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https://api.github.com/repos/huggingface/datasets/issues/5143 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5143/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5143/comments | https://api.github.com/repos/huggingface/datasets/issues/5143/events | https://github.com/huggingface/datasets/issues/5143 | 1,416,837,186 | I_kwDODunzps5UczhC | 5,143 | DownloadManager Git LFS support | {
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"Hey ! Actually it works, just pass the right URL ;)\r\nThe URL must be the one with “/resolve/”\r\n\r\ne.g. https://huggingface.co/datasets/imagenet-1k/resolve/main/data/test_images.tar.gz\r\n\r\nYou can even pass a relative path to the dl_manager instead, like `dl_manager.download(\"data/test_images.tar.gz\")`",
... | 2022-10-20T15:29:29Z | 2022-10-20T17:17:10Z | 2022-10-20T17:17:10Z | CONTRIBUTOR | null | null | null | ### Feature request
Maybe I'm mistaken but the `DownloadManager` does not support extracting git lfs files out of the box right?
Using `dl_manager.download()` or `dl_manager.download_and_extract()` still returns lfs files afaict.
Is there a good way to write a dataset loading script for a repo with lfs files?
### Motivation
/
### Your contribution
/ | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hey @mariosasko . Can you please help me with why the tests keep failing. I have reviewed the code changes multiple times but can't spot any mistakes. ",
"You can fix this failure by formatting your code with the `make style` comm... | 2022-10-20T09:52:52Z | 2022-10-25T18:06:56Z | 2022-10-25T15:56:45Z | CONTRIBUTOR | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks @mariosasko ,i commited the changes as you said.\r\n\r\n"
] | 2022-10-19T19:30:05Z | 2022-10-25T15:59:25Z | 2022-10-25T15:56:58Z | CONTRIBUTOR | null | 0 | {
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"I removed all the invalid task_ids in datasts without namespace, based on the <s>(internal)</s> types.ts",
"(Types.ts is not internal it's public)",
"I have opened PRs to fix the task_ids in all datasets within a namespace as well.\r\n\r\nWorking on task_categories...",
"For future reference: this fix had so... | 2022-10-19T09:41:42Z | 2022-11-10T05:25:58Z | 2022-10-25T06:17:00Z | MEMBER | null | null | null | ## Describe
Once we have agreed on a common naming for task tags for all open source projects, we should align on them.
## Steps
- [x] Align task tags in canonical datasets
- [x] task_categories: 4 datasets
- [x] task_ids (by @lhoestq)
- [x] Open PRs in community datasets
- [x] task_categories: 451 datasets
- [x] task_ids: 556 datasets
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-19T07:58:27Z | 2022-10-20T08:12:21Z | 2022-10-20T08:10:00Z | MEMBER | null | 0 | {
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} | Todo:
- [x] Update docs:
- [x] Datasets on GitHub (legacy)
- [x] Load: offline
- [x] About dataset load:
- [x] Maintaining integrity
- [x] Security
- [x] Update docstrings:
- [x] Inspect:
- [x] get_dataset_config_info
- [x] get_dataset_split_names
- [x] Load:
- [x] dataset_module_factory
- [x] load_dataset_builder
- [x] load_dataset
- [x] Remove `ADD_NEW_DATASET.md`
- [x] Update `.github/ISSUE_TEMPLATE/config.yml`
Fix #5135. | {
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As discussed in:
- https://github.com/huggingface/hub-docs/pull/423#pullrequestreview-1146083701
we should update our docs once dataset scripts have been transferred to the Hub (and removed from GitHub):
- #4974
Concretely:
- [x] Datasets on GitHub (legacy): https://huggingface.co/docs/datasets/main/en/share#datasets-on-github-legacy
- [x] ADD_NEW_DATASET: https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md
- ...
This PR complements the work of:
- #5067
This PR is a follow-up of PRs:
- #3777
CC: @julien-c | {
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"hey ,i would like to work on this issue . Please assign it to me.",
"hey @mariosasko , i made a pr for this issue. Could you please review it.\r\nAlso i found multiple `OSError` in `extract.py` file which i thought could be replaced too but wasn't sure about them.\r\nPlease do tell if that also needs to be done.... | 2022-10-18T17:53:46Z | 2022-10-25T15:56:59Z | 2022-10-25T15:56:59Z | CONTRIBUTOR | null | null | null | According to the official Python docs, `OSError` should be thrown in the following situations:
> This exception is raised when a system function returns a system-related error, including I/O failures such as “file not found” or “disk full” (not for illegal argument types or other incidental errors).
Hence, it makes more sense to raise `ImportError` instead of `OSError` when the required extraction/decompression library is not installed. | {
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"Hi! The Torch ops in your snippet are not equivalent to the NumPy ones, hence the difference. You can get the same behavior by replacing the line `feature = torch.mean(feature, dim=1)` with `feature = feature.squeeze().mean(1)` .",
"> Hi! The Torch ops in your snippet are not equivalent to the NumPy ones, hence ... | 2022-10-18T17:53:35Z | 2022-10-19T04:15:45Z | 2022-10-19T04:15:44Z | NONE | null | null | null | ## Describe the bug
I'm doing a torch.mean() operation in data preprocessing, and it's not working.
## Steps to reproduce the bug
```
from transformers import pipeline
import torch
import numpy as np
from datasets import load_dataset
device = 'cuda:0'
raw_dataset = load_dataset("glue", "sst2")
feature_extraction = pipeline('feature-extraction', 'bert-base-uncased', device=device)
def extracted_data(examples):
# feature = torch.tensor(feature_extraction(examples['sentence'], batch_size=16), device=device)
# feature = torch.mean(feature, dim=1)
feature = np.asarray(feature_extraction(examples['sentence'], batch_size=16)).squeeze().mean(1)
print(feature.shape)
return {'feature': feature}
extracted_dataset = raw_dataset.map(extracted_data, batched=True, batch_size=16)
```
## Results
When running with torch.mean(), the shape printed out is [16, seq_len, 768], which is exactly the same before the operation. While numpy works just fine, which gives [16, 768].
## Environment info
- `datasets` version: 2.6.1
- Platform: Linux-4.4.0-142-generic-x86_64-with-glibc2.31
- Python version: 3.10.6
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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"I can take this! #self-assign",
"#self-assign",
"@lazarust i'm already working on this issue :smile: ",
"#self-assign",
"hey @mariosasko , i made a pr for this issue. Could you please review it."
] | 2022-10-18T17:41:05Z | 2022-10-25T15:56:46Z | 2022-10-25T15:56:46Z | CONTRIBUTOR | null | null | null | The `num_proc` parameter is only present in `DownloadManager.extract` but not in `StreamingDownloadManager.extract`, making it impossible to support streaming in the dataset scripts that use it (`openwebtext` and `the_pile_stack_exchange`). We can avoid this situation by deprecating this parameter and passing `DownloadConfig`'s `num_proc` to `map_nested` instead, as it's done in `DownloadManager.download`. | {
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"any updates on this? It happens to me on [OpenWikiText-20%](https://huggingface.co/datasets/Bingsu/openwebtext_20p) dataset, but not on [OpenWebText-10k](https://huggingface.co/datasets/stas/openwebtext-10k). This is really strange because I don't change anything else in my running script.\r\n\r\ntransformers vers... | 2022-10-18T16:44:00Z | 2023-08-08T08:42:40Z | 2023-07-21T14:41:51Z | NONE | null | null | null | See issue here: https://github.com/huggingface/transformers/issues/19702 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-18T15:31:24Z | 2022-10-19T11:53:02Z | 2022-10-19T11:50:46Z | CONTRIBUTOR | null | 0 | {
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"Hi! Unfortunately, I can't reproduce this issue locally (in Python 3.7/3.10) or in Colab. I would assume this is due to a bug we fixed in the latest release, but your version is up-to-date, so I'm not sure if there is something we can do to help...",
"Hi, 方子东. I tried running the code with exact the same configu... | 2022-10-18T11:15:24Z | 2022-10-19T03:02:26Z | 2022-10-19T03:02:26Z | NONE | null | null | null | ## Describe the bug
When invoke `cast` or `class_encode_column` to a colunm renamed by `rename_column` , it will convert all the variables in this column into one variable. I also run this script in version 2.5.2, this bug does not appear. So I switched to the older version.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("amazon_reviews_multi", "en")
data = dataset['train']
data = data.remove_columns(
[
"review_id",
"product_id",
"reviewer_id",
"review_title",
"language",
"product_category",
]
)
data = data.rename_column("review_body", "text")
data1 = data.class_encode_column("stars")
print(set(data1.data.columns[0]))
# output: {<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>}
data = data.rename_column("stars", "label")
print(set(data.data.columns[0]))
# output: {<pyarrow.Int32Scalar: 5>, <pyarrow.Int32Scalar: 4>, <pyarrow.Int32Scalar: 1>, <pyarrow.Int32Scalar: 3>, <pyarrow.Int32Scalar: 2>}
data2 = data.class_encode_column("label")
print(set(data2.data.columns[0]))
# output: {<pyarrow.Int64Scalar: 0>}
```
## Expected results
the last print should be:
{<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>}
## Actual results
but it output:
{<pyarrow.Int64Scalar: 0>}
## Environment info
- `datasets` version: 2.6.1
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.10.6
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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"_The documentation is not available anymore as the PR was closed or merged._",
"> I think we should also modify one of the metadata files in the `folder_based_builder` tests to make sure \"./\" is ignored now in the `file_name`\r\n\r\n@mariosasko what do you mean here? I'm not sure which metadata file I should m... | 2022-10-18T08:22:48Z | 2022-10-28T13:07:38Z | 2022-10-28T13:05:06Z | CONTRIBUTOR | null | 0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5127). All of your documentation changes will be reflected on that endpoint.",
"Should we close this PR?"
] | 2022-10-17T16:50:22Z | 2024-01-11T06:27:04Z | 2024-01-08T14:25:43Z | MEMBER | null | 1 | {
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} | I added a first draft of the `IterableDataset.to_wds` method.
You can use it to savea dataset loaded in streamign mode as a webdataset locally.
The API can be further improved to allow to export to a cloud storage like the HF Hub.
I also included sharding with a default max shard size of 500MB (uncompressed), and it is single-processed fo rnow.
Choosing the number of shards is not implemented yet - though if we know the size of the `IterableDataset` this is probably doable`.
For example
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="train", streaming=True)
>>> ds.to_wds("output_dir", compress=True)
>>> import webdataset as wds
>>> ds = wds.WebDataset("output_dir/rotten_tomatoes-train-000000.tar.gz").decode()
>>> next(iter(ds))
{'__key__': '0',
'__url__': 'output_dir/rotten_tomatoes-train-000000.tar.gz',
'label.cls': 1,
'text.txt': 'the rock is destined to be the 21st century\'s new ..., jean-claud van damme or steven segal .'}
```
### Implementation details
The WebDataset format is made of TAR archives containing a series of files per example. For example one pair of `image.jpg` and `label.cls` for image classification.
WebDataset automatically decodes serialized data based on the extension of the files, and output a dictionary. For example `{"image.png": np.array(...), "label.cls": 0}` if you choose the numpy decoding.
To use the automatic decoding, I store each field of each example as a file with its corresponding extension (jpg, json, cls, etc.)
While this is useful to end up with a dictionary with one key per column and appropriate decoding, it can create huge TAR archives if the dataset is made of small samples of text - probably because of useless TAR metadata for each file. This also makes loading super slow: iterating on SQuAD takes 50sec vs 7sec using `datasets` in streaming mode.
