | 0/5 [00:00<?, ?ba/s]
executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
executed [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]
executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]
executed [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]
executed [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]
executed [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]
executed [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]
executed [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]
executed [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]
#0: 100%|██████████| 5/5 [00:00<00:00, 59.85ba/s]
executed [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]
#1: 100%|██████████| 5/5 [00:00<00:00, 60.85ba/s]
#0: 0%| | 0/1 [00:00<?, ?ba/s]
#1: 0%| | 0/1 [00:00<?, ?ba/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
#0: 100%|██████████| 1/1 [00:00<00:00, 69.32ba/s]
executed [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]
#1: 100%|██████████| 1/1 [00:00<00:00, 70.93ba/s]
#0: 0%| | 0/2 [00:00<?, ?ba/s]
#1: 0%| | 0/2 [00:00<?, ?ba/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]
#0: 100%|██████████| 2/2 [00:00<00:00, 63.25ba/s]
executed [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]
executed [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]
#1: 100%|██████████| 2/2 [00:00<00:00, 57.69ba/s]
##############################
#0: 0%| | 0/5 [00:00<?, ?ba/s]
#1: 0%| | 0/5 [00:00<?, ?ba/s]
executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
executed [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]
executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]
executed [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]
executed [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]
executed [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]
executed [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]
executed [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]
#0: 100%|██████████| 5/5 [00:00<00:00, 58.10ba/s]
executed [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]
executed [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]
#1: 100%|██████████| 5/5 [00:00<00:00, 57.19ba/s]
#0: 0%| | 0/1 [00:00<?, ?ba/s]
#1: 0%| | 0/1 [00:00<?, ?ba/s]
executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
#0: 100%|██████████| 1/1 [00:00<00:00, 60.10ba/s]
executed [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]
#1: 100%|██████████| 1/1 [00:00<00:00, 53.82ba/s]
#0: 0%| | 0/2 [00:00<?, ?ba/s]
#1: 0%| | 0/2 [00:00<?, ?ba/s]
executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]
executed [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]
#0: 100%|██████████| 2/2 [00:00<00:00, 72.76ba/s]
executed [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]
#1: 100%|██████████| 2/2 [00:00<00:00, 71.55ba/s]
- Datasets: 1.6.1
- Python: 3.8.3 (default, May 19 2020, 18:47:26)
[GCC 7.3.0]
- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10
```
## Expected results
Caching should work.
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Fix #2319. | {
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https://api.github.com/repos/huggingface/datasets/issues/2320 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2320/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2320/comments | https://api.github.com/repos/huggingface/datasets/issues/2320/events | https://github.com/huggingface/datasets/pull/2320 | 876,257,026 | MDExOlB1bGxSZXF1ZXN0NjMwNDM5NjI5 | 2,320 | Set default name in init_dynamic_modules | {
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Close #2318. | {
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https://api.github.com/repos/huggingface/datasets/issues/2319 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2319/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2319/comments | https://api.github.com/repos/huggingface/datasets/issues/2319/events | https://github.com/huggingface/datasets/issues/2319 | 876,251,376 | MDU6SXNzdWU4NzYyNTEzNzY= | 2,319 | UnicodeDecodeError for OSCAR (Afrikaans) | {
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"Thanks for reporting, @sgraaf.\r\n\r\nI am going to have a look at it. \r\n\r\nI guess the expected codec is \"UTF-8\". Normally, when no explicitly codec is passed, Python uses one which is platform-dependent. For Linux machines, the default codec is `utf_8`, which is OK. However for Windows machine, the default ... | 1,620,206,572,000 | 1,620,212,251,000 | 1,620,211,855,000 | NONE | null | ## Describe the bug
When loading the [OSCAR dataset](https://huggingface.co/datasets/oscar) (specifically `unshuffled_deduplicated_af`), I encounter a `UnicodeDecodeError`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_af")
```
## Expected results
Anything but an error, really.
## Actual results
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("oscar", "unshuffled_deduplicated_af")
Downloading: 14.7kB [00:00, 4.91MB/s]
Downloading: 3.07MB [00:00, 32.6MB/s]
Downloading and preparing dataset oscar/unshuffled_deduplicated_af (download: 62.93 MiB, generated: 163.38 MiB, post-processed: Unknown size, total: 226.32 MiB) to C:\Users\sgraaf\.cache\huggingface\datasets\oscar\unshuffled_deduplicated_af\1.0.0\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464...
