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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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} | 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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_No response_
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] | null | [] | 2022-06-06T23:13:15Z | 2022-12-15T13:36:40Z | 2022-06-08T07:16:16Z | NONE | null | null | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/4449 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4449/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4449/comments | https://api.github.com/repos/huggingface/datasets/issues/4449/events | https://github.com/huggingface/datasets/issues/4449 | 1,261,262,326 | I_kwDODunzps5LLVX2 | 4,449 | Rj | {
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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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] | open | false | null | [] | null | [] | 2022-06-05T05:32:56Z | 2022-06-05T15:14:27Z | null | NONE | null | null | null | **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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} | 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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} | To fix the rendering of `**kwargs` in docstrings, a parentheses must be added afterwards.
See:
- huggingface/doc-builder/issues/235 | {
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_No response_
### Description
_No response_
### Owner
_No response_ | {
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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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] | null | [] | 2022-06-02T18:57:12Z | 2022-06-07T18:50:55Z | 2022-06-07T18:50:55Z | MEMBER | null | null | null | ### 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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} | 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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] | closed | false | null | [] | null | [] | 2022-06-02T16:35:56Z | 2022-06-03T08:44:17Z | 2022-06-03T08:44:16Z | NONE | null | null | null | ## 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:
```
Β³ PHONCODE.DOC
Β³ PROMPTS.TXT
Β³ SPKRINFO.TXT
Β³ SPKRSENT.TXT
Β³ 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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} | As pointed out by @sgugger:
- huggingface/doc-builder/issues/235 | {
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https://api.github.com/repos/huggingface/datasets/issues/4437 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4437/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4437/comments | https://api.github.com/repos/huggingface/datasets/issues/4437/events | https://github.com/huggingface/datasets/pull/4437 | 1,258,249,582 | PR_kwDODunzps44-uRW | 4,437 | Add missing columns to `blended_skill_talk` | {
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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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- #4422 | {
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] | closed | false | null | [] | null | [] | 2022-06-02T01:51:49Z | 2022-06-02T23:59:26Z | 2022-06-02T23:59:18Z | NONE | null | null | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/4434 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4434/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4434/comments | https://api.github.com/repos/huggingface/datasets/issues/4434/events | https://github.com/huggingface/datasets/pull/4434 | 1,256,207,321 | PR_kwDODunzps443mAr | 4,434 | Fix dummy dataset generation script for handling nested types of _URLs | {
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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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https://api.github.com/repos/huggingface/datasets/issues/4432 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4432/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4432/comments | https://api.github.com/repos/huggingface/datasets/issues/4432/events | https://github.com/huggingface/datasets/pull/4432 | 1,255,523,720 | PR_kwDODunzps441JmK | 4,432 | Fix builder docstring | {
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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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https://api.github.com/repos/huggingface/datasets/issues/4431 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4431/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4431/comments | https://api.github.com/repos/huggingface/datasets/issues/4431/events | https://github.com/huggingface/datasets/pull/4431 | 1,254,618,948 | PR_kwDODunzps44x5aG | 4,431 | Add personaldialog datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/4430 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4430/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4430/comments | https://api.github.com/repos/huggingface/datasets/issues/4430/events | https://github.com/huggingface/datasets/issues/4430 | 1,254,412,591 | I_kwDODunzps5KxNEv | 4,430 | Add ability to load newer, cleaner version of Multi-News | {
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] | closed | false | null | [] | null | [] | 2022-05-31T21:00:44Z | 2022-06-07T17:14:44Z | 2022-06-07T17:14:44Z | CONTRIBUTOR | null | null | null | **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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https://api.github.com/repos/huggingface/datasets/issues/4429 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4429/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4429/comments | https://api.github.com/repos/huggingface/datasets/issues/4429/events | https://github.com/huggingface/datasets/pull/4429 | 1,254,184,358 | PR_kwDODunzps44whxN | 4,429 | Update builder docstring for deprecated/added arguments | {
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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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https://api.github.com/repos/huggingface/datasets/issues/4428 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4428/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4428/comments | https://api.github.com/repos/huggingface/datasets/issues/4428/events | https://github.com/huggingface/datasets/issues/4428 | 1,254,092,818 | I_kwDODunzps5Kv_AS | 4,428 | Errors when building dummy data if you use nested _URLS | {
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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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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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It seems that all tests are passed | {
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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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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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] | closed | false | null | [] | null | [] | 2022-05-30T12:13:18Z | 2022-09-30T16:01:37Z | 2022-09-30T16:01:37Z | CONTRIBUTOR | null | null | null | **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 π€ `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 π€
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] | null | [] | 2022-05-29T16:28:27Z | 2022-09-16T14:44:19Z | 2022-09-16T14:44:19Z | NONE | null | null | null | ### 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[βtextβ])
```
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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} | Hi, I am trying to add a new dataset lccc.
