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the previous example was using a model for translation and the way it was setup was not really the right way to use the model. | {
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First, we should address:
- #6644 | {
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} | [] | open | false | null | [] | null | 1 | "2024-02-06T16:41:00" | "2024-02-08T10:39:54" | null | NONE | null | null | null | ### Describe the bug
I am working on a retrieval project and encountering I have encountered two issues in the hugging face faiss integration:
1. I am trying to pass in a dataset with a faiss index to the Huggingface trainer. The code works for a cpu faiss index, but doesn't for a gpu one, getting error:
```
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 1543, in train
return inner_training_loop(
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 1555, in _inner_training_loop
train_dataloader = self.get_train_dataloader()
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 831, in get_train_dataloader
train_dataset = self._remove_unused_columns(train_dataset, description="training")
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 725, in _remove_unused_columns
return dataset.remove_columns(ignored_columns)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 592, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 557, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/fingerprint.py", line 481, in wrapper
out = func(dataset, *args, **kwargs)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2146, in remove_columns
dataset = copy.deepcopy(self)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 161, in deepcopy
rv = reductor(4)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/__init__.py", line 556, in index_getstate
return {"this": serialize_index(self).tobytes()}
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/__init__.py", line 1607, in serialize_index
write_index(index, writer)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/swigfaiss.py", line 9843, in write_index
return _swigfaiss.write_index(*args)
RuntimeError: Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /project/faiss/faiss/impl/index_write.cpp:590: don't know how to serialize this type of index
```
The index was created with the add_faiss_index method
```
train_dataset.add_faiss_index(
column='embeddings',
index_name='embeddings',
string_factory=faiss_index_string,
train_size=config.faiss_train_size,
device=0, # Use -1 for CPU, or specify GPU device ID
faiss_verbose=True
)
```
2. Athough faiss is written to be compatible on the gpu for searching [https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU) I am getting error when trying to use the hugggingface code to do the search on gpu. This seems to be caused by this line https://github.com/huggingface/datasets/blob/f9975f636542df7f95c27065ea93147440d690b7/src/datasets/search.py#L376 producing error
```
total_scores, total_examples = self.dataset.get_nearest_examples_batch('embeddings', embeddings, k=self.k)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 773, in get_nearest_examples_batch
total_scores, total_indices = self.search_batch(index_name, queries, k, **kwargs)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 727, in search_batch
return self._indexes[index_name].search_batch(queries, k, **kwargs)
File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 376, in search_batch
if not queries.flags.c_contiguous:
AttributeError: 'Tensor' object has no attribute 'flags'
```
### Steps to reproduce the bug
```
train_dataset.add_faiss_index(
column='embeddings',
index_name='embeddings',
string_factory=faiss_index_string,
train_size=config.faiss_train_size,
device=0, # Use -1 for CPU, or specify GPU device ID
faiss_verbose=True
)
Trainer(
model=model,
args=args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=data_collator,
tokenizer=tokenizer
)
train_dataset.get_nearest_examples_batch('embeddings', embeddings, k=self.k)
```
### Expected behavior
I would expect the faiss database code to be gpu compatible
### Environment info
huggingface Version: 2.16.1 | {
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} | [] | closed | false | null | [] | null | 2 | "2024-02-05T17:28:57" | "2024-02-06T09:50:19" | "2024-02-06T09:50:19" | NONE | null | null | null | ### Describe the bug
Differently sized object is saved than it is loaded.
### Steps to reproduce the bug
Hi, I save dataset in a following way:
```
dataset = load_dataset("json",
data_files={
"train": os.path.join(input_folder, f"{task_meta_type}_{task_type}_train.jsonl"),
"test": os.path.join(input_folder, f"{task_meta_type}_{task_type}_test.jsonl")})
print(os.path.join(output_folder, f"{task_meta_type}_{task_type}"))
print(f"Length of train dataset: {len(dataset['train'])}")
print(f"Length of test dataset: {len(dataset['test'])}")
dataset.save_to_disk(os.path.join(output_folder, f"{task_meta_type}_{task_type}"))
```
this yields output
```
.data/hf_dataset/propaganda_zanr
Length of train dataset: 7642
Length of test dataset: 1000
```
Everything looks fine.
Then I load the dataset
```python
from datasets import load_dataset
dataset_path = ".data/hf_dataset/propaganda_zanr"
dataset = load_dataset(dataset_path)
print(f"Length of train dataset: {len(dataset['train'])}")
print(f"Length of test dataset: {len(dataset['test'])}")
```
this prints
```
Generating train split: 1 examples [00:00, 72.10 examples/s]
Generating test split: 1 examples [00:00, 100.69 examples/s]
Length of train dataset: 1
Length of test dataset: 1
```
I dont' understand :(
### Expected behavior
same object is loaded
### Environment info
datasets==2.16.1 | {
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] | null | 1 | "2024-02-04T08:49:31" | "2024-02-06T09:26:07" | "2024-02-06T09:11:45" | NONE | null | null | null | ### Describe the bug
unicodedecodeerror: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte
### Steps to reproduce the bug
```
import sys
sys.getdefaultencoding()
'utf-8'
from datasets import load_dataset
print(f"Train dataset size: {len(dataset['train'])}")
print(f"Test dataset size: {len(dataset['test'])}")
Resolving data files: 100%
159/159 [00:00<00:00, 9909.28it/s]
Using custom data configuration samsum-0b1209637541c9e6
Downloading and preparing dataset json/samsum to C:/Users/Administrator/.cache/huggingface/datasets/json/samsum-0b1209637541c9e6/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...
