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I_kwDODunzps48Qwjd

ArrowInvalid: Can only convert 1-dimensional array values

closed

NONE

## Describe the bug For the ViT and LayoutLMv2 demo notebooks in my [Transformers-Tutorials repo](https://github.com/NielsRogge/Transformers-Tutorials), people reported an ArrowInvalid issue after applying the following function to a Dataset: ``` def preprocess_data(examples): images = [Image.open(path).convert("RGB") for path in examples['image_path']] words = examples['words'] boxes = examples['bboxes'] word_labels = examples['ner_tags'] encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels, padding="max_length", truncation=True) return encoded_inputs ``` ``` Full trace: --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) <ipython-input-8-0fc3efc6f0c2> in <module>() 27 28 train_dataset = datasets['train'].map(preprocess_data, batched=True, remove_columns=datasets['train'].column_names, ---> 29 features=features) 30 test_dataset = datasets['test'].map(preprocess_data, batched=True, remove_columns=datasets['test'].column_names, 31 features=features) 13 frames /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 1701 new_fingerprint=new_fingerprint, 1702 disable_tqdm=disable_tqdm, -> 1703 desc=desc, 1704 ) 1705 else: /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 183 } 184 # apply actual function --> 185 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 186 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 187 # re-apply format to the output /usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 396 # Call actual function 397 --> 398 out = func(self, *args, **kwargs) 399 400 # Update fingerprint of in-place transforms + update in-place history of transforms /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2063 writer.write_table(batch) 2064 else: -> 2065 writer.write_batch(batch) 2066 if update_data and writer is not None: 2067 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 409 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) 410 typed_sequence_examples[col] = typed_sequence --> 411 pa_table = pa.Table.from_pydict(typed_sequence_examples) 412 self.write_table(pa_table, writer_batch_size) 413 /usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 106 storage = numpy_to_pyarrow_listarray(self.data, type=type.value_type) 107 else: --> 108 storage = pa.array(self.data, type.storage_dtype) 109 out = pa.ExtensionArray.from_storage(type, storage) 110 elif isinstance(self.data, np.ndarray): /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array() /usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Can only convert 1-dimensional array values ``` It can be fixed by adding the following line: ```diff def preprocess_data(examples): images = [Image.open(path).convert("RGB") for path in examples['image_path']] words = examples['words'] boxes = examples['bboxes'] word_labels = examples['ner_tags'] encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels, padding="max_length", truncation=True) + encoded_inputs["image"] = np.array(encoded_inputs["image"]) return encoded_inputs ``` However, would be great if this can be fixed within Datasets itself.

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https://github.com/huggingface/datasets/pull/2986

PR_kwDODunzps4scSHR

Refac module factory + avoid etag requests for hub datasets

closed

MEMBER

## Refactor the module factory When trying to extend the `data_files` logic to avoid doing unnecessary ETag requests, I noticed that the module preparation mechanism needed a refactor: - the function was 600 lines long - it was not readable - it contained many different cases that made it complex to maintain - it was hard to properly test it - it was hard to extend without breaking anything The module preparation mechanism is in charge of taking the name of a dataset or a metric given by the user (ex: "squad", "accuracy", "lhoestq/test", "path/to/my/script.py", "path/to/my/data/directory", "json", "csv") and return a module (possibly downloaded from the Hub) that contains the dataset builder or the metric class to use. ### Implementation details I decided to separate all these use cases into different dataset/metric module factories. First, the metric module factories: - **CanonicalMetricModuleFactory**: "accuracy", "rouge", ... - **LocalMetricModuleFactory**: "path/to/my/metric.py" Then, the dataset module factories: - **CanonicalDatasetModuleFactory**: "squad", "glue", ... - **CommunityDatasetModuleFactoryWithScript**: "lhoestq/test" - **CommunityDatasetModuleFactoryWithoutScript**: "lhoestq/demo1" - **PackagedDatasetModuleFactory**: "json", "csv", ... - **LocalDatasetModuleFactoryWithScript**: "path/to/my/script.py" - **LocalDatasetModuleFactoryWithoutScript**: "path/to/my/data/directory" And finally, additional factories when users have no internet: - **CachedDatasetModuleFactory** - **CachedMetricModuleFactory** ### Breaking changes One thing is that I still don't know at what extent we want to keep backward compatibility for `prepare_module`. For now I just kept it (except I removed two parameters) just in case, but it's not used anywhere anymore. ## Avoid etag requests for hub datasets To do this I added a class `DataFilesDict` that can be hashed to define the cache directory of the dataset. It contains the usual data files formatted as `{"train": ["train.txt"]}` for example. But each list of file is a `DataFilesList` that also has a `origin_metadata` attribute that contains metadata about the origin of each file: - for URLs: it stores the ETags of the files - for local files: it stores the last modification data - for files from a Hugging Face repository on the Hub: it stores the pattern (`*`, `*.csv`, "train.txt", etc.) and the commit sha of the repository (so there're no ETag requests !) This way if any file changes, the hash of the `DataFilesDict` changes too ! You can instantiate a `DataFilesDict` by using patterns for local/remote files or files in a HF repository: - for local/remote files: `DataFilesDict.from_local_or_remote(patterns)` - for files in a HF repository: `DataFilesDict.from_hf_repo(patterns, dataset_info)` Fix #2859 ## TODO Fix the latest test: - [x] fix the call to dataset_info in offline mode (related to https://github.com/huggingface/huggingface_hub/issues/372) Add some more tests: - [x] test all the factories - [x] test the new data files logic Other: - [x] docstrings - [x] comments

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https://github.com/huggingface/datasets/pull/2985

PR_kwDODunzps4sbbbo

add new dataset kan_hope

closed

CONTRIBUTOR

## Adding a Dataset - **Name:** *KanHope* - **Description:** *A code-mixed English-Kannada dataset for Hope speech detection* - **Task:** *Binary Text Classification* - **Paper:** *https://arxiv.org/abs/2108.04616* - **Data:** *https://github.com/adeepH/kan_hope/tree/main/dataset* - **Motivation:** *The dataset is amongst the very few resources available for code-mixed low-resourced Dravidian languages of India*

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I_kwDODunzps48OsRm

Exceeded maximum rows when reading large files

closed

NONE

## Describe the bug A clear and concise description of what the bug is. When using `load_dataset` with json files, if the files are too large, there will be "Exceeded maximum rows" error. ## Steps to reproduce the bug ```python dataset = load_dataset('json', data_files=data_files) # data files have 3M rows in a single file ``` ## Expected results No error ## Actual results ``` ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 134 with open(file, encoding="utf-8") as f: --> 135 dataset = json.load(f) 136 except json.JSONDecodeError: ~/anaconda3/envs/python/lib/python3.9/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 292 """ --> 293 return loads(fp.read(), 294 cls=cls, object_hook=object_hook, ~/anaconda3/envs/python/lib/python3.9/json/__init__.py in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 345 parse_constant is None and object_pairs_hook is None and not kw): --> 346 return _default_decoder.decode(s) 347 if cls is None: ~/anaconda3/envs/python/lib/python3.9/json/decoder.py in decode(self, s, _w) 339 if end != len(s): --> 340 raise JSONDecodeError("Extra data", s, end) 341 return obj JSONDecodeError: Extra data: line 2 column 1 (char 20321) During handling of the above exception, another exception occurred: ArrowInvalid Traceback (most recent call last) <ipython-input-20-ab3718a6482f> in <module> ----> 1 dataset = load_dataset('json', data_files=data_files) ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs) 841 842 # Download and prepare data --> 843 builder_instance.download_and_prepare( 844 download_config=download_config, 845 download_mode=download_mode, ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 606 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 607 if not downloaded_from_gcs: --> 608 self._download_and_prepare( 609 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 610 ) ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 684 try: 685 # Prepare split will record examples associated to the split --> 686 self._prepare_split(split_generator, **prepare_split_kwargs) 687 except OSError as e: 688 raise OSError( ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _prepare_split(self, split_generator) 1153 generator = self._generate_tables(**split_generator.gen_kwargs) 1154 with ArrowWriter(features=self.info.features, path=fpath) as writer: -> 1155 for key, table in utils.tqdm( 1156 generator, unit=" tables", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET) 1157 ): ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 135 dataset = json.load(f) 136 except json.JSONDecodeError: --> 137 raise e 138 raise ValueError( 139 f"Not able to read records in the JSON file at {file}. " ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 114 while True: 115 try: --> 116 pa_table = paj.read_json( 117 BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) 118 ) ~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json() ~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Exceeded maximum rows ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Linux - Python version: 3.9 - PyArrow version: 4.0.1

