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https://api.github.com/repos/huggingface/datasets/issues/4360
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| 1,237,239,096
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PR_kwDODunzps434izs
| 4,360
|
Fix example in opus_ubuntu, Add license info
|
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2022-05-16T14:22:28Z
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2022-06-01T13:06:07Z
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2022-06-01T12:57:09Z
|
CONTRIBUTOR
| null | null | null |
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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PR_kwDODunzps434Pb6
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|
Fix Version equality
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2022-05-16T13:19:26Z
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2022-05-24T16:25:37Z
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2022-05-24T16:17:14Z
|
MEMBER
| null | null | null |
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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I_kwDODunzps5JvWAs
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Missing dataset tags and sections in some dataset cards
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2022-05-16T13:18:16Z
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2022-05-30T15:36:52Z
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CONTRIBUTOR
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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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PR_kwDODunzps4333b9
| 4,357
|
Fix warning in push_to_hub
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2022-05-16T11:50:17Z
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2022-05-16T15:18:49Z
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2022-05-16T15:10:41Z
|
MEMBER
| null | null | null |
Fix warning:
```
FutureWarning: 'shard_size' was renamed to 'max_shard_size' in version 2.1.1 and will be removed in 2.4.0.
```
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PR_kwDODunzps433OsB
| 4,356
|
Fix dataset builder default version
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2022-05-16T09:05:10Z
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2022-05-30T13:56:58Z
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2022-05-30T13:47:54Z
|
MEMBER
| null | null | null |
Currently, when using a custom config (subclass of `BuilderConfig`), default version set at the builder level is ignored: we must set default version in the custom config class.
However, when loading a dataset with `config_kwargs` (for a configuration not present in `BUILDER_CONFIGS`), the default version set in the custom config is ignored and "0.0.0" is used instead:
```python
ds = load_dataset("wikipedia", language="co", date="20220501", beam_runner="DirectRunner")
```
generates the following config:
```python
WikipediaConfig(name='20220501.co', version=0.0.0, data_dir=None, data_files=None, description='Wikipedia dataset for co, parsed from 20220501 dump.')
```
with version "0.0.0" instead of "2.0.0".
See as a counter-example, when the config is present in `BUILDER_CONFIGS`:
```python
ds = load_dataset("wikipedia", "20220301.fr", beam_runner="DirectRunner")
```
generates the following config:
```python
WikipediaConfig(name='20220301.fr', version=2.0.0, data_dir=None, data_files=None, description='Wikipedia dataset for fr, parsed from 20220301 dump.')
```
with correct version "2.0.0", as set in the custom config class.
The reason for this is that `DatasetBuilder` has a default VERSION ("0.0.0") that overwrites the default version set at the custom config class.
This PR:
- Removes the default VERSION at `DatasetBuilder` (set to None, so that the class attribute exists but it does not override the custom config default version).
- Note that the `BuilderConfig` class already sets a default version = "0.0.0"; no need to pass this from the builder.
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PR_kwDODunzps433EgP
| 4,355
|
Fix warning in upload_file
|
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2022-05-16T08:21:31Z
|
2022-05-16T11:28:02Z
|
2022-05-16T11:19:57Z
|
MEMBER
| null | null | null |
Fix warning:
```
FutureWarning: Pass path_or_fileobj='...' as keyword args. From version 0.7 passing these as positional arguments will result in an error
```
|
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I_kwDODunzps5Jsgif
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Problems with WMT dataset
|
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2022-05-15T20:58:26Z
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2022-07-11T14:54:02Z
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2022-07-11T14:54:01Z
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NONE
| null | null |
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## Describe the bug
I am trying to load WMT15 dataset and to define which data-sources to use for train/validation/test splits, but unfortunately it seems that the official documentation at [https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)](https://huggingface.co/datasets/wmt15#:~:text=Versions%20exists%20for,wmt_translate%22%2C%20config%3Dconfig)) doesn't work anymore.
## Steps to reproduce the bug
```shell
>>> import datasets
>>> a = datasets.translate.wmt.WmtConfig()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: module 'datasets' has no attribute 'translate'
>>> a = datasets.wmt.WmtConfig()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: module 'datasets' has no attribute 'wmt'
```
## Expected results
To load WMT15 with given data-sources.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-5.10.0-10-amd64-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
|
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PR_kwDODunzps43016x
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Don't strip proceeding hyphen
|
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2022-05-14T18:25:29Z
|
2022-05-16T18:51:38Z
|
2022-05-16T13:52:11Z
|
CONTRIBUTOR
| null | null | null |
Closes #4320.
|
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I_kwDODunzps5JrS2a
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|
When using `dataset.map()` if passed `Features` types do not match what is returned from the mapped function, execution does not except in an obvious way
|
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2022-05-16T15:09:17Z
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NONE
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## Describe the bug
Recently I was trying to using `.map()` to preprocess a dataset. I defined the expected Features and passed them into `.map()` like `dataset.map(preprocess_data, features=features)`. My expected `Features` keys matched what came out of `preprocess_data`, but the types i had defined for them did not match the types that came back. Because of this, i ended up in tracebacks deep inside arrow_dataset.py and arrow_writer.py with exceptions that [did not make clear what the problem was](https://github.com/huggingface/datasets/issues/4349). In short i ended up with overflows and the OS killing processes when Arrow was attempting to write. It wasn't until I dug into `def write_batch` and the loop that loops over cols that I figured out what was going on.
It seems like `.map()` could set a boolean that it's checked that for at least 1 instance from the dataset, the returned data's types match the types provided by the `features` param and error out with a clear exception if they don't. This would make the cause of the issue much more understandable and save people time. This could be construed as a feature but it feels more like a bug to me.
## Steps to reproduce the bug
I don't have explicit code to repro the bug, but ill show an example
Code prior to the fix:
```python
def preprocess(examples):
# returns an encoded data dict with keys that match the features, but the types do not match
...
def get_encoded_data(data):
dataset = Dataset.from_pandas(data)
unique_labels = data['audit_type'].unique().tolist()
features = Features({
'image': Array3D(dtype="uint8", shape=(3, 224, 224))),
'input_ids': Sequence(feature=Value(dtype='int64'))),
'attention_mask': Sequence(Value(dtype='int64'))),
'token_type_ids': Sequence(Value(dtype='int64'))),
'bbox': Array2D(dtype="int64", shape=(512, 4))),
'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels),
})
encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names)
```
The Features set that fixed it:
```python
features = Features({
'image': Sequence(Array3D(dtype="uint8", shape=(3, 224, 224))),
'input_ids': Sequence(Sequence(feature=Value(dtype='int64'))),
'attention_mask': Sequence(Sequence(Value(dtype='int64'))),
'token_type_ids': Sequence(Sequence(Value(dtype='int64'))),
'bbox': Sequence(Array2D(dtype="int64", shape=(512, 4))),
'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels),
})
```
The difference between my original code (which was based on documentation) and the working code is the addition of the `Sequence(...)` to 4/5 features as I am working with paginated data and the doc examples are not.
## Expected results
Dataset.map() attempts to validate the data types for each Feature on the first iteration and errors out if they are not validated.
## Actual results
Specify the actual results or traceback.
Based on the value of `writer_batch_size`, execution errors out when Arrow attempts to write because the types do not match, though its error messages dont make this obvious
Example errors:
```
OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB.
(offset overflow while concatenating arrays)
```
```
zsh: killed python doc_classification.py
UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
datasets version: 2.1.0
Platform: macOS-12.2.1-arm64-arm-64bit
Python version: 3.9.12
PyArrow version: 6.0.1
Pandas version: 1.4.2
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I_kwDODunzps5JqxqB
| 4,351
|
Add optional progress bar for .save_to_disk(..) and .load_from_disk(..) when working with remote filesystems
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2022-05-14T11:30:42Z
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2022-12-14T18:22:59Z
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2022-12-14T18:22:59Z
|
CONTRIBUTOR
| null | null |
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**Is your feature request related to a problem? Please describe.**
When working with large datasets stored on remote filesystems(such as s3), the process of uploading a dataset could take really long time. For instance: I was uploading a re-processed version of wmt17 en-ru to my s3 bucket and it took like 35 minutes(and that's given that I have a fiber optic connection). The only output during that process was a progress bar for flattening indices and then ~35 minutes of complete silence.
**Describe the solution you'd like**
I want to be able to enable a progress bar when calling .save_to_disk(..) and .load_from_disk(..), it would track either amount of bytes sent/received or amount of records written/loaded, and will give some ETA. Basically just tqdm.
