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I_kwDODunzps52waK6

User token is printed out!

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This line prints user token on command line! Is it safe? https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/load.py#L2091

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I_kwDODunzps52vQlx

Datasets does not load HuggingFace Repository properly

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### Describe the bug Dear Datasets team, We just have published a dataset on Huggingface: https://huggingface.co/ai4privacy However, when trying to read it using the Dataset library we get an error. As I understand jsonl files are compatible, could you please clarify how we can solve the issue? Please let me know and we would be more than happy to adapt the structure of the repository or meta data so it works easier: `from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k")` `Downloading readme: 100% 11.8k/11.8k [00:00<00:00, 512kB/s] Downloading data files: 100% 1/1 [00:11<00:00, 11.16s/it] Downloading data: 100% 64.3M/64.3M [00:02<00:00, 32.9MB/s] Downloading data: 100% 113M/113M [00:03<00:00, 35.0MB/s] Downloading data: 100% 97.7M/97.7M [00:02<00:00, 46.1MB/s] Downloading data: 100% 90.8M/90.8M [00:02<00:00, 44.9MB/s] Downloading data: 100% 7.63k/7.63k [00:00<00:00, 41.0kB/s] Downloading data: 100% 1.03k/1.03k [00:00<00:00, 9.44kB/s] Extracting data files: 100% 1/1 [00:00<00:00, 29.26it/s] Generating train split: 209261/0 [00:05<00:00, 41201.25 examples/s] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1939 ) -> 1940 writer.write_table(table) 1941 num_examples_progress_update += len(table) 8 frames [/usr/local/lib/python3.10/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_table(self, pa_table, writer_batch_size) 571 pa_table = pa_table.combine_chunks() --> 572 pa_table = table_cast(pa_table, self._schema) 573 if self.embed_local_files: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in table_cast(table, schema) 2327 if table.schema != schema: -> 2328 return cast_table_to_schema(table, schema) 2329 elif table.schema.metadata != schema.metadata: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_table_to_schema(table, schema) 2285 if sorted(table.column_names) != sorted(features): -> 2286 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") 2287 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] ValueError: Couldn't cast JOBTYPE: int64 PHONEIMEI: int64 ACCOUNTNAME: int64 VEHICLEVIN: int64 GENDER: int64 CURRENCYCODE: int64 CREDITCARDISSUER: int64 JOBTITLE: int64 SEX: int64 CURRENCYSYMBOL: int64 IP: int64 EYECOLOR: int64 MASKEDNUMBER: int64 SECONDARYADDRESS: int64 JOBAREA: int64 ACCOUNTNUMBER: int64 language: string BITCOINADDRESS: int64 MAC: int64 SSN: int64 EMAIL: int64 ETHEREUMADDRESS: int64 DOB: int64 VEHICLEVRM: int64 IPV6: int64 AMOUNT: int64 URL: int64 PHONENUMBER: int64 PIN: int64 TIME: int64 CREDITCARDNUMBER: int64 FIRSTNAME: int64 IBAN: int64 BIC: int64 COUNTY: int64 STATE: int64 LASTNAME: int64 ZIPCODE: int64 HEIGHT: int64 ORDINALDIRECTION: int64 MIDDLENAME: int64 STREET: int64 USERNAME: int64 CURRENCY: int64 PREFIX: int64 USERAGENT: int64 CURRENCYNAME: int64 LITECOINADDRESS: int64 CREDITCARDCVV: int64 AGE: int64 CITY: int64 PASSWORD: int64 BUILDINGNUMBER: int64 IPV4: int64 NEARBYGPSCOORDINATE: int64 DATE: int64 COMPANYNAME: int64 to {'masked_text': Value(dtype='string', id=None), 'unmasked_text': Value(dtype='string', id=None), 'privacy_mask': Value(dtype='string', id=None), 'span_labels': Value(dtype='string', id=None), 'bio_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'tokenised_text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} because column names don't match The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) [<ipython-input-2-f1c6811e9c83>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("ai4privacy/pii-masking-200k") [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2151 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, **prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1959 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset` Thank you and have a great day ahead ### Steps to reproduce the bug Open Google Colab Notebook: Run command: !pip3 install datasets Run code: from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k") ### Expected behavior Download the dataset successfully from HuggingFace to the notebook so that we can start working with it ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.19.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3

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I_kwDODunzps52uukh

using DownloadManager to download from local filesystem and disable_progress_bar, there will be an exception

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### Describe the bug i'm using datasets.download.download_manager.DownloadManager to download files like "file:///a/b/c.txt", and i disable_progress_bar() to disable bar. there will be an exception as follows: `AttributeError: 'function' object has no attribute 'close' Exception ignored in: <function TqdmCallback.__del__ at 0x7fa8683d84c0> Traceback (most recent call last): File "/home/protoss.gao/.local/lib/python3.9/site-packages/fsspec/callbacks.py", line 233, in __del__ self.tqdm.close()` i check your source code in datasets/utils/file_utils.py:348 you define TqdmCallback derive from fsspec.callbacks.TqdmCallback but in the newest fsspec code [https://github.com/fsspec/filesystem_spec/blob/master/fsspec/callbacks.py](url) , line 146, in this case, _DEFAULT_CALLBACK will take effect, but in line 234, it calls "close()" function which _DEFAULT_CALLBACK don't have such thing. so i think the class "TqdmCallback" in datasets/utils/file_utils.py may override "__del__" function or report this bug to fsspec. ### Steps to reproduce the bug as i said ### Expected behavior no exception ### Environment info datasets: 2.14.4 python: 3.9 platform: x86_64

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I_kwDODunzps52ugb8

IterableDataset lost but not keep columns when map function adding columns with names in remove_columns

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### Describe the bug IterableDataset lost but not keep columns when map function adding columns with names in remove_columns, Dataset not. May be related to the code below: https://github.com/huggingface/datasets/blob/06c3ffb8d068b6307b247164b10f7c7311cefed4/src/datasets/iterable_dataset.py#L750-L756 ### Steps to reproduce the bug ```python dataset: IterableDataset = load_dataset("Anthropic/hh-rlhf", streaming=True, split="train") column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected'] # map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx} dataset = dataset.map(map_fn, batched=True, remove_columns=column_names) next(iter(dataset)) # output # {'prompt': 'xxx, 'history': xxx} ``` ```python # when load_dataset with streaming=False, the column_names are kept: dataset: Dataset = load_dataset("Anthropic/hh-rlhf", streaming=False, split="train") column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected'] # map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx} dataset = dataset.map(map_fn, batched=True, remove_columns=column_names) next(iter(dataset)) # output # {'prompt': 'xxx, 'history': xxx, "chosen": xxx, "rejected": xxx} ``` ### Expected behavior IterableDataset keep columns when map function adding columns with names in remove_columns ### Environment info datasets==2.14.6

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I_kwDODunzps52tBff

Loading the dataset from private S3 bucket gives "TypeError: cannot pickle '_contextvars.Context' object"

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### Describe the bug I'm trying to read the parquet file from the private s3 bucket using the `load_dataset` function, but I receive `TypeError: cannot pickle '_contextvars.Context' object` error I'm working on a machine with `~/.aws/credentials` file. I can't give credentials and the path to a file in a private bucket for obvious reasons, but I'll try to give all possible outputs. ### Steps to reproduce the bug ```python import s3fs from datasets import load_dataset from aiobotocore.session import get_session DATA_PATH = "s3://bucket_name/path/validation.parquet" fs = s3fs.S3FileSystem(session=get_session()) ``` `fs.stat` returns the data, so we can say that fs is working and we have all permissions ```python fs.stat(DATA_PATH) # Returns: # {'ETag': '"123123a-19"', # 'LastModified': datetime.datetime(2023, 11, 1, 10, 16, 57, tzinfo=tzutc()), # 'size': 312237170, # 'name': 'bucket_name/path/validation.parquet', # 'type': 'file', # 'StorageClass': 'STANDARD', # 'VersionId': 'Abc.HtmsC9h.as', # 'ContentType': 'binary/octet-stream'} ``` ```python fs.storage_options # Returns: # {'session': <aiobotocore.session.AioSession at 0x7f9193fa53c0>} ``` ```python ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options) ``` <details> <summary>Returns such error (expandable)</summary> ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[88], line 1 ----> 1 ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/load.py:2153, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2150 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, 2156 verification_mode=verification_mode, 2157 try_from_hf_gcs=try_from_hf_gcs, 2158 num_proc=num_proc, 2159 storage_options=storage_options, 2160 ) 2162 # Build dataset for splits 2163 keep_in_memory = ( 2164 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2165 ) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 **prepare_split_kwargs, 958 **download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:1027, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 1025 split_dict = SplitDict(dataset_name=self.dataset_name) 1026 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) -> 1027 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 1029 # Checksums verification 1030 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums: File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py:34, in Parquet._split_generators(self, dl_manager) 32 if not self.config.data_files: 33 raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}") ---> 34 data_files = dl_manager.download_and_extract(self.config.data_files) 35 if isinstance(data_files, (str, list, tuple)): 36 files = data_files File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:565, in DownloadManager.download_and_extract(self, url_or_urls) 549 def download_and_extract(self, url_or_urls): 550 """Download and extract given `url_or_urls`. 551 552 Is roughly equivalent to: (...) 563 extracted_path(s): `str`, extracted paths of given URL(s). 564 """ --> 565 return self.extract(self.download(url_or_urls)) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:420, in DownloadManager.download(self, url_or_urls) 401 def download(self, url_or_urls): 402 """Download given URL(s). 403 404 By default, only one process is used for download. Pass customized `download_config.num_proc` to change this behavior. (...) 418 ``` 419 """ --> 420 download_config = self.download_config.copy() 421 download_config.extract_compressed_file = False 422 if download_config.download_desc is None: File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in DownloadConfig.copy(self) 93 def copy(self) -> "DownloadConfig": ---> 94 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in <dictcomp>(.0) 93 def copy(self) -> "DownloadConfig": ---> 94 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (2 times), deepcopy at line 146 (2 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: deepcopy at line 146 (1 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:206, in _deepcopy_list(x, memo, deepcopy) 204 append = y.append 205 for a in x: --> 206 append(deepcopy(a, memo)) 207 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:238, in _deepcopy_method(x, memo) 237 def _deepcopy_method(x, memo): # Copy instance methods --> 238 return type(x)(x.__func__, deepcopy(x.__self__, memo)) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (3 times), deepcopy at line 146 (3 times), deepcopy at line 172 (3 times), _reconstruct at line 271 (2 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (1 times), deepcopy at line 146 (1 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:265, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 263 if deep and args: 264 args = (deepcopy(arg, memo) for arg in args) --> 265 y = func(*args) 266 if deep: 267 memo[id(x)] = y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:264, in <genexpr>(.0) 262 deep = memo is not None 263 if deep and args: --> 264 args = (deepcopy(arg, memo) for arg in args) 265 y = func(*args) 266 if deep: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:161, in deepcopy(x, memo, _nil) 159 reductor = getattr(x, "__reduce_ex__", None) 160 if reductor is not None: --> 161 rv = reductor(4) 162 else: 163 reductor = getattr(x, "__reduce__", None) TypeError: cannot pickle '_contextvars.Context' object ``` </details> ### Expected behavior If I choose to load the file from the public bucket with `anon=True` passed - everything works, so I expected loading from the private bucket to work as well ### Environment info - `datasets` version: 2.14.6 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.10.13 - Huggingface_hub version: 0.19.1 - PyArrow version: 14.0.1 - Pandas version: 1.5.3 - s3fs version: 2023.10.0 - fsspec version: 2023.10.0 - aiobotocore version: 2.7.0

