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1,326,034,767 | https://api.github.com/repos/huggingface/datasets/issues/4780/labels{/name} | Instead of importing `apache_beam` at the module level, import it in the method `_build_pcollection`.
Fix #4779. | 2022-08-02T16:16:33Z | 4,780 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-08-02T15:34:54Z | https://api.github.com/repos/huggingface/datasets/issues/4780/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4780/timeline | Remove apache_beam import from module level in natural_questions dataset | https://api.github.com/repos/huggingface/datasets/issues/4780/events | null | {
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1,325,997,225 | https://api.github.com/repos/huggingface/datasets/issues/4779/labels{/name} | ## Describe the bug
When loading "natural_questions", the package "apache_beam" is required:
```
ImportError: To be able to use natural_questions, you need to install the following dependency: apache_beam.
Please install it using 'pip install apache_beam' for instance'
```
This requirement is unnecessary, once there exists preprocessed data and the script just needs to download it.
## Steps to reproduce the bug
```python
load_dataset("natural_questions", "dev", split="validation", revision="main")
```
## Expected results
No ImportError raised.
## Actual results
```
ImportError Traceback (most recent call last)
[<ipython-input-3-c938e7c05d02>](https://localhost:8080/#) in <module>()
----> 1 from datasets import load_dataset; ds = load_dataset("natural_questions", "dev", split="validation", revision="main")
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1732 revision=revision,
1733 use_auth_token=use_auth_token,
-> 1734 **config_kwargs,
1735 )
1736
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1504 download_mode=download_mode,
1505 data_dir=data_dir,
-> 1506 data_files=data_files,
1507 )
1508
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1245 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1246 ) from None
-> 1247 raise e1 from None
1248 else:
1249 raise FileNotFoundError(
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1180 download_config=download_config,
1181 download_mode=download_mode,
-> 1182 dynamic_modules_path=dynamic_modules_path,
1183 ).get_module()
1184 elif path.count("/") == 1: # community dataset on the Hub
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self)
490 base_path=hf_github_url(path=self.name, name="", revision=revision),
491 imports=imports,
--> 492 download_config=self.download_config,
493 )
494 additional_files = [(config.DATASETDICT_INFOS_FILENAME, dataset_infos_path)] if dataset_infos_path else []
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in _download_additional_modules(name, base_path, imports, download_config)
214 _them_str = "them" if len(needs_to_be_installed) > 1 else "it"
215 raise ImportError(
--> 216 f"To be able to use {name}, you need to install the following {_depencencies_str}: "
217 f"{', '.join(needs_to_be_installed)}.\nPlease install {_them_str} using 'pip install "
218 f"{' '.join(needs_to_be_installed.values())}' for instance'"
ImportError: To be able to use natural_questions, you need to install the following dependency: apache_beam.
Please install it using 'pip install apache_beam' for instance'
```
## Environment info
Colab notebook.
| 2022-08-02T16:03:18Z | 4,779 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,324,928,750 | https://api.github.com/repos/huggingface/datasets/issues/4778/labels{/name} | This PR clarifies the local loading script section to include how to load a dataset after you've modified the local loading script (closes #4732). | 2022-08-23T16:32:26Z | 4,778 | null | https://api.github.com/repos/huggingface/datasets | false | [
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4778). All of your documentation changes will be reflected on that endpoint.",
"I would rather have a section in the docs that explains how to modify the script of an existing dataset (`inspect_dataset` + modification + `load_d... | {
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1,324,548,784 | https://api.github.com/repos/huggingface/datasets/issues/4777/labels{/name} | Related to:
- https://github.com/huggingface/transformers/issues/18379
Fix partially #4776. | 2022-08-02T17:35:14Z | 4,777 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-08-01T14:50:50Z | https://api.github.com/repos/huggingface/datasets/issues/4777/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4777/timeline | Require torchaudio<0.12.0 to avoid RuntimeError | https://api.github.com/repos/huggingface/datasets/issues/4777/events | null | {
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1,324,493,860 | https://api.github.com/repos/huggingface/datasets/issues/4776/labels{/name} | Current version of `torchaudio` (0.12.0) raises a RuntimeError when trying to use `sox_io` backend but non-Python dependency `sox` is not installed:
https://github.com/pytorch/audio/blob/2e1388401c434011e9f044b40bc8374f2ddfc414/torchaudio/backend/sox_io_backend.py#L21-L29
```python
def _fail_load(
filepath: str,
frame_offset: int = 0,
num_frames: int = -1,
normalize: bool = True,
channels_first: bool = True,
format: Optional[str] = None,
) -> Tuple[torch.Tensor, int]:
raise RuntimeError("Failed to load audio from {}".format(filepath))
```
Maybe we should raise a more actionable error message so that the user knows how to fix it.
UPDATE:
- this is an incompatibility of latest torchaudio (0.12.0) and the sox backend
TODO:
- [x] as a temporary solution, we should recommend installing torchaudio<0.12.0
- #4777
- #4785
- [ ] however, a stable solution must be found for torchaudio>=0.12.0
Related to:
- https://github.com/huggingface/transformers/issues/18379 | 2023-03-02T15:58:16Z | 4,776 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-08-01T14:11:23Z | https://api.github.com/repos/huggingface/datasets/issues/4776/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4776/timeline | RuntimeError when using torchaudio 0.12.0 to load MP3 audio file | https://api.github.com/repos/huggingface/datasets/issues/4776/events | null | {
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1,324,136,486 | https://api.github.com/repos/huggingface/datasets/issues/4775/labels{/name} | ### Link
_No response_
### Description
_No response_
### Owner
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1,323,375,844 | https://api.github.com/repos/huggingface/datasets/issues/4774/labels{/name} | ## Describe the bug
I use load_dataset() (I tried with [wiki](https://huggingface.co/datasets/wikipedia) and my own json data) and use set_transform/with_transform for preprocessing. But it hangs at the end of the 1st epoch if dataloader_num_workers>=1. No problem with single worker.
## Steps to reproduce the bug
```python
train_dataset = datasets.load_dataset("wikipedia", "20220301.en",
split='train',
cache_dir=model_args.cache_dir,
streaming=False)
train_dataset.set_transform(psg_parse_fn)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.train_batch_size,
sampler=DistributedSampler(train_dataset),
collate_fn=data_collator,
drop_last=args.dataloader_drop_last,
num_workers=args.dataloader_num_workers,
)
```
## Expected results
## Actual results
It simply hangs. The ending step is num_example/batch_size (one epoch).
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Linux-5.4.170+-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 8.0.0
- Pandas version: 1.4.1 | 2022-07-31T06:36:43Z | 4,774 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,322,796,721 | https://api.github.com/repos/huggingface/datasets/issues/4773/labels{/name} | This PR describes loading a dataset from the Hub by specifying a relative path in `data_dir` or `data_files` in `load_dataset` (see #4757). | 2022-08-25T18:36:45Z | 4,773 | null | https://api.github.com/repos/huggingface/datasets | false | [
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for the feedback!\r\n\r\nI agree that adding it to `load_hub.mdx` is probably a bit too specific, especially for beginners reading the tutorials. Since this clarification is closely related to loading from the Hub (the only di... | {
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1,322,693,123 | https://api.github.com/repos/huggingface/datasets/issues/4772/labels{/name} | ## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.
The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator
dataset = load_dataset('glue', 'mrpc', split='train')
tf_dataset = dataset.to_tf_dataset(
columns=["sentence1", "sentence2", "idx"],
label_cols=["label"],
batch_size=16,
collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```
## Expected results
No assertion error.
## Actual results
```
AssertionError: in user code:
File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels *
assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- 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: 1.4.3
| 2022-09-12T11:24:46Z | 4,772 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"cc @Rocketknight1 ",
"Hi @lehrig, this is caused by the data collator renaming \"label\" to \"labels\". If you set `label_cols=[\"labels\"]` in the call it will work correctly. However, I agree that the cause of the bug is not obvious, so I'll see if I can make a PR to clarify things when the collator renames co... | {
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1,322,600,725 | https://api.github.com/repos/huggingface/datasets/issues/4771/labels{/name} | This PR removes instructions to generate dummy data since that is no longer necessary for datasets that are uploaded to the Hub instead of our GitHub repo.
Close #4744 | 2022-08-03T00:04:01Z | 4,771 | null | https://api.github.com/repos/huggingface/datasets | false | [
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1,322,147,855 | https://api.github.com/repos/huggingface/datasets/issues/4770/labels{/name} | By defaul -> By default | 2022-07-29T16:02:07Z | 4,770 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-29T11:46:12Z | https://api.github.com/repos/huggingface/datasets/issues/4770/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4770/timeline | fix typo | https://api.github.com/repos/huggingface/datasets/issues/4770/events | null | {
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} | PR_kwDODunzps48UEBT | [
"good catch thanks ! Can you check if the same typo is also present in `add_elasticsearch_index` ? It has a very similar signature",
"> good catch thanks ! Can you check if the same typo is also present in `add_elasticsearch_index` ? It has a very similar signature\r\n\r\nfixed"
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1,322,121,554 | https://api.github.com/repos/huggingface/datasets/issues/4769/labels{/name} | ## Describe the bug
datasets fail to process SQuADv1.1 with max_seq_length=128, doc_stride=96 when calling datasets["train"].train_dataset.map().
## Steps to reproduce the bug
I used huggingface[ TF2 question-answering examples](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering). And my scripts are as follows:
```
python run_qa.py \
--model_name_or_path $BERT_DIR \
--dataset_name $SQUAD_DIR \
--do_train \
--do_eval \
--per_device_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 128 \
--doc_stride 96 \
--output_dir $OUTPUT \
--save_steps 10000 \
--overwrite_cache \
--overwrite_output_dir \
```
## Expected results
Normally process SQuADv1.1 datasets with max_seq_length=128, doc_stride=96.
## Actual results
```
INFO:__main__:Padding all batches to max length because argument was set or we're on TPU.
WARNING:datasets.fingerprint:Parameter 'function'=<function main.<locals>.prepare_train_features at 0x7f15bc2d07a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
0%| | 0/88 [00:00<?, ?ba/s]thread '<unnamed>' panicked at 'assertion failed: stride < max_len', /__w/tokenizers/tokenizers/tokenizers/src/tokenizer/encoding.rs:311:9
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
0%| | 0/88 [00:00<?, ?ba/s]
Traceback (most recent call last):
File "run_qa.py", line 743, in <module>
main()
File "run_qa.py", line 485, in main
load_from_cache_file=not data_args.overwrite_cache,
File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2394, in map
desc=desc,
File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 551, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 518, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2768, in _map_single
offset=offset,
File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2644, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2336, in decorated
result = f(decorated_item, *args, **kwargs)
File "run_qa.py", line 410, in prepare_train_features
padding=padding,
File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2512, in __call__
**kwargs,
File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2703, in batch_encode_plus
**kwargs,
File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py", line 429, in _batch_encode_plus
is_pretokenized=is_split_into_words,
pyo3_runtime.PanicException: assertion failed: stride < max_len
Traceback (most recent call last):
File "./data/SQuADv1.1/evaluate-v1.1.py", line 92, in <module>
with open(args.prediction_file) as prediction_file:
FileNotFoundError: [Errno 2] No such file or directory: './output/bert_base_squadv1.1_tf2/eval_predictions.json'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Ubuntu, pytorch=1.11.0, tensorflow-gpu=2.9.1
- Python version: 2.7
- PyArrow version: 8.0.0
| 2022-07-29T11:18:24Z | 4,769 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,321,913,645 | https://api.github.com/repos/huggingface/datasets/issues/4768/labels{/name} | Once `rouge-score` has made the 0.1.2 release to fix their issue https://github.com/google-research/google-research/issues/1212, we can unpin it.
