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https://api.github.com/repos/huggingface/datasets/issues/3507 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3507/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3507/comments | https://api.github.com/repos/huggingface/datasets/issues/3507/events | https://github.com/huggingface/datasets/issues/3507 | 1,091,214,808 | I_kwDODunzps5BCp3Y | 3,507 | Discuss whether support canonical datasets w/o dataset_infos.json and/or dummy data | {
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"IMO, the data streaming test is good enough of a test that the dataset works correctly (assuming that we can more or less ensure that if streaming works then the non-streaming case will also work), so that for datasets that have a working dataset preview, we can remove the dummy data IMO. On the other hand, it seems like not all datasets have streaming enabled yet and for those datasets (if they are used a lot), I think it would be nice to continue testing some dummy data.\r\n\r\nI don't really have an opinion regarding the JSON metadata as I don't know enough about it.\r\n\r\n"
] | 1,640,883,865,000 | 1,640,884,461,000 | null | MEMBER | null | I open this PR to have a public discussion about this topic and make a decision.
As previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?
On the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:
- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)
- we are migrating canonical datasets to the Hub
Do we really need to continue testing them in out CI?
Also note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).
Feel free to ping other people for the discussion.
CC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw | {
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https://api.github.com/repos/huggingface/datasets/issues/3506 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3506/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3506/comments | https://api.github.com/repos/huggingface/datasets/issues/3506/events | https://github.com/huggingface/datasets/pull/3506 | 1,091,166,595 | PR_kwDODunzps4wZpot | 3,506 | Allows DatasetDict.filter to have batching option | {
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- Fixes: #3503
We extends `.filter( ... batched: bool)` support to DatasetDict. | {
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https://api.github.com/repos/huggingface/datasets/issues/3505 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3505/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3505/comments | https://api.github.com/repos/huggingface/datasets/issues/3505/events | https://github.com/huggingface/datasets/issues/3505 | 1,091,150,820 | I_kwDODunzps5BCaPk | 3,505 | cast_column function not working with map function in streaming mode for Audio features | {
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] | open | false | null | [] | null | [] | 1,640,875,921,000 | 1,640,877,038,000 | null | NONE | null | ## Describe the bug
I am trying to use Audio class for loading audio features using custom dataset. I am able to cast 'audio' feature into 'Audio' format with cast_column function. On using map function, I am not getting 'Audio' casted feature but getting path of audio file only.
I am getting features of 'audio' of string type with load_dataset call. After using cast_column 'audio' feature is converted into 'Audio' type. But in map function I am not able to get Audio type for audio feature & getting string type data containing path of file only. So I am not able to use processor in encode function.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset, Audio
from transformers import Wav2Vec2Processor
def encode(batch, processor):
print("Audio: ",batch['audio'])
batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values
return batch
def print_ds(ds):
iterator = iter(ds)
for d in iterator:
print("Data: ",d)
break
processor = Wav2Vec2Processor.from_pretrained(pretrained_model_path)
dataset = load_dataset("custom_dataset.py","train",data_files={'train':'train_path.txt'},
data_dir="data", streaming=True, split="train")
print("Features: ",dataset.features)
print_ds(dataset)
dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
print("Features: ",dataset.features)
print_ds(dataset)
dataset = dataset.map(lambda x: encode(x,processor))
print("Features: ",dataset.features)
print_ds(dataset)
```
## Expected results
map function not printing Audio type features be used with processor function and getting error in processor call due to this.
## Actual results
# after load_dataset call
Features: {'sentence': Value(dtype='string', id=None), 'audio': Value(dtype='string', id=None)}
Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': 'data/0116_003.wav'}
# after cast_column call
Features: {'sentence': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None)}
Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': {'path': 'data/0116_003.wav', 'array': array([ 1.2662281e-06, 1.0264218e-06, -1.3615092e-06, ...,
1.3017889e-02, 1.0085563e-02, 4.8155054e-03], dtype=float32), 'sampling_rate': 16000}}
# after map call
Features: None
Audio: data/0116_003.wav
Traceback (most recent call last):
File "demo2.py", line 36, in <module>
print_ds(dataset)
File "demo2.py", line 11, in print_ds
for d in iterator:
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 341, in __iter__
for key, example in self._iter():
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 338, in _iter
yield from ex_iterable
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 192, in __iter__
yield key, self.function(example)
File "demo2.py", line 32, in <lambda>
dataset = dataset.map(lambda x: batch_encode(x,processor))
File "demo2.py", line 6, in batch_encode
batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values
TypeError: string indices must be integers
## Environment info
- `datasets` version: 1.17.0
- Platform: Linux-4.14.243 with-debian-bullseye-sid
- Python version: 3.7.9
- PyArrow version: 6.0.1
| {
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https://api.github.com/repos/huggingface/datasets/issues/3504 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3504/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3504/comments | https://api.github.com/repos/huggingface/datasets/issues/3504/events | https://github.com/huggingface/datasets/issues/3504 | 1,090,682,230 | I_kwDODunzps5BAn12 | 3,504 | Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst | {
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] | open | false | null | [] | null | [] | 1,640,802,200,000 | 1,640,802,200,000 | null | NONE | null | ## Describe the bug
I am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https://huggingface.co/course/chapter5/4?fw=pt).
https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
# This takes a few minutes to run, so go grab a tea or coffee while you wait :)
data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
```
I also tried with `wget` as follows.
```
wget https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst
```
## Expected results
I expect to be able to download this file.
## Actual results
Traceback
```
---------------------------------------------------------------------------
timeout Traceback (most recent call last)
/usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self)
158 try:
--> 159 conn = connection.create_connection(
160 (self._dns_host, self.port), self.timeout, **extra_kw
/usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
83 if err is not None:
---> 84 raise err
85
/usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
73 sock.bind(source_address)
---> 74 sock.connect(sa)
75 return sock
timeout: timed out
During handling of the above exception, another exception occurred:
ConnectTimeoutError Traceback (most recent call last)
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
664 # Make the request on the httplib connection object.