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I_kwDODunzps52pCW1

CI Build PR Documentation is broken: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions'

closed

MEMBER

Our CI Build PR Documentation is broken. See: https://github.com/huggingface/datasets/actions/runs/6799554060/job/18486828777?pr=6390 ``` ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' ```

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I_kwDODunzps52onbX

ConfigNamesError on a simple CSV file

closed

CONTRIBUTOR

See https://huggingface.co/datasets/Nguyendo1999/mmath/discussions/1 ``` Error code: ConfigNamesError Exception: TypeError Message: __init__() missing 1 required positional argument: 'dtype' Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1512, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1489, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1039, in get_module dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 468, in from_dataset_card_data dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 399, in _from_yaml_dict yaml_data["features"] = Features._from_yaml_list(yaml_data["features"]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1838, in _from_yaml_list return cls.from_dict(from_yaml_inner(yaml_data)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1690, in from_dict obj = generate_from_dict(dic) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1345, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1345, in <dictcomp> return {key: generate_from_dict(value) for key, value in obj.items()} File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1353, in generate_from_dict return class_type(**{k: v for k, v in obj.items() if k in field_names}) TypeError: __init__() missing 1 required positional argument: 'dtype' ``` This is the CSV file: https://huggingface.co/datasets/Nguyendo1999/mmath/blob/dbcdd7c2c6fc447f852ec136a7532292802bb46f/math_train.csv

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I_kwDODunzps52nn-B

Cannot import datasets on google colab (python 3.10.12)

open

NONE

### Describe the bug I'm trying A full colab demo notebook of zero-shot-distillation from https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation but i got this type of error when importing datasets on my google colab (python version is 3.10.12)  I found the same problem that have been solved in [#3326 ] but it seem still error on the google colab. I can't try on my local using jupyter notebook because of my laptop resource doesn't fulfill the requirements. Please can anyone help me solve this problem. Thank you 😅 ### Steps to reproduce the bug Error: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) [<ipython-input-8-b6e092f83978>](https://localhost:8080/#) in <cell line: 1>() ----> 1 from datasets import load_dataset 2 3 # Print all the available datasets 4 from huggingface_hub import list_datasets 5 print([dataset.id for dataset in list_datasets()]) 6 frames [/usr/lib/python3.10/functools.py](https://localhost:8080/#) in update_wrapper(wrapper, wrapped, assigned, updated) 59 # Issue #17482: set __wrapped__ last so we don't inadvertently copy it 60 # from the wrapped function when updating __dict__ ---> 61 wrapper.__wrapped__ = wrapped 62 # Return the wrapper so this can be used as a decorator via partial() 63 return wrapper AttributeError: readonly attribute ``` ### Expected behavior Run success on Google Colab (free) ### Environment info Windows 11 x64, Google Colab free

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I_kwDODunzps52iU6t

dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") not working

open

NONE

### Describe the bug ``` (datasets) mruserbox@guru-X99:/media/10TB_HHD/_LLM_DATASETS$ python dataset.py Downloading readme: 100%|███████████████████████████████████| 360/360 [00:00<00:00, 2.16MB/s] Downloading data: 100%|█████████████████████████████████| 65.1M/65.1M [00:19<00:00, 3.38MB/s] Downloading data: 100%|█████████████████████████████████| 6.35k/6.35k [00:00<00:00, 20.7kB/s] Downloading data: 100%|█████████████████████████████████| 7.29M/7.29M [00:01<00:00, 3.99MB/s] Downloading data files: 100%|██████████████████████████████████| 3/3 [00:21<00:00, 7.14s/it] Extracting data files: 100%|█████████████████████████████████| 3/3 [00:00<00:00, 1624.23it/s] Generating train split: 100%|█████████████| 314294/314294 [00:00<00:00, 668186.58 examples/s] Generating validation split: 120 examples [00:00, 100422.28 examples/s] Generating test split: 100%|████████████████| 34922/34922 [00:00<00:00, 754683.41 examples/s] Traceback (most recent call last): File "/media/10TB_HHD/_LLM_DATASETS/dataset.py", line 3, in <module> dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 1067, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/utils/info_utils.py", line 93, in verify_splits raise UnexpectedSplits(str(set(recorded_splits) - set(expected_splits))) datasets.utils.info_utils.UnexpectedSplits: {'validation'} ``` ### Steps to reproduce the bug Name: `dataset.py` Code: ``` from datasets import load_dataset dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") ``` ### Expected behavior Run without errors ### Environment info ``` name: datasets channels: - defaults dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - bzip2=1.0.8=h7b6447c_0 - ca-certificates=2023.08.22=h06a4308_0 - ld_impl_linux-64=2.38=h1181459_1 - libffi=3.4.4=h6a678d5_0 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - libuuid=1.41.5=h5eee18b_0 - ncurses=6.4=h6a678d5_0 - openssl=3.0.12=h7f8727e_0 - python=3.10.13=h955ad1f_0 - readline=8.2=h5eee18b_0 - setuptools=68.0.0=py310h06a4308_0 - sqlite=3.41.2=h5eee18b_0 - tk=8.6.12=h1ccaba5_0 - wheel=0.41.2=py310h06a4308_0 - xz=5.4.2=h5eee18b_0 - zlib=1.2.13=h5eee18b_0 - pip: - aiohttp==3.8.6 - aiosignal==1.3.1 - async-timeout==4.0.3 - attrs==23.1.0 - certifi==2023.7.22 - charset-normalizer==3.3.2 - click==8.1.7 - datasets==2.14.6 - dill==0.3.7 - filelock==3.13.1 - frozenlist==1.4.0 - fsspec==2023.10.0 - huggingface-hub==0.19.0 - idna==3.4 - multidict==6.0.4 - multiprocess==0.70.15 - numpy==1.26.1 - openai==0.27.8 - packaging==23.2 - pandas==2.1.3 - pip==23.3.1 - platformdirs==4.0.0 - pyarrow==14.0.1 - python-dateutil==2.8.2 - pytz==2023.3.post1 - pyyaml==6.0.1 - requests==2.31.0 - six==1.16.0 - tomli==2.0.1 - tqdm==4.66.1 - typer==0.9.0 - typing-extensions==4.8.0 - tzdata==2023.3 - urllib3==2.0.7 - xxhash==3.4.1 - yarl==1.9.2 prefix: /home/mruserbox/miniconda3/envs/datasets ```

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I_kwDODunzps52hzC1

Safely load datasets by disabling execution of dataset loading script

open

NONE

### Feature request Is there a way to disable execution of dataset loading script using `load_dataset`? This is a security vulnerability that could lead to arbitrary code execution. Any suggested workarounds are welcome as well. ### Motivation This is a security vulnerability that could lead to arbitrary code execution. ### Your contribution n/a

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I_kwDODunzps52hBh3

TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array

open

NONE

### Describe the bug Hi, I am preprocessing a large custom dataset with numpy arrays. I am running into this TypeError during writing in a dataset.map() function. I've tried decreasing writer batch size, but this error persists. This error does not occur for smaller datasets. Thank you! ### Steps to reproduce the bug Traceback (most recent call last): File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/multiprocess/pool.py", line 125, in worker result = (True, func(*args, **kwds)) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1354, in _write_generator_to_queue for i, result in enumerate(func(**kwargs)): File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3493, in _map_single writer.write_batch(batch) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 555, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 243, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 184, in __arrow_array__ out = numpy_to_pyarrow_listarray(data) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/features/features.py", line 1394, in numpy_to_pyarrow_listarray values = pa.ListArray.from_arrays(offsets, values) File "pyarrow/array.pxi", line 2004, in pyarrow.lib.ListArray.from_arrays TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array ### Expected behavior Type should not be a ChunkedArray ### Environment info datasets v2.14.5 arrow v1.2.3 pyarrow v12.0.1

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I_kwDODunzps52eLUI

Raise a different exception for inexisting dataset vs files without known extension

open

CONTRIBUTOR

See https://github.com/huggingface/datasets-server/issues/2082#issuecomment-1805716557 We have the same error for: - https://huggingface.co/datasets/severo/a_dataset_that_does_not_exist: a dataset that does not exist - https://huggingface.co/datasets/severo/test_files_without_extension: a dataset with files without a known extension ``` >>> import datasets >>> datasets.get_dataset_config_names('severo/a_dataset_that_does_not_exist') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/a_dataset_that_does_not_exist/a_dataset_that_does_not_exist.py or any data file in the same directory. Couldn't find 'severo/a_dataset_that_does_not_exist' on the Hugging Face Hub either: FileNotFoundError: Dataset 'severo/a_dataset_that_does_not_exist' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`. >>> datasets.get_dataset_config_names('severo/test_files_without_extension') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/test_files_without_extension/test_files_without_extension.py or any data file in the same directory. Couldn't find 'severo/test_files_without_extension' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in severo/test_files_without_extension. ``` To differentiate, we must parse the error message (only the end is different). We should have a different exception for these two errors.

