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https://api.github.com/repos/huggingface/datasets/issues/3548 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3548/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3548/comments | https://api.github.com/repos/huggingface/datasets/issues/3548/events | https://github.com/huggingface/datasets/issues/3548 | 1,096,409,512 | I_kwDODunzps5BWeGo | 3,548 | Specify the feature types of a dataset on the Hub without needing a dataset script | {
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} | **Is your feature request related to a problem? Please describe.**
Currently if I upload a CSV with paths to audio files, the column type is string instead of Audio.
**Describe the solution you'd like**
I'd like to be able to specify the types of the column, so that when loading the dataset I directly get the features types I want.
The feature types could read from the `dataset_infos.json` for example.
**Describe alternatives you've considered**
Create a dataset script to specify the features, but that seems complicated for a simple thing.
cc @abidlabs | {
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} | ## Describe the bug
In offline mode, one can still access previously-cached datasets. This fails with datasets created with `push_to_hub`.
## Steps to reproduce the bug
in Python:
```
import datasets
mpwiki = datasets.load_dataset("teven/matched_passages_wikidata")
```
in bash:
```
export HF_DATASETS_OFFLINE=1
```
in Python:
```
import datasets
mpwiki = datasets.load_dataset("teven/matched_passages_wikidata")
```
## Expected results
`datasets` should find the previously-cached dataset.
## Actual results
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'teven/matched_passages_wikidata': Offline mode is enabled
## Environment info
- `datasets` version: 1.16.2.dev0
- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 3.0.0
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] | open | false | null | [] | null | 0 | 2022-01-06T23:02:25Z | 2022-01-06T23:02:25Z | null | CONTRIBUTOR | null | null | {
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} | Hello,
**Is your feature request related to a problem? Please describe.**
My use case is that I have one writer that adds columns and multiple workers reading the same `Dataset`. Each worker should have access to columns added by the writer when they reload the dataset.
I understand that we shouldn't overwrite an arrow file as this could cause Segfault and so on. Before 1.16, I was able to overwrite the dataset and that would work most of the time with some retries.
**Describe the solution you'd like**
I was thinking that if we could append `Dataset._data_files`, when the workers reload the Dataset, they would get the new columns.
**Describe alternatives you've considered**
I currently need to
1. Save multiple "versions" of the dataset and load the latest.
2. Try working with cache files to get the latest columns.
**Additional context**
I think this would be a great addition to HFDataset as Parquet supports multi-files input out of the box!
I can make a PR myself with some pointers as needed :) | null | {
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Currently, I can load a metric implemented by me by providing the local path to the file in `load_metric`.
However, there is no option to do it with the metric uploaded to the hub.
This means that if I want to allow other users to use it, they must download it first which makes the usage less smooth.
**Describe the solution you'd like**
Load metrics from the hub just like datasets are loaded.
In order to not break stuff, the convention can be to put the metric file in a "metrics" folder in the hub.
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* more details about the data structure
* this dataset does not contain annotations | {
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} | **Is your feature request related to a problem? Please describe.**
We should support 7-zip compressed data files:
- [x] in `extract`:
- #4672
- [ ] in `iter_archive`: for streaming mode
both in streaming and non-streaming modes.
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https://api.github.com/repos/huggingface/datasets/issues/3540 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3540/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3540/comments | https://api.github.com/repos/huggingface/datasets/issues/3540/events | https://github.com/huggingface/datasets/issues/3540 | 1,094,900,336 | I_kwDODunzps5BQtpw | 3,540 | How to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset? | {
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} | Hi,
I use torch.utils.data.Dataset to define my own data, but I need to use the 'map' function of datasets.arrow_dataset.Dataset later, so I hope to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset.
Here is an example.
```
from torch.utils.data import Dataset
from datasets.arrow_dataset import Dataset as HFDataset
class ADataset(Dataset):
def __init__(self, data):
super().__init__()
self.data = data
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return self.len
class MDataset():
def __init__(self, tokenizer: AutoTokenizer, data_args, training_args):
self.train_dataset = ADataset(data_args)
self.tokenizer = tokenizer
self.data_args = data_args
self.train_dataset = self.train_dataset.map(
self.process_function,
batched=True,
remove_columns=column_names,
load_from_cache_file=True,
desc="Running tokenizer on train dataset",
)
def process_function(self, examples):
sentences = [" ".join(sample[0][3]) for sample in examples]
tokenized = self.tokenizer(
sentences,
max_length=self.max_seq_len,
padding=self.padding,
truncation=True)
```
But it would raise an ERROR, AttributeError: 'ADataset' object has no attribute 'map'.
so how to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset?
Thanks in advance!
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https://api.github.com/repos/huggingface/datasets/issues/3538 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3538/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3538/comments | https://api.github.com/repos/huggingface/datasets/issues/3538/events | https://github.com/huggingface/datasets/pull/3538 | 1,094,756,755 | PR_kwDODunzps4wlLmD | 3,538 | Readme usage update | {
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] | null | 0 | 2022-01-05T21:26:28Z | 2022-01-05T23:34:25Z | 2022-01-05T23:24:15Z | CONTRIBUTOR | null | null | null | Noticing that the recent commit throws a lot of errors in the automatic checks. It looks to me that those errors are simply errors that were already there (metadata issues), unrelated to what I've just changed, but worth another look to make sure. | {
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https://api.github.com/repos/huggingface/datasets/issues/3537 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3537/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3537/comments | https://api.github.com/repos/huggingface/datasets/issues/3537/events | https://github.com/huggingface/datasets/pull/3537 | 1,094,738,734 | PR_kwDODunzps4wlH1d | 3,537 | added PII statements and license links to data cards | {
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} | [] | closed | false | null | [] | null | 0 | 2022-01-05T20:59:21Z | 2022-01-05T22:02:37Z | 2022-01-05T22:02:37Z | CONTRIBUTOR | null | null | null | Updates for the following datacards:
multilingual_librispeech
openslr
speech commands
superb
timit_asr
vctk | {
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https://api.github.com/repos/huggingface/datasets/issues/3536 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3536/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3536/comments | https://api.github.com/repos/huggingface/datasets/issues/3536/events | https://github.com/huggingface/datasets/pull/3536 | 1,094,645,771 | PR_kwDODunzps4wk0Yb | 3,536 | update `pretty_name` for all datasets | {
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} | [] | closed | false | null | [] | null | 1 | 2022-01-05T18:45:05Z | 2022-07-10T14:36:54Z | 2022-01-12T22:59:45Z | CONTRIBUTOR | null | null | null | This PR updates `pretty_name` for all datasets. Previous PR #3498 had done this for only first 200 datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/3535 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3535/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3535/comments | https://api.github.com/repos/huggingface/datasets/issues/3535/events | https://github.com/huggingface/datasets/pull/3535 | 1,094,633,214 | PR_kwDODunzps4wkxv0 | 3,535 | Add SVHN dataset | {
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} | [] | closed | false | null | [] | null | 0 | 2022-01-05T18:29:09Z | 2022-01-12T14:14:35Z | 2022-01-12T14:14:35Z | COLLABORATOR | null | null | null | Add the SVHN dataset.
Additional notes:
* compared to the TFDS implementation, exposes additional the "full numbers" config
* adds the streaming support for `os.path.splitext` and `scipy.io.loadmat`
* adds `h5py` to the requirements list for the dummy data test | {
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https://api.github.com/repos/huggingface/datasets/issues/3534 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3534/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3534/comments | https://api.github.com/repos/huggingface/datasets/issues/3534/events | https://github.com/huggingface/datasets/pull/3534 | 1,094,352,449 | PR_kwDODunzps4wj3LE | 3,534 | Update wiki_dpr README.md | {
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} | [] | closed | false | null | [] | null | 0 | 2022-01-05T13:29:44Z | 2022-02-17T13:45:56Z | 2022-01-05T14:16:51Z | MEMBER | null | null | null | Some infos of wiki_dpr were missing as noted in https://github.com/huggingface/datasets/issues/3510, I added them and updated the tags and the examples
Close #3510. | {
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} | When I want to look at all datasets of the category: `speech-processing` *i.e.* https://huggingface.co/datasets?task_categories=task_categories:speech-processing&sort=downloads , then the following dataset doesn't show up for some reason:
- https://huggingface.co/datasets/speech_commands
even thought it's task tags seem correct:
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https://api.github.com/repos/huggingface/datasets/issues/3532 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3532/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3532/comments | https://api.github.com/repos/huggingface/datasets/issues/3532/events | https://github.com/huggingface/datasets/pull/3532 | 1,094,035,066 | PR_kwDODunzps4wi1ft | 3,532 | Give clearer instructions to add the YAML tags | {
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CC: @julien-c @VictorSanh | {
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https://api.github.com/repos/huggingface/datasets/issues/3531 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3531/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3531/comments | https://api.github.com/repos/huggingface/datasets/issues/3531/events | https://github.com/huggingface/datasets/issues/3531 | 1,094,033,280 | I_kwDODunzps5BNZ-A | 3,531 | Give clearer instructions to add the YAML tags | {
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} | ## Describe the bug
As reported by @julien-c, many community datasets contain the line `YAML tags:` at the top of the YAML section in the header of the README file. See e.g.: https://huggingface.co/datasets/bigscience/P3/commit/a03bea08cf4d58f268b469593069af6aeb15de32
Maybe we should give clearer instruction/hints in the README template.
