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load_dataset processing failed with OS error after downloading a dataset
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## Describe the bug
After downloading a dataset like opus100, there is a bug that
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Steps to reproduce the bug
```python
from datasets import load_dataset
this_dataset = load_dataset('opus100', 'af-en')
```
## Expected results
there is no error when running load_dataset.
## Actual results
Specify the actual results or traceback.
Traceback (most recent call last):
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prep
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 989, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 952, in encode_example
example = cast_to_python_objects(example)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 219, in cast_to_python_ob
return _cast_to_python_objects(obj)[0]
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 165, in _cast_to_python_o
import torch
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 188, in <module>
_load_global_deps()
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 141, in _load_global_deps
ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
File "/home/anaconda3/lib/python3.6/ctypes/__init__.py", line 348, in __init__
self._handle = _dlopen(self._name, mode)
OSError: dlopen: cannot load any more object with static TLS
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "download_hub_opus100.py", line 9, in <module>
this_dataset = load_dataset('opus100', language_pair)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepa
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 658, in _download_and_prep
+ str(e)
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid
- Python version: 3.6.6
- PyArrow version: 3.0.0
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Batched `map` not allowed to return 0 items
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## Describe the bug
I'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.
However, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L100), but no elements were returned in this case.
For this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.
## Steps to reproduce the bug
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
# The following line simulates no matches found, so we return an empty batch
result = {'key': []}
return result
filtered_dataset = dataset.map(
select_rows,
remove_columns = dataset.column_names,
batched = True,
num_proc = 1,
desc = "Selecting rows with images that exist"
)
```
The code above immediately triggers the exception. If we use the following instead:
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
result = {'key': []} # or defaultdict or whatever
# code to check for condition and append elements to result
# some_items_found will be set to True if there were any matching elements in the batch
return result if some_items_found else {}
```
Then it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.
In my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.
## Expected results
The dataset would be filtered and only the matching fields would be returned.
## Actual results
An exception is encountered, as described. Using a workaround makes it fail further along the line.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
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Enum used in map functions will raise a RecursionError with dill.
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## Describe the bug
Enums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https://github.com/uqfoundation/dill/issues/250#issuecomment-852566284
In my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from enum import Enum
class A(Enum):
a = 'a'
def main():
a = A.a
def f(x):
return {} if a == a.a else x
ds = load_dataset('cnn_dailymail', '3.0.0')['test']
ds = ds.map(f, num_proc=15)
if __name__ == "__main__":
main()
```
## Expected results
The known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood.
## Actual results
```python
File "/home/xxxx/miniconda3/lib/python3.8/site-packages/dill/_dill.py", line 1373, in save_type
pickler.save_reduce(_create_type, (type(obj), obj.__name__,
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 690, in save_reduce
save(args)
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 899, in save_tuple
save(element)
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 534, in save
self.framer.commit_frame()
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 220, in commit_frame
if f.tell() >= self._FRAME_SIZE_TARGET or force:
RecursionError: maximum recursion depth exceeded while calling a Python object
```
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10
- Python version: 3.8.5
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Support multi-worker with streaming dataset (IterableDataset).
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CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.**
The current `.map` does not support multi-process, CPU can become bottleneck if the pre-processing is complex (e.g. t5 span masking).
**Describe the solution you'd like**
Ideally `.map` should support multi-worker like tfds, with `AUTOTUNE`.
**Describe alternatives you've considered**
A simpler solution is to shard the dataset and process it in parallel with pytorch dataloader. The shard does not need to be of equal size.
* https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset
**Additional context**
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load_dataset("financial_phrasebank") NonMatchingChecksumError
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## Describe the bug
Attempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("financial_phrasebank", 'sentences_allagree')
```
## Expected results
I expect to see the financial_phrasebank dataset downloaded successfully
## Actual results
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip']
## Environment info
- `datasets` version: 1.9.0
- Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6
- Python version: 3.7.10
- PyArrow version: 4.0.1
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Fix docstrings
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MEMBER
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Fix rendering of some docstrings.
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Minor reorganization of the code, so that additional patching functions (not related to streaming) might be created.
In relation with the initial approach followed in #2631.
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MEMBER
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It was not using `open` in the builder. Therefore `pyarrow.json.read_json` was downloading the full file to start yielding rows.
Moreover, it appeared that `pyarrow.json.read_json` was not really suited for streaming as it was downloading too much data and failing if `block_size` was not properly configured (related to #2573).
So I switched to using `open` which is extended to support reading from remote file progressively, and I removed the pyarrow json reader which was not practical.
Instead, I'm using the classical `json.loads` from the standard library.
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MEMBER
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It was not using open in the builder. Therefore pd.read_pickle could fail when streaming from a private repo for example.
Indeed, when streaming, open is extended to support reading from remote files and handles authentication to the HF Hub
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MDExOlB1bGxSZXF1ZXN0Njg4Njk5OTM5
| 2,635
|
Streaming for the CSV loader
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| 2021-07-13T09:08:58Z
| 2021-07-13T15:19:38Z
| 2021-07-13T15:19:37Z
|
MEMBER
| null | null | null |
It was not using `open` in the builder. Therefore `pd.read_csv` was downloading the full file to start yielding rows.
Indeed, when streaming, `open` is extended to support reading from remote file progressively.
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MDExOlB1bGxSZXF1ZXN0Njg4NDk2Mzc2
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Inject ASR template for lj_speech dataset
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|
MEMBER
| null | null | null |
Related to: #2565, #2633.
cc: @lewtun
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Update ASR tags
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| 2021-07-12T19:58:31Z
| 2021-07-13T05:45:26Z
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|
MEMBER
| null | null | null |
This PR updates the ASR tags of the 5 datasets added in #2565 following the change of task categories in #2620
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MDExOlB1bGxSZXF1ZXN0Njg4MDQyMjcw
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add image-classification task template
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| 2021-07-12T17:41:03Z
| 2021-07-13T15:44:28Z
| 2021-07-13T15:28:16Z
|
CONTRIBUTOR
| null | null | null |
Snippet below is the tl;dr, but you can try it out directly here:
[](https://colab.research.google.com/gist/nateraw/005c025d41f0e48ae3d4ee61c0f20b70/image-classification-task-template-demo.ipynb)
```python
from datasets import load_dataset
ds = load_dataset('nateraw/image-folder', data_files='PetImages/')
# DatasetDict({
# train: Dataset({
# features: ['file', 'labels'],
# num_rows: 23410
# })
# })
ds = ds.prepare_for_task('image-classification')
# DatasetDict({
# train: Dataset({
# features: ['image_file_path', 'labels'],
# num_rows: 23410
# })
# })
```
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Delete extracted files when loading dataset
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| 2021-07-12T16:39:33Z
| 2021-07-19T09:08:19Z
| 2021-07-19T09:08:19Z
|
MEMBER
| null | null | null |
Close #2481, close #2604, close #2591.
cc: @stas00, @thomwolf, @BirgerMoell
|
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MDU6SXNzdWU5NDIxMDI5NTY=
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|
Progress bars are not properly rendered in Jupyter notebook
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| 2021-07-12T14:07:13Z
| 2022-02-03T15:55:33Z
| 2022-02-03T15:55:33Z
|
MEMBER
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## Describe the bug
The progress bars are not Jupyter widgets; regular progress bars appear (like in a terminal).
## Steps to reproduce the bug
```python
ds.map(tokenize, num_proc=10)
```
## Expected results
Jupyter widgets displaying the progress bars.
## Actual results
Simple plane progress bars.
cc: Reported by @thomwolf
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Load datasets from the Hub without requiring a dataset script
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| 2021-08-25T14:18:08Z
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MEMBER
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As a user I would like to be able to upload my csv/json/text/parquet/etc. files in a dataset repository on the Hugging Face Hub and be able to load this dataset with `load_dataset` without having to implement a dataset script.
Moreover I would like to be able to specify which file goes into which split using the `data_files` argument.
This feature should be compatible with private repositories and dataset streaming.
This can be implemented by checking the extension of the files in the dataset repository and then by using the right dataset builder that is already packaged in the library (csv/json/text/parquet/etc.)