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I_kwDODunzps52c-ot

Issue with pyarrow 14.0.1

closed

CONTRIBUTOR

See https://github.com/huggingface/datasets-server/pull/2089 for reference ``` from datasets import (Array2D, Dataset, Features) feature_type = Array2D(shape=(2, 2), dtype="float32") content = [[0.0, 0.0], [0.0, 0.0]] features = Features({"col": feature_type}) dataset = Dataset.from_dict({"col": [content]}, features=features) ``` generates ``` /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:648: FutureWarning: pyarrow.PyExtensionType is deprecated and will refuse deserialization by default. Instead, please derive from pyarrow.ExtensionType and implement your own serialization mechanism. pa.PyExtensionType.__init__(self, self.storage_dtype) /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:1661: RuntimeWarning: pickle-based deserialization of pyarrow.PyExtensionType subclasses is disabled by default; if you only ingest trusted data files, you may re-enable this using `pyarrow.PyExtensionType.set_auto_load(True)`. In the future, Python-defined extension subclasses should derive from pyarrow.ExtensionType (not pyarrow.PyExtensionType) and implement their own serialization mechanism. obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:1661: FutureWarning: pyarrow.PyExtensionType is deprecated and will refuse deserialization by default. Instead, please derive from pyarrow.ExtensionType and implement your own serialization mechanism. obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 924, in from_dict return cls(pa_table, info=info, split=split) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 693, in __init__ inferred_features = Features.from_arrow_schema(arrow_table.schema) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1661, in from_arrow_schema obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1661, in <dictcomp> obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1381, in generate_from_arrow_type return Value(dtype=_arrow_to_datasets_dtype(pa_type)) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 111, in _arrow_to_datasets_dtype raise ValueError(f"Arrow type {arrow_type} does not have a datasets dtype equivalent.") ValueError: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent. ```

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I_kwDODunzps52Z1ec

Add ability to set lock type

open

NONE

### Feature request Allow setting file lock type, maybe from an environment variable Currently, it only depends on whether fnctl is available: https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/utils/filelock.py#L463-L470C16 ### Motivation In my environment, flock isn't supported on a network attached drive ### Your contribution I'll be happy to submit a pr.

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I_kwDODunzps52XyXs

TorchFormatter images (H, W, C) instead of (C, H, W) format

open

NONE

### Describe the bug Using .set_format("torch") leads to images having shape (H, W, C), the same as in numpy. However, pytorch normally uses (C, H, W) format. Maybe I'm missing something but this makes the format a lot less useful as I then have to permute it anyways. If not using the format it is possible to directly use torchvision transforms but any non-transformed value will not be a tensor. Is there a reason for this choice? ### Steps to reproduce the bug ```python from datasets import Dataset, Features, Audio, Image images = ["path/to/image.png"] * 10 features = Features({"image": Image()}) ds = Dataset.from_dict({"image": images}, features=features) ds = ds.with_format("torch") ds[0]["image"].shape ``` ```python torch.Size([512, 512, 4]) ``` ### Expected behavior ```python from datasets import Dataset, Features, Audio, Image images = ["path/to/image.png"] * 10 features = Features({"image": Image()}) ds = Dataset.from_dict({"image": images}, features=features) ds = ds.with_format("torch") ds[0]["image"].shape ``` ```python torch.Size([4, 512, 512]) ``` ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-6.5.9-100.fc37.x86_64-x86_64-with-glibc2.31 - Python version: 3.11.6 - Huggingface_hub version: 0.18.0 - PyArrow version: 14.0.1 - Pandas version: 2.1.2

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I_kwDODunzps52T19r

Filter occasionally hangs

open

NONE

### Describe the bug A call to `.filter` occasionally hangs (after the filter is complete, according to tqdm) There is a trace produced ``` Exception ignored in: <function Dataset.__del__ at 0x7efb48130c10> Traceback (most recent call last): File "/usr/lib/python3/dist-packages/datasets/arrow_dataset.py", line 1366, in __del__ if hasattr(self, "_indices"): File "/usr/lib/python3/dist-packages/composer/core/engine.py", line 123, in sigterm_handler sys.exit(128 + signal) SystemExit: 143 ``` but I'm not sure if the trace is actually from `datasets`, or from surrounding code that is trying to clean up after datasets gets stuck. Unfortunately I can't reproduce this issue anywhere close to reliably. It happens infrequently when using `num_procs > 1`. Anecdotally I started seeing it when using larger datasets (~10M samples). ### Steps to reproduce the bug N/A see description ### Expected behavior map/filter calls always complete sucessfully ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.4.0-137-generic-x86_64-with-glibc2.31 - Python version: 3.10.13 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.2

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I_kwDODunzps52RxOJ

`push_to_hub` is not robust to hub closing connection

open

NONE

### Describe the bug Like to #6172, `push_to_hub` will crash if Hub resets the connection and raise the following error: ``` Pushing dataset shards to the dataset hub: 32%|███▏ | 54/171 [06:38<14:23, 7.38s/it] Traceback (most recent call last): File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 715, in urlopen httplib_response = self._make_request( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 467, in _make_request six.raise_from(e, None) File "<string>", line 3, in raise_from File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 462, in _make_request httplib_response = conn.getresponse() File "/usr/lib/python3.8/http/client.py", line 1348, in getresponse response.begin() File "/usr/lib/python3.8/http/client.py", line 316, in begin version, status, reason = self._read_status() File "/usr/lib/python3.8/http/client.py", line 285, in _read_status raise RemoteDisconnected("Remote end closed connection without" http.client.RemoteDisconnected: Remote end closed connection without response During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/adapters.py", line 486, in send resp = conn.urlopen( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 799, in urlopen retries = retries.increment( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/util/retry.py", line 550, in increment raise six.reraise(type(error), error, _stacktrace) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/packages/six.py", line 769, in reraise raise value.with_traceback(tb) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 715, in urlopen httplib_response = self._make_request( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 467, in _make_request six.raise_from(e, None) File "<string>", line 3, in raise_from File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 462, in _make_request httplib_response = conn.getresponse() File "/usr/lib/python3.8/http/client.py", line 1348, in getresponse response.begin() File "/usr/lib/python3.8/http/client.py", line 316, in begin version, status, reason = self._read_status() File "/usr/lib/python3.8/http/client.py", line 285, in _read_status raise RemoteDisconnected("Remote end closed connection without" urllib3.exceptions.ProtocolError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 383, in _wrapped_lfs_upload lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 223, in lfs_upload _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action["href"]) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 319, in _upload_multi_part else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 375, in _upload_parts_iteratively part_upload_res = http_backoff("PUT", part_upload_url, data=fileobj_slice) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_http.py", line 258, in http_backoff response = session.request(method=method, url=url, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/sessions.py", line 589, in request resp = self.send(prep, **send_kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/sessions.py", line 703, in send r = adapter.send(request, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_http.py", line 63, in send return super().send(request, *args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/adapters.py", line 501, in send raise ConnectionError(err, request=request) requests.exceptions.ConnectionError: (ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')), '(Request ID: 2bab8c06-b701-4266-aead-fe2e0dc0e3ed)') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "convert_to_hf.py", line 116, in <module> main() File "convert_to_hf.py", line 108, in main audio_dataset.push_to_hub( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py", line 1641, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 5308, in _push_parquet_shards_to_hub _retry( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 290, in _retry return func(*func_args, **func_kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, *args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 3221, in upload_file commit_info = self.create_commit( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, *args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 2695, in create_commit upload_lfs_files( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 393, in upload_lfs_files _wrapped_lfs_upload(filtered_actions[0]) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 385, in _wrapped_lfs_upload raise RuntimeError(f"Error while uploading '{operation.path_in_repo}' to the Hub.") from exc RuntimeError: Error while uploading 'batch_19/train-00054-of-00171-932beb4082c034bf.parquet' to the Hub. ``` The function should retry if the operations fails, or at least offer a way to recover after such a failure. Right now, calling the function again will start sending all the parquets files leading to duplicates in the repository, with no guarantee that it will actually be pushed. Previously, it would crash with an error 400 #4677 . ### Steps to reproduce the bug Any large dataset pushed the hub: ```py audio_dataset.push_to_hub( repo_id="org/dataset", ) ``` ### Expected behavior `push_to_hub` should have an option for max retries or resume. ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.15.0-1044-aws-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.16.4 - PyArrow version: 13.0.0 - Pandas version: 2.0.3

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I_kwDODunzps52OoGQ

Index 339 out of range for dataset of size 339 <-- save_to_file()

