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PR_kwDODunzps46YEEa

Fix evaluation metadata for ncbi_disease

closed

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This PR fixes the task in the evaluation metadata and removes the metrics info as we've decided this is not a great way to propagate this information downstream.

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I_kwDODunzps5Ml-Mj

Dataset Viewer issue for mlsum

closed

MEMBER

### Link https://huggingface.co/datasets/mlsum/viewer/de/train ### Description There's seems to be a problem with the download / streaming of this dataset: ``` Server error Status code: 400 Exception: BadZipFile Message: File is not a zip file ``` ### Owner No

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I_kwDODunzps5MlTJY

Dataset sharding non-contiguous?

closed

CONTRIBUTOR

## Describe the bug I'm not sure if this is a bug; more likely normal behavior but i wanted to double check. Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset? This might be related to this pull request (https://github.com/huggingface/datasets/pull/4466) but I have to admit I did not properly look into the changes made. ## Steps to reproduce the bug ```python max_shard_size = convert_file_size_to_int('300MB') dataset_nbytes = dataset.data.nbytes num_shards = int(dataset_nbytes / max_shard_size) + 1 num_shards = max(num_shards, 1) print(f"{num_shards=}") for shard_index in range(num_shards): shard = dataset.shard(num_shards=num_shards, index=shard_index) shard.to_parquet(f"tokenized/tokenized-{shard_index:03d}.parquet") os.listdir('tokenized/') ``` ## Expected results I expected the shards to match the order of the data of the original dataset; i.e. `dataset[10]` being the same as `shard_1[10]` for example ## Actual results Only the first element is the same; i.e. `dataset[0]` is the same as `shard_1[0]` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31 - Python version: 3.10.4 - PyArrow version: 8.0.0 - Pandas version: 1.4.2

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I_kwDODunzps5MlQGe

Dataset Viewer issue for sst2

closed

MEMBER

### Link https://huggingface.co/datasets/sst2 ### Description Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem): ``` Status code: 400 Exception: Exception Message: Give up after 5 attempts with ConnectionError ``` ### Owner No

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I_kwDODunzps5MkkoI

XNLI cache reload is very slow

closed

CONTRIBUTOR

### Reproduce Using `2.3.3.dev0` `from datasets import load_dataset` `load_dataset("xnli", "en")` Turn off Internet `load_dataset("xnli", "en")` I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the library trying to download when there is no Internet. If I leave it running it works but takes way longer than when there is Internet. I would expect loading from cache to take the same amount of time regardless of whether there is Internet. ``` --------------------------------------------------------------------------- gaierror Traceback (most recent call last) /opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self) 174 conn = connection.create_connection( --> 175 (self._dns_host, self.port), self.timeout, **extra_kw 176 ) /opt/conda/lib/python3.7/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 71 ---> 72 for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM): 73 af, socktype, proto, canonname, sa = res /opt/conda/lib/python3.7/socket.py in getaddrinfo(host, port, family, type, proto, flags) 751 addrlist = [] --> 752 for res in _socket.getaddrinfo(host, port, family, type, proto, flags): 753 af, socktype, proto, canonname, sa = res gaierror: [Errno -3] Temporary failure in name resolution During handling of the above exception, another exception occurred: KeyboardInterrupt Traceback (most recent call last) /tmp/ipykernel_33/3594208039.py in <module> ----> 1 load_dataset("xnli", "en") /opt/conda/lib/python3.7/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, **config_kwargs) 1673 revision=revision, 1674 use_auth_token=use_auth_token, -> 1675 **config_kwargs, 1676 ) 1677 /opt/conda/lib/python3.7/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, **config_kwargs) 1494 download_mode=download_mode, 1495 data_dir=data_dir, -> 1496 data_files=data_files, 1497 ) 1498 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1182 download_config=download_config, 1183 download_mode=download_mode, -> 1184 dynamic_modules_path=dynamic_modules_path, 1185 ).get_module() 1186 elif path.count("/") == 1: # community dataset on the Hub /opt/conda/lib/python3.7/site-packages/datasets/load.py in __init__(self, name, revision, download_config, download_mode, dynamic_modules_path) 506 self.dynamic_modules_path = dynamic_modules_path 507 assert self.name.count("/") == 0 --> 508 increase_load_count(name, resource_type="dataset") 509 510 def download_loading_script(self, revision: Optional[str]) -> str: /opt/conda/lib/python3.7/site-packages/datasets/load.py in increase_load_count(name, resource_type) 166 if not config.HF_DATASETS_OFFLINE and config.HF_UPDATE_DOWNLOAD_COUNTS: 167 try: --> 168 head_hf_s3(name, filename=name + ".py", dataset=(resource_type == "dataset")) 169 except Exception: 170 pass /opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in head_hf_s3(identifier, filename, use_cdn, dataset, max_retries) 93 return http_head( 94 hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset), ---> 95 max_retries=max_retries, 96 ) 97 /opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries) 445 allow_redirects=allow_redirects, 446 timeout=timeout, --> 447 max_retries=max_retries, 448 ) 449 return response /opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params) 366 tries += 1 367 try: --> 368 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params) 369 success = True 370 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err: /opt/conda/lib/python3.7/site-packages/requests/api.py in request(method, url, **kwargs) 59 # cases, and look like a memory leak in others. 60 with sessions.Session() as session: ---> 61 return session.request(method=method, url=url, **kwargs) 62 63 /opt/conda/lib/python3.7/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 527 } 528 send_kwargs.update(settings) --> 529 resp = self.send(prep, **send_kwargs) 530 531 return resp /opt/conda/lib/python3.7/site-packages/requests/sessions.py in send(self, request, **kwargs) 643 644 # Send the request --> 645 r = adapter.send(request, **kwargs) 646 647 # Total elapsed time of the request (approximately) /opt/conda/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 448 decode_content=False, 449 retries=self.max_retries, --> 450 timeout=timeout 451 ) 452 /opt/conda/lib/python3.7/site-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) 708 body=body, 709 headers=headers, --> 710 chunked=chunked, 711 ) 712 /opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 384 # Trigger any extra validation we need to do. 385 try: --> 386 self._validate_conn(conn) 387 except (SocketTimeout, BaseSSLError) as e: 388 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout. /opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn) 1038 # Force connect early to allow us to validate the connection. 1039 if not getattr(conn, "sock", None): # AppEngine might not have `.sock` -> 1040 conn.connect() 1041 1042 if not conn.is_verified: /opt/conda/lib/python3.7/site-packages/urllib3/connection.py in connect(self) 356 def connect(self): 357 # Add certificate verification --> 358 self.sock = conn = self._new_conn() 359 hostname = self.host 360 tls_in_tls = False /opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self) 173 try: 174 conn = connection.create_connection( --> 175 (self._dns_host, self.port), self.timeout, **extra_kw 176 ) 177 KeyboardInterrupt: ```

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PR_kwDODunzps46Wh0-

Add evaluation data for amazon_reviews_multi

closed

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I_kwDODunzps5Mjloa

Document link #load_dataset_enhancing_performance points to nowhere

closed

NONE

## Describe the bug A clear and concise description of what the bug is.  The [load_dataset_enhancing_performance](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#load_dataset_enhancing_performance) link [here](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.load_from_disk.keep_in_memory) points to nowhere, I guess it should point to https://huggingface.co/docs/datasets/v2.3.2/en/cache#improve-performance?

