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PR_kwDODunzps45XlWW

Extend support for streaming datasets that use xml.dom.minidom.parse

closed

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This PR extends the support in streaming mode for datasets that use `xml.dom.minidom.parse`, by patching that function. This PR adds support for streaming datasets like "Yaxin/SemEval2015". Fix #4453.

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PR_kwDODunzps45Vnzu

Use config_id to check split sizes instead of config name

closed

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Fix https://github.com/huggingface/datasets/issues/4462

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I_kwDODunzps5LZ5Qz

NonMatchingSplitsSizesError when passing a dataset configuration parameter

closed

MEMBER

As noticed in https://github.com/huggingface/datasets/pull/4125 when a dataset config class has a parameter that reduces the number of examples (e.g. named `max_examples`), then loading the dataset and passing `max_examples` raises `NonMatchingSplitsSizesError`. This is because it will check for expected the number of examples of the config with the same name without taking into account the `max_examples` parameter. This can be fixed by checking the expected number of examples using the **config id** instead of name. Indeed the config id corresponds to the config name + an optional suffix that depends on the config parameters

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I_kwDODunzps5LY1LD

AttributeError: module 'datasets' has no attribute 'load_dataset'

closed

NONE

## Describe the bug I have piped install datasets, but this package doesn't have these attributes: load_dataset, load_metric. ## Environment info - `datasets` version: 1.9.0 - Platform: Linux-5.13.0-44-generic-x86_64-with-debian-bullseye-sid - Python version: 3.6.13 - PyArrow version: 6.0.1

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https://github.com/huggingface/datasets/pull/4460

PR_kwDODunzps45UHIs

Drop Python 3.6 support

open

CONTRIBUTOR

Remove the fallback imports/checks in the code needed for Python 3.6 and update the requirements/CI files. Also, use Python types for the NumPy dtype wherever possible to avoid deprecation warnings in newer NumPy versions.

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PR_kwDODunzps45UFc8

Add and fix language tags for udhr dataset

closed

MEMBER

Related to #4362.

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PR_kwDODunzps45QZCU

First draft of the docs for TF + Datasets

closed

MEMBER

I might cc a few of the other TF people to take a look when this is closer to being finished, but it's still a draft for now.

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I_kwDODunzps5LS4jp

Workflow for Tabular data

open

MEMBER

Tabular data are treated very differently than data for NLP, audio, vision, etc. and therefore the worflow for tabular data in `datasets` is not ideal. For example for tabular data, it is common to use pandas/spark/dask to process the data, and then load the data into X and y (X is an array of features and y an array of labels), then train_test_split and finally feed the data to a machine learning model. In `datasets` the workflow is different: we use load_dataset, then map, then train_test_split (if we only have a train split) and we end up with columnar dataset splits, not formatted as X and y. Right now, it is already possible to convert a dataset from and to pandas, but there are still many things that could improve the workflow for tabular data: - be able to load the data into X and y - be able to load a dataset from the output of spark or dask (as far as I know it's usually csv or parquet files on S3/GCS/HDFS etc.) - support "unsplit" datasets explicitly, instead of putting everything in "train" by default cc @adrinjalali @merveenoyan feel free to complete/correct this :) Feel free to also share ideas of APIs that would be super intuitive in your opinion !

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PR_kwDODunzps45O5F9

Update data URLs in fever dataset

closed

MEMBER

As stated in their website, data owners updated their URLs on 28/04/2022. This PR updates the data URLs. Fix #4452.

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I_kwDODunzps5LQuQd

Dataset Viewer issue for Yaxin/SemEval2015

closed

NONE

### Link _No response_ ### Description the link could not visit ### Owner _No response_

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I_kwDODunzps5LQuC5

Dataset Viewer issue for Yaxin/SemEval2015

closed

NONE

### Link _No response_ ### Description _No response_ ### Owner _No response_

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I_kwDODunzps5LQKx2

Trying to load FEVER dataset results in NonMatchingChecksumError

closed

NONE

## Describe the bug Trying to load the `fever` dataset fails with `datasets.utils.info_utils.NonMatchingChecksumError`. I tried with `download_mode="force_redownload"` but that did not fix the error. I also tried with `ignore_verification=True` but then that raised a `json.decoder.JSONDecodeError`. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('fever', 'v1.0') # Fails with NonMatchingChecksumError dataset = load_dataset('fever', 'v1.0', download_mode="force_redownload") # Fails with NonMatchingChecksumError dataset = load_dataset('fever', 'v1.0', ignore_verification=True)` # Fails with JSONDecodeError ``` ## Expected results I expect this call to return with no error raised. ## Actual results With `ignore_verification=False`: ``` *** datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://s3-eu-west-1.amazonaws.com/fever.public/train.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev_public.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_test.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_test.jsonl'] ``` With `ignore_verification=True`: ``` *** json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.3.dev0 - Platform: Linux-4.15.0-50-generic-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.2

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PR_kwDODunzps45LkGc

Use newer version of multi-news with fixes

closed

CONTRIBUTOR

Closes #4430.