open

NONE

### Describe the bug When saving out some Audio() data. The data is audio recordings with associated 'sentences'. (They use the audio 'bytes' approach because they're clips within audio files). Code is below the traceback (I can't upload the voice audio/text (it's not even me)). ``` Traceback (most recent call last): File "/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py", line 156, in <module> create_dataset(args) File "/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py", line 138, in create_dataset hf_dataset.save_to_disk(args.outds, max_shard_size='50MB') File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 1531, in save_to_disk for kwargs in kwargs_per_job: File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 1508, in <genexpr> "shard": self.shard(num_shards=num_shards, index=shard_idx, contiguous=True), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 4609, in shard return self.select( ^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 556, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/fingerprint.py", line 511, in wrapper out = func(dataset, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 3797, in select return self._select_contiguous(start, length, new_fingerprint=new_fingerprint) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 556, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/fingerprint.py", line 511, in wrapper out = func(dataset, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 3857, in _select_contiguous _check_valid_indices_value(start, len(self)) File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 648, in _check_valid_indices_value raise IndexError(f"Index {index} out of range for dataset of size {size}.") IndexError: Index 339 out of range for dataset of size 339. ``` ### Steps to reproduce the bug (I had to set the default max batch size down due to a different bug... or maybe it's related: https://github.com/huggingface/datasets/issues/5717) ```python3 #!/usr/bin/env python3 import argparse import os from pathlib import Path import soundfile as sf import datasets datasets.config.DEFAULT_MAX_BATCH_SIZE=35 from datasets import Features, Array2D, Value, Dataset, Sequence, Audio import numpy as np import librosa import sys import soundfile as sf import io import logging logging.basicConfig(level=logging.DEBUG, filename='debug.log', filemode='w', format='%(name)s - %(levelname)s - %(message)s') # Define the arguments for the command-line interface def parse_args(): parser = argparse.ArgumentParser(description="Create a Huggingface dataset from labeled audio files.") parser.add_argument("--indir_labeled", action="append", help="Directory containing labeled audio files.", required=True) parser.add_argument("--outds", help="Path to save the dataset file.", required=True) parser.add_argument("--max_clips", type=int, help="Max count of audio samples to add to the dataset.", default=None) parser.add_argument("-r", "--sr", type=int, help="Sample rate for the audio files.", default=16000) parser.add_argument("--no-resample", action="store_true", help="Disable resampling of the audio files.") parser.add_argument("--max_clip_secs", type=float, help="Max length of audio clips in seconds.", default=3.0) parser.add_argument("-v", "--verbose", action='count', default=1, help="Increase verbosity") return parser.parse_args() # Convert the NumPy arrays to audio bytes in WAV format def numpy_to_bytes(audio_array, sampling_rate=16000): with io.BytesIO() as bytes_io: sf.write(bytes_io, audio_array, samplerate=sampling_rate, format='wav', subtype='FLOAT') # float32 return bytes_io.getvalue() # Function to find audio and label files in a directory def find_audio_label_pairs(indir_labeled): audio_label_pairs = [] for root, _, files in os.walk(indir_labeled): for file in files: if file.endswith(('.mp3', '.wav', '.aac', '.flac')): audio_path = Path(root) / file if args.verbose>1: print(f'File: {audio_path}') label_path = audio_path.with_suffix('.labels.txt') if label_path.exists(): if args.verbose>0: print(f' Pair: {audio_path}') audio_label_pairs.append((audio_path, label_path)) return audio_label_pairs def process_audio_label_pair(audio_path, label_path, sampling_rate, no_resample, max_clip_secs): # Read the label file with open(label_path, 'r') as label_file: labels = label_file.readlines() # Load the full audio file full_audio, current_sr = sf.read(audio_path) if not no_resample and current_sr != sampling_rate: # You can use librosa.resample here if librosa is available full_audio = librosa.resample(full_audio, orig_sr=current_sr, target_sr=sampling_rate) audio_segments = [] sentences = [] # Process each label for label in labels: start_secs, end_secs, label_text = label.strip().split('\t') start_sample = int(float(start_secs) * sampling_rate) end_sample = int(float(end_secs) * sampling_rate) # Extract segment and truncate or pad to max_clip_secs audio_segment = full_audio[start_sample:end_sample] max_samples = int(max_clip_secs * sampling_rate) if len(audio_segment) > max_samples: # Truncate audio_segment = audio_segment[:max_samples] elif len(audio_segment) < max_samples: # Pad padding = np.zeros(max_samples - len(audio_segment), dtype=audio_segment.dtype) audio_segment = np.concatenate((audio_segment, padding)) audio_segment = numpy_to_bytes(audio_segment) audio_data = { 'path': str(audio_path), 'bytes': audio_segment, } audio_segments.append(audio_data) sentences.append(label_text) return audio_segments, sentences # Main function to create the dataset def create_dataset(args): audio_label_pairs = [] for indir in args.indir_labeled: audio_label_pairs.extend(find_audio_label_pairs(indir)) # Initialize our dataset data dataset_data = { 'path': [], # This will be a list of strings 'audio': [], # This will be a list of dictionaries 'sentence': [], # This will be a list of strings } # Process each audio-label pair and add the data to the dataset for audio_path, label_path in audio_label_pairs[:args.max_clips]: audio_segments, sentences = process_audio_label_pair(audio_path, label_path, args.sr, args.no_resample, args.max_clip_secs) if audio_segments and sentences: for audio_data, sentence in zip(audio_segments, sentences): if args.verbose>1: print(f'Appending {audio_data["path"]}') dataset_data['path'].append(audio_data['path']) dataset_data['audio'].append({ 'path': audio_data['path'], 'bytes': audio_data['bytes'], }) dataset_data['sentence'].append(sentence) features = Features({ 'path': Value('string'), # Path is redundant in common voice set also 'audio': Audio(sampling_rate=16000), 'sentence': Value('string'), }) hf_dataset = Dataset.from_dict(dataset_data, features=features) for key in dataset_data: for i, item in enumerate(dataset_data[key]): if item is None or (isinstance(item, bytes) and len(item) == 0): logging.error(f"Invalid {key} at index {i}: {item}") import ipdb; ipdb.set_trace(context=16); pass hf_dataset.save_to_disk(args.outds, max_shard_size='50MB') # try: # hf_dataset.save_to_disk(args.outds) # except TypeError as e: # # If there's a TypeError, log the exception and the dataset data that might have caused it # logging.exception("An error occurred while saving the dataset.") # import ipdb; ipdb.set_trace(context=16); pass # for key in dataset_data: # logging.debug(f"{key} length: {len(dataset_data[key])}") # if key == 'audio': # # Log the first 100 bytes of the audio data to avoid huge log files # for i, audio in enumerate(dataset_data[key]): # logging.debug(f"Audio {i}: {audio['bytes'][:100]}") # raise # Run the script if __name__ == "__main__": args = parse_args() create_dataset(args) ``` ### Expected behavior It shouldn't fail. ### Environment info - `datasets` version: 2.14.7.dev0 - Platform: Linux-6.1.0-13-amd64-x86_64-with-glibc2.36 - Python version: 3.11.2 - `huggingface_hub` version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.2 - `fsspec` version: 2023.9.2

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I_kwDODunzps52Fbzd

How to create 3d medical imgae dataset?

open

NONE

### Feature request I am newer to huggingface, after i look up `datasets` docs, I can't find how to create the dataset contains 3d medical image (ends with '.mhd', '.dcm', '.nii') ### Motivation help us to upload 3d medical dataset to huggingface! ### Your contribution I'll submit a PR if I find a way to add this feature

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I_kwDODunzps52B9IU

How to load existing downloaded dataset ?

open

NONE

Hi @mariosasko @lhoestq @katielink Thanks for your contribution and hard work. ### Feature request First, I download a dataset as normal by: ``` from datasets import load_dataset dataset = load_dataset('username/data_name', cache_dir='data') ``` The dataset format in `data` directory will be: ``` -data |-data_name |-test-00000-of-00001-bf4c733542e35fcb.parquet |-train-00000-of-00001-2a1df75c6bce91ab.parquet ``` Then I use SCP to clone this dataset into another machine, and then try: ``` from datasets import load_dataset dataset = load_dataset('data/data_name') # load from local path ``` This leads to re-generating training and validation split for each time, and the disk quota will be duplicated occupation. How can I just load the dataset without generating and saving these splits again? ### Motivation I do not want to download the same dataset in two machines, scp is much faster and better than HuggingFace API. I hope we can directly load the downloaded datasets (.parquest) ### Your contribution Please refer to the feature

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I_kwDODunzps52Aop-

Formatting overhead

closed

NONE

### Describe the bug Hi! I very recently noticed that my training time is dominated by batch formatting. Using Lightning's profilers, I located the bottleneck within `datasets.formatting.formatting` and then narrowed it down with `line-profiler`. It turns out that almost all of the overhead is due to creating new instances of `self.python_arrow_extractor`. I admit I'm confused why that could be the case - as far as I can tell there's no complex `__init__` logic to execute.  ### Steps to reproduce the bug 1. Set up a dataset `ds` with potentially several (4+) columns (not sure if this is necessary, but it did at one point of the investigation make overhead worse) 2. Process it using a custom transform, `ds = ds.with_transform(transform_func)` 3. Decorate this function https://github.com/huggingface/datasets/blob/main/src/datasets/formatting/formatting.py#L512 with `@profile` from https://pypi.org/project/line-profiler/ 4. Profile with `$ kernprof -l script_to_profile.py` ### Expected behavior Batch formatting should have acceptable overhead. ### Environment info ``` datasets=2.14.6 pyarrow=14.0.0 ```

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I_kwDODunzps51-dky

Get an error when i try to concatenate the squad dataset with my own dataset

closed

NONE

### Describe the bug Hello, I'm new here and I need to concatenate the squad dataset with my own dataset i created. I find the following error when i try to do it: Traceback (most recent call last): Cell In[9], line 1 concatenated_dataset = concatenate_datasets([train_dataset, dataset1]) File ~\anaconda3\Lib\site-packages\datasets\combine.py:213 in concatenate_datasets return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis) File ~\anaconda3\Lib\site-packages\datasets\arrow_dataset.py:6002 in _concatenate_map_style_datasets _check_if_features_can_be_aligned([dset.features for dset in dsets]) File ~\anaconda3\Lib\site-packages\datasets\features\features.py:2122 in _check_if_features_can_be_aligned raise ValueError( ValueError: The features can't be aligned because the key answers of features {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} has unexpected type - Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) (expected either {'answer_start': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'text': Value(dtype='string', id=None)} or Value("null"). ### Steps to reproduce the bug ```python from huggingface_hub import notebook_login from datasets import load_dataset notebook_login("mymailadresse", "mypassword") squad = load_dataset("squad", split="train[:5000]") squad = squad.train_test_split(test_size=0.2) dataset1 = squad["train"] import json mybase = [ { "id": "1", "context": "She lives in Nantes", "question": "Where does she live?", "answers": { "text": "Nantes", "answer_start": [13], } } ] # Save the data to a JSON file json_file_path = r"C:\Users\mypath\thefile.json" with open(json_file_path, "w", encoding= "utf-8") as json_file: json.dump(mybase, json_file, indent=4) # Load the JSON file as a dataset custom_dataset = load_dataset("json", data_files=json_file_path) # Access the train split train_dataset = custom_dataset["train"] from datasets import concatenate_datasets # Concatenate the datasets concatenated_dataset = concatenate_datasets([train_dataset, dataset1]) ``` ### Expected behavior I would expect the two datasets to be concatenated without error. The len(dataset1) is equal to 4000 and the len(train_dataset) is equal to 1 so I would exepect concatenated_dataset to be created and having lenght 4001. ### Environment info Python 3.11.4 and using windows Thank you for your help

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I_kwDODunzps519u4N

Load the local dataset folder from other place

open

NONE

This is from https://github.com/huggingface/diffusers/issues/5573

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I_kwDODunzps516MZN

imagenet-1k downloads over and over

closed

NONE

### Describe the bug What could be causing this? ``` $ python3 Python 3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> load_dataset("imagenet-1k") Downloading builder script: 100%|██████████| 4.72k/4.72k [00:00<00:00, 7.51MB/s] Downloading readme: 100%|███████████████████| 85.4k/85.4k [00:00<00:00, 510kB/s] Downloading extra modules: 100%|████████████| 46.4k/46.4k [00:00<00:00, 300kB/s] Downloading data: 100%|████████████████████| 29.1G/29.1G [19:36<00:00, 24.8MB/s] Downloading data: 100%|████████████████████| 29.3G/29.3G [08:38<00:00, 56.5MB/s] Downloading data: 100%|████████████████████| 29.0G/29.0G [09:26<00:00, 51.2MB/s] Downloading data: 100%|████████████████████| 29.2G/29.2G [09:38<00:00, 50.6MB/s] Downloading data: 100%|███████████████████▉| 29.2G/29.2G [09:37<00:00, 44.1MB/s^Downloading data: 0%| | 106M/29.1G [00:05<23:49, 20.3MB/s] ``` ### Steps to reproduce the bug See above commands/code ### Expected behavior imagenet-1k is downloaded ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-6.2.0-34-generic-x86_64-with-glibc2.17 - Python version: 3.8.13 - Huggingface_hub version: 0.15.1 - PyArrow version: 14.0.0 - Pandas version: 1.5.2

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I_kwDODunzps513L3f

Add CheXpert dataset for vision

open

NONE

### Feature request ### Name **CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison** ### Paper https://arxiv.org/abs/1901.07031 ### Data https://stanfordaimi.azurewebsites.net/datasets/8cbd9ed4-2eb9-4565-affc-111cf4f7ebe2 ### Motivation CheXpert is one of the fundamental models in medical image classification and can serve as a viable pre-training dataset for radiology classification or low-scale ablation / exploratory studies. This could also serve as a good pre-training dataset for Kaggle competitions. ### Your contribution Would love to make a PR and pre-process / get this into 🤗