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https://api.github.com/repos/huggingface/datasets/issues/3530 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3530/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3530/comments | https://api.github.com/repos/huggingface/datasets/issues/3530/events | https://github.com/huggingface/datasets/pull/3530 | 1,093,894,732 | PR_kwDODunzps4wiZCw | 3,530 | Update README.md | {
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] | null | 0 | 2022-01-04T23:48:11Z | 2022-01-05T12:49:41Z | 2022-01-05T12:49:40Z | CONTRIBUTOR | null | null | null | Updating license with appropriate capitalization & a link.
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https://api.github.com/repos/huggingface/datasets/issues/3526 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3526/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3526/comments | https://api.github.com/repos/huggingface/datasets/issues/3526/events | https://github.com/huggingface/datasets/pull/3526 | 1,093,833,446 | PR_kwDODunzps4wiMaQ | 3,526 | Update license to bookcorpus dataset card | {
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] | closed | false | null | [] | null | 1 | 2022-01-04T22:33:45Z | 2022-05-06T16:27:37Z | 2022-05-06T16:27:37Z | NONE | null | null | {
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} | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("wmt19", 'zh-en')
```
## Expected results
The dataset should download.
## Actual results
`ConnectionError: Couldn't reach ftp://cwmt-wmt:[email protected]/parallel/casia2015.zip`
## Environment info
- `datasets` version: 1.15.1
- Platform: Linux
- Python version: 3.8
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https://api.github.com/repos/huggingface/datasets/issues/3521 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3521/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3521/comments | https://api.github.com/repos/huggingface/datasets/issues/3521/events | https://github.com/huggingface/datasets/pull/3521 | 1,093,797,947 | PR_kwDODunzps4wiFCs | 3,521 | Vivos license update | {
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https://api.github.com/repos/huggingface/datasets/issues/3520 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3520/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3520/comments | https://api.github.com/repos/huggingface/datasets/issues/3520/events | https://github.com/huggingface/datasets/pull/3520 | 1,093,747,753 | PR_kwDODunzps4wh6oD | 3,520 | Audio datacard update - first pass | {
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] | null | 2 | 2022-01-04T20:58:25Z | 2022-01-05T12:30:21Z | 2022-01-05T12:30:20Z | CONTRIBUTOR | null | null | null | Filling out data card "Personal and Sensitive Information" for speech datasets to note PII concerns | {
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https://api.github.com/repos/huggingface/datasets/issues/3519 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3519/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3519/comments | https://api.github.com/repos/huggingface/datasets/issues/3519/events | https://github.com/huggingface/datasets/pull/3519 | 1,093,655,205 | PR_kwDODunzps4whnXH | 3,519 | CC100: Using HTTPS for the data source URL fixes load_dataset() | {
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} | [] | closed | false | null | [] | null | 0 | 2022-01-04T18:45:54Z | 2022-01-05T17:28:34Z | 2022-01-05T17:28:34Z | CONTRIBUTOR | null | null | null | Without this change the following script (with any lang parameter) consistently fails. After changing to the HTTPS URL, the script works as expected.
```python
from datasets import load_dataset
dataset = load_dataset("cc100", lang="en")
```
This is the error produced by the previous script:
```sh
Using custom data configuration en-lang=en
Downloading and preparing dataset cc100/en to /home/antti/.cache/huggingface/datasets/cc100/en-lang=en/0.0.0/526ac20780de5e074cf73a7466e868cb67f960b48f6de42ff6a6c4e71910d71b...
Traceback (most recent call last):
File "/home/antti/tmp/cc100/cc100.py", line 3, in <module>
dataset = load_dataset("cc100", lang="en")
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/load.py", line 1694, in load_dataset
builder_instance.download_and_prepare(
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/builder.py", line 595, in download_and_prepare
self._download_and_prepare(
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/builder.py", line 661, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/antti/.cache/huggingface/modules/datasets_modules/datasets/cc100/526ac20780de5e074cf73a7466e868cb67f960b48f6de42ff6a6c4e71910d71b/cc100.py", line 117, in _split_generators
path = dl_manager.download_and_extract(download_url)
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 308, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 251, in map_nested
return function(data_struct)
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache
raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach http://data.statmt.org/cc-100/en.txt.xz (error 503)
```
Note that I get the same behavior also using curl on the command line. The plain HTTP "curl -L http://data.statmt.org/cc-100/en.txt.xz" fails with "503 Service unavailable", but the with the HTTPS version of the URL curl starts downloading the file.
My guess is that the server does overly aggressive rate-limitting. When a client requests an HTTP URL, it (sensibly) gets redirected to the HTTPS equivalent, but now the server notices two requests coming from the same client (the original HTTP and the redirected HTTPS) during a brief time windows and rate-limitter kicks in and blocks the second request! If the client initally uses the HTTPS URL there's only one incoming request which the rate-limitter allows. | {
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https://api.github.com/repos/huggingface/datasets/issues/3518 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3518/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3518/comments | https://api.github.com/repos/huggingface/datasets/issues/3518/events | https://github.com/huggingface/datasets/issues/3518 | 1,093,063,455 | I_kwDODunzps5BJtMf | 3,518 | Add PubMed Central Open Access dataset | {
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] | null | 3 | 2022-01-04T06:54:35Z | 2022-01-17T15:25:57Z | 2022-01-17T15:25:57Z | MEMBER | null | null | {
"completed": 0,
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} | ## Adding a Dataset
- **Name:** PubMed Central Open Access
- **Description:** The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under license terms that allow reuse.
- **Paper:** *link to the dataset paper if available*
- **Data:** https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Before, the download was failing | {
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} | ## Describe the bug
I am trying to load a dataset called `evidence_infer_treatment`. The first subset (`1.1`) works fine but the second returns an error (`2.0`). It downloads a file but crashes during the checksums.
## Steps to reproduce the bug
```python
>>> from datasets import load_dataset
>>> load_dataset("evidence_infer_treatment", "2.0")
Downloading and preparing dataset evidence_infer_treatment/2.0 (download: 34.84 MiB, generated: 91.46 MiB, post-processed: Unknown size, total: 126.30 MiB) to /home/victor_huggingface_co/.cache/huggingface/datasets/evidence_infer_treatment/2.0/2.0.0/6812655bfd26cbaa58c84eab098bf6403694b06c6ae2ded603c55681868a1e24...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/load.py", line 1669, in load_dataset
use_auth_token=use_auth_token,
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 594, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 664, in _download_and_prepare
self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 33, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'http://evidence-inference.ebm-nlp.com/v2.0.tar.gz'}
```
I did try to pass the argument `ignore_verifications=True` but run into an error when trying to build the dataset:
```python
>>> load_dataset("evidence_infer_treatment", "2.0", ignore_verifications=True, download_mode="force_redownload")
Downloading and preparing dataset evidence_infer_treatment/2.0 (download: 34.84 MiB, generated: 91.46 MiB, post-processed: Unknown size, total: 126.30 MiB) to /home/victor_huggingface_co/.cache/huggingface/datasets/evidence_infer_treatment/2.0/2.0.0/6812655bfd26cbaa58c84eab098bf6403694b06c6ae2ded603c55681868a1e24...
Downloading: 164MB [00:23, 6.98MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/load.py", line 1669, in load_dataset
use_auth_token=use_auth_token,
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 594, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 681, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 1080, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 1032, in encode_example
return encode_nested_example(self, example)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 807, 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/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 807, in <dictcomp>
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 829, in encode_nested_example
list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]]
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 829, in <listcomp>
list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]]
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 828, in encode_nested_example
for k, dict_tuples in utils.zip_dict(schema.feature, *obj):
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 136, in zip_dict
yield key, tuple(d[key] for d in dicts)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 136, in <genexpr>
yield key, tuple(d[key] for d in dicts)
KeyError: ''
```
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-5.0.0-1020-gcp-x86_64-with-debian-buster-sid
- Python version: 3.7.11
- PyArrow version: 6.0.1
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**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
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**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
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https://api.github.com/repos/huggingface/datasets/issues/3510 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3510/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3510/comments | https://api.github.com/repos/huggingface/datasets/issues/3510/events | https://github.com/huggingface/datasets/issues/3510 | 1,091,997,004 | I_kwDODunzps5BFo1M | 3,510 | `wiki_dpr` details for Open Domain Question Answering tasks | {
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Thanks for creating the `wiki_dpr` dataset!
I am currently trying to use the dataset for context retrieval using DPR on NQ questions and need details about what each of the files and data instances mean, which version of the Wikipedia dump it uses, etc. Please respond at your earliest convenience regarding the same! Thanks a ton!
P.S.: (If one of @thomwolf @lewtun @lhoestq could respond, that would be even better since they have the first-hand details of the dataset. If anyone else has those, please reach out! Thanks!) | {
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] | closed | false | null | [] | null | 17 | 2021-12-30T17:04:25Z | 2022-11-04T15:31:38Z | 2022-11-04T15:31:37Z | MEMBER | null | null | {
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} | I open this PR to have a public discussion about this topic and make a decision.
As previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?