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 81.0/81.0 [00:00<00:00, 40.5kB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 66.0M/66.0M [00:18<00:00, 3.50MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\load.py", line 745, in load_dataset
builder_instance.download_and_prepare(
File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\builder.py", line 574, in download_and_prepare
self._download_and_prepare(
File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\builder.py", line 979, in _prepare_split
for key, record in utils.tqdm(
File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\tqdm\std.py", line 1133, in __iter__
for obj in iterable:
File "C:\Users\sgraaf\.cache\huggingface\modules\datasets_modules\datasets\oscar\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464\oscar.py", line 359, in _generate_examples
for line in f:
File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\encodings\cp1252.py", line 23, in decode
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 7454: character maps to <undefined>
```
## Versions
Paste the output of the following code:
```python
import datasets
import sys
import platform
print(f"""
- Datasets: {datasets.__version__}
- Python: {sys.version}
- Platform: {platform.platform()}
""")
```
- Datasets: 1.6.2
- Python: 3.9.4 (tags/v3.9.4:1f2e308, Apr 6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]
- Platform: Windows-10-10.0.19041-SP0 | {
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"Hi @richardliaw, \r\n\r\nFirst, thanks for the compliments.\r\n\r\nIn relation with your request, currently, the dynamic modules path is obtained this way:\r\n```python\r\nfrom datasets.load import init_dynamic_modules, MODULE_NAME_FOR_DYNAMIC_MODULES\r\n\r\ndynamic_modules_path = init_dynamic_modules(MODULE_NAME_... | 1,620,204,048,000 | 1,620,290,745,000 | 1,620,287,874,000 | NONE | null | **Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
This is an awesome library.
It seems like the dynamic module path in this library has broken some of hyperparameter tuning functionality: https://discuss.huggingface.co/t/using-hyperparameter-search-in-trainer/785/34
This is because Ray will spawn new processes, and each process will load modules by path. However, we need to explicitly inform Ray to load the right modules, or else it will error upon import.
I'd like an API to obtain the dynamic paths. This will allow us to support this functionality in this awesome library while being future proof.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
`datasets.get_dynamic_paths -> List[str]` will be sufficient for my use case.
By offering this API, we will be able to address the following issues (by patching the ray integration sufficiently):
https://github.com/huggingface/blog/issues/106
https://github.com/huggingface/transformers/issues/11565
https://discuss.huggingface.co/t/using-hyperparameter-search-in-trainer/785/34
https://discuss.huggingface.co/t/using-hyperparameter-search-in-trainer/785/35
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https://api.github.com/repos/huggingface/datasets/issues/2317 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2317/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2317/comments | https://api.github.com/repos/huggingface/datasets/issues/2317/events | https://github.com/huggingface/datasets/pull/2317 | 875,767,318 | MDExOlB1bGxSZXF1ZXN0NjMwMDQxNzc4 | 2,317 | Fix incorrect version specification for the pyarrow package | {
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Simply, I put a comma between the version bounds.
Fix #2316. | {
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"Fixed by #2317."
] | 1,620,155,711,000 | 1,620,209,403,000 | 1,620,209,403,000 | CONTRIBUTOR | null | ## Describe the bug
The pyarrow dependency is incorrectly specified in setup.py file, in [this line](https://github.com/huggingface/datasets/blob/3a3e5a4da20bfcd75f8b6a6869b240af8feccc12/setup.py#L77).
Also as a snippet:
```python
"pyarrow>=1.0.0<4.0.0",
```
## Steps to reproduce the bug
```bash
pip install "pyarrow>=1.0.0<4.0.0"
```
## Expected results
It is expected to get a pyarrow version between 1.0.0 (inclusive) and 4.0.0 (exclusive).
## Actual results
pip ignores the specified versions since there is a missing comma between the lower and upper limits. Therefore, pip installs the latest pyarrow version from PYPI, which is 4.0.0.