I haven't taken a look at alternatives for text datasets made out of small samples, but for image datasets this can already be used to run some benchmarks. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5126). All of your documentation changes will be reflected on that endpoint.",
"I have removed the reference to the Issue in the PR title, so that we avoid to have both references (to the issue and to the PR) in the merge commi... | 2022-10-17T15:11:02Z | 2022-11-14T14:40:18Z | 2022-11-14T14:40:18Z | CONTRIBUTOR | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-17T13:38:47Z | 2022-10-17T14:23:27Z | 2022-10-17T14:21:09Z | CONTRIBUTOR | null | 0 | {
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"@lhoestq I tested the script without accelerator, and I confirm this is due to datasets part as this gets similar results without accelerator.",
"Hi ! You said it works on 1 GPU but doesn't wortk without accelerator - what's the difference between running on 1 GPU and running without accelerator in your case ?"... | 2022-10-17T03:28:16Z | 2023-05-14T06:55:20Z | null | NONE | null | null | null | ## Describe the bug
Hi. I am using this dataloader, which is for processing large datasets in streaming mode mentioned in one of examples of huggingface. I am using it to read c4: https://github.com/huggingface/transformers/blob/b48ac1a094e572d6076b46a9e4ed3e0ebe978afc/examples/research_projects/codeparrot/scripts/codeparrot_training.py#L22
During using multi-gpu in accelerator in one node, the code freezes, but works for 1 GPU:
```
10/16/2022 14:18:46 - INFO - datasets.info - Loading Dataset Infos from /home/jack/.cache/huggingface/modules/datasets_modules/datasets/c4/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01
Steps: 0%| | 0/400000 [00:00<?, ?it/s]10/16/2022 14:18:47 - INFO - torch.utils.data.dataloader - Shared seed (135290893754684706) sent to store on rank 0
```
# Code to reproduce
please run this code with `accelerate launch code.py`
```
from accelerate import Accelerator
from accelerate.logging import get_logger
from datasets import load_dataset
from torch.utils.data.dataloader import DataLoader
import torch
from datasets import load_dataset
from transformers import AutoTokenizer
import torch
from accelerate.logging import get_logger
from torch.utils.data import IterableDataset
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
logger = get_logger(__name__)
class ConstantLengthDataset(IterableDataset):
"""
Iterable dataset that returns constant length chunks of tokens from stream of text files.
Args:
tokenizer (Tokenizer): The processor used for proccessing the data.
dataset (dataset.Dataset): Dataset with text files.
infinite (bool): If True the iterator is reset after dataset reaches end else stops.
max_seq_length (int): Length of token sequences to return.
num_of_sequences (int): Number of token sequences to keep in buffer.
chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.
"""
def __init__(
self,
tokenizer,
dataset,
infinite=False,
max_seq_length=1024,
num_of_sequences=1024,
chars_per_token=3.6,
):
self.tokenizer = tokenizer
# self.concat_token_id = tokenizer.bos_token_id
self.dataset = dataset
self.max_seq_length = max_seq_length
self.epoch = 0
self.infinite = infinite
self.current_size = 0
self.max_buffer_size = max_seq_length * chars_per_token * num_of_sequences
self.content_field = "text"
def __iter__(self):
iterator = iter(self.dataset)
more_examples = True
while more_examples:
buffer, buffer_len = [], 0
while True:
if buffer_len >= self.max_buffer_size:
break
try:
buffer.append(next(iterator)[self.content_field])
buffer_len += len(buffer[-1])
except StopIteration:
if self.infinite:
iterator = iter(self.dataset)
self.epoch += 1
logger.info(f"Dataset epoch: {self.epoch}")
else:
more_examples = False
break
tokenized_inputs = self.tokenizer(buffer, truncation=False)["input_ids"]
all_token_ids = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input)
for i in range(0, len(all_token_ids), self.max_seq_length):
input_ids = all_token_ids[i : i + self.max_seq_length]
if len(input_ids) == self.max_seq_length:
self.current_size += 1
yield torch.tensor(input_ids)
def shuffle(self, buffer_size=1000):
return ShufflerIterDataPipe(self, buffer_size=buffer_size)
def create_dataloaders(tokenizer, accelerator):
ds_kwargs = {"streaming": True}
# In distributed training, the load_dataset function gaurantees that only one process
# can concurrently download the dataset.
datasets = load_dataset(
"c4",
"en",
cache_dir="cache_dir",
**ds_kwargs,
)
train_data, valid_data = datasets["train"], datasets["validation"]
with accelerator.main_process_first():
train_data = train_data.shuffle(buffer_size=10000, seed=None)
train_dataset = ConstantLengthDataset(
tokenizer,
train_data,
infinite=True,
max_seq_length=256,
)
valid_dataset = ConstantLengthDataset(
tokenizer,
valid_data,
infinite=False,
max_seq_length=256,
)
train_dataset = train_dataset.shuffle(buffer_size=10000)
train_dataloader = DataLoader(train_dataset, batch_size=160, shuffle=True)
eval_dataloader = DataLoader(valid_dataset, batch_size=160)
return train_dataloader, eval_dataloader
def main():
# Accelerator.
logging_dir = "data_save_dir/log"
accelerator = Accelerator(
gradient_accumulation_steps=1,
mixed_precision="bf16",
log_with="tensorboard",
logging_dir=logging_dir,
)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("test")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
# Load datasets and create dataloaders.
train_dataloader, _ = create_dataloaders(tokenizer, accelerator)
train_dataloader = accelerator.prepare(train_dataloader)
for step, batch in enumerate(train_dataloader, start=1):
print(step)
accelerator.end_training()
if __name__ == "__main__":
main()
```
## Results expected
Being able to run the code for streamining datasets with multi-gpu
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.2
- Platform: linux
- Python version: 3.9.12
- PyArrow version: 9.0.0
@lhoestq I do not have any idea why this freezing happens, and I removed the streaming mode and this was working fine, so I know this is caused by streaming mode of the dataloader part not working well with multi-gpu setting. Since datasets are large, I hope to keep the streamining mode. I very much appreciate your help.
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"As mentioned in https://github.com/huggingface/datasets/issues/5105 I think we just need to keep the existing files instead of deleting them.\r\nThe `dataset_info.json` file contains the split names anyway, so we know which files belong to the dataset, and which ones don't."
] | 2022-10-17T01:30:37Z | 2022-11-05T12:23:53Z | 2022-11-05T12:23:53Z | NONE | null | 0 | {
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} | Fixes: #5105
I think removing the directory with warning is a better solution for this issue. Because if we decide to keep existing files in directory, then we should deal with the case providing same directory for several datasets! Which we know is not possible since `dataset_info.json` exists in that directory. | {
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"Adding \"function\" to the kwargs to ignore when computing the fingerprint will break `map` caching. Indeed passing two different function would result in two different datasets that have the same fingerprint - and the cache wouldn't be able to distinguish them.\r\n\r\nE.g this code would reload ds1 from the cache... | 2022-10-17T00:03:43Z | 2022-10-17T12:39:36Z | 2022-10-17T12:39:36Z | NONE | null | 0 | {
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"@albertvillanova Thanks for your comment. What do you think about creating 2 `pbar` for each case? I see the `pbar_iterable` is initialized differently. Maybe `pbar` can also be initialized like that.",
"@albertvillanova Another solution I implemented is to change `pbar_iterable` and add the `zip` to it. I updat... | 2022-10-16T22:19:18Z | 2022-10-23T10:27:53Z | 2022-10-19T08:53:17Z | CONTRIBUTOR | null | 0 | {
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} | This PR solves #5117, by wrapping the entire `zip` clause in tqdm.
For more information, please checkout this Stack Overflow thread:
https://stackoverflow.com/questions/41171191/tqdm-progressbar-and-zip-built-in-do-not-work-together | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-16T17:36:49Z | 2022-10-19T09:48:19Z | 2022-10-19T09:45:59Z | CONTRIBUTOR | null | 0 | {
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"Thanks for reporting, @david1542."
] | 2022-10-16T16:50:08Z | 2022-10-19T09:10:08Z | 2022-10-19T09:10:08Z | CONTRIBUTOR | null | null | null | ## Describe the bug
I wanted to install `datasets` dependencies on my M1 (in order to start contributing to the project). However, I got an error regarding `tensorflow`.
On M1, `tensorflow-macos` needs to be installed instead. Can we add a conditional requirement, so that `tensorflow-macos` would be installed on M1?
## Steps to reproduce the bug
Fresh clone this project (on m1), create a virtualenv and run this:
```python
pip install -e ".[dev]"
```
## Expected results
Installation should be smooth, and all the dependencies should be installed on M1.
## Actual results
You should receive an error, saying pip couldn't find a version that matches this pattern:
```
tensorflow>=2.3,!=2.6.0,!=2.6.1
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.6.2.dev0
- Platform: macOS-12.6-arm64-arm-64bit
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.5.0
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"Hi @echatzikyriakidis, thanks for submitting the issue.\r\nWhich shell are you using exactly? I tried to run the command you sent, but I don't see colors at all 🧐\r\n\r\nI tried from bash and zsh as well.",
"Hi @david1542 ,\r\n\r\nI use Google Colab.\r\n",
"Got it. I [created a PR](https://github.com/huggingf... | 2022-10-14T16:12:30Z | 2022-10-23T12:58:41Z | 2022-10-23T12:58:41Z | NONE | null | null | null | ## Describe the bug
Progress bars after transformative operations turn in red and never be completed to 100%
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset('rotten_tomatoes', split='test').filter(lambda o: True)
```
## Expected results
Progress bar should be 100% and green
## Actual results
Progress bar turn in red and never completed to 100%
## Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.14
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-14T15:53:13Z | 2022-10-19T13:05:49Z | 2022-10-19T13:03:22Z | CONTRIBUTOR | null | 0 | {
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} | Use YAML instead of markdown (more expressive) for the issue templates. In addition, update their structure/fields to be more aligned with Transformers.
PS: also removes the "add_dataset" PR template, as we no longer accept such PRs. | {
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https://api.github.com/repos/huggingface/datasets/issues/5115 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5115/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5115/comments | https://api.github.com/repos/huggingface/datasets/issues/5115/events | https://github.com/huggingface/datasets/pull/5115 | 1,409,250,020 | PR_kwDODunzps5Az9Pm | 5,115 | Fix iter_batches | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I also ran the code in https://github.com/huggingface/datasets/issues/5111 and it works fine now :)",
"This is ready for review :)"
] | 2022-10-14T12:06:14Z | 2022-10-14T15:02:15Z | 2022-10-14T14:59:58Z | MEMBER | null | 0 | {
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} | The `pa.Table.to_reader()` method available in `pyarrow>=8.0.0` may return chunks of size < `max_chunksize`, therefore `iter_batches` can return batches smaller than the `batch_size` specified by the user
Therefore batched `map` couldn't always use batches of the right size, e.g. this fails because it runs only on one batch of one element:
```python
from datasets import Dataset, concatenate_datasets
ds = concatenate_datasets([Dataset.from_dict({"a": [i]}) for i in range(10)])
ds2 = ds.map(lambda _: {}, batched=True)
assert list(ds2) == list(ds)
```
This was introduced in https://github.com/huggingface/datasets/pull/5030
Close https://github.com/huggingface/datasets/issues/5111
This will require a patch release along with https://github.com/huggingface/datasets/pull/5113
TODO:
- [x] fix tests
- [x] add more tests | {
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"Hi Hubert! Could you please probably create a publicly available `gs://` dataset link? I think this would be easier for others to directly start to debug.",
"What seems to work is to change the line to:\r\n```\r\nfs.download(src_dataset_path, dataset_path.parent.as_posix(), recursive=True)\r\n```"
] | 2022-10-14T11:54:53Z | 2022-11-19T07:13:10Z | null | CONTRIBUTOR | null | null | null | ## Describe the bug
The function load_from_disk fails when using a remote filesystem because of a wrong temporary path generation in the load_from_disk method of arrow_dataset.py:
```python
if is_remote_filesystem(fs):
src_dataset_path = extract_path_from_uri(dataset_path)
dataset_path = Dataset._build_local_temp_path(src_dataset_path)
fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True)
```
If _dataset_path_ is `gs://speech/mydataset/train`, then _src_dataset_path_ will be `speech/mydataset/train` and _dataset_path_ will be something like `/var/folders/9s/gf0b/T/tmp6t/speech/mydataset/train`
Then, after downloading the **folder** _src_dataset_path_, you will get a path like `/var/folders/9s/gf0b/T/tmp6t/speech/mydataset/train/train/state.json` (notice we have train twice)
Instead of downloading the remote folder we should be downloading all the files in the folder for the path to be right:
```python
fs.download(os.path.join(src_dataset_path,*), dataset_path.as_posix(), recursive=True)
```
## Steps to reproduce the bug
```python
fs = gcsfs.GCSFileSystem(**storage_options)
dataset = load_from_disk("common_voice_processed") # loading local dataset previously saved locally, works fine
dataset.save_to_disk(output_dir, fs=fs) #works fine
dataset = load_from_disk(output_dir, fs=fs) # crashes
```
## Expected results
The dataset is loaded
## Actual results
FileNotFoundError: [Errno 2] No such file or directory: '/var/folders/9s/gf0b9jz15d517yrf7m3nvlxr0000gn/T/tmp6t5e221_/speech/datasets/tests/common_voice_processed/train/state.json'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets-2.6.1.dev0
- Platform: mac os monterey 12.5.1
- Python version: 3.8.13
- PyArrow version:pyarrow==9.0.0
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"_The documentation is not available anymore as the PR was closed or merged._",
"I think a patch release will be necessary.",
"I'm also fixing https://github.com/huggingface/datasets/issues/5111 which will lalso require a patch release"
] | 2022-10-14T11:30:03Z | 2022-10-24T06:21:09Z | 2022-10-14T12:11:44Z | MEMBER | null | 0 | {
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} | This PR fixes a bug introduced by:
- #5030
Fix #5112. | {
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"The issue is here:\r\nhttps://github.com/huggingface/datasets/blob/3ad9644b9a2e4558dd1d0f1e43c67658674e6228/src/datasets/arrow_dataset.py#L2964",
"@PartiallyTyped, @Muennighoff: the issue is fixed.\r\n\r\nWe are planning to make a patch release today.",
"Thanks a lot for the swift response! For a brief moment ... | 2022-10-14T10:35:47Z | 2022-10-14T13:55:03Z | 2022-10-14T12:11:45Z | MEMBER | null | null | null | ## Describe the bug
As reported by @PartiallyTyped (and by @Muennighoff):
- https://github.com/huggingface/datasets/issues/5111#issuecomment-1278652524
There is an issue with the indices of a filtered dataset.
