Datasets:

VanHoan

/

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PR_kwDODunzps45QZCU

First draft of the docs for TF + Datasets

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closed

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['_The documentation is not available anymore as the PR was closed or merged._' 'Some links are still missing I think :)' 'This is probably quite close to being ready, so cc some TF people @gante @amyeroberts @merveenoyan just so they see it! No need for a full review, but if you have any comments or suggestions feel free.' 'Thanks ! We plan to make a new release later today for `to_tf_dataset` FYI, so I think we can merge it soon and include this documentation in the new release']

2022-06-07 16:06:48

2022-06-14 16:08:41

2022-06-14 15:59:08

MEMBER

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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

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open

[]

['I use below to load a dataset:\r\n```\r\ndataset = datasets.load_dataset("scikit-learn/auto-mpg")\r\ndf = pd.DataFrame(dataset["train"])\r\n```\r\nTBH as said, tabular folk split their own dataset, they sometimes have two splits, sometimes three. Maybe somehow avoiding it for tabular datasets might be good for later. (it\'s just UX improvement) ']

2022-06-07 12:48:22

2022-07-01 09:57:47

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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https://github.com/huggingface/datasets/pull/4455

PR_kwDODunzps45O5F9

Update data URLs in fever dataset

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closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-06-07 10:40:54

2022-06-08 07:24:54

2022-06-08 07:16:17

MEMBER

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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

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closed

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["Closing since it's a duplicate of https://github.com/huggingface/datasets/issues/4453"]

2022-06-07 03:31:46

2022-06-07 11:53:11

2022-06-07 11:53:11

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

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[]

closed

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["I understand that the issue is that a remote file (URL) is being loaded as a local file. Right @albertvillanova @lhoestq?\r\n\r\n```\r\nMessage: [Errno 2] No such file or directory: 'https://raw.githubusercontent.com/YaxinCui/ABSADataset/main/SemEval2015Task12Corrected/train/restaurants_train.xml'\r\n```" '`xml.dom.minidom.parse` is not supported in streaming mode. I opened a PR here to fix it:\r\nhttps://huggingface.co/datasets/Yaxin/SemEval2015/discussions/1\r\n\r\nPlease review the PR @WithYouTo and let me know if it works !' "Additionally, I'm also patching our library, so that we support streaming datasets that use `xml.dom.minidom.parse`."]

2022-06-07 03:30:08

2022-06-09 08:34:16

2022-06-09 08:34:16

NONE

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I_kwDODunzps5LQKx2

Trying to load FEVER dataset results in NonMatchingChecksumError

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closed

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['Thanks for reporting @santhnm2. We are fixing it.\r\n\r\nData owners updated their URLs recently. We have to align with them, otherwise you do not download anything (that is why ignore_verifications does not work).']

2022-06-06 23:13:15

2022-06-08 07:16:16

2022-06-08 07:16:16

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

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._' 'Awesome thanks @mariosasko!']

2022-06-06 16:57:08

2022-06-07 17:40:01

2022-06-07 17:14:44

CONTRIBUTOR

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Closes #4430.

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PR_kwDODunzps45Kzwh

Update README.md of fquad

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-06-06 13:52:41

2022-06-06 14:51:49

2022-06-06 14:43:03

MEMBER

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I_kwDODunzps5LLVX2

Rj

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[]

closed

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[]

2022-06-06 02:24:32

2022-06-06 15:44:50

2022-06-06 15:44:50

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]

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open

[]

["Hi! The [datasets_sql](https://github.com/mariosasko/datasets_sql) package lets you easily find distinct rows in a dataset (an example with `SELECT DISTINCT` is in the readme). Deduplication is (still) not part of the official API because it's hard to implement for datasets bigger than RAM while only using the native PyArrow ops.\r\n\r\n(Btw, this is a duplicate of https://github.com/huggingface/datasets/issues/2514)"]

2022-06-05 05:32:56

2022-06-05 15:14:27

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

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-06-03 15:22:34

2022-06-06 15:50:25

2022-06-06 15:41:37

CONTRIBUTOR

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

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PR_kwDODunzps45E1Qb

Add missing kwargs to docstrings

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-06-03 15:10:27

2022-06-03 16:10:09

2022-06-03 16:01:29

MEMBER

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PR_kwDODunzps45EjtA

Fix missing args in docstring of load_dataset_builder

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-06-03 13:55:50

2022-06-03 14:35:32

2022-06-03 14:27:09

MEMBER

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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

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-06-03 10:29:02

2022-06-03 11:01:28

2022-06-03 10:52:46

MEMBER

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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

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open

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['If I understand correctly, this is due to the key `split` missing in the line https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L41 of the script.\r\nMaybe @albertvillanova could confirm.' "I'm having a look." 'Indeed there are several issues in this dataset loading script.\r\n\r\nThe one pointed out by @severo: for the default configuration "crops": https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L244\r\n- The download manager downloads `_URL`\r\n- But `_URL` is not defined: https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L41\r\n ```python\r\n _URL = {\'train\': []}\r\n ```\r\n- Afterwards, for each split, a different key in `_ULR` is used, but it only contains one key: "train"\r\n - "valid" key: https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L260\r\n - "test key: https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/blob/main/nimrod-uk-1km.py#L269\r\n \r\nThese keys do not exist inside `_URL`, thus the error message reported in the viewer: \r\n```\r\nException: KeyError\r\nMessage: \'valid\'\r\n```' 'Would anyone want to submit a Hub PR (or open a Discussion for the authors to be aware) to this dataset? https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km' "Hi, I'm the main author for that dataset, so I'll work on updating it! I was working on debugging some stuff awhile ago, which is what broke it. " "I've opened a Discussion page, so that we can ask/answer and propose fixes until the script works properly: https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km/discussions/1\r\n\r\nCC: @julien-c @jacobbieker "]

2022-06-03 08:17:16

2022-06-07 08:23:52

NONE

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

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I_kwDODunzps5LBIxc

Dataset Viewer issue for amazon_polarity

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closed

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['Thanks, looking at it' "Not sure what happened 😬, but it's fixed"]

2022-06-02 19:18:38

2022-06-07 18:50:37

2022-06-07 18:50:37

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

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["Not sure what happened 😬, but it's fixed"]

2022-06-02 18:57:12

2022-06-07 18:50:55

2022-06-07 18:50:55

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

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closed

[]

['_The documentation is not available anymore as the PR was closed or merged._' '> 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?\r\n\r\nWe plan to address this with end-to-end examples (for each modality) more focused on preprocessing than the ones in the Transformers docs.']

2022-06-02 17:42:03

2022-06-23 16:33:19

2022-06-23 16:23:02

MEMBER

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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

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closed

[]

['To have some context, please see:\r\n- #4145\r\n\r\nPlease, also note that we have recently made some fixes to the script, which are in our GitHub master branch but not yet released:\r\n- #4422\r\n- #4425 \r\n- #4436' "Thanks Albert! I'll try pulling `datasets` from the git repo instead of PyPI, and/or just wait for the next release.\r\n" "I'm closing this issue then. Please, feel free to reopen it again if the problem persists."]