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I_kwDODunzps5MinJi

Add UFSC OCPap dataset

closed

NONE

## Adding a Dataset - **Name:** UFSC OCPap: Papanicolaou Stained Oral Cytology Dataset (v4) - **Description:** The UFSC OCPap dataset comprises 9,797 labeled images of 1200x1600 pixels acquired from 5 slides of cancer diagnosed and 3 healthy of oral brush samples, from distinct patients. - **Paper:** https://dx.doi.org/10.2139/ssrn.4119212 - **Data:** https://data.mendeley.com/datasets/dr7ydy9xbk/1 - **Motivation:** real data of pap stained oral cytology samples 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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PR_kwDODunzps46UqUN

Support streaming bookcorpus dataset

closed

MEMBER

Support streaming bookcorpus dataset.

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PR_kwDODunzps46UmZQ

Support streaming allocine dataset

closed

MEMBER

Support streaming allocine dataset. Fix #4562.

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I_kwDODunzps5MhOvl

Dataset Viewer issue for allocine

closed

MEMBER

### Link https://huggingface.co/datasets/allocine ### Description Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed: ``` Status code: 400 Exception: AttributeError Message: 'TarContainedFile' object has no attribute 'readable' ``` ### Owner No

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PR_kwDODunzps46TnVe

Add evaluation data to acronym_identification

closed

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PR_kwDODunzps46TY9n

Add evaluation metadata to imagenet-1k

closed

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PR_kwDODunzps46TV7-

Add action names in schema_guided_dstc8 dataset card

closed

MEMBER

As aseked in https://huggingface.co/datasets/schema_guided_dstc8/discussions/1, I added the action names in the dataset card

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PR_kwDODunzps46THl_

Add evaluation metadata to wmt14

closed

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PR_kwDODunzps46TGZK

Add evaluation metadata to wmt16

closed

MEMBER

Just to confirm: we should add this metadata via GitHub and not Hub PRs for canonical datasets right?

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I_kwDODunzps5MgBbh

Dataset Viewer issue for conll2003

closed

MEMBER

### Link https://huggingface.co/datasets/conll2003/viewer/conll2003/test ### Description Seems like a cache problem with this config / split: ``` Server error Status code: 400 Exception: FileNotFoundError Message: [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/conll2003/__init__.py' ``` ### Owner No

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I_kwDODunzps5Mf-sD

Dataset Viewer issue for xtreme

closed

MEMBER

### Link https://huggingface.co/datasets/xtreme/viewer/PAN-X.de/test ### Description There seems to be a problem with the cache of this config / split: ``` Server error Status code: 400 Exception: FileNotFoundError Message: [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/xtreme/349258adc25bb45e47de193222f95e68a44f7a7ab53c4283b3f007208a11bf7e/xtreme.py' ``` ### Owner No

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PR_kwDODunzps46Sv_f

Fix WMT dataset loading issue and docs update (Re-opened)

closed

CONTRIBUTOR

This PR is a fix for #4354 Changes are made for `wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t`. And READMEs are updated for the corresponding datasets. Let me know, if any additional changes are required. Thanks

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PR_kwDODunzps46Q1q7

Stop dropping columns in to_tf_dataset() before we load batches

closed

MEMBER

`to_tf_dataset()` dropped unnecessary columns before loading batches from the dataset, but this is causing problems when using a transform, because the dropped columns might be needed to compute the transform. Since there's no real way to check which columns the transform might need, we skip dropping columns and instead drop keys from the batch after we load it. cc @amyeroberts and https://github.com/huggingface/notebooks/pull/202

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PR_kwDODunzps46QSHV

Tell users to upload on the hub directly

closed

MEMBER

As noted in https://github.com/huggingface/datasets/pull/4534, it is still not clear that it is recommended to add datasets on the Hugging Face Hub directly instead of GitHub, so I updated some docs. Moreover since users won't be able to get reviews from us on the Hub, I added a paragraph to tell users that they can open a discussion and tag `datasets` maintainers for reviews. Finally I removed the _previous good reasons_ to add a dataset on GitHub to only keep this one: > In some rare cases it makes more sense to open a PR on GitHub. For example when you are not the author of the dataset and there is no clear organization / namespace that you can put the dataset under. Does it sound good to you @albertvillanova @julien-c ?

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PR_kwDODunzps46QAV-

Perform hidden file check on relative data file path

closed

CONTRIBUTOR

Fix #4549

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I_kwDODunzps5Mb3sp

imdb source error

closed

NONE

## Describe the bug imdb dataset not loading ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("imdb") ``` ## Expected results ## Actual results ```bash 06/23/2022 14:45:18 - INFO - datasets.builder - Dataset not on Hf google storage. Downloading and preparing it from source 06/23/2022 14:46:34 - INFO - datasets.utils.file_utils - HEAD request to http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz timed out, retrying... [1.0] ..... ConnectionError: Couldn't reach http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz (ConnectTimeout(MaxRetryError("HTTPConnectionPool(host='ai.stanford.edu', port=80): Max retries exceeded with url: /~amaas/data/sentiment/aclImdb_v1.tar.gz (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x7f2d750cf690>, 'Connection to ai.stanford.edu timed out. (connect timeout=100)'))"))) ``` ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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I_kwDODunzps5MbosP

FileNotFoundError when passing a data_file inside a directory starting with double underscores

closed

MEMBER

Bug experienced in the `accelerate` CI: https://github.com/huggingface/accelerate/runs/7016055148?check_suite_focus=true This is related to https://github.com/huggingface/datasets/pull/4505 and the changes from https://github.com/huggingface/datasets/pull/4412

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I_kwDODunzps5MbRhw

Metadata.jsonl for Imagefolder is ignored if it's in a parent directory to the splits directories/do not have "{split}_" prefix

closed

CONTRIBUTOR

If data contains a single `metadata.jsonl` file for several splits, it won't be included in a dataset's `data_files` and therefore ignored. This happens when a directory is structured like as follows: ``` train/ file_1.jpg file_2.jpg test/ file_3.jpg file_4.jpg metadata.jsonl ``` or like as follows: ``` train_file_1.jpg train_file_2.jpg test_file_3.jpg test_file_4.jpg metadata.jsonl ``` The same for HF repos. because it's ignored by the patterns [here](https://github.com/huggingface/datasets/blob/master/src/datasets/data_files.py#L29) @lhoestq @mariosasko Do you think it's better to add this functionality in `data_files.py` or just specifically in imagefolder/audiofolder code? In `data_files.py` would me more general but I don't know if there are any other cases when that might be needed.