## Steps to reproduce the bug
```python
ds = Dataset.from_dict({"num": [0, 1, 2, 3]})
ds = ds.filter(lambda num: num % 2 == 0, input_columns="num", batch_size=2)
assert all(item["num"] % 2 == 0 for item in ds)
```
## Expected results
The indices of the filtered dataset should correspond to the examples with "language" equals to "english".
## Actual results
Indices to items with other languages are included in the filtered dataset indices
## Preliminar investigation
It seems a bug introduced by:
- #5030
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"Same bug exists with `num_proc=1` on colab. `3.7.14 (default, Sep 8 2022, 00:06:44) [GCC 7.5.0]` ",
"Thanks for reporting, @loubnabnl and for the additional information, @PartiallyTyped.\r\n\r\nHowever, I'm not able to reproduce this issue, neither locally nor on Colab:\r\n```\r\nDataset({\r\n features: ['re... | 2022-10-13T17:00:55Z | 2022-10-17T08:26:59Z | 2022-10-14T14:59:59Z | NONE | null | null | null | ## Describe the bug
When mapping is used on a dataset with more than one process, there is a weird behavior when trying to use `filter` , it's like only the samples from one worker are retrieved, one needs to specify the same `num_proc` in filter for it to work properly. This doesn't happen with `datasets` version 2.5.2
In the code below the data is filtered differently when we increase `num_proc` used in `map` although the datsets before and after mapping have identical elements.
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
def preprocess(example):
return example
ds = load_dataset("codeparrot/codeparrot-clean-valid", split="train").select([i for i in range(10)])
ds1 = ds.map(preprocess, num_proc=2)
ds2 = ds.map(preprocess)
# the datasets elements are the same
for i in range(len(ds1)):
assert ds1[i]==ds2[i]
print(f'Target column before filtering {ds1["autogenerated"]}')
print(f'Target column before filtering {ds2["autogenerated"]}')
print(f"datasets version {datasets.__version__}")
ds_filtered_1 = ds1.filter(lambda x: not x["autogenerated"])
ds_filtered_2 = ds2.filter(lambda x: not x["autogenerated"])
# all elements in Target column are false so they should all be kept, but for ds2 only the first 5=num_samples/num_proc are kept
print(ds_filtered_1)
print(ds_filtered_2)
```
```
Target column before filtering [False, False, False, False, False, False, False, False, False, False]
Target column before filtering [False, False, False, False, False, False, False, False, False, False]
Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 5
})
Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 10
})
```
## Expected results
Increasing `num_proc` in mapping shouldn't alter filtering. With the previous version 2.5.2 this doesn't happen
## Actual results
Filtering doesn't work properly when we increase `num_proc` in mapping but not when calling `filter`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.6.0
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
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"The hash used for caching is computed by pickling recursively the function passed to `map`. Maybe some objects don't have the same hash across sessions. In particular you can check the hash of your model using\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\nobj = AutoModel.from_config(config=config, ad... | 2022-10-13T09:12:58Z | 2022-10-17T10:38:45Z | 2022-10-17T10:38:45Z | CONTRIBUTOR | null | null | null | ## Describe the bug
The cache loading is not working as expected for some class methods with a model stored in an attribute.
The new fingerprint for `_map_single` is not the same at each run. The hasher generate a different hash for the class method.
This comes from `dumps` function in `datasets.utils.py_utils` which generates a different dump at each run.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import AutoConfig, AutoModel, AutoTokenizer
dataset = load_dataset("ethos", "binary")
BASE_MODELNAME = "sentence-transformers/all-MiniLM-L6-v2"
class Object:
def __init__(self):
config = AutoConfig.from_pretrained(BASE_MODELNAME)
self.bert = AutoModel.from_config(config=config, add_pooling_layer=False)
self.tok = AutoTokenizer.from_pretrained(BASE_MODELNAME)
def tokenize(self, examples):
tokenized_texts = self.tok(
examples["text"],
padding="max_length",
truncation=True,
max_length=256,
)
return tokenized_texts
instance = Object()
result = dict()
for phase in ["train"]:
result[phase] = dataset[phase].map(instance.tokenize, batched=True, load_from_cache_file=True, num_proc=2)
```
## Expected results
Load cache instead of recompute result.
## Actual results
Result recomputed from scratch at each run.
The cache works fine when deleting `bert` attribute.
## Environment info
- `datasets` version: 2.5.3.dev0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.13
- PyArrow version: 7.0.0
- Pandas version: 1.5.0
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"I would also like to add a test, but am not sure whether it should go into `test_builder` (more natural imo) or `test_load` (which already contains a lot of the things I have to import to run my current testing setup). For reference, what I run to test that it works looks like:\r\n\r\n```\r\nimport os\r\nfrom path... | 2022-10-12T19:59:17Z | 2022-12-01T15:37:09Z | 2022-11-09T17:11:43Z | CONTRIBUTOR | null | 0 | {
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} | This PR adds the multiprocessing part of #2650 (but not the caching of already-computed arrow files). On the other side, loading of sharded arrow files still needs to be implemented (sharded parquet files can already be loaded). | {
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https://api.github.com/repos/huggingface/datasets/issues/5106 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5106/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5106/comments | https://api.github.com/repos/huggingface/datasets/issues/5106/events | https://github.com/huggingface/datasets/pull/5106 | 1,406,635,758 | PR_kwDODunzps5ArM6G | 5,106 | Fix task template reload from dict | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Just wondering if there might be other data classes default values missed that could cause an issue... Apart from feature-like classes and tasks, I don't see any others though...\r\n\r\nI think we're good ! `asdict` is used on the ... | 2022-10-12T18:33:49Z | 2022-10-13T09:59:07Z | 2022-10-13T09:56:51Z | MEMBER | null | 0 | {
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} | Since #4926 the JSON dumps are simplified and it made task template dicts empty by default.
I fixed this by always including the task name which is needed to reload a task from a dict | {
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"cc @lhoestq ",
"Thanks for reporting, @cakiki.\r\n\r\nI would say the deletion of the dir is an expected behavior though...",
"`dask.to_parquet` has an \"overwrite\" parameter and default is `False`, we could also have something similar",
"Thank you both for your feedback!\r\n\r\n@albertvillanova I think I m... | 2022-10-12T11:53:33Z | 2022-10-20T11:53:59Z | null | CONTRIBUTOR | null | null | null | ## Describe the bug
The builder correctly creates the `output_dir` folder if it doesn't exist, but if the folder exists everything within it is deleted. Specifying `"."` as the `output_dir` deletes everything in your current dir but also leads to **another bug** whose traceback is the following:
```
Traceback (most recent call last)
Input In [11], in <cell line: 1>()
----> 1 rotten_tomatoes_builder.download_and_prepare(output_dir=".", max_shard_size="200MB", file_format="parquet")
File ~/BIGSCIENCE/env/lib/python3.9/site-packages/datasets/builder.py:818, 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, storage_options, **download_and_prepare_kwargs)
File /usr/lib/python3.9/contextlib.py:124, in _GeneratorContextManager.__exit__(self, type, value, traceback)
122 if type is None:
123 try:
--> 124 next(self.gen)
125 except StopIteration:
126 return False
File ~/BIGSCIENCE/env/lib/python3.9/site-packages/datasets/builder.py:760, in incomplete_dir(dirname)
File /usr/lib/python3.9/shutil.py:722, in rmtree(path, ignore_errors, onerror)
720 os.rmdir(path)
721 except OSError:
--> 722 onerror(os.rmdir, path, sys.exc_info())
723 else:
724 try:
725 # symlinks to directories are forbidden, see bug #1669
File /usr/lib/python3.9/shutil.py:720, in rmtree(path, ignore_errors, onerror)
718 _rmtree_safe_fd(fd, path, onerror)
719 try:
--> 720 os.rmdir(path)
721 except OSError:
722 onerror(os.rmdir, path, sys.exc_info())
OSError: [Errno 22] Invalid argument: '/home/christopher/BIGSCIENCE/.'
```
## Steps to reproduce the bug
```python
rotten_tomatoes_builder = load_dataset_builder("rotten_tomatoes")
rotten_tomatoes_builder.download_and_prepare(output_dir="./test_folder", max_shard_size="200MB", file_format="parquet")
```
If `test_folder` contains any files they will all be deleted
## Expected results
Either a warning that all files will be deleted, but preferably that they not be deleted at all.
## Actual results
N/A
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.15.0-48-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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"Hi, thanks for reporting! The last line should be `dataset = Dataset.from_generator(my_gen)`.",
"Can I work on this one?"
] | 2022-10-11T14:28:58Z | 2022-10-12T11:31:56Z | 2022-10-12T11:31:56Z | NONE | null | null | null | ## Describe the bug
In HOW-TO-GUIDES > Load > [Python generator](https://huggingface.co/docs/datasets/v2.5.2/en/loading#python-generator), the code example defines the `my_gen` function, but when creating the dataset, an undefined `my_dict` is passed in.
```Python
>>> from datasets import Dataset
>>> def my_gen():
... for i in range(1, 4):
... yield {"a": i}
>>> dataset = Dataset.from_generator(my_dict)
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-11T11:57:21Z | 2022-10-12T15:32:59Z | 2022-10-12T15:30:38Z | MEMBER | null | 0 | {
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"`datasets` doesn't seem to urlencode the directory names here\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/utils/file_utils.py#L109-L111\r\n\r\nfor example we should have\r\n```python\r\nfrom datasets.utils.file_utils import hf_hub_url\r\n\r\nurl = hf_hu... | 2022-10-11T10:05:32Z | 2022-10-13T13:14:20Z | 2022-10-13T13:14:20Z | NONE | null | null | null | ## Describe the bug
dataset files with `#` symbol their paths aren't read correctly.