2022-06-02 16:35:56

2022-06-03 08:44:17

2022-06-03 08:44:16

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

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-06-02 14:21:10

2022-06-02 16:40:55

2022-06-02 16:32:27

MEMBER

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

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PR_kwDODunzps44-uRW

Add missing columns to `blended_skill_talk`

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closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-06-02 14:16:26

2022-06-06 15:49:56

2022-06-06 15:41:25

CONTRIBUTOR

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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

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-06-02 06:45:04

2022-06-02 09:32:56

2022-06-02 09:24:27

MEMBER

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Related to: - #4422

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I_kwDODunzps5K89_o

Load a local cached dataset that has been modified

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closed

[]

['Hi! `datasets` caches every modification/loading, so you can either rerun the pipeline up to the `map` call or use `Dataset.from_file(modified_dataset)` to load the dataset directly from the cache file.' 'Awesome, hvala Mario! This works. ']

2022-06-02 01:51:49

2022-06-02 23:59:26

2022-06-02 23:59:18

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

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closed

[]

[]

2022-06-01 14:53:15

2022-06-07 12:08:28

2022-06-07 09:24:09

CONTRIBUTOR

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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`

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[]

closed

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['_The documentation is not available anymore as the PR was closed or merged._' 'Added back the `[:]` and a comment to explain why this is needed. ']

2022-06-01 12:09:56

2022-06-09 10:34:54

2022-06-09 10:26:07

CONTRIBUTOR

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Fix #4348

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PR_kwDODunzps441JmK

Fix builder docstring

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-06-01 09:45:30

2022-06-02 17:43:47

2022-06-02 17:35:15

MEMBER

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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

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closed

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['These test errors are related to issue #4428 \r\n' '_The documentation is not available anymore as the PR was closed or merged._' 'I only made a trivial modification in my commit https://github.com/huggingface/datasets/pull/4431/commits/402c893d35224d7828176717233909ac5f1e7b3e\r\n\r\nI have submitted a PR #4434 for the about issue.' "> Awesome thanks for adding this dataset :)\r\n> \r\n> I just have one comment about the licensing.\r\n> \r\n> Also it seems that you already have the dataset in https://huggingface.co/datasets/silver/personal_dialog, so it's unnecessary to add it here\r\n\r\nThank you very much for your comment.\r\n\r\nSo, should I close this PR?" "Thanks for fixing the licensing section :)\r\n\r\n> So, should I close this PR?\r\n\r\nYes you can close this PR, it's better if your dataset is under your namespace at https://huggingface.co/datasets/silver/personal_dialog :)\r\n\r\nDon't forget to update the licensing section on https://huggingface.co/datasets/silver/personal_dialog as well"]

2022-06-01 01:20:40

2022-06-11 12:40:23

2022-06-11 12:31:16

CONTRIBUTOR

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It seems that all tests are passed

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I_kwDODunzps5KxNEv

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

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closed

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['Hi! Our versioning is based on Git revisions (the `revision` param in `load_dataset`), so you can just replace the old URL with the new one and open a PR :). I can also give you some pointers if needed.' '@mariosasko Awesome thanks! I will do that. Looks like this new version of the data is not available as a zip but as three files (train/dev/test). How is this usually handled in HF Datasets, should `_URL` be a dict with keys `train`, `val`, `test` perhaps?' 'Yes! Let me help you with more detailed instructions.\r\n\r\nIn the first step, we need to update the URLs. One of the possible dictionary structures is as follows:\r\n```python\r\n_URLs = {\r\n "train": {"src": "https://drive.google.com/uc?export=download&id=1wHAWDOwOoQWSj7HYpyJ3Aeud8WhhaJ7P", "tgt": "https://drive.google.com/uc?export=download&id=1QVgswwhVTkd3VLCzajK6eVkcrSWEK6kq"}\r\n "val": ...\r\n "test": ...\r\n}\r\n```\r\n\r\n(You can use this page to generate direct download links: https://sites.google.com/site/gdocs2direct/)\r\n\r\nThen we move to the `split_generators` method:\r\n```python\r\ndef _split_generators(self, dl_manager):\r\n """Returns SplitGenerators."""\r\n files = dl_manager.download(_URLs)\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN,\r\n gen_kwargs={"src_file": files["train"]["src"], "tgt_file": files["train"]["tgt"]},\r\n ),\r\n ... # same for val and test\r\n ]\r\n```\r\nFinally, we adjust the signature of `_generate_examples`:\r\n```python\r\ndef _generate_examples(self, src_file, tgt_file):\r\n """Yields examples."""\r\n with open(src_file, encoding="utf-8") as src_f, open(\r\n tgt_file, encoding="utf-8"\r\n ) as tgt_f:\r\n ... # the rest is the same\r\n```\r\n\r\nAnd that\'s it!\r\n\r\nPS: Let me know if you need help updating the dummy data and regenerating the metadata file.' 'Awesome! Thanks for the detailed help, that was straightforward with your instruction. However, I think I am being blocked by this issue: https://github.com/huggingface/datasets/issues/4428' 'Feel free to open a PR, and I can fix this manually.' 'Awsome, done in #4451!']

2022-05-31 21:00:44

2022-06-07 17:14:44

2022-06-07 17:14:44

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

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['_The documentation is not available anymore as the PR was closed or merged._' '@mishig25 is investigating why deprecated/added do not affect the enclosed text format when used in args docstring: no special formatting appears: \r\n- https://moon-ci-docs.huggingface.co/docs/datasets/pr_4429/en/package_reference/builder_classes#datasets.DatasetBuilder' '@albertvillanova please check now 👍 \r\nhttps://moon-ci-docs.huggingface.co/docs/datasets/pr_4429/en/package_reference/builder_classes#datasets.DatasetBuilder\r\n\r\n<img width="500" alt="Screenshot 2022-06-06 at 10 20 34" src="https://user-images.githubusercontent.com/11827707/172123471-fab97138-c903-4a71-ab7f-c90e5e43c58f.png">\r\n' 'Thanks @mishig25.\r\n\r\nJust one question: is it expected to have the deprecated box right edge not filling all the page width (contrary to the added box)?' '> Just one question: is it expected to have the deprecated box right edge not filling all the page width (contrary to the added box)?\r\n\r\nYes, that is expected 😊 because the depreacted box is being bounded by its parent box (the box for `name` argument in the screenshot above)']

2022-05-31 17:37:25

2022-06-08 11:40:18

2022-06-08 11:31:45

MEMBER

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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

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closed

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[]

2022-05-31 16:10:57

2022-06-07 09:24:09

2022-06-07 09:24:09

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

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closed

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-05-31 14:31:21

2022-06-01 12:37:42

2022-06-01 12:29:02

MEMBER

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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

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closed

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["Hi! Indeed the column is missing, but you shouldn't get an error? Have you made some modifications (locally) to the loading script? I've opened a PR to add the missing columns to the script. "]

2022-05-31 13:40:16

2022-06-03 16:25:25

2022-06-03 16:25:25

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

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-05-31 10:10:04

2022-06-01 14:15:30

2022-06-01 14:06:51

MEMBER

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Related to #4422.

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PR_kwDODunzps44uZBD

Fix DuplicatedKeysError in timit_asr dataset

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2022-05-31 08:47:45

2022-05-31 13:50:50

2022-05-31 13:42:31

MEMBER

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Fix #4422.