**Describe alternatives you've considered**
- Save dataset to tmp folder at the disk and then upload it using custom wrapper over botocore, which will work with progress bar, like [this](https://alexwlchan.net/2021/04/s3-progress-bars/).
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PR_kwDODunzps43zKIV
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Add a new metric: CTC_Consistency
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2022-05-13T17:31:19Z
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2022-05-19T10:23:04Z
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2022-05-19T10:23:03Z
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NONE
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Add CTC_Consistency metric
Do I also need to modify the `test_metric_common.py` file to make it run on test?
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| 1,235,474,765
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I_kwDODunzps5Jo9lN
| 4,349
|
Dataset.map()'s fails at any value of parameter writer_batch_size
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2022-05-13T16:55:12Z
|
2022-06-02T12:51:11Z
|
2022-05-14T15:08:08Z
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NONE
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## Describe the bug
If the the value of `writer_batch_size` is less than the total number of instances in the dataset it will fail at that same number of instances. If it is greater than the total number of instances, it fails on the last instance.
Context:
I am attempting to fine-tune a pre-trained HuggingFace transformers model called LayoutLMv2. This model takes three inputs: document images, words and word bounding boxes. [The Processor for this model has two options](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor), the default is passing a document to the Processor and allowing it to create images of the document and use PyTesseract to perform OCR and generate words/bounding boxes. The other option is to provide `revision="no_ocr"` to the pre-trained model which allows you to use your own OCR results (in my case, Amazon Textract) so you have to provide the image, words and bounding boxes yourself. I am using this second option which might be good context for the bug.
I am using the Dataset.map() paradigm to create these three inputs, encode them and save the dataset. Note that my documents (data instances) on average are fairly large and can range from 1 page up to 300 pages.
Code I am using is provided below
## Steps to reproduce the bug
I do not have explicit sample code, but I will paste the code I'm using in case reading it helps. When `.map()` is called, the dataset has 2933 rows, many of which represent large pdf documents.
```python
def get_encoded_data(data):
dataset = Dataset.from_pandas(data)
unique_labels = data['label'].unique()
features = Features({
'image': Array3D(dtype="int64", shape=(3, 224, 224)),
'input_ids': Sequence(feature=Value(dtype='int64')),
'attention_mask': Sequence(Value(dtype='int64')),
'token_type_ids': Sequence(Value(dtype='int64')),
'bbox': Array2D(dtype="int64", shape=(512, 4)),
'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels),
})
encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names, writer_batch_size=dataset.num_rows+1)
encoded_dataset.save_to_disk(TRAINING_DATA_PATH + ENCODED_DATASET_NAME)
encoded_dataset.set_format(type="torch")
return encoded_dataset
```
```python
PROCESSOR = LayoutLMv2Processor.from_pretrained(MODEL_PATH, revision="no_ocr", use_fast=False)
def preprocess_data(examples):
directory = os.path.join(FILES_PATH, examples['file_location'])
images_dir = os.path.join(directory, PDF_IMAGE_DIR)
textract_response_path = os.path.join(directory, 'textract.json')
doc_meta_path = os.path.join(directory, 'doc_meta.json')
textract_document = get_textract_document(textract_response_path, doc_meta_path)
images, words, bboxes = get_doc_training_data(images_dir, textract_document)
encoded_inputs = PROCESSOR(images, words, boxes=bboxes, padding="max_length", truncation=True)
# https://github.com/NielsRogge/Transformers-Tutorials/issues/36
encoded_inputs["image"] = np.array(encoded_inputs["image"])
encoded_inputs["label"] = examples['label_id']
return encoded_inputs
```
## Expected results
My expectation is that `writer_batch_size` allows one to simply trade off performance and memory requirements, not that it must be a specific number for `.map()` to function correctly.
## Actual results
If writer_batch_size is set to a value less than the number of rows, I get either:
```
OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB.
(offset overflow while concatenating arrays)
```
or simply
```
zsh: killed python doc_classification.py
UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
```
If it is greater than the number of rows, i get the `zsh: killed` error above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: macOS-12.2.1-arm64-arm-64bit
- Python version: 3.9.12
- PyArrow version: 6.0.1
- Pandas version: 1.4.2
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`inspect` functions can't fetch dataset script from the Hub
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|
MEMBER
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The `inspect_dataset` and `inspect_metric` functions are unable to retrieve a dataset or metric script from the Hub and store it locally at the specified `local_path`:
```py
>>> from datasets import inspect_dataset
>>> inspect_dataset('rotten_tomatoes', local_path='path/to/my/local/folder')
FileNotFoundError: Couldn't find a dataset script at /content/rotten_tomatoes/rotten_tomatoes.py or any data file in the same directory.
```
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PR_kwDODunzps43yihq
| 4,347
|
Support remote cache_dir
|
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2022-05-13T14:26:35Z
|
2022-05-25T16:35:23Z
|
2022-05-25T16:27:03Z
|
MEMBER
| null | null | null |
This PR implements complete support for remote `cache_dir`. Before, the support was just partial.
This is useful to create datasets using Apache Beam (parallel data processing) builder with `cache_dir` in a remote bucket, e.g., for Wikipedia dataset.
|
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| 1,235,067,062
|
I_kwDODunzps5JnaC2
| 4,346
|
GH Action to build documentation never ends
|
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2022-05-13T10:44:44Z
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2022-05-13T11:22:00Z
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2022-05-13T11:22:00Z
|
MEMBER
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## Describe the bug
See: https://github.com/huggingface/datasets/runs/6418035586?check_suite_focus=true
I finally forced the cancel of the workflow.
|
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| 1,235,062,787
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PR_kwDODunzps43xrky
| 4,345
|
Fix never ending GH Action to build documentation
|
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2022-05-13T10:40:10Z
|
2022-05-13T11:29:43Z
|
2022-05-13T11:22:00Z
|
MEMBER
| null | null | null |
There was an unclosed code block introduced by:
- #4313
https://github.com/huggingface/datasets/pull/4313/files#diff-f933ce41f71c6c0d1ce658e27de62cbe0b45d777e9e68056dd012ac3eb9324f7R538
This causes the "Make documentation" step in the "Build documentation" workflow to never finish.
- I think this issue should also be addressed in the `doc-builder` lib.
Fix #4346.
|
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PR_kwDODunzps43xFEn
| 4,344
|
Fix docstring in DatasetDict::shuffle
|
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2022-05-13T08:06:00Z
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2022-05-25T09:23:43Z
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2022-05-24T15:35:21Z
|
CONTRIBUTOR
| null | null | null |
I think due to #1626, the docstring contained this error ever since `seed` was added.
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I_kwDODunzps5Jmogo
| 4,343
|
Metrics documentation is not accessible in the datasets doc UI
|
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2022-05-13T07:46:30Z
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2022-06-03T08:50:25Z
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2022-06-03T08:50:25Z
|
CONTRIBUTOR
| null | null |
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**Is your feature request related to a problem? Please describe.**
Search for a metric name like "seqeval" yields no results on https://huggingface.co/docs/datasets/master/en/index . One needs to go look in `datasets/metrics/README.md` to find the doc. Even in the `README.md`, it can be hard to understand what the metric expects as an input, for example for `squad` there is a [key `id`](https://github.com/huggingface/datasets/blob/1a4c185663a6958f48ec69624473fdc154a36a9d/metrics/squad/squad.py#L42) documented only in the function doc but not in the `README.md`, and one needs to go look into the code to understand what the metric expects.
**Describe the solution you'd like**
Have the documentation for metrics appear as well in the doc UI, e.g. this https://github.com/huggingface/datasets/blob/1a4c185663a6958f48ec69624473fdc154a36a9d/metrics/squad/squad.py#L21-L63
I know there are plans to migrate metrics to the evaluate library, but just pointing this out.
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PR_kwDODunzps43woHm
| 4,342
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Fix failing CI on Windows for sari and wiki_split metrics
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2022-05-13T05:03:38Z
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2022-05-13T05:47:42Z
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2022-05-13T05:47:42Z
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MEMBER
| null | null | null |
This PR adds `sacremoses` as explicit tests dependency (required by sari and wiki_split metrics).
Before, this library was installed as a third-party dependency, but this is no longer the case for Windows.