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PR_kwDODunzps4saw_v

added SwissJudgmentPrediction dataset

closed

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PR_kwDODunzps4saVLh

Add the Math Aptitude Test of Heuristics dataset.

closed

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PR_kwDODunzps4sZ4ke

add wit dataset

open

CONTRIBUTOR

Resolves #2902 based on conversation there - would also close #2810. Open to suggestions/help 😀 CC @hassiahk @lhoestq @yjernite

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I_kwDODunzps48MXJK

OpenSLR 25: ASR data for Amharic, Swahili and Wolof

open

CONTRIBUTOR

## Adding a Dataset - **Name:** *SLR25* - **Description:** *Subset 25 from OpenSLR. Other subsets have been added to https://huggingface.co/datasets/openslr, 25 covers Amharic, Swahili and Wolof data* - **Paper:** *https://www.openslr.org/25/ has citations for each of the three subsubsets. * - **Data:** *Currently the three links to the .tar.bz2 files can be found a thttps://www.openslr.org/25/* - **Motivation:** *Increase ASR data for underrepresented African languages. Also, other subsets of OpenSLR speech recognition have been uploaded, so this would be easy.* https://github.com/huggingface/datasets/blob/master/datasets/openslr/openslr.py already has been created for various other OpenSLR subsets, this should be relatively straightforward to do.

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I_kwDODunzps48Lctj

ValueError when computing f1 metric with average None

closed

NONE

## Describe the bug When I try to compute the f1 score for each class in a multiclass classification problem, I get a ValueError. The same happens with recall and precision. I traced the error to the `.item()` in these scripts, which is probably there for the other averages. E.g. from f1.py: ```python return { "f1": f1_score( references, predictions, labels=labels, pos_label=pos_label, average=average, sample_weight=sample_weight, ).item(), } ``` Since the result is an array with more than one item, the `.item()` throws the error. I didn't submit a PR because this might be needed for the other averages, I'm not very familiar with the library ## Steps to reproduce the bug ```python from datasets import load_metric metric = load_metric("f1") metric.add_batch(predictions=[2,34,1,34,1,2,3], references=[23,52,1,3,523,5,8]) metric.compute(average=None) ``` ## Expected results `array([0.66666667, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ])` ## Actual results ValueError: can only convert an array of size 1 to a Python scalar ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.9.5 - PyArrow version: 5.0.0

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I_kwDODunzps48LBML

Run CI tests against non-production server

open

MEMBER

Currently, the CI test suite performs requests to the HF production server. As discussed with @elishowk, we should refactor our tests to use the HF staging server instead, like `huggingface_hub` and `transformers`.

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I_kwDODunzps48KeWE

Impossible to load compressed csv

closed

CONTRIBUTOR

## Describe the bug It is not possible to load from a compressed csv anymore. ## Steps to reproduce the bug ```python load_dataset('csv', data_files=['/path/to/csv.bz2']) ``` ## Problem and possible solution This used to work, but the commit that broke it is [this one](https://github.com/huggingface/datasets/commit/ad489d4597381fc2d12c77841642cbeaecf7a2e0#diff-6f60f8d0552b75be8b3bfd09994480fd60dcd4e7eb08d02f721218c3acdd2782). `pandas` usually gets the compression information from the filename itself (which was previously directly passed). Now, since it gets a file descriptor, it might be good to auto-infer the compression or let the user pass the `compression` kwarg to `load_dataset` (or maybe warn the user if the file ends with a commonly known compression scheme?). ## Environment info - `datasets` version: 1.10.0 (and over) - Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.17 - Python version: 3.8.10 - PyArrow version: 3.0.0

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I_kwDODunzps48Hr7R

Can't load dataset

closed

NONE

I'm trying to load a wikitext dataset ``` from datasets import load_dataset raw_datasets = load_dataset("wikitext") ``` ValueError: Config name is missing. Please pick one among the available configs: ['wikitext-103-raw-v1', 'wikitext-2-raw-v1', 'wikitext-103-v1', 'wikitext-2-v1'] Example of usage: `load_dataset('wikitext', 'wikitext-103-raw-v1')`. If I try ``` from datasets import load_dataset raw_datasets = load_dataset("wikitext-2-v1") ``` FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.12.1/datasets/wikitext-2-v1/wikitext-2-v1.py #### Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic (colab) - Python version: 3.7.12 - PyArrow version: 3.0.0

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https://github.com/huggingface/datasets/pull/2975

PR_kwDODunzps4sVAOt

ignore dummy folder and dataset_infos.json

closed

CONTRIBUTOR

Fixes #2877 Added the `dataset_infos.json` to the ignored files list and also added check to ignore files which have parent directory as `dummy`. Let me know if it is correct. Thanks :)

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PR_kwDODunzps4sUZCX

Actually disable dummy labels by default

closed

CONTRIBUTOR

So I might have just changed the docstring instead of the actual default argument value and not realized. @lhoestq I'm sorry >.>

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https://github.com/huggingface/datasets/pull/2973

PR_kwDODunzps4sTRvk

Fix JSON metadata of masakhaner dataset

closed

MEMBER

Fix #2971.

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I_kwDODunzps48EfDK

OSError: Not enough disk space.

closed

CONTRIBUTOR

## Describe the bug I'm trying to download `natural_questions` dataset from the Internet, and I've specified the cache_dir which locates in a mounted disk and has enough disk space. However, even though the space is enough, the disk space checking function still reports the space of root `/` disk having no enough space. The file system structure is like below. The root `/` has `115G` disk space available, and the `sda1` is mounted to `/mnt`, which has `1.2T` disk space available: ``` / /mnt/sda1/path/to/args.dataset_cache_dir ``` ## Steps to reproduce the bug ```python dataset_config = DownloadConfig( cache_dir=os.path.abspath(args.dataset_cache_dir), resume_download=True, ) dataset = load_dataset("natural_questions", download_config=dataset_config) ``` ## Expected results Can download the dataset without an error. ## Actual results The following error raised: ``` OSError: Not enough disk space. Needed: 134.92 GiB (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Ubuntu 18.04 - Python version: 3.8.10 - PyArrow version:

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I_kwDODunzps48EDqK

masakhaner dataset load problem

closed

CONTRIBUTOR

## Describe the bug Masakhaner dataset is not loading ## Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset("masakhaner",'amh') ``` ## Expected results Expected the return of a dataset ## Actual results ``` NonMatchingSplitsSizesError Traceback (most recent call last) <ipython-input-3-a6abc1161d4c> in <module>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("masakhaner",'amh') 3 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_splits(expected_splits, recorded_splits) 72 ] 73 if len(bad_splits) > 0: ---> 74 raise NonMatchingSplitsSizesError(str(bad_splits)) 75 logger.info("All the splits matched successfully.") 76 NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=639927, num_examples=1751, dataset_name='masakhaner'), 'recorded': SplitInfo(name='train', num_bytes=639911, num_examples=1750, dataset_name='masakhaner')}, {'expected': SplitInfo(name='validation', num_bytes=92768, num_examples=251, dataset_name='masakhaner'), 'recorded': SplitInfo(name='validation', num_bytes=92753, num_examples=250, dataset_name='masakhaner')}, {'expected': SplitInfo(name='test', num_bytes=184286, num_examples=501, dataset_name='masakhaner'), 'recorded': SplitInfo(name='test', num_bytes=184271, num_examples=500, dataset_name='masakhaner')}] ``` ## Environment info Google Colab