All tests are passed. | {
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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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https://api.github.com/repos/huggingface/datasets/issues/4414 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4414/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4414/comments | https://api.github.com/repos/huggingface/datasets/issues/4414/events | https://github.com/huggingface/datasets/pull/4414 | 1,250,546,888 | PR_kwDODunzps44klhY | 4,414 | Rename DatasetBuilder config_name | {
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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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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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https://api.github.com/repos/huggingface/datasets/issues/4412 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4412/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4412/comments | https://api.github.com/repos/huggingface/datasets/issues/4412/events | https://github.com/huggingface/datasets/pull/4412 | 1,249,490,179 | PR_kwDODunzps44hFvq | 4,412 | Skip hidden files/directories in data files resolution and `iter_files` | {
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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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https://api.github.com/repos/huggingface/datasets/issues/4410 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4410/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4410/comments | https://api.github.com/repos/huggingface/datasets/issues/4410/events | https://github.com/huggingface/datasets/pull/4410 | 1,249,148,457 | PR_kwDODunzps44f_Td | 4,410 | Remove Google Drive URL in spider dataset | {
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Fix #4401. | {
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https://api.github.com/repos/huggingface/datasets/issues/4409 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4409/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4409/comments | https://api.github.com/repos/huggingface/datasets/issues/4409/events | https://github.com/huggingface/datasets/pull/4409 | 1,249,083,179 | PR_kwDODunzps44fxiH | 4,409 | Update: add using pcm bytes (#4323) | {
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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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https://api.github.com/repos/huggingface/datasets/issues/4407 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4407/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4407/comments | https://api.github.com/repos/huggingface/datasets/issues/4407/events | https://github.com/huggingface/datasets/issues/4407 | 1,248,671,778 | I_kwDODunzps5KbTgi | 4,407 | Dataset Viewer issue for conll2012_ontonotesv5 | {
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] | null | [] | 2022-05-25T20:18:33Z | 2022-06-07T18:39:16Z | 2022-06-07T18:39:16Z | NONE | null | null | null | ### Link
https://huggingface.co/datasets/conll2012_ontonotesv5
### Description
Dataset viewer outage.
### Owner
No | {
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https://api.github.com/repos/huggingface/datasets/issues/4406 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4406/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4406/comments | https://api.github.com/repos/huggingface/datasets/issues/4406/events | https://github.com/huggingface/datasets/pull/4406 | 1,248,626,622 | PR_kwDODunzps44ePLU | 4,406 | Improve language tag for PIAF dataset | {
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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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https://api.github.com/repos/huggingface/datasets/issues/4405 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4405/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4405/comments | https://api.github.com/repos/huggingface/datasets/issues/4405/events | https://github.com/huggingface/datasets/issues/4405 | 1,248,574,087 | I_kwDODunzps5Ka7qH | 4,405 | [TypeError: Couldn't cast array of type] Cannot process dataset in v2.2.2 | {
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] | closed | false | null | [] | null | [] | 2022-05-25T18:56:43Z | 2022-06-07T14:27:20Z | 2022-06-07T14:27:20Z | NONE | null | null | null | ## 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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] | closed | false | null | [] | null | [] | 2022-05-25T18:56:08Z | 2022-09-13T15:09:30Z | 2022-06-16T10:47:53Z | NONE | null | null | null | **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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https://api.github.com/repos/huggingface/datasets/issues/4402 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4402/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4402/comments | https://api.github.com/repos/huggingface/datasets/issues/4402/events | https://github.com/huggingface/datasets/pull/4402 | 1,248,078,067 | PR_kwDODunzps44cdR5 | 4,402 | Skip identical files in `push_to_hub` instead of overwriting | {
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} | [] | closed | false | null | [] | null | [] | 2022-05-25T13:12:51Z | 2022-05-25T15:16:36Z | 2022-05-25T15:08:03Z | CONTRIBUTOR | null | false | {