--> 665 httplib_response = self._make_request(
666 conn,
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
375 try:
--> 376 self._validate_conn(conn)
377 except (SocketTimeout, BaseSSLError) as e:
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
995 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
--> 996 conn.connect()
997
/usr/lib/python3/dist-packages/urllib3/connection.py in connect(self)
313 # Add certificate verification
--> 314 conn = self._new_conn()
315 hostname = self.host
/usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self)
163 except SocketTimeout:
--> 164 raise ConnectTimeoutError(
165 self,
ConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')
During handling of the above exception, another exception occurred:
MaxRetryError Traceback (most recent call last)
/usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
438 if not chunked:
--> 439 resp = conn.urlopen(
440 method=request.method,
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
718
--> 719 retries = retries.increment(
720 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
/usr/lib/python3/dist-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)
435 if new_retry.is_exhausted():
--> 436 raise MaxRetryError(_pool, url, error or ResponseError(cause))
437
MaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))
During handling of the above exception, another exception occurred:
ConnectTimeout Traceback (most recent call last)
/tmp/ipykernel_15104/606583593.py in <module>
3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :)
4 data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
----> 5 pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
6 pubmed_dataset
~/.local/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, revision, use_auth_token, task, streaming, script_version, **config_kwargs)
1655
1656 # Create a dataset builder
-> 1657 builder_instance = load_dataset_builder(
1658 path=path,
1659 name=name,
~/.local/lib/python3.8/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)
1492 download_config = download_config.copy() if download_config else DownloadConfig()
1493 download_config.use_auth_token = use_auth_token
-> 1494 dataset_module = dataset_module_factory(
1495 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files
1496 )
~/.local/lib/python3.8/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)
1116 # Try packaged
1117 if path in _PACKAGED_DATASETS_MODULES:
-> 1118 return PackagedDatasetModuleFactory(
1119 path, data_files=data_files, download_config=download_config, download_mode=download_mode
1120 ).get_module()
~/.local/lib/python3.8/site-packages/datasets/load.py in get_module(self)
773 else get_patterns_locally(str(Path().resolve()))
774 )
--> 775 data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)
776 module_path, hash = _PACKAGED_DATASETS_MODULES[self.name]
777 builder_kwargs = {"hash": hash, "data_files": data_files}
~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
576 for key, patterns_for_key in patterns.items():
577 out[key] = (
--> 578 DataFilesList.from_local_or_remote(
579 patterns_for_key,
580 base_path=base_path,
~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
545 base_path = base_path if base_path is not None else str(Path().resolve())
546 data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
--> 547 origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)
548 return cls(data_files, origin_metadata)
549
~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token)
492 data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None
493 ) -> Tuple[str]:
--> 494 return thread_map(
495 partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token),
496 data_files,
~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs)
92 """
93 from concurrent.futures import ThreadPoolExecutor
---> 94 return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
95
96
~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs)
74 map_args.update(chunksize=chunksize)
75 with PoolExecutor(**pool_kwargs) as ex:
---> 76 return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))
77
78
~/.local/lib/python3.8/site-packages/tqdm/notebook.py in __iter__(self)
252 def __iter__(self):
253 try:
--> 254 for obj in super(tqdm_notebook, self).__iter__():
255 # return super(tqdm...) will not catch exception
256 yield obj
~/.local/lib/python3.8/site-packages/tqdm/std.py in __iter__(self)
1171 # (note: keep this check outside the loop for performance)
1172 if self.disable:
-> 1173 for obj in iterable:
1174 yield obj
1175 return
/usr/lib/python3.8/concurrent/futures/_base.py in result_iterator()
617 # Careful not to keep a reference to the popped future
618 if timeout is None:
--> 619 yield fs.pop().result()
620 else:
621 yield fs.pop().result(end_time - time.monotonic())
/usr/lib/python3.8/concurrent/futures/_base.py in result(self, timeout)
442 raise CancelledError()
443 elif self._state == FINISHED:
--> 444 return self.__get_result()
445 else:
446 raise TimeoutError()
/usr/lib/python3.8/concurrent/futures/_base.py in __get_result(self)
387 if self._exception:
388 try:
--> 389 raise self._exception
390 finally:
391 # Break a reference cycle with the exception in self._exception
/usr/lib/python3.8/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token)
483 if isinstance(data_file, Url):
484 data_file = str(data_file)
--> 485 return (request_etag(data_file, use_auth_token=use_auth_token),)
486 else:
487 data_file = str(data_file.resolve())
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in request_etag(url, use_auth_token)
489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]:
490 headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)
--> 491 response = http_head(url, headers=headers, max_retries=3)
492 response.raise_for_status()
493 etag = response.headers.get("ETag") if response.ok else None
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
474 headers = copy.deepcopy(headers) or {}
475 headers["user-agent"] = get_datasets_user_agent(user_agent=headers.get("user-agent"))
--> 476 response = _request_with_retry(
477 method="HEAD",
478 url=url,
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
408 if tries > max_retries:
--> 409 raise err
410 else:
411 logger.info(f"{method} request to {url} timed out, retrying... [{tries/max_retries}]")
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
403 tries += 1
404 try:
--> 405 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
406 success = True
407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/usr/lib/python3/dist-packages/requests/api.py in request(method, url, **kwargs)
58 # cases, and look like a memory leak in others.
59 with sessions.Session() as session:
---> 60 return session.request(method=method, url=url, **kwargs)
61
62
/usr/lib/python3/dist-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
531 }
532 send_kwargs.update(settings)
--> 533 resp = self.send(prep, **send_kwargs)
534
535 return resp
/usr/lib/python3/dist-packages/requests/sessions.py in send(self, request, **kwargs)
644
645 # Send the request
--> 646 r = adapter.send(request, **kwargs)
647
648 # Total elapsed time of the request (approximately)
/usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
502 # TODO: Remove this in 3.0.0: see #2811
503 if not isinstance(e.reason, NewConnectionError):
--> 504 raise ConnectTimeout(e, request=request)
505
506 if isinstance(e.reason, ResponseError):
ConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))
```
## Environment info
- `datasets` version: 1.17.0
- Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 6.0.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/3503 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3503/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3503/comments | https://api.github.com/repos/huggingface/datasets/issues/3503/events | https://github.com/huggingface/datasets/issues/3503 | 1,090,472,735 | I_kwDODunzps5A_0sf | 3,503 | Batched in filter throws error | {
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] | null | [] | 1,640,779,264,000 | 1,640,876,412,000 | null | NONE | null | I hope this is really a bug, I could not find it among the open issues
## Describe the bug
using `batched=False` in DataSet.filter throws error
```python
TypeError: filter() got an unexpected keyword argument 'batched'
```
but in the docs it is lister as an argument.
## Steps to reproduce the bug
```python
task = "mnli"
max_length = 128
tokenizer = AutoTokenizer.from_pretrained("./pretrained_models/pretrained_models_drozd/sl250.m.gsic.titech.ac.jp:8000/21.11.17_06.30.32_roberta-base_a0057/checkpoints/smpl_400M/hf/")
dataset = load_dataset("glue", task)
task_to_keys = {
"cola": ("sentence", None),
"mnli": ("premise", "hypothesis"),
"mnli-mm": ("premise", "hypothesis"),
"mrpc": ("sentence1", "sentence2"),
"qnli": ("question", "sentence"),
"qqp": ("question1", "question2"),
"rte": ("sentence1", "sentence2"),
"sst2": ("sentence", None),
"stsb": ("sentence1", "sentence2"),
"wnli": ("sentence1", "sentence2"),
}
##### tokenization_parameters
sentence1_key, sentence2_key = task_to_keys[task]
def preprocess_function(examples, max_length):
if sentence2_key is None:
return tokenizer(
examples[sentence1_key], truncation=True, max_length=max_length
)
return tokenizer(
examples[sentence1_key],
examples[sentence2_key],
truncation=False,
padding="max_length",
max_length=max_length,
)
encoded_dataset = dataset.map(
lambda x: preprocess_function(x, max_length=max_length), batched=False
)
encoded_dataset.filter(lambda x: len(x['input_ids']) <= max_length, batched=False)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1, 1.17.0
- Platform: ubuntu
- Python version: 3.8.12
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https://api.github.com/repos/huggingface/datasets/issues/3502 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3502/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3502/comments | https://api.github.com/repos/huggingface/datasets/issues/3502/events | https://github.com/huggingface/datasets/pull/3502 | 1,090,438,558 | PR_kwDODunzps4wXSLi | 3,502 | Add QuALITY | {
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https://api.github.com/repos/huggingface/datasets/issues/3501 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3501/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3501/comments | https://api.github.com/repos/huggingface/datasets/issues/3501/events | https://github.com/huggingface/datasets/pull/3501 | 1,090,413,758 | PR_kwDODunzps4wXM8H | 3,501 | Update pib dataset card | {
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} | [] | closed | false | null | [] | null | [] | 1,640,772,880,000 | 1,640,776,401,000 | null | MEMBER | null | Related to #3496 | {
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https://api.github.com/repos/huggingface/datasets/issues/3500 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3500/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3500/comments | https://api.github.com/repos/huggingface/datasets/issues/3500/events | https://github.com/huggingface/datasets/pull/3500 | 1,090,406,133 | PR_kwDODunzps4wXLTB | 3,500 | Docs: Add VCTK dataset description | {
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} | [] | open | false | null | [] | null | [] | 1,640,772,125,000 | 1,640,772,125,000 | null | CONTRIBUTOR | null | This PR is a very minor followup to #1837, with only docs changes (single comment string). | {
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https://api.github.com/repos/huggingface/datasets/issues/3499 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3499/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3499/comments | https://api.github.com/repos/huggingface/datasets/issues/3499/events | https://github.com/huggingface/datasets/issues/3499 | 1,090,132,618 | I_kwDODunzps5A-hqK | 3,499 | Adjusting chunk size for streaming datasets | {
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] | open | false | null | [] | null | [] | 1,640,726,273,000 | 1,640,726,273,000 | null | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
I want to use mc4 which I cannot save locally, so I stream it. However, I want to process the entire dataset and filter some documents from it. With the current chunk size of around 1000 documents (right?) I hit a performance bottleneck because of the frequent decompressing.