Fix #4341.
|
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I_kwDODunzps5JmKH3
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Failing CI on Windows for sari and wiki_split metrics
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## Describe the bug
Our CI is failing from yesterday on Windows for metrics: sari and wiki_split
```
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_sari - ...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_wiki_split
```
See: https://app.circleci.com/pipelines/github/huggingface/datasets/11928/workflows/79daa5e7-65c9-4e85-829b-00d2bfbd076a/jobs/71594
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Fix irc_disentangle dataset script
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2022-05-13T02:37:57Z
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2022-05-24T15:37:30Z
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2022-05-24T15:37:29Z
|
NONE
| null | null | null |
updated extracted dataset's repo's latest commit hash (included in tarball's name), and updated the related data_infos.json
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PR_kwDODunzps43v0WT
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Dataset loader for the MSLR2022 shared task
|
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2022-05-12T21:23:41Z
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2022-07-18T17:19:27Z
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2022-07-18T16:58:34Z
|
CONTRIBUTOR
| null | null | null |
This PR adds a dataset loader for the [MSLR2022 Shared Task](https://github.com/allenai/mslr-shared-task). Both the MS^2 and Cochrane datasets can be loaded with this dataloader:
```python
from datasets import load_dataset
ms2 = load_dataset("mslr2022", "ms2")
cochrane = load_dataset("mslr2022", "cochrane")
```
Usage looks like:
```python
>>> ms2 = load_dataset("mslr2022", "ms2", split="validation")
>>> ms2.keys()
dict_keys(['review_id', 'pmid', 'title', 'abstract', 'target', 'background', 'reviews_info'])
>>> ms2[0].target
'Conclusions SC therapy is effective for PAH in pre clinical studies .\nThese results may help to st and ardise pre clinical animal studies and provide a theoretical basis for clinical trial design in the future .'
```
I have tested this works with the following command:
```bash
datasets-cli test datasets/mslr2022 --save_infos --all_configs
```
However I have having a little trouble generating the dummy data
```bash
datasets-cli dummy_data datasets/mslr2022 --auto_generate
```
errors out with the following stack trace:
```
Couldn't generate dummy file 'datasets/mslr2022/dummy/ms2/1.0.0/dummy_data/mslr_data.tar.gz/mslr_data/ms2/convert_to_cochrane.py'. Ignore that if this file is not useful for dummy data.
Traceback (most recent call last):
File "/Users/johngiorgi/.pyenv/versions/datasets/bin/datasets-cli", line 11, in <module>
load_entry_point('datasets', 'console_scripts', 'datasets-cli')()
File "/Users/johngiorgi/Documents/dev/datasets/src/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/Users/johngiorgi/Documents/dev/datasets/src/datasets/commands/dummy_data.py", line 319, in run
keep_uncompressed=self._keep_uncompressed,
File "/Users/johngiorgi/Documents/dev/datasets/src/datasets/commands/dummy_data.py", line 361, in _autogenerate_dummy_data
dataset_builder._prepare_split(split_generator, check_duplicate_keys=False)
File "/Users/johngiorgi/Documents/dev/datasets/src/datasets/builder.py", line 1146, in _prepare_split
desc=f"Generating {split_info.name} split",
File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/Users/johngiorgi/.cache/huggingface/modules/datasets_modules/datasets/mslr2022/b4becd2f52cf18255d4934d7154c2a1127fb393371b87b3c1fc2c8b35a777cea/mslr2022.py", line 149, in _generate_examples
reviews_info_df = pd.read_csv(reviews_info_filepath, index_col=0)
File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/util/_decorators.py", line 311, in wrapper
return func(*args, **kwargs)
File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 586, in read_csv
return _read(filepath_or_buffer, kwds)
File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 488, in _read
return parser.read(nrows)
File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 1047, in read
index, columns, col_dict = self._engine.read(nrows)
File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 224, in read
chunks = self._reader.read_low_memory(nrows)
File "pandas/_libs/parsers.pyx", line 801, in pandas._libs.parsers.TextReader.read_low_memory
File "pandas/_libs/parsers.pyx", line 857, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 843, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 1925, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: EOF inside string starting at row 2
```
I think this may have to do with unusual line terminators in the original data. When I open it in VSCode, it complains:
```
The file 'dev-inputs.csv' contains one or more unusual line terminator characters, like Line Separator (LS) or Paragraph Separator (PS).
It is recommended to remove them from the file. This can be configured via `editor.unusualLineTerminators`.
```
Tagging the organizers of the shared task in case they want to sanity check this or add any info to the model card :) @lucylw @jayded
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Eval metadata Batch 4: Tweet Eval, Tweets Hate Speech Detection, VCTK, Weibo NER, Wisesight Sentiment, XSum, Yahoo Answers Topics, Yelp Polarity, Yelp Review Full
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CONTRIBUTOR
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Adding evaluation metadata for:
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CONTRIBUTOR
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Adding evaluation metadata for:
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2022-05-16T16:24:59Z
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CONTRIBUTOR
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Adding evaluation metadata for :
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2022-05-16T16:23:09Z
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CONTRIBUTOR
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Adding evaluation metadata for:
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2023-09-24T10:02:46Z
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CONTRIBUTOR
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Adding eval metadata for billsum
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I_kwDODunzps5JiueU
| 4,327
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`wikipedia` pre-processed datasets
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2022-05-12T11:25:42Z
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2022-08-31T08:26:57Z
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2022-08-31T08:26:57Z
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NONE
| null | null |
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## Describe the bug
[Wikipedia](https://huggingface.co/datasets/wikipedia) dataset readme says that certain subsets are preprocessed. However it seems like they are not available. When I try to load them it takes a really long time, and it seems like it's processing them.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikipedia", "20220301.en")
```
## Expected results
To load the dataset
## Actual results
Takes a very long time to load (after downloading)
After `Downloading data files: 100%`. It takes hours and gets killed.
Tried `wikipedia.simple` and it got processed after ~30mins.
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PR_kwDODunzps43tjWy
| 4,326
|
Fix type hint and documentation for `new_fingerprint`
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2022-05-12T11:05:08Z
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2022-06-01T13:04:45Z
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2022-06-01T12:56:18Z
|
CONTRIBUTOR
| null | null | null |
Currently, there are no type hints nor `Optional` for the argument `new_fingerprint` in several methods of `datasets.arrow_dataset.Dataset`.
There was some documentation missing as well.
Note that pylance is happy with the type hints, but pyright does not detect that `new_fingerprint` is set within the decorator.
The modifications in this PR are fine since here https://github.com/huggingface/datasets/blob/aa743886221d76afb409d263e1b136e7a71fe2b4/src/datasets/fingerprint.py#L446-L454
for the non-inplace case we make sure to auto-generate a new fingerprint (as indicated in the doc).
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I_kwDODunzps5Jinrf
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|
Dataset Viewer issue for strombergnlp/offenseval_2020, strombergnlp/polstance
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2022-05-12T10:59:08Z
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2022-05-13T10:57:15Z
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2022-05-13T10:57:02Z
|
CONTRIBUTOR
| null | null |
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### Link
https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train
### Description
The viewer isn't running for these two datasets. I left it overnight because a wait sometimes helps things get loaded, and the error messages have all gone, but the datasets are still turning up blank in viewer. Maybe it needs a bit more time.
* https://huggingface.co/datasets/strombergnlp/polstance/viewer/PolStance/train
* https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train
While offenseval_2020 is gated w. prompt, the other gated previews I have run fine in Viewer, e.g. https://huggingface.co/datasets/strombergnlp/shaj , so I'm a bit stumped!
### Owner
Yes
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I_kwDODunzps5JigCG
| 4,324
|
Support >1 PWC dataset per dataset card
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2022-05-12T10:29:07Z
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2022-05-13T11:25:29Z
| null |
CONTRIBUTOR
| null | null |
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**Is your feature request related to a problem? Please describe.**
Some datasets cover more than one dataset on PapersWithCode. For example, the OffensEval 2020 challenge involved five languages, and there's one dataset to cover all five datasets, [`strombergnlp/offenseval_2020`](https://huggingface.co/datasets/strombergnlp/offenseval_2020). However, the yaml `paperswithcode_id:` dataset card entry only supports one value; when multiple are added, the PWC link disappears from the dataset page.
Because the link from a PapersWithCode dataset to a Hugging Face Hub entry can't be entered manually and seems to be scraped, this means end users don't have a way of getting a dataset reader link to appear on all the PWC datasets supported by one HF Hub Dataset reader.
It's not super unusual to have papers introduce multiple parallel variants of a dataset and would be handy to reflect this, so e.g. dataset maintainers can DRY, and so dataset users can keep what they're doing simple.
**Describe the solution you'd like**
I'd like `paperswithcode_id:` to support lists and be able to connect with multiple PWC datasets.
**Describe alternatives you've considered**
De-normalising the datasets on HF Hub to create multiple readers for each variation on a task, i.e. instead of a single `offenseval_2020`, having `offenseval_2020_ar`, `offenseval_2020_da`, `offenseval_2020_gr`, ...