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PR_kwDODunzps46Ot5u

[CI] Fix some warnings

closed

MEMBER

There are some warnings in the CI that are annoying, I tried to remove most of them

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PR_kwDODunzps46Oe_K

[CI] fixing seqeval install in ci by pinning setuptools-scm

closed

MEMBER

The latest setuptools-scm version supported on 3.6 is 6.4.2. However for some reason circleci has version 7, which doesn't work. I fixed this by pinning the version of setuptools-scm in the circleci job Fix https://github.com/huggingface/datasets/issues/4544

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PR_kwDODunzps46KV-y

Make DuplicateKeysError more user friendly [For Issue #2556]

closed

CONTRIBUTOR

# What does this PR do? ## Summary *DuplicateKeysError error does not provide any information regarding the examples which have the same the key.* *This information is very helpful for debugging the dataset generator script.* ## Additions - ## Changes - Changed `DuplicateKeysError Class` in `src/datasets/keyhash.py` to add current index and duplicate_key_indices to error message. - Changed `check_duplicate_keys` function in `src/datasets/arrow_writer.py` to find indices of examples with duplicate hash if duplicate keys are found. ## Deletions - ## To do : - [x] Find way to find and print path `<Path to Dataset>` in Error message ## Issues Addressed : Fixes #2556

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I_kwDODunzps5MUuJ0

[CI] seqeval installation fails sometimes on python 3.6

closed

MEMBER

The CI sometimes fails to install seqeval, which cause the `seqeval` metric tests to fail. The installation fails because of this error: ``` Collecting seqeval Downloading seqeval-1.2.2.tar.gz (43 kB) |███████▌ | 10 kB 42.1 MB/s eta 0:00:01 |███████████████ | 20 kB 53.3 MB/s eta 0:00:01 |██████████████████████▌ | 30 kB 67.2 MB/s eta 0:00:01 |██████████████████████████████ | 40 kB 76.1 MB/s eta 0:00:01 |████████████████████████████████| 43 kB 10.0 MB/s Preparing metadata (setup.py) ... - error ERROR: Command errored out with exit status 1: command: /home/circleci/.pyenv/versions/3.6.15/bin/python3.6 -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py'"'"'; __file__='"'"'/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-pip-egg-info-pf54_vqy cwd: /tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/ Complete output (22 lines): Traceback (most recent call last): File "<string>", line 1, in <module> File "/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py", line 56, in <module> 'Programming Language :: Python :: Implementation :: PyPy' File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/__init__.py", line 143, in setup return distutils.core.setup(**attrs) File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/distutils/core.py", line 108, in setup _setup_distribution = dist = klass(attrs) File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/dist.py", line 442, in __init__ k: v for k, v in attrs.items() File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/distutils/dist.py", line 281, in __init__ self.finalize_options() File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/dist.py", line 601, in finalize_options ep.load()(self, ep.name, value) File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/pkg_resources/__init__.py", line 2346, in load return self.resolve() File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/pkg_resources/__init__.py", line 2352, in resolve module = __import__(self.module_name, fromlist=['__name__'], level=0) File "/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/.eggs/setuptools_scm-7.0.2-py3.6.egg/setuptools_scm/__init__.py", line 5 from __future__ import annotations ^ SyntaxError: future feature annotations is not defined ---------------------------------------- WARNING: Discarding https://files.pythonhosted.org/packages/9d/2d/233c79d5b4e5ab1dbf111242299153f3caddddbb691219f363ad55ce783d/seqeval-1.2.2.tar.gz#sha256=f28e97c3ab96d6fcd32b648f6438ff2e09cfba87f05939da9b3970713ec56e6f (from https://pypi.org/simple/seqeval/). Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output. ``` for example in https://app.circleci.com/pipelines/github/huggingface/datasets/12665/workflows/93878eb9-a923-4b35-b2e7-c5e9b22f10ad/jobs/75300 Here is a diff of the pip install logs until the error is reached: https://www.diffchecker.com/VkQDLeQT This could be caused by the latest updates of setuptools-scm

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PR_kwDODunzps46IiEp

[CI] Fix upstream hub test url

closed

MEMBER

Some tests were still using moon-stagign instead of hub-ci. I also updated the token to use one dedicated to `datasets`

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PR_kwDODunzps46HyPK

Fix timestamp conversion from Pandas to Python datetime in streaming mode

closed

MEMBER

Arrow accepts both pd.Timestamp and datetime.datetime objects to create timestamp arrays. However a timestamp array is always converted to datetime.datetime objects. This created an inconsistency between streaming in non-streaming. e.g. the `ett` dataset outputs datetime.datetime objects in non-streaming but pd.timestamp in streaming. I fixed this by always converting pd.Timestamp to datetime.datetime during the example encoding step. I fixed the same issue for pd.Timedelta as well. Finally I added an extra step of conversion for Series and DataFrame to take this into account in case such data are passed as Series or DataFrame. Fix https://github.com/huggingface/datasets/issues/4533 Related to https://github.com/huggingface/datasets-server/issues/397

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I_kwDODunzps5MTW5e

Avoid splitting by` .py` for the file.

closed

NONE

https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272 Hello, Thanks you for this library . I was using it and I had one edge case. my home folder name ends with `.py` it is `/home/espoir.py` so anytime I am running the code to load a local module this code here it is failing because after splitting it is trying to save the code to my home directory. Step to reproduce. - If you have a home folder which ends with `.py` - load a module with a local folder `qa_dataset = load_dataset("src/data/build_qa_dataset.py")` it is failed A possible workaround would be to use pathlib at the mentioned line ` meta_path = Path(importable_local_file).parent.joinpath("metadata.json")` this can alivate the issue . Let me what are your thought on this and I can try to fix it by A PR.