On the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:
- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)
- we are migrating canonical datasets to the Hub
Do we really need to continue testing them in out CI?
Also note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).
Feel free to ping other people for the discussion.
CC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw | {
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https://api.github.com/repos/huggingface/datasets/issues/3506 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3506/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3506/comments | https://api.github.com/repos/huggingface/datasets/issues/3506/events | https://github.com/huggingface/datasets/pull/3506 | 1,091,166,595 | PR_kwDODunzps4wZpot | 3,506 | Allows DatasetDict.filter to have batching option | {
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} | [] | closed | false | null | [] | null | 0 | 2021-12-30T15:22:22Z | 2022-01-04T10:24:28Z | 2022-01-04T10:24:27Z | CONTRIBUTOR | null | null | null | - Related to: #3244
- Fixes: #3503
We extends `.filter( ... batched: bool)` support to DatasetDict. | {
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https://api.github.com/repos/huggingface/datasets/issues/3505 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3505/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3505/comments | https://api.github.com/repos/huggingface/datasets/issues/3505/events | https://github.com/huggingface/datasets/issues/3505 | 1,091,150,820 | I_kwDODunzps5BCaPk | 3,505 | cast_column function not working with map function in streaming mode for Audio features | {
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} | ## Describe the bug
I am trying to use Audio class for loading audio features using custom dataset. I am able to cast 'audio' feature into 'Audio' format with cast_column function. On using map function, I am not getting 'Audio' casted feature but getting path of audio file only.
I am getting features of 'audio' of string type with load_dataset call. After using cast_column 'audio' feature is converted into 'Audio' type. But in map function I am not able to get Audio type for audio feature & getting string type data containing path of file only. So I am not able to use processor in encode function.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset, Audio
from transformers import Wav2Vec2Processor
def encode(batch, processor):
print("Audio: ",batch['audio'])
batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values
return batch
def print_ds(ds):
iterator = iter(ds)
for d in iterator:
print("Data: ",d)
break
processor = Wav2Vec2Processor.from_pretrained(pretrained_model_path)
dataset = load_dataset("custom_dataset.py","train",data_files={'train':'train_path.txt'},
data_dir="data", streaming=True, split="train")
print("Features: ",dataset.features)
print_ds(dataset)
dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
print("Features: ",dataset.features)
print_ds(dataset)
dataset = dataset.map(lambda x: encode(x,processor))
print("Features: ",dataset.features)
print_ds(dataset)
```
## Expected results
map function not printing Audio type features be used with processor function and getting error in processor call due to this.
## Actual results
# after load_dataset call
Features: {'sentence': Value(dtype='string', id=None), 'audio': Value(dtype='string', id=None)}
Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': 'data/0116_003.wav'}
# after cast_column call
Features: {'sentence': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None)}
Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': {'path': 'data/0116_003.wav', 'array': array([ 1.2662281e-06, 1.0264218e-06, -1.3615092e-06, ...,
1.3017889e-02, 1.0085563e-02, 4.8155054e-03], dtype=float32), 'sampling_rate': 16000}}
# after map call
Features: None
Audio: data/0116_003.wav
Traceback (most recent call last):
File "demo2.py", line 36, in <module>
print_ds(dataset)
File "demo2.py", line 11, in print_ds
for d in iterator:
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 341, in __iter__
for key, example in self._iter():
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 338, in _iter
yield from ex_iterable
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 192, in __iter__
yield key, self.function(example)
File "demo2.py", line 32, in <lambda>
dataset = dataset.map(lambda x: batch_encode(x,processor))
File "demo2.py", line 6, in batch_encode
batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values
TypeError: string indices must be integers
## Environment info
- `datasets` version: 1.17.0
- Platform: Linux-4.14.243 with-debian-bullseye-sid
- Python version: 3.7.9
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3504 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3504/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3504/comments | https://api.github.com/repos/huggingface/datasets/issues/3504/events | https://github.com/huggingface/datasets/issues/3504 | 1,090,682,230 | I_kwDODunzps5BAn12 | 3,504 | Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst | {
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"completed": 0,
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"total": 0
} | ## Describe the bug
I am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https://huggingface.co/course/chapter5/4?fw=pt).
https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
# This takes a few minutes to run, so go grab a tea or coffee while you wait :)
data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
```
I also tried with `wget` as follows.
```
wget https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst
```
## Expected results
I expect to be able to download this file.
## Actual results
Traceback
```
---------------------------------------------------------------------------
timeout Traceback (most recent call last)
/usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self)
158 try:
--> 159 conn = connection.create_connection(
160 (self._dns_host, self.port), self.timeout, **extra_kw
/usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
83 if err is not None:
---> 84 raise err
85
/usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
73 sock.bind(source_address)
---> 74 sock.connect(sa)
75 return sock
timeout: timed out
During handling of the above exception, another exception occurred:
ConnectTimeoutError Traceback (most recent call last)
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
664 # Make the request on the httplib connection object.
--> 665 httplib_response = self._make_request(
666 conn,
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
375 try:
--> 376 self._validate_conn(conn)
377 except (SocketTimeout, BaseSSLError) as e:
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
995 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
--> 996 conn.connect()
997
/usr/lib/python3/dist-packages/urllib3/connection.py in connect(self)
313 # Add certificate verification
--> 314 conn = self._new_conn()
315 hostname = self.host
/usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self)
163 except SocketTimeout:
--> 164 raise ConnectTimeoutError(
165 self,
ConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')
During handling of the above exception, another exception occurred:
MaxRetryError Traceback (most recent call last)
/usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
438 if not chunked:
--> 439 resp = conn.urlopen(
440 method=request.method,
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
718
--> 719 retries = retries.increment(
720 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
/usr/lib/python3/dist-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)
435 if new_retry.is_exhausted():
--> 436 raise MaxRetryError(_pool, url, error or ResponseError(cause))
437
MaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))
During handling of the above exception, another exception occurred:
ConnectTimeout Traceback (most recent call last)
/tmp/ipykernel_15104/606583593.py in <module>
3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :)
4 data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
----> 5 pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
6 pubmed_dataset
~/.local/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)
1655
1656 # Create a dataset builder
-> 1657 builder_instance = load_dataset_builder(
1658 path=path,
1659 name=name,
~/.local/lib/python3.8/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)
1492 download_config = download_config.copy() if download_config else DownloadConfig()
1493 download_config.use_auth_token = use_auth_token
-> 1494 dataset_module = dataset_module_factory(
1495 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files
1496 )
~/.local/lib/python3.8/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)
1116 # Try packaged
1117 if path in _PACKAGED_DATASETS_MODULES:
-> 1118 return PackagedDatasetModuleFactory(
1119 path, data_files=data_files, download_config=download_config, download_mode=download_mode
1120 ).get_module()
~/.local/lib/python3.8/site-packages/datasets/load.py in get_module(self)
773 else get_patterns_locally(str(Path().resolve()))
774 )
--> 775 data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)
776 module_path, hash = _PACKAGED_DATASETS_MODULES[self.name]
777 builder_kwargs = {"hash": hash, "data_files": data_files}
~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
576 for key, patterns_for_key in patterns.items():
577 out[key] = (
--> 578 DataFilesList.from_local_or_remote(
579 patterns_for_key,
580 base_path=base_path,
~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
545 base_path = base_path if base_path is not None else str(Path().resolve())
546 data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
--> 547 origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)
548 return cls(data_files, origin_metadata)
549
~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token)
492 data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None
493 ) -> Tuple[str]:
--> 494 return thread_map(
495 partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token),
496 data_files,
~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs)
92 """
93 from concurrent.futures import ThreadPoolExecutor
---> 94 return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
95
96
~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs)
74 map_args.update(chunksize=chunksize)
75 with PoolExecutor(**pool_kwargs) as ex:
---> 76 return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))
77
78
~/.local/lib/python3.8/site-packages/tqdm/notebook.py in __iter__(self)
252 def __iter__(self):
253 try:
--> 254 for obj in super(tqdm_notebook, self).__iter__():
255 # return super(tqdm...) will not catch exception
256 yield obj
~/.local/lib/python3.8/site-packages/tqdm/std.py in __iter__(self)
1171 # (note: keep this check outside the loop for performance)
1172 if self.disable:
-> 1173 for obj in iterable:
1174 yield obj
1175 return
/usr/lib/python3.8/concurrent/futures/_base.py in result_iterator()
617 # Careful not to keep a reference to the popped future
618 if timeout is None:
--> 619 yield fs.pop().result()
620 else:
621 yield fs.pop().result(end_time - time.monotonic())
/usr/lib/python3.8/concurrent/futures/_base.py in result(self, timeout)
442 raise CancelledError()
443 elif self._state == FINISHED:
--> 444 return self.__get_result()
445 else:
446 raise TimeoutError()
/usr/lib/python3.8/concurrent/futures/_base.py in __get_result(self)
387 if self._exception:
388 try:
--> 389 raise self._exception
390 finally:
391 # Break a reference cycle with the exception in self._exception
/usr/lib/python3.8/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token)
483 if isinstance(data_file, Url):
484 data_file = str(data_file)
--> 485 return (request_etag(data_file, use_auth_token=use_auth_token),)
486 else:
487 data_file = str(data_file.resolve())
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in request_etag(url, use_auth_token)
489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]:
490 headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)
--> 491 response = http_head(url, headers=headers, max_retries=3)
492 response.raise_for_status()
493 etag = response.headers.get("ETag") if response.ok else None
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
474 headers = copy.deepcopy(headers) or {}
475 headers["user-agent"] = get_datasets_user_agent(user_agent=headers.get("user-agent"))
--> 476 response = _request_with_retry(
477 method="HEAD",
478 url=url,
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
408 if tries > max_retries:
--> 409 raise err
410 else:
411 logger.info(f"{method} request to {url} timed out, retrying... [{tries/max_retries}]")
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
403 tries += 1
404 try:
--> 405 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
406 success = True
407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/usr/lib/python3/dist-packages/requests/api.py in request(method, url, **kwargs)
58 # cases, and look like a memory leak in others.