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Use ETag of remote data files
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| 2021-07-12T14:08:34Z
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MEMBER
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Use ETag of remote data files to create config ID.
Related to #2616.
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Minor fix tests with Windows paths
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MEMBER
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Minor fix tests with Windows paths.
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MDExOlB1bGxSZXF1ZXN0Njg3MzY4OTMz
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Use correct logger in metrics.py
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COLLABORATOR
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Fixes #2624
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MDU6SXNzdWU5NDE0Mzk5MjI=
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⚛️😇⚙️🔑
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can't set verbosity for `metric.py`
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## Describe the bug
```
[2021-07-10 20:13:11,528][datasets.utils.filelock][INFO] - Lock 139705371374976 acquired on /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.lock
[2021-07-10 20:13:11,529][datasets.arrow_writer][INFO] - Done writing 32 examples in 6100 bytes /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.
[2021-07-10 20:13:11,531][datasets.arrow_dataset][INFO] - Set __getitem__(key) output type to python objects for no columns (when key is int or slice) and don't output other (un-formatted) columns.
[2021-07-10 20:13:11,543][/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric.py][INFO] - Removing /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow
```
As you can see, `datasets` logging come from different places.
`filelock`, `arrow_writer` & `arrow_dataset` comes from `datasets.*` which are expected
However, `metric.py` logging comes from `/conda/envs/myenv/lib/python3.8/site-packages/datasets/`
So when setting `datasets.utils.logging.set_verbosity_error()`, it still logs the last message which is annoying during evaluation.
I had to do
```
logging.getLogger("/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric").setLevel(logging.ERROR)
```
to fully mute these messages
## Expected results
it shouldn't log these messages when setting `datasets.utils.logging.set_verbosity_error()`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: tried both 1.8.0 & 1.9.0
- Platform: Ubuntu 18.04.5 LTS
- Python version: 3.8.10
- PyArrow version: 3.0.0
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[Metrics] added wiki_split metrics
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| 2021-07-10T14:51:50Z
| 2021-07-14T14:28:13Z
| 2021-07-12T22:34:31Z
|
CONTRIBUTOR
| null | null | null |
Fixes: #2606
This pull request adds combine metrics for the wikisplit or English sentence split task
Reviewer: @patrickvonplaten
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Integration with AugLy
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| 2021-07-10T00:03:09Z
| 2023-07-20T13:18:48Z
| 2023-07-20T13:18:47Z
|
NONE
| null | null |
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|
**Is your feature request related to a problem? Please describe.**
Facebook recently launched a library, [AugLy](https://github.com/facebookresearch/AugLy) , that has a unified API for augmentations for image, video and text.
It would be pretty exciting to have it hooked up to HF libraries so that we can make NLP models robust to misspellings or to punctuation, or emojis etc. Plus, with Transformers supporting more CV use cases, having augmentations support becomes crucial.
**Describe the solution you'd like**
The biggest difference between augmentations and preprocessing is that preprocessing happens only once, but you are running augmentations once per epoch. AugLy operates on text directly, so this breaks the typical workflow where we would run the tokenizer once, set format to pt tensors and be ready for the Dataloader.
**Describe alternatives you've considered**
One possible way of implementing these is to make a custom Dataset class where getitem(i) runs the augmentation and the tokenizer every time, though this would slow training down considerably given we wouldn't even run the tokenizer in batches.
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Use prefix to allow exceed Windows MAX_PATH
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| 2021-07-09T16:39:53Z
| 2021-07-16T15:28:12Z
| 2021-07-16T15:28:11Z
|
MEMBER
| null | null | null |
By using this prefix, you can exceed the Windows MAX_PATH limit.
See: https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces
Related to #2524, #2220.
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Add speech processing tasks
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| 2021-07-09T16:07:29Z
| 2021-07-12T18:32:59Z
| 2021-07-12T17:32:02Z
|
MEMBER
| null | null | null |
This PR replaces the `automatic-speech-recognition` task category with a broader `speech-processing` category.
The tasks associated with this category are derived from the [SUPERB benchmark](https://arxiv.org/abs/2105.01051), and ASR is included in this set.
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Add ASR task for SUPERB
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| 2021-07-15T08:55:58Z
| 2021-07-13T12:40:18Z
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MEMBER
| null | null | null |
This PR starts building up the SUPERB benchmark by including the ASR task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/v0.2.0/downstream#asr-automatic-speech-recognition).
Usage:
```python
from datasets import load_dataset
asr = load_dataset("superb", "asr")
# DatasetDict({
# train: Dataset({
# features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
# num_rows: 28539
# })
# validation: Dataset({
# features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
# num_rows: 2703
# })
# test: Dataset({
# features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
# num_rows: 2620
# })
# })
```
I've used the GLUE benchmark as a guide for filling out the README.
To move fast during the evaluation PoC I propose to merge one task at a time, so we can continue building the training / evaluation framework in parallel.
Note: codewise this PR is ready for review - I'll add the missing YAML tags once #2620 is merged :)
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MDU6SXNzdWU5NDA4NTI2NDA=
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`filelock.py` Error
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| 2024-06-21T06:14:07Z
| 2023-11-23T19:06:19Z
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## Describe the bug
It seems that the `filelock.py` went error.
```
>>> ds=load_dataset('xsum')
^CTraceback (most recent call last):
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
OSError: [Errno 37] No locks available
```
According to error log, it is OSError, but there is an `except` in the `_acquire` function.
```
def _acquire(self):
open_mode = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
fd = os.open(self._lock_file, open_mode)
except (IOError, OSError):
pass
else:
self._lock_file_fd = fd
return None
```
I don't know why it stucked rather than `pass` directly.
I am not quite familiar with filelock operation, so any help is highly appriciated.
## Steps to reproduce the bug
```python
ds = load_dataset('xsum')
```
## Expected results
A clear and concise description of the expected results.
## Actual results
```
>>> ds=load_dataset('xsum')
^CTraceback (most recent call last):
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
OSError: [Errno 37] No locks available
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 818, in load_dataset
use_auth_token=use_auth_token,
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 470, in prepare_module
with FileLock(lock_path):
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 323, in __enter__
self.acquire()
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 272, in acquire
self._acquire()
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
KeyboardInterrupt
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-135-generic-x86_64-with-debian-buster-sid
- Python version: 3.6.13
- PyArrow version: 4.0.1
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Fix missing EOL issue in to_json for old versions of pandas
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| 2021-07-09T15:05:45Z
| 2021-07-12T14:09:00Z
| 2021-07-09T15:28:33Z
|
MEMBER
| null | null | null |
Some versions of pandas don't add an EOL at the end of the output of `to_json`.
Therefore users could end up having two samples in the same line
Close https://github.com/huggingface/datasets/issues/2615
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Support remote data files
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| 2021-07-09T16:13:41Z
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MEMBER
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Add support for (streaming) remote data files:
```python
data_files = f"https://huggingface.co/datasets/{repo_id}/resolve/main/{relative_file_path}"
ds = load_dataset("json", split="train", data_files=data_files, streaming=True)
```
cc: @thomwolf
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| 2021-07-09T14:02:05Z
| 2021-07-09T15:29:07Z
| 2021-07-09T15:28:33Z
|
CONTRIBUTOR
| null | null |
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## Describe the bug
When exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default
## Steps to reproduce the bug
This what I'm running:
in python:
```
from datasets import load_dataset
ptb = load_dataset("ptb_text_only")
ptb["train"].to_json("ptb.jsonl")
```
then out of python:
```
head -10000 ptb.jsonl
```
## Expected results
Properly separated lines
## Actual results
The last line is a concatenation of two lines
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyArrow version: 4.0.1
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MDExOlB1bGxSZXF1ZXN0Njg2Nzg2NTg3
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|
Convert numpy scalar to python float in Pearsonr output
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| 2021-07-09T13:22:55Z
| 2021-07-12T14:13:02Z
| 2021-07-09T14:04:38Z
|
MEMBER
| null | null | null |
Following of https://github.com/huggingface/datasets/pull/2612
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MDExOlB1bGxSZXF1ZXN0Njg2Nzg0MzY0
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Use ndarray.item instead of ndarray.tolist
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| 2021-07-09T13:19:35Z
| 2021-07-12T14:12:57Z
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|
MEMBER
| null | null | null |
This PR follows up on #2612 to use `numpy.ndarray.item` instead of `numpy.ndarray.tolist` as the latter is somewhat confusing to the developer (even though it works).
