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https://api.github.com/repos/huggingface/datasets/issues/2661 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2661/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2661/comments | https://api.github.com/repos/huggingface/datasets/issues/2661/events | https://github.com/huggingface/datasets/pull/2661 | 946,446,967 | MDExOlB1bGxSZXF1ZXN0NjkxNjE5MzAz | 2,661 | Add SD task for SUPERB | {
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"I make a summary about our discussion with @lewtun and @Narsil on the agreed schema for this dataset and the additional steps required to generate the 2D array labels:\r\n- The labels for this dataset are a 2D array:\r\n Given an example:\r\n ```python\r\n {\"record_id\": record_id, \"file\": file, \"start\": s... | 1,626,453,801,000 | 1,628,096,633,000 | 1,628,096,633,000 | MEMBER | null | Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization).
TODO:
- [x] Generate the LibriMix corpus
- [x] Prepare the corpus for diarization
- [x] Upload these files to the superb-data repo
- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script
- [x] README: tags + description sections
- ~~Add DER metric~~ (we leave the DER metric for a follow-up PR)
Related to #2619.
Close #2653.
cc: @lewtun | {
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https://api.github.com/repos/huggingface/datasets/issues/2660 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2660/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2660/comments | https://api.github.com/repos/huggingface/datasets/issues/2660/events | https://github.com/huggingface/datasets/pull/2660 | 946,316,180 | MDExOlB1bGxSZXF1ZXN0NjkxNTA4NzE0 | 2,660 | Move checks from _map_single to map | {
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"@lhoestq This one has been open for a while. Could you please take a look?",
"@lhoestq Ready for the final review!",
"I forgot to update the signature of `DatasetDict.map`, so did that now."
] | 1,626,443,613,000 | 1,630,937,543,000 | 1,630,937,543,000 | CONTRIBUTOR | null | The goal of this PR is to remove duplicated checks in the `map` logic to execute them only once whenever possible (`fn_kwargs`, `input_columns`, ...). Additionally, this PR improves the consistency (to align it with `input_columns`) of the `remove_columns` check by adding support for a single string value, which is then wrapped into a list. | {
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https://api.github.com/repos/huggingface/datasets/issues/2659 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2659/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2659/comments | https://api.github.com/repos/huggingface/datasets/issues/2659/events | https://github.com/huggingface/datasets/pull/2659 | 946,155,407 | MDExOlB1bGxSZXF1ZXN0NjkxMzcwNzU3 | 2,659 | Allow dataset config kwargs to be None | {
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The dataset config kwargs that were set to None we simply ignored.
This was an issue when None has some meaning for certain parameters of certain builders, like the `sep` parameter of the "csv" builder that allows to infer to separator.
cc @SBrandeis | {
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Related to https://github.com/huggingface/datasets/pull/2656
cc @SBrandeis | {
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"This is not the default in pandas right ?\r\nWe try to align our CSV loader with the pandas API.\r\n\r\nMoreover according to their documentation, the python parser is used when sep is None, which might not be the fastest one.\r\n\r\nMaybe users could just specify `sep=None` themselves ?\r\nIn this case we should ... | 1,626,358,146,000 | 1,626,431,006,000 | 1,626,431,006,000 | CONTRIBUTOR | null | Passing `sep=None` to pandas's `read_csv` lets pandas guess the CSV file's separator
This PR allows users to use this pandas's feature by passing `sep=None` to `Dataset.from_csv`:
```python
Dataset.from_csv(
...,
sep=None
)
``` | {
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"Note that this subset requires us to:\r\n\r\n* generate the LibriMix corpus from LibriSpeech\r\n* prepare the corpus for diarization\r\n\r\nAs suggested by @lhoestq we should perform these steps locally and add the prepared data to this public repo on the Hub: https://huggingface.co/datasets/superb/superb-data\r\n... | 1,626,335,500,000 | 1,628,096,632,000 | 1,628,096,632,000 | MEMBER | null | Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization).
Steps:
- [x] Generate the LibriMix corpus
- [x] Prepare the corpus for diarization
- [x] Upload these files to the superb-data repo
- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script
- [ ] README: tags + description sections
Related to #2619.
cc: @lewtun
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https://api.github.com/repos/huggingface/datasets/issues/2652 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2652/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2652/comments | https://api.github.com/repos/huggingface/datasets/issues/2652/events | https://github.com/huggingface/datasets/pull/2652 | 944,865,924 | MDExOlB1bGxSZXF1ZXN0NjkwMjg0MTI4 | 2,652 | Fix logging docstring | {
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} | [] | closed | false | null | [] | null | [] | 1,626,304,798,000 | 1,626,608,466,000 | 1,626,343,051,000 | CONTRIBUTOR | null | Remove "no tqdm bars" from the docstring in the logging module to align it with the changes introduced in #2534. | {
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https://api.github.com/repos/huggingface/datasets/issues/2651 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2651/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2651/comments | https://api.github.com/repos/huggingface/datasets/issues/2651/events | https://github.com/huggingface/datasets/issues/2651 | 944,796,961 | MDU6SXNzdWU5NDQ3OTY5NjE= | 2,651 | Setting log level higher than warning does not suppress progress bar | {
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"Hi,\r\n\r\nyou can suppress progress bars by patching logging as follows:\r\n```python\r\nimport datasets\r\nimport logging\r\ndatasets.logging.get_verbosity = lambda: logging.NOTSET\r\n# map call ...\r\n```",
"Thank you, it worked :)",
"See https://github.com/huggingface/datasets/issues/2528 for reference",
... | 1,626,296,811,000 | 1,627,045,390,000 | 1,626,320,495,000 | NONE | null | ## Describe the bug
I would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well).