59 with sessions.Session() as session:
---> 60 return session.request(method=method, url=url, **kwargs)
61
62
/usr/lib/python3/dist-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
531 }
532 send_kwargs.update(settings)
--> 533 resp = self.send(prep, **send_kwargs)
534
535 return resp
/usr/lib/python3/dist-packages/requests/sessions.py in send(self, request, **kwargs)
644
645 # Send the request
--> 646 r = adapter.send(request, **kwargs)
647
648 # Total elapsed time of the request (approximately)
/usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
502 # TODO: Remove this in 3.0.0: see #2811
503 if not isinstance(e.reason, NewConnectionError):
--> 504 raise ConnectTimeout(e, request=request)
505
506 if isinstance(e.reason, ResponseError):
ConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))
```
## Environment info
- `datasets` version: 1.17.0
- Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 6.0.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/3503 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3503/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3503/comments | https://api.github.com/repos/huggingface/datasets/issues/3503/events | https://github.com/huggingface/datasets/issues/3503 | 1,090,472,735 | I_kwDODunzps5A_0sf | 3,503 | Batched in filter throws error | {
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] | null | 0 | 2021-12-29T12:01:04Z | 2022-01-04T10:24:27Z | 2022-01-04T10:24:27Z | CONTRIBUTOR | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | I hope this is really a bug, I could not find it among the open issues
## Describe the bug
using `batched=False` in DataSet.filter throws error
```python
TypeError: filter() got an unexpected keyword argument 'batched'
```
but in the docs it is lister as an argument.
## Steps to reproduce the bug
```python
task = "mnli"
max_length = 128
tokenizer = AutoTokenizer.from_pretrained("./pretrained_models/pretrained_models_drozd/sl250.m.gsic.titech.ac.jp:8000/21.11.17_06.30.32_roberta-base_a0057/checkpoints/smpl_400M/hf/")
dataset = load_dataset("glue", task)
task_to_keys = {
"cola": ("sentence", None),
"mnli": ("premise", "hypothesis"),
"mnli-mm": ("premise", "hypothesis"),
"mrpc": ("sentence1", "sentence2"),
"qnli": ("question", "sentence"),
"qqp": ("question1", "question2"),
"rte": ("sentence1", "sentence2"),
"sst2": ("sentence", None),
"stsb": ("sentence1", "sentence2"),
"wnli": ("sentence1", "sentence2"),
}
##### tokenization_parameters
sentence1_key, sentence2_key = task_to_keys[task]
def preprocess_function(examples, max_length):
if sentence2_key is None:
return tokenizer(
examples[sentence1_key], truncation=True, max_length=max_length
)
return tokenizer(
examples[sentence1_key],
examples[sentence2_key],
truncation=False,
padding="max_length",
max_length=max_length,
)
encoded_dataset = dataset.map(
lambda x: preprocess_function(x, max_length=max_length), batched=False
)
encoded_dataset.filter(lambda x: len(x['input_ids']) <= max_length, batched=False)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1, 1.17.0
- Platform: ubuntu
- Python version: 3.8.12
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https://api.github.com/repos/huggingface/datasets/issues/3502 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3502/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3502/comments | https://api.github.com/repos/huggingface/datasets/issues/3502/events | https://github.com/huggingface/datasets/pull/3502 | 1,090,438,558 | PR_kwDODunzps4wXSLi | 3,502 | Add QuALITY | {
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] | closed | false | null | [] | null | 1 | 2021-12-29T10:58:46Z | 2022-10-03T09:36:14Z | 2022-10-03T09:36:14Z | CONTRIBUTOR | null | null | null | Fixes #3441. | {
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https://api.github.com/repos/huggingface/datasets/issues/3501 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3501/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3501/comments | https://api.github.com/repos/huggingface/datasets/issues/3501/events | https://github.com/huggingface/datasets/pull/3501 | 1,090,413,758 | PR_kwDODunzps4wXM8H | 3,501 | Update pib dataset card | {
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} | [] | closed | false | null | [] | null | 0 | 2021-12-29T10:14:40Z | 2021-12-29T11:13:21Z | 2021-12-29T11:13:21Z | MEMBER | null | null | null | Related to #3496 | {
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https://api.github.com/repos/huggingface/datasets/issues/3500 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3500/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3500/comments | https://api.github.com/repos/huggingface/datasets/issues/3500/events | https://github.com/huggingface/datasets/pull/3500 | 1,090,406,133 | PR_kwDODunzps4wXLTB | 3,500 | Docs: Add VCTK dataset description | {
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} | [] | closed | false | null | [] | null | 0 | 2021-12-29T10:02:05Z | 2022-01-04T10:46:02Z | 2022-01-04T10:25:09Z | CONTRIBUTOR | null | null | null | This PR is a very minor followup to #1837, with only docs changes (single comment string). | {
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https://api.github.com/repos/huggingface/datasets/issues/3499 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3499/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3499/comments | https://api.github.com/repos/huggingface/datasets/issues/3499/events | https://github.com/huggingface/datasets/issues/3499 | 1,090,132,618 | I_kwDODunzps5A-hqK | 3,499 | Adjusting chunk size for streaming datasets | {
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] | closed | false | null | [] | null | 2 | 2021-12-28T21:17:53Z | 2022-05-06T16:29:05Z | 2022-05-06T16:29:05Z | CONTRIBUTOR | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | **Is your feature request related to a problem? Please describe.**
I want to use mc4 which I cannot save locally, so I stream it. However, I want to process the entire dataset and filter some documents from it. With the current chunk size of around 1000 documents (right?) I hit a performance bottleneck because of the frequent decompressing.
**Describe the solution you'd like**
I would appreciate a parameter in the load_dataset function, that allows me to set the chunksize myself (to a value like 100'000 in my case). Like that, I hope to improve the processing time.
| {
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https://api.github.com/repos/huggingface/datasets/issues/3498 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3498/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3498/comments | https://api.github.com/repos/huggingface/datasets/issues/3498/events | https://github.com/huggingface/datasets/pull/3498 | 1,090,096,332 | PR_kwDODunzps4wWL5U | 3,498 | update `pretty_name` for first 200 datasets | {
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} | [] | closed | false | null | [] | null | 0 | 2021-12-28T19:50:07Z | 2022-07-10T14:36:53Z | 2022-01-05T16:38:21Z | CONTRIBUTOR | null | null | null | I made a script some time back to fetch `pretty_names` from `papers_with_code` dataset along with some other rules incase that dataset wasn't available on `papers_with_code`. Updating them in the `README` of `datasets`. Took only the first 200 datasets into consideration and after some eyeballing, most of them were looking good to me! | {
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https://api.github.com/repos/huggingface/datasets/issues/3497 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3497/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3497/comments | https://api.github.com/repos/huggingface/datasets/issues/3497/events | https://github.com/huggingface/datasets/issues/3497 | 1,090,050,148 | I_kwDODunzps5A-Nhk | 3,497 | Changing sampling rate in audio dataset and subsequently mapping with `num_proc > 1` leads to weird bug | {
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] | null | 2 | 2021-12-28T18:03:49Z | 2022-01-21T13:22:27Z | 2022-01-21T13:22:27Z | CONTRIBUTOR | null | null | {
"completed": 0,
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} | Running:
```python
from datasets import load_dataset, DatasetDict
import datasets
from transformers import AutoFeatureExtractor
raw_datasets = DatasetDict()
raw_datasets["train"] = load_dataset("common_voice", "ab", split="train")
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
raw_datasets = raw_datasets.cast_column(
"audio", datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
)
num_workers = 16
def prepare_dataset(batch):
sample = batch["audio"]
inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
batch["input_values"] = inputs.input_values[0]
batch["input_length"] = len(batch["input_values"])
return batch
raw_datasets.map(
prepare_dataset,
remove_columns=next(iter(raw_datasets.values())).column_names,
num_proc=16,
desc="preprocess datasets",
)
```
gives
```bash
File "/home/patrick/experiments/run_bug.py", line 25, in <module>
raw_datasets.map(
File "/home/patrick/python_bin/datasets/dataset_dict.py", line 492, in map
{
File "/home/patrick/python_bin/datasets/dataset_dict.py", line 493, in <dictcomp>
k: dataset.map(
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2139, in map
shards = [
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2140, in <listcomp>
self.shard(num_shards=num_proc, index=rank, contiguous=True, keep_in_memory=keep_in_memory)
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 3164, in shard
return self.select(
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 485, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/patrick/python_bin/datasets/fingerprint.py", line 411, in wrapper
out = func(self, *args, **kwargs)
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2756, in select
return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2667, in _new_dataset_with_indices
return Dataset(
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 659, in __init__
raise ValueError(
ValueError: External features info don't match the dataset:
Got
{'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)}
with type
struct<client_id: string, path: string, audio: string, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string>
but expected something like
{'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': {'path': Value(dtype='string', id=None), 'bytes': Value(dtype='binary', id=None)}, 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)}
with type
struct<client_id: string, path: string, audio: struct<path: string, bytes: binary>, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string>
```
Versions:
```python
- `datasets` version: 1.16.2.dev0
- Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 6.0.1
```
and `transformers`:
```
- `transformers` version: 4.16.0.dev0
- Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33
- Python version: 3.9.7
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} | [] | closed | false | null | [] | null | 3 | 2021-12-28T16:01:55Z | 2022-01-03T14:42:28Z | 2021-12-29T08:42:57Z | MEMBER | null | null | null | This PR:
- Updates version of pib dataset: from 0.0.0 to 1.3.0
- Makes the dataset streamable
Fix #3491.