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PR_kwDODunzps46GfWv

Replace deprecated logging.warn with logging.warning

closed

CONTRIBUTOR

Replace `logging.warn` (deprecated in [Python 2.7, 2011](https://github.com/python/cpython/commit/04d5bc00a219860c69ea17eaa633d3ab9917409f)) with `logging.warning` (added in [Python 2.3, 2003](https://github.com/python/cpython/commit/6fa635df7aa88ae9fd8b41ae42743341316c90f7)). * https://docs.python.org/3/library/logging.html#logging.Logger.warning * https://github.com/python/cpython/issues/57444

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I_kwDODunzps5MQj56

Dataset Viewer issue for Pile of Law

closed

NONE

### Link https://huggingface.co/datasets/pile-of-law/pile-of-law ### Description Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines and so would like to turn off dataset previews. But we do not want to collect user emails because it would violate single blind review, allowing us to deduce potential reviewers' identities. Is there a way that we can turn off the dataset viewer without collecting identity information? Thanks so much! ### Owner Yes

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PR_kwDODunzps46ESJn

Fix WMT dataset loading issue and docs update

closed

CONTRIBUTOR

This PR is a fix for #4354 Changes are made for `wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t`. And READMEs are updated for the corresponding datasets. As I am on a M1 Mac, I am not able to create a virtual `dev` environment using `pip install -e ".[dev]"`. Issue is with `tensorflow-text` not supported on M1s and there is no supporting repo by Apple or Google. So, if I was needed to perform local testing, I am not able to do that. Let me know, if any additional changes are required. Thanks

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PR_kwDODunzps46C2z6

Properly raise FileNotFound even if the dataset is private

closed

MEMBER

`tests/test_load.py::test_load_streaming_private_dataset` was failing because the hub now returns 401 when getting the HfApi.dataset_info of a dataset without authentication. `load_dataset` was raising ConnectionError, while it should be FileNoteFoundError since it first checks for local files before checking the Hub. Moreover when use_auth_token is not set (default is False), we should not pass `token=None` to HfApi.dataset_info, or it will use the local token by default - instead it should use no token. It's currently not possible to ask for no token to be used, so as a workaround I simply set token="no-token"

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PR_kwDODunzps46BnXq

Add `batch_size` parameter when calling `add_faiss_index` and `add_faiss_index_from_external_arrays`

closed

CONTRIBUTOR

Currently, even though the `batch_size` when adding vectors to the FAISS index can be tweaked in `FaissIndex.add_vectors()`, the function `ArrowDataset.add_faiss_index` doesn't have either the parameter `batch_size` to be propagated to the nested `FaissIndex.add_vectors` function or `*args, **kwargs`, so on, this PR adds the `batch_size` parameter to both `ArrowDataset.add_faiss_index` and `ArrowDataset.add_faiss_index_from_external_arrays`. This is useful so as to tweak the `batch_size` according to the VM specifications.

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PR_kwDODunzps46AFK_

Add `tldr_news` dataset

closed

NONE

This PR aims at adding support for a news dataset: `tldr news`. This dataset is based on the daily [tldr tech newsletter](https://tldr.tech/newsletter) and contains a `headline` as well as a `content` for every piece of news contained in a newsletter.

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I_kwDODunzps5MILNi

Timestamp not returned as datetime objects in streaming mode

closed

MEMBER

As reported in (internal) https://github.com/huggingface/datasets-server/issues/397 ```python >>> from datasets import load_dataset >>> dataset = load_dataset("ett", name="h2", split="test", streaming=True) >>> d = next(iter(dataset)) >>> d['start'] Timestamp('2016-07-01 00:00:00') ``` while loading in non-streaming mode it returns `datetime.datetime(2016, 7, 1, 0, 0)`

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PR_kwDODunzps459kB7

Add Video feature

closed

CONTRIBUTOR

The following adds a `Video` feature for encoding/decoding videos on the fly from in memory bytes. It uses my own `encoded-video` library which is basically `pytorchvideo`'s encoded video but with all the `torch` specific stuff stripped out. Because of that, and because the tool I used under the hood is not very mature, I leave this as a draft idea that we can use to build off of.

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I_kwDODunzps5MHkzc

Dataset Viewer issue for CSV datasets

closed

CONTRIBUTOR

### Link https://huggingface.co/datasets/scikit-learn/breast-cancer-wisconsin ### Description I'm populating CSV datasets [here](https://huggingface.co/scikit-learn) but the viewer is not enabled and it looks for a dataset loading script, the datasets aren't on queue as well. You can replicate the problem by simply uploading any CSV dataset. ### Owner Yes

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PR_kwDODunzps458n_S

Add AudioFolder packaged loader

closed

CONTRIBUTOR

will close #3964 AudioFolder is almost identical to ImageFolder except for inferring labels is not the default behavior (`drop_labels` is set to True in config), the option of inferring them is preserved though. The weird thing is happening with the `test_data_files_with_metadata_and_archives` when `streaming` is `True`. Here is the log from the CI: ``` ../.pyenv/versions/3.6.15/lib/python3.6/site-packages/datasets/features/audio.py:237: in _decode_non_mp3_path_like array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) ../.pyenv/versions/3.6.15/lib/python3.6/site-packages/librosa/util/decorators.py:88: in inner_f return f(*args, **kwargs) ../.pyenv/versions/3.6.15/lib/python3.6/site-packages/librosa/core/audio.py:176: in load raise (exc) ../.pyenv/versions/3.6.15/lib/python3.6/site-packages/librosa/core/audio.py:155: in load context = sf.SoundFile(path) ../.pyenv/versions/3.6.15/lib/python3.6/site-packages/soundfile.py:629: in __init__ self._file = self._open(file, mode_int, closefd) ../.pyenv/versions/3.6.15/lib/python3.6/site-packages/soundfile.py:1184: in _open "Error opening {0!r}: ".format(self.name)) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ err = 72 prefix = "Error opening <zipfile.ZipExtFile name='audio_file.wav' mode='r' compress_type=deflate>: " def _error_check(err, prefix=""): """Pretty-print a numerical error code if there is an error.""" if err != 0: err_str = _snd.sf_error_number(err) > raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace')) E RuntimeError: Error opening <zipfile.ZipExtFile name='audio_file.wav' mode='r' compress_type=deflate>: Error in WAV file. No 'data' chunk marker. ``` I hadn't been able to reproduce this locally until I created the same test environment (I mean with `pip install .[tests]`) with python3.6. The same env but with python3.8 passes the test! I didn't manage to figure out what's wrong, I also tried simply to replace the test wav file and still got the same error. Versions of `soundfile`, `librosa` and `libsndfile` are identical. Might it be something with zip compression? Sounds weird but I don't have any other ideas... TODO: - [x] align with #4622 - [x] documentation - [x] tests for AutoFolder?