**Describe the solution you'd like**
I would appreciate a parameter in the load_dataset function, that allows me to set the chunksize myself (to a value like 100'000 in my case). Like that, I hope to improve the processing time.
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https://api.github.com/repos/huggingface/datasets/issues/3498 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3498/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3498/comments | https://api.github.com/repos/huggingface/datasets/issues/3498/events | https://github.com/huggingface/datasets/pull/3498 | 1,090,096,332 | PR_kwDODunzps4wWL5U | 3,498 | update `pretty_name` for all datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/3497 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3497/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3497/comments | https://api.github.com/repos/huggingface/datasets/issues/3497/events | https://github.com/huggingface/datasets/issues/3497 | 1,090,050,148 | I_kwDODunzps5A-Nhk | 3,497 | Changing sampling rate in audio dataset and subsequently mapping with `num_proc > 1` leads to weird bug | {
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"Same error occures when using max samples with https://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py"
] | 1,640,714,629,000 | 1,640,725,234,000 | null | MEMBER | null | Running:
```python
from datasets import load_dataset, DatasetDict
import datasets
from transformers import AutoFeatureExtractor
raw_datasets = DatasetDict()
raw_datasets["train"] = load_dataset("common_voice", "ab", split="train")
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
raw_datasets = raw_datasets.cast_column(
"audio", datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
)
num_workers = 16
def prepare_dataset(batch):
sample = batch["audio"]
inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
batch["input_values"] = inputs.input_values[0]
batch["input_length"] = len(batch["input_values"])
return batch
raw_datasets.map(
prepare_dataset,
remove_columns=next(iter(raw_datasets.values())).column_names,
num_proc=16,
desc="preprocess datasets",
)
```
gives
```bash
File "/home/patrick/experiments/run_bug.py", line 25, in <module>
raw_datasets.map(
File "/home/patrick/python_bin/datasets/dataset_dict.py", line 492, in map
{
File "/home/patrick/python_bin/datasets/dataset_dict.py", line 493, in <dictcomp>
k: dataset.map(
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2139, in map
shards = [
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2140, in <listcomp>
self.shard(num_shards=num_proc, index=rank, contiguous=True, keep_in_memory=keep_in_memory)
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 3164, in shard
return self.select(
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 485, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/patrick/python_bin/datasets/fingerprint.py", line 411, in wrapper
out = func(self, *args, **kwargs)
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2756, in select
return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2667, in _new_dataset_with_indices
return Dataset(
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 659, in __init__
raise ValueError(
ValueError: External features info don't match the dataset:
Got
{'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)}
with type
struct<client_id: string, path: string, audio: string, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string>
but expected something like
{'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': {'path': Value(dtype='string', id=None), 'bytes': Value(dtype='binary', id=None)}, 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)}
with type
struct<client_id: string, path: string, audio: struct<path: string, bytes: binary>, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string>
```
Versions:
```python
- `datasets` version: 1.16.2.dev0
- Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 6.0.1
```
and `transformers`:
```
- `transformers` version: 4.16.0.dev0
- Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33
- Python version: 3.9.7
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3496 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3496/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3496/comments | https://api.github.com/repos/huggingface/datasets/issues/3496/events | https://github.com/huggingface/datasets/pull/3496 | 1,089,989,155 | PR_kwDODunzps4wV1_w | 3,496 | Update version of pib dataset and make it streamable | {
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"It seems like there is still an error: `Message: 'TarContainedFile' object has no attribute 'readable'`\r\n\r\nhttps://huggingface.co/datasets/pib/viewer",
"@severo I was wondering about that...\r\n\r\nIt works fine when I run it in streaming mode in my terminal:\r\n```python\r\nIn [3]: from datasets import load_dataset; ds = load_dataset(\"pib\", \"gu-pa\", split=\"train\", streaming=True); item = next(iter(ds))\r\n\r\nIn [4]: item\r\nOut[4]: \r\n{'translation': {'gu': 'એવો નિર્ણય લેવાયો હતો કે ખંતપૂર્વકની કામગીરી હાથ ધરવા, કાયદેસર અને ટેકનિકલ મૂલ્યાંકન કરવા, વેન્ચર કેપિટલ ઇન્વેસ્ટમેન્ટ સમિતિની બેઠક યોજવા વગેરે એઆઇએફને કરવામાં આવેલ પ્રતિબદ્ધતાના 0.50 ટકા સુધી અને બાકીની રકમ એફએફએસને પૂર્ણ કરવામાં આવશે.',\r\n 'pa': 'ਇਹ ਵੀ ਫੈਸਲਾ ਕੀਤਾ ਗਿਆ ਕਿ ਐੱਫਆਈਆਈ ਅਤੇ ਬਕਾਏ ਲਈ ਕੀਤੀਆਂ ਗਈਆਂ ਵਚਨਬੱਧਤਾਵਾਂ ਦੇ 0.50 % ਦੀ ਸੀਮਾ ਤੱਕ ਐੱਫਈਐੱਸ ਨੂੰ ਮਿਲਿਆ ਜਾਏਗਾ, ਇਸ ਨਾਲ ਉੱਦਮ ਪੂੰਜੀ ਨਿਵੇਸ਼ ਕਮੇਟੀ ਦੀ ਬੈਠਕ ਦਾ ਆਯੋਜਨ ਉਚਿਤ ਸਾਵਧਾਨੀ, ਕਾਨੂੰਨੀ ਅਤੇ ਤਕਨੀਕੀ ਮੁੱਲਾਂਕਣ ਲਈ ਸੰਚਾਲਨ ਖਰਚ ਆਦਿ ਦੀ ਪੂਰਤੀ ਹੋਵੇਗੀ।'}}\r\n```"
] | 1,640,707,315,000 | 1,640,795,865,000 | null | MEMBER | null | This PR:
- Updates version of pib dataset: from 0.0.0 to 1.3.0
- Makes the dataset streamable
Fix #3491.