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PR_kwDODunzps44trdP

Add new dataset MMChat

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['_The documentation is not available anymore as the PR was closed or merged._' "Thanks ! As for https://github.com/huggingface/datasets/pull/4431 please also update the licensing section in https://huggingface.co/datasets/silver/mmchat ;)\r\n\r\nThen if it's fine for you feel free to close this PR"]

2022-05-31 04:45:07

2022-06-11 12:40:52

2022-06-11 12:31:42

CONTRIBUTOR

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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

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["Thanks for reporting, @bhaddow.\r\n\r\nI'm fixing it." 'Thanks for the quick fix!' "@bhaddow we have also made a fix so that you don't have to convert to uppercase the file extensions of the LDC data.\r\n\r\nWould you mind checking if it works OK now for you and reporting if there are any issues? Thanks. " 'Hi @albertvillanova -It loads fine on a copy of the data from deepai - although I have to remove the copies of the .WAV files (with extension .WAV,wav). On a copy of the data that was obtained from the LDC, the glob still fails to find the files. The LDC copy looks like it was copied from CD, in 2004, so the structure may be different to a current download.' 'Ah, if I change the train/ and test/ directories to TRAIN/ and TEST/ then it works!' "Thanks for your investigation and report, @bhaddow. I'm adding another fix for the TRAIN/train and TEST/test directory names."]

2022-05-30 22:00:22

2022-06-02 06:34:05

2022-05-31 13:42:31

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

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2022-05-30 19:19:40

2022-06-06 15:22:50

2022-06-06 15:22:50

CONTRIBUTOR

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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

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["If you call `metric.compute` in a distributed setup like yours, then `metric.compute` is called in each process. `metric.compute` first calls `metric.add_batch`, and it looks like your error appears at that stage.\r\n\r\nTo make sure that all the processes have started writing their predictions/references at the same time, each process waits for process 0 to lock `slurm-{world_size}-0.arrow.lock`. Process 0 locks this file when `metric.add_batch` is called, so here when `metric.compute` is called.\r\n\r\nTherefore your error can happen when process 0 takes too much time to call `metric.compute` compared to process 3 (>100 seconds by default). I haven't tried running your code but could it be the case ?\r\n\r\nI guess it could also happen if you run multiple times the same distributed job at the same time with the same `experiment_id` because they would collide.\r\n" "We've finally been able to isolate the problem, it wasn't a timing problem, but rather a file locking one. \r\nThe locks produced by calling `flock` where not visible between nodes (so the master node couldn't check other node's locks nor the other way around). \r\n\r\nWe are now having issues with the pre-processing in our runner script, but are not related with the rendezvous process during the evaluation phase. We will let you know about it once we address it. \r\n\r\nOur solution to the rendezvous is as follows:\r\n- We solved the problem by calling `lockf` instead of `flock`.\r\n- We had to change slightly the `_check_all_processes_locks` method so that the main process (i.e. process 0) didn't check it's own lock (because `lockf` permits recursive locks and thus checking it only replaced the current lock with a new one). \r\n\r\nWe use a shared file system between nodes using GPFS in our cluster setup. Maybe the difference between the behavior we see with respect to your usage in multi-node executions comes from that fact. Which file system scheme do you use for the multi-node executions? \r\n\r\n`lockf` seems to work in more settings than `flock`, so maybe we could write a PR so you could test it in your environment. " "Cool, I'm glad you managed to make evaluation work :)\r\n\r\nI'm not completely aware of the differences between lockf and flock, but I've read somewhere that flock is preferable over lockf in multithreading and multiprocessing situations. Here we definitely are in such a situation so unless it is super important I don't think we will switch to lockf"]

2022-05-30 13:24:05

2022-06-08 13:03:05

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`

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[]

["Hi! If the only goal is to improve readability, it's better to use `assertTupleEqual` than `assertSequenceEqual` for Python tuples. Also, note that this function is called internally by `assertEqual`, but I guess we can accept a PR to be more verbose." "Hi @mariosasko, right! I'll update the issue title/desc with `assertTupleEqual` even though as you said it seems to be internally using `assertEqual` so I'm not sure whether it's worth it or not...\r\n\r\nhttps://docs.python.org/3/library/unittest.html#unittest.TestCase.assertTupleEqual" 'I thought we were supposed to move gradually from `unittest` to `pytest`...']

2022-05-30 12:13:18

2022-06-15 09:45:04

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

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closed

[]

[]

2022-05-30 10:10:40

2022-05-30 14:58:18

2022-05-30 14:58:18

CONTRIBUTOR

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I_kwDODunzps5Knvuj

how to convert a dict generator into a huggingface dataset.