Downloading data files: 100%
3/3 [00:00<00:00, 119.99it/s]
Extracting data files: 100%
3/3 [00:00<00:00, 9.54it/s]
Generating train split:
88392/0 [00:15<00:00, 86848.17 examples/s]
Generating test split:
0/0 [00:00<?, ? examples/s]
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\packaged_modules\json\json.py:132, in Json._generate_tables(self, files)
131 try:
--> 132 pa_table = paj.read_json(
133 io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
134 )
135 break
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\_json.pyx:290, in pyarrow._json.read_json()
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\error.pxi:100, in pyarrow.lib.check_status()
ArrowInvalid: JSON parse error: Invalid value. in row 0
During handling of the above exception, another exception occurred:
UnicodeDecodeError Traceback (most recent call last)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1819, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1818 _time = time.time()
-> 1819 for _, table in generator:
1820 if max_shard_size is not None and writer._num_bytes > max_shard_size:
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\packaged_modules\json\json.py:153, in Json._generate_tables(self, files)
152 with open(file, encoding="utf-8") as f:
--> 153 dataset = json.load(f)
154 except json.JSONDecodeError:
File ~\AppData\Local\Programs\Python\Python310\lib\json\__init__.py:293, in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
276 """Deserialize ``fp`` (a ``.read()``-supporting file-like object containing
277 a JSON document) to a Python object.
278
(...)
291 kwarg; otherwise ``JSONDecoder`` is used.
292 """
--> 293 return loads(fp.read(),
294 cls=cls, object_hook=object_hook,
295 parse_float=parse_float, parse_int=parse_int,
296 parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)
File ~\AppData\Local\Programs\Python\Python310\lib\codecs.py:322, in BufferedIncrementalDecoder.decode(self, input, final)
321 data = self.buffer + input
--> 322 (result, consumed) = self._buffer_decode(data, self.errors, final)
323 # keep undecoded input until the next call
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
Cell In[81], line 5
1 from datasets import load_dataset
3 # Load dataset from the hub
4 #dataset = load_dataset("json",data_files="C:/Users/Administrator/Desktop/samsum/samsum/data/corpus/train.json",field="data")
----> 5 dataset = load_dataset('json',"samsum")
6 #dataset = load_dataset("samsum")
7 print(f"Train dataset size: {len(dataset['train'])}")
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py:1758, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)
1755 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1757 # Download and prepare data
-> 1758 builder_instance.download_and_prepare(
1759 download_config=download_config,
1760 download_mode=download_mode,
1761 ignore_verifications=ignore_verifications,
1762 try_from_hf_gcs=try_from_hf_gcs,
1763 num_proc=num_proc,
1764 )
1766 # Build dataset for splits
1767 keep_in_memory = (
1768 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1769 )
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:860, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
858 if num_proc is not None:
859 prepare_split_kwargs["num_proc"] = num_proc
--> 860 self._download_and_prepare(
861 dl_manager=dl_manager,
862 verify_infos=verify_infos,
863 **prepare_split_kwargs,
864 **download_and_prepare_kwargs,
865 )
866 # Sync info
867 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:953, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
949 split_dict.add(split_generator.split_info)
951 try:
952 # Prepare split will record examples associated to the split
--> 953 self._prepare_split(split_generator, **prepare_split_kwargs)
954 except OSError as e:
955 raise OSError(
956 "Cannot find data file. "
957 + (self.manual_download_instructions or "")
958 + "\nOriginal error:\n"
959 + str(e)
960 ) from None
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1708, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1706 gen_kwargs = split_generator.gen_kwargs
1707 job_id = 0
-> 1708 for job_id, done, content in self._prepare_split_single(
1709 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1710 ):
1711 if done:
1712 result = content
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1851, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1849 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1850 e = e.__context__
-> 1851 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1853 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
can't load dataset
### Environment info
dataset:samsum
system :win10
gpu:m40 24G | {
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https://api.github.com/repos/huggingface/datasets/issues/6640 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6640/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6640/comments | https://api.github.com/repos/huggingface/datasets/issues/6640/events | https://github.com/huggingface/datasets/issues/6640 | 2,115,864,531 | I_kwDODunzps5-HYfT | 6,640 | Sign Language Support | {
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] | open | false | null | [] | null | 0 | "2024-02-02T21:54:51" | "2024-02-02T21:54:51" | null | NONE | null | null | null | ### Feature request
Currently, there are only several Sign Language labels, I would like to propose adding all the Signed Languages as new labels which are described in this ISO standard: https://www.evertype.com/standards/iso639/sign-language.html
### Motivation
Datasets currently only have labels for several signed languages. There are more signed languages in the world. Furthermore, some signed languages that have a lot of online data cannot be found because of this reason (for instance, German Sign Language, and there is no German Sign Language label on huggingface datasets even though there are a lot of readily available sign language datasets exist for German Sign Language, which are used very frequently in Sign Language Processing papers, and models.)
### Your contribution
I can submit a PR for this as well, adding the ISO codes and languages to the labels in datasets. | {
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} | A first step towards https://github.com/huggingface/datasets/issues/6529 | {
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https://api.github.com/repos/huggingface/datasets/issues/6638 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6638/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6638/comments | https://api.github.com/repos/huggingface/datasets/issues/6638/events | https://github.com/huggingface/datasets/issues/6638 | 2,113,329,257 | I_kwDODunzps599thp | 6,638 | Cannot download wmt16 dataset | {
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} | [] | closed | false | null | [] | null | 1 | "2024-02-01T19:41:42" | "2024-02-01T20:07:29" | "2024-02-01T20:07:29" | NONE | null | null | null | ### Describe the bug
As of this morning (PST) 2/1/2024, seeing the wmt16 dataset is missing from opus , could you suggest an alternative?