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I_kwDODunzps48Cso5

Magnet’s

closed

NONE

## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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I_kwDODunzps48COzL

medical-dialog error

closed

NONE

## Describe the bug A clear and concise description of what the bug is. When I attempt to download the huggingface datatset medical_dialog it errors out midway through ## Steps to reproduce the bug ```python raw_datasets = load_dataset("medical_dialog", "en", split="train", download_mode="force_redownload", data_dir="./Medical-Dialogue-Dataset-English") ``` ## Expected results A clear and concise description of the expected results. No error ## Actual results ``` 3 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_splits(expected_splits, recorded_splits) 72 ] 73 if len(bad_splits) > 0: ---> 74 raise NonMatchingSplitsSizesError(str(bad_splits)) 75 logger.info("All the splits matched successfully.") 76 NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='medical_dialog'), 'recorded': SplitInfo(name='train', num_bytes=295097913, num_examples=229674, dataset_name='medical_dialog')}] ``` Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.21.1 - Platform: colab - Python version: colab 3.7 - PyArrow version: N/A

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I_kwDODunzps48CMwQ

`DatasetDict` cannot be exported to parquet if the splits have different features

closed

MEMBER

## Describe the bug I'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. For `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file. ## Steps to reproduce the bug The following works as expected: ```python from datasets import load_dataset ds = load_dataset("lhoestq/custom_squad") ds['train'].to_parquet("./ds/train/split.parquet") ds['validation'].to_parquet("./ds/validation/split.parquet") brand_new_dataset = load_dataset("ds") ``` Modifying a single split to add a new feature ends up in a crash: ```python from datasets import load_dataset ds = load_dataset("lhoestq/custom_squad") def identical_answers(e): e['identical_answers'] = len(set(e['answers']['text'])) == 1 return e ds['validation'] = ds['validation'].map(identical_answers) ds['train'].to_parquet("./ds/train/split.parquet") ds['validation'].to_parquet("./ds/validation/split.parquet") brand_new_dataset = load_dataset("ds") ``` ``` File "/home/lysandre/.config/JetBrains/PyCharm2021.2/scratches/datasets/upload_dataset.py", line 26, in <module> brand_new_dataset = load_dataset("ds") File "/home/lysandre/Workspaces/Python/datasets/src/datasets/load.py", line 1151, in load_dataset builder_instance.download_and_prepare( File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 642, in download_and_prepare self._download_and_prepare( File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 732, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 1194, in _prepare_split writer.write_table(table) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_writer.py", line 428, in write_table pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_writer.py", line 428, in <listcomp> pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "pyarrow/table.pxi", line 1257, in pyarrow.lib.Table.__getitem__ File "pyarrow/table.pxi", line 1833, in pyarrow.lib.Table.column File "pyarrow/table.pxi", line 1808, in pyarrow.lib.Table._ensure_integer_index KeyError: 'Field "identical_answers" does not exist in table schema' ``` It does work, however, to use the `save_to_disk` and `load_from_disk` methods: ```py from datasets import load_from_disk ds = load_dataset("lhoestq/custom_squad") def identical_answers(e): e['identical_answers'] = len(set(e['answers']['text'])) == 1 return e ds['validation'] = ds['validation'].map(identical_answers) ds.save_to_disk("local_path") brand_new_dataset = load_from_disk("local_path") ``` ## Expected results The saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files. If it's helpful, I've traced a possible patch to the `write_table` method here: https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/arrow_writer.py#L424-L425 The writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features: https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/arrow_writer.py#L254-L255 but the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`: https://github.com/huggingface/datasets/blob/26ff41aa3a642e46489db9e95be1e9a8c4e64bea/src/datasets/builder.py#L1190 Updating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise: ``` File "/home/lysandre/.config/JetBrains/PyCharm2021.2/scratches/datasets/upload_dataset.py", line 27, in <module> brand_new_dataset = load_dataset("ds") File "/home/lysandre/Workspaces/Python/datasets/src/datasets/load.py", line 1163, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 819, in as_dataset datasets = utils.map_nested( File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 207, in map_nested mapped = [ File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 208, in <listcomp> _single_map_nested((function, obj, types, None, True)) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py", line 143, in _single_map_nested return function(data_struct) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 850, in _build_single_dataset ds = self._as_dataset( File "/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py", line 920, in _as_dataset dataset_kwargs = ArrowReader(self._cache_dir, self.info).read( File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 217, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 238, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 173, in _read_files pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 308, in _get_table_from_filename table = ArrowReader.read_table(filename, in_memory=in_memory) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py", line 327, in read_table return table_cls.from_file(filename) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/table.py", line 458, in from_file table = _memory_mapped_arrow_table_from_file(filename) File "/home/lysandre/Workspaces/Python/datasets/src/datasets/table.py", line 45, in _memory_mapped_arrow_table_from_file pa_table = opened_stream.read_all() File "pyarrow/ipc.pxi", line 563, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 114, in pyarrow.lib.check_status OSError: Header-type of flatbuffer-encoded Message is not RecordBatch. ``` ## Environment info - `datasets` version: 1.12.2.dev0 - Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33 - Python version: 3.9.7 - PyArrow version: 5.0.0

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I_kwDODunzps48CJLV

Adding vision-and-language datasets (e.g., VQA, VCR) to Datasets

closed

NONE

**Is your feature request related to a problem? Please describe.** Would you like to add any vision-and-language datasets (e.g., VQA, VCR) to Huggingface Datasets? **Describe the solution you'd like** N/A **Describe alternatives you've considered** N/A **Additional context** This is Da Yin at UCLA. Recently, we have published an EMNLP 2021 paper about geo-diverse visual commonsense reasoning (https://arxiv.org/abs/2109.06860). We propose a new dataset called GD-VCR, a vision-and-language dataset to evaluate how well V&L models perform on scenarios involving geo-location-specific commonsense. We hope to have our V&L dataset incorporated into Huggingface to further promote our project, but I haven't seen much V&L datasets in the current package. Is it possible to add V&L datasets, and if so, how should we prepare for the loading? Thank you very much!

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PR_kwDODunzps4sRRMs

Upload greek-legal-code dataset

closed

CONTRIBUTOR

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I_kwDODunzps48BuJ5

Invalid download URL of WMT17 `zh-en` data

open

NONE

## Describe the bug Partial data (wmt17 zh-en) cannot be downloaded due to an invalid URL. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('wmt17','zh-en') ``` ## Expected results ConnectionError: Couldn't reach ftp://cwmt-wmt:cwmt-wmt@datasets.nju.edu.cn/parallel/casia2015.zip