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PR_kwDODunzps45Kzwh

Update README.md of fquad

closed

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I_kwDODunzps5LLVX2

Rj

closed

NONE

import android.content.DialogInterface; import android.database.Cursor; import android.os.Bundle; import android.view.View; import android.widget.ArrayAdapter; import android.widget.Button; import android.widget.EditText; import android.widget.Toast; import androidx.appcompat.app.AlertDialog; import androidx.appcompat.app.AppCompatActivity; public class MainActivity extends AppCompatActivity { private EditText editTextID; private EditText editTextName; private EditText editTextNum; private String name; private int number; private String ID; private dbHelper db; @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_main); db = new dbHelper(this); editTextID = findViewById(R.id.editText1); editTextName = findViewById(R.id.editText2); editTextNum = findViewById(R.id.editText3); Button buttonSave = findViewById(R.id.button); Button buttonRead = findViewById(R.id.button2); Button buttonUpdate = findViewById(R.id.button3); Button buttonDelete = findViewById(R.id.button4); Button buttonSearch = findViewById(R.id.button5); Button buttonDeleteAll = findViewById(R.id.button6); buttonSave.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { name = editTextName.getText().toString(); String num = editTextNum.getText().toString(); if (name.isEmpty() || num.isEmpty()) { Toast.makeText(MainActivity.this, "Cannot Submit Empty Fields", Toast.LENGTH_SHORT).show(); } else { number = Integer.parseInt(num); try { // Insert Data db.insertData(name, number); // Clear the fields editTextID.getText().clear(); editTextName.getText().clear(); editTextNum.getText().clear(); } catch (Exception e) { e.printStackTrace(); } } } }); buttonRead.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { final ArrayAdapter<String> adapter = new ArrayAdapter<>(MainActivity.this, android.R.layout.simple_list_item_1); String name; String num; String id; try { Cursor cursor = db.readData(); if (cursor != null && cursor.getCount() > 0) { while (cursor.moveToNext()) { id = cursor.getString(0); // get data in column index 0 name = cursor.getString(1); // get data in column index 1 num = cursor.getString(2); // get data in column index 2 // Add SQLite data to listView adapter.add("ID :- " + id + "\n" + "Name :- " + name + "\n" + "Number :- " + num + "\n\n"); } } else { adapter.add("No Data"); } cursor.close(); } catch (Exception e) { e.printStackTrace(); } // show the saved data in alertDialog AlertDialog.Builder builder = new AlertDialog.Builder(MainActivity.this); builder.setTitle("SQLite saved data"); builder.setIcon(R.mipmap.app_icon_foreground); builder.setAdapter(adapter, new DialogInterface.OnClickListener() { @Override public void onClick(DialogInterface dialog, int which) { } }); builder.setPositiveButton("OK", new DialogInterface.OnClickListener() { @Override public void onClick(DialogInterface dialog, int which) { dialog.cancel(); } }); AlertDialog dialog = builder.create(); dialog.show(); } }); buttonUpdate.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { name = editTextName.getText().toString(); String num = editTextNum.getText().toString(); ID = editTextID.getText().toString(); if (name.isEmpty() || num.isEmpty() || ID.isEmpty()) { Toast.makeText(MainActivity.this, "Cannot Submit Empty Fields", Toast.LENGTH_SHORT).show(); } else { number = Integer.parseInt(num); try { // Update Data db.updateData(ID, name, number); // Clear the fields editTextID.getText().clear(); editTextName.getText().clear(); editTextNum.getText().clear(); } catch (Exception e) { e.printStackTrace(); } } } }); buttonDelete.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { ID = editTextID.getText().toString(); if (ID.isEmpty()) { Toast.makeText(MainActivity.this, "Please enter the ID", Toast.LENGTH_SHORT).show(); } else { try { // Delete Data db.deleteData(ID); // Clear the fields editTextID.getText().clear(); editTextName.getText().clear(); editTextNum.getText().clear(); } catch (Exception e) { e.printStackTrace(); } } } }); buttonDeleteAll.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { // Delete all data // You can simply delete all the data by calling this method --> db.deleteAllData(); // You can try this also AlertDialog.Builder builder = new AlertDialog.Builder(MainActivity.this); builder.setIcon(R.mipmap.app_icon_foreground); builder.setTitle("Delete All Data"); builder.setCancelable(false); builder.setMessage("Do you really need to delete your all data ?"); builder.setPositiveButton("Yes", new DialogInterface.OnClickListener() { @Override public void onClick(DialogInterface dialog, int which) { // User confirmed , now you can delete the data db.deleteAllData(); // Clear the fields editTextID.getText().clear(); editTextName.getText().clear(); editTextNum.getText().clear(); } }); builder.setNegativeButton("No", new DialogInterface.OnClickListener() { @Override public void onClick(DialogInterface dialog, int which) { // user not confirmed dialog.cancel(); } }); AlertDialog dialog = builder.create(); dialog.show(); } }); buttonSearch.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { ID = editTextID.getText().toString(); if (ID.isEmpty()) { Toast.makeText(MainActivity.this, "Please enter the ID", Toast.LENGTH_SHORT).show(); } else { try { // Search data Cursor cursor = db.searchData(ID); if (cursor.moveToFirst()) { editTextName.setText(cursor.getString(1)); editTextNum.setText(cursor.getString(2)); Toast.makeText(MainActivity.this, "Data successfully searched", Toast.LENGTH_SHORT).show(); } else { Toast.makeText(MainActivity.this, "ID not found", Toast.LENGTH_SHORT).show(); editTextNum.setText("ID Not found"); editTextName.setText("ID not found"); } cursor.close(); } catch (Exception e) { e.printStackTrace(); } } } }); } }

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I_kwDODunzps5LKNDx

New Preprocessing Feature - Deduplication [Request]

open

NONE

**Is your feature request related to a problem? Please describe.** Many large datasets are full of duplications and it has been shown that deduplicating datasets can lead to better performance while training, and more truthful evaluation at test-time. A feature that allows one to easily deduplicate a dataset can be cool! **Describe the solution you'd like** We can define a function and keep only the first/last data-point that yields the value according to this function. **Describe alternatives you've considered** The clear alternative is to repeat a clear boilerplate every time someone want to deduplicate a dataset.

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PR_kwDODunzps45E4A-

Minor fixes/improvements in `scene_parse_150` card

closed

CONTRIBUTOR

Add `paperswithcode_id` and fix some links in the `scene_parse_150` card.

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PR_kwDODunzps45E1Qb

Add missing kwargs to docstrings

closed

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PR_kwDODunzps45EjtA

Fix missing args in docstring of load_dataset_builder

closed

MEMBER

Currently, the docstring of `load_dataset_builder` only contains the first parameter `path` (no other): - https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/loading_methods#datasets.load_dataset_builder.path

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PR_kwDODunzps45D2XX

Fix kwargs in docstrings

closed

MEMBER

To fix the rendering of `**kwargs` in docstrings, a parentheses must be added afterwards. See: - huggingface/doc-builder/issues/235

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I_kwDODunzps5LFBE-

Dataset Viewer issue for openclimatefix/nimrod-uk-1km

open

NONE

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I_kwDODunzps5LBIxc

Dataset Viewer issue for amazon_polarity

closed

MEMBER

### Link https://huggingface.co/datasets/amazon_polarity/viewer/amazon_polarity/test ### Description For some reason the train split is OK but the test split is not for this dataset: ``` Server error Status code: 400 Exception: FileNotFoundError Message: [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/amazon_polarity/__init__.py' ``` ### Owner No

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I_kwDODunzps5LBDvQ

Dataset Viewer issue for aeslc

closed

MEMBER

### Link https://huggingface.co/datasets/aeslc ### Description The dataset viewer can't find `dataset_infos.json` in it's cache: ``` Server error Status code: 400 Exception: FileNotFoundError Message: [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/aeslc/eb8e30234cf984a58ebe9f205674597ac1db2ec91e7321cd7f36864f7e3671b8/dataset_infos.json' ``` ### Owner No

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PR_kwDODunzps44_io_

Update docs around audio and vision

closed

MEMBER

As part of the strategy to center the docs around the different modalities, this PR updates the quickstart to include audio and vision examples. This improves the developer experience by making audio and vision content more discoverable, enabling users working in these modalities to also quickly get started without digging too deeply into the docs. Other changes include: - Moved the installation guide to the Get Started section because it should be part of a user's onboarding to the library before exploring tutorials or how-to's. - Updated the native TF code at creating a `tf.data.Dataset` because it was throwing an error. The `to_tensor()` bit was redundant and removing it fixed the error (please double-check me here!). - Added some UI components to the quickstart so it's easier for users to navigate directly to the relevant section with context about what to expect. - Reverted to the code tabs for content that don't have any framework-specific text. I think this saves space compared to the code blocks. We'll still use the code blocks if the `torch` text is different from the `tf` text. Let me know what you think, especially if we should include some code samples for training a model in the audio/vision sections. I left this out since we already showed it in the NLP section. I want to keep the focus on using Datasets to load and process a dataset, and not so much the training part. Maybe we can add links to the Transformers docs instead?