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['@albertvillanova @lhoestq , could you please help me on this issue. ' 'Hi ! As mentioned on the [forum](https://discuss.huggingface.co/t/how-to-wrap-a-generator-with-hf-dataset/18464), the simplest for now would be to define a [dataset script](https://huggingface.co/docs/datasets/dataset_script) which can contain your generator. But we can also explore adding something like `ds = Dataset.from_iterable(seqio_dataset)`' '@lhoestq , hey i did as you instructed, but sadly i cannot get pass through the download_manager, as i dont have anything to download. i was skipping the ` def _split_generators(self, dl_manager):` function. but i cannot get around it. I get a `NotImplementedError: `\r\n\r\nthe following is my code for the same: \r\n\r\n\r\n\r\n```\r\nimport datasets \r\nimport functools\r\nimport glob \r\nfrom datasets import load_from_disk\r\nimport seqio\r\nimport tensorflow as tf\r\nimport t5.data\r\nfrom datasets import load_dataset\r\nfrom t5.data import postprocessors\r\nfrom t5.data import preprocessors\r\nfrom t5.evaluation import metrics\r\nfrom seqio import FunctionDataSource, utils\r\n\r\nTaskRegistry = seqio.TaskRegistry\r\n\r\ndata_path = glob.glob("/home/stephen/Desktop/MEGA_CORPUS/COMBINED_CORPUS/*", recursive=False)\r\n\r\n\r\ndef gen_dataset(split, shuffle=False, seed=None, column="text", dataset_path=None):\r\n dataset = load_from_disk(dataset_path)\r\n if shuffle:\r\n if seed:\r\n dataset = dataset.shuffle(seed=seed)\r\n else:\r\n dataset = dataset.shuffle()\r\n while True:\r\n for item in dataset[str(split)]:\r\n yield item[column]\r\n\r\n\r\ndef dataset_fn(split, shuffle_files, seed=None, dataset_path=None):\r\n return tf.data.Dataset.from_generator(\r\n functools.partial(gen_dataset, split, shuffle_files, seed, dataset_path=dataset_path),\r\n output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_path)\r\n )\r\n\r\n@utils.map_over_dataset\r\ndef target_to_key(x, key_map, target_key):\r\n """Assign the value from the dataset to target_key in key_map"""\r\n return {**key_map, target_key: x}\r\n\r\n\r\n_CITATION = "Not ready yet"\r\n_DESCRIPTION = "a custom seqio based mixed samples on a given temperature value, that again returns a dataset in HF dataset format well samples on the Mixture temperature"\r\n_HOMEPAGE = "ldcil.org"\r\n\r\nclass CustomSeqio(datasets.GeneratorBasedBuilder):\r\n\r\n def _info(self):\r\n return datasets.DatasetInfo(\r\n description=_DESCRIPTION,\r\n features=datasets.Features(\r\n {\r\n "text": datasets.Value("string"),\r\n }\r\n ),\r\n homepage="https://ldcil.org",\r\n citation=_CITATION,)\r\n\r\ndef generate_examples(self):\r\n seqio_train_list = []\r\n for lang in data_path:\r\n dataset_name = lang.split("/")[-1]\r\n dataset_shapes = None \r\n\r\n TaskRegistry.add(\r\n str(dataset_name),\r\n source=seqio.FunctionDataSource(\r\n dataset_fn=functools.partial(dataset_fn, dataset_path=lang),\r\n splits=("train", "test"),\r\n caching_permitted=False,\r\n num_input_examples=dataset_shapes,\r\n ),\r\n preprocessors=[\r\n functools.partial(\r\n target_to_key, key_map={\r\n "targets": None,\r\n }, target_key="targets")],\r\n output_features={"targets": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)},\r\n metric_fns=[]\r\n )\r\n\r\n seqio_train_dataset = seqio.get_mixture_or_task(dataset_name).get_dataset(\r\n sequence_length=None,\r\n split="train",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n seqio_train_list.append(seqio_train_dataset)\r\n \r\n lang_name_list = []\r\n for lang in data_path:\r\n lang_name = lang.split("/")[-1]\r\n lang_name_list.append(lang_name)\r\n\r\n seqio_mixture = seqio.MixtureRegistry.add(\r\n "seqio_mixture",\r\n lang_name_list,\r\n default_rate=0.7)\r\n \r\n seqio_mixture_dataset = seqio.get_mixture_or_task("seqio_mixture").get_dataset(\r\n sequence_length=None,\r\n split="train",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n\r\n for id, ex in enumerate(seqio_mixture_dataset):\r\n yield id, {"text": ex["targets"].numpy().decode()}\r\n```\r\n\r\nand i load it by:\r\n\r\n`seqio_mixture = load_dataset("seqio_loader")`' '@lhoestq , just to make things clear ... \r\n\r\nthe following is my original code, thats not in the HF dataset loading script: \r\n\r\n```\r\nimport functools\r\nimport seqio\r\nimport tensorflow as tf\r\nimport t5.data\r\nfrom datasets import load_from_disk\r\nfrom t5.data import postprocessors\r\nfrom t5.data import preprocessors\r\nfrom t5.evaluation import metrics\r\nfrom seqio import FunctionDataSource, utils\r\nimport glob \r\n\r\nTaskRegistry = seqio.TaskRegistry\r\n\r\n\r\n\r\ndef gen_dataset(split, shuffle=False, seed=None, column="text", dataset_path=None):\r\n dataset = load_from_disk(dataset_path)\r\n if shuffle:\r\n if seed:\r\n dataset = dataset.shuffle(seed=seed)\r\n else:\r\n dataset = dataset.shuffle()\r\n while True:\r\n for item in dataset[str(split)]:\r\n yield item[column]\r\n\r\n\r\ndef dataset_fn(split, shuffle_files, seed=None, dataset_path=None):\r\n return tf.data.Dataset.from_generator(\r\n functools.partial(gen_dataset, split, shuffle_files, seed, dataset_path=dataset_path),\r\n output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_path)\r\n )\r\n\r\n\r\n@utils.map_over_dataset\r\ndef target_to_key(x, key_map, target_key):\r\n """Assign the value from the dataset to target_key in key_map"""\r\n return {**key_map, target_key: x}\r\n\r\ndata_path = glob.glob("/home/stephen/Desktop/MEGA_CORPUS/COMBINED_CORPUS/*", recursive=False)\r\n\r\nseqio_train_list = []\r\n\r\nfor lang in data_path:\r\n dataset_name = lang.split("/")[-1]\r\n dataset_shapes = None \r\n\r\n TaskRegistry.add(\r\n str(dataset_name),\r\n source=seqio.FunctionDataSource(\r\n dataset_fn=functools.partial(dataset_fn, dataset_path=lang),\r\n splits=("train", "test"),\r\n caching_permitted=False,\r\n num_input_examples=dataset_shapes,\r\n ),\r\n preprocessors=[\r\n functools.partial(\r\n target_to_key, key_map={\r\n "targets": None,\r\n }, target_key="targets")],\r\n output_features={"targets": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)},\r\n metric_fns=[]\r\n )\r\n\r\n seqio_train_dataset = seqio.get_mixture_or_task(dataset_name).get_dataset(\r\n sequence_length=None,\r\n split="train",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n seqio_train_list.append(seqio_train_dataset)\r\n\r\nlang_name_list = []\r\nfor lang in data_path:\r\n lang_name = lang.split("/")[-1]\r\n lang_name_list.append(lang_name)\r\n\r\nseqio_mixture = seqio.MixtureRegistry.add(\r\n "seqio_mixture",\r\n lang_name_list,\r\n default_rate=0.7\r\n)\r\n\r\nseqio_mixture_dataset = seqio.get_mixture_or_task("seqio_mixture").get_dataset(\r\n sequence_length=None,\r\n split="train",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n\r\nfor _, ex in zip(range(15), seqio_mixture_dataset):\r\n print(ex["targets"].numpy().decode())\r\n```\r\n\r\nwhere the seqio_mixture_dataset is the generator that i wanted to be wrapped in HF dataset. \r\n\r\nalso additionally, could you please tell me how do i set the `default_rate=0.7` args where `seqio_mixture` is defined to be made as a custom option in the HF load_dataset() method,\r\n\r\nmaybe like this: \r\n`seqio_mixture_dataset = datasets.load_dataset("seqio_loader",temperature=0.5)`' 'I like the idea of having `Dataset.from_iterable(iterable)` in the API. The only problem is that we also want to make this part cachable, which is tricky if `iterable` is a generator. \r\n\r\nSome resources on this issue:\r\n* https://github.com/uqfoundation/dill/issues/311\r\n* https://stackoverflow.com/questions/7180212/why-cant-generators-be-pickled\r\n* https://github.com/tonyroberts/generator_tools - python package for pickling generators; pickles bytecode, so it creates version-specific dumps' 'For the caching maybe we can have `Dataset.from_generator` as TF and pickle+hash the generator function (not the generator object itself) ?\r\n\r\nAnd then keep `Dataset.from_iterable` fo pickable objects like lists' '@lhoestq, @mariosasko do you too have any examples where the dataset is a generator and needs to be wrapped into hf dataset ? ' '@lhoestq, following to my previous question ... what possibly could be done in this [link1](https://github.com/huggingface/datasets/issues/4417#issuecomment-1146627404) [link2](https://github.com/huggingface/datasets/issues/4417#issuecomment-1146627593) case? do you have any ideas? ' '@lhoestq +1 for the `Dataset.from_generator` idea.\r\n\r\nHaving thought about it, let\'s avoid adding `Dataset.from_iterable` to the API since dictionaries are technically iteralbles ("iterable" is a broad term in Python), and we already provide `Dataset.from_dict`. And for lists maybe we can add `Dataset.from_list` similar to `pa.Table.from_pylist`. WDYT?\r\n' 'Hi @StephennFernandes!\r\n\r\nTo fix the issues in the copied code, rename `generate_examples` to` _generate_examples` and add one level of indentation as this is a method of `GeneratorBasedBuilder` and define `_split_generators` as follows (again as a method of `GeneratorBasedBuilder):\r\n```python\r\n def _split_generators(self, dl_manager):\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN,\r\n gen_kwargs={},\r\n ),\r\n ]\r\n```\r\n\r\nAnd if you are feeling extra adventurous, you can try to use ArrowWriter to directly create a cache file:\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets.arrow_writer import ArrowWriter\r\n\r\nwriter = ArrowWriter(path="path/to/cache_file.arrow", writer_batch_size=1000)\r\n\r\nwith writer:\r\n for ex in generator:\r\n writer.write(ex) \r\n writer.finalize()\r\n\r\ndset = Dataset.from_file("path/to/cache_file.arrow")\r\n```\r\n\r\n' 'I have a problem which I think is very similar: I would like to "stream" data to a HF Array (memory-mapped) Dataset, where the final size of the dataset is unknown, but could be much larger than what fits into memory.\r\nWhat I want to end up with is an Array Dataset which I can open using `Dataset.load_from_disk(dataset_path="somename")` and use e.g. as the training set. \r\n\r\nFor this I would have thought there should be an API which allows me to open/create the dataset (and define the features etc), then write examples to the dataset, but I could not find a way to do this. \r\n\r\nI tried doing this and it looks like it works, but it feels very hacky and I am not sure if this might fail to update some of the fields in the json files which may turn out to be important:\r\n```\r\nfrom datasets import Dataset, Features, ClassLabel, Sequence, Value\r\nfrom datasets.arrow_writer import ArrowWriter \r\n# 1) define the features\r\nfeatures = Features(dict(\r\n id=Value(dtype="string"),\r\n tokens=Sequence(feature=Value(dtype="string")),\r\n ner_tags=Sequence(feature=ClassLabel(names=[\'O\', \'B-corporation\', \'I-corporation\', \'B-creative-work\', \'I-creative-work\', \'B-group\', \'I-group\', \'B-location\', \'I-location\', \'B-person\', \'I-person\', \'B-product\', \'I-product\'])),\r\n))\r\n# 2) create empty dataset for examples with these features and store to disk\r\nempty = dict(\r\n id = [],\r\n tokens = [],\r\n ner_tags = [],\r\n)\r\nds = Dataset.from_dict(empty, features=features)\r\nds.save_to_disk(dataset_path="debug_ds1")\r\n\r\n# 3) directly write all the examples to the arrow dataset \r\nwith ArrowWriter(path="debug_ds1/dataset.arrow") as writer: \r\n writer.write(dict(id=0, tokens=["a", "b"], ner_tags=[0, 0])) \r\n writer.write(dict(id=1, tokens=["x", "y"], ner_tags=[1, 0])) \r\n writer.finalize() \r\n \r\nds2 = Dataset.load_from_disk(dataset_path="debug_ds1")\r\nlen(ds2)\r\n```\r\nIs there a cleaner/proper way to do this?\r\n\r\nI like the sound of `Dataset.from_iterable` or `Dataset.from_generator` (should not from iterable be able to handle from generator too as all generators are iterables?) but how would I define the features for me examples there? ' "Hi @johann-petrak! You can pass the features directly to ArrowWriter's initializer like so `ArrowWriter(..., features=features)`.\r\n\r\nAnd the reason why I prefer `Dataset.from_generator` over `Dataset.from_iterable` is mentioned in one of my previous comments." '@mariosasko so at the moment we still have to create a fake `Dataset` first and then use `ArrowWriter` to write an actual dataset? I\'m using the latest version of `datasets` on pypi but my final file is always empty. Is there anything wrong with the code below?\r\n\r\n```python\r\n total = 0\r\n with ArrowWriter(path=str(final_data_path), features=features) as writer:\r\n for batch in loader:\r\n for traj in batch:\r\n for generator in question_generators:\r\n for xi in generator(traj):\r\n # print(f"Question: {xi.question}, answer: {xi.answer}")\r\n total += 1\r\n writer.write(\r\n {\r\n "id": f"qa_{total}",\r\n "question": xi.question,\r\n "answer": xi.answer,\r\n }\r\n )\r\n writer.finalize()\r\n print(f"Total #questions = {total}") # this prints 402\r\n```' 'This works for me if I then (actually I also close the writer: `writer.close()`) open the Arrow file as a dataset using `ds=Dataset.from_file(final_data_path)` then `ds.save_to_disk(somedir)`. The Dataset created that way contains the expected examples.' "Oh thanks. That did the trick I believe. Shouldn't ArrowWriter have a context manager that does these operations?" 'You can just use `Dataset.from_file` to get your dataset, no need to do an extra `save_to_disk` somewhere else ;)' 'I was thinking that `save_to_disk` is necessary when one wants to re-use that dataset as a proper HF dataset later, no?\r\nAt least what I wanted to achieve is create a dataset that can be opened like any other local or remote dataset. ' "`save_to_disk`/`load_from_disk` is indeed more general, e.g. it supports datasets that consist in several files, and saves some extra info in a dataset_info.json file (description, citation, split sizes, etc.)\r\n\r\nIf you have one single file it's fine to simply do `.from_file()`"]