Related to:
- #4735 | 2022-07-29T16:42:28Z | 4,768 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-29T08:17:40Z | https://api.github.com/repos/huggingface/datasets/issues/4768/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4768/timeline | Unpin rouge_score test dependency | https://api.github.com/repos/huggingface/datasets/issues/4768/events | null | {
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1,321,787,428 | https://api.github.com/repos/huggingface/datasets/issues/4765/labels{/name} | After latest release, `map_nested` docstring needs being updated with the right version for versionchanged and versionadded. | 2022-07-29T11:51:25Z | 4,765 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-29T05:44:32Z | https://api.github.com/repos/huggingface/datasets/issues/4765/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4765/timeline | Fix version in map_nested docstring | https://api.github.com/repos/huggingface/datasets/issues/4765/events | null | {
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1,321,295,961 | https://api.github.com/repos/huggingface/datasets/issues/4764/labels{/name} | Replace the old CircleCI badge with a new one for GH Actions. | 2022-07-29T11:36:37Z | 4,764 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-28T18:04:20Z | https://api.github.com/repos/huggingface/datasets/issues/4764/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4764/timeline | Update CI badge | https://api.github.com/repos/huggingface/datasets/issues/4764/events | null | {
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1,321,295,876 | https://api.github.com/repos/huggingface/datasets/issues/4763/labels{/name} | `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! | 2022-09-08T19:17:54Z | 4,763 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-28T18:04:15Z | https://api.github.com/repos/huggingface/datasets/issues/4763/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4763/timeline | More rigorous shape inference in to_tf_dataset | https://api.github.com/repos/huggingface/datasets/issues/4763/events | null | {
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1,321,261,733 | https://api.github.com/repos/huggingface/datasets/issues/4762/labels{/name} | `IterableDataset._resolve_features` was returning the features sorted alphabetically by column name, which is not consistent with non-streaming. I changed this and used the order of columns from the data themselves. It was causing some inconsistencies in the dataset viewer as well.
I also fixed `interleave_datasets` that was not filling missing columns with None, because it was not using the columns from `IterableDataset._resolve_features`
cc @severo | 2022-09-09T17:17:39Z | 4,762 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-28T17:28:11Z | https://api.github.com/repos/huggingface/datasets/issues/4762/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4762/timeline | Improve features resolution in streaming | https://api.github.com/repos/huggingface/datasets/issues/4762/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Just took your comment into account @mariosasko , let me know if it's good for you now :)"
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1,321,068,411 | https://api.github.com/repos/huggingface/datasets/issues/4761/labels{/name} | While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works.
Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360 | 2023-07-21T02:07:10Z | 4,761 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-07-28T14:57:03Z | https://api.github.com/repos/huggingface/datasets/issues/4761/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4761/timeline | parallel searching in multi-gpu setting using faiss | https://api.github.com/repos/huggingface/datasets/issues/4761/events | null | {
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} | [] | null | null | CONTRIBUTOR | null | null | I_kwDODunzps5Oved7 | [
"And I don't see any speed up when increasing the number of GPUs while calling `get_nearest_examples_batch`.",
"Hi ! Yes search_batch uses FAISS search which happens in parallel across the GPUs\r\n\r\n> And I don't see any speed up when increasing the number of GPUs while calling get_nearest_examples_batch.\r\n\r... | {
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1,320,878,223 | https://api.github.com/repos/huggingface/datasets/issues/4760/labels{/name} | ## Describe the bug
I can't retrieve a cached dataset with offline mode enabled
## Steps to reproduce the bug
To reproduce my issue, first, you'll need to run a script that will cache the dataset
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "0"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
then, you can try to reload it in offline mode:
```python
import os
os.environ["HF_DATASETS_OFFLINE"] = "1"
import datasets
datasets.logging.set_verbosity_info()
ds_name = "SaulLu/toy_struc_dataset"
ds = datasets.load_dataset(ds_name)
print(ds)
```
## Expected results
I would have expected the 2nd snippet not to return any errors
## Actual results
The 2nd snippet returns:
```
Traceback (most recent call last):
File "/home/lucile_huggingface_co/sandbox/evaluate/test_cache_datasets.py", line 8, in <module>
ds = datasets.load_dataset(ds_name)
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1723, in load_dataset
builder_instance = load_dataset_builder(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1500, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/lucile_huggingface_co/anaconda3/envs/evaluate-dev/lib/python3.8/site-packages/datasets/load.py", line 1241, in dataset_module_factory
raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'SaulLu/toy_struc_dataset': Offline mode is enabled.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
Maybe I'm misunderstanding something in the use of the offline mode (see [doc](https://huggingface.co/docs/datasets/v2.4.0/en/loading#offline)), is that the case?
| 2024-03-25T16:24:45Z | 4,760 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | completed | CONTRIBUTOR | 2024-01-23T10:58:22Z | null | I_kwDODunzps5OuwCP | [
"Hi @SaulLu, thanks for reporting.\r\n\r\nI think offline mode is not supported for datasets containing only data files (without any loading script). I'm having a look into this...",
"Thanks for your feedback! \r\n\r\nTo give you a little more info, if you don't set the offline mode flag, the script will load the... | {
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1,320,783,300 | https://api.github.com/repos/huggingface/datasets/issues/4759/labels{/name} | ### Link
https://huggingface.co/datasets/Toygar/turkish-offensive-language-detection
### Description
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
Hi, I provided train.csv, test.csv and valid.csv files. However, viewer says dataset does not exist.
Should I need to do anything else?
### Owner
Yes | 2022-07-28T13:17:56Z | 4,759 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"I refreshed the dataset viewer manually, it's fixed now. Sorry for the inconvenience.\r\n<img width=\"1557\" alt=\"Capture d’écran 2022-07-28 à 09 17 39\" src=\"https://user-images.githubusercontent.com/1676121/181514666-92d7f8e1-ddc1-4769-84f3-f1edfdb902e8.png\">\r\n\r\n"
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1,320,602,532 | https://api.github.com/repos/huggingface/datasets/issues/4757/labels{/name} | As discussed with @ydshieh, when passing a relative path as `data_dir` to `load_dataset` of a dataset hosted on the Hub, the relative path is transformed to the corresponding URL of the Hub dataset.
Currently, we mention this in our docs here: [Create a dataset loading script > Download data files and organize splits](https://huggingface.co/docs/datasets/v2.4.0/en/dataset_script#download-data-files-and-organize-splits)
> If the data files live in the same folder or repository of the dataset script, you can just pass the relative paths to the files instead of URLs.
Maybe we should document better how relative paths are handled, not only when creating a dataset loading script, but also when passing to `load_dataset`:
- `data_dir`
- `data_files`
CC: @stevhliu | 2022-08-25T18:34:24Z | 4,757 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,319,687,044 | https://api.github.com/repos/huggingface/datasets/issues/4755/labels{/name} | ## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because each tokenizer only looks at its share of the samples, and maps to the index _within its share_, but then `Dataset.map` collates them together.
## Steps to reproduce the bug
1. Make a dataset of 3 strings.
2. Tokenize via Dataset.map with n_proc = 8
3. Inspect the `overflow_to_sample_mapping` field
## Expected results
`[0, 1, 2]`
## Actual results
`[0, 0, 0]`
Notes:
1. I have not yet extracted a minimal example, but the above works reliably
2. If the dataset is large, I've yet to determine if this bug still happens a. not at all b. always c. on the small, leftover batch at the end.
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"I've built a minimal example that shows this bug without `n_proc`. It seems like it's a problem any way of using **tokenizers, `overflow_to_sample_mapping`, and Dataset.map, with a small batch size**:\r\n\r\n```\r\nimport datasets\r\nimport transformers\r\npretrained = 'deepset/tinyroberta-squad2'\r\ntokenizer = t... | {
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1,319,681,541 | https://api.github.com/repos/huggingface/datasets/issues/4754/labels{/name} | Following https://github.com/huggingface/datasets/pull/4753 there was still a "unknown" langauge tag in `wikipedia` so the job at https://github.com/huggingface/datasets/runs/7542567336?check_suite_focus=true failed for wikipedia | 2022-07-27T15:03:00Z | 4,754 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-27T14:50:12Z | https://api.github.com/repos/huggingface/datasets/issues/4754/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4754/timeline | Remove "unkown" language tags | https://api.github.com/repos/huggingface/datasets/issues/4754/events | null | {
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1,319,571,745 | https://api.github.com/repos/huggingface/datasets/issues/4753/labels{/name} | Following (internal) https://github.com/huggingface/moon-landing/pull/3509, we need to move the bcp47 tags to `language_bcp47` and keep the `language` tag for iso 639 1-2-3 codes. In particular I made sure that all the tags in `languages` are not longer than 3 characters. I moved the rest to `language_bcp47` and fixed some of them.
After this PR is merged I think we can simplify the language validation from the DatasetMetadata class (and keep it bare-bone just for the tagging app)
PS: the CI is failing because of missing content in dataset cards that are unrelated to this PR | 2022-07-27T14:50:03Z | 4,753 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-27T13:31:16Z | https://api.github.com/repos/huggingface/datasets/issues/4753/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4753/timeline | Add `language_bcp47` tag | https://api.github.com/repos/huggingface/datasets/issues/4753/events | null | {
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1,319,464,409 | https://api.github.com/repos/huggingface/datasets/issues/4752/labels{/name} | ## Describe the bug
When running the `datasets-cli test` it would seem that some config properties in a DatasetInfo get mangled, leading to issues, e.g., about the ClassLabel.
## Steps to reproduce the bug
In summary, what I want to do is create three configs:
- unfiltered: no classlabel, no tasks. Gets data from unfiltered.json.gz (I'd want this without splits, just one chunk of data, but that does not seem possible?)
- filtered_sentiment: `review_sentiment` as ClassLabel, TextClassification task with `review_sentiment` as label. Gets train/test split from respective json.gz files
- filtered_rating: `review_rating0` as ClassLabel, TextClassification task with `review_rating0` as label. Gets train/test split from respective json.gz files
This might be a bit tedious to reproduce, so I am sorry, but these are the steps:
- Clone datasets -> `datasets/` and install it
- Clone `https://huggingface.co/datasets/BramVanroy/hebban-reviews` into `datasets/datasets` so that you have a new folder `datasets/datasets/hebban-reviews/`.
- Replace the HebbanReviews class with this new one:
```python
class HebbanReviews(datasets.GeneratorBasedBuilder):
"""The Hebban book reviews dataset."""
BUILDER_CONFIGS = [
HebbanReviewsConfig(
name="unfiltered",
description=_HEBBAN_REVIEWS_UNFILTERED_DESCRIPTION,
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_sentiment",
description=f"This config has the negative, neutral, and positive sentiment scores as ClassLabel in the 'review_sentiment' column.\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
),
HebbanReviewsConfig(
name="filtered_rating",
description=f"This config has the 5-class ratings as ClassLabel in the 'review_rating0' column (which is a variant of 'review_rating' that starts counting from 0 instead of 1).\n{_HEBBAN_REVIEWS_FILTERED_DESCRIPTION}",
version=datasets.Version(_HEBBAN_VERSION)
)
]
DEFAULT_CONFIG_NAME = "filtered_sentiment"
_URLS = {
"train": "train.jsonl.gz",
"test": "test.jsonl.gz",
"unfiltered": "unfiltered.jsonl.gz",
}
def _info(self):
features = {
"review_title": datasets.Value("string"),
"review_text": datasets.Value("string"),
"review_text_without_quotes": datasets.Value("string"),
"review_n_quotes": datasets.Value("int32"),
"review_n_tokens": datasets.Value("int32"),
"review_rating": datasets.Value("int32"),
"review_rating0": datasets.Value("int32"),
"review_author_url": datasets.Value("string"),
"review_author_type": datasets.Value("string"),
"review_n_likes": datasets.Value("int32"),
"review_n_comments": datasets.Value("int32"),
"review_url": datasets.Value("string"),
"review_published_date": datasets.Value("string"),
"review_crawl_date": datasets.Value("string"),
"lid": datasets.Value("string"),
"lid_probability": datasets.Value("float32"),
"review_sentiment": datasets.features.ClassLabel(names=["negative", "neutral", "positive"]),
"review_sentiment_label": datasets.Value("string"),
"book_id": datasets.Value("int32"),
}
if self.config.name == "filtered_sentiment":
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_sentiment")]
elif self.config.name == "filtered_rating":
# For CrossEntropy, our classes need to start at index 0 -- not 1
features["review_rating0"] = datasets.features.ClassLabel(names=["1", "2", "3", "4", "5"])
features["review_sentiment"] = datasets.Value("int32")
task_templates = [datasets.TextClassification(text_column="review_text_without_quotes", label_column="review_rating0")]
elif self.config.name == "unfiltered": # no ClassLabels in unfiltered
features["review_sentiment"] = datasets.Value("int32")
task_templates = None
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
print("AT INFO", self.config.name, task_templates)
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(features),
homepage="https://huggingface.co/datasets/BramVanroy/hebban-reviews",
citation=_HEBBAN_REVIEWS_CITATION,
task_templates=task_templates,
license="cc-by-4.0"
)
def _split_generators(self, dl_manager):
if self.config.name.startswith("filtered"):
files = dl_manager.download_and_extract({"train": "train.jsonl.gz",
"test": "test.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": files["test"]
},
),
]
elif self.config.name == "unfiltered":
files = dl_manager.download_and_extract({"train": "unfiltered.jsonl.gz"})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": files["train"]
},
),
]
else:
raise ValueError(f"Unsupported config {self.config.name}. Expected one of 'filtered_sentiment' (default),"
f" 'filtered_rating', or 'unfiltered'")
def _generate_examples(self, data_file):
lines = Path(data_file).open(encoding="utf-8").readlines()
for line_idx, line in enumerate(lines):
row = json.loads(line)
yield line_idx, row
```
- finally, run `datasets-cli test ./datasets/hebban-reviews/ --save_infos --all_configs` from within the topmost `datasets` directory
## Expected results
Succeeding tests for three different configs.