**Additional context**
Hope that's enough
**Priority**
Low
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Audio can not find value["bytes"]
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2022-05-12T08:31:58Z
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2022-07-07T13:16:08Z
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2022-07-07T13:16:08Z
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CONTRIBUTOR
| null | null |
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## Describe the bug
I wrote down _generate_examples like:
but where is the bytes?
## Expected results
value["bytes"] is not None, so i can make datasets with bytes, not path
## bytes looks like:
blah blah~~
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blah blah~~
that function not return None
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:2.2.1
- Platform:ubuntu 18.04
- Python version:3.6.9
- PyArrow version:6.0.1
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PR_kwDODunzps43s1wy
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Added stratify option to train_test_split function.
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2022-05-12T08:00:31Z
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2022-11-22T14:53:55Z
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2022-05-25T20:43:51Z
|
CONTRIBUTOR
| null | null | null |
This PR adds `stratify` option to `train_test_split` method. I took reference from scikit-learn's `StratifiedShuffleSplit` class for implementing stratified split and integrated the changes as were suggested by @lhoestq.
It fixes #3452.
@lhoestq Please review and let me know, if any changes are required.
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PR_kwDODunzps43ryW7
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Adding dataset enwik8
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2022-05-11T23:25:02Z
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2022-06-01T14:27:30Z
|
2022-06-01T14:04:06Z
|
CONTRIBUTOR
| null | null | null |
Because I regularly work with enwik8, I would like to contribute the dataset loader 🤗
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I_kwDODunzps5JgUYg
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Multi-news dataset loader attempts to strip wrong character from beginning of summaries
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2022-05-11T21:36:41Z
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2022-05-16T13:52:10Z
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2022-05-16T13:52:10Z
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CONTRIBUTOR
| null | null |
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## Describe the bug
The `multi_news.py` data loader has [a line which attempts to strip `"- "` from the beginning of summaries](https://github.com/huggingface/datasets/blob/aa743886221d76afb409d263e1b136e7a71fe2b4/datasets/multi_news/multi_news.py#L97). The actual character in the multi-news dataset, however, is `"– "`, which is different, e.g. `"– " != "- "`.
I would have just opened a PR to fix the mistake, but I am wondering what the motivation for stripping this character is? AFAICT most approaches just leave it in, e.g. the current SOTA on this dataset, [PRIMERA](https://huggingface.co/allenai/PRIMERA-multinews) (you can see its in the generated summaries of the model in their [example notebook](https://github.com/allenai/PRIMER/blob/main/Evaluation_Example.ipynb)).
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
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- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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PR_kwDODunzps43q0UY
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Adding eval metadata for ade v2
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CONTRIBUTOR
| null | null | null |
Adding metadata to allow evaluation
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Don't check f.loc in _get_extraction_protocol_with_magic_number
|
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2022-05-11T16:27:09Z
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2022-05-11T16:57:02Z
|
2022-05-11T16:46:31Z
|
MEMBER
| null | null | null |
`f.loc` doesn't always exist for file-like objects in python. I removed it since it was not necessary anyway (we always seek the file to 0 after reading the magic number)
Fix https://github.com/huggingface/datasets/issues/4310
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| 1,232,737,401
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PR_kwDODunzps43qBzh
| 4,317
|
Fix cnn_dailymail (dm stories were ignored)
|
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2022-05-11T14:25:25Z
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2022-05-11T16:00:09Z
|
2022-05-11T15:52:37Z
|
MEMBER
| null | null | null |
https://github.com/huggingface/datasets/pull/4188 introduced a bug in `datasets` 2.2.0: DailyMail stories are ignored when generating the dataset.
I fixed that, and removed the google drive link (it has annoying quota limitations issues)
We can do a patch release after this is merged
|
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PR_kwDODunzps43p1Za
| 4,316
|
Support passing config_kwargs to CLI run_beam
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2022-05-11T13:53:37Z
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2022-05-11T14:36:49Z
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2022-05-11T14:28:31Z
|
MEMBER
| null | null | null |
This PR supports passing `config_kwargs` to CLI run_beam, so that for example for "wikipedia" dataset, we can pass:
```
--date 20220501 --language ca
```
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| 1,232,549,330
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PR_kwDODunzps43pZ6p
| 4,315
|
Fix CLI run_beam namespace
|
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2022-05-11T12:21:00Z
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2022-05-11T13:13:00Z
|
2022-05-11T13:05:08Z
|
MEMBER
| null | null | null |
Currently, it raises TypeError:
```
TypeError: __init__() got an unexpected keyword argument 'namespace'
```
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PR_kwDODunzps43oqXD
| 4,314
|
Catch pull error when mirroring
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2022-05-11T09:38:35Z
|
2022-05-11T12:54:07Z
|
2022-05-11T12:46:42Z
|
MEMBER
| null | null | null |
Catch pull errors when mirroring so that the script continues to update the other datasets.
The error will still be printed at the end of the job. In this case the job also fails, and asks to manually update the datasets that failed.
|
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PR_kwDODunzps43m4qB
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Add API code examples for Builder classes
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MEMBER
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This PR adds API code examples for the Builder classes.
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PR_kwDODunzps43mlug
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added TR-News dataset
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2022-05-10T20:33:00Z
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2022-10-03T09:36:45Z
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2022-10-03T09:36:45Z
|
NONE
| null | null | null | null |
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PR_kwDODunzps43ln8-
| 4,311
|
[Imagefolder] Docs + Don't infer labels from file names when there are metadata + Error messages when metadata and images aren't linked correctly
|
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2022-05-10T15:52:15Z
|
2022-05-10T17:19:42Z
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2022-05-10T17:11:47Z
|
MEMBER
| null | null | null |
I updated the `docs/source/image_process.mdx` documentation and added an example for image captioning and object detection using `ImageFolder`.
While doing so I also improved a few aspects:
- we don't need to infer labels from file names when there are metadata - they can just be in the metadata if necessary
- raise informative error messages when metadata and images aren't linked correctly:
- when an image is missing a metadata file
- when a metadata file is missing an image
I added some tests for these changes as well
cc @mariosasko
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I_kwDODunzps5JZHMH
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Loading dataset with streaming: '_io.BufferedReader' object has no attribute 'loc'
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## Describe the bug
Loading a datasets with `load_dataset` and `streaming=True` returns `AttributeError: '_io.BufferedReader' object has no attribute 'loc'`. Notice that loading with `streaming=False` works fine.
In the following steps we load parquet files but the same happens with pickle files. The problem seems to come from `fsspec` lib, I put in the environment info also `s3fs` and `fsspec` versions since I'm loading from an s3 bucket.