CC: @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/3495 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3495/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3495/comments | https://api.github.com/repos/huggingface/datasets/issues/3495/events | https://github.com/huggingface/datasets/issues/3495 | 1,089,983,632 | I_kwDODunzps5A99SQ | 3,495 | Add VoxLingua107 | {
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] | open | false | null | [] | null | [] | 1,640,706,703,000 | 1,640,706,703,000 | null | CONTRIBUTOR | null | ## Adding a Dataset
- **Name:** VoxLingua107
- **Description:** VoxLingua107 is a speech dataset for training spoken language identification models.
- **Paper:** https://arxiv.org/abs/2011.12998
- **Data:** http://bark.phon.ioc.ee/voxlingua107/
- **Motivation:** 107 languages, totaling 6628 hours for the train split.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| {
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https://api.github.com/repos/huggingface/datasets/issues/3494 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3494/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3494/comments | https://api.github.com/repos/huggingface/datasets/issues/3494/events | https://github.com/huggingface/datasets/pull/3494 | 1,089,983,103 | PR_kwDODunzps4wV0vB | 3,494 | Clone full repo to detect new tags when mirroring datasets on the Hub | {
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"Good catch !!",
"The CI fail is unrelated to this PR and fixed on master, merging :)"
] | 1,640,706,647,000 | 1,640,707,641,000 | null | MEMBER | null | The new releases of `datasets` were not detected because the shallow clone in the CI wasn't getting the git tags.
By cloning the full repository we can properly detect a new release, and tag all the dataset repositories accordingly
cc @SBrandeis | {
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https://api.github.com/repos/huggingface/datasets/issues/3493 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3493/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3493/comments | https://api.github.com/repos/huggingface/datasets/issues/3493/events | https://github.com/huggingface/datasets/pull/3493 | 1,089,967,286 | PR_kwDODunzps4wVxfr | 3,493 | Fix VCTK encoding | {
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https://api.github.com/repos/huggingface/datasets/issues/3492 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3492/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3492/comments | https://api.github.com/repos/huggingface/datasets/issues/3492/events | https://github.com/huggingface/datasets/pull/3492 | 1,089,952,943 | PR_kwDODunzps4wVufr | 3,492 | Add `gzip` for `to_json` | {
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https://api.github.com/repos/huggingface/datasets/issues/3491 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3491/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3491/comments | https://api.github.com/repos/huggingface/datasets/issues/3491/events | https://github.com/huggingface/datasets/issues/3491 | 1,089,918,018 | I_kwDODunzps5A9tRC | 3,491 | Update version of pib dataset | {
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Related to bigscience-workshop/data_tooling#130
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https://api.github.com/repos/huggingface/datasets/issues/3490 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3490/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3490/comments | https://api.github.com/repos/huggingface/datasets/issues/3490/events | https://github.com/huggingface/datasets/issues/3490 | 1,089,730,181 | I_kwDODunzps5A8_aF | 3,490 | Does datasets support load text from HDFS? | {
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} | [] | open | false | null | [] | null | [] | 1,640,681,762,000 | 1,640,682,973,000 | null | NONE | null | The raw text data is stored on HDFS due to the dataset's size is too large to store on my develop machine,
so I wander does datasets support read data from hdfs? | {
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} | [] | open | false | null | [] | null | [] | 1,640,629,256,000 | 1,640,681,995,000 | null | CONTRIBUTOR | null | Like in `join([... for s in ...])`. Also changed other things that I saw:
* Use a `with` statement for many `open` that missed them, so the files don't remain open.
* Remove unused variables.
* Many HTTP links converted into HTTPS (verified).
* Remove unnecessary "r" mode arg in `open` (double-checked it was actually the default in each case).
* Remove Python 2 style of using `super`.
* Run `pyupgrade $(find . -name "*.py" -type f) --py36-plus` (which already does some of the previous points).
* Run `dos2unix $(find . -name "*.py" -type f)` (CRLF to LF line endings).
* Fix typos. | {
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] | open | false | null | [] | null | [] | 1,640,622,540,000 | 1,640,622,540,000 | null | MEMBER | null | ## Describe the bug
There is an ssue with `StreamingDownloadManager._extract`.
I don't know how the test `test_streaming_gg_drive_gzipped` passes:
For
```python
TEST_GG_DRIVE_GZIPPED_URL = "https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz"
urlpath = StreamingDownloadManager().download_and_extract(TEST_GG_DRIVE_GZIPPED_URL)
```
gives `urlpath`:
```python
'gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz::https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz'
```
The gzip path makes no sense: `gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz`
## Steps to reproduce the bug
```python
from datasets.utils.streaming_download_manager import StreamingDownloadManager
dl_manager = StreamingDownloadManager()
urlpath = dl_manager.extract("https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz")
print(urlpath)
```
## Expected results
The query parameters should not be set as part of the path.
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https://api.github.com/repos/huggingface/datasets/issues/3487 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3487/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3487/comments | https://api.github.com/repos/huggingface/datasets/issues/3487/events | https://github.com/huggingface/datasets/pull/3487 | 1,089,209,031 | PR_kwDODunzps4wTVeN | 3,487 | Update ADD_NEW_DATASET.md | {
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"You can add columns that you wish to set into `torch` format using `dataset.set_format(\"torch\", ['id', 'abc'])` so that input batch of the transform only contains those columns",
"Sorry, I miss `output_all_columns` args and thought after `dataset.set_format(\"torch\", columns=columns)` I can only get specific columns I assigned."
] | 1,640,589,595,000 | 1,640,596,027,000 | null | NONE | null | **Is your feature request related to a problem? Please describe.**
When using `dataset.set_format("torch")`, I must make sure every columns in datasets can convert to `torch`, however, sometimes I want to keep some string columns.
**Describe the solution you'd like**
skip columns which cannot set to specific format when set_format instead of raise an error.
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] | open | false | null | [] | null | [] | 1,640,571,362,000 | 1,640,571,362,000 | null | NONE | null | As describe in https://github.com/huggingface/datasets/issues/2005#issuecomment-793716753 and mentioned by @mariosasko in [image feature example](https://colab.research.google.com/drive/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c#scrollTo=ow3XHDvf2I0B&line=1&uniqifier=1), IMO make shape verifcaiton to use ArrayXD instead of nested lists for map can help user reduce unnecessary cast. I notice datasets have done something special for `input_ids` and `attention_mask` which is also unnecessary after this feature added. | {
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https://api.github.com/repos/huggingface/datasets/issues/3482 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3482/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3482/comments | https://api.github.com/repos/huggingface/datasets/issues/3482/events | https://github.com/huggingface/datasets/pull/3482 | 1,088,317,921 | PR_kwDODunzps4wQqE1 | 3,482 | Fix duplicate keys in NewsQA | {
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"Flaky tests?"
] | 1,640,343,719,000 | 1,640,536,193,000 | null | CONTRIBUTOR | null | * Fix duplicate keys in NewsQA when loading from CSV files.
* Fix s/narqa/newsqa/ in the download manually error message.
* Make the download manually error message show nicely when printed. Otherwise, is hard to read due to spacing issues.
* Fix the format of the license text.