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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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] | null | [] | 2022-05-25T07:45:07Z | 2022-05-26T06:40:12Z | 2022-05-26T06:40:12Z | NONE | null | null | null | ## 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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] | closed | false | null | [] | null | [] | 2022-05-25T03:10:44Z | 2022-10-24T06:10:27Z | 2022-05-25T03:26:36Z | NONE | null | null | null | ## 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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] | closed | false | null | [] | null | [] | 2022-05-24T18:03:01Z | 2022-09-12T15:30:43Z | 2022-09-12T15:30:43Z | CONTRIBUTOR | null | null | null | ## 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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] | closed | false | null | [] | null | [] | 2022-05-24T14:41:34Z | 2022-06-14T16:01:56Z | 2022-06-14T16:01:56Z | CONTRIBUTOR | null | null | null | 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 π€ `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 π€ `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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} | 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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} | As commented in:
- https://github.com/huggingface/datasets/issues/4386#issuecomment-1134902372
CC: @StevenTang1998 | {
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] | closed | false | null | [] | null | [] | 2022-05-23T16:34:05Z | 2022-10-03T09:39:08Z | 2022-10-03T09:36:56Z | CONTRIBUTOR | null | false | {
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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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] | open | false | null | [] | null | [] | 2022-05-23T14:04:37Z | 2022-06-06T16:08:01Z | null | NONE | null | null | null | ## 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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> Youβre using a [deprecated Docker convenience image.](https://discuss.circleci.com/t/legacy-convenience-image-deprecation/41034) Upgrade to a next-gen Docker convenience image.
This PR updates to next-generation convenience image.
Related to:
- #2955 | {
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} | This PR tweaks the keys in the metadata that are used to define the column mapping for question answering datasets. This is needed in order to faithfully reconstruct column names like `answers.text` and `answers.answer_start` from the keys in AutoTrain.
As observed in https://github.com/huggingface/datasets/pull/4367 we cannot use periods `.` in the keys of the YAML tags, so a decision was made to use a flat mapping with underscores. For QA datasets, however, it's handy to be able to reconstruct the nesting -- hence this PR.
cc @sashavor | {
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} | Since Python 3.8, the typing module:
- raises an AttributeError when trying to access `__args__` on any type, e.g.: `List.__args__`
- provides the `get_args` function instead: `get_args(List)`
This PR implements a fix for Python >=3.8 whereas maintaining backward compatibility. | {
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Fix #4386. | {
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} | Now the builder name attribute is set from from the builder class name.
This PR sets the builder name attribute from the module name instead. Some motivating reasons:
- The dataset ID is relevant and unique among all datasets and this is directly related to the repository name, i.e., the name of the directory containing the dataset
- The name of the module (i.e. the file containing the loading loading script) is already relevant for loading: it must have the same name as its containing directory (related to the dataset ID), as we search for it using its directory name
- On the other hand, the name of the builder class is not relevant for loading: in our code, we just search for a class which is subclass of `DatasetBuilder` (independently of its name). We do not put any constraint on the naming of the builder class and indeed it can have a name completely different from its module/direcotry/dataset_id
IMO it makes more sense to align the caching directory name with the dataset_id/directory/module name instead of the builder class name.
Fix #4381. | {
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https://android.googlesource.com/device/google/accessory/adk2012/#:~:text=git%20clone%20https%3A//android.googlesource.com/device/google/accessory/adk2012 | {
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The script of wiki_auto_asset_turk for GEM may be out of date.
## Steps to reproduce the bug
```python
import datasets
datasets.load_dataset('gem', 'wiki_auto_asset_turk')
```
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/load.py", line 1731, in load_dataset
builder_instance.download_and_prepare(
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 640, in download_and_prepare
self._download_and_prepare(
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 1158, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/builder.py", line 707, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/tangtianyi/.cache/huggingface/modules/datasets_modules/datasets/gem/982a54473b12c6a6e40d4356e025fb7172a5bb2065e655e2c1af51f2b3cf4ca1/gem.py", line 538, in _split_generators
dl_dir = dl_manager.download_and_extract(_URLs[self.config.name])
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 416, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 294, in download
downloaded_path_or_paths = map_nested(
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 351, in map_nested
mapped = [
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 352, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 288, in _single_map_nested
return function(data_struct)
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 320, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 234, in cached_path
output_path = get_from_cache(
File "/home/tangtianyi/miniconda3/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 579, in get_from_cache
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://github.com/facebookresearch/asset/raw/master/dataset/asset.test.orig
``` | {
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} | Regression test for future releases of `dill`.