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I_kwDODunzps5MGJPz

Memory leak when iterating a Dataset

closed

MEMBER

e## Describe the bug It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop) ## Steps to reproduce the bug ```python import gc import logging import time import pyarrow from datasets import load_dataset from tqdm import trange import os, psutil logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) process = psutil.Process(os.getpid()) print(process.memory_info().rss) # output: 633507840 bytes corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus'] # or "BeIR/trec-covid" for a smaller dataset print(process.memory_info().rss) # output: 698601472 bytes logger.info("Applying method to all examples in all splits") for i in trange(0, len(corpus), 1000): batch = corpus[i:i+1000] data = pyarrow.total_allocated_bytes() if data > 0: logger.info(f"{i}/{len(corpus)}: {data}") print(process.memory_info().rss) # output: 3788247040 bytes del batch gc.collect() print(process.memory_info().rss) # output: 3788247040 bytes logger.info("Done...") time.sleep(100) ``` ## Expected results Limited memory usage, and memory to be freed after processing ## Actual results Memory leak  You can see how the memory allocation keeps increasing until it reaches a steady state when we hit the `time.sleep(100)`, which showcases that even the garbage collector couldn't free the allocated memory ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 8.0.0 - Pandas version: 1.4.2

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I_kwDODunzps5MFx5w

Dataset Viewer issue for vadis/sv-ident

closed

MEMBER

### Link https://huggingface.co/datasets/vadis/sv-ident ### Description The dataset preview does not work: ``` Server Error Status code: 400 Exception: Status400Error Message: The dataset does not exist. ``` However, the dataset is streamable and works locally: ```python In [1]: from datasets import load_dataset; ds = load_dataset("sv-ident.py", split="train", streaming=True); item = next(iter(ds)); item Using custom data configuration default Out[1]: {'sentence': 'Our point, however, is that so long as downward (favorable) comparisons overwhelm the potential for unfavorable comparisons, system justification should be a likely outcome amongst the disadvantaged.', 'is_variable': 1, 'variable': ['exploredata-ZA5400_VarV66', 'exploredata-ZA5400_VarV53'], 'research_data': ['ZA5400'], 'doc_id': '73106', 'uuid': 'b9fbb80f-3492-4b42-b9d5-0254cc33ac10', 'lang': 'en'} ``` CC: @e-tornike ### Owner No

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PR_kwDODunzps452hgh

Update download url and improve card of `cats_vs_dogs` dataset

closed

CONTRIBUTOR

Improve the download URL (reported here: https://huggingface.co/datasets/cats_vs_dogs/discussions/1), remove the `image_file_path` column (not used in Transformers, so it should be safe) and add more info to the card.

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I_kwDODunzps5L_boN

Datasets method `.map` not hashing

closed

CONTRIBUTOR

## Describe the bug Datasets method `.map` not hashing, even with an empty no-op function ## Steps to reproduce the bug ```python from datasets import load_dataset # download 9MB dummy dataset ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean") def prepare_dataset(batch): return(batch) ds = ds.map( prepare_dataset, num_proc=1, desc="preprocess train dataset", ) ``` ## Expected results Hashed and cached dataset preprocessing ## Actual results Does not hash properly: ``` Parameter 'function'=<function prepare_dataset at 0x7fccb68e9280> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.3.dev0 - Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 8.0.0 - Pandas version: 1.4.2 cc @lhoestq

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I_kwDODunzps5L_RzM

Failure to hash `dataclasses` - results in functions that cannot be hashed or cached in `.map`