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I_kwDODunzps47_5ZQ

Error when calculating Matthews Correlation Coefficient loaded with `load_metric`

closed

NONE

## Describe the bug After loading the metric named "[Matthews Correlation Coefficient](https://huggingface.co/metrics/matthews_correlation)" from `🤗datasets`, the `.compute` method fails with the following exception `AttributeError: 'float' object has no attribute 'item'` (complete stack trace can be provided if required). ## Steps to reproduce the bug ```python import torch predictions = torch.ones((10,)) references = torch.zeros((10,)) from datasets import load_metric METRIC = load_metric("matthews_correlation") result = METRIC.compute(predictions=predictions, references=references) ``` ## Expected results We should expect a Python `dict` as it follows: ``` { "matthews_correlation": float() } ``` as defined in https://github.com/huggingface/datasets/blob/master/metrics/matthews_correlation/matthews_correlation.py, so the fix will imply removing `.item()`, since the value returned by the `scikit-learn` function is not a `torch.Tensor` but a `float`, which means that the `.item()` will fail. ## Actual results ``` Traceback (most recent call last): File "/home/alvaro.bartolome/XXX/xxx/cli.py", line 59, in main app() File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/typer/main.py", line 214, in __call__ return get_command(self)(*args, **kwargs) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1137, in __call__ return self.main(*args, **kwargs) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1062, in main rv = self.invoke(ctx) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1668, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1404, in invoke return ctx.invoke(self.callback, **ctx.params) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 763, in invoke return __callback(*args, **kwargs) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/typer/main.py", line 500, in wrapper return callback(**use_params) # type: ignore File "/home/alvaro.bartolome/XXX/xxx/cli.py", line 43, in train metrics = trainer.evaluate() File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/transformers/trainer.py", line 2051, in evaluate output = eval_loop( File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/transformers/trainer.py", line 2292, in evaluation_loop metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels)) File "/home/alvaro.bartolome/XXX/xxx/metrics.py", line 20, in compute_metrics res = METRIC.compute(predictions=predictions, references=eval_preds.label_ids) File "/home/alvaro.bartolome/miniconda3/envs/lang/lib/python3.9/site-packages/datasets/metric.py", line 402, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/alvaro.bartolome/.cache/huggingface/modules/datasets_modules/metrics/matthews_correlation/0275f1e9a4d318e3ea8cdd87547ee0d58d894966616052e3d18444ac8ddd2357/matthews_correlation.py", line 88, in _compute "matthews_correlation": matthews_corrcoef(references, predictions, sample_weight=sample_weight).item(), AttributeError: 'float' object has no attribute 'item' ``` ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-4.15.0-1113-azure-x86_64-with-glibc2.23 - Python version: 3.9.7 - PyArrow version: 5.0.0

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I_kwDODunzps47_1K9

raise TypeError( TypeError: Provided `function` which is applied to all elements of table returns a variable of type <class 'list'>. Make sure provided `function` returns a variable of type `dict` to update the dataset or `None` if you are only interested in side effects.

closed

NONE

## Describe the bug A clear and concise description of what the bug is. I am trying to use Dataset to load my file in order to use Bert embeddings model baut when I finished loading using dataset and I want to pass to the tokenizer using the function map; I get the following error : raise TypeError( TypeError: Provided `function` which is applied to all elements of table returns a variable of type <class 'list'>. Make sure provided `function` returns a variable of type `dict` to update the dataset or `None` if you are only interested in side effects. I was able to load my file using dataset before but since this morning , I keep getting this erreor. ## Steps to reproduce the bug ```python # Xtrain, ytrain, filename, len_labels = read_file_2(fic) # Xtrain, lge_size = get_flaubert_layer(Xtrain, path_to_model_lge) data_preprocessed = make_new_traindata(Xtrain) my_dict = {"verbatim": data_preprocessed[1], "label": ytrain} # lemme avec conjonction dataset = Dataset.from_dict(my_dict) ``` ## Expected results A clear and concise description of the expected results. ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:

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I_kwDODunzps47_tni

Enable splits during streaming the dataset

open

CONTRIBUTOR

## Describe the Problem I'd like to stream only a specific percentage or part of the dataset. I want to do splitting when I'm streaming dataset as well. ## Solution Enabling splits when `streaming = True` as well. `e.g. dataset = load_dataset('dataset', split='train[:100]', streaming = True)` ## Alternatives Below is the alternative of doing it. `dataset = load_dataset("dataset", split='train', streaming = True).take(100)`

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PR_kwDODunzps4sPTXV

Fix CI doc build

closed

MEMBER

Pin `fsspec`. Before the issue: 'fsspec-2021.8.1', 's3fs-2021.8.1' Generating the issue: 'fsspec-2021.9.0', 's3fs-0.5.1'

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PR_kwDODunzps4sOl0Y

Support pandas 1.3 new `read_csv` parameters

closed

CONTRIBUTOR

Support two new arguments introduced in pandas v1.3.0: - `encoding_errors` - `on_bad_lines` `read_csv` reference: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html

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PR_kwDODunzps4sMihl

Added computer vision tasks

open

CONTRIBUTOR

Added various image processing/computer vision tasks.

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PR_kwDODunzps4sLTaB

Add security policy to the project

closed

MEMBER

Add security policy to the project, as recommended by GitHub: https://docs.github.com/en/code-security/getting-started/adding-a-security-policy-to-your-repository Close #2953.

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I_kwDODunzps475RLx

MultiWOZ Dataset NonMatchingChecksumError

open

NONE

## Describe the bug The checksums for the downloaded MultiWOZ dataset and source MultiWOZ dataset aren't matching. ## Steps to reproduce the bug Both of the below dataset versions yield the checksum error: ```python from datasets import load_dataset dataset = load_dataset('multi_woz_v22', 'v2.2') dataset = load_dataset('multi_woz_v22', 'v2.2_active_only') ``` ## Expected results For the above calls to `load_dataset` to work. ## Actual results NonMatchingChecksumError. Traceback: > Traceback (most recent call last): File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-15-4e91280e112e>", line 1, in <module> dataset = load_dataset('multi_woz_v22', 'v2.2') File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/load.py", line 847, in load_dataset builder_instance.download_and_prepare( File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/builder.py", line 615, in download_and_prepare self._download_and_prepare( File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare verify_checksums( File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json'] ## Environment info - `datasets` version: 1.11.0 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.10 - PyArrow version: 5.0.0

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I_kwDODunzps473H-_

Cache problem in the `load_dataset` method for local compressed file(s)

open

NONE

## Describe the bug Cache problem in the `load_dataset` method: when modifying a compressed file in a local folder `load_dataset` doesn't detect the change and load the previous version. ## Steps to reproduce the bug To test it directly, I have prepared a [Google Colaboratory notebook](https://colab.research.google.com/drive/11Em_Amoc-aPGhSBIkSHU2AvEh24nVayy?usp=sharing) that shows this behavior. For this example, I have created a toy dataset at: https://huggingface.co/datasets/SaulLu/toy_struc_dataset This dataset is composed of two versions: - v1 on commit `a6beb46` which has a single example `{'id': 1, 'value': {'tag': 'a', 'value': 1}}` in file `train.jsonl.gz` - v2 on commit `e7935f4` (`main` head) which has a single example `{'attr': 1, 'id': 1, 'value': 'a'}` in file `train.jsonl.gz` With a terminal, we can start to get the v1 version of the dataset ```bash git lfs install git clone https://huggingface.co/datasets/SaulLu/toy_struc_dataset cd toy_struc_dataset git checkout a6beb46 ``` Then we can load it with python and look at the content: ```python from datasets import load_dataset path = "/content/toy_struc_dataset" dataset = load_dataset(path, data_files={"train": "*.jsonl.gz"}) print(dataset["train"][0]) ``` Output ``` {'id': 1, 'value': {'tag': 'a', 'value': 1}} # This is the example in v1 ``` With a terminal, we can now start to get the v1 version of the dataset ```bash git checkout main ``` Then we can load it with python and look at the content: ```python from datasets import load_dataset path = "/content/toy_struc_dataset" dataset = load_dataset(path, data_files={"train": "*.jsonl.gz"}) print(dataset["train"][0]) ``` Output ``` {'id': 1, 'value': {'tag': 'a', 'value': 1}} # This is the example in v1 (not v2) ``` ## Expected results The last output should have been ``` {"id":1, "value": "a", "attr": 1} # This is the example in v2 ``` ## Ideas As discussed offline with Quentin, if the cache hash was ever sensitive to changes in a compressed file we would probably not have the problem anymore. This situation leads me to suggest 2 other features: - to also have an `load_from_cache_file` argument in the "load_dataset" method - to reorganize the cache so that we can delete the caches related to a dataset (cf issue #ToBeFilledSoon) And thanks again for this great library :hugs: ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0