Related to #4379. | {
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} | This PR performs a refactoring of the download functionalities, by proposing a modular solution and moving them to their own package "download". Some motivating arguments:
- understandability: from a logical partitioning of the library, it makes sense to have all download functionalities grouped together instead of scattered in a much larger directory containing many more different functionalities
- abstraction: the level of abstraction of "download" (higher) is not the same as "utils" (lower); putting different levels of abstraction together, makes dependencies more intricate (potential circular dependencies) and the system more tightly coupled; when the levels of abstraction are clearly separated, the dependencies flow in a neat direction from higher to lower
- architectural: "download" is a domain-specific functionality of our library/application (a dataset builder performs several actions: download, generate dataset and cache it); these functionalities are at the core of our library; on the other hand, "utils" are always a low-level set of functionalities, not directly related to our domain/business core logic (all libraries have "utils"), thus at the periphery of our lib architecture
Also note that when a library is not architecturally designed following simple, neat, clean principles, this has a negative impact on extensibility, making more and more difficult to make enhancements.
As a concrete example in this case, please see: https://app.circleci.com/pipelines/github/huggingface/datasets/12185/workflows/ff25a790-8e3f-45a1-aadd-9d79dfb73c4d/jobs/72860
- After an extension, a circular import is found
- Diving into the cause of this circular import, see the dependency flow, which should be from higher to lower levels of abstraction:
```
ImportError while loading conftest '/home/circleci/datasets/tests/conftest.py'.
tests/conftest.py:12: in <module>
import datasets
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/datasets/__init__.py:37: in <module>
from .arrow_dataset import Dataset, concatenate_datasets
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/datasets/arrow_dataset.py:59: in <module>
from . import config
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/datasets/config.py:8: in <module>
from .utils.logging import get_logger
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/datasets/utils/__init__.py:30: in <module>
from .download_manager import DownloadConfig, DownloadManager, DownloadMode
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/datasets/utils/download_manager.py:39: in <module>
from .py_utils import NestedDataStructure, map_nested, size_str
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/datasets/utils/py_utils.py:608: in <module>
if config.DILL_VERSION < version.parse("0.3.5"):
E AttributeError: module 'datasets.config' has no attribute 'DILL_VERSION'
```
Imports:
- datasets
- Dataset: lower level than datasets
- config: lower level than Dataset
- logger: lower level than config
- DownloadManager: !!! HIGHER level of abstraction than logger!!
Why when importing logger we require importing DownloadManager?!?
- Logically, it does not make sense
- This is due to an error in the design/architecture of our library:
- To import the logger, we need to import it from `.utils.logging`
- To import `.utils.logging` we need to import `.utils`
- The import of `.utils` require the import of all its submodules defined in `utils.__init__.py`, among them: `.utils.download_manager`!
When putting `logging` and `download_manager` both inside `utils`, in order to import `logging` we need to import `download_manager` first: this is a strong coupling between modules and moreover between modules at different levels of abstraction (to import a lower level module, we require to import a higher level module). Additionally, it is clear that is makes no sense that in order to import `logging` we require to import `download_manager` first. | {
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L
## Expected L
A clear and concise lmll
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version: | {
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- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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The two datasets `mteb/mtop_intent` and `mteb/mtop_domain `use both the same cache folder `.cache/huggingface/datasets/mteb___mtop`. So if you first load `mteb/mtop_intent` then datasets will not load `mteb/mtop_domain`.
If you delete this cache folder and flip the order how you load the two datasets , you will get the opposite datasets loaded (difference is here in terms of the label and label_text).
## Steps to reproduce the bug
```python
import datasets
dataset = datasets.load_dataset("mteb/mtop_intent", "en")
print(dataset['train'][0])
dataset = datasets.load_dataset("mteb/mtop_domain", "en")
print(dataset['train'][0])
```
## Expected results
```
Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_intent/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35)
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 920.14it/s]
{'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'}
Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_domain/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35)
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 1307.59it/s]
{'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 0, 'label_text': 'messaging'}
```
## Actual results
```
Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35)
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 920.14it/s]
{'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'}
Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35)
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 1307.59it/s]
{'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.1
- Platform: macOS-12.1-arm64-arm-64bit
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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CC: @sgugger | {
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] | null | [] | 2022-05-20T13:48:36Z | 2022-05-21T15:53:26Z | 2022-05-20T17:06:27Z | MEMBER | null | null | null | ## Describe the bug
As reported by @sgugger, latest dill release is breaking things with Datasets.
