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MDU6SXNzdWU4NjE5MDQzMDY=

Error loading wikihow dataset

closed

CONTRIBUTOR

## Describe the bug When attempting to load wikihow into a dataset with ```python from datasets import load_dataset dataset = load_dataset('wikihow', data_dir='./wikihow') ``` I get the message: ``` AttributeError: 'BuilderConfig' object has no attribute 'filename' ``` at the end of a [full stack trace](https://gist.github.com/odellus/602c3b2de52f541d353b1022f320ffc2). ## Steps to reproduce the bug I have followed the instructions for creating a wikihow dataset. The [wikihow dataset site](https://huggingface.co/datasets/wikihow) says to use ```python from datasets import load_dataset dataset = load_dataset('wikihow') ``` to load the dataset. I do so and I get the message ``` AssertionError: The dataset wikihow with config all requires manual data. Please follow the manual download instructions: You need to manually download two wikihow files. An overview of which files to download can be seen at https://github.com/mahnazkoupaee/WikiHow-Dataset. You need to download the following two files manually: 1) https://ucsb.app.box.com/s/ap23l8gafpezf4tq3wapr6u8241zz358 and save the file under <path/to/folder>/wikihowAll.csv 2) https://ucsb.app.box.com/s/7yq601ijl1lzvlfu4rjdbbxforzd2oag and save the file under <path/to/folder>/wikihowSep.csv The <path/to/folder> can e.g. be "~/manual_wikihow_data". Wikihow can then be loaded using the following command `datasets.load_dataset("wikihow", data_dir="<path/to/folder>")`. . Manual data can be loaded with `datasets.load_dataset(wikihow, data_dir='<path/to/manual/data>') ``` So I create a directory `./wikihow` and download `wikihowAll.csv` and `wikihowSep.csv` into the new directory. Then I run ```python from datasets import load_dataset dataset = load_dataset('wikihow', data_dir='./wikihow') ``` that's when I get the [stack trace](https://gist.github.com/odellus/602c3b2de52f541d353b1022f320ffc2) ## Expected results I expected it to load the downloaded files into a dataset. ## Actual results ```python Using custom data configuration default-data_dir=.%2Fwikihow Downloading and preparing dataset wikihow/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/azureuser/.cache/huggingface/datasets/wikihow/default-data_dir=.%2Fwikihow/0.0.0/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2... --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-9-5e4d40142f30> in <module> ----> 1 dataset = load_dataset('wikihow',data_dir='./wikihow') ~/.local/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs) 745 try_from_hf_gcs=try_from_hf_gcs, 746 base_path=base_path,--> 747 use_auth_token=use_auth_token, 748 ) 749 ~/.local/lib/python3.6/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) 577 if not downloaded_from_gcs: 578 self._download_and_prepare( --> 579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 580 ) 581 # Sync info ~/.local/lib/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 632 split_dict = SplitDict(dataset_name=self.name) 633 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 634 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 635 636 # Checksums verification ~/.cache/huggingface/modules/datasets_modules/datasets/wikihow/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2/wikihow.py in _split_generators(self, dl_manager) 132 133 path_to_manual_file = os.path.join( --> 134 os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), self.config.filename 135 ) 136 AttributeError: 'BuilderConfig' object has no attribute 'filename' ``` ## Versions Paste the output of the following code: ```python import datasets import sys import platform print(f""" - Datasets: {datasets.__version__} - Python: {sys.version} - Platform: {platform.platform()} """) ``` ``` - Datasets: 1.5.0 - Python: 3.6.9 (default, Jan 26 2021, 15:33:00) [GCC 8.4.0] - Platform: Linux-5.4.0-1046-azure-x86_64-with-Ubuntu-18.04-bionic ```

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https://github.com/huggingface/datasets/pull/2238

MDExOlB1bGxSZXF1ZXN0NjE4MTY5NzM5

NLU evaluation data

closed

CONTRIBUTOR

New intent classification dataset from https://github.com/xliuhw/NLU-Evaluation-Data

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MDU6SXNzdWU4NjE0Mjc0Mzk=

Update Dataset.dataset_size after transformed with map

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After loading a dataset, if we transform it by using `.map` its `dataset_size` attirbute is not updated.

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MDU6SXNzdWU4NjEzODgxNDU=

Request to add StrategyQA dataset

open

NONE

## Request to add StrategyQA dataset - **Name:** StrategyQA - **Description:** open-domain QA [(project page)](https://allenai.org/data/strategyqa) - **Paper:** [url](https://arxiv.org/pdf/2101.02235.pdf) - **Data:** [here](https://allenai.org/data/strategyqa) - **Motivation:** uniquely-formulated dataset that also includes a question-decomposition breakdown and associated Wikipedia annotations for each step. Good for multi-hop reasoning modeling.

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MDExOlB1bGxSZXF1ZXN0NjE3Nzc0NDUw

Update README.md

closed

CONTRIBUTOR

Adding relevant citations (paper accepted at AAAI 2020 & EMNLP 2020) to the benchmark

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MDExOlB1bGxSZXF1ZXN0NjE3MzI4NDU3

Fix bash snippet formatting in ADD_NEW_DATASET.md

closed

CONTRIBUTOR

This PR indents the paragraphs around the bash snippets in ADD_NEW_DATASET.md to fix formatting.