## Actual results
I printed out the values that are given to `DatasetInfo` for config name and task_templates, as you can see. There, as expected, I get `unfiltered None`. I also modified datasets/info.py and added this line [at L.170](https://github.com/huggingface/datasets/blob/f5847a304aa1b38b3a3c54a8318b4df60f1299bc/src/datasets/info.py#L170):
```python
print("INTERNALLY AT INFO.PY", self.config_name, self.task_templates)
```
to my surprise, here I get `unfiltered [TextClassification(task='text-classification', text_column='review_text_without_quotes', label_column='review_sentiment')]`. So one way or another, here I suddenly see that `unfiltered` now does have a task_template -- even though that is not what is written in the data loading script, as the first print statement correctly shows.
I do not quite understand how, but it seems that the config name and task_templates get mixed.
This ultimately leads to the following error, but this trace may not be very useful in itself:
```
Traceback (most recent call last):
File "C:\Users\bramv\.virtualenvs\hebban-U6poXNQd\Scripts\datasets-cli-script.py", line 33, in <module>
sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
File "c:\dev\python\hebban\datasets\src\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\dev\python\hebban\datasets\src\datasets\commands\test.py", line 144, in run
builder.as_dataset()
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 899, in as_dataset
datasets = map_nested(
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 393, in map_nested
mapped = [
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 394, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "c:\dev\python\hebban\datasets\src\datasets\utils\py_utils.py", line 330, in _single_map_nested
return function(data_struct)
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 930, in _build_single_dataset
ds = self._as_dataset(
File "c:\dev\python\hebban\datasets\src\datasets\builder.py", line 1006, in _as_dataset
return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File "c:\dev\python\hebban\datasets\src\datasets\arrow_dataset.py", line 661, in __init__
info = info.copy() if info is not None else DatasetInfo()
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 286, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File "<string>", line 20, in __init__
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 176, in __post_init__
self.task_templates = [
File "c:\dev\python\hebban\datasets\src\datasets\info.py", line 177, in <listcomp>
template.align_with_features(self.features) for template in (self.task_templates)
File "c:\dev\python\hebban\datasets\src\datasets\tasks\text_classification.py", line 22, in align_with_features
raise ValueError(f"Column {self.label_column} is not a ClassLabel.")
ValueError: Column review_sentiment is not a ClassLabel.
```
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3 | 2022-08-08T18:20:50Z | 4,752 | null | https://api.github.com/repos/huggingface/datasets | null | [
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} | [] | null | null | CONTRIBUTOR | null | null | I_kwDODunzps5OpW3Z | [
"I've narrowed down the issue to the `dataset_module_factory` which already creates a `dataset_infos.json` file down in the `.cache/modules/dataset_modules/..` folder. That JSON file already contains the wrong task_templates for `unfiltered`.",
"Ugh. Found the issue: apparently `datasets` was reusing the already ... | {
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1,319,440,903 | https://api.github.com/repos/huggingface/datasets/issues/4751/labels{/name} | This PR aims to add relevant information like the Description, Language and citation information of the clinic oos dataset card. | 2022-07-28T10:53:21Z | 4,751 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-27T11:44:28Z | https://api.github.com/repos/huggingface/datasets/issues/4751/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4751/timeline | Added dataset information in clinic oos dataset card | https://api.github.com/repos/huggingface/datasets/issues/4751/events | null | {
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1,319,333,645 | https://api.github.com/repos/huggingface/datasets/issues/4750/labels{/name} | Hi,
I would like to create a loading script for a benchmark comprising multiple huggingface datasets.
The function _split_generators needs to return the files for the respective dataset. However, the files are not always in the same location for each dataset. I want to just make a wrapper dataset that provides a single interface to all the underlying datasets.
I thought about downloading the files with the load_dataset function and then providing the link to the cached file. But this seems a bit inelegant to me. What approach would you propose to do this?
Please let me know if you have any questions.
Cheers,
Joel | 2022-07-27T13:58:07Z | 4,750 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-07-27T10:13:38Z | https://api.github.com/repos/huggingface/datasets/issues/4750/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4750/timeline | Easily create loading script for benchmark comprising multiple huggingface datasets | https://api.github.com/repos/huggingface/datasets/issues/4750/events | null | {
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} | [] | null | completed | CONTRIBUTOR | 2022-07-27T13:58:07Z | null | I_kwDODunzps5Oo28N | [
"Hi ! I think the simplest is to copy paste the `_split_generators` code from the other datasets and do a bunch of if-else, as in the glue dataset: https://huggingface.co/datasets/glue/blob/main/glue.py#L467",
"Ok, I see. Thank you"
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1,318,874,913 | https://api.github.com/repos/huggingface/datasets/issues/4748/labels{/name} | This PR follows up on #4710 to separate the object detection and image classification guides. It expands a little more on the original guide to include a more complete example of loading and transforming a whole dataset. | 2022-07-27T17:28:21Z | 4,748 | null | https://api.github.com/repos/huggingface/datasets | false | [
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1,318,586,932 | https://api.github.com/repos/huggingface/datasets/issues/4747/labels{/name} | Following https://github.com/huggingface/datasets/pull/4724 (needs to be merged first)
It's good practice to shard parquet files to enable parallelism with spark/dask/etc.
I added the `max_shard_size` parameter to `download_and_prepare` (default to 500MB for parquet, and None for arrow).
```python
from datasets import *
output_dir = "./output_dir" # also supports "s3://..."
builder = load_dataset_builder("squad")
builder.download_and_prepare(output_dir, file_format="parquet", max_shard_size="5MB")
```
### Implementation details
The examples are written to a parquet file until `ParquetWriter._num_bytes > max_shard_size`. When this happens, a new writer is instantiated to start writing the next shard. At the end, all the shards are renamed to include the total number of shards in their names: `{builder.name}-{split}-{shard_id:05d}-of-{num_shards:05d}.parquet`
I also added the `MAX_SHARD_SIZE` config variable (default to 500MB)
TODO:
- [x] docstrings
- [x] docs
- [x] tests
cc @severo | 2022-09-15T13:43:55Z | 4,747 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-26T18:05:01Z | https://api.github.com/repos/huggingface/datasets/issues/4747/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4747/timeline | Shard parquet in `download_and_prepare` | https://api.github.com/repos/huggingface/datasets/issues/4747/events | null | {
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"This is ready for review cc @mariosasko :) please let me know what you think !"
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1,318,486,599 | https://api.github.com/repos/huggingface/datasets/issues/4746/labels{/name} | ### Link
_No response_
### Description
_No response_
### Owner
_No response_ | 2022-09-08T08:15:22Z | 4,746 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"The dataset is empty, as far as I can tell: there are no files in the repository at https://huggingface.co/datasets/yanekyuk/wikikey/tree/main\r\n\r\nMaybe the viewer can display a better message for empty datasets",
"OK. Closing as it's not an error. We will work on making the error message a lot clearer."
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1,318,016,655 | https://api.github.com/repos/huggingface/datasets/issues/4745/labels{/name} | I am working with a large collection of private datasets, it would be convenient for me to be able to list them.
I would envision extending the convention of using `use_auth_token` keyword argument to `list_datasets` function, then calling:
```
list_datasets(use_auth_token="my_token")
```
would return the list of all datasets I have permissions to view, including private ones. The only current alternative I see is to use the hub website to manually obtain the list of dataset names - this is in the context of BigScience where respective private spaces contain hundreds of datasets, so not very convenient to list manually. | 2023-07-25T15:01:49Z | 4,745 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Thanks for opening this issue :)\r\n\r\nIf it can help, I think you can already use `huggingface_hub` to achieve this:\r\n```python\r\n>>> from huggingface_hub import HfApi\r\n>>> [ds_info.id for ds_info in HfApi().list_datasets(use_auth_token=token) if ds_info.private]\r\n['bigscience/xxxx', 'bigscience-catalogue... | {
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1,317,822,345 | https://api.github.com/repos/huggingface/datasets/issues/4744/labels{/name} | In our docs, we indicate to generate the dummy data: https://huggingface.co/docs/datasets/dataset_script#testing-data-and-checksum-metadata
However:
- dummy data makes sense only for datasets in our GitHub repo: so that we can test their loading with our CI
- for datasets on the Hub:
- they do not pass any CI test requiring dummy data
- there are no instructions on how they can test their dataset locally using the dummy data
- the generation of the dummy data assumes our GitHub directory structure:
- the dummy data will be generated under `./datasets/<dataset_name>/dummy` even if locally there is no `./datasets` directory (which is the usual case). See issue:
- #4742
CC: @stevhliu | 2022-08-02T23:50:30Z | 4,744 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"gi... | null | completed | MEMBER | 2022-08-02T23:50:30Z | null | I_kwDODunzps5OjF-J | [
"Note that for me personally, conceptually all the dummy data (even for \"canonical\" datasets) should be superseded by `datasets-server`, which performs some kind of CI/CD of datasets (including the canonical ones)",
"I totally agree: next step should be rethinking if dummy data makes sense for canonical dataset... | {
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1,317,362,561 | https://api.github.com/repos/huggingface/datasets/issues/4743/labels{/name} | This PR updates the `map` docs for processing text to include `return_tensors="np"` to make it run faster (see #4676). | 2022-07-27T16:22:04Z | 4,743 | null | https://api.github.com/repos/huggingface/datasets | false | [
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1,317,260,663 | https://api.github.com/repos/huggingface/datasets/issues/4742/labels{/name} | ## Describe the bug
To finalize my dataset, I wanted to create dummy data as per the guide and I ran
```shell
datasets-cli dummy_data datasets/hebban-reviews --auto_generate
```
where hebban-reviews is [this repo](https://huggingface.co/datasets/BramVanroy/hebban-reviews). And even though the scripts runs and shows a message at the end that it succeeded, I cannot find the dummy data anywhere. Where is it?
## Expected results
To see the dummy data in the datasets' folder or in the folder where I ran the command.
## Actual results
I see the following message but I cannot find the dummy data anywhere.
```
Dummy data generation done and dummy data test succeeded for config 'filtered''.
Automatic dummy data generation succeeded for all configs of '.\datasets\hebban-reviews\'
```
## Environment info
- `datasets` version: 2.4.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.8
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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"Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst of all, please note that you do not need the dummy data: this was the case when we were adding datasets to the `datasets` library (on this GitHub repo), so that we could test the correct loading of all datasets with our CI. However, this is no longer the case for ... | {
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1,316,621,272 | https://api.github.com/repos/huggingface/datasets/issues/4741/labels{/name} | Fix #4681 | 2022-07-25T12:50:36Z | 4,741 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-25T10:41:27Z | https://api.github.com/repos/huggingface/datasets/issues/4741/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4741/timeline | Fix to dict conversion of `DatasetInfo`/`Features` | https://api.github.com/repos/huggingface/datasets/issues/4741/events | null | {
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1,316,478,007 | https://api.github.com/repos/huggingface/datasets/issues/4740/labels{/name} | As previously discussed:
Before, multiprocessing was not used in `map_nested` if `num_proc` was greater than or equal to `len(iterable)`.
- Multiprocessing was not used e.g. when passing `num_proc=20` but having 19 files to download
- As by default, `DownloadManager` sets `num_proc=16`, before multiprocessing was only used when `len(iterable)>16` by default
Now, if `num_proc` is greater than or equal to ``len(iterable)``, `num_proc` is set to ``len(iterable)`` and multiprocessing is used.
- We pass the variable `parallel_min_length=16`, so that multiprocessing is only used if at least 16 files to be downloaded
- ~As by default, `DownloadManager` sets `num_proc=16`, now multiprocessing is used when `len(iterable)>1` by default~
See discussion below.
~After having had to fix some tests (87602ac), I am wondering:~
- ~do we want to have multiprocessing by default?~
- ~please note that `DownloadManager.download` sets `num_proc=16` by default~
- ~or would it be better to ask the user to set it explicitly if they want multiprocessing (and default to `num_proc=1`)?~
Fix #4636.