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I_kwDODunzps51p-XM

Support pyarrow 14.0.0

closed

MEMBER

Support pyarrow 14.0.0 by fixing the root cause of: - #6374 and revert: - #6375

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I_kwDODunzps51p74s

Caching problem when deleting a dataset

open

MEMBER

### Describe the bug Pushing a dataset with n + m features to a repo which was deleted, but contained n features, will fail. ### Steps to reproduce the bug 1. Create a dataset with n features per row 2. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)` 3. Go on the hub, delete the repo at `YOUR_PATH` 4. Update your local dataset to have n + m features per row 5. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)` will fail because of a mismatch in features number ### Expected behavior Step 5 should work or display a message to indicate the cache has not been cleared ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.15.0-88-generic-x86_64-with-glibc2.31 - Python version: 3.10.10 - Huggingface_hub version: 0.16.4 - PyArrow version: 11.0.0 - Pandas version: 2.0.0

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I_kwDODunzps51pqyU

CI is broken: TypeError: Couldn't cast array

closed

MEMBER

See: https://github.com/huggingface/datasets/actions/runs/6730567226/job/18293518039 ``` FAILED tests/test_table.py::test_cast_sliced_fixed_size_array_to_features - TypeError: Couldn't cast array of type fixed_size_list<item: int32>[3] to Sequence(feature=Value(dtype='int64', id=None), length=3, id=None) ```

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I_kwDODunzps51lqeb

`Dataset.from_generator` should not try to download from HF GCS

closed

CONTRIBUTOR

### Describe the bug When using [`Dataset.from_generator`](https://github.com/huggingface/datasets/blob/c9c1166e1cf81d38534020f9c167b326585339e5/src/datasets/arrow_dataset.py#L1072) with `streaming=False`, the internal logic will call [`download_and_prepare`](https://github.com/huggingface/datasets/blob/main/src/datasets/io/generator.py#L47) which will attempt to download from HF GCS which is redundant, because user has already provided the generator from which the data should be drawn. If someone attempts to call `Dataset.from_generator` from an environment that doesn't have external internet access (for example internal production machine) and doesn't set `HF_DATASETS_OFFLINE=1`, this will result in process being stuck at building connection. ### Steps to reproduce the bug ```python import datasets def gen(): for _ in range(100): yield {"text": "dummy text"} dataset = datasets.Dataset.from_generator(gen) ``` A minimum example executed on any environment that doesn't have access to HF GCS can result in the error ### Expected behavior `try_from_hf_gcs` should be set to False here https://github.com/huggingface/datasets/blob/c9c1166e1cf81d38534020f9c167b326585339e5/src/datasets/io/generator.py#L51 ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-3.10.0-1160.90.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.17.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3

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I_kwDODunzps51i3W1

TensorDataset format does not work with Trainer from transformers

open

NONE

### Describe the bug The model was built to do fine tunning on BERT model for relation extraction. trainer.train() returns an error message ```TypeError: vars() argument must have __dict__ attribute``` when it has `train_dataset` generated from `torch.utils.data.TensorDataset` However, in the document, the required data format is `torch.utils.data.TensorDataset`.  Transformers trainer is supposed to accept the train_dataset in the format of torch.utils.data.TensorDataset, but it returns error message *"TypeError: vars() argument must have __dict__ attribute"* ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-30-5df728c929a2> in <cell line: 1>() ----> 1 trainer.train() 2 trainer.evaluate(test_dataset) 9 frames /usr/local/lib/python3.10/dist-packages/transformers/data/data_collator.py in <listcomp>(.0) 107 108 if not isinstance(features[0], Mapping): --> 109 features = [vars(f) for f in features] 110 first = features[0] 111 batch = {} TypeError: vars() argument must have __dict__ attribute ``` ### Steps to reproduce the bug Create train_dataset using `torch.utils.data.TensorDataset`, for instance, ```train_dataset = torch.utils.data.TensorDataset(train_input_ids, train_attention_masks, train_labels)``` Feed this `train_dataset` to your trainer and run trainer.train ``` trainer = Trainer(model, training_args, train_dataset=train_dataset, eval_dataset=dev_dataset, compute_metrics=compute_metrics, ) ``` ### Expected behavior Trainer should start training ### Environment info It is running on Google Colab - `datasets` version: 2.14.6 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.17.3 - PyArrow version: 9.0.0 - Pandas version: 1.5.3

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I_kwDODunzps51hzC8

Multi process map did not load cache file correctly

open

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### Describe the bug When I was training model on Multiple GPUs by DDP, the dataset is tokenized multiple times after main process.   Code is modified from [run_clm.py](https://github.com/huggingface/transformers/blob/7d8ff3629b2725ec43ace99c1a6e87ac1978d433/examples/pytorch/language-modeling/run_clm.py#L484) ### Steps to reproduce the bug ``` block_size = data_args.block_size IGNORE_INDEX = -100 Ignore_Input = False def tokenize_function(examples): sources = [] targets = [] for instruction, inputs, output in zip(examples['instruction'], examples['input'], examples['output']): source = instruction + inputs target = f"{output}{tokenizer.eos_token}" sources.append(source) targets.append(target) tokenized_sources = tokenizer(sources, return_attention_mask=False) tokenized_targets = tokenizer(targets, return_attention_mask=False, add_special_tokens=False ) all_input_ids = [] all_labels = [] for s, t in zip(tokenized_sources['input_ids'], tokenized_targets['input_ids']): if len(s) > block_size and Ignore_Input == False: # print(s) continue input_ids = torch.LongTensor(s + t)[:block_size] if Ignore_Input: labels = torch.LongTensor([IGNORE_INDEX] * len(s) + t)[:block_size] else: labels = input_ids assert len(input_ids) == len(labels) all_input_ids.append(input_ids) all_labels.append(labels) results = { 'input_ids': all_input_ids, 'labels': all_labels, } return results with training_args.main_process_first(desc="dataset map tokenization ", local=False): # print('local_rank',training_args.local_rank) if not data_args.streaming: tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on dataset ", ) else: tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, desc="Running tokenizer on dataset " ) ``` ### Expected behavior This code should only tokenize the dataset in the main process, and the other processes load the dataset after waiting ### Environment info transformers == 4.34.1 datasets == 2.14.5

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I_kwDODunzps51bxJy

with_format() function returns bytes instead of PIL images even when image column is not part of "columns"

closed

NONE

### Describe the bug When using the with_format() function on a dataset containing images, even if the image column is not part of the columns provided in the function, its type will be changed to bytes. Here is a minimal reproduction of the bug: https://colab.research.google.com/drive/1hyaOspgyhB41oiR1-tXE3k_gJCdJUQCf?usp=sharing ### Steps to reproduce the bug 1. Load the image dataset 2. apply with_format(columns=["text"]) 3. Check the type of images in the "image" column before and after applying with_format ### Expected behavior The type should stay the same, but it does not ### Environment info datasets==2.14.6

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I_kwDODunzps51bfTo

Parquet size grows exponential for categorical data

closed

NONE

### Describe the bug It seems that when saving a data frame with a categorical column inside the size can grow exponentially. This seems to happen because when we save the categorical data to parquet, we are saving the data + all the categories existing in the original data. This happens even when the categories are not present in the original data. ### Steps to reproduce the bug To reproduce the bug, it is enough to run this script: ``` import pandas as pd import os if __name__ == "__main__": for n in [10, 1e2, 1e3, 1e4, 1e5]: for n_col in [1, 10, 100, 1000, 10000]: input = pd.DataFrame([{"{i}": f"{i}_cat" for col in range(n_col)} for i in range(int(n))]) input.iloc[0:100].to_parquet("a.parquet") for col in input.columns: input[col] = input[col].astype("category") input.iloc[0:100].to_parquet("b.parquet") a_size_mb = os.stat("a.parquet").st_size / (1024 * 1024) b_size_mb = os.stat("b.parquet").st_size / (1024 * 1024) print(f"{n} {n_col} {a_size_mb} {b_size_mb} {100*b_size_mb/a_size_mb:.2f}") ``` That produces this output: <img width="464" alt="Screenshot 2023-10-31 at 11 25 25" src="https://github.com/huggingface/datasets/assets/82567957/2b8a9284-7f9e-4c10-a006-0a27236ebd15"> ### Expected behavior In my opinion either: 1. The two file should have (almost) the same size 2. There should be warning telling the user that such difference in size is possible ### Environment info Python 3.8.18 pandas==2.0.3 numpy==1.24.4

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I_kwDODunzps51XqHq

ArrowNotImplementedError: Unsupported cast from string to list using function cast_list

closed

NONE

Hi, I am trying to load a local csv dataset(similar to explodinggradients_fiqa) using load_dataset. When I try to pass features, I am facing the mentioned issue. CSV Data sample(golden_dataset.csv): Question | Context | answer | groundtruth "what is abc?" | "abc is this and that" | "abc is this " | "abc is this and that" ``` import csv # built it based on https://huggingface.co/datasets/explodinggradients/fiqa/viewer/ragas_eval?row=0 mydict = [ {'question' : "what is abc?", 'contexts': ["abc is this and that"], 'answer': "abc is this " , 'groundtruth': ["abc is this and that"]}, {'question' : "what is abc?", 'contexts': ["abc is this and that"], 'answer': "abc is this " , 'groundtruth': ["abc is this and that"]}, {'question' : "what is abc?", 'contexts': ["abc is this and that"], 'answer': "abc is this " , 'groundtruth': ["abc is this and that"]} ] fields = ['question', 'contexts', 'answer', 'ground_truths'] with open('golden_dataset.csv', 'w', newline='\n') as file: writer = csv.DictWriter(file, fieldnames = fields) writer.writeheader() for row in mydict: writer.writerow(row) ``` Retrieved dataset: DatasetDict({ train: Dataset({ features: ['question', 'contexts', 'answer', 'ground_truths'], num_rows: 1 }) }) Code to reproduce issue: ``` from datasets import load_dataset, Features, Sequence, Value encode_features = Features( { "question": Value(dtype='string', id=0), "contexts": Sequence(feature=Value(dtype='string', id=1)), "answer": Value(dtype='string', id=2), "ground_truths": Sequence(feature=Value(dtype='string',id=3)), } ) eval_dataset = load_dataset('csv', data_files='/golden_dataset.csv', features = encode_features ) ``` Error trace: ``` --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1925, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1924 _time = time.time() -> 1925 for _, table in generator: 1926 if max_shard_size is not None and writer._num_bytes > max_shard_size: File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/packaged_modules/csv/csv.py:192, in Csv._generate_tables(self, files) 189 # Uncomment for debugging (will print the Arrow table size and elements) 190 # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") 191 # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) --> 192 yield (file_idx, batch_idx), self._cast_table(pa_table) 193 except ValueError as e: File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/packaged_modules/csv/csv.py:167, in Csv._cast_table(self, pa_table) 165 if all(not require_storage_cast(feature) for feature in self.config.features.values()): 166 # cheaper cast --> 167 pa_table = pa.Table.from_arrays([pa_table[field.name] for field in schema], schema=schema) 168 else: 169 # more expensive cast; allows str <-> int/float or str to Audio for example File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/table.pxi:3781, in pyarrow.lib.Table.from_arrays() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/table.pxi:1449, in pyarrow.lib._sanitize_arrays() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/array.pxi:354, in pyarrow.lib.asarray() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/table.pxi:551, in pyarrow.lib.ChunkedArray.cast() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/compute.py:400, in cast(arr, target_type, safe, options, memory_pool) 399 options = CastOptions.safe(target_type) --> 400 return call_function("cast", [arr], options, memory_pool) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/_compute.pyx:572, in pyarrow._compute.call_function() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/_compute.pyx:367, in pyarrow._compute.Function.call() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/error.pxi:121, in pyarrow.lib.check_status() ArrowNotImplementedError: Unsupported cast from string to list using function cast_list The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[57], line 1 ----> 1 eval_dataset = load_dataset('csv', data_files='/golden_dataset.csv', features = encode_features ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/load.py:2153, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2150 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, 2156 verification_mode=verification_mode, 2157 try_from_hf_gcs=try_from_hf_gcs, 2158 num_proc=num_proc, 2159 storage_options=storage_options, 2160 ) 2162 # Build dataset for splits 2163 keep_in_memory = ( 2164 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2165 ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 **prepare_split_kwargs, 958 **download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1049, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 1045 split_dict.add(split_generator.split_info) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, **prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( 1052 "Cannot find data file. " 1053 + (self.manual_download_instructions or "") 1054 + "\nOriginal error:\n" 1055 + str(e) 1056 ) from None File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1813, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): 1816 if done: 1817 result = content File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1958, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` Environment Info: datasets version: 2.14.5 Python version: 3.10.8 PyArrow version: 12.0.1 Pandas version: 2.0.3 I have also tried to load dataset first and then use cast_column, or save_to_disk and load_from_disk.