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MDExOlB1bGxSZXF1ZXN0NjE3MDYwMTkw

Fix `xnli` dataset tuple key

closed

CONTRIBUTOR

Closes #2229 The `xnli` dataset yields a tuple key in case of `ar` which is inconsistant with the acceptable key types (str/int). The key was thus ported to `str` keeping the original information intact.

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MDExOlB1bGxSZXF1ZXN0NjE3MDQyNTI4

Start filling GLUE dataset card

closed

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The dataset card was pretty much empty. I added the descriptions (mainly from TFDS since the script is the same), and I also added the tasks tags as well as examples for a subset of the tasks. cc @sgugger

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MDExOlB1bGxSZXF1ZXN0NjE2ODYyNTEx

Fix map when removing columns on a formatted dataset

closed

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This should fix issue #2226 The `remove_columns` argument was ignored on formatted datasets

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MDU6SXNzdWU4NTk4MTcxNTk=

Keys yielded while generating dataset are not being checked

closed

CONTRIBUTOR

The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not. Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation: https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/datasets/xnli/xnli.py#L196 Even after having a tuple as key, the dataset is generated without any warning. Also, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example): ``` >>> import datasets >>> nik = datasets.load_dataset('anli') Downloading and preparing dataset anli/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\Users\nikhil\.cache\huggingface\datasets\anli\plain_text\0.1.0\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299... 0 examples [00:00, ? examples/s]1 {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: "Rete filoviaria di Parma" ) forms part of the public transport network of the city and "comune" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''} 2021-04-16 12:38:14.483968: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll 1 examples [00:01, 1.87s/ examples]1 {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': "Sharron Macready was a popular character through the 1980's.", 'label': 'neutral', 'reason': ''} 1 {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': "Bastedo didn't keep any pets because of her views on animal rights.", 'label': 'neutral', 'reason': ''} 1 {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''} 1 {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''} ``` Here also, the dataset was generated successfuly even hough it had same keys without any warning. The reason appears to stem from here: https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/src/datasets/builder.py#L988 Here, although it has access to every key, but it is not being checked and the example is written directly: https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/src/datasets/builder.py#L992 I would like to take this issue if you allow me. Thank You!

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MDU6SXNzdWU4NTk4MTA2MDI=

`xnli` dataset creating a tuple key while yielding instead of `str` or `int`

closed

CONTRIBUTOR

When using `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key: https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/datasets/xnli/xnli.py#L196 Since, community datasets in Tensorflow Datasets also use HF datasets, this causes a Tuple key error while loading HF's `xnli` dataset. I'm up for sending a fix for this, I think we can simply use `file_idx + "_" + row_idx` as a unique key instead of a tuple.

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MDExOlB1bGxSZXF1ZXN0NjE2ODE2MTQz

[WIP] Add ArrayXD support for fixed size list.

open

NONE

Add support for fixed size list for ArrayXD when shape is known . See https://github.com/huggingface/datasets/issues/2146 Since offset are not stored anymore, the file size is now roughly equal to the actual data size.

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MDExOlB1bGxSZXF1ZXN0NjE2Nzk1NjMx

Use update_metadata_with_features decorator in class_encode_column method

closed

CONTRIBUTOR

Following @mariosasko 's comment

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MDU6SXNzdWU4NTk3MjAzMDI=

Batched map fails when removing all columns

closed

NONE

Hi @lhoestq , I'm hijacking this issue, because I'm currently trying to do the approach you recommend: > Currently the optimal setup for single-column computations is probably to do something like > > ```python > result = dataset.map(f, input_columns="my_col", remove_columns=dataset.column_names) > ``` Here is my code: (see edit, in which I added a simplified version ``` This is the error: ```bash pyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000 ``` I wonder why this error occurs, when I delete every column? Can you give me a hint? ### Edit: I preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error? I can iterate over the complete dataset and print every sample before calling map. There seems to be no other problem with the dataset. I tried to simplify the code that crashes: ```python # works log.debug(dataset.column_names) log.debug(dataset) for i, sample in enumerate(dataset): log.debug(i, sample) # crashes counted_dataset = dataset.map( lambda x: {"a": list(range(20))}, input_columns=column, remove_columns=dataset.column_names, load_from_cache_file=False, num_proc=num_workers, batched=True, ) ``` ``` pyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000 ``` Edit2: May this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error: ```python # crashes counted_dataset = dataset.map( lambda x: {"a": list(range(20))}, input_columns=column, remove_columns=dataset.column_names, load_from_cache_file=False, num_proc=num_workers, batched=True, features=datasets.Features( { "a": datasets.Sequence(datasets.Value("int32")) } ) ) ``` ``` File "env/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1704, in _map_single writer.write_batch(batch) File "env/lib/python3.8/site-packages/datasets/arrow_writer.py", line 312, in write_batch col_type = schema.field(col).type if schema is not None else None File "pyarrow/types.pxi", line 1341, in pyarrow.lib.Schema.field KeyError: 'Column tokens does not exist in schema' ``` _Originally posted by @villmow in https://github.com/huggingface/datasets/issues/2193#issuecomment-820230874_

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MDExOlB1bGxSZXF1ZXN0NjE1NzAzMTY4

fixed one instance of 'train' to 'test'

closed

CONTRIBUTOR

I believe this should be 'test' instead of 'train'

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MDU6SXNzdWU4NTc5ODMzNjE=

Raise error if Windows max path length is not disabled

open

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On startup, raise an error if Windows max path length is not disabled; ask the user to disable it. Linked to discussion in #2220.