```
______________ ExamplesTests.test_run_speech_recognition_seq2seq _______________
self = <multiprocess.pool.ApplyResult object at 0x7fa5981a1cd0>, timeout = None
def get(self, timeout=None):
self.wait(timeout)
if not self.ready():
raise TimeoutError
if self._success:
return self._value
else:
> raise self._value
E TypeError: '>' not supported between instances of 'NoneType' and 'float'
```
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} | There was a bug in filepath segment:
- wrong: `jkkummerfeld-irc-disentanglement-fd379e9`
- right: `jkkummerfeld-irc-disentanglement-35f0a40`
Also there was a bug in the checksum of the downloaded file.
This PR fixes these issues.
Fix partially #4376.
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```
Server error
Status code: 400
Exception: ValueError
Message: Cannot seek streaming HTTP file
```
it appears to give the same message for the "channel_two" data as well.
I get a Checksums error when using `load_data()` with this dataset. Even with the `download_mode` and `ignore_verifications` options set. i referenced the issue here: https://github.com/huggingface/datasets/issues/3807 | {
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} | ### Issue
It's currently not possible to properly stream a dataset using multiple `torch.utils.data.DataLoader` workers:
- the `TorchIterableDataset` can't be pickled and passed to the subprocesses: https://github.com/huggingface/datasets/issues/3950
- streaming extension is failing: https://github.com/huggingface/datasets/issues/3951
- `fsspec` doesn't work out of the box in subprocesses
### Solution in this PR
I fixed these to enable passing an `IterableDataset` to a `torch.utils.data.DataLoader` with `num_workers > 0`.
I also had to shard the `IterableDataset` to give each worker a shard, otherwise data would be duplicated. This is implemented in `TorchIterableDataset.__iter__` and uses the new `IterableDataset._iter_shard(shard_idx)` method
I also had to do a few changes the patching that enable streaming in dataset scripts:
- the patches are now always applied - not just for streaming mode. They're applied when a builder is instantiated
- I improved it to also check for renamed modules or attributes (ex: pandas vs pd)
- I grouped all the patches of pathlib.Path into a class `xPath`, so that `Path` outside of dataset scripts stay unchanged - otherwise I didn't change the content of the extended Path methods for streaming
- I fixed a bug with the `pd.read_csv` patch, opening the file in "rb" mode was missing and causing some datasets to not work in streaming mode, and compression inference was missing
### A few details regarding `fsspec` in multiprocessing
From https://github.com/fsspec/filesystem_spec/pull/963#issuecomment-1131709948 :
> Non-async instances might be safe in the forked child, if they hold no open files/sockets etc.; I'm not sure any implementations pass this test!
> If any async instance has been created, the newly forked processes must:
> 1. discard references to locks, threads and event loops and make new ones
> 2. not use any async fsspec instances from the parent process
> 3. clear all class instance caches
Therefore in a DataLoader's worker, I clear the reference to the loop and thread (1). We should be fine for 2 and 3 already since we don't use fsspec class instances from the parent process.
Fix https://github.com/huggingface/datasets/issues/3950
Fix https://github.com/huggingface/datasets/issues/3951
TODO:
- [x] fix tests | {
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] | open | false | null | [] | null | [] | 2022-05-19T14:18:05Z | 2022-06-06T16:14:28Z | null | NONE | null | null | null | ## processing a custom dataset loaded as .txt file is extremely slow, compared to a dataset of similar volume from the hub
I have a large .txt file of 22 GB which i load into HF dataset
`lang_dataset = datasets.load_dataset("text", data_files="hi.txt")`
further i use a pre-processing function to clean the dataset
`lang_dataset["train"] = lang_dataset["train"].map(
remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)`
the following processing takes astronomical time to process, while hoging all the ram.
similar dataset of same size that's available in the huggingface hub works completely fine. which runs the same processing function and has the same amount of data.
`lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", use_auth_token=True)`
the hours predicted to preprocess are as follows:
huggingface hub dataset: 6.5 hrs
custom loaded dataset: 7000 hrs
note: both the datasets are almost actually same, just provided by different sources with has +/- some samples, only one is hosted on the HF hub and the other is downloaded in a text format.