2022-05-29 16:28:27

2022-07-18 12:29:34

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

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['_The documentation is not available anymore as the PR was closed or merged._' 'Thank you very much for your help @albertvillanova .\r\n\r\nI think I have fixed all the comments.\r\n\r\nPlease let me know if this PR need further modification ;)' '@albertvillanova Thank you very much for your kind help.\r\nThese suggestions make the code looks more pythonic.\r\n\r\nI have commited these changes' "Hi ! The dataset seems to be a duplicate of https://huggingface.co/datasets/silver/lccc - next time no need to add it on github if it's already available on huggingface.co ;)" "> Hi ! The dataset seems to be a duplicate of https://huggingface.co/datasets/silver/lccc - next time no need to add it on github if it's already available on huggingface.co ;)\r\n\r\nOK, sorry for the inconvenience. I have closed another two PRs since these datasets are already available on huggingface.co" "It's fine, thanks @silverriver for adding these datasets !"]

2022-05-29 12:27:19

2022-06-15 10:28:59

2022-06-02 09:13:46

CONTRIBUTOR

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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

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-05-27 17:03:42

2022-06-07 12:42:25

2022-06-07 12:33:52

CONTRIBUTOR

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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

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-05-27 09:28:02

2022-05-31 15:07:21

2022-05-31 14:58:51

MEMBER

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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

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closed

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['Thanks for reporting @dgcnz.\r\n\r\nI have checked that the dataset works fine in streaming mode.\r\n\r\nAdditionally, other datasets containing timestamps are properly rendered by the viewer: https://huggingface.co/datasets/blbooks\r\n\r\nI have tried to force the refresh of the preview, but the endpoint is not responsive: Connection timed out\r\n\r\nCC: @severo ' "I've just resent the refresh of the preview to the new endpoint, without success.\r\n\r\nCC: @severo " 'Fixed!\r\n\r\nhttps://huggingface.co/datasets/ett/viewer/h1/test\r\n\r\n<img width="982" alt="Capture d’écran 2022-06-15 à 09 30 22" src="https://user-images.githubusercontent.com/1676121/173769035-a075d753-ecfc-4a43-b54b-973105d464d3.png">\r\n']