* Reformat the code to make it simpler. | {
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https://api.github.com/repos/huggingface/datasets/issues/3481 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3481/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3481/comments | https://api.github.com/repos/huggingface/datasets/issues/3481/events | https://github.com/huggingface/datasets/pull/3481 | 1,088,308,343 | PR_kwDODunzps4wQoJu | 3,481 | Fix overriding of filesystem info | {
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This generated a bug for filesystem methods that use `self.info()`, like e.g. `fs.isfile()`.
This PR:
- Adds tests for `fs.isfile` (that use `fs.info`).
- Fixes custom `BaseCompressedFileFileSystem.info` by removing its overriding. | {
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https://api.github.com/repos/huggingface/datasets/issues/3480 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3480/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3480/comments | https://api.github.com/repos/huggingface/datasets/issues/3480/events | https://github.com/huggingface/datasets/issues/3480 | 1,088,267,110 | I_kwDODunzps5A3aNm | 3,480 | the compression format requested when saving a dataset in json format is not respected | {
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"Thanks for reporting @SaulLu.\r\n\r\nAt first sight I think the problem is caused because `pandas` only takes into account the `compression` parameter if called with a non-null file path or buffer. And in our implementation, we call pandas `to_json` with `None` `path_or_buf`.\r\n\r\nWe should fix this:\r\n- either handling directly the `compression` parameter ourselves\r\n- or refactoring to pass non-null path or buffer to pandas\r\n\r\nCC: @lhoestq",
"I was thinking if we can handle the `compression` parameter by ourselves? Compression types will be similar to what `pandas` offer. Initially, we can try this with 2-3 compression types and see how good/bad it is? Let me know if it sounds good, I can raise a PR for this next week",
"Hi ! Thanks for your help @bhavitvyamalik :)\r\nMaybe let's start with `gzip` ? I think it's the most common use case, then if we're fine with it we can add other compression methods"
] | 1,640,337,831,000 | 1,640,622,318,000 | null | NONE | null | ## Describe the bug
In the documentation of the `to_json` method, it is stated in the parameters that
> **to_json_kwargs – Parameters to pass to pandas’s pandas.DataFrame.to_json.
however when we pass for example `compression="gzip"`, the saved file is not compressed.
Would you also have expected compression to be applied? :relaxed:
## Steps to reproduce the bug
```python
my_dict = {"a": [1, 2, 3], "b": [1, 2, 3]}
```
### Result with datasets
```python
from datasets import Dataset
dataset = Dataset.from_dict(my_dict)
dataset.to_json("dic_with_datasets.jsonl.gz", compression="gzip")
!cat dic_with_datasets.jsonl.gz
```
output
```
{"a":1,"b":1}
{"a":2,"b":2}
{"a":3,"b":3}
```
Note: I would expected to see binary data here
### Result with pandas
```python
import pandas as pd
df = pd.DataFrame(my_dict)
df.to_json("dic_with_pandas.jsonl.gz", lines=True, orient="records", compression="gzip")
!cat dic_with_pandas.jsonl.gz
```
output
```
4��a�dic_with_pandas.jsonl��VJT�2�QJ��\� ��g��yƵ���������)���
```
Note: It looks like binary data
## Expected results
I would have expected that the saved result with datasets would also be a binary file
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.11
- PyArrow version: 5.0.0
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"You're right, we have an issue today with the datasets preview. We're investigating.",
"It should be fixed now. Thanks for reporting.",
"Down again. ",
"Fixed for good."
] | 1,640,333,928,000 | 1,640,356,066,000 | null | NONE | null | ## Dataset viewer issue for '*french_book_reviews*'
**Link:** https://huggingface.co/datasets/Abirate/french_book_reviews
**short description of the issue**
For my dataset, the dataset preview is no longer functional (it used to work: The dataset had been added the day before and it was fine...)
And, after looking over the datasets, I discovered that this issue affects all Hugging Face datasets (as of yesterday, December 23, 2021, around 10 p.m. (CET)).
**Am I the one who added this dataset** : Yes
**Note**: here a screenshot showing the issue
**And here for glue dataset :**
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https://api.github.com/repos/huggingface/datasets/issues/3478 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3478/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3478/comments | https://api.github.com/repos/huggingface/datasets/issues/3478/events | https://github.com/huggingface/datasets/pull/3478 | 1,087,860,180 | PR_kwDODunzps4wPMWq | 3,478 | Extend support for streaming datasets that use os.walk | {
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"Nice. I'll update the dataset viewer once merged, and test on these four datasets"
] | 1,640,277,775,000 | 1,640,343,020,000 | null | MEMBER | null | This PR extends the support in streaming mode for datasets that use `os.walk`, by patching that function.
This PR adds support for streaming mode to datasets:
1. autshumato
1. code_x_glue_cd_code_to_text
1. code_x_glue_tc_nl_code_search_adv
1. nchlt
CC: @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/3477 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3477/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3477/comments | https://api.github.com/repos/huggingface/datasets/issues/3477/events | https://github.com/huggingface/datasets/pull/3477 | 1,087,850,253 | PR_kwDODunzps4wPKPX | 3,477 | Use `iter_files` instead of `str(Path(...)` in image dataset | {
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"`iter_archive` is about to support ZIP archives. I think we should use this no ?\r\n\r\nsee #3347 https://github.com/huggingface/datasets/pull/3379",
"I was interested in the support for isfile/dir in remote.\r\n\r\nAnyway, `iter_files` will be available for community users.",
"I'm not a big fan of having two functions that do the same thing. What do you think ?",
"They do not do the same thing:\r\n- One iterates over files in a directory\r\n- The other I guess will iterate over the members of an archive",
"Makes sense ! Sounds good then - sorry for my misunderstanding\r\n\r\nNote that `iter_archive` will be more performant for data streaming that `iter_files` thanks to the buffering so maybe in the future we can `iter_archive` for some of these datasets",
"Yes, @lhoestq I agree with you: once `iter_archive` supports zip files, it will be more suitable than `iter_files` for these 2 datasets.\r\n\r\nAnyway, this PR also implements `isfile`/`isdir` in streaming mode, besides fixing `iter_files`. And I'm interested in having those in master.\r\n\r\nMaybe, could we merge this PR into master and take note to refactor the datasets to use `iter_archive` once zip is supported?\r\nOther option could be to split this PR into 2..."
] | 1,640,276,815,000 | 1,640,704,502,000 | null | CONTRIBUTOR | null | Use `iter_files` in the `beans` and the `cats_vs_dogs` dataset scripts as suggested by @albertvillanova.
