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https://api.github.com/repos/huggingface/datasets/issues/4717 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4717/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4717/comments | https://api.github.com/repos/huggingface/datasets/issues/4717/events | https://github.com/huggingface/datasets/issues/4717 | 1,309,512,483 | I_kwDODunzps5ODZMj | 4,717 | Dataset Viewer issue for LawalAfeez/englishreview-ds-mini | {
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"It's currently working, as far as I understand\r\n\r\nhttps://huggingface.co/datasets/LawalAfeez/englishreview-ds-mini/viewer/LawalAfeez--englishreview-ds-mini/train\r\n\r\n<img width=\"1556\" alt=\"Capture d’écran 2022-07-19 à 09 24 01\" src=\"https://user-images.githubusercontent.com/1676121/179761130-2d7980b9... | 2022-07-19T13:19:39Z | 2022-07-20T08:32:57Z | 2022-07-20T08:32:57Z | NONE | null | null | null | ### Link
_No response_
### Description
Unable to view the split data
### Owner
_No response_ | {
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https://api.github.com/repos/huggingface/datasets/issues/6220 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6220/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6220/comments | https://api.github.com/repos/huggingface/datasets/issues/6220/events | https://github.com/huggingface/datasets/pull/6220 | 1,884,285,980 | PR_kwDODunzps5ZspRb | 6,220 | Set dev version | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6220). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma... | 2023-09-06T15:40:33Z | 2023-09-06T15:52:33Z | 2023-09-06T15:41:13Z | MEMBER | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/2073 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2073/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2073/comments | https://api.github.com/repos/huggingface/datasets/issues/2073/events | https://github.com/huggingface/datasets/pull/2073 | 834,192,501 | MDExOlB1bGxSZXF1ZXN0NTk1MDYyMzQ2 | 2,073 | Fixes check of TF_AVAILABLE and TORCH_AVAILABLE | {
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} | # What is this PR doing
This PR implements the checks if `Tensorflow` and `Pytorch` are available the same way as `transformers` does it. I added the additional checks for the different `Tensorflow` and `torch` versions. #2068 | {
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https://api.github.com/repos/huggingface/datasets/issues/1710 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1710/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1710/comments | https://api.github.com/repos/huggingface/datasets/issues/1710/events | https://github.com/huggingface/datasets/issues/1710 | 781,914,951 | MDU6SXNzdWU3ODE5MTQ5NTE= | 1,710 | IsADirectoryError when trying to download C4 | {
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"I haven't tested C4 on my side so there so there may be a few bugs in the code/adjustments to make.\r\nHere it looks like in c4.py, line 190 one of the `files_to_download` is `'/'` which is invalid.\r\nValid files are paths to local files or URLs to remote files.",
"Fixed once processed data is used instead:\r\n... | 2021-01-08T07:31:30Z | 2022-08-04T11:56:10Z | 2022-08-04T11:55:04Z | NONE | null | null | null | **TLDR**:
I fail to download C4 and see a stacktrace originating in `IsADirectoryError` as an explanation for failure.
How can the problem be fixed?
**VERBOSE**:
I use Python version 3.7 and have the following dependencies listed in my project:
```
datasets==1.2.0
apache-beam==2.26.0
```
When running the following code, where `/data/huggingface/unpacked/` contains a single unzipped `wet.paths` file manually downloaded as per the instructions for C4:
```
from datasets import load_dataset
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
```
I get the following stacktrace:
```
/Users/fredriko/venv/misc/bin/python /Users/fredriko/source/misc/main.py
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/fredriko/.cache/huggingface/datasets/c4/en/2.3.0/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283...
Traceback (most recent call last):
File "/Users/fredriko/source/misc/main.py", line 3, in <module>
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 1066, in _download_and_prepare
pipeline=pipeline,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 582, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/fredriko/.cache/huggingface/modules/datasets_modules/datasets/c4/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283/c4.py", line 190, in _split_generators
file_paths = dl_manager.download_and_extract(files_to_download)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 258, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 189, in download
self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 117, in _record_sizes_checksums
self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 80, in get_size_checksum_dict
with open(path, "rb") as f:
IsADirectoryError: [Errno 21] Is a directory: '/'
Process finished with exit code 1
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5572 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5572/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5572/comments | https://api.github.com/repos/huggingface/datasets/issues/5572/events | https://github.com/huggingface/datasets/issues/5572 | 1,597,257,624 | I_kwDODunzps5fNDeY | 5,572 | Datasets 2.10.0 does not reuse the dataset cache | {
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download_mode="reuse_dataset_if_exists" will always consider that a dataset doesn't exist.
Specifically, upon losing an internet connection trying to load a dataset for a second time in ten seconds, a connection error results, showing a breakpoint of:
```
File ~/jupyterlab/.direnv/python-3.9.6/lib/python3.9/site-packages/datasets/load.py:1174, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1165 except Exception as e: # noqa: catch any exception of hf_hub and consider that the dataset doesn't exist
1166 if isinstance(
1167 e,
1168 (
(...)
1172 ),
1173 ):
-> 1174 raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})")
1175 elif "404" in str(e):
1176 msg = f"Dataset '{path}' doesn't exist on the Hub"
ConnectionError: Couldn't reach 'lsb/tenk' on the Hub (ConnectionError)
```
This has been around since at least v2.0.
### Steps to reproduce the bug
```
from datasets import load_dataset
import numpy as np
tenk = load_dataset("lsb/tenk") # ten thousand integers
print(np.average(tenk['train']['a'])) # prints 4999.5
### now disconnect your internet
tenk_too = load_dataset("lsb/tenk", download_mode="reuse_dataset_if_exists")
# Raises ConnectionError: Couldn't reach 'lsb/tenk' on the Hub (ConnectionError)
```
### Expected behavior
I expected that I would be able to reuse the dataset I just downloaded.
### Environment info
- `datasets` version: 2.10.0
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.5.2 | {
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} | Adding the ASSIN dataset, a Portuguese language dataset for Natural Language Inference and Semantic Similarity Scoring | {
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https://api.github.com/repos/huggingface/datasets/issues/5195 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5195/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5195/comments | https://api.github.com/repos/huggingface/datasets/issues/5195/events | https://github.com/huggingface/datasets/pull/5195 | 1,434,290,689 | PR_kwDODunzps5CHhF2 | 5,195 | [wip testing docs] | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5195). All of your documentation changes will be reflected on that endpoint."
] | 2022-11-03T08:37:34Z | 2023-04-04T15:10:37Z | 2023-04-04T15:10:33Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5150 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5150/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5150/comments | https://api.github.com/repos/huggingface/datasets/issues/5150/events | https://github.com/huggingface/datasets/issues/5150 | 1,420,684,999 | I_kwDODunzps5Ure7H | 5,150 | Problems after upgrading to 2.6.1 | {
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"Hi! I can't reproduce the error following these steps. Can you please provide a reproducible example?",
"I faced the same issue:\r\n\r\n### Repro\r\n```\r\n!pip install datasets==2.6.1\r\nimport datasets as Dataset\r\ndataset = Dataset.from_pandas(dataframe)\r\ndataset.save_to_disk(local)\r\n\r\n!pip install dat... | 2022-10-24T11:32:36Z | 2023-01-03T15:26:00Z | null | NONE | null | null | null | ### Describe the bug
Loading a dataset_dict from disk with `load_from_disk` is now creating a `KeyError "length"` that was not occurring in v2.5.2.
Context:
- Each individual dataset in the dict is created with `Dataset.from_pandas`
- The dataset_dict is create from a dict of `Dataset`s, e.g., `DatasetDict({"train": train_ds, "validation": val_ds})
- The pandas dataframe, besides text columns, has a column with a dictionary inside and potentially different keys in each row. Correctly the `Dataset.from_pandas` function adds `key: None` to all dictionaries in each row so that the schema can be correctly inferred.
### Steps to reproduce the bug
Steps to reproduce:
- Upgrade to datasets==2.6.1
- Create a dataset from pandas dataframe with `Dataset.from_pandas`
- Create a dataset_dict from a dict of `Dataset`s, e.g., `DatasetDict({"train": train_ds, "validation": val_ds})
- Save to disk with the `save` function
### Expected behavior
Same as in v2.5.2, that is load from disk without errors
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.4.209-129.367.amzn2int.x86_64-x86_64-with-glibc2.26
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/3443 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3443/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3443/comments | https://api.github.com/repos/huggingface/datasets/issues/3443/events | https://github.com/huggingface/datasets/pull/3443 | 1,082,052,833 | PR_kwDODunzps4v8QDX | 3,443 | Extend iter_archive to support file object input | {
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} | This PR adds support to passing a file object to `[Streaming]DownloadManager.iter_archive`.
With this feature, we can iterate over a tar file inside another tar file. | {
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https://api.github.com/repos/huggingface/datasets/issues/5990 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5990/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5990/comments | https://api.github.com/repos/huggingface/datasets/issues/5990/events | https://github.com/huggingface/datasets/issues/5990 | 1,774,389,854 | I_kwDODunzps5pwwpe | 5,990 | Pushing a large dataset on the hub consistently hangs | {
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"Hi @AntreasAntoniou , sorry to know you are facing this issue. To help debugging it, could you tell me:\r\n- What is the total dataset size?\r\n- Is it always failing on the same shard or is the hanging problem happening randomly?\r\n- Were you able to save the dataset as parquet locally? This would help us determ... | 2023-06-10T14:46:47Z | 2023-08-17T09:54:11Z | null | NONE | null | null | null | ### Describe the bug
Once I have locally built a large dataset that I want to push to hub, I use the recommended approach of .push_to_hub to get the dataset on the hub, and after pushing a few shards, it consistently hangs. This has happened over 40 times over the past week, and despite my best efforts to try and catch this happening and kill a process and restart, it seems to be extremely time wasting -- so I came to you to report this and to seek help.
I already tried installing hf_transfer, but it doesn't support Byte file uploads so I uninstalled it.
### Reproduction
```python
import multiprocessing as mp
import pathlib
from math import ceil
import datasets
import numpy as np
from tqdm.auto import tqdm
from tali.data.data import select_subtitles_between_timestamps
from tali.utils import load_json
tali_dataset_dir = "/data/"
if __name__ == "__main__":
full_dataset = datasets.load_dataset(
"Antreas/TALI", num_proc=mp.cpu_count(), cache_dir=tali_dataset_dir
)
def data_generator(set_name, percentage: float = 1.0):
dataset = full_dataset[set_name]
for item in tqdm(dataset):
video_list = item["youtube_content_video"]
video_list = np.random.choice(
video_list, int(ceil(len(video_list) * percentage))
)
if len(video_list) == 0:
continue
captions = item["youtube_subtitle_text"]
captions = select_subtitles_between_timestamps(
subtitle_dict=load_json(
captions.replace(
"/data/",
tali_dataset_dir,
)
),
starting_timestamp=0,
ending_timestamp=100000000,
)
for video_path in video_list:
temp_path = video_path.replace("/data/", tali_dataset_dir)
video_path_actual: pathlib.Path = pathlib.Path(temp_path)
if video_path_actual.exists():
item["youtube_content_video"] = open(video_path_actual, "rb").read()
item["youtube_subtitle_text"] = captions
yield item
train_generator = lambda: data_generator("train", percentage=0.1)
val_generator = lambda: data_generator("val")
test_generator = lambda: data_generator("test")
train_data = datasets.Dataset.from_generator(
train_generator,
num_proc=mp.cpu_count(),
writer_batch_size=5000,
cache_dir=tali_dataset_dir,
)
val_data = datasets.Dataset.from_generator(
val_generator,
writer_batch_size=5000,
num_proc=mp.cpu_count(),
cache_dir=tali_dataset_dir,
)
test_data = datasets.Dataset.from_generator(
test_generator,
writer_batch_size=5000,
num_proc=mp.cpu_count(),
cache_dir=tali_dataset_dir,
)
dataset = datasets.DatasetDict(
{
"train": train_data,
"val": val_data,
"test": test_data,
}
)
succesful_competion = False
while not succesful_competion:
try:
dataset.push_to_hub(repo_id="Antreas/TALI-small", max_shard_size="5GB")
succesful_competion = True
except Exception as e:
print(e)
```
### Logs
```shell
Pushing dataset shards to the dataset hub: 33%|██████████████████████████████████████▎ | 7/21 [24:33<49:06, 210.45s/it]
Error while uploading 'data/val-00007-of-00021-6b216a984af1a4c8.parquet' to the Hub.
Pushing split train to the Hub.
Resuming upload of the dataset shards.
Pushing dataset shards to the dataset hub: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 46/46 [42:10<00:00, 55.01s/it]
Pushing split val to the Hub.
Resuming upload of the dataset shards.
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00, 1.55ba/s]
Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:23<00:00, 23.51s/it]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.39ba/s]
Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:30<00:00, 30.19s/it]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.28ba/s]
Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:24<00:00, 24.08s/it]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.42ba/s]
Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:23<00:00, 23.97s/it]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.49ba/s]
Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.54ba/s^
Upload 1 LFS files: 0%| | 0/1 [04:42<?, ?it/s]
Pushing dataset shards to the dataset hub: 52%|████████████████████████████████████████████████████████████▏ | 11/21 [17:23<15:48, 94.82s/it]
That's where it got stuck
```
### System info
```shell
- huggingface_hub version: 0.15.1
- Platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.35
- Python version: 3.10.11
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /root/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: Antreas
- Configured git credential helpers: store
- FastAI: N/A
- Tensorflow: N/A
- Torch: 2.1.0.dev20230606+cu121
- Jinja2: 3.1.2
- Graphviz: N/A
- Pydot: N/A
- Pillow: 9.5.0
- hf_transfer: N/A
- gradio: N/A
- numpy: 1.24.3
- ENDPOINT: https://huggingface.co
- HUGGINGFACE_HUB_CACHE: /root/.cache/huggingface/hub
- HUGGINGFACE_ASSETS_CACHE: /root/.cache/huggingface/assets
- HF_TOKEN_PATH: /root/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
```
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https://api.github.com/repos/huggingface/datasets/issues/4484 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4484/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4484/comments | https://api.github.com/repos/huggingface/datasets/issues/4484/events | https://github.com/huggingface/datasets/pull/4484 | 1,269,383,811 | PR_kwDODunzps45jywZ | 4,484 | Better ImportError message when a dataset script dependency is missing | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Discussed offline with @mariosasko, merging :)",
"Fwiw, i think this same issue is occurring on the datasets website page, where preview isn't available due to the `bigbench` import error",
"For the preview of BigBench datasets, ... | 2022-06-13T12:44:37Z | 2022-07-08T14:30:44Z | 2022-06-13T13:50:47Z | MEMBER | null | 0 | {
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} | When a depenency is missing for a dataset script, an ImportError message is shown, with a tip to install the missing dependencies. This message is not ideal at the moment: it may show duplicate dependencies, and is not very readable.