2022-05-27 02:12:35

2022-06-15 07:30:46

2022-06-15 07:30:46

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`

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['_The documentation is not available anymore as the PR was closed or merged._' 'This PR (via new release) broke many transformers tests.\r\n\r\nI will try to post a summary shortly.\r\n\r\ncc: @ydshieh ' 'So now it can\'t handle a local path via: `--train_file tests/deepspeed/../fixtures/tests_samples/wmt_en_ro/train.json` even though it\'s there. it works just fine if I change the path to not have `..`\r\n\r\nYou can reproduce the original problem with:\r\n\r\n```\r\n$ cd transformers \r\n$ python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --train_file tests/fixtures/tests_samples/wmt_en_ro/train.json --validation_file tests/deepspeed/../fixtures/tests_samples/wmt_en_ro/val.json --output_dir /tmp/tmp5o5to4k0 --overwrite_output_dir --max_source_length 32 --max_target_length 32 --val_max_target_length 32 --warmup_steps 8 --predict_with_generate --save_steps 0 --eval_steps 1 --group_by_length --label_smoothing_factor 0.1 --source_lang en --target_lang ro --report_to none --source_prefix "translate English to Romanian: " --fp16 --do_train --num_train_epochs 1 --max_train_samples 16 --per_device_train_batch_size 2 --learning_rate 3e-3\r\n[...]\r\nTraceback (most recent call last):\r\n File "examples/pytorch/translation/run_translation.py", line 656, in <module>\r\n main()\r\n File "examples/pytorch/translation/run_translation.py", line 346, in main\r\n raw_datasets = load_dataset(\r\n File "/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/load.py", line 1656, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File "/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/load.py", line 1439, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File "/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/load.py", line 1097, in dataset_module_factory\r\n return PackagedDatasetModuleFactory(\r\n File "/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/load.py", line 743, in get_module\r\n data_files = DataFilesDict.from_local_or_remote(\r\n File "/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/data_files.py", line 588, in from_local_or_remote\r\n DataFilesList.from_local_or_remote(\r\n File "/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/data_files.py", line 556, in from_local_or_remote\r\n data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n File "/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls\r\n for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):\r\n File "/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally\r\n raise FileNotFoundError(error_msg)\r\nFileNotFoundError: Unable to find \'/mnt/nvme0/code/huggingface/transformers-master/tests/deepspeed/../fixtures/tests_samples/wmt_en_ro/val.json\' at /mnt/nvme0/code/huggingface/transformers-master\r\n```' 'will apply a workaround to `transformers` tests here https://github.com/huggingface/transformers/pull/17721\r\n' 'This has been fixed with https://github.com/huggingface/datasets/pull/4505, will do a patch release tomorrow for `datasets` ;)' 'Thank you for the quick fix, @lhoestq ']

2022-05-26 12:10:28

2022-06-15 17:11:25

2022-06-01 13:04:16

CONTRIBUTOR

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Fix #4115

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PR_kwDODunzps44g_yL

Update `_format_columns` in `remove_columns`

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['🤗 This PR closes https://github.com/huggingface/datasets/issues/4398' '_The documentation is not available anymore as the PR was closed or merged._' 'Hi! Thanks for reporting and providing a fix. I made a small change to make the fix easier to understand.' "Hi, @mariosasko thanks! It makes sense, sorry I'm not that familiar with `datasets` code 😩 " "Sure @albertvillanova I'll do that later today and ping you once done, thanks! :hugs:" 'Hi again @albertvillanova! Let me know if those tests are fine 🤗 ' 'Hi @alvarobartt,\r\n\r\nI think your tests are failing. I don\'t know why previously, after your last commit, the CI tests were not triggered. \r\n\r\nIn order to force the re-running of the CI tests, I had to edit your file using the GitHub UI.\r\n\r\nFirst I tried to do it using my terminal, but I don\'t have push right to your PR branch: I would ask you next time you open a PR, please mark the checkbox "Allow edits from maintainers": https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork#enabling-repository-maintainer-permissions-on-existing-pull-requests' 'Hi @albertvillanova, let me check those again! And regarding that checkbox I thought it was already checked so my bad there 😩 ' '@albertvillanova again it seems that the tests were not automatically triggered, but I tested those locally and now they work, as previously those were failing as I created an assertion as `self.assertEqual` over an empty list that was compared as `None` while the value was `[]` so I updated it to be `self.assertListEqual` and changed the comparison value to `[]`.' '@lhoestq any idea why the CI is not triggered?' '@alvarobartt I have tested locally and the tests continue failing.\r\n\r\nI think there is a basis error: `new_dset._format_columns` is always `None` in those cases.\r\n' "You're right @albertvillanova I was indeed running the tests with `datasets==2.2.0` rather than with the branch version, I'll check it again! Sorry for the inconvenience..." "> @alvarobartt I have tested locally and the tests continue failing.\r\n> \r\n> I think there is a basis error: `new_dset._format_columns` is always `None` in those cases.\r\n\r\nIn order to have some regressions tests for the fixed scenario, I've manually updated the value of `_format_columns` in the `ArrowDataset` so as to check whether it's updated or not right after calling `remove_columns`, and it does behave as expected, so with the latest version of this branch the reported issue doesn't occur anymore." "Hi again @albertvillanova sorry I was on leave! I'll do that ASAP :hugs:" "@albertvillanova, does it make sense to add regression tests for `DatasetDict`? As `DatasetDict` doesn't have the attribute `_format_columns`, when we call `remove_columns` over a `DatasetDict` it removes the columns and updates the attributes of each split which is an `ArrowDataset`.\r\n\r\nSo on, we can either:\r\n- Update first the `_format_columns` attribute of each split and then remove the columns over the `DatasetDict`\r\n- Loop over the splits of `DatasetDict` and remove the columns right after updating `_format_columns` of each `ArrowDataset`.\r\n\r\nI assume that the best regression test is the one implemented (mentioned first above), let me know if there's a better way to do that 👍🏻 " "I think there's already a decorator to support transmitting the right `_format_columns`: `@transmit_format`, have you tried adding this decorator to `remove_columns` ?" "> I think there's already a decorator to support transmitting the right `_format_columns`: `@transmit_format`, have you tried adding this decorator to `remove_columns` ?\r\n\r\nHi @lhoestq I can check now!" 'It worked indeed @lhoestq, thanks for the proposal and the review! 🤗 ' "Oops, I forgot about `@transmit_format`'s existence. From what I see, we should also use this decorator in `flatten`, `rename_column` and `rename_columns`. \r\n\r\n@alvarobartt Let me know if you'd like to work on this (in a subsequent PR)." 'Sure @mariosasko I can prepare another PR to add those too, thanks! ']

2022-05-26 11:40:06

2022-06-14 19:05:37

2022-06-14 16:01:56

CONTRIBUTOR

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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

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-05-26 06:17:35

2022-05-26 06:48:42

2022-05-26 06:40:12

MEMBER

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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)