Judging from the `numpy` docs, `ndarray.item` is closer to what we want: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html#numpy-ndarray-item
PS. Sorry for the duplicate work here. I should have read the numpy docs more carefully in #2612
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Return Python float instead of numpy.float64 in sklearn metrics
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MEMBER
| null | null | null |
This PR converts the return type of all `sklearn` metrics to be Python `float` instead of `numpy.float64`.
The reason behind this is that our Hub evaluation framework relies on converting benchmark-specific metrics to YAML ([example](https://huggingface.co/datasets/autonlp/autonlp-benchmark-raft-neelalex__raft-test-neelalex__raft-predictions-3/blob/main/README.md#L11)) and the `numpy.float64` format produces garbage like:
```python
import yaml
from datasets import load_metric
metric = load_metric("accuracy")
score = metric.compute(predictions=[0,1], references=[0,1])
print(yaml.dump(score["accuracy"])) # output below
# !!python/object/apply:numpy.core.multiarray.scalar
# - !!python/object/apply:numpy.dtype
# args:
# - f8
# - false
# - true
# state: !!python/tuple
# - 3
# - <
# - null
# - null
# - null
# - -1
# - -1
# - 0
# - !!binary |
# AAAAAAAA8D8=
```
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More consistent naming
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| 2021-07-13T17:13:19Z
| 2021-07-13T16:08:30Z
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COLLABORATOR
| null | null | null |
As per @stas00's suggestion in #2500, this PR inserts a space between the logo and the lib name (`🤗Datasets` -> `🤗 Datasets`) for consistency with the Transformers lib. Additionally, more consistent names are used for Datasets Hub, etc.
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Add missing WikiANN language tags
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MEMBER
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Add missing language tags for WikiANN datasets.
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MDExOlB1bGxSZXF1ZXN0Njg1ODA3MTMz
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Fix potential DuplicatedKeysError
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| 2021-07-08T08:38:04Z
| 2021-07-12T14:13:16Z
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MEMBER
| null | null | null |
Fix potential DiplicatedKeysError by ensuring keys are unique.
We should promote as a good practice, that the keys should be programmatically generated as unique, instead of read from data (which might be not unique).
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MDExOlB1bGxSZXF1ZXN0Njg1MjAwMDYw
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Support streaming JSON files
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MEMBER
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Use open in JSON dataset builder, so that it can be patched with xopen for streaming.
Close #2607.
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Streaming local gzip compressed JSON line files is not working
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| 2021-07-08T16:08:41Z
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MEMBER
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## Describe the bug
Using streaming to iterate on local gzip compressed JSON files raise a file not exist error
## Steps to reproduce the bug
```python
from datasets import load_dataset
streamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)
next(iter(streamed_dataset))
```
## Actual results
```
FileNotFoundError Traceback (most recent call last)
<ipython-input-6-27a664e29784> in <module>
----> 1 next(iter(streamed_dataset))
~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self)
336
337 def __iter__(self):
--> 338 for key, example in self._iter():
339 if self.features:
340 # we encode the example for ClassLabel feature types for example
~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in _iter(self)
333 else:
334 ex_iterable = self._ex_iterable
--> 335 yield from ex_iterable
336
337 def __iter__(self):
~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self)
76
77 def __iter__(self):
---> 78 for key, example in self.generate_examples_fn(**self.kwargs):
79 yield key, example
80
~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in wrapper(**kwargs)
282 def wrapper(**kwargs):
283 python_formatter = PythonFormatter()
--> 284 for key, table in generate_tables_fn(**kwargs):
285 batch = python_formatter.format_batch(table)
286 for i, example in enumerate(_batch_to_examples(batch)):
~/Documents/GitHub/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files, original_files)
85 file,
86 read_options=self.config.pa_read_options,
---> 87 parse_options=self.config.pa_parse_options,
88 )
89 except pa.ArrowInvalid as err:
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json._get_reader()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_input_stream()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_native_file()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile.__cinit__()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile._open_readable()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
FileNotFoundError: [Errno 2] Failed to open local file 'gzip://file-000000000000.json::/Users/thomwolf/github-dataset/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory
```
## Environment info
- `datasets` version: 1.9.1.dev0
- Platform: Darwin-19.6.0-x86_64-i386-64bit
- Python version: 3.7.7
- PyArrow version: 1.0.0
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MDU6SXNzdWU5Mzg3NjM2ODQ=
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[Metrics] addition of wiki_split metrics
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|
CONTRIBUTOR
| null | null |
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**Is your feature request related to a problem? Please describe.**
While training the model on sentence split the task in English we require to evaluate the trained model on `Exact Match`, `SARI` and `BLEU` score
like this
While training we require metrics which can give all the output
Currently, we don't have an exact match for text normalized data
**Describe the solution you'd like**
A custom metrics for wiki_split that can calculate these three values and provide it in the form of a single dictionary
For exact match, we can refer to [this](https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py)
**Describe alternatives you've considered**
Two metrics are already present one more can be added for an exact match then we can run all three metrics in training script
#self-assign
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Make any ClientError trigger retry in streaming mode (e.g. ClientOSError)
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|
MEMBER
| null | null | null |
During the FLAX sprint some users have this error when streaming datasets:
```python
aiohttp.client_exceptions.ClientOSError: [Errno 104] Connection reset by peer
```
This error must trigger a retry instead of directly crashing
Therefore I extended the error type that triggers the retry to be the base aiohttp error type: `ClientError`
In particular both `ClientOSError` and `ServerDisconnectedError` inherit from `ClientError`.
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MDU6SXNzdWU5Mzg2MDIyMzc=
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Add option to delete temporary files (e.g. extracted files) when loading dataset
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| 14
| 2021-07-07T07:56:16Z
| 2021-07-19T09:08:18Z
| 2021-07-19T09:08:18Z
|
MEMBER
| null | null |
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I'm loading a dataset constituted of 44 GB of compressed JSON files.
When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables
Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.
I can maybe tackle this one in the JSON script unless you want a more general solution.
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MDExOlB1bGxSZXF1ZXN0Njg0OTQ0ODcz
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Fix DuplicatedKeysError in omp
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| 2021-07-12T14:10:41Z
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MEMBER
| null | null | null |
Close #2598.
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MDExOlB1bGxSZXF1ZXN0Njg0OTE5MjMy
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Remove import of transformers
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| 2021-07-07T06:58:18Z
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MEMBER
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When pickling a tokenizer within multiprocessing, check that is instance of transformers PreTrainedTokenizerBase without importing transformers.
Related to huggingface/transformers#12549 and #502.
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MDExOlB1bGxSZXF1ZXN0Njg0NTQyNjY5
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Fix `filter` with multiprocessing in case all samples are discarded
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CONTRIBUTOR
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Fixes #2600
Also I moved the check for `num_proc` larger than dataset size added in #2566 up so that multiprocessing is not used with one process.
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Crash when using multiprocessing (`num_proc` > 1) on `filter` and all samples are discarded
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## Describe the bug
If `filter` is applied to a dataset using multiprocessing (`num_proc` > 1) and all sharded datasets are empty afterwards (due to all samples being discarded), the program crashes.
## Steps to reproduce the bug
```python
from datasets import Dataset
data = Dataset.from_dict({'id': [0,1]})
data.filter(lambda x: False, num_proc=2)
```
## Expected results
An empty table should be returned without crashing.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/user/venv/lib/python3.8/site-packages/datasets/fingerprint.py", line 397, in wrapper
out = func(self, *args, **kwargs)
File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2143, in filter
return self.map(
File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1738, in map
result = concatenate_datasets(transformed_shards)
File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3267, in concatenate_datasets
table = concat_tables(tables_to_concat, axis=axis)
File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 853, in concat_tables
return ConcatenationTable.from_tables(tables, axis=axis)
File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 713, in from_tables
blocks = to_blocks(tables[0])
IndexError: list index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.12.11-300.fc34.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.8.10
- PyArrow version: 3.0.0
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CONTRIBUTOR
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Add other supported export formats than CSV in the docs.