CC: @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/3495 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3495/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3495/comments | https://api.github.com/repos/huggingface/datasets/issues/3495/events | https://github.com/huggingface/datasets/issues/3495 | 1,089,983,632 | I_kwDODunzps5A99SQ | 3,495 | Add VoxLingua107 | {
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] | open | false | null | [] | null | 0 | 2021-12-28T15:51:43Z | 2021-12-28T15:51:43Z | null | CONTRIBUTOR | null | null | {
"completed": 0,
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} | ## Adding a Dataset
- **Name:** VoxLingua107
- **Description:** VoxLingua107 is a speech dataset for training spoken language identification models.
- **Paper:** https://arxiv.org/abs/2011.12998
- **Data:** http://bark.phon.ioc.ee/voxlingua107/
- **Motivation:** 107 languages, totaling 6628 hours for the train split.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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https://api.github.com/repos/huggingface/datasets/issues/3494 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3494/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3494/comments | https://api.github.com/repos/huggingface/datasets/issues/3494/events | https://github.com/huggingface/datasets/pull/3494 | 1,089,983,103 | PR_kwDODunzps4wV0vB | 3,494 | Clone full repo to detect new tags when mirroring datasets on the Hub | {
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} | [] | closed | false | null | [] | null | 2 | 2021-12-28T15:50:47Z | 2021-12-28T16:07:21Z | 2021-12-28T16:07:20Z | MEMBER | null | null | null | The new releases of `datasets` were not detected because the shallow clone in the CI wasn't getting the git tags.
By cloning the full repository we can properly detect a new release, and tag all the dataset repositories accordingly
cc @SBrandeis | {
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https://api.github.com/repos/huggingface/datasets/issues/3493 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3493/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3493/comments | https://api.github.com/repos/huggingface/datasets/issues/3493/events | https://github.com/huggingface/datasets/pull/3493 | 1,089,967,286 | PR_kwDODunzps4wVxfr | 3,493 | Fix VCTK encoding | {
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} | [] | closed | false | null | [] | null | 0 | 2021-12-28T15:23:36Z | 2021-12-28T15:48:18Z | 2021-12-28T15:48:17Z | MEMBER | null | null | null | utf-8 encoding was missing in the VCTK dataset builder added in #3351 | {
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https://api.github.com/repos/huggingface/datasets/issues/3492 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3492/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3492/comments | https://api.github.com/repos/huggingface/datasets/issues/3492/events | https://github.com/huggingface/datasets/pull/3492 | 1,089,952,943 | PR_kwDODunzps4wVufr | 3,492 | Add `gzip` for `to_json` | {
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} | [] | closed | false | null | [] | null | 0 | 2021-12-28T15:01:11Z | 2022-07-10T14:36:52Z | 2022-01-05T13:03:36Z | CONTRIBUTOR | null | null | null | (Partially) closes #3480. I have added `gzip` compression for `to_json`. I realised we can run into this compression problem with `to_csv` as well. `IOHandler` can be used for `to_csv` too. Please let me know if any changes are required. | {
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} | On the Hub we have v0, while there exists v1.3.
Related to bigscience-workshop/data_tooling#130
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so I wander does datasets support read data from hdfs? | null | {
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} | [] | open | false | null | [] | null | 1 | 2021-12-27T18:20:56Z | 2022-07-06T15:19:49Z | null | CONTRIBUTOR | null | null | null | Like in `join([... for s in ...])`. Also changed other things that I saw:
* Use a `with` statement for many `open` that missed them, so the files don't remain open.
* Remove unused variables.
* Many HTTP links converted into HTTPS (verified).
* Remove unnecessary "r" mode arg in `open` (double-checked it was actually the default in each case).
* Remove Python 2 style of using `super`.
* Run `pyupgrade $(find . -name "*.py" -type f) --py36-plus` (which already does some of the previous points).
* Run `dos2unix $(find . -name "*.py" -type f)` (CRLF to LF line endings).
* Fix typos. | null | {
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} | ## Describe the bug
There is an ssue with `StreamingDownloadManager._extract`.
I don't know how the test `test_streaming_gg_drive_gzipped` passes:
For
```python
TEST_GG_DRIVE_GZIPPED_URL = "https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz"
urlpath = StreamingDownloadManager().download_and_extract(TEST_GG_DRIVE_GZIPPED_URL)
```
gives `urlpath`:
```python
'gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz::https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz'
```
The gzip path makes no sense: `gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz`
## Steps to reproduce the bug
```python
from datasets.utils.streaming_download_manager import StreamingDownloadManager
dl_manager = StreamingDownloadManager()
urlpath = dl_manager.extract("https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz")
print(urlpath)
```
## Expected results
The query parameters should not be set as part of the path.
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} | **Is your feature request related to a problem? Please describe.**
When using `dataset.set_format("torch")`, I must make sure every columns in datasets can convert to `torch`, however, sometimes I want to keep some string columns.
**Describe the solution you'd like**
skip columns which cannot set to specific format when set_format instead of raise an error.
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} | As describe in https://github.com/huggingface/datasets/issues/2005#issuecomment-793716753 and mentioned by @mariosasko in [image feature example](https://colab.research.google.com/drive/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c#scrollTo=ow3XHDvf2I0B&line=1&uniqifier=1), IMO make shape verifcaiton to use ArrayXD instead of nested lists for map can help user reduce unnecessary cast. I notice datasets have done something special for `input_ids` and `attention_mask` which is also unnecessary after this feature added. | null | {
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* Fix s/narqa/newsqa/ in the download manually error message.
* Make the download manually error message show nicely when printed. Otherwise, is hard to read due to spacing issues.
* Fix the format of the license text.
* Reformat the code to make it simpler. | {
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This generated a bug for filesystem methods that use `self.info()`, like e.g. `fs.isfile()`.
This PR:
- Adds tests for `fs.isfile` (that use `fs.info`).
- Fixes custom `BaseCompressedFileFileSystem.info` by removing its overriding. | {
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} | ## Describe the bug
In the documentation of the `to_json` method, it is stated in the parameters that
> **to_json_kwargs – Parameters to pass to pandas’s pandas.DataFrame.to_json.
however when we pass for example `compression="gzip"`, the saved file is not compressed.
Would you also have expected compression to be applied? :relaxed:
## Steps to reproduce the bug
```python
my_dict = {"a": [1, 2, 3], "b": [1, 2, 3]}
```
### Result with datasets
```python
from datasets import Dataset
dataset = Dataset.from_dict(my_dict)
dataset.to_json("dic_with_datasets.jsonl.gz", compression="gzip")
!cat dic_with_datasets.jsonl.gz
```
output
```
{"a":1,"b":1}
{"a":2,"b":2}
{"a":3,"b":3}
```
Note: I would expected to see binary data here
### Result with pandas
```python
import pandas as pd
df = pd.DataFrame(my_dict)
df.to_json("dic_with_pandas.jsonl.gz", lines=True, orient="records", compression="gzip")
!cat dic_with_pandas.jsonl.gz
```
output
```
4��a�dic_with_pandas.jsonl��VJT�2�QJ��\� ��g��yƵ���������)���
```
Note: It looks like binary data
## Expected results
I would have expected that the saved result with datasets would also be a binary file
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.11
- PyArrow version: 5.0.0
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} | ## Dataset viewer issue for '*french_book_reviews*'
**Link:** https://huggingface.co/datasets/Abirate/french_book_reviews
**short description of the issue**
For my dataset, the dataset preview is no longer functional (it used to work: The dataset had been added the day before and it was fine...)
And, after looking over the datasets, I discovered that this issue affects all Hugging Face datasets (as of yesterday, December 23, 2021, around 10 p.m. (CET)).
**Am I the one who added this dataset** : Yes
**Note**: here a screenshot showing the issue
**And here for glue dataset :**
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} | [] | closed | false | null | [] | null | 1 | 2021-12-23T16:42:55Z | 2021-12-24T10:50:20Z | 2021-12-24T10:50:19Z | MEMBER | null | null | null | This PR extends the support in streaming mode for datasets that use `os.walk`, by patching that function.