According to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0.
I also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work.
## Steps to reproduce the bug
```python
import datasets
from datasets.utils.logging import set_verbosity_error
set_verbosity_error()
def dummy_map(batch):
return batch
common_voice_train = datasets.load_dataset("common_voice", "de", split="train")
common_voice_test = datasets.load_dataset("common_voice", "de", split="test")
common_voice_train.map(dummy_map)
```
## Expected results
- The progress bar for `.map` call won't be shown
## Actual results
- The progress bar for `.map` is still shown
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.5
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2645 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2645/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2645/comments | https://api.github.com/repos/huggingface/datasets/issues/2645/events | https://github.com/huggingface/datasets/issues/2645 | 944,374,284 | MDU6SXNzdWU5NDQzNzQyODQ= | 2,645 | load_dataset processing failed with OS error after downloading a dataset | {
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"Hi ! It looks like an issue with pytorch.\r\n\r\nCould you try to run `import torch` and see if it raises an error ?",
"> Hi ! It looks like an issue with pytorch.\r\n> \r\n> Could you try to run `import torch` and see if it raises an error ?\r\n\r\nIt works. Thank you!"
] | 1,626,265,433,000 | 1,626,341,642,000 | 1,626,341,642,000 | NONE | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/2644 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2644/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2644/comments | https://api.github.com/repos/huggingface/datasets/issues/2644/events | https://github.com/huggingface/datasets/issues/2644 | 944,254,748 | MDU6SXNzdWU5NDQyNTQ3NDg= | 2,644 | Batched `map` not allowed to return 0 items | {
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"Hi ! Thanks for reporting. Indeed it looks like type inference makes it fail. We should probably just ignore this step until a non-empty batch is passed.",
"Sounds good! Do you want me to propose a PR? I'm quite busy right now, but if it's not too urgent I could take a look next week.",
"Sure if you're interes... | 1,626,256,699,000 | 1,627,311,315,000 | 1,627,311,315,000 | CONTRIBUTOR | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/2640 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2640/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2640/comments | https://api.github.com/repos/huggingface/datasets/issues/2640/events | https://github.com/huggingface/datasets/pull/2640 | 943,591,055 | MDExOlB1bGxSZXF1ZXN0Njg5MjAxMDkw | 2,640 | Fix docstrings | {
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https://api.github.com/repos/huggingface/datasets/issues/2639 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2639/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2639/comments | https://api.github.com/repos/huggingface/datasets/issues/2639/events | https://github.com/huggingface/datasets/pull/2639 | 943,527,463 | MDExOlB1bGxSZXF1ZXN0Njg5MTQ3NDE5 | 2,639 | Refactor patching to specific submodule | {
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In relation with the initial approach followed in #2631. | {
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"A note is that I think we should add a few indicator of status (as mentioned by @stas00 in #2649), probably at the (1) downloading, (2) extracting and (3) reading steps. In particular when loading many very large files it's interesting to know a bit where we are in the process.",
"I tested locally, and the built... | 1,626,187,026,000 | 1,626,451,172,000 | 1,626,451,171,000 | MEMBER | null | 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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https://api.github.com/repos/huggingface/datasets/issues/2636 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2636/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2636/comments | https://api.github.com/repos/huggingface/datasets/issues/2636/events | https://github.com/huggingface/datasets/pull/2636 | 943,044,514 | MDExOlB1bGxSZXF1ZXN0Njg4NzEyMTY4 | 2,636 | Streaming for the Pandas loader | {
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Indeed, when streaming, open is extended to support reading from remote files and handles authentication to the HF Hub | {
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Indeed, when streaming, `open` is extended to support reading from remote file progressively. | {
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cc: @lewtun | {
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https://api.github.com/repos/huggingface/datasets/issues/2632 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2632/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2632/comments | https://api.github.com/repos/huggingface/datasets/issues/2632/events | https://github.com/huggingface/datasets/pull/2632 | 942,293,727 | MDExOlB1bGxSZXF1ZXN0Njg4MDQyMjcw | 2,632 | add image-classification task template | {
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"Awesome!",
"Thanks for adding a new task template - great work @nateraw 🚀 !"
] | 1,626,111,663,000 | 1,626,191,068,000 | 1,626,190,096,000 | CONTRIBUTOR | 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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https://api.github.com/repos/huggingface/datasets/issues/2631 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2631/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2631/comments | https://api.github.com/repos/huggingface/datasets/issues/2631/events | https://github.com/huggingface/datasets/pull/2631 | 942,242,271 | MDExOlB1bGxSZXF1ZXN0Njg3OTk3MzM2 | 2,631 | Delete extracted files when loading dataset | {
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"Sure @stas00, it is still a draft pull request. :)",
"Yes, I noticed it after reviewing - my apologies.",
"The problem with this approach is that it also deletes the downloaded files (if they need not be extracted). 😟 ",
"> The problem with this approach is that it also deletes the downloaded files (if they... | 1,626,107,973,000 | 1,626,685,699,000 | 1,626,685,699,000 | MEMBER | null | Close #2481, close #2604, close #2591.
cc: @stas00, @thomwolf, @BirgerMoell | {
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"This is so cool, let us know if we can help with anything on the hub side (@Pierrci @elishowk) 🎉 "
] | 1,626,079,517,000 | 1,629,901,088,000 | 1,629,901,088,000 | MEMBER | null | 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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Related to #2616. | {
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https://api.github.com/repos/huggingface/datasets/issues/2624 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2624/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2624/comments | https://api.github.com/repos/huggingface/datasets/issues/2624/events | https://github.com/huggingface/datasets/issues/2624 | 941,318,247 | MDU6SXNzdWU5NDEzMTgyNDc= | 2,624 | can't set verbosity for `metric.py` | {
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"Thanks @thomas-happify for reporting and thanks @mariosasko for the fix."