closed

CONTRIBUTOR

Dataclasses cannot be hashed. As a result, they cannot be hashed or cached if used in the `.map` method. Dataclasses are used extensively in Transformers examples scripts: (c.f. [CTC example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py)). Since dataclasses cannot be hashed, one has to define separate variables prior to passing dataclass attributes to the `.map` method: ```python phoneme_language = data_args.phoneme_language ``` in the example https://github.com/huggingface/transformers/blob/3c7e56fbb11f401de2528c1dcf0e282febc031cd/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L603-L630 ## Steps to reproduce the bug ```python from dataclasses import dataclass, field from datasets.fingerprint import Hasher @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ phoneme_language: str = field( default=None, metadata={"help": "The name of the phoneme language to use."} ) data_args = DataTrainingArguments(phoneme_language ="foo") Hasher.hash(data_args) phoneme_language = data_args.phoneme_language Hasher.hash(phoneme_language) ``` ## Expected results A hash. ## Actual results <details> <summary> Traceback </summary> ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) Input In [1], in <cell line: 16>() 10 phoneme_language: str = field( 11 default=None, metadata={"help": "The name of the phoneme language to use."} 12 ) 14 data_args = DataTrainingArguments(phoneme_language ="foo") ---> 16 Hasher.hash(data_args) 18 phoneme_language = data_args. phoneme_language 20 Hasher.hash(phoneme_language) File ~/datasets/src/datasets/fingerprint.py:237, in Hasher.hash(cls, value) 235 return cls.dispatch[type(value)](cls, value) 236 else: --> 237 return cls.hash_default(value) File ~/datasets/src/datasets/fingerprint.py:230, in Hasher.hash_default(cls, value) 228 @classmethod 229 def hash_default(cls, value: Any) -> str: --> 230 return cls.hash_bytes(dumps(value)) File ~/datasets/src/datasets/utils/py_utils.py:564, in dumps(obj) 562 file = StringIO() 563 with _no_cache_fields(obj): --> 564 dump(obj, file) 565 return file.getvalue() File ~/datasets/src/datasets/utils/py_utils.py:539, in dump(obj, file) 537 def dump(obj, file): 538 """pickle an object to a file""" --> 539 Pickler(file, recurse=True).dump(obj) 540 return File ~/hf/lib/python3.8/site-packages/dill/_dill.py:620, in Pickler.dump(self, obj) 618 raise PicklingError(msg) 619 else: --> 620 StockPickler.dump(self, obj) 621 return File /usr/lib/python3.8/pickle.py:487, in _Pickler.dump(self, obj) 485 if self.proto >= 4: 486 self.framer.start_framing() --> 487 self.save(obj) 488 self.write(STOP) 489 self.framer.end_framing() File /usr/lib/python3.8/pickle.py:603, in _Pickler.save(self, obj, save_persistent_id) 599 raise PicklingError("Tuple returned by %s must have " 600 "two to six elements" % reduce) 602 # Save the reduce() output and finally memoize the object --> 603 self.save_reduce(obj=obj, *rv) File /usr/lib/python3.8/pickle.py:687, in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj) 684 raise PicklingError( 685 "args[0] from __newobj__ args has the wrong class") 686 args = args[1:] --> 687 save(cls) 688 save(args) 689 write(NEWOBJ) File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id) 558 f = self.dispatch.get(t) 559 if f is not None: --> 560 f(self, obj) # Call unbound method with explicit self 561 return 563 # Check private dispatch table if any, or else 564 # copyreg.dispatch_table File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1838, in save_type(pickler, obj, postproc_list) 1836 postproc_list = [] 1837 postproc_list.append((setattr, (obj, '__qualname__', obj_name))) -> 1838 _save_with_postproc(pickler, (_create_type, ( 1839 type(obj), obj.__name__, obj.__bases__, _dict 1840 )), obj=obj, postproc_list=postproc_list) 1841 log.info("# %s" % _t) 1842 else: File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1140, in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list) 1137 pickler._postproc[id(obj)] = postproc_list 1139 # TODO: Use state_setter in Python 3.8 to allow for faster cPickle implementations -> 1140 pickler.save_reduce(*reduction, obj=obj) 1142 if is_pickler_dill: 1143 # pickler.x -= 1 1144 # print(pickler.x*' ', 'pop', obj, id(obj)) 1145 postproc = pickler._postproc.pop(id(obj)) File /usr/lib/python3.8/pickle.py:692, in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj) 690 else: 691 save(func) --> 692 save(args) 693 write(REDUCE) 695 if obj is not None: 696 # If the object is already in the memo, this means it is 697 # recursive. In this case, throw away everything we put on the 698 # stack, and fetch the object back from the memo. File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id) 558 f = self.dispatch.get(t) 559 if f is not None: --> 560 f(self, obj) # Call unbound method with explicit self 561 return 563 # Check private dispatch table if any, or else 564 # copyreg.dispatch_table File /usr/lib/python3.8/pickle.py:901, in _Pickler.save_tuple(self, obj) 899 write(MARK) 900 for element in obj: --> 901 save(element) 903 if id(obj) in memo: 904 # Subtle. d was not in memo when we entered save_tuple(), so 905 # the process of saving the tuple's elements must have saved (...) 909 # could have been done in the "for element" loop instead, but 910 # recursive tuples are a rare thing. 911 get = self.get(memo[id(obj)][0]) File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id) 558 f = self.dispatch.get(t) 559 if f is not None: --> 560 f(self, obj) # Call unbound method with explicit self 561 return 563 # Check private dispatch table if any, or else 564 # copyreg.dispatch_table File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1251, in save_module_dict(pickler, obj) 1248 if is_dill(pickler, child=False) and pickler._session: 1249 # we only care about session the first pass thru 1250 pickler._first_pass = False -> 1251 StockPickler.save_dict(pickler, obj) 1252 log.info("# D2") 1253 return File /usr/lib/python3.8/pickle.py:971, in _Pickler.save_dict(self, obj) 968 self.write(MARK + DICT) 970 self.memoize(obj) --> 971 self._batch_setitems(obj.items()) File /usr/lib/python3.8/pickle.py:997, in _Pickler._batch_setitems(self, items) 995 for k, v in tmp: 996 save(k) --> 997 save(v) 998 write(SETITEMS) 999 elif n: File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id) 558 f = self.dispatch.get(t) 559 if f is not None: --> 560 f(self, obj) # Call unbound method with explicit self 561 return 563 # Check private dispatch table if any, or else 564 # copyreg.dispatch_table File ~/datasets/src/datasets/utils/py_utils.py:862, in save_function(pickler, obj) 859 if state_dict: 860 state = state, state_dict --> 862 dill._dill._save_with_postproc( 863 pickler, 864 ( 865 dill._dill._create_function, 866 (obj.__code__, globs, obj.__name__, obj.__defaults__, closure), 867 state, 868 ), 869 obj=obj, 870 postproc_list=postproc_list, 871 ) 872 else: 873 closure = obj.func_closure File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1153, in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list) 1151 dest, source = reduction[1] 1152 if source: -> 1153 pickler.write(pickler.get(pickler.memo[id(dest)][0])) 1154 pickler._batch_setitems(iter(source.items())) 1155 else: 1156 # Updating with an empty dictionary. Same as doing nothing. KeyError: 140434581781568 ``` </details> ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.3.dev0 - Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.2 cc @lhoestq

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PR_kwDODunzps45zhqa

Create new sections for audio and vision in guides

closed

MEMBER

This PR creates separate sections in the guides for audio, vision, text, and general usage so it is easier for users to find loading, processing, or sharing guides specific to the dataset type they're working with. It'll also allow us to scale the docs to additional dataset types - like time series, tabular, etc. - while keeping our docs information architecture. Some other changes include: - ~Experimented with decorating text with some CSS to highlight guides specific to each modality. Hopefully, it'll be easier for users to find and realize that these different docs exist!~ Will experiment with this in a different PR. - Added deprecation warning for Metrics and redirect to Evaluate. - Updated `set_format` section to recommend using the new `to_tf_dataset` function if you need to convert to a TensorFlow dataset. - Reorganized `toctree` to nest general usage, audio, vision, and text sections under the how-to guides. - A quick review and edit to the Load and Process docs for clarity.

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PR_kwDODunzps45zMnB

Patch tests for hfh v0.8.0

closed

MEMBER

This PR patches testing utilities that would otherwise fail with hfh v0.8.0.

