id
int64 599M
3.26B
| number
int64 1
7.7k
| title
stringlengths 1
290
| body
stringlengths 0
228k
โ | state
stringclasses 2
values | html_url
stringlengths 46
51
| 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
โ | user
dict | labels
listlengths 0
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bool 2
classes | comments
listlengths 0
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|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,425,460,168
| 7,067
|
Convert_to_parquet fails for datasets with multiple configs
|
If the dataset has multiple configs, when using the `datasets-cli convert_to_parquet` command to avoid issues with the data viewer caused by loading scripts, the conversion process only successfully converts the data corresponding to the first config. When it starts converting the second config, it throws an error:
```
Traceback (most recent call last):
File "/opt/anaconda3/envs/dl/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/datasets_cli.py", line 41, in main
service.run()
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/convert_to_parquet.py", line 83, in run
dataset.push_to_hub(
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/dataset_dict.py", line 1713, in push_to_hub
api.create_branch(repo_id, branch=revision, token=token, repo_type="dataset", exist_ok=True)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 5503, in create_branch
hf_raise_for_status(response)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 358, in hf_raise_for_status
raise BadRequestError(message, response=response) from e
huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-669fc665-7c2e80d75f4337496ee95402;731fcdc7-0950-4eec-99cf-ce047b8d003f)
Bad request:
Invalid reference for a branch: refs/pr/1
```
|
closed
|
https://github.com/huggingface/datasets/issues/7067
| 2024-07-23T15:09:33
| 2024-07-30T10:51:02
| 2024-07-30T10:51:02
|
{
"login": "HuangZhen02",
"id": 97585031,
"type": "User"
}
|
[] | false
|
[] |
2,425,125,160
| 7,066
|
One subset per file in repo ?
|
Right now we consider all the files of a dataset to be the same data, e.g.
```
single_subset_dataset/
โโโ train0.jsonl
โโโ train1.jsonl
โโโ train2.jsonl
```
but in cases like this, each file is actually a different subset of the dataset and should be loaded separately
```
many_subsets_dataset/
โโโ animals.jsonl
โโโ trees.jsonl
โโโ metadata.jsonl
```
It would be nice to detect those subsets automatically using a simple heuristic. For example we can group files together if their paths names are the same except some digits ?
|
open
|
https://github.com/huggingface/datasets/issues/7066
| 2024-07-23T12:43:59
| 2025-06-26T08:24:50
| null |
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | false
|
[] |
2,424,734,953
| 7,065
|
Cannot get item after loading from disk and then converting to iterable.
|
### Describe the bug
The dataset generated from local file works fine.
```py
root = "/home/data/train"
file_list1 = glob(os.path.join(root, "*part1.flac"))
file_list2 = glob(os.path.join(root, "*part2.flac"))
ds = (
Dataset.from_dict({"part1": file_list1, "part2": file_list2})
.cast_column("part1", Audio(sampling_rate=None, mono=False))
.cast_column("part2", Audio(sampling_rate=None, mono=False))
)
ids = ds.to_iterable_dataset(128)
ids = ids.shuffle(buffer_size=10000, seed=42)
dataloader = DataLoader(ids, num_workers=4, batch_size=8, persistent_workers=True)
for batch in dataloader:
break
```
But after saving it to disk and then loading it from disk, I cannot get data as expected.
```py
root = "/home/data/train"
file_list1 = glob(os.path.join(root, "*part1.flac"))
file_list2 = glob(os.path.join(root, "*part2.flac"))
ds = (
Dataset.from_dict({"part1": file_list1, "part2": file_list2})
.cast_column("part1", Audio(sampling_rate=None, mono=False))
.cast_column("part2", Audio(sampling_rate=None, mono=False))
)
ds.save_to_disk("./train")
ds = datasets.load_from_disk("./train")
ids = ds.to_iterable_dataset(128)
ids = ids.shuffle(buffer_size=10000, seed=42)
dataloader = DataLoader(ids, num_workers=4, batch_size=8, persistent_workers=True)
for batch in dataloader:
break
```
After a long time waiting, an error occurs:
```
Loading dataset from disk: 100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 165/165 [00:00<00:00, 6422.18it/s]
Traceback (most recent call last):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1133, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/queues.py", line 113, in get
if not self._poll(timeout):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 257, in poll
return self._poll(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 424, in _poll
r = wait([self], timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 931, in wait
ready = selector.select(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/selectors.py", line 416, in select
fd_event_list = self._selector.poll(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 3490529) is killed by signal: Killed.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module>
cli.main()
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main
run()
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file
runpy.run_path(target, run_name="__main__")
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path
return _run_module_code(code, init_globals, run_name,
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code
exec(code, run_globals)
File "/home/hanzerui/workspace/NetEase/test/test_datasets.py", line 60, in <module>
for batch in dataloader:
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 631, in __next__
data = self._next_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1329, in _next_data
idx, data = self._get_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1295, in _get_data
success, data = self._try_get_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1146, in _try_get_data
raise RuntimeError(f'DataLoader worker (pid(s) {pids_str}) exited unexpectedly') from e
RuntimeError: DataLoader worker (pid(s) 3490529) exited unexpectedly
```
It seems that streaming is not supported by `laod_from_disk`, so does that mean I cannot convert it to iterable?
### Steps to reproduce the bug
1. Create a `Dataset` from local files with `from_dict`
2. Save it to disk with `save_to_disk`
3. Load it from disk with `load_from_disk`
4. Convert to iterable with `to_iterable_dataset`
5. Loop the dataset
### Expected behavior
Get items faster than the original dataset generated from dict.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.10.14
- `huggingface_hub` version: 0.23.2
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
|
open
|
https://github.com/huggingface/datasets/issues/7065
| 2024-07-23T09:37:56
| 2024-07-23T09:37:56
| null |
{
"login": "happyTonakai",
"id": 21305646,
"type": "User"
}
|
[] | false
|
[] |
2,424,613,104
| 7,064
|
Add `batch` method to `Dataset` class
|
This PR introduces a new `batch` method to the `Dataset` class, aligning its functionality with the `IterableDataset.batch()` method (implemented in #7054). The implementation uses as well the existing `map` method for efficient batching of examples.
Key changes:
- Add `batch` method to `Dataset` class in `arrow_dataset.py`
- Utilize `map` method for batching
Closes #7063
Once the approach is approved, i will create the tests and update the documentation.
|
closed
|
https://github.com/huggingface/datasets/pull/7064
| 2024-07-23T08:40:43
| 2024-07-25T13:51:25
| 2024-07-25T13:45:20
|
{
"login": "lappemic",
"id": 61876623,
"type": "User"
}
|
[] | true
|
[] |
2,424,488,648
| 7,063
|
Add `batch` method to `Dataset`
|
### Feature request
Add a `batch` method to the Dataset class, similar to the one recently implemented for `IterableDataset` in PR #7054.
### Motivation
A batched iteration speeds up data loading significantly (see e.g. #6279)
### Your contribution
I plan to open a PR to implement this.
|
closed
|
https://github.com/huggingface/datasets/issues/7063
| 2024-07-23T07:36:59
| 2024-07-25T13:45:21
| 2024-07-25T13:45:21
|
{
"login": "lappemic",
"id": 61876623,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
2,424,467,484
| 7,062
|
Avoid calling http_head for non-HTTP URLs
|
Avoid calling `http_head` for non-HTTP URLs, by adding and `else` statement.
Currently, it makes an unnecessary HTTP call (which adds latency) for non-HTTP protocols, like FTP, S3,...
I discovered this while working in an unrelated issue.
|
closed
|
https://github.com/huggingface/datasets/pull/7062
| 2024-07-23T07:25:09
| 2024-07-23T14:28:27
| 2024-07-23T14:21:08
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,423,786,881
| 7,061
|
Custom Dataset | Still Raise Error while handling errors in _generate_examples
|
### Describe the bug
I follow this [example](https://discuss.huggingface.co/t/error-handling-in-iterabledataset/72827/3) to handle errors in custom dataset. I am writing a dataset script which read jsonl files and i need to handle errors and continue reading files without raising exception and exit the execution.
```
def _generate_examples(self, filepaths):
errors=[]
id_ = 0
for filepath in filepaths:
try:
with open(filepath, 'r') as f:
for line in f:
json_obj = json.loads(line)
yield id_, json_obj
id_ += 1
except Exception as exc:
logger.error(f"error occur at filepath: {filepath}")
errors.append(error)
```
seems the logger.error is printed but still exception is raised the the run is exit.
```
Downloading and preparing dataset custom_dataset/default to /home/myuser/.cache/huggingface/datasets/custom_dataset/default-a14cdd566afee0a6/1.0.0/acfcc9fb9c57034b580c4252841
ERROR: datasets_modules.datasets.custom_dataset.acfcc9fb9c57034b580c4252841bb890a5617cbd28678dd4be5e52b81188ad02.custom_dataset: 2024-07-22 10:47:42,167: error occur at filepath: '/home/myuser/ds/corrupted-file.jsonl
Traceback (most recent call last):
File "/home/myuser/.cache/huggingface/modules/datasets_modules/datasets/custom_dataset/ac..2/custom_dataset.py", line 48, in _generate_examples
json_obj = json.loads(line)
File "myenv/lib/python3.8/json/__init__.py", line 357, in loads
return _default_decoder.decode(s)
File "myenv/lib/python3.8/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "myenv/lib/python3.8/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Invalid control character at: line 1 column 4 (char 3)
Generating train split: 0 examples [00:06, ? examples/s]>
RemoteTraceback:
"""
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1637, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "myenv/lib/python3.8/site-packages/datasets/arrow_writer.py", line 594, in finalize
raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "myenv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 1353, in
_write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1646, 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
"""
The above exception was the direct cause of the following exception:
โ โ
โ myenv/lib/python3.8/site-packages/datasets/utils/py_utils. โ
โ py:1377 in <listcomp> โ
โ โ
โ 1374 โ โ โ โ if all(async_result.ready() for async_result in async_results) and queue โ
โ 1375 โ โ โ โ โ break โ
โ 1376 โ โ # we get the result in case there's an error to raise โ
โ โฑ 1377 โ โ [async_result.get() for async_result in async_results] โ
โ 1378 โ
โ โ
โ โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ locals โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ
โ โ .0 = <list_iterator object at 0x7f2cc1f0ce20> โ โ
โ โ async_result = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> โ โ
โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ โ
โ โ
โ myenv/lib/python3.8/site-packages/multiprocess/pool.py:771 โ
โ in get โ
โ โ
โ 768 โ โ if self._success: โ
โ 769 โ โ โ return self._value โ
โ 770 โ โ else: โ
โ โฑ 771 โ โ โ raise self._value โ
โ 772 โ โ
โ 773 โ def _set(self, i, obj): โ
โ 774 โ โ self._success, self._value = obj โ
โ โ
โ โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ locals โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ
โ โ self = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> โ โ
โ โ timeout = None โ โ
โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ โ
DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
same as above
### Expected behavior
should handle error and continue reading remaining files
### Environment info
python 3.9
|
open
|
https://github.com/huggingface/datasets/issues/7061
| 2024-07-22T21:18:12
| 2024-09-09T14:48:07
| null |
{
"login": "hahmad2008",
"id": 68266028,
"type": "User"
}
|
[] | false
|
[] |
2,423,188,419
| 7,060
|
WebDataset BuilderConfig
|
This PR adds `WebDatasetConfig`.
Closes #7055
|
closed
|
https://github.com/huggingface/datasets/pull/7060
| 2024-07-22T15:41:07
| 2024-07-23T13:28:44
| 2024-07-23T13:28:44
|
{
"login": "hlky",
"id": 106811348,
"type": "User"
}
|
[] | true
|
[] |
2,422,827,892
| 7,059
|
None values are skipped when reading jsonl in subobjects
|
### Describe the bug
I have been fighting against my machine since this morning only to find out this is some kind of a bug.
When loading a dataset composed of `metadata.jsonl`, if you have nullable values (Optional[str]), they can be ignored by the parser, shifting things around.
E.g., let's take this example
Here are two version of a same dataset:
[not-buggy.tar.gz](https://github.com/user-attachments/files/16333532/not-buggy.tar.gz)
[buggy.tar.gz](https://github.com/user-attachments/files/16333553/buggy.tar.gz)
### Steps to reproduce the bug
1. Load the `buggy.tar.gz` dataset
2. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]`
3. Load the `not-buggy.tar.gz` dataset
4. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]`
### Expected behavior
Both should have 4 baseline entries:
1. Buggy should have None followed by three lists
2. Non-Buggy should have four lists, and the first one should be an empty list.
One does not work, 2 works. Despite accepting None in another position than the first one.
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.0
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
|
open
|
https://github.com/huggingface/datasets/issues/7059
| 2024-07-22T13:02:42
| 2024-07-22T13:02:53
| null |
{
"login": "PonteIneptique",
"id": 1929830,
"type": "User"
}
|
[] | false
|
[] |
2,422,560,355
| 7,058
|
New feature type: Document
|
It would be useful for PDF.
https://github.com/huggingface/dataset-viewer/issues/2991#issuecomment-2242656069
|
open
|
https://github.com/huggingface/datasets/issues/7058
| 2024-07-22T10:49:20
| 2024-07-22T10:49:20
| null |
{
"login": "severo",
"id": 1676121,
"type": "User"
}
|
[] | false
|
[] |
2,422,498,520
| 7,057
|
Update load_hub.mdx
| null |
closed
|
https://github.com/huggingface/datasets/pull/7057
| 2024-07-22T10:17:46
| 2024-07-22T10:34:14
| 2024-07-22T10:28:10
|
{
"login": "severo",
"id": 1676121,
"type": "User"
}
|
[] | true
|
[] |
2,422,192,257
| 7,056
|
Make `BufferShuffledExamplesIterable` resumable
|
This PR aims to implement a resumable `BufferShuffledExamplesIterable`.
Instead of saving the entire buffer content, which is very memory-intensive, the newly implemented `BufferShuffledExamplesIterable` saves only the minimal state necessary for recovery, e.g., the random generator states and the state of the first example in the buffer dict.
The idea is that since the buffer size is limited, even if the entire buffer is discarded, we can rebuild it as long as the state of the oldest example is recorded. For buffer size $B$, the expected distance between when an example is pushed and when it is yielded is
$d = \sum_{k=1}^{\infty} k\frac{1}{B} (1 - \frac{1}{B} )^{k-1} =B$.
Simulation experiments support these claims:
```py
from random import randint
BUFFER_SIZE = 1024
dists = []
buffer = []
for i in range(10000000):
if i < BUFFER_SIZE:
buffer.append(i)
else:
index = randint(0, BUFFER_SIZE - 1)
dists.append(i - buffer[index])
buffer[index] = i
print(f"MIN DIST: {min(dists)}\nMAX DIST: {max(dists)}\nAVG DIST: {sum(dists) / len(dists):.2f}\n")
```
which produces the following output:
```py
MIN DIST: 1
MAX DIST: 15136
AVG DIST: 1023.95
```
The overall time for reconstructing the buffer and recovery should not be too long.
The following code mimics the cases of resuming online tokenization by `datasets` and `StatefulDataLoader` under distributed scenarios,
```py
import pickle
import time
from itertools import chain
from typing import Any, Dict, List
import torch
from datasets import load_dataset
from torchdata.stateful_dataloader import StatefulDataLoader
from tqdm import tqdm
from transformers import AutoTokenizer, DataCollatorForLanguageModeling
tokenizer = AutoTokenizer.from_pretrained('fla-hub/gla-1.3B-100B')
tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
torch.manual_seed(42)
def tokenize(examples: Dict[str, List[Any]]) -> Dict[str, List[List[int]]]:
input_ids = tokenizer(examples['text'])['input_ids']
input_ids = list(chain(*input_ids))
total_length = len(input_ids)
chunk_size = 2048
total_length = (total_length // chunk_size) * chunk_size
# the last chunk smaller than chunk_size will be discarded
return {'input_ids': [input_ids[i: i+chunk_size] for i in range(0, total_length, chunk_size)]}
batch_size = 16
num_workers = 5
context_length = 2048
rank = 1
world_size = 32
prefetch_factor = 2
steps = 2048
path = 'fla-hub/slimpajama-test'
dataset = load_dataset(
path=path,
split='train',
streaming=True,
trust_remote_code=True
)
dataset = dataset.map(tokenize, batched=True, remove_columns=next(iter(dataset)).keys())
dataset = dataset.shuffle(seed=42)
loader = StatefulDataLoader(dataset=dataset,
batch_size=batch_size,
collate_fn=data_collator,
num_workers=num_workers,
persistent_workers=False,
prefetch_factor=prefetch_factor)
start = time.time()
for i, batch in tqdm(enumerate(loader)):
if i == 0:
print(f'{i}\n{batch["input_ids"]}')
if i == steps - 1:
print(f'{i}\n{batch["input_ids"]}')
state_dict = loader.state_dict()
if i == steps:
print(f'{i}\n{batch["input_ids"]}')
break
print(f"{time.time() - start:.2f}s elapsed")
print(f"{len(pickle.dumps(state_dict)) / 1024**2:.2f}MB states in total")
for worker in state_dict['_snapshot']['_worker_snapshots'].keys():
print(f"{worker} {len(pickle.dumps(state_dict['_snapshot']['_worker_snapshots'][worker])) / 1024**2:.2f}MB")
print(state_dict['_snapshot']['_worker_snapshots']['worker_0']['dataset_state'])
loader = StatefulDataLoader(dataset=dataset,
batch_size=batch_size,
collate_fn=data_collator,
num_workers=num_workers,
persistent_workers=False,
prefetch_factor=prefetch_factor)
print("Loading state dict")
loader.load_state_dict(state_dict)
start = time.time()
for batch in loader:
print(batch['input_ids'])
break
print(f"{time.time() - start:.2f}s elapsed")
```
and the outputs are
```py
0
tensor([[ 909, 395, 19082, ..., 13088, 16232, 395],
[ 601, 28705, 28770, ..., 28733, 923, 288],
[21753, 15071, 13977, ..., 9369, 28723, 415],
...,
[21763, 28751, 20300, ..., 28781, 28734, 4775],
[ 354, 396, 10214, ..., 298, 429, 28770],
[ 333, 6149, 28768, ..., 2773, 340, 351]])
2047
tensor([[28723, 415, 3889, ..., 272, 3065, 2609],
[ 403, 3214, 3629, ..., 403, 21163, 16434],
[28723, 13, 28749, ..., 28705, 28750, 28734],
...,
[ 2778, 2251, 28723, ..., 354, 684, 429],
[ 5659, 298, 1038, ..., 5290, 297, 22153],
[ 938, 28723, 1537, ..., 9123, 28733, 12154]])
2048
tensor([[ 769, 278, 12531, ..., 28721, 19309, 28739],
[ 415, 23347, 622, ..., 3937, 2426, 28725],
[28745, 4345, 28723, ..., 338, 28725, 583],
...,
[ 1670, 28709, 5809, ..., 28734, 28760, 393],
[ 340, 1277, 624, ..., 325, 28790, 1329],
[ 523, 1144, 3409, ..., 359, 359, 17422]])
65.97s elapsed
0.00MB states in total
worker_0 0.00MB
worker_1 0.00MB
worker_2 0.00MB
worker_3 0.00MB
worker_4 0.00MB
{'ex_iterable': {'ex_iterable': {'shard_idx': 0, 'shard_example_idx': 14000}, 'num_examples_since_previous_state': 166, 'previous_state_example_idx': 7394, 'previous_state': {'shard_idx': 0, 'shard_example_idx': 13000}}, 'num_taken': 6560, 'global_example_idx': 7560, 'buffer_state_dict': {'num_taken': 6560, 'global_example_idx': 356, 'index_offset': 0, 'first_state': {'ex_iterable': {'shard_idx': 0, 'shard_example_idx': 1000}, 'num_examples_since_previous_state': 356, 'previous_state_example_idx': 0, 'previous_state': {'shard_idx': 0, 'shard_example_idx': 0}}, 'bit_generator_state': {'state': {'state': 274674114334540486603088602300644985544, 'inc': 332724090758049132448979897138935081983}, 'bit_generator': 'PCG64', 'has_uint32': 0, 'uinteger': 0}}}
Loading state dict
tensor([[ 769, 278, 12531, ..., 28721, 19309, 28739],
[ 415, 23347, 622, ..., 3937, 2426, 28725],
[28745, 4345, 28723, ..., 338, 28725, 583],
...,
[ 1670, 28709, 5809, ..., 28734, 28760, 393],
[ 340, 1277, 624, ..., 325, 28790, 1329],
[ 523, 1144, 3409, ..., 359, 359, 17422]])
24.60s elapsed
```
Not sure if this PR complies with the `datasets` code style. Looking for your help @lhoestq, also very willing to further improve the code if any suggestions are given.
|
closed
|
https://github.com/huggingface/datasets/pull/7056
| 2024-07-22T07:50:02
| 2025-01-31T05:34:20
| 2025-01-31T05:34:19
|
{
"login": "yzhangcs",
"id": 18402347,
"type": "User"
}
|
[] | true
|
[] |
2,421,708,891
| 7,055
|
WebDataset with different prefixes are unsupported
|
### Describe the bug
Consider a WebDataset with multiple images for each item where the number of images may vary: [example](https://huggingface.co/datasets/bigdata-pw/fashion-150k)
Due to this [code](https://github.com/huggingface/datasets/blob/87f4c2088854ff33e817e724e75179e9975c1b02/src/datasets/packaged_modules/webdataset/webdataset.py#L76-L80) an error is given.
```
The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
```
The purpose of this check is unclear because PyArrow supports different keys.
Removing the check allows the dataset to be loaded and there's no issue when iterating through the dataset.
```
>>> from datasets import load_dataset
>>> path = "shards/*.tar"
>>> dataset = load_dataset("webdataset", data_files={"train": path}, split="train", streaming=True)
Resolving data files: 100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 152/152 [00:00<00:00, 56458.93it/s]
>>> dataset
IterableDataset({
features: ['__key__', '__url__', '1.jpg', '2.jpg', '3.jpg', '4.jpg', 'json'],
n_shards: 152
})
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("bigdata-pw/fashion-150k")
```
### Expected behavior
Dataset loads without error
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.14.0-467.el9.x86_64-x86_64-with-glibc2.34
- Python version: 3.9.19
- `huggingface_hub` version: 0.23.4
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
|
closed
|
https://github.com/huggingface/datasets/issues/7055
| 2024-07-22T01:14:19
| 2024-07-24T13:26:30
| 2024-07-23T13:28:46
|
{
"login": "hlky",
"id": 106811348,
"type": "User"
}
|
[] | false
|
[] |
2,418,548,995
| 7,054
|
Add batching to `IterableDataset`
|
I've taken a try at implementing a batched `IterableDataset` as requested in issue #6279. This PR adds a new `BatchedExamplesIterable` class and a `.batch()` method to the `IterableDataset` class.
The main changes are:
1. A new `BatchedExamplesIterable` that groups examples into batches.
2. A `.batch()` method for `IterableDataset` to easily create batched versions.
3. Support for shuffling and sharding to work with PyTorch DataLoader and multiple workers.
I'm not sure if this is exactly what you had in mind and also have not fully tested it atm, so I'd really appreciate your feedback. Does this seem like it's heading in the right direction? I'm happy to make any changes or explore different approaches if needed.
Pinging @lhoestq
|
closed
|
https://github.com/huggingface/datasets/pull/7054
| 2024-07-19T10:11:47
| 2024-07-23T13:25:13
| 2024-07-23T10:34:28
|
{
"login": "lappemic",
"id": 61876623,
"type": "User"
}
|
[] | true
|
[] |
2,416,423,791
| 7,053
|
Datasets.datafiles resolve_pattern `TypeError: can only concatenate tuple (not "str") to tuple`
|
### Describe the bug
in data_files.py, line 332,
`fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options)`
If we run the code on AWS, as fs.protocol will be a tuple like: `('file', 'local')`
So, `isinstance(fs.protocol, str) == False` and
`protocol_prefix = fs.protocol + "://" if fs.protocol != "file" else ""` will raise
`TypeError: can only concatenate tuple (not "str") to tuple`.
### Steps to reproduce the bug
Steps to reproduce:
1. Run on a cloud server like AWS,
2. `import datasets.data_files as datafile`
3. datafile.resolve_pattern('path/to/dataset', '.')
4. `TypeError: can only concatenate tuple (not "str") to tuple`
### Expected behavior
Should return path of the dataset, with fs.protocol at the beginning
### Environment info
- `datasets` version: 2.14.0
- Platform: Linux-3.10.0-1160.119.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.19
- Huggingface_hub version: 0.23.5
- PyArrow version: 16.1.0
- Pandas version: 1.1.5
|
closed
|
https://github.com/huggingface/datasets/issues/7053
| 2024-07-18T13:42:35
| 2024-07-18T15:17:42
| 2024-07-18T15:16:18
|
{
"login": "MatthewYZhang",
"id": 48289218,
"type": "User"
}
|
[] | false
|
[] |
2,411,682,730
| 7,052
|
Adding `Music` feature for symbolic music modality (MIDI, abc)
|
โ ๏ธ (WIP) โ ๏ธ
### What this PR does
This PR adds a `Music` feature for the symbolic music modality, in particular [MIDI](https://en.wikipedia.org/wiki/Musical_Instrument_Digital_Interface) and [abc](https://en.wikipedia.org/wiki/ABC_notation) files.
###ย Motivations
These two file formats are widely used in the [Music Information Retrieval (MIR)](https://en.wikipedia.org/wiki/Music_information_retrieval) for tasks such as music generation, music transcription, music synthesis or music transcription. Having a dedicated feature in the datasets library would allow to both encourage researchers to share datasets of this modality as well as making them more easily usable for end users, benefitting from the perks of the library.
These file formats are supported by [symusic](https://github.com/Yikai-Liao/symusic), a lightweight Python library with C bindings (using nanobind) allowing to efficiently read, write and manipulate them. The library is actively developed, and can in the future also implement other file formats such as [musicXML](https://en.wikipedia.org/wiki/MusicXML). As such, this PR relies on it.
The music data can then easily be tokenized with appropriate tokenizers such as [MidiTok](https://github.com/Natooz/MidiTok) or converted to pianorolls matrices by symusic.
**Jul 16th 2024:**
* the tests for the `Music` feature are currently failing due to non-supported access to the LazyBatch in `test_dataset_with_music_feature_map` and `test_dataset_with_music_feature_map_resample_music` (see TODOs). I am a beginner with pyArrow, I'll take any advice to make this work;
* additional tests including the `Music` feature with parquet and WebDataset should be implemented. As of right now, I am waiting for your feedback before taking further steps;
* a `MusicFolder` should also be implemented to comply with the usages of the `Image` and `Audio` features, waiting for your feedback too.
CCing @lhoestq and @albertvillanova
|
closed
|
https://github.com/huggingface/datasets/pull/7052
| 2024-07-16T17:26:04
| 2024-07-29T06:47:55
| 2024-07-29T06:47:55
|
{
"login": "Natooz",
"id": 56734983,
"type": "User"
}
|
[] | true
|
[] |
2,409,353,929
| 7,051
|
How to set_epoch with interleave_datasets?
|
Let's say I have dataset A which has 100k examples, and dataset B which has 100m examples.
I want to train on an interleaved dataset of A+B, with stopping_strategy='all_exhausted' so dataset B doesn't repeat any examples. But every time A is exhausted I want it to be reshuffled (eg. calling set_epoch)
Of course I want to interleave as IterableDatasets / streaming mode so B doesn't have to get tokenized completely at the start.
How could I achieve this? I was thinking something like, if I wrap dataset A in some new IterableDataset with from_generator() and manually call set_epoch before interleaving it? But I'm not sure how to keep the number of shards in that dataset...
Something like
```
dataset_a = load_dataset(...)
dataset_b = load_dataset(...)
def epoch_shuffled_dataset(ds):
# How to make this maintain the number of shards in ds??
for epoch in itertools.count():
ds.set_epoch(epoch)
yield from iter(ds)
shuffled_dataset_a = IterableDataset.from_generator(epoch_shuffled_dataset, gen_kwargs={'ds': dataset_a})
interleaved = interleave_datasets([shuffled_dataset_a, dataset_b], probs, stopping_strategy='all_exhausted')
```
|
closed
|
https://github.com/huggingface/datasets/issues/7051
| 2024-07-15T18:24:52
| 2024-08-05T20:58:04
| 2024-08-05T20:58:04
|
{
"login": "jonathanasdf",
"id": 511073,
"type": "User"
}
|
[] | false
|
[] |
2,409,048,733
| 7,050
|
add checkpoint and resume title in docs
|
(minor) just to make it more prominent in the docs page for the soon-to-be-released new torchdata
|
closed
|
https://github.com/huggingface/datasets/pull/7050
| 2024-07-15T15:38:04
| 2024-07-15T16:06:15
| 2024-07-15T15:59:56
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
2,408,514,366
| 7,049
|
Save nparray as list
|
### Describe the bug
When I use the `map` function to convert images into features, datasets saves nparray as a list. Some people use the `set_format` function to convert the column back, but doesn't this lose precision?
### Steps to reproduce the bug
the map function
```python
def convert_image_to_features(inst, processor, image_dir):
image_file = inst["image_url"]
file = image_file.split("/")[-1]
image_path = os.path.join(image_dir, file)
image = Image.open(image_path)
image = image.convert("RGBA")
inst["pixel_values"] = processor(images=image, return_tensors="np")["pixel_values"]
return inst
```
main function
```python
map_fun = partial(
convert_image_to_features, processor=processor, image_dir=image_dir
)
ds = ds.map(map_fun, batched=False, num_proc=20)
print(type(ds[0]["pixel_values"])
```
### Expected behavior
(type < list>)
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-4.19.91-009.ali4000.alios7.x86_64-x86_64-with-glibc2.35
- Python version: 3.11.5
- `huggingface_hub` version: 0.23.4
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0
|
closed
|
https://github.com/huggingface/datasets/issues/7049
| 2024-07-15T11:36:11
| 2024-07-18T11:33:34
| 2024-07-18T11:33:34
|
{
"login": "Sakurakdx",
"id": 48399040,
"type": "User"
}
|
[] | false
|
[] |
2,408,487,547
| 7,048
|
ImportError: numpy.core.multiarray when using `filter`
|
### Describe the bug
I can't apply the filter method on my dataset.
### Steps to reproduce the bug
The following snippet generates a bug:
```python
from datasets import load_dataset
ami = load_dataset('kamilakesbi/ami', 'ihm')
ami['train'].filter(
lambda example: example["file_name"] == 'EN2001a'
)
```
I get the following error:
`ImportError: numpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it).`
### Expected behavior
It should work properly!
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
|
closed
|
https://github.com/huggingface/datasets/issues/7048
| 2024-07-15T11:21:04
| 2024-07-16T10:11:25
| 2024-07-16T10:11:25
|
{
"login": "kamilakesbi",
"id": 45195979,
"type": "User"
}
|
[] | false
|
[] |
2,406,495,084
| 7,047
|
Save Dataset as Sharded Parquet
|
### Feature request
`to_parquet` currently saves the dataset as one massive, monolithic parquet file, rather than as several small parquet files. It should shard large datasets automatically.
### Motivation
This default behavior makes me very sad because a program I ran for 6 hours saved its results using `to_parquet`, putting the entire billion+ row dataset into a 171 GB *single shard parquet file* which pyarrow, apache spark, etc. all cannot work with without completely exhausting the memory of my system. I was previously able to work with larger-than-memory parquet files, but not this one. I *assume* the reason why this is happening is because it is a single shard. Making sharding the default behavior puts datasets in parity with other frameworks, such as spark, which automatically shard when a large dataset is saved as parquet.
### Your contribution
I could change the logic here https://github.com/huggingface/datasets/blob/bf6f41e94d9b2f1c620cf937a2e85e5754a8b960/src/datasets/io/parquet.py#L109-L158
to use `pyarrow.dataset.write_dataset`, which seems to support sharding, or periodically open new files. We would only shard if the user passed in a path rather than file handle.
|
open
|
https://github.com/huggingface/datasets/issues/7047
| 2024-07-12T23:47:51
| 2024-07-17T12:07:08
| null |
{
"login": "tom-p-reichel",
"id": 43631024,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
2,405,485,582
| 7,046
|
Support librosa and numpy 2.0 for Python 3.10
|
Support librosa and numpy 2.0 for Python 3.10 by installing soxr 0.4.0b1 pre-release:
- https://github.com/dofuuz/python-soxr/releases/tag/v0.4.0b1
- https://github.com/dofuuz/python-soxr/issues/28
|
closed
|
https://github.com/huggingface/datasets/pull/7046
| 2024-07-12T12:42:47
| 2024-07-12T13:04:40
| 2024-07-12T12:58:17
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,405,447,858
| 7,045
|
Fix tensorflow min version depending on Python version
|
Fix tensorflow min version depending on Python version.
Related to:
- #6991
|
closed
|
https://github.com/huggingface/datasets/pull/7045
| 2024-07-12T12:20:23
| 2024-07-12T12:38:53
| 2024-07-12T12:33:00
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,405,002,987
| 7,044
|
Mark tests that require librosa
|
Mark tests that require `librosa`.
Note that `librosa` is an optional dependency (installed with `audio` option) and we should be able to test environments without that library installed. This is the case if we want to test Numpy 2.0, which is currently incompatible with `librosa` due to its dependency on `soxr`:
- https://github.com/dofuuz/python-soxr/issues/28
|
closed
|
https://github.com/huggingface/datasets/pull/7044
| 2024-07-12T08:06:59
| 2024-07-12T09:06:32
| 2024-07-12T09:00:09
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,404,951,714
| 7,043
|
Add decorator as explicit test dependency
|
Add decorator as explicit test dependency.
We use `decorator` library in our CI test since PR:
- #4845
However we did not add it as an explicit test requirement, and we depended on it indirectly through other libraries' dependencies.
I discovered this while testing Numpy 2.0 and removing incompatible libraries.
|
closed
|
https://github.com/huggingface/datasets/pull/7043
| 2024-07-12T07:35:23
| 2024-07-12T08:12:55
| 2024-07-12T08:07:10
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,404,605,836
| 7,042
|
Improved the tutorial by adding a link for loading datasets
|
Improved the tutorial by letting readers know about loading datasets with common files and including a link. I left the local files section alone because the methods were already listed with code snippets.
|
closed
|
https://github.com/huggingface/datasets/pull/7042
| 2024-07-12T03:49:54
| 2024-08-15T10:07:44
| 2024-08-15T10:01:59
|
{
"login": "AmboThom",
"id": 41874659,
"type": "User"
}
|
[] | true
|
[] |
2,404,576,038
| 7,041
|
`sort` after `filter` unreasonably slow
|
### Describe the bug
as the tittle says ...
### Steps to reproduce the bug
`sort` seems to be normal.
```python
from datasets import Dataset
import random
nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]
ds = Dataset.from_list(nums)
print("start sort")
ds = ds.sort("k")
print("finish sort")
```
but `sort` after `filter` is extremely slow.
```python
from datasets import Dataset
import random
nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]
ds = Dataset.from_list(nums)
ds = ds.filter(lambda x:x > 100, input_columns="k")
print("start sort")
ds = ds.sort("k")
print("finish sort")
```
### Expected behavior
Is this a bug, or is it a misuse of the `sort` function?
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-3.10.0-1127.19.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2023.10.0
|
closed
|
https://github.com/huggingface/datasets/issues/7041
| 2024-07-12T03:29:27
| 2025-04-29T09:49:25
| 2025-04-29T09:49:25
|
{
"login": "Tobin-rgb",
"id": 56711045,
"type": "User"
}
|
[] | false
|
[] |
2,402,918,335
| 7,040
|
load `streaming=True` dataset with downloaded cache
|
### Describe the bug
We build a dataset which contains several hdf5 files and write a script using `h5py` to generate the dataset. The hdf5 files are large and the processed dataset cache takes more disk space. So we hope to try streaming iterable dataset. Unfortunately, `h5py` can't convert a remote URL into a hdf5 file descriptor. So we use `fsspec` as an interface like below:
```python
def _generate_examples(self, filepath, split):
for file in filepath:
with fsspec.open(file, "rb") as fs:
with h5py.File(fs, "r") as fp:
# for event_id in sorted(list(fp.keys())):
event_ids = list(fp.keys())
......
```
### Steps to reproduce the bug
The `fsspec` works, but it takes 10+ min to print the first 10 examples, which is even longer than the downloading time. I'm not sure if it just caches the whole hdf5 file and generates the examples.
### Expected behavior
So does the following make sense so far?
1. download the files
```python
dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True)
```
2. load the iterable dataset faster (using the raw file cache at path `.cache/huggingface/datasets/downloads`)
```python
dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True, streaming=true)
```
I made some tests, but the code above can't get the expected result. I'm not sure if this is supported. I also find the issue #6327 . It seemed similar to mine, but I couldn't find a solution.
### Environment info
- `datasets` = 2.18.0
- `h5py` = 3.10.0
- `fsspec` = 2023.10.0
|
open
|
https://github.com/huggingface/datasets/issues/7040
| 2024-07-11T11:14:13
| 2024-07-11T14:11:56
| null |
{
"login": "wanghaoyucn",
"id": 39429965,
"type": "User"
}
|
[] | false
|
[] |
2,402,403,390
| 7,039
|
Fix export to JSON when dataset larger than batch size
|
Fix export to JSON (`lines=False`) when dataset larger than batch size.
Fix #7037.
|
open
|
https://github.com/huggingface/datasets/pull/7039
| 2024-07-11T06:52:22
| 2024-09-28T06:10:00
| null |
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,400,192,419
| 7,037
|
A bug of Dataset.to_json() function
|
### Describe the bug
When using the Dataset.to_json() function, an unexpected error occurs if the parameter is set to lines=False. The stored data should be in the form of a list, but it actually turns into multiple lists, which causes an error when reading the data again.
The reason is that to_json() writes to the file in several segments based on the batch size. This is not a problem when lines=True, but it is incorrect when lines=False, because writing in several times will produce multiple lists(when len(dataset) > batch_size).
### Steps to reproduce the bug
try this code:
```python
from datasets import load_dataset
import json
train_dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")["train"]
output_path = "./harmless-base_hftojs.json"
print(len(train_dataset))
train_dataset.to_json(output_path, lines=False, force_ascii=False, indent=2)
with open(output_path, encoding="utf-8") as f:
data = json.loads(f.read())
```
it raise error: json.decoder.JSONDecodeError: Extra data: line 4003 column 1 (char 1373709)
Extra square brackets have appeared here:
<img width="265" alt="image" src="https://github.com/huggingface/datasets/assets/26499566/81492332-386d-42e8-88d1-b6d4ae3682cc">
### Expected behavior
The code runs normally.
### Environment info
datasets=2.20.0
|
open
|
https://github.com/huggingface/datasets/issues/7037
| 2024-07-10T09:11:22
| 2024-09-22T13:16:07
| null |
{
"login": "LinglingGreat",
"id": 26499566,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
2,400,035,672
| 7,036
|
Fix doc generation when NamedSplit is used as parameter default value
|
Fix doc generation when `NamedSplit` is used as parameter default value.
Fix #7035.
|
closed
|
https://github.com/huggingface/datasets/pull/7036
| 2024-07-10T07:58:46
| 2024-07-26T07:58:00
| 2024-07-26T07:51:52
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,400,021,225
| 7,035
|
Docs are not generated when a parameter defaults to a NamedSplit value
|
While generating the docs, we get an error when some parameter defaults to a `NamedSplit` value, like:
```python
def call_function(split=Split.TRAIN):
...
```
The error is: ValueError: Equality not supported between split train and <class 'inspect._empty'>
See: https://github.com/huggingface/datasets/actions/runs/9869660902/job/27254359863?pr=7015
```
Building the MDX files: 97%|โโโโโโโโโโ| 58/60 [00:00<00:00, 91.94it/s]
Traceback (most recent call last):
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 197, in build_mdx_files
content, new_anchors, source_files, errors = resolve_autodoc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 123, in resolve_autodoc
doc = autodoc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 499, in autodoc
method_doc, check = document_object(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 395, in document_object
signature = format_signature(obj)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 126, in format_signature
if param.default != inspect._empty:
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 136, in __ne__
return not self.__eq__(other)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 379, in __eq__
raise ValueError(f"Equality not supported between split {self} and {other}")
ValueError: Equality not supported between split train and <class 'inspect._empty'>
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/datasets/datasets/.venv/bin/doc-builder", line 8, in <module>
sys.exit(main())
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/doc_builder_cli.py", line 47, in main
args.func(args)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/build.py", line 102, in build_command
build_doc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 367, in build_doc
anchors_mapping, source_files_mapping = build_mdx_files(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 230, in build_mdx_files
raise type(e)(f"There was an error when converting {file} to the MDX format.\n" + e.args[0]) from e
ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format.
Equality not supported between split train and <class 'inspect._empty'>
```
|
closed
|
https://github.com/huggingface/datasets/issues/7035
| 2024-07-10T07:51:24
| 2024-07-26T07:51:53
| 2024-07-26T07:51:53
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "maintenance",
"color": "d4c5f9"
}
] | false
|
[] |
2,397,525,974
| 7,034
|
chore: fix typos in docs
| null |
closed
|
https://github.com/huggingface/datasets/pull/7034
| 2024-07-09T08:35:05
| 2024-08-13T08:22:25
| 2024-08-13T08:16:22
|
{
"login": "hattizai",
"id": 150505746,
"type": "User"
}
|
[] | true
|
[] |
2,397,419,768
| 7,033
|
`from_generator` does not allow to specify the split name
|
### Describe the bug
I'm building train, dev, and test using `from_generator`; however, in all three cases, the logger prints `Generating train split:`
It's not possible to change the split name since it seems to be hardcoded: https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/generator/generator.py
### Steps to reproduce the bug
```
In [1]: from datasets import Dataset
In [2]: def gen():
...: yield {"pokemon": "bulbasaur", "type": "grass"}
...:
In [3]: ds = Dataset.from_generator(gen)
Generating train split: 1 examples [00:00, 133.89 examples/s]
```
### Expected behavior
It should be possible to specify any split name
### Environment info
- `datasets` version: 2.19.2
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.5
- `huggingface_hub` version: 0.23.3
- PyArrow version: 15.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2023.10.0
|
closed
|
https://github.com/huggingface/datasets/issues/7033
| 2024-07-09T07:47:58
| 2024-07-26T12:56:16
| 2024-07-26T09:31:56
|
{
"login": "pminervini",
"id": 227357,
"type": "User"
}
|
[] | false
|
[] |
2,395,531,699
| 7,032
|
Register `.zstd` extension for zstd-compressed files
|
For example, https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0 dataset files have `.zstd` extension which is currently ignored (only `.zst` is registered).
|
closed
|
https://github.com/huggingface/datasets/pull/7032
| 2024-07-08T12:39:50
| 2024-07-12T15:07:03
| 2024-07-12T15:07:03
|
{
"login": "polinaeterna",
"id": 16348744,
"type": "User"
}
|
[] | true
|
[] |
2,395,401,692
| 7,031
|
CI quality is broken: use ruff check instead
|
CI quality is broken: https://github.com/huggingface/datasets/actions/runs/9838873879/job/27159697027
```
error: `ruff <path>` has been removed. Use `ruff check <path>` instead.
```
|
closed
|
https://github.com/huggingface/datasets/issues/7031
| 2024-07-08T11:42:24
| 2024-07-08T11:47:29
| 2024-07-08T11:47:29
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | false
|
[] |
2,393,411,631
| 7,030
|
Add option to disable progress bar when reading a dataset ("Loading dataset from disk")
|
### Feature request
Add an option in load_from_disk to disable the progress bar even if the number of files is larger than 16.
### Motivation
I am reading a lot of datasets that it creates lots of logs.
<img width="1432" alt="image" src="https://github.com/huggingface/datasets/assets/57996478/8d4bbf03-6b89-44b6-937c-932f01b4eb2a">
### Your contribution
Seems like an easy fix to make. I can create a PR if necessary.
|
closed
|
https://github.com/huggingface/datasets/issues/7030
| 2024-07-06T05:43:37
| 2024-07-13T14:35:59
| 2024-07-13T14:35:59
|
{
"login": "yuvalkirstain",
"id": 57996478,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
2,391,366,696
| 7,029
|
load_dataset on AWS lambda throws OSError(30, 'Read-only file system') error
|
### Describe the bug
I'm using AWS lambda to run a python application. I run the `load_dataset` function with cache_dir="/tmp" and is still throws the OSError(30, 'Read-only file system') error. Is even updated all the HF envs to point to /tmp dir but the issue still persists. I can confirm that the I can write to /tmp directory.
### Steps to reproduce the bug
```python
d = load_dataset(
path=hugging_face_link,
split=split,
token=token,
cache_dir="/tmp/hugging_face_cache",
)
```
### Expected behavior
Everything written to the file system as part of the load_datasets function should be in the /tmp directory.
### Environment info
datasets version: 2.16.1
Platform: Linux-5.10.216-225.855.amzn2.x86_64-x86_64-with-glibc2.26
Python version: 3.11.9
huggingface_hub version: 0.19.4
PyArrow version: 16.1.0
Pandas version: 2.2.2
fsspec version: 2023.10.0
|
open
|
https://github.com/huggingface/datasets/issues/7029
| 2024-07-04T19:15:16
| 2024-07-17T12:44:03
| null |
{
"login": "sugam-nexusflow",
"id": 171606538,
"type": "User"
}
|
[] | false
|
[] |
2,391,077,531
| 7,028
|
Fix ci
|
...after last pr errors
|
closed
|
https://github.com/huggingface/datasets/pull/7028
| 2024-07-04T15:11:08
| 2024-07-04T15:26:35
| 2024-07-04T15:19:16
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
2,391,013,330
| 7,027
|
Missing line from previous pr
| null |
closed
|
https://github.com/huggingface/datasets/pull/7027
| 2024-07-04T14:34:29
| 2024-07-04T14:40:46
| 2024-07-04T14:34:36
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
2,390,983,889
| 7,026
|
Fix check_library_imports
|
move it to after the `trust_remote_code` check
Note that it only affects local datasets that already exist on disk, not datasets loaded from HF directly
|
closed
|
https://github.com/huggingface/datasets/pull/7026
| 2024-07-04T14:18:38
| 2024-07-04T14:28:36
| 2024-07-04T14:20:02
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
2,390,488,546
| 7,025
|
feat: support non streamable arrow file binary format
|
Support Arrow files (`.arrow`) that are in non streamable binary file formats.
|
closed
|
https://github.com/huggingface/datasets/pull/7025
| 2024-07-04T10:11:12
| 2024-07-31T06:15:50
| 2024-07-31T06:09:31
|
{
"login": "kmehant",
"id": 15800200,
"type": "User"
}
|
[] | true
|
[] |
2,390,141,626
| 7,024
|
Streaming dataset not returning data
|
### Describe the bug
I'm deciding to post here because I'm still not sure what the issue is, or if I am using IterableDatasets wrongly.
I'm following the guide on here https://huggingface.co/learn/cookbook/en/fine_tuning_code_llm_on_single_gpu pretty much to a tee and have verified that it works when I'm fine-tuning on the provided dataset.
However, I'm doing some data preprocessing steps (filtering out entries), when I try to swap out the dataset for mine, it fails to train. However, I eventually fixed this by simply setting `stream=False` in `load_dataset`.
Coud this be some sort of network / firewall issue I'm facing?
### Steps to reproduce the bug
I made a post with greater description about how I reproduced this problem before I found my workaround: https://discuss.huggingface.co/t/problem-with-custom-iterator-of-streaming-dataset-not-returning-anything/94551
Here is the problematic dataset snippet, which works when streaming=False (and with buffer keyword removed from shuffle)
```
commitpackft = load_dataset(
"chargoddard/commitpack-ft-instruct", split="train", streaming=True
).filter(lambda example: example["language"] == "Python")
def form_template(example):
"""Forms a template for each example following the alpaca format for CommitPack"""
example["content"] = (
"### Human: " + example["instruction"] + " " + example["input"] + " ### Assistant: " + example["output"]
)
return example
dataset = commitpackft.map(
form_template,
remove_columns=["id", "language", "license", "instruction", "input", "output"],
).shuffle(
seed=42, buffer_size=10000
) # remove everything since its all inside "content" now
validation_data = dataset.take(4000)
train_data = dataset.skip(4000)
```
The annoying part about this is that it only fails during training and I don't know when it will fail, except that it always fails during evaluation.
### Expected behavior
The expected behavior is that I should be able to get something from the iterator when called instead of getting nothing / stuck in a loop somewhere.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.31
- Python version: 3.11.7
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
|
open
|
https://github.com/huggingface/datasets/issues/7024
| 2024-07-04T07:21:47
| 2024-07-04T07:21:47
| null |
{
"login": "johnwee1",
"id": 91670254,
"type": "User"
}
|
[] | false
|
[] |
2,388,090,424
| 7,023
|
Remove dead code for pyarrow < 15.0.0
|
Remove dead code for pyarrow < 15.0.0.
Code is dead since the merge of:
- #6892
Fix #7022.
|
closed
|
https://github.com/huggingface/datasets/pull/7023
| 2024-07-03T09:05:03
| 2024-07-03T09:24:46
| 2024-07-03T09:17:35
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,388,064,650
| 7,022
|
There is dead code after we require pyarrow >= 15.0.0
|
There are code lines specific for pyarrow versions < 15.0.0.
However, we require pyarrow >= 15.0.0 since the merge of PR:
- #6892
Those code lines are now dead code and should be removed.
|
closed
|
https://github.com/huggingface/datasets/issues/7022
| 2024-07-03T08:52:57
| 2024-07-03T09:17:36
| 2024-07-03T09:17:36
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "maintenance",
"color": "d4c5f9"
}
] | false
|
[] |
2,387,948,935
| 7,021
|
Fix casting list array to fixed size list
|
Fix casting list array to fixed size list.
This bug was introduced in [datasets-2.17.0](https://github.com/huggingface/datasets/releases/tag/2.17.0) by PR: https://github.com/huggingface/datasets/pull/6283/files#diff-1cb2b66aa9311d729cfd83013dad56cf5afcda35b39dfd0bfe9c3813a049eab0R1899
- #6283
Fix #7020.
|
closed
|
https://github.com/huggingface/datasets/pull/7021
| 2024-07-03T07:58:57
| 2024-07-03T08:47:49
| 2024-07-03T08:41:55
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,387,940,990
| 7,020
|
Casting list array to fixed size list raises error
|
When trying to cast a list array to fixed size list, an AttributeError is raised:
> AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length'
Steps to reproduce the bug:
```python
import pyarrow as pa
from datasets.table import array_cast
arr = pa.array([[0, 1]])
array_cast(arr, pa.list_(pa.int64(), 2))
```
Stack trace:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-12-6cb90a1d8216> in <module>
3
4 arr = pa.array([[0, 1]])
----> 5 array_cast(arr, pa.list_(pa.int64(), 2))
~/huggingface/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
1802 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1803 else:
-> 1804 return func(array, *args, **kwargs)
1805
1806 return wrapper
~/huggingface/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_primitive_to_str, allow_decimal_to_str)
1920 else:
1921 array_values = array.values[
-> 1922 array.offset * pa_type.length : (array.offset + len(array)) * pa_type.length
1923 ]
1924 return pa.FixedSizeListArray.from_arrays(_c(array_values, pa_type.value_type), pa_type.list_size)
AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length'
```
|
closed
|
https://github.com/huggingface/datasets/issues/7020
| 2024-07-03T07:54:49
| 2024-07-03T08:41:56
| 2024-07-03T08:41:56
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
2,385,793,897
| 7,019
|
Support pyarrow large_list
|
Allow Polars round trip by supporting pyarrow large list.
Fix #6834, fix #6984.
Supersede and close #4800, close #6835, close #6986.
|
closed
|
https://github.com/huggingface/datasets/pull/7019
| 2024-07-02T09:52:52
| 2024-08-12T14:49:45
| 2024-08-12T14:43:45
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,383,700,286
| 7,018
|
`load_dataset` fails to load dataset saved by `save_to_disk`
|
### Describe the bug
This code fails to load the dataset it just saved:
```python
from datasets import load_dataset
from transformers import AutoTokenizer
MODEL = "google-bert/bert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
dataset = load_dataset("yelp_review_full")
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
tokenized_datasets.save_to_disk("dataset")
tokenized_datasets = load_dataset("dataset/") # raises
```
It raises `ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('test'): ('json', {})}`.
I believe this bug is caused by the [logic that tries to infer dataset format](https://github.com/huggingface/datasets/blob/9af8dd3de7626183a9a9ec8973cebc672d690400/src/datasets/load.py#L556). It counts the most common file extension. However, a small dataset can fit in a single `.arrow` file and have two JSON metadata files, causing the format to be inferred as JSON:
```shell
$ ls -l dataset/test
-rw-r--r-- 1 sliedes sliedes 191498784 Jul 1 13:55 data-00000-of-00001.arrow
-rw-r--r-- 1 sliedes sliedes 1730 Jul 1 13:55 dataset_info.json
-rw-r--r-- 1 sliedes sliedes 249 Jul 1 13:55 state.json
```
### Steps to reproduce the bug
Execute the code above.
### Expected behavior
The dataset is loaded successfully.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.9.3-arch1-1-x86_64-with-glibc2.39
- Python version: 3.12.4
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
|
open
|
https://github.com/huggingface/datasets/issues/7018
| 2024-07-01T12:19:19
| 2025-05-24T05:21:12
| null |
{
"login": "sliedes",
"id": 2307997,
"type": "User"
}
|
[] | false
|
[] |
2,383,647,419
| 7,017
|
Support fsspec 2024.6.1
|
Support fsspec 2024.6.1.
|
closed
|
https://github.com/huggingface/datasets/pull/7017
| 2024-07-01T11:57:15
| 2024-07-01T12:12:32
| 2024-07-01T12:06:24
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,383,262,608
| 7,016
|
`drop_duplicates` method
|
### Feature request
`drop_duplicates` method for huggingface datasets (similiar in simplicity to the `pandas` one)
### Motivation
Ease of use
### Your contribution
I don't think i am good enough to help
|
open
|
https://github.com/huggingface/datasets/issues/7016
| 2024-07-01T09:01:06
| 2024-07-20T06:51:58
| null |
{
"login": "MohamedAliRashad",
"id": 26205298,
"type": "User"
}
|
[
{
"name": "duplicate",
"color": "cfd3d7"
},
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
2,383,151,220
| 7,015
|
add split argument to Generator
|
## Actual
When creating a multi-split dataset using generators like
```python
datasets.DatasetDict({
"val": datasets.Dataset.from_generator(
generator=generator_val,
features=features
),
"test": datasets.Dataset.from_generator(
generator=generator_test,
features=features,
)
})
```
It displays (for both test and val)
```
Generating train split
```
## Expected
I would like to be able to improve this behavior by doing
```python
datasets.DatasetDict({
"val": datasets.Dataset.from_generator(
generator=generator_val,
features=features,
split="val"
),
"test": datasets.Dataset.from_generator(
generator=generator_test,
features=features,
split="test"
)
})
```
It would display
```
Generating val split
```
and
```
Generating test split
```
## Proposal
Current PR is adding an explicit `split` argument and replace the implicit "train" split in the following classes/function :
* Generator
* from_generator
* AbstractDatasetInputStream
* GeneratorDatasetInputStream
Please share your feedbacks
|
closed
|
https://github.com/huggingface/datasets/pull/7015
| 2024-07-01T08:09:25
| 2024-07-26T09:37:51
| 2024-07-26T09:31:56
|
{
"login": "piercus",
"id": 156736,
"type": "User"
}
|
[] | true
|
[] |
2,382,985,847
| 7,014
|
Skip faiss tests on Windows to avoid running CI for 360 minutes
|
Skip faiss tests on Windows to avoid running CI for 360 minutes.
Fix #7013.
Revert once the underlying issue is fixed.
|
closed
|
https://github.com/huggingface/datasets/pull/7014
| 2024-07-01T06:45:35
| 2024-07-01T07:16:36
| 2024-07-01T07:10:27
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,382,976,738
| 7,013
|
CI is broken for faiss tests on Windows: node down: Not properly terminated
|
Faiss tests on Windows make the CI run indefinitely until maximum execution time (360 minutes) is reached.
See: https://github.com/huggingface/datasets/actions/runs/9712659783
```
test (integration, windows-latest, deps-minimum)
The job running on runner GitHub Actions 60 has exceeded the maximum execution time of 360 minutes.
test (integration, windows-latest, deps-latest)
The job running on runner GitHub Actions 238 has exceeded the maximum execution time of 360 minutes.
```
```
____________________________ tests/test_search.py _____________________________
[gw1] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw1' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
____________________________ tests/test_search.py _____________________________
[gw2] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw2' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
```
```
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw0] node down: Not properly terminated
[gw0] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw0
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw1] node down: Not properly terminated
[gw1] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw1
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw2] node down: Not properly terminated
[gw2] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw2
```
|
closed
|
https://github.com/huggingface/datasets/issues/7013
| 2024-07-01T06:40:03
| 2024-07-01T07:10:28
| 2024-07-01T07:10:28
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "maintenance",
"color": "d4c5f9"
}
] | false
|
[] |
2,380,934,047
| 7,012
|
Raise an error when a nested object is expected to be a mapping that displays the object
| null |
closed
|
https://github.com/huggingface/datasets/pull/7012
| 2024-06-28T18:10:59
| 2024-07-11T02:06:16
| 2024-07-11T02:06:16
|
{
"login": "sebbyjp",
"id": 22511797,
"type": "User"
}
|
[] | true
|
[] |
2,379,785,262
| 7,011
|
Re-enable raising error from huggingface-hub FutureWarning in CI
|
Re-enable raising error from huggingface-hub FutureWarning in tests, once that the fix in transformers
- https://github.com/huggingface/transformers/pull/31007
was just released yesterday in transformers-4.42.0: https://github.com/huggingface/transformers/releases/tag/v4.42.0
Fix #7010.
|
closed
|
https://github.com/huggingface/datasets/pull/7011
| 2024-06-28T07:28:32
| 2024-06-28T12:25:25
| 2024-06-28T12:19:28
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,379,777,480
| 7,010
|
Re-enable raising error from huggingface-hub FutureWarning in CI
|
Re-enable raising error from huggingface-hub FutureWarning in CI, which was disabled by PR:
- #6876
Note that this can only be done once transformers releases the fix:
- https://github.com/huggingface/transformers/pull/31007
|
closed
|
https://github.com/huggingface/datasets/issues/7010
| 2024-06-28T07:23:40
| 2024-06-28T12:19:30
| 2024-06-28T12:19:29
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "maintenance",
"color": "d4c5f9"
}
] | false
|
[] |
2,379,619,132
| 7,009
|
Support ruff 0.5.0 in CI
|
Support ruff 0.5.0 in CI and revert:
- #7007
Fix #7008.
|
closed
|
https://github.com/huggingface/datasets/pull/7009
| 2024-06-28T05:37:36
| 2024-06-28T07:17:26
| 2024-06-28T07:11:17
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,379,591,141
| 7,008
|
Support ruff 0.5.0 in CI
|
Support ruff 0.5.0 in CI.
Also revert:
- #7007
|
closed
|
https://github.com/huggingface/datasets/issues/7008
| 2024-06-28T05:11:26
| 2024-06-28T07:11:18
| 2024-06-28T07:11:18
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "maintenance",
"color": "d4c5f9"
}
] | false
|
[] |
2,379,588,676
| 7,007
|
Fix CI by temporarily pinning ruff < 0.5.0
|
As a hotfix for CI, temporarily pin ruff upper version < 0.5.0.
Fix #7006.
Revert once root cause is fixed.
|
closed
|
https://github.com/huggingface/datasets/pull/7007
| 2024-06-28T05:09:17
| 2024-06-28T05:31:21
| 2024-06-28T05:25:17
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,379,581,543
| 7,006
|
CI is broken after ruff-0.5.0: E721
|
After ruff-0.5.0 release (https://github.com/astral-sh/ruff/releases/tag/0.5.0), our CI is broken due to E721 rule.
See: https://github.com/huggingface/datasets/actions/runs/9707641618/job/26793170961?pr=6983
> src/datasets/features/features.py:844:12: E721 Use `is` and `is not` for type comparisons, or `isinstance()` for isinstance checks
|
closed
|
https://github.com/huggingface/datasets/issues/7006
| 2024-06-28T05:03:28
| 2024-06-28T05:25:18
| 2024-06-28T05:25:18
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "maintenance",
"color": "d4c5f9"
}
] | false
|
[] |
2,378,424,349
| 7,005
|
EmptyDatasetError: The directory at /metadata.jsonl doesn't contain any data files
|
### Describe the bug
while trying to load custom dataset from jsonl file, I get the error: "metadata.jsonl doesn't contain any data files"
### Steps to reproduce the bug
This is my [metadata_v2.jsonl](https://github.com/user-attachments/files/16016011/metadata_v2.json) file. I have this file in the folder with all images mentioned in that json(l) file.
Through below mentioned command I am trying to load_dataset so that I can upload it as mentioned here on the [official website](https://huggingface.co/docs/datasets/en/image_dataset#upload-dataset-to-the-hub).
````
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="path/to/jsonl/metadata.jsonl")
````
error:
````
EmptyDatasetError Traceback (most recent call last)
Cell In[18], line 3
1 from datasets import load_dataset
----> 3 dataset = load_dataset("imagefolder",
4 data_dir="path/to/jsonl/file/metadata.jsonl")
5 dataset[0]["objects"]
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2594, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2589 verification_mode = VerificationMode(
2590 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
2591 )
2593 # Create a dataset builder
-> 2594 builder_instance = load_dataset_builder(
2595 path=path,
2596 name=name,
2597 data_dir=data_dir,
2598 data_files=data_files,
2599 cache_dir=cache_dir,
2600 features=features,
2601 download_config=download_config,
2602 download_mode=download_mode,
2603 revision=revision,
2604 token=token,
2605 storage_options=storage_options,
2606 trust_remote_code=trust_remote_code,
2607 _require_default_config_name=name is None,
2608 **config_kwargs,
2609 )
2611 # Return iterable dataset in case of streaming
2612 if streaming:
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2266, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
2264 download_config = download_config.copy() if download_config else DownloadConfig()
2265 download_config.storage_options.update(storage_options)
-> 2266 dataset_module = dataset_module_factory(
2267 path,
2268 revision=revision,
2269 download_config=download_config,
2270 download_mode=download_mode,
2271 data_dir=data_dir,
2272 data_files=data_files,
2273 cache_dir=cache_dir,
2274 trust_remote_code=trust_remote_code,
2275 _require_default_config_name=_require_default_config_name,
2276 _require_custom_configs=bool(config_kwargs),
2277 )
2278 # Get dataset builder class from the processing script
2279 builder_kwargs = dataset_module.builder_kwargs
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1805, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
1782 # We have several ways to get a dataset builder:
1783 #
1784 # - if path is the name of a packaged dataset module
(...)
1796
1797 # Try packaged
1798 if path in _PACKAGED_DATASETS_MODULES:
1799 return PackagedDatasetModuleFactory(
1800 path,
1801 data_dir=data_dir,
1802 data_files=data_files,
1803 download_config=download_config,
1804 download_mode=download_mode,
-> 1805 ).get_module()
1806 # Try locally
1807 elif path.endswith(filename):
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1140, in PackagedDatasetModuleFactory.get_module(self)
1135 def get_module(self) -> DatasetModule:
1136 base_path = Path(self.data_dir or "").expanduser().resolve().as_posix()
1137 patterns = (
1138 sanitize_patterns(self.data_files)
1139 if self.data_files is not None
-> 1140 else get_data_patterns(base_path, download_config=self.download_config)
1141 )
1142 data_files = DataFilesDict.from_patterns(
1143 patterns,
1144 download_config=self.download_config,
1145 base_path=base_path,
1146 )
1147 supports_metadata = self.name in _MODULE_SUPPORTS_METADATA
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/data_files.py:503, in get_data_patterns(base_path, download_config)
501 return _get_data_files_patterns(resolver)
502 except FileNotFoundError:
--> 503 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None
EmptyDatasetError: The directory at path/to/jsonl/file/metadata.jsonl doesn't contain any data files`
```
### Expected behavior
It should be able load the whole file in a format of "dataset" inside the dataset variable. But it gives error "The directory at "path/to/jsonl/metadata.jsonl" doesn't contain any data files."
### Environment info
I am using conda environment.
|
closed
|
https://github.com/huggingface/datasets/issues/7005
| 2024-06-27T15:08:26
| 2024-06-28T09:56:19
| 2024-06-28T09:56:19
|
{
"login": "Aki1991",
"id": 117731544,
"type": "User"
}
|
[] | false
|
[] |
2,376,064,264
| 7,004
|
Fix WebDatasets KeyError for user-defined Features when a field is missing in an example
|
Fixes: https://github.com/huggingface/datasets/issues/6900
Not sure if this needs any addition stuff before merging
|
closed
|
https://github.com/huggingface/datasets/pull/7004
| 2024-06-26T18:58:05
| 2024-06-29T00:15:49
| 2024-06-28T09:30:12
|
{
"login": "ProGamerGov",
"id": 10626398,
"type": "User"
}
|
[] | true
|
[] |
2,373,084,132
| 7,003
|
minor fix for bfloat16
| null |
closed
|
https://github.com/huggingface/datasets/pull/7003
| 2024-06-25T16:10:04
| 2024-06-25T16:16:11
| 2024-06-25T16:10:10
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
2,373,010,351
| 7,002
|
Fix dump of bfloat16 torch tensor
|
close https://github.com/huggingface/datasets/issues/7000
|
closed
|
https://github.com/huggingface/datasets/pull/7002
| 2024-06-25T15:38:09
| 2024-06-25T16:10:16
| 2024-06-25T15:51:52
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
2,372,930,879
| 7,001
|
Datasetbuilder Local Download FileNotFoundError
|
### Describe the bug
So I was trying to download a dataset and save it as parquet and I follow the [tutorial](https://huggingface.co/docs/datasets/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage) of Huggingface. However, during the excution I face a FileNotFoundError.
I debug the code and it seems there is a bug there:
So first it creates a .incomplete folder and before moving its contents the following code deletes the directory
[Code](https://github.com/huggingface/datasets/blob/98fdc9e78e6d057ca66e58a37f49d6618aab8130/src/datasets/builder.py#L984)
hence as a result I face with:
``` FileNotFoundError: [Errno 2] No such file or directory: '~/data/Parquet/.incomplete '```
### Steps to reproduce the bug
```
from datasets import load_dataset_builder
from pathlib import Path
parquet_dir = "~/data/Parquet/"
Path(parquet_dir).mkdir(parents=True, exist_ok=True)
builder = load_dataset_builder(
"rotten_tomatoes",
)
builder.download_and_prepare(parquet_dir, file_format="parquet")
```
### Expected behavior
Downloads the files and saves as parquet
### Environment info
Ubuntu,
Python 3.10
```
datasets 2.19.1
```
|
open
|
https://github.com/huggingface/datasets/issues/7001
| 2024-06-25T15:02:34
| 2024-06-25T15:21:19
| null |
{
"login": "purefall",
"id": 12601271,
"type": "User"
}
|
[] | false
|
[] |
2,372,887,585
| 7,000
|
IterableDataset: Unsupported ScalarType BFloat16
|
### Describe the bug
`IterableDataset.from_generator` crashes when using BFloat16:
```
File "/usr/local/lib/python3.11/site-packages/datasets/utils/_dill.py", line 169, in _save_torchTensor
args = (obj.detach().cpu().numpy(),)
^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: Got unsupported ScalarType BFloat16
```
### Steps to reproduce the bug
```python
import torch
from datasets import IterableDataset
def demo(x):
yield {"x": x}
x = torch.tensor([1.], dtype=torch.bfloat16)
dataset = IterableDataset.from_generator(
demo,
gen_kwargs=dict(x=x),
)
example = next(iter(dataset))
print(example)
```
### Expected behavior
Code sample should print:
```python
{'x': tensor([1.], dtype=torch.bfloat16)}
```
### Environment info
```
datasets==2.20.0
torch==2.2.2
```
|
closed
|
https://github.com/huggingface/datasets/issues/7000
| 2024-06-25T14:43:26
| 2024-06-25T16:04:00
| 2024-06-25T15:51:53
|
{
"login": "stoical07",
"id": 170015089,
"type": "User"
}
|
[] | false
|
[] |
2,372,124,589
| 6,999
|
Remove tasks
|
Remove tasks, as part of the 3.0 release.
|
closed
|
https://github.com/huggingface/datasets/pull/6999
| 2024-06-25T09:06:16
| 2024-08-21T09:07:07
| 2024-08-21T09:01:18
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,371,973,926
| 6,998
|
Fix tests using hf-internal-testing/librispeech_asr_dummy
|
Fix tests using hf-internal-testing/librispeech_asr_dummy once that dataset has been converted to Parquet.
Fix #6997.
|
closed
|
https://github.com/huggingface/datasets/pull/6998
| 2024-06-25T07:59:44
| 2024-06-25T08:22:38
| 2024-06-25T08:13:42
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,371,966,127
| 6,997
|
CI is broken for tests using hf-internal-testing/librispeech_asr_dummy
|
CI is broken: https://github.com/huggingface/datasets/actions/runs/9657882317/job/26637998686?pr=6996
```
FAILED tests/test_inspect.py::test_get_dataset_config_names[hf-internal-testing/librispeech_asr_dummy-expected4] - AssertionError: assert ['clean'] == ['clean', 'other']
Right contains one more item: 'other'
Full diff:
[
'clean',
- 'other',
]
FAILED tests/test_inspect.py::test_get_dataset_default_config_name[hf-internal-testing/librispeech_asr_dummy-None] - AssertionError: assert 'clean' is None
```
Note that repository was recently converted to Parquet: https://huggingface.co/datasets/hf-internal-testing/librispeech_asr_dummy/commit/5be91486e11a2d616f4ec5db8d3fd248585ac07a
|
closed
|
https://github.com/huggingface/datasets/issues/6997
| 2024-06-25T07:55:44
| 2024-06-25T08:13:43
| 2024-06-25T08:13:43
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "maintenance",
"color": "d4c5f9"
}
] | false
|
[] |
2,371,841,671
| 6,996
|
Remove deprecated code
|
Remove deprecated code, as part of the 3.0 release.
First merge:
- [x] #6983
- [x] #6987
- [x] #6999
|
closed
|
https://github.com/huggingface/datasets/pull/6996
| 2024-06-25T06:54:40
| 2024-08-21T09:42:52
| 2024-08-21T09:35:06
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,370,713,475
| 6,995
|
ImportError when importing datasets.load_dataset
|
### Describe the bug
I encountered an ImportError while trying to import `load_dataset` from the `datasets` module in Hugging Face. The error message indicates a problem with importing 'CommitInfo' from 'huggingface_hub'.
### Steps to reproduce the bug
1. pip install git+https://github.com/huggingface/datasets
2. from datasets import load_dataset
### Expected behavior
ImportError Traceback (most recent call last)
Cell In[7], [line 1](vscode-notebook-cell:?execution_count=7&line=1)
----> [1](vscode-notebook-cell:?execution_count=7&line=1) from datasets import load_dataset
[3](vscode-notebook-cell:?execution_count=7&line=3) train_set = load_dataset("mispeech/speechocean762", split="train")
[4](vscode-notebook-cell:?execution_count=7&line=4) test_set = load_dataset("mispeech/speechocean762", split="test")
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py:[1](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:1)7
1 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
[2](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:2) #
[3](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:3) # Licensed under the Apache License, Version 2.0 (the "License");
(...)
[12](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:12) # See the License for the specific language governing permissions and
[13](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:13) # limitations under the License.
[15](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:15) __version__ = "2.20.1.dev0"
---> [17](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:17) from .arrow_dataset import Dataset
[18](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:18) from .arrow_reader import ReadInstruction
[19](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:19) from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py:63
[61](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:61) import pyarrow.compute as pc
[62](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:62) from fsspec.core import url_to_fs
---> [63](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:63) from huggingface_hub import (
[64](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:64) CommitInfo,
[65](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:65) CommitOperationAdd,
...
[70](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:70) )
[71](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:71) from huggingface_hub.hf_api import RepoFile
[72](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:72) from multiprocess import Pool
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (d:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?580889ab-0f61-4f37-9214-eaa2b3807f85) or open in a [text editor](command:workbench.action.openLargeOutput?580889ab-0f61-4f37-9214-eaa2b3807f85). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
### Environment info
Leo@DESKTOP-9NHUAMI MSYS /d/Anaconda3/envs/CS224S/Lib/site-packages/huggingface_hub
$ datasets-cli env
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "D:\Anaconda3\envs\CS224S\Scripts\datasets-cli.exe\__main__.py", line 4, in <module>
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py", line 17, in <module>
from .arrow_dataset import Dataset
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py", line 63, in <module>
from huggingface_hub import (
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
(CS224S)
|
closed
|
https://github.com/huggingface/datasets/issues/6995
| 2024-06-24T17:07:22
| 2024-11-14T01:42:09
| 2024-06-25T06:11:37
|
{
"login": "Leo-Lsc",
"id": 124846947,
"type": "User"
}
|
[] | false
|
[] |
2,370,491,689
| 6,994
|
Fix incorrect rank value in data splitting
|
Fix #6990.
|
closed
|
https://github.com/huggingface/datasets/pull/6994
| 2024-06-24T15:07:47
| 2024-06-26T04:37:35
| 2024-06-25T16:19:17
|
{
"login": "yzhangcs",
"id": 18402347,
"type": "User"
}
|
[] | true
|
[] |
2,370,444,104
| 6,993
|
less script docs
|
+ mark as legacy in some parts of the docs since we'll not build new features for script datasets
|
closed
|
https://github.com/huggingface/datasets/pull/6993
| 2024-06-24T14:45:28
| 2024-07-08T13:10:53
| 2024-06-27T09:31:21
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
2,367,890,622
| 6,992
|
Dataset with streaming doesn't work with proxy
|
### Describe the bug
I'm currently trying to stream data using dataset since the dataset is too big but it hangs indefinitely without loading the first batch. I use AIMOS which is a supercomputer that uses proxy to connect to the internet. I assume it has to do with the network configurations. I've already set up both HTTP_PROXY and HTTPS_PROXY. streaming = False works fine.
### Steps to reproduce the bug
use load_dataset with streaming = True in AIMOS
### Expected behavior
does not hang indefinitely and loads batches to start training run
### Environment info
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
_pytorch_select 2.0 cuda_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
abseil-cpp 20220623.0 h9888cd1_6 conda-forge
absl-py 1.0.0 py311h399429b_0 https://ftp.osuosl.org/pub/open-ce/1.10.0
aiofiles 23.2.1 pyhd8ed1ab_0 conda-forge
aiohttp 3.8.6 py311hf118e41_0
aiosignal 1.2.0 pyhd3eb1b0_0
archspec 0.2.3 pyhd8ed1ab_0 conda-forge
arrow-cpp 11.0.0 ha3edaa6_5_cpu conda-forge
async-timeout 4.0.2 py311h6ffa863_0
attrs 23.1.0 py311h6ffa863_0
av 10.0.0 py311he6153ed_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
aws-c-auth 0.6.24 hb81f6d7_5 conda-forge
aws-c-cal 0.5.20 h3c2b4d9_6 conda-forge
aws-c-common 0.8.11 h4194056_0 conda-forge
aws-c-compression 0.2.16 ha19333d_3 conda-forge
aws-c-event-stream 0.2.18 h12a9399_6 conda-forge
aws-c-http 0.7.4 ha2cde00_2 conda-forge
aws-c-io 0.13.17 h9189062_2 conda-forge
aws-c-mqtt 0.8.6 h40d1a04_6 conda-forge
aws-c-s3 0.2.4 hbdbe4f0_3 conda-forge
aws-c-sdkutils 0.1.7 ha19333d_3 conda-forge
aws-checksums 0.1.14 ha19333d_3 conda-forge
aws-crt-cpp 0.19.7 hd018011_7 conda-forge
aws-sdk-cpp 1.10.57 hb9575ba_4 conda-forge
blas 1.0 openblas
blinker 1.8.2 pyhd8ed1ab_0 conda-forge
boltons 23.0.0 py311h6ffa863_0
boost-cpp 1.82.0 h25e6d66_2
bottleneck 1.3.5 py311h34f6284_0
brotli 1.0.9 hf118e41_7
brotli-bin 1.0.9 hf118e41_7
brotli-python 1.0.9 py311h4a02239_7
bzip2 1.0.8 h7b6447c_0
c-ares 1.19.1 hf118e41_0
ca-certificates 2024.6.2 h0f6029e_0 conda-forge
cachetools 5.3.3 pyhd8ed1ab_0 conda-forge
certifi 2024.6.2 pyhd8ed1ab_0 conda-forge
cffi 1.15.1 py311hf118e41_3
charset-normalizer 2.0.4 pyhd3eb1b0_0
click 8.1.7 unix_pyh707e725_0 conda-forge
conda 24.5.0 py311h1af927a_0 conda-forge
conda-content-trust 0.2.0 py311h6ffa863_0
conda-libmamba-solver 23.11.1 py311h6ffa863_0
conda-package-handling 2.2.0 py311h6ffa863_0
conda-package-streaming 0.9.0 py311h6ffa863_0
contourpy 1.0.5 py311h25e6d66_0
cryptography 41.0.3 py311hb0e80e7_0
cudatoolkit 11.8.0 hedcfb66_13 conda-forge
cudnn 8.9.2_11.8 h9ceb136_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
cycler 0.11.0 pyhd3eb1b0_0
datasets 2.12.0 py311h6ffa863_0
dill 0.3.6 py311h6ffa863_0
distro 1.9.0 pyhd8ed1ab_0 conda-forge
ffmpeg 4.2.2 opence_0 https://ftp.osuosl.org/pub/open-ce/1.10.0
filelock 3.9.0 py311h6ffa863_0
fmt 9.1.0 h25e6d66_0
fonttools 4.25.0 pyhd3eb1b0_0
freetype 2.12.1 hd23a775_0
frozendict 2.4.4 py311hb02d432_0 conda-forge
frozenlist 1.4.0 py311hf118e41_0
fsspec 2023.9.2 py311h6ffa863_0
gflags 2.2.2 he6710b0_0
giflib 5.2.1 hf118e41_3
glog 0.6.0 hbe088e0_0 conda-forge
gmp 6.3.0 h46f38da_0 conda-forge
gmpy2 2.1.5 py311h2758da7_1 conda-forge
google-auth 2.30.0 pyhff2d567_0 conda-forge
google-auth-oauthlib 0.5.3 pyhd8ed1ab_0 conda-forge
grpc-cpp 1.51.1 h8ba971d_1 conda-forge
grpcio 1.54.3 py311h414e0d3_0 https://ftp.osuosl.org/pub/open-ce/1.10.0
huggingface_hub 0.17.3 py311h6ffa863_0
icu 73.1 h4a02239_0
idna 3.4 py311h6ffa863_0
importlib-metadata 6.0.0 py311h6ffa863_0
jinja2 3.1.4 pyhd8ed1ab_0 conda-forge
jpeg 9e hf118e41_1
jsonpatch 1.32 pyhd3eb1b0_0
jsonpointer 2.1 pyhd3eb1b0_0
kiwisolver 1.4.4 py311h4a02239_0
krb5 1.20.1 hc019ccd_1
lame 3.100 hb283c62_1003 conda-forge
lcms2 2.12 h2045e0b_0
ld_impl_linux-ppc64le 2.38 hec883e6_1
lerc 3.0 h29c3540_0
leveldb 1.23 h24532b4_1 conda-forge
libabseil 20220623.0 cxx17_h9235812_6 conda-forge
libarchive 3.6.2 hd8ab008_2
libarrow 11.0.0 h837770b_5_cpu conda-forge
libboost 1.82.0 haf51a6a_2
libbrotlicommon 1.0.9 hf118e41_7
libbrotlidec 1.0.9 hf118e41_7
libbrotlienc 1.0.9 hf118e41_7
libcrc32c 1.1.2 h3b9df90_0 conda-forge
libcurl 8.4.0 h4d62439_0
libdeflate 1.17 hf118e41_1
libedit 3.1.20221030 hf118e41_0
libev 4.33 h140841e_1
libevent 2.1.10 h19c23f1_4 conda-forge
libexpat 2.6.2 h46f38da_0 conda-forge
libffi 3.4.4 h4a02239_0
libgcc-ng 13.2.0 h31e42bb_10 conda-forge
libgfortran-ng 11.2.0 hb3889a9_1
libgfortran5 11.2.0 h1234567_1
libgomp 13.2.0 h31e42bb_10 conda-forge
libgoogle-cloud 2.7.0 h11140b6_1 conda-forge
libgrpc 1.51.1 h4d29a31_1 conda-forge
libmamba 1.5.3 h7c6fafd_0
libmambapy 1.5.3 py311h828bf7b_0
libnghttp2 1.57.0 h44e5816_0
libnsl 2.0.1 ha17a0cc_0 conda-forge
libopenblas 0.3.23 hc5a31fb_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
libopus 1.3.1 h4e0d66e_1 conda-forge
libpng 1.6.39 hf118e41_0
libprotobuf 3.21.12 h1776448_0 https://ftp.osuosl.org/pub/open-ce/1.10.0
libsolv 0.7.24 h0f529ac_0
libsqlite 3.45.3 hd4bbf49_0 conda-forge
libssh2 1.10.0 h50fa78f_2
libstdcxx-ng 13.2.0 h262982c_10 conda-forge
libthrift 0.18.0 h82f1162_0 conda-forge
libtiff 4.5.1 h4a02239_0
libutf8proc 2.8.0 hb283c62_0 conda-forge
libuuid 2.38.1 h4194056_0 conda-forge
libvpx 1.13.1 h46f38da_0 conda-forge
libwebp 1.3.2 h0f96ee2_0
libwebp-base 1.3.2 hf118e41_0
libxcrypt 4.4.36 ha17a0cc_1 conda-forge
libxml2 2.10.4 h18e3229_1
libzlib 1.2.13 h1f2b957_6 conda-forge
llvm-openmp 14.0.6 hc028133_0 https://ftp.osuosl.org/pub/open-ce/1.10.0
lmdb 0.9.31 ha17a0cc_1 conda-forge
lz4-c 1.9.4 h4a02239_0
markdown 3.4.4 pyhd8ed1ab_0 conda-forge
markupsafe 2.1.5 py311h32d8acf_0 conda-forge
matplotlib 3.8.0 py311h6ffa863_0
matplotlib-base 3.8.0 py311h52e1fcc_0
menuinst 2.1.1 py311h1af927a_0 conda-forge
mpc 1.3.1 heaf1863_0 conda-forge
mpfr 4.2.1 haad2271_1 conda-forge
mpmath 1.3.0 pyhd8ed1ab_0 conda-forge
multidict 6.0.2 py311hf118e41_0
multiprocess 0.70.14 py311h6ffa863_0
munkres 1.1.4 py_0
mypy_extensions 1.0.0 pyha770c72_0 conda-forge
nccl 2.18.3 cuda11.8_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
ncurses 6.4 h4a02239_0
nest-asyncio 1.6.0 pyhd8ed1ab_0 conda-forge
networkx 2.8.8 pyhd8ed1ab_0 conda-forge
nomkl 3.0 0 https://ftp.osuosl.org/pub/open-ce/1.10.0
numactl 2.0.16 hba61f60_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
numexpr 2.8.7 py311hc46fc55_0
numpy 1.24.3 py311h148a09e_0
numpy-base 1.24.3 py311h06b82f6_0
oauthlib 3.2.2 pyhd8ed1ab_0 conda-forge
openjpeg 2.4.0 hfe35807_0
openssl 3.3.1 h1f2b957_0 conda-forge
orc 1.8.2 h341c9a4_2 conda-forge
packaging 23.1 py311h6ffa863_0
pandas 2.1.1 py311h52e1fcc_0
pcre2 10.42 h280155c_0
pillow 10.0.1 py311he33076b_0
pip 23.3 py311h6ffa863_0
platformdirs 4.2.2 pyhd8ed1ab_0 conda-forge
pluggy 1.0.0 py311h6ffa863_1
pooch 1.8.2 pyhd8ed1ab_0 conda-forge
protobuf 4.21.12 py311ha7baec7_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
psutil 5.9.8 py311hd26027c_0 conda-forge
pyarrow 11.0.0 py311h04a18d5_1
pyasn1 0.6.0 pyhd8ed1ab_0 conda-forge
pyasn1-modules 0.4.0 pyhd8ed1ab_0 conda-forge
pybind11-abi 4 hd3eb1b0_1
pycosat 0.6.6 py311hf118e41_0
pycparser 2.21 pyhd3eb1b0_0
pyjwt 2.8.0 pyhd8ed1ab_1 conda-forge
pyopenssl 23.2.0 py311h6ffa863_0
pyparsing 3.0.9 py311h6ffa863_0
pyre-extensions 0.0.30 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 py311h6ffa863_0
python 3.11.8 h3332dee_0_cpython conda-forge
python-dateutil 2.8.2 pyhd3eb1b0_0
python-tzdata 2023.3 pyhd3eb1b0_0
python-xxhash 2.0.2 py311hf118e41_1
python_abi 3.11 4_cp311 conda-forge
pytorch 2.0.1 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
pytorch-base 2.0.1 cuda11.8_py311_pb4.21.12_4 https://ftp.osuosl.org/pub/open-ce/1.10.0
pytz 2023.3.post1 py311h6ffa863_0
pyu2f 0.1.5 pyhd8ed1ab_0 conda-forge
pyyaml 6.0.1 py311hf118e41_0
re2 2023.02.01 h883269e_0 conda-forge
readline 8.2 hf118e41_0
regex 2023.10.3 py311hf118e41_0
reproc 14.2.4 h29c3540_1
reproc-cpp 14.2.4 h29c3540_1
requests 2.31.0 py311h6ffa863_0
requests-oauthlib 2.0.0 pyhd8ed1ab_0 conda-forge
responses 0.13.3 pyhd3eb1b0_0
rsa 4.9 pyhd8ed1ab_0 conda-forge
ruamel.yaml 0.17.21 py311hf118e41_0
s2n 1.3.37 h5e47323_0 conda-forge
safetensors 0.4.0 py311hda16d9e_0
scipy 1.11.1 py311hd69e9bb_0 https://ftp.osuosl.org/pub/open-ce/1.10.0
sentencepiece 0.1.97 h1e74c73_py311_pb4.21.12_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
setuptools 68.0.0 py311h6ffa863_0
six 1.16.0 pyhd3eb1b0_1
snappy 1.1.9 h29c3540_0
sqlite 3.41.2 hf118e41_0
sympy 1.12.1 pypyh2585a3b_103 conda-forge
tabulate 0.8.10 pyhd8ed1ab_0 conda-forge
tensorboard 2.13.0 pyhab0730d_pb4.21.12_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
tensorboard-data-server 0.7.0 pyh6f84499_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
tensorboard-plugin-wit 1.6.0 pyh9f0ad1d_0 conda-forge
tk 8.6.13 hd4bbf49_0 conda-forge
tokenizers 0.13.3 py311h3d4f45a_0
torchdata 0.6.0 py311_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
torchsnapshot 0.1.0 pyhd8ed1ab_0 conda-forge
torchtext-base 0.15.2 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
torchtnt 0.2.4 pyhd8ed1ab_0 conda-forge
torchvision-base 0.15.2 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
tornado 6.3.3 py311hf118e41_0
tqdm 4.65.0 py311h7837921_0
transformers 4.32.1 py311h6ffa863_0
truststore 0.8.0 py311h6ffa863_0
typing-extensions 4.7.1 py311h6ffa863_0
typing_extensions 4.7.1 py311h6ffa863_0
typing_inspect 0.9.0 pyhd8ed1ab_0 conda-forge
tzdata 2023c h04d1e81_0
urllib3 1.26.18 py311h6ffa863_0
utf8proc 2.6.1 h140841e_0
werkzeug 2.3.8 pyhd8ed1ab_0 conda-forge
wheel 0.41.2 py311h6ffa863_0
xxhash 0.8.0 h140841e_3
xz 5.4.2 hf118e41_0
yaml 0.2.5 h7b6447c_0
yaml-cpp 0.8.0 h4a02239_0
yarl 1.8.1 py311hf118e41_0
zipp 3.11.0 py311h6ffa863_0
zlib 1.2.13 h1f2b957_6 conda-forge
zstandard 0.19.0 py311hf118e41_0
zstd 1.5.5 h57e4825_0
|
open
|
https://github.com/huggingface/datasets/issues/6992
| 2024-06-22T16:12:08
| 2024-06-25T15:43:05
| null |
{
"login": "YHL04",
"id": 57779173,
"type": "User"
}
|
[] | false
|
[] |
2,367,711,094
| 6,991
|
Unblock NumPy 2.0
|
Fixes https://github.com/huggingface/datasets/issues/6980
|
closed
|
https://github.com/huggingface/datasets/pull/6991
| 2024-06-22T09:19:53
| 2024-12-25T17:57:34
| 2024-07-12T12:04:53
|
{
"login": "NeilGirdhar",
"id": 730137,
"type": "User"
}
|
[] | true
|
[] |
2,366,660,785
| 6,990
|
Problematic rank after calling `split_dataset_by_node` twice
|
### Describe the bug
I'm trying to split `IterableDataset` by `split_dataset_by_node`.
But when doing split on a already split dataset, the resulting `rank` is greater than `world_size`.
### Steps to reproduce the bug
Here is the minimal code for reproduction:
```py
>>> from datasets import load_dataset
>>> from datasets.distributed import split_dataset_by_node
>>> dataset = load_dataset('fla-hub/slimpajama-test', split='train', streaming=True)
>>> dataset = split_dataset_by_node(dataset, 1, 32)
>>> dataset._distributed
DistributedConfig(rank=1, world_size=32)
>>> dataset = split_dataset_by_node(dataset, 1, 15)
>>> dataset._distributed
DistributedConfig(rank=481, world_size=480)
```
As you can see, the second rank 481 > 480, which is problematic.
### Expected behavior
I think this error comes from this line @lhoestq
https://github.com/huggingface/datasets/blob/a6ccf944e42c1a84de81bf326accab9999b86c90/src/datasets/iterable_dataset.py#L2943-L2944
We may need to obtain the rank first. Then the above code gives
```py
>>> dataset._distributed
DistributedConfig(rank=16, world_size=480)
```
### Environment info
datasets==2.20.0
|
closed
|
https://github.com/huggingface/datasets/issues/6990
| 2024-06-21T14:25:26
| 2024-06-25T16:19:19
| 2024-06-25T16:19:19
|
{
"login": "yzhangcs",
"id": 18402347,
"type": "User"
}
|
[] | false
|
[] |
2,365,556,449
| 6,989
|
cache in nfs error
|
### Describe the bug
- When reading dataset, a cache will be generated to the ~/. cache/huggingface/datasets directory
- When using .map and .filter operations, runtime cache will be generated to the /tmp/hf_datasets-* directory
- The default is to use the path of tempfile.tempdir
- If I modify this path to the NFS disk, an error will be reported, but the program will continue to run
- https://github.com/huggingface/datasets/blob/main/src/datasets/config.py#L257
```
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap
self.run()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server
server.serve_forever()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever
sys.exit(0)
SystemExit: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers
finalizer()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd
onerror(os.unlink, fullname, sys.exc_info())
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd
os.unlink(entry.name, dir_fd=topfd)
OSError: [Errno 16] Device or resource busy: '.nfs000000038330a012000030b4'
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap
self.run()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server
server.serve_forever()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever
sys.exit(0)
SystemExit: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers
finalizer()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd
onerror(os.unlink, fullname, sys.exc_info())
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd
os.unlink(entry.name, dir_fd=topfd)
OSError: [Errno 16] Device or resource busy: '.nfs0000000400064d4a000030e5'
```
### Steps to reproduce the bug
```
import os
import time
import tempfile
from datasets import load_dataset
def add_column(sample):
# print(type(sample))
# time.sleep(0.1)
sample['__ds__stats__'] = {'data': 123}
return sample
def filt_column(sample):
# print(type(sample))
if len(sample['content']) > 10:
return True
else:
return False
if __name__ == '__main__':
input_dir = '/mnt/temp/CN/small' # some json dataset
dataset = load_dataset('json', data_dir=input_dir)
temp_dir = '/media/release/release/temp/temp' # a nfs folder
os.makedirs(temp_dir, exist_ok=True)
# change huggingface-datasets runtime cache in nfs๏ผdefault in /tmp๏ผ
tempfile.tempdir = temp_dir
aa = dataset.map(add_column, num_proc=64)
aa = aa.filter(filt_column, num_proc=64)
print(aa)
```
### Expected behavior
no error occur
### Environment info
datasets==2.18.0
ubuntu 20.04
|
open
|
https://github.com/huggingface/datasets/issues/6989
| 2024-06-21T02:09:22
| 2025-01-29T11:44:04
| null |
{
"login": "simplew2011",
"id": 66729924,
"type": "User"
}
|
[] | false
|
[] |
2,364,129,918
| 6,988
|
[`feat`] Move dataset card creation to method for easier overriding
|
Hello!
## Pull Request overview
* Move dataset card creation to method for easier overriding
## Details
It's common for me to fully automatically download, reformat, and upload a dataset (e.g. see https://huggingface.co/datasets?other=sentence-transformers), but one aspect that I cannot easily automate is the dataset card generation. This is because during `push_to_hub`, the dataset card is created in 3 lines of code in a much larger method. To automatically generate a dataset card, I need to either:
1. Subclass `Dataset`/`DatasetDict`, copy the entire `push_to_hub` method to override the ~3 lines used to generate the dataset card. This is not viable as the method is likely to change over time.
2. Use `push_to_hub` normally, then separately download the pushed (but empty) dataset card, update it, and reupload the modified dataset. This works fine, but prevents me from being able to return a `Dataset` to my users which will automatically use a nice dataset card.
So, in this PR I'm proposing to move the dataset generation into another method so that it can be overridden more easily. For example, imagine the following use case:
````python
import json
from typing import Any, Dict, Optional
from datasets import Dataset, load_dataset
from datasets.info import DatasetInfosDict, DatasetInfo
from datasets.utils.metadata import MetadataConfigs
from huggingface_hub import DatasetCardData, DatasetCard
TEMPLATE = r"""---
{dataset_card_data}
---
# Dataset Card for {source_dataset_name} with mined hard negatives
This dataset is a collection of {column_one}-{column_two}-negative triplets from the {source_dataset_name} dataset. See [{source_dataset_name}](https://huggingface.co/datasets/{source_dataset_id}) for additional information. This dataset can be used directly with Sentence Transformers to train embedding models.
## Mining Parameters
The negative samples have been mined using the following parameters:
- `range_min`: {range_min}, i.e. we skip the {range_min} most similar samples
- `range_max`: {range_max}, i.e. we only look at the top {range_max} most similar samples
- `margin`: {margin}, i.e. we require negative similarity + margin < positive similarity, so negative samples can't be more similar than the known true answer
- `sampling_strategy`: {sampling_strategy}, i.e. whether to randomly sample from the candidate negatives or take the "top" negatives
- `num_negatives`: {num_negatives}, i.e. we mine {num_negatives} negatives per question-answer pair
## Dataset Format
- Columns: {column_one}, {column_two}, negative
- Column types: str, str, str
- Example:
```python
{example}
```
"""
class HNMDataset(Dataset):
@classmethod
def from_dict(cls, *args, mining_kwargs: Dict[str, Any], **kwargs) -> "HNMDataset":
dataset = super().from_dict(*args, **kwargs)
dataset.mining_kwargs = mining_kwargs
return dataset
def _create_dataset_card(
self,
dataset_card_data: DatasetCardData,
dataset_card: Optional[DatasetCard],
config_name: str,
info_to_dump: DatasetInfo,
metadata_config_to_dump: MetadataConfigs,
) -> DatasetCard:
if dataset_card:
return dataset_card
DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data)
MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data)
dataset_card_data.tags = ["sentence-transformers"]
dataset_name = self.mining_kwargs["source_dataset"].info.dataset_name
# Very messy, just as an example:
dataset_id = list(self.mining_kwargs["source_dataset"].info.download_checksums.keys())[0].removeprefix("hf://datasets/").split("@")[0]
content = TEMPLATE.format(**{
"dataset_card_data": str(dataset_card_data),
"source_dataset_name": dataset_name,
"source_dataset_id": dataset_id,
"range_min": self.mining_kwargs["range_min"],
"range_max": self.mining_kwargs["range_max"],
"margin": self.mining_kwargs["margin"],
"sampling_strategy": self.mining_kwargs["sampling_strategy"],
"num_negatives": self.mining_kwargs["num_negatives"],
"column_one": self.column_names[0],
"column_two": self.column_names[1],
"example": json.dumps(self[0], indent=4),
})
return DatasetCard(content)
source_dataset = load_dataset("sentence-transformers/gooaq", split="train[:100]")
dataset = HNMDataset.from_dict({
"query": source_dataset["question"],
"answer": source_dataset["answer"],
# "negative": ... <- In my case, this column would be 'mined' automatically with these parameters
}, mining_kwargs={
"range_min": 10,
"range_max": 20,
"max_score": 0.9,
"margin": 0.1,
"sampling_strategy": "random",
"num_negatives": 3,
"source_dataset": source_dataset,
})
dataset.push_to_hub("tomaarsen/mining_demo", private=True)
````
In this script, I've created a subclass which stores some additional information about how the dataset was generated. It's a bit hacky (e.g. setting a `mining_kwargs` parameter in `from_dict` that wasn't created in `__init__`, but that's just a consequence of how the `from_...` methods don't accept kwargs), but it allows me to create a "hard negatives mining" function that returns a dataset which people can use locally like normal, but if they choose to upload it, then it'll automatically include some information, e.g.: https://huggingface.co/datasets/tomaarsen/mining_demo
This allows others to actually find this dataset (e.g. via the `sentence-transformers` tag) and get an idea of the quality, source, etc. by looking at the model card.
## Note
I'm not fixed on this solution whatsoever: I am also completely fine with other solutions, e.g. a `dataset.set_dataset_card_creator` method that allows me to provide a function without even having to subclass anything. I'm open to all ideas :)
cc @albertvillanova @lhoestq
cc @LysandreJik
- Tom Aarsen
|
open
|
https://github.com/huggingface/datasets/pull/6988
| 2024-06-20T10:47:57
| 2024-06-21T16:04:58
| null |
{
"login": "tomaarsen",
"id": 37621491,
"type": "User"
}
|
[] | true
|
[] |
2,363,728,190
| 6,987
|
Remove beam
|
Remove beam, as part of the 3.0 release.
|
closed
|
https://github.com/huggingface/datasets/pull/6987
| 2024-06-20T07:27:14
| 2024-06-26T19:41:55
| 2024-06-26T19:35:42
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,362,584,179
| 6,986
|
Add large_list type support in string_to_arrow
|
add large_list type support in string_to_arrow() and _arrow_to_datasets_dtype() in features.py
Fix #6984
|
closed
|
https://github.com/huggingface/datasets/pull/6986
| 2024-06-19T14:54:25
| 2024-08-12T14:43:48
| 2024-08-12T14:43:47
|
{
"login": "arthasking123",
"id": 16257131,
"type": "User"
}
|
[] | true
|
[] |
2,362,378,276
| 6,985
|
AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'
|
### Describe the bug
I have been struggling with this for two days, any help would be appreciated. Python 3.10
```
from setfit import SetFitModel
from huggingface_hub import login
access_token_read = "cccxxxccc"
# Authenticate with the Hugging Face Hub
login(token=access_token_read)
# Load the models from the Hugging Face Hub
trainer_relv = SetFitModel.from_pretrained("snowdere/trainer_relevance")
trainer_trust = SetFitModel.from_pretrained("snowdere/trainer_trust")
trainer_sent = SetFitModel.from_pretrained("snowdere/trainer_sent")
trainer_topic = SetFitModel.from_pretrained("snowdere/trainer_topic")
```
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 from setfit import SetFitModel
2 from huggingface_hub import login
4 access_token_read = "ccsddsds"
File /opt/conda/lib/python3.10/site-packages/setfit/__init__.py:7
4 import os
5 import warnings
----> 7 from .data import get_templated_dataset, sample_dataset
8 from .model_card import SetFitModelCardData
9 from .modeling import SetFitHead, SetFitModel
File /opt/conda/lib/python3.10/site-packages/setfit/data.py:5
3 import pandas as pd
4 import torch
----> 5 from datasets import Dataset, DatasetDict, load_dataset
6 from torch.utils.data import Dataset as TorchDataset
8 from . import logging
File /opt/conda/lib/python3.10/site-packages/datasets/__init__.py:18
1 # ruff: noqa
2 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
3 #
(...)
13 # See the License for the specific language governing permissions and
14 # limitations under the License.
16 __version__ = "2.19.0"
---> 18 from .arrow_dataset import Dataset
19 from .arrow_reader import ReadInstruction
20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:76
73 from tqdm.contrib.concurrent import thread_map
75 from . import config
---> 76 from .arrow_reader import ArrowReader
77 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
78 from .data_files import sanitize_patterns
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_reader.py:29
26 from typing import TYPE_CHECKING, List, Optional, Union
28 import pyarrow as pa
---> 29 import pyarrow.parquet as pq
30 from tqdm.contrib.concurrent import thread_map
32 from .download.download_config import DownloadConfig
File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20
1 # Licensed to the Apache Software Foundation (ASF) under one
2 # or more contributor license agreements. See the NOTICE file
3 # distributed with this work for additional information
(...)
17
18 # flake8: noqa
---> 20 from .core import *
File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33
30 import pyarrow as pa
32 try:
---> 33 import pyarrow._parquet as _parquet
34 except ImportError as exc:
35 raise ImportError(
36 "The pyarrow installation is not built with support "
37 f"for the Parquet file format ({str(exc)})"
38 ) from None
File /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet()
AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'
```
setfit: 1.0.3
transformers: 4.41.2
lingua-language-detector: 2.0.2
polars: 0.20.31
lightning: None
google-cloud-bigquery: 3.24.0
shapely: 2.0.4
pyarrow: 16.0.0
### Steps to reproduce the bug
I have tried all version combinations for Dataset and Pyarrow, the all have the same error since a few days ago. This is accross multiple scripts I have.
### Expected behavior
Just ron normally.
### Environment info
3.10
|
closed
|
https://github.com/huggingface/datasets/issues/6985
| 2024-06-19T13:22:28
| 2025-03-14T18:47:53
| 2024-06-25T05:40:51
|
{
"login": "firmai",
"id": 26666267,
"type": "User"
}
|
[] | false
|
[] |
2,362,143,554
| 6,984
|
Convert polars DataFrame back to datasets
|
### Feature request
This returns error.
```python
from datasets import Dataset
dsdf = Dataset.from_dict({"x": [[1, 2], [3, 4, 5]], "y": ["a", "b"]})
Dataset.from_polars(dsdf.to_polars())
```
ValueError: Arrow type large_list<item: int64> does not have a datasets dtype equivalent.
### Motivation
When datasets contain Sequence data type, it will be converted to Arrow type large_list. However, the reverse (from large_list to Sequence) does not work.
### Your contribution
No
|
closed
|
https://github.com/huggingface/datasets/issues/6984
| 2024-06-19T11:38:48
| 2024-08-12T14:43:46
| 2024-08-12T14:43:46
|
{
"login": "ljw20180420",
"id": 38550511,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
2,361,806,201
| 6,983
|
Remove metrics
|
Remove all metrics, as part of the 3.0 release.
Note they are deprecated since 2.5.0 version.
|
closed
|
https://github.com/huggingface/datasets/pull/6983
| 2024-06-19T09:08:55
| 2024-06-28T06:57:38
| 2024-06-28T06:51:30
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,361,661,469
| 6,982
|
cannot split dataset when using load_dataset
|
### Describe the bug
when I use load_dataset methods to load mozilla-foundation/common_voice_7_0, it can successfully download and extracted the dataset but It cannot generating the arrow document,
This bug happened in my server, my laptop, so as #6906 , but it won't happen in the google colab. I work for it for days, even I load the datasets from local path, it can Generatingโtrainโsplit and validation split but bug happen again in testโsplit.
### Steps to reproduce the bug
from datasets import load_dataset, load_metric, Audio
common_voice_train = load_dataset("mozilla-foundation/common_voice_7_0", "ja", split="train", token=selftoken, trust_remote_code=True)
### Expected behavior
```
{
"name": "ValueError",
"message": "Instruction \"train\" corresponds to no data!",
"stack": "---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[2], line 3
1 from datasets import load_dataset, load_metric, Audio
----> 3 common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"train\",token='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')#,trust_remote_code=True)#,streaming=True)
4 common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"test\",token='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')#,trust_remote_code=True)#,streaming=True)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\load.py:2626, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2622 # Build dataset for splits
2623 keep_in_memory = (
2624 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2625 )
-> 2626 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
2627 # Rename and cast features to match task schema
2628 if task is not None:
2629 # To avoid issuing the same warning twice
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1266, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)
1263 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS)
1265 # Create a dataset for each of the given splits
-> 1266 datasets = map_nested(
1267 partial(
1268 self._build_single_dataset,
1269 run_post_process=run_post_process,
1270 verification_mode=verification_mode,
1271 in_memory=in_memory,
1272 ),
1273 split,
1274 map_tuple=True,
1275 disable_tqdm=True,
1276 )
1277 if isinstance(datasets, dict):
1278 datasets = DatasetDict(datasets)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\utils\\py_utils.py:484, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, batched, batch_size, types, disable_tqdm, desc)
482 if batched:
483 data_struct = [data_struct]
--> 484 mapped = function(data_struct)
485 if batched:
486 mapped = mapped[0]
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1296, in DatasetBuilder._build_single_dataset(self, split, run_post_process, verification_mode, in_memory)
1293 split = Split(split)
1295 # Build base dataset
-> 1296 ds = self._as_dataset(
1297 split=split,
1298 in_memory=in_memory,
1299 )
1300 if run_post_process:
1301 for resource_file_name in self._post_processing_resources(split).values():
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1370, in DatasetBuilder._as_dataset(self, split, in_memory)
1368 if self._check_legacy_cache():
1369 dataset_name = self.name
-> 1370 dataset_kwargs = ArrowReader(cache_dir, self.info).read(
1371 name=dataset_name,
1372 instructions=split,
1373 split_infos=self.info.splits.values(),
1374 in_memory=in_memory,
1375 )
1376 fingerprint = self._get_dataset_fingerprint(split)
1377 return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\arrow_reader.py:256, in BaseReader.read(self, name, instructions, split_infos, in_memory)
254 msg = f'Instruction \"{instructions}\" corresponds to no data!'
255 #msg = f'Instruction \"{self._path}\",\"{name}\",\"{instructions}\",\"{split_infos}\" corresponds to no data!'
--> 256 raise ValueError(msg)
257 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
ValueError: Instruction \"train\" corresponds to no data!"
}
```
### Environment info
Environment:
python 3.9
windows 11 pro
VScode+jupyter
|
closed
|
https://github.com/huggingface/datasets/issues/6982
| 2024-06-19T08:07:16
| 2024-07-08T06:20:16
| 2024-07-08T06:20:16
|
{
"login": "cybest0608",
"id": 17721894,
"type": "User"
}
|
[] | false
|
[] |
2,361,520,022
| 6,981
|
Update docs on trust_remote_code defaults to False
|
Update docs on trust_remote_code defaults to False.
The docs needed to be updated due to this PR:
- #6954
|
closed
|
https://github.com/huggingface/datasets/pull/6981
| 2024-06-19T07:12:21
| 2024-06-19T14:32:59
| 2024-06-19T14:26:37
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,360,909,930
| 6,980
|
Support NumPy 2.0
|
### Feature request
Support NumPy 2.0.
### Motivation
NumPy introduces the Array API, which bridges the gap between machine learning libraries. Many clients of HuggingFace are eager to start using the Array API.
Besides that, NumPy 2 provides a cleaner interface than NumPy 1.
### Tasks
NumPy 2.0 was released for testing so that libraries could ensure compatibility [since mid-March](https://github.com/numpy/numpy/issues/24300#issuecomment-1986815755). What needs to be done for HuggingFace to support Numpy 2?
- [x] Fix use of `array`: https://github.com/huggingface/datasets/pull/6976
- [ ] Remove [NumPy version limit](https://github.com/huggingface/datasets/pull/6975): https://github.com/huggingface/datasets/pull/6991
|
closed
|
https://github.com/huggingface/datasets/issues/6980
| 2024-06-18T23:30:22
| 2024-07-12T12:04:54
| 2024-07-12T12:04:53
|
{
"login": "NeilGirdhar",
"id": 730137,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
2,360,175,363
| 6,979
|
How can I load partial parquet files only?
|
I have a HUGE dataset about 14TB, I unable to download all parquet all. I just take about 100 from it.
dataset = load_dataset("xx/", data_files="data/train-001*-of-00314.parquet")
How can I just using 000 - 100 from a 00314 from all partially?
I search whole net didn't found a solution, **this is stupid if they didn't support it, and I swear I wont using stupid parquet any more**
|
closed
|
https://github.com/huggingface/datasets/issues/6979
| 2024-06-18T15:44:16
| 2024-06-21T17:09:32
| 2024-06-21T13:32:50
|
{
"login": "lucasjinreal",
"id": 21303438,
"type": "User"
}
|
[] | false
|
[] |
2,359,511,469
| 6,978
|
Fix regression for pandas < 2.0.0 in JSON loader
|
A regression was introduced for pandas < 2.0.0 in PR:
- #6914
As described in pandas docs, the `dtype_backend` parameter was first added in pandas 2.0.0: https://pandas.pydata.org/docs/reference/api/pandas.read_json.html
This PR fixes the regression by passing (or not) the `dtype_backend` parameter depending on pandas version.
Maybe, in a future 3.0 `datasets` release, we could just require pandas > 2.0.
Reported by:
- #6977
|
closed
|
https://github.com/huggingface/datasets/pull/6978
| 2024-06-18T10:26:34
| 2024-06-19T06:23:24
| 2024-06-19T05:50:18
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,359,295,045
| 6,977
|
load json file error with v2.20.0
|
### Describe the bug
```
load_dataset(path="json", data_files="./test.json")
```
```
Generating train split: 0 examples [00:00, ? examples/s]
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 132, in _generate_tables
pa_table = paj.read_json(
File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1997, in _prepare_split_single
for _, table in generator:
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 155, in _generate_tables
df = pd.read_json(f, dtype_backend="pyarrow")
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper
return func(*args, **kwargs)
TypeError: read_json() got an unexpected keyword argument 'dtype_backend'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/app/t1.py", line 11, in <module>
load_dataset(path=data_path, data_files="./t2.json")
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2616, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1124, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1884, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 2040, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
```
```
import pandas as pd
with open("./test.json", "r") as f:
df = pd.read_json(f, dtype_backend="pyarrow")
```
```
Traceback (most recent call last):
File "/app/t3.py", line 3, in <module>
df = pd.read_json(f, dtype_backend="pyarrow")
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper
return func(*args, **kwargs)
TypeError: read_json() got an unexpected keyword argument 'dtype_backend'
```
### Steps to reproduce the bug
.
### Expected behavior
.
### Environment info
```
datasets 2.20.0
pandas 1.5.3
```
|
closed
|
https://github.com/huggingface/datasets/issues/6977
| 2024-06-18T08:41:01
| 2024-06-18T10:06:10
| 2024-06-18T10:06:09
|
{
"login": "xiaoyaolangzhi",
"id": 15037766,
"type": "User"
}
|
[] | false
|
[] |
2,357,107,203
| 6,976
|
Ensure compatibility with numpy 2.0.0
|
Following the conversion guide, copy=False is no longer required and will result in an error: https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword.
The following fix should resolve the issue.
error found during testing on the MTEB repository e.g. [here](https://github.com/embeddings-benchmark/mteb/pull/938)
|
closed
|
https://github.com/huggingface/datasets/pull/6976
| 2024-06-17T11:29:22
| 2024-06-19T14:30:32
| 2024-06-19T14:04:34
|
{
"login": "KennethEnevoldsen",
"id": 23721977,
"type": "User"
}
|
[] | true
|
[] |
2,357,003,959
| 6,975
|
Set temporary numpy upper version < 2.0.0 to fix CI
|
Set temporary numpy upper version < 2.0.0 to fix CI. See: https://github.com/huggingface/datasets/actions/runs/9546031216/job/26308072017
```
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.
```
|
closed
|
https://github.com/huggingface/datasets/pull/6975
| 2024-06-17T10:36:54
| 2024-06-17T12:49:53
| 2024-06-17T12:43:56
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,355,517,362
| 6,973
|
IndexError during training with Squad dataset and T5-small model
|
### Describe the bug
I am encountering an IndexError while training a T5-small model on the Squad dataset using the transformers and datasets libraries. The error occurs even with a minimal reproducible example, suggesting a potential bug or incompatibility.
### Steps to reproduce the bug
1.Install the required libraries: !pip install transformers datasets
2.Run the following code:
!pip install transformers datasets
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments, Trainer, DataCollatorWithPadding
# Load a small, publicly available dataset
from datasets import load_dataset
dataset = load_dataset("squad", split="train[:100]") # Use a small subset for testing
# Load a pre-trained model and tokenizer
model_name = "t5-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Define a basic data collator
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
per_device_train_batch_size=2,
num_train_epochs=1,
)
# Create a trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset,
data_collator=data_collator,
)
# Train the model
trainer.train()
### Expected behavior
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
[<ipython-input-23-f13a4b23c001>](https://localhost:8080/#) in <cell line: 34>()
32
33 # Train the model
---> 34 trainer.train()
10 frames
[/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_index_key(key, size)
427 if isinstance(key, int):
428 if (key < 0 and key + size < 0) or (key >= size):
--> 429 raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
430 return
431 elif isinstance(key, slice):
IndexError: Invalid key: 42 is out of bounds for size 0
### Environment info
transformers version:4.41.2
datasets version:1.18.4
Python version:3.10.12
|
closed
|
https://github.com/huggingface/datasets/issues/6973
| 2024-06-16T07:53:54
| 2024-07-01T11:25:40
| 2024-07-01T11:25:40
|
{
"login": "ramtunguturi36",
"id": 151521233,
"type": "User"
}
|
[] | false
|
[] |
2,353,531,912
| 6,972
|
Fix webdataset pickling
|
...by making tracked iterables picklable.
This is important to make streaming datasets compatible with multiprocessing e.g. for parallel data loading
|
closed
|
https://github.com/huggingface/datasets/pull/6972
| 2024-06-14T14:43:02
| 2024-06-14T15:43:43
| 2024-06-14T15:37:35
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
2,351,830,856
| 6,971
|
packaging: Remove useless dependencies
|
Revert changes in #6396 and #6404. CVE-2023-47248 has been fixed since PyArrow v14.0.1. Meanwhile Python requirements requires `pyarrow>=15.0.0`.
|
closed
|
https://github.com/huggingface/datasets/pull/6971
| 2024-06-13T18:43:43
| 2024-06-14T14:03:34
| 2024-06-14T13:57:24
|
{
"login": "daskol",
"id": 9336514,
"type": "User"
}
|
[] | true
|
[] |
2,351,380,029
| 6,970
|
Set dev version
| null |
closed
|
https://github.com/huggingface/datasets/pull/6970
| 2024-06-13T14:59:45
| 2024-06-13T15:06:18
| 2024-06-13T14:59:56
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,351,351,436
| 6,969
|
Release: 2.20.0
| null |
closed
|
https://github.com/huggingface/datasets/pull/6969
| 2024-06-13T14:48:20
| 2024-06-13T15:04:39
| 2024-06-13T14:55:53
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
2,351,331,417
| 6,968
|
Use `HF_HUB_OFFLINE` instead of `HF_DATASETS_OFFLINE`
|
To use `datasets` offline, one can use the `HF_DATASETS_OFFLINE` environment variable. This PR makes `HF_HUB_OFFLINE` the recommended environment variable for offline training. Goal is to be more consistent with the rest of HF ecosystem and have a single config value to set.
The changes are backward-compatible meaning that:
- `HF_DATASETS_OFFLINE` environment is still taken into account, though not documented
- `datasets.config.HF_DATASETS_OFFLINE` still exists, though it is not used anymore (in favor of `datasets.config.HF_HUB_OFFLINE`)
**Note:** it might break things in downstream libraries if they were monkeypatching `datasets.config.HF_DATASETS_OFFLINE` in their CI tests (for instance). Not much of a problem IMO.
|
closed
|
https://github.com/huggingface/datasets/pull/6968
| 2024-06-13T14:39:40
| 2024-06-13T17:31:37
| 2024-06-13T17:25:37
|
{
"login": "Wauplin",
"id": 11801849,
"type": "User"
}
|
[] | true
|
[] |
2,349,146,398
| 6,967
|
Method to load Laion400m
|
### Feature request
Large datasets like Laion400m are provided as embeddings. The provided methods in load_dataset are not straightforward for loading embedding files, i.e. img_emb_XX.npy ; XX = 0 to 99
### Motivation
The trial and experimentation is the key pivot of HF. It would be great if HF can load embeddings files s,ealessly.
### Your contribution
I cam write the loader with some help.
|
open
|
https://github.com/huggingface/datasets/issues/6967
| 2024-06-12T16:04:04
| 2024-06-12T16:04:04
| null |
{
"login": "humanely",
"id": 6862868,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
2,348,934,466
| 6,966
|
Remove underlines between badges
|
## Before:
<img width="935" alt="image" src="https://github.com/huggingface/datasets/assets/35881688/93666e72-059b-4180-9e1d-ff176a3d9dac">
## After:
<img width="956" alt="image" src="https://github.com/huggingface/datasets/assets/35881688/75df7c3e-f473-44f0-a872-eeecf6a85fe2">
|
closed
|
https://github.com/huggingface/datasets/pull/6966
| 2024-06-12T14:32:11
| 2024-06-19T14:16:21
| 2024-06-19T14:10:11
|
{
"login": "andrewhong04",
"id": 35881688,
"type": "User"
}
|
[] | true
|
[] |
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