## Steps to reproduce the bug
```
import datasets
import psutil
import sys
import glob
from fastcore.utils import listify
import re
import gc
def remove_non_indic_sentences(example):
tmp_ls = []
eng_regex = r'[. a-zA-Z0-9ΓΓΓ
ΓΆΓ€Γ₯ _.,!"\'\/$]*'
for e in listify(example['text']):
matches = re.findall(eng_regex, e)
for match in (str(match).strip() for match in matches if match not in [""," ", " ", ",", " ,", ", ", " , "]):
if len(list(match.split(" "))) > 2:
e = re.sub(match," ",e,count=1)
tmp_ls.append(e)
gc.collect()
example['clean_text'] = tmp_ls
return example
lang_dataset = datasets.load_dataset("text", data_files="hi.txt")
lang_dataset["train"] = lang_dataset["train"].map(
remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)
## same thing work much faster when loading similar dataset from hub
lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", split="train", use_auth_token=True)
lang_dataset["train"] = lang_dataset["train"].map(
remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)
```
## Actual results
similar dataset of same size that's available in the huggingface hub works completely fine. which runs the same processing function and has the same amount of data.
`lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", use_auth_token=True)
**the hours predicted to preprocess are as follows:**
huggingface hub dataset: 6.5 hrs
custom loaded dataset: 7000 hrs
**i even tried the following:**
- sharding the large 22gb text files into smaller files and loading
- saving the file to disk and then loading
- using lesser num_proc
- using smaller batch size
- processing without batches ie : without `batched=True`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2.dev0
- Platform: Ubuntu 20.04 LTS
- Python version: 3.9.7
- PyArrow version:8.0.0
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} | Many datasets have dots in their config names. However it causes issues with the YAML tags of the dataset cards since we can't have dots in YAML keys.
I fix this, I removed the tags separations per config name completely, and have a single flat YAML for all configurations. Dataset search doesn't use this info anyway. I removed all the config names used as YAML keys, and I moved them in under a new `config:` key.
This is related to https://github.com/huggingface/datasets/pull/2362 (internal https://github.com/huggingface/moon-landing/issues/946).
Also removing the dots in the YAML keys would allow us to do as in https://github.com/huggingface/datasets/pull/4302 which removes a hack that replaces all the dots by underscores in the YAML tags.
I also added a test in the CI that checks that all the YAML tags to make sure that:
- they can be parsed using a YAML parser
- they contain only valid YAML tags like languages or task_ids | {
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when I run the order:
nohup python3 custom_service.pyc > service.log 2>&1&
the log:
nohup: εΏ½η₯θΎε
₯
Traceback (most recent call last):
File "/home/xfz/p3_custom_test/custom_service.py", line 55, in <module>
File "/home/xfz/p3_custom_test/custom_service.py", line 48, in doInitialize
File "custom_impl.py", line 286, in custom_setup
File "custom_impl.py", line 127, in create_es_index
File "/usr/local/lib/python3.7/site-packages/elasticsearch/_sync/client/__init__.py", line 345, in __init__
ssl_show_warn=ssl_show_warn,
File "/usr/local/lib/python3.7/site-packages/elasticsearch/_sync/client/utils.py", line 105, in client_node_configs
node_configs = hosts_to_node_configs(hosts)
File "/usr/local/lib/python3.7/site-packages/elasticsearch/_sync/client/utils.py", line 154, in hosts_to_node_configs
node_configs.append(host_mapping_to_node_config(host))
File "/usr/local/lib/python3.7/site-packages/elasticsearch/_sync/client/utils.py", line 221, in host_mapping_to_node_config
return NodeConfig(**options) # type: ignore
TypeError: __init__() missing 1 required positional argument: 'scheme'
[1]+ ιεΊ 1 nohup python3 custom_service.pyc > service.log 2>&1
custom_service_pyc can't running
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This is related to https://github.com/huggingface/datasets/pull/2362 (internal https://github.com/huggingface/moon-landing/issues/946).
Also removing the dots in the config names would allow us to merge https://github.com/huggingface/datasets/pull/4302 which removes a hack that replaces all the dots by underscores in the YAML tags.