```
Downloading data files: 0%| | 0/4 [00:00<?, ?it/s]Traceback (most recent call last):
File "test.py", line 2, in <module>
raw_datasets = load_dataset("wmt16","ro-en",split="train")
File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 2153, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 954, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1717, in _download_and_prepare
super()._download_and_prepare(
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1027, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/746749a11d25c02058042da7502d973ff410e73457f3d305fc1177dc0e8c4227/wmt_utils.py", line 754, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 565, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 428, in download
downloaded_path_or_paths = map_nested(
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 464, in map_nested
mapped = [
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 465, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 384, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 384, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 367, in _single_map_nested
return function(data_struct)
File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 454, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 182, in cached_path
output_path = get_from_cache(
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 596, in get_from_cache
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz
```
### Steps to reproduce the bug
```
from datasets import load_dataset
raw_datasets = load_dataset("wmt16","ro-en",split="train")
```
### Expected behavior
Expect the dataset to be downloaded/ at least a clean exit with error explaining dataset is missing and a suggestion for next steps
### Environment info
- `datasets` version: 2.14.7
- Platform: Linux-5.15.0-92-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.17.3
- PyArrow version: 15.0.0
- Pandas version: 2.0.1
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https://api.github.com/repos/huggingface/datasets/issues/6637 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6637/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6637/comments | https://api.github.com/repos/huggingface/datasets/issues/6637/events | https://github.com/huggingface/datasets/issues/6637 | 2,113,025,975 | I_kwDODunzps598je3 | 6,637 | 'with_format' is extremely slow when used together with 'interleave_datasets' or 'shuffle' on IterableDatasets | {
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} | [] | open | false | null | [] | null | 1 | "2024-02-01T17:16:54" | "2024-02-05T10:43:47" | null | NONE | null | null | null | ### Describe the bug
If you:
1. Interleave two iterable datasets together with the interleave_datasets function, or shuffle an iterable dataset
2. Set the output format to torch tensors with .with_format('torch')
Then iterating through the dataset becomes over 100x slower than it is if you don't apply the torch formatting.
### Steps to reproduce the bug
```python
import datasets
import torch
from tqdm import tqdm
rand_a = torch.randn(3,224,224)
rand_b = torch.randn(3,224,224)
a = torch.stack([rand_a] * 1000)
b = torch.stack([rand_b] * 1000)
features = datasets.Features({"tensor": datasets.Array3D(shape=(3,224,224), dtype="float32")})
ds_a = datasets.Dataset.from_dict({"tensor": a}, features=features).to_iterable_dataset()
ds_b = datasets.Dataset.from_dict({"tensor": b}, features=features).to_iterable_dataset()
# Iterating through either dataset with torch formatting is really fast (2000it/s on my machine)
for example in tqdm(ds_a.with_format('torch')):
pass
# Iterating through either dataset shuffled is also pretty fast (100it/s on my machine)
for example in tqdm(ds_a.shuffle()):
pass
# Iterating through this interleaved dataset is pretty fast (200it/s on my machine)
ds_fast = datasets.interleave_datasets([ds_a, ds_b])
for example in tqdm(ds_fast):
pass
# Iterating through either dataset with torch formatting *after shuffling* is really slow... (<2it/s on my machine)
for example in tqdm(ds_a.shuffle().with_format('torch')):
pass
# Iterating through this torch formatted interleaved dataset is also really slow (<2it/s on my machine)...
ds_slow = datasets.interleave_datasets([ds_a, ds_b]).with_format('torch')
for example in tqdm(ds_slow):
pass
# Even doing this is way faster!! (70it/s on my machine)
for example in tqdm(ds_fast):
test = torch.tensor(example['tensor'])
```
### Expected behavior
Applying torch formatting to the interleaved dataset shouldn't increase the time taken to iterate through the dataset by very much, since even explicitly converting every example is over 70x faster than calling .with_format('torch').
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-6.5.0-15-generic-x86_64-with-glibc2.38
- Python version: 3.11.6
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0
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https://api.github.com/repos/huggingface/datasets/issues/6636 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6636/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6636/comments | https://api.github.com/repos/huggingface/datasets/issues/6636/events | https://github.com/huggingface/datasets/pull/6636 | 2,110,781,097 | PR_kwDODunzps5lm4zI | 6,636 | Faster column validation and reordering | {
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} | [] | closed | false | null | [] | null | 3 | "2024-01-31T19:08:28" | "2024-02-07T19:39:00" | "2024-02-06T23:03:38" | CONTRIBUTOR | null | 0 | {
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} | I work with bioinformatics data and often these tables have thousands and even tens of thousands of features. These tables are also accompanied by metadata that I do not want to pass in the model. When I perform `set_format('pt', columns=large_column_list)` , it can take several minutes before it finishes. The culprit is when the following check is performed: `any(col not in self._data.column_names for col in columns)`. Replacing this by `set(columns) - (self._data.column_names)` is more efficient. | {
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} | [] | closed | false | null | [] | null | 2 | "2024-01-31T17:55:21" | "2024-02-07T16:48:55" | "2024-02-07T16:41:04" | MEMBER | null | 0 | {
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} | Fix getting the info for script-based datasets with Parquet export with a single config not named "default".
E.g.
```python
from datasets import load_dataset_builder
b = load_dataset_builder("bookcorpus")
print(b.info.features)
# should print {'text': Value(dtype='string', id=None)}
```
I fixed this by setting the default config name when there is only one config. | {
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} | Support `data_dir` parameter in `push_to_hub`.
