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NonMatchingChecksumError for hendrycks_test
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[] | null | 2 | 2023-01-26T00:43:23Z | 2023-01-27T05:44:31Z | 2023-01-26T07:41:58Z |
NONE
| null |
### Describe the bug
The checksum of the file has likely changed on the remote host.
### Steps to reproduce the bug
`dataset = nlp.load_dataset("hendrycks_test", "anatomy")`
### Expected behavior
no error thrown
### Environment info
- `datasets` version: 2.2.1
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1
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Imagefolder docs: mention support of CSV and ZIP
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Concatenate on axis=1 with misaligned blocks
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[] | null | 4 | 2023-01-25T12:33:22Z | 2023-01-26T09:37:00Z | 2023-01-26T09:27:19Z |
MEMBER
| null |
Allow to concatenate on axis 1 two tables made of misaligned blocks.
For example if the first table has 2 row blocks of 3 rows each, and the second table has 3 row blocks or 2 rows each.
To do that, I slice the row blocks to re-align the blocks.
Fix https://github.com/huggingface/datasets/issues/5413
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Discrepancy in `nyu_depth_v2` dataset
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[] | null | 37 | 2023-01-24T19:15:46Z | 2023-02-06T20:52:00Z | null |
CONTRIBUTOR
| null |
### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-side comparison,
I tried to find the origin of this issue but sadly as I mentioned in tensorflow/datasets/issues/4674, the download link from `fast-depth` doesn't work anymore hence couldn't verify if the error originated there or during porting data from there to HF.
Hi @sayakpaul, as you worked on huggingface/datasets/issues/5255, if you still have access to that data could you please share the data or perhaps checkout this issue?
### Steps to reproduce the bug
This [notebook](https://colab.research.google.com/drive/1K3ZU8XUPRDOYD38MQS9nreQXJYitlKSW?usp=sharing#scrollTo=UEW7QSh0jf0i) from @sayakpaul could be used to generate depth maps and actual ground truths could be checked from this [dataset](https://www.kaggle.com/datasets/awsaf49/nyuv2-bts-dataset) from BTS repo.
> Note: BTS dataset has only 36K data compared to the train-test 50K. They sampled the data as adjacent frames look quite the same
### Expected behavior
Expected depth maps should be smooth rather than discrete/clipped.
### Environment info
- `datasets` version: 2.8.1.dev0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
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Document that removing all the columns returns an empty document and the num_row is lost
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Disable aiohttp requoting of redirection URL
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MEMBER
| null |
The library `aiohttp` performs a requoting of redirection URLs that unquotes the single quotation mark character: `%27` => `'`
This is a problem for our Hugging Face Hub, which requires exact URL from location header.
Specifically, in the query component of the URL (`https://netloc/path?query`), the value for `response-content-disposition` contains `%27`:
```
response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27sample.jsonl.gz%3B+filename%3D%22sample.jsonl.gz%22%3B
```
and after the requoting, the `%27` characters get unquoted to `'`:
```
response-content-disposition=attachment%3B+filename*%3DUTF-8''sample.jsonl.gz%3B+filename%3D%22sample.jsonl.gz%22%3B
```
This PR disables the `aiohttp` requoting of redirection URLs.
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slice split while streaming
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NONE
| null |
### Describe the bug
When using the `load_dataset` function with streaming set to True, slicing splits is apparently not supported.
Did I miss this in the documentation?
### Steps to reproduce the bug
`load_dataset("lhoestq/demo1",revision=None, streaming=True, split="train[:3]")`
causes ValueError: Bad split: train[:3]. Available splits: ['train', 'test'] in builder.py, line 1213, in as_streaming_dataset
### Expected behavior
The first 3 entries of the dataset as a stream
### Environment info
- `datasets` version: 2.8.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.10.9
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
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| 5,457 |
prebuilt dataset relies on `downloads/extracted`
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[] | null | 2 | 2023-01-24T02:09:32Z | 2023-01-24T18:14:10Z | null |
MEMBER
| null |
### Describe the bug
I pre-built the dataset:
```
python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
```
and it can be used just fine.
now I wipe out `downloads/extracted` and it no longer works.
```
rm -r ~/.cache/huggingface/datasets/downloads
```
That is I can still load it:
```
python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
No config specified, defaulting to: general-pmd-synthetic-testing/100.unique
Found cached dataset general-pmd-synthetic-testing (/home/stas/.cache/huggingface/datasets/HuggingFaceM4___general-pmd-synthetic-testing/100.unique/1.1.1/86bc445e3e48cb5ef79de109eb4e54ff85b318cd55c3835c4ee8f86eae33d9d2)
```
but if I try to use it:
```
E stderr: Traceback (most recent call last):
E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/main.py", line 116, in <module>
E stderr: train_loader, val_loader = get_dataloaders(
E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/dataset.py", line 170, in get_dataloaders
E stderr: train_loader = get_dataloader_from_config(
E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/dataset.py", line 443, in get_dataloader_from_config
E stderr: dataloader = get_dataloader(
E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/dataset.py", line 264, in get_dataloader
E stderr: is_pmd = "meta" in hf_dataset[0] and "source" in hf_dataset[0]
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/arrow_dataset.py", line 2601, in __getitem__
E stderr: return self._getitem(
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/arrow_dataset.py", line 2586, in _getitem
E stderr: formatted_output = format_table(
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 634, in format_table
E stderr: return formatter(pa_table, query_type=query_type)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 406, in __call__
E stderr: return self.format_row(pa_table)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 442, in format_row
E stderr: row = self.python_features_decoder.decode_row(row)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 225, in decode_row
E stderr: return self.features.decode_example(row) if self.features else row
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1846, in decode_example
E stderr: return {
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1847, in <dictcomp>
E stderr: column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1304, in decode_nested_example
E stderr: return decode_nested_example([schema.feature], obj)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1296, in decode_nested_example
E stderr: if decode_nested_example(sub_schema, first_elmt) != first_elmt:
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1309, in decode_nested_example
E stderr: return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/image.py", line 144, in decode_example
E stderr: image = PIL.Image.open(path)
E stderr: File "/home/stas/anaconda3/envs/py38-pt113/lib/python3.8/site-packages/PIL/Image.py", line 3092, in open
E stderr: fp = builtins.open(filename, "rb")
E stderr: FileNotFoundError: [Errno 2] No such file or directory: '/mnt/nvme0/code/data/cache/huggingface/datasets/downloads/extracted/134227b9b94c4eccf19b205bf3021d4492d0227b9be6c2ddb6bf517d8d55a8cb/data/101/images_01.jpg'
```
Only if I wipe out the cached dir and rebuild then it starts working as `download/extracted` is back again with extracted files.
```
rm -r ~/.cache/huggingface/datasets/HuggingFaceM4___general-pmd-synthetic-testing
python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
```
I think there are 2 issues here:
1. why does it still rely on extracted files after `arrow` files were printed - did I do something incorrectly when creating this dataset?
2. why doesn't the dataset know that it has been gutted and loads just fine? If it has a dependency on `download/extracted` then `load_dataset` should check if it's there and fail or force rebuilding. I am sure this could be a very expensive operation, so probably really solving #1 will not require this check. and this second item is probably an overkill. Other than perhaps if it had an optional `check_consistency` flag to do that.
### Environment info
datasets@main
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PR_kwDODunzps5IXq92
| 5,456 |
feat: tqdm for `to_parquet`
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[] | null | 2 | 2023-01-23T22:05:38Z | 2023-01-24T11:26:47Z | 2023-01-24T11:17:12Z |
CONTRIBUTOR
| null |
As described in #5418
I noticed also that the `to_json` function supports multi-workers whereas `to_parquet`, is that not possible/not needed with Parquet or something that hasn't been implemented yet?
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PR_kwDODunzps5IUvAZ
| 5,455 |
Single TQDM bar in multi-proc map
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[] | null | 12 | 2023-01-23T12:49:40Z | 2023-02-13T20:23:34Z | 2023-02-13T20:16:38Z |
CONTRIBUTOR
| null |
Use the "shard generator approach with periodic progress updates" (used in `save_to_disk` and multi-proc `load_dataset`) in `Dataset.map` to enable having a single TQDM progress bar in the multi-proc mode.
Closes https://github.com/huggingface/datasets/issues/771, closes https://github.com/huggingface/datasets/issues/3177
TODO:
- [x] cleaner refactor of the `_map_single` decorators now that they also have to wrap generator functions (decorate `map` instead of `map_single` with the `transmit_` decorators and predict the shards' fingerprint in `map`)
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I_kwDODunzps5cjzoz
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Save and resume the state of a DataLoader
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[] | null | 2 | 2023-01-23T10:58:54Z | 2023-01-24T01:45:48Z | null |
MEMBER
| null |
It would be nice when using `datasets` with a PyTorch DataLoader to be able to resume a training from a DataLoader state (e.g. to resume a training that crashed)
What I have in mind (but lmk if you have other ideas or comments):
For map-style datasets, this requires to have a PyTorch Sampler state that can be saved and reloaded per node and worker.
For iterable datasets, this requires to save the state of the dataset iterator, which includes:
- the current shard idx and row position in the current shard
- the epoch number
- the rng state
- the shuffle buffer
Right now you can already resume the data loading of an iterable dataset by using `IterableDataset.skip` but it takes a lot of time because it re-iterates on all the past data until it reaches the resuming point.
cc @stas00 @sgugger
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Fix base directory while extracting insecure TAR files
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[] | null | 3 | 2023-01-23T08:57:40Z | 2023-01-24T01:34:20Z | 2023-01-23T10:10:42Z |
MEMBER
| null |
This PR fixes the extraction of insecure TAR files by changing the base path against which TAR members are compared:
- from: "."
- to: `output_path`
This PR also adds tests for extracting insecure TAR files.
Related to:
- #5441
- #5452
@stas00 please note this PR addresses just one of the issues you pointed out: the use of the cwd by the extractor. The other issues (actionable error messages, raise instead of log error) should be addressed in other PRs.
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PR_kwDODunzps5ITcA3
| 5,452 |
Swap log messages for symbolic/hard links in tar extractor
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[] | null | 2 | 2023-01-23T07:53:38Z | 2023-01-23T09:40:55Z | 2023-01-23T08:31:17Z |
MEMBER
| null |
The log messages do not match their if-condition. This PR swaps them.
Found while investigating:
- #5441
CC: @lhoestq
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ImageFolder BadZipFile: Bad offset for central directory
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[] | null | 2 | 2023-01-22T23:50:12Z | 2023-02-10T16:31:37Z | 2023-02-10T16:31:36Z |
NONE
| null |
### Describe the bug
I'm getting the following exception:
```
lib/python3.10/zipfile.py:1353 in _RealGetContents │
│ │
│ 1350 │ │ # self.start_dir: Position of start of central directory │
│ 1351 │ │ self.start_dir = offset_cd + concat │
│ 1352 │ │ if self.start_dir < 0: │
│ ❱ 1353 │ │ │ raise BadZipFile("Bad offset for central directory") │
│ 1354 │ │ fp.seek(self.start_dir, 0) │
│ 1355 │ │ data = fp.read(size_cd) │
│ 1356 │ │ fp = io.BytesIO(data) │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
BadZipFile: Bad offset for central directory
Extracting data files: 35%|█████████████████▊ | 38572/110812 [00:10<00:20, 3576.26it/s]
```
### Steps to reproduce the bug
```
load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
),
```
### Expected behavior
loads the dataset
### Environment info
datasets==2.8.0
Python 3.10.8
Linux 129-146-3-202 5.15.0-52-generic #58~20.04.1-Ubuntu SMP Thu Oct 13 13:09:46 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux
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I_kwDODunzps5cdAz1
| 5,450 |
to_tf_dataset with a TF collator causes bizarrely persistent slowdown
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[] | null | 7 | 2023-01-20T16:08:37Z | 2023-02-13T14:13:34Z | 2023-02-13T14:13:34Z |
MEMBER
| null |
### Describe the bug
This will make more sense if you take a look at [a Colab notebook that reproduces this issue.](https://colab.research.google.com/drive/1rxyeciQFWJTI0WrZ5aojp4Ls1ut18fNH?usp=sharing)
Briefly, there are several datasets that, when you iterate over them with `to_tf_dataset` **and** a data collator that returns `tf` tensors, become very slow. We haven't been able to figure this one out - it can be intermittent, and we have no idea what could possibly cause it. The weirdest thing is that **the slowdown affects other attempts to access the underlying dataset**. If you try to iterate over the `tf.data.Dataset`, then interrupt execution, and then try to iterate over the original dataset, the original dataset is now also very slow! This is true even if the dataset format is not set to `tf` - the iteration is slow even though it's not calling TF at all!
There is a simple workaround for this - we can simply get our data collators to return `np` tensors. When we do this, the bug is never triggered and everything is fine. In general, `np` is preferred for this kind of preprocessing work anyway, when the preprocessing is not going to be compiled into a pure `tf.data` pipeline! However, the issue is fascinating, and the TF team were wondering if anyone in datasets (cc @lhoestq @mariosasko) might have an idea of what could cause this.
### Steps to reproduce the bug
Run the attached Colab.
### Expected behavior
The slowdown should go away, or at least not persist after we stop iterating over the `tf.data.Dataset`
### Environment info
The issue occurs on multiple versions of Python and TF, both on local machines and on Colab.
All testing was done using the latest versions of `transformers` and `datasets` from `main`
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| 5,449 |
Support fsspec 2023.1.0 in CI
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[] | null | 2 | 2023-01-20T12:53:17Z | 2023-01-20T13:32:50Z | 2023-01-20T13:26:03Z |
MEMBER
| null |
Support fsspec 2023.1.0 in CI.
In the 2023.1.0 fsspec release, they replaced the type of `fsspec.registry`:
- from `ReadOnlyRegistry`, with an attribute called `target`
- to `MappingProxyType`, without that attribute
Consequently, we need to change our `mock_fsspec` fixtures, that were using the `target` attribute.
Fix #5448.
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Support fsspec 2023.1.0 in CI
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MEMBER
| null |
Once we find out the root cause of:
- #5445
we should revert the temporary pin on fsspec introduced by:
- #5447
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Fix CI by temporarily pinning fsspec < 2023.1.0
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MEMBER
| null |
Temporarily pin fsspec < 2023.1.0
Fix #5445.
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PR_kwDODunzps5IMyka
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test v0.12.0.rc0
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[] | null | 5 | 2023-01-20T10:05:19Z | 2023-01-20T10:43:22Z | 2023-01-20T10:13:48Z |
CONTRIBUTOR
| null |
DO NOT MERGE.
Only to test the CI.
cc @lhoestq @albertvillanova
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CI tests are broken: AttributeError: 'mappingproxy' object has no attribute 'target'
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MEMBER
| null |
CI tests are broken, raising `AttributeError: 'mappingproxy' object has no attribute 'target'`. See: https://github.com/huggingface/datasets/actions/runs/3966497597/jobs/6797384185
```
...
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://top_level-date=2019-10-0[1-4]/*-expected_paths4] - AttributeError: 'mappingproxy' object has no attribute 'target'
===== 2076 passed, 19 skipped, 15 warnings, 47 errors in 115.54s (0:01:55) =====
```
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[] | null | 6 | 2023-01-20T01:19:18Z | 2023-01-25T15:43:22Z | null |
NONE
| null |
### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from the Python docs for reference:
* INFO: Confirmation that things are working as expected.
* WARNING: An indication that something unexpected happened, or indicative of some problem in the near future (e.g. ‘disk space low’). The software is still working as expected.
In theory, a user should be able to resolve things such that there are no warnings.
### Steps to reproduce the bug
Load any dataset that's already cached.
### Expected behavior
No output when log level is at the default WARNING level.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 9.0.0
- Pandas version: 1.5.2
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Update share tutorial
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[] | null | 2 | 2023-01-20T01:09:14Z | 2023-01-20T15:44:45Z | 2023-01-20T15:37:30Z |
MEMBER
| null |
Based on feedback from discussion #5423, this PR updates the sharing tutorial with a mention of writing your own dataset loading script to support more advanced dataset creation options like multiple configs.
I'll open a separate PR to update the *Create a Dataset card* with the new Hub metadata UI update 😄
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OneDrive Integrations with HF Datasets
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[] | null | 1 | 2023-01-19T23:12:08Z | 2023-01-20T18:05:52Z | null |
NONE
| null |
### Feature request
First of all , I would like to thank all community who are developed DataSet storage and make it free available
How to integrate our Onedrive account or any other possible storage clouds (like google drive,...) with the **HF** datasets section.
For example, if I have **50GB** on my **Onedrive** account and I want to move between drive and Hugging face repo or vis versa
### Motivation
make the dataset section more flexible with other possible storage
like the integration between Google Collab and Google drive the storage
### Your contribution
Can be done using Hugging face CLI
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resolving a weird tar extract issue
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[] | null | 4 | 2023-01-19T02:17:21Z | 2023-01-20T16:49:22Z | null |
MEMBER
| null |
ok, every so often, I have been getting a strange failure on dataset install:
```
$ python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
No config specified, defaulting to: general-pmd-synthetic-testing/100.unique
Downloading and preparing dataset general-pmd-synthetic-testing/100.unique (download: 3.21 KiB, generated: 16.01 MiB, post-processed: Unknown size, total: 16.02 MiB) to /home/stas/.cache/huggingface/datasets/HuggingFaceM4___general-pmd-synthetic-testing/100.unique/1.1.1/86bc445e3e48cb5ef79de109eb4e54ff85b318cd55c3835c4ee8f86eae33d9d2...
Extraction of data is blocked (illegal path)
Extraction of data/1 is blocked (illegal path)
Extraction of data/1/text.null is blocked (illegal path)
[...]
```
I had no idea what to do with that - what in the world does **illegal path** mean?
I started looking at the code in `TarExtractor` and added a debug print of `base` so that told me that there was a problem with the current directory - which was a clone of one of the hf repos.
This particular dataset extracts into a directory `data` and the current dir I was running the tests from already had `data` in it which was a symbolic link to another partition and somehow all that `badpath` code was blowing up there.
https://github.com/huggingface/datasets/blob/80eb8db74f49b7ee9c0f73a819c22177fabd61db/src/datasets/utils/extract.py#L113-L114
I tried hard to come up with a repro, but no matter what I tried it only fails in that particular clone directory that has a `data` symlink and not anywhere else.
In any case, in this PR I'm proposing to at least give a user a hint of what seems to be an issue.
I'm not at all happy with the info I got with this proposed change, but at least it gave me a hint that `TarExtractor` tries to extract into the current directory without any respect to pre-existing files. Say what?
https://github.com/huggingface/datasets/blob/80eb8db74f49b7ee9c0f73a819c22177fabd61db/src/datasets/utils/extract.py#L110
why won't it use the `datasets` designated directory for that? There would never be a problem if it were to do that.
I had to look at all those `resolved`, `badpath` calls and see what it did and why it failed, since it was far from obvious. It appeared like it resolved a symlink and compared it to the original path which of course wasn't matching.
So perhaps you have a better solution than what I proposed in this PR. I think that code line I quoted is the one that should be fixed instead.
But if you can't think of a better solution let's merge this at least so that the user will have a clue that the current dir is somehow involved.
p.s. I double checked that if I remove the pre-existing `data` symlink in the current dir I'm running the dataset install command from, the problem goes away too.
Thanks.
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Fix documentation about batch samplers
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NONE
| null |
### Feature request
Please add the common voice 12_0 datasets. Apart from English, a significant amount of audio-data has been added to the other minor-language datasets.
### Motivation
The dataset link:
https://commonvoice.mozilla.org/en/datasets
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Update actions/checkout in CD Conda release
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MEMBER
| null |
This PR updates the "checkout" GitHub Action to its latest version, as previous ones are deprecated: https://github.blog/changelog/2022-09-22-github-actions-all-actions-will-begin-running-on-node16-instead-of-node12/
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I_kwDODunzps5bmkYY
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Can't load png dataset with 4 channel (RGBA)
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[] | null | 3 | 2023-01-17T18:22:27Z | 2023-01-18T20:20:15Z | 2023-01-18T20:20:15Z |
NONE
| null |
I try to create dataset which contains about 9000 png images 64x64 in size, and they are all 4-channel (RGBA). When trying to use load_dataset() then a dataset is created from only 2 images. What exactly interferes I can not understand.
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Revert container image pin in CI benchmarks
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CONTRIBUTOR
| null |
Closes #5433, reverts #5432, and also:
* Uses [ghcr.io container images](https://cml.dev/doc/self-hosted-runners/#docker-images) for extra speed
* Updates `actions/checkout` to `v3` (note that `v2` is [deprecated](https://github.blog/changelog/2022-09-22-github-actions-all-actions-will-begin-running-on-node16-instead-of-node12/))
* Follows the new naming convention for environment variables introduced with [iterative/cml#1272](https://github.com/iterative/cml/pull/1272)
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Wrong statement in "Load a Dataset in Streaming mode" leads to data leakage
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[] | null | 4 | 2023-01-17T10:04:16Z | 2023-01-19T09:56:03Z | 2023-01-19T09:56:03Z |
NONE
| null |
### Describe the bug
In the [Split your dataset with take and skip](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#split-your-dataset-with-take-and-skip), it states:
> Using take (or skip) prevents future calls to shuffle from shuffling the dataset shards order, otherwise the taken examples could come from other shards. In this case it only uses the shuffle buffer. Therefore it is advised to shuffle the dataset before splitting using take or skip. See more details in the [Shuffling the dataset: shuffle](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#iterable-dataset-shuffling) section.`
>> \# You can also create splits from a shuffled dataset
>> train_dataset = shuffled_dataset.skip(1000)
>> eval_dataset = shuffled_dataset.take(1000)
Where the shuffled dataset comes from:
`shuffled_dataset = dataset.shuffle(buffer_size=10_000, seed=42)`
At least in Tensorflow 2.9/2.10/2.11, [docs](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) states the `reshuffle_each_iteration` argument is `True` by default. This means the dataset would be shuffled after each epoch, and as a result **the validation data would leak into training test**.
### Steps to reproduce the bug
N/A
### Expected behavior
The `reshuffle_each_iteration` argument should be set to `False`.
### Environment info
Tensorflow 2.9/2.10/2.11
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sample_dataset module not found
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Support latest Docker image in CI benchmarks
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MEMBER
| null |
Once we find out the root cause of:
- #5431
we should revert the temporary pin on the Docker image version introduced by:
- #5432
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Fix CI benchmarks by temporarily pinning Docker image version
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[] | null | 2 | 2023-01-17T07:15:31Z | 2023-01-17T08:58:22Z | 2023-01-17T08:51:17Z |
MEMBER
| null |
This PR fixes CI benchmarks, by temporarily pinning Docker image version, instead of "latest" tag.
It also updates deprecated `cml-send-comment` command and using `cml comment create` instead.
Fix #5431.
|
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CI benchmarks are broken: Unknown arguments: runnerPath, path
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MEMBER
| null |
Our CI benchmarks are broken, raising `Unknown arguments` error: https://github.com/huggingface/datasets/actions/runs/3932397079/jobs/6724905161
```
Unknown arguments: runnerPath, path
```
Stack trace:
```
100%|██████████| 500/500 [00:01<00:00, 338.98ba/s]
Updating lock file 'dvc.lock'
To track the changes with git, run:
git add dvc.lock
To enable auto staging, run:
dvc config core.autostage true
Use `dvc push` to send your updates to remote storage.
cml send-comment <markdown file>
Global Options:
--log Logging verbosity
[string] [choices: "error", "warn", "info", "debug"] [default: "info"]
--driver Git provider where the repository is hosted
[string] [choices: "github", "gitlab", "bitbucket"] [default: infer from the
environment]
--repo Repository URL or slug
[string] [default: infer from the environment]
--driver-token, --token CI driver personal/project access token (PAT)
[string] [default: infer from the environment]
--help Show help [boolean]
Options:
--target Comment type (`commit`, `pr`, `commit/f00bar`,
`pr/42`, `issue/1337`),default is automatic (`pr`
but fallback to `commit`). [string]
--watch Watch for changes and automatically update the
comment [boolean]
--publish Upload any local images found in the Markdown
report [boolean] [default: true]
--publish-url Self-hosted image server URL
[string] [default: "https://asset.cml.dev/"]
--publish-native, --native Uses driver's native capabilities to upload assets
instead of CML's storage; not available on GitHub
[boolean]
--watermark-title Hidden comment marker (used for targeting in
subsequent `cml comment update`); "{workflow}" &
"{run}" are auto-replaced [string] [default: ""]
Unknown arguments: runnerPath, path
Error: Process completed with exit code 1.
```
Issue reported to iterative/cml:
- iterative/cml#1319
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I_kwDODunzps5bi093
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Support Apache Beam >= 2.44.0
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MEMBER
| null |
Once we find out the root cause of:
- #5426
we should revert the temporary pin on apache-beam introduced by:
- #5429
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Fix CI by temporarily pinning apache-beam < 2.44.0
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[] | null | 1 | 2023-01-16T16:20:09Z | 2023-01-16T16:51:42Z | 2023-01-16T16:49:03Z |
MEMBER
| null |
Temporarily pin apache-beam < 2.44.0
Fix #5426.
|
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Load/Save FAISS index using fsspec
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[] | null | 2 | 2023-01-16T16:08:12Z | 2023-01-19T16:34:34Z | null |
CONTRIBUTOR
| null |
### Feature request
From what I understand `faiss` already support this [link](https://github.com/facebookresearch/faiss/wiki/Index-IO,-cloning-and-hyper-parameter-tuning#generic-io-support)
I would like to use a stream as input to `Dataset.load_faiss_index` and `Dataset.save_faiss_index`.
### Motivation
In my case, I'm saving faiss index in cloud storage and use `fsspec` to load them. It would be ideal if I could send the stream directly instead of copying the file locally (or mounting the bucket) and then load the index.
### Your contribution
I can submit the PR
|
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Unable to download dataset id_clickbait
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NONE
| null |
### Describe the bug
I tried to download dataset `id_clickbait`, but receive this error message.
```
FileNotFoundError: Couldn't find file at https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/k42j7x2kpn-1.zip
```
When i open the link using browser, i got this XML data.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<Error><Code>NoSuchBucket</Code><Message>The specified bucket does not exist</Message><BucketName>md-datasets-cache-zipfiles-prod</BucketName><RequestId>NVRM6VEEQD69SD00</RequestId><HostId>W/SPDxLGvlCGi0OD6d7mSDvfOAUqLAfvs9nTX50BkJrjMny+X9Jnqp/Li2lG9eTUuT4MUkAA2jjTfCrCiUmu7A==</HostId></Error>
```
### Steps to reproduce the bug
Code snippet:
```
from datasets import load_dataset
load_dataset('id_clickbait', 'annotated')
load_dataset('id_clickbait', 'raw')
```
Link to Kaggle notebook: https://www.kaggle.com/code/ilosvigil/bug-check-on-id-clickbait-dataset
### Expected behavior
Successfully download and load `id_newspaper` dataset.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5
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CI tests are broken: SchemaInferenceError
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MEMBER
| null |
CI is broken, raising a `SchemaInferenceError`: see https://github.com/huggingface/datasets/actions/runs/3930901593/jobs/6721492004
```
FAILED tests/test_beam.py::BeamBuilderTest::test_download_and_prepare_sharded - datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
```
Stack trace:
```
______________ BeamBuilderTest.test_download_and_prepare_sharded _______________
[gw1] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
self = <tests.test_beam.BeamBuilderTest testMethod=test_download_and_prepare_sharded>
@require_beam
def test_download_and_prepare_sharded(self):
import apache_beam as beam
original_write_parquet = beam.io.parquetio.WriteToParquet
expected_num_examples = len(get_test_dummy_examples())
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = DummyBeamDataset(cache_dir=tmp_cache_dir, beam_runner="DirectRunner")
with patch("apache_beam.io.parquetio.WriteToParquet") as write_parquet_mock:
write_parquet_mock.side_effect = partial(original_write_parquet, num_shards=2)
> builder.download_and_prepare()
tests/test_beam.py:97:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/builder.py:864: in download_and_prepare
**download_and_prepare_kwargs,
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/builder.py:1976: in _download_and_prepare
num_examples, num_bytes = beam_writer.finalize(metrics.query(m_filter))
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:694: in finalize
shard_num_bytes, _ = parquet_to_arrow(source, destination)
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:740: in parquet_to_arrow
num_bytes, num_examples = writer.finalize()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <datasets.arrow_writer.ArrowWriter object at 0x7f6dcbb3e810>
close_stream = True
def finalize(self, close_stream=True):
self.write_rows_on_file()
# In case current_examples < writer_batch_size, but user uses finalize()
if self._check_duplicates:
self.check_duplicate_keys()
# Re-intializing to empty list for next batch
self.hkey_record = []
self.write_examples_on_file()
# If schema is known, infer features even if no examples were written
if self.pa_writer is None and self.schema:
self._build_writer(self.schema)
if self.pa_writer is not None:
self.pa_writer.close()
self.pa_writer = None
if close_stream:
self.stream.close()
else:
if close_stream:
self.stream.close()
> raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
E datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:593: SchemaInferenceError
```
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Sort on multiple keys with datasets.Dataset.sort()
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[] | null | 9 | 2023-01-16T09:22:26Z | 2023-02-03T13:42:23Z | null |
NONE
| null |
### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggested solution:
> ... having something similar to pandas and be able to specify multiple columns for sorting. We’re already using pandas under the hood to do the sorting in datasets.
The suggested workaround:
> convert your dataset to pandas and use `df.sort_values()`
### Motivation
Preserved ordering when sorting is very handy when one needs to sort on multiple columns, A and B, so that e.g. whenever A is equal for two or more rows, B is kept sorted.
Having a parameter to do this in 🤗datasets would be cleaner than going through pandas and back, and it wouldn't add much complexity to the library.
Alternatives:
- the possibility to specify multiple keys to sort by with decreasing priority (suggested solution),
- the ability to provide a key function for sorting, so that one can manually specify the sorting criteria.
### Your contribution
I'll be happy to contribute by submitting a PR. Will get documented on `CONTRIBUTING.MD`.
Would love to get thoughts on this, if anyone has anything to add.
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I_kwDODunzps5bdQGE
| 5,424 |
When applying `ReadInstruction` to custom load it's not DatasetDict but list of Dataset?
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[] | null | 1 | 2023-01-16T06:54:28Z | 2023-01-19T15:09:14Z | null |
NONE
| null |
### Describe the bug
I am loading datasets from custom `tsv` files stored locally and applying split instructions for each split. Although the ReadInstruction is being applied correctly and I was expecting it to be `DatasetDict` but instead it is a list of `Dataset`.
### Steps to reproduce the bug
Steps to reproduce the behaviour:
1. Import
`from datasets import load_dataset, ReadInstruction`
2. Instruction to load the dataset
```
instructions = [
ReadInstruction(split_name="train", from_=0, to=10, unit='%', rounding='closest'),
ReadInstruction(split_name="dev", from_=0, to=10, unit='%', rounding='closest'),
ReadInstruction(split_name="test", from_=0, to=5, unit='%', rounding='closest')
]
```
3. Load
`dataset = load_dataset('csv', data_dir="data/", data_files={"train":"train.tsv", "dev":"dev.tsv", "test":"test.tsv"}, delimiter="\t", split=instructions)`
### Expected behavior
**Current behaviour**
:
**Expected behaviour**
### Environment info
``datasets==2.8.0
``
`Python==3.8.5
`
`Platform - Ubuntu 20.04.4 LTS`
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I_kwDODunzps5bZZoX
| 5,422 |
Datasets load error for saved github issues
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[] | null | 2 | 2023-01-14T17:29:38Z | 2023-01-16T13:10:30Z | null |
NONE
| null |
### Describe the bug
Loading a previously downloaded & saved dataset as described in the HuggingFace course:
issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train")
Gives this error:
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
A work-around I found was to use streaming.
### Steps to reproduce the bug
Reproduce by executing the code provided:
https://huggingface.co/course/chapter5/5?fw=pt
From the heading:
'let’s create a function that can download all the issues from a GitHub repository'
### Expected behavior
No error
### Environment info
Datasets version 2.8.0. Note that version 2.6.1 gives the same error (related to null timestamp).
**[EDIT]**
This is the complete error trace confirming the issue is related to the timestamp (`Couldn't cast array of type timestamp[s] to null`)
```
Using custom data configuration default-950028611d2860c8
Downloading and preparing dataset json/default to [...]/.cache/huggingface/datasets/json/default-950028611d2860c8/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...
Downloading data files: 100%|██████████| 1/1 [00:00<?, ?it/s]
Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 500.63it/s]
Generating train split: 2619 examples [00:00, 7155.72 examples/s]Traceback (most recent call last):
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\arrow_writer.py", line 567, in write_table
pa_table = table_cast(pa_table, self._schema)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2282, in table_cast
return cast_table_to_schema(table, schema)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2241, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2241, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1807, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1807, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2035, in cast_array_to_feature
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2035, in <listcomp>
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1809, in wrapper
return func(array, *args, **kwargs)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2101, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1809, in wrapper
return func(array, *args, **kwargs)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1990, in array_cast
raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
TypeError: Couldn't cast array of type timestamp[s] to null
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\pydevconsole.py", line 364, in runcode
coro = func()
File "<input>", line 1, in <module>
File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "[...]\PycharmProjects\TransformersTesting\dataset_issues.py", line 20, in <module>
issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train")
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\load.py", line 1757, in load_dataset
builder_instance.download_and_prepare(
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 860, in download_and_prepare
self._download_and_prepare(
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 953, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1706, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1849, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
Generating train split: 2619 examples [00:19, 7155.72 examples/s]
```
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Support case-insensitive Hub dataset name in load_dataset
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[] | null | 1 | 2023-01-13T13:07:07Z | 2023-01-13T20:12:32Z | 2023-01-13T20:12:32Z |
CONTRIBUTOR
| null |
### Feature request
The dataset name on the Hub is case-insensitive (see https://github.com/huggingface/moon-landing/pull/2399, internal issue), i.e., https://huggingface.co/datasets/GLUE redirects to https://huggingface.co/datasets/glue.
Ideally, we could load the glue dataset using the following:
```
from datasets import load_dataset
load_dataset('GLUE', 'cola')
```
It breaks because the loading script `GLUE.py` does not exist (`glue.py` should be selected instead).
Minor additional comment: in other cases without a loading script, we can load the dataset, but the automatically generated config name depends on the casing:
- `load_dataset('severo/danish-wit')` generates the config name `severo--danish-wit-e6fda5b070deb133`, while
- `load_dataset('severo/danish-WIT')` generates the config name `severo--danish-WIT-e6fda5b070deb133`
### Motivation
To follow the same UX on the Hub and in the datasets library.
### Your contribution
...
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ci: 🎡 remove two obsolete issue templates
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[] | null | 3 | 2023-01-13T12:58:43Z | 2023-01-13T13:36:00Z | 2023-01-13T13:29:01Z |
CONTRIBUTOR
| null |
add-dataset is not needed anymore since the "canonical" datasets are on the Hub. And dataset-viewer is managed within the datasets-server project.
See https://github.com/huggingface/datasets/issues/new/choose
<img width="1245" alt="Capture d’écran 2023-01-13 à 13 59 58" src="https://user-images.githubusercontent.com/1676121/212325813-2d4c30e2-343e-4aa2-8cce-b2b77f45628e.png">
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label_column='labels' in datasets.TextClassification and 'label' or 'label_ids' in transformers.DataColator
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[] | null | 1 | 2023-01-13T09:40:07Z | 2023-01-19T15:46:51Z | null |
NONE
| null |
### Describe the bug
When preparing a dataset for a task using `datasets.TextClassification`, the output feature is named `labels`. When preparing the trainer using the `transformers.DataCollator` the default column name is `label` if binary or `label_ids` if multi-class problem.
It is required to rename the column accordingly to the expected name : `label` or `label_ids`
### Steps to reproduce the bug
```python
from datasets import TextClassification, AutoTokenized, DataCollatorWithPadding
ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0'))
print(ds_prepared)
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
ds_tokenized = ds_prepared.map(lambda x: tokenizer(x['text'], truncation=True), batched=True)
print(ds_tokenized)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
tf_data = model.prepare_tf_dataset(ds_tokenized, shuffle=True, batch_size=16, collate_fn=data_collator)
print(tf_data)
```
### Expected behavior
Without renaming the the column, the target column is not in the final tf_data since it is not in the column name expected by the data_collator.
To correct this, we have to rename the column:
```python
ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0')).rename_column('labels', 'label')
```
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.6
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
- `transformers` version: 4.26.0.dev0
- Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.11.1
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): 2.11.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
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Add ProgressBar for `to_parquet`
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CONTRIBUTOR
| null |
### Feature request
Add a progress bar for `Dataset.to_parquet`, similar to how `to_json` works.
### Motivation
It's a bit frustrating to not know how long a dataset will take to write to file and if it's stuck or not without a progress bar
### Your contribution
Sure I can help if needed
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Fix RuntimeError: Sharding is ambiguous for this dataset
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[] | null | 4 | 2023-01-10T08:43:19Z | 2023-01-18T17:12:17Z | 2023-01-18T14:09:02Z |
MEMBER
| null |
This PR fixes the RuntimeError: Sharding is ambiguous for this dataset.
The error for ambiguous sharding will be raised only if num_proc > 1.
Fix #5415, fix #5414.
Fix https://huggingface.co/datasets/ami/discussions/3.
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RuntimeError: Sharding is ambiguous for this dataset
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MEMBER
| null |
### Describe the bug
When loading some datasets, a RuntimeError is raised.
For example, for "ami" dataset: https://huggingface.co/datasets/ami/discussions/3
```
.../huggingface/datasets/src/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)
1415 fpath = path_join(self._output_dir, fname)
1416
-> 1417 num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs)
1418 if num_input_shards <= 1 and num_proc is not None:
1419 logger.warning(
.../huggingface/datasets/src/datasets/utils/sharding.py in _number_of_shards_in_gen_kwargs(gen_kwargs)
10 lists_lengths = {key: len(value) for key, value in gen_kwargs.items() if isinstance(value, list)}
11 if len(set(lists_lengths.values())) > 1:
---> 12 raise RuntimeError(
13 (
14 "Sharding is ambiguous for this dataset: "
RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key samples_paths has length 6
- key ids has length 7
- key verification_ids has length 6
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.
```
This behavior was introduced when implementing multiprocessing by PR:
- #5107
### Steps to reproduce the bug
```python
ds = load_dataset("ami", "microphone-single", split="train", revision="2d7620bb7c3f1aab9f329615c3bdb598069d907a")
```
### Expected behavior
No error raised.
### Environment info
Since datasets 2.7.0
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I_kwDODunzps5a8Nm6
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Sharding error with Multilingual LibriSpeech
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NONE
| null |
### Describe the bug
Loading the German Multilingual LibriSpeech dataset results in a RuntimeError regarding sharding with the following stacktrace:
```
Downloading and preparing dataset multilingual_librispeech/german to /home/nithin/datadrive/cache/huggingface/datasets/facebook___multilingual_librispeech/german/2.1.0/1904af50f57a5c370c9364cc337699cfe496d4e9edcae6648a96be23086362d0...
Downloading data files: 100%
3/3 [00:00<00:00, 107.23it/s]
Downloading data files: 100%
1/1 [00:00<00:00, 35.08it/s]
Downloading data files: 100%
6/6 [00:00<00:00, 303.36it/s]
Downloading data files: 100%
3/3 [00:00<00:00, 130.37it/s]
Downloading data files: 100%
1049/1049 [00:00<00:00, 4491.40it/s]
Downloading data files: 100%
37/37 [00:00<00:00, 1096.78it/s]
Downloading data files: 100%
40/40 [00:00<00:00, 1003.93it/s]
Extracting data files: 100%
3/3 [00:11<00:00, 2.62s/it]
Generating train split:
469942/0 [34:13<00:00, 273.21 examples/s]
Output exceeds the size limit. Open the full output data in a text editor
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-14-74fa6d092bdc> in <module>
----> 1 mls = load_dataset(MLS_DATASET,
2 LANGUAGE,
3 cache_dir="~/datadrive/cache/huggingface/datasets",
4 ignore_verifications=True)
/anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)
1755
1756 # Download and prepare data
-> 1757 builder_instance.download_and_prepare(
1758 download_config=download_config,
1759 download_mode=download_mode,
/anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/builder.py in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
858 if num_proc is not None:
859 prepare_split_kwargs["num_proc"] = num_proc
--> 860 self._download_and_prepare(
861 dl_manager=dl_manager,
862 verify_infos=verify_infos,
/anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs)
1609
1610 def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs):
...
RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_archives has length 1049
- key local_extracted_archive has length 1049
- key limited_ids_paths has length 1
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.
```
### Steps to reproduce the bug
Here is the code to reproduce it:
```python
from datasets import load_dataset
MLS_DATASET = "facebook/multilingual_librispeech"
LANGUAGE = "german"
mls = load_dataset(MLS_DATASET,
LANGUAGE,
cache_dir="~/datadrive/cache/huggingface/datasets",
ignore_verifications=True)
```
### Expected behavior
The expected behaviour is that the dataset is successfully loaded.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.4.0-1094-azure-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 10.0.1
- Pandas version: 1.2.4
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I_kwDODunzps5a32zd
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concatenate_datasets fails when two dataset with shards > 1 and unequal shard numbers
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NONE
| null |
### Describe the bug
When using `concatenate_datasets([dataset1, dataset2], axis = 1)` to concatenate two datasets with shards > 1, it fails:
```
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/combine.py", line 182, in concatenate_datasets
return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 5499, in _concatenate_map_style_datasets
table = concat_tables([dset._data for dset in dsets], axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1778, in concat_tables
return ConcatenationTable.from_tables(tables, axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1483, in from_tables
blocks = _extend_blocks(blocks, table_blocks, axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1477, in _extend_blocks
result[i].extend(row_blocks)
IndexError: list index out of range
```
### Steps to reproduce the bug
dataset = concatenate_datasets([dataset1, dataset2], axis = 1)
### Expected behavior
The datasets are correctly concatenated.
### Environment info
datasets==2.8.0
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load_dataset() cannot find dataset_info.json with multiple training runs in parallel
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NONE
| null |
### Describe the bug
I have a custom local dataset in JSON form. I am trying to do multiple training runs in parallel. The first training run runs with no issue. However, when I start another run on another GPU, the following code throws this error.
If there is a workaround to ignore the cache I think that would solve my problem too.
I am using datasets version 2.8.0.
### Steps to reproduce the bug
1. Start training run of GPU 0 loading dataset from
```
load_dataset(
"json",
data_files=tr_dataset_path,
split=f"train",
download_mode="force_redownload",
)
```
2. While GPU 0 is training, start an identical run on GPU 1. GPU 1 will produce the following error:
```
Traceback (most recent call last):
File "/local-scratch1/data/mt/code/qq/train.py", line 198, in <module>
main()
File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1130, in __call__
return self.main(*args, **kwargs)
File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1055, in main
rv = self.invoke(ctx)
File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 760, in invoke
return __callback(*args, **kwargs)
File "/local-scratch1/data/mt/code/qq/train.py", line 113, in main
load_dataset(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/load.py", line 1734, in load_dataset
builder_instance = load_dataset_builder(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/load.py", line 1518, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/builder.py", line 366, in __init__
self.info = DatasetInfo.from_directory(self._cache_dir)
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/info.py", line 313, in from_directory
with fs.open(path_join(dataset_info_dir, config.DATASET_INFO_FILENAME), "r", encoding="utf-8") as f:
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/spec.py", line 1094, in open
self.open(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/spec.py", line 1106, in open
f = self._open(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 175, in _open
return LocalFileOpener(path, mode, fs=self, **kwargs)
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 273, in __init__
self._open()
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 278, in _open
self.f = open(self.path, mode=self.mode)
FileNotFoundError: [Errno 2] No such file or directory: '/home/username/.cache/huggingface/datasets/json/default-43d06a4aedb25e6d/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/dataset_info.json'
```
### Expected behavior
Expected behavior: 2nd GPU training run should run the same as 1st GPU training run.
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.8.0
- Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.10
- Python version: 3.8.15
- PyArrow version: 9.0.0
- Pandas version: 1.5.2
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PR_kwDODunzps5G23-T
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Update docs of S3 filesystem with async aiobotocore
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[] | null | 2 | 2023-01-06T23:19:17Z | 2023-01-18T11:18:59Z | 2023-01-18T11:12:04Z |
CONTRIBUTOR
| null |
[s3fs has migrated to all async calls](https://github.com/fsspec/s3fs/commit/0de2c6fb3d87c08ea694de96dca0d0834034f8bf).
Updating documentation to use `AioSession` while using s3fs for download manager as well as working with datasets
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PR_kwDODunzps5GvnJH
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Map-style Dataset to IterableDataset
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[] | null | 22 | 2023-01-05T18:12:17Z | 2023-02-01T18:11:45Z | 2023-02-01T16:36:01Z |
MEMBER
| null |
Added `ds.to_iterable()` to get an iterable dataset from a map-style arrow dataset.
It also has a `num_shards` argument to split the dataset before converting to an iterable dataset. Sharding is important to enable efficient shuffling and parallel loading of iterable datasets.
TODO:
- [x] tests
- [x] docs
Fix https://github.com/huggingface/datasets/issues/5265
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| 5,409 |
Fix deprecation warning when use_auth_token passed to download_and_prepare
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[] | null | 2 | 2023-01-05T09:10:58Z | 2023-01-06T11:06:16Z | 2023-01-06T10:59:13Z |
MEMBER
| null |
The `DatasetBuilder.download_and_prepare` argument `use_auth_token` was deprecated in:
- #5302
However, `use_auth_token` is still passed to `download_and_prepare` in our built-in `io` readers (csv, json, parquet,...).
This PR fixes it, so that no deprecation warning is raised.
Fix #5407.
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[] | null | 2 | 2023-01-05T01:59:59Z | 2023-01-06T13:22:19Z | 2023-01-06T13:22:18Z |
NONE
| null |
### Describe the bug
I follow the [blog post](https://huggingface.co/blog/fine-tune-whisper#building-a-demo) to finetune a Cantonese transcribe model.
When using map function to prepare dataset, following warning pop out:
`common_voice = common_voice.map(prepare_dataset,
remove_columns=common_voice.column_names["train"], num_proc=1)`
> Parameter 'function'=<function prepare_dataset at 0x000001D1D9D79A60> 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.
I read https://github.com/huggingface/datasets/issues/4521 and https://github.com/huggingface/datasets/issues/3178 but cannot solve the issue.
### Steps to reproduce the bug
```python
from datasets import load_dataset, DatasetDict
common_voice = DatasetDict()
common_voice["train"] = load_dataset("mozilla-foundation/common_voice_11_0", "zh-HK",
split="train+validation")
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "zh-HK",
split="test")
common_voice = common_voice.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "path", "segment", "up_votes"])
from transformers import WhisperFeatureExtractor, WhisperTokenizer, WhisperProcessor
feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")
tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="chinese", task="transcribe")
processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="chinese", task="transcribe")
from datasets import Audio
common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000))
def prepare_dataset(batch):
# load and resample audio data from 48 to 16kHz
audio = batch["audio"]
# compute log-Mel input features from input audio array
batch["input_features"] = feature_extractor(audio["array"],
sampling_rate=audio["sampling_rate"]).input_features[0]
# encode target text to label ids
batch["labels"] = tokenizer(batch["sentence"]).input_ids
return batch
common_voice = common_voice.map(prepare_dataset,
remove_columns=common_voice.column_names["train"], num_proc=1)
```
### Expected behavior
Should be no warning shown.
### Environment info
- `datasets` version: 2.7.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5
- dill version: 0.3.4
- multiprocess version: 0.70.12.2
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Datasets.from_sql() generates deprecation warning
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NONE
| null |
### Describe the bug
Calling `Datasets.from_sql()` generates a warning:
`.../site-packages/datasets/builder.py:712: FutureWarning: 'use_auth_token' was deprecated in version 2.7.1 and will be removed in 3.0.0. Pass 'use_auth_token' to the initializer/'load_dataset_builder' instead.`
### Steps to reproduce the bug
Any valid call to `Datasets.from_sql()` will produce the deprecation warning.
### Expected behavior
No warning.
The fix should be simply to remove the parameter `use_auth_token` from the call to `builder.download_and_prepare()` at line 43 of `io/sql.py` (it is set to `None` anyway, and is not needed).
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-4.15.0-169-generic-x86_64-with-glibc2.27
- Python version: 3.9.15
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
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[2.6.1][2.7.0] Upgrade `datasets` to fix `TypeError: can only concatenate str (not "int") to str`
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MEMBER
| null |
`datasets` 2.6.1 and 2.7.0 started to stop supporting datasets like IMDB, ConLL or MNIST datasets.
When loading a dataset using 2.6.1 or 2.7.0, you may this error when loading certain datasets:
```python
TypeError: can only concatenate str (not "int") to str
```
This is because we started to update the metadata of those datasets to a format that is not supported in 2.6.1 and 2.7.0
This change is required or those datasets won't be supported by the Hugging Face Hub.
Therefore if you encounter this error or if you're using `datasets` 2.6.1 or 2.7.0, we encourage you to update to a newer version.
For example, versions 2.6.2 and 2.7.1 patch this issue.
```python
pip install -U datasets
```
All the datasets affected are the ones with a ClassLabel feature type and YAML "dataset_info" metadata. More info [here](https://github.com/huggingface/datasets/issues/5275).
We apologize for the inconvenience.
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| 1,517,879,386 |
I_kwDODunzps5aeQBa
| 5,405 |
size_in_bytes the same for all splits
|
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[] | null | 1 | 2023-01-03T20:25:48Z | 2023-01-04T09:22:59Z | null |
NONE
| null |
### Describe the bug
Hi, it looks like whenever you pull a dataset and get size_in_bytes, it returns the same size for all splits (and that size is the combined size of all splits). It seems like this shouldn't be the intended behavior since it is misleading. Here's an example:
```
>>> from datasets import load_dataset
>>> x = load_dataset("glue", "wnli")
Found cached dataset glue (/Users/breakend/.cache/huggingface/datasets/glue/wnli/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1097.70it/s]
>>> x["train"].size_in_bytes
186159
>>> x["validation"].size_in_bytes
186159
>>> x["test"].size_in_bytes
186159
>>>
```
### Steps to reproduce the bug
```
>>> from datasets import load_dataset
>>> x = load_dataset("glue", "wnli")
>>> x["train"].size_in_bytes
186159
>>> x["validation"].size_in_bytes
186159
>>> x["test"].size_in_bytes
186159
```
### Expected behavior
The expected behavior is that it should return the separate sizes for all splits.
### Environment info
- `datasets` version: 2.7.1
- Platform: macOS-12.5-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
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I_kwDODunzps5adDl7
| 5,404 |
Better integration of BIG-bench
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[] | null | 1 | 2023-01-03T15:37:57Z | 2023-02-09T20:30:26Z | null |
MEMBER
| null |
### Feature request
Ideally, it would be nice to have a maintained PyPI package for `bigbench`.
### Motivation
We'd like to allow anyone to access, explore and use any task.
### Your contribution
@lhoestq has opened an issue in their repo:
- https://github.com/google/BIG-bench/issues/906
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| 5,403 |
Replace one letter import in docs
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[] | null | 4 | 2023-01-03T14:26:32Z | 2023-01-03T15:06:18Z | 2023-01-03T14:59:01Z |
CONTRIBUTOR
| null |
This PR updates a code example for consistency across the docs based on [feedback from this comment](https://github.com/huggingface/transformers/pull/20925/files/9fda31634d203a47d3212e4e8d43d3267faf9808#r1058769500):
"In terms of style we usually stay away from one-letter imports like this (even if the community uses them) as they are not always known by beginners and one letter is very undescriptive. Here it wouldn't change anything to use albumentations instead of A."
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I_kwDODunzps5acdSV
| 5,402 |
Missing state.json when creating a cloud dataset using a dataset_builder
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[] | null | 3 | 2023-01-03T13:39:59Z | 2023-01-04T17:23:57Z | null |
NONE
| null |
### Describe the bug
Using `load_dataset_builder` to create a builder, run `download_and_prepare` do upload it to S3. However when trying to load it, there are missing `state.json` files. Complete example:
```python
from aiobotocore.session import AioSession as Session
from datasets import load_from_disk, load_datase, load_dataset_builder
import s3fs
storage_options = {"session": Session()}
fs = s3fs.S3FileSystem(**storage_options)
output_dir = "s3://bucket/imdb"
builder = load_dataset_builder("imdb")
builder.download_and_prepare(output_dir, storage_options=storage_options)
load_from_disk(output_dir, fs=fs) # ERROR
# [Errno 2] No such file or directory: '/tmp/tmpy22yys8o/bucket/imdb/state.json'
```
As a comparison, if you use the non lazy `load_dataset`, it works and the S3 folder has different structure + state.json files. Example:
```python
from aiobotocore.session import AioSession as Session
from datasets import load_from_disk, load_dataset, load_dataset_builder
import s3fs
storage_options = {"session": Session()}
fs = s3fs.S3FileSystem(**storage_options)
output_dir = "s3://bucket/imdb"
dataset = load_dataset("imdb",)
dataset.save_to_disk(output_dir, fs=fs)
load_from_disk(output_dir, fs=fs) # WORKS
```
You still want the 1st option for the laziness and the parquet conversion. Thanks!
### Steps to reproduce the bug
```python
from aiobotocore.session import AioSession as Session
from datasets import load_from_disk, load_datase, load_dataset_builder
import s3fs
storage_options = {"session": Session()}
fs = s3fs.S3FileSystem(**storage_options)
output_dir = "s3://bucket/imdb"
builder = load_dataset_builder("imdb")
builder.download_and_prepare(output_dir, storage_options=storage_options)
load_from_disk(output_dir, fs=fs) # ERROR
# [Errno 2] No such file or directory: '/tmp/tmpy22yys8o/bucket/imdb/state.json'
```
BTW, you need the AioSession as s3fs is now based on aiobotocore, see https://github.com/fsspec/s3fs/issues/385.
### Expected behavior
Expected to be able to load the dataset from S3.
### Environment info
```
s3fs 2022.11.0
s3transfer 0.6.0
datasets 2.8.0
aiobotocore 2.4.2
boto3 1.24.59
botocore 1.27.59
```
python 3.7.15.
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| 5,401 |
Support Dataset conversion from/to Spark
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MEMBER
| null |
This PR implements Spark integration by supporting `Dataset` conversion from/to Spark `DataFrame`.
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Support streaming datasets with os.path.exists and Path.exists
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MEMBER
| null |
Support streaming datasets with `os.path.exists` and `pathlib.Path.exists`.
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I_kwDODunzps5aVW8L
| 5,399 |
Got disconnected from remote data host. Retrying in 5sec [2/20]
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[] | null | 0 | 2023-01-01T13:00:11Z | 2023-01-02T07:21:52Z | 2023-01-02T07:21:52Z |
NONE
| null |
### Describe the bug
While trying to upload my image dataset of a CSV file type to huggingface by running the below code. The dataset consists of a little over 100k of image-caption pairs
### Steps to reproduce the bug
```
df = pd.read_csv('x.csv', encoding='utf-8-sig')
features = Features({
'link': Image(decode=True),
'caption': Value(dtype='string'),
})
#make sure u r logged in to HF
ds = Dataset.from_pandas(df, features=features)
ds.features
ds.push_to_hub("x/x")
```
I got the below error and It always stops at the same progress
```
100%|██████████| 4/4 [23:53<00:00, 358.48s/ba]
100%|██████████| 4/4 [24:37<00:00, 369.47s/ba]%|▍ | 1/22 [00:06<02:09, 6.16s/it]
100%|██████████| 4/4 [25:00<00:00, 375.15s/ba]%|▉ | 2/22 [25:54<2:36:15, 468.80s/it]
100%|██████████| 4/4 [24:53<00:00, 373.29s/ba]%|█▎ | 3/22 [51:01<4:07:07, 780.39s/it]
100%|██████████| 4/4 [24:01<00:00, 360.34s/ba]%|█▊ | 4/22 [1:17:00<5:04:07, 1013.74s/it]
100%|██████████| 4/4 [23:59<00:00, 359.91s/ba]%|██▎ | 5/22 [1:41:07<5:24:06, 1143.90s/it]
100%|██████████| 4/4 [24:16<00:00, 364.06s/ba]%|██▋ | 6/22 [2:05:14<5:29:15, 1234.74s/it]
100%|██████████| 4/4 [25:24<00:00, 381.10s/ba]%|███▏ | 7/22 [2:29:38<5:25:52, 1303.52s/it]
100%|██████████| 4/4 [25:24<00:00, 381.24s/ba]%|███▋ | 8/22 [2:56:02<5:23:46, 1387.58s/it]
100%|██████████| 4/4 [25:08<00:00, 377.23s/ba]%|████ | 9/22 [3:22:24<5:13:17, 1445.97s/it]
100%|██████████| 4/4 [24:11<00:00, 362.87s/ba]%|████▌ | 10/22 [3:48:24<4:56:02, 1480.19s/it]
100%|██████████| 4/4 [24:44<00:00, 371.11s/ba]%|█████ | 11/22 [4:12:42<4:30:10, 1473.66s/it]
100%|██████████| 4/4 [24:35<00:00, 368.81s/ba]%|█████▍ | 12/22 [4:37:34<4:06:29, 1478.98s/it]
100%|██████████| 4/4 [24:02<00:00, 360.67s/ba]%|█████▉ | 13/22 [5:03:24<3:45:04, 1500.45s/it]
100%|██████████| 4/4 [24:07<00:00, 361.78s/ba]%|██████▎ | 14/22 [5:27:33<3:17:59, 1484.97s/it]
100%|██████████| 4/4 [23:39<00:00, 354.85s/ba]%|██████▊ | 15/22 [5:51:48<2:52:10, 1475.82s/it]
Pushing dataset shards to the dataset hub: 73%|███████▎ | 16/22 [6:16:58<2:28:37, 1486.31s/it]Got disconnected from remote data host. Retrying in 5sec [1/20]
Got disconnected from remote data host. Retrying in 5sec [2/20]
Got disconnected from remote data host. Retrying in 5sec [3/20]
Got disconnected from remote data host. Retrying in 5sec [4/20]
Got disconnected from remote data host. Retrying in 5sec [5/20]
Got disconnected from remote data host. Retrying in 5sec [6/20]
Got disconnected from remote data host. Retrying in 5sec [7/20]
Got disconnected from remote data host. Retrying in 5sec [8/20]
Got disconnected from remote data host. Retrying in 5sec [9/20]
...
Got disconnected from remote data host. Retrying in 5sec [19/20]
Got disconnected from remote data host. Retrying in 5sec [20/20]
75%|███████▌ | 3/4 [24:47<08:15, 495.86s/ba]
Pushing dataset shards to the dataset hub: 73%|███████▎ | 16/22 [6:41:46<2:30:39, 1506.65s/it]
Output exceeds the size limit. Open the full output data in a text editor
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-1-dbf8530779e9> in <module>
16 ds.features
```
### Expected behavior
I was trying to upload an image dataset and expected it to be fully uploaded
### Environment info
- `datasets` version: 2.8.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.9
- PyArrow version: 10.0.1
- Pandas version: 1.3.5
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| 1,514,425,231 |
I_kwDODunzps5aREuP
| 5,398 |
Unpin pydantic
|
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[] | null | 0 | 2022-12-30T10:37:31Z | 2022-12-30T10:43:41Z | 2022-12-30T10:43:41Z |
MEMBER
| null |
Once `pydantic` fixes their issue in their 1.10.3 version, unpin it.
See issue:
- #5394
See temporary fix:
- #5395
|
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| 1,514,412,246 |
PR_kwDODunzps5GYirs
| 5,397 |
Unpin pydantic test dependency
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[] | null | 2 | 2022-12-30T10:22:09Z | 2022-12-30T10:53:11Z | 2022-12-30T10:43:40Z |
MEMBER
| null |
Once pydantic-1.10.3 has been yanked, we can unpin it: https://pypi.org/project/pydantic/1.10.3/
See reply by pydantic team https://github.com/pydantic/pydantic/issues/4885#issuecomment-1367819807
```
v1.10.3 has been yanked.
```
in response to spacy request: https://github.com/pydantic/pydantic/issues/4885#issuecomment-1367810049
```
On behalf of spacy-related packages: would it be possible for you to temporarily yank v1.10.3?
To address this and be compatible with v1.10.4, we'd have to release new versions of a whole series of packages and nearly everyone (including me) is currently on vacation. Even if v1.10.4 is released with a fix, pip would still back off to v1.10.3 for spacy, etc. because of its current pins for typing_extensions. If it could instead back off to v1.10.2, we'd have a bit more breathing room to make the updates on our end.
```
Close #5398.
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| 5,396 |
Fix checksum verification
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[] | null | 7 | 2022-12-29T19:45:17Z | 2023-02-13T11:11:22Z | 2023-02-13T11:11:22Z |
CONTRIBUTOR
| null |
Expected checksum was verified against checksum dict (not checksum).
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Temporarily pin pydantic test dependency
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[] | null | 3 | 2022-12-29T19:34:19Z | 2022-12-30T06:36:57Z | 2022-12-29T21:00:26Z |
MEMBER
| null |
Temporarily pin `pydantic` until a permanent solution is found.
Fix #5394.
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| 5,394 |
CI error: TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers'
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MEMBER
| null |
### Describe the bug
While installing the dependencies, the CI raises a TypeError:
```
Traceback (most recent call last):
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 183, in _run_module_as_main
mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 142, in _get_module_details
return _get_module_details(pkg_main_name, error)
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 109, in _get_module_details
__import__(pkg_name)
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/__init__.py", line 6, in <module>
from .errors import setup_default_warnings
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/errors.py", line 2, in <module>
from .compat import Literal
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/compat.py", line 3, in <module>
from thinc.util import copy_array
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/thinc/__init__.py", line 5, in <module>
from .config import registry
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/thinc/config.py", line 2, in <module>
import confection
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/confection/__init__.py", line 10, in <module>
from pydantic import BaseModel, create_model, ValidationError, Extra
File "pydantic/__init__.py", line 2, in init pydantic.__init__
File "pydantic/dataclasses.py", line 46, in init pydantic.dataclasses
# | None | Attribute is set to None. |
File "pydantic/main.py", line 121, in init pydantic.main
TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers'
```
See: https://github.com/huggingface/datasets/actions/runs/3793736481/jobs/6466356565
### Steps to reproduce the bug
```shell
pip install .[tests,metrics-tests]
python -m spacy download en_core_web_sm
```
### Expected behavior
No error.
### Environment info
See: https://github.com/huggingface/datasets/actions/runs/3793736481/jobs/6466356565
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PR_kwDODunzps5GTg0a
| 5,393 |
Finish deprecating the fs argument
|
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[] | null | 6 | 2022-12-28T15:33:17Z | 2023-01-18T12:42:33Z | 2023-01-18T12:35:32Z |
CONTRIBUTOR
| null |
See #5385 for some discussion on this
The `fs=` arg was depcrecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in `2.8.0` (to be removed in `3.0.0`). There are a few other places where the `fs=` arg was still used (functions/methods in `datasets.info` and `datasets.load`). This PR adds a similar behavior, warnings and the `storage_options=` arg to these functions and methods.
One question: should the "deprecated" / "added" versions be `2.8.1` for the docs/warnings on these? Right now I'm going with "fs was deprecated in 2.8.0" but "storage_options= was added in 2.8.1" where appropriate.
@mariosasko
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Fix Colab notebook link
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MEMBER
| null |
Fix notebook link to open in Colab.
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Whisper Event - RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 [2:52:21<00:00, 10.34s/it]
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[] | null | 1 | 2022-12-25T15:17:14Z | 2023-01-05T12:56:02Z | null |
NONE
| null |
Done in a VM with a GPU (Ubuntu) following the [Whisper Event - PYTHON](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event#python-script) instructions.
Attempted using [RuntimeError: he size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 - WEB](https://discuss.huggingface.co/t/trainer-runtimeerror-the-size-of-tensor-a-462-must-match-the-size-of-tensor-b-448-at-non-singleton-dimension-1/26010/10 ) - another person experiencing the same issue. But could not resolve the issue with the google/fleurs data. __Not clear what can be modified in the PY code to resolve the input data size mismatch, as the training data is already very small__.
Tried posting on Discord, @sanchit-gandhi and @vaibhavs10. Was hoping that the event is over and some input/help is now available. [Hugging Face - whisper-small-amet](https://huggingface.co/drmeeseeks/whisper-small-amet).
The paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) am_et is a low resource language (Table E), with the WER results ranging from 120-229, based on model size. (Whisper small WER=120.2).
# ---> Initial Training Output
/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
warnings.warn(
[INFO|trainer.py:1641] 2022-12-18 05:23:28,799 >> ***** Running training *****
[INFO|trainer.py:1642] 2022-12-18 05:23:28,799 >> Num examples = 446
[INFO|trainer.py:1643] 2022-12-18 05:23:28,799 >> Num Epochs = 72
[INFO|trainer.py:1644] 2022-12-18 05:23:28,799 >> Instantaneous batch size per device = 16
[INFO|trainer.py:1645] 2022-12-18 05:23:28,799 >> Total train batch size (w. parallel, distributed & accumulation) = 32
[INFO|trainer.py:1646] 2022-12-18 05:23:28,799 >> Gradient Accumulation steps = 2
[INFO|trainer.py:1647] 2022-12-18 05:23:28,800 >> Total optimization steps = 1000
[INFO|trainer.py:1648] 2022-12-18 05:23:28,801 >> Number of trainable parameters = 241734912
# ---> Error
14% 9/65 [07:07<48:34, 52.04s/it][INFO|configuration_utils.py:523] 2022-12-18 05:03:07,941 >> Generate config GenerationConfig {
"begin_suppress_tokens": [
220,
50257
],
"bos_token_id": 50257,
"decoder_start_token_id": 50258,
"eos_token_id": 50257,
"max_length": 448,
"pad_token_id": 50257,
"transformers_version": "4.26.0.dev0",
"use_cache": false
}
Traceback (most recent call last):
File "run_speech_recognition_seq2seq_streaming.py", line 629, in <module>
main()
File "run_speech_recognition_seq2seq_streaming.py", line 578, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1534, in train
return inner_training_loop(
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1859, in _inner_training_loop
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2122, in _maybe_log_save_evaluate
metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 78, in evaluate
return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2818, in evaluate
output = eval_loop(
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 3000, in evaluation_loop
loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 213, in prediction_step
outputs = model(**inputs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1197, in forward
outputs = self.model(
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1066, in forward
decoder_outputs = self.decoder(
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 873, in forward
hidden_states = inputs_embeds + positions
RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1
100% 1000/1000 [2:52:21<00:00, 10.34s/it]
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I_kwDODunzps5Z9vfx
| 5,390 |
Error when pushing to the CI hub
|
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[] | null | 5 | 2022-12-23T13:36:37Z | 2022-12-23T20:29:02Z | 2022-12-23T20:29:02Z |
CONTRIBUTOR
| null |
### Describe the bug
Note that it's a special case where the Hub URL is "https://hub-ci.huggingface.co", which does not appear if we do the same on the Hub (https://huggingface.co).
The call to `dataset.push_to_hub(` fails:
```
Pushing dataset shards to the dataset hub: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.93s/it]
Traceback (most recent call last):
File "reproduce_hubci.py", line 16, in <module>
dataset.push_to_hub(repo_id=repo_id, private=False, token=USER_TOKEN, embed_external_files=True)
File "/home/slesage/hf/datasets/src/datasets/arrow_dataset.py", line 5025, in push_to_hub
HfApi(endpoint=config.HF_ENDPOINT).upload_file(
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1346, in upload_file
raise err
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1337, in upload_file
r.raise_for_status()
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_DATASETS_SERVER_USER__/bug-16718047265472/upload/main/README.md
```
### Steps to reproduce the bug
```python
# reproduce.py
from datasets import Dataset
import time
USER = "__DUMMY_DATASETS_SERVER_USER__"
USER_TOKEN = "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"
dataset = Dataset.from_dict({"a": [1, 2, 3]})
repo_id = f"{USER}/bug-{int(time.time() * 10e3)}"
dataset.push_to_hub(repo_id=repo_id, private=False, token=USER_TOKEN, embed_external_files=True)
```
```bash
$ HF_ENDPOINT="https://hub-ci.huggingface.co" python reproduce.py
```
### Expected behavior
No error and the dataset should be uploaded to the Hub with the README file (which generates the error).
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.0-1026-aws-x86_64-with-glibc2.35
- Python version: 3.9.15
- PyArrow version: 7.0.0
- Pandas version: 1.5.2
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PR_kwDODunzps5GHsOo
| 5,389 |
Fix link in `load_dataset` docstring
|
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[] | null | 6 | 2022-12-23T13:26:31Z | 2023-01-25T19:00:43Z | 2023-01-24T16:33:38Z |
CONTRIBUTOR
| null |
Fix https://github.com/huggingface/datasets/issues/5387, fix https://github.com/huggingface/datasets/issues/4566
|
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Getting Value Error while loading a dataset..
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[] | null | 4 | 2022-12-23T08:16:43Z | 2022-12-29T08:36:33Z | 2022-12-27T17:59:09Z |
NONE
| null |
### Describe the bug
I am trying to load a dataset using Hugging Face Datasets load_dataset method. I am getting the value error as show below. Can someone help with this? I am using Windows laptop and Google Colab notebook.
```
WARNING:datasets.builder:Using custom data configuration default-a1d9e8eaedd958cd
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-12-5b4fdcb8e6d5>](https://localhost:8080/#) in <module>
6 )
7
----> 8 next(iter(law_dataset_streamed))
17 frames
[/usr/local/lib/python3.8/dist-packages/fsspec/core.py](https://localhost:8080/#) in get_compression(urlpath, compression)
485 compression = infer_compression(urlpath)
486 if compression is not None and compression not in compr:
--> 487 raise ValueError("Compression type %s not supported" % compression)
488 return compression
489
ValueError: Compression type zstd not supported
```
### Steps to reproduce the bug
```
!pip install zstandard
from datasets import load_dataset
lds = load_dataset(
"json",
data_files="https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst",
split="train",
streaming=True,
)
```
### Expected behavior
I expect an iterable object as the output 'lds' to be created.
### Environment info
Windows laptop with Google Colab notebook
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Missing documentation page : improve-performance
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[] | null | 1 | 2022-12-23T01:12:57Z | 2023-01-24T16:33:40Z | 2023-01-24T16:33:40Z |
NONE
| null |
### Describe the bug
Trying to access https://huggingface.co/docs/datasets/v2.8.0/en/package_reference/cache#improve-performance, the page is missing.
The link is in here : https://huggingface.co/docs/datasets/v2.8.0/en/package_reference/loading_methods#datasets.load_dataset.keep_in_memory
### Steps to reproduce the bug
Access the page and see it's missing.
### Expected behavior
Not missing page
### Environment info
Doesn't matter
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`max_shard_size` in `datasets.push_to_hub()` breaks with large files
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[] | null | 2 | 2022-12-22T21:50:58Z | 2022-12-26T23:45:51Z | 2022-12-26T23:45:51Z |
NONE
| null |
### Describe the bug
`max_shard_size` parameter for `datasets.push_to_hub()` works unreliably with large files, generating shard files that are way past the specified limit.
In my private dataset, which contains unprocessed images of all sizes (up to `~100MB` per file), I've encountered cases where `max_shard_size='100MB'` results in shard files that are `>2GB` in size. Setting `max_shard_size` to another value, such as `1GB` or `500MB` does not fix this problem.
**The real problem is this:** When the shard file size grows too big, the entire dataset breaks because of #4721 and ultimately https://issues.apache.org/jira/browse/ARROW-5030. Since `max_shard_size` does not let one accurately control the size of the shard files, it becomes very easy to build a large dataset without any warnings that it will be broken -- even when you think you are mitigating this problem by setting `max_shard_size`.
```
File " /path/to/sd-test-suite-v1/venv/lib/site-packages/datasets/builder.py", line 1763, in _prepare_split_single
for _, table in generator:
File " /path/to/sd-test-suite-v1/venv/lib/site-packages/datasets/packaged_modules/parquet/parquet.py", line 69, in _generate_tables
for batch_idx, record_batch in enumerate(
File "pyarrow/_parquet.pyx", line 1323, 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
1. Clone [example repo](https://github.com/salieri/hf-dataset-shard-size-bug)
2. Follow steps in [README.md](https://github.com/salieri/hf-dataset-shard-size-bug/blob/main/README.md)
3. After uploading the dataset, you will see that the shard file size varies between `30MB` and `200MB` -- way beyond the `max_shard_size='75MB'` limit (example: `train-00003-of-00131...` is `155MB` in [here](https://huggingface.co/datasets/slri/shard-size-test/tree/main/data))
(Note that this example repo does not generate shard files that are so large that they would trigger #4721)
### Expected behavior
The shard file size should remain below or equal to `max_shard_size`.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.10.157-139.675.amzn2.aarch64-aarch64-with-glibc2.17
- Python version: 3.7.15
- PyArrow version: 10.0.1
- Pandas version: 1.3.5
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Is `fs=` deprecated in `load_from_disk()` as well?
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[] | null | 3 | 2022-12-22T21:00:45Z | 2023-01-23T10:50:05Z | 2023-01-23T10:50:04Z |
CONTRIBUTOR
| null |
### Describe the bug
The `fs=` argument was deprecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in favor of automagically figuring it out via fsspec:
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L1339-L1340
Is there a reason the same thing shouldn't also apply to `datasets.load.load_from_disk()` as well ?
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/load.py#L1779
### Steps to reproduce the bug
n/a
### Expected behavior
n/a
### Environment info
n/a
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Handle 0-dim tensors in `cast_to_python_objects`
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[] | null | 2 | 2022-12-22T16:15:30Z | 2023-01-13T16:10:15Z | 2023-01-13T16:00:52Z |
CONTRIBUTOR
| null |
Fix #5229
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IterableDataset missing column_names, differs from Dataset interface
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open
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[] | null | 3 | 2022-12-22T05:27:02Z | 2022-12-23T02:57:31Z | null |
NONE
| null |
### Describe the bug
The documentation on [Stream](https://huggingface.co/docs/datasets/v1.18.2/stream.html) seems to imply that IterableDataset behaves just like a Dataset. However, examples like
```
dataset.map(augment_data, batched=True, remove_columns=dataset.column_names, ...)
```
will not work because `.column_names` does not exist on IterableDataset. I cannot find any clear explanation on why this is not available, is it an oversight? We do have `iterable_ds.features` available.
### Steps to reproduce the bug
See above
### Expected behavior
Dataset and IterableDataset would be expected to have the same interface, with any differences noted in the documentation.
### Environment info
n/a
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Raise from disconnect error in xopen
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[] | null | 3 | 2022-12-20T15:52:44Z | 2023-01-26T09:51:13Z | 2023-01-26T09:42:45Z |
MEMBER
| null |
this way we can know the cause of the disconnect
related to https://github.com/huggingface/datasets/issues/5374
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I_kwDODunzps5ZrNLT
| 5,381 |
Wrong URL for the_pile dataset
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[] | null | 1 | 2022-12-20T12:40:14Z | 2023-02-15T16:24:57Z | 2023-02-15T16:24:57Z |
NONE
| null |
### Describe the bug
When trying to load `the_pile` dataset from the library, I get a `FileNotFound` error.
### Steps to reproduce the bug
Steps to reproduce:
Run:
```
from datasets import load_dataset
dataset = load_dataset("the_pile")
```
I get the output:
"name": "FileNotFoundError",
"message": "Unable to resolve any data file that matches '['**']' at /storage/store/work/lgrinszt/memorization/the_pile with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'BLP', 'BMP', 'DIB', 'BUFR', 'CUR', 'PCX', 'DCX', 'DDS', 'PS', 'EPS', 'FIT', 'FITS', 'FLI', 'FLC', 'FTC', 'FTU', 'GBR', 'GIF', 'GRIB', 'H5', 'HDF', 'PNG', 'APNG', 'JP2', 'J2K', 'JPC', 'JPF', 'JPX', 'J2C', 'ICNS', 'ICO', 'IM', 'IIM', 'TIF', 'TIFF', 'JFIF', 'JPE', 'JPG', 'JPEG', 'MPG', 'MPEG', 'MSP', 'PCD', 'PXR', 'PBM', 'PGM', 'PPM', 'PNM', 'PSD', 'BW', 'RGB', 'RGBA', 'SGI', 'RAS', 'TGA', 'ICB', 'VDA', 'VST', 'WEBP', 'WMF', 'EMF', 'XBM', 'XPM', 'aiff', 'au', 'avr', 'caf', 'flac', 'htk', 'svx', 'mat4', 'mat5', 'mpc2k', 'ogg', 'paf', 'pvf', 'raw', 'rf64', 'sd2', 'sds', 'ircam', 'voc', 'w64', 'wav', 'nist', 'wavex', 'wve', 'xi', 'mp3', 'opus', 'AIFF', 'AU', 'AVR', 'CAF', 'FLAC', 'HTK', 'SVX', 'MAT4', 'MAT5', 'MPC2K', 'OGG', 'PAF', 'PVF', 'RAW', 'RF64', 'SD2', 'SDS', 'IRCAM', 'VOC', 'W64', 'WAV', 'NIST', 'WAVEX', 'WVE', 'XI', 'MP3', 'OPUS', 'zip']"
### Expected behavior
`the_pile` dataset should be dowloaded.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-4.15.0-112-generic-x86_64-with-glibc2.27
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
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I_kwDODunzps5Zq2JL
| 5,380 |
Improve dataset `.skip()` speed in streaming mode
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[] | null | 8 | 2022-12-20T11:25:23Z | 2023-01-17T08:44:56Z | null |
CONTRIBUTOR
| null |
### Feature request
Add extra information to the `dataset_infos.json` file to include the number of samples/examples in each shard, for example in a new field `num_examples` alongside `num_bytes`. The `.skip()` function could use this information to ignore the download of a shard when in streaming mode, which AFAICT it should speed up the skipping process.
### Motivation
When resuming from a checkpoint after a crashed run, using `dataset.skip()` is very convenient to recover the exact state of the data and to not train again over the same examples (assuming same seed, no shuffling). However, I have noticed that for audio datasets in streaming mode this is very costly in terms of time, as shards need to be downloaded every time before skipping the right number of examples.
### Your contribution
I took a look already at the code, but it seems a change like this is way deeper than I am able to manage, as it touches the library in several parts. I could give it a try but might need some guidance on the internals.
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PR_kwDODunzps5F1r2k
| 5,379 |
feat: depth estimation dataset guide.
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MEMBER
| null |
This PR adds a guide for prepping datasets for depth estimation.
PR to add documentation images is up here: https://huggingface.co/datasets/huggingface/documentation-images/discussions/22
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I_kwDODunzps5Zo4CU
| 5,378 |
The dataset "the_pile", subset "enron_emails" , load_dataset() failure
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[] | null | 1 | 2022-12-20T02:19:13Z | 2022-12-20T07:52:54Z | 2022-12-20T07:52:54Z |
NONE
| null |
### Describe the bug
When run
"datasets.load_dataset("the_pile","enron_emails")" failure
### Steps to reproduce the bug
Run below code in python cli:
>>> import datasets
>>> datasets.load_dataset("the_pile","enron_emails")
### Expected behavior
Load dataset "the_pile", "enron_emails" successfully.
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.7.1
- Platform: Linux-5.15.0-53-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- PyArrow version: 10.0.0
- Pandas version: 1.4.3
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Add a parallel implementation of to_tf_dataset()
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[] | null | 32 | 2022-12-19T19:40:27Z | 2023-01-25T16:28:44Z | 2023-01-25T16:21:40Z |
MEMBER
| null |
Hey all! Here's a first draft of the PR to add a multiprocessing implementation for `to_tf_dataset()`. It worked in some quick testing for me, but obviously I need to do some much more rigorous testing/benchmarking, and add some proper library tests.
The core idea is that we do everything using `multiprocessing` and `numpy`, and just wrap a `tf.data.Dataset` around the output. We could also rewrite the existing single-threaded implementation based on this code, which might simplify it a bit.
Checklist:
- [X] Add initial draft
- [x] Check that it works regardless of whether the `collate_fn` or dataset returns `tf` or `np` arrays
- [x] Check that it works with `tf.string` return data
- [x] Check indices are correctly reshuffled each epoch
- [x] Make sure workers don't try to initialize a GPU device!!
- [x] Check `fit()` with multiple epochs works fine and that the progress bar is correct
- [x] Check there are no memory leaks or zombie processes
- [x] Benchmark performance
- [x] Tweak params for dataset inference - can we speed things up there a bit?
- [x] Add tests to the library
- [x] Add a PR to `transformers` to expose the `num_workers` argument via `prepare_tf_dataset` (will merge after this one is released)
- [x] Stop TF console spam!! (almost)
- [x] Add a method for creating SHM that doesn't crash if it was left and still linked
- [x] Add a barrier for Py <= 3.7 because it doesn't support SharedMemory
- [x] Support string dtypes by converting them into fixed-width character arrays
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set dev version
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Release: 2.8.0
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Using too many threads results in: Got disconnected from remote data host. Retrying in 5sec
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CONTRIBUTOR
| null |
### Describe the bug
`streaming_download_manager` seems to disconnect if too many runs access the same underlying dataset 🧐
The code works fine for me if I have ~100 runs in parallel, but disconnects once scaling to 200.
Possibly related:
- https://github.com/huggingface/datasets/pull/3100
- https://github.com/huggingface/datasets/pull/3050
### Steps to reproduce the bug
Running
```python
c4 = datasets.load_dataset("c4", "en", split="train", streaming=True).skip(args.start).take(args.end-args.start)
df = pd.DataFrame(c4, index=None)
```
with different start & end arguments on 200 CPUs in parallel yields:
```
WARNING:datasets.load:Using the latest cached version of the module from /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01 (last modified on Mon Dec 12 10:45:02 2022) since it couldn't be found locally at c4.
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [1/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [2/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [3/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [4/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [5/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [6/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [7/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [8/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [9/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [10/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [11/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [12/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [13/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [14/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [15/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [16/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [17/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [18/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [19/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [20/20]
╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/dec-2022-tasky/inference │
│ _c4.py:68 in <module> │
│ │
│ 65 │ model.eval() │
│ 66 │ │
│ 67 │ c4 = datasets.load_dataset("c4", "en", split="train", streaming=Tru │
│ ❱ 68 │ df = pd.DataFrame(c4, index=None) │
│ 69 │ texts = df["text"].to_list() │
│ 70 │ preds = batch_inference(texts, batch_size=args.batch_size) │
│ 71 │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/site-packages/pandas/core/frame.p │
│ y:684 in __init__ │
│ │
│ 681 │ │ # For data is list-like, or Iterable (will consume into list │
│ 682 │ │ elif is_list_like(data): │
│ 683 │ │ │ if not isinstance(data, (abc.Sequence, ExtensionArray)): │
│ ❱ 684 │ │ │ │ data = list(data) │
│ 685 │ │ │ if len(data) > 0: │
│ 686 │ │ │ │ if is_dataclass(data[0]): │
│ 687 │ │ │ │ │ data = dataclasses_to_dicts(data) │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:751 in __iter__ │
│ │
│ 748 │ │ yield from ex_iterable.shard_data_sources(shard_idx) │
│ 749 │ │
│ 750 │ def __iter__(self): │
│ ❱ 751 │ │ for key, example in self._iter(): │
│ 752 │ │ │ if self.features: │
│ 753 │ │ │ │ # `IterableDataset` automatically fills missing colum │
│ 754 │ │ │ │ # This is done with `_apply_feature_types`. │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:741 in _iter │
│ │
│ 738 │ │ │ ex_iterable = self._ex_iterable.shuffle_data_sources(self │
│ 739 │ │ else: │
│ 740 │ │ │ ex_iterable = self._ex_iterable │
│ ❱ 741 │ │ yield from ex_iterable │
│ 742 │ │
│ 743 │ def _iter_shard(self, shard_idx: int): │
│ 744 │ │ if self._shuffling: │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:617 in __iter__ │
│ │
│ 614 │ │ self.n = n │
│ 615 │ │
│ 616 │ def __iter__(self): │
│ ❱ 617 │ │ yield from islice(self.ex_iterable, self.n) │
│ 618 │ │
│ 619 │ def shuffle_data_sources(self, generator: np.random.Generator) -> │
│ 620 │ │ """Doesn't shuffle the wrapped examples iterable since it wou │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:594 in __iter__ │
│ │
│ 591 │ │
│ 592 │ def __iter__(self): │
│ 593 │ │ #ex_iterator = iter(self.ex_iterable) │
│ ❱ 594 │ │ yield from islice(self.ex_iterable, self.n, None) │
│ 595 │ │ #for _ in range(self.n): │
│ 596 │ │ # next(ex_iterator) │
│ 597 │ │ #yield from islice(ex_iterator, self.n, None) │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/iterable_dataset.py:106 in __iter__ │
│ │
│ 103 │ │ self.kwargs = kwargs │
│ 104 │ │
│ 105 │ def __iter__(self): │
│ ❱ 106 │ │ yield from self.generate_examples_fn(**self.kwargs) │
│ 107 │ │
│ 108 │ def shuffle_data_sources(self, generator: np.random.Generator) -> │
│ 109 │ │ return ShardShuffledExamplesIterable(self.generate_examples_f │
│ │
│ /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/d │
│ f532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01/c4.py:89 in │
│ _generate_examples │
│ │
│ 86 │ │ for filepath in filepaths: │
│ 87 │ │ │ logger.info("generating examples from = %s", filepath) │
│ 88 │ │ │ with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8" │
│ ❱ 89 │ │ │ │ for line in f: │
│ 90 │ │ │ │ │ if line: │
│ 91 │ │ │ │ │ │ example = json.loads(line) │
│ 92 │ │ │ │ │ │ yield id_, example │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:313 in read1 │
│ │
│ 310 │ │ │
│ 311 │ │ if size < 0: │
│ 312 │ │ │ size = io.DEFAULT_BUFFER_SIZE │
│ ❱ 313 │ │ return self._buffer.read1(size) │
│ 314 │ │
│ 315 │ def peek(self, n): │
│ 316 │ │ self._check_not_closed() │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/_compression.py:68 in readinto │
│ │
│ 65 │ │
│ 66 │ def readinto(self, b): │
│ 67 │ │ with memoryview(b) as view, view.cast("B") as byte_view: │
│ ❱ 68 │ │ │ data = self.read(len(byte_view)) │
│ 69 │ │ │ byte_view[:len(data)] = data │
│ 70 │ │ return len(data) │
│ 71 │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:493 in read │
│ │
│ 490 │ │ │ │ self._new_member = False │
│ 491 │ │ │ │
│ 492 │ │ │ # Read a chunk of data from the file │
│ ❱ 493 │ │ │ buf = self._fp.read(io.DEFAULT_BUFFER_SIZE) │
│ 494 │ │ │ │
│ 495 │ │ │ uncompress = self._decompressor.decompress(buf, size) │
│ 496 │ │ │ if self._decompressor.unconsumed_tail != b"": │
│ │
│ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:96 in read │
│ │
│ 93 │ │ │ read = self._read │
│ 94 │ │ │ self._read = None │
│ 95 │ │ │ return self._buffer[read:] + \ │
│ ❱ 96 │ │ │ │ self.file.read(size-self._length+read) │
│ 97 │ │
│ 98 │ def prepend(self, prepend=b''): │
│ 99 │ │ if self._read is None: │
│ │
│ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │
│ lib/python3.9/site-packages/datasets/download/streaming_download_manager.py: │
│ 365 in read_with_retries │
│ │
│ 362 │ │ │ │ ) │
│ 363 │ │ │ │ time.sleep(config.STREAMING_READ_RETRY_INTERVAL) │
│ 364 │ │ else: │
│ ❱ 365 │ │ │ raise ConnectionError("Server Disconnected") │
│ 366 │ │ return out │
│ 367 │ │
│ 368 │ file_obj.read = read_with_retries │
╰──────────────────────────────────────────────────────────────────────────────╯
ConnectionError: Server Disconnected
```
### Expected behavior
There should be no disconnect I think.
### Environment info
```
datasets=2.7.0
Python 3.9.12
```
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Simplify skipping
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CONTRIBUTOR
| null |
Was hoping to find a way to speed up the skipping as I'm running into bottlenecks skipping 100M examples on C4 (it takes 12 hours to skip), but didn't find anything better than this small change :(
Maybe there's a way to directly skip whole shards to speed it up? 🧐
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Fix streaming pandas.read_excel
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[] | null | 2 | 2022-12-17T12:58:52Z | 2023-01-06T11:50:58Z | 2023-01-06T11:43:37Z |
MEMBER
| null |
This PR fixes `xpandas_read_excel`:
- Support passing a path string, besides a file-like object
- Support passing `use_auth_token`
- First assumes the host server supports HTTP range requests; only if a ValueError is thrown (Cannot seek streaming HTTP file), then it preserves previous behavior (see [#3355](https://github.com/huggingface/datasets/pull/3355)).
Fix https://huggingface.co/datasets/bigbio/meqsum/discussions/1
Fix:
- https://github.com/bigscience-workshop/biomedical/issues/801
Related to:
- #3355
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Add a robustness benchmark dataset for vision
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MEMBER
| null |
### Name
ImageNet-C
### Paper
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
### Data
https://github.com/hendrycks/robustness
### Motivation
It's a known fact that vision models are brittle when they meet with slightly corrupted and perturbed data. This is also correlated to the robustness aspects of vision models.
Researchers use different benchmark datasets to evaluate the robustness aspects of vision models. ImageNet-C is one of them.
Having this dataset in 🤗 Datasets would allow researchers to evaluate and study the robustness aspects of vision models. Since the metric associated with these evaluations is top-1 accuracy, researchers should be able to easily take advantage of the evaluation benchmarks on the Hub and perform comprehensive reporting.
ImageNet-C is a large dataset. Once it's in, it can act as a reference and we can also reach out to the authors of the other robustness benchmark datasets in vision, such as ObjectNet, WILDS, Metashift, etc. These datasets cater to different aspects. For example, ObjectNet is related to assessing how well a model performs under sub-population shifts.
Related thread: https://huggingface.slack.com/archives/C036H4A5U8Z/p1669994598060499
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PR_kwDODunzps5Fqaj-
| 5,369 |
Distributed support
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[] | null | 4 | 2022-12-16T17:43:47Z | 2023-01-16T13:36:12Z | 2023-01-16T13:33:32Z |
MEMBER
| null |
To split your dataset across your training nodes, you can use the new [`datasets.distributed.split_dataset_by_node`]:
```python
import os
from datasets.distributed import split_dataset_by_node
ds = split_dataset_by_node(ds, rank=int(os.environ["RANK"]), world_size=int(os.environ["WORLD_SIZE"]))
```
This works for both map-style datasets and iterable datasets.
The dataset is split for the node at rank `rank` in a pool of nodes of size `world_size`.
For map-style datasets:
Each node is assigned a chunk of data, e.g. rank 0 is given the first chunk of the dataset.
For iterable datasets:
If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`),
then the shards are evenly assigned across the nodes, which is the most optimized.
Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples.
This can also be combined with a `torch.utils.data.DataLoader` if you want each node to use multiple workers to load the data.
This also supports shuffling. At each epoch, the iterable dataset shards are reshuffled across all the nodes - you just have to call `iterable_ds.set_epoch(epoch_number)`.
TODO:
- [x] docs for usage in PyTorch
- [x] unit tests
- [x] integration tests with torch.distributed.launch
Related to https://github.com/huggingface/transformers/issues/20770
Close https://github.com/huggingface/datasets/issues/5360
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PR_kwDODunzps5FpZyx
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Align remove columns behavior and input dict mutation in `map` with previous behavior
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[] | null | 1 | 2022-12-16T14:28:47Z | 2022-12-16T16:28:08Z | 2022-12-16T16:25:12Z |
CONTRIBUTOR
| null |
Align the `remove_columns` behavior and input dict mutation in `map` with the behavior before https://github.com/huggingface/datasets/pull/5252.
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PR_kwDODunzps5FlevK
| 5,367 |
Fix remove columns from lazy dict
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[] | null | 1 | 2022-12-15T22:04:12Z | 2022-12-15T22:27:53Z | 2022-12-15T22:24:50Z |
MEMBER
| null |
This was introduced in https://github.com/huggingface/datasets/pull/5252 and causing the transformers CI to break: https://app.circleci.com/pipelines/github/huggingface/transformers/53886/workflows/522faf2e-a053-454c-94f8-a617fde33393/jobs/648597
Basically this code should return a dataset with only one column:
```python
from datasets import *
ds = Dataset.from_dict({"a": range(5)})
def f(x):
x["b"] = x["a"]
return x
ds = ds.map(f, remove_columns=["a"])
assert ds.column_names == ["b"]
```
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ExamplesIterable fixes
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[] | null | 1 | 2022-12-15T14:23:05Z | 2022-12-15T14:44:47Z | 2022-12-15T14:41:45Z |
MEMBER
| null |
fix typing and ExamplesIterable.shard_data_sources
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fix: image array should support other formats than uint8
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CONTRIBUTOR
| null |
Currently images that are provided as ndarrays, but not in `uint8` format are going to loose data. Namely, for example in a depth image where the data is in float32 format, the type-casting to uint8 will basically make the whole image blank.
`PIL.Image.fromarray` [does support mode `F`](https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes).
although maybe some further metadata could be supplied via the [Image](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/main_classes#datasets.Image) object.
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Support for writing arrow files directly with BeamWriter
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CONTRIBUTOR
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Make it possible to write Arrow files directly with `BeamWriter` rather than converting from Parquet to Arrow, which is sub-optimal, especially for big datasets for which Beam is primarily used.
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Dataset.from_generator() crashes on simple example
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Run 'GPT-J' failure due to download dataset fail (' ConnectionError: Couldn't reach http://eaidata.bmk.sh/data/enron_emails.jsonl.zst ' )
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NONE
| null |
### Describe the bug
Run model "GPT-J" with dataset "the_pile" fail.
The fail out is as below:
Looks like which is due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst" unreachable .
### Steps to reproduce the bug
Steps to reproduce this issue:
git clone https://github.com/huggingface/transformers
cd transformers
python examples/pytorch/language-modeling/run_clm.py --model_name_or_path EleutherAI/gpt-j-6B --dataset_name the_pile --dataset_config_name enron_emails --do_eval --output_dir /tmp/output --overwrite_output_dir
### Expected behavior
This issue looks like due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst " couldn't be reached.
Is there another way to download the dataset "the_pile" ?
Is there another way to cache the dataset "the_pile" but not let the hg to download it when runtime ?
### Environment info
huggingface_hub version: 0.11.1
Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35
Python version: 3.9.12
Running in iPython ?: No
Running in notebook ?: No
Running in Google Colab ?: No
Token path ?: /home/taosy/.huggingface/token
Has saved token ?: False
Configured git credential helpers:
FastAI: N/A
Tensorflow: N/A
Torch: N/A
Jinja2: N/A
Graphviz: N/A
Pydot: N/A
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