This is especially problematic since "conda env export" fails due to incorrect version specification. Here is the conda error as well:
```bash
conda env export
InvalidVersionSpec: Invalid version '1.0.0<4.0.0': invalid character(s)
```
## Fix suggestion
Put a comma between the version limits which means replacing the line in setup.py file with the following:
```python
"pyarrow>=1.0.0,<4.0.0",
```
## Versions
Paste the output of the following code:
```python
- Datasets: 1.6.2
- Python: 3.7.10 (default, Feb 26 2021, 18:47:35)
[GCC 7.3.0]
- Platform: Linux-5.4.0-42-generic-x86_64-with-debian-buster-sid
```
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https://api.github.com/repos/huggingface/datasets/issues/2315 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2315/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2315/comments | https://api.github.com/repos/huggingface/datasets/issues/2315/events | https://github.com/huggingface/datasets/pull/2315 | 875,742,200 | MDExOlB1bGxSZXF1ZXN0NjMwMDIyMDYy | 2,315 | Datasets cli improvements | {
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"Additionally, I've deleted the points that are not very relevant for this repo (I guess the deleted points originate from the transformers repo). With this change, running `datasets-cli` is identical to copy-pasting the code from `bug_report.md`, but is more elegant because it doesn't require launching the REPL an... | 1,620,154,511,000 | 1,620,664,611,000 | 1,620,664,610,000 | CONTRIBUTOR | null | This PR:
* replaces the code from the `bug_report.md` that was used to get relevant system info with a dedicated command (a more elegant approach than copy-pasting the code IMO)
* removes the `download` command (copied from the transformers repo?)
* adds missing help messages to the cli commands
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https://api.github.com/repos/huggingface/datasets/issues/2314 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2314/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2314/comments | https://api.github.com/repos/huggingface/datasets/issues/2314/events | https://github.com/huggingface/datasets/pull/2314 | 875,729,271 | MDExOlB1bGxSZXF1ZXN0NjMwMDExODc4 | 2,314 | Minor refactor prepare_module | {
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"@lhoestq this is the PR that I mentioned to you, which can be considered as a first step in refactoring `prepare_module`.",
"closing in favor of #2986 "
] | 1,620,153,446,000 | 1,634,116,054,000 | 1,634,116,054,000 | MEMBER | null | Start to refactor `prepare_module` to try to decouple functionality.
This PR does:
- extract function `_initialize_dynamic_modules_namespace_package`
- extract function `_find_module_in_github_or_s3`
- some renaming of variables
- use of f-strings | {
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- neither its returned result is used
- nor it raises any exception, as exceptions are catched and returned (not raised)
This PR removes it. | {
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"Merging then 😄 "
] | 1,620,133,073,000 | 1,620,135,793,000 | 1,620,135,792,000 | CONTRIBUTOR | null | Cherry-picked from #2255 | {
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"Hi @lhoestq , I am not sure about the error message:\r\n```\r\n#!/bin/bash -eo pipefail\r\n./scripts/datasets_metadata_validator.py\r\nWARNING:root:❌ Failed to validate 'datasets/openslr/README.md':\r\n__init__() got an unexpected keyword argument 'SLR32'\r\nINFO:root:❌ Failed on 1 files.\r\n\r\nExited with code e... | 1,620,119,283,000 | 1,620,381,055,000 | 1,620,381,055,000 | CONTRIBUTOR | null | Add large speech datasets for Sinhala, Bengali and Nepali. | {
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https://api.github.com/repos/huggingface/datasets/issues/2309 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2309/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2309/comments | https://api.github.com/repos/huggingface/datasets/issues/2309/events | https://github.com/huggingface/datasets/pull/2309 | 874,644,990 | MDExOlB1bGxSZXF1ZXN0NjI5MTU4NjQx | 2,309 | Fix conda release | {
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To fix this I had to:
- add the --single-version-externally-managed tag to the build stage (suggestion from [here](https://stackoverflow.com/a/64825075))
- set the python version of the conda build stage to 3.8 since 3.9 isn't supported
- sync the evrsion requirement of `huggingface_hub`
With these changes I'm working on uploading all missing versions until 1.6.2 to conda
EDIT: I managed to build and upload all missing versions until 1.6.2 to conda :) | {
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https://api.github.com/repos/huggingface/datasets/issues/2302 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2302/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2302/comments | https://api.github.com/repos/huggingface/datasets/issues/2302/events | https://github.com/huggingface/datasets/pull/2302 | 873,961,435 | MDExOlB1bGxSZXF1ZXN0NjI4NjIzMDQ3 | 2,302 | Add SubjQA dataset | {
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"I'm not sure why the windows test fails, but looking at the logs it looks like some caching issue on one of the metrics ... maybe re-run and 🤞 ?",
"Hi @lewtun, thanks for adding this dataset!\r\n\r\nIf the dataset is going to be referenced heavily, I think it's worth spending some time to make the dataset card ... | 1,619,967,080,000 | 1,620,638,479,000 | 1,620,638,479,000 | MEMBER | null | Hello datasetters 🙂!