I also added a test in the CI that checks that all the YAML tags to make sure that:
- they can be parsed using a YAML parser
- they contain only valid YAML tags like `languages` or `task_ids`
- they contain valid config names (no invalid characters `<>:/\|?*.`) | {
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} | This PR adds `table_cast` to the packaged loaders to fix casting to the `Image`/`Audio`, `ArrayND` and `ClassLabel` types. If these types are not present in the `builder.config.features` dictionary, the built-in `pa.Table.cast` is used for better performance. Additionally, this PR adds `cast_storage` to `ClassLabel` to support the string to int conversion in `table_cast` and ensure that integer labels are in a valid range.
Fix https://github.com/huggingface/datasets/issues/4210
This PR is also a solution for these (popular) discussions: https://discuss.huggingface.co/t/converting-string-label-to-int/2816 and https://discuss.huggingface.co/t/class-labels-for-custom-datasets/15130/2
TODO:
* [x] tests | {
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```
Status code: 400
Exception: AttributeError
Message: 'NoneType' object has no attribute 'split'
``` | {
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] | closed | false | null | [] | null | [] | 2022-05-17T13:47:09Z | 2022-06-08T19:11:21Z | 2022-06-08T19:11:21Z | CONTRIBUTOR | null | null | null | ## Describe the bug
Loading `udhr` fails due to a checksum mismatch for some source files. Looks like both of the source files on unicode.org have changed:
size + checksum in datasets repo:
```
(hfdev) leon@blade:~/datasets/datasets/udhr$ jq .default.download_checksums < dataset_infos.json
{
"https://unicode.org/udhr/assemblies/udhr_xml.zip": {
"num_bytes": 2273633,
"checksum": "0565fa62c2ff155b84123198bcc967edd8c5eb9679eadc01e6fb44a5cf730fee"
},
"https://unicode.org/udhr/assemblies/udhr_txt.zip": {
"num_bytes": 2107471,
"checksum": "087b474a070dd4096ae3028f9ee0b30dcdcb030cc85a1ca02e143be46327e5e5"
}
}
```
size + checksum regenerated from current source files:
```
(hfdev) leon@blade:~/datasets/datasets/udhr$ rm dataset_infos.json
(hfdev) leon@blade:~/datasets/datasets/udhr$ datasets-cli test --save_infos udhr.py
Using custom data configuration default
Testing builder 'default' (1/1)
Downloading and preparing dataset udhn/default (download: 4.18 MiB, generated: 6.15 MiB, post-processed: Unknown size, total: 10.33 MiB) to /home/leon/.cache/huggingface/datasets/udhn/default/0.0.0/ad74b91fa2b3c386e5751b0c52bdfda76d334f76731142fd432d4acc2e2fde66...
Dataset udhn downloaded and prepared to /home/leon/.cache/huggingface/datasets/udhn/default/0.0.0/ad74b91fa2b3c386e5751b0c52bdfda76d334f76731142fd432d4acc2e2fde66. Subsequent calls will reuse this data.
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 686.69it/s]
Dataset Infos file saved at dataset_infos.json
Test successful.
(hfdev) leon@blade:~/datasets/datasets/udhr$ jq .default.download_checksums < dataset_infos.json
{
"https://unicode.org/udhr/assemblies/udhr_xml.zip": {
"num_bytes": 2389690,
"checksum": "a3350912790196c6e1b26bfd1c8a50e8575f5cf185922ecd9bd15713d7d21438"
},
"https://unicode.org/udhr/assemblies/udhr_txt.zip": {
"num_bytes": 2215441,
"checksum": "cb87ecb25b56f34e4fd6f22b323000524fd9c06ae2a29f122b048789cf17e9fe"
}
}
(hfdev) leon@blade:~/datasets/datasets/udhr$
```
--- is unicode.org a sustainable hosting solution for this dataset?
## Steps to reproduce the bug
```python
from datasets import load_dataset
udhr = load_dataset("udhr")
```
## Expected results
That a Dataset object containing the UDHR data will be returned.