] | 1,625,948,625,000 | 1,626,069,269,000 | 1,626,069,269,000 | NONE | null | ## 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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"Looks all good to me thanks :)\r\nJust did some minor corrections in the docstring"
] | 1,625,928,710,000 | 1,626,272,893,000 | 1,626,129,271,000 | CONTRIBUTOR | null | Fixes: #2606
This pull request adds combine metrics for the wikisplit or English sentence split task
Reviewer: @patrickvonplaten | {
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https://api.github.com/repos/huggingface/datasets/issues/2621 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2621/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2621/comments | https://api.github.com/repos/huggingface/datasets/issues/2621/events | https://github.com/huggingface/datasets/pull/2621 | 940,916,446 | MDExOlB1bGxSZXF1ZXN0Njg2OTE1Mzcw | 2,621 | Use prefix to allow exceed Windows MAX_PATH | {
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"Does this mean the `FileNotFoundError` that avoids infinite loop can be removed?",
"Yes, I think so...",
"Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?",
" > Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?\r\n\r\nWhat about converting relative p... | 1,625,848,793,000 | 1,626,449,292,000 | 1,626,449,291,000 | MEMBER | 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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"Are there any `task_categories:automatic-speech-recognition` dataset for which we should update the tags ?",
"> Are there any `task_categories:automatic-speech-recognition` dataset for which we should update the tags ?\r\n\r\nYes there's a few - I'll fix them tomorrow :)"
] | 1,625,846,849,000 | 1,626,114,779,000 | 1,626,111,122,000 | MEMBER | 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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"Wait until #2620 is merged before pushing the README tags in this PR",
"> Thanks!\r\n> \r\n> One question: aren't you adding `task_templates` to the `_info` method (and to the `dataset_infos.json`?\r\n\r\ngreat catch! i've now added the asr task template (along with a mapping from superb task -> template) and up... | 1,625,843,985,000 | 1,626,339,358,000 | 1,626,180,018,000 | MEMBER | 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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Therefore users could end up having two samples in the same line
Close https://github.com/huggingface/datasets/issues/2615 | {
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"@lhoestq maybe we could also use (if available) the ETag of the remote file in `create_config_id`?",
"> @lhoestq maybe we could also use (if available) the ETag of the remote file in `create_config_id`?\r\n\r\nSure ! We can get the ETag with\r\n```python\r\nheaders = get_authentication_headers_for_url(url, use_a... | 1,625,839,658,000 | 1,625,847,221,000 | 1,625,847,221,000 | MEMBER | null | 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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"Thanks for reporting @TevenLeScao! I'm having a look...",
"(not sure what just happened on the assignations sorry)",
"For some reason this happens (both `datasets` version are on master) only on Python 3.6 and not Python 3.8.",
"@TevenLeScao we are using `pandas` to serialize the dataset to JSON Lines. So it... | 1,625,839,325,000 | 1,625,844,547,000 | 1,625,844,513,000 | MEMBER | null | ## 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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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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"I opened an issue on the `sklearn` repo to understand why `numpy.float64` is the default: https://github.com/scikit-learn/scikit-learn/discussions/20490",
"It could be surprising at first to use `tolist()` on numpy scalars but it works ^^",
"did the same for Pearsonr here: https://github.com/huggingface/datase... | 1,625,824,089,000 | 1,626,099,173,000 | 1,625,835,834,000 | MEMBER | 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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"Finally, I'm splitting this PR."