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I_kwDODunzps51WuWN

dataset.transform() hangs indefinitely while finetuning the stable diffusion XL

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NONE

### Describe the bug Multi-GPU fine-tuning the stable diffusion X by following https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/README_sdxl.md hangs indefinitely. ### Steps to reproduce the bug accelerate launch train_text_to_image_sdxl.py --pretrained_model_name_or_path=$MODEL_NAME --pretrained_vae_model_name_or_path=$VAE_NAME --dataset_name=$DATASET_NAME --enable_xformers_memory_efficient_attention --resolution=512 --center_crop --random_flip --proportion_empty_prompts=0.2 --train_batch_size=1 --gradient_accumulation_steps=4 --gradient_checkpointing --max_train_steps=10000 --use_8bit_adam --learning_rate=1e-06 --lr_scheduler="constant" --lr_warmup_steps=0 --mixed_precision="fp16" --report_to="wandb" --validation_prompt="a cute Sundar Pichai creature" --validation_epochs 5 --checkpointing_steps=5000 --output_dir="sdxl-pokemon-model" ### Expected behavior It should start the training as it does for the single GPU training. I opened the issue in diffusers **https://github.com/huggingface/diffusers/issues/5534 but it does seem to be an issue with the Pokemon dataset. I added some debug prints ``` print("==========HERE3=============") with accelerator.main_process_first(): print(accelerator.is_main_process) print("===========Here3.1===========") if args.max_train_samples is not None: dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) print("===========Here3.2===========") # Set the training transforms train_dataset = dataset["train"].with_transform(preprocess_train) print("==========HERE4=============") Corresponding Output Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 1 Local process index: 1 Device: cuda:1 Mixed precision type: fp16 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 2 Local process index: 2 Device: cuda:2 Mixed precision type: fp16 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 0 Local process index: 0 Device: cuda:0 Mixed precision type: fp16 You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. {‘variance_type’, ‘clip_sample_range’, ‘thresholding’, ‘dynamic_thresholding_ratio’} was not found in config. Values will be initialized to default values. {‘attention_type’, ‘reverse_transformer_layers_per_block’, ‘dropout’} was not found in config. Values will be initialized to default values. ==========HERE1============= ==========HERE1============= ==========HERE1============= ==========HERE2============= ==========HERE2============= ==========HERE2============= ==========HERE3============= True ===========Here3.1=========== ===========Here3.2=========== ==========HERE3============= ==========HERE3========= ``` ### Environment info _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_kmp_llvm conda-forge absl-py 2.0.0 pypi_0 pypi accelerate 0.24.0 pypi_0 pypi aiohttp 3.8.6 pypi_0 pypi aiosignal 1.3.1 pypi_0 pypi appdirs 1.4.4 pyh9f0ad1d_0 conda-forge async-timeout 4.0.3 pypi_0 pypi attrs 23.1.0 pypi_0 pypi bitsandbytes 0.41.1 pypi_0 pypi blas 1.0 mkl blessings 1.7 py39h06a4308_1002 brotli-python 1.0.9 py39h6a678d5_7 bzip2 1.0.8 h7b6447c_0 ca-certificates 2023.08.22 h06a4308_0 cachetools 5.3.2 pypi_0 pypi certifi 2023.7.22 py39h06a4308_0 cffi 1.15.1 py39h5eee18b_3 charset-normalizer 2.0.4 pyhd3eb1b0_0 click 8.1.7 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lcms2 2.12 h3be6417_0 ld_impl_linux-64 2.38 h1181459_1 lerc 3.0 h295c915_0 libcublas 11.10.3.66 0 nvidia libcufft 10.7.2.124 h4fbf590_0 nvidia libcufile 1.8.0.34 0 nvidia libcurand 10.3.4.52 0 nvidia libcusolver 11.4.0.1 0 nvidia libcusparse 11.7.4.91 0 nvidia libdeflate 1.17 h5eee18b_1 libffi 3.4.4 h6a678d5_0 libgcc-ng 13.2.0 h807b86a_2 conda-forge libgfortran-ng 13.2.0 h69a702a_2 conda-forge libgfortran5 13.2.0 ha4646dd_2 conda-forge libiconv 1.16 h7f8727e_2 libidn2 2.3.4 h5eee18b_0 libnpp 11.7.4.75 0 nvidia libnvjpeg 11.8.0.2 0 nvidia libpng 1.6.39 h5eee18b_0 libprotobuf 3.20.3 he621ea3_0 libstdcxx-ng 13.2.0 h7e041cc_2 conda-forge libtasn1 4.19.0 h5eee18b_0 libtiff 4.5.1 h6a678d5_0 libunistring 0.9.10 h27cfd23_0 libwebp 1.3.2 h11a3e52_0 libwebp-base 1.3.2 h5eee18b_0 llvm-openmp 14.0.6 h9e868ea_0 lz4-c 1.9.4 h6a678d5_0 markdown 3.5 pypi_0 pypi markupsafe 2.1.3 pypi_0 pypi mkl 2023.1.0 h213fc3f_46343 mkl-service 2.4.0 py39h5eee18b_1 mkl_fft 1.3.8 py39h5eee18b_0 mkl_random 1.2.4 py39hdb19cb5_0 multidict 6.0.4 pypi_0 pypi multiprocess 0.70.15 pypi_0 pypi ncurses 6.4 h6a678d5_0 nettle 3.7.3 hbbd107a_1 numpy 1.26.0 py39h5f9d8c6_0 numpy-base 1.26.0 py39hb5e798b_0 nvidia-ml 7.352.0 pyhd3eb1b0_0 oauthlib 3.2.2 pypi_0 pypi openh264 2.1.1 h4ff587b_0 openjpeg 2.4.0 h3ad879b_0 openssl 3.0.11 h7f8727e_2 packaging 23.2 pypi_0 pypi pandas 2.1.1 pypi_0 pypi pathtools 0.1.2 py_1 conda-forge pillow 10.0.1 py39ha6cbd5a_0 pip 23.3 py39h06a4308_0 protobuf 4.23.4 pypi_0 pypi psutil 5.9.6 pypi_0 pypi pyarrow 13.0.0 pypi_0 pypi pyasn1 0.5.0 pypi_0 pypi pyasn1-modules 0.3.0 pypi_0 pypi pycparser 2.21 pyhd3eb1b0_0 pyopenssl 23.2.0 py39h06a4308_0 pysocks 1.7.1 py39h06a4308_0 python 3.9.18 h955ad1f_0 python-dateutil 2.8.2 pypi_0 pypi python_abi 3.9 2_cp39 conda-forge pytorch 1.13.1 py3.9_cuda11.7_cudnn8.5.0_0 pytorch pytorch-cuda 11.7 h778d358_5 pytorch pytorch-mutex 1.0 cuda pytorch pytz 2023.3.post1 pypi_0 pypi pyyaml 6.0.1 pypi_0 pypi readline 8.2 h5eee18b_0 regex 2023.10.3 pypi_0 pypi requests 2.31.0 py39h06a4308_0 requests-oauthlib 1.3.1 pypi_0 pypi rsa 4.9 pypi_0 pypi safetensors 0.4.0 pypi_0 pypi scipy 1.11.3 py39h5f9d8c6_0 sentry-sdk 1.32.0 pyhd8ed1ab_0 conda-forge setproctitle 1.1.10 py39h3811e60_1004 conda-forge setuptools 68.0.0 py39h06a4308_0 six 1.16.0 pyh6c4a22f_0 conda-forge smmap 5.0.0 pyhd8ed1ab_0 conda-forge sqlite 3.41.2 h5eee18b_0 tbb 2021.8.0 hdb19cb5_0 tensorboard 2.15.0 pypi_0 pypi tensorboard-data-server 0.7.2 pypi_0 pypi tk 8.6.12 h1ccaba5_0 tokenizers 0.14.1 pypi_0 pypi torchaudio 0.13.1 py39_cu117 pytorch torchtriton 2.1.0 py39 pytorch torchvision 0.14.1 py39_cu117 pytorch tqdm 4.66.1 pypi_0 pypi transformers 4.34.1 pypi_0 pypi typing_extensions 4.7.1 py39h06a4308_0 tzdata 2023.3 pypi_0 pypi urllib3 1.26.18 py39h06a4308_0 wandb 0.15.12 pyhd8ed1ab_0 conda-forge wcwidth 0.2.8 pypi_0 pypi werkzeug 3.0.1 pypi_0 pypi wheel 0.41.2 py39h06a4308_0 xformers 0.0.22.post7 py39_cu11.7.1_pyt1.13.1 xformers xxhash 3.4.1 pypi_0 pypi xz 5.4.2 h5eee18b_0 yaml 0.2.5 h7f98852_2 conda-forge yarl 1.9.2 pypi_0 pypi zipp 3.17.0 pypi_0 pypi zlib 1.2.13 h5eee18b_0 zstd 1.5.5 hc292b87_0