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https://github.com/huggingface/datasets/pull/2223

MDExOlB1bGxSZXF1ZXN0NjE1MjE4MDIz

Set test cache config

closed

MEMBER

Currently, running the tests populates the default cache directory `"~/.cache"`. This PR monkey-patches the config to set the cache directory within the temporary test directory, avoiding side effects.

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MDExOlB1bGxSZXF1ZXN0NjE1MTk5MTM5

Fix too long WindowsFileLock name

closed

MEMBER

Fix WindowsFileLock name longer than allowed MAX_PATH by shortening the basename.

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MDExOlB1bGxSZXF1ZXN0NjE1MTg4MTE5

Add SLR70 - SLR80 and SLR86 to OpenSLR dataset

closed

CONTRIBUTOR

I would like to add SLR70, SLR71, SLR72, SLR73, SLR74, SLR75, SLR76, SLR77, SLR78, SLR79, SLR80 and SLR86 to OpenSLR dataset. The languages are: Nigerian English, Chilean Spanish, Columbian Spanish, Peruvian Spanish, Puerto Rico Spanish, Venezuelan Spanish, Basque, Galician, Gujarati and Kannada.

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https://github.com/huggingface/datasets/pull/2220

MDExOlB1bGxSZXF1ZXN0NjE1MTM4NDQz

Fix infinite loop in WindowsFileLock

closed

MEMBER

Raise exception to avoid infinite loop.

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MDExOlB1bGxSZXF1ZXN0NjE0NzYxMzA3

Added CUAD dataset

closed

CONTRIBUTOR

Dataset link : https://github.com/TheAtticusProject/cuad/ Working on README.md currently. Closes #2084 and [#1](https://github.com/TheAtticusProject/cuad/issues/1).

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MDU6SXNzdWU4NTcyMzg0MzU=

Duplicates in the LAMA dataset

open

NONE

I observed duplicates in the LAMA probing dataset, see a minimal code below. ``` >>> import datasets >>> dataset = datasets.load_dataset('lama') No config specified, defaulting to: lama/trex Reusing dataset lama (/home/anam/.cache/huggingface/datasets/lama/trex/1.1.0/97deffae13eca0a18e77dfb3960bb31741e973586f5c1fe1ec0d6b5eece7bddc) >>> train_dataset = dataset['train'] >>> train_dataset[0] {'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi ʒyl tʁɔʃy]; 12 March 1815 – 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'} >>> train_dataset[1] {'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi ʒyl tʁɔʃy]; 12 March 1815 – 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'} ``` I checked the original data available at https://dl.fbaipublicfiles.com/LAMA/data.zip. This particular duplicated comes from: ``` {"uuid": "40b2ed1c-0961-482e-844e-32596b6117c8", "obj_uri": "Q150", "obj_label": "French", "sub_uri": "Q441235", "sub_label": "Louis Jules Trochu", "predicate_id": "P103", "evidences": [{"sub_surface": "Louis Jules Trochu", "obj_surface": "French", "masked_sentence": "Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician."}, {"sub_surface": "Louis Jules Trochu", "obj_surface": "French", "masked_sentence": "Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician."}]} ``` What is the best way to deal with these duplicates if I want to use `datasets` to probe with LAMA?

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MDExOlB1bGxSZXF1ZXN0NjE0NTAxNjIz

Revert breaking change in cache_files property

closed

MEMBER

#2025 changed the format of `Dataset.cache_files`. Before it was formatted like ```python [{"filename": "path/to/file.arrow", "start": 0, "end": 1337}] ``` and it was changed to ```python ["path/to/file.arrow"] ``` since there's no start/end offsets available anymore. To make this less breaking, I'm setting the format back to a list of dicts: ```python [{"filename": "path/to/file.arrow"}] ```

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MDExOlB1bGxSZXF1ZXN0NjE0NDU0MjE1

added real label for glue/mrpc to test set

closed

MEMBER

Added real label to `glue.py` `mrpc` task for test split.

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MDExOlB1bGxSZXF1ZXN0NjE0MjUyNTEy

Add datasets SLR35 and SLR36 to OpenSLR

closed

CONTRIBUTOR

I would like to add [SLR35](https://openslr.org/35/) (18GB) and [SLR36](https://openslr.org/36/) (22GB) which are Large Javanese and Sundanese ASR training data set collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia.

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MDU6SXNzdWU4NTYzMzM2NTc=

load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'

closed

NONE

I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package. ```python >>> from datasets import load_metric >>> metric = load_metric("glue", "sst2") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/ext3/miniconda3/lib/python3.8/site-packages/datasets-1.2.1-py3.8.egg/datasets/load.py", line 502, in load_metric File "/ext3/miniconda3/lib/python3.8/site-packages/datasets-1.2.1-py3.8.egg/datasets/load.py", line 66, in import_main_class File "/ext3/miniconda3/lib/python3.8/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1014, in _gcd_import File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 783, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/ns4008/.cache/huggingface/modules/datasets_modules/metrics/glue/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de/glue.py", line 105, in <module> @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) AttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings' ```

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MDExOlB1bGxSZXF1ZXN0NjEzNjcwODk2