CC: @nateraw | 2022-07-28T10:53:23Z | 4,740 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-25T08:44:19Z | https://api.github.com/repos/huggingface/datasets/issues/4740/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4740/timeline | Fix multiprocessing in map_nested | https://api.github.com/repos/huggingface/datasets/issues/4740/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@lhoestq as a workaround to preserve previous behavior, the parameter `multiprocessing_min_length=16` is passed from `download` to `map_nested`, so that multiprocessing is only used if at least 16 files to be downloaded.\r\n\r\nNote ... | {
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1,316,400,915 | https://api.github.com/repos/huggingface/datasets/issues/4739/labels{/name} | Deprecate metrics:
- deprecate public functions: `load_metric`, `list_metrics` and `inspect_metric`: docstring and warning
- test deprecation warnings are issues
- deprecate metrics in all docs
- remove mentions to metrics in docs and README
- deprecate internal functions/classes
Maybe we should also stop testing metrics? | 2022-07-28T11:44:27Z | 4,739 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-25T07:35:55Z | https://api.github.com/repos/huggingface/datasets/issues/4739/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4739/timeline | Deprecate metrics | https://api.github.com/repos/huggingface/datasets/issues/4739/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
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1,315,222,166 | https://api.github.com/repos/huggingface/datasets/issues/4738/labels{/name} | This PR:
- Implements separate unit/integration tests
- A fail in integration tests does not cancel the rest of the jobs
- We should implement more robust integration tests: work in progress in a subsequent PR
- For the moment, test involving network requests are marked as integration: to be evolved | 2022-07-26T20:19:22Z | 4,738 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-22T16:48:00Z | https://api.github.com/repos/huggingface/datasets/issues/4738/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4738/timeline | Use CI unit/integration tests | https://api.github.com/repos/huggingface/datasets/issues/4738/events | null | {
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1,315,011,004 | https://api.github.com/repos/huggingface/datasets/issues/4737/labels{/name} | ```
from datasets import load_dataset
dataset = load_dataset("scene_parse_150", "scene_parsing")
FileNotFoundError: Couldn't find file at http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip
```
| 2022-09-01T15:37:11Z | 4,737 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi! The server with the data seems to be down. I've reported this issue (https://github.com/CSAILVision/sceneparsing/issues/34) in the dataset repo. ",
"The URL seems to work now, and therefore the script as well."
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1,314,931,996 | https://api.github.com/repos/huggingface/datasets/issues/4736/labels{/name} | ### Link
https://huggingface.co/datasets/deepklarity/huggingface-spaces-dataset/viewer/deepklarity--huggingface-spaces-dataset/train
### Description
Hi Team,
I'm getting the following error on a uploaded dataset. I'm getting the same status for a couple of hours now. The dataset size is `<1MB` and the format is csv, so I'm not sure if it's supposed to take this much time or not.
```
Status code: 400
Exception: Status400Error
Message: The split is being processed. Retry later.
```
Is there any explicit step to be taken to get the viewer to work?
### Owner
Yes | 2022-07-22T13:46:38Z | 4,736 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Thanks for reporting. You're right, workers were under-provisioned due to a manual error, and the job queue was full. It's fixed now."
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1,314,501,641 | https://api.github.com/repos/huggingface/datasets/issues/4735/labels{/name} | Temporarily pin `rouge_score` (to avoid latest version 0.7.0) until the issue is fixed.
Fix #4734 | 2022-07-22T07:58:14Z | 4,735 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-22T07:18:21Z | https://api.github.com/repos/huggingface/datasets/issues/4735/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4735/timeline | Pin rouge_score test dependency | https://api.github.com/repos/huggingface/datasets/issues/4735/events | null | {
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1,314,495,382 | https://api.github.com/repos/huggingface/datasets/issues/4734/labels{/name} | ## Describe the bug
After the today release of `rouge_score-0.0.7` it seems no longer importable. Our CI fails: https://github.com/huggingface/datasets/runs/7463218591?check_suite_focus=true
```
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_bigbench
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_builder_configs_bigbench
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_bigbench
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_rouge
```
with errors:
```
> from rouge_score import rouge_scorer
E ModuleNotFoundError: No module named 'rouge_score'
```
```
E ImportError: To be able to use rouge, you need to install the following dependency: rouge_score.
E Please install it using 'pip install rouge_score' for instance'
```
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"We have added a comment on an existing issue opened in their repo: https://github.com/google-research/google-research/issues/1212#issuecomment-1192267130\r\n- https://github.com/google-research/google-research/issues/1212"
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1,314,479,616 | https://api.github.com/repos/huggingface/datasets/issues/4733/labels{/name} | ## Describe the bug
A clear and concise description of what the bug is.
Loading Rouge metric gives error after latest rouge-score==0.0.7 release.
Downgrading rougemetric==0.0.4 works fine.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
## Expected results
A clear and concise description of the expected results.
from rouge_score import rouge_scorer, scoring
should run
## Actual results
Specify the actual results or traceback.
File "/root/.cache/huggingface/modules/datasets_modules/metrics/rouge/0ffdb60f436bdb8884d5e4d608d53dbe108e82dac4f494a66f80ef3f647c104f/rouge.py", line 21, in <module>
from rouge_score import rouge_scorer, scoring
ImportError: cannot import name 'rouge_scorer' from 'rouge_score' (unknown location)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version:3.9
- PyArrow version:
| 2022-07-22T09:08:02Z | 4,733 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Fixed by:\r\n- #4735"
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1,314,371,566 | https://api.github.com/repos/huggingface/datasets/issues/4732/labels{/name} | As reported by @TrentBrick here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191858596, it could be more clear that loading a dataset by passing its name does not use the (modified) local script of it.
What he did:
- he installed `datasets` from source
- he modified locally `datasets/the_pile/the_pile.py` loading script
- he tried to load it but using `load_dataset("the_pile")` instead of `load_dataset("datasets/the_pile")`
- as explained here https://github.com/huggingface/datasets/issues/4725#issuecomment-1191040245:
- the former does not use the local script, but instead it downloads a copy of `the_pile.py` from our GitHub, caches it locally (inside `~/.cache/huggingface/modules`) and uses that.
He suggests adding a more clear explanation about this. He suggests adding it maybe in [Installation > source](https://huggingface.co/docs/datasets/installation))
CC: @stevhliu | 2022-08-23T16:32:23Z | 4,732 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Thanks for the feedback!\r\n\r\nI think since this issue is closely related to loading, I can add a clearer explanation under [Load > local loading script](https://huggingface.co/docs/datasets/main/en/loading#local-loading-script).",
"That makes sense but I think having a line about it under https://huggingface.... | {
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1,313,421,263 | https://api.github.com/repos/huggingface/datasets/issues/4730/labels{/name} | ## Describe the bug
Loading into memory the validation split of imagenet-1k takes much more RAM than expected. Assuming ImageNet-1k is 150 GB, split is 50000 validation images and 1,281,167 train images, I would expect only about 6 GB loaded in RAM.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imagenet-1k", split="validation")
print(dataset)
"""prints
Dataset({
features: ['image', 'label'],
num_rows: 50000
})
"""
pipe_inputs = dataset["image"]
# and wait :-)
```
## Expected results
Use only < 10 GB RAM when loading the images.
## Actual results
```
Using custom data configuration default
Reusing dataset imagenet-1k (/home/fxmarty/.cache/huggingface/datasets/imagenet-1k/default/1.0.0/a1e9bfc56c3a7350165007d1176b15e9128fcaf9ab972147840529aed3ae52bc)
Killed
```
## Environment info
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
- datasets commit: 4e4222f1b6362c2788aec0dd2cd8cede6dd17b80
| 2022-07-21T16:41:04Z | 4,730 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"My bad, `482 * 418 * 50000 * 3 / 1000000 = 30221 MB` ( https://stackoverflow.com/a/42979315 ).\r\n\r\nMeanwhile `256 * 256 * 50000 * 3 / 1000000 = 9830 MB`. We are loading the non-cropped images and that is why we take so much RAM."
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1,313,374,015 | https://api.github.com/repos/huggingface/datasets/issues/4729/labels{/name} | This PR refactors `test_upstream_hub` by removing unittests and using the following pytest Hub fixtures:
- `ci_hub_config`
- `set_ci_hub_access_token`: to replace setUp/tearDown
- `temporary_repo` context manager: to replace `try... finally`
- `cleanup_repo`: to delete repo accidentally created if one of the tests fails
This is a preliminary work done to manage unit/integration tests separately. | 2022-07-22T15:09:49Z | 4,729 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-21T14:43:13Z | https://api.github.com/repos/huggingface/datasets/issues/4729/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4729/timeline | Refactor Hub tests | https://api.github.com/repos/huggingface/datasets/issues/4729/events | null | {
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1,312,897,454 | https://api.github.com/repos/huggingface/datasets/issues/4728/labels{/name} | I tried both codes below to download the financial phrasebank dataset (https://huggingface.co/datasets/financial_phrasebank) with the sentences_allagree subset. However, the code gives a 403 error when executed from multiple machines locally or on the cloud.
```
from datasets import load_dataset, DownloadMode
load_dataset(path='financial_phrasebank',name='sentences_allagree',download_mode=DownloadMode.FORCE_REDOWNLOAD)
```
```
from datasets import load_dataset, DownloadMode
load_dataset(path='financial_phrasebank',name='sentences_allagree')
```
**Error**
ConnectionError: Couldn't reach https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip (error 403)
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"Hi @rohitvincent, thanks for reporting.\r\n\r\nUnfortunately I'm not able to reproduce your issue:\r\n```python\r\nIn [2]: from datasets import load_dataset, DownloadMode\r\n ...: load_dataset(path='financial_phrasebank',name='sentences_allagree', download_mode=\"force_redownload\")\r\nDownloading builder script... | {
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1,312,645,391 | https://api.github.com/repos/huggingface/datasets/issues/4727/labels{/name} | ### Link
https://huggingface.co/datasets/TheNoob3131/mosquito-data/viewer/TheNoob3131--mosquito-data/test
### Description
Dataset preview not showing with large files. Says 'split cache is empty' even though there are train and test splits.
### Owner
_No response_ | 2022-07-21T07:51:56Z | 4,727 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,312,082,175 | https://api.github.com/repos/huggingface/datasets/issues/4726/labels{/name} | The Markdown link fails to render if it is in the same line as the `<span>`. This PR implements @mishig25's fix by using `<a href=" ">` instead.
 | 2022-07-21T14:33:18Z | 4,726 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-20T22:57:27Z | https://api.github.com/repos/huggingface/datasets/issues/4726/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4726/timeline | Fix broken link to the Hub | https://api.github.com/repos/huggingface/datasets/issues/4726/events | null | {
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1,311,907,096 | https://api.github.com/repos/huggingface/datasets/issues/4725/labels{/name} | https://github.com/huggingface/datasets/pull/3627 changed the Eleuther AI Pile dataset URL from https://the-eye.eu/ to https://mystic.the-eye.eu/ but the latter is now broken and the former works again.
Note that when I git clone the repo and use `pip install -e .` and then edit the URL back the codebase doesn't seem to use this edit so the mystic URL is also cached somewhere else that I can't find? | 2022-07-22T06:09:46Z | 4,725 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Thanks for reporting, @TrentBrick. We are addressing the change with their data host server.\r\n\r\nOn the meantime, if you would like to work with your fixed local copy of the_pile script, you should use:\r\n```python\r\nload_dataset(\"path/to/your/local/the_pile/the_pile.py\",...\r\n```\r\ninstead of just `load_... | {
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1,311,127,404 | https://api.github.com/repos/huggingface/datasets/issues/4724/labels{/name} | Download a dataset as Parquet in a cloud storage can be useful for streaming mode and to use with spark/dask/ray.
This PR adds support for `fsspec` URIs like `s3://...`, `gcs://...` etc. and ads the `file_format` to save as parquet instead of arrow:
```python
from datasets import *
cache_dir = "s3://..."
builder = load_dataset_builder("crime_and_punish", cache_dir=cache_dir)
builder.download_and_prepare(file_format="parquet")
```
EDIT: actually changed the API to
```python
from datasets import *
builder = load_dataset_builder("crime_and_punish")
builder.download_and_prepare("s3://...", file_format="parquet")
```
credentials to cloud storage can be passed using the `storage_options` argument in
For consistency with the BeamBasedBuilder, I name the parquet files `{builder.name}-{split}-xxxxx-of-xxxxx.parquet`. I think this is fine since we'll need to implement parquet sharding after this PR, so that a dataset can be used efficiently with dask for example.