## Steps to reproduce the bug
The data in folder `c#`of this [dataset](https://huggingface.co/datasets/loubnabnl/bigcode_csharp) can't be loaded. While the folder `c_sharp` with the same data is loaded properly
```python
ds = load_dataset('loubnabnl/bigcode_csharp', split="train", data_files=["data/c#/*"])
```
```
FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/27a3166cff4bb18e11919cafa6f169c0f57483de/data/c#/data_0003.jsonl
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.2
- Platform: macOS-12.2.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
cc @lhoestq | {
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"It can be solved temporarily by remove `resolve` in \r\nhttps://github.com/huggingface/datasets/blob/bef23be3d9543b1ca2da87ab2f05070201044ddc/src/datasets/data_files.py#L278",
"Hi, thanks for reporting and suggesting a fix! We still need to account for `.`/`..` in the file path, so a more robust fix would be `P... | 2022-10-11T06:10:58Z | 2022-11-14T14:40:20Z | 2022-11-14T14:40:20Z | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
Like this: #4015
When there are **symbolic links** to pictures in the data folder, the parent folder name of the **real file** will be used as the class name instead of the parent folder of the symbolic link itself. Can you give an option to decide whether to enable symbolic link tracking?
This is inconsistent with the `torchvision.datasets.ImageFolder` behavior.
For example:
It use `others` in green circle as class label but not `abnormal`, I wish `load_dataset` not use the real file parent as label.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context about the feature request here.
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"Thanks for reporting, @catalys1.\r\n\r\nThis seems a duplicate of:\r\n- #3310 \r\n\r\nThe source of the problem is in PyArrow:\r\n- [ARROW-15141: [C++] Fatal error condition occurred in aws_thread_launch](https://issues.apache.org/jira/browse/ARROW-15141)\r\n- [ARROW-17501: [C++] Fatal error condition occurred in ... | 2022-10-10T20:29:04Z | 2022-10-11T06:56:01Z | 2022-10-11T06:56:00Z | NONE | null | null | null | ## Describe the bug
When using datasets, at the very end of my jobs the program crashes (see trace below).
It doesn't seem to affect anything, as it appears to happen as the program is closing down. Just importing `datasets` is enough to cause the error.
## Steps to reproduce the bug
This is sufficient to reproduce the problem:
```bash
python -c "import datasets"
```
## Expected results
Program should run to completion without an error.
## Actual results
```bash
Fatal error condition occurred in /opt/vcpkg/buildtrees/aws-c-io/src/9e6648842a-364b708815.clean/source/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS
Exiting Application
################################################################################
Stack trace:
################################################################################
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x200af06) [0x150dff547f06]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x20028e5) [0x150dff53f8e5]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x1f27e09) [0x150dff464e09]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x150dff548a3d]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x1f25948) [0x150dff462948]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x150dff548a3d]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x1ee0b46) [0x150dff41db46]
/u/user/miniconda3/envs/env/lib/python3.10/site-packages/pyarrow/libarrow.so.900(+0x194546a) [0x150dfee8246a]
/lib64/libc.so.6(+0x39b0c) [0x150e15eadb0c]
/lib64/libc.so.6(on_exit+0) [0x150e15eadc40]
/u/user/miniconda3/envs/env/bin/python(+0x28db18) [0x560ae370eb18]
/u/user/miniconda3/envs/env/bin/python(+0x28db4b) [0x560ae370eb4b]
/u/user/miniconda3/envs/env/bin/python(+0x28db90) [0x560ae370eb90]
/u/user/miniconda3/envs/env/bin/python(_PyRun_SimpleFileObject+0x1e6) [0x560ae37123e6]
/u/user/miniconda3/envs/env/bin/python(_PyRun_AnyFileObject+0x44) [0x560ae37124c4]
/u/user/miniconda3/envs/env/bin/python(Py_RunMain+0x35d) [0x560ae37135bd]
/u/user/miniconda3/envs/env/bin/python(Py_BytesMain+0x39) [0x560ae37137d9]
/lib64/libc.so.6(__libc_start_main+0xf3) [0x150e15e97493]
/u/user/miniconda3/envs/env/bin/python(+0x2125d4) [0x560ae36935d4]
Aborted (core dumped)
```
## Environment info
- `datasets` version: 2.5.1
- Platform: Linux-4.18.0-348.23.1.el8_5.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.4
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
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"The transfer of the dummy dataset to the dummy org works as expected:\r\n```python\r\nIn [1]: from datasets import load_dataset; ds = load_dataset(\"dummy_canonical_dataset\", download_mode=\"force_redownload\"); ds\r\nDownloading builder script: 100%|███████████████████████████████████████████████████████████████... | 2022-10-10T15:44:31Z | 2024-01-11T10:03:35Z | null | MEMBER | null | null | null | As discussed during our @huggingface/datasets meeting, we are planning to move some "canonical" dataset scripts under their corresponding organization namespace (if this does not exist).
On the contrary, if the dataset already exists under the organization namespace, we are deprecating the canonical one (and eventually delete it).
First, we should test it using a dummy dataset/organization.
TODO:
- [x] Test with a dummy dataset
- [x] Create dummy canonical dataset: https://huggingface.co/datasets/dummy_canonical_dataset
- [x] Create dummy organization: https://huggingface.co/dummy-canonical-org
- [x] Transfer dummy canonical dataset to dummy organization
- [ ] Transfer datasets
- [x] babi_qa => facebook
- [x] blbooks => TheBritishLibrary/blbooks
- [x] blbooksgenre => TheBritishLibrary/blbooksgenre
- [x] common_gen => allenai
- [x] commonsense_qa => tau
- [x] competition_math => hendrycks/competition_math
- [x] cord19 => allenai
- [x] emotion => dair-ai
- [ ] gem => GEM
- [x] hendrycks_test => cais/mmlu
- [x] indonlu => indonlp
- [ ] multilingual_librispeech => facebook
- It already exists "facebook/multilingual_librispeech"
- [ ] oscar => oscar-corpus
- [x] peer_read => allenai
- [x] qasper => allenai
- [x] reddit => webis/tldr-17
- [x] russian_super_glue => russiannlp
- [x] rvl_cdip => aharley
- [x] s2orc => allenai
- [x] scicite => allenai
- [x] scifact => allenai
- [x] scitldr => allenai
- [x] swiss_judgment_prediction => rcds
- [x] the_pile => EleutherAI
- [ ] wmt14, wmt15, wmt16, wmt17, wmt18, wmt19,... => wmt
- [ ] Deprecate (and eventually remove) datasets that cannot be transferred because they already exist
- [x] banking77 => PolyAI
- [x] common_voice => mozilla-foundation
- [x] german_legal_entity_recognition => elenanereiss
- ...
EDIT: the list above is continuously being updated | {
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https://api.github.com/repos/huggingface/datasets/issues/5095 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5095/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5095/comments | https://api.github.com/repos/huggingface/datasets/issues/5095/events | https://github.com/huggingface/datasets/pull/5095 | 1,403,221,408 | PR_kwDODunzps5Afzsq | 5,095 | Fix tutorial (#5093) | {
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"Oops I merged without linking to the hacktoberfest issue - not sure if it counts in this case\r\n\r\nsorry about that..\r\n\r\nNext time you can just mention \"Close #XXXX\" in your issue to link it",
"It should :) (the `hacktoberfest` repo topic is all that matters)"
] | 2022-10-10T13:55:15Z | 2022-10-10T17:50:52Z | 2022-10-10T15:32:20Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5094 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5094/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5094/comments | https://api.github.com/repos/huggingface/datasets/issues/5094/events | https://github.com/huggingface/datasets/issues/5094 | 1,403,214,950 | I_kwDODunzps5To1xm | 5,094 | Multiprocessing with `Dataset.map` and `PyTorch` results in deadlock | {
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"Hi ! Could it be an Out of Memory issue that could have killed one of the processes ? can you check your memory ?",
"Hi! I don't think it is a memory issue. I'm monitoring the main and spawn python processes and threads with `htop` and the memory does not peak. Besides, the example I've posted above should not b... | 2022-10-10T13:50:56Z | 2023-07-24T15:29:13Z | 2023-07-24T15:29:13Z | NONE | null | null | null | ## Describe the bug
There seems to be an issue with using multiprocessing with `datasets.Dataset.map` (i.e. setting `num_proc` to a value greater than one) combined with a function that uses `torch` under the hood. The subprocesses that `datasets.Dataset.map` spawns [a this step](https://github.com/huggingface/datasets/blob/1b935dab9d2f171a8c6294269421fe967eb55e34/src/datasets/arrow_dataset.py#L2663) go into wait mode forever.
## Steps to reproduce the bug
The below code goes into deadlock when `NUMBER_OF_PROCESSES` is greater than one.
```python
NUMBER_OF_PROCESSES = 2
from transformers import AutoTokenizer, AutoModel
from datasets import load_dataset
dataset = load_dataset("glue", "mrpc", split="train")
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model.to("cpu")
def cls_pooling(model_output):
return model_output.last_hidden_state[:, 0]
def generate_embeddings_batched(examples):
sentences_batch = list(examples['sentence1'])
encoded_input = tokenizer(
sentences_batch, padding=True, truncation=True, return_tensors="pt"
)
encoded_input = {k: v.to("cpu") for k, v in encoded_input.items()}
model_output = model(**encoded_input)
embeddings = cls_pooling(model_output)
examples['embeddings'] = embeddings.detach().cpu().numpy() # 64, 384
return examples
embeddings_dataset = dataset.map(
generate_embeddings_batched,
batched=True,
batch_size=10,
num_proc=NUMBER_OF_PROCESSES
)
```
While debugging it I've seen that it gets "stuck" when calling `torch.nn.Embedding.forward` but some testing shows that the same happens with other functions from `torch.nn`.
## Environment info
- Platform: Linux-5.14.0-1052-oem-x86_64-with-glibc2.31
- Python version: 3.9.14
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
Not sure if this is a HF problem, a PyTorch problem or something I'm doing wrong..
Thanks!
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"Hi, thanks for reporting! This line should be replaced with \r\n```python\r\ndataset = dataset.map(lambda examples: tokenizer(examples[\"text\"], return_tensors=\"np\"), batched=True)\r\n```\r\nfor it to work (the `return_tensors` part inside the `tokenizer` call).",
"Can I work on this?",
"Fixed in https://gi... | 2022-10-10T10:23:53Z | 2022-10-10T17:51:15Z | 2022-10-10T17:51:14Z | MEMBER | null | null | null | ## Describe the bug
In the "Process text data" tutorial, [`map` has `return_tensors` as kwarg](https://huggingface.co/docs/datasets/main/en/nlp_process#map). It does not seem to appear in the [function documentation](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.map), nor to work.
## Steps to reproduce the bug
MWE:
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
from datasets import load_dataset
dataset = load_dataset("lhoestq/demo1", split="train")
dataset = dataset.map(lambda examples: tokenizer(examples["review"]), batched=True, return_tensors="pt")
```
## Expected results
return_tensors to be a valid kwarg :smiley:
## Actual results
```python
>> TypeError: map() got an unexpected keyword argument 'return_tensors'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.14.0-1052-oem-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Good catch, @lewtun. Thanks for the fix.\r\n> \r\n> Do you know if there are other absolute paths in the docs that should be fixed as well?\r\n\r\nI found a few more in [0d4796b](https://github.com/huggingface/datasets/pull/5092/co... | 2022-10-10T07:24:27Z | 2022-10-11T13:25:45Z | 2022-10-11T13:23:23Z | MEMBER | null | 0 | {
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} | This PR replaces the absolute paths in the landing page tiles with relative ones so that one can test navigation both locally in and in future PRs (see [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5084/en/index) for an example PR where the links don't work).
I encountered this while working on the `optimum` docs and figured I'd fix it elsewhere too :)
Internal Slack thread: https://huggingface.slack.com/archives/C02GLJ5S0E9/p1665129710176619 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-07T12:39:44Z | 2022-10-09T13:19:15Z | 2022-10-09T13:16:57Z | CONTRIBUTOR | null | 0 | {
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PS: ~~This PR contains a parameter link, so https://github.com/huggingface/doc-builder/pull/311 needs to be merged before it's "ready for review".~~ Done! | {
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"Nice!!"