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| 2021-07-06T14:00:52Z
| 2021-07-07T12:56:35Z
| 2021-07-07T12:56:35Z
|
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| null | null |
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## Describe the bug
The omp dataset cannot be downloaded because of a DuplicatedKeysError
## Steps to reproduce the bug
from datasets import load_dataset
omp = load_dataset('omp', 'posts_labeled')
print(omp)
## Expected results
This code should download the omp dataset and print the dictionary
## Actual results
Downloading and preparing dataset omp/posts_labeled (download: 1.27 MiB, generated: 13.31 MiB, post-processed: Unknown size, total: 14.58 MiB) to /home/erika_distefano/.cache/huggingface/datasets/omp/posts_labeled/1.1.0/2fe5b067be3bff1d4588d5b0cbb9b5b22ae1b9d5b026a8ff572cd389f862735b...
0 examples [00:00, ? examples/s]2021-07-06 09:43:55.868815: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0
Traceback (most recent call last):
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 990, in _prepare_split
writer.write(example, key)
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 338, in write
self.check_duplicate_keys()
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 3326
Keys should be unique and deterministic in nature
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "hf_datasets.py", line 32, in <module>
omp = load_dataset('omp', 'posts_labeled')
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 992, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 409, in finalize
self.check_duplicate_keys()
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 3326
Keys should be unique and deterministic in nature
## Environment info
- `datasets` version: 1.8.0
- Platform: Ubuntu 18.04.4 LTS
- Python version: 3.6.9
- PyArrow version: 3.0.0
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| 937,917,770
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MDExOlB1bGxSZXF1ZXN0Njg0Mzk0MDIz
| 2,597
|
Remove redundant prepare_module
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| 2021-07-06T13:47:45Z
| 2021-07-12T14:10:52Z
| 2021-07-07T13:01:46Z
|
MEMBER
| null | null | null |
I have noticed that after implementing `load_dataset_builder` (#2500), there is a redundant call to `prepare_module`.
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| 937,598,914
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MDU6SXNzdWU5Mzc1OTg5MTQ=
| 2,596
|
Transformer Class on dataset
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| 2021-07-06T07:27:15Z
| 2022-11-02T14:26:09Z
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|
NONE
| null | null |
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Just wondering if you have intenttion to create
TransformerClass :
dataset --> dataset
and make determnistic transformation (ie not fit).
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|
ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets
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Error traceback:
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-8-a7b592d3bca0> in <module>()
1 from datasets import load_dataset, load_metric
2
----> 3 common_voice_train = load_dataset("common_voice", "pa-IN", split="train+validation")
4 common_voice_test = load_dataset("common_voice", "pa-IN", split="test")
9 frames
/root/.cache/huggingface/modules/datasets_modules/datasets/common_voice/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b/common_voice.py in <module>()
19
20 import datasets
---> 21 from datasets.tasks import AutomaticSpeechRecognition
22
23
ModuleNotFoundError: No module named 'datasets.tasks'
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MDExOlB1bGxSZXF1ZXN0NjgzODc0NjIz
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Fix BibTeX entry
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| 2021-07-05T18:24:10Z
| 2021-07-06T04:59:38Z
| 2021-07-06T04:59:38Z
|
MEMBER
| null | null | null |
Fix BibTeX entry.
|
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| 937,242,137
|
MDExOlB1bGxSZXF1ZXN0NjgzODMwMjcy
| 2,593
|
Support pandas 1.3.0 read_csv
|
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| 2021-07-05T16:40:04Z
| 2021-07-05T17:14:14Z
| 2021-07-05T17:14:14Z
|
MEMBER
| null | null | null |
Workaround for this issue in pandas 1.3.0 : https://github.com/pandas-dev/pandas/issues/42387
The csv reader raises an error:
```python
/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py in _refine_defaults_read(dialect, delimiter, delim_whitespace, engine, sep, error_bad_lines, warn_bad_lines, on_bad_lines, names, prefix, defaults)
1304
1305 if names is not lib.no_default and prefix is not lib.no_default:
-> 1306 raise ValueError("Specified named and prefix; you can only specify one.")
1307
1308 kwds["names"] = None if names is lib.no_default else names
ValueError: Specified named and prefix; you can only specify one.
```
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| 937,060,559
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MDExOlB1bGxSZXF1ZXN0NjgzNjc2MjA4
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|
Add c4.noclean infos
|
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| 2021-07-05T12:51:40Z
| 2021-07-05T13:15:53Z
| 2021-07-05T13:15:52Z
|
MEMBER
| null | null | null |
Adding the data files checksums and the dataset size of the c4.noclean configuration of the C4 dataset
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| 936,957,975
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MDU6SXNzdWU5MzY5NTc5NzU=
| 2,591
|
Cached dataset overflowing disk space
|
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| 2021-07-05T10:43:19Z
| 2021-07-19T09:08:19Z
| 2021-07-19T09:08:19Z
|
CONTRIBUTOR
| null | null |
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I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).
The cache folder is 500gb (and now my disk space is full).
Is there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files).
This might not technically be a bug, but I was unsure and I felt that the bug was the closest one.
Traceback (most recent call last):
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/multiprocess/pool.py", line 121, in worker
result = (True, func(*args, **kwds))
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 186, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/fingerprint.py", line 397, in wrapper
out = func(self, *args, **kwargs)
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1983, in _map_single
writer.finalize()
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_writer.py", line 418, in finalize
self.pa_writer.close()
File "pyarrow/ipc.pxi", line 402, in pyarrow.lib._CRecordBatchWriter.close
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
OSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device
"""
The above exception was the direct cause of the following exception:
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| 936,954,348
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MDExOlB1bGxSZXF1ZXN0NjgzNTg1MDg2
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|
Add language tags
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| 2021-07-05T10:39:57Z
| 2021-07-05T10:58:48Z
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MEMBER
| null | null | null |
This PR adds some missing language tags needed for ASR datasets in #2565
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MDExOlB1bGxSZXF1ZXN0NjgzNDc0OTQ0
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Support multilabel metrics
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| 2021-07-05T08:19:25Z
| 2022-07-29T10:56:25Z
| 2021-07-08T08:40:15Z
|
MEMBER
| null | null | null |
Currently, multilabel metrics are not supported because `predictions` and `references` are defined as `Value("int32")`.
This PR creates a new feature type `OptionalSequence` which can act as either `Value("int32")` or `Sequence(Value("int32"))`, depending on the data passed.
Close #2554.
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Fix test_is_small_dataset
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MEMBER
| null | null | null |
Remove environment variable fixture `env_max_in_memory_dataset_size`. This fixture does not work because env variable is read in datasets.config when first loading datasets, and it is never reread during tests.
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Add aiohttp to tests extras require
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MEMBER
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Currently, none of the streaming tests are runned within our CI test suite, because the streaming tests require aiohttp and this is missing from our tests extras require dependencies.
Our CI test suite should be exhaustive and test all the library functionalities.
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Fix misalignment in SQuAD
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MEMBER
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Fix misalignment between:
- the answer text and
- the answer_start within the context
by keeping original leading blank spaces in the context.
Fix #2585.
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sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index
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| 2021-07-04T15:39:49Z
| 2021-07-07T13:18:51Z
| 2021-07-07T13:18:51Z
|
NONE
| null | null |
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## Describe the bug
The built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start'].
For example:
id = '56d1f453e7d4791d009025bd'
answers = {'text': ['Pure Land'], 'answer_start': [146]}
However the actual text in context at location 146 is 'ure Land,'
Which is an off-by-one error from the correct answer.