Note that images/audio files are not embedded yet in the parquet files, this will added in a subsequent PR
TODO:
- [x] docs
- [x] tests | 2022-09-05T17:27:25Z | 4,724 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-20T13:39:02Z | https://api.github.com/repos/huggingface/datasets/issues/4724/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4724/timeline | Download and prepare as Parquet for cloud storage | https://api.github.com/repos/huggingface/datasets/issues/4724/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Added some docs for dask and took your comments into account\r\n\r\ncc @philschmid if you also want to take a look :)",
"Just noticed that it would be more convenient to pass the output dir to download_and_prepare directly, to bypa... | {
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1,310,970,604 | https://api.github.com/repos/huggingface/datasets/issues/4723/labels{/name} | Previously, fixture modules `hub_fixtures` and `s3_fixtures`:
- were both at the root test directory
- were imported using `import *`
- as a side effect, the modules `os` and `pytest` were imported from `s3_fixtures` into `conftest`
This PR:
- puts both fixture modules in a dedicated directory `fixtures`
- renames both to: `fixtures.hub` and `fixtures.s3`
- imports them into `conftest` as plugins, using the `pytest_plugins`: this avoids the `import *`
- additionally creates a new fixture module `fixtures.files` with all file-related fixtures | 2022-07-21T14:37:11Z | 4,723 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-20T12:15:22Z | https://api.github.com/repos/huggingface/datasets/issues/4723/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4723/timeline | Refactor conftest fixtures | https://api.github.com/repos/huggingface/datasets/issues/4723/events | null | {
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1,310,785,916 | https://api.github.com/repos/huggingface/datasets/issues/4722/labels{/name} | `href="/docs/datasets/quickstart#audio"` implicitly goes to `href="/docs/datasets/{$LATEST_STABLE_VERSION}/quickstart#audio"`. Therefore, https://huggingface.co/docs/datasets/quickstart#audio #audio hashlink does not work since the new docs were not added to v2.3.2 (LATEST_STABLE_VERSION)
to preserve the version, it should be just `href="#audio"`, which will implicilty go to curren_page + #audio element | 2022-07-20T17:02:33Z | 4,722 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-20T10:04:37Z | https://api.github.com/repos/huggingface/datasets/issues/4722/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4722/timeline | Docs: Fix same-page haslinks | https://api.github.com/repos/huggingface/datasets/issues/4722/events | null | {
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1,310,253,552 | https://api.github.com/repos/huggingface/datasets/issues/4721/labels{/name} | ## Describe the bug
I am fine tuning a wav2vec2 model following the script here using my own dataset: https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
Loading my Audio dataset from the hub which was originally generated from disk results in the following PyArrow error:
```sh
File "/home/ubuntu/w2v2/run_speech_recognition_ctc.py", line 227, in main
raw_datasets = load_dataset(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/load.py", line 1679, in load_dataset
builder_instance.download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 1268, in _prepare_split
for key, table in logging.tqdm(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables
for batch_idx, record_batch in enumerate(
File "pyarrow/_parquet.pyx", line 1309, in iter_batches
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
```
## Steps to reproduce the bug
I created a dataset from a JSON lines manifest of `audio_filepath`, `text`, and `duration`.
When creating the dataset, I do something like this:
```python
import json
from datasets import Dataset, Audio
# manifest_lines is a list of dicts w/ "audio_filepath", "duration", and "text
for line in manifest_lines:
line = line.strip()
if line:
line_dict = json.loads(line)
manifest_dict["audio"].append(f"{root_path}/{line_dict['audio_filepath']}")
manifest_dict["duration"].append(line_dict["duration"])
manifest_dict["transcription"].append(line_dict["text"])
# Create a HF dataset
dataset = Dataset.from_dict(manifest_dict).cast_column(
"audio", Audio(sampling_rate=16_000),
)
# From the docs for saving to disk
# https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.save_to_disk
def read_audio_file(example):
with open(example["audio"]["path"], "rb") as f:
return {"audio": {"bytes": f.read()}}
dataset = dataset.map(read_audio_file, num_proc=70)
dataset.save_to_disk(f"/audio-data/hf/{artifact_name}")
dataset.push_to_hub(f"{org-name}/{artifact_name}", max_shard_size="5GB", private=True)
```
Then when I call `load_dataset()` in my training script, with the same dataset I generated above, and download from the huggingface hub I get the above stack trace.
I am able to load the dataset fine if I use `load_from_disk()`.
## Expected results
`load_dataset()` should behave just like `load_from_disk()` and not cause any errors.
## Actual results
See above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
I am using the `huggingface/transformers-pytorch-gpu:latest` image
- `datasets` version: 2.3.0
- Platform: Docker/Ubuntu 20.04
- Python version: 3.8
- PyArrow version: 8.0.0
| 2022-07-22T14:11:47Z | 4,721 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi ! It looks like a bug in `pyarrow`. If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nTo achieve that you can try to lower the value of `max_shard_size` and also don't use `map` before `push_to_hub`.\r\n\r\nDo you have a minimum reproducible example that we can... | {
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1,309,980,195 | https://api.github.com/repos/huggingface/datasets/issues/4720/labels{/name} | ### Link
https://huggingface.co/datasets/shamikbose89/lancaster_newsbooks
### Description
Status code: 400
Exception: ValueError
Message: Cannot seek streaming HTTP file
I am able to use the dataset loading script locally and it also runs when I'm using the one from the hub, but the viewer still doesn't load
### Owner
Yes | 2022-09-08T16:47:21Z | 4,720 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-07-19T20:00:07Z | https://api.github.com/repos/huggingface/datasets/issues/4720/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4720/timeline | Dataset Viewer issue for shamikbose89/lancaster_newsbooks | https://api.github.com/repos/huggingface/datasets/issues/4720/events | null | {
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"It seems like the list of splits could not be obtained:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"shamikbose89/lancaster_newsbooks\", \"default\")\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n File \"/home/slesage/h... | {
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1,309,854,492 | https://api.github.com/repos/huggingface/datasets/issues/4719/labels{/name} | 
So my dataset is public in the Huggingface Hub, but when I try to load it using the load_dataset command, it shows that it is downloading the files, but throws a ValueError. When I went to my directory to see if the files were downloaded, the folder was blank.
Here is the error below:
ValueError Traceback (most recent call last)
Input In [8], in <cell line: 3>()
1 from datasets import load_dataset
----> 3 dataset = load_dataset("TheNoob3131/mosquito-data", split="train")
File ~\Anaconda3\lib\site-packages\datasets\load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
Is the dataset in the wrong format or is there some security permission that I should enable? | 2022-07-20T06:46:57Z | 4,719 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-07-19T17:47:37Z | https://api.github.com/repos/huggingface/datasets/issues/4719/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4719/timeline | Issue loading TheNoob3131/mosquito-data dataset | https://api.github.com/repos/huggingface/datasets/issues/4719/events | null | {
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"I am also getting a ValueError: 'Couldn't cast' at the bottom. Is this because of some delimiter issue? My dataset is on the Huggingface Hub. If you could look at it, that would be greatly appreciated.",
"Hi @thenerd31, thanks for reporting.\r\n\r\nPlease note that your issue is not caused by the Hugging Face Da... | {
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1,309,520,453 | https://api.github.com/repos/huggingface/datasets/issues/4718/labels{/name} | This PR:
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1,309,512,483 | https://api.github.com/repos/huggingface/datasets/issues/4717/labels{/name} | ### Link
_No response_
### Description
Unable to view the split data
### Owner
_No response_ | 2022-07-20T08:32:57Z | 4,717 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,309,455,838 | https://api.github.com/repos/huggingface/datasets/issues/4716/labels{/name} | Added the "tags" YAML tag, so that users can specify data domain/topics keywords for dataset search | 2022-07-20T13:44:50Z | 4,716 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-19T12:34:31Z | https://api.github.com/repos/huggingface/datasets/issues/4716/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4716/timeline | Support "tags" yaml tag | https://api.github.com/repos/huggingface/datasets/issues/4716/events | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"IMO `DatasetMetadata` shouldn't crash with attributes that it doesn't know, btw",
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1,309,405,980 | https://api.github.com/repos/huggingface/datasets/issues/4715/labels{/name} | We're now using `part-of-speech` and not `part-of-speech-tagging`, see discussion here: https://github.com/huggingface/datasets/commit/114c09aff2fa1519597b46fbcd5a8e0c0d3ae020#r78794777 | 2022-07-19T12:54:34Z | 4,715 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-19T11:52:54Z | https://api.github.com/repos/huggingface/datasets/issues/4715/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4715/timeline | Fix POS tags | https://api.github.com/repos/huggingface/datasets/issues/4715/events | null | {
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"CI failures are about missing content in the dataset cards or bad tags, and this is unrelated to this PR. Merging :)"
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1,309,265,682 | https://api.github.com/repos/huggingface/datasets/issues/4714/labels{/name} | This PR:
- makes `NamedSplit` sortable: so that `sorted()` can be called on them
- removes unnecessary `sorted()` on `dict.keys()`: `dict_keys` view is already like a `set`
- removes unnecessary casting of `NamedSplit` to `str` | 2022-07-22T09:39:45Z | 4,714 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-19T09:48:28Z | https://api.github.com/repos/huggingface/datasets/issues/4714/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4714/timeline | Fix named split sorting and remove unnecessary casting | https://api.github.com/repos/huggingface/datasets/issues/4714/events | null | {
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"hahaha what a timing, I added my comment right after you merged x)\r\n\r\nyou can ignore my (nit), it's fine",
"Sorry, just too sync... :sweat_smile: "
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1,309,184,756 | https://api.github.com/repos/huggingface/datasets/issues/4713/labels{/name} | The `sox` OS package needs being installed manually using the distribution package manager.
This PR adds this explanation to the docs. | 2022-07-21T08:16:59Z | 4,713 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-19T08:42:35Z | https://api.github.com/repos/huggingface/datasets/issues/4713/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4713/timeline | Document installation of sox OS dependency for audio | https://api.github.com/repos/huggingface/datasets/issues/4713/events | null | {
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1,309,177,302 | https://api.github.com/repos/huggingface/datasets/issues/4712/labels{/name} | Highlight that the licence granted by Amazon only covers non-commercial research use. | 2022-07-27T16:09:40Z | 4,712 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-19T08:36:20Z | https://api.github.com/repos/huggingface/datasets/issues/4712/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4712/timeline | Highlight non-commercial license in amazon_reviews_multi dataset card | https://api.github.com/repos/huggingface/datasets/issues/4712/events | null | {
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1,309,138,570 | https://api.github.com/repos/huggingface/datasets/issues/4711/labels{/name} | Currently, in our docs for Audio/Vision/Text, we explain how to:
- Load data
- Process data
However we only explain how to *Create a dataset loading script* for text data.
I think it would be useful that we add the same for Audio/Vision as these have some specificities different from Text.
See, for example:
- #4697
- and comment there: https://github.com/huggingface/datasets/issues/4697#issuecomment-1191502492
CC: @stevhliu
| 2023-07-25T16:07:52Z | 4,711 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"I'm closing this issue as both the Audio and Image sections now have a \"Create dataset\" page that contains the info about writing the loading script version of a dataset."
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1,308,958,525 | https://api.github.com/repos/huggingface/datasets/issues/4710/labels{/name} | The following adds a quick guide on how to process object detection datasets with `albumentations`. | 2022-07-21T20:10:35Z | 4,710 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-19T04:23:46Z | https://api.github.com/repos/huggingface/datasets/issues/4710/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4710/timeline | Add object detection processing tutorial | https://api.github.com/repos/huggingface/datasets/issues/4710/events | null | {
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"Great idea! Now that we have more than one task, it makes sense to separate image classification and object detection so it'll be easier for users to follow.",
"@lhoestq do we want to do that in this PR, or should we merge it and l... | {
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1,308,633,093 | https://api.github.com/repos/huggingface/datasets/issues/4709/labels{/name} | ## Adding a Dataset
- **Name:** WMT21 & WMT22
- **Description:** We are going to have three tracks: two small tasks and a large task.
The small tracks evaluate translation between fairly related languages and English (all pairs). The large track uses 101 languages.