] | 2022-10-07T12:31:56Z | 2022-10-08T07:07:36Z | 2022-10-08T07:07:36Z | MEMBER | null | null | null | ## Describe the bug
We have discovered that sometimes there were sync issues between GitHub and Hub datasets, after a merge commit to main branch.
For example:
- this merge commit: https://github.com/huggingface/datasets/commit/d74a9e8e4bfff1fed03a4cab99180a841d7caf4b
- was not properly synced with the Hub: https://github.com/huggingface/datasets/actions/runs/3002495269/jobs/4819769684
```
[main 9e641de] Add Papers with Code ID to scifact dataset (#4941)
Author: Albert Villanova del Moral <albertvillanova@users.noreply.huggingface.co>
1 file changed, 42 insertions(+), 14 deletions(-)
push failed !
GitCommandError(['git', 'push'], 1, b'remote: ---------------------------------------------------------- \nremote: Sorry, your push was rejected during YAML metadata verification: \nremote: - Error: "license" does not match any of the allowed types \nremote: ---------------------------------------------------------- \nremote: Please find the documentation at: \nremote: https://huggingface.co/docs/hub/models-cards#model-card-metadata \nremote: ---------------------------------------------------------- \nTo [https://huggingface.co/datasets/scifact.git\n](https://huggingface.co/datasets/scifact.git/n) ! [remote rejected] main -> main (pre-receive hook declined)\nerror: failed to push some refs to \'[https://huggingface.co/datasets/scifact.git\](https://huggingface.co/datasets/scifact.git/)'', b'')
```
We are reviewing sync issues in previous commits to recover them and repushing to the Hub.
TODO: Review
- [x] #4941
- scifact
- [x] #4931
- scifact
- [x] #4753
- wikipedia
- [x] #4554
- wmt17, wmt19, wmt_t2t
- Fixed with "Release 2.4.0" commit: https://github.com/huggingface/datasets/commit/401d4c4f9b9594cb6527c599c0e7a72ce1a0ea49
- https://huggingface.co/datasets/wmt17/commit/5c0afa83fbbd3508ff7627c07f1b27756d1379ea
- https://huggingface.co/datasets/wmt19/commit/b8ad5bf1960208a376a0ab20bc8eac9638f7b400
- https://huggingface.co/datasets/wmt_t2t/commit/b6d67191804dd0933476fede36754a436b48d1fc
- [x] #4607
- [x] #4416
- lccc
- Fixed with "Release 2.3.0" commit: https://huggingface.co/datasets/lccc/commit/8b1f8cf425b5653a0a4357a53205aac82ce038d1
- [x] #4367
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] | open | false | null | [] | null | [] | 2022-10-07T08:07:03Z | 2022-10-07T08:07:03Z | null | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
When a process (`map`, `filter`, etc.) crashes part-way through, you lose all progress.
**Describe the solution you'd like**
It would be good if the cache reflected the partial progress, so that after we restart the script, the process can restart where it left off.
**Describe alternatives you've considered**
Doing processing outside of `datasets`, by writing the dataset to json files and building a restart mechanism myself.
**Additional context**
N/A
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"Hi @junwang-wish, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce the bug. Which version of `datasets` are you using? Does the problem persist if you update `datasets`?\r\n```shell\r\npip install -U datasets\r\n``` ",
"Thanks @albertvillanova I updated `datasets` from `2.5.1` to `2.5.2` and... | 2022-10-07T02:16:58Z | 2022-10-07T14:43:16Z | null | NONE | null | null | null | ## Describe the bug
I have a local file `*.json.gz` and it can be read by `pandas.read_json(lines=True)`, but cannot be read by `load_datasets("json")` (resulting in 0 lines)
## Steps to reproduce the bug
```python
fpath = '/data/junwang/.cache/general/57b6f2314cbe0bc45dda5b78f0871df2/test.json.gz'
ds_panda = DatasetDict(
test=Dataset.from_pandas(
pd.read_json(fpath, lines=True)
)
)
ds_direct = load_dataset(
'json', data_files={
'test': fpath
}, features=Features(
text_input=Value(dtype="string", id=None),
text_output=Value(dtype="string", id=None)
)
)
len(ds_panda['test']), len(ds_direct['test'])
```
## Expected results
Lines of `ds_panda['test']` and `ds_direct['test']` should match.
## Actual results
```
Using custom data configuration default-c0ef2598760968aa
Downloading and preparing dataset json/default to /data/junwang/.cache/huggingface/datasets/json/default-c0ef2598760968aa/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab...
Dataset json downloaded and prepared to /data/junwang/.cache/huggingface/datasets/json/default-c0ef2598760968aa/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab. Subsequent calls will reuse this data.
(62087, 0)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Ubuntu 18.04.4 LTS
- Python version: 3.8.13
- PyArrow version: 9.0.0
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-07T01:07:00Z | 2022-10-07T18:43:03Z | 2022-10-07T18:40:26Z | CONTRIBUTOR | null | 0 | {
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"FYI @lewtun ",
"Hi @km5ar, thanks for reporting.\r\n\r\nThis should be fixed in the notebook:\r\n- the filename `datasets-issues-with-hf-doc-builder.jsonl` no longer exists on the repo; instead, current filename is `datasets-issues-with-comments.jsonl`\r\n- see: https://huggingface.co/datasets/lewtun/github-issu... | 2022-10-06T19:48:58Z | 2022-10-07T15:12:01Z | 2022-10-07T15:12:01Z | NONE | null | null | null | ## Describe the bug
I was following chap 5 from huggingface course: https://huggingface.co/course/chapter5/6?fw=tf
However, I'm not able to download the datasets, with a 404 erros
<img width="1160" alt="iShot2022-10-06_15 54 50" src="https://user-images.githubusercontent.com/54015474/194406327-ae62c2f3-1da5-4686-8631-13d879a0edee.png">
## Steps to reproduce the bug
```python
from huggingface_hub import hf_hub_url
data_files = hf_hub_url(
repo_id="lewtun/github-issues",
filename="datasets-issues-with-hf-doc-builder.jsonl",
repo_type="dataset",
)
from datasets import load_dataset
issues_dataset = load_dataset("json", data_files=data_files, split="train")
issues_dataset
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
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- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.12
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"~~It seems like #5043 fix (merged recently) is the root cause of such behaviour. When we empty indices mapping (because the dataset length equals to zero), we can no longer get column item like: `ds_filter_2['sentence']` which uses\r\n`ds_filter_1._indices.column(0)`~~\r\n\r\n**UPDATE:**\r\nEmpty datasets are retu... | 2022-10-06T18:18:49Z | 2022-10-07T19:06:02Z | 2022-10-07T18:40:26Z | NONE | null | null | null | ## Describe the bug
When filtering a dataset twice, where the first result is an empty dataset, the second dataset seems corrupted.
## Steps to reproduce the bug
```python
datasets = load_dataset("glue", "sst2")
dataset_split = datasets['validation']
ds_filter_1 = dataset_split.filter(lambda x: False) # Some filtering condition that leads to an empty dataset
assert ds_filter_1.num_rows == 0
sentences = ds_filter_1['sentence']
assert len(sentences) == 0
ds_filter_2 = ds_filter_1.filter(lambda x: False) # Some other filtering condition
assert ds_filter_2.num_rows == 0
assert 'sentence' in ds_filter_2.column_names
sentences = ds_filter_2['sentence']
```
## Expected results
The last line should be returning an empty list, same as 4 lines above.
## Actual results
The last line currently raises `IndexError: index out of bounds`.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.2
- Platform: macOS-11.6.6-x86_64-i386-64bit
- Python version: 3.9.11
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- Pandas version: 1.4.1
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5084). All of your documentation changes will be reflected on that endpoint.",
"Actually I'm not happy with this implementation. It always require the iterable dataset to have definite `features`, which removes a lot of flexibi... | 2022-10-06T16:53:38Z | 2023-09-24T10:06:51Z | 2022-12-20T17:19:52Z | MEMBER | null | 1 | {
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} | This code now returns a numpy array:
```python
from datasets import load_dataset
ds = load_dataset("imagenet-1k", split="train", streaming=True).with_format("np")
print(next(iter(ds))["image"])
```
It also works with "arrow", "pandas", "torch", "tf" and "jax"
### Implementation details:
I'm using the existing code to format an Arrow Table to the right output format for simplicity.
Therefore it's probbaly not the most optimized approach.
For example to output PyTorch tensors it does this for every example:
python data -> arrow table -> numpy extracted data -> pytorch formatted data
### Releasing this feature
Even though I consider this as a bug/inconsistency, this change is a breaking change.
And I'm sure some users were relying on the torch iterable dataset to return PIL Image and used data collators to convert to pytorch.
So I guess this is `datasets` 3.0 ?
### TODO
- [x] merge https://github.com/huggingface/datasets/pull/5072
- [ ] docs
- [ ] tests
Close https://github.com/huggingface/datasets/issues/5083 | {
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"hii @lhoestq, can you assign this issue to me? Though i am new to open source still I would love to put my best foot forward. I can see there isn't anyone right now assigned to this issue.",
"Hi @zutarich ! This issue was fixed by #5852 - sorry I forgot to close it\r\n\r\nFeel free to look for other issues and p... | 2022-10-06T15:14:58Z | 2023-10-09T12:42:15Z | 2023-10-09T12:42:15Z | MEMBER | null | null | null | Right now `IterableDataset` doesn't do any formatting.
In particular this code should return a numpy array:
```python
from datasets import load_dataset
ds = load_dataset("imagenet-1k", split="train", streaming=True).with_format("np")
print(next(iter(ds))["image"])
```
Right now it returns a PIL.Image.
Setting `streaming=False` does return a numpy array after #5072 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi @mariosasko , I have added a test for the `keep_in_memory` version. I have also removed the `Compatible with temp_seed` part in the scope of `dset_shuffled`, please verify if that makes sense."
] | 2022-10-06T11:10:46Z | 2022-10-07T14:35:34Z | 2022-10-07T14:32:54Z | CONTRIBUTOR | null | 0 | {
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"url": "https://api.github.com/repos/huggingface/datasets/pulls/5082"
} | Fixing #514 .
Hello @mariosasko 👋, I have implemented what you have recommanded to fix the keep in memory problem for shuffle on the issue #514 . | {
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https://api.github.com/repos/huggingface/datasets/issues/5081 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5081/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5081/comments | https://api.github.com/repos/huggingface/datasets/issues/5081/events | https://github.com/huggingface/datasets/issues/5081 | 1,399,340,050 | I_kwDODunzps5TaDwS | 5,081 | Bug loading `sentence-transformers/parallel-sentences` | {
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"tagging @nreimers ",
"The dataset is sadly not really compatible to be loaded with `load_dataset`. So far it is better to git clone it and to use the files directly.\r\n\r\nA data loading script would be needed to be added to this dataset. But this was too much overhead / not really intuitive how to create it.",... | 2022-10-06T10:47:51Z | 2022-10-11T10:00:48Z | null | CONTRIBUTOR | null | null | null | ## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("sentence-transformers/parallel-sentences")
```
raises this:
```
/home/phmay/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:697: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'
return pd.read_csv(xopen(filepath_or_buffer, "rb", use_auth_token=use_auth_token), **kwargs)
/home/phmay/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:697: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'
return pd.read_csv(xopen(filepath_or_buffer, "rb", use_auth_token=use_auth_token), **kwargs)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In [4], line 1
----> 1 dataset = load_dataset("sentence-transformers/parallel-sentences", split="train")
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/load.py:1693, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1690 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1692 # Download and prepare data
-> 1693 builder_instance.download_and_prepare(
1694 download_config=download_config,
1695 download_mode=download_mode,
1696 ignore_verifications=ignore_verifications,
1697 try_from_hf_gcs=try_from_hf_gcs,
1698 use_auth_token=use_auth_token,
1699 )
1701 # Build dataset for splits
1702 keep_in_memory = (
1703 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1704 )
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/builder.py:807, in DatasetBuilder.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, storage_options, **download_and_prepare_kwargs)
801 if not downloaded_from_gcs:
802 prepare_split_kwargs = {
803 "file_format": file_format,
804 "max_shard_size": max_shard_size,
805 **download_and_prepare_kwargs,
806 }
--> 807 self._download_and_prepare(
808 dl_manager=dl_manager,
809 verify_infos=verify_infos,
810 **prepare_split_kwargs,
811 **download_and_prepare_kwargs,
812 )
813 # Sync info
814 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/builder.py:898, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
894 split_dict.add(split_generator.split_info)
896 try:
897 # Prepare split will record examples associated to the split
--> 898 self._prepare_split(split_generator, **prepare_split_kwargs)
899 except OSError as e:
900 raise OSError(
901 "Cannot find data file. "
902 + (self.manual_download_instructions or "")
903 + "\nOriginal error:\n"
904 + str(e)
905 ) from None
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/builder.py:1513, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, max_shard_size)
1506 shard_id += 1
1507 writer = writer_class(
1508 features=writer._features,
1509 path=fpath.replace("SSSSS", f"{shard_id:05d}"),
1510 storage_options=self._fs.storage_options,
1511 embed_local_files=embed_local_files,
1512 )
-> 1513 writer.write_table(table)
1514 finally:
1515 num_shards = shard_id + 1
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/arrow_writer.py:540, in ArrowWriter.write_table(self, pa_table, writer_batch_size)
538 if self.pa_writer is None:
539 self._build_writer(inferred_schema=pa_table.schema)
--> 540 pa_table = table_cast(pa_table, self._schema)
541 if self.embed_local_files:
542 pa_table = embed_table_storage(pa_table)
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/table.py:2044, in table_cast(table, schema)
2032 """Improved version of pa.Table.cast.