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I_kwDODunzps5LAi4_

TIMIT won't load after manual download: Errors about files that don't exist

closed

NONE

## Describe the bug I get the message from HuggingFace that it must be downloaded manually. From the URL provided in the message, I got to UPenn page for manual download. (UPenn apparently want $250? for the dataset??) ...So, ok, I obtained a copy from a friend and also a smaller version from Kaggle. But in both cases the HF dataloader fails; it is looking for files that don't exist anywhere in the dataset: it is looking for files with lower-case letters like "**test*" (all the filenames in both my copies are uppercase) and certain file extensions that exclude the .DOC which is provided in TIMIT: ## Steps to reproduce the bug ```python data = load_dataset('timit_asr', 'clean')['train'] ``` ## Expected results The dataset should load with no errors. ## Actual results This error message: ``` File "/home/ubuntu/envs/data2vec/lib/python3.9/site-packages/datasets/data_files.py", line 201, in resolve_patterns_locally_or_by_urls raise FileNotFoundError(error_msg) FileNotFoundError: Unable to resolve any data file that matches '['**test*', '**eval*']' at /home/ubuntu/datasets/timit with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] ``` But this is a strange sort of error: why is it looking for lower-case file names when all the TIMIT dataset filenames are uppercase? Why does it exclude .DOC files when the only parts of the TIMIT data set with "TEST" in them have ".DOC" extensions? ...I wonder, how was anyone able to get this to work in the first place? The files in the dataset look like the following: ``` ³ PHONCODE.DOC ³ PROMPTS.TXT ³ SPKRINFO.TXT ³ SPKRSENT.TXT ³ TESTSET.DOC ``` ...so why are these being excluded by the dataset loader? ## Environment info - `datasets` version: 2.2.2 - Platform: Linux-5.4.0-1060-aws-x86_64-with-glibc2.27 - Python version: 3.9.9 - PyArrow version: 8.0.0 - Pandas version: 1.4.2

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PR_kwDODunzps44-vhC

Fix docstring of inspect_dataset

closed

MEMBER

As pointed out by @sgugger: - huggingface/doc-builder/issues/235

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PR_kwDODunzps44-uRW

Add missing columns to `blended_skill_talk`

closed

CONTRIBUTOR

Adds the missing columns to `blended_skill_talk` to align the loading logic with [ParlAI](https://github.com/facebookresearch/ParlAI/blob/main/parlai/tasks/blended_skill_talk/build.py). Fix #4426

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PR_kwDODunzps449FsU

Fix directory names for LDC data in timit_asr dataset

closed

MEMBER

Related to: - #4422

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I_kwDODunzps5K89_o

Load a local cached dataset that has been modified

closed

NONE

## Describe the bug I have loaded a dataset as follows: ``` d = load_dataset("emotion", split="validation") ``` Afterwards I make some modifications to the dataset via a `map` call: ``` d.map(some_update_func, cache_file_name=modified_dataset) ``` This generates a cached version of the dataset on my local system in the same directory as the original download of the data (/path/to/cache). Running an `ls` returns: ``` modified_dataset dataset_info.json emotion-test.arrow emotion-train.arrow emotion-validation.arrow ``` as expected. However, when I try to load up the modified cached dataset via a call to ``` modified = load_dataset("emotion", split="validation", data_files="/path/to/cache/modified_dataset") ``` it simply redownloads a new version of the dataset and dumps to a new cache rather than loading up the original modified dataset: ``` Using custom data configuration validation-cdbf51685638421b Downloading and preparing dataset emotion/validation to ... ``` How am I supposed to load the original modified local cache copy of the dataset? ## Environment info - `datasets` version: 2.2.2 - Platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.2

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PR_kwDODunzps443mAr

Fix dummy dataset generation script for handling nested types of _URLs

closed

CONTRIBUTOR

It seems that when user specify nested _URLs structures in their dataset script. An error will be raised when generating dummy dataset. I think the types of all elements in `dummy_data_dict.values()` should be checked because they may have different types. Linked to issue #4428 PS: I am not sure whether my code fix this issue in a proper way.

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PR_kwDODunzps442P5L

Fix script fetching and local path handling in `inspect_dataset` and `inspect_metric`

closed

CONTRIBUTOR

Fix #4348

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PR_kwDODunzps441JmK

Fix builder docstring

closed

MEMBER

Currently, the args of `DatasetBuilder` do not appear in the docs: https://huggingface.co/docs/datasets/v2.1.0/en/package_reference/builder_classes#datasets.DatasetBuilder

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PR_kwDODunzps44x5aG

Add personaldialog datasets

closed

CONTRIBUTOR

It seems that all tests are passed

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I_kwDODunzps5KxNEv

Add ability to load newer, cleaner version of Multi-News

closed

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** The [Multi-News dataloader points to the original version of the Multi-News dataset](https://github.com/huggingface/datasets/blob/12540dd75015678ec6019f258d811ee107439a73/datasets/multi_news/multi_news.py#L47), but this has [known errors in it](https://github.com/Alex-Fabbri/Multi-News/issues/11). There exists a [newer version which fixes some of these issues](https://drive.google.com/open?id=1jwBzXBVv8sfnFrlzPnSUBHEEAbpIUnFq). Unfortunately I don't think you can just replace this old URL with the new one, otherwise this could lead to issues with reproducibility. **Describe the solution you'd like** Add a new version to the Multi-News dataloader that points to the updated dataset which has fixes for some known issues. **Describe alternatives you've considered** Replace the current URL to the original version to the dataset with the URL to the version with fixes. **Additional context** Would be happy to make a PR for this, could someone maybe point me to another dataloader that has multiple versions so I can see how this is handled in `datasets`?