I improved it from
```
ImportError: To be able to use bigbench, you need to install the following dependencies['bigbench', 'bigbench', 'bigbench', 'bigbench'] using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz" bigbench bigbench bigbench' for instance'
```
to
```
ImportError: To be able to use bigbench, you need to install the following dependency: bigbench.
Please install it using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz"' for instance'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5818 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5818/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5818/comments | https://api.github.com/repos/huggingface/datasets/issues/5818/events | https://github.com/huggingface/datasets/issues/5818 | 1,695,052,555 | I_kwDODunzps5lCHML | 5,818 | Ability to update a dataset | {
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"This [reply](https://discuss.huggingface.co/t/how-do-i-add-things-rows-to-an-already-saved-dataset/27423) from @mariosasko on the forums may be useful :)",
"In this case, I think we can avoid the `PermissionError` by unpacking the underlying `ConcatenationTable` and saving only the newly added data blocks (in ne... | 2023-05-04T01:08:13Z | 2023-05-04T20:43:39Z | null | NONE | null | null | null | ### Feature request
The ability to load a dataset, add or change something, and save it back to disk.
Maybe it's possible, but I can't work out how to do it, e.g. this fails:
```py
import datasets
dataset = datasets.load_from_disk("data/test1")
dataset = dataset.add_item({"text": "A new item"})
dataset.save_to_disk("data/test1")
```
With the error:
```
PermissionError: Tried to overwrite /mnt/c/Users/david/py/learning/mini_projects/data_sorting_and_filtering/data/test1 but a dataset can't overwrite itself.
```
### Motivation
My use case is that I want to process a dataset in a particular way but it doesn't fit in memory if I do it in one go. So I want to perform a loop and at each step in the loop, process one shard and append it to an ever-growing dataset. The code in the loop will load a dataset, add some rows, then save it again.
Maybe I'm just thinking about things incorrectly and there's a better approach. FWIW I can't use `dataset.map()` to do the task because that doesn't work with `num_proc` when adding rows, so is confined to a single process which is too slow.
The only other way I can think of is to create a new file each time, but surely that's not how people do this sort of thing.
### Your contribution
na | {
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https://api.github.com/repos/huggingface/datasets/issues/5017 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5017/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5017/comments | https://api.github.com/repos/huggingface/datasets/issues/5017/events | https://github.com/huggingface/datasets/issues/5017 | 1,384,022,463 | I_kwDODunzps5SfoG_ | 5,017 | xcsr: X-CSQA simply uses english for all alleged non-english data | {
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"Thanks for reporting, @thesofakillers. Good catch. We are fixing this. "
] | 2022-09-23T16:11:54Z | 2022-09-26T10:57:31Z | 2022-09-26T10:57:31Z | NONE | null | null | null | ## Describe the bug
All the alleged non-english subcollections for the X-CSQA task in the [xcsr benchmark dataset ](https://huggingface.co/datasets/xcsr) seem to be copies of the english subcollection, rather than translations. This is in contrast to the data description:
> we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR
## Steps to reproduce the bug
```python
# let's say you want to load the french X-CSQA subcollection
french = datasets.load_dataset("xcsr", "X-CSQA-fr")
# for good measure, let's load english too
english = datasets.load_dataset("xcsr", "X-CSQA-en")
# let's inspect
"".join(english['test'][0]['question']['stem'])
# output: 'The people wanted to stop the parade, so what did they set up to thwart it?'
"".join(french['test'][0]['question']['stem'])
# output: 'The people wanted to stop the parade, so what did they set up to thwart it?'
# what? Why are they both in english?
# I've checked this for validation and train splits too, across many datapoints. It's all the same english dataset
# maybe i need to look better?
french['test'].unique('lang')
# output: ['en']
# no, it's all english
```
## Expected results
Accessing a subcollection in language X should return a subcollection containg samples in language X
## Actual results
Accessing a subcollection in language X returns a subcollection containing samples in English.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.1
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
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https://api.github.com/repos/huggingface/datasets/issues/1449 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1449/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1449/comments | https://api.github.com/repos/huggingface/datasets/issues/1449/events | https://github.com/huggingface/datasets/pull/1449 | 761,083,210 | MDExOlB1bGxSZXF1ZXN0NTM1ODA0MzEy | 1,449 | add W&I + LOCNESS dataset (BEA-2019 workshop shared task on GEC) [PROPER] | {
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"linter your code with flake8 and also run the commands present in Makefile for proper formatting \r\n",
"merging since the CI is fixed on master"
] | 2020-12-10T09:51:08Z | 2020-12-11T17:07:46Z | 2020-12-11T17:07:46Z | CONTRIBUTOR | null | 0 | {
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} | - **Name:** W&I + LOCNESS dataset (from the BEA-2019 workshop shared task on GEC)
- **Description:** https://www.cl.cam.ac.uk/research/nl/bea2019st/#data
- **Paper:** https://www.aclweb.org/anthology/W19-4406/
- **Motivation:** This is a recent dataset (actually two in one) for grammatical error correction and is used for benchmarking in this field of NLP.
### Checkbox
- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass.
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https://api.github.com/repos/huggingface/datasets/issues/2279 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2279/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2279/comments | https://api.github.com/repos/huggingface/datasets/issues/2279/events | https://github.com/huggingface/datasets/issues/2279 | 870,431,662 | MDU6SXNzdWU4NzA0MzE2NjI= | 2,279 | Compatibility with Ubuntu 18 and GLIBC 2.27? | {
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"From the trace this seems like an error in the tokenizer library instead.\r\n\r\nDo you mind opening an issue at https://github.com/huggingface/tokenizers instead?",
"Hi @tginart, thanks for reporting.\r\n\r\nI think this issue is already open at `tokenizers` library: https://github.com/huggingface/tokenizers/is... | 2021-04-28T22:08:07Z | 2021-04-29T07:42:42Z | 2021-04-29T07:42:42Z | NONE | null | null | null | ## Describe the bug
For use on Ubuntu systems, it seems that datasets requires GLIBC 2.29. However, Ubuntu 18 runs with GLIBC 2.27 and it seems [non-trivial to upgrade GLIBC to 2.29 for Ubuntu 18 users](https://www.digitalocean.com/community/questions/how-install-glibc-2-29-or-higher-in-ubuntu-18-04).
I'm not sure if there is anything that can be done about this, but I'd like to confirm that using huggingface/datasets requires either an upgrade to Ubuntu 19/20 or a hand-rolled install of a higher version of GLIBC.
## Steps to reproduce the bug
1. clone the transformers repo
2. move to examples/pytorch/language-modeling
3. run example command:
```python run_clm.py --model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train --do_eval --output_dir /tmp/test-clm```
## Expected results
As described in the transformers repo.
## Actual results
```Traceback (most recent call last):
File "run_clm.py", line 34, in <module>
from transformers import (
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/__init__.py", line 2487, in __getattr__
return super().__getattr__(name)
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/file_utils.py", line 1699, in __getattr__
module = self._get_module(self._class_to_module[name])
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/__init__.py", line 2481, in _get_module
return importlib.import_module("." + module_name, self.__name__)
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/__init__.py", line 19, in <module>
from . import (
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/layoutlm/__init__.py", line 23, in <module>
from .tokenization_layoutlm import LayoutLMTokenizer
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/layoutlm/tokenization_layoutlm.py", line 19, in <module>
from ..bert.tokenization_bert import BertTokenizer
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/bert/tokenization_bert.py", line 23, in <module>
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/tokenization_utils.py", line 26, in <module>
from .tokenization_utils_base import (
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 68, in <module>
from tokenizers import AddedToken
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/tokenizers/__init__.py", line 79, in <module>
from .tokenizers import (
ImportError: /lib/x86_64-linux-gnu/libm.so.6: version `GLIBC_2.29' not found (required by /home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/tokenizers/tokenizers.cpython-37m-x86_64-linux-gnu.so)
```
## Versions
Paste the output of the following code:
```
- Datasets: 1.6.1
- Python: 3.7.10 (default, Feb 26 2021, 18:47:35)
[GCC 7.3.0]
- Platform: Linux-4.15.0-128-generic-x86_64-with-debian-buster-sid
```
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https://api.github.com/repos/huggingface/datasets/issues/6180 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6180/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6180/comments | https://api.github.com/repos/huggingface/datasets/issues/6180/events | https://github.com/huggingface/datasets/pull/6180 | 1,867,032,578 | PR_kwDODunzps5Yy1r- | 6,180 | Use `hf-internal-testing` repos for hosting test dataset repos | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-08-25T13:10:26Z | 2023-08-25T16:58:02Z | 2023-08-25T16:46:22Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/1520 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1520/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1520/comments | https://api.github.com/repos/huggingface/datasets/issues/1520/events | https://github.com/huggingface/datasets/pull/1520 | 764,140,938 | MDExOlB1bGxSZXF1ZXN0NTM4MzU5MTA5 | 1,520 | ru_reviews dataset adding | {
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"Hi @lhoestq \r\n\r\nI have added the readme as well \r\n\r\nPlease do have a look at it when suitable ",
"Chatted with @darshan-gandhi on Slack about parsing examples into a separate text and sentiment field",
"Thanks for your contribution, @darshan-gandhi. Are you still interested in adding this dataset?\r\n... | 2020-12-12T18:13:06Z | 2022-10-03T09:38:42Z | 2022-10-03T09:38:42Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/2443 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2443/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2443/comments | https://api.github.com/repos/huggingface/datasets/issues/2443/events | https://github.com/huggingface/datasets/issues/2443 | 909,983,574 | MDU6SXNzdWU5MDk5ODM1NzQ= | 2,443 | Some tests hang on Windows | {
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"Hi ! That would be nice indeed to at least have a warning, since we don't handle the max path length limit.\r\nAlso if we could have an error instead of an infinite loop I'm sure windows users will appreciate that",
"Unfortunately, I know this problem very well... 😅 \r\n\r\nI remember having proposed to throw a... | 2021-06-03T00:27:30Z | 2021-06-28T08:47:39Z | 2021-06-28T08:47:39Z | CONTRIBUTOR | null | null | null | Currently, several tests hang on Windows if the max path limit of 260 characters is not disabled. This happens due to the changes introduced by #2223 that cause an infinite loop in `WindowsFileLock` described in #2220. This can be very tricky to debug, so I think now is a good time to address these issues/PRs. IMO throwing an error is too harsh, but maybe we can emit a warning in the top-level `__init__.py ` on startup if long paths are not enabled.
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https://api.github.com/repos/huggingface/datasets/issues/4478 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4478/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4478/comments | https://api.github.com/repos/huggingface/datasets/issues/4478/events | https://github.com/huggingface/datasets/issues/4478 | 1,268,358,213 | I_kwDODunzps5LmZxF | 4,478 | Dataset slow during model training | {
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"Hi ! cc @Rocketknight1 maybe you know better ?\r\n\r\nI'm not too familiar with `tf.data.experimental.save`. Note that `datasets` uses memory mapping, so depending on your hardware and the disk you are using you can expect performance differences with a dataset loaded in RAM",
"Hi @lehrig, I suspect what's happe... | 2022-06-11T19:40:19Z | 2022-06-14T12:04:31Z | null | NONE | null | null | null | ## Describe the bug
While migrating towards 🤗 Datasets, I encountered an odd performance degradation: training suddenly slows down dramatically. I train with an image dataset using Keras and execute a `to_tf_dataset` just before training.
First, I have optimized my dataset following https://discuss.huggingface.co/t/solved-image-dataset-seems-slow-for-larger-image-size/10960/6, which actually improved the situation from what I had before but did not completely solve it.
Second, I saved and loaded my dataset using `tf.data.experimental.save` and `tf.data.experimental.load` before training (for which I would have expected no performance change). However, I ended up with the performance I had before tinkering with 🤗 Datasets.
Any idea what's the reason for this and how to speed-up training with 🤗 Datasets?
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
import os
dataset_dir = "./dataset"
prep_dataset_dir = "./prepdataset"
model_dir = "./model"
# Load Data
dataset = load_dataset("Lehrig/Monkey-Species-Collection", "downsized")
def read_image_file(example):
with open(example["image"].filename, "rb") as f:
example["image"] = {"bytes": f.read()}
return example
dataset = dataset.map(read_image_file)
dataset.save_to_disk(dataset_dir)
# Preprocess
from datasets import (
Array3D,
DatasetDict,
Features,
load_from_disk,
Sequence,
Value
)
import numpy as np
from transformers import ImageFeatureExtractionMixin
dataset = load_from_disk(dataset_dir)
num_classes = dataset["train"].features["label"].num_classes
one_hot_matrix = np.eye(num_classes)
feature_extractor = ImageFeatureExtractionMixin()
def to_pixels(image):
image = feature_extractor.resize(image, size=size)
image = feature_extractor.to_numpy_array(image, channel_first=False)
image = image / 255.0
return image
def process(examples):
examples["pixel_values"] = [
to_pixels(image) for image in examples["image"]
]
examples["label"] = [
one_hot_matrix[label] for label in examples["label"]
]
return examples
features = Features({
"pixel_values": Array3D(dtype="float32", shape=(size, size, 3)),
"label": Sequence(feature=Value(dtype="int32"), length=num_classes)
})
prep_dataset = dataset.map(
process,
remove_columns=["image"],
batched=True,
batch_size=batch_size,
num_proc=2,
features=features,
)
prep_dataset = prep_dataset.with_format("numpy")
# Split
train_dev_dataset = prep_dataset['test'].train_test_split(
test_size=test_size,
shuffle=True,
seed=seed
)
train_dev_test_dataset = DatasetDict({
'train': train_dev_dataset['train'],
'dev': train_dev_dataset['test'],
'test': prep_dataset['test'],
})
train_dev_test_dataset.save_to_disk(prep_dataset_dir)
# Train Model
import datetime
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.applications import InceptionV3
from tensorflow.keras.layers import Dense, Dropout, GlobalAveragePooling2D, BatchNormalization
from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping
from transformers import DefaultDataCollator
dataset = load_from_disk(prep_data_dir)
data_collator = DefaultDataCollator(return_tensors="tf")
train_dataset = dataset["train"].to_tf_dataset(
columns=['pixel_values'],
label_cols=['label'],
shuffle=True,
batch_size=batch_size,
collate_fn=data_collator
)
validation_dataset = dataset["dev"].to_tf_dataset(
columns=['pixel_values'],
label_cols=['label'],
shuffle=False,
batch_size=batch_size,
collate_fn=data_collator
)
print(f'{datetime.datetime.now()} - Saving Data')
tf.data.experimental.save(train_dataset, model_dir+"/train")
tf.data.experimental.save(validation_dataset, model_dir+"/val")
print(f'{datetime.datetime.now()} - Loading Data')
train_dataset = tf.data.experimental.load(model_dir+"/train")
validation_dataset = tf.data.experimental.load(model_dir+"/val")
shape = np.shape(dataset["train"][0]["pixel_values"])
backbone = InceptionV3(
include_top=False,
weights='imagenet',
input_shape=shape
)
for layer in backbone.layers:
layer.trainable = False
model = Sequential()
model.add(backbone)
model.add(GlobalAveragePooling2D())
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(64, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(10, activation='softmax'))
model.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy']
)
print(model.summary())
earlyStopping = EarlyStopping(
monitor='val_loss',
patience=10,
verbose=0,
mode='min'
)
mcp_save = ModelCheckpoint(
f'{model_dir}/best_model.hdf5',
save_best_only=True,
monitor='val_loss',
mode='min'
)
reduce_lr_loss = ReduceLROnPlateau(
monitor='val_loss',
factor=0.1,
patience=7,
verbose=1,
min_delta=0.0001,
mode='min'
)
hist = model.fit(
train_dataset,
epochs=epochs,
validation_data=validation_dataset,
callbacks=[earlyStopping, mcp_save, reduce_lr_loss]
)
```
## Expected results
Same performance when training without my "save/load hack" or a good explanation/recommendation about the issue.