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PR_kwDODunzps45zBl0

Add tags for task_ids:summarization-* and task_categories:summarization*

closed

CONTRIBUTOR

yaml header at top of README.md file was edited to add task tags because I couldn't find the existing tags in the json separate Pull Request will modify dataset_infos.json to add these tags The Enron dataset (dataset id aeslc) is only tagged with: arxiv:1906.03497' languages:en pretty_name:AESLC Using the email subject_line field as a label or target variable it possible to create models for the following task_ids (in order of relevance): 'task_ids:summarization' 'task_ids:summarization-other-conversations-summarization' "task_ids:other-other-query-based-multi-document-summarization" 'task_ids:summarization-other-aspect-based-summarization' 'task_ids:summarization--other-headline-generation' The subject might also be used for the task_category "task_categories:summarization" E-mail chains might be used for the task category "task_categories:dialogue-system"

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PR_kwDODunzps45ykYX

Fix hashing for python 3.9

closed

MEMBER

In python 3.9, pickle hashes the `glob_ids` dictionary in addition to the `globs` of a function. Therefore the test at `tests/test_fingerprint.py::RecurseDumpTest::test_recurse_dump_for_function_with_shuffled_globals` is currently failing for python 3.9 To make hashing deterministic when the globals are not in the same order, we also need to make the order of `glob_ids` deterministic. Right now we don't have a CI to test python 3.9 but we should definitely have one. For this PR in particular I ran the tests locally using python 3.9 and they're passing now. Fix https://github.com/huggingface/datasets/issues/4506

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PR_kwDODunzps45x5mB

Add uppercased versions of image file extensions for automatic module inference

closed

CONTRIBUTOR

Adds the uppercased versions of the image file extensions to the supported extensions. Another approach would be to call `.lower()` on extensions while resolving data files, but uppercased extensions are not something we want to encourage out of the box IMO unless they are commonly used (as they are in the vision domain) Note that there is a slight discrepancy between the image file resolution and `imagefolder` as the latter calls `.lower()` on file extensions leading to some image file extensions being ignored by the resolution but not by the loader (e.g. `pNg`). Such extensions should also be discouraged, so I'm ignoring that case too. Fix #4514.

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I_kwDODunzps5L6CXO

Allow .JPEG as a file extension

closed

NONE

## Describe the bug When loading image data, HF datasets seems to recognize `.jpg` and `.jpeg` file extensions, but not e.g. .JPEG. As the naming convention .JPEG is used in important datasets such as imagenet, I would welcome if according extensions like .JPEG or .JPG would be allowed. ## Steps to reproduce the bug ```python # use bash to create 2 sham datasets with jpeg and JPEG ext !mkdir dataset_a !mkdir dataset_b !wget https://upload.wikimedia.org/wikipedia/commons/7/71/Dsc_%28179253513%29.jpeg -O example_img.jpeg !cp example_img.jpeg ./dataset_a/ !mv example_img.jpeg ./dataset_b/example_img.JPEG from datasets import load_dataset # working df1 = load_dataset("./dataset_a", ignore_verifications=True) #not working df2 = load_dataset("./dataset_b", ignore_verifications=True) # show print(df1, df2) ``` ## Expected results ``` DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 1 }) }) DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 1 }) }) ``` ## Actual results ``` FileNotFoundError: Unable to resolve any data file that matches '['**']' at /..PATH../dataset_b with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] ``` I know that it can be annoying to allow seemingly arbitrary numbers of file extensions. But I think this one would be really welcome.

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PR_kwDODunzps45xTqv

Update Google Cloud Storage documentation and add Azure Blob Storage example

closed

CONTRIBUTOR

While I was going through the 🤗 Datasets documentation of the Cloud storage filesystems at https://huggingface.co/docs/datasets/filesystems, I realized that the Google Cloud Storage documentation could be improved e.g. bullet point says "Load your dataset" when the actual call was to "Save your dataset", in-line code comment was mentioning "s3 bucket" instead of "gcs bucket", and some more in-line comments could be included. Also, I think that mixing Google Cloud Storage documentation with AWS S3's one was a little bit confusing, so I moved all those to the end of the document under an h2 tab named "Other filesystems", with an h3 for "Google Cloud Storage". Besides that, I was currently working with Azure Blob Storage and found out that the URL to [adlfs](https://github.com/fsspec/adlfs) was common for both filesystems Azure Blob Storage and Azure DataLake Storage, as well as the URL, which was updated even though the redirect was working fine, so I decided to group those under the same row in the column of supported filesystems. And took also the change to add a small documentation entry as for Google Cloud Storage but for Azure Blob Storage, as I assume that AWS S3, GCP Cloud Storage, and Azure Blob Storage, are the most used cloud storage providers. Let me know if you're OK with these changes, or whether you want me to roll back some of those! :hugs:

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PR_kwDODunzps45xEDN

Add links to vision tasks scripts in ADD_NEW_DATASET template

closed

CONTRIBUTOR

Add links to vision dataset scripts in the ADD_NEW_DATASET template.

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PR_kwDODunzps45w7RN

Support all negative values in ClassLabel

closed

MEMBER

We usually use -1 to represent a missing label, but we should also support any negative values (some users use -100 for example). This is a regression from `datasets` 2.3 Fix https://github.com/huggingface/datasets/issues/4508

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PR_kwDODunzps45wq6o

Add regression test for `ArrowWriter.write_batch` when batch is empty

closed

CONTRIBUTOR

As spotted by @cccntu in #4502, there's a logic bug in `ArrowWriter.write_batch` as the if-statement to handle the empty batches as detailed in the docstrings of the function ("Ignores the batch if it appears to be empty, preventing a potential schema update of unknown types."), the current if-statement is not handling properly `writer.write_batch({})` as an error is triggered. Also, if we add a regression test in `test_arrow_writer.py::test_write_batch` before applying the fix, the test will fail as when trying to write an empty batch as follows: ``` =================================================================================== short test summary info =================================================================================== FAILED tests/test_arrow_writer.py::test_write_batch[None-None] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[None-1] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[None-10] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields1-None] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields1-1] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields1-10] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields2-None] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields2-1] - ValueError: Schema and number of arrays unequal FAILED tests/test_arrow_writer.py::test_write_batch[fields2-10] - ValueError: Schema and number of arrays unequal ======================================================================== 9 failed, 73 deselected, 7 warnings in 0.81s ========================================================================= ``` So the batch is not ignored when empty, as `batch_examples={}` won't match the condition `if batch_examples: ...`.

