id
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3.26B
| number
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| created_at
timestamp[s]date 2020-04-14 10:18:02
2025-07-23 08:04:53
| updated_at
timestamp[s]date 2020-04-27 16:04:17
2025-07-23 18:53:44
| closed_at
timestamp[s]date 2020-04-14 12:01:40
2025-07-23 16:44:42
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|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,615,029,615
| 5,621
|
Adding Oracle Cloud to docs
|
Adding Oracle Cloud's fsspec implementation to the list of supported cloud storage providers.
|
closed
|
https://github.com/huggingface/datasets/pull/5621
| 2023-03-08T10:22:50
| 2023-03-11T00:57:18
| 2023-03-11T00:49:56
|
{
"login": "ahosler",
"id": 29129502,
"type": "User"
}
|
[] | true
|
[] |
1,613,460,520
| 5,620
|
Bump pyarrow to 8.0.0
|
Fix those for Pandas 2.0 (tested [here](https://github.com/huggingface/datasets/actions/runs/4346221280/jobs/7592010397) with pandas==2.0.0.rc0):
```python
=========================== short test summary info ============================
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_in_memory - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
- Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
- Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_on_disk - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
- Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
- Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
===== 2 failed, 2137 passed, 18 skipped, 32 warnings in 212.76s (0:03:32) ======
```
EDIT: also for performance - with 8.0 we can use `.to_reader()`
|
closed
|
https://github.com/huggingface/datasets/pull/5620
| 2023-03-07T13:31:53
| 2023-03-08T14:01:27
| 2023-03-08T13:54:22
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,613,439,709
| 5,619
|
unpin fsspec
|
close https://github.com/huggingface/datasets/issues/5618
|
closed
|
https://github.com/huggingface/datasets/pull/5619
| 2023-03-07T13:22:41
| 2023-03-07T13:47:01
| 2023-03-07T13:39:02
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,612,977,934
| 5,618
|
Unpin fsspec < 2023.3.0 once issue fixed
|
Unpin `fsspec` upper version once root cause of our CI break is fixed.
See:
- #5614
|
closed
|
https://github.com/huggingface/datasets/issues/5618
| 2023-03-07T08:41:51
| 2023-03-07T13:39:03
| 2023-03-07T13:39:03
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | false
|
[] |
1,612,947,422
| 5,617
|
Fix CI by temporarily pinning fsspec < 2023.3.0
|
As a hotfix for our CI, temporarily pin `fsspec`:
Fix #5616.
Until root cause is fixed, see:
- #5614
|
closed
|
https://github.com/huggingface/datasets/pull/5617
| 2023-03-07T08:18:20
| 2023-03-07T08:44:55
| 2023-03-07T08:37:28
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,612,932,508
| 5,616
|
CI is broken after fsspec-2023.3.0 release
|
As reported by @lhoestq, our CI is broken after `fsspec` 2023.3.0 release:
```
FAILED tests/test_filesystem.py::test_compression_filesystems[Bz2FileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
At index 0 diff: {'name': 'file.txt', 'size': 70, 'type': 'file', 'created': 1678175677.1887748, 'islink': False, 'mode': 33188, 'uid': 1001, 'gid': 123, 'mtime': 1678175677.1887748, 'ino': 286957, 'nlink': 1} != 'file.txt'
Full diff:
[
- 'file.txt',
+ {'created': 1678175677.1887748,
+ 'gid': 123,
+ 'ino': 286957,
+ 'islink': False,
+ 'mode': 33188,
+ 'mtime': 1678175677.1887748,
+ 'name': 'file.txt',
+ 'nlink': 1,
+ 'size': 70,
+ 'type': 'file',
+ 'uid': 1001},
]
```
Also:
```
FAILED tests/test_filesystem.py::test_compression_filesystems[GzipFileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
FAILED tests/test_filesystem.py::test_compression_filesystems[Lz4FileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
FAILED tests/test_filesystem.py::test_compression_filesystems[XzFileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
FAILED tests/test_filesystem.py::test_compression_filesystems[ZstdFileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
===== 5 failed, 2134 passed, 18 skipped, 38 warnings in 157.21s (0:02:37) ======
```
See:
- fsspec/filesystem_spec#1205
|
closed
|
https://github.com/huggingface/datasets/issues/5616
| 2023-03-07T08:06:39
| 2023-03-07T08:37:29
| 2023-03-07T08:37:29
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,612,552,653
| 5,615
|
IterableDataset.add_column is unable to accept another IterableDataset as a parameter.
|
### Describe the bug
`IterableDataset.add_column` occurs an exception when passing another `IterableDataset` as a parameter.
The method seems to accept only eager evaluated values.
https://github.com/huggingface/datasets/blob/35b789e8f6826b6b5a6b48fcc2416c890a1f326a/src/datasets/iterable_dataset.py#L1388-L1391
I wrote codes below to make it.
```py
def add_column(dataset: IterableDataset, name: str, add_dataset: IterableDataset, key: str) -> IterableDataset:
iter_add_dataset = iter(add_dataset)
def add_column_fn(example):
if name in example:
raise ValueError(f"Error when adding {name}: column {name} is already in the dataset.")
return {name: next(iter_add_dataset)[key]}
return dataset.map(add_column_fn)
```
Is there other way to do it? Or is it intended?
### Steps to reproduce the bug
Thie codes below occurs `NotImplementedError`
```py
from datasets import IterableDataset
def gen(num):
yield {f"col{num}": 1}
yield {f"col{num}": 2}
yield {f"col{num}": 3}
ids1 = IterableDataset.from_generator(gen, gen_kwargs={"num": 1})
ids2 = IterableDataset.from_generator(gen, gen_kwargs={"num": 2})
new_ids = ids1.add_column("new_col", ids1)
for row in new_ids:
print(row)
```
### Expected behavior
`IterableDataset.add_column` is able to task `IterableDataset` and lazy evaluated values as a parameter since IterableDataset is lazy evalued.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-3.10.0-1160.36.2.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.7
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5615
| 2023-03-07T01:52:00
| 2023-03-09T15:24:05
| 2023-03-09T15:23:54
|
{
"login": "zsaladin",
"id": 6466389,
"type": "User"
}
|
[
{
"name": "wontfix",
"color": "ffffff"
}
] | false
|
[] |
1,611,896,357
| 5,614
|
Fix archive fs test
| null |
closed
|
https://github.com/huggingface/datasets/pull/5614
| 2023-03-06T17:28:09
| 2023-03-07T13:27:50
| 2023-03-07T13:20:57
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,611,875,473
| 5,613
|
Version mismatch with multiprocess and dill on Python 3.10
|
### Describe the bug
Grabbing the latest version of `datasets` and `apache-beam` with `poetry` using Python 3.10 gives a crash at runtime. The crash is
```
File "/Users/adpauls/sc/git/DSI-transformers/data/NQ/create_NQ_train_vali.py", line 1, in <module>
import datasets
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/__init__.py", line 43, in <module>
from .arrow_dataset import Dataset
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 65, in <module>
from .arrow_reader import ArrowReader
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 30, in <module>
from .download.download_config import DownloadConfig
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/__init__.py", line 9, in <module>
from .download_manager import DownloadManager, DownloadMode
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/download_manager.py", line 35, in <module>
from ..utils.py_utils import NestedDataStructure, map_nested, size_str
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 40, in <module>
import multiprocess.pool
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 609, in <module>
class ThreadPool(Pool):
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 611, in ThreadPool
from .dummy import Process
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/dummy/__init__.py", line 87, in <module>
class Condition(threading._Condition):
AttributeError: module 'threading' has no attribute '_Condition'. Did you mean: 'Condition'?
```
I think this is a bad interaction of versions from `dill`, `multiprocess`, `apache-beam`, and `threading` from the Python (3.10) standard lib. Upgrading `multiprocess` to a version that does not crash like this is not possible because `apache-beam` pins `dill` to and old version:
```
Because multiprocess (0.70.10) depends on dill (>=0.3.2)
and apache-beam (2.45.0) depends on dill (>=0.3.1.1,<0.3.2), multiprocess (0.70.10) is incompatible with apache-beam (2.45.0).
And because no versions of apache-beam match >2.45.0,<3.0.0, multiprocess (0.70.10) is incompatible with apache-beam (>=2.45.0,<3.0.0).
So, because yyy depends on both apache-beam (^2.45.0) and multiprocess (0.70.10), version solving failed.
```
Perhaps it is not right to file a bug here, but I'm not totally sure whose fault it is. And in any case, this is an immediate blocker to using `datasets` out of the box.
Possibly related to https://github.com/huggingface/datasets/issues/5232.
### Steps to reproduce the bug
Steps to reproduce:
1. Make a poetry project with this configuration
```
[tool.poetry]
name = "yyy"
version = "0.1.0"
description = ""
authors = ["Adam Pauls <adpauls@gmail.com>"]
readme = "README.md"
packages = [{ include = "xxx" }]
[tool.poetry.dependencies]
python = ">=3.10,<3.11"
datasets = "^2.10.1"
apache-beam = "^2.45.0"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
```
2. `poetry install`.
3. `poetry run python -c "import datasets"`.
### Expected behavior
Script runs.
### Environment info
Python 3.10. Here are the versions installed by `poetry`:
```
•• Installing frozenlist (1.3.3)
• Installing idna (3.4)
• Installing multidict (6.0.4)
• Installing aiosignal (1.3.1)
• Installing async-timeout (4.0.2)
• Installing attrs (22.2.0)
• Installing certifi (2022.12.7)
• Installing charset-normalizer (3.1.0)
• Installing six (1.16.0)
• Installing urllib3 (1.26.14)
• Installing yarl (1.8.2)
• Installing aiohttp (3.8.4)
• Installing dill (0.3.1.1)
• Installing docopt (0.6.2)
• Installing filelock (3.9.0)
• Installing numpy (1.22.4)
• Installing pyparsing (3.0.9)
• Installing protobuf (3.19.4)
• Installing packaging (23.0)
• Installing python-dateutil (2.8.2)
• Installing pytz (2022.7.1)
• Installing pyyaml (6.0)
• Installing requests (2.28.2)
• Installing tqdm (4.65.0)
• Installing typing-extensions (4.5.0)
• Installing cloudpickle (2.2.1)
• Installing crcmod (1.7)
• Installing fastavro (1.7.2)
• Installing fasteners (0.18)
• Installing fsspec (2023.3.0)
• Installing grpcio (1.51.3)
• Installing hdfs (2.7.0)
• Installing httplib2 (0.20.4)
• Installing huggingface-hub (0.12.1)
• Installing multiprocess (0.70.9)
• Installing objsize (0.6.1)
• Installing orjson (3.8.7)
• Installing pandas (1.5.3)
• Installing proto-plus (1.22.2)
• Installing pyarrow (9.0.0)
• Installing pydot (1.4.2)
• Installing pymongo (3.13.0)
• Installing regex (2022.10.31)
• Installing responses (0.18.0)
• Installing xxhash (3.2.0)
• Installing zstandard (0.20.0)
• Installing apache-beam (2.45.0)
• Installing datasets (2.10.1)
```
|
open
|
https://github.com/huggingface/datasets/issues/5613
| 2023-03-06T17:14:41
| 2024-04-05T20:13:52
| null |
{
"login": "adampauls",
"id": 1243668,
"type": "User"
}
|
[] | false
|
[] |
1,611,262,510
| 5,612
|
Arrow map type in parquet files unsupported
|
### Describe the bug
When I try to load parquet files that were processed with Spark, I get the following issue:
`ValueError: Arrow type map<string, string ('warc_headers')> does not have a datasets dtype equivalent.`
Strangely, loading the dataset with `streaming=True` solves the issue.
### Steps to reproduce the bug
The dataset is private, but this can be reproduced with any dataset that has Arrow maps.
### Expected behavior
Loading the dataset no matter whether streaming is True or not.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-5.15.0-1029-gcp-x86_64-with-glibc2.31
- Python version: 3.10.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
|
open
|
https://github.com/huggingface/datasets/issues/5612
| 2023-03-06T12:03:24
| 2024-03-15T18:56:12
| null |
{
"login": "TevenLeScao",
"id": 26709476,
"type": "User"
}
|
[] | false
|
[] |
1,611,197,906
| 5,611
|
add Dataset.to_list
|
close https://github.com/huggingface/datasets/issues/5606
This PR is for adding the `Dataset.to_list` method.