Additional changes:
* Fix `iter_files` in `MockDownloadManager` (see this [CI error](https://app.circleci.com/pipelines/github/huggingface/datasets/9247/workflows/2657ff8a-b531-4fd9-a9fc-6541a72e8d83/jobs/57028))
* Add support for `os.path.isdir` and `os.path.isfile` in streaming (`os.path.isfile` is needed in `StreamingDownloadManager`'s `iter_files` to make `cats_vs_dogs` streamable)
TODO:
- [ ] add tests for `xisdir` and `xisfile` | {
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This PR adds support for streaming mode to datasets:
1. ami
1. assin
1. assin2
1. counter
1. enriched_web_nlg
1. europarl_bilingual
1. hyperpartisan_news_detection
1. polsum
1. qa4mre
1. quail
1. ted_talks_iwslt
1. udhr
1. web_nlg
1. winograd_wsc
CC: @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/3475 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3475/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3475/comments | https://api.github.com/repos/huggingface/datasets/issues/3475/events | https://github.com/huggingface/datasets/issues/3475 | 1,087,352,041 | I_kwDODunzps5Az6zp | 3,475 | The rotten_tomatoes dataset of movie reviews contains some reviews in Spanish | {
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"Hi @puzzler10, thanks for reporting.\r\n\r\nPlease note this dataset is not hosted on Hugging Face Hub. See: \r\nhttps://github.com/huggingface/datasets/blob/c8f914473b041833fd47178fa4373cdcb56ac522/datasets/rotten_tomatoes/rotten_tomatoes.py#L42\r\n\r\nIf there are issues with the source data of a dataset, you should contact the data owners/creators instead. In the homepage associated with this dataset (http://www.cs.cornell.edu/people/pabo/movie-review-data/), you can find the authors of the dataset and how to contact them:\r\n> If you have any questions or comments regarding this site, please send email to Bo Pang or Lillian Lee.\r\n\r\nP.S.: Please also note that the example you gave of non-English review is in Portuguese (not Spanish). ;)",
"Maybe best to just put a quick sentence in the dataset description that highlights this? "
] | 1,640,231,803,000 | 1,640,305,383,000 | null | NONE | null | ## Describe the bug
See title. I don't think this is intentional and they probably should be removed. If they stay the dataset description should be at least updated to make it clear to the user.
## Steps to reproduce the bug
Go to the [dataset viewer](https://huggingface.co/datasets/viewer/?dataset=rotten_tomatoes) for the dataset, set the offset to 4160 for the train dataset, and scroll through the results. I found ones at index 4166 and 4173. There's others too (e.g. index 2888) but those two are easy to find like that.
## Expected results
English movie reviews only.
## Actual results
Example of a Spanish movie review (4173):
> "É uma pena que , mais tarde , o próprio filme abandone o tom de paródia e passe a utilizar os mesmos clichês que havia satirizado "
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This PR enables image decoding in `Dataset.__iter__`
Close https://github.com/huggingface/datasets/issues/3473 | {
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"As discussed, I remember I set `decoded=False` here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed.",
"> I set decoded=False here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed\r\n\r\nhttps://github.com/huggingface/datasets/pull/3430 will add more control to decoding, so I think it's OK to enable decoding in `__iter__` for now. After we merge the linked PR, the user can easily disable it again.",
"@mariosasko I wonder why there is no issue in `Audio` feature with decoding disabled in `__iter__`, whereas there is in `Image` feature.\r\n\r\nEnabling decoding in `__iter__` will make fail Audio regressions tests: https://github.com/huggingface/datasets/runs/4608657230?check_suite_focus=true\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/features/test_audio.py::test_dataset_with_audio_feature_map_is_not_decoded\r\nFAILED tests/features/test_audio.py::test_dataset_with_audio_feature_map_is_decoded\r\n========================= 2 failed, 15 passed in 8.37s =========================",
"Please also note that the regression tests were implemented in accordance with the specifications:\r\n- when doing a `map` (wich calls `__iter__`) of a function that doesn't access the audio field, the decoding should be disabled; this is why the decoding is disabled in `__iter__` (and only enabled in `__getitem__`).",
"> I wonder why there is no issue in Audio feature with decoding disabled in __iter__, whereas there is in Image feature.\r\n\r\n@albertvillanova Not sure if I understand this part. Currently, both the Image and the Audio feature don't decode data in `__iter__`, so their behavior is aligned there.\r\n",
"Therefore, this is not an issue, neither for Audio nor Image feature.\r\n\r\nCould you please elaborate more on the expected use case? @lhoestq @NielsRogge \r\n\r\nThe expected use cases (in accordance with the specs: see #2324):\r\n- decoding should be enabled when accessing a specific item (`__getitem__`)\r\n- decoding should be disabled while iterating (`__iter__`) to allow preprocessing of non-audio/image features (like label or text, for example) using `.map`\r\n- decoding should be enabled in a `.map` only if the `.map` function accesses the audio/image feature (implemented using `LazyDict`)",
"For me it's not an issue, actually. I just (mistakenly) tried to iterate over a PyTorch Dataset instead of a PyTorch DataLoader, \r\n\r\ni.e. I did this:\r\n\r\n`batch = next(iter(train_ds)) `\r\n\r\nwhereas I actually wanted to do\r\n\r\n`batch = next(iter(train_dataloader))`\r\n\r\nand then it turned out that in the first case, the image was a string of bytes rather than a Pillow image, hence Quentin opened an issue.",
"Thanks @NielsRogge for the context.\r\n\r\nSo IMO everything is working as expected.\r\n\r\nI'm closing this issue. Feel free to reopen it again if further changes of the specs should be addressed.",
"Thanks for the details :)\r\n\r\nI still think that it's unexpected to get different results when doing\r\n```python\r\nfor i in range(len(dataset)):\r\n sample = dataset[i]\r\n```\r\nand\r\n```python\r\nfor sample in dataset:\r\n pass\r\n```\r\neven though I understand that if you don't need to decode the data, then decoding image or audio data when iterating is a waste of time and resources.\r\n\r\nBut in this case users can still drop the column that need decoding to get the full speed back no ?"
] | 1,640,186,792,000 | 1,640,614,401,000 | null | MEMBER | null | ## Describe the bug
If I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.
## Steps to reproduce the bug
```python
from datasets import load_dataset
import PIL
mnist = load_dataset("mnist", split="train")
first_image = mnist[0]["image"]
assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # passes
first_image = next(iter(mnist))["image"]
assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # fails
```
## Expected results
The image should be decoded, as a PIL Image
## Actual results
We get a dictionary
```
{'bytes': b'\x89PNG\r\n\x1a\n\x00..., 'path': None}
```
## Environment info
- `datasets` version: 1.17.1.dev0
- Platform: Darwin-20.6.0-x86_64-i386-64bit
- Python version: 3.7.2
- PyArrow version: 6.0.0
The bug also exists in 1.17.0
## Investigation
I think the issue is that decoding is disabled in `__iter__`:
https://github.com/huggingface/datasets/blob/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6/src/datasets/arrow_dataset.py#L1651-L1661
Do you remember why it was disabled in the first place @albertvillanova ?
Also cc @mariosasko @NielsRogge
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- Yields stripped text
- Fix path for Windows
- Adds license
- Adds more info in dataset card
Close bigscience-workshop/data_tooling#279 | {
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Currently, `ClassLabel` docstring rendering is not right. | {
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https://api.github.com/repos/huggingface/datasets/issues/3469 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3469/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3469/comments | https://api.github.com/repos/huggingface/datasets/issues/3469/events | https://github.com/huggingface/datasets/pull/3469 | 1,085,882,664 | PR_kwDODunzps4wIrOV | 3,469 | Fix METEOR missing NLTK's omw-1.4 | {
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"I also modified the doctest call to raise the exception that doctest may catch, instead of `doctest.UnexpectedException`.\r\nThis will make debugging easier if it happens again"
] | 1,640,096,351,000 | 1,640,098,348,000 | null | MEMBER | null | NLTK 3.6.6 now requires `omw-1.4` to be downloaded for METEOR to work.