This allows users to organize the data files according to their specific needs. For example, "wikimedia/wikipedia" files could be organized by year and/or date, e.g. "2024/20240101/20240101.en". | {
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} | The cache used to only check for the latest cache directory with a given config_name, but it was wrong (e.g. `default-data_dir=data%2Ffortran-data_dir=data%2Ffortran` instead of `default-data_dir=data%2Ffortran`)
I fixed this by not passing the `config_kwargs` to the parent Builder `__init__`, and passing the config_id forged from the `config_kwargs` directly
close https://github.com/huggingface/datasets/issues/6609 | {
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} | reported in https://github.com/huggingface/evaluate/issues/542
cc @stas00 @williamberrios
close https://github.com/huggingface/datasets/issues/6589 | {
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} | This behavior is aligned with:
- the behavior of `datasets` before merging #6519
- the behavior described in the corresponding docstring
- the behavior of `huggingface_hub.create_repo`
Revert "Support push_to_hub canonical datasets (#6519)"
- This reverts commit a887ee78835573f5d80f9e414e8443b4caff3541.
Fix #6597. | {
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} | Support passing `--num_proc` to CLI test.
This was really useful recently to run the command on `pubmed`: https://huggingface.co/datasets/pubmed/discussions/11 | {
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https://api.github.com/repos/huggingface/datasets/issues/6627 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6627/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6627/comments | https://api.github.com/repos/huggingface/datasets/issues/6627/events | https://github.com/huggingface/datasets/pull/6627 | 2,105,735,816 | PR_kwDODunzps5lVr-t | 6,627 | Disable `tqdm` bars in non-interactive environments | {
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} | Replace `disable=False` with `disable=None` in the `tqdm` bars to disable them in non-interactive environments (by default).
For more info, see a [similar PR](https://github.com/huggingface/huggingface_hub/pull/2000) in `huggingface_hub`. | {
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} | e.g. dashes '-' are not allowed in split names
This should add an error message on datasets with unsupported split names like https://huggingface.co/datasets/open-source-metrics/test
cc @AndreaFrancis | {
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https://huggingface.co/datasets/laion/laion-coco | {
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] | open | false | null | [] | null | 6 | "2024-01-27T23:46:13" | "2024-02-02T09:42:10" | null | NONE | null | null | null | ### Feature request
Let’s say I have a dataset with 5 samples with values [1, 2, 3, 4, 5], with 2 GPUs (for DDP) and batch size of 2. This dataset is an `IterableDataset` since I am streaming it.
Now I split the dataset using `split_dataset_by_node` to ensure it doesn’t get repeated. And since it’s already splitted, I don’t have to use `DistributedSampler` (also they don't work with iterable datasets anyway)?
But in this case I noticed that the:
First iteraton:
first GPU will get → [1, 2]
first GPU will get → [3, 4]
Second iteraton:
first GPU will get → [5]
first GPU will get → Nothing
which actually creates an issue since in case of `DistributedSampler`, the samples are repeated internally to ensure non of the GPUs at any iteration is missing any data for gradient sync.
So my questions are:
1. Here since splitting is happening before hand, how to make sure each GPU get’s a batch at each iteration to avoid gradient sync issues?
2. Do we need to use `DistributedSampler`? If yes, how?
3. in the docstrings of `split_dataset_by_node`, this is mentioned: *"If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples."* Can you explain the last part here?
4. If `dataset.n_shards % world_size != 0`, is it possible to shard the streaming dataset on the fly to avoid the case where data is missing?
### Motivation
Somehow streaming datasets should work with DDP since for big LLMs a lot of data is required and DDP/multi-node is mostly used to train such models and streaming can actually help solve the data part of it.
### Your contribution
Yes, I can help in submitting the PR once we get mutual understanding on how it should behave. | {
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https://api.github.com/repos/huggingface/datasets/issues/6622 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6622/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6622/comments | https://api.github.com/repos/huggingface/datasets/issues/6622/events | https://github.com/huggingface/datasets/issues/6622 | 2,103,780,697 | I_kwDODunzps59ZSVZ | 6,622 | multi-GPU map does not work | {
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Here is the code for single-GPU processing: https://pastebin.com/bfmEeK2y
Here is the code for multi-GPU processing: https://pastebin.com/gQ7i5AQy
Here is the video showing that the multi-GPU mapping does not work as expected (there are so many things wrong here, it's better to watch the 3-minute video than explain here):
https://youtu.be/RNbdPkSppc4
### Steps to reproduce the bug
-
### Expected behavior
-
### Environment info
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https://api.github.com/repos/huggingface/datasets/issues/6620 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6620/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6620/comments | https://api.github.com/repos/huggingface/datasets/issues/6620/events | https://github.com/huggingface/datasets/issues/6620 | 2,103,110,536 | I_kwDODunzps59WuuI | 6,620 | wiki_dpr.py error (ID mismatch between lines {id} and vector {vec_id} | {
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] | null | 1 | "2024-01-27T01:00:09" | "2024-02-06T09:40:19" | "2024-02-06T09:40:19" | NONE | null | null | null | ### Describe the bug
I'm trying to run a rag example, and the dataset is wiki_dpr.
wiki_dpr download and extracting have been completed successfully.
However, at the generating train split stage, an error from wiki_dpr.py keeps popping up.
Especially in "_generate_examples" :
1. The following error occurs in the line **id, text, title = line.strip().split("\t")**
ValueError: not enough values to unpack (expected 3, got 2)
-> This part handles exceptions so that even if an error occurs, it passes.
2. **ID mismatch between lines {id} and vector {vec_id}**
This error seems to occur at the line " assert int(id) == int(vec_id),".
After I handled the exception in the split error, generating train split progressed to 80%, but an id mismatch error occurred at about the 16200000th vector id.
Debugging is even more difficult because it takes a long time to download and split wiki_dpr. I need help. thank you in advance!!
### Steps to reproduce the bug
Occurs in the generating train split step when running the rag example in the transformers repository.
Specifically, it is an error in wiki_dpr.py.
### Expected behavior
.