## Steps to reproduce the bug
```python
import datasets
def check_context_answer_alignment(example):
for a_idx in range(len(example['answers']['text'])):
# check raw dataset for answer consistency between context and answer
answer_text = example['answers']['text'][a_idx]
a_st_idx = example['answers']['answer_start'][a_idx]
a_end_idx = a_st_idx + len(example['answers']['text'][a_idx])
answer_text_from_context = example['context'][a_st_idx:a_end_idx]
if answer_text != answer_text_from_context:
#print(example['id'])
return False
return True
dataset = datasets.load_dataset('squad_v2', split='train', keep_in_memory=True)
start_len = len(dataset)
dataset = dataset.filter(check_context_answer_alignment,
num_proc=1,
keep_in_memory=True)
end_len = len(dataset)
print('{} instances contain mis-alignment between the answer text and answer index.'.format(start_len - end_len))
```
## Expected results
This code should result in 0 rows being filtered out from the dataset.
## Actual results
This filter command results in 258 rows being flagged as containing a discrepancy between the text contained within answers['text'] and the text in example['context'] at the answers['answer_start'] location.
This code will reproduce the problem and produce the following count:
"258 instances contain mis-alignment between the answer text and answer index."
## Environment info
Steps to rebuilt the Conda environment:
```
# create a virtual environment to stuff all these packages into
conda create -n round8 python=3.8 -y
# activate the virtual environment
conda activate round8
# install pytorch (best done through conda to handle cuda dependencies)
conda install pytorch torchvision torchtext cudatoolkit=11.1 -c pytorch-lts -c nvidia
pip install jsonpickle transformers datasets matplotlib
```
OS: Ubuntu 20.04
Python 3.8
Result of `conda env export`:
```
name: round8
channels:
- pytorch-lts
- nvidia
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=4.5=1_gnu
- blas=1.0=mkl
- brotlipy=0.7.0=py38h27cfd23_1003
- bzip2=1.0.8=h7b6447c_0
- ca-certificates=2021.5.25=h06a4308_1
- certifi=2021.5.30=py38h06a4308_0
- cffi=1.14.5=py38h261ae71_0
- chardet=4.0.0=py38h06a4308_1003
- cryptography=3.4.7=py38hd23ed53_0
- cudatoolkit=11.1.74=h6bb024c_0
- ffmpeg=4.2.2=h20bf706_0
- freetype=2.10.4=h5ab3b9f_0
- gmp=6.2.1=h2531618_2
- gnutls=3.6.15=he1e5248_0
- idna=2.10=pyhd3eb1b0_0
- intel-openmp=2021.2.0=h06a4308_610
- jpeg=9b=h024ee3a_2
- lame=3.100=h7b6447c_0
- lcms2=2.12=h3be6417_0
- ld_impl_linux-64=2.35.1=h7274673_9
- libffi=3.3=he6710b0_2
- libgcc-ng=9.3.0=h5101ec6_17
- libgomp=9.3.0=h5101ec6_17
- libidn2=2.3.1=h27cfd23_0
- libopus=1.3.1=h7b6447c_0
- libpng=1.6.37=hbc83047_0
- libstdcxx-ng=9.3.0=hd4cf53a_17
- libtasn1=4.16.0=h27cfd23_0
- libtiff=4.2.0=h85742a9_0
- libunistring=0.9.10=h27cfd23_0
- libuv=1.40.0=h7b6447c_0
- libvpx=1.7.0=h439df22_0
- libwebp-base=1.2.0=h27cfd23_0
- lz4-c=1.9.3=h2531618_0
- mkl=2021.2.0=h06a4308_296
- mkl-service=2.3.0=py38h27cfd23_1
- mkl_fft=1.3.0=py38h42c9631_2
- mkl_random=1.2.1=py38ha9443f7_2
- ncurses=6.2=he6710b0_1
- nettle=3.7.3=hbbd107a_1
- ninja=1.10.2=hff7bd54_1
- numpy=1.20.2=py38h2d18471_0
- numpy-base=1.20.2=py38hfae3a4d_0
- olefile=0.46=py_0
- openh264=2.1.0=hd408876_0
- openssl=1.1.1k=h27cfd23_0
- pillow=8.2.0=py38he98fc37_0
- pip=21.1.2=py38h06a4308_0
- pycparser=2.20=py_2
- pyopenssl=20.0.1=pyhd3eb1b0_1
- pysocks=1.7.1=py38h06a4308_0
- python=3.8.10=h12debd9_8
- pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0
- readline=8.1=h27cfd23_0
- requests=2.25.1=pyhd3eb1b0_0
- setuptools=52.0.0=py38h06a4308_0
- six=1.16.0=pyhd3eb1b0_0
- sqlite=3.35.4=hdfb4753_0
- tk=8.6.10=hbc83047_0
- torchtext=0.9.1=py38
- torchvision=0.9.1=py38_cu111
- typing_extensions=3.7.4.3=pyha847dfd_0
- urllib3=1.26.4=pyhd3eb1b0_0
- wheel=0.36.2=pyhd3eb1b0_0
- x264=1!157.20191217=h7b6447c_0
- xz=5.2.5=h7b6447c_0
- zlib=1.2.11=h7b6447c_3
- zstd=1.4.9=haebb681_0
- pip:
- click==8.0.1
- cycler==0.10.0
- datasets==1.8.0
- dill==0.3.4
- filelock==3.0.12
- fsspec==2021.6.0
- huggingface-hub==0.0.8
- joblib==1.0.1
- jsonpickle==2.0.0
- kiwisolver==1.3.1
- matplotlib==3.4.2
- multiprocess==0.70.12.2
- packaging==20.9
- pandas==1.2.4
- pyarrow==3.0.0
- pyparsing==2.4.7
- python-dateutil==2.8.1
- pytz==2021.1
- regex==2021.4.4
- sacremoses==0.0.45
- tokenizers==0.10.3
- tqdm==4.49.0
- transformers==4.6.1
- xxhash==2.0.2
prefix: /home/mmajurski/anaconda3/envs/round8
```
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MDExOlB1bGxSZXF1ZXN0NjgyODY2Njc1
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wi_locness: reference latest leaderboard on codalab
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| 2021-07-02T20:26:22Z
| 2021-07-05T09:06:14Z
| 2021-07-05T09:06:14Z
|
CONTRIBUTOR
| null | null | null |
The dataset's author asked me to put this codalab link into the dataset's README.
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MDU6SXNzdWU5MzYwMzQ5NzY=
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Error iteration over IterableDataset using Torch DataLoader
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| 2021-07-02T19:55:58Z
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| 2021-07-05T23:48:23Z
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NONE
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## Describe the bug
I have an IterableDataset (created using streaming=True) and I am trying to create batches using Torch DataLoader class by passing this IterableDataset to it. This throws error which is pasted below. I can do the same by using Torch IterableDataset. One thing I noticed is that in the former case when I look at the dataloader.sampler class I get torch.utils.data.sampler.SequentialSampler while the latter one gives torch.utils.data.dataloader._InfiniteConstantSampler.
I am not sure if this is how it is meant to be used, but that's what seemed reasonable to me.
## Steps to reproduce the bug
1. Does not work.
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
>>> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)
>>> dataloader.sampler
<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>
>>> for batch in dataloader:
... print(batch)
```
2. Works.
```python
import torch
from torch.utils.data import Dataset, IterableDataset, DataLoader
class CustomIterableDataset(IterableDataset):
'Characterizes a dataset for PyTorch'
def __init__(self, data):
'Initialization'
self.data = data
def __iter__(self):
return iter(self.data)
data = list(range(12))
dataset = CustomIterableDataset(data)
dataloader = DataLoader(dataset, batch_size=4)
print("dataloader: ", dataloader.sampler)
for batch in dataloader:
print(batch)
```
## Expected results
To get batches of data with the batch size as 4. Output from the latter one (2) though Datasource is different here so actual data is different.
dataloader: <torch.utils.data.dataloader._InfiniteConstantSampler object at 0x7f1cc29e2c50>
tensor([0, 1, 2, 3])
tensor([4, 5, 6, 7])
tensor([ 8, 9, 10, 11])
## Actual results
<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>
...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
data = self._next_data()
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 474, in _next_data
index = self._next_index() # may raise StopIteration
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 427, in _next_index
return next(self._sampler_iter) # may raise StopIteration
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 227, in __iter__
for idx in self.sampler:
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 67, in __iter__
return iter(range(len(self.data_source)))
TypeError: object of type 'IterableDataset' has no len()
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: '1.8.1.dev0'
- Platform: Linux
- Python version: Python 3.6.8
- PyArrow version: '3.0.0'
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Add skip and take
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| 2021-07-02T15:10:19Z
| 2021-07-05T16:06:40Z
| 2021-07-05T16:06:39Z
|
MEMBER
| null | null | null |
As discussed in https://github.com/huggingface/datasets/pull/2375#discussion_r657084544 I added the `IterableDataset.skip` and `IterableDataset.take` methods that allows to do basic splitting of iterable datasets.