## Actual results
```
>>> d = load_dataset('udhr')
Using custom data configuration default
Downloading and preparing dataset udhn/default (download: 4.18 MiB, generated: 6.15 MiB, post-processed: Unknown size, total: 10.33 MiB) to /home/leon/.cache/huggingface/datasets/udhn/default/0.0.0/ad74b91fa2b3c386e5751b0c52bdfda76d334f76731142fd432d4acc2e2fde66...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/leon/.local/lib/python3.9/site-packages/datasets/load.py", line 1731, in load_dataset
builder_instance.download_and_prepare(
File "/home/leon/.local/lib/python3.9/site-packages/datasets/builder.py", line 613, in download_and_prepare
self._download_and_prepare(
File "/home/leon/.local/lib/python3.9/site-packages/datasets/builder.py", line 1117, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/leon/.local/lib/python3.9/site-packages/datasets/builder.py", line 684, in _download_and_prepare
verify_checksums(
File "/home/leon/.local/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://unicode.org/udhr/assemblies/udhr_xml.zip', 'https://unicode.org/udhr/assemblies/udhr_txt.zip']
>>>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.1 commit/4110fb6034f79c5fb470cf1043ff52180e9c63b7
- Platform: Linux Ubuntu 20.04
- Python version: 3.9.12
- PyArrow version: 8.0.0
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https://api.github.com/repos/huggingface/datasets/issues/4360 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4360/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4360/comments | https://api.github.com/repos/huggingface/datasets/issues/4360/events | https://github.com/huggingface/datasets/pull/4360 | 1,237,239,096 | PR_kwDODunzps434izs | 4,360 | Fix example in opus_ubuntu, Add license info | {
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} | [] | closed | false | null | [] | null | [] | 2022-05-16T14:22:28Z | 2022-06-01T13:06:07Z | 2022-06-01T12:57:09Z | CONTRIBUTOR | null | false | {
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} | This PR
* fixes a typo in the example for the`opus_ubuntu` dataset where it's mistakenly referred to as `ubuntu`
* adds the declared license info for this corpus' origin
* adds an example instance
* updates the data origin type | {
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https://api.github.com/repos/huggingface/datasets/issues/4359 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4359/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4359/comments | https://api.github.com/repos/huggingface/datasets/issues/4359/events | https://github.com/huggingface/datasets/pull/4359 | 1,237,149,578 | PR_kwDODunzps434Pb6 | 4,359 | Fix Version equality | {
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} | [] | closed | false | null | [] | null | [] | 2022-05-16T13:19:26Z | 2022-05-24T16:25:37Z | 2022-05-24T16:17:14Z | MEMBER | null | false | {
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} | I think `Version` equality should align with other similar cases in Python, like:
```python
In [1]: "a" == 5, "a" == None
Out[1]: (False, False)
In [2]: "a" != 5, "a" != None
Out[2]: (True, True)
```
With this PR, we will get:
```python
In [3]: Version("1.0.0") == 5, Version("1.0.0") == None
Out[3]: (False, False)
In [4]: Version("1.0.0") != 5, Version("1.0.0") != None
Out[4]: (True, True)
```
Note I found this issue when `doc-builder` tried to compare:
```python
if param.default != inspect._empty
```
where `param.default` is an instance of `Version`. | {
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https://api.github.com/repos/huggingface/datasets/issues/4358 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4358/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4358/comments | https://api.github.com/repos/huggingface/datasets/issues/4358/events | https://github.com/huggingface/datasets/issues/4358 | 1,237,147,692 | I_kwDODunzps5JvWAs | 4,358 | Missing dataset tags and sections in some dataset cards | {
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] | open | false | null | [] | null | [] | 2022-05-16T13:18:16Z | 2022-05-30T15:36:52Z | null | CONTRIBUTOR | null | null | null | Summary of CircleCI errors for different dataset metadata:
- **BoolQ**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'
- **Conllpp**: expected some content in section `Citation Information` but it is empty.
- **GLUE**: 'annotations_creators', 'language_creators', 'source_datasets' :['unknown'] are not registered tags
- **ConLL2003**: field 'task_ids': ['part-of-speech-tagging'] are not registered tags for 'task_ids'
- **Hate_speech18:** Expected some content in section `Data Instances` but it is empty, Expected some content in section `Data Splits` but it is empty
- **Jjigsaw_toxicity_pred**: `Citation Information` but it is empty.
- **LIAR** : `Data Instances`,`Data Fields`, `Data Splits`, `Citation Information` are empty.
- **MSRA NER** : Dataset Summary`, `Data Instances`, `Data Fields`, `Data Splits`, `Citation Information` are empty.
- **sem_eval_2010_task_8**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'
- **sms_spam**: `Data Instances` and`Data Splits` are empty.
- **Quora** : Expected some content in section `Citation Information` but it is empty, missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'
- **sentiment140**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids' | {
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