2033
2034 It supports casting to feature types stored in the schema metadata.
(...)
2041 table (:obj:`pyarrow.Table`): the casted table
2042 """
2043 if table.schema != schema:
-> 2044 return cast_table_to_schema(table, schema)
2045 elif table.schema.metadata != schema.metadata:
2046 return table.replace_schema_metadata(schema.metadata)
File ~/miniconda3/envs/paraphrase-mining/lib/python3.9/site-packages/datasets/table.py:2005, in cast_table_to_schema(table, schema)
2003 features = Features.from_arrow_schema(schema)
2004 if sorted(table.column_names) != sorted(features):
-> 2005 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match")
2006 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
2007 return pa.Table.from_arrays(arrays, schema=schema)
ValueError: Couldn't cast
Action taken on Parliament's resolutions: see Minutes: string
Následný postup na základě usnesení Parlamentu: viz zápis: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 742
to
{'Membership of Parliament: see Minutes': Value(dtype='string', id=None), 'Състав на Парламента: вж. протоколи': Value(dtype='string', id=None)}
because column names don't match
```
## Expected results
no error
## Actual results
error
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version: Python 3.9.13
- PyArrow version: pyarrow 9.0.0
- transformers 4.22.2
- datasets 2.5.2 | {
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"There is some discussion in https://github.com/huggingface/huggingface_hub/pull/1088 if it can help :)"
] | 2022-10-06T05:51:58Z | 2022-10-06T14:26:05Z | null | MEMBER | null | null | null | ## Is your feature request related to a problem?
As previously discussed in our meeting with @Wauplin and agreed on our last datasets team sync meeting, I'm investigating how `datasets` can use `hfh` for caching.
## Describe the solution you'd like
Due to the peculiarities of the `datasets` cache, I would propose adopting `hfh` caching system in stages.
First, we could easily start using `hfh` caching for:
- dataset Python scripts
- dataset READMEs
- dataset infos JSON files (now deprecated)
Second, we could also use `hfh` caching for data files downloaded from the Hub.
Further investigation is needed for:
- files downloaded from non-Hub hosts
- extracted files from downloaded archive/compressed files
- generated Arrow files
## Additional context
Docs about the `hfh` caching system:
- [Manage huggingface_hub cache-system](https://huggingface.co/docs/huggingface_hub/main/en/how-to-cache)
- [Cache-system reference](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/cache)
The `transformers` library has already adopted `hfh` for caching. See:
- huggingface/transformers#18438
- huggingface/transformers#18857
- huggingface/transformers#18966
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-06T01:39:35Z | 2022-10-07T14:35:43Z | 2022-10-07T14:33:10Z | CONTRIBUTOR | null | 0 | {
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} | Closes #5074
Replaces `AssertionError` in the following files with more descriptive exceptions:
- `src/datasets/arrow_reader.py`
- `src/datasets/builder.py`
- `src/datasets/utils/version.py`
The issue listed more files that needed to be fixed, but the rest of them were contained in the top-level `datasets` directory, which was removed when #4974 was merged | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-05T20:22:44Z | 2022-10-06T08:12:23Z | 2022-10-06T08:09:41Z | MEMBER | null | 0 | {
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] | 2022-10-05T16:42:36Z | 2022-10-06T05:31:22Z | 2022-10-06T05:29:06Z | MEMBER | null | 0 | {
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] | 2022-10-05T14:46:29Z | 2022-10-07T14:35:57Z | 2022-10-07T14:33:27Z | CONTRIBUTOR | null | 0 | {
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Ready for review
Description of Changes:
Fixes #5075
Changes proposed in this pull request:
- Throw EnvironmentError instead of OSError in `push_to_hub` when the Hub token is not present. | {
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"@mariosasko I've raised a PR #5076 against this issue. Please help to review. Thanks."
] | 2022-10-05T14:14:18Z | 2022-10-07T14:33:28Z | 2022-10-07T14:33:28Z | CONTRIBUTOR | null | null | null | Throw EnvironmentError instead of OSError ([link](https://github.com/huggingface/datasets/blob/6ad430ba0cdeeb601170f732d4bd977f5c04594d/src/datasets/arrow_dataset.py#L4306) to the line) in `push_to_hub` when the Hub token is not present. | {
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"Hi, can I pick up this issue?",
"#self-assign",
"Looks like the top-level `datasource` directory was removed when https://github.com/huggingface/datasets/pull/4974 was merged, so there are 3 source files to fix."
] | 2022-10-05T14:03:55Z | 2022-10-07T14:33:11Z | 2022-10-07T14:33:11Z | CONTRIBUTOR | null | null | null | Replace the AssertionErrors with more meaningful errors such as ValueError, TypeError, etc.
The files with AssertionErrors that need to be replaced:
```
src/datasets/arrow_reader.py
src/datasets/builder.py
src/datasets/utils/version.py
``` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-05T13:51:47Z | 2022-10-11T16:55:07Z | 2022-10-11T16:49:23Z | CONTRIBUTOR | null | 0 | {
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} | Hello! @mariosasko
This pull request relates to issue #5050 and intends to add the format to datasets loaded from disk.
All I did was add a set_format in the Dataset.load_from_disk, as DatasetDict.load_from_disk relies on the first.
I don't know if I should add a test and where, so let me know if I should and I can work on that as well!
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"_The documentation is not available anymore as the PR was closed or merged._",
"I just added a consolidation step so that numpy arrays or tensors of images are stacked together if the shapes match, instead of having lists of tensors\r\n\r\nFeel free to review @mariosasko :)",
"I added a few lines in the docs a... | 2022-10-05T13:07:03Z | 2022-10-10T13:24:10Z | 2022-10-10T13:21:32Z | MEMBER | null | 0 | {
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} | Added support for image and audio formatting for numpy, torch, tf and jax.
For images, the dtype used is the one of the image (the one returned by PIL.Image), e.g. uint8
I also added support for string, binary and None types. In particular for torch and jax, strings are kept unchanged (previously it was returning an error because you can't create a tensor of strings) | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Super, thanks a lot for adding this support, Albert!"
] | 2022-10-05T06:28:39Z | 2022-10-06T14:43:12Z | 2022-10-06T14:40:26Z | MEMBER | null | 0 | {
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} | This PR supports defining a default config name, even if no predefined allowed config names are set.
Fix #5070.
CC: @stas00 | {
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"Thank you for creating this feature request, Albert.\r\n\r\nFor context this is the datatest where Albert has been helping me to switch to on-the-fly split config https://huggingface.co/datasets/HuggingFaceM4/cm4-synthetic-testing\r\n\r\nand the attempt to switch on-the-fly splits was here: https://huggingface.co/... | 2022-10-04T19:49:35Z | 2022-10-06T14:40:26Z | 2022-10-06T14:40:26Z | MEMBER | null | null | null | **Is your feature request related to a problem? Please describe.**
As discussed with @stas00, we could support defining a default config name, even if no predefined allowed config names are set. That is, support `DEFAULT_CONFIG_NAME`, even when `BUILDER_CONFIGS` is not defined.
**Additional context**
In order to support creating configs on the fly **by name** (not using kwargs), the list of allowed builder configs `BUILDER_CONFIGS` must not be set.
However, if so, then `DEFAULT_CONFIG_NAME` is not supported.
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-04T14:16:05Z | 2022-10-06T06:14:43Z | 2022-10-06T06:12:12Z | MEMBER | null | 0 | {
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} | This PR updates the `CONTRIBUTING.md` guide, once the all dataset scripts have been removed from the GitHub repo and transferred to the HF Hub:
- #4974
See diff here: https://github.com/huggingface/datasets/commit/e3291ecff9e54f09fcee3f313f051a03fdc3d94b
Additionally, this PR fixes the line separator that by some previous mistake was CRLF instead of LF. | {
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] | 2022-10-04T11:20:05Z | 2022-10-06T15:13:51Z | 2022-10-06T15:11:29Z | MEMBER | null | 0 | {
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This has been a recurring issue. See, e.g.:
- #5060
- #3191 | {
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https://api.github.com/repos/huggingface/datasets/issues/5065 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5065/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5065/comments | https://api.github.com/repos/huggingface/datasets/issues/5065/events | https://github.com/huggingface/datasets/pull/5065 | 1,396,003,362 | PR_kwDODunzps5AHxlQ | 5,065 | Ci py3.10 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Does it sound good to you @albertvillanova ?"
] | 2022-10-04T10:13:51Z | 2022-11-29T15:28:05Z | 2022-11-29T15:25:26Z | MEMBER | null | 0 | {
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} | Added a CI job for python 3.10
Some dependencies don't work on 3.10 like apache beam, so I remove them from the extras in this case.
I also removed some s3 fixtures that we don't use anymore (and that don't work on 3.10 anyway) | {
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https://api.github.com/repos/huggingface/datasets/issues/5064 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5064/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5064/comments | https://api.github.com/repos/huggingface/datasets/issues/5064/events | https://github.com/huggingface/datasets/pull/5064 | 1,395,978,143 | PR_kwDODunzps5AHsP0 | 5,064 | Align signature of create/delete_repo with latest hfh | {
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] | 2022-10-04T09:54:53Z | 2022-10-07T17:02:11Z | 2022-10-07T16:59:30Z | MEMBER | null | 0 | {
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Related to:
- #5063
CC: @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/5063 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5063/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5063/comments | https://api.github.com/repos/huggingface/datasets/issues/5063/events | https://github.com/huggingface/datasets/pull/5063 | 1,395,895,463 | PR_kwDODunzps5AHasG | 5,063 | Align signature of list_repo_files with latest hfh | {
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This is already the case for `dataset_info`.
CC: @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/5062 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5062/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5062/comments | https://api.github.com/repos/huggingface/datasets/issues/5062/events | https://github.com/huggingface/datasets/pull/5062 | 1,395,739,417 | PR_kwDODunzps5AG6SA | 5,062 | Fix CI hfh token warning | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"good catch !"
] | 2022-10-04T06:36:54Z | 2022-10-04T08:58:15Z | 2022-10-04T08:42:31Z | MEMBER | null | 0 | {
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} | In our CI, we get warnings from `hfh` about using deprecated `token`: https://github.com/huggingface/datasets/actions/runs/3174626525/jobs/5171672431
```
tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_private
tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub
tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files
tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files_with_max_shard_size
tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_overwrite_files
C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\huggingface_hub\utils\_deprecation.py:97: FutureWarning: Deprecated argument(s) used in 'dataset_info': token. Will not be supported from version '0.12'.
warnings.warn(message, FutureWarning)
```
This PR fixes the tests in `TestPushToHub` so that we fix these warnings.