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PR_kwDODunzps45wkDl

Support skipping Parquet to Arrow conversion when using Beam

closed

MEMBER

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I_kwDODunzps5L3CZJ

cast_storage method from datasets.features

closed

NONE

## Describe the bug A bug occurs when mapping a function to a dataset object. I ran the same code with the same data yesterday and it worked just fine. It works when i run locally on an old version of datasets. ## Steps to reproduce the bug Steps are: - load whatever datset - write a preprocessing function such as "tokenize_and_align_labels" written in https://huggingface.co/docs/transformers/tasks/token_classification - map the function on dataset and get "ValueError: Class label -100 less than -1" from cast_storage method from datasets.features # Sample code to reproduce the bug def tokenize_and_align_labels(examples): tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True, max_length=38,padding="max_length") labels = [] for i, label in enumerate(examples[f"labels"]): word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word. previous_word_idx = None label_ids = [] for word_idx in word_ids: # Set the special tokens to -100. if word_idx is None: label_ids.append(-100) elif word_idx != previous_word_idx: # Only label the first token of a given word. label_ids.append(label[word_idx]) else: label_ids.append(-100) previous_word_idx = word_idx labels.append(label_ids) tokenized_inputs["labels"] = labels return tokenized_inputs tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") dt = dataset.map(tokenize_and_align_labels, batched=True) ## Expected results New dataset objects should load and do on older versions. ## Actual results "ValueError: Class label -100 less than -1" from cast_storage method from datasets.features ## Environment info everything works fine on older installations of datasets/transformers Issue arises when installing datasets on google collab under python3.7 I can't manage to find the exact output you're requirering but version printed is datasets-2.3.2

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I_kwDODunzps5L2pP8

How to let `load_dataset` return a `Dataset` instead of `DatasetDict` in customized loading script

closed

NONE

If the dataset does not need splits, i.e., no training and validation split, more like a table. How can I let the `load_dataset` function return a `Dataset` object directly rather than return a `DatasetDict` object with only one key-value pair. Or I can paraphrase the question in the following way: how to skip `_split_generators` step in `DatasetBuilder` to let `as_dataset` gives a single `Dataset` rather than a list`[Dataset]`? Many thanks for any help.

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I_kwDODunzps5L2REf

Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results

closed

NONE

## Describe the bug Sometimes I get messages about not being able to hash a method: `Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform datasets.arrow_dataset.Dataset. _map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.` Whilst the function looks like this: ```python @staticmethod def _separate_speaker_id_from_dialogue(example: arrow_dataset.Example): speaker_id, dialogue = tuple(zip(*(example["dialogue"]))) example["speaker_id"] = speaker_id example["dialogue"] = dialogue return example ``` This is the first step in my preprocessing pipeline, but sometimes the message about failure to hash is not appearing on the first step, but then appears on a later step. This error is sometimes causing a failure to use cached data, instead of re-running all steps again. ## Steps to reproduce the bug ```python import copy import datasets from datasets import arrow_dataset def main(): dataset = datasets.load_dataset("blended_skill_talk") res = dataset.map(method) print(res) def method(example: arrow_dataset.Example): example['previous_utterance_copy'] = copy.deepcopy(example['previous_utterance']) return example if __name__ == '__main__': main() ``` Run with: ``` python -m reproduce_error ``` ## Expected results Dataset is mapped and cached correctly. ## Actual results The code outputs this at some point: `Parameter 'function'=<function method at 0x7faa83d2a160> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Ubuntu 20.04.3 - Python version: 3.9.12 - PyArrow version: 8.0.0 - Datasets version: 2.3.1

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PR_kwDODunzps45uH-o

Fix double dots in data files

closed

MEMBER

As mentioned in https://github.com/huggingface/transformers/pull/17715 `data_files` can't find a file if the path contains double dots `/../`. This has been introduced in https://github.com/huggingface/datasets/pull/4412, by trying to ignore hidden files and directories (i.e. if they start with a dot) I fixed this and added a test cc @sgugger @ydshieh

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PR_kwDODunzps45twLR

Refactor and add metadata to fever dataset

closed

MEMBER

Related to: #4452 and #3792.

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I_kwDODunzps5L1pOk

Logic bug in arrow_writer?

closed

CONTRIBUTOR

https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488 I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows: ``` - if batch_examples and len(next(iter(batch_examples.values()))) == 0: + if not batch_examples or len(next(iter(batch_examples.values()))) == 0: return ``` @lhoestq

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PR_kwDODunzps45th2M

Corrected broken links in doc

closed

CONTRIBUTOR

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PR_kwDODunzps45tdxk

Add `concatenate_datasets` for iterable datasets

closed

MEMBER

`concatenate_datasets` currently only supports lists of `datasets.Dataset`, not lists of `datasets.IterableDataset` like `interleave_datasets` Fix https://github.com/huggingface/datasets/issues/2564 I also moved `_interleave_map_style_datasets` from combine.py to arrow_dataset.py, since the logic depends a lot on the `Dataset` object internals And I moved `concatenate_datasets` from arrow_dataset.py to combine.py to have it with `interleave_datasets` (though it's also copied in arrow_dataset module for backward compatibility for now)

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PR_kwDODunzps45s6Jh

fix ETT m1/m2 test/val dataset

closed

CONTRIBUTOR

https://huggingface.co/datasets/ett/discussions/1

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I_kwDODunzps5L0rbF

WER and CER > 1

closed

NONE

## Describe the bug It seems that in some cases in which the `prediction` is longer than the `reference` we may have word/character error rate higher than 1 which is a bit odd. If it's a real bug I think I can solve it with a PR changing [this](https://github.com/huggingface/datasets/blob/master/metrics/wer/wer.py#L105) line to ```python return min(incorrect / total, 1.0) ``` ## Steps to reproduce the bug ```python from datasets import load_metric wer = load_metric("wer") wer_value = wer.compute(predictions=["Hi World vka"], references=["Hello"]) print(wer_value) ``` ## Expected results ``` 1.0 ``` ## Actual results ``` 3.0 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.0 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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PR_kwDODunzps45sYns

Re-add download_manager module in utils

closed

MEMBER

https://github.com/huggingface/datasets/pull/4384 moved `datasets.utils.download_manager` to `datasets.download.download_manager` This breaks `evaluate` which imports `DownloadMode` from `datasets.utils.download_manager` This PR re-adds `datasets.utils.download_manager` without circular imports. We could also show a message that says that accessing it is deprecated, but I think we can do this in a subsequent PR, and just focus on doing a patch release for now

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PR_kwDODunzps45sUnW

Replace `assertEqual` with `assertTupleEqual` in unit tests for verbosity

closed

CONTRIBUTOR

As detailed in #4419 and as suggested by @mariosasko, we could replace the `assertEqual` assertions with `assertTupleEqual` when the assertion is between Tuples, in order to make the tests more verbose.

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PR_kwDODunzps45sAgO

Fix patching module that doesn't exist

closed

MEMBER

Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true When trying to patch `scipy.io.loadmat`: ```python ModuleNotFoundError: No module named 'scipy' ``` Instead it shouldn't raise an error and do nothing Bug introduced by #4375 Fix https://github.com/huggingface/datasets/issues/4494

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