Fix lc_quad download checksum

closed

CONTRIBUTOR

Fixes #2211

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MDU6SXNzdWU4NTU5OTkxMzM=

Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset

closed

NONE

I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running: ```Python fquad = load_dataset("fquad") ``` which produces the following error: ``` Using custom data configuration default Downloading and preparing dataset fquad/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to /root/.cache/huggingface/datasets/fquad/default/0.1.0/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061... --------------------------------------------------------------------------- ConnectionError Traceback (most recent call last) <ipython-input-48-a2721797e23b> in <module>() ----> 1 fquad = load_dataset("fquad") 11 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token) 614 raise FileNotFoundError("Couldn't find file at {}".format(url)) 615 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") --> 616 raise ConnectionError("Couldn't reach {}".format(url)) 617 618 # Try a second time ConnectionError: Couldn't reach https://storage.googleapis.com/illuin/fquad/train.json.zip ``` Does anyone know why that is and how to fix it?

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MDU6SXNzdWU4NTU5ODg0MTA=

Getting checksum error when trying to load lc_quad dataset

closed

NONE

I'm having issues loading the [lc_quad](https://huggingface.co/datasets/fquad) dataset by running: ```Python lc_quad = load_dataset("lc_quad") ``` which is giving me the following error: ``` Using custom data configuration default Downloading and preparing dataset lc_quad/default (download: 3.69 MiB, generated: 19.77 MiB, post-processed: Unknown size, total: 23.46 MiB) to /root/.cache/huggingface/datasets/lc_quad/default/2.0.0/5a98fe174603f5dec6df07edf1c2b4d2317210d2ad61f5a393839bca4d64e5a7... --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-42-404ace83f73c> in <module>() ----> 1 lc_quad = load_dataset("lc_quad") 3 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 37 if len(bad_urls) > 0: 38 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 39 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 40 logger.info("All the checksums matched successfully" + for_verification_name) 41 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/AskNowQA/LC-QuAD2.0/archive/master.zip'] ``` Does anyone know why this could be and how I fix it?

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MDU6SXNzdWU4NTU3MDk0MDA=

dataloading slow when using HUGE dataset

closed

NONE

Hi, When I use datasets with 600GB data, the dataloading speed increases significantly. I am experimenting with two datasets, and one is about 60GB and the other 600GB. Simply speaking, my code uses `datasets.set_format("torch")` function and let pytorch-lightning handle ddp training. When looking at the pytorch-lightning supported profile of two different runs, I see that fetching a batch(`get_train_batch`) consumes an unreasonable amount of time when data is large. What could be the cause? * 60GB data ``` Action | Mean duration (s) |Num calls | Total time (s) | Percentage % | ------------------------------------------------------------------------------------------------------------------------------------ Total | - |_ | 200.33 | 100 % | ------------------------------------------------------------------------------------------------------------------------------------ run_training_epoch | 71.994 |1 | 71.994 | 35.937 | run_training_batch | 0.64373 |100 | 64.373 | 32.133 | optimizer_step_and_closure_0 | 0.64322 |100 | 64.322 | 32.108 | training_step_and_backward | 0.61004 |100 | 61.004 | 30.452 | model_backward | 0.37552 |100 | 37.552 | 18.745 | model_forward | 0.22813 |100 | 22.813 | 11.387 | training_step | 0.22759 |100 | 22.759 | 11.361 | get_train_batch | 0.066385 |100 | 6.6385 | 3.3138 | ``` * 600GB data ``` Action | Mean duration (s) |Num calls | Total time (s) | Percentage % | ------------------------------------------------------------------------------------------------------------------------------------ Total | - |_ | 3285.6 | 100 % | ------------------------------------------------------------------------------------------------------------------------------------ run_training_epoch | 1397.9 |1 | 1397.9 | 42.546 | run_training_batch | 7.2596 |100 | 725.96 | 22.095 | optimizer_step_and_closure_0 | 7.2589 |100 | 725.89 | 22.093 | training_step_and_backward | 7.223 |100 | 722.3 | 21.984 | model_backward | 6.9662 |100 | 696.62 | 21.202 | get_train_batch | 6.322 |100 | 632.2 | 19.241 | model_forward | 0.24902 |100 | 24.902 | 0.75789 | training_step | 0.2485 |100 | 24.85 | 0.75633 | ```

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MDExOlB1bGxSZXF1ZXN0NjEzMzQwMTI2

Add code of conduct to the project

closed

MEMBER

Add code of conduct to the project and link it from README and CONTRIBUTING. This was already done in `transformers`.

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MDExOlB1bGxSZXF1ZXN0NjEzMTAxMzMw

Remove Python2 leftovers

closed

CONTRIBUTOR

This PR removes Python2 leftovers since this project aims for Python3.6+ (and as of 2020 Python2 is no longer officially supported)

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MDU6SXNzdWU4NTUyNjczODM=

making labels consistent across the datasets

closed

NONE

Hi For accessing the labels one can type ``` >>> a.features['label'] ClassLabel(num_classes=3, names=['entailment', 'neutral', 'contradiction'], names_file=None, id=None) ``` The labels however are not consistent with the actual labels sometimes, for instance in case of XNLI, the actual labels are 0,1,2, but if one try to access as above they are entailment, neutral,contradiction, it would be great to have the labels consistent. thanks

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MDU6SXNzdWU4NTUyNTI0MTU=

Got pyarrow error when loading a dataset while adding special tokens into the tokenizer

closed

NONE

I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below: Traceback (most recent call last): File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1687, in _map_single writer.write(example) File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 296, in write self.write_on_file() File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 270, in write_on_file pa_array = pa.array(typed_sequence) File "pyarrow/array.pxi", line 222, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 108, in __arrow_array__ out = out.cast(pa.list_(self.optimized_int_type)) File "pyarrow/array.pxi", line 810, in pyarrow.lib.Array.cast File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/pyarrow/compute.py", line 281, in cast return call_function("cast", [arr], options) File "pyarrow/_compute.pyx", line 465, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 294, in pyarrow._compute.Function.call File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127 Do you have any idea about it?