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PR_kwDODunzps4sHuRu

Update legacy Python image for CI tests in Linux

closed

MEMBER

Instead of legacy, use next-generation convenience images, built from the ground up with CI, efficiency, and determinism in mind. Here are some of the highlights: - Faster spin-up time - In Docker terminology, these next-gen images will generally have fewer and smaller layers. Using these new images will lead to faster image downloads when a build starts, and a higher likelihood that the image is already cached on the host. - Improved reliability and stability - The existing legacy convenience images are rebuilt practically every day with potential changes from upstream that we cannot always test fast enough. This leads to frequent breaking changes, which is not the best environment for stable, deterministic builds. Next-gen images will only be rebuilt for security and critical-bugs, leading to more stable and deterministic images. More info: https://circleci.com/docs/2.0/circleci-images

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PR_kwDODunzps4sHa8O

Run tests in parallel

closed

MEMBER

Run CI tests in parallel to speed up the test suite. Speed up results: - Linux: from `7m 30s` to `5m 32s` - Windows: from `13m 52s` to `11m 10s`

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I_kwDODunzps47xQC1

Trying to get in touch regarding a security issue

closed

NONE

Hey there! I'd like to report a security issue but cannot find contact instructions on your repository. If not a hassle, might you kindly add a `SECURITY.md` file with an email, or another contact method? GitHub [recommends](https://docs.github.com/en/code-security/getting-started/adding-a-security-policy-to-your-repository) this best practice to ensure security issues are responsibly disclosed, and it would serve as a simple instruction for security researchers in the future. Thank you for your consideration, and I look forward to hearing from you! (cc @huntr-helper)

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PR_kwDODunzps4sDU8S

Fix missing conda deps

closed

MEMBER

`aiohttp` was added as a dependency in #2662 but was missing for the conda build, which causes the 1.12.0 and 1.12.1 to fail. Fix #2932.

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PR_kwDODunzps4r-lGs

Dummy labels no longer on by default in `to_tf_dataset`

closed

CONTRIBUTOR

After more experimentation, I think I have a way to do things that doesn't depend on adding `dummy_labels` - they were quite a hacky solution anyway!

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PR_kwDODunzps4r-AKu

Fix fn kwargs in filter

closed

MEMBER

#2836 broke the `fn_kwargs` parameter of `filter`, as mentioned in https://github.com/huggingface/datasets/issues/2927 I fixed that and added a test to make sure it doesn't happen again (for either map or filter) Fix #2927

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PR_kwDODunzps4r90Pt

Introduce web and wiki config in triviaqa dataset

closed

CONTRIBUTOR

The TriviaQA paper suggests that the two subsets (Wikipedia and Web) should be treated differently. There are also different leaderboards for the two sets on CodaLab. For that reason, introduce additional builder configs in the trivia_qa dataset.

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PR_kwDODunzps4r9PdV

Fix minor URL format in scitldr dataset

closed

MEMBER

While investigating issue #2918, I found this minor format issues in the URLs (if runned in a Windows machine).

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PR_kwDODunzps4r9GIP

Don't use old, incompatible cache for the new `filter`

closed

MEMBER

#2836 changed `Dataset.filter` and the resulting data that are stored in the cache are different and incompatible with the ones of the previous `filter` implementation. However the caching mechanism wasn't able to differentiate between the old and the new implementation of filter (only the method name was taken into account). This is an issue because anyone that update `datasets` and re-runs some code that uses `filter` would see an error, because the cache would try to load an incompatible `filter` result. To fix this I added the notion of versioning for dataset transform in the caching mechanism, and bumped the version of the `filter` implementation to 2.0.0 This way the new `filter` outputs are now considered different from the old ones from the caching point of view. This should fix #2943 cc @anton-l

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PR_kwDODunzps4r89f8

Update meteor score from nltk update

closed

MEMBER

It looks like there were issues in NLTK on the way the METEOR score was computed. A fix was added in NLTK at https://github.com/nltk/nltk/pull/2763, and therefore the scoring function no longer returns the same values. I updated the score of the example in the docs

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I_kwDODunzps47pFLz

Protect master branch

closed

MEMBER

After accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.: - 00cc036fea7c7745cfe722360036ed306796a3f2 - 13ae8c98602bbad8197de3b9b425f4c78f582af1 - ... I propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future: - [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch - Currently, simple merge commits are already disabled - I propose to disable rebase merging as well - ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~ - ~~This protection would reject direct pushes to master branch~~ - ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~ - [x] Protect the master branch only from direct pushing of **merge commits** - GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch). - No need to disable/re-enable this protection on each release This purpose of this Issue is to open a discussion about this problem and to agree in a solution.

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I_kwDODunzps47oxhy

Add `remove_columns` to `IterableDataset `

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. ```python from datasets import load_dataset dataset = load_dataset("c4", 'realnewslike', streaming =True, split='train') dataset = dataset.remove_columns('url') ``` ``` AttributeError: 'IterableDataset' object has no attribute 'remove_columns' ``` **Describe the solution you'd like** It would be nice to have `.remove_columns()` to match the `Datasets` api. **Describe alternatives you've considered** This can be done with a single call to `.map()`, I can try to help add this. 🤗

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I_kwDODunzps47oDUr

Backwards compatibility broken for cached datasets that use `.filter()`

closed

CONTRIBUTOR

## Describe the bug After upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with `ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}` Related feature: https://github.com/huggingface/datasets/pull/2836 :question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) ## Workaround Remove the cache for the given dataset, e.g. `rm -rf ~/.cache/huggingface/datasets/librispeech_asr`. ## Steps to reproduce the bug 1. Delete `~/.cache/huggingface/datasets/librispeech_asr` if it exists. 2. `pip install datasets==1.11.0` and run the following snippet: ```python from datasets import load_dataset ids = ["1272-141231-0000"] ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") ds = ds.filter(lambda x: x["id"] in ids) ``` 3. `pip install datasets==1.12.1` and re-run the code again ## Expected results Same result as with the previous `datasets` version. ## Actual results ```bash Reusing dataset librispeech_asr (./.cache/huggingface/datasets/librispeech_asr/clean/2.1.0/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1) Loading cached processed dataset at ./.cache/huggingface/datasets/librispeech_asr/clean/2.1.0/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1/cache-cd1c29844fdbc87a.arrow Traceback (most recent call last): File "./repos/transformers/src/transformers/models/wav2vec2/try_dataset.py", line 5, in <module> ds = ds.filter(lambda x: x["id"] in ids) File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 185, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "./envs/transformers/lib/python3.8/site-packages/datasets/fingerprint.py", line 398, in wrapper out = func(self, *args, **kwargs) File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2169, in filter indices = self.map( File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1686, in map return self._map_single( File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 185, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "./envs/transformers/lib/python3.8/site-packages/datasets/fingerprint.py", line 398, in wrapper out = func(self, *args, **kwargs) File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1896, in _map_single return Dataset.from_file(cache_file_name, info=info, split=self.split) File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 343, in from_file return cls( File "./envs/transformers/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 282, in __init__ self.info.features = self.info.features.reorder_fields_as(inferred_features) File "./envs/transformers/lib/python3.8/site-packages/datasets/features.py", line 1151, in reorder_fields_as return Features(recursive_reorder(self, other)) File "./envs/transformers/lib/python3.8/site-packages/datasets/features.py", line 1140, in recursive_reorder raise ValueError(f"Keys mismatch: between {source} and {target}" + stack_position) ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)} Process finished with exit code 1 ``` ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17 - Python version: 3.8.10 - PyArrow version: 5.0.0

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PR_kwDODunzps4r7tY6

Add SEDE dataset

closed

CONTRIBUTOR

This PR adds the SEDE dataset for the task of realistic Text-to-SQL, following the instructions of how to add a database and a dataset card. Please see our paper for more details: https://arxiv.org/abs/2106.05006