This PR adds support for streaming mode to datasets:
1. autshumato
1. code_x_glue_cd_code_to_text
1. code_x_glue_tc_nl_code_search_adv
1. nchlt
CC: @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/3477 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3477/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3477/comments | https://api.github.com/repos/huggingface/datasets/issues/3477/events | https://github.com/huggingface/datasets/pull/3477 | 1,087,850,253 | PR_kwDODunzps4wPKPX | 3,477 | Use `iter_files` instead of `str(Path(...)` in image dataset | {
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} | [] | closed | false | null | [] | null | 6 | 2021-12-23T16:26:55Z | 2021-12-28T15:15:02Z | 2021-12-28T15:15:02Z | COLLABORATOR | null | null | null | Use `iter_files` in the `beans` and the `cats_vs_dogs` dataset scripts as suggested by @albertvillanova.
Additional changes:
* Fix `iter_files` in `MockDownloadManager` (see this [CI error](https://app.circleci.com/pipelines/github/huggingface/datasets/9247/workflows/2657ff8a-b531-4fd9-a9fc-6541a72e8d83/jobs/57028))
* Add support for `os.path.isdir` and `os.path.isfile` in streaming (`os.path.isfile` is needed in `StreamingDownloadManager`'s `iter_files` to make `cats_vs_dogs` streamable)
TODO:
- [ ] add tests for `xisdir` and `xisfile` | {
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This PR adds support for streaming mode to datasets:
1. ami
1. assin
1. assin2
1. counter
1. enriched_web_nlg
1. europarl_bilingual
1. hyperpartisan_news_detection
1. polsum
1. qa4mre
1. quail
1. ted_talks_iwslt
1. udhr
1. web_nlg
1. winograd_wsc
CC: @severo | {
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https://api.github.com/repos/huggingface/datasets/issues/3475 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3475/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3475/comments | https://api.github.com/repos/huggingface/datasets/issues/3475/events | https://github.com/huggingface/datasets/issues/3475 | 1,087,352,041 | I_kwDODunzps5Az6zp | 3,475 | The rotten_tomatoes dataset of movie reviews contains some reviews in Spanish | {
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} | ## Describe the bug
See title. I don't think this is intentional and they probably should be removed. If they stay the dataset description should be at least updated to make it clear to the user.
## Steps to reproduce the bug
Go to the [dataset viewer](https://huggingface.co/datasets/viewer/?dataset=rotten_tomatoes) for the dataset, set the offset to 4160 for the train dataset, and scroll through the results. I found ones at index 4166 and 4173. There's others too (e.g. index 2888) but those two are easy to find like that.
## Expected results
English movie reviews only.
## Actual results
Example of a Spanish movie review (4173):
> "É uma pena que , mais tarde , o próprio filme abandone o tom de paródia e passe a utilizar os mesmos clichês que havia satirizado "
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} | [] | closed | false | null | [] | null | 0 | 2021-12-22T15:34:49Z | 2023-09-24T09:54:04Z | 2021-12-28T16:08:10Z | MEMBER | null | null | null | If I iterate over a vision dataset, the images are not decoded, and the dictionary with the bytes is returned.
This PR enables image decoding in `Dataset.__iter__`
Close https://github.com/huggingface/datasets/issues/3473 | {
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https://api.github.com/repos/huggingface/datasets/issues/3473 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3473/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3473/comments | https://api.github.com/repos/huggingface/datasets/issues/3473/events | https://github.com/huggingface/datasets/issues/3473 | 1,086,937,610 | I_kwDODunzps5AyVoK | 3,473 | Iterating over a vision dataset doesn't decode the images | {
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] | closed | false | null | [] | null | 9 | 2021-12-22T15:26:32Z | 2021-12-27T14:13:21Z | 2021-12-23T15:21:57Z | MEMBER | null | null | {
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} | ## Describe the bug
If I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.
## Steps to reproduce the bug
```python
from datasets import load_dataset
import PIL
mnist = load_dataset("mnist", split="train")
first_image = mnist[0]["image"]
assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # passes
first_image = next(iter(mnist))["image"]
assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # fails
```
## Expected results
The image should be decoded, as a PIL Image
## Actual results
We get a dictionary
```
{'bytes': b'\x89PNG\r\n\x1a\n\x00..., 'path': None}
```
## Environment info
- `datasets` version: 1.17.1.dev0
- Platform: Darwin-20.6.0-x86_64-i386-64bit
- Python version: 3.7.2
- PyArrow version: 6.0.0
The bug also exists in 1.17.0
## Investigation
I think the issue is that decoding is disabled in `__iter__`:
https://github.com/huggingface/datasets/blob/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6/src/datasets/arrow_dataset.py#L1651-L1661
Do you remember why it was disabled in the first place @albertvillanova ?
Also cc @mariosasko @NielsRogge
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} | [] | closed | false | null | [] | null | 0 | 2021-12-22T15:06:03Z | 2021-12-22T16:52:53Z | 2021-12-22T16:52:52Z | COLLABORATOR | null | null | null | Fix `str(Path(...))` conversion in streaming on Linux. This should fix the streaming of the `beans` and `cats_vs_dogs` datasets. | {
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} | [] | closed | false | null | [] | null | 0 | 2021-12-22T08:41:30Z | 2021-12-22T10:12:08Z | 2021-12-22T10:12:07Z | MEMBER | null | null | null | This PR:
- Yields stripped text
- Fix path for Windows
- Adds license
- Adds more info in dataset card
Close bigscience-workshop/data_tooling#279 | {
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Currently, `ClassLabel` docstring rendering is not right. | {
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https://api.github.com/repos/huggingface/datasets/issues/3469 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3469/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3469/comments | https://api.github.com/repos/huggingface/datasets/issues/3469/events | https://github.com/huggingface/datasets/pull/3469 | 1,085,882,664 | PR_kwDODunzps4wIrOV | 3,469 | Fix METEOR missing NLTK's omw-1.4 | {
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This should fix the CI on master | {
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https://api.github.com/repos/huggingface/datasets/issues/3468 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3468/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3468/comments | https://api.github.com/repos/huggingface/datasets/issues/3468/events | https://github.com/huggingface/datasets/pull/3468 | 1,085,871,301 | PR_kwDODunzps4wIozO | 3,468 | Add COCO dataset | {
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] | closed | false | null | [] | null | 7 | 2021-12-21T14:07:50Z | 2023-09-24T09:33:31Z | 2022-10-03T09:36:08Z | COLLABORATOR | null | null | null | This PR adds the MS COCO dataset. Compared to the [TFDS](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/object_detection/coco.py) script, this implementation adds 8 additional configs to cover the tasks other than object detection.
Some notes:
* the data exposed by TFDS is contained in the `2014`, `2015`, `2017` and `2017_panoptic_segmentation` configs here
* I've updated `encode_nested_example` for easier handling of missing values (cc @lhoestq @albertvillanova; will add tests if you are OK with the changes in `features.py`)
* this implementation should fix https://github.com/huggingface/datasets/pull/3377#issuecomment-985559427
TODOs:
- [x] dataset card
- [ ] dummy data
cc @merveenoyan
Closes #2526 | {
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https://api.github.com/repos/huggingface/datasets/issues/3467 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3467/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3467/comments | https://api.github.com/repos/huggingface/datasets/issues/3467/events | https://github.com/huggingface/datasets/pull/3467 | 1,085,870,665 | PR_kwDODunzps4wIoqd | 3,467 | Push dataset infos.json to Hub | {
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This PR fixes this by also pushing a `dataset_infos.json` file to the Hub, that stores the feature types.
Other minor changes:
- renamed the `___` separator to `--`, since `--` is now disallowed in a name in the back-end.
I tested this feature with datasets like conll2003 that has feature types like `ClassLabel` that were previously lost.
Close https://github.com/huggingface/datasets/issues/3394
I would like to include this in today's release (though not mandatory), so feel free to comment/suggest changes | {
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"completed": 0,
"percent_completed": 0,
"total": 0
} | ## Describe the bug
I wanted to load cnn_dailymail dataset from huggingface datasets on Google Colab, but I am getting an error while loading it.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0', ignore_verifications = True)
```
## Expected results
Expecting to load 'cnn_dailymail' dataset.
## Actual results
`NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
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} | ## Describe the bug
A clear and concise description of what the bug is.
using latest datasets=datasets-1.16.1-py3-none-any.whl
process my own multilingual dataset by following codes, and the number of rows in all dataset is 306000, the max_length of each sentence is 256:
then I get this error:
I have seen the issue in #2134 and #2150, so I don't understand why latest repo still can't deal with big dataset.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: linux docker
- Python version: 3.6
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Fix #3462.
Close bigscience-workshop/data_tooling#107 | {
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} | Please note also: the HuggingFace version at https://huggingface.co/datasets/swahili_news is outdated. An updated version, with deduplicated text and official splits, can be found at https://zenodo.org/record/5514203.