## Actual results
Performance slower without my "save/load hack".
**Epoch Breakdown (without my "save/load hack"):**
- Epoch 1/10
41s 2s/step - loss: 1.6302 - accuracy: 0.5048 - val_loss: 1.4713 - val_accuracy: 0.3273 - lr: 0.0010
- Epoch 2/10
32s 2s/step - loss: 0.5357 - accuracy: 0.8510 - val_loss: 1.0447 - val_accuracy: 0.5818 - lr: 0.0010
- Epoch 3/10
36s 3s/step - loss: 0.3547 - accuracy: 0.9231 - val_loss: 0.6245 - val_accuracy: 0.7091 - lr: 0.0010
- Epoch 4/10
36s 3s/step - loss: 0.2721 - accuracy: 0.9231 - val_loss: 0.3395 - val_accuracy: 0.9091 - lr: 0.0010
- Epoch 5/10
32s 2s/step - loss: 0.1676 - accuracy: 0.9856 - val_loss: 0.2187 - val_accuracy: 0.9636 - lr: 0.0010
- Epoch 6/10
42s 3s/step - loss: 0.2066 - accuracy: 0.9615 - val_loss: 0.1635 - val_accuracy: 0.9636 - lr: 0.0010
- Epoch 7/10
32s 2s/step - loss: 0.1814 - accuracy: 0.9423 - val_loss: 0.1418 - val_accuracy: 0.9636 - lr: 0.0010
- Epoch 8/10
32s 2s/step - loss: 0.1301 - accuracy: 0.9856 - val_loss: 0.1388 - val_accuracy: 0.9818 - lr: 0.0010
- Epoch 9/10
loss: 0.1102 - accuracy: 0.9856 - val_loss: 0.1185 - val_accuracy: 0.9818 - lr: 0.0010
- Epoch 10/10
32s 2s/step - loss: 0.1013 - accuracy: 0.9808 - val_loss: 0.0978 - val_accuracy: 0.9818 - lr: 0.0010
**Epoch Breakdown (with my "save/load hack"):**
- Epoch 1/10
13s 625ms/step - loss: 3.0478 - accuracy: 0.1146 - val_loss: 2.3061 - val_accuracy: 0.0727 - lr: 0.0010
- Epoch 2/10
0s 80ms/step - loss: 2.3105 - accuracy: 0.2656 - val_loss: 2.3085 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 3/10
0s 77ms/step - loss: 1.8608 - accuracy: 0.3542 - val_loss: 2.3130 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 4/10
1s 98ms/step - loss: 1.8677 - accuracy: 0.3750 - val_loss: 2.3157 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 5/10
1s 204ms/step - loss: 1.5561 - accuracy: 0.4583 - val_loss: 2.3049 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 6/10
1s 210ms/step - loss: 1.4657 - accuracy: 0.4896 - val_loss: 2.2944 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 7/10
1s 205ms/step - loss: 1.4018 - accuracy: 0.5312 - val_loss: 2.2917 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 8/10
1s 207ms/step - loss: 1.2370 - accuracy: 0.5729 - val_loss: 2.2814 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 9/10
1s 214ms/step - loss: 1.1190 - accuracy: 0.6250 - val_loss: 2.2733 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 10/10
1s 207ms/step - loss: 1.1484 - accuracy: 0.6302 - val_loss: 2.2624 - val_accuracy: 0.0909 - lr: 0.0010
## Environment info
- `datasets` version: 2.2.2
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
- TensorFlow: 2.8.0
- GPU (used during training): Tesla V100-SXM2-32GB
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"Thanks, looking at it!",
"Finally fixed! We updated the dataset viewer and it fixed the issue.\r\n\r\nhttps://huggingface.co/datasets/universal_dependencies/viewer/aqz_tudet/train\r\n\r\n<img width=\"1561\" alt=\"Capture d’écran 2022-09-07 à 13 29 18\" src=\"https://user-images.githubusercontent.com/1676121/18... | 2022-06-29T08:50:29Z | 2022-09-07T11:29:28Z | 2022-09-07T11:29:27Z | NONE | null | null | null | ### Link
https://huggingface.co/datasets/universal_dependencies
### Description
invalid json response body at https://datasets-server.huggingface.co/splits?dataset=universal_dependencies reason: Unexpected token I in JSON at position 0
### Owner
_No response_ | {
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https://api.github.com/repos/huggingface/datasets/issues/4883 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4883/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4883/comments | https://api.github.com/repos/huggingface/datasets/issues/4883/events | https://github.com/huggingface/datasets/issues/4883 | 1,349,083,235 | I_kwDODunzps5QaWBj | 4,883 | With dataloader RSS memory consumed by HF datasets monotonically increases | {
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"Are you sure there is a leak? How can I see it? You shared the script but not the output which you believe should indicate a leak.\r\n\r\nI modified your reproduction script to print only once per try as your original was printing too much info and you absolutely must add `gc.collect()` when doing any memory measu... | 2022-08-24T08:42:54Z | 2023-10-16T14:32:08Z | null | CONTRIBUTOR | null | null | null | ## Describe the bug
When the HF datasets is used in conjunction with PyTorch Dataloader, the RSS memory of the process keeps on increasing when it should stay constant.
## Steps to reproduce the bug
Run and observe the output of this snippet which logs RSS memory.
```python
import psutil
import os
from transformers import BertTokenizer
from datasets import load_dataset
from torch.utils.data import DataLoader
BATCH_SIZE = 32
NUM_TRIES = 10
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def transform(x):
x.update(tokenizer(x["text"], return_tensors="pt", max_length=64, padding="max_length", truncation=True))
x.pop("text")
x.pop("label")
return x
dataset = load_dataset("imdb", split="train")
dataset.set_transform(transform)
train_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)
mem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
count = 0
while count < NUM_TRIES:
for idx, batch in enumerate(train_loader):
mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
print(count, idx, mem_after - mem_before)
count += 1
```
## Expected results
Memory should not increase after initial setup and loading of the dataset
## Actual results
Memory continuously increases as can be seen in the log.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 7.0.0
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https://api.github.com/repos/huggingface/datasets/issues/4864 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4864/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4864/comments | https://api.github.com/repos/huggingface/datasets/issues/4864/events | https://github.com/huggingface/datasets/issues/4864 | 1,344,410,043 | I_kwDODunzps5QIhG7 | 4,864 | Allow pathlib PoxisPath in Dataset.read_json | {
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"This same error will occur using `ds = datasets.load_dataset('json', data_files=['test.jsonl'])`",
"@cccntu I want to make a quick fix for this, but I am struggling to find where the json dataset builder is. Do you know?",
"@vvvm23 I think you mean think:\r\n```python\r\nds = datasets.load_dataset('json', data... | 2022-08-19T12:59:17Z | 2023-03-12T11:25:49Z | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
```
from pathlib import Path
from datasets import Dataset
ds = Dataset.read_json(Path('data.json'))
```
causes an error
```
AttributeError: 'PosixPath' object has no attribute 'decode'
```
**Describe the solution you'd like**
It should be able to accept PosixPath and read the json from inside. | {
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https://api.github.com/repos/huggingface/datasets/issues/1811 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1811/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1811/comments | https://api.github.com/repos/huggingface/datasets/issues/1811/events | https://github.com/huggingface/datasets/issues/1811 | 799,211,060 | MDU6SXNzdWU3OTkyMTEwNjA= | 1,811 | Unable to add Multi-label Datasets | {
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"Thanks for adding this dataset! As far as I know `supervised_keys` is mostly a holdover from TFDS, but isn't really used, so feel free to drop it (@lhoestq or @thomwolf correct me if I'm wrong). It definitely shouldn't be blocking :) ",
"I can confirm that it comes from TFDS and is not used at the moment.",
"... | 2021-02-02T11:50:56Z | 2021-02-18T14:16:31Z | 2021-02-18T14:16:31Z | CONTRIBUTOR | null | null | null | I am trying to add [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as
`supervised_keys=("img", "fine_label")` raises no issue. But trying `supervised_keys=("img", "fine_label","coarse_label")` leads to this error :
```python
Traceback (most recent call last):
File "test_script.py", line 2, in <module>
d = load_dataset('./datasets/cifar100')
File "~/datasets/src/datasets/load.py", line 668, in load_dataset
**config_kwargs,
File "~/datasets/src/datasets/builder.py", line 896, in __init__
super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
File "~/datasets/src/datasets/builder.py", line 247, in __init__
info.update(self._info())
File "~/.cache/huggingface/modules/datasets_modules/datasets/cifar100/61d2489b2d4a4abc34201432541b7380984ec714e290817d9a1ee318e4b74e0f/cifar100.py", line 79, in _info
citation=_CITATION,
File "<string>", line 19, in __init__
File "~/datasets/src/datasets/info.py", line 136, in __post_init__
self.supervised_keys = SupervisedKeysData(*self.supervised_keys)
TypeError: __init__() takes from 1 to 3 positional arguments but 4 were given
```
Is there a way I can fix this?
Also, what does adding `supervised_keys` do? Is it necessary? How would I specify `supervised_keys` for a multi-input, multi-label dataset?
Thanks,
Gunjan | {
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"Sounds good ! Can I help with anything ?"
] | 2022-09-23T08:48:10Z | 2022-10-05T07:15:57Z | 2022-10-05T07:15:57Z | MEMBER | null | null | null | Before merging:
- #4974
TODO:
- [x] Create label: ["dataset contribution"](https://github.com/huggingface/datasets/pulls?q=label%3A%22dataset+contribution%22)
- [x] Create project: [Datasets: Transfer datasets to Hub](https://github.com/orgs/huggingface/projects/22/)
- [x] PRs:
- [x] Add dataset: we should recommend transfer all additions of datasets to the Hub, under the appropriate namespace; no more additions of datasets on GitHub
- [x] Update dataset: in general, we should merge bug fixes; enhancements should be considered on a case-by-case basis, depending on whether there is a more suitable namespace on the Hub
- [ ] Issues
Finally:
- [x] #4974
Let me know what you think! :hugs: | {
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- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). | {
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https://api.github.com/repos/huggingface/datasets/issues/2672 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2672/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2672/comments | https://api.github.com/repos/huggingface/datasets/issues/2672/events | https://github.com/huggingface/datasets/pull/2672 | 947,294,605 | MDExOlB1bGxSZXF1ZXN0NjkyMjk2NDQ4 | 2,672 | Fix potential DuplicatedKeysError in LibriSpeech | {
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- Fix unnecessary path join.
- Fix potential DiplicatedKeysError by ensuring keys are unique.
We should promote as a good practice, that the keys should be programmatically generated as unique, instead of read from data (which might be not unique). | {
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https://api.github.com/repos/huggingface/datasets/issues/3632 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3632/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3632/comments | https://api.github.com/repos/huggingface/datasets/issues/3632/events | https://github.com/huggingface/datasets/issues/3632 | 1,115,027,185 | I_kwDODunzps5Cdfbx | 3,632 | Adding CC-100: Monolingual Datasets from Web Crawl Data (Datasets links are invalid) | {
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"Hi @AnzorGozalishvili,\r\n\r\nMaybe their site was temporarily down, but it seems to work fine now.\r\n\r\nCould you please try again and confirm if the problem persists? ",
"Hi @albertvillanova \r\nI checked and it works. \r\nIt seems that it was really temporarily down.\r\nThanks!"
] | 2022-01-26T13:35:37Z | 2022-02-10T06:58:11Z | 2022-02-10T06:58:11Z | CONTRIBUTOR | null | null | null | ## Describe the bug
The dataset links are no longer valid for CC-100. It seems that the website which was keeping these files are no longer accessible and therefore this dataset became unusable.
Check out the dataset [homepage](http://data.statmt.org/cc-100/) which isn't accessible.
Also the URLs for dataset file per language isn't accessible: http://data.statmt.org/cc-100/<language code here>.txt.xz (language codes: am, sr, ka, etc.)
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("cc100", "ka")
```
It throws 503 error.
## Expected results
It should successfully download and load dataset but it throws an exception because the dataset files are no longer accessible.
## Environment info
Run from google colab. Just installed the library using pip:
```!pip install -U datasets```
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https://api.github.com/repos/huggingface/datasets/issues/4708 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4708/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4708/comments | https://api.github.com/repos/huggingface/datasets/issues/4708/events | https://github.com/huggingface/datasets/pull/4708 | 1,308,279,700 | PR_kwDODunzps47lewm | 4,708 | Fix require torchaudio and refactor test requirements | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-18T17:24:28Z | 2022-07-22T06:30:56Z | 2022-07-22T06:18:11Z | MEMBER | null | 0 | {
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} | Currently there is a bug in `require_torchaudio` (indeed it is requiring `sox` instead):
```python
def require_torchaudio(test_case):
if find_spec("sox") is None:
...