## Steps to reproduce the bug
```python
from datasets import load_dataset
# path is the path to parquet files
data_files = {"train": path + "meta_train.parquet.gzip", "test": path + "meta_test.parquet.gzip"}
dataset = load_dataset("parquet", data_files=data_files, streaming=True)
```
## Expected results
A dataset object `datasets.dataset_dict.DatasetDict`
## Actual results
```
AttributeError Traceback (most recent call last)
<command-562086> in <module>
11
12 data_files = {"train": path + "meta_train.parquet.gzip", "test": path + "meta_test.parquet.gzip"}
---> 13 dataset = load_dataset("parquet", data_files=data_files, streaming=True)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1679 if streaming:
1680 extend_dataset_builder_for_streaming(builder_instance, use_auth_token=use_auth_token)
-> 1681 return builder_instance.as_streaming_dataset(
1682 split=split,
1683 use_auth_token=use_auth_token,
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/builder.py in as_streaming_dataset(self, split, base_path, use_auth_token)
904 )
905 self._check_manual_download(dl_manager)
--> 906 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
907 # By default, return all splits
908 if split is None:
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py in _split_generators(self, dl_manager)
30 if not self.config.data_files:
31 raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}")
---> 32 data_files = dl_manager.download_and_extract(self.config.data_files)
33 if isinstance(data_files, (str, list, tuple)):
34 files = data_files
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in download_and_extract(self, url_or_urls)
798
799 def download_and_extract(self, url_or_urls):
--> 800 return self.extract(self.download(url_or_urls))
801
802 def iter_archive(self, urlpath_or_buf: Union[str, io.BufferedReader]) -> Iterable[Tuple]:
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in extract(self, path_or_paths)
776
777 def extract(self, path_or_paths):
--> 778 urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)
779 return urlpaths
780
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
312 num_proc = 1
313 if num_proc <= 1 or len(iterable) <= num_proc:
--> 314 mapped = [
315 _single_map_nested((function, obj, types, None, True, None))
316 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0)
313 if num_proc <= 1 or len(iterable) <= num_proc:
314 mapped = [
--> 315 _single_map_nested((function, obj, types, None, True, None))
316 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
317 ]
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args)
267 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
268 else:
--> 269 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
270 if isinstance(data_struct, list):
271 return mapped
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0)
267 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
268 else:
--> 269 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
270 if isinstance(data_struct, list):
271 return mapped
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args)
249 # Singleton first to spare some computation
250 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 251 return function(data_struct)
252
253 # Reduce logging to keep things readable in multiprocessing with tqdm
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _extract(self, urlpath)
781 def _extract(self, urlpath: str) -> str:
782 urlpath = str(urlpath)
--> 783 protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)
784 if protocol is None:
785 # no extraction
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _get_extraction_protocol(urlpath, use_auth_token)
371 urlpath, kwargs = urlpath, {}
372 with fsspec.open(urlpath, **kwargs) as f:
--> 373 return _get_extraction_protocol_with_magic_number(f)
374
375
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _get_extraction_protocol_with_magic_number(f)
335 def _get_extraction_protocol_with_magic_number(f) -> Optional[str]:
336 """read the magic number from a file-like object and return the compression protocol"""
--> 337 prev_loc = f.loc
338 magic_number = f.read(MAGIC_NUMBER_MAX_LENGTH)
339 f.seek(prev_loc)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/fsspec/implementations/local.py in __getattr__(self, item)
337
338 def __getattr__(self, item):
--> 339 return getattr(self.f, item)
340
341 def __enter__(self):
AttributeError: '_io.BufferedReader' object has no attribute 'loc'
```
## Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-1071-aws-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
- `fsspec` version: 2021.08.1
- `s3fs` version: 2021.08.1
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PR_kwDODunzps43lKpm
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[WIP] Add TEDLIUM dataset
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2022-05-10T14:12:47Z
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2022-06-17T12:54:40Z
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2022-06-17T11:44:01Z
|
CONTRIBUTOR
| null | null | null |
Adds the TED-LIUM dataset https://www.tensorflow.org/datasets/catalog/tedlium#tedliumrelease3
TODO:
- [x] Port `tedium.py` from TF datasets using `convert_dataset.sh` script
- [x] Make `load_dataset` work
- [ ] ~~Run `datasets-cli` command to generate `dataset_infos.json`~~
- [ ] ~~Create dummy data for continuous testing~~
- [ ] ~~Dummy data tests~~
- [ ] ~~Real data tests~~
- [ ] Create the metadata JSON
- [ ] Close PR and add directly to the Hub under LIUM org
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Remove unused multiprocessing args from test CLI
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MEMBER
| null | null | null |
Multiprocessing is not used in the test CLI.
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PR_kwDODunzps43k-Wo
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Add packaged builder configs to the documentation
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2022-05-10T13:55:54Z
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MEMBER
| null | null | null |
Add the packaged builders configurations to the docs reference is useful to show the list of all parameters one can use when loading data in many formats: CSV, JSON, etc.
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I_kwDODunzps5JYam0
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`load_dataset` does not work with certain filename.
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NONE
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## Describe the bug
This is a weird bug that took me some time to find out.
I have a JSON dataset that I want to load with `load_dataset` like this:
```
data_files = dict(train="train.json.zip", val="val.json.zip")
dataset = load_dataset("json", data_files=data_files, field="data")
```
## Expected results
No error.
## Actual results
The val file is loaded as expected, but the train file throws JSON decoding error:
```
╭──────────────────────────── Traceback (most recent call last) ────────────────────────────╮
│ <ipython-input-74-97947e92c100>:5 in <module> │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/load.py:1687 in │
│ load_dataset │
│ │
│ 1684 │ try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES │
│ 1685 │ │
│ 1686 │ # Download and prepare data │
│ ❱ 1687 │ builder_instance.download_and_prepare( │
│ 1688 │ │ download_config=download_config, │
│ 1689 │ │ download_mode=download_mode, │
│ 1690 │ │ ignore_verifications=ignore_verifications, │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:605 in │
│ download_and_prepare │
│ │
│ 602 │ │ │ │ │ │ except ConnectionError: │
│ 603 │ │ │ │ │ │ │ logger.warning("HF google storage unreachable. Downloa │
│ 604 │ │ │ │ │ if not downloaded_from_gcs: │
│ ❱ 605 │ │ │ │ │ │ self._download_and_prepare( │
│ 606 │ │ │ │ │ │ │ dl_manager=dl_manager, verify_infos=verify_infos, **do │
│ 607 │ │ │ │ │ │ ) │
│ 608 │ │ │ │ │ # Sync info │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:694 in │
│ _download_and_prepare │
│ │
│ 691 │ │ │ │
│ 692 │ │ │ try: │
│ 693 │ │ │ │ # Prepare split will record examples associated to the split │
│ ❱ 694 │ │ │ │ self._prepare_split(split_generator, **prepare_split_kwargs) │
│ 695 │ │ │ except OSError as e: │
│ 696 │ │ │ │ raise OSError( │
│ 697 │ │ │ │ │ "Cannot find data file. " │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:1151 in │
│ _prepare_split │
│ │
│ 1148 │ │ │
│ 1149 │ │ generator = self._generate_tables(**split_generator.gen_kwargs) │
│ 1150 │ │ with ArrowWriter(features=self.info.features, path=fpath) as writer: │
│ ❱ 1151 │ │ │ for key, table in logging.tqdm( │
│ 1152 │ │ │ │ generator, unit=" tables", leave=False, disable=True # not loggin │
│ 1153 │ │ │ ): │
│ 1154 │ │ │ │ writer.write_table(table) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/notebook.py:257 in │
│ __iter__ │
│ │
│ 254 │ │
│ 255 │ def __iter__(self): │
│ 256 │ │ try: │
│ ❱ 257 │ │ │ for obj in super(tqdm_notebook, self).__iter__(): │
│ 258 │ │ │ │ # return super(tqdm...) will not catch exception │
│ 259 │ │ │ │ yield obj │
│ 260 │ │ # NB: except ... [ as ...] breaks IPython async KeyboardInterrupt │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/std.py:1183 in │
│ __iter__ │
│ │
│ 1180 │ │ # If the bar is disabled, then just walk the iterable │
│ 1181 │ │ # (note: keep this check outside the loop for performance) │
│ 1182 │ │ if self.disable: │
│ ❱ 1183 │ │ │ for obj in iterable: │
│ 1184 │ │ │ │ yield obj │
│ 1185 │ │ │ return │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/packaged_modules/j │
│ son/json.py:90 in _generate_tables │
│ │
│ 87 │ │ │ # If the file is one json object and if we need to look at the list of │
│ 88 │ │ │ if self.config.field is not None: │
│ 89 │ │ │ │ with open(file, encoding="utf-8") as f: │
│ ❱ 90 │ │ │ │ │ dataset = json.load(f) │
│ 91 │ │ │ │ │
│ 92 │ │ │ │ # We keep only the field we are interested in │
│ 93 │ │ │ │ dataset = dataset[self.config.field] │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:293 in load │
│ │
│ 290 │ To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` │
│ 291 │ kwarg; otherwise ``JSONDecoder`` is used. │
│ 292 │ """ │
│ ❱ 293 │ return loads(fp.read(), │
│ 294 │ │ cls=cls, object_hook=object_hook, │
│ 295 │ │ parse_float=parse_float, parse_int=parse_int, │
│ 296 │ │ parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:357 in loads │
│ │
│ 354 │ if (cls is None and object_hook is None and │
│ 355 │ │ │ parse_int is None and parse_float is None and │
│ 356 │ │ │ parse_constant is None and object_pairs_hook is None and not kw): │
│ ❱ 357 │ │ return _default_decoder.decode(s) │
│ 358 │ if cls is None: │
│ 359 │ │ cls = JSONDecoder │
│ 360 │ if object_hook is not None: │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:337 in decode │
│ │
│ 334 │ │ containing a JSON document). │
│ 335 │ │ │
│ 336 │ │ """ │
│ ❱ 337 │ │ obj, end = self.raw_decode(s, idx=_w(s, 0).end()) │
│ 338 │ │ end = _w(s, end).end() │
│ 339 │ │ if end != len(s): │
│ 340 │ │ │ raise JSONDecodeError("Extra data", s, end) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:353 in raw_decode │
│ │
│ 350 │ │ │
│ 351 │ │ """ │
│ 352 │ │ try: │
│ ❱ 353 │ │ │ obj, end = self.scan_once(s, idx) │
│ 354 │ │ except StopIteration as err: │
│ 355 │ │ │ raise JSONDecodeError("Expecting value", s, err.value) from None │
│ 356 │ │ return obj, end │
╰───────────────────────────────────────────────────────────────────────────────────────────╯
JSONDecodeError: Unterminated string starting at: line 85 column 20 (char 60051)
```
However, when I rename the `train.json.zip` to other names (like `training.json.zip`, or even to `train.json`), everything works fine; when I unzip the file to `train.json`, it works as well.