Thank you in advance.
|
closed
|
https://github.com/huggingface/datasets/pull/5611
| 2023-03-06T11:21:57
| 2023-03-27T13:34:19
| 2023-03-27T13:26:38
|
{
"login": "kyoto7250",
"id": 50972773,
"type": "User"
}
|
[] | true
|
[] |
1,610,698,006
| 5,610
|
use datasets streaming mode in trainer ddp mode cause memory leak
|
### Describe the bug
use datasets streaming mode in trainer ddp mode cause memory leak
### Steps to reproduce the bug
import os
import time
import datetime
import sys
import numpy as np
import random
import torch
from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler,DistributedSampler,BatchSampler
torch.manual_seed(42)
from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, GPT2Model,DataCollatorForLanguageModeling,AutoModelForCausalLM
from transformers import AdamW, get_linear_schedule_with_warmup
hf_model_path ='./Wenzhong-GPT2-110M'
tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path)
tokenizer.add_special_tokens({'pad_token': '<|pad|>'})
from datasets import load_dataset
gpus=8
max_len = 576
batch_size_node = 17
save_step = 5000
gradient_accumulation = 2
dataloader_num = 4
max_step = 351000*1000//batch_size_node//gradient_accumulation//gpus
#max_step = -1
print("total_step:%d"%(max_step))
import datasets
datasets.version
dataset = load_dataset("text", data_files="./gpt_data_v1/*",split='train',cache_dir='./dataset_cache',streaming=True)
print('load over')
shuffled_dataset = dataset.shuffle(seed=42)
print('shuffle over')
def dataset_tokener(example,max_lenth=max_len):
example['text'] = list(map(lambda x : x.strip()+'<|endoftext|>',example['text'] ))
return tokenizer(example['text'], truncation=True, max_length=max_lenth, padding="longest")
new_new_dataset = shuffled_dataset.map(dataset_tokener, batched=True, remove_columns=["text"])
print('map over')
configuration = GPT2Config.from_pretrained(hf_model_path, output_hidden_states=False)
model = AutoModelForCausalLM.from_pretrained(hf_model_path)
model.resize_token_embeddings(len(tokenizer))
seed_val = 42
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
from transformers import Trainer,TrainingArguments
import os
print("strat train")
training_args = TrainingArguments(output_dir="./test_trainer",
num_train_epochs=1.0,
report_to="none",
do_train=True,
dataloader_num_workers=dataloader_num,
local_rank=int(os.environ.get('LOCAL_RANK', -1)),
overwrite_output_dir=True,
logging_strategy='steps',
logging_first_step=True,
logging_dir="./logs",
log_on_each_node=False,
per_device_train_batch_size=batch_size_node,
warmup_ratio=0.03,
save_steps=save_step,
save_total_limit=5,
gradient_accumulation_steps=gradient_accumulation,
max_steps=max_step,
disable_tqdm=False,
data_seed=42
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=new_new_dataset,
eval_dataset=None,
tokenizer=tokenizer,
data_collator=DataCollatorForLanguageModeling(tokenizer,mlm=False),
#compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
#preprocess_logits_for_metrics=preprocess_logits_for_metrics
#if training_args.do_eval and not is_torch_tpu_available()
#else None,
)
trainer.train(resume_from_checkpoint=True)
### Expected behavior
use the train code uppper
my dataset ./gpt_data_v1 have 1000 files, each file size is 120mb
start cmd is : python -m torch.distributed.launch --nproc_per_node=8 my_train.py
here is result:
here is memory usage monitor in 12 hours
every dataloader work allocate over 24gb cpu memory
according to memory usage monitor in 12 hours,sometime small memory releases, but total memory usage is increase.
i think datasets streaming mode should not used so much memery,so maybe somewhere has memory leak.
### Environment info
pytorch 1.11.0
py 3.8
cuda 11.3
transformers 4.26.1
datasets 2.9.0
|
open
|
https://github.com/huggingface/datasets/issues/5610
| 2023-03-06T05:26:49
| 2024-03-07T01:11:32
| null |
{
"login": "gromzhu",
"id": 15223544,
"type": "User"
}
|
[] | false
|
[] |
1,610,062,862
| 5,609
|
`load_from_disk` vs `load_dataset` performance.
|
### Describe the bug
I have downloaded `openwebtext` (~12GB) and filtered out a small amount of junk (it's still huge). Now, I would like to use this filtered version for future work. It seems I have two choices:
1. Use `load_dataset` each time, relying on the cache mechanism, and re-run my filtering.
2. `save_to_disk` and then use `load_from_disk` to load the filtered version.
The performance of these two approaches is wildly different:
* Using `load_dataset` takes about 20 seconds to load the dataset, and a few seconds to re-filter (thanks to the brilliant filter/map caching)
* Using `load_from_disk` takes 14 minutes! And the second time I tried, the session just crashed (on a machine with 32GB of RAM)
I don't know if you'd call this a bug, but it seems like there shouldn't need to be two methods to load from disk, or that they should not take such wildly different amounts of time, or that one should not crash. Or maybe that the docs could offer some guidance about when to pick which method and why two methods exist, or just how do most people do it?
Something I couldn't work out from reading the docs was this: can I modify a dataset from the hub, save it (locally) and use `load_dataset` to load it? This [post seemed to suggest that the answer is no](https://discuss.huggingface.co/t/save-and-load-datasets/9260).
### Steps to reproduce the bug
See above
### Expected behavior
Load times should be about the same.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
open
|
https://github.com/huggingface/datasets/issues/5609
| 2023-03-05T05:27:15
| 2023-07-13T18:48:05
| null |
{
"login": "davidgilbertson",
"id": 4443482,
"type": "User"
}
|
[] | false
|
[] |
1,609,996,563
| 5,608
|
audiofolder only creates dataset of 13 rows (files) when the data folder it's reading from has 20,000 mp3 files.
|
### Describe the bug
x = load_dataset("audiofolder", data_dir="x")
When running this, x is a dataset of 13 rows (files) when it should be 20,000 rows (files) as the data_dir "x" has 20,000 mp3 files. Does anyone know what could possibly cause this (naming convention of mp3 files, etc.)
### Steps to reproduce the bug
x = load_dataset("audiofolder", data_dir="x")
### Expected behavior
x = load_dataset("audiofolder", data_dir="x") should create a dataset of 20,000 rows (files).
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5608
| 2023-03-05T00:14:45
| 2023-03-12T00:02:57
| 2023-03-12T00:02:57
|
{
"login": "jcho19",
"id": 107211437,
"type": "User"
}
|
[] | false
|
[] |
1,609,166,035
| 5,607
|
Fix outdated `verification_mode` values
|
~I think it makes sense not to save `dataset_info.json` file to a dataset cache directory when loading dataset with `verification_mode="no_checks"` because otherwise when next time the dataset is loaded **without** `verification_mode="no_checks"`, it will be loaded successfully, despite some values in info might not correspond to the ones in the repo which was the reason for using `verification_mode="no_checks"` first.~
Updated values of `verification_mode` to the current ones in some places ("none" -> "no_checks", "all" -> "all_checks")
|
closed
|
https://github.com/huggingface/datasets/pull/5607
| 2023-03-03T19:50:29
| 2023-03-09T17:34:13
| 2023-03-09T17:27:07
|
{
"login": "polinaeterna",
"id": 16348744,
"type": "User"
}
|
[] | true
|
[] |
1,608,911,632
| 5,606
|
Add `Dataset.to_list` to the API
|
Since there is `Dataset.from_list` in the API, we should also add `Dataset.to_list` to be consistent.
Regarding the implementation, we can re-use `Dataset.to_dict`'s code and replace the `to_pydict` calls with `to_pylist`.
|
closed
|
https://github.com/huggingface/datasets/issues/5606
| 2023-03-03T16:17:10
| 2023-03-27T13:26:40
| 2023-03-27T13:26:40
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
},
{
"name": "good first issue",
"color": "7057ff"
}
] | false
|
[] |
1,608,865,460
| 5,605
|
Update README logo
| null |
closed
|
https://github.com/huggingface/datasets/pull/5605
| 2023-03-03T15:46:31
| 2023-03-03T21:57:18
| 2023-03-03T21:50:17
|
{
"login": "gary149",
"id": 3841370,
"type": "User"
}
|
[] | true
|
[] |
1,608,304,775
| 5,604
|
Problems with downloading The Pile
|
### Describe the bug
The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.
Here are the downloaded files:
They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/).
Alternatively, can I somehow download the files by myself and use the datasets preparing script?
### Steps to reproduce the bug
dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets')
### Expected behavior
The files should be downloaded correctly.
### Environment info
- `datasets` version: 2.10.1
- Platform: Windows-10-10.0.22623-SP0
- Python version: 3.10.5
- PyArrow version: 9.0.0
- Pandas version: 1.4.2
|
closed
|
https://github.com/huggingface/datasets/issues/5604
| 2023-03-03T09:52:08
| 2023-10-14T02:15:52
| 2023-03-24T12:44:25
|
{
"login": "sentialx",
"id": 11065386,
"type": "User"
}
|
[] | false
|
[] |
1,607,143,509
| 5,603
|
Don't compute checksums if not necessary in `datasets-cli test`
|
we only need them if there exists a `dataset_infos.json`
|
closed
|
https://github.com/huggingface/datasets/pull/5603
| 2023-03-02T16:42:39
| 2023-03-03T15:45:32
| 2023-03-03T15:38:28
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,607,054,110
| 5,602
|
Return dict structure if columns are lists - to_tf_dataset
|
This PR introduces new logic to `to_tf_dataset` affecting the returned data structure, enabling a dictionary structure to be returned, even if only one feature column is selected.
If the passed in `columns` or `label_cols` to `to_tf_dataset` are a list, they are returned as a dictionary, respectively. If they are a string, the tensor is returned.
An outline of the behaviour:
```
dataset,to_tf_dataset(columns=["col_1"], label_cols="col_2")
# ({'col_1': col_1}, col_2}
dataset,to_tf_dataset(columns="col1", label_cols="col_2")
# (col1, col2)
dataset,to_tf_dataset(columns="col1")
# col1
dataset,to_tf_dataset(columns=["col_1"], labels=["col_2"])
# ({'col1': tensor}, {'col2': tensor}}
dataset,to_tf_dataset(columns="col_1", labels=["col_2"])
# (col1, {'col2': tensor}}
```
## Motivation
Currently, when calling `to_tf_dataset`, the returned dataset will always return a tuple structure if a single feature column is used. This can cause issues when calling `model.fit` on models which train without labels e.g. [TFVitMAEForPreTraining](https://github.com/huggingface/transformers/blob/b6f47b539377ac1fd845c7adb4ccaa5eb514e126/src/transformers/models/vit_mae/modeling_vit_mae.py#L849). Specifically, [this line](https://github.com/huggingface/transformers/blob/d9e28d91a8b2d09b51a33155d3a03ad9fcfcbd1f/src/transformers/modeling_tf_utils.py#L1521) where it's assumed the input `x` is a dictionary if there is no label.
## Example
Previous behaviour
```python
In [1]: import tensorflow as tf
...: from datasets import load_dataset
...:
...:
...: def transform(batch):
...: def _transform_img(img):
...: img = img.convert("RGB")
...: img = tf.keras.utils.img_to_array(img)
...: img = tf.image.resize(img, (224, 224))
...: img /= 255.0
...: img = tf.transpose(img, perm=[2, 0, 1])
...: return img
...: batch['pixel_values'] = [_transform_img(pil_img) for pil_img in batch['img']]
...: return batch
...:
...:
...: def collate_fn(examples):
...: pixel_values = tf.stack([example["pixel_values"] for example in examples])
...: return {"pixel_values": pixel_values}
...:
...:
...: dataset = load_dataset('cifar10')['train']
...: dataset = dataset.with_transform(transform)
...: dataset.to_tf_dataset(batch_size=8, columns=['pixel_values'], collate_fn=collate_fn)
Out[1]: <PrefetchDataset element_spec=TensorSpec(shape=(None, 3, 224, 224), dtype=tf.float32, name=None)>
```
New behaviour
```python
In [1]: import tensorflow as tf
...: from datasets import load_dataset
...:
...:
...: def transform(batch):
...: def _transform_img(img):
...: img = img.convert("RGB")
...: img = tf.keras.utils.img_to_array(img)
...: img = tf.image.resize(img, (224, 224))
...: img /= 255.0
...: img = tf.transpose(img, perm=[2, 0, 1])
...: return img
...: batch['pixel_values'] = [_transform_img(pil_img) for pil_img in batch['img']]
...: return batch
...:
...:
...: def collate_fn(examples):
...: pixel_values = tf.stack([example["pixel_values"] for example in examples])
...: return {"pixel_values": pixel_values}
...:
...:
...: dataset = load_dataset('cifar10')['train']
...: dataset = dataset.with_transform(transform)
...: dataset.to_tf_dataset(batch_size=8, columns=['pixel_values'], collate_fn=collate_fn)
Out[1]: <PrefetchDataset element_spec={'pixel_values': TensorSpec(shape=(None, 3, 224, 224), dtype=tf.float32, name=None)}>
```
|
open
|
https://github.com/huggingface/datasets/pull/5602
| 2023-03-02T15:51:12
| 2023-04-12T15:54:53
| null |
{
"login": "amyeroberts",
"id": 22614925,
"type": "User"
}
|
[] | true
|
[] |
1,606,685,976
| 5,601
|
Authorization error
|
### Describe the bug
Get `Authorization error` when try to push data into hugginface datasets hub.
### Steps to reproduce the bug
I did all steps in the [tutorial](https://huggingface.co/docs/datasets/share),
1. `huggingface-cli login` with WRITE token
2. `git lfs install`
3. `git clone https://huggingface.co/datasets/namespace/your_dataset_name`
4.
```
cp /somewhere/data/*.json .
git lfs track *.json
git add .gitattributes
git add *.json
git commit -m "add json files"
```
but when I execute `git push` I got the error:
```
Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done.
batch response: Authorization error.
error: failed to push some refs to 'https://huggingface.co/datasets/zeusfsx/ukrainian-news'
```
Size of data ~100Gb. I have five json files - different parts.