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I_kwDODunzps47mszH

OSCAR unshuffled_original_ko: NonMatchingSplitsSizesError

open

NONE

## Describe the bug Cannot download OSCAR `unshuffled_original_ko` due to `NonMatchingSplitsSizesError`. ## Steps to reproduce the bug ```python >>> dataset = datasets.load_dataset('oscar', 'unshuffled_original_ko') NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=25292102197, num_examples=7345075, dataset_name='oscar'), 'recorded': SplitInfo(name='train', num_bytes=25284578514, num_examples=7344907, dataset_name='oscar')}] ``` ## Expected results Loading is successful. ## Actual results Loading throws above error. ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 5.0.0

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PR_kwDODunzps4r6EUF

add swedish_medical_ner dataset

closed

CONTRIBUTOR

Adding the Swedish Medical NER dataset, listed in "Biomedical Datasets - BigScience Workshop 2021"

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PR_kwDODunzps4r58Gu

MENYO-20k repo has moved, updating URL

closed

CONTRIBUTOR

Dataset repo moved to https://github.com/uds-lsv/menyo-20k_MT, now editing URL to match. https://github.com/uds-lsv/menyo-20k_MT/blob/master/data/train.tsv is the file we're looking for

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PR_kwDODunzps4r5qwa

Take namespace into account in caching

closed

MEMBER

Loading a dataset "username/dataset_name" hosted by a user on the hub used to cache the dataset only taking into account the dataset name, and ignorign the username. Because of this, if a user later loads "dataset_name" without specifying the username, it would reload the dataset from the cache instead of failing. I changed the dataset cache and module cache mechanism to include the username in the name of the cache directory that is used: <s> `~/.cache/huggingface/datasets/username/dataset_name` for the data `~/.cache/huggingface/modules/datasets_modules/datasets/username/dataset_name` for the python files </s> EDIT: actually using three underscores: `~/.cache/huggingface/datasets/username___dataset_name` for the data `~/.cache/huggingface/modules/datasets_modules/datasets/username___dataset_name` for the python files This PR should fix the issue https://github.com/huggingface/datasets/issues/2842 cc @stas00

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I_kwDODunzps47k-V1

load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied

open

NONE

## Describe the bug Standard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11. ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset('wiki_bio') ``` ## Expected results It is expected that the dataset downloads without any errors. ## Actual results PermissionError see trace below: ``` Using custom data configuration default Downloading and preparing dataset wiki_bio/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\Users\username\.cache\huggingface\datasets\wiki_bio\default\1.1.0\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Users\username\.conda\envs\hf\lib\site-packages\datasets\load.py", line 1112, in load_dataset builder_instance.download_and_prepare( File "C:\Users\username\.conda\envs\hf\lib\site-packages\datasets\builder.py", line 644, in download_and_prepare self._save_info() File "C:\Users\username\.conda\envs\hf\lib\contextlib.py", line 120, in __exit__ next(self.gen) File "C:\Users\username\.conda\envs\hf\lib\site-packages\datasets\builder.py", line 598, in incomplete_dir os.rename(tmp_dir, dirname) PermissionError: [WinError 5] Access is denied: 'C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9' ``` By commenting out the os.rename() [L604](https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L604) and the shutil.rmtree() [L607](https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed. It seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https://github.com/conan-io/conan/issues/6560) with similar issue with os.rename() if it helps debug this issue. ## Environment info - `datasets` version: 1.12.1 - Platform: Windows-10-10.0.22449-SP0 - Python version: 3.8.12 - PyArrow version: 5.0.0

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PR_kwDODunzps4r5knb

Check that array is not Float as nan != nan

closed

CONTRIBUTOR

The Exception wants to check for issues with StructArrays/ListArrays but catches FloatArrays with value nan as nan != nan. Pass on FloatArrays as we should not raise an Exception for them.

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PR_kwDODunzps4r5j8B

Add Jigsaw unintended Bias

closed

CONTRIBUTOR

Hi, Here's a first attempt at this dataset. Would be great if it could be merged relatively quickly as it is needed for Bigscience-related stuff. This requires manual download, and I had some trouble generating dummy_data in this setting, so welcoming feedback there.

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I_kwDODunzps47ktCl

to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows

closed

MEMBER

To reproduce: ```python import datasets as ds import weakref import gc d = ds.load_dataset("mnist", split="train") ref = weakref.ref(d._data.table) tfd = d.to_tf_dataset("image", batch_size=1, shuffle=False, label_cols="label") del tfd, d gc.collect() assert ref() is None, "Error: there is at least one reference left" ``` This causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards. Moreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this. cc @Rocketknight1

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PR_kwDODunzps4r5MHs

Replace script_version with revision

closed

MEMBER

As discussed in https://github.com/huggingface/datasets/pull/2718#discussion_r707013278, the parameter name `script_version` is no longer applicable to datasets without loading script (i.e., datasets only with raw data files). This PR replaces the parameter name `script_version` with `revision`. This way, we are also aligned with: - Transformers: `AutoTokenizer.from_pretrained(..., revision=...)` - Hub: `HfApi.dataset_info(..., revision=...)`, `HfApi.upload_file(..., revision=...)`

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I_kwDODunzps47kGD2

Conda build fails

closed

MEMBER

## Describe the bug Current `datasets` version in conda is 1.9 instead of 1.12. The build of the conda package fails.

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PR_kwDODunzps4r1-JH

Fix bug in to_tf_dataset

closed

CONTRIBUTOR

Replace `set_format()` to `with_format()` so that we don't alter the original dataset in `to_tf_dataset()`

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I_kwDODunzps47fqBH

Mutable columns argument breaks set_format

closed

CONTRIBUTOR

## Describe the bug If you pass a mutable list to the `columns` argument of `set_format` and then change the list afterwards, the returned columns also change. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("glue", "cola") column_list = ["idx", "label"] dataset.set_format("python", columns=column_list) column_list[1] = "foo" # Change the list after we call `set_format` dataset['train'][:4].keys() ``` ## Expected results ```python dict_keys(['idx', 'label']) ``` ## Actual results ```python dict_keys(['idx']) ```

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PR_kwDODunzps4r015C

Add regression test for null Sequence

closed

MEMBER

Relates to #2892 and #2900.

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PR_kwDODunzps4r0yUb

Update BibTeX entry

closed

MEMBER

Update BibTeX entry.

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I_kwDODunzps47dwCY

Datasets 1.12 dataset.filter TypeError: get_indices_from_mask_function() got an unexpected keyword argument