## Environment info
```
- `datasets` version: 2.1.0
- Platform: Linux-4.4.0-131-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
```
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PR_kwDODunzps43kt4P
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Fixes FrugalScore
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"default": false,
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2022-05-10T12:44:06Z
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2022-09-22T16:42:06Z
| null |
CONTRIBUTOR
| null | null | null |
There are two minor modifications in this PR:
1) `predictions` and `references` are swapped. Basically Frugalscore is commutative, however some tiny differences can occur if we swap the references and the predictions. I decided to swap them just to obtain the exact results as reported in the paper.
2) I switched to dynamic padding that was was used in the training, forcing the padding to `max_length` introduces errors for some reason that I ignore.
@lhoestq
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I_kwDODunzps5JYEmL
| 4,304
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Language code search does direct matches
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2022-05-10T11:59:16Z
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2022-05-10T12:38:42Z
| null |
CONTRIBUTOR
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## Describe the bug
Hi. Searching for bcp47 tags that are just the language prefix (e.g. `sq` or `da`) excludes datasets that have added extra information in their language metadata (e.g. `sq-AL` or `da-bornholm`). The example codes given in the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging) encourages addition of the additional codes ("_expected format is BCP47 tags separated for ';' e.g. 'en-US;fr-FR'_") but this would lead to those datasets being hidden in datasets search.
## Steps to reproduce the bug
1. Add a dataset using a variant tag (e.g. [`sq-AL`](https://huggingface.co/datasets?languages=languages:sq-AL))
2. Look for datasets using the full code
3. Note that they're missing when just the language is searched for (e.g. [`sq`](https://huggingface.co/datasets?languages=languages:sq))
Some datasets are already affected by this - e.g. `AmazonScience/massive` is listed under `sq-AL` but not `sq`.
One workaround is for dataset creators to add an additional root language tag to dataset YAML metadata, but it's unclear how to communicate this. It might be possible to index the search on `languagecode.split('-')[0]` but I wanted to float this issue before trying to write any code :)
## Expected results
Datasets using longer bcp47 tags also appear under searches for just the language code; e.g. Quebecois datasets (`fr-CA`) would come up when looking for French datasets with no region specification (`fr`), or US English (`en-US`) datasets would come up when searching for English datasets (`en`).
## Actual results
The language codes seem to be directly string matched, excluding datasets with specific language tags from non-specific searches.
## Environment info
(web app)
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Fix: Add missing comma
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PR_kwDODunzps43jPE5
| 4,302
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Remove hacking license tags when mirroring datasets on the Hub
|
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2022-05-10T05:52:46Z
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2022-05-20T09:48:30Z
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2022-05-20T09:40:20Z
|
MEMBER
| null | null | null |
Currently, when mirroring datasets on the Hub, the license tags are hacked: removed of characters "." and "$". On the contrary, this hacking is not applied to community datasets on the Hub. This generates multiple variants of the same tag on the Hub.
I guess this hacking is no longer necessary:
- it is not applied to community datasets
- all canonical datasets are validated by maintainers before being merged: CI + maintainers make sure license tags are the right ones
Fix #4298.
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PR_kwDODunzps43idlE
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Add ImageNet-Sketch dataset
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2022-05-09T23:38:45Z
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2022-05-23T18:14:14Z
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2022-05-23T18:05:29Z
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CONTRIBUTOR
| null | null | null |
This PR adds the ImageNet-Sketch dataset and resolves #3953 .
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PR_kwDODunzps43iA86
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Add API code examples for loading methods
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2022-05-09T21:30:26Z
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2022-05-25T16:23:15Z
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2022-05-25T09:20:13Z
|
MEMBER
| null | null | null |
This PR adds API code examples for loading methods, let me know if I've missed any important parameters we should showcase :)
I was a bit confused about `inspect_dataset` and `inspect_metric`. The `path` parameter says it will accept a dataset identifier from the Hub. But when I try the identifier `rotten_tomatoes`, it gives me:
```py
from datasets import inspect_dataset
inspect_dataset('rotten_tomatoes', local_path='/content/rotten_tomatoes')
FileNotFoundError: Couldn't find a dataset script at /content/rotten_tomatoes/rotten_tomatoes.py or any data file in the same directory.
```
Does the user need to have an existing copy of `rotten_tomatoes.py` on their local drive (in which case, it seems like the same option as the first option in `path`)?
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PR_kwDODunzps43h5RP
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Remove manual download from imagenet-1k
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2022-05-09T20:49:18Z
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2022-05-25T14:54:59Z
|
2022-05-25T14:46:16Z
|
COLLABORATOR
| null | null | null |
Remove the manual download code from `imagenet-1k` to make it a regular dataset.
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I_kwDODunzps5JTHcm
| 4,298
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Normalise license names
|
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2022-05-09T13:51:32Z
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2022-05-20T09:51:50Z
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2022-05-20T09:51:50Z
|
CONTRIBUTOR
| null | null |
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**Is your feature request related to a problem? Please describe.**
When browsing datasets, the Licenses tag cloud (bottom left of e.g. https://huggingface.co/datasets) has multiple variants of the same license. This means the options exclude datasets arbitrarily, giving users artificially low recall. The cause of the dupes is probably due to a bit of variation in metadata.
**Describe the solution you'd like**
I'd like the licenses in metadata to follow the same standard as much as possible, to remove this problem. I'd like to go ahead and normalise the dataset metadata to follow the format & values given in [src/datasets/utils/resources/licenses.json](https://github.com/huggingface/datasets/blob/master/src/datasets/utils/resources/licenses.json) .
**Describe alternatives you've considered**
None
**Additional context**
None
**Priority**
Low
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I_kwDODunzps5JTEZK
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Datasets YAML tagging space is down
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2022-05-09T13:45:05Z
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2022-05-09T14:44:25Z
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2022-05-09T14:44:25Z
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CONTRIBUTOR
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## Describe the bug
The neat hf spaces app for generating YAML tags for dataset `README.md`s is down
## Steps to reproduce the bug
1. Visit https://huggingface.co/spaces/huggingface/datasets-tagging
## Expected results
There'll be a HF spaces web app for generating dataset metadata YAML
## Actual results
There's an error message; here's the step where it breaks:
```
Step 18/29 : RUN pip install -r requirements.txt
---> Running in e88bfe7e7e0c
Defaulting to user installation because normal site-packages is not writeable
Collecting git+https://github.com/huggingface/datasets.git@update-task-list (from -r requirements.txt (line 4))
Cloning https://github.com/huggingface/datasets.git (to revision update-task-list) to /tmp/pip-req-build-bm8t0r0k
Running command git clone --filter=blob:none --quiet https://github.com/huggingface/datasets.git /tmp/pip-req-build-bm8t0r0k
WARNING: Did not find branch or tag 'update-task-list', assuming revision or ref.
Running command git checkout -q update-task-list
error: pathspec 'update-task-list' did not match any file(s) known to git
error: subprocess-exited-with-error
× git checkout -q update-task-list did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× git checkout -q update-task-list did not run successfully.
│ exit code: 1
╰─> See above for output.
```
## Environment info
- Platform: Linux / Brave
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Fix URL query parameters in compression hop path when streaming
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2022-05-09T11:18:22Z
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2022-07-06T15:19:53Z
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MEMBER
| null | null | null |
Fix #3488.
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PR_kwDODunzps43fieR
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Fix missing lz4 dependency for tests
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2022-05-09T10:53:20Z
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2022-05-09T11:21:22Z
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2022-05-09T11:13:44Z
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MEMBER
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Currently, `lz4` is not defined as a dependency for tests. Therefore, all tests marked with `@require_lz4` are skipped.
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PR_kwDODunzps43fTXA
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Fix CLI run_beam save_infos
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2022-05-09T09:47:43Z
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2022-05-10T07:04:04Z
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2022-05-10T06:56:10Z
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MEMBER
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Currently, it raises TypeError:
```
TypeError: _download_and_prepare() got an unexpected keyword argument 'save_infos'
```
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PR_kwDODunzps43dRt9
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Fix wrong map parameter name in cache docs
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2022-05-08T07:27:46Z
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2022-06-14T16:49:00Z
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2022-06-14T16:07:00Z
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CONTRIBUTOR
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The `load_from_cache` parameter of `map` should be `load_from_cache_file`.