### Environment info
python 3.8 | {
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cannot import name 'DEFAULT_CIPHERS' from 'urllib3.util.ssl_' this is the error i received
### Steps to reproduce the bug
from datasets import load_dataset
### Expected behavior
No errors
### Environment info
python 3.11.5 | {
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close https://github.com/huggingface/datasets/issues/5477 | {
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https://api.github.com/repos/huggingface/datasets/issues/6614 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6614/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6614/comments | https://api.github.com/repos/huggingface/datasets/issues/6614/events | https://github.com/huggingface/datasets/issues/6614 | 2,098,884,520 | I_kwDODunzps59Gm-o | 6,614 | `datasets/downloads` cleanup tool | {
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] | open | false | null | [] | null | 0 | "2024-01-24T18:52:10" | "2024-01-24T18:55:09" | null | CONTRIBUTOR | null | null | null | ### Feature request
Splitting off https://github.com/huggingface/huggingface_hub/issues/1997 - currently `huggingface-cli delete-cache` doesn't take care of cleaning `datasets` temp files
e.g. I discovered having millions of files under `datasets/downloads` cache, I had to do:
```
sudo find /data/huggingface/datasets/downloads -type f -mtime +3 -exec rm {} \+
sudo find /data/huggingface/datasets/downloads -type d -empty -delete
```
could the cleanup be integrated into `huggingface-cli` or a different tool provided to keep the folders tidy and not consume inodes and space
e.g. there were tens of thousands of `.lock` files - I don't know why they never get removed - lock files should be temporary for the duration of the operation requiring the lock and not remain after the operation finished, IMHO.
Also I think one should be able to nuke `datasets/downloads` w/o hurting the cache, but I think there are some datasets that rely on files extracted under this dir - or at least they did in the past - which is very difficult to manage since one has no idea what is safe to delete and what not.
Thank you
@Wauplin (requested to be tagged) | {
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https://api.github.com/repos/huggingface/datasets/issues/6612 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6612/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6612/comments | https://api.github.com/repos/huggingface/datasets/issues/6612/events | https://github.com/huggingface/datasets/issues/6612 | 2,098,078,210 | I_kwDODunzps59DiIC | 6,612 | cnn_dailymail repeats itself | {
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} | [] | closed | false | null | [] | null | 1 | "2024-01-24T11:38:25" | "2024-02-01T08:14:50" | "2024-02-01T08:14:50" | NONE | null | null | null | ### Describe the bug
When I try to load `cnn_dailymail` dataset, it takes longer than usual and when I checked the dataset it's 3x bigger than it's supposed to be.
Check https://huggingface.co/datasets/cnn_dailymail: it says 287k rows for train. But when I check length of train split it says 861339.
Also I checked data:
```
>>> ds['train']['highlights'][0]
"Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday . Young actor says he has no plans to fritter his cash away . Radcliffe's earnings from first five Potter films have been held in trust fund ."````
>>> ds['train']['highlights'][0]
"Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday . Young actor says he has no plans to fritter his cash away . Radcliffe's earnings from first five Potter films have been held in trust fund ."````
>>> ds['train']['highlights'][287113]
"Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\nYoung actor says he has no plans to fritter his cash away .\nRadcliffe's earnings from first five Potter films have been held in trust fund ."````
>>> ds['train']['highlights'][574226]
"Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\nYoung actor says he has no plans to fritter his cash away .\nRadcliffe's earnings from first five Potter films have been held in trust fund ."
```
The datasets seems to be updated 6 days ago to convert it to Parquet. Probably, there is some issue with backward compatability.
### Steps to reproduce the bug
1.
```
from datasets import load_dataset
ds = load_dataset('cnn_dailymail', '3.0.0')
len(ds['train'])
```
### Expected behavior
It should not repeat itself.
### Environment info
datasets==2.13.2
Python==3.7.13 | {
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https://api.github.com/repos/huggingface/datasets/issues/6611 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6611/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6611/comments | https://api.github.com/repos/huggingface/datasets/issues/6611/events | https://github.com/huggingface/datasets/issues/6611 | 2,096,004,858 | I_kwDODunzps587n76 | 6,611 | `load_from_disk` with large dataset from S3 runs into `botocore.exceptions.ClientError` | {
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} | [] | open | false | null | [] | null | 0 | "2024-01-23T12:37:57" | "2024-01-23T12:37:57" | null | NONE | null | null | null | ### Describe the bug
When loading a large dataset (>1000GB) from S3 I run into the following error:
```
Traceback (most recent call last):
File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 113, in _error_wrapper
return await func(*args, **kwargs)
File "/home/alp/.local/lib/python3.10/site-packages/aiobotocore/client.py", line 383, in _make_api_call
raise error_class(parsed_response, operation_name)
botocore.exceptions.ClientError: An error occurred (RequestTimeTooSkewed) when calling the GetObject operation: The difference between the request time and the current time is too large.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/alp/phoneme-classification.monorepo/aws_sagemaker/data_processing/inspect_final_dataset.py", line 13, in <module>
dataset = load_from_disk("s3://speech-recognition-processed-data/whisper/de/train_data/", storage_options=storage_options)
File "/home/alp/.local/lib/python3.10/site-packages/datasets/load.py", line 1902, in load_from_disk
return Dataset.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options)
File "/home/alp/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1686, in load_from_disk
fs.download(src_dataset_path, [dest_dataset_path.as](http://dest_dataset_path.as/)_posix(), recursive=True)
File "/home/alp/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1480, in download
return self.get(rpath, lpath, recursive=recursive, **kwargs)
File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper
return sync(self.loop, func, *args, **kwargs)
File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync
raise return_result
File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner
result[0] = await coro
File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 604, in _get
return await _run_coros_in_chunks(
File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 257, in _run_coros_in_chunks
await asyncio.gather(*chunk, return_exceptions=return_exceptions),
File "/usr/lib/python3.10/asyncio/tasks.py", line 408, in wait_for
return await fut
File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 1193, in _get_file
body, content_length = await _open_file(range=0)
File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 1184, in _open_file
resp = await self._call_s3(
File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 348, in _call_s3
return await _error_wrapper(
File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 140, in _error_wrapper
raise err
PermissionError: The difference between the request time and the current time is too large.