closed

NONE

## Describe the bug Upgrading to 1.12 caused `dataset.filter` call to fail with > get_indices_from_mask_function() got an unexpected keyword argument valid_rel_labels ## Steps to reproduce the bug ```pythondef filter_good_rows( ex: Dict, valid_rel_labels: Set[str], valid_ner_labels: Set[str], tokenizer: PreTrainedTokenizerFast, ) -> bool: """Get the good rows""" encoding = get_encoding_for_text(text=ex["text"], tokenizer=tokenizer) ex["encoding"] = encoding for relation in ex["relations"]: if not is_valid_relation(relation, valid_rel_labels): return False for span in ex["spans"]: if not is_valid_span(span, valid_ner_labels, encoding): return False return True def get_dataset(): loader_path = str(Path(__file__).parent / "prodigy_dataset_builder.py") ds = load_dataset( loader_path, name="prodigy-dataset", data_files=sorted(file_paths), cache_dir=cache_dir, )["train"] valid_ner_labels = set(vocab.ner_category) valid_relations = set(vocab.relation_types.keys()) ds = ds.filter( filter_good_rows, fn_kwargs=dict( valid_rel_labels=valid_relations, valid_ner_labels=valid_ner_labels, tokenizer=vocab.tokenizer, ), keep_in_memory=True, num_proc=num_proc, ) ``` `ds` is a `DatasetDict` produced by a jsonl dataset. This runs fine on 1.11 but fails on 1.12 **Stack Trace** ## Expected results I expect 1.12 datasets filter to filter the dataset without raising as it does on 1.11 ## Actual results ``` tf_ner_rel_lib/dataset.py:695: in load_prodigy_arrow_datasets_from_jsonl ds = ds.filter( ../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:185: in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/fingerprint.py:398: in wrapper out = func(self, *args, **kwargs) ../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:2169: in filter indices = self.map( ../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:1686: in map return self._map_single( ../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:185: in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/fingerprint.py:398: in wrapper out = func(self, *args, **kwargs) ../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:2048: in _map_single batch = apply_function_on_filtered_inputs( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ inputs = {'_input_hash': [2108817714, 1477695082, -1021597032, 2130671338, -1260483858, -1203431639, ...], '_task_hash': [18070...ons', 'relations', 'relations', ...], 'answer': ['accept', 'accept', 'accept', 'accept', 'accept', 'accept', ...], ...} indices = [0, 1, 2, 3, 4, 5, ...], check_same_num_examples = False, offset = 0 def apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples=False, offset=0): """Utility to apply the function on a selection of columns.""" nonlocal update_data fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns] if offset == 0: effective_indices = indices else: effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset processed_inputs = ( > function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) ) E TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'valid_rel_labels' ../../../../.pyenv/versions/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:1939: TypeError ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Mac - Python version: 3.8.9 - PyArrow version: pyarrow==5.0.0

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I_kwDODunzps47dBTt

Error when downloading datasets to non-traditional cache directories

open

NONE

## Describe the bug When the cache directory is linked (soft link) to a directory on a NetApp device, the download fails. ## Steps to reproduce the bug ```bash ln -s /path/to/netapp/.cache ~/.cache ``` ```python load_dataset("imdb") ``` ## Expected results Successfully loading IMDB dataset ## Actual results ``` datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=33432835, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='test', num_bytes=32650697, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='test', num_bytes=659932, num_examples=503, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67106814, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.1.2 - Platform: Ubuntu - Python version: 3.8 ## Extra notes Stranger yet, trying to debug the phenomenon, I found the range of results to vary a lot without clear direction: - With `cache_dir="/path/to/netapp/.cache"` the same thing happens. - However, when linking `~/netapp/` to `/path/to/netapp` *and* setting `cache_dir="~/netapp/.cache/huggingface/datasets"` - it does work - On the other hand, when linking `~/.cache` to `~/netapp/.cache` without using `cache_dir`, it does work anymore. While I could test it only for a NetApp device, it might have to do with any other mounted FS. Thanks :)

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PR_kwDODunzps4rzJ9s

Add tutorial for no-code dataset upload

closed

CONTRIBUTOR

This PR is for a tutorial for uploading a dataset to the Hub. It relies on the Hub UI elements to upload a dataset, introduces the online tagging tool for creating tags, and the Dataset card template to get a head start on filling it out. The addition of this tutorial should make it easier for beginners to upload a dataset without accessing the terminal or knowing Git.

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I_kwDODunzps47cshB

"File name too long" error for file locks

closed

NONE

## Describe the bug Getting the following error when calling `load_dataset("gar1t/test")`: ``` OSError: [Errno 36] File name too long: '<user>/.cache/huggingface/datasets/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock' ``` ## Steps to reproduce the bug Where the user cache dir (e.g. `~/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4): ```python from datasets import load_dataset load_dataset("gar1t/test") ``` ## Expected results Expect the function to return without an error. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<python_venv>/lib/python3.9/site-packages/datasets/load.py", line 1112, in load_dataset builder_instance.download_and_prepare( File "<python_venv>/lib/python3.9/site-packages/datasets/builder.py", line 644, in download_and_prepare self._save_info() File "<python_venv>/lib/python3.9/site-packages/datasets/builder.py", line 765, in _save_info with FileLock(lock_path): File "<python_venv>/lib/python3.9/site-packages/datasets/utils/filelock.py", line 323, in __enter__ self.acquire() File "<python_venv>/lib/python3.9/site-packages/datasets/utils/filelock.py", line 272, in acquire self._acquire() File "<python_venv>/lib/python3.9/site-packages/datasets/utils/filelock.py", line 403, in _acquire fd = os.open(self._lock_file, open_mode) OSError: [Errno 36] File name too long: '<user>/.cache/huggingface/datasets/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock' ``` ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 5.0.0

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I_kwDODunzps47cmCm

Loading an autonlp dataset raises in normal mode but not in streaming mode

open

CONTRIBUTOR

## Describe the bug The same dataset (from autonlp) raises an error in normal mode, but does not raise in streaming mode ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("severo/autonlp-data-sentiment_detection-3c8bcd36", split="train", streaming=False) ## raises an error load_dataset("severo/autonlp-data-sentiment_detection-3c8bcd36", split="train", streaming=True) ## does not raise an error ``` ## Expected results Both calls should raise the same error ## Actual results Call with streaming=False: ``` 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 5825.42it/s] Using custom data configuration autonlp-data-sentiment_detection-3c8bcd36-fe30267462d1d42b Downloading and preparing dataset json/autonlp-data-sentiment_detection-3c8bcd36 to /home/slesage/.cache/huggingface/datasets/json/autonlp-data-sentiment_detection-3c8bcd36-fe30267462d1d42b/0.0.0/d75ead8d5cfcbe67495df0f89bd262f0023257fbbbd94a730313295f3d756d50... 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 15923.71it/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 3346.88it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/load.py", line 1112, in load_dataset builder_instance.download_and_prepare( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/builder.py", line 636, in download_and_prepare self._download_and_prepare( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/builder.py", line 726, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/builder.py", line 1187, in _prepare_split writer.write_table(table) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/arrow_writer.py", line 418, in write_table pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.8/site-packages/datasets/arrow_writer.py", line 418, in <listcomp> pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "pyarrow/table.pxi", line 1249, in pyarrow.lib.Table.__getitem__ File "pyarrow/table.pxi", line 1825, in pyarrow.lib.Table.column File "pyarrow/table.pxi", line 1800, in pyarrow.lib.Table._ensure_integer_index KeyError: 'Field "splits" does not exist in table schema' ``` Call with `streaming=False`: ``` 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 6000.43it/s] Using custom data configuration autonlp-data-sentiment_detection-3c8bcd36-fe30267462d1d42b 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 46916.15it/s] 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 148734.18it/s] ``` ## Environment info - `datasets` version: 1.12.1.dev0 - Platform: Linux-5.11.0-1017-aws-x86_64-with-glibc2.29 - Python version: 3.8.11 - PyArrow version: 4.0.1

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https://github.com/huggingface/datasets/pull/2922

PR_kwDODunzps4ry6-s

Fix conversion of multidim arrays in list to arrow

closed

MEMBER

Arrow only supports 1-dim arrays. Previously we were converting all the numpy arrays to python list before instantiating arrow arrays to workaround this limitation. However in #2361 we started to keep numpy arrays in order to keep their dtypes. It works when we pass any multi-dim numpy array (the conversion to arrow has been added on our side), but not for lists of multi-dim numpy arrays. In this PR I added two strategies: - one that takes a list of multi-dim numpy arrays on returns an arrow array in an optimized way (more common case) - one that takes a list of possibly very nested data (lists, dicts, tuples) containing multi-dim arrays. This one is less optimized since it converts all the multi-dim numpy arrays into lists of 1-d arrays for compatibility with arrow. This strategy is simpler that just trying to create the arrow array from a possibly very nested data structure, but in the future we can improve it if needed. Fix https://github.com/huggingface/datasets/issues/2921