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PR_kwDODunzps43bhrp
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Add API code examples for remaining main classes
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2022-05-25T18:05:13Z
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2022-05-25T17:56:36Z
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MEMBER
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This PR adds API code examples for the remaining functions in the Main classes. I wasn't too familiar with some of the functions (`decode_batch`, `decode_column`, `decode_example`, etc.) so please feel free to add an example of usage and I can fill in the rest :)
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Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message
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2022-05-09T08:25:58Z
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2022-05-09T08:25:58Z
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CONTRIBUTOR
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### Link
https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train
### Description
The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss?
### Owner
Yes
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PR_kwDODunzps43Zr08
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Update paper link in medmcqa dataset card
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CONTRIBUTOR
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Updating readme in medmcqa dataset.
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PR_kwDODunzps43XLKi
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Add missing `faiss` import to fix https://github.com/huggingface/datasets/issues/4287
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2022-05-05T15:21:49Z
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2022-05-10T12:09:48Z
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MEMBER
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This PR fixes the issue recently mentioned in https://github.com/huggingface/datasets/issues/4287 🤗
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"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None
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MEMBER
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## Describe the bug
When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception.
All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required.
## Steps to reproduce the bug
```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
ds = load_dataset('crime_and_punish', split='train[:100]')
ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()})
ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None`
```
## Expected results
A new column named `embeddings` in the dataset that we're adding the index to.
## Actual results
An exception is triggered with the following message `NameError: name 'faiss' is not defined`.
## Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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Add Lahnda language tag
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COLLABORATOR
| null | null | null |
This language is present in [Wikimedia's WIT](https://huggingface.co/datasets/wikimedia/wit_base) dataset.
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Update LexGLUE README.md
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2022-05-05T08:36:50Z
|
2022-05-05T13:39:04Z
|
2022-05-05T13:33:35Z
|
CONTRIBUTOR
| null | null | null |
Update the leaderboard based on the latest results presented in the ACL 2022 version of the article.
|
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I_kwDODunzps5JFlaX
| 4,284
|
Issues in processing very large datasets
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2022-05-05T05:01:09Z
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2023-07-25T15:12:38Z
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2023-07-25T15:12:38Z
|
NONE
| null | null |
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## Describe the bug
I'm trying to add a feature called "subgraph" to CNN/DM dataset (modifications on run_summarization.py of Huggingface Transformers script) --- I'm not quite sure if I'm doing it the right way, though--- but the main problem appears when the training starts where the error ` [OSError: [Errno 12] Cannot allocate memory]` appears. I suppose this problem roots in RAM issues and how the dataset is loaded during training, but I have no clue of what I can do to fix it. Observing the dataset's cache directory, I see that it takes ~600GB of memory and that's why I believe special care is needed when loading it into the memory.
Here are my modifications to `run_summarization.py` code.
```
# loading pre-computed dictionary where keys are 'id' of article and values are corresponding subgraph
graph_data_train = get_graph_data('train')
graph_data_validation = get_graph_data('val')
...
...
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function_train,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
```
And here is the modified preprocessed function:
```
def preprocess_function_train(examples):
inputs, targets, sub_graphs, ids = [], [], [], []
for i in range(len(examples[text_column])):
if examples[text_column][i] is not None and examples[summary_column][i] is not None:
# if examples['doc_id'][i] in graph_data.keys():
inputs.append(examples[text_column][i])
targets.append(examples[summary_column][i])
sub_graphs.append(graph_data_train[examples['id'][i]])
ids.append(examples['id'][i])
inputs = [prefix + inp for inp in inputs]
model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True,
sub_graphs=sub_graphs, ids=ids)
# Setup the tokenizer for targets
with tokenizer.as_target_tokenizer():
labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
# padding in the loss.
if padding == "max_length" and data_args.ignore_pad_token_for_loss:
labels["input_ids"] = [
[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
]
model_inputs["labels"] = labels["input_ids"]
return model_inputs
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux Ubuntu
- Python version: 3.6
- PyArrow version: 6.0.1
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| 1,225,686,988
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PR_kwDODunzps43Tnxo
| 4,283
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Fix filesystem docstring
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2022-05-04T17:42:42Z
|
2022-05-06T16:32:02Z
|
2022-05-06T06:22:17Z
|
MEMBER
| null | null | null |
This PR untangles the `S3FileSystem` docstring so the [parameters](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes#parameters) are properly displayed.
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PR_kwDODunzps43TZYL
| 4,282
|
Don't do unnecessary list type casting to avoid replacing None values by empty lists
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2022-05-04T16:37:01Z
|
2022-05-06T10:43:58Z
|
2022-05-06T10:37:00Z
|
MEMBER
| null | null | null |
In certain cases, `None` values are replaced by empty lists when casting feature types.
It happens every time you cast an array of nested lists like [None, [0, 1, 2, 3]] to a different type (to change the integer precision for example). In this case you'd get [[], [0, 1, 2, 3]] for example. This issue comes from PyArrow, see the discussion in https://github.com/huggingface/datasets/issues/3676
This issue also happens when no type casting is needed, because casting is supposed to be a no-op in this case. But as https://github.com/huggingface/datasets/issues/3676 shown, it's not the case and `None` are replaced by empty lists even if we cast to the exact same type.
In this PR I just workaround this bug in the case where no type casting is needed. In particular, I only call `pa.ListArray.from_arrays` only when necessary.
I also added a warning when some `None` are effectively replaced by empty lists. I wanted to raise an error in this case, but maybe we should wait a major update to do so
This PR fixes this particular case, that is occurring in `run_qa.py` in `transformers`:
```python
from datasets import Dataset
ds = Dataset.from_dict({"a": range(4)})
ds = ds.map(lambda x: {"b": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=["a"])
print(ds.to_pandas())
# before:
# b
# 0 [None, [0]]
# 1 [[], [0]]
# 2 [[], [0]]
# 3 [[], [0]]
#
# now:
# b
# 0 [None, [0]]
# 1 [None, [0]]
# 2 [None, [0]]
# 3 [None, [0]]
```
cc @sgugger
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Remove a copy-paste sentence in dataset cards
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MEMBER
| null | null | null |
Remove the following copy-paste sentence from dataset cards:
```
We show detailed information for up to 5 configurations of the dataset.
```
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Add missing features to commonsense_qa dataset
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2022-05-04T14:24:26Z
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2022-05-06T14:23:57Z
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2022-05-06T14:16:46Z
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MEMBER
| null | null | null |
Fix partially #4275.
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PR_kwDODunzps43SXw5
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|
Update minimal PyArrow version warning
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2022-05-05T08:50:58Z
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2022-05-05T08:43:47Z
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COLLABORATOR
| null | null | null |
Update the minimal PyArrow version warning (should've been part of #4250).
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PR_kwDODunzps43RyTs
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Add missing features to openbookqa dataset for additional config
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2022-05-04T09:22:50Z
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2022-05-06T13:13:20Z
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2022-05-06T13:06:01Z
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MEMBER
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Fix partially #4276.
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PR_kwDODunzps43RZV9
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Enable label alignment for token classification datasets
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2022-05-04T07:15:16Z
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2022-05-06T15:42:15Z
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2022-05-06T15:36:31Z
|
MEMBER
| null | null | null |
This PR extends the `Dataset.align_labels_with_mapping()` method to support alignment of label mappings between datasets and models for token classification (e.g. NER).
Example of usage:
```python
from datasets import load_dataset
ner_ds = load_dataset("conll2003", split="train")
# returns [3, 0, 7, 0, 0, 0, 7, 0, 0]
ner_ds[0]["ner_tags"]
# hypothetical model mapping with O <--> B-LOC
label2id = {
"B-LOC": "0",
"B-MISC": "7",
"B-ORG": "3",
"B-PER": "1",
"I-LOC": "6",
"I-MISC": "8",
"I-ORG": "4",
"I-PER": "2",
"O": "5"
}
ner_aligned_ds = ner_ds.align_labels_with_mapping(label2id, "ner_tags")
# returns [3, 5, 7, 5, 5, 5, 7, 5, 5]
ner_aligned_ds[0]["ner_tags"]
```
Context: we need this in AutoTrain to automatically align datasets / models during evaluation. cc @abhishekkrthakur
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I_kwDODunzps5JAz4E
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OpenBookQA has missing and inconsistent field names
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2022-05-04T05:51:52Z
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2022-10-11T17:11:53Z
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2022-10-05T13:50:03Z
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CONTRIBUTOR
| null | null |
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## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanScore'],
- 'clarity': row['clarity'],
- 'turkIdAnonymized': row['turkIdAnonymized']
3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Expected results
The structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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CommonSenseQA has missing and inconsistent field names
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2022-05-04T11:41:18Z
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CONTRIBUTOR
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## Describe the bug
In short, CommonSenseQA implementation is inconsistent with the original dataset.