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I_kwDODunzps51Kcn2

Add support for `Sequence(Audio/Image)` feature in `push_to_hub`

open

CONTRIBUTOR

### Feature request Allow for `Sequence` of `Image` (or `Audio`) to be embedded inside the shards. ### Motivation Currently, thanks to #3685, when `embed_external_files` is set to True (which is the default) in `push_to_hub`, features of type `Image` and `Audio` are embedded inside the arrow/parquet shards, instead of only storing paths to the files. I've noticed that this behavior does not extend to `Sequence` of `Image`, when working with a [dataset of timelapse images](https://huggingface.co/datasets/1aurent/Human-Embryo-Timelapse). ### Your contribution I'll submit a PR if I find a way to add this feature

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I_kwDODunzps51JUwX

Stuck in "Resolving data files..."

open

NONE

### Describe the bug I have an image dataset with 300k images, the size of image is 768 * 768. When I run `dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')` in second time, it takes 50 minutes to finish "Resolving data files" part, what's going on in this part? From my understand, after Arrow files been created in the first run, the second run should not take time longer than one or two minutes. ### Steps to reproduce the bug # Run following code two times dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train') ### Expected behavior Fast dataset building ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.35 - Python version: 3.10.11 - Huggingface_hub version: 0.17.3 - PyArrow version: 10.0.1 - Pandas version: 1.5.3

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I_kwDODunzps51H75D

Mounting datasets cache fails due to absolute paths.

open

NONE

### Describe the bug Creating a datasets cache and mounting this into, for example, a docker container, renders the data unreadable due to absolute paths written into the cache. ### Steps to reproduce the bug 1. Create a datasets cache by downloading some data 2. Mount the dataset folder into a docker container or remote system. 3. (Edit) Set `HF_HOME` or `HF_DATASET_CACHE` to point to the mounted cache. 4. Attempt to access the data from within the docker container. 5. An error is thrown saying no file exists at \<absolute path to original cache location\> ### Expected behavior The data is loaded without error ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.4.0-162-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.16.4 - PyArrow version: 13.0.0 - Pandas version: 2.0.3

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I_kwDODunzps51Gj2r

Allow passing a multiprocessing context to functions that support `num_proc`

open

CONTRIBUTOR

### Feature request Allow specifying [a multiprocessing context](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods) to functions that support `num_proc` or use multiprocessing pools. For example, the following could be done: ```python dataset = dataset.map(_func, num_proc=2, mp_context=multiprocess.get_context("spawn")) ``` Or at least the multiprocessing start method ("fork", "spawn", "fork_server" or `None`): ```python dataset = dataset.map(_func, num_proc=2, mp_start_method="spawn") ``` Another option could be passing the `pool` as an argument. ### Motivation By default, `multiprocess` (the `multiprocessing`-fork library that this repo uses) uses the "fork" start method for multiprocessing pools (for the default context). It could be changed by using `set_start_method`. However, this conditions the multiprocessing start method from all processing in a Python program that uses the default context, because [you can't call that function more than once](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods:~:text=set_start_method()%20should%20not%20be%20used%20more%20than%20once%20in%20the%20program.). My proposal is to allow using a different multiprocessing context, not to condition the whole Python program. One reason to change the start method is that "fork" (the default) makes child processes likely deadlock if thread pools were created before (and also this is not supported by POSIX). For example, this happens when using PyTorch because OpenMP threads are used for CPU intra-op parallelism, which is enabled by default (e.g., for context see [`torch.set_num_threads`](https://pytorch.org/docs/stable/generated/torch.set_num_threads.html)). This can also be fixed by setting `torch.set_num_threads(1)` (or similarly by other methods) but this conditions the whole Python program as it can only be set once to guarantee its behavior (similarly to). There are noticeable performance differences when setting this number to 1 even when using GPU(s). Using, e.g., a "spawn" start method would solve this issue. For more context, see: * https://discuss.huggingface.co/t/dataset-map-stuck-with-torch-set-num-threads-set-to-2-or-larger/37984 * https://discuss.huggingface.co/t/using-num-proc-1-in-dataset-map-hangs/44310 ### Your contribution I'd be happy to review a PR that makes such a change. And if you really don't have the bandwidth for it, I'd consider creating one.

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I_kwDODunzps51CGC8

`IterableDataset.from_spark` does not support multiple workers in pytorch `Dataloader`

open

NONE

### Describe the bug Looks like `IterableDataset.from_spark` does not support multiple workers in pytorch `Dataloader` if I'm not missing anything. Also, returns not consistent error messages, which probably depend on the nondeterministic order of worker executions Some exampes I've encountered: ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__ yield from self.generate_examples_fn() File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn df_with_partition_id = df.select("*", pyspark.sql.functions.spark_partition_id().alias("part_id")) File "/databricks/spark/python/pyspark/instrumentation_utils.py", line 54, in wrapper logger.log_failure( File "/databricks/spark/python/pyspark/databricks/usage_logger.py", line 70, in log_failure self.logger.recordFunctionCallFailureEvent( File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1322, in __call__ return_value = get_return_value( File "/databricks/spark/python/pyspark/errors/exceptions/captured.py", line 188, in deco return f(*a, **kw) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/protocol.py", line 342, in get_return_value return OUTPUT_CONVERTER[type](answer[2:], gateway_client) KeyError: 'c' ``` ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__ yield from self.generate_examples_fn() File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn df_with_partition_id = df.select("*", pyspark.sql.functions.spark_partition_id().alias("part_id")) File "/databricks/spark/python/pyspark/sql/utils.py", line 162, in wrapped return f(*args, **kwargs) File "/databricks/spark/python/pyspark/sql/functions.py", line 4893, in spark_partition_id return _invoke_function("spark_partition_id") File "/databricks/spark/python/pyspark/sql/functions.py", line 98, in _invoke_function return Column(jf(*args)) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1322, in __call__ return_value = get_return_value( File "/databricks/spark/python/pyspark/errors/exceptions/captured.py", line 188, in deco return f(*a, **kw) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/protocol.py", line 342, in get_return_value return OUTPUT_CONVERTER[type](answer[2:], gateway_client) KeyError: 'm' ``` ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__ yield from self.generate_examples_fn() File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn df_with_partition_id = df.select("*", pyspark.sql.functions.spark_partition_id().alias("part_id")) File "/databricks/spark/python/pyspark/sql/utils.py", line 162, in wrapped return f(*args, **kwargs) File "/databricks/spark/python/pyspark/sql/functions.py", line 4893, in spark_partition_id return _invoke_function("spark_partition_id") File "/databricks/spark/python/pyspark/sql/functions.py", line 97, in _invoke_function jf = _get_jvm_function(name, SparkContext._active_spark_context) File "/databricks/spark/python/pyspark/sql/functions.py", line 88, in _get_jvm_function return getattr(sc._jvm.functions, name) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1725, in __getattr__ raise Py4JError(message) py4j.protocol.Py4JError: functions does not exist in the JVM ``` ### Steps to reproduce the bug ```python import pandas as pd import numpy as np batch_size = 16 pdf = pd.DataFrame({ key: np.random.rand(16*100) for key in ['feature', 'target'] }) test_df = spark.createDataFrame(pdf) from datasets import IterableDataset from torch.utils.data import DataLoader ids = IterableDataset.from_spark(test_df) for batch in DataLoader(ids, batch_size=16, num_workers=4): for k, b in batch.items(): print(k, b.shape, sep='\t') print('\n') ``` ### Expected behavior For `num_workers` equal to 0 or 1 works fine as expected: ``` feature torch.Size([16]) target torch.Size([16]) feature torch.Size([16]) target torch.Size([16]) .... ``` Expected to support workers >1. ### Environment info Databricks 13.3 LTS ML runtime - Spark 3.4.1 pyspark==3.4.1 py4j==0.10.9.7 datasets==2.13.1 and also tested with datasets==2.14.6

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I_kwDODunzps50-5uy

load_dataset save_to_disk load_from_disk error

closed

NONE

### Describe the bug datasets version： 2.10.1 I `load_dataset `and `save_to_disk` sucessfully on windows10( **and I `load_from_disk(/LLM/data/wiki)` succcesfully on windows10**), and I copy the dataset `/LLM/data/wiki` into a ubuntu system, but when I `load_from_disk(/LLM/data/wiki)` on ubuntu, something weird happens: ``` load_from_disk('/LLM/data/wiki') File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/load.py", line 1874, in load_from_disk return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/dataset_dict.py", line 1309, in load_from_disk dataset_dict[k] = Dataset.load_from_disk( File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1543, in load_from_disk fs_token_paths = fsspec.get_fs_token_paths(dataset_path, storage_options=storage_options) File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 610, in get_fs_token_paths chain = _un_chain(urlpath0, storage_options or {}) File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 325, in _un_chain cls = get_filesystem_class(protocol) File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/registry.py", line 232, in get_filesystem_class raise ValueError(f"Protocol not known: {protocol}") ValueError: Protocol not known: /LLM/data/wiki ``` It seems that something went wrong on the arrow file? How can I solve this , since currently I can not save_to_disk on ubuntu system ### Steps to reproduce the bug datasets version： 2.10.1 ### Expected behavior datasets version： 2.10.1 ### Environment info datasets version： 2.10.1

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I_kwDODunzps509kL5

Error loading wikitext data raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")

closed

NONE

I was trying to load the wiki dataset, but i got this error traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train') File "/home/aelkordy/.conda/envs/prune_llm/lib/python3.9/site-packages/datasets/load.py", line 1804, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) File "/home/aelkordy/.conda/envs/prune_llm/lib/python3.9/site-packages/datasets/builder.py", line 1108, in as_dataset raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.") NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.

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I_kwDODunzps5077-T

Different objects are returned from calls that should be returning the same kind of object.

open

NONE

### Describe the bug 1. dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=training_args.cache_dir, split='train[:1%]') 2. dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=training_args.cache_dir) The only difference I would expect these calls to have is the size of the dataset. But, while 2. returns a dictionary with "train" key in it, 1. returns a dataset WITHOUT any initial "train" keyword. Both calls are to be used within exactly the same context. They should return identically structured datasets of different size. ### Steps to reproduce the bug See above. ### Expected behavior Expect both calls to return the same structured Dataset structure but with different number of elements, i.e. call 1. should have 1% of the data of the call 2.0 ### Environment info Ubuntu 20.04 gcc 9.x.x. It is really irrelevant.