### Expected behavior
All my data pushed into hub
### Environment info
- `datasets` version: 2.10.1
- Platform: macOS-13.2.1-arm64-arm-64bit
- Python version: 3.10.10
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5601
| 2023-03-02T12:08:39
| 2023-03-14T16:55:35
| 2023-03-14T16:55:34
|
{
"login": "OleksandrKorovii",
"id": 107404835,
"type": "User"
}
|
[] | false
|
[] |
1,606,585,596
| 5,600
|
Dataloader getitem not working for DreamboothDatasets
|
### Describe the bug
Dataloader getitem is not working as before (see example of [DreamboothDatasets](https://github.com/huggingface/peft/blob/main/examples/lora_dreambooth/train_dreambooth.py#L451C14-L529))
moving Datasets to 2.8.0 solved the issue.
### Steps to reproduce the bug
1- using DreamBoothDataset to load some images
2- error after loading when trying to visualise the images
### Expected behavior
I was expecting a numpy array of the image
### Environment info
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
|
closed
|
https://github.com/huggingface/datasets/issues/5600
| 2023-03-02T11:00:27
| 2023-03-13T17:59:35
| 2023-03-13T17:59:35
|
{
"login": "salahiguiliz",
"id": 76955987,
"type": "User"
}
|
[] | false
|
[] |
1,605,018,478
| 5,598
|
Fix push_to_hub with no dataset_infos
|
As reported in https://github.com/vijaydwivedi75/lrgb/issues/10, `push_to_hub` fails if the remote repository already exists and has a README.md without `dataset_info` in the YAML tags
cc @clefourrier
|
closed
|
https://github.com/huggingface/datasets/pull/5598
| 2023-03-01T13:54:06
| 2023-03-02T13:47:13
| 2023-03-02T13:40:17
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,604,928,721
| 5,597
|
in-place dataset update
|
### Motivation
For the circumstance that I creat an empty `Dataset` and keep appending new rows into it, I found that it leads to creating a new dataset at each call. It looks quite memory-consuming. I just wonder if there is any more efficient way to do this.
```python
from datasets import Dataset
ds = Dataset.from_list([])
ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: [],
>>> num_rows: 0
>>> })
ds = ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: ['a', 'b'],
>>> num_rows: 1
>>> })
```
### Feature request
Call for in-place dataset update functions, that update the existing `Dataset` in place without creating a new copy. The interface is supposed to keep the same style as PyTorch, such as the in-place version of a `function` is named `function_`. For example, the in-pace version of `add_item`, i.e., `add_item_`, immediately updates the `Dataset`.
```python
from datasets import Dataset
ds = Dataset.from_list([])
ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: [],
>>> num_rows: 0
>>> })
ds.add_item_({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: ['a', 'b'],
>>> num_rows: 1
>>> })
```
### Related Functions
* `.map`
* `.filter`
* `.add_item`
|
closed
|
https://github.com/huggingface/datasets/issues/5597
| 2023-03-01T12:58:18
| 2023-03-02T13:30:41
| 2023-03-02T03:47:00
|
{
"login": "speedcell4",
"id": 3585459,
"type": "User"
}
|
[
{
"name": "wontfix",
"color": "ffffff"
}
] | false
|
[] |
1,604,919,993
| 5,596
|
[TypeError: Couldn't cast array of type] Can only load a subset of the dataset
|
### Describe the bug
I'm trying to load this [dataset](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues) which consists of jsonl files and I get the following error:
```
casted_values = _c(array.values, feature[0])
File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 1839, in wrapper
return func(array, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 2132, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
struct<type: string, action: string, datetime: timestamp[s], author: string, title: string, description: string, comment_id: int64, comment: string, labels: list<item: string>>
to
{'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)}
```
But I can succesfully load a subset of the dataset, for example this works:
```python
ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train", data_files=[f"data/data-{x}.jsonl" for x in range(10)])
```
and `ds.features` returns:
```
{'repo': Value(dtype='string', id=None),
'org': Value(dtype='string', id=None),
'issue_id': Value(dtype='int64', id=None),
'issue_number': Value(dtype='int64', id=None),
'pull_request': {'user_login': Value(dtype='string', id=None),
'repo': Value(dtype='string', id=None),
'number': Value(dtype='int64', id=None)},
'events': [{'type': Value(dtype='string', id=None),
'action': Value(dtype='string', id=None),
'datetime': Value(dtype='timestamp[s]', id=None),
'author': Value(dtype='string', id=None),
'title': Value(dtype='string', id=None),
'description': Value(dtype='string', id=None),
'comment_id': Value(dtype='int64', id=None),
'comment': Value(dtype='string', id=None)}]}
```
So I'm not sure if there's an issue with just some of the files. Grateful if you have any suggestions to fix the issue.
Side note:
I saw this related [issue](https://github.com/huggingface/datasets/issues/3637) and tried to write a loading script to have `events` as a `Sequence` and not `list` [here](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/blob/main/loading.py) (the script was renamed). It worked with a subset locally but doesn't for the remote dataset it can't find https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/resolve/main/data.
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train")
```
### Expected behavior
Load the entire dataset succesfully.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
|
closed
|
https://github.com/huggingface/datasets/issues/5596
| 2023-03-01T12:53:08
| 2023-12-05T03:22:00
| 2023-03-02T11:12:11
|
{
"login": "loubnabnl",
"id": 44069155,
"type": "User"
}
|
[] | false
|
[] |
1,604,070,629
| 5,595
|
Unpins sqlAlchemy
|
Closes #5477
|
closed
|
https://github.com/huggingface/datasets/pull/5595
| 2023-03-01T01:33:45
| 2023-04-04T08:20:19
| 2023-04-04T08:19:14
|
{
"login": "lazarust",
"id": 46943923,
"type": "User"
}
|
[] | true
|
[] |
1,603,980,995
| 5,594
|
Error while downloading the xtreme udpos dataset
|
### Describe the bug
Hi,
I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed
```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4...
Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s]
Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last):
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single
for key, record in generator:
File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples
yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs)
File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples
for path, file in filepath:
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__
yield from self.generator(*self.args, **self.kwargs)
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path
yield from cls._iter_tar(f)
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar
for tarinfo in stream:
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__
tarinfo = self.next()
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next
raise ReadError("unexpected end of data")
tarfile.ReadError: unexpected end of data
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module>
main()
File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main
train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload")
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset
builder_instance.download_and_prepare(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare
self._download_and_prepare(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare
super()._download_and_prepare(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
```
train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload")
```
### Expected behavior
Download the udpos dataset
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
|
closed
|
https://github.com/huggingface/datasets/issues/5594
| 2023-02-28T23:40:53
| 2023-11-04T20:45:56
| 2023-07-24T14:22:18
|
{
"login": "simran-khanuja",
"id": 24687672,
"type": "User"
}
|
[] | false
|
[] |
1,603,619,124
| 5,592
|
Fix docstring example
|
Fixes #5581 to use the correct output for the `set_format` method.
|
closed
|
https://github.com/huggingface/datasets/pull/5592
| 2023-02-28T18:42:37
| 2023-02-28T19:26:33
| 2023-02-28T19:19:15
|
{
"login": "stevhliu",
"id": 59462357,
"type": "User"
}
|
[] | true
|
[] |
1,603,571,407
| 5,591
|
set dev version
| null |
closed
|
https://github.com/huggingface/datasets/pull/5591
| 2023-02-28T18:09:05
| 2023-02-28T18:16:31
| 2023-02-28T18:09:15
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,603,549,504
| 5,590
|
Release: 2.10.1
| null |
closed
|
https://github.com/huggingface/datasets/pull/5590
| 2023-02-28T17:58:11
| 2023-02-28T18:16:27
| 2023-02-28T18:06:08
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,603,535,704
| 5,589
|
Revert "pass the dataset features to the IterableDataset.from_generator"
|
This reverts commit b91070b9c09673e2e148eec458036ab6a62ac042 (temporarily)
It hurts iterable dataset performance a lot (e.g. x4 slower because it encodes+decodes images unnecessarily). I think we need to fix this before re-adding it
cc @mariosasko @Hubert-Bonisseur
|
closed
|
https://github.com/huggingface/datasets/pull/5589
| 2023-02-28T17:52:04
| 2023-09-24T10:07:33
| 2023-03-21T14:18:18
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,603,304,766
| 5,588
|
Flatten dataset on the fly in `save_to_disk`
|
Flatten a dataset on the fly in `save_to_disk` instead of doing it with `flatten_indices` to avoid creating an additional cache file.
(this is one of the sub-tasks in https://github.com/huggingface/datasets/issues/5507)
|
closed
|
https://github.com/huggingface/datasets/pull/5588
| 2023-02-28T15:37:46
| 2023-02-28T17:28:35
| 2023-02-28T17:21:17
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,603,139,420
| 5,587
|
Fix `sort` with indices mapping
|
Fixes the `key` range in the `query_table` call in `sort` to account for an indices mapping
Fix #5586
|
closed
|
https://github.com/huggingface/datasets/pull/5587
| 2023-02-28T14:05:08
| 2023-02-28T17:28:57
| 2023-02-28T17:21:58
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,602,961,544
| 5,586
|
.sort() is broken when used after .filter(), only in 2.10.0
|
### Describe the bug
Hi, thank you for your support!
It seems like the addition of multiple key sort (#5502) in 2.10.0 broke the `.sort()` method.
After filtering a dataset with `.filter()`, the `.sort()` seems to refer to the query_table index of the previous unfiltered dataset, resulting in an IndexError.
This only happens with the 2.10.0 release.
### Steps to reproduce the bug
```Python
from datasets import load_dataset
# dataset with length of 1104
ds = load_dataset('glue', 'ax')['test']
ds = ds.filter(lambda x: x['idx'] > 1100)
ds.sort('premise')
print('Done')
```
File "/home/dongkeun/datasets_test/test.py", line 5, in <module>
ds.sort('premise')
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 528, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 511, in wrapper
out = func(dataset, *args, **kwargs)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3959, in sort
sort_table = query_table(
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 588, in query_table
_check_valid_index_key(key, size)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 537, in _check_valid_index_key
_check_valid_index_key(max(key), size=size)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 531, in _check_valid_index_key
raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 1103 is out of bounds for size 3
### Expected behavior
It should sort the dataset and print "Done". Which it does on 2.9.0.
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5586
| 2023-02-28T12:18:09
| 2023-02-28T18:17:26
| 2023-02-28T17:21:59
|
{
"login": "MattYoon",
"id": 57797966,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,602,190,030
| 5,585
|
Cache is not transportable
|
### Describe the bug
I would like to share cache between two machines (a Windows host machine and a WSL instance).
I run most my code in WSL. I have just run out of space in the virtual drive. Rather than expand the drive size, I plan to move to cache to the host Windows machine, thereby sharing the downloads.
I'm hoping that I can just copy/paste the cache files, but I notice that a lot of the file names start with the path name, e.g. `_home_davidg_.cache_huggingface_datasets_conll2003_default-451...98.lock` where `home/davidg` is where the cache is in WSL.
This seems to suggest that the cache is not portable/cannot be centralised or shared. Is this the case, or are the files that start with path names not integral to the caching mechanism? Because copying the cache files _seems_ to work, but I'm not filled with confidence that something isn't going to break.
A related issue, when trying to load a dataset that should come from cache (running in WSL, pointing to cache on the Windows host) it seemed to work fine, but it still uses a WSL directory for `.cache\huggingface\modules\datasets_modules`. I see nothing in the docs about this, or how to point it to a different place.
I have asked a related question on the forum: https://discuss.huggingface.co/t/is-datasets-cache-operating-system-agnostic/32656
### Steps to reproduce the bug
View the cache directory in WSL/Windows.
### Expected behavior
Cache can be shared between (virtual) machines and be transportable.
It would be nice to have a simple way to say "Dear Hugging Face packages, please put ALL your cache in `blah/de/blah`" and have all the Hugging Face packages respect that single location.
### Environment info
```
- `datasets` version: 2.9.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- ```
|
closed
|
https://github.com/huggingface/datasets/issues/5585
| 2023-02-28T00:53:06
| 2023-02-28T21:26:52
| 2023-02-28T21:26:52
|
{
"login": "davidgilbertson",
"id": 4443482,
"type": "User"
}
|
[] | false
|
[] |
1,601,821,808
| 5,584
|
Unable to load coyo700M dataset
|
### Describe the bug
Seeing this error when downloading https://huggingface.co/datasets/kakaobrain/coyo-700m:
```ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.```
Full stack trace
```Downloading and preparing dataset parquet/kakaobrain--coyo-700m to /root/.cache/huggingface/datasets/kakaobrain___parquet/kakaobrain--coyo-700m-ae729692ae3e0073/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...
Downloading data files: 100%
1/1 [00:00<00:00, 63.35it/s]
Extracting data files: 100%
1/1 [00:00<00:00, 5.00it/s]
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
[/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1859 _time = time.time()
-> 1860 for _, table in generator:
1861 if max_shard_size is not None and writer._num_bytes > max_shard_size:
9 frames
ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
[/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1891 e = e.__context__
-> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1893
1894 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset```
### Steps to reproduce the bug
```
from datasets import load_dataset
hf_dataset = load_dataset("kakaobrain/coyo-700m")
```
### Expected behavior
The above commands load the dataset successfully. Or handles exception and continue loading the remainder.
### Environment info
colab. any
|
closed
|
https://github.com/huggingface/datasets/issues/5584
| 2023-02-27T19:35:03
| 2023-02-28T07:27:59
| 2023-02-28T07:27:58
|
{
"login": "manuaero",
"id": 3059998,
"type": "User"
}
|
[] | false
|
[] |
1,601,583,625
| 5,583
|
Do no write index by default when exporting a dataset
|
Ensures all the writers that use Pandas for conversion (JSON, CSV, SQL) do not export `index` by default (https://github.com/huggingface/datasets/pull/5490 only did this for CSV)
|
closed
|
https://github.com/huggingface/datasets/pull/5583
| 2023-02-27T17:04:46
| 2023-02-28T13:52:15
| 2023-02-28T13:44:04
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,600,932,092
| 5,582
|
Add column_names to IterableDataset
|
This PR closes #5383
* Add column_names property to IterableDataset
* Add multiple tests for this new property
|
closed
|
https://github.com/huggingface/datasets/pull/5582
| 2023-02-27T10:50:07
| 2023-03-13T19:10:22
| 2023-03-13T19:03:32
|
{
"login": "patrickloeber",
"id": 50772274,
"type": "User"
}
|
[] | true
|
[] |
1,600,675,489
| 5,581
|
[DOC] Mistaken docs on set_format
|
### Describe the bug
https://huggingface.co/docs/datasets/v2.10.0/en/package_reference/main_classes#datasets.Dataset.set_format
<img width="700" alt="image" src="https://user-images.githubusercontent.com/36224762/221506973-ae2e3991-60a7-4d4e-99f8-965c6eb61e59.png">
While actually running it will result in:
<img width="1094" alt="image" src="https://user-images.githubusercontent.com/36224762/221507032-007dab82-8781-4319-b21a-e6e4d40d97b3.png">
### Steps to reproduce the bug
_
### Expected behavior
_
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
|
closed
|
https://github.com/huggingface/datasets/issues/5581
| 2023-02-27T08:03:09
| 2023-02-28T19:19:17
| 2023-02-28T19:19:17
|
{
"login": "NightMachinery",
"id": 36224762,
"type": "User"
}
|
[
{
"name": "good first issue",
"color": "7057ff"
}
] | false
|
[] |
1,600,431,792
| 5,580
|
Support cloud storage in load_dataset via fsspec
|
Closes https://github.com/huggingface/datasets/issues/5281
This PR uses fsspec to support datasets on cloud storage (tested manually with GCS). ETags are currently unsupported for cloud storage. In general, a much larger refactor could be done to just use fsspec for all schemes (ftp, http/s, s3, gcs) to unify the interfaces here, but I ultimately opted to leave that out of this PR
I didn't create a GCS filesystem class in `datasets.filesystems` since the S3 one appears to be a wrapper around `s3fs.S3FileSystem` and mainly used to generate docs.
|
closed
|
https://github.com/huggingface/datasets/pull/5580
| 2023-02-27T04:06:05
| 2024-11-27T01:25:39
| 2023-03-11T00:55:40
|
{
"login": "dwyatte",
"id": 2512762,
"type": "User"
}
|
[] | true
|
[] |
1,599,732,211
| 5,579
|
Add instructions to create `DataLoader` from augmented dataset in object detection guide
|
The following adds instructions on how to create a `DataLoader` from the guide on how to use object detection with augmentations (#4710). I am open to hearing any suggestions for improvement !
|
closed
|
https://github.com/huggingface/datasets/pull/5579
| 2023-02-25T14:53:17
| 2023-03-23T19:24:59
| 2023-03-23T19:24:50
|
{
"login": "Laurent2916",
"id": 21087104,
"type": "User"
}
|
[] | true
|
[] |
1,598,863,119
| 5,578
|
Add `huggingface_hub` version to env cli command
|
Add the `huggingface_hub` version to the `env` command's output.
|
closed
|
https://github.com/huggingface/datasets/pull/5578
| 2023-02-24T15:37:43
| 2023-02-27T17:28:25
| 2023-02-27T17:21:09
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,598,587,665
| 5,577
|
Cannot load `the_pile_openwebtext2`
|
### Describe the bug
I met the same bug mentioned in #3053 which is never fixed. Because several `reddit_scores` are larger than `int8` even `int16`. https://huggingface.co/datasets/the_pile_openwebtext2/blob/main/the_pile_openwebtext2.py#L62
### Steps to reproduce the bug
```python3
from datasets import load_dataset
dataset = load_dataset("the_pile_openwebtext2")
```
### Expected behavior
load as normal.
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5577
| 2023-02-24T13:01:48
| 2023-02-24T14:01:09
| 2023-02-24T14:01:09
|
{
"login": "wjfwzzc",
"id": 5126316,
"type": "User"
}
|
[] | false
|
[] |
1,598,582,744
| 5,576
|
I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value("int8"))`, but the actual values can be well outside the max range for 8-bit integers.
|
I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value("int8"))`, but the actual values can be well outside the max range for 8-bit integers.
I worked around this by downloading the `the_pile_openwebtext2.py` and editing it to use local files and drop reddit scores as a column (not needed for my purposes).
_Originally posted by @tc-wolf in https://github.com/huggingface/datasets/issues/3053#issuecomment-1281392422_
|
closed
|
https://github.com/huggingface/datasets/issues/5576
| 2023-02-24T12:57:49
| 2023-02-24T12:58:31
| 2023-02-24T12:58:18
|
{
"login": "wjfwzzc",
"id": 5126316,
"type": "User"
}
|
[] | false
|
[] |
1,598,396,552
| 5,575
|
Metadata for each column
|
### Feature request
Being able to put some metadata for each column as a string or any other type.
### Motivation
I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata
### Your contribution
Maybe we could map another relational like database as the metadata?
|
open
|
https://github.com/huggingface/datasets/issues/5575
| 2023-02-24T10:53:44
| 2024-01-05T21:48:35
| null |
{
"login": "parsa-ra",
"id": 11356471,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,598,104,691
| 5,574
|
c4 dataset streaming fails with `FileNotFoundError`
|
### Describe the bug
Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("c4", "en", split="train", streaming=True)
next(iter(dataset))
```
causes a
```
FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz
```
I can download this file manually though e.g. by entering this URL in a browser.
There is an underlying HTTP 403 status code:
```
aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX')
```
### Expected behavior
This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5574
| 2023-02-24T07:57:32
| 2023-12-18T07:32:32
| 2023-02-27T04:03:38
|
{
"login": "krasserm",
"id": 202907,
"type": "User"
}
|
[] | false
|
[] |
1,597,400,836
| 5,573
|
Use soundfile for mp3 decoding instead of torchaudio
|
I've removed `torchaudio` completely and switched to use `soundfile` for everything. With the new version of `soundfile` package this should work smoothly because the `libsndfile` C library is bundled, in Linux wheels too.
Let me know if you think it's too harsh and we should continue to support `torchaudio` decoding.
I decided that we can drop it completely because:
1. it's always something wrong with `torchaudio` (for example recently https://github.com/huggingface/datasets/issues/5488 )
2. the results of mp3 decoding are different depending on `torchaudio` version
3. `soundfile` is slightly faster then the latest `torchaudio`
4. anyway users can pass any custom decoding function with any library they want if needed (worth putting a snippet in the docs).
cc @sanchit-gandhi @vaibhavad
|
closed
|
https://github.com/huggingface/datasets/pull/5573
| 2023-02-23T19:19:44
| 2023-02-28T20:25:14
| 2023-02-28T20:16:02
|
{
"login": "polinaeterna",
"id": 16348744,
"type": "User"
}
|
[] | true
|
[] |
1,597,257,624
| 5,572
|
Datasets 2.10.0 does not reuse the dataset cache
|
### Describe the bug
download_mode="reuse_dataset_if_exists" will always consider that a dataset doesn't exist.
Specifically, upon losing an internet connection trying to load a dataset for a second time in ten seconds, a connection error results, showing a breakpoint of:
```
File ~/jupyterlab/.direnv/python-3.9.6/lib/python3.9/site-packages/datasets/load.py:1174, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1165 except Exception as e: # noqa: catch any exception of hf_hub and consider that the dataset doesn't exist
1166 if isinstance(
1167 e,
1168 (
(...)
1172 ),
1173 ):
-> 1174 raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})")
1175 elif "404" in str(e):
1176 msg = f"Dataset '{path}' doesn't exist on the Hub"
ConnectionError: Couldn't reach 'lsb/tenk' on the Hub (ConnectionError)
```
This has been around since at least v2.0.
### Steps to reproduce the bug
```
from datasets import load_dataset
import numpy as np
tenk = load_dataset("lsb/tenk") # ten thousand integers
print(np.average(tenk['train']['a'])) # prints 4999.5
### now disconnect your internet
tenk_too = load_dataset("lsb/tenk", download_mode="reuse_dataset_if_exists")
# Raises ConnectionError: Couldn't reach 'lsb/tenk' on the Hub (ConnectionError)
```
### Expected behavior
I expected that I would be able to reuse the dataset I just downloaded.
### Environment info
- `datasets` version: 2.10.0
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.5.2
|
closed
|
https://github.com/huggingface/datasets/issues/5572
| 2023-02-23T17:28:11
| 2023-02-23T18:03:55
| 2023-02-23T18:03:55
|
{
"login": "lsb",
"id": 45281,
"type": "User"
}
|
[] | false
|
[] |
1,597,198,953
| 5,571
|
load_dataset fails for JSON in windows
|
### Describe the bug
Steps:
1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method.
2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json"
3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON.
4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py.
raise InvalidConfigName(
f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. "
f"They could create issues when creating a directory for this config on Windows filesystem."
6. When I bring the data to the current directory, it works fine.
### Steps to reproduce the bug
Steps:
1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method.
2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json"
3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON.
4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py.
raise InvalidConfigName(
f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. "
f"They could create issues when creating a directory for this config on Windows filesystem."
6. When I bring the data to the current directory, it works fine.
### Expected behavior
Should be able to read from a path different than current directory in Windows machine.
### Environment info
datasets version: 2.3.1
python version: 3.8
Windows OS
|
closed
|
https://github.com/huggingface/datasets/issues/5571
| 2023-02-23T16:50:11
| 2023-02-24T13:21:47
| 2023-02-24T13:21:47
|
{
"login": "abinashsahu",
"id": 11876897,
"type": "User"
}
|
[] | false
|
[] |
1,597,190,926
| 5,570
|
load_dataset gives FileNotFoundError on imagenet-1k if license is not accepted on the hub
|
### Describe the bug
When calling ```load_dataset('imagenet-1k')``` FileNotFoundError is raised, if not logged in and if logged in with huggingface-cli but not having accepted the licence on the hub. There is no error once accepting.
### Steps to reproduce the bug
```
from datasets import load_dataset
imagenet = load_dataset("imagenet-1k", split="train", streaming=True)
FileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub
```
tested on a colab notebook.
### Expected behavior
I would expect a specific error indicating that I have to login then accept the dataset licence.
I find this bug very relevant as this code is on a guide on the [Huggingface documentation for Datasets](https://huggingface.co/docs/datasets/about_mapstyle_vs_iterable)
### Environment info
google colab cpu-only instance
|
closed
|
https://github.com/huggingface/datasets/issues/5570
| 2023-02-23T16:44:32
| 2023-07-24T15:18:50
| 2023-07-24T15:18:50
|
{
"login": "buoi",
"id": 38630200,
"type": "User"
}
|
[] | false
|
[] |
1,597,132,383
| 5,569
|
pass the dataset features to the IterableDataset.from_generator function
|
[5558](https://github.com/huggingface/datasets/issues/5568)
|
closed
|
https://github.com/huggingface/datasets/pull/5569
| 2023-02-23T16:06:04
| 2023-02-24T14:06:37
| 2023-02-23T18:15:16
|
{
"login": "bruno-hays",
"id": 48770768,
"type": "User"
}
|
[] | true
|
[] |
1,596,900,532
| 5,568
|
dataset.to_iterable_dataset() loses useful info like dataset features
|
### Describe the bug
Hello,
I like the new `to_iterable_dataset` feature but I noticed something that seems to be missing.
When using `to_iterable_dataset` to transform your map style dataset into iterable dataset, you lose valuable metadata like the features.
These metadata are useful if you want to interleave iterable datasets, cast columns etc.
### Steps to reproduce the bug
```python
dataset = load_dataset("lhoestq/demo1")["train"]
print(dataset.features)
# {'id': Value(dtype='string', id=None), 'package_name': Value(dtype='string', id=None), 'review': Value(dtype='string', id=None), 'date': Value(dtype='string', id=None), 'star': Value(dtype='int64', id=None), 'version_id': Value(dtype='int64', id=None)}
dataset = dataset.to_iterable_dataset()
print(dataset.features)
# None
```
### Expected behavior
Keep the relevant information
### Environment info
datasets==2.10.0
|
closed
|
https://github.com/huggingface/datasets/issues/5568
| 2023-02-23T13:45:33
| 2023-02-24T13:22:36
| 2023-02-24T13:22:36
|
{
"login": "bruno-hays",
"id": 48770768,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
},
{
"name": "good first issue",
"color": "7057ff"
}
] | false
|
[] |
1,595,916,674
| 5,566
|
Directly reading parquet files in a s3 bucket from the load_dataset method
|
### Feature request
Right now, we have to read the get the parquet file to the local storage. So having ability to read given the bucket directly address would be benificial
### Motivation
In a production set up, this feature can help us a lot. So we do not need move training datafiles in between storage.
### Your contribution
I am willing to help if there's anyway.
|
open
|
https://github.com/huggingface/datasets/issues/5566
| 2023-02-22T22:13:40
| 2023-02-23T11:03:29
| null |
{
"login": "shamanez",
"id": 16892570,
"type": "User"
}
|
[
{
"name": "duplicate",
"color": "cfd3d7"
},
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,595,281,752
| 5,565
|
Add writer_batch_size for ArrowBasedBuilder
|
This way we can control the size of the record_batches/row_groups of arrow/parquet files.
This can be useful for `datasets-server` to keep control of the row groups size which can affect random access performance for audio/image/video datasets
Right now having 1,000 examples per row group cause some image datasets to be pretty slow for random access (e.g. 4seconds for `beans` to get 20 rows)
|
closed
|
https://github.com/huggingface/datasets/pull/5565
| 2023-02-22T15:09:30
| 2023-03-10T13:53:03
| 2023-03-10T13:45:43
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,595,064,698
| 5,564
|
Set dev version
| null |
closed
|
https://github.com/huggingface/datasets/pull/5564
| 2023-02-22T13:00:09
| 2023-02-22T13:09:26
| 2023-02-22T13:00:25
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,595,049,025
| 5,563
|
Release: 2.10.0
| null |
closed
|
https://github.com/huggingface/datasets/pull/5563
| 2023-02-22T12:48:52
| 2023-02-22T13:05:55
| 2023-02-22T12:56:48
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,594,625,539
| 5,562
|
Update csv.py
|
Removed mangle_dup_cols=True from BuilderConfig.
It triggered following deprecation warning:
/usr/local/lib/python3.8/dist-packages/datasets/download/streaming_download_manager.py:776: 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)
Further documentation of pandas: https://pandas.pydata.org/docs/whatsnew/v1.4.0.html#mangle-dupe-cols-in-read-csv-no-longer-renames-unique-columns-conflicting-with-target-names
At first sight it seems like this flag is resolved internally, it might need some more research.
|
closed
|
https://github.com/huggingface/datasets/pull/5562
| 2023-02-22T07:56:10
| 2023-02-23T11:07:49
| 2023-02-23T11:00:58
|
{
"login": "xdoubleu",
"id": 54279069,
"type": "User"
}
|
[] | true
|
[] |
1,593,862,388
| 5,561
|
Add pre-commit config yaml file to enable automatic code formatting
|
@huggingface/datasets do you think it would be useful? Motivation - sometimes PRs are like 30% "fix: style" commits :)
If so - I need to double check the config but for me locally it works as expected.
|
closed
|
https://github.com/huggingface/datasets/pull/5561
| 2023-02-21T17:35:07
| 2023-02-28T15:37:22
| 2023-02-23T18:23:29
|
{
"login": "polinaeterna",
"id": 16348744,
"type": "User"
}
|
[] | true
|
[] |
1,593,809,978
| 5,560
|
Ensure last tqdm update in `map`
|
This PR modifies `map` to:
* ensure the TQDM bar gets the last progress update
* when a map function fails, avoid throwing a chained exception in the single-proc mode
|
closed
|
https://github.com/huggingface/datasets/pull/5560
| 2023-02-21T16:56:17
| 2023-02-21T18:26:23
| 2023-02-21T18:19:09
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,593,676,489
| 5,559
|
Fix map suffix_template
|
#5455 introduced a small bug that lead `map` to ignore the `suffix_template` argument and not put suffixes to cached files in multiprocessing.
I fixed this and also improved a few things:
- regarding logging: "Loading cached processed dataset" is now logged only once even in multiprocessing (it used to be logged `num_proc` times)
- regarding new_fingerprint: I made sure that the returned dataset satisfies `ds._fingerprint==new_fingerprint` if `new_fingerprint` is passed to `map`
|
closed
|
https://github.com/huggingface/datasets/pull/5559
| 2023-02-21T15:26:26
| 2023-02-21T17:21:37
| 2023-02-21T17:14:29
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,593,655,815
| 5,558
|
Remove instructions for `ffmpeg` system package installation on Colab
|
Colab now has Ubuntu 20.04 which already has `ffmpeg` of required (>4) version.
|
closed
|
https://github.com/huggingface/datasets/pull/5558
| 2023-02-21T15:13:36
| 2023-03-01T13:46:04
| 2023-02-23T13:50:27
|
{
"login": "polinaeterna",
"id": 16348744,
"type": "User"
}
|
[] | true
|
[] |
1,593,545,324
| 5,557
|
Add filter desc
|
Otherwise it would show a `Map` progress bar, since it uses `map` under the hood
|
closed
|
https://github.com/huggingface/datasets/pull/5557
| 2023-02-21T14:04:42
| 2023-02-21T14:19:54
| 2023-02-21T14:12:39
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,593,246,936
| 5,556
|
Use default audio resampling type
|
...instead of relying on the optional librosa dependency `resampy`.
It was only used for `_decode_non_mp3_file_like` anyway and not for the other ones - removing it fixes consistency between decoding methods (except torchaudio decoding)
Therefore I think it is a better solution than adding `resampy` as a dependency in https://github.com/huggingface/datasets/pull/5554
cc @polinaeterna
|
closed
|
https://github.com/huggingface/datasets/pull/5556
| 2023-02-21T10:45:50
| 2023-02-21T12:49:50
| 2023-02-21T12:42:52
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,592,469,938
| 5,555
|
`.shuffle` throwing error `ValueError: Protocol not known: parent`
|
### Describe the bug
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In [16], line 1
----> 1 train_dataset = train_dataset.shuffle()
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs)
544 self_format = {
545 "type": self._format_type,
546 "format_kwargs": self._format_kwargs,
547 "columns": self._format_columns,
548 "output_all_columns": self._output_all_columns,
549 }
550 # apply actual function
--> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
553 # re-apply format to the output
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
482 # Update fingerprint of in-place transforms + update in-place history of transforms
484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3616, in Dataset.shuffle(self, seed, generator, keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint)
3610 return self._new_dataset_with_indices(
3611 fingerprint=new_fingerprint, indices_cache_file_name=indices_cache_file_name
3612 )
3614 permutation = generator.permutation(len(self))
-> 3616 return self.select(
3617 indices=permutation,
3618 keep_in_memory=keep_in_memory,
3619 indices_cache_file_name=indices_cache_file_name if not keep_in_memory else None,
3620 writer_batch_size=writer_batch_size,
3621 new_fingerprint=new_fingerprint,
3622 )
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs)
544 self_format = {
545 "type": self._format_type,
546 "format_kwargs": self._format_kwargs,
547 "columns": self._format_columns,
548 "output_all_columns": self._output_all_columns,
549 }
550 # apply actual function
--> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
553 # re-apply format to the output
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
482 # Update fingerprint of in-place transforms + update in-place history of transforms
484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3266, in Dataset.select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
3263 return self._select_contiguous(start, length, new_fingerprint=new_fingerprint)
3265 # If not contiguous, we need to create a new indices mapping
-> 3266 return self._select_with_indices_mapping(
3267 indices,
3268 keep_in_memory=keep_in_memory,
3269 indices_cache_file_name=indices_cache_file_name,
3270 writer_batch_size=writer_batch_size,
3271 new_fingerprint=new_fingerprint,
3272 )
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs)
544 self_format = {
545 "type": self._format_type,
546 "format_kwargs": self._format_kwargs,
547 "columns": self._format_columns,
548 "output_all_columns": self._output_all_columns,
549 }
550 # apply actual function
--> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
553 # re-apply format to the output
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
482 # Update fingerprint of in-place transforms + update in-place history of transforms
484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3389, in Dataset._select_with_indices_mapping(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
3387 logger.info(f"Caching indices mapping at {indices_cache_file_name}")
3388 tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False)
-> 3389 writer = ArrowWriter(
3390 path=tmp_file.name, writer_batch_size=writer_batch_size, fingerprint=new_fingerprint, unit="indices"
3391 )
3393 indices = indices if isinstance(indices, list) else list(indices)
3395 size = len(self)
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_writer.py:315, in ArrowWriter.__init__(self, schema, features, path, stream, fingerprint, writer_batch_size, hash_salt, check_duplicates, disable_nullable, update_features, with_metadata, unit, embed_local_files, storage_options)
312 self._disable_nullable = disable_nullable
314 if stream is None:
--> 315 fs_token_paths = fsspec.get_fs_token_paths(path, storage_options=storage_options)
316 self._fs: fsspec.AbstractFileSystem = fs_token_paths[0]
317 self._path = (
318 fs_token_paths[2][0]
319 if not is_remote_filesystem(self._fs)
320 else self._fs.unstrip_protocol(fs_token_paths[2][0])
321 )
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:593, in get_fs_token_paths(urlpath, mode, num, name_function, storage_options, protocol, expand)
591 else:
592 urlpath = stringify_path(urlpath)
--> 593 chain = _un_chain(urlpath, storage_options or {})
594 if len(chain) > 1:
595 inkwargs = {}
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:330, in _un_chain(path, kwargs)
328 for bit in reversed(bits):
329 protocol = split_protocol(bit)[0] or "file"
--> 330 cls = get_filesystem_class(protocol)
331 extra_kwargs = cls._get_kwargs_from_urls(bit)
332 kws = kwargs.get(protocol, {})
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/registry.py:240, in get_filesystem_class(protocol)
238 if protocol not in registry:
239 if protocol not in known_implementations:
--> 240 raise ValueError("Protocol not known: %s" % protocol)
241 bit = known_implementations[protocol]
242 try:
ValueError: Protocol not known: parent
```
This is what the `train_dataset` object looks like
```
Dataset({
features: ['label', 'input_ids', 'attention_mask'],
num_rows: 364166
})
```
### Steps to reproduce the bug
The `train_dataset` obj is created by concatenating two datasets
And then shuffle is called, but it throws the mentioned error.
### Expected behavior
Should shuffle the dataset properly.
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-1022-aws-x86_64-with-glibc2.31
- Python version: 3.9.13
- PyArrow version: 10.0.0
- Pandas version: 1.4.4
|
open
|
https://github.com/huggingface/datasets/issues/5555
| 2023-02-20T21:33:45
| 2023-02-27T09:23:34
| null |
{
"login": "prabhakar267",
"id": 10768588,
"type": "User"
}
|
[] | false
|
[] |
1,592,285,062
| 5,554
|
Add resampy dep
|
In librosa 0.10 they removed the `resmpy` dependency and set it to optional.
However it is necessary for resampling. I added it to the "audio" extra dependencies.
|
closed
|
https://github.com/huggingface/datasets/pull/5554
| 2023-02-20T18:15:43
| 2023-09-24T10:07:29
| 2023-02-21T12:43:38
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,592,236,998
| 5,553
|
improved message error row formatting
|
Solves #5539
|
closed
|
https://github.com/huggingface/datasets/pull/5553
| 2023-02-20T17:29:14
| 2023-02-21T13:08:25
| 2023-02-21T12:58:12
|
{
"login": "Plutone11011",
"id": 26489385,
"type": "User"
}
|
[] | true
|
[] |
1,592,186,703
| 5,552
|
Make tiktoken tokenizers hashable
|
Fix for https://discord.com/channels/879548962464493619/1075729627546406912/1075729627546406912
|
closed
|
https://github.com/huggingface/datasets/pull/5552
| 2023-02-20T16:50:09
| 2023-02-21T13:20:42
| 2023-02-21T13:13:05
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,592,140,836
| 5,551
|
Suggest scikit-learn instead of sklearn
|
This is kinda unimportant fix but, the suggested `pip install sklearn` does not work.
The current error message if sklearn is not installed:
```
ImportError: To be able to use [dataset name], you need to install the following dependency: sklearn.
Please install it using 'pip install sklearn' for instance.
```
|
closed
|
https://github.com/huggingface/datasets/pull/5551
| 2023-02-20T16:16:57
| 2023-02-21T13:27:57
| 2023-02-21T13:21:07
|
{
"login": "osbm",
"id": 74963545,
"type": "User"
}
|
[] | true
|
[] |
1,591,409,475
| 5,550
|
Resolve four broken refs in the docs
|
Hello!
## Pull Request overview
* Resolve 4 broken references in the docs
## The problems
Two broken references [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.class_encode_column):
---
One broken reference [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.unique):
---
One missing reference [here](https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.DatasetDict.class_encode_column):
- Tom Aarsen
|
closed
|
https://github.com/huggingface/datasets/pull/5550
| 2023-02-20T08:52:11
| 2023-02-20T15:16:13
| 2023-02-20T15:09:13
|
{
"login": "tomaarsen",
"id": 37621491,
"type": "User"
}
|
[] | true
|
[] |
1,590,836,848
| 5,549
|
Apply ruff flake8-comprehension checks
|
Fix #5548
Apply ruff's flake8-comprehension checks for better performance, and more readable code.
|
closed
|
https://github.com/huggingface/datasets/pull/5549
| 2023-02-19T20:09:28
| 2023-02-23T14:06:39
| 2023-02-23T13:59:39
|
{
"login": "Skylion007",
"id": 2053727,
"type": "User"
}
|
[] | true
|
[] |
1,590,835,479
| 5,548
|
Apply flake8-comprehensions to codebase
|
### Feature request
Apply ruff flake8 comprehension checks to codebase.
### Motivation
This should strictly improve the performance / readability of the codebase by removing unnecessary iteration, function calls, etc. This should generate better Python bytecode which should strictly improve performance.
I already applied this fixes to PyTorch and Sympy with little issue and have opened PRs to diffusers and transformers todo this as well.
### Your contribution
Making a PR.
|
closed
|
https://github.com/huggingface/datasets/issues/5548
| 2023-02-19T20:05:38
| 2023-02-23T13:59:41
| 2023-02-23T13:59:41
|
{
"login": "Skylion007",
"id": 2053727,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,590,468,200
| 5,547
|
Add JAX device selection when formatting
|
## What's in this PR?
After exploring for a while the JAX integration in 🤗`datasets`, I found out that, even though JAX prioritizes the TPU and GPU as the default device when available, the `JaxFormatter` doesn't let you specify the device where you want to place the `jax.Array`s in case you don't want to rely on JAX's default array placement.
So on, I've included the `device` param in `JaxFormatter` but there are some things to take into consideration:
* A formatted `Dataset` is copied with `copy.deepcopy` which means that if one adds the param `device` in `JaxFormatter` as a `jaxlib.xla_extension.Device`, it "fails" because that object cannot be serialized (instead of serializing the param adds a random hash instead). That's the reason why I added a function `_map_devices_to_str` to basically create a mapping of strings to `jaxlib.xla_extension.Device`s so that `self.device` is a string and not a `jaxlib.xla_extension.Device`.
* To create a `jax.Array` in a device you need to either create it in the default device and then move it to the desired device with `jax.device_put` or directly create it in the device you want with `jax.default_device()` context manager.
* JAX will create an array by default in `jax.devices()[0]`
More information on JAX device management is available at https://jax.readthedocs.io/en/latest/faq.html#controlling-data-and-computation-placement-on-devices
## What's missing in this PR?
I've tested it both locally in CPU (Mac M2 and Mac M1, as no GPU support for Mac yet), and in GPU and TPU in Google Colab, let me know if you want me to provide you the Notebook for the latter.
But I did not implement any integration test as I wanted to get your feedback first.
|
closed
|
https://github.com/huggingface/datasets/pull/5547
| 2023-02-18T20:57:40
| 2023-02-21T16:10:55
| 2023-02-21T16:04:03
|
{
"login": "alvarobartt",
"id": 36760800,
"type": "User"
}
|
[] | true
|
[] |
1,590,346,349
| 5,546
|
Downloaded datasets do not cache at $HF_HOME
|
### Describe the bug
In the huggingface course (https://huggingface.co/course/chapter3/2?fw=pt) it said that if we set HF_HOME, downloaded datasets would be cached at specified address but it does not. downloaded models from checkpoint names are downloaded and cached at HF_HOME but this is not the case for datasets, they are still cached at ~/.cache/huggingface/datasets.
### Steps to reproduce the bug
Run the following code
```
from datasets import load_dataset
raw_datasets = load_dataset("glue", "mrpc")
raw_datasets
```
it downloads and store dataset at ~/.cache/huggingface/datasets
### Expected behavior
to cache dataset at HF_HOME.
### Environment info
python 3.10.6
Kubuntu 22.04
HF_HOME located on a separate partition
|
closed
|
https://github.com/huggingface/datasets/issues/5546
| 2023-02-18T13:30:35
| 2023-07-24T14:22:43
| 2023-07-24T14:22:43
|
{
"login": "ErfanMoosaviMonazzah",
"id": 79091831,
"type": "User"
}
|
[] | false
|
[] |
1,590,315,972
| 5,545
|
Added return methods for URL-references to the pushed dataset
|
Hi,
I was missing the ability to easily open the pushed dataset and it seemed like a quick fix.
Maybe we also want to log this info somewhere, but let me know if I need to add that too.
Cheers,
David
|
open
|
https://github.com/huggingface/datasets/pull/5545
| 2023-02-18T11:26:25
| 2023-12-18T16:57:56
| null |
{
"login": "davidberenstein1957",
"id": 25269220,
"type": "User"
}
|
[] | true
|
[] |
1,588,951,379
| 5,543
|
the pile datasets url seems to change back
|
### Describe the bug
in #3627, the host url of the pile dataset became `https://mystic.the-eye.eu`. Now the new url is broken, but `https://the-eye.eu` seems to work again.
### Steps to reproduce the bug
```python3
from datasets import load_dataset
dataset = load_dataset("bookcorpusopen")
```
shows
```python3
ConnectionError: Couldn't reach https://mystic.the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz (ProxyError(MaxRetryError("HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_pr
eliminary_components/books1.tar.gz (Caused by ProxyError('Cannot connect to proxy.', OSError('Tunnel connection failed: 504 Gateway Timeout')))")))
```
### Expected behavior
Downloading as normal.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 6.0.1
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5543
| 2023-02-17T08:40:11
| 2023-02-21T06:37:00
| 2023-02-20T08:41:33
|
{
"login": "wjfwzzc",
"id": 5126316,
"type": "User"
}
|
[] | false
|
[] |
1,588,633,724
| 5,542
|
Avoid saving sparse ChunkedArrays in pyarrow tables
|
Fixes https://github.com/huggingface/datasets/issues/5541
|
closed
|
https://github.com/huggingface/datasets/pull/5542
| 2023-02-17T01:52:38
| 2023-02-17T19:20:49
| 2023-02-17T11:12:32
|
{
"login": "marioga",
"id": 6591505,
"type": "User"
}
|
[] | true
|
[] |
1,588,633,555
| 5,541
|
Flattening indices in selected datasets is extremely inefficient
|
### Describe the bug
If we perform a `select` (or `shuffle`, `train_test_split`, etc.) operation on a dataset , we end up with a dataset with an `indices_table`. Currently, flattening such dataset consumes a lot of memory and the resulting flat dataset contains ChunkedArrays with as many chunks as there are rows. This is extremely inefficient and slows down the operations on the flat dataset, e.g., saving/loading the dataset to disk becomes really slow.
Perhaps more importantly, loading the dataset back from disk basically loads the whole table into RAM, as it cannot take advantage of memory mapping.
### Steps to reproduce the bug
The following script reproduces the issue:
```python
import gc
import os
import psutil
import tempfile
import time
from datasets import Dataset
DATASET_SIZE = 5000000
def profile(func):
def wrapper(*args, **kwargs):
mem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
start = time.time()
# Run function here
out = func(*args, **kwargs)
end = time.time()
mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
print(f"{func.__name__} -- RAM memory used: {mem_after - mem_before} MB -- Total time: {end - start:.6f} s")
return out
return wrapper
def main():
ds = Dataset.from_list([{'col': i} for i in range(DATASET_SIZE)])
print(f"Num chunks for original ds: {ds.data['col'].num_chunks}")
with tempfile.TemporaryDirectory() as tmpdir:
path1 = os.path.join(tmpdir, 'ds1')
print("Original ds save/load")
profile(ds.save_to_disk)(path1)
ds_loaded = profile(Dataset.load_from_disk)(path1)
print(f"Num chunks for original ds after reloading: {ds_loaded.data['col'].num_chunks}")
print("")
ds_select = ds.select(reversed(range(len(ds))))
print(f"Num chunks for selected ds: {ds_select.data['col'].num_chunks}")
del ds
del ds_loaded
gc.collect()
# This would happen anyway when we call save_to_disk
ds_select = profile(ds_select.flatten_indices)()
print(f"Num chunks for selected ds after flattening: {ds_select.data['col'].num_chunks}")
print("")
path2 = os.path.join(tmpdir, 'ds2')
print("Selected ds save/load")
profile(ds_select.save_to_disk)(path2)
del ds_select
gc.collect()
ds_select_loaded = profile(Dataset.load_from_disk)(path2)
print(f"Num chunks for selected ds after reloading: {ds_select_loaded.data['col'].num_chunks}")
if __name__ == '__main__':
main()
```
Sample result:
```
Num chunks for original ds: 1
Original ds save/load
save_to_disk -- RAM memory used: 0.515625 MB -- Total time: 0.253888 s
load_from_disk -- RAM memory used: 42.765625 MB -- Total time: 0.015176 s
Num chunks for original ds after reloading: 5000
Num chunks for selected ds: 1
flatten_indices -- RAM memory used: 4852.609375 MB -- Total time: 46.116774 s
Num chunks for selected ds after flattening: 5000000
Selected ds save/load
save_to_disk -- RAM memory used: 1326.65625 MB -- Total time: 42.309825 s
load_from_disk -- RAM memory used: 2085.953125 MB -- Total time: 11.659137 s
Num chunks for selected ds after reloading: 5000000
```
### Expected behavior
Saving/loading the dataset should be much faster and consume almost no extra memory thanks to pyarrow memory mapping.
### Environment info
- `datasets` version: 2.9.1.dev0
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5541
| 2023-02-17T01:52:24
| 2023-02-22T13:15:20
| 2023-02-17T11:12:33
|
{
"login": "marioga",
"id": 6591505,
"type": "User"
}
|
[] | false
|
[] |
1,588,438,344
| 5,540
|
Tutorial for creating a dataset
|
A tutorial for creating datasets based on the folder-based builders and `from_dict` and `from_generator` methods. I've also mentioned loading scripts as a next step, but I think we should keep the tutorial focused on the low-code methods. Let me know what you think! 🙂
|
closed
|
https://github.com/huggingface/datasets/pull/5540
| 2023-02-16T22:09:35
| 2023-02-17T18:50:46
| 2023-02-17T18:41:28
|
{
"login": "stevhliu",
"id": 59462357,
"type": "User"
}
|
[] | true
|
[] |
1,587,970,083
| 5,539
|
IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
|
### Describe the bug
When dataset contains a 0-dim tensor, formatting.py raises a following error and fails.
```bash
Traceback (most recent call last):
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 501, in format_row
return _unnest(formatted_batch)
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in _unnest
return {key: array[0] for key, array in py_dict.items()}
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in <dictcomp>
return {key: array[0] for key, array in py_dict.items()}
IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
```
### Steps to reproduce the bug
Load whichever dataset and add transform method to add 0-dim tensor. Or create/find a dataset containing 0-dim tensor. E.g.
```python
from datasets import load_dataset
import torch
dataset = load_dataset("lambdalabs/pokemon-blip-captions", split='train')
def t(batch):
return {"test": torch.tensor(1)}
dataset.set_transform(t)
d_0 = dataset[0]
```
### Expected behavior
Extractor will correctly get a row from the dataset, even if it contains 0-dim tensor.
### Environment info
`datasets==2.8.0`, but it looks like it is also applicable to main branch version (as of 16th February)
|
closed
|
https://github.com/huggingface/datasets/issues/5539
| 2023-02-16T16:08:51
| 2023-02-22T10:30:30
| 2023-02-21T13:03:57
|
{
"login": "aalbersk",
"id": 41912135,
"type": "User"
}
|
[
{
"name": "good first issue",
"color": "7057ff"
}
] | false
|
[] |
1,587,732,596
| 5,538
|
load_dataset in seaborn is not working for me. getting this error.
|
TimeoutError Traceback (most recent call last)
~\anaconda3\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args)
1345 try:
-> 1346 h.request(req.get_method(), req.selector, req.data, headers,
1347 encode_chunked=req.has_header('Transfer-encoding'))
~\anaconda3\lib\http\client.py in request(self, method, url, body, headers, encode_chunked)
1278 """Send a complete request to the server."""
-> 1279 self._send_request(method, url, body, headers, encode_chunked)
1280
~\anaconda3\lib\http\client.py in _send_request(self, method, url, body, headers, encode_chunked)
1324 body = _encode(body, 'body')
-> 1325 self.endheaders(body, encode_chunked=encode_chunked)
1326
~\anaconda3\lib\http\client.py in endheaders(self, message_body, encode_chunked)
1273 raise CannotSendHeader()
-> 1274 self._send_output(message_body, encode_chunked=encode_chunked)
1275
~\anaconda3\lib\http\client.py in _send_output(self, message_body, encode_chunked)
1033 del self._buffer[:]
-> 1034 self.send(msg)
1035
~\anaconda3\lib\http\client.py in send(self, data)
973 if self.auto_open:
--> 974 self.connect()
975 else:
~\anaconda3\lib\http\client.py in connect(self)
1440
-> 1441 super().connect()
1442
~\anaconda3\lib\http\client.py in connect(self)
944 """Connect to the host and port specified in __init__."""
--> 945 self.sock = self._create_connection(
946 (self.host,self.port), self.timeout, self.source_address)
~\anaconda3\lib\socket.py in create_connection(address, timeout, source_address)
843 try:
--> 844 raise err
845 finally:
~\anaconda3\lib\socket.py in create_connection(address, timeout, source_address)
831 sock.bind(source_address)
--> 832 sock.connect(sa)
833 # Break explicitly a reference cycle
TimeoutError: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond
During handling of the above exception, another exception occurred:
URLError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_12220/2927704185.py in <module>
1 import seaborn as sn
----> 2 iris = sn.load_dataset('iris')
~\anaconda3\lib\site-packages\seaborn\utils.py in load_dataset(name, cache, data_home, **kws)
594 if name not in get_dataset_names():
595 raise ValueError(f"'{name}' is not one of the example datasets.")
--> 596 urlretrieve(url, cache_path)
597 full_path = cache_path
598 else:
~\anaconda3\lib\urllib\request.py in urlretrieve(url, filename, reporthook, data)
237 url_type, path = _splittype(url)
238
--> 239 with contextlib.closing(urlopen(url, data)) as fp:
240 headers = fp.info()
241
~\anaconda3\lib\urllib\request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context)
212 else:
213 opener = _opener
--> 214 return opener.open(url, data, timeout)
215
216 def install_opener(opener):
~\anaconda3\lib\urllib\request.py in open(self, fullurl, data, timeout)
515
516 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
--> 517 response = self._open(req, data)
518
519 # post-process response
~\anaconda3\lib\urllib\request.py in _open(self, req, data)
532
533 protocol = req.type
--> 534 result = self._call_chain(self.handle_open, protocol, protocol +
535 '_open', req)
536 if result:
~\anaconda3\lib\urllib\request.py in _call_chain(self, chain, kind, meth_name, *args)
492 for handler in handlers:
493 func = getattr(handler, meth_name)
--> 494 result = func(*args)
495 if result is not None:
496 return result
~\anaconda3\lib\urllib\request.py in https_open(self, req)
1387
1388 def https_open(self, req):
-> 1389 return self.do_open(http.client.HTTPSConnection, req,
1390 context=self._context, check_hostname=self._check_hostname)
1391
~\anaconda3\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args)
1347 encode_chunked=req.has_header('Transfer-encoding'))
1348 except OSError as err: # timeout error
-> 1349 raise URLError(err)
1350 r = h.getresponse()
1351 except:
URLError: <urlopen error [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond>
|
closed
|
https://github.com/huggingface/datasets/issues/5538
| 2023-02-16T14:01:58
| 2023-02-16T14:44:36
| 2023-02-16T14:44:36
|
{
"login": "reemaranibarik",
"id": 125575109,
"type": "User"
}
|
[] | false
|
[] |
1,587,567,464
| 5,537
|
Increase speed of data files resolution
|
Certain datasets like `bigcode/the-stack-dedup` have so many files that loading them takes forever right from the data files resolution step.
`datasets` uses file patterns to check the structure of the repository but it takes too much time to iterate over and over again on all the data files.
This comes from `resolve_patterns_in_dataset_repository` which calls `_resolve_single_pattern_in_dataset_repository`, which iterates on all the files at
```python
glob_iter = [PurePath(filepath) for filepath in fs.glob(PurePath(pattern).as_posix()) if fs.isfile(filepath)]
```
but calling `glob` on such a dataset is too expensive. Indeed it calls `ls()` in `hffilesystem.py` too many times.
Maybe `glob` can be more optimized in `hffilesystem.py`, or the data files resolution can directly be implemented in the filesystem by checking its `dir_cache` ?
|
closed
|
https://github.com/huggingface/datasets/issues/5537
| 2023-02-16T12:11:45
| 2023-12-15T13:12:31
| 2023-12-15T13:12:31
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
},
{
"name": "good second issue",
"color": "BDE59C"
}
] | false
|
[] |
1,586,930,643
| 5,536
|
Failure to hash function when using .map()
|
### Describe the bug
_Parameter 'function'=<function process at 0x7f1ec4388af0> of the transform datasets.arrow_dataset.Dataset.\_map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed._
This issue with `.map()` happens for me consistently, as also described in closed issue #4506
Dataset indices can be individually serialized using dill and pickle without any errors. I'm using tiktoken to encode in the function passed to map(). Similarly, indices can be individually encoded without error.
### Steps to reproduce the bug
```py
from datasets import load_dataset
import tiktoken
dataset = load_dataset("stas/openwebtext-10k")
enc = tiktoken.get_encoding("gpt2")
tokenized = dataset.map(
process,
remove_columns=['text'],
desc="tokenizing the OWT splits",
)
def process(example):
ids = enc.encode(example['text'])
ids.append(enc.eot_token)
out = {'ids': ids, 'len': len(ids)}
return out
```
### Expected behavior
Should encode simple text objects.
### Environment info
Python versions tried: both 3.8 and 3.10.10
`PYTHONUTF8=1` as env variable
Datasets tried:
- stas/openwebtext-10k
- rotten_tomatoes
- local text file
OS: Ubuntu Linux 20.04
Package versions:
- torch 1.13.1
- dill 0.3.4 (if using 0.3.6 - same issue)
- datasets 2.9.0
- tiktoken 0.2.0
|
closed
|
https://github.com/huggingface/datasets/issues/5536
| 2023-02-16T03:12:07
| 2023-09-08T21:06:01
| 2023-02-16T14:56:41
|
{
"login": "venzen",
"id": 6916056,
"type": "User"
}
|
[] | false
|
[] |
1,586,520,369
| 5,535
|
Add JAX-formatting documentation
|
## What's in this PR?
As a follow-up of #5522, I've created this entry in the documentation to explain how to use `.with_format("jax")` and why is it useful.
@lhoestq Feel free to drop any feedback and/or suggestion, as probably more useful features can be included there!
|
closed
|
https://github.com/huggingface/datasets/pull/5535
| 2023-02-15T20:35:11
| 2023-02-20T10:39:42
| 2023-02-20T10:32:39
|
{
"login": "alvarobartt",
"id": 36760800,
"type": "User"
}
|
[] | true
|
[] |
1,586,177,862
| 5,534
|
map() breaks at certain dataset size when using Array3D
|
### Describe the bug
`map()` magically breaks when using a `Array3D` feature and mapping it. I created a very simple dummy dataset (see below). When filtering it down to 95 elements I can apply map, but it breaks when filtering it down to just 96 entries with the following exception:
```
Traceback (most recent call last):
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3255, in _map_single
writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_writer.py", line 581, in finalize
self.write_examples_on_file()
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_writer.py", line 440, in write_examples_on_file
batch_examples[col] = array_concat(arrays)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1931, in array_concat
return _concat_arrays(arrays)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1901, in _concat_arrays
return array_type.wrap_array(_concat_arrays([array.storage for array in arrays]))
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1922, in _concat_arrays
_concat_arrays([array.values for array in arrays]),
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1922, in _concat_arrays
_concat_arrays([array.values for array in arrays]),
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1920, in _concat_arrays
return pa.ListArray.from_arrays(
File "pyarrow/array.pxi", line 1997, in pyarrow.lib.ListArray.from_arrays
File "pyarrow/array.pxi", line 1527, in pyarrow.lib.Array.validate
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Negative offsets in list array
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2815, in map
return self._map_single(
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 546, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 513, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/fingerprint.py", line 480, in wrapper
out = func(self, *args, **kwargs)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3259, in _map_single
writer.finalize()
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_writer.py", line 581, in finalize
self.write_examples_on_file()
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/arrow_writer.py", line 440, in write_examples_on_file
batch_examples[col] = array_concat(arrays)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1931, in array_concat
return _concat_arrays(arrays)
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1901, in _concat_arrays
return array_type.wrap_array(_concat_arrays([array.storage for array in arrays]))
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1922, in _concat_arrays
_concat_arrays([array.values for array in arrays]),
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1922, in _concat_arrays
_concat_arrays([array.values for array in arrays]),
File "/home/arbi01/miniconda3/envs/tmp9/lib/python3.9/site-packages/datasets/table.py", line 1920, in _concat_arrays
return pa.ListArray.from_arrays(
File "pyarrow/array.pxi", line 1997, in pyarrow.lib.ListArray.from_arrays
File "pyarrow/array.pxi", line 1527, in pyarrow.lib.Array.validate
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Negative offsets in list array
```
### Steps to reproduce the bug
1. put following dataset loading script into: debug/debug.py
```python
import datasets
import numpy as np
class DEBUG(datasets.GeneratorBasedBuilder):
"""DEBUG dataset."""
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"id": datasets.Value("uint8"),
"img_data": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"),
},
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN)]
def _generate_examples(self):
for i in range(149):
image_np = np.zeros(shape=(3, 224, 224), dtype=np.int8).tolist()
yield f"id_{i}", {"id": i, "img_data": image_np}
```
2. try the following code:
```python
import datasets
def add_dummy_col(ex):
ex["dummy"] = "test"
return ex
ds = datasets.load_dataset(path="debug", split="train")
# works
ds_filtered_works = ds.filter(lambda example: example["id"] < 95)
print(f"filtered result size: {len(ds_filtered_works)}")
# output:
# filtered result size: 95
ds_mapped_works = ds_filtered_works.map(add_dummy_col)
# fails
ds_filtered_error = ds.filter(lambda example: example["id"] < 96)
print(f"filtered result size: {len(ds_filtered_error)}")
# output:
# filtered result size: 96
ds_mapped_error = ds_filtered_error.map(add_dummy_col)
```
### Expected behavior
The example code does not fail.
### Environment info
Python 3.9.16 (main, Jan 11 2023, 16:05:54); [GCC 11.2.0] :: Anaconda, Inc. on linux
datasets 2.9.0
|
open
|
https://github.com/huggingface/datasets/issues/5534
| 2023-02-15T16:34:25
| 2023-03-03T16:31:33
| null |
{
"login": "ArneBinder",
"id": 3375489,
"type": "User"
}
|
[] | false
|
[] |
1,585,885,871
| 5,533
|
Add reduce function
|
This PR closes #5496 .
I tried to imitate the `reduce`-method from `functools`, i.e. the function input must be a binary operation. I assume that the input type has an empty element, i.e. `input_type()` is defined, as the acumulant is instantiated as this object - im not sure that is this a reasonable assumption?
If `batched= True` the reduction of each shard is _not_ returned, but the reduction of the entire dataset. I was unsure wether this was an intuitive API, or it would make more sense to return the reduction of each shard?
|
closed
|
https://github.com/huggingface/datasets/pull/5533
| 2023-02-15T13:44:01
| 2024-11-25T14:33:27
| 2023-02-28T14:46:12
|
{
"login": "AJDERS",
"id": 38854604,
"type": "User"
}
|
[] | true
|
[] |
1,584,505,128
| 5,532
|
train_test_split in arrow_dataset does not ensure to keep single classes in test set
|
### Describe the bug
When I have a dataset with very few (e.g. 1) examples per class and I call the train_test_split function on it, sometimes the single class will be in the test set. thus will never be considered for training.
### Steps to reproduce the bug
```
import numpy as np
from datasets import Dataset
data = [
{'label': 0, 'text': "example1"},
{'label': 1, 'text': "example2"},
{'label': 1, 'text': "example3"},
{'label': 1, 'text': "example4"},
{'label': 0, 'text': "example5"},
{'label': 1, 'text': "example6"},
{'label': 2, 'text': "example7"},
{'label': 2, 'text': "example8"}
]
for _ in range(10):
data_set = Dataset.from_list(data)
data_set = data_set.train_test_split(test_size=0.5)
data_set["train"]
unique_labels_train = np.unique(data_set["train"][:]["label"])
unique_labels_test = np.unique(data_set["test"][:]["label"])
assert len(unique_labels_train) >= len(unique_labels_test)
```
### Expected behavior
I expect to have every available class at least once in my training set.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 11.0.0
- Pandas version: 1.3.5
|
closed
|
https://github.com/huggingface/datasets/issues/5532
| 2023-02-14T16:52:29
| 2023-02-15T16:09:19
| 2023-02-15T16:09:19
|
{
"login": "Ulipenitz",
"id": 37191008,
"type": "User"
}
|
[] | false
|
[] |
1,584,387,276
| 5,531
|
Invalid Arrow data from JSONL
|
This code fails:
```python
from datasets import Dataset
ds = Dataset.from_json(path_to_file)
ds.data.validate()
```
raises
```python
ArrowInvalid: Column 2: In chunk 1: Invalid: Struct child array #3 invalid: Invalid: Length spanned by list offsets (4064) larger than values array (length 4063)
```
This causes many issues for @TevenLeScao:
- `map` fails because it fails to rewrite invalid arrow arrays
```python
~/Desktop/hf/datasets/src/datasets/arrow_writer.py in write_examples_on_file(self)
438 if all(isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) for row in self.current_examples):
439 arrays = [row[0][col] for row in self.current_examples]
--> 440 batch_examples[col] = array_concat(arrays)
441 else:
442 batch_examples[col] = [
~/Desktop/hf/datasets/src/datasets/table.py in array_concat(arrays)
1885
1886 if not _is_extension_type(array_type):
-> 1887 return pa.concat_arrays(arrays)
1888
1889 def _offsets_concat(offsets):
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.concat_arrays()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowIndexError: array slice would exceed array length
```
- `to_dict()` **segfaults** ⚠️
```python
/Users/runner/work/crossbow/crossbow/arrow/cpp/src/arrow/array/data.cc:99: Check failed: (off) <= (length) Slice offset greater
than array length
```
To reproduce: unzip the archive and run the above code using `sanity_oscar_en.jsonl`
[sanity_oscar_en.jsonl.zip](https://github.com/huggingface/datasets/files/10734124/sanity_oscar_en.jsonl.zip)
PS: reading using pandas and converting to Arrow works though (note that the dataset lives in RAM in this case):
```python
ds = Dataset.from_pandas(pd.read_json(path_to_file, lines=True))
ds.data.validate()
```
|
open
|
https://github.com/huggingface/datasets/issues/5531
| 2023-02-14T15:39:49
| 2023-02-14T15:46:09
| null |
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,582,938,241
| 5,530
|
Add missing license in `NumpyFormatter`
|
## What's in this PR?
As discussed with @lhoestq in https://github.com/huggingface/datasets/pull/5522, the license for `NumpyFormatter` at `datasets/formatting/np_formatter.py` was missing, but present on the rest of the `formatting/*.py` files. So this PR is basically to include it there.
|
closed
|
https://github.com/huggingface/datasets/pull/5530
| 2023-02-13T19:33:23
| 2023-02-14T14:40:41
| 2023-02-14T12:23:58
|
{
"login": "alvarobartt",
"id": 36760800,
"type": "User"
}
|
[] | true
|
[] |
1,582,501,233
| 5,529
|
Fix `datasets.load_from_disk`, `DatasetDict.load_from_disk` and `Dataset.load_from_disk`
|
## What's in this PR?
After playing around a little bit with 🤗`datasets` in Google Cloud Storage (GCS), I found out some things that should be fixed IMO in the code:
* `datasets.load_from_disk` is not checking whether `state.json` is there too when trying to load a `Dataset`, just `dataset_info.json` is checked
* `DatasetDict.load_from_disk` is not checking whether `state.json` is there too when redirecting the user to load it as `datasets.load_from_disk`, just `dataset_info.json` is checked, which is misleading, as it won't be loadable that way either
* `Dataset.load_from_disk` is missing the `extract_path_from_uri` call before checking in the `fs` whether `dataset_info.json` and `dataset_dict.json` exist, which when using `gcsfs` leads to 400 error code (not blocking) due to `gcsfs.retry.HttpError: Invalid bucket name: 'gs:', 400`
* And, finally, the exception messages are a little bit misleading / incomplete IMO so I've tried to include all the relevant information in the messages to avoid issues when interpreting the exceptions
|
closed
|
https://github.com/huggingface/datasets/pull/5529
| 2023-02-13T14:54:55
| 2023-02-23T18:14:32
| 2023-02-23T18:05:26
|
{
"login": "alvarobartt",
"id": 36760800,
"type": "User"
}
|
[] | true
|
[] |
1,582,195,085
| 5,528
|
Push to hub in a pull request
|
Fixes #5492.
Introduce new kwarg `create_pr` in `push_to_hub`, which is passed to `HFapi.upload_file`.
|
open
|
https://github.com/huggingface/datasets/pull/5528
| 2023-02-13T11:43:47
| 2023-10-06T21:58:02
| null |
{
"login": "AJDERS",
"id": 38854604,
"type": "User"
}
|
[] | true
|
[] |
1,581,228,531
| 5,527
|
Fix benchmarks CI - pin protobuf
|
fix https://github.com/huggingface/datasets/actions/runs/4156059127/jobs/7189576331
|
closed
|
https://github.com/huggingface/datasets/pull/5527
| 2023-02-12T11:51:25
| 2023-02-13T10:29:03
| 2023-02-13T09:24:16
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,580,488,133
| 5,526
|
Allow loading/saving of FAISS index using fsspec
|
Fixes #5428
Allow loading/saving of FAISS index using fsspec:
1. Simply use BufferedIOWriter/Reader to Read/Write indices on fsspec stream.
2. Needed `mockfs` in the test, so I took it out of the `TestCase`. Let me know if that makes sense.
I can work on the documentation once the code changes are approved.
|
closed
|
https://github.com/huggingface/datasets/pull/5526
| 2023-02-10T23:37:14
| 2023-03-27T15:26:46
| 2023-03-27T15:18:20
|
{
"login": "Dref360",
"id": 8976546,
"type": "User"
}
|
[] | true
|
[] |
1,580,342,729
| 5,525
|
TypeError: Couldn't cast array of type string to null
|
### Describe the bug
Processing a dataset I alredy uploaded to the Hub (https://huggingface.co/datasets/tj-solergibert/Europarl-ST) I found that for some splits and some languages (test split, source_lang = "nl") after applying a map function I get the mentioned error.
I alredy tried reseting the shorter strings (reset_cortas function). It only happends with NL, PL, RO and PT. It does not make sense since when processing the other languages I also use the corpus of those that fail and it does not cause any errors.
I suspect that the error may be in this direction:
We use cast_array_to_feature to support casting to custom types like Audio and Image # Also, when trying type "string", we don't want to convert integers or floats to "string". # We only do it if trying_type is False - since this is what the user asks for.
### Steps to reproduce the bug
Here I link a colab notebook to reproduce the error:
https://colab.research.google.com/drive/1JCrS7FlGfu_kFqChMrwKZ_bpabnIMqbP?authuser=1#scrollTo=FBAvlhMxIzpA
### Expected behavior
Data processing does not fail. A correct example can be seen here: https://huggingface.co/datasets/tj-solergibert/Europarl-ST-processed-mt-en
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
|
closed
|
https://github.com/huggingface/datasets/issues/5525
| 2023-02-10T21:12:36
| 2023-02-14T17:41:08
| 2023-02-14T09:35:49
|
{
"login": "TJ-Solergibert",
"id": 74564958,
"type": "User"
}
|
[] | false
|
[] |
1,580,219,454
| 5,524
|
[INVALID PR]
|
Hi to whoever is reading this! 🤗
## What's in this PR?
~~Basically, I've removed the 🤗`datasets` installation as `python -m pip install ".[quality]" in the `check_code_quality` job in `.github/workflows/ci.yaml`, as we don't need to install the whole package to run the CI, unless that's done on purpose e.g. to check that the Python package installation succeeds before running the tests over the matrix of os?~~
~~So I just wanted to check whether the time was reduced doing this (which I assume it will), plus whether this is something that can be improved, or just discarded in case you're also using that step to make sure that the package can be installed.~~
## What's missing?
~~I was just wondering whether you consider replacing `isort` and `flake8` with `ruff` (if possible), since it's way faster, more information at [`ruff`](https://github.com/charliermarsh/ruff). Before creating this PR the average time of the `check_code_quality` job was around 40s.~~
## Edit
Sorry for the inconvenience this may have caused, didn't realise that the config is defined in `setup.cfg` and `pyproject.toml`, so running those without installing the Python package leads to failure, my bad 😞
|
closed
|
https://github.com/huggingface/datasets/pull/5524
| 2023-02-10T19:35:50
| 2023-02-10T19:51:45
| 2023-02-10T19:49:12
|
{
"login": "alvarobartt",
"id": 36760800,
"type": "User"
}
|
[] | true
|
[] |
1,580,193,015
| 5,523
|
Checking that split name is correct happens only after the data is downloaded
|
### Describe the bug
Verification of split names (=indexing data by split) happens after downloading the data. So when the split name is incorrect, users learn about that only after the data is fully downloaded, for large datasets it might take a lot of time.
### Steps to reproduce the bug
Load any dataset with random split name, for example:
```python
from datasets import load_dataset
load_dataset("mozilla-foundation/common_voice_11_0", "en", split="blabla")
```
and the download will start smoothly, despite there is no split named "blabla".
### Expected behavior
Raise error when split name is incorrect.
### Environment info
`datasets==2.9.1.dev0`
|
open
|
https://github.com/huggingface/datasets/issues/5523
| 2023-02-10T19:13:03
| 2023-02-10T19:14:50
| null |
{
"login": "polinaeterna",
"id": 16348744,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,580,183,124
| 5,522
|
Minor changes in JAX-formatting docstrings & type-hints
|
Hi to whoever is reading this! 🤗
## What's in this PR?
I was exploring the code regarding the `JaxFormatter` implemented in 🤗`datasets`, and found some things that IMO could be changed. Those are mainly regarding the docstrings and the type-hints based on `jax`'s 0.4.1 release where `jax.Array` was introduced as the default type for JAX-arrays (instead of `jnp.DeviceArray`, `jnp.SharedDeviceArray`, and `jnp.GlobalDeviceArray`). Even though `isinstance(..., jax.Array)` also works with lower versions such as e.g. `0.3.25`.
More information about the latter at [`jax` v0.4.1 - Release Notes](https://github.com/google/jax/releases/tag/jax-v0.4.1) and [jax.Array migration - JAX documentation](https://jax.readthedocs.io/en/latest/jax_array_migration.html).
## What's missing?
* Do you want me to write an entry in the documentation on how to use 🤗`datasets` with JAX as https://huggingface.co/docs/datasets/use_with_pytorch with PyTorch?
* Do we need to actually include `pyarrow` under the `TYPE_CHECKING` when needed? I just did it for JAX, but if we are OK with that, I can do that with the rest of the formatters, just LMK.
* Should the License header be included in `datasets.formatting.np_formatter`? If so, do I include the one from 2020 e.g. https://github.com/huggingface/datasets/blob/b065547654efa0ec633cf373ac1512884c68b2e1/src/datasets/formatting/tf_formatter.py#L1-L13
* Is there any reason why `jnp.array` is being used instead of `jnp.asarray`? There's no difference between both, just that `jnp.asarray` has `copy=False` as default, even though `numpy` to `jax.numpy` conversion is not zero-copy, but just asking :)
|
closed
|
https://github.com/huggingface/datasets/pull/5522
| 2023-02-10T19:05:00
| 2023-02-15T14:48:27
| 2023-02-15T13:19:06
|
{
"login": "alvarobartt",
"id": 36760800,
"type": "User"
}
|
[] | true
|
[] |
1,578,418,289
| 5,521
|
Fix bug when casting empty array to class labels
|
Fix https://github.com/huggingface/datasets/issues/5520.
|
closed
|
https://github.com/huggingface/datasets/pull/5521
| 2023-02-09T18:47:59
| 2023-02-13T20:40:48
| 2023-02-12T11:17:17
|
{
"login": "marioga",
"id": 6591505,
"type": "User"
}
|
[] | true
|
[] |
1,578,417,074
| 5,520
|
ClassLabel.cast_storage raises TypeError when called on an empty IntegerArray
|
### Describe the bug
`ClassLabel.cast_storage` raises `TypeError` when called on an empty `IntegerArray`.
### Steps to reproduce the bug
Minimal steps:
```python
import pyarrow as pa
from datasets import ClassLabel
ClassLabel(names=['foo', 'bar']).cast_storage(pa.array([], pa.int64()))
```
In practice, this bug arises in situations like the one below:
```python
from datasets import ClassLabel, Dataset, Features, Sequence
dataset = Dataset.from_dict({'labels': [[], []]}, features=Features({'labels': Sequence(ClassLabel(names=['foo', 'bar']))}))
# this raises TypeError
dataset.map(batched=True, batch_size=1)
```
### Expected behavior
`ClassLabel.cast_storage` should return an empty Int64Array.
### Environment info
- `datasets` version: 2.9.1.dev0
- Platform: Linux-4.15.0-1032-aws-x86_64-with-glibc2.27
- Python version: 3.10.6
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
|
closed
|
https://github.com/huggingface/datasets/issues/5520
| 2023-02-09T18:46:52
| 2023-02-12T11:17:18
| 2023-02-12T11:17:18
|
{
"login": "marioga",
"id": 6591505,
"type": "User"
}
|
[] | false
|
[] |
1,578,341,785
| 5,519
|
Lint code with `ruff`
|
EDIT:
Use `ruff` for linting instead of `isort` and `flake8` ~~`black`~~ to be consistent with [`transformers`](https://github.com/huggingface/transformers/pull/21480) and [`hfh`](https://github.com/huggingface/huggingface_hub/pull/1323).
TODO:
- [x] ~Merge the community contributors' PR to avoid having to run `make style` on their PR branches~ (we have some new PRs, but fixing those shouldn't be too big of a problem)
|
closed
|
https://github.com/huggingface/datasets/pull/5519
| 2023-02-09T17:50:21
| 2024-06-01T15:35:02
| 2023-02-14T16:18:38
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,578,203,962
| 5,518
|
Remove py.typed
|
Fix https://github.com/huggingface/datasets/issues/3841
|
closed
|
https://github.com/huggingface/datasets/pull/5518
| 2023-02-09T16:22:29
| 2023-02-13T13:55:49
| 2023-02-13T13:48:40
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
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