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MDExOlB1bGxSZXF1ZXN0NjEzMDAwMzYw

Updating citation information on LinCE readme

closed

CONTRIBUTOR

Hi! I just updated the citation information in this PR. It had an additional bibtex from one of the datasets used in LinCE and then the LinCE bibtex. I removed the former and added a link that shows the full list of citations for each dataset. Thanks!

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MDExOlB1bGxSZXF1ZXN0NjEyOTU1MzM2

Add configurable options to `seqeval` metric

closed

CONTRIBUTOR

Fixes #2148 Adds options to use strict mode, different schemes of evaluation, sample weight and adjust zero_division behavior, if encountered. `seqeval` provides schemes as objects, hence dynamic import from string, to avoid making the user do the import (thanks to @albertvillanova for the `importlib` idea).

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MDExOlB1bGxSZXF1ZXN0NjEyODg4MzA5

updated banking77 train and test data

closed

NONE

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MDExOlB1bGxSZXF1ZXN0NjEyNDM2ODMx

Add classes GenerateMode, DownloadConfig and Version to the documentation

closed

MEMBER

Add documentation for classes `GenerateMode`, `DownloadConfig` and `Version`. Update the docstring of `load_dataset` to create cross-reference links to the classes. Related to #2187.

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MDExOlB1bGxSZXF1ZXN0NjEyNDM1NTE3

Fix ArrowWriter overwriting features in ArrowBasedBuilder

closed

MEMBER

This should fix the issues with CSV loading experienced in #2153 and #2200. The CSV builder is an ArrowBasedBuilder that had an issue with its ArrowWriter used to write the arrow file from the csv data. The writer wasn't initialized with the features passed by the user. Therefore the writer was inferring the features from the arrow data, discarding the features passed by the user. I fixed that and I updated the tests

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MDU6SXNzdWU4NTQ0NDk2NTY=

_prepare_split will overwrite DatasetBuilder.info.features

closed

NONE

Hi, here is my issue: I initialized a Csv datasetbuilder with specific features: ``` def get_dataset_features(data_args): features = {} if data_args.text_features: features.update({text_feature: hf_features.Value("string") for text_feature in data_args.text_features.strip().split(",")}) if data_args.num_features: features.update({text_feature: hf_features.Value("float32") for text_feature in data_args.num_features.strip().split(",")}) if data_args.label_classes: features["label"] = hf_features.ClassLabel(names=data_args.label_classes.strip().split(",")) else: features["label"] = hf_features.Value("float32") return hf_features.Features(features) datasets = load_dataset(extension, data_files=data_files, sep=data_args.delimiter, header=data_args.header, column_names=data_args.column_names.split(",") if data_args.column_names else None, features=get_dataset_features(data_args=data_args)) ``` The `features` is printout as below before `builder_instance.as_dataset` is called: ``` {'label': ClassLabel(num_classes=2, names=['unacceptable', 'acceptable'], names_file=None, id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)} ```` But after the `builder_instance.as_dataset` is called for Csv dataset builder, the `features` is changed to: ``` {'label': Value(dtype='int64', id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)} ``` After digged into the code, I releazed that in `ArrowBasedBuilder._prepare_split`, the DatasetBuilder's info's features will be overwrited by `ArrowWriter`'s `_features`. But `ArrowWriter` is initailized without passing `features`. So my concern is: It's this overwrite must be done, or, should it be an option to pass features in `_prepare_split` function?

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MDExOlB1bGxSZXF1ZXN0NjEyMzY0ODU3

Fix backward compatibility in Dataset.load_from_disk

closed

MEMBER

Fix backward compatibility when loading from disk an old dataset saved to disk with indices using key "_indices_data_files". Related to #2195.

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MDExOlB1bGxSZXF1ZXN0NjEyMzE0MTIz

added file_permission in load_dataset

closed

CONTRIBUTOR

As discussed in #2065 I've added `file_permission` argument in `load_dataset`. Added mainly 2 things here: 1) Permission of downloaded datasets when converted to .arrow files can be changed with argument `file_permission` argument in `load_dataset` (default is 0o644 only) 2) Incase the user uses `map` later on to generate another cache file of dataset, it ensures the permissions of newly generated file are similar to that of` *-train.arrow` file inside cache_dir for that dataset.

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MDExOlB1bGxSZXF1ZXN0NjEyMzEzMzQw

fix missing indices_files in load_form_disk

closed

MEMBER

This should fix #2195 `load_from_disk` was failing if there was no "_indices_files" field in state.json. This can happen if the dataset has no indices mapping

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MDU6SXNzdWU4NTQxMjYxMTQ=

`load_dataset` caches two arrow files?

closed

NONE

Hi, I am using datasets to load large json file of 587G. I checked the cached folder and found that there are two arrow files created: * `cache-ed205e500a7dc44c.arrow` - 355G * `json-train.arrow` - 582G Why is the first file created? If I delete it, would I still be able to `load_from_disk`?