This should fix the CI on master | {
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"The CI failures other than a missing dummy data file and missing fields in the card are unrelated to this PR. ",
"Thanks a lot for this great work and fixing TFDS based script @mariosasko 🤗 will generate the dummy dataset and write the model card tomorrow!",
"@mariosasko I added the dataset card, I'm on the dummy data rn. "
] | 1,640,095,670,000 | 1,640,183,236,000 | null | CONTRIBUTOR | null | This PR adds the MS COCO dataset. Compared to the [TFDS](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/object_detection/coco.py) script, this implementation adds 8 additional configs to cover the tasks other than object detection.
Some notes:
* the data exposed by TFDS is contained in the `2014`, `2015`, `2017` and `2017_panoptic_segmentation` configs here
* I've updated `encode_nested_example` for easier handling of missing values (cc @lhoestq @albertvillanova; will add tests if you are OK with the changes in `features.py`)
* this implementation should fix https://github.com/huggingface/datasets/pull/3377#issuecomment-985559427
TODOs:
- [x] dataset card
- [ ] dummy data
cc @merveenoyan
Closes #2526 | {
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"The change from `___` to `--` was allowed by https://github.com/huggingface/moon-landing/pull/1657"
] | 1,640,095,633,000 | 1,640,106,010,000 | null | MEMBER | null | When doing `push_to_hub`, the feature types are lost (see issue https://github.com/huggingface/datasets/issues/3394).
This PR fixes this by also pushing a `dataset_infos.json` file to the Hub, that stores the feature types.
Other minor changes:
- renamed the `___` separator to `--`, since `--` is now disallowed in a name in the back-end.
I tested this feature with datasets like conll2003 that has feature types like `ClassLabel` that were previously lost.
Close https://github.com/huggingface/datasets/issues/3394
I would like to include this in today's release (though not mandatory), so feel free to comment/suggest changes | {
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"Hi Albert,\r\nThank you for your comments.\r\nI hope I have uploaded my local git repo to include the dummy files and style reworkings.\r\nAdded YAML in Readme as well.\r\n\r\nPlease check again.\r\n\r\nHope it works now :)"
] | 1,640,085,442,000 | 1,640,789,625,000 | null | CONTRIBUTOR | null | Added crass dataset | {
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"Hi @talha1503, thanks for reporting.\r\n\r\nIt seems there is an issue with one of the data files hosted at Google Drive:\r\n```\r\nGoogle Drive - Quota exceeded\r\n\r\nSorry, you can't view or download this file at this time.\r\n\r\nToo many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.\r\n```\r\n\r\nAs you probably know, Hugging Face does not host the data, and in this case the data owner decided to host their data at Google Drive, which has quota limits.\r\n\r\nIs there anything we could do, @lhoestq @mariosasko?",
"This looks related to https://github.com/huggingface/datasets/issues/996"
] | 1,640,057,541,000 | 1,640,097,343,000 | null | NONE | null | ## Describe the bug
I wanted to load cnn_dailymail dataset from huggingface datasets on Google Colab, but I am getting an error while loading it.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0', ignore_verifications = True)
```
## Expected results
Expecting to load 'cnn_dailymail' dataset.
## Actual results
`NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
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] | open | false | null | [] | null | [] | 1,640,057,341,000 | 1,640,057,341,000 | null | NONE | null | ## Describe the bug
A clear and concise description of what the bug is.
using latest datasets=datasets-1.16.1-py3-none-any.whl
process my own multilingual dataset by following codes, and the number of rows in all dataset is 306000, the max_length of each sentence is 256:
then I get this error:
I have seen the issue in #2134 and #2150, so I don't understand why latest repo still can't deal with big dataset.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: linux docker
- Python version: 3.6
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https://api.github.com/repos/huggingface/datasets/issues/3463 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3463/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3463/comments | https://api.github.com/repos/huggingface/datasets/issues/3463/events | https://github.com/huggingface/datasets/pull/3463 | 1,085,078,795 | PR_kwDODunzps4wGB4P | 3,463 | Update swahili_news dataset | {
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Fix #3462.
Close bigscience-workshop/data_tooling#107 | {
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] | null | [] | 1,640,022,241,000 | 1,640,067,842,000 | null | MEMBER | null | Please note also: the HuggingFace version at https://huggingface.co/datasets/swahili_news is outdated. An updated version, with deduplicated text and official splits, can be found at https://zenodo.org/record/5514203.
## Adding a Dataset
- **Name:** swahili_news
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Related to:
- bigscience-workshop/data_tooling#107
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https://api.github.com/repos/huggingface/datasets/issues/3461 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3461/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3461/comments | https://api.github.com/repos/huggingface/datasets/issues/3461/events | https://github.com/huggingface/datasets/pull/3461 | 1,085,007,346 | PR_kwDODunzps4wFzDP | 3,461 | Fix links in metrics description | {
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Related to #3437. | {
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https://api.github.com/repos/huggingface/datasets/issues/3460 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3460/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3460/comments | https://api.github.com/repos/huggingface/datasets/issues/3460/events | https://github.com/huggingface/datasets/pull/3460 | 1,085,002,469 | PR_kwDODunzps4wFyCf | 3,460 | Don't encode lists as strings when using `Value("string")` | {
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This PR fixes this and should fix the issue with WER showing low values if the input format is not right. | {
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"I think this is a duplicate of [#3190](https://github.com/huggingface/datasets/issues/3190)?",
"Upgrading the datasets version as per #3190 fixes this bug. \r\nI'm Marking as closed."
] | 1,640,017,009,000 | 1,640,018,097,000 | null | NONE | null | ## Describe the bug
When using dataset.select to select a subset of a dataset, dataset._indices are set to indicate which elements are now considered in the dataset.
The same thing happens when you shuffle the dataset; dataset._indices are set to indicate what the new order of the data is.
However, if you then use a dataset.filter, that filter interacts with those dataset._indices values in a non-intuitive manner.
https://huggingface.co/docs/datasets/_modules/datasets/arrow_dataset.html#Dataset.filter
Effectively, it looks like the original set of _indices were discared and overwritten by the set created during the filter operation.
I think this is actually an issue with how the map function handles dataset._indices. Ideally it should use the _indices it gets passed, and then return an updated _indices which reflect the map transformation applied to the starting _indices.
## Steps to reproduce the bug
```python
dataset = load_dataset('imdb', split='train', keep_in_memory=True)
dataset = dataset.shuffle(keep_in_memory=True)
dataset = dataset.select(range(0, 10), keep_in_memory=True)
print("initial 10 elements")
print(dataset['label']) # -> [1, 1, 0, 1, 0, 0, 0, 1, 0, 0]
dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True)
print("filtered 10 elements looking for label 0")
print(dataset['label']) # -> [1, 1, 1, 1, 1, 1]
```
## Actual results
```
$ python indices_bug.py
initial 10 elements
[1, 1, 0, 1, 0, 0, 0, 1, 0, 0]
filtered 10 elements looking for label 0
[1, 1, 1, 1, 1, 1]
```
This code block first shuffles the dataset (to get a mix of label 0 and label 1).
Then it selects just the first 10 elements (the number of elements does not matter, 10 is just easy to visualize). The important part is that you select some subset of the dataset.
Finally, a filter is applied to pull out just the elements with `label == 0`.
The bug is that you cannot combine any dataset operation which sets the dataset._indices with filter.
In this case I have 2, shuffle and subset.
If you just use a single dataset._indices operation (in this case shuffle) the bug still shows up.
The shuffle sets the dataset._indices and then filter uses those indices in the map, then overwrites dataset._indices with the filter results.