```
The bug was introduced by:
- #3685
- Commit: https://github.com/huggingface/datasets/pull/3685/commits/b5a3e7122d49c4dcc9333b1d8d18a833fc04b940
which moved
```python
require_sndfile = pytest.mark.skipif(
# In Windows and OS X, soundfile installs sndfile
(sys.platform != "linux" and find_spec("soundfile") is None)
# In Linux, soundfile throws RuntimeError if sndfile not installed with distribution package manager
or (sys.platform == "linux" and find_library("sndfile") is None),
reason="Test requires 'sndfile': `pip install soundfile`; "
"Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`",
)
require_sox = pytest.mark.skipif(
find_library("sox") is None,
reason="Test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`",
)
require_torchaudio = pytest.mark.skipif(find_spec("torchaudio") is None, reason="Test requires 'torchaudio'")
```
to
```python
def require_sndfile(test_case):
"""
Decorator marking a test that requires soundfile.
These tests are skipped when soundfile isn't installed.
"""
if (sys.platform != "linux" and find_spec("soundfile") is None) or (
sys.platform == "linux" and find_library("sndfile") is None
):
test_case = unittest.skip(
"test requires 'sndfile': `pip install soundfile`; "
"Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`",
)(test_case)
return test_case
def require_sox(test_case):
"""
Decorator marking a test that requires sox.
These tests are skipped when sox isn't installed.
"""
if find_library("sox") is None:
return unittest.skip("test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`")(
test_case
)
return test_case
def require_torchaudio(test_case):
"""
Decorator marking a test that requires torchaudio.
These tests are skipped when torchaudio isn't installed.
"""
if find_spec("sox") is None:
return unittest.skip("test requires 'torchaudio'")(test_case)
return test_case
```
This PR;
- fixes the bug in `require_torchaudio`
- refactors the test requirements back to using `pytest` instead of `unittest`
- the text in `pytest.skipif` `reason` can be used if needed in a test body: `require_torchaudio.kwargs["reason"]` | {
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"Reverted the multi-input-text-classification tag from task_categories and added it as task_ids @lhoestq ",
"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-04-27T18:28:14Z | 2022-05-04T10:43:53Z | 2022-05-04T10:36:37Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/1757 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1757/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1757/comments | https://api.github.com/repos/huggingface/datasets/issues/1757/events | https://github.com/huggingface/datasets/issues/1757 | 790,466,509 | MDU6SXNzdWU3OTA0NjY1MDk= | 1,757 | FewRel | {
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"+1",
"@dspoka Please check the following link : https://github.com/thunlp/FewRel\r\nThis link mentions two versions of the datasets. Also, this one seems to be the official link.\r\n\r\nI am assuming this is the correct link and implementing based on the same.",
"Hi @lhoestq,\r\n\r\nThis issue can be closed, I... | 2021-01-20T23:56:03Z | 2021-03-09T02:52:05Z | 2021-03-08T14:34:52Z | NONE | null | null | null | ## Adding a Dataset
- **Name:** FewRel
- **Description:** Large-Scale Supervised Few-Shot Relation Classification Dataset
- **Paper:** @inproceedings{han2018fewrel,
title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation},
author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong},
booktitle={EMNLP},
year={2018}}
- **Data:** https://github.com/ProKil/FewRel
- **Motivation:** relationship extraction dataset that's been used by some state of the art systems that should be incorporated.
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/2582 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2582/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2582/comments | https://api.github.com/repos/huggingface/datasets/issues/2582/events | https://github.com/huggingface/datasets/pull/2582 | 935,859,104 | MDExOlB1bGxSZXF1ZXN0NjgyNzAzNzg3 | 2,582 | Add skip and take | {
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"@lhoestq looks good. I tried with https://huggingface.co/datasets/vblagoje/wikipedia_snippets_streamed and it worked nicely. I would add more unit tests for edge cases. What happens if the n is larger than the total number of samples? Just to make sure these cases are handled properly. ",
"Yup I'll add the tests... | 2021-07-02T15:10:19Z | 2021-07-05T16:06:40Z | 2021-07-05T16:06:39Z | MEMBER | null | 0 | {
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} | As discussed in https://github.com/huggingface/datasets/pull/2375#discussion_r657084544 I added the `IterableDataset.skip` and `IterableDataset.take` methods that allows to do basic splitting of iterable datasets.
You can create new dataset with the first `n` examples using `IterableDataset.take()`, or you can get a dataset with the rest of the examples by skipping the first `n` examples with `IterableDataset.skip()`
One implementation detail:
Using `take` (or `skip`) prevents future dataset shuffling from shuffling the dataset shards, otherwise the taken examples could come from other shards. In this case it only uses the shuffle buffer.
I would have loved to allow the shards of the taken examples to be shuffled anyway, but since we don't know in advance the length of each shard we don't know what shards to take or skip.
I think this is ok though since users can shuffle before doing take or skip. I mentioned this in the documentation
cc @vblagoje @lewtun | {
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} | This will speed up the loading of the datasets where the number of data files is large (can easily happen with `imagefoler`, for instance) | {
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https://api.github.com/repos/huggingface/datasets/issues/3372 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3372/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3372/comments | https://api.github.com/repos/huggingface/datasets/issues/3372/events | https://github.com/huggingface/datasets/issues/3372 | 1,069,948,178 | I_kwDODunzps4_xh0S | 3,372 | [SEO improvement] Add Dataset Metadata to make datasets indexable | {
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] | closed | false | null | [] | null | [] | 2021-12-02T20:21:07Z | 2022-03-18T09:36:48Z | 2022-03-18T09:36:48Z | CONTRIBUTOR | null | null | null | Some people who host datasets on github seem to include a table of metadata at the end of their README.md to make the dataset indexable by [Google Dataset Search](https://datasetsearch.research.google.com/) (See [here](https://github.com/google-research/google-research/tree/master/goemotions#dataset-metadata) and [here](https://github.com/cvdfoundation/google-landmark#dataset-metadata)). This could be a useful addition to canonical datasets; perhaps even community datasets.
I'll include a screenshot (as opposed to markdown) as an example so as not to have a github issue indexed as a dataset:
> 
**_PS: It might very well be the case that this is already covered by some other markdown magic I'm not aware of._**
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https://api.github.com/repos/huggingface/datasets/issues/3245 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3245/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3245/comments | https://api.github.com/repos/huggingface/datasets/issues/3245/events | https://github.com/huggingface/datasets/pull/3245 | 1,048,726,062 | PR_kwDODunzps4uSqqq | 3,245 | Fix load_from_disk temporary directory | {
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} | `load_from_disk` uses `tempfile.TemporaryDirectory()` instead of our `get_temporary_cache_files_directory()` function. This can cause the temporary directory to be deleted before the dataset object is garbage collected.
In practice, it prevents anyone from using methods like `shuffle` on a dataset loaded this way, because it can't write the shuffled indices in a directory that doesn't exist anymore.
In this PR I switch to using `get_temporary_cache_files_directory()` and I update the tests.
cc @mariosasko since you worked on `get_temporary_cache_files_directory()` | {
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https://api.github.com/repos/huggingface/datasets/issues/5301 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5301/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5301/comments | https://api.github.com/repos/huggingface/datasets/issues/5301/events | https://github.com/huggingface/datasets/pull/5301 | 1,464,749,156 | PR_kwDODunzps5DuCzR | 5,301 | Return a split Dataset in load_dataset | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5301). All of your documentation changes will be reflected on that endpoint.",
"Just noticed that now we have to deal with indexed & split datasets. The remaining tests are failing because one should be able to get an indexed d... | 2022-11-25T16:35:54Z | 2023-09-24T10:06:15Z | 2023-02-21T13:13:13Z | MEMBER | null | 1 | {
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} | ...instead of a DatasetDict.
```python
# now supported
ds = load_dataset("squad")
ds[0]
for example in ds:
pass
# still works
ds["train"]
ds["validation"]
# new
ds.splits # Dict[str, Dataset] | None
# soon to be supported (not in this PR)
ds = load_dataset("dataset_with_no_splits")
ds[0]
for example in ds:
pass
```
I implemented `Dataset.__getitem__` and `IterableDataset.__getitem__` to be able to get a split from a dataset.
The splits are defined by the `ds.info.splits` dictionary.
Therefore a dataset is a table that optionally has some splits defined in the dataset info. And a split dataset is the concatenation of all its splits.
I made as little breaking changes as possible. Notable breaking changes:
- `load_dataset("potato").keys() / .items() / .values() /` don't work anymore, since we don't return a dict
- same for `for split_name in load_dataset("potato")`, since we now iterate on the examples
- ..
TODO:
- [x] Update push_to_hub
- [x] Update save_to_disk/load_from_disk
- [ ] check for other breaking changes
- [ ] fix existing tests
- [ ] add new tests
- [ ] docs
This is related to https://github.com/huggingface/datasets/issues/5189, to extend `load_dataset` to return datasets without splits | {
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"Supporting this could be useful (I remember a use-case for this on the Hub). Do you agree @polinaeterna? \r\n\r\nImplementing this should be possible if we iterate over metadata files and build image/audio file paths instead of iterating over image/audio files and looking for the corresponding entries in metadata ... | 2023-04-16T16:01:05Z | 2023-09-22T15:12:02Z | null | NONE | null | null | null | ### Feature request
Extend the `imagefolder` dataloading script to support loading multiple images per dataset entry.
This only really makes sense if a metadata file is present.
Currently you can use the following format (example `metadata.jsonl`:
```
{'file_name': 'path_to_image.png', 'metadata': ...}
...
```
which will return a batch with key `image` and any other metadata.
I would propose extending `file_name` to also accept a list of files, which would return a batch with key `images` and any other metadata.
### Motivation
This is useful for example in segmentation tasks in computer vision models, or in text-to-image models that also accept conditioning signals such as another image, feature map, or similar. Currently if I want to do this, I would need to write a custom dataset, rather than just use `imagefolder`.
### Your contribution
Would be open to doing a PR, but also happy for someone else to take it as I am not familiar with the datasets library. | {
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https://api.github.com/repos/huggingface/datasets/issues/4518 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4518/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4518/comments | https://api.github.com/repos/huggingface/datasets/issues/4518/events | https://github.com/huggingface/datasets/pull/4518 | 1,274,010,628 | PR_kwDODunzps45zMnB | 4,518 | Patch tests for hfh v0.8.0 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-06-16T19:45:32Z | 2022-06-17T16:15:57Z | 2022-06-17T16:06:07Z | MEMBER | null | 0 | {
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"Hi @frgfm, thanks for reporting.\r\n\r\nAs the error message says, streaming mode is not supported out of the box when the dataset contains TAR archive files.\r\n\r\nTo make the dataset streamable, you have to use `dl_manager.iter_archive`.\r\n\r\nThere are several examples in other datasets, e.g. food101: https:/... | 2022-07-18T02:51:09Z | 2022-08-01T15:10:57Z | 2022-08-01T15:10:57Z | NONE | null | null | null | ### Link
https://huggingface.co/datasets/frgfm/imagenette
### Description
Hello there :wave:
Thanks for the amazing work you've done with HF Datasets! I've just started playing with it, and managed to upload my first dataset. But for the second one, I'm having trouble with the preview since there is some archive extraction involved :sweat_smile:
Basically, I get a:
```
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
I've tried several things and checked this issue https://github.com/huggingface/datasets/issues/4181 as well, but no luck so far!
Could you point me in the right direction please? :pray:
### Owner
Yes | {
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https://api.github.com/repos/huggingface/datasets/issues/1466 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1466/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1466/comments | https://api.github.com/repos/huggingface/datasets/issues/1466/events | https://github.com/huggingface/datasets/pull/1466 | 761,554,357 | MDExOlB1bGxSZXF1ZXN0NTM2MjA0OTMx | 1,466 | Add Turkish News Category Dataset (270K).Updates were made for review… | {
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"@SBrandeis, What exactly is it that makes the tests fail? Can you help me please?",
"These errors\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_ajgt_twitter_ar\r\nFAILED tests/test_dataset_com... | 2020-12-10T19:41:12Z | 2020-12-11T14:27:15Z | 2020-12-11T14:27:15Z | CONTRIBUTOR | null | 0 | {
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} | This PR adds the **Turkish News Categories Dataset (270K)** dataset which is a text classification dataset by me and @yavuzKomecoglu. Turkish news dataset consisting of **273601 news in 17 categories**, compiled from printed media and news websites between 2010 and 2017 by the [Interpress](https://www.interpress.com/) media monitoring company.
**Note**: Resubmitted as a clean version of the previous Pull Request(#1419). @SBrandeis @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/3831 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3831/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3831/comments | https://api.github.com/repos/huggingface/datasets/issues/3831/events | https://github.com/huggingface/datasets/issues/3831 | 1,160,501,000 | I_kwDODunzps5FK9cI | 3,831 | when using to_tf_dataset with shuffle is true, not all completed batches are made | {
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"Maybe @Rocketknight1 can help here",
"Hi @greenned, this is expected behaviour for `to_tf_dataset`. By default, we drop the smaller 'remainder' batch during training (i.e. when `shuffle=True`). If you really want to keep that batch, you can set `drop_remainder=False` when calling `to_tf_dataset()`.",
"@Rocketk... | 2022-03-06T02:43:50Z | 2022-03-08T15:18:56Z | 2022-03-08T15:18:56Z | NONE | null | null | null | ## Describe the bug
when converting a dataset to tf_dataset by using to_tf_dataset with shuffle true, the remainder is not converted to one batch
## Steps to reproduce the bug
this is the sample code below
https://colab.research.google.com/drive/1_oRXWsR38ElO1EYF9ayFoCU7Ou1AAej4?usp=sharing
## Expected results
regardless of shuffle is true or not, 67 rows dataset should be 5 batches when batch size is 16.
## Actual results
4 batches
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/5566 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5566/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5566/comments | https://api.github.com/repos/huggingface/datasets/issues/5566/events | https://github.com/huggingface/datasets/issues/5566 | 1,595,916,674 | I_kwDODunzps5fH8GC | 5,566 | Directly reading parquet files in a s3 bucket from the load_dataset method | {
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"Hi ! I think is in the scope of this other issue: to https://github.com/huggingface/datasets/issues/5281 "
] | 2023-02-22T22:13:40Z | 2023-02-23T11:03:29Z | null | NONE | null | null | null | ### Feature request
Right now, we have to read the get the parquet file to the local storage. So having ability to read given the bucket directly address would be benificial
### Motivation
In a production set up, this feature can help us a lot. So we do not need move training datafiles in between storage.