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MDU6SXNzdWU4NTQwNzAxOTQ=

KeyError: '_indices_files' in `arrow_dataset.py`

closed

NONE

After pulling the latest master, I'm getting a crash when `load_from_disk` tries to load my local dataset. Trace: ``` Traceback (most recent call last): File "load_data.py", line 11, in <module> dataset = load_from_disk(SRC) File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/load.py", line 784, in load_from_disk return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory) File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/dataset_dict.py", line 692, in load_from_disk dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory) File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 634, in load_from_disk if state["_indices_files"]: KeyError: '_indices_files' ``` I believe this is the line causing the error since there may not be a `_indices_files` key in the older versions: https://github.com/huggingface/datasets/blob/b70141e3c5149430951773aaa0155555c5fb3e76/src/datasets/arrow_dataset.py#L634 May I suggest using `state.get()` instead of directly indexing the dictionary? @lhoestq

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MDU6SXNzdWU4NTM5MDk0NTI=

py3.7: TypeError: can't pickle _LazyModule objects

closed

CONTRIBUTOR

While this works fine with py3.8, under py3.7, with a totally new conda env and transformers install: ``` git clone https://github.com/huggingface/transformers cd transformers pip install -e .[testing] export BS=1; rm -rf /tmp/test-clm; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0 python \ examples/language-modeling/run_clm.py --model_name_or_path distilgpt2 --dataset_name wikitext \ --dataset_config_name wikitext-2-raw-v1 --do_train --max_train_samples 1 \ --per_device_train_batch_size $BS --output_dir /tmp/test-clm --block_size 128 --logging_steps 1 \ --fp16 ``` ``` Traceback (most recent call last): File "examples/language-modeling/run_clm.py", line 453, in <module> main() File "examples/language-modeling/run_clm.py", line 336, in main load_from_cache_file=not data_args.overwrite_cache, File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/dataset_dict.py", line 303, in map for k, dataset in self.items() File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp> for k, dataset in self.items() File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1259, in map update_data=update_data, File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 157, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 158, in wrapper self._fingerprint, transform, kwargs_for_fingerprint File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 105, in update_fingerprint hasher.update(transform_args[key]) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 57, in update self.m.update(self.hash(value).encode("utf-8")) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 53, in hash return cls.hash_default(value) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 46, in hash_default return cls.hash_bytes(dumps(value)) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 389, in dumps dump(obj, file) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 361, in dump Pickler(file, recurse=True).dump(obj) File "/home/stas/anaconda3/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 556, in save_function obj=obj, File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/stas/anaconda3/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 524, in save rv = reduce(self.proto) TypeError: can't pickle _LazyModule objects ``` ``` $ python --version Python 3.7.4 $ python -m torch.utils.collect_env Collecting environment information... PyTorch version: 1.8.0.dev20210110+cu110 Is debug build: False CUDA used to build PyTorch: 11.0 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.2 LTS (x86_64) GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0 Clang version: 10.0.0-4ubuntu1 CMake version: version 3.16.3 ``` Thanks.

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MDU6SXNzdWU4NTM3MjU3MDc=

Filtering/mapping on one column is very slow

closed

CONTRIBUTOR

I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation. I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that— I'm not very familiar with the pyarrow API. I know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset. PS: This is definitely not a "dataset request." I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.

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MDExOlB1bGxSZXF1ZXN0NjExNjE5NTY0

Fix typo in huggingface hub

closed

MEMBER

pip knows how to resolve to `huggingface_hub`, but conda doesn't! The `packaging` dependency is also required for the build to complete.

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MDExOlB1bGxSZXF1ZXN0NjExNDY1Nzc0

Refactorize tests to use Dataset as context manager

closed

MEMBER

Refactorize Dataset tests to use Dataset as context manager.

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MDU6SXNzdWU4NTMxODE1NjQ=

News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs

closed

NONE

I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi. ``` train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]') val_ds = load_dataset("news_commentary", "ar-en", split='train[98%:]') # filtering out examples that are not ar-en translations but ar-hi val_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True) ``` * I'm fairly new to using datasets so I might be doing something wrong

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MDU6SXNzdWU4NTMwNTI4OTE=

save_to_disk doesn't work when we use concatenate_datasets function before creating the final dataset_object.

closed

NONE

As you can see, it saves the entire dataset. @lhoestq You can check by going through the following example, ``` from datasets import load_from_disk,concatenate_datasets loaded_data=load_from_disk('/home/gsir059/HNSW-ori/my_knowledge_dataset') n=20 kb_list=[loaded_data.shard(n, i, contiguous=True) for i in range(n)] final_dataset=concatenate_datasets([kb_list[1],kb_list[2]]) final_dataset.save_to_disk('/home/gsir059/haha/k.arrow') ```

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MDU6SXNzdWU4NTMwNDQxNjY=

Duplicate data in Timit dataset

closed

NONE

I ran a simple code to list all texts in Timit dataset and the texts were all the same. Is this dataset corrupted? **Code:** timit = load_dataset("timit_asr") print(*timit['train']['text'], sep='\n') **Result:** Would such an act of refusal be useful? Would such an act of refusal be useful? Would such an act of refusal be useful? Would such an act of refusal be useful? ... ... Would such an act of refusal be useful?