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["@lhoestq Maybe I'm missing something, but what's the reason to read and encode PCM files to WAV in `Audio.encode_example`. Isn't the whole purpose of the decodable types to operate on raw files whenever possible? IMO this PR should only modify `Audio.decode_example` to support PCM files/bytes decoding." 'Because the PCM file is not enough, we also need the `sampling_rate` associated to it. Therefore the two alternatives are either:\r\n- convert to WAV\r\n- add a `sampling_rate` field to the Audio arrow storage (not sure how it would behave for backward compatibility though)' "But [`scipy.io.wavfile.read`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.wavfile.read.html), which is used for reading such files, returns a file's sampling rate. The only tricky part is [resampling](https://stackoverflow.com/questions/33682490/how-to-read-a-wav-file-using-scipy-at-a-different-sampling-rate) to a different sampling rate than the default one." "How does it get the sampling rate of a PCM file then ? According to [SO](https://stackoverflow.com/a/57027667/17517845) it's not possible to infer it from the file alone" '> Awesome thanks ! Could you also add tests in `tests/features/test_audio.py` ?\r\n> \r\n> Maybe add a small pcm file in `tests/features/data` and check that everything works as expected in tests cases like `test_audio_encode_example_pcm` and `test_audio_decode_example_pcm` for example.\r\n\r\n@lhoestq how can i test test_audio.py? where is "__main__" func?\r\ndo you have some example or guideline?' '> But [`scipy.io.wavfile.read`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.wavfile.read.html), which is used for reading such files, returns a file\'s sampling rate. The only tricky part is [resampling](https://stackoverflow.com/questions/33682490/how-to-read-a-wav-file-using-scipy-at-a-different-sampling-rate) to a different sampling rate than the default one.\r\n\r\n@mariosasko @lhoestq \r\nthanks for comment!\r\n\r\nFirst of all, "PCM file" can not read alone to any audio library.\r\n"PCM file" has not any audio META information header. (it just purely audio byte data. therefore, we don\'t have to encoding and decoding)\r\nbut, "PCM file" is audio extension, so we can use `datasets.Audio`\r\n\r\nif you want to read "PCM file" to audio file likely, it have to needs additional parameter. (channel, sampling_rate, else....)\r\nbut, in many situation, we only know sampling_rate for PCM\r\n\r\nand, if we want to use `datasets.Audio` for "PCM file", we must process encode_example.\r\ntherefore, i have to use sampling_rate for encoding for making wav-style byte. (we only know sampling_rate)\r\n\r\nIn my source code, I don\'t compare sampling rate(`datasets.Audio\'s self.sampling_rate` and `read pcm sampling_rate(value["sampling_rate"])`) and checking mono\r\n@mariosasko ! do you want to process resampling and making mono? then i can modify my source\r\n' 'There is no "main" function in test scripts :) To run a test script you must use the `pytest` command:\r\n```\r\npytest tests/features/test_audio.py\r\n```\r\n\r\nto run only one function you can also do\r\n```\r\npytest tests/features/test_audio.py::test_audio_feature_type_to_arrow\r\n```\r\nfor example' '@lhoestq\r\nmaybe, if i write test code, i have to commit test_audio.py and send pr?\r\nbecause, we need to keep `test_audio_encode_example_pcm` and `test_audio_decode_example_pcm` method after my pr merged?' 'You can add your tests in this PR with the other changes you did' '@lhoestq \r\ntest complete & commit my test_audio.py\r\n\r\nAND, some change in my code.\r\n\r\naudio.py\r\ni think "sampling_rate" is already Audio object initial variable. so, we don`t have to use input parameter.\r\n\r\ntest_audio.py\r\nwe can check "PCM" file to path (exactly, extenstion)\r\nso, test case has to know `path`. if only have `bytes`, we don`t know that is "PCM" or not' '@lhoestq\r\nand, why circleci raised exception?\r\nmaybe, [repo](https://huggingface.co/api/datasets/lhoestq/_dummy?full=true) url is not found!\r\nPLZ, CHK!' '@lhoestq\r\nhello????' '@lhoestq \r\ntest_audio.py\r\nif we don`t use path in pcm, test-case need to be changed\r\nso, we check path just None' "i'm merge branch already and `multiprocess` in `setup.py` but circleci error only win version\r\n\r\nhow can i fixed it?" '@lhoestq thx for comment!\r\ntest_audio.py test complete. it runs sucessfully\r\nand, self.get("sampling_rate") -> value.get("sampling_rate") changed\r\n\r\nand, some comment is not agreed to me, plz check my sub comment!' '_The documentation is not available anymore as the PR was closed or merged._']

2022-05-26 04:26:36

2022-07-07 13:27:29

2022-07-07 13:16:09

CONTRIBUTOR

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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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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-05-25 20:32:19

2022-05-25 20:45:11

2022-05-25 20:36:47

MEMBER

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I_kwDODunzps5KbTgi

Dataset Viewer issue for conll2012_ontonotesv5

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closed

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['Thanks for reporting, @jiangwy99.\r\n\r\nI guess this could be addressed only once we fix our issue with irresponsive backend endpoint.\r\n\r\nCC: @severo ' "I've just sent the forcing of the refresh of the preview to the new endpoint." 'Fixed, thanks for the patience. The issue was the amount of RAM allowed to extract the first rows of the dataset was not sufficient.']

2022-05-25 20:18:33

2022-06-07 18:39:16

2022-06-07 18:39:16

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

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closed

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[]

2022-05-25 19:41:55

2022-05-27 14:51:23

2022-05-27 14:51:23

NONE

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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

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closed

[]

['And if the problem is that the way I am to construct the {Entity Type: list of spans} makes entity types without any spans hard to handle, is there a better way to meet the demand? Although I have verified that to make entity types without any spans to behave like `entity_chunk[label] = [[""]]` can perform normally, I still wonder if there is a more elegant way?']

2022-05-25 18:56:43

2022-06-07 14:27:20

2022-06-07 14:27:20

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

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closed

[]

["Hi! You can already use `dset.builder_name` and `dset.config_name` for that purpose. And when it comes to versioning, it's better to use `dset._fingerprint` than the `version` attribute as the former represents a deterministic hash that encodes all the mutable ops executed on a dataset, and the latter stays the same unless it's manually updated after each op."]

2022-05-25 18:56:08

2022-06-16 10:47:53

2022-06-16 10:47:53

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

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-05-25 16:46:15

2022-05-31 08:33:36

2022-05-31 08:25:02

MEMBER

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PR_kwDODunzps44cdR5

Skip identical files in `push_to_hub` instead of overwriting

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-05-25 13:12:51

2022-05-25 15:16:36

2022-05-25 15:08:03

CONTRIBUTOR

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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

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closed

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["Thanks for reporting, @OmarAlaaeldein.\r\n\r\nDatasets hosted at Google Drive give problems quite often due to a change in their service:\r\n- #3786 \r\n\r\nRelated to:\r\n- #3906\r\n\r\nI'm having a look." 'We have made a Pull Request to replace the Google Drive URL. This fix will be accessible in our next `datasets` library release.\r\n\r\nIn the meantime, once the PR merged into master, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset("...", download_mode="force_redownload")\r\n```']

2022-05-25 07:45:07

2022-05-26 06:40:12

2022-05-26 06:40:12

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.

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closed

[]

[]

2022-05-25 03:10:44

2022-05-25 03:26:36

2022-05-25 03:26:36

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

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open

[]

['Ok, so\r\n```\r\nos.path.basename("/home/user/")\r\n```\r\ngives `\'\'` while \r\n```\r\nos.path.basename("/home/user")\r\n```\r\ngives `user`. \r\nThe code should check if the last char is a slash.\r\n' 'The fix is:\r\n```\r\n"name": os.path.basename(self.path[:-1] if self.path[-1] == "/" else self.path)\r\n```' 'I came through the same issue , just removing the last slash in the dataset path fixed it for me, may be this repo moderators could accept this as an accepted answer atleast if this could not be integrated\r\n\r\n> The fix is:\r\n> \r\n> ```\r\n> "name": os.path.basename(self.path[:-1] if self.path[-1] == "/" else self.path)\r\n> ```\r\n\r\n@apohllo consider making a pull request on this \r\n\r\nThanks for the amazing contributions from huggingface people !!\r\n']

2022-05-24 18:03:01

2022-08-16 13:25:44

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`

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closed

[]