```
The usual problem for this error is that the time on my local machine is out of sync with the current time. However, this is not the case here. I checked the time and even reset it with no success. See resources here:
- https://stackoverflow.com/questions/4770635/s3-error-the-difference-between-the-request-time-and-the-current-time-is-too-la
- https://stackoverflow.com/questions/25964491/aws-s3-upload-fails-requesttimetooskewed
The error does not appear when loading a smaller dataset (e.g. our test set) from the same s3 path.
### Steps to reproduce the bug
1. Create large dataset
2. Try loading it from s3 using:
```
dataset = load_from_disk("s3://...", storage_options=storage_options)
```
### Expected behavior
Load dataset without running into this error.
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.19.3
- PyArrow version: 12.0.1
- Pandas version: 2.0.3 | {
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} | [] | closed | false | null | [] | null | 2 | "2024-01-23T09:32:32" | "2024-01-25T02:15:23" | "2024-01-25T02:15:23" | NONE | null | null | null | ### Describe the bug
I am working with the following demo code:
```
from datasets import load_dataset
from datasets.features import Sequence, Value, ClassLabel, Features
ais_dataset = load_dataset("/data/ryan.gao/ais_dataset_cache/raw/1978/")
ais_dataset = ais_dataset["train"]
def add_class(example):
example["my_labeled_bbox"] = {"bbox": [100,100,200,200], "label": "cat"}
return example
ais_dataset = ais_dataset.map(add_class, batched=False, num_proc=32)
ais_dataset = ais_dataset.cast_column("my_labeled_bbox", Sequence(
{
"bbox": Sequence(Value(dtype="int64")),
"label": ClassLabel(names=["cat", "dog"])
}))
print(ais_dataset[0])
```
However, executing this code results in an error:
```
File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2111, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
int64
to
Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)
```
Upon examining the source code in datasets/table.py at line 2035:
```
if isinstance(feature, Sequence) and isinstance(feature.feature, dict):
feature = {
name: Sequence(subfeature, length=feature.length) for name, subfeature in feature.feature.items()
}
```
I noticed that if subfeature is of type Sequence, the code results in Sequence(Sequence(...), ...) and Sequence(ClassLabel(...), ...), which appears to be the source of the error.
### Steps to reproduce the bug
run my demo code
### Expected behavior
no exception
### Environment info
python 3.9
datasets: 2.16.1 | {
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] | null | 5 | "2024-01-23T01:47:19" | "2024-02-06T17:21:25" | "2024-02-06T17:21:25" | NONE | null | null | null | ### Describe the bug
Dear huggingfacers,
I'm trying to use a subset of the-stack dataset. When I run the command the first time
```
dataset = load_dataset(
path='bigcode/the-stack',
data_dir='data/fortran',
split='train' )
```
It downloads the files and caches them normally.
Nevertheless, since my compute nodes are not online (`HF_DATASETS_OFFLINE=1`) . Whenever I try to run the command again, the library is passing the wrong cache path:
`Cache directory for the-stack doesn't exist at /Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data%2Ffortran-data_dir=data%2Ffortran`
when the right path is:
`'/Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data\%2Ffortran`
Not sure why those redundancies are included in the path. If I try adding the correct path through the the cache_dir argument it throws an error:
ConnectionError: Couldn't reach the Hugging Face Hub for dataset 'bigcode/the-stack': Offline mode is enabled.
Your help with this issue is greatly appreciated. Thanks a lot for the great work.
### Steps to reproduce the bug
1:
`dataset = load_dataset(
path='bigcode/the-stack',
data_dir='data/fortran',
split='train' )`
2:
`HF_DATASETS_OFFLINE=1`
3:
`dataset = load_dataset(
path='bigcode/the-stack',
data_dir='data/fortran',
split='train' )`
### Expected behavior
being able to use the cached data
### Environment info
several different systems | {
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} | [] | closed | false | null | [] | null | 2 | "2024-01-22T15:19:16" | "2024-01-29T16:43:11" | "2024-01-29T16:36:53" | CONTRIBUTOR | null | 0 | {
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} | Fixes https://github.com/huggingface/datasets/issues/6566
Let me know if there's any tests I need to clear. | {
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} | [] | closed | false | null | [] | null | 2 | "2024-01-19T18:34:47" | "2024-01-26T15:11:38" | "2024-01-26T15:05:34" | CONTRIBUTOR | null | 0 | {
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} | Closes https://github.com/huggingface/datasets/issues/6604, closes https://github.com/huggingface/datasets/issues/2775 | {
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https://api.github.com/repos/huggingface/datasets/issues/6605 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6605/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6605/comments | https://api.github.com/repos/huggingface/datasets/issues/6605/events | https://github.com/huggingface/datasets/issues/6605 | 2,090,188,376 | I_kwDODunzps58lb5Y | 6,605 | ELI5 no longer available, but referenced in example code | {
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} | [] | closed | false | null | [] | null | 1 | "2024-01-19T10:21:52" | "2024-02-01T17:58:23" | "2024-02-01T17:58:22" | NONE | null | null | null | Here, an example code is given:
https://huggingface.co/docs/transformers/tasks/language_modeling
This code + article references the ELI5 dataset.
ELI5 is no longer available, as the ELI5 dataset page states: https://huggingface.co/datasets/eli5
"Defunct: Dataset "eli5" is defunct and no longer accessible due to unavailability of the source data.