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PR_kwDODunzps44whxN

Update builder docstring for deprecated/added arguments

closed

MEMBER

This PR updates the builder docstring with deprecated/added directives for arguments name/config_name. Follow up of: - #4414 - huggingface/doc-builder#233 First merge: - #4432

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I_kwDODunzps5Kv_AS

Errors when building dummy data if you use nested _URLS

closed

CONTRIBUTOR

## Describe the bug When making dummy data with the `datasets-cli dummy_data` tool, an error will be raised if you use a nested _URLS in your dataset script. Traceback (most recent call last): File "/home/name/LCCC/datasets/src/datasets/commands/datasets_cli.py", line 43, in <module> main() File "/home/name/LCCC/datasets/src/datasets/commands/datasets_cli.py", line 39, in main service.run() File "/home/name/LCCC/datasets/src/datasets/commands/dummy_data.py", line 311, in run self._autogenerate_dummy_data( File "/home/name/LCCC/datasets/src/datasets/commands/dummy_data.py", line 337, in _autogenerate_dummy_data dataset_builder._split_generators(dl_manager) File "/home/name/.cache/huggingface/modules/datasets_modules/datasets/personal_dialog/559332bced5eeafa7f7efc2a7c10ce02cee2a8116bbab4611c35a50ba2715b77/personal_dialog.py", line 108, in _split_generators data_dir = dl_manager.download_and_extract(urls) File "/home/name/LCCC/datasets/src/datasets/commands/dummy_data.py", line 56, in download_and_extract dummy_output = self.mock_download_manager.download(url_or_urls) File "/home/name/LCCC/datasets/src/datasets/download/mock_download_manager.py", line 130, in download return self.download_and_extract(data_url) File "/home/name/LCCC/datasets/src/datasets/download/mock_download_manager.py", line 122, in download_and_extract return self.create_dummy_data_dict(dummy_file, data_url) File "/home/name/LCCC/datasets/src/datasets/download/mock_download_manager.py", line 165, in create_dummy_data_dict if isinstance(first_value, str) and len(set(dummy_data_dict.values())) < len(dummy_data_dict.values()): TypeError: unhashable type: 'list' ## Steps to reproduce the bug You can use my dataset script implemented here: https://github.com/silverriver/datasets/blob/2ecd36760c40b8e29b1137cd19b5bad0e19c76fd/datasets/personal_dialog/personal_dialog.py ```python datasets_cli dummy_data datasets/personal_dialog --auto_generate ``` You can change https://github.com/silverriver/datasets/blob/2ecd36760c40b8e29b1137cd19b5bad0e19c76fd/datasets/personal_dialog/personal_dialog.py#L54 to ``` "train": "https://huggingface.co/datasets/silver/personal_dialog/resolve/main/dev_random.jsonl.gz" ``` before runing the above script to avoid downloading a large training data. ## Expected results The dummy data should be generated ## Actual results An error is raised. It seems that in https://github.com/huggingface/datasets/blob/12540dd75015678ec6019f258d811ee107439a73/src/datasets/download/mock_download_manager.py#L165 We only check if the first item of dummy_data_dict.values() is str. However, dummy_data_dict.values() may have the type of [str, list, list]. A simple fix would be changing https://github.com/huggingface/datasets/blob/12540dd75015678ec6019f258d811ee107439a73/src/datasets/download/mock_download_manager.py#L165 to ```python if all([isinstance(value, str) for value in dummy_data_dict.values()]) and len(set(dummy_data_dict.values())) < len(dummy_data_dict.values()): ``` But I don't know if this kinds of change may bring any side effect since I am not sure about the detail logic here. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Linux - Python version: Python 3.9.10 - PyArrow version: 7.0.0

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PR_kwDODunzps44vyGg

Add HF.co for PRs/Issues for specific datasets

closed

MEMBER

As in https://github.com/huggingface/transformers/pull/17485, issues and PR for datasets under a namespace have to be on the HF Hub

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I_kwDODunzps5KvM1P

Add loading variable number of columns for different splits

closed

NONE

**Is your feature request related to a problem? Please describe.** The original dataset `blended_skill_talk` consists of different sets of columns for the different splits: (test/valid) splits have additional data column `label_candidates` that the (train) doesn't have. When loading such data, an exception occurs at table.py:cast_table_to_schema, because of mismatched columns.

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PR_kwDODunzps44uuDq

Make extensions case-insensitive in timit_asr dataset

closed

MEMBER

Related to #4422.

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PR_kwDODunzps44uZBD

Fix DuplicatedKeysError in timit_asr dataset

closed

MEMBER

Fix #4422.

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PR_kwDODunzps44trdP

Add new dataset MMChat

closed

CONTRIBUTOR

Hi, I am adding a new dataset MMChat. It seems that all tests are passed

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I_kwDODunzps5KsX-P

Cannot load timit_asr data set

closed

NONE

## Describe the bug I am trying to load the timit_asr data set. I have tried with a copy from the LDC, and a copy from deepai. In both cases they fail with a "duplicate key" error. With the LDC version I have to convert the file extensions all to upper-case before I can load it at all. ## Steps to reproduce the bug ```python timit = datasets.load_dataset("timit_asr", data_dir = "/path/to/dataset") # Sample code to reproduce the bug ``` ## Expected results The data set should load without error. It worked for me before the LDC url change. ## Actual results ``` datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: SA1 Keys should be unique and deterministic in nature ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - `datasets` version: 2.2.2 - Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 8.0.0 - Pandas version: 1.4.2

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PR_kwDODunzps44szxR

Add extractor for bzip2-compressed files

closed

CONTRIBUTOR

This change enables loading bzipped datasets, just like any other compressed dataset.

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I_kwDODunzps5Kq0in

Metric evaluation problems in multi-node, shared file system

open

NONE

## Describe the bug Metric evaluation fails in multi-node within a shared file system, because the master process cannot find the lock files from other nodes. (This issue was originally mentioned in the transformers repo https://github.com/huggingface/transformers/issues/17412) ## Steps to reproduce the bug 1. clone [this huggingface model](https://huggingface.co/PereLluis13/wav2vec2-xls-r-300m-ca-lm) and replace the `run_speech_recognition_ctc.py` script with the version in the gist [here](https://gist.github.com/gullabi/3f66094caa8db1c1e615dd35bd67ec71#file-run_speech_recognition_ctc-py). 2. Setup the `venv` according to the requirements of the model file plus `datasets==2.0.0`, `transformers==4.18.0` and `torch==1.9.0` 3. Launch the runner in a distributed environment which has a shared file system for two nodes, preferably with SLURM. Example [here](https://gist.github.com/gullabi/3f66094caa8db1c1e615dd35bd67ec71) Specifically for the datasets, for the distributed setup the `load_metric` is called as: ``` process_id=int(os.environ["RANK"]) num_process=int(os.environ["WORLD_SIZE"]) eval_metrics = {metric: load_metric(metric, process_id=process_id, num_process=num_process, experiment_id="slurm") for metric in data_args.eval_metrics} ``` ## Expected results The training should not fail, due to the failure of the `Metric.compute()` step. ## Actual results For the test I am executing the world size is 4, with 2 GPUs in 2 nodes. However the process is not finding the necessary lock files ``` File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 841, in <module> main() File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 792, in main train_result = trainer.train(resume_from_checkpoint=checkpoint) File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 1497, in train self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval) File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 1624, in _maybe_log_save_evaluate metrics = self.evaluate(ignore_keys=ignore_keys_for_eval) File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 2291, in evaluate metric_key_prefix=metric_key_prefix, File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/transformers/trainer.py", line 2535, in evaluation_loop metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels)) File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 742, in compute_metrics metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()} File "/gpfs/projects/bsc88/speech/asr/wav2vec2-xls-r-300m-ca-lm/run_speech_recognition_ctc.py", line 742, in <dictcomp> metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()} File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 419, in compute self.add_batch(**inputs) File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 465, in add_batch self._init_writer() File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 552, in _init_writer self._check_rendez_vous() # wait for master to be ready and to let everyone go File "/gpfs/projects/bsc88/projects/speech-tech-resources/venv_amd_speech/lib/python3.7/site-packages/datasets/metric.py", line 342, in _check_rendez_vous ) from None ValueError: Expected to find locked file /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-0.arrow.lock from process 3 but it doesn't exist. ``` When I look at the cache directory, I can see all the lock files in principle: ``` /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-0.arrow /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-0.arrow.lock /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-1.arrow /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-1.arrow.lock /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-2.arrow /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-2.arrow.lock /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-3.arrow /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-3.arrow.lock /home/bsc88/bsc88474/.cache/huggingface/metrics/wer/default/slurm-4-rdv.lock ``` I see that there was another related issue here https://github.com/huggingface/datasets/issues/1942, but it seems to have resolved via https://github.com/huggingface/datasets/pull/1966. Let me know if there is problem with how I am calling the `load_metric` or whether I need to make changes to the `.compute()` steps. ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-147.8.1.el8_1.x86_64-x86_64-with-centos-8.1.1911-Core - Python version: 3.7.4 - PyArrow version: 7.0.0 - Pandas version: 1.3.0