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I_kwDODunzps506SIZ

Can't load ds = load_dataset("imdb")

closed

NONE

### Describe the bug I did `from datasets import load_dataset, load_metric` and then `ds = load_dataset("imdb")` and it gave me the error: ExpectedMoreDownloadedFiles: {'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'} I tried doing `ds = load_dataset("imdb",download_mode="force_redownload")` as well as reinstalling dataset. I still face this problem. ### Steps to reproduce the bug 1. from datasets import load_dataset, load_metric 2. ds = load_dataset("imdb") ### Expected behavior It should load and give me this when I run `ds` DatasetDict({ train: Dataset({ features: ['text', 'label'], num_rows: 25000 }) test: Dataset({ features: ['text', 'label'], num_rows: 25000 }) unsupervised: Dataset({ features: ['text', 'label'], num_rows: 50000 }) }) ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.4.0-164-generic-x86_64-with-glibc2.17 - Python version: 3.8.18 - Huggingface_hub version: 0.16.2 - PyArrow version: 13.0.0 - Pandas version: 2.0.2

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I_kwDODunzps505pUY

Parquet stream-conversion fails to embed images/audio files from gated repos

open

CONTRIBUTOR

it seems to be an issue with datasets not passing the token to embed_table_storage when generating a dataset See https://github.com/huggingface/datasets-server/issues/2010

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I_kwDODunzps50xAqj

Incorrect example code in 'Create a dataset' docs

closed

NONE

### Describe the bug On [this](https://huggingface.co/docs/datasets/create_dataset) page, the example code for loading in images and audio is incorrect. Currently, examples are: ``` python from datasets import ImageFolder dataset = load_dataset("imagefolder", data_dir="/path/to/pokemon") ``` and ``` python from datasets import AudioFolder dataset = load_dataset("audiofolder", data_dir="/path/to/folder") ``` I'm pretty sure the imports are wrong and should be: ``` python from datasets import load_dataset dataset = load_dataset("audiofolder", data_dir="/path/to/folder") ``` I am happy to update this if this is right but just wanted to check before making any changes. ### Steps to reproduce the bug Go to https://huggingface.co/docs/datasets/create_dataset ### Expected behavior N/A ### Environment info N/A

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I_kwDODunzps50sEBe

support squad structure datasets using a YAML parameter

open

NONE

### Feature request Since the squad structure is widely used, I think it could be beneficial to support it using a YAML parameter. could you implement automatic data loading of squad-like data using squad JSON format, to read it from JSON files and view it in the correct squad structure. The dataset structure should be like this: https://huggingface.co/datasets/squad Columns:id,title,context,question,answers ### Motivation Dataset repo requires arbitrary Python code execution ### Your contribution The dataset structure should be like this: https://huggingface.co/datasets/squad Columns:id,title,context,question,answers train and dev sets in squad structure JSON files

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I_kwDODunzps50oRe3

Support fsspec 2023.10.0

open

MEMBER

Once root issue is fixed, remove temporary pin of fsspec < 2023.10.0 introduced by: - #6331 Related to issue: - #6330 As @ZachNagengast suggested, the issue might be related to: - https://github.com/fsspec/filesystem_spec/pull/1381

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I_kwDODunzps50lwEu

Latest fsspec==2023.10.0 issue with streaming datasets

closed

CONTRIBUTOR

### Describe the bug Loading a streaming dataset with this version of fsspec fails with the following error: `NotImplementedError: Loading a streaming dataset cached in a LocalFileSystem is not supported yet.` I suspect the issue is with this PR https://github.com/fsspec/filesystem_spec/pull/1381 ### Steps to reproduce the bug 1. Upgrade fsspec to version `2023.10.0` 2. Attempt to load a streaming dataset e.g. `load_dataset("laion/gpt4v-emotion-dataset", split="train", streaming=True)` 3. Observe the following exception: ``` File "/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/datasets/load.py", line 2146, in load_dataset return builder_instance.as_streaming_dataset(split=split) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/datasets/builder.py", line 1318, in as_streaming_dataset raise NotImplementedError( NotImplementedError: Loading a streaming dataset cached in a LocalFileSystem is not supported yet. ``` ### Expected behavior Should stream the dataset as normal. ### Environment info datasets@main fsspec==2023.10.0

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I_kwDODunzps50lAZk

شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی

closed

NONE

شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی

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I_kwDODunzps50lAXw

شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی

closed

NONE

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I_kwDODunzps50jh2j

FileNotFoundError when trying to load the downloaded dataset with `load_dataset(..., streaming=True)`

closed

NONE

### Describe the bug Hi, I'm trying to load the dataset `togethercomputer/RedPajama-Data-1T-Sample` with `load_dataset` in streaming mode, i.e., `streaming=True`, but `FileNotFoundError` occurs. ### Steps to reproduce the bug I've downloaded the dataset and save it to the cache dir in advance. My hope is loading the files in offline environment and without taking too much hours to prepross the entire data before running into the training process. So I try the following code to load the files streamingly ```py dataset = load_dataset('togethercomputer/RedPajama-Data-1T-Sample', streaming=True) print(next(iter(dataset['train']))) ``` Sadly, it raises the following: ``` FileNotFoundError: [Errno 2] No such file or directory: 'CURRENT_CODE_PATH/arxiv_sample.jsonl' ``` I've noticed that the dataset can be properly found in the begining ``` Using the latest cached version of the module from /root/.cache/huggingface/modules/datasets_modules/datasets/togethercomputer--RedPajama-Data-1T-Sample/6ea3bc8ec2e84ec6d2df1930942e9028ace8c5b9d9143823cf911c50bbd92039 (last modified on Sat Oct 21 20:12:57 2023) since it couldn't be found locally at togethercomputer/RedPajama-Data-1T-Sample., or remotely on the Hugging Face Hub. ``` But it seems that the paths couldn't be properly parsed when loading iteratively. How should I fix this error. I've tried specifying `data_files` or `data_dir` as `.../arxiv_sample.jsonl` but none of them works. Thanks. ### Expected behavior Properly load the dataset. ### Environment info `datasets==2.14.5`

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I_kwDODunzps50iN2f

Conversion to Arrow fails due to wrong type heuristic

closed

NONE

### Describe the bug I have a list of dictionaries with valid/JSON-serializable values. One key is the denominator for a paragraph. In 99.9% of cases its a number, but there are some occurences of '1a', '2b' and so on. If trying to convert this list to a dataset with `Dataset.from_list()`, I always get `ArrowInvalid: Could not convert '1' with type str: tried to convert to int64`, presumably because pyarrow tries to convert the keys to integers. Is there any way to circumvent this and fix dtypes? I didn't find anything in the documentation. ### Steps to reproduce the bug * create a list of dicts with one key being a string of an integer for the first few thousand occurences and try to convert to dataset. ### Expected behavior There shouldn't be an error (e.g. some flag to turn off automatic str to numeric conversion). ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.0-84-generic-x86_64-with-glibc2.35 - Python version: 3.9.18 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.1

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I_kwDODunzps50e21c

Loading dataset from large GCS bucket very slow since 2.14

open

NONE

### Describe the bug Since updating to >2.14 we have very slow access to our parquet files on GCS when loading a dataset (>30 min vs 3s). Our GCS bucket has many objects and resolving globs is very slow. I could track down the problem to this change: https://github.com/huggingface/datasets/blame/bade7af74437347a760830466eb74f7a8ce0d799/src/datasets/data_files.py#L348 The underlying implementation with gcsfs is really slow. Could you go back to the old way if we are simply giving the parquet files and no glob pattern? Thank you. ### Steps to reproduce the bug Load a dataset from a GCS bucket that has many files. ### Expected behavior Used to be fast (3s) in 2.13 ### Environment info datasets==2.14.5

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I_kwDODunzps50YpdM

Dataset slice splits can't load training and validation at the same time

open

NONE

### Describe the bug According to the [documentation](https://huggingface.co/docs/datasets/v2.14.5/loading#slice-splits) is should be possible to run the following command: `train_test_ds = datasets.load_dataset("bookcorpus", split="train+test")` to load the train and test sets from the dataset. However executing the equivalent code: `speech_commands_v1 = load_dataset("superb", "ks", split="train+test")` only yields the following output: > Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 54175 > }) Where loading the dataset without the split argument yields: > DatasetDict({ > train: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 51094 > }) > validation: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 6798 > }) > test: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 3081 > }) > }) Thus, the API seems to be broken in this regard. This is a bit annoying since I want to be able to use the split argument with `split="train[:10%]+test[:10%]"` to have smaller dataset to work with when validating my model is working correctly. ### Steps to reproduce the bug `speech_commands_v1 = load_dataset("superb", "ks", split="train+test")` ### Expected behavior > DatasetDict({ > train: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 51094 > }) > test: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 3081 > }) > }) ### Environment info ``` import datasets print(datasets.__version__) ``` > 2.14.5 ``` import sys print(sys.version) ``` > 3.9.17 (main, Jul 5 2023, 20:41:20) > [GCC 11.2.0]

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I_kwDODunzps50WrVV

Datasets.map is severely broken

open

NONE

### Describe the bug Regardless of how many cores I used, I have 16 or 32 threads, map slows down to a crawl at around 80% done, lingers maybe until 97% extremely slowly and NEVER finishes the job. It just hangs. After watching this for 27 hours I control-C out of it. Until the end one process appears to be doing something, but it never ends. I saw some comments about fast tokenizers using Rust and all and tried different variations. NOTHING works. ### Steps to reproduce the bug Running it without breaking the dataset into parts results in the same behavior. The loop was an attempt to see if this was a RAM issue. for idx in range(100): dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=cache_dir, split=f'train[{idx}%:{idx+1}%]') dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=False, num_proc=1, remove_columns=["text", "meta"]) dataset.save_to_disk(training_args.cache_dir + f"/training_data_{idx}") ### Expected behavior I expect map to run at more or less the same speed it starts with and FINISH its processing. ### Environment info Python 3.8, same with 3.10 makes no difference. Ubuntu 20.04,

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I_kwDODunzps50WKHk

sentiment140 dataset unavailable

closed

NONE

### Describe the bug loading the dataset using load_dataset("sentiment140") returns the following error ConnectionError: Couldn't reach http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip (error 403) ### Steps to reproduce the bug Run the following code (version should not matter). ``` from datasets import load_dataset data = load_dataset("sentiment140") ``` ### Expected behavior The dataset should be loaded just like any other. The main issue is that it is no longer hosted by stanford. It is still available from a [Google Drive Link](https://docs.google.com/file/d/0B04GJPshIjmPRnZManQwWEdTZjg/edit). ### Environment info - `datasets` version: 2.14.5 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.10.8 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.1

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I_kwDODunzps50Vh3z

Hub datasets with CSV metadata raise ArrowInvalid: JSON parse error: Invalid value. in row 0

closed

MEMBER

When trying to load a Hub dataset that contains a CSV metadata file, it raises an `ArrowInvalid` error: ``` E pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0 pyarrow/error.pxi:100: ArrowInvalid ``` See: https://huggingface.co/datasets/lukarape/public_small_papers/discussions/1

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