## Adding a Dataset
- **Name:** swahili_news
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Related to:
- bigscience-workshop/data_tooling#107
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Related to #3437. | {
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This PR fixes this and should fix the issue with WER showing low values if the input format is not right. | {
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https://api.github.com/repos/huggingface/datasets/issues/3459 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3459/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3459/comments | https://api.github.com/repos/huggingface/datasets/issues/3459/events | https://github.com/huggingface/datasets/issues/3459 | 1,084,969,672 | I_kwDODunzps5Aq1LI | 3,459 | dataset.filter overwriting previously set dataset._indices values, resulting in the wrong elements being selected. | {
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] | closed | false | null | [] | null | 2 | 2021-12-20T16:16:49Z | 2021-12-20T16:34:57Z | 2021-12-20T16:34:57Z | NONE | null | null | {
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} | ## Describe the bug
When using dataset.select to select a subset of a dataset, dataset._indices are set to indicate which elements are now considered in the dataset.
The same thing happens when you shuffle the dataset; dataset._indices are set to indicate what the new order of the data is.
However, if you then use a dataset.filter, that filter interacts with those dataset._indices values in a non-intuitive manner.
https://huggingface.co/docs/datasets/_modules/datasets/arrow_dataset.html#Dataset.filter
Effectively, it looks like the original set of _indices were discared and overwritten by the set created during the filter operation.
I think this is actually an issue with how the map function handles dataset._indices. Ideally it should use the _indices it gets passed, and then return an updated _indices which reflect the map transformation applied to the starting _indices.
## Steps to reproduce the bug
```python
dataset = load_dataset('imdb', split='train', keep_in_memory=True)
dataset = dataset.shuffle(keep_in_memory=True)
dataset = dataset.select(range(0, 10), keep_in_memory=True)
print("initial 10 elements")
print(dataset['label']) # -> [1, 1, 0, 1, 0, 0, 0, 1, 0, 0]
dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True)
print("filtered 10 elements looking for label 0")
print(dataset['label']) # -> [1, 1, 1, 1, 1, 1]
```
## Actual results
```
$ python indices_bug.py
initial 10 elements
[1, 1, 0, 1, 0, 0, 0, 1, 0, 0]
filtered 10 elements looking for label 0
[1, 1, 1, 1, 1, 1]
```
This code block first shuffles the dataset (to get a mix of label 0 and label 1).
Then it selects just the first 10 elements (the number of elements does not matter, 10 is just easy to visualize). The important part is that you select some subset of the dataset.
Finally, a filter is applied to pull out just the elements with `label == 0`.
The bug is that you cannot combine any dataset operation which sets the dataset._indices with filter.
In this case I have 2, shuffle and subset.
If you just use a single dataset._indices operation (in this case shuffle) the bug still shows up.
The shuffle sets the dataset._indices and then filter uses those indices in the map, then overwrites dataset._indices with the filter results.
```python
dataset = load_dataset('imdb', split='train', keep_in_memory=True)
dataset = dataset.shuffle(keep_in_memory=True)
dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True)
dataset = dataset.select(range(0, 10), keep_in_memory=True)
print(dataset['label']) # -> [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
```
## Expected results
In an ideal world, the dataset filter would respect any dataset._indices values which had previously been set.
If you use dataset.filter with the base dataset (where dataset._indices has not been set) then the filter command works as expected.
## Environment info
Here are the commands required to rebuild the conda environment from scratch.
```
# create a virtual environment
conda create -n dataset_indices python=3.8 -y
# activate the virtual environment
conda activate dataset_indices
# install huggingface datasets
conda install datasets
```
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.11.0-41-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 3.0.0
### Full Conda Environment
```
$ conda env export
name: dasaset_indices
channels:
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=4.5=1_gnu
- abseil-cpp=20210324.2=h2531618_0
- aiohttp=3.8.1=py38h7f8727e_0
- aiosignal=1.2.0=pyhd3eb1b0_0
- arrow-cpp=3.0.0=py38h6b21186_4
- attrs=21.2.0=pyhd3eb1b0_0
- aws-c-common=0.4.57=he6710b0_1
- aws-c-event-stream=0.1.6=h2531618_5
- aws-checksums=0.1.9=he6710b0_0
- aws-sdk-cpp=1.8.185=hce553d0_0
- bcj-cffi=0.5.1=py38h295c915_0
- blas=1.0=mkl
- boost-cpp=1.73.0=h27cfd23_11
- bottleneck=1.3.2=py38heb32a55_1
- brotli=1.0.9=he6710b0_2
- brotli-python=1.0.9=py38heb0550a_2
- brotlicffi=1.0.9.2=py38h295c915_0
- brotlipy=0.7.0=py38h27cfd23_1003
- bzip2=1.0.8=h7b6447c_0
- c-ares=1.17.1=h27cfd23_0
- ca-certificates=2021.10.26=h06a4308_2
- certifi=2021.10.8=py38h06a4308_0
- cffi=1.14.6=py38h400218f_0
- conllu=4.4.1=pyhd3eb1b0_0
- cryptography=36.0.0=py38h9ce1e76_0
- dataclasses=0.8=pyh6d0b6a4_7
- dill=0.3.4=pyhd3eb1b0_0
- double-conversion=3.1.5=he6710b0_1
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- frozenlist=1.2.0=py38h7f8727e_0
- gflags=2.2.2=he6710b0_0
- glog=0.5.0=h2531618_0
- gmp=6.2.1=h2531618_2
- grpc-cpp=1.39.0=hae934f6_5
- huggingface_hub=0.0.17=pyhd3eb1b0_0
- icu=58.2=he6710b0_3
- idna=3.3=pyhd3eb1b0_0
- importlib-metadata=4.8.2=py38h06a4308_0
- importlib_metadata=4.8.2=hd3eb1b0_0
- intel-openmp=2021.4.0=h06a4308_3561
- krb5=1.19.2=hac12032_0
- ld_impl_linux-64=2.35.1=h7274673_9
- libboost=1.73.0=h3ff78a5_11
- libcurl=7.80.0=h0b77cf5_0
- libedit=3.1.20210910=h7f8727e_0
- libev=4.33=h7f8727e_1
- libevent=2.1.8=h1ba5d50_1
- libffi=3.3=he6710b0_2
- libgcc-ng=9.3.0=h5101ec6_17
- libgomp=9.3.0=h5101ec6_17
- libnghttp2=1.46.0=hce63b2e_0
- libprotobuf=3.17.2=h4ff587b_1
- libssh2=1.9.0=h1ba5d50_1
- libstdcxx-ng=9.3.0=hd4cf53a_17
- libthrift=0.14.2=hcc01f38_0
- libxml2=2.9.12=h03d6c58_0
- libxslt=1.1.34=hc22bd24_0
- lxml=4.6.3=py38h9120a33_0
- lz4-c=1.9.3=h295c915_1
- mkl=2021.4.0=h06a4308_640
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- multiprocess=0.70.12.2=py38h7f8727e_0
- multivolumefile=0.2.3=pyhd3eb1b0_0
- ncurses=6.3=h7f8727e_2
- numexpr=2.7.3=py38h22e1b3c_1
- numpy=1.21.2=py38h20f2e39_0
- numpy-base=1.21.2=py38h79a1101_0
- openpyxl=3.0.9=pyhd3eb1b0_0
- openssl=1.1.1l=h7f8727e_0
- orc=1.6.9=ha97a36c_3
- packaging=21.3=pyhd3eb1b0_0
- pip=21.2.4=py38h06a4308_0
- py7zr=0.16.1=pyhd3eb1b0_1
- pycparser=2.21=pyhd3eb1b0_0
- pycryptodomex=3.10.1=py38h27cfd23_1
- pyopenssl=21.0.0=pyhd3eb1b0_1
- pyparsing=3.0.4=pyhd3eb1b0_0
- pyppmd=0.16.1=py38h295c915_0
- pysocks=1.7.1=py38h06a4308_0
- python=3.8.12=h12debd9_0
- python-dateutil=2.8.2=pyhd3eb1b0_0
- python-xxhash=2.0.2=py38h7f8727e_0
- pyzstd=0.14.4=py38h7f8727e_3
- re2=2020.11.01=h2531618_1
- readline=8.1=h27cfd23_0
- requests=2.26.0=pyhd3eb1b0_0
- setuptools=58.0.4=py38h06a4308_0
- six=1.16.0=pyhd3eb1b0_0
- snappy=1.1.8=he6710b0_0
- sqlite=3.36.0=hc218d9a_0
- texttable=1.6.4=pyhd3eb1b0_0
- tk=8.6.11=h1ccaba5_0
- typing_extensions=3.10.0.2=pyh06a4308_0
- uriparser=0.9.3=he6710b0_1
- utf8proc=2.6.1=h27cfd23_0
- wheel=0.37.0=pyhd3eb1b0_1
- xxhash=0.8.0=h7f8727e_3
- xz=5.2.5=h7b6447c_0
- zipp=3.6.0=pyhd3eb1b0_0
- zlib=1.2.11=h7f8727e_4
- zstd=1.4.9=haebb681_0
- pip:
- async-timeout==4.0.2
- charset-normalizer==2.0.9
- datasets==1.16.1
- fsspec==2021.11.1
- huggingface-hub==0.2.1
- multidict==5.2.0
- pandas==1.3.5
- pyarrow==6.0.1
- pytz==2021.3
- pyyaml==6.0
- tqdm==4.62.3
- typing-extensions==4.0.1
- urllib3==1.26.7
- yarl==1.7.2
```
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https://api.github.com/repos/huggingface/datasets/issues/3457 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3457/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3457/comments | https://api.github.com/repos/huggingface/datasets/issues/3457/events | https://github.com/huggingface/datasets/issues/3457 | 1,084,862,121 | I_kwDODunzps5Aqa6p | 3,457 | Add CMU Graphics Lab Motion Capture dataset | {
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] | open | false | null | [] | null | 3 | 2021-12-20T14:34:39Z | 2022-03-16T16:53:09Z | null | CONTRIBUTOR | null | null | {
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} | ## Adding a Dataset
- **Name:** CMU Graphics Lab Motion Capture database
- **Description:** The database contains free motions which you can download and use.