] | 1,625,733,484,000 | 1,626,099,196,000 | 1,625,848,928,000 | MEMBER | 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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Close #2607. | {
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"Updating to pyarrow-4.0.1 didn't fix the issue",
"Here is an exemple dataset with 2 of these compressed JSON files: https://huggingface.co/datasets/thomwolf/github-python",
"Hi @thomwolf, thanks for reporting.\r\n\r\nIt seems this might be due to the fact that the JSON Dataset builder uses `pyarrow.json` (`paj... | 1,625,657,793,000 | 1,626,774,619,000 | 1,625,760,521,000 | MEMBER | null | ## 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
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"#take"
] | 1,625,655,364,000 | 1,626,129,271,000 | 1,626,129,271,000 | CONTRIBUTOR | null | **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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```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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"Hi !\r\nIf we want something more general, we could either\r\n1. delete the extracted files after the arrow data generation automatically, or \r\n2. delete each extracted file during the arrow generation right after it has been closed.\r\n\r\nSolution 2 is better to save disk space during the arrow generation. Is ... | 1,625,644,576,000 | 1,626,685,698,000 | 1,626,685,698,000 | MEMBER | null | 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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https://api.github.com/repos/huggingface/datasets/issues/2602 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2602/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2602/comments | https://api.github.com/repos/huggingface/datasets/issues/2602/events | https://github.com/huggingface/datasets/pull/2602 | 938,555,712 | MDExOlB1bGxSZXF1ZXN0Njg0OTE5MjMy | 2,602 | Remove import of transformers | {
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Related to huggingface/transformers#12549 and #502. | {
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https://api.github.com/repos/huggingface/datasets/issues/2601 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2601/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2601/comments | https://api.github.com/repos/huggingface/datasets/issues/2601/events | https://github.com/huggingface/datasets/pull/2601 | 938,096,396 | MDExOlB1bGxSZXF1ZXN0Njg0NTQyNjY5 | 2,601 | Fix `filter` with multiprocessing in case all samples are discarded | {
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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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] | closed | false | null | [] | null | [] | 1,625,590,405,000 | 1,625,662,231,000 | 1,625,662,231,000 | CONTRIBUTOR | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/2599 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2599/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2599/comments | https://api.github.com/repos/huggingface/datasets/issues/2599/events | https://github.com/huggingface/datasets/pull/2599 | 937,980,229 | MDExOlB1bGxSZXF1ZXN0Njg0NDQ2MTYx | 2,599 | Update processing.rst with other export formats | {
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https://api.github.com/repos/huggingface/datasets/issues/2598 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2598/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2598/comments | https://api.github.com/repos/huggingface/datasets/issues/2598/events | https://github.com/huggingface/datasets/issues/2598 | 937,930,632 | MDU6SXNzdWU5Mzc5MzA2MzI= | 2,598 | Unable to download omp dataset | {
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"Hi @erikadistefano , thanks for reporting the issue.\r\n\r\nI have created a Pull Request that should fix it. \r\n\r\nOnce merged into master, feel free to update your installed `datasets` library (either by installing it from our GitHub master branch or waiting until our next release) to be able to load omp datas... | 1,625,580,052,000 | 1,625,662,595,000 | 1,625,662,595,000 | NONE | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/2597 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2597/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2597/comments | https://api.github.com/repos/huggingface/datasets/issues/2597/events | https://github.com/huggingface/datasets/pull/2597 | 937,917,770 | MDExOlB1bGxSZXF1ZXN0Njg0Mzk0MDIz | 2,597 | Remove redundant prepare_module | {
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https://api.github.com/repos/huggingface/datasets/issues/2595 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2595/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2595/comments | https://api.github.com/repos/huggingface/datasets/issues/2595/events | https://github.com/huggingface/datasets/issues/2595 | 937,483,120 | MDU6SXNzdWU5Mzc0ODMxMjA= | 2,595 | ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets | {
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"Hi @profsatwinder.\r\n\r\nIt looks like you are using an old version of `datasets`. Please update it with `pip install -U datasets` and indicate if the problem persists.",
"@albertvillanova Thanks for the information. I updated it to 1.9.0 and the issue is resolved. Thanks again. "
] | 1,625,541,655,000 | 1,625,551,189,000 | 1,625,551,189,000 | NONE | null | 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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https://api.github.com/repos/huggingface/datasets/issues/2594 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2594/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2594/comments | https://api.github.com/repos/huggingface/datasets/issues/2594/events | https://github.com/huggingface/datasets/pull/2594 | 937,294,772 | MDExOlB1bGxSZXF1ZXN0NjgzODc0NjIz | 2,594 | Fix BibTeX entry | {
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https://api.github.com/repos/huggingface/datasets/issues/2593 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2593/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2593/comments | https://api.github.com/repos/huggingface/datasets/issues/2593/events | https://github.com/huggingface/datasets/pull/2593 | 937,242,137 | MDExOlB1bGxSZXF1ZXN0NjgzODMwMjcy | 2,593 | Support pandas 1.3.0 read_csv | {
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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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https://api.github.com/repos/huggingface/datasets/issues/2592 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2592/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2592/comments | https://api.github.com/repos/huggingface/datasets/issues/2592/events | https://github.com/huggingface/datasets/pull/2592 | 937,060,559 | MDExOlB1bGxSZXF1ZXN0NjgzNjc2MjA4 | 2,592 | Add c4.noclean infos | {
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"Hi! I'm transferring this issue over to `datasets`",