More precisely, we need to:
1. Add the dataset matching "id" field. The current dataset, instead, regenerates monotonically increasing id.
2. The [“question”][“stem”] field is flattened into "question". We should match the original dataset and unflatten it
3. Add the missing "question_concept" field in the question tree node
4. Anything else? Go over the data structure of the newly repaired CommonSenseQA and make sure it matches the original
## Expected results
Every data item of the CommonSenseQA should structurally and data-wise match the original CommonSenseQA dataset.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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2022-05-03T17:44:56Z
|
2022-05-04T06:58:35Z
|
2022-05-04T06:58:35Z
|
NONE
| null | null |
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## Describe the bug
in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable".
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PR_kwDODunzps43PC5V
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Fix style in openbookqa dataset
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2022-05-03T15:21:34Z
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2022-05-06T08:38:06Z
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2022-05-03T16:20:52Z
|
MEMBER
| null | null | null |
CI in PR:
- #4259
was green, but after merging it to master, a code quality error appeared.
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PR_kwDODunzps43Nzwh
| 4,269
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Add license and point of contact to big_patent dataset
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2022-05-03T09:24:07Z
|
2022-05-06T08:38:09Z
|
2022-05-03T11:16:19Z
|
MEMBER
| null | null | null |
Update metadata of big_patent dataset with:
- license
- point of contact
|
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| 1,223,331,964
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I_kwDODunzps5I6pB8
| 4,268
|
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
|
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"default": false,
"description": "A bug in a dataset script provided in the library",
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"name": "dataset bug",
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[] | null | 10
|
2022-05-02T20:34:25Z
|
2022-05-06T15:53:30Z
|
2022-05-03T11:23:48Z
|
NONE
| null | null |
{
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|
## Describe the bug
Error generated when attempting to download dataset
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
```
## Expected results
A clear and concise description of the expected results.
## Actual results
```
ExpectedMoreDownloadedFiles Traceback (most recent call last)
[<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
3 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
31 return
32 if len(set(expected_checksums) - set(recorded_checksums)) > 0:
---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
34 if len(set(recorded_checksums) - set(expected_checksums)) > 0:
35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
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| 1,223,214,275
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PR_kwDODunzps43LzOR
| 4,267
|
Replace data URL in SAMSum dataset within the same repository
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2022-05-06T08:38:13Z
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2022-05-02T19:03:49Z
|
MEMBER
| null | null | null |
Replace data URL with one in the same repository.
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PR_kwDODunzps43LeXK
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Add HF Speech Bench to Librispeech Dataset Card
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2022-05-02T16:59:31Z
|
2022-05-05T08:47:20Z
|
2022-05-05T08:40:09Z
|
CONTRIBUTOR
| null | null | null |
Adds the HF Speech Bench to Librispeech Dataset Card in place of the Papers With Code Leaderboard. Should improve usage and visibility of this leaderboard! Wondering whether this can also be done for [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) and [8](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) through someone with permissions?
cc @patrickvonplaten: more leaderboard promotion!
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PR_kwDODunzps43KLnD
| 4,263
|
Rename imagenet2012 -> imagenet-1k
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2022-05-02T10:26:21Z
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2022-05-02T17:50:46Z
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2022-05-02T16:32:57Z
|
MEMBER
| null | null | null |
On the Hugging Face Hub, users refer to imagenet2012 (from #4178 ) as imagenet-1k in their model tags.
To correctly link models to imagenet, we should rename this dataset `imagenet-1k`.
Later we can add `imagenet-21k` as a new dataset if we want.
Once this one is merged we can delete the `imagenet2012` dataset repository on the Hub.
EDIT: to complete the rationale on why we should name it `imagenet-1k`:
If users specifically added the tag `imagenet-1k` , then it could be for two reasons (not sure which one is predominant), either they
- wanted to make it explicit that it’s not 21k -> the distinction is important for the community
- or they have been following this convention from other models -> the convention implicitly exists already
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Add YAML tags to Dataset Card rotten tomatoes
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2022-05-01T11:59:08Z
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2022-05-03T14:27:33Z
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2022-05-03T14:20:35Z
|
CONTRIBUTOR
| null | null | null |
The dataset card for the rotten tomatoes / MR movie review dataset had some missing YAML tags. Hopefully, this also improves the visibility of this dataset now that paperswithcode and huggingface link to eachother.
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I_kwDODunzps5I1HeD
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data leakage in `webis/conclugen` dataset
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2022-04-30T17:43:37Z
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2022-05-03T06:04:26Z
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2022-05-03T06:04:26Z
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NONE
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## Describe the bug
Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results.
Furthermore, all splits contain duplicate samples.
## Steps to reproduce the bug
```python
from datasets import load_dataset
training = load_dataset("webis/conclugen", "base", split="train")
validation = load_dataset("webis/conclugen", "base", split="validation")
testing = load_dataset("webis/conclugen", "base", split="test")
# collect which sample id's are present in the training split
ids_validation = list()
ids_testing = list()
for train_sample in training:
train_argument = train_sample["argument"]
train_conclusion = train_sample["conclusion"]
train_id = train_sample["id"]
# test if current sample is in validation split
if train_argument in validation["argument"]:
for validation_sample in validation:
validation_argument = validation_sample["argument"]
validation_conclusion = validation_sample["conclusion"]
validation_id = validation_sample["id"]
if train_argument == validation_argument and train_conclusion == validation_conclusion:
ids_validation.append(validation_id)
# test if current sample is in test split
if train_argument in testing["argument"]:
for testing_sample in testing:
testing_argument = testing_sample["argument"]
testing_conclusion = testing_sample["conclusion"]
testing_id = testing_sample["id"]
if train_argument == testing_argument and train_conclusion == testing_conclusion:
ids_testing.append(testing_id)
```
## Expected results
Length of both lists `ids_validation` and `ids_testing` should be zero.
## Actual results
Length of `ids_validation` = `2556`
Length of `ids_testing` = `287`
Furthermore, there seems to be duplicate samples in (at least) the *training* split, since:
`print(len(set(ids_validation)))` = `950`
`print(len(set(ids_testing)))` = `101`
All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: macOS-12.3.1-arm64-arm-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0
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PR_kwDODunzps43HSfs
| 4,260
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Add mr_polarity movie review sentiment classification
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2022-04-30T13:19:33Z
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2022-04-30T14:16:25Z
|
2022-04-30T14:16:25Z
|
CONTRIBUTOR
| null | null | null |
Add the MR (Movie Review) dataset. The original dataset contains sentences from Rotten Tomatoes labeled as either "positive" or "negative".
Homepage: [https://www.cs.cornell.edu/people/pabo/movie-review-data/](https://www.cs.cornell.edu/people/pabo/movie-review-data/)
paperswithcode: [https://paperswithcode.com/dataset/mr](https://paperswithcode.com/dataset/mr)
- [ ] I was not able to generate dummy data, the original dataset files have ".pos" and ".neg" as file extensions so the auto-generator does not work. Is it fine like this or should dummy data be added?
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https://api.github.com/repos/huggingface/datasets/issues/4259
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| 1,221,768,025
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PR_kwDODunzps43HHGc
| 4,259
|
Fix bug in choices labels in openbookqa dataset
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2022-04-30T07:41:39Z
|
2022-05-04T06:31:31Z
|
2022-05-03T15:14:21Z
|
CONTRIBUTOR
| null | null | null |
This PR fixes the Bug in the openbookqa dataset as mentioned in this issue #3550.
Fix #3550.
cc. @lhoestq @mariosasko
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| 1,221,637,727
|
PR_kwDODunzps43Gstg
| 4,258
|
Fix/start token mask issue and update documentation
|
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2022-04-29T22:42:44Z
|
2022-05-02T16:33:20Z
|
2022-05-02T16:26:12Z
|
CONTRIBUTOR
| null | null | null |
This pr fixes a couple bugs:
1) the perplexity was calculated with a 0 in the attention mask for the start token, which was causing high perplexity scores that were not correct
2) the documentation was not updated
|
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