### Your contribution
I am willing to help if there's anyway. | {
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https://api.github.com/repos/huggingface/datasets/issues/5718 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5718/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5718/comments | https://api.github.com/repos/huggingface/datasets/issues/5718/events | https://github.com/huggingface/datasets/pull/5718 | 1,658,958,406 | PR_kwDODunzps5N2IZC | 5,718 | Reorder default data splits to have validation before test | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"After this CI error: https://github.com/huggingface/datasets/actions/runs/4639528358/jobs/8210492953?pr=5718\r\n```\r\nFAILED tests/test_data_files.py::test_get_data_files_patterns[data_file_per_split4] - AssertionError: assert ['ran... | 2023-04-07T16:01:26Z | 2023-04-27T14:43:13Z | 2023-04-27T14:35:52Z | MEMBER | null | 0 | {
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} | This PR reorders data splits, so that by default validation appears before test.
The default order becomes: [train, validation, test] instead of [train, test, validation]. | {
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https://api.github.com/repos/huggingface/datasets/issues/5987 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5987/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5987/comments | https://api.github.com/repos/huggingface/datasets/issues/5987/events | https://github.com/huggingface/datasets/issues/5987 | 1,773,047,909 | I_kwDODunzps5prpBl | 5,987 | Why max_shard_size is not supported in load_dataset and passed to download_and_prepare | {
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"Can you explain your use case for `max_shard_size`? \r\n\r\nOn some systems, there is a limit to the size of a memory-mapped file, so we could consider exposing this parameter in `load_dataset`.",
"In my use case, users may choose a proper size to balance the cost and benefit of using large shard size. (On azure... | 2023-06-25T04:19:13Z | 2023-06-29T16:06:08Z | 2023-06-29T16:06:08Z | CONTRIBUTOR | null | null | null | ### Describe the bug
https://github.com/huggingface/datasets/blob/a8a797cc92e860c8d0df71e0aa826f4d2690713e/src/datasets/load.py#L1809
What I can to is break the `load_dataset` and use `load_datset_builder` + `download_and_prepare` instead.
### Steps to reproduce the bug
https://github.com/huggingface/datasets/blob/a8a797cc92e860c8d0df71e0aa826f4d2690713e/src/datasets/load.py#L1809
### Expected behavior
Users can define the max shard size.
### Environment info
datasets==2.13.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/3109 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3109/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3109/comments | https://api.github.com/repos/huggingface/datasets/issues/3109/events | https://github.com/huggingface/datasets/pull/3109 | 1,030,543,284 | PR_kwDODunzps4tZXmC | 3,109 | Update BibTeX entry | {
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https://api.github.com/repos/huggingface/datasets/issues/2049 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2049/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2049/comments | https://api.github.com/repos/huggingface/datasets/issues/2049/events | https://github.com/huggingface/datasets/pull/2049 | 830,978,687 | MDExOlB1bGxSZXF1ZXN0NTkyNDE2MzQ0 | 2,049 | Fix text-classification tags | {
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"LGTM, thanks for fixing."
] | 2021-03-13T19:51:42Z | 2021-03-16T15:47:46Z | 2021-03-16T15:47:46Z | CONTRIBUTOR | null | 0 | {
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} | There are different tags for text classification right now: `text-classification` and `text_classification`:
.
This PR fixes it.
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https://api.github.com/repos/huggingface/datasets/issues/4212 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4212/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4212/comments | https://api.github.com/repos/huggingface/datasets/issues/4212/events | https://github.com/huggingface/datasets/pull/4212 | 1,214,498,582 | PR_kwDODunzps42udRf | 4,212 | [Common Voice] Make sure bytes are correctly deleted if `path` exists | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"cool that you noticed that we store unnecessary bytes again :D "
] | 2022-04-25T13:18:26Z | 2022-04-26T22:54:28Z | 2022-04-26T22:48:27Z | MEMBER | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/4097 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4097/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4097/comments | https://api.github.com/repos/huggingface/datasets/issues/4097/events | https://github.com/huggingface/datasets/pull/4097 | 1,193,205,751 | PR_kwDODunzps41qPEu | 4,097 | Updating FrugalScore metric card | {
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https://api.github.com/repos/huggingface/datasets/issues/5143 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5143/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5143/comments | https://api.github.com/repos/huggingface/datasets/issues/5143/events | https://github.com/huggingface/datasets/issues/5143 | 1,416,837,186 | I_kwDODunzps5UczhC | 5,143 | DownloadManager Git LFS support | {
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"Hey ! Actually it works, just pass the right URL ;)\r\nThe URL must be the one with “/resolve/”\r\n\r\ne.g. https://huggingface.co/datasets/imagenet-1k/resolve/main/data/test_images.tar.gz\r\n\r\nYou can even pass a relative path to the dl_manager instead, like `dl_manager.download(\"data/test_images.tar.gz\")`",
... | 2022-10-20T15:29:29Z | 2022-10-20T17:17:10Z | 2022-10-20T17:17:10Z | CONTRIBUTOR | null | null | null | ### Feature request
Maybe I'm mistaken but the `DownloadManager` does not support extracting git lfs files out of the box right?
Using `dl_manager.download()` or `dl_manager.download_and_extract()` still returns lfs files afaict.
Is there a good way to write a dataset loading script for a repo with lfs files?
### Motivation
/
### Your contribution
/ | {
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https://api.github.com/repos/huggingface/datasets/issues/6013 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6013/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6013/comments | https://api.github.com/repos/huggingface/datasets/issues/6013/events | https://github.com/huggingface/datasets/issues/6013 | 1,796,083,437 | I_kwDODunzps5rDg7t | 6,013 | [FR] `map` should reuse unchanged columns from the previous dataset to avoid disk usage | {
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"You can use the `remove_columns` parameter in `map` to avoid duplicating the columns (and save disk space) and then concatenate the original dataset with the map result:\r\n```python\r\nfrom datasets import concatenate_datasets\r\n# dummy example\r\nds_new = ds.map(lambda x: {\"new_col\": x[\"col\"] + 2}, remove_c... | 2023-07-10T06:42:20Z | 2023-07-10T15:37:52Z | null | CONTRIBUTOR | null | null | null | ### Feature request
Currently adding a new column with `map` will cause all the data in the dataset to be duplicated and stored/cached on the disk again. It should reuse unchanged columns.
### Motivation
This allows having datasets with different columns but sharing some basic columns. Currently, these datasets would become too expensive to store and one would need some kind of on-the-fly join; which also doesn't seem implemented.
### Your contribution
_ | {
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https://api.github.com/repos/huggingface/datasets/issues/1319 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1319/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1319/comments | https://api.github.com/repos/huggingface/datasets/issues/1319/events | https://github.com/huggingface/datasets/pull/1319 | 759,565,923 | MDExOlB1bGxSZXF1ZXN0NTM0NTQ5ODU5 | 1,319 | adding wili-2018 language identification dataset | {
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"@lhoestq Not sure what happened, I just changed the py file but it is showing some TensorFlow error now.",
"You can ignore it.\r\nIt's caused by the Tensorflow update that happened 30min ago. They added breaking changes.\r\nI'm working on a fix on the master branch right now\r\n",
"oh okay, btw I have made the... | 2020-12-08T16:00:09Z | 2020-12-14T21:20:32Z | 2020-12-14T21:20:32Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/1764 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1764/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1764/comments | https://api.github.com/repos/huggingface/datasets/issues/1764/events | https://github.com/huggingface/datasets/issues/1764 | 791,486,860 | MDU6SXNzdWU3OTE0ODY4NjA= | 1,764 | Connection Issues | {
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"Academic WIFI was blocking."
] | 2021-01-21T20:56:09Z | 2021-01-21T21:00:19Z | 2021-01-21T21:00:02Z | NONE | null | null | null | Today, I am getting connection issues while loading a dataset and the metric.
```
Traceback (most recent call last):
File "src/train.py", line 180, in <module>
train_dataset, dev_dataset, test_dataset = create_race_dataset()
File "src/train.py", line 130, in create_race_dataset
train_dataset = load_dataset("race", "all", split="train")
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 591, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
max_retries=download_config.max_retries,
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/race/race.py
```
Or
```
Traceback (most recent call last):
File "src/train.py", line 105, in <module>
rouge = datasets.load_metric("rouge")
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 500, in load_metric
dataset=False,
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
max_retries=download_config.max_retries,
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/metrics/rouge/rouge.py
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2683 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2683/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2683/comments | https://api.github.com/repos/huggingface/datasets/issues/2683/events | https://github.com/huggingface/datasets/issues/2683 | 948,721,379 | MDU6SXNzdWU5NDg3MjEzNzk= | 2,683 | Cache directories changed due to recent changes in how config kwargs are handled | {
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"gists... | null | [] | 2021-07-20T14:37:57Z | 2021-07-20T16:27:15Z | 2021-07-20T16:27:15Z | MEMBER | null | null | null | Since #2659 I can see weird cache directory names with hashes in the config id, even though no additional config kwargs are passed. For example:
```python
from datasets import load_dataset_builder
c4_builder = load_dataset_builder("c4", "en")
print(c4_builder.cache_dir)
# /Users/quentinlhoest/.cache/huggingface/datasets/c4/en-174d3b7155eb68db/0.0.0/...
# instead of
# /Users/quentinlhoest/.cache/huggingface/datasets/c4/en/0.0.0/...
```
This issue could be annoying since it would simply ignore old cache directories for users, and regenerate datasets
cc @stas00 this is what you experienced a few days ago
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https://api.github.com/repos/huggingface/datasets/issues/5677 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5677/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5677/comments | https://api.github.com/repos/huggingface/datasets/issues/5677/events | https://github.com/huggingface/datasets/issues/5677 | 1,644,828,606 | I_kwDODunzps5iChe- | 5,677 | Dataset.map() crashes when any column contains more than 1000 empty dictionaries | {
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... | null | [] | 2023-03-29T00:01:31Z | 2023-07-07T14:01:14Z | 2023-07-07T14:01:14Z | NONE | null | null | null | ### Describe the bug
`Dataset.map()` crashes any time any column contains more than `writer_batch_size` (default 1000) empty dictionaries, regardless of whether the column is being operated on. The error does not occur if the dictionaries are non-empty.
### Steps to reproduce the bug
Example:
```
import datasets
def add_one(example):
example["col2"] += 1
return example
n = 1001 # crashes
# n = 999 # works
ds = datasets.Dataset.from_dict({"col1": [{}] * n, "col2": [1] * n})
ds = ds.map(add_one, writer_batch_size=1000)
```
### Expected behavior
Above code should not crash
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.10
- Python version: 3.8.15
- PyArrow version: 9.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5255 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5255/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5255/comments | https://api.github.com/repos/huggingface/datasets/issues/5255/events | https://github.com/huggingface/datasets/issues/5255 | 1,452,631,517 | I_kwDODunzps5WlWXd | 5,255 | Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI | {
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"Also cc @mariosasko and @lhoestq ",
"Cool ! Let us know if you have questions or if we can help :)\r\n\r\nI guess we'll also have to create the NYU CS Department on the Hub ?",
"> I guess we'll also have to create the NYU CS Department on the Hub ?\r\n\r\nYes, you're right! Let me add it to my profile first, a... | 2022-11-17T03:22:22Z | 2022-12-17T12:20:38Z | 2022-12-17T12:20:37Z | MEMBER | null | null | null | ### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it. | {
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"Hi! I like this idea. For consistency with `load_dataset`, we can use `fsspec`'s `TqdmCallback` in `.load_from_disk` to monitor the number of bytes downloaded, and in `.save_to_disk`, we can track the number of saved shards for consistency with `push_to_hub` (after we implement https://github.com/huggingface/data... | 2022-05-14T11:30:42Z | 2022-12-14T18:22:59Z | 2022-12-14T18:22:59Z | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
When working with large datasets stored on remote filesystems(such as s3), the process of uploading a dataset could take really long time. For instance: I was uploading a re-processed version of wmt17 en-ru to my s3 bucket and it took like 35 minutes(and that's given that I have a fiber optic connection). The only output during that process was a progress bar for flattening indices and then ~35 minutes of complete silence.
**Describe the solution you'd like**
I want to be able to enable a progress bar when calling .save_to_disk(..) and .load_from_disk(..), it would track either amount of bytes sent/received or amount of records written/loaded, and will give some ETA. Basically just tqdm.
**Describe alternatives you've considered**
- Save dataset to tmp folder at the disk and then upload it using custom wrapper over botocore, which will work with progress bar, like [this](https://alexwlchan.net/2021/04/s3-progress-bars/). | {
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https://api.github.com/repos/huggingface/datasets/issues/6272 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6272/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6272/comments | https://api.github.com/repos/huggingface/datasets/issues/6272/events | https://github.com/huggingface/datasets/issues/6272 | 1,920,831,487 | I_kwDODunzps5yfY__ | 6,272 | Duplicate `data_files` when named `<split>/<split>.parquet` | {
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"Also reported in https://github.com/huggingface/datasets/issues/6259",
"I think it's best to drop duplicates with a `set` (as a temporary fix) and improve the patterns when/if https://github.com/fsspec/filesystem_spec/pull/1382 gets merged. @lhoestq Do you have some other ideas?",
"Alternatively we could just... | 2023-10-01T15:43:56Z | 2023-10-05T10:32:27Z | null | MEMBER | null | null | null | e.g. with `u23429/stock_1_minute_ticker`
```ipython
In [1]: from datasets import *
In [2]: b = load_dataset_builder("u23429/stock_1_minute_ticker")
Downloading readme: 100%|██████████████████████████| 627/627 [00:00<00:00, 246kB/s]
In [3]: b.config.data_files
Out[3]:
{NamedSplit('train'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet',
'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet'],
NamedSplit('validation'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet',
'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet'],
NamedSplit('test'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet',
'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet']}
```
This bug issue is present in the current `datasets` 2.14.5 and also on `main` even after https://github.com/huggingface/datasets/pull/6244 cc @mariosasko | {
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https://api.github.com/repos/huggingface/datasets/issues/1469 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1469/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1469/comments | https://api.github.com/repos/huggingface/datasets/issues/1469/events | https://github.com/huggingface/datasets/pull/1469 | 761,611,315 | MDExOlB1bGxSZXF1ZXN0NTM2MjUzMDk4 | 1,469 | ADD: Wino_bias dataset | {
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"merging since the CI is fixed on master"
] | 2020-12-10T20:59:45Z | 2020-12-13T19:13:57Z | 2020-12-13T19:13:57Z | CONTRIBUTOR | null | 0 | {
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Removed manual downloading of dataset. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-04-14T11:53:06Z | 2022-04-15T14:47:42Z | 2022-04-15T14:41:22Z | MEMBER | null | 0 | {
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"Hi @StellaAthena, thanks for reporting.\r\n\r\nPlease note, that our `datasets` library performs several operations in order to load a dataset, among them:\r\n- it downloads all the required files: for C4 \"en\", 378.69 GB of JSON GZIPped files\r\n- it parses their content to generate the dataset\r\n- it caches th... | 2022-04-07T17:41:30Z | 2023-06-26T22:01:29Z | 2023-06-26T22:01:29Z | NONE | null | null | null | ## Describe the bug
C4-en is a 300 GB dataset. However, when I try to download it through the hub it takes over _six hours_ to generate the train/test split from the downloaded files. This is an absurd amount of time and an unnecessary waste of resources.