"I'm using the datasets concatenate dataset to combine the datasets and then train.\r\ntrain_dataset = concatenate_datasets([dataset1, dataset2, common_voice_train])\r\n\r\n",
"Hi @BirgerMoell.\r\n\r\nYou have several options:\r\n- to set caching to be store... | 1,625,481,799,000 | 1,626,685,699,000 | 1,626,685,699,000 | CONTRIBUTOR | null | 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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"Hi ! Thanks for the fix :)\r\n\r\nIf I understand correctly, `OptionalSequence` doesn't have an associated arrow type that we know in advance unlike the other feature types, because it depends on the type of the examples.\r\n\r\nFor example, I tested this and it raises an error:\r\n```python\r\nimport datasets as ... | 1,625,473,165,000 | 1,626,099,130,000 | 1,625,733,615,000 | MEMBER | 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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Our CI test suite should be exhaustive and test all the library functionalities. | {
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- 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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https://api.github.com/repos/huggingface/datasets/issues/2585 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2585/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2585/comments | https://api.github.com/repos/huggingface/datasets/issues/2585/events | https://github.com/huggingface/datasets/issues/2585 | 936,484,419 | MDU6SXNzdWU5MzY0ODQ0MTk= | 2,585 | sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index | {
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"Hi @mmajurski, thanks for reporting this issue.\r\n\r\nIndeed this misalignment arises because the source dataset context field contains leading blank spaces (and these are counted within the answer_start), while our datasets loading script removes these leading blank spaces.\r\n\r\nI'm going to fix our script so ... | 1,625,413,189,000 | 1,625,663,931,000 | 1,625,663,931,000 | NONE | null | ## 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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"Hi ! This is because you first need to format the dataset for pytorch:\r\n\r\n```python\r\n>>> import torch\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n>>> torch_iterable_dataset = dataset.with_format(\"torch\")\r... | 1,625,255,758,000 | 1,626,771,885,000 | 1,625,528,903,000 | NONE | null | ## 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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"@lhoestq looks good. I tried with https://huggingface.co/datasets/vblagoje/wikipedia_snippets_streamed and it worked nicely. I would add more unit tests for edge cases. What happens if the n is larger than the total number of samples? Just to make sure these cases are handled properly. ",
"Yup I'll add the tests... | 1,625,238,619,000 | 1,625,501,200,000 | 1,625,501,199,000 | MEMBER | 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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https://api.github.com/repos/huggingface/datasets/issues/2581 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2581/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2581/comments | https://api.github.com/repos/huggingface/datasets/issues/2581/events | https://github.com/huggingface/datasets/pull/2581 | 935,783,588 | MDExOlB1bGxSZXF1ZXN0NjgyNjQwMDY4 | 2,581 | Faster search_batch for ElasticsearchIndex due to threading | {
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} | [] | 1,625,233,327,000 | 1,626,099,226,000 | 1,626,083,571,000 | CONTRIBUTOR | null | Hey,
I think it makes sense to perform search_batch threaded, so ES can perform search in parallel.
Cheers! | {
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https://api.github.com/repos/huggingface/datasets/issues/2580 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2580/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2580/comments | https://api.github.com/repos/huggingface/datasets/issues/2580/events | https://github.com/huggingface/datasets/pull/2580 | 935,767,421 | MDExOlB1bGxSZXF1ZXN0NjgyNjI2MTkz | 2,580 | Fix Counter import | {
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https://api.github.com/repos/huggingface/datasets/issues/2579 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2579/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2579/comments | https://api.github.com/repos/huggingface/datasets/issues/2579/events | https://github.com/huggingface/datasets/pull/2579 | 935,486,894 | MDExOlB1bGxSZXF1ZXN0NjgyMzkyNjYx | 2,579 | Fix BibTeX entry | {
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cc: @abhishekkrthakur @thomwolf | {
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https://api.github.com/repos/huggingface/datasets/issues/2578 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2578/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2578/comments | https://api.github.com/repos/huggingface/datasets/issues/2578/events | https://github.com/huggingface/datasets/pull/2578 | 935,187,497 | MDExOlB1bGxSZXF1ZXN0NjgyMTQ0OTY2 | 2,578 | Support Zstandard compressed files | {
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"> What if people want to run some tests without having zstandard ?\r\n> Usually what we do is add a decorator @require_zstandard for example\r\n\r\n@lhoestq I think I'm missing something here...\r\n\r\nTests are a *development* tool (to ensure we deliver a good quality lib), not something we offer to the end users... | 1,625,170,954,000 | 1,628,693,184,000 | 1,625,482,227,000 | MEMBER | null | Close #2572.
cc: @thomwolf | {
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https://api.github.com/repos/huggingface/datasets/issues/2576 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2576/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2576/comments | https://api.github.com/repos/huggingface/datasets/issues/2576/events | https://github.com/huggingface/datasets/pull/2576 | 934,986,761 | MDExOlB1bGxSZXF1ZXN0NjgxOTc5MTA1 | 2,576 | Add mC4 | {
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} | [] | closed | false | null | [] | null | [] | 1,625,154,685,000 | 1,625,237,456,000 | 1,625,237,455,000 | MEMBER | 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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https://api.github.com/repos/huggingface/datasets/issues/2575 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2575/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2575/comments | https://api.github.com/repos/huggingface/datasets/issues/2575/events | https://github.com/huggingface/datasets/pull/2575 | 934,876,496 | MDExOlB1bGxSZXF1ZXN0NjgxODg0OTgy | 2,575 | Add C4 | {
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} | [] | closed | false | null | [] | null | [] | 1,625,147,888,000 | 1,625,237,423,000 | 1,625,237,423,000 | MEMBER | 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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"I think the failing test has nothing to do with my PR..."