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I_kwDODunzps5Kqfdg

Update `unittest` assertions over tuples from `assertEqual` to `assertTupleEqual`

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** So this is more a readability improvement rather than a proposal, wouldn't it be better to use `assertTupleEqual` over the tuples rather than `assertEqual`? As `unittest` added that function in `v3.1`, as detailed at https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertTupleEqual, so maybe it's worth updating. Find an example of an `assertEqual` over a tuple in 🤗 `datasets` unit tests over an `ArrowDataset` at https://github.com/huggingface/datasets/blob/0bb47271910c8a0b628dba157988372307fca1d2/tests/test_arrow_dataset.py#L570 **Describe the solution you'd like** Start slowly replacing all the `assertEqual` statements with `assertTupleEqual` if the assertion is done over a Python tuple, as we're doing with the Python lists using `assertListEqual` rather than `assertEqual`. **Additional context** If so, please let me know and I'll try to go over the tests and create a PR if applicable, otherwise, if you consider this should stay as `assertEqual` rather than `assertSequenceEqual` feel free to close this issue! Thanks 🤗

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PR_kwDODunzps44q9pG

Add dataset MMChat

closed

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I_kwDODunzps5Knvuj

how to convert a dict generator into a huggingface dataset.

open

NONE

### Link _No response_ ### Description Hey there, I have used seqio to get a well distributed mixture of samples from multiple dataset. However the resultant output from seqio is a python generator dict, which I cannot produce back into huggingface dataset. The generator contains all the samples needed for training the model but I cannot convert it into a huggingface dataset. The code looks like this: ``` for ex in seqio_data: print(ex[“text”]) ``` I need to convert the seqio_data (generator) into huggingface dataset. the complete seqio code goes here: ``` import functools import seqio import tensorflow as tf import t5.data from datasets import load_dataset from t5.data import postprocessors from t5.data import preprocessors from t5.evaluation import metrics from seqio import FunctionDataSource, utils TaskRegistry = seqio.TaskRegistry def gen_dataset(split, shuffle=False, seed=None, column="text", dataset_params=None): dataset = load_dataset(**dataset_params) if shuffle: if seed: dataset = dataset.shuffle(seed=seed) else: dataset = dataset.shuffle() while True: for item in dataset[str(split)]: yield item[column] def dataset_fn(split, shuffle_files, seed=None, dataset_params=None): return tf.data.Dataset.from_generator( functools.partial(gen_dataset, split, shuffle_files, seed, dataset_params=dataset_params), output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_name) ) @utils.map_over_dataset def target_to_key(x, key_map, target_key): """Assign the value from the dataset to target_key in key_map""" return {**key_map, target_key: x} dataset_name = 'oscar-corpus/OSCAR-2109' subset= 'mr' dataset_params = {"path": dataset_name, "language":subset, "use_auth_token":True} dataset_shapes = None TaskRegistry.add( "oscar_marathi_corpus", source=seqio.FunctionDataSource( dataset_fn=functools.partial(dataset_fn, dataset_params=dataset_params), splits=("train", "validation"), caching_permitted=False, num_input_examples=dataset_shapes, ), preprocessors=[ functools.partial( target_to_key, key_map={ "targets": None, }, target_key="targets")], output_features={"targets": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)}, metric_fns=[] ) dataset = seqio.get_mixture_or_task("oscar_marathi_corpus").get_dataset( sequence_length=None, split="train", shuffle=True, num_epochs=1, shard_info=seqio.ShardInfo(index=0, num_shards=10), use_cached=False, seed=42 ) for _, ex in zip(range(5), dataset): print(ex['targets'].numpy().decode()) ``` ### Owner _No response_

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PR_kwDODunzps44o7sF

Add LCCC dataset

closed

CONTRIBUTOR

Hi, I am trying to add a new dataset lccc. All tests are passed.

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PR_kwDODunzps44mIJk

Update `dataset_infos.json` with new split info in `Dataset.push_to_hub` to avoid verification error

closed

CONTRIBUTOR

Update `dataset_infos.json` when pushing splits one by one via `Dataset.push_to_hub` to avoid the splits verification error. TODO: ~~- [ ] handle token + `{Audio, Image}.embed_storage`~~ - [x] tests

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PR_kwDODunzps44klhY

Rename DatasetBuilder config_name

closed

MEMBER

This PR renames the DatasetBuilder keyword argument `name` to `config_name` so that: - it avoids confusion with the attribute `DatasetBuilder.name`, which is different - it aligns with the Dataset property name `config_name`, defined in `DatasetInfoMixin.config_name` Other simpler possibility could be to rename it to just `config` instead. Please note I have only renamed this argument of DatasetBuilder because I think this refactoring has a low impact on users: we can assume this is not a public facing parameter, but private or related to the inners of our library. It would have a major impact to rename it also in: - load_dataset - load_dataset_builder: although this could also be assumed as inners... - in our CLI commands Besides the naming of `name`, I also find really confusing the naming of `path` in `load_dataset`. IMHO, they should have a more simpler and precise meaning (currently, they are too vague). I would propose (maybe for next major release): ``` load_dataset(dataset, config,... ``` instead of ``` load_dataset(path, name,... ```

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I_kwDODunzps5KhXNu

Dataset Viewer issue for ett

closed

NONE

### Link https://huggingface.co/datasets/ett ### Description Timestamp is not JSON serializable. ``` Status code: 500 Exception: Status500Error Message: Type is not JSON serializable: Timestamp ``` ### Owner No

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PR_kwDODunzps44hFvq

Skip hidden files/directories in data files resolution and `iter_files`

closed

CONTRIBUTOR

Fix #4115

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PR_kwDODunzps44g_yL

Update `_format_columns` in `remove_columns`

closed

CONTRIBUTOR

As explained at #4398, when calling `dataset.add_faiss_index` under certain conditions when calling a sequence of operations `cast_column`, `map`, and `remove_columns`, it fails as it's trying to look for already removed columns. So on, after testing some possible fixes, it seems that setting the dataset format right after removing the columns seems to be working fine, so I had to add a call to `.set_format` in the `remove_columns` function. Hope this helps!