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I_kwDODunzps47cfpw

Using a list of multi-dim numpy arrays raises an error "can only convert 1-dimensional array values"

closed

MEMBER

This error has been introduced in https://github.com/huggingface/datasets/pull/2361 To reproduce: ```python import numpy as np from datasets import Dataset d = Dataset.from_dict({"a": [np.zeros((2, 2))]}) ``` raises ```python Traceback (most recent call last): File "playground/ttest.py", line 5, in <module> d = Dataset.from_dict({"a": [np.zeros((2, 2))]}).with_format("torch") File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/arrow_dataset.py", line 458, in from_dict pa_table = InMemoryTable.from_pydict(mapping=mapping) File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/table.py", line 365, in from_pydict return cls(pa.Table.from_pydict(*args, **kwargs)) File "pyarrow/table.pxi", line 1639, in pyarrow.lib.Table.from_pydict File "pyarrow/array.pxi", line 332, in pyarrow.lib.asarray File "pyarrow/array.pxi", line 223, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/arrow_writer.py", line 107, in __arrow_array__ out = pa.array(self.data, type=type) File "pyarrow/array.pxi", line 306, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Can only convert 1-dimensional array values

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PR_kwDODunzps4ry4_u

Fix unwanted tqdm bar when accessing examples

closed

MEMBER

A change in #2814 added bad progress bars in `map_nested`. Now they're disabled by default Fix #2919

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I_kwDODunzps47bvU_

Unwanted progress bars when accessing examples

closed

MEMBER

When accessing examples from a dataset formatted for pytorch, some progress bars appear when accessing examples: ```python In [1]: import datasets as ds In [2]: d = ds.Dataset.from_dict({"a": [0, 1, 2]}).with_format("torch") In [3]: d[0] 100%|████████████████████████████████| 1/1 [00:00<00:00, 3172.70it/s] Out[3]: {'a': tensor(0)} ``` This is because the pytorch formatter calls `map_nested` that uses progress bars cc @sgugger

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I_kwDODunzps47bfqz

`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming

closed

CONTRIBUTOR

## Describe the bug Trying to load the `"FullText"` config of the `"scitldr"` dataset with `streaming=True` raises an error from `aiohttp`: ```python ClientPayloadError: 400, message='Can not decode content-encoding: gzip' ``` cc @lhoestq ## Steps to reproduce the bug ```python from datasets import load_dataset iter_dset = iter( load_dataset("scitldr", name="FullText", split="test", streaming=True) ) next(iter_dset) ``` ## Expected results Returns the first sample of the dataset ## Actual results Calling `__next__` crashes with the following Traceback: ```python ----> 1 next(dset_iter) ~\miniconda3\envs\datasets\lib\site-packages\datasets\iterable_dataset.py in __iter__(self) 339 340 def __iter__(self): --> 341 for key, example in self._iter(): 342 if self.features: 343 # we encode the example for ClassLabel feature types for example ~\miniconda3\envs\datasets\lib\site-packages\datasets\iterable_dataset.py in _iter(self) 336 else: 337 ex_iterable = self._ex_iterable --> 338 yield from ex_iterable 339 340 def __iter__(self): ~\miniconda3\envs\datasets\lib\site-packages\datasets\iterable_dataset.py in __iter__(self) 76 77 def __iter__(self): ---> 78 for key, example in self.generate_examples_fn(**self.kwargs): 79 yield key, example 80 ~\.cache\huggingface\modules\datasets_modules\datasets\scitldr\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\scitldr.py in _generate_examples(self, filepath, split) 162 163 with open(filepath, encoding="utf-8") as f: --> 164 for id_, row in enumerate(f): 165 data = json.loads(row) 166 if self.config.name == "AIC": ~\miniconda3\envs\datasets\lib\site-packages\fsspec\implementations\http.py in read(self, length) 496 else: 497 length = min(self.size - self.loc, length) --> 498 return super().read(length) 499 500 async def async_fetch_all(self): ~\miniconda3\envs\datasets\lib\site-packages\fsspec\spec.py in read(self, length) 1481 # don't even bother calling fetch 1482 return b"" -> 1483 out = self.cache._fetch(self.loc, self.loc + length) 1484 self.loc += len(out) 1485 return out ~\miniconda3\envs\datasets\lib\site-packages\fsspec\caching.py in _fetch(self, start, end) 378 elif start < self.start: 379 if self.end - end > self.blocksize: --> 380 self.cache = self.fetcher(start, bend) 381 self.start = start 382 else: ~\miniconda3\envs\datasets\lib\site-packages\fsspec\asyn.py in wrapper(*args, **kwargs) 86 def wrapper(*args, **kwargs): 87 self = obj or args[0] ---> 88 return sync(self.loop, func, *args, **kwargs) 89 90 return wrapper ~\miniconda3\envs\datasets\lib\site-packages\fsspec\asyn.py in sync(loop, func, timeout, *args, **kwargs) 67 raise FSTimeoutError 68 if isinstance(result[0], BaseException): ---> 69 raise result[0] 70 return result[0] 71 ~\miniconda3\envs\datasets\lib\site-packages\fsspec\asyn.py in _runner(event, coro, result, timeout) 23 coro = asyncio.wait_for(coro, timeout=timeout) 24 try: ---> 25 result[0] = await coro 26 except Exception as ex: 27 result[0] = ex ~\miniconda3\envs\datasets\lib\site-packages\fsspec\implementations\http.py in async_fetch_range(self, start, end) 538 if r.status == 206: 539 # partial content, as expected --> 540 out = await r.read() 541 elif "Content-Length" in r.headers: 542 cl = int(r.headers["Content-Length"]) ~\miniconda3\envs\datasets\lib\site-packages\aiohttp\client_reqrep.py in read(self) 1030 if self._body is None: 1031 try: -> 1032 self._body = await self.content.read() 1033 for trace in self._traces: 1034 await trace.send_response_chunk_received( ~\miniconda3\envs\datasets\lib\site-packages\aiohttp\streams.py in read(self, n) 342 async def read(self, n: int = -1) -> bytes: 343 if self._exception is not None: --> 344 raise self._exception 345 346 # migration problem; with DataQueue you have to catch ClientPayloadError: 400, message='Can not decode content-encoding: gzip' ``` ## Environment info - `datasets` version: 1.12.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.8.5 - PyArrow version: 2.0.0 - aiohttp version: 3.7.4.post0

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I_kwDODunzps47baX6

windows download abnormal

closed

NONE

## Describe the bug The script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why?? ## Steps to reproduce the bug ```python3.7 + windows  # Sample code to reproduce the bug ``` ## Expected results It can be downloaded normally. ## Actual results it cann't ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:1.11.0 - Platform:windows - Python version:3.7 - PyArrow version:

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PR_kwDODunzps4rx5ua

Add OpenAI's pass@k code evaluation metric

closed

CONTRIBUTOR

This PR introduces the `code_eval` metric which implements [OpenAI's code evaluation harness](https://github.com/openai/human-eval) introduced in the [Codex paper](https://arxiv.org/abs/2107.03374). It is heavily based on the original implementation and just adapts the interface to follow the `predictions`/`references` convention. The addition of this metric should enable the evaluation against the code evaluation datasets added in #2897 and #2893. A few open questions: - The implementation makes heavy use of multiprocessing which this PR does not touch. Is this conflicting with multiprocessing natively integrated in `datasets`? - This metric executes generated Python code and as such it poses dangers of executing malicious code. OpenAI addresses this issue by 1) commenting the `exec` call in the code so the user has to actively uncomment it and read the warning and 2) suggests using a sandbox environment (gVisor container). Should we add a similar safeguard? E.g. a prompt that needs to be answered when initialising the metric? Or at least a warning message? - Naming: the implementation sticks to the `predictions`/`references` naming, however, the references are not reference solutions but unittest to test the solution. While reference solutions are also available they are not used. Should the naming be adapted?

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PR_kwDODunzps4rxfWb

Fix fsspec AbstractFileSystem access

closed

CONTRIBUTOR

This addresses the issue from #2914 by changing the way fsspec's AbstractFileSystem is accessed.

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