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https://api.github.com/repos/huggingface/datasets/issues/2570 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2570/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2570/comments | https://api.github.com/repos/huggingface/datasets/issues/2570/events | https://github.com/huggingface/datasets/pull/2570 | 933,402,521 | MDExOlB1bGxSZXF1ZXN0NjgwNjEzNzc0 | 2,570 | Minor fix docs format for bertscore | {
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- link to README
- format of KWARGS_DESCRIPTION | {
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https://api.github.com/repos/huggingface/datasets/issues/2569 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2569/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2569/comments | https://api.github.com/repos/huggingface/datasets/issues/2569/events | https://github.com/huggingface/datasets/issues/2569 | 933,015,797 | MDU6SXNzdWU5MzMwMTU3OTc= | 2,569 | Weights of model checkpoint not initialized for RobertaModel for Bertscore | {
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"Hi @suzyahyah, thanks for reporting.\r\n\r\nThe message you get is indeed not an error message, but a warning coming from Hugging Face `transformers`. The complete warning message is:\r\n```\r\nSome weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.wei... | 1,624,992,923,000 | 1,625,123,339,000 | 1,625,038,549,000 | NONE | null | 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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} | [] | closed | false | null | [] | null | [] | 1,624,987,164,000 | 1,625,132,014,000 | 1,625,132,013,000 | MEMBER | 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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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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## 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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https://api.github.com/repos/huggingface/datasets/issues/2565 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2565/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2565/comments | https://api.github.com/repos/huggingface/datasets/issues/2565/events | https://github.com/huggingface/datasets/pull/2565 | 932,445,439 | MDExOlB1bGxSZXF1ZXN0Njc5Nzg3NTI4 | 2,565 | Inject templates for ASR datasets | {
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"Wait until #2567 is merged so we can benefit from the tagger :)",
"thanks for the feedback @lhoestq! i've added the new language codes and this PR should be ready for a merge :)"
] | 1,624,960,921,000 | 1,625,495,186,000 | 1,625,495,186,000 | MEMBER | 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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https://api.github.com/repos/huggingface/datasets/issues/2563 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2563/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2563/comments | https://api.github.com/repos/huggingface/datasets/issues/2563/events | https://github.com/huggingface/datasets/issues/2563 | 932,387,639 | MDU6SXNzdWU5MzIzODc2Mzk= | 2,563 | interleave_datasets for map-style datasets | {
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"followers_url": "https://api.git... | null | [] | 1,624,957,044,000 | 1,625,132,013,000 | 1,625,132,013,000 | MEMBER | null | 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 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2562/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2562/comments | https://api.github.com/repos/huggingface/datasets/issues/2562/events | https://github.com/huggingface/datasets/pull/2562 | 932,333,436 | MDExOlB1bGxSZXF1ZXN0Njc5NjkyMjQ2 | 2,562 | Minor fix in loading metrics docs | {
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https://api.github.com/repos/huggingface/datasets/issues/2560 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2560/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2560/comments | https://api.github.com/repos/huggingface/datasets/issues/2560/events | https://github.com/huggingface/datasets/pull/2560 | 932,143,634 | MDExOlB1bGxSZXF1ZXN0Njc5NTMyODk4 | 2,560 | fix Dataset.map when num_procs > num rows | {
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"Hi ! Thanks for fixing this :)\r\n\r\nLooks like you have tons of changes due to code formatting.\r\nWe're using `black` for this, with a custom line length. To run our code formatting, you just need to run\r\n```\r\nmake style\r\n```\r\n\r\nThen for the windows error in the CI, I'm looking into it. It's probably ... | 1,624,933,451,000 | 1,624,978,818,000 | 1,624,978,411,000 | CONTRIBUTOR | 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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https://api.github.com/repos/huggingface/datasets/issues/2558 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2558/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2558/comments | https://api.github.com/repos/huggingface/datasets/issues/2558/events | https://github.com/huggingface/datasets/pull/2558 | 931,736,647 | MDExOlB1bGxSZXF1ZXN0Njc5MTg0Njk1 | 2,558 | Update: WebNLG - update checksums | {
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} | [] | closed | false | null | [] | null | [] | 1,624,896,997,000 | 1,624,900,997,000 | 1,624,900,996,000 | MEMBER | 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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https://api.github.com/repos/huggingface/datasets/issues/2557 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2557/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2557/comments | https://api.github.com/repos/huggingface/datasets/issues/2557/events | https://github.com/huggingface/datasets/pull/2557 | 931,633,823 | MDExOlB1bGxSZXF1ZXN0Njc5MDk4ODg3 | 2,557 | Fix `fever` keys | {
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I fixed it by taking into account the file index as well. | {
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https://api.github.com/repos/huggingface/datasets/issues/2555 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2555/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2555/comments | https://api.github.com/repos/huggingface/datasets/issues/2555/events | https://github.com/huggingface/datasets/pull/2555 | 931,585,485 | MDExOlB1bGxSZXF1ZXN0Njc5MDU4ODM3 | 2,555 | Fix code_search_net keys | {
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"Fix #2552."
] | 1,624,887,623,000 | 1,630,571,083,000 | 1,624,889,435,000 | MEMBER | 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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https://api.github.com/repos/huggingface/datasets/issues/2554 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2554/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2554/comments | https://api.github.com/repos/huggingface/datasets/issues/2554/events | https://github.com/huggingface/datasets/issues/2554 | 931,453,855 | MDU6SXNzdWU5MzE0NTM4NTU= | 2,554 | Multilabel metrics not supported | {
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"Hi @GuillemGSubies, thanks for reporting.\r\n\r\nI have made a PR to fix this issue and allow metrics to be computed also for multilabel classification problems.",
"Looks nice, thank you very much! 🚀 ",
"Sorry for reopening but I just noticed that the `_compute` method for the F1 metric is still not good enou... | 1,624,878,586,000 | 1,634,128,153,000 | 1,625,733,615,000 | NONE | null | 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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https://api.github.com/repos/huggingface/datasets/issues/2553 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2553/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2553/comments | https://api.github.com/repos/huggingface/datasets/issues/2553/events | https://github.com/huggingface/datasets/issues/2553 | 931,365,926 | MDU6SXNzdWU5MzEzNjU5MjY= | 2,553 | load_dataset("web_nlg") NonMatchingChecksumError | {
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"Hi ! Thanks for reporting. This is due to the WebNLG repository that got updated today.\r\nI just pushed a fix at #2558 - this shouldn't happen anymore in the future.",
"This is fixed on `master` now :)\r\nWe'll do a new release soon !"