## Steps to reproduce the bug
```python
c4 = datasets.load("c4", "en")
```
## Expected results
I would like to be able to download pre-split data.
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.13.0-35-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
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https://api.github.com/repos/huggingface/datasets/issues/1384 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1384/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1384/comments | https://api.github.com/repos/huggingface/datasets/issues/1384/events | https://github.com/huggingface/datasets/pull/1384 | 760,331,767 | MDExOlB1bGxSZXF1ZXN0NTM1MTgxMjg1 | 1,384 | Add News Commentary Dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/2045 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2045/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2045/comments | https://api.github.com/repos/huggingface/datasets/issues/2045/events | https://github.com/huggingface/datasets/pull/2045 | 830,351,527 | MDExOlB1bGxSZXF1ZXN0NTkxODc2Mjcz | 2,045 | Preserve column ordering in Dataset.rename_column | {
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"Not sure why CI isn't triggered.\r\n\r\n@lhoestq Can you please help me with this? ",
"I don't know how to trigger it manually, but an empty commit should do the job"
] | 2021-03-12T18:26:47Z | 2021-03-16T14:48:05Z | 2021-03-16T14:35:05Z | CONTRIBUTOR | null | 0 | {
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} | Currently `Dataset.rename_column` doesn't necessarily preserve the order of the columns:
```python
>>> from datasets import Dataset
>>> d = Dataset.from_dict({'sentences': ["s1", "s2"], 'label': [0, 1]})
>>> d
Dataset({
features: ['sentences', 'label'],
num_rows: 2
})
>>> d.rename_column('sentences', 'text')
Dataset({
features: ['label', 'text'],
num_rows: 2
})
```
This PR fixes this. | {
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"That's interesting, can you tell me what you think would be useful to access to inspect a dataset?\r\n\r\nYou can filter them in the hub with the search by the way: https://huggingface.co/datasets have you seen it?",
"Hi @thomwolf \r\nthank you, I was not aware of this, I was looking into the data viewer linked ... | 2020-12-24T15:26:34Z | 2022-10-04T14:57:33Z | 2022-10-04T14:57:33Z | NONE | null | null | null | Hi
Is there a way I could get all NLI datasets/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/5164 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5164/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5164/comments | https://api.github.com/repos/huggingface/datasets/issues/5164/events | https://github.com/huggingface/datasets/pull/5164 | 1,422,813,247 | PR_kwDODunzps5BhL4J | 5,164 | WIP: drop labels in Image and Audio folders by default | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"close in favor of https://github.com/huggingface/datasets/pull/5192"
] | 2022-10-25T17:21:49Z | 2022-11-16T14:21:16Z | 2022-11-02T14:03:02Z | CONTRIBUTOR | null | 1 | {
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} | will fix https://github.com/huggingface/datasets/issues/5153 and redundant labels displaying for most of the images datasets on the Hub (which are used just to store files)
TODO: discuss adding `drop_labels` (and `drop_metadata`) params to yaml | {
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https://api.github.com/repos/huggingface/datasets/issues/2040 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2040/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2040/comments | https://api.github.com/repos/huggingface/datasets/issues/2040/events | https://github.com/huggingface/datasets/issues/2040 | 830,169,387 | MDU6SXNzdWU4MzAxNjkzODc= | 2,040 | ValueError: datasets' indices [1] come from memory and datasets' indices [0] come from disk | {
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"Hi ! To help me understand the situation, can you print the values of `load_from_disk(PATH_DATA_CLS_A)['train']._indices_data_files` and `load_from_disk(PATH_DATA_CLS_B)['train']._indices_data_files` ?\r\nThey should both have a path to an arrow file\r\n\r\nAlso note that from #2025 concatenating datasets will no... | 2021-03-12T14:27:00Z | 2021-08-04T18:00:43Z | 2021-08-04T18:00:43Z | NONE | null | null | null | Hi there,
I am trying to concat two datasets that I've previously saved to disk via `save_to_disk()` like so (note that both are saved as `DataDict`, `PATH_DATA_CLS_*` are `Path`-objects):
```python
concatenate_datasets([load_from_disk(PATH_DATA_CLS_A)['train'], load_from_disk(PATH_DATA_CLS_B)['train']])
```
Yielding the following error:
```python
ValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.
However datasets' indices [1] come from memory and datasets' indices [0] come from disk.
```
Been trying to solve this for quite some time now. Both `DataDict` have been created by reading in a `csv` via `load_dataset` and subsequently processed using the various `datasets` methods (i.e. filter, map, remove col, rename col). Can't figure out tho...
`load_from_disk(PATH_DATA_CLS_A)['train']` yields:
```python
Dataset({
features: ['labels', 'text'],
num_rows: 785
})
```
`load_from_disk(PATH_DATA_CLS_B)['train']` yields:
```python
Dataset({
features: ['labels', 'text'],
num_rows: 3341
})
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3914 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3914/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3914/comments | https://api.github.com/repos/huggingface/datasets/issues/3914/events | https://github.com/huggingface/datasets/pull/3914 | 1,168,777,880 | PR_kwDODunzps40aq2r | 3,914 | Use templates for doc-builidng jobs | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3914). All of your documentation changes will be reflected on that endpoint.",
"You can ignore the CI failures btw, they're unrelated to this PR"
] | 2022-03-14T18:53:06Z | 2022-03-17T15:02:59Z | 2022-03-17T15:02:58Z | CONTRIBUTOR | null | 0 | {
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} | This PR updates the jobs for all doc-building related things by using the templates introduced on `doc-builder`. By putting those once there, we make sure every repo gets the latest fixes on the doc-building github actions :-)
Note: all libraries must share the same docker image for those doc-building jobs. For now, all the one used (`huggingface/transformers-doc-builder`) contains all extra steps of the datasets install for docbuling (mainly libsndfile) but if in the future some additional steps are necessary on top of `pip install -e .[dev]`, this docker image will need to be updated with the extra deps. | {
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https://api.github.com/repos/huggingface/datasets/issues/1250 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1250/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1250/comments | https://api.github.com/repos/huggingface/datasets/issues/1250/events | https://github.com/huggingface/datasets/pull/1250 | 758,491,704 | MDExOlB1bGxSZXF1ZXN0NTMzNjU2NTI4 | 1,250 | added Nergrit dataset | {
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} | Nergrit Corpus is a dataset collection for Indonesian Named Entity Recognition, Statement Extraction, and Sentiment Analysis. This PR is only for the Named Entity Recognition. | {
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https://api.github.com/repos/huggingface/datasets/issues/3811 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3811/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3811/comments | https://api.github.com/repos/huggingface/datasets/issues/3811/events | https://github.com/huggingface/datasets/pull/3811 | 1,158,234,407 | PR_kwDODunzps4z4dHS | 3,811 | Update dev doc gh workflows | {
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https://api.github.com/repos/huggingface/datasets/issues/1471 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1471/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1471/comments | https://api.github.com/repos/huggingface/datasets/issues/1471/events | https://github.com/huggingface/datasets/pull/1471 | 761,842,512 | MDExOlB1bGxSZXF1ZXN0NTM2NDUyMzcy | 1,471 | Adding the HAREM dataset | {
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"Thanks for the changes !\r\n\r\nSorry if I wasn't clear about the suggestion of adding the `raw` dataset as well.\r\nBy `raw` I meant the dataset with its original features, i.e. not tokenized to follow the conll format for NER.\r\nThe `raw` dataset has data fields `doc_text`, `doc_id` and `entities`.",
"Alright... | 2020-12-11T03:21:10Z | 2020-12-22T10:37:33Z | 2020-12-22T10:37:33Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/3415 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3415/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3415/comments | https://api.github.com/repos/huggingface/datasets/issues/3415/events | https://github.com/huggingface/datasets/issues/3415 | 1,076,472,534 | I_kwDODunzps5AKarW | 3,415 | Non-deterministic tests: CI tests randomly fail | {
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"I think it might come from two different issues:\r\n1. Google Drive is an unreliable host, mainly because of quota limitations\r\n2. the staging environment can sometimes raise some errors\r\n\r\nFor Google Drive tests we could set up some retries with backup URLs if necessary I guess.\r\nFor staging on the other ... | 2021-12-10T06:08:59Z | 2022-03-31T16:38:51Z | 2022-03-31T16:38:51Z | MEMBER | null | null | null | ## Describe the bug
Some CI tests fail randomly.
1. In https://github.com/huggingface/datasets/pull/3375/commits/c10275fe36085601cb7bdb9daee9a8f1fc734f48, there were 3 failing tests, only on Linux:
```
=========================== short test summary info ============================
FAILED tests/test_streaming_download_manager.py::test_streaming_dl_manager_get_extraction_protocol[https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh-zip]
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive - Fi...
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped
= 3 failed, 3553 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 192.79s (0:03:12) =
```
2. After re-running the CI (without any change in the code) in https://github.com/huggingface/datasets/pull/3375/commits/57bfe1f342cd3c59d2510b992d5f06a0761eb147, there was only 1 failing test (one on Linux and a different one on Windows):
- On Linux:
```
=========================== short test summary info ============================
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped
= 1 failed, 3555 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 199.76s (0:03:19) =
```
- On Windows:
```
=========================== short test summary info ===========================
FAILED tests/test_load.py::test_load_dataset_builder_for_community_dataset_without_script
= 1 failed, 3551 passed, 2954 skipped, 2 xfailed, 1 xpassed, 121 warnings in 478.58s (0:07:58) =
```
The test `tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped` passes locally.
3. After re-running again the CI (without any change in the code) in https://github.com/huggingface/datasets/pull/3375/commits/39f32f2119cf91b86867216bb5c356c586503c6a, ALL the tests passed.
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https://api.github.com/repos/huggingface/datasets/issues/4763 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4763/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4763/comments | https://api.github.com/repos/huggingface/datasets/issues/4763/events | https://github.com/huggingface/datasets/pull/4763 | 1,321,295,876 | PR_kwDODunzps48RMKi | 4,763 | More rigorous shape inference in to_tf_dataset | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-28T18:04:15Z | 2022-09-08T19:17:54Z | 2022-09-08T19:15:41Z | MEMBER | null | 0 | {
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} | `tf.data` needs to know the shape of tensors emitted from a `tf.data.Dataset`. Although `None` dimensions are possible, overusing them can cause problems - Keras uses the dataset tensor spec at compile-time, and so saying that a dimension is `None` when it's actually constant can hurt performance, or even cause training to fail for dimensions that are needed to determine the shape of weight tensors!
The compromise I used here was to sample several batches from the underlying dataset and apply the `collate_fn` to them, and then to see which dimensions were "empirically variable". There's an obvious problem here, though - if you sample 10 batches and they all have the same shape on a certain dimension, there's still a small chance that the 11th batch will be different, and Keras will throw an error if a dataset tries to emit a tensor whose shape doesn't match the spec.
I encountered this bug in practice once or twice for datasets that were mostly-but-not-totally constant on a given dimension, and I still don't have a perfect solution, but this PR should greatly reduce the risk. It samples many more batches, and also samples very small batches (size 2) - this increases the variability, making it more likely that a few outlier samples will be detected.
Ideally, of course, we'd determine the full output shape analytically, but that's surprisingly tricky when the `collate_fn` can be any arbitrary Python code! | {
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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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} | Updated the license information with the link to the license text | {
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https://api.github.com/repos/huggingface/datasets/issues/1723 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1723/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1723/comments | https://api.github.com/repos/huggingface/datasets/issues/1723/events | https://github.com/huggingface/datasets/pull/1723 | 783,982,100 | MDExOlB1bGxSZXF1ZXN0NTUzMjQ4MzU1 | 1,723 | ADD S3 support for downloading and uploading processed datasets | {
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"I created the documentation for `FileSystem Integration for cloud storage` with loading and saving datasets to/from a filesystem with an example of using `datasets.filesystem.S3Filesystem`. I added a note on the `Saving a processed dataset on disk and reload` saying that it is also possible to use other filesystem... | 2021-01-12T07:17:34Z | 2021-01-26T17:02:08Z | 2021-01-26T17:02:08Z | MEMBER | null | 0 | {
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} | # What does this PR do?
This PR adds the functionality to load and save `datasets` from and to s3.
You can save `datasets` with either `Dataset.save_to_disk()` or `DatasetDict.save_to_disk`.
You can load `datasets` with either `load_from_disk` or `Dataset.load_from_disk()`, `DatasetDict.load_from_disk()`.
Loading `csv` or `json` datasets from s3 is not implemented.
To save/load datasets to s3 you either need to provide an `aws_profile`, which is set up on your machine, per default it uses the `default` profile or you have to pass an `aws_access_key_id` and `aws_secret_access_key`.
The implementation was done with the `fsspec` and `boto3`.