Here's an interesting dataset about extractive question-answering on _subjective_ product / restaurant reviews. It's quite challenging for models fine-tuned on SQuAD and provides a nice example of domain adaptation (i.e. fine-tuning a SQuAD model on this domain gives better performance).
I found a bug in the start/end indices that I've proposed a fix for here: https://github.com/megagonlabs/SubjQA/pull/2
Unfortunately, the dataset creators are unresponsive, so for now I am using my fork as the source. Will update the URL if/when the creators respond. | {
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https://api.github.com/repos/huggingface/datasets/issues/2301 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2301/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2301/comments | https://api.github.com/repos/huggingface/datasets/issues/2301/events | https://github.com/huggingface/datasets/issues/2301 | 873,941,266 | MDU6SXNzdWU4NzM5NDEyNjY= | 2,301 | Unable to setup dev env on Windows | {
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"Hi @gchhablani, \r\n\r\nThere are some 3rd-party dependencies that require to build code in C. In this case, it is the library `python-Levenshtein`.\r\n\r\nOn Windows, in order to be able to build C code, you need to install at least `Microsoft C++ Build Tools` version 14. You can find more info here: https://visu... | 1,619,961,642,000 | 1,620,055,081,000 | 1,620,055,054,000 | CONTRIBUTOR | null | Hi
I tried installing the `".[dev]"` version on Windows 10 after cloning.
Here is the error I'm facing:
```bat
(env) C:\testing\datasets>pip install -e ".[dev]"
Obtaining file:///C:/testing/datasets
Requirement already satisfied: numpy>=1.17 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.19.5)
Collecting pyarrow>=0.17.1
Using cached pyarrow-4.0.0-cp37-cp37m-win_amd64.whl (13.3 MB)
Requirement already satisfied: dill in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.3.1.1)
Collecting pandas
Using cached pandas-1.2.4-cp37-cp37m-win_amd64.whl (9.1 MB)
Requirement already satisfied: requests>=2.19.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2.25.1)
Requirement already satisfied: tqdm<4.50.0,>=4.27 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (4.49.0)
Requirement already satisfied: xxhash in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2.0.2)
Collecting multiprocess
Using cached multiprocess-0.70.11.1-py37-none-any.whl (108 kB)
Requirement already satisfied: fsspec in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2021.4.0)
Collecting huggingface_hub<0.1.0
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Collecting pytest-xdist
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Collecting keras-preprocessing~=1.1.2
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Collecting opt-einsum~=3.3.0
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Collecting pathspec<1,>=0.8.1
Using cached pathspec-0.8.1-py2.py3-none-any.whl (28 kB)
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Collecting appdirs
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Collecting mypy-extensions>=0.4.3
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Using cached beautifulsoup4-4.9.3-py3-none-any.whl (115 kB)
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Collecting python-Levenshtein
Using cached python-Levenshtein-0.12.2.tar.gz (50 kB)
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Collecting portalocker==2.0.0
Using cached portalocker-2.0.0-py2.py3-none-any.whl (11 kB)
Requirement already satisfied: scikit-learn>=0.21.3 in c:\programdata\anaconda3\envs\env\lib\site-packages (from seqeval->datasets==1.5.0.dev0) (0.24.2)
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Building wheels for collected packages: python-Levenshtein
Building wheel for python-Levenshtein (setup.py) ... error
ERROR: Command errored out with exit status 1:
command: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\VKC~1\AppData\Local\Temp\pip-wheel-8jh7fm18'
cwd: C:\Users\VKC~1\AppData\Local\Temp\pip-install-ynt_dbm4\python-levenshtein_c02e7e6f9def4629a475349654670ae9\
Complete output (27 lines):
running bdist_wheel
running build
running build_py
creating build
creating build\lib.win-amd64-3.7
creating build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\StringMatcher.py -> build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\__init__.py -> build\lib.win-amd64-3.7\Levenshtein
running egg_info
writing python_Levenshtein.egg-info\PKG-INFO
writing dependency_links to python_Levenshtein.egg-info\dependency_links.txt
writing entry points to python_Levenshtein.egg-info\entry_points.txt
writing namespace_packages to python_Levenshtein.egg-info\namespace_packages.txt
writing requirements to python_Levenshtein.egg-info\requires.txt
writing top-level names to python_Levenshtein.egg-info\top_level.txt
reading manifest file 'python_Levenshtein.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no previously-included files matching '*pyc' found anywhere in distribution
warning: no previously-included files matching '*so' found anywhere in distribution