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MDU6SXNzdWU4NTI5Mzk3MzY=

Question (potential issue?) related to datasets caching

open

NONE

I thought I had disabled datasets caching in my code, as follows: ``` from datasets import set_caching_enabled ... def main(): # disable caching in datasets set_caching_enabled(False) ``` However, in my log files I see messages like the following: ``` 04/07/2021 18:34:42 - WARNING - datasets.builder - Using custom data configuration default-888a87931cbc5877 04/07/2021 18:34:42 - WARNING - datasets.builder - Reusing dataset csv (xxxx/cache-transformers/datasets/csv/default-888a87931cbc5877/0.0.0/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93 ``` Can you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!

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MDExOlB1bGxSZXF1ZXN0NjExMDMxNzE0

GEM: new challenge sets

closed

MEMBER

This PR updates the GEM dataset to: - remove extraneous fields in WikiAuto after https://github.com/huggingface/datasets/pull/2171 fixed the source - add context and services to Schema Guided Dialog - Add new or update challenge sets for MLSUM ES and DE, XSUM, and SGD

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MDU6SXNzdWU4NTI2ODQzOTU=

.map() and distributed training

closed

MEMBER

Hi, I have a question regarding distributed training and the `.map` call on a dataset. I have a local dataset "my_custom_dataset" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`. `dataset` is then tokenized: ```python datasets = load_from_disk(dataset_path=my_path) [...] def tokenize_function(examples): return tokenizer(examples[text_column_name]) logger.info("Mapping dataset to tokenized dataset.") tokenized_datasets = datasets.map( tokenize_function, batched=True, num_proc=preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=True, ) ``` I am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path/train` (there is only a train split). When I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect. Everything so far was done by launching a **single process script**. I now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. I tried adding the `cache_file_name` argument: `cache_file_name={"train": my_path/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it. **My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training. - I am following the same structure as the examples of transformers (more specifically [run_clm.py](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_clm.py) in my case) - I am using 1.5.0 version of datasets if that matters.

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MDExOlB1bGxSZXF1ZXN0NjEwODIxMTc0

Implementation of class_encode_column

closed

CONTRIBUTOR

Addresses #2176 I'm happy to discuss the API and internals!

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MDExOlB1bGxSZXF1ZXN0NjEwNzU3MjUz

Fix s3fs tests for py36 and py37+

closed

MEMBER

Recently several changes happened: 1. latest versions of `fsspec` require python>3.7 for async features 2. `s3fs` added a dependency on `aiobotocore`, which is not compatible with the `moto` s3 mock context manager This PR fixes both issues, by pinning `fsspec` and `s3fs` for python 3.6, and by using `moto` in server mode to support running the tests on python>=3.7 with the latest version of `fsspec` and `s3fs`. cc @philschmid

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MDExOlB1bGxSZXF1ZXN0NjEwNjQ2MDIy

Set default in-memory value depending on the dataset size

closed

MEMBER

Set a default value for `in_memory` depending on the size of the dataset to be loaded. Close #2179. TODO: - [x] Add a section in the docs about this. - ~Add a warning if someone tries to specify `cache_file_name=` in `map`, `filter` etc. on a dataset that is in memory, since the computation is not going to be cached in this case.~

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MDU6SXNzdWU4NTIyNjE2MDc=

Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries)

closed

NONE

Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project. When loading a huge json file of 500GB, pyarrow complains as follows: ``` Traceback (most recent call last): File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 531, in incomplete_dir yield tmp_dir File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 573, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 650, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 1027, in _prepare_split for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose): File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__ for obj in iterable: File "/app/.cache/huggingface/modules/datasets_modules/datasets/json/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641/json.py", line 83, in _generate_tables parse_options=self.config.pa_parse_options, File "pyarrow/_json.pyx", line 247, in pyarrow._json.read_json File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?) ``` When using only a small portion of the sample file, say first 100 lines, it works perfectly well.. I see that it is the error from pyarrow, but could you give me a hint or possible solutions? #369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance!

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MDExOlB1bGxSZXF1ZXN0NjEwNTQxOTA2

Add tel to xtreme tatoeba

closed

MEMBER

This should fix issue #2149

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MDU6SXNzdWU4NTIyMzc5NTc=

Load small datasets in-memory instead of using memory map

closed

MEMBER

Currently all datasets are loaded using memory mapping by default in `load_dataset`. However this might not be necessary for small datasets. If a dataset is small enough, then it can be loaded in-memory and: - its memory footprint would be small so it's ok - in-memory computations/queries would be faster - the caching on-disk would be disabled, making computations even faster (no I/O bound because of the disk) - but running the same computation a second time would recompute everything since there would be no cached results on-disk. But this is probably fine since computations would be fast anyway + users should be able to provide a cache filename if needed. Therefore, maybe the default behavior of `load_dataset` should be to load small datasets in-memory and big datasets using memory mapping.