You can create new dataset with the first `n` examples using `IterableDataset.take()`, or you can get a dataset with the rest of the examples by skipping the first `n` examples with `IterableDataset.skip()`
One implementation detail:
Using `take` (or `skip`) prevents future dataset shuffling from shuffling the dataset shards, otherwise the taken examples could come from other shards. In this case it only uses the shuffle buffer.
I would have loved to allow the shards of the taken examples to be shuffled anyway, but since we don't know in advance the length of each shard we don't know what shards to take or skip.
I think this is ok though since users can shuffle before doing take or skip. I mentioned this in the documentation
cc @vblagoje @lewtun
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MDExOlB1bGxSZXF1ZXN0NjgyNjQwMDY4
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Faster search_batch for ElasticsearchIndex due to threading
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| 2021-07-02T13:42:07Z
| 2021-07-12T14:13:46Z
| 2021-07-12T09:52:51Z
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NONE
| null | null | null |
Hey,
I think it makes sense to perform search_batch threaded, so ES can perform search in parallel.
Cheers!
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MDExOlB1bGxSZXF1ZXN0NjgyNjI2MTkz
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Fix Counter import
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| 2021-07-02T13:21:48Z
| 2021-07-02T14:37:47Z
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MEMBER
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Import from `collections` instead of `typing`.
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MDExOlB1bGxSZXF1ZXN0NjgyMzkyNjYx
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Fix BibTeX entry
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| 2021-07-02T07:10:40Z
| 2021-07-02T07:33:44Z
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|
MEMBER
| null | null | null |
Add missing contributor to BibTeX entry.
cc: @abhishekkrthakur @thomwolf
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Support Zstandard compressed files
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| 2021-07-01T20:22:34Z
| 2021-08-11T14:46:24Z
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|
MEMBER
| null | null | null |
Close #2572.
cc: @thomwolf
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Add mC4
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| 2021-07-01T15:51:25Z
| 2021-07-02T14:50:56Z
| 2021-07-02T14:50:55Z
|
MEMBER
| null | null | null |
AllenAI is now hosting the processed C4 and mC4 dataset in this repo: https://huggingface.co/datasets/allenai/c4
Thanks a lot to them !
In this PR I added the mC4 dataset builder. It supports 108 languages
You can load it with
```python
from datasets import load_dataset
en_mc4 = load_dataset("mc4", "en")
fr_mc4 = load_dataset("mc4", "fr")
en_and_fr_mc4 = load_dataset("mc4", languages=["en", "fr"])
```
It also supports streaming, if you don't want to download hundreds of GB of data:
```python
en_mc4 = load_dataset("mc4", "en", streaming=True)
```
Regarding the dataset_infos.json, I will add them once I have them.
Also we can work on the dataset card at that will be at https://huggingface.co/datasets/mc4
For now I just added a link to https://huggingface.co/datasets/allenai/c4 as well as a few sections
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MDExOlB1bGxSZXF1ZXN0NjgxODg0OTgy
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Add C4
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| 2021-07-01T13:58:08Z
| 2021-07-02T14:50:23Z
| 2021-07-02T14:50:23Z
|
MEMBER
| null | null | null |
The old code for the C4 dataset was to generate the C4 with Apache Beam, as in Tensorflow Datasets.
However AllenAI is now hosting the processed C4 dataset in this repo: https://huggingface.co/datasets/allenai/c4
Thanks a lot to them for their amazing work !
In this PR I changed the script to download and prepare the data directly from this repo.
It has 4 variants: en, en.noblocklist, en.noclean, realnewslike
You can load it with
```python
from datasets import load_dataset
c4 = load_dataset("c4", "en")
```
It also supports streaming, if you don't want to download hundreds of GB of data:
```python
c4 = load_dataset("c4", "en", streaming=True)
```
Regarding the dataset_infos.json, I haven't added the infos for en.noclean. I will add them once I have them.
Also we can work on the dataset card at https://huggingface.co/datasets/c4
For now I just added a link to https://huggingface.co/datasets/allenai/c4 as well as a few sections
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MDExOlB1bGxSZXF1ZXN0NjgxNjczMzYy
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Add streaming in load a dataset docs
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| 2021-07-01T09:32:53Z
| 2021-07-01T14:12:22Z
| 2021-07-01T14:12:21Z
|
MEMBER
| null | null | null |
Mention dataset streaming on the "loading a dataset" page of the documentation
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MDU6SXNzdWU5MzQ1ODQ3NDU=
| 2,573
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Finding right block-size with JSON loading difficult for user
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| 2021-07-01T08:48:35Z
| 2021-07-01T19:10:53Z
| null |
MEMBER
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As reported by @thomwolf, while loading a JSON Lines file with "json" loading script, he gets
> json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 383)
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Support Zstandard compressed files
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Add support for Zstandard compressed files: https://facebook.github.io/zstd/
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MDExOlB1bGxSZXF1ZXN0NjgwOTQ2NzQ1
| 2,571
|
Filter expected warning log from transformers
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| 2021-06-30T14:48:19Z
| 2021-07-02T04:08:17Z
| 2021-07-02T04:08:17Z
|
MEMBER
| null | null | null |
Close #2569.
|
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Minor fix docs format for bertscore
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| 2021-06-30T07:42:12Z
| 2021-06-30T15:31:01Z
| 2021-06-30T15:31:01Z
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| null | null | null |
Minor fix docs format for bertscore:
- link to README
- format of KWARGS_DESCRIPTION
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Weights of model checkpoint not initialized for RobertaModel for Bertscore
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| 2021-07-01T07:08:59Z
| 2021-06-30T07:35:49Z
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NONE
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When applying bertscore out of the box,
```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']```
Following the typical usage from https://huggingface.co/docs/datasets/loading_metrics.html
```
from datasets import load_metric
metric = load_metric('bertscore')
# Example of typical usage
for batch in dataset:
inputs, references = batch
predictions = model(inputs)
metric.add_batch(predictions=predictions, references=references)
score = metric.compute(lang="en")
#score = metric.compute(model_type="roberta-large") # gives the same error
```
I am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https://github.com/Tiiiger/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo....
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27
- Python version: 3.9.5
- PyArrow version: 3.0.0
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Add interleave_datasets for map-style datasets
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| 2021-06-29T17:19:24Z
| 2021-07-01T09:33:34Z
| 2021-07-01T09:33:33Z
|
MEMBER
| null | null | null |
### Add interleave_datasets for map-style datasets
Add support for map-style datasets (i.e. `Dataset` objects) in `interleave_datasets`.
It was only supporting iterable datasets (i.e. `IterableDataset` objects).
### Implementation details
It works by concatenating the datasets and then re-order the indices to make the new dataset.
### TODO
- [x] tests
- [x] docs
Close #2563
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Add ASR task and new languages to resources
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| 2021-06-29T17:18:01Z
| 2021-07-01T09:42:23Z
| 2021-07-01T09:42:09Z
|
MEMBER
| null | null | null |
This PR adds a new `automatic-speech-recognition` task to the list of supported tasks in `tasks.json` and also includes a few new languages missing from `common_voice`.
Note: I used the [Papers with Code list](https://www.paperswithcode.com/area/speech/speech-recognition) as inspiration for the ASR subtasks
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fix Dataset.map when num_procs > num rows
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| 2021-07-01T09:11:13Z
| 2021-07-01T09:11:13Z
|
CONTRIBUTOR
| null | null | null |
closes #2470
## Testing notes
To run updated tests:
```sh
pytest tests/test_arrow_dataset.py -k "BaseDatasetTest and test_map_multiprocessing" -s
```
With Python code (to view warning):
```python
from datasets import Dataset
dataset = Dataset.from_dict({"x": ["sample"]})
print(len(dataset))
dataset.map(lambda x: x, num_proc=10)
```
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Inject templates for ASR datasets
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| 2021-07-05T14:26:26Z
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MEMBER
| null | null | null |
This PR adds ASR templates for 5 of the most common speech datasets on the Hub, where "common" is defined by the number of models trained on them.