### Example `aws_profile` :
<details>
```python
dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
```
</details>
### Example `aws_access_key_id` and `aws_secret_access_key` :
<details>
```python
dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk",
aws_access_key_id="fake_access_key",
aws_secret_access_key="fake_secret_key"
)
load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk",
aws_access_key_id="fake_access_key",
aws_secret_access_key="fake_secret_key"
)
```
</details>
If you want to load a dataset from a public s3 bucket you can pass `anon=True`
### Example `anon=True` :
<details>
```python
dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
load_from_disk("s3://moto-mock-s3-bucketdatasets/sdk",anon=True)
```
</details>
### Full Example
```python
import datasets
dataset = datasets.load_dataset("imdb")
print(f"DatasetDict contains {len(dataset)} datasets")
print(f"train Dataset has the size of: {len(dataset['train'])}")
dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
remote_dataset = datasets.load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm")
print(f"DatasetDict contains {len(remote_dataset)} datasets")
print(f"train Dataset has the size of: {len(remote_dataset['train'])}")
```
Related to #878
I would also adjust the documentation after the code would be reviewed, as long as I leave the PR in "draft" status. Something that we can consider is renaming the functions and changing the `_disk` maybe to `_filesystem`
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https://api.github.com/repos/huggingface/datasets/issues/1575 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1575/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1575/comments | https://api.github.com/repos/huggingface/datasets/issues/1575/events | https://github.com/huggingface/datasets/pull/1575 | 767,076,374 | MDExOlB1bGxSZXF1ZXN0NTM5OTEzNzgx | 1,575 | Hind_Encorp all done | {
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"ALL TEST PASSED locally @yjernite ",
"@rahul-art kindly run the following from the datasets folder \r\n\r\n```\r\nmake style \r\nflake8 datasets\r\n\r\n```\r\n",
"@skyprince999 I did that before it says all done \r\n",
"I did that again it gives the same output all done and then I synchronized my changes ... | 2020-12-15T01:36:02Z | 2020-12-16T15:15:17Z | 2020-12-16T15:15:17Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/2686 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2686/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2686/comments | https://api.github.com/repos/huggingface/datasets/issues/2686/events | https://github.com/huggingface/datasets/pull/2686 | 948,811,669 | MDExOlB1bGxSZXF1ZXN0NjkzNTk4OTE3 | 2,686 | Fix bad config ids that name cache directories | {
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} | `data_dir=None` was considered a dataset config parameter, hence creating a special config_id for all dataset being loaded.
Since the config_id is used to name the cache directories, this leaded to datasets being regenerated for users.
I fixed this by ignoring the value of `data_dir` when it's `None` when computing the config_id.
I also added a test to make sure the cache directories are not unexpectedly renamed in the future.
Fix https://github.com/huggingface/datasets/issues/2683 | {
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https://api.github.com/repos/huggingface/datasets/issues/5805 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5805/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5805/comments | https://api.github.com/repos/huggingface/datasets/issues/5805/events | https://github.com/huggingface/datasets/issues/5805 | 1,688,558,577 | I_kwDODunzps5kpVvx | 5,805 | Improve `Create a dataset` tutorial | {
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"I can work on this. The link to the tutorial seems to be broken though @polinaeterna. ",
"@isunitha98selvan would be great, thank you! which link are you talking about? I think it should work: https://huggingface.co/docs/datasets/create_dataset"
] | 2023-04-28T13:26:22Z | 2023-06-23T14:58:44Z | null | CONTRIBUTOR | null | null | null | Our [tutorial on how to create a dataset](https://huggingface.co/docs/datasets/create_dataset) is a bit misleading.
1. In **Folder-based builders** section it says that we have two folder-based builders as standard builders, but we also have similar builders (that can be created from directory with data of required format) for `csv`, `json/jsonl`, `parquet` and `txt` files. We have info about these loaders in separate [guide for loading](https://huggingface.co/docs/datasets/loading#local-and-remote-files) but it's worth briefly mentioning them in the beginning tutorial because they are more common and for consistency. Would be helpful to add the link to the full guide.
2. **From local files** section lists methods for creating a dataset from in-memory data which are also described in [loading guide](https://huggingface.co/docs/datasets/loading#inmemory-data).
Maybe we should actually rethink and restructure this tutorial somehow. | {
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https://api.github.com/repos/huggingface/datasets/issues/4067 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4067/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4067/comments | https://api.github.com/repos/huggingface/datasets/issues/4067/events | https://github.com/huggingface/datasets/pull/4067 | 1,186,731,905 | PR_kwDODunzps41U7qc | 4,067 | Update datasets task tags to align tags with models | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Looks good, but I think we are missing some scripts with outdated tags (RedCaps, MNIST, ...).",
"Just updated the tags of vision datasets :)\r\nWe can figure out one for image datasets without labels like PASS - not sure how to nam... | 2022-03-30T16:49:32Z | 2022-04-13T17:37:27Z | 2022-04-13T17:31:11Z | MEMBER | null | 0 | {
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} | **Requires https://github.com/huggingface/datasets/pull/4066 to be merged first**
Following https://github.com/huggingface/datasets/pull/4066 we need to update many dataset tags to use the new ones. This PR takes case of this and is quite big - feel free to review only certain tags if you don't want to spend too much time on it.
Note that the CI will never be green for this PR, because many dataset cards have missing tags or sections, and fixing them is out of scope of this PR (the CI on master will be green anyway) | {
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https://api.github.com/repos/huggingface/datasets/issues/5824 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5824/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5824/comments | https://api.github.com/repos/huggingface/datasets/issues/5824/events | https://github.com/huggingface/datasets/pull/5824 | 1,697,152,148 | PR_kwDODunzps5P1rIZ | 5,824 | Fix incomplete docstring for `BuilderConfig` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-05T07:34:28Z | 2023-05-05T12:39:14Z | 2023-05-05T12:31:54Z | CONTRIBUTOR | null | 0 | {
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} | Fixes #5820
Also fixed a couple of typos I spotted | {
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https://api.github.com/repos/huggingface/datasets/issues/3631 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3631/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3631/comments | https://api.github.com/repos/huggingface/datasets/issues/3631/events | https://github.com/huggingface/datasets/issues/3631 | 1,114,833,662 | I_kwDODunzps5CcwL- | 3,631 | Labels conflict when loading a local CSV file. | {
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"Hi @pichljan, thanks for reporting.\r\n\r\nThis should be fixed. I'm looking at it. "
] | 2022-01-26T10:00:33Z | 2022-02-11T23:02:31Z | 2022-02-11T23:02:31Z | NONE | null | null | null | ## Describe the bug
I am trying to load a local CSV file with a separate file containing label names. It is successfully loaded for the first time, but when I try to load it again, there is a conflict between provided labels and the cached dataset info. Disabling caching globally and/or using `download_mode="force_redownload"` did not help.
## Steps to reproduce the bug
```python
load_dataset('csv', data_files='data/my_data.csv',
features=Features(text=Value(dtype='string'),
label=ClassLabel(names_file='data/my_data_labels.txt')))
```
`my_data.csv` file has the following structure:
```
text,label
"example1",0
"example2",1
...
```
and the `my_data_labels.txt` looks like this:
```
label1
label2
...
```
## Expected results
Successfully loaded dataset.
## Actual results
```python
File "/usr/local/lib/python3.8/site-packages/datasets/load.py", line 1706, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 766, in as_dataset
datasets = utils.map_nested(
File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 261, in map_nested
mapped = [
File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 262, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 797, in _build_single_dataset
ds = self._as_dataset(
File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 872, in _as_dataset
return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File "/usr/local/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 638, in __init__
inferred_features = Features.from_arrow_schema(arrow_table.schema)
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1242, in from_arrow_schema
return Features.from_dict(metadata["info"]["features"])
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1271, in from_dict
obj = generate_from_dict(dic)
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1076, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1076, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1083, in generate_from_dict
return class_type(**{k: v for k, v in obj.items() if k in field_names})
File "<string>", line 7, in __init__
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 776, in __post_init__
raise ValueError("Please provide either names or names_file but not both.")
ValueError: Please provide either names or names_file but not both.
```
## Environment info
- `datasets` version: 1.18.0
- Python version: 3.8.2
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} | Instead of legacy, use next-generation convenience images, built from the ground up with CI, efficiency, and determinism in mind. Here are some of the highlights:
- Faster spin-up time - In Docker terminology, these next-gen images will generally have fewer and smaller layers. Using these new images will lead to faster image downloads when a build starts, and a higher likelihood that the image is already cached on the host.
- Improved reliability and stability - The existing legacy convenience images are rebuilt practically every day with potential changes from upstream that we cannot always test fast enough. This leads to frequent breaking changes, which is not the best environment for stable, deterministic builds. Next-gen images will only be rebuilt for security and critical-bugs, leading to more stable and deterministic images.
More info: https://circleci.com/docs/2.0/circleci-images | {
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} | https://github.com/huggingface/datasets/pull/4188 introduced a bug in `datasets` 2.2.0: DailyMail stories are ignored when generating the dataset.
I fixed that, and removed the google drive link (it has annoying quota limitations issues)
We can do a patch release after this is merged | {
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https://api.github.com/repos/huggingface/datasets/issues/4683 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4683/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4683/comments | https://api.github.com/repos/huggingface/datasets/issues/4683/events | https://github.com/huggingface/datasets/pull/4683 | 1,305,443,253 | PR_kwDODunzps47cLkm | 4,683 | Update create dataset card docs | {
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Let me know what you think! :) | {
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"I make a summary about our discussion with @lewtun and @Narsil on the agreed schema for this dataset and the additional steps required to generate the 2D array labels:\r\n- The labels for this dataset are a 2D array:\r\n Given an example:\r\n ```python\r\n {\"record_id\": record_id, \"file\": file, \"start\": s... | 2021-07-16T16:43:21Z | 2021-08-04T17:03:53Z | 2021-08-04T17:03:53Z | MEMBER | null | 0 | {
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} | Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization).
TODO:
- [x] Generate the LibriMix corpus
- [x] Prepare the corpus for diarization
- [x] Upload these files to the superb-data repo
- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script
- [x] README: tags + description sections
- ~~Add DER metric~~ (we leave the DER metric for a follow-up PR)
Related to #2619.
Close #2653.
cc: @lewtun | {
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https://api.github.com/repos/huggingface/datasets/issues/1763 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1763/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1763/comments | https://api.github.com/repos/huggingface/datasets/issues/1763/events | https://github.com/huggingface/datasets/pull/1763 | 791,389,763 | MDExOlB1bGxSZXF1ZXN0NTU5NDU3MTY1 | 1,763 | PAWS-X: Fix csv Dictreader splitting data on quotes | {
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```python
from datasets import load_dataset
# load english paws-x dataset
datasets = load_dataset('paws-x', 'en')
print(len(datasets['train'])) # outputs 49202 but official dataset has 49401 pairs
print(datasets['train'].unique('label')) # outputs [1, 0, -1] but labels are binary [0,1]
```
changed `data = csv.DictReader(f, delimiter="\t")` to `data = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)` in the dataloader to make csv module not split by quotes.
The results are as expected for all languages after the change. | {
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https://api.github.com/repos/huggingface/datasets/issues/1496 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1496/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1496/comments | https://api.github.com/repos/huggingface/datasets/issues/1496/events | https://github.com/huggingface/datasets/pull/1496 | 763,091,663 | MDExOlB1bGxSZXF1ZXN0NTM3NTc4NzQw | 1,496 | Add Multi-Dimensional Gender Bias classification data | {
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} | https://parl.ai/projects/md_gender/
Mostly has the ABOUT dimension since the others are inferred from other datasets in most cases.
I tried to keep the dummy data small but one of the configs has 140 splits ( > 56KB data) | {
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https://api.github.com/repos/huggingface/datasets/issues/1519 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1519/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1519/comments | https://api.github.com/repos/huggingface/datasets/issues/1519/events | https://github.com/huggingface/datasets/pull/1519 | 764,107,360 | MDExOlB1bGxSZXF1ZXN0NTM4MzM5OTg5 | 1,519 | Initial commit for AQuaMuSe | {
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"@yjernite Thank you for your help, generating the dummy data 🤗 Having that all the tests have passed 👍🏻",
"merging since the CI is fixed on master",
"Thank you :)"
] | 2020-12-12T17:46:16Z | 2020-12-18T13:50:42Z | 2020-12-17T17:03:30Z | CONTRIBUTOR | null | 0 | {
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} | There is an issue in generation of dummy data. Tests on real data have passed locally. | {
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https://api.github.com/repos/huggingface/datasets/issues/1406 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1406/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1406/comments | https://api.github.com/repos/huggingface/datasets/issues/1406/events | https://github.com/huggingface/datasets/pull/1406 | 760,581,330 | MDExOlB1bGxSZXF1ZXN0NTM1Mzg5NDk5 | 1,406 | Add Portuguese Hate Speech dataset | {
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"@lhoestq done! (The failing tests don't seem to be related)",
"merging since the CI is fixed on master"
] | 2020-12-09T18:48:16Z | 2020-12-14T18:06:42Z | 2020-12-14T16:22:20Z | CONTRIBUTOR | null | 0 | {
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} | Binary Portuguese Hate Speech dataset from [this paper](https://www.aclweb.org/anthology/W19-3510/). | {
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https://api.github.com/repos/huggingface/datasets/issues/3708 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3708/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3708/comments | https://api.github.com/repos/huggingface/datasets/issues/3708/events | https://github.com/huggingface/datasets/issues/3708 | 1,132,968,402 | I_kwDODunzps5Dh7nS | 3,708 | Loading JSON gets stuck with many workers/threads | {
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"Hi ! Note that it does `block_size *= 2` until `block_size > len(batch)`, so it doesn't loop indefinitely. What do you mean by \"get stuck indefinitely\" then ? Is this the actual call to `paj.read_json` that hangs ?\r\n\r\n> increasing the `chunksize` argument decreases the chance of getting stuck\r\n\r\nCould yo... | 2022-02-11T18:50:48Z | 2023-06-16T11:24:12Z | null | MEMBER | null | null | null | ## Describe the bug
Loading a JSON dataset with `load_dataset` can get stuck when running on a machine with many CPUs. This is especially an issue when loading a large dataset on a large machine.
## Steps to reproduce the bug
I originally created the following script to reproduce the issue:
```python
from datasets import load_dataset
from multiprocessing import Process
from tqdm import tqdm
import datasets
from transformers import set_seed
def run_tasks_in_parallel(tasks, ds_list):
for _ in tqdm(range(1000)):
print('new batch')
running_tasks = [Process(target=task, args=(ds, i)) for i, (task, ds) in enumerate(zip(tasks, ds_list))]
for running_task in running_tasks:
running_task.start()
for running_task in running_tasks:
running_task.join()
def get_dataset():
dataset_name = 'transformersbook/codeparrot'
ds = load_dataset(dataset_name+'-train', split="train", streaming=True)
ds = ds.shuffle(buffer_size=1000, seed=1)
return iter(ds)
def get_next_element(ds, process_id, N=10000):
for _ in range(N):
_ = next(ds)['content']
print(f'process {process_id} done')
return
set_seed(1)
datasets.utils.logging.set_verbosity_debug()
n_processes = 8
tasks = [get_next_element for _ in range(n_processes)]
args = [get_dataset() for _ in range(n_processes)]
run_tasks_in_parallel(tasks, args)
```
Today I noticed that it can happen when running it on a single process on a machine with many cores without streaming. So just `load_dataset("transformersbook/codeparrot-train")` alone might cause the issue after waiting long enough or trying many times. It's a slightly random process which makes it especially hard to track down. When I encountered it today it had already processed 17GB of data (the size of the cache folder when it got stuck) before getting stuck.