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PR_kwDODunzps44f_Td

Remove Google Drive URL in spider dataset

closed

MEMBER

The `spider` dataset is distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) license. Fix #4401.

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PR_kwDODunzps44fxiH

Update: add using pcm bytes (#4323)

open

NONE

first of all, please look #4323 why i can not use {"path","array","sampling_rate"} because sf.write(format="wav") and sf.read(BytesIO) is changed my pcm data value maybe, i think wav got header but, pcm is not. and variable naming, pcm data is "byte" type. so, "array" name is not fair i think so, i use scipy lib and numpy (that is huggingface dependency) and refer to @lhoestq answered, 1. encode -> using sampling_rate and pcm byte -> wav style byte (scipy.wavfile.write to byte) 2. byte converting using fairseq style pcm audio read [FileAudioDataset](https://github.com/facebookresearch/fairseq/blob/main/fairseq/data/audio/raw_audio_dataset.py) 4. decode -> read wavfile.read that way is not screw up my pcm byte to float data, and another audio type(wav) safety please check!

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PR_kwDODunzps44ecNI

Update imagenet gate

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I_kwDODunzps5KbTgi

Dataset Viewer issue for conll2012_ontonotesv5

closed

NONE

### Link https://huggingface.co/datasets/conll2012_ontonotesv5 ### Description Dataset viewer outage. ### Owner No

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PR_kwDODunzps44ePLU

Improve language tag for PIAF dataset

closed

NONE

Hi, As pointed out by @lhoestq in this discussion (https://huggingface.co/datasets/asi/wikitext_fr/discussions/1), it is not yet possible to edit datasets outside of a namespace with the Hub PR feature and that you have to go through GitHub. This modification should allow better referencing since only the xx language tags are currently taken into account and not the xx-xx.

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I_kwDODunzps5Ka7qH

[TypeError: Couldn't cast array of type] Cannot process dataset in v2.2.2

closed

NONE

## Describe the bug I am trying to process the [conll2012_ontonotesv5](https://huggingface.co/datasets/conll2012_ontonotesv5) dataset in `datasets` v2.2.2 and am running into a type error when casting the features. ## Steps to reproduce the bug ```python import os from typing import ( List, Dict, ) from collections import ( defaultdict, ) from dataclasses import ( dataclass, ) from datasets import ( load_dataset, ) @dataclass class ConllConverter: path: str name: str cache_dir: str def __post_init__( self, ): self.dataset = load_dataset( path=self.path, name=self.name, cache_dir=self.cache_dir, ) def convert( self, ): class_label = self.dataset["train"].features["sentences"][0]["named_entities"].feature # label_set = list(set([ # label.split("-")[1] if label != "O" else label for label in class_label.names # ])) def prepare_chunk(token, entity): assert len(token) == len(entity) # Sequence length length = len(token) # Variable used entity_chunk = defaultdict(list) idx = flag = 0 # While loop while idx < length: if entity[idx] == "O": flag += 1 idx += 1 else: iob_tp, lab_tp = entity[idx].split("-") assert iob_tp == "B" idx += 1 while idx < length and entity[idx].startswith("I-"): idx += 1 entity_chunk[lab_tp].append(token[flag: idx]) flag = idx entity_chunk = dict(entity_chunk) # for label in label_set: # if label != "O" and label not in entity_chunk.keys(): # entity_chunk[label] = None return entity_chunk def prepare_features( batch: Dict[str, List], ) -> Dict[str, List]: sentence = [ sent for doc_sent in batch["sentences"] for sent in doc_sent ] feature = { "sentence": list(), } for sent in sentence: token = sent["words"] entity = class_label.int2str(sent["named_entities"]) entity_chunk = prepare_chunk(token, entity) sent_feat = { "token": token, "entity": entity, "entity_chunk": entity_chunk, } feature["sentence"].append(sent_feat) return feature column_names = self.dataset.column_names["train"] dataset = self.dataset.map( function=prepare_features, with_indices=False, batched=True, batch_size=3, remove_columns=column_names, num_proc=1, ) dataset.save_to_disk( dataset_dict_path=os.path.join("data", self.path, self.name) ) if __name__ == "__main__": converter = ConllConverter( path="conll2012_ontonotesv5", name="english_v4", cache_dir="cache", ) converter.convert() ``` ## Expected results I want to use the dataset to perform NER task and to change the label list into a {Entity Type: list of spans} format. ## Actual results <details> <summary>Traceback</summary> ```python Traceback (most recent call last): | 0/81 [00:00<?, ?ba/s] File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/multiprocess/pool.py", line 125, in worker result = (True, func(*args, **kwds)) File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 532, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 499, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2751, in _map_single writer.write_batch(batch) File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_writer.py", line 503, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_writer.py", line 198, in __arrow_array__ out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1675, in wrapper return func(array, *args, **kwargs) File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1793, in cast_array_to_feature arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1793, in <listcomp> arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1675, in wrapper return func(array, *args, **kwargs) File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1844, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type struct<CARDINAL: list<item: list<item: string>>, DATE: list<item: list<item: string>>, EVENT: list<item: list<item: string>>, FAC: list<item: list<item: string>>, GPE: list<item: list<item: string>>, LANGUAGE: list<item: list<item: string>>, LAW: list<item: list<item: string>>, LOC: list<item: list<item: string>>, MONEY: list<item: list<item: string>>, NORP: list<item: list<item: string>>, ORDINAL: list<item: list<item: string>>, ORG: list<item: list<item: string>>, PERCENT: list<item: list<item: string>>, PERSON: list<item: list<item: string>>, QUANTITY: list<item: list<item: string>>, TIME: list<item: list<item: string>>, WORK_OF_ART: list<item: list<item: string>>> to {'CARDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'DATE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'EVENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'FAC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'GPE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LAW': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LOC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'MONEY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'NORP': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORG': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERCENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERSON': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PRODUCT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'QUANTITY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'TIME': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'WORK_OF_ART': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None)} """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home2/jiangwangyi/workspace/work/Entity/dataconverter.py", line 110, in <module> converter.convert() File "/home2/jiangwangyi/workspace/work/Entity/dataconverter.py", line 91, in convert dataset = self.dataset.map( File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/dataset_dict.py", line 770, in map { File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/dataset_dict.py", line 771, in <dictcomp> k: dataset.map( File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2459, in map transformed_shards[index] = async_result.get() File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/multiprocess/pool.py", line 771, in get raise self._value TypeError: Couldn't cast array of type struct<CARDINAL: list<item: list<item: string>>, DATE: list<item: list<item: string>>, EVENT: list<item: list<item: string>>, FAC: list<item: list<item: string>>, GPE: list<item: list<item: string>>, LANGUAGE: list<item: list<item: string>>, LAW: list<item: list<item: string>>, LOC: list<item: list<item: string>>, MONEY: list<item: list<item: string>>, NORP: list<item: list<item: string>>, ORDINAL: list<item: list<item: string>>, ORG: list<item: list<item: string>>, PERCENT: list<item: list<item: string>>, PERSON: list<item: list<item: string>>, QUANTITY: list<item: list<item: string>>, TIME: list<item: list<item: string>>, WORK_OF_ART: list<item: list<item: string>>> to {'CARDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'DATE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'EVENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'FAC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'GPE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LAW': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LOC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'MONEY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'NORP': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORG': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERCENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERSON': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PRODUCT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'QUANTITY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'TIME': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'WORK_OF_ART': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None)} ``` </details> ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.2 - Platform: Ubuntu 18.04 - Python version: 3.9.7 - PyArrow version: 7.0.0