Reddit recently [changed the terms of access](https://www.reddit.com/r/reddit/comments/12qwagm/an_update_regarding_reddits_api/) to its API, making the source data for this dataset unavailable.
"
Please change the example code to use a different dataset. | {
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} | [] | closed | false | null | [] | null | 2 | "2024-01-19T06:32:25" | "2024-01-26T15:05:35" | "2024-01-26T15:05:35" | NONE | null | null | null | ### Describe the bug
The transform fingerprinting logic relies on the `random` library for random bits when the function is not hashable (e.g. bound methods as used in `trl`: https://github.com/huggingface/trl/blob/main/trl/trainer/dpo_trainer.py#L356). This causes collisions when the training code sets a fixed random seed, which is common practice: https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-beta/sft/config_full.yaml#L45.
This results in fingerprint collisions which leads to silently loading incorrect cache files corresponding to completely different datasets.
### Steps to reproduce the bug
n/a
### Expected behavior
Use `uuid` v4 instead of `random.getrandbits()`
### Environment info
`datasets` main branch | {
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} | [] | open | false | null | [] | null | 2 | "2024-01-18T23:08:30" | "2024-01-28T04:01:15" | null | NONE | null | null | null | ### Describe the bug
In the documentation `datasets.Dataset.map` arg `cache_file_name` is said to be a string, but it doesn't work.
### Steps to reproduce the bug
1. pick a dataset
2. write a map function
3. do `ds.map(..., cache_file_name='some_filename')`
4. it crashes
### Expected behavior
It will tell you the filename you specified does not exist or it will generate a new file and tell you the filename does not exist.
### Environment info
- `datasets` version: 2.16.0
- Platform: Linux-5.10.201-168.748.amzn2int.x86_64-x86_64-with-glibc2.26
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.2
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.12.2 | {
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} | [] | open | false | null | [] | null | 0 | "2024-01-18T23:00:47" | "2024-01-18T23:00:47" | null | NONE | null | null | null | ### Describe the bug
At `save_to_disk` step, the `max_shard_size` by default is `500MB`. However, one row of the dataset might be larger than `500MB` then the saving will throw an index error. Without looking at the source code, the bug is due to wrong calculation of number of shards which i think is
`total_size / min(max_shard_size, row_size)` which should be `total_size / max(max_shard_size, row_size)`
The fix is setting a larger `max_shard_size`
### Steps to reproduce the bug
1. create a dataset with large dense tensors per row
2. set a small `max_shard_size` say 1MB
3. `save_to_disk`
### Expected behavior
```
raise IndexError(f"Index {index} out of range for dataset of size {size}.")
IndexError: Index 10 out of range for dataset of size 10.
```
### Environment info
- `datasets` version: 2.16.0
- Platform: Linux-5.10.201-168.748.amzn2int.x86_64-x86_64-with-glibc2.26
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.2
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.12.2 | {
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} | [] | open | false | null | [] | null | 2 | "2024-01-18T14:48:27" | "2024-01-23T14:42:32" | null | NONE | null | null | null | ### Describe the bug
I wanted to be able to save a HF dataset for translations and load it again in another script, but I'm a bit confused with the documentation and the result I've got so I'm opening this issue to ask if this behavior is as expected.
### Steps to reproduce the bug
The documentation I've mainly consulted is https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/loading_methods#datasets.load_dataset and https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset (where I've found `.to_csv()`)
```python
# Load a dataset of translations
test_dataset = load_dataset("opus100", name="en-fr", split="test")
# Save with .to_csv()
test_csv_path = "try_testset_save.csv"
test_dataset.to_csv(test_csv_path)
# Load dataset from the CSV
loaded_dataset = load_dataset("csv", data_files=test_csv_path)
print(test_dataset_fromfile[0]["translation"])
print(test_dataset_fromfile[0]["translation"]["en"])
```
```
Creating CSV from Arrow format: 100%
2/2 [00:00<00:00, 47.99ba/s]
Downloading data files: 100%
1/1 [00:00<00:00, 65.33it/s]
Extracting data files: 100%
1/1 [00:00<00:00, 42.10it/s]
Generating train split:
2000/0 [00:00<00:00, 47486.09 examples/s]
{'en': "She wasn't going to vaccinate her kid against polio, no way.", 'fr': 'Elle ne vaccinerait pas son enfant contre la polio. Pas question.'}
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[29], line 11
9 loaded_dataset = load_dataset("csv", data_files=test_csv_path)
10 print(test_dataset_fromfile[0]["translation"])
---> 11 print(test_dataset_fromfile[0]["translation"]["en"])
TypeError: string indices must be integers, not 'str'
```
### Expected behavior
Each translation was saved as a stringified dict like `"{'en': ""She wasn't going to vaccinate her kid against polio, no way."", 'fr': 'Elle ne vaccinerait pas son enfant contre la polio. Pas question.'}"` where I would have expected 2 columns (1st with English segments, and 2nd with French segments), and I was expecting `load_dataset` to infer the type of feature automatically as I haven't seen anything about it in the documentation.
Do you have an example of how to effectively save and load datasets of translations ?
### Environment info
- `datasets` version: 2.15.0
- Platform: Linux-3.10.0-1160.36.2.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.11.5
- `huggingface_hub` version: 0.16.4
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6599 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6599/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6599/comments | https://api.github.com/repos/huggingface/datasets/issues/6599/events | https://github.com/huggingface/datasets/issues/6599 | 2,086,684,664 | I_kwDODunzps58YEf4 | 6,599 | Easy way to segment into 30s snippets given an m4a file and a vtt file | {
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] | closed | false | null | [] | null | 2 | "2024-01-17T17:51:40" | "2024-01-23T10:42:17" | "2024-01-22T15:35:49" | NONE | null | null | null | ### Feature request
Uploading datasets is straightforward thanks to the ability to push Audio to hub. However, it would be nice if the data (text and audio) could be segmented when being pushed (if not possible already).