```python
dataset = load_dataset('imdb', split='train', keep_in_memory=True)
dataset = dataset.shuffle(keep_in_memory=True)
dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True)
dataset = dataset.select(range(0, 10), keep_in_memory=True)
print(dataset['label']) # -> [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
```
## Expected results
In an ideal world, the dataset filter would respect any dataset._indices values which had previously been set.
If you use dataset.filter with the base dataset (where dataset._indices has not been set) then the filter command works as expected.
## Environment info
Here are the commands required to rebuild the conda environment from scratch.
```
# create a virtual environment
conda create -n dataset_indices python=3.8 -y
# activate the virtual environment
conda activate dataset_indices
# install huggingface datasets
conda install datasets
```
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.11.0-41-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 3.0.0
### Full Conda Environment
```
$ conda env export
name: dasaset_indices
channels:
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=4.5=1_gnu
- abseil-cpp=20210324.2=h2531618_0
- aiohttp=3.8.1=py38h7f8727e_0
- aiosignal=1.2.0=pyhd3eb1b0_0
- arrow-cpp=3.0.0=py38h6b21186_4
- attrs=21.2.0=pyhd3eb1b0_0
- aws-c-common=0.4.57=he6710b0_1
- aws-c-event-stream=0.1.6=h2531618_5
- aws-checksums=0.1.9=he6710b0_0
- aws-sdk-cpp=1.8.185=hce553d0_0
- bcj-cffi=0.5.1=py38h295c915_0
- blas=1.0=mkl
- boost-cpp=1.73.0=h27cfd23_11
- bottleneck=1.3.2=py38heb32a55_1
- brotli=1.0.9=he6710b0_2
- brotli-python=1.0.9=py38heb0550a_2
- brotlicffi=1.0.9.2=py38h295c915_0
- brotlipy=0.7.0=py38h27cfd23_1003
- bzip2=1.0.8=h7b6447c_0
- c-ares=1.17.1=h27cfd23_0
- ca-certificates=2021.10.26=h06a4308_2
- certifi=2021.10.8=py38h06a4308_0
- cffi=1.14.6=py38h400218f_0
- conllu=4.4.1=pyhd3eb1b0_0
- cryptography=36.0.0=py38h9ce1e76_0
- dataclasses=0.8=pyh6d0b6a4_7
- dill=0.3.4=pyhd3eb1b0_0
- double-conversion=3.1.5=he6710b0_1
- et_xmlfile=1.1.0=py38h06a4308_0
- filelock=3.4.0=pyhd3eb1b0_0
- frozenlist=1.2.0=py38h7f8727e_0
- gflags=2.2.2=he6710b0_0
- glog=0.5.0=h2531618_0
- gmp=6.2.1=h2531618_2
- grpc-cpp=1.39.0=hae934f6_5
- huggingface_hub=0.0.17=pyhd3eb1b0_0
- icu=58.2=he6710b0_3
- idna=3.3=pyhd3eb1b0_0
- importlib-metadata=4.8.2=py38h06a4308_0
- importlib_metadata=4.8.2=hd3eb1b0_0
- intel-openmp=2021.4.0=h06a4308_3561
- krb5=1.19.2=hac12032_0
- ld_impl_linux-64=2.35.1=h7274673_9
- libboost=1.73.0=h3ff78a5_11
- libcurl=7.80.0=h0b77cf5_0
- libedit=3.1.20210910=h7f8727e_0
- libev=4.33=h7f8727e_1
- libevent=2.1.8=h1ba5d50_1
- libffi=3.3=he6710b0_2
- libgcc-ng=9.3.0=h5101ec6_17
- libgomp=9.3.0=h5101ec6_17
- libnghttp2=1.46.0=hce63b2e_0
- libprotobuf=3.17.2=h4ff587b_1
- libssh2=1.9.0=h1ba5d50_1
- libstdcxx-ng=9.3.0=hd4cf53a_17
- libthrift=0.14.2=hcc01f38_0
- libxml2=2.9.12=h03d6c58_0
- libxslt=1.1.34=hc22bd24_0
- lxml=4.6.3=py38h9120a33_0
- lz4-c=1.9.3=h295c915_1
- mkl=2021.4.0=h06a4308_640
- mkl-service=2.4.0=py38h7f8727e_0
- mkl_fft=1.3.1=py38hd3c417c_0
- mkl_random=1.2.2=py38h51133e4_0
- multiprocess=0.70.12.2=py38h7f8727e_0
- multivolumefile=0.2.3=pyhd3eb1b0_0
- ncurses=6.3=h7f8727e_2
- numexpr=2.7.3=py38h22e1b3c_1
- numpy=1.21.2=py38h20f2e39_0
- numpy-base=1.21.2=py38h79a1101_0
- openpyxl=3.0.9=pyhd3eb1b0_0
- openssl=1.1.1l=h7f8727e_0
- orc=1.6.9=ha97a36c_3
- packaging=21.3=pyhd3eb1b0_0
- pip=21.2.4=py38h06a4308_0
- py7zr=0.16.1=pyhd3eb1b0_1
- pycparser=2.21=pyhd3eb1b0_0
- pycryptodomex=3.10.1=py38h27cfd23_1
- pyopenssl=21.0.0=pyhd3eb1b0_1
- pyparsing=3.0.4=pyhd3eb1b0_0
- pyppmd=0.16.1=py38h295c915_0
- pysocks=1.7.1=py38h06a4308_0
- python=3.8.12=h12debd9_0
- python-dateutil=2.8.2=pyhd3eb1b0_0
- python-xxhash=2.0.2=py38h7f8727e_0
- pyzstd=0.14.4=py38h7f8727e_3
- re2=2020.11.01=h2531618_1
- readline=8.1=h27cfd23_0
- requests=2.26.0=pyhd3eb1b0_0
- setuptools=58.0.4=py38h06a4308_0
- six=1.16.0=pyhd3eb1b0_0
- snappy=1.1.8=he6710b0_0
- sqlite=3.36.0=hc218d9a_0
- texttable=1.6.4=pyhd3eb1b0_0
- tk=8.6.11=h1ccaba5_0
- typing_extensions=3.10.0.2=pyh06a4308_0
- uriparser=0.9.3=he6710b0_1
- utf8proc=2.6.1=h27cfd23_0
- wheel=0.37.0=pyhd3eb1b0_1
- xxhash=0.8.0=h7f8727e_3
- xz=5.2.5=h7b6447c_0
- zipp=3.6.0=pyhd3eb1b0_0
- zlib=1.2.11=h7f8727e_4
- zstd=1.4.9=haebb681_0
- pip:
- async-timeout==4.0.2
- charset-normalizer==2.0.9
- datasets==1.16.1
- fsspec==2021.11.1
- huggingface-hub==0.2.1
- multidict==5.2.0
- pandas==1.3.5
- pyarrow==6.0.1
- pytz==2021.3
- pyyaml==6.0
- tqdm==4.62.3
- typing-extensions==4.0.1
- urllib3==1.26.7
- yarl==1.7.2
```
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https://api.github.com/repos/huggingface/datasets/issues/3458 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3458/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3458/comments | https://api.github.com/repos/huggingface/datasets/issues/3458/events | https://github.com/huggingface/datasets/pull/3458 | 1,084,926,025 | PR_kwDODunzps4wFiRb | 3,458 | Fix duplicated tag in wikicorpus dataset card | {
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