I also fixed a bunch of the tags in the READMEs 😎
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Currently `concatenate_datasets` only works for map-style `Dataset`.
It would be nice to have it work for `IterableDataset` objects as well.
It would simply chain the iterables of the iterable datasets.
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interleave_datasets for map-style datasets
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MEMBER
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Currently the `interleave_datasets` functions only works for `IterableDataset`.
Let's make it work for map-style `Dataset` objects as well.
It would work the same way: either alternate between the datasets in order or randomly given probabilities specified by the user.
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https://api.github.com/repos/huggingface/datasets/issues/2562
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MDExOlB1bGxSZXF1ZXN0Njc5NjkyMjQ2
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|
Minor fix in loading metrics docs
|
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| 2021-06-29T07:55:11Z
| 2021-06-29T17:21:22Z
| 2021-06-29T17:21:22Z
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MEMBER
| null | null | null |
Make some minor fixes in "Loading metrics" docs.
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MDU6SXNzdWU5MzIzMjE3MjU=
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|
Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`
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| 2022-08-04T11:58:36Z
| 2022-08-04T11:58:36Z
|
CONTRIBUTOR
| null | null |
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## Describe the bug
If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.
## Steps to reproduce the bug
- Create a local dataset builder class
- load the local builder class file using `load_dataset` and let the cache build
- update the file's content
- The cache should rebuilt.
## Expected results
With `ignore_verifications=True`, `load_dataset` should pick up existing cache.
## Actual results
Creates new cache.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.7
- PyArrow version: 3.0.0
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MDExOlB1bGxSZXF1ZXN0Njc5NTMyODk4
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fix Dataset.map when num_procs > num rows
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| 2021-06-29T02:24:11Z
| 2021-06-29T15:00:18Z
| 2021-06-29T14:53:31Z
|
CONTRIBUTOR
| null | null | null |
closes #2470
## Testing notes
To run updated tests:
```sh
pytest tests/test_arrow_dataset.py -k "BaseDatasetTest and test_map_multiprocessing" -s
```
With Python code (to view warning):
```python
from datasets import Dataset
dataset = Dataset.from_dict({"x": ["sample"]})
print(len(dataset))
dataset.map(lambda x: x, num_proc=10)
```
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MDU6SXNzdWU5MzE4NDk3MjQ=
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Memory usage consistently increases when processing a dataset with `.map`
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| 2023-07-20T13:34:10Z
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|
CONTRIBUTOR
| null | null |
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## Describe the bug
I have a HF dataset with image paths stored in it and I am trying to load those image paths using `.map` with `num_proc=80`. I am noticing that the memory usage consistently keeps on increasing with time. I tried using `DEFAULT_WRITER_BATCH_SIZE=10` in the builder to decrease arrow writer's batch size but that doesn't seem to help.
## Steps to reproduce the bug
Providing code as it is would be hard. I can provide a MVP if that helps.
## Expected results
Memory usage should become consistent after some time following the launch of processing.
## Actual results
Memory usage keeps on increasing.
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.7
- PyArrow version: 3.0.0
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Update: WebNLG - update checksums
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| 2021-06-28T16:16:37Z
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MEMBER
| null | null | null |
The master branch changed so I computed the new checksums.
I also pinned a specific revision so that it doesn't happen again in the future.
Fix https://github.com/huggingface/datasets/issues/2553
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MDExOlB1bGxSZXF1ZXN0Njc5MDk4ODg3
| 2,557
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Fix `fever` keys
|
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| 2021-06-28T14:27:02Z
| 2021-06-28T16:11:30Z
| 2021-06-28T16:11:29Z
|
MEMBER
| null | null | null |
The keys has duplicates since they were reset to 0 after each file.
I fixed it by taking into account the file index as well.
|
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Better DuplicateKeysError error to help the user debug the issue
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| 2022-06-28T09:26:04Z
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MEMBER
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As mentioned in https://github.com/huggingface/datasets/issues/2552 it would be nice to improve the error message when a dataset fails to build because there are duplicate example keys.
The current one is
```python
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 48
Keys should be unique and deterministic in nature
```
and we could have something that guides the user to debugging the issue:
```python
DuplicateKeysError: both 42th and 1337th examples have the same keys `48`.
Please fix the dataset script at <path/to/the/dataset/script>
```
|
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MDExOlB1bGxSZXF1ZXN0Njc5MDU4ODM3
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|
Fix code_search_net keys
|
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| 2021-06-28T13:40:23Z
| 2021-09-02T08:24:43Z
| 2021-06-28T14:10:35Z
|
MEMBER
| null | null | null |
There were duplicate keys in the `code_search_net` dataset, as reported in https://github.com/huggingface/datasets/issues/2552
I fixed the keys (it was an addition of the file and row indices, which was causing collisions)
Fix #2552.
|
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|
Multilabel metrics not supported
|
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| 2021-06-28T11:09:46Z
| 2021-10-13T12:29:13Z
| 2021-07-08T08:40:15Z
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NONE
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When I try to use a metric like F1 macro I get the following error:
```
TypeError: int() argument must be a string, a bytes-like object or a number, not 'list'
```
There is an explicit casting here:
https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/src/datasets/features.py#L274
And looks like this is because here
https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/metrics/f1/f1.py#L88
the features can only be integers, so we cannot use that F1 for multilabel. Instead, if I create the following F1 (ints replaced with sequence of ints), it will work:
```python
class F1(datasets.Metric):
def _info(self):
return datasets.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("int32")),
"references": datasets.Sequence(datasets.Value("int32")),
}
),
reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"],
)
def _compute(self, predictions, references, labels=None, pos_label=1, average="binary", sample_weight=None):
return {
"f1": f1_score(
references,
predictions,
labels=labels,
pos_label=pos_label,
average=average,
sample_weight=sample_weight,
),
}
```
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load_dataset("web_nlg") NonMatchingChecksumError
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| 2021-06-28T09:26:46Z
| 2021-06-28T17:23:39Z
| 2021-06-28T17:23:16Z
|
NONE
| null | null |
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Hi! It seems the WebNLG dataset gives a NonMatchingChecksumError.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('web_nlg', name="release_v3.0_en", split="dev")
```
Gives
```
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip']
```
## Environment info
- `datasets` version: 1.8.0
- Platform: macOS-11.3.1-x86_64-i386-64bit
- Python version: 3.9.4
- PyArrow version: 3.0.0
Also tested on Linux, with python 3.6.8
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MDU6SXNzdWU5MzEzNTQ2ODc=
| 2,552
|
Keys should be unique error on code_search_net
|
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| 2021-06-28T09:15:20Z
| 2021-09-06T14:08:30Z
| 2021-09-02T08:25:29Z
|
MEMBER
| null | null |
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## Describe the bug
Loading `code_search_net` seems not possible at the moment.
## Steps to reproduce the bug
```python
>>> load_dataset('code_search_net')
Downloading: 8.50kB [00:00, 3.09MB/s]
Downloading: 19.1kB [00:00, 10.1MB/s]
No config specified, defaulting to: code_search_net/all
Downloading and preparing dataset code_search_net/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to /Users/thomwolf/.cache/huggingface/datasets/code_search_net/all/1.0.0/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...
Traceback (most recent call last):
File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/builder.py", line 1067, in _prepare_split
writer.write(example, key)
File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 343, in write
self.check_duplicate_keys()
File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 354, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 48
Keys should be unique and deterministic in nature
```
## Environment info
- `datasets` version: 1.8.1.dev0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 2.0.0
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| 930,967,978
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MDExOlB1bGxSZXF1ZXN0Njc4NTQzMjg1
| 2,551
|
Fix FileSystems documentation
|
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| 2021-06-27T16:18:42Z
| 2021-06-28T13:09:55Z
| 2021-06-28T13:09:54Z
|
CONTRIBUTOR
| null | null | null |
### What this fixes:
This PR resolves several issues I discovered in the documentation on the `datasets.filesystems` module ([this page](https://huggingface.co/docs/datasets/filesystems.html)).