Here's my current understanding of the error. As far as I can tell it happens in the following block: https://github.com/huggingface/datasets/blob/be701e9e89ab38022612c7263edc015bc7feaff9/src/datasets/packaged_modules/json/json.py#L119-L139
When the try on line 121 fails and the `block_size` is increased it can happen that it can't read the JSON again and gets stuck indefinitely. A hint that points in that direction is that increasing the `chunksize` argument decreases the chance of getting stuck and vice versa. Maybe it is an issue with a lock on the file that is not properly released.
## Expected results
Read a JSON before the end of the universe.
## Actual results
Read a JSON not before the end of the universe.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.10
- PyArrow version: 7.0.0
@lhoestq we dicsussed this a while ago. @albertvillanova we discussed this today :)
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https://api.github.com/repos/huggingface/datasets/issues/5596 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5596/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5596/comments | https://api.github.com/repos/huggingface/datasets/issues/5596/events | https://github.com/huggingface/datasets/issues/5596 | 1,604,919,993 | I_kwDODunzps5fqSK5 | 5,596 | [TypeError: Couldn't cast array of type] Can only load a subset of the dataset | {
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"Apparently some JSON objects have a `\"labels\"` field. Since this field is not present in every object, you must specify all the fields types in the README.md\r\n\r\nEDIT: actually specifying the feature types doesn’t solve the issue, it raises an error because “labels” is missing in the data",
"We've updated t... | 2023-03-01T12:53:08Z | 2023-04-19T10:19:37Z | 2023-03-02T11:12:11Z | NONE | null | null | null | ### Describe the bug
I'm trying to load this [dataset](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues) which consists of jsonl files and I get the following error:
```
casted_values = _c(array.values, feature[0])
File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 1839, in wrapper
return func(array, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 2132, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
struct<type: string, action: string, datetime: timestamp[s], author: string, title: string, description: string, comment_id: int64, comment: string, labels: list<item: string>>
to
{'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)}
```
But I can succesfully load a subset of the dataset, for example this works:
```python
ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train", data_files=[f"data/data-{x}.jsonl" for x in range(10)])
```
and `ds.features` returns:
```
{'repo': Value(dtype='string', id=None),
'org': Value(dtype='string', id=None),
'issue_id': Value(dtype='int64', id=None),
'issue_number': Value(dtype='int64', id=None),
'pull_request': {'user_login': Value(dtype='string', id=None),
'repo': Value(dtype='string', id=None),
'number': Value(dtype='int64', id=None)},
'events': [{'type': Value(dtype='string', id=None),
'action': Value(dtype='string', id=None),
'datetime': Value(dtype='timestamp[s]', id=None),
'author': Value(dtype='string', id=None),
'title': Value(dtype='string', id=None),
'description': Value(dtype='string', id=None),
'comment_id': Value(dtype='int64', id=None),
'comment': Value(dtype='string', id=None)}]}
```
So I'm not sure if there's an issue with just some of the files. Grateful if you have any suggestions to fix the issue.
Side note:
I saw this related [issue](https://github.com/huggingface/datasets/issues/3637) and tried to write a loading script to have `events` as a `Sequence` and not `list` [here](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/blob/main/loading.py) (the script was renamed). It worked with a subset locally but doesn't for the remote dataset it can't find https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/resolve/main/data.
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train")
```
### Expected behavior
Load the entire dataset succesfully.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/2239 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2239/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2239/comments | https://api.github.com/repos/huggingface/datasets/issues/2239/events | https://github.com/huggingface/datasets/issues/2239 | 861,904,306 | MDU6SXNzdWU4NjE5MDQzMDY= | 2,239 | Error loading wikihow dataset | {
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"Hi @odellus, thanks for reporting.\r\n\r\nThe `wikihow` dataset has 2 versions:\r\n- `all`: Consisting of the concatenation of all paragraphs as the articles and the bold lines as the reference summaries.\r\n- `sep`: Consisting of each paragraph and its summary.\r\n\r\nTherefore, in order to load it, you have to s... | 2021-04-19T21:02:31Z | 2021-04-20T16:33:11Z | 2021-04-20T16:33:11Z | CONTRIBUTOR | null | null | null | ## Describe the bug
When attempting to load wikihow into a dataset with
```python
from datasets import load_dataset
dataset = load_dataset('wikihow', data_dir='./wikihow')
```
I get the message:
```
AttributeError: 'BuilderConfig' object has no attribute 'filename'
```
at the end of a [full stack trace](https://gist.github.com/odellus/602c3b2de52f541d353b1022f320ffc2).
## Steps to reproduce the bug
I have followed the instructions for creating a wikihow dataset. The [wikihow dataset site](https://huggingface.co/datasets/wikihow) says to use
```python
from datasets import load_dataset
dataset = load_dataset('wikihow')
```
to load the dataset. I do so and I get the message
```
AssertionError: The dataset wikihow with config all requires manual data.
Please follow the manual download instructions: You need to manually download two wikihow files. An overview of which files to download can be seen at https://github.com/mahnazkoupaee/WikiHow-Dataset.
You need to download the following two files manually:
1) https://ucsb.app.box.com/s/ap23l8gafpezf4tq3wapr6u8241zz358 and save the file under <path/to/folder>/wikihowAll.csv
2) https://ucsb.app.box.com/s/7yq601ijl1lzvlfu4rjdbbxforzd2oag and save the file under <path/to/folder>/wikihowSep.csv
The <path/to/folder> can e.g. be "~/manual_wikihow_data".
Wikihow can then be loaded using the following command `datasets.load_dataset("wikihow", data_dir="<path/to/folder>")`.
.
Manual data can be loaded with `datasets.load_dataset(wikihow, data_dir='<path/to/manual/data>')
```
So I create a directory `./wikihow` and download `wikihowAll.csv` and `wikihowSep.csv` into the new directory.
Then I run
```python
from datasets import load_dataset
dataset = load_dataset('wikihow', data_dir='./wikihow')
```
that's when I get the [stack trace](https://gist.github.com/odellus/602c3b2de52f541d353b1022f320ffc2)
## Expected results
I expected it to load the downloaded files into a dataset.
## Actual results
```python
Using custom data configuration default-data_dir=.%2Fwikihow
Downloading and preparing dataset wikihow/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/azureuser/.cache/huggingface/datasets/wikihow/default-data_dir=.%2Fwikihow/0.0.0/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2... ---------------------------------------------------------------------------
AttributeError
Traceback (most recent call last)
<ipython-input-9-5e4d40142f30> in <module>
----> 1 dataset = load_dataset('wikihow',data_dir='./wikihow')
~/.local/lib/python3.6/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, script_version, use_auth_token, **config_kwargs)
745 try_from_hf_gcs=try_from_hf_gcs,
746 base_path=base_path,-->
747 use_auth_token=use_auth_token,
748 )
749
~/.local/lib/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
577 if not downloaded_from_gcs:
578 self._download_and_prepare( -->
579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
580 )
581 # Sync info
~/.local/lib/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
632 split_dict = SplitDict(dataset_name=self.name)
633 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) -->
634 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
635
636 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/wikihow/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2/wikihow.py in _split_generators(self, dl_manager)
132
133 path_to_manual_file = os.path.join(
--> 134 os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), self.config.filename
135 )
136
AttributeError: 'BuilderConfig' object has no attribute 'filename'
```
## Versions
Paste the output of the following code:
```python
import datasets
import sys
import platform
print(f"""
- Datasets: {datasets.__version__}
- Python: {sys.version}
- Platform: {platform.platform()}
""")
```
```
- Datasets: 1.5.0
- Python: 3.6.9 (default, Jan 26 2021, 15:33:00) [GCC 8.4.0]
- Platform: Linux-5.4.0-1046-azure-x86_64-with-Ubuntu-18.04-bionic
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3292 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3292/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3292/comments | https://api.github.com/repos/huggingface/datasets/issues/3292/events | https://github.com/huggingface/datasets/issues/3292 | 1,056,962,554 | I_kwDODunzps4-__f6 | 3,292 | Not able to load 'wikipedia' dataset | {
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"gists... | null | [
"Hi ! Indeed it looks like the code snippet on the Hugging face Hub doesn't show the second parameter\r\n\r\n\r\n\r\nThanks for reporting, I'm taking a look\r\n"
] | 2021-11-18T05:41:18Z | 2021-11-19T16:49:29Z | 2021-11-19T16:49:29Z | NONE | null | null | null | ## Describe the bug
I am following the instruction for loading the wikipedia dataset using datasets. However getting the below error.
## Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset("wikipedia")
```
## Expected results
A clear and concise description of the expected results.
## Actual results
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)
339 "Config name is missing."
340 "\nPlease pick one among the available configs: %s" % list(self.builder_configs.keys())
--> 341 + "\nExample of usage:\n\t`{}`".format(example_of_usage)
342 )
343 builder_config = self.BUILDER_CONFIGS[0]
ValueError: Config name is missing.
Please pick one among the available configs: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']
Example of usage:
`load_dataset('wikipedia', '20200501.aa')`
I think the other parameter is missing in the load_dataset function that is not shown in the instruction. | {
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"Maybe we can change the disk space calculating API from `shutil.disk_usage` to `os.statvfs` in UNIX-like system, which can provide correct results.\r\n```\r\nstatvfs = os.statvfs('path')\r\navail_space_bytes = statvfs.f_frsize * statvfs.f_bavail\r\n```",
"Hi @qqaatw, thanks for reporting.\r\n\r\nCould you pleas... | 2021-09-27T07:41:22Z | 2022-08-29T23:21:36Z | 2021-09-28T06:43:15Z | CONTRIBUTOR | null | null | null | ## Describe the bug
I'm trying to download `natural_questions` dataset from the Internet, and I've specified the cache_dir which locates in a mounted disk and has enough disk space. However, even though the space is enough, the disk space checking function still reports the space of root `/` disk having no enough space.
The file system structure is like below. The root `/` has `115G` disk space available, and the `sda1` is mounted to `/mnt`, which has `1.2T` disk space available:
```
/
/mnt/sda1/path/to/args.dataset_cache_dir
```
## Steps to reproduce the bug
```python
dataset_config = DownloadConfig(
cache_dir=os.path.abspath(args.dataset_cache_dir),
resume_download=True,
)
dataset = load_dataset("natural_questions", download_config=dataset_config)
```
## Expected results
Can download the dataset without an error.
## Actual results
The following error raised:
```
OSError: Not enough disk space. Needed: 134.92 GiB (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Ubuntu 18.04
- Python version: 3.8.10
- PyArrow version:
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https://api.github.com/repos/huggingface/datasets/issues/3474 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3474/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3474/comments | https://api.github.com/repos/huggingface/datasets/issues/3474/events | https://github.com/huggingface/datasets/pull/3474 | 1,086,945,384 | PR_kwDODunzps4wMMt0 | 3,474 | Decode images when iterating | {
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} | [] | closed | false | null | [] | null | [] | 2021-12-22T15:34:49Z | 2023-09-24T09:54:04Z | 2021-12-28T16:08:10Z | MEMBER | null | 0 | {
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} | 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/2141 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2141/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2141/comments | https://api.github.com/repos/huggingface/datasets/issues/2141/events | https://github.com/huggingface/datasets/pull/2141 | 843,914,790 | MDExOlB1bGxSZXF1ZXN0NjAzMjM2MjUw | 2,141 | added spans field for the wikiann datasets | {
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"Hi @lhoestq \r\nThanks a lot for taking time checking it. I update \"dataset_infos.json\", I added description to the function of _generate_samples in wikiann.py but I was not sure about the format to write in README. thanks. ",
"Thanks !\r\n\r\nFor the fields description in the dataset card, something like thi... | 2021-03-29T23:38:26Z | 2021-03-31T13:27:50Z | 2021-03-31T13:27:50Z | CONTRIBUTOR | null | 0 | {
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} | Hi @lhoestq
I tried to add spans to the wikiann datasets.
Thanks a lot for kindly having a look.
This addresses https://github.com/huggingface/datasets/issues/2130.
Best regards
Rabeeh | {
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"You're right, we have an issue today with the datasets preview. We're investigating.",
"It should be fixed now. Thanks for reporting.",
"Down again. ",
"Fixed for good."
] | 2021-12-24T08:18:48Z | 2021-12-24T14:27:46Z | 2021-12-24T14:27:46Z | NONE | null | null | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/1740 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1740/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1740/comments | https://api.github.com/repos/huggingface/datasets/issues/1740/events | https://github.com/huggingface/datasets/pull/1740 | 787,264,605 | MDExOlB1bGxSZXF1ZXN0NTU2MDA5NjM1 | 1,740 | add id_liputan6 dataset | {
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} | id_liputan6 is a large-scale Indonesian summarization dataset. The articles were harvested from an online news portal, and obtain 215,827 document-summary pairs: https://arxiv.org/abs/2011.00679 | {
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https://api.github.com/repos/huggingface/datasets/issues/5705 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5705/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5705/comments | https://api.github.com/repos/huggingface/datasets/issues/5705/events | https://github.com/huggingface/datasets/issues/5705 | 1,653,500,383 | I_kwDODunzps5ijmnf | 5,705 | Getting next item from IterableDataset took forever. | {
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"Hi! It can take some time to iterate over Parquet files as big as yours, convert the samples to Python, and find the first one that matches a filter predicate before yielding it...",
"Thanks @mariosasko, I figured it was the filter operation. I'm closing this issue because it is not a bug, it is the expected beh... | 2023-04-04T09:16:17Z | 2023-04-05T23:35:41Z | 2023-04-05T23:35:41Z | NONE | null | null | null | ### Describe the bug
I have a large dataset, about 500GB. The format of the dataset is parquet.
I then load the dataset and try to get the first item
```python
def get_one_item():
dataset = load_dataset("path/to/datafiles", split="train", cache_dir=".", streaming=True)
dataset = dataset.filter(lambda example: example['text'].startswith('Ar'))
print(next(iter(dataset)))
```
However, this function never finish. I waited ~10mins, the function was still running so I killed the process. I'm now using `line_profiler` to profile how long it would take to return one item. I'll be patient and wait for as long as it needs.
I suspect the filter operation is the reason why it took so long. Can I get some possible reasons behind this?
### Steps to reproduce the bug
Unfortunately without my data files, there is no way to reproduce this bug.
### Expected behavior
With `IteralbeDataset`, I expect the first item to be returned instantly.
### Environment info
- datasets version: 2.11.0
- python: 3.7.12 | {
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