['It works if we either remove the `ds = ds.cast_column("id", Value("int32"))` line from the code above, or if instead calling `ds.remove_columns()` we remove the columns inside each mapping as `ds.map(..., remove_columns=[...])` instead of right after the mapping.\r\n\r\nBoth of those solutions seem to fix the issue, so the root cause of it may be around that. Sorry I cannot provide you more insights, in case I get to fix it I\'ll submit a PR, in the meanwhile the code that I\'m using as a workaround is the following:\r\n\r\n```python\r\nfrom transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\nimport torch\r\n\r\ntorch.set_grad_enabled(False)\r\nctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")\r\nctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")\r\n\r\nfrom datasets import load_dataset, Value\r\n\r\nds = load_dataset("csv", data_files=["sample.csv"], split="train")\r\nds = ds.cast_column("id", Value("int32"))\r\nds = ds.map(lambda x: {"inputs": f"{ctx_tokenizer.sep_token}".join(["title", "summary"])}, remove_columns=["title", "summary"])\r\n\r\ndef generate_embeddings(x):\r\n return {"embeddings": ctx_encoder(**ctx_tokenizer(x["inputs"], return_tensors="pt"))[0][0].numpy()}\r\n\r\nds = ds.map(generate_embeddings, remove_columns=["inputs"])\r\nds.add_faiss_index(column="embeddings")\r\n```' 'FYI the main reason I want to use `dataset.remove_columns` rather than the function inside `dataset.map` is because according to the 🤗 Datasets documentation, it\'s faster.\r\n\r\n"🤗 Datasets also has a [Dataset.remove_columns()](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/main_classes#datasets.Dataset.remove_columns) method that is functionally identical, but faster, because it doesn’t copy the data of the remaining columns."\r\n\r\nMore information at https://huggingface.co/docs/datasets/process#map' 'Here I\'m presenting all the scenarios so that you can further investigate the issue:\r\n\r\n- ✅ `cast_column` -> `map` with `remove_columns` -> `map` with `remove_columns` -> `add_faiss_index`\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset("csv", data_files=["sample.csv"], split="train")\r\n ds = ds.cast_column("id", Value("int32"))\r\n ds = ds.map(lambda x: {"inputs": f"{ctx_tokenizer.sep_token}".join(["title", "summary"])}, remove_columns=["title", "summary"])\r\n \r\n def generate_embeddings(x):\r\n return {"embeddings": ctx_encoder(**ctx_tokenizer(x["inputs"], return_tensors="pt"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings, remove_columns=["inputs"])\r\n ds.add_faiss_index(column="embeddings")\r\n ```\r\n\r\n- ❌ `cast_column` -> `map` -> `remove_columns` -> `map` -> `remove_columns` -> `add_faiss_index`\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset("csv", data_files=["sample.csv"], split="train")\r\n ds = ds.cast_column("id", Value("int32"))\r\n ds = ds.map(lambda x: {"inputs": f"{ctx_tokenizer.sep_token}".join(["title", "summary"])})\r\n ds = ds.remove_columns(["title", "summary"])\r\n \r\n def generate_embeddings(x):\r\n return {"embeddings": ctx_encoder(**ctx_tokenizer(x["inputs"], return_tensors="pt"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings)\r\n ds = ds.remove_columns(["inputs"])\r\n ds.add_faiss_index(column="embeddings")\r\n ```\r\n\r\n- ❌ `cast_column` -> `map` with `remove_columns` -> `map` -> `remove_columns` -> `add_faiss_index`\r\n\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset("csv", data_files=["sample.csv"], split="train")\r\n ds = ds.cast_column("id", Value("int32"))\r\n ds = ds.map(lambda x: {"inputs": f"{ctx_tokenizer.sep_token}".join(["title", "summary"])}, remove_columns=["title", "summary"])\r\n \r\n def generate_embeddings(x):\r\n return {"embeddings": ctx_encoder(**ctx_tokenizer(x["inputs"], return_tensors="pt"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings)\r\n ds = ds.remove_columns(["inputs"])\r\n ds.add_faiss_index(column="embeddings")\r\n ```\r\n\r\n- ✅ `cast_column` -> `map` -> `remove_columns` -> `map` with `remove_columns` -> `add_faiss_index`\r\n\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset("csv", data_files=["sample.csv"], split="train")\r\n ds = ds.cast_column("id", Value("int32"))\r\n ds = ds.map(lambda x: {"inputs": f"{ctx_tokenizer.sep_token}".join(["title", "summary"])})\r\n ds = ds.remove_columns(["title", "summary"])\r\n \r\n def generate_embeddings(x):\r\n return {"embeddings": ctx_encoder(**ctx_tokenizer(x["inputs"], return_tensors="pt"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings, remove_columns=["inputs"])\r\n ds.add_faiss_index(column="embeddings")\r\n ```\r\n\r\n- ✅ `map` -> `remove_columns` -> `map` -> `remove_columns` -> `add_faiss_index`\r\n\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset("csv", data_files=["sample.csv"], split="train")\r\n ds = ds.map(lambda x: {"inputs": f"{ctx_tokenizer.sep_token}".join(["title", "summary"])})\r\n ds = ds.remove_columns(["title", "summary"])\r\n \r\n def generate_embeddings(x):\r\n return {"embeddings": ctx_encoder(**ctx_tokenizer(x["inputs"], return_tensors="pt"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings)\r\n ds = ds.remove_columns(["inputs"])\r\n ds.add_faiss_index(column="embeddings")\r\n ```' "So on, I've created #4411 so as to fix the bug with `remove_columns` under certain conditions before `add_faiss_index`, which means that the scenarios not working above are already working fine."]

2022-05-24 14:41:34

2022-06-14 16:01:56

2022-06-14 16:01:56

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

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[]

closed

[]

['_The documentation is not available anymore as the PR was closed or merged._']

2022-05-24 13:54:23

2022-05-25 14:02:52

2022-05-25 13:54:08

MEMBER

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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

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['_The documentation is not available anymore as the PR was closed or merged._']

2022-05-23 17:16:12

2022-05-24 05:49:11

2022-05-24 05:41:00

MEMBER

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

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PR_kwDODunzps44TrBA

Add Pascal VOC dataset

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['The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4395). All of your documentation changes will be reflected on that endpoint.' 'Some CI fails are unrelated to your PR and fixed on master, feel free to merge master into your branch :)']

2022-05-23 16:34:05

2022-07-06 15:19:53

CONTRIBUTOR

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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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I_kwDODunzps5KOJMZ

trainer became extremely slow after reload dataset by `load_from_disk`

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["I tried to make the dataset much more smaller (100000 rows) , then the speed became `33.88it/s` from`8.62s/it`. It's nearly 200 times... Do you have any idea? Thank you!" "Similar issue: https://github.com/huggingface/transformers/issues/8818\r\n\r\nI changed `RandomSampler` to `SequentialSampler` in the `trainer.py`, but the speed didn't become faster." 'I changed\r\n```\r\ntokenized_datasets = load_from_disk(\r\n "/pathto/dataset"\r\n )\r\n```\r\nto\r\n```\r\ntokenized_datasets = load_from_disk(\r\n "/pathto/dataset", keep_in_memory=True\r\n )\r\n```\r\nand obtained normal speed. It\'s seems that the problem is on the os\'s speed limit.' "Hi ! Currently `save_to_disk` saves one big Arrow file, which causes some slow downs. This has been discussed in #3735 and we'll implement sharding pretty soon to solve this\r\n\r\nFor now you can try splitting and saving your dataset in several Arrow files. Then you can load them one by one and use `concatenate_datasets` to have your big dataset again and hopefully with a better speed"]

2022-05-23 14:04:37

2022-06-06 16:08:01

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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PR_kwDODunzps44RxWN

Update CI deprecated legacy image

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