### What were the issues?
When I originally tried implementing the code examples I faced several bugs attributed to:
- out of date [botocore](https://github.com/boto/botocore) call signatures
- capitalization errors in the `S3FileSystem` class name (written as `S3Filesystem` in one place)
- call signature errors for the `S3FileSystem` class constructor (uses parameter `sessions` instead of `session` in some places) (see [`s3fs`](https://s3fs.readthedocs.io/en/latest/api.html#s3fs.core.S3FileSystem) for where this constructor signature is defined)
### Testing/reviewing notes
Instructions for generating the documentation locally: [here](https://github.com/huggingface/datasets/tree/master/docs#generating-the-documentation).
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MDU6SXNzdWU5MzA5NTEyODc=
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|
Allow for incremental cumulative metric updates in a distributed setup
|
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|
CONTRIBUTOR
| null | null |
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Currently, using a metric allows for one of the following:
- Per example/batch metrics
- Cumulative metrics over the whole data
What I'd like is to have an efficient way to get cumulative metrics over the examples/batches added so far, in order to display it as part of the progress bar during training/evaluation.
Since most metrics are just an average of per-example metrics (which aren't?), an efficient calculation can be done as follows:
`((score_cumulative * n_cumulative) + (score_new * n_new)) / (n_cumulative+ n_new)`
where `n` and `score` refer to number of examples and metric score, `cumulative` refers to the cumulative metric and `new` refers to the addition of new examples.
If you don't want to add this capability in the library, a simple solution exists so users can do it themselves:
It is easy to implement for a single process setup, but in a distributed one there is no way to get the correct `n_new`.
The solution for this is to return the number of examples that was used to compute the metrics in `.compute()` by adding the following line here:
https://github.com/huggingface/datasets/blob/5a3221785311d0ce86c2785b765e86bd6997d516/src/datasets/metric.py#L402-L403
```
output["number_of_examples"] = len(predictions)
```
and also remove the log message here so it won't spam:
https://github.com/huggingface/datasets/blob/3db67f5ff6cbf807b129d2b4d1107af27623b608/src/datasets/metric.py#L411
If this change is ok with you, I'll open a pull request.
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Handling unlabeled datasets
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| 2021-06-25T21:07:57Z
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NONE
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Hi!
Is there a way for datasets to produce unlabeled instances (e.g., the `ClassLabel` can be nullable).
For example, I want to use the MNLI dataset reader ( https://github.com/huggingface/datasets/blob/master/datasets/multi_nli/multi_nli.py ) on a file that doesn't have the `gold_label` field. I tried setting `"label": data.get("gold_label")`, but got the following error:
```
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 989, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 953, in encode_example
return encode_nested_example(self, example)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 848, in encode_nested_example
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 848, in <dictcomp>
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 875, in encode_nested_example
return schema.encode_example(obj)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 653, in encode_example
if not -1 <= example_data < self.num_classes:
TypeError: '<=' not supported between instances of 'int' and 'NoneType'
```
What's the proper way to handle reading unlabeled datasets, especially for downstream usage with Transformers?
|
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| 929,232,831
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MDU6SXNzdWU5MjkyMzI4MzE=
| 2,548
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Field order issue in loading json
|
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## Describe the bug
The `load_dataset` function expects columns in alphabetical order when loading json files.
Similar bug was previously reported for csv in #623 and fixed in #684.
## Steps to reproduce the bug
For a json file `j.json`,
```
{"c":321, "a": 1, "b": 2}
```
Running the following,
```
f= datasets.Features({'a': Value('int32'), 'b': Value('int32'), 'c': Value('int32')})
json_data = datasets.load_dataset('json', data_files='j.json', features=f)
```
## Expected results
A successful load.
## Actual results
```
File "pyarrow/table.pxi", line 1409, in pyarrow.lib.Table.cast
ValueError: Target schema's field names are not matching the table's field names: ['c', 'a', 'b'], ['a', 'b', 'c']
```
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 3.0.0
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MDU6SXNzdWU5MjkxOTIzMjk=
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Dataset load_from_disk is too slow
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@lhoestq
## Describe the bug
It's not normal that I have to wait 7-8 hours for a dataset to be loaded from disk, as there are no preprocessing steps, it's only loading it with load_from_disk. I have 96 cpus, however only 1 is used for this, which is inefficient. Moreover, its usage is at 1%... This is happening in the context of a language model training, therefore I'm wasting 100$ each time I have to load the dataset from disk again (because the spot instance was stopped by aws and I need to relaunch it for example).
## Steps to reproduce the bug
Just get the oscar in spanish (around 150GGB) and try to first save in disk and then load the processed dataset. It's not dependent on the task you're doing, it just depends on the size of the text dataset.
## Expected results
I expect the dataset to be loaded in a normal time, by using the whole machine for loading it, I mean if you store the dataset in multiple files (.arrow) and then load it from multiple files, you can use multiprocessing for that and therefore don't waste so much time.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Ubuntu 18
- Python version: 3.8
I've seen you're planning to include a streaming mode for load_dataset, but that only saves the downloading and processing time, that's not being a problem for me, you cannot save the pure loading from disk time, therefore that's not a solution for my use case or for anyone who wants to use your library for training a language model.
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Add license to the Cambridge English Write & Improve + LOCNESS dataset card
|
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| 2021-06-24T10:39:29Z
| 2021-06-24T10:52:01Z
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|
MEMBER
| null | null | null |
As noticed in https://github.com/huggingface/datasets/pull/2539, the licensing information was missing for this dataset.
I added it and I also filled a few other empty sections.
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MDExOlB1bGxSZXF1ZXN0Njc2OTMxOTYw
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Fix DuplicatedKeysError in drop dataset
|
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| 2021-06-24T09:10:39Z
| 2021-06-24T14:57:08Z
| 2021-06-24T14:57:08Z
|
MEMBER
| null | null | null |
Close #2542.
cc: @VictorSanh.
|
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MDExOlB1bGxSZXF1ZXN0Njc2ODM1MjYz
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|
Fix logging levels
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"user_view_type": "public"
}
|
[] |
closed
| false
| null |
[] | null | 0
| 2021-06-24T06:41:36Z
| 2021-06-25T13:40:19Z
| 2021-06-25T13:40:19Z
|
MEMBER
| null | null | null |
Sometimes default `datasets` logging can be too verbose. One approach could be reducing some logging levels, from info to debug, or from warning to info.
Close #2543.
cc: @stas00
|
{
"avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4",
"events_url": "https://api.github.com/users/albertvillanova/events{/privacy}",
"followers_url": "https://api.github.com/users/albertvillanova/followers",
"following_url": "https://api.github.com/users/albertvillanova/following{/other_user}",
"gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}",
"gravatar_id": "",
"html_url": "https://github.com/albertvillanova",
"id": 8515462,
"login": "albertvillanova",
"node_id": "MDQ6VXNlcjg1MTU0NjI=",
"organizations_url": "https://api.github.com/users/albertvillanova/orgs",
"received_events_url": "https://api.github.com/users/albertvillanova/received_events",
"repos_url": "https://api.github.com/users/albertvillanova/repos",
"site_admin": false,
"starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions",
"type": "User",
"url": "https://api.github.com/users/albertvillanova",
"user_view_type": "public"
}
|
{
"+1": 0,
"-1": 0,
"confused": 0,
"eyes": 0,
"heart": 0,
"hooray": 0,
"laugh": 0,
"rocket": 0,
"total_count": 0,
"url": "https://api.github.com/repos/huggingface/datasets/issues/2544/reactions"
}
|
https://api.github.com/repos/huggingface/datasets/issues/2544/timeline
| null | null | false
|
{
"diff_url": "https://github.com/huggingface/datasets/pull/2544.diff",
"html_url": "https://github.com/huggingface/datasets/pull/2544",
"merged_at": "2021-06-25T13:40:19Z",
"patch_url": "https://github.com/huggingface/datasets/pull/2544.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/2544"
}
| true
| 1
|
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