- **Paper:** /
- **Data:** https://statmt.org/wmt21/large-scale-multilingual-translation-task.html https://statmt.org/wmt22/large-scale-multilingual-translation-task.html
- **Motivation:** Many more languages than previous WMT versions - Could be very high impact
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
I could also tackle this. I saw the existing logic for WMT models is a bit complex (datasets are stored on the wmt account & retrieved in separate wmt datasets afaict). How long do you think it would take me? @lhoestq
| 2023-06-20T09:02:11Z | 4,709 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi ! That would be awesome to have them indeed, thanks for opening this issue\r\n\r\nI just added you to the WMT org on the HF Hub if you're interested in adding those datasets.\r\n\r\nFeel free to create a dataset repository for each dataset and upload the data files there :) preferably in ZIP archives instead of... | {
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1,308,279,700 | https://api.github.com/repos/huggingface/datasets/issues/4708/labels{/name} | 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"]` | 2022-07-22T06:30:56Z | 4,708 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-18T17:24:28Z | https://api.github.com/repos/huggingface/datasets/issues/4708/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4708/timeline | Fix require torchaudio and refactor test requirements | https://api.github.com/repos/huggingface/datasets/issues/4708/events | null | {
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1,308,251,405 | https://api.github.com/repos/huggingface/datasets/issues/4707/labels{/name} | ### Link
_No response_
### Description
Getting this error when trying to view dataset preview:
Message: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/TheNoob3131/mosquito-data/resolve/8aceebd6c4a359d216d10ef020868bd9e8c986dd/0_Africa_train.csv')
### Owner
_No response_ | 2022-07-18T19:44:46Z | 4,707 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Thanks for reporting. I refreshed the dataset viewer and it now works as expected.\r\n\r\nhttps://huggingface.co/datasets/TheNoob3131/mosquito-data\r\n\r\n<img width=\"1135\" alt=\"Capture d’écran 2022-07-18 à 13 15 22\" src=\"https://user-images.githubusercontent.com/1676121/179566497-e47f1a27-fd84-4a8d-9d7f-2e... | {
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1,308,198,454 | https://api.github.com/repos/huggingface/datasets/issues/4706/labels{/name} | As reported in https://huggingface.co/muibk, there are empty examples in xtreme/bucc18.de
I applied your fix @mustaszewski
I also used a dict to make the dataset generation much faster | 2022-07-19T06:41:14Z | 4,706 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-18T16:22:46Z | https://api.github.com/repos/huggingface/datasets/issues/4706/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4706/timeline | Fix empty examples in xtreme dataset for bucc18 config | https://api.github.com/repos/huggingface/datasets/issues/4706/events | null | {
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1,308,161,794 | https://api.github.com/repos/huggingface/datasets/issues/4705/labels{/name} | As reported in https://huggingface.co/datasets/crd3/discussions/1#62cc377073b2512b81662794, each split of the dataset was containing the same data. This issues comes from a bug in the dataset script
I fixed it and also uploaded the data to hf.co to make the dataset work in streaming mode | 2022-07-21T17:18:44Z | 4,705 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-18T15:53:44Z | https://api.github.com/repos/huggingface/datasets/issues/4705/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4705/timeline | Fix crd3 | https://api.github.com/repos/huggingface/datasets/issues/4705/events | null | {
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1,308,147,876 | https://api.github.com/repos/huggingface/datasets/issues/4704/labels{/name} | Currently, if `zstandard` or `lz4` are not installed, `test_compression_filesystems` and `test_streaming_dl_manager_extract_all_supported_single_file_compression_types` are skipped for all compression format parameters.
This PR fixes these tests, so that if `zstandard` or `lz4` are not installed, the tests are skipped only for the corresponding compression parameters (`zstd` or `lz4`), whereas the tests are not skipped for all the other compression parameters (`gzip`, `xz` and `bz2`).
Related to:
- #4688 | 2022-07-19T13:02:31Z | 4,704 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-18T15:41:40Z | https://api.github.com/repos/huggingface/datasets/issues/4704/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4704/timeline | Skip tests only for lz4/zstd params if not installed | https://api.github.com/repos/huggingface/datasets/issues/4704/events | null | {
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1,307,844,097 | https://api.github.com/repos/huggingface/datasets/issues/4703/labels{/name} | Make the cast in `from_pandas` more robust (as it was done for the packaged modules in https://github.com/huggingface/datasets/pull/4364)
This should be useful in situations like [this one](https://discuss.huggingface.co/t/loading-custom-audio-dataset-and-fine-tuning-model/8836/4). | 2022-07-22T11:17:42Z | 4,703 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-18T11:55:49Z | https://api.github.com/repos/huggingface/datasets/issues/4703/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4703/timeline | Make cast in `from_pandas` more robust | https://api.github.com/repos/huggingface/datasets/issues/4703/events | null | {
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1,307,793,811 | https://api.github.com/repos/huggingface/datasets/issues/4702/labels{/name} | **Is your feature request related to a problem? Please describe.**
## The problem
The datasets hub currently has `8,239` datasets. These datasets span a wide range of different modalities and tasks (currently with a bias towards textual data).
There are various ways of identifying datasets that may be relevant for a particular use case:
- searching
- various filters
Currently, however, there isn't an easy way to identify datasets belonging to a specific domain. For example, I want to browse machine learning datasets related to 'social science' or 'climate change research'.
The ability to identify datasets relating to a specific domain has come up in discussions around the [BigLA](https://github.com/bigscience-workshop/lam/) datasets hackathon https://github.com/bigscience-workshop/lam/discussions/31#discussioncomment-3123610. As part of the hackathon, we're currently collecting datasets related to Libraries, Archives and Museums and making them available via the hub. We currently do this under a Hugging Face organization (https://huggingface.co/biglam). However, going forward, I can see some of these datasets being migrated to sit under an organization that is the custodian of the dataset (for example, a national library the data was originally from). At this point, it becomes more difficult to quickly identify datasets from this domain without relying on search.
This is also related to some existing issues on Github related to metadata on the hub:
- https://github.com/huggingface/datasets/issues/3625
- https://github.com/huggingface/datasets/issues/3877
**Describe the solution you'd like**
### Some possible solutions that may help with this:
#### Enable domain tags (from a controlled vocabulary)
- This would add metadata field to the YAML for the domain a dataset relates to
- Advantages:
- the list is controlled, allowing it to be more easily integrated into the datasets tag app (https://huggingface.co/space/huggingface/datasets-tagging)
- the controlled vocabulary could align with an existing controlled vocabulary
- this additional metadata can be used to perform filtering by domain
- disadvantages
- choosing the best controlled vocab may be difficult
- there are many datasets that are likely to fit into the 'machine learning' domain (i.e. there is a long tail of datasets that aren't in more 'generic' machine learning domain
#### Enable topic tags (user-generated)
Enable 'free form' topic tags for datasets and models. This would be closer to GitHub's repository topics which can be chosen from a controlled list (https://github.com/topics/) but can also be more user/org specific. This could potentially be useful for organizations to also manage their own models and datasets as the number they hold in their org grows. For example, they may create 'topic tags' for a specific project, so it's clearer which datasets /models are related to that project.
#### Collections
This solution would likely be the biggest shift and may require significant changes in the hub fronted. Collections could work in several different ways but would include:
Users can curate particular datasets, models, spaces, etc., into a collection. For example, they may create a collection of 'historic newspapers suitable for training language models'. These collections would not be mutually exclusive, i.e. a dataset can belong to zero, one or many collections. Collections can also potentially be nested under other collections.
This is fairly common on other data reposotiores for example the following collections:
<img width="293" alt="Screenshot 2022-07-18 at 11 50 44" src="https://user-images.githubusercontent.com/8995957/179496445-963ed122-5e26-4574-96e8-41081bce3e2b.png">
all belong under a higher level collection (https://bl.iro.bl.uk/collections/353c908d-b495-4413-b047-87236d2573e3?locale=en).
There are different models one could use for how these collections could be created:
- only within an org
- for any dataset/model
- the owner or a dataset/model has to agree to be added to a collection
- a collection owner can have people suggest additions to their collection
- other models....
These collections could be thematic, related to particular training approaches, curate models with particular inference properties etc. Whilst some of these features may duplicate current/or future tag filters on the hub, they offer the advantage of being flexible and not having to predict what users will want to do upfront.
There is also potential for automating the creation of these collections based on existing metadata. For example, one could collect models trained on a collection of datasets so for example, if we had a collection of 'historic newspapers suitable for training language models' that contained 30 datasets, we could create another collection 'historic newspaper language models' that takes any model on the hub whose metadata says it used one or more of those 30 datasets.
There is also the option of exploring ML approaches to suggest models/datasets may be relevant to a particular collection.
This approach is likely to be quite difficult to implement well and would require significant thought. There is also likely to be a benefit in doing quite a bit of upfront work in curating useful collections to demonstrate the benefits of collections.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
It is possible to collate this information externally, i.e. one could link back to the relevant models/datasets from an external platform.
**Additional context**
Add any other context about the feature request here.
I'm cc'ing others involved in the BigLAM hackathon who may also have thoughts @cakiki @clancyoftheoverflow @albertvillanova | 2024-02-12T09:53:43Z | 4,702 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi! I added a link to this issue in our internal request for adding keywords/topics to the Hub, which is identical to the `topic tags` solution. The `collections` solution seems too complex (as you point out). Regarding the `domain tags` solution, we primarily focus on machine learning, so I'm not sure if it's a g... | {
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1,307,689,625 | https://api.github.com/repos/huggingface/datasets/issues/4701/labels{/name} | Added more information in the README about contributors and encouraged reading the thesis for more infos | 2022-07-28T10:33:05Z | 4,701 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-18T09:48:03Z | https://api.github.com/repos/huggingface/datasets/issues/4701/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4701/timeline | Added more information in the README about contributors of the Arabic Speech Corpus | https://api.github.com/repos/huggingface/datasets/issues/4701/events | null | {
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1,307,555,592 | https://api.github.com/repos/huggingface/datasets/issues/4699/labels{/name} | I fixed a few errors when it occurs while streaming the private dataset on the Huggingface Hub.
```
from datasets import load_dataset
dataset = load_dataset(<repo_id>, use_auth_token=<private_token>, streaming=True)
for d in dataset['train']:
print(d)
break # this is for checking
```
This code is an example for streaming private datasets.
when the version of the datasets is 2.2.2, it works well but datasets>2.2.2 occurs error like this,
```
/usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self)
1007 status=self.status,
1008 message=self.reason,
→ 1009 headers=self.headers,
1010 )
1011
ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/.../train-00000-of-00001-168b451062c67c34.parquet')
```
(this is an example on the dataset has `parquet` extenstion)
It seems that the `xisfile `module in `download/streaming_download_manager.py` couldn't recognize the file on "https://huggingface.co/~".
so I add three lines.
With this change, there is no error anymore(but this code is ad-hoc). | 2022-07-20T13:10:44Z | 4,699 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-18T08:03:41Z | https://api.github.com/repos/huggingface/datasets/issues/4699/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4699/timeline | Fix Authentification Error while streaming | https://api.github.com/repos/huggingface/datasets/issues/4699/events | null | {
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"Hi, thanks for working on this, but the fix for this has already been merged in https://github.com/huggingface/datasets/pull/4608."
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1,307,539,585 | https://api.github.com/repos/huggingface/datasets/issues/4698/labels{/name} | Fixes #4637 | 2023-01-19T10:11:38Z | 4,698 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-18T07:47:39Z | https://api.github.com/repos/huggingface/datasets/issues/4698/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4698/timeline | Enable streaming dataset to use the "all" split | https://api.github.com/repos/huggingface/datasets/issues/4698/events | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4698). All of your documentation changes will be reflected on that endpoint.",
"@albertvillanova \r\nAdding the validation split causes these two `assert_called_once` assertions to fail with `AssertionError: Expected 'ArrowWrit... | {
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1,307,332,253 | https://api.github.com/repos/huggingface/datasets/issues/4697/labels{/name} | ### 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 | 2022-08-01T15:10:57Z | 4,697 | null | https://api.github.com/repos/huggingface/datasets | null | [
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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:/... | {
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1,307,183,099 | https://api.github.com/repos/huggingface/datasets/issues/4696/labels{/name} | ## Describe the bug
Cannot load LinCE dataset due to a connection error
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("lince", "ner_spaeng")
```
A notebook with this code and corresponding error can be found at https://colab.research.google.com/drive/1pgX3bNB9amuUwAVfPFm-XuMV5fEg-cD2
## Expected results
It should load the dataset
## Actual results
```python
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-2-fc551ddcebef> in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("lince", "ner_spaeng")
10 frames
/usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
-> 1684 use_auth_token=use_auth_token,
1685 )
1686
/usr/local/lib/python3.7/dist-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)
703 if not downloaded_from_gcs:
704 self._download_and_prepare(
--> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1219
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1222
1223 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
772
773 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/lince/10d41747f55f0849fa84ac579ea1acfa7df49aa2015b60426bc459c111b3d589/lince.py in _split_generators(self, dl_manager)
481 def _split_generators(self, dl_manager):
482 """Returns SplitGenerators."""
--> 483 lince_dir = dl_manager.download_and_extract(f"{_LINCE_URL}/{self.config.name}.zip")
484 data_dir = os.path.join(lince_dir, self.config.data_dir)
485 return [
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download_and_extract(self, url_or_urls)
429 extracted_path(s): `str`, extracted paths of given URL(s).