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MDExOlB1bGxSZXF1ZXN0NjEwNTA1Mjg1

Fix cast memory usage by using map on subtables

closed

MEMBER

The `cast` operation on a pyarrow Table may create new arrays in memory. This is an issue since users expect memory mapped datasets to not fill up the RAM. To fix that I used `map` to write a new arrow file on disk when cast is used. To make things more convenient I introduced the `arrow` formatting of a dataset, to make it return pyarrow tables instead of python dicts. This way one can use pyarrow transforms directly when using `map`. edit: we'll use the same mechanism for `filter`

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MDExOlB1bGxSZXF1ZXN0NjEwMzc5MDYx

add social thumbnial

closed

MEMBER

# What does this PR do? I added OpenGraph/ Twitter Card support to the docs to create nice social thumbnails.  To be able to add these I needed to install `sphinxext-opengraph`. I came across this [issue](https://github.com/readthedocs/readthedocs.org/issues/1758) on the readthedocs repo saying that since someone has built this plugin they are not integrating and providing documentation to it. That's why I added it for creating the documentation. The repository can be found [here](https://github.com/wpilibsuite/sphinxext-opengraph/tree/main). P.S. It seemed that `make style` never ran for `docs/` i hope the changes are okay otherwise I'll revert it.

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MDU6SXNzdWU4NTE4NjU3OTU=

Converting a Value to a ClassLabel

closed

NONE

Hi! In the docs for `cast`, it's noted that `For non-trivial conversion, e.g. string <-> ClassLabel you should use map() to update the Dataset.` Would it be possible to have an example that demonstrates such a string <-> ClassLabel conversion using `map`? Thanks!

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MDU6SXNzdWU4NTE4MzYwOTY=

dataset.search_batch() function outputs all -1 indices sometime.

closed

NONE

I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, "IVF65536_HNSW32,Flat")**. During the retrieval phase exactly in [this line of retrieval_rag.py](https://github.com/huggingface/transformers/blob/master/src/transformers/models/rag/retrieval_rag.py#L231) an error issue when all retrieved indices are -1. Please refer to the screenshot of a PID worker.  Here, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ? Is this a problem of the index, where the faiss can't find any similar vector? Is there documentation on the output index being -1? @lhoestq

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MDExOlB1bGxSZXF1ZXN0NjA5ODE2OTQ2

Pin docutils for better doc

closed

CONTRIBUTOR

The latest release of docutils make the navbar in the documentation weird and the Markdown wrongly interpreted:  We had the same problem in Transformers and solved it by pinning docutils (a dep of sphinx). You can see the version after the change [here](https://32769-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html).

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MDExOlB1bGxSZXF1ZXN0NjA5Nzk2NzI2

Add OpenSLR dataset

closed

CONTRIBUTOR

OpenSLR (https://openslr.org/) is a site devoted to hosting speech and language resources, such as training corpora for speech recognition, and software related to speech recognition. There are around 80 speech datasets listed in OpenSLR, currently this PR includes only 9 speech datasets SLR41, SLR42, SLR43, SLR44, SLR63, SLR64, SLR65, SLR66 and SLR69 (Javanese, Khmer, Nepali and Sundanese, Malayalam, Marathi, Tamil, Telugu and Catalan). I can add other speech datasets gradually next time.

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MDExOlB1bGxSZXF1ZXN0NjA5Njg4ODgx

Pin fsspec lower than 0.9.0

closed

MEMBER

Today's release of `fsspec` 0.9.0 implied a new release of `s3fs` 0.6.0 but this version breaks the CI (see [here](https://app.circleci.com/pipelines/github/huggingface/datasets/5312/workflows/490f3240-cd1c-4dd1-bb60-b416771c5584/jobs/32734) for example) I'm pinning `fsspec` until this has been resolved

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MDExOlB1bGxSZXF1ZXN0NjA5NTY4MDcw

Fixed the link to wikiauto training data.

closed

CONTRIBUTOR

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MDU6SXNzdWU4NTA5MTMyMjg=

Wikipedia historic dumps are deleted but hf/datasets hardcodes dump date

open

NONE

Wikimedia does not keep all historical dumps. For example, as of today https://dumps.wikimedia.org/kowiki/ only provides ``` 20201220/ 02-Feb-2021 01:36 - 20210101/ 21-Feb-2021 01:26 - 20210120/ 02-Mar-2021 01:25 - 20210201/ 21-Mar-2021 01:26 - 20210220/ 02-Apr-2021 01:26 - 20210301/ 03-Mar-2021 08:10 - 20210320/ 21-Mar-2021 18:13 - 20210401/ 03-Apr-2021 10:08 - latest/ 03-Apr-2021 10:08 - ``` However, the wikipedia dataset provided in the library, only supports the following configs, none of which are applicable anymore when disregarding the cached datasets: ``` ValueError: BuilderConfig 20210401.ko not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu'] ``` The cached datasets: ``` % aws s3 --no-sign-request --endpoint-url https://storage.googleapis.com ls s3://huggingface-nlp/cache/datasets/wikipedia/ PRE 20200501.de/ PRE 20200501.en/ PRE 20200501.fr/ PRE 20200501.frr/ PRE 20200501.it/ PRE 20200501.simple/ ```

https://api.github.com/repos/huggingface/datasets/issues/2170/timeline

https://api.github.com/repos/huggingface/datasets/issues/2169

https://api.github.com/repos/huggingface/datasets

https://api.github.com/repos/huggingface/datasets/issues/2169/labels{/name}

https://api.github.com/repos/huggingface/datasets/issues/2169/comments

https://api.github.com/repos/huggingface/datasets/issues/2169/events