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I_kwDODunzps5Ka7Xj

Dataset should have a `.name` field

closed

NONE

**Is your feature request related to a problem? Please describe.** If building pipelines that can evaluate on more than one dataset, it would be nice to be able to log results of things like `Evaluating on {dataset.name}` or `results for {dataset.name} are: {results}` Without some way of concisely identifying a dataset from the dataset object, tools which might run on more than one dataset must be passed the dataset object _and_ the name/id of the dataset being used. **Describe the solution you'd like** The DatasetInfo class should have a `name` field which is the name of a dataset. then for a given dataset if it evolves in time the `version` can be updated but its different versions of the same dataset with a unique `name`. The name could then all be accessed by `dataset.name` **Describe alternatives you've considered** For my own purposes I am considering making `NamedDataset[Dataset]` where the subclass just has a .name field. **Additional context** My guess is that most usecases are not working with more than one dataset in a given pipeline so a name is not really needed. This has surprised me though as one of the advantages of a standard dataset interface is to be able to build pipelines which can be passed in a dataset and separate responsibilities of the dataset loading from the train or eval pipeline.

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PR_kwDODunzps44dcpl

Uncomment logging deactivation for ArrowBasedBuilder

closed

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PR_kwDODunzps44cdR5

Skip identical files in `push_to_hub` instead of overwriting

closed

CONTRIBUTOR

Skip identical files instead of overwriting them to save bandwidth and circumvent (user-side/server-side) errors, which can arise when working with large datasets due to long-lasting HTTP connections, by repeating calls to `push_to_hub` to resume an upload. To be able to check if an upload can be resumed, this PR modifies the shard naming scheme from: ``` data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].parquet ``` to: ``` data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]-<SHARD_FINGERPRINT>.parquet ``` cc @LysandreJik

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I_kwDODunzps5KXlQx

"NonMatchingChecksumError" when importing 'spider' dataset

closed

NONE

## Describe the bug When importing 'spider' dataset [https://huggingface.co/datasets/spider] an error occurs ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('spider') ``` ## Expected results Dataset object ## Actual results NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0'] ## Environment info - `datasets` version: 2.2.2 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.7.11 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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I_kwDODunzps5KWeDN

load dataset wikitext-2-raw-v1 failed. Could not reach wikitext-2-raw-v1.py.

closed

NONE

## Describe the bug Could not reach wikitext-2-raw-v1.py ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("wikitext-2-raw-v1") ``` ## Expected results Download `wikitext-2-raw-v1` dataset successfully. ## Actual results ``` File "load_datasets.py", line 13, in <module> load_dataset("wikitext-2-raw-v1") File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1715, in load_dataset **config_kwargs, File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1536, in load_dataset_builder data_files=data_files, File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1282, in dataset_module_factory raise e1 from None File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1224, in dataset_module_factory dynamic_modules_path=dynamic_modules_path, File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 559, in get_module local_path = self.download_loading_script(revision) File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 539, in download_loading_script return cached_path(file_path, download_config=download_config) File "/root/miniconda3/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 246, in cached_path download_desc=download_config.download_desc, File "/root/miniconda3/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 582, in get_from_cache raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})") ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.2.2/datasets/wikitext-2-raw-v1/wikitext-2-raw-v1.py (ReadTimeout(ReadTimeoutError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Read timed out. (read timeout=100)",),)) ``` I tried to download wikitext-2-raw-v1.py by chrome and got:  ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.2 - Platform: CentOS 7 - Python version: 3.6 - PyArrow version: 3.0.0

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I_kwDODunzps5KUuvL

LocalDatasetModuleFactoryWithoutScript extracts invalid builder name

open

NONE

## Describe the bug Trying to load a local dataset raises an error indicating that the config builder has to have a name. No error should be reported, since the call is completly valid. ## Steps to reproduce the bug ```python load_dataset("./data/some-dataset/", name="some-name") ``` ## Expected results The dataset should be loaded. ## Actual results ``` Traceback (most recent call last): File "train_lquad.py", line 19, in <module> load(tokenize_target_function, tokenize_target_function, {}, tokenizer) File "train_lquad.py", line 14, in load dataset = load_dataset("./data/lquad/", name="lquad") File "/net/pr2/scratch/people/plgapohl/python-3.8.6/lib/python3.8/site-packages/datasets/load.py", line 1708, in load_dataset builder_instance = load_dataset_builder( File "/net/pr2/scratch/people/plgapohl/python-3.8.6/lib/python3.8/site-packages/datasets/load.py", line 1560, in load_dataset_builder builder_instance: DatasetBuilder = builder_cls( File "/net/pr2/scratch/people/plgapohl/python-3.8.6/lib/python3.8/site-packages/datasets/builder.py", line 269, in __init__ self.config, self.config_id = self._create_builder_config( File "/net/pr2/scratch/people/plgapohl/python-3.8.6/lib/python3.8/site-packages/datasets/builder.py", line 403, in _create_builder_config raise ValueError(f"BuilderConfig must have a name, got {builder_config.name}") ValueError: BuilderConfig must have a name, got ``` ## Environment info - `datasets` version: 2.2.2 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-glibc2.2.5 - Python version: 3.8.6 - PyArrow version: 8.0.0 - Pandas version: 1.4.2 The error is probably in line 795 in load.py: ``` builder_kwargs = { "hash": hash, "data_files": data_files, "name": os.path.basename(self.path), "base_path": self.path, **builder_kwargs, } ``` `os.path.basename` for a directory returns an empty string, rather than the name of the directory.