- **Data:** http://mocap.cs.cmu.edu/
- **Motivation:** Nice motion capture dataset
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). | null | {
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https://api.github.com/repos/huggingface/datasets/issues/3456 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3456/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3456/comments | https://api.github.com/repos/huggingface/datasets/issues/3456/events | https://github.com/huggingface/datasets/pull/3456 | 1,084,687,973 | PR_kwDODunzps4wEwXz | 3,456 | [WER] Better error message for wer | {
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} | [] | closed | false | null | [] | null | 4 | 2021-12-20T11:38:40Z | 2021-12-20T16:53:37Z | 2021-12-20T16:53:36Z | CONTRIBUTOR | null | null | null | Currently we have the following problem when using the WER. When the input format to the WER metric is wrong, instead of throwing an error message a word-error-rate is computed which is incorrect. E.g. when doing the following:
```python
from datasets import load_metric
wer = load_metric("wer")
target_str = ["hello this is nice", "hello the weather is bloomy"]
pred_str = [["hello it's nice"], ["hello it's the weather"]]
print("Wrong:", wer.compute(predictions=pred_str, references=target_str))
print("Correct", wer.compute(predictions=[x[0] for x in pred_str], references=target_str))
```
We get:
```
Wrong: 1.0
Correct 0.5555555555555556
```
meaning that we get a word-error rate for incorrectly passed input formats. We should raise an error here instead so that people don't spent hours fixing a model while it's their incorrect evaluation metric is the problem for a low WER. | {
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"completed": 0,
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} | **Is your feature request related to a problem? Please describe.**
It requires a lot of effort to improve a datasheet.
**Describe the solution you'd like**
UI or at least a link to the place where the code that needs to be edited is (and an easy way to edit this code directly from the site, without cloning, branching, makefile etc.)
**Describe alternatives you've considered**
The current Ux is to have the 8 steps for contribution while One just wishes to change a line a type etc.
| {
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- Adds tests to DownloadManager and StreamingDownloadManager `iter_archive` for both path and file inputs
- Fixes bugs in `iter_archive` introduced in:
- #3443
Fix #3453. | {
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} | ## Describe the bug
After the merge of:
- #3443
the method `iter_archive` throws a ValueError:
```
ValueError: read of closed file
```
## Steps to reproduce the bug
```python
for path, file in dl_manager.iter_archive(archive_path):
pass
```
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is there any other way implement the stratify option while splitting the dataset? as it is important point to be considered while splitting the dataset. | {
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} | [] | closed | false | null | [] | null | 2 | 2021-12-17T17:12:11Z | 2021-12-21T10:25:46Z | 2021-12-20T14:09:51Z | MEMBER | null | null | null | Let's have a script that updates the dataset repositories on staging for now. This way we can make sure it works fine before going in prod.
Related to https://github.com/huggingface/datasets/issues/3341
The script runs on each commit on `master`. It checks the datasets that were changed, and it pushes the changes to the corresponding repositories on the Hub.
If there's a new dataset, then a new repository is created.
If the commit is a new release of `datasets`, it also pushes the tag to all the repositories. | {
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https://api.github.com/repos/huggingface/datasets/issues/3450 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3450/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3450/comments | https://api.github.com/repos/huggingface/datasets/issues/3450/events | https://github.com/huggingface/datasets/issues/3450 | 1,083,450,158 | I_kwDODunzps5AlCMu | 3,450 | Unexpected behavior doing Split + Filter | {
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} | ## Describe the bug
I observed unexpected behavior when applying 'train_test_split' followed by 'filter' on dataset. Elements of the training dataset eventually end up in the test dataset (after applying the 'filter')
## Steps to reproduce the bug
```
from datasets import Dataset
import pandas as pd
dic = {'x': [1,2,3,4,5,6,7,8,9], 'y':['q','w','e','r','t','y','u','i','o']}
df = pd.DataFrame.from_dict(dic)
dataset = Dataset.from_pandas(df)
split_dataset = dataset.train_test_split(test_size=0.5, shuffle=False, seed=42)
train_dataset = split_dataset["train"]
eval_dataset = split_dataset["test"]
eval_dataset_2 = eval_dataset.filter(lambda example: example['x'] % 2 == 0)
print( eval_dataset['x'])
print(eval_dataset_2['x'])
```
One observes that elements in eval_dataset2 are actually coming from the training dataset...
## Expected results
The expected results would be that the filtered eval dataset would only contain elements from the original eval dataset.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows 10
- Python version: 3.7
- PyArrow version: 5.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3449 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3449/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3449/comments | https://api.github.com/repos/huggingface/datasets/issues/3449/events | https://github.com/huggingface/datasets/issues/3449 | 1,083,373,018 | I_kwDODunzps5AkvXa | 3,449 | Add `__add__()`, `__iadd__()` and similar to `Dataset` class | {
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} | **Is your feature request related to a problem? Please describe.**
No.
**Describe the solution you'd like**
I would like to be able to concatenate datasets as follows:
```python
>>> dataset["train"] += dataset["validation"]
```
... instead of using `concatenate_datasets()`:
```python
>>> raw_datasets["train"] = concatenate_datasets([raw_datasets["train"], raw_datasets["validation"]])
>>> del raw_datasets["validation"]
```
**Describe alternatives you've considered**
Well, I have considered `concatenate_datasets()` 😀
**Additional context**
N.a.
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https://api.github.com/repos/huggingface/datasets/issues/3448 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3448/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3448/comments | https://api.github.com/repos/huggingface/datasets/issues/3448/events | https://github.com/huggingface/datasets/issues/3448 | 1,083,231,080 | I_kwDODunzps5AkMto | 3,448 | JSONDecodeError with HuggingFace dataset viewer | {
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} | ## Dataset viewer issue for 'pubmed_neg'
**Link:** https://huggingface.co/datasets/IGESML/pubmed_neg
I am getting the error:
Status code: 400
Exception: JSONDecodeError
Message: Expecting property name enclosed in double quotes: line 61 column 2 (char 1202)
I have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue.
Am I the one who added this dataset ? Yes
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"completed": 0,
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} | ## Describe the bug
According to https://huggingface.co/docs/datasets/loading_datasets.html#loading-a-dataset-builder, setting HF_DATASETS_OFFLINE to 1 should make datasets to "run in full offline mode". It didn't work for me. At the very beginning, datasets still tried to download "custom data configuration" for JSON, despite I have run the program once and cached all data into the same --cache_dir.
"Downloading" is not an issue when running with local disk, but crashes often with cloud storage because (1) multiply GPU processes try to access the same file, AND (2) FileLocker fails to synchronize all processes, due to storage throttling. 99% of times, when the main process releases FileLocker, the file is not actually ready for access in cloud storage and thus triggers "FileNotFound" errors for all other processes. Well, another way to resolve the problem is to investigate super reliable cloud storage, but that's out of scope here.
## Steps to reproduce the bug
```
export HF_DATASETS_OFFLINE=1
python run_clm.py --model_name_or_path=models/gpt-j-6B --train_file=trainpy.v2.train.json --validation_file=trainpy.v2.eval.json --cache_dir=datacache/trainpy.v2
```
## Expected results
datasets should stop all "downloading" behavior but reuse the cached JSON configuration. I think the problem here is part of the cache directory path, "default-471372bed4b51b53", is randomly generated, and it could change if some parameters changed. And I didn't find a way to use a fixed path to ensure datasets to reuse cached data every time.
## Actual results
The logging shows datasets are still downloading into "datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426".
```
12/16/2021 10:25:59 - WARNING - datasets.builder - Using custom data configuration default-471372bed4b51b53
12/16/2021 10:25:59 - INFO - datasets.builder - Generating dataset json (datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426)
Downloading and preparing dataset json/default to datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...
100%|██████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 17623.13it/s]
12/16/2021 10:25:59 - INFO - datasets.utils.download_manager - Downloading took 0.0 min
12/16/2021 10:26:00 - INFO - datasets.utils.download_manager - Checksum Computation took 0.0 min
100%|███████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1206.99it/s]
12/16/2021 10:26:00 - INFO - datasets.utils.info_utils - Unable to verify checksums.
12/16/2021 10:26:00 - INFO - datasets.builder - Generating split train
12/16/2021 10:26:01 - INFO - datasets.builder - Generating split validation
12/16/2021 10:26:02 - INFO - datasets.utils.info_utils - Unable to verify splits sizes.
Dataset json downloaded and prepared to datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426. Subsequent calls will reuse this data.
100%|█████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 53.54it/s]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux
- Python version: 3.8.10
- PyArrow version: 6.0.1
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