] | 1,624,872,406,000 | 1,624,901,019,000 | 1,624,900,996,000 | NONE | null | 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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https://api.github.com/repos/huggingface/datasets/issues/2552 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2552/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2552/comments | https://api.github.com/repos/huggingface/datasets/issues/2552/events | https://github.com/huggingface/datasets/issues/2552 | 931,354,687 | MDU6SXNzdWU5MzEzNTQ2ODc= | 2,552 | Keys should be unique error on code_search_net | {
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"Two questions:\r\n- with `datasets-cli env` we don't have any information on the dataset script version used. Should we give access to this somehow? Either as a note in the Error message or as an argument with the name of the dataset to `datasets-cli env`?\r\n- I don't really understand why the id is duplicated in... | 1,624,871,720,000 | 1,630,937,310,000 | 1,630,571,129,000 | MEMBER | null | ## 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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} | [] | closed | false | null | [] | null | [] | 1,624,810,722,000 | 1,624,885,795,000 | 1,624,885,794,000 | CONTRIBUTOR | 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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https://api.github.com/repos/huggingface/datasets/issues/2550 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2550/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2550/comments | https://api.github.com/repos/huggingface/datasets/issues/2550/events | https://github.com/huggingface/datasets/issues/2550 | 930,951,287 | MDU6SXNzdWU5MzA5NTEyODc= | 2,550 | Allow for incremental cumulative metric updates in a distributed setup | {
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] | closed | false | null | [] | null | [] | 1,624,806,058,000 | 1,632,663,759,000 | 1,632,663,759,000 | CONTRIBUTOR | null | 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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"Hi @nelson-liu,\r\n\r\nYou can pass the parameter `features` to `load_dataset`: https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset\r\n\r\nIf you look at the code of the MNLI script you referred in your question (https://github.com/huggingface/datasets/blob/master/datasets/multi_nli/multi... | 1,624,595,543,000 | 1,624,655,277,000 | 1,624,655,276,000 | NONE | null | 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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https://api.github.com/repos/huggingface/datasets/issues/2548 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2548/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2548/comments | https://api.github.com/repos/huggingface/datasets/issues/2548/events | https://github.com/huggingface/datasets/issues/2548 | 929,232,831 | MDU6SXNzdWU5MjkyMzI4MzE= | 2,548 | Field order issue in loading json | {
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"Hi @luyug, thanks for reporting.\r\n\r\nThe good news is that we fixed this issue only 9 days ago: #2507.\r\n\r\nThe patch is already in the master branch of our repository and it will be included in our next `datasets` release version 1.9.0.\r\n\r\nFeel free to reopen the issue if the problem persists."
] | 1,624,541,393,000 | 1,624,545,403,000 | 1,624,545,245,000 | NONE | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/2546 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2546/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2546/comments | https://api.github.com/repos/huggingface/datasets/issues/2546/events | https://github.com/huggingface/datasets/pull/2546 | 929,091,689 | MDExOlB1bGxSZXF1ZXN0Njc2OTk2MjQ0 | 2,546 | Add license to the Cambridge English Write & Improve + LOCNESS dataset card | {
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} | [] | closed | false | null | [] | null | [] | 1,624,531,169,000 | 1,624,531,921,000 | 1,624,531,921,000 | MEMBER | 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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https://api.github.com/repos/huggingface/datasets/issues/2545 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2545/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2545/comments | https://api.github.com/repos/huggingface/datasets/issues/2545/events | https://github.com/huggingface/datasets/pull/2545 | 929,016,580 | MDExOlB1bGxSZXF1ZXN0Njc2OTMxOTYw | 2,545 | Fix DuplicatedKeysError in drop dataset | {
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cc: @VictorSanh. | {
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https://api.github.com/repos/huggingface/datasets/issues/2544 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2544/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2544/comments | https://api.github.com/repos/huggingface/datasets/issues/2544/events | https://github.com/huggingface/datasets/pull/2544 | 928,900,827 | MDExOlB1bGxSZXF1ZXN0Njc2ODM1MjYz | 2,544 | Fix logging levels | {
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} | [] | closed | false | null | [] | null | [] | 1,624,516,896,000 | 1,624,628,419,000 | 1,624,628,419,000 | MEMBER | 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 | {
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"Hi @stas00, thanks for pointing out this issue with logging.\r\n\r\nI agree that `datasets` can sometimes be too verbose... I can create a PR and we could discuss there the choice of the log levels for different parts of the code."
] | 1,624,476,415,000 | 1,624,628,419,000 | 1,624,628,419,000 | CONTRIBUTOR | null | In https://github.com/huggingface/transformers/pull/12276 we are now changing the examples to have `datasets` on the same log level as `transformers`, so that one setting can do a consistent logging across all involved components.
The trouble is that now we get a ton of these:
```
06/23/2021 12:15:31 - INFO - datasets.utils.filelock - Lock 139627640431136 acquired on /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock
06/23/2021 12:15:31 - INFO - datasets.arrow_writer - Done writing 50 examples in 12280 bytes /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.
06/23/2021 12:15:31 - INFO - datasets.arrow_dataset - 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.