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I_kwDODunzps5KTp_9

Calling `cast_column`/`remove_columns` and a sequence of `map` operations ends up making `faiss` fail with `ValueError`

closed

CONTRIBUTOR

First of all, sorry in advance for the unclear title, but this bug is weird to explain (at least for me), so I tried my best to summarize all the information in this issue. ## Describe the bug Calling a certain combination of operations over a 🤗 `Dataset` and then trying to calculate the `faiss` index with `.add_faiss_index` ends up throwing an exception while trying to set the format back of a previously removed column. But this just happens over certain conditions... I'll present some scenarios below! ## Steps to reproduce the bug Assuming the following dataset named `sample.csv` with some IMDb data: ```csv id,title,summary 1877830,"The Batman","When a sadistic serial killer begins murdering key political figures in Gotham, Batman is forced to investigate the city's hidden corruption and question his family's involvement." 9419884,"Doctor Strange in the Multiverse of Madness","Doctor Strange teams up with a mysterious teenage girl from his dreams who can travel across multiverses, to battle multiple threats, including other-universe versions of himself, which threaten to wipe out millions across the multiverse. They seek help from Wanda the Scarlet Witch, Wong and others." 11138512,"The Northman","From visionary director Robert Eggers comes The Northman, an action-filled epic that follows a young Viking prince on his quest to avenge his father's murder." 1745960,"Top Gun: Maverick","After more than thirty years of service as one of the Navy's top aviators, Pete Mitchell is where he belongs, pushing the envelope as a courageous test pilot and dodging the advancement in rank that would ground him." ``` We'll be able to reproduce the bug using the following piece of code: ```python # Sample code to reproduce the bug from transformers import DPRContextEncoder, DPRContextEncoderTokenizer import torch torch.set_grad_enabled(False) ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base") from datasets import load_dataset, Value ds = load_dataset("csv", data_files=["sample.csv"], split="train") ds = ds.cast_column("id", Value("int32")) # from `int64` to `int32` ds = ds.map(lambda x: {"inputs": f"{ctx_tokenizer.sep_token}".join(["title", "summary"])}) ds = ds.remove_columns(["title", "summary"]) def generate_embeddings(x): return {"embeddings": ctx_encoder(**ctx_tokenizer(x["inputs"], return_tensors="pt"))[0][0].numpy()} ds = ds.map(generate_embeddings) ds = ds.remove_columns("inputs") ds.add_faiss_index(column="embeddings") # It fails here! ``` The code above is an adaptation of https://huggingface.co/docs/datasets/faiss_es, for the sake of presenting the bug with a simple example. ## Expected results Ideally, the `faiss` index should be calculated over the 🤗 `Dataset` and no exception should be triggered. ## Actual results But what happens instead is that a `ValueError: Columns ['inputs'] not in the dataset. Current columns in the dataset: ['id', 'embeddings']`, which makes no sense as that column has been previously dropped. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.2 - Platform: Linux-5.4.0-1074-azure-x86_64-with-glibc2.31 - Python version: 3.9.5 - PyArrow version: 8.0.0 - Pandas version: 1.4.2

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PR_kwDODunzps44XcG3

Fix dependency on dill version

closed

MEMBER

We had to make a hotfix by pinning dill: - #4380 because from version 0.3.5, our custom `save_function` pickling function was raising an exception: - #4379 This PR fixes this by implementing our custom `save_function` depending on the version of dill. CC: @anivegesana This PR needs first being merged: - [x] #4384 - so that a circular import is fixed It is also convenient to merge first: - [x] #4385

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PR_kwDODunzps44T0Di

Fix URL in gem dataset for totto config

closed

MEMBER

As commented in: - https://github.com/huggingface/datasets/issues/4386#issuecomment-1134902372 CC: @StevenTang1998

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https://github.com/huggingface/datasets/pull/4395

PR_kwDODunzps44TrBA

Add Pascal VOC dataset

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CONTRIBUTOR

This PR adds the Pascal VOC dataset in the same way TFDS has it added. I believe we can iterate on this dataset and in future versions include more data, such as segmentation masks, but for now I think it is a good idea to just add it the same way as TFDS to get a solid first version out there.

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https://github.com/huggingface/datasets/issues/4394

I_kwDODunzps5KOJMZ

trainer became extremely slow after reload dataset by `load_from_disk`

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NONE

## Describe the bug Due to memory problem, I need to save my tokenized datasets locally by CPU and reload it by multi GPU for running training script. However, after I reload it by `load_from_disk` and start training, the speed is extremely slow. It says I need about 1500 hours with 8 A100 cards. Before this, I can run the whole script in one day with a single A100 card. Since I am try to pre-train a BERT, **my dataset is very large(29058165 rows)** ## Steps to reproduce the bug ```python tokenized_datasets.save_to_disk( "/pathto/dataset" ) tokenized_datasets = load_from_disk( "/pathto/dataset" ) trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets["train"] if training_args.do_train else None, eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None, tokenizer=tokenizer, data_collator=data_collator, ) train_result = trainer.train(resume_from_checkpoint=checkpoint) ``` ## Expected results Without the save and reload process, I only need about one day to run the whole script with one A100 card. ## Actual results ``` [INFO|trainer.py:1290] 2022-05-23 22:49:46,266 >> ***** Running training ***** [INFO|trainer.py:1291] 2022-05-23 22:49:46,266 >> Num examples = 29058165 [INFO|trainer.py:1292] 2022-05-23 22:49:46,266 >> Num Epochs = 5 [INFO|trainer.py:1293] 2022-05-23 22:49:46,266 >> Instantaneous batch size per device = 16 [INFO|trainer.py:1294] 2022-05-23 22:49:46,266 >> Total train batch size (w. parallel, distributed & accumulation) = 256 [INFO|trainer.py:1295] 2022-05-23 22:49:46,266 >> Gradient Accumulation steps = 2 [INFO|trainer.py:1296] 2022-05-23 22:49:46,266 >> Total optimization steps = 567540 0%| | 1/567540 [00:09<1544:49:04, 9.80s/it] 0%| | 2/567540 [00:17<1320:00:17, 8.37s/it] 0%| | 3/567540 [00:26<1393:10:17, 8.84s/it] 0%| | 4/567540 [00:34<1344:56:33, 8.53s/it] 0%| | 5/567540 [00:43<1359:36:12, 8.62s/it] ``` ## Environment info ``` torch 1.11.0+cu113 torchaudio 0.11.0+cu113 torchvision 0.12.0+cu113 transformers 4.18.0 datasets 2.2.2 ```

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