Continuation of:
- #5031
CC: @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/5061 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5061/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5061/comments | https://api.github.com/repos/huggingface/datasets/issues/5061/events | https://github.com/huggingface/datasets/issues/5061 | 1,395,476,770 | I_kwDODunzps5TLUki | 5,061 | `_pickle.PicklingError: logger cannot be pickled` in multiprocessing `map` | {
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"This is maybe related to python 3.10, do you think you could try on 3.8 ?\r\n\r\nIn the meantime we'll keep improving the support for 3.10. Let me add a dedicated CI",
"I did some binary search and seems like the root cause is either `multiprocess` or `dill`. python 3.10 is fine. Specifically:\r\n- `multiprocess... | 2022-10-03T23:51:38Z | 2023-07-21T14:43:35Z | 2023-07-21T14:43:34Z | NONE | null | null | null | ## Describe the bug
When I `map` with multiple processes, this error occurs. The `.name` of the `logger` that fails to pickle in the final line is `datasets.fingerprint`.
```
File "~/project/dataset.py", line 204, in <dictcomp>
split: dataset.map(
File ".../site-packages/datasets/arrow_dataset.py", line 2489, in map
transformed_shards[index] = async_result.get()
File ".../site-packages/multiprocess/pool.py", line 771, in get
raise self._value
File ".../site-packages/multiprocess/pool.py", line 537, in _handle_tasks
put(task)
File ".../site-packages/multiprocess/connection.py", line 214, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File ".../site-packages/multiprocess/reduction.py", line 54, in dumps
cls(buf, protocol, *args, **kwds).dump(obj)
File ".../site-packages/dill/_dill.py", line 620, in dump
StockPickler.dump(self, obj)
File ".../pickle.py", line 487, in dump
self.save(obj)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../pickle.py", line 902, in save_tuple
save(element)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../site-packages/dill/_dill.py", line 1963, in save_function
_save_with_postproc(pickler, (_create_function, (
File ".../site-packages/dill/_dill.py", line 1140, in _save_with_postproc
pickler.save_reduce(*reduction, obj=obj)
File ".../pickle.py", line 717, in save_reduce
save(state)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../pickle.py", line 887, in save_tuple
save(element)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../site-packages/dill/_dill.py", line 1251, in save_module_dict
StockPickler.save_dict(pickler, obj)
File ".../pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File ".../pickle.py", line 998, in _batch_setitems
save(v)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../site-packages/dill/_dill.py", line 1963, in save_function
_save_with_postproc(pickler, (_create_function, (
File ".../site-packages/dill/_dill.py", line 1140, in _save_with_postproc
pickler.save_reduce(*reduction, obj=obj)
File ".../pickle.py", line 717, in save_reduce
save(state)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../pickle.py", line 887, in save_tuple
save(element)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../site-packages/dill/_dill.py", line 1251, in save_module_dict
StockPickler.save_dict(pickler, obj)
File ".../pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File ".../pickle.py", line 998, in _batch_setitems
save(v)
File ".../pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File ".../site-packages/dill/_dill.py", line 1963, in save_function
_save_with_postproc(pickler, (_create_function, (
File ".../site-packages/dill/_dill.py", line 1154, in _save_with_postproc
pickler._batch_setitems(iter(source.items()))
File ".../pickle.py", line 998, in _batch_setitems
save(v)
File ".../pickle.py", line 578, in save
rv = reduce(self.proto)
File ".../logging/__init__.py", line 1774, in __reduce__
raise pickle.PicklingError('logger cannot be pickled')
_pickle.PicklingError: logger cannot be pickled
```
## Steps to reproduce the bug
Sorry I failed to have a minimal reproducible example, but the offending line on my end is
```python
dataset.map(
lambda examples: self.tokenize(examples), # this doesn't matter, lambda e: [1] * len(...) also breaks. In fact I'm pretty sure it breaks before executing this lambda
batched=True,
num_proc=4,
)
```
This does work when `num_proc=1`, so it's likely a multiprocessing thing.
## Expected results
`map` succeeds
## Actual results
The error trace above.
## Environment info
- `datasets` version: 1.16.1 and 2.5.1 both failed
- Platform: Ubuntu 20.04.4 LTS
- Python version: 3.10.4
- PyArrow version: 9.0.0
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https://api.github.com/repos/huggingface/datasets/issues/5060 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5060/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5060/comments | https://api.github.com/repos/huggingface/datasets/issues/5060/events | https://github.com/huggingface/datasets/issues/5060 | 1,395,382,940 | I_kwDODunzps5TK9qc | 5,060 | Unable to Use Custom Dataset Locally | {
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"Hi ! I opened a PR in your repo to fix this :)\r\nhttps://huggingface.co/datasets/zpn/pubchem_selfies/discussions/7\r\n\r\nbasically you need to use `open` for streaming to work properly",
"Thank you so much for this! Naive question, is this a feature of `open` or have you all overloaded it to be able to read fr... | 2022-10-03T21:55:16Z | 2022-10-06T14:29:18Z | 2022-10-06T14:29:17Z | CONTRIBUTOR | null | null | null | ## Describe the bug
I have uploaded a [dataset](https://huggingface.co/datasets/zpn/pubchem_selfies) and followed the instructions from the [dataset_loader](https://huggingface.co/docs/datasets/dataset_script#download-data-files-and-organize-splits) tutorial. In that tutorial, it says
```
If the data files live in the same folder or repository of the dataset script,
you can just pass the relative paths to the files instead of URLs.
```
Accordingly, I put the [relative path](https://huggingface.co/datasets/zpn/pubchem_selfies/blob/main/pubchem_selfies.py#L76) to the data to be used. I was able to test the dataset and generate the metadata locally with `datasets-cli test path/to/<your-dataset-loading-script> --save_infos --all_configs`
However, if I try to load the data using `load_dataset`, I get the following error
```
with gzip.open(filepath, mode="rt") as f:
File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 58, in open
binary_file = GzipFile(filename, gz_mode, compresslevel)
File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 173, in __init__
fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
FileNotFoundError: [Errno 2] No such file or directory: 'https://huggingface.co/datasets/zpn/pubchem_selfies/resolve/main/data/Compound_021000001_021500000/Compound_021000001_021500000_SELFIES.jsonl.gz'
```
## Steps to reproduce the bug
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("zpn/pubchem_selfies", streaming=True)
>>> t = dataset["train"]
>>> for item in t:
...... print(item)
...... break
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/zachnussbaum/env/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 723, in __iter__
for key, example in self._iter():
File "/Users/zachnussbaum/env/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 713, in _iter
yield from ex_iterable
File "/Users/zachnussbaum/env/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 113, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/Users/zachnussbaum/.cache/huggingface/modules/datasets_modules/datasets/zpn--pubchem_selfies/d2571f35996765aea70fd3f3f8e3882d59c401fb738615c79282e2eb1d9f7a25/pubchem_selfies.py", line 475, in _generate_examples
with gzip.open(filepath, mode="rt") as f:
File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 58, in open
binary_file = GzipFile(filename, gz_mode, compresslevel)
File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 173, in __init__
fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
FileNotFoundError: [Errno 2] No such file or directory: 'https://huggingface.co/datasets/zpn/pubchem_selfies/resolve/main/data/Compound_021000001_021500000/Compound_021000001_021500000_SELFIES.jsonl.gz'
````
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.1
- Platform: macOS-12.5.1-x86_64-i386-64bit
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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https://api.github.com/repos/huggingface/datasets/issues/5059 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5059/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5059/comments | https://api.github.com/repos/huggingface/datasets/issues/5059/events | https://github.com/huggingface/datasets/pull/5059 | 1,395,050,876 | PR_kwDODunzps5AEoX7 | 5,059 | Fix typo | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-03T17:05:25Z | 2022-10-03T17:34:40Z | 2022-10-03T17:32:27Z | MEMBER | null | 0 | {
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} | Fixes a small typo :) | {
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https://api.github.com/repos/huggingface/datasets/issues/5058 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5058/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5058/comments | https://api.github.com/repos/huggingface/datasets/issues/5058/events | https://github.com/huggingface/datasets/pull/5058 | 1,394,962,424 | PR_kwDODunzps5AEVWn | 5,058 | Mark CI tests as xfail when 502 error | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-03T15:53:55Z | 2022-10-04T10:03:23Z | 2022-10-04T10:01:23Z | MEMBER | null | 0 | {
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} | To make CI more robust, we could mark as xfail when the Hub raises a 502 error (besides 500 error):
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_skip_identical_files
- https://github.com/huggingface/datasets/actions/runs/3174626525/jobs/5171672431
```
> raise HTTPError(http_error_msg, response=self)
E requests.exceptions.HTTPError: 502 Server Error: Bad Gateway for url: https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16648055339047.git/info/lfs/objects/batch
```
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_overwrite_files
- https://github.com/huggingface/datasets/actions/runs/3145587033/jobs/5113074889
```
> raise HTTPError(http_error_msg, response=self)
E requests.exceptions.HTTPError: 502 Server Error: Bad Gateway for url: https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16643866807322.git/info/lfs/objects/verify
```
Currently, we mark as xfail when 500 error:
- #4845 | {
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https://api.github.com/repos/huggingface/datasets/issues/5057 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5057/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5057/comments | https://api.github.com/repos/huggingface/datasets/issues/5057/events | https://github.com/huggingface/datasets/pull/5057 | 1,394,827,216 | PR_kwDODunzps5AD4c6 | 5,057 | Support `converters` in `CsvBuilder` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-03T14:23:21Z | 2022-10-04T11:19:28Z | 2022-10-04T11:17:32Z | CONTRIBUTOR | null | 0 | {
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} | Add the `converters` param to `CsvBuilder`, to help in situations like [this one](https://discuss.huggingface.co/t/typeerror-in-load-dataset-related-to-a-sequence-of-strings/23545).
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https://api.github.com/repos/huggingface/datasets/issues/5056 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5056/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5056/comments | https://api.github.com/repos/huggingface/datasets/issues/5056/events | https://github.com/huggingface/datasets/pull/5056 | 1,394,713,173 | PR_kwDODunzps5ADfxN | 5,056 | Fix broken URL's (GEM) | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5056). All of your documentation changes will be reflected on that endpoint.",
"Thanks, @manandey. We have removed all dataset scripts from this repo. Subsequent PRs should be opened directly on the Hugging Face Hub."
] | 2022-10-03T13:13:22Z | 2022-10-04T13:49:00Z | 2022-10-04T13:48:59Z | CONTRIBUTOR | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-03T10:30:14Z | 2022-10-03T13:43:55Z | 2022-10-03T13:41:32Z | MEMBER | null | 0 | {
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} | While working on https://github.com/huggingface/datasets/pull/5018 I noticed a small bug introduced in #4926 regarding backward compatibility for dataset_infos.json
Indeed, when a dataset repo had both dataset_infos.json and README.md, the JSON file was ignored. This is unexpected: in practice it should be ignored only if the README.md has a dataset_info field, which has precedence over the data in the JSON file. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-03T05:19:13Z | 2022-10-03T09:26:49Z | 2022-10-03T09:24:30Z | MEMBER | null | 0 | {
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- https://github.com/modestyachts/squadshifts-website/issues/1
Additionally, we have updated the mention in their website to our `datasets` library (they were referring old name `nlp`):
- https://github.com/modestyachts/squadshifts-website/pull/2#event-7500953009 | {
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https://api.github.com/repos/huggingface/datasets/issues/5053 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5053/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5053/comments | https://api.github.com/repos/huggingface/datasets/issues/5053/events | https://github.com/huggingface/datasets/issues/5053 | 1,393,739,882 | I_kwDODunzps5TEshq | 5,053 | Intermittent JSON parse error when streaming the Pile | {
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"Maybe #2838 can help. In this PR we allow to skip bad chunks of JSON data to not crash the training\r\n\r\nDid you have warning messages before the error ?\r\n\r\nsomething like this maybe ?\r\n```\r\n03/24/2022 02:19:46 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host... | 2022-10-02T11:56:46Z | 2022-10-04T17:59:03Z | null | NONE | null | null | null | ## Describe the bug
I have an intermittent error when streaming the Pile, where I get a JSON parse error which causes my program to crash.