warning: no previously-included files matching '.project' found anywhere in distribution
warning: no previously-included files matching '.pydevproject' found anywhere in distribution
writing manifest file 'python_Levenshtein.egg-info\SOURCES.txt'
copying Levenshtein\_levenshtein.c -> build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\_levenshtein.h -> build\lib.win-amd64-3.7\Levenshtein
running build_ext
building 'Levenshtein._levenshtein' extension
error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
----------------------------------------
ERROR: Failed building wheel for python-Levenshtein
Running setup.py clean for python-Levenshtein
Failed to build python-Levenshtein
Installing collected packages: python-Levenshtein, pytest-forked, pyppmd, pymongo, pyflakes, pydot, pycryptodome, pycodestyle, pyarrow, portalocker, pathspec, pandas, opt-einsum, oauth2client, nltk, mypy-extensions, multivolumefile, multiprocess, moto, mccabe, matplotlib, keras-preprocessing, huggingface-hub, hdfs, h5py, google-pasta, gast, flatbuffers, fastavro, execnet, et-xmlfile, entrypoints, crcmod, beautifulsoup4, bcj-cffi, avro-python3, astunparse, appdirs, zstandard, tldextract, tensorflow, sklearn, seqeval, sacrebleu, rouge-score, rarfile, pytest-xdist, py7zr, openpyxl, mwparserfromhell, lxml, langdetect, jiwer, isort, flake8, elasticsearch, datasets, conllu, bs4, black, bert-score, apache-beam
Running setup.py install for python-Levenshtein ... error
ERROR: Command errored out with exit status 1:
command: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\VKC~1\AppData\Local\Temp\pip-record-v7l7zitb\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\ProgramData\Anaconda3\envs\env\Include\python-Levenshtein'
cwd: C:\Users\VKC~1\AppData\Local\Temp\pip-install-ynt_dbm4\python-levenshtein_c02e7e6f9def4629a475349654670ae9\
Complete output (27 lines):
running install
running build
running build_py
creating build
creating build\lib.win-amd64-3.7
creating build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\StringMatcher.py -> build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\__init__.py -> build\lib.win-amd64-3.7\Levenshtein
running egg_info
writing python_Levenshtein.egg-info\PKG-INFO
writing dependency_links to python_Levenshtein.egg-info\dependency_links.txt
writing entry points to python_Levenshtein.egg-info\entry_points.txt
writing namespace_packages to python_Levenshtein.egg-info\namespace_packages.txt
writing requirements to python_Levenshtein.egg-info\requires.txt
writing top-level names to python_Levenshtein.egg-info\top_level.txt
reading manifest file 'python_Levenshtein.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no previously-included files matching '*pyc' found anywhere in distribution
warning: no previously-included files matching '*so' found anywhere in distribution
warning: no previously-included files matching '.project' found anywhere in distribution
warning: no previously-included files matching '.pydevproject' found anywhere in distribution
writing manifest file 'python_Levenshtein.egg-info\SOURCES.txt'
copying Levenshtein\_levenshtein.c -> build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\_levenshtein.h -> build\lib.win-amd64-3.7\Levenshtein
running build_ext
building 'Levenshtein._levenshtein' extension
error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
----------------------------------------
ERROR: Command errored out with exit status 1: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\VKC~1\AppData\Local\Temp\pip-record-v7l7zitb\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\ProgramData\Anaconda3\envs\env\Include\python-Levenshtein' Check the logs for full command output.
```
Here are conda and python versions:
```bat
(env) C:\testing\datasets>conda --version
conda 4.9.2
(env) C:\testing\datasets>python --version
Python 3.7.10
```
Please help me out. Thanks. | {
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"Hi @lhoestq,\r\n\r\nOnce that #2578 has been merged, I would like to ask you to have a look at this PR: it implements the same logic as the one in #2578 but for all the other file compression formats.\r\n\r\nThanks.",
"I think all is done @lhoestq ;)"
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The recordbatches of the `IndexedTableMixin` used to speed up queries to the table were copied in memory during a table deepcopy.
This resulted in `concatenate_datasets`, `load_from_disk` and other methods to always bring the data in memory.
I fixed the copy similarly to #2287 and updated the test to make sure it doesn't happen again (added a test for deepcopy + make sure that the immutable arrow objects are passed to the copied table without being copied).
The issue was not caught by our tests because the total allocated bytes value in PyArrow isn't updated when deepcopying recordbatches: the copy in memory wasn't detected. This behavior looks like a bug in PyArrow, I'll open a ticket on JIRA.
Thanks @samsontmr , @TaskManager91 and @mariosasko for the help
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