06/23/2021 12:15:31 - INFO - datasets.utils.filelock - Lock 139627640431136 released on /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock
```
May I suggest that these can be `log.debug` as it's no informative to the user.
More examples: these are not informative - too much information:
```
06/23/2021 12:14:26 - INFO - datasets.load - Checking /home/stas/.cache/huggingface/datasets/downloads/459933f1fe47711fad2f6ff8110014ff189120b45ad159ef5b8e90ea43a174fa.e23e7d1259a8c6274a82a42a8936dd1b87225302c6dc9b7261beb3bc2daac640.py for additional imports.
06/23/2021 12:14:27 - INFO - datasets.builder - Constructing Dataset for split train, validation, test, from /home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a
```
While these are:
```
06/23/2021 12:14:27 - INFO - datasets.info - Loading Dataset Infos from /home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt16/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a
06/23/2021 12:14:27 - WARNING - datasets.builder - Reusing dataset wmt16 (/home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a)
```
I also realize that `transformers` examples don't have do use `info` for `datasets` to let the default `warning` keep logging to less noisy.
But I think currently the log levels are slightly misused and skewed by 1 level. Many `warnings` will better be `info`s and most `info`s be `debug`.
e.g.:
```
06/23/2021 12:14:27 - WARNING - datasets.builder - Reusing dataset wmt16 (/home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a)
```
why is this a warning? it is informing me that the cache is used, there is nothing to be worried about. I'd have it as `info`.
Warnings are typically something that's bordering error or the first thing to check when things don't work as expected.
infrequent info is there to inform of the different stages or important events.
Everything else is debug.
At least the way I understand things.
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"very much related: https://github.com/huggingface/datasets/pull/2333",
"Hi @VictorSanh, thank you for reporting this issue with duplicated keys.\r\n\r\n- The issue with \"adversarial_qa\" was fixed 23 days ago: #2433. Current version of `datasets` (1.8.0) includes the patch.\r\n- I am investigating the issue wit... | 1,624,473,676,000 | 1,624,657,805,000 | 1,624,546,628,000 | MEMBER | null | ## Describe the bug
Failure to generate the datasets (`drop` and subset `adversarialQA` from `adversarial_qa`) because of duplicate keys.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("drop")
load_dataset("adversarial_qa", "adversarialQA")
```
## Expected results
The examples keys should be unique.
## Actual results
```bash
>>> load_dataset("drop")
Using custom data configuration default
Downloading and preparing dataset drop/default (download: 7.92 MiB, generated: 111.88 MiB, post-processed: Unknown size, total: 119.80 MiB) to /home/hf/.cache/huggingface/datasets/drop/default/0.1.0/7a94f1e2bb26c4b5c75f89857c06982967d7416e5af935a9374b9bccf5068026...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/load.py", line 751, in load_dataset
use_auth_token=use_auth_token,
File "/home/hf/dev/promptsource/.venv/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/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 992, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/arrow_writer.py", line 409, in finalize
self.check_duplicate_keys()
File "/home/hf/dev/promptsource/.venv/lib/python3.7/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: 28553293-d719-441b-8f00-ce3dc6df5398
Keys should be unique and deterministic in nature
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.7.0
- Platform: Linux-5.4.0-1044-gcp-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyArrow version: 3.0.0
| {
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https://api.github.com/repos/huggingface/datasets/issues/2541 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2541/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2541/comments | https://api.github.com/repos/huggingface/datasets/issues/2541/events | https://github.com/huggingface/datasets/pull/2541 | 928,529,078 | MDExOlB1bGxSZXF1ZXN0Njc2NTIwNDgx | 2,541 | update discofuse link cc @ekQ | {
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"The CI is failing because the dataset tags for `discofuse` are missing. I'm merging this PR since this is unrelated to this PR, but feel free to open another PR to add the tags here if you have some time:\r\n\r\nhttps://github.com/huggingface/datasets/blob/19408f9fab85c79b966085574cd2da3b90959179/datasets/discofus... | 1,624,472,698,000 | 1,624,890,891,000 | 1,624,890,890,000 | MEMBER | null | Updating the discofuse link: https://github.com/google-research-datasets/discofuse/commit/fd4b120cb3dd19a417e7f3b5432010b574b5eeee | {
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https://api.github.com/repos/huggingface/datasets/issues/2540 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2540/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2540/comments | https://api.github.com/repos/huggingface/datasets/issues/2540/events | https://github.com/huggingface/datasets/pull/2540 | 928,433,892 | MDExOlB1bGxSZXF1ZXN0Njc2NDM5NTM1 | 2,540 | Remove task templates if required features are removed during `Dataset.map` | {
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} | [] | closed | false | null | [] | null | [] | 1,624,465,225,000 | 1,624,545,675,000 | 1,624,541,643,000 | MEMBER | null | This PR fixes a bug reported by @craffel where removing a dataset's columns during `Dataset.map` triggered a `KeyError` because the `TextClassification` template tried to access the removed columns during `DatasetInfo.__post_init__`:
```python
from datasets import load_dataset
# `yelp_polarity` comes with a `TextClassification` template
ds = load_dataset("yelp_polarity", split="test")
ds
# Dataset({
# features: ['text', 'label'],
# num_rows: 38000
# })
# Triggers KeyError: 'label' - oh noes!
ds.map(lambda x: {"inputs": 0}, remove_columns=ds.column_names)
```
I wrote a unit test to make sure I could reproduce the error and then patched a fix. | {
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