430 """
--> 431 return self.extract(self.download(url_or_urls))
432
433 def get_recorded_sizes_checksums(self):
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in download(self, url_or_urls)
313 num_proc=download_config.num_proc,
314 disable_tqdm=not is_progress_bar_enabled(),
--> 315 desc="Downloading data files",
316 )
317 duration = datetime.now() - start_time
/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
346 # Singleton
347 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 348 return function(data_struct)
349
350 disable_tqdm = disable_tqdm or not logging.is_progress_bar_enabled()
/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py in _download(self, url_or_filename, download_config)
333 # append the relative path to the base_path
334 url_or_filename = url_or_path_join(self._base_path, url_or_filename)
--> 335 return cached_path(url_or_filename, download_config=download_config)
336
337 def iter_archive(self, path_or_buf: Union[str, io.BufferedReader]):
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
195 use_auth_token=download_config.use_auth_token,
196 ignore_url_params=download_config.ignore_url_params,
--> 197 download_desc=download_config.download_desc,
198 )
199 elif os.path.exists(url_or_filename):
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
532 if head_error is not None:
--> 533 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
534 elif response is not None:
535 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (ConnectTimeout(MaxRetryError("HTTPSConnectionPool(host='ritual.uh.edu', port=443): Max retries exceeded with url: /lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/ner_spaeng.zip (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7feb1c45a690>, 'Connection to ritual.uh.edu timed out. (connect timeout=100)'))")))
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
| 2022-07-18T09:20:40Z | 4,696 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | completed | NONE | 2022-07-18T07:24:22Z | null | I_kwDODunzps5N6gf7 | [
"Hi @finiteautomata, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"lince\", \"ner_spaeng\")\r\nDownloading builder script: 20.8kB [00:00, 9.09MB/s] ... | {
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1,307,134,701 | https://api.github.com/repos/huggingface/datasets/issues/4695/labels{/name} | This PR adds MANtIS dataset.
Arxiv: [https://arxiv.org/abs/1912.04639](https://arxiv.org/abs/1912.04639)
Github: [https://github.com/Guzpenha/MANtIS](https://github.com/Guzpenha/MANtIS)
README and dataset tags are WIP. | 2022-09-30T14:39:30Z | 4,695 | null | https://api.github.com/repos/huggingface/datasets | true | [
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for your contribution, @bhavitvyamalik. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets... | {
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1,306,958,380 | https://api.github.com/repos/huggingface/datasets/issues/4694/labels{/name} | ### Feature request
Any documentations for the the `load_dataset(streaming=True)` for (multi-node multi-GPU) DDP training?
### Motivation
Given a bunch of data files, it is expected to split them onto different GPUs. Is there a guide or documentation?
### Your contribution
Does it requires manually split on data files for each worker in `DatasetBuilder._split_generator()`? What is`IterableDatasetShard` expected to do? | 2023-04-26T18:21:09Z | 4,694 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi ! According to https://huggingface.co/docs/datasets/use_with_pytorch#stream-data you can use the pytorch DataLoader with `num_workers>0` to distribute the shards across your workers (it uses `torch.utils.data.get_worker_info()` to get the worker ID and select the right subsets of shards to use)\r\n\r\n<s> EDIT:... | {
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1,306,788,322 | https://api.github.com/repos/huggingface/datasets/issues/4693/labels{/name} | update `samsum` script after #4672 was merged (citation is also updated) | 2022-09-23T11:40:11Z | 4,693 | null | https://api.github.com/repos/huggingface/datasets | false | [
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"_The documentation is not available anymore as the PR was closed or merged._",
"We are closing PRs to dataset scripts because we are moving them to the Hub.\r\n\r\nThanks anyway.\r\n\r\n"
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1,306,609,680 | https://api.github.com/repos/huggingface/datasets/issues/4692/labels{/name} | ## Describe the bug
A clear and concise description of what the bug is.
When I create a dataset, then add a column to the created dataset through the `dataset.add_column` feature and then try to cast a column of the dataset (this column contains image paths) with `Image()` by using the `cast_column()` feature, I get the following error - ``` TypeError: Couldn't cast array of type
string
to
{'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)} ```
When I try and cast the same column, but without doing the `add_column` in the previous step, it works as expected.
## Steps to reproduce the bug
```python
from datasets import Dataset, Image
data_dict = {
"img_path": ["https://picsum.photos/200/300"]
}
dataset = Dataset.from_dict(data_dict)
#NOTE Comment out this line and use cast_column and it works properly
dataset = dataset.add_column("yeet", [1])
#NOTE This line fails to execute properly if `add_column` is called before
dataset = dataset.cast_column("img_path", Image())
# #NOTE This is my current workaround. This seems to work fine with/without `add_column`. While
# # running this, make sure to comment out the `cast_column` line
# new_features = dataset.features.copy()
# new_features["img_path"] = Image()
# dataset = dataset.cast(new_features)
print(dataset)
print(dataset.features)
print(dataset[0])
```
## Expected results
A clear and concise description of the expected results.
Able to successfully use `cast_column` to cast a column containing img_paths to now be Image() features after modifying the dataset using `add_column` in a previous step
## Actual results
Specify the actual results or traceback.
```
Traceback (most recent call last):
File "/home/surya/Desktop/hf_bug_test.py", line 14, in <module>
dataset = dataset.cast_column("img_path", Image())
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1580, in cast_column
dataset._data = dataset._data.cast(dataset.features.arrow_schema)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1487, in cast
new_tables.append(subtable.cast(subschema, *args, **kwargs))
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 834, in cast
return InMemoryTable(table_cast(self.table, *args, **kwargs))
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1897, in table_cast
return cast_table_to_schema(table, schema)
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1880, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1673, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/surya/anaconda3/envs/snap_test/lib/python3.9/site-packages/datasets/table.py", line 1846, 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
string
to
{'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Ubuntu 20.04.3 LTS
- Python version: 3.9.7
- PyArrow version: 7.0.0
| 2022-07-19T13:36:24Z | 4,692 | null | https://api.github.com/repos/huggingface/datasets | null | [
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1,306,389,656 | https://api.github.com/repos/huggingface/datasets/issues/4691/labels{/name} | ### Link
https://huggingface.co/datasets/rajistics/indian_food_images/viewer/rajistics--indian_food_images/train
### Description
I have a train/test split in my dataset
<img width="410" alt="Screen Shot 2022-07-15 at 11 44 42 AM" src="https://user-images.githubusercontent.com/6808012/179293215-7b419ec3-3527-46f2-8dad-adbc5568cfa0.png">
t
The dataset viewer works for the test split (images of indian food), but does not show my train split. My guess is maybe there is some corrupt image file that is guessing this. But I have no idea.
The original dataset was pulled from here: https://www.kaggle.com/datasets/l33tc0d3r/indian-food-classification?resource=download-directory
### Owner
Yes | 2022-07-18T15:02:03Z | 4,691 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi, thanks for reporting. I triggered a refresh of the preview for this dataset, and it works now. I'm not sure what occurred.\r\n<img width=\"1019\" alt=\"Capture d’écran 2022-07-18 à 11 01 52\" src=\"https://user-images.githubusercontent.com/1676121/179541327-f62ecd5e-a18a-4d91-b316-9e2ebde77a28.png\">\r\n\r\n... | {
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1,306,321,975 | https://api.github.com/repos/huggingface/datasets/issues/4690/labels{/name} | This PR:
- Refactors base extractors as subclasses of `BaseExtractor`:
- this is an abstract class defining the interface with:
- `is_extractable`: abstract class method
- `extract`: abstract static method
- Implements abstract `MagicNumberBaseExtractor` (as subclass of `BaseExtractor`):
- this has a default implementation of `is_extractable`
- this improves performance (reducing the number of file reads) by allowing passing already read `magic_number`
- Refactors `Extractor`:
- reads magic number from file only once
This PR deprecates:
```python
is_extractable, extractor = self.extractor.is_extractable(input_path, return_extractor=True)
self.extractor.extract(input_path, output_path, extractor=extractor)
```
and uses more Pythonic instead:
```python
extractor_format = self.extractor.infer_extractor_format(input_path)
self.extractor.extract(input_path, output_path, extractor_format)
``` | 2022-07-18T08:46:56Z | 4,690 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-15T17:47:48Z | https://api.github.com/repos/huggingface/datasets/issues/4690/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4690/timeline | Refactor base extractors | https://api.github.com/repos/huggingface/datasets/issues/4690/events | null | {
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1,306,230,203 | https://api.github.com/repos/huggingface/datasets/issues/4689/labels{/name} | This PR:
- Adds all compression formats to `test_extractor`
- Tests each base extractor for all compression formats
Note that all compression formats are tested except "rar". | 2022-07-15T17:47:02Z | 4,689 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-15T16:29:55Z | https://api.github.com/repos/huggingface/datasets/issues/4689/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4689/timeline | Test extractors for all compression formats | https://api.github.com/repos/huggingface/datasets/issues/4689/events | null | {
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1,306,100,488 | https://api.github.com/repos/huggingface/datasets/issues/4688/labels{/name} | Currently, if `zstandard` is not installed, `test_extractor` is skipped for all compression format parameters.
This PR fixes `test_extractor` so that if `zstandard` is not installed, `test_extractor` is skipped only for the `zstd` compression parameter, that is, it is not skipped for all the other compression parameters (`gzip`, `xz`,...). | 2022-07-15T15:27:53Z | 4,688 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-15T14:23:47Z | https://api.github.com/repos/huggingface/datasets/issues/4688/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4688/timeline | Skip test_extractor only for zstd param if zstandard not installed | https://api.github.com/repos/huggingface/datasets/issues/4688/events | null | {
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1,306,021,415 | https://api.github.com/repos/huggingface/datasets/issues/4687/labels{/name} | Currently, new CI (on GitHub Actions) is only triggered on pull requests branches when the base branch is main.
This PR also triggers the CI when a PR is merged to main branch. | 2022-07-15T13:47:21Z | 4,687 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-15T13:11:29Z | https://api.github.com/repos/huggingface/datasets/issues/4687/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4687/timeline | Trigger CI also on push to main | https://api.github.com/repos/huggingface/datasets/issues/4687/events | null | {
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1,305,974,924 | https://api.github.com/repos/huggingface/datasets/issues/4686/labels{/name} | Fix #2832 | 2023-09-24T10:05:34Z | 4,686 | null | https://api.github.com/repos/huggingface/datasets | true | [] | 2022-07-15T12:24:29Z | https://api.github.com/repos/huggingface/datasets/issues/4686/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4686/timeline | Align logging with Transformers (again) | https://api.github.com/repos/huggingface/datasets/issues/4686/events | null | {
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"I wasn't aware of https://github.com/huggingface/datasets/pull/1845 before opening this PR. This issue seems much more complex now ..."
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1,305,861,708 | https://api.github.com/repos/huggingface/datasets/issues/4685/labels{/name} | This PR:
- Removes an unused method from `DummyTestFS`
- Refactors `mock_fsspec` to make it simpler | 2022-07-15T13:05:03Z | 4,685 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-15T10:23:12Z | https://api.github.com/repos/huggingface/datasets/issues/4685/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4685/timeline | Fix mock fsspec | https://api.github.com/repos/huggingface/datasets/issues/4685/events | null | {
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1,305,554,654 | https://api.github.com/repos/huggingface/datasets/issues/4684/labels{/name} | 
Hi, if I want to change some values of the dataset, or add new columns to it, how can I do it?
For example, I want to change all the labels of the SST2 dataset to `0`:
```python
from datasets import load_dataset
data = load_dataset('glue','sst2')
data['train']['label'] = [0]*len(data)
```
I will get the error:
```
TypeError: 'Dataset' object does not support item assignment
``` | 2023-03-20T15:50:41Z | 4,684 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi! One option is use `map` with a function that overwrites the labels (`dset = dset.map(lamba _: {\"label\": 0}, features=dset.features`)). Or you can use the `remove_column` + `add_column` combination (`dset = dset.remove_columns(\"label\").add_column(\"label\", [0]*len(data)).cast(dset.features)`, but note that... | {
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1,305,443,253 | https://api.github.com/repos/huggingface/datasets/issues/4683/labels{/name} | This PR proposes removing the [online dataset card creator](https://huggingface.co/datasets/card-creator/) in favor of simply copy/pasting a template and using the [Datasets Tagger app](https://huggingface.co/spaces/huggingface/datasets-tagging) to generate the tags. The Tagger app provides more guidance by showing all possible values a user can select in the dropdown menus, whereas the online dataset card creator doesn't, which can make it difficult to know what tag values to input.
Let me know what you think! :) | 2022-07-18T17:26:00Z | 4,683 | null | https://api.github.com/repos/huggingface/datasets | false | [
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"_The documentation is not available anymore as the PR was closed or merged._"
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1,304,788,215 | https://api.github.com/repos/huggingface/datasets/issues/4682/labels{/name} | I have a dataset online (CloverSearch/cc-news-mutlilingual) that has a bunch of columns, two of which are "score_title_maintext" and "score_title_description". the original files are jsonl formatted. I was trying to iterate through via streaming mode and grab all "score_title_description" values, but I kept getting key not found after a certain point of iteration. I found that some json objects in the file don't have "score_title_description". And in SOME cases, this returns a NONE and in others it just gets a key error. Why is there an inconsistency here and how can I fix it? | 2022-07-14T13:26:47Z | 4,682 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-07-14T13:26:47Z | https://api.github.com/repos/huggingface/datasets/issues/4682/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4682/timeline | weird issue/bug with columns (dataset iterable/stream mode) | https://api.github.com/repos/huggingface/datasets/issues/4682/events | null | {
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1,304,617,484 | https://api.github.com/repos/huggingface/datasets/issues/4681/labels{/name} | ## Describe the bug
Loading an image dataset with `imagefolder` throws `IndexError: list index out of range` when the given folder contains a non-image file (like a csv).
## Steps to reproduce the bug
Put a csv file in a folder with images and load it:
```python
import datasets
datasets.load_dataset("imagefolder", data_dir=path/to/folder)
```
## Expected results
I would expect a better error message, like `Unsupported file` or even the dataset loader just ignoring every file that is not an image in that case.
## Actual results
Here is the whole traceback:
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.11.0-051100-generic-x86_64-with-glibc2.27
- Python version: 3.9.9
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| 2022-07-25T12:37:54Z | 4,681 | null | https://api.github.com/repos/huggingface/datasets | null | [
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... | null | completed | NONE | 2022-07-25T12:37:54Z | null | I_kwDODunzps5NwuIM | [
"Hi, thanks for reporting! If there are no examples in ImageFolder, the `label` column is of type `ClassLabel(names=[])`, which leads to an error in [this line](https://github.com/huggingface/datasets/blob/c15b391942764152f6060b59921b09cacc5f22a6/src/datasets/arrow_writer.py#L387) as `asdict(info)` calls `Features(... | {
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1,304,534,770 | https://api.github.com/repos/huggingface/datasets/issues/4680/labels{/name} | ### Link
https://huggingface.co/datasets/codeparrot/xlcost-text-to-code
### Description
Error
```
Server Error
Status code: 400
Exception: TypeError
Message: 'NoneType' object is not iterable
```
Before I did a minor change in the dataset script (removing some comments), the viewer was working but not properely, it wasn't showing the dataset subsets. But the data can be loaded successfully.
Thanks!
### Owner
Yes | 2022-07-18T16:37:00Z | 4,680 | null | https://api.github.com/repos/huggingface/datasets | null | [] | 2022-07-14T09:45:50Z | https://api.github.com/repos/huggingface/datasets/issues/4680/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4680/timeline | Dataset Viewer issue for codeparrot/xlcost-text-to-code | https://api.github.com/repos/huggingface/datasets/issues/4680/events | null | {
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"There seems to be an issue with the `C++-snippet-level` config:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"codeparrot/xlcost-text-to-code\", \"C++-snippet-level\")\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/... | {
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1,303,980,648 | https://api.github.com/repos/huggingface/datasets/issues/4679/labels{/name} | Added the ability for a DatasetDict to remove additional nested layers within its features to avoid conflicts when collating. It is meant to accompany [this PR](https://github.com/huggingface/transformers/pull/18119) to resolve the same issue [#15505](https://github.com/huggingface/transformers/issues/15505).
@stas00 @lhoestq | 2022-07-21T15:55:26Z | 4,679 | null | https://api.github.com/repos/huggingface/datasets | false | [] | 2022-07-13T21:49:37Z | https://api.github.com/repos/huggingface/datasets/issues/4679/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4679/timeline | Added method to remove excess nesting in a DatasetDict | https://api.github.com/repos/huggingface/datasets/issues/4679/events | null | {
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"Hi ! I think the issue you linked is closed and suggests to use `remove_columns`.\r\n\r\nMoreover if you end up with a dataset with an unnecessarily nested data, please modify your processing functions to not output nested data, or use `map(..., batched=True)` if you function take batches as input",
"Hi @lhoestq... | {
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1,303,741,432 | https://api.github.com/repos/huggingface/datasets/issues/4678/labels{/name} | ## Describe the bug
I am trying to pass a streaming version of c4 to a dataloader, but it can't be passed after I call `dataset.take(n)`. Some functions such as `shuffle()` can be applied without breaking the dataloader but not take.
## Steps to reproduce the bug
```python
import datasets
import torch
dset = datasets.load_dataset(path='c4', name='en', split="train", streaming=True)
dset = dset.take(50_000)
dset = dset.with_format("torch")
num_workers = 8
batch_size = 512
loader = torch.utils.data.DataLoader(dataset=dset,
batch_size=batch_size,
num_workers=num_workers)
for batch in loader:
...
```
## Expected results
No error thrown when iterating over the dataloader
## Actual results
Original Traceback (most recent call last):
File "/usr/local/lib/python3.9/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.9/dist-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
data.append(next(self.dataset_iter))
File "/root/.local/lib/python3.9/site-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py", line 48, in __iter__
for key, example in self._iter_shard(shard_idx):
File "/root/.local/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 586, in _iter_shard
yield from ex_iterable.shard_data_sources(shard_idx)
File "/root/.local/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 60, in shard_data_sources
raise NotImplementedError(f"{type(self)} doesn't implement shard_data_sources yet")
NotImplementedError: <class 'datasets.iterable_dataset.TakeExamplesIterable'> doesn't implement shard_data_sources yet
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.31
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| 2022-07-14T13:07:21Z | 4,678 | null | https://api.github.com/repos/huggingface/datasets | null | [
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"Hi! Calling `take` on an iterable/streamable dataset makes it not possible to shard the dataset, which in turn disables multi-process loading (attempts to split the workload over the shards), so to go past this limitation, you can either use single-process loading in `DataLoader` (`num_workers=None`) or fetch the ... | {
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1,302,258,440 | https://api.github.com/repos/huggingface/datasets/issues/4677/labels{/name} | ## Describe the bug
When pushing a dataset, the client errors randomly with `Bad Request for url:...`.
At the next call, a new parquet file is created for each shard.
The client may fail at any random shard.
## Steps to reproduce the bug
```python
dataset.push_to_hub("ORG/DATASET", private=True, branch="main")
```
## Expected results
Push all the dataset to the Hub with no duplicates.
If it fails, it should retry or fail, but continue from the last failed shard.
## Actual results
```
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
testing.ipynb Cell 29 in <cell line: 1>()
----> [1](testing.ipynb?line=0) dataset.push_to_hub("ORG/DATASET", private=True, branch="main")
File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4297, in Dataset.push_to_hub(self, repo_id, split, private, token, branch, max_shard_size, shard_size, embed_external_files)
4291 warnings.warn(
4292 "'shard_size' was renamed to 'max_shard_size' in version 2.1.1 and will be removed in 2.4.0.",
4293 FutureWarning,
4294 )
4295 max_shard_size = shard_size
-> 4297 repo_id, split, uploaded_size, dataset_nbytes, repo_files, deleted_size = self._push_parquet_shards_to_hub(
4298 repo_id=repo_id,
4299 split=split,
4300 private=private,
4301 token=token,
4302 branch=branch,
4303 max_shard_size=max_shard_size,
4304 embed_external_files=embed_external_files,
4305 )
4306 organization, dataset_name = repo_id.split("/")
4307 info_to_dump = self.info.copy()
File ~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:4195, in Dataset._push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, embed_external_files)
4193 shard.to_parquet(buffer)
4194 uploaded_size += buffer.tell()
-> 4195 _retry(
4196 api.upload_file,
4197 func_kwargs=dict(
4198 path_or_fileobj=buffer.getvalue(),
4199 path_in_repo=shard_path_in_repo,
4200 repo_id=repo_id,
4201 token=token,
4202 repo_type="dataset",
4203 revision=branch,
4204 identical_ok=False,
4205 ),
4206 exceptions=HTTPError,
4207 status_codes=[504],
4208 base_wait_time=2.0,
4209 max_retries=5,
4210 max_wait_time=20.0,
4211 )
4212 shards_path_in_repo.append(shard_path_in_repo)
4214 # Cleanup to remove unused files
File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:284, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
282 except exceptions as err:
283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
--> 284 raise err
285 else:
286 sleep_time = min(max_wait_time, base_wait_time * 2**retry) # Exponential backoff
File ~/.local/lib/python3.9/site-packages/datasets/utils/file_utils.py:281, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)
279 while True:
280 try:
--> 281 return func(*func_args, **func_kwargs)
282 except exceptions as err:
283 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):
File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1967, in HfApi.upload_file(self, path_or_fileobj, path_in_repo, repo_id, token, repo_type, revision, identical_ok, commit_message, commit_description, create_pr)
1957 commit_message = (
1958 commit_message
1959 if commit_message is not None
1960 else f"Upload {path_in_repo} with huggingface_hub"
1961 )
1962 operation = CommitOperationAdd(
1963 path_or_fileobj=path_or_fileobj,
1964 path_in_repo=path_in_repo,
1965 )
-> 1967 pr_url = self.create_commit(
1968 repo_id=repo_id,
1969 repo_type=repo_type,
1970 operations=[operation],
1971 commit_message=commit_message,
1972 commit_description=commit_description,
1973 token=token,
1974 revision=revision,
1975 create_pr=create_pr,
1976 )
1977 if pr_url is not None:
1978 re_match = re.match(REGEX_DISCUSSION_URL, pr_url)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/hf_api.py:1844, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads)
1836 commit_url = f"{self.endpoint}/api/{repo_type}s/{repo_id}/commit/{revision}"
1838 commit_resp = requests.post(
1839 url=commit_url,
1840 headers={"Authorization": f"Bearer {token}"},
1841 json=commit_payload,
1842 params={"create_pr": 1} if create_pr else None,
1843 )
-> 1844 _raise_for_status(commit_resp)
1845 return commit_resp.json().get("pullRequestUrl", None)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:84, in _raise_for_status(request)
76 if request.status_code == 401:
77 # The repo was not found and the user is not Authenticated
78 raise RepositoryNotFoundError(
79 f"401 Client Error: Repository Not Found for url: {request.url}. If the"
80 " repo is private, make sure you are authenticated. (Request ID:"
81 f" {request_id})"
82 )
---> 84 _raise_with_request_id(request)
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:95, in _raise_with_request_id(request)
92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str):
93 e.args = (e.args[0] + f" (Request ID: {request_id})",) + e.args[1:]
---> 95 raise e
File ~/.local/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py:90, in _raise_with_request_id(request)
88 request_id = request.headers.get("X-Request-Id")
89 try:
---> 90 request.raise_for_status()
91 except Exception as e:
92 if request_id is not None and len(e.args) > 0 and isinstance(e.args[0], str):
File ~/.local/lib/python3.9/site-packages/requests/models.py:1021, in Response.raise_for_status(self)
1016 http_error_msg = (
1017 f"{self.status_code} Server Error: {reason} for url: {self.url}"
1018 )
1020 if http_error_msg:
-> 1021 raise HTTPError(http_error_msg, response=self)
HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/ORG/DATASET/commit/main (Request ID: a_F0IQAHJdxGKVRYyu1cF)
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.13.0-1025-aws-x86_64-with-glibc2.31
- Python version: 3.9.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
| 2023-02-07T13:54:10Z | 4,677 | null | https://api.github.com/repos/huggingface/datasets | null | [
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] | 2022-07-12T15:56:44Z | https://api.github.com/repos/huggingface/datasets/issues/4677/comments | null | https://api.github.com/repos/huggingface/datasets/issues/4677/timeline | Random 400 Client Error when pushing dataset | https://api.github.com/repos/huggingface/datasets/issues/4677/events | null | {
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"did you ever fix this? I'm experiencing the same",
"I am having the same issue. Even the simple example from the documentation gives me the 400 Error\r\n\r\n\r\n> from datasets import load_dataset\r\n> \r\n> dataset = load_dataset(\"stevhliu/demo\")\r\n> dataset.push_to_hub(\"processed_demo\")\r\n\r\n\r\n`reques... | {
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