### Motivation
It's easy to create a vtt file from an audio file. If there could be auto-segmenting, this would make the creation of datasets much faster.
### Your contribution
I have made a custom script to do this but it's not all that clean - uses librosa and pydub. | {
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} | [] | open | false | null | [] | null | 4 | "2024-01-16T15:16:01" | "2024-02-05T05:44:44" | null | NONE | null | null | null | ### Describe the bug
I receive this error message when using `load_dataset` with "csv" path and `dataset_files=s3://...`:
```
TypeError: Session.__init__() got an unexpected keyword argument 'hf'
```
I found a similar issue here: https://stackoverflow.com/questions/77596258/aws-issue-load-dataset-from-s3-fails-with-unexpected-keyword-argument-error-in
Full stacktrace:
```
.../site-packages/datasets/load.py:2549: in load_dataset
builder_instance.download_and_prepare(
.../site-packages/datasets/builder.py:1005: in download_and_prepare
self._download_and_prepare(
.../site-packages/datasets/builder.py:1078: in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
.../site-packages/datasets/packaged_modules/csv/csv.py:147: in _split_generators
data_files = dl_manager.download_and_extract(self.config.data_files)
.../site-packages/datasets/download/download_manager.py:562: in download_and_extract
return self.extract(self.download(url_or_urls))
.../site-packages/datasets/download/download_manager.py:426: in download
downloaded_path_or_paths = map_nested(
.../site-packages/datasets/utils/py_utils.py:466: in map_nested
mapped = [
.../site-packages/datasets/utils/py_utils.py:467: in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
.../site-packages/datasets/utils/py_utils.py:387: in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
.../site-packages/datasets/utils/py_utils.py:387: in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
.../site-packages/datasets/utils/py_utils.py:370: in _single_map_nested
return function(data_struct)
.../site-packages/datasets/download/download_manager.py:451: in _download
out = cached_path(url_or_filename, download_config=download_config)
.../site-packages/datasets/utils/file_utils.py:188: in cached_path
output_path = get_from_cache(
...1/site-packages/datasets/utils/file_utils.py:511: in get_from_cache
response = fsspec_head(url, storage_options=storage_options)
.../site-packages/datasets/utils/file_utils.py:316: in fsspec_head
fs, _, paths = fsspec.get_fs_token_paths(url, storage_options=storage_options)
.../site-packages/fsspec/core.py:622: in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
.../site-packages/fsspec/registry.py:290: in filesystem
return cls(**storage_options)
.../site-packages/fsspec/spec.py:79: in __call__
obj = super().__call__(*args, **kwargs)
.../site-packages/s3fs/core.py:187: in __init__
self.s3 = self.connect()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <s3fs.core.S3FileSystem object at 0x1500a1310>, refresh = True
def connect(self, refresh=True):
"""
Establish S3 connection object.
Parameters
----------
refresh : bool
Whether to create new session/client, even if a previous one with
the same parameters already exists. If False (default), an
existing one will be used if possible
"""
if refresh is False:
# back compat: we store whole FS instance now
return self.s3
anon, key, secret, kwargs, ckwargs, token, ssl = (
self.anon, self.key, self.secret, self.kwargs,
self.client_kwargs, self.token, self.use_ssl)
if not self.passed_in_session:
> self.session = botocore.session.Session(**self.kwargs)
E TypeError: Session.__init__() got an unexpected keyword argument 'hf'
```
### Steps to reproduce the bug
1. Assuming a valid CSV file located at `s3://bucket/data.csv`
2. Run the below code:
```
storage_options = {
"key": "...",
"secret": "...",
"client_kwargs": {
"endpoint_url": "...",
}
}
load_dataset("csv", data_files="s3://bucket/data.csv", storage_options=storage_options)
```
Encountered in version `2.16.1` but also reproduced in `2.16.0` and `2.15.0`.
Note: I encountered this in a unit test using a `moto` mock for S3, however since the error occurs before the session is instantiated, it should not be the issue.
### Expected behavior
No exception is raised, the boto3 session is created successfully, and the CSV file is downloaded successfully and returned as a dataset.
===
After some research I found that `DownloadConfig` has a `__post_init__` method that always forces this value to be set in its `storage_options`, even though in case of an S3 location the storage options get passed on to the S3 Session which does not expect this parameter. I assume this parameter is needed when reading from the huggingface hub and should not be set in this context.
Unfortunately there is nothing the user can do to work around it. Even if you manually do something like:
```
download_config = DownloadConfig()
del download_config.storage_options["hf"]
load_dataset("csv", data_files="s3://bucket/data.csv", download_config=download_config)
```
the library will still reinsert this parameter when `download_config = self.download_config.copy()` in line 418 of `download_manager.py` (`DownloadManager.download`).
Therefore `load_dataset` currently cannot be used to read a dataset in CSV format from an S3 location.
### Environment info
- `datasets` version: 2.16.1
- Platform: macOS-14.2.1-arm64-arm-64bit
- Python version: 3.11.7
- `huggingface_hub` version: 0.20.2
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0
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] | null | 6 | "2024-01-16T11:27:07" | "2024-02-05T12:29:37" | "2024-02-05T12:29:37" | MEMBER | null | null | null | While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace.
## Steps to reproduce the bug
The command:
```python
commit_info = ds.push_to_hub(
"caner",
config_name="default",
commit_message="Convert dataset to Parquet",
commit_description="Convert dataset to Parquet.",
create_pr=True,
token=token,
)
```
creates the additional dataset `albertvillanova/caner`. | {
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