This is intermittent - when I rerun the program with the same random seed it does not crash in the same way. The exact point this happens also varied - it happened to me 11B tokens and 4 days into a training run, and now just happened 2 minutes into one, but I can't reliably reproduce it.
I'm using a remote machine with 8 A6000 GPUs via runpod.io
## Expected results
I have a DataLoader which can iterate through the whole Pile
## Actual results
Stack trace:
```
Failed to read file 'zstd://12.jsonl::https://the-eye.eu/public/AI/pile/train/12.jsonl.zst' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Invalid value. in row 0
```
I'm currently using HuggingFace accelerate, which also gave me the following stack trace, but I've also experienced this problem intermittently when using DataParallel, so I don't think it's to do with parallelisation
```
Traceback (most recent call last):
File "ddp_script.py", line 1258, in <module>
main()
File "ddp_script.py", line 1143, in main
for c, batch in tqdm.tqdm(enumerate(data_iter)):
File "/opt/conda/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 503, in __iter__
next_batch, next_batch_info, next_skip = self._fetch_batches(main_iterator)
File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 454, in _fetch_batches
broadcast_object_list(batch_info)
File "/opt/conda/lib/python3.7/site-packages/accelerate/utils/operations.py", line 333, in broadcast_object_list
torch.distributed.broadcast_object_list(object_list, src=from_process)
File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1900, in broadcast_object_list
object_list[i] = _tensor_to_object(obj_view, obj_size)
File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1571, in _tensor_to_object
return _unpickler(io.BytesIO(buf)).load()
_pickle.UnpicklingError: invalid load key, '@'.
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset(
cfg["dataset_name"], streaming=True, split="train")
dataset = dataset.remove_columns("meta")
dataset = dataset.map(tokenize_and_concatenate, batched=True)
dataset = dataset.with_format(type="torch")
train_data_loader = DataLoader(
dataset, batch_size=cfg["batch_size"], num_workers=3)
for batch in train_data_loader:
continue
```
`tokenize_and_concatenate` is a custom tokenization function I defined on the GPT-NeoX tokenizer to tokenize the text, separated by endoftext tokens, and reshape to have length batch_size, I don't think this is related to tokenization:
```
import numpy as np
import einops
import torch
def tokenize_and_concatenate(examples):
texts = examples["text"]
full_text = tokenizer.eos_token.join(texts)
div = 20
length = len(full_text) // div
text_list = [full_text[i * length: (i + 1) * length]
for i in range(div)]
tokens = tokenizer(text_list, return_tensors="np", padding=True)[
"input_ids"
].flatten()
tokens = tokens[tokens != tokenizer.pad_token_id]
n = len(tokens)
curr_batch_size = n // (seq_len - 1)
tokens = tokens[: (seq_len - 1) * curr_batch_size]
tokens = einops.rearrange(
tokens,
"(batch_size seq) -> batch_size seq",
batch_size=curr_batch_size,
seq=seq_len - 1,
)
prefix = np.ones((curr_batch_size, 1), dtype=np.int64) * \
tokenizer.bos_token_id
return {
"text": np.concatenate([prefix, tokens], axis=1)
}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-5.4.0-105-generic-x86_64-with-debian-buster-sid
- Python version: 3.7.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
ZStandard data:
Version: 0.18.0
Summary: Zstandard bindings for Python
Home-page: https://github.com/indygreg/python-zstandard
Author: Gregory Szorc
Author-email: gregory.szorc@gmail.com
License: BSD
Location: /opt/conda/lib/python3.7/site-packages
Requires:
Required-by: | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I added a test and moved the `streaming` param from `read` to `__init_`. Then, I also decided to update the `read` method of the rest of the packaged modules to account for this param. \r\n\r\n@hamid-vakilzadeh Are you OK with these ... | 2022-09-30T22:14:05Z | 2022-10-05T12:51:48Z | 2022-10-05T12:10:48Z | CONTRIBUTOR | null | 0 | {
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This resolves issues #4988.
I added a method `from_generator` to class `IterableDataset`.
I modified the `read` method of input stream generator to also return Iterable_dataset.
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"_The documentation is not available anymore as the PR was closed or merged._"
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} | Reverts the removal of `task_templates` in the folder-based builders. I also added the `AudioClassifaction` task for consistency.
This is needed to fix https://github.com/huggingface/transformers/issues/19177.
I think we should soon deprecate and remove the current task API (and investigate if it's possible to integrate the `train eval index` API), but we need to update the Transformers examples before that so we don't break them.
cc @NielsRogge | {
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"Hi, can I work on this?",
"Hi, sure! Let us know if you need some pointers/help."
] | 2022-09-30T12:40:07Z | 2022-10-11T16:49:24Z | 2022-10-11T16:49:24Z | CONTRIBUTOR | null | null | null | Even though we save the `format` state in `save_to_disk`, we don't restore it in `load_from_disk`. We should fix that.
Reported here: https://discuss.huggingface.co/t/save-to-disk-loses-formatting-information/23815 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-30T12:24:27Z | 2022-10-03T11:00:11Z | 2022-10-03T10:58:15Z | CONTRIBUTOR | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@JamesLYC88 here is the fix! Thanks again!",
"Thanks, @albertvillanova. When do you expect that this change will take effect when someone downloads the dataset?",
"The change is immediately available now, since this change we mad... | 2022-09-30T09:47:12Z | 2022-09-30T16:30:25Z | 2022-09-30T16:21:41Z | CONTRIBUTOR | null | 0 | {
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} | Fix for a critical bug in the EURLEX dataset label list to make LexGLUE EURLEX results replicable.
In LexGLUE (Chalkidis et al., 2022), the following is mentioned w.r.t. EUR-LEX: _"It supports four different label granularities, comprising 21, 127, 567, 7390 EuroVoc concepts, respectively. We use the 100 most frequent concepts from level 2 [...]”._ The current label list has all 127 labels, which leads to different (lower) results, as communicated by users.
Thanks! | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-30T08:47:29Z | 2022-09-30T10:23:22Z | 2022-09-30T09:34:28Z | MEMBER | null | 0 | {
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} | Reported in https://github.com/huggingface/datasets/pull/3878
I updated the number of examples | {
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"Hi! Unfortunately, I can't reproduce this behavior. Instead, I get `ValueError: audio at 2063_fe9936e7-62b2-4e62-a276-acbd344480ce_1.wav doesn't have metadata in /audio-data/metadata.csv`, which can be fixed by removing the `./` from the file name.\r\n\r\n(Link to a Colab that tries to reproduce this behavior: htt... | 2022-09-29T19:17:23Z | 2022-10-28T13:05:07Z | 2022-10-28T13:05:07Z | NONE | null | null | null | ## Describe the bug
When audio files are at the same level as the metadata (`metadata.csv` or `metadata.jsonl` ), the `load_dataset` returns a `DatasetDict` with no rows but the correct columns.
https://github.com/huggingface/datasets/blob/1ea4d091b7a4b83a85b2eeb8df65115d39af3766/docs/source/audio_dataset.mdx?plain=1#L88
## Steps to reproduce the bug
`metadata.csv`:
```csv
file_name,duration,transcription
./2063_fe9936e7-62b2-4e62-a276-acbd344480ce_1.wav,10.768,hello
```
```python
>>> audio_dataset = load_dataset("audiofolder", data_dir="/audio-data/")
>>> audio_dataset
DatasetDict({
train: Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 0
})
validation: Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 0
})
})
```
I've tried, with no success,:
- setting `split` to something else so I don't get a `DatasetDict`,
- removing the `./`,
- using `.jsonl`.
## Expected results
```
Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 1
})
```
## Actual results
```
DatasetDict({
train: Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 0
})
validation: Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 0
})
})
```
## Environment info
- `datasets` version: 2.5.1
- Platform: Linux-5.13.0-1025-aws-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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"Could you share the error you got please ? Maybe the full stack trace if you have it ?\r\n\r\nMaybe `push_to_hub` be implemented as a single commit @Wauplin ? This way if it fails, the repo is still at the previous (valid) state instead of ending-up in an invalid/incimplete state.",
"> Maybe push_to_hub be imple... | 2022-09-29T18:08:12Z | 2023-10-16T13:30:49Z | 2023-10-16T13:30:49Z | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
I pushed a modification of a large dataset (remove a column) to the hub. The push was interrupted after some files were committed to the repo. This left the dataset to raise an error on load_dataset() (ValueError couldn’t cast … because column names don’t match). Only by specifying the previous (complete) commit as revision=commit_hash in load_data(), I was able to repair this and after a successful, complete push, the dataset loads without error again.
**Describe the solution you'd like**
Would it make sense to detect an incomplete push_to_hub() and automatically revert to the previous commit/revision?
**Describe alternatives you've considered**
Leave everything as is, the revision parameter in load_dataset() allows to manually fix this problem.
**Additional context**
Provide useful defaults
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"I agree the situation is not ideal and it would be awesome to use `load_dataset` to reload a dataset saved locally !\r\n\r\nFor context:\r\n\r\n- `load_dataset` works in three steps: download the dataset, then prepare it as an arrow dataset, and finally return a memory mapped arrow dataset. In particular it create... | 2022-09-29T17:37:12Z | 2024-02-12T15:03:27Z | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
Is it possible to make `load_dataset` more universal similar to `from_pretrained` in `transformers` so that it can handle the hub, and the local path datasets of all supported types?
Currently one has to choose a different loader depending on how the dataset has been created.
e.g. this won't work:
```
$ git clone https://huggingface.co/datasets/severo/test-parquet
$ python -c 'from datasets import load_dataset; ds=load_dataset("test-parquet"); \
ds.save_to_disk("my_dataset"); load_dataset("my_dataset")'
[...]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/load.py", line 1746, in load_dataset
builder_instance.download_and_prepare(
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/builder.py", line 1277, in _prepare_split
writer.write_table(table)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_writer.py", line 524, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/table.py", line 2005, in table_cast
return cast_table_to_schema(table, schema)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/table.py", line 1968, in cast_table_to_schema
raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match")
ValueError: Couldn't cast
_data_files: list<item: struct<filename: string>>
child 0, item: struct<filename: string>
child 0, filename: string
```
both times the dataset is being loaded from disk. Why does it fail the second time?
Why can't `save_to_disk` generate a dataset that can be immediately loaded by `load_dataset`?
e.g. the simplest hack would be to have `save_to_disk` add some flag to the saved dataset, that tells `load_dataset` to internally call `load_from_disk`. like having `save_to_disk` create a `load_me_with_load_from_disk.txt` file ;) and `load_dataset` will support that feature from saved datasets from new `datasets` versions. The old ones will still need to use `load_from_disk` explicitly. Unless the flag is not needed and one can immediately tell by looking at the saved dataset that it was saved via `save_to_disk` and thus use `load_from_disk` internally.
The use-case is defining a simple API where the user only ever needs to pass a `dataset_name_or_path` and it will always just work. Currently one needs to manually add additional switches telling the system whether to use one loading method or the other which works but it's not smooth.
Thank you! | {
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- supports streaming
- fixes the description section of the dataset card | {
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Fix #5039. | {
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"Thanks for reporting, @DanielHesslow. We are fixing it. ",
"@albertvillanova thanks for taking care of this so quickly!",
"The dataset metadata is fixed. You can download it normally."
] | 2022-09-29T06:56:20Z | 2022-09-29T10:23:30Z | 2022-09-29T10:04:20Z | NONE | null | null | null | Hi,
The checksum for [hendrycks_test](https://huggingface.co/datasets/hendrycks_test) does not compare correctly, I guess it has been updated on the remote.
```
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://people.eecs.berkeley.